@article {1359, title = {Association between chromosome 9p21 variants and the ankle-brachial index identified by a meta-analysis of 21 genome-wide association studies.}, journal = {Circ Cardiovasc Genet}, volume = {5}, year = {2012}, month = {2012 Feb 01}, pages = {100-12}, abstract = {

BACKGROUND: Genetic determinants of peripheral arterial disease (PAD) remain largely unknown. To identify genetic variants associated with the ankle-brachial index (ABI), a noninvasive measure of PAD, we conducted a meta-analysis of genome-wide association study data from 21 population-based cohorts.

METHODS AND RESULTS: Continuous ABI and PAD (ABI <=0.9) phenotypes adjusted for age and sex were examined. Each study conducted genotyping and imputed data to the ≈2.5 million single nucleotide polymorphisms (SNPs) in HapMap. Linear and logistic regression models were used to test each SNP for association with ABI and PAD using additive genetic models. Study-specific data were combined using fixed effects inverse variance weighted meta-analyses. There were a total of 41 692 participants of European ancestry (≈60\% women, mean ABI 1.02 to 1.19), including 3409 participants with PAD and with genome-wide association study data available. In the discovery meta-analysis, rs10757269 on chromosome 9 near CDKN2B had the strongest association with ABI (β=-0.006, P=2.46{\texttimes}10(-8)). We sought replication of the 6 strongest SNP associations in 5 population-based studies and 3 clinical samples (n=16 717). The association for rs10757269 strengthened in the combined discovery and replication analysis (P=2.65{\texttimes}10(-9)). No other SNP associations for ABI or PAD achieved genome-wide significance. However, 2 previously reported candidate genes for PAD and 1 SNP associated with coronary artery disease were associated with ABI: DAB21P (rs13290547, P=3.6{\texttimes}10(-5)), CYBA (rs3794624, P=6.3{\texttimes}10(-5)), and rs1122608 (LDLR, P=0.0026).

CONCLUSIONS: Genome-wide association studies in more than 40 000 individuals identified 1 genome wide significant association on chromosome 9p21 with ABI. Two candidate genes for PAD and 1 SNP for coronary artery disease are associated with ABI.

}, keywords = {Adult, Age Factors, Aged, Aged, 80 and over, Alleles, Ankle Brachial Index, Chromosomes, Human, Pair 9, Cohort Studies, Cyclin-Dependent Kinase Inhibitor p15, Female, Genome-Wide Association Study, Genotype, HapMap Project, Humans, Logistic Models, Male, Middle Aged, Peripheral Vascular Diseases, Phenotype, Polymorphism, Single Nucleotide, Risk Factors, Sex Factors}, issn = {1942-3268}, doi = {10.1161/CIRCGENETICS.111.961292}, author = {Murabito, Joanne M and White, Charles C and Kavousi, Maryam and Sun, Yan V and Feitosa, Mary F and Nambi, Vijay and Lamina, Claudia and Schillert, Arne and Coassin, Stefan and Bis, Joshua C and Broer, Linda and Crawford, Dana C and Franceschini, Nora and Frikke-Schmidt, Ruth and Haun, Margot and Holewijn, Suzanne and Huffman, Jennifer E and Hwang, Shih-Jen and Kiechl, Stefan and Kollerits, Barbara and Montasser, May E and Nolte, Ilja M and Rudock, Megan E and Senft, Andrea and Teumer, Alexander and van der Harst, Pim and Vitart, Veronique and Waite, Lindsay L and Wood, Andrew R and Wassel, Christina L and Absher, Devin M and Allison, Matthew A and Amin, Najaf and Arnold, Alice and Asselbergs, Folkert W and Aulchenko, Yurii and Bandinelli, Stefania and Barbalic, Maja and Boban, Mladen and Brown-Gentry, Kristin and Couper, David J and Criqui, Michael H and Dehghan, Abbas and den Heijer, Martin and Dieplinger, Benjamin and Ding, Jingzhong and D{\"o}rr, Marcus and Espinola-Klein, Christine and Felix, Stephan B and Ferrucci, Luigi and Folsom, Aaron R and Fraedrich, Gustav and Gibson, Quince and Goodloe, Robert and Gunjaca, Grgo and Haltmayer, Meinhard and Heiss, Gerardo and Hofman, Albert and Kieback, Arne and Kiemeney, Lambertus A and Kolcic, Ivana and Kullo, Iftikhar J and Kritchevsky, Stephen B and Lackner, Karl J and Li, Xiaohui and Lieb, Wolfgang and Lohman, Kurt and Meisinger, Christa and Melzer, David and Mohler, Emile R and Mudnic, Ivana and Mueller, Thomas and Navis, Gerjan and Oberhollenzer, Friedrich and Olin, Jeffrey W and O{\textquoteright}Connell, Jeff and O{\textquoteright}Donnell, Christopher J and Palmas, Walter and Penninx, Brenda W and Petersmann, Astrid and Polasek, Ozren and Psaty, Bruce M and Rantner, Barbara and Rice, Ken and Rivadeneira, Fernando and Rotter, Jerome I and Seldenrijk, Adrie and Stadler, Marietta and Summerer, Monika and Tanaka, Toshiko and Tybjaerg-Hansen, Anne and Uitterlinden, Andr{\'e} G and van Gilst, Wiek H and Vermeulen, Sita H and Wild, Sarah H and Wild, Philipp S and Willeit, Johann and Zeller, Tanja and Zemunik, Tatijana and Zgaga, Lina and Assimes, Themistocles L and Blankenberg, Stefan and Boerwinkle, Eric and Campbell, Harry and Cooke, John P and de Graaf, Jacqueline and Herrington, David and Kardia, Sharon L R and Mitchell, Braxton D and Murray, Anna and M{\"u}nzel, Thomas and Newman, Anne B and Oostra, Ben A and Rudan, Igor and Shuldiner, Alan R and Snieder, Harold and van Duijn, Cornelia M and V{\"o}lker, Uwe and Wright, Alan F and Wichmann, H-Erich and Wilson, James F and Witteman, Jacqueline C M and Liu, Yongmei and Hayward, Caroline and Borecki, Ingrid B and Ziegler, Andreas and North, Kari E and Cupples, L Adrienne and Kronenberg, Florian} } @article {1382, title = {Carotid intima-media thickness progression to predict cardiovascular events in the general population (the PROG-IMT collaborative project): a meta-analysis of individual participant data.}, journal = {Lancet}, volume = {379}, year = {2012}, month = {2012 Jun 02}, pages = {2053-62}, abstract = {

BACKGROUND: Carotid intima-media thickness (cIMT) is related to the risk of cardiovascular events in the general population. An association between changes in cIMT and cardiovascular risk is frequently assumed but has rarely been reported. Our aim was to test this association.

METHODS: We identified general population studies that assessed cIMT at least twice and followed up participants for myocardial infarction, stroke, or death. The study teams collaborated in an individual participant data meta-analysis. Excluding individuals with previous myocardial infarction or stroke, we assessed the association between cIMT progression and the risk of cardiovascular events (myocardial infarction, stroke, vascular death, or a combination of these) for each study with Cox regression. The log hazard ratios (HRs) per SD difference were pooled by random effects meta-analysis.

FINDINGS: Of 21 eligible studies, 16 with 36,984 participants were included. During a mean follow-up of 7{\textperiodcentered}0 years, 1519 myocardial infarctions, 1339 strokes, and 2028 combined endpoints (myocardial infarction, stroke, vascular death) occurred. Yearly cIMT progression was derived from two ultrasound visits 2-7 years (median 4 years) apart. For mean common carotid artery intima-media thickness progression, the overall HR of the combined endpoint was 0{\textperiodcentered}97 (95\% CI 0{\textperiodcentered}94-1{\textperiodcentered}00) when adjusted for age, sex, and mean common carotid artery intima-media thickness, and 0{\textperiodcentered}98 (0{\textperiodcentered}95-1{\textperiodcentered}01) when also adjusted for vascular risk factors. Although we detected no associations with cIMT progression in sensitivity analyses, the mean cIMT of the two ultrasound scans was positively and robustly associated with cardiovascular risk (HR for the combined endpoint 1{\textperiodcentered}16, 95\% CI 1{\textperiodcentered}10-1{\textperiodcentered}22, adjusted for age, sex, mean common carotid artery intima-media thickness progression, and vascular risk factors). In three studies including 3439 participants who had four ultrasound scans, cIMT progression did not correlate between occassions (reproducibility correlations between r=-0{\textperiodcentered}06 and r=-0{\textperiodcentered}02).

INTERPRETATION: The association between cIMT progression assessed from two ultrasound scans and cardiovascular risk in the general population remains unproven. No conclusion can be derived for the use of cIMT progression as a surrogate in clinical trials.

FUNDING: Deutsche Forschungsgemeinschaft.

}, keywords = {Cardiovascular Diseases, Carotid Intima-Media Thickness, Disease Progression, Follow-Up Studies, Humans, Myocardial Infarction, Prognosis, Risk Assessment, Stroke}, issn = {1474-547X}, doi = {10.1016/S0140-6736(12)60441-3}, author = {Lorenz, Matthias W and Polak, Joseph F and Kavousi, Maryam and Mathiesen, Ellisiv B and V{\"o}lzke, Henry and Tuomainen, Tomi-Pekka and Sander, Dirk and Plichart, Matthieu and Catapano, Alberico L and Robertson, Christine M and Kiechl, Stefan and Rundek, Tatjana and Desvarieux, Mo{\"\i}se and Lind, Lars and Schmid, Caroline and DasMahapatra, Pronabesh and Gao, Lu and Ziegelbauer, Kathrin and Bots, Michiel L and Thompson, Simon G} } @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 {6544, title = {Genetic association study of QT interval highlights role for calcium signaling pathways in myocardial repolarization.}, journal = {Nat Genet}, volume = {46}, year = {2014}, month = {2014 Aug}, pages = {826-36}, abstract = {

The QT interval, an electrocardiographic measure reflecting myocardial repolarization, is a heritable trait. QT prolongation is a risk factor for ventricular arrhythmias and sudden cardiac death (SCD) and could indicate the presence of the potentially lethal mendelian long-QT syndrome (LQTS). Using a genome-wide association and replication study in up to 100,000 individuals, we identified 35 common variant loci associated with QT interval that collectively explain \~{}8-10\% of QT-interval variation and highlight the importance of calcium regulation in myocardial repolarization. Rare variant analysis of 6 new QT interval-associated loci in 298 unrelated probands with LQTS identified coding variants not found in controls but of uncertain causality and therefore requiring validation. Several newly identified loci encode proteins that physically interact with other recognized repolarization proteins. Our integration of common variant association, expression and orthogonal protein-protein interaction screens provides new insights into cardiac electrophysiology and identifies new candidate genes for ventricular arrhythmias, LQTS and SCD.

}, keywords = {Adult, Aged, Arrhythmias, Cardiac, Calcium Signaling, Death, Sudden, Cardiac, Electrocardiography, Female, Genetic Predisposition to Disease, Genome-Wide Association Study, Genotype, Heart Ventricles, Humans, Long QT Syndrome, Male, Middle Aged, Myocardium, Polymorphism, Single Nucleotide}, issn = {1546-1718}, doi = {10.1038/ng.3014}, author = {Arking, Dan E and Pulit, Sara L and Crotti, Lia and van der Harst, Pim and Munroe, Patricia B and Koopmann, Tamara T and Sotoodehnia, Nona and Rossin, Elizabeth J and Morley, Michael and Wang, Xinchen and Johnson, Andrew D and Lundby, Alicia and Gudbjartsson, Daniel F and Noseworthy, Peter A and Eijgelsheim, Mark and Bradford, Yuki and Tarasov, Kirill V and D{\"o}rr, Marcus and M{\"u}ller-Nurasyid, Martina and Lahtinen, Annukka M and Nolte, Ilja M and Smith, Albert Vernon and Bis, Joshua C and Isaacs, Aaron and Newhouse, Stephen J and Evans, Daniel S and Post, Wendy S and Waggott, Daryl and Lyytik{\"a}inen, Leo-Pekka and Hicks, Andrew A and Eisele, Lewin and Ellinghaus, David and Hayward, Caroline and Navarro, Pau and Ulivi, Sheila and Tanaka, Toshiko and Tester, David J and Chatel, St{\'e}phanie and Gustafsson, Stefan and Kumari, Meena and Morris, Richard W and Naluai, {\r A}sa T and Padmanabhan, Sandosh and Kluttig, Alexander and Strohmer, Bernhard and Panayiotou, Andrie G and Torres, Maria and Knoflach, Michael and Hubacek, Jaroslav A and Slowikowski, Kamil and Raychaudhuri, Soumya and Kumar, Runjun D and Harris, Tamara B and Launer, Lenore J and Shuldiner, Alan R and Alonso, Alvaro and Bader, Joel S and Ehret, Georg and Huang, Hailiang and Kao, W H Linda and Strait, James B and Macfarlane, Peter W and Brown, Morris and Caulfield, Mark J and Samani, Nilesh J and Kronenberg, Florian and Willeit, Johann and Smith, J Gustav and Greiser, Karin H and Meyer Zu Schwabedissen, Henriette and Werdan, Karl and Carella, Massimo and Zelante, Leopoldo and Heckbert, Susan R and Psaty, Bruce M and Rotter, Jerome I and Kolcic, Ivana and Polasek, Ozren and Wright, Alan F and Griffin, Maura and Daly, Mark J and Arnar, David O and Holm, Hilma and Thorsteinsdottir, Unnur and Denny, Joshua C and Roden, Dan M and Zuvich, Rebecca L and Emilsson, Valur and Plump, Andrew S and Larson, Martin G and O{\textquoteright}Donnell, Christopher J and Yin, Xiaoyan and Bobbo, Marco and D{\textquoteright}Adamo, Adamo P and Iorio, Annamaria and Sinagra, Gianfranco and Carracedo, Angel and Cummings, Steven R and Nalls, Michael A and Jula, Antti and Kontula, Kimmo K and Marjamaa, Annukka and Oikarinen, Lasse and Perola, Markus and Porthan, Kimmo and Erbel, Raimund and Hoffmann, Per and J{\"o}ckel, Karl-Heinz and K{\"a}lsch, Hagen and N{\"o}then, Markus M and den Hoed, Marcel and Loos, Ruth J F and Thelle, Dag S and Gieger, Christian and Meitinger, Thomas and Perz, Siegfried and Peters, Annette and Prucha, Hanna and Sinner, Moritz F and Waldenberger, Melanie and de Boer, Rudolf A and Franke, Lude and van der Vleuten, Pieter A and Beckmann, Britt Maria and Martens, Eimo and Bardai, Abdennasser and Hofman, Nynke and Wilde, Arthur A M and Behr, Elijah R and Dalageorgou, Chrysoula and Giudicessi, John R and Medeiros-Domingo, Argelia and Barc, Julien and Kyndt, Florence and Probst, Vincent and Ghidoni, Alice and Insolia, Roberto and Hamilton, Robert M and Scherer, Stephen W and Brandimarto, Jeffrey and Margulies, Kenneth and Moravec, Christine E and del Greco M, Fabiola and Fuchsberger, Christian and O{\textquoteright}Connell, Jeffrey R and Lee, Wai K and Watt, Graham C M and Campbell, Harry and Wild, Sarah H and El Mokhtari, Nour E and Frey, Norbert and Asselbergs, Folkert W and Mateo Leach, Irene and Navis, Gerjan and van den Berg, Maarten P and van Veldhuisen, Dirk J and Kellis, Manolis and Krijthe, Bouwe P and Franco, Oscar H and Hofman, Albert and Kors, Jan A and Uitterlinden, Andr{\'e} G and Witteman, Jacqueline C M and Kedenko, Lyudmyla and Lamina, Claudia and Oostra, Ben A and Abecasis, Goncalo R and Lakatta, Edward G and Mulas, Antonella and Orr{\`u}, Marco and Schlessinger, David and Uda, Manuela and Markus, Marcello R P and V{\"o}lker, Uwe and Snieder, Harold and Spector, Timothy D and Arnl{\"o}v, Johan and Lind, Lars and Sundstr{\"o}m, Johan and Syv{\"a}nen, Ann-Christine and Kivimaki, Mika and K{\"a}h{\"o}nen, Mika and Mononen, Nina and Raitakari, Olli T and Viikari, Jorma S and Adamkova, Vera and Kiechl, Stefan and Brion, Maria and Nicolaides, Andrew N and Paulweber, Bernhard and Haerting, Johannes and Dominiczak, Anna F and Nyberg, Fredrik and Whincup, Peter H and Hingorani, Aroon D and Schott, Jean-Jacques and Bezzina, Connie R and Ingelsson, Erik and Ferrucci, Luigi and Gasparini, Paolo and Wilson, James F and Rudan, Igor and Franke, Andre and M{\"u}hleisen, Thomas W and Pramstaller, Peter P and Lehtim{\"a}ki, Terho J and Paterson, Andrew D and Parsa, Afshin and Liu, Yongmei and van Duijn, Cornelia M and Siscovick, David S and Gudnason, Vilmundur and Jamshidi, Yalda and Salomaa, Veikko and Felix, Stephan B and Sanna, Serena and Ritchie, Marylyn D and Stricker, Bruno H and Stefansson, Kari and Boyer, Laurie A and Cappola, Thomas P and Olsen, Jesper V and Lage, Kasper and Schwartz, Peter J and K{\"a}{\"a}b, Stefan and Chakravarti, Aravinda and Ackerman, Michael J and Pfeufer, Arne and de Bakker, Paul I W and Newton-Cheh, Christopher} } @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 {6612, title = {Inflammatory markers and extent and progression of early atherosclerosis: Meta-analysis of individual-participant-data from 20 prospective studies of the PROG-IMT collaboration.}, journal = {Eur J Prev Cardiol}, volume = {23}, year = {2016}, month = {2016 Jan}, pages = {194-205}, abstract = {

BACKGROUND: Large-scale epidemiological evidence on the role of inflammation in early atherosclerosis, assessed by carotid ultrasound, is lacking. We aimed to quantify cross-sectional and longitudinal associations of inflammatory markers with common-carotid-artery intima-media thickness (CCA-IMT) in the general population.

METHODS: Information on high-sensitivity C-reactive protein, fibrinogen, leucocyte count and CCA-IMT was available in 20 prospective cohort studies of the PROG-IMT collaboration involving 49,097 participants free of pre-existing cardiovascular disease. Estimates of associations were calculated within each study and then combined using random-effects meta-analyses.

RESULTS: Mean baseline CCA-IMT amounted to 0.74 mm (SD = 0.18) and mean CCA-IMT progression over a mean of 3.9 years to 0.011 mm/year (SD = 0.039). Cross-sectional analyses showed positive linear associations between inflammatory markers and baseline CCA-IMT. After adjustment for traditional cardiovascular risk factors, mean differences in baseline CCA-IMT per one-SD higher inflammatory marker were: 0.0082 mm for high-sensitivity C-reactive protein (p < 0.001); 0.0072 mm for fibrinogen (p < 0.001); and 0.0025 mm for leucocyte count (p = 0.033). {\textquoteright}Inflammatory load{\textquoteright}, defined as the number of elevated inflammatory markers (i.e. in upper two quintiles), showed a positive linear association with baseline CCA-IMT (p < 0.001). Longitudinal associations of baseline inflammatory markers and changes therein with CCA-IMT progression were null or at most weak. Participants with the highest {\textquoteright}inflammatory load{\textquoteright} had a greater CCA-IMT progression (p = 0.015).

CONCLUSION: Inflammation was independently associated with CCA-IMT cross-sectionally. The lack of clear associations with CCA-IMT progression may be explained by imprecision in its assessment within a limited time period. Our findings for {\textquoteright}inflammatory load{\textquoteright} suggest important combined effects of the three inflammatory markers on early atherosclerosis.

}, issn = {2047-4881}, doi = {10.1177/2047487314560664}, author = {Willeit, Peter and Thompson, Simon G and Agewall, Stefan and Bergstr{\"o}m, G{\"o}ran and Bickel, Horst and Catapano, Alberico L and Chien, Kuo-Liong and de Groot, Eric and Empana, Jean-Philippe and Etgen, Thorleif and Franco, Oscar H and Iglseder, Bernhard and Johnsen, Stein H and Kavousi, Maryam and Lind, Lars and Liu, Jing and Mathiesen, Ellisiv B and Norata, Giuseppe D and Olsen, Michael H and Papagianni, Aikaterini and Poppert, Holger and Price, Jackie F and Sacco, Ralph L and Yanez, David N and Zhao, Dong and Schminke, Ulf and B{\"u}lb{\"u}l, Alpaslan and Polak, Joseph F and Sitzer, Matthias and Hofman, Albert and Grigore, Liliana and D{\"o}rr, Marcus and Su, Ta-Chen and Ducimetiere, Pierre and Xie, Wuxiang and Ronkainen, Kimmo and Kiechl, Stefan and Rundek, Tatjana and Robertson, Christine and Fagerberg, Bj{\"o}rn and Bokemark, Lena and Steinmetz, Helmuth and Ikram, M Arfan and V{\"o}lzke, Henry and Lin, Hung-Ju and Plichart, Matthieu and Tuomainen, Tomi-Pekka and Desvarieux, Mo{\"\i}se and McLachlan, Stela and Schmidt, Caroline and Kauhanen, Jussi and Willeit, Johann and Lorenz, Matthias W and Sander, Dirk} } @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 {7574, title = {Automatic identification of variables in epidemiological datasets using logic regression.}, journal = {BMC Med Inform Decis Mak}, volume = {17}, year = {2017}, month = {2017 Apr 13}, pages = {40}, abstract = {

BACKGROUND: For an individual participant data (IPD) meta-analysis, multiple datasets must be transformed in a consistent format, e.g. using uniform variable names. When large numbers of datasets have to be processed, this can be a time-consuming and error-prone task. Automated or semi-automated identification of variables can help to reduce the workload and improve the data quality. For semi-automation high sensitivity in the recognition of matching variables is particularly important, because it allows creating software which for a target variable presents a choice of source variables, from which a user can choose the matching one, with only low risk of having missed a correct source variable.

METHODS: For each variable in a set of target variables, a number of simple rules were manually created. With logic regression, an optimal Boolean combination of these rules was searched for every target variable, using a random subset of a large database of epidemiological and clinical cohort data (construction subset). In a second subset of this database (validation subset), this optimal combination rules were validated.

RESULTS: In the construction sample, 41 target variables were allocated on average with a positive predictive value (PPV) of 34\%, and a negative predictive value (NPV) of 95\%. In the validation sample, PPV was 33\%, whereas NPV remained at 94\%. In the construction sample, PPV was 50\% or less in 63\% of all variables, in the validation sample in 71\% of all variables.

CONCLUSIONS: We demonstrated that~the application of logic regression in a complex data management task in large epidemiological IPD meta-analyses is feasible. However, the performance of the algorithm is poor, which may require backup strategies.

}, issn = {1472-6947}, doi = {10.1186/s12911-017-0429-1}, author = {Lorenz, Matthias W and Abdi, Negin Ashtiani and Scheckenbach, Frank and Pflug, Anja and B{\"u}lb{\"u}l, Alpaslan and Catapano, Alberico L and Agewall, Stefan and Ezhov, Marat and Bots, Michiel L and Kiechl, Stefan and Orth, Andreas} } @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} } @article {7846, title = {Predictive value for cardiovascular events of common carotid intima media thickness and its rate of change in individuals at high cardiovascular risk - Results from the PROG-IMT collaboration.}, journal = {PLoS One}, volume = {13}, year = {2018}, month = {2018}, pages = {e0191172}, abstract = {

AIMS: Carotid intima media thickness (CIMT) predicts cardiovascular (CVD) events, but the predictive value of CIMT change is debated. We assessed the relation between CIMT change and events in individuals at high cardiovascular risk.

METHODS AND RESULTS: From 31 cohorts with two CIMT scans (total n = 89070) on average 3.6 years apart and clinical follow-up, subcohorts were drawn: (A) individuals with at least 3 cardiovascular risk factors without previous CVD events, (B) individuals with carotid plaques without previous CVD events, and (C) individuals with previous CVD events. Cox regression models were fit to estimate the hazard ratio (HR) of the combined endpoint (myocardial infarction, stroke or vascular death) per standard deviation (SD) of CIMT change, adjusted for CVD risk factors. These HRs were pooled across studies. In groups A, B and C we observed 3483, 2845 and 1165 endpoint events, respectively. Average common CIMT was 0.79mm (SD 0.16mm), and annual common CIMT change was 0.01mm (SD 0.07mm), both in group A. The pooled HR per SD of annual common CIMT change (0.02 to 0.43mm) was 0.99 (95\% confidence interval: 0.95-1.02) in group A, 0.98 (0.93-1.04) in group B, and 0.95 (0.89-1.04) in group C. The HR per SD of common CIMT (average of the first and the second CIMT scan, 0.09 to 0.75mm) was 1.15 (1.07-1.23) in group A, 1.13 (1.05-1.22) in group B, and 1.12 (1.05-1.20) in group C.

CONCLUSIONS: We confirm that common CIMT is associated with future CVD events in individuals at high risk. CIMT change does not relate to future event risk in high-risk individuals.

}, keywords = {Aged, Cardiovascular Diseases, Carotid Intima-Media Thickness, Female, Humans, Intersectoral Collaboration, Male, Middle Aged, Prognosis, Risk Factors}, issn = {1932-6203}, doi = {10.1371/journal.pone.0191172}, author = {Lorenz, Matthias W and Gao, Lu and Ziegelbauer, Kathrin and Norata, Giuseppe Danilo and Empana, Jean Philippe and Schmidtmann, Irene and Lin, Hung-Ju and McLachlan, Stela and Bokemark, Lena and Ronkainen, Kimmo and Amato, Mauro and Schminke, Ulf and Srinivasan, Sathanur R and Lind, Lars and Okazaki, Shuhei and Stehouwer, Coen D A and Willeit, Peter and Polak, Joseph F and Steinmetz, Helmuth and Sander, Dirk and Poppert, Holger and Desvarieux, Mo{\"\i}se and Ikram, M Arfan and Johnsen, Stein Harald and Staub, Daniel and Sirtori, Cesare R and Iglseder, Bernhard and Beloqui, Oscar and Engstr{\"o}m, Gunnar and Friera, Alfonso and Rozza, Francesco and Xie, Wuxiang and Parraga, Grace and Grigore, Liliana and Plichart, Matthieu and Blankenberg, Stefan and Su, Ta-Chen and Schmidt, Caroline and Tuomainen, Tomi-Pekka and Veglia, Fabrizio and V{\"o}lzke, Henry and Nijpels, Giel and Willeit, Johann and Sacco, Ralph L and Franco, Oscar H and Uthoff, Heiko and Hedblad, Bo and Suarez, Carmen and Izzo, Raffaele and Zhao, Dong and Wannarong, Thapat and Catapano, Alberico and Ducimetiere, Pierre and Espinola-Klein, Christine and Chien, Kuo-Liong and Price, Jackie F and Bergstr{\"o}m, G{\"o}ran and Kauhanen, Jussi and Tremoli, Elena and D{\"o}rr, Marcus and Berenson, Gerald and Kitagawa, Kazuo and Dekker, Jacqueline M and Kiechl, Stefan and Sitzer, Matthias and Bickel, Horst and Rundek, Tatjana and Hofman, Albert and Mathiesen, Ellisiv B and Castelnuovo, Samuela and Landecho, Manuel F and Rosvall, Maria and Gabriel, Rafael and de Luca, Nicola and Liu, Jing and Baldassarre, Damiano and Kavousi, Maryam and de Groot, Eric and Bots, Michiel L and Yanez, David N and Thompson, Simon G} } @article {7664, title = {Risk thresholds for alcohol consumption: combined analysis of individual-participant data for 599 912 current drinkers in 83 prospective studies.}, journal = {Lancet}, volume = {391}, year = {2018}, month = {2018 04 14}, pages = {1513-1523}, abstract = {

BACKGROUND: Low-risk limits recommended for alcohol consumption vary substantially across different national guidelines. To define thresholds associated with lowest risk for all-cause mortality and cardiovascular disease, we studied individual-participant data from 599 912 current drinkers without previous cardiovascular disease.

METHODS: We did a combined analysis of individual-participant data from three large-scale data sources in 19 high-income countries (the Emerging Risk Factors Collaboration, EPIC-CVD, and the UK Biobank). We characterised dose-response associations and calculated hazard ratios (HRs) per 100 g per week of alcohol (12{\textperiodcentered}5 units per week) across 83 prospective studies, adjusting at least for study or centre, age, sex, smoking, and diabetes. To be eligible for the analysis, participants had to have information recorded about their alcohol consumption amount and status (ie, non-drinker vs current drinker), plus age, sex, history of diabetes and smoking status, at least 1 year of follow-up after baseline, and no baseline history of cardiovascular disease. The main analyses focused on current drinkers, whose baseline alcohol consumption was categorised into eight predefined groups according to the amount in grams consumed per week. We assessed alcohol consumption in relation to all-cause mortality, total cardiovascular disease, and several cardiovascular disease subtypes. We corrected HRs for estimated long-term variability in alcohol consumption using 152 640 serial alcohol assessments obtained some years apart (median interval 5{\textperiodcentered}6 years [5th-95th percentile 1{\textperiodcentered}04-13{\textperiodcentered}5]) from 71 011 participants from 37 studies.

FINDINGS: In the 599 912 current drinkers included in the analysis, we recorded 40 310 deaths and 39 018 incident cardiovascular disease events during 5{\textperiodcentered}4 million person-years of follow-up. For all-cause mortality, we recorded a positive and curvilinear association with the level of alcohol consumption, with the minimum mortality risk around or below 100 g per week. Alcohol consumption was roughly linearly associated with a higher risk of stroke (HR per 100 g per week higher consumption 1{\textperiodcentered}14, 95\% CI, 1{\textperiodcentered}10-1{\textperiodcentered}17), coronary disease excluding myocardial infarction (1{\textperiodcentered}06, 1{\textperiodcentered}00-1{\textperiodcentered}11), heart failure (1{\textperiodcentered}09, 1{\textperiodcentered}03-1{\textperiodcentered}15), fatal hypertensive disease (1{\textperiodcentered}24, 1{\textperiodcentered}15-1{\textperiodcentered}33); and fatal aortic aneurysm (1{\textperiodcentered}15, 1{\textperiodcentered}03-1{\textperiodcentered}28). By contrast, increased alcohol consumption was log-linearly associated with a lower risk of myocardial infarction (HR 0{\textperiodcentered}94, 0{\textperiodcentered}91-0{\textperiodcentered}97). In comparison to those who reported drinking >0-<=100 g per week, those who reported drinking >100-<=200 g per week, >200-<=350 g per week, or >350 g per week had lower life expectancy at age 40 years of approximately 6 months, 1-2 years, or 4-5 years, respectively.

INTERPRETATION: In current drinkers of alcohol in high-income countries, the threshold for lowest risk of all-cause mortality was about 100 g/week. For cardiovascular disease subtypes other than myocardial infarction, there were no clear risk thresholds below which lower alcohol consumption stopped being associated with lower disease risk. These data support limits for alcohol consumption that are lower than those recommended in most current guidelines.

FUNDING: UK Medical Research Council, British Heart Foundation, National Institute for Health Research, European Union Framework 7, and European Research Council.

}, issn = {1474-547X}, doi = {10.1016/S0140-6736(18)30134-X}, author = {Wood, Angela M and Kaptoge, Stephen and Butterworth, Adam S and Willeit, Peter and Warnakula, Samantha and Bolton, Thomas and Paige, Ellie and Paul, Dirk S and Sweeting, Michael and Burgess, Stephen and Bell, Steven and Astle, William and Stevens, David and Koulman, Albert and Selmer, Randi M and Verschuren, W M Monique and Sato, Shinichi and Nj{\o}lstad, Inger and Woodward, Mark and Salomaa, Veikko and Nordestgaard, B{\o}rge G and Yeap, Bu B and Fletcher, Astrid and Melander, Olle and Kuller, Lewis H and Balkau, Beverley and Marmot, Michael and Koenig, Wolfgang and Casiglia, Edoardo and Cooper, Cyrus and Arndt, Volker and Franco, Oscar H and Wennberg, Patrik and Gallacher, John and de la C{\'a}mara, Agustin G{\'o}mez and V{\"o}lzke, Henry and Dahm, Christina C and Dale, Caroline E and Bergmann, Manuela M and Crespo, Carlos J and van der Schouw, Yvonne T and Kaaks, Rudolf and Simons, Leon A and Lagiou, Pagona and Schoufour, Josje D and Boer, Jolanda M A and Key, Timothy J and Rodriguez, Beatriz and Moreno-Iribas, Conchi and Davidson, Karina W and Taylor, James O and Sacerdote, Carlotta and Wallace, Robert B and Quiros, J Ramon and Tumino, Rosario and Blazer, Dan G and Linneberg, Allan and Daimon, Makoto and Panico, Salvatore and Howard, Barbara and Skeie, Guri and Strandberg, Timo and Weiderpass, Elisabete and Nietert, Paul J and Psaty, Bruce M and Kromhout, Daan and Salamanca-Fernandez, Elena and Kiechl, Stefan and Krumholz, Harlan M and Grioni, Sara and Palli, Domenico and Huerta, Jos{\'e} M and Price, Jackie and Sundstr{\"o}m, Johan and Arriola, Larraitz and Arima, Hisatomi and Travis, Ruth C and Panagiotakos, Demosthenes B and Karakatsani, Anna and Trichopoulou, Antonia and K{\"u}hn, Tilman and Grobbee, Diederick E and Barrett-Connor, Elizabeth and van Schoor, Natasja and Boeing, Heiner and Overvad, Kim and Kauhanen, Jussi and Wareham, Nick and Langenberg, Claudia and Forouhi, Nita and Wennberg, Maria and Despr{\'e}s, Jean-Pierre and Cushman, Mary and Cooper, Jackie A and Rodriguez, Carlos J and Sakurai, Masaru and Shaw, Jonathan E and Knuiman, Matthew and Voortman, Trudy and Meisinger, Christa and Tj{\o}nneland, Anne and Brenner, Hermann and Palmieri, Luigi and Dallongeville, Jean and Brunner, Eric J and Assmann, Gerd and Trevisan, Maurizio and Gillum, Richard F and Ford, Ian and Sattar, Naveed and Lazo, Mariana and Thompson, Simon G and Ferrari, Pietro and Leon, David A and Smith, George Davey and Peto, Richard and Jackson, Rod and Banks, Emily and Di Angelantonio, Emanuele and Danesh, John} }