Skip to main content
Full access
Editorials
Published Online: 1 November 2022

Harnessing the Power of Population Cohorts to Study the Relationship Between Endocrine-Metabolic Disorders and Depression

Publication: American Journal of Psychiatry
Being diagnosed with an endocrine-metabolic disorder such as type 1 diabetes, type 2 diabetes, polycystic ovary syndrome (PCOS), or obesity is known to increase the risk of a subsequent diagnosis of major depressive disorder (hereafter referred to as depression), and vice versa (1, 2). However, the causal and functional relationship between endocrine-metabolic disorders and depression is still poorly understood, and the studies to date have at times reached seemingly contradictory conclusions. For instance, recent causal inference studies have suggested that type 2 diabetes and obesity can lead to a subsequent depression diagnosis (3, 4), whereas another study found higher (relative) carbohydrate intake to reduce depression risk in a causal manner (5). Furthermore, the gut microbiome, shown to be influenced by the host’s genetics (6), may be associated with both diabetes and depression (7, 8). Taken together, the literature depicts a remarkably complex relationship between endocrine-metabolic disorders and depression, with various potential pathways of bidirectional correlations (see Figure 1).
FIGURE 1. Simplified overview of several potential causal relationships driving comorbidity between depression and endocrine-metabolic disorders
To help solve this important puzzle, Leone et al., reporting in this issue of the Journal (9), utilize a population cohort of 2.2 million individuals born in Sweden and their first-degree relatives to characterize the genetic and environmental relationship between endocrine-metabolic disorders and depression. The authors performed two types of analyses. First, they examined the association between endocrine-metabolic disorders and depression in case subjects and the extent to which these conditions coaggregated in family members, who, to some degree, share both genetics and environment. Despite the low observed frequency of some of the endocrine-metabolic conditions and their co-occurrences, the relatively large sample size in the Swedish registers allowed for the detection of significant positive associations between all endocrine-metabolic disorders and depression in case subjects as well as in their family members. Second, the authors performed a quantitative analysis using the siblings to estimate (additive) heritabilities, as well as genetic and environmental correlations between endocrine-metabolic disorders and depression. The authors found genetic correlations for the sibling pairs (see the article’s supplementary Table S12) to be 0.31 between type 2 diabetes and depression, 0.21 between obesity and depression, and 0.34 between PCOS and depression. However, the estimated genetic correlation between autoimmune disorders and depression was smaller, with 0.13 for hypothyroidism and approximately 0 for type 1 diabetes. Interestingly, shared environmental effects for siblings were small, suggesting that the parental environment does not play a large role in the observed associations. The large sample size of the quantitative genetic analysis (more than 778,000 sibling pairs) allowed Leone et al. to estimate the genetic overlap with more accuracy than previous studies, clearly demonstrating a positive association between endocrine-metabolic disorders and depression that is due to both genetics and the environment. These results are consistent with recent findings (10, 11) and bring clarity to a previously unclear picture of how these serious conditions with notable population prevalence relate to each other.
Recent efforts toward erasing the boundaries between psychiatric and somatic disorders have highlighted the inaccuracy of the dichotomous classification of the former as solely falling under the domain of the brain and the nervous system. One such example is the negative genetic correlations reported for anorexia nervosa with metabolic (e.g., type 2 diabetes, insulin resistance, circulating leptin levels) and anthropometric measures (e.g., body mass index, overweight, obesity), reestablishing this deadly eating disorder as a metabo-psychiatric disorder as opposed to one having a purely psychiatric origin (12). Considering our improved understanding of the complex interplay between monoamines (13), cytokines, and the microbiome (14) influencing the risk of depression, Leone and colleagues’ findings also point toward the need for a more comprehensive evaluation of the pathophysiology of depression. Importantly, this study is a crucial first step toward setting the path for future work establishing a causal relationship between endocrine-metabolic disorders and depression. Although making a causal inference, especially in a retrospective cohort study, is very difficult, individual medical trajectories could provide important clues, such as the temporal relationship between two diagnoses (i.e., the order of diagnosis). With the goal of identifying modifiable causal risk factors that could be targeted in public health efforts and preventive treatments, understanding the causal relationship between comorbid conditions such as endocrine-metabolic disorders and depression is critical.

Population Cohorts Facilitate Accurate and Unbiased Risk Estimates

The Leone et al. study is a reminder of how powerful population register data sets are for studying disease etiologies. For more than half a century, the Nordic countries (Denmark, Finland, Iceland, Norway, and Sweden) have collected individual-level data, including medical, social, financial, and geographical information, and incorporated them into their respective population registers. These registers have become and invaluable resource for medical and public health research, as well as demographic and economic research. The sample sizes available in these population-wide registers allow inferences to be made with unprecedented certainty and impressive detail. The broad inclusion of individuals in registers reduces ascertainment biases, which translates to more accurate population risk estimates, otherwise difficult to obtain. These data have proven to be incredibly valuable for identifying risk factors and understanding medication response and long-term side effects, as well as studying demography and health economics. Indeed, register-based research has often influenced public health policies directly.

Genetic Data Increase the Value of Population Cohorts for All Researchers

A major benefit of the population registers is that once new individual-level information is added to the registers, it can be linked with any other register information available in the cohort, thereby enriching the entire data set. With genetics playing an important role in many diseases and health outcomes, adding individual-level genotypes to population register data offers new research opportunities. Future efforts could expand on the work by Leone et al. by incorporating methods such as Mendelian randomization (15) through the utilization of individual-level genetic data to elucidate the causal mechanism behind the comorbidity of endocrine-metabolic disorders and depression, as well as estimating individual variants associated with the outcome of interest. Summary statistics from external genome-wide association studies (GWASs) could also be used to examine genetic relationships with thousands of other relevant health outcomes. In addition, the family information available in the registers could be used to increase power in GWASs (16). Moreover, individual-level genotypes can be used to study gene-by-environment interactions, as well as to account for population structure and other potential confounders in epidemiological studies. These examples highlight the synergies of making more data available in population registers.

More Register-Based Research Helps the Field Move Forward

There is no doubt that the population registers of the Nordic and other countries are a rich and comprehensive source of data; however, they are also distinct and subject to differing national regulations. The information collected also varies, as well as how it is structured in different registers. Therefore, to better actualize the benefits of these population registers, there is a clear need to standardize the data format across countries. The widespread use of ICD-10 codes goes a long way, but agreeing on standardization protocols for the data in general would significantly improve coordination efforts across Nordic countries. Such protocols would encourage international collaborations by, for instance, enabling cross-country validation of research findings, as well as enabling more conclusive meta-analyses.
Finally, perhaps the most important limitation of register-based research is data access, which is often complicated, even for experienced researchers. The UK Biobank project is an example of providing broad access to external researchers, which, as a result, established the UK Biobank as the single most studied genetic data set, with over 28,000 registered researchers from 98 countries, and numerous examples of findings directly impacting clinical care. Inspired by the UK Biobank, we envision that providing secure access for external researchers to population register data could similarly boost efforts to harness the full research potential of population registers. These initiatives should of course safeguard individual privacy, adhere to the European Union’s General Data Protection Regulation and other relevant regulations, and take geopolitical concerns into account. Advances in the sharing of population register data would improve our understanding of both the etiology and the comorbidity of diseases and consequently benefit not only the population from which the data originated, but society as a whole.

References

1.
Milaneschi Y, Simmons WK, van Rossum EFC, et al: Depression and obesity: evidence of shared biological mechanisms. Mol Psychiatry 2019; 24:18–33
2.
Luppino FS, de Wit LM, Bouvy PF, et al: Overweight, obesity, and depression: a systematic review and meta-analysis of longitudinal studies. Arch Gen Psychiatry 2010; 67:220–229
3.
Speed MS, Jefsen OH, Børglum AD, et al: Investigating the association between body fat and depression via Mendelian randomization. Transl Psychiatry 2019; 9:184
4.
Bergmans RS, Rapp A, Kelly KM, et al: Understanding the relationship between type 2 diabetes and depression: lessons from genetically informative study designs. Diabet Med 2021; 38:e14399
5.
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 (Online ahead of print July 18, 2022)
6.
Lopera-Maya EA, Kurilshikov A, van der Graaf A, et al: Effect of host genetics on the gut microbiome in 7,738 participants of the Dutch Microbiome Project. Nat Genet 2022; 54:143–151
7.
Gurung M, Li Z, You H, et al: Role of gut microbiota in type 2 diabetes pathophysiology. EBioMedicine 2020; 51:102590
8.
Qin Y, Havulinna AS, Liu Y, et al: Combined effects of host genetics and diet on human gut microbiota and incident disease in a single population cohort. Nat Genet 2022; 54:134–142
9.
Leone M, Kuja-Halkola R, Leval A, et al: Genetic and environmental contribution to the co-occurrence of endocrine-metabolic disorders and depression: a nationwide Swedish study of siblings. Am J Psychiatry 2022; 179:824–832
10.
Hagenaars SP, Coleman JRI, Choi SW, et al: Genetic comorbidity between major depression and cardio-metabolic traits, stratified by age at onset of major depression. Am J Med Genet B Neuropsychiatr Genet 2020; 183:309–330
11.
Torgersen K, Rahman Z, Bahrami S, et al: Shared genetic loci between depression and cardiometabolic traits. Plos Genet 2022; 18:e1010161
12.
Watson HJ, Yilmaz Z, Thornton LM, et al: Genome-wide association study identifies eight risk loci and implicates metabo-psychiatric origins for anorexia nervosa. Nat Genet 2019; 51:1207–1214
13.
Tafet GE, Bernardini R: Psychoneuroendocrinological links between chronic stress and depression. Prog Neuropsychopharmacol Biol Psychiatry 2003; 27:893–903
14.
Winter G, Hart RA, Charlesworth RPG, et al: Gut microbiome and depression: what we know and what we need to know. Rev Neurosci 2018; 29:629–643
15.
Sanderson E, Glymour MM, Holmes MV, et al: Mendelian randomization. Nat Rev Methods Primers 2022; 2:1–21
16.
Pedersen EM, Agerbo E, Plana-Ripoll O, et al: Accounting for age of onset and family history improves power in genome-wide association studies. Am J Hum Genet 2022; 109:417–432

Information & Authors

Information

Published In

Go to American Journal of Psychiatry
Go to American Journal of Psychiatry
American Journal of Psychiatry
Pages: 788 - 790

History

Accepted: 21 September 2022
Published online: 1 November 2022
Published in print: November 01, 2022

Keywords

  1. Depressive Disorders
  2. Neuroendocrinology
  3. Metabolism

Authors

Details

Anders Jespersen, Ph.D. [email protected]
National Center for Register-Based Research (Jespersen, Yilmaz, Vilhjálmsson), Department of Biomedicine (Yilmaz), and Bioinformatics Research Center (Vilhjálmsson), Aarhus University, Aarhus, Denmark; Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm (Yilmaz).
Zeynep Yilmaz, Ph.D.
National Center for Register-Based Research (Jespersen, Yilmaz, Vilhjálmsson), Department of Biomedicine (Yilmaz), and Bioinformatics Research Center (Vilhjálmsson), Aarhus University, Aarhus, Denmark; Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm (Yilmaz).
Bjarni J. Vilhjálmsson, Ph.D. [email protected]
National Center for Register-Based Research (Jespersen, Yilmaz, Vilhjálmsson), Department of Biomedicine (Yilmaz), and Bioinformatics Research Center (Vilhjálmsson), Aarhus University, Aarhus, Denmark; Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm (Yilmaz).

Notes

Send correspondence to Dr. Jespersen ([email protected]) and Dr. Vilhjálmsson ([email protected]).

Competing Interests

Dr. Vilhjálmsson is a member of the scientific advisory board for Allelica. The other authors report no financial relationships with commercial interests.

Metrics & Citations

Metrics

Citations

Export Citations

If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.

For more information or tips please see 'Downloading to a citation manager' in the Help menu.

Format
Citation style
Style
Copy to clipboard

View Options

View options

PDF/EPUB

View PDF/EPUB

Get Access

Login options

Already a subscriber? Access your subscription through your login credentials or your institution for full access to this article.

Personal login Institutional Login Open Athens login

Not a subscriber?

Subscribe Now / Learn More

PsychiatryOnline subscription options offer access to the DSM-5-TR® library, books, journals, CME, and patient resources. This all-in-one virtual library provides psychiatrists and mental health professionals with key resources for diagnosis, treatment, research, and professional development.

Need more help? PsychiatryOnline Customer Service may be reached by emailing [email protected] or by calling 800-368-5777 (in the U.S.) or 703-907-7322 (outside the U.S.).

Media

Figures

Other

Tables

Share

Share

Share article link

Share