Osteosarcopenia as a risk factor for depression: Longitudinal findings from the SHARE study
Bibliographic record
Abstract
Background Osteosarcopenia (i.e., the co-existence of osteoporosis and sarcopenia) and depression are highly prevalent among older people. However, the association between osteosarcopenia and depression in older people is largely unknown. Therefore, the present study aims to investigate this possible association in a representative sample of the older adult population in Europe and Israel. Methods Osteosarcopenia was defined as the concomitant presence of osteoporosis and sarcopenia; depressive symptoms in the SHARE study were self-reported using the EURO-D scale. The association between the presence of osteosarcopenia at baseline in people free from depression and incident depression during 12 years of follow-up was analyzed using a Cox's regression analysis, adjusting for several baseline covariates. Results 16,452 participants were included (mean age 63.7, SD 9.6; females 50.6 %). During the follow-up period, 5056 participants (31.1 % of the initial population) became depressed. People affected by osteosarcopenia became depressed in more than half of the cases compared to a quarter of controls. After adjusting for several potential baseline confounding variables, only sarcopenia (HR, hazard ratio = 1.17; 95 % CI, confidence intervals 1.04–1.32; p = 0.009) and osteosarcopenia (HR = 1.27; CI 95 % 1.12–1.58; p = 0.003) were significantly associated with a higher risk of depression. Limitations Definition of sarcopenia using an anthropometric equation; definition of depression using the EURO-D scale. Conclusions The present study identified a significant association between osteosarcopenia and depression over 12 years of follow-up, mainly driven by sarcopenia. If future research confirms the present findings, it may then be prudent to target those with osteosarcopenia to aid in the prevention of onset depression.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".