Weakness Thresholds Are Differentially Linked to Cognitive Function by Obesity Status in Older Americans
Bibliographic record
Abstract
Background: Weakness can be operationalized with several thresholds, which in turn, could impact associations with cognitive impairment when considering obesity status. Objective: We examined the associations of absolute, normalized, and collective weakness thresholds on future cognitive impairment by obesity status in older adults. Methods: We performed a secondary data analysis on the 2006–2018 waves of the Health and Retirement Study. A spring-type dynamometer collected handgrip strength (HGS). Males were categorized weak if their HGS was <35.5-kg (absolute), <0.45-kg/kg (body mass normalized), or <1.05-kg/kg/m 2 (body mass index (BMI) normalized), while females were defined as weak if their HGS was <20.0-kg, <0.337-kg/kg, or <0.79-kg/kg/m 2 . The modified Telephone Interview of Cognitive Status examined cognitive function. Persons scoring ≤10 had a cognitive impairment. Obesity was categorized as BMI ≥30 kg/m 2 . Results: We included 7,532 and 3,584 persons aged ≥65-years living without and with obesity, respectively. Those without obesity but beneath the absolute weakness threshold had 1.54 (95% confidence interval (CI): 1.24–1.91) greater odds for future cognitive impairment. Persons with obesity and beneath each threshold also had greater odds for future cognitive impairment: 1.89 (95% CI: 1.28–2.78) for absolute, 2.17 (95% CI: 1.02–4.62) for body mass normalized, and 1.75 (95% CI: 1.10–2.80) for BMI normalized. Older Americans without obesity but underneath all the weakness thresholds had 1.32 (95% CI: 1.00–1.74) greater odds for impairment in cognitive function, while persons with obesity had 2.76 (95% CI: 1.29–5.93) greater odds. Conclusions: There should be consideration for how body size and different weakness thresholds may influence future cognitive outcomes.
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 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 | 0.003 |
| 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 | 0.001 |
| 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".