Associations of healthy aging index and all-cause and cause-specific mortality: a prospective cohort study of UK Biobank participants.

The healthy aging index (HAI) has been recently developed as a surrogate measure of biological age. However, to what extent the HAI is associated with all-cause and cause-specific mortality and whether this association differs in younger and older adults remains unknown. We aimed to quantify the association between the HAI and mortality in a population of UK adults. In the prospective cohort study, data are obtained from the UK Biobank. Five HAI components (systolic blood pressure, reaction time, cystatin C, serum glucose, forced vital capacity) were scored 0 (healthiest), 1, and 2 (unhealthiest) according to sex-specific tertiles or clinically relevant cut-points and summed to construct the HAI (range 0-10). Cox proportional hazard regression models were used to estimate the associations of the HAI with the risk of all-cause and cause-specific mortality. 387,794 middle-aged and older participants were followed up for a median of 8.9 years (IQR 8.3-9.5). A total of 14,112 all-cause deaths were documented. After adjustments, each 1-point increase in the HAI was related to a higher risk of all-cause mortality (hazards ratio [HR], 1.17; 95%CI, 1.15-1.18). Such association was stronger among adults younger than 60 years (1.19, 1.17-1.21) than that among those 60 years and older (1.15, 1.14-1.17) (P interaction < 0.001). For each unit increment of the HAI, the multivariate-adjusted HRs for risk of death were 1.28 (1.25-1.31) for cardiovascular diseases, 1.09 (1.07-1.10) for cancer, 1.36 (1.29-1.44) for digestive disease, 1.42 (1.35-1.48) for respiratory disease, 1.42 (1.33-1.51) for infectious diseases, and 1.15 (1.09-1.21) for neurodegenerative disease, respectively. Our findings indicate that the HAI is positively associated with all-cause and cause-specific mortality independent of chronological age. Our results further underscore the importance of effective early-life interventions to slow aging and prevent premature death.

Identifying Potential Causal Effects of Telomere Length on Health Outcomes: A Phenome-Wide Investigation and Mendelian Randomization Study.

BACKGROUND: Telomere length has been linked to various health outcomes. To comprehensively investigate the causal effects of telomere length throughout the human disease spectrum, we conducted a phenome-wide Mendelian randomization study (MR-PheWAS) and a systematic review of MR studies. METHODS: We conducted a PheWAS to screen for associations between telomere length and 1 035 phenotypes in the UK Biobank (n = 408 354). The exposure of interest was the genetic risk score (GRS) of telomere length. Observed associations passing multiple testing corrections were assessed for causality by 2-sample MR analysis. A systematic review of MR studies on telomere length was performed to harmonize the published evidence and complement our findings. RESULTS: Of the 1 035 phenotypes tested, PheWAS identified 29 and 78 associations of telomere length GRS at a Bonferroni- and false discovery rate-corrected threshold; 24 and 66 distinct health outcomes were causal in the following principal MR analysis. The replication MR using data from the FinnGen study provided evidence of causal effects of genetically instrumented telomere length on 28 out of 66 outcomes, including decreased risks of 5 diseases in respiratory diseases, digestive diseases, and myocardial infarction, and increased risks of 23 diseases, mainly comprised neoplasms, diseases of the genitourinary system, and essential hypertension. A systematic review of 53 MR studies found evidence to support 16 out of the 66 outcomes. CONCLUSIONS: This large-scale MR-PheWAS identified a wide range of health outcomes that were possibly affected by telomere length, and suggested that susceptibility to telomere length may vary across disease categories.

Proteome-Wide Mendelian Randomization Analysis Identified Potential Drug Targets for Atrial Fibrillation.

Background Finding effective and safe therapeutic drugs for atrial fibrillation (AF) is an important concern for clinicians. Proteome-wide Mendelian randomization analysis provides new ideas for finding potential drug targets. Methods and Results Using a proteome-wide Mendelian randomization approach, we assessed the genetic predictive causality between thousands of proteins and AF risk and found that genetically predicted plasma levels of phosphomevalonate kinase, tumor necrosis factor ligand superfamily member 12, sulfhydryl oxidase 2, interleukin-6 receptor subunit alpha, and low-affinity immunoglobulin gamma Fc region receptor II-b might decrease AF risk, while genetically predicted plasma levels of beta-mannosidase, collagen alpha-1(XV) chain, ANXA4 (annexin A4), COF2 (cofilin-2), and RAB1A (Ras-related protein Rab-1A) might increase AF risk (P<3.4×10-5). By using different Mendelian randomization methods and instrumental variable selection thresholds, we performed sensitivity analyses in 30 scenarios to test the robustness of positive findings. Replication analyses were also performed in independent samples to further avoid false-positive findings. Drugs targeting tumor necrosis factor ligand superfamily member 12, interleukin-6 receptor subunit alpha, low-affinity immunoglobulin gamma Fc region receptor II-b, and annexin A4 are approved or in development. The results of the phenome-wide Mendelian randomization analysis showed that changing the plasma levels of phosphomevalonate kinase, cofilin-2, annexin A4, Ras-related protein Rab-1A, sulfhydryl oxidase 2, and collagen alpha-1(XV) chain did not increase the risk of other diseases while decreasing the risk of AF. Conclusions We found a significant causal association between genetically predicted levels of 10 plasma proteins and AF risk. Four of these proteins have drugs targeting them that are approved or in development, and our results suggest the potential for these drugs to treat AF or cause AF. Sulfhydryl oxidase 2, low-affinity immunoglobulin gamma Fc region receptor II-b, and beta-mannosidase have not been suggested by previous laboratory or epidemiological studies to be associated with AF and may reveal new pathophysiological pathways as well as therapeutic targets for AF.

Associations of Modified Healthy Aging Index With Major Adverse Cardiac Events, Major Coronary Events, and Ischemic Heart Disease.

Background The Healthy Aging Index (HAI) has been regarded as useful in capturing the health status of multiple organ systems. However, to what extent the HAI is associated with major cardiovascular events remains largely unknown. The authors constructed a modified HAI (mHAI) to quantify the association of physiological aging with major vascular events and explored how the effects of a healthy lifestyle can modify this association. Methods and Results The participants with either missing values of any individual mHAI component or major illnesses such as heart attack, angina and stroke, and self-reported cancer at baseline were excluded. The mHAI components include systolic blood pressure, reaction time, forced vital capacity, serum cystatin c, and serum glucose. The authors used Cox proportional hazard models to quantify the association of mHAI with major adverse cardiac events, major coronary events, and ischemic heart disease. Cumulative incidence at 5 and 10 years was estimated, and joint analyses were stratified by age group and 4 mHAI categories. The mHAI was significantly correlated with major cardiovascular events, which is a better reflection of the aging level of the body than chronological age. An mHAI was calculated in 338 044 participants aged 38 to 73 years in the UK Biobank. Each point increase in the mHAI was associated with a 44% higher risk of major adverse cardiac events (adjusted hazard ratio [aHR], 1.44 [95% CI, 1.40-1.49]), 44% higher risk of major coronary events (aHR, 1.44 [95% CI, 1.40-1.48]), and 36% higher risk of ischemic heart disease (aHR, 1.36 [95% CI, 1.33-1.39]). The percentage of population-attribution risk was 51% (95% CI, 47-55) for major adverse cardiac events, 49% (95% CI, 45-53) for major coronary events, and 47% (95% CI, 44-50) for ischemic heart disease, which means that a substantial portion of these events could be prevented. Systolic blood pressure was the factor most significantly associated with major adverse cardiac events (aHR, 1.94 [95% CI, 1.82-2.08]; percentage of population-attribution risk, 36%), major coronary events (aHR, 2.01 [95% CI, 1.85-2.17]; percentage of population-attribution risk, 38%), and ischemic heart disease (aHR, 1.80 [95% CI, 1.71-1.89]; percentage of population-attribution risk, 32%). A healthy lifestyle significantly attenuated mHAI associations with incidence of vascular events. Conclusions Our findings indicate that higher mHAI is associated with increased major vascular events. A healthy lifestyle may attenuate these associations.

Genetic support of a causal relationship between cannabis use and educational attainment: a two-sample Mendelian randomization study of European ancestry.

BACKGROUND AND AIMS: Excessive cannabis use may lead to lower educational attainment. However, this association may be due to confounders and reverse causality. We tested the potential causal relationship between cannabis use disorder (CUD) or life-time cannabis use (LCU) and educational attainment. DESIGN: Bidirectional two-sample Mendelian randomization (MR) study was conducted. Our primary method was inverse-variance weighted (IVW) MR, with a series of sensitivity analyses. Multivariable MR (MVMR) was performed to estimate any direct effect independent of intelligence, smoking initiation or attention deficit hyperactivity disorder (ADHD). SETTING AND PARTICIPANTS: European ancestry individuals. The sample sizes of the genome-wide association study ranged from 55 374 to 632 802 participants. MEASUREMENTS: Genetic variants of CUD, LCU or educational attainment. FINDINGS: Using univariable MR, we found evidence of a potential causal effect of genetic liability to CUD on a lower educational attainment [MR, 95% confidence interval (CI)inverse variance weighted (IVW) = -1.2 month (-1.9 month, -0.5 month); P = 0.0008]. However, we found no evidence of an effect of genetic liability to LCU on educational attainment [MR, 95% CIIVW = 0.5 month (-1.5 month, 2.6 month), P = 0.6032]. Reverse direction analysis suggested that genetic liability to higher educational attainment had a potential causal effect on lower risk of CUD [odds ratio (OR), 95% CIIVW = 0.39 (0.29, 0.52), P = 1.69 × 10-10 ]. We also found evidence of potential causal effect from genetic liability to higher educational attainment to higher risk of LCU [OR, 95% CIIVW = 1.35 (1.11, 1.66), P = 0.0033]. CONCLUSIONS: Genetic liability to cannabis use disorder may lead to lower educational attainment. Genetic liability to higher educational attainment may also lead to higher life-time cannabis use risk and lower cannabis use disorder risk. However, the bidirectional effect between cannabis use disorder and educational attainment may be due to shared risk factors (e.g. attention-deficit hyperactivity disorder).

Associations of polysocial risk score, lifestyle and genetic factors with incident type 2 diabetes: a prospective cohort study.

AIM/HYPOTHESIS: We aimed to investigate the association between polysocial risk score (PsRS), an estimator of individual-level exposure to cumulative social risks, and incident type 2 diabetes in the UK Biobank study. METHODS: This study includes 319,832 participants who were free of diabetes, cardiovascular disease and cancer at baseline in the UK Biobank study. The PsRS was calculated by counting the 12 social determinants of health from three social risk domains (namely socioeconomic status, psychosocial factors, and neighbourhood and living environment) that had a statistically significant association with incident type 2 diabetes after Bonferroni correction. A healthy lifestyle score was calculated using information on smoking status, alcohol intake, physical activity, diet quality and sleep quality. A genetic risk score was calculated using 403 SNPs that showed significant genome-wide associations with type 2 diabetes in people of European descent. The Cox proportional hazards model was used to analyse the association between the PsRS and incident type 2 diabetes. RESULTS: During a median follow-up period of 8.7 years, 4427 participants were diagnosed with type 2 diabetes. After adjustment for major confounders, an intermediate PsRS (4-6) and high PsRS (≥7) was associated with higher risks of developing type 2 diabetes with the HRs being 1.38 (95% CI 1.26, 1.52) and 2.02 (95% CI 1.83, 2.22), respectively, compared with those with a low PsRS (≤3). In addition, an intermediate to high PsRS accounted for approximately 34% (95% CI 29, 39) of new-onset type 2 diabetes cases. A healthy lifestyle slightly, but significantly, mitigated PsRS-related risks of type 2 diabetes (pinteraction=0.030). In addition, the additive interactions between PsRS and genetic predisposition led to 15% (95% CI 13, 17; p<0.001) of new-onset type 2 diabetes cases (pinteraction<0.001). CONCLUSIONS/INTERPRETATION: A higher PsRS was related to increased risks of type 2 diabetes. Adherence to a healthy lifestyle may attenuate elevated diabetes risks due to social vulnerability. Genetic susceptibility and disadvantaged social status may act synergistically, resulting in additional risks for type 2 diabetes.

Smoking Status and Type 2 Diabetes, and Cardiovascular Disease: A Comprehensive Analysis of Shared Genetic Etiology and Causal Relationship.

OBJECTIVE: This study aimed to explore shared genetic etiology and the causality between smoking status and type 2 diabetes (T2D), cardiovascular diseases (CVDs), and related metabolic traits. METHODS: Using summary statistics from publicly available genome-wide association studies (GWASs), we estimated genetic correlations between smoking status and T2D, 6 major CVDs, and 8 related metabolic traits with linkage disequilibrium score regression (LDSC) analysis; identified shared genetic loci with large-scale genome-wide cross-trait meta-analysis; explored potential shared biological mechanisms with a series of post-GWAS analyses; and determined causality with Mendelian randomization (MR). RESULTS: We found significant positive genetic associations with smoking status for T2D (Rg = 0.170, p = 9.39 × 10-22), coronary artery disease (CAD) (Rg = 0.234, p = 1.96 × 10-27), myocardial infarction (MI) (Rg = 0.226, p = 1.08 × 10-17), and heart failure (HF) (Rg = 0.276, p = 8.43 × 10-20). Cross-trait meta-analysis and transcriptome-wide association analysis of smoking status identified 210 loci (32 novel loci) and 354 gene-tissue pairs jointly associated with T2D, 63 loci (12 novel loci) and 37 gene-tissue pairs with CAD, 38 loci (6 novel loci) and 17 gene-tissue pairs with MI, and 28 loci (3 novel loci) and one gene-tissue pair with HF. The shared loci were enriched in the exo-/endocrine, cardiovascular, nervous, digestive, and genital systems. Furthermore, we observed that smoking status was causally related to a higher risk of T2D (ß = 0.385, p = 3.31 × 10-3), CAD (ß = 0.670, p = 7.86 × 10-11), MI (ß = 0.725, p = 2.32 × 10-9), and HF (ß = 0.520, p = 1.53 × 10-6). CONCLUSIONS: Our findings provide strong evidence on shared genetic etiology and causal associations between smoking status and T2D, CAD, MI, and HF, underscoring the potential shared biological mechanisms underlying the link between smoking and T2D and CVDs. This work opens up a new way of more effective and timely prevention of smoking-related T2D and CVDs.