Immunometabolism has emerged as a rapidly growing field of research, holding significant promise for personalised medicine and precision immunotherapy. This review explores the intricate relationship between immune function and metabolic processes, emphasising their profound impact on various immune-related disorders. Understanding how metabolic dysregulation contributes to the pathogenesis of these disorders remains a critical research gap. Therefore, this review aims to bridge that gap by examining the key metabolic pathways involved and their specific implications in immune cell function. Key metabolic pathways, including glycolysis, mitochondrial metabolism, fatty acid metabolism, and amino acid metabolism, are discussed in the context of immune cell function. Dysregulation of these pathways can disrupt immune cell activation, differentiation, and overall function, contributing to disease pathogenesis. Understanding these metabolic alterations\' molecular mechanisms is essential for developing targeted therapeutic interventions. The review also emphasises the importance of personalised medicine in immune-related disorders. The unique metabolic profiles of individuals can influence treatment outcomes, highlighting the need for tailored approaches. Integrating metabolic profiling into clinical practice can enhance treatment efficacy and improve patient outcomes. Investigating the clinical significance of immunometabolism in diverse disease contexts will facilitate the translation of research findings into clinical practice. Moreover, refining treatment strategies based on individual metabolic profiles will contribute to advancing precision immunotherapy.

It has been proposed that type 2 diabetes and schizophrenia-spectrum disorders share overlapping genetic backgrounds. Therefore, we aimed to perform a systematic review and meta-analysis of studies comparing fasting levels of glucose and insulin, the Homeostatic Model Assessment of Insulin Resistance (HOMA-IR), glucose levels during the oral glucose tolerance test (OGTT) and the levels of glycated hemoglobin (HbA1c) in unaffected first-degree relatives of patients with schizophrenia and controls. Online searches covered the publication period from database inception until May 8th 2020. Meta-analyses were performed using random-effects models with Hedges\' g as the effect size estimate. Out of 2556 records identified, 12 studies representing 672 relatives of schizophrenia patients and 6446 controls were found to be eligible. There were no significant differences in fasting levels of glucose (g = 0.54, 95%CI = -0.26 to 1.35, p = 0.188) and insulin (g = 0.07, 95%CI = -0.14 to 0.29, p = 0.491), HOMA-IR (g = 0.12, 95%CI = -0.19 to 0.43, p = 0.433), and the levels of HbA1c (g = 0.38, 95%CI = -0.02 to 0.77, p = 0.061) between relatives of schizophrenia patients and controls. Two studies demonstrated significantly higher 2-hour glucose levels during OGTT in relatives of patients with schizophrenia (g = 0.90, 95%CI = 0.49 to 1.31, p < 0.001). Our findings do not support the hypothesis that familial liability to psychosis is related to altered fasting parameters of glucose homeostasis. However, this population might show impaired glucose tolerance. More studies are needed to confirm these observations.

Skeletal muscle is recognized as a tissue with high metabolic capacity given its key roles in glucose and lipid metabolism. Although low muscle mass has been associated with metabolic disorders in adults, it is not clear if this body composition phenotype is related to metabolic health status earlier in life. In this review, we aim to clarify whether having low muscle mass is associated with increased risk of metabolic dysregulation in the pediatric population. Fifteen original articles investigating the relationship between body composition measures of muscle mass and single or clustered metabolic risk factors in children and adolescents were critically evaluated. Despite a growing body of evidence supporting low muscle mass as a risk factor for metabolic health in children and adolescents, conflicting associations were reported. Differences in body composition techniques, muscle mass indices, and clinical methods used to assess metabolic biomarkers may have contributed to a lack of a consistent conclusion. Moreover, most studies did not control for potential biological and lifestyle confounders. Future studies using precise, reproducible techniques to evaluate body composition and metabolic biomarkers are required to determine the implications of low muscle mass on metabolic health during childhood and adolescence.

Background: Antipsychotic-induced weight gain (AIWG) and other adverse metabolic effects represent serious side effects faced by many patients with psychosis that can lead to numerous comorbidities and which reduce the lifespan. While the pathophysiology of AIWG remains poorly understood, numerous studies have reported a positive association between AIWG and the therapeutic benefit of antipsychotic medications. Objectives: To review the literature to (1) determine if AIWG is consistently associated with therapeutic benefit and (2) investigate which variables may mediate such an association. Data Sources: MEDLINE, Google Scholar, Cochrane Database and PsycINFO databases were searched for articles containing all the following exploded MESH terms: schizophrenia [AND] antipsychotic agents/neuroleptics [AND] (weight gain [OR] lipids [OR] insulin [OR] leptin) [AND] treatment outcome. Results were limited to full-text, English journal articles. Results: Our literature search uncovered 31 independent studies which investigated an AIWG-therapeutic benefit association with a total of 6063 enrolled individuals diagnosed with schizophrenia or another serious mental illness receiving antipsychotic medications. Twenty-two studies found a positive association while, 10 studies found no association and one study reported a negative association. Study variables including medication compliance, sex, ethnicity, or prior antipsychotic exposure did not appear to consistently affect the AIWG-therapeutic benefit relationship. In contrast, there was some evidence that controlling for baseline BMI/psychopathology, duration of treatment and specific agent studied [i.e., olanzapine (OLZ) or clozapine (CLZ)] strengthened the relationship between AIWG and therapeutic benefit. Limitations: There were limitations of the reviewed studies in that many had small sample sizes, and/or were retrospective. The heterogeneity of the studies also made comparisons difficult and publication bias was not controlled for. Conclusions: An AIWG-therapeutic benefit association may exist and is most likely to be observed in OLZ and CLZ-treated patients. The clinical meaningfulness of this association remains unclear and weight gain and other metabolic comorbidities should be identified and treated to the same targets as the general population. Further research should continue to explore the links between therapeutic benefit and metabolic health with emphasis on both pre-clinical work and well-designed prospective clinical trials examining metabolic parameters associated, but also occurring independently to AIWG.

Dyslipidaemia is one of the most prevalent metabolic disturbances observed in schizophrenia patients and has been largely attributed to the effects of poor lifestyle habits and adverse effects of antipsychotic treatment. However, less is known whether patients with first-episode non-affective psychosis (FENP) present subthreshold indices of dyslipidaemia. Therefore, we tested the hypothesis whether subclinical lipid profile alterations occur already in antipsychotic-naïve FENP patients.
In this systematic review and meta-analysis we adhered to the PRISMA guidelines and searched PubMed, CINAHL Complete, Academic Search Complete, ERIC and Health Source: Nursing/Academic Edition from database inception to Dec 12, 2016, for case-control studies measuring the levels of total cholesterol, low- and high-density lipoproteins (LDL and HDL) and triglycerides in patients with FENP and controls. W calculated effect size (ES) estimates as Hedges\' g and pooled data using random- or fixed-effects models depending on heterogeneity. Our study was registered in the PROSPERO database (CRD42016051732).
Out of 2466 records identified, 19 studies representing 1803 participants were finally included in our systematic review and meta-analysis. Pooled analysis revealed that FENP patients had significantly lower levels of total cholesterol [ES=-0.16 (95% CI: -0.27, -0.06), p=0.003], LDL [ES=-0.13 (95% CI: -0.24, -0.01), p=0.034] and HDL [ES=-0.27 (95% CI: -0.49, -0.05), p=0.018] as well as significantly higher levels of triglycerides [ES=0.22 (95% CI: 0.11, 0.32), p<0.001] compared to controls. After removing single studies in sensitivity analysis, ES estimate for LDL levels was insignificant.
Antipsychotic-naïve patients with FENP present subclinical dyslipidaemia. Future studies should disentangle whether our findings reflect disease-specific mechanisms.