Metabolic Syndrome and Cognitive Impairment among High Socioeconomic, Nondemented Older US Adults
Bibliographic record
Abstract
OBJECTIVES: Nondemented cognitive impairment (CI) presents opportunities for early interventions among individuals at risk for dementia. Identification of modifiable determinants is paramount to the development of effective clinical interventions. Metabolic syndrome (MetS) was theorized as a risk factor, but current research yields inconsistent findings. Few studies have examined the association between MetS and CI among US populations, and global results may be ungeneralizable. We investigated the MetS-CI association among high socioeconomic, nondemented older US adults, examining the roles of sociodemographic, clinical, behavioral, and genetic factors. DESIGN: Cross-sectional. SETTING: Cooper Clinic of Dallas, Texas: Cooper Center Longitudinal Study (2009-2017). PARTICIPANTS: A total of 5200 dementia-free older adult Cooper Clinic patients. MEASUREMENTS: CI was detected with a Montreal Cognitive Assessment (MoCA) score lower than 26. MetS was established based on National Cholesterol Education Program Adult Treatment Panel guidelines. Unadjusted and multivariable log-binomial regression were used to assess the MetS-CI association, with modification assessment by age, sex, education, cardiorespiratory fitness (CRF), and apolipoprotein-ε4 carrier status (APOE-ε4). RESULTS: MetS was not associated with CI when adjusting for age, sex, minority status, education, and marital status (prevalence ratio [PR] = 1.09; 95% confidence interval = .97-1.23) or when additionally adjusting for body mass index, CRF, alcohol consumption, current smoking status, and APOE-ε4 (PR = 1.07; 95% confidence interval = .80-1.45). The association was not modified by age, sex, CRF, or APOE-ε4 (P for interaction >.05). CONCLUSION: In contrast with some global and US studies, MetS and CI were not associated among our study population of nondemented older US adults. MetS may not be a suitable intervention target for poor cognitive outcomes among high socioeconomic older US adults, although separate MetS components may have different recommendations. Future studies should explore more diverse older US populations. If replicated, these findings would inform clinical efforts to reduce the burden of poor cognitive outcomes in the United States.
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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 | 0.002 |
| 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 | 0.000 | 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".