Understanding the relationship between major depressive disorder (MDD) and other medical conditions is critical for developing biomarkers that aid in the diagnosis, treatment, and prevention of MDD. Candidate biomarkers for depression have expanded beyond monoamine targets and now include pathways across the entire body.1 Particular attention, however, has focused on biomarkers related to metabolic, cardiovascular, neurologic, and immunologic processes, although many large clinical trials in depression contributing to this knowledge base exclude patients with significant medical comorbidity. These candidate biomarkers include laboratory tests that are already readily available like high-sensitivity C-reactive protein and hemoglobin A1c, as well as molecular and imaging markers that are not yet used at all in clinical care. It is also increasingly recognized that diseases related to these systems are disproportionately prevalent in patients with MDD. Together, these observations raise two important questions. First, which medical conditions are more prevalent in patients with depression? Second, when comorbidity exists, does one disease cause the other, or are there common risk factors? Biomarkers for both depression and these medical comorbidities across the trajectory of the disease courses are necessary to elucidate answers to these questions.
Epidemiology of Medical Comorbidity With MDD
In a longitudinal analysis from the Canadian National Population Health Survey, the presence of any long-term medical condition was associated with nearly double the risk of developing a depressive episode at 2-year follow-up.2 Similar results have been found in specific disorders, such as diabetes, myocardial infarcts, cerebrovascular disease, HIV, cancer, and multiple sclerosis.3 Other studies have examined this association in the opposite direction—the onset of medical conditions in patients with MDD. For example, patients with a history of depression had about twice the risk of developing obesity.4
These relationships become more complex when one considers how much the presentation of the same illness can vary between patients. Depression is a heterogenous syndrome, as are the most common medical comorbidities. The Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-5),5 requires 5 of 9 possible symptoms to make a diagnosis of MDD; for any two patients, the symptom overlap may be minimal. Additionally, because symptoms like sleep and appetite can be either a deficit or excess, overlap may be nonexistent. These criteria only address core symptoms of depression, whereas many measures of depression like the Inventory of Depressive Symptomatology assess 30 different items.6 This includes several somatic symptoms, such as gastrointestinal discomfort and libido. In addition to the presence of symptoms, depression can also vary based on intensity, duration of illness, extent of disability and treatment resistance. Finally, the DSM-5 defines several depressive disorders, including bipolar disorders, persistent depressive disorder, substance/medication-induced depressive disorder, depressive disorder due to another medical condition, other specified depressive disorder, unspecified depressive disorder, and adjustment disorder with depressed mood.5
Depressive Disorder Due to Medical Conditions or Treatments
Because medical conditions and medical treatments can initiate symptoms of depression, it can be challenging to determine where best to attribute individual symptoms. The DSM-5 distinguishes MDD from depressive disorder due to another medical condition when the onset of depressive symptoms is best explained by a medical illness.5 A detailed symptom history coupled with appropriate laboratory testing is necessary to make this diagnosis. Onset of depressive symptoms should follow the onset of the medical problem and respond well to medical treatment. In some cases, however, a medical condition may be contributing to the severity of an otherwise independent depressive episode, or symptoms may not fully resolve with medical treatment. In these cases, true comorbidity should be considered rather than a medically induced depressive disorder.
Anemia, sleep apnea, and thyroid dysfunction are among the most common medical conditions that directly cause depressive symptoms.7 Like MDD, hypothyroidism alone can present with decreased energy, anhedonia, and sleep disturbance. Hyperthyroidism can also present with sleep disturbance but is more so associated with more anxious and/or irritable mood. Anemia can also present with symptoms of depression, notably fatigue. These conditions all have readily available laboratory tests (thyroid-stimulating hormone, hemoglobin) that can act as biomarkers for the medical condition; although they may correlate with depressive symptoms, they are not necessarily concordant. Additionally, it remains unclear how to incorporate these tests alongside other depression biomarkers. Hemoglobin may be problematic with functional magnetic resonance imaging because this test relies on blood flow. Finally, sleep apnea is a medical cause of depressive symptoms. Hypoxic events during sleep disrupt normal sleep architecture, which can result in debilitating fatigue. This fatigue, much like with anemia and hypothyroidism, can lead to other depressive symptoms, such as decreased interest in pleasurable activities and excessive guilt related to functional impairment from fatigue.
Treatments for certain medical problems have also been associated with onset or worsening of depressive symptoms.5 Depressive symptoms are a well-described side effect of interferon therapy.8 Fortunately, newer therapies for hepatitis C and multiple sclerosis have largely, but not completely replaced the use of interferon. Corticosteroid use is also associated with changes in mood. Acute treatment is associated with manic symptoms, whereas withdrawal from acute corticosteroid treatment is associated with onset of depressive symptoms. Chronic corticosteroid use on the other hand is primarily associated with depressive symptoms.9 According to DSM-5, depressive symptoms that are best explained by a medical treatment are categorized as a substance/medication-induced depressive disorder, rather than MDD.
Trajectory of Depression and Chronic Medical Conditions
It is much more difficult to tease out the relationship between other medical conditions, particularly chronic illnesses, with depression. Conditions like type 2 diabetes are framed as gradually controlled over time rather than cured,10 and hence linking improvements with depressive symptoms is challenging. As mentioned previously, most of these conditions have some objective biologic marker of current disease state (eg, hemoglobin A1c for diabetes) but factors like severity, duration, disability, and treatment resistance are not necessarily captured in these markers. Hence biomarkers for these conditions do not always provide the complete history of disease burden. A patient with type 2 diabetes may currently have a well-controlled hemoglobin A1c, but still suffer from grave diabetic neuropathy due to years of poorly controlled glucose. Additionally, acute events like stroke may have very specific anatomical lesions that vary among patients. Two patients may have a similar amount of damaged tissue, but depressive sequalae may vary depending on the brain circuits that are affected.11
So, when considering the impact of a medical illness on depression, time course of disease can be relevant. Conditions like myocardial infarcts or cerebrovascular events can have rapid and intense impacts on human physiology with prolonged sequelae. In contrast, diabetes or hypertension have more insidious onset with culminative worsening over time. Finally, conditions like multiple sclerosis or asthma can manifest with relapsing and remitting patterns. On top of this, depression itself can manifest in all these patterns.12 Major life stressors can trigger the onset of major depressive episodes, but for many, onset is gradual and persistent. For most patients, however, depressive symptoms have clear episodic worsening and improvement. Connecting these variable patterns of depressive illness with the heterogenous course of medical illness makes interpreting cross-sectional biomarkers exceptionally challenging.
The significance of time course depends on the type of biomarker. Genetic markers like single nucleotide polymorphisms (SNPs) are stable across the lifespan; therefore, their assay is not dependent on when in the disease course they are analyzed.13 Thus, they can help define disease risk, but offer little insight into predicting disease burden at a given moment. Furthermore, they do not incorporate environmental factors that can influence disease course, such as early life trauma. In contrast, cytokines and metabolomic markers are much more fluid and state dependent.1 Unlike SNPs, they are influenced by environmental factors like chronic stress. These markers are well suited for clinical use, as they can change over time and inform clinicians about disease progression and improvement. Both types of markers may be useful tools for predicting treatment response, but none are currently validated for use in clinical care. SNP markers are already commercially available as predictors of antidepressant metabolism, but their utility remains debated.14
To date there are no single genetic markers that are sensitive or specific for predicting onset of depression. Instead, there are numerous markers that are enriched in depressed populations.15 Interestingly, some of these are independently associated with other medical conditions. Replication of SNP findings itself is challenging but can be further complicated when looking at patients with specific medical comorbidity. For example, several SNPs previously associated with depression were analyzed in a large cohort of patients with type 2 diabetes.16 Three of these a priori SNPs were enriched with depressive symptoms, but in the opposite direction as previously described.
Shared Pathophysiology and Mechanisms
Although there is a bidirectional epidemiologic relationship between medical illnesses and depression, causality remains unclear. That is, does depression lead to other conditions, do these conditions lead to depression, or are there common risk factors?
Insulin resistance and obesity are associated with each other but also independently with depression. Much of this work has focused on dysregulation of the hypothalamic pituitary axis, including its role as a mediator between early life adversity, depressive symptoms, and metabolic disease.17 Chronic hyperactivity of this axis leads to cortisol resistance and increased inflammatory cytokines signaling that negatively impact both insulin resistance and depressive symptoms. In addition, molecular pathways first recognized in diabetes are being studied in the context of depression, and cognitive dysfunction related to depression is increasingly recognized as a sequalae of diabetes. Recent work has implicated both sphingolipids and adipokines as relevant to both depressive symptoms18 and treatment response.19 Selective attention to negative stimuli is a common cognitive symptom of MDD.2,20 Interestingly, both body mass index and waist circumference have also been negatively associated with accuracy and processing time on selective attention tasks.21,22
Serotonin signaling may explain some of the overlap between depression and cardiovascular disease due to the role of serotonin in platelet activation.23 In a study that compared to both healthy controls and people who are not depressed with ischemic heart disease, people who are depressed had higher levels of platelet factor 4 and beta-thromboglobulin, suggesting increased platelet activation.24 Additionally, polymorphisms in the serotonin transporter gene have also been associated with additional cardiac events after an acute myocardial infarction.25
Mechanisms of depression associated with neurologic conditions may be more direct. This includes direct injury from stroke that impairs monoamine signaling.26 Other pathways may be relevant too, especially given the emerging role of glutamate in depression.27 In support of this, glutamate-mediated excitotoxicity after stroke has been associated with onset of depressive symptoms.28 Finally, the neuroinflammatory response after stroke,29 but also in other neurologic conditions, such as multiple sclerosis,30 may mediate shared pathophysiology. In addition to direct neuroinflammatory mechanisms, peripheral inflammation has been extensively studied with depression. Because of its depth, this topic is covered in a companion piece to this article.31 Potentially, biomarkers that establish excitotoxicity versus inflammatory pathophysiology could predict whether a patient would benefit more from a glutamate-based therapy or treatment that targets inflammation.
Finally, it is also important to note the psychosocial impact of medical conditions. For example, a phenomenon called “diabetes distress” has been associated with the onset of type 2 diabetes. This includes stress related to taking daily medication, tracking blood glucose, and worry over the long-term impact on a patient's health.32 Furthermore, functional impact can occur related to disability from a medical condition. Diabetic wounds, or worse, amputations, can have a profound impact on a patient's mobility and behavior. For patients with a baseline high level of activity, medical disability can cause a dramatic swing in their activity level that results in significant physiologic and psychologic changes. This complete history may be relevant to care but may not be captured using a cross-sectional biomarker.
The complex role of medical health in depression remains a major hurdle for the development of biomarkers to personalize depression management and prevention. In addition to directly impacting brain physiology, it is increasingly clear that there are systemic contributions to MDD. The immense complexity of these contributions presents many challenges, but also exciting opportunities for biomarker discovery. In the search for clinically deployable biomarkers, peripheral markers are simply more accessible than brained-based biomarkers and most readily available for integration into clinical care. To date, most of the work in this field is limited to cross-sectional assays of individual markers that do not capture the full patient medical and psychiatric history. Longitudinal biomarker studies that comprehensively capture the diversity of both depressive symptoms and medical illness, such as Texas Resilience Against Depression,33 are needed.
- Gadad BS, Jha MK, Czysz A, et al. Peripheral biomarkers of major depression and antidepressant treatment response: current knowledge and future outlooks. J Affect Disord. 2018;233:3–14. doi:10.1016/j.jad.2017.07.001 [CrossRef]. PMID:28709695
- Patten SB. Long-term medical conditions and major depression in a Canadian population study at waves 1 and 2. J Affect Disord. 2001;63(1–3):35–41. doi:10.1016/S0165-0327(00)00186-5 [CrossRef] PMID:11246078
- Katon WJ. Clinical and health services relationships between major depression, depressive symptoms, and general medical illness. Biol Psychiatry. 2003;54(3):216–226. doi:10.1016/S0006-3223(03)00273-7 [CrossRef] PMID:12893098
- 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(3):220–229. doi:10.1001/archgenpsychiatry.2010.2 [CrossRef] PMID:20194822
- American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders. 5th ed. Arlington, VA: American Psychiatric Publishing; 2013.
- Rush AJ, Gullion CM, Basco MR, Jarrett RB, Trivedi MH. The Inventory of Depressive Symptomatology (IDS): psychometric properties. Psychol Med. 1996;26(3):477–486. doi:10.1017/S0033291700035558 [CrossRef] PMID:8733206
- Cosci F, Fava GA, Sonino N. Mood and anxiety disorders as early manifestations of medical illness: a systematic review. Psychother Psychosom. 2015;84(1):22–29. doi:10.1159/000367913 [CrossRef] PMID:25547421
- Loftis JM, Hauser P. The phenomenology and treatment of interferon-induced depression. J Affect Disord. 2004;82(2):175–190. doi:10.1016/j.jad.2004.04.002 [CrossRef]
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- Buse JB, Caprio S, Cefalu WT, et al. How do we define cure of diabetes?Diabetes Care. 2009;32(11):2133–2135. doi:10.2337/dc09-9036 [CrossRef] PMID:19875608
- Whyte EM, Mulsant BH. Post stroke depression: epidemiology, pathophysiology, and biological treatment. Biol Psychiatry. 2002;52(3):253–264. doi:10.1016/S0006-3223(02)01424-5 [CrossRef] PMID:12182931
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- Rosenblat JD, Lee Y, McIntyre RS. Does pharmacogenomic testing improve clinical outcomes for major depressive disorder? a systematic review of clinical trials and cost-effectiveness studies. J Clin Psychiatry. 2017;78(6):720–729. doi:10.4088/JCP.15r10583 [CrossRef] PMID:28068459
- Lubke GH, Hottenga JJ, Walters R, et al. Estimating the genetic variance of major depressive disorder due to all single nucleotide polymorphisms. Biol Psychiatry. 2012;72(8):707–709. doi:10.1016/j.biopsych.2012.03.011 [CrossRef] PMID:22520966
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- Toups MS, Trivedi MH. Role of metabolic dysfunction in treatment resistance of major depressive disorder. Neuropsychiatry. 2011;1(5):441–455. Accessed May 14, 2020. http://www.jneuropsychiatry.org/peer-review/role-of-metabolic-dysfunction-in-treatment-resistance-of-major-depressive-disorder.pdf
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- Czysz AH, South C, Gadad BS, et al. Can targeted metabolomics predict depression recovery? Results from the CO-MED trial. Transl Psychiatry. 2019;9(1):11. doi:10.1038/s41398-018-0349-6 [CrossRef] PMID:30664617
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