Biological age in UK Biobank: biomarker composition and prediction of mortality, coronary heart disease and hospital admissions

AbstractBackgroundAge is the strongest risk factor for most chronic diseases, and yet individuals may age at different rates biologically. A biological age formed from biomarkers may be a stronger risk factor than chronological age and understanding what factors contribute to it could provide insight into new opportunities for disease prevention.Methods and findingsAmong 480,019 UK Biobank participants aged 40-70 recruited in 2006-2010 and followed up for 6-12 years via linked death registry and secondary care records, a subpopulation of 141,254 (29.4%) non-smoking adults in good health and with no medication use or disease history at baseline were identified. Independent components of 72 biomarkers measured at baseline were characterised by principal component analysis. The Klemera Doubal method (KDM), which derived a weighted sum of biomarker principal components based on the strengths of their linear associations with chronological age, was used to derive sex-specific biological ages in this healthy subpopulation. The proportions of the overall biological and chronological age effects on mortality, coronary heart disease and age-related non-fatal hospital admissions (based on a hospital frailty index) that were explained by biological age were assessed using log-likelihoods of proportional hazards models.Reduced lung function, reduced kidney function, slower reaction time, lower insulin-like-growth factor 1, lower hand grip strength and higher blood pressure were key contributors to biological age (explaining the highest percentages of its variance) in both men and women, while lower albumin, higher sex hormone-binding globulin and lower muscle mass in men, and higher liver enzymes, blood lipids and HbA1c in women were also important. Across both sexes, a 51-principal component biological age explained 66%, 80% and 63% of the age effects on mortality, coronary heart disease and hospital admissions, respectively. Restricting the biological age to the 12-13 key biomarkers corresponding to the 10 most importantly contributing principal components resulted in little change in these proportions for women, but a reduction to 53%, 63% and 50%, respectively, for men.ConclusionsThis study identified that markers of impaired function in a range of organs account for a substantial proportion of the apparent effect of age on disease and hospital admissions. It supports a broader, multi-system approach to research and prevention of diseases of ageing.

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

The Journals of Gerontology Series A ◽

10.1093/gerona/glab069 ◽

2021 ◽

Author(s):

Mei Sum Chan ◽

Matthew Arnold ◽

Alison Offer ◽

Imen Hammami ◽

Marion Mafham ◽

...

Keyword(s):

Principal Components ◽

Hospital Admissions ◽

Later Life ◽

Chronological Age ◽

Uk Biobank ◽

Hand Grip ◽

Cox Proportional Hazard ◽

Prediction Of Mortality ◽

Age Related ◽

Cox Proportional Hazard Models

Abstract 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.

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Prediction of biological age and all-cause mortality by 12-lead electrocardiogram in patients without structural heart disease

10.21203/rs.3.rs-27441/v1 ◽

2020 ◽

Author(s):

Naomi Hirota ◽

Shinya Suzuki ◽

Takuto Arita ◽

Naoharu Yagi ◽

Takayuki Otsuka ◽

...

Keyword(s):

Principal Component Analysis ◽

Heart Disease ◽

Ventricular Tachyarrhythmia ◽

Principal Component ◽

Structural Heart Disease ◽

Biological Age ◽

Chronological Age ◽

Increased Risk ◽

All Cause Mortality ◽

Electrocardiogram Ecg

Abstract Background: The 12-lead electrocardiogram (ECG) is affected by not only the cardiovascular but also the non-cardiovascular status. Whether ECG can be the determinant of biological age (BA) and the gap between chronological age (CA) and ECG-predicted BA reflect differences in prognosis are unclear. Methods: In the Shinken Database 2010 – 2017 (n = 19170), 12-lead ECG was analyzed in 13005 patients excluding those with structural heart disease or having pacing beats, atrial or ventricular tachyarrhythmia, and indeterminate axis (R axis > 180˚) on index ECG. The prediction model of BA was developed by principal component analysis with 438 ECG parameters. The gap between ECG-predicted BA and CA was calculated (AgeDiff = ECG-predicted BA − CA). Results: The ECG-predicted BA was significantly correlated with CA (r = 0.967). Patients with a positively wide AgeDiff had a higher incidence of all-cause mortality compared to those with a narrow AgeDiff or those with negative AgeDiff. The risk of AgeDiff > 0 for all-cause mortality compared with AgeDiff ≤ 0 was 1.78 (95%CI: 1.00 − 3.16), which increased according to the aging and became the highest in patients with CA of 71 − 80 years. Conclusion: Our data suggested that 12-lead ECG can be a tool to estimate BA. The gap between ECG-predicted BA and CA allowed estimation of increased risk of all-cause mortality in patients without structural heart disease.

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