Sex differences in obesity and cognitive function in a cognitively normal aging Chinese Han population
Bibliographic record
Abstract
Wei Li,* Qi Qiu,* Lin Sun, Ling Yue, Tao Wang, Xia Li, Shifu Xiao Alzheimer’s Disease and Related Disorders Center, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China *These authors contributed equally to this work Background: Sex differences in Alzheimer’s disease and mild cognitive impairment have been well recognized. However, sex differences in cognitive function and obesity in cognitively normal aging Chinese Han population have not attracted much attention. Objective: The aim of this study was to investigate the relationship between sex, obesity, and cognitive function in an elderly Chinese population with normal cognitive function. Subjects and methods: A total of 228 cognitively normal aging participants (males/females =93/135) entered this study. Their general demographic information (sex, age, and education) was collected by standardized questionnaire. Apolipoprotein E (APOE) genotype and serum lipid levels were measured. The Montreal Cognitive Assessment (MoCA) was used to assess participants’ cognitive function. Results: The prevalence of obesity in elderly women (18/133, 13.5%) was significantly higher than that in men (5/92, 5.4%, P=0.009). Regression analyses showed that obesity was associated with drinking alcohol (OR =13.695, P=0.045) and triglyceride (OR =1.436, P=0.048) in women and limited to low-density lipoprotein (OR =11.829, P=0.023) in men. Women performed worse on the naming score for MoCA than men (P<0.01). Stepwise linear regression analysis showed that education (t=3.689, P<0.001) and smoking (t=2.031, P=0.045) were related to the score of naming in female, while high-density lipoprotein (t=–2.077, P=0.041) was related to the score of naming in male; however, no correlation was found between body mass index and cognitive function in both male and female (P>0.05). Conclusion: Our finding suggests that there are significant sex differences in obesity and specific cognitive domains in aging Chinese Han population with normal cognitive function. Keywords: sex difference, obesity, cognitive function, lipids, elderly Chinese
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".