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• W2947171305 abstract "The capacity of HDLs to accept cholesterol effluxing from macrophages has been proposed as a new biomarker of HDLs' anti-atherogenic function. Whether cholesterol efflux capacity (CEC) is independent of HDL cholesterol (HDL-C) as a biomarker for coronary heart disease (CHD) risk in a generally healthy primary-prevention population remains unanswered. Therefore, in this nested case-control study, we simultaneously assessed CEC (using J774 cells) and plasma HDL-C levels as predictors of CHD in healthy middle-aged and older men not receiving treatment affecting blood lipid concentrations. We used risk-set sampling of participants free of disease at baseline from the Health Professionals Follow-Up Study, and matched cases (n = 701) to controls 1:1 for age, smoking, and blood sampling date. We applied conditional logistic regression models to calculate the multivariable relative risk and 95% CIs of CHD over 16 years of follow-up. CEC and HDL-C were correlated (r = 0.50, P < 0.0001). The risk (95% CI) of CHD per one SD higher CEC was 0.82 (0.71–0.96), but completely attenuated to 1.08 (0.85–1.37) with HDL-C in the model. The association per one SD between HDL-C and CHD (0.66; 0.58–0.76) was essentially unchanged (0.68; 0.53–0.88) after adjustment for CEC. These findings indicate that CEC's ability to predict CHD may not be independent of HDL-C in a cohort of generally healthy men. The capacity of HDLs to accept cholesterol effluxing from macrophages has been proposed as a new biomarker of HDLs' anti-atherogenic function. Whether cholesterol efflux capacity (CEC) is independent of HDL cholesterol (HDL-C) as a biomarker for coronary heart disease (CHD) risk in a generally healthy primary-prevention population remains unanswered. Therefore, in this nested case-control study, we simultaneously assessed CEC (using J774 cells) and plasma HDL-C levels as predictors of CHD in healthy middle-aged and older men not receiving treatment affecting blood lipid concentrations. We used risk-set sampling of participants free of disease at baseline from the Health Professionals Follow-Up Study, and matched cases (n = 701) to controls 1:1 for age, smoking, and blood sampling date. We applied conditional logistic regression models to calculate the multivariable relative risk and 95% CIs of CHD over 16 years of follow-up. CEC and HDL-C were correlated (r = 0.50, P < 0.0001). The risk (95% CI) of CHD per one SD higher CEC was 0.82 (0.71–0.96), but completely attenuated to 1.08 (0.85–1.37) with HDL-C in the model. The association per one SD between HDL-C and CHD (0.66; 0.58–0.76) was essentially unchanged (0.68; 0.53–0.88) after adjustment for CEC. These findings indicate that CEC's ability to predict CHD may not be independent of HDL-C in a cohort of generally healthy men. Cholesterol efflux capacity (CEC) has gained attention as a novel biomarker proposed to reflect function of HDL and improve risk prediction of coronary heart disease (CHD) (1.Rohatgi A. High-density lipoprotein function measurement in human studies: focus on cholesterol efflux capacity.Prog. Cardiovasc. Dis. 2015; 58: 32-40Crossref PubMed Scopus (66) Google Scholar, 2.Cahill L.E. Bertoia M.L. Aroner S.A. Mukamal K.J. Jensen M.K. New and emerging biomarkers in cardiovascular disease.Curr. Diab. Rep. 2015; 15: 88Crossref PubMed Scopus (15) Google Scholar). CEC measures the ability of HDL to receive cholesterol from macrophages, which is the first key step of reverse cholesterol transport, the process in which peripherally deposited cholesterol is taken up and carried by HDL to the liver for excretion (3.Rader D.J. Alexander E.T. Weibel G.L. Billheimer J. Rothblat G.H. The role of reverse cholesterol transport in animals and humans and relationship to atherosclerosis.J. Lipid Res. 2009; 50: S189-S194Abstract Full Text Full Text PDF PubMed Scopus (452) Google Scholar, 4.Fisher E.A. Feig J.E. Hewing B. Hazen S.L. Smith J.D. High-density lipoprotein function, dysfunction, and reverse cholesterol transport.Arterioscler. Thromb. Vasc. Biol. 2012; 32: 2813-2820Crossref PubMed Scopus (273) Google Scholar, 5.Rosenson R.S. Brewer Jr., H.B. Davidson W.S. Fayad Z.A. Fuster V. Goldstein J. Hellerstein M. Jiang X.C. Phillips M.C. Rader D.J. et al.Cholesterol efflux and atheroprotection: advancing the concept of reverse cholesterol transport.Circulation. 2012; 125: 1905-1919Crossref PubMed Scopus (681) Google Scholar). Reverse cholesterol transport is considered a primary function of HDL, but other functions likely also contribute to its cardioprotection, including macrophage foam cell formation, endothelial activation of endothelial nitric oxide synthase, monocyte adhesion, and platelet aggregation (4.Fisher E.A. Feig J.E. Hewing B. Hazen S.L. Smith J.D. High-density lipoprotein function, dysfunction, and reverse cholesterol transport.Arterioscler. Thromb. Vasc. Biol. 2012; 32: 2813-2820Crossref PubMed Scopus (273) Google Scholar, 6.Rye K.A. Barter P.J. Cardioprotective functions of HDLs.J. Lipid Res. 2014; 55: 168-179Abstract Full Text Full Text PDF PubMed Scopus (205) Google Scholar). Low plasma levels of cholesterol in HDL [HDL cholesterol (HDL-C)] are strongly associated with CHD risk in observational studies (7.Emerging Risk Factors Collaborators Di Angelantonio E. Sarwar N. Perry P. Kaptoge S. Ray K.K. Thompson A. Wood A.M. Lewington S. Sattar N. et al.Major lipids, apolipoproteins, and risk of vascular disease.JAMA. 2009; 302: 1993-2000Crossref PubMed Scopus (1965) Google Scholar), yet solid evidence of causality is lacking. For example, therapeutics that raise total HDL-C do not substantially lower CHD risk (8.HPS2-THRIVE Collaborative Group Landray M.J. Haynes R. Hopewell J.C. Parish S. Aung T. Tomson J. Wallendszus L. Craig M. Jiang L. et al.Effects of extended-release niacin with laropiprant in high-risk patients.N. Engl. J. Med. 2014; 371: 203-212Crossref PubMed Scopus (1166) Google Scholar, 9.AIM HIGH Investigators Boden W.E. Probstfield J.L. Anderson T. Chaitman B.R. Desvignes-Nickens P. Koprowicz K. McBride R. Teo K. Weintraub W. Niacin in patients with low HDL cholesterol levels receiving intensive statin therapy.N. Engl. J. Med. 2011; 365: 2255-2267Crossref PubMed Scopus (2267) Google Scholar, 10.HPS3/TIMI55–REVEAL Collaborative Group Bowman L. Hopewell J.C. Chen F. Wallendszus K. Stevens W. Collins R. Wiviott S.D. Cannon C.P. Braunwald E. et al.Effects of anacetrapib in patients with atherosclerotic vascular disease.N. Engl. J. Med. 2017; 377: 1217-1227Crossref PubMed Scopus (605) Google Scholar, 11.Lincoff A.M. Nicholls S.J. Riesmeyer J.S. Barter P.J. Brewer H.B. Fox K.A.A. Gibson C.M. Granger C. Menon V. Montalescot G. ACCELERATE Investigators et al.Evacetrapib and cardiovascular outcomes in high-risk vascular disease.N. Engl. J. Med. 2017; 376: 1933-1942Crossref PubMed Scopus (443) Google Scholar). Further, human genetic studies of inherited variation that predispose to elevated HDL-C levels do not support a causal relationship between HDL-C and CHD (12.Voight B.F. Peloso G.M. Orho-Melander M. Frikke-Schmidt R. Barbalic M. Jensen M.K. Hindy G. Holm H. Ding E.L. Johnson T. et al.Plasma HDL cholesterol and risk of myocardial infarction: a mendelian randomisation study.Lancet. 2012; 380: 572-580Abstract Full Text Full Text PDF PubMed Scopus (1648) Google Scholar). Therefore, understanding of the relationship between CHD risk and the traditional measures of HDL-C or apoA-I concentration could be advanced by biomarkers such as CEC that reflect functional properties of HDL. CEC has a strong inverse association with prevalent carotid intima-media thickness (13.Khera A.V. Cuchel M. de la Llera-Moya M. Rodrigues A. Burke M.F. Jafri K. French B.C. Phillips J.A. Mucksavage M.L. Wilensky R.L. et al.Cholesterol efflux capacity, high-density lipoprotein function, and atherosclerosis.N. Engl. J. Med. 2011; 364: 127-135Crossref PubMed Scopus (1533) Google Scholar) and also with risk of incident CVD (14.Saleheen D. Scott R. Javad S. Zhao W. Rodrigues A. Picataggi A. Lukmanova D. Mucksavage M.L. Luben R. Billheimer J. et al.Association of HDL cholesterol efflux capacity with incident coronary heart disease events: a prospective case-control study.Lancet Diabetes Endocrinol. 2015; 3: 507-513Abstract Full Text Full Text PDF PubMed Scopus (348) Google Scholar, 15.Rohatgi A. Khera A. Berry J.D. Givens E.G. Ayers C.R. Wedin K.E. Neeland I.J. Yuhanna I.S. Rader D.R. de Lemos J.A. et al.HDL cholesterol efflux capacity and incident cardiovascular events.N. Engl. J. Med. 2014; 371: 2383-2393Crossref PubMed Scopus (970) Google Scholar). However, some studies, especially those with longitudinal designs, have reported contrary findings (16.Li X.M. Tang W.H. Mosior M.K. Huang Y. Wu Y. Matter W. Gao V. Schmitt D. Didonato J.A. Fisher E.A. et al.Paradoxical association of enhanced cholesterol efflux with increased incident cardiovascular risks.Arterioscler. Thromb. Vasc. Biol. 2013; 33: 1696-1705Crossref PubMed Scopus (255) Google Scholar, 17.de Vries R. Groen A.K. Dullaart R.P. Cholesterol efflux capacity and atherosclerosis.N. Engl. J. Med. 2011; 364: 1473-1474; author reply 1474–1475PubMed Google Scholar). Most studies on CEC to date have been conducted in populations on lipid-altering medications and with HDL-C concentrations only weakly associated with risk of CHD or CVD mortality (13.Khera A.V. Cuchel M. de la Llera-Moya M. Rodrigues A. Burke M.F. Jafri K. French B.C. Phillips J.A. Mucksavage M.L. Wilensky R.L. et al.Cholesterol efflux capacity, high-density lipoprotein function, and atherosclerosis.N. Engl. J. Med. 2011; 364: 127-135Crossref PubMed Scopus (1533) Google Scholar, 15.Rohatgi A. Khera A. Berry J.D. Givens E.G. Ayers C.R. Wedin K.E. Neeland I.J. Yuhanna I.S. Rader D.R. de Lemos J.A. et al.HDL cholesterol efflux capacity and incident cardiovascular events.N. Engl. J. Med. 2014; 371: 2383-2393Crossref PubMed Scopus (970) Google Scholar, 18.Potočnjak I. Degoricija V. Trbušić M. Terešak S.D. Radulović B. Pregartner G. Berghold A. Tiran B. Marsche G. Frank S. Metrics of high-density lipoprotein function and hospital mortality in acute heart failure patients.PLoS One. 2016; 11: e0157507Crossref PubMed Scopus (22) Google Scholar, 19.Kopecky C. Ebtehaj S. Genser B. Drechsler C. Krane V. Antlanger M. Kovarik J.J. Kaltenecker C.C. Parvizi M. Wanner C. et al.HDL cholesterol efflux does not predict cardiovascular risk in hemodialysis patients.J. Am. Soc. Nephrol. 2017; 28: 769-775Crossref PubMed Scopus (42) Google Scholar, 20.Javaheri A. Molina M. Zamani P. Rodrigues A. Novak E. Chambers S. Stutman P. Maslanek W. Williams M. Lilly S.M. et al.Cholesterol efflux capacity of high-density lipoprotein correlates with survival and allograft vasculopathy in cardiac transplant recipients.J. Heart Lung Transplant. 2016; 35: 1295-1302Abstract Full Text Full Text PDF PubMed Scopus (10) Google Scholar). Prospective examination of the association between CEC and risk of incident CHD in a generally healthy population sample where HDL-C has the expected strong inverse association with risk of CHD is imperative. Until such assessment, it remains unresolved to what extent the association between CEC and risk of CHD is independent of HDL-C levels. The objective of the present study was to investigate the relationship between CEC and risk of incident CHD alone and in conjunction with HDL-C in a prospective nested case-control study of middle-aged and older men who were free of CVD at blood sample collection and represent a sample not confounded by therapeutics affecting blood lipid concentrations. The Health Professionals Follow-up Study (HPFS) is a prospective cohort of 51,529 male health professionals (dentists, optometrists, osteopathic physicians, pharmacists, podiatrists, and veterinarians) who were 40 to 75 years of age at baseline in 1986. Between 1993 and 1995, blood samples were collected from participants free of CVD and cancer; thus, this blood collection time serves as the baseline of our present prospective nested case-control study of CHD, which is nested within the larger HPFS cohort study. Information on anthropometric and lifestyle factors was obtained through self-administered questionnaires every 2 years and diet every 4 years as part of the larger prospective HPFS cohort study, so baseline anthropometric and lifestyle variables for the present nested case-control study were derived from the 1994 questionnaire (closest to blood collection) with missing information substituted from the two previous questionnaires. Men who had an incident myocardial infarction (MI) or fatal CHD between the date of blood draw and January 2010 were identified as cases in our present nested case-control study. Using risk-set sampling, controls were selected randomly and matched 1:1 on age, smoking, and month of blood draw among participants who were free of CVD at the time CHD was diagnosed in the case patient. The majority (>95%) of participants in this case-control sample were Caucasian. The validity of the questionnaires and the reproducibility of the measurements have been previously reported (21.Rimm E.B. Giovannucci E.L. Stampfer M.J. Colditz G.A. Litin L.B. Willett W.C. Reproducibility and validity of an expanded self-administered semiquantitative food frequency questionnaire among male health professionals.Am. J. Epidemiol. 1992; 135: 1114-1126, discussion 1127–1136Crossref PubMed Scopus (1764) Google Scholar). Three participants were missing cholesterol values, and two had missing alcohol intake data, leaving a final sample of 696 controls and 701 cases. The study protocol was approved by the institutional review boards of the Brigham and Women's Hospital and Harvard T. H. Chan School of Public Health, and those of participating registries as required. Informed consent was obtained and the study abided by the Declaration of Helsinki principles. Incident CHD in our study was defined as nonfatal MI and fatal CHD. MI was confirmed based on the criteria of the World Health Organization (symptoms plus either diagnostic electrocardiographic changes or altered levels of cardiac enzymes) (22.Rose G.A. Blackburn H. Cardiovascular Survey Methods..WHO Monograph. 1982; 58 (World Health Organization, Geneva, Switzerland.)Google Scholar). As previously reported (23.Rimm E.B. Giovannucci E.L. Willett W.C. Colditz G.A. Ascherio A. Rosner B. Stampfer M.J. Prospective study of alcohol consumption and risk of coronary disease in men.Lancet. 1991; 338: 464-468Abstract PubMed Scopus (1059) Google Scholar), nonfatal events were confirmed through review of medical records by physicians blinded to the participants' questionnaire reports. Deaths were identified from state vital records and the National Death Index or reported by the participant's next of kin or the postal system. Fatal CHD was confirmed by an examination of hospital or autopsy records (by the listing of CHD as the cause of death on the death certificate or if CHD was the underlying and most plausible cause of death). Participants underwent phlebotomy and returned ethylenediaminetetraacetic acid-preserved tubes of whole blood in a Styrofoam container with an icepack to the Brigham and Women's Hospital/Harvard Cohorts Biorepository of the Brigham and Women's Hospital, Harvard Medical School via overnight courier. Upon arrival, whole blood samples were centrifuged to separate plasma, buffy coat, and red blood cells, which were stored in cryotubes as in the vapor phase of liquid nitrogen freezers at less than −130°C. Details about the Brigham and Women's Hospital/Harvard Cohorts Biorepository have been described elsewhere (24.Hankinson S.E. Willett W.C. Manson J.E. Colditz G.A. Hunter D.J. Spiegelman D. Barbieri R.L. Speizer F.E. Plasma sex steroid hormone levels and risk of breast cancer in postmenopausal women.J. Natl. Cancer Inst. 1998; 90: 1292-1299Crossref PubMed Scopus (589) Google Scholar). The biomarkers in this sample generally showed excellent stability and reproducibility during simulated transport and storage (25.Pai J.K. Curhan G.C. Cannuscio C.C. Rifai N. Ridker P.M. Rimm E.B. Stability of novel plasma markers associated with cardiovascular disease: processing within 36 hours of specimen collection.Clin. Chem. 2002; 48: 1781-1784Crossref PubMed Scopus (75) Google Scholar). Standard methods were employed by the Rifai laboratory at Harvard University to measure serum lipids (26.Fung T.T. Hu F.B. Yu J. Chu N.F. Spiegelman D. Tofler G.H. Willett W.C. Rimm E.B. Leisure-time physical activity, television watching, and plasma biomarkers of obesity and cardiovascular disease risk.Am. J. Epidemiol. 2000; 152: 1171-1178Crossref PubMed Scopus (184) Google Scholar). Total plasma cholesterol [coefficient of variation (CV) of 1.8%] was measured by the esterase-oxidase method, and triglycerides (TGs) (5.1% CV) were measured by an enzymatic procedure. LDL cholesterol (LDL-C) (2.7% CV) was determined by a homogenous direct method and HDL-C (2.2% CV) by enzymatic assay (27.Shai I. Rimm E.B. Schulze M.B. Rifai N. Stampfer M.J. Hu F.B. Moderate alcohol intake and markers of inflammation and endothelial dysfunction among diabetic men.Diabetologia. 2004; 47: 1760-1767Crossref PubMed Scopus (65) Google Scholar). The CEC assay (9.2% CV) was performed by the Rader laboratory at the University of Pennsylvania, using methods similar to those described by Khera et al. (13.Khera A.V. Cuchel M. de la Llera-Moya M. Rodrigues A. Burke M.F. Jafri K. French B.C. Phillips J.A. Mucksavage M.L. Wilensky R.L. et al.Cholesterol efflux capacity, high-density lipoprotein function, and atherosclerosis.N. Engl. J. Med. 2011; 364: 127-135Crossref PubMed Scopus (1533) Google Scholar) with a few small changes to improve throughput and simplify the assay for use in large populations: J774 cells derived from a murine macrophage cell line were labeled with 2 μCi of 3H-cholesterol (Perkin Elmer) per milliliter of medium overnight and without the presence of an acetyl-CoA acetyltransferase inhibitor, as preliminary data indicated that <4% of total cellular cholesterol was cholesterol ester. ABCA1 was upregulated for 4 h by incubation with 0.3 mM 8-(4-chlorophenylthio)-cyclic AMP. Efflux media containing the equivalent of 2% apoB-depleted plasma were then incubated for 2 h at 37°C in a non-CO2 incubator. A pooled plasma control was included on each of the 24-well plates for normalization. The percent cholesterol efflux was calculated by the following formula: (cpm of 3H-cholesterol in media – cpm of 3H-cholesterol in baseline control)/(cpm of 3H-cholesterol in cells + cpm of 3H-cholesterol in the media) × 100, yielding a normalized percentage (15.Rohatgi A. Khera A. Berry J.D. Givens E.G. Ayers C.R. Wedin K.E. Neeland I.J. Yuhanna I.S. Rader D.R. de Lemos J.A. et al.HDL cholesterol efflux capacity and incident cardiovascular events.N. Engl. J. Med. 2014; 371: 2383-2393Crossref PubMed Scopus (970) Google Scholar). In our within-person pilot study of 44 participants who provided two measures 1–2 years apart, CEC was well-correlated between draws (Spearman r = 0.61) and had strong intraclass correlations (r = 0.62). To account for the potential variation in CEC by batch (because there were 24 batches), values of each CEC exposure were recalibrated to represent the average distribution across batches using the Rosner recalibration method (28.Rosner B. Cook N. Portman R. Daniels S. Falkner B. Determination of blood pressure percentiles in normal-weight children: some methodological issues.Am. J. Epidemiol. 2008; 167: 653-666Crossref PubMed Scopus (287) Google Scholar, 29.Rice M.S. Tworoger S.S. Rosner B.A. Pollak M.N. Hankinson S.E. Tamimi R.M. Insulin-like growth factor-1, insulin-like growth factor-binding protein-3, growth hormone, and mammographic density in the Nurses' Health Studies.Breast Cancer Res. Treat. 2012; 136: 805-812Crossref PubMed Scopus (28) Google Scholar). The Rosner method assumes that all batches combined represent an average batch, regresses the values, and calibrates the values within each batch by adding the resulting value of the coefficients for that batch minus the average of the batch coefficients. Therefore, log-transformed CEC variables were regressed on batch using linear regression models, and then we used the back-transformed measures of CEC in our analyses as recalibrated levels accounting for the variability between batches. CEC was examined as a continuous variable (per one SD in CEC) and also in quintiles, the cut-points of which were derived from only the controls. Participant characteristics were compared between cases and controls using t-tests for continuous variables and χ2 tests for categorical variables. For HbA1c and TGs, we used log-transformation to normalize the distributions. Relative risks (RRs) of CHD were estimated by incidence rate ratios using conditional logistic regression conditional on matching factors (age in years, smoking, month of blood draw). Analyses were adjusted for fasting status (yes/no), BMI (categories: <20, 20–24, 25–29, 30–34, and ≥35 kg/m2), alcohol intake (categories: nondrinker, 0.1–4.9 g of alcohol per day (g/day), 5.0–14.9 g/day, 15.0–29.9 g/day, ≥30.0 g/day), parental MI before the age of 60 (yes/no), history of high cholesterol (yes/no), and the history of high blood pressure (yes/no), TG, and LDL-C. Additionally, the multivariable model further included HDL-C to observe the influence of HDL-C on the ability of CEC to predict risk of CHD. Similarly, the association between HDL-C and risk of CHD was determined with and without adjustment for CEC. Although we had collected data on (lipid-lowering, blood pressure-lowering, blood sugar-lowering) medication use, such medication use in this sample of relatively healthy men was uncommon, and adjustment for it did not alter our results. We further ran analyses stratified by established CHD risk factors. All analyses were conducted in SAS version 9.4 at a two-tailed α level of 0.05. CEC was normally distributed in both cases and controls separately and combined. As expected, cases had higher mean BMI, total cholesterol, LDL-C, apoB, TG, and HbA1c and a greater prevalence of hypertension, diabetes, hypercholesterolemia, family history of CHD, and medication use at baseline as compared with controls (Table 1). Cases also had a lower mean HDL-C and drank less alcohol than controls. CEC (mean, SD; unit is normalized percentage) was significantly lower in cases (0.97, 0.14) as compared with controls (0.99, 0.14). CEC was highly correlated with HDL-C (r = 0.50, P < 0.0001 in all participants and r = 0.53, P < 0.0001 in controls only) (Table 2). CEC was most strongly associated with total cholesterol (r = 0.34, P < 0.0001), alcohol intake (r = 0.25, P < 0.0001), and LDL-C (r = 0.15, P < 0.0001) (in all participants). CEC did not correlate with diabetes, hypertension, TGs, or HbA1c (all r < 0.10).TABLE 1Baseline (1994) characteristics of HPFS CHD case-control study participantsControlsCasesPNumber of SubjectsMean (SD) or %Number of SubjectsMean (SD) or %CECaCEC is calculated as a normalized percentage using the formula: (cpm of 3H-cholesterol in media – cpm of 3H-cholesterol in baseline control)/(cpm of 3H-cholesterol in cells + cpm of 3H-cholesterol in the media) × 100.6960.99 (0.14)7010.97 (0.14)0.01Demographic and anthropometric factors Age (years)bCase-control matching factor.69663.0 (8.7)70163.0 (8.7)0.99 Fasting ≥8 h (yes)696419 (60%)701415 (59%)0.70 BMI69625.7 (3.4)70126.2 (3.3)0.005Medical history History of diabetes69620 (3%)70159 (8%)<0.0001 History of hypertension696181 (26%)701259 (37%)<0.0001 History of hypercholesterolemia696269 (37%)701347 (50%)<0.0001 Cholesterol-lowering medication use69645 (6%)70154 (8%)0.36 Parental CHD before age 60696232 (33%)701284 (41%)0.005Lipid-related markers Total cholesterol (mg/dl)696199.2 (33.5)701205.9 (37.4)0.0004 HDL-C (mg/dl)69646.9 (12.6)70142.4 (11.6)<0.0001 LDL-C (mg/dl)696129.0 (30.7)701135.0 (33.7)0.0005 apoB (mg/dl)41890.1 (20.4)42398.4 (23.5)<0.0001 TGs (mg/dl)cTGs, hsCRP, and HbA1c were the only skewed variables. For all subsequent tables/analyses these three skewed variables will be log-transformed.696129.1 (65.2)701157.0 (92.3)<0.0001 HbA1c (%)cTGs, hsCRP, and HbA1c were the only skewed variables. For all subsequent tables/analyses these three skewed variables will be log-transformed.6945.65 (0.56)6995.89 (1.03)<0.0001 hsCRP (mg/l)cTGs, hsCRP, and HbA1c were the only skewed variables. For all subsequent tables/analyses these three skewed variables will be log-transformed.6962.12 (4.45)7002.33 (3.82)<0.0001Lifestyle factors Activity (MET-hours/week) Quintile 1 (≤9.0)139 (20%)172 (25%) Quintile 2 (9.1–19.1)140 (20%)127 (18%) Quintile 3 (19.2–33.1)139 (20%)128 (18%)0.33 Quintile 4 (33.2–56.8)149 (20%)139 (20%) Quintile 5 (>56.8)139 (20%)135 (19%) Current SmokingbCase-control matching factor.69644 (6%)70151 (7%)0.46 Alcohol intake Nondrinker147 (21%)196 (28%) 0.1–4.9 g/day145 (21%)179 (25%) 5.0–14.9 g/day200 (29%)168 (24%)0.0006 15.0–29.9 g/day108 (15%)88 (13%) ≥30 g/day96 (14%)70 (10%) Diet quality scoredDiet quality score is the Alternate Healthy Eating Index 2010. Quintile 1 (≤44.69)138 (20%)162 (23%) Quintile 2 (44.70–51.29)140 (20%)144 (21%) Quintile 3 (51.30–58.12)140 (20%)161 (23%)0.11 Quintile 4 (58.13–64.57)138 (20%)122 (17%) Quintile 5 (>64.57)140 (20%)112 (16%)Quintiles are based on the distributions of controls only. hsCRP, high-sensitivity C-reactive protein; MET, metabolic equivalent task.a CEC is calculated as a normalized percentage using the formula: (cpm of 3H-cholesterol in media – cpm of 3H-cholesterol in baseline control)/(cpm of 3H-cholesterol in cells + cpm of 3H-cholesterol in the media) × 100.b Case-control matching factor.c TGs, hsCRP, and HbA1c were the only skewed variables. For all subsequent tables/analyses these three skewed variables will be log-transformed.d Diet quality score is the Alternate Healthy Eating Index 2010. Open table in a new tab TABLE 2Cross-sectional Spearman correlates with CEC in all participants and in only controlsCorrelation (P) with CECaCEC is calculated as a normalized percentage using the formula: (cpm of 3H-cholesterol in media – cpm of 3H-cholesterol in baseline control)/(cpm of 3H-cholesterol in cells + cpm of 3H-cholesterol in the media) × 100.All ParticipantsControls OnlyAge (years)−0.01 (0.57)0.01 (0.73)Fasting ≥8 h at blood draw−0.01 (0.70)−0.04 (0.33)BMI−0.11 (<0.0001)−0.13 (0.0004)History of diabetes−0.04 (0.15)−0.07 (0.05)History of hypertension0.02 (0.54)−0.02 (0.61)History of hypercholesterolemia0.14 (<0.0001)0.15 (<0.0001)Cholesterol-lowering medication use (%)0.07 (0.008)0.07 (0.08)Parental CHD before age 600.05 (0.09)0.05 (0.21)Total cholesterol (mg/dl)0.34 (<0.0001)0.34 (<0.0001)HDL-C (mg/dl)0.50 (<0.0001)0.53 (<0.0001)LDL-C (mg/dl)0.15 (<0.0001)0.15 (0.0001)apoB (mg/dl)0.14 (<0.0001)0.08 (0.09)TGs (mg/dl)†0.02 (0.44)−0.04 (0.31)HbA1cbLog-transformed because the variable has a skewed distribution.−0.05 (0.06)−0.07 (0.09)hsCRP (mg/l)bLog-transformed because the variable has a skewed distribution.−0.07 (0.008)−0.007 (0.85)Physical activity (quintiles)0.06 (0.02)0.10 (0.01)Current smoker−0.03 (0.26)−0.03 (0.51)Alcohol (categories)0.25 (<0.0001)0.28 (<0.0001)Diet quality scorecDiet quality score is the Alternate Healthy Eating Index 2010. (quintiles)0.06 (0.02)0.04 (0.21)hsCRP, high-sensitivity C-reactive protein.a CEC is calculated as a normalized percentage using the formula: (cpm of 3H-cholesterol in media – cpm of 3H-cholesterol in baseline control)/(cpm of 3H-cholesterol in cells + cpm of 3H-cholesterol in the media) × 100.b Log-transformed because the variable has a skewed distribution.c Diet quality score is the Alternate Healthy Eating Index 2010. Open table in a new tab Quintiles are based on the distributions of controls only. hsCRP, high-sensitivity C-reactive protein; MET, metabolic equivalent task. hsCRP, high-sensitivity C-reactive protein. Accounting for matching factors (age, smoking, and date of blood draw) and all covariates except blood lipids, the RR (95% CI) of CHD per one SD of CEC was 0.89 (0.77–1.02) (Table 3). After progressive covariate adjustment for LDL-C and log-transformed TG, the RR was 0.82 (0.71–0.95), which suggests an important inverse association between CEC and risk of CHD. However, after further adjustment for HDL-C, the inverse RR of CHD for efflux capacity was completely attenuated to 1.08 (0.85–1.37) and no longer significant. In a model that included HDL-C as a covariate, but not LDL-C or TG, the RR (95% CI) of CHD per one SD of CEC was 1.25 (1.04–1.50) (P = 0.02), indicating that adjusting for HDL-C can reverse the inverse association between efflux and CHD. In contrast, the inverse association between HDL-C and CHD (0.68; 0.60–0.78) was unchanged by mutual adjustment for CEC, LDL-C, and TG (after adjustment: 0.68; 0.53–0.88) in the fully adjusted model.TABLE 3RRs and 95% CIs for CHD per one SD of CEC and HDL-CCECaCEC is calculated as a normalized percentage using the formula: (cpm of 3H-cholesterol in media – cpm of 3H-cholesterol in baseline control)/(cpm of 3H-cholesterol in cells + cpm of 3H-cholesterol in the media) × 100.HDL-CRR95% CIPRR95% CIPMultivariable modelbAdjusted for fasting status, history of diabetes and hypertension, parental CHD before age 60, BMI (<20, 20–24, 25–29, 30–34, ≥35 kg/m2), and alcohol intake (nondrinker, 0.1–4.9 g of alcohol per day (g/day), 5.0–14.9 g/day, 15.0–29.9 g/day, ≥30.0 g/day). Matching factors (cases and controls were matched on age, smoking, and date of blood draw) are incorporated into the analysis using conditional logistic regression.0.890.77–1.020.090.680.60–0.78<0.0001+ LDL0.840.73–0.980.020.680.60–0.78<0.0001+ logTG0.820.71–0.950.010.660.58–0.76<0.0001+ HDL-C or CEC1.080.85–1.370.520" @default.
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• W2947171305 date "2019-08-01" @default.
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• W2947171305 title "Cholesterol efflux capacity, HDL cholesterol, and risk of coronary heart disease: a nested case-control study in men" @default.
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