Association Between BMI and Neurocognitive Functions Among Middle-aged Obese Adults: Preliminary Findings Using Machine-learning (ML)-based Approach
Bibliographic record
Abstract
Background Studies suggest that obesity predisposes individuals to developing cognitive dysfunction and an increased risk of dementia, but the nature of the relationship remains largely unexplored for better prognostic predictors. Purpose This study, the first of its kind in Indian participants with obesity, was intended to explore the use of quantification of different neurocognitive indices with increasing body mass index (BMI) among middle-aged participants with obesity. Additionally, machine-learning models were used to analyse the predictive performance of BMI for different cognitive functions. Methods In the cross-sectional analytical study, a total of 137 ( n = 137) participants were included. Out of the total, 107 healthy obese (BMI = 23.0–30.0 kg m −2 ; age between 36 and 55 years of both genders) were recruited from the out-patient department of the Department of Endocrinology and General Medicine, and 30 participants were recruited as the control group, between March 2023 to February 2024. The participants underwent neuropsychological assessments, including mini-mental state examination (MMSE), Montreal cognitive assessment (MoCA) and serum levels of brain-derived neurotrophic factor (BDNF). Results Significant ( p < .05) differences were observed for neurocognitive functions for the obese group versus the control group. According to the correlation heatmaps, BMI was significantly ( p < .05) negatively associated with BDNF. Multivariate linear regression analysis revealed a substantial ( p < .05) decline in BDNF with a change in BMI, accenting its significant impact on cognitive ageing. Additionally, consistent decreasing trends were observed across the MoCA and MMSE, confirming the robustness of the findings across diverse analytical methodologies. Furthermore, the linear regression model and super vector machine model contributed additional evidence to the consistency of the trends in cognitive decline linked to BMI variations. Conclusion The preliminary results of the present study support that increased BMI is an important physiological indicator that influences neurocognition and neuroplasticity in individuals with obesity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".