Metabolomic profiles discriminating anxiety from depression

Probiotics may be useful for drug-induced liver dysfunction in patients with depression - A secondary analysis of a randomized clinical trial

BACKGROUND & AIMS

There is a need to identify new treatment options for depression with its comorbidities. Depression often coexists with liver steatosis and the two may share a pathophysiological overlap, including inflammation and microbiota changes. Probiotics might represent a safe option as an adjunctive therapy in patients with depression and possible liver steatosis. The paper presents the secondary analysis of a clinical trial of the effect of probiotic supplementation on the levels of non-invasive markers of liver steatosis and fibrosis in adult patients with depressive disorders.

METHODS

The research had a two-arm, parallel-group, prospective, randomized, double-blind, controlled design on probiotics in depression. 116 participants received a probiotic preparation containing Lactobacillus helveticus Rosell®-52 and Bifidobacterium longum Rosell®-175 over 60 days. Here, data from 92 subjects was analyzed. The following were assessed: alanine aminotransferase (ALT), alanine aminotransferase/aspartate aminotransferase (ALT/AST) ratio, Hepatic Steatosis Index, Framingham Steatosis Index, as well as non-invasive biomarkers of liver fibrosis (AST to Platelet Ratio Index, Fibosis-4 Index), or baseline socio-demographic, clinical, and laboratory parameters.

RESULTS

The probiotics did not influence liver steatosis and fibrosis parameters compared with placebo (p = 0.940 for HSI). However, the subgroup analysis revealed significant differences in liver-related parameters when stratified by the main diagnosis group (better improvement in steatosis indices after probiotics in depressive episode than mixed depression and anxiety disorder patients) or psychotropic medications use (better improvement in ALT-based indices after probiotics in antidepressant-treated subjects than those non-antidepressant-treated). The interplay between probiotics, medications, clinical and metabolic profiles of depression, and the changes in liver-related parameters has been discussed.

CONCLUSIONS

Multiple factors may modulate the postulated hepatoprotective properties of probiotics efficacy in patients with depression. Further studies with larger sample sizes, different probiotic strains, and longer intervention period are necessary to assess the real significance of probiotics for liver health in this population.

CLINICALTRIALS

GOV IDENTIFIER

NCT04756544.

High persistence and low treatment rates of metabolic syndrome in patients with mood and anxiety disorders: A naturalistic follow-up study

BACKGROUND

Patients with affective and anxiety disorders are at risk of metabolic syndrome (MetS) and, consequently, cardiovascular disease and premature death. In this study, the course and treatment of MetS was investigated using longitudinal data from a naturalistic sample of affective- and anxiety-disordered outpatients (Monitoring Outcome of psychiatric PHARmacotherapy [MOPHAR]).

METHODS

Demographics, clinical characteristics, medication use, and MetS components were obtained for n = 2098 patients at baseline and, in a FU-subsample of n = 507 patients, after a median follow-up (FU) of 11 months. Furthermore, pharmacological treatment rates of MetS were investigated at baseline and FU. Finally, demographic and clinical determinants of change in MetS (component) scores were investigated.

RESULTS

At baseline, 34.6 % of n = 2098 patients had MetS, 41.4 % of whom received treatment. Of patients with persisting MetS, 46.1 % received treatment for one (or more) MetS component(s) at baseline, and 56.6 % received treatment at FU. Treatment rates of solely elevated blood pressure and reduced HDL-cholesterol did significantly, but modestly, improve. Higher age, male sex, smoking behavior, low education, diabetes, and depressive versus anxiety disorder were predictors of worse outcome at FU on at least one MetS component.

LIMITATIONS

We did not have data on lifestyle interventions as a form of treatment, which might partly have explained the observed low pharmacotherapeutic treatment rates.

CONCLUSION

MetS (components) show high persistence rates in affective- and anxiety-disordered patients, and are, despite adequate monitoring, undertreated over time. This indicates that adherence and implementation of monitoring protocols should be crucially improved in psychiatric outpatients in secondary care.

Metabolites and depressive symptoms: Network- and longitudinal analyses from the Finnish Depression and Metabolic Syndrome in Adults (FDMSA) Study

BACKGROUND

Depression is associated with metabolic abnormalities linked to metabolic syndrome and tissue inflammation, but the interplay between metabolic markers and their association with subsequent depression is unknown. Therefore, we aimed to describe the network of metabolites and their prospective association with depressive symptoms.

METHODS

The Finnish Depression and Metabolic Syndrome in Adults (FDMSA) cohort, originally a prospective case-control study, comprised a group with Beck Depression Inventory (BDI)-I scores ≥10 at baseline, and controls (n = 319, BDI-I < 10); mean (sd) follow-up time: 7.4 (0.7) years. Serum metabolic biomarkers were determined by proton nuclear magnetic resonance (NMR), and depressive symptoms sum-score by using the BDI-I. We examined the prospective associations between metabolites at baseline and BDI score at follow-up utilizing multivariate linear regression, parsimonious predictions models and network analysis.

RESULTS

Some metabolites tended to be either negatively (e.g. histidine) or positively associated (e.g. glycoprotein acetylation, creatinine and triglycerides in very large high density lipoproteins [XL-HDL-TG]) with depressive symptoms. None of the associations were significant after correction for multiple testing. The network analysis suggested high correlation among the metabolites, but that none of the metabolites directly influenced subsequent depressive symptoms.

LIMITATIONS

Although the sample size may be considered satisfactory in a prospective context, we cannot exclude the possibility that our study was underpowered.

CONCLUSIONS

Our results suggest that the investigated metabolic biomarkers are not a driving force in the development of depressive symptoms. These findings should be confirmed in studies with larger samples and studies that account for the heterogeneity of depressive disorders.

Systematic identification of the role of gut microbiota in mental disorders: a TwinsUK cohort study

Mental disorders are complex disorders influenced by multiple genetic, environmental, and biological factors. Specific microbiota imbalances seem to affect mental health status. However, the mechanisms by which microbiota disturbances impact the presence of depression, stress, anxiety, and eating disorders remain poorly understood. Currently, there are no robust biomarkers identified. We proposed a novel pyramid-layer design to accurately identify microbial/metabolomic signatures underlying mental disorders in the TwinsUK registry. Monozygotic and dizygotic twins discordant for mental disorders were screened, in a pairwise manner, for differentially abundant bacterial genera and circulating metabolites. In addition, multivariate analyses were performed, accounting for individual-level confounders. Our pyramid-layer study design allowed us to overcome the limitations of cross-sectional study designs with significant confounder effects and resulted in an association of the abundance of genus Parabacteroides with the diagnosis of mental disorders. Future research should explore the potential role of Parabacteroides as a mediator of mental health status. Our results indicate the potential role of the microbiome as a modifier in mental disorders that might contribute to the development of novel methodologies to assess personal risk and intervention strategies.

Prevalence and correlates of severe anxiety in patients with first hospitalization for major depressive disorder combined with dyslipidemia: a large sample cross-sectional study

Background

Anxiety symptoms and dyslipidemia are common co-morbidities in patients with major depressive disorder (MDD), and there are complex pathophysiologic as well as clinical mechanisms underlying the association between the three. In this study, we investigated the prevalence and associated factors of severe anxiety in first-time hospitalized patients with MDD with dyslipidemia.

Methods

We included 708 patients with major depressive disorder with comorbid dyslipidemia and collected their sociodemographic and general clinical data as well as biochemical parameters such as lipids, thyroid function, and blood glucose. We also completed the Hamilton Anxiety Scale (HAMA), Hamilton Depression Scale (HAMD), Positive Symptom Scale (PSS), and Clinical General Impression Scale (CGI) to assess their clinical symptoms.

Results

The prevalence of severe anxiety disorder was 11.02% in patients with MDD with comorbid dyslipidemia. Suicidal history, female gender, body mass index (BMI), HAMD score, PSS score, and FT4 level were risk factors for the development of severe anxiety symptoms. Higher HAMD scores, higher PSS scores, and suicidal history were influential factors in exacerbating severe anxiety symptoms.

Conclusion

This study reports and identifies the prevalence of severe anxiety symptoms in first-time hospitalized MDD patients with dyslipidemia, as well as risk factors for anxiety symptoms and factors influencing their severity, and these identified factors may be potentially helpful and informative in preventing and intervening in severe anxiety disorders in this target population.

The contribution of polyamine pathway to determinations of diagnosis for treatment-resistant depression: A metabolomic analysis

OBJECTIVES

Approximately one-third of major depressive disorder (MDD) patients do not respond to standard antidepressants and develop treatment-resistant depression (TRD). We aimed to reveal metabolic differences and discover promising potential biomarkers in TRD.

METHODS

Our study recruited 108 participants including healthy controls (n = 40) and patients with TRD (n = 35) and first-episode drug-naive MDD (DN-MDD) (n = 33). Plasma samples were presented to ultra performance liquid chromatography-tandem mass spectrometry. Then, a machine-learning algorithm was conducted to facilitate the selection of potential biomarkers.

RESULTS

TRD appeared to be a distinct metabolic disorder from DN-MDD and healthy controls (HCs). Compared to HCs, 199 and 176 differentially expressed metabolites were identified in TRD and DN-MDD, respectively. Of all the metabolites that were identified, spermine, spermidine, and carnosine were considered the most promising biomarkers for diagnosing TRD and DN-MDD patients, with the resulting area under the ROC curve of 0.99, 0.99, and 0.93, respectively. Metabolic pathway analysis yielded compelling evidence of marked changes or imbalances in both polyamine metabolism and energy metabolism, which could potentially represent the primary altered pathways associated with MDD. Additionally, L-glutamine, Beta-alanine, and spermine were correlated with HAMD score.

CONCLUSIONS

A more disordered metabolism structure is found in TRD than in DN-MDD and HCs. Future investigations should prioritize the comprehensive analysis of potential roles played by these differential metabolites and disturbances in polyamine pathways in the pathophysiology of TRD and depression.

A network analysis of depressive symptoms and metabolomics

BACKGROUND

Depression is associated with metabolic alterations including lipid dysregulation, whereby associations may vary across individual symptoms. Evaluating these associations using a network perspective yields a more complete insight than single outcome-single predictor models.

METHODS

We used data from the Netherlands Study of Depression and Anxiety (N = 2498) and leveraged networks capturing associations between 30 depressive symptoms (Inventory of Depressive Symptomatology) and 46 metabolites. Analyses involved 4 steps: creating a network with Mixed Graphical Models; calculating centrality measures; bootstrapping for stability testing; validating central, stable associations by extra covariate-adjustment; and validation using another data wave collected 6 years later.

RESULTS

The network yielded 28 symptom-metabolite associations. There were 15 highly-central variables (8 symptoms, 7 metabolites), and 3 stable links involving the symptoms Low energy (fatigue), and Hypersomnia. Specifically, fatigue showed consistent associations with higher mean diameter for VLDL particles and lower estimated degree of (fatty acid) unsaturation. These remained present after adjustment for lifestyle and health-related factors and using another data wave.

CONCLUSIONS

The somatic symptoms Fatigue and Hypersomnia and cholesterol and fatty acid measures showed central, stable, and consistent relationships in our network. The present analyses showed how metabolic alterations are more consistently linked to specific symptom profiles.

The association between adolescent depression and dyslipidemia

BACKGROUND

Children and adolescents with major depressive disorder (MDD) are at increased risk for premature cardiovascular disease (CVD). Whether adolescents with MDD manifest evidence of dyslipidemia, a key risk factor for CVD, is unknown.

METHODS

Youth recruited through an ambulatory psychiatry clinic and the community, were categorized following diagnostic interview as MDD or as healthy controls [HC]. CVD risk factors including high density lipoprotein (HDL), low density lipoprotein (LDL), and triglyceride concentrations were collected. Depression severity was measured using the Center for Epidemiological Studies Depression Scale for Children. The associations of diagnostic group as well as depressive symptom severity with lipid concentrations were examined using multiple regression analyses. Models were adjusted for age, sex, and standardized Body Mass Index.

RESULTS

Participants (n = 243) were 68 % female with a mean age of 15.04 ± 1.81 years. MDD and HC participants had comparable levels of dyslipidemia (MDD: 48 %, HC: 46 %, p > .7) and hypertriglyceridemia (MDD: 34 %, HC: 30 %, p > .7). Among depressed adolescents, greater depressive symptoms were associated with higher total cholesterol concentrations in unadjusted models only. Greater depressive symptoms were associated with higher HDL concentrations and a lower triglyceride-to-HDL ratio, after adjusting for covariates.

LIMITATIONS

Cross-sectional design.

CONCLUSIONS

Adolescents with clinically significant depressive symptoms manifested similar levels of dyslipidemia as healthy youth. Future studies examining the prospective trajectories of depressive symptoms and lipid concentrations are needed to determine the point at which dyslipidemia emerges in the course of MDD, and the mechanism of the association that imparts increased CVD risk for depressed youth.

Metabolomics and Self-Reported Depression, Anxiety, and Phobic Symptoms in the VA Normative Aging Study

Traditional approaches to understanding metabolomics in mental illness have focused on investigating a single disorder or comparisons between diagnoses, but a growing body of evidence suggests substantial mechanistic overlap in mental disorders that could be reflected by the metabolome. In this study, we investigated associations between global plasma metabolites and abnormal scores on the depression, anxiety, and phobic anxiety subscales of the Brief Symptom Inventory (BSI) among 405 older males who participated in the Normative Aging Study (NAS). Our analysis revealed overlapping and distinct metabolites associated with each mental health dimension subscale and four metabolites belonging to xenobiotic, carbohydrate, and amino acid classes that were consistently associated across all three symptom dimension subscales. Furthermore, three of these four metabolites demonstrated a higher degree of alteration in men who reported poor scores in all three dimensions compared to men with poor scores in only one, suggesting the potential for shared underlying biology but a differing degree of perturbation when depression and anxiety symptoms co-occur. Our findings implicate pathways of interest relevant to the overlap of mental health conditions in aging veterans and could represent clinically translatable targets underlying poor mental health in this high-risk population.

Metabolomics signatures of depression: the role of symptom profiles

Depression shows a metabolomic signature overlapping with that of cardiometabolic conditions. Whether this signature is linked to specific depression profiles remains undetermined. Previous research suggested that metabolic alterations cluster more consistently with depressive symptoms of the atypical spectrum related to energy alterations, such as hyperphagia, weight gain, hypersomnia, fatigue and leaden paralysis. We characterized the metabolomic signature of an "atypical/energy-related" symptom (AES) profile and evaluated its specificity and consistency. Fifty-one metabolites measured using the Nightingale platform in 2876 participants from the Netherlands Study of Depression and Anxiety were analyzed. An 'AES profile' score was based on five items of the Inventory of Depressive Symptomatology (IDS) questionnaire. The AES profile was significantly associated with 31 metabolites including higher glycoprotein acetyls (β = 0.13, p = 1.35*10^-12), isoleucine (β = 0.13, p = 1.45*10^-10), very-low-density lipoproteins cholesterol (β = 0.11, p = 6.19*10^-9) and saturated fatty acid levels (β = 0.09, p = 3.68*10^-10), and lower high-density lipoproteins cholesterol (β = -0.07, p = 1.14*10^-4). The metabolites were not significantly associated with a summary score of all other IDS items not included in the AES profile. Twenty-five AES-metabolites associations were internally replicated using data from the same subjects (N = 2015) collected at 6-year follow-up. We identified a specific metabolomic signature-commonly linked to cardiometabolic disorders-associated with a depression profile characterized by atypical, energy-related symptoms. The specific clustering of a metabolomic signature with a clinical profile identifies a more homogenous subgroup of depressed patients at higher cardiometabolic risk, and may represent a valuable target for interventions aiming at reducing depression's detrimental impact on health.

Normal range CAG repeat size variations in the HTT gene are associated with an adverse lipoprotein profile partially mediated by body mass index

Tandem cytosine-adenine-guanine (CAG) repeat sizes of 36 or more in the huntingtin gene (HTT) cause Huntington's disease (HD). Apart from neuropsychiatric complications, the disease is also accompanied by metabolic dysregulation and weight loss, which contribute to a progressive functional decline. Recent studies also reported an association between repeats below the pathogenic threshold (<36) for HD and body mass index (BMI), suggesting that HTT repeat sizes in the non-pathogenic range are associated with metabolic dysregulation. In this study, we hypothesized that HTT repeat sizes < 36 are associated with metabolite levels, possibly mediated through reduced BMI. We pooled data from three European cohorts (n = 10 228) with genotyped HTT CAG repeat size and metabolomic measurements. All 145 metabolites were measured on the same targeted platform in all studies. Multilevel mixed-effects analysis using the CAG repeat size in HTT identified 67 repeat size metabolite associations. Overall, the metabolomic profile associated with larger CAG repeat sizes in HTT were unfavorable-similar to those of higher risk of coronary artery disease and type 2 diabetes-and included elevated levels of amino acids, fatty acids, low-density lipoprotein (LDL)-, very low-density lipoprotein- and intermediate density lipoprotein (IDL)-related metabolites while with decreased levels of very large high-density lipoprotein (HDL)-related metabolites. Furthermore, the associations of 50 metabolites, in particular, specific very large HDL-related metabolites, were mediated by lower BMI. However, no mediation effect was found for 17 metabolites related to LDL and IDL. In conclusion, our findings indicate that large non-pathogenic CAG repeat sizes in HTT are associated with an unfavorable metabolomic profile despite their association with a lower BMI.

Serum metabolomic profiling revealed potential diagnostic biomarkers in patients with panic disorder

BACKGROUND

Currently, specific metabolites and diagnostic biomarkers of panic disorder (PD) patients have not been identified in clinical practice. The aim of this study was to explore metabolites and metabolic pathways in serum through a metabolomics method.

METHODS

Fifty-five PD patients who completed 2 weeks of inpatient treatment and 55 healthy control subjects (HCs) matched for age, sex and BMI were recruited. Ultra-high performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS) was used to detect metabolites in serum. Multivariate Statistical Analysis was used to identify differential metabolites. The relevant biometabolic pathways were further identified by the online tool MetaboAnalyst 5.0.

RESULTS

43 different metabolites in PD patients compared to HCs (P < 0.05) were screened. Pathway analysis showed that these small molecules were mainly associated with amino acid metabolism. 14 metabolites were significantly changed after 2 weeks of drug treatment (P < 0.05), which were mainly associated with tryptophan metabolism.

CONCLUSION

In conclusion, our analysis of metabolomics of PD patients at baseline and two weeks after treatment screened for differential metabolites that could be potential diagnostic biomarkers involved in PD pathogenesis and influence some biometabolic pathways such as phenylalanine metabolism and tryptophan metabolism. In the future, we can summarize and observe the dynamic changes of differential metabolites that appear more frequently in similar studies to further explore the underlying mechanisms of PD evolution.

Exploring the role of ketone bodies in the diagnosis and treatment of psychiatric disorders

In recent times, advances in the field of metabolomics have shed greater light on the role of metabolic disturbances in neuropsychiatric conditions. The following review explores the role of ketone bodies and ketosis in both the diagnosis and treatment of three major psychiatric disorders: major depressive disorder, anxiety disorders, and schizophrenia. Distinction is made between the potential therapeutic effects of the ketogenic diet and exogenous ketone preparations, as exogenous ketones in particular offer a standardized, reproducible manner for inducing ketosis. Compelling associations between symptoms of mental distress and dysregulation in central nervous system ketone metabolism have been demonstrated in preclinical studies with putative neuroprotective effects of ketone bodies being elucidated, including effects on inflammasomes and the promotion of neurogenesis in the central nervous system. Despite emerging pre-clinical data, clinical research on ketone body effectiveness as a treatment option for psychiatric disorders remains lacking. This gap in understanding warrants further investigating, especially considering that safe and acceptable ways of inducing ketosis are readily available.

Specific metabolic syndrome components predict cognition and social functioning in people with type 2 diabetes mellitus and severe mental disorders

OBJECTIVE

Obesity and metabolic diseases such as metabolic syndrome (MetS) are more prevalent in people with type 2 diabetes mellitus (T2DM), major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SZ). MetS components might be associated with neurocognitive and functional impairments in these individuals. The predictive and discriminatory validity of MetS and its components regarding those outcomes were assessed from prospective and transdiagnostic perspectives.

METHODS

Metabolic syndrome components and neurocognitive and social functioning were assessed in 165 subjects, including 30 with SZ, 42 with BD, 35 with MDD, 30 with T2DM, and 28 healthy controls (HCs). A posteriori, individuals were classified into two groups. The MetS group consisted of those who met at least three of the following criteria: abdominal obesity (AO), elevated triglycerides (TG), reduced high-density lipoprotein cholesterol (HDL), elevated blood pressure (BP), and elevated fasting glucose (FPG); the remaining participants comprised the No-MetS group. Mixed one-way analysis of covariance and linear and binary logistic regression analyses were performed.

RESULTS

Cognitive impairment was significantly greater in the MetS group (n = 82) than in the No-MetS group (n = 83), with small effect sizes (p < 0.05; η²p = 0.02 - 0.03). In both groups, the most robust associations between MetS components and neurocognitive and social functioning were observed with TG and FPG (p < 0.05). There was also evidence for a significant relationship between cognition and BP in the MetS group (p < 0.05). The combination of TG, FPG, elevated systolic BP and HDL best classified individuals with greater cognitive impairment (p < 0.001), and TG was the most accurate (p < 0.0001).

CONCLUSIONS

Specific MetS components are significantly associated with cognitive impairment across somatic and psychiatric disorders. Our findings provide further evidence on the summative effect of MetS components to predict cognition and social functioning and allow the identification of individuals with worse outcomes. Transdiagnostic, lifestyle-based therapeutic interventions targeted at that group hold the potential to improve health outcomes.

Metabolomics of Major Depressive Disorder: A Systematic Review of Clinical Studies

Although the understanding of the pathophysiology of major depressive disorder (MDD) has advanced greatly, this has not been translated into improved outcomes. To date, no biomarkers have been identified for the diagnosis, prognosis, and therapeutic management of MDD. Thus, we aim to review the biomarkers that are differentially expressed in MDD. A systematic review was conducted in January 2022 in the PubMed/MEDLINE, Scopus, Embase, PsycINFO, and Gale Academic OneFile databases for clinical studies published from January 2001 onward using the following terms: "Depression" OR "Depressive disorder" AND "Metabolomic." Multiple metabolites were found at altered levels in MDD, demonstrating the involvement of cellular signaling metabolites, components of the cell membrane, neurotransmitters, inflammatory and immunological mediators, hormone activators and precursors, and sleep controllers. Kynurenine and acylcarnitine were identified as consistent with depression and response to treatment. The most consistent evidence found was regarding kynurenine and acylcarnitine. Although the data obtained allow us to identify how metabolic pathways are affected in MDD, there is still not enough evidence to propose changes to current diagnostic and therapeutic actions. Some limitations are the heterogeneity of studies on metabolites, methods for detection, analyzed body fluids, and treatments used. The experiments contemplated in the review identified increased or reduced levels of metabolites, but not necessarily increased or reduced the activity of the associated pathways. The information acquired through metabolomic analyses does not specify whether the changes identified in the metabolites are a cause or a consequence of the pathology.

Discovery and validation of acetyl-L-carnitine in serum for diagnosis of major depressive disorder and remission status through metabolomic approach

Major depressive disorder (MDD) is one of the most common psychiatric disorders that accompany psychophysiological and mood changes. However, the pathophysiology-based disease mechanism of MDD is not yet fully understood, and diagnosis is also conducted through interviews with clinicians and patients. Diagnosis and treatment of MDD are limited due to the absence of biomarkers underlying the pathophysiological mechanisms of MDD. Although various attempts have been made to discover metabolite biomarkers for the diagnosis and treatment response of MDD, problems with sample size and consistency of results have limited clinical application. In addition, it was reported that future biomarker studies must consider exposure to antidepressants, which is the main cause of heterogeneity in depression subgroups. Therefore, the purpose of this study is to discover and validate biomarkers for the diagnosis of depression in consideration of exposure to drug treatment including antidepressants that contribute to the heterogeneity of the MDD subgroup. In the biomarker discovery and validation set, the disease group consisted of a mixture of patients exposed and unexposed to drug treatment including antidepressants for the treatment of MDD. The serum metabolites that differed between the MDD patients and the control group were profiled using mass spectrometry. The validation set including the remission group was used to verify the effectiveness as a biomarker for the diagnosis of depression and determination of remission status. The presence of different metabolites between the two groups was confirmed through serum metabolite profiling between the MDD patient group and the control group. Finally, Acetylcarnitine was selected as a biomarker. In validation, acetylcarnitine was significantly decreased in MDD and was distinguished from remission status. This study confirmed that the discovered acetylcarnitine has potential as a biomarker for diagnosing depression and determining remission status, regardless of exposure to drug treatment including antidepressants.

Childhood adversities and the comorbidity between mood and general medical disorders in adults: Results from the WHO World Mental Health Survey Portugal

Objective

Childhood adversities have been linked to poor health outcomes in adults, including both mood and general medical disorders. Here we tested the hypothesis that childhood adversities specifically increase the risk of comorbidity between mood and general medical disorders, rather than increasing the risk of either one independently.

Methods

Mood disorders (DSM-IV major depressive, dysthymic and bipolar disorders), childhood adversities and general medical disorders were assessed in 2060 adults in the WHO World Mental Health Survey Portugal. Discrete-time survival analyses were used to investigate the association between mood disorders and subsequent first-onset general medical disorders and between general medical disorders and subsequent first-onset mood disorders, in adults. Discrete-time survival and multinomial regression analyses were used to test the influence of childhood adversities on the comorbidity between mood disorders and general medical disorders. Anxiety disorders were used as a psychiatric control.

Results

Adult-onset mood disorders were found to precede the onset of diabetes (OR:1.8; 95% CI:1.2-2.9), arthritis (OR:1.6; 95% CI:1.1-2.3) and seasonal allergies (OR:1.6; 95% CI:1.1-2.5) while adult-onset hypertension was found to precede the onset of mood disorders (OR:1.7; 95% CI:1.2-2.6). Maladaptive family functioning (abuse, neglect and parental maladjustment), was associated with mood disorders (OR:1.5; 95% CI:1.2-1.9), hypertension (OR:1.4; 95% CI:1.1-1.7), arthritis (OR:1.3; 95% CI:1.0-1.6) and seasonal allergies (OR:1.5; 95% CI:1.1-2.0) in adulthood. Finally, the effect of maladaptive family functioning in predicting comorbid mood disorders and arthritis significantly differed from its effect in predicting only arthritis (p ​= ​0.01), which was not observed for other comorbidities. Maladaptive family functioning further predicted comorbid anxiety disorders and hypertension.

Conclusion

Childhood adversities may be a specific risk factor for comorbid mood disorders and arthritis in adults.

Targeting Metabolic Dysfunction for the Treatment of Mood Disorders: Review of the Evidence

Major depressive disorder (MDD) and bipolar disorder (BD) are often chronic with many patients not responding to available treatments. As these mood disorders are frequently associated with metabolic dysfunction, there has been increased interest in novel treatments that would target metabolic pathways. The objectives of this scoping review were to synthesize evidence on the impact on mood symptoms of lipid lowering agents and anti-diabetics drugs, while also reviewing current knowledge on the association between mood disorders and dyslipidemia or hyperglycemia. We propose that metabolic dysfunction is prevalent in both MDD and BD and it may contribute to the development of these disorders through a variety of pathophysiological processes including inflammation, brain structural changes, hormonal alterations, neurotransmitter disruptions, alteration on brain cholesterol, central insulin resistance, and changes in gut microbiota. Current evidence is conflicting on the use of statins, polyunsaturated fatty acids, thiazolidinediones, glucagon-like peptide agonists, metformin, or insulin for the treatment of MDD and BD. Given the paucity of high-quality randomized controlled trials, additional studies are needed before any of these medications can be repurposed in routine clinical practice. Future trials need to enrich patient recruitment, include evaluations of mechanism of action, and explore differential effects on specific symptom domains such as anhedonia, suicidality, and cognition.