The global increase in life expectancy has sparked growing interest in the factors that contribute to exceptional longevity. Between 1990 and 2015, the number of centenarians worldwide more than quadrupled. This study aimed to analyse the relationship between blood-based biomarkers and the likelihood of reaching 100 years of age in Catalonia (2015-2022), and to examine how biomarker variations during COVID-19 affected longevity. Using a retrospective cohort study based on primary care electronic health records from Catalonia, we compared centenarians with individuals aged 92 or older who died before reaching 100 years of age. We analysed anaemia, cholesterol, glycemia, kidney function, and liver function biomarkers. We employed multiple strategies to control for confounding including matching without replacement, adjusting for both observed confounders at both the individual and contextual level, and unobserved confounders, in particular spatial dependence. Our findings reveal that centenarians exhibit higher rates of chronic conditions, greater socioeconomic disadvantage, and increased neighbourhood inequality in urban areas. Biologically, longevity was linked to intermediate levels of ferritin and cholesterol, alongside lower glucose, creatinine, and uric acid levels. Glycaemic balance, indicated by HbA1c and fasting glucose, emerged as a key factor in survival to extreme old age. Additionally, biomarker improvements during the pandemic correlated with an increased likelihood of reaching centenarian age. These results emphasize the complex interplay between biological, behavioural, and contextual factors in determining longevity. While biomarkers provide valuable insights, they are insufficient indicators of healthy ageing. Future research should integrate multiple dimensions, among them, environmental, and social determinants for uncovering the mechanisms of longevity.

Comparing biomarker profiles measured at similar ages, but earlier in life, among exceptionally long-lived individuals and their shorter-lived peers can improve our understanding of aging processes. This study aimed to (i) describe and compare biomarker profiles at similar ages between 64 and 99 among individuals eventually becoming centenarians and their shorter-lived peers, (ii) investigate the association between specific biomarker values and the chance of reaching age 100, and (iii) examine to what extent centenarians have homogenous biomarker profiles earlier in life. Participants in the population-based AMORIS cohort with information on blood-based biomarkers measured during 1985-1996 were followed in Swedish register data for up to 35 years. We examined biomarkers of metabolism, inflammation, liver, renal, anemia, and nutritional status using descriptive statistics, logistic regression, and cluster analysis. In total, 1224 participants (84.6% females) lived to their 100th birthday. Higher levels of total cholesterol and iron and lower levels of glucose, creatinine, uric acid, aspartate aminotransferase, gamma-glutamyl transferase, alkaline phosphatase, lactate dehydrogenase, and total iron-binding capacity were associated with reaching 100 years. Centenarians overall displayed rather homogenous biomarker profiles. Already from age 65 and onwards, centenarians displayed more favorable biomarker values in commonly available biomarkers than individuals dying before age 100. The differences in biomarker values between centenarians and non-centenarians more than one decade prior death suggest that genetic and/or possibly modifiable lifestyle factors reflected in these biomarker levels may play an important role for exceptional longevity.

Objectives: To determine reference values for laboratory tests in individuals aged 85 and older.

Design: Cross-sectional cohort study.

Setting: International.

Participants: Long Life Family Study (LLFS) participants (N~5,000, age: range 25-110, median 67, 45% male).

Measurements: Serum biomarkers were selected based on association with aging-related diseases and included complete blood count, lipids (triglycerides, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, total cholesterol), 25-hydroxyvitamin D2 and D3, vitamin D epi-isomer, diabetes mellitus-related biomarkers (adiponectin, insulin, insulin-like growth factor 1, glucose, glycosylated hemoglobin, soluble receptor for advanced glycation endproduct), kidney disease-related biomarkers (albumin, creatinine, cystatin), endocrine biomarkers (dehydroepiandrosterone, sex-hormone binding globulin, testosterone), markers of inflammation (interleukin 6, high-sensitivity C-reactive protein, N-terminal pro b-type natriuretic peptide), ferritin, and transferrin.

Results: Of 38 measured biomarkers, 34 were significantly correlated with age. Summary statistics were generated for all biomarkers according to sex and 5-year age increments from 50 and up after excluding participants with diseases and treatments that were associated with biomarkers. A biomarker data set was also generated that will be useful for other investigators seeking to compare biomarker levels between studies.

Conclusion: Levels of several biomarkers change with older age in healthy individuals. The descriptive statistics identified herein will be useful in future studies and, if replicated in additional studies, might also become useful in clinical practice. The availability of the reference data set will facilitate appropriate calibration of biomarkers measured in different laboratories.

Background: Lifespan and the proportion of older people in the population are increasing, with far reaching consequences for the social, political and economic landscape. Unless accompanied by an increase in health span, increases in age-related diseases will increase the burden on health care resources. Intervention studies to enhance healthy ageing need appropriate outcome measures, such as blood-borne biomarkers, which are easily obtainable, cost-effective, and widely accepted. To date there have been no systematic reviews of blood-borne biomarkers of mortality.

Aim: To conduct a systematic review to identify available blood-borne biomarkers of mortality that can be used to predict healthy ageing post-retirement.

Methods: Four databases (Medline, Embase, Scopus, Web of Science) were searched. We included prospective cohort studies with a minimum of two years follow up and data available for participants with a mean age of 50 to 75 years at baseline.

Results: From a total of 11,555 studies identified in initial searches, 23 fulfilled the inclusion criteria. Fifty-one blood borne biomarkers potentially predictive of mortality risk were identified. In total, 20 biomarkers were associated with mortality risk. Meta-analyses of mortality risk showed significant associations with C-reactive protein (Hazard ratios for all-cause mortality 1.42, p<0.001; Cancer-mortality 1.62, p<0.009; CVD-mortality 1.31, p = 0.033), N Terminal-pro brain natriuretic peptide (Hazard ratios for all-cause mortality 1.43, p<0.001; CHD-mortality 1.58, p<0.001; CVD-mortality 1.67, p<0.001) and white blood cell count (Hazard ratios for all-cause mortality 1.36, p = 0.001). There was also evidence that brain natriuretic peptide, cholesterol fractions, erythrocyte sedimentation rate, fibrinogen, granulocytes, homocysteine, intercellular adhesion molecule-1, neutrophils, osteoprotegerin, procollagen type III aminoterminal peptide, serum uric acid, soluble urokinase plasminogen activator receptor, tissue inhibitor of metalloproteinases 1 and tumour necrosis factor receptor II may predict mortality risk. There was equivocal evidence for the utility of 14 biomarkers and no association with mortality risk for CD40 ligand, cortisol, dehydroepiandrosterone, ferritin, haemoglobin, interleukin-12, monocyte chemoattractant protein 1, matrix metalloproteinase 9, myelopereoxidase, P-selectin, receptor activator of nuclear factor KappaB ligand, sex hormone binding globulin, testosterone, transferrin, and thyroid stimulating hormone and thyroxine.

Conclusions: Twenty biomarkers should be prioritised as potential predictors of mortality in future studies. More studies using standardised protocols and reporting methods, and which focus on mortality rather than risk of disease or health status as an outcome, are needed.

Genetic studies have thus far identified a limited number of loci associated with human longevity by applying age at death or survival up to advanced ages as phenotype. As an alternative approach, one could first try to identify biomarkers of healthy ageing and the genetic variants associated with these traits and subsequently determine the association of these variants with human longevity. In the present study, we used this approach by testing whether the 35 baseline serum parameters measured in the Leiden Longevity Study (LLS) meet the proposed criteria for a biomarker of healthy ageing. The LLS consists of 421 families with long-lived siblings of European descent, who were recruited together with their offspring and the spouses of the offspring (controls). To test the four criteria for a biomarker of healthy ageing in the LLS, we determined the association of the serum parameters with chronological age, familial longevity, general practitioner-reported general health, and mortality. Out of the 35 serum parameters, we identified glucose, insulin, and triglycerides as biomarkers of healthy ageing, meeting all four criteria in the LLS. We subsequently showed that the genetic variants previously associated with these parameters are significantly enriched in the largest genome-wide association study for human longevity. In conclusion, we showed that biomarkers of healthy ageing can be used to leverage genetic studies into human longevity. We identified several genetic variants influencing the variation in glucose, insulin and triglycerides that contribute to human longevity.

As increasing numbers of individuals reach very advanced age, it is important to understand the influence of modifiable lifestyle factors such as diet and nutrition on both the achievement of exceptional longevity as well as the maintenance of optimal functional capacity. This includes determining the most appropriate biomarkers for monitoring changes in health and nutrition status and response to therapy in oldest old individuals. In an earlier work (Hausman et al., Maturitas 2011;68:203-9), we summarized studies of dietary intake and patterns of long-lived peoples and presented the current knowledge regarding vitamin B12, folate, 25(OH) vitamin D and other specific indicators of nutritional status in centenarians. The present review focuses on less specific biochemical indices of health and nutritional status and summarizes studies comparing protein, lipid and hematological biomarkers in centenarians and older adult controls. Such studies, from many countries worldwide, are often small, convenience samples of 'healthy' and/or community-dwelling centenarians, although a few population-based studies including participants with a broader range of physical and cognitive functioning are also presented. Though heterogeneous in design and demographic region, these studies typically show lower levels of protein and hematological indicators and improved levels of some lipid biomarkers in centenarians as compared with regionally matched older adult controls. As these biomarkers can be influenced by many factors interpretation of results must be approached with caution. Importantly, studies examining potential associations of these biomarkers with cognitive, mental and physical function must carefully control for potential confounders including genetics and chronic disease, an increasing burden at advanced age.

Lifespan is a complex trait, and longitudinal data for humans are naturally scarce. We report the results of Cox regression and Pearson correlation analyses using data of the Study of Health in Pomerania (SHIP), with mortality data of 1518 participants (113 of which died), over a time span of more than 10 years. We found that in the Cox regression model based on the Bayesian information criterion, apart from chronological age of the participant, six baseline variables were considerably associated with higher mortality rates: smoking, mean attachment loss (i.e. loss of tooth supporting tissue), fibrinogen concentration, albumin/creatinine ratio, treated gastritis, and medication during the last 7 days. Except for smoking, the causative contribution of these variables to mortality was deemed inconclusive. In turn, four variables were found to be associated with decreased mortality rates: treatment of benign prostatic hypertrophy, treatment of dyslipidemia, IGF-1 and being female. Here, being female was an undisputed causative variable, the causal role of IFG-1 was deemed inconclusive, and the treatment effects were deemed protective to the degree that treated subjects feature better survival than respective controls. Using Cox modeling based on the Akaike information criterion, diabetes, mean corpuscular hemoglobin concentration, red blood cell count and serum calcium were also associated with mortality. The latter two, together with albumin and fibrinogen, aligned with an"integrated albunemia" model of aging proposed recently.

Frailty describes a geriatric syndrome characterized by an increased vulnerability. Although a variety of potential blood-based biomarkers have been discussed for its characterization, a reliable protocol considering blood-based biomarkers for this purpose is still missing. However, a comprehensive overview on these biomarkers is necessary to understand potential molecular pathways to frailty. We, therefore, performed a mapping review to identify those blood-based biomarkers most consistently associated with frailty in community-dwelling older adults as well as possible analytical gaps according to the available literature. A proposed weighted correlation index (CI) describing the direction and consistency of the association considering the number of available publications as well as the size of the study populations was calculated for each biomarker. Overall, 72 manuscripts were critically reviewed reporting on a total of 82 biomarkers. The most consistent positive association with at least 3 articles addressing the respective biomarker in unadjusted and fully adjusted models was shown for interleukin 6 (IL-6), high-sensitivity C-reactive protein (hs-CRP), neopterin, white blood cells count, glycated hemoglobin A1c (HbA1c) and sex hormone binding-globuline (SHBG) with a CI ≥ 0.7, while for negative association hemoglobin, 25-hydroxy vitamin D, free testosterone in men and albumin with a CI ≤ -0.7 were identified.

Multi-marker approaches are well suited for untangling the intrinsic complexity of aging and related conditions. Herein, we quantified (1) baseline concentrations of a panel of geroscience biomarkers pertaining to four biological domains (i.e., metabolism, inflammation, vascular/organ dysfunction and cellular senescence, and neurodegeneration) in individuals aged ≥60 years; (2) investigated linear and non-linear changes in biomarker levels over a 6-year period according to age and sex; and (3) described the relationships among geroscience biomarkers at baseline and follow-up. We found that repeated measures of age-dependent changes of 47 blood-borne biomarkers over 6 years had differential associations depending on the biological domains. The most relevant biomolecules in the associations between age and repeated assessments were (1) adiponectin, C-peptide, renin (metabolism), (2) CXCL10, IL-1α, IL-1β, IL-6, IL-10, IL-12p70, MPO (inflammation), (3) cystatin C, MMP7, MMP12, VCAM-1 (vascular/organ dysfunction and cellular senescence), and (4) S100B and Tau protein (neurodegeneration). Among these molecules, a negative association with increasing age was found for IL-1α, IL-1β, IL-12p70, S100B, and Tau protein. Non-linear relationships were also identified with age for IGFBP-1, leptin, β2M, TNFRSF1B, fibrinogen, GDF-15, N-cadherin, and BDNF. Our results indicate that inflammatory and metabolic biomolecules are strongly associated with aging over 6 years of follow-up. Whether the biological pathways reflected by these biomarkers contribute to the aging process or are associated with negative health-related events needs to be explored through comprehensive multi-omics longitudinal analysis in larger cohorts.

Background: There is no criterion reference for assessing healthy ageing and this creates difficulties when conducting and comparing research on ageing across studies. A cardinal feature of ageing is loss of function which translates into wide-ranging consequences for the individual and for family, carers and society. We undertook comprehensive reviews of the literature searching for biomarkers of ageing on five ageing-related domains including physical capability and cognitive, physiological and musculoskeletal, endocrine and immune functions. Where available, we used existing systematic reviews, meta-analyses and other authoritative reports such as the recently launched NIH Toolbox for assessment of neurological and behavioural function, which includes test batteries for cognitive and motor function (the latter described here as physical capability). We invited international experts to comment on our draft recommendations. In addition, we hosted an experts workshop in Newcastle, UK, on 22-23 October 2012, aiming to help capture the state-of-the-art in this complex area and to provide an opportunity for the wider ageing research community to critique the proposed panel of biomarkers.

Discussion: Here we have identified important biomarkers of healthy ageing classified as subdomains of the main areas proposed. Cardiovascular and lung function, glucose metabolism and musculoskeletal function are key subdomains of physiological function. Strength, locomotion, balance and dexterity are key physical capability subdomains. Memory, processing speed and executive function emerged as key subdomains of cognitive function. Markers of the HPA-axis, sex hormones and growth hormones were important biomarkers of endocrine function. Finally, inflammatory factors were identified as important biomarkers of immune function. We present recommendations for a panel of biomarkers that address these major areas of function which decline during ageing. This biomarker panel may have utility in epidemiological studies of human ageing, in health surveys of older people and as outcomes in intervention studies that aim to promote healthy ageing. Further, the inclusion of the same common panel of measures of healthy ageing in diverse study designs and populations may enhance the value of those studies by allowing the harmonisation of surrogate endpoints or outcome measures, thus facilitating less equivocal comparisons between studies and the pooling of data across studies.