Nutrition, the gut microbiota and immunity are all important factors in the maintenance of health. However, there is a growing realization of the complex interplay between these elements coalescing in a nutrition–gut microbiota–immunity axis. This regulatory axis is critical for health with disruption being implicated in a broad range of diseases, including autoimmune disorders, allergies and mental health disorders. This new perspective continues to underpin a growing number of innovative therapeutic strategies targeting different elements of this axis to treat relevant diseases. This review describes the inter-relationships between nutrition, the gut microbiota and immunity. It then details several human diseases where disruption of the nutrition–gut microbiota–immunity axis has been identified and presents examples of how the various elements may be targeted therapeutically as alternate treatment strategies for these diseases.

Machine Learning (ML) models have been used to predict common mental disorders (CMDs) and may provide insights into the key modifiable factors that can identify and predict CMD risk and be targeted through interventions. This systematic review aimed to synthesise evidence from ML studies predicting CMDs, evaluate their performance, and establish the potential benefit of incorporating lifestyle data in ML models alongside biological and/or demographic-environmental factors. This systematic review adheres to the PRISMA statement (Prospero CRD42023401194). Databases searched included MEDLINE, EMBASE, PsycInfo, IEEE Xplore, Engineering Village, Web of Science, and Scopus from database inception to 28/08/24. Included studies used ML methods with feature importance to predict CMDs in adults. Risk of bias (ROB) was assessed using PROBAST. Model performance metrics were compared. The ten most important variables reported by each study were assigned to broader categories to evaluate their frequency across studies. 117 studies were included (111 model development-only, 16 development and validation). Deep learning methods showed best accuracy for predicting CMD cases. Studies commonly incorporated features from multiple categories (n = 56), and frequently identified demographic-environmental predictors in their top ten most important variables (63/69 models). These tended to be in combination with psycho-social and biological variables (n = 15). Lifestyle data were infrequently examined as sole predictors of CMDs across included studies (4.27 %). Studies commonly had high heterogeneity and ROB ratings. This review is the first to evaluate the utility of diagnostic ML for CMDs, assess their ROB, and evaluate predictor types. CMDs were able to be predicted, however studies had high ROB and lifestyle data were underutilised, precluding full identification of a robust predictor set.

Depression is a risk factor for cardiometabolic diseases, but the extent to which this is attributable to genetic (rather than environmental) factors is unclear. Mendelian randomisation (MR) uses genetic variants to infer causality. We aimed to investigate the bidirectional association between respective genetic susceptibility to depression and cardiometabolic diseases. We conducted a systematic review and meta-analysis of MR studies (unidirectional or bidirectional) in adults that assessed associations between genetic variants related to depression and any cardiometabolic disease. A comprehensive literature search of MEDLINE, EMBASE, PsycINFO, CINAHL, Scopus, Web of Science, and Cochrane Library was performed to capture relevant articles published between database inception to June 20, 2024. This search was updated on April 23, 2025. Reference lists of included articles were manually searched for additional records. Articles published in English or translated into English were included. Primary outcomes of interest included cardiovascular diseases (six) and metabolic diseases (three). Contextually similar studies were pooled and sub-grouped based on population ancestry, study design, and outcome definition. A customised risk of bias tools for Mendelian randomisation was used to assess the risk of bias across five domains. Heterogeneity was assessed via I2 and tau-squared (τ2) statistics and publication bias was assessed via visual inspection of contour-enhanced funnel plots. This work is registered with PROSPERO, CRD42023460334. We included 79 studies (294 MR analyses) in the systematic review and 21 studies (29 MR analyses) in the meta-analysis. For the primary outcomes eligible for pooling, genetic susceptibility to depression was associated with increased odds of coronary artery disease (odds ratio 1.12, 95% CI 1.05–1.19; k = 3, τ² = 0.00, I² = 0%), myocardial infarction (1.13, 1.10–1.17; k = 3, τ² = 0.00, I² = 0%), and elevated triglycerides (1.10, 1.06–1.14; k = 3, τ² = 0.00, I² = 0); but not with diabetes (1.08, 0.96–1.21; k = 3, τ² = 0.01, I² = 75%), high-density lipoproteins (0.97, 0.92–1.01; k = 3, τ² = 0.00, I² = 37%), or low-density lipoproteins (1.12, 0.99–1.28; k = 4, τ² = 0.00, I² = 87%). Leave-one-out analyses showed that some studies altered the results more than the others. Our findings suggest that depression and cardiometabolic diseases are associated with each other. Though the observed effects are small and of limited immediate clinical relevance, our findings reflect the genetic component of the association separately to the behavioural and environmental influences. Treatment strategies identifying those at risk could benefit both diseases; however, high heterogeneity warrants cautious interpretation of our findings. None.

Depression contributes substantially to disease burden in the United States. Diet is a modifiable risk factor, yet surprisingly the population-level diet-depression association has never been comprehensively evaluated over time. We aimed to assess prevalence of suboptimal dietary intakes and depression, and the association between diet and depression over time using National Health and Nutrition Examination Survey (2005–2018) data. Complete-case logistic regression assessed associations between diet (following Global Burden of Disease definitions, plus ultra-processed food [UPF]; measured using 24-hour recalls) and odds of depression (defined as PHQ-9 ≥ 10). Depression prevalence (7.69%) was consistent from 2007–2018. Prevalence of suboptimal milk intake increased over time (p = 0.003), but suboptimal processed meat (p = 0.038), red meat (p = 0.022), nuts/seeds (p = 0.001), and polyunsaturated fat intake decreased (p < 0.001). Higher fiber intake was consistently associated with lower odds of depression from 2005–2018, while higher UPF intakes were associated with higher odds from 2007–2018. All other dietary risks were associated at various years but not consistently at each wave. Given previously observed causal relationships between diet and depression, improving population-level fiber intake and reducing UPF consumption may plausibly reduce depression burden in the United States. These results support the Global Burden of Disease Lifestyle And mental Disorders project (DERR2-10.2196/65576).

Background: Mental disorders are leading contributors to disease burden worldwide. Nutritional psychiatry research supports an important role for diet in the prevention and management of mental disorders, but the relevance of specific dietary exposures requires further investigation. We examined associations of Global Burden of Disease-defined food groups and nutrients with subsequent depression and anxiety outcomes in UK Biobank participants, following the Global Burden of Disease Lifestyle And mental Disorders Taskforce protocol. Methods: We used data from the UK Biobank, a large biomedical cohort of more than 500,000 UK participants. Dietary intake was assessed using repeated 24-hour dietary recalls collected between 2011 and 2012 and classified according to Global Burden of Disease dietary exposure definitions. Depression and anxiety outcomes were assessed using the Patient Health Questionnaire-9 and Generalised Anxiety Disorder-7 in the 2016 Mental Wellbeing Questionnaire, after excluding participants with pre-existing depression or anxiety from the respective models. Associations were estimated using complete-case log-binomial regression models with robust standard errors, adjusting for demographic, socioeconomic, and lifestyle covariates. Results: Analyses included 46,503 participants in depression models and 48,656 participants in anxiety models. Overall, dietary intake was generally suboptimal, with many participants not meeting theoretical minimum risk exposure levels across several dietary factors. In fully adjusted models, higher intakes of wholegrains and fibre were associated with lower depression risk, whereas higher intakes of sugar-sweetened beverages and sodium were associated with higher depression risk. For anxiety, higher intakes of sugar-sweetened beverages, sodium and legumes were associated with higher risk, although the legume association was attenuated in post-hoc analyses excluding baked beans. Higher intakes of red meat, processed meat and trans fatty acids were associated with lower anxiety risk; however, the trans fatty acid association was attenuated in post-hoc analyses. Conclusions: In this large UK Biobank cohort, selected dietary exposures were associated with subsequent depression and anxiety outcomes. Findings were most consistent for wholegrains, fibre, sugar-sweetened beverages, and sodium, suggesting that specific aspects of diet quality may be relevant to later mental health. Associations for other dietary exposures were less consistent across outcomes, adjustment sets, and subgroup analyses. Further research is needed to confirm these findings and clarify potential mechanisms.