A Biomarker-based Biological Age in UK Biobank: Composition and Prediction of Mortality and Hospital Admissions.

BACKGROUND: Chronological age is the strongest risk factor for most chronic diseases. Developing a biomarker-based age and understanding its most important contributing biomarkers may shed light on the effects of age on later-life health and inform opportunities for disease prevention. METHODS: A subpopulation of 141 254 individuals healthy at baseline were studied, from among 480 019 UK Biobank participants aged 40-70 recruited in 2006-2010, and followed up for 6-12 years via linked death and secondary care records. Principal components of 72 biomarkers measured at baseline were characterized and used to construct sex-specific composite biomarker ages using the Klemera Doubal method, which derived a weighted sum of biomarker principal components based on their linear associations with chronological age. Biomarker importance in the biomarker ages was assessed by the proportion of the variation in the biomarker ages that each explained. The proportions of the overall biomarker and chronological age effects on mortality and age-related hospital admissions explained by the biomarker ages were compared using likelihoods in Cox proportional hazard models. RESULTS: Reduced lung function, kidney function, reaction time, insulin-like growth factor 1, hand grip strength, and higher blood pressure were key contributors to the derived biomarker age in both men and women. The biomarker ages accounted for >65% and >84% of the apparent effect of age on mortality and hospital admissions for the healthy and whole populations, respectively, and significantly improved prediction of mortality (p < .001) and hospital admissions (p < 1 × 10-10) over chronological age alone. CONCLUSIONS: This study suggests that a broader, multisystem approach to research and prevention of diseases of aging warrants consideration.

Governing AI-Driven Health Research: Are IRBs Up to the Task?

Many are calling for concrete mechanisms of oversight for health research involving artificial intelligence (AI). In response, institutional review boards (IRBs) are being turned to as a familiar model of governance. Here, we examine the IRB model as a form of ethics oversight for health research that uses AI. We consider the model's origins, analyze the challenges IRBs are facing in the contexts of both industry and academia, and offer concrete recommendations for how these committees might be adapted in order to provide an effective mechanism of oversight for health-related AI research.

Design, methods, and reporting of impact studies of cardiovascular clinical prediction rules are suboptimal: a systematic review.

OBJECTIVES: To evaluate design, methods, and reporting of impact studies of cardiovascular clinical prediction rules (CPRs). STUDY DESIGN AND SETTING: We conducted a systematic review. Impact studies of cardiovascular CPRs were identified by forward citation and electronic database searches. We categorized the design of impact studies as appropriate for randomized and nonrandomized experiments, excluding uncontrolled before-after study. For impact studies with appropriate study design, we assessed the quality of methods and reporting. We compared the quality of methods and reporting between impact and matched control studies. RESULTS: We found 110 impact studies of cardiovascular CPRs. Of these, 65 (59.1%) used inappropriate designs. Of 45 impact studies with appropriate design, 31 (68.9%) had substantial risk of bias. Mean number of reporting domains that impact studies with appropriate study design adhered to was 10.2 of 21 domains (95% confidence interval, 9.3 and 11.1). The quality of methods and reporting was not clearly different between impact and matched control studies. CONCLUSION: We found most impact studies either used inappropriate study design, had substantial risk of bias, or poorly complied with reporting guidelines. This appears to be a common feature of complex interventions. Users of CPRs should critically evaluate evidence showing the effectiveness of CPRs.

Socio-economic inequalities in life expectancy of older adults with and without multimorbidity: a record linkage study of 1.1 million people in England.

BACKGROUND: Age of onset of multimorbidity and its prevalence are well documented. However, its contribution to inequalities in life expectancy has yet to be quantified. METHODS: A cohort of 1.1 million English people aged 45 and older were followed up from 2001 to 2010. Multimorbidity was defined as having 2 or more of 30 major chronic diseases. Multi-state models were used to estimate years spent healthy and with multimorbidity, stratified by sex, smoking status and quintiles of small-area deprivation. RESULTS: Unequal rates of multimorbidity onset and subsequent survival contributed to higher life expectancy at age 65 for the least (Q1) compared with most (Q5) deprived: there was a 2-year gap in healthy life expectancy for men [Q1: 7.7 years (95% confidence interval: 6.4-8.5) vs Q5: 5.4 (4.4-6.0)] and a 3-year gap for women [Q1: 8.6 (7.5-9.4) vs Q5: 5.9 (4.8-6.4)]; a 1-year gap in life expectancy with multimorbidity for men [Q1: 10.4 (9.9-11.2) vs Q5: 9.1 (8.7-9.6)] but none for women [Q1: 11.6 (11.1-12.4) vs Q5: 11.5 (11.1-12.2)]. Inequalities were attenuated but not fully attributable to socio-economic differences in smoking prevalence: multimorbidity onset was latest for never smokers and subsequent survival was longer for never and ex smokers. CONCLUSIONS: The association between social disadvantage and multimorbidity is complex. By quantifying socio-demographic and smoking-related contributions to multimorbidity onset and subsequent survival, we provide evidence for more equitable allocation of prevention and health-care resources to meet local needs.