Prediction of Personal Glycemic Responses to Food for Individuals With Type 1 Diabetes Through Integration of Clinical and Microbial Data

Impacts of non-nutritive sweeteners on the human microbiome

Replacing sugar with non-nutritive sweeteners (NNS) is a common dietary strategy for reducing the caloric content and glycemic index of foods and beverages. However, the efficacy of this strategy in preventing and managing metabolic syndrome and its associated comorbidities remains uncertain. Human cohort studies suggest that NNS contribute to, rather than prevent, metabolic syndrome, whereas randomized controlled trials yield heterogeneous outcomes, ranging from beneficial to detrimental impacts on cardiometabolic health. The World Health Organization recently issued a conditional recommendation against using NNS, citing the need for additional evidence causally linking sweeteners to health effects. One proposed mechanism through which NNS induce metabolic derangements is through disruption of the gut microbiome, a link strongly supported by evidence in preclinical models. This review summarizes the evidence for similar effects in interventional and observational trials in humans. The limited available data highlight heterogeneity between trials, as some, but not all, find NNS consumption associated with microbiome modulation as well as metabolic effects independent of sweetener type. In other trials, the lack of microbiome changes coincides with the absence of metabolic effects. We discuss the hypothesis that the impacts of NNS on health are personalized and microbiome dependent. Thus, a precision nutrition approach may help resolve the conflicting reports regarding NNS impacts on the microbiome and health. This review also discusses additional factors contributing to study heterogeneity that should be addressed in future clinical trials to clarify the relationship between NNS, the microbiome, and health to better inform dietary guidelines and public health policies.

Predicting Postprandial Glycemic Responses With Limited Data in Type 1 and Type 2 Diabetes

BACKGROUND

A core challenge in managing diabetes is predicting glycemic responses to meals. Prior work identified significant interindividual variation in responses and developed personalized forecasts. However, intraindividual variation is still not well understood, and the most accurate approaches require invasive microbiome data. We aimed to investigate (1) whether postprandial glycemic responses (PPGRs) can be predicted with limited data and (2) sources of intraindividual variation.

METHODS

We used data collected from 397 people with Type 1 Diabetes (T1DEXI) and 100 people with Type 2 Diabetes (ShanghaiT2DM) who wore continuous glucose monitors (CGMs) and logged meals. Using dietary, demographic, and temporal features, we predicted 2 hours PPGR, and peak 2 hours postprandial glucose rise (Glumax). We evaluated the contribution of food features (eg, macronutrients, food category) and use of personal training data and investigated intraindividual variability in responses.

RESULTS

We achieved comparable accuracy to prior work for PPGR (T1DEXI R = 0.61, ShanghaiT2DM R = 0.72) and Glumax (T1DEXI R = 0.64, ShanghaiT2DM R = 0.73), without using invasive data like microbiome. Including food category features led to higher accuracy than macronutrients alone. Analysis of glycemic responses to duplicate meals identified time of day (PPGR: T1DEXI P < .05 for lunch, ShanghaiT2DM P < .001 for lunch and dinner) and menstrual cycle (Glumax: P < .05 for perimenstrual) as sources of variability.

CONCLUSIONS

We demonstrate that in individuals with T1D and T2D, glycemic responses to meals can be predicted without personalized training data or invasive physiological data.

Personalized prediction of glycemic responses to food in women with diet-treated gestational diabetes: the role of the gut microbiota

We developed a prediction model for postprandial glycemic response (PPGR) in pregnant women, including those with diet-treated gestational diabetes mellitus (GDM) and healthy women, and explored the role of gut microbiota in improving prediction accuracy. The study involved 105 pregnant women (77 with GDM, 28 healthy), who underwent continuous glucose monitoring (CGM) for 7 days, provided food diaries, and gave stool samples for microbiome analysis. Machine learning models were created using CGM data, meal content, lifestyle factors, biochemical parameters, and microbiota data (16S rRNA gene sequence analysis). Adding microbiome data increased the explained variance in peak glycemic levels (GLUmax) from 34 to 42% and in incremental area under the glycemic curve (iAUC120) from 50 to 52%. The final model showed better correlation with measured PPGRs than one based only on carbohydrate count (r = 0.72 vs. r = 0.51 for iAUC120). Although microbiome features were important, their contribution to model performance was modest.

The impact of gut microbiome on immune and metabolic homeostasis in type 1 diabetes: Clinical insights for prevention and treatment strategies

Type 1 diabetes (T1D) is a complex disease triggered by a combination of genetic and environmental factors, where abnormal autoimmune responses lead to progressive damage of the pancreatic β cells and severe glucose metabolism disorder. Recent studies have increasingly highlighted the close link between gut microbiota dysbiosis and the development of T1D. This review delves into existing population studies to explore the intricate interactions between the gut microbiota and the immune and metabolic homeostasis in T1D. It summarizes how changes in the structure and function of the gut microbiota are closely associated with the onset and progression of T1D across its natural course and clinical stages. More importantly, based on evidence accumulated from clinical observations and trials, we pioneer the discussion on gut microbiota-based T1D prevention and treatment strategies, this not only enriches our understanding of the complex pathological mechanisms of T1D but also provides potential directions for developing novel prevention and treatment strategies.

Use of Machine Learning to Predict Individual Postprandial Glycemic Responses to Food Among Individuals With Type 2 Diabetes in India: Protocol for a Prospective Cohort Study

BACKGROUND

Type 2 diabetes (T2D) is a leading cause of premature morbidity and mortality globally and affects more than 100 million people in the world's most populous country, India. Nutrition is a critical and evidence-based component of effective blood glucose control and most dietary advice emphasizes carbohydrate and calorie reduction. Emerging global evidence demonstrates marked interindividual differences in postprandial glucose response (PPGR) although no such data exists in India and previous studies have primarily evaluated PPGR variation in individuals without diabetes.

OBJECTIVE

This prospective cohort study seeks to characterize the PPGR variability among individuals with diabetes living in India and to identify factors associated with these differences.

METHODS

Adults with T2D and a hemoglobin A1c of ≥7 are being enrolled from 14 sites around India. Participants wear a continuous glucose monitor, eat a series of standardized meals, and record all free-living foods, activities, and medication use for a 14-day period. The study's primary outcome is PPGR, calculated as the incremental area under the curve 2 hours after each logged meal.

RESULTS

Data collection commenced in May 2022, and the results will be ready for publication by September 2025. Results from our study will generate data to facilitate the creation of machine learning models to predict individual PPGR responses and to facilitate the prescription of personalized diets for individuals with T2D.

CONCLUSIONS

This study will provide the first large scale examination variability in blood glucose responses to food in India and will be among the first to estimate PPGR variability for individuals with T2D in any jurisdiction.

TRIAL REGISTRATION

Clinical Trials Registry-India CTRI/2022/02/040619; https://tinyurl.com/mrywf6bf.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/59308.

Machine learning and artificial intelligence within pediatric autoimmune diseases: applications, challenges, future perspective

INTRODUCTION

Autoimmune disorders affect 4.5% to 9.4% of children, significantly reducing their quality of life. The diagnosis and prognosis of autoimmune diseases are uncertain because of the variety of onset and development. Machine learning can identify clinically relevant patterns from vast amounts of data. Hence, its introduction has been beneficial in the diagnosis and management of patients.

AREAS COVERED

This narrative review was conducted through searching various electronic databases, including PubMed, Scopus, and Web of Science. This study thoroughly explores the current knowledge and identifies the remaining gaps in the applications of machine learning specifically in the context of pediatric autoimmune and related diseases.

EXPERT OPINION

Machine learning algorithms have the potential to completely change how pediatric autoimmune disorders are identified, treated, and managed. Machine learning can assist physicians in making more precise and fast judgments, identifying new biomarkers and therapeutic targets, and personalizing treatment strategies for each patient by utilizing massive datasets and powerful analytics.

Connecting precision nutrition with the Food is Medicine approach

Two initiatives are reshaping how we can approach and address the persistent and widely prevalent challenge of malnutrition, the leading global risk factor for morbidity and mortality. First is the focus on precision nutrition to identify inter- and intra-individual variation in our responses to diet, and its determinants. Second is the Food is Medicine (FIM) approach, an umbrella term for programs and services that link nutrition and health through the provision of food (e.g., tailored meals, produce prescriptions) and access to healthcare services. This article outlines how interventions and programs using FIM can synergize with precision nutrition approaches to make individual- or population-level tailored nutrition accessible and affordable, help to reduce the risk of metabolic diseases, and improve quality of life.

Prediction of personalised postprandial glycaemic response in type 1 diabetes mellitus

Objectives

Patients with type 1 diabetes (T1D) face unique challenges in glycaemic control due to the complexity and uniqueness of the dietary structure in China, especially in terms of postprandial glycaemic response (PPGR). This study aimed to establish a personalized model for predicting PPGR in patients with T1D.

Materials and methods

Data provided by the First People's Hospital of Yunnan Province, 13 patients with T1D, were recruited and provided with an intervention for at least two weeks. All patients were asked to wear a continuous glucose monitoring (CGM) device under free-living conditions during the study period. To tackle the challenge of incomplete data from wearable devices for CGM measurements, the GAIN method was used in this paper to achieve a more rational interpolation process. In this study, patients' PPGRs were calculated, and a LightGBM prediction model was constructed based on a Bayesian hyperparameter optimisation algorithm and a random search algorithm, which integrated glucose measurement, insulin dose, dietary nutrient content, blood measurement and anthropometry as inputs.

Results

The experimental outcomes revealed that the PPGR prediction model presented in this paper demonstrated superior accuracy (R=0.63) compared to both the carbohydrate content only model (R=0.14) and the baseline model emulating the standard of care for insulin administration (R=0.43). In addition, the interpretation of the model using the SHAP method showed that blood glucose levels at meals and blood glucose trends 30 minutes before meals were the most important features of the model.

Conclusion

The proposed model offers a heightened precision in predicting PPGR in patients with T1D, so it can better guide the diet plan and insulin intake dose of patients with T1D.

Therapy-Related Satisfaction and Quality of Life for Japanese People with Diabetes Using Rapid-Acting Insulin Analogs: A Web-Based Survey

INTRODUCTION

People with diabetes require insulin to regulate blood glucose (BG); rapid-acting insulin analogs (RAIA) represent one approach for BG management. New fast-acting RAIA administered at the start of a meal suppress postprandial BG better than conventional RAIA. New RAIA are expected to confer higher treatment satisfaction and improved quality of life (QOL) than conventional RAIA.

METHODS

This cross-sectional, web-based survey in Japan (November 2022) included people with diabetes (type 1/2), aged ≥ 18 years, registered in the Rakuten Insight Diabetes Panel, using new and/or conventional RAIA. RAIA-specific satisfaction was evaluated by questions on RAIA use (scores: 1 [not at all satisfied]; 7 [very satisfied]) and QOL by the Diabetes Therapy-Related (DTR)-QOL questionnaire (scores: 0-100, 100 = best) for the whole population (primary endpoint) and for new versus conventional RAIA users (secondary endpoint). Multiple regression models were used to compare new versus conventional RAIA users.

RESULTS

The analysis population comprised 217 people with diabetes (new RAIA, n = 109; conventional RAIA, n = 108). Mean (standard deviation) RAIA-specific satisfaction scores ranged from 5.1 (1.2) to 5.4 (1.2); DTR-QOL total score was 51.6 (20.4). RAIA satisfaction scores were numerically higher for new versus conventional RAIA users; no difference in DTR-QOL total score was observed. DTR-QOL satisfaction with treatment domain score was significantly higher in new versus conventional RAIA users (least squares mean difference [standard error]: 7.3 [3.1]; 95% confidence interval: 1.2, 13.4; P = 0.0197). RAIA-specific satisfaction was higher among patients who discussed BG sufficiently with their doctor versus those who did not.

CONCLUSIONS

New RAIA users have greater treatment satisfaction than conventional RAIA users. QOL was similar among new and conventional RAIA users, except for satisfaction with treatment, which was significantly higher among new RAIA users. Detailed explanations from the doctor to the person with diabetes about the relationship between new RAIA and BG status are essential. A graphical plain language summary is available with this article.

Postprandial Glucose Control With Different Hybrid Closed-Loop Systems According to Type of Meal in Adults With Type 1 Diabetes

BACKGROUND

Hybrid Closed-Loop Systems (HCLs) may not perform optimally on postprandial glucose control. We evaluated how first-generation and advanced HCLs manage meals varying in carbohydrates, fat, and protein.

METHOD

According to a cross-sectional design, seven-day food records and HCLs reports from 120 adults with type 1 diabetes (MiniMed670G: n = 40, MiniMed780G: n = 49, Control-IQ [C-IQ]: n = 31) were analyzed. Breakfasts (n = 570), lunches (n = 658), and dinners (n = 619) were divided according to the median of their carbohydrate (g)/fat (g) plus protein (g) ratio (C/FP). After breakfast (4-hour), lunch (6-hour), and dinner (6-hour), continuous glucose monitoring (CGM) metrics and early and late glucose incremental area under the curves (iAUCs) and delivered insulin doses were evaluated. The association of C/FP and HCLs with postprandial glucose and insulin patterns was analyzed by univariate analysis of variance (ANOVA) with a two-factor design.

RESULTS

Postprandial glucose time-in-range 70 to 180 mg/dL was optimal after breakfast (78.3 ± 26.9%), lunch (72.7 ± 26.1%), and dinner (70.8 ± 27.3%), with no significant differences between HCLs. Independent of C/FP, late glucose-iAUC after lunch was significantly lower in C-IQ users than 670G and 780G (P < .05), with no significant differences at breakfast and dinner. Postprandial insulin pattern (Ins3-6h minus Ins0-3h) differed by type of HCLs at lunch (P = .026) and dinner (P < .001), being the early insulin dose (Ins0-3h) higher than the late dose (Ins3-6h) in 670G and 780G users with an opposite pattern in C-IQ users.

CONCLUSIONS

Independent of different proportions of dietary carbohydrates, fat, and protein, postprandial glucose response was similar in users of different HCLs, although obtained through different automatic insulin delivery patterns.

Transforming the cardiometabolic disease landscape: Multimodal AI-powered approaches in prevention and management

The rise of artificial intelligence (AI) has revolutionized various scientific fields, particularly in medicine, where it has enabled the modeling of complex relationships from massive datasets. Initially, AI algorithms focused on improved interpretation of diagnostic studies such as chest X-rays and electrocardiograms in addition to predicting patient outcomes and future disease onset. However, AI has evolved with the introduction of transformer models, allowing analysis of the diverse, multimodal data sources existing in medicine today. Multimodal AI holds great promise in more accurate disease risk assessment and stratification as well as optimizing the key driving factors in cardiometabolic disease: blood pressure, sleep, stress, glucose control, weight, nutrition, and physical activity. In this article we outline the current state of medical AI in cardiometabolic disease, highlighting the potential of multimodal AI to augment personalized prevention and treatment strategies in cardiometabolic disease.

Continuous glucose monitoring for automatic real-time assessment of eating events and nutrition: a scoping review

Background

Accurate dietary assessment remains a challenge, particularly in free-living settings. Continuous glucose monitoring (CGM) shows promise in optimizing the assessment and monitoring of ingestive activity (IA, i.e., consumption of calorie-containing foods/beverages), and it might enable administering dietary Just-In-Time Adaptive Interventions (JITAIs).

Objective

In a scoping review, we aimed to answer the following questions: (1) Which CGM approaches to automatically detect IA in (near-)real-time have been investigated? (2) How accurate are these approaches? (3) Can they be used in the context of JITAIs?

Methods

We systematically searched four databases until October 2023 and included publications in English or German that used CGM-based approaches for human (all ages) IA detection. Eligible publications included a ground-truth method as a comparator. We synthesized the evidence qualitatively and critically appraised publication quality.

Results

Of 1,561 potentially relevant publications identified, 19 publications (17 studies, total N = 311; for 2 studies, 2 publications each were relevant) were included. Most publications included individuals with diabetes, often using meal announcements and/or insulin boluses accompanying meals. Inpatient and free-living settings were used. CGM-only approaches and CGM combined with additional inputs were deployed. A broad range of algorithms was tested. Performance varied among the reviewed methods, ranging from unsatisfactory to excellent (e.g., 21% vs. 100% sensitivity). Detection times ranged from 9.0 to 45.0 min.

Conclusion

Several CGM-based approaches are promising for automatically detecting IA. However, response times need to be faster to enable JITAIs aimed at impacting acute IA. Methodological issues and overall heterogeneity among articles prevent recommending one single approach; specific cases will dictate the most suitable approach.

Impact of gut microbiota and associated mechanisms on postprandial glucose levels in patients with diabetes

Diabetes and its complications are serious medical and global burdens, often manifesting as postprandial hyperglycemia. In recent years, considerable research attention has focused on relationships between the gut microbiota and circulating postprandial glucose (PPG). Different population studies have suggested that PPG is closely related to the gut microbiota which may impact PPG via short-chain fatty acids (SCFAs), bile acids (BAs) and trimethylamine N-oxide (TMAO). Studies now show that gut microbiota models can predict PPG, with individualized nutrition intervention strategies used to regulate gut microbiota and improve glucose metabolism to facilitate the precision treatment of diabetes. However, few studies have been conducted in patients with diabetes. Therefore, little is known about the relationships between the gut microbiota and PPG in this cohort. Thus, more research is required to identify key gut microbiota and associated metabolites and pathways impacting PPG to provide potential therapeutic targets for PPG.

Precision medicine for cardiometabolic disease: a framework for clinical translation

Cardiometabolic disease is a major threat to global health. Precision medicine has great potential to help to reduce the burden of this common and complex disease cluster, and to enhance contemporary evidence-based medicine. Its key pillars are diagnostics; prediction (of the primary disease); prevention (of the primary disease); prognosis (prediction of complications of the primary disease); treatment (of the primary disease or its complications); and monitoring (of risk exposure, treatment response, and disease progression or remission). To contextualise precision medicine in both research and clinical settings, and to encourage the successful translation of discovery science into clinical practice, in this Series paper we outline a model (the EPPOS model) that builds on contemporary evidence-based approaches; includes precision medicine that improves disease-related predictions by stratifying a cohort into subgroups of similar characteristics, or using participants' characteristics to model treatment outcomes directly; includes personalised medicine with the use of a person's data to objectively gauge the efficacy, safety, and tolerability of therapeutics; and subjectively tailors medical decisions to the individual's preferences, circumstances, and capabilities. Precision medicine requires a well functioning system comprised of multiple stakeholders, including health-care recipients, health-care providers, scientists, health economists, funders, innovators of medicines and technologies, regulators, and policy makers. Powerful computing infrastructures supporting appropriate analysis of large-scale, well curated, and accessible health databases that contain high-quality, multidimensional, time-series data will be required; so too will prospective cohort studies in diverse populations designed to generate novel hypotheses, and clinical trials designed to test them. Here, we carefully consider these topics and describe a framework for the integration of precision medicine in cardiometabolic disease.

Gene-based microbiome representation enhances host phenotype classification

With the concomitant advances in both the microbiome and machine learning fields, the gut microbiome has become of great interest for the potential discovery of biomarkers to be used in the classification of the host health status. Shotgun metagenomics data derived from the human microbiome is composed of a high-dimensional set of microbial features. The use of such complex data for the modeling of host-microbiome interactions remains a challenge as retaining de novo content yields a highly granular set of microbial features. In this study, we compared the prediction performances of machine learning approaches according to different types of data representations derived from shotgun metagenomics. These representations include commonly used taxonomic and functional profiles and the more granular gene cluster approach. For the five case-control datasets used in this study (Type 2 diabetes, obesity, liver cirrhosis, colorectal cancer, and inflammatory bowel disease), gene-based approaches, whether used alone or in combination with reference-based data types, allowed improved or similar classification performances as the taxonomic and functional profiles. In addition, we show that using subsets of gene families from specific functional categories of genes highlight the importance of these functions on the host phenotype. This study demonstrates that both reference-free microbiome representations and curated metagenomic annotations can provide relevant representations for machine learning based on metagenomic data. IMPORTANCE Data representation is an essential part of machine learning performance when using metagenomic data. In this work, we show that different microbiome representations provide varied host phenotype classification performance depending on the dataset. In classification tasks, untargeted microbiome gene content can provide similar or improved classification compared to taxonomical profiling. Feature selection based on biological function also improves classification performance for some pathologies. Function-based feature selection combined with interpretable machine learning algorithms can generate new hypotheses that can potentially be assayed mechanistically. This work thus proposes new approaches to represent microbiome data for machine learning that can potentiate the findings associated with metagenomic data.

Associations of Dietary Intake with the Intestinal Microbiota and Short-Chain Fatty Acids Among Young Adults with Type 1 Diabetes and Overweight or Obesity

BACKGROUND

Diet, a key component of type 1 diabetes (T1D) management, modulates the intestinal microbiota and its metabolically active byproducts-including SCFA-through fermentation of dietary carbohydrates such as fiber. However, the diet-microbiome relationship remains largely unexplored in longstanding T1D.

OBJECTIVES

We evaluated whether increased carbohydrate intake, including fiber, is associated with increased SCFA-producing gut microbes, SCFA, and intestinal microbial diversity among young adults with longstanding T1D and overweight or obesity.

METHODS

Young adult men and women with T1D for ≥1 y, aged 19-30 y, and BMI of 27.0-39.9 kg/m2 at baseline provided stool samples at baseline and 3, 6, and 9 mo of a randomized dietary weight loss trial. Diet was assessed by 1-2 24-h recalls. The abundance of SCFA-producing microbes was measured using 16S rRNA gene sequencing. GC-MS measured fecal SCFA (acetate, butyrate, propionate, and total) concentrations. Adjusted and Bonferroni-corrected generalized estimating equations modeled associations of dietary fiber (total, soluble, and pectins) and carbohydrate (available carbohydrate, and fructose) with microbiome-related outcomes. Primary analyses were restricted to data collected before COVID-19 interruptions.

RESULTS

Fiber (total and soluble) and carbohydrates (available and fructose) were positively associated with total SCFA and acetate concentrations (n = 40 participants, 52 visits). Each 10 g/d of total and soluble fiber intake was associated with an additional 8.8 μmol/g (95% CI: 4.5, 12.8 μmol/g; P = 0.006) and 24.0 μmol/g (95% CI: 12.9, 35.1 μmol/g; P = 0.003) of fecal acetate, respectively. Available carbohydrate intake was positively associated with SCFA producers Roseburia and Ruminococcus gnavus. All diet variables except pectin were inversely associated with normalized abundance of Bacteroides and Alistipes. Fructose was inversely associated with Akkermansia abundance.

CONCLUSIONS

In young adults with longstanding T1D, fiber and carbohydrate intake were associated positively with fecal SCFA but had variable associations with SCFA-producing gut microbes. Controlled feeding studies should determine whether gut microbes and SCFA can be directly manipulated in T1D.

Emerging trends and focus on the link between gut microbiota and type 1 diabetes: A bibliometric and visualization analysis

OBJECTIVE

To conduct the first thorough bibliometric analysis to evaluate and quantify global research regarding to the gut microbiota and type 1 diabetes (T1D).

METHODS

A literature search for research studies on gut microbiota and T1D was conducted using the Web of Science Core Collection (WoSCC) database on 24 September 2022. VOSviewer software and the packages Bibliometrix R and ggplot used in RStudio were applied to perform the bibliometric and visualization analysis.

RESULTS

A total of 639 publications was extracted using the terms "gut microbiota" and "type 1 diabetes" (and their synonyms in MeSH). Ultimately, 324 articles were included in the bibliometric analysis. The United States and European countries are the main contributors to this field, and the top 10 most influential institutions are all based in the United States, Finland and Denmark. The three most influential researchers in this field are Li Wen, Jorma Ilonen and Mikael Knip. Historical direct citation analysis showed the evolution of the most cited papers in the field of T1D and gut microbiota. Clustering analysis defined seven clusters, covering the current main topics in both basic and clinical research on T1D and gut microbiota. The most commonly found high-frequency keywords in the period from 2018 to 2021 were "metagenomics," "neutrophils" and "machine learning."

CONCLUSION

The application of multi-omics and machine learning approaches will be a necessary future step for better understanding gut microbiota in T1D. Finally, the future outlook for customized therapy toward reshaping gut microbiota of T1D patients remains promising.

Dietary treatment of type 1 diabetes: Beyond carbohydrate counting to fight cardiovascular risk

AIMS

Type 1 diabetes (T1D) is tied to an increased risk of cardiovascular morbidity and mortality. Dietary treatment would be an elective therapeutic strategy to fight this risk. However, it is not known what the best dietary approach is. We revisited the currently available literature on the nutritional treatment of T1D in the light of their potential comprehensive effects on the management of cardio-metabolic risk factors (body weight, fasting and postprandial glucose and lipid metabolism).

DATA SYNTHESIS

Nutritional research in T1D is mainly focused on blood glucose control, with most of the trials aiming at evaluating the acute effects of nutrients on postprandial glycemic response. The effects of the quantity and quality of nutrients and some specific foods on other metabolic risk factors have been explored mainly in cross-sectional analysis. Very few well-designed nutritional trials evaluated the best dietary approach to comprehensively manage cardiovascular risk by targeting along with blood glucose control, overweight, fasting and postprandial dyslipidemia. Therefore, the current best practice guidance for the dietary management of cardiovascular risk in T1D is generally based on evidence from patients with type 2 diabetes.

CONCLUSIONS

Well-conducted nutritional trials specifically designed for T1D are needed to identify the best dietary treatment to fight cardiovascular risk in these patients.

Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive review

Diabetes as a metabolic illness can be characterized by increased amounts of blood glucose. This abnormal increase can lead to critical detriment to the other organs such as the kidneys, eyes, heart, nerves, and blood vessels. Therefore, its prediction, prognosis, and management are essential to prevent harmful effects and also recommend more useful treatments. For these goals, machine learning algorithms have found considerable attention and have been developed successfully. This review surveys the recently proposed machine learning (ML) and deep learning (DL) models for the objectives mentioned earlier. The reported results disclose that the ML and DL algorithms are promising approaches for controlling blood glucose and diabetes. However, they should be improved and employed in large datasets to affirm their applicability.

Machine learning modeling practices to support the principles of AI and ethics in nutrition research

BACKGROUND

Nutrition research is relying more on artificial intelligence and machine learning models to understand, diagnose, predict, and explain data. While artificial intelligence and machine learning models provide powerful modeling tools, failure to use careful and well-thought-out modeling processes can lead to misleading conclusions and concerns surrounding ethics and bias.

METHODS

Based on our experience as reviewers and journal editors in nutrition and obesity, we identified the most frequently omitted best practices from statistical modeling and how these same practices extend to machine learning models. We next addressed areas required for implementation of machine learning that are not included in commercial software packages.

RESULTS

Here, we provide a tutorial on best artificial intelligence and machine learning modeling practices that can reduce potential ethical problems with a checklist and guiding principles to aid nutrition researchers in developing, evaluating, and implementing artificial intelligence and machine learning models in nutrition research.

CONCLUSION

The quality of AI/ML modeling in nutrition research requires iterative and tailored processes to mitigate against potential ethical problems or to predict conclusions that are free of bias.

Gut Microbiota and Antidiabetic Drugs: Perspectives of Personalized Treatment in Type 2 Diabetes Mellitus

Alterations in the composition and function of the gut microbiota have been reported in patients with type 2 diabetes mellitus (T2DM). Emerging studies show that prescribed antidiabetic drugs distort the gut microbiota signature associated with T2DM. Even more importantly, accumulated evidence provides support for the notion that gut microbiota, in turn, mediates the efficacy and safety of antidiabetic drugs. In this review, we highlight the current state-of-the-art knowledge on the crosstalk and interactions between gut microbiota and antidiabetic drugs, including metformin, α-glucosidase inhibitors, glucagon-like peptide-1 receptor agonists, dipeptidyl peptidase-4 inhibitors, sodium-glucose cotransporter 2 inhibitors, traditional Chinese medicines and other antidiabetic drugs, as well as address corresponding microbial-based therapeutics, aiming to provide novel preventative strategies and personalized therapeutic targets in T2DM.

The Gut Microbiome of Adults With Type 1 Diabetes and Its Association With the Host Glycemic Control

OBJECTIVE

Previous studies have demonstrated an association between gut microbiota composition and type 1 diabetes (T1D) pathogenesis. However, little is known about the composition and function of the gut microbiome in adults with longstanding T1D or its association with host glycemic control.

RESEARCH DESIGN AND METHODS

We performed a metagenomic analysis of the gut microbiome obtained from fecal samples of 74 adults with T1D, 14.6 ± 9.6 years following diagnosis, and compared their microbial composition and function to 296 age-matched healthy control subjects (1:4 ratio). We further analyzed the association between microbial taxa and indices of glycemic control derived from continuous glucose monitoring measurements and blood tests and constructed a prediction model that solely takes microbiome features as input to evaluate the discriminative power of microbial composition for distinguishing individuals with T1D from control subjects.

RESULTS

Adults with T1D had a distinct microbial signature that separated them from control subjects when using prediction algorithms on held-out subjects (area under the receiver operating characteristic curve = 0.89 ± 0.03). Linear discriminant analysis showed several bacterial species with significantly higher scores in T1D, including Prevotella copri and Eubacterium siraeum, and species with higher scores in control subjects, including Firmicutes bacterium and Faecalibacterium prausnitzii (P < 0.05, false discovery rate corrected for all). On the functional level, several metabolic pathways were significantly lower in adults with T1D. Several bacterial taxa and metabolic pathways were associated with the host's glycemic control.

CONCLUSIONS

We identified a distinct gut microbial signature in adults with longstanding T1D and associations between microbial taxa, metabolic pathways, and glycemic control indices. Additional mechanistic studies are needed to identify the role of these bacteria for potential therapeutic strategies.