Identification of 12 genetic loci associated with human healthspan

The relationship between 11 different polygenic longevity scores, parental lifespan, and disease diagnosis in the UK Biobank

Large-scale genome-wide association studies (GWAS) strongly suggest that most traits and diseases have a polygenic component. This observation has motivated the development of disease-specific "polygenic scores (PGS)" that are weighted sums of the effects of disease-associated variants identified from GWAS that correlate with an individual's likelihood of expressing a specific phenotype. Although most GWAS have been pursued on disease traits, leading to the creation of refined "Polygenic Risk Scores" (PRS) that quantify risk to diseases, many GWAS have also been pursued on extreme human longevity, general fitness, health span, and other health-positive traits. These GWAS have discovered many genetic variants seemingly protective from disease and are often different from disease-associated variants (i.e., they are not just alternative alleles at disease-associated loci) and suggest that many health-positive traits also have a polygenic basis. This observation has led to an interest in "polygenic longevity scores (PLS)" that quantify the "risk" or genetic predisposition of an individual towards health. We derived 11 different PLS from 4 different available GWAS on lifespan and then investigated the properties of these PLS using data from the UK Biobank (UKB). Tests of association between the PLS and population structure, parental lifespan, and several cancerous and non-cancerous diseases, including death from COVID-19, were performed. Based on the results of our analyses, we argue that PLS are made up of variants not only robustly associated with parental lifespan, but that also contribute to the genetic architecture of disease susceptibility, morbidity, and mortality.

Ambient air pollution, lifestyle, and genetic predisposition on all-cause and cause-specific mortality: A prospective cohort study

BACKGROUND

Although it is widely acknowledged that long-term exposure to ambient air pollution is closely related to the risk of mortality, there were inconsistencies in terms of cause-specific mortality and it is still unknown whether lifestyle and genetic susceptibility could modify the association.

METHODS

This population-based prospective cohort study involved 461,112 participants from the UK Biobank. The land-use regression model was used to estimate the concentrations of particulate matter (PM2.5, PMcoarse, PM10), and nitrogen oxides (NO2 and NOx). The association between air pollution and mortality was evaluated using Cox proportional hazard models. Furthermore, a lifestyle score incorporated with smoking status, physical activity, alcohol consumption, and diet behaviors, and polygenic risk score using 12 genetic variants, were developed to assess the modifying effect of air pollution on mortality outcomes.

RESULTS

During a median follow-up of 14.0 years, 33,903 deaths were recorded, including 17,083 (2835; 14,248), 6970, 2429, and 1287 deaths due to cancer (lung cancer, non-lung cancer), cardiovascular disease (CVD), respiratory and digestive disease, respectively. Each interquartile range (IQR) increase in PM2.5, NO2 and NOx was associated with 7 %, 6 % and 5 % higher risk of all-cause mortality, respectively. Specifically, for cause-specific mortality, each IQR increase in PM2.5, NO2 and NOx was also linked to mortality due to cancer (lung cancer and non-lung cancer), CVD, respiratory and digestive disease. Furthermore, additive and multiplicative interactions were identified between high ambient air pollution and unhealthy lifestyle on mortality. In addition, associations between air pollution and mortality were modified by lifestyle behaviors.

CONCLUSION

Long-term exposure to air pollutants increased the risk of all-cause and cause-specific mortality, which was modified by lifestyle behaviors. In addition, we also revealed a synergistically detrimental effect between air pollution and an unhealthy lifestyle, suggesting the significance of joint air pollution management and adherence to a healthy lifestyle on public health.

A lipidomic based metabolic age score captures cardiometabolic risk independent of chronological age

BACKGROUND

Metabolic ageing biomarkers may capture the age-related shifts in metabolism, offering a precise representation of an individual's overall metabolic health.

METHODS

Utilising comprehensive lipidomic datasets from two large independent population cohorts in Australia (n = 14,833, including 6630 males, 8203 females), we employed different machine learning models, to predict age, and calculated metabolic age scores (mAge). Furthermore, we defined the difference between mAge and age, termed mAgeΔ, which allow us to identify individuals sharing similar age but differing in their metabolic health status.

FINDINGS

Upon stratification of the population into quintiles by mAgeΔ, we observed that participants in the top quintile group (Q5) were more likely to have cardiovascular disease (OR = 2.13, 95% CI = 1.62-2.83), had a 2.01-fold increased risk of 12-year incident cardiovascular events (HR = 2.01, 95% CI = 1.45-2.57), and a 1.56-fold increased risk of 17-year all-cause mortality (HR = 1.56, 95% CI = 1.34-1.79), relative to the individuals in the bottom quintile group (Q1). Survival analysis further revealed that men in the Q5 group faced the challenge of reaching a median survival rate due to cardiovascular events more than six years earlier and reaching a median survival rate due to all-cause mortality more than four years earlier than men in the Q1 group.

INTERPRETATION

Our findings demonstrate that the mAge score captures age-related metabolic changes, predicts health outcomes, and has the potential to identify individuals at increased risk of metabolic diseases.

FUNDING

The specific funding of this article is provided in the acknowledgements section.

A proteomic signature of healthspan

The focus of aging research has shifted from increasing lifespan to enhancing healthspan to reduce the time spent living with disability. Despite significant efforts to develop biomarkers of aging, few studies have focused on biomarkers of healthspan. We developed a proteomics-based signature of healthspan (healthspan proteomic score (HPS)) using data from the UK Biobank Pharma Proteomics Project (53,018 individuals and 2920 proteins). A lower HPS was associated with higher mortality risk and several age-related conditions, such as COPD, diabetes, heart failure, cancer, myocardial infarction, dementia, and stroke. HPS showed superior predictive accuracy for these outcomes compared to chronological age and biological age measures. Proteins associated with HPS were enriched in hallmark pathways such as immune response, inflammation, cellular signaling, and metabolic regulation. Our findings demonstrate the validity of HPS, making it a valuable tool for assessing healthspan and as a potential surrogate marker in geroscience-guided studies.

Mendelian randomization evidence for the causal effect of mental well-being on healthy aging

Mental well-being relates to multitudinous lifestyle behaviours and morbidities and underpins healthy aging. Thus far, causal evidence on whether and in what pattern mental well-being impacts healthy aging and the underlying mediating pathways is unknown. Applying genetic instruments of the well-being spectrum and its four dimensions including life satisfaction, positive affect, neuroticism and depressive symptoms (n = 80,852 to 2,370,390), we performed two-sample Mendelian randomization analyses to estimate the causal effect of mental well-being on the genetically independent phenotype of aging (aging-GIP), a robust and representative aging phenotype, and its components including resilience, self-rated health, healthspan, parental lifespan and longevity (n = 36,745 to 1,012,240). Analyses were adjusted for income, education and occupation. All the data were from the largest available genome-wide association studies in populations of European descent. Better mental well-being spectrum (each one Z-score higher) was causally associated with a higher aging-GIP (β [95% confidence interval (CI)] in different models ranging from 1.00 [0.82-1.18] to 1.07 [0.91-1.24] standard deviations (s.d.)) independent of socioeconomic indicators. Similar association patterns were seen for resilience (β [95% CI] ranging from 0.97 [0.82-1.12] to 1.04 [0.91-1.17] s.d.), self-rated health (0.61 [0.43-0.79] to 0.76 [0.59-0.93] points), healthspan (odds ratio [95% CI] ranging from 1.23 [1.02-1.48] to 1.35 [1.11-1.65]) and parental lifespan (1.77 [0.010-3.54] to 2.95 [1.13-4.76] years). Two-step Mendelian randomization mediation analyses identified 33 out of 106 candidates as mediators between the well-being spectrum and the aging-GIP: mainly lifestyles (for example, TV watching and smoking), behaviours (for example, medication use) and diseases (for example, heart failure, attention-deficit hyperactivity disorder, stroke, coronary atherosclerosis and ischaemic heart disease), each exhibiting a mediation proportion of >5%. These findings underscore the importance of mental well-being in promoting healthy aging and inform preventive targets for bridging aging disparities attributable to suboptimal mental health.

Biomarkers of Aging and Relevant Evaluation Techniques: A Comprehensive Review

The risk of developing chronic illnesses and disabilities is increasing with age. To predict and prevent aging, biomarkers relevant to the aging process must be identified. This paper reviews the known molecular, cellular, and physiological biomarkers of aging. Moreover, we discuss the currently available technologies for identifying these biomarkers, and their applications and potential in aging research. We hope that this review will stimulate further research and innovation in this emerging and fast-growing field.

Adding value for clients during work disability assessments: A qualitative exploration from the perspective of medical examiners

BACKGROUND

Value-based healthcare delivery focuses on optimizing care provided by measuring the healthcare outcomes which are most important to the clients relative to the total care costs. However, the understanding of what adds value for clients during work disability assessment is lacking.

OBJECTIVE

To explore what medical examiners (MEs) perceive as valuable during the work disability assessment process, by exploring possible: 1) facilitators, 2) barriers and 3) opportunities to add value for the client during the work disability assessment.

METHODS

For this explorative qualitative study, 7 semi-structured interviews were conducted with MEs in the Netherlands. Thematic coding was performed for all interviews.

RESULTS

A large variety of facilitators (n = 22), barriers (n = 17) and opportunities (n = 11) were identified and inductively subdivided into four main themes: 1) coherent process, including all time related aspects, 2) interdisciplinary collaboration, including all aspects related to the collaboration between the ME and other professionals, 3) client-centred interaction, including all aspects related to the supportive interplay from the ME towards the client, and 4) information provision on all aspects during the work disability assessment process towards the client to ensure a valuable work disability assessment process.

CONCLUSIONS

The overview of identified possible facilitators, barriers and opportunities to add value for clients from the perspective of the ME may stimulate improvement in the current work disability assessment practice and to better match the client needs.

Using genetics and proteomics data to identify proteins causally related to COVID-19, healthspan and lifespan: a Mendelian randomization study

BACKGROUND

COVID-19 pandemic poses a heavy burden on public health and accounts for substantial mortality and morbidity. Proteins are building blocks of life, but specific proteins causally related to COVID-19, healthspan and lifespan have not been systematically examined.

METHODS

We conducted a Mendelian randomization study to assess the effects of 1,361 plasma proteins on COVID-19, healthspan and lifespan, using large GWAS of severe COVID-19 (up to 13,769 cases and 1,072,442 controls), COVID-19 hospitalization (32,519 cases and 2,062,805 controls) and SARS-COV2 infection (122,616 cases and 2,475,240 controls), healthspan (n = 300,477) and parental lifespan (~0.8 million of European ancestry).

RESULTS

We identified 35, 43, and 63 proteins for severe COVID, COVID-19 hospitalization, and SARS-COV2 infection, and 4, 32, and 19 proteins for healthspan, father's attained age, and mother's attained age. In addition to some proteins reported previously, such as SFTPD related to severe COVID-19, we identified novel proteins involved in inflammation and immunity (such as ICAM-2 and ICAM-5 which affect COVID-19 risk, CXCL9, HLA-DRA and LILRB4 for healthspan and lifespan), apoptosis (such as FGFR2 and ERBB4 which affect COVID-19 risk and FOXO3 which affect lifespan) and metabolism (such as PCSK9 which lowers lifespan). We found 2, 2 and 3 proteins shared between COVID-19 and healthspan/lifespan, such as CXADR and LEFTY2, shared between severe COVID-19 and healthspan/lifespan. Three proteins affecting COVID-19 and seven proteins affecting healthspan/lifespan are targeted by existing drugs.

CONCLUSIONS

Our study provided novel insights into protein targets affecting COVID-19, healthspan and lifespan, with implications for developing new treatment and drug repurposing.

Associations between sleep-behavioral traits and healthspan: A one-sample Mendelian randomization study based on 388,909 participants of the UK-Biobank

BACKGROUND

Although the association between sleep behavior and morbidity and mortality risk has been reported before, there is still uncertainty whether the observed associations are causal or confounding. Therefore, we investigated the causal relationships between sleep-behavioral traits and terminated healthspan risk using Mendelian randomization (MR).

METHODS

We conducted a one-sample MR analysis to evaluate causality between six sleep-behavioral traits (sleep duration, chronotype/morningness, napping, sleeplessness/insomnia, and getting up from bed) and risk of healthspan termination among 388, 909 UK Biobank (UKB) participants. Instrumental variables for sleep behaviors (N = 590) were obtained from recent genome-wide association studies (GWAS). We defined healthspan based on eight predominant health-terminating events associated with longevity (congestive heart failure, myocardial infarction, chronic obstructive pulmonary disease, stroke, dementia, diabetes, cancer, and death). We further constructed a sleep score and a weighted genetic risk score to increase the predictive ability of the sleep-behavioral traits. Cox regression models and Inverse Probability Treatment Weighting (IPTW) were implemented, followed by MR to assess causation. We used inverse-variance-weighted MR to estimate causal effects, and weighted-median and MR-egger for sensitivity analysis to test the pleiotropic effects.

RESULTS

In IPTW, we observed a decreased risk of terminated healthspan for healthy sleep behaviors such as 'sleep duration 7-8h/d' (Hazard ratio, HR = 0.93; 95 % confidence interval, CI: 0.92-0.96; P < 0.001); 'morningness' (HR = 0.95; 95%CI: 0.93-0.98; P < 0.01); 'napping' (HR = 0.93; 95%CI: 0.91-0.94; P < 0.001); 'easy getting up from bed' (HR = 0.91; 95%CI: 0.88-0.93; P < 0.001); and, 'never/rarely experience sleeplessness/insomnia' (HR = 0.94; 95%CI: 0.92-0.96; P < 0.001). MR results further indicated causal associations between healthy sleep duration (OR = 0.98; 95%CI: 0.97-1.00; P = 0.036) and insomnia (OR = 1.02; 95%CI: 1.01-1.03; P < 0.001) with terminated healthspan. MR-egger did not suggest any potential pleiotropy.

CONCLUSION

This study supports abnormal sleep duration and insomnia as potential causal risk factors for terminated healthspan. Thus, healthy sleep behavior is valuable for the extension of healthspan, and well-designed and tailored sleep health interventions are warranted.

DNA methylation marker identification and poly-methylation risk score in prediction of healthspan termination

Aim: To elucidate the epigenetic consequences of DNA methylation in healthspan termination (HST), considering the current limited understanding. Materials & methods: Genetically predicted DNA methylation models were established (n = 2478). These models were applied to genome-wide association study data on HST. Then, a poly-methylation risk score (PMRS) was established in 241,008 individuals from the UK Biobank. Results: Of the 63,046 CpGs from the prediction models, 13 novel CpGs were associated with HST. Furthermore, people with high PMRSs showed higher HST risk (hazard ratio: 1.18; 95% CI: 1.13-1.25). Conclusion: The study indicates that DNA methylation may influence HST by regulating the expression of genes (e.g., PRMT6, CTSK). PMRSs have a promising application in discriminating subpopulations to facilitate early prevention.

Novel insights into transfer RNA-derived small RNA (tsRNA) in cardio-metabolic diseases

Cardio-metabolic diseases, including a cluster of metabolic disorders and their secondary affections on cardiovascular physiology, are gradually brought to the forefront by researchers due to their high prevalence and mortality, as well as an unidentified pathogenesis. tRNA-derived small RNAs (tsRNAs), cleaved by several specific enzymes and once considered as some "metabolic junks" in the past, have been proved to possess numerous functions in human bodies. More interestingly, such a potential also seems to influence the progression of cardio-metabolic diseases to some extent. In this review, the biogenesis, classification and mechanisms of tsRNAs will be discussed based on some latest studies, and their relations with several cardio-metabolic diseases will be highlighted in sequence. Lastly, some future prospects, such as their clinical applications as biomarkers and therapeutic targets will also be mentioned, in order to provide researchers with a comprehensive understanding of the research status of tsRNAs as well as its association with cardio-metabolic diseases, thus presenting as a beacon to indicate directions for the next stage of study.

Mendelian randomization reveals apolipoprotein B shortens healthspan and possibly increases risk for Alzheimer's disease

Apolipoprotein B-100 (APOB) is a component of fat- and cholesterol-transporting molecules in the bloodstream. It is the main lipoprotein in low-density lipoprotein cholesterol (LDL) and has been implicated in conditions that end healthspan (the interval between birth and onset of chronic disease). However, APOB's direct relationship with healthspan remains uncertain. With Mendelian randomization, we show that higher levels of APOB and LDL shorten healthspan in humans. Multivariable Mendelian randomization of APOB and LDL on healthspan suggests that the predominant trait accounting for the relationship is APOB. In addition, we provide preliminary evidence that APOB increases risk for Alzheimer's disease, a condition that ends healthspan. If these relationships are causal, they suggest that interventions to improve healthspan in aging populations could include strategies targeting APOB. Ultimately, given that more than 44 million people currently suffer from Alzheimer's disease worldwide, such interventions are needed.

Causality-enriched epigenetic age uncouples damage and adaptation

Machine learning models based on DNA methylation data can predict biological age but often lack causal insights. By harnessing large-scale genetic data through epigenome-wide Mendelian randomization, we identified CpG sites potentially causal for aging-related traits. Neither the existing epigenetic clocks nor age-related differential DNA methylation are enriched in these sites. These CpGs include sites that contribute to aging and protect against it, yet their combined contribution negatively affects age-related traits. We established a new framework to introduce causal information into epigenetic clocks, resulting in DamAge and AdaptAge-clocks that track detrimental and adaptive methylation changes, respectively. DamAge correlates with adverse outcomes, including mortality, while AdaptAge is associated with beneficial adaptations. These causality-enriched clocks exhibit sensitivity to short-term interventions. Our findings provide a detailed landscape of CpG sites with putative causal links to lifespan and healthspan, facilitating the development of aging biomarkers, assessing interventions, and studying reversibility of age-associated changes.

The Gompertz Law emerges naturally from the inter-dependencies between sub-components in complex organisms

Understanding and facilitating healthy aging has become a major goal in medical research and it is becoming increasingly acknowledged that there is a need for understanding the aging phenotype as a whole rather than focusing on individual factors. Here, we provide a universal explanation for the emergence of Gompertzian mortality patterns using a systems approach to describe aging in complex organisms that consist of many inter-dependent subsystems. Our model relates to the Sufficient-Component Cause Model, widely used within the field of epidemiology, and we show that including inter-dependencies between subsystems and modeling the temporal evolution of subsystem failure results in Gompertizan mortality on the population level. Our model also provides temporal trajectories of mortality-risk for the individual. These results may give insight into understanding how biological age evolves stochastically within the individual, and how this in turn leads to a natural heterogeneity of biological age in a population.

Geroscience-Centric Perspective for Geriatric Psychiatry: Integrating Aging Biology With Geriatric Mental Health Research

The geroscience hypothesis asserts that physiological aging is caused by a small number of biological pathways. Despite the explosion of geroscience research over the past couple of decades, the research on how serious mental illnesses (SMI) affects the biological aging processes is still in its infancy. In this review, we aim to provide a critical appraisal of the emerging literature focusing on how we measure biological aging systematically, and in the brain and how SMIs affect biological aging measures in older adults. We will also review recent developments in the field of cellular senescence and potential targets for interventions for SMIs in older adults, based on the geroscience hypothesis.

Trend of incidence rate of age-related diseases: results from the National Health Insurance Service-National Sample Cohort (NHIS-NSC) database in Korea: a cross- sectional study

BACKGROUND

This study aimed to identify and select age-related diseases (ARDs) in Korea, which is about to have a super-aged society, and to elucidate patterns in their incidence rates.

METHODS

The National Health Insurance Service-National Sample Cohort, comprising 1 million health insurance and medical benefit beneficiaries in Korea from 2002 to 2019, was utilized. We selected 14 diseases with high disease burden and prevalence among Koreans from the 92 diseases defined in the Global Burden of Diseases, Injuries, and Risk Factors Study as ARDs. The annual incidence rate represented the number of patients newly diagnosed with an ARD each year from 2006 to 2019, excluding those with a history of ARD diagnosis from 2002 to 2005. The incidence rate by age was categorized into 10-year units based on age as of 2019. The number of patients with ARDs in each age group was used as the numerator, and the incidence rate for each age group was calculated with the age group as the denominator.

RESULTS

Regarding the annual incidence rates of ARDs from 2006 to 2019, chronic obstructive pulmonary disease, congestive heart failure, and ischemic heart disease decreased annually, whereas dyslipidemia, chronic kidney disease, cataracts, hearing loss, and Parkinson's disease showed a significant increase. Hypertension, diabetes, cerebrovascular disease, osteoporosis, osteoarthritis, and age-related macular degeneration initially displayed a gradual decrease in incidence but exhibited a tendency to increase after 2015. Concerning age-specific incidence rates of ARDs, two types of curves emerged. The first type, characterized by an exponential increase with age, was exemplified by congestive heart failure. The second type, marked by an exponential increase peaking between ages 60 and 80, followed by stability or decrease, was observed in 13 ARDs, excluding congestive heart failure. However, hypertension, ischemic heart disease, cerebrovascular disease, chronic obstructive pulmonary disease, and hearing loss in men belonged to the first type.

CONCLUSIONS

From an epidemiological perspective, there are similar characteristics in age-specific ARDs that increase with age, reaching a peak followed by a plateau or decrease in Koreans.

Measuring healthy ageing: current and future tools

Human ageing is a complex, multifactorial process characterised by physiological damage, increased risk of age-related diseases and inevitable functional deterioration. As the population of the world grows older, placing significant strain on social and healthcare resources, there is a growing need to identify reliable and easy-to-employ markers of healthy ageing for early detection of ageing trajectories and disease risk. Such markers would allow for the targeted implementation of strategies or treatments that can lessen suffering, disability, and dependence in old age. In this review, we summarise the healthy ageing scores reported in the literature, with a focus on the past 5 years, and compare and contrast the variables employed. The use of approaches to determine biological age, molecular biomarkers, ageing trajectories, and multi-omics ageing scores are reviewed. We conclude that the ideal healthy ageing score is multisystemic and able to encompass all of the potential alterations associated with ageing. It should also be longitudinal and able to accurately predict ageing complications at an early stage in order to maximize the chances of successful early intervention.

Molecular bases of comorbidities: present and future perspectives

Co-occurrence of diseases decreases patient quality of life, complicates treatment choices, and increases mortality. Analyses of electronic health records present a complex scenario of comorbidity relationships that vary by age, sex, and cohort under study. The study of similarities between diseases using 'omics data, such as genes altered in diseases, gene expression, proteome, and microbiome, are fundamental to uncovering the origin of, and potential treatment for, comorbidities. Recent studies have produced a first generation of genetic interpretations for as much as 46% of the comorbidities described in large cohorts. Integrating different sources of molecular information and using artificial intelligence (AI) methods are promising approaches for the study of comorbidities. They may help to improve the treatment of comorbidities, including the potential repositioning of drugs.

Healthy lifestyle in late-life, longevity genes, and life expectancy among older adults: a 20-year, population-based, prospective cohort study

BACKGROUND

Lifestyle and longevity genes have different and important roles in the human lifespan; however, the association between a healthy lifestyle in late-life and life expectancy mediated by genetic risk is yet to be elucidated. We aimed to investigate the associations of healthy lifestyle in late-life and genetic risk with life expectancy among older adults.

METHODS

A weighted healthy lifestyle score was constructed from the following variables: current non-smoking, non-harmful alcohol consumption, regular physical activity, and a healthy diet. Participants were recruited from the Chinese Longitudinal Healthy Longevity Survey, a prospective community-based cohort study that took place between 1998 and 2018. Eligible participants were aged 65 years and older with available information on lifestyle factors at baseline, and then were categorised into unhealthy (bottom tertile of the weighted healthy lifestyle score), intermediate (middle tertile), and healthy (top tertile) lifestyle groups. A genetic risk score was constructed based on 11 lifespan loci among 9633 participants, divided by the median and classified into low and high genetic risk groups. Stratified Cox proportional hazard regression was used to estimate the interaction between genetic and lifestyle factors on all-cause mortality risk.

FINDINGS

Between Jan 13, 1998, and Dec 31, 2018, 36 164 adults aged 65 years and older were recruited, among whom a total of 27 462 deaths were documented during a median follow-up of 3·12 years (IQR 1·62-5·94) and included in the lifestyle association analysis. Compared with the unhealthy lifestyle category, participants in the healthy lifestyle group had a lower all-cause mortality risk (hazard ratio [HR] 0·56 [95% CI 0·54-0·57]; p<0·0001). The highest mortality risk was observed in individuals in the high genetic risk and unhealthy lifestyle group (HR 1·80 [95% CI 1·63-1·98]; p<0·0001). The absolute risk reduction was greater for participants in the high genetic risk group. A healthy lifestyle was associated with a gain of 3·84 years (95% CI 3·05-4·64) at the age of 65 years in the low genetic risk group, and 4·35 years (3·70-5·06) in the high genetic risk group.

INTERPRETATION

A healthy lifestyle, even in late-life, was associated with lower mortality risk and longer life expectancy among Chinese older adults, highlighting the importance of a healthy lifestyle in extending the lifespan, especially for individuals with high genetic risk.

FUNDING

National Natural Science Foundation of China.

TRANSLATION

For the Mandarin translation of the abstract see Supplementary Materials section.

Discovering Biological Mechanisms of Exceptional Human Health Span and Life Span

Humans age at different rates and families with exceptional longevity provide an opportunity to understand why some people age slower than others. Unique features exhibited by centenarians include a family history of extended life span, compression of morbidity with resultant extension of health span, and longevity-associated biomarker profiles. These biomarkers, including low-circulating insulin-like growth factor 1 (IGF-1) and elevated high-density lipoprotein (HDL) cholesterol levels, are associated with functional genotypes that are enriched in centenarians, suggesting that they may be causative for longevity. While not all genetic discoveries from centenarians have been validated, in part due to exceptional life span being a rare phenotype in the general population, the APOE2 and FOXO3a genotypes have been confirmed in a number of populations with exceptional longevity. However, life span is now recognized as a complex trait and genetic research methods to study longevity are rapidly extending beyond classical Mendelian genetics to polygenic inheritance methodologies. Moreover, newer approaches are suggesting that pathways that have been recognized for decades to control life span in animals may also regulate life span in humans. These discoveries led to strategic development of therapeutics that may delay aging and prolong health span.

Applications of multi-omics analysis in human diseases

Multi-omics usually refers to the crossover application of multiple high-throughput screening technologies represented by genomics, transcriptomics, single-cell transcriptomics, proteomics and metabolomics, spatial transcriptomics, and so on, which play a great role in promoting the study of human diseases. Most of the current reviews focus on describing the development of multi-omics technologies, data integration, and application to a particular disease; however, few of them provide a comprehensive and systematic introduction of multi-omics. This review outlines the existing technical categories of multi-omics, cautions for experimental design, focuses on the integrated analysis methods of multi-omics, especially the approach of machine learning and deep learning in multi-omics data integration and the corresponding tools, and the application of multi-omics in medical researches (e.g., cancer, neurodegenerative diseases, aging, and drug target discovery) as well as the corresponding open-source analysis tools and databases, and finally, discusses the challenges and future directions of multi-omics integration and application in precision medicine. With the development of high-throughput technologies and data integration algorithms, as important directions of multi-omics for future disease research, single-cell multi-omics and spatial multi-omics also provided a detailed introduction. This review will provide important guidance for researchers, especially who are just entering into multi-omics medical research.

Multivariate genome-wide analysis of aging-related traits identifies novel loci and new drug targets for healthy aging

The concept of aging is complex, including many related phenotypes such as healthspan, lifespan, extreme longevity, frailty and epigenetic aging, suggesting shared biological underpinnings; however, aging-related endpoints have been primarily assessed individually. Using data from these traits and multivariate genome-wide association study methods, we modeled their underlying genetic factor ('mvAge'). mvAge (effective n = ~1.9 million participants of European ancestry) identified 52 independent variants in 38 genomic loci. Twenty variants were novel (not reported in input genome-wide association studies). Transcriptomic imputation identified age-relevant genes, including VEGFA and PHB1. Drug-target Mendelian randomization with metformin target genes showed a beneficial impact on mvAge (P value = 8.41 × 10-5). Similarly, genetically proxied thiazolidinediones (P value = 3.50 × 10-10), proprotein convertase subtilisin/kexin 9 inhibition (P value = 1.62 × 10-6), angiopoietin-like protein 4, beta blockers and calcium channel blockers also had beneficial Mendelian randomization estimates. Extending the drug-target Mendelian randomization framework to 3,947 protein-coding genes prioritized 122 targets. Together, these findings will inform future studies aimed at improving healthy aging.

Measuring healthy ageing: current and future tools

Human ageing is a complex, multifactorial process characterised by physiological damage, increased risk of age-related diseases and inevitable functional deterioration. As the population of the world grows older, placing significant strain on social and healthcare resources, there is a growing need to identify reliable and easy-to-employ markers of healthy ageing for early detection of ageing trajectories and disease risk. Such markers would allow for the targeted implementation of strategies or treatments that can lessen suffering, disability, and dependence in old age. In this review, we summarise the healthy ageing scores reported in the literature, with a focus on the past 5 years, and compare and contrast the variables employed. The use of approaches to determine biological age, molecular biomarkers, ageing trajectories, and multi-omics ageing scores are reviewed. We conclude that the ideal healthy ageing score is multisystemic and able to encompass all of the potential alterations associated with ageing. It should also be longitudinal and able to accurately predict ageing complications at an early stage in order to maximize the chances of successful early intervention.

Polygenic risk for termination of the 'healthspan' and its interactions with lifestyle factors: A prospective cohort study based on 288,359 participants

OBJECTIVES

To investigate whether a polygenic risk score (PRS) and its interactions with lifestyle factors are associated with termination of the 'healthspan' (the number of years living without serious diseases or degeneration).

DESIGN, EXPOSURES AND PARTICIPANTS

Death or the incidence of any of seven independent morbidities (cancer, congestive heart failure, myocardial infarction, chronic obstructive pulmonary disease, stroke, dementia, and diabetes) strongly associated with aging were considered to define the termination of the healthspan. A total of 288,359 healthy participants from the UK Biobank were included in this prospective cohort study to evaluate the associations between PRS, lifestyle, and healthspan. The PRS was generated by weighting 12 healthspan-related genetic loci, which and scores were then categorized into three groups in Cox regression models. A lifestyle index was developed that incorporated body mass index (BMI), alcohol consumption, diet, smoking, and physical activity, and these scores were also categorized into three groups. The risk of termination of the healthspan was calculated across the different PRS and lifestyle index groups using Cox regression models. Interactions were estimated with the marginal effect of lifestyle on the risk of termination of healthspan across values of the moderator PRS using kernel estimation.

RESULTS

During an average follow-up of 9.83 years, 68,903 healthspan-termination events occurred. It was calculated that people with high polygenic risk could reduce their risk of healthspan termination by 40 % if they maintain a favorable lifestyle. The marginal effect of lifestyle on the risk of healthspan termination increased with growing genetic risk. Smoking and diet showed monotonic changes in opposite directions, while BMI, physical activity, and alcohol had a U-shaped interaction with genetic risk.

CONCLUSIONS

Favorable lifestyle can attenuate the risk of termination of the healthspan, especially for people with high genetic risk. The improvement afforded by ideal lifestyle behaviors varies for each individual.

Female "Paradox" in Atrial Fibrillation-Role of Left Truncation Due to Competing Risks

Female sex in patients with atrial fibrillation (AF) is a controversial and paradoxical risk factor for stroke-controversial because it increases the risk of stroke only among older women of some ethnicities and paradoxical because it appears to contradict male predominance in cardiovascular diseases. However, the underlying mechanism remains unclear. We conducted simulations to examine the hypothesis that this sex difference is generated non-causally through left truncation due to competing risks (CR) such as coronary artery diseases, which occur more frequently among men than among women and share common unobserved causes with stroke. We modeled the hazards of stroke and CR with correlated heterogeneous risk. We assumed that some people died of CR before AF diagnosis and calculated the hazard ratio of female sex in the left-truncated AF population. In this situation, female sex became a risk factor for stroke in the absence of causal roles. The hazard ratio was attenuated in young populations without left truncation and in populations with low CR and high stroke incidence, which is consistent with real-world observations. This study demonstrated that spurious risk factors can be identified through left truncation due to correlated CR. Female sex in patients with AF may be a paradoxical risk factor for stroke.

Multi-omic underpinnings of epigenetic aging and human longevity

Biological aging is accompanied by increasing morbidity, mortality, and healthcare costs; however, its molecular mechanisms are poorly understood. Here, we use multi-omic methods to integrate genomic, transcriptomic, and metabolomic data and identify biological associations with four measures of epigenetic age acceleration and a human longevity phenotype comprising healthspan, lifespan, and exceptional longevity (multivariate longevity). Using transcriptomic imputation, fine-mapping, and conditional analysis, we identify 22 high confidence associations with epigenetic age acceleration and seven with multivariate longevity. FLOT1, KPNA4, and TMX2 are novel, high confidence genes associated with epigenetic age acceleration. In parallel, cis-instrument Mendelian randomization of the druggable genome associates TPMT and NHLRC1 with epigenetic aging, supporting transcriptomic imputation findings. Metabolomics Mendelian randomization identifies a negative effect of non-high-density lipoprotein cholesterol and associated lipoproteins on multivariate longevity, but not epigenetic age acceleration. Finally, cell-type enrichment analysis implicates immune cells and precursors in epigenetic age acceleration and, more modestly, multivariate longevity. Follow-up Mendelian randomization of immune cell traits suggests lymphocyte subpopulations and lymphocytic surface molecules affect multivariate longevity and epigenetic age acceleration. Our results highlight druggable targets and biological pathways involved in aging and facilitate multi-omic comparisons of epigenetic clocks and human longevity.

Programmed ageing: decline of stem cell renewal, immunosenescence, and Alzheimer's disease

The characteristic maximum lifespan varies enormously across animal species from a few hours to hundreds of years. This argues that maximum lifespan, and the ageing process that itself dictates lifespan, are to a large extent genetically determined. Although controversial, this is supported by firm evidence that semelparous species display evolutionarily programmed ageing in response to reproductive and environmental cues. Parabiosis experiments reveal that ageing is orchestrated systemically through the circulation, accompanied by programmed changes in hormone levels across a lifetime. This implies that, like the circadian and circannual clocks, there is a master 'clock of age' (circavital clock) located in the limbic brain of mammals that modulates systemic changes in growth factor and hormone secretion over the lifespan, as well as systemic alterations in gene expression as revealed by genomic methylation analysis. Studies on accelerated ageing in mice, as well as human longevity genes, converge on evolutionarily conserved fibroblast growth factors (FGFs) and their receptors, including KLOTHO, as well as insulin-like growth factors (IGFs) and steroid hormones, as key players mediating the systemic effects of ageing. Age-related changes in these and multiple other factors are inferred to cause a progressive decline in tissue maintenance through failure of stem cell replenishment. This most severely affects the immune system, which requires constant renewal from bone marrow stem cells. Age-related immune decline increases risk of infection whereas lifespan can be extended in germfree animals. This and other evidence suggests that infection is the major cause of death in higher organisms. Immune decline is also associated with age-related diseases. Taking the example of Alzheimer's disease (AD), we assess the evidence that AD is caused by immunosenescence and infection. The signature protein of AD brain, Aβ, is now known to be an antimicrobial peptide, and Aβ deposits in AD brain may be a response to infection rather than a cause of disease. Because some cognitively normal elderly individuals show extensive neuropathology, we argue that the location of the pathology is crucial - specifically, lesions to limbic brain are likely to accentuate immunosenescence, and could thus underlie a vicious cycle of accelerated immune decline and microbial proliferation that culminates in AD. This general model may extend to other age-related diseases, and we propose a general paradigm of organismal senescence in which declining stem cell proliferation leads to programmed immunosenescence and mortality.

Aging Hallmarks and the Role of Oxidative Stress

Aging is a complex biological process accompanied by a progressive decline in the physical function of the organism and an increased risk of age-related chronic diseases such as cardiovascular diseases, cancer, and neurodegenerative diseases. Studies have established that there exist nine hallmarks of the aging process, including (i) telomere shortening, (ii) genomic instability, (iii) epigenetic modifications, (iv) mitochondrial dysfunction, (v) loss of proteostasis, (vi) dysregulated nutrient sensing, (vii) stem cell exhaustion, (viii) cellular senescence, and (ix) altered cellular communication. All these alterations have been linked to sustained systemic inflammation, and these mechanisms contribute to the aging process in timing not clearly determined yet. Nevertheless, mitochondrial dysfunction is one of the most important mechanisms contributing to the aging process. Mitochondria is the primary endogenous source of reactive oxygen species (ROS). During the aging process, there is a decline in ATP production and elevated ROS production together with a decline in the antioxidant defense. Elevated ROS levels can cause oxidative stress and severe damage to the cell, organelle membranes, DNA, lipids, and proteins. This damage contributes to the aging phenotype. In this review, we summarize recent advances in the mechanisms of aging with an emphasis on mitochondrial dysfunction and ROS production.

Current Trends and Approaches to the Search for Genetic Determinants of Aging and Longevity

Aging is a natural process of extinction of the body and the main aspect that determines the life expectancy for individuals who have survived to the post-reproductive period. The process of aging is accompanied by certain physiological, immune, and metabolic changes in the body, as well as the development of age-related diseases. The contribution of genetic factors to human life expectancy is estimated at about 25-30%. Despite the success in identifying genes and metabolic pathways that may be involved in the life extension process in model organisms, the key question remains to what extent these data can be extrapolated to humans, for example, because of the complexity of its biological and sociocultural systems, as well as possible species differences in life expectancy and causes of mortality. New molecular genetic methods have significantly expanded the possibilities for searching for genetic factors of human life expectancy and identifying metabolic pathways of aging, the interaction of genes and transcription factors, the regulation of gene expression at the level of transcription, and epigenetic modifications. The review presents the latest research and current strategies for studying the genetic basis of human aging and longevity: the study of individual candidate genes in genetic population studies, variations identified by the GWAS method, immunogenetic differences in aging, and genomic studies to identify factors of "healthy aging." Understanding the mechanisms of the interaction between factors affecting the life expectancy and the possibility of their regulation can become the basis for developing comprehensive measures to achieve healthy longevity.

Supplementary Information

The online version contains supplementary material available at 10.1134/S1022795422120067.

Association between gut microbiota and longevity: a genetic correlation and mendelian randomization study

BACKGROUND

Longevity is one of the most complex phenotypes, and its genetic basis remains unclear. This study aimed to explore the genetic correlation and potential causal association between gut microbiota and longevity.

RESULTS

Linkage disequilibrium score (LDSC) regression analysis and a bi-directional two-sample Mendelian Randomization (MR) analysis were performed to analyze gut microbiota and longevity-related traits. LDSC analysis detected four candidate genetic correlations, including Veillonella (genetic correlation = 0.5578, P = 4.67 × 10^- 2) and Roseburia (genetic correlation = 0.4491, P = 2.67 × 10^- 2) for longevity, Collinsella (genetic correlation = 0.3144, P = 4.07 × 10^- 2) for parental lifespan and Sporobacter (genetic correlation = 0.2092, P = 3.53 × 10^- 2) for healthspan. Further MR analysis observed suggestive causation between Collinsella and parental longevity (father's age at death) (weighted median: b = 1.79 × 10^- 3, P = 3.52 × 10^- 2). Reverse MR analysis also detected several causal effects of longevity-related traits on gut microbiota, such as longevity and Sporobacter (IVW: b = 7.02 × 10^- 1, P = 4.21 × 10^- 25). Statistical insignificance of the heterogeneity test and pleiotropy test supported the validity of the MR study.

CONCLUSION

Our study found evidence that gut microbiota is causally associated with longevity, or vice versa, providing novel clues for understanding the roles of gut microbiota in aging development.

Measuring biological age using omics data

Age is the key risk factor for diseases and disabilities of the elderly. Efforts to tackle age-related diseases and increase healthspan have suggested targeting the ageing process itself to 'rejuvenate' physiological functioning. However, achieving this aim requires measures of biological age and rates of ageing at the molecular level. Spurred by recent advances in high-throughput omics technologies, a new generation of tools to measure biological ageing now enables the quantitative characterization of ageing at molecular resolution. Epigenomic, transcriptomic, proteomic and metabolomic data can be harnessed with machine learning to build 'ageing clocks' with demonstrated capacity to identify new biomarkers of biological ageing.

What Is an Aging-Related Disease? An Epidemiological Perspective

There are no established or standardized definitions of aging-related disease. Data from the Global Burden of Diseases, Injuries, and Risk Factors Study 2019 were used to model the relationship between age and incidence of diseases. Clustering analysis identified 4 groups of noncommunicable diseases: Group A diseases with an exponential increase in incidence with age; Group B diseases with an exponential increase in incidence that usually peaked in late life which then declined or plateaued at the oldest ages; and Groups C and D diseases with an onset in earlier life and where incidence was stable or decreased in old age. From an epidemiological perspective, Group A diseases are "aging-related diseases" because there is an exponential association between age and incidence, and the slope of the incidence curves remains positive throughout old age. These included the major noncommunicable diseases dementia, stroke, and ischemic heart disease. Whether any of the other diseases are aging-related is uncertain because their incidence either does not change or more often decreases in old age. Only biological studies can determine how the aging process contributes to any of these diseases and this may lead to a reclassification of disease on the basis of whether they are directly caused by or are in continuity with the biological changes of aging. In the absence of this mechanistic data, we propose the term "aging-related disease" should be used with precision based on epidemiological evidence.

Co-Inheritance of Variation in All-Cause Mortality and Biochemical Risk Factors

Biomarkers may be useful endophenotypes for genetic studies if they share genetic sources of variation with the outcome, for example, with all-cause mortality. Australian adult study participants who had reported their parental survival information were included in the study: 14,169 participants had polygenic risk scores (PRS) from genotyping and up to 13,365 had biomarker results. We assessed associations between participants' biomarker results and parental survival, and between biomarker results and eight parental survival PRS at varying p-value cut-offs. Survival in parents was associated with participants' serum bilirubin, C-reactive protein, HDL cholesterol, triglycerides and uric acid, and with LDL cholesterol for participants' fathers but not for their mothers. PRS for all-cause mortality were associated with liver function tests (alkaline phosphatase, butyrylcholinesterase, gamma-glutamyl transferase), metabolic tests (LDL and HDL cholesterol, triglycerides, uric acid), and acute-phase reactants (C-reactive protein, globulins). Association between offspring biomarker results and parental survival demonstrates the existence of familial effects common to both, while associations between biomarker results and PRS for mortality favor at least a partial genetic cause of this covariation. Identification of genetic loci affecting mortality-associated biomarkers offers a route to the identification of additional loci affecting mortality.

Lipoprotein(a) and cardiovascular and valvular diseases: A genetic epidemiological perspective

Rates of atherosclerotic cardiovascular diseases (CVD) in the Western world have spectacularly decreased over the past 50 years. However, a substantial proportion of high-risk patients still develop heart attacks, strokes and valvular heart diseases despite benefiting from state-of-the-art treatments including lipid-lowering therapies. Over the past 10-15 years, it has become increasingly clear that Lipoprotein(a) (Lp[a]) is a critical component of this so-called residual risk. Genetic association studies revealed that Lp(a) is robustly, independently and causally associated with a broad range of cardiovascular and valvular heart diseases. Up to 1 billion people around the globe may have an Lp(a) level that places them in a high-risk category. Lp(a) is strongly associated with calcific aortic valve stenosis (CAVS), coronary artery disease (CAD), peripheral arterial disease (PAD) and to a lesser extent with ischemic stroke (IS) and heart failure (HF). Because of this strong association with cardiovascular and valvular heart diseases, Lp(a) even emerged as one of the most important genetic determinants of human lifespan and healthspan. Here, we review the evidence from the largest and most informative genetic association studies and prospective studies that have investigated the association between Lp(a) and human lifespan, healthspan, CVD, CAVS and non-cardiovascular diseases. We present Lp(a) threshold values that may be clinically relevant and identify other cardiovascular risk factors that may modulate the absolute risk of CVD in individuals with high Lp(a) levels. Finally, we identify key clinical and research questions that require further investigation to eventually and optimally reduce CVD risk in patients with high Lp(a) levels.

Shared genomic architectures of COVID-19 and antisocial behavior

Little is known about the genetics of norm violation and aggression in relation to coronavirus disease 2019 (COVID-19). To investigate this, we used summary statistics from genome-wide association studies and linkage disequilibrium score regression to calculate a matrix of genetic correlations (r_gs) for antisocial behavior (ASB), COVID-19, and various health and behavioral traits. After false-discovery rate correction, ASB was genetically correlated with COVID-19 (r_g = 0.51; P = 1.54E-02) and 19 other traits. ASB and COVID-19 were both positively genetically correlated with having a noisy workplace, doing heavy manual labor, chronic obstructive pulmonary disease, and genitourinary diseases. ASB and COVID-19 were both inversely genetically correlated with average income, education years, healthspan, verbal reasoning, lifespan, cheese intake, and being breastfed as a baby. But keep in mind that r_gs are not necessarily causal. And, if causal, their prevailing directions of effect (which causes which) are indiscernible from r_gs alone. Moreover, the SNP-heritability ([Formula: see text]) estimates for two measures of COVID-19 were very small, restricting the overlap of genetic variance in absolute terms between ASB and COVID-19. Nonetheless, our findings suggest that those with antisocial tendencies possibly have a higher risk of exposure to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) than those without antisocial tendencies. This may have been especially true early in the pandemic before vaccines against SARS-CoV-2 were available and before the emergence of the highly transmissible Omicron variant.

Genetic associations with healthy ageing among Chinese adults

The genetic basis of overall healthy ageing, especially among the East-Asian population is understudied. We conducted a genome-wide association study among 1618 Singapore Chinese elderly participants (65 years or older) ascertained to have aged healthily and compared their genome-wide genotypes to 6221 participants who did not age healthily, after a 20-year follow-up. Two genetic variants were identified (PMeta < 2.59 × 10-8) to be associated with healthy aging, including the LRP1B locus previously associated in long-lived individuals without cognitive decline. Our study sheds additional insights on the genetic basis of healthy ageing.

Mitochondrial and metabolic dysfunction in ageing and age-related diseases

Organismal ageing is accompanied by progressive loss of cellular function and systemic deterioration of multiple tissues, leading to impaired function and increased vulnerability to death. Mitochondria have become recognized not merely as being energy suppliers but also as having an essential role in the development of diseases associated with ageing, such as neurodegenerative and cardiovascular diseases. A growing body of evidence suggests that ageing and age-related diseases are tightly related to an energy supply and demand imbalance, which might be alleviated by a variety of interventions, including physical activity and calorie restriction, as well as naturally occurring molecules targeting conserved longevity pathways. Here, we review key historical advances and progress from the past few years in our understanding of the role of mitochondria in ageing and age-related metabolic diseases. We also highlight emerging scientific innovations using mitochondria-targeted therapeutic approaches.

Biological mechanisms of aging predict age-related disease co-occurrence in patients

Genetic, environmental, and pharmacological interventions into the aging process can confer resistance to multiple age-related diseases in laboratory animals, including rhesus monkeys. These findings imply that individual mechanisms of aging might contribute to the co-occurrence of age-related diseases in humans and could be targeted to prevent these conditions simultaneously. To address this question, we text mined 917,645 literature abstracts followed by manual curation and found strong, non-random associations between age-related diseases and aging mechanisms in humans, confirmed by gene set enrichment analysis of GWAS data. Integration of these associations with clinical data from 3.01 million patients showed that age-related diseases associated with each of five aging mechanisms were more likely than chance to be present together in patients. Genetic evidence revealed that innate and adaptive immunity, the intrinsic apoptotic signaling pathway and activity of the ERK1/2 pathway were associated with multiple aging mechanisms and diverse age-related diseases. Mechanisms of aging hence contribute both together and individually to age-related disease co-occurrence in humans and could potentially be targeted accordingly to prevent multimorbidity.

A catalogue of omics biological ageing clocks reveals substantial commonality and associations with disease risk

Biological age (BA), a measure of functional capacity and prognostic of health outcomes that discriminates between individuals of the same chronological age (chronAge), has been estimated using a variety of biomarkers. Previous comparative studies have mainly used epigenetic models (clocks), we use ~1000 participants to compare fifteen omics ageing clocks, with correlations of 0.21-0.97 with chronAge, even with substantial sub-setting of biomarkers. These clocks track common aspects of ageing with 95% of the variance in chronAge being shared among clocks. The difference between BA and chronAge - omics clock age acceleration (OCAA) - often associates with health measures. One year’s OCAA typically has the same effect on risk factors/10-year disease incidence as 0.09/0.25 years of chronAge. Epigenetic and IgG glycomics clocks appeared to track generalised ageing while others capture specific risks. We conclude BA is measurable and prognostic and that future work should prioritise health outcomes over chronAge.

Mendelian randomization of genetically independent aging phenotypes identifies LPA and VCAM1 as biological targets for human aging

Length and quality of life are important to us all, yet identification of promising drug targets for human aging using genetics has had limited success. In the present study, we combine six European-ancestry genome-wide association studies of human aging traits-healthspan, father and mother lifespan, exceptional longevity, frailty index and self-rated health-in a principal component framework that maximizes their shared genetic architecture. The first principal component (aging-GIP1) captures both length of life and indices of mental and physical wellbeing. We identify 27 genomic regions associated with aging-GIP1, and provide additional, independent evidence for an effect on human aging for loci near HTT and MAML3 using a study of Finnish and Japanese survival. Using proteome-wide, two-sample, Mendelian randomization and colocalization, we provide robust evidence for a detrimental effect of blood levels of apolipoprotein(a) and vascular cell adhesion molecule 1 on aging-GIP1. Together, our results demonstrate that combining multiple aging traits using genetic principal components enhances the power to detect biological targets for human aging.

Utility of polygenic embryo screening for disease depends on the selection strategy

Polygenic risk scores (PRSs) have been offered since 2019 to screen in vitro fertilization embryos for genetic liability to adult diseases, despite a lack of comprehensive modeling of expected outcomes. Here we predict, based on the liability threshold model, the expected reduction in complex disease risk following polygenic embryo screening for a single disease. A strong determinant of the potential utility of such screening is the selection strategy, a factor that has not been previously studied. When only embryos with a very high PRS are excluded, the achieved risk reduction is minimal. In contrast, selecting the embryo with the lowest PRS can lead to substantial relative risk reductions, given a sufficient number of viable embryos. We systematically examine the impact of several factors on the utility of screening, including: variance explained by the PRS, number of embryos, disease prevalence, parental PRSs, and parental disease status. We consider both relative and absolute risk reductions, as well as population-averaged and per-couple risk reductions, and also examine the risk of pleiotropic effects. Finally, we confirm our theoretical predictions by simulating 'virtual' couples and offspring based on real genomes from schizophrenia and Crohn's disease case-control studies. We discuss the assumptions and limitations of our model, as well as the potential emerging ethical concerns.

Genetic and phenotypic analysis of the causal relationship between aging and COVID-19

Background

Epidemiological studies revealed that the elderly and those with comorbidities are most affected by COVID-19, but it is important to investigate shared genetic mechanisms between COVID-19 risk and aging.

Methods

We conducted a multi-instrument Mendelian Randomization analysis of multiple lifespan-related traits and COVID-19. Aging clock models were applied to the subjects with different COVID-19 conditions in the UK-Biobank cohort. We performed a bivariate genomic scan for age-related COVID-19 and Mendelian Randomization analysis of 389 immune cell traits to investigate their effect on lifespan and COVID-19 risk.

Results

We show that the genetic variation that supports longer life is significantly associated with the lower risk of COVID-19 infection and hospitalization. The odds ratio is 0.31 (P = 9.7 × 10⁻⁶) and 0.46 (P = 3.3 × 10⁻⁴), respectively, per additional 10 years of life. We detect an association between biological age acceleration and future incidence and severity of COVID-19 infection. Genetic profiling of age-related COVID-19 infection indicates key contributions of Notch signaling and immune system development. We reveal a negative correlation between the effects of immune cell traits on lifespan and COVID-19 risk. We find that lower B-cell CD19 levels are indicative of an increased risk of COVID-19 and decreased life expectancy, which is further validated by COVID-19 clinical data.

Conclusions

Our analysis suggests that the factors that accelerate aging lead to an increased COVID-19 risk and point to the importance of Notch signaling and B cells in both. Interventions that target these factors to reduce biological age may reduce the risk of COVID-19.

Older adults and those with comorbidities are more likely to develop severe COVID-19 if infected with SARS-CoV-2. In this study, we investigate the genetic factors underlying the link between aging and COVID-19. Using data on the genetic variation between individuals and statistical methods to allow us to determine causality, we find that genetic variation associated with longer lifespan is associated with reduced risk of COVID-19 infection and hospitalization. We also find that acceleration of biological age (i.e., the age of your body based on physiological measurements rather than time) is associated with future incidence and severity of COVID-19, and identify some of the key cells and molecules involved in aging-related COVID-19 risk. Our study helps to characterize the relationship between aging and COVID-19, which may help to identify strategies to protect or treat older adults.

Ying et al. conduct a multi-instrument Mendelian randomization study looking at the link between aging and COVID-19 risk. They observe an association between genetic variation implicated in longevity and decreased risk of COVID-19 infection and hospitalization, with Notch signaling and immune system development loci found to be important in aging-related COVID-19 risk.

Human genetic analyses of organelles highlight the nucleus in age-related trait heritability

Most age-related human diseases are accompanied by a decline in cellular organelle integrity, including impaired lysosomal proteostasis and defective mitochondrial oxidative phosphorylation. An open question, however, is the degree to which inherited variation in or near genes encoding each organelle contributes to age-related disease pathogenesis. Here, we evaluate if genetic loci encoding organelle proteomes confer greater-than-expected age-related disease risk. As mitochondrial dysfunction is a 'hallmark' of aging, we begin by assessing nuclear and mitochondrial DNA loci near genes encoding the mitochondrial proteome and surprisingly observe a lack of enrichment across 24 age-related traits. Within nine other organelles, we find no enrichment with one exception: the nucleus, where enrichment emanates from nuclear transcription factors. In agreement, we find that genes encoding several organelles tend to be 'haplosufficient,' while we observe strong purifying selection against heterozygous protein-truncating variants impacting the nucleus. Our work identifies common variation near transcription factors as having outsize influence on age-related trait risk, motivating future efforts to determine if and how this inherited variation then contributes to observed age-related organelle deterioration.

Association between housing type and accelerated biological aging in different sexes: moderating effects of health behaviors

Introduction: Despite associated with multiple geriatric disorders, whether housing type, an indicator of socioeconomic status (SES) and environmental factors, is associated with accelerated biological aging is unknown. Furthermore, although individuals with low-SES have higher body mass index (BMI) and are more likely to smoke, whether BMI and smoking status moderate the association between SES and biological aging is unclear. We examined these questions in urbanized low-SES older community-dwelling adults.

Methods: First, we analyzed complete blood count data using the cox proportional hazards model and derived measures for biological age (BA) and biological age acceleration (BAA, the higher the more accelerated aging) (N = 376). Subsequently, BAA was regressed on housing type, controlling for covariates, including four other SES indicators. Interaction terms between housing type and BMI/smoking status were separately added to examine their moderating effects. Total sample and sex-stratified analyses were performed.

Results: There were significant differences between men and women in housing type and BAA. Compared to residents in ≥3 room public or private housing, older adults resided in 1–2 room public housing had a higher BAA. Furthermore, BMI attenuated the association between housing type and BAA. In sex-stratified analyses, the main and interaction effects were only significant in women. In men, smoking status instead aggravated the association between housing type and BAA.

Conclusion: Controlling for other SES indicators, housing type is an independent socio-environmental determinant of BA and BAA in a low-SES urbanized population. There were also sex differences in the moderating effects of health behaviors on biological aging.

Exploring domains, clinical implications and environmental associations of a deep learning marker of biological ageing

Deep Neural Networks (DNN) have been recently developed for the estimation of Biological Age (BA), the hypothetical underlying age of an organism, which can differ from its chronological age (CA). Although promising, these population-specific algorithms warrant further characterization and validation, since their biological, clinical and environmental correlates remain largely unexplored. Here, an accurate DNN was trained to compute BA based on 36 circulating biomarkers in an Italian population (N = 23,858; age ≥ 35 years; 51.7% women). This estimate was heavily influenced by markers of metabolic, heart, kidney and liver function. The resulting Δage (BA-CA) significantly predicted mortality and hospitalization risk for all and specific causes. Slowed biological aging (Δage < 0) was associated with higher physical and mental wellbeing, healthy lifestyles (e.g. adherence to Mediterranean diet) and higher socioeconomic status (educational attainment, household income and occupational status), while accelerated aging (Δage > 0) was associated with smoking and obesity. Together, lifestyles and socioeconomic variables explained ~48% of the total variance in Δage, potentially suggesting the existence of a genetic basis. These findings validate blood-based biological aging as a marker of public health in adult Italians and provide a robust body of knowledge on its biological architecture, clinical implications and potential environmental influences.

Comparative Analysis of Mammal Genomes Unveils Key Genomic Variability for Human Life Span

The enormous mammal's lifespan variation is the result of each species' adaptations to their own biological trade-offs and ecological conditions. Comparative genomics have demonstrated that genomic factors underlying both, species lifespans and longevity of individuals, are in part shared across the tree of life. Here, we compared protein-coding regions across the mammalian phylogeny to detect individual amino acid (AA) changes shared by the most long-lived mammals and genes whose rates of protein evolution correlate with longevity. We discovered a total of 2,737 AA in 2,004 genes that distinguish long- and short-lived mammals, significantly more than expected by chance (P = 0.003). These genes belong to pathways involved in regulating lifespan, such as inflammatory response and hemostasis. Among them, a total 1,157 AA showed a significant association with maximum lifespan in a phylogenetic test. Interestingly, most of the detected AA positions do not vary in extant human populations (81.2%) or have allele frequencies below 1% (99.78%). Consequently, almost none of these putatively important variants could have been detected by genome-wide association studies. Additionally, we identified four more genes whose rate of protein evolution correlated with longevity in mammals. Crucially, SNPs located in the detected genes explain a larger fraction of human lifespan heritability than expected, successfully demonstrating for the first time that comparative genomics can be used to enhance interpretation of human genome-wide association studies. Finally, we show that the human longevity-associated proteins are significantly more stable than the orthologous proteins from short-lived mammals, strongly suggesting that general protein stability is linked to increased lifespan.

Lipoprotein Proteomics and Aortic Valve Transcriptomics Identify Biological Pathways Linking Lipoprotein(a) Levels to Aortic Stenosis

Lipoprotein(a) (Lp(a)) is one of the most important risk factors for the development of calcific aortic valve stenosis (CAVS). However, the mechanisms through which Lp(a) causes CAVS are currently unknown. Our objectives were to characterize the Lp(a) proteome and to identify proteins that may be differentially associated with Lp(a) in patients with versus without CAVS. Our second objective was to identify genes that may be differentially regulated by exposure to high versus low Lp(a) levels in explanted aortic valves from patients with CAVS. We isolated Lp(a) from the blood of 21 patients with CAVS and 22 volunteers and performed untargeted label-free analysis of the Lp(a) proteome. We also investigated the transcriptomic signature of calcified aortic valves from patients who underwent aortic valve replacement with high versus low Lp(a) levels (n = 118). Proteins involved in the protein activation cascade, platelet degranulation, leukocyte migration, and response to wounding may be associated with Lp(a) depending on CAVS status. The transcriptomic analysis identified genes involved in cardiac aging, chondrocyte development, and inflammation as potentially influenced by Lp(a). Our multi-omic analyses identified biological pathways through which Lp(a) may cause CAVS, as well as key molecular events that could be triggered by Lp(a) in CAVS development.

Associations Between Sleep Quality and Health Span: A Prospective Cohort Study Based on 328,850 UK Biobank Participants

OBJECTIVE

To examine the associations between sleep quality and health span using a prospective cohort design based on the UK Biobank (UKB).

MATERIALS AND METHODS

This longitudinal cohort study enrolled 328,850 participants aged between 37 and 73 years from UKB to examine the associations between sleep quality and risk of terminated health span. End of health span was defined by eight events strongly associated with longevity (cancer, death, congestive heart failure, myocardial infarction, chronic obstructive pulmonary disease, stroke, dementia, and diabetes), and a sleep score was generated according to five sleep behavioral factors (sleep duration, chronotype, sleeplessness, daytime sleepiness, and snoring) to characterize sleep quality. The hazard ratio (HR) and 95% confidence intervals (CIs) were calculated by multivariate-adjusted Cox proportional hazards model. Moreover, we calculated population attributable risk percentage (PAR%) to reflect the public health significance of healthy sleep quality.

RESULTS

Compared with poor sleep quality, participants with healthy sleep quality had a 15% (HR: 0.85, 95% CI: 0.81-0.88) reduced risk of terminated health span, and those of less-healthy sleep quality had a 12% (HR: 0.88, 95% CI: 0.85-0.92) reduced risk. Linear trend results indicated that the risk of terminated health span decreased by 4% for every additional sleep score. Nearly 15% health span termination events in this cohort would have been prevented if a healthy sleep behavior pattern was adhered to (PAR%: 15.30, 95% CI: 12.58-17.93).

CONCLUSION

Healthy sleep quality was associated with a reduced risk of premature end of health span, suggesting healthy sleep behavior may extend health span. However, further studies are suggested for confirmation of causality and potential mechanism.

Immune age and biological age as determinants of vaccine responsiveness among elderly populations: the Human Immunomics Initiative research program

The Human Immunomics Initiative (HII), a joint project between the Harvard T.H. Chan School of Public Health and the Human Vaccines Project (HVP), focuses on studying immunity and the predictability of immuneresponsiveness to vaccines in aging populations. This paper describes the hypotheses and methodological approaches of this new collaborative initiative. Central to our thinking is the idea that predictors of age-related non-communicable diseases are the same as predictors for infectious diseases like COVID-19 and influenza. Fundamental to our approach is to differentiate between chronological, biological and immune age, and to use existing large-scale population cohorts. The latter provide well-typed phenotypic data on individuals’ health status over time, readouts of routine clinical biochemical biomarkers to determine biological age, and bio-banked plasma samples to deep phenotype humoral immune responses as biomarkers of immune age. The first phase of the program involves 1. the exploration of biological age, humoral biomarkers of immune age, and genetics in a large multigenerational cohort, and 2. the subsequent development of models of immunity in relation to health status in a second, prospective cohort of an aging population. In the second phase, vaccine responses and efficacy of licensed COVID-19 vaccines in the presence and absence of influenza-, pneumococcal- and pertussis vaccines routinely offered to elderly, will be studied in older aged participants of prospective population-based cohorts in different geographical locations who will be selected for representing distinct biological and immune ages. The HII research program is aimed at relating vaccine responsiveness to biological and immune age, and identifying aging-related pathways crucial to enhance vaccine effectiveness in aging populations.

A Compendium of Age-Related PheWAS and GWAS Traits for Human Genetic Association Studies, Their Networks and Genetic Correlations

The rich data from the genome-wide association studies (GWAS) and phenome-wide association studies (PheWAS) offer an unprecedented opportunity to identify the biological underpinnings of age-related disease (ARD) risk and multimorbidity. Surprisingly, however, a comprehensive list of ARDs remains unavailable due to the lack of a clear definition and selection criteria. We developed a method to identify ARDs and to provide a compendium of ARDs for genetic association studies. Querying 1,358 electronic medical record-derived traits, we first defined ARDs and age-related traits (ARTs) based on their prevalence profiles, requiring a unimodal distribution that shows an increasing prevalence after the age of 40 years, and which reaches a maximum peak at 60 years of age or later. As a result, we identified a list of 463 ARDs and ARTs in the GWAS and PheWAS catalogs. We next translated the ARDs and ARTs to their respective 276 Medical Subject Headings diseases and 45 anatomy terms. The most abundant disease categories are neoplasms (48 terms), cardiovascular diseases (44 terms), and nervous system diseases (27 terms). Employing data from a human symptoms-disease network, we found 6 symptom-shared disease groups, representing cancers, heart diseases, brain diseases, joint diseases, eye diseases, and mixed diseases. Lastly, by overlaying our ARD and ART list with genetic correlation data from the UK Biobank, we found 54 phenotypes in 2 clusters with high genetic correlations. Our compendium of ARD and ART is a highly useful resource, with broad applicability for studies of the genetics of aging, ARD, and multimorbidity.