@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 {7451, title = {REPEATED MEASUREMENTS OF BLOOD PRESSURE AND CHOLESTEROL IMPROVES CARDIOVASCULAR DISEASE RISK PREDICTION: AN INDIVIDUAL-PARTICIPANT-DATA META-ANALYSIS.}, journal = {Am J Epidemiol}, year = {2017}, month = {2017 May 26}, abstract = {

The 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.

}, issn = {1476-6256}, doi = {10.1093/aje/kwx149}, author = {Paige, Ellie and Barrett, Jessica and Pennells, Lisa and Sweeting, Michael and Willeit, Peter and Di Angelantonio, Emanuele and Gudnason, Vilmundur and Nordestgaard, B{\o}rge G and Psaty, Bruce M and Goldbourt, Uri and Best, Lyle G and Assmann, Gerd and Salonen, Jukka T and Nietert, Paul J and Verschuren, Wm Monique and Brunner, Eric J and Kronmal, Richard A and Salomaa, Veikko and Bakker, Stephan Jl and Dagenais, Gilles R and Sato, Shinichi and Jansson, Jan-H{\r a}kan and Willeit, Johann and Onat, Altan and de la C{\'a}mara, Agustin G{\'o}mez and Roussel, Ronan and V{\"o}lzke, Henry and Dankner, Rachel and Tipping, Robert W and Meade, Tom W and Donfrancesco, Chiara and Kuller, Lewis H and Peters, Annette and Gallacher, John and Kromhout, Daan and Iso, Hiroyasu and Knuiman, Matthew and Casiglia, Edoardo and Kavousi, Maryam and Palmieri, Luigi and Sundstr{\"o}m, Johan and Davis, Barry R and Nj{\o}lstad, Inger and Couper, David and Danesh, John and Thompson, Simon G and Wood, Angela} } @article {7923, title = {Equalization of four cardiovascular risk algorithms after systematic recalibration: individual-participant meta-analysis of 86 prospective studies.}, journal = {Eur Heart J}, year = {2018}, month = {2018 Nov 22}, abstract = {

Aims: 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 {\textquoteright}recalibration{\textquoteright}, 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 {\textquoteright}high{\textquoteright} 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.

}, issn = {1522-9645}, doi = {10.1093/eurheartj/ehy653}, author = {Pennells, Lisa and Kaptoge, Stephen and Wood, Angela and Sweeting, Mike and Zhao, Xiaohui and White, Ian and Burgess, Stephen and Willeit, Peter and Bolton, Thomas and Moons, Karel G M and van der Schouw, Yvonne T and Selmer, Randi and Khaw, Kay-Tee and Gudnason, Vilmundur and Assmann, Gerd and Amouyel, Philippe and Salomaa, Veikko and Kivimaki, Mika and Nordestgaard, B{\o}rge G and Blaha, Michael J and Kuller, Lewis H and Brenner, Hermann and Gillum, Richard F and Meisinger, Christa and Ford, Ian and Knuiman, Matthew W and Rosengren, Annika and Lawlor, Debbie A and V{\"o}lzke, Henry and Cooper, Cyrus and Mar{\'\i}n Iba{\~n}ez, Alejandro and Casiglia, Edoardo and Kauhanen, Jussi and Cooper, Jackie A and Rodriguez, Beatriz and Sundstr{\"o}m, Johan and Barrett-Connor, Elizabeth and Dankner, Rachel and Nietert, Paul J and Davidson, Karina W and Wallace, Robert B and Blazer, Dan G and Bj{\"o}rkelund, Cecilia and Donfrancesco, Chiara and Krumholz, Harlan M and Nissinen, Aulikki and Davis, Barry R and Coady, Sean and Whincup, Peter H and J{\o}rgensen, Torben and Ducimetiere, Pierre and Trevisan, Maurizio and Engstr{\"o}m, Gunnar and Crespo, Carlos J and Meade, Tom W and Visser, Marjolein and Kromhout, Daan and Kiechl, Stefan and Daimon, Makoto and Price, Jackie F and G{\'o}mez de la C{\'a}mara, Agustin and Wouter Jukema, J and Lamarche, Beno{\^\i}t and Onat, Altan and Simons, Leon A and Kavousi, Maryam and Ben-Shlomo, Yoav and Gallacher, John and Dekker, Jacqueline M and Arima, Hisatomi and Shara, Nawar and Tipping, Robert W and Roussel, Ronan and Brunner, Eric J and Koenig, Wolfgang and Sakurai, Masaru and Pavlovic, Jelena and Gansevoort, Ron T and Nagel, Dorothea and Goldbourt, Uri and Barr, Elizabeth L M and Palmieri, Luigi and Nj{\o}lstad, Inger and Sato, Shinichi and Monique Verschuren, W M and Varghese, Cherian V and Graham, Ian and Onuma, Oyere and Greenland, Philip and Woodward, Mark and Ezzati, Majid and Psaty, Bruce M and Sattar, Naveed and Jackson, Rod and Ridker, Paul M and Cook, Nancy R and D{\textquoteright}Agostino, Ralph B and Thompson, Simon G and Danesh, John and Di Angelantonio, Emanuele} }