Prevention of type 2 diabetes (T2D) is a great challenge worldwide. The aim of this evidence synthesis was to summarize the available evidence in order to update the European Association for the Study of Diabetes (EASD) clinical practice guidelines for nutrition therapy. We conducted a systematic review and, where appropriate, meta-analyses of randomized controlled trials (RCTs) carried out in people with impaired glucose tolerance (IGT) (six studies) or dysmetabolism (one study) to answer the following questions: What is the evidence that T2D is preventable by lifestyle changes? What is the optimal diet (with a particular focus on diet quality) for prevention, and does the prevention of T2D result in a lower risk of late complications of T2D? The Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach was applied to assess the certainty of the trial evidence. Altogether seven RCTs (N = 4090) fulfilled the eligibility criteria and were included in the meta-analysis. The diagnosis of incident diabetes was based on an oral glucose tolerance test (OGTT). The overall risk reduction of T2D by the lifestyle interventions was 0.53 (95% CI 0.41; 0.67). Most of the trials aimed to reduce weight, increase physical activity, and apply a diet relatively low in saturated fat and high in fiber. The PREDIMED trial that did not meet eligibility criteria for inclusion in the meta-analysis was used in the final assessment of diet quality. We conclude that T2D is preventable by changing lifestyle and the risk reduction is sustained for many years after the active intervention (high certainty of evidence). Healthy dietary changes based on the current recommendations and the Mediterranean dietary pattern can be recommended for the long-term prevention of diabetes. There is limited or insufficient data to show that prevention of T2D by lifestyle changes results in a lower risk of cardiovascular and microvascular complications.

Fasting glucose and insulin are intermediate traits for type 2 diabetes. Here we explore the role of coding variation on these traits by analysis of variants on the HumanExome BeadChip in 60,564 non-diabetic individuals and in 16,491 T2D cases and 81,877 controls. We identify a novel association of a low-frequency nonsynonymous SNV in GLP1R (A316T; rs10305492; MAF=1.4%) with lower FG (β=−0.09±0.01 mmol l⁻¹, P=3.4 × 10⁻¹²), T2D risk (OR[95%CI]=0.86[0.76–0.96], P=0.010), early insulin secretion (β=−0.07±0.035 pmol_insulin mmol_glucose⁻¹, P=0.048), but higher 2-h glucose (β=0.16±0.05 mmol l⁻¹, P=4.3 × 10⁻⁴). We identify a gene-based association with FG at G6PC2 (p_SKAT=6.8 × 10⁻⁶) driven by four rare protein-coding SNVs (H177Y, Y207S, R283X and S324P). We identify rs651007 (MAF=20%) in the first intron of ABO at the putative promoter of an antisense lncRNA, associating with higher FG (β=0.02±0.004 mmol l⁻¹, P=1.3 × 10⁻⁸). Our approach identifies novel coding variant associations and extends the allelic spectrum of variation underlying diabetes-related quantitative traits and T2D susceptibility.

Some studies document relationships of the incidence of gestational diabetes mellitus (GDM) with individual components of the diet, but studies exploring relationships with patterns of eating are lacking. This observational study aimed to explore a possible relationship between the incidence of GDM and the Mediterranean diet (MedDiet) pattern of eating. In 10 Mediterranean countries, 1076 consecutive pregnant women underwent a 75-g OGTT at the 24th–32nd week of gestation, interpreted both by the ADA_2010 and the International Association of the Diabetes and Pregnancy Study Groups (IADPSG)_2012 criteria. The dietary habits were assessed by a previously validated questionnaire and a Mediterranean Diet Index (MDI) was computed, reflecting the degree of adherence to the MedDiet pattern of eating: a higher MDI denoting better adherence. After adjustment for age, BMI, diabetes in the family, weight gain and energy intake, subjects with GDM, by either criterion, had lower MDI (ADA_2010, 5.8 vs 6.3, P=0.028; IADPSG_2012, 5.9 vs 6.4, P<0.001). Moreover, the incidence of GDM was lower in subjects with better adherence to the MedDiet (higher tertile of MDI distribution), 8.0% vs 12.3%, OR=0.618, P=0.030 by ADA_2010 and 24.3% vs 32.8%, OR=0.655, P=0.004 by IADPSG_2012 criteria. In subjects without GDM, MDI was negatively correlated with both fasting plasma glucose and AUC glucose, P<0.001 for both. Adherence to a MedDiet pattern of eating is associated with lower incidence of GDM and better degree of glucose tolerance, even in women without GDM. The possibility to use MedDiet for the prevention of GDM deserves further testing with intervention studies.

OBJECTIVE—To investigate the role of dietary factors in the development of type 2 diabetes. RESEARCH DESIGN AND METHODS—In the context of the Multinational MGSD Nutrition Study, three groups of subjects were studied: 204 subjects with recently diagnosed diabetes (RDM), 42 subjects with undiagnosed diabetes (UDM) (American Diabetes Association criteria—fasting plasma glucose [FPG] ≥126 mg/dl), and 55 subjects with impaired fasting glucose (IFG) (FPG ≥110 and <126 mg/dl). Each group was compared with a control group of nondiabetic subjects, matched one by one for center, sex, age, and BMI. Nutritional habits were evaluated by a dietary history method, validated against the 3-day diet diary. In RDM, the questionnaire referred to the nutritional habits before the diagnosis of diabetes. Demographic data were collected, and anthropometrical and biochemical measurements were taken. RESULTS—Compared with control subjects, RDM more frequently had a family history of diabetes (49.0 vs. 14.2%; P < 0.001), exercised less (exercise index 53.5 vs. 64.4; P < 0.01), and more frequently had sedentary professions (47.5 vs. 27.4%; P < 0.001). Carbohydrates contributed less to their energy intake (53.5 vs. 55.1%; P < 0.05), whereas total fat (30.2 ± 0.5 vs. 27.8 ± 0.5%; P < 0.001) and animal fat (12.2 ± 0.3 vs. 10.8 ± 0.3%; P < 0.01) contributed more and the plant-to-animal fat ratio was lower (1.5 ± 0.1 vs. 1.8 ± 0.1; P < 0.01). UDM more frequently had a family history of diabetes (38.1 vs. 19.0%; P < 0.05) and sedentary professions (58.5 vs. 34.1%; P < 0.05), carbohydrates contributed less to their energy intake (47.6 ± 1.7 vs. 52.8 ± 1.4%; P < 0.05), total fat (34.7 ± 1.5 vs. 30.4 ± 1.2%; P < 0.05) and animal fat (14.2 ± 0.9 vs. 10.6 ± 0.7%; P < 0.05) contributed more, and the plant-to-animal fat ratio was lower (1.6 ± 0.2 vs. 2.3 ± 0.4; P < 0.05). IFG differed only in the prevalence of family history of diabetes (32.7 vs. 16.4%; P < 0.05). CONCLUSIONS—Our data support the view that increased animal fat intake is associated with the presence of diabetes.

The estimation of long-term diabetes complications risk is essential in the process of medical decision making. Guidelines for the management of Type 2 Diabetes Mellitus (T2DM) advocate calculating the Cardiovascular Disease (CVD) risk to initiate appropriate treatment. The objective of this study is to investigate the use of sophisticated machine learning techniques towards the development of personalized models able to predict the risk of fatal or non-fatal CVD incidence in T2DM patients. The important challenge of handling the unbalanced nature of the available dataset is addressed, by applying novel ensemble strategies. Hybrid Wavelet Neural Networks (HWNNs) and Self-Organizing Maps (SOMs) constitute the primary models for building ensembles following a sub-sampling approach. Different methods for combining the decisions of the primary models are applied and comparatively assessed. Data from the 5-year follow up of 560 patients with T2DM are used for development and evaluation purposes. The highest discrimination performance (Area Under the Curve (AUC): 71.48%) is achieved by taking into account both the HWNN- and SOM- based primary models' outputs. The proposed method is superior to the Binomial Linear Regression (BLR) model justifying the need to apply more sophisticated techniques in order to produce reliable CVD risk scores.