Biological Age Research Articles

Neonatal multimorbidity and the phenotype of premature aging in preterm infants.

Multimorbidity is the co-occurrence of multiple chronic health problems, associated with aging, frailty, and poor functioning. Children born preterm experience more multimorbid conditions in early life compared to term-born peers. Though neonatal multimorbidity is linked to poor health-related quality of life, functional outcomes, and peer group participation, gaps in our theoretical understanding and conceptualization remain. Drawing from life course epidemiology and the Developmental Origins of Heath and Disease models, we offer a framework that neonatal multimorbidity reflects maturational vulnerability posed by preterm birth. The impact of such vulnerability on health and development may be further amplified by adverse exposures and interventions within the environment of the neonatal intensive care unit. This can be exacerbated by disadvantaged home or community contexts after discharge. Uncovering the physiologic and social antecedents of multiple morbid conditions in the neonatal period and their biological underpinnings will allow for more accurate risk-prediction, counseling, and care planning for preterm infants and their families. According to this framework, the maturational vulnerability to multimorbidity imparted by preterm birth and its negative effects on health and development are not predetermined or static. Elucidating pathways of early biologic and physical aging will lead to improvements in care and outcomes. IMPACT: Multimorbidity is associated with significant frailty and dysfunction among older adults and is indicative of early physiologic aging. Preterm infants commonly experience multimorbidities in the newborn period, an underrecognized threat to long-term health and development. We offer a novel framework incorporating multimorbidity, early cellular aging, and life course health development to innovate risk-prediction, care-planning, and therapeutics.

When to Trust Epigenetic Clocks: Avoiding False Positives in Aging Interventions.

Recent human studies have suggested that aging interventions can reduce aging biomarkers related to morbidity and mortality risk. Such biomarkers may potentially serve as early, rapid indicators of effects on healthspan. An increasing number of studies are measuring intervention effects on epigenetic clocks, commonly used aging biomarkers based on DNA methylation profiles. However, with dozens of clocks to choose from, different clocks may not agree on the effect of an intervention. Furthermore, changes in some clocks may simply be the result of technical noise causing a false positive result. To address these issues, we measured the variability between 6 popular epigenetic clocks across a range of longitudinal datasets containing either an aging intervention or an age-accelerating event. We further compared them to the same clocks re-trained to have high test-retest reliability. We find the newer generation of clocks, trained on mortality or rate-of-aging, capture aging events more reliably than those clocks trained on chronological age, as these show consistent effects (or lack thereof) across multiple clocks including high-reliability versions, and including after multiple testing correction. In contrast, clocks trained on chronological age frequently show sporadic changes that are not replicable when using high-reliability versions of those same clocks, or when using newer generations of clocks and these results do not survive multiple-testing correction. These are likely false positive results, and we note that some of these clock changes were previously published, suggesting the literature should be re-examined. This work lays the foundation for future clinical trials that aim to measure aging interventions with epigenetic clocks, by establishing when to attribute a given change in biological age to a bona fide change in the aging process.

Revealing the spatial pattern of brain hemodynamic sensitivity to healthy aging through sparse DCM.

Age-related changes in the BOLD response could reflect neuro-vascular coupling modifications rather than simply impairments in neural functioning. In this study, we propose the use of a sparse dynamic causal model (sDCM) to decouple neuronal and vascular factors in the BOLD signal, with the aim of characterizing the whole-brain spatial pattern of hemodynamic sensitivity to healthy aging, as well as to test the role of hemodynamic features as independent predictors in an age-classification model. sDCM was applied to the resting-state fMRI data of a cohort of 126 healthy individuals in a wide age range (31 females), providing reliable estimates of the hemodynamic response function (HRF) for each subject and each region of interest. Then, some features characterizing each HRF curve were extracted and used to fit a multivariate logistic regression model predicting the age class of each individual. Ultimately, we tested the final predictive model on an independent dataset of 338 healthy subjects (173 females) selected from the Human Connectome Project Aging (HCP-A) and Development (HCP-D) cohorts. Our results entail the spatial heterogeneity of the age effects on the hemodynamic component, since its impact resulted to be strongly region- and population-specific, discouraging any space-invariant corrective procedures that attempt to correct for vascular factors when carrying out functional studies involving groups with different ages. Moreover, we demonstrated that a strong interaction exists between some specific hemodynamic features and age, further supporting the essential role of the hemodynamic factor as independent predictor of biological aging, rather than a simple confounding variable.Significance statement By inferring region-wise hemodynamic profiles at the individual level, this is the first study providing an exhaustive whole-brain characterization of the hemodynamic sensitivity to healthy aging, reporting further evidence of the vascular changes across the adult lifespan. Using a predictive framework, we analysed the statistical influence of advancing age on individual regional hemodynamic attributes, offering a quantitative evaluation of the diverse hemodynamic bias across different brain regions. We then unveiled a specific set of hemodynamic predictors to discriminate young from elderly people, mainly describing vascular properties of right-hemispheric areas. This suggests the asymmetric nature of vascular degeneration processes affecting the human brain at the latest stage of life, other than a potential biomarker that could be relevant for brain-age prediction.

Association of biological aging with prostate cancer: insights from the National Health and Nutrition Examination Survey.

The link between biological aging and prostate cancer (PCa) risk, particularly as indicated by elevated prostate-specific antigen (PSA) levels, remains uncertain. This study utilized data from the National Health and Nutrition Examination Survey (2001-2010) to explore this association. Biological age was assessed using Klemera-Doubal method age (KDMAge) and phenotypic age (PhenoAge). PCa was identified through self-reported diagnoses, and highly probable PCa was determined by PSA levels. We analyzed the prevalence of PCa and PSA-defined highly probable PCa across quartiles of biological age measures using weighted chi-square and linear trend tests. Associations were evaluated using weighted multiple logistic regression models. Among 7,209 and 6,682 males analyzed, the overall weighted prevalence of PCa was 2.86%, increasing to 9.60% in those aged 65 and above. A significant rise in PCa prevalence was observed with higher quartiles of KDMAge or PhenoAge (P for trend < 0.001), particularly in those under 65. In this younger group, higher PhenoAge acceleration quartiles were linked to increased PCa prevalence and higher risk of PCa (OR = 1.50, P = 0.015) as well as highly probable PCa in those without a diagnosis (OR = 1.28, P = 0.031). These findings suggest that accelerated biological aging is associated with an increased risk of PCa and may indicate early risk as signaled by PSA levels, even in those without a PCa diagnosis.

An explainable machine learning estimated biological age based on morphological parameters of the spine.

Accurately estimating biological age is beneficial for measuring aging and predicting risk. It is widely accepted that the prevalence of spine compression increases significantly with age. However, biological age based on vertebral morphological data is rarely reported. In this study, a total of 2,364 participants from the National Health and Nutrition Examination Survey were enrolled, and morphological parameters of the spine were collected from lateral radiographs scanned by dual energy X-ray absorptiometry. The biological age of the spine, called SpineAge, was calculated with the parameters by machine learning models. The SHapley Additive exPlanation was used for better interpreting each parameter's contribution. Besides, an Accelerated Aging Index (AAI) was defined as SpineAge minus chronological age and was used to quantify the accelerating aging degree of the spine. The results indicated that the SpineAge performed better than chronological age did in predicting 2-year and 5-year all-cause mortality. After adjusting all covariates, there was a significant association between AAI and all-cause mortality risk. Specifically, each 1-year increase in AAI was associated with a 25.9% increase in all-cause mortality risk (Hazards ratio, 1.259; 95% CI, 1.087-1.457; P < 0.001). Considering the first quartile of AAI as a reference, the mortality risks for the second, third, and fourth quartiles were 2.389 (95% CI, 1.064-5.364; P = 0.035), 5.911 (95% CI, 2.241-15.590; P < 0.001) and 22.925 (95% CI, 4.744-110.769; P < 0.001) times higher, respectively. Our study developed a novel and highly applicable biological-age predictor for predicting individualized long-term prognosis and facilitating personalized care.

Is castration leading to biological aging in dogs? Assessment of lipid peroxidation, inflammation, telomere length, mitochondrial DNA copy number, and expression of telomerase and age-related genes

BackgroundBiological aging is a complex process influenced by various factors, including reproductive status and castration. This study aimed to evaluate the impact of castration on biological aging in dogs.MethodFifteen male crossbred dogs were randomly divided into a sham-operation control group (n = 5) and a castrated group (n = 10). Blood samples were collected at weeks 0, 4, 8, 12, 16, and 18 post-surgery. Malondialdehyde (MDA as indicator of Lipid peroxidation), C-reactive protein (as an indicator of inflammation), telomere length, mitochondrial DNA (mtDNA) copy number, and the expression of age-related (P16, P21, TBX2) and telomerase-related (TERT) genes were assessed in blood samples.ResultsPlasma MDA levels were higher in the control group at weeks 16 and 18, while CRP levels were higher only at week 18. Telomere length and mtDNA copy number were lower in the control group at week 18. Gene expression analysis showed that P16 was lower in the control group at weeks 8 and 12, P21 and TERT were lower at weeks 16 and 18, and TBX2 was lower at weeks 16 and 18. The TBX2/P16 ratio was lower in the control group at weeks 16 and 18 but higher at week 12, while the TBX2/P21 ratio did not differ between groups.ConclusionCastration appears to have a protective effect against biological aging in dogs, as evidenced by lower lipid peroxidation, inflammation, and age-related changes in telomere length, mtDNA copy number, and gene expression.

Biologically informed machine learning modeling of immune cells to reveal physiological and pathological aging process

The immune system undergoes progressive functional remodeling from neonatal stages to old age. Therefore, understanding how aging shapes immune cell function is vital for precise treatment of patients at different life stages. Here, we constructed the first transcriptomic atlas of immune cells encompassing human lifespan, ranging from newborns to supercentenarians, and comprehensively examined gene expression signatures involving cell signaling, metabolism, differentiation, and functions in all cell types to investigate immune aging changes. By comparing immune cell composition among different age groups, HLA highly expressing NK cells and CD83 positive B cells were identified with high percentages exclusively in the teenager (Tg) group, whereas unknown_T cells were exclusively enriched in the supercentenarian (Sc) group. Notably, we found that the biological age (BA) of pediatric COVID-19 patients with multisystem inflammatory syndrome accelerated aging according to their chronological age (CA). Besides, we proved that inflammatory shift- myeloid abundance and signature correlate with the progression of complications in Kawasaki disease (KD). The shift- myeloid signature was also found to be associated with KD treatment resistance, and effective therapies improve treatment outcomes by reducing this signaling. Finally, based on those age-related immune cell compositions, we developed a novel BA prediction model PHARE (https://xiazlab.org/phare/), which can apply to both scRNA-seq and bulk RNA-seq data. Using this model, we found patients with coronary artery disease (CAD) also exhibit accelerated aging compared to healthy individuals. Overall, our study revealed changes in immune cell proportions and function associated with aging, both in health and disease, and provided a novel tool for successfully capturing features that accelerate or delay aging.

Artificial intelligence-enabled electrocardiogram for mortality and cardiovascular risk estimation: a model development and validation study

Artificial intelligence (AI)-enabled electrocardiography (ECG) can be used to predict risk of future disease and mortality but has not yet been adopted into clinical practice. Existing model predictions do not have actionability at an individual patient level, explainability, or biological plausibi. We sought to address these limitations of previous AI-ECG approaches by developing the AI-ECG risk estimator (AIRE) platform. The AIRE platform was developed in a secondary care dataset (Beth Israel Deaconess Medical Center [BIDMC]) of 1 163 401 ECGs from 189 539 patients with deep learning and a discrete-time survival model to create a patient-specific survival curve with a single ECG. Therefore, AIRE predicts not only risk of mortality, but also time-to-mortality. AIRE was validated in five diverse, transnational cohorts from the USA, Brazil, and the UK (UK Biobank [UKB]), including volunteers, primary care patients, and secondary care patients. AIRE accurately predicts risk of all-cause mortality (BIDMC C-index 0·775, 95% CI 0·773-0·776; C-indices on external validation datasets 0·638-0·773), future ventricular arrhythmia (BIDMC C-index 0·760, 95% CI 0·756-0·763; UKB C-index 0·719, 95% CI 0·635-0·803), future atherosclerotic cardiovascular disease (0·696, 0·694-0·698; 0·643, 0·624-0·662), and future heart failure (0·787, 0·785-0·789; 0·768, 0·733-0·802). Through phenome-wide and genome-wide association studies, we identified candidate biological pathways for the prediction of increased risk, including changes in cardiac structure and function, and genes associated with cardiac structure, biological ageing, and metabolic syndrome. AIRE is an actionable, explainable, and biologically plausible AI-ECG risk estimation platform that has the potential for use worldwide across a wide range of clinical contexts for short-term and long-term risk estimation. British Heart Foundation, National Institute for Health and Care Research, and Medical Research Council.

The environmental impact on aging: Insights from buccal mucosa and molecular biomarkers

Buccal epithelial cells serve as a primary barrier against the inhalation and ingestion of harmful substances, working alongside immune system cells such as natural killer cells to protect the body from health-damaging factors. These epithelial cells can also be used as an alternative tissue source for monitoring the genotoxic effects of external factors such as chemical exposure. This assessment can be performed using molecular biomarkers of aging, which reflect biological age and indicate cellular aging acceleration due to internal and external damage factors, such as environmental hazards. In contrast to chronological age, which merely reflects the passage of time, biological age accounts for individual variation in aging processes. Molecular biomarkers are crucial for distinguishing between normal and pathological processes in the body and for identifying the effects of external factors such as chemical exposures. The identification of specific biomarkers enhances the ability to detect and monitor adverse biological responses and accelerated aging. This review aims to highlight the routes through which environmental hazards enter the body, the application of buccal epithelial cells in assessing genetic modifications, and the introduction of potential molecular biomarkers. However, further research is necessary to elucidate the roles of these biomarkers in determining aging rates and individual variability. Understanding their implications may also help identify new therapeutic targets for preventing premature aging, treating age-related diseases, and developing potential treatments.

In utero and childhood exposure to the great Chinese famine and risk of aging in adulthood

Background Early-life exposure to famine may influence the occurrence of chronic diseases and aging in midlife among those exposed. This study aims to explore the relationship between exposure to the Chinese Great Famine and aging in middle-aged individuals. Methods Participants born in 1963–1965 (unexposed), 1959–1961 (in utero exposure), and 1955–1957 (childhood exposure) from the Kailuan Study were included. Their biological age at 2010, 2014, and 2018 was investigated, and age acceleration (biological age minus actual age) was calculated to assess aging. Logistic regression analysis was employed to describe the relationship between famine exposure and the aging risk. Subgroup and sensitivity analysis were conducted to explore differences and stability in this relationship among different groups. Results A total of 17,543 participants were included in this study. Among them, 12,762 (72.7%) were male, and 4,781 (27.3%) were female, with 2,543 participants experiencing aging events. Compared to unexposed participants, those exposed during childhood and in utero exhibited a 1.69-fold (OR = 1.69, 95%CI: 1.53–1.87) and 1.22-fold (OR = 1.22, 95%CI: 1.08–1.37) increased risk of aging. Subgroup analysis revealed an interaction with income (P for interaction = 0.008), and additional interaction analysis suggested that increasing income could partially mitigate the detrimental effects of early-life famine exposure. Furthermore, experiencing famine in severely affected regions exacerbated the risk of aging (OR = 1.41, 95%CI: 1.21–1.63). Conclusion Exposure to famine in utero or during childhood may elevate the risk of midlife aging among exposed individuals, and these relationships are influenced by the severity of famine exposure. Increasing income may also help mitigate these effects.Trial registration: Kailuan study, ChiCTRTNRC11001489. Registered July 19, 2015 Retrospectively registered, https//www.chictr.org.cn/showprojEN.html?proj=8050&u_atoken=af46a0dee8d73f320bb5459ab7bbcfa9&u_asig=1a0c381017255295896468605e00cf .

Systematic Review of Associations between Anxiety, Depression, and Functional/Biological aging among Cancer Survivors.

Evidence suggests a mind-body component to aging, through which psychological distress from anxiety and depression drives molecular changes that promote early decline (ie, accelerated aging). Cancer survivors experience particularly high rates of anxiety and depression. Some survivors also have accelerated aging, though the relationships between anxiety/depression and aging are not clear. A synthesis of evidence is needed to understand the state of the science and impending priorities. PubMed, Embase, CINAHL, Web of Science, and PsycNet databases were searched for studies that measured associations between depression, anxiety, and non-chronological aging in cancer survivors (2012 to 2022). Data were methodologically evaluated. Survivorship studies were included if they were peer-reviewed, published in English from 2012-2022, and measured associations between anxiety/depression and aging. 51 studies were included. Just over half were cross-sectional (53%). Foci included functional (n = 35, 69%) and biological (n = 16, 31%). Functional aging measures included frailty, sarcopenia, geriatric assessment, and cognition. Biological aging measures included telomere length, telomerase, age-related inflammatory blood-based biomarkers, renal insufficiency, anemia, and DNA methylation. 223 associations were tested. Associations between anxiety, depression and aging were generally positive, though with varying strengths. Most compelling were associations between functional aging-depression. There were concerns for selection and measurement biases. Findings suggest positive associations between anxiety, depression and aging among cancer survivors. Future work is needed to clarify temporality, develop a consensus on the operationalization of aging, and diversify cohorts.

Hemoglobin-oxygen affinity changes in neonatal blood transfusions: RBC selection insights.

Despite preterm newborns often requiring blood transfusions, we have an incomplete understanding of the impact of adult packed red blood cell (pRBC) transfusions on fetal red blood cell (RBC) oxygen affinity. We investigated the influence of adult pRBC on oxygen binding in fetal RBCs obtained from the umbilical cord of preterm newborns. This included exploring the influence of the biological age of adult pRBCs on the oxygen affinity of fetal blood. Cord blood samples from preterm infants were titrated with young (Y-RBC) and old (O-RBC) adult pRBCs using an in vitro transfusion model. Parameters, including oxygen affinity (p50), hemoglobin variants, and red blood cell indices, were measured. The titration of cord blood with adult pRBCs (n = 19) resulted in a concentration-dependent decrease in p50, indicating an increased oxygen affinity. Hemoglobin variant analysis revealed a shift in composition, with a decrease in fetal hemoglobin (HbF) and an increase in adult hemoglobin (HbA) following titration. The impact of biological age was evident, as O-RBCs had a more pronounced effect on p50 values compared to Y-RBCs. Understanding the physiologic implications of transfusing preterm infants with adult pRBCs is important for optimizing transfusion practices in newborns. In an in vitro model, titrating adult pRBC into fetal blood significantly affects oxygen binding Oxygen affinity of fetal blood is significantly increased after titration of adult pRBC. Compared to older RBC subpopulations of adult pRBC, oxygen-binding properties of younger RBC subpopulations of adult pRBC more closely approximate the oxygen affinity of fetal blood. This work highlights the importance of investigating the influence of adult pRBC transfusion on fetal RBC oxygen binding. This may have implications for morbidities in the newborn period related to oxygen physiology.

Association between the Planetary Health Diet Index and biological aging among the U.S. population.

The Planetary Health Diet (PHD) is a novel dietary pattern proposed by the EAT-Lancet Commission in 2019, yet a limited study has investigated the anti-aging effects of PHD to date. This study aimed to explore the association between adherence to PHD, as quantified by the Planetary Health Diet Index (PHDI), and biological aging in American populations. Data were obtained from the National Health and Nutrition Examination Survey (NHANES) for 1999-2018. Food consumption information was relied on two 24-h diet recall questionnaires. The biological aging condition was comprehensively assessed by four biological markers, including phenotypic age, biological age, telomere length, and klotho concentration. Weighted multivariate linear models, restricted cubic spline (RCS), and subgroup analysis were subsequently carried out to evaluate the influence of PHDI on biological aging. 44,925 participants with complete data were finally enrolled in our study. The fully adjusted models showed decreased 0.20 years in phenotypic age [-0.20 (-0.31, -0.10)] and declined 0.54 years in biological age [-0.54 (-0.69, -0.38)] correlated with PHDI per 10 scores increment. Klotho concentration [6.2 (1.0, 11.0)] was positively related to PHDI. In Model 2, telomere length increased by 0.02 bp for every 10-point rise in PHDI. Besides, the RCS analysis results exhibited a curvilinear relationship between PHDI and four indicators. Our study explored a significant correlation between PHDI and biological aging, indicating that adherence to PHD may prevent biological aging.

Biological aging mediates the association between volatile organic compounds and cardiovascular disease

BackgroundEvidence for the relationship between individual and combined volatile organic compounds (VOCs) and cardiovascular disease (CVD) is limited. Besides, the mediating role of biological aging (BA) has not been studied. Therefore, this study aimed to examine the association between VOCs and CVD risk and to explore the mediating effects of BA.MethodsLogistic regression models were used to investigate the relationships of metabolites of volatile organic compounds (mVOCs) and BA with CVD. In addition, weighted quantile sum (WQS) regression, adaptive elastic networks, and Environmental Risk Score (AENET-ERS) were utilized to assess overall associations of mixed VOCs co-exposure with CVD. Mediation analyses were used to identify potential mediating effects of BA.ResultsIn the single-pollutant model, CYMA was shown to be associated with an increased risk of CVD. Additionally, we identified significantly positive associations between the WQS index and CVD (odds ratio (OR) = 1.292, 95% confidence interval (CI): 1.006, 1.660), and DHBMA had the greatest contribution for CVD (0.246). Furthermore, the AENET-ERS results showed that 8 mVOCs were significantly associated with CVD, and ERS was related to an elevated risk of CVD (OR = 1.538, 95%CI: 1.255, 1.884). Three BA indicators mediated the association of the mVOCs mixture with CVD, with mediating effect proportions of 11.32%, 34.34%, and 7.92%, respectively.ConclusionThe risk of CVD was found to increase with both individual and combined exposure to VOCs. BA mediates the positive effects of VOCs on CVD, suggesting that this pathway may be one of the mechanisms of CVD.

Lifetime chronic stress exposures, stress hormones, and biological aging: Results from the Midlife in the United States (MIDUS) study

Psychosocial stress and adversity have been linked to accelerated aging and increased risk for age-related diseases. Animal and in vitro studies have shown that exposure to stress hormones (catecholamines, glucocorticoids) can impact biological aging processes such as DNA damage and cellular senescence, suggesting they play a key role in links between stress and aging; however, these associations have not been well investigated in humans. We examined cross-sectional associations between chronic stress exposures, stress hormones, and biological aging markers in midlife adults and whether stress hormones mediated associations between stress and aging. Participants were 531 adults aged 26–78 years (Mage = 53.9, 50.1% female) in the nationally representative Midlife in the United States Refresher cohort. They reported chronic stress exposures in childhood and adulthood (Stressful Life Event Inventory) and provided 12-hour urine samples used to assess norepinephrine, epinephrine, and cortisol. RNA sequencing of peripheral blood mononuclear cells derived aging biomarkers: the DNA damage response (DDR; 30-gene composite), cellular senescence signal p16INK4a (CDKN2A), and the pro-inflammatory senescence-associated secretory phenotype (SASP; 57-gene composite). Regression models adjusting for age, sex, race/ethnicity, BMI, smoking status, alcohol use, and medications revealed that more childhood exposures were associated with higher norepinephrine (β = 0.09, p = 0.04), independent from adult exposures. Higher norepinephrine was associated with elevated DDR expression (β = 0.17, p < 0.001). Higher norepinephrine (β = 0.14, p = 0.003) and epinephrine (β = 0.10, p = 0.02) were both associated with elevated SASP expression. Statistical mediation analyses implicated elevated norepinephrine as a plausible mediator of associations between childhood exposures and both DDR (unstandardized b = 0.005, 95% CI [0.0002, 0.011]) and SASP (b = 0.002, 95% CI [0.0001, 0.05]). Findings provide preliminary evidence in humans that stress hormones may impact key biological aging processes and may be a mechanism linking chronic stress exposures in childhood to accelerated aging later in life.

Epigenetic aging and fecundability: the Norwegian Mother, Father and Child Cohort Study.

Is there an association between male or female epigenetic age acceleration (EAA) or deceleration (EAD) and fecundability? We do not find compelling evidence of an association between EAA or EAD and fecundability. Prior research has shown that female accelerated epigenetic aging is associated with unfavorable clinical fecundity outcomes and use of in vitro fertilization, and that epigenetic aging in sperm cells is associated with unfavorable sperm parameters. Studies of epigenetic aging and fecundability among individuals who conceive naturally are lacking. This study is based on the Norwegian Mother, Father and Child Cohort Study (MoBa), a population-based pregnancy cohort which recruited pregnant couples between 1999 and 2008. We used data from 1657 couples (women and men) with planned naturally conceived pregnancies and available blood samples. Methylation levels were measured in DNA from blood samples taken recruitment (at ∼18 gestational weeks) from pregnant women and their partners using the Illumina Methylation EPIC Array. To obtain a measure of EAA/EAD, we performed a linear regression of each of seven different established epigenetic biomarkers (DNAmAge by Horvath, DNAmAge by Hannum et al., PhenoAge by Levine et al., DunedinPoAm by Belsky et al., DunedinPACE by Belsky et al., DNAmTL by Lu et al., and GrimAge by Lu et al.) against chronological age. We fitted proportional probability regression models to obtain fecundability ratios (FRs) for each standard deviation increase in epigenetic aging, and obtained crude and adjusted (for body mass index, smoking, and education level) estimates. Results were evaluated at a false discovery rate (FDR) of 5%. We evaluated all models for non-linear associations using categories of epigenetic age where appropriate. Although the DunedinPACE clock in males demonstrated slightly increasing fecundability with increasing EAA (adjusted FR 1.05 per one standard deviation increase in EAA, 95% CI 1.00-1.10), this was not robust when evaluated at an FDR of 5%. We found evidence of non-linearity between biological aging and fecundability in two models in females and three models in males, but non-linear associations were weak and conflicting. As MoBa is a pregnancy cohort, our findings may not be generalizable to all couples attempting conception. Fecundability is a couple-level measure, and any impacts of epigenetic aging in each partner may be obscured by effects of the other partner. Our findings contrast with those of prior studies, which have indicated an association between EAA and unfavorable clinical fertility outcomes in populations using fertility treatments, possibly due to less important effects of epigenetic aging among couples who conceive naturally. More research is needed on the association between blood-based EAA and clinical fertility parameters in both sexes. The study was supported by the Research Council of Norway through its Medical Student Research Program funding scheme (project number 271555/F20), its Centres of Excellence funding scheme (project number 262700), and a grant from the Women's Health Program (320656). Co-funding was also received from the Strategic Research Council (SRC), FLUX consortium, decision numbers 345130 and 345131; the National Institute on Aging (R01AG075208); grants to the Max Planck-University of Helsinki Center from the Max Planck Society (decision number 5714240218), Jane and Aatos Erkko Foundation, Faculty of Social Sciences at the University of Helsinki, and Cities of Helsinki, Vantaa, and Espoo; and the European Research Council; and the European Research Council (ERC Synergy, BIOSFER, grant number 101071773, and the Horizon 2020 research and innovation program, grant number 947684). The authors declare no conflicts of interest. N/A.

A review of artificial intelligence-based brain age estimation and its applications for related diseases.

The study of brain age has emerged over the past decade, aiming to estimate a person's age based on brain imaging scans. Ideally, predicted brain age should match chronological age in healthy individuals. However, brain structure and function change in the presence of brain-related diseases. Consequently, brain age also changes in affected individuals, making the brain age gap (BAG)-the difference between brain age and chronological age-a potential biomarker for brain health, early screening, and identifying age-related cognitive decline and disorders. With the recent successes of artificial intelligence in healthcare, it is essential to track the latest advancements and highlight promising directions. This review paper presents recent machine learning techniques used in brain age estimation (BAE) studies. Typically, BAE models involve developing a machine learning regression model to capture age-related variations in brain structure from imaging scans of healthy individuals and automatically predict brain age for new subjects. The process also involves estimating BAG as a measure of brain health. While we discuss recent clinical applications of BAE methods, we also review studies of biological age that can be integrated into BAE research. Finally, we point out the current limitations of BAE's studies.

Integrating aging biomarkers and immune function to predict kidney health: insights from the future of families and child wellbeing study.

Biomarkers of biological aging predict health outcomes more accurately than chronological age. This study examines the relationship between aging biomarkers, immune function, and kidney health using the Future of Families and Child Wellbeing Study Biomarker Dataset. Using Wave 5 (year 9) and Wave 6 (year 15), we examined biomarker data from a total of 4898 individuals. The panel of aging biomarkers, comprised of epigenetic clocks (GrimAge, Horvath), immune function markers (CD8 + T cells, plasmablasts), and metabolic indicators (GDF-15, leptin), was evaluated in depth. We used principal component analysis (PCA) and K-means clustering for subtype identification. A random forest regressor was employed to predict kidney function using Cystatin C levels, and the importance of features was assessed. Three clusters with unique biological age and immune function profiles were identified. Cluster 1 had younger biological age markers. In Cluster 2, both GrimAge and GDF-15 levels were significantly increased, indicating an elevated risk for age-related diseases. According to predictive modeling, GrimAge, Pack Years, and immune function markers had the strongest influence on Cystatin C levels (R2 = 0.856). The incorporation of immune aging markers enhanced the predictive power, emphasizing the importance of immunosenescence in renal health. Aging biomarkers and immune function significantly impact kidney health prediction. The study results call for the utilization of extensive biomarker tests for individualized elderly care and early recognition of kidney deterioration. Clinical practice can be improved by incorporating biological age assessments for the prevention and management of age-related diseases.

The Importance of Biosecurity in Emerging Biotechnologies and Synthetic Biology.

The age of synthetic biology is ushering in new technologies for the advancement of society, human health, and agriculture. It appears that synthetic biology has integrated engineering paradigms into biological contexts. The combined use of new biotechnology and synthetic biology raises concerns about biosafety, biosecurity, and even cyberbiosecurity. For example, synthetic biology increases the possibility of designing, developing, and deploying pathogenic bioweapons in new and different ways than natural pathogens, as well as manipulating the genome. Evaluation of new technologies and platforms that enable creative or destructive manipulation of biological materials, systems, and organisms is important to identify potential security opportunities and vulnerabilities. This issue poses challenges to the medical community and civilian populations worldwide, creating a growing need to implement and enforce standardized biosafety and biosecurity regulations to protect humans, animals, plants, and the environment. It is critical to establish rules and management guidelines, provide strong leadership at the individual and institutional levels, and utilize established biosafety and biosecurity tools to mitigate the risks associated with synthetic biology. This review addresses the current state of synthetic biology, focusing on the concepts of biosafety, biosecurity, and cyberbiosecurity, as well as enhancing the standardization, regulation, and management of biosecurity in synthetic biology. In this review, the current situation in the Middle East region has been discussed and the challenges and opportunities encountered by synthetic biology researchers in this area is explored. The Middle East region is vulnerable to bioterrorism due to various factors. However, some countries in this strategically important region face challenges as they lack the necessary resources to effectively combat this significant global threat. These attacks are not limited to a specific border or area; they can affect multiple countries or have a global impact.

A hematology-based clock derived from the Study of Longitudinal Aging in Mice to estimate biological age.

Biological clocks and other molecular biomarkers of aging are difficult to implement widely in a clinical setting. In this study, we used routinely collected hematological markers to develop an aging clock to predict blood age and determine whether the difference between predicted age and chronologic age (aging gap) is associated with advanced aging in mice. Data from 2,562 mice of both sexes and three strains were drawn from two longitudinal studies of aging. Eight hematological variables and two metabolic indices were collected longitudinally (12,010 observations). Blood age was predicted using a deep neural network. Blood age was significantly correlated with chronological age, and aging gap was positively associated with mortality risk and frailty. Platelets were identified as the strongest age predictor by the deep neural network. An aging clock based on routinely collected blood measures has the potential to provide a practical clinical tool to better understand individual variability in the aging process.