@article {1116, title = {Lipoprotein(a) concentration and the risk of coronary heart disease, stroke, and nonvascular mortality.}, journal = {JAMA}, volume = {302}, year = {2009}, month = {2009 Jul 22}, pages = {412-23}, abstract = {

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.

}, keywords = {Cause of Death, Coronary Disease, Humans, Lipoprotein(a), Risk Factors, Stroke}, issn = {1538-3598}, doi = {10.1001/jama.2009.1063}, author = {Erqou, Sebhat and Kaptoge, Stephen and Perry, Philip L and Di Angelantonio, Emanuele and Thompson, Alexander and White, Ian R and Marcovina, Santica M and Collins, Rory and Thompson, Simon G and Danesh, John} } @article {1151, title = {C-reactive protein concentration and risk of coronary heart disease, stroke, and mortality: an individual participant meta-analysis.}, journal = {Lancet}, volume = {375}, year = {2010}, month = {2010 Jan 09}, pages = {132-40}, abstract = {

BACKGROUND: 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.

}, keywords = {Alcohol Drinking, Biomarkers, Blood Pressure, Body Mass Index, C-Reactive Protein, Cholesterol, Coronary Disease, Databases, Factual, Diabetes Mellitus, Female, Fibrinogen, Humans, Interleukin-6, Leukocyte Count, Lung Diseases, Male, Middle Aged, Motor Activity, Neoplasms, Regression Analysis, Risk Assessment, Risk Factors, Serum Albumin, Sex Factors, Smoking, Stroke, Triglycerides}, issn = {1474-547X}, doi = {10.1016/S0140-6736(09)61717-7}, author = {Kaptoge, Stephen and Di Angelantonio, Emanuele and Lowe, Gordon and Pepys, Mark B and Thompson, Simon G and Collins, Rory and Danesh, John} } @article {583, title = {Lipoprotein-associated phospholipase A(2) and risk of coronary disease, stroke, and mortality: collaborative analysis of 32 prospective studies.}, journal = {Lancet}, volume = {375}, year = {2010}, month = {2010 May 01}, pages = {1536-44}, abstract = {

BACKGROUND: 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.

}, keywords = {1-Alkyl-2-acetylglycerophosphocholine Esterase, Blood Pressure, Coronary Disease, Female, Humans, Linear Models, Lipids, Male, Middle Aged, Prospective Studies, Risk Assessment, Risk Factors, Stroke, Systole}, issn = {1474-547X}, doi = {10.1016/S0140-6736(10)60319-4}, author = {Thompson, Alexander and Gao, Pei and Orfei, Lia and Watson, Sarah and Di Angelantonio, Emanuele and Kaptoge, Stephen and Ballantyne, Christie and Cannon, Christopher P and Criqui, Michael and Cushman, Mary and Hofman, Albert and Packard, Chris and Thompson, Simon G and Collins, Rory and Danesh, John} } @article {1198, title = {Triglyceride-mediated pathways and coronary disease: collaborative analysis of 101 studies.}, journal = {Lancet}, volume = {375}, year = {2010}, month = {2010 May 08}, pages = {1634-9}, abstract = {

BACKGROUND: 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.

}, keywords = {Apolipoprotein A-V, Apolipoproteins, Apolipoproteins A, Coronary Disease, Gene Frequency, Genotype, Humans, Lipids, Lipoproteins, HDL, Lipoproteins, LDL, Lipoproteins, VLDL, Mendelian Randomization Analysis, Particle Size, Polymorphism, Single Nucleotide, Promoter Regions, Genetic, Risk Factors, Triglycerides}, issn = {1474-547X}, doi = {10.1016/S0140-6736(10)60545-4}, author = {Sarwar, Nadeem and Sandhu, Manjinder S and Ricketts, Sally L and Butterworth, Adam S and Di Angelantonio, Emanuele and Boekholdt, S Matthijs and Ouwehand, Willem and Watkins, Hugh and Samani, Nilesh J and Saleheen, Danish and Lawlor, Debbie and Reilly, Muredach P and Hingorani, Aroon D and Talmud, Philippa J and Danesh, John} } @article {1272, title = {Diabetes mellitus, fasting glucose, and risk of cause-specific death.}, journal = {N Engl J Med}, volume = {364}, year = {2011}, month = {2011 Mar 03}, pages = {829-841}, abstract = {

BACKGROUND: 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.).

}, keywords = {Blood Glucose, Cause of Death, Diabetes Mellitus, Female, Humans, Hyperglycemia, Life Expectancy, Male, Middle Aged, Risk, Survival Analysis}, issn = {1533-4406}, doi = {10.1056/NEJMoa1008862}, author = {Rao Kondapally Seshasai, Sreenivasa and Kaptoge, Stephen and Thompson, Alexander and Di Angelantonio, Emanuele and Gao, Pei and Sarwar, Nadeem and Whincup, Peter H and Mukamal, Kenneth J and Gillum, Richard F and Holme, Ingar and Nj{\o}lstad, Inger and Fletcher, Astrid and Nilsson, Peter and Lewington, Sarah and Collins, Rory and Gudnason, Vilmundur and Thompson, Simon G and Sattar, Naveed and Selvin, Elizabeth and Hu, Frank B and Danesh, John} } @article {1563, title = {Separate and combined associations of body-mass index and abdominal adiposity with cardiovascular disease: collaborative analysis of 58 prospective studies.}, journal = {Lancet}, volume = {377}, year = {2011}, month = {2011 Mar 26}, pages = {1085-95}, abstract = {

BACKGROUND: 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.

}, keywords = {Age Factors, Blood Pressure, Body Mass Index, Cardiovascular Diseases, Cholesterol, Cholesterol, HDL, Diabetes Mellitus, Female, Humans, Male, Middle Aged, Obesity, Abdominal, Proportional Hazards Models, Prospective Studies, Risk Assessment, Sex Factors, Smoking, Systole, Waist Circumference, Waist-Hip Ratio}, issn = {1474-547X}, doi = {10.1016/S0140-6736(11)60105-0}, author = {Wormser, David and Kaptoge, Stephen and Di Angelantonio, Emanuele and Wood, Angela M and Pennells, Lisa and Thompson, Alex and Sarwar, Nadeem and Kizer, Jorge R and Lawlor, Debbie A and Nordestgaard, B{\o}rge G and Ridker, Paul and Salomaa, Veikko and Stevens, June and Woodward, Mark and Sattar, Naveed and Collins, Rory and Thompson, Simon G and Whitlock, Gary and Danesh, John} } @article {1399, title = {Lipid-related markers and cardiovascular disease prediction.}, journal = {JAMA}, volume = {307}, year = {2012}, month = {2012 Jun 20}, pages = {2499-506}, abstract = {

CONTEXT: 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{\textquoteright}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.

}, keywords = {Aged, Biomarkers, Cardiovascular Diseases, Cholesterol, HDL, Cohort Studies, Female, Humans, Lipoproteins, Male, Middle Aged, Risk Assessment}, issn = {1538-3598}, doi = {10.1001/jama.2012.6571}, author = {Di Angelantonio, Emanuele and Gao, Pei and Pennells, Lisa and Kaptoge, Stephen and Caslake, Muriel and Thompson, Alexander and Butterworth, Adam S and Sarwar, Nadeem and Wormser, David and Saleheen, Danish and Ballantyne, Christie M and Psaty, Bruce M and Sundstr{\"o}m, Johan and Ridker, Paul M and Nagel, Dorothea and Gillum, Richard F and Ford, Ian and Ducimetiere, Pierre and Kiechl, Stefan and Koenig, Wolfgang and Dullaart, Robin P F and Assmann, Gerd and D{\textquoteright}Agostino, Ralph B and Dagenais, Gilles R and Cooper, Jackie A and Kromhout, Daan and Onat, Altan and Tipping, Robert W and G{\'o}mez-de-la-C{\'a}mara, Agust{\'\i}n and Rosengren, Annika and Sutherland, Susan E and Gallacher, John and Fowkes, F Gerry R and Casiglia, Edoardo and Hofman, Albert and Salomaa, Veikko and Barrett-Connor, Elizabeth and Clarke, Robert and Brunner, Eric and Jukema, J Wouter and Simons, Leon A and Sandhu, Manjinder and Wareham, Nicholas J and Khaw, Kay-Tee and Kauhanen, Jussi and Salonen, Jukka T and Howard, William J and Nordestgaard, B{\o}rge G and Wood, Angela M and Thompson, Simon G and Boekholdt, S Matthijs and Sattar, Naveed and Packard, Chris and Gudnason, Vilmundur and Danesh, John} } @article {6559, title = {Glycated hemoglobin measurement and prediction of cardiovascular disease.}, journal = {JAMA}, volume = {311}, year = {2014}, month = {2014 Mar 26}, pages = {1225-33}, abstract = {

IMPORTANCE: 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.

}, keywords = {Aged, C-Reactive Protein, Cholesterol, HDL, Coronary Disease, Diabetes Mellitus, Female, Glycated Hemoglobin A, Humans, Male, Middle Aged, Predictive Value of Tests, Prospective Studies, Risk Assessment, Stroke}, issn = {1538-3598}, doi = {10.1001/jama.2014.1873}, author = {Di Angelantonio, Emanuele and Gao, Pei and Khan, Hassan and Butterworth, Adam S and Wormser, David and Kaptoge, Stephen and Kondapally Seshasai, Sreenivasa Rao and Thompson, Alex and Sarwar, Nadeem and Willeit, Peter and Ridker, Paul M and Barr, Elizabeth L M and Khaw, Kay-Tee and Psaty, Bruce M and Brenner, Hermann and Balkau, Beverley and Dekker, Jacqueline M and Lawlor, Debbie A and Daimon, Makoto and Willeit, Johann and Nj{\o}lstad, Inger and Nissinen, Aulikki and Brunner, Eric J and Kuller, Lewis H and Price, Jackie F and Sundstr{\"o}m, Johan and Knuiman, Matthew W and Feskens, Edith J M and Verschuren, W M M and Wald, Nicholas and Bakker, Stephan J L and Whincup, Peter H and Ford, Ian and Goldbourt, Uri and G{\'o}mez-de-la-C{\'a}mara, Agust{\'\i}n and Gallacher, John and Simons, Leon A and Rosengren, Annika and Sutherland, Susan E and Bj{\"o}rkelund, Cecilia and Blazer, Dan G and Wassertheil-Smoller, Sylvia and Onat, Altan and Mar{\'\i}n Iba{\~n}ez, Alejandro and Casiglia, Edoardo and Jukema, J Wouter and Simpson, Lara M and Giampaoli, Simona and Nordestgaard, B{\o}rge G and Selmer, Randi and Wennberg, Patrik and Kauhanen, Jussi and Salonen, Jukka T and Dankner, Rachel and Barrett-Connor, Elizabeth and Kavousi, Maryam and Gudnason, Vilmundur and Evans, Denis and Wallace, Robert B and Cushman, Mary and D{\textquoteright}Agostino, Ralph B and Umans, Jason G and Kiyohara, Yutaka and Nakagawa, Hidaeki and Sato, Shinichi and Gillum, Richard F and Folsom, Aaron R and van der Schouw, Yvonne T and Moons, Karel G and Griffin, Simon J and Sattar, Naveed and Wareham, Nicholas J and Selvin, Elizabeth and Thompson, Simon G and Danesh, John} } @article {6811, title = {Association of Cardiometabolic Multimorbidity With Mortality.}, journal = {JAMA}, volume = {314}, year = {2015}, month = {2015 Jul 7}, pages = {52-60}, abstract = {

IMPORTANCE: 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.

}, keywords = {Adult, Aged, Comorbidity, Diabetes Mellitus, Female, Humans, Life Expectancy, Male, Middle Aged, Mortality, Myocardial Infarction, Risk Factors, Stroke}, issn = {1538-3598}, doi = {10.1001/jama.2015.7008}, author = {Di Angelantonio, Emanuele and Kaptoge, Stephen and Wormser, David and Willeit, Peter and Butterworth, Adam S and Bansal, Narinder and O{\textquoteright}Keeffe, Linda M and Gao, Pei and Wood, Angela M and Burgess, Stephen and Freitag, Daniel F and Pennells, Lisa and Peters, Sanne A and Hart, Carole L and H{\r a}heim, Lise Lund and Gillum, Richard F and Nordestgaard, B{\o}rge G and Psaty, Bruce M and Yeap, Bu B and Knuiman, Matthew W and Nietert, Paul J and Kauhanen, Jussi and Salonen, Jukka T and Kuller, Lewis H and Simons, Leon A and van der Schouw, Yvonne T and Barrett-Connor, Elizabeth and Selmer, Randi and Crespo, Carlos J and Rodriguez, Beatriz and Verschuren, W M Monique and Salomaa, Veikko and Sv{\"a}rdsudd, Kurt and van der Harst, Pim and Bj{\"o}rkelund, Cecilia and Wilhelmsen, Lars and Wallace, Robert B and Brenner, Hermann and Amouyel, Philippe and Barr, Elizabeth L M and Iso, Hiroyasu and Onat, Altan and Trevisan, Maurizio and D{\textquoteright}Agostino, Ralph B and Cooper, Cyrus and Kavousi, Maryam and Welin, Lennart and Roussel, Ronan and Hu, Frank B and Sato, Shinichi and Davidson, Karina W and Howard, Barbara V and Leening, Maarten J G and Leening, Maarten and Rosengren, Annika and D{\"o}rr, Marcus and Deeg, Dorly J H and Kiechl, Stefan and Stehouwer, Coen D A and Nissinen, Aulikki and Giampaoli, Simona and Donfrancesco, Chiara and Kromhout, Daan and Price, Jackie F and Peters, Annette and Meade, Tom W and Casiglia, Edoardo and Lawlor, Debbie A and Gallacher, John and Nagel, Dorothea and Franco, Oscar H and Assmann, Gerd and Dagenais, Gilles R and Jukema, J Wouter and Sundstr{\"o}m, Johan and Woodward, Mark and Brunner, Eric J and Khaw, Kay-Tee and Wareham, Nicholas J and Whitsel, Eric A and Nj{\o}lstad, Inger and Hedblad, Bo and Wassertheil-Smoller, Sylvia and Engstr{\"o}m, Gunnar and Rosamond, Wayne D and Selvin, Elizabeth and Sattar, Naveed and Thompson, Simon G and Danesh, John} } @article {8567, title = {Natriuretic peptides and integrated risk assessment for cardiovascular disease: an individual-participant-data meta-analysis.}, journal = {Lancet Diabetes Endocrinol}, volume = {4}, year = {2016}, month = {2016 10}, pages = {840-9}, abstract = {

BACKGROUND: 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{\textperiodcentered}5\%, and >=7{\textperiodcentered}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{\textperiodcentered}76 (95\% CI 1{\textperiodcentered}56-1{\textperiodcentered}98) for the combination of coronary heart disease and stroke and 2{\textperiodcentered}00 (1{\textperiodcentered}77-2{\textperiodcentered}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{\textperiodcentered}012 (0{\textperiodcentered}010-0{\textperiodcentered}014) and a net reclassification improvement of 0{\textperiodcentered}027 (0{\textperiodcentered}019-0{\textperiodcentered}036) for the combination of coronary heart disease and stroke and a C-index increase of 0{\textperiodcentered}019 (0{\textperiodcentered}016-0{\textperiodcentered}022) and a net reclassification improvement of 0{\textperiodcentered}028 (0{\textperiodcentered}019-0{\textperiodcentered}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.

}, keywords = {Aged, Biomarkers, Cardiovascular Diseases, Female, Humans, Male, Middle Aged, Natriuretic Peptide, Brain, Peptide Fragments, Prospective Studies, Risk Assessment}, issn = {2213-8595}, doi = {10.1016/S2213-8587(16)30196-6}, author = {Willeit, Peter and Kaptoge, Stephen and Welsh, Paul and Butterworth, Adam and Chowdhury, Rajiv and Spackman, Sarah and Pennells, Lisa and Gao, Pei and Burgess, Stephen and Freitag, Daniel and Sweeting, Michael and Wood, Angela and Cook, Nancy and Judd, Suzanne and Trompet, Stella and Nambi, Vijay and Olsen, Michael and Everett, Brendan and Kee, Frank and Arnl{\"o}v, Johan and Salomaa, Veikko and Levy, Daniel and Kauhanen, Jussi and Laukkanen, Jari and Kavousi, Maryam and Ninomiya, Toshiharu and Casas, Juan-Pablo and Daniels, Lori and Lind, Lars and Kistorp, Caroline and Rosenberg, Jens and Mueller, Thomas and Rubattu, Speranza and Panagiotakos, Demosthenes and Franco, Oscar and de Lemos, James and Luchner, Andreas and Kizer, Jorge and Kiechl, Stefan and Salonen, Jukka and Goya Wannamethee, S and de Boer, Rudolf and Nordestgaard, B{\o}rge and Andersson, Jonas and J{\o}rgensen, Torben and Melander, Olle and Ballantyne, Christie and DeFilippi, Christopher and Ridker, Paul and Cushman, Mary and Rosamond, Wayne and Thompson, Simon and Gudnason, Vilmundur and Sattar, Naveed and Danesh, John and Di Angelantonio, Emanuele} } @article {7573, title = {Exome-wide association study of plasma lipids in >300,000 individuals.}, journal = {Nat Genet}, volume = {49}, year = {2017}, month = {2017 Dec}, pages = {1758-1766}, abstract = {

We 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.

}, keywords = {Coronary Artery Disease, Diabetes Mellitus, Type 2, Exome, Genetic Association Studies, Genetic Predisposition to Disease, Genetic Variation, Genotype, Humans, Lipids, Macular Degeneration, Phenotype, Risk Factors}, issn = {1546-1718}, doi = {10.1038/ng.3977}, author = {Liu, Dajiang J and Peloso, Gina M and Yu, Haojie and Butterworth, Adam S and Wang, Xiao and Mahajan, Anubha and Saleheen, Danish and Emdin, Connor and Alam, Dewan and Alves, Alexessander Couto and Amouyel, Philippe and Di Angelantonio, Emanuele and Arveiler, Dominique and Assimes, Themistocles L and Auer, Paul L and Baber, Usman and Ballantyne, Christie M and Bang, Lia E and Benn, Marianne and Bis, Joshua C and Boehnke, Michael and Boerwinkle, Eric and Bork-Jensen, Jette and Bottinger, Erwin P and Brandslund, Ivan and Brown, Morris and Busonero, Fabio and Caulfield, Mark J and Chambers, John C and Chasman, Daniel I and Chen, Y Eugene and Chen, Yii-Der Ida and Chowdhury, Rajiv and Christensen, Cramer and Chu, Audrey Y and Connell, John M and Cucca, Francesco and Cupples, L Adrienne and Damrauer, Scott M and Davies, Gail and Deary, Ian J and Dedoussis, George and Denny, Joshua C and Dominiczak, Anna and Dub{\'e}, Marie-Pierre and Ebeling, Tapani and Eiriksdottir, Gudny and Esko, T{\~o}nu and Farmaki, Aliki-Eleni and Feitosa, Mary F and Ferrario, Marco and Ferrieres, Jean and Ford, Ian and Fornage, Myriam and Franks, Paul W and Frayling, Timothy M and Frikke-Schmidt, Ruth and Fritsche, Lars G and Frossard, Philippe and Fuster, Valentin and Ganesh, Santhi K and Gao, Wei and Garcia, Melissa E and Gieger, Christian and Giulianini, Franco and Goodarzi, Mark O and Grallert, Harald and Grarup, Niels and Groop, Leif and Grove, Megan L and Gudnason, Vilmundur and Hansen, Torben and Harris, Tamara B and Hayward, Caroline and Hirschhorn, Joel N and Holmen, Oddgeir L and Huffman, Jennifer and Huo, Yong and Hveem, Kristian and Jabeen, Sehrish and Jackson, Anne U and Jakobsdottir, Johanna and Jarvelin, Marjo-Riitta and Jensen, Gorm B and J{\o}rgensen, Marit E and Jukema, J Wouter and Justesen, Johanne M and Kamstrup, Pia R and Kanoni, Stavroula and Karpe, Fredrik and Kee, Frank and Khera, Amit V and Klarin, Derek and Koistinen, Heikki A and Kooner, Jaspal S and Kooperberg, Charles and Kuulasmaa, Kari and Kuusisto, Johanna and Laakso, Markku and Lakka, Timo and Langenberg, Claudia and Langsted, Anne and Launer, Lenore J and Lauritzen, Torsten and Liewald, David C M and Lin, Li An and Linneberg, Allan and Loos, Ruth J F and Lu, Yingchang and Lu, Xiangfeng and M{\"a}gi, Reedik and M{\"a}larstig, Anders and Manichaikul, Ani and Manning, Alisa K and M{\"a}ntyselk{\"a}, Pekka and Marouli, Eirini and Masca, Nicholas G D and Maschio, Andrea and Meigs, James B and Melander, Olle and Metspalu, Andres and Morris, Andrew P and Morrison, Alanna C and Mulas, Antonella and M{\"u}ller-Nurasyid, Martina and Munroe, Patricia B and Neville, Matt J and Nielsen, Jonas B and Nielsen, Sune F and Nordestgaard, B{\o}rge G and Ordovas, Jose M and Mehran, Roxana and O{\textquoteright}Donnell, Christoper J and Orho-Melander, Marju and Molony, Cliona M and Muntendam, Pieter and Padmanabhan, Sandosh and Palmer, Colin N A and Pasko, Dorota and Patel, Aniruddh P and Pedersen, Oluf and Perola, Markus and Peters, Annette and Pisinger, Charlotta and Pistis, Giorgio and Polasek, Ozren and Poulter, Neil and Psaty, Bruce M and Rader, Daniel J and Rasheed, Asif and Rauramaa, Rainer and Reilly, Dermot F and Reiner, Alex P and Renstrom, Frida and Rich, Stephen S and Ridker, Paul M and Rioux, John D and Robertson, Neil R and Roden, Dan M and Rotter, Jerome I and Rudan, Igor and Salomaa, Veikko and Samani, Nilesh J and Sanna, Serena and Sattar, Naveed and Schmidt, Ellen M and Scott, Robert A and Sever, Peter and Sevilla, Raquel S and Shaffer, Christian M and Sim, Xueling and Sivapalaratnam, Suthesh and Small, Kerrin S and Smith, Albert V and Smith, Blair H and Somayajula, Sangeetha and Southam, Lorraine and Spector, Timothy D and Speliotes, Elizabeth K and Starr, John M and Stirrups, Kathleen E and Stitziel, Nathan and Strauch, Konstantin and Stringham, Heather M and Surendran, Praveen and Tada, Hayato and Tall, Alan R and Tang, Hua and Tardif, Jean-Claude and Taylor, Kent D and Trompet, Stella and Tsao, Philip S and Tuomilehto, Jaakko and Tybjaerg-Hansen, Anne and van Zuydam, Natalie R and Varbo, Anette and Varga, Tibor V and Virtamo, Jarmo and Waldenberger, Melanie and Wang, Nan and Wareham, Nick J and Warren, Helen R and Weeke, Peter E and Weinstock, Joshua and Wessel, Jennifer and Wilson, James G and Wilson, Peter W F and Xu, Ming and Yaghootkar, Hanieh and Young, Robin and Zeggini, Eleftheria and Zhang, He and Zheng, Neil S and Zhang, Weihua and Zhang, Yan and Zhou, Wei and Zhou, Yanhua and Zoledziewska, Magdalena and Howson, Joanna M M and Danesh, John and McCarthy, Mark I and Cowan, Chad A and Abecasis, Goncalo and Deloukas, Panos and Musunuru, Kiran and Willer, Cristen J and Kathiresan, Sekar} } @article {8109, title = {A catalog of genetic loci associated with kidney function from analyses of a million individuals.}, journal = {Nat Genet}, volume = {51}, year = {2019}, month = {2019 06}, pages = {957-972}, abstract = {

Chronic 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.

}, keywords = {Chromosome Mapping, European Continental Ancestry Group, Genetic Association Studies, Genetic Predisposition to Disease, Genome-Wide Association Study, Glomerular Filtration Rate, Humans, Inheritance Patterns, Kidney Function Tests, Phenotype, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Quantitative Trait, Heritable, Renal Insufficiency, Chronic, Uromodulin}, issn = {1546-1718}, doi = {10.1038/s41588-019-0407-x}, author = {Wuttke, Matthias and Li, Yong and Li, Man and Sieber, Karsten B and Feitosa, Mary F and Gorski, Mathias and Tin, Adrienne and Wang, Lihua and Chu, Audrey Y and Hoppmann, Anselm and Kirsten, Holger and Giri, Ayush and Chai, Jin-Fang and Sveinbjornsson, Gardar and Tayo, Bamidele O and Nutile, Teresa and Fuchsberger, Christian and Marten, Jonathan and Cocca, Massimiliano and Ghasemi, Sahar and Xu, Yizhe and Horn, Katrin and Noce, Damia and van der Most, Peter J and Sedaghat, Sanaz and Yu, Zhi and Akiyama, Masato and Afaq, Saima and Ahluwalia, Tarunveer S and Almgren, Peter and Amin, Najaf and Arnl{\"o}v, Johan and Bakker, Stephan J L and Bansal, Nisha and Baptista, Daniela and Bergmann, Sven and Biggs, Mary L and Biino, Ginevra and Boehnke, Michael and Boerwinkle, Eric and Boissel, Mathilde and Bottinger, Erwin P and Boutin, Thibaud S and Brenner, Hermann and Brumat, Marco and Burkhardt, Ralph and Butterworth, Adam S and Campana, Eric and Campbell, Archie and Campbell, Harry and Canouil, Micka{\"e}l and Carroll, Robert J and Catamo, Eulalia and Chambers, John C and Chee, Miao-Ling and Chee, Miao-Li and Chen, Xu and Cheng, Ching-Yu and Cheng, Yurong and Christensen, Kaare and Cifkova, Renata and Ciullo, Marina and Concas, Maria Pina and Cook, James P and Coresh, Josef and Corre, Tanguy and Sala, Cinzia Felicita and Cusi, Daniele and Danesh, John and Daw, E Warwick and de Borst, Martin H and De Grandi, Alessandro and de Mutsert, Ren{\'e}e and de Vries, Aiko P J and Degenhardt, Frauke and Delgado, Graciela and Demirkan, Ayse and Di Angelantonio, Emanuele and Dittrich, Katalin and Divers, Jasmin and Dorajoo, Rajkumar and Eckardt, Kai-Uwe and Ehret, Georg and Elliott, Paul and Endlich, Karlhans and Evans, Michele K and Felix, Janine F and Foo, Valencia Hui Xian and Franco, Oscar H and Franke, Andre and Freedman, Barry I and Freitag-Wolf, Sandra and Friedlander, Yechiel and Froguel, Philippe and Gansevoort, Ron T and Gao, He and Gasparini, Paolo and Gaziano, J Michael and Giedraitis, Vilmantas and Gieger, Christian and Girotto, Giorgia and Giulianini, Franco and G{\"o}gele, Martin and Gordon, Scott D and Gudbjartsson, Daniel F and Gudnason, Vilmundur and Haller, Toomas and Hamet, Pavel and Harris, Tamara B and Hartman, Catharina A and Hayward, Caroline and Hellwege, Jacklyn N and Heng, Chew-Kiat and Hicks, Andrew A and Hofer, Edith and Huang, Wei and Hutri-K{\"a}h{\"o}nen, Nina and Hwang, Shih-Jen and Ikram, M Arfan and Indridason, Olafur S and Ingelsson, Erik and Ising, Marcus and Jaddoe, Vincent W V and Jakobsdottir, Johanna and Jonas, Jost B and Joshi, Peter K and Josyula, Navya Shilpa and Jung, Bettina and K{\"a}h{\"o}nen, Mika and Kamatani, Yoichiro and Kammerer, Candace M and Kanai, Masahiro and Kastarinen, Mika and Kerr, Shona M and Khor, Chiea-Chuen and Kiess, Wieland and Kleber, Marcus E and Koenig, Wolfgang and Kooner, Jaspal S and K{\"o}rner, Antje and Kovacs, Peter and Kraja, Aldi T and Krajcoviechova, Alena and Kramer, Holly and Kr{\"a}mer, Bernhard K and Kronenberg, Florian and Kubo, Michiaki and Kuhnel, Brigitte and Kuokkanen, Mikko and Kuusisto, Johanna and La Bianca, Martina and Laakso, Markku and Lange, Leslie A and Langefeld, Carl D and Lee, Jeannette Jen-Mai and Lehne, Benjamin and Lehtim{\"a}ki, Terho and Lieb, Wolfgang and Lim, Su-Chi and Lind, Lars and Lindgren, Cecilia M and Liu, Jun and Liu, Jianjun and Loeffler, Markus and Loos, Ruth J F and Lucae, Susanne and Lukas, Mary Ann and Lyytik{\"a}inen, Leo-Pekka and M{\"a}gi, Reedik and Magnusson, Patrik K E and Mahajan, Anubha and Martin, Nicholas G and Martins, Jade and M{\"a}rz, Winfried and Mascalzoni, Deborah and Matsuda, Koichi and Meisinger, Christa and Meitinger, Thomas and Melander, Olle and Metspalu, Andres and Mikaelsdottir, Evgenia K and Milaneschi, Yuri and Miliku, Kozeta and Mishra, Pashupati P and Mohlke, Karen L and Mononen, Nina and Montgomery, Grant W and Mook-Kanamori, Dennis O and Mychaleckyj, Josyf C and Nadkarni, Girish N and Nalls, Mike A and Nauck, Matthias and Nikus, Kjell and Ning, Boting and Nolte, Ilja M and Noordam, Raymond and O{\textquoteright}Connell, Jeffrey and O{\textquoteright}Donoghue, Michelle L and Olafsson, Isleifur and Oldehinkel, Albertine J and Orho-Melander, Marju and Ouwehand, Willem H and Padmanabhan, Sandosh and Palmer, Nicholette D and Palsson, Runolfur and Penninx, Brenda W J H and Perls, Thomas and Perola, Markus and Pirastu, Mario and Pirastu, Nicola and Pistis, Giorgio and Podgornaia, Anna I and Polasek, Ozren and Ponte, Belen and Porteous, David J and Poulain, Tanja and Pramstaller, Peter P and Preuss, Michael H and Prins, Bram P and Province, Michael A and Rabelink, Ton J and Raffield, Laura M and Raitakari, Olli T and Reilly, Dermot F and Rettig, Rainer and Rheinberger, Myriam and Rice, Kenneth M and Ridker, Paul M and Rivadeneira, Fernando and Rizzi, Federica and Roberts, David J and Robino, Antonietta and Rossing, Peter and Rudan, Igor and Rueedi, Rico and Ruggiero, Daniela and Ryan, Kathleen A and Saba, Yasaman and Sabanayagam, Charumathi and Salomaa, Veikko and Salvi, Erika and Saum, Kai-Uwe and Schmidt, Helena and Schmidt, Reinhold and Sch{\"o}ttker, Ben and Schulz, Christina-Alexandra and Schupf, Nicole and Shaffer, Christian M and Shi, Yuan and Smith, Albert V and Smith, Blair H and Soranzo, Nicole and Spracklen, Cassandra N and Strauch, Konstantin and Stringham, Heather M and Stumvoll, Michael and Svensson, Per O and Szymczak, Silke and Tai, E-Shyong and Tajuddin, Salman M and Tan, Nicholas Y Q and Taylor, Kent D and Teren, Andrej and Tham, Yih-Chung and Thiery, Joachim and Thio, Chris H L and Thomsen, Hauke and Thorleifsson, Gudmar and Toniolo, Daniela and T{\"o}njes, Anke and Tremblay, Johanne and Tzoulaki, Ioanna and Uitterlinden, Andr{\'e} G and Vaccargiu, Simona and van Dam, Rob M and van der Harst, Pim and van Duijn, Cornelia M and Velez Edward, Digna R and Verweij, Niek and Vogelezang, Suzanne and V{\"o}lker, Uwe and Vollenweider, Peter and Waeber, G{\'e}rard and Waldenberger, Melanie and Wallentin, Lars and Wang, Ya Xing and Wang, Chaolong and Waterworth, Dawn M and Bin Wei, Wen and White, Harvey and Whitfield, John B and Wild, Sarah H and Wilson, James F and Wojczynski, Mary K and Wong, Charlene and Wong, Tien-Yin and Xu, Liang and Yang, Qiong and Yasuda, Masayuki and Yerges-Armstrong, Laura M and Zhang, Weihua and Zonderman, Alan B and Rotter, Jerome I and Bochud, Murielle and Psaty, Bruce M and Vitart, Veronique and Wilson, James G and Dehghan, Abbas and Parsa, Afshin and Chasman, Daniel I and Ho, Kevin and Morris, Andrew P and Devuyst, Olivier and Akilesh, Shreeram and Pendergrass, Sarah A and Sim, Xueling and B{\"o}ger, Carsten A and Okada, Yukinori and Edwards, Todd L and Snieder, Harold and Stefansson, Kari and Hung, Adriana M and Heid, Iris M and Scholz, Markus and Teumer, Alexander and K{\"o}ttgen, Anna and Pattaro, Cristian} } @article {9941, title = {Cardiac Troponins and Cardiovascular Disease Risk Prediction: An Individual-Participant-Data Meta-Analysis.}, journal = {J Am Coll Cardiol}, volume = {85}, year = {2025}, month = {2025 Apr 15}, pages = {1471-1484}, abstract = {

BACKGROUND: The extent to which high-sensitivity cardiac troponin can predict cardiovascular disease (CVD) is uncertain.

OBJECTIVES: We aimed to quantify the potential advantage of adding information on cardiac troponins to conventional risk factors in the prevention of CVD.

METHODS: We meta-analyzed individual-participant data from 15 cohorts, comprising 62,150 participants without prior CVD. We calculated HRs, measures of risk discrimination, and reclassification after adding cardiac troponin T (cTnT) or I (cTnI) to conventional risk factors. The primary outcome was first-onset CVD (ie, coronary heart disease or stroke). We then modeled the implications of initiating statin therapy using incidence rates from 2.1 million individuals from the United Kingdom.

RESULTS: Among participants with cTnT or cTnI measurements, 8,133 and 3,749 incident CVD events occurred during a median follow-up of 11.8 and 9.8 years, respectively. HRs for CVD per 1-SD higher concentration were 1.31 (95\%~CI: 1.25-1.37) for cTnT and 1.26 (95\%~CI: 1.19-1.33) for cTnI. Addition of cTnT or cTnI to conventional risk factors was associated with C-index increases of 0.015 (95\%~CI: 0.012-0.018) and 0.012 (95\%~CI: 0.009-0.015) and continuous net reclassification improvements of 6\% and 5\% in cases and 22\% and 17\% in noncases. One additional CVD event would be prevented for every 408 and 473 individuals screened based on statin therapy in those whose CVD risk is reclassified from intermediate to high risk after cTnT or cTnI measurement, respectively.

CONCLUSIONS: Measurement of cardiac troponin results in a modest improvement in the prediction of first-onset CVD that may translate into population health benefits if used at scale.

}, keywords = {Biomarkers, Cardiovascular Diseases, Female, Humans, Hydroxymethylglutaryl-CoA Reductase Inhibitors, Male, Middle Aged, Risk Assessment, Risk Factors, Troponin I, Troponin T}, issn = {1558-3597}, doi = {10.1016/j.jacc.2025.02.016}, author = {Shah, Anoop S V and Keene, Spencer J and Pennells, Lisa and Kaptoge, Stephen and Kimenai, Dorien M and Walker, Matthew and Halley, Julianne D and Rocha, Sara and Hoogeveen, Ron C and Gudnason, Vilmundur and Bakker, Stephan J L and Wannamethee, Sasiwarang G and Pareek, Manan and Eggers, Kai M and Jukema, J Wouter and Hankey, Graeme J and deLemos, James A and Ford, Ian and Omland, Torbj{\o}rn and Lyngbakken, Magnus Nakrem and Psaty, Bruce M and deFilippi, Christopher R and Wood, Angela M and Danesh, John and Welsh, Paul and Sattar, Naveed and Mills, Nicholas L and Di Angelantonio, Emanuele} }