The elderly population is rapidly growing around the world, and the age group 80 years and older is expanding faster than any other demographic group (Colby and Ortman, 2015). Advanced age is one of the most powerful predictors shared by many chronic diseases that are highly prevalent in the older population and negatively impact function and quality of life. Understanding mechanisms by which aging increase the risk of chronic morbidity is of utmost importance for the development of new intervention strategies aimed at improving health-span in the general population.

While, at the population level, the overall risk of pathology increases with chronological age, the age when pathology first emerges clinically, the rates of pathology development and the age at death are highly variable between individuals. Hence, two individuals of the same chronological age may lay on very different health trajectories. While the characterization of risk factors has proven useful for the identification of individuals at high risk of developing specific diseases, there is currently no robust method to distinguish between individuals at risk of global deterioration of health status. For example, chronic inflammation is the only biomarker that predicts the development of multimorbidity (Fabbri et al., 2015). Thus, the development of biomarkers that can discriminate, at an early stage, individuals on different health trajectories with aging is a critical challenge in aging research.

Over the last few years, data from a large number of molecular analytes have been used to screen biomarkers associated with chronological age, specific chronic diseases and global health in relatively large populations (Horvath, 2013; Tanaka et al., 2018; Sun et al., 2018). Among these molecular analytes, proteins are particularly attractive because they are direct biological effectors, and dynamic changes with aging or disease are complex and manifold. Indeed, previous studies on circulating proteins have already shown great promise as potential biomarker signatures of human diseases (Tanaka et al., 2018). Past studies have used various proteomic platforms to identify novel age- and disease-related protein biomarkers in plasma, serum, and cerebral spinal fluid (Tanaka et al., 2018; Sun et al., 2018; Menni et al., 2015; Baird et al., 2015; Di Narzo et al., 2017). While several promising candidate biomarkers have been reported, reproducibility of the findings and the clinical utility of these biomarkers is still uncertain. Highly robust and clinically useful biomarkers will emerge following repeated studies in genetically and geographically diverse cohorts.

Since aging is a complex process, it is likely that multiple biomarkers are needed to capture the physiological mechanisms of accelerated aging that eventually lead to the multiplicity of adverse outcomes that typically occur in older persons. Perhaps the best example is the estimation of ‘epigenetic clock’ signatures that were developed by examining DNA methylation across a large number of DNA sites to single out those that in a weighted average reproducibly predict chronological age (Horvath, 2013; Bocklandt et al., 2011; Human Microbiome Project Consortium, 2012; Weidner et al., 2014). Positive deviations of the epigenetic clock from chronological age are meant to estimate biological age acceleration and have, in fact, been shown that they predict pathologic age-related traits, such as diabetes, cardiovascular disease, cancer, and premature death (Fauci et al., 2008; Chen et al., 2016a; Levine et al., 2015; Perna et al., 2016). Other epigenetic biomarkers, still based on DNA methylation, were developed by tuning them against health-related characteristics, including risk factors of cardiovascular disease, mortality and lifespan (Levine et al., 2018; Lu et al., 2019). While these tools have clearly demonstrated that the aging process determines predictable, non-stochastic epigenetic changes, the predictive power of epigenetic biomarkers are still too limited for extensive clinical use. In addition, the mechanisms for such age-related changes in DNA methylation and their association with health outcomes is unknown, as it is challenging to draw conclusions related to functional pathways. Additionally, ideal clinically useful biomarkers should be directly measurable in highly accessible and non-invasive biological specimens such as plasma or serum.

Our group recently developed an accurate signature of age using data from 76 plasma proteins in a population of 240 healthy subjects between 22 and 93 years of age. This proteomic signature of age was correlated with age-related clinical parameters such as inflammation and adiposity (Tanaka et al., 2018). A clear advantage of protein biomarkers compared to other biomarkers is that they are direct biological effectors, and their identification provides important clues to the underlying biological mechanism involved in their association with age and health outcomes. However, whether this proteomic clock is associated with clinically relevant aging outcomes could not be determined because of limited sample size and because the initial study was restricted to extremely healthy individuals.

To address the limitations of our previous US-based study, here we examined associations between age and abundances of 1301 proteins measured using an aptamer-based method on a representative population sample of 997 participants in the Italy-based InCHIANTI study. We aimed to identify novel age-associated proteins, as well as confirm previously reported age-associated proteins, and to uncover their relationship with age-related outcomes. By using a genetically and geographically distinct cohort, we also aimed to crystallize the most robust biomarkers in diverse populations. We used a Mendelian Randomization (MR) approach to explore genetic evidence of causal relationship between age-associated proteins with key chronic diseases. We also explored DNA methylation as an underlying epigenetic mechanism for age-related changes in protein abundance and show that, for some proteins, methylation can partially explain the observed age associations. We further investigated whether proteomic data can be used to predict chronological age in a geographically distinct cohort using the algorithm from our previous study (Tanaka et al., 2018). Moreover, we assessed that the deviation between predicted and observed age may reflect rates of aging and could be used as a measure of accelerated biological aging.

A proteomic analysis of chronological age was conducted to identify individuals who are experiencing accelerated health deterioration with aging based on their proteomic profile. The proteomic analysis identified 651 age-associated plasma proteins, many of which had a strong age-relationship independent of the presence of chronic diseases. Many of these proteins were previously described, 224 additional novel age-associated proteins are reported in the InCHIANTI study. Sex-stratified analysis showed that for most proteins, age associations were similar in men and women. Proteins that systematically change with aging are also biomarkers of health longitudinally and risk biomarkers for mortality and accumulation of multimorbidity over time in all subjects as well in persons that appear to be healthy by being free of any chronic diseases at the time of the evaluation. Analysis of DNA methylation data showed that methylation is one of the molecular mechanisms of age-associated differences in protein expression. Exploration of publicly available genetic data to assess the genetic relationship between age-associated protein and aging disease supported the clinical importance of these proteins. Finally, age acceleration measure using proteomic signature of age, which can be interpreted as a proxy measure for biological age, is associated with comorbidity and predicts future rate of change in multimorbidity as well as all-cause mortality over a 18 year follow-up period. These data support the notion that circulating proteins are useful biomarkers to monitor trajectories of health and to identify those individuals who are aging faster than their chronological age. These individuals may be targeted for in depth diagnostic workup to identify novel opportunities for prevention of the impending deterioration of health. Not only are plasma proteins ideal clinical aging biomarker candidates as they are easily accessible, proteins are direct biological effectors and can highlight important mechanisms underlying aging.

Results from several studies that have investigated the relationship between plasma protein and age are consistent in their findings (Tanaka et al., 2018; Sun et al., 2018; Menni et al., 2015; Lehallier et al., 2019). The current study in a general Italian population yielded results highly correlated with those from our previous study in a highly selective healthy population from the US, and therefore highlighting that this cluster of age-related proteins is not population specific but rather reflects some general physiological mechanisms of aging, which may have important clinical implications. In our study, 32.6% of the age-associated proteins were predictive of all-cause mortality and 45.3% were associated with multimorbidity independent of chronological age, suggesting that their change may reflect a shared biological process between aging and disease. This observation is consistent with the geroscience paradigm that accelerated aging has a causative role in multiple chronic diseases (Sierra and Kohanski, 2017). The protein most strongly associated with chronological age in the current study was pleiotrophin (PTN), which is a heparin-binding growth factor involved in angiogenesis, tumorigenesis, and neuromodulation (Bertram et al., 2019; Souttou et al., 1998). In our study, PTN was not predictive of either mortality or multimorbidity after adjusting for age. The second most significant protein in our analysis was growth-differentiation factor 15 (GDF15), a member of the transforming growth factor‐β cytokine superfamily. GDF15 was the most significant age-associated protein predictive of mortality and multimorbidity. In humans, GDF15 has been suggested as a biomarker of cardiovascular disease mortality, but more recently associated with all-cause mortality (Lindholm et al., 2018; ActiFE study group et al., 2019). There is growing evidence in both human and animal models that GDF15 plays an important role beyond cardiovascular disease and has been linked to weight homeostasis, fibrosis, diabetes, cardiovascular disease, renal disease, mobility disability and cancer (Baek and Eling, 2019; Bidadkosh et al., 2017; Daniels et al., 2011; Ho et al., 2013; Wallentin et al., 2013; Osawa et al., 2020).

More recently, GDF15 has been identified as a highly robust senescence-associated secretory phenotype (SASP) factor in multiple senescence inducers and cell types (Basisty et al., 2020) as well as a driver of senescence-associated colon cancer metastasis (Guo et al., 2019). Increase of cellular senescence burden is hypothesized to be one of the fundamental biological mechanisms underlying aging, thus confirmation of GDF15 as a SASP factor (Basisty et al., 2020) supports the utility of this protein as a global biomarker of health and lifespan. We have previously reported an enrichment of SASP factors among circulating protein biomarkers of aging (Tanaka et al., 2018). Here we have also identified numerous SASP components or other senescence-associated proteins among biomarkers of aging, mortality, and multimorbidity. Among the most robust SASP components we identified 11 proteins in this study - GDF15, MMP1, STC1, IGFBP2, IGFBP4, TIMP1, TIMP2, CST3, CTSB, CLEC11A, and POSTN – which are both core SASP components (secreted by multiple types of senescent cells and senescence inducers) and also associated with all-cause mortality and multimorbidity. IL-6, a classical SASP factor in multiple senescent cell types (Coppé et al., 2008), was also associated with all-cause mortality. Several other top SASP components that we identified in plasma are also previously reported health biomarkers (Basisty et al., 2020). In addition to aging, MMP1 is a biomarker for several cancers, pulmonary fibrosis and potentially Alzheimer’s disease (Bhat et al., 2012; Chen et al., 2016b; Rosas et al., 2008), whereas STC1 is a diagnostic and prognostic biomarker for cancers, pulmonary fibrosis, renal ischemia/reperfusion injury and Alzheimer’s disease (Chang et al., 2015; Ohkouchi et al., 2015; Pan et al., 2015; Shahim et al., 2017).

The recent INTERVAL and LonGenity studies show that there were unique non-linear patterns in the expression proteins across lifespan (Lehallier et al., 2019). More specifically there were three waves of changes in the proteome in the fourth, sixth and eight decade. Moreover, it was shown that the proteins that changed in the older decades were associated with age-related diseases while the proteins that change in the fourth were not. We explored whether similar patterns can be observed in the InCHIANTI study, and strikingly we found a peak shift in the proteome in the eighth decade at 78 years of age replicating the same exact timing of the INTERVAL and LonGenity study. T in the younger ages, however our study may have been limited in identifying a wave in ages before 60 due to the small sample size resulting from the sampling design in the InCHIANTI study. We also found that the proteins that were associated with age in the older years were associated with both all-cause mortality and multimorbidity. In our study, the proteins identified through linear modeling were also enriched in proteins associated with all-cause mortality and multimorbidity thus the linear models did capture important aging biomarkers. However, these data suggest that more emphasis on proteins that change in later years may lead to discovery of clinically important proteomic biomarkers of age.

MR explores causal relationships between exposure and outcome using the random assortment of genetic alleles associated with exposures of interest, often described as a ‘natural’ RCT study. Two sample MR utilizes large databases of genome-wide association study results to infer causal relationships using genetic data as instrument variables. In this study, we find that genetically elevated brain type natriuretic peptide (NPPB) was protective against hypertension. Circulating NPPB reduces blood pressure and plasma volume through its effect on the adrenal gland, kidney, brain and vasculature (Levin et al., 1998). Circulating NPPB levels are found at high concentrations in patients with heart failure and was shown to be a useful diagnostic tool in an acute setting (Davis et al., 1994; Gardner, 2003). A genome-wide association study of circulating NPPB identified a variants in the NPPB locus, and these variants were also associated with lower systolic and diastolic blood pressure as well as lower risk of hypertension (Newton-Cheh et al., 2009). The genetic instrument for NPPB used in our study is mapped to the same locus as the variants used in the previous report, and our results are consistent with their findings. We also found genetically higher matrix metalloproteinase-12 (MMP12) to be protective of coronary heart disease, MI, and ischemic stroke. MMP12 is a metalloprotease produced primarily by macrophages and is involved in inflammation and tissue damage (Shapiro et al., 1993). While circulating levels of MMP12 have been linked to increased risk of cardiovascular disease, genetically increased MMP12 has been linked to reduced cardiovascular disease risk in the current report as well as others (Sun et al., 2018; Chong et al., 2019). The apparent discrepant association between the genetic instrument for NPPB and MMP12 with their respective age-associated disease may signify reverse-causation where proteins levels are altered in response to development of disease or may reflect possible compensatory protein where protein levels increase with age to protect against development of disease (Bucur et al., 2020). The discrepancy of the direction of associations should be explored further. We found genetically increased proteinase inhibitor 3 (PI), or elafin, was associated with increased odds of having MI, both ischemic and cardioembolic stroke, lunch cancer and rheumatoid arthritis. PI3 is a skin-derived serine protease inhibitor that plays an important role in hydrolyzing connective tissue components including elastin and collagen through the inhibition of elastase (Schalkwijk et al., 1990; Wiedow et al., 1990). PI3 has also been linked to lung tissue repair and is a candidate biomarker for lung diseases including acute respiratory lung disease and asthma (Wang et al., 2017; Tsai et al., 2016), as well as modulator of inflammation through the inhibition of neutrophil derived elastases (Alam et al., 2012). Our data support the involvement of PI3 in cardiovascular, lung and immunological diseases through the control of inflammation and tissue repair. We found genetically higher SPARC related modular calcium binding 1 (SMOC1) to be associated with higher bone mineral density, hemoglobin and RDW. Our results are consistent with results from genome-wide association studies of bone mineral density and osteoporosis, where SMOC1 has been identified as the candidate gene underlying the signal on chromosome 14 (Zhang et al., 2014; Qiu et al., 2019). Animal studies have shown that SMOC1 knockout results in disruption of ocular and limb development through reduction in interdigital apoptosis and disruption in BMP signaling. Interestingly, SMOC1 was identified in a genome-wide association study of blood traits in mice, however, this locus has not been a major locus in human genetic studies of blood parameters (Davis et al., 2013). Further studies are needed to explore the link between SMOC1 and blood traits found in our study. Finally, we find genetically higher growth hormone receptor (GHR) was associated with bone mineral density. GHR knockout animals display disproportional bone growth and lower bone mineral density (Sjögren et al., 2000). In humans, administration of growth hormone has been shown to improve bone mineral density in postmenopausal women (Joseph et al., 2008).

Our data suggest that the proteomic signature of age (PROage) can be effectively used to assess an individual’s rate of aging (PROaccel). What was surprising is that the proteomic age signature developed by our group is strikingly similar to another proteomic age signature created in a geographically distinct population by Lehallier and colleagues (Lehallier et al., 2019). This suggests that the circulating age proteome, at least as captured by this aptamer-based platform, is very robust. Using this proteomic signature, subjects who were predicted to be older than their chronological age had higher prevalence of comorbidities, more rapid increase in co-morbidities over time, and higher risk of all-cause mortality. In recent years, several measures of rates of aging have been developed using methylation data. We compared the proteomic signature with three epigenetic clocks. One of the first phase of epigenetic clocks developed by Horvath, 2013, is a multi-tissue methylation signature (mDNA_HORVATH) that was designed to predict chronological age. The second is an epigenetic marker for phenotypic age developed by Levine and colleagues (Levine et al., 2018) that was derived using clinical biomarkers, such as albumin, creatinine, and glucose (mDNA_PHENO). Phenotype age was designed to predict all-cause mortality, cause-specific mortality, and other aging measures. The final epigenetic clock is one developed by Ake and colleagues (Lu et al., 2019) based on biomarkers and risk factors of mortality including smoking, seven biomarkers including GDF15 and PAI-I (mDNA_GRIM). Interestingly, aging rate based on proteomic clock (PROaccel) was not correlated with aging rates based on mDNA_HORVATH (mDNA_HORVATHACCEL), but was moderately correlated with rates based on mDNA_PHENO (mDNA_PHENOACCEL) and mDNA_GRIM (mDNA_GRIMACCEL). Since both PROaccel and mDNA_GRIMACCEL both include GDF15, these two biological aging measures may be capturing similar aging mechanisms. We compared the PROaccel against three methylation age measures in their ability to predict all-cause mortality. PROaccel performed better than mDNA_HORVATHACCEL, but was outperformed by mDNA_PHENOACCEL and mDNA_GRIMACCEL. This would suggest that creating a proteomic signature of aging phenotypes, rather than chronological age, could result in a more robust proteomic biomarker for aging and should be explored in future studies. However, our biomarker panels include many SASP proteins that may be utilized as important benchmarks for clinically assessing senescent cell burden. As the development of drugs that selectively eliminate senescent cells (senolytics) or modify the SASP (senomorphics) has quickly advanced in recent years, with early trials now in progress, using biomarker panels that include markers of senescence will be an important step in translating these interventions to the clinic.

There are several limitations to this study. While the associations of individual proteins with age have been consistent across different populations, the associations of the PROaccel with morbidity and mortality should be confirmed in additional, larger studies. It would also be important to validate whether PROage measure can be confirmed using proteomic data generated using different platforms. Since proteomic measurements were only conducted cross-sectionally, we could not determine whether PROaccel is a sensitive measure of changes in health status over time. Longitudinal measurement of proteomic data will also be useful determining the trajectories of protein abundances, which could be a more sensitive in capturing rates of aging.

In summary, in this study 651 age-associated proteins were identified that are also predictive of important aging outcomes including all-cause mortality and multimorbidity. Further examination of the function of these proteins individually or collectively may further shed light to the mechanisms underlying the aging process. Finally, a proteomic signature of age based on abundances of 76 proteins captures the rate of aging and may be useful in identifying individuals who are aging faster than their chronological age. These findings should be confirmed in a longitudinal perspective, with good representation of individual men and women across the lifespan. Finally, clinical studies that use in depth characterization of physiological and pathologic characteristics of individuals who are ‘older’ compared to those who are ‘younger’ than their chronological aging may confirm whether a proteomic signature of aging may be a useful tool in clinical practice.

Phenotypic data and source codes used for this manuscript are provided. Due to the contents of the InCHIANTI study consent forms, proteomic and DNA methylation data cannot be made publicly available. Researchers can seek access to these data through the submission of proposals and subsequent approval through the InCHIANTI study website (http://inchiantistudy.net/).

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Author details

1. Toshiko Tanaka

   Translational Gerontology Branch, National Institute on Aging, NIH, Baltimore, United States

   Contribution

   Conceptualization, Formal analysis, Visualization, Writing - original draft, Writing - review and editing

   For correspondence

   tanakato@mail.nih.gov

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4161-3829

2. Nathan Basisty

   The Buck Institute for Research on Aging, Novato, United States

   Contribution

   Formal analysis, Visualization, Writing - review and editing

   Competing interests

   No competing interests declared

3. Giovanna Fantoni

   National Institute on Aging, Intramural Research Program, Clinical Research Core, NIH, Baltimore, United States

   Contribution

   Data curation, Writing - review and editing

   Competing interests

   No competing interests declared

4. Julián Candia

   Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, United States

   Contribution

   Data curation, Formal analysis

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5793-8989

5. Ann Z Moore

   Translational Gerontology Branch, National Institute on Aging, NIH, Baltimore, United States

   Contribution

   Writing - review and editing

   Competing interests

   No competing interests declared

6. Angelique Biancotto

   Precision Immunology, Immunology & Inflammation Research Therapeutic Area, Sanofi, Cambridge, United States

   Contribution

   Data curation

   Competing interests

   No competing interests declared

7. Birgit Schilling

   The Buck Institute for Research on Aging, Novato, United States

   Contribution

   Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-9907-2749

8. Stefania Bandinelli

   Geriatric Unit, Azienda Sanitaria toscana centro, Firenze, Italy

   Contribution

   Data curation, Supervision, Writing - original draft

   Competing interests

   No competing interests declared

9. Luigi Ferrucci

   Translational Gerontology Branch, National Institute on Aging, NIH, Baltimore, United States

   Contribution

   Conceptualization, Supervision

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-6273-1613

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