The impact of cardiovascular health and frailty on mortality for males and females across the life course
Bibliographic record
Abstract
BACKGROUND: The effect of frailty and poor cardiovascular health on mortality for males and females is not fully elucidated. We investigated whether the combined burden of frailty and poor cardiovascular health is associated with all-cause and cardiovascular disease (CVD) mortality by sex and age. METHODS: We analyzed data of 35,207 non-institutionalized US residents aged 20-85 years old (mean age [standard deviation]: 46.6 [16.7 years], 51.4% female, 70.8% White, 10.3% Black, 13.2% Hispanic) from the National Health and Nutrition Examination Survey (1999-2015). Cardiovascular health was measured with the American Heart Association's Life's Simple 7 score (LS7). A 33-item frailty index (FI) was constructed to exclude cardiovascular health deficits. We grouped the FI into 0.1 increments (non-frail: FI < 0.10, very mildly frail: 0.1 ≤ FI < 0.20, mildly frail: 0.20 ≤ FI < 0.30, and moderately/severely frail: FI ≥ 0.30) and LS7 into tertiles (T1[poor] = 0-7, T2[intermediate] = 8-9, T3[ideal] = 10-14). All-cause and CVD mortality data were analyzed up to 16 years. All regression models were stratified by sex. RESULTS: The average FI was 0.09 (SD 0.10); 29.6% were at least very mildly frail, and the average LS7 was 7.9 (2.3). Mortality from all-causes and CVD were 8.5% (4228/35,207) and 6.1% (2917/35,207), respectively. The median length of follow-up was 8.1 years. The combined burden of frailty and poor cardiovascular health on mortality risk varied according to age in males (FI*age interaction p = 0.01; LS7*age interaction p < 0.001) but not in females. In females, poor FI and LS7 combined to predict all-cause and CVD mortality in a dose-response manner. All-cause and CVD mortality risk was greater for older males (60 and 70 years old) who were at least mildly frail and had intermediate cardiovascular health or worse (hazard ratio [lower/higher confidence interval ranges] range: all-cause mortality = 2.02-5.30 [1.20-4.04, 3.15-6.94]; CVD-related mortality = 2.22-7.16 [1.03-4.46, 4.49-11.50]) but not for younger males (30, 40, and 50 years old). CONCLUSIONS: The combined burden of frailty and LS7 on mortality is similar across all ages in females. In males, this burden is greater among older people. Adding frailty to assessments of overall cardiovascular health may identify more individuals at risk for mortality and better inform decisions to implement preventative or treatment approaches.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".