TY - JOUR T1 - Separate and combined associations of body-mass index and abdominal adiposity with cardiovascular disease: collaborative analysis of 58 prospective studies. JF - Lancet Y1 - 2011 A1 - Wormser, David A1 - Kaptoge, Stephen A1 - Di Angelantonio, Emanuele A1 - Wood, Angela M A1 - Pennells, Lisa A1 - Thompson, Alex A1 - Sarwar, Nadeem A1 - Kizer, Jorge R A1 - Lawlor, Debbie A A1 - Nordestgaard, Børge G A1 - Ridker, Paul A1 - Salomaa, Veikko A1 - Stevens, June A1 - Woodward, Mark A1 - Sattar, Naveed A1 - Collins, Rory A1 - Thompson, Simon G A1 - Whitlock, Gary A1 - Danesh, John KW - Age Factors KW - Blood Pressure KW - Body Mass Index KW - Cardiovascular Diseases KW - Cholesterol KW - Cholesterol, HDL KW - Diabetes Mellitus KW - Female KW - Humans KW - Male KW - Middle Aged KW - Obesity, Abdominal KW - Proportional Hazards Models KW - Prospective Studies KW - Risk Assessment KW - Sex Factors KW - Smoking KW - Systole KW - Waist Circumference KW - Waist-Hip Ratio AB -

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.

VL - 377 IS - 9771 U1 - http://www.ncbi.nlm.nih.gov/pubmed/21397319?dopt=Abstract ER - TY - JOUR T1 - Lipid-related markers and cardiovascular disease prediction. JF - JAMA Y1 - 2012 A1 - Di Angelantonio, Emanuele A1 - Gao, Pei A1 - Pennells, Lisa A1 - Kaptoge, Stephen A1 - Caslake, Muriel A1 - Thompson, Alexander A1 - Butterworth, Adam S A1 - Sarwar, Nadeem A1 - Wormser, David A1 - Saleheen, Danish A1 - Ballantyne, Christie M A1 - Psaty, Bruce M A1 - Sundström, Johan A1 - Ridker, Paul M A1 - Nagel, Dorothea A1 - Gillum, Richard F A1 - Ford, Ian A1 - Ducimetiere, Pierre A1 - Kiechl, Stefan A1 - Koenig, Wolfgang A1 - Dullaart, Robin P F A1 - Assmann, Gerd A1 - D'Agostino, Ralph B A1 - Dagenais, Gilles R A1 - Cooper, Jackie A A1 - Kromhout, Daan A1 - Onat, Altan A1 - Tipping, Robert W A1 - Gómez-de-la-Cámara, Agustín A1 - Rosengren, Annika A1 - Sutherland, Susan E A1 - Gallacher, John A1 - Fowkes, F Gerry R A1 - Casiglia, Edoardo A1 - Hofman, Albert A1 - Salomaa, Veikko A1 - Barrett-Connor, Elizabeth A1 - Clarke, Robert A1 - Brunner, Eric A1 - Jukema, J Wouter A1 - Simons, Leon A A1 - Sandhu, Manjinder A1 - Wareham, Nicholas J A1 - Khaw, Kay-Tee A1 - Kauhanen, Jussi A1 - Salonen, Jukka T A1 - Howard, William J A1 - Nordestgaard, Børge G A1 - Wood, Angela M A1 - Thompson, Simon G A1 - Boekholdt, S Matthijs A1 - Sattar, Naveed A1 - Packard, Chris A1 - Gudnason, Vilmundur A1 - Danesh, John KW - Aged KW - Biomarkers KW - Cardiovascular Diseases KW - Cholesterol, HDL KW - Cohort Studies KW - Female KW - Humans KW - Lipoproteins KW - Male KW - Middle Aged KW - Risk Assessment AB -

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'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.

VL - 307 IS - 23 U1 - http://www.ncbi.nlm.nih.gov/pubmed/22797450?dopt=Abstract ER - TY - JOUR T1 - Glycated hemoglobin measurement and prediction of cardiovascular disease. JF - JAMA Y1 - 2014 A1 - Di Angelantonio, Emanuele A1 - Gao, Pei A1 - Khan, Hassan A1 - Butterworth, Adam S A1 - Wormser, David A1 - Kaptoge, Stephen A1 - Kondapally Seshasai, Sreenivasa Rao A1 - Thompson, Alex A1 - Sarwar, Nadeem A1 - Willeit, Peter A1 - Ridker, Paul M A1 - Barr, Elizabeth L M A1 - Khaw, Kay-Tee A1 - Psaty, Bruce M A1 - Brenner, Hermann A1 - Balkau, Beverley A1 - Dekker, Jacqueline M A1 - Lawlor, Debbie A A1 - Daimon, Makoto A1 - Willeit, Johann A1 - Njølstad, Inger A1 - Nissinen, Aulikki A1 - Brunner, Eric J A1 - Kuller, Lewis H A1 - Price, Jackie F A1 - Sundström, Johan A1 - Knuiman, Matthew W A1 - Feskens, Edith J M A1 - Verschuren, W M M A1 - Wald, Nicholas A1 - Bakker, Stephan J L A1 - Whincup, Peter H A1 - Ford, Ian A1 - Goldbourt, Uri A1 - Gómez-de-la-Cámara, Agustín A1 - Gallacher, John A1 - Simons, Leon A A1 - Rosengren, Annika A1 - Sutherland, Susan E A1 - Björkelund, Cecilia A1 - Blazer, Dan G A1 - Wassertheil-Smoller, Sylvia A1 - Onat, Altan A1 - Marín Ibañez, Alejandro A1 - Casiglia, Edoardo A1 - Jukema, J Wouter A1 - Simpson, Lara M A1 - Giampaoli, Simona A1 - Nordestgaard, Børge G A1 - Selmer, Randi A1 - Wennberg, Patrik A1 - Kauhanen, Jussi A1 - Salonen, Jukka T A1 - Dankner, Rachel A1 - Barrett-Connor, Elizabeth A1 - Kavousi, Maryam A1 - Gudnason, Vilmundur A1 - Evans, Denis A1 - Wallace, Robert B A1 - Cushman, Mary A1 - D'Agostino, Ralph B A1 - Umans, Jason G A1 - Kiyohara, Yutaka A1 - Nakagawa, Hidaeki A1 - Sato, Shinichi A1 - Gillum, Richard F A1 - Folsom, Aaron R A1 - van der Schouw, Yvonne T A1 - Moons, Karel G A1 - Griffin, Simon J A1 - Sattar, Naveed A1 - Wareham, Nicholas J A1 - Selvin, Elizabeth A1 - Thompson, Simon G A1 - Danesh, John KW - Aged KW - C-Reactive Protein KW - Cholesterol, HDL KW - Coronary Disease KW - Diabetes Mellitus KW - Female KW - Glycated Hemoglobin A KW - Humans KW - Male KW - Middle Aged KW - Predictive Value of Tests KW - Prospective Studies KW - Risk Assessment KW - Stroke AB -

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.

VL - 311 IS - 12 U1 - http://www.ncbi.nlm.nih.gov/pubmed/24668104?dopt=Abstract ER - TY - JOUR T1 - Association of Cardiometabolic Multimorbidity With Mortality. JF - JAMA Y1 - 2015 A1 - Di Angelantonio, Emanuele A1 - Kaptoge, Stephen A1 - Wormser, David A1 - Willeit, Peter A1 - Butterworth, Adam S A1 - Bansal, Narinder A1 - O'Keeffe, Linda M A1 - Gao, Pei A1 - Wood, Angela M A1 - Burgess, Stephen A1 - Freitag, Daniel F A1 - Pennells, Lisa A1 - Peters, Sanne A A1 - Hart, Carole L A1 - Håheim, Lise Lund A1 - Gillum, Richard F A1 - Nordestgaard, Børge G A1 - Psaty, Bruce M A1 - Yeap, Bu B A1 - Knuiman, Matthew W A1 - Nietert, Paul J A1 - Kauhanen, Jussi A1 - Salonen, Jukka T A1 - Kuller, Lewis H A1 - Simons, Leon A A1 - van der Schouw, Yvonne T A1 - Barrett-Connor, Elizabeth A1 - Selmer, Randi A1 - Crespo, Carlos J A1 - Rodriguez, Beatriz A1 - Verschuren, W M Monique A1 - Salomaa, Veikko A1 - Svärdsudd, Kurt A1 - van der Harst, Pim A1 - Björkelund, Cecilia A1 - Wilhelmsen, Lars A1 - Wallace, Robert B A1 - Brenner, Hermann A1 - Amouyel, Philippe A1 - Barr, Elizabeth L M A1 - Iso, Hiroyasu A1 - Onat, Altan A1 - Trevisan, Maurizio A1 - D'Agostino, Ralph B A1 - Cooper, Cyrus A1 - Kavousi, Maryam A1 - Welin, Lennart A1 - Roussel, Ronan A1 - Hu, Frank B A1 - Sato, Shinichi A1 - Davidson, Karina W A1 - Howard, Barbara V A1 - Leening, Maarten J G A1 - Leening, Maarten A1 - Rosengren, Annika A1 - Dörr, Marcus A1 - Deeg, Dorly J H A1 - Kiechl, Stefan A1 - Stehouwer, Coen D A A1 - Nissinen, Aulikki A1 - Giampaoli, Simona A1 - Donfrancesco, Chiara A1 - Kromhout, Daan A1 - Price, Jackie F A1 - Peters, Annette A1 - Meade, Tom W A1 - Casiglia, Edoardo A1 - Lawlor, Debbie A A1 - Gallacher, John A1 - Nagel, Dorothea A1 - Franco, Oscar H A1 - Assmann, Gerd A1 - Dagenais, Gilles R A1 - Jukema, J Wouter A1 - Sundström, Johan A1 - Woodward, Mark A1 - Brunner, Eric J A1 - Khaw, Kay-Tee A1 - Wareham, Nicholas J A1 - Whitsel, Eric A A1 - Njølstad, Inger A1 - Hedblad, Bo A1 - Wassertheil-Smoller, Sylvia A1 - Engström, Gunnar A1 - Rosamond, Wayne D A1 - Selvin, Elizabeth A1 - Sattar, Naveed A1 - Thompson, Simon G A1 - Danesh, John KW - Adult KW - Aged KW - Comorbidity KW - Diabetes Mellitus KW - Female KW - Humans KW - Life Expectancy KW - Male KW - Middle Aged KW - Mortality KW - Myocardial Infarction KW - Risk Factors KW - Stroke AB -

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.

VL - 314 IS - 1 U1 - http://www.ncbi.nlm.nih.gov/pubmed/26151266?dopt=Abstract ER -