The association between depressive symptoms and high-sensitivity C-reactive protein: Is body mass index a moderator?
Bibliographic record
Abstract
Depression and obesity are highly comorbid conditions with shared biological mechanisms. It remains unclear how depressive symptoms and body mass index (BMI) interact in relation to inflammation. This cross-sectional study investigated the independent associations of depressive symptoms and BMI with high sensitivity C-reactive protein (hs-CRP), as well as the moderating role of BMI on the depressive symptoms-hs-CRP association. Participants (n = 8,827) from the 2015-2018 National Health and Nutrition Examination Surveys were aged ≥20 with a BMI ≥18.5 kg/m2, completed the Depression Screener, and had hs-CRP data. Multivariable linear regression was used to analyze hs-CRP in relation to depressive symptoms and BMI. An interaction term was included to examine whether the depressive symptoms-hs-CRP relationship differs depending on BMI. There was a slight, albeit non-significant, increase in hs-CRP levels with each one-point increase in depressive symptoms (aCoef.Estm. = 0.01, 95% CI = -0.05, 0.06, p = 0.754). Participants with overweight (aCoef.Estm. = 1.07, 95% CI = 0.61, 1.53, p < 0.001) or obese (aCoef.Estm. = 3.51, 95% CI = 3.04, 3.98, p < 0.001) BMIs had higher mean hs-CRP levels than those with a healthy BMI. There were no significant interactions between depressive symptoms and overweight (aCoef.Estm. = 0.04, 95% CI = -0.04, 0.13, p = 0.278) or obese (aCoef.Estm. = 0.11, 95% CI = -0.01, 0.22, p = 0.066) BMI indicating a lack of difference in the depressive symptoms-hs-CRP association across participants in the healthy versus overweight and obese ranges. This study suggests that BMI might not act as a moderator in the association between depressive symptoms and hs-CRP. Results should be replicated in larger samples. Further research is warranted to understand underlying mechanisms.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".