Metabolomic and inflammatory signatures of symptom dimensions in major depression

Brydges, Christopher; Bhattacharyya, Sudeepa; Dehkordi, Siamak Mahmoudian; Milaneschi, Yuri; Penninx, Brenda W. J. H.; Jansen, Rick; Kristal, Bruce S.; Han, Xianlin; Arnold, Matthias; Kastenmüller, Gabi; Bekhbat, Mandakh; Mayberg, Helen S.; Craighead, W. Edward; Rush, A. John; Fiehn, Oliver; Dunlop, Boadie W.; Kaddurah‐Daouk, Rima

doi:10.1016/j.bbi.2022.02.003

Cited by 53 publications

(21 citation statements)

References 107 publications

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“…Depressive symptoms such as Sleeping too much, Low energy level and Increase in weight form a subgroup referred to as atypical, energy-related symptoms. These share associations with cardiometabolic diseases (Alshehri et al, 2021;Lamers et al, 2018), and inflammatory and metabolic dysregulations (Brydges et al, 2022;Lamers et al, 2020;Milaneschi et al, 2020), which is congruent with the present findings. This clustering of atypical energy-related symptoms sharing associations with heightened levels of inflammation is referred to as Immunometabolic Depression (IMD).…”

Section: Discussionsupporting

confidence: 93%

A network analysis of depressive symptoms and metabolomics

et al. 2023

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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.

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Section: Discussionsupporting

confidence: 93%

A network analysis of depressive symptoms and metabolomics

et al. 2023

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“…Furthermore, we added two sensitivity analyses in which we first investigated the impact of the inclusion of increased sleepiness symptom among atypical energy-related symptom profile (i.e., by adding it as an extra symptom to the score) on the results. Second, to further confirm the specificity of the associations detected for AES, we derived similarly to previous work [15][16][17] a melancholic symptom profile score (0-24 range) including the following melancholic features [24]: diurnal variation (mood worse in the morning), early morning awakening, distinct quality of mood, excessive guilt, decreased appetite, decreased weight, psychomotor agitation and psychomotor retardation. All analyses were done using R version 4.0.2, and for the meta-analysis step, package (rmeta) was used.…”

Section: B Association Between Grs-mua Grs-mha and Atypical Energy-re...mentioning

confidence: 89%

“…We expect that metabolic dysregulations may represent the shared link connecting obesity with the AES profile. Studies have shown that the atypical energy-related symptom profile is associated with an adverse immuno-metabolic profile, such as BMI and CRP [15,16], and biomarkers of neurotoxicity (kynurenine and quinolinic acid) related to low grade inflammation [17]. In the present study, we used genomics to separate the effect of adiposity from that of metabolic dysregulations to examine whether the link between obesity and AES is dependent on metabolic dysregulations.…”

Section: Introductionmentioning

confidence: 99%

The association between adiposity and atypical energy-related symptoms of depression: a role for metabolic dysregulations

Alshehri

Kanamori

Mutsert

et al. 2022

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Background: Adiposity has been shown to be linked with atypical energy-related symptoms (AES) of depression. We used genomics to separate the effect of adiposity from that of metabolic dysregulations to examine whether the link between obesity and AES is dependent on the presence of metabolic dysregulations. Method: Data were from NEO (n=5734 individuals) and NESDA (n=2238 individuals) cohorts, in which the Inventory of Depressive Symptomatology (IDS-SR30) was assessed. AES profile was based on four symptoms: increased appetite, increased weight, low energy level, and leaden paralysis. We estimated associations between AES and two genetic risk scores (GRS) indexing increasing total body fat with (metabolically unhealthy adiposity, GRS-MUA) and without (metabolically healthy adiposity, GRS-MHA) metabolic dysregulations. Results: GRS-MUA and GRS-MHA were both associated with higher total body fat in NEO study, but divergently associated with biomarkers of metabolic health (e.g. fasting glucose and HDL) in both cohorts. In the pooled results, per standard deviation, GRS-MUA was specifically associated with a higher AES score (β=0.03 , 95%CI: 0.01; 0.05), while there was no association between GRS-MHA and AES (β=-0.01, 95%CI: -0.03; 0.01). Conclusion: These results suggest that the established link between adiposity and AES profile emerges in the presence of metabolic dysregulations, which may represent the connecting substrate be-tween the two conditions.

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“…153 metabolites met quality control criteria in both the PGRN-AMPS and CO-MED datasets ( Supplementary Table S1 ). The p180 platform has demonstrated utility in characterizing related psychiatric phenotypes, including MDD and antidepressant response ( Gupta et al, 2016 ; Neavin et al, 2016 ; Athreya et al, 2018 ; Bhattacharyya et al, 2019 ; Czysz et al, 2019 ; Ahmed et al, 2020 ; Joyce et al, 2021 ; MahmoudianDehkordi et al, 2021 ; Brydges et al, 2022 ), schizophrenia ( Parksepp et al, 2020 ), and psychosis ( Kriisa et al, 2017 ; Balotsev et al, 2019 ), but has not yet been investigated in the context of suicidality. Therefore, all metabolites meeting quality control criteria were included in the current integration analysis.…”

Section: Methodsmentioning

confidence: 99%

Network science approach elucidates integrative genomic-metabolomic signature of antidepressant response and lifetime history of attempted suicide in adults with major depressive disorder

et al. 2022

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Background: Individuals with major depressive disorder (MDD) and a lifetime history of attempted suicide demonstrate lower antidepressant response rates than those without a prior suicide attempt. Identifying biomarkers of antidepressant response and lifetime history of attempted suicide may help augment pharmacotherapy selection and improve the objectivity of suicide risk assessments. Towards this goal, this study sought to use network science approaches to establish a multi-omics (genomic and metabolomic) signature of antidepressant response and lifetime history of attempted suicide in adults with MDD.Methods: Single nucleotide variants (SNVs) which associated with suicide attempt(s) in the literature were identified and then integrated with a) p180-assayed metabolites collected prior to antidepressant pharmacotherapy and b) a binary measure of antidepressant response at 8 weeks of treatment using penalized regression-based networks in 245 ‘Pharmacogenomics Research Network Antidepressant Medication Study (PGRN-AMPS)’ and 103 ‘Combining Medications to Enhance Depression Outcomes (CO-MED)’ patients with major depressive disorder. This approach enabled characterization and comparison of biological profiles and associated antidepressant treatment outcomes of those with (N = 46) and without (N = 302) a self-reported lifetime history of suicide attempt.Results: 351 SNVs were associated with suicide attempt(s) in the literature. Intronic SNVs in the circadian genes CLOCK and ARNTL (encoding the CLOCK:BMAL1 heterodimer) were amongst the top network analysis features to differentiate patients with and without a prior suicide attempt. CLOCK and ARNTL differed in their correlations with plasma phosphatidylcholines, kynurenine, amino acids, and carnitines between groups. CLOCK and ARNTL-associated phosphatidylcholines showed a positive correlation with antidepressant response in individuals without a prior suicide attempt which was not observed in the group with a prior suicide attempt.Conclusion: Results provide evidence for a disturbance between CLOCK:BMAL1 circadian processes and circulating phosphatidylcholines, kynurenine, amino acids, and carnitines in individuals with MDD who have attempted suicide. This disturbance may provide mechanistic insights for differential antidepressant pharmacotherapy outcomes between patients with MDD with versus without a lifetime history of attempted suicide. Future investigations of CLOCK:BMAL1 metabolic regulation in the context of suicide attempts may help move towards biologically-augmented pharmacotherapy selection and stratification of suicide risk for subgroups of patients with MDD and a lifetime history of attempted suicide.

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Metabolomic and inflammatory signatures of symptom dimensions in major depression

Cited by 53 publications

References 107 publications

A network analysis of depressive symptoms and metabolomics

A network analysis of depressive symptoms and metabolomics

The association between adiposity and atypical energy-related symptoms of depression: a role for metabolic dysregulations

Network science approach elucidates integrative genomic-metabolomic signature of antidepressant response and lifetime history of attempted suicide in adults with major depressive disorder

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