Association Between Bone Mineral Density and Depressive Symptoms in a Population-Based Sample
Bibliographic record
Abstract
OBJECTIVE: This analysis was conducted to determine the relationship between bone mineral density (BMD) and depressive symptoms in a population-based cohort. METHODS: Data were extracted from the second phase of the Dallas Heart Study (DHS-2), a large, multiethnic population sample in Dallas County, Texas, from September 1, 2007, to December 31, 2009. Depressive symptom severity was measured with the 16-item Quick Inventory of Depressive Symptomatology-Self Report (QIDS-SR₁₆), which is derived from DSM-IV major depressive disorder criteria. BMD was measured using dual-energy x-ray absorptiometry. Multiple linear regressions examined the relationship between QIDS-SR₁₆ score and BMD controlling for age, body mass index, sex, ethnicity, smoking status, alcohol use status, serum 25-hydroxyvitamin D concentration, antidepressant use, and physical activity as measured by total vigorous and moderate metabolic equivalents. Subgroup analyses explored differences related to age. RESULTS: QIDS-SR₁₆ score was not a significant predictor of either lumbar spine or total hip T-score (β = -0.01, P = .61 and β = -0.02, P = .39) in the overall population (n = 2,285). There was a significant negative interaction term between age and QIDS-SR₁₆ group (β = -0.01, P = .01). In participants aged 60 years or older (n = 465), QIDS-SR₁₆ score was a significant predictor of BMD at the lumbar spine and total hip (β = -0.14, P = .003 and β = -0.12, P = .006, respectively). CONCLUSIONS: QIDS-SR₁₆ score did not significantly predict BMD in the overall DHS-2 sample. There was, however, a significant association observed in participants aged ≥ 60 years. Results suggest that diagnosis and treatment of depressive symptoms may be of clinical importance in older individuals, a subgroup at high risk for osteoporosis and fractures.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".