The risks of biomarker-based epidemiology: Associations of circulating calcium levels with age, mortality, and frailty vary substantially across populations

Biological Aging and Venous Thromboembolism: A Review of Telomeres and Beyond

Although venous thromboembolism (VTE) is the third most common cardiovascular disease, and the risk of VTE increases sharply with advancing age, approximately 40% of VTE cases are currently classified as unprovoked, highlighting the importance of risk factor research. While chronological aging is associated with the risk of VTE, the association with biological aging remains unclear. Biological aging is highly complex, influenced by several dysregulated cellular and biochemical mechanisms. In the last decade, advancements in omics methodologies provided insights into the molecular complexity of biological aging. Techniques such as high-throughput genomics, epigenomics, transcriptomics, proteomics, and metabolomics analyses identified and quantified numerous epigenetic markers, transcripts, proteins, and metabolites. These methods have also revealed the molecular alterations organisms undergo as they age. Despite the progress, there is still a lack of consensus regarding the methods for assessing and validating these biomarkers, and their application lacks standardization. This review gives an overview of biomarkers of biological aging, including telomere length, and their potential role for VTE. Furthermore, we critically examine the advantages and disadvantages of the proposed methods and discuss possible future directions for investigating biological aging in VTE.

Moving towards the detection of frailty with biomarkers: A population health study

Aging adults experience increased health vulnerability and compromised abilities to cope with stressors, which are the clinical manifestations of frailty. Frailty is complex, and efforts to identify biomarkers to detect frailty and pre-frailty in the clinical setting are rarely reproduced across cohorts. We developed a predictive model incorporating biological and clinical frailty measures to identify robust biomarkers across data sets. Data were from two large cohorts of older adults: "Invecchiare in Chianti (Aging in Chianti, InCHIANTI Study") (n = 1453) from two small towns in Tuscany, Italy, and replicated in the Atherosclerosis Risk in Communities Study (ARIC) (n = 6508) from four U.S. communities. A complex systems approach to biomarker selection with a tree-boosting machine learning (ML) technique for supervised learning analysis was used to examine biomarker population differences across both datasets. Our approach compared predictors with robust, pre-frail, and frail participants and examined the ability to detect frailty status by race. Unique biomarker features identified in the InCHIANTI study allowed us to predict frailty with a model accuracy of 0.72 (95% confidence interval (CI) 0.66-0.80). Replication models in ARIC maintained a model accuracy of 0.64 (95% CI 0.66-0.72). Frail and pre-frail Black participant models maintained a lower model accuracy. The predictive panel of biomarkers identified in this study may improve the ability to detect frailty as a complex aging syndrome in the clinical setting. We propose several concrete next steps to keep research moving toward detecting frailty with biomarker-based detection methods.

Biomarkers of aging for the identification and evaluation of longevity interventions

With the rapid expansion of aging biology research, the identification and evaluation of longevity interventions in humans have become key goals of this field. Biomarkers of aging are critically important tools in achieving these objectives over realistic time frames. However, the current lack of standards and consensus on the properties of a reliable aging biomarker hinders their further development and validation for clinical applications. Here, we advance a framework for the terminology and characterization of biomarkers of aging, including classification and potential clinical use cases. We discuss validation steps and highlight ongoing challenges as potential areas in need of future research. This framework sets the stage for the development of valid biomarkers of aging and their ultimate utilization in clinical trials and practice.

Dietary profiling of physical frailty in older age phenotypes using a machine learning approach: the Salus in Apulia Study

PURPOSE

Growing awareness of the biological and clinical value of nutrition in frailty settings calls for further efforts to investigate dietary gaps to act sooner to achieve focused management of aging populations. We cross-sectionally examined the eating habits of an older Mediterranean population to profile dietary features most associated with physical frailty.

METHODS

Clinical and physical examination, routine biomarkers, medical history, and anthropometry were analyzed in 1502 older adults (65 +). CHS criteria were applied to classify physical frailty, and a validated Food Frequency Questionnaire to assess diet. The population was subdivided by physical frailty status (frail or non-frail). Raw and adjusted logistic regression models were applied to three clusters of dietary variables (food groups, macronutrients, and micronutrients), previously selected by a LASSO approach to better predict diet-related frailty determinants.

RESULTS

A lower consumption of wine (OR 0.998, 95% CI 0.997-0.999) and coffee (OR 0.994, 95% CI 0.989-0.999), as well as a cluster of macro and micronutrients led by PUFAs (OR 0.939, 95% CI 0.896-0.991), zinc (OR 0.977, 95% CI 0.952-0.998), and coumarins (OR 0.631, 95% CI 0.431-0.971), was predictive of non-frailty, but higher legumes intake (OR 1.005, 95%CI 1.000-1.009) of physical frailty, regardless of age, gender, and education level.

CONCLUSIONS

Higher consumption of coffee and wine, as well as PUFAs, zinc, and coumarins, as opposed to legumes, may work well in protecting against a physical frailty profile of aging in a Mediterranean setting. Longitudinal investigations are needed to better understand the causal potential of diet as a modifiable contributor to frailty during aging.

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.

Plasma Growth and Differentiation Factor 15 Predict Longitudinal Changes in Bone Parameters in Women, but Not in Men

Bone fragility can progress with aging, but biomarkers to detect emerging osteopenia have not been fully elucidated. Growth/differentiation factor 15 (GDF-15) has pleiotropic roles in a broad range of age-related conditions, but its association with osteopenia is unknown. We examined the relationship between plasma GDF-15 levels and rate of change in bone parameters over 9 years of follow-up in 596 adults in the InCHIANTI study (baseline age, 65-94 years; women, 52.4%; mean follow-up, 7.0 ± 3.0 years). Plasma GDF-15 concentrations were measured using the 1.3k HTS SOMAscan assay. Eight bone parameters were measured in the right tibia by peripheral quantitative computed tomography; total bone density, trabecular bone density, medullary plus trabecular bone density, cortical bone density, total bone area, cortical bone area, medullary bone area, and minimum moment of inertia (mMOI). We ran sex-specific linear mixed-effect models with random intercepts and slopes adjusted for age, age-squared, education, body mass index, the rate of change in weight, smoking, sedentary behavior, cross-sectional areas of calf muscles and fat, 25-hydroxyvitamin D, parathyroid hormone, calcium, diabetes mellitus, and follow-up time. We found a significant association of "baseline GDF-15 × time" in models predicting cortical bone density and the mMOI in women, suggesting that the rates of decline in these bone parameters increased with higher GDF-15 (false discovery rate <0.05). Higher plasma levels GDF-15 predicted an accelerated decline in bone parameters in women, but was less associated in men. Furthermore studies are needed to understand the mechanisms underlying these sex differences.

Multidimensional associations between nutrient intake and healthy ageing in humans

BACKGROUND

Little is known about how normal variation in dietary patterns in humans affects the ageing process. To date, most analyses of the problem have used a unidimensional paradigm, being concerned with the effects of a single nutrient on a single outcome. Perhaps then, our ability to understand the problem has been complicated by the fact that both nutrition and the physiology of ageing are highly complex and multidimensional, involving a high number of functional interactions. Here we apply the multidimensional geometric framework for nutrition to data on biological ageing from 1560 older adults followed over four years to assess on a large-scale how nutrient intake associates with the ageing process.

RESULTS

Ageing and age-related loss of homeostasis (physiological dysregulation) were quantified via the integration of blood biomarkers. The effects of diet were modelled using the geometric framework for nutrition, applied to macronutrients and 19 micronutrients/nutrient subclasses. We observed four broad patterns: (1) The optimal level of nutrient intake was dependent on the ageing metric used. Elevated protein intake improved/depressed some ageing parameters, whereas elevated carbohydrate levels improved/depressed others; (2) There were non-linearities where intermediate levels of nutrients performed well for many outcomes (i.e. arguing against a simple more/less is better perspective); (3) There is broad tolerance for nutrient intake patterns that don't deviate too much from norms ('homeostatic plateaus'). (4) Optimal levels of one nutrient often depend on levels of another (e.g. vitamin E and vitamin C). Simpler linear/univariate analytical approaches are insufficient to capture such associations. We present an interactive tool to explore the results in the high-dimensional nutritional space.

CONCLUSION

Using multidimensional modelling techniques to test the effects of nutrient intake on physiological dysregulation in an aged population, we identified key patterns of specific nutrients associated with minimal biological ageing. Our approach presents a roadmap for future studies to explore the full complexity of the nutrition-ageing landscape.

Improving the Identification of Frailty in Long-Term Care Residents: A Cross-Sectional Study

PURPOSE

To compare the capacity of blood myostatin concentration and physical, cognitive, and affective function tests to predict frailty among long-term care (LTC) residents.

METHODS

This cross-sectional analysis used baseline data from three randomized controlled trials involving 260 older adults in 14 LTC centers. Serum myostatin levels were analyzed by enzyme-linked immunosorbent assay. Frailty, physical fitness, cognitive and affective functions were assessed using validated tests and scales.

RESULTS

The Timed Up and Go, gait speed, 6-minute walk, and Berg Balance Scale had excellent capabilities in identifying frail individuals in accordance with Fried's Frailty Phenotype (FFP). The best tests for identifying frailty in accordance with the Clinical Frailty Scale (CFS) were Timed Up and Go and Berg Balance Scale. For the Tilburg Frailty Indicator (TFI), the best tests were Quality of Life in Alzheimer's Disease (QoL-AD) and Goldberg Anxiety. Myostatin, along with physical, cognitive, and affective function tests, improved the capability of the hand grip, arm-curl, Montreal Cognitive Assessment, Goldberg Anxiety, Goldberg Depression, and QoL-AD to identify frailty according to FFP, while myostatin improved CFS-defined frailty identification by the hand grip, arm-curl, 6-minute walk test, Berg Balance Scale, 30-second chair-stand, gait speed, Montreal Cognitive Assessment, Goldberg Anxiety, and De Jong-Gierveld Loneliness Scale.

CONCLUSION

Among LTC residents, serum myostatin was associated with being frail according to FFP and CFS. However, this measure was less discriminating of frailty than physical fitness tests (for FFP and CFS) and affective function parameters (for TFI). However, evaluated concurrently with physical, cognitive, and affective parameters, myostatin improved the capabilities of these measures to predict CFS-defined frailty.

Associations of Serum Albumin With Disability in Activities of Daily Living, Mobility and Objective Physical Functioning Regardless of Vitamin D: Cross-Sectional Findings From the Chinese Longitudinal Healthy Longevity Survey

Objective

To examine the associations of serum albumin, a nutrition indicator, with disability in activities of daily living (ADL), mobility, and objective physical functioning among Chinese older adults.

Materials and Methods

Cross-sectional data of 2233 older adults (≥65 years) who participated in the 2011/2012 main survey of the Chinese Longitudinal Healthy Longevity Survey (CLHLS) and the 2012 biomarker sub-study was used. Serum albumin was measured by immunoturbidimetric assay. Physical functioning included subjectively (ADL and mobility) and objectively measured disability (standing up from a chair, picking up a book from the floor, and turning around 360°). Multivariable logistic regression models were performed.

Results

After adjusting for age and sex, compared with participants in the lowest quartile group of serum albumin, those in the highest quartile group had 45% lower odds of disability in ADL (odds ratio [OR]: 0.55; 95% confidence interval [CI]: 0.38, 0.80); 48% lower odds of disability in mobility (OR: 0.52; 95% CI: 0.38, 0.71); 46% lower odds of disability in standing up from a chair (OR: 0.54; 95% CI: 0.34, 0.85); and 37% lower odds of disability in picking up a book from the floor (OR: 0.63; 95% CI: 0.40, 0.97). We did not observe a statistically significant interaction effect between serum albumin and vitamin D on disability in physical functioning.

Conclusion

Serum albumin level was associated with physical functioning among Chinese older adults, regardless of vitamin D level. The findings indicate that appropriate management of poor nutritional status, in particular low serum albumin levels, may contribute to maintaining physical functioning in older adults.

Development of a diagnostic model focusing on nutritional indicators for frailty classification in people with chronic heart failure

AIMS

Frailty has a great impact on the quality of life of patients with chronic heart failure (CHF), which needs to be judged in time. To develop a diagnostic model based on nutritional indicators to judge the frailty status of patients with chronic heart failure (Frailty-CHF).

METHODS AND RESULTS

In the data collection part of this study, questionnaire method and biomedical measurement method were adopted. The trace elements in serum samples were detected by high performance liquid chromatography, chemiluminescence, and inductively coupled plasma mass spectrometry. We used Excel for data consolidation, and then imported the data into R software for modelling. Lasso method was used for variable screening, and Logistics regression fitting model was used after variables were determined. The internal validation of the model was completed by Bootstrap re-sampling. A total of 123 patients were included in this study. After variables' screening, age, nutritional status-heart failure, New York Heart Association Functional Class (NYHA), micronutrients B12, Ca, folic acid, and Se were included in the model, the c statistic and Brier score of the original model were 0.9697 and 0.0685, respectively. After Bootstrap re-sampling adjustment, the c statistic and Brier score were 0.8503 and 0.1690.

CONCLUSION

In this study, a diagnostic model of age, nutritional status-heart failure, NYHA, the micronutrients B12, Ca, folic acid, and Se was established. It could help healthcare professionals better identify the frailty status in patients with CHF.

Identification of biomarkers for physical frailty and sarcopenia through a new multi-marker approach: results from the BIOSPHERE study

Physical frailty and sarcopenia (PF&S) is a prototypical geriatric condition characterized by reduced physical function and low muscle mass. The aim of the present study was to provide an initial selection of biomarkers for PF&S using a novel multivariate analytic strategy. Two-hundred community-dwellers, 100 with PF&S and 100 non-physically frail, non-sarcopenic (nonPF&S) controls aged 70 and older were enrolled as part of the BIOmarkers associated with Sarcopenia and Physical frailty in EldeRly pErsons (BIOSPHERE) study. A panel of 74 serum analytes involved in inflammation, muscle growth and remodeling, neuromuscular junction damage, and amino acid metabolism was assayed. Biomarker selection was accomplished through sequential and orthogonalized covariance selection (SO-CovSel) analysis. Separate SO-CovSel models were constructed for the whole study population and for the two genders. The model with the best prediction ability obtained with the smallest number of variables was built using seven biomolecules. This model allowed correct classification of 80.6 ± 5.3% PF&S participants and 79.9 ± 5.1% nonPF&S controls. The PF&S biomarker profile was characterized by higher serum levels of asparagine, aspartic acid, and citrulline. Higher serum concentrations of platelet-derived growth factor BB, heat shock protein 72 (Hsp72), myeloperoxidase, and α-aminobutyric acid defined the profile of nonPF&S participants. Gender-specific SO-CovSel models identified a "core" biomarker profile of PF&S, characterized by higher serum levels of aspartic acid and Hsp72 and lower concentrations of macrophage inflammatory protein 1β, with peculiar signatures in men and women.SO-CovSel analysis allowed identifying a set of potential biomarkers for PF&S. The adoption of such an innovative multivariate approach could help address the complex pathophysiology of PF&S, translate biomarker discovery from bench to bedside, and unveil novel targets for interventions.

Prediction of Mortality in Hemodialysis Patients Using Moving Multivariate Distance

There is an increasingly widespread use of biomarkers in network physiology to evaluate an organism’s physiological state. A recent study showed that albumin variability increases before death in chronic hemodialysis patients. We hypothesized that a multivariate statistical approach would better allow us to capture signals of impending physiological collapse/death. We proposed a Moving Multivariate Distance (MMD), based on the Mahalanobis distance, to quantify the variability of the multivariate biomarker profile as a whole from one visit to the next. Biomarker profiles from a visit were used as the reference to calculate MMD at the subsequent visit. We selected 16 biomarkers (of which 11 are measured every 2 weeks) from blood samples of 763 chronic kidney disease patients hemodialyzed at the CHUS hospital in Quebec, who visited the hospital regularly (∼every 2 weeks) to perform routine blood tests. MMD tended to increase markedly preceding death, indicating an increasing intraindividual multivariate variability presaging a critical transition. In survival analysis, the hazard ratio between the 97.5th percentile and the 2.5th percentile of MMD reached as high as 21.1 [95% CI: 14.3, 31.2], showing that higher variability indicates substantially higher mortality risk. Multivariate approaches to early warning signs of critical transitions hold substantial clinical promise to identify early signs of critical transitions, such as risk of death in hemodialysis patients; future work should also explore whether the MMD approach works in other complex systems (i.e., ecosystems, economies), and should compare it to other multivariate approaches to quantify system variability.

Robust Physiological Metrics From Sparsely Sampled Networks

Physiological and biochemical networks are highly complex, involving thousands of nodes as well as a hierarchical structure. True network structure is also rarely known. This presents major challenges for applying classical network theory to these networks. However, complex systems generally share the property of having a diffuse or distributed signal. Accordingly, we should predict that system state can be robustly estimated with sparse sampling, and with limited knowledge of true network structure. In this review, we summarize recent findings from several methodologies to estimate system state via a limited sample of biomarkers, notably Mahalanobis distance, principal components analysis, and cluster analysis. While statistically simple, these methods allow novel characterizations of system state when applied judiciously. Broadly, system state can often be estimated even from random samples of biomarkers. Furthermore, appropriate methods can detect emergent underlying physiological structure from this sparse data. We propose that approaches such as these are a powerful tool to understand physiology, and could lead to a new understanding and mapping of the functional implications of biological variation.

Glutathione Serum Levels and Rate of Multimorbidity Development in Older Adults

We aimed to investigate the association between baseline levels of total serum glutathione (tGSH) and rate of chronic disease accumulation over time. The study population (n = 2,596) was derived from a population-based longitudinal study on ≥60-year-olds living in Stockholm. Participants were clinically assessed at baseline, 3- and 6-year follow-ups. Multimorbidity was measured as the number of chronic conditions from a previously built list of 60 diseases. Linear mixed models were applied to analyze the association between baseline tGSH levels and the rate of multimorbidity development over 6 years. We found that at baseline, participants with ≥4 diseases had lower tGSH levels than participants with no chronic conditions (3.3 vs 3.6 µmol/L; p < .001). At follow-up, baseline levels of tGSH were inversely associated with the rate of multimorbidity development (β * time: -0.044, p < .001) after adjusting for age, sex, education, levels of serum creatinine, C-reactive protein, albumin, body mass index, smoking, and time of dropout or death. In conclusion, serum levels of tGSH are inversely associated with multimorbidity development; the association exists above and beyond the link between tGSH and specific chronic conditions. Our findings support the hypothesis that tGSH is a biomarker of multisystem dysregulation that eventually leads to multimorbidity.

Circulating Mitochondrial-Derived Vesicles, Inflammatory Biomarkers and Amino Acids in Older Adults With Physical Frailty and Sarcopenia: A Preliminary BIOSPHERE Multi-Marker Study Using Sequential and Orthogonalized Covariance Selection - Linear Discriminant Analysis

Physical frailty and sarcopenia (PF&S) is a prototypical geriatric condition characterized by reduced physical function and low muscle mass. The multifaceted pathophysiology of this condition recapitulates all hallmarks of aging making the identification of specific biomarkers challenging. In the present study, we explored the relationship among three processes that are thought to be involved in PF&S (i.e., systemic inflammation, amino acid dysmetabolism, and mitochondrial dysfunction). We took advantage of the well-characterized cohort of older adults recruited in the “BIOmarkers associated with Sarcopenia and Physical frailty in EldeRly pErsons” (BIOSPHERE) study to preliminarily combine in a multi-platform analytical approach inflammatory biomolecules, amino acids and derivatives, and mitochondrial-derived vesicle (MDV) cargo molecules to evaluate their performance as possible biomarkers for PF&S. Eleven older adults aged 70 years and older with PF&S and 10 non-sarcopenic non-frail controls were included in the analysis based on the availability of the three categories of biomolecules. A sequential and orthogonalized covariance selection—linear discriminant analysis (SO-CovSel–LDA) approach was used for biomarkers selection. Of the 75 analytes assayed, 16 had concentrations below the detection limit. Within the remaining 59 biomolecules, So-CovSel–LDA selected a set comprising two amino acids (phosphoethanolamine and tryptophan), two cytokines (interleukin 1 receptor antagonist and macrophage inflammatory protein 1β), and MDV-derived nicotinamide adenine dinucleotide reduced form:ubiquinone oxidoreductase subunit S3 as the best predictors for discriminating older people with and without PF&S. The evaluation of these biomarkers in larger cohorts and their changes over time or in response to interventions may unveil specific pathogenetic pathways of PF&S and identify new biological targets for drug development.

Association of Blood Chemistry Quantifications of Biological Aging With Disability and Mortality in Older Adults

Quantification of biological aging has been proposed for population surveillance of age-related decline in system integrity and evaluation of geroprotective therapies. However, methods of quantifying biological aging have been little studied in geriatric populations. We analyzed three clinical-biomarker-algorithm methods to quantify biological aging. Klemera-Doubal method Biological Age and homeostatic dysregulation algorithms were parameterized from analysis of U.S. National Health and Nutrition Examination Surveys (NHANES) data (N = 36,207) based on published methods. Levine method Biological Age was adapted from published analysis of NHANES data. Algorithms were applied to biomarker data from the Duke Established Populations for Epidemiologic Studies of the Elderly (Duke-EPESE) cohort of older adults (N = 1,374, aged 71-102 years, 35% male, 52% African American). We tested associations of biological aging measures with participant reported Activities of daily living (ADL), instrumental activities of daily living (IADL) dependencies, and mortality. We evaluated the sensitivity of results to the demographic composition of reference samples and biomarker sets used to develop biological aging algorithms. African American and white Duke-EPESE participants with more advanced biological aging reported dependence in more ADLs and IADLs and were at increased risk of death over follow-up through 2017. Effect sizes were similar across algorithms, but were strongest for Levine method Biological Age (per-quintile increase in ADL incidence rate ratio = 1.25, 95% confidence interval [1.17-1.37], IADL incidence rate ratio = 1.23 [1.15-1.32], mortality hazard ratio = 1.12 [1.08-1.16]). Results were insensitive to demographic composition of reference samples, but modestly sensitive to the biomarker sets used to develop biological aging algorithms. Blood-chemistry-based quantifications of biological aging show promise for evaluating the effectiveness of interventions to extend healthy life span in older adults.

Healthspan pathway maps in C. elegans and humans highlight transcription, proliferation/biosynthesis and lipids

The molecular basis of aging and of aging-associated diseases is being unraveled at an increasing pace. An extended healthspan, and not merely an extension of lifespan, has become the aim of medical practice. Here, we define health based on the absence of diseases and dysfunctions. Based on an extensive review of the literature, in particular for humans and C. elegans, we compile a list of features of health and of the genes associated with them. These genes may or may not be associated with survival/lifespan. In turn, survival/lifespan genes that are not known to be directly associated with health are not considered. Clusters of these genes based on molecular interaction data give rise to maps of healthspan pathways for humans and for C. elegans. Overlaying healthspan-related gene expression data onto the healthspan pathway maps, we observe the downregulation of (pro-inflammatory) Notch signaling in humans and of proliferation in C. elegans. We identify transcription, proliferation/biosynthesis and lipids as a common theme on the annotation level, and proliferation-related kinases on the gene/protein level. Our literature-based data corpus, including visualization, should be seen as a pilot investigation of the molecular underpinnings of health in two different species. Web address: http://pathways.h2020awe.eu.

An Emergent Integrated Aging Process Conserved Across Primates

Aging is a complex process emerging from integrated physiological networks. Recent work using principal component analysis (PCA) of multisystem biomarkers proposed a novel fundamental physiological process, "integrated albunemia," which was consistent across human populations and more strongly associated with age and mortality risk than individual biomarkers. Here we tested for integrated albunemia and associations with age and mortality across six diverse nonhuman primate species and humans. PCA of 13 physiological biomarkers recovered in all species a primary axis of variation (PC1) resembling integrated albunemia, which increased with age in all but one species but was less predictive of mortality risk. Within species, PC1 scores were often reliably recovered with a minimal biomarker subset and usually stable between sexes. Even among species, correlations in PC1 structure were often strong, but the effect of phylogeny was inconclusive. Thus, integrated albunemia likely reflects an evolutionarily conserved process across primates and appears to be generally associated with aging but not necessarily with negative impacts on survival. Integrated albunemia is unlikely to be the only conserved emergent physiological process; our findings hence have implications both for the evolution of the aging process and of physiological networks more generally.

Aging Fits the Disease Criteria of the International Classification of Diseases

The disease criteria used by the World Health Organization (WHO) were applied to human biological aging in order to assess whether aging can be classified as a disease. These criteria were developed for the 11th revision of the International Classification of Diseases (ICD) and included disease diagnostics, mechanisms, course and outcomes, known interventions, and linkage to genetic and environmental factors. RESULTS: Biological aging can be diagnosed with frailty indices, functional, blood-based biomarkers. A number of major causal mechanisms of human aging involved in various organs have been described, such as inflammation, replicative cellular senescence, immune senescence, proteostasis failures, mitochondrial dysfunctions, fibrotic propensity, hormonal aging, body composition changes, etc. We identified a number of clinically proven interventions, as well as genetic and environmental factors of aging. Therefore, aging fits the ICD-11 criteria and can be considered a disease. Our proposal was submitted to the ICD-11 Joint Task force, and this led to the inclusion of the extension code for "Ageing-related" (XT9T) into the "Causality" section of the ICD-11. This might lead to greater focus on biological aging in global health policy and might provide for more opportunities for the new therapy developers.

The metabolomics side of frailty: Toward personalized medicine for the aged

Frailty encompasses several domains (i.e., metabolic, physical, cognitive). The multisystem derangements underlying frailty pathophysiology, its phenotypic heterogeneity, and the fluctuations of individuals across severity states have hampered a comprehensive appraisal of the condition. Circulating biomarkers emerged as an alleged tool for capturing this complexity and, as proxies for organismal metabolic changes, may hold the advantages of: 1) supporting diagnosis, 2) tracking the progression, 3) assisting healthcare professionals in clinical and therapeutic decision-making, and 4) verifying the efficacy of an intervention before measurable clinical manifestations occur. Among available analytical tools, metabolomics are able to identify and quantify the (ideally) whole repertoire of small molecules in biological matrices (i.e., cells, tissues, and biological fluids). Results of metabolomics analysis may define the final output of genome-environment interactions at the individual level. This entire collection of metabolites is called "metabolome" and is highly dynamic. Here, we discuss how monitoring the dynamics of metabolic profiles may provide a read-out of the environmental and clinical disturbances affecting cell homeostasis in frailty-associated conditions.

An Information Theory Approach for the Analysis of Individual and Combined Evaluation Parameters of Multiple Age-Related Diseases

In view of the frequent presence of several aging-related diseases in geriatric patients, there is a need to develop analytical methodologies that would be able to perform diagnostic evaluation of several diseases at once by individual or combined evaluation parameters and select the most informative parameters or parameter combinations. So far there have been no established formal methods to enable such capabilities. We develop a new formal method for the evaluation of multiple age-related diseases by calculating the informative values (normalized mutual information) of particular parameters or parameter combinations on particular diseases, and then combine the ranks of informative values to provide an overall estimation (or correlation) on several diseases at once. Using this methodology, we evaluate a geriatric cohort, with several common age-related diseases, including cognitive and physical impairments (dementia, chronic obstructive pulmonary disease-COPD and ischemic heart disease), utilizing a set of evaluation parameters (such as demographic data and blood biomarkers) routinely available in geriatric clinical practice. This method permitted us to establish the most informative parameters and parameter combinations for several diseases at once. Combinations of evaluation parameters were shown to be more informative than individual parameters. This method, with additional clinical data, may help establish the most informative parameters and parameter combinations for the diagnostic evaluation of multiple age-related diseases and enhance specific assessment for older multi-morbid patients and treatments against old-age multimorbidity.

A framework for selection of blood-based biomarkers for geroscience-guided clinical trials: report from the TAME Biomarkers Workgroup

Recent advances indicate that biological aging is a potentially modifiable driver of late-life function and chronic disease and have led to the development of geroscience-guided therapeutic trials such as TAME (Targeting Aging with MEtformin). TAME is a proposed randomized clinical trial using metformin to affect molecular aging pathways to slow the incidence of age-related multi-morbidity and functional decline. In trials focusing on clinical end-points (e.g., disease diagnosis or death), biomarkers help show that the intervention is affecting the underlying aging biology before sufficient clinical events have accumulated to test the study hypothesis. Since there is no standard set of biomarkers of aging for clinical trials, an expert panel was convened and comprehensive literature reviews conducted to identify 258 initial candidate biomarkers of aging and age-related disease. Next selection criteria were derived and applied to refine this set emphasizing: (1) measurement reliability and feasibility; (2) relevance to aging; (3) robust and consistent ability to predict all-cause mortality, clinical and functional outcomes; and (4) responsiveness to intervention. Application of these selection criteria to the current literature resulted in a short list of blood-based biomarkers proposed for TAME: IL-6, TNFα-receptor I or II, CRP, GDF15, insulin, IGF1, cystatin C, NT-proBNP, and hemoglobin A1c. The present report provides a conceptual framework for the selection of blood-based biomarkers for use in geroscience-guided clinical trials. This work also revealed the scarcity of well-vetted biomarkers for human studies that reflect underlying biologic aging hallmarks, and the need to leverage proposed trials for future biomarker discovery and validation.