Aging can be conceptualized in different ways. While chronological age is measured by date of birth, biological age reflects the relative aging of an individual’s physiological condition. Biological aging can be estimated by various cellular indices (López-Otín et al., 2013). Commonly used indices are based on telomere length, DNA methylation patterns (epigenetic age), variation in transcription (transcriptomic age) as well as alterations in the metabolome (metabolomic age) and in the proteome (proteomic age) (see Han et al., 2019; Xia et al., 2017 and Jylhävä et al., 2017 for recent reviews). Biological aging is defined as the residuals of regressing predicted biological age on chronological age: a positive value indicates that the biological age is larger than the chronological age. Advanced biological aging (i.e. an increased biological clock) has been associated to poor somatic health, including the onset of aging-related somatic diseases such as cardiovascular disease, diabetes, and cognitive decline (Xia et al., 2017). Advanced biological aging has also been correlated to mental health: childhood trauma (Li et al., 2017), psychological stress, and psychiatric disorders (Darrow et al., 2016; Han et al., 2018). Specifically, telomere length has been most extensively researched and was found to be shorter in various somatic conditions (Jin et al., 2018), all-cause mortality (Mons et al., 2017; Wang et al., 2018) and a range of psychiatric disorders (Lindqvist et al., 2015). Advanced epigenetic aging has also been linked to worse somatic health, mortality (Marioni et al., 2015), depressive disorder (Han et al., 2018; Whalley, 2017), and post-traumatic stress disorder (Wolf et al., 2018), although some studies have found associations with the opposite direction of effect (Verhoeven et al., 2018; Boks et al., 2015). Advanced transcriptomic aging was found in those with higher blood pressure, cholesterol levels, fasting glucose, and body mass index (BMI) (Peters et al., 2015). Advanced metabolomic aging increases risk on future cardiovascular disease, mortality, and functionality (Akker et al., 2019).

While all biological clocks aim to measure the biological aging process, there is limited evidence for cross-correlations among different clocks. Belsky and colleagues (Belsky et al., 2017) recently showed low agreement between eleven quantifications of biological aging including telomere length, epigenetic aging, and biomarker-composites. In contrast, Hastings et al., 2019 showed relatively strong correlations (r > 0.50) between three physiological composite biological clocks (i.e. homeostatic dysregulation, Klemer and Doubal’s method and Levine’s method), but not with telomere length. Other studies showed that telomere length was not correlated with epigenetic aging (Han et al., 2018; Marioni et al., 2018), although cell type composition adjustments revealed a modest association (Chen et al., 2017). Further, both Hannum and Horvath epigenetic clocks (Hannum et al., 2013; Horvath et al., 2012) showed modest correlations to a transcriptomic clock.

Most previous studies, however, have separately considered the relation between a single biological clock and different somatic and mental health conditions. To date, extensive integrated analyses across multiple cellular and molecular aging markers in one study are lacking and it remains unknown to what extent different biological clocks are similarly associated to different health determinants. In addition, most studies did not examine health in its full range and, consequently, whether both somatic and mental health are associated with biological aging remains elusive. As it is unlikely that a single biological clock can fully capture the complexity of the aging process (Cole et al., 2019), a composite index, that integrates the different biological clocks and thereby aging at several molecular levels, may reveal the strongest health impact. Therefore, there is an additional need to integrate different biological clocks and test whether such a ‘composite clock’ outperforms single biological blocks in its association with health determinants.

To develop a better understanding of the mechanisms underlying biological aging, this study aimed to examine (1) the intercorrelations between biological aging indicators based on different molecular levels ranging from DNA to metabolites, namely telomere length, epigenetic, transcriptomic, proteomic and metabolomic clocks; (2) the relationships between different biological aging indicators with both somatic and mental health determinants; and (3) whether a composite biological clock outperforms single biological aging indicators in its association with health. For the five biological aging indicators and the composite clock, associations were computed with a wide panel of lifestyle (e.g. alcohol use, physical activity, smoking), somatic health (functional indicators, BMI, metabolic syndrome, chronic diseases) and mental health (childhood trauma, depression status) determinants.

In this study, we examined five biological clocks based on telomere length and four omics levels from a large, clinically well-characterized cohort. We demonstrated significant intercorrelations between three pairs of biological aging indicators, illustrating the complex and multifactorial processes of biological aging. Furthermore, we observed both overlapping and unique associations between the individual biological aging indicators and different lifestyle, somatic and mental health determinants. Separate linear regressions showed that male sex, high BMI, smoking, and metabolic syndrome were consistently associated with more advanced levels of biological aging across at least four of the biological clocks. Strikingly, depression was associated to more advanced levels of epigenetic, transcriptomic and proteomic aging, signifying that both somatic and mental health are associated with advanced biological aging. Finally, by integrating a composite index of all biological aging indicators we were able to obtain larger effect sizes with for example physical disability and childhood trauma exposure, underscoring the broad impact of determinants on cumulative multi-system biological aging.

The range of correlations among the biological aging indicators considered in this study indicates that the correlates of chronological age in different molecular layers were not strongly correlated, suggesting that biological aging may be differently manifested at certain cellular levels. Consistent with prior studies, we showed weak correlations between different biological aging indicators (Li et al., 2020) and we confirm the absent relationship between telomere length and epigenetic aging (Marioni et al., 2018; Belsky et al., 2017; Breitling et al., 2016), but also show lack of associations with transcriptomic, proteomic or metabolomic aging. However, we do confirm an earlier finding showing a significant but modest correlation between epigenetic and transcriptomic aging (Peters et al., 2015). The correlation between metabolomic and proteomic aging may partly be explained by the fact that both data were obtained from platforms that were aimed at probing central inflammation lipid processes, rather than the full proteome or metabolome. Nevertheless, we can infer that only some biological aging indicators show overlap, while most of them seem to be tracking distinctive parts of the aging process, even if they are associated with the same somatic or health determinants.

Our study showed that several of the determinants considered exhibited consistent associations with different biological aging indicators. First, male sex was associated with shorter telomere length and advanced epigenetic, transcriptomic, and metabolomic aging, in line with a large body of literature that shows advanced biological aging and earlier mortality in males compared to females (Austad and Fischer, 2016). Second, high BMI was consistently related to all biological aging indicators, showing that the more overweight or obese, the higher the biological age (Gielen et al., 2018), also after controlling for sex. Earlier studies showed similar associations between high BMI and shorter telomere length (Gielen et al., 2018), and older epigenetic (Horvath and Raj, 2018) and transcriptomic aging signatures (Peters et al., 2015). Third, our analyses showed similarly consistent associations between the prevalence of metabolic syndrome and advanced levels of aging. Further, all but epigenetic aging was advanced with respect to cigarette smoking.

Major depressive disorder (MDD) status was consistently related to advanced aging in three (epigenetic, transcriptomic, proteomic) out of the five biological aging indicators. In contrast, a recent study (N > 1000) in young adults (20–39 years) did not show associations between mental health (as measured by the CIDI) and biological aging (indicated by telomere length, homeostatic dysregulation, Klemer and Doubal’s method and Levine’s method) (Hastings et al., 2019), but it seems possible that this sample was too young to fully develop aging-related manifestations of mental health problems, or lacked age variation. It is likely that our data (obtained from participants 18–64 years) may have been more sensitive in picking up associations with mental health due to increased variation in both chronological age (i.e. inclusion of older persons), as well as symptom severity. To further examine whether the results were consistent across participants with and without depressive psychopathology, we repeated all models in post-hoc analyses and added an interaction term between current depression status and health determinants. There was an overall consistent pattern of non-significant interaction terms for most health determinants and biological aging, although only higher BMI was significantly associated to advanced epigenetic aging in the psychopathology group. However, taken together, the results suggest that findings are not different in persons with and without mental disorders. We observed some significant associations between biological aging and medication use. The design of the current observational study cannot conclusively prove whether this is a direct medication effect or confounding by indication: the patient group using antidepressant medication is also the group that is more chronically and severely depressed. This is similar for the metabolic syndrome related medication. Future studies using randomized clinical trial designs are needed to investigate the mechanism of action of direct pharmacological effects of medication on biological aging.

Furthermore, we computed a composite index by summing up the five biological aging indicators studied here. In other words, this integrative metric contains cumulative independent signal from the individual markers and dependent shared signal – with possible reduced noise due to the summation – between them. Given that this composite index demonstrates larger effect sizes for BMI, sex, smoking, depression severity, and metabolic syndrome than the individual aging indicators, it is suggested that being biologically old at multiple cellular levels has a cumulative multi-systemic effect. When integrated, the composite index reveals stronger (i.e. greater cumulative betas for the composite index than individual clocks) converging associations with sex, BMI, metabolic syndrome and current MDD. This provides further support for the hypothesis that not one biological clock sufficiently captures the biological aging process and that not all clocks are under the control of one unitary aging process. There is abundant room for further progress in determining whether biological aging can be modified by intervening on these determinants.

Nonetheless, the question remains which biological mechanism could plausibly link the current quantification of biological aging and its lifestyle, somatic, and mental health determinants. Part of this answer requires discussion on the features used to build the different clocks: the proteomic and metabolomic clocks mostly measure inflammatory or metabolic factors, two highly integrated processes in aging and aging-related diseases (Frasca et al., 2017). Previous studies suggest immune-mediated mechanisms (specifically inflammatory signaling) connecting metabolic syndrome (Révész et al., 2015), mental health disorders (Wohleb et al., 2016), and aging (Révész et al., 2018). Moreover, MDD is a condition in which inflammation, obesity, and premature or advanced aging co-occur and converge. It might therefore be speculated that immunity and 'inflammaging' (Franceschi et al., 2018) may tie together the currently observed associations.

This study did not include existing biological clocks. While the application of established algorithms would increase generalizability of our findings, there are several reasons why it was not optimal to implement previously published algorithms in the NESDA data. First and foremost, generated omics data are platform-dependent and the existing epigenetic (Horvath, 2013) and gene expression (Peters et al., 2015) clocks rely on arrays with different coverage of probes as was used in NESDA, that also target different parts of genes. Second, a subsample of NESDA was part of the previously published metabolomic clock (Akker et al., 2019), thus application of this model to the current dataset would result in overfitting. The current proteomic platform has not been used before to train a biological clock. Moreover, there is currently no validated gold standard for calculating transcriptomic, proteomic, or metabolomic clocks. Importantly, in spite of these limitations, we have followed an alternative but consistent methodological approach for training our omics-based biological clocks, leveraging the advantage to compare, combine, and integrate these clocks within the same population. However, we emphasize the need for epidemiological replication of these determinants in other datasets (e.g. those including different ethnicities) and we recognize that data harmonization and pooling are important strategies on the scientific research agenda that may overcome this limitation in the future.

Since no previously published algorithms were used, we trained our own clocks using ridge regression with cross-validation. This approach relies on the assumption that the determined cross-sectional correlation between the omics patterns and chronological age arise mainly as a consequence of biological aging, and is independent from potential secular trends (Nelson et al., 2020; Belsky, 2015; Belsky et al., 2020). As common to cross-sectional studies, it is, however, impossible to completely rule out potential cohort effects or uncontrolled individual differences and results should be interpreted in light of this limitation. Future longitudinal studies are needed to identify patterns of biological changes that go beyond their ability to predict age at the time of sampling. While the current study only used chronological age as criterion endpoint, it is important to mention that other epigenetic clocks exist that are trained to predict other potential criteria such as phenotypic markers of age (DNAm PhenoAge) (Levine et al., 2018) or a composite biomarker that was derived from DNAm surrogates and smoking in pack-years (GrimAge) (Hillary et al., 2019). Such clocks were developed to lead to improved predictions of risk of mortality.

More research is needed to elucidate whether: (1) physiological disturbances, such as loss of inflammatory control associated with somatic and psychopathology, accelerate biological aging over time, (2) advanced biological aging precedes and constitutes a vulnerability factor that causes somatic and psychopathology, or (3) somatic and psychopathology and biological aging processes are not causally linked, but share underlying etiological roots (e.g. shared genetic risks or environmental factors) (Han et al., 2019). Yet, it could conceivably be hypothesized that dysregulation of immunoinflammatory control may be related to metabolic outcomes, aging, and depression (Diniz and Vieira, 2018), providing scope as to why some of these determinants converge across different platforms and multiple biological levels.

Here, we used a large cohort that was well-characterized in terms of demographics, lifestyle, and both somatic and mental health assessments, to study and integrate five biological clocks across multiple levels of analysis. This is particularly important as we show that the determinants of biological aging encompass several different domains. Moreover, our sample was adequately powered to detect statistically significant associations, limiting the possibility for chance findings and increasing probability for identifying robust biological age determinants. On the other hand, an obvious limitation is the cross-sectional nature of this study that prevents us from drawing any conclusions on whether the determinants accelerate the aging trajectory over time, the other way around, or whether ‘third’ variables effect this association.

Another aspect that limits the interpretability of our findings in the context of increased risk of developing aging-related diseases and mortality was the relatively young age of the current sample. To illustrate, we were unable to predict future incidence of chronic disease or mortality from baseline biological aging, likely due to the low numbers of mortality and disease onset (Supplementary file 3), for example the number of deceased cases ranged from 64 (TL) to 27 (proteomic clock). Previous studies that have associated biological aging with mortality risk commonly include aging cohorts (Danish longitudinal twin study with mean age of 86.1 years; Framingham Offspring Study with mean age 61.0 years; Swedish population cohort SATSA with mean age 63.6 years; German population cohort ESTHER with mean age 62.5 years; Lothian Birth Cohorts with mean age >69.5 years; Normative Aging Study with mean age 71.7 years) (Marioni et al., 2018; Li et al., 2020; Christiansen et al., 2016; Perna et al., 2016; Murabito et al., 2018; Chen et al., 2016). Before definitively interpreting a ‘clock’ as a measure of biological aging, further independent studies are needed to establish that the clock changes with advancing age and forecasts disease, disability and mortality.

According to European law (GDPR) data containing potentially identifying or sensitive patient information are restricted; our data involving clinical participants are not freely available in a public repository. However, we highly value scientific collaboration, therefore, in principle, NESDA data are available to all scientific researchers working at non-commercial research organizations worldwide. Researchers can request either existing data for data analyses or bioanalysis. Please visit the online data overview for an extensive overview of the available data and NESDA's current output (http://www.nesda.nl). Data are available upon request via the NESDA Data Access Committee (nesda@ggzingeest.nl). Gene expression data used for this study are available at dbGaP, accession number phs000486.v1.p1 (https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000486.v1.p1). As agreed with NIH and approved by the local IRB, the data was scheduled to be deposited in the NIH controlled access repository dbGAP. However, dbGAP is currently full and as soon as the new NIH controlled access repository comes online data will be deposited.

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Author details

1. Rick Jansen

   Department of Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Public Health Research Institute and Amsterdam Neuroscience, Amsterdam, Netherlands

   Contribution

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

   Contributed equally with

   Laura KM Han

   For correspondence

   ri.jansen@ggzingeest.nl

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3333-6737

2. Laura KM Han

   Department of Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Public Health Research Institute and Amsterdam Neuroscience, Amsterdam, Netherlands

   Contribution

   Writing - original draft, Writing - review and editing

   Contributed equally with

   Rick Jansen

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-9647-3723

3. Josine E Verhoeven

   Department of Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Public Health Research Institute and Amsterdam Neuroscience, Amsterdam, Netherlands

   Contribution

   Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

4. Karolina A Aberg

   Center for Biomarker Research and Precision Medicine, Virginia Commonwealth University, Richmond, United States

   Contribution

   Writing - review and editing

   Competing interests

   No competing interests declared

5. Edwin CGJ van den Oord

   Center for Biomarker Research and Precision Medicine, Virginia Commonwealth University, Richmond, United States

   Contribution

   Writing - review and editing

   Competing interests

   No competing interests declared

6. Yuri Milaneschi

   Department of Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Public Health Research Institute and Amsterdam Neuroscience, Amsterdam, Netherlands

   Contribution

   Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

7. Brenda WJH Penninx

   Department of Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Public Health Research Institute and Amsterdam Neuroscience, Amsterdam, Netherlands

   Contribution

   Writing - original draft, Writing - review and editing

   Competing interests

   has received research funding (not related to the current paper) from Boehringer Ingelheim and Jansen Research.

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