Prevalence and Diagnostic Strategies for Sarcopenia in Menopausal and Non-Menopausal Women: A Cross-Sectional Comparative Study
Bibliographic record
Abstract
Miaoyuan Li,1,2,* Xuewen Yan,3,* Rongshun Wu,4 Yanting Liao,5 Jing Zhang,3 Yufang Ye,3 Chenxia Xu6 1The First Clinical Medical College of Jinan University, Guangzhou, 510280, People’s Republic of China; 2Department of Urology, Zhongshan People’s Hospital, Zhongshan, 528400, People’s Republic of China; 3Women’s Health Department, Boai Hospital of Zhongshan, Zhongshan, 528400, People’s Republic of China; 4Department of Critical Care, Zhongshan People’s Hospital, Zhongshan, 528400, People’s Republic of China; 5Gynecology Department, Boai Hospital of Zhongshan, Zhongshan, 528400, People’s Republic of China; 6Prenatal Diagnosis Center, Boai Hospital of Zhongshan, Zhongshan, 528400, People’s Republic of China*These authors contributed equally to this workCorrespondence: Chenxia Xu, Prenatal Diagnosis Center, Boai Hospital of Zhongshan, No. 6 Chenggui Road, East District, Zhongshan, Guangdong, 528400, People’s Republic of China, Email xuchenxia709@foxmail.comObjective: This study aimed to compare the prevalence of sarcopenia between postmenopausal and premenopausal women and evaluate the diagnostic performance of various screening methods combining anthropometric measurements and functional assessments.Methods: A total of 1630 women (794 postmenopausal, 836 premenopausal) were included. Data on upper arm circumference (AC), calf circumference (CC), and SARC-F questionnaire scores were collected. ROC curve analysis was performed to assess the sensitivity, specificity, and accuracy of individual and combined diagnostic models.Results: The overall prevalence of sarcopenia was 6.44%, with a higher prevalence in postmenopausal women (7.43%) than in premenopausal women (5.50%). Sarcopenic individuals had lower BMI and poorer functional performance. Among postmenopausal women, the SARC-F + CC combination demonstrated the highest diagnostic accuracy (AUC = 91.2), while in premenopausal women, the SARC-F + AC model was most effective (AUC = 85.53). The SARC-F + CC combination showed the best sensitivity (89.0%) and specificity (75.0%) across all participants.Conclusion: Sarcopenia is more prevalent in postmenopausal women, with menopause identified as a key risk factor. Combining SARC-F with calf circumference enhances diagnostic accuracy and is recommended for early screening in primary healthcare settings to facilitate timely interventions and improve patient outcomes.Keywords: sarcopenia, menopause, non-menopausal, prevalence, diagnostic methods, upper arm circumference, calf circumference, SARC-F questionnaire
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".