Deep serum lipidomics identifies evaluative and predictive biomarkers for individualized glycemic responses following low-energy diet-induced weight loss: a PREVention of diabetes through lifestyle Intervention and population studies in Europe and around the World (PREVIEW) substudy.

BACKGROUND: Weight loss through lifestyle interventions, notably low-energy diets, offers glycemic benefits in populations with overweight-associated prediabetes. However, >50% of these individuals fail to achieve normoglycemia after weight loss. Circulating lipids hold potential for evaluating dietary impacts and predicting diabetes risk. OBJECTIVES: This study sought to identify serum lipids that could serve as evaluative or predictive biomarkers for individual glycemic changes following diet-induced weight loss. METHODS: We studied 104 participants with overweight-associated prediabetes, who lost ≥8% weight via a low-energy diet over 8 wk. High-coverage lipidomics was conducted in serum samples before and after the dietary intervention. The lipidomic recalibration was assessed using differential lipid abundance comparisons and partial least squares discriminant analyses. Associations between lipid changes and clinical characteristics were determined by Spearman correlation and Bootstrap Forest of ensemble machine learning model. Baseline lipids, predictive of glycemic parameters changes postweight loss, were assessed using Bootstrap Forest analyses. RESULTS: We quantified 439 serum lipid species and 9 related organic acids. Dietary intervention significantly reduced diacylglycerols, ceramides, lysophospholipids, and ether-linked phosphatidylethanolamine. In contrast, acylcarnitines, short-chain fatty acids, organic acids, and ether-linked phosphatidylcholine increased significantly. Changes in certain lipid species (e.g., saturated and monounsaturated fatty acid-containing glycerolipids, sphingadienine-based very long-chain sphingolipids, and organic acids) were closely associated with clinical glycemic parameters. Six baseline bioactive sphingolipids primarily predicted changes in fasting plasma glucose. In addition, a number of baseline lipid species, mainly diacylglycerols and triglycerides, were predictive of clinical changes in hemoglobin A1c, insulin and homeostasis model assessment of insulin resistance. CONCLUSIONS: Newly discovered serum lipidomic alterations and the associated changes in lipid-clinical variables suggest broad metabolic reprogramming related to diet-mediated glycemic control. Novel lipid predictors of glycemic outcomes could facilitate early stratification of individuals with prediabetes who are metabolically less responsive to weight loss, enabling more tailored intervention strategies beyond 1-size-fits-all lifestyle modification advice. The PREVIEW lifestyle intervention study was registered at clinicaltrials.gov as NCT01777893 (https://clinicaltrials.gov/study/NCT01777893).

The Decreasing Trend in Dietary Glycaemic Index and Glycaemic Load in Australian Children and Adolescents between 1995 and 2012.

This study aims to examine whether there were changes between 1995⁻2012 in the dietary glycaemic index (dGI) and glycaemic load (dGL) in Australian children (<16 years) according to three national surveys in 1995 (1995NS), 2007 (2007NS), and 2011⁻2012 (2012NS). Glycaemic index (GI) values of foods were assigned using published methodology. Plausible 24-h recall data from the 1995NS, 2007NS and 2012NS (weighted n = 2475, 4373 and 1691 respectively) were compared for differences in dGI and dGL, and the contribution to dGL from different foods using one-way ANOVA with Bonferroni post hoc comparisons and linear regression. Decreasing trends across surveys were found in dGI and dGL (p < 0.001). Between 1995 and 2012, dGI and dGL per Megajoule (MJ) dropped by 2% and 6% respectively. The per capita dGL contribution from breads and bread rolls, fruit and vegetable juices, sweetened beverages and potatoes showed strong decreasing trends (R² > 0.7). Our findings suggest that dGI and dGL of Australian youths declined between 1995 to 2012, which may be due to increased awareness of the GI concept and healthy diet, widened food choices and immigrants with diverse dietary habits. This may lower the future risks of chronic degenerative diseases in Australian youths.

Validity of carbohydrate, glycaemic index and glycaemic load data obtained using a semi-quantitative food-frequency questionnaire.

OBJECTIVE: To assess the ability of a food-frequency questionnaire (FFQ) to rank Australians according to their intake of total carbohydrate, sugar, starch, fibre, glycaemic index (GI) and glycaemic load (GL). DESIGN: Cross-sectional sample from a population cohort. SETTING: Two postcode areas west of Sydney, Australia. SUBJECTS: From 1992 to 1994, a total of 2868 older Australians provided dietary data using a 145-item Willett-derived FFQ. A representative sub-sample of 78 subjects completed three 4-day weighed food records (WFRs). Pearson and Spearman correlations, Bland-Altman plots and weighted kappa values were calculated. RESULTS: Compared with the WFR, the FFQ provided higher mean estimates of all nutrients except starch and GI. All Pearson and/or Spearman correlations were greater than 0.5, except for GL. For GI, sugar, starch and fibre, the regression lines from the Bland-Altman analysis indicated a non-significant linear trend (P = 0.07, P = 0.36, P = 0.28 and P = 0.10, respectively). For GL and total carbohydrate, however, there was a significant linear trend (P = 0.006 and P < 0.0001, respectively), indicating that as the GL and carbohydrate intake of individuals increased, so did the magnitude of the error between the FFQ and WFR. Weighted kappa values all indicated moderate to good agreement, with the exception of GL which was only fair. The proportions of subjects correctly classified within one quintile for all of the nutrients were over 50% and gross misclassification was low (<10%). CONCLUSION: This FFQ was able to rank individuals according to their intakes of total carbohydrate, sugar, starch, fibre and GI, but not as well for GL.