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Gestational diabetes mellitus (GDM) is defined as “glucose intolerance that is first diagnosed during pregnancy”. Mothers with GDM and their infants may experience both short and long term complications. Dietary intervention is the first therapeutic strategy. If good glycaemic control is not achieved, insulin therapy is recommended. There is no consensus on which nutritional approach should be used in GDM. In the last few years, there has been growing evidence of the benefits of a low glycaemic index (LGI) diet on diabetes and cardiovascular disease. The effect of a LGI diet on GDM incidence has been investigated as well. Several studies observed a lower incidence of GDM in LGI diet arms, without adverse maternal and fetal outcomes. The main positive effect of the LGI diet was the reduction of 2-h post-prandial glucose (PPG). Several studies have also evaluated the effect of the LGI diet in GDM treatment. Overall, the LGI diet might have beneficial effects on certain outcomes, such as 2-h PPG, fasting plasma glucose and lipid profile in patients with GDM. Indeed, most studies observed a significant reduction in insulin requirement. Overall, according to current evidence, the LGI nutritional approach is safe and it might therefore be considered in clinical care for GDM.

Consumers' interest towards glycaemic index (GI) is expanding due to their aspiration towards informed buying and increased awareness regarding diet. This is especially the case for people engaged in various physical activities. Their diet, besides general principles of a healthy balanced diet, should also meet the needs of training. Consumption of carbohydrates with different GI before, during and after training affects athletic performance, with high GI food being preferred. The aim was to compare the effect of honey and two different sports energy products on glycaemia and their potential in terms of sports performance. GI of acacia honey and two sports energy products (gel and jelly) was determined according to the method ISO 26642: 2010. Ten healthy males aged 23.2 years (20 to 27 years old) who are minimum 4 hours per week engaged in at least one sport were enrolled. Glycaemia was the least affected by honey (GI = 89.6 ± 37.1), while jelly increased glycaemia the most (GI = 162.1 ± 60.9), and the difference was statistically significant (p = 0.008). Honey also showed good acceptability, and did not differ in subjective satiety score to gel or jelly. The results show that honey can be a good alternative to various sports energy products, and a more affordable one.

There are no published data regarding the overall dietary glycaemic index (GI) and glycaemic load (GL) of Australian children and adolescents. We therefore aim to describe the dietary GI and GL of participants of the 2007 Australian National Children's Nutrition and Physical Activity Survey (2007ANCNPAS), and to identify the main foods contributing to their GL. Children, aged 2–16 years, who provided two 24 h recalls in the 2007ANCNPAS were included. A final dataset of 4184 participants was analysed. GI of each food item was assigned using a previously published method. GL was calculated, and food groups contributing to the GL were described by age group and sex. The weighted mean dietary GI and GL of the participants were 54 (sd 5) and 136 (sd 44), respectively. Among the nutrients examined, Ca had the highest inverse relationship with GI (P < 0·001), while percentage energy from starch was most positively associated with GI. The association between fibre density and GI was modest, and percentage energy from sugar had an inverse relationship with GI. Daily dietary GL contributed by energy-dense and/or nutrient-poor (EDNP) items in subjects aged 14–16 years was more than doubled that of subjects aged 2–3 years. To conclude, Australian children and adolescents were having a high-GI dietary pattern characterised by high-starchy food intake and low Ca intake. A significant proportion of their dietary GL was from EDNP foods. Efforts to reduce dietary GI and GL in children and adolescents should focus on energy-dense starchy foods.

To assess the glycaemic response after ingestion of two specialised oral and enteral nutrition formulas for glycaemic control. The participants were sixteen healthy volunteers, aged 21–49 years, with normal glucose tolerance. The volunteers attended the tests fasting for 10 h, for 5 weeks, and consumed the reference food – glucose solution – for 3 weeks, and the two formulas DiamaxO and DiamaxIG in the following weeks, in amounts equivalent to 25 g of available carbohydrates. During the period of 120 min, seven blood samples were taken through capillary blood sampling to determine the glycaemic response. The glycaemic index (GI) was calculated according to the trapezoidal rule, ignoring areas below the fasting line. The glycaemic load (GL) was determined by the formula GL = ((GI(glucose = reference) × ‘g’ of available carbohydrate per serving]/100. The formulas showed low GI and GL. GI = 37·8 and GL = 6·6 for DiamaxO and GI = 21·5 and GL = 3·5 for DiamaxIG. The peak of the glycaemic response occurred 30 min after ingestion, with a marked difference in blood glucose between the Diamax products in relation to glucose. Differences were also significant at times 15, 45, 60 and 90 min in relation to glucose (ANOVA with post hoc Bonferroni, P < 0·005), but not between the two products. However, the AUC and the GI of DiamaxIG are significantly smaller than that of the DiamaxO second t test (P = 0·0059). The glycaemic response to the products is quite reduced, presenting a curve with a little accentuated shape, without high peak, especially in the modified product.

The glycemic index (GI) has been included in the Canadian clinical practice guidelines for type 2 diabetes (T2D) management since 2003, and even longer in other parts of the world (e.g., Australia). Despite this, dietitians have reported that GI is “too difficult for patients to understand and apply.” They have called for diverse GI-utility data and evidence-informed education materials. To address these concerns, we developed and evaluated a GI education workshop and supporting materials, using the Kirkpatrick Model, for a T2D population. Participants (n = 29) with T2D attended a dietitian-facilitated workshop and received education materials. A mixed-form questionnaire (GIQ) and 3-day-diet-record were used to capture patient demographics, satisfaction, knowledge, and application, prior to and immediately after the workshop, 1-week, and 4-weeks post-education. Dietary GI was significantly lower at 1 and 4 weeks post-education (mean ± SEM; both 54 ± 1), compared to pre-education (58 ± 1; p ≤ 0.001). Participants (28/29) were satisfied with the intervention. The GI knowledge score was significantly higher post-education at baseline (83.5 ± 3.4%; p ≤ 0.001), week one (87.5 ± 2.6%; p = 0.035), and week four (87.6 ± 3.8%; p = 0.011) when compared to pre-education (53.6 ± 5.1%). A significant reduction in dietary GI was achieved by participants living with T2D, after completing the workshop, and they were able to acquire and apply GI knowledge in a relatively short period.

Type 2 diabetes mellitus (T2DM) remains a significant global public health concern, largely driven by poor dietary habits, physical inactivity, and rising obesity rates. In recent years, personalised nutrition (PN) emerged as a promising approach to T2DM prevention and management. This narrative review synthesises current evidence on tailored dietary strategies, including the glycaemic index (GI), glycaemic load (GL), food insulin index (FII), and precision nutrition tools. It further explores their impact on glycaemic control, insulin sensitivity, and adherence to dietary interventions. A structured review of peer-reviewed and grey literature was conducted, taking into account behavioural, cultural, and systemic implementation factors. Although evidence supports the efficacy of PN in improving metabolic outcomes, implementation in low- and middle-income countries (LMICs) remains limited due to infrastructural, financial, and contextual challenges. This review emphasises the need for context-specific, scalable solutions integrated into primary healthcare systems.

Previous studies have found direct associations between glycaemic index (GI) and glycaemic load (GL) with chronic diseases. However, this evidence has not been consistent in relation to mortality, and most data regarding this association come from high-income and low-carbohydrate-intake populations. The aim of this study was to evaluate the association between the overall GI and dietary GL and all-cause mortality, CVD and breast cancer mortality in Mexico. Participants from the Mexican Teachers’ Cohort (MTC) study in 2006–2008 were followed for a median of 10 years. Overall GI and dietary GL were calculated from a validated FFQ. Deaths were identified by the cross-linkage of MTC participants with two national mortality registries. Cox proportional hazard models were used to estimate the impact of GI and GL on mortality. We identified 1198 deaths. Comparing the lowest and highest quintile, dietary GI and GL appeared to be marginally associated with all-cause mortality; GI, 1·12 (95 % CI: 0·93, 1·35); GL, 1·12 (95 % CI: 0·87, 1·44). Higher GI and GL were associated with increased risk of CVD mortality, GI, 1·30 (95 % CI: 0·82, 2·08); GL, 1·64 (95 % CI: 0·87, 3·07) and with greater risk of breast cancer mortality; GI, 2·13 (95 % CI: 1·12, 4·06); GL, 2·43 (95 % CI: 0·90, 6·59). It is necessary to continue the improvement of carbohydrate quality indicators to better guide consumer choices and to lead the Mexican population to limit excessive intake of low-quality carbohydrate foods.

There is a lack of information from Middle Eastern countries regarding diet–disease associations. We examined dietary glycaemic and insulinemic potential in relation to risk of incident diabetes among a large group of Iranian adults. The present study was carried out using data from Yazd Health Study-Taghzieh Mardom Yazd, a prospective cohort study on adults aged 20–70 years in Yazd Greater Area, Iran. This study was initiated in 2014–2016 (baseline examination), and data are collected prospectively at one 5-year intervals. Data on demographic characteristics, dietary intakes and potential confounders were gathered by interview. During the follow-up phase of the study, diabetes incidence was confirmed by laboratory tests and physician diagnoses. This study included a total of 6178 participants in the cross-sectional analysis and 5105 subjects in the prospective phase. Examining the cross-sectional phase, we failed to find any significant association between dietary glycaemic index (GI) and glycaemic load (GL) as well as dietary insulin index (DII) or dietary insulin load (DIL) and prevalence of diabetes. No significant relationship was also seen between DII/DIL and risk of T2DM in the prospective phase; however, in the stratified analysis by BMI status, there was an inverse significant association between DII and risk of type 2 diabetes mellitus (hazard ratio for T3 v. T1: 0·19; 95 % CI: 0·04, 0·92; P = 0·03) in non-obese (BMI ≥ 25 kg/m2) and overweight (BMI < 25 kg/m2) participants. No significant association was found between dietary GI/ dietary GL/ DII/ or DIL with risk of T2DM. More research, particularly with a longer follow-up duration, is needed to confirm these findings.

•The present systematic review and meta-analysis included 41 randomized controlled trials.•Epigallocatechin gallate supplementation notably reduced decrease FBS, HA1c and HOMA-IR in diabetic patient. Epigallocatechin gallate (EGCG), a major catechin found in green tea, has been suggested to influence glycemic control. This systematic review and meta-analysis aim to evaluate the effects of EGCG on the glycemic index (GI) and related glycemic parameters. Using predefined keywords, online databases (PubMed, Scopus, Web of Science Core Collection, and Google Scholar) were searched for relevant studies, published from inception up to May 2024. Initially 1994 studies were obtained out of which 41 RCTs were decided to be included for further analyses. The meta-analysis demonstrated that EGCG supplementation led to statistically significant, but modest, reductions in fasting blood glucose (FBG), HbA1c, and HOMA-IR. Notably, the reduction in HbA1c (WMD: −0.18%, 95% CI: −0.35, −0.02; P = 0.029) was small and may not equate to clinically meaningful benefits for all populations. Furthermore, the effect on fasting insulin was not statistically significant (WMD: −0.50; 95% CI: −1.46, 0.47; P = 0.313), indicating a lack of robust or consistent impact on this parameter across studies. Although EGCG supplementation is associated with improvements in some glycemic parameters, these effects especially for HbA1c and fasting insulin are modest and may not be clinically meaningful for most population. Therefore, current evidence does not strongly support the use of EGCG as a stand-alone intervention for glycemic control.

Aims/hypothesis A type 2 diabetes-risk-increasing variant, MTNR1B (melatonin receptor 1B) rs10830963, regulates the circadian function and may influence the variability in metabolic responses to dietary carbohydrates. We investigated whether the effects of carbohydrate quantity and dietary glycaemic index (GI) on glycaemic response during OGTTs varied by the risk G allele of MTNR1B- rs10830963. Methods This study included participants ( n =150) of a randomised crossover-controlled feeding trial of four diets with high/low GI levels and high/low carbohydrate content for 5 weeks. The MTNR1B- rs10830963 (C/G) variant was genotyped. Glucose response during 2 h OGTT was measured at baseline and the end of each diet intervention. Results Among the four study diets, carrying the risk G allele (CG/GG vs CC genotype) of MTNR1B -rs10830963 was associated with the largest AUC of glucose during 2 h OGTT after consuming a high-carbohydrate/high-GI diet (β 134.32 [SE 45.69] mmol/l × min; p =0.004). The risk G-allele carriers showed greater increment of glucose during 0–60 min (β 1.26 [0.47] mmol/l; p =0.008) or 0–90 min (β 1.10 [0.50] mmol/l; p =0.028) after the high-carbohydrate/high-GI diet intervention, but not after consuming the other three diets. At high carbohydrate content, reducing GI levels decreased 60 min post-OGTT glucose (mean –0.67 [95% CI: –1.18, –0.17] mmol/l) and the increment of glucose during 0–60 min (mean –1.00 [95% CI: –1.67, –0.33] mmol/l) and 0–90 min, particularly in the risk G-allele carriers ( p interaction <0.05 for all). Conclusions/interpretation Our study shows that carrying the risk G allele of MTNR1B -rs10830963 is associated with greater glycaemic responses after consuming a diet with high carbohydrates and high GI levels. Reducing GI in a high-carbohydrate diet may decrease post-OGTT glucose concentrations among the risk G-allele carriers. Graphical Abstract