The aim of this systematic review and meta-analysis was to synthesize the knowledge about the relation between intake of 12 major food groups and risk of type 2 diabetes (T2D). We conducted a systematic search in PubMed, Embase, Medline (Ovid), Cochrane Central, and Google Scholar for prospective studies investigating the association between whole grains, refined grains, vegetables, fruits, nuts, legumes, eggs, dairy, fish, red meat, processed meat, and sugar-sweetened beverages (SSB) on risk of T2D. Summary relative risks were estimated using a random effects model by contrasting categories, and for linear and non-linear dose–response relationships. Six out of the 12 food-groups showed a significant relation with risk of T2D, three of them a decrease of risk with increasing consumption (whole grains, fruits, and dairy), and three an increase of risk with increasing consumption (red meat, processed meat, and SSB) in the linear dose–response meta-analysis. There was evidence of a non-linear relationship between fruits, vegetables, processed meat, whole grains, and SSB and T2D risk. Optimal consumption of risk-decreasing foods resulted in a 42% reduction, and consumption of risk-increasing foods was associated with a threefold T2D risk, compared to non-consumption. The meta-evidence was graded “low” for legumes and nuts; “moderate” for refined grains, vegetables, fruit, eggs, dairy, and fish; and “high” for processed meat, red meat, whole grains, and SSB. Among the investigated food groups, selecting specific optimal intakes can lead to a considerable change in risk of T2D.

Background: Despite growing evidence for food-based dietary patterns' potential to reduce cardiovascular disease risk, knowledge about the amounts of food associated with the greatest change in risk of specific cardiovascular outcomes and about the quality of meta-evidence is limited. Therefore, the aim of this meta-analysis was to synthesize the knowledge about the relation between intake of 12 major food groups (whole grains, refined grains, vegetables, fruits, nuts, legumes, eggs, dairy, fish, red meat, processed meat, and sugar-sweetened beverages [SSB]) and the risk of coronary heart disease (CHD), stroke and heart failure (HF). Methods: We conducted a systematic search in PubMed and Embase up to March 2017 for prospective studies. Summary risk ratios (RRs) and 95% confidence intervals (95% CI) were estimated using a random effects model for highest versus lowest intake categories, as well as for linear and non-linear relationships. Results: Overall, 123 reports were included in the meta-analyses. An inverse association was present for whole grains (RR_CHD: 0.95 (95% CI: 0.92–0.98), RR_HF: 0.96 (0.95–0.97)), vegetables and fruits (RR_CHD: 0.97 (0.96–0.99), and 0.94 (0.90–0.97); RR_stroke: 0.92 (0.86–0.98), and 0.90 (0.84–0.97)), nuts (RR_CHD: 0.67 (0.43–1.05)), and fish consumption (RR_CHD: 0.88 (0.79–0.99), RR_stroke: 0.86 (0.75–0.99), and RR_HF: 0.80 (0.67–0.95)), while a positive association was present for egg (RR_HF: 1.16 (1.03–1.31)), red meat (RR_CHD: 1.15 (1.08–1.23), RR_stroke: 1.12 (1.06–1.17), RR_HF: 1.08 (1.02–1.14)), processed meat (RR_CHD: 1.27 (1.09–1.49), RR_stroke: 1.17 (1.02–1.34), RR_HF: 1.12 (1.05–1.19)), and SSB consumption (RR_CHD: 1.17 (1.11–1.23), RR_stroke: 1.07 (1.02–1.12), RR_HF: 1.08 (1.05–1.12)) in the linear dose-response meta-analysis. There were clear indications for non-linear dose-response relationships between whole grains, fruits, nuts, dairy, and red meat and CHD. Conclusion: An optimal intake of whole grains, vegetables, fruits, nuts, legumes, dairy, fish, red and processed meat, eggs and SSB showed an important lower risk of CHD, stroke, and HF.

Background: Suboptimal diet is one of the most important factors in preventing early death and disability worldwide.

Objective: To summarise the evidence of associations between dietary factors and incidence of type 2 diabetes and to evaluate the strength and validity of these associations. Design: Umbrella review of systematic reviews with meta-analyses of prospective observational studies. Data sources: PubMed, Web of Science, and Embase, searched up to August 2018. Eligibility criteria: Systematic reviews with meta-analyses reporting summary risk estimates for the associations between incidence of type 2 diabetes and dietary behaviours or diet quality indices, food groups, foods, beverages, alcoholic beverages, macronutrients, and micronutrients. Results: 53 publications were included, with 153 adjusted summary hazard ratios on dietary behaviours or diet quality indices (n=12), food groups and foods (n=56), beverages (n=10), alcoholic beverages (n=12), macronutrients (n=32), and micronutrients (n=31), regarding incidence of type 2 diabetes. Methodological quality was high for 75% (n=115) of meta-analyses, moderate for 23% (n=35), and low for 2% (n=3). Quality of evidence was rated high for an inverse association for type 2 diabetes incidence with increased intake of whole grains (for an increment of 30 g/day, adjusted summary hazard ratio 0.87 (95% confidence interval 0.82 to 0.93)) and cereal fibre (for an increment of 10 g/day, 0.75 (0.65 to 0.86)), as well as for moderate intake of total alcohol (for an intake of 12-24 g/day v no consumption, 0.75 (0.67 to 0.83)). Quality of evidence was also high for the association for increased incidence of type 2 diabetes with higher intake of red meat (for an increment of 100 g/day, 1.17 (1.08 to 1.26)), processed meat (for an increment of 50 g/day, 1.37 (1.22 to 1.54)), bacon (per two slices/day, 2.07 (1.40 to 3.05)), and sugar sweetened beverages (for an increase of one serving/day, 1.26 (1.11 to 1.43)). Conclusions: Overall, the association between dietary factors and type 2 diabetes has been extensively studied, but few of the associations were graded as high quality of evidence. Further factors are likely to be important in type 2 diabetes prevention; thus, more well conducted research, with more detailed assessment of diet, is needed. Systematic review registration: PROSPERO CRD42018088106.

Aims/hypothesis: The term prediabetes is used for individuals who have impaired glucose metabolism whose glucose or HbA_1c levels are not yet high enough to be diagnosed as diabetes. Prediabetes may already be associated with an increased risk of chronic ‘diabetes-related’ complications. This umbrella review aimed to provide a systematic overview of the available evidence from meta-analyses of prospective observational studies on the associations between prediabetes and incident diabetes-related complications in adults and to evaluate their strength and certainty. Methods: For this umbrella review, systematic reviews with meta-analyses reporting summary risk estimates for the associations between prediabetes (based on fasting or 2 h postload glucose or on HbA_1c) and incidence of diabetes-related complications, comorbidities and mortality risk were included. PubMed, Web of Science, the Cochrane Library and Epistemonikos were searched up to 17 June 2021. Summary risk estimates were recalculated using a random effects model. The certainty of evidence was evaluated by applying the GRADE tool. This study is registered with PROSPERO, CRD42020153227. Results: Ninety-five meta-analyses from 16 publications were identified. In the general population, prediabetes was associated with a 6–101% increased risk for all-cause mortality and the incidence of cardiovascular outcomes, CHD, stroke, heart failure, atrial fibrillation and chronic kidney disease, as well as total cancer, total liver cancer, hepatocellular carcinoma, breast cancer and all-cause dementia with moderate certainty of evidence. No associations between prediabetes and incident depressive symptoms and cognitive impairment were observed (with low or very low certainty of evidence). The association with all-cause mortality was stronger for prediabetes defined by impaired glucose tolerance than for prediabetes defined by HbA_1c. Conclusions/interpretation: Prediabetes was positively associated with risk of all-cause mortality and the incidence of cardiovascular outcomes, CHD, stroke, chronic kidney disease, cancer and dementia. Further high-quality studies, particularly on HbA_1c-defined prediabetes and other relevant health outcomes (e. g. neuropathy) are required to support the evidence. Graphical

This meta-analysis summarizes the evidence of a prospective association between the intake of foods [whole grains, refined grains, vegetables, fruit, nuts, legumes, eggs, dairy, fish, red meat, processed meat, and sugar-sweetened beverages (SSBs)] and risk of general overweight/obesity, abdominal obesity, and weight gain. PubMed and Web of Science were searched for prospective observational studies until August 2018. Summary RRs and 95% CIs were estimated from 43 reports for the highest compared with the lowest intake categories, as well as for linear and nonlinear relations focusing on each outcome separately: overweight/obesity, abdominal obesity, and weight gain. The quality of evidence was evaluated with use of the NutriGrade tool. In the dose-response meta-analysis, inverse associations were found for whole-grain (RR_{overweight/obesity}: 0.93; 95% CI: 0.89, 0.96), fruit (RR_{overweight/obesity}: 0.93; 95% CI: 0.86, 1.00; RR_{weight gain}: 0.91; 95% CI: 0.86, 0.97), nut (RR_{abdominal obesity}: 0.42; 95% CI: 0.31, 0.57), legume (RR_{overweight/obesity}: 0.88; 95% CI: 0.84, 0.93), and fish (RR_{abdominal obesity}: 0.83; 95% CI: 0.71, 0.97) consumption and positive associations were found for refined grains (RR_{overweight/obesity}: 1.05; 95% CI: 1.00, 1.10), red meat (RR_{abdominal obesity}: 1.10; 95% CI: 1.04, 1.16; RR_{weight gain}: 1.14; 95% CI: 1.03, 1.26), and SSBs (RR_{overweight/obesity}: 1.05; 95% CI: 1.00, 1.11; RR_{abdominal obesity}: 1.12; 95% CI: 1.04, 1.20). The dose-response meta-analytical findings provided very low to low quality of evidence that certain food groups have an impact on different measurements of adiposity risk. To improve the quality of evidence, better-designed observational studies, inclusion of intervention trials, and use of novel statistical methods (e.g., substitution analyses or network meta-analyses) are needed.