Insulin resistance and obesity, and their association with depression in relatively young people: findings from a large UK birth cohort.
Level 3 - non-randomized controlled study
Prospective longitudinal birth cohort study
PubMed 30854996 · doi:10.1017/S0033291719000308
What was done
Researchers analyzed data from 3,208 participants in the Avon Longitudinal Study of Parents and Children (ALSPAC) UK birth cohort to evaluate cross-sectional and longitudinal relationships between measures of disordered glucose and insulin homeostasis (insulin resistance, impaired glucose tolerance), BMI at ages 9 and 18 years, and depression (depressive symptoms and depressive episodes) at age 18 years. Regression models were adjusted for sociodemographic characteristics, lifestyle factors, and childhood inflammatory markers (interleukin-6 and C-reactive protein at age 9 years).
What was found
Cross-sectionally at age 18, insulin resistance and BMI were positively associated with depression, but these associations appeared to be explained by sociodemographic and lifestyle factors. Longitudinally, measures of glucose/insulin homeostasis and BMI at age 9 were not associated with depression at age 18. Adjustment for IL-6 and C-reactive protein did not attenuate the observed associations between insulin resistance/BMI and depression. Exact effect estimates and confidence intervals were not reported in the abstract.
Why it matters
These results indicate that the co-occurrence of metabolic dysfunction, elevated BMI, and depression in young people is primarily contemporaneous and driven by shared lifestyle and sociodemographic factors rather than early childhood metabolic or inflammatory precursors.
Limits
The abstract provides no point estimates, odds ratios, or confidence intervals. The authors explicitly note that longitudinal analyses may have been underpowered, and observational findings from a single UK birth cohort cannot establish causality or necessarily generalize to other populations.
Cited by
- partial Insulin resistance in early life predicts the future onset of psychiatric disorders and is associated with more severe disease progression in large cohort datasets.