Estimation Of Biological Age Research Articles

DNA Methylation-derived biological age and long-term mortality risk in subjects with type 2 diabetes

BackgroundIndividuals with type 2 diabetes (T2D) face an increased mortality risk, not fully captured by canonical risk factors. Biological age estimation through DNA methylation (DNAm), i.e. the epigenetic clocks, is emerging as a possible tool to improve risk stratification for multiple outcomes. However, whether these tools predict mortality independently of canonical risk factors in subjects with T2D is unknown.MethodsAmong a cohort of 568 T2D patients followed for 16.8 years, we selected a subgroup of 50 subjects, 27 survived and 23 deceased at present, passing the quality check and balanced for all risk factors after propensity score matching. We analyzed DNAm from peripheral blood leukocytes using the Infinium Human MethylationEPIC BeadChip (Illumina) to evaluate biological aging through previously validated epigenetic clocks and assess the DNAm-estimated levels of selected inflammatory proteins and blood cell counts. We tested the associations of these estimates with mortality using two-stage residual-outcome regression analysis, creating a reference model on data from the group of survived patients. ResultsDeceased subjects had higher median epigenetic age expressed with DNAmPhenoAge algorithm (57.49 [54.72; 60.58] years. vs. 53.40 [49.73; 56.75] years; p = 0.012), and accelerated DunedinPoAm pace of aging (1.05 [1.02; 1.11] vs. 1.02 [0.98; 1.06]; p = 0.012). DNAm PhenoAge (HR 1.16, 95% CI 1.05–1.28; p = 0.004) and DunedinPoAm (HR 3.65, 95% CI 1.43–9.35; p = 0.007) showed an association with mortality independently of canonical risk factors. The epigenetic predictors of 3 chronic inflammation-related proteins, i.e. CXCL10, CXCL11 and enRAGE, C-reactive protein methylation risk score and DNAm-based estimates of exhausted CD8 + T cell counts were higher in deceased subjects when compared to survived.ConclusionsThese findings suggest that biological aging, as estimated through existing epigenetic tools, is associated with mortality risk in individuals with T2D, independently of common risk factors and that increased DNAm-surrogates of inflammatory protein levels characterize deceased T2D patients. Replication in larger cohorts is needed to assess the potential of this approach to refine mortality risk in T2D.

Towards Healthy Longevity: Comprehensive Insights from Molecular Targets and Biomarkers to Biological Clocks.

Aging is a complex and time-dependent decline in physiological function that affects most organisms, leading to increased risk of age-related diseases. Investigating the molecular underpinnings of aging is crucial to identify geroprotectors, precisely quantify biological age, and propose healthy longevity approaches. This review explores pathways that are currently being investigated as intervention targets and aging biomarkers spanning molecular, cellular, and systemic dimensions. Interventions that target these hallmarks may ameliorate the aging process, with some progressing to clinical trials. Biomarkers of these hallmarks are used to estimate biological aging and risk of aging-associated disease. Utilizing aging biomarkers, biological aging clocks can be constructed that predict a state of abnormal aging, age-related diseases, and increased mortality. Biological age estimation can therefore provide the basis for a fine-grained risk stratification by predicting all-cause mortality well ahead of the onset of specific diseases, thus offering a window for intervention. Yet, despite technological advancements, challenges persist due to individual variability and the dynamic nature of these biomarkers. Addressing this requires longitudinal studies for robust biomarker identification. Overall, utilizing the hallmarks of aging to discover new drug targets and develop new biomarkers opens new frontiers in medicine. Prospects involve multi-omics integration, machine learning, and personalized approaches for targeted interventions, promising a healthier aging population.

Evaluation of DNAmAge in paired fresh, frozen, and formalin-fixed paraffin-embedded heart tissues.

The continued development in methylome analysis has enabled a more precise assessment of DNA methylation, but treatment of target tissue prior to analysis may affect DNA analysis. Prediction of age based on methylation levels in the genome (DNAmAge) has gained much interest in disease predisposition (biological age estimation), but also in chronological donor age estimation in crime case samples. Various epigenetic clocks were designed to predict the age. However, it remains unknown how the storage of the tissues affects the DNAmAge estimation. In this study, we investigated the storage method impact of DNAmAge by the comparing the DNAmAge of the two commonly used storage methods, freezing and formalin-fixation and paraffin-embedding (FFPE) to DNAmAge of fresh tissue. This was carried out by comparing paired heart tissue samples of fresh tissue, samples stored by freezing and FFPE to chronological age and whole blood samples from the same individuals. Illumina EPIC beadchip array was used for methylation analysis and the DNAmAge was evaluated with the following epigenetic clocks: Horvath, Hannum, Levine, Horvath skin+blood clock (Horvath2), PedBE, Wu, BLUP, EN, and TL. We observed differences in DNAmAge among the storage conditions. FFPE samples showed a lower DNAmAge compared to that of frozen and fresh samples. Additionally, the DNAmAge of the heart tissue was lower than that of the whole blood and the chronological age. This highlights caution when evaluating DNAmAge for FFPE samples as the results were underestimated compared with fresh and frozen tissue samples. Furthermore, the study also emphasizes the need for a DNAmAge model based on heart tissue samples for an accurate age estimation.

Artificial Intelligence Assessment of Biological Age From Transthoracic Echocardiography: Discrepancies with Chronologic Age Predict Significant Excess Mortality

Age and sex can be estimated using artificial intelligence on the basis of various sources. The aims of this study were to test whether convolutional neural networks could be trained to estimate age and predict sex using standard transthoracic echocardiography and to evaluate the prognostic implications. The algorithm was trained on 76,342 patients, validated in 22,825 patients, and tested in 20,960 patients. It was then externally validated using data from a different hospital (n=556). Finally, a prospective cohort of handheld point-of-care ultrasound devices (n=319; ClinicalTrials.gov identifier NCT05455541) was used to confirm the findings. A multivariate Cox regression model was used to investigate the association between age estimation and chronologic age with overall survival. The mean absolute error in age estimation was 4.9years, with a Pearson correlation coefficient of 0.922. The probabilistic value of sex had an overall accuracy of 96.1% and an area under the curve of 0.993. External validation and prospective study cohorts yielded consistent results. Finally, survival analysis demonstrated that age prediction ≥5years vs chronologic age was associated with an independent 34% increased risk for death during follow-up (P<.001). Applying artificial intelligence to standard transthoracic echocardiography allows the prediction of sex and the estimation of age. Machine-based estimation is an independent predictor of overall survival and, with further evaluation, can be used for risk stratification and estimation of biological age.

Accurate estimation of biological age and its application in disease prediction using a multimodal image Transformer system.

Aging in an individual refers to the temporal change, mostly decline, in the body's ability to meet physiological demands. Biological age (BA) is a biomarker of chronological aging and can be used to stratify populations to predict certain age-related chronic diseases. BA can be predicted from biomedical features such as brain MRI, retinal, or facial images, but the inherent heterogeneity in the aging process limits the usefulness of BA predicted from individual body systems. In this paper, we developed a multimodal Transformer-based architecture with cross-attention which was able to combine facial, tongue, and retinal images to estimate BA. We trained our model using facial, tongue, and retinal images from 11,223 healthy subjects and demonstrated that using a fusion of the three image modalities achieved the most accurate BA predictions. We validated our approach on a test population of 2,840 individuals with six chronic diseases and obtained significant difference between chronological age and BA (AgeDiff) than that of healthy subjects. We showed that AgeDiff has the potential to be utilized as a standalone biomarker or conjunctively alongside other known factors for risk stratification and progression prediction of chronic diseases. Our results therefore highlight the feasibility of using multimodal images to estimate and interrogate the aging process.

REPRODUCIBILITY OF BRAIN AGE SALIENCIES ACROSS DEEP NEURAL NETWORK ARCHITECTURES

Abstract The brain’s biological age (BA) reflects structural changes related to neuroanatomic senescence. BA is estimated from magnetic resonance images (MRIs) using convolutional neural networks (CNNs), which provide interpretability through saliency maps. By conveying the importance of brain regions to BA estimation, these maps reveal sex differences in brain-aging. We compared two CNN architectures to assess the reproducibility of such differences. Both regression CNNs had T1-weighted MRIs as inputs; outputs were estimated BAs. One CNN was trained on males and females separately, whereas the other was trained on both sexes. The dataset used consisted of T1-weighted MRIs from 5,851 cognitively normal individuals (3,142 females) aged 22 to 95 years, sampled from the Alzheimer’s Disease Neuroimaging Initiative (N = 510), Human Connectome Project Aging (N = 508) and Young Adult (N = 1,112), and UK Biobank (N = 3,721). For both models, compared to the average saliency for a null distribution, males’ BA estimation relied significantly (p &amp;lt; 0.05) more on the right lateral temporal lobe and superior frontal gyrus. Females’ BA estimation depended significantly (p &amp;lt;; 0.05) more on the right posterior and bilateral occipital regions, and medial parietal lobe. Simulated alterations in brain morphometry indicated saliencies correctly revealed regions with aging-related dynamics, which confirm prior findings on sex differences in brain-aging. Variations in CNN models’ saliencies did not affect overall anatomic patterns, suggesting that CNNs can capture brain-aging patterns robustly despite architecture differences. Novel statistical models for formal comparison of CNN saliencies should be developed to accommodate their nonlinear nature.

THE LINK BETWEEN VIOLENCE EXPOSURE AND BIOLOGICAL AGING IN MID-LIFE MUSLIMS IN ISRAEL

Abstract Trauma and accumulated chronic stress throughout life are associated with negative health outcomes; more specifically, exposure to violence has been linked to an accelerated aging state. In Israel’s Arab sector, rates of violence have been steadily rising for the past several years. Homicide rates, relative to population size, are seven times higher among Arabs. We aimed to determine the effects of exposure to violence on estimation of biological age as a measure of rate of aging within the ethnic minority of Muslims in Israel. We have conducted a cross-sectional study that included 375 participants age 50.5±6.9 years, of which 61% women. Biological age was estimated using the Klemera-Doubal method and physiological biomarkers; exposure to violence was measured using the Screen for Adolescent Violence Exposure questionnaire followed by a principal component analysis. The average total score of violence exposure was 1.83±0.36 (range 1-5, 1 corresponds with lower exposure). We found a positive and significant relationship among biological age, the “direct evidence of violence” component, and the “evidence of severe violence in the immediate environment” component (r = 0.119, p&amp;lt;0.05; r = 0.111, p&amp;lt;0.05, respectively). These results demonstrate that exposure to violence negatively affects health and accelerates aging within Israel’s Muslim minority. Comprehensive, culturally- and gender-adapted programs are required to reduce violence and address its contribution to the negative aging trajectory in this population.

Echocardiographic age and sex estimation using artificial intelligence predicts overall survival

Abstract Background Transthoracic echocardiography (TTE) is increasingly used by point-of-care ultrasound (POCUS) devices. Age and sex can be estimated by artificial intelligence (AI) based on various sources. The current study aimed to test whether deep learning (DL) networks could be trained to predict a patient's age and sex using a TTE. We hypothesized that DL-predicted age would be superior to chronological age in predicting long-term survival. Methods The analysis was based on 122,014 unique patients who underwent TTE evaluation between 2007 and 2021 at large tertiary medical center. Survival data were available for all subjects from the National Population Register. Three mutually exclusive cohorts were used for training (N=76,342[62%]), validation (N=22,825[19%]) and testing (N=22,847[19%]). A fourth cohort (N=650), from a different hospital, was used for external validation. The testing cohort was used to evaluate the accuracy in estimating age and sex. We divided the patients into two mutually exclusive groups; (1) the patients whose machine-estimated age was ≥ 5 years of their chronological age (20% of the cohort), (2) all other patients. A multivariate Cox regression model was used to investigate the association between age estimation and chronological age with overall survival. Results The final test cohort included 18,447 unique patients, of whom 10,640 (58%) were men with a median age of 66 (IQR 54-76). The machine learning model was able to estimate age with an average error of 4.9 years, a Root Mean Square Error (RMSE) of 6.3 and a Pearson correlation coefficient (r) of 0.922. Sex was estimated as a probabilistic value with total accuracy of 96.1% and an area under the curve (AUC) of 0.993. Consistent results were obtained with external validation from a different hospital (RMSE = 7.04, (r)=0.897 and an AUC of 0.988). During a median follow-up of 4.3 (IQR, 2.1-6.3) years, 5147 (27.9%) patients died. A multivariate Cox regression model with adjustment for age, sex, and ejection fraction demonstrated that age prediction ≥ 5 years of chronological age was associated with an independent and significant 48% increased risk of death during follow-up (95% CI 1.38-1.59, p&amp;lt;0.001). When machine’s age prediction was used as a continuous variable in the same model, each year above the chronological age was associated with an independent and significant 8% increased risk of death (95% CI 1.05-1.10, p&amp;lt;0.001). Similarly, a higher probability of male sex among female subjects was also associated with a significant 63% increased risk of death in the same model (95% CI 1.38-1.92, p&amp;lt;0.001). Conclusions Applying AI to the standard TTE allows the prediction of a patient's sex and estimation of age. Machine-based estimation is an independent predictor of overall survival and, with further evaluation, can be used for risk stratification and estimation of biological age at the point of care.Kaplan-Meier survival curve by delta-ageKaplan-Meier survival curve by sex

Epigenetics insights from perceived facial aging

Facial aging is the most visible manifestation of aging. People desire to look younger than others of the same chronological age. Hence, perceived age is often used as a visible marker of aging, while biological age, often estimated by methylation markers, is used as an objective measure of age. Multiple epigenetics-based clocks have been developed for accurate estimation of general biological age and the age of specific organs, including the skin. However, it is not clear whether the epigenetic biomarkers (CpGs) used in these clocks are drivers of aging processes or consequences of aging. In this proof-of-concept study, we integrate data from GWAS on perceived facial aging and EWAS on CpGs measured in blood. By running EW Mendelian randomization, we identify hundreds of putative CpGs that are potentially causal to perceived facial aging with similar numbers of damaging markers that causally drive or accelerate facial aging and protective methylation markers that causally slow down or protect from aging. We further demonstrate that while candidate causal CpGs have little overlap with known epigenetics-based clocks, they affect genes or proteins with known functions in skin aging, such as skin pigmentation, elastin, and collagen levels. Overall, our results suggest that blood methylation markers reflect facial aging processes, and thus can be used to quantify skin aging and develop anti-aging solutions that target the root causes of aging.

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.

An uncertainty-aware and sex-prior guided biological age estimation from orthopantomogram images.

Bone age, as a measure of biological age (BA), plays an important role in a variety of fields, including forensics, orthodontics, sports, and immigration. Despite its significance, accurate estimation of BA remains a challenge due to the uncertainty error between BA and chronological age (CA) caused by individual diversity and the difficult integration of multiple factors, such as sex, and identified or measured anatomical structures, into the estimation process. To address problems, we propose an uncertainty-aware and sex-prior guided biological age estimation from orthopantomogram images (OPGs), named UASP-BAE, which models uncertainty errors while setting sex dimorphism as tractive features to enhance age-related specific features, aiming to improve the accuracy of BA estimation. Furthermore, considering the global relevance of the anatomic structure, such as the mandible, teeth, maxillary sinus, etc., a cross-attention module based on CNN and self-attention is proposed to mine the local texture and global semantic features of OPGs. Moreover, we design a novel age composition loss by cross-entropy, probability bias, and regression functions, aiming at evaluating BA's uncertainty errors and results to obtain an accurate and robust model. On 10703 OPGs from 5.00 to 25.00 years of age, our model had a best MAE value of 0.8005 years and higher than the comparison popular algorithms, which also demonstrates the method's potential for improved accuracy in BA estimation.

Estimated Age Calculation with Tooth Coronal Index (TCI) Method Using Orthopanthomography in Malang City Population

Age estimation is an important aspect in forensics because it plays a role in determining individual identity. In cases of forensic identification of bodies that are difficult to identify visually or in poor condition, radiography is the choice of non-invasive method that can be used for age estimation, one of which is panoramic radiography. The Tooth Coronal Index (TCI) method is one method that can be used to identify a person's biological age through panoramic radiography. The purpose of this study was to compare the chronological age with the biological age obtained from the measurement of the TCI method on the mandibular premolars using panoramic radiography in the Malang City population. TCI measurement was done by measuring the height of the pulp chamber (Coronal Height) and the height of the crown of the tooth (Coronal Pulp Cavity Height) vertically from the Cemento Enamel Junction (CEJ) on the mandibular premolars. Then the value is entered in the regression equation that has been determined to obtain an estimate of biological age. Then, the results of the estimated biological age are compared with their chronological age. At last, the difference between biological and chronological age is calculated to determine the approximate age. The mean difference between chronological age and biological age was ± 1.32 years and there is no significant difference between the biological age and the chronological age (p value 0.11). TCI method on the mandibular premolars using panoramic radiography can be used to estimate age in Malang City Population.

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.

Image-Based Biological Heart Age Estimation Reveals Differential Aging Patterns Across Cardiac Chambers.

Biological heart age estimation can provide insights into cardiac aging. However, existing studies do not consider differential aging across cardiac regions. To estimate biological age of the left ventricle (LV), right ventricle (RV), myocardium, left atrium, and right atrium using magnetic resonance imaging radiomics phenotypes and to investigate determinants of aging by cardiac region. Cross-sectional. A total of 18,117 healthy UK Biobank participants including 8338 men (mean age=64.2± 7.5) and 9779 women (mean age=63.0 ± 7.4). A 1.5T/balanced steady-state free precession. An automated algorithm was used to segment the five cardiac regions, from which radiomic features were extracted. Bayesian ridge regression was used to estimate biological age of each cardiac region with radiomics features as predictors and chronological age as the output. The "age gap" was the difference between biological and chronological age. Linear regression was used to calculate associations of age gap from each cardiac region with socioeconomic, lifestyle, body composition, blood pressure and arterial stiffness, blood biomarkers, mental well-being, multiorgan health, and sex hormone exposures (n= 49). Multiple testing correction with false discovery method (threshold=5%). The largest model error was with RV and the smallest with LV age (mean absolute error in men: 5.26 vs. 4.96 years). There were 172 statistically significant age gap associations. Greater visceral adiposity was the strongest correlate of larger age gaps, for example, myocardial age gap in women (Beta=0.85, P= 1.69 × 10-26 ). Poor mental health associated with large age gaps, for example, "disinterested" episodes and myocardial age gap in men (Beta=0.25, P= 0.001), as did a history of dental problems (eg LV in men Beta=0.19, P= 0.02). Higher bone mineral density was the strongest associate of smaller age gaps, for example, myocardial age gap in men (Beta=-1.52, P= 7.44 × 10-6 ). This work demonstrates image-based heart age estimation as a novel method for understanding cardiac aging. 1. Stage 1.

Systematic estimation of biological age of in vitro cell culture systems by an age-associated marker panel.

Aging is a process that affects almost all multicellular organisms and since our population ages with increasing prevalence of age-related diseases, it is important to study basic processes involved in aging. Many studies have been published so far using different and often single age markers to estimate the biological age of organisms or different cell culture systems. However, comparability of studies is often hampered by the lack of a uniform panel of age markers. Consequently, we here suggest an easy-to-use biomarker-based panel of classical age markers to estimate the biological age of cell culture systems that can be used in standard cell culture laboratories. This panel is shown to be sensitive in a variety of aging conditions. We used primary human skin fibroblasts of different donor ages and additionally induced either replicative senescence or artificial aging by progerin overexpression. Using this panel, highest biological age was found for artificial aging by progerin overexpression. Our data display that aging varies depending on cell line and aging model and even from individual to individual showing the need for comprehensive analyses.

Conceptual Overview of Biological Age Estimation.

Chronological age is an imperfect measure of the aging process, which is affected by a wide range of genetic and environmental exposures. Biological age estimates may be derived using mathematical modelling with biomarkers set as predictors and chronological age as the output. The difference between biological and chronological age is denoted the "age gap" and considered a complementary indicator of aging. The utility of the "age gap" metric is assessed through examination of its associations with exposures of interest and the demonstration of additional information provided by this metric over chronological age alone. This paper reviews the key concepts of biological age estimation, the age gap metric, and approaches to assessment of model performance in this context. We further discuss specific challenges for the field, in particular the limited generalisability of effect sizes across studies owing to dependency of the age gap metric on pre-processing and model building methods. The discussion will be centred on brain age estimation, but the concepts are transferable to all biological age estimation.

Estimating the Biological Age and the Magnitude of Lifestyle Determinants of Ageing among Nigerian Adults

Considering the various limitations of using chronological age, biological age estimation is becoming increasingly recognized as one of the novel public health and clinical strategies for preventing and controlling the rising global prevalence of noncommunicable diseases (NCDs) and for achieving healthy ageing. The objectives of this study are to estimate the biological age and compare it to the chronological age of Nigerian adults. Also, to score the magnitude of some of the lifestyle determinants of biological age among the study population. This cross-sectional study uses simple random sampling technique to select 82 Nigerian adults for the study, while standardized instruments were used to collect data. The P value for the study was set at 0.05 level of significance. The result of the study noticed poor mean Mediterranean Diet Adherence (MDAQ) score of 7.0 ± 2.28 and mean International Physical Activity (IPAQ) score of 1.3 ± 0.51. There was also suboptimal mean Pittsburgh Sleep Quality Index (PSQI) score of 5.9 ± 3.01, mean Perceive Stress Scale-4 (PSS-4) score of 6.3 ± 2.79, and mean Social Connectedness Scale (SCS) score of 15.2 ± 4.13. Furthermore, the estimated biological age of the respondents (45.9 years, ±10.31), was higher than their chronological age (43.2 years, ±8.92). The study concluded that the magnitudes of the lifestyle determinants of ageing are high enough to result in accelerated biological ageing among the study population. Such development, if not mitigated, may result in a significant increase in the prevalence of NCDs and premature deaths in the near future. Keywords: Accelerated Ageing, Biological Age, Chronological Age, Lifestyle determinants of ageing, Health Promotion Intervention.

AnthropoAge, a novel approach to integrate body composition into the estimation of biological age.

Aging is believed to occur across multiple domains, one of which is body composition; however, attempts to integrate it into biological age (BA) have been limited. Here, we consider the sex-dependent role of anthropometry for the prediction of 10-year all-cause mortality using data from 18,794 NHANES participants to generate and validate a new BA metric. Our data-driven approach pointed to sex-specific contributors for BA estimation: WHtR, arm and thigh circumferences for men; weight, WHtR, thigh circumference, subscapular and triceps skinfolds for women. We used these measurements to generate AnthropoAge, which predicted all-cause mortality (AUROC 0.876, 95%CI 0.864-0.887) and cause-specific mortality independently of ethnicity, sex, and comorbidities; AnthropoAge was a better predictor than PhenoAge for cerebrovascular, Alzheimer, and COPD mortality. A metric of age acceleration was also derived and used to assess sexual dimorphisms linked to accelerated aging, where women had an increase in overall body mass plus an important subcutaneous to visceral fat redistribution, and men displayed a marked decrease in fat and muscle mass. Finally, we showed that consideration of multiple BA metrics may identify unique aging trajectories with increased mortality (HR for multidomain acceleration 2.43, 95%CI 2.25-2.62) and comorbidity profiles. A simplified version of AnthropoAge (S-AnthropoAge) was generated using only BMI and WHtR, all results were preserved using this metric. In conclusion, AnthropoAge is a useful proxy of BA that captures cause-specific mortality and sex dimorphisms in body composition, and it could be used for future multidomain assessments of aging to better characterize the heterogeneity of this phenomenon.

Utility of the Age Discrepancy between Frailty-Based Biological Age and Expected Life Age in Patients with Urological Cancers.

Background: The estimation of biological age is challenging in patients with cancers. We aimed to investigate frailty-based biological ages using frailty-discriminant scores (FDS) and examined the effect of biological-expected life age discrepancy on the prognosis of patients with urological cancers. Methods: We retrospectively evaluated frailty in 1035 patients having urological cancers. Their frailty-based biological age was then defined by the FDS, which is a comprehensive frailty assessment tool, using 1790 noncancer individuals as controls. An expected life age (=chronological age + life expectancy) was subsequently calculated using the 2019 life expectancy table. The primary outcome was the estimation of the biological-expected life age discrepancy between the frailty-based biological age and expected life age in patients with urological cancers. Secondary outcomes were the evaluation of the effect of the biological-expected life age discrepancy on overall survival. Results: We included 405, 466, and 164 patients diagnosed with prostate cancer, urothelial carcinoma, and renal cell carcinoma, respectively. The median chronological age, life expectancy, and estimated frailty-based biological age were 71, 17, and 83 years, respectively. The biological-expected life age discrepancy in any urological cancers, localized diseases, and metastatic diseases was -4.8, -6.3, and +0.15 years, respectively. The biological-expected life age discrepancy of &gt;5 years was significantly associated with poor overall survival. Conclusions: The biological-expected life age discrepancy between frailty-based biological age and expected life age may be helpful in understanding the role of frailty and patient/doctor conversation.

Multimodal brain age prediction fusing morphometric and imaging data and association with cardiovascular risk factors.

The difference between the chronological and biological brain age, called the brain age gap (BAG), has been identified as a promising biomarker to detect deviation from normal brain aging and to indicate the presence of neurodegenerative diseases. Moreover, the BAG has been shown to encode biological information about general health, which can be measured through cardiovascular risk factors. Current approaches for biological brain age estimation, and therefore BAG estimation, either depend on hand-crafted, morphological measurements extracted from brain magnetic resonance imaging (MRI) or on direct analysis of brain MRI images. The former can be processed with traditional machine learning models while the latter is commonly processed with convolutional neural networks (CNNs). Using a multimodal setting, this study aims to compare both approaches in terms of biological brain age prediction accuracy and biological information captured in the BAG. T1-weighted MRI, containing brain tissue information, and magnetic resonance angiography (MRA), providing information about brain arteries, from 1,658 predominantly healthy adults were used. The volumes, surface areas, and cortical thickness of brain structures were extracted from the T1-weighted MRI data, while artery density and thickness within the major blood flow territories and thickness of the major arteries were extracted from MRA data. Independent multilayer perceptron and CNN models were trained to estimate the brain age from the hand-crafted features and image data, respectively. Next, both approaches were fused to assess the benefits of combining image data and hand-crafted features for brain age prediction. The combined model achieved a mean absolute error of 4 years between the chronological and predicted biological brain age. Among the independent models, the lowest mean absolute error was observed for the CNN using T1-weighted MRI data (4.2 years). When evaluating the BAGs obtained using the different approaches and imaging modalities, diverging associations between cardiovascular risk factors were found. For example, BAGs obtained from the CNN models showed an association with systolic blood pressure, while BAGs obtained from hand-crafted measurements showed greater associations with obesity markers. In conclusion, the use of more diverse sources of data can improve brain age estimation modeling and capture more diverse biological deviations from normal aging.