Key result
Self-reported anthropometrics strongly correlate with measured values and reliably estimate cardiometabolic biomarker associations.
Why the study?
Misreporting of self-reported anthropometric data may bias associations with health outcomes, and few validation studies have compared self-reported and measured waist circumference.
Are self-reported anthropometric measures reliable for estimating associations with cardiometabolic biomarkers compared to measured values in adults?
Cohort (n=39,514)
Are self-reported anthropometric measures reliable for estimating associations with cardiometabolic biomarkers compared to measured values in adults?
Self-reported anthropometric measures, including waist circumference, are highly concordant with measured values and reliable for estimating associations with cardiometabolic biomarkers in epidemiological studies.
Answers are grounded in this paper and cite their sources.
Self-reported measures may suffice for population studies; leaves open precision for individual cardiometabolic risk assessment.
General obesity is a recognized risk factor for various metabolically related diseases, including hypertension, dyslipidemia, and pre-diabetes. In epidemiological studies, anthropometric variables such as height and weight are often self-reported. However, misreporting of self-reported data may bias estimates of associations between anthropometry and health outcomes. Further, few validation studies have compared self-reported and measured waist circumference (WC). This study aimed to quantify the agreement between self-reported and measured height, weight, body mass index (BMI), WC, and waist-to-height ratio (WHtR), and to investigate associations of these anthropometric measures with cardiometabolic biomarkers. A total of 39,514 participants aged above 18 years were included into the Diet, Cancer, and Health-Next Generation Cohort in 2015-19. Self-reported and measured anthropometric variables, blood pressure, and cardiometabolic biomarkers (HbA1c, lipid profiles, C-reactive protein and creatinine) were collected by standard procedures. Pearson correlations (r) and Lin's concordance correlations were applied to evaluate misreporting. Misreporting by age, sex and smoking status was investigated in linear regression models. Multivariable regression models and Receiver Operating Characteristic analyses assessed associations of self-reported and measured anthropometry with cardiometabolic biomarkers. Self-reported height was overreported by 1.07 cm, and weight was underreported by 0.32 kg on average. Self-reported BMI and WC were 0.42 kg/m2 and 0.2 cm lower than measured, respectively. Self-reported and measured height, weight, BMI, WC and WtHR were strongly correlated (r = 0.98, 0.99, 0.98, 0.88, 0.86, respectively). Age, sex, smoking, and BMI contributed to misreporting of all anthropometric measures. Associations between self-reported or measured anthropometric measures and cardiometabolic biomarkers were similar in direction and strength. Concordance between self-reported and measured anthropometric measures, including WC, was very high. Self-reported anthropometric measures were reliable when estimating associations with cardiometabolic biomarkers.
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Zhang et al. (2023) reported a cohort. Self-reported anthropometric measures vs. Measured anthropometric measures was evaluated on Agreement between self-reported and measured anthropometric variables (height, weight, BMI, WC, WHtR). Self-reported anthropometric measures were strongly correlated with measured values (r=0.86-0.99) and were reliable when estimating associations with cardiometabolic biomarkers.
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