Independent and interactive impacts of hypertension and diabetes mellitus on verbal memory: A coordinated analysis of longitudinal data from England, Sweden, and the United States.
Bibliographic record
Abstract
The importance of preventing and controlling hypertension (HTN) and diabetes mellitus (DM) to mitigate risks to physical health has long been understood by health care professionals. More recently, a growing body of evidence implicates HTN and DM in age-related cognitive decline and risk for dementia, though consensus has yet to be reached on whether older adults living with comorbid HTN and DM are at heightened risk for cognitive impairment. The present study sought to contribute to this topic through a coordinated analysis of 3 longitudinal studies of aging from England, Sweden, and the United States (total N = 12,513). Identical multilevel linear growth models were fit to each to estimate the impact of baseline disease status on initial level and change in verbal declarative memory performance. Overall, few associations between HTN, DM, and cognition were observed. Rate of decline was steeper for Swedish participants with independent HTN but attenuated for their American counterparts. Americans with comorbid HTN and DM showed attenuated decline. Treatment with medication was substantially less prevalent in the earlier-born and lower-educated Swedish sample, which may help to explain our pattern of results. In addition, those living with multiple conditions may be more likely to receive treatment, mitigating cognitive decline. Our results present a nuanced view of the interactions between HTN, DM, and cognition, and lead us to recommend consideration of treatment status or proxies such as birth cohort and education, in combination with age at assessment and specific measure used to interpret research in this area. (PsycINFO Database Record
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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 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".