Summary: Those with a genetic predisposition to clinical depression are more likely to exhibit physical symptoms including migraines, chronic pain, and fatigue, researchers report.
Source:: University of Queensland
People with higher genetic risk of clinical depression are more likely to have physical symptoms such as chronic pain, fatigue and migraine, University of Queensland researchers have found.
Dr. Enda Byrne conducted the research at UQ’s Institute for Molecular Bioscience, finding that depression was a serious disorder with lifetime risks of poor health.
“A large proportion of people with clinically-diagnosed depression present initially to doctors with physical symptoms that cause distress and can severely impact on people’s quality of life,” he said.
“Our research aimed to better understand the biological basis of depression and found that assessing a broad range of symptoms was important,” Dr. Byrne said.
“Ultimately, our research aimed to better understand the genetic risks and generate more accurate risk scores for use in research and healthcare.”
Despite recent breakthroughs, Dr. Byrne said finding additional genetic risk factors was difficult because of the variety of patient ages, their symptoms, responses to treatment and additional mental and physical disorders.
“Previous genetic studies have included participants who report having seen a doctor for worries or tension—but who may not meet the ‘official’ criteria for a diagnosis of depression,” Dr. Byrne said
In collaboration with QIMR Berghofer Medical Research Institute, his team analyzed data from more than 15,000 volunteers who provided details of their mental health history, symptoms of depression and a DNA sample using a saliva kit.
“We wanted to see how genetic risk factors based on clinical definitions of depression differed—from those based on a single question to those based on a doctor’s consultation about mental health problems,” Dr. Byrne said.
The research found that participants with higher genetic risk for clinical depression are more likely to experience physical symptoms such as chronic pain, fatigue and migraine.
“It is also linked to higher rates of somatic symptoms—that is, physical symptoms that cause distress and can severely impact on people’s quality of life,” Dr. Byrne said.
“Our results highlight the need for larger studies investigating the broad range of symptoms experienced by people with depression.”
Polygenic Risk Scores Derived From Varying Definitions of Depression and Risk of Depression
Genetic studies with broad definitions of depression may not capture genetic risk specific to major depressive disorder (MDD), raising questions about how depression should be operationalized in future genetic studies.
To use a large, well-phenotyped single study of MDD to investigate how different definitions of depression used in genetic studies are associated with estimation of MDD and phenotypes of MDD, using polygenic risk scores (PRSs).
Design, Setting, and Participants
In this case-control polygenic risk score analysis, patients meeting diagnostic criteria for a diagnosis of MDD were drawn from the Australian Genetics of Depression Study, a cross-sectional, population-based study of depression, and controls and patients with self-reported depression were drawn from QSkin, a population-based cohort study. Data analyzed herein were collected before September 2018, and data analysis was conducted from September 10, 2020, to January 27, 2021.
Main Outcome and Measures
Polygenic risk scores generated from genome-wide association studies using different definitions of depression were evaluated for estimation of MDD in and within individuals with MDD for an association with age at onset, adverse childhood experiences, comorbid psychiatric and somatic disorders, and current physical and mental health.
Participants included 12 106 (71% female; mean age, 42.3 years; range, 18-88 years) patients meeting criteria for MDD and 12 621 (55% female; mean age, 60.9 years; range, 43-87 years) control participants with no history of psychiatric disorders. The effect size of the PRS was proportional to the discovery sample size, with the largest study having the largest effect size with the odds ratio for MDD (1.75; 95% CI, 1.73-1.77) per SD of PRS and the PRS derived from ICD-10 codes documented in hospitalization records in a population health cohort having the lowest odds ratio (1.14; 95% CI, 1.12-1.16). When accounting for differences in sample size, the PRS from a genome-wide association study of patients meeting diagnostic criteria for MDD and control participants was the best estimator of MDD, but not in those with self-reported depression, and associations with higher odds ratios with childhood adverse experiences and measures of somatic distress.
Conclusions and Relevance
These findings suggest that increasing sample sizes, regardless of the depth of phenotyping, may be most informative for estimating risk of depression. The next generation of genome-wide association studies should, like the Australian Genetics of Depression Study, have both large sample sizes and extensive phenotyping to capture genetic risk factors for MDD not identified by other definitions of depression.