Age- and sex-dependent upper reference limits for the high-sensitivity cardiac troponin T assay. - Gore, M Odette; Seliger, Stephen L; deFilippi, Christopher R; Nambi, Vijay; Christenson, Robert H; Hashim, Ibrahim A; Hoogeveen, Ron C; Ayers, Colby R; Sun, Wensheng; McGuire, Darren K; Ballantyne, Christie M; de Lemos, James A (J Am Coll Cardiol, 2014)
OBJECTIVES: The study sought to determine the 99th percentile upper reference limit for the high-sensitivity cardiac troponin T assay (hs-cTnT) in 3 large independent cohorts. BACKGROUND: The presently recommended 14 ng/l cut point for the diagnosis of myocardial infarction using the hs-cTnT assay was derived from small studies of presumably healthy individuals, with relatively little phenotypic characterization. METHODS: Data were included from 3 well-characterized population-based studies: the Dallas Heart Study (DHS), the Atherosclerosis Risk in Communities (ARIC) Study, and the Cardiovascular Health Study (CHS). Within each cohort, reference subcohorts were defined excluding individuals with recent hospitalization, overt cardiovascular disease, and kidney disease (subcohort 1), and further excluding those with subclinical structural heart disease (subcohort 2). Data were analyzed stratified by age, sex, and race. RESULTS: The 99th percentile values for the hs-cTnT assay in DHS, ARIC, and CHS were 18, 22, and 36 ng/l (subcohort 1) and 14, 21, and 28 ng/l (subcohort 2), respectively. These differences in 99th percentile values paralleled age differences across cohorts. Analyses within sex/age strata yielded similar results between cohorts. Within each cohort, 99th percentile values increased with age and were higher in men. More than 10% of men 65 to 74 years of age with no cardiovascular disease in our study had cardiac troponin T values above the current myocardial infarction threshold. CONCLUSIONS: Use of a uniform 14 ng/l cutoff for the hs-cTnT assay may lead to over-diagnosis of myocardial infarction, particularly in men and the elderly. Clinical validation is needed of new age- and sex-specific cutoff values for this assay.
Keywords: Adult; Age Factors; Aged; Biological Assay; Biomarkers; Blood Chemical Analysis; Cohort Studies; Cross-Sectional Studies; Female; Humans; Male; Middle Aged; Myocardial Infarction; Reference Values; Risk Assessment; Sensitivity and Specificity; Severity of Illness Index; Sex Factors; Troponin T

Performance of the Pooled Cohort Equations to Estimate Atherosclerotic Cardiovascular Disease Risk by Body Mass Index. - Khera, Rohan; Pandey, Ambarish; Ayers, Colby R; Carnethon, Mercedes R; Greenland, Philip; Ndumele, Chiadi E; Nambi, Vijay; Seliger, Stephen L; Chaves, Paulo H M; Safford, Monika M; Cushman, Mary; Xanthakis, Vanessa; et al. (JAMA Netw Open, 2020)
Importance: Obesity is a global health challenge and a risk factor for atherosclerotic cardiovascular disease (ASVCD). Performance of the pooled cohort equations (PCE) for ASCVD risk by body mass index (BMI; calculated as weight in kilograms divided by height in meters squared) is unknown. Objective: To assess performance of the PCE across clinical BMI categories. Design, Setting, and Participants: This cohort study used pooled individual-level data from 8 community-based, prospective, longitudinal cohort studies with 10-year ASCVD event follow-up from 1996 to 2016. We included all adults ages 40 to 79 years without baseline ASCVD or statin use, resulting in a sample size of 37 311 participants. Data were analyzed from August 2017 to July 2020. Exposures: Participant BMI category: underweight (<18.5), normal weight (18.5 to <25), overweight (25 to <30), mild obesity (30 to <35), and moderate to severe obesity (≥35). Main Outcomes and Measures: Discrimination (Harrell C statistic) and calibration (Nam-D'Agostino χ2 goodness-of-fit test) of the PCE across BMI categories. Improvement in discrimination and net reclassification with addition of BMI, waist circumference, and high-sensitivity C-reactive protein (hsCRP) to the PCE. Results: Among 37 311 participants (mean [SD] age, 58.6 [11.8] years; 21 897 [58.7%] women), 380 604 person-years of follow-up were conducted. Mean (SD) baseline BMI was 29.0 (6.2), and 360 individuals (1.0%) were in the underweight category, 9937 individuals (26.6%) were in the normal weight category, 13 601 individuals (36.4%) were in the overweight category, 7783 individuals (20.9%) were in the mild obesity category, and 5630 individuals (15.1%) were in the moderate to severe obesity category. Median (interquartile range [IQR]) 10-year estimated ASCVD risk was 7.1% (2.5%-15.4%), and 3709 individuals (9.9%) developed ASCVD over a median (IQR) 10.8 [8.5-12.6] years. The PCE overestimated ASCVD risk in the overall cohort (estimated/observed [E/O] risk ratio, 1.22; 95% CI, 1.18-1.26) and across all BMI categories except the underweight category. Calibration was better near the clinical decision threshold in all BMI groups but worse among individuals with moderate or severe obesity (E/O risk ratio, 1.36; 95% CI, 1.25-1.47) and among those with the highest estimated ASCVD risk ≥20%. The PCE C statistic overall was 0.760 (95% CI, 0.753-0.767), with lower discrimination in the moderate or severe obesity group (C statistic, 0.742; 95% CI, 0.721-0.763) compared with the normal-range BMI group (C statistic, 0.785; 95% CI, 0.772-0.798). Waist circumference (hazard ratio, 1.07 per 1-SD increase; 95% CI, 1.03-1.11) and hsCRP (hazard ratio, 1.07 per 1-SD increase; 95% CI, 1.05-1.09), but not BMI, were associated with increased ASCVD risk when added to the PCE. However, these factors did not improve model performance (C statistic, 0.760; 95% CI, 0.753-0.767) with or without added metrics. Conclusions and Relevance: These findings suggest that the PCE had acceptable model discrimination and were well calibrated at clinical decision thresholds but overestimated risk of ASCVD for individuals in overweight and obese categories, particularly individuals with high estimated risk. Incorporation of the usual clinical measures of obesity did not improve risk estimation of the PCE. Future research is needed to determine whether incorporation of alternative high-risk obesity markers (eg, weight trajectory or measures of visceral or ectopic fat) into the PCE may improve risk prediction.
Keywords: Adult; Aged; Body Mass Index; Cardiovascular Diseases; Cohort Studies; Correlation of Data; Female; Humans; Longitudinal Studies; Male; Middle Aged; Proportional Hazards Models; Prospective Studies; Risk Assessment; Risk Factors