Association Between Body Mass Index and Cognitive Impairment in Elderly Subjects with Type 2 Diabetes Mellitus: A Cross-Sectional Study
Bibliographic record
Abstract
Background. Chronic illnesses such as Type 2 diabetes mellitus (T2DM) and obesity have been implicated as risk factors in the development of cognitive impairment (CI), but despite this, definite association between the two conditions in increasing cognitive impairment risk is not well defined. Objective. This study aims to examine the association between body mass index (BMI) and cognitive impairment (CI) in elderly patients with Type 2 diabetes mellitus. Methods. This is a cross-sectional study conducted in the outpatient clinics of a private hospital in Manila which included elderly patients with Type 2 diabetes. BMI categories of the subjects were determined using the Asia-Pacific criteria and the Montreal Cognitive Assessment – Philippines (MOCA-P) was administered to subjects who fulfilled the inclusion criteria. Descriptive statistics were used to determine the prevalence of impaired cognition among subjects while risk ratio analysis was used to determine the correlation between BMI and CI. Correlation analysis and linear regression analysis were used to determine the presence of association between cognition (measured by MOCA-P scores) and BMI. For all analysis, a 95% level of significance was used. Results. A total of 109 subjects from the outpatient clinics were included in the study. A high percentage of the study population (90.83%) had CI based on MOCA-P scores. Subjects that belonged to the extremes of BMI- underweight and obese class 2 had higher incidence of CI compared to the other groups. Underweight subjects had 1.103 (95% CI: 1.038 to 1.172) times likelihood of having impaired cognition (p-value 0.0016), while obese 2 subjects had 1.110 (95% CI: 1.040 to 1.184) times likelihood of having impaired cognition (p-value 0.0016). Regression analysis revealed that in subjects with diabetes of less than 10 years, cognition scores were negatively correlated to BMI (p-value 0.0454). Correlation analysis revealed that at the general population level, regardless of the external factors, increasing or decreasing BMI did not have significant effect on cognition scores. Conclusion. Subjects who belonged to the extremes of BMI- underweight and obese class 2 – had higher incidence of CI compared to the other BMI groups. Among subjects with T2DM duration of less than 10 years, cognition scores tend to be negatively correlated to BMI. Keywords. Type 2 diabetes mellitus, cognitive impairment, BMI
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| 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".