BODY MASS INDEX AND COGNITIVE DECLINE AMONG KOREAN OLDER ADULTS: AN 8-YEAR FOLLOW-UP STUDY
Bibliographic record
Abstract
Objective: This study examined the longitudinal trajectory of the relation between body mass index (BMI) and cognitive decline among Korean older adults. Methods: Participants were a nationally representative sample of 5,549 Korean adults aged 60 or older from the Korean Longitudinal Study of Aging (KLOSA: 2006–2014). Our outcome variable was cognitive functioning measued with the Korean Mini-Mental State Examination (K-MMSE). Growth curve modeling analysis was conducted to examine the longitudinal association between BMI and cognitive functioning. Results: Results from growth curve modeling analysis showed that after adjusting for covariates, underweight increased a risk for cognitive decline among Korean older adults over the 8-year period (p < .05). On the contrary, overweight or obese older adults had a reduced risk for cognitive decline over the 8-year period, after adjusting for covariates (ps < .001). A growth curve figure displayed a negative linear pattern of the relation between BMI and cognitive decline, indicating that the declining pattern of cognitive functioning scores reduced as BMI increased over the 8-year period. Discussion: Findings suggest that compared to healthy weight, low BMI could be a risk factor for cognitive dysfunction, whereas high BMI could function as a protective factor for cognitive dysfunction in late adulthood. Additional research examining reasons for this longitudinal trajectory is needed. Implications for research and clinical practice are discussed.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".