Obesity moderates the complex relationships between inflammation, oxidative stress, sleep quality and depressive symptoms
Bibliographic record
Abstract
The relationship between obesity and depression is complex. This study assessed the impact of body mass index (BMI) on the link between BMI, inflammation, oxidative stress, sleep quality and self-reported depressive symptoms. We used data from the U.S. National Health and Nutritional Examination Survey 2005–2008 cycles ( n = 9133; ≥20y). Depressive symptoms and sleep quality were determined from questionnaires. C-reactive Protein (CRP) was used as a biomarker of inflammation and γ-glutamyltransferase was used to assess oxidative stress. The relationship between depressive symptoms, sleep quality, and biomarkers were assessed with regression models. The moderating effects of BMI and sex were tested. BMI was a significant moderator of the relationship between γ-glutamyltransferase and depressive symptoms ( p = 0.02), but not CRP or sleep quality. Higher BMI increased odds of depressive symptoms in women (OR (95% CI): 3.92 (1.85–8.30) for BMI ≥25 to < 30 kg/m 2 ; 3.17 (1.53–6.58) for BMI ≥30 to < 35 kg/m 2 ; and 7.38 (2.11–25.76) for BMI ≥35 kg/m 2 ). BMI was also a significant moderator of γ-glutamyltransferase levels in those with vs without depressive symptoms. Those with depressive symptoms had 24% poorer sleep quality compared to those without depressive symptoms after adjusting for inflammation, oxidative stress and other confounders. The link between oxidative stress and depressive symptoms may be particularly relevant for females and people living with obesity. People with depressive symptoms also have a substantial reduction in sleep quality. Thus, research should examine these relationships prospectively to inform and improve the mental health of the adult population in developed countries.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".