03982nas a2200421 4500008004100000022001400041245012400055210006900179260001300248300001100261490000700272520282000279653000903099653001003108653002803118653004303146653002103189653002103210653002803231653001103259653001803270653001103288653001103299653001703310653000903327653002603336653001803362100001903380700001403399700001603413700001503429700001503444700001703459700001703476700001503493700001703508856003503525 1992 eng d a0009-732200aLipoprotein lipids in older people. Results from the Cardiovascular Health Study. The CHS Collaborative Research Group.0 aLipoprotein lipids in older people Results from the Cardiovascul c1992 Sep a858-690 v863 a
BACKGROUND: Cardiovascular disease is the leading cause of death and disability in older people. There is little information about the distributions of risk factors in older populations. This article describes the distribution and correlates of lipoprotein lipids in people greater than or equal to 65 years old.
METHODS AND RESULTS: Lipoprotein lipid concentrations were measured in 2,106 men (M) and 2,732 women (F) who were participants in the Cardiovascular Health Study, a population-based epidemiological study. Distributions of lipids by age and sex and bivariate and multivariate relations among lipids and other variables were determined in cross-sectional analyses. Mean concentrations of lipids were cholesterol: M, 5.20 +/- 0.93 mmol/l (201 +/- 36 mg/dl) and F, 5.81 +/- 0.98 mmol/l (225 +/- 38 mg/dl); triglyceride (TG): M, 1.58 +/- 0.85 mmol/l (140 +/- 75 mg/dl) and F, 1.57 +/- 0.78 mmol/l (139 +/- 69 mg/dl); high density lipoprotein cholesterol (HDL-C): M, 1.23 +/- 0.33 mmol/l (48 +/- 16 mg/dl), and F, 1.53 +/- 0.41 mmol/l (59 +/- 16 mg/dl); low density lipoprotein cholesterol (LDL-C): M, 3.27 +/- 0.85 mmol/l (127 +/- 33 mg/dl) and F, 3.57 +/- 0.93 mmol/l (138 +/- 36 mg/dl). The total cholesterol to HDL-C ratios were M, 4.49 +/- 1.29 and F, 4.05 +/- 1.22. TG, total cholesterol, and LDL-C concentrations were lower with increasing age, the last more evident in men than in women. TG concentration was positively associated with obesity (in women), central fat patterning, glucose intolerance, use of beta-blockers (in men), and use of estrogens (in women) and negatively associated with age, renal function, alcohol use, and socioeconomic status. In general, HDL-C had opposite relations with these variables, except that estrogen use was associated with higher HDL-C concentrations. LDL-C concentration was associated with far fewer variables than the other lipids but was negatively associated with age in men and women and positively correlated with obesity and central fat patterning and negatively correlated with renal function and estrogen use in women. There were no differences in total cholesterol and LDL-C concentrations among participants with and without prevalent coronary heart disease and stroke, but TG concentration was higher and HDL-C lower in men with both coronary heart disease and stroke and in women with coronary heart disease.
CONCLUSIONS: Cholesterol and cholesterol/HDL-C ratio were lower and HDL-C higher than previously reported values in older people, suggesting that lipid risk profiles may be improving in older Americans. TG and HDL-C concentrations, and to a lesser extent LDL-C, were associated with potentially important modifiable factors such as obesity, glucose intolerance, renal function, and medication use.
10aAged10aAging10aCardiovascular Diseases10aCardiovascular Physiological Phenomena10aCholesterol, HDL10aCholesterol, LDL10aCross-Sectional Studies10aFemale10aHealth Status10aHumans10aLipids10aLipoproteins10aMale10aMultivariate Analysis10aTriglycerides1 aEttinger, W, H1 aWahl, P W1 aKuller, L H1 aBush, T, L1 aTracy, R P1 aManolio, T A1 aBorhani, N O1 aWong, N, D1 aO'Leary, D H uhttps://chs-nhlbi.org/node/78102981nas a2200433 4500008004100000022001400041245007100055210006900126260001300195300001200208490000700220520183300227653000902060653002202069653002102091653001602112653002102128653001602149653002102165653002102186653001902207653002102226653002802247653001702275653001102292653001102303653001202314653002202326653002302348653002302371653000902394653001602403653002602419100001902445700001802464700001402482700001502496856003602511 1994 eng d a0022-142200aHigh density lipoprotein cholesterol subfractions in older people.0 aHigh density lipoprotein cholesterol subfractions in older peopl c1994 May aM116-220 v493 aBACKGROUND: High density lipoprotein (HDL) may be an important risk factor for cardiovascular disease in older people. HDL is heterogeneous with several subfractions. This article describes the distribution and correlates of HDL2 cholesterol (C) and HDL3-C in older people.
METHODS: HDL subfraction cholesterols were measured in 1,127 females and 825 males > or = 65 years old who participated in the Cardiovascular Health Study. Distributions of HDL subfraction cholesterols and bivariate and multivariate relationships were determined in cross-sectional analyses.
RESULTS: Mean (+/- SD) concentrations of HDL subfractions were: HDL3-C (M .98 +/- .25, F 1.2 +/- .29 mmol/l), HDL2-C (M .09 +/- .08, F .13 +/- .09 mmol/l). HDL2-C, but not HDL3-C, was slightly higher with age. Using multivariate analysis, both HDL2-C and HDL3-C (in females) were inversely correlated with triglyceride, body weight, and fasting insulin; HDL3-C was inversely correlated with central fat distribution in women. Both HDL2-C and HDL3-C were lower in participants with prevalent cardiovascular disease. However, only HDL3-C was significantly inversely related to carotid stenosis, as measured by ultrasound.
CONCLUSIONS: The slight increase in HDL-C with age appears to be due to an increase in the HDL2-C subfraction. HDL-C subfractions are independently related to triglyceride levels, body weight, and insulin concentrations in older people, all potentially modifiable risk factors. Both HDL2-C and HDL3-C are lower in older people with prevalent cardiovascular disease, although only HDL3-C was correlated with carotid atherosclerosis. These findings are consistent with the hypothesis that HDL subfractions are important risk factors for atherosclerotic cardiovascular disease in the elderly.
10aAged10aAged, 80 and over10aAlcohol Drinking10aBody Weight10aCarotid Stenosis10aCholesterol10aCholesterol, HDL10aCholesterol, LDL10aCohort Studies10aCoronary Disease10aCross-Sectional Studies10aDrug Therapy10aFemale10aHumans10aInsulin10aLipoproteins, HDL10aLipoproteins, HDL210aLipoproteins, HDL310aMale10aSex Factors10aSocioeconomic Factors1 aEttinger, W, H1 aVerdery, R, B1 aWahl, P W1 aFried, L P uhttps://chs-nhlbi.org/node/143202895nas a2200361 4500008004100000022001400041245009200055210006900147260001300216300001100229490000700240520190600247653000902153653002202162653002002184653002802204653002102232653002102253653002102274653002802295653002202323653001102345653001102356653001902367653001702386653000902403653001502412653001702427100001602444700001802460700001902478856003602497 1995 eng d a0002-861400aHigh density lipoprotein cholesterol is associated with serum cortisol in older people.0 aHigh density lipoprotein cholesterol is associated with serum co c1995 Dec a1345-90 v433 aOBJECTIVE: To determine the associations between serum cortisol and HDL cholesterol, other lipoprotein lipids and cardiovascular risk factors, carotid atherosclerosis, and clinical heart disease in older people.
DESIGN: A cross-sectional, observational, ancillary study of the Cardiovascular Health Study (CHS).
POPULATION: A total of 245 community-dwelling people, 65 to 89 years old, were recruited consecutively for a 2-month period from the CHS cohort in Forsyth County, North Carolina.
METHODS: Cortisol was measured by radioimmunoassay in serum collected between 7:00 and 10:00 AM after an overnight fast. Cortisol levels were correlated with lipoprotein lipids, insulin, glucose, body mass index, waist-hip ratio, prevalent coronary heart disease, hypertension, diabetes, and carotid atherosclerosis by B-mode ultrasound.
RESULTS: Serum cortisol was correlated negatively (r = -.24) with body mass index and waist-hip ratio (r = -.16) but was not related significantly to fasting insulin or glucose. Cortisol was not associated significantly with triglyceride and low density lipoprotein cholesterol but showed a positive correlation (r = .21) with high density lipoprotein cholesterol. The relationship between cortisol and high density lipoprotein cholesterol persisted after adjustment for gender, body mass index, waist-hip ratio, cigarette and alcohol use, triglyceride level, and diabetes. There was a trend toward a negative correlation between cortisol and measures of carotid atherosclerosis, but no significant relationship was indicated between cortisol and prevalent coronary heart disease, hypertension, or diabetes.
CONCLUSION: Endogenous glucocorticoid levels correlated with HDL cholesterol levels and may play a role in the physiologic regulation of high density lipoprotein levels in older people.
10aAged10aBody Constitution10aBody Mass Index10aCardiovascular Diseases10aCarotid Stenosis10aCholesterol, HDL10aCoronary Disease10aCross-Sectional Studies10aDiabetes Mellitus10aFemale10aHumans10aHydrocortisone10aHypertension10aMale10aPrevalence10aRisk Factors1 aVarma, V, K1 aRushing, J, T1 aEttinger, W, H uhttps://chs-nhlbi.org/node/141203950nas a2200565 4500008004100000022001400041245013900055210006900194260001300263300001100276490000700287520238100294653001002675653000902685653002102694653001902715653002702734653002902761653002102790653002102811653001902832653002102851653002202872653002602894653002402920653001102944653001102955653000902966653002702975653002503002653002403027653002403051653001703075653002403092653001203116653001803128653001703146653002003163100001703183700001603200700001803216700001703234700001703251700001803268700001303286700001603299700001603315700001703331856003603348 1996 eng d a0039-249900aThickening of the carotid wall. A marker for atherosclerosis in the elderly? Cardiovascular Health Study Collaborative Research Group.0 aThickening of the carotid wall A marker for atherosclerosis in t c1996 Feb a224-310 v273 aBACKGROUND AND PURPOSE: We investigated the relationships between prevalent coronary heart disease (CHD), clinically manifest atherosclerotic disease (ASD), and major established risk factors for atherosclerosis and intima-media thickness (IMT) in the common carotid arteries (CCA) and internal carotid arteries (ICA) separately and in combination in older adults. We wished to determine whether a noninvasive measurement can serve as an indicator of clinically manifest atherosclerotic disease and to determine which of the two variables, CCA IMT or ICA IMT, is a better correlate.
METHODS: IMT of the CCA and ICA was measured with duplex ultrasound in 5117 of 5201 individuals enrolled in the Cardiovascular Health Study, a study of the risk factors and the natural history of cardiovascular disease in adults aged 65 years or more. Histories of CHD, peripheral arterial disease, and cerebrovascular disease were obtained during baseline examination. Risk factors included cholesterol levels, cigarette smoking, elevated blood pressure, diabetes, age, and sex. Relationships between risk factors and IMT were studied by multiple regression analysis and canonical variate analysis. Prediction of prevalent CHD and ASD by IMT measurements in CCAs and ICAs were made by logistic regression, adjusting for age and sex.
RESULTS: IMT measurements of the CCAs and ICAs were greater in persons with CHD and ASD than those without, even after controlling for sex (P < .001). IMT measurements in the ICA were greater than those in the CCA. Risk factors for ASD accounted for 17% and 18% of the variability in IMT in the CCA and ICA, respectively. These same risk factors accounted for 25% of the variability of a composite measurement consisting of the sum of the ICA IMT and CCA IMT. The ability to predict CHD and ASD was greater for ICA IMT (odds ratio [confidence interval]: 1.36 [1.31 to 1.41] and 1.35 [1.25 to 1.44], respectively) than for CCA IMT (1.09 [1.05 to 1.13] and 1.17 [1.09 to 1.25]).
CONCLUSIONS: Whereas CCA IMT is associated with major risk factors for atherosclerosis and existing CHD and ASD in older adults, this association is not as strong as that for ICA IMT. The combination of these measures relates more strongly to existing CHD and ASD and cerebrovascular disease risk factors than either taken alone.
10aAdult10aAged10aArteriosclerosis10aBlood Pressure10aCarotid Artery, Common10aCarotid Artery, Internal10aCholesterol, HDL10aCholesterol, LDL10aCohort Studies10aCoronary Disease10aDiabetes Mellitus10aDiabetic Angiopathies10aElectrocardiography10aFemale10aHumans10aMale10aMedical History Taking10aPhysical Examination10aProspective Studies10aRegression Analysis10aRisk Factors10aSex Characteristics10aSmoking10aTunica Intima10aTunica Media10aUltrasonography1 aO'Leary, D H1 aPolak, J, F1 aKronmal, R, A1 aSavage, P, J1 aBorhani, N O1 aKittner, S, J1 aTracy, R1 aGardin, J M1 aPrice, T, R1 aFurberg, C D uhttps://chs-nhlbi.org/node/145303565nas a2200517 4500008004100000022001400041245022800055210006900283260001300352300001300365490000700378520201100385653002202396653003902418653000902457653001002466653002102476653002002497653004302517653002602560653002102586653002102607653002102628653001902649653004002668653001102708653001502719653001802734653001102752653001702763653000902780653001602789653002402805653001702829653001602846653001202862653001802874653001702892653002002909100001402929700001702943700001602960700001802976700001702994856003603011 1997 eng d a0039-249900aDoes the association of risk factors and atherosclerosis change with age? An analysis of the combined ARIC and CHS cohorts. The Atherosclerosis Risk in Communities (ARIC) and Cardiovascular Health Study (CHS) investigators.0 aDoes the association of risk factors and atherosclerosis change c1997 Sep a1693-7010 v283 aINTRODUCTION: A decrease in the estimated relative risk of cerebrovascular and cardiovascular diseases associated with known disease risk factors has been observed among elderly cohorts, perhaps suggesting that continued risk factor management in the elderly may not be as efficacious as with younger age groups. In this paper, the differential magnitude of the association of risk factors with atherosclerosis across the age spectrum from 45 years to older than 75 years is presented.
METHODS: Subclinical atherosclerosis as measured by carotid ultrasonography and risk factor prevalence were assessed using similar methods among participants aged 45 to 64 years in the Atherosclerosis Risk in Communities (ARIC) study and among participants 65 years and older in the Cardiovascular Health Study (CHS). Pooling these two cohorts provided data on the relationship of risk factors and atherosclerosis on nearly 19,000 participants over a broad age range. Regression analyses were used to assess the consistency of the magnitude of the association of risk factors with atherosclerosis across the age spectrum separately for black and white participants in cross-sectional analyses.
RESULTS: As expected, each of the risk factors was globally (across all ages) associated with increased atherosclerosis. However, the magnitude of the association did not differ across the age spectrum for hypertension, low density lipoprotein cholesterol (LDL-c), fibrinogen, or body mass index (BMI). For whites, there was a significantly greater impact of smoking and HDL-C among older age strata but a smaller impact of diabetes. For black women, the impact of HDL-C decreased among the older age strata.
CONCLUSIONS: These data suggest that most risk factors continue to be associated with increased atherosclerosis at older ages, possibly suggesting a continued value in investigation of strategies to reduce atherosclerosis by controlling risk factors at older ages.
10aAfrican Americans10aAfrican Continental Ancestry Group10aAged10aAging10aArteriosclerosis10aBody Mass Index10aCardiovascular Physiological Phenomena10aCardiovascular System10aCarotid Arteries10aCholesterol, HDL10aCholesterol, LDL10aCohort Studies10aEuropean Continental Ancestry Group10aFemale10aFibrinogen10aHealth Status10aHumans10aHypertension10aMale10aMiddle Aged10aRegression Analysis10aRisk Factors10aSex Factors10aSmoking10aTunica Intima10aTunica Media10aUltrasonography1 aHoward, G1 aManolio, T A1 aBurke, G, L1 aWolfson, S, K1 aO'Leary, D H uhttps://chs-nhlbi.org/node/148603000nas a2200409 4500008004100000022001400041245008400055210006900139260001600208300001200224490000700236520195300243653001902196653000902215653001802224653002202242653002102264653002502285653001102310653001102321653001402332653001202346653000902358653002402367653000902391653001802400653001202418100001702430700001702447700001602464700001602480700001502496700001502511700001202526700001702538856003502555 1999 eng d a0027-887400aIncreased blood glucose and insulin, body size, and incident colorectal cancer.0 aIncreased blood glucose and insulin body size and incident color c1999 Jul 07 a1147-540 v913 aBACKGROUND: Abdominal obesity--an elevated level of visceral adipose tissue--has been linked to colorectal cancer. Furthermore, elevated levels of visceral adipose tissue have been associated with hyperinsulinemia, and insulin is a growth factor in the colon. We assessed whether waist circumference, a surrogate measure of visceral adipose tissue, and metabolic parameters associated with visceral adipose tissue were related to colorectal cancer.
METHODS: In the Cardiovascular Health Study cohort, we examined the relationship of baseline measurements of body size, glucose, insulin, and lipoproteins to incident colorectal cancer. All P values are two-sided.
RESULTS: Among 5849 participants, 102 incident cases of colorectal cancer were identified. Individuals in the highest quartile of fasting glucose had a nearly twofold increased risk of colorectal cancer (relative risk [RR] = 1.8; 95% confidence interval [CI] = 1.0-3.1), and the linear trend RR (LT RR = 1.2; 95% CI = 1.0-1.5) for fasting glucose level was statistically significant (P =. 02). Glucose and insulin levels 2 hours after oral glucose challenge also exhibited statistically significant associations with colorectal cancer (2-hour glucose levels: RR = 2.4 [95% CI = 1.2-4. 7]/LT RR = 1.3 [95% CI = 1.0-1.6; P =.02]; 2-hour insulin levels: RR = 2.0 [95% CI = 1.0-3.8]/LT RR = 1.2 [95% CI = 1.0-1.5; P =.04]). Analysis of fasting insulin levels suggested a threshold effect, with values above the median associated with colorectal cancer (RR = 1.6; 95% CI = 1.1-2.4; P =.02). Higher levels of waist circumference were also statistically significantly associated with colorectal cancer (RR = 1.9; 95% CI = 1.1-3.3; P =.02).
CONCLUSIONS: These data provide, to our knowledge, the first direct evidence of an association between elevated visceral adipose tissue level, its associated metabolic effects, and colorectal cancer.
10aAdipose Tissue10aAged10aBlood Glucose10aBody Constitution10aCholesterol, HDL10aColorectal Neoplasms10aFemale10aHumans10aIncidence10aInsulin10aMale10aProspective Studies10aRisk10aTriglycerides10aViscera1 aSchoen, R, E1 aTangen, C, M1 aKuller, L H1 aBurke, G, L1 aCushman, M1 aTracy, R P1 aDobs, A1 aSavage, P, J uhttps://chs-nhlbi.org/node/59403261nas a2200397 4500008004100000022001400041245008400055210006900139260001300208300001300221490000700234520216800241653002102409653000902430653002102439653002102460653002102481653001502502653002802517653001102545653001102556653002802567653000902595653001502604653002402619653001702643100002402660700002002684700001702704700002302721700002102744700002202765700002102787700002002808856003502828 2002 eng d a0085-253800aCardiovascular disease risk status in elderly persons with renal insufficiency.0 aCardiovascular disease risk status in elderly persons with renal c2002 Sep a997-10040 v623 aBACKGROUND: Renal insufficiency has been independently associated with incident cardiovascular disease events in some, but not all, prospective studies. We determined the prevalence of elevated cardiovascular disease risk status among elderly persons with renal insufficiency.
METHODS: This study is a cross-sectional analysis using data collected at the baseline visit of the Cardiovascular Health Study, which enrolled 5888 community dwelling adults aged 65 years or older from four clinical centers in the United States. Renal insufficiency was defined as a serum creatinine level > or =1.3 mg/dL in women and > or =1.5 mg/dL in men. The outcomes of this study included prevalent cardiovascular disease [prior coronary heart disease (CHD) or stroke], subclinical cardiovascular disease (abnormal values of ankle-arm index, carotid ultrasound, and echocardiography) and elevated cardiovascular risk based upon a diagnosis of diabetes and the Framingham equations. The association between renal insufficiency and cardiovascular risk status was estimated with and without adjustment for other cardiovascular predictors.
RESULTS: Among the 5808 participants with creatinine levels measured at entry, 15.9% of men (N = 394), and 7.6% of women (N = 254) had renal insufficiency. The prevalence of either clinical or subclinical cardiovascular disease was 64% in persons with renal insufficiency compared with 43% in those without it [odds ratio (OR) 2.34; 95% confidence interval (95% CI), 1.96, 2.80]. After adjustment for other cardiovascular risk factors, renal insufficiency remained significantly associated with clinical and subclinical cardiovascular disease (adjusted OR 1.43; 95% CI, 1.18, 1.75), but the magnitude of association was substantially reduced. After combining clinical and subclinical cardiovascular disease, diabetes, and an estimated risk>20% by the Framingham equations, 78% of men and 61% of women with renal insufficiency had elevated cardiovascular risk status.
CONCLUSIONS: Renal insufficiency is a marker for elevated cardiovascular disease risk in community dwelling elderly adults.
10aAge Distribution10aAged10aCholesterol, HDL10aCholesterol, LDL10aCoronary Disease10aCreatinine10aCross-Sectional Studies10aFemale10aHumans10aKidney Failure, Chronic10aMale10aPrevalence10aProspective Studies10aRisk Factors1 aShlipak, Michael, G1 aFried, Linda, F1 aCrump, Casey1 aBleyer, Anthony, J1 aManolio, Teri, A1 aTracy, Russell, P1 aFurberg, Curt, D1 aPsaty, Bruce, M uhttps://chs-nhlbi.org/node/69702882nas a2200385 4500008004100000022001400041245006800055210006700123260001300190300001200203490000700215520181900222653000902041653002102050653001602071653002102087653002102108653002202129653002102151653002802172653001102200653001102211653003602222653000902258653001502267653001702282100002402299700002202323700002102345700002202366700002402388700002502412700002402437856003502461 2007 eng d a0021-972X00aAlcohol consumption and lipoprotein subclasses in older adults.0 aAlcohol consumption and lipoprotein subclasses in older adults c2007 Jul a2559-660 v923 aCONTEXT: Limited evidence suggests that alcohol intake may be associated with lipoprotein subclass distribution, which could mediate its relationship with coronary heart disease.
OBJECTIVES: The objective was to determine the relationship of alcohol intake with lipoprotein particle subclasses.
DESIGN, SETTING, AND PARTICIPANTS: The study included a cross-sectional analysis of 1850 participants of the Cardiovascular Health Study aged 65 yr and older and free of clinical cardiovascular disease.
MAIN OUTCOME MEASURE: Lipoprotein subclass distribution was measured with nuclear magnetic resonance spectroscopy, according to self-reported alcohol intake.
RESULTS: Alcohol intake was associated with total low-density lipoprotein (LDL) particles in a U-shaped manner. Consumers of one or more drinks per week had the highest number of large LDL particles, whereas consumers of 7-13 drinks per week had the lowest number of small LDL particles. Alcohol intake was strongly positively associated with large- and medium-sized high-density lipoprotein (HDL) particles but had an inverse relationship with concentrations of small HDL particles and small- and medium-sized very-low-density lipoprotein particles. Average particle sizes of all three lipoproteins were positively associated with alcohol intake. Associations were generally stronger among women than men but in similar directions. Beverage type did not consistently modify these findings.
CONCLUSIONS: Alcohol intake is associated with less total LDL particles, lower levels of small LDL, HDL, and very-low-density lipoprotein particles, and higher levels of large LDL and medium- and large-sized HDL particles in older adults free of prevalent clinical cardiovascular disease.
10aAged10aAlcohol Drinking10aCholesterol10aCholesterol, HDL10aCholesterol, LDL10aCholesterol, VLDL10aCoronary Disease10aCross-Sectional Studies10aFemale10aHumans10aMagnetic Resonance Spectroscopy10aMale10aPrevalence10aRisk Factors1 aMukamal, Kenneth, J1 aMackey, Rachel, H1 aKuller, Lewis, H1 aTracy, Russell, P1 aKronmal, Richard, A1 aMittleman, Murray, A1 aSiscovick, David, S uhttps://chs-nhlbi.org/node/95902656nas a2200433 4500008004100000022001400041245010700055210006900162260001300231300001100244490000700255520141500262653000901677653002201686653002101708653003401729653002001763653002601783653001101809653001101820653001701831653002401848653000901872653001301881653002301894653002601917653001201943653003101955653002401986653002402010653001802034100002202052700002402074700002002098700002202118700002402140700002302164856003502187 2007 eng d a1935-554800aCosts of the metabolic syndrome in elderly individuals: findings from the Cardiovascular Health Study.0 aCosts of the metabolic syndrome in elderly individuals findings c2007 Oct a2553-80 v303 aOBJECTIVE: The cardiovascular consequences of the metabolic syndrome and its component risk factors have been documented in elderly individuals. Little is known about how the metabolic syndrome and its individual components translate into long-term medical costs.
RESEARCH DESIGN AND METHODS: We used log-linear regression models to assess the independent contributions of the metabolic syndrome and its individual components to 10-year medical costs among 3,789 individuals aged > or = 65 years in the Cardiovascular Health Study.
RESULTS: As defined by the National Cholesterol Education Program Third Adult Treatment Panel report, the metabolic syndrome was present in 47% of the sample. Total costs to Medicare were 20% higher among participants with the metabolic syndrome ($40,873 vs. $33,010; P < 0.001). Controlling for age, sex, race/ethnicity, and other covariates, we found that abdominal obesity, low HDL cholesterol, and elevated blood pressure were associated with 15% (95% CI 4.3-26.7), 16% (1.7-31.8), and 20% (10.1-31.7) higher costs, respectively. When added to the model, the metabolic syndrome composite variable did not contribute significantly (P = 0.32).
CONCLUSIONS: Abdominal obesity, low HDL cholesterol, and hypertension but not the metabolic syndrome per se are important predictors of long-term costs in the Medicare population.
10aAged10aAged, 80 and over10aCholesterol, HDL10aContinental Population Groups10aCost of Illness10aDiabetic Angiopathies10aFemale10aHumans10aHypertension10aInterviews as Topic10aMale10aMedicare10aMetabolic Syndrome10aMultivariate Analysis10aObesity10aPatient Education as Topic10aProspective Studies10aRegression Analysis10aUnited States1 aCurtis, Lesley, H1 aHammill, Bradley, G1 aBethel, Angelyn1 aAnstrom, Kevin, J1 aGottdiener, John, S1 aSchulman, Kevin, A uhttps://chs-nhlbi.org/node/96903297nas a2200469 4500008004100000022001400041245013000055210006900185260001300254300001000267490000700277520190900284653005102193653000902244653002002253653002002273653002402293653002802317653002102345653002102366653001902387653002802406653002402434653001102458653001802469653001102487653003402498653002102532653000902553653002102562653002402583653001702607653002402624653001802648100002102666700002402687700001702711700001802728700002502746700002002771856003602791 2008 eng d a1532-541500aDistribution and correlates of lipoprotein-associated phospholipase A2 in an elderly cohort: the Cardiovascular Health Study.0 aDistribution and correlates of lipoproteinassociated phospholipa c2008 May a792-90 v563 aOBJECTIVES: To determine whether high levels of lipoprotein-associated phospholipase A(2) (Lp-PLA(2)) are associated with prevalent cardiovascular disease (CVD) and to evaluate factors most influencing Lp-PLA(2) levels in a community-based cohort of older adults.
DESIGN: Cross-sectional.
SETTING: The Cardiovascular Health Study (CHS), a population-based cohort study of men and women aged 65 and older.
PARTICIPANTS: Five thousand five hundred thirty-one CHS participants.
MEASUREMENTS: Levels of Lp-PLA(2) activity were determined using stored blood samples from the baseline examination.
RESULTS: Mean Lp-PLA(2) was higher in participants with electrocardiographically determined ventricular conduction defect and major Q-wave abnormality and was positively correlated with left ventricular (LV) mass. It was high in those with echocardiographically determined abnormal LV ejection fraction, which persisted after adjustment. Mean Lp-PLA(2) was also higher in participants with mild renal insufficiency and kidney disease. After multivariable adjustment, there was a modest but significant 27% greater risk of prevalent CHF per standard deviation increment of Lp-PLA(2) and a modest but significant 12% greater risk of prevalent myocardial infarction. Lp-PLA(2) was weakly but mainly most strongly correlated with cholesterol and lipoproteins, but those correlations were not especially strong. Lp-PLA(2) was weakly positively correlated with soluble intercellular adhesion molecule-1 but not interleukin-6. In total, all factors considered could explain only 29% of Lp-PLA(2) activity.
CONCLUSION: Novel findings in the study are the associations, in those aged 65 and older, between Lp-PLA(2) activity and LV dysfunction, CHF, and renal disease. CVD risk factors only minimally explain levels of Lp-PLA(2).
10a1-Alkyl-2-acetylglycerophosphocholine Esterase10aAged10aAtherosclerosis10aBody Mass Index10aCardiac Output, Low10aCardiovascular Diseases10aCholesterol, HDL10aCholesterol, LDL10aCohort Studies10aCross-Sectional Studies10aElectrocardiography10aFemale10aHeart Failure10aHumans10aHypertrophy, Left Ventricular10aLong QT Syndrome10aMale10aReference Values10aRenal Insufficiency10aRisk Factors10aStatistics as Topic10aTriglycerides1 aFurberg, Curt, D1 aNelson, Jeanenne, J1 aSolomon, Cam1 aCushman, Mary1 aJenny, Nancy, Swords1 aPsaty, Bruce, M uhttps://chs-nhlbi.org/node/102202289nas a2200337 4500008004100000022001400041245013800055210006900193260001600262300001200278490000800290520128900298653000901587653002301596653002101619653001101640653001101651653001401662653002501676653000901701653001601710653003201726653001801758653002701776100002701803700002101830700002301851700002301874700001801897856003601915 2008 eng d a1528-002000aHigh-density lipoprotein cholesterol and venous thromboembolism in the Longitudinal Investigation of Thromboembolism Etiology (LITE).0 aHighdensity lipoprotein cholesterol and venous thromboembolism i c2008 Oct 01 a2675-800 v1123 aWe determined prospectively the risk of venous thromboembolism (VTE) in relation to baseline high-density lipoprotein cholesterol (HDL-c) in 19 049 participants of the Longitudinal Investigation of Thromboembolism Etiology (LITE), which was composed of 14 490 participants of the Atherosclerosis Risk in Communities (ARIC) study and 4559 participants of the Cardiovascular Health Study (CHS). In addition, we determined the risk of VTE in relation to baseline subfractions of HDL (HDL(2) and HDL(3)) and apolipoprotein A-I (apoA-I) in 14 488 participants of the ARIC study. Age-adjusted incidence rates of VTE by HDL-c quartile ranged from 1.64 to 1.91 per 1000 person-years in men and 1.40 to 1.94 per 1000 person-years in women; however, there was no apparent trend of VTE incidence across HDL-c quartiles for either sex. The multivariate adjusted hazard ratios of VTE by HDL-c quartiles (with quartile 4 as the reference) were nonsignificant for both sexes and ranged between 0.91 and 0.99 for men and 0.78 and 1.22 for women. Results did not differ in separate evaluations of idiopathic and secondary VTE. In the ARIC study, there was no trend of VTE hazard ratios across quartiles of HDL(2), HDL(3), or apoA-I. Low HDL-c does not appear to be an important VTE risk factor.
10aAged10aApolipoprotein A-I10aCholesterol, HDL10aFemale10aHumans10aIncidence10aLongitudinal Studies10aMale10aMiddle Aged10aProportional Hazards Models10aUnited States10aVenous Thromboembolism1 aChamberlain, Alanna, M1 aFolsom, Aaron, R1 aHeckbert, Susan, R1 aRosamond, Wayne, D1 aCushman, Mary uhttps://chs-nhlbi.org/node/104003161nas a2200433 4500008004100000022001400041245017600055210006900231260001300300300001100313490000700324520179400331653000902125653002802134653002102162653001402183653001102197653002902208653001102237653002702248653004602275653002202321653001802343653000902361653003602370653003202406653002802438653003202466653001802498100001902516700002202535700002302557700002702580700002002607700001702627700002202644700002502666856003602691 2009 eng d a1880-387300aAssociation of genetic variation in serum amyloid-A with cardiovascular disease and interactions with IL6, IL1RN, IL1beta and TNF genes in the Cardiovascular Health Study.0 aAssociation of genetic variation in serum amyloidA with cardiova c2009 Aug a419-300 v163 aAIM: Since inflammation is an important contributor to atherosclerosis, gene variants mediating inflammation are of interest. We investigated gene variants in acute phase serum amyloid-A (SAA), a sensitive indicator of inflammatory activity, and their associations with cardiovascular disease (CVD) and HDL cholesterol. Interaction of the SAA genes with genetic variants of their regulators, IL-1, IL-6 and TNF-alpha in influencing CVD was also explored.
METHODS: SNPs characterizing common variation in the SAA1 and SAA2 genes were genotyped in European-(EA) and African-American (AA) participants (n=3969 and n=719) of the Cardiovascular Health Study. Using linear and Cox proportional hazards regression, we assessed associations of SNPs with baseline carotid artery intima-media thickness (cIMT) and risk of incident myocardial infarction, ischemic stroke, total CVD events or mortality during 14 years of follow-up.
RESULTS: No associations between SAA SNPs and outcomes were observed in EA, with the exception of total CVD events; each rs4638289 minor allele was associated with an increased risk in obese individuals, HR=1.2 (95%CI: 0.981.4; p=0.086) and decreased risk among non-obese, HR=0.9 (95%CI: 0.80.99; p=0.026). In AA, we observed modest associations between SAA SNPs and cIMT, potentially modified by HDL. SAA SNPs were also associated with lower HDL in EA and AA. Suggestive gene-gene interaction findings for cIMT in AA and CVD mortality in EA were not significant in subsequent model selection tests.
CONCLUSION: Associations of SAA SNPs with cIMT, HDL and total CVD events were identified, unadjusted for multiple testing. These findings should be regarded as hypothesis-generating until confirmed by other studies.
10aAged10aCardiovascular Diseases10aCholesterol, HDL10aCytokines10aFemale10aGene Regulatory Networks10aHumans10aInflammation Mediators10aInterleukin 1 Receptor Antagonist Protein10aInterleukin-1beta10aInterleukin-610aMale10aPolymorphism, Single Nucleotide10aProportional Hazards Models10aSerum Amyloid A Protein10aTumor Necrosis Factor-alpha10aTunica Intima1 aCarty, Cara, L1 aHeagerty, Patrick1 aHeckbert, Susan, R1 aEnquobahrie, Daniel, A1 aJarvik, Gail, P1 aDavis, Scott1 aTracy, Russell, P1 aReiner, Alexander, P uhttps://chs-nhlbi.org/node/112602609nas a2200445 4500008004100000022001400041245007800055210006900133260000900202300001100211490000700222520142700229653000901656653002201665653001501687653002301702653002101725653001501746653002201761653001601783653001101799653002201810653001101832653002401843653001801867653002001885653000901905653001501914653003301929653001701962653001501979100002001994700001602014700001802030700001702048700002402065700001802089700002002107856003602127 2009 eng d a1421-967000aChange in cardiovascular risk factors with progression of kidney disease.0 aChange in cardiovascular risk factors with progression of kidney c2009 a334-410 v293 aBACKGROUND: Prior studies evaluating the relationship of kidney disease with cardiovascular risk factors have been limited by their cross-sectional design. We evaluated the change in lipids, inflammatory and procoagulant biomarkers with decline in kidney function in a nested case-cohort study in the Cardiovascular Health Study, a community-based study of adults aged >65 years.
METHODS: Individuals with an increase in serum creatinine >or=0.3 mg/dl (baseline to 3 years later, n = 207) were matched to controls of similar age, race, gender, diabetes and baseline serum creatinine, but whose change in creatinine was <0.3 mg/dl. Baseline and change in risk factors were analyzed with conditional logistic regression.
RESULTS: Changes in C-reactive protein were similar. In contrast, cases had larger increases in fibrinogen (OR 1.38 per standard deviation, 95% confidence interval 1.08-1.76) and factor VIII [1.38 (1.10-1.72)] and larger decreases in HDL [OR 0.80 (0.64, 1.00)]. Change in interleukin-6 was greater in cases than controls, but this did not persist after multivariate adjustment. However, in linear regression, change in interleukin-6 was correlated with change in creatinine.
CONCLUSION: Cardiovascular risk factors and kidney function may change concurrently. This could lead to an increased risk of cardiovascular disease as kidney function worsens.
10aAged10aAged, 80 and over10aBiomarkers10aC-Reactive Protein10aCholesterol, HDL10aCreatinine10aDiabetes Mellitus10aFactor VIII10aFemale10aFollow-Up Studies10aHumans10aHypertension, Renal10aInterleukin-610aLogistic Models10aMale10aPrevalence10aRenal Insufficiency, Chronic10aRisk Factors10aVasculitis1 aFried, Linda, F1 aKatz, Ronit1 aCushman, Mary1 aSarnak, Mark1 aShlipak, Michael, G1 aKuller, Lewis1 aNewman, Anne, B uhttps://chs-nhlbi.org/node/106109616nas a2202929 4500008004100000022001400041245007900055210006900134260001600203300001100219490000800230520140900238653002201647653001201669653003701681653002101718653002101739653002801760653001101788653004001799653001101839653001701850653003401867653001301901653001101914653002101925653001101946653001001957653000901967653000901976653003901985653001402024653003602038653002602074653003102100653001802131100002402149700002002173700002102193700002502214700002602239700002102265700002602286700002002312700002302332700002302355700002902378700002402407700001802431700002002449700001902469700002302488700001902511700002402530700002502554700002102579700001902600700002502619700001902644700001802663700001602681700001702697700001602714700002002730700001702750700002002767700002002787700001802807700001602825700001702841700002402858700003002882700002102912700002102933700001802954700002002972700001402992700001803006700002003024700002103044700001803065700002103083700002003104700003003124700002103154700002303175700002203198700002303220700002403243700002503267700002003292700002403312700002403336700002203360700002803382700001703410700002303427700002203450700002003472700001703492700002603509700002003535700002003555700002203575700001903597700002003616700002503636700002003661700001703681700002403698700001803722700002003740700002003760700002003780700001903800700001603819700001903835700002003854700001703874700002003891700002603911700001903937700001903956700002703975700002304002700002204025700002004047700002104067700001904088700003004107700002404137700002604161700002504187700002204212700002004234700002204254700001904276700001804295700002404313700001904337700002104356700002704377700001704404700001804421700002004439700002304459700002204482700002404504700002204528700002304550700002104573700002204594700002404616700002004640700001804660700001904678700001904697700001904716700002704735700002604762700002004788700001604808700001804824700002404842700001904866700002104885700002804906700001804934700002204952700002204974700002404996700002205020700002305042700002205065700002005087700002205107700001905129700002005148700002205168700002305190700002005213700002205233700001805255700002205273700002005295700002405315700002305339700002505362700001905387700001805406700002205424700002405446700002105470700002305491700002005514700001905534700002505553700002305578700002405601700002405625700001905649700002505668700002805693700002905721700002405750700002105774700002005795700001805815700001805833700002105851700002705872700002005899700002205919700002305941700002305964700002105987700001606008700002806024700002406052700002206076700002106098700002106119700002406140700002206164700001806186700002206204700002506226700002006251700002206271700002106293700002306314700002006337700002106357700002406378700002206402700002406424700002506448700002506473700002006498700002106518700002306539700002006562700002506582700002106607700002206628856003606650 2010 eng d a1476-468700aBiological, clinical and population relevance of 95 loci for blood lipids.0 aBiological clinical and population relevance of 95 loci for bloo c2010 Aug 05 a707-130 v4663 aPlasma concentrations of total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol and triglycerides are among the most important risk factors for coronary artery disease (CAD) and are targets for therapeutic intervention. We screened the genome for common variants associated with plasma lipids in >100,000 individuals of European ancestry. Here we report 95 significantly associated loci (P < 5 x 10(-8)), with 59 showing genome-wide significant association with lipid traits for the first time. The newly reported associations include single nucleotide polymorphisms (SNPs) near known lipid regulators (for example, CYP7A1, NPC1L1 and SCARB1) as well as in scores of loci not previously implicated in lipoprotein metabolism. The 95 loci contribute not only to normal variation in lipid traits but also to extreme lipid phenotypes and have an impact on lipid traits in three non-European populations (East Asians, South Asians and African Americans). Our results identify several novel loci associated with plasma lipids that are also associated with CAD. Finally, we validated three of the novel genes-GALNT2, PPP1R3B and TTC39B-with experiments in mouse models. Taken together, our findings provide the foundation to develop a broader biological understanding of lipoprotein metabolism and to identify new therapeutic opportunities for the prevention of CAD.
10aAfrican Americans10aAnimals10aAsian Continental Ancestry Group10aCholesterol, HDL10aCholesterol, LDL10aCoronary Artery Disease10aEurope10aEuropean Continental Ancestry Group10aFemale10aGenetic Loci10aGenome-Wide Association Study10aGenotype10aHumans10aLipid Metabolism10aLipids10aLiver10aMale10aMice10aN-Acetylgalactosaminyltransferases10aPhenotype10aPolymorphism, Single Nucleotide10aProtein Phosphatase 110aReproducibility of Results10aTriglycerides1 aTeslovich, Tanya, M1 aMusunuru, Kiran1 aSmith, Albert, V1 aEdmondson, Andrew, C1 aStylianou, Ioannis, M1 aKoseki, Masahiro1 aPirruccello, James, P1 aRipatti, Samuli1 aChasman, Daniel, I1 aWiller, Cristen, J1 aJohansen, Christopher, T1 aFouchier, Sigrid, W1 aIsaacs, Aaron1 aPeloso, Gina, M1 aBarbalic, Maja1 aRicketts, Sally, L1 aBis, Joshua, C1 aAulchenko, Yurii, S1 aThorleifsson, Gudmar1 aFeitosa, Mary, F1 aChambers, John1 aOrho-Melander, Marju1 aMelander, Olle1 aJohnson, Toby1 aLi, Xiaohui1 aGuo, Xiuqing1 aLi, Mingyao1 aCho, Yoon, Shin1 aGo, Min, Jin1 aKim, Young, Jin1 aLee, Jong-Young1 aPark, Taesung1 aKim, Kyunga1 aSim, Xueling1 aOng, Rick, Twee-Hee1 aCroteau-Chonka, Damien, C1 aLange, Leslie, A1 aSmith, Joshua, D1 aSong, Kijoung1 aZhao, Jing, Hua1 aYuan, Xin1 aLuan, Jian'an1 aLamina, Claudia1 aZiegler, Andreas1 aZhang, Weihua1 aZee, Robert, Y L1 aWright, Alan, F1 aWitteman, Jacqueline, C M1 aWilson, James, F1 aWillemsen, Gonneke1 aWichmann, H-Erich1 aWhitfield, John, B1 aWaterworth, Dawn, M1 aWareham, Nicholas, J1 aWaeber, Gérard1 aVollenweider, Peter1 aVoight, Benjamin, F1 aVitart, Veronique1 aUitterlinden, André, G1 aUda, Manuela1 aTuomilehto, Jaakko1 aThompson, John, R1 aTanaka, Toshiko1 aSurakka, Ida1 aStringham, Heather, M1 aSpector, Tim, D1 aSoranzo, Nicole1 aSmit, Johannes, H1 aSinisalo, Juha1 aSilander, Kaisa1 aSijbrands, Eric, J G1 aScuteri, Angelo1 aScott, James1 aSchlessinger, David1 aSanna, Serena1 aSalomaa, Veikko1 aSaharinen, Juha1 aSabatti, Chiara1 aRuokonen, Aimo1 aRudan, Igor1 aRose, Lynda, M1 aRoberts, Robert1 aRieder, Mark1 aPsaty, Bruce, M1 aPramstaller, Peter, P1 aPichler, Irene1 aPerola, Markus1 aPenninx, Brenda, W J H1 aPedersen, Nancy, L1 aPattaro, Cristian1 aParker, Alex, N1 aParé, Guillaume1 aOostra, Ben, A1 aO'Donnell, Christopher, J1 aNieminen, Markku, S1 aNickerson, Deborah, A1 aMontgomery, Grant, W1 aMeitinger, Thomas1 aMcPherson, Ruth1 aMcCarthy, Mark, I1 aMcArdle, Wendy1 aMasson, David1 aMartin, Nicholas, G1 aMarroni, Fabio1 aMangino, Massimo1 aMagnusson, Patrik, K E1 aLucas, Gavin1 aLuben, Robert1 aLoos, Ruth, J F1 aLokki, Marja-Liisa1 aLettre, Guillaume1 aLangenberg, Claudia1 aLauner, Lenore, J1 aLakatta, Edward, G1 aLaaksonen, Reijo1 aKyvik, Kirsten, O1 aKronenberg, Florian1 aKönig, Inke, R1 aKhaw, Kay-Tee1 aKaprio, Jaakko1 aKaplan, Lee, M1 aJohansson, Asa1 aJarvelin, Marjo-Riitta1 aJanssens, Cecile, J W1 aIngelsson, Erik1 aIgl, Wilmar1 aHovingh, Kees1 aHottenga, Jouke-Jan1 aHofman, Albert1 aHicks, Andrew, A1 aHengstenberg, Christian1 aHeid, Iris, M1 aHayward, Caroline1 aHavulinna, Aki, S1 aHastie, Nicholas, D1 aHarris, Tamara, B1 aHaritunians, Talin1 aHall, Alistair, S1 aGyllensten, Ulf1 aGuiducci, Candace1 aGroop, Leif, C1 aGonzalez, Elena1 aGieger, Christian1 aFreimer, Nelson, B1 aFerrucci, Luigi1 aErdmann, Jeanette1 aElliott, Paul1 aEjebe, Kenechi, G1 aDöring, Angela1 aDominiczak, Anna, F1 aDemissie, Serkalem1 aDeloukas, Panagiotis1 aGeus, Eco, J C1 ade Faire, Ulf1 aCrawford, Gabriel1 aCollins, Francis, S1 aChen, Yii-der, I1 aCaulfield, Mark, J1 aCampbell, Harry1 aBurtt, Noel, P1 aBonnycastle, Lori, L1 aBoomsma, Dorret, I1 aBoekholdt, Matthijs1 aBergman, Richard, N1 aBarroso, Inês1 aBandinelli, Stefania1 aBallantyne, Christie, M1 aAssimes, Themistocles, L1 aQuertermous, Thomas1 aAltshuler, David1 aSeielstad, Mark1 aWong, Tien, Y1 aTai, E-Shyong1 aFeranil, Alan, B1 aKuzawa, Christopher, W1 aAdair, Linda, S1 aTaylor, Herman, A1 aBorecki, Ingrid, B1 aGabriel, Stacey, B1 aWilson, James, G1 aHolm, Hilma1 aThorsteinsdottir, Unnur1 aGudnason, Vilmundur1 aKrauss, Ronald, M1 aMohlke, Karen, L1 aOrdovas, Jose, M1 aMunroe, Patricia, B1 aKooner, Jaspal, S1 aTall, Alan, R1 aHegele, Robert, A1 aKastelein, John, J P1 aSchadt, Eric, E1 aRotter, Jerome, I1 aBoerwinkle, Eric1 aStrachan, David, P1 aMooser, Vincent1 aStefansson, Kari1 aReilly, Muredach, P1 aSamani, Nilesh, J1 aSchunkert, Heribert1 aCupples, Adrienne, L1 aSandhu, Manjinder, S1 aRidker, Paul, M1 aRader, Daniel, J1 aDuijn, Cornelia, M1 aPeltonen, Leena1 aAbecasis, Goncalo, R1 aBoehnke, Michael1 aKathiresan, Sekar uhttps://chs-nhlbi.org/node/122104142nas a2200757 4500008004100000022001400041245008700055210006900142260001300211300001100224490000600235520194100241653002202182653002102204653002102225653001902246653002302265653004002288653003202328653001302360653001102373653001402384653001902398653003602417653002002453653001802473100002002491700002202511700001802533700002302551700002602574700002202600700002402622700001602646700002002662700001802682700002102700700001902721700002202740700002402762700002502786700002002811700001802831700002302849700002402872700002902896700002302925700002002948700002802968700002102996700002003017700002103037700001903058700002103077700002203098700002203120700002203142700002703164700002103191700002203212700002403234700002103258700002303279710004603302856003603348 2010 eng d a1942-326800aCandidate gene association resource (CARe): design, methods, and proof of concept.0 aCandidate gene association resource CARe design methods and proo c2010 Jun a267-750 v33 aBACKGROUND: The National Heart, Lung, and Blood Institute's Candidate Gene Association Resource (CARe), a planned cross-cohort analysis of genetic variation in cardiovascular, pulmonary, hematologic, and sleep-related traits, comprises >40,000 participants representing 4 ethnic groups in 9 community-based cohorts. The goals of CARe include the discovery of new variants associated with traits using a candidate gene approach and the discovery of new variants using the genome-wide association mapping approach specifically in African Americans.
METHODS AND RESULTS: CARe has assembled DNA samples for >40,000 individuals self-identified as European American, African American, Hispanic, or Chinese American, with accompanying data on hundreds of phenotypes that have been standardized and deposited in the CARe Phenotype Database. All participants were genotyped for 7 single-nucleotide polymorphisms (SNPs) selected based on prior association evidence. We performed association analyses relating each of these SNPs to lipid traits, stratified by sex and ethnicity, and adjusted for age and age squared. In at least 2 of the ethnic groups, SNPs near CETP, LIPC, and LPL strongly replicated for association with high-density lipoprotein cholesterol concentrations, PCSK9 with low-density lipoprotein cholesterol levels, and LPL and APOA5 with serum triglycerides. Notably, some SNPs showed varying effect sizes and significance of association in different ethnic groups.
CONCLUSIONS: The CARe Pilot Study validates the operational framework for phenotype collection, SNP genotyping, and analytic pipeline of the CARe project and validates the planned candidate gene study of approximately 2000 biological candidate loci in all participants and genome-wide association study in approximately 8000 African American participants. CARe will serve as a valuable resource for the scientific community.
10aAfrican Americans10aCholesterol, HDL10aCholesterol, LDL10aCohort Studies10aDatabases, Genetic10aEuropean Continental Ancestry Group10aGenetic Association Studies10aGenotype10aHumans10aPhenotype10aPilot Projects10aPolymorphism, Single Nucleotide10aResearch Design10aTriglycerides1 aMusunuru, Kiran1 aLettre, Guillaume1 aYoung, Taylor1 aFarlow, Deborah, N1 aPirruccello, James, P1 aEjebe, Kenechi, G1 aKeating, Brendan, J1 aYang, Qiong1 aChen, Ming-Huei1 aLapchyk, Nina1 aCrenshaw, Andrew1 aZiaugra, Liuda1 aRachupka, Anthony1 aBenjamin, Emelia, J1 aCupples, Adrienne, L1 aFornage, Myriam1 aFox, Ervin, R1 aHeckbert, Susan, R1 aHirschhorn, Joel, N1 aNewton-Cheh, Christopher1 aNizzari, Marcia, M1 aPaltoo, Dina, N1 aPapanicolaou, George, J1 aPatel, Sanjay, R1 aPsaty, Bruce, M1 aRader, Daniel, J1 aRedline, Susan1 aRich, Stephen, S1 aRotter, Jerome, I1 aTaylor, Herman, A1 aTracy, Russell, P1 aVasan, Ramachandran, S1 aWilson, James, G1 aKathiresan, Sekar1 aFabsitz, Richard, R1 aBoerwinkle, Eric1 aGabriel, Stacey, B1 aNHLBI Candidate Gene Association Resource uhttps://chs-nhlbi.org/node/118803584nas a2200481 4500008004100000022001400041245009300055210006900148260001300217300001100230490000700241520226000248653000902508653002202517653002102539653002002560653001602580653002102596653002202617653000902639653002602648653003302674653001102707653001502718653001102733653002302744653001502767653001102782653000902793653003202802653002402834653001702858653001602875653001802891100002502909700001702934700001902951700002402970700001902994700002403013700002903037856003603066 2010 eng d a1938-320700aCirculating palmitoleic acid and risk of metabolic abnormalities and new-onset diabetes.0 aCirculating palmitoleic acid and risk of metabolic abnormalities c2010 Dec a1350-80 v923 aBACKGROUND: Animal experiments suggest that circulating palmitoleic acid (cis-16:1n-7) from adipocyte de novo fatty acid synthesis may directly regulate insulin resistance and metabolic dysregulation.
OBJECTIVE: We investigated the independent determinants of circulating palmitoleate in free-living humans and whether palmitoleate is related to lower metabolic risk and the incidence of diabetes.
DESIGN: In a prospective cohort of 3630 US men and women in the Cardiovascular Health Study, plasma phospholipid fatty acids, anthropometric variables, blood lipids, inflammatory markers, and glucose and insulin concentrations were measured between 1992 and 2006 by using standardized methods. Independent determinants of plasma phospholipid palmitoleate and relations of palmitoleate with metabolic risk factors were investigated by using multivariable-adjusted linear regression. Relations with incident diabetes (296 incident cases) were investigated by using Cox proportional hazards.
RESULTS: The mean (± SD) palmitoleate value was 0.49 ± 0.20% (range: 0.11-2.55%) of total fatty acids. Greater body mass index, carbohydrate intake, protein intake, and alcohol use were each independent lifestyle correlates of higher palmitoleate concentrations. In multivariable analyses that adjusted for these factors and other potential confounders, higher palmitoleate concentrations were independently associated with lower LDL cholesterol (P < 0.001), higher HDL cholesterol (P < 0.001), lower total:HDL-cholesterol ratio (P = 0.04), and lower fibrinogen (P < 0.001). However, palmitoleate was also associated with higher triglycerides (P < 0.001) and (in men only) with greater insulin resistance (P < 0.001). Palmitoleate was not significantly associated with incident diabetes.
CONCLUSIONS: Adiposity (energy imbalance), carbohydrate consumption, and alcohol use-even within typical ranges-are associated with higher circulating palmitoleate concentrations. Circulating palmitoleate is robustly associated with multiple metabolic risk factors but in mixed directions, perhaps related to divergent lifestyle determinants or endogenous sources (liver, adipose tissue) of fatty acid synthesis.
10aAged10aAged, 80 and over10aAlcohol Drinking10aBody Mass Index10aCholesterol10aCholesterol, HDL10aDiabetes Mellitus10aDiet10aDietary Carbohydrates10aFatty Acids, Monounsaturated10aFemale10aFibrinogen10aHumans10aInsulin Resistance10aLife Style10aLipids10aMale10aProportional Hazards Models10aProspective Studies10aRisk Factors10aSex Factors10aTriglycerides1 aMozaffarian, Dariush1 aCao, Haiming1 aKing, Irena, B1 aLemaitre, Rozenn, N1 aSong, Xiaoling1 aSiscovick, David, S1 aHotamisligil, Gökhan, S uhttps://chs-nhlbi.org/node/123803132nas a2200469 4500008004100000022001400041245015000055210006900205260001300274300001000287490000700297520179300304653001602097653000902113653002202122653001902144653002802163653002102191653002102212653002202233653001102255653001902266653001102285653002502296653001102321653000902332653003202341653001602373653001202389653002602401653001802427100002002445700002102465700002202486700002202508700002202530700001802552700001902570700001702589700002002606856003602626 2010 eng d a1941-722500aCombined association of lipids and blood pressure in relation to incident cardiovascular disease in the elderly: the cardiovascular health study.0 aCombined association of lipids and blood pressure in relation to c2010 Feb a161-70 v233 aBACKGROUND: Hypertension and dyslipidemia are highly prevalent in the elderly. We studied the combined impact of both conditions on cardiovascular disease (CVD) events.
METHODS: We studied 4,311 participants aged 65-98 (61.2% female) from the Cardiovascular Health Study (CHS), a longitudinal epidemiologic study, with no prior CVD. We evaluated the relation of low-density lipoprotein (LDL), high-density lipoprotein (HDL), or non-HDL-cholesterol combined with blood pressure (BP) categories to incident CVD-including coronary heart disease (CHD) (angina, myocardial infarction (MI), angioplasty, coronary bypass surgery, or CHD death), stroke, claudication, and CVD death over 15 years.
RESULTS: CVD incidence (per 1,000 person years) ranged from 38.4 when BP <120/80 mm Hg and LDL-C <100 mg/dl to 94.8 when BP >or=160/100 mm Hg and LDL-C >or=160 mg/dl, and from 28.9 when BP <120/80 mm Hg and HDL >60 mg/dl to 87.1 for a BP >or=160/100 and HDL-C <40 mg/dl. Compared with those with BP <120/80 mm Hg with either LDL-C <100 mg/dl or HDL-C >60 mg/dl, hazard ratios (HRs) for CVD events were 2.1 when BP >or=160/100 mm Hg and LDL-C >or=160 mg/dl and 2.1 when BP >or=160/100 and HDL-C <40 mg/dl (all P < 0.01), with similar results for non-HDL-C. Elevated BP was associated with increased risk across all lipid levels. Increased LDL-C added risk mainly when BP <140/90 mm Hg, but lower HDL-C also predicted CVD in those with higher BP.
CONCLUSION: Increased BP confers increased risks for CVD in elderly persons across all lipid levels. Although increased LDL-C added risk mainly when BP <140/90 mm Hg, low HDL-C added risk also in those with hypertension. These results document the importance of combined hypertension and dyslipidemia.
10aAge Factors10aAged10aAged, 80 and over10aBlood Pressure10aCardiovascular Diseases10aCholesterol, HDL10aCholesterol, LDL10aDiabetes Mellitus10aFemale10aHealth Surveys10aHumans10aLikelihood Functions10aLipids10aMale10aProportional Hazards Models10aSex Factors10aSmoking10aSocioeconomic Factors10aUnited States1 aWong, Nathan, D1 aLopez, Victor, A1 aRoberts, Craig, S1 aSolomon, Henry, A1 aBurke, Gregory, L1 aKuller, Lewis1 aTracy, Russell1 aYanez, David1 aPsaty, Bruce, M uhttps://chs-nhlbi.org/node/114504243nas a2200433 4500008004100000022001400041245010700055210006900162260001600231300001000247490000800257520302000265653001403285653000903299653002303308653001603331653002103347653001903368653003003387653003303417653002103450653001103471653001103482653001403493653002303507653000903530653002403539653001703563653001803580653001803598100002503616700001703641700001903658700002403677700001903701700002403720700002903744856003603773 2010 eng d a1539-370400aTrans-palmitoleic acid, metabolic risk factors, and new-onset diabetes in U.S. adults: a cohort study.0 aTranspalmitoleic acid metabolic risk factors and newonset diabet c2010 Dec 21 a790-90 v1533 aBACKGROUND: Palmitoleic acid (cis-16:1n-7), which is produced by endogenous fat synthesis, has been linked to both beneficial and deleterious metabolic effects, potentially confounded by diverse determinants and tissue sources of endogenous production. Trans-palmitoleate (trans-16:1n-7) represents a distinctly exogenous source of 16:1n-7, unconfounded by endogenous synthesis or its determinants, that may be uniquely informative.
OBJECTIVE: To investigate whether circulating trans-palmitoleate is independently related to lower metabolic risk and incident type 2 diabetes.
DESIGN: Prospective cohort study from 1992 to 2006.
SETTING: Four U.S. communities.
PATIENTS: 3736 adults in the Cardiovascular Health Study.
MEASUREMENTS: Anthropometric characteristics and levels of plasma phospholipid fatty acids, blood lipids, inflammatory markers, and glucose-insulin measured at baseline in 1992 and dietary habits measured 3 years earlier. Multivariate-adjusted models were used to investigate how demographic, clinical, and lifestyle factors independently related to plasma phospholipid trans-palmitoleate; how trans-palmitoleate related to major metabolic risk factors; and how trans-palmitoleate related to new-onset diabetes (304 incident cases). Findings were validated for metabolic risk factors in an independent cohort of 327 women.
RESULTS: In multivariate analyses, whole-fat dairy consumption was most strongly associated with higher trans-palmitoleate levels. Higher trans-palmitoleate levels were associated with slightly lower adiposity and, independently, with higher high-density lipoprotein cholesterol levels (1.9% across quintiles; P = 0.040), lower triglyceride levels (-19.0%; P < 0.001), a lower total cholesterol-HDL cholesterol ratio (-4.7%; P < 0.001), lower C-reactive protein levels (-13.8%; P = 0.05), and lower insulin resistance (-16.7%, P < 0.001). Trans-palmitoleate was also associated with a substantially lower incidence of diabetes, with multivariate hazard ratios of 0.41 (95% CI, 0.27 to 0.64) and 0.38 (CI, 0.24 to 0.62) in quintiles 4 and 5 versus quintile 1 (P for trend < 0.001). Findings were independent of estimated dairy consumption or other fatty acid dairy biomarkers. Protective associations with metabolic risk factors were confirmed in the validation cohort.
LIMITATION: Results could be affected by measurement error or residual confounding.
CONCLUSION: Circulating trans-palmitoleate is associated with lower insulin resistance, presence of atherogenic dyslipidemia, and incident diabetes. Our findings may explain previously observed metabolic benefits of dairy consumption and support the need for detailed further experimental and clinical investigation.
PRIMARY FUNDING SOURCE: National Heart, Lung, and Blood Institute and National Institute of Diabetes and Digestive and Kidney Diseases of the National Institutes of Health.
10aAdiposity10aAged10aC-Reactive Protein10aCholesterol10aCholesterol, HDL10aDairy Products10aDiabetes Mellitus, Type 210aFatty Acids, Monounsaturated10aFeeding Behavior10aFemale10aHumans10aIncidence10aInsulin Resistance10aMale10aProspective Studies10aRisk Factors10aTriglycerides10aUnited States1 aMozaffarian, Dariush1 aCao, Haiming1 aKing, Irena, B1 aLemaitre, Rozenn, N1 aSong, Xiaoling1 aSiscovick, David, S1 aHotamisligil, Gökhan, S uhttps://chs-nhlbi.org/node/125704370nas a2200613 4500008004100000022001400041245016100055210006900216260001600285300001200301490000800313520260000321653001602921653001902937653002002956653002802976653001603004653002103020653002203041653001103063653001103074653000903085653001603094653002303110653003203133653002403165653002003189653001603209653001203225653001203237653002403249653002003273110004003293700001903333700002103352700003003373700002003403700001903423700001903442700001903461700002003480700002203500700002803522700001703550700002003567700001803587700001903605700001903624700001803643700002303661700001903684700001703703856003603720 2011 eng d a1474-547X00aSeparate and combined associations of body-mass index and abdominal adiposity with cardiovascular disease: collaborative analysis of 58 prospective studies.0 aSeparate and combined associations of bodymass index and abdomin c2011 Mar 26 a1085-950 v3773 aBACKGROUND: Guidelines differ about the value of assessment of adiposity measures for cardiovascular disease risk prediction when information is available for other risk factors. We studied the separate and combined associations of body-mass index (BMI), waist circumference, and waist-to-hip ratio with risk of first-onset cardiovascular disease.
METHODS: We used individual records from 58 cohorts to calculate hazard ratios (HRs) per 1 SD higher baseline values (4.56 kg/m(2) higher BMI, 12.6 cm higher waist circumference, and 0.083 higher waist-to-hip ratio) and measures of risk discrimination and reclassification. Serial adiposity assessments were used to calculate regression dilution ratios.
RESULTS: Individual records were available for 221,934 people in 17 countries (14,297 incident cardiovascular disease outcomes; 1.87 million person-years at risk). Serial adiposity assessments were made in up to 63,821 people (mean interval 5.7 years [SD 3.9]). In people with BMI of 20 kg/m(2) or higher, HRs for cardiovascular disease were 1.23 (95% CI 1.17-1.29) with BMI, 1.27 (1.20-1.33) with waist circumference, and 1.25 (1.19-1.31) with waist-to-hip ratio, after adjustment for age, sex, and smoking status. After further adjustment for baseline systolic blood pressure, history of diabetes, and total and HDL cholesterol, corresponding HRs were 1.07 (1.03-1.11) with BMI, 1.10 (1.05-1.14) with waist circumference, and 1.12 (1.08-1.15) with waist-to-hip ratio. Addition of information on BMI, waist circumference, or waist-to-hip ratio to a cardiovascular disease risk prediction model containing conventional risk factors did not importantly improve risk discrimination (C-index changes of -0.0001, -0.0001, and 0.0008, respectively), nor classification of participants to categories of predicted 10-year risk (net reclassification improvement -0.19%, -0.05%, and -0.05%, respectively). Findings were similar when adiposity measures were considered in combination. Reproducibility was greater for BMI (regression dilution ratio 0.95, 95% CI 0.93-0.97) than for waist circumference (0.86, 0.83-0.89) or waist-to-hip ratio (0.63, 0.57-0.70).
INTERPRETATION: BMI, waist circumference, and waist-to-hip ratio, whether assessed singly or in combination, do not importantly improve cardiovascular disease risk prediction in people in developed countries when additional information is available for systolic blood pressure, history of diabetes, and lipids.
FUNDING: British Heart Foundation and UK Medical Research Council.
10aAge Factors10aBlood Pressure10aBody Mass Index10aCardiovascular Diseases10aCholesterol10aCholesterol, HDL10aDiabetes Mellitus10aFemale10aHumans10aMale10aMiddle Aged10aObesity, Abdominal10aProportional Hazards Models10aProspective Studies10aRisk Assessment10aSex Factors10aSmoking10aSystole10aWaist Circumference10aWaist-Hip Ratio1 aEmerging Risk Factors Collaboration1 aWormser, David1 aKaptoge, Stephen1 aDi Angelantonio, Emanuele1 aWood, Angela, M1 aPennells, Lisa1 aThompson, Alex1 aSarwar, Nadeem1 aKizer, Jorge, R1 aLawlor, Debbie, A1 aNordestgaard, Børge, G1 aRidker, Paul1 aSalomaa, Veikko1 aStevens, June1 aWoodward, Mark1 aSattar, Naveed1 aCollins, Rory1 aThompson, Simon, G1 aWhitlock, Gary1 aDanesh, John uhttps://chs-nhlbi.org/node/156303420nas a2200517 4500008004100000022001400041245023700055210006900292260001600361300001000377490000600387520182700393653000902220653002202229653002802251653002102279653002102300653004002321653001102361653002502372653003402397653001302431653001102444653000902455653001602464653003602480653001702516653001102533653001802544100001902562700002102581700002002602700002302622700001802645700001902663700002302682700002302705700001402728700002002742700001602762700002002778700001902798700002502817700002402842856003602866 2012 eng d a1942-326800aAssociations between incident ischemic stroke events and stroke and cardiovascular disease-related genome-wide association studies single nucleotide polymorphisms in the Population Architecture Using Genomics and Epidemiology study.0 aAssociations between incident ischemic stroke events and stroke c2012 Apr 01 a210-60 v53 aBACKGROUND: Genome-wide association studies (GWAS) have identified loci associated with ischemic stroke (IS) and cardiovascular disease (CVD) in European-descent individuals, but their replication in different populations has been largely unexplored.
METHODS AND RESULTS: Nine single nucleotide polymorphisms (SNPs) selected from GWAS and meta-analyses of stroke, and 86 SNPs previously associated with myocardial infarction and CVD risk factors, including blood lipids (high density lipoprotein [HDL], low density lipoprotein [LDL], and triglycerides), type 2 diabetes, and body mass index (BMI), were investigated for associations with incident IS in European Americans (EA) N=26 276, African-Americans (AA) N=8970, and American Indians (AI) N=3570 from the Population Architecture using Genomics and Epidemiology Study. Ancestry-specific fixed effects meta-analysis with inverse variance weighting was used to combine study-specific log hazard ratios from Cox proportional hazards models. Two of 9 stroke SNPs (rs783396 and rs1804689) were significantly associated with [corrected] IS hazard in AA; none were significant in this large EA cohort. Of 73 CVD risk factor SNPs tested in EA, 2 (HDL and triglycerides SNPs) were associated with IS. In AA, SNPs associated with LDL, HDL, and BMI were significantly associated with IS (3 of 86 SNPs tested). Out of 58 SNPs tested in AI, 1 LDL SNP was significantly associated with IS.
CONCLUSIONS: Our analyses showing lack of replication in spite of reasonable power for many stroke SNPs and differing results by ancestry highlight the need to follow up on GWAS findings and conduct genetic association studies in diverse populations. We found modest IS associations with BMI and lipids SNPs, though these findings require confirmation.
10aAged10aAged, 80 and over10aCardiovascular Diseases10aCholesterol, HDL10aCholesterol, LDL10aEuropean Continental Ancestry Group10aFemale10aGenetics, Population10aGenome-Wide Association Study10aGenomics10aHumans10aMale10aMiddle Aged10aPolymorphism, Single Nucleotide10aRisk Factors10aStroke10aTriglycerides1 aCarty, Cara, L1 aBůzková, Petra1 aFornage, Myriam1 aFranceschini, Nora1 aCole, Shelley1 aHeiss, Gerardo1 aHindorff, Lucia, A1 aHoward, Barbara, V1 aMann, Sue1 aMartin, Lisa, W1 aZhang, Ying1 aMatise, Tara, C1 aPrentice, Ross1 aReiner, Alexander, P1 aKooperberg, Charles uhttps://chs-nhlbi.org/node/137103528nas a2200721 4500008004100000022001400041245014600055210006900201260000900270300001100279490000600290520139200296653002201688653002801710653004001738653002101778653002101799653002301820653001901843653001901862653003401881653001301915653001101928653002301939653003601962653002801998100001902026700001302045700001902058700001602077700002902093700002202122700002302144700002202167700002302189700002102212700002102233700002202254700002102276700001802297700002202315700002502337700002302362700002202385700001702407700002002424700002402444700001202468700002302480700002002503700002602523700002202549700002802571700002402599700001802623700002102641700002102662700002702683700001902710700002202729700001902751856003602770 2012 eng d a1932-620300aEvaluation of the metabochip genotyping array in African Americans and implications for fine mapping of GWAS-identified loci: the PAGE study.0 aEvaluation of the metabochip genotyping array in African America c2012 ae356510 v73 aThe Metabochip is a custom genotyping array designed for replication and fine mapping of metabolic, cardiovascular, and anthropometric trait loci and includes low frequency variation content identified from the 1000 Genomes Project. It has 196,725 SNPs concentrated in 257 genomic regions. We evaluated the Metabochip in 5,863 African Americans; 89% of all SNPs passed rigorous quality control with a call rate of 99.9%. Two examples illustrate the value of fine mapping with the Metabochip in African-ancestry populations. At CELSR2/PSRC1/SORT1, we found the strongest associated SNP for LDL-C to be rs12740374 (p = 3.5 × 10(-11)), a SNP indistinguishable from multiple SNPs in European ancestry samples due to high correlation. Its distinct signal supports functional studies elsewhere suggesting a causal role in LDL-C. At CETP we found rs17231520, with risk allele frequency 0.07 in African Americans, to be associated with HDL-C (p = 7.2 × 10(-36)). This variant is very rare in Europeans and not tagged in common GWAS arrays, but was identified as associated with HDL-C in African Americans in a single-gene study. Our results, one narrowing the risk interval and the other revealing an associated variant not found in Europeans, demonstrate the advantages of high-density genotyping of common and rare variation for fine mapping of trait loci in African American samples.
10aAfrican Americans10aCardiovascular Diseases10aCholesterol Ester Transfer Proteins10aCholesterol, HDL10aCholesterol, LDL10aChromosomes, Human10aCohort Studies10aGene Frequency10aGenome-Wide Association Study10aGenotype10aHumans10aMetabolic Diseases10aPolymorphism, Single Nucleotide10aQuantitative Trait Loci1 aBuyske, Steven1 aWu, Ying1 aCarty, Cara, L1 aCheng, Iona1 aAssimes, Themistocles, L1 aDumitrescu, Logan1 aHindorff, Lucia, A1 aMitchell, Sabrina1 aAmbite, Jose, Luis1 aBoerwinkle, Eric1 aBůzková, Petra1 aCarlson, Chris, S1 aCochran, Barbara1 aDuggan, David1 aEaton, Charles, B1 aFesinmeyer, Megan, D1 aFranceschini, Nora1 aHaessler, Jeffrey1 aJenny, Nancy1 aKang, Hyun, Min1 aKooperberg, Charles1 aLin, Yi1 aLe Marchand, Loïc1 aMatise, Tara, C1 aRobinson, Jennifer, G1 aRodriguez, Carlos1 aSchumacher, Fredrick, R1 aVoight, Benjamin, F1 aYoung, Alicia1 aManolio, Teri, A1 aMohlke, Karen, L1 aHaiman, Christopher, A1 aPeters, Ulrike1 aCrawford, Dana, C1 aNorth, Kari, E uhttps://chs-nhlbi.org/node/663403315nas a2200529 4500008004100000022001400041245007000055210006700125260001300192300001200205490000700217520186700224653000902091653002202100653001802122653001902140653002302159653002802182653002102210653002102231653001902252653001502271653001202286653001102298653003102309653001102340653002302351653001502374653000902389653001402398653003002412653003202442653002402474653001702498653001802515653002402533100002002557700001602577700002402593700002002617700001902637700002202656700002402678700002302702700002402725856003602749 2012 eng d a1935-554800aInsulin resistance, cystatin C, and mortality among older adults.0 aInsulin resistance cystatin C and mortality among older adults c2012 Jun a1355-600 v353 aOBJECTIVE: Insulin resistance is a risk factor for cardiovascular and noncardiovascular diseases. Impaired kidney function is linked with insulin resistance and may affect relationships of insulin resistance with health outcomes.
RESEARCH DESIGN AND METHODS: We performed a cohort study of 3,138 Cardiovascular Health Study participants (age ≥ 65 years) without diabetes. Insulin sensitivity index (ISI) was calculated from fasting and 2-h postload insulin and glucose concentrations. Associations of ISI and fasting insulin concentration with all-cause mortality were tested using Cox proportional hazards models, adjusting for demographic variables, prevalent cardiovascular disease, lifestyle variables, waist circumference, and LDL cholesterol. Subsequent models were additionally adjusted for or stratified by glomerular filtration rate estimated using serum cystatin C (eGFR).
RESULTS: A total of 1,810 participants died during the 14.7-year median follow-up. Compared with the highest quartile of ISI, the lowest quartile (most insulin resistant) was associated with 21% (95% CI 6-41) and 11% (-3 to 29) higher risks of death without and with adjustment for eGFR, respectively. Compared with the lowest quartile of fasting insulin concentration, the highest quartile was associated with 22% (4-43) and 4% (-12 to 22) higher risks of death without and with adjustment for eGFR, respectively. Similar attenuation by eGFR was observed when blood pressure, triglycerides, HDL cholesterol, and C-reactive protein were included in models.
CONCLUSIONS: Insulin resistance measured as ISI or fasting insulin concentration is associated with increased risk of death among older adults, adjusting for conventional confounding characteristics. Impaired kidney function may mediate or confound this relationship.
10aAged10aAged, 80 and over10aBlood Glucose10aBlood Pressure10aC-Reactive Protein10aCardiovascular Diseases10aCholesterol, HDL10aCholesterol, LDL10aCohort Studies10aCystatin C10aFasting10aFemale10aGlomerular Filtration Rate10aHumans10aInsulin Resistance10aLife Style10aMale10aMortality10aPredictive Value of Tests10aProportional Hazards Models10aRenal Insufficiency10aRisk Factors10aTriglycerides10aWaist Circumference1 ade Boer, Ian, H1 aKatz, Ronit1 aChonchol, Michel, B1 aFried, Linda, F1 aIx, Joachim, H1 aKestenbaum, Bryan1 aMukamal, Kenneth, J1 aPeralta, Carmen, A1 aSiscovick, David, S uhttps://chs-nhlbi.org/node/137204973nas a2200937 4500008004100000022001400041245006500055210006300120260001600183300001300199490000800212520237500220653000902595653001502604653002802619653002102647653001902668653001102687653001102698653001702709653000902726653001602735653002002751110004002771700003002811700001302841700001902854700002102873700002002894700002402914700002502938700001902963700001902982700002103001700002803022700002003050700002203070700002003092700002003112700002303132700001403155700002403169700001903193700002103212700002503233700001803258700002503276700002403301700002203325700001903347700001603366700002303382700003503405700002203440700002503462700002003487700002103507700002203528700001903550700002003569700003003589700001903619700001803638700001903656700002003675700002203695700002503717700001803742700002003760700002203780700002303802700002803825700002003853700002303873700002403896700001903920700001903939700002403958700001703982856003603999 2012 eng d a1538-359800aLipid-related markers and cardiovascular disease prediction.0 aLipidrelated markers and cardiovascular disease prediction c2012 Jun 20 a2499-5060 v3073 aCONTEXT: The value of assessing various emerging lipid-related markers for prediction of first cardiovascular events is debated.
OBJECTIVE: To determine whether adding information on apolipoprotein B and apolipoprotein A-I, lipoprotein(a), or lipoprotein-associated phospholipase A2 to total cholesterol and high-density lipoprotein cholesterol (HDL-C) improves cardiovascular disease (CVD) risk prediction.
DESIGN, SETTING, AND PARTICIPANTS: Individual records were available for 165,544 participants without baseline CVD in 37 prospective cohorts (calendar years of recruitment: 1968-2007) with up to 15,126 incident fatal or nonfatal CVD outcomes (10,132 CHD and 4994 stroke outcomes) during a median follow-up of 10.4 years (interquartile range, 7.6-14 years).
MAIN OUTCOME MEASURES: Discrimination of CVD outcomes and reclassification of participants across predicted 10-year risk categories of low (<10%), intermediate (10%-<20%), and high (≥20%) risk.
RESULTS: The addition of information on various lipid-related markers to total cholesterol, HDL-C, and other conventional risk factors yielded improvement in the model's discrimination: C-index change, 0.0006 (95% CI, 0.0002-0.0009) for the combination of apolipoprotein B and A-I; 0.0016 (95% CI, 0.0009-0.0023) for lipoprotein(a); and 0.0018 (95% CI, 0.0010-0.0026) for lipoprotein-associated phospholipase A2 mass. Net reclassification improvements were less than 1% with the addition of each of these markers to risk scores containing conventional risk factors. We estimated that for 100,000 adults aged 40 years or older, 15,436 would be initially classified at intermediate risk using conventional risk factors alone. Additional testing with a combination of apolipoprotein B and A-I would reclassify 1.1%; lipoprotein(a), 4.1%; and lipoprotein-associated phospholipase A2 mass, 2.7% of people to a 20% or higher predicted CVD risk category and, therefore, in need of statin treatment under Adult Treatment Panel III guidelines.
CONCLUSION: In a study of individuals without known CVD, the addition of information on the combination of apolipoprotein B and A-I, lipoprotein(a), or lipoprotein-associated phospholipase A2 mass to risk scores containing total cholesterol and HDL-C led to slight improvement in CVD prediction.
10aAged10aBiomarkers10aCardiovascular Diseases10aCholesterol, HDL10aCohort Studies10aFemale10aHumans10aLipoproteins10aMale10aMiddle Aged10aRisk Assessment1 aEmerging Risk Factors Collaboration1 aDi Angelantonio, Emanuele1 aGao, Pei1 aPennells, Lisa1 aKaptoge, Stephen1 aCaslake, Muriel1 aThompson, Alexander1 aButterworth, Adam, S1 aSarwar, Nadeem1 aWormser, David1 aSaleheen, Danish1 aBallantyne, Christie, M1 aPsaty, Bruce, M1 aSundström, Johan1 aRidker, Paul, M1 aNagel, Dorothea1 aGillum, Richard, F1 aFord, Ian1 aDucimetiere, Pierre1 aKiechl, Stefan1 aKoenig, Wolfgang1 aDullaart, Robin, P F1 aAssmann, Gerd1 aD'Agostino, Ralph, B1 aDagenais, Gilles, R1 aCooper, Jackie, A1 aKromhout, Daan1 aOnat, Altan1 aTipping, Robert, W1 aGómez-de-la-Cámara, Agustín1 aRosengren, Annika1 aSutherland, Susan, E1 aGallacher, John1 aFowkes, Gerry, R1 aCasiglia, Edoardo1 aHofman, Albert1 aSalomaa, Veikko1 aBarrett-Connor, Elizabeth1 aClarke, Robert1 aBrunner, Eric1 aJukema, Wouter1 aSimons, Leon, A1 aSandhu, Manjinder1 aWareham, Nicholas, J1 aKhaw, Kay-Tee1 aKauhanen, Jussi1 aSalonen, Jukka, T1 aHoward, William, J1 aNordestgaard, Børge, G1 aWood, Angela, M1 aThompson, Simon, G1 aBoekholdt, Matthijs1 aSattar, Naveed1 aPackard, Chris1 aGudnason, Vilmundur1 aDanesh, John uhttps://chs-nhlbi.org/node/139903041nas a2200541 4500008004100000022001400041245007600055210006900131260000900200300001100209490000600220520147100226653002201697653002101719653002101740653004001761653003201801653001701833653001101850653003601861653001801897100002001915700002401935700002201959700002101981700002102002700002102023700002202044700002502066700002202091700002102113700001902134700002002153700002802173700002102201700001502222700002202237700002202259700002302281700002002304700002502324700002202349700002102371700002802392700002202420700002102442856003602463 2012 eng d a1932-620300aMulti-ethnic analysis of lipid-associated loci: the NHLBI CARe project.0 aMultiethnic analysis of lipidassociated loci the NHLBI CARe proj c2012 ae364730 v73 aBACKGROUND: Whereas it is well established that plasma lipid levels have substantial heritability within populations, it remains unclear how many of the genetic determinants reported in previous studies (largely performed in European American cohorts) are relevant in different ethnicities.
METHODOLOGY/PRINCIPAL FINDINGS: We tested a set of ∼50,000 polymorphisms from ∼2,000 candidate genes and genetic loci from genome-wide association studies (GWAS) for association with low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglycerides (TG) in 25,000 European Americans and 9,000 African Americans in the National Heart, Lung, and Blood Institute (NHLBI) Candidate Gene Association Resource (CARe). We replicated associations for a number of genes in one or both ethnicities and identified a novel lipid-associated variant in a locus harboring ICAM1. We compared the architecture of genetic loci associated with lipids in both African Americans and European Americans and found that the same genes were relevant across ethnic groups but the specific associated variants at each gene often differed.
CONCLUSIONS/SIGNIFICANCE: We identify or provide further evidence for a number of genetic determinants of plasma lipid levels through population association studies. In many loci the determinants appear to differ substantially between African Americans and European Americans.
10aAfrican Americans10aCholesterol, HDL10aCholesterol, LDL10aEuropean Continental Ancestry Group10aGenetic Association Studies10aGenetic Loci10aHumans10aPolymorphism, Single Nucleotide10aTriglycerides1 aMusunuru, Kiran1 aRomaine, Simon, P R1 aLettre, Guillaume1 aWilson, James, G1 aVolcik, Kelly, A1 aTsai, Michael, Y1 aTaylor, Herman, A1 aSchreiner, Pamela, J1 aRotter, Jerome, I1 aRich, Stephen, S1 aRedline, Susan1 aPsaty, Bruce, M1 aPapanicolaou, George, J1 aOrdovas, Jose, M1 aLiu, Kiang1 aKrauss, Ronald, M1 aGlazer, Nicole, L1 aGabriel, Stacey, B1 aFornage, Myriam1 aCupples, Adrienne, L1 aBuxbaum, Sarah, G1 aBoerwinkle, Eric1 aBallantyne, Christie, M1 aKathiresan, Sekar1 aRader, Daniel, J uhttps://chs-nhlbi.org/node/138821848nas a2207177 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2012 eng d a1553-740400aNovel loci for adiponectin levels and their influence on type 2 diabetes and metabolic traits: a multi-ethnic meta-analysis of 45,891 individuals.0 aNovel loci for adiponectin levels and their influence on type 2 c2012 ae10026070 v83 aCirculating levels of adiponectin, a hormone produced predominantly by adipocytes, are highly heritable and are inversely associated with type 2 diabetes mellitus (T2D) and other metabolic traits. We conducted a meta-analysis of genome-wide association studies in 39,883 individuals of European ancestry to identify genes associated with metabolic disease. We identified 8 novel loci associated with adiponectin levels and confirmed 2 previously reported loci (P = 4.5×10(-8)-1.2×10(-43)). Using a novel method to combine data across ethnicities (N = 4,232 African Americans, N = 1,776 Asians, and N = 29,347 Europeans), we identified two additional novel loci. Expression analyses of 436 human adipocyte samples revealed that mRNA levels of 18 genes at candidate regions were associated with adiponectin concentrations after accounting for multiple testing (p<3×10(-4)). We next developed a multi-SNP genotypic risk score to test the association of adiponectin decreasing risk alleles on metabolic traits and diseases using consortia-level meta-analytic data. This risk score was associated with increased risk of T2D (p = 4.3×10(-3), n = 22,044), increased triglycerides (p = 2.6×10(-14), n = 93,440), increased waist-to-hip ratio (p = 1.8×10(-5), n = 77,167), increased glucose two hours post oral glucose tolerance testing (p = 4.4×10(-3), n = 15,234), increased fasting insulin (p = 0.015, n = 48,238), but with lower in HDL-cholesterol concentrations (p = 4.5×10(-13), n = 96,748) and decreased BMI (p = 1.4×10(-4), n = 121,335). These findings identify novel genetic determinants of adiponectin levels, which, taken together, influence risk of T2D and markers of insulin resistance.
10aAdiponectin10aAfrican Americans10aAsian Continental Ancestry Group10aCholesterol, HDL10aDiabetes Mellitus, Type 210aEuropean Continental Ancestry Group10aFemale10aGene Expression10aGenetic Predisposition to Disease10aGenome-Wide Association Study10aGlucose Tolerance Test10aHumans10aInsulin Resistance10aMale10aMetabolic Networks and Pathways10aPolymorphism, Single Nucleotide10aWaist-Hip Ratio1 aDastani, Zari1 aHivert, Marie-France1 aTimpson, Nicholas1 aPerry, John, R B1 aYuan, Xin1 aScott, Robert, A1 aHenneman, Peter1 aHeid, Iris, M1 aKizer, Jorge, R1 aLyytikäinen, Leo-Pekka1 aFuchsberger, Christian1 aTanaka, Toshiko1 aMorris, Andrew, P1 aSmall, Kerrin1 aIsaacs, Aaron1 aBeekman, Marian1 aCoassin, Stefan1 aLohman, Kurt1 aQi, Lu1 aKanoni, Stavroula1 aPankow, James, S1 aUh, Hae-Won1 aWu, Ying1 aBidulescu, Aurelian1 aRasmussen-Torvik, Laura, J1 aGreenwood, Celia, M T1 aLadouceur, Martin1 aGrimsby, Jonna1 aManning, Alisa, K1 aLiu, Ching-Ti1 aKooner, Jaspal1 aMooser, Vincent, E1 aVollenweider, Peter1 aKapur, Karen, A1 aChambers, John1 aWareham, Nicholas, J1 aLangenberg, Claudia1 aFrants, Rune1 aWillems-Vandijk, Ko1 aOostra, Ben, A1 aWillems, Sara, M1 aLamina, Claudia1 aWinkler, Thomas, W1 aPsaty, Bruce, M1 aTracy, Russell, P1 aBrody, Jennifer1 aChen, Ida1 aViikari, Jorma1 aKähönen, Mika1 aPramstaller, Peter, P1 aEvans, David, M1 aSt Pourcain, Beate1 aSattar, Naveed1 aWood, Andrew, R1 aBandinelli, Stefania1 aCarlson, Olga, D1 aEgan, Josephine, M1 aBöhringer, Stefan1 avan Heemst, Diana1 aKedenko, Lyudmyla1 aKristiansson, Kati1 aNuotio, Marja-Liisa1 aLoo, Britt-Marie1 aHarris, Tamara1 aGarcia, Melissa1 aKanaya, Alka1 aHaun, Margot1 aKlopp, Norman1 aWichmann, H-Erich1 aDeloukas, Panos1 aKatsareli, Efi1 aCouper, David, J1 aDuncan, Bruce, B1 aKloppenburg, Margreet1 aAdair, Linda, S1 aBorja, Judith, B1 aWilson, James, G1 aMusani, Solomon1 aGuo, Xiuqing1 aJohnson, Toby1 aSemple, Robert1 aTeslovich, Tanya, M1 aAllison, Matthew, A1 aRedline, Susan1 aBuxbaum, Sarah, G1 aMohlke, Karen, L1 aMeulenbelt, Ingrid1 aBallantyne, Christie, M1 aDedoussis, George, V1 aHu, Frank, B1 aLiu, Yongmei1 aPaulweber, Bernhard1 aSpector, Timothy, D1 aSlagboom, Eline1 aFerrucci, Luigi1 aJula, Antti1 aPerola, Markus1 aRaitakari, Olli1 aFlorez, Jose, C1 aSalomaa, Veikko1 aEriksson, Johan, G1 aFrayling, Timothy, M1 aHicks, Andrew, A1 aLehtimäki, Terho1 aSmith, George Davey1 aSiscovick, David, S1 aKronenberg, Florian1 aDuijn, Cornelia1 aLoos, Ruth, J F1 aWaterworth, Dawn, M1 aMeigs, James, B1 aDupuis, Josée1 aRichards, Brent1 aVoight, Benjamin, F1 aScott, Laura, J1 aSteinthorsdottir, Valgerdur1 aDina, Christian1 aWelch, Ryan, P1 aZeggini, Eleftheria1 aHuth, Cornelia1 aAulchenko, Yurii, S1 aThorleifsson, Gudmar1 aMcCulloch, Laura, J1 aFerreira, Teresa1 aGrallert, Harald1 aAmin, Najaf1 aWu, Guanming1 aWiller, Cristen, J1 aRaychaudhuri, Soumya1 aMcCarroll, Steve, A1 aHofmann, Oliver, M1 aSegrè, Ayellet, V1 aHoek, Mandy1 aNavarro, Pau1 aArdlie, Kristin1 aBalkau, Beverley1 aBenediktsson, Rafn1 aBennett, Amanda, J1 aBlagieva, Roza1 aBoerwinkle, Eric1 aBonnycastle, Lori, L1 aBoström, Kristina, Bengtsson1 aBravenboer, Bert1 aBumpstead, Suzannah1 aBurtt, Noel, P1 aCharpentier, Guillaume1 aChines, Peter, S1 aCornelis, Marilyn1 aCrawford, Gabe1 aDoney, Alex, S F1 aElliott, Katherine, S1 aElliott, Amanda, L1 aErdos, Michael, R1 aFox, Caroline, S1 aFranklin, Christopher, S1 aGanser, Martha1 aGieger, Christian1 aGrarup, Niels1 aGreen, Todd1 aGriffin, Simon1 aGroves, Christopher, J1 aGuiducci, Candace1 aHadjadj, Samy1 aHassanali, Neelam1 aHerder, Christian1 aIsomaa, Bo1 aJackson, Anne, U1 aJohnson, Paul, R V1 aJørgensen, Torben1 aKao, Wen, H L1 aKong, Augustine1 aKraft, Peter1 aKuusisto, Johanna1 aLauritzen, Torsten1 aLi, Man1 aLieverse, Aloysius1 aLindgren, Cecilia, M1 aLyssenko, Valeriya1 aMarre, Michel1 aMeitinger, Thomas1 aMidthjell, Kristian1 aMorken, Mario, A1 aNarisu, Narisu1 aNilsson, Peter1 aOwen, Katharine, R1 aPayne, Felicity1 aPetersen, Ann-Kristin1 aPlatou, Carl1 aProença, Christine1 aProkopenko, Inga1 aRathmann, Wolfgang1 aRayner, William1 aRobertson, Neil, R1 aRocheleau, Ghislain1 aRoden, Michael1 aSampson, Michael, J1 aSaxena, Richa1 aShields, Beverley, M1 aShrader, Peter1 aSigurdsson, Gunnar1 aSparsø, Thomas1 aStrassburger, Klaus1 aStringham, Heather, M1 aSun, Qi1 aSwift, Amy, J1 aThorand, Barbara1 aTichet, Jean1 aTuomi, Tiinamaija1 avan Dam, Rob, M1 avan Haeften, Timon, W1 avan Herpt, Thijs1 avan Vliet-Ostaptchouk, Jana, V1 aWalters, Bragi, G1 aWeedon, Michael, N1 aWijmenga, Cisca1 aWitteman, Jacqueline1 aBergman, Richard, N1 aCauchi, Stephane1 aCollins, Francis, S1 aGloyn, Anna, L1 aGyllensten, Ulf1 aHansen, Torben1 aHide, Winston, A1 aHitman, Graham, A1 aHofman, Albert1 aHunter, David, J1 aHveem, Kristian1 aLaakso, Markku1 aMorris, Andrew, D1 aPalmer, Colin, N A1 aRudan, Igor1 aSijbrands, Eric1 aStein, Lincoln, D1 aTuomilehto, Jaakko1 aUitterlinden, Andre1 aWalker, Mark1 aWatanabe, Richard, M1 aAbecasis, Goncalo, R1 aBoehm, Bernhard, O1 aCampbell, Harry1 aDaly, Mark, J1 aHattersley, Andrew, T1 aPedersen, Oluf1 aBarroso, Inês1 aGroop, Leif1 aSladek, Rob1 aThorsteinsdottir, Unnur1 aWilson, James, F1 aIllig, Thomas1 aFroguel, Philippe1 aDuijn, Cornelia, M1 aStefansson, Kari1 aAltshuler, David1 aBoehnke, Michael1 aMcCarthy, Mark, I1 aSoranzo, Nicole1 aWheeler, Eleanor1 aGlazer, Nicole, L1 aBouatia-Naji, Nabila1 aMägi, Reedik1 aRandall, Joshua1 aElliott, Paul1 aRybin, Denis1 aDehghan, Abbas1 aHottenga, Jouke Jan1 aSong, Kijoung1 aGoel, Anuj1 aLajunen, Taina1 aDoney, Alex1 aCavalcanti-Proença, Christine1 aKumari, Meena1 aTimpson, Nicholas, J1 aZabena, Carina1 aIngelsson, Erik1 aAn, Ping1 aO'Connell, Jeffrey1 aLuan, Jian'an1 aElliott, Amanda1 aMcCarroll, Steven, A1 aRoccasecca, Rosa Maria1 aPattou, François1 aSethupathy, Praveen1 aAriyurek, Yavuz1 aBarter, Philip1 aBeilby, John, P1 aBen-Shlomo, Yoav1 aBergmann, Sven1 aBochud, Murielle1 aBonnefond, Amélie1 aBorch-Johnsen, Knut1 aBöttcher, Yvonne1 aBrunner, Eric1 aBumpstead, Suzannah, J1 aChen, Yii-Der Ida1 aChines, Peter1 aClarke, Robert1 aCoin, Lachlan, J M1 aCooper, Matthew, N1 aCrisponi, Laura1 aDay, Ian, N M1 aGeus, Eco, J C1 aDelplanque, Jerome1 aFedson, Annette, C1 aFischer-Rosinsky, Antje1 aForouhi, Nita, G1 aFranzosi, Maria Grazia1 aGalan, Pilar1 aGoodarzi, Mark, O1 aGraessler, Jürgen1 aGrundy, Scott1 aGwilliam, Rhian1 aHallmans, Göran1 aHammond, Naomi1 aHan, Xijing1 aHartikainen, Anna-Liisa1 aHayward, Caroline1 aHeath, Simon, C1 aHercberg, Serge1 aHillman, David, R1 aHingorani, Aroon, D1 aHui, Jennie1 aHung, Joe1 aKaakinen, Marika1 aKaprio, Jaakko1 aKesaniemi, Antero, Y1 aKivimaki, Mika1 aKnight, Beatrice1 aKoskinen, Seppo1 aKovacs, Peter1 aKyvik, Kirsten Ohm1 aLathrop, Mark, G1 aLawlor, Debbie, A1 aLe Bacquer, Olivier1 aLecoeur, Cécile1 aLi, Yun1 aMahley, Robert1 aMangino, Massimo1 aMartínez-Larrad, María Teresa1 aMcAteer, Jarred, B1 aMcPherson, Ruth1 aMeisinger, Christa1 aMelzer, David1 aMeyre, David1 aMitchell, Braxton, D1 aMukherjee, Sutapa1 aNaitza, Silvia1 aNeville, Matthew, J1 aOrrù, Marco1 aPakyz, Ruth1 aPaolisso, Giuseppe1 aPattaro, Cristian1 aPearson, Daniel1 aPeden, John, F1 aPedersen, Nancy, L1 aPfeiffer, Andreas, F H1 aPichler, Irene1 aPolasek, Ozren1 aPosthuma, Danielle1 aPotter, Simon, C1 aPouta, Anneli1 aProvince, Michael, A1 aRayner, Nigel, W1 aRice, Kenneth1 aRipatti, Samuli1 aRivadeneira, Fernando1 aRolandsson, Olov1 aSandbaek, Annelli1 aSandhu, Manjinder1 aSanna, Serena1 aSayer, Avan Aihie1 aScheet, Paul1 aSeedorf, Udo1 aSharp, Stephen, J1 aShields, Beverley1 aSigurðsson, Gunnar1 aSijbrands, Eric, J G1 aSilveira, Angela1 aSimpson, Laila1 aSingleton, Andrew1 aSmith, Nicholas, L1 aSovio, Ulla1 aSwift, Amy1 aSyddall, Holly1 aSyvänen, Ann-Christine1 aTönjes, Anke1 aUitterlinden, André, G1 aDijk, Ko Willems1 aVarma, Dhiraj1 aVisvikis-Siest, Sophie1 aVitart, Veronique1 aVogelzangs, Nicole1 aWaeber, Gérard1 aWagner, Peter, J1 aWalley, Andrew1 aWard, Kim, L1 aWatkins, Hugh1 aWild, 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Masahiro1 aPirruccello, James, P1 aChasman, Daniel, I1 aJohansen, Christopher, T1 aFouchier, Sigrid, W1 aPeloso, Gina, M1 aBarbalic, Maja1 aRicketts, Sally, L1 aBis, Joshua, C1 aFeitosa, Mary, F1 aOrho-Melander, Marju1 aMelander, Olle1 aLi, Xiaohui1 aLi, Mingyao1 aCho, Yoon Shin1 aGo, Min Jin1 aKim, Young, Jin1 aLee, Jong-Young1 aPark, Taesung1 aKim, Kyunga1 aSim, Xueling1 aOng, Rick Twee-Hee1 aCroteau-Chonka, Damien, C1 aLange, Leslie, A1 aSmith, Joshua, D1 aZiegler, Andreas1 aZhang, Weihua1 aZee, Robert, Y L1 aWhitfield, John, B1 aThompson, John, R1 aSurakka, Ida1 aSpector, Tim, D1 aSmit, Johannes, H1 aSinisalo, Juha1 aScott, James1 aSaharinen, Juha1 aSabatti, Chiara1 aRose, Lynda, M1 aRoberts, Robert1 aRieder, Mark1 aParker, Alex, N1 aParé, Guillaume1 aO'Donnell, Christopher, J1 aNieminen, Markku, S1 aNickerson, Deborah, A1 aMontgomery, Grant, W1 aMcArdle, Wendy1 aMasson, David1 aMartin, Nicholas, G1 aMarroni, Fabio1 aLucas, Gavin1 aLuben, Robert1 aLokki, Marja-Liisa1 aLettre, 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aStrachan, David, P1 aReilly, Muredach, P1 aSamani, Nilesh, J1 aSchunkert, Heribert1 aCupples, Adrienne, L1 aSandhu, Manjinder, S1 aRidker, Paul, M1 aRader, Daniel, J1 aKathiresan, Sekar1 aDIAGRAM+ Consortium1 aMAGIC Consortium1 aGLGC Investigators1 aMuTHER Consortium1 aDIAGRAM Consortium1 aGIANT Consortium1 aGlobal B Pgen Consortium1 aProcardis Consortium1 aMAGIC investigators1 aGLGC Consortium uhttps://chs-nhlbi.org/node/137810597nas a2203385 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2013 eng d a1546-171800aCommon variants associated with plasma triglycerides and risk for coronary artery disease.0 aCommon variants associated with plasma triglycerides and risk fo c2013 Nov a1345-520 v453 aTriglycerides are transported in plasma by specific triglyceride-rich lipoproteins; in epidemiological studies, increased triglyceride levels correlate with higher risk for coronary artery disease (CAD). However, it is unclear whether this association reflects causal processes. We used 185 common variants recently mapped for plasma lipids (P < 5 × 10(-8) for each) to examine the role of triglycerides in risk for CAD. First, we highlight loci associated with both low-density lipoprotein cholesterol (LDL-C) and triglyceride levels, and we show that the direction and magnitude of the associations with both traits are factors in determining CAD risk. Second, we consider loci with only a strong association with triglycerides and show that these loci are also associated with CAD. Finally, in a model accounting for effects on LDL-C and/or high-density lipoprotein cholesterol (HDL-C) levels, the strength of a polymorphism's effect on triglyceride levels is correlated with the magnitude of its effect on CAD risk. These results suggest that triglyceride-rich lipoproteins causally influence risk for CAD.
10aBiological Transport10aCholesterol, HDL10aCholesterol, LDL10aCoronary Artery Disease10aHumans10aPolymorphism, Single Nucleotide10aRisk Factors10aTriglycerides1 aDo, Ron1 aWiller, Cristen, J1 aSchmidt, Ellen, M1 aSengupta, Sebanti1 aGao, Chi1 aPeloso, Gina, M1 aGustafsson, Stefan1 aKanoni, Stavroula1 aGanna, Andrea1 aChen, Jin1 aBuchkovich, Martin, L1 aMora, Samia1 aBeckmann, Jacques, S1 aBragg-Gresham, Jennifer, L1 aChang, Hsing-Yi1 aDemirkan, Ayse1 aHertog, Heleen, M Den1 aDonnelly, Louise, A1 aEhret, Georg, B1 aEsko, Tõnu1 aFeitosa, Mary, F1 aFerreira, Teresa1 aFischer, Krista1 aFontanillas, Pierre1 aFraser, Ross, M1 aFreitag, Daniel, F1 aGurdasani, Deepti1 aHeikkilä, Kauko1 aHyppönen, Elina1 aIsaacs, Aaron1 aJackson, Anne, U1 aJohansson, Asa1 aJohnson, Toby1 aKaakinen, Marika1 aKettunen, Johannes1 aKleber, Marcus, E1 aLi, Xiaohui1 aLuan, Jian'an1 aLyytikäinen, Leo-Pekka1 aMagnusson, Patrik, K E1 aMangino, Massimo1 aMihailov, Evelin1 aMontasser, May, E1 aMüller-Nurasyid, Martina1 aNolte, Ilja, M1 aO'Connell, Jeffrey, R1 aPalmer, Cameron, D1 aPerola, Markus1 aPetersen, Ann-Kristin1 aSanna, Serena1 aSaxena, Richa1 aService, Susan, K1 aShah, Sonia1 aShungin, Dmitry1 aSidore, Carlo1 aSong, Ci1 aStrawbridge, Rona, J1 aSurakka, Ida1 aTanaka, Toshiko1 aTeslovich, Tanya, M1 aThorleifsson, Gudmar1 avan den Herik, Evita, G1 aVoight, Benjamin, F1 aVolcik, Kelly, A1 aWaite, Lindsay, L1 aWong, Andrew1 aWu, Ying1 aZhang, Weihua1 aAbsher, Devin1 aAsiki, Gershim1 aBarroso, Inês1 aBeen, Latonya, F1 aBolton, Jennifer, L1 aBonnycastle, Lori, L1 aBrambilla, Paolo1 aBurnett, Mary, S1 aCesana, Giancarlo1 aDimitriou, Maria1 aDoney, Alex, S F1 aDöring, Angela1 aElliott, Paul1 aEpstein, Stephen, E1 aEyjolfsson, Gudmundur, Ingi1 aGigante, Bruna1 aGoodarzi, Mark, O1 aGrallert, Harald1 aGravito, Martha, L1 aGroves, Christopher, J1 aHallmans, Göran1 aHartikainen, Anna-Liisa1 aHayward, Caroline1 aHernandez, Dena1 aHicks, Andrew, A1 aHolm, Hilma1 aHung, Yi-Jen1 aIllig, Thomas1 aJones, Michelle, R1 aKaleebu, Pontiano1 aKastelein, John, J P1 aKhaw, Kay-Tee1 aKim, Eric1 aKlopp, Norman1 aKomulainen, Pirjo1 aKumari, Meena1 aLangenberg, Claudia1 aLehtimäki, Terho1 aLin, Shih-Yi1 aLindström, Jaana1 aLoos, Ruth, J F1 aMach, François1 aMcArdle, Wendy, L1 aMeisinger, Christa1 aMitchell, Braxton, D1 aMüller, Gabrielle1 aNagaraja, Ramaiah1 aNarisu, Narisu1 aNieminen, Tuomo, V M1 aNsubuga, Rebecca, N1 aOlafsson, Isleifur1 aOng, Ken, K1 aPalotie, Aarno1 aPapamarkou, Theodore1 aPomilla, Cristina1 aPouta, Anneli1 aRader, Daniel, J1 aReilly, Muredach, P1 aRidker, Paul, M1 aRivadeneira, Fernando1 aRudan, Igor1 aRuokonen, Aimo1 aSamani, Nilesh1 aScharnagl, Hubert1 aSeeley, Janet1 aSilander, Kaisa1 aStančáková, Alena1 aStirrups, Kathleen1 aSwift, Amy, J1 aTiret, Laurence1 aUitterlinden, André, G1 avan Pelt, Joost1 aVedantam, Sailaja1 aWainwright, Nicholas1 aWijmenga, Cisca1 aWild, Sarah, H1 aWillemsen, Gonneke1 aWilsgaard, Tom1 aWilson, James, F1 aYoung, Elizabeth, H1 aZhao, Jing Hua1 aAdair, Linda, S1 aArveiler, Dominique1 aAssimes, Themistocles, L1 aBandinelli, Stefania1 aBennett, Franklyn1 aBochud, Murielle1 aBoehm, Bernhard, O1 aBoomsma, Dorret, I1 aBorecki, Ingrid, B1 aBornstein, Stefan, R1 aBovet, Pascal1 aBurnier, Michel1 aCampbell, Harry1 aChakravarti, Aravinda1 aChambers, John, C1 aChen, Yii-Der Ida1 aCollins, Francis, S1 aCooper, Richard, S1 aDanesh, John1 aDedoussis, George1 ade Faire, Ulf1 aFeranil, Alan, B1 aFerrieres, Jean1 aFerrucci, Luigi1 aFreimer, Nelson, B1 aGieger, Christian1 aGroop, Leif, C1 aGudnason, Vilmundur1 aGyllensten, Ulf1 aHamsten, Anders1 aHarris, Tamara, B1 aHingorani, Aroon1 aHirschhorn, Joel, N1 aHofman, Albert1 aHovingh, Kees1 aHsiung, Chao, Agnes1 aHumphries, Steve, E1 aHunt, Steven, C1 aHveem, Kristian1 aIribarren, Carlos1 aJarvelin, Marjo-Riitta1 aJula, Antti1 aKähönen, Mika1 aKaprio, Jaakko1 aKesäniemi, Antero1 aKivimaki, Mika1 aKooner, Jaspal, S1 aKoudstaal, Peter, J1 aKrauss, Ronald, M1 aKuh, Diana1 aKuusisto, Johanna1 aKyvik, Kirsten, O1 aLaakso, Markku1 aLakka, Timo, A1 aLind, Lars1 aLindgren, Cecilia, M1 aMartin, Nicholas, G1 aMärz, Winfried1 aMcCarthy, Mark, I1 aMcKenzie, Colin, A1 aMeneton, Pierre1 aMetspalu, Andres1 aMoilanen, Leena1 aMorris, Andrew, D1 aMunroe, Patricia, B1 aNjølstad, Inger1 aPedersen, Nancy, L1 aPower, Chris1 aPramstaller, Peter, P1 aPrice, Jackie, F1 aPsaty, Bruce, M1 aQuertermous, Thomas1 aRauramaa, Rainer1 aSaleheen, Danish1 aSalomaa, Veikko1 aSanghera, Dharambir, K1 aSaramies, Jouko1 aSchwarz, Peter, E H1 aSheu, Wayne, H-H1 aShuldiner, Alan, R1 aSiegbahn, Agneta1 aSpector, Tim, D1 aStefansson, Kari1 aStrachan, David, P1 aTayo, Bamidele, O1 aTremoli, Elena1 aTuomilehto, Jaakko1 aUusitupa, Matti1 aDuijn, Cornelia, M1 aVollenweider, Peter1 aWallentin, Lars1 aWareham, Nicholas, J1 aWhitfield, John, B1 aWolffenbuttel, Bruce, H R1 aAltshuler, David1 aOrdovas, Jose, M1 aBoerwinkle, Eric1 aPalmer, Colin, N A1 aThorsteinsdottir, Unnur1 aChasman, Daniel, I1 aRotter, Jerome, I1 aFranks, Paul, W1 aRipatti, Samuli1 aCupples, Adrienne, L1 aSandhu, Manjinder, S1 aRich, Stephen, S1 aBoehnke, Michael1 aDeloukas, Panos1 aMohlke, Karen, L1 aIngelsson, Erik1 aAbecasis, Goncalo, R1 aDaly, Mark, J1 aNeale, Benjamin, M1 aKathiresan, Sekar uhttps://chs-nhlbi.org/node/801410661nas a2203397 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2013 eng d a1546-171800aDiscovery and refinement of loci associated with lipid levels.0 aDiscovery and refinement of loci associated with lipid levels c2013 Nov a1274-12830 v453 aLevels of low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, triglycerides and total cholesterol are heritable, modifiable risk factors for coronary artery disease. To identify new loci and refine known loci influencing these lipids, we examined 188,577 individuals using genome-wide and custom genotyping arrays. We identify and annotate 157 loci associated with lipid levels at P < 5 × 10(-8), including 62 loci not previously associated with lipid levels in humans. Using dense genotyping in individuals of European, East Asian, South Asian and African ancestry, we narrow association signals in 12 loci. We find that loci associated with blood lipid levels are often associated with cardiovascular and metabolic traits, including coronary artery disease, type 2 diabetes, blood pressure, waist-hip ratio and body mass index. Our results demonstrate the value of using genetic data from individuals of diverse ancestry and provide insights into the biological mechanisms regulating blood lipids to guide future genetic, biological and therapeutic research.
10aAfrican Continental Ancestry Group10aAsian Continental Ancestry Group10aCholesterol, HDL10aCholesterol, LDL10aCoronary Artery Disease10aEuropean Continental Ancestry Group10aGenetic Predisposition to Disease10aGenome-Wide Association Study10aGenotype10aHumans10aLipids10aTriglycerides1 aWiller, Cristen, J1 aSchmidt, Ellen, M1 aSengupta, Sebanti1 aPeloso, Gina, M1 aGustafsson, Stefan1 aKanoni, Stavroula1 aGanna, Andrea1 aChen, Jin1 aBuchkovich, Martin, L1 aMora, Samia1 aBeckmann, Jacques, S1 aBragg-Gresham, Jennifer, L1 aChang, Hsing-Yi1 aDemirkan, Ayse1 aHertog, Heleen, M Den1 aDo, Ron1 aDonnelly, Louise, A1 aEhret, Georg, B1 aEsko, Tõnu1 aFeitosa, Mary, F1 aFerreira, Teresa1 aFischer, Krista1 aFontanillas, Pierre1 aFraser, Ross, M1 aFreitag, Daniel, F1 aGurdasani, Deepti1 aHeikkilä, Kauko1 aHyppönen, Elina1 aIsaacs, Aaron1 aJackson, Anne, U1 aJohansson, Asa1 aJohnson, Toby1 aKaakinen, Marika1 aKettunen, Johannes1 aKleber, Marcus, E1 aLi, Xiaohui1 aLuan, Jian'an1 aLyytikäinen, Leo-Pekka1 aMagnusson, Patrik, K E1 aMangino, Massimo1 aMihailov, Evelin1 aMontasser, May, E1 aMüller-Nurasyid, Martina1 aNolte, Ilja, M1 aO'Connell, Jeffrey, R1 aPalmer, Cameron, D1 aPerola, Markus1 aPetersen, Ann-Kristin1 aSanna, Serena1 aSaxena, Richa1 aService, Susan, K1 aShah, Sonia1 aShungin, Dmitry1 aSidore, Carlo1 aSong, Ci1 aStrawbridge, Rona, J1 aSurakka, Ida1 aTanaka, Toshiko1 aTeslovich, Tanya, M1 aThorleifsson, Gudmar1 avan den Herik, Evita, G1 aVoight, Benjamin, F1 aVolcik, Kelly, A1 aWaite, Lindsay, L1 aWong, Andrew1 aWu, Ying1 aZhang, Weihua1 aAbsher, Devin1 aAsiki, Gershim1 aBarroso, Inês1 aBeen, Latonya, F1 aBolton, Jennifer, L1 aBonnycastle, Lori, L1 aBrambilla, Paolo1 aBurnett, Mary, S1 aCesana, Giancarlo1 aDimitriou, Maria1 aDoney, Alex, S F1 aDöring, Angela1 aElliott, Paul1 aEpstein, Stephen, E1 aEyjolfsson, Gudmundur, Ingi1 aGigante, Bruna1 aGoodarzi, Mark, O1 aGrallert, Harald1 aGravito, Martha, L1 aGroves, Christopher, J1 aHallmans, Göran1 aHartikainen, Anna-Liisa1 aHayward, Caroline1 aHernandez, Dena1 aHicks, Andrew, A1 aHolm, Hilma1 aHung, Yi-Jen1 aIllig, Thomas1 aJones, Michelle, R1 aKaleebu, Pontiano1 aKastelein, John, J P1 aKhaw, Kay-Tee1 aKim, Eric1 aKlopp, Norman1 aKomulainen, Pirjo1 aKumari, Meena1 aLangenberg, Claudia1 aLehtimäki, Terho1 aLin, Shih-Yi1 aLindström, Jaana1 aLoos, Ruth, J F1 aMach, François1 aMcArdle, Wendy, L1 aMeisinger, Christa1 aMitchell, Braxton, D1 aMüller, Gabrielle1 aNagaraja, Ramaiah1 aNarisu, Narisu1 aNieminen, Tuomo, V M1 aNsubuga, Rebecca, N1 aOlafsson, Isleifur1 aOng, Ken, K1 aPalotie, Aarno1 aPapamarkou, Theodore1 aPomilla, Cristina1 aPouta, Anneli1 aRader, Daniel, J1 aReilly, Muredach, P1 aRidker, Paul, M1 aRivadeneira, Fernando1 aRudan, Igor1 aRuokonen, Aimo1 aSamani, Nilesh1 aScharnagl, Hubert1 aSeeley, Janet1 aSilander, Kaisa1 aStančáková, Alena1 aStirrups, Kathleen1 aSwift, Amy, J1 aTiret, Laurence1 aUitterlinden, André, G1 avan Pelt, Joost1 aVedantam, Sailaja1 aWainwright, Nicholas1 aWijmenga, Cisca1 aWild, Sarah, H1 aWillemsen, Gonneke1 aWilsgaard, Tom1 aWilson, James, F1 aYoung, Elizabeth, H1 aZhao, Jing Hua1 aAdair, Linda, S1 aArveiler, Dominique1 aAssimes, Themistocles, L1 aBandinelli, Stefania1 aBennett, Franklyn1 aBochud, Murielle1 aBoehm, Bernhard, O1 aBoomsma, Dorret, I1 aBorecki, Ingrid, B1 aBornstein, Stefan, R1 aBovet, Pascal1 aBurnier, Michel1 aCampbell, Harry1 aChakravarti, Aravinda1 aChambers, John, C1 aChen, Yii-Der Ida1 aCollins, Francis, S1 aCooper, Richard, S1 aDanesh, John1 aDedoussis, George1 ade Faire, Ulf1 aFeranil, Alan, B1 aFerrieres, Jean1 aFerrucci, Luigi1 aFreimer, Nelson, B1 aGieger, Christian1 aGroop, Leif, C1 aGudnason, Vilmundur1 aGyllensten, Ulf1 aHamsten, Anders1 aHarris, Tamara, B1 aHingorani, Aroon1 aHirschhorn, Joel, N1 aHofman, Albert1 aHovingh, Kees1 aHsiung, Chao, Agnes1 aHumphries, Steve, E1 aHunt, Steven, C1 aHveem, Kristian1 aIribarren, Carlos1 aJarvelin, Marjo-Riitta1 aJula, Antti1 aKähönen, Mika1 aKaprio, Jaakko1 aKesäniemi, Antero1 aKivimaki, Mika1 aKooner, Jaspal, S1 aKoudstaal, Peter, J1 aKrauss, Ronald, M1 aKuh, Diana1 aKuusisto, Johanna1 aKyvik, Kirsten, O1 aLaakso, Markku1 aLakka, Timo, A1 aLind, Lars1 aLindgren, Cecilia, M1 aMartin, Nicholas, G1 aMärz, Winfried1 aMcCarthy, Mark, I1 aMcKenzie, Colin, A1 aMeneton, Pierre1 aMetspalu, Andres1 aMoilanen, Leena1 aMorris, Andrew, D1 aMunroe, Patricia, B1 aNjølstad, Inger1 aPedersen, Nancy, L1 aPower, Chris1 aPramstaller, Peter, P1 aPrice, Jackie, F1 aPsaty, Bruce, M1 aQuertermous, Thomas1 aRauramaa, Rainer1 aSaleheen, Danish1 aSalomaa, Veikko1 aSanghera, Dharambir, K1 aSaramies, Jouko1 aSchwarz, Peter, E H1 aSheu, Wayne, H-H1 aShuldiner, Alan, R1 aSiegbahn, Agneta1 aSpector, Tim, D1 aStefansson, Kari1 aStrachan, David, P1 aTayo, Bamidele, O1 aTremoli, Elena1 aTuomilehto, Jaakko1 aUusitupa, Matti1 aDuijn, Cornelia, M1 aVollenweider, Peter1 aWallentin, Lars1 aWareham, Nicholas, J1 aWhitfield, John, B1 aWolffenbuttel, Bruce, H R1 aOrdovas, Jose, M1 aBoerwinkle, Eric1 aPalmer, Colin, N A1 aThorsteinsdottir, Unnur1 aChasman, Daniel, I1 aRotter, Jerome, I1 aFranks, Paul, W1 aRipatti, Samuli1 aCupples, Adrienne, L1 aSandhu, Manjinder, S1 aRich, Stephen, S1 aBoehnke, Michael1 aDeloukas, Panos1 aKathiresan, Sekar1 aMohlke, Karen, L1 aIngelsson, Erik1 aAbecasis, Goncalo, R1 aGlobal Lipids Genetics Consortium uhttps://chs-nhlbi.org/node/615403070nas a2200445 4500008004100000022001400041245015700055210006900212260001300281300001100294490000800305520172400313653005102037653002202088653000902110653001502119653002302134653002802157653002102185653002102206653004002227653001102267653001102278653001402289653001702303653001802320653000902338653001602347653003202363653002402395653001702419100002202436700002202458700001702480700002202497700002702519700002202546700002002568856003602588 2013 eng d a1879-148400aImpact of inflammatory biomarkers on relation of high density lipoprotein-cholesterol with incident coronary heart disease: cardiovascular Health Study.0 aImpact of inflammatory biomarkers on relation of high density li c2013 Dec a246-510 v2313 aBACKGROUND: Inflammatory factors and low HDL-C relate to CHD risk, but whether inflammation attenuates any protective association of high HDL-C is unknown.
OBJECTIVE: Investigate inflammatory markers' individual and collective impact on the association of HDL-C with incident coronary heart disease (CHD).
METHODS: In 3888 older adults without known cardiovascular disease (CVD), we examined if the inflammatory markers C-reactive protein (CRP), interleukin-6 (IL-6), and lipoprotein-associated phospholipase A2 (Lp-PLA₂) modify the relation of HDL-C with CHD. HDL-C, CRP, IL-6, and Lp-PLA₂ values were grouped as using gender-specific tertiles. Also, an inflammation index of z-score sums for CRP, IL-6, and Lp-PLA₂ was categorized into tertiles. We calculated CHD incidence for each HDL-C/inflammation group and performed Cox regression, adjusted for standard CVD risk factors and triglycerides to examine the relationship of combined HDL-C-inflammation groups with incident events.
RESULTS: CHD incidence (per 1000 person years) was higher for higher levels of CRP, IL-6, and the index, and lower for higher levels of HDL-C. Compared to high HDL-C/low-inflammation categories (referent), adjusted HRs for incident CHD were increased for those with high HDL-C and high CRP (HR = 1.50, p < 0.01) or highest IL-6 tertile (HR = 1.40, p < 0.05), but not with highest Lp-PLA₂ tertile. Higher CHD incidence was similarly seen for those with intermediate or low HDL-C accompanied by high CRP, high IL-6, or a high inflammatory index.
CONCLUSION: The protective relation of high HDL-C for incident CHD appears to be attenuated by greater inflammation.
10a1-Alkyl-2-acetylglycerophosphocholine Esterase10aAfrican Americans10aAged10aBiomarkers10aC-Reactive Protein10aCardiovascular Diseases10aCholesterol, HDL10aCoronary Disease10aEuropean Continental Ancestry Group10aFemale10aHumans10aIncidence10aInflammation10aInterleukin-610aMale10aMiddle Aged10aProportional Hazards Models10aProspective Studies10aRisk Factors1 aTehrani, David, M1 aGardin, Julius, M1 aYanez, David1 aHirsch, Calvin, H1 aLloyd-Jones, Donald, M1 aStein, Phyllis, K1 aWong, Nathan, D uhttps://chs-nhlbi.org/node/616702686nas a2200697 4500008004100000022001400041245012800055210006900183260001300252300001200265490000800277520066900285653002100954653002100975653001900996653001801015653001101033653001901044653003301063653002501096653003401121653001101155653002101166653000901187653003601196653001501232653001201247653001801259653001601277100002201293700001901315700002301334700002301357700002101380700002101401700002801422700002201450700002301472700002101495700001901516700002101535700002401556700001601580700002201596700002201618700002201640700002601662700002301688700002101711700002101732700002301753700002201776700002301798700002701821700001901848700002401867700001901891700002001910700002201930856003601952 2013 eng d a1432-120300aNo evidence of interaction between known lipid-associated genetic variants and smoking in the multi-ethnic PAGE population.0 aNo evidence of interaction between known lipidassociated genetic c2013 Dec a1427-310 v1323 aGenome-wide association studies (GWAS) have identified many variants that influence high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and/or triglycerides. However, environmental modifiers, such as smoking, of these known genotype-phenotype associations are just recently emerging in the literature. We have tested for interactions between smoking and 49 GWAS-identified variants in over 41,000 racially/ethnically diverse samples with lipid levels from the Population Architecture Using Genomics and Epidemiology (PAGE) study. Despite their biological plausibility, we were unable to detect significant SNP × smoking interactions.
10aCholesterol, HDL10aCholesterol, LDL10aCohort Studies10aEthnic Groups10aFemale10aGene Frequency10aGene-Environment Interaction10aGenetics, Population10aGenome-Wide Association Study10aHumans10aLipid Metabolism10aMale10aPolymorphism, Single Nucleotide10aPrevalence10aSmoking10aTriglycerides10aYoung Adult1 aDumitrescu, Logan1 aCarty, Cara, L1 aFranceschini, Nora1 aHindorff, Lucia, A1 aCole, Shelley, A1 aBůzková, Petra1 aSchumacher, Fredrick, R1 aEaton, Charles, B1 aGoodloe, Robert, J1 aDuggan, David, J1 aHaessler, Jeff1 aCochran, Barbara1 aHenderson, Brian, E1 aCheng, Iona1 aJohnson, Karen, C1 aCarlson, Chris, S1 aLove, Shelly-Anne1 aBrown-Gentry, Kristin1 aNato, Alejandro, Q1 aQuibrera, Miguel1 aShohet, Ralph, V1 aAmbite, Jose, Luis1 aWilkens, Lynne, R1 aLe Marchand, Loïc1 aHaiman, Christopher, A1 aBuyske, Steven1 aKooperberg, Charles1 aNorth, Kari, E1 aFornage, Myriam1 aCrawford, Dana, C uhttps://chs-nhlbi.org/node/629203114nas a2200433 4500008004100000022001400041245009600055210006900151260001300220300001100233490000700244520194700251653000902198653002202207653002002229653001502249653001902264653002802283653003502311653002102346653002202367653001102389653001102400653000902411653002602420653002402446653001702470653001102487100001802498700001602516700001602532700001302548700001402561700002002575700001702595700001502612700001702627856003602644 2013 eng d a1432-042800aPrediction and classification of cardiovascular disease risk in older adults with diabetes.0 aPrediction and classification of cardiovascular disease risk in c2013 Feb a275-830 v563 aAIMS/HYPOTHESIS: We sought to derive and validate a cardiovascular disease (CVD) prediction algorithm for older adults with diabetes, and evaluate the incremental benefit of adding novel circulating biomarkers and measures of subclinical atherosclerosis.
METHODS: As part of the Cardiovascular Health Study (CHS), a population-based cohort of adults aged ≥65 years, we examined the 10 year risk of myocardial infarction, stroke and cardiovascular death in 782 older adults with diabetes, in whom 265 events occurred. We validated predictive models in 843 adults with diabetes, who were followed for 7 years in a second cohort, the Multi-Ethnic Study of Atherosclerosis (MESA); here 71 events occurred.
RESULTS: The best fitting standard model included age, smoking, systolic blood pressure, total and HDL-cholesterol, creatinine and the use of glucose-lowering agents; however, this model had a C statistic of 0.64 and poorly classified risk in men. Novel biomarkers did not improve discrimination or classification. The addition of ankle-brachial index, electrocardiographic left ventricular hypertrophy and internal carotid intima-media thickness modestly improved discrimination (C statistic 0.68; p = 0.002) and classification (net reclassification improvement [NRI] 0.12; p = 0.01), mainly in those remaining free of CVD. Results were qualitatively similar in the MESA, with a change in C statistic from 0.65 to 0.68 and an NRI of 0.09 upon inclusion of subclinical disease measures.
CONCLUSIONS/INTERPRETATION: Standard clinical risk factors and novel biomarkers poorly discriminate and classify CVD risk in older adults with diabetes. The inclusion of subclinical atherosclerotic measures modestly improves these features, but to develop more robust risk prediction, a better understanding of the pathophysiology and determinants of CVD in this patient group is needed.
10aAged10aAged, 80 and over10aAtherosclerosis10aBiomarkers10aBlood Pressure10aCardiovascular Diseases10aCarotid Intima-Media Thickness10aCholesterol, HDL10aDiabetes Mellitus10aFemale10aHumans10aMale10aMyocardial Infarction10aRegression Analysis10aRisk Factors10aStroke1 aMukamal, K, J1 aKizer, J, R1 aDjoussé, L1 aIx, J, H1 aZieman, S1 aSiscovick, D, S1 aSibley, C, T1 aTracy, R P1 aArnold, A, M uhttps://chs-nhlbi.org/node/585905266nas a2201201 4500008004100000022001400041245015400055210006900209260001300278300001300291490000600304520181500310653002202125653002202147653002102169653002102190653004002211653003402251653001102285653002202296653002202318653002702340653002602367653001802393100001302411700002202424700002102446700002102467700001902488700001802507700002102525700002102546700002502567700001902592700001602611700002102627700003002648700002202678700002202700700002302722700001702745700002402762700002302786700001402809700002002823700002202843700001802865700002302883700001202906700002202918700002802940700002502968700001902993700002603012700002103038700002503059700002003084700001703104700001803121700002003139700001903159700001903178700002103197700002003218700002803238700002103266700002603287700002403313700001803337700002103355700001703376700001703393700002003410700002003430700002303450700002603473700002203499700001903521700001903540700001403559700002103573700002003594700002003614700002103634700001903655700002403674700002103698700002203719700002003741700002303761700002003784700002003804700002103824700002703845700002103872700002403893700002903917700002203946700002003968700001903988700002104007856003604028 2013 eng d a1553-740400aTrans-ethnic fine-mapping of lipid loci identifies population-specific signals and allelic heterogeneity that increases the trait variance explained.0 aTransethnic finemapping of lipid loci identifies populationspeci c2013 Mar ae10033790 v93 aGenome-wide association studies (GWAS) have identified ~100 loci associated with blood lipid levels, but much of the trait heritability remains unexplained, and at most loci the identities of the trait-influencing variants remain unknown. We conducted a trans-ethnic fine-mapping study at 18, 22, and 18 GWAS loci on the Metabochip for their association with triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C), respectively, in individuals of African American (n = 6,832), East Asian (n = 9,449), and European (n = 10,829) ancestry. We aimed to identify the variants with strongest association at each locus, identify additional and population-specific signals, refine association signals, and assess the relative significance of previously described functional variants. Among the 58 loci, 33 exhibited evidence of association at P<1 × 10(-4) in at least one ancestry group. Sequential conditional analyses revealed that ten, nine, and four loci in African Americans, Europeans, and East Asians, respectively, exhibited two or more signals. At these loci, accounting for all signals led to a 1.3- to 1.8-fold increase in the explained phenotypic variance compared to the strongest signals. Distinct signals across ancestry groups were identified at PCSK9 and APOA5. Trans-ethnic analyses narrowed the signals to smaller sets of variants at GCKR, PPP1R3B, ABO, LCAT, and ABCA1. Of 27 variants reported previously to have functional effects, 74% exhibited the strongest association at the respective signal. In conclusion, trans-ethnic high-density genotyping and analysis confirm the presence of allelic heterogeneity, allow the identification of population-specific variants, and limit the number of candidate SNPs for functional studies.
10aAfrican Americans10aApolipoproteins A10aCholesterol, HDL10aCholesterol, LDL10aEuropean Continental Ancestry Group10aGenome-Wide Association Study10aHumans10aLipoproteins, HDL10aLipoproteins, LDL10aProprotein Convertases10aSerine Endopeptidases10aTriglycerides1 aWu, Ying1 aWaite, Lindsay, L1 aJackson, Anne, U1 aSheu, Wayne, H-H1 aBuyske, Steven1 aAbsher, Devin1 aArnett, Donna, K1 aBoerwinkle, Eric1 aBonnycastle, Lori, L1 aCarty, Cara, L1 aCheng, Iona1 aCochran, Barbara1 aCroteau-Chonka, Damien, C1 aDumitrescu, Logan1 aEaton, Charles, B1 aFranceschini, Nora1 aGuo, Xiuqing1 aHenderson, Brian, E1 aHindorff, Lucia, A1 aKim, Eric1 aKinnunen, Leena1 aKomulainen, Pirjo1 aLee, Wen-Jane1 aLe Marchand, Loïc1 aLin, Yi1 aLindström, Jaana1 aLingaas-Holmen, Oddgeir1 aMitchell, Sabrina, L1 aNarisu, Narisu1 aRobinson, Jennifer, G1 aSchumacher, Fred1 aStančáková, Alena1 aSundvall, Jouko1 aSung, Yun-Ju1 aSwift, Amy, J1 aWang, Wen-Chang1 aWilkens, Lynne1 aWilsgaard, Tom1 aYoung, Alicia, M1 aAdair, Linda, S1 aBallantyne, Christie, M1 aBůzková, Petra1 aChakravarti, Aravinda1 aCollins, Francis, S1 aDuggan, David1 aFeranil, Alan, B1 aHo, Low-Tone1 aHung, Yi-Jen1 aHunt, Steven, C1 aHveem, Kristian1 aJuang, Jyh-Ming, J1 aKesäniemi, Antero, Y1 aKuusisto, Johanna1 aLaakso, Markku1 aLakka, Timo, A1 aLee, I-Te1 aLeppert, Mark, F1 aMatise, Tara, C1 aMoilanen, Leena1 aNjølstad, Inger1 aPeters, Ulrike1 aQuertermous, Thomas1 aRauramaa, Rainer1 aRotter, Jerome, I1 aSaramies, Jouko1 aTuomilehto, Jaakko1 aUusitupa, Matti1 aWang, Tzung-Dau1 aBoehnke, Michael1 aHaiman, Christopher, A1 aChen, Yii-der, I1 aKooperberg, Charles1 aAssimes, Themistocles, L1 aCrawford, Dana, C1 aHsiung, Chao, A1 aNorth, Kari, E1 aMohlke, Karen, L uhttps://chs-nhlbi.org/node/662902102nas a2200469 4500008004100000022001400041245008200055210006900137260001300206300001200219490000700231520073300238653002100971653002600992653002301018653002201041653001801063653003401081653001301115653001701128653001101145653002401156100002401180700001901204700002301223700001801246700001201264700001801276700001701294700001301311700001801324700001901342700001601361700001901377700003001396700002001426700002501446700001901471700002101490710008501511856003601596 2013 eng d a1546-171800aWhole-genome sequence-based analysis of high-density lipoprotein cholesterol.0 aWholegenome sequencebased analysis of highdensity lipoprotein ch c2013 Aug a899-9010 v453 aWe describe initial steps for interrogating whole-genome sequence data to characterize the genetic architecture of a complex trait, levels of high-density lipoprotein cholesterol (HDL-C). We report whole-genome sequencing and analysis of 962 individuals from the Cohorts for Heart and Aging Research in Genetic Epidemiology (CHARGE) studies. From this analysis, we estimate that common variation contributes more to heritability of HDL-C levels than rare variation, and screening for mendelian variants for dyslipidemia identified individuals with extreme HDL-C levels. Whole-genome sequencing analyses highlight the value of regulatory and non-protein-coding regions of the genome in addition to protein-coding regions.
10aCholesterol, HDL10aComputational Biology10aDatabases, Genetic10aGenetic Variation10aGenome, Human10aGenome-Wide Association Study10aGenomics10aHeterozygote10aHumans10aOpen Reading Frames1 aMorrison, Alanna, C1 aVoorman, Arend1 aJohnson, Andrew, D1 aLiu, Xiaoming1 aYu, Jin1 aLi, Alexander1 aMuzny, Donna1 aYu, Fuli1 aRice, Kenneth1 aZhu, Chengsong1 aBis, Joshua1 aHeiss, Gerardo1 aO'Donnell, Christopher, J1 aPsaty, Bruce, M1 aCupples, Adrienne, L1 aGibbs, Richard1 aBoerwinkle, Eric1 aCohorts for Heart and Aging Research in Genetic Epidemiology (CHARGE) Consortium uhttps://chs-nhlbi.org/node/628305943nas a2201657 4500008004100000022001400041245014100055210006900196260001600265300001100281490000700292520126000299653005101559653001001610653003901620653000901659653001201668653001201680653002101692653002101713653001901734653002101753653004001774653001101814653001901825653003201844653001701876653002201893653001101915653001801926653000901944653000901953653002301962653003601985653001602021653001402037653002702051653001602078653001802094100002002112700001802132700001902150700001902169700002402188700002402212700002302236700002502259700002002284700002002304700001802324700002602342700002102368700001702389700002502406700002102431700001702452700001702469700001802486700002102504700002002525700001802545700001902563700002302582700001602605700001902621700002502640700002402665700002202689700002202711700002602733700002402759700002502783700002602808700002102834700001902855700002802874700002202902700002002924700002602944700002502970700002502995700001903020700002103039700002303060700001803083700001803101700002003119700002403139700001903163700002503182700002003207700001803227700002903245700002003274700001503294700002103309700002203330700002303352700001903375700002203394700001603416700002103432700002403453700002003477700001803497700002103515700001903536700001503555700002103570700002203591700001903613700002503632700002103657700001903678700001603697700002403713700002203737700002103759700001703780700001903797700002503816700002203841700001903863700001803882700002303900700001703923700002403940700002103964700002303985700002404008700002104032700002004053700002204073700003004095700001804125700002104143700002204164700002504186710003804211856003604249 2014 eng d a1537-660500aAssociation of low-frequency and rare coding-sequence variants with blood lipids and coronary heart disease in 56,000 whites and blacks.0 aAssociation of lowfrequency and rare codingsequence variants wit c2014 Feb 06 a223-320 v943 aLow-frequency coding DNA sequence variants in the proprotein convertase subtilisin/kexin type 9 gene (PCSK9) lower plasma low-density lipoprotein cholesterol (LDL-C), protect against risk of coronary heart disease (CHD), and have prompted the development of a new class of therapeutics. It is uncertain whether the PCSK9 example represents a paradigm or an isolated exception. We used the "Exome Array" to genotype >200,000 low-frequency and rare coding sequence variants across the genome in 56,538 individuals (42,208 European ancestry [EA] and 14,330 African ancestry [AA]) and tested these variants for association with LDL-C, high-density lipoprotein cholesterol (HDL-C), and triglycerides. Although we did not identify new genes associated with LDL-C, we did identify four low-frequency (frequencies between 0.1% and 2%) variants (ANGPTL8 rs145464906 [c.361C>T; p.Gln121*], PAFAH1B2 rs186808413 [c.482C>T; p.Ser161Leu], COL18A1 rs114139997 [c.331G>A; p.Gly111Arg], and PCSK7 rs142953140 [c.1511G>A; p.Arg504His]) with large effects on HDL-C and/or triglycerides. None of these four variants was associated with risk for CHD, suggesting that examples of low-frequency coding variants with robust effects on both lipids and CHD will be limited.
10a1-Alkyl-2-acetylglycerophosphocholine Esterase10aAdult10aAfrican Continental Ancestry Group10aAged10aAlleles10aAnimals10aCholesterol, HDL10aCholesterol, LDL10aCohort Studies10aCoronary Disease10aEuropean Continental Ancestry Group10aFemale10aGene Frequency10aGenetic Association Studies10aGenetic Code10aGenetic Variation10aHumans10aLinear Models10aMale10aMice10aMice, Inbred C57BL10aMicrotubule-Associated Proteins10aMiddle Aged10aPhenotype10aSequence Analysis, DNA10aSubtilisins10aTriglycerides1 aPeloso, Gina, M1 aAuer, Paul, L1 aBis, Joshua, C1 aVoorman, Arend1 aMorrison, Alanna, C1 aStitziel, Nathan, O1 aBrody, Jennifer, A1 aKhetarpal, Sumeet, A1 aCrosby, Jacy, R1 aFornage, Myriam1 aIsaacs, Aaron1 aJakobsdottir, Johanna1 aFeitosa, Mary, F1 aDavies, Gail1 aHuffman, Jennifer, E1 aManichaikul, Ani1 aDavis, Brian1 aLohman, Kurt1 aJoon, Aron, Y1 aSmith, Albert, V1 aGrove, Megan, L1 aZanoni, Paolo1 aRedon, Valeska1 aDemissie, Serkalem1 aLawson, Kim1 aPeters, Ulrike1 aCarlson, Christopher1 aJackson, Rebecca, D1 aRyckman, Kelli, K1 aMackey, Rachel, H1 aRobinson, Jennifer, G1 aSiscovick, David, S1 aSchreiner, Pamela, J1 aMychaleckyj, Josyf, C1 aPankow, James, S1 aHofman, Albert1 aUitterlinden, André, G1 aHarris, Tamara, B1 aTaylor, Kent, D1 aStafford, Jeanette, M1 aReynolds, Lindsay, M1 aMarioni, Riccardo, E1 aDehghan, Abbas1 aFranco, Oscar, H1 aPatel, Aniruddh, P1 aLu, Yingchang1 aHindy, George1 aGottesman, Omri1 aBottinger, Erwin, P1 aMelander, Olle1 aOrho-Melander, Marju1 aLoos, Ruth, J F1 aDuga, Stefano1 aMerlini, Piera, Angelica1 aFarrall, Martin1 aGoel, Anuj1 aAsselta, Rosanna1 aGirelli, Domenico1 aMartinelli, Nicola1 aShah, Svati, H1 aKraus, William, E1 aLi, Mingyao1 aRader, Daniel, J1 aReilly, Muredach, P1 aMcPherson, Ruth1 aWatkins, Hugh1 aArdissino, Diego1 aZhang, Qunyuan1 aWang, Judy1 aTsai, Michael, Y1 aTaylor, Herman, A1 aCorrea, Adolfo1 aGriswold, Michael, E1 aLange, Leslie, A1 aStarr, John, M1 aRudan, Igor1 aEiriksdottir, Gudny1 aLauner, Lenore, J1 aOrdovas, Jose, M1 aLevy, Daniel1 aChen, Y-D, Ida1 aReiner, Alexander, P1 aHayward, Caroline1 aPolasek, Ozren1 aDeary, Ian, J1 aBorecki, Ingrid, B1 aLiu, Yongmei1 aGudnason, Vilmundur1 aWilson, James, G1 aDuijn, Cornelia, M1 aKooperberg, Charles1 aRich, Stephen, S1 aPsaty, Bruce, M1 aRotter, Jerome, I1 aO'Donnell, Christopher, J1 aRice, Kenneth1 aBoerwinkle, Eric1 aKathiresan, Sekar1 aCupples, Adrienne, L1 aNHLBI GO Exome Sequencing Project uhttps://chs-nhlbi.org/node/659003125nas a2200553 4500008004100000022001400041245011100055210006900166260000900235300001200244490000600256520150100262653002101763653001101784653003401795653001101829653000901840653001601849653003601865100003001901700002901931700002001960700002201980700002102002700002202023700002202045700002402067700001202091700002302103700001902126700002202145700001802167700001902185700001902204700002602223700002802249700002102277700002802298700002402326700002002350700002502370700002702395700002002422700001802442700002802460700002402488700002302512856003602535 2014 eng d a1932-620300aThe challenges of genome-wide interaction studies: lessons to learn from the analysis of HDL blood levels.0 achallenges of genomewide interaction studies lessons to learn fr c2014 ae1092900 v93 aGenome-wide association studies (GWAS) have revealed 74 single nucleotide polymorphisms (SNPs) associated with high-density lipoprotein cholesterol (HDL) blood levels. This study is, to our knowledge, the first genome-wide interaction study (GWIS) to identify SNP×SNP interactions associated with HDL levels. We performed a GWIS in the Rotterdam Study (RS) cohort I (RS-I) using the GLIDE tool which leverages the massively parallel computing power of Graphics Processing Units (GPUs) to perform linear regression on all genome-wide pairs of SNPs. By performing a meta-analysis together with Rotterdam Study cohorts II and III (RS-II and RS-III), we were able to filter 181 interaction terms with a p-value<1 · 10-8 that replicated in the two independent cohorts. We were not able to replicate any of these interaction term in the AGES, ARIC, CHS, ERF, FHS and NFBC-66 cohorts (Ntotal = 30,011) when adjusting for multiple testing. Our GWIS resulted in the consistent finding of a possible interaction between rs774801 in ARMC8 (ENSG00000114098) and rs12442098 in SPATA8 (ENSG00000185594) being associated with HDL levels. However, p-values do not reach the preset Bonferroni correction of the p-values. Our study suggest that even for highly genetically determined traits such as HDL the sample sizes needed to detect SNP×SNP interactions are large and the 2-step filtering approaches do not yield a solution. Here we present our analysis plan and our reservations concerning GWIS.
10aCholesterol, HDL10aFemale10aGenome-Wide Association Study10aHumans10aMale10aMiddle Aged10aPolymorphism, Single Nucleotide1 avan Leeuwen, Elisabeth, M1 aSmouter, Françoise, A S1 aKam-Thong, Tony1 aKarbalai, Nazanin1 aSmith, Albert, V1 aHarris, Tamara, B1 aLauner, Lenore, J1 aSitlani, Colleen, M1 aLi, Guo1 aBrody, Jennifer, A1 aBis, Joshua, C1 aWhite, Charles, C1 aJaiswal, Alok1 aOostra, Ben, A1 aHofman, Albert1 aRivadeneira, Fernando1 aUitterlinden, André, G1 aBoerwinkle, Eric1 aBallantyne, Christie, M1 aGudnason, Vilmundur1 aPsaty, Bruce, M1 aCupples, Adrienne, L1 aJarvelin, Marjo-Riitta1 aRipatti, Samuli1 aIsaacs, Aaron1 aMüller-Myhsok, Bertram1 aKarssen, Lennart, C1 aDuijn, Cornelia, M uhttps://chs-nhlbi.org/node/660702696nas a2200421 4500008004100000022001400041245008100055210006900136260001600205300001200221490000600233520156000239653000901799653001501808653002101823653002101844653001101865653001101876653001801887653000901905653001601914653002601930653001401956653003201970653002002002653001702022653001702039653001802056653001802074100002102092700001802113700001902131700002302150700001602173700002202189700002702211856003602238 2014 eng d a2047-998000aCoronary heart disease risks associated with high levels of HDL cholesterol.0 aCoronary heart disease risks associated with high levels of HDL c2014 Mar 13 ae0005190 v33 aBACKGROUND: The association between high-density lipoprotein cholesterol (HDL-C) and coronary heart disease (CHD) events is not well described in individuals with very high levels of HDL-C (>80 mg/dL).
METHODS AND RESULTS: Using pooled data from 6 community-based cohorts we examined CHD and total mortality risks across a broad range of HDL-C, including values in excess of 80 mg/dL. We used Cox proportional hazards models with penalized splines to assess multivariable, adjusted, sex-stratified associations of HDL-C with the hazard for CHD events and total mortality, using HDL-C 45 mg/dL and 55 mg/dL as the referent in men and women, respectively. Analyses included 11 515 men and 12 925 women yielding 307 245 person-years of follow-up. In men, the association between HDL-C and CHD events was inverse and linear across most HDL-C values; however at HDL-C values >90 mg/dL there was a plateau effect in the pattern of association. In women, the association between HDL-C and CHD events was inverse and linear across lower values of HDL-C, however at HDL-C values >75 mg/dL there were no further reductions in the hazard ratio point estimates for CHD. In unadjusted models there were increased total mortality risks in men with very high HDL-C, however mortality risks observed in participants with very high HDL-C were attenuated after adjustment for traditional risk factors.
CONCLUSIONS: We did not observe further reductions in CHD risk with HDL-C values higher than 90 mg/dL in men and 75 mg/dL in women.
10aAged10aBiomarkers10aCholesterol, HDL10aCoronary Disease10aFemale10aHumans10aLinear Models10aMale10aMiddle Aged10aMultivariate Analysis10aPrognosis10aProportional Hazards Models10aRisk Assessment10aRisk Factors10aTime Factors10aUnited States10aUp-Regulation1 aWilkins, John, T1 aNing, Hongyan1 aStone, Neil, J1 aCriqui, Michael, H1 aZhao, Lihui1 aGreenland, Philip1 aLloyd-Jones, Donald, M uhttps://chs-nhlbi.org/node/636605794nas a2201213 4500008004100000022001400041245007800055210006900133260001600202300001200218490000800230520237200238653000902610653002302619653002102642653002102663653002202684653001102706653002602717653001102743653000902754653001602763653003002779653002402809653002002833653001102853110004002864700003002904700001302934700001702947700002502964700001902989700002103008700003703029700001903066700001903085700001903104700002003123700002503143700001803168700002003186700002103206700002103227700002603248700002203274700001903296700002003315700002103335700002203356700002103378700002103399700002103420700002203441700002403463700002403487700002303511700001903534700002503553700002203578700001403600700001903614700003503633700002003668700002003688700002203708700002503730700002503755700001903780700003203799700001603831700003103847700002203878700001903900700002103919700002203940700002803962700001803990700002104008700002004029700002204049700002004071700003004091700002004121700002404141700001704165700002304182700001804205700002504223700002004248700002104268700002204289700001904311700002304330700002104353700002204374700002004396700002204416700001904438700002504457700002204482700002304504700001704527856003604544 2014 eng d a1538-359800aGlycated hemoglobin measurement and prediction of cardiovascular disease.0 aGlycated hemoglobin measurement and prediction of cardiovascular c2014 Mar 26 a1225-330 v3113 aIMPORTANCE: The value of measuring levels of glycated hemoglobin (HbA1c) for the prediction of first cardiovascular events is uncertain.
OBJECTIVE: To determine whether adding information on HbA1c values to conventional cardiovascular risk factors is associated with improvement in prediction of cardiovascular disease (CVD) risk.
DESIGN, SETTING, AND PARTICIPANTS: Analysis of individual-participant data available from 73 prospective studies involving 294,998 participants without a known history of diabetes mellitus or CVD at the baseline assessment.
MAIN OUTCOMES AND MEASURES: Measures of risk discrimination for CVD outcomes (eg, C-index) and reclassification (eg, net reclassification improvement) of participants across predicted 10-year risk categories of low (<5%), intermediate (5% to <7.5%), and high (≥ 7.5%) risk.
RESULTS: During a median follow-up of 9.9 (interquartile range, 7.6-13.2) years, 20,840 incident fatal and nonfatal CVD outcomes (13,237 coronary heart disease and 7603 stroke outcomes) were recorded. In analyses adjusted for several conventional cardiovascular risk factors, there was an approximately J-shaped association between HbA1c values and CVD risk. The association between HbA1c values and CVD risk changed only slightly after adjustment for total cholesterol and triglyceride concentrations or estimated glomerular filtration rate, but this association attenuated somewhat after adjustment for concentrations of high-density lipoprotein cholesterol and C-reactive protein. The C-index for a CVD risk prediction model containing conventional cardiovascular risk factors alone was 0.7434 (95% CI, 0.7350 to 0.7517). The addition of information on HbA1c was associated with a C-index change of 0.0018 (0.0003 to 0.0033) and a net reclassification improvement of 0.42 (-0.63 to 1.48) for the categories of predicted 10-year CVD risk. The improvement provided by HbA1c assessment in prediction of CVD risk was equal to or better than estimated improvements for measurement of fasting, random, or postload plasma glucose levels.
CONCLUSIONS AND RELEVANCE: In a study of individuals without known CVD or diabetes, additional assessment of HbA1c values in the context of CVD risk assessment provided little incremental benefit for prediction of CVD risk.
10aAged10aC-Reactive Protein10aCholesterol, HDL10aCoronary Disease10aDiabetes Mellitus10aFemale10aGlycated Hemoglobin A10aHumans10aMale10aMiddle Aged10aPredictive Value of Tests10aProspective Studies10aRisk Assessment10aStroke1 aEmerging Risk Factors Collaboration1 aDi Angelantonio, Emanuele1 aGao, Pei1 aKhan, Hassan1 aButterworth, Adam, S1 aWormser, David1 aKaptoge, Stephen1 aSeshasai, Sreenivasa, Rao Kondap1 aThompson, Alex1 aSarwar, Nadeem1 aWilleit, Peter1 aRidker, Paul, M1 aBarr, Elizabeth, L M1 aKhaw, Kay-Tee1 aPsaty, Bruce, M1 aBrenner, Hermann1 aBalkau, Beverley1 aDekker, Jacqueline, M1 aLawlor, Debbie, A1 aDaimon, Makoto1 aWilleit, Johann1 aNjølstad, Inger1 aNissinen, Aulikki1 aBrunner, Eric, J1 aKuller, Lewis, H1 aPrice, Jackie, F1 aSundström, Johan1 aKnuiman, Matthew, W1 aFeskens, Edith, J M1 aVerschuren, W, M M1 aWald, Nicholas1 aBakker, Stephan, J L1 aWhincup, Peter, H1 aFord, Ian1 aGoldbourt, Uri1 aGómez-de-la-Cámara, Agustín1 aGallacher, John1 aSimons, Leon, A1 aRosengren, Annika1 aSutherland, Susan, E1 aBjörkelund, Cecilia1 aBlazer, Dan, G1 aWassertheil-Smoller, Sylvia1 aOnat, Altan1 aIbañez, Alejandro, Marín1 aCasiglia, Edoardo1 aJukema, Wouter1 aSimpson, Lara, M1 aGiampaoli, Simona1 aNordestgaard, Børge, G1 aSelmer, Randi1 aWennberg, Patrik1 aKauhanen, Jussi1 aSalonen, Jukka, T1 aDankner, Rachel1 aBarrett-Connor, Elizabeth1 aKavousi, Maryam1 aGudnason, Vilmundur1 aEvans, Denis1 aWallace, Robert, B1 aCushman, Mary1 aD'Agostino, Ralph, B1 aUmans, Jason, G1 aKiyohara, Yutaka1 aNakagawa, Hidaeki1 aSato, Shinichi1 aGillum, Richard, F1 aFolsom, Aaron, R1 aSchouw, Yvonne, T1 aMoons, Karel, G1 aGriffin, Simon, J1 aSattar, Naveed1 aWareham, Nicholas, J1 aSelvin, Elizabeth1 aThompson, Simon, G1 aDanesh, John uhttps://chs-nhlbi.org/node/655903245nas a2200517 4500008004100000022001400041245011100055210006900166260001300235300001100248490000800259520180600267653000902073653002202082653001902104653002302123653002802146653002102174653002102195653002702216653002202243653001102265653001102276653001702287653001102304653002002315653001102335653000902346653003102355653001202386653002202398653001702420100002302437700002402460700002402484700001602508700002502524700002102549700001902570700002002589700002402609700001802633700002002651700002002671856003602691 2014 eng d a1879-148400aRisk factors for cardiovascular disease across the spectrum of older age: the Cardiovascular Health Study.0 aRisk factors for cardiovascular disease across the spectrum of o c2014 Nov a336-420 v2373 aOBJECTIVE: The associations of some risk factors with cardiovascular disease (CVD) are attenuated in older age; whereas others appear robust. The present study aimed to compare CVD risk factors across older age.
METHODS: Participants (n = 4883) in the Cardiovascular Health Study free of prevalent CVD, were stratified into three age groups: 65-74, 75-84, 85+ years. Traditional risk factors included systolic blood pressure (BP), LDL-cholesterol, HDL-cholesterol, obesity, and diabetes. Novel risk factors included kidney function, C-reactive protein (CRP), and N-terminal pro-B-type natriuretic peptide (NT pro-BNP).
RESULTS: There were 1498 composite CVD events (stroke, myocardial infarction, and cardiovascular death) over 5 years. The associations of high systolic BP and diabetes appeared strongest, though both were attenuated with age (p-values for interaction = 0.01 and 0.002, respectively). The demographic-adjusted hazard ratios (HR) for elevated systolic BP were 1.79 (95% confidence interval: 1.49, 2.15), 1.59 (1.37, 1.85) and 1.10 (0.86, 1.41) in participants aged 65-74, 75-84, 85+, and for diabetes, 2.36 (1.89, 2.95), 1.55 (1.27, 1.89), 1.51 (1.10, 2.09). The novel risk factors had consistent associations with the outcome across the age spectrum; low kidney function: 1.69 (1.31, 2.19), 1.61 (1.36, 1.90), and 1.57 (1.16, 2.14) for 65-74, 75-84, and 85+ years, respectively; elevated CRP: 1.54 (1.28, 1.87), 1.33 (1.13, 1.55), and 1.51 (1.15, 1.97); elevated NT pro-BNP: 2.67 (1.96, 3.64), 2.71 (2.25, 3.27), and 2.18 (1.43, 3.45).
CONCLUSIONS: The associations of most traditional risk factors with CVD were minimal in the oldest old, whereas diabetes, eGFR, CRP, and NT pro-BNP were associated with CVD across older age.
10aAged10aAged, 80 and over10aBlood Pressure10aC-Reactive Protein10aCardiovascular Diseases10aCholesterol, HDL10aCholesterol, LDL10aDiabetes Complications10aDiabetes Mellitus10aFemale10aHumans10aInflammation10aKidney10aKidney Diseases10aLipids10aMale10aNatriuretic Peptide, Brain10aObesity10aPeptide Fragments10aRisk Factors1 aOdden, Michelle, C1 aShlipak, Michael, G1 aWhitson, Heather, E1 aKatz, Ronit1 aKearney, Patricia, M1 adeFilippi, Chris1 aShastri, Shani1 aSarnak, Mark, J1 aSiscovick, David, S1 aCushman, Mary1 aPsaty, Bruce, M1 aNewman, Anne, B uhttps://chs-nhlbi.org/node/658808133nas a2201957 4500008004100000022001400041245012800055210006900183260001600252300001100268490000800279520256000287653000902847653002002856653001602876653002102892653002102913653003002934653001102964653002002975653001102995653004103006653005103047653000903098653001603107653003603123653004203159653001703201100002403218700001803242700003103260700002303291700002503314700001503339700001803354700002003372700002303392700002103415700002103436700001803457700002203475700001503497700002803512700002103540700001703561700002203578700001403600700001803614700001903632700002203651700001903673700001503692700001703707700002003724700002603744700002103770700002203791700002503813700002103838700003103859700002203890700002403912700002503936700002103961700002303982700002404005700001904029700002304048700002304071700002104094700002504115700002004140700001904160700001804179700002104197700001404218700001904232700002604251700002404277700002004301700001504321700002004336700003404356700002104390700002304411700002604434700002204460700002604482700001804508700001904526700001904545700002204564700002204586700002304608700002004631700002004651700002104671700002104692700002304713700002104736700002004757700002404777700002104801700002304822700002004845700002204865700001904887700001904906700003704925700002104962700002104983700002205004700002305026700002205049700002405071700002105095700002505116700002005141700002405161700001805185700001805203700002105221700002905242700001905271700002105290700002305311700002005334700002305354700001905377700001805396700001705414700001505431700001905446700002205465700002305487700001505510700002105525700002605546700002305572700002205595700001805617700001805635700002505653700002005678700002105698700002205719700002105741700002405762700002405786700001905810700002505829700002405854700002705878700002105905700001805926700001905944700002105963700002005984700002406004700002406028700001906052710002306071710002106094710002406115856003606139 2015 eng d a1474-547X00aHMG-coenzyme A reductase inhibition, type 2 diabetes, and bodyweight: evidence from genetic analysis and randomised trials.0 aHMGcoenzyme A reductase inhibition type 2 diabetes and bodyweigh c2015 Jan 24 a351-610 v3853 aBACKGROUND: Statins increase the risk of new-onset type 2 diabetes mellitus. We aimed to assess whether this increase in risk is a consequence of inhibition of 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR), the intended drug target.
METHODS: We used single nucleotide polymorphisms in the HMGCR gene, rs17238484 (for the main analysis) and rs12916 (for a subsidiary analysis) as proxies for HMGCR inhibition by statins. We examined associations of these variants with plasma lipid, glucose, and insulin concentrations; bodyweight; waist circumference; and prevalent and incident type 2 diabetes. Study-specific effect estimates per copy of each LDL-lowering allele were pooled by meta-analysis. These findings were compared with a meta-analysis of new-onset type 2 diabetes and bodyweight change data from randomised trials of statin drugs. The effects of statins in each randomised trial were assessed using meta-analysis.
FINDINGS: Data were available for up to 223 463 individuals from 43 genetic studies. Each additional rs17238484-G allele was associated with a mean 0·06 mmol/L (95% CI 0·05-0·07) lower LDL cholesterol and higher body weight (0·30 kg, 0·18-0·43), waist circumference (0·32 cm, 0·16-0·47), plasma insulin concentration (1·62%, 0·53-2·72), and plasma glucose concentration (0·23%, 0·02-0·44). The rs12916 SNP had similar effects on LDL cholesterol, bodyweight, and waist circumference. The rs17238484-G allele seemed to be associated with higher risk of type 2 diabetes (odds ratio [OR] per allele 1·02, 95% CI 1·00-1·05); the rs12916-T allele association was consistent (1·06, 1·03-1·09). In 129 170 individuals in randomised trials, statins lowered LDL cholesterol by 0·92 mmol/L (95% CI 0·18-1·67) at 1-year of follow-up, increased bodyweight by 0·24 kg (95% CI 0·10-0·38 in all trials; 0·33 kg, 95% CI 0·24-0·42 in placebo or standard care controlled trials and -0·15 kg, 95% CI -0·39 to 0·08 in intensive-dose vs moderate-dose trials) at a mean of 4·2 years (range 1·9-6·7) of follow-up, and increased the odds of new-onset type 2 diabetes (OR 1·12, 95% CI 1·06-1·18 in all trials; 1·11, 95% CI 1·03-1·20 in placebo or standard care controlled trials and 1·12, 95% CI 1·04-1·22 in intensive-dose vs moderate dose trials).
INTERPRETATION: The increased risk of type 2 diabetes noted with statins is at least partially explained by HMGCR inhibition.
FUNDING: The funding sources are cited at the end of the paper.
10aAged10aBody Mass Index10aBody Weight10aCholesterol, HDL10aCholesterol, LDL10aDiabetes Mellitus, Type 210aFemale10aGenetic Testing10aHumans10aHydroxymethylglutaryl CoA Reductases10aHydroxymethylglutaryl-CoA Reductase Inhibitors10aMale10aMiddle Aged10aPolymorphism, Single Nucleotide10aRandomized Controlled Trials as Topic10aRisk Factors1 aSwerdlow, Daniel, I1 aPreiss, David1 aKuchenbaecker, Karoline, B1 aHolmes, Michael, V1 aEngmann, Jorgen, E L1 aShah, Tina1 aSofat, Reecha1 aStender, Stefan1 aJohnson, Paul, C D1 aScott, Robert, A1 aLeusink, Maarten1 aVerweij, Niek1 aSharp, Stephen, J1 aGuo, Yiran1 aGiambartolomei, Claudia1 aChung, Christina1 aPeasey, Anne1 aAmuzu, Antoinette1 aLi, KaWah1 aPalmen, Jutta1 aHoward, Philip1 aCooper, Jackie, A1 aDrenos, Fotios1 aLi, Yun, R1 aLowe, Gordon1 aGallacher, John1 aStewart, Marlene, C W1 aTzoulaki, Ioanna1 aBuxbaum, Sarah, G1 avan der A, Daphne, L1 aForouhi, Nita, G1 aOnland-Moret, Charlotte, N1 aSchouw, Yvonne, T1 aSchnabel, Renate, B1 aHubacek, Jaroslav, A1 aKubinova, Ruzena1 aBaceviciene, Migle1 aTamosiunas, Abdonas1 aPajak, Andrzej1 aTopor-Madry, Roman1 aStepaniak, Urszula1 aMalyutina, Sofia1 aBaldassarre, Damiano1 aSennblad, Bengt1 aTremoli, Elena1 ade Faire, Ulf1 aVeglia, Fabrizio1 aFord, Ian1 aJukema, Wouter1 aWestendorp, Rudi, G J1 ade Borst, Gert, Jan1 ade Jong, Pim, A1 aAlgra, Ale1 aSpiering, Wilko1 avan der Zee, Anke, H Maitland1 aKlungel, Olaf, H1 ade Boer, Anthonius1 aDoevendans, Pieter, A1 aEaton, Charles, B1 aRobinson, Jennifer, G1 aDuggan, David1 aKjekshus, John1 aDowns, John, R1 aGotto, Antonio, M1 aKeech, Anthony, C1 aMarchioli, Roberto1 aTognoni, Gianni1 aSever, Peter, S1 aPoulter, Neil, R1 aWaters, David, D1 aPedersen, Terje, R1 aAmarenco, Pierre1 aNakamura, Haruo1 aMcMurray, John, J V1 aLewsey, James, D1 aChasman, Daniel, I1 aRidker, Paul, M1 aMaggioni, Aldo, P1 aTavazzi, Luigi1 aRay, Kausik, K1 aSeshasai, Sreenivasa, Rao Kondap1 aManson, JoAnn, E1 aPrice, Jackie, F1 aWhincup, Peter, H1 aMorris, Richard, W1 aLawlor, Debbie, A1 aSmith, George Davey1 aBen-Shlomo, Yoav1 aSchreiner, Pamela, J1 aFornage, Myriam1 aSiscovick, David, S1 aCushman, Mary1 aKumari, Meena1 aWareham, Nick, J1 aVerschuren, W, M Monique1 aRedline, Susan1 aPatel, Sanjay, R1 aWhittaker, John, C1 aHamsten, Anders1 aDelaney, Joseph, A1 aDale, Caroline1 aGaunt, Tom, R1 aWong, Andrew1 aKuh, Diana1 aHardy, Rebecca1 aKathiresan, Sekar1 aCastillo, Berta, A1 aHarst, Pim1 aBrunner, Eric, J1 aTybjaerg-Hansen, Anne1 aMarmot, Michael, G1 aKrauss, Ronald, M1 aTsai, Michael1 aCoresh, Josef1 aHoogeveen, Ronald, C1 aPsaty, Bruce, M1 aLange, Leslie, A1 aHakonarson, Hakon1 aDudbridge, Frank1 aHumphries, Steve, E1 aTalmud, Philippa, J1 aKivimaki, Mika1 aTimpson, Nicholas, J1 aLangenberg, Claudia1 aAsselbergs, Folkert, W1 aVoevoda, Mikhail1 aBobak, Martin1 aPikhart, Hynek1 aWilson, James, G1 aReiner, Alex, P1 aKeating, Brendan, J1 aHingorani, Aroon, D1 aSattar, Naveed1 aDIAGRAM Consortium1 aMAGIC Consortium1 aInterAct Consortium uhttps://chs-nhlbi.org/node/686305080nas a2201129 4500008004100000022001400041245007200055210006900127260001600196300001100212490000700223520191200230653002502142653002102167653002802188653001102216653001902227653001302246653002602259653001102285653000902296653003702305653001602342653003602358653002002394653001802414100002302432700002702455700001902482700001902501700002502520700002702545700002202572700002502594700001702619700002402636700002302660700002602683700002802709700001602737700002102753700001502774700002002789700002802809700001502837700002102852700001802873700002102891700002602912700002602938700002202964700001902986700002103005700002203026700001803048700002003066700002103086700001903107700002403126700002103150700002603171700002203197700002403219700002103243700002203264700001903286700002403305700002003329700002103349700001803370700001803388700001803406700002003424700002103444700002103465700001803486700002403504700001903528700001803547700002003565700002103585700002103606700002503627700002103652700002503673700002003698700002403718700001903742700002203761700002103783700002203804700002403826700002403850700001903874710002103893856003603914 2015 eng d a1522-964500aMendelian randomization of blood lipids for coronary heart disease.0 aMendelian randomization of blood lipids for coronary heart disea c2015 Mar 01 a539-500 v363 aAIMS: To investigate the causal role of high-density lipoprotein cholesterol (HDL-C) and triglycerides in coronary heart disease (CHD) using multiple instrumental variables for Mendelian randomization.
METHODS AND RESULTS: We developed weighted allele scores based on single nucleotide polymorphisms (SNPs) with established associations with HDL-C, triglycerides, and low-density lipoprotein cholesterol (LDL-C). For each trait, we constructed two scores. The first was unrestricted, including all independent SNPs associated with the lipid trait identified from a prior meta-analysis (threshold P < 2 × 10(-6)); and the second a restricted score, filtered to remove any SNPs also associated with either of the other two lipid traits at P ≤ 0.01. Mendelian randomization meta-analyses were conducted in 17 studies including 62,199 participants and 12,099 CHD events. Both the unrestricted and restricted allele scores for LDL-C (42 and 19 SNPs, respectively) associated with CHD. For HDL-C, the unrestricted allele score (48 SNPs) was associated with CHD (OR: 0.53; 95% CI: 0.40, 0.70), per 1 mmol/L higher HDL-C, but neither the restricted allele score (19 SNPs; OR: 0.91; 95% CI: 0.42, 1.98) nor the unrestricted HDL-C allele score adjusted for triglycerides, LDL-C, or statin use (OR: 0.81; 95% CI: 0.44, 1.46) showed a robust association. For triglycerides, the unrestricted allele score (67 SNPs) and the restricted allele score (27 SNPs) were both associated with CHD (OR: 1.62; 95% CI: 1.24, 2.11 and 1.61; 95% CI: 1.00, 2.59, respectively) per 1-log unit increment. However, the unrestricted triglyceride score adjusted for HDL-C, LDL-C, and statin use gave an OR for CHD of 1.01 (95% CI: 0.59, 1.75).
CONCLUSION: The genetic findings support a causal effect of triglycerides on CHD risk, but a causal role for HDL-C, though possible, remains less certain.
10aCase-Control Studies10aCholesterol, HDL10aCoronary Artery Disease10aFemale10aGene Frequency10aGenotype10aGenotyping Techniques10aHumans10aMale10aMendelian Randomization Analysis10aMiddle Aged10aPolymorphism, Single Nucleotide10aRisk Assessment10aTriglycerides1 aHolmes, Michael, V1 aAsselbergs, Folkert, W1 aPalmer, Tom, M1 aDrenos, Fotios1 aLanktree, Matthew, B1 aNelson, Christopher, P1 aDale, Caroline, E1 aPadmanabhan, Sandosh1 aFinan, Chris1 aSwerdlow, Daniel, I1 aTragante, Vinicius1 avan Iperen, Erik, P A1 aSivapalaratnam, Suthesh1 aShah, Sonia1 aElbers, Clara, C1 aShah, Tina1 aEngmann, Jorgen1 aGiambartolomei, Claudia1 aWhite, Jon1 aZabaneh, Delilah1 aSofat, Reecha1 aMcLachlan, Stela1 aDoevendans, Pieter, A1 aBalmforth, Anthony, J1 aHall, Alistair, S1 aNorth, Kari, E1 aAlmoguera, Berta1 aHoogeveen, Ron, C1 aCushman, Mary1 aFornage, Myriam1 aPatel, Sanjay, R1 aRedline, Susan1 aSiscovick, David, S1 aTsai, Michael, Y1 aKarczewski, Konrad, J1 aHofker, Marten, H1 aVerschuren, Monique1 aBots, Michiel, L1 aSchouw, Yvonne, T1 aMelander, Olle1 aDominiczak, Anna, F1 aMorris, Richard1 aBen-Shlomo, Yoav1 aPrice, Jackie1 aKumari, Meena1 aBaumert, Jens1 aPeters, Annette1 aThorand, Barbara1 aKoenig, Wolfgang1 aGaunt, Tom, R1 aHumphries, Steve, E1 aClarke, Robert1 aWatkins, Hugh1 aFarrall, Martin1 aWilson, James, G1 aRich, Stephen, S1 ade Bakker, Paul, I W1 aLange, Leslie, A1 aSmith, George, Davey1 aReiner, Alex, P1 aTalmud, Philippa, J1 aKivimaki, Mika1 aLawlor, Debbie, A1 aDudbridge, Frank1 aSamani, Nilesh, J1 aKeating, Brendan, J1 aHingorani, Aroon, D1 aCasas, Juan, P1 aUCLEB consortium uhttps://chs-nhlbi.org/node/656804529nas a2200901 4500008004100000022001400041245012300055210006900178260000900247300001300256490000700269520197100276653001002247653002102257653000902278653002802287653003502315653002102350653002102371653001602392653003402408653002202442653001802464653001802482653001102500653002202511653001802533653001102551653001702562653001402579653001802593653000902611653001602620653002602636653001502662653003202677653001702709653001202726653001102738100002602749700002802775700001902803700002802822700002702850700002202877700002202899700002602921700002202947700001902969700002502988700002603013700001603039700002203055700001403077700002003091700002203111700003003133700001703163700002403180700002603204700001803230700002003248700002303268700002303291700002303314700002103337700002503358700002503383700002803408700001903436700002003455700002203475700002103497700002503518700002103543700002703564856003603591 2015 eng d a1932-620300aRace/Ethnic Differences in the Associations of the Framingham Risk Factors with Carotid IMT and Cardiovascular Events.0 aRaceEthnic Differences in the Associations of the Framingham Ris c2015 ae01323210 v103 aBACKGROUND: Clinical manifestations and outcomes of atherosclerotic disease differ between ethnic groups. In addition, the prevalence of risk factors is substantially different. Primary prevention programs are based on data derived from almost exclusively White people. We investigated how race/ethnic differences modify the associations of established risk factors with atherosclerosis and cardiovascular events.
METHODS: We used data from an ongoing individual participant meta-analysis involving 17 population-based cohorts worldwide. We selected 60,211 participants without cardiovascular disease at baseline with available data on ethnicity (White, Black, Asian or Hispanic). We generated a multivariable linear regression model containing risk factors and ethnicity predicting mean common carotid intima-media thickness (CIMT) and a multivariable Cox regression model predicting myocardial infarction or stroke. For each risk factor we assessed how the association with the preclinical and clinical measures of cardiovascular atherosclerotic disease was affected by ethnicity.
RESULTS: Ethnicity appeared to significantly modify the associations between risk factors and CIMT and cardiovascular events. The association between age and CIMT was weaker in Blacks and Hispanics. Systolic blood pressure associated more strongly with CIMT in Asians. HDL cholesterol and smoking associated less with CIMT in Blacks. Furthermore, the association of age and total cholesterol levels with the occurrence of cardiovascular events differed between Blacks and Whites.
CONCLUSION: The magnitude of associations between risk factors and the presence of atherosclerotic disease differs between race/ethnic groups. These subtle, yet significant differences provide insight in the etiology of cardiovascular disease among race/ethnic groups. These insights aid the race/ethnic-specific implementation of primary prevention.
10aAdult10aAge Distribution10aAged10aCarotid Artery Diseases10aCarotid Intima-Media Thickness10aCholesterol, HDL10aCholesterol, LDL10aComorbidity10aContinental Population Groups10aDiabetes Mellitus10aDyslipidemias10aEthnic Groups10aFemale10aFollow-Up Studies10aGlobal Health10aHumans10aHypertension10aIncidence10aLinear Models10aMale10aMiddle Aged10aMyocardial Infarction10aPrevalence10aProportional Hazards Models10aRisk Factors10aSmoking10aStroke1 aGijsberts, Crystel, M1 aGroenewegen, Karlijn, A1 aHoefer, Imo, E1 aEijkemans, Marinus, J C1 aAsselbergs, Folkert, W1 aAnderson, Todd, J1 aBritton, Annie, R1 aDekker, Jacqueline, M1 aEngström, Gunnar1 aEvans, Greg, W1 ade Graaf, Jacqueline1 aGrobbee, Diederick, E1 aHedblad, Bo1 aHolewijn, Suzanne1 aIkeda, Ai1 aKitagawa, Kazuo1 aKitamura, Akihiko1 ade Kleijn, Dominique, P V1 aLonn, Eva, M1 aLorenz, Matthias, W1 aMathiesen, Ellisiv, B1 aNijpels, Giel1 aOkazaki, Shuhei1 aO'Leary, Daniel, H1 aPasterkamp, Gerard1 aPeters, Sanne, A E1 aPolak, Joseph, F1 aPrice, Jacqueline, F1 aRobertson, Christine1 aRembold, Christopher, M1 aRosvall, Maria1 aRundek, Tatjana1 aSalonen, Jukka, T1 aSitzer, Matthias1 aStehouwer, Coen, D A1 aBots, Michiel, L1 aRuijter, Hester, M den uhttps://chs-nhlbi.org/node/687605111nas a2200877 4500008004100000022001400041245025000055210006900305260001300374300001100387490000800398520248000406653002202886653003902908653002102947653001902968653000902987653002002996653002603016653002403042653001603066653003103082653001103113653001103124653003603135653003003171653001803201100001303219700002003232700002003252700002003272700002403292700001903316700002103335700001403356700001703370700001303387700001903400700002103419700002303440700002003463700002303483700002103506700002003527700002803547700002303575700001503598700002203613700002103635700001303656700002503669700002003694700002503714700001603739700002203755700002603777700002003803700001903823700002503842700002203867700002503889700002103914700002003935700002003955700002503975700001704000700002204017700003004039700002004069700002404089700001804113700002104131700002104152700002404173856003604197 2016 eng d a1938-320700aInteraction of methylation-related genetic variants with circulating fatty acids on plasma lipids: a meta-analysis of 7 studies and methylation analysis of 3 studies in the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium.0 aInteraction of methylationrelated genetic variants with circulat c2016 Feb a567-780 v1033 aBACKGROUND: DNA methylation is influenced by diet and single nucleotide polymorphisms (SNPs), and methylation modulates gene expression.
OBJECTIVE: We aimed to explore whether the gene-by-diet interactions on blood lipids act through DNA methylation.
DESIGN: We selected 7 SNPs on the basis of predicted relations in fatty acids, methylation, and lipids. We conducted a meta-analysis and a methylation and mediation analysis with the use of data from the CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) consortium and the ENCODE (Encyclopedia of DNA Elements) consortium.
RESULTS: On the basis of the meta-analysis of 7 cohorts in the CHARGE consortium, higher plasma HDL cholesterol was associated with fewer C alleles at ATP-binding cassette subfamily A member 1 (ABCA1) rs2246293 (β = -0.6 mg/dL, P = 0.015) and higher circulating eicosapentaenoic acid (EPA) (β = 3.87 mg/dL, P = 5.62 × 10(21)). The difference in HDL cholesterol associated with higher circulating EPA was dependent on genotypes at rs2246293, and it was greater for each additional C allele (β = 1.69 mg/dL, P = 0.006). In the GOLDN (Genetics of Lipid Lowering Drugs and Diet Network) study, higher ABCA1 promoter cg14019050 methylation was associated with more C alleles at rs2246293 (β = 8.84%, P = 3.51 × 10(18)) and lower circulating EPA (β = -1.46%, P = 0.009), and the mean difference in methylation of cg14019050 that was associated with higher EPA was smaller with each additional C allele of rs2246293 (β = -2.83%, P = 0.007). Higher ABCA1 cg14019050 methylation was correlated with lower ABCA1 expression (r = -0.61, P = 0.009) in the ENCODE consortium and lower plasma HDL cholesterol in the GOLDN study (r = -0.12, P = 0.0002). An additional mediation analysis was meta-analyzed across the GOLDN study, Cardiovascular Health Study, and the Multi-Ethnic Study of Atherosclerosis. Compared with the model without the adjustment of cg14019050 methylation, the model with such adjustment provided smaller estimates of the mean plasma HDL cholesterol concentration in association with both the rs2246293 C allele and EPA and a smaller difference by rs2246293 genotypes in the EPA-associated HDL cholesterol. However, the differences between 2 nested models were NS (P > 0.05).
CONCLUSION: We obtained little evidence that the gene-by-fatty acid interactions on blood lipids act through DNA methylation.
10aApolipoproteins E10aATP Binding Cassette Transporter 110aCholesterol, HDL10aCohort Studies10aDiet10aDNA Methylation10aEicosapentaenoic Acid10aEpigenesis, Genetic10aFatty Acids10aGene Expression Regulation10aHumans10aLipids10aPolymorphism, Single Nucleotide10aPromoter Regions, Genetic10aTriglycerides1 aMa, Yiyi1 aFollis, Jack, L1 aSmith, Caren, E1 aTanaka, Toshiko1 aManichaikul, Ani, W1 aChu, Audrey, Y1 aSamieri, Cecilia1 aZhou, Xia1 aGuan, Weihua1 aWang, Lu1 aBiggs, Mary, L1 aChen, Yii-der, I1 aHernandez, Dena, G1 aBorecki, Ingrid1 aChasman, Daniel, I1 aRich, Stephen, S1 aFerrucci, Luigi1 aIrvin, Marguerite, Ryan1 aAslibekyan, Stella1 aZhi, Degui1 aTiwari, Hemant, K1 aClaas, Steven, A1 aSha, Jin1 aKabagambe, Edmond, K1 aLai, Chao-Qiang1 aParnell, Laurence, D1 aLee, Yu-Chi1 aAmouyel, Philippe1 aLambert, Jean-Charles1 aPsaty, Bruce, M1 aKing, Irena, B1 aMozaffarian, Dariush1 aMcKnight, Barbara1 aBandinelli, Stefania1 aTsai, Michael, Y1 aRidker, Paul, M1 aDing, Jingzhong1 aMstat, Kurt, Lohmant1 aLiu, Yongmei1 aSotoodehnia, Nona1 aBarberger-Gateau, Pascale1 aSteffen, Lyn, M1 aSiscovick, David, S1 aAbsher, Devin1 aArnett, Donna, K1 aOrdovas, Jose, M1 aLemaitre, Rozenn, N uhttps://chs-nhlbi.org/node/695110097nas a2203265 4500008004100000022001400041245010400055210006900159260001500228300000800243490000700251520106700258653001501325653001001340653003901350653000901389653002201398653003701420653001101457653002901468653001601497653002101513653002101534653004001555653001301595653001101608653001701619653003401636653001301670653002301683653001101706653002901717653002101746653001101767653000901778653002201787653003601809653001601845653002001861653002601881653002601907653001801933653001601951100002801967700002001995700002102015700001802036700002102054700002302075700002402098700002302122700001902145700001902164700002002183700002202203700002602225700002302251700001702274700002002291700002302311700002102334700001702355700002302372700002102395700001702416700001402433700002002447700002402467700002102491700002202512700002102534700002502555700002802580700001702608700002002625700002102645700001702666700002102683700002402704700001702728700001902745700002202764700002002786700001302806700001902819700002502838700001502863700002202878700002102900700001402921700001802935700002102953700001902974700001902993700002103012700002203033700002803055700001903083700002103102700001903123700002303142700001703165700002603182700002303208700002703231700002003258700001803278700001803296700001703314700001603331700001903347700001603366700002103382700002103403700001803424700001603442700002203458700002203480700002403502700001803526700002303544700002103567700002203588700001903610700002003629700002203649700002103671700002503692700002303717700002103740700002303761700002103784700001703805700002203822700001403844700002003858700002003878700002003898700002003918700001903938700001903957700002203976700002103998700002804019700002104047700002204068700002304090700002404113700002204137700001904159700002404178700001904202700002004221700001704241700001804258700002104276700002204297700002104319700002104340700002104361700002204382700002204404700001904426700002404445700002704469700002204496700002004518700002804538700002004566700002604586700002704612700001704639700002304656700002004679700002404699700001904723700002304742700001904765700002304784700002104807700001604828700002604844700001904870700001604889700002504905700002104930700002304951700001604974700002304990700002005013700001705033700001905050700002305069700002405092700001405116700002005130700002105150700002305171700002805194700002405222700002205246700002005268700002605288700002105314700002005335700002105355700001305376700001705389700001905406700001405425700002305439700002105462700002105483700002205504700001805526700001805544700001805562700001605580700002305596700002205619700002105641700002205662700001905684700001905703700001905722700002205741700002705763700002905790700002705819700002205846700001705868700001505885700003505900700002405935700002605959700001605985700002506001700001906026700001706045700001506062700001806077700002206095700002006117700002406137700002206161700002306183700002106206700001706227700001906244700002606263700002006289700001906309700002006328700001806348700002406366700001806390700002906408700002506437700001906462700002306481700002206504700002706526700002106553700002006574700001806594700002406612700002006636700002506656700002306681700002406704700002006728700002006748710002706768856003606795 2019 eng d a2041-172300aMulti-ancestry study of blood lipid levels identifies four loci interacting with physical activity.0 aMultiancestry study of blood lipid levels identifies four loci i c2019 01 22 a3760 v103 aMany genetic loci affect circulating lipid levels, but it remains unknown whether lifestyle factors, such as physical activity, modify these genetic effects. To identify lipid loci interacting with physical activity, we performed genome-wide analyses of circulating HDL cholesterol, LDL cholesterol, and triglyceride levels in up to 120,979 individuals of European, African, Asian, Hispanic, and Brazilian ancestry, with follow-up of suggestive associations in an additional 131,012 individuals. We find four loci, in/near CLASP1, LHX1, SNTA1, and CNTNAP2, that are associated with circulating lipid levels through interaction with physical activity; higher levels of physical activity enhance the HDL cholesterol-increasing effects of the CLASP1, LHX1, and SNTA1 loci and attenuate the LDL cholesterol-increasing effect of the CNTNAP2 locus. The CLASP1, LHX1, and SNTA1 regions harbor genes linked to muscle function and lipid metabolism. Our results elucidate the role of physical activity interactions in the genetic contribution to blood lipid levels.
10aAdolescent10aAdult10aAfrican Continental Ancestry Group10aAged10aAged, 80 and over10aAsian Continental Ancestry Group10aBrazil10aCalcium-Binding Proteins10aCholesterol10aCholesterol, HDL10aCholesterol, LDL10aEuropean Continental Ancestry Group10aExercise10aFemale10aGenetic Loci10aGenome-Wide Association Study10aGenotype10aHispanic Americans10aHumans10aLIM-Homeodomain Proteins10aLipid Metabolism10aLipids10aMale10aMembrane Proteins10aMicrotubule-Associated Proteins10aMiddle Aged10aMuscle Proteins10aNerve Tissue Proteins10aTranscription Factors10aTriglycerides10aYoung Adult1 aKilpeläinen, Tuomas, O1 aBentley, Amy, R1 aNoordam, Raymond1 aSung, Yun, Ju1 aSchwander, Karen1 aWinkler, Thomas, W1 aJakupović, Hermina1 aChasman, Daniel, I1 aManning, Alisa1 aNtalla, Ioanna1 aAschard, Hugues1 aBrown, Michael, R1 aFuentes, Lisa, de Las1 aFranceschini, Nora1 aGuo, Xiuqing1 aVojinovic, Dina1 aAslibekyan, Stella1 aFeitosa, Mary, F1 aKho, Minjung1 aMusani, Solomon, K1 aRichard, Melissa1 aWang, 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John1 ade Mutsert, Renée1 ade Silva, Janaka1 ade Vries, Paul, S1 aDemirkan, Ayse1 aDing, Jingzhong1 aEaton, Charles, B1 aFaul, Jessica, D1 aFriedlander, Yechiel1 aGabriel, Kelley, P1 aGhanbari, Mohsen1 aGiulianini, Franco1 aGu, Chi, Charles1 aGu, Dongfeng1 aHarris, Tamara, B1 aHe, Jiang1 aHeikkinen, Sami1 aHeng, Chew-Kiat1 aHunt, Steven, C1 aIkram, Arfan, M1 aJonas, Jost, B1 aKoh, Woon-Puay1 aKomulainen, Pirjo1 aKrieger, Jose, E1 aKritchevsky, Stephen, B1 aKutalik, Zoltán1 aKuusisto, Johanna1 aLangefeld, Carl, D1 aLangenberg, Claudia1 aLauner, Lenore, J1 aLeander, Karin1 aLemaitre, Rozenn, N1 aLewis, Cora, E1 aLiang, Jingjing1 aLiu, Jianjun1 aMägi, Reedik1 aManichaikul, Ani1 aMeitinger, Thomas1 aMetspalu, Andres1 aMilaneschi, Yuri1 aMohlke, Karen, L1 aMosley, Thomas, H1 aMurray, Alison, D1 aNalls, Mike, A1 aNang, Ei-Ei, Khaing1 aNelson, Christopher, P1 aNona, Sotoodehnia1 aNorris, Jill, M1 aNwuba, Chiamaka, Vivian1 aO'Connell, Jeff1 aPalmer, Nicholette, D1 aPapanicolau, George, J1 aPazoki, Raha1 aPedersen, Nancy, L1 aPeters, Annette1 aPeyser, Patricia, A1 aPolasek, Ozren1 aPorteous, David, J1 aPoveda, Alaitz1 aRaitakari, Olli, T1 aRich, Stephen, S1 aRisch, Neil1 aRobinson, Jennifer, G1 aRose, Lynda, M1 aRudan, Igor1 aSchreiner, Pamela, J1 aScott, Robert, A1 aSidney, Stephen, S1 aSims, Mario1 aSmith, Jennifer, A1 aSnieder, Harold1 aSofer, Tamar1 aStarr, John, M1 aSternfeld, Barbara1 aStrauch, Konstantin1 aTang, Hua1 aTaylor, Kent, D1 aTsai, Michael, Y1 aTuomilehto, Jaakko1 aUitterlinden, André, G1 avan der Ende, Yldau1 avan Heemst, Diana1 aVoortman, Trudy1 aWaldenberger, Melanie1 aWennberg, Patrik1 aWilson, Gregory1 aXiang, Yong-Bing1 aYao, Jie1 aYu, Caizheng1 aYuan, Jian-Min1 aZhao, Wei1 aZonderman, Alan, B1 aBecker, Diane, M1 aBoehnke, Michael1 aBowden, Donald, W1 ade Faire, Ulf1 aDeary, Ian, J1 aElliott, Paul1 aEsko, Tõnu1 aFreedman, Barry, I1 aFroguel, Philippe1 aGasparini, Paolo1 aGieger, Christian1 aKato, Norihiro1 aLaakso, Markku1 aLakka, Timo, A1 aLehtimäki, Terho1 aMagnusson, Patrik, K E1 aOldehinkel, Albertine, J1 aPenninx, Brenda, W J H1 aSamani, Nilesh, J1 aShu, Xiao-Ou1 aHarst, Pim1 avan Vliet-Ostaptchouk, Jana, V1 aVollenweider, Peter1 aWagenknecht, Lynne, E1 aWang, Ya, X1 aWareham, Nicholas, J1 aWeir, David, R1 aWu, Tangchun1 aZheng, Wei1 aZhu, Xiaofeng1 aEvans, Michele, K1 aFranks, Paul, W1 aGudnason, Vilmundur1 aHayward, Caroline1 aHorta, Bernardo, L1 aKelly, Tanika, N1 aLiu, Yongmei1 aNorth, Kari, E1 aPereira, Alexandre, C1 aRidker, Paul, M1 aTai, Shyong, E1 avan Dam, Rob, M1 aFox, Ervin, R1 aKardia, Sharon, L R1 aLiu, Ching-Ti1 aMook-Kanamori, Dennis, O1 aProvince, Michael, A1 aRedline, Susan1 aDuijn, Cornelia, M1 aRotter, Jerome, I1 aKooperberg, Charles, B1 aGauderman, James1 aPsaty, Bruce, M1 aRice, Kenneth1 aMunroe, Patricia, B1 aFornage, Myriam1 aCupples, Adrienne, L1 aRotimi, Charles, N1 aMorrison, Alanna, C1 aRao, Dabeeru, C1 aLoos, Ruth, J F1 aLifeLines Cohort Study uhttps://chs-nhlbi.org/node/797601045nas 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