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.

  • Article
  • Published:

Bidirectional two-sample Mendelian randomization analysis identifies causal associations between relative carbohydrate intake and depression

Nature Human Behaviour volume 6, pages 1569–1576 (2022)Cite this article

Subjects

Abstract

Growing evidence suggests that relative carbohydrate intake affects depression; however, the association between carbohydrates and depression remains controversial. To test this, we performed a two-sample bidirectional Mendelian randomization (MR) analysis using genetic variants associated with relative carbohydrate intake (N = 268,922) and major depressive disorder (N = 143,265) from the largest available genome-wide association studies. MR evidence suggested a causal relationship between higher relative carbohydrate intake and lower depression risk (odds ratio, 0.42 for depression per one-standard-deviation increment in relative carbohydrate intake; 95% confidence interval, 0.28 to 0.62; P = 1.49 × 10−5). Multivariable MR indicated that the protective effect of relative carbohydrate intake on depression persisted after conditioning on other diet compositions. The mediation analysis via two-step MR showed that this effect was partly mediated by body mass index, with a mediated proportion of 15.4% (95% confidence interval, 6.7% to 24.1%). These findings may inform prevention strategies and interventions directed towards relative carbohydrate intake and depression.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Fig. 1: MR plots for the relationship of relative carbohydrate intake (N = 268,922) with MDD (N = 143,265).
Fig. 2: Mediation analysis of the effect of relative carbohydrate intake on MDD via potential mediators.

Similar content being viewed by others

Data availability

All GWAS summary statistics analysed in this study are publicly available for download by qualified researchers. The GWASs for relative intake of carbohydrate, fat and protein can be obtained through the SSGAC data portal (https://www.thessgac.org/). The GWASs for MDD were provided by the PGC (https://www.med.unc.edu/pgc). All data generated in the current study can be obtained from the Supplementary Information.

Code availability

All the analyses used in this study were conducted using PLINK version 1.07 and R packages TwoSampleMR (version 0.4.10), MRPRESSO (version 1.0), RadialMR (version 0.4) and MendelianRandomization (version 0.5.1). The code to reproduce all results reported in the manuscript is available at https://github.com/studentyaoshi/MR.

References

  1. Fact Sheets: Depression (World Health Organization, 2021); https://www.who.int/news-room/fact-sheets/detail/depression

  2. Walker, E. R., McGee, R. E. & Druss, B. G. Mortality in mental disorders and global disease burden implications: a systematic review and meta-analysis. JAMA Psychiatry 72, 1259–1259 (2015).

    Article  Google Scholar 

  3. Hasin, D. S. et al. Epidemiology of adult DSM-5 major depressive disorder and its specifiers in the United States. JAMA Psychiatry 75, 336–346 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Hammen, C. Risk factors for depression: an autobiographical review. Annu. Rev. Clin. Psychol. 14, 1–28 (2018).

    Article  PubMed  Google Scholar 

  5. Carnegie, R. et al. Mendelian randomisation for nutritional psychiatry. Lancet Psychiatry 7, 208–216 (2020).

    Article  PubMed  Google Scholar 

  6. Huang, Q. Y., Liu, H., Suzuki, K., Ma, S. H. & Liu, C. H. Linking what we eat to our mood: a review of diet, dietary antioxidants, and depression. Antioxidants (Basel) 8, 376 (2019).

    Article  CAS  Google Scholar 

  7. Christensen, L. The effect of food intake on mood. Clin. Nutr. 20, 161–166 (2001).

    Article  CAS  Google Scholar 

  8. Christensen, L. & Somers, S. Comparison of nutrient intake among depressed and nondepressed individuals. Int. J. Eat. Disord. 20, 105–109 (1996).

    Article  CAS  PubMed  Google Scholar 

  9. Mantantzis, K., Schlaghecken, F., Sunram-Lea, S. I. & Maylor, E. A. Sugar rush or sugar crash? A meta-analysis of carbohydrate effects on mood. Neurosci. Biobehav. Rev. 101, 45–67 (2019).

    Article  CAS  PubMed  Google Scholar 

  10. Hu, D. Q., Cheng, L. X. & Jiang, W. J. Sugar-sweetened beverages consumption and the risk of depression: a meta-analysis of observational studies. J. Affect. Disord. 245, 348–355 (2019).

    Article  PubMed  Google Scholar 

  11. Rogers, P. J. A healthy body, a healthy mind: long-term impact of diet on mood and cognitive function. Proc. Nutr. Soc. 60, 135–143 (2001).

    Article  CAS  PubMed  Google Scholar 

  12. Pellegrin, K. L. et al. Average daily nutrient intake and mood among obese women. Nutr. Res. 18, 1103–1112 (1998).

    Article  CAS  Google Scholar 

  13. Rosenthal, N. E. et al. Psychobiological effects of carbohydrate- and protein-rich meals in patients with seasonal affective disorder and normal controls. Biol. Psychiatry 25, 1029–1040 (1989).

    Article  CAS  PubMed  Google Scholar 

  14. Brinkworth, G. D., Buckley, J. D., Noakes, M., Clifton, P. M. & Wilson, C. J. Long-term effects of a very low-carbohydrate diet and a low-fat diet on mood and cognitive function. Arch. Intern. Med. 169, 1873–1880 (2009).

    Article  CAS  PubMed  Google Scholar 

  15. Daneshzad, E., Keshavarz, S. A., Qorbani, M., Larijani, B. & Azadbakht, L. Association between a low-carbohydrate diet and sleep status, depression, anxiety, and stress score. J. Sci. Food Agr. 100, 2946–2952 (2020).

    Article  CAS  Google Scholar 

  16. Lemmens, S. G., Born, J. M., Martens, E. A., Martens, M. J. & Westerterp-Plantenga, M. S. Influence of consumption of a high-protein vs. high-carbohydrate meal on the physiological cortisol and psychological mood response in men and women. PLoS ONE 6, e16826 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Lawlor, D. A., Harbord, R. M., Sterne, J. A. C., Timpson, N. & Smith, G. D. Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat. Med. 27, 1133–1163 (2008).

    Article  PubMed  Google Scholar 

  18. Davies, N. M., Holmes, M. V. & Davey Smith, G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. Brit. Med. J. 362, k601 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Meddens, S. F. W. et al. Genomic analysis of diet composition finds novel loci and associations with health and lifestyle. Mol. Psychiatry 26, 2056–2069 (2021).

    Article  CAS  PubMed  Google Scholar 

  20. Wray, N. R. et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat. Genet. 50, 668–681 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Merino, J. et al. Genetic analysis of dietary intake identifies new loci and functional links with metabolic traits. Nat. Hum. Behav. 6, 155–163 (2022).

    Article  PubMed  Google Scholar 

  22. Hartwig, F. P., Smith, G. D. & Bowden, J. Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption. Int. J. Epidemiol. 46, 1985–1998 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Artigas, F. Serotonin receptors involved in antidepressant effects. Pharmacol. Ther. 137, 119–131 (2013).

    Article  CAS  PubMed  Google Scholar 

  24. Leyton, M., Young, S. N. & Benkelfat, C. Relapse of depression after rapid depletion of tryptophan. Lancet 349, 1840–1841 (1997).

    Article  CAS  PubMed  Google Scholar 

  25. Jenkins, T. A., Nguyen, J. C. D., Polglaze, K. E. & Bertrand, P. P. Influence of tryptophan and serotonin on mood and cognition with a possible role of the gut–brain axis. Nutrients 8, 56 (2016).

    Article  PubMed Central  Google Scholar 

  26. Fernstrom, J. D. et al. The ingestion of different dietary proteins by humans induces large changes in the plasma tryptophan ratio, a predictor of brain tryptophan uptake and serotonin synthesis. Clin. Nutr. 32, 1073–1076 (2013).

    Article  CAS  PubMed  Google Scholar 

  27. Markus, C. R. Effects of carbohydrates on brain tryptophan availability and stress performance. Biol. Psychol. 76, 83–90 (2007).

    Article  CAS  PubMed  Google Scholar 

  28. Fernstrom, J. D. & Wurtman, R. J. Brain-serotonin content—physiological regulation by plasma neutral amino-acids. Science 178, 414–416 (1972).

    Article  CAS  PubMed  Google Scholar 

  29. Rondanelli, M., Opizzi, A., Faliva, M., Bucci, M. & Perna, S. Relationship between the absorption of 5-hydroxytryptophan from an integrated diet, by means of Griffonia simplicifolia extract, and the effect on satiety in overweight females after oral spray administration. Eat. Weight Disord. 17, E22–E28 (2012).

    CAS  PubMed  Google Scholar 

  30. Mikulska, J., Juszczyk, G., Gawronska-Grzywacz, M. & Herbet, M. HPA axis in the pathomechanism of depression and schizophrenia: new therapeutic strategies based on its participation. Brain Sci. 11, 1298 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Herrick, K. et al. Maternal consumption of a high-meat, low-carbohydrate diet in late pregnancy: relation to adult cortisol concentrations in the offspring. J. Clin. Endocrinol. Metab. 88, 3554–3560 (2003).

    Article  CAS  PubMed  Google Scholar 

  32. Anderson, K. E. et al. Diet–hormone interactions: protein/carbohydrate ratio alters reciprocally the plasma levels of testosterone and cortisol and their respective binding globulins in man. Life Sci. 40, 1761–1768 (1987).

    Article  CAS  PubMed  Google Scholar 

  33. Rodriguez, A. C. I. et al. Hypothalamic–pituitary–adrenal axis dysregulation and cortisol activity in obesity: a systematic review. Psychoneuroendocrinology 62, 301–318 (2015).

    Article  Google Scholar 

  34. Pedrini, S. et al. Dietary composition modulates brain mass and solubilizable Aβ levels in a mouse model of aggressive Alzheimer’s amyloid pathology. Mol. Neurodegener. 4, 40 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Wurtman, J. & Wurtman, R. The trajectory from mood to obesity. Curr. Obes. Rep. 7, 1–5 (2018).

    Article  PubMed  Google Scholar 

  36. Freuer, D., Meisinger, C. & Linseisen, J. Causal relationship between dietary macronutrient composition and anthropometric measures: a bidirectional two-sample Mendelian randomization analysis. Clin. Nutr. 40, 4120–4131 (2021).

    Article  CAS  PubMed  Google Scholar 

  37. Hartwig, F. P. et al. Body mass index and psychiatric disorders: a Mendelian randomization study. Sci. Rep. 6, 32730 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Qi, G. H. & Chatterjee, N. Mendelian randomization analysis using mixture models for robust and efficient estimation of causal effects. Nat. Commun. 10, 1941 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Tyrrell, J. et al. Using genetics to understand the causal influence of higher BMI on depression. Int. J. Epidemiol. 48, 834–848 (2019).

    Article  PubMed  Google Scholar 

  40. van den Broek, N. et al. Causal associations between body mass index and mental health: a Mendelian randomisation study. J. Epidemiol. Community Health 72, 708–710 (2018).

    Article  PubMed  Google Scholar 

  41. Casanova, F. et al. Higher adiposity and mental health: causal inference using Mendelian randomization. Hum. Mol. Genet. 30, 2371–2382 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Speed, M. S., Jefsen, O. H., Borglum, A. D., Speed, D. & Ostergaard, S. D. Investigating the association between body fat and depression via Mendelian randomization. Transl. Psychiatry 9, 184 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Zhang, M., Chen, J., Yin, Z., Wang, L. & Peng, L. The association between depression and metabolic syndrome and its components: a bidirectional two-sample Mendelian randomization study. Transl. Psychiatry 11, 633 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Pistis, G. et al. Obesity and atypical depression symptoms: findings from Mendelian randomization in two European cohorts. Transl. Psychiatry 11, 96 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  45. Hemani, G., Bowden, J. & Smith, G. D. Evaluating the potential role of pleiotropy in Mendelian randomization studies. Hum. Mol. Genet. 27, R195–R208 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Brumpton, B. et al. Avoiding dynastic, assortative mating, and population stratification biases in Mendelian randomization through within-family analyses. Nat. Commun. 11, 3519 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Didelez, V. & Sheehan, N. Mendelian randomization as an instrumental variable approach to causal inference. Stat. Methods Med. Res. 16, 309–330 (2007).

    Article  PubMed  Google Scholar 

  48. Angrist, J. D., Imbens, G. W. & Rubin, D. B. Identification of causal effects using instrumental variables. J. Am. Stat. Assoc. 91, 444–455 (1996).

    Article  Google Scholar 

  49. Willett, W. Nutritional Epidemiology 3rd edn (Oxford Univ. Press, 2013).

  50. Burgess, S., Davies, N. M. & Thompson, S. G. Bias due to participant overlap in two-sample Mendelian randomization. Genet. Epidemiol. 40, 597–608 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Hoffmann, T. J. et al. A large multiethnic genome-wide association study of adult body mass index identifies novel loci. Genetics 210, 499–515 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Shungin, D. et al. New genetic loci link adipose and insulin biology to body fat distribution. Nature 518, 187–196 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Lu, Y. et al. New loci for body fat percentage reveal link between adiposity and cardiometabolic disease risk. Nat. Commun. 7, 10495 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Staley, J. R. et al. PhenoScanner: a database of human genotype–phenotype associations. Bioinformatics 32, 3207–3209 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Kamat, M. A. et al. PhenoScanner V2: an expanded tool for searching human genotype–phenotype associations. Bioinformatics 35, 4851–4853 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Kim, H. et al. Associations between smoking, alcohol consumption, physical activity and depression in middle-aged premenopausal and postmenopausal women. Front. Psychiatry 12, 761761 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  58. Chrzastek, Z. et al. Association of lower nutritional status and education level with the severity of depression symptoms in older adults—a cross sectional survey. Nutrients 13, 515 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  59. Bowden, J. et al. Improving the visualization, interpretation and analysis of two-sample summary data Mendelian randomization via the Radial plot and Radial regression. Int. J. Epidemiol. 47, 1264–1278 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  60. Burgess, S., Thompson, S. G. & CRP CHD Genetics Collaboration Avoiding bias from weak instruments in Mendelian randomization studies. Int. J. Epidemiol. 40, 755–764 (2011).

    Article  PubMed  Google Scholar 

  61. Palmer, T. M. et al. Using multiple genetic variants as instrumental variables for modifiable risk factors. Stat. Methods Med. Res. 21, 223–242 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Burgess, S. Sample size and power calculations in Mendelian randomization with a single instrumental variable and a binary outcome. Int. J. Epidemiol. 43, 922–929 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  63. Burgess, S. & Thompson, S. G. Mendelian Randomization: Methods for Using Genetic Variants in Causal Estimation (CRC, 2015).

  64. Palmer, T. M. et al. Instrumental variable estimation of causal risk ratios and causal odds ratios in Mendelian randomization analyses. Am. J. Epidemiol. 173, 1392–1403 (2011).

    Article  PubMed  Google Scholar 

  65. Burgess, S., Butterworth, A. & Thompson, S. G. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet. Epidemiol. 37, 658–665 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  66. Bowden, J. et al. A framework for the investigation of pleiotropy in two-sample summary data Mendelian randomization. Stat. Med. 36, 1783–1802 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  67. Bowden, J., Davey Smith, G. & Burgess, S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int. J. Epidemiol. 44, 512–525 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  68. Bowden, J., Davey Smith, G., Haycock, P. C. & Burgess, S. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genet. Epidemiol. 40, 304–314 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  69. Zhao, Q. Y., Wang, J. S., Hemani, G., Bowden, J. & Small, D. S. Statistical inference in two-sample summary-data mendelian randomization using robust adjusted profile score. Ann. Stat. 48, 1742–1769 (2020).

    Article  Google Scholar 

  70. Verbanck, M., Chen, C. Y., Neale, B. & Do, R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat. Genet. 50, 693–698 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Hemani, G., Tilling, K. & Smith, G. D. Orienting the causal relationship between imprecisely measured traits using GWAS summary data. PLoS Genet. 13, e1007081 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  72. Sanderson, E., Davey Smith, G., Windmeijer, F. & Bowden, J. An examination of multivariable Mendelian randomization in the single-sample and two-sample summary data settings. Int. J. Epidemiol. 48, 713–727 (2019).

    Article  PubMed  Google Scholar 

  73. Burgess, S. & Thompson, S. G. Multivariable Mendelian randomization: the use of pleiotropic genetic variants to estimate causal effects. Am. J. Epidemiol. 181, 251–260 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  74. Rees, J. M. B., Wood, A. M. & Burgess, S. Extending the MR-Egger method for multivariable Mendelian randomization to correct for both measured and unmeasured pleiotropy. Stat. Med. 36, 4705–4718 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  75. VanderWeele, T. J. Mediation analysis: a practitioner’s guide. Annu. Rev. Public Health 37, 17–32 (2016).

    Article  PubMed  Google Scholar 

  76. Carter, A. R. et al. Understanding the consequences of education inequality on cardiovascular disease: Mendelian randomisation study. Brit. Med. J. 365, l1855 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This study was supported by the National Natural Science Foundation of China (grant nos 82101601 (S.Y.), 32170616 (T.-L.Y.) and 82170896 (Y.G.)), the Natural Science Basic Research Program of Shaanxi Province (grant no. 2021JC-02 (T.-L.Y.)), Innovation Capability Support Program of Shaanxi Province (grant no. 2022TD-44 (T.-L.Y.)), China Postdoctoral Science Foundation (grant nos 2021M702612 (S.Y.), 2020M683454 (S.-S.D.) and 2021T140546 (S.-S.D.)) and the Fundamental Research Funds for the Central Universities. This study was also supported by the High-Performance Computing Platform of Xi’an Jiaotong University. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Author notes

  1. These authors contributed equally: Shi Yao, Meng Zhang, Shan-Shan Dong.

Authors and Affiliations

  1. National and Local Joint Engineering Research Center of Biodiagnosis and Biotherapy, the Second Affiliated Hospital, Xi’an Jiaotong University, Xi’an, P. R. China

    Shi Yao & Tie-Lin Yang

  2. Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, P. R. China

    Meng Zhang, Shan-Shan Dong, Jia-Hao Wang, Kun Zhang, Jing Guo, Yan Guo & Tie-Lin Yang

Authors
  1. Shi Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Meng Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shan-Shan Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jia-Hao Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Kun Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jing Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yan Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Tie-Lin Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

S.Y. and T.-L.Y. designed the study. Y.G. and T.-L.Y. supervised the study. M.Z. participated in the data collection. S.Y., M.Z., J.-H.W., K.Z. and J.G. performed the data analyses. S.Y. and M.Z. prepared the tables and figures. S.Y. wrote the paper. S.-S.D. and Y.G. critically revised the content. All authors contributed to editing the paper.

Corresponding authors

Correspondence to Yan Guo or Tie-Lin Yang.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Human Behaviour thanks Julien Vaucher and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional information

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

Supplementary information

Supplementary Information

Supplementary Tables 1–19 and Figs. 1–5.

Reporting Summary

Peer Review File

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yao, S., Zhang, M., Dong, SS. et al. Bidirectional two-sample Mendelian randomization analysis identifies causal associations between relative carbohydrate intake and depression. Nat Hum Behav 6, 1569–1576 (2022). https://doi.org/10.1038/s41562-022-01412-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41562-022-01412-9

This article is cited by

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