03609nas a2200337 4500008004100000022001400041245010800055210006900163260001600232300001100248490000800259520261400267653001902881653002102900653001102921653001902932653001702951653001102968110004002979700001803019700002103037700002103058700003003079700002403109700001803133700002603151700001803177700002303195700001703218856003603235 2009 eng d a1538-359800aLipoprotein(a) concentration and the risk of coronary heart disease, stroke, and nonvascular mortality.0 aLipoproteina concentration and the risk of coronary heart diseas c2009 Jul 22 a412-230 v3023 a
CONTEXT: Circulating concentration of lipoprotein(a) (Lp[a]), a large glycoprotein attached to a low-density lipoprotein-like particle, may be associated with risk of coronary heart disease (CHD) and stroke.
OBJECTIVE: To assess the relationship of Lp(a) concentration with risk of major vascular and nonvascular outcomes.
STUDY SELECTION: Long-term prospective studies that recorded Lp(a) concentration and subsequent major vascular morbidity and/or cause-specific mortality published between January 1970 and March 2009 were identified through electronic searches of MEDLINE and other databases, manual searches of reference lists, and discussion with collaborators.
DATA EXTRACTION: Individual records were provided for each of 126,634 participants in 36 prospective studies. During 1.3 million person-years of follow-up, 22,076 first-ever fatal or nonfatal vascular disease outcomes or nonvascular deaths were recorded, including 9336 CHD outcomes, 1903 ischemic strokes, 338 hemorrhagic strokes, 751 unclassified strokes, 1091 other vascular deaths, 8114 nonvascular deaths, and 242 deaths of unknown cause. Within-study regression analyses were adjusted for within-person variation and combined using meta-analysis. Analyses excluded participants with known preexisting CHD or stroke at baseline.
DATA SYNTHESIS: Lipoprotein(a) concentration was weakly correlated with several conventional vascular risk factors and it was highly consistent within individuals over several years. Associations of Lp(a) with CHD risk were broadly continuous in shape. In the 24 cohort studies, the rates of CHD in the top and bottom thirds of baseline Lp(a) distributions, respectively, were 5.6 (95% confidence interval [CI], 5.4-5.9) per 1000 person-years and 4.4 (95% CI, 4.2-4.6) per 1000 person-years. The risk ratio for CHD, adjusted for age and sex only, was 1.16 (95% CI, 1.11-1.22) per 3.5-fold higher usual Lp(a) concentration (ie, per 1 SD), and it was 1.13 (95% CI, 1.09-1.18) following further adjustment for lipids and other conventional risk factors. The corresponding adjusted risk ratios were 1.10 (95% CI, 1.02-1.18) for ischemic stroke, 1.01 (95% CI, 0.98-1.05) for the aggregate of nonvascular mortality, 1.00 (95% CI, 0.97-1.04) for cancer deaths, and 1.00 (95% CI, 0.95-1.06) for nonvascular deaths other than cancer.
CONCLUSION: Under a wide range of circumstances, there are continuous, independent, and modest associations of Lp(a) concentration with risk of CHD and stroke that appear exclusive to vascular outcomes.
10aCause of Death10aCoronary Disease10aHumans10aLipoprotein(a)10aRisk Factors10aStroke1 aEmerging Risk Factors Collaboration1 aErqou, Sebhat1 aKaptoge, Stephen1 aPerry, Philip, L1 aDi Angelantonio, Emanuele1 aThompson, Alexander1 aWhite, Ian, R1 aMarcovina, Santica, M1 aCollins, Rory1 aThompson, Simon, G1 aDanesh, John uhttps://chs-nhlbi.org/node/111603743nas a2200553 4500008004100000022001400041245013700055210006900192260001600261300001100277490000800288520220500296653002102501653001502522653001902537653002002556653002302576653001602599653002102615653002302636653002202659653001102681653001502692653001102707653001802718653002002736653001802756653000902774653001602783653001902799653001402818653002402832653002002856653001702876653001802893653001602911653001202927653001102939653001802950110004002968700002103008700003003029700001703059700001903076700002303095700001803118700001703136856003603153 2010 eng d a1474-547X00aC-reactive protein concentration and risk of coronary heart disease, stroke, and mortality: an individual participant meta-analysis.0 aCreactive protein concentration and risk of coronary heart disea c2010 Jan 09 a132-400 v3753 aBACKGROUND: Associations of C-reactive protein (CRP) concentration with risk of major diseases can best be assessed by long-term prospective follow-up of large numbers of people. We assessed the associations of CRP concentration with risk of vascular and non-vascular outcomes under different circumstances.
METHODS: We meta-analysed individual records of 160 309 people without a history of vascular disease (ie, 1.31 million person-years at risk, 27 769 fatal or non-fatal disease outcomes) from 54 long-term prospective studies. Within-study regression analyses were adjusted for within-person variation in risk factor levels.
RESULTS: Log(e) CRP concentration was linearly associated with several conventional risk factors and inflammatory markers, and nearly log-linearly with the risk of ischaemic vascular disease and non-vascular mortality. Risk ratios (RRs) for coronary heart disease per 1-SD higher log(e) CRP concentration (three-fold higher) were 1.63 (95% CI 1.51-1.76) when initially adjusted for age and sex only, and 1.37 (1.27-1.48) when adjusted further for conventional risk factors; 1.44 (1.32-1.57) and 1.27 (1.15-1.40) for ischaemic stroke; 1.71 (1.53-1.91) and 1.55 (1.37-1.76) for vascular mortality; and 1.55 (1.41-1.69) and 1.54 (1.40-1.68) for non-vascular mortality. RRs were largely unchanged after exclusion of smokers or initial follow-up. After further adjustment for fibrinogen, the corresponding RRs were 1.23 (1.07-1.42) for coronary heart disease; 1.32 (1.18-1.49) for ischaemic stroke; 1.34 (1.18-1.52) for vascular mortality; and 1.34 (1.20-1.50) for non-vascular mortality.
INTERPRETATION: CRP concentration has continuous associations with the risk of coronary heart disease, ischaemic stroke, vascular mortality, and death from several cancers and lung disease that are each of broadly similar size. The relevance of CRP to such a range of disorders is unclear. Associations with ischaemic vascular disease depend considerably on conventional risk factors and other markers of inflammation.
FUNDING: British Heart Foundation, UK Medical Research Council, BUPA Foundation, and GlaxoSmithKline.
10aAlcohol Drinking10aBiomarkers10aBlood Pressure10aBody Mass Index10aC-Reactive Protein10aCholesterol10aCoronary Disease10aDatabases, Factual10aDiabetes Mellitus10aFemale10aFibrinogen10aHumans10aInterleukin-610aLeukocyte Count10aLung Diseases10aMale10aMiddle Aged10aMotor Activity10aNeoplasms10aRegression Analysis10aRisk Assessment10aRisk Factors10aSerum Albumin10aSex Factors10aSmoking10aStroke10aTriglycerides1 aEmerging Risk Factors Collaboration1 aKaptoge, Stephen1 aDi Angelantonio, Emanuele1 aLowe, Gordon1 aPepys, Mark, B1 aThompson, Simon, G1 aCollins, Rory1 aDanesh, John uhttps://chs-nhlbi.org/node/115103615nas a2200493 4500008004100000022001400041245014900055210006900204260001600273300001200289490000800301520218300309653005102492653001902543653002102562653001102583653001102594653001802605653001102623653000902634653001602643653002402659653002002683653001702703653001102720653001202731110003602743700002402779700001302803700001502816700001802831700003002849700002102879700002502900700002702925700002002952700001802972700001902990700001903009700002303028700001803051700001703069856003503086 2010 eng d a1474-547X00aLipoprotein-associated phospholipase A(2) and risk of coronary disease, stroke, and mortality: collaborative analysis of 32 prospective studies.0 aLipoproteinassociated phospholipase A2 and risk of coronary dise c2010 May 01 a1536-440 v3753 aBACKGROUND: Lipoprotein-associated phospholipase A(2) (Lp-PLA(2)), an inflammatory enzyme expressed in atherosclerotic plaques, is a therapeutic target being assessed in trials of vascular disease prevention. We investigated associations of circulating Lp-PLA(2) mass and activity with risk of coronary heart disease, stroke, and mortality under different circumstances.
METHODS: With use of individual records from 79 036 participants in 32 prospective studies (yielding 17 722 incident fatal or non-fatal outcomes during 474 976 person-years at risk), we did a meta-analysis of within-study regressions to calculate risk ratios (RRs) per 1 SD higher value of Lp-PLA(2) or other risk factor. The primary outcome was coronary heart disease.
FINDINGS: Lp-PLA(2) activity and mass were associated with each other (r=0.51, 95% CI 0.47-0.56) and proatherogenic lipids. We noted roughly log-linear associations of Lp-PLA(2) activity and mass with risk of coronary heart disease and vascular death. RRs, adjusted for conventional risk factors, were: 1.10 (95% CI 1.05-1.16) with Lp-PLA(2) activity and 1.11 (1.07-1.16) with Lp-PLA(2) mass for coronary heart disease; 1.08 (0.97-1.20) and 1.14 (1.02-1.27) for ischaemic stroke; 1.16 (1.09-1.24) and 1.13 (1.05-1.22) for vascular mortality; and 1.10 (1.04-1.17) and 1.10 (1.03-1.18) for non-vascular mortality, respectively. RRs with Lp-PLA(2) did not differ significantly in people with and without initial stable vascular disease, apart from for vascular death with Lp-PLA(2) mass. Adjusted RRs for coronary heart disease were 1.10 (1.02-1.18) with non-HDL cholesterol and 1.10 (1.00-1.21) with systolic blood pressure.
INTERPRETATION: Lp-PLA(2) activity and mass each show continuous associations with risk of coronary heart disease, similar in magnitude to that with non-HDL cholesterol or systolic blood pressure in this population. Associations of Lp-PLA(2) mass and activity are not exclusive to vascular outcomes, and the vascular associations depend at least partly on lipids.
FUNDING: UK Medical Research Council, GlaxoSmithKline, and British Heart Foundation.
10a1-Alkyl-2-acetylglycerophosphocholine Esterase10aBlood Pressure10aCoronary Disease10aFemale10aHumans10aLinear Models10aLipids10aMale10aMiddle Aged10aProspective Studies10aRisk Assessment10aRisk Factors10aStroke10aSystole1 aLp-PLA(2) Studies Collaboration1 aThompson, Alexander1 aGao, Pei1 aOrfei, Lia1 aWatson, Sarah1 aDi Angelantonio, Emanuele1 aKaptoge, Stephen1 aBallantyne, Christie1 aCannon, Christopher, P1 aCriqui, Michael1 aCushman, Mary1 aHofman, Albert1 aPackard, Chris1 aThompson, Simon, G1 aCollins, Rory1 aDanesh, John uhttps://chs-nhlbi.org/node/58304053nas a2200529 4500008004100000022001400041245009600055210006900151260001600220300001100236490000800247520243900255653002302694653002002717653002202737653002102759653001902780653001302799653001102812653001102823653002202834653002202856653002302878653003702901653001802938653003602956653003002992653001703022653001803039110009403057700001903151700002503170700002303195700002503218700003003243700002403273700002103297700001803318700002203336700002103358700001903379700002403398700002403422700002403446700001703470856003603487 2010 eng d a1474-547X00aTriglyceride-mediated pathways and coronary disease: collaborative analysis of 101 studies.0 aTriglyceridemediated pathways and coronary disease collaborative c2010 May 08 a1634-90 v3753 aBACKGROUND: Whether triglyceride-mediated pathways are causally relevant to coronary heart disease is uncertain. We studied a genetic variant that regulates triglyceride concentration to help judge likelihood of causality.
METHODS: We assessed the -1131T>C (rs662799) promoter polymorphism of the apolipoprotein A5 (APOA5) gene in relation to triglyceride concentration, several other risk factors, and risk of coronary heart disease. We compared disease risk for genetically-raised triglyceride concentration (20,842 patients with coronary heart disease, 35,206 controls) with that recorded for equivalent differences in circulating triglyceride concentration in prospective studies (302 430 participants with no history of cardiovascular disease; 12,785 incident cases of coronary heart disease during 2.79 million person-years at risk). We analysed -1131T>C in 1795 people without a history of cardiovascular disease who had information about lipoprotein concentration and diameter obtained by nuclear magnetic resonance spectroscopy.
FINDINGS: The minor allele frequency of -1131T>C was 8% (95% CI 7-9). -1131T>C was not significantly associated with several non-lipid risk factors or LDL cholesterol, and it was modestly associated with lower HDL cholesterol (mean difference per C allele 3.5% [95% CI 2.6-4.6]; 0.053 mmol/L [0.039-0.068]), lower apolipoprotein AI (1.3% [0.3-2.3]; 0.023 g/L [0.005-0.041]), and higher apolipoprotein B (3.2% [1.3-5.1]; 0.027 g/L [0.011-0.043]). By contrast, for every C allele inherited, mean triglyceride concentration was 16.0% (95% CI 12.9-18.7), or 0.25 mmol/L (0.20-0.29), higher (p=4.4x10(-24)). The odds ratio for coronary heart disease was 1.18 (95% CI 1.11-1.26; p=2.6x10(-7)) per C allele, which was concordant with the hazard ratio of 1.10 (95% CI 1.08-1.12) per 16% higher triglyceride concentration recorded in prospective studies. -1131T>C was significantly associated with higher VLDL particle concentration (mean difference per C allele 12.2 nmol/L [95% CI 7.7-16.7]; p=9.3x10(-8)) and smaller HDL particle size (0.14 nm [0.08-0.20]; p=7.0x10(-5)), factors that could mediate the effects of triglyceride.
INTERPRETATION: These data are consistent with a causal association between triglyceride-mediated pathways and coronary heart disease.
FUNDING: British Heart Foundation, UK Medical Research Council, Novartis.
10aApolipoprotein A-V10aApolipoproteins10aApolipoproteins A10aCoronary Disease10aGene Frequency10aGenotype10aHumans10aLipids10aLipoproteins, HDL10aLipoproteins, LDL10aLipoproteins, VLDL10aMendelian Randomization Analysis10aParticle Size10aPolymorphism, Single Nucleotide10aPromoter Regions, Genetic10aRisk Factors10aTriglycerides1 aTriglyceride Coronary Disease Genetics Consortium and Emerging Risk Factors Collaboration1 aSarwar, Nadeem1 aSandhu, Manjinder, S1 aRicketts, Sally, L1 aButterworth, Adam, S1 aDi Angelantonio, Emanuele1 aBoekholdt, Matthijs1 aOuwehand, Willem1 aWatkins, Hugh1 aSamani, Nilesh, J1 aSaleheen, Danish1 aLawlor, Debbie1 aReilly, Muredach, P1 aHingorani, Aroon, D1 aTalmud, Philippa, J1 aDanesh, John uhttps://chs-nhlbi.org/node/119803669nas a2200529 4500008004100000022001400041245007400055210006900129260001600198300001200214490000800226520220200234653001802436653001902454653002202473653001102495653001102506653001802517653002002535653000902555653001602564653000902580653002202589100003702611700002102648700002402669700003002693700001302723700001902736700002202755700002402777700002302801700001702824700002102841700002102862700001902883700002102902700001802923700002402941700002302965700001902988700002203007700001703029700001703046710004003063856003603103 2011 eng d a1533-440600aDiabetes mellitus, fasting glucose, and risk of cause-specific death.0 aDiabetes mellitus fasting glucose and risk of causespecific deat c2011 Mar 03 a829-8410 v3643 aBACKGROUND: The extent to which diabetes mellitus or hyperglycemia is related to risk of death from cancer or other nonvascular conditions is uncertain.
METHODS: We calculated hazard ratios for cause-specific death, according to baseline diabetes status or fasting glucose level, from individual-participant data on 123,205 deaths among 820,900 people in 97 prospective studies.
RESULTS: After adjustment for age, sex, smoking status, and body-mass index, hazard ratios among persons with diabetes as compared with persons without diabetes were as follows: 1.80 (95% confidence interval [CI], 1.71 to 1.90) for death from any cause, 1.25 (95% CI, 1.19 to 1.31) for death from cancer, 2.32 (95% CI, 2.11 to 2.56) for death from vascular causes, and 1.73 (95% CI, 1.62 to 1.85) for death from other causes. Diabetes (vs. no diabetes) was moderately associated with death from cancers of the liver, pancreas, ovary, colorectum, lung, bladder, and breast. Aside from cancer and vascular disease, diabetes (vs. no diabetes) was also associated with death from renal disease, liver disease, pneumonia and other infectious diseases, mental disorders, nonhepatic digestive diseases, external causes, intentional self-harm, nervous-system disorders, and chronic obstructive pulmonary disease. Hazard ratios were appreciably reduced after further adjustment for glycemia measures, but not after adjustment for systolic blood pressure, lipid levels, inflammation or renal markers. Fasting glucose levels exceeding 100 mg per deciliter (5.6 mmol per liter), but not levels of 70 to 100 mg per deciliter (3.9 to 5.6 mmol per liter), were associated with death. A 50-year-old with diabetes died, on average, 6 years earlier than a counterpart without diabetes, with about 40% of the difference in survival attributable to excess nonvascular deaths.
CONCLUSIONS: In addition to vascular disease, diabetes is associated with substantial premature death from several cancers, infectious diseases, external causes, intentional self-harm, and degenerative disorders, independent of several major risk factors. (Funded by the British Heart Foundation and others.).
10aBlood Glucose10aCause of Death10aDiabetes Mellitus10aFemale10aHumans10aHyperglycemia10aLife Expectancy10aMale10aMiddle Aged10aRisk10aSurvival Analysis1 aSeshasai, Sreenivasa, Rao Kondap1 aKaptoge, Stephen1 aThompson, Alexander1 aDi Angelantonio, Emanuele1 aGao, Pei1 aSarwar, Nadeem1 aWhincup, Peter, H1 aMukamal, Kenneth, J1 aGillum, Richard, F1 aHolme, Ingar1 aNjølstad, Inger1 aFletcher, Astrid1 aNilsson, Peter1 aLewington, Sarah1 aCollins, Rory1 aGudnason, Vilmundur1 aThompson, Simon, G1 aSattar, Naveed1 aSelvin, Elizabeth1 aHu, Frank, B1 aDanesh, John1 aEmerging Risk Factors Collaboration uhttps://chs-nhlbi.org/node/127204370nas 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/156304973nas 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/139905794nas 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/655906729nas a2201369 4500008004100000022001400041245006600055210006500121260001500186300001000201490000800211520297400219653001003193653000903203653001603212653002203228653001103250653001103261653002003272653000903292653001603301653001403317653002603331653001703357653001103374110004003385700003003425700002103455700001903476700001903495700002503514700002103539700002303560700001303583700002003596700002103616700002303637700001903660700002103679700002003700700002403720700002303744700002803767700002003795700001603815700002403831700002103855700002003876700002203896700002103918700002003939700002203959700003003981700001804011700002204029700002304051700002904074700002004103700002104123700001504144700002504159700002104184700002304205700002104228700002204249700002504271700001804296700001604314700002304330700002504353700001804378700002004396700001904416700001904435700001704454700001904471700002404490700002304514700002604537700002104563700002204584700001804606700002104624700001904645700002504664700002204689700002204711700002504733700001904758700002104777700002004798700001804818700002204836700002204858700002004880700002004900700002104920700001804941700002404959700001904983700002205002700001905024700002105043700001805064700002505082700002105107700002105128700001605149700003205165700002205197700002305219700002205242700001905264700002305283700001705306856003605323 2015 eng d a1538-359800aAssociation of Cardiometabolic Multimorbidity With Mortality.0 aAssociation of Cardiometabolic Multimorbidity With Mortality c2015 Jul 7 a52-600 v3143 aIMPORTANCE: The prevalence of cardiometabolic multimorbidity is increasing.
OBJECTIVE: To estimate reductions in life expectancy associated with cardiometabolic multimorbidity.
DESIGN, SETTING, AND PARTICIPANTS: Age- and sex-adjusted mortality rates and hazard ratios (HRs) were calculated using individual participant data from the Emerging Risk Factors Collaboration (689,300 participants; 91 cohorts; years of baseline surveys: 1960-2007; latest mortality follow-up: April 2013; 128,843 deaths). The HRs from the Emerging Risk Factors Collaboration were compared with those from the UK Biobank (499,808 participants; years of baseline surveys: 2006-2010; latest mortality follow-up: November 2013; 7995 deaths). Cumulative survival was estimated by applying calculated age-specific HRs for mortality to contemporary US age-specific death rates.
EXPOSURES: A history of 2 or more of the following: diabetes mellitus, stroke, myocardial infarction (MI).
MAIN OUTCOMES AND MEASURES: All-cause mortality and estimated reductions in life expectancy.
RESULTS: In participants in the Emerging Risk Factors Collaboration without a history of diabetes, stroke, or MI at baseline (reference group), the all-cause mortality rate adjusted to the age of 60 years was 6.8 per 1000 person-years. Mortality rates per 1000 person-years were 15.6 in participants with a history of diabetes, 16.1 in those with stroke, 16.8 in those with MI, 32.0 in those with both diabetes and MI, 32.5 in those with both diabetes and stroke, 32.8 in those with both stroke and MI, and 59.5 in those with diabetes, stroke, and MI. Compared with the reference group, the HRs for all-cause mortality were 1.9 (95% CI, 1.8-2.0) in participants with a history of diabetes, 2.1 (95% CI, 2.0-2.2) in those with stroke, 2.0 (95% CI, 1.9-2.2) in those with MI, 3.7 (95% CI, 3.3-4.1) in those with both diabetes and MI, 3.8 (95% CI, 3.5-4.2) in those with both diabetes and stroke, 3.5 (95% CI, 3.1-4.0) in those with both stroke and MI, and 6.9 (95% CI, 5.7-8.3) in those with diabetes, stroke, and MI. The HRs from the Emerging Risk Factors Collaboration were similar to those from the more recently recruited UK Biobank. The HRs were little changed after further adjustment for markers of established intermediate pathways (eg, levels of lipids and blood pressure) and lifestyle factors (eg, smoking, diet). At the age of 60 years, a history of any 2 of these conditions was associated with 12 years of reduced life expectancy and a history of all 3 of these conditions was associated with 15 years of reduced life expectancy.
CONCLUSIONS AND RELEVANCE: Mortality associated with a history of diabetes, stroke, or MI was similar for each condition. Because any combination of these conditions was associated with multiplicative mortality risk, life expectancy was substantially lower in people with multimorbidity.
10aAdult10aAged10aComorbidity10aDiabetes Mellitus10aFemale10aHumans10aLife Expectancy10aMale10aMiddle Aged10aMortality10aMyocardial Infarction10aRisk Factors10aStroke1 aEmerging Risk Factors Collaboration1 aDi Angelantonio, Emanuele1 aKaptoge, Stephen1 aWormser, David1 aWilleit, Peter1 aButterworth, Adam, S1 aBansal, Narinder1 aO'Keeffe, Linda, M1 aGao, Pei1 aWood, Angela, M1 aBurgess, Stephen1 aFreitag, Daniel, F1 aPennells, Lisa1 aPeters, Sanne, A1 aHart, Carole, L1 aHåheim, Lise, Lund1 aGillum, Richard, F1 aNordestgaard, Børge, G1 aPsaty, Bruce, M1 aYeap, Bu, B1 aKnuiman, Matthew, W1 aNietert, Paul, J1 aKauhanen, Jussi1 aSalonen, Jukka, T1 aKuller, Lewis, H1 aSimons, Leon, A1 aSchouw, Yvonne, T1 aBarrett-Connor, Elizabeth1 aSelmer, Randi1 aCrespo, Carlos, J1 aRodriguez, Beatriz1 aVerschuren, W, M Monique1 aSalomaa, Veikko1 aSvärdsudd, Kurt1 aHarst, Pim1 aBjörkelund, Cecilia1 aWilhelmsen, Lars1 aWallace, Robert, B1 aBrenner, Hermann1 aAmouyel, Philippe1 aBarr, Elizabeth, L M1 aIso, Hiroyasu1 aOnat, Altan1 aTrevisan, Maurizio1 aD'Agostino, Ralph, B1 aCooper, Cyrus1 aKavousi, Maryam1 aWelin, Lennart1 aRoussel, Ronan1 aHu, Frank, B1 aSato, Shinichi1 aDavidson, Karina, W1 aHoward, Barbara, V1 aLeening, Maarten, J G1 aLeening, Maarten1 aRosengren, Annika1 aDörr, Marcus1 aDeeg, Dorly, J H1 aKiechl, Stefan1 aStehouwer, Coen, D A1 aNissinen, Aulikki1 aGiampaoli, Simona1 aDonfrancesco, Chiara1 aKromhout, Daan1 aPrice, Jackie, F1 aPeters, Annette1 aMeade, Tom, W1 aCasiglia, Edoardo1 aLawlor, Debbie, A1 aGallacher, John1 aNagel, Dorothea1 aFranco, Oscar, H1 aAssmann, Gerd1 aDagenais, Gilles, R1 aJukema, Wouter1 aSundström, Johan1 aWoodward, Mark1 aBrunner, Eric, J1 aKhaw, Kay-Tee1 aWareham, Nicholas, J1 aWhitsel, Eric, A1 aNjølstad, Inger1 aHedblad, Bo1 aWassertheil-Smoller, Sylvia1 aEngström, Gunnar1 aRosamond, Wayne, D1 aSelvin, Elizabeth1 aSattar, Naveed1 aThompson, Simon, G1 aDanesh, John uhttps://chs-nhlbi.org/node/681105631nas a2200937 4500008004100000022001400041245013000055210006900185260001200254300001000266490000600276520305400282653000903336653001503345653002803360653001103388653001103399653000903410653001603419653003103435653002203466653002403488653002003512100001903532700002103551700001603572700002203588700001903610700002003629700001903649700001303668700002103681700002003702700002203722700001703744700001603761700001803777700002003795700001703815700001903832700002103851700001503872700001903887700002003906700001703926700002003943700002003963700002003983700002404003700002204027700001804049700001504067700002204082700002004104700002004124700002204144700003004166700001804196700002004214700002104234700001704255700001904272700001904291700002204310700002004332700002504352700002104377700002304398700001904421700002504440700002704465700001704492700001804509700002004527700002004547700002404567700001904591700001704610700003004627856003604657 2016 eng d a2213-859500aNatriuretic peptides and integrated risk assessment for cardiovascular disease: an individual-participant-data meta-analysis.0 aNatriuretic peptides and integrated risk assessment for cardiova c2016 10 a840-90 v43 aBACKGROUND: Guidelines for primary prevention of cardiovascular diseases focus on prediction of coronary heart disease and stroke. We assessed whether or not measurement of N-terminal-pro-B-type natriuretic peptide (NT-proBNP) concentration could enable a more integrated approach than at present by predicting heart failure and enhancing coronary heart disease and stroke risk assessment.
METHODS: In this individual-participant-data meta-analysis, we generated and harmonised individual-participant data from relevant prospective studies via both de-novo NT-proBNP concentration measurement of stored samples and collection of data from studies identified through a systematic search of the literature (PubMed, Scientific Citation Index Expanded, and Embase) for articles published up to Sept 4, 2014, using search terms related to natriuretic peptide family members and the primary outcomes, with no language restrictions. We calculated risk ratios and measures of risk discrimination and reclassification across predicted 10 year risk categories (ie, <5%, 5% to <7·5%, and ≥7·5%), adding assessment of NT-proBNP concentration to that of conventional risk factors (ie, age, sex, smoking status, systolic blood pressure, history of diabetes, and total and HDL cholesterol concentrations). Primary outcomes were the combination of coronary heart disease and stroke, and the combination of coronary heart disease, stroke, and heart failure.
FINDINGS: We recorded 5500 coronary heart disease, 4002 stroke, and 2212 heart failure outcomes among 95 617 participants without a history of cardiovascular disease in 40 prospective studies. Risk ratios (for a comparison of the top third vs bottom third of NT-proBNP concentrations, adjusted for conventional risk factors) were 1·76 (95% CI 1·56-1·98) for the combination of coronary heart disease and stroke and 2·00 (1·77-2·26) for the combination of coronary heart disease, stroke, and heart failure. Addition of information about NT-proBNP concentration to a model containing conventional risk factors was associated with a C-index increase of 0·012 (0·010-0·014) and a net reclassification improvement of 0·027 (0·019-0·036) for the combination of coronary heart disease and stroke and a C-index increase of 0·019 (0·016-0·022) and a net reclassification improvement of 0·028 (0·019-0·038) for the combination of coronary heart disease, stroke, and heart failure.
INTERPRETATION: In people without baseline cardiovascular disease, NT-proBNP concentration assessment strongly predicted first-onset heart failure and augmented coronary heart disease and stroke prediction, suggesting that NT-proBNP concentration assessment could be used to integrate heart failure into cardiovascular disease primary prevention.
FUNDING: British Heart Foundation, Austrian Science Fund, UK Medical Research Council, National Institute for Health Research, European Research Council, and European Commission Framework Programme 7.
10aAged10aBiomarkers10aCardiovascular Diseases10aFemale10aHumans10aMale10aMiddle Aged10aNatriuretic Peptide, Brain10aPeptide Fragments10aProspective Studies10aRisk Assessment1 aWilleit, Peter1 aKaptoge, Stephen1 aWelsh, Paul1 aButterworth, Adam1 aChowdhury, Raj1 aSpackman, Sarah1 aPennells, Lisa1 aGao, Pei1 aBurgess, Stephen1 aFreitag, Daniel1 aSweeting, Michael1 aWood, Angela1 aCook, Nancy1 aJudd, Suzanne1 aTrompet, Stella1 aNambi, Vijay1 aOlsen, Michael1 aEverett, Brendan1 aKee, Frank1 aArnlöv, Johan1 aSalomaa, Veikko1 aLevy, Daniel1 aKauhanen, Jussi1 aLaukkanen, Jari1 aKavousi, Maryam1 aNinomiya, Toshiharu1 aCasas, Juan-Pablo1 aDaniels, Lori1 aLind, Lars1 aKistorp, Caroline1 aRosenberg, Jens1 aMueller, Thomas1 aRubattu, Speranza1 aPanagiotakos, Demosthenes1 aFranco, Oscar1 ade Lemos, James1 aLuchner, Andreas1 aKizer, Jorge1 aKiechl, Stefan1 aSalonen, Jukka1 aWannamethee, Goya1 ade Boer, Rudolf1 aNordestgaard, Børge1 aAndersson, Jonas1 aJørgensen, Torben1 aMelander, Olle1 aBallantyne, Christie1 aDeFilippi, Christopher1 aRidker, Paul1 aCushman, Mary1 aRosamond, Wayne1 aThompson, Simon1 aGudnason, Vilmundur1 aSattar, Naveed1 aDanesh, John1 aDi Angelantonio, Emanuele uhttps://chs-nhlbi.org/node/856709668nas a2203061 4500008004100000022001400041245007500055210006900130260001300199300001400212490000700226520116500233653002801398653003001426653001001456653003201466653003801498653002201536653001301558653001101571653001101582653002501593653001401618653001701632100002001649700002001669700001501689700002501704700001501729700002001744700002101764700001801785700001601803700003101819700002201850700003001872700002401902700002901926700001801955700001701973700002801990700001702018700001902035700001902054700002102073700002102094700002302115700002402138700002102162700001802183700002002201700002302221700002202244700002302266700001702289700002202306700001902328700002402347700001902371700002102390700002102411700002502432700002302457700001702480700001802497700002202515700002102537700002102558700002402579700002002603700002402623700001602647700002502663700002102688700002002709700002002729700001402749700002002763700002002783700002502803700002502828700002202853700002302875700002102898700002202919700001302941700002302954700002202977700002302999700002203022700002103044700001803065700001603083700002003099700002403119700001903143700002203162700002203184700002403206700002303230700002203253700001403275700002003289700002003309700002103329700002603350700002703376700002003403700002503423700001903448700002503467700002103492700002203513700001903535700001503554700001903569700001803588700002503606700002203631700002403653700002003677700002203697700001903719700001603738700002403754700001903778700002203797700002303819700002403842700001603866700002103882700002003903700001803923700001803941700001803959700002303977700002104000700002204021700002404043700002004067700002504087700002004112700002004132700001904152700002104171700002204192700002404214700002104238700003004259700002404289700002104313700002204334700002104356700002804377700002104405700001904426700002904445700002504474700002204499700002204521700002504543700002304568700001804591700002304609700001904632700001904651700002004670700002404690700002004714700001904734700001804753700002004771700002104791700001804812700002104830700002204851700002004873700002004893700002104913700002004934700001904954700002304973700001804996700002205014700001605036700002005052700002205072700001805094700001905112700002205131700002105153700001705174700002305191700002605214700001705240700002805257700002105285700002105306700002005327700002605347700002205373700002405395700002805419700001905447700002605466700002105492700002405513700002605537700002305563700001705586700001805603700001405621700002405635700002005659700002005679700002005699700002305719700002605742700002705768700001805795700002005813700001905833700002605852700001405878700002105892700002105913700002005934700002205954700002105976700002105997700002306018700001306041700002306054700001706077700002406094700001406118700001906132700001806151700001506169700001406184700001706198700002806215700002406243700001706267700002206284700001906306700002206325700002006347700002006367700002306387700002206410710003406432710002906466710002406495710002006519710003106539856003606570 2017 eng d a1546-171800aExome-wide association study of plasma lipids in >300,000 individuals.0 aExomewide association study of plasma lipids in 300000 individua c2017 Dec a1758-17660 v493 aWe screened variants on an exome-focused genotyping array in >300,000 participants (replication in >280,000 participants) and identified 444 independent variants in 250 loci significantly associated with total cholesterol (TC), high-density-lipoprotein cholesterol (HDL-C), low-density-lipoprotein cholesterol (LDL-C), and/or triglycerides (TG). At two loci (JAK2 and A1CF), experimental analysis in mice showed lipid changes consistent with the human data. We also found that: (i) beta-thalassemia trait carriers displayed lower TC and were protected from coronary artery disease (CAD); (ii) excluding the CETP locus, there was not a predictable relationship between plasma HDL-C and risk for age-related macular degeneration; (iii) only some mechanisms of lowering LDL-C appeared to increase risk for type 2 diabetes (T2D); and (iv) TG-lowering alleles involved in hepatic production of TG-rich lipoproteins (TM6SF2 and PNPLA3) tracked with higher liver fat, higher risk for T2D, and lower risk for CAD, whereas TG-lowering alleles involved in peripheral lipolysis (LPL and ANGPTL4) had no effect on liver fat but decreased risks for both T2D and CAD.
10aCoronary Artery Disease10aDiabetes Mellitus, Type 210aExome10aGenetic Association Studies10aGenetic Predisposition to Disease10aGenetic Variation10aGenotype10aHumans10aLipids10aMacular Degeneration10aPhenotype10aRisk Factors1 aLiu, Dajiang, J1 aPeloso, Gina, M1 aYu, Haojie1 aButterworth, Adam, S1 aWang, Xiao1 aMahajan, Anubha1 aSaleheen, Danish1 aEmdin, Connor1 aAlam, Dewan1 aAlves, Alexessander, Couto1 aAmouyel, Philippe1 aDi Angelantonio, Emanuele1 aArveiler, Dominique1 aAssimes, Themistocles, L1 aAuer, Paul, L1 aBaber, Usman1 aBallantyne, Christie, M1 aBang, Lia, E1 aBenn, Marianne1 aBis, Joshua, C1 aBoehnke, Michael1 aBoerwinkle, Eric1 aBork-Jensen, Jette1 aBottinger, Erwin, P1 aBrandslund, Ivan1 aBrown, Morris1 aBusonero, Fabio1 aCaulfield, Mark, J1 aChambers, John, C1 aChasman, Daniel, I1 aChen, Eugene1 aChen, Yii-Der Ida1 aChowdhury, Raj1 aChristensen, Cramer1 aChu, Audrey, Y1 aConnell, John, M1 aCucca, Francesco1 aCupples, Adrienne, L1 aDamrauer, Scott, M1 aDavies, Gail1 aDeary, Ian, J1 aDedoussis, George1 aDenny, Joshua, C1 aDominiczak, Anna1 aDubé, Marie-Pierre1 aEbeling, Tapani1 aEiriksdottir, Gudny1 aEsko, Tõnu1 aFarmaki, Aliki-Eleni1 aFeitosa, Mary, F1 aFerrario, Marco1 aFerrieres, Jean1 aFord, Ian1 aFornage, Myriam1 aFranks, Paul, W1 aFrayling, Timothy, M1 aFrikke-Schmidt, Ruth1 aFritsche, Lars, G1 aFrossard, Philippe1 aFuster, Valentin1 aGanesh, Santhi, K1 aGao, Wei1 aGarcia, Melissa, E1 aGieger, Christian1 aGiulianini, Franco1 aGoodarzi, Mark, O1 aGrallert, Harald1 aGrarup, Niels1 aGroop, Leif1 aGrove, Megan, L1 aGudnason, Vilmundur1 aHansen, Torben1 aHarris, Tamara, B1 aHayward, Caroline1 aHirschhorn, Joel, N1 aHolmen, Oddgeir, L1 aHuffman, Jennifer1 aHuo, Yong1 aHveem, Kristian1 aJabeen, Sehrish1 aJackson, Anne, U1 aJakobsdottir, Johanna1 aJarvelin, Marjo-Riitta1 aJensen, Gorm, B1 aJørgensen, Marit, E1 aJukema, Wouter1 aJustesen, Johanne, M1 aKamstrup, Pia, R1 aKanoni, Stavroula1 aKarpe, Fredrik1 aKee, Frank1 aKhera, Amit, V1 aKlarin, Derek1 aKoistinen, Heikki, A1 aKooner, Jaspal, S1 aKooperberg, Charles1 aKuulasmaa, Kari1 aKuusisto, Johanna1 aLaakso, Markku1 aLakka, Timo1 aLangenberg, Claudia1 aLangsted, Anne1 aLauner, Lenore, J1 aLauritzen, Torsten1 aLiewald, David, C M1 aLin, Li, An1 aLinneberg, Allan1 aLoos, Ruth, J F1 aLu, Yingchang1 aLu, Xiangfeng1 aMägi, Reedik1 aMälarstig, Anders1 aManichaikul, Ani1 aManning, Alisa, K1 aMäntyselkä, Pekka1 aMarouli, Eirini1 aMasca, Nicholas, G D1 aMaschio, Andrea1 aMeigs, James, B1 aMelander, Olle1 aMetspalu, Andres1 aMorris, Andrew, P1 aMorrison, Alanna, C1 aMulas, Antonella1 aMüller-Nurasyid, Martina1 aMunroe, Patricia, B1 aNeville, Matt, J1 aNielsen, Jonas, B1 aNielsen, Sune, F1 aNordestgaard, Børge, G1 aOrdovas, Jose, M1 aMehran, Roxana1 aO'Donnell, Christoper, J1 aOrho-Melander, Marju1 aMolony, Cliona, M1 aMuntendam, Pieter1 aPadmanabhan, Sandosh1 aPalmer, Colin, N A1 aPasko, Dorota1 aPatel, Aniruddh, P1 aPedersen, Oluf1 aPerola, Markus1 aPeters, Annette1 aPisinger, Charlotta1 aPistis, Giorgio1 aPolasek, Ozren1 aPoulter, Neil1 aPsaty, Bruce, M1 aRader, Daniel, J1 aRasheed, Asif1 aRauramaa, Rainer1 aReilly, Dermot, F1 aReiner, Alex, P1 aRenstrom, Frida1 aRich, Stephen, S1 aRidker, Paul, M1 aRioux, John, D1 aRobertson, Neil, R1 aRoden, Dan, M1 aRotter, Jerome, I1 aRudan, Igor1 aSalomaa, Veikko1 aSamani, Nilesh, J1 aSanna, Serena1 aSattar, Naveed1 aSchmidt, Ellen, M1 aScott, Robert, A1 aSever, Peter1 aSevilla, Raquel, S1 aShaffer, Christian, M1 aSim, Xueling1 aSivapalaratnam, Suthesh1 aSmall, Kerrin, S1 aSmith, Albert, V1 aSmith, Blair, H1 aSomayajula, Sangeetha1 aSoutham, Lorraine1 aSpector, Timothy, D1 aSpeliotes, Elizabeth, K1 aStarr, John, M1 aStirrups, Kathleen, E1 aStitziel, Nathan1 aStrauch, Konstantin1 aStringham, Heather, M1 aSurendran, Praveen1 aTada, Hayato1 aTall, Alan, R1 aTang, Hua1 aTardif, Jean-Claude1 aTaylor, Kent, D1 aTrompet, Stella1 aTsao, Philip, S1 aTuomilehto, Jaakko1 aTybjaerg-Hansen, Anne1 avan Zuydam, Natalie, R1 aVarbo, Anette1 aVarga, Tibor, V1 aVirtamo, Jarmo1 aWaldenberger, Melanie1 aWang, Nan1 aWareham, Nick, J1 aWarren, Helen, R1 aWeeke, Peter, E1 aWeinstock, Joshua1 aWessel, Jennifer1 aWilson, James, G1 aWilson, Peter, W F1 aXu, Ming1 aYaghootkar, Hanieh1 aYoung, Robin1 aZeggini, Eleftheria1 aZhang, He1 aZheng, Neil, S1 aZhang, Weihua1 aZhang, Yan1 aZhou, Wei1 aZhou, Yanhua1 aZoledziewska, Magdalena1 aHowson, Joanna, M M1 aDanesh, John1 aMcCarthy, Mark, I1 aCowan, Chad, A1 aAbecasis, Goncalo1 aDeloukas, Panos1 aMusunuru, Kiran1 aWiller, Cristen, J1 aKathiresan, Sekar1 aCharge Diabetes Working Group1 aEPIC-InterAct Consortium1 aEPIC-CVD Consortium1 aGOLD Consortium1 aVA Million Veteran Program uhttps://chs-nhlbi.org/node/757303467nas a2200673 4500008004100000022001400041245015500055210006900210260001600279520146600295100001701761700002101778700001901799700002201818700001901840700003001859700002401889700002801913700002001941700001901961700001801980700001801998700002202016700002102038700002802059700002102087700002402108700002002132700002402152700002402176700001902200700002402219700002002243700001602263700003502279700001902314700001902333700002002352700002302372700001802395700002502413700002102438700002002459700002002479700001902499700001802518700002102536700002202557700002002579700002002599700002202619700002002641700002102661700001802682700001702700700002302717700001702740856003602757 2017 eng d a1476-625600aREPEATED MEASUREMENTS OF BLOOD PRESSURE AND CHOLESTEROL IMPROVES CARDIOVASCULAR DISEASE RISK PREDICTION: AN INDIVIDUAL-PARTICIPANT-DATA META-ANALYSIS.0 aREPEATED MEASUREMENTS OF BLOOD PRESSURE AND CHOLESTEROL IMPROVES c2017 May 263 aThe added value of incorporating information from repeated measurements of blood pressure and cholesterol for cardiovascular disease (CVD) risk prediction has not been rigorously assessed. We used data from the Emerging Risk Factors Collaboration on 191,445 adults (38 cohorts from across 17 countries with data from 1962-2014) with > 1 million measurements of systolic blood pressure, total cholesterol and high-density lipoprotein cholesterol; over a median 12 years of follow-up, 21,170 CVD events occurred. Risk prediction models using cumulative means of repeated measurements and summary measures from longitudinal modelling of the repeated measurements were compared to models using measurements from a single time point. Risk discrimination (C-index) and net reclassification were calculated, and changes in C-indices were meta-analysed across studies. Compared to the single time point model, the cumulative means and the longitudinal models increased the C-index by 0.0040 (95% CI: 0.0023, 0.0057) and 0.0023 (0.0005, 0.0042), respectively. Reclassification was also improved in both models; compared to the single time point model, overall net reclassification improvements were 0.0369 (0.0303, 0.0436) for the cumulative means model and 0.0177 (0.0110, 0.0243) for the longitudinal model. In conclusion, incorporating repeated measurements of blood pressure and cholesterol into CVD risk prediction models slightly improves risk prediction.
1 aPaige, Ellie1 aBarrett, Jessica1 aPennells, Lisa1 aSweeting, Michael1 aWilleit, Peter1 aDi Angelantonio, Emanuele1 aGudnason, Vilmundur1 aNordestgaard, Børge, G1 aPsaty, Bruce, M1 aGoldbourt, Uri1 aBest, Lyle, G1 aAssmann, Gerd1 aSalonen, Jukka, T1 aNietert, Paul, J1 aVerschuren, Wm, Monique1 aBrunner, Eric, J1 aKronmal, Richard, A1 aSalomaa, Veikko1 aBakker, Stephan, Jl1 aDagenais, Gilles, R1 aSato, Shinichi1 aJansson, Jan-Håkan1 aWilleit, Johann1 aOnat, Altan1 ade la Cámara, Agustin, Gómez1 aRoussel, Ronan1 aVölzke, Henry1 aDankner, Rachel1 aTipping, Robert, W1 aMeade, Tom, W1 aDonfrancesco, Chiara1 aKuller, Lewis, H1 aPeters, Annette1 aGallacher, John1 aKromhout, Daan1 aIso, Hiroyasu1 aKnuiman, Matthew1 aCasiglia, Edoardo1 aKavousi, Maryam1 aPalmieri, Luigi1 aSundström, Johan1 aDavis, Barry, R1 aNjølstad, Inger1 aCouper, David1 aDanesh, John1 aThompson, Simon, G1 aWood, Angela uhttps://chs-nhlbi.org/node/745105662nas a2201321 4500008004100000022001400041245015200055210006900207260001600276520185700292100001902149700002102168700001702189700001902206700001802225700001502243700002102258700001902279700001902298700002202317700002202339700001802361700001802379700002402397700001802421700002202439700002002461700001902481700002802500700002202528700002102550700002102571700002302592700002302615700001402638700002402652700002202676700002202698700001902720700001802739700003102757700002202788700002002810700002202830700002302852700002202875700003002897700002002927700002102947700002402968700002302992700001903015700002503034700002503059700002403084700002203108700002003130700001603150700002203166700002303188700002403211700002303235700002203258700002203280700001803302700002203320700001903342700001903361700001903380700002103399700003503420700001903455700002203474700001603496700002003512700002003532700002103552700002003573700002603593700002003619700001703639700002303656700001903679700002103698700002103719700002003740700002103760700002303781700002003804700001903824700002503843700002003868700002103888700001903909700002903928700002503957700001603982700001703998700002204015700001904037700001804056700002004074700001904094700001704113700002004130700001904150700002504169700002304194700001704217700003004234710004004264856003604304 2018 eng d a1522-964500aEqualization of four cardiovascular risk algorithms after systematic recalibration: individual-participant meta-analysis of 86 prospective studies.0 aEqualization of four cardiovascular risk algorithms after system c2018 Nov 223 aAims: There is debate about the optimum algorithm for cardiovascular disease (CVD) risk estimation. We conducted head-to-head comparisons of four algorithms recommended by primary prevention guidelines, before and after 'recalibration', a method that adapts risk algorithms to take account of differences in the risk characteristics of the populations being studied.
Methods and results: Using individual-participant data on 360 737 participants without CVD at baseline in 86 prospective studies from 22 countries, we compared the Framingham risk score (FRS), Systematic COronary Risk Evaluation (SCORE), pooled cohort equations (PCE), and Reynolds risk score (RRS). We calculated measures of risk discrimination and calibration, and modelled clinical implications of initiating statin therapy in people judged to be at 'high' 10 year CVD risk. Original risk algorithms were recalibrated using the risk factor profile and CVD incidence of target populations. The four algorithms had similar risk discrimination. Before recalibration, FRS, SCORE, and PCE over-predicted CVD risk on average by 10%, 52%, and 41%, respectively, whereas RRS under-predicted by 10%. Original versions of algorithms classified 29-39% of individuals aged ≥40 years as high risk. By contrast, recalibration reduced this proportion to 22-24% for every algorithm. We estimated that to prevent one CVD event, it would be necessary to initiate statin therapy in 44-51 such individuals using original algorithms, in contrast to 37-39 individuals with recalibrated algorithms.
Conclusion: Before recalibration, the clinical performance of four widely used CVD risk algorithms varied substantially. By contrast, simple recalibration nearly equalized their performance and improved modelled targeting of preventive action to clinical need.
1 aPennells, Lisa1 aKaptoge, Stephen1 aWood, Angela1 aSweeting, Mike1 aZhao, Xiaohui1 aWhite, Ian1 aBurgess, Stephen1 aWilleit, Peter1 aBolton, Thomas1 aMoons, Karel, G M1 aSchouw, Yvonne, T1 aSelmer, Randi1 aKhaw, Kay-Tee1 aGudnason, Vilmundur1 aAssmann, Gerd1 aAmouyel, Philippe1 aSalomaa, Veikko1 aKivimaki, Mika1 aNordestgaard, Børge, G1 aBlaha, Michael, J1 aKuller, Lewis, H1 aBrenner, Hermann1 aGillum, Richard, F1 aMeisinger, Christa1 aFord, Ian1 aKnuiman, Matthew, W1 aRosengren, Annika1 aLawlor, Debbie, A1 aVölzke, Henry1 aCooper, Cyrus1 aIbañez, Alejandro, Marín1 aCasiglia, Edoardo1 aKauhanen, Jussi1 aCooper, Jackie, A1 aRodriguez, Beatriz1 aSundström, Johan1 aBarrett-Connor, Elizabeth1 aDankner, Rachel1 aNietert, Paul, J1 aDavidson, Karina, W1 aWallace, Robert, B1 aBlazer, Dan, G1 aBjörkelund, Cecilia1 aDonfrancesco, Chiara1 aKrumholz, Harlan, M1 aNissinen, Aulikki1 aDavis, Barry, R1 aCoady, Sean1 aWhincup, Peter, H1 aJørgensen, Torben1 aDucimetiere, Pierre1 aTrevisan, Maurizio1 aEngström, Gunnar1 aCrespo, Carlos, J1 aMeade, Tom, W1 aVisser, Marjolein1 aKromhout, Daan1 aKiechl, Stefan1 aDaimon, Makoto1 aPrice, Jackie, F1 ade la Cámara, Agustin, Gómez1 aJukema, Wouter1 aLamarche, Benoît1 aOnat, Altan1 aSimons, Leon, A1 aKavousi, Maryam1 aBen-Shlomo, Yoav1 aGallacher, John1 aDekker, Jacqueline, M1 aArima, Hisatomi1 aShara, Nawar1 aTipping, Robert, W1 aRoussel, Ronan1 aBrunner, Eric, J1 aKoenig, Wolfgang1 aSakurai, Masaru1 aPavlovic, Jelena1 aGansevoort, Ron, T1 aNagel, Dorothea1 aGoldbourt, Uri1 aBarr, Elizabeth, L M1 aPalmieri, Luigi1 aNjølstad, Inger1 aSato, Shinichi1 aVerschuren, W, M Monique1 aVarghese, Cherian, V1 aGraham, Ian1 aOnuma, Oyere1 aGreenland, Philip1 aWoodward, Mark1 aEzzati, Majid1 aPsaty, Bruce, M1 aSattar, Naveed1 aJackson, Rod1 aRidker, Paul, M1 aCook, Nancy, R1 aD'Agostino, Ralph, B1 aThompson, Simon, G1 aDanesh, John1 aDi Angelantonio, Emanuele1 aEmerging Risk Factors Collaboration uhttps://chs-nhlbi.org/node/792308059nas a2201585 4500008004100000022001400041245015200055210006900207260001500276300001400291490000800305520349800313100002003811700002103831700002503852700001903877700002403896700001903920700001703939700001803956700002203974700002103996700001704017700001904034700001904053700002004072700002104092700002904113700001904142700002104161700001904182700002004201700002804221700001604249700002104265700001904286700002104305700002104326700002004347700002104367700002204388700001804410700001804428700002104446700002104467700002004488700003504508700001904543700002304562700002204585700002504607700002204632700002204654700001804676700002004694700001904714700002404733700002304757700002004780700002304800700002604823700002404849700002104873700002404894700002304918700001804941700002004959700001904979700002104998700001905019700002205038700002005060700001605080700002105096700002605117700002105143700002005164700001905184700003105203700001905234700002405253700001705277700002005294700002105314700001805335700002205353700002205375700002005397700002005417700003305437700002205470700002605492700001805518700002605536700003005562700002405592700001905616700001705635700002005652700001805672700002405690700001805714700002005732700002605752700001805778700002205796700002505818700002005843700002205863700002105885700002005906700002305926700002205949700002105971700002005992700002406012700002106036700001806057700002306075700002306098700001406121700001906135700001806154700002306172700002006195700001906215700002406234700001806258700001706276700001706293700003006310700001706340710008006357856003606437 2018 eng d a1474-547X00aRisk thresholds for alcohol consumption: combined analysis of individual-participant data for 599 912 current drinkers in 83 prospective studies.0 aRisk thresholds for alcohol consumption combined analysis of ind c2018 04 14 a1513-15230 v3913 aBACKGROUND: Low-risk limits recommended for alcohol consumption vary substantially across different national guidelines. To define thresholds associated with lowest risk for all-cause mortality and cardiovascular disease, we studied individual-participant data from 599 912 current drinkers without previous cardiovascular disease.
METHODS: We did a combined analysis of individual-participant data from three large-scale data sources in 19 high-income countries (the Emerging Risk Factors Collaboration, EPIC-CVD, and the UK Biobank). We characterised dose-response associations and calculated hazard ratios (HRs) per 100 g per week of alcohol (12·5 units per week) across 83 prospective studies, adjusting at least for study or centre, age, sex, smoking, and diabetes. To be eligible for the analysis, participants had to have information recorded about their alcohol consumption amount and status (ie, non-drinker vs current drinker), plus age, sex, history of diabetes and smoking status, at least 1 year of follow-up after baseline, and no baseline history of cardiovascular disease. The main analyses focused on current drinkers, whose baseline alcohol consumption was categorised into eight predefined groups according to the amount in grams consumed per week. We assessed alcohol consumption in relation to all-cause mortality, total cardiovascular disease, and several cardiovascular disease subtypes. We corrected HRs for estimated long-term variability in alcohol consumption using 152 640 serial alcohol assessments obtained some years apart (median interval 5·6 years [5th-95th percentile 1·04-13·5]) from 71 011 participants from 37 studies.
FINDINGS: In the 599 912 current drinkers included in the analysis, we recorded 40 310 deaths and 39 018 incident cardiovascular disease events during 5·4 million person-years of follow-up. For all-cause mortality, we recorded a positive and curvilinear association with the level of alcohol consumption, with the minimum mortality risk around or below 100 g per week. Alcohol consumption was roughly linearly associated with a higher risk of stroke (HR per 100 g per week higher consumption 1·14, 95% CI, 1·10-1·17), coronary disease excluding myocardial infarction (1·06, 1·00-1·11), heart failure (1·09, 1·03-1·15), fatal hypertensive disease (1·24, 1·15-1·33); and fatal aortic aneurysm (1·15, 1·03-1·28). By contrast, increased alcohol consumption was log-linearly associated with a lower risk of myocardial infarction (HR 0·94, 0·91-0·97). In comparison to those who reported drinking >0-≤100 g per week, those who reported drinking >100-≤200 g per week, >200-≤350 g per week, or >350 g per week had lower life expectancy at age 40 years of approximately 6 months, 1-2 years, or 4-5 years, respectively.
INTERPRETATION: In current drinkers of alcohol in high-income countries, the threshold for lowest risk of all-cause mortality was about 100 g/week. For cardiovascular disease subtypes other than myocardial infarction, there were no clear risk thresholds below which lower alcohol consumption stopped being associated with lower disease risk. These data support limits for alcohol consumption that are lower than those recommended in most current guidelines.
FUNDING: UK Medical Research Council, British Heart Foundation, National Institute for Health Research, European Union Framework 7, and European Research Council.
1 aWood, Angela, M1 aKaptoge, Stephen1 aButterworth, Adam, S1 aWilleit, Peter1 aWarnakula, Samantha1 aBolton, Thomas1 aPaige, Ellie1 aPaul, Dirk, S1 aSweeting, Michael1 aBurgess, Stephen1 aBell, Steven1 aAstle, William1 aStevens, David1 aKoulman, Albert1 aSelmer, Randi, M1 aVerschuren, W, M Monique1 aSato, Shinichi1 aNjølstad, Inger1 aWoodward, Mark1 aSalomaa, Veikko1 aNordestgaard, Børge, G1 aYeap, Bu, B1 aFletcher, Astrid1 aMelander, Olle1 aKuller, Lewis, H1 aBalkau, Beverley1 aMarmot, Michael1 aKoenig, Wolfgang1 aCasiglia, Edoardo1 aCooper, Cyrus1 aArndt, Volker1 aFranco, Oscar, H1 aWennberg, Patrik1 aGallacher, John1 ade la Cámara, Agustin, Gómez1 aVölzke, Henry1 aDahm, Christina, C1 aDale, Caroline, E1 aBergmann, Manuela, M1 aCrespo, Carlos, J1 aSchouw, Yvonne, T1 aKaaks, Rudolf1 aSimons, Leon, A1 aLagiou, Pagona1 aSchoufour, Josje, D1 aBoer, Jolanda, M A1 aKey, Timothy, J1 aRodriguez, Beatriz1 aMoreno-Iribas, Conchi1 aDavidson, Karina, W1 aTaylor, James, O1 aSacerdote, Carlotta1 aWallace, Robert, B1 aQuiros, Ramon1 aTumino, Rosario1 aBlazer, Dan, G1 aLinneberg, Allan1 aDaimon, Makoto1 aPanico, Salvatore1 aHoward, Barbara1 aSkeie, Guri1 aStrandberg, Timo1 aWeiderpass, Elisabete1 aNietert, Paul, J1 aPsaty, Bruce, M1 aKromhout, Daan1 aSalamanca-Fernandez, Elena1 aKiechl, Stefan1 aKrumholz, Harlan, M1 aGrioni, Sara1 aPalli, Domenico1 aHuerta, José, M1 aPrice, Jackie1 aSundström, Johan1 aArriola, Larraitz1 aArima, Hisatomi1 aTravis, Ruth, C1 aPanagiotakos, Demosthenes, B1 aKarakatsani, Anna1 aTrichopoulou, Antonia1 aKühn, Tilman1 aGrobbee, Diederick, E1 aBarrett-Connor, Elizabeth1 avan Schoor, Natasja1 aBoeing, Heiner1 aOvervad, Kim1 aKauhanen, Jussi1 aWareham, Nick1 aLangenberg, Claudia1 aForouhi, Nita1 aWennberg, Maria1 aDesprés, Jean-Pierre1 aCushman, Mary1 aCooper, Jackie, A1 aRodriguez, Carlos, J1 aSakurai, Masaru1 aShaw, Jonathan, E1 aKnuiman, Matthew1 aVoortman, Trudy1 aMeisinger, Christa1 aTjønneland, Anne1 aBrenner, Hermann1 aPalmieri, Luigi1 aDallongeville, Jean1 aBrunner, Eric, J1 aAssmann, Gerd1 aTrevisan, Maurizio1 aGillum, Richard, F1 aFord, Ian1 aSattar, Naveed1 aLazo, Mariana1 aThompson, Simon, G1 aFerrari, Pietro1 aLeon, David, A1 aSmith, George Davey1 aPeto, Richard1 aJackson, Rod1 aBanks, Emily1 aDi Angelantonio, Emanuele1 aDanesh, John1 aEmerging Risk Factors Collaboration/EPIC-CVD/UK Biobank Alcohol Study Group uhttps://chs-nhlbi.org/node/766413143nas a2204261 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2019 eng d a1546-171800aA catalog of genetic loci associated with kidney function from analyses of a million individuals.0 acatalog of genetic loci associated with kidney function from ana c2019 06 a957-9720 v513 aChronic kidney disease (CKD) is responsible for a public health burden with multi-systemic complications. Through trans-ancestry meta-analysis of genome-wide association studies of estimated glomerular filtration rate (eGFR) and independent replication (n = 1,046,070), we identified 264 associated loci (166 new). Of these, 147 were likely to be relevant for kidney function on the basis of associations with the alternative kidney function marker blood urea nitrogen (n = 416,178). Pathway and enrichment analyses, including mouse models with renal phenotypes, support the kidney as the main target organ. A genetic risk score for lower eGFR was associated with clinically diagnosed CKD in 452,264 independent individuals. Colocalization analyses of associations with eGFR among 783,978 European-ancestry individuals and gene expression across 46 human tissues, including tubulo-interstitial and glomerular kidney compartments, identified 17 genes differentially expressed in kidney. Fine-mapping highlighted missense driver variants in 11 genes and kidney-specific regulatory variants. These results provide a comprehensive priority list of molecular targets for translational research.
10aChromosome Mapping10aEuropean Continental Ancestry Group10aGenetic Association Studies10aGenetic Predisposition to Disease10aGenome-Wide Association Study10aGlomerular Filtration Rate10aHumans10aInheritance Patterns10aKidney Function Tests10aPhenotype10aPolymorphism, Single Nucleotide10aQuantitative Trait Loci10aQuantitative Trait, Heritable10aRenal Insufficiency, Chronic10aUromodulin1 aWuttke, Matthias1 aLi, Yong1 aLi, Man1 aSieber, Karsten, B1 aFeitosa, Mary, F1 aGorski, Mathias1 aTin, Adrienne1 aWang, Lihua1 aChu, Audrey, Y1 aHoppmann, Anselm1 aKirsten, Holger1 aGiri, Ayush1 aChai, Jin-Fang1 aSveinbjornsson, Gardar1 aTayo, Bamidele, O1 aNutile, Teresa1 aFuchsberger, Christian1 aMarten, Jonathan1 aCocca, Massimiliano1 aGhasemi, Sahar1 aXu, Yizhe1 aHorn, Katrin1 aNoce, Damia1 avan der Most, Peter, J1 aSedaghat, Sanaz1 aYu, Zhi1 aAkiyama, Masato1 aAfaq, Saima1 aAhluwalia, Tarunveer, S1 aAlmgren, Peter1 aAmin, Najaf1 aArnlöv, Johan1 aBakker, Stephan, J L1 aBansal, Nisha1 aBaptista, Daniela1 aBergmann, Sven1 aBiggs, Mary, L1 aBiino, Ginevra1 aBoehnke, Michael1 aBoerwinkle, Eric1 aBoissel, Mathilde1 aBottinger, Erwin, P1 aBoutin, Thibaud, S1 aBrenner, Hermann1 aBrumat, Marco1 aBurkhardt, Ralph1 aButterworth, Adam, S1 aCampana, Eric1 aCampbell, Archie1 aCampbell, Harry1 aCanouil, Mickaël1 aCarroll, Robert, J1 aCatamo, Eulalia1 aChambers, John, C1 aChee, Miao-Ling1 aChee, Miao-Li1 aChen, Xu1 aCheng, Ching-Yu1 aCheng, Yurong1 aChristensen, Kaare1 aCifkova, Renata1 aCiullo, Marina1 aConcas, Maria, Pina1 aCook, James, P1 aCoresh, Josef1 aCorre, Tanguy1 aSala, Cinzia, Felicita1 aCusi, Daniele1 aDanesh, John1 aDaw, Warwick1 ade Borst, Martin, H1 aDe Grandi, Alessandro1 ade Mutsert, Renée1 ade Vries, Aiko, P J1 aDegenhardt, Frauke1 aDelgado, Graciela1 aDemirkan, Ayse1 aDi Angelantonio, Emanuele1 aDittrich, Katalin1 aDivers, Jasmin1 aDorajoo, Rajkumar1 aEckardt, Kai-Uwe1 aEhret, Georg1 aElliott, Paul1 aEndlich, Karlhans1 aEvans, Michele, K1 aFelix, Janine, F1 aFoo, Valencia, Hui Xian1 aFranco, Oscar, H1 aFranke, Andre1 aFreedman, Barry, I1 aFreitag-Wolf, Sandra1 aFriedlander, Yechiel1 aFroguel, Philippe1 aGansevoort, Ron, T1 aGao, He1 aGasparini, Paolo1 aGaziano, Michael1 aGiedraitis, Vilmantas1 aGieger, Christian1 aGirotto, Giorgia1 aGiulianini, Franco1 aGögele, Martin1 aGordon, Scott, D1 aGudbjartsson, Daniel, F1 aGudnason, Vilmundur1 aHaller, Toomas1 aHamet, Pavel1 aHarris, Tamara, B1 aHartman, Catharina, A1 aHayward, Caroline1 aHellwege, Jacklyn, N1 aHeng, Chew-Kiat1 aHicks, Andrew, A1 aHofer, Edith1 aHuang, Wei1 aHutri-Kähönen, Nina1 aHwang, Shih-Jen1 aIkram, Arfan, M1 aIndridason, Olafur, S1 aIngelsson, Erik1 aIsing, Marcus1 aJaddoe, Vincent, W V1 aJakobsdottir, Johanna1 aJonas, Jost, B1 aJoshi, Peter, K1 aJosyula, Navya, Shilpa1 aJung, Bettina1 aKähönen, Mika1 aKamatani, Yoichiro1 aKammerer, Candace, M1 aKanai, Masahiro1 aKastarinen, Mika1 aKerr, Shona, M1 aKhor, Chiea-Chuen1 aKiess, Wieland1 aKleber, Marcus, E1 aKoenig, Wolfgang1 aKooner, Jaspal, S1 aKörner, Antje1 aKovacs, Peter1 aKraja, Aldi, T1 aKrajcoviechova, Alena1 aKramer, Holly1 aKrämer, Bernhard, K1 aKronenberg, Florian1 aKubo, Michiaki1 aKuhnel, Brigitte1 aKuokkanen, Mikko1 aKuusisto, Johanna1 aLa Bianca, Martina1 aLaakso, Markku1 aLange, Leslie, A1 aLangefeld, Carl, D1 aLee, Jeannette, Jen-Mai1 aLehne, Benjamin1 aLehtimäki, Terho1 aLieb, Wolfgang1 aLim, Su-Chi1 aLind, Lars1 aLindgren, Cecilia, M1 aLiu, Jun1 aLiu, Jianjun1 aLoeffler, Markus1 aLoos, Ruth, J F1 aLucae, Susanne1 aLukas, Mary, Ann1 aLyytikäinen, Leo-Pekka1 aMägi, Reedik1 aMagnusson, Patrik, K E1 aMahajan, Anubha1 aMartin, Nicholas, G1 aMartins, Jade1 aMärz, Winfried1 aMascalzoni, Deborah1 aMatsuda, Koichi1 aMeisinger, Christa1 aMeitinger, Thomas1 aMelander, Olle1 aMetspalu, Andres1 aMikaelsdottir, Evgenia, K1 aMilaneschi, Yuri1 aMiliku, Kozeta1 aMishra, Pashupati, P1 aMohlke, Karen, L1 aMononen, Nina1 aMontgomery, Grant, W1 aMook-Kanamori, Dennis, O1 aMychaleckyj, Josyf, C1 aNadkarni, Girish, N1 aNalls, Mike, A1 aNauck, Matthias1 aNikus, Kjell1 aNing, Boting1 aNolte, Ilja, M1 aNoordam, Raymond1 aO'Connell, Jeffrey1 aO'Donoghue, Michelle, L1 aOlafsson, Isleifur1 aOldehinkel, Albertine, J1 aOrho-Melander, Marju1 aOuwehand, Willem, H1 aPadmanabhan, Sandosh1 aPalmer, Nicholette, D1 aPalsson, Runolfur1 aPenninx, Brenda, W J H1 aPerls, Thomas1 aPerola, Markus1 aPirastu, Mario1 aPirastu, Nicola1 aPistis, Giorgio1 aPodgornaia, Anna, I1 aPolasek, Ozren1 aPonte, Belen1 aPorteous, David, J1 aPoulain, Tanja1 aPramstaller, Peter, P1 aPreuss, Michael, H1 aPrins, Bram, P1 aProvince, Michael, A1 aRabelink, Ton, J1 aRaffield, Laura, M1 aRaitakari, Olli, T1 aReilly, Dermot, F1 aRettig, Rainer1 aRheinberger, Myriam1 aRice, Kenneth, M1 aRidker, Paul, M1 aRivadeneira, Fernando1 aRizzi, Federica1 aRoberts, David, J1 aRobino, Antonietta1 aRossing, Peter1 aRudan, Igor1 aRueedi, Rico1 aRuggiero, Daniela1 aRyan, Kathleen, A1 aSaba, Yasaman1 aSabanayagam, Charumathi1 aSalomaa, Veikko1 aSalvi, Erika1 aSaum, Kai-Uwe1 aSchmidt, Helena1 aSchmidt, Reinhold1 aSchöttker, Ben1 aSchulz, Christina-Alexandra1 aSchupf, Nicole1 aShaffer, Christian, M1 aShi, Yuan1 aSmith, Albert, V1 aSmith, Blair, H1 aSoranzo, Nicole1 aSpracklen, Cassandra, N1 aStrauch, Konstantin1 aStringham, Heather, M1 aStumvoll, Michael1 aSvensson, Per, O1 aSzymczak, Silke1 aTai, E-Shyong1 aTajuddin, Salman, M1 aTan, Nicholas, Y Q1 aTaylor, Kent, D1 aTeren, Andrej1 aTham, Yih-Chung1 aThiery, Joachim1 aThio, Chris, H L1 aThomsen, Hauke1 aThorleifsson, Gudmar1 aToniolo, Daniela1 aTönjes, Anke1 aTremblay, Johanne1 aTzoulaki, Ioanna1 aUitterlinden, André, G1 aVaccargiu, Simona1 avan Dam, Rob, M1 aHarst, Pim1 aDuijn, Cornelia, M1 aEdward, Digna, R Velez1 aVerweij, Niek1 aVogelezang, Suzanne1 aVölker, Uwe1 aVollenweider, Peter1 aWaeber, Gérard1 aWaldenberger, Melanie1 aWallentin, Lars1 aWang, Ya, Xing1 aWang, Chaolong1 aWaterworth, Dawn, M1 aBin Wei, Wen1 aWhite, Harvey1 aWhitfield, John, B1 aWild, Sarah, H1 aWilson, James, F1 aWojczynski, Mary, K1 aWong, Charlene1 aWong, Tien-Yin1 aXu, Liang1 aYang, Qiong1 aYasuda, Masayuki1 aYerges-Armstrong, Laura, M1 aZhang, Weihua1 aZonderman, Alan, B1 aRotter, Jerome, I1 aBochud, Murielle1 aPsaty, Bruce, M1 aVitart, Veronique1 aWilson, James, G1 aDehghan, Abbas1 aParsa, Afshin1 aChasman, Daniel, I1 aHo, Kevin1 aMorris, Andrew, P1 aDevuyst, Olivier1 aAkilesh, Shreeram1 aPendergrass, Sarah, A1 aSim, Xueling1 aBöger, Carsten, A1 aOkada, Yukinori1 aEdwards, Todd, L1 aSnieder, Harold1 aStefansson, Kari1 aHung, Adriana, M1 aHeid, Iris, M1 aScholz, Markus1 aTeumer, Alexander1 aKöttgen, Anna1 aPattaro, Cristian1 aLifeLines Cohort Study1 aV. A. Million Veteran Program uhttps://chs-nhlbi.org/node/810905243nas a2201489 4500008004100000022001400041245006800055210006300123260001600186300001800202490000800220520113000228100002201358700001701380700001801397700002101415700001801436700001501454700002001469700002301489700002301512700002501535700002201560700001601582700002201598700002801620700002301648700002201671700001801693700001901711700002201730700001701752700001701769700002001786700002001806700002101826700002001847700002301867700002401890700002301914700002601937700002901963700002301992700002002015700001702035700003002052700001602082700002002098700001802118700001602136700002202152700002202174700002002196700001702216700001802233700002002251700001202271700002402283700001902307700001902326700002402345700001502369700002002384700002002404700002002424700002302444700002002467700002402487700002302511700002102534700002202555700002102577700001702598700002802615700002102643700002002664700002102684700001802705700002402723700002402747700001702771700002102788700001902809700002002828700002002848700002302868700002102891700002902912700002302941700002202964700002002986700002803006700002303034700001403057700002403071700002103095700001703116700001903133700002503152700001803177700001203195700001803207700002103225700002003246700002303266700001503289700001203304700002003316700002203336700002003358700001903378700002503397700002003422700002203442700002003464700002303484700001803507700002203525700002503547700002503572700002403597700002203621700002303643700002003666710003103686856003603717 2020 eng d a1097-417200aThe Polygenic and Monogenic Basis of Blood Traits and Diseases.0 aPolygenic and Monogenic Basis of Blood Traits and Diseases c2020 Sep 03 a1214-1231.e110 v1823 aBlood cells play essential roles in human health, underpinning physiological processes such as immunity, oxygen transport, and clotting, which when perturbed cause a significant global health burden. Here we integrate data from UK Biobank and a large-scale international collaborative effort, including data for 563,085 European ancestry participants, and discover 5,106 new genetic variants independently associated with 29 blood cell phenotypes covering a range of variation impacting hematopoiesis. We holistically characterize the genetic architecture of hematopoiesis, assess the relevance of the omnigenic model to blood cell phenotypes, delineate relevant hematopoietic cell states influenced by regulatory genetic variants and gene networks, identify novel splice-altering variants mediating the associations, and assess the polygenic prediction potential for blood traits and clinical disorders at the interface of complex and Mendelian genetics. These results show the power of large-scale blood cell trait GWAS to interrogate clinically meaningful variants across a wide allelic spectrum of human variation.
1 aVuckovic, Dragana1 aBao, Erik, L1 aAkbari, Parsa1 aLareau, Caleb, A1 aMousas, Abdou1 aJiang, Tao1 aChen, Ming-Huei1 aRaffield, Laura, M1 aTardaguila, Manuel1 aHuffman, Jennifer, E1 aRitchie, Scott, C1 aMegy, Karyn1 aPonstingl, Hannes1 aPenkett, Christopher, J1 aAlbers, Patrick, K1 aWigdor, Emilie, M1 aSakaue, Saori1 aMoscati, Arden1 aManansala, Regina1 aLo, Ken, Sin1 aQian, Huijun1 aAkiyama, Masato1 aBartz, Traci, M1 aBen-Shlomo, Yoav1 aBeswick, Andrew1 aBork-Jensen, Jette1 aBottinger, Erwin, P1 aBrody, Jennifer, A1 avan Rooij, Frank, J A1 aChitrala, Kumaraswamy, N1 aWilson, Peter, W F1 aChoquet, Helene1 aDanesh, John1 aDi Angelantonio, Emanuele1 aDimou, Niki1 aDing, Jingzhong1 aElliott, Paul1 aEsko, Tõnu1 aEvans, Michele, K1 aFelix, Stephan, B1 aFloyd, James, S1 aBroer, Linda1 aGrarup, Niels1 aGuo, Michael, H1 aGuo, Qi1 aGreinacher, Andreas1 aHaessler, Jeff1 aHansen, Torben1 aHowson, Joanna, M M1 aHuang, Wei1 aJorgenson, Eric1 aKacprowski, Tim1 aKähönen, Mika1 aKamatani, Yoichiro1 aKanai, Masahiro1 aKarthikeyan, Savita1 aKoskeridis, Fotios1 aLange, Leslie, A1 aLehtimäki, Terho1 aLinneberg, Allan1 aLiu, Yongmei1 aLyytikäinen, Leo-Pekka1 aManichaikul, Ani1 aMatsuda, Koichi1 aMohlke, Karen, L1 aMononen, Nina1 aMurakami, Yoshinori1 aNadkarni, Girish, N1 aNikus, Kjell1 aPankratz, Nathan1 aPedersen, Oluf1 aPreuss, Michael1 aPsaty, Bruce, M1 aRaitakari, Olli, T1 aRich, Stephen, S1 aRodriguez, Benjamin, A T1 aRosen, Jonathan, D1 aRotter, Jerome, I1 aSchubert, Petra1 aSpracklen, Cassandra, N1 aSurendran, Praveen1 aTang, Hua1 aTardif, Jean-Claude1 aGhanbari, Mohsen1 aVölker, Uwe1 aVölzke, Henry1 aWatkins, Nicholas, A1 aWeiss, Stefan1 aCai, Na1 aKundu, Kousik1 aWatt, Stephen, B1 aWalter, Klaudia1 aZonderman, Alan, B1 aCho, Kelly1 aLi, Yun1 aLoos, Ruth, J F1 aKnight, Julian, C1 aGeorges, Michel1 aStegle, Oliver1 aEvangelou, Evangelos1 aOkada, Yukinori1 aRoberts, David, J1 aInouye, Michael1 aJohnson, Andrew, D1 aAuer, Paul, L1 aAstle, William, J1 aReiner, Alexander, P1 aButterworth, Adam, S1 aOuwehand, Willem, H1 aLettre, Guillaume1 aSankaran, Vijay, G1 aSoranzo, Nicole1 aVA Million Veteran Program uhttps://chs-nhlbi.org/node/849005226nas a2201465 4500008004100000022001400041245010900055210006900164260001600233300001800249490000800267520110800275100002001383700002301403700001801426700001801444700002501462700001901487700001901506700001501525700001801540700002201558700001701580700001501597700002201612700002501634700001701659700001701676700001701693700002101710700002301731700001901754700002001773700002001793700002101813700002001834700002301854700002401877700002301901700002601924700003101950700001501981700002001996700001902016700001702035700003002052700001602082700002002098700001802118700001602136700002202152700002002174700001702194700001802211700002002229700002402249700001902273700001902292700002402311700002002335700001502355700002002370700002002390700002002410700002302430700002002453700002402473700002202497700002102519700002202540700002102562700002102583700001702604700002802621700002102649700002202670700002002692700002102712700001802733700002402751700002402775700002002799700001702819700002402836700002102860700001902881700002002900700002002920700002302940700002202963700002102985700002903006700002303035700002203058700002003080700002803100700002303128700001403151700002403165700002503189700002103214700001703235700001903252700002503271700002303296700002303319700001203342700002503354700002803379700002803407700001703435700002003452700002203472700002503494700002303519700002303542700002003565700002003585700002303605700002503628700001803653700002203671710003103693856003603724 2020 eng d a1097-417200aTrans-ethnic and Ancestry-Specific Blood-Cell Genetics in 746,667 Individuals from 5 Global Populations.0 aTransethnic and AncestrySpecific BloodCell Genetics in 746667 In c2020 Sep 03 a1198-1213.e140 v1823 aMost loci identified by GWASs have been found in populations of European ancestry (EUR). In trans-ethnic meta-analyses for 15 hematological traits in 746,667 participants, including 184,535 non-EUR individuals, we identified 5,552 trait-variant associations at p < 5 × 10, including 71 novel associations not found in EUR populations. We also identified 28 additional novel variants in ancestry-specific, non-EUR meta-analyses, including an IL7 missense variant in South Asians associated with lymphocyte count in vivo and IL-7 secretion levels in vitro. Fine-mapping prioritized variants annotated as functional and generated 95% credible sets that were 30% smaller when using the trans-ethnic as opposed to the EUR-only results. We explored the clinical significance and predictive value of trans-ethnic variants in multiple populations and compared genetic architecture and the effect of natural selection on these blood phenotypes between populations. Altogether, our results for hematological traits highlight the value of a more global representation of populations in genetic studies.
1 aChen, Ming-Huei1 aRaffield, Laura, M1 aMousas, Abdou1 aSakaue, Saori1 aHuffman, Jennifer, E1 aMoscati, Arden1 aTrivedi, Bhavi1 aJiang, Tao1 aAkbari, Parsa1 aVuckovic, Dragana1 aBao, Erik, L1 aZhong, Xue1 aManansala, Regina1 aLaplante, Véronique1 aChen, Minhui1 aLo, Ken, Sin1 aQian, Huijun1 aLareau, Caleb, A1 aBeaudoin, Mélissa1 aHunt, Karen, A1 aAkiyama, Masato1 aBartz, Traci, M1 aBen-Shlomo, Yoav1 aBeswick, Andrew1 aBork-Jensen, Jette1 aBottinger, Erwin, P1 aBrody, Jennifer, A1 avan Rooij, Frank, J A1 aChitrala, Kumaraswamynaidu1 aCho, Kelly1 aChoquet, Helene1 aCorrea, Adolfo1 aDanesh, John1 aDi Angelantonio, Emanuele1 aDimou, Niki1 aDing, Jingzhong1 aElliott, Paul1 aEsko, Tõnu1 aEvans, Michele, K1 aFloyd, James, S1 aBroer, Linda1 aGrarup, Niels1 aGuo, Michael, H1 aGreinacher, Andreas1 aHaessler, Jeff1 aHansen, Torben1 aHowson, Joanna, M M1 aHuang, Qin, Qin1 aHuang, Wei1 aJorgenson, Eric1 aKacprowski, Tim1 aKähönen, Mika1 aKamatani, Yoichiro1 aKanai, Masahiro1 aKarthikeyan, Savita1 aKoskeridis, Fotis1 aLange, Leslie, A1 aLehtimäki, Terho1 aLerch, Markus, M1 aLinneberg, Allan1 aLiu, Yongmei1 aLyytikäinen, Leo-Pekka1 aManichaikul, Ani1 aMartin, Hilary, C1 aMatsuda, Koichi1 aMohlke, Karen, L1 aMononen, Nina1 aMurakami, Yoshinori1 aNadkarni, Girish, N1 aNauck, Matthias1 aNikus, Kjell1 aOuwehand, Willem, H1 aPankratz, Nathan1 aPedersen, Oluf1 aPreuss, Michael1 aPsaty, Bruce, M1 aRaitakari, Olli, T1 aRoberts, David, J1 aRich, Stephen, S1 aRodriguez, Benjamin, A T1 aRosen, Jonathan, D1 aRotter, Jerome, I1 aSchubert, Petra1 aSpracklen, Cassandra, N1 aSurendran, Praveen1 aTang, Hua1 aTardif, Jean-Claude1 aTrembath, Richard, C1 aGhanbari, Mohsen1 aVölker, Uwe1 aVölzke, Henry1 aWatkins, Nicholas, A1 aZonderman, Alan, B1 aWilson, Peter, W F1 aLi, Yun1 aButterworth, Adam, S1 aGauchat, Jean-François1 aChiang, Charleston, W K1 aLi, Bingshan1 aLoos, Ruth, J F1 aAstle, William, J1 aEvangelou, Evangelos1 avan Heel, David, A1 aSankaran, Vijay, G1 aOkada, Yukinori1 aSoranzo, Nicole1 aJohnson, Andrew, D1 aReiner, Alexander, P1 aAuer, Paul, L1 aLettre, Guillaume1 aVA Million Veteran Program uhttps://chs-nhlbi.org/node/8481