Exercise-induced improvement of glycemic fluctuation and its relationship with fat and muscle distribution in type 2 diabetes.

AIMS: Management of blood glucose fluctuation is essential for diabetes. Exercise is a key therapeutic strategy for diabetes patients, although little is known about determinants of glycemic response to exercise training. We aimed to investigate the effect of combined aerobic and resistance exercise training on blood glucose fluctuation in type 2 diabetes patients and explore the predictors of exercise-induced glycemic response. MATERIALS AND METHODS: Fifty sedentary diabetes patients were randomly assigned to control or exercise group. Participants in the control group maintained sedentary lifestyle for 2 weeks, and those in the exercise group specifically performed combined exercise training for 1 week. All participants received dietary guidance based on a recommended diet chart. Glycemic fluctuation was measured by flash continuous glucose monitoring. Baseline fat and muscle distribution were accurately quantified through magnetic resonance imaging (MRI). RESULTS: Combined exercise training decreased SD of sensor glucose (SDSG, exercise-pre vs exercise-post, mean 1.35 vs 1.10 mmol/L, p = .006) and coefficient of variation (CV, mean 20.25 vs 17.20%, p = .027). No significant change was observed in the control group. Stepwise multiple linear regression showed that baseline MRI-quantified fat and muscle distribution, including visceral fat area (ß = -0.761, p = .001) and mid-thigh muscle area (ß = 0.450, p = .027), were significantly independent predictors of SDSG change in the exercise group, as well as CV change. CONCLUSIONS: Combined exercise training improved blood glucose fluctuation in diabetes patients. Baseline fat and muscle distribution were significant factors that influence glycemic response to exercise, providing new insights into personalized exercise intervention for diabetes.

DRAC 2022: A public benchmark for diabetic retinopathy analysis on ultra-wide optical coherence tomography angiography images.

We described a challenge named "DRAC - Diabetic Retinopathy Analysis Challenge" in conjunction with the 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022). Within this challenge, we provided the DRAC datset, an ultra-wide optical coherence tomography angiography (UW-OCTA) dataset (1,103 images), addressing three primary clinical tasks: diabetic retinopathy (DR) lesion segmentation, image quality assessment, and DR grading. The scientific community responded positively to the challenge, with 11, 12, and 13 teams submitting different solutions for these three tasks, respectively. This paper presents a concise summary and analysis of the top-performing solutions and results across all challenge tasks. These solutions could provide practical guidance for developing accurate classification and segmentation models for image quality assessment and DR diagnosis using UW-OCTA images, potentially improving the diagnostic capabilities of healthcare professionals. The dataset has been released to support the development of computer-aided diagnostic systems for DR evaluation.

Resistant starch intake facilitates weight loss in humans by reshaping the gut microbiota.

Emerging evidence suggests that modulation of gut microbiota by dietary fibre may offer solutions for metabolic disorders. In a randomized placebo-controlled crossover design trial (ChiCTR-TTRCC-13003333) in 37 participants with overweight or obesity, we test whether resistant starch (RS) as a dietary supplement influences obesity-related outcomes. Here, we show that RS supplementation for 8 weeks can help to achieve weight loss (mean -2.8 kg) and improve insulin resistance in individuals with excess body weight. The benefits of RS are associated with changes in gut microbiota composition. Supplementation with Bifidobacterium adolescentis, a species that is markedly associated with the alleviation of obesity in the study participants, protects male mice from diet-induced obesity. Mechanistically, the RS-induced changes in the gut microbiota alter the bile acid profile, reduce inflammation by restoring the intestinal barrier and inhibit lipid absorption. We demonstrate that RS can facilitate weight loss at least partially through B. adolescentis and that the gut microbiota is essential for the action of RS.

A deep learning system for predicting time to progression of diabetic retinopathy.

Diabetic retinopathy (DR) is the leading cause of preventable blindness worldwide. The risk of DR progression is highly variable among different individuals, making it difficult to predict risk and personalize screening intervals. We developed and validated a deep learning system (DeepDR Plus) to predict time to DR progression within 5 years solely from fundus images. First, we used 717,308 fundus images from 179,327 participants with diabetes to pretrain the system. Subsequently, we trained and validated the system with a multiethnic dataset comprising 118,868 images from 29,868 participants with diabetes. For predicting time to DR progression, the system achieved concordance indexes of 0.754-0.846 and integrated Brier scores of 0.153-0.241 for all times up to 5 years. Furthermore, we validated the system in real-world cohorts of participants with diabetes. The integration with clinical workflow could potentially extend the mean screening interval from 12 months to 31.97 months, and the percentage of participants recommended to be screened at 1-5 years was 30.62%, 20.00%, 19.63%, 11.85% and 17.89%, respectively, while delayed detection of progression to vision-threatening DR was 0.18%. Altogether, the DeepDR Plus system could predict individualized risk and time to DR progression over 5 years, potentially allowing personalized screening intervals.

Sex- and age-specific associations between abdominal fat and non-alcoholic fatty liver disease: a prospective cohort study.

Obesity is closely related to non-alcoholic fatty liver disease (NAFLD). Although sex differences in body fat distribution have been well demonstrated, little is known about the sex-specific associations between adipose tissue and the development of NAFLD. Using community-based cohort data, we evaluated the associations between magnetic resonance imaging-quantified areas of abdominal adipose tissue, including visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT), and incident NAFLD in 2830 participants (1205 males and 1625 females) aged 55-70 years. During a 4.6-year median follow-up, the cumulative incidence rates of NAFLD increased with areas of VAT and SAT both in males and females. Further analyses showed that the abovementioned positive associations were stronger in males than in females, especially in participants under 60 years old. In contrast, these sex differences disappeared in those over 60 years old. Furthermore, the risk of developing NAFLD increased nonlinearly with increasing fat area in a sex-specific pattern. Additionally, sex-specific potential mediators, such as insulin resistance, lipid metabolism, inflammation, and adipokines, may exist in the associations between adipose tissue and NAFLD. This study showed that the associations between abdominal fat and the risk of NAFLD were stratified by sex and age, highlighting the potential need for sex- and age-specific management of NAFLD.

Artificial intelligence in diabetes management: Advancements, opportunities, and challenges.

The increasing prevalence of diabetes, high avoidable morbidity and mortality due to diabetes and diabetic complications, and related substantial economic burden make diabetes a significant health challenge worldwide. A shortage of diabetes specialists, uneven distribution of medical resources, low adherence to medications, and improper self-management contribute to poor glycemic control in patients with diabetes. Recent advancements in digital health technologies, especially artificial intelligence (AI), provide a significant opportunity to achieve better efficiency in diabetes care, which may diminish the increase in diabetes-related health-care expenditures. Here, we review the recent progress in the application of AI in the management of diabetes and then discuss the opportunities and challenges of AI application in clinical practice. Furthermore, we explore the possibility of combining and expanding upon existing digital health technologies to develop an AI-assisted digital health-care ecosystem that includes the prevention and management of diabetes.

Effect of virtual reality-based exercise and physical exercise on adolescents with overweight and obesity: study protocol for a randomised controlled trial.

INTRODUCTION: Obesity is a complex and multifactorial disease that has affected many adolescents in recent decades. Clinical practice guidelines recommend exercise as the key treatment option for adolescents with overweight and obesity. However, the effects of virtual reality (VR) exercise on the physical and brain health of adolescents with overweight and obese remain unclear. This study aims to evaluate the effects of physical and VR exercises on physical and brain outcomes and explore the differences in benefits between them. Moreover, we will apply a multiomics analysis to investigate the mechanism underlying the effects of physical and VR exercises on adolescents with overweight and obesity. METHODS AND ANALYSIS: This randomised controlled clinical trial will include 220 adolescents with overweight and obesity aged between 11 and 17 years. The participants will be randomised into five groups after screening. Participants in the exercise groups will perform an exercise programme by adding physical or VR table tennis or soccer classes to routine physical education classes in schools three times a week for 8 weeks. Participants in the control group will maintain their usual physical activity. The primary outcome will be the change in body fat mass measured using bioelectrical impedance analysis. The secondary outcomes will include changes in other physical health-related parameters, brain health-related parameters and multiomics variables. ETHICS AND DISSEMINATION: This study was approved by the Ethics Committee of Shanghai Sixth People's Hospital and registered in the Chinese Clinical Trial Registry. Dissemination of the findings will include peer-reviewed publications, conference presentations and media releases. TRIAL REGISTRATION NUMBER: ChiCTR2300068786.

Resistant starch decreases intrahepatic triglycerides in patients with NAFLD via gut microbiome alterations.

Non-alcoholic fatty liver disease (NAFLD) is a hepatic manifestation of metabolic dysfunction for which effective interventions are lacking. To investigate the effects of resistant starch (RS) as a microbiota-directed dietary supplement for NAFLD treatment, we coupled a 4-month randomized placebo-controlled clinical trial in individuals with NAFLD (ChiCTR-IOR-15007519) with metagenomics and metabolomics analysis. Relative to the control (n = 97), the RS intervention (n = 99) resulted in a 9.08% absolute reduction of intrahepatic triglyceride content (IHTC), which was 5.89% after adjusting for weight loss. Serum branched-chain amino acids (BCAAs) and gut microbial species, in particular Bacteroides stercoris, significantly correlated with IHTC and liver enzymes and were reduced by RS. Multi-omics integrative analyses revealed the interplay among gut microbiota changes, BCAA availability, and hepatic steatosis, with causality supported by fecal microbiota transplantation and monocolonization in mice. Thus, RS dietary supplementation might be a strategy for managing NAFLD by altering gut microbiota composition and functionality.

TMM-Nets: Transferred Multi- to Mono-Modal Generation for Lupus Retinopathy Diagnosis.

Rare diseases, which are severely underrepresented in basic and clinical research, can particularly benefit from machine learning techniques. However, current learning-based approaches usually focus on either mono-modal image data or matched multi-modal data, whereas the diagnosis of rare diseases necessitates the aggregation of unstructured and unmatched multi-modal image data due to their rare and diverse nature. In this study, we therefore propose diagnosis-guided multi-to-mono modal generation networks (TMM-Nets) along with training and testing procedures. TMM-Nets can transfer data from multiple sources to a single modality for diagnostic data structurization. To demonstrate their potential in the context of rare diseases, TMM-Nets were deployed to diagnose the lupus retinopathy (LR-SLE), leveraging unmatched regular and ultra-wide-field fundus images for transfer learning. The TMM-Nets encoded the transfer learning from diabetic retinopathy to LR-SLE based on the similarity of the fundus lesions. In addition, a lesion-aware multi-scale attention mechanism was developed for clinical alerts, enabling TMM-Nets not only to inform patient care, but also to provide insights consistent with those of clinicians. An adversarial strategy was also developed to refine multi- to mono-modal image generation based on diagnostic results and the data distribution to enhance the data augmentation performance. Compared to the baseline model, the TMM-Nets showed 35.19% and 33.56% F1 score improvements on the test and external validation sets, respectively. In addition, the TMM-Nets can be used to develop diagnostic models for other rare diseases.

Effects of Exercise Intervention on Type 2 Diabetes Patients With Abdominal Obesity and Low Thigh Circumference (EXTEND): Study Protocol for a Randomized Controlled Trial.

Introduction: Type 2 diabetes patients have abdominal obesity and low thigh circumference. Previous studies have mainly focused on the role of exercise in reducing body weight and fat mass, improving glucose and lipid metabolism, with a lack of evaluation on the loss of muscle mass, diabetes complications, energy metabolism, and brain health. Moreover, whether the potential physiological benefit of exercise for diabetes mellitus is related to the modulation of the microbiota-gut-brain axis remains unclear. Multi-omics approaches and multidimensional evaluations may help systematically and comprehensively correlate physical exercise and the metabolic benefits. Methods and Analysis: This study is a randomized controlled clinical trial. A total of 100 sedentary patients with type 2 diabetes will be allocated to either an exercise or a control group in a 1:1 ratio. Participants in the exercise group will receive a 16-week combined aerobic and resistance exercise training, while those in the control group will maintain their sedentary lifestyle unchanged. Additionally, all participants will receive a diet administration to control the confounding effects of diet. The primary outcome will be the change in body fat mass measured using bioelectrical impedance analysis. The secondary outcomes will include body fat mass change rate (%), and changes in anthropometric indicators (body weight, waist, hip, and thigh circumference), clinical biochemical indicators (glycated hemoglobin, blood glucose, insulin sensitivity, blood lipid, liver enzyme, and renal function), brain health (appetite, mood, and cognitive function), immunologic function, metagenomics, metabolomics, energy expenditure, cardiopulmonary fitness, exercise-related indicators, fatty liver, cytokines (fibroblast growth factor 21, fibroblast growth factor 19, adiponectin, fatty acid-binding protein 4, and lipocalin 2), vascular endothelial function, autonomic nervous function, and glucose fluctuation. Discussion: This study will evaluate the effect of a 16-week combined aerobic and resistance exercise regimen on patients with diabetes. The results will provide a comprehensive evaluation of the physiological effects of exercise, and reveal the role of the microbiota-gut-brain axis in exercise-induced metabolic benefits to diabetes. Clinical Trial Registration: http://www.chictr.org.cn/searchproj.aspx, identifier ChiCTR2100046148.

DeepDRiD: Diabetic Retinopathy-Grading and Image Quality Estimation Challenge.

We described a challenge named "Diabetic Retinopathy (DR)-Grading and Image Quality Estimation Challenge" in conjunction with ISBI 2020 to hold three sub-challenges and develop deep learning models for DR image assessment and grading. The scientific community responded positively to the challenge, with 34 submissions from 574 registrations. In the challenge, we provided the DeepDRiD dataset containing 2,000 regular DR images (500 patients) and 256 ultra-widefield images (128 patients), both having DR quality and grading annotations. We discussed details of the top 3 algorithms in each sub-challenges. The weighted kappa for DR grading ranged from 0.93 to 0.82, and the accuracy for image quality evaluation ranged from 0.70 to 0.65. The results showed that image quality assessment can be used as a further target for exploration. We also have released the DeepDRiD dataset on GitHub to help develop automatic systems and improve human judgment in DR screening and diagnosis.

Risk assessment with gut microbiome and metabolite markers in NAFLD development.

A growing body of evidence suggests interplay between the gut microbiota and the pathogenesis of nonalcoholic fatty liver disease (NAFLD). However, the role of the gut microbiome in early detection of NAFLD is unclear. Prospective studies are necessary for identifying reliable, microbiome markers for early NAFLD. We evaluated 2487 individuals in a community-based cohort who were followed up 4.6 years after initial clinical examination and biospecimen sampling. Metagenomic and metabolomic characterizations using stool and serum samples taken at baseline were performed for 90 participants who progressed to NAFLD and 90 controls who remained NAFLD free at the follow-up visit. Cases and controls were matched for gender, age, body mass index (BMI) at baseline and follow-up, and 4-year BMI change. Machine learning models integrating baseline microbial signatures (14 features) correctly classified participants (auROCs of 0.72 to 0.80) based on their NAFLD status and liver fat accumulation at the 4-year follow up, outperforming other prognostic clinical models (auROCs of 0.58 to 0.60). We confirmed the biological relevance of the microbiome features by testing their diagnostic ability in four external NAFLD case-control cohorts examined by biopsy or magnetic resonance spectroscopy, from Asia, Europe, and the United States. Our findings raise the possibility of using gut microbiota for early clinical warning of NAFLD development.

SparseVoxNet: 3-D Object Recognition With Sparsely Aggregation of 3-D Dense Blocks.

Automatic recognition of 3-D objects in a 3-D model by convolutional neural network (CNN) methods has been successfully applied to various tasks, e.g., robotics and augmented reality. Three-dimensional object recognition is mainly performed by analyzing the object using multi-view images, depth images, graphs, or volumetric data. In some cases, using volumetric data provides the most promising results. However, existing recognition techniques on volumetric data have many drawbacks, such as losing object details on converting points to voxels and the large size of the input volume data that leads to substantial 3-D CNNs. Using point clouds could also provide very promising results; however, point-cloud-based methods typically need sparse data entry and time-consuming training stages. Thus, using volumetric could be a more efficient and flexible recognizer for our special case in the School of Medicine, Shanghai Jiao Tong University. In this article, we propose a novel solution to 3-D object recognition from volumetric data using a combination of three compact CNN models, low-cost SparseNet, and feature representation technique. We achieve an optimized network by estimating extra geometrical information comprising the surface normal and curvature into two separated neural networks. These two models provide supplementary information to each voxel data that consequently improve the results. The primary network model takes advantage of all the predicted features and uses these features in Random Forest (RF) for recognition purposes. Our method outperforms other methods in training speed in our experiments and provides an accurate result as good as the state-of-the-art.

Mixed-Weight Neural Bagging for Detecting m6A Modifications in SARS-CoV-2 RNA Sequencing.

OBJECTIVE: The m6A modification is the most common ribonucleic acid (RNA) modification, playing a role in prompting the virus's gene mutation and protein structure changes in the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Nanopore single-molecule direct RNA sequencing (DRS) provides data support for RNA modification detection, which can preserve the potential m6A signature compared to second-generation sequencing. However, due to insufficient DRS data, there is a lack of methods to find m6A RNA modifications in DRS. Our purpose is to identify m6A modifications in DRS precisely. METHODS: We present a methodology for identifying m6A modifications that incorporated mapping and extracted features from DRS data. To detect m6A modifications, we introduce an ensemble method called mixed-weight neural bagging (MWNB), trained with 5-base RNA synthetic DRS containing modified and unmodified m6A. RESULTS: Our MWNB model achieved the highest classification accuracy of 97.85% and AUC of 0.9968. Additionally, we applied the MWNB model to the COVID-19 dataset; the experiment results reveal a strong association with biomedical experiments. CONCLUSION: Our strategy enables the prediction of m6A modifications using DRS data and completes the identification of m6A modifications on the SARS-CoV-2. SIGNIFICANCE: The Corona Virus Disease 2019 (COVID-19) outbreak has significantly influence, caused by the SARS-CoV-2. An RNA modification called m6A is connected with viral infections. The appearance of m6A modifications related to several essential proteins affects proteins' structure and function. Therefore, finding the location and number of m6A RNA modifications is crucial for subsequent analysis of the protein expression profile.

ECSU-Net: An Embedded Clustering Sliced U-Net Coupled With Fusing Strategy for Efficient Intervertebral Disc Segmentation and Classification.

Automatic vertebra segmentation from computed tomography (CT) image is the very first and a decisive stage in vertebra analysis for computer-based spinal diagnosis and therapy support system. However, automatic segmentation of vertebra remains challenging due to several reasons, including anatomic complexity of spine, unclear boundaries of the vertebrae associated with spongy and soft bones. Based on 2D U-Net, we have proposed an Embedded Clustering Sliced U-Net (ECSU-Net). ECSU-Net comprises of three modules named segmentation, intervertebral disc extraction (IDE) and fusion. The segmentation module follows an instance embedding clustering approach, where our three sliced sub-nets use axis of CT images to generate a coarse 2D segmentation along with embedding space with the same size of the input slices. Our IDE module is designed to classify vertebra and find the inter-space between two slices of segmented spine. Our fusion module takes the coarse segmentation (2D) and outputs the refined 3D results of vertebra. A novel adaptive discriminative loss (ADL) function is introduced to train the embedding space for clustering. In the fusion strategy, three modules are integrated via a learnable weight control component, which adaptively sets their contribution. We have evaluated classical and deep learning methods on Spineweb dataset-2. ECSU-Net has provided comparable performance to previous neural network based algorithms achieving the best segmentation dice score of 95.60% and classification accuracy of 96.20%, while taking less time and computation resources.

The role of FGF21 in the pathogenesis of cardiovascular disease.

ABSTRACT: The morbidity and mortality of cardiovascular diseases (CVDs) are increasing worldwide and seriously threaten human life and health. Fibroblast growth factor 21 (FGF21), a metabolic regulator, regulates glucose and lipid metabolism and may exert beneficial effects on the cardiovascular system. In recent years, FGF21 has been found to act directly on the cardiovascular system and may be used as an early biomarker of CVDs. The present review highlights the recent progress in understanding the relationship between FGF21 and CVDs including coronary heart disease, myocardial ischemia, cardiomyopathy, and heart failure and also explores the related mechanism of the cardioprotective effect of FGF21. FGF21 plays an important role in the prediction, treatment, and improvement of prognosis in CVDs. This cardioprotective effect of FGF21 may be achieved by preventing endothelial dysfunction and lipid accumulating, inhibiting cardiomyocyte apoptosis and regulating the associated oxidative stress, inflammation and autophagy. In conclusion, FGF21 is a promising target for the treatment of CVDs, however, its clinical application requires further clarification of the precise role of FGF21 in CVDs.

The relationship of neutrophil elastase and proteinase 3 with risk factors, and chronic complications in type 2 diabetes: A Fenofibrate Intervention and Event Lowering in Diabetes (FIELD) sub-study.

INTRODUCTION: Neutrophil elastase (NE) and proteinase 3 (PR3) are novel inflammation biomarkers. We investigated their associations with chronic complications, determinants of biomarker levels and effects of fenofibrate in patients with type 2 diabetes mellitus (T2DM) from Fenofibrate Intervention and Event Lowering in Diabetes study. METHODS: Plasma NE and PR3 levels were quantified at baseline (n = 2000), and relationships with complications over 5-years assessed. Effects of fenofibrate on biomarker levels (n = 200) were determined at four follow-up visits. RESULTS: Higher waist-to-hip ratio, homocysteine and C-reactive protein and lower apoA-II were determinants of higher NE and PR3 levels. Higher NE levels were associated with on-trial stroke and cardiovascular mortality, and higher PR3 levels with on-trial stroke, but associations were not significant after adjustment for confounding factors. Although higher NE and PR3 levels were associated with baseline total microvascular disease, only NE levels were associated with on-trial neuropathy or amputation. These associations were not significant after adjusting for multiple comparisons. NE and PR3 levels did not change with fenofibrate. CONCLUSIONS: In T2DM plasma NE and PR3 levels are associated with vascular risk factors, and total microvascular disease at baseline, but on rigorous analyses were not associated with on-trial complications. Levels were not changed by fenofibrate.

FGF21/adiponectin ratio predicts deterioration in glycemia: a 4.6-year prospective study in China.

BACKGROUND: The fibroblast growth factor (FGF) 21-adiponectin pathway is involved in the regulation of insulin resistance. However, the relationship between the FGF21-adiponectin pathway and type 2 diabetes in humans is unclear. Here, we investigated the association of FGF21/adiponectin ratio with deterioration in glycemia in a prospective cohort study. METHODS: We studied 6361 subjects recruited from the prospective Shanghai Nicheng Cohort Study in China. The association between baseline FGF21/adiponectin ratio and new-onset diabetes and incident prediabetes was evaluated using multiple logistic regression analysis. RESULTS: At baseline, FGF21/adiponectin ratio levels increased progressively with the deterioration in glycemic control from normal glucose tolerance to prediabetes and diabetes (p for trend < 0.001). Over a median follow-up of 4.6 years, 195 subjects developed new-onset diabetes and 351 subjects developed incident prediabetes. Elevated baseline FGF21/adiponectin ratio was a significant predictor of new-onset diabetes independent of traditional risk factors, especially in subjects with prediabetes (odds ratio, 1.367; p = 0.001). Moreover, FGF21/adiponectin ratio predicted incident prediabetes (odds ratio, 1.185; p = 0.021) while neither FGF21 nor adiponectin were independent predictors of incident prediabetes (both p > 0.05). Furthermore, net reclassification improvement and integrated discrimination improvement analyses showed that FGF21/adiponectin ratio provided a better performance in diabetes risk prediction than the use of FGF21 or adiponectin alone. CONCLUSIONS: FGF21/adiponectin ratio independently predicted the onset of prediabetes and diabetes, with the potential to be a useful biomarker of deterioration in glycemia.

Decreased Abundance of Akkermansia muciniphila Leads to the Impairment of Insulin Secretion and Glucose Homeostasis in Lean Type 2 Diabetes.

Although obesity occurs in most of the patients with type 2 diabetes (T2D), a fraction of patients with T2D are underweight or have normal weight. Several studies have linked the gut microbiome to obesity and T2D, but the role of gut microbiota in lean individuals with T2D having unique clinical characteristics remains unclear. A metagenomic and targeted metabolomic analysis is conducted in 182 lean and abdominally obese individuals with and without newly diagnosed T2D. The abundance of Akkermansia muciniphila (A. muciniphila) significantly decreases in lean individuals with T2D than without T2D, but not in the comparison of obese individuals with and without T2D. Its abundance correlates inversely with serum 3ß-chenodeoxycholic acid (ßCDCA) levels and positively with insulin secretion and fibroblast growth factor 15/19 (FGF15/19) concentrations. The supplementation with A. muciniphila is sufficient to protect mice against high sucrose-induced impairment of glucose intolerance by decreasing ßCDCA and increasing insulin secretion and FGF15/19. Furthermore, ßCDCA inhibits insulin secretion and FGF15/19 expression. These findings suggest that decreased abundance of A. muciniphila is linked to the impairment of insulin secretion and glucose homeostasis in lean T2D, paving the way for new therapeutic options for the prevention or treatment of diabetes.