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1.
Fine-tuning the gut ecosystem: the current landscape and outlook of artificial microbiome therapeutics.
Porcari, S, Fusco, W, Spivak, I, Fiorani, M, Gasbarrini, A, Elinav, E, Cammarota, G, Ianiro, G
The lancet. Gastroenterology & hepatology. 2024;(5):460-475
Abstract
The gut microbiome is acknowledged as a key determinant of human health, and technological progress in the past two decades has enabled the deciphering of its composition and functions and its role in human disorders. Therefore, manipulation of the gut microbiome has emerged as a promising therapeutic option for communicable and non-communicable disorders. Full exploitation of current therapeutic microbiome modulators (including probiotics, prebiotics, and faecal microbiota transplantation) is hindered by several factors, including poor precision, regulatory and safety issues, and the impossibility of providing reproducible and targeted treatments. Artificial microbiota therapeutics (which include a wide range of products, such as microbiota consortia, bacteriophages, bacterial metabolites, and engineered probiotics) have appeared as an evolution of current microbiota modulators, as they promise safe and reproducible effects, with variable levels of precision via different pathways. We describe the landscape of artificial microbiome therapeutics, from those already on the market to those still in the pipeline, and outline the major challenges for positioning these therapeutics in clinical practice.
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2.
The Role of Artificial Intelligence in Deciphering Diet-Disease Relationships: Case Studies.
Cohen, Y, Valdés-Mas, R, Elinav, E
Annual review of nutrition. 2023;:225-250
Abstract
Modernization of society from a rural, hunter-gatherer setting into an urban and industrial habitat, with the associated dietary changes, has led to an increased prevalence of cardiometabolic and additional noncommunicable diseases, such as cancer, inflammatory bowel disease, and neurodegenerative and autoimmune disorders. However, while dietary sciences have been rapidly evolving to meet these challenges, validation and translation of experimental results into clinical practice remain limited for multiple reasons, including inherent ethnic, gender, and cultural interindividual variability, among other methodological, dietary reporting-related, and analytical issues. Recently, large clinical cohorts with artificial intelligence analytics have introduced new precision and personalized nutrition concepts that enable one to successfully bridge these gaps in a real-life setting. In this review, we highlight selected examples of case studies at the intersection between diet-disease research and artificial intelligence. We discuss their potential and challenges and offer an outlook toward the transformation of dietary sciences into individualized clinical translation.
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3.
Impact of caloric restriction on the gut microbiota.
Kern, L, Kviatcovsky, D, He, Y, Elinav, E
Current opinion in microbiology. 2023;:102287
Abstract
Caloric restriction (CR) and related time-restricted diets have been popularized as means of preventing metabolic disease while improving general well-being. However, evidence as to their long-term efficacy, adverse effects, and mechanisms of activity remains incompletely understood. The gut microbiota is modulated by such dietary approaches, yet causal evidence to its possible downstream impacts on host metabolism remains elusive. Herein, we discuss the positive and adverse influences of restrictive dietary interventions on gut microbiota composition and function, and their collective impacts on host health and disease risk. We highlight known mechanisms of microbiota influences on the host, such as modulation of bioactive metabolites, while discussing challenges in achieving mechanistic dietary-microbiota insights, including interindividual variability in dietary responses as well as other methodological and conceptual challenges. In all, causally understanding the impact of CR approaches on the gut microbiota may enable to better decode their overall influences on human physiology and disease.
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Small Intestinal Microbiota Oscillations, Host Effects and Regulation-A Zoom into Three Key Effector Molecules.
Ratiner, K, Fachler-Sharp, T, Elinav, E
Biology. 2023;(1)
Abstract
The gut microbiota features a unique diurnal rhythmicity which contributes to modulation of host physiology and homeostasis. The composition and activity of the microbiota and its secreted molecules influence the intestinal milieu and neighboring organs, such as the liver. Multiple immune-related molecules have been linked to the diurnal microbiota-host interaction, including Reg3γ, IgA, and MHCII, which are secreted or expressed on the gut surface and directly interact with intestinal bacteria. These molecules are also strongly influenced by dietary patterns, such as high-fat diet and time-restricted feeding, which are already known to modulate microbial rhythms and peripheral clocks. Herein, we use Reg3γ, IgA, and MHCII as test cases to highlight the divergent effects mediated by the diurnal activity of the gut microbiota and their downstream host effects. We further highlight current challenges and conflicts, remaining questions, and perspectives toward a holistic understanding of the microbiome's impacts on circadian human behavior.
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5.
Effects of personalized diets by prediction of glycemic responses on glycemic control and metabolic health in newly diagnosed T2DM: a randomized dietary intervention pilot trial.
Rein, M, Ben-Yacov, O, Godneva, A, Shilo, S, Zmora, N, Kolobkov, D, Cohen-Dolev, N, Wolf, BC, Kosower, N, Lotan-Pompan, M, et al
BMC medicine. 2022;20(1):56
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Plain language summary
Type 2 diabetes mellitus (T2DM) affects around 10% of the global population. The primary goal in its management is to improve glycemic control. Modifying the diet can help, but many patients fail to achieve improvements with diet alone. The aim of the randomized dietary intervention pilot trial is to evaluate the effects of a personalized postprandial-targeting (PPT) diet on glycemic control and metabolic health in 23 adults with newly diagnosed T2DM, as compared to the commonly recommended Mediterranean-style (MED) diet. The PPT diet led to significant lower levels of continuous-glucose-monitoring (CGM)-based measures as compared to the MED diet. In the additional 6-months intervention, metabolic parameters were further improved and 61% of the participants exhibited diabetes remission. Improvements in clinical outcomes were also accompanied by changes in the gut microbiome. These findings may be useful for the design of larger studies in the future that may have implications for dietary advice in clinical practice.
Abstract
BACKGROUND Dietary modifications are crucial for managing newly diagnosed type 2 diabetes mellitus (T2DM) and preventing its health complications, but many patients fail to achieve clinical goals with diet alone. We sought to evaluate the clinical effects of a personalized postprandial-targeting (PPT) diet on glycemic control and metabolic health in individuals with newly diagnosed T2DM as compared to the commonly recommended Mediterranean-style (MED) diet. METHODS We enrolled 23 adults with newly diagnosed T2DM (aged 53.5 ± 8.9 years, 48% males) for a randomized crossover trial of two 2-week-long dietary interventions. Participants were blinded to their assignment to one of the two sequence groups: either PPT-MED or MED-PPT diets. The PPT diet relies on a machine learning algorithm that integrates clinical and microbiome features to predict personal postprandial glucose responses (PPGR). We further evaluated the long-term effects of PPT diet on glycemic control and metabolic health by an additional 6-month PPT intervention (n = 16). Participants were connected to continuous glucose monitoring (CGM) throughout the study and self-recorded dietary intake using a smartphone application. RESULTS In the crossover intervention, the PPT diet lead to significant lower levels of CGM-based measures as compared to the MED diet, including average PPGR (mean difference between diets, - 19.8 ± 16.3 mg/dl × h, p < 0.001), mean glucose (mean difference between diets, - 7.8 ± 5.5 mg/dl, p < 0.001), and daily time of glucose levels > 140 mg/dl (mean difference between diets, - 2.42 ± 1.7 h/day, p < 0.001). Blood fructosamine also decreased significantly more during PPT compared to MED intervention (mean change difference between diets, - 16.4 ± 37 μmol/dl, p < 0.0001). At the end of 6 months, the PPT intervention leads to significant improvements in multiple metabolic health parameters, among them HbA1c (mean ± SD, - 0.39 ± 0.48%, p < 0.001), fasting glucose (- 16.4 ± 24.2 mg/dl, p = 0.02) and triglycerides (- 49 ± 46 mg/dl, p < 0.001). Importantly, 61% of the participants exhibited diabetes remission, as measured by HbA1c < 6.5%. Finally, some clinical improvements were significantly associated with gut microbiome changes per person. CONCLUSION In this crossover trial in subjects with newly diagnosed T2DM, a PPT diet improved CGM-based glycemic measures significantly more than a Mediterranean-style MED diet. Additional 6-month PPT intervention further improved glycemic control and metabolic health parameters, supporting the clinical efficacy of this approach. TRIAL REGISTRATION ClinicalTrials.gov number, NCT01892956.
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Personalized Postprandial Glucose Response-Targeting Diet Versus Mediterranean Diet for Glycemic Control in Prediabetes.
Ben-Yacov, O, Godneva, A, Rein, M, Shilo, S, Kolobkov, D, Koren, N, Cohen Dolev, N, Travinsky Shmul, T, Wolf, BC, Kosower, N, et al
Diabetes care. 2021;(9):1980-1991
Abstract
OBJECTIVE To compare the clinical effects of a personalized postprandial-targeting (PPT) diet versus a Mediterranean (MED) diet on glycemic control and metabolic health in prediabetes. RESEARCH DESIGN AND METHODS We randomly assigned adults with prediabetes (n = 225) to follow a MED diet or a PPT diet for a 6-month dietary intervention and additional 6-month follow-up. The PPT diet relies on a machine learning algorithm that integrates clinical and microbiome features to predict personal postprandial glucose responses. During the intervention, all participants were connected to continuous glucose monitoring (CGM) and self-reported dietary intake using a smartphone application. RESULTS Among 225 participants randomized (58.7% women, mean ± SD age 50 ± 7 years, BMI 31.3 ± 5.8 kg/m2, HbA1c, 5.9 ± 0.2% [41 ± 2.4 mmol/mol], fasting plasma glucose 114 ± 12 mg/dL [6.33 ± 0.67 mmol/L]), 200 (89%) completed the 6-month intervention. A total of 177 participants also contributed 12-month follow-up data. Both interventions reduced the daily time with glucose levels >140 mg/dL (7.8 mmol/L) and HbA1c levels, but reductions were significantly greater in PPT compared with MED. The mean 6-month change in "time above 140" was -0.3 ± 0.8 h/day and -1.3 ± 1.5 h/day for MED and PPT, respectively (95% CI between-group difference -1.29 to -0.66, P < 0.001). The mean 6-month change in HbA1c was -0.08 ± 0.19% (-0.9 ± 2.1 mmol/mol) and -0.16 ± 0.24% (-1.7 ± 2.6 mmol/mol) for MED and PPT, respectively (95% CI between-group difference -0.14 to -0.02, P = 0.007). The significant between-group differences were maintained at 12-month follow-up. No significant differences were noted between the groups in a CGM-measured oral glucose tolerance test. CONCLUSIONS In this clinical trial in prediabetes, a PPT diet improved glycemic control significantly more than a MED diet as measured by daily time of glucose levels >140 mg/dL (7.8 mmol/L) and HbA1c. These findings may have implications for dietary advice in clinical practice.
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Harnessing SmartPhones to Personalize Nutrition in a Time of Global Pandemic.
Zmora, N, Elinav, E
Nutrients. 2021;(2)
Abstract
The soar in COVID-19 cases around the globe has forced many to adapt to social distancing and self-isolation. In order to reduce contact with healthcare facilities and other patients, the CDC has advocated the use of telemedicine, i.e., electronic information and telecommunication technology. While these changes may disrupt normal behaviors and routines and induce anxiety, resulting in decreased vigilance to healthy diet and physical activity and reluctance to seek medical attention, they may just as well be circumvented using modern technology. Indeed, as the beginning of the pandemic a plethora of alternatives to conventional physical interactions were introduced. In this Perspective, we portray the role of SmartPhone applications (apps) in monitoring healthy nutrition, from their basic functionality as food diaries required for simple decision-making and nutritional interventions, through more advanced purposes, such as multi-dimensional data-mining and development of machine learning algorithms. Finally, we will delineate the emerging field of personalized nutrition and introduce pioneering technologies and concepts yet to be incorporated in SmartPhone-based dietary surveillance.
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Breakthroughs and Bottlenecks in Microbiome Research.
Liwinski, T, Leshem, A, Elinav, E
Trends in molecular medicine. 2021;(4):298-301
Abstract
Over the past 15 years, the research community has witnessed unprecedented progress in microbiome research. We review this increasing knowledge and first attempts of its clinical application, and also limitations and challenges faced by the research community, in mechanistically understanding host-microbiome interactions and integrating these insights into clinical practice.
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Commensal inter-bacterial interactions shaping the microbiota.
Kern, L, Abdeen, SK, Kolodziejczyk, AA, Elinav, E
Current opinion in microbiology. 2021;:158-171
Abstract
The gut microbiota, a complex ecosystem of microorganisms of different kingdoms, impacts host physiology and disease. Within this ecosystem, inter-bacterial interactions and their impacts on microbiota community structure and the eukaryotic host remain insufficiently explored. Microbiota-related inter-bacterial interactions range from symbiotic interactions, involving exchange of nutrients, enzymes, and genetic material; competition for nutrients and space, mediated by biophysical alterations and secretion of toxins and anti-microbials; to predation of overpopulating bacteria. Collectively, these understudied interactions hold important clues as to forces shaping microbiota diversity, niche formation, and responses to signals perceived from the host, incoming pathogens and the environment. In this review, we highlight the roles and mechanisms of selected inter-bacterial interactions in the microbiota, and their potential impacts on the host and pathogenic infection. We discuss challenges in mechanistically decoding these complex interactions, and prospects of harnessing them as future targets for rational microbiota modification in a variety of diseases.
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Reporting guidelines for human microbiome research: the STORMS checklist.
Mirzayi, C, Renson, A, , , , , Zohra, F, Elsafoury, S, Geistlinger, L, Kasselman, LJ, Eckenrode, K, van de Wijgert, J, et al
Nature medicine. 2021;(11):1885-1892
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Abstract
The particularly interdisciplinary nature of human microbiome research makes the organization and reporting of results spanning epidemiology, biology, bioinformatics, translational medicine and statistics a challenge. Commonly used reporting guidelines for observational or genetic epidemiology studies lack key features specific to microbiome studies. Therefore, a multidisciplinary group of microbiome epidemiology researchers adapted guidelines for observational and genetic studies to culture-independent human microbiome studies, and also developed new reporting elements for laboratory, bioinformatics and statistical analyses tailored to microbiome studies. The resulting tool, called 'Strengthening The Organization and Reporting of Microbiome Studies' (STORMS), is composed of a 17-item checklist organized into six sections that correspond to the typical sections of a scientific publication, presented as an editable table for inclusion in supplementary materials. The STORMS checklist provides guidance for concise and complete reporting of microbiome studies that will facilitate manuscript preparation, peer review, and reader comprehension of publications and comparative analysis of published results.