High Prevalence and Clinical Correlates of Sarcopenia and Metabolic Syndrome in Outpatients with Major Depressive Disorder: A Cross-Sectional Study
Bibliographic record
Abstract
Ching-I Hung,1,2 Le-Yung Wang,1,2 Huang-Li Lin,1,2 Ching-Hui Yang3 1Department of Psychiatry, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan; 2Chang Gung University College of Medicine, Taoyuan, Taiwan; 3Department of Nursing, Mackay Medical College, New Taipei City, TaiwanCorrespondence: Ching-I Hung, Department of Psychiatry, Chang Gung Memorial Hospital, 5 Fu-Shing St, Kweishan, Taoyuan, 333, Taiwan, Tel +886-3-3281200 ext 2439, Fax +886-3-3280267, Email a36250@cgmh.org.twObjective: This study aimed to investigate the percentages, clinical characteristics, and risk factors of sarcopenia and metabolic syndrome (MetS) among outpatients with major depressive disorder (MDD).Methods: This study enrolled 225 MDD outpatients based on DSM-V and age- and sex-matched health controls (HCs). Sarcopenia was diagnosed based on the 2019 Asian Working Group for Sarcopenia diagnostic criteria. Three psychometric scales and the Montreal Cognitive Assessment were used to evaluate the severities of depression, anxiety, and somatic symptoms and cognitive function, respectively. Binary logistic regression models were used to investigate the risk factors of the two disorders.Results: MDD subjects had significantly higher percentages of sarcopenia (41.3% vs 15.6%), MetS (40.9% vs 28.9%), and sarcopenia and/or MetS (68.9% vs 40.4%) than HCs. For elderly MDD subjects, 89.3% suffered from sarcopenia and/or MetS. MDD subjects had higher unqualified percentages in handgrip strength and three physical performance tests than HCs. MDD subjects with sarcopenia had worse depression than those without. MDD subjects with MetS had poorer cognitive function than those without. MDD subjects with only sarcopenia had worse mood symptoms than those with only MetS. After controlling for age and other variables, full remission of depression decreased the risk of sarcopenia. Being overweight decreased and increased the risk of sarcopenia and MetS, respectively.Conclusions: Most MDD outpatients, especially elderly outpatients, suffered from sarcopenia and/or MetS. The two disorders should be screened in MDD patients. Treatment of depression might decrease the risk of sarcopenia.Keywords: depression, sarcopenia, metabolic syndrome, cardiovascular disease
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 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".