Cognitive Dysfunction in Obese Individuals With or Without Metabolic Risk Factors (I12-5A)
Bibliographic record
Abstract
Objective: To document the association between components of metabolic syndrome (MS) and cognitive impairment in metabolically healthy obese (MHO) and metabolically unhealthy obese (MAO) individuals. Background: MS may be associated with development of cognitive impairment. Whether ‘healthy obese phenotype’, obesity in the absence of metabolic risk factors is associated with cognitive impairment or not remains to be determined. Methods: 60 obese individuals aged 49±10 (52[percnt] male) were enrolled. Obesity was defined as BMI>30. MS was defined according to ATP III criteria. Obese individuals were divided into two groups: group 1, MHO ( 2 components of MS). Cognitive dysfunction was determined by Montreal cognitive assessment score (MOCA 11-21 dementia, MOCA 19-25 mild cognitive impairment, or MOCA 25-30 normal test score). CRP, Fibro scan, CAP, LFTS and components of MS were measured. Results: Among 30 MAO individuals 13[percnt] developed dementia, 51[percnt] had mild cognitive impairment and 36 [percnt] had normal cognitive score as compared to 3[percnt], 7[percnt], and 90[percnt] in MHO group respectively. There was a significant difference in liver stiffness (normal< 6.0 kpa, 7 ± 3 vs. 5.2 ± 2.7 kpa, p<0.001), liver fat measurement (CAP normal <270 db/m, 337 ± 51 vs. 280 ± 20, p<0.001) and CRP (9 ± 6 vs. 7 ± 6 P<0.001) levels between the two groups respectively. Correlations between cognitive score and components of MS were strong with blood pressure (r=-0.252), abdominal girth (r=-0.26), and liver stiffness measurements (r=-0.256). Multivariate analysis accounting for confounders showed that abdominal girth (T=-2.1, p<0.04) and age (T=-3.0, p<0.009) were the two most powerful predictors of cognitive dysfunction. Conclusion: An association exists between metabolic risk factors and cognitive impairment in obese individuals (50 [percnt] of MAO). Additional studies are needed to determine if treatment of patients with MAO would improve cognitive impairment.
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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 | 0.000 |
| 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) | 0.002 | 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".