Abstract
INTRODUCTION: Obesity is associated with a state of chronic inflammation, and increased cardiometabolic disease risk. The present study examined the relationship between body mass index (BMI) and cardiometabolic and inflammatory biomarkers among normal weight, overweight, and obese Canadian adults. METHODS: Subjects (n = 1805, aged 18 to 79 years) from the Canadian Health Measures Survey (CHMS) were examined for associations between BMI, cardiometabolic markers (apolipoprotein [Apo] A1, ApoB, low-density lipoprotein cholesterol [LDL-C], high-density lipoprotein cholesterol [HDL-C], total cholesterol, total cholesterol/HDL ratio [total:HDL-C ratio], triglycerides, and glycosylated hemoglobin [HbA(1c)]), inflammatory factors (C-reactive protein [CRP], fibrinogen, and homocysteine), and 25-hydroxyvitamin D [25(OH)D]. Bootstrap weights for variance and sampling weights for point estimates were applied to account for the complex survey design. Linear regression models adjusted for age, sex, physical activity, smoking status, and ethnicity (in addition to season of clinic visit, for vitamin D analyses only) were used to examine the association between cardiometabolic markers, inflammatory factors, and BMI in Canadian adults. RESULTS: All biomarkers were significantly associated with BMI (P ≤ 0.001). ApoA1 (β = -0.31, P < 0.0001), HDL-C (β = -0.61, P < 0.0001), and 25(OH)D (β = -0.25, P < 0.0001) were inversely associated with BMI, while all other biomarkers showed positive linear associations. Distinct patterns of association were noted among normal weight, overweight, and obese groups, excluding CRP which showed a significant positive association with BMI in the overall population (β = 2.80, P < 0.0001) and in the normal weight (β = 3.20, P = 0.02), overweight (β = 3.53, P = 0.002), and obese (β = 2.22, P = 0.0002) groups. CONCLUSIONS: There is an apparent profile of cardiometabolic and inflammatory biomarkers that emerges as BMI increases from normal weight to obesity. Understanding these profiles may permit developing an effective approach for early risk prediction for cardiometabolic disease.