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

Clinical biomarker-based biological ageing and future risk of neurological disorders in the UK Biobank

BACKGROUND

Many common neurological disorders are associated with advancing chronological age, but their association with biological age (BA) remains poorly understood.

METHODS

We studied 325 870 participants in the UK Biobank without a diagnosed neurological condition at baseline and generated three previously-described measures of BA based on 18 routinely measured clinical biomarkers (PhenoAge, Klemera-Doubal method age (KDMAge), homeostatic dysregulation age). Using survival models, we assessed the effect of advanced BA on incident neurological diagnoses, including all-cause and cause-specific dementia, ischaemic stroke, Parkinson's disease and motor neuron disease.

RESULTS

During a mean follow-up of 9.0 years, there were 1397 incident cases of dementia and 2515 of ischaemic stroke, with smaller case numbers of other diagnoses. The strongest associations with a 1 SD in BA residual were seen for all-cause dementia (KDMAge HR=1.19, 95% CI=1.11 to 1.26), vascular dementia (1.41, 1.25 to 1.60) and ischaemic stroke (1.39, 1.34 to 1.46). Weaker associations were seen for Alzheimer's disease and motor neuron disease, while, in contrast, HRs for Parkinson's disease tended to be <1. Results were largely consistent after adjustment for disease-specific covariates including common cardiometabolic risk factors.

CONCLUSIONS

Advanced BA calculated from routine clinical biomarker results increases the risk of subsequent neurological diagnoses including all-cause dementia and ischaemic stroke.

A biological age model based on physical examination data to predict mortality in a Chinese population

Biological age could be reflective of an individual's health status and aging degree. Limited estimations of biological aging based on physical examination data in the Chinese population have been developed to quantify the rate of aging. We developed and validated a novel aging measure (Balanced-AGE) based on readily available physical health examination data. In this study, a repeated sub-sampling approach was applied to address the data imbalance issue, and this approach significantly improved the performance of biological age (Balanced-AGE) in predicting all-cause mortality with a 10-year time-dependent AUC of 0.908 for all-cause mortality. This mortality prediction tool was found to be effective across different subgroups by age, sex, smoking, and alcohol consumption status. Additionally, this study revealed that individuals who were underweight, smokers, or drinkers had a higher extent of age acceleration. The Balanced-AGE may serve as an effective and generally applicable tool for health assessment and management among the elderly population.

Geroscience and pathology: a new frontier in understanding age-related diseases

Geroscience, a burgeoning discipline at the intersection of aging and disease, aims to unravel the intricate relationship between the aging process and pathogenesis of age-related diseases. This paper explores the pivotal role played by geroscience in reshaping our understanding of pathology, with a particular focus on age-related diseases. These diseases, spanning cardiovascular and cerebrovascular disorders, malignancies, and neurodegenerative conditions, significantly contribute to the morbidity and mortality of older individuals. We delve into the fundamental cellular and molecular mechanisms underpinning aging, including mitochondrial dysfunction and cellular senescence, and elucidate their profound implications for the pathogenesis of various age-related diseases. Emphasis is placed on the importance of assessing key biomarkers of aging and biological age within the realm of pathology. We also scrutinize the interplay between cellular senescence and cancer biology as a central area of focus, underscoring its paramount significance in contemporary pathological research. Moreover, we shed light on the integration of anti-aging interventions that target fundamental aging processes, such as senolytics, mitochondria-targeted treatments, and interventions that influence epigenetic regulation within the domain of pathology research. In conclusion, the integration of geroscience concepts into pathological research heralds a transformative paradigm shift in our understanding of disease pathogenesis and promises breakthroughs in disease prevention and treatment.

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.

Biological age estimation using circulating blood biomarkers

Biological age captures physiological deterioration better than chronological age and is amenable to interventions. Blood-based biomarkers have been identified as suitable candidates for biological age estimation. This study aims to improve biological age estimation using machine learning models and a feature-set of 60 circulating biomarkers available from the UK Biobank (n = 306,116). We implement an Elastic-Net derived Cox model with 25 selected biomarkers to predict mortality risk (C-Index = 0.778; 95% CI [0.767-0.788]), which outperforms the well-known blood-biomarker based PhenoAge model (C-Index = 0.750; 95% CI [0.739-0.761]), providing a C-Index lift of 0.028 representing an 11% relative increase in predictive value. Importantly, we then show that using common clinical assay panels, with few biomarkers, alongside imputation and the model derived on the full set of biomarkers, does not substantially degrade predictive accuracy from the theoretical maximum achievable for the available biomarkers. Biological age is estimated as the equivalent age within the same-sex population which corresponds to an individual's mortality risk. Values ranged between 20-years younger and 20-years older than individuals' chronological age, exposing the magnitude of ageing signals contained in blood markers. Thus, we demonstrate a practical and cost-efficient method of estimating an improved measure of Biological Age, available to the general population.

The NHANES Biological Age Index demonstrates accelerated aging in MS patients

BACKGROUND

Chronological age is associated with disability accumulation in multiple sclerosis (MS). Biological age may give more precise estimates of aging pathways associations with MS severity. Both normal aging and accelerated aging from MS may negatively impact disease course. Multi-marker indices of aging, such as the NHANES biological age index (BAI), may be more robust than single biomarkers in capturing biological age and are strongly associated with mortality risk and aging-related diseases.

OBJECTIVE

We sought to investigate whether the NHANES BAI, utilizing readily available measures in the clinic, captures accelerating aging and correlates with disability in MS participants.

METHODS

We conducted a prospective, cross-sectional case-control pilot study. Consecutive patients who met the 2017 McDonald's Criteria for MS were recruited from May 2020 to May 2022 along with age-similar healthy controls. BAI components included blood pressure, FEV1, serum creatinine, C-reactive protein, blood-urea nitrogen, albumin, alkaline phosphatase, cholesterol, CMV IgG, and hemoglobin A1c. The index was calculated using the Klemara and Doubal method. Spearman correlation and multivariable linear regression models were used to assess the association between BAI and MS clinical outcomes.

RESULTS

A total of 51 MS (68.6% female) and 38 control (68.4% female) participants were recruited. BAI correlated with chronological age (CA) in MS (r2=0.90,p<0.0001) and control participants (r2 =0.87,p<0.0001). The mean BAI was 1.4 years older than CA in MS participants (range +15 to -10.5 years) and 2.2 years younger in control participants (range +11.2 to -14.1 years). In unadjusted Spearman analyses, BAI correlated with the timed 25-foot walk (T25FW, rhos=0.31, p = 0.045) and symbol digit modalities test (SDMT rhos = 0.35, p = 0.018). In a multivariable regression model, a 5-year older BAI was associated with a 1.2-point lower score on SDMT (95%CI -2.2 to -0.25, p = 0.014).

CONCLUSIONS

MS participants were biologically older than their own chronological age and age-similar controls. In this modest-sized pilot sample, there was strongest correlation for MS outcome measures between BAI and the SDMT. These results support further study of the BAI as a marker of biological age variability in MS.

Anthropometric and Body Circumference Determinants for Hand Grip Strength: A Population-Based Mon-Timeline Study

Background

Hand grip strength (HGS) is a tool for diagnosing sarcopenia. In this study, we examined some anthropometric and body circumference measurements as determinants for HGS.

Methods

This cross-sectional study was conducted with participants (Mongolians, n = 1080, aged 18-70, mean age of 41.2 ± 13.9 years, 33.7% of men) from the "Mon-Timeline" cohort study. To measure HGS, a digital grip strength dynamometer was used.

Results

Mean HGS in men was 40.1 ± 10.4 kg and in women was 24.5 ± 5.6 kg. Correlation analysis showed that the strongest correlation with HGS was height (r = 0.712, p < 0.001). Moreover, HGS was inversely correlated with age (r = -0.239, p < 0.001) and thigh circumference (r = -0.070, p < 0.01), while it was positively correlated with body weight (r = 0.309, p < 0.001), neck circumference (r = 0.427, p < 0.001), upper arm circumference (r = 0.108, p < 0.0001), lower arm circumference (r = 0.413, p < 0.0001), and calf circumference (r = 0.117, p < 0.0001). In the multivariate linear regression analysis (unstandardized B coefficient, 95% CI), age (-0.159, -0.188; -0.129), sex (-9.262, -10.459; -8.064), height (0.417, 0.357; 0.478), lower arm circumference (1.003, 0.736; 1.270), and calf circumference (-0.162, -0.309; -0.015) were significantly associated with HGS.

Conclusions

When detecting sarcopenia using HGS, it is important to take into account variables such as body height and body circumference.

Heterogeneous aging across multiple organ systems and prediction of chronic disease and mortality

Biological aging of human organ systems reflects the interplay of age, chronic disease, lifestyle and genetic risk. Using longitudinal brain imaging and physiological phenotypes from the UK Biobank, we establish normative models of biological age for three brain and seven body systems. Here we find that an organ's biological age selectively influences the aging of other organ systems, revealing a multiorgan aging network. We report organ age profiles for 16 chronic diseases, where advanced biological aging extends from the organ of primary disease to multiple systems. Advanced body age associates with several lifestyle and environmental factors, leukocyte telomere lengths and mortality risk, and predicts survival time (area under the curve of 0.77) and premature death (area under the curve of 0.86). Our work reveals the multisystem nature of human aging in health and chronic disease. It may enable early identification of individuals at increased risk of aging-related morbidity and inform new strategies to potentially limit organ-specific aging in such individuals.

No signs of neurodegenerative effects in 15q11.2 BP1-BP2 copy number variant carriers in the UK Biobank

The 15q11.2 BP1-BP2 copy number variant (CNV) is associated with altered brain morphology and risk for atypical development, including increased risk for schizophrenia and learning difficulties for the deletion. However, it is still unclear whether differences in brain morphology are associated with neurodevelopmental or neurodegenerative processes. This study derived morphological brain MRI measures in 15q11.2 BP1-BP2 deletion (n = 124) and duplication carriers (n = 142), and matched deletion-controls (n = 496) and duplication-controls (n = 568) from the UK Biobank study to investigate the association with brain morphology and estimates of brain ageing. Further, we examined the ageing trajectory of age-affected measures (i.e., cortical thickness, surface area, subcortical volume, reaction time, hand grip strength, lung function, and blood pressure) in 15q11.2 BP1-BP2 CNV carriers compared to non-carriers. In this ageing population, the results from the machine learning models showed that the estimated brain age gaps did not differ between the 15q11.2 BP1-BP2 CNV carriers and non-carriers, despite deletion carriers displaying thicker cortex and lower subcortical volume compared to the deletion-controls and duplication carriers, and lower surface area compared to the deletion-controls. Likewise, the 15q11.2 BP1-BP2 CNV carriers did not deviate from the ageing trajectory on any of the age-affected measures examined compared to non-carriers. Despite altered brain morphology in 15q11.2 BP1-BP2 CNV carriers, the results did not show any clear signs of apparent altered ageing in brain structure, nor in motor, lung or heart function. The results do not indicate neurodegenerative effects in 15q11.2 BP1-BP2 CNV carriers.

Identifying the Biomarker Profile of Pre-Frail and Frail People: A Cross-Sectional Analysis from UK Biobank

OBJECTIVE

This study aimed to compare the biomarker profile of pre-frail and frail adults in the UK Biobank cohort by sex.

METHODS

In total, 202,537 participants (67.8% women, aged 37 to 73 years) were included in this cross-sectional analysis. Further, 31 biomarkers were investigated in this study. Frailty was defined using a modified version of the Frailty Phenotype. Multiple linear regression analyses were performed to explore the biomarker profile of pre-frail and frail individuals categorized by sex.

RESULTS

Lower concentrations of apoA1, total, LDL, and HDL cholesterol, albumin, eGFRcys, vitamin D, total bilirubin, apoB, and testosterone (differences ranged from -0.30 to -0.02 per 1-SD change), as well as higher concentrations of triglycerides, GGT, cystatin C, CRP, ALP, and phosphate (differences ranged from 0.01 to 0.53 per 1-SD change), were identified both in pre-frail and frail men and women. However, some of the associations differed by sex. For instance, higher rheumatoid factor and urate concentrations were identified in pre-frail and frail women, while lower calcium, total protein, and IGF-1 concentrations were identified in pre-frail women and frail women and men. When the analyses were further adjusted for CRP, similar results were found.

CONCLUSIONS

Several biomarkers were linked to pre-frailty and frailty. Nonetheless, some of the associations differed by sex. Our findings contribute to a broader understanding of the pathophysiology of frailty as currently defined.

Progress in biological age research

Biological age (BA) is a common model to evaluate the function of aging individuals as it may provide a more accurate measure of the extent of human aging than chronological age (CA). Biological age is influenced by the used biomarkers and standards in selected aging biomarkers and the statistical method to construct BA. Traditional used BA estimation approaches include multiple linear regression (MLR), principal component analysis (PCA), Klemera and Doubal's method (KDM), and, in recent years, deep learning methods. This review summarizes the markers for each organ/system used to construct biological age and published literature using methods in BA research. Future research needs to explore the new aging markers and the standard in select markers and new methods in building BA models.

Effect of dietary inflammatory potential on the aging acceleration for cardiometabolic disease: A population-based study

BACKGROUND/AIM

Optimized dietary patterns have been considered an important determinant of delaying aging in cardiometabolic disease (CMD). Dietary pattern with high-level dietary inflammatory potential is a key risk factor for cardiometabolic disease, and has drawn increasing attention. The aim of this study was to investigate whether dietary pattern with high dietary inflammatory potential was associated with aging acceleration in cardiometabolic disease.

MATERIALS AND METHODS

We analyzed the cross-sectional data from six survey cycles (1999-2000, 2001-2002, 2003-2004, 2005-2006, 2007-2008, and 2009-2010) of the National Health and Nutritional Examination Surveys (NHANES). A total of 16,681 non-institutionalized adults and non-pregnant females with CMD were included in this study. Dietary inflammatory index (DII) was used to assess the dietary inflammatory potential. The two age acceleration biomarkers were calculated by the residuals from regressing chronologic age on Klemera-Doubal method biological age (KDM BioAge) or Phenotypic Age (PhenoAge), termed "KDMAccel" and "PhenoAgeAccel." A multivariable linear regression accounting for multistage survey design and sampling weights was used in different models to investigate the association between DII and aging acceleration. Four sensitivity analyses were used to ensure the robustness of our results. Besides, we also analyzed the anti-aging effects of DASH-type dietary pattern and "Life's Simple 7".

RESULTS

For 16,681 participants with CMD, compared with the first tertile of DII after adjusting for all potential confounders, the patients with second tertile of DII showed a 1.02-years increase in KDMAccel and 0.63-years increase in PhenoAgeAccel (KDMAccel, β = 1.02, 95% CI = 0.64 to 1.41, P < 0.001; PhenoAgeAccel, β = 0.63, 95% CI = 0.44 to 0.82, P < 0.001), while the patients with the third tertile of DII showed a 1.48-years increase in KDMAccel and 1.22-years increase in PhenoAgeAccel (KDMAccel, β = 1.48, 95% CI = 1.02 to 1.94, P < 0.001; PhenoAgeAccel, β = 1.22, 95% CI = 1.01 to 1.43, P < 0.001). In addition, DASH-type dietary pattern was associated with a 0.57-years reduction in KDMAccel (β = -0.57, 95% CI = -1.08 to -0.06, P = 0.031) and a 0.54-years reduction in PhenoAgeAccel (β = -0.54, 95% CI = -0.80 to -0.28, P < 0.001). The each one-unit increase in CVH score was associated with a 1.58-years decrease in KDMAccel (β = -1.58, 95% CI = -1.68 to -1.49, P < 0.001) and a 0.36-years in PhenoAgeAccel (β = -0.36, 95% CI = -0.41 to -0.31, P < 0.001).

CONCLUSION

Among CMD, the dietary pattern with high dietary inflammatory potential was association with aging acceleration, and the anti-aging potential of DASH-type dietary pattern and "Life's Simple 7" should also be given attention, but these observations require future prospective validation.

The Predicted Value of Kidney Injury Molecule-1 (KIM-1) in Healthy People

Purpose

Recent studies have focused on whether kidney injury molecule-1 (KIM-1) might serve as a marker of acute kidney tubular injury. Our study analyzed the levels of KIM-1 in the healthy population of different ages to explore the correlation between KIM-1 and age. Moreover, we constructed a model to predict kidney age.

Methods

A cross-sectional study was conducted by Huashan Hospital, Shanghai, China, between April 2020 and December 2020. KIM-1 and other kidney biomarkers were measured in 176 healthy individuals ranging from 26 to 91 years old. Statistical correlated analyses for urinary KIM-1, creatinine (uCREA), potassium (K), sodium (Na) and chlorine (Cl), plasmic renin, angiotensin-2 (AngII) and aldosterone (ALD), and serum microalbuminuria (MALB), β2-microglobulin (B2MG), cystatin C (CYSC), urea nitrogen (BUN), creatinine (CREA), and glucose (GLU) were performed to assess the correlation between age and kidney biomarkers. All variables were selected as independent variables for the prediction of age by multiple linear regression.

Results

KIM-1 positively correlated with age in kidney healthy people (r = 0.41, p < 0.05), whether among females (r = 0.51, p < 0.05) or males (r = 0.27, p < 0.05). It was much related to K (r = 0.34), B2MG (r = 0.28), and CL (r = 0.23). The predicted model was constructed with eGFR, Cl, ALD, CYSC, KIM-1, BUN, GLU and AngII, reaching an adjusted R² of 69.5% and a standard error of the estimated 7.84 years.

Conclusion

The level of urinary KIM-1 increases with age in healthy people. The model constructed by KIM-1 and the other 7 biomarkers can predict kidney age in healthy people.

All-Subset Analysis Improves the Predictive Accuracy of Biological Age for All-Cause Mortality in Chinese and U.S. Populations

BACKGROUND

Klemera-Doubal's method (KDM) is an advanced and widely applied algorithm for estimating biological age (BA), but it has no uniform paradigm for biomarker processing. This article proposed all subsets of biomarkers for estimating BAs and assessed their association with mortality to determine the most predictive subset and BA.

METHODS

Clinical biomarkers, including those from physical examinations and blood assays, were assessed in the China Health and Nutrition Survey (CHNS) 2009 wave. Those correlated with chronological age (CA) were combined to produce complete subsets, and BA was estimated by KDM from each subset of biomarkers. A Cox proportional hazards regression model was used to examine and compare each BA's effect size and predictive capacity for all-cause mortality. Validation analysis was performed in the Chinese Longitudinal Healthy Longevity Survey (CLHLS) and National Health and Nutrition Examination Survey (NHANES). KD-BA and Levine's BA were compared in all cohorts.

RESULTS

A total of 130 918 panels of BAs were estimated from complete subsets comprising 3-17 biomarkers, whose Pearson coefficients with CA varied from 0.39 to 1. The most predictive subset consisted of 5 biomarkers, whose estimated KD-BA had the most predictive accuracy for all-cause mortality. Compared with Levine's BA, the accuracy of the best-fitting KD-BA in predicting death varied among specific populations.

CONCLUSION

All-subset analysis could effectively reduce the number of redundant biomarkers and significantly improve the accuracy of KD-BA in predicting all-cause mortality.

Aging Challenges. Perceived Age – a New Predictor of Longevity?

The ageing process is accompanied by the manifestation of many characteristics, so-called biomarkers, which can be quantified and used to assess a patient's health status. One of these signs is the progressive decline of a human's facial look, which is described by the concept of 'perceived age'. Facial aging is the most important parameter of perceived age. However, over the years, researchers have identified risk factors that affect the facial skin, including smoking, systematic consumption of alcoholic beverages, overweight or underweight, environmental conditions, and psychosocial determinants. The influence of psychological state on the appearance and life prognosis is shown. The authors presented data from the international literature on the study of perceived age. The frontiers of using perceived age as a biomarker of aging were Danish scientists who developed the main methodological approaches to determine this indicator. One such methodology used in population studies has been the clinical technique of assessing perceived age through photography. The review presents this methodology in detail, with its advantages and modifications. The authors conclude that the measurement of an individual's perceived age can serve not only as a prognostic indicator,  but also over time can become a   useful marker of the effectiveness of various treatments. Until now perceived age has hardly been studied in population studies, the authors presented data from the works of V.A.  Labunskaya, G.V.  Serikov,  T.A.  Shkurko who develop the direction related to psychology of perceived age  and in their studies use social-psychological approaches of appearance assessment.

Patterns-of-Care Analysis for Radiotherapy of Elderly Head-and-Neck Cancer Patients: A Trinational Survey in Germany, Austria and Switzerland

OBJECTIVES

The number of elderly head-and-neck squamous cell carcinoma (HNSCC) patients is increasing, and clinical trials defining the standard of care either excluded or underrepresented elderly patients. This leaves physicians with a challenging and highly individual treatment decision largely lacking clinical evidence.

METHODS

A tri-national patterns-of-care survey was sent to all members of the German (DEGRO), Austrian (ÖGRO), and Swiss (SRO/SSRO) national societies of radiation oncology. The online questionnaire consisted of 27 questions on the treatment of elderly HNSCC patients, including 6 case-based questions. Frequency distributions and subgroup comparisons were calculated using SPSS statistics software.

RESULTS

A total of 132 answers were collected, including 46(35%) form universities, 52(39%) from non-university-hospitals and 34(26%) from private practices. 83(63%) treat 1-5 and 42(32%) >5 elderly HNSCC patients per month. Target volumes are defined analog current guidelines by 65(50%) of responders and altered based on age/comorbidities or tumor stage by 36(28%) and 28(22%), respectively. Chemotherapy is routinely administered by 108(84%) if indicated, with weekly 40mg/m² of cisplatin being the favored regimen by 68(53%) in the definitive situation and 60(47%) in the adjuvant setting. Hypofractionation and hyperfractionation/acceleration are used by 26(20%) and 11(9%), respectively. Only 7(5%) clinicians routinely recommend inpatient treatment for elderly HNSCC patients. In a typical definitive patient case, 73(63%) responders recommended chemoradiation with bilateral elective node irradiation analog current guidelines. In an adjuvant example case recommendations regarding elective volume and chemotherapy were heterogeneous. Differences between responders' institutions concern the frequency of PET-CT in staging, preventive port-catheter and PEG implantation, the choice of chemotherapy regimens and the use of alternative fractionations.

CONCLUSION

Treatment of elderly HNSCC-patients in the German-speaking countries mainly follows guidelines established for younger patients. Algorithms for patient stratification and treatment de-escalation for "unfit" elderly patients are needed.