Relationships Between Body Composition and Cognitive Impairment in Hospitalised Middle-Aged Type 2 Diabetic Patients
Bibliographic record
Abstract
Yao-Shuang Li,1,2,* Yan-Lan Liu,2,* Jun-Jia Wang,2,3 Nahal Haghbin,4 Xiao-He Wang,5 Wei-Ran Jiang,6 Hui-Na Qiu,2 Long-Fei Xia,1,2 Fan Wu,2,7 Chen-Ying Lin,1,2 Jing-Bo Li,2 Jing-Na Lin2 1Tianjin Union Medical Center, Tianjin Medical University, Tianjin, People’s Republic of China; 2Department of Endocrinology, Tianjin Union Medical Center, Nankai University Affiliated Hospital, Tianjin, People’s Republic of China; 3Tianjin Union Medical Center, Tianjin University of Traditional Chinese Medicine, Tianjin, People’s Republic of China; 4School of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China; 5Institute of Non-Communicable Diseases Control and Prevention, Tianjin Center for Disease Control and Prevention, Tianjin, People’s Republic of China; 6Eastman Institute for Oral Health, University of Rochester Medical Center, Rochester, NY, USA; 7Tianjin Union Medical Center, School of Medicine, Nankai University, Tianjin, People’s Republic of China*These authors contributed equally to this workCorrespondence: Jing-Na Lin; Jing-Bo Li, Email 13207628978@163.com; doctorljb@aliyun.comObjective: The aim of this study was to elucidate the relationship between specific body composition and the risk of Cognitive Impairment (CI) in middle-aged Type 2 Diabetes Mellitus (T2DM) patients.Methods: This cross-sectional study included 504 hospitalized patients with T2DM from the Department of Endocrinology and Metabolism of the Tianjin Union Medical Center. Subjects were grouped by sex, and cognitive status was assessed using the Montreal Cognitive Assessment (MoCA). The relationship between body composition and cognitive ability was investigated with the use of linear regression analysis. The association between body composition and CI risk was determined by logistic regression analysis.Results: The prevalence of CI was 39.3% in middle-aged T2DM patients. After adjusting for age, education, marriage status, carotid atherosclerosis, cerebrovascular disease and hemoglobin, multiple linear regression analysis showed that lean mass index (LMI), body mass index (BMI) and appendicular skeletal muscle index (SMI) were significant predictors for the MoCA scores in men (p < 0.05). In addition, BMI (OR 0.913, 95% CI 0.840– 0.992) and LMI (OR 0.820, 95% CI 0.682– 0.916) were independent protective factors for CI in males. After adjusted for age, education, marriage status, dietary control of diabetes and cerebrovascular disease, visceral obesity (VO, OR 1.950, 95% CI 1.033– 3.684) and abdominal obesity (AO, OR 2.537, 95% CI 1.191– 5.403) were risk factors for CI in female patients.Conclusion: The results suggest that there may be different mechanisms underlying the relationship of body compositions and cognitive performance between middle-aged male and female patients with T2DM. In addition, our finding of potential determinants of cognitive impairment may facilitate the development of intervention programs for middle-aged type 2 diabetic patients. Nevertheless, more large prospective studies looking at cognition and changes in body composition over time are needed in the future to further support their association.Keywords: cognitive impairment, diabetes, middle age, body composition, sex
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".