Cardiovascular risk in aging adults with double deficits in social support: A gender-sensitive, cross-sectional analysis of the CLSA cohort
Bibliographic record
Abstract
BACKGROUND: Synergistic effects of diverse social supports (informational, tangible, emotional and belonging) on cardiovascular disease risk factors (CVRF), by gender, is unknown. AIM: To quantify gender differences in the singular and combined associations of four different forms of social support with cardiovascular disease risk factors (CVRF) in aging adults. METHODS: Cross-sectional study of 28,779 adults (45-85 years) in the Canadian Longitudinal Study on Aging Comprehensive cohort (2011-15); independent variables were self-reported measures of informational, tangible, emotional and belonging support; dependent variables were clinically measured BMI, waist circumference and blood pressure. We used stratified multivariable linear and logistic regression with principal component regression with cross-product terms to post-estimate adjusted means and 95% CIs for combined associations. RESULTS: All low-low support combinations were consistently associated with the highest adjusted mean BMI and WC levels among women. Adjusted mean BMI differences were largest among women with low informational and low tangible supports (27.95 kg/m2 [27.93, 27.97]), compared to women with high informational and high tangible supports (27.34 kg/m2 [27.30, 27.38]). Similarly, the greatest difference in adjusted mean WC was seen among women with low informational and low emotional supports (88.69 cm [88.62, 88.76]) compared to the high-high combination (86.88 cm [86.75, 87.01]). Women with low availability of informational support, with or without deficits in a second support type, had the highest adjusted mean SBP levels (range: 119.94 to 119.95 mmHg). Among men, mean CVRFs were not consistently worse for combinations of dual deficits in social support. Results were null for DBP. CONCLUSION: Women with two deficits in social supports, particularly combinations with low informational support, showed worse CVRF measures than one social support deficit. Results indicated no antagonistic/synergistic effects of social support on CVRFs. Heart health care and prevention for aging women would benefit from ensuring informational support with other supports is available.
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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".