@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 {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 {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} } @article {8049, title = {Admixture mapping identifies novel loci for obstructive sleep apnea in Hispanic/Latino Americans.}, journal = {Hum Mol Genet}, volume = {28}, year = {2019}, month = {2019 02 15}, pages = {675-687}, abstract = {

Obstructive sleep apnea (OSA) is a common disorder associated with increased risk of cardiovascular disease and mortality. Its prevalence and severity vary across ancestral background. Although OSA traits are heritable, few genetic associations have been identified. To identify genetic regions associated with OSA and improve statistical power, we applied admixture mapping on three primary OSA traits [the apnea hypopnea index (AHI), overnight average oxyhemoglobin saturation (SaO2) and percentage time SaO2~<~90\%] and a secondary trait (respiratory event duration) in a Hispanic/Latino American population study of 11~575 individuals with significant variation in ancestral background. Linear mixed models were performed using previously inferred African, European and Amerindian local genetic ancestry markers. Global African ancestry was associated with a lower AHI, higher SaO2 and shorter event duration. Admixture mapping analysis of the primary OSA traits identified local African ancestry at the chromosomal region 2q37 as genome-wide significantly associated with AHI (P~<~5.7~{\texttimes}~10-5), and European and Amerindian ancestries at 18q21 suggestively associated with both AHI and percentage time SaO2~<~90\% (P~<~10-3). Follow-up joint ancestry-SNP association analyses identified novel variants in ferrochelatase (FECH), significantly associated with AHI and percentage time SaO2~<~90\% after adjusting for multiple tests (P~<~8~{\texttimes}~10-6). These signals contributed to the admixture mapping associations and were replicated in independent cohorts. In this first admixture mapping study of OSA, novel associations with variants in the iron/heme metabolism pathway suggest a role for iron in influencing respiratory traits underlying OSA.

}, issn = {1460-2083}, doi = {10.1093/hmg/ddy387}, author = {Wang, Heming and Cade, Brian E and Sofer, Tamar and Sands, Scott A and Chen, Han and Browning, Sharon R and Stilp, Adrienne M and Louie, Tin L and Thornton, Timothy A and Johnson, W Craig and Below, Jennifer E and Conomos, Matthew P and Evans, Daniel S and Gharib, Sina A and Guo, Xiuqing and Wood, Alexis C and Mei, Hao and Yaffe, Kristine and Loredo, Jose S and Ramos, Alberto R and Barrett-Connor, Elizabeth and Ancoli-Israel, Sonia and Zee, Phyllis C and Arens, Raanan and Shah, Neomi A and Taylor, Kent D and Tranah, Gregory J and Stone, Katie L and Hanis, Craig L and Wilson, James G and Gottlieb, Daniel J and Patel, Sanjay R and Rice, Ken and Post, Wendy S and Rotter, Jerome I and Sunyaev, Shamil R and Cai, Jianwen and Lin, Xihong and Purcell, Shaun M and Laurie, Cathy C and Saxena, Richa and Redline, Susan and Zhu, Xiaofeng} }