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Background and Aims: The relationship between alcohol consumption and age-related diseases is inconsistent. Biological age (BA) serves as both a precursor and a predictor of age-related diseases; however, longitudinal associations between alcohol consumption and BA in middle-aged and older people remain unclear. We measured whether there was a longitudinal association between drinking frequency and pure alcohol intake with BA among middle-aged and older people., Design and Setting and Participants: This study involved two prospective cohort studies, set in Southwestern China and the United Kingdom. A total of 8046 participants from the China Multi-Ethnic Cohort study (CMEC) and 5412 participants from the UK Biobank (UKB), aged 30-79 years, took part, with complete data from two waves of clinical biomarkers., Measurements: BA was calculated by the Klemera Doubal's method. Accelerated BA equalled BA minus chronological age. Drinking frequency and pure alcohol intake were obtained through self-reported questionnaires. Drinking frequency in the past year was classified as current non-drinking, occasional (monthly drinking) and regular (weekly drinking)., Findings: Compared with consistent current non-drinkers, more frequent drinkers [CMEC: β = 0.46, 95% confidence interval (CI) = 0.13-0.80; UKB: β = 0.65, 95% CI = 0.01-1.29)], less frequent drinkers (CMEC: β = 0.62, 95% CI = 0.37-0.87; UKB: β = 0.54, 95% CI = -0.01-1.09), consistent occasional drinkers (CMEC: β = 0.51, 95% CI = 0.23-0.79; UKB: β = 0.63, 95% CI = 0.13-1.13) and consistent regular drinkers (CMEC: β = 0.56, 95% CI = 0.17-0.95; UKB: β = 0.46, 95% CI = 0.00-0.91) exhibited increased accelerated BA. A non-linear relationship between pure alcohol intake and accelerated BA was observed among consistent regular drinkers., Conclusions: In middle-aged and older people, any change in drinking frequency and any amount of pure alcohol intake seem to be positively associated with acceleration of biological ageing, compared with maintaining abstinence., (© 2024 Society for the Study of Addiction.)

Background: Suicidal behaviors, including suicide deaths and attempts, are major public health concerns. However, previous suicide models required a huge amount of input features, resulting in limited applicability in clinical practice., Objective: We aimed to construct applicable models (ie, with limited features) for short- and long-term suicidal behavior prediction. We further validated these models among individuals with different genetic risks of suicide., Methods: Based on the prospective cohort of UK Biobank, we included 223 (0.06%) eligible cases of suicide attempts or deaths, according to hospital inpatient or death register data within 1 year from baseline and randomly selected 4460 (1.18%) controls (1:20) without such records. We similarly identified 833 (0.22%) cases of suicidal behaviors 1 to 6 years from baseline and 16,660 (4.42%) corresponding controls. Based on 143 input features, mainly including sociodemographic, environmental, and psychosocial factors; medical history; and polygenic risk scores (PRS) for suicidality, we applied a bagged balanced light gradient-boosting machine (LightGBM) with stratified 10-fold cross-validation and grid-search to construct the full prediction models for suicide attempts or deaths within 1 year or between 1 and 6 years. The Shapley Additive Explanations (SHAP) approach was used to quantify the importance of input features, and the top 20 features with the highest SHAP values were selected to train the applicable models. The external validity of the established models was assessed among 50,310 individuals who participated in UK Biobank repeated assessments both overall and by the level of PRS for suicidality., Results: Individuals with suicidal behaviors were on average 56 years old, with equal sex distribution. The application of these full models in the external validation data set demonstrated good model performance, with the area under the receiver operating characteristic (AUROC) curves of 0.919 and 0.892 within 1 year and between 1 and 6 years, respectively. Importantly, the applicable models with the top 20 most important features showed comparable external-validated performance (AUROC curves of 0.901 and 0.885) as the full models, based on which we found that individuals in the top quintile of predicted risk accounted for 91.7% (n=11) and 80.7% (n=25) of all suicidality cases within 1 year and during 1 to 6 years, respectively. We further obtained comparable prediction accuracy when applying these models to subpopulations with different genetic susceptibilities to suicidality. For example, for the 1-year risk prediction, the AUROC curves were 0.907 and 0.885 for the high (>2nd tertile of PRS) and low (<1st) genetic susceptibilities groups, respectively., Conclusions: We established applicable machine learning-based models for predicting both the short- and long-term risk of suicidality with high accuracy across populations of varying genetic risk for suicide, highlighting a cost-effective method of identifying individuals with a high risk of suicidality., (©Junren Wang, Jiajun Qiu, Ting Zhu, Yu Zeng, Huazhen Yang, Yanan Shang, Jin Yin, Yajing Sun, Yuanyuan Qu, Unnur A Valdimarsdóttir, Huan Song. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 20.02.2023.)

Background: Cardiometabolic multimorbidity (CMM) is becoming increasingly common in patients with hypertension, and it is well established that healthy lifestyle plays a key role in the prevention of hypertension. However, the association between combined lifestyle factors and CMM in patients with hypertension is uncertain., Methods: This prospective analysis included the data (obtained from the UK biobank) of participants with hypertension who did not have coronary heart disease (CHD), stroke, or diabetes. The outcome was the occurrence of CMM, defined as ≥ 1 disease of CHD, stroke, and diabetes that occurred in participants with hypertension. Four lifestyle factors (smoking, alcohol consumption, diet, and physical activity) were assessed using a weighted healthy lifestyle score, and participants were divided into four groups: the very unhealthy, unhealthy, healthy, and very healthy groups. The flexible parameter Royston-Parmar proportional hazard model was used to estimate hazard ratios (HRs) between lifestyles and CMM, as well as the difference in CMM-free life expectancy., Results: During a median follow-up of 12.2 years, 9812 (18.4%) of the 53,397 hypertensive patients occurred CMM. Compared with the very unhealthy group, the very healthy group had a 41% reduction in the risk for CMM in hypertensive patients and a 32-50% reduction in the risk for specific cardiometabolic diseases such as CHD, stroke, and diabetes. For each lifestyle factor, non-smoking had the greatest protective effect against CMM (HR: 0.64, 95% confidence interval (CI) 0.60-0.68). A lifestyle combining multiple healthy factors extended CMM-free life expectancy (e.g., six years longer at age 45 years for participants in the very healthy group)., Conclusions: Combined healthy lifestyle factors were associated with a lower risk for CMM in hypertensive patients. This suggests that combined healthy lifestyle should be supported to decrease disease burden., (© 2022. The Author(s).)

Background Although the association between shift work and individual cardiometabolic diseases has been well studied, its role in the progression to cardiometabolic multimorbidity (CMM) remains unclear. In this study, we investigate the association between shift work and the incidence of CMM in patients with hypertension. Methods and Results This study is a population-based and prospective cohort study on 36 939 UK Biobank participants. We used competing risk models to examine the association between shift work and the risk of CMM, which was defined as coexistence of hypertension and diabetes, coronary heart disease, or stroke in our study. We also investigated the association between the frequency and duration of shift work and CMM risks. In addition, we conducted a cross-classification analysis with the combination of frequency and duration of shift work, chronotype and sleep duration as the exposure metrics. During a median follow-up of 11.6 years, a total of 5935 participants developed CMM. We found that usually/always night shift workers were associated with a 16% higher risk of CMM compared with day workers (hazard ratio [HR], 1.16 [95% CI, 1.02-1.31]). We also found that a higher frequency of night shifts (>10/month) was associated with increased risk of CMM (HR, 1.19 [95% CI, 1.06-1.34]) that was more pronounced for >10/month in combination with a morning chronotype or <7 hours or >8 hours of sleep duration (HR, 1.26 [95% CI, 1.02-1.56]; HR, 1.43 [95% CI, 1.19-1.72], respectively). Conclusions We find that night shift work is associated with higher CMM risk in patients with hypertension.

Background: As the population ages, cognitive decline and dementia have become major health concerns in the UK. Loneliness has been linked to cognitive decline, but the reverse causality of this association remains unclear. This study aims to examine whether there is a bidirectional relationship between loneliness and cognitive function in older English adults (age 50 years and over) over a 10-year follow-up., Methods: Data came from a nationally representative sample of 5885 participants in the English Longitudinal Study of Ageing (ELSA), free of stroke or dementia and followed every 2 years up to wave 7 (2014-15). At each wave, cognitive function was measured with word recall and verbal fluency tests, and loneliness was measured with the abridged version of the revised UCLA Loneliness Scale. Bivariate dual change score models were used to assess the multivariate associations between loneliness and cognitive function, used interchangeably as exposures and outcomes., Results: Greater loneliness at baseline was associated with poorer memory [β intercept = -0.03, standard error (SE) = 0.01, P  =  0.016] and verbal fluency (β intercept = -0.01, SE  =  001, P =  0.027) at baseline, and with a stronger linear rate of decline in both memory (β linear slope = -0.07, SE  =  001, P  ≤ 0.001) and verbal fluency (β linear slope = -0.09, SE  =  0.03, P =  0.003) over a 10-year follow-up period, although the performance on verbal fluency did not change substantially on average over this period. We also found that higher baseline memory, but not verbal fluency, predicted a slower change in loneliness (β linear slope = -0.01, SE  =  001, P =  0.004) and that a linear decline in memory was associated with an acceleration in loneliness (β quadratic slope = -0.02, SE  =  001, P  ≤ 0.001) during follow-up., Conclusions: Higher loneliness is associated with poorer cognitive function at baseline and contributes to a worsening in memory and verbal fluency over a decade. These factors seem, however, to be partially intertwined, since baseline memory and its rate of decline also contribute to an increase in loneliness over time., (© The Author(s) 2019. Published by Oxford University Press on behalf of the International Epidemiological Association.)

Primary open-angle glaucoma (POAG) is a leading cause of irreversible vision loss, yet much of the genetic risk remains unaccounted for, especially in African-Americans who have a higher risk for developing POAG. We conduct a multiethnic genome-wide association study (GWAS) of POAG in the GERA cohort, with replication in the UK Biobank (UKB), and vice versa, GWAS in UKB with replication in GERA. We identify 24 loci (P < 5.0 × 10 -8 ), including 14 novel, of which 9 replicate (near FMNL2, PDE7B, TMTC2, IKZF2, CADM2, DGKG, ANKH, EXOC2, and LMX1B). Functional studies support intraocular pressure-related influences of FMNL2 and LMX1B, with certain Lmx1b mutations causing high IOP and glaucoma resembling POAG in mice. The newly identified loci increase the proportion of variance explained in each GERA race/ethnicity group, with the largest gain in African-Americans (0.5-3.1%). A meta-analysis combining GERA and UKB identifies 24 additional loci. Our study provides important insights into glaucoma pathogenesis.