Objective To evaluate the existing meta-analytic evidence of associations between exposure to ultra-processed foods, as defined by the Nova food classification system, and adverse health outcomes. Design Systematic umbrella review of existing meta-analyses. Data sources MEDLINE, PsycINFO, Embase, and the Cochrane Database of Systematic Reviews, as well as manual searches of reference lists from 2009 to June 2023. Eligibility criteria for selecting studies Systematic reviews and meta-analyses of cohort, case-control, and/or cross sectional study designs. To evaluate the credibility of evidence, pre-specified evidence classification criteria were applied, graded as convincing (“class I”), highly suggestive (“class II”), suggestive (“class III”), weak (“class IV”), or no evidence (“class V”). The quality of evidence was assessed using the GRADE (Grading of Recommendations, Assessment, Development, and Evaluations) framework, categorised as “high,” “moderate,” “low,” or “very low” quality. Results The search identified 45 unique pooled analyses, including 13 dose-response associations and 32 non-dose-response associations (n=9 888 373). Overall, direct associations were found between exposure to ultra-processed foods and 32 (71%) health parameters spanning mortality, cancer, and mental, respiratory, cardiovascular, gastrointestinal, and metabolic health outcomes. Based on the pre-specified evidence classification criteria, convincing evidence (class I) supported direct associations between greater ultra-processed food exposure and higher risks of incident cardiovascular disease related mortality (risk ratio 1.50, 95% confidence interval 1.37 to 1.63; GRADE=very low) and type 2 diabetes (dose-response risk ratio 1.12, 1.11 to 1.13; moderate), as well as higher risks of prevalent anxiety outcomes (odds ratio 1.48, 1.37 to 1.59; low) and combined common mental disorder outcomes (odds ratio 1.53, 1.43 to 1.63; low). Highly suggestive (class II) evidence indicated that greater exposure to ultra-processed foods was directly associated with higher risks of incident all cause mortality (risk ratio 1.21, 1.15 to 1.27; low), heart disease related mortality (hazard ratio 1.66, 1.51 to 1.84; low), type 2 diabetes (odds ratio 1.40, 1.23 to 1.59; very low), and depressive outcomes (hazard ratio 1.22, 1.16 to 1.28; low), together with higher risks of prevalent adverse sleep related outcomes (odds ratio 1.41, 1.24 to 1.61; low), wheezing (risk ratio 1.40, 1.27 to 1.55; low), and obesity (odds ratio 1.55, 1.36 to 1.77; low). Of the remaining 34 pooled analyses, 21 were graded as suggestive or weak strength (class III-IV) and 13 were graded as no evidence (class V). Overall, using the GRADE framework, 22 pooled analyses were rated as low quality, with 19 rated as very low quality and four rated as moderate quality. Conclusions Greater exposure to ultra-processed food was associated with a higher risk of adverse health outcomes, especially cardiometabolic, common mental disorder, and mortality outcomes. These findings provide a rationale to develop and evaluate the effectiveness of using population based and public health measures to target and reduce dietary exposure to ultra-processed foods for improved human health. They also inform and provide support for urgent mechanistic research. Systematic review registration PROSPERO CRD42023412732.

Medicinal cannabis might have a role in supporting the mental health of people with cancer. This systematic review and meta-analysis examined the efficacy and safety of medicinal cannabis, compared with any control, as an intervention for depression, anxiety, and stress symptoms in people living with cancer. A secondary aim was to examine the effect of low versus high Δ⁹-tetrahydrocannabinol (THC) dose on these outcomes. Five databases were systematically searched, and complemented with a snowball search from inception to May 2023, for any type of interventional study that included humans of any age with any cancer type. Primary outcomes were incidence and severity of depression, anxiety, and stress symptoms. Secondary outcomes were mood, cognition, quality of life, appetite, nutrition status, gastrointestinal symptoms, and adverse events. Data were pooled using Review Manager. Evidence was appraised using Cochrane risk of bias tools. Confidence in the estimated effect of pooled outcomes was assessed using Grading of Recommendations, Assessment, Development and Evaluation (GRADE). Fifteen studies (n = 11 randomized trials, n = 4 non-randomized trials) of 18 interventions (N = 1898 total participants; 100 % ≥18 years of age) were included. Ten studies examined THC (70 % synthetic), two synthetic cannabidiol with or without THC, and six whole-plant extracts. No clinically significant effects of medicinal cannabis were found on primary outcomes. The likelihood of anxiety events increased with higher-dose synthetic THC compared with a lower dose (OR: 2.0; 95 % CI: 1.4, 2.9; p < 0.001; Confidence: very low). Medicinal cannabis (THC, cannabidiol, and whole-plant extract) increased the likelihood of improved appetite (OR: 12.3; 95 % CI: 3.5, 45.5; p < 0.001; n = 3 interventions; Confidence: moderate) and reduced severity of appetite loss (SMD: -0.4; 95 % CI: -0.8, -0.1; p = 0.009; Confidence: very low). There was very low confidence that higher doses of synthetic THC increased the likelihood of any adverse event (OR: 0.5; 95 % CI: 0.3, 0.7; p < 0.001). Medicinal cannabis had no effect on emotional functioning, mood changes, confusion, disorientation, quality of life, and gastrointestinal symptoms. Confidence in findings was limited by some studies having high or unclear risk of bias and imprecise pooled estimates. There was insufficient evidence to determine the efficacy and safety of medicinal cannabis as a therapeutic intervention for depression, anxiety, or stress in people with active cancer. Further research should explore whether medicinal cannabis might improve and maintain appetite and if high-dose synthetic THC might increase the incidence of side-effects, including anxiety. To inform clinical practice, well-powered and rigorously designed trials are warranted that evaluate the effects of medicinal cannabis prescribed to target anxiety, depression, and stress.

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.

Due to the high prevalence of depression among young adults, identifying prevention strategies during young adulthood is crucial. Dietary polyphenols have been associated with depression in older cohorts; however, the association remains unclear, particularly in young adults. This study aimed to assess the prospective association between the intake of total polyphenols, polyphenol classes and polyphenol subclasses with depressive symptoms in young adults. Data from 1484 Raine Study Generation 2 participants (52·7 % female; baseline mean age ( sd ): 20 (0·5)) at the −20, −22, and −27 year follow-ups ( n 964, 979 and 1094, respectively), with overlap across follow-ups, were used. Energy-adjusted polyphenol intake was estimated from FFQ data using our expansion of the AUSNUT 2011–13 and Phenol-Explorer to include polyphenol content data and categorised into quartiles. The primary outcome was self-reported depressive symptoms assessed via the twenty-one-item Depression, Anxiety and Stress Scale averaged across the three time points. Linear mixed-effects models were used to assess the association between the polyphenol intake exposures and depressive symptoms. Sociodemographic characteristics and lifestyle- and health-related behaviours were adjusted for. Participants in the highest quartiles for flavonol and hydroxybenzoic acid intake had lower depressive symptoms across time than participants in the lowest quartiles (flavonols (Q4 v. Q1 mean difference: −1.38; 95% CI −2.48, −0.28); hydroxybenzoic acids (Q4 v. Q1: −1.40; 95% CI −2.53, −0.27). We found no evidence of a highest v. lowest association for all other polyphenol categories. Future studies are required to investigate whether increasing polyphenol intake could protect against depression in young adults.

Introduction: There is a lack of detailed polyphenol intake data collected at the national level, both in Australia and globally. Hence, the aim of this study was to estimate the intake of total polyphenols, polyphenol classes, and polyphenol subclasses in a nationally representative sample of Australians across the lifespan. Methods: Polyphenol intake was estimated from secondary analyses of two 24‐h dietary recalls collected as part of the 2011–12 National Nutrition and Physical Activity Survey component of the Australian Health Survey ( N = 7735) using an Australian‐specific polyphenol database. Polyphenol content data were previously incorporated into this database using Phenol‐Explorer. Intake of total polyphenols, polyphenol classes, and polyphenol subclasses was reported and compared by sex and age using median regression models, both before and after adjusting for energy intake. Results: The estimated total polyphenol intake of Australians aged 2+ years [mean (SD) age: 40.9 (22.7) years] was median (IQR) 479.07 (529.81) mg/day when unadjusted for energy intake and 253.18 (276.29) mg per 1000 kcal/day when adjusted. The most consumed polyphenol class was flavonoids, providing 64.5% and 55.6% of total polyphenols before and after adjusting for energy intake. Among major food groups, the top contributor to total polyphenols was non‐alcoholic beverages, primarily consisting of tea, coffee, and coffee substitutes. Discussion: This study offers an Australian‐wide comprehensive description of polyphenol intake. By addressing key gaps in the literature, this study contributes to the limited global and Australian research on polyphenol intake. Further, this study includes findings that align with other population‐wide polyphenol intake studies from Europe, the United States, and the United Kingdom that have also reported tea and coffee as primary sources of polyphenols.