Plasma proteomic signature of age in healthy humans

Exposure to ambient air pollution mixture and senescence-associated secretory phenotype proteins among middle-aged and older women

Our study aimed to investigate the impact of environmental exposures, such as ambient air pollutants, on systemic inflammation and cellular senescence in middle-aged and older women. We utilized epidemiological data linked with exposure data of six air pollutants (particulate matters [PM10, PM2.5], sulphur dioxide [SO2], nitrogen dioxide [NO2], carbon monoxide [CO], and ozone [O3]) and blood samples of 380 peri- and postmenopausal women participants of the Korean Genome and Epidemiology Study. We measured blood high-sensitivity C-reactive protein (hsCRP) and age-related 27 circulatory senescence-associated secretory phenotypes (SASP) produced by senescent cells. We employed single exposure models to explore the general pattern of association between air pollution exposure and proteomic markers. Using quantile g-computation models, we assessed the association of six air pollutant mixtures with hsCRP and SASP proteins. In single-exposure, single-period models, nine out of the 27 SASP proteins including IFN-γ (β = 0.04, 95% CI: 0.01, 0.07 per interquartile range-increase), IL-8 (0.15, 95% CI: 0.09, 0.20), and MIP1α (0.11, 95% CI: 0.04, 0.18) were positively associated with the average level of O3 over one week. Among the age-related SASP proteins, IFN-γ (0.11, 95% CI: 0.03, 0.20) and IL-8 (0.22, 95% CI: 0.05, 0.39) were positively associated with exposure to air pollutant mixture over one week. The MIP1β was higher with an increasing one-month average concentration of the air pollutant mixture (0.11, 95% CI: 0.00, 0.21). The IL-8 showed consistently positive association with the ambient air pollutant mixture for the exposure periods ranging from one week to one year. O3 predominantly showed positive weights in the associations between air pollutant mixtures and IL-8. These findings underscore the potential of proteomic indicators as markers for biological aging attributed to short-term air pollution exposure.

Anti-cytomegalovirus antibody levels stratify human immune profiles across the lifespan

Human cytomegalovirus (hCMV) is a ubiquitous latent persistent herpesvirus infecting 60-90% of the population worldwide. hCMV carriage in immunocompetent people is asymptomatic; thus, hCMV can be considered a component of normative aging. However, hCMV powerfully modulates many features of the immune, and likely other, systems and organs. Questions remain as to how hCMV carriage affects the human host. We used anti-CMV antibody titers as a stratifying criterion to examine the impact of "intensity" of hCMV infection as a potential biomarker of aging, inflammation, and immune homeostasis in a cohort of 247 participants stratified into younger (21-40 years) and older (> 65 years of age) groups. We showed that anti-CMV antibody titers increased with age and directly correlated to increased levels of soluble tumor necrosis factor (sTNFR) I in younger but not older participants. CD8 + cell numbers were reduced in the older group due to the loss in CD8 + T naïve (Tn) cells. In CMV carriers and, in particular, in anti-CMV Ab-high participants, this loss was mitigated or reversed by an increase in the numbers of CD8 + T effector memory (Tem) and T effector memory reexpressing CD45RA (Temra) cells. Analysis of CD38, HLA-DR, and CD57 expression revealed subset (CD4 or CD8)-specific changes that correlated with anti-CMV Ab levels. In addition, anti-CMV Ab levels predicted anti-CMV CD8 T cell responsiveness to different CMV open reading frames (ORFs) selectively in older participants, which correlated to the transcriptional order of expression of specific CMV ORFs. Implications of these results for the potential predictive value of anti-CMV Ab titers during aging are discussed.

Inflammatory age and its impact on age-related health in older Chinese adults

INTRODUCTION

A standardized measure for inflammaging is lacking. We introduced the inflammatory age (iAge) as a quantification method and explored its associations with age-related traits and diseases in an older Chinese cohort.

METHODS

Inflammatory markers including white blood cell count (WBC), neutrophils, lymphocytes, monocytes, C-reactive protein, platelets and albumin were measured. Quantitative real-time polymerase chain reaction was used to measure telomere length. Traditional multivariable linear, partial least squares, and logistic regression were used.

RESULTS

iAge was constructed based on WBC, neutrophils, monocytes and albumin, which were associated with telomere length independently. A higher iAge indicated a heavier aging-related inflammation burden. Per 1-year increase in iAge was associated with higher body mass index (β 0.86 (95 % CI 0.67, 1.05) kg/m2), waist circumference (β 2.37 (95 % CI 1.85, 2.90) cm), glycosylated hemoglobin A1c (β 0.06 (95 % CI 0.02, 0.10) %), systolic blood pressure (β 1.06 (95 % CI 0.10, 2.03) mmHg), triglycerides (β 0.05 (95 % CI 0.01, 0.08) mmol/L), 10-year cardiovascular diseases risk (β 0.05 (95 % CI 0.02, 0.08) %), diabetes (OR 1.22 (95 % CI 1.02, 1.46)), hypertension (OR 1.21 (95 % CI 1.04, 1.42)) and metabolic syndrome risks (OR 1.25 (95 % CI 1.04, 1.51)), and lower fasting plasma glucose (β -0.016 (95 % CI -0.024, -0.007) mmol/L), total cholesterol (β -0.06 (95 % CI -0.12, -0.01) mmol/L) and high-density lipoprotein cholesterol (β -0.05 (95 % CI -0.07, -0.03) mmol/L).

CONCLUSION

The newly introduced iAge, derived from inflammatory markers and telomere length, aligns with various metabolic dysfunctions and age-related disease risks, underscoring its potential ability in identifying aging-related phenotypes.

Prognostic Significance and Biologic Associations of Senescence-Associated Secretory Phenotype Biomarkers in Heart Failure

BACKGROUND

The role of cellular senescence in human heart failure (HF) remains unclear. The senescence-associated secretory phenotype (SASP) is composed of proteins released by senescent cells. We assessed the prognostic significance and biologic pathways associated with the SASP in human HF using a plasma proteomics approach.

METHODS AND RESULTS

We measured 25 known SASP proteins among 2248 PHFS (Penn HF Study) participants using the SOMAScan V4 assay. We extracted the common variance in these proteins to generate SASP factor scores and assessed the relationship between these SASP factor scores and (1) all-cause death and (2) the composite of death or HF hospital admission. We also assessed the relationship of each SASP factor to 4746 other proteins, correcting for multiple comparisons, followed by pathway analyses. Two SASP factors were identified. Both factors were associated with older age, lower estimated glomerular filtration rate, and more advanced New York Heart Association class, among other clinical variables. Both SASP factors exhibited a significant positive association with the risk of death independent of the Meta-Analysis of Global-Group in Chronic HF score and NT-proBNP (N-terminal pro-B-type natriuretic peptide) levels. The 2 identified SASP factors were associated with 1201 and 1554 proteins, respectively, belonging to various pathways including the coagulation system, complement system, acute phase response signaling, and retinoid X receptor-related pathways that regulate cell metabolism.

CONCLUSIONS

Increased SASP components are independently associated with adverse outcomes in HF. Biologic pathways associated with SASP are predominantly related to coagulation, inflammation, and cell metabolism.

A roadmap to the molecular human linking multiomics with population traits and diabetes subtypes

In-depth multiomic phenotyping provides molecular insights into complex physiological processes and their pathologies. Here, we report on integrating 18 diverse deep molecular phenotyping (omics-) technologies applied to urine, blood, and saliva samples from 391 participants of the multiethnic diabetes Qatar Metabolomics Study of Diabetes (QMDiab). Using 6,304 quantitative molecular traits with 1,221,345 genetic variants, methylation at 470,837 DNA CpG sites, and gene expression of 57,000 transcripts, we determine (1) within-platform partial correlations, (2) between-platform mutual best correlations, and (3) genome-, epigenome-, transcriptome-, and phenome-wide associations. Combined into a molecular network of > 34,000 statistically significant trait-trait links in biofluids, our study portrays "The Molecular Human". We describe the variances explained by each omics in the phenotypes (age, sex, BMI, and diabetes state), platform complementarity, and the inherent correlation structures of multiomics data. Further, we construct multi-molecular network of diabetes subtypes. Finally, we generated an open-access web interface to "The Molecular Human" ( http://comics.metabolomix.com ), providing interactive data exploration and hypotheses generation possibilities.

[Different levels and clinical significance of growth differentiation factor-15 in patients with atrial fibrillation]

OBJECTIVE

To measure the concentration of growth differentiation factor-15 (GDF-15) in the serum of patients with atrial fibrillation (AF), to study the correlations between the levels of GDF-15 and different factors including basic clinical information, biochemical examinations, and atrial structure, and further to explore the association between GDF-15 and AF types and structural remodeling.

METHODS

AF patients who were admitted to the ward of the Department of Cardiology at Peking University Third Hospital between October 2017 and October 2019 were prospectively enrolled. Patients admitted to the ward at the same time with sinus rhythm and no prior AF history were enrolled in the control group. Clinical information and blood samples of the patients were collected. Enzyme-linked immunosorbent assay was used to measure the concentration of GDF-15. SPSS 23.0 was used for statistical analysis.

RESULTS

In the study, 156 AF patients (64 persistent AF and 92 paroxysmal AF) and 38 patients of the control group were included. Serum GDF-15 levels in the AF group were significantly higher than in the control group [1 112 (723, 1 525) ng/L vs. 697 (499, 825) ng/L, P < 0.001]. Serum GDF-15 levels in the persistent AF group were significantly higher than in the paroxysmal AF group [1 140 (858, 1 708) ng/L vs. 1 090 (662, 1 374) ng/L, P=0.047]. The area under the curve (AUC) of serum GDF-15 levels for prediction of AF was 0.736 (95%CI: 0.651-0.822, P < 0.001). The cut-off value was 843.2 ng/L with a sensitivity of 68.2% and a specificity of 78.9%. The AUC of serum GDF-15 levels for prediction of persistent AF was 0.594 (95%CI: 0.504-0.684, P=0.047). The cut-off va-lue was 771.5 ng/L with a sensitivity of 82.8% and a specificity of 35.9%. Spearman rank correlation analysis showed that the serum GDF-15 levels were positively correlated with age (r=0.480, P < 0.001), left atrial pressure (LAP, r=0.300, P < 0.001), and also negatively correlated with left atrial appendage flow velocity (LAAV, r=-0.252, P=0.002). Multiple linear regression analysis showed that age and LAP affected the GDF-15 levels significantly (P < 0.05). Logistic regression analysis suggested GDF-15 (OR=1.002, 95%CI: 1.001-1.003, P=0.004) and left atrial diameter (LAD, OR=1.400, 95%CI: 1.214-1.616, P < 0.001) were independent predictors of AF.

CONCLUSIONS

Serum GDF-15 levels are higher in AF patients. Meanwhile, serum GDF-15 levels are higher in persistent AF patients than paroxysmal AF patients. GDF-15 is associated with AF and atrial structural remodeling.

Associations of Serum GDF-15 Levels with Physical Performance, Mobility Disability, Cognition, Cardiovascular Disease, and Mortality in Older Adults

Background

Growth differentiation factor 15 (GDF-15) is a member of the TGFβ superfamily secreted by many cell types and found at higher blood concentrations as chronological age increases (1). Given the emergence of GDF-15 as a key protein associated with aging, it is important to understand the multitude of conditions with which circulating GDF-15 is associated.

Methods

We pooled data from 1,174 randomly selected Health ABC Study (Health ABC) participants and 1,503 Cardiovascular Health Study (CHS) participants to evaluate the risk of various conditions and age-related outcomes across levels of GDF-15. The primary outcomes were (1) risk of mobility disability and falls; (2) impaired cognitive function; (3) and increased risk of cardiovascular disease and total mortality.

Results

The pooled study cohort had a mean age of 75.4 +/-4.4 years. Using a Bonferroni-corrected threshold, our analyses show that high levels of GDF-15 were associated with a higher risk of severe mobility disability (HR: 2.13 [1.64, 2.77]), coronary heart disease (HR: 1.47 [1.17, 1.83]), atherosclerotic cardiovascular disease (HR: 1.56 [1.22, 1.98]), heart failure (HR: 2.09 [1.66, 2.64]), and mortality (HR: 1.81 [1.53, 2.15]) when comparing the highest and lowest quartiles. For CHS participants, analysis of extreme quartiles in fully adjusted models revealed a 3.5-fold higher risk of dementia (HR: 3.50 [1.97, 6.22]).

Conclusions

GDF-15 is associated with several age-related outcomes and diseases, including mobility disability, impaired physical and cognitive performance, dementia, cardiovascular disease, and mortality. Each of these findings demonstrates the importance of GDF-15 as a potential biomarker for many aging-related conditions.

Proteomics and machine learning in the prediction and explanation of low pectoralis muscle area

Low muscle mass is associated with numerous adverse outcomes independent of other associated comorbid diseases. We aimed to predict and understand an individual's risk for developing low muscle mass using proteomics and machine learning. We identified eight biomarkers associated with low pectoralis muscle area (PMA). We built three random forest classification models that used either clinical measures, feature selected biomarkers, or both to predict development of low PMA. The area under the receiver operating characteristic curve for each model was: clinical-only = 0.646, biomarker-only = 0.740, and combined = 0.744. We displayed the heterogenetic nature of an individual's risk for developing low PMA and identified two distinct subtypes of participants who developed low PMA. While additional validation is required, our methods for identifying and understanding individual and group risk for low muscle mass could be used to enable developments in the personalized prevention of low muscle mass.

Activity Prediction Modeling Based on a Combination of Growth Differentiation Factor 15 and Serum Biomarker Levels in Dermatomyositis and Polymyositis

AIMS

Growth differentiation factor 15 (GDF15) plays an important role in multiple inflammatory disorders. We aimed to analyze serum GDF15 levels in adult patients with idiopathic inflammatory myopathies (IIMs).

METHODS

Serum GDF15 levels were measured in 179 adult patients with IIMs and 76 healthy controls (HCs). The association between GDF15 levels and disease variables was analyzed using Spearman's rank correlation. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the discriminatory ability of GDF15 and the GDF15-to-lymphocyte ratio (GLR). Machine learning methods were applied to build predictive models.

RESULTS

GDF15 levels and GLR were significantly elevated in patients with adult IIMs than in HCs. Compared with patients in remission, both GDF15 and GLR were significantly higher in myositis patients in an active phase. GDF15 levels correlated positively with myositis disease activity indices and negatively correlated with lymphocyte and platelet counts. ROC curve analysis revealed that GDF15 levels and GLR outperformed muscle enzymes and distinguished well between patients with active disease and those in remission. Furthermore, even in the normal muscle enzyme group, GDF15 levels and GLR were also well-distinguished between patients with active disease and those in remission. Using machine learning, a logistic regression model of GDF15 combined with creatine kinase and lymphocyte count was constructed and had a reliable predictive value for disease activity.

CONCLUSIONS

GDF15, particularly GLR, was significantly correlated with disease activity in adult patients with IIMs. They could serve as useful biochemical markers for evaluating disease activity, monitoring disease progression, and guiding treatment in adult patients with IIMs.

Long COVID as a disease of accelerated biological aging: An opportunity to translate geroscience interventions

It has been four years since long COVID-the protracted consequences that survivors of COVID-19 face-was first described. Yet, this entity continues to devastate the quality of life of an increasing number of COVID-19 survivors without any approved therapy and a paucity of clinical trials addressing its biological root causes. Notably, many of the symptoms of long COVID are typically seen with advancing age. Leveraging this similarity, we posit that Geroscience-which aims to target the biological drivers of aging to prevent age-associated conditions as a group-could offer promising therapeutic avenues for long COVID. Bearing this in mind, this review presents a translational framework for studying long COVID as a state of effectively accelerated biological aging, identifying research gaps and offering recommendations for future preclinical and clinical studies.

Growth Differentiation Factor 15 as a Biomarker of Cardiovascular Risk in Chronic Musculoskeletal Pain

BACKGROUND

It is unknown whether growth differentiation factor 15 (GDF-15) is associated with chronic musculoskeletal pain (CMP) and whether or not its association with incident cardiovascular disease (CVD) changes according to CMP status.

METHODS

In total, 1 957 randomly selected adults aged ≥65 years without prior CVD were followed up between 2015 and 2023. CMP was classified according to its intensity, frequency, and interference with daily activities. The association between GDF-15 levels and CMP was assessed using linear models with progressive inclusion of potential confounders, whereas the association between GDF-15 and CVD risk was evaluated with Cox proportional hazard models with similar adjustment and interaction terms between GDF-15 and CMP. The incremental predictive performance of GDF-15 over standard predictors was evaluated using discrimination and risk reclassification metrics.

RESULTS

GDF-15 concentrations were 6.90% (95% confidence interval [CI]: 2.56; 11.25) higher in individuals with CMP, and up to 8.89% (4.07; 15.71) and 15.79% (8.43; 23.16) higher in those with ≥3 CMP locations and interfering pain. These increased levels were influenced by a higher prevalence of cardiometabolic risk factors, functional impairments, depressive symptoms, and greater levels of inflammation in individuals with CMP. In fully adjusted models, a twofold increase in GDF-15 was associated with a 1.49 increased risk (95% CI: 1.08; 2.05) of a CVD event in individuals with CMP, but not among those without CMP (1.02 [0.77; 1.35]); p-interaction 0.041. Adding GDF-15 to models including the Framingham Risk Score improved predictive performance among individuals with CMP.

CONCLUSIONS

We provide evidence that GDF-15 could serve as a biomarker to assess CMP, as well as to predict CVD incidence in individuals with CMP.

Associations of plasma proteomics and age-related outcomes with brain age in a diverse cohort

Machine learning models are increasingly being used to estimate "brain age" from neuroimaging data. The gap between chronological age and the estimated brain age gap (BAG) is potentially a measure of accelerated and resilient brain aging. Brain age calculated in this fashion has been shown to be associated with mortality, measures of physical function, health, and disease. Here, we estimate the BAG using a voxel-based elastic net regression approach, and then, we investigate its associations with mortality, cognitive status, and measures of health and disease in participants from Atherosclerosis Risk in Communities (ARIC) study who had a brain MRI at visit 5 of the study. Finally, we used the SOMAscan assay containing 4877 proteins to examine the proteomic associations with the MRI-defined BAG. Among N = 1849 participants (age, 76.4 (SD 5.6)), we found that increased values of BAG were strongly associated with increased mortality and increased severity of the cognitive status. Strong associations with mortality persisted when the analyses were performed in cognitively normal participants. In addition, it was strongly associated with BMI, diabetes, measures of physical function, hypertension, prevalent heart disease, and stroke. Finally, we found 33 proteins associated with BAG after a correction for multiple comparisons. The top proteins with positive associations to brain age were growth/differentiation factor 15 (GDF-15), Sushi, von Willebrand factor type A, EGF, and pentraxin domain-containing protein 1 (SEVP 1), matrilysin (MMP7), ADAMTS-like protein 2 (ADAMTS), and heat shock 70 kDa protein 1B (HSPA1B) while EGF-receptor (EGFR), mast/stem-cell-growth-factor-receptor (KIT), coagulation-factor-VII, and cGMP-dependent-protein-kinase-1 (PRKG1) were negatively associated to brain age. Several of these proteins were previously associated with dementia in ARIC. These results suggest that circulating proteins implicated in biological aging, cellular senescence, angiogenesis, and coagulation are associated with a neuroimaging measure of brain aging.

Proteomic aging clock (PAC) predicts age-related outcomes in middle-aged and older adults

Beyond mere prognostication, optimal biomarkers of aging provide insights into qualitative and quantitative features of biological aging and might, therefore, offer useful information for the testing and, ultimately, clinical use of gerotherapeutics. We aimed to develop a proteomic aging clock (PAC) for all-cause mortality risk as a proxy of biological age. Data were from the UK Biobank Pharma Proteomics Project, including 53,021 participants aged between 39 and 70 years and 2923 plasma proteins assessed using the Olink Explore 3072 assay®. 10.9% of the participants died during a mean follow-up of 13.3 years, with the mean age at death of 70.1 years. The Spearman correlation between PAC proteomic age and chronological age was 0.77. PAC showed robust age-adjusted associations and predictions for all-cause mortality and the onset of various diseases in general and disease-free participants. The proteins associated with PAC proteomic age deviation were enriched in several processes related to the hallmarks of biological aging. Our results expand previous findings by showing that biological age acceleration, based on PAC, strongly predicts all-cause mortality and several incident disease outcomes. Particularly, it facilitates the evaluation of risk for multiple conditions in a disease-free population, thereby, contributing to the prevention of initial diseases, which vary among individuals and may subsequently lead to additional comorbidities.

Secretory Phenotype in Peripheral Blood Mononuclear Cells of Elderly Patients with Rheumatoid Arthritis

This study aims to investigate the expression differences of peripheral blood mononuclear cells (PBMCs) in patients with elderly rheumatoid arthritis (ERA). Differentially expressed genes (DEGs) of PBMCs between young patients with RA (RA_Y) and elderly patients with RA (RA_A) were identified by RNA sequencing using the DESeq2 package, followed by bioinformatics analysis. The overlapped targets of the current DEGs and proteomic differentially expressed proteins (another set of unpublished data) were identified and further validated. The bioinformatics analysis revealed significant transcriptomic heterogeneity between RA_A and RA_Y. A total of 348 upregulated and 363 downregulated DEGs were identified. Gene functional enrichment analysis indicated that the DEGs, which represented senescence phenotype for patients with ERA, were enriched in pathways such as Phosphatidylinositol3 kinase/AKT serine-threonine protein kinase (PI3K/Akt) signaling, Mitogen-activated protein kinases (MAPK) signaling, toll-like receptor family, neutrophil degranulation, and immune-related pathways. Gene set enrichment analysis further confirmed the activation of humoral immune response pathways in RA_A. Quantitative polymerase chain reaction validated the expression of five representative DEGs such as SPTA1, SPTB, VNN1, TNXB, and KRT1 in PBMCs of patients with ERA. Patients with ERA have significant senescence phenotype differences versus the young patients. The DEGs identified may facilitate exploring the biomarkers of senescence in RA.

Cross-platform proteomics signatures of extreme old age

In previous work, we used a SomaLogic platform targeting approximately 5000 proteins to generate a serum protein signature of centenarians that we validated in independent studies that used the same technology. We set here to validate and possibly expand the results by profiling the serum proteome of a subset of individuals included in the original study using liquid chromatography tandem mass spectrometry (LC-MS/MS). Following pre-processing, the LC-MS/MS data provided quantification of 398 proteins, with only 266 proteins shared by both platforms. At 1% FDR statistical significance threshold, the analysis of LC-MS/MS data detected 44 proteins associated with extreme old age, including 23 of the original analysis. To identify proteins for which associations between expression and extreme-old age were conserved across platforms, we performed inter-study conservation testing of the 266 proteins quantified by both platforms using a method that accounts for the correlation between the results. From these tests, a total of 80 proteins reached 5% FDR statistical significance, and 26 of these proteins had concordant pattern of gene expression in whole blood generated in an independent set. This signature of 80 proteins points to blood coagulation, IGF signaling, extracellular matrix (ECM) organization, and complement cascade as important pathways whose protein level changes provide evidence for age-related adjustments that distinguish centenarians from younger individuals. The comparison with blood transcriptomics also highlights a possible role for neutrophil degranulation in aging.

Spatiotemporal transcriptomic profiling and modeling of mouse brain at single-cell resolution reveals cell proximity effects of aging and rejuvenation

Old age is associated with a decline in cognitive function and an increase in neurodegenerative disease risk1. Brain aging is complex and accompanied by many cellular changes2-20. However, the influence that aged cells have on neighboring cells and how this contributes to tissue decline is unknown. More generally, the tools to systematically address this question in aging tissues have not yet been developed. Here, we generate spatiotemporal data at single-cell resolution for the mouse brain across lifespan, and we develop the first machine learning models based on spatial transcriptomics ('spatial aging clocks') to reveal cell proximity effects during brain aging and rejuvenation. We collect a single-cell spatial transcriptomics brain atlas of 4.2 million cells from 20 distinct ages and across two rejuvenating interventions-exercise and partial reprogramming. We identify spatial and cell type-specific transcriptomic fingerprints of aging, rejuvenation, and disease, including for rare cell types. Using spatial aging clocks and deep learning models, we find that T cells, which infiltrate the brain with age, have a striking pro-aging proximity effect on neighboring cells. Surprisingly, neural stem cells have a strong pro-rejuvenating effect on neighboring cells. By developing computational tools to identify mediators of these proximity effects, we find that pro-aging T cells trigger a local inflammatory response likely via interferon-γ whereas pro-rejuvenating neural stem cells impact the metabolism of neighboring cells possibly via growth factors (e.g. vascular endothelial growth factor) and extracellular vesicles, and we experimentally validate some of these predictions. These results suggest that rare cells can have a drastic influence on their neighbors and could be targeted to counter tissue aging. We anticipate that these spatial aging clocks will not only allow scalable assessment of the efficacy of interventions for aging and disease but also represent a new tool for studying cell-cell interactions in many spatial contexts.

Proteomic Aging Clocks and the Risk of Mortality among Longer-Term Cancer Survivors in the Atherosclerosis Risk in Communities (ARIC) Study

Background

We constructed a new proteomic aging clock (PAC) and computed the published Lehallier's PAC to estimate biological age. We tested PACs' associations with mortality in longer-term cancer survivors and cancer-free participants.

Methods

ARIC measured 4,712 proteins using SomaScan in plasma samples collected at multiple visits, including Visit 5 (2011-13), from 806 cancer survivors and 3,699 cancer-free participants (aged 66-90). In the training set (N=2,466 randomly selected cancer-free participants), we developed the new PAC using elastic net regression and computed Lehallier's PAC. Age acceleration was calculated as residuals after regressing each PAC on chronological age after excluding the training set. We used multivariable-adjusted Cox proportional hazards regression to examine the associations of age acceleration with all-cause, cardiovascular disease (CVD), and cancer mortality.

Results

Both PACs were correlated with chronological age [r=0.70-0.75]. Age acceleration for these two PACs was similarly associated with all-cause mortality in cancer survivors [hazard ratios (HRs) per 1 SD=1.40-1.42, p<0.01]. The associations with all-cause mortality were similar in cancer survivors and cancer-free participants for both PACs [p-interactions=0.20-0.62]. There were also associations with all-cause mortality in breast cancer survivors for both PACs [HRs=1.54-1.72, p<0.01] and colorectal cancer survivors for the new PAC [HR=1.96, p=0.03]. Additionally, the new PAC was associated with cancer mortality in all cancer survivors. Finally, HRs=1.42-1.61 [p<0.01] for CVD mortality in cancer-free participants for two PACs but the association was insignificant in cancer survivors perhaps due to a limited number of outcomes.

Conclusion

PACs hold promise as potential biomarkers for premature mortality in cancer survivors.

Serum growth differentiation factor 15 as a biomarker for malnutrition in patients with acute exacerbation of chronic obstructive pulmonary disease

Background

Chronic obstructive pulmonary disease (COPD) is a common respiratory disease that often coexists with malnutrition during acute exacerbation (AECOPD) and significantly affects the prognosis. Previous studies have shown that growth differentiation factor 15 (GDF15) levels promote appetite suppression, weight loss, and muscle weakness, and are markedly high in peripheral blood following inflammatory stimulation. However, it is still unknown whether serum GDF15 levels can be used to predict malnutrition in patients with AECOPD.

Methods

A total of 142 patients admitted to the Department of Respiratory Medicine at Anshun People's Hospital between December 2022 and August 2023 were selected for this study. The participants were divided into two groups: malnutrition group (n = 44) and non-malnutrition group (n = 98) based on a body mass index (BMI) < 18.5 kg/m2, according to the Global Leadership Initiative on Malnutrition (GLIM) criteria. Serum GDF15 levels were measured using the enzyme-linked immunosorbent assay (ELISA) and compared between the two groups. Spearman correlation analysis was used to examine the association between serum GDF15 levels, baseline data, and clinical indicators. Binary logistic regression was used to identify the independent risk factors for AECOPD combined with malnutrition. The predictive value of serum GDF15, albumin (ALB), and a combination of these was evaluated to identify malnutrition in patients with AECOPD using a receiver operating characteristic (ROC) curve.

Results

Serum GDF15 levels in patients with malnutrition and AECOPD were significantly higher than those in patients without malnutrition, whereas the serum ALB levels were significantly lower than those in patients without malnutrition (p < 0.001). Moreover, serum GDF15 levels were negatively correlated with BMI (r = -0.562, p < 0.001), mid-arm circumference (r = -0.505, p < 0.001), calf circumference (r = -0.490, p < 0.001), total protein (r = -0.486, p < 0.001), ALB (r = -0.445, p < 0.001), and prognostic nutritional index (r = -0.276, p = 0.001), and positively correlated with C-reactive protein (r = 0.318, p < 0.001), COPD assessment test score (r = 0.286, p = 0.001), modified medical research council classification (r = 0.310, p < 0.001), and global initiative for chronic obstructive pulmonary disease grade (r = 0.177, p = 0.035). Furthermore, serum GDF15 levels were an independent risk factor for malnutrition in patients with AECOPD (OR = 1.010, 95% CI, 1.003∼1.016). The optimal cut-off value of serum GDF15 level was 1,092.885 pg/mL, with a sensitivity of 65.90% and a specificity of 89.80%, while the serum ALB level was 36.15 g/L, with a sensitivity of 86.40% and a specificity of 65.00%, as well as a combined sensitivity of 84.10% and a specificity of 73.90%. Serum GDF15 and serum ALB levels had a good predictive ability (AUC = 0.856, AUC = 0.887), and the ROC revealed a greater combined prediction value for the two (AUC = 0.935).

Conclusion

Serum GDF15 levels could be used as a potential biomarker in the prediction of malnutrition in patients with AECOPD, offering a guidance for future clinical evaluation of malnutrition.

A unified metric of human immune health

Immunological health has been challenging to characterize but could be defined as the absence of immune pathology. While shared features of some immune diseases and the concept of immunologic resilience based on age-independent adaptation to antigenic stimulation have been developed, general metrics of immune health and its utility for assessing clinically healthy individuals remain ill defined. Here we integrated transcriptomics, serum protein, peripheral immune cell frequency and clinical data from 228 patients with 22 monogenic conditions impacting key immunological pathways together with 42 age- and sex-matched healthy controls. Despite the high penetrance of monogenic lesions, differences between individuals in diverse immune parameters tended to dominate over those attributable to disease conditions or medication use. Unsupervised or supervised machine learning independently identified a score that distinguished healthy participants from patients with monogenic diseases, thus suggesting a quantitative immune health metric (IHM). In ten independent datasets, the IHM discriminated healthy from polygenic autoimmune and inflammatory disease states, marked aging in clinically healthy individuals, tracked disease activities and treatment responses in both immunological and nonimmunological diseases, and predicted age-dependent antibody responses to immunizations with different vaccines. This discriminatory power goes beyond that of the classical inflammatory biomarkers C-reactive protein and interleukin-6. Thus, deviations from health in diverse conditions, including aging, have shared systemic immune consequences, and we provide a web platform for calculating the IHM for other datasets, which could empower precision medicine.

Promises and Challenges of populational Proteomics in Health and Disease

Advances in proteomic assay technologies have significantly increased coverage and throughput, enabling recent increases in the number of large-scale population-based proteomic studies of human plasma and serum. Improvements in multiplexed protein assays have facilitated the quantification of thousands of proteins over a large dynamic range, a key requirement for detecting the lowest-ranging, and potentially the most disease-relevant, blood-circulating proteins. In this perspective, we examine how populational proteomic datasets in conjunction with other concurrent omic measures can be leveraged to better understand the genomic and non-genomic correlates of the soluble proteome, constructing biomarker panels for disease prediction, among others. Mass spectrometry workflows are discussed as they are becoming increasingly competitive with affinity-based array platforms in terms of speed, cost, and proteome coverage due to advances in both instrumentation and workflows. Despite much success, there remain considerable challenges such as orthogonal validation and absolute quantification. We also highlight emergent challenges associated with study design, analytical considerations, and data integration as population-scale studies are run in batches and may involve longitudinal samples collated over many years. Lastly, we take a look at the future of what the nascent next-generation proteomic technologies might provide to the analysis of large sets of blood samples, as well as the difficulties in designing large-scale studies that will likely require participation from multiple and complex funding sources and where data sharing, study designs, and financing must be solved.

Cerebrospinal fluid proteomic profile of frailty: Results from the PROLIPHYC cohort

Frailty is a clinical state reflecting a decrease in physiological reserve capacities, known to affect numerous biological pathways and is associated with health issues, including neurodegenerative diseases. However, how global protein expression is affected in the central nervous system in frail subject remains underexplored. In this post hoc cross-sectional biomarker analysis, we included 90 adults (52-85 years) suspected of normal pressure hydrocephalus (NPH) and presenting with markers of neurodegenerative diseases. We investigated the human proteomic profile of cerebrospinal fluid associated with frailty defined by an established cumulated frailty index (FI, average = 0.32), not enriched for neurology clinical features. Using a label-free quantitative proteomic approach, we identified and quantified 999 proteins of which 13 were positively associated with frailty. Pathway analysis with the top positively frailty-associated proteins revealed enrichment for proteins related to inflammation and immune response. Among the 60 proteins negatively associated with frailty, functional pathways enriched included neurogenesis, synaptogenesis and neuronal guidance. We constructed a frailty prediction model using ridge regression with 932 standardized proteins. Our results showed that the "proteomic model" could become an equivalent predictor of FI in order to study chronological age. This study represents the first comprehensive exploration of the proteomic profile of frailty within cerebrospinal fluid. It sheds light on the physiopathology of frailty, particularly highlighting processes of neuroinflammation and inhibition of neurogenesis. Our findings unveil a range of biological mechanisms that are dysregulated in frailty, in NPH subjects at risk of neurodegenerative impairment, offering new perspectives on frailty phenotyping and prediction.

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.

Analysis of the aging-related biomarker in a nonhuman primate model using multilayer omics

BACKGROUND

Aging is a prominent risk factor for diverse diseases; therefore, an in-depth understanding of its physiological mechanisms is required. Nonhuman primates, which share the closest genetic relationship with humans, serve as an ideal model for exploring the complex aging process. However, the potential of the nonhuman primate animal model in the screening of human aging markers is still not fully exploited. Multiomics analysis of nonhuman primate peripheral blood offers a promising approach to evaluate new therapies and biomarkers. This study explores aging-related biomarker through multilayer omics, including transcriptomics (mRNA, lncRNA, and circRNA) and proteomics (serum and serum-derived exosomes) in rhesus monkeys (Macaca mulatta).

RESULTS

Our findings reveal that, unlike mRNAs and circRNAs, highly expressed lncRNAs are abundant during the key aging period and are associated with cancer pathways. Comparative analysis highlighted exosomal proteins contain more types of proteins than serum proteins, indicating that serum-derived exosomes primarily regulate aging through metabolic pathways. Finally, eight candidate aging biomarkers were identified, which may serve as blood-based indicators for detecting age-related brain changes.

CONCLUSIONS

Our results provide a comprehensive understanding of nonhuman primate blood transcriptomes and proteomes, offering novel insights into the aging mechanisms for preventing or treating age-related diseases.

Towards Healthy Longevity: Comprehensive Insights from Molecular Targets and Biomarkers to Biological Clocks

Aging is a complex and time-dependent decline in physiological function that affects most organisms, leading to increased risk of age-related diseases. Investigating the molecular underpinnings of aging is crucial to identify geroprotectors, precisely quantify biological age, and propose healthy longevity approaches. This review explores pathways that are currently being investigated as intervention targets and aging biomarkers spanning molecular, cellular, and systemic dimensions. Interventions that target these hallmarks may ameliorate the aging process, with some progressing to clinical trials. Biomarkers of these hallmarks are used to estimate biological aging and risk of aging-associated disease. Utilizing aging biomarkers, biological aging clocks can be constructed that predict a state of abnormal aging, age-related diseases, and increased mortality. Biological age estimation can therefore provide the basis for a fine-grained risk stratification by predicting all-cause mortality well ahead of the onset of specific diseases, thus offering a window for intervention. Yet, despite technological advancements, challenges persist due to individual variability and the dynamic nature of these biomarkers. Addressing this requires longitudinal studies for robust biomarker identification. Overall, utilizing the hallmarks of aging to discover new drug targets and develop new biomarkers opens new frontiers in medicine. Prospects involve multi-omics integration, machine learning, and personalized approaches for targeted interventions, promising a healthier aging population.

Sex and aging signatures of proteomics in human cerebrospinal fluid identify distinct clusters linked to neurodegeneration

Sex and age are major risk factors for chronic diseases. Recent studies examining age-related molecular changes in plasma provided insights into age-related disease biology. Cerebrospinal fluid (CSF) proteomics can provide additional insights into brain aging and neurodegeneration. By comprehensively examining 7,006 aptamers targeting 6,139 proteins in CSF obtained from 660 healthy individuals aged from 43 to 91 years old, we subsequently identified significant sex and aging effects on 5,097 aptamers in CSF. Many of these effects on CSF proteins had different magnitude or even opposite direction as those on plasma proteins, indicating distinctive CSF-specific signatures. Network analysis of these CSF proteins revealed not only modules associated with healthy aging but also modules showing sex differences. Through subsequent analyses, several modules were highlighted for their proteins implicated in specific diseases. Module 2 and 6 were enriched for many aging diseases including those in the circulatory systems, immune mechanisms, and neurodegeneration. Together, our findings fill a gap of current aging research and provide mechanistic understanding of proteomic changes in CSF during a healthy lifespan and insights for brain aging and diseases.

SenNet recommendations for detecting senescent cells in different tissues

Once considered a tissue culture-specific phenomenon, cellular senescence has now been linked to various biological processes with both beneficial and detrimental roles in humans, rodents and other species. Much of our understanding of senescent cell biology still originates from tissue culture studies, where each cell in the culture is driven to an irreversible cell cycle arrest. By contrast, in tissues, these cells are relatively rare and difficult to characterize, and it is now established that fully differentiated, postmitotic cells can also acquire a senescence phenotype. The SenNet Biomarkers Working Group was formed to provide recommendations for the use of cellular senescence markers to identify and characterize senescent cells in tissues. Here, we provide recommendations for detecting senescent cells in different tissues based on a comprehensive analysis of existing literature reporting senescence markers in 14 tissues in mice and humans. We discuss some of the recent advances in detecting and characterizing cellular senescence, including molecular senescence signatures and morphological features, and the use of circulating markers. We aim for this work to be a valuable resource for both seasoned investigators in senescence-related studies and newcomers to the field.

Prognostic significance of growth differentiation factor-15 across age in chronic heart failure

AIMS

Growth differentiation factor-15 (GDF15), a cytokine in the transforming growth factor family, is up-regulated in stress and inflammatory conditions and is elevated in patients with heart failure (HF). However, the age-specific attributes and prognostic significance of GDF15 across age remain unknown in chronic HF (CHF).

METHODS AND RESULTS

Serum levels of GDF15 were examined in 942 hypertensive patients (median 68 years) with CHF from the SUPPORT trial across the four age groups [under 50 (n = 73), 51-59 (n = 158), 60-69 (n = 296), and 70-79 years (n = 415)] and in the continuous spectrum. Clinical correlates of GDF15 were explored using the classic stepwise and LASSO (least absolute shrinkage and selection operator) regression approaches. Interaction terms with age were tested in the LASSO regression approach. The associations with the composite outcome of HF hospitalization or all-cause death were investigated across ages. Median GDF15 levels (pg/mL) increased along with aging, from 691 in under 50 years to 855 in 51-59 years, 1114 in 60-69 years, and 1516 in 70-79 years (trend P < 0.001). Age, sex, systolic blood pressure, history of diabetes, ischaemic heart disease, left ventricular (LV) end-systolic dimension, LV ejection fraction, estimated glomerular filtration rate, haemoglobin, N-terminal pro-brain natriuretic peptide (NT-proBNP), troponin, C-reactive protein, and the use of angiotensin-converting enzyme inhibitors, diuretics, and statins were mutually selected as clinical covariates of GDF15. The LASSO regression analysis identified significant interactions between age and the history of diabetes and NT-proBNP, with particularly robust associations in patients aged between 60 and 70 years. During the mean follow-up of 8.6 years, 474 composite endpoints of HF hospitalization or death occurred. GDF15 was associated with a higher risk of HF hospitalization or all-cause death [adjusted hazard ratio 1.84 (95% confidence interval 1.45-2.33)], with a particularly heightened risk in patients aged around 70 years (Pinteraction = 0.0008). The model with GDF15 on top of other established risk factors yielded marginally higher C-statistics compared with the model without GDF15 (0.803 and 0.796, P = 0.045). The additive value of GDF15 on top of other established risk factors appeared similar across ages. A universal cut-off value of 1400 pg/mL performed well in discriminating between those with and without HF hospitalization or death.

CONCLUSIONS

Some clinical correlates of GDF15 have an interaction with age. GDF15 is an important determinant of cardiovascular endpoints, particularly in patients aged around 70 years. The additive value of GDF15 appeared consistent across ages, suggesting the use of a universal cut-off value.

Plasma proteomic signature of human longevity

The identification of protein targets that exhibit anti-aging clinical potential could inform interventions to lengthen the human health span. Most previous proteomics research has been focused on chronological age instead of longevity. We leveraged two large population-based prospective cohorts with long follow-ups to evaluate the proteomic signature of longevity defined by survival to 90 years of age. Plasma proteomics was measured using a SOMAscan assay in 3067 participants from the Cardiovascular Health Study (discovery cohort) and 4690 participants from the Age Gene/Environment Susceptibility-Reykjavik Study (replication cohort). Logistic regression identified 211 significant proteins in the CHS cohort using a Bonferroni-adjusted threshold, of which 168 were available in the replication cohort and 105 were replicated (corrected p value <0.05). The most significant proteins were GDF-15 and N-terminal pro-BNP in both cohorts. A parsimonious protein-based prediction model was built using 33 proteins selected by LASSO with 10-fold cross-validation and validated using 27 available proteins in the validation cohort. This protein model outperformed a basic model using traditional factors (demographics, height, weight, and smoking) by improving the AUC from 0.658 to 0.748 in the discovery cohort and from 0.755 to 0.802 in the validation cohort. We also found that the associations of 169 out of 211 proteins were partially mediated by physical and/or cognitive function. These findings could contribute to the identification of biomarkers and pathways of aging and potential therapeutic targets to delay aging and age-related diseases.

The AccelerAge framework: a new statistical approach to predict biological age based on time-to-event data

Aging is a multifaceted and intricate physiological process characterized by a gradual decline in functional capacity, leading to increased susceptibility to diseases and mortality. While chronological age serves as a strong risk factor for age-related health conditions, considerable heterogeneity exists in the aging trajectories of individuals, suggesting that biological age may provide a more nuanced understanding of the aging process. However, the concept of biological age lacks a clear operationalization, leading to the development of various biological age predictors without a solid statistical foundation. This paper addresses these limitations by proposing a comprehensive operationalization of biological age, introducing the "AccelerAge" framework for predicting biological age, and introducing previously underutilized evaluation measures for assessing the performance of biological age predictors. The AccelerAge framework, based on Accelerated Failure Time (AFT) models, directly models the effect of candidate predictors of aging on an individual's survival time, aligning with the prevalent metaphor of aging as a clock. We compare predictors based on the AccelerAge framework to a predictor based on the GrimAge predictor, which is considered one of the best-performing biological age predictors, using simulated data as well as data from the UK Biobank and the Leiden Longevity Study. Our approach seeks to establish a robust statistical foundation for biological age clocks, enabling a more accurate and interpretable assessment of an individual's aging status.

Integrated proteomic and genomic analysis to identify predictive biomarkers for valproate response in bipolar disorder: a 6-month follow-up study

BACKGROUND

Several genetic studies have been undertaken to elucidate the intricate interplay between genetics and drug responses in bipolar disorder (BD). However, there has been notably limited research on biomarkers specifically linked to valproate, with only a few studies investigating integrated proteomic and genomic factors in response to valproate treatment. Therefore, this study aimed to identify biological markers for the therapeutic response to valproate treatment in BD. Patients with BD in remission were assessed only at baseline, whereas those experiencing acute mood episodes were evaluated at three points (baseline, 8 ± 2 weeks, and 6 ± 1 months). The response to valproate treatment was measured using the Alda scale, with individuals scoring an Alda A score ≥ 5 categorized into the acute-valproate responder (acute-VPAR) group. We analyzed 158 peptides (92 proteins) from peripheral blood samples using multiple reaction monitoring mass spectrometry, and proteomic result-guided candidate gene association analyses, with 1,627 single nucleotide variants (SNVs), were performed using the Korean chip.

RESULTS

The markers of 37 peptides (27 protein) showed temporal upregulation, indicating possible association with response to valproate treatment. A total of 58 SNVs in 22 genes and 37 SNVs in 16 genes showed nominally significant associations with the Alda A continuous score and the acute-VPAR group, respectively. No SNVs reached the genome-wide significance threshold; however, three SNVs (rs115788299, rs11563197, and rs117669164) in the secreted phosphoprotein 2 gene reached a gene-based false discovery rate-corrected significance threshold with response to valproate treatment. Significant markers were associated with the pathophysiological processes of bipolar disorders, including the immune response, acute phase reaction, and coagulation cascade. These results suggest that valproate effectively suppresses mechanisms associated with disease progression.

CONCLUSIONS

The markers identified in this study could be valuable indicators of the underlying mechanisms associated with response to valproate treatment.

Senescence-Associated Alterations in Matrisome of Mesenchymal Stem Cells

The process of aging is intimately linked to alterations at the tissue and cellular levels. Currently, the role of senescent cells in the tissue microenvironment is still being investigated. Despite common characteristics, different cell populations undergo distinctive morphofunctional changes during senescence. Mesenchymal stem cells (MSCs) play a pivotal role in maintaining tissue homeostasis. A multitude of studies have examined alterations in the cytokine profile that determine their regulatory function. The extracellular matrix (ECM) of MSCs is a less studied aspect of their biology. It has been shown to modulate the activity of neighboring cells. Therefore, investigating age-related changes in the MSC matrisome is crucial for understanding the mechanisms of tissue niche ageing. This study conducted a broad proteomic analysis of the matrisome of separated fractions of senescent MSCs, including the ECM, conditioned medium (CM), and cell lysate. This is the first time such an analysis has been conducted. It has been established that there is a shift in production towards regulatory molecules and a significant downregulation of the main structural and adhesion proteins of the ECM, particularly collagens, fibulins, and fibrilins. Additionally, a decrease in the levels of cathepsins, galectins, S100 proteins, and other proteins with cytoprotective, anti-inflammatory, and antifibrotic properties has been observed. However, the level of inflammatory proteins and regulators of profibrotic pathways increases. Additionally, there is an upregulation of proteins that can directly cause prosenescent effects on microenvironmental cells (SERPINE1, THBS1, and GDF15). These changes confirm that senescent MSCs can have a negative impact on other cells in the tissue niche, not only through cytokine signals but also through the remodeled ECM.

Different roles of circulating and intramuscular GDF15 as markers of skeletal muscle health

Introduction

Growth Differentiation Factor 15 (GDF15) is a mitokine expressed in response to various stresses whose circulating levels increase with age and are associated with numerous pathological conditions, including muscle wasting and sarcopenia. However, the use of circulating GDF15 (c-GDF15) as a biomarker of sarcopenia is still debated. Moreover, the role of GDF15 intracellular precursor, pro-GDF15, in human skeletal muscle (SM-GDF15) is not totally understood. In order to clarify these points, the association of both forms of GDF15 with parameters of muscle strength, body composition, metabolism and inflammation was investigated.

Methods

the levels of c-GDF15 and SM-GDF15 were evaluated in plasma and muscle biopsies, respectively, of healthy subjects (HS) and patients with lower limb mobility impairment (LLMI), either young (<40 years-old) or old (>70 years-old). Other parameters included in the analysis were Isometric Quadriceps Strength (IQS), BMI, lean and fat mass percentage, Vastus lateralis thickness, as well as circulating levels of Adiponectin, Leptin, Resistin, IGF-1, Insulin, IL6, IL15 and c-PLIN2. Principal Component Analysis (PCA), Canonical Discriminant Analysis (CDA) and Receiving Operating Characteristics (ROC) analysis were performed.

Results

c-GDF15 but not SM-GDF15 levels resulted associated with decreased IQS and IGF-1 levels in both HS and LLMI, while only in LLMI associated with increased levels of Resistin. Moreover, in LLMI both c-GDF15 and SM-GDF15 levels were associated with IL-6 levels, but interestingly SM-GDF15 is lower in LLMI with respect to HS. Furthermore, a discrimination of the four groups of subjects based on these parameters was possible with PCA and CDA. In particular HS, LLMI over 70 years or under 40 years of age were discriminated based on SM-GDF15, c-GDF15 and Insulin levels, respectively.

Conclusion

our data support the idea that c-GDF15 level could be used as a biomarker of decreased muscle mass and strength. Moreover, it is suggested that c-GDF15 has a different diagnostic significance with respect to SM-GDF15, which is likely linked to a healthy and active state.

The impact of ageing mechanisms on musculoskeletal system diseases in the elderly

Ageing is an inevitable process that affects various tissues and organs of the human body, leading to a series of physiological and pathological changes. Mechanisms such as telomere depletion, stem cell depletion, macrophage dysfunction, and cellular senescence gradually manifest in the body, significantly increasing the incidence of diseases in elderly individuals. These mechanisms interact with each other, profoundly impacting the quality of life of older adults. As the ageing population continues to grow, the burden on the public health system is expected to intensify. Globally, the prevalence of musculoskeletal system diseases in elderly individuals is increasing, resulting in reduced limb mobility and prolonged suffering. This review aims to elucidate the mechanisms of ageing and their interplay while exploring their impact on diseases such as osteoarthritis, osteoporosis, and sarcopenia. By delving into the mechanisms of ageing, further research can be conducted to prevent and mitigate its effects, with the ultimate goal of alleviating the suffering of elderly patients in the future.

A metabolic atlas of mouse aging

Humans are living longer, but this is accompanied by an increased incidence of age-related chronic diseases. Many of these diseases are influenced by age-associated metabolic dysregulation, but how metabolism changes in multiple organs during aging in males and females is not known. Answering this could reveal new mechanisms of aging and age-targeted therapeutics. In this study, we describe how metabolism changes in 12 organs in male and female mice at 5 different ages. Organs show distinct patterns of metabolic aging that are affected by sex differently. Hydroxyproline shows the most consistent change across the dataset, decreasing with age in 11 out of 12 organs investigated. We also developed a metabolic aging clock that predicts biological age and identified alpha-ketoglutarate, previously shown to extend lifespan in mice, as a key predictor of age. Our results reveal fundamental insights into the aging process and identify new therapeutic targets to maintain organ health.

In vitro heterochronic parabiosis identifies pigment epithelium-derived factor as a systemic mediator of rejuvenation by young blood

Several decades of heterochronic parabiosis (HCPB) studies have demonstrated the restorative impact of young blood, and deleterious influence of aged blood, on physiological function and homeostasis across tissues, although few of the factors responsible for these observations have been identified. Here we develop an in vitro HCPB system to identify these circulating factors, using replicative lifespan (RLS) of primary human fibroblasts as an endpoint of cellular health. We find that RLS is inversely correlated with serum donor age and sensitive to the presence or absence of specific serum components. Through in vitro HCPB, we identify the secreted protein pigment epithelium-derived factor (PEDF) as a circulating factor that extends RLS of primary human fibroblasts and declines with age in mammals. Systemic administration of PEDF to aged mice reverses age-related functional decline and pathology across several tissues, improving cognitive function and reducing hepatic fibrosis and renal lipid accumulation. Together, our data supports PEDF as a systemic mediator of the effect of young blood on organismal health and homeostasis and establishes our in vitro HCPB system as a valuable screening platform for the identification of candidate circulating factors involved in aging and rejuvenation.

Focus on senescence: Clinical significance and practical applications

The older population is increasing worldwide, and life expectancy is continuously rising, predominantly thanks to medical and technological progress. Healthspan refers to the number of years an individual can live in good health. From a gerontological viewpoint, the mission is to extend the life spent in good health, promoting well-being and minimizing the impact of aging-related diseases to slow the aging process. Biologically, aging is a malleable process characterized by an intra- and inter-individual heterogeneous and dynamic balance between accumulating damage and repair mechanisms. Cellular senescence is a key component of this process, with senescent cells accumulating in different tissues and organs, leading to aging and age-related disease susceptibility over time. Removing senescent cells from the body or slowing down the burden rate has been proposed as an efficient way to reduce age-dependent deterioration. In animal models, senotherapeutic molecules can extend life expectancy and lifespan by either senolytic or senomorphic activity. Much research shows that dietary and physical activity-driven lifestyle interventions protect against senescence. This narrative review aims to summarize the current knowledge on targeting senescent cells to reduce the risk of age-related disease in animal models and their translational potential for humans. We focused on studies that have examined the potential role of senotherapeutics in slowing the aging process and modifying age-related disease burdens. The review concludes with a general discussion of the mechanisms underlying this unique trajectory and its implications for future research.

Gastric GDF15 levels are regulated by age, sex, and nutritional status in rodents and humans

AIM

Growth differentiation factor 15 (GDF15) is a stress response cytokine that has been proposed as a relevant metabolic hormone. Descriptive studies have shown that plasma GDF15 levels are regulated by short term changes in nutritional status, such as fasting, or in obesity. However, few data exist regarding how GDF15 levels are regulated in peripheral tissues. The aim of the present work was to study the variations on gastric levels of GDF15 and its precursor under different physiological conditions, such as short-term changes in nutritional status or overfeeding achieved by HFD. Moreover, we also address the sex- and age-dependent alterations in GDF15 physiology.

METHODS

The levels of gastric and plasma GDF15 and its precursor were measured in lean and obese mice, rats and humans by western blot, RT-PCR, ELISA, immunohistochemistry and by an in vitro organ culture system.

RESULTS

Our results show a robust regulation of gastric GDF15 production by fasting in rodents. In obesity an increase in GDF15 secretion from the stomach is reflected with an increase in circulating levels of GDF15 in rats and humans. Moreover, gastric GDF15 levels increase with age in both rats and humans. Finally, gastric GDF15 levels display sexual dimorphism, which could explain the difference in circulating GFD15 levels between males and females, observed in both humans and rodents.

CONCLUSIONS

Our results provide clear evidence that gastric GDF15 is a critical contributor of circulating GDF15 levels and can explain some of the metabolic effects induced by GDF15.

Organ-specific aging in the plasma proteome predicts disease

In their recent Nature paper, Oh et al. use 4979 plasma proteins collected across multiple cohorts, publicly available gene expression data, and machine learning models to identify 11 organ-specific aging scores that are linked to organ-specific disease and mortality risk, including heart failure, cognitive decline, and Alzheimer's disease.

Growth and differentiation factor-15: A link between inflammaging and cardiovascular disease

Age-related disorders are closely linked to the accumulation of senescent cells. The senescence-associated secretory phenotype (SASP) sustains and progresses chronic inflammation, which is involved in cellular and tissue dysfunction. SASP-related growth and differentiation factor-15 (GDF-15) is an immunoregulatory cytokine that is coupled to aging and thus may have a regulatory role in the development and maintenance of atherosclerosis, a major cause of cardiovascular disease (CVD). Although the effects of GDF-15 are tissue-specific and dependent on microenvironmental changes such as inflammation, available data suggest that GDF-15 has a significant role in CVD. Thus, GDF-15 is a promising biomarker and potential therapeutic target for atherosclerotic CVD.

Psychobiological regulation of plasma and saliva GDF15 dynamics in health and mitochondrial diseases

GDF15 (growth differentiation factor 15) is a marker of cellular energetic stress linked to physical-mental illness, aging, and mortality. However, questions remain about its dynamic properties and measurability in human biofluids other than blood. Here, we examine the natural dynamics and psychobiological regulation of plasma and saliva GDF15 in four human studies representing 4,749 samples from 188 individuals. We show that GDF15 protein is detectable in saliva (8% of plasma concentration), likely produced by salivary glands secretory duct cells. Using a brief laboratory socio-evaluative stressor paradigm, we find that psychosocial stress increases plasma (+3.5-5.9%) and saliva GDF15 (+43%) with distinct kinetics, within minutes. Moreover, saliva GDF15 exhibits a robust awakening response, declining by ~40-89% within 30-45 minutes from its peak level at the time of waking up. Clinically, individuals with genetic mitochondrial OxPhos diseases show elevated baseline plasma and saliva GDF15, and post-stress GDF15 levels in both biofluids correlate with multi-system disease severity, exercise intolerance, and the subjective experience of fatigue. Taken together, our data establish that saliva GDF15 is dynamic, sensitive to psychological states, a clinically relevant endocrine marker of mitochondrial diseases. These findings also point to a shared psychobiological pathway integrating metabolic and mental stress.

Multiplex proteomics identifies inflammation-related plasma biomarkers for aging and cardio-metabolic disorders

BACKGROUND

Cardio-metabolic disorders (CMDs) are common in aging people and are pivotal risk factors for cardiovascular diseases (CVDs). Inflammation is involved in the pathogenesis of CVDs and aging, but the underlying inflammatory molecular phenotypes in CMDs and aging are still unknown.

METHOD

We utilized multiple proteomics to detect 368 inflammatory proteins in the plasma of 30 subjects, including healthy young individuals, healthy elderly individuals, and elderly individuals with CMDs, by Proximity Extension Assay technology (PEA, O-link). Protein-protein interaction (PPI) network and functional modules were constructed to explore hub proteins in differentially expressed proteins (DEPs). The correlation between proteins and clinical traits of CMDs was analyzed and diagnostic value for CMDs of proteins was evaluated by ROC curve analysis.

RESULT

Our results revealed that there were 161 DEPs (adjusted p < 0.05) in normal aging and EGF was the most differentially expressed hub protein in normal aging. Twenty-eight DEPs were found in elderly individuals with CMDs and MMP1 was the most differentially expressed hub protein in CMDs. After the intersection of DEPs in aging and CMDs, there were 10 overlapping proteins: SHMT1, MVK, EGLN1, SLC39A5, NCF2, CXCL6, IRAK4, REG4, PTPN6, and PRDX5. These proteins were significantly correlated with the level of HDL-C, TG, or FPG in plasma. They were verified to have good diagnostic value for CMDs in aging with an AUC > 0.7. Among these, EGLN1, NCF2, REG4, and SLC39A2 were prominently increased both in normal aging and aging with CMDs.

CONCLUSION

Our results could reveal molecular markers for normal aging and CMDs, which need to be further expanded the sample size and to be further investigated to predict their significance for CVDs.

Proteomic aging clock (PAC) predicts age-related outcomes in middle-aged and older adults

Beyond mere prognostication, optimal biomarkers of aging provide insights into qualitative and quantitative features of biological aging and might, therefore, offer useful information for the testing and, ultimately, clinical use of gerotherapeutics. We aimed to develop a proteomic aging clock (PAC) for all-cause mortality risk as a proxy of biological age. Data were from the UK Biobank Pharma Proteomics Project, including 53,021 participants aged between 39 and 70 years and 2,923 plasma proteins assessed using the Olink Explore 3072 assay®. The Spearman correlation between PAC proteomic age and chronological age was 0.77. A total of 10.9% of the participants died during a mean follow-up of 13.3 years, with the mean age at death 70.1 years. We developed a proteomic aging clock (PAC) for all-cause mortality risk as a surrogate of BA using a combination of least absolute shrinkage and selection operator (LASSO) penalized Cox regression and Gompertz proportional hazards models. PAC showed robust age-adjusted associations and predictions for all-cause mortality and the onset of various diseases in general and disease-free participants. The proteins associated with PAC were enriched in several processes related to the hallmarks of biological aging. Our results expand previous findings by showing that age acceleration, based on PAC, strongly predicts all-cause mortality and several incident disease outcomes. Particularly, it facilitates the evaluation of risk for multiple conditions in a disease-free population, thereby, contributing to the prevention of initial diseases, which vary among individuals and may subsequently lead to additional comorbidities.

Cross-platform proteomics signatures of extreme old age

In previous work we used a Somalogic platform targeting approximately 5000 proteins to generate a serum protein signature of centenarians that we validated in independent studies that used the same technology. We set here to validate and possibly expand the results by profiling the serum proteome of a subset of individuals included in the original study using liquid chromatography tandem mass spectrometry (LC-MS/MS). Following pre-processing, the LC-MS/MS data provided quantification of 398 proteins, with only 266 proteins shared by both platforms. At 1% FDR statistical significance threshold, the analysis of LC-MS/MS data detected 44 proteins associated with extreme old age, including 23 of the original analysis. To identify proteins for which associations between expression and extreme-old age were conserved across platforms, we performed inter-study conservation testing of the 266 proteins quantified by both platforms using a method that accounts for the correlation between the results. From these tests, a total of 80 proteins reached 5% FDR statistical significance, and 26 of these proteins had concordant pattern of gene expression in whole blood. This signature of 80 proteins points to blood coagulation, IGF signaling, extracellular matrix (ECM) organization, and complement cascade as important pathways whose protein level changes provide evidence for age-related adjustments that distinguish centenarians from younger individuals.

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.

Influence of Growth Differentiation Factor 15 on Intraocular Pressure in Mice

Growth differentiation factor 15 (GDF15), a stress-sensitive cytokine, and a distant member of the transforming growth factor β superfamily, has been shown to exhibit increased levels with aging, and in various age-related pathologies. Although GDF15 levels are elevated in the aqueous humor (AH) of glaucoma (optic nerve atrophy) patients, the possible role of this cytokine in the modulation of intraocular pressure (IOP) or AH outflow is unknown. The current study addresses this question using transgenic mice expressing human GDF15 and GDF15 null mice, and by perfusing enucleated mouse eyes with recombinant human GDF15 (rhGDF15). Treatment of primary cultures of human trabecular meshwork cells with a telomerase inhibitor, an endoplasmic reticulum stress-inducing agent, hydrogen peroxide, or an autophagy inhibitor resulted in significant elevation in GDF15 levels relative to the respective control cells. rhGDF15 stimulated modest but significant increases in the expression of genes encoding the extracellular matrix, cell adhesion proteins, and chemokine receptors (C-C chemokine receptor type 2) in human trabecular meshwork cells compared with controls, as deduced from the differential transcriptional profiles using RNA-sequencing analysis. There was a significant increase in IOP in transgenic mice expressing human GDF15, but not in GDF15 null mice, compared with the respective wild-type control mice. The AH outflow facility was decreased in enucleated wild-type mouse eyes perfused with rhGDF15. Light microcopy-based histologic examination of the conventional AH outflow pathway tissues did not reveal identifiable differences between the GDF15-targeted and control mice. Taken together, these results reveal the modest elevation of IOP in mice expressing human GDF15 possibly stemming from decreased AH outflow through the trabecular pathway.

ATAC-clock: An aging clock based on chromatin accessibility

The establishment of aging clocks highlighted the strong link between changes in DNA methylation and aging. Yet, it is not known if other epigenetic features could be used to predict age accurately. Furthermore, previous studies have observed a lack of effect of age-related changes in DNA methylation on gene expression, putting the interpretability of DNA methylation-based aging clocks into question. In this study, we explore the use of chromatin accessibility to construct aging clocks. We collected blood from 159 human donors and generated chromatin accessibility, transcriptomic, and cell composition data. We investigated how chromatin accessibility changes during aging and constructed a novel aging clock with a median absolute error of 5.27 years. The changes in chromatin accessibility used by the clock were strongly related to transcriptomic alterations, aiding clock interpretation. We additionally show that our chromatin accessibility clock performs significantly better than a transcriptomic clock trained on matched samples. In conclusion, we demonstrate that the clock relies on cell-intrinsic chromatin accessibility alterations rather than changes in cell composition. Further, we present a new approach to construct epigenetic aging clocks based on chromatin accessibility, which bear a direct link to age-related transcriptional alterations, but which allow for more accurate age predictions than transcriptomic clocks.

Is There a Role for Growth and Differentiation Factor-15 in B-Cell Lymphoproliferative Neoplasms?

Growth and differentiation factor-15 (GDF-15) correlates with worse outcome of many tumours and any cause mortality. Data about its role in lymphoproliferative neoplasms (LPN) are scarce. Our research aimed to reveal the correlation between GDF-15 and standard laboratory parameters of LPN activity, and to get insight into the possible value of this cytokine assessment in lymphoma patients. Prospective research included 40 patients treated for aggressive or indolent LPN, and 31 with indolent LPN on "watch and wait" regimen. Analyses were performed before and after treatment in treated patients and on two separate occasions in the "watch and wait" group. ELISA technique with R&D assays according to the manufacturer manual, from stored sera at - 70 °C was used for GDF-15 level measurement. Statistical analyses were performed by IBM SPSS Statistics 22 using descriptive and inferential statistics. As appropriate, differences between groups were assessed by two tailed t-test, Mann-Whitney or x2 test. Spearman Rank Order Correlation was done to correlate GDF-15 with standard laboratory markers of disease activity. All tests are two-tailed with significance level p < 0. 05. GDF-15 (p = 0.028) and fibrinogen (p = 0.001) concentrations increased after treatment in indolent lymphoma patients while β2 microglobulin decreased (p < 0.001). GDF-15 positively correlated with β2microglobulin before (p < 0.001) and after (p = 0.031) therapy. There were no differences in any of the aforementioned parameters in the "watch and wait" group during observation. A positive correlation between GDF-15 and β2 microglobulin in patients with indolent LPN who need treatment suggests potential value in risk assessment.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12288-023-01695-6.

Application of AI in biological age prediction

The development of anti-aging interventions requires quantitative measurement of biological age. Machine learning models, known as "aging clocks," are built by leveraging diverse aging biomarkers that vary across lifespan to predict biological age. In addition to traditional aging clocks harnessing epigenetic signatures derived from bulk samples, emerging technologies allow the biological age estimating at single-cell level to dissect cellular diversity in aging tissues. Moreover, imaging-based aging clocks are increasingly employed with the advantage of non-invasive measurement, making it suitable for large-scale human cohort studies. To fully capture the features in the ever-growing multi-modal and high-dimensional aging-related data and uncover disease associations, deep-learning based approaches, which are effective to learn complex and non-linear relationships without relying on pre-defined features, are increasingly applied. The use of big data and AI-based aging clocks has achieved high accuracy, interpretability and generalizability, guiding clinical applications to delay age-related diseases and extend healthy lifespans.

Accelerated Aging in Cancer Survivors: Cellular Senescence, Frailty, and Possible Opportunities for Interventions

The population of cancer survivors has markedly increased due to the rapid improvements in cancer treatment. However, cancer survivors experience accelerated aging, which leads to chronic diseases and other age-related conditions, such as frailty. Those conditions may persist years after cancer diagnosis and treatment. Cellular senescence, a hallmark of aging, is one of the mechanisms that contribute to accelerated aging in cancer survivors. Several aging measures, including measures based on clinical markers and biomarkers, have been proposed to estimate the aging process, and some of them have shown associations with mortality and frailty in cancer survivors. Several anti-aging interventions, including lifestyle changes and anti-aging drugs, have been proposed. Future research, particularly in large-scale studies, is needed to determine the efficiency of these aging measures and anti-aging interventions before considering their application in clinics. This review focuses on the mechanisms of cellular senescence and accelerated aging in cancer survivors, assessment of the aging process using clinical markers and biomarkers, and the high prevalence of frailty in that population, as well as possible opportunities for anti-aging interventions. A deeper understanding of aging measures and anti-aging interventions in cancer survivors will contribute to the development of effective strategies to mitigate accelerated aging in cancer survivors and improve their quality of life.

Comprehensive proteomics of CSF, plasma, and urine identify DDC and other biomarkers of early Parkinson's disease

Parkinson's disease (PD) starts at the molecular and cellular level long before motor symptoms appear, yet there are no early-stage molecular biomarkers for diagnosis, prognosis prediction, or monitoring therapeutic response. This lack of biomarkers greatly impedes patient care and translational research-L-DOPA remains the standard of care more than 50 years after its introduction. Here, we performed a large-scale, multi-tissue, and multi-platform proteomics study to identify new biomarkers for early diagnosis and disease monitoring in PD. We analyzed 4877 cerebrospinal fluid, blood plasma, and urine samples from participants across seven cohorts using three orthogonal proteomics methods: Olink proximity extension assay, SomaScan aptamer precipitation assay, and liquid chromatography-mass spectrometry proteomics. We discovered that hundreds of proteins were upregulated in the CSF, blood, or urine of PD patients, prodromal PD patients with DAT deficit and REM sleep behavior disorder or anosmia, and non-manifesting genetic carriers of LRRK2 and GBA mutations. We nominate multiple novel hits across our analyses as promising markers of early PD, including DOPA decarboxylase (DDC), also known as L-aromatic acid decarboxylase (AADC), sulfatase-modifying factor 1 (SUMF1), dipeptidyl peptidase 2/7 (DPP7), glutamyl aminopeptidase (ENPEP), WAP four-disulfide core domain 2 (WFDC2), and others. DDC, which catalyzes the final step in dopamine synthesis, particularly stands out as a novel hit with a compelling mechanistic link to PD pathogenesis. DDC is consistently upregulated in the CSF and urine of treatment-naïve PD, prodromal PD, and GBA or LRRK2 carrier participants by all three proteomics methods. We show that CSF DDC levels correlate with clinical symptom severity in treatment-naïve PD patients and can be used to accurately diagnose PD and prodromal PD. This suggests that urine and CSF DDC could be a promising diagnostic and prognostic marker with utility in both clinical care and translational research.