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Background The association of social isolation or lack of social network ties in older adults is unknown. This knowledge gap is important since the risk of heart failure (HF) and social isolation increase with age. The study examines whether social isolation is associated with incident HF in older women, and examines depressive symptoms as a potential mediator and age and race and ethnicity as effect modifiers. Methods and Results This study included 44 174 postmenopausal women of diverse race and ethnicity from the WHI (Women's Health Initiative) study who underwent annual assessment for HF adjudication from baseline enrollment (1993-1998) through 2018. We conducted a mediation analysis to examine depressive symptoms as a potential mediator and further examined effect modification by age and race and ethnicity. Incident HF requiring hospitalization was the main outcome. Social isolation was a composite variable based on marital/partner status, religious ties, and community ties. Depressive symptoms were assessed using CES-D (Center for Epidemiology Studies-Depression). Over a median follow-up of 15.0 years, we analyzed data from 36 457 women, and 2364 (6.5%) incident HF cases occurred; 2510 (6.9%) participants were socially isolated. In multivariable analyses adjusted for sociodemographic, behavioral, clinical, and general health/functioning; socially isolated women had a higher risk of incident HF than nonisolated women (HR, 1.23; 95% CI, 1.08-1.41). Adding depressive symptoms in the model did not change this association (HR, 1.22; 95% CI, 1.07-1.40). Neither race and ethnicity nor age moderated the association between social isolation and incident HF. Conclusions Socially isolated older women are at increased risk for developing HF, independent of traditional HF risk factors. Registration URL: http://www.clinicaltrials.gov; Unique identifier: NCT00000611.

Background The association of social isolation or lack of social network ties in older adults is unknown. This knowledge gap is important since the risk of heart failure (HF) and social isolation increase with age. The study examines whether social isolation is associated with incident HF in older women, and examines depressive symptoms as a potential mediator and age and race and ethnicity as effect modifiers. Methods and Results This study included 44 174 postmenopausal women of diverse race and ethnicity from the WHI (Women's Health Initiative) study who underwent annual assessment for HF adjudication from baseline enrollment (1993-1998) through 2018. We conducted a mediation analysis to examine depressive symptoms as a potential mediator and further examined effect modification by age and race and ethnicity. Incident HF requiring hospitalization was the main outcome. Social isolation was a composite variable based on marital/partner status, religious ties, and community ties. Depressive symptoms were assessed using CES-D (Center for Epidemiology Studies-Depression). Over a median follow-up of 15.0 years, we analyzed data from 36 457 women, and 2364 (6.5%) incident HF cases occurred; 2510 (6.9%) participants were socially isolated. In multivariable analyses adjusted for sociodemographic, behavioral, clinical, and general health/functioning; socially isolated women had a higher risk of incident HF than nonisolated women (HR, 1.23; 95% CI, 1.08-1.41). Adding depressive symptoms in the model did not change this association (HR, 1.22; 95% CI, 1.07-1.40). Neither race and ethnicity nor age moderated the association between social isolation and incident HF. Conclusions Socially isolated older women are at increased risk for developing HF, independent of traditional HF risk factors. Registration URL: http://www.clinicaltrials.gov; Unique identifier: NCT00000611.

BackgroundCognitive impairment and decline may signal the increased risk of incident cardiovascular disease (CVD). We examined associations of global cognitive function, as measured by the Modified Mini-Mental State Examination (3MS) and changes in 3MS over time, with incident CVD, individual CVD outcomes, CVD death, and all-cause mortality.MethodsA total of 5,596 women (≥ 60) from the Women's Health Initiative Memory Study free of CVD at baseline were followed for an average of 7.1 years. The 3MS was measured at baseline and annually thereafter. Cox proportional hazards regressions were used to model associations between baseline 3MS and changes in 3MS and time to events.ResultsIn the fully-adjusted models for every 5-point lower baseline 3MS score, the risk was 12% greater for incident CVD, 37% for HF, 35% for CVD death, and 24% for all-cause mortality. No significant relationships were found for coronary heart disease (CHD), angina, stroke/transient ischemic attack (TIA), or coronary revascularization. When change in 3MS was added as a time-varying covariate in the fully-adjusted models, for every 1-point/year greater decline in 3MS, the risk was 4% greater for incident CVD, 10% for CHD, 9% for Stroke/TIA, 17% for CVD death, and 13% for all-cause mortality.ConclusionsIn older women free of prevalent CVD at baseline, lower baseline global cognitive function or decline in global cognitive function over time, increased risk of incident CVD, CVD death, and all-cause mortality.

Adjustment for covariates is a time-honored tool in statistical analysis and is often implemented by including the covariates that one intends to adjust as additional predictors in a model. This adjustment often does not work well when the underlying model is misspecified. We consider here the situation where we compare a response between two groups. This response may depend on a covariate for which the distribution differs between the two groups one intends to compare. This creates the potential that observed differences are due to differences in covariate levels rather than "genuine" population differences that cannot be explained by covariate differences. We propose a bootstrap-based adjustment method. Bootstrap weights are constructed with the aim of aligning bootstrap-weighted empirical distributions of the covariate between the two groups. Generally, the proposed weighted-bootstrap algorithm can be used to align or match the values of an explanatory variable as closely as desired to those of a given target distribution. We illustrate the proposed bootstrap adjustment method in simulations and in the analysis of data on the fecundity of historical cohorts of French-Canadian women.