While the pathogenesis of depression has been extensively studied, the mechanisms underlying appetite suppression in depression remain poorly understood. This study aimed to elucidate the role of the microRNA-124–3p (miR-124–3p)/neuropeptide Y (NPY) axis in regulating appetite during depression. We examined the relationship between body weight fluctuations and serum miR-124–3p/NPY levels in major depressive disorder (MDD) patients and assessed correlations between NPY levels and appetite scores. Chronic unpredictable mild stress (CUMS) and lipopolysaccharide (LPS)-induced depression mouse models were established to monitor food intake and body weight fluctuations. Hippocampal and cortical tissues were collected to measure miR-124–3p and NPY expression. The miR-124–3p antagomir (inhibitor) and agomir (activator) were subsequently administered to LPS-induced mice to evaluate changes in NPY expression. Clinical studies have confirmed the interactions among miR-124–3p, NPY, and appetite regulation. MDD patients presented elevated serum levels of miR-124–3p, reduced NPY content, and a positive correlation between NPY levels and both appetite scores and body weight. In depression model mice, miR-124–3p expression was increased in the hippocampus and cortex, whereas NPY expression was decreased. Inhibition of miR-124–3p activity enhanced NPY expression, leading to weight gain. Conversely, the activation of miR-124–3p reduces NPY expression, resulting in weight loss. Our study demonstrated that miR-124–3p negatively regulates NPY to mediate appetite suppression in depression. Targeting this axis could provide novel therapeutic strategies for managing weight loss and appetite dysfunction in MDD patients.

BackgroundShort-chain fatty acids (SCFAs) have been shown to improve depression-like behavior in animal models. However, their predictive value for depression outcomes in humans remains unclear. If SCFAs are predictive, this would provide strong justification for their use in diagnostic and intervention strategies. Therefore, the aim of our study was to determine whether baseline SCFA levels predict remission from depression over a six-month period. Secondary objectives included identifying the SCFA most strongly associated with depression outcomes and assessing the relationship between SCFA levels and inflammatory markers.MethodsA case-control study was conducted, including a pre-selected subset of 50 patients with major depressive disorder (MDD) at baseline, assessed at two time points (25 remitted, 25 non-remitted after six months), and 25 matched healthy controls (CTRL) from a convenience sample. The study was conducted at a university hospital, with fasting SCFA levels measured from high-quality blood samples. Depression severity was assessed using the Montgomery-Åsberg Depression Rating Scale (MADRS), and plasma concentrations of acetate, butyrate, and propionate were measured using gas chromatography-mass spectrometry at baseline and follow-up.ResultsAt baseline, plasma concentrations of propionate (t(71.2) = −2.38, P = 0.01, Cohen's d = 0.53) and butyrate (t(62.5) = −1.77, P = 0.041, Cohen's d = 0.41) were significantly lower in MDD participants compared to HC. Acetate levels did not differ significantly between groups (t(44.6) = −0.65, P = 0.26, Cohen's d = 0.16). Within the MDD group, baseline butyrate levels were associated with remission at follow-up (β = 2.79 ± 1.41, χ2(1) = 4.29, P = 0.038, OR = 16.3, 95 % CI: 1.01–262.4, AUC ROC = 0.67). Neither acetate (P = 0.43), propionate (P = 0.24), nor C-reactive protein (CRP) (P = 0.83) significantly predicted depression outcomes. Lasso regression identified butyrate as the primary predictor of depression severity at follow-up (β = 2.90 ± 1.39, χ2 = 4.38, P = 0.036).ConclusionPatients with MDD exhibited lower baseline levels of butyrate and propionate compared to healthy controls. Higher baseline butyrate levels were associated with a greater likelihood of remission at follow-up. These findings suggest that butyrate may play a role in depression recovery, emphasizing the need for future studies to explore the therapeutic potential of butyrate-enhancing interventions in depression treatment.

Anxiety disorders are the most prevalent type of mental health disorders and are characterised by excessive fear and worry. Despite affecting one in four individuals within their lifetime, there remains a gap in our understanding regarding the underlying pathophysiology of anxiety disorders, which limits the development of novel treatment options. Exploring blood-based biomarkers of anxiety disorder offers the potential to predict the risk of clinically significant anxiety in the general population, increase our understanding of anxiety pathophysiology, and to reveal options for preventative treatment. Here, using psychosocial variables in combination with blood and urine biomarkers, reported in the UK Biobank, we sought to predict future anxiety onset. Machine learning accurately predicted (ROC AUC: ∼0.83) ICD-10-coded anxiety diagnoses up to 5 years (mean 3.5 years) after blood sampling, against lifetime anxiety-free controls. Analysis of the blood biochemistry measures indicated that anxious individuals were more anaemic and exhibited higher levels of markers of systemic inflammation than controls. However, blood biomarkers alone were not predictive of resilience or susceptibility to anxiety disorders in a subset of individuals rigorously matched for a wide range of psychosocial covariates (ROC AUC: ∼0.50). Overall, we demonstrate that the integration of biological and psychosocial risk factors is an effective tool to screen for and predict anxiety disorder onset in the general population.

This study examined the regional distribution of glial activation in essential workers with neurological post-acute sequelae of coronavirus disease 2019 (COVID-19) infections (N-PASC). We injected ≤185 MBq of [18F]-FEPPA as an intravenous bolus and positron-emission tomography over 2h. To measure distribution volume (VT) we recruited 24 essential workers (14N-PASC, 10 Never-COVID-19 Controls, of whom 22 successfully placed arterial lines). Individuals with low binding affinity were excluded from this study, and VT was adjusted for translocator protein genotype. Analyses that passed the false discovery rate are reported. Participants at midlife survived mild to moderate COVID-19 without hospitalization but reported onset of post-acute sequelae of COVID-19 (PASC) for, on average, 22 months before undergoing neuroimaging. Hippocampal VT was higher (VT=1.70, 95% C.I.=[1.30-2.21], p=0.001) in participants with persistent brain fog after COVID-19, reflecting an increase of 10.58mL/cm3 in VT (area under the receiver-operating curve, AUC=0.95 [0.85-1.00]). At a cutoff of 10.6, sensitivity/specificity/accuracy were 0.88/0.93/0.91. The results from this study imply that neuroimmune response is a distinct and identifiable characteristic of brain fog after COVID-19. Results suggest that [18F]-FEPPA could be used to support N-PASC diagnosis.