Pre-treatment allostatic load and metabolic dysregulation predict SSRI response in major depressive disorder: a preliminary report

Ghrelin is associated with an elevated mood after an overnight fast in depression

BACKGROUND

Major depressive disorder (MDD) comprises subtypes with distinct symptom profiles. For example, patients with melancholic and atypical MDD differ in the direction of appetite and body weight changes as well as mood reactivity. Despite reported links to altered energy metabolism, the role of circulating neuropeptides from the gut in modulating such symptoms remains largely elusive.

METHODS

We collected data from 103 participants, including 52 patients with MDD and 51 healthy control participants (HCP). After an overnight fast, we measured plasma levels of (acyl and des-acyl) ghrelin and participants reported their current metabolic and mood states using visual analog scales (VAS). Furthermore, they completed symptom-related questionnaires (i.e., STAI-T).

RESULTS

Patients with atypical versus melancholic MDD reported less negative affect (p = 0.025). Higher levels of acyl ghrelin (corrected for BMI) were associated with improved mood (p = 0.012), specifically in patients with MDD. These associations of ghrelin were not mood-item specific and exceeded correlations with trait markers of negative affectivity. In contrast to associations with mood state, higher levels of ghrelin were not associated with increased hunger per se or changes in appetite in patients with MDD.

LIMITATIONS

The study is limited by the cross-sectional design without an intervention.

CONCLUSIONS

Our results reveal potentially mood-enhancing effects of ghrelin in fasting individuals that exceed associations with metabolic state ratings. These associations with circulating neuropeptides might help explain anti-depressive effects of fasting interventions and could complement conventional treatments in patients with melancholic MDD.

Association of subclinical hypothyroidism with metabolic syndrome and its components among outpatients with first-episode drug-naïve major depressive disorder: a large-scale cross-sectional study

Both metabolic syndrome (MetS) and subclinical hypothyroidism (SCH) are prevalent in major depressive disorder (MDD) patients. However, their relationship in this population remains unknown. The study assessed the association between SCH and MetS in 1706 first-episode drug-naïve (FEDN) MDD patients. We also compared the relationship between MetS and clinical symptoms in patients with and without comorbid SCH. The Positive and Negative Syndrome Scale positive subscale, the Hamilton Anxiety Rating Scale, and the Hamilton Depression Rating Scale were used to detect clinical symptoms. Serum levels of free triiodothyronine, free thyroxine, thyroid stimulating hormone (TSH), anti-thyroglobulin, thyroid peroxidases antibody, cholesterol, triglycerides, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and fasting glucose were measured. The Area Under the Curve (AUC) was used to test the performance of serum TSH in identifying MetS patients. The prevalence of MetS and SCH was 34.5% (n = 585) and 61% (n = 1034), respectively. The presence of SCH increased the risk of MetS, hyperglycemia, hypertension, obesity, and low HDL-C by 4.91, 3.51, 3.54, 2.02, and 2.34 times, respectively. Serum TSH had a nice ability to distinguish MetS patients from non-MetS patients (AUC value = 0.77). MetS and its components exhibited a positive association with clinical profiles only in SCH patients, but not in non-SCH patients. Taken together, our study suggested SCH was closely related to MetS and might play a vital role in the relationship between MetS and clinical symptoms. Regular thyroid function checks might help early detect MetS.

Shorter telomere length predicts poor antidepressant response and poorer cardiometabolic indices in major depression

Telomere length (TL) is a marker of biological aging, and shorter telomeres have been associated with several medical and psychiatric disorders, including cardiometabolic dysregulation and Major Depressive Disorder (MDD). In addition, studies have shown shorter TL to be associated with poorer response to certain psychotropic medications, and our previous work suggested shorter TL and higher telomerase activity (TA) predicts poorer response to Selective Serotonin Reuptake Inhibitor (SSRI) treatment. Using a new group of unmedicated medically healthy individuals with MDD (n = 48), we sought to replicate our prior findings demonstrating that peripheral blood mononuclear cell (PBMC) TL and TA predict response to SSRI treatment and to identify associations between TL and TA with biological stress mediators and cardiometabolic risk indices. Our results demonstrate that longer pre-treatment TL was associated with better response to SSRI treatment (β = .407 p = .007). Additionally, we observed that TL had a negative relationship with allostatic load (β = - .320 p = .017) and a cardiometabolic risk score (β = - .300 p = .025). Our results suggest that PBMC TL reflects, in part, the cumulative effects of physiological stress and cardiovascular risk in MDD and may be a biomarker for predicting SSRI response.

Association between thyroid dysfunction, metabolic disturbances, and clinical symptoms in first-episode, untreated Chinese patients with major depressive disorder: Undirected and Bayesian network analyses

AIMS

Thyroid dysfunction and metabolic disturbances are common in major depressive disorder (MDD) patients. We aimed to assess the relationship between thyroid dysfunction, metabolic disturbances, and clinical symptoms in Chinese first-episode, drug-naïve (FEDN) MDD patients using undirected and Bayesian network methods.

METHODS

1718 FEDN MDD patients were recruited. Serum levels of free triiodothyronine (FT3), free thyroxine (FT4), thyroid stimulating hormone (TSH), anti-thyroglobulin (TgAb), thyroid peroxidases antibody (TPOAb), total cholesterol (TC), total triglycerides (TG), high-density lipoprotein (HDL-C), low-density lipoprotein (LDL-C), and glucose were assessed. Blood pressure and body mass index were measured. Hamilton Rating Scale for Depression (HAMD), Hamilton Rating Scale for Anxiety, and positive subscale of Positive And Negative Syndrome Scales were used to detect clinical symptoms. An undirected network with EBICglasso default and a directed acyclic graph (DAG) using the Bayesian network approach was conducted.

RESULTS

The prevalence rates of clinical symptoms, thyroid dysfunction, and metabolic dysfunction were as follows: anxiety (n=894, 52%), psychotic symptoms (171, 10%), subclinical hypothyroidism (SCH, n=1041, 61%), abnormal TgAb (n=297, 17%), abnormal TPOAb (n=438, 25%), hyperthyroidism (n=5, 0.3%), hypothyroidism (n=3, 0.2%), hyperglycemia (n=241, 14%), hypertriglyceridemia (n=668, 39%), low HDL-C (n=429, 25%), hypercholesterolemia (421, 25%), abnormal TC (357, 21%), abnormal LDL-C (185, 11%). overweight or obesity (n=1026, 60%), and hypertension (n=92, 5.4%). Both networks demonstrated serum TSH and TC levels and the severity of depression played an important role in the pathophysiology of MDD.

CONCLUSIONS

MDD patients may have thyroid and metabolic dysfunction in the early stage. Targeting hypercholesterolemia, depressive symptoms, and SCH in MDD patients may hold promise in reducing clinical symptoms, metabolic disturbances, and thyroid dysfunction.

Computational approaches to treatment response prediction in major depression using brain activity and behavioral data: A systematic review

Major depressive disorder is a heterogeneous diagnostic category with multiple available treatments. With the goal of optimizing treatment selection, researchers are developing computational models that attempt to predict treatment response based on various pretreatment measures. In this paper, we review studies that use brain activity data to predict treatment response. Our aim is to highlight and clarify important methodological differences between various studies that relate to the incorporation of domain knowledge, specifically within two approaches delineated as data-driven and theory-driven. We argue that theory-driven generative modeling, which explicitly models information processing in the brain and thus can capture disease mechanisms, is a promising emerging approach that is only beginning to be utilized in treatment response prediction. The predictors extracted via such models could improve interpretability, which is critical for clinical decision-making. We also identify several methodological limitations across the reviewed studies and provide suggestions for addressing them. Namely, we consider problems with dichotomizing treatment outcomes, the importance of investigating more than one treatment in a given study for differential treatment response predictions, the need for a patient-centered approach for defining treatment outcomes, and finally, the use of internal and external validation methods for improving model generalizability.

Depressive symptoms, childhood maltreatment, and allostatic load: The importance of sex differences

OBJECTIVE

Roughly half the individuals who experience childhood maltreatment also experience depressive symptoms in adulthood; however, many current treatments are ineffective. Allostatic load (AL) offers a method of exploring this relationship through the lens of biometric dysregulation that may explain these increased odds for depressive symptoms in adulthood. We attempted to expand the limited research base on AL, depressive symptoms, and childhood maltreatment by examining how these variables are associated in a U.S. community sample.

METHOD

Data were acquired through secondary analysis of the Midlife in the United States Refresher biomarker survey (n = 691). Depression severity, measured by the Center for Epidemiologic Studies Depression Scale, served as the dependent variable, while summed scores for the Childhood Trauma Questionnaire, and a 16 measure AL index served as independent variables. All analyses were stratified by sex (n male = 347, n female = 344). The Perceived Stress Scale was included to control for recent stress. An OLS regression was used to understand the relationship between depression and predictor variables before then being tested for the possible mediating role of AL.

RESULTS

Comparison of OLS models yielded notable differences in predicting depressive symptoms between males and females, namely that while maltreatment was significant for both groups, AL was only significant for females. Mediation by allostatic load was not significant for males or females.

CONCLUSION

We explored the possible mediation of childhood maltreatment and adulthood depressive symptoms by AL. While our study did not confirm mediation, this was the first known study to explore these relationships in a U.S., community sample. Sex stratification reveals a clear need for accounting for sex differences in predictor variables for future studies.