Exploring Risk Factors Related to Low Calf Circumference in Older Adults With Multimorbidity: Cross-Sectional Latent Class Analysis
Bibliographic record
Abstract
Background: As the global population continues to age, the prevalence of sarcopenia is gradually increasing, and the loss of skeletal muscle mass is one of the manifestations of sarcopenia. Low calf circumference (CC) is often used as a predictor of poor skeletal muscle mass or sarcopenia. Older adults usually have a combination of multiple chronic diseases. There is a lack of evidence to explore the risk factors for low CC with multimorbidity in Chinese, community-dwelling, older adults. Objective: This study aimed to explore the risk factors and potential categories in older adult patients with low CC and multimorbidity from an individual-centered perspective. Methods: We selected 15,874 participants from the Chinese Longitudinal Healthy Longevity Survey in 2018 and screened for low CC in older adult patients. The individual-centered latent class analysis was used to classify potential multimorbidity groups. Multiple logistic regression was used to explore the risk factors associated with low CC and multimorbidity by applying the elastic net to screen for reliable risk variables. Results: A total of 7956 older individuals were eligible for the study, of whom 3960 (49.8%) were aged >90 years and 2166 (27.2%) had multimorbidity with low CC. The prevalence of multimorbidity increases between the ages of 65 and 89 years. However, the majority of older adults remain in reasonably good health beyond the age of 90 years. Five multimorbidity groups were identified by latent class analysis: multisystem morbidity diseases (78/2166, 3.6%), arthritis-rheumatism or rheumatoid diseases (400/2166, 18.47%), diabetes-hypertension diseases (330/2166, 15.23%), respiratory-heart diseases (347/2166, 16.02%), and cardiovascular diseases (1011/2166, 46.68%). Through 12 variables screened by the elastic net, multiple logistic regression showed different impacts on multimorbidity groups, including demographic background, behavioral characteristics, and physical and mental health factors. In particular, older patients who self-report poor health and live in urban areas need more attention. Conclusions: The results revealed that low CC is a common phenomenon among community-dwelling older adults, and a substantial proportion also present with multimorbidity. In the older adult population with low CC, the proportion of multimorbidity does not simply increase with age. Multimorbidity in low CC has been identified in 5 potential groups. Different groups have distinctive risk factors. Public health authorities should pay attention to low CC in older adult patients with multimorbidity and carry out targeted interventions, thereby enhancing health outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".