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The Semmelweis Study is a prospective occupational cohort study that seeks to enroll all employees of Semmelweis University (Budapest, Hungary) aged 25 years and older, with a population of 8866 people, 70.5% of whom are women. The study builds on the successful experiences of the Whitehall II study and aims to investigate the complex relationships between lifestyle, environmental, and occupational risk factors, and the development and progression of chronic age-associated diseases. An important goal of the Semmelweis Study is to identify groups of people who are aging unsuccessfully and therefore have an increased risk of developing age-associated diseases. To achieve this, the study takes a multidisciplinary approach, collecting economic, social, psychological, cognitive, health, and biological data. The Semmelweis Study comprises a baseline data collection with open healthcare data linkage, followed by repeated data collection waves every 5 years. Data are collected through computer-assisted self-completed questionnaires, followed by a physical health examination, physiological measurements, and the assessment of biomarkers. This article provides a comprehensive overview of the Semmelweis Study, including its origin, context, objectives, design, relevance, and expected contributions.

The search for biomarkers that quantify biological aging (particularly ‘omic’-based biomarkers) has intensified in recent years. Such biomarkers could predict aging-related outcomes and could serve as surrogate endpoints for the evaluation of interventions promoting healthy aging and longevity. However, no consensus exists on how biomarkers of aging should be validated before their translation to the clinic. Here, we review current efforts to evaluate the predictive validity of omic biomarkers of aging in population studies, discuss challenges in comparability and generalizability and provide recommendations to facilitate future validation of biomarkers of aging. Finally, we discuss how systematic validation can accelerate clinical translation of biomarkers of aging and their use in gerotherapeutic clinical trials.

Despite the prominence of the weathering hypothesis as a mechanism underlying racialized inequities in morbidity and mortality, the life course social and economic determinants of Black–White disparities in biological aging remain inadequately understood. This study uses data from the Health and Retirement Study (n = 6,782), multivariable regression, and Kitagawa–Blinder–Oaxaca decomposition to assess Black–White disparities across three measures of biological aging: PhenoAge, Klemera–Doubal biological age, and homeostatic dysregulation. It also examines the contributions of racial differences in life course socioeconomic and stress exposures and vulnerability to those exposures to Black–White disparities in biological aging. Across the outcomes, Black individuals exhibited accelerated biological aging relative to White individuals. Decomposition analyses showed that racial differences in life course socioeconomic exposures accounted for roughly 27% to 55% of the racial disparities across the biological aging measures, and racial disparities in psychosocial stress exposure explained 7% to 11%. We found less evidence that heterogeneity in the associations between social exposures and biological aging by race contributed substantially to Black–White disparities in biological aging. Our findings offer new evidence of the role of life course social exposures in generating disparities in biological aging, with implications for understanding age patterns of morbidity and mortality risks.

Measures of biological age (BA) integrate information across organ systems to quantify “biological aging,” i.e., inter-individual differences in aging-related health decline. While longevity and lifespan aggregate in families, reflecting transmission of genes and environments across generations, little is known about intergenerational continuity of biological aging or the extent to which this continuity may be modified by environmental factors. Using data from the Jerusalem Perinatal Study (JPS), we tested if differences in offspring BA were related to mortality in their parents. We measured BA using biomarker data collected from 1473 offspring during clinical exams in 2007–2009, at age 32 ± 1.1. Parental mortality was obtained from population registry data for the years 2004–2016. We fitted parametric survival models to investigate the associations between offspring BA and parental all-cause and cause-specific mortality. We explored potential differences in these relationships by socioeconomic position (SEP) and offspring sex. Participants’ BAs widely varied (SD= 6.95). Among those measured to be biologically older, parents had increased all-cause mortality (HR= 1.10, 95% CI: 1.08, 1.13), diabetes mortality (HR= 1.19, 95% CI: 1.08, 1.30), and cancer mortality (HR= 1.07, 95% CI: 1.02, 1.13). The association with all-cause mortality was stronger for families with low compared with high SEP (Pinteraction= 0.04) and for daughters as compared to sons (Pinteraction< 0.001). Using a clinical-biomarker-based BA estimate, observable by young adulthood prior to the onset of aging-related diseases, we demonstrate intergenerational continuity of the aging process. Furthermore, variation in this familial aggregation according to household socioeconomic position (SEP) at offspring birth and between families of sons and daughters proposes that the environment alters individuals’ aging trajectory set by their parents.

Discrimination is a social determinant of health and health disparities for which the biological mechanisms remain poorly understood. This study investigated the hypothesis that discrimination contributes to poor health outcomes by accelerating biological processes of aging. We analyzed survey and blood DNA methylation data from the Midlife in the United States (MIDUS) study (N = 1967). We used linear regression analysis to test associations of everyday, major, and workplace discrimination with biological aging measured by the DunedinPACE, PhenoAge, and GrimAge2 epigenetic clocks. MIDUS participants who reported more discrimination tended to exhibit a faster pace of aging and older biological age as compared to peers who reported less discrimination. Effect-sizes for associations tended to be larger for the DunedinPACE pace-of-aging clock (effect-size range r = 0.1–0.2) as compared with the PhenoAge and GrimAge2 biological-age clocks (effect-sizes r < 0.1) and for experiences of everyday and major discrimination as compared with workplace discrimination. Smoking status and body-mass index accounted for roughly half of observed association between discrimination and biological aging. Reports of discrimination were more strongly associated with accelerated biological aging among White as compared with Black participants, although Black participants reported more discrimination overall and tended to exhibit older biological age and faster biological aging. Findings support the hypothesis that experiences of interpersonal discrimination contribute to accelerated biological aging and suggest that structural and individual-level interventions to reduce discrimination and promote adaptive coping have potential to support healthy aging and build health equity.

Methods to quantify biological aging are emerging as new measurement tools for epidemiology and population science and have been proposed as surrogate measures for healthy lifespan extension in geroscience clinical trials. Publicly available software packages to compute biological aging measurements from DNA methylation data have accelerated dissemination of these measures and generated rapid gains in knowledge about how different measures perform in a range of datasets. Biological age measures derived from blood chemistry data were introduced at the same time as the DNA methylation measures and, in multiple studies, demonstrate superior performance to these measures in prediction of healthy lifespan. However, their dissemination has been slow by comparison, resulting in a significant gap in knowledge. We developed a software package to help address this knowledge gap. The BioAge R package, available for download at GitHub (http://github.com/dayoonkwon/BioAge), implements three published methods to quantify biological aging based on analysis of chronological age and mortality risk: Klemera-Doubal biological age, PhenoAge, and homeostatic dysregulation. The package allows users to parametrize measurement algorithms using custom sets of biomarkers, to compare the resulting measurements to published versions of the Klemera-Doubal method and PhenoAge algorithms, and to score the measurements in new datasets. We applied BioAge to safety lab data from the CALERIE™ randomized controlled trial, the first-ever human trial of long-term calorie restriction in healthy, non-obese adults, to test effects of intervention on biological aging. Results contribute evidence that CALERIE intervention slowed biological aging. BioAge is a toolkit to facilitate measurement of biological age for geroscience.

An individual’s brainAGE is the difference between chronological age and age predicted from machine-learning models of brain-imaging data. BrainAGE has been proposed as a biomarker of age-related deterioration of the brain. Having an older brainAGE has been linked to Alzheimer’s, dementia, and mortality. However, these findings are largely based on cross-sectional associations which can confuse age differences with cohort differences. To illuminate the validity of brainAGE as a biomarker of accelerated brain aging, a study is needed of a large cohort all born in the same year who nevertheless vary on brainAGE. In the Dunedin Study, a population-representative 1972–73 birth cohort, we measured brainAGE at age 45 years, as well as the pace of biological aging and cognitive decline in longitudinal data from childhood to midlife (N= 869). In this cohort, all chronological age 45 years, brainAGE was measured reliably (ICC = 0.81) and ranged from 24 to 72 years. Those with older midlife brainAGEs tended to have poorer cognitive function in both adulthood and childhood, as well as impaired brain health at age 3. Furthermore, those with older brainAGEs had an accelerated pace of biological aging, older facial appearance, and early signs of cognitive decline from childhood to midlife. These findings help to validate brainAGE as a potential surrogate biomarker for midlife intervention studies that seek to measure dementia-prevention efforts in midlife. However, the findings also caution against the assumption that brainAGE scores represent only age-related deterioration of the brain as they may also index central nervous system variation present since childhood.

An intriguing effect of short-term caloric restriction (CR) is the expansion of certain stem cell populations, including muscle stem cells (satellite cells), which facilitate an accelerated regenerative program after injury. Here, we utilized the MetRSL274G(MetRS) transgenic mouse to identify liver-secreted plasminogen as a candidate for regulating satellite cell expansion during short-term CR. Knockdown of circulating plasminogen prevents satellite cell expansion during short-term CR. Furthermore, loss of the plasminogen receptor KT (Plg-RKT) is also sufficient to prevent CR-related satellite cell expansion, consistent with direct signaling of plasminogen through the plasminogen receptor Plg-RKT/ERK kinase to promote proliferation of satellite cells. Importantly, we are able to replicate many of these findings in human participants from the CALERIE trial. Our results demonstrate that CR enhances liver protein secretion of plasminogen, which signals directly to the muscle satellite cell through Plg-RKTto promote proliferation and subsequent muscle resilience during CR.

Little is known about the genetic architecture of traits affecting educational attainment other than cognitive ability. We used genomic structural equation modeling and prior genome-wide association studies (GWASs) of educational attainment (n?=?1,131,881) and cognitive test performance (n?=?257,841) to estimate SNP associations with educational attainment variation that is independent of cognitive ability. We identified 157 genome-wide-significant loci and a polygenic architecture accounting for 57% of genetic variance in educational attainment. Noncognitive genetics were enriched in the same brain tissues and cell types as cognitive performance, but showed different associations with gray-matter brain volumes. Noncognitive genetics were further distinguished by associations with personality traits, less risky behavior and increased risk for certain psychiatric disorders. For socioeconomic success and longevity, noncognitive and cognitive-performance genetics demonstrated associations of similar magnitude. By conducting a GWAS of a phenotype that was not directly measured, we offer a view of genetic architecture of noncognitive skills influencing educational success.

Childhood maltreatment and psychiatric morbidity have each been associated with accelerated biological aging primarily through cross-sectional studies. Using data from a prospective longitudinal study of individuals with histories of childhood maltreatment and control participants followed into midlife, we tested 2 hypotheses examining whether 1) psychiatric symptoms mediate the relationship between childhood maltreatment and biological aging and 2) psychiatric symptoms of anxiety, depression, or posttraumatic stress disorder (PTSD) act in conjunction with childhood maltreatment to exacerbate the association of child maltreatment to aging.

The field of molecular epidemiology of aging involves the application of molecular methods to measure aging processes and their genetic determinants in human cohorts. Over the last decade, the field has undergone rapid progress with a dramatic increase in the number of papers published. The aim of this review is to give an overview of the research field, with a specific focus on new developments, opportunities, and challenges. Aging occurs at multiple hierarchical levels. There is increasing consensus that aging-related changes at the molecular level cause declines in physiological integrity, functional capacity, and ultimately lifespan. Molecular epidemiology studies seek to quantify this process. Telomere length, composite scores integrating clinical biomarkers, and omics clocks are among the most well-studied metrics in molecular epidemiology studies. New developments in the field include bigger data and hypothesis-free analysis together with new modes of collaborations in interdisciplinary teams and open access norms around data sharing. Key challenges facing the field are the lack of a gold standard by which to evaluate molecular measures of aging, inconsistency in which metrics of aging are measured and analyzed across studies, and a need for more longitudinal data necessary to observe change over time.

Background:Behavioral lifestyle factors are promising for prevention of numerous diseases and mortality risk by contributing to overall healthy ageing. Healthy diet and regular physical activity have been associated with longer telomere length, indicating younger biological aging. More recently, algorithm-based measurements of biological aging, which integrate information across panels of molecular or clinical indicators, have proven both more technically reliable and more precise in their predictions of morbidity and mortality than telomere length. The aim of this study was to examine the association of diet, physical activity, and their interaction with biological ageing measured by PhenoAge blood chemistry algorithm.Methods:This study included adult participants (≥20 years-old) from the National Health and Nutrition Examination Survey (NHANES), 1999-2018. We evaluated adherence to a Mediterranean diet (MeDi) and level of leisure time physical activity (LTPA) using standard instruments. We measured biological aging using the PhenoAge algorithm, developed using data from NAHNES III (1988-1994) and validated as a predictor of morbidity/mortality in the NHANES data we analyzed. We defined PhenoAge advancement as the difference between biological age derived from eight blood biomarkers and chronological age, with higher score indicating being biologically older. We evaluated the associations of MeDi and LTPA with PhenoAge in adjusted linear regressions. We explored synergies between these two health behaviors and heterogeneity in their associations across strata of sex age, gender, and body mass index (BMI).Results:In total, 38,617 participants (mean age 47.1 years, 51% women) were included. Both higher adherence to MeDi and higher level of LTPA were associated with lower PhenoAge advancement, in mutually adjusted model controlled for demographic, socioeconomic and lifestyle variables (β = 0.15 SD [95% CI, -0.18; -0.11] for high vs low tertile of adherence to MeDi, and β = 0.12 SD [-0.15; -0.09] forhigh LTPA level vs sedentarity). The associations of diet and physical activity phenotypes with biological aging were independent and additive (p-interaction >0.05). Stratified analyses indicated consistent significant associations across strata of age, gender, and BMI, although stronger associations were observed among women and participants with normal weight than among their respective counterparts for MeDi (p-interaction = 0.001 and 0.04, respectively) and among older participants for LTPA (p-interaction <0.001). Sensitivity analysis using the Healthy Eating Index 2015 did not change the results.Conclusion:Healthy diet and regular physical activity are independently associated with lower clinically defined biological aging, regardless of age, sex, and BMI category.

•What is the primary question addressed by this study?Could the effect of loneliness on physical health consequences differ by its patterns and duration and whether there are sex differences in its effect.•What is the main finding of this study?Chronic loneliness is an independent risk factor for new-onset ADL disability in both men and women, but transient loneliness is not.Chronic loneliness was associated with the risk of IADL disability in women, not in men.•What is the meaning of the finding?Tailored interventions are needed for the right people at the right time to avoid loneliness chronicity and prevent functional disability.

Mental disorders traditionally have been viewed as distinct, episodic, and categorical conditions. This view has been challenged by evidence that many disorders are sequentially comorbid, recurrent/chronic, and exist on a continuum. Using the Dunedin Multidisciplinary Health and Development Study, we examined the structure of psychopathology, taking into account dimensionality, persistence, co-occurrence, and sequential comorbidity of mental disorders across 20 years, from adolescence to midlife. Psychiatric disorders were initially explained by three higher-order factors (Internalizing, Externalizing, and Thought Disorder) but explained even better with one General Psychopathology dimension. We have called this dimension the p factor because it conceptually parallels a familiar dimension in psychological science: the g factor of general intelligence. Higher p scores are associated with more life impairment, greater familiality, worse developmental histories, and more compromised early-life brain function. The p factor explains why it is challenging to find causes, consequences, biomarkers, and treatments with specificity to individual mental disorders. Transdiagnostic approaches may improve research.

Occupational characteristics are associated with late-life cognition. However, little is known about the association between occupational factors and cognition in early adulthood, especially when controlling for early life socioeconomic status (SES) and cognition in childhood. Importantly, sex may shape the impact of occupational characteristics that provide cognitive stimulation given that education, occupational status, and workplace experiences differ by sex.

Genome-wide association studies (GWAS) have discovered genetic variants that predispose individuals to asthma. To integrate these new discoveries with emerging models of asthma pathobiology, we aimed to test how genetic discoveries relate to developmental and biological characteristics of asthma.

OBJECTIVE To test how genomic loci identified in genome-wide association studies influence the development of obesity. DESIGN A 38-year prospective longitudinal study of a representative birth cohort. SETTING The Dunedin Multidisciplinary Health and Development Study, Dunedin, New Zealand. PARTICIPANTS One thousand thirty-seven male and female study members. MAIN EXPOSURES We assessed genetic risk with a multilocus genetic risk score. The genetic risk score was composed of single-nucleotide polymorphisms identified in genome-wide association studies of obesity-related phenotypes. We assessed family history from parent body mass index data collected when study members were 11 years of age. MAIN OUTCOME MEASURES Body mass index growth curves, developmental phenotypes of obesity, and adult obesity outcomes were defined from anthropometric assessments at birth and at 12 subsequent in-person interviews through 38 years of age. RESULTS Individuals with higher genetic risk scores were more likely to be chronically obese in adulthood. Genetic risk first manifested as rapid growth during early childhood. Genetic risk was unrelated to birth weight. After birth, children at higher genetic risk gained weight more rapidly and reached adiposity rebound earlier and at a higher body mass index. In turn, these developmental phenotypes predicted adult obesity, mediating about half the genetic effect on adult obesity risk. Genetic associations with growth and obesity risk were independent of family history, indicating that the genetic risk score could provide novel information to clinicians. CONCLUSIONS Genetic variation linked with obesity risk operates, in part, through accelerating growth in the early childhood years after birth. Etiological research and prevention strategies should target early childhood to address the obesity epidemic.

OBJECTIVES To estimate the prevalence of major eye diseases and low vision or blindness in a national sample of male US Union Army veterans from 1890 to 1910 and to compare these prevalence rates with contemporary rates for the same diseases and visual status. DESIGN Longitudinal histories of 16 022 white Union Army veterans receiving disability pensions from 1890 to 1910 were developed from pension board examination records. Prevalence rates of trachoma, corneal opacities, cataract, diseases of the retina and optic nerve, and low vision or blindness were calculated in 1895 and 1910. Changes in prevalence by age were examined. RESULTS By 1910, 11.9% of veterans had low vision or were blind in both eyes. Prevalence of cataract increased with age, resulting in 13.1% of veterans having had cataract in one or both eyes. Rates of trachoma were 3.2% in 1895 and 4.8% in 1910. Rates of corneal opacity were 3.0% and 5.1%, respectively. Glaucoma was rarely diagnosed from 1890 to 1910, but diseases of the optic nerve were reported in 2.0% of veterans in 1895 and 3.6% in 1910. CONCLUSIONS This study documents substantial reductions in the prevalence of low vision or blindness and changes in the composition of eye diseases from an era in which there were few effective therapies for eye diseases to the present.Arch Ophthalmol. 2008;126(2):246-250--

DNA methylation plays an important role in both normal human development and risk of disease. The most utilized method of assessing DNA methylation uses BeadChips, generating an epigenome-wide “snapshot” of >450,000 observations (probe measurements) per assay. However, the reliability of each of these measurements is not equal, and little consideration is paid to consequences for research. We correlated repeat measurements of the same DNA samples using the Illumina HumanMethylation450K and the Infinium MethylationEPIC BeadChips in 350 blood DNA samples. Probes that were reliably measured were more heritable and showed consistent associations with environmental exposures, gene expression, and greater cross-tissue concordance. Unreliable probes were less replicable and generated an unknown volume of false negatives. This serves as a lesson for working with DNA methylation data, but the lessons are equally applicable to working with other data: as we advance toward generating increasingly greater volumes of data, failure to document reliability risks harming reproducibility.

OBJECTIVE:s To test whether frequent bullying victimisation in childhood increases the likelihood of self harming in early adolescence, and to identify which bullied children are at highest risk of self harm. Design The Environmental Risk (E-Risk) longitudinal study of a nationally representative UK cohort of 1116 twin pairs born in 1994-95 (2232 children). Setting England and Wales, United Kingdom. Participants Children assessed at 5, 7, 10, and 12 years of age. MAIN OUTCOME MEASURES: Relative risks of children’s self harming behaviour in the six months before their 12th birthday. RESULTS: Self harm data were available for 2141 children. Among children aged 12 who had self harmed (2.9%; n=62), more than half were victims of frequent bullying (56%; n=35). Exposure to frequent bullying predicted higher rates of self harm even after children’s pre-morbid emotional and behavioural problems, low IQ, and family environmental risks were taken into account (bullying victimisation reported by mother: adjusted relative risk 1.92, 95% confidence interval 1.18 to 3.12; bullying victimisation reported by child: 2.44, 1.36 to 4.40). Victimised twins were more likely to self harm than were their non-victimised twin sibling (bullying victimisation reported by mother: 13/162 v 3/162, ratio=4.3, 95% confidence interval 1.3 to 14.0; bullying victimisation reported by child: 12/144 v 7/144, ratio=1.7, 0.71 to 4.1). Compared with bullied children who did not self harm, bullied children who self harmed were distinguished by a family history of attempted/completed suicide, concurrent mental health problems, and a history of physical maltreatment by an adult. CONCLUSIONS: Prevention of non-suicidal self injury in young adolescents should focus on helping bullied children to cope more appropriately with their distress. Programmes should target children who have additional mental health problems, have a family history of attempted/completed suicide, or have been maltreated by an adult.