Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Brief Communication
  • Published:

Mitochondrial and psychosocial stress-related regulation of FGF21 in humans

Nature Metabolism (2025)Cite this article

Subjects

Abstract

Fibroblast growth factor 21 (FGF21) is a metabolic hormone induced by fasting, metabolic stress and mitochondrial oxidative phosphorylation (OxPhos) defects that cause mitochondrial diseases (MitoD). Here we report that acute psychosocial stress alone (without physical exertion) decreases serum FGF21 by an average of 20% (P < 0.0001) in healthy controls, but increases FGF21 by 32% (P < 0.0001) in people with MitoD, pointing to a functional FGF21 interaction between the stress response and OxPhos capacity. We further define co-activation patterns between FGF21 and stress-related neuroendocrine hormones and report associations between FGF21 and psychosocial factors related to stress and wellbeing. Overall, these results highlight a potential role for FGF21 as a stress hormone involved in meeting the energetic needs of psychosocial stress.

Access through your institution
Buy or subscribe

This is a preview of subscription content, access via your institution

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Elevated serum FGF21 in MitoD is exacerbated in response to acute mental stress and correlated with disease severity.
Fig. 2: Differential patterns of co-regulation between FGF21 and hormones of the HPA, HPG and SNS axes and correlations between FGF21 and psychosocial factors in Controls and MitoD.

Data availability

All source data for the analyses in this paper are available for download. Source data for steroids and catecholamines is available at http://osf.io/pc9f7. MiSBIE study information, detailed protocols and procedures are available at www.picardlab.org/MiSBIE. Source data are provided with this paper.

Code availability

Code used for the analyses is available at https://github.com/mitopsychobio/FGF21_MitoD_Kurade.git.

References

  1. Tezze, C., Romanello, V. & Sandri, M. FGF21 as modulator of metabolism in health and disease. Front. Physiol. 10, 419 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Flippo, K. H. & Potthoff, M. J. Metabolic messengers: FGF21. Nat. Metab. 3, 309–317 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Forsström, S. et al. Fibroblast growth factor 21 drives dynamics of local and systemic stress responses in mitochondrial myopathy with mtDNA deletions. Cell Metab. 30, 1040–1054.e7 (2019).

    Article  PubMed  Google Scholar 

  4. Keipert, S. & Ost, M. Stress-induced FGF21 and GDF15 in obesity and obesity resistance. Trends Endocrinol. Metab. 32, 904–915 (2021).

    Article  CAS  PubMed  Google Scholar 

  5. Prida, E. et al. Liver brain interactions: focus on FGF21 a systematic review. Int. J. Mol. Sci. 23, 13318 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Lehtonen, J. M. et al. FGF21 is a biomarker for mitochondrial translation and mtDNA maintenance disorders. Neurology 87, 2290–2299 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Lin, Y. et al. Accuracy of FGF-21 and GDF-15 for the diagnosis of mitochondrial disorders: a meta-analysis. Ann. Clin. Transl. Neurol. 7, 1204–1213 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Bobba-Alves, N., Juster, R. P. & Picard, M. The energetic cost of allostasis and allostatic load. Psychoneuroendocrinology 146, 105951 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Russell, G. & Lightman, S. The human stress response. Nat. Rev. Endocrinol. 15, 525–534 (2019).

    Article  PubMed  Google Scholar 

  10. Bobba-Alves, N. et al. Cellular allostatic load is linked to increased energy expenditure and accelerated biological aging. Psychoneuroendocrinology 155, 106322 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Usui, N. et al. Roles of fibroblast growth factor 21 in the control of depression-like behaviours after social defeat stress in male rodents. J. Neuroendocrinol. 33, e13026 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Christaki, E. V. et al. Circulating FGF21 vs. stress markers in girls during childhood and adolescence, and in their caregivers: intriguing inter-relations between overweight/obesity, emotions, behavior, and the cared–caregiver relationship. Children 9, 821 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Mason, B. L. et al. Fibroblast growth factor 21 (FGF21) is increased in MDD and interacts with body mass index (BMI) to affect depression trajectory. Transl. Psychiatry 12, 16 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Kelly, C. et al. A platform to map the mind–mitochondria connection and the hallmarks of psychobiology: the MiSBIE study. Trends Endocrinol. Metab. 35, 884–901 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Zhang, X. et al. Serum FGF21 levels are increased in obesity and are independently associated with the metabolic syndrome in humans. Diabetes 57, 1246–1253 (2008).

    Article  CAS  PubMed  Google Scholar 

  16. Hanks, L. J. et al. Circulating levels of fibroblast growth factor-21 increase with age independently of body composition indices among healthy individuals. J. Clin. Transl. Endocrinol. 2, 77–82 (2015).

    PubMed  PubMed Central  Google Scholar 

  17. Wu, C.-T. & Ryan, K. K. Context matters for addressing controversies in FGF21 biology. Trends Endocrinol. Metab. 35, 280–281 (2024).

    Article  CAS  PubMed  Google Scholar 

  18. Huang, Q. et al. The mitochondrial disease biomarker GDF15 is dynamic, quantifiable in saliva, and correlates with disease severity. Mol. Genet. Metab. 145, 109179 (2025).

    Article  CAS  PubMed  Google Scholar 

  19. Huang, Q. et al. The energetic stress marker GDF15 is induced by acute psychosocial stress. Preprint at bioRxiv https://doi.org/10.1101/2024.04.19.590241 (2025).

  20. Picard, M. et al. Mitochondrial functions modulate neuroendocrine, metabolic, inflammatory, and transcriptional responses to acute psychological stress. Proc. Natl Acad. Sci. 112, E6614–E6623 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Deng, Y.-T. et al. Atlas of the plasma proteome in health and disease in 53,026 adults. Cell 188, 253–271.e7 (2025).

    Article  CAS  PubMed  Google Scholar 

  22. Shaulson, E. D., Cohen, A. A. & Picard, M. The brain–body energy conservation model of aging. Nat. Aging 4, 1354–1371 (2024).

    Article  PubMed  Google Scholar 

  23. Kelly, C. et al. Perceived association of mood and symptom severity in adults with mitochondrial diseases. Mitochondrion 84, 102033 (2025).

    Article  CAS  PubMed  Google Scholar 

  24. Allen, A. P. et al. The Trier Social Stress Test: principles and practice. Neurobiol. Stress 6, 113–126 (2017).

    Article  PubMed  Google Scholar 

  25. Gao, W. et al. Quantitative analysis of steroid hormones in human hair using a column-switching LC–APCI–MS/MS assay. J. Chromatogr. B 928, 1–8 (2013).

    Article  CAS  Google Scholar 

  26. Gao, W., Stalder, T. & Kirschbaum, C. Quantitative analysis of estradiol and six other steroid hormones in human saliva using a high throughput liquid chromatography–tandem mass spectrometry assay. Talanta 143, 353–358 (2015).

    Article  CAS  PubMed  Google Scholar 

  27. Kirschbaum, C. & Hellhammer, D. H. Salivary cortisol in psychobiological research: an overview. Neuropsychobiology 22, 150–169 (1989).

    Article  CAS  PubMed  Google Scholar 

  28. Fisk, J. D. et al. Measuring the functional impact of fatigue: initial validation of the fatigue impact scale. Clin. Infect. Dis. 18, S79–S83 (1994).

    Article  PubMed  Google Scholar 

  29. Cohen, S., Kamarck, T. & Mermelstein, R. A global measure of perceived stress. J. Health Soc. Behav. 24, 385–396 (1983).

    Article  CAS  PubMed  Google Scholar 

  30. Schulz, P. & Schlotz, W. The Trier Inventory for the Assessment of Chronic Stress (TICS): scale construction, statistical testing, and validation of the scale work overload. Diagnostica 45, 8–19 (1999).

    Article  Google Scholar 

  31. Kanner, A. D., Coyne, J. C., Schaefer, C. & Lazarus, R. S. Comparison of two modes of stress measurement: Daily Hassles and Uplifts versus Major Life Events. J. Behav. Med 4, 1–39 (1981).

    Article  CAS  PubMed  Google Scholar 

  32. Bernstein, D. P., Ahluvalia, T., Pogge, D. & Handelsman, L. Validity of the Childhood Trauma Questionnaire in an adolescent psychiatric population. J. Am. Acad. Child Adolesc. Psychiatry 36, 340–348 (1997).

    Article  CAS  PubMed  Google Scholar 

  33. Norbeck, J. S. Modification of life event questionnaires for use with female respondents. Res Nurs. Health 7, 61–71 (1984).

    Article  CAS  PubMed  Google Scholar 

  34. Slavich, G. M. & Shields, G. S. Assessing lifetime stress exposure using the Stress and Adversity Inventory for Adults (Adult STRAIN): an overview and initial validation. Psychosom. Med. 80, 17–27 (2018).

    Article  PubMed  Google Scholar 

  35. Spielberger, C. D., Gorsuch, R. L. & Lushene. R. E. Manual for the State–Trait Anxiety Inventory (Consulting Psychologists Press, 1970).

  36. Beck A. T., Steer, R. A. & Brown, G. K. Manual for the Beck Depression Inventory (Psychological Corporation, 1987).

  37. Maslach, C., Jackson, S. E. & Leiter, M. P. Maslach Burnout Inventory Manual 3rd edn (Consulting Psychologists Press, 1996).

  38. Blevins, C. A., Weathers, F. W., Davis, M. T., Witte, T. K. & Domino, J. L. The Posttraumatic Stress Disorder Checklist for DSM-5 (PCL-5): development and initial psychometric evaluation. J. Trauma Stress 28, 489–498 (2015).

    Article  PubMed  Google Scholar 

  39. Ryff, C. D. & Keyes, C. L. The structure of psychological well-being revisited. J. Pers. Soc. Psychol. 69, 719–727 (1995).

    Article  CAS  PubMed  Google Scholar 

  40. Antonovsky, A. The structure and properties of the sense of coherence scale. Soc. Sci. Med. 36, 725–733 (1993).

    Article  CAS  PubMed  Google Scholar 

  41. Fredrickson, B. L., Tugade, M. M., Waugh, C. E. & Larkin, G. R. What good are positive emotions in crises? A prospective study of resilience and emotions following the terrorist attacks on the United States on September 11th, 2001. J. Personal. Soc. Psychol. 84, 365–376 (2003).

    Article  Google Scholar 

  42. Adler, N. E., Epel, E. S., Castellazzo, G. & Ickovics, J. R. Relationship of subjective and objective social status with psychological and physiological functioning: preliminary data in healthy white women. Health Psychol. 19, 586–592 (2000).

    Article  CAS  PubMed  Google Scholar 

  43. Sarason, I. G., Levine, H. M., Basham, R. B. & Sarason, B. R. Assessing social support: the Social Support Questionnaire. J. Personal. Soc. Psychol. 44, 127–139 (1983).

    Article  Google Scholar 

  44. Zimet, G. D., Dahlem, N. W., Zimet, S. G. & Farley, G. K. The multidimensional scale of perceived social support. J. Personal. Assess. 52, 30–41 (1988).

    Article  Google Scholar 

  45. Funk, J. L. & Rogge, R. D. Testing the ruler with item response theory: increasing precision of measurement for relationship satisfaction with the Couples Satisfaction Index. J. Fam. Psychol. 21, 572–583 (2007).

    Article  PubMed  Google Scholar 

  46. Russell, D. W. UCLA Loneliness Scale (version 3): reliability, validity, and factor structure. J. Pers. Assess. 66, 20–40 (1996).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

The MiSBIE study was supported by the National Institutes of Health (grants R21MH113011, R01MH122706, RF1AG076821 and R01MH137190), the Seed Grant Program for MR Studies of the Zuckerman Mind Brain Behavior Institute at Columbia University, the Robert N. Butler Columbia Aging Center Fellowship Program at the Mailman School of Public Health, the National Center for Advancing Translational Sciences and National Institutes of Health (through grant numbers UL1TR001873 and P30CA013696), the Columbia Irving Institute Scholars program, and the Wharton Fund and the Baszucki Group (to M.P.). We are grateful to G. Liu for assistance with the MiSBIE study database.

Author information

Authors and Affiliations

  1. Division of Behavioral Medicine, Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA

    Mangesh Kurade, Natalia Bobba-Alves, Catherine Kelly, Alexander Behnke, Quinn Conklin, Caroline Trumpff & Martin Picard

  2. Clinical and Biological Psychology, Institute of Psychology and Education, Ulm University, Ulm, Germany

    Alexander Behnke

  3. German Center for Mental Health (DZPG), partner site Mannheim-Heidelberg-Ulm, Ulm, Germany

    Alexander Behnke

  4. Department of Psychiatry and Addiction, University of Montreal, Montreal, Quebec, Canada

    Robert-Paul Juster

  5. H. Houston Merritt Center for Neuromuscular and Mitochondrial Disorders, Columbia Translational Neuroscience Initiative, Department of Neurology, Columbia University Medical Center, New York, NY, USA

    Michio Hirano & Martin Picard

  6. Robert N Butler Columbia Aging Center, Columbia University Mailman School of Public Health, New York, NY, USA

    Martin Picard

  7. New York State Psychiatric Institute, New York, NY, USA

    Martin Picard

Authors
  1. Mangesh Kurade
    View author publications

    Search author on:PubMed Google Scholar

  2. Natalia Bobba-Alves
    View author publications

    Search author on:PubMed Google Scholar

  3. Catherine Kelly
    View author publications

    Search author on:PubMed Google Scholar

  4. Alexander Behnke
    View author publications

    Search author on:PubMed Google Scholar

  5. Quinn Conklin
    View author publications

    Search author on:PubMed Google Scholar

  6. Robert-Paul Juster
    View author publications

    Search author on:PubMed Google Scholar

  7. Michio Hirano
    View author publications

    Search author on:PubMed Google Scholar

  8. Caroline Trumpff
    View author publications

    Search author on:PubMed Google Scholar

  9. Martin Picard
    View author publications

    Search author on:PubMed Google Scholar

Contributions

M.P., R.-P.J., C.T. and M.H. designed the MiSBIE study. M.K. performed FGF21 assays and analysed data. C.K. recruited participants, performed study visits, collected study data and performed data quality control. N.B.-A. analysed steroid and catecholamine hormones data. C.K. and C.T. analysed psychosocial questionnaires. A.B. provided statistical guidance and performed the regression models. M.H. supervised the clinical portion of the study. M.K., Q.C. and M.P. drafted the manuscript. All authors reviewed the final version of the manuscript.

Corresponding author

Correspondence to Martin Picard.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Metabolism thanks Michael van der Kooij and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Jean Nakhle, in collaboration with the Nature Metabolism team.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 FGF21 and receptor-complex expression across the human body.

Mean transcript levels (normalized Transcript Per Million, nTPM) for (a) FGF21 together with its (b) high affinity receptor FGFR1 (all isoforms) and its obligate co-receptor (c) β-Klotho (KLB) expression across 48 human tissues, from the GTEx v8 RNAseq dataset. Datapoints represent the mean from 21 to 803 individuals per tissue. Tissues are grouped and color-coded by their systemic functions and location. The five tissues with the highest and lowest expression are annotated. See Supplemental Table 2 for corresponding values. Two-sided Wilcoxon signed rank test showed that liver FGF21 expression is 104.2-fold higher (p < 0.0001) than the second highest tissue, skeletal muscle, and 123.7-fold higher than the average of all human tissues. Note: The relatively uniform expression of FGFR1 (all isoforms) and KLB across vascular, digestive, and glandular tissues, along with two brain regions is consistent with circulating FGF21 having a broad signaling in human physiology.

Extended Data Fig. 2 Morning to afternoon dynamics of FGF21 across groups.

(a) Two-sided Mann-Whitney’s U-test comparing fasting and fed FGF21 levels (pg/ml) between Controls (n = 65) and MitoD subgroups (3243A>G, n = 18; MELAS, n = 3-4; Deletion, n = 12). In the AM–Fasting state, the 3243A>G group was 0.8-fold higher (M = 427.6 pg/ml, P = 0.004) than Controls (M = 235 pg/ml), whereas the Deletion group was 3.6-fold higher (M = 1088 pg/ml, P < 0.0001). In the PM–Fed state, all MitoD groups were significantly higher than Controls (M =109 pg/ml): 3243A>G was 1.8-fold higher (M = 304 pg/ml, P = 0.0005), MELAS was 1.6-fold higher (M = 284 pg/ml, P = 0.006), and Deletion was 8.5-fold higher (M = 1035 pg/ml, P < 0.0001). Error bars represent standard error of the mean (SEM) (b) Hedge’s g comparing MitoD subgroups to Controls at each time point. (c) Fasting (AM) to fed (PM) change in individual participants’ FGF21 (pg/ml) levels with the group difference expressed as percent difference between time points. Two-sided paired Wilcoxon tests showed significant decreases from fasting-AM to fed-PM levels in Controls (n = 61, P < 0.0001, Median: AM = 173.8 pg/ml, PM = 79.08 pg/ml) and the MitoD group combined (n = 30, P = 0.0005, Median: AM = 535.4 pg/ml, PM = 309.1 pg/ml). When split by subgroup, there was a significant decrease in the m.3243A>G point mutation group only (n = 18, P = 0.004, Median: AM = 307 pg/ml, PM = 191.6 pg/ml).

Source data

Extended Data Fig. 3 ROC Analyses examining FGF21 as a Diagnostic Biomarker for Mitochondrial Disease.

Receiver operating characteristic (ROC) curve analyses for the prediction of mitochondrial disease based on the serum levels of FGF21 measured by enzyme-linked immunosorbent assay. Plots depict percentage sensitivity plotted against percentage specificity to assess diagnostic performance of serum FGF21 in distinguishing MitoD from healthy participants. (a) Two curves comparing the diagnostic performance of fasting (AM) versus fed (PM) measures of serum FGF21, showing that fed (PM) FGF21 shows enhanced performance with AUC = 0.86. (b) Two plots comparing the diagnostic performance of fed and fasting FGF21 for Deletion and Mutation (m.3243A>G + MELAS) groups separately, alongside a groups-combined curve. Curves demonstrate highest discriminative ability for the Deletion group in the fed state (AUC = 0.98). (c) Comparison of FGF21 and GDF15 diagnostic performance in the fed state and (d) the performance of FGF21 and GDF15 combined. The combined curve was generated using individual FGF21 and GDF15 measures and calculating predicted probabilities by logistic regression for each participant measurement at PM-fed time point.

Source data

Extended Data Fig. 4 FGF21 dynamics stratified by major clinical symptoms in participants with mitochondrial disease.

Two-sided Mann-Whitney’s U-tests comparing (a) baseline (PM‑fed, pre‑stress) serum FGF21 concentrations in MitoD participants who either do (Symptom +) or do not (Symptom –) carry a given clinical symptom and (b) FGF21 reactivity, expressed as the net percent change from baseline. Bar graphs represent mean FGF21 values. Error bars represent standard error of the mean (SEM).

Source data

Extended Data Fig. 5 Demographic and metabolic variations in serum FGF21 levels by group: Analysis by sex, age, body composition, and metabolic markers.

(a) Violin plot showing FGF21 (pg/ml) between females and males for each group. Solid line marks median and dashed lines mark first and third quartiles (IQR). A two-sided Kruskal-Wallis test with Dunn’s multiple comparisons revealed no difference in FGF21 levels between females (n = 64) and males (n = 34). (b) Two-sided Spearman’s correlations between age and fasting FGF21 (pg/ml) by group. Error bands represent 95% confidence intervals. (c) Two-sided Spearman’s correlations between fasting FGF21 levels (pg/ml) and different body composition factors in Controls and MitoD participants. Percent body fat was significantly correlated with FGF21 in both groups (Controls, n = 63, P = 0.046, and MitoD, n = 34, P = 0.013), whereas fat mass was only significantly correlated in Controls (n = 63, P = 0.015). Error bands represent 95% confidence intervals. (d) Forrest plot depicting two-sided Spearman’s correlations coefficients between fasting FGF21 and metabolic biomarkers in Controls and MitoD participants; all measurements are Log10 transformed. Circles represent Spearman’s coefficients and error bars represent 95% confidence intervals. In Controls (n = 65), FGF21 was positively correlated with blood glucose (P < 0.0001), total cholesterol (P = 0.016), and insulin (P = 0.026), and negatively correlated with HDL (P = 0.0043). The MitoD group showed no significant correlation with any metabolic biomarkers. P < 0.05 (*), P < 0.01 (**), P < 0.001 (***), P < 0.0001 (****).

Source data

Extended Data Fig. 6 Mixed-effects models comparing MitoD to Controls over time with individual and group FGF21 trajectories in response to the speech task.

(a) A mixed effects model with Restricted Maximum Likelihood (REML) estimation showed significant effects of Group, F(1,100) = 44.34, P < 0.0001, Time, F(1.2,109.9) = 13.05, P = 0.0002, and a significant Group X Time interaction, F(7, 640) = 12, P < 0.0001, n = 102, indicating that the trajectory of serum FGF21 over time varied significantly in the MitoD group compared to Controls. Using Dunnett’s tests to compare each subsequent time point to baseline (−5min), we found that Controls showed significant differences at 5mins (P = 0.0021, n = 63), 10mins (P = 0.0003, n = 60), 20mins (P = 0.0007, n = 61), and 30mins (P = 0.0014, n = 61), whereas the MitoD group showed significant differences at 10mins (P = 0.0091, n = 33), 20mins (P = 0.006, n = 32), 30mins (P = 0.0004, n = 31), 60mins (P = 0.0013, n = 30), and 90mins (P = 0.0052, n = 28). Each timepoint represents mean FGF21 (pg/ml). (b-c) Group and individual trajectories for each subgroup are show relative to baseline values (Value/Baseline). Each data point represents a unique biological replicate that is derived as an average from two technical ELISA replicates from each participant (unit of study = individual participant). Control = 65 participants, m.3243A>G = 17 participants, Deletion = 12, MELAS = 3. Relative change between time points of the same group was calculated as the value at a given time point divided by the baseline value (−5min). For each group, paired Wilcoxon tests were used to compare the FGF21 peak to its own baseline measurements. Error bars represent standard error of the mean (SEM). (b) Average percent change in FGF21 (pg/ml) in Controls as a group, as well as individual trajectories for each participant. Individual timepoint represent mean FGF21 (pg/ml). Error bars represent standard error of mean (SEM). (c) Average percent change for each MitoD subgroup relative to their baseline, as well as individual trajectories for each participant. Individual time point represent mean FGF21 (pg/ml). Error bars represent standard error of mean (SEM). P < 0.05 (*), P < 0.01 (**), P < 0.001 (***), P < 0.0001 (****).

Source data

Extended Data Fig. 7 Stress-induced FGF21, GDF15, and cortisol trajectories in the MiSBIE Study.

Schematic depicting distinct average trajectories of FGF21, GDF15, and cortisol as percent change from baseline after the Tier Social Stress Test (TSST) in Controls (Healthy Mitochondria) vs MitoD (Mitochondrial Diseases). Preliminary data is available at ClinicalTrials.gov #NCT04831424.

Extended Data Fig. 8 Psychosocial stress enhances correlations between FGF21 and disease severity indices in patients with MitoD.

(a) Scatter plots illustrating two-sided Spearman’s correlations between FGF21 and composite scores of various disease severity indices at fasting (AM) and post-stress (+90min). Error bands represent 95% confidence intervals (b) Spearman’s correlation matrix showing two-sided correlations between serum FGF21 levels across time points and subscales of disease severity indices. NMDAS = The Newcastle Mitochondrial Disease Adult Scale.

Source data

Extended Data Fig. 9 Differences in Controls based on FGF21 stress-reactivity.

(a) Controls classified as having a net positive or net negative FGF21 stress-reaction based on percent Area Under the Curve (AUC). AUC calculations were derived from baseline corrected values expressed as percent change relative to baseline FGF21, with both positive and negative peaks included in this calculation, resulting in a net percent AUC for each participant. Scatter plots show two-sided Spearman’s correlations between FGF21 reactivity and (b) cortisol reactivity (P = 0.011, n = 64), (c) cortisone reactivity (P = 0.0049, n = 55), (d) baseline testosterone levels (P = 0.019, n = 48), (e) percent body fat (P = 0.0005, n = 61), and (f) fasting insulin levels (P = 0.0069, n = 64). Reactivity for cortisol and cortisone was calculated as Percent Reactivity = ((Max_Reactivity_Value - Baseline_Value) / Baseline_Value) * 100; Baseline testosterone = measurements (pg/ml) at −5min of the TSST time course; Fasting insulin = measurements (U/ml) at the fasting (AM) time point. Bar graphs for two-sided unpaired Mann-Whitney’s U tests with error bars representing standard error of the mean (SEM) show that Controls with positive reactivity have (b) 80% higher mean cortisol reactivity (P = 0.026, n = 44 vs 17), (c) 30% higher mean cortisone reactivity (P = 0.0023, n = 37 vs 18), and (e) 6.6% higher mean percent body fat (P = 0.0056, n = 42 vs 19). (d) Inversely, Controls with higher FGF21 reactivity had 17.61 pg/ml lower mean testosterone at baseline (P = 0.0066, n = 34 vs 14). (f) There was no statistically significant difference between high and low FGF21 reactors on baseline insulin (P = 0.16, n = 45 vs 19).

Source data

Extended Data Fig. 10 Forrest plots of fasting and fed FGF21 levels with psychosocial self-report measures.

Forrest plots illustrating associations between psychosocial self-report measures categorized as (a) positive or (b) negative and fasting or fed FGF21 levels adjusted for age and percent fat. The group-specific associations were computed in linear regression models adjusting for age and group-specific percent fat. Circles represent the size of the adjusted bivariate association between FGF21 and the self-report measures as Cohen’s r value with 95% CI error bars. Fasting is presented on the left and fed on the right, with the comparison showing that the differences between Controls and MitoD are more apparent in the fed state. They are organized from smallest (bottom) to largest (top) association for the MitoD group in the fed state. Diamonds represent the overall Cohen’s r for each group represented as mean and standard error of the mean (SEM). P < 0.05 (*), P < 0.01 (**), P < 0.001 (***), P < 0.0001 (****).

Source data

Supplementary information

Supplementary Information

Supplementary Tables 1 and 2.

Reporting Summary

Source data

Source Data Fig. 1

Statistical source data.

Source Data Fig. 2

Statistical source data.

Source Data Extended Data Fig. 2

Statistical source data.

Source Data Extended Data Fig. 3

Statistical source data.

Source Data Extended Data Fig. 4

Statistical source data.

Source Data Extended Data Fig. 5

Statistical source data.

Source Data Extended Data Fig. 6

Statistical source data.

Source Data Extended Data Fig. 8

Statistical source data.

Source Data Extended Data Fig. 9

Statistical source data.

Source Data Extended Data Fig. 10

Statistical source data.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kurade, M., Bobba-Alves, N., Kelly, C. et al. Mitochondrial and psychosocial stress-related regulation of FGF21 in humans. Nat Metab (2025). https://doi.org/10.1038/s42255-025-01388-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s42255-025-01388-6

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing