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.

1. 1. Sullivan PF
   2. Wright F
   3. Wang W
   4. Sun W
   5. Zou F
   6. Batista A
   7. Madar V
   8. Via K
   9. Brooks A
   10. Tischfield J
   11. Wang Q
   12. Qu A
   13. Kochar J
   14. D'Ambrosia D
   15. Penninx B
   16. Smit J
   17. Jansen R
   18. van Grootheest G
   19. Boomsma D
   20. deGeus E
   21. Willemsen G
   22. Hottenga J-J

   (2014) dbGAP

   ID phs000486.v1.p1. Integration of Genomics and Transcriptomics in unselected Twins and in Major Depression.

   https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000486.v1.p1

1. 1. Aberg KA
   2. Dean B
   3. Shabalin AA
   4. Chan RF
   5. Han LKM
   6. Zhao M
   7. van Grootheest G
   8. Xie LY
   9. Milaneschi Y
   10. Clark SL
   11. Turecki G
   12. Penninx B
   13. van den Oord E

   (2020) Methylome-wide association findings for major depressive disorder overlap in blood and brain and replicate in independent brain samples

   Molecular Psychiatry 25:1344–1354.

   https://doi.org/10.1038/s41380-018-0247-6

   • PubMed
   • Google Scholar
2. Preprint

   1. Akker E
   2. Trompet S
   3. Wolf J
   4. Beekman M
   5. Suchiman HED

   (2019) Predicting biological age based on the BBMRI-NL 1 H-NMR metabolomics repository

   bioRxiv.

   https://doi.org/10.1101/632919

   • Google Scholar
3. 1. Austad SN
   2. Fischer KE

   (2016) Sex differences in lifespan

   Cell Metabolism 23:1022–1033.

   https://doi.org/10.1016/j.cmet.2016.05.019

   • PubMed
   • Google Scholar
4. 1. Babor TF
   2. Kranzler HR
   3. Lauerman RJ

   (1989) Early detection of harmful alcohol consumption: comparison of clinical, laboratory, and self-report screening procedures

   Addictive Behaviors 14:139–157.

   https://doi.org/10.1016/0306-4603(89)90043-9

   • PubMed
   • Google Scholar
5. 1. BBMRI-NL Metabolomics Consortium
   2. Bot M
   3. Milaneschi Y
   4. Al-Shehri T
   5. Amin N
   6. Garmaeva S
   7. Onderwater GLJ
   8. Pool R
   9. Thesing CS
   10. Vijfhuizen LS
   11. Vogelzangs N
   12. Arts ICW
   13. Demirkan A
   14. van Duijn C
   15. van Greevenbroek M
   16. van der Kallen CJH
   17. Köhler S
   18. Ligthart L
   19. van den Maagdenberg A
   20. Mook-Kanamori DO
   21. de Mutsert R
   22. Tiemeier H
   23. Schram MT
   24. Stehouwer CDA
   25. Terwindt GM
   26. Willems van Dijk K
   27. Fu J
   28. Zhernakova A
   29. Beekman M
   30. Slagboom PE
   31. Boomsma DI
   32. Penninx B

   (2020) Metabolomics profile in depression: a pooled analysis of 230 metabolic markers in 5283 cases with depression and 10,145 controls

   Biological Psychiatry 87:409–418.

   https://doi.org/10.1016/j.biopsych.2019.08.016

   • PubMed
   • Google Scholar
6. 1. Belsky DW

   (2015) Reply to Newman: quantification of biological aging in young adults is not the same thing as the onset of obesity

   PNAS 112:E7164–E7165.

   https://doi.org/10.1073/pnas.1518878112

   • PubMed
   • Google Scholar
7. 1. Belsky DW
   2. Moffitt TE
   3. Cohen AA
   4. Corcoran DL
   5. Levine ME
   6. Prinz JA
   7. Schaefer J
   8. Sugden K
   9. Williams B
   10. Poulton R
   11. Caspi A

   (2017) Eleven telomere, epigenetic clock, and Biomarker-Composite quantifications of biological aging: do they measure the same thing?

   American Journal of Epidemiology 187:1220–1230.

   https://doi.org/10.1093/aje/kwx346

   • Google Scholar
8. 1. Belsky DW
   2. Caspi A
   3. Arseneault L
   4. Baccarelli A
   5. Corcoran DL
   6. Gao X
   7. Hannon E
   8. Harrington HL
   9. Rasmussen LJH
   10. Houts R
   11. Huffman K
   12. Kraus WE
   13. Kwon D
   14. Mill J
   15. Pieper CF
   16. Prinz JA
   17. Poulton R
   18. Schwartz J
   19. Sugden K
   20. Vokonas P
   21. Williams BS
   22. Moffitt TE

   (2020) Quantification of the pace of biological aging in humans through a blood test, the DunedinPoAm DNA methylation algorithm

   eLife 9:e54870.

   https://doi.org/10.7554/eLife.54870

   • Google Scholar
9. 1. Boks MP
   2. Mierlo H
   3. Rutten BPF
   4. Radstake TRDJ
   5. De Witte L
   6. Geuze E
   7. Horvath S
   8. Schalkwyk LC
   9. Vinkers CH
   10. Broen JCA
   11. Vermetten E

   (2015) Longitudinal changes of telomere length and epigenetic age related to traumatic stress and post-traumatic stress disorder

   Psychoneuroendocrinology 51:506–512.

   https://doi.org/10.1016/j.psyneuen.2014.07.011

   • Google Scholar
10. 1. Breitling LP
    2. Saum K-U
    3. Perna L
    4. Schöttker B
    5. Holleczek B
    6. Brenner H

    (2016) Frailty is associated with the epigenetic clock but not with telomere length in a German cohort

    Clinical Epigenetics 8:21.

    https://doi.org/10.1186/s13148-016-0186-5

    • Google Scholar
11. 1. Cawthon RM

    (2002) Telomere measurement by quantitative PCR

    Nucleic Acids Research 30:47.

    https://doi.org/10.1093/nar/30.10.e47

    • Google Scholar
12. 1. Chen BH
    2. Marioni RE
    3. Colicino E
    4. Peters MJ
    5. Ward-Caviness CK
    6. Tsai P-C
    7. Roetker NS
    8. Just AC
    9. Demerath EW
    10. Guan W
    11. Bressler J
    12. Fornage M
    13. Studenski S
    14. Vandiver AR
    15. Moore AZ
    16. Tanaka T
    17. Kiel DP
    18. Liang L
    19. Vokonas P
    20. Schwartz J
    21. Lunetta KL
    22. Murabito JM
    23. Bandinelli S
    24. Hernandez DG
    25. Melzer D
    26. Nalls M
    27. Pilling LC
    28. Price TR
    29. Singleton AB
    30. Gieger C
    31. Holle R
    32. Kretschmer A
    33. Kronenberg F
    34. Kunze S
    35. Linseisen J
    36. Meisinger C
    37. Rathmann W
    38. Waldenberger M
    39. Visscher PM
    40. Shah S
    41. Wray NR
    42. McRae AF
    43. Franco OH
    44. Hofman A
    45. Uitterlinden AG
    46. Absher D
    47. Assimes T
    48. Levine ME
    49. Lu AT
    50. Tsao PS
    51. Hou L
    52. Manson JE
    53. Carty CL
    54. LaCroix AZ
    55. Reiner AP
    56. Spector TD
    57. Feinberg AP
    58. Levy D
    59. Baccarelli A
    60. van Meurs J
    61. Bell JT
    62. Peters A
    63. Deary IJ
    64. Pankow JS
    65. Ferrucci L
    66. Horvath S

    (2016) DNA methylation-based measures of biological age: meta-analysis predicting time to death

    Aging 8:1844–1865.

    https://doi.org/10.18632/aging.101020

    • Google Scholar
13. 1. Chen BH
    2. Carty CL
    3. Kimura M
    4. Kark JD
    5. Chen W
    6. Li S
    7. Zhang T
    8. Kooperberg C
    9. Levy D
    10. Assimes T
    11. Absher D
    12. Horvath S
    13. Reiner AP
    14. Aviv A

    (2017) Leukocyte telomere length, T cell composition and DNA methylation age

    Aging 9:1983–1995.

    https://doi.org/10.18632/aging.101293

    • Google Scholar
14. 1. Christiansen L
    2. Lenart A
    3. Tan Q
    4. Vaupel JW
    5. Aviv A
    6. McGue M
    7. Christensen K

    (2016) DNA methylation age is associated with mortality in a longitudinal Danish twin study

    Aging Cell 15:149–154.

    https://doi.org/10.1111/acel.12421

    • PubMed
    • Google Scholar
15. 1. Clark SL
    2. Hattab MW
    3. Chan RF
    4. Shabalin AA
    5. Han LKM
    6. Zhao M
    7. Smit JH
    8. Jansen R
    9. Milaneschi Y
    10. Xie LY
    11. van Grootheest G
    12. Penninx B
    13. Aberg KA
    14. van den Oord E

    (2020) A methylation study of long-term depression risk

    Molecular Psychiatry 25:1334–1343.

    https://doi.org/10.1038/s41380-019-0516-z

    • PubMed
    • Google Scholar
16. 1. Cole JH
    2. Marioni RE
    3. Harris SE
    4. Deary IJ

    (2019) Brain age and other bodily 'ages': implications for neuropsychiatry

    Molecular Psychiatry 24:266–281.

    https://doi.org/10.1038/s41380-018-0098-1

    • PubMed
    • Google Scholar
17. 1. Craig CL
    2. Marshall AL
    3. Sjöström M
    4. Bauman AE
    5. Booth ML
    6. Ainsworth BE
    7. Pratt M
    8. Ekelund U
    9. Yngve A
    10. Sallis JF
    11. Oja P

    (2003) International physical activity questionnaire: 12-country reliability and validity

    Medicine & Science in Sports & Exercise 35:1381–1395.

    https://doi.org/10.1249/01.MSS.0000078924.61453.FB

    • PubMed
    • Google Scholar
18. 1. Darrow SM
    2. Verhoeven JE
    3. Révész D
    4. Lindqvist D
    5. Penninx BWJH
    6. Delucchi KL
    7. Wolkowitz OM
    8. Mathews CA

    (2016) The Association Between Psychiatric Disorders and Telomere Length

    Psychosomatic Medicine 78:776–787.

    https://doi.org/10.1097/PSY.0000000000000356

    • Google Scholar
19. 1. de Graaf R
    2. Bijl RV
    3. Smit F
    4. Vollebergh WAM
    5. Spijker J

    (2002) Risk Factors for 12-Month Comorbidity of Mood, Anxiety, and Substance Use Disorders: Findings From the Netherlands Mental Health Survey and Incidence Study

    American Journal of Psychiatry 159:620–629.

    https://doi.org/10.1176/appi.ajp.159.4.620

    • Google Scholar
20. 1. Diniz BS
    2. Vieira EM

    (2018) Stress, Inflammation, and Aging: An Association Beyond Chance

    The American Journal of Geriatric Psychiatry 26:964–965.

    https://doi.org/10.1016/j.jagp.2018.06.013

    • Google Scholar
21. 1. Fehrmann RSN
    2. Jansen RC
    3. Veldink JH
    4. Westra H-J
    5. Arends D
    6. Bonder MJ
    7. Fu J
    8. Deelen P
    9. Groen HJM
    10. Smolonska A
    11. Weersma RK
    12. Hofstra RMW
    13. Buurman WA
    14. Rensen S
    15. Wolfs MGM
    16. Platteel M
    17. Zhernakova A
    18. Elbers CC
    19. Festen EM
    20. Trynka G
    21. Hofker MH
    22. Saris CGJ
    23. Ophoff RA
    24. van den Berg LH
    25. van Heel DA
    26. Wijmenga C
    27. te Meerman GJ
    28. Franke L

    (2011) Trans-eQTLs reveal that independent genetic variants associated with a complex phenotype converge on intermediate genes, with a major role for the HLA

    PLOS Genetics 7:e1002197.

    https://doi.org/10.1371/journal.pgen.1002197

    • Google Scholar
22. 1. Fischer K
    2. Kettunen J
    3. Würtz P
    4. Haller T
    5. Havulinna AS
    6. Kangas AJ
    7. Soininen P
    8. Esko T
    9. Tammesoo M-L
    10. Mägi R
    11. Smit S
    12. Palotie A
    13. Ripatti S
    14. Salomaa V
    15. Ala-Korpela M
    16. Perola M
    17. Metspalu A

    (2014) Biomarker Profiling by Nuclear Magnetic Resonance Spectroscopy for the Prediction of All-Cause Mortality: An Observational Study of 17,345 Persons

    PLOS Medicine 11:e1001606.

    https://doi.org/10.1371/journal.pmed.1001606

    • Google Scholar
23. 1. Franceschi C
    2. Garagnani P
    3. Parini P
    4. Giuliani C
    5. Santoro A

    (2018) Inflammaging: a new immune–metabolic viewpoint for age-related diseases

    Nature Reviews Endocrinology 14:576–590.

    https://doi.org/10.1038/s41574-018-0059-4

    • Google Scholar
24. 1. Frasca D
    2. Blomberg BB
    3. Paganelli R

    (2017) Aging, Obesity, and Inflammatory Age-Related Diseases

    Frontiers in Immunology 8:1–10.

    https://doi.org/10.3389/fimmu.2017.01745

    • Google Scholar
25. 1. Gaspersz R
    2. Lamers F
    3. Beekman ATF
    4. van Hemert AM
    5. Schoevers RA
    6. Penninx BWJH

    (2018) The Impact of Depressive Disorder Symptoms and Subtypes on 6-Year Incidence of Somatic Diseases

    Psychotherapy and Psychosomatics 87:308–310.

    https://doi.org/10.1159/000491933

    • Google Scholar
26. 1. Gerrits MM
    2. van Oppen P
    3. van Marwijk HW
    4. van der Horst H
    5. Penninx BW

    (2013) The Impact of Chronic Somatic Diseases on the Course of Depressive and Anxiety Disorders

    Psychotherapy and Psychosomatics 82:64–66.

    https://doi.org/10.1159/000338636

    • Google Scholar
27. 1. Gielen M
    2. Hageman GJ
    3. Antoniou EE
    4. Nordfjall K
    5. Mangino M
    6. Balasubramanyam M
    7. de Meyer T
    8. Hendricks AE
    9. Giltay EJ
    10. Hunt SC
    11. Nettleton JA
    12. Salpea KD
    13. Diaz VA
    14. Farzaneh-Far R
    15. Atzmon G
    16. Harris SE
    17. Hou L
    18. Gilley D
    19. Hovatta I
    20. Kark JD
    21. Nassar H
    22. Kurz DJ
    23. Mather KA
    24. Willeit P
    25. Zheng Y-L
    26. Pavanello S
    27. Demerath EW
    28. Rode L
    29. Bunout D
    30. Steptoe A
    31. Boardman L
    32. Marti A
    33. Needham B
    34. Zheng W
    35. Ramsey-Goldman R
    36. Pellatt AJ
    37. Kaprio J
    38. Hofmann JN
    39. Gieger C
    40. Paolisso G
    41. Hjelmborg JBH
    42. Mirabello L
    43. Seeman T
    44. Wong J
    45. van der Harst P
    46. Broer L
    47. Kronenberg F
    48. Kollerits B
    49. Strandberg T
    50. Eisenberg DTA
    51. Duggan C
    52. Verhoeven JE
    53. Schaakxs R
    54. Zannolli R
    55. dos Reis RMR
    56. Charchar FJ
    57. Tomaszewski M
    58. Mons U
    59. Demuth I
    60. Molli AEI
    61. Cheng G
    62. Krasnienkov D
    63. D'Antono B
    64. Kasielski M
    65. McDonnell BJ
    66. Ebstein RP
    67. Sundquist K
    68. Pare G
    69. Chong M
    70. Zeegers MP
    71. TELOMAAS group

    (2018) Body mass index is negatively associated with telomere length: a collaborative cross-sectional meta-analysis of 87 observational studies

    The American Journal of Clinical Nutrition 108:453–475.

    https://doi.org/10.1093/ajcn/nqy107

    • Google Scholar
28. 1. Guralnik JM
    2. Fried LP
    3. Salive ME

    (1996) Disability as a Public Health Outcome in the Aging Population

    Annual Review of Public Health 17:25–46.

    https://doi.org/10.1146/annurev.pu.17.050196.000325

    • Google Scholar
29. 1. Han LKM
    2. Aghajani M
    3. Clark SL
    4. Chan RF
    5. Hattab MW
    6. Shabalin AA
    7. Zhao M
    8. Kumar G
    9. Xie LY
    10. Jansen R
    11. Milaneschi Y
    12. Dean B
    13. Aberg KA
    14. van den Oord E
    15. Penninx B

    (2018) Epigenetic aging in major depressive disorder

    The American Journal of Psychiatry 175:774–782.

    https://doi.org/10.1176/appi.ajp.2018.17060595

    • PubMed
    • Google Scholar
30. 1. Han LKM
    2. Verhoeven JE
    3. Tyrka AR
    4. Penninx BWJH
    5. Wolkowitz OM
    6. Månsson KNT
    7. Lindqvist D
    8. Boks MP
    9. Révész D
    10. Mellon SH
    11. Picard M

    (2019) Accelerating research on biological aging and mental health: Current challenges and future directions

    Psychoneuroendocrinology 106:293–311.

    https://doi.org/10.1016/j.psyneuen.2019.04.004

    • Google Scholar
31. 1. Hannum G
    2. Guinney J
    3. Zhao L
    4. Zhang L
    5. Hughes G
    6. Sadda S
    7. Klotzle B
    8. Bibikova M
    9. Fan J-B
    10. Gao Y
    11. Deconde R
    12. Chen M
    13. Rajapakse I
    14. Friend S
    15. Ideker T
    16. Zhang K

    (2013) Genome-wide Methylation Profiles Reveal Quantitative Views of Human Aging Rates

    Molecular Cell 49:359–367.

    https://doi.org/10.1016/j.molcel.2012.10.016

    • Google Scholar
32. 1. Hastings WJ
    2. Shalev I
    3. Belsky DW

    (2019) Comparability of biological aging measures in the National Health and Nutrition Examination Study, 1999–2002

    Psychoneuroendocrinology 106:171–178.

    https://doi.org/10.1016/j.psyneuen.2019.03.012

    • Google Scholar
33. 1. Hillary RF
    2. Stevenson AJ
    3. Cox SR
    4. McCartney DL
    5. Harris SE
    6. Seeboth A
    7. Higham J
    8. Sproul D
    9. Taylor AM
    10. Redmond P
    11. Corley J
    12. Pattie A
    13. Hernández M
    14. Muñoz-Maniega S
    15. Bastin ME
    16. Wardlaw JM
    17. Horvath S
    18. Ritchie CW
    19. Spires-Jones TL
    20. McIntosh AM
    21. Evans KL
    22. Deary IJ
    23. Marioni RE

    (2019) An epigenetic predictor of death captures multi-modal measures of brain health

    Molecular Psychiatry 380:2019.

    https://doi.org/10.1038/s41380-019-0616-9

    • Google Scholar
34. 1. Horvath S
    2. Zhang Y
    3. Langfelder P
    4. Kahn RS
    5. Boks MPM
    6. van Eijk K
    7. van den Berg LH
    8. Ophoff RA

    (2012) Aging effects on DNA methylation modules in human brain and blood tissue

    Genome Biology 13:R97.

    https://doi.org/10.1186/gb-2012-13-10-r97

    • Google Scholar
35. 1. Horvath S

    (2013) DNA methylation age of human tissues and cell types

    Genome Biology 14:R115.

    https://doi.org/10.1186/gb-2013-14-10-r115

    • Google Scholar
36. 1. Horvath S
    2. Raj K

    (2018) DNA methylation-based biomarkers and the epigenetic clock theory of ageing

    Nature Reviews Genetics 19:371–384.

    https://doi.org/10.1038/s41576-018-0004-3

    • PubMed
    • Google Scholar
37. 1. Jansen R
    2. Batista S
    3. Brooks AI
    4. Tischfield JA
    5. Willemsen G
    6. van Grootheest G
    7. Hottenga J-J
    8. Milaneschi Y
    9. Mbarek H
    10. Madar V
    11. Peyrot W
    12. Vink JM
    13. Verweij CL
    14. de Geus EJC
    15. Smit JH
    16. Wright FA
    17. Sullivan PF
    18. Boomsma DI
    19. Penninx BWJH

    (2014) Sex differences in the human peripheral blood transcriptome

    BMC Genomics 15:33.

    https://doi.org/10.1186/1471-2164-15-33

    • Google Scholar
38. 1. Jansen R
    2. Hottenga J-J
    3. Nivard MG
    4. Abdellaoui A
    5. Laport B
    6. de Geus EJ
    7. Wright FA
    8. Penninx BWJH
    9. Boomsma DI

    (2017) Conditional eQTL analysis reveals allelic heterogeneity of gene expression

    Human Molecular Genetics 26:1444–1451.

    https://doi.org/10.1093/hmg/ddx043

    • Google Scholar
39. 1. Jin X
    2. Pan B
    3. Dang X
    4. Wu H
    5. Xu D

    (2018) Relationship between short telomere length and stroke

    Medicine 97:e12489.

    https://doi.org/10.1097/MD.0000000000012489

    • Google Scholar
40. 1. Jylhävä J
    2. Pedersen NL
    3. Hägg S

    (2017) Biological age predictors

    EBioMedicine 21:29–36.

    https://doi.org/10.1016/j.ebiom.2017.03.046

    • PubMed
    • Google Scholar
41. 1. Lamers F
    2. Bot M
    3. Jansen R
    4. Chan MK
    5. Cooper JD
    6. Bahn S
    7. Penninx BWJH

    (2016) Serum proteomic profiles of depressive subtypes

    Translational Psychiatry 6:e851.

    https://doi.org/10.1038/tp.2016.115

    • Google Scholar
42. 1. Langmead B
    2. Salzberg SL

    (2012) Fast gapped-read alignment with bowtie 2

    Nature Methods 9:357–359.

    https://doi.org/10.1038/nmeth.1923

    • PubMed
    • Google Scholar
43. 1. Levine ME
    2. Lu AT
    3. Quach A
    4. Chen BH
    5. Assimes TL
    6. Bandinelli S
    7. Hou L
    8. Baccarelli AA
    9. Stewart JD
    10. Li Y
    11. Whitsel EA
    12. Wilson JG
    13. Reiner AP
    14. Aviv A
    15. Lohman K
    16. Liu Y
    17. Ferrucci L
    18. Horvath S

    (2018) An epigenetic biomarker of aging for lifespan and healthspan

    Aging 10:573–591.

    https://doi.org/10.18632/aging.101414

    • Google Scholar
44. 1. Li Z
    2. He Y
    3. Wang D
    4. Tang J
    5. Chen X

    (2017) Association between childhood trauma and accelerated telomere erosion in adulthood: A meta-analytic study

    Journal of Psychiatric Research 93:64–71.

    https://doi.org/10.1016/j.jpsychires.2017.06.002

    • Google Scholar
45. 1. Li X
    2. Ploner A
    3. Wang Y
    4. Magnusson PK
    5. Reynolds C
    6. Finkel D
    7. Pedersen NL
    8. Jylhävä J
    9. Hägg S

    (2020) Longitudinal trajectories, correlations and mortality associations of nine biological ages across 20-years follow-up

    eLife 9:e51507.

    https://doi.org/10.7554/eLife.51507

    • PubMed
    • Google Scholar
46. 1. Lindqvist D
    2. Epel ES
    3. Mellon SH
    4. Penninx BW
    5. Révész D
    6. Verhoeven JE
    7. Reus VI
    8. Lin J
    9. Mahan L
    10. Hough CM
    11. Rosser R
    12. Bersani FS
    13. Blackburn EH
    14. Wolkowitz OM

    (2015) Psychiatric disorders and leukocyte telomere length: Underlying mechanisms linking mental illness with cellular aging

    Neuroscience & Biobehavioral Reviews 55:333–364.

    https://doi.org/10.1016/j.neubiorev.2015.05.007

    • Google Scholar
47. 1. López-Otín C
    2. Blasco MA
    3. Partridge L
    4. Serrano M
    5. Kroemer G

    (2013) The Hallmarks of Aging

    Cell 153:1194–1217.

    https://doi.org/10.1016/j.cell.2013.05.039

    • Google Scholar
48. 1. Marioni RE
    2. Shah S
    3. McRae AF
    4. Chen BH
    5. Colicino E
    6. Harris SE
    7. Gibson J
    8. Henders AK
    9. Redmond P
    10. Cox SR
    11. Pattie A
    12. Corley J
    13. Murphy L
    14. Martin NG
    15. Montgomery GW
    16. Feinberg AP
    17. Fallin MD
    18. Multhaup ML
    19. Jaffe AE
    20. Joehanes R
    21. Schwartz J
    22. Just AC
    23. Lunetta KL
    24. Murabito JM
    25. Starr JM
    26. Horvath S
    27. Baccarelli AA
    28. Levy D
    29. Visscher PM
    30. Wray NR
    31. Deary IJ

    (2015) DNA methylation age of blood predicts all-cause mortality in later life

    Genome Biology 16:25.

    https://doi.org/10.1186/s13059-015-0584-6

    • Google Scholar
49. 1. Marioni RE
    2. Harris SE
    3. Shah S
    4. McRae AF
    5. von Zglinicki T
    6. Martin-Ruiz C
    7. Wray NR
    8. Visscher PM
    9. Deary IJ

    (2018) The epigenetic clock and telomere length are independently associated with chronological age and mortality

    International Journal of Epidemiology 45:424–432.

    https://doi.org/10.1093/ije/dyw041

    • PubMed
    • Google Scholar
50. 1. Milaneschi Y
    2. Penninx BW

    (2014) Depression in older persons with mobility limitations

    Current Pharmaceutical Design 20:3114–3118.

    https://doi.org/10.2174/13816128113196660060

    • PubMed
    • Google Scholar
51. 1. Mons U
    2. Müezzinler A
    3. Schöttker B
    4. Dieffenbach AK
    5. Butterbach K
    6. Schick M
    7. Peasey A
    8. De Vivo I
    9. Trichopoulou A
    10. Boffetta P
    11. Brenner H

    (2017) Leukocyte telomere length and All-Cause, cardiovascular disease, and Cancer mortality: results from Individual-Participant-Data Meta-Analysis of 2 large prospective cohort studies

    American Journal of Epidemiology 185:1317–1326.

    https://doi.org/10.1093/aje/kww210

    • PubMed
    • Google Scholar
52. 1. Murabito JM
    2. Zhao Q
    3. Larson MG
    4. Rong J
    5. Lin H
    6. Benjamin EJ
    7. Levy D
    8. Lunetta KL

    (2018) Measures of biologic age in a community sample predict mortality and Age-Related disease: the framingham offspring study

    The Journals of Gerontology: Series A 73:757–762.

    https://doi.org/10.1093/gerona/glx144

    • Google Scholar
53. 1. Nelson PG
    2. Promislow DEL
    3. Masel J

    (2020) Biomarkers for aging identified in Cross-sectional studies tend to be Non-causative

    The Journals of Gerontology: Series A 75:466–472.

    https://doi.org/10.1093/gerona/glz174

    • Google Scholar
54. 1. NESDA Research Consortium
    2. Penninx BW
    3. Beekman AT
    4. Smit JH
    5. Zitman FG
    6. Nolen WA
    7. Spinhoven P
    8. Cuijpers P
    9. De Jong PJ
    10. Van Marwijk HW
    11. Assendelft WJ
    12. Van Der Meer K
    13. Verhaak P
    14. Wensing M
    15. De Graaf R
    16. Hoogendijk WJ
    17. Ormel J
    18. Van Dyck R

    (2008) The netherlands study of depression and anxiety (NESDA): rationale, objectives and methods

    International Journal of Methods in Psychiatric Research 17:121–140.

    https://doi.org/10.1002/mpr.256

    • PubMed
    • Google Scholar
55. 1. Perna L
    2. Zhang Y
    3. Mons U
    4. Holleczek B
    5. Saum K-U
    6. Brenner H

    (2016) Epigenetic age acceleration predicts cancer, cardiovascular, and all-cause mortality in a German case cohort

    Clinical Epigenetics 8:64.

    https://doi.org/10.1186/s13148-016-0228-z

    • Google Scholar
56. 1. Peters MJ
    2. Joehanes R
    3. Pilling LC
    4. Schurmann C
    5. Conneely KN
    6. Powell J
    7. Reinmaa E
    8. Sutphin GL
    9. Zhernakova A
    10. Schramm K
    11. Wilson YA
    12. Kobes S
    13. Tukiainen T
    14. Ramos YF
    15. Göring HHH
    16. Fornage M
    17. Liu Y
    18. Gharib SA
    19. Stranger BE
    20. De Jager PL
    21. Aviv A
    22. Levy D
    23. Murabito JM
    24. Munson PJ
    25. Huan T
    26. Hofman A
    27. Uitterlinden AG
    28. Rivadeneira F
    29. van Rooij J
    30. Stolk L
    31. Broer L
    32. Verbiest MMPJ
    33. Jhamai M
    34. Arp P
    35. Metspalu A
    36. Tserel L
    37. Milani L
    38. Samani NJ
    39. Peterson P
    40. Kasela S
    41. Codd V
    42. Peters A
    43. Ward-Caviness CK
    44. Herder C
    45. Waldenberger M
    46. Roden M
    47. Singmann P
    48. Zeilinger S
    49. Illig T
    50. Homuth G
    51. Grabe H-J
    52. Völzke H
    53. Steil L
    54. Kocher T
    55. Murray A
    56. Melzer D
    57. Yaghootkar H
    58. Bandinelli S
    59. Moses EK
    60. Kent JW
    61. Curran JE
    62. Johnson MP
    63. Williams-Blangero S
    64. Westra H-J
    65. McRae AF
    66. Smith JA
    67. Kardia SLR
    68. Hovatta I
    69. Perola M
    70. Ripatti S
    71. Salomaa V
    72. Henders AK
    73. Martin NG
    74. Smith AK
    75. Mehta D
    76. Binder EB
    77. Nylocks KM
    78. Kennedy EM
    79. Klengel T
    80. Ding J
    81. Suchy-Dicey AM
    82. Enquobahrie DA
    83. Brody J
    84. Rotter JI
    85. Chen Y-DI
    86. Houwing-Duistermaat J
    87. Kloppenburg M
    88. Slagboom PE
    89. Helmer Q
    90. den Hollander W
    91. Bean S
    92. Raj T
    93. Bakhshi N
    94. Wang QP
    95. Oyston LJ
    96. Psaty BM
    97. Tracy RP
    98. Montgomery GW
    99. Turner ST
    100. Blangero J
    101. Meulenbelt I
    102. Ressler KJ
    103. Yang J
    104. Franke L
    105. Kettunen J
    106. Visscher PM
    107. Neely GG
    108. Korstanje R
    109. Hanson RL
    110. Prokisch H
    111. Ferrucci L
    112. Esko T
    113. Teumer A
    114. van Meurs JBJ
    115. Johnson AD

    (2015) The transcriptional landscape of age in human peripheral blood

    Nature Communications 6:8570.

    https://doi.org/10.1038/ncomms9570

    • Google Scholar
57. 1. Révész D
    2. Milaneschi Y
    3. Verhoeven JE
    4. Penninx BW

    (2014) Telomere length as a marker of cellular aging is associated with prevalence and progression of metabolic syndrome

    The Journal of Clinical Endocrinology & Metabolism 99:4607–4615.

    https://doi.org/10.1210/jc.2014-1851

    • PubMed
    • Google Scholar
58. 1. Révész D
    2. Milaneschi Y
    3. Verhoeven JE
    4. Lin J
    5. Penninx BW

    (2015) Longitudinal associations between metabolic syndrome components and telomere shortening

    The Journal of Clinical Endocrinology & Metabolism 100:3050–3059.

    https://doi.org/10.1210/JC.2015-1995

    • PubMed
    • Google Scholar
59. 1. Révész D
    2. Verhoeven JE
    3. Picard M
    4. Lin J
    5. Sidney S
    6. Epel ES
    7. Penninx B
    8. Puterman E

    (2018) Associations between cellular aging markers and metabolic syndrome: findings from the CARDIA study

    The Journal of Clinical Endocrinology & Metabolism 103:148–157.

    https://doi.org/10.1210/jc.2017-01625

    • PubMed
    • Google Scholar
60. 1. Rush AJ
    2. Gullion CM
    3. Basco MR
    4. Jarrett RB
    5. Trivedi MH

    (1996) The inventory of depressive symptomatology (IDS): psychometric properties

    Psychological Medicine 26:477–486.

    https://doi.org/10.1017/S0033291700035558

    • PubMed
    • Google Scholar
61. 1. Shabalin AA
    2. Hattab MW
    3. Clark SL
    4. Chan RF
    5. Kumar G
    6. Aberg KA
    7. van den Oord E

    (2018) RaMWAS: fast methylome-wide association study pipeline for enrichment platforms

    Bioinformatics 34:2283–2285.

    https://doi.org/10.1093/bioinformatics/bty069

    • PubMed
    • Google Scholar
62. 1. Soininen P
    2. Kangas AJ
    3. Würtz P
    4. Suna T
    5. Ala-Korpela M

    (2015) Quantitative serum nuclear magnetic resonance metabolomics in cardiovascular epidemiology and genetics

    Circulation. Cardiovascular Genetics 8:192–206.

    https://doi.org/10.1161/CIRCGENETICS.114.000216

    • PubMed
    • Google Scholar
63. 1. Verhoeven JE
    2. Révész D
    3. Epel ES
    4. Lin J
    5. Wolkowitz OM
    6. Penninx BW

    (2014) Major depressive disorder and accelerated cellular aging: results from a large psychiatric cohort study

    Molecular Psychiatry 19:895–901.

    https://doi.org/10.1038/mp.2013.151

    • PubMed
    • Google Scholar
64. 1. Verhoeven JE
    2. Yang R
    3. Wolkowitz OM
    4. Bersani FS
    5. Lindqvist D
    6. Mellon SH
    7. Yehuda R
    8. Flory JD
    9. Lin J
    10. Abu-Amara D
    11. Makotkine I
    12. Marmar C
    13. Jett M
    14. Hammamieh R

    (2018) Epigenetic age in male Combat-Exposed war veterans: associations with posttraumatic stress disorder status

    Molecular Neuropsychiatry 4:90–99.

    https://doi.org/10.1159/000491431

    • Google Scholar
65. 1. Wang Q
    2. Zhan Y
    3. Pedersen NL
    4. Fang F
    5. Hägg S

    (2018) Telomere length and All-Cause mortality: a Meta-analysis

    Ageing Research Reviews 48:11–20.

    https://doi.org/10.1016/j.arr.2018.09.002

    • PubMed
    • Google Scholar
66. Preprint

    1. Whalley HC

    (2017) Accelerated epigenetic ageing in major depressive disorder

    bioRxiv.

    https://doi.org/10.1101/210666

    • Google Scholar
67. 1. Wohleb ES
    2. Franklin T
    3. Iwata M
    4. Duman RS

    (2016) Integrating neuroimmune systems in the neurobiology of depression

    Nature Reviews Neuroscience 17:497–511.

    https://doi.org/10.1038/nrn.2016.69

    • Google Scholar
68. 1. Wolf EJ
    2. Maniates H
    3. Nugent N
    4. Maihofer AX
    5. Armstrong D
    6. Ratanatharathorn A
    7. Ashley-Koch AE
    8. Garrett M
    9. Kimbrel NA
    10. Lori A
    11. Aiello AE
    12. Baker DG
    13. Beckham JC
    14. Boks MP
    15. Galea S
    16. Geuze E
    17. Hauser MA
    18. Kessler RC
    19. Koenen KC
    20. Miller MW
    21. Ressler KJ
    22. Risbrough V
    23. Rutten BPF
    24. Stein MB
    25. Ursano RJ
    26. Vermetten E
    27. Vinkers CH
    28. Uddin M
    29. Smith AK
    30. Nievergelt CM
    31. Logue MW

    (2018) Traumatic stress and accelerated DNA methylation age: A meta-analysis

    Psychoneuroendocrinology 92:123–134.

    https://doi.org/10.1016/j.psyneuen.2017.12.007

    • Google Scholar
69. 1. Wright FA
    2. Sullivan PF
    3. Brooks AI
    4. Zou F
    5. Sun W
    6. Xia K
    7. Madar V
    8. Jansen R
    9. Chung W
    10. Zhou Y-H
    11. Abdellaoui A
    12. Batista S
    13. Butler C
    14. Chen G
    15. Chen T-H
    16. D'Ambrosio D
    17. Gallins P
    18. Ha MJ
    19. Hottenga JJ
    20. Huang S
    21. Kattenberg M
    22. Kochar J
    23. Middeldorp CM
    24. Qu A
    25. Shabalin A
    26. Tischfield J
    27. Todd L
    28. Tzeng J-Y
    29. van Grootheest G
    30. Vink JM
    31. Wang Q
    32. Wang W
    33. Wang W
    34. Willemsen G
    35. Smit JH
    36. de Geus EJ
    37. Yin Z
    38. Penninx BWJH
    39. Boomsma DI

    (2014) Heritability and genomics of gene expression in peripheral blood

    Nature Genetics 46:430–437.

    https://doi.org/10.1038/ng.2951

    • Google Scholar
70. 1. Wurtz P
    2. Makinen V-P
    3. Soininen P
    4. Kangas AJ
    5. Tukiainen T
    6. Kettunen J
    7. Savolainen MJ
    8. Tammelin T
    9. Viikari JS
    10. Ronnemaa T
    11. Kahonen M
    12. Lehtimaki T
    13. Ripatti S
    14. Raitakari OT
    15. Jarvelin M-R
    16. Ala-Korpela M

    (2012) Metabolic Signatures of Insulin Resistance in 7,098 Young Adults

    Diabetes 61:1372–1380.

    https://doi.org/10.2337/db11-1355

    • Google Scholar
71. 1. Würtz P
    2. Havulinna AS
    3. Soininen P
    4. Tynkkynen T
    5. Prieto-Merino D
    6. Tillin T
    7. Ghorbani A
    8. Artati A
    9. Wang Q
    10. Tiainen M
    11. Kangas AJ
    12. Kettunen J
    13. Kaikkonen J
    14. Mikkilä V
    15. Jula A
    16. Kähönen M
    17. Lehtimäki T
    18. Lawlor DA
    19. Gaunt TR
    20. Hughes AD
    21. Sattar N
    22. Illig T
    23. Adamski J
    24. Wang TJ
    25. Perola M
    26. Ripatti S
    27. Vasan RS
    28. Raitakari OT
    29. Gerszten RE
    30. Casas J-P
    31. Chaturvedi N
    32. Ala-Korpela M
    33. Salomaa V

    (2015) Metabolite profiling and cardiovascular event risk

    Circulation 131:774–785.

    https://doi.org/10.1161/CIRCULATIONAHA.114.013116

    • Google Scholar
72. 1. Würtz P
    2. Cook S
    3. Wang Q
    4. Tiainen M
    5. Tynkkynen T
    6. Kangas AJ
    7. Soininen P
    8. Laitinen J
    9. Viikari J
    10. Kähönen M
    11. Lehtimäki T
    12. Perola M
    13. Blankenberg S
    14. Zeller T
    15. Männistö S
    16. Salomaa V
    17. Järvelin M-R
    18. Raitakari OT
    19. Ala-Korpela M
    20. Leon DA

    (2016) Metabolic profiling of alcohol consumption in 9778 young adults

    International Journal of Epidemiology 45:1493–1506.

    https://doi.org/10.1093/ije/dyw175

    • Google Scholar
73. 1. Xia X
    2. Chen W
    3. McDermott J
    4. Han J-DJ

    (2017) Molecular and phenotypic biomarkers of aging

    F1000Research 6:860.

    https://doi.org/10.12688/f1000research.10692.1

    • Google Scholar

Acceptance summary:

The paper by Jansen et al. investigates somatic and mental health using molecular substrates from the same individuals to create five different multi-omics biological aging clocks. This is exactly the kind of data needed to advance the field and to understand how different layers of data can be integrated to understand biological aging processes.

Decision letter after peer review:

Thank you for submitting your article "An integrative study of five biological clocks in somatic and mental health" for consideration by eLife. Your article has been reviewed by four peer reviewers, including Sara Hägg as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Jessica Tyler as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Daniel W Belsky (Reviewer #2); Erik van den Akker (Reviewer #3); David G Le Couteur (Reviewer #4).

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

In this article, Jansen et al. take some steps toward addressing a knowledge gap on different molecular data used to derive biological aging clocks. They train algorithms to predict chronological age based on several molecular substrates assayed from blood samples: DNA methylation, gene expression, metabolomics, proteomics, and also telomere length. They then test correlations among the several derived measures and compare their associations with various exposures and criterion endpoints relevant to the aging process. The main finding is that the age-correlated features of the different substrates are (a) not very well correlated with one another and (b) largely non-overlapping in their information about health and exposure history. The authors also show that combining information across substrates produces a superior measurement as compared to substrate-specific measures.

Revisions for this paper:

1) Using established clocks

The authors talk about the problem of using their results in other studies since the omics platforms are not always available in other cohorts. It is not clear why it was necessary to train the algorithms in the NESDA cohort. There are published "clocks" for all of the substrates analyzed here (many examples are cited in the Introduction). For example, is there any way that the authors can calculate some of the standard epigenetic clocks (Horvath, Hannum, PhenoAge, GrimAge etc) perhaps not using the online calculator but using standalone scripts for these clocks adapted to your data format? Then, the results would be much more interesting for a wider audience and generalizable to other studies as well. The manuscript would be substantially stronger if these clocks were used in place of bespoke versions derived from the data used for testing hypotheses. If it is not feasible to implement published clocks in the NESDA data, this needs to be explained to the reader.

2) Comments on the algorithms used

If established clocks cannot be used, the alternative strategy of training and testing clocks within a single dataset needs to be presented as the alternative along with specific acknowledgement of the limitations of this approach. The Ridge-regression method used to train the clocks requires the assumption that patterning of molecular markers across the chronological age distribution in the sample reflects biological changes that occur with aging. That assumption has important limitations in any sample, for example see Nelson et al., 2020 on mortality selection or discussion of cohort effects in Belsky et al., 2015 or 2020 . But, depending on how age relates to sampling in the NESDA, there could be further challenges here.

Please also specify whether the feature selection for CpGs/genes was done on the whole dataset, prior to cross-validation, or within the cross-validation loop. If the first, this would lead to reporting overoptimistic performances (overtraining), if the latter, OK; please state so in the manuscript. Please also indicate what step size was used.

Would Mahalanobis distance be a better/more interesting way of analysing the data (eg Bello and Dumancas, Curr Aging Sci, 2017)?

A further consideration to be addressed if the NESDA data are to be used in training the "clocks": prediction of chronological age is only one criterion endpoint used to develop biological aging measures. Recent DNA methylation algorithms including the PhenoAge Clock and the GrimAge Clock were developed from analysis of physiology and mortality data along with chronological age. Some acknowledgement is needed that chronological age is only one of several potential criteria on which to train these measures.

Finally, algorithms trained by applying machine learning analysis to fit high-dimensional molecular data to chronological age variation are hypothesized to measure biological processes of aging. But this is a hypothesis, not a fact. Before we can interpret a "clock" as a measure of biological aging, we must establish that it changes with advancing age, forecasts disease, disability, and mortality, and indicates more advanced/delayed aging in individuals with exposure histories linked to shorter/longer healthy lifespan. The authors should be commended for undertaking some of this testing in NESDA, although using established clocks would be a better alternative. Hence, caution is warranted in interpretation of findings.

3) The NESDA cohort

More detail on NESDA is required to help the reader understand its appropriateness as a setting for comparing measures of aging. How was the sample selected? When were biological measurements collected and what was the extent of attrition from the baseline sample at those time points? In addition, it is not clear when the various exposure and health outcome measurements were collected relative to the biological measurements used to compose the clocks. For each participant, were all the analyses done in a blood sample taken at the same time, or were the different methods applied to bloods taken at different times? How long ago were the samples taken? Are there any storage effects that might influence analyses? A figure illustrating the timeline of data collection would greatly improve clarity of the analysis design.

4) The composite index

The composite index of the 5 clocks was a nice addition to the results. Ideally, clocks, including the composite, are scaled prior to associations with outcomes, as effect sizes are directly compared in the paper. Please indicate whether this has been done; if not, evaluations should be made on the basis of significance only, if so, great! Please state so in the Materials and methods.

Would the results have been similar if the 5 clocks were used combined in multivariate models instead? For example, what happens when all five clocks are put in the same model as predictors with BMI as health outcome? Will they all still be important in the association or are some redundant? Alternatively, it might be useful to compare the current operationalization of the multi-substrate composite to one derived from a factor analysis instead. This is information that is useful to understand the stability of the results but now somehow missed using only one composite index.

5) The Results

The biggest concern with the study is the generalizability given that the samples come from a clinical cohort with individuals suffering from depression and anxiety disorders. About 26% of the study participants were healthy controls and should then be representing a more general population. Moreover, age range is 20-65 years, so probably misses the age groups where biological changes of old age become dominant. Different participants were analysed for each biomarker, and there was a full dataset on only a fraction (approx. 1/3) of the participants that had individual tests done. Any data on ethnicities in NESDA? It is important to perform sensitivity analyses in selected groups of NESDA addressing these concerns in all associations and conclude if the effects are similar or changed in any important way. This also needs to be addressed in the Discussion section.

Another issue is the direction of effects, since these samples and associations are based on cross-sectional data, the authors correctly state that no conclusion can be made on the cause and consequence pathways. However, the biological clocks are used as outcomes in the linear regression models, why? For somatic health and chronic conditions, these are often treated as outcomes in the models using biological age as predictors. In Figure 3, intuitively this is interpreted as the health determinants are the outcomes.

If NESDA is a longitudinal cohort collected about 15 years ago, is there no other follow-up data on somatic and mental health that can be used then using biological age as predictor and health as outcome?

The authors should be somewhat more circumspect in interpreting the clocks they derive. A conclusion of the article is that biological aging proceeds differently across different molecular substrates. This takes the derived measures too literally. Instead, the finding is that the correlates of chronological age in different molecular substrates are not very well correlated with one another.

Given metabolic syndrome and depression are often medicated conditions, are there any data on medications, or any suggestion that medications might influence biomarkers?

6) Data availability

The statement on data availability is not good enough. Why can some gene expression data be released but not other data on these individuals? Data that are anonymized (the identifier key is thrown away) are not considered as sensitive data and it should hence be possible to release more data in this manner.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

Thank you for resubmitting your work entitled "An integrative study of five biological clocks in somatic and mental health" for further consideration by eLife. Your revised article has been evaluated by Jessica Tyler (Senior Editor) and Sara Hägg (Reviewing Editor).

The current version of the manuscript represents a highly responsive revision that addressed most comments. There are some remaining issues that need to be addressed before acceptance, as outlined below:

– The mortality association should be mentioned already in the result section as an additional analysis.

– The biological aging indicator is not clearly described in all figure legends, if it is the residulized age version or not.

– State the direction of effect for the medication analysis in the result section, not just p-values.

– The metabolomic platform is "Brainshake" in Materials and methods?

Essential revisions:

1) Using established clocks

The authors talk about the problem of using their results in other studies since the omics platforms are not always available in other cohorts. It is not clear why it was necessary to train the algorithms in the NESDA cohort. There are published "clocks" for all of the substrates analyzed here (many examples are cited in the Introduction). For example, is there any way that the authors can calculate some of the standard epigenetic clocks (Horvath, Hannum, PhenoAge, GrimAge etc) perhaps not using the online calculator but using standalone scripts for these clocks adapted to your data format? Then, the results would be much more interesting for a wider audience and generalizable to other studies as well. The manuscript would be substantially stronger if these clocks were used in place of bespoke versions derived from the data used for testing hypotheses. If it is not feasible to implement published clocks in the NESDA data, this needs to be explained to the reader.

We thank the reviewers for this comment and explain below why we do not use standard epigenetic clocks (or other published clocks using the omics levels presented here), which, if possible, would indeed have facilitated interpretation and replication of our results.

It is important to note that commonly used methods for assaying DNA methylation depend on the Illumina arrays, platforms that generate variables indicating the percentage of methylated CpGs (values range from 0 to 1, indicating no methylation to 100% methylation), whereas the current study used optimized Methyl-CpG binding domain sequencing, that generate quantitative scores (e.g. scores ranging from 0-20) representing the number of fragments covering a CpG that is proportional to the level of methylation occurring in its locus. Optimized MBD-seq has a more comprehensive coverage of the methylome (interrogation of 94% instead of 2-4% of all 28 million common CpG sites in blood). Computing existing array-based epigenetic clocks (e.g. Horvath) using the MBD-seq data would be suboptimal compared to computing MBD-seq based clocks because: (1) MBD-seq covers much more CpGs than the array-based methylation, and restricting to array based CpGs means not leveraging that coverage, and (2) using only the CpGs used for the Horvath clock from the MBD-seq method does not generate equivalent predictive power.

As for the other omics-based clocks that we computed: there are existing gene expression based clocks (Peters et al., 2015) however, gene expression measures are also platform dependent. The mentioned existing clock used Illumina arrays, while we generated gene expression data from Affymetrix arrays, which (in many cases) target different parts of the gene and not completely overlapping gene sets. The proteomic clock is based on a selected number of proteins from a particular platform with 172 proteins. To the best of our knowledge, this platform has not been used before to compute a proteomic clock. The metabolomic clock was created using the Nightingale platform: this platform has been used before to compute biological age, and an algorithm to compute biological age was provided (van den Akker et al., 2019). However, NESDA was part of this study (and thus part of the “training set” to compute the algorithm): using this algorithm in NESDA may lead to overfitting. More importantly, the above mentioned transcriptomic and metabolomic clocks have not been validated by other independent studies. For the above outlined reasons, we believe that the previously established clocks are suboptimal and/or not suitable for the current available data. While we agree that this comes at the cost of generalizability of findings, we also ask for the reviewers understanding that applying existing clocks is not optimal with the available data. Importantly, by using one consistent approach for calculating the biological clocks using different omics data, we increase the comparability of clocks within our own study.

We now clarify this and write the following in the Discussion to emphasize the limitations of this strategy:

“This study did not include existing biological clocks. […] 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.”

2) Comments on the algorithms used

If established clocks cannot be used, the alternative strategy of training and testing clocks within a single dataset needs to be presented as the alternative along with specific acknowledgement of the limitations of this approach. The Ridge-regression method used to train the clocks requires the assumption that patterning of molecular markers across the chronological age distribution in the sample reflects biological changes that occur with aging. That assumption has important limitations in any sample, for example see Nelson et al., 2020 on mortality selection or discussion of cohort effects in Belsky et al., 2015 or 2020 . But, depending on how age relates to sampling in the NESDA, there could be further challenges here.

We thank the reviewers for the notion of these references, and now write in the Discussion:

“Since no previously published algorithms were used, we trained our own clocks using ridge regression with cross-validation. […] Future longitudinal studies are needed to identify patterns of biological changes that go beyond their ability to predict age at the time of sampling.”

Please also specify whether the feature selection for CpGs/genes was done on the whole dataset, prior to cross-validation, or within the cross-validation loop. If the first, this would lead to reporting overoptimistic performances (overtraining), if the latter, OK; please state so in the manuscript. Please also indicate what step size was used.

We now clarified this in the Materials and methods section:

“We increased the number of sites included in the elastic net in steps (steps for CpGs: 0, 100, 1000, 10 000, 80 000, 100 000, steps for gene expression 100, 500, 1000, 1200, 1400). […] This approach resulted in 80,000 CpGs (mapping to 2,976 genes) for the epigenetic clock, and 1,200 probes (mapping to 767 genes) for the transcriptomic clock.”

Would Mahalanobis distance be a better/more interesting way of analysing the data (eg Bello and Dumancas, Curr Aging Sci, 2017)?

We thank the reviewer for this suggestion, but in order to make our results comparable to the most commonly previously used methods with similar research designs, we choose not to use the Mahalanobis distance.

A further consideration to be addressed if the NESDA data are to be used in training the "clocks": prediction of chronological age is only one criterion endpoint used to develop biological aging measures. Recent DNA methylation algorithms including the PhenoAge Clock and the GrimAge Clock were developed from analysis of physiology and mortality data along with chronological age. Some acknowledgement is needed that chronological age is only one of several potential criteria on which to train these measures.

We now acknowledge this in the Discussion:

“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)⁴³ or a composite biomarker that was derived from DNAm surrogates and smoking in pack-years (GrimAge)⁴⁴. Such clocks were developed to lead to improved predictions of risk of mortality.”

Finally, algorithms trained by applying machine learning analysis to fit high-dimensional molecular data to chronological age variation are hypothesized to measure biological processes of aging. But this is a hypothesis, not a fact. Before we can interpret a "clock" as a measure of biological aging, we must establish that it changes with advancing age, forecasts disease, disability, and mortality, and indicates more advanced/delayed aging in individuals with exposure histories linked to shorter/longer healthy lifespan. The authors should be commended for undertaking some of this testing in NESDA, although using established clocks would be a better alternative. Hence, caution is warranted in interpretation of findings.

The reviewer is correct on these points, it is also our understanding that we cannot conclusively state that the biological clocks considered in the current cross-sectional study in fact reflect biological processes of ongoing aging. We agree that this limitation did not receive enough attention in the previous version of the manuscript. We have to acknowledge that our sample is rather young (average age=41, range 18-65 years). Despite the fact that we have longitudinal data on our respondents, it is to be expected that the power to look at aging-related outcomes such as mortality and disease onset in our sample is still limited. However, we now performed additional analyses in which we associated biological aging with mortality and chronic disease onset outcomes. These analyses showed no significant associations with biological aging indicators. We added these findings to the Results section and revised the text to reflect that caution is warranted in interpretation of our findings.

We now write in the Materials and methods section:

“Longitudinal analysis of mortality and chronic disease onset

As NESDA is a longitudinal study, with several follow-up measurement waves, we conducted post-hoc analyses on the relationship between the biological aging indicators and subsequent outcomes after six years of follow-up duration. […] For each biological clock we computed longitudinal analyses, using a linear model with mortality or chronic disease onset as outcome, and the biological clock residualized for chronological age as predictor, while correcting for sex.”

And further in the Discussion:

“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. […] 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.”

3) The NESDA cohort

More detail on NESDA is required to help the reader understand its appropriateness as a setting for comparing measures of aging. How was the sample selected? When were biological measurements collected and what was the extent of attrition from the baseline sample at those time points? In addition, it is not clear when the various exposure and health outcome measurements were collected relative to the biological measurements used to compose the clocks. For each participant, were all the analyses done in a blood sample taken at the same time, or were the different methods applied to bloods taken at different times? How long ago were the samples taken? Are there any storage effects that might influence analyses? A figure illustrating the timeline of data collection would greatly improve clarity of the analysis design.

We thank the reviewer for their comment to add more detailed information of NESDA in the manuscript. We now point out in the revised description that all health determinants and blood-based biological clocks were collected at the same timepoint, making a figure illustrating the timeline of data collection seem redundant as there is also no attrition to report. However, we now improved the clarity of the analysis design by adding a section on the timing of data collection, storage, and detailed report of the baseline interview. The Materials and methods section now reads:

“Data used were from the Netherlands Study of Depression and Anxiety (NESDA), an ongoing longitudinal cohort study examining course and consequences of depressive and anxiety disorders. […] Data to derive different biological clocks was available for different subsamples and all based on the same fasting blood draw from participants in the morning between 8:30 and 9:30 after which samples were stored in a -80°C freezer or – for RNA – transferred into PAXgene tubes (Qiagen, Valencia, California, USA) and stored at −20°C. To create biological clocks, we used telomere length (N=2936), DNA methylation (N=1130, MBD-seq, 28M CpGs), gene expression (N=1990, Affymetrix U219 micro arrays, >20K genes), proteins (N=1837, Myriad RBM DiscoveryMAP 250+, 171 proteins) and metabolites (N=2910, Brainshake platform, 231 metabolites), see Table 1 and details in the following sections.”

4) The composite index

The composite index of the 5 clocks was a nice addition to the results. Ideally, clocks, including the composite, are scaled prior to associations with outcomes, as effect sizes are directly compared in the paper. Please indicate whether this has been done; if not, evaluations should be made on the basis of significance only, if so, great! Please state so in the Materials and methods.

We indeed standardized all our clocks prior to association analyses. So, the reported associations are standardized betas that can be compared across individual biological aging indicators. We better indicated this now in the Materials and methods:

“Standardized betas from these models are reported (by scaling predictor and outcome).”

And

“A composite index of biological aging was made by scaling each of the five biological indicators and taking the sum, in the 653 samples that had data for all five omics levels.”

Would the results have been similar if the 5 clocks were used combined in multivariate models instead? For example, what happens when all five clocks are put in the same model as predictors with BMI as health outcome? Will they all still be important in the association or are some redundant? Alternatively, it might be useful to compare the current operationalization of the multi-substrate composite to one derived from a factor analysis instead. This is information that is useful to understand the stability of the results but now somehow missed using only one composite index.

We agree that there are multiple strategies to compute a composite index. As an alternative we now also report the associations between the first PC of a PCA analysis of the 5 biological aging indicators, and the health determinants. This PC reflects the strongest biological aging correlations, which is between metabolomic and proteomic aging. Thus, this PC is a weighted sum of the indicators, all positive weights, but with highest weights for metabolomic and proteomic aging indicators. Therefore, the associations between this PC and the health outcomes, is very similar to the associations between the health determinants and metabolomic and proteomic aging. Moreover, PCA analysis gives the explained variance of multiple independent dimensions derived from the underlying data, and therefore also helps to answer the reviewers question about redundancy of the biological clocks. We added to the Results:

“As an alternative approach, Principal Component Analysis (PCA) was used to compute an alternative composite index. […] The five PC’s each explain more than 15% of variance (the first 2 PC’s more than 25% each), indicating the multidimensionality and non-redundancy of the five biological clocks.”

5) The Results

The biggest concern with the study is the generalizability given that the samples come from a clinical cohort with individuals suffering from depression and anxiety disorders. About 26% of the study participants were healthy controls and should then be representing a more general population. Moreover, age range is 20-65 years, so probably misses the age groups where biological changes of old age become dominant. Different participants were analysed for each biomarker, and there was a full dataset on only a fraction (approx. 1/3) of the participants that had individual tests done. Any data on ethnicities in NESDA? It is important to perform sensitivity analyses in selected groups of NESDA addressing these concerns in all associations and conclude if the effects are similar or changed in any important way. This also needs to be addressed in the Discussion section.

We would like to emphasize that sample size is important for testing robust associations, so we wanted to make optimal use of the current sample sizes to examine individual health determinant associations. The rationale to additionally test all associations in a fully overlapping sample with all omics data (N=653) was to compare whether shared and unique associations would be (much) different, but this analysis showed very similar results to those with a maximum number of samples per omics level, indicating high sensitivity of the results.

Furthermore, although we believe that poorer mental or somatic health outcomes will be similarly associated to advanced aging in both control and patient groups, we can see how associations might be confounded by diagnostic status. We therefore repeated the same separate univariate analyses, but included an additional interaction term between current depression status and health determinant in the models. Overall, these interaction terms were not significant. These findings can be found in the supplement.

We now write in the Discussion:

“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 have also added the following sentence to the Materials and methods:

“More than 94% of the NESDA participants were from North European origin.”

And the following sentence in the Discussion:

“We also 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.”

Another issue is the direction of effects, since these samples and associations are based on cross-sectional data, the authors correctly state that no conclusion can be made on the cause and consequence pathways. However, the biological clocks are used as outcomes in the linear regression models, why? For somatic health and chronic conditions, these are often treated as outcomes in the models using biological age as predictors. In Figure 3, intuitively this is interpreted as the health determinants are the outcomes.

In cross sectional analysis, interchanging predictor and outcome will not significantly change the interpretation of associations between these variables in a linear model. Since we wanted to find determinants for biological aging, while correcting for sex, we used biological aging as outcomes. We now clarify in the legend of Figure 3 that biological clocks residualized for age were used as outcomes.

If NESDA is a longitudinal cohort collected about 15 years ago, is there no other follow-up data on somatic and mental health that can be used then using biological age as predictor and health as outcome?

We appreciate this comment and have conducted additional analyses to examine whether biological aging at baseline predicts the presence of somatic disease and mortality rates 6 years later in those who were initially disease-free at baseline. Please see response 2.

The authors should be somewhat more circumspect in interpreting the clocks they derive. A conclusion of the article is that biological aging proceeds differently across different molecular substrates. This takes the derived measures too literally. Instead, the finding is that the correlates of chronological age in different molecular substrates are not very well correlated with one another.

We assume the reviewers refer to this sentence in the Discussion: “Biological aging seems to be differently manifested at certain cellular levels, as suggested by the range of correlations among the biological clocks considered in this study.”

To make clearer that findings from the biological clocks are only suggesting findings for biological aging we changed this sentence into:

“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.”

Given metabolic syndrome and depression are often medicated conditions, are there any data on medications, or any suggestion that medications might influence biomarkers?

We thank the reviewer for this suggestion, and provide additional analyses in which we verified if antidepressant medication (SSRIs, TCAs, or other antidepressants) or metabolic syndrome related medication (anti-diabetic, fibrates, or anti-hypertensives), were associated with the biological clocks.

The findings can be read in the Results section:

“To verify if the results were confounded by medication use, we computed associations between antidepressant medication (SSRIs, TCAs, or other antidepressants), metabolic syndrome related medication (“metabolic medication”: anti-diabetic, fibrates, or anti-hypertensives) and biological aging (Supplementary file 1). After FDR correction, we found that metabolomic aging was associated with the use of metabolic medication (P=2.35e-3), and antidepressant use with proteomic (P=7.16e-5) and transcriptomic aging (P=8.1e-3). The design of the current observational study cannot conclusively prove whether this is a direct medication effect or confounding by indication.”

We believe, however, that these findings are not indicating direct medication effects per se. In fact, these could be interpreted as confirmation of our disease findings. E.g. the patient group using antidepressant medication is also the group that is more chronically and severely depressed. Therefore, using the current data from an observational cohort, we are unable to discern the effects of depression, depression severity, or direct effects of antidepressants. The same is true for the findings of metabolic medication.

We have added the following sentence to the Discussion:

“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.”

6) Data availability

The statement on data availability is not good enough. Why can some gene expression data be released but not other data on these individuals? Data that are anonymized (the identifier key is thrown away) are not considered as sensitive data and it should hence be possible to release more data in this manner.

We have updated our data availability section.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

The current version of the manuscript represents a highly responsive revision that addressed most comments. There are some remaining issues that need to be addressed before acceptance, as outlined below:

– The mortality association should be mentioned already in the result section as an additional analysis.

We now write in the Results section:

“Association between biological aging indicators and mortality in longitudinal analysis

We conducted post-hoc analyses on the relationship between the biological aging indicators and subsequent outcomes after six years of follow-up duration. Mortality data and self-reported somatic disease onset (in the categories cardiometabolic, respiratory, musculoskeletal, digestive, neurological and endocrine diseases, and cancer) was gathered at each measurement wave. There were no significant associations between chronic disease onset or mortality and baseline biological aging, likely due to the low numbers of mortality and disease onset (Supplementary file 3).”

– The biological aging indicator is not clearly described in all figure legends, if it is the residulized age version or not.

We added to the legends of Figure 3 and Figure 4:

“All biological aging indicators were age-regressed, only telomere length was not.”

– State the direction of effect for the medication analysis in the result section, not just p-values.

We now write in the Results section:

“After FDR correction, we found that metabolomic aging was associated with the increased use of metabolic medication (Β=0.153, P=2.35e-3), and antidepressant use with proteomic (Β=0.208, P=7.16e-5) and transcriptomic aging (Β=0.129, P=8.1e-3). The design of the current observational study cannot conclusively prove whether this is a direct medication effect or confounding by indication.”

– The metabolomic platform is "Brainshake" in Materials and methods?

We corrected this to “Nightingale platform” (formerly known as “Brainshake”).

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.

• 3,425

  Page views

• 427

  Downloads

• 25

  Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.