Cross-sectional population associations between detailed adiposity measures and C-reactive protein levels at age 6 years: the Generation R Study

Objectives: High body mass index is associated with increased C-reactive protein levels in childhood and adulthood. Little is known about the associations of detailed adiposity measures with C-reactive protein levels in childhood. We examined the associations of general and abdominal adiposity measures with C-reactive protein levels at school age. To gain insight into the direction of causality, we used genetic risk scores based on known genetic variants in adults as proxies for child adiposity measures and C-reactive protein levels. Methods: Within a population-based cohort study among 4338 children at the median age of 6.2 years, we measured body mass index, fat mass percentage, android/gynoid fat mass ratio and preperitoneal abdominal fat mass. We also measured C-reactive protein blood levels and defined increased levels as ⩾3.0 mg l –1 . Single-nucleotide polymorphisms (SNPs) for the weighted genetic risk scores were extracted from large genome-wide association studies on adult body mass index, waist–hip ratio and C-reactive protein levels. Results: All fat mass measures were associated with increased C-reactive protein levels, even after adjusting for multiple confounders. Fat mass percentage was most strongly associated with increased C-reactive protein levels (odds ratio 1.46 (95% confidence interval 1.30–1.65) per increase standard deviation scores in fat mass percentage). The association was independent of body mass index. The genetic risk score based on adult body mass index SNPs, but not adult waist–hip ratio SNPs, tended to be associated with increased C-reactive protein levels at school age. The genetic risk score based on adult C-reactive protein level SNPs was not associated with adiposity measures at school age. Conclusion: Our results suggest that higher general and abdominal fat mass may lead to increased C-reactive protein levels at school age. Further studies are needed to replicate these results and explore the causality and long-term consequences.