Extensive research has explored altered structural and functional networks in major depressive disorder (MDD). However, studies examining the relationships between structure and function yielded heterogeneous and inconclusive results. Recent work has suggested that the structure-function relationship is not uniform throughout the brain but varies across different levels of functional hierarchy. This study aims to investigate changes in structure-function couplings (SFC) and their relevance to antidepressant response in MDD from a functional hierarchical perspective.

We compared regional SFC between individuals with MDD (n = 258) and healthy controls (HC, n = 99) using resting-state functional magnetic resonance imaging and diffusion tensor imaging. We also compared antidepressant non-responders (n = 55) and responders (n = 68, defined by a reduction in depressive severity of >50%). To evaluate variations in altered and response-associated SFC across the functional hierarchy, we ranked significantly different regions by their principal gradient values and assessed patterns of increase or decrease along the gradient axis. The principal gradient value, calculated from 219 healthy individuals in the Human Connectome Project, represents a region's position along the principal gradient axis.

Compared to HC, MDD patients exhibited increased SFC in unimodal regions (lower principal gradient) and decreased SFC in transmodal regions (higher principal gradient) (p < 0.001). Responders primarily had higher SFC in unimodal regions and lower SFC in attentional networks (median principal gradient) (p < 0.001).

Our findings reveal opposing SFC alterations in low-level unimodal and high-level transmodal networks, underscoring spatial variability in MDD pathology. Moreover, hierarchy-specific antidepressant effects provide valuable insights into predicting treatment outcomes.

The close association among thyroid metabolism, mood disorders and behavior has long been known. The role of basal thyroid axis in antidepressant treatment response is less known.

The aim of the present study was to study the association of basal serum thyrotropin (TSH) levels, with antidepressant treatment response in major depressive disorder.

Thirty-one depressed adult outpatients were included. Major depressive episode was diagnosed through the MINI (DSM-IV-TR) interview. Clinical symptomatology and blood samples were assessed at baseline, and at 4- and 8-weeks of either escitalopram or sertraline. Treatment response was defined by an improvement ≥50% in MADRS scores at 4-, and 8-weeks. Basal TSH levels were included in a linear regression model as predictor of treatment response.

Twenty-seven patients finished 8-weeks of treatment. Response to treatment was of 74% at 4-weeks, and 63% at 8-weeks of antidepressant treatment. Basal median TSH levels were between normal ranges (M+SD=1.85+1,02 mlU/L). Basal TSH levels not correlated with basal MADRS scores, but with higher MADRS scores at week-4 (r=0,415, p=0,031) and at week-8 (r=0,392, p=0,043). Moreover, less baseline TSH levels trend to be a significant good predictor for treatment response at 4-weeks (R2=.116, p=.083); and a good predictor at 8-weeks treatment (R2=.147, p=.049).

Baseline TSH levels even within the normal range may play a role in predicting antidepressant response.

No significant relationships.

Major depressive disorder (MDD) constitutes the leading cause of disability worldwide. Although efficacious antidepressant pharmacotherapies exist for MDD, only about 40-60% of the patients respond to initial treatment. However, there is still a lack of robustly established and applicable biomarkers for antidepressant response in everyday clinical practice.

This study targets the assessment of the vasopressin (AVP) surrogate marker Copeptin (CoP), as a potential peripheral hypothalamic-level biomarker of antidepressant treatment response in MDD.

We measured baseline and dynamic levels of plasma CoP along with plasma ACTH and cortisol (CORT) in drug-naive outpatients with MDD before and after overnight manipulation of the hypothalamic-pituitary-adrenal (HPA) axis [i.e., stimulation (metyrapone) and suppression (dexamethasone)] on three consecutive days and their association with treatment response to 4 weeks of escitalopram treatment.

Our findings suggest significantly higher baseline and post-metyrapone plasma CoP levels in future non-responders, a statistically significant invert association between baseline CoP levels and probability of treatment response and a potential baseline plasma CoP cut-off level of above 2.9 pmol/L for future non-response screening. Baseline and dynamic plasma ACTH and CORT levels showed no association with treatment response.

This pilot study provide first evidence in humans that CoP may represent a novel, clinically easily applicable, endocrine biomarker of antidepressant response, based on a single-measurement, cut-off level. These findings, underline the role of the vasopressinergic system in the pathophysiology of MDD and may represent a significant new tool in the clinical and biological phenotyping of MDD enhancing individual-tailored therapies.

Major depressive disorder (MDD) is associated with increased allostatic load (AL; a measure of physiological costs of repeated/chronic stress-responding) and metabolic dysregulation (MetD; a measure of metabolic health and precursor to many medical illnesses). Though AL and MetD are associated with poor somatic health outcomes, little is known regarding their relationship with antidepressant-treatment outcomes.

We determined pre-treatment AL and MetD in 67 healthy controls and 34 unmedicated, medically healthy MDD subjects. Following this, MDD subjects completed 8-weeks of open-label selective serotonin reuptake inhibitor (SSRI) antidepressant treatment and were categorized as ‘Responders’ (⩾50% improvement in depression severity ratings) or ‘Non-responders’ (<50% improvement). Logistic and linear regressions were performed to determine if pre-treatment AL or MetD scores predicted SSRI-response. Secondary analyses examined cross-sectional differences between MDD and control groups.

Pre-treatment AL and MetD scores significantly predicted continuous antidepressant response (i.e. absolute decreases in depression severity ratings) (p = 0.012 and 0.014, respectively), as well as post-treatment status as a Responder or Non-responder (p = 0.022 and 0.040, respectively), such that higher pre-treatment AL and MetD were associated with poorer SSRI-treatment outcomes. Pre-treatment AL and MetD of Responders were similar to Controls, while those of Non-responders were significantly higher than both Responders (p = 0.025 and 0.033, respectively) and Controls (p = 0.039 and 0.001, respectively).

These preliminary findings suggest that indices of metabolic and hypothalamic-pituitary-adrenal-axis dysregulation are associated with poorer SSRI-treatment response. To our knowledge, this is the first study to demonstrate that these markers of medical disease risk also predict poorer antidepressant outcomes.

Genomewide association studies (GWASs) on antidepressant efficacy have yielded modest results. A possible reason is that response is influenced by other factors, which possibly interact with genetic variation. We used a GWAS model to predict antidepressant response, by including predictors previously known to affect response, such as quality of life (QoL). We also evaluated the association between genes, previously implicated in gene–environment (G × E) interactions, and response using an enrichment analysis.

We examined a sample of 1426 depressed patients from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial: 774 responders, 652 non-responders and 418 865 single nucleotide polymorphisms (SNPs) were analysed. First, in a GWAS model, we investigated whether genetic variations interact with patients' levels of QoL to predict response, after controlling for demographic characteristics, severity and population stratification. Second, we conducted an enrichment analysis exploring whether candidate genes that have emerged from prior G × E interaction studies on depression are associated with treatment response.

The GWAS model, with QoL as a moderator, yielded one SNP (rs520210) associated with response in the NEDD4L gene (p = 3.64 × 10−8). In the Caucasian sample only, we observed a drop in significance for this SNP. The enrichment analysis showed that SNPs within serotonergic genes contained more significant markers that predicted response, compared with a random set of genes in the genome.

Our findings point to possible target genes, which are proposed for further independent replication. Our enrichment analysis provides further support, in a genomewide context, of the role of serotonergic genes in influencing antidepressant response.

Response and remission defined by cut-off values on the last observed depression severity score are commonly used as outcome criteria in clinical trials, but ignore the time course of symptomatic change and may lead to inefficient analyses. We explore alternative categorization of outcome by naturally occurring trajectories of symptom change.

Growth mixture models were applied to repeated measurements of depression severity in 807 participants with major depression treated for 12 weeks with escitalopram or nortriptyline in the part-randomized Genome-based Therapeutic Drugs for Depression study. Latent trajectory classes were validated as outcomes in drug efficacy comparison and pharmacogenetic analyses.

The final two-piece growth mixture model categorized participants into a majority (75%) following a gradual improvement trajectory and the remainder following a trajectory with rapid initial improvement. The rapid improvement trajectory was over-represented among nortriptyline-treated participants and showed an antidepressant-specific pattern of pharmacogenetic associations. In contrast, conventional response and remission favoured escitalopram and produced chance results in pharmacogenetic analyses. Controlling for drop-out reduced drug differences on response and remission but did not affect latent trajectory results.

Latent trajectory mixture models capture heterogeneity in the development of clinical response after the initiation of antidepressants and provide an outcome that is distinct from traditional endpoint measures. It differentiates between antidepressants with different modes of action and is robust against bias due to differential discontinuation.