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Multidimensional predictors of antidepressant responses: Integrating mitochondrial, genetic, metabolic and environmental factors with clinical outcomes.

ABSTRACT: Major depressive disorder (MDD) is a primary psychiatric illness worldwide; there is a dearth of new mechanistic models for the development of better therapeutic strategies. Although we continue to discover individual biological factors, a major challenge is the identification of integrated, multidimensional traits underlying the complex heterogeneity of depression and treatment outcomes. Here, we set out to ascertain the emergence of the novel mitochondrial mediator of epigenetic function acetyl-L-carnitine (LAC) in relation to previously described individual predictors of antidepressant responses to the insulin-sensitizing agent pioglitazone. Herein, we report that i) subjects with MDD and shorter leukocyte telomere length (LTL) show decreased levels of LAC, increased BMI, and a history of specific types of childhood trauma; and that ii) these multidimensional factors spanning mitochondrial metabolism, cellular aging, metabolic function, and childhood trauma provide more detailed signatures to predict longitudinal changes in depression severity in response to pioglitazone than individual factors. The findings of multidimensional signatures involved in the pathophysiology of depression and their role in predicting treatment outcomes provide a starting point for the development of a mechanistic framework linking biological networks and environmental factors to clinical outcomes in pursuit of personalized medicine strategies to effectively treat MDD.

SUBMITTER: Nasca C

PROVIDER: S-EPMC8592929 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature

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Multidimensional predictors of antidepressant responses: Integrating mitochondrial, genetic, metabolic and environmental factors with clinical outcomes.

Nasca Carla C Barnhill Olivia O DeAngelis Paolo P Watson Kathleen K Lin Jue J Beasley James J Young Sarah P SP Myoraku Alison A Dobbin Josh J Bigio Benedetta B McEwen Bruce B Rasgon Natalie N

Neurobiology of stress 20211009

Major depressive disorder (MDD) is a primary psychiatric illness worldwide; there is a dearth of new mechanistic models for the development of better therapeutic strategies. Although we continue to discover individual biological factors, a major challenge is the identification of integrated, multidimensional traits underlying the complex heterogeneity of depression and treatment outcomes. Here, we set out to ascertain the emergence of the novel mitochondrial mediator of epigenetic function acety ...[more]

PMID: 34815985

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Project description:BackgroundThe psychological structural model of major depressive disorder (MDD) is complex and multifaceted, consisting of the environment factors (EF), cognitive function (CF), and behavioral-emotional manifestations (BEM). Currently, the diagnosis and treatment of MDD is difficult to improve the disease state as a whole through an intervention point. Constructing the psychological structural model of MDD and finding the critical node of the psychological structural model are meaningful for the diagnosis and treatment of MDD.Methods308 MDD patients (MDDs) participated in this research. They completed assessments of 34 psychological factors, including EF, CF, BEM and interoception function (IF). Pearson correlation was used to investigate the relationship between IF and each factor in the depressive multidimensional psychological structural model (EF, CF and BEM) which was constructed by structural equation modeling (SEM). Critical nodes were identified by the goodness of fit of the model. The depressive multidimensional psychological structural model (EF, CF and BEM) was created through SEM. IF was added to the depressive multidimensional psychological structural model to further verify the pathways and effects of interoception in the network.ResultsIF was significantly correlated with all psychological factors. In the depressive multidimensional psychological structural model, EF (βdirect = 0.163, p = 0.033) and BEM (βdirect = 0.230, p = 0.003) can directly influence MDD. When adding IF to the model, interoception predicted all the factors in the model. The poorer EF led to the lower IF (βdirect = - 0.346, p < 0.001). Interoception dysfunction increased the risk of the CF (βdirect = -0.525, p = 0.002) and BEM (βdirect = - 0.250, p = 0.031) of the patients. EF had the largest total effect on MDD (βdirect = 0.365, βindirect = 0.150, βtotal = 0.515), IF (βdirect = - 0.309, βindirect = - 0.126, βtotal = - 0.434) are second only to EF.ConclusionsIF is a part of the psychological structural model of MDD that predicts EF, CF, and BEM. It could be a potential intervention point to improve the depressive state as a whole.

Project description:BackgroundThere is growing evidence about the role of nurses in patient outcomes in several healthcare settings. However, there is still a lack of evidence about the transitional care setting. We aimed to assess the association between patient characteristics identified in a multidimensional nursing assessment and outcomes of mortality and acute hospitalization during community hospital stay.MethodsA retrospective observational study was performed on patients consecutively admitted to a community hospital (CH) in Loreto (Ancona, Italy) between January 1st, 2018 and May 31st, 2019. The nursing assessment included sociodemographic characteristics, functional status, risk of falls (Conley Score) and pressure damage (Norton scale), nursing diagnoses, presence of pressure sores, feeding tubes, urinary catheters or vascular access devices and comorbidities. Two logistic regression models were developed to assess the association between patient characteristics identified in a multidimensional nursing assessment and outcomes of mortality and acute hospitalization during CH stay.ResultsWe analyzed data from 298 patients. The mean age was 83 ± 9.9 years; 60.4% (n = 180) were female. The overall mean length of stay was 42.8 ± 36 days (32 ± 32 days for patients who died and 33.9 ± 35 days for patients who had an acute hospitalization, respectively). An acute hospitalization was reported for 13.4% (n = 40) of patients and 21.8% (n = 65) died. An increased risk of death was related to female sex (OR 2.25, 95% CI 1.10-4.62), higher Conley Score (OR 1.19; 95% CI 1.03-1.37) and having a vascular access device (OR 3.64, 95% CI 1.82-7.27). A higher Norton score was associated with a decreased risk of death (OR 0.71, 95% CI 0.62-0.81). The risk for acute hospitalization was correlated with younger age (OR 0.94, 95% CI 0.91-0.97), having a vascular access device (OR 2.33, 95% CI 1.02-5.36), impaired walking (OR 2.50, 95% CI 1.03-6.06) and it is inversely correlated with a higher Conley score (OR 0.84, 95% CI 0.77-0.98).ConclusionUsing a multidimensional nursing assessment enables identification of risk of nearness of end of life and acute hospitalization to target care and treatment. The present study adds further knowledge on this topic and confirms the importance of nursing assessment to evaluate the risk of patients' adverse outcome development.

Project description:Background: Numerous studies have focused on the PI3K/AKT/mTOR pathway in estrogen receptor positive (ER) breast cancer (BC), as a linear signal transduction pathway and reported its association with worse clinical outcomes. To gain better insight into the relative contribution of each of the signalling pathways which lie downstream to PI3K (namely AKT and mTOR) to BC outcomes, we have developed a novel in silico approach which assessed the activation of each of these signalling pathways separately. Methods: By analysing gene expression and matched proteomic data in ER-positive patients from the TCGA repository, we developed gene signatures that reflect the level of expression of phosphorylated-Serine473-AKT (pAKT) and phosphorylated-Serine2448-mTOR (p-mTOR) separately, capturing their corresponding level of pathway activation. Using pooled analysis of gene-expression data from over 7,000 patients with ER-positive early BC, we then investigated the association between pathway activation and outcomes. Results: Our analysis revealed that the pAKT pathway activation (as measured by the developed signature) was associated with luminal A BC while the p-mTOR pathway activation was more associated with luminal B BC (Kruskal-Wallis test p<10-10). pAKT pathway activation was significantly associated with better outcomes (multivariable HR, 0.79; 95% CI, 0.74 to 0.85; p=2.5x10-10) whereas p-mTOR pathway activation showed worse outcomes (multivariable HR, 1.1; 95% CI, 1.1 to 1.2; p=9.9x10-4). Similar associations between activation and outcomes were obtained when the analysis was performed in patients who were treated with hormonal therapy. Additionally, pAKT pathway activation predicted better outcomes irrespective of the BC molecular subtypes (luminal A multivariable HR, 0.85; 95% CI, 0.75 to 0.96; p=0.01; luminal B HR, 0.91; 95% CI, 0.83 to 0.99; p=0.033). pAKT pathway activation was associated with PIK3CA mutations (p=0.0001) while p-mTOR pathway activation was associated with p53 mutations (p=0.04). Finally, we show that pAKT pathway activation was associated with less response to Everolimus. Conclusions: Our data show that pAKT and p-mTOR pathway activation have differing impact on prognosis and suggest that they are not linearly connected in luminal breast cancers. These findings add to our understanding of signalling through the PI3K pathway and could have important implications for the treatment of luminal BC.

Project description:BackgroundProfiling patients on a proposed 'immunometabolic depression' (IMD) dimension, described as a cluster of atypical depressive symptoms related to energy regulation and immunometabolic dysregulations, may optimise personalised treatment.AimsTo test the hypothesis that baseline IMD features predict poorer treatment outcomes with antidepressants.MethodData on 2551 individuals with depression across the iSPOT-D (n = 967), CO-MED (n = 665), GENDEP (n = 773) and EMBARC (n = 146) clinical trials were used. Predictors included baseline severity of atypical energy-related symptoms (AES), body mass index (BMI) and C-reactive protein levels (CRP, three trials only) separately and aggregated into an IMD index. Mixed models on the primary outcome (change in depressive symptom severity) and logistic regressions on secondary outcomes (response and remission) were conducted for the individual trial data-sets and pooled using random-effects meta-analyses.ResultsAlthough AES severity and BMI did not predict changes in depressive symptom severity, higher baseline CRP predicted smaller reductions in depressive symptoms (n = 376, βpooled = 0.06, P = 0.049, 95% CI 0.0001-0.12, I2 = 3.61%); this was also found for an IMD index combining these features (n = 372, βpooled = 0.12, s.e. = 0.12, P = 0.031, 95% CI 0.01-0.22, I2= 23.91%), with a higher - but still small - effect size compared with CRP. Confining analyses to selective serotonin reuptake inhibitor users indicated larger effects of CRP (βpooled = 0.16) and the IMD index (βpooled = 0.20). Baseline IMD features, both separately and combined, did not predict response or remission.ConclusionsDepressive symptoms of people with more IMD features improved less when treated with antidepressants. However, clinical relevance is limited owing to small effect sizes in inconsistent associations. Whether these patients would benefit more from treatments targeting immunometabolic pathways remains to be investigated.

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Multidimensional predictors of antidepressant responses: Integrating mitochondrial, genetic, metabolic and environmental factors with clinical outcomes.

Publications

Multidimensional predictors of antidepressant responses: Integrating mitochondrial, genetic, metabolic and environmental factors with clinical outcomes.

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