Search

Frailty is an aging-related clinical phenotype defined as a state in which there is an increase in a person's vulnerability for dependency and/or mortality when exposed to a stressor. While underlying mechanisms leading to the occurrence of frailty are complex, the importance of genetic factors has not been fully investigated. We conducted a large-scale genome-wide association study (GWAS) of frailty, as defined by the five criteria (weight loss, exhaustion, physical activity, walking speed, and grip strength) captured in the Fried Frailty Score (FFS), in 386,565 European descent participants enrolled in the UK Biobank (mean age 57 [SD 8] years, 208,481 [54%] females). We identified 37 independent, novel loci associated with the FFS (p < 5 × 10–8), including seven loci without prior described associations with other traits. The variants associated with FFS were significantly enriched in brain tissues as well as aging-related pathways. Our post-GWAS bioinformatic analyses revealed significant genetic correlations between FFS and cardiovascular-, neurological-, and inflammation-related diseases/traits, and subsequent Mendelian Randomization analyses identified causal associations with chronic pain, obesity, diabetes, education-related traits, joint disorders, and depressive/neurological, metabolic, and respiratory diseases. The GWAS signals were replicated in the Health and Retirement Study (HRS, n = 9,720, mean age 73 [SD 7], 5,582 [57%] females), where the polygenic risk score built from UKB GWAS was significantly associated with the FFS in HRS individuals (OR per SD of the score 1.27, 95% CI 1.22–1.31, p = 1.3 × 10–11). These results provide new insight into the biology of frailty by comprehensively evaluating its genetic architecture. [ABSTRACT FROM AUTHOR]

Background and Objectives: Mounting evidence points to a strong connection between cardiovascular risk during middle age and brain health later in life. The American Heart Association's Life's Essential 8 (LE8) constitutes a research and public health construct capturing key determinants of cardiovascular health. However, the overall effect of the LE8 on global, clinically relevant metrics of brain health is still unknown. We tested the hypothesis that worse LE8 profiles are associated with higher composite risk of the most important clinical endpoints related to poor brain health., Methods: We conducted a two-stage (discovery and replication) prospective study using data from the UK Biobank (UKB) and All of Us (AoU), 2 large population studies in the United Kingdom and the United States, respectively. The primary exposure was the LE8 score, a validated tool that captures 8 modifiable cardiovascular risk factors (blood pressure, glucose, cholesterol, body mass index, smoking, physical activity, diet, and sleep duration), organized in 3 categories (optimal, intermediate, and poor). The primary outcome was a composite of stroke, dementia, or late-life depression. We evaluated associations using multivariable Cox proportional hazard models., Results: The discovery stage included 316,127 UKB participants (mean age 56, 52% female). Over a mean (SD) follow-up time of 4.9 (0.4) years, the unadjusted risk of the composite outcome was 0.7% (95% CI 0.61-0.74), 1.2% (95% CI 1.11-1.22), and 1.8% (95% CI 1.70-1.91) in participants with optimal, intermediate, and poor cardiovascular health, respectively ( p < 0.001). This association remained significant in multivariable Cox models (intermediate vs optimal cardiovascular health hazard ratio [HR], 1.37; 95% CI 1.24-1.52, and poor vs optimal cardiovascular health HR, 2.11; 95% CI 1.88-2.36, p trend <0.001). The replication stage included 68,407 AoU participants (mean age 56, 60% female). Over a mean (SD) follow-up time of 2.9 (1.41) years, the unadjusted risk of the composite outcome was 2.8% (95% CI 2.49-3.05), 6% (95% CI 5.76-6.22), and 9.7% (95% CI 9.24-10.24) in participants with optimal, intermediate, and poor cardiovascular health, respectively ( p < 0.001). This association remained significant in multivariable Cox models (intermediate vs optimal cardiovascular health, HR 1.35; 95% CI 1.21-1.51, and poor vs optimal cardiovascular health, HR 1.94; 95% CI 1.72-2.18; p trend <0.001)., Discussion: Among middle-aged adults enrolled in 2 large population studies, poor cardiovascular health profiles were associated with two-fold higher risk of developing a composite outcome that captures the most important diseases related to poor brain health. Because the evaluated risk factors are all modifiable, our findings highlight the potential brain health benefits of using the Life's Essential 8 to guide cardiovascular health optimization.

Background: The implementation of preventive therapies among patients with stroke remains inadequately explored, especially when compared with patients with myocardial infarction (MI), despite sharing similar vascular risk profiles. We tested the hypothesis that participants with a history of stroke have a worse cardiovascular prevention profile in comparison to participants with MI., Methods and Results: In cross-sectional analyses within the UK Biobank and All of Us Research Program, involving 14 760 (9193 strokes, 5567 MIs) and 7315 (2948 strokes, 4367 MIs) participants, respectively, we evaluated cardiovascular prevention profiles assessing low-density lipoprotein (<100 mg/dL), blood pressure (systolic, <140 mm Hg; and diastolic, <90 mm Hg), statin and antiplatelet use, and a cardiovascular prevention score that required meeting at least 3 of these criteria. The results revealed that, within the UK Biobank, patients with stroke had significantly lower odds of meeting all the preventive criteria compared with patients with MI: low-density lipoprotein control (odds ratio [OR], 0.73 [95% CI, 0.68-0.78]; P <0.001), blood pressure control (OR, 0.63 [95% CI, 0.59-0.68]; P <0.001), statin use (OR, 0.45 [95% CI, 0.42-0.48]; P <0.001), antiplatelet therapy use (OR, 0.30 [95% CI, 0.27-0.32]; P <0.001), and cardiovascular prevention score (OR, 0.42 [95% CI, 0.39-0.45]; P <0.001). Similar patterns were observed in the All of Us Research Program, with significant differences across all comparisons ( P <0.05), and further analysis suggested that the odds of having a good cardiovascular prevention score were influenced by race and ethnicity as well as neighborhood deprivation levels (interaction P <0.05 in both cases)., Conclusions: In 2 independent national cohorts, patients with stroke showed poorer cardiovascular prevention profiles and lower adherence to guideline-directed therapies compared with patients with MI. These findings underscore the need to explore the reasons behind the underuse of secondary prevention in vulnerable stroke survivors.

Competing Interests: Conflicts of interest None disclosed.

Background and Purpose: Animal and observational studies indicate that smoking is a risk factor for aneurysm formation and rupture, leading to nontraumatic subarachnoid hemorrhage (SAH). However, a definitive causal relationship between smoking and the risk of SAH has not been established. Using Mendelian randomization (MR) analyses, we tested the hypothesis that smoking is causally linked to the risk of SAH., Methods: We conducted a 1-sample MR study using data from the UK Biobank, a large cohort study that enrolled over 500 000 Britons aged 40 to 69 from 2006 to 2010. Participants of European descent were included. SAH cases were ascertained using a combination of self-reported, electronic medical record, and death registry data. As the instrument, we built a polygenic risk score using independent genetic variants known to associate ( P <5 ×10 - 8 ) with smoking behavior. This polygenic risk score represents the genetic susceptibility to smoking initiation. The primary MR analysis utilized the ratio method. Secondary MR analyses included the inverse variance weighted and weighted median methods., Results: A total of 408 609 study participants were evaluated (mean age, 57 [SD 8], female sex, 220 937 [54%]). Among these, 132 566 (32%) ever smoked regularly, and 904 (0.22%) had a SAH. Each additional SD of the smoking polygenic risk score was associated with 21% increased risk of smoking (odds ratio [OR], 1.21 [95% CI, 1.20-1.21]; P <0.001) and a 10% increased risk of SAH (OR, 1.10 [95% CI, 1.03-1.17]; P =0.006). In the primary MR analysis, genetic susceptibility to smoking was associated with a 63% increase in the risk of SAH (OR, 1.63 [95% CI, 1.15-2.31]; P =0.006). Secondary analyses using the inverse variance weighted method (OR, 1.57 [95% CI, 1.13-2.17]; P =0.007) and the weighted median method (OR, 1.74 [95% CI, 1.06-2.86]; P =0.03) yielded similar results. There was no significant pleiotropy (MR-Egger intercept P =0.39; MR Pleiotropy Residual Sum and Outlier global test P =0.69)., Conclusions: These findings provide evidence for a causal link between smoking and the risk of SAH.