Abuqwider, Jumana (2026) POSTPRANDIAL GLUCOSE RESPONSE IN PATIENTS WITH TYPE 1 DIABETES: POSSIBLE ROLE OF DIET AND GUT MICROBIOTA COMPOSITION IN MOVING TOWARD A PERSONALIZED APPROACH. [Tesi di dottorato]

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Abstract

Background Type 1 diabetes mellitus (T1D) is an autoimmune disease characterized by immune-mediated destruction of pancreatic β-cells, resulting in absolute insulin deficiency. Despite major technological advances, including continuous glucose monitoring (CGM) and automated insulin delivery systems, optimal management of postprandial glucose responses (PPGR) remains a significant clinical challenge. PPGR is influenced by a wide range of intra- and inter-individual factors, including meal composition and timing, habitual dietary patterns, and gut microbiome composition and function. A deeper understanding of how these factors interact to shape postprandial glycaemic dynamics may enable more personalized therapeutic strategies and improved metabolic control in individuals with T1D. Objectives The objectives of this thesis were to: (i) systematically review the current evidence and identify knowledge gaps regarding the relationship between gut microbiome characteristics and glycaemic control in T1D; (ii) evaluate the performance of commercially available hybrid closed-loop (artificial pancreas) insulin delivery systems in managing postprandial glucose responses following meals with differing macronutrient composition; (iii) investigate the associations between habitual dietary patterns, gut microbiome composition, and glycaemic control, with a particular focus on short-chain fatty acids, adherence to the Mediterranean diet, and consumption of ultra-processed foods; and (iv) develop a predictive model integrating gut microbiome profiles to improve the prediction of postprandial glucose responses in individuals with T1D. Materials and Methods To identify the determinants of PPGR variability and overall glycaemic patterns in T1D, one systematic review and five experimental studies were conducted at the Diabetes Unit of the University Hospital Federico II of Naples. The study population included adults aged 18–65 years with T1D treated with multiple daily insulin injections, insulin pump therapy, or automated insulin delivery systems, and with HbA1c values <8.5%. Assessments included global and 10 postprandial glycaemic control derived from CGM, dietary intake evaluated through food records and the EPIC food frequency questionnaire, and gut microbiome profiling. Random forest regression models were developed to predict PPGR. Statistical analyses included t-tests, analysis of variance (ANOVA), correlation analyses, PERMANOVA, linear discriminant analysis effect size (LEfSe), and machine-learning modelling. Results New-generation automated insulin delivery systems demonstrated comparable efficacy in managing postprandial glucose responses, despite substantial differences in insulin delivery patterns. Higher circulating propionate levels were associated with improved glycaemic control in women with T1D, characterized by increased time in range (70–180 mg/dL), reduced time above range (>180 mg/dL), and lower glucose management indicator values, highlighting a sex-specific effect. Greater adherence to the Mediterranean diet was associated with better metabolic control and a more favourable gut microbiome composition. Ultra-processed food consumption was associated with an atherogenic lipid profile, characterized by higher triglyceride concentrations and lower HDL cholesterol levels; these associations were driven by specific ultra-processed food subgroups and accompanied by distinct gut microbiome features. Finally, a machine learning–based random forest regression model successfully predicted postprandial glucose responses using gut microbiome profiles. Conclusions Postprandial glucose responses and overall glycaemic control in T1D arise from a complex interplay of nutritional, behavioural, and individual biological factors. Consideration of carbohydrate content alone is insufficient to explain the observed variability in postprandial glycaemic responses, underscoring the need for more personalized management strategies. Integrating clinical, dietary, and gut microbiome data within artificial intelligence–based models represents a promising approach toward truly personalized prediction and optimization of glycaemic control in individuals with T1D.

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