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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.

OBJECTIVE Despite technological advances, results from various clinical trials have repeatedly shown that many individuals with type 1 diabetes (T1D) do not achieve their glycemic goals. One of the major challenges in disease management is the administration of an accurate amount of insulin for each meal that will match the expected postprandial glycemic response (PPGR). The objective of this study was to develop a prediction model for PPGR in individuals with T1D. RESEARCH DESIGN AND METHODS We recruited individuals with T1D who were using continuous glucose monitoring and continuous subcutaneous insulin infusion devices simultaneously to a prospective cohort and profiled them for 2 weeks. Participants were asked to report real-time dietary intake using a designated mobile app. We measured their PPGRs and devised machine learning algorithms for PPGR prediction, which integrate glucose measurements, insulin dosages, dietary habits, blood parameters, anthropometrics, exercise, and gut microbiota. Data of the PPGR of 900 healthy individuals to 41,371 meals were also integrated into the model. The performance of the models was evaluated with 10-fold cross validation. RESULTS A total of 121 individuals with T1D, 75 adults and 46 children, were included in the study. PPGR to 6,377 meals was measured. Our PPGR prediction model substantially outperforms a baseline model with emulation of standard of care (correlation of R = 0.59 compared with R = 0.40 for predicted and observed PPGR respectively; P < 10−10). The model was robust across different subpopulations. Feature attribution analysis revealed that glucose levels at meal initiation, glucose trend 30 min prior to meal, meal carbohydrate content, and meal’s carbohydrate-to-fat ratio were the most influential features for the model. CONCLUSIONS Our model enables a more accurate prediction of PPGR and therefore may allow a better adjustment of the required insulin dosage for meals. It can be further implemented in closed loop systems and may lead to rationally designed nutritional interventions personally tailored for individuals with T1D on the basis of meals with expected low glycemic response.

Background We evaluated whether integration of novel diets for donors and patients, in addition to faecal transplantation [FT], could increase FT remission rate in refractory ulcerative colitis [UC]. Methods This was a blinded, randomised, controlled trial in adults with active UC, defined by a simple clinical colitis activity index [SCCAI] of ≥5 and ≤11 and endoscopic Mayo score 2–3, refractory to medication. Group 1 received free diet and single donor standard FT by colonoscopy on Day 1and rectal enemas on Days 2 and 14 without dietary conditioning of the donor. Group 2 received FT as above but with dietary pre-conditioning of the donor for 14 days and a UC Exclusion Diet [UCED] for the patients. Group 3 received the UCED alone. The primary endpoint was Week 8 clinical steroid-free remission, defined as SCCAI Results Of 96 planned patients, 62 were enrolled. Remission Week 8 Group 1 was 2/17 [11.8%], Group 2 was 4/19 [21.1%], Group 3 was 6/15 [40%] [non-significant]. Endoscopic remission Group 1 was 2/17 [12%], Group 2 was 3/19 [16%], Group 3 was 4/15 [27%] [Group 1 vs 3 p = 0.38]. Mucosal healing [Mayo 0] was achieved only in Group 3 [3/15, 20%] vs 0/36 FT patients [p = 0.022]. Exacerbation of disease occurred in 3/17 [17.6%] of Group 1, 4/19 [21.1%] of Group 2, and 1/15 [6.7%] of Group 3 [Group 2 vs 3, p = 0.35]. Conclusions UCED alone appeared to achieve higher clinical remission and mucosal healing than single donor FT with or without diet. The study was stopped for futility by a safety monitoring board.

ImportanceInterindividual variability in postprandial glycemic response (PPGR) to the same foods may explain why low glycemic index or load and low-carbohydrate diet interventions have mixed weight loss outcomes. A precision nutrition approach that estimates personalized PPGR to specific foods may be more efficacious for weight loss.ObjectiveTo compare a standardized low-fat vs a personalized diet regarding percentage of weight loss in adults with abnormal glucose metabolism and obesity.Design, Setting, and ParticipantsThe Personal Diet Study was a single-center, population-based, 6-month randomized clinical trial with measurements at baseline (0 months) and 3 and 6 months conducted from February 12, 2018, to October 28, 2021. A total of 269 adults aged 18 to 80 years with a body mass index (calculated as weight in kilograms divided by height in meters squared) ranging from 27 to 50 and a hemoglobin A1c level ranging from 5.7% to 8.0% were recruited. Individuals were excluded if receiving medications other than metformin or with evidence of kidney disease, assessed as an estimated glomerular filtration rate of less than 60 mL/min/1.73 m2 using the Chronic Kidney Disease Epidemiology Collaboration equation, to avoid recruiting patients with advanced type 2 diabetes.InterventionsParticipants were randomized to either a low-fat diet (Main Outcomes and MeasuresThe primary outcome was the percentage of weight loss from baseline to 6 months. Secondary outcomes included changes in body composition (fat mass, fat-free mass, and percentage of body weight), resting energy expenditure, and adaptive thermogenesis. Data were collected at baseline and 3 and 6 months. Analysis was based on intention to treat using linear mixed modeling.ResultsOf a total of 204 adults randomized, 199 (102 in the personalized group vs 97 in the standardized group) contributed data (mean [SD] age, 58 [11] years; 133 women [66.8%]; mean [SD] body mass index, 33.9 [4.8]). Weight change at 6 months was −4.31% (95% CI, −5.37% to −3.24%) for the standardized group and −3.26% (95% CI, −4.25% to −2.26%) for the personalized group, which was not significantly different (difference between groups, 1.05% [95% CI, −0.40% to 2.50%]; P = .16). There were no between-group differences in body composition and adaptive thermogenesis; however, the change in resting energy expenditure was significantly greater in the standardized group from 0 to 6 months (difference between groups, 92.3 [95% CI, 0.9-183.8] kcal/d; P = .05).Conclusions and RelevanceA personalized diet targeting a reduction in PPGR did not result in greater weight loss compared with a low-fat diet at 6 months. Future studies should assess methods of increasing dietary self-monitoring adherence and intervention exposure.Trial RegistrationClinicalTrials.gov Identifier: NCT03336411

The 10 K is a large-scale prospective longitudinal cohort and biobank that was established in Israel. The primary aims of the study include development of prediction models for disease onset and progression and identification of novel molecular markers with a diagnostic, prognostic and therapeutic value. The recruitment was initiated in 2018 and is expected to complete in 2021. Between 28/01/2019 and 13/12/2020, 4,629 from the expected 10,000 participants were recruited (46 %). Follow-up visits are scheduled every year for a total of 25 years. The cohort includes individuals between the ages of 40 and 70 years. Predefined medical conditions were determined as exclusions. Information collected at baseline includes medical history, lifestyle and nutritional habits, vital signs, anthropometrics, blood tests results, Electrocardiography, Ankle–brachial pressure index (ABI), liver US and Dual-energy X-ray absorptiometry (DXA) tests. Molecular profiling includes transcriptome, proteome, gut and oral microbiome, metabolome and immune system profiling. Continuous measurements include glucose levels using a continuous glucose monitoring device for 2 weeks and sleep monitoring by a home sleep apnea test device for 3 nights. Blood and stool samples are collected and stored at − 80 °C in a storage facility for future research. Linkage is being established with national disease registries.

ObjectiveTo explore the interplay between dietary modifications, microbiome composition and host metabolic responses in a dietary intervention setting of a personalised postprandial-targeting (PPT) diet versus a Mediterranean (MED) diet in pre-diabetes.DesignIn a 6-month dietary intervention, adults with pre-diabetes were randomly assigned to follow an MED or PPT diet (based on a machine-learning algorithm for predicting postprandial glucose responses). Data collected at baseline and 6 months from 200 participants who completed the intervention included: dietary data from self-recorded logging using a smartphone application, gut microbiome data from shotgun metagenomics sequencing of faecal samples, and clinical data from continuous glucose monitoring, blood biomarkers and anthropometrics.ResultsPPT diet induced more prominent changes to the gut microbiome composition, compared with MED diet, consistent with overall greater dietary modifications observed. Particularly, microbiome alpha-diversity increased significantly in PPT (p=0.007) but not in MED arm (p=0.18). Post hoc analysis of changes in multiple dietary features, including food-categories, nutrients and PPT-adherence score across the cohort, demonstrated significant associations between specific dietary changes and species-level changes in microbiome composition. Furthermore, using causal mediation analysis we detect nine microbial species that partially mediate the association between specific dietary changes and clinical outcomes, including three species (fromBacteroidales,Lachnospiraceae,Oscillospiralesorders) that mediate the association between PPT-adherence score and clinical outcomes of hemoglobin A1c (HbA1c), high-density lipoprotein cholesterol (HDL-C) and triglycerides. Finally, using machine-learning models trained on dietary changes and baseline clinical data, we predict personalised metabolic responses to dietary modifications and assess features importance for clinical improvement in cardiometabolic markers of blood lipids, glycaemic control and body weight.ConclusionsOur findings support the role of gut microbiome in modulating the effects of dietary modifications on cardiometabolic outcomes, and advance the concept of precision nutrition strategies for reducing comorbidities in pre-diabetes.Trial registration numberNCT03222791.

Complex diseases, such as coronary artery disease (CAD), are often multifactorial, caused by multiple underlying pathological mechanisms. Here, to study the multifactorial nature of CAD, we performed comprehensive clinical and multi-omic profiling, including serum metabolomics and gut microbiome data, for 199 patients with acute coronary syndrome (ACS) recruited from two major Israeli hospitals, and validated these results in a geographically distinct cohort. ACS patients had distinct serum metabolome and gut microbial signatures as compared with control individuals, and were depleted in a previously unknown bacterial species of the Clostridiaceae family. This bacterial species was associated with levels of multiple circulating metabolites in control individuals, several of which have previously been linked to an increased risk of CAD. Metabolic deviations in ACS patients were found to be person specific with respect to their potential genetic or environmental origin, and to correlate with clinical parameters and cardiovascular outcomes. Moreover, metabolic aberrations in ACS patients linked to microbiome and diet were also observed to a lesser extent in control individuals with metabolic impairment, suggesting the involvement of these aberrations in earlier dysmetabolic phases preceding clinically overt CAD. Finally, a metabolomics-based model of body mass index (BMI) trained on the non-ACS cohort predicted higher-than-actual BMI when applied to ACS patients, and the excess BMI predictions independently correlated with both diabetes mellitus (DM) and CAD severity, as defined by the number of vessels involved. These results highlight the utility of the serum metabolome in understanding the basis of risk-factor heterogeneity in CAD.

Atopic dermatitis (AD) is a remitting relapsing chronic eczematous pruritic disease. Several studies suggest that gut microbiota may influence AD by immune system regulation.We performed the first in-human efficacy and safety assessment of fecal microbiota transplantation (FMT) for AD adult patients. All patients received 2 placebo transplantations followed by 4 FMTs each 2 weeks apart. AD severity and fecal microbiome profile were evaluated by the Scoring Atopic Dermatitis Score (SCORAD), the weekly frequency of topical corticosteroids usage, and gut microbiota metagenomic analysis, at the study beginning, before every FMT, and 1-8 months after the last FMT.Nine patients completed the study protocol. There was no significant change in the SCORAD score following the two placebo transplants. The average SCORAD score significantly decreased from baseline at Weeks 4-12 (before and 2 weeks after 4 times of FMT) (59.2 ± 34.9%, Wilcoxon p = .011), 50% and 75% decrease was achieved by 7 (77%) and 4 (44%) patients, respectively. At Week 18 (8 weeks after the last FMT) the average SCORAD score decreased from baseline at Week 4 (85.5 ± 8.4%, Wilcoxon p = .018), 50% and 75% decrease was achieved by 7 (77%) and 6 (66.7%) patients respectively. Weekly topical corticosteroids usage was diminished during the study and follow-up period as well. Two patients had a quick relapse and were switched to a different treatment. Two patients developed exacerbations alleviated after an additional fifth FMT. Metagenomic analysis of the fecal microbiota of patients and donors showed bacterial strains transmission from donors to patients. No adverse events were recorded during the study and follow-up period.FMT may be a safe and effective therapeutic intervention for AD patients, associated with transfer of specific microbial species from the donors to the patients. Further studies are required to reconfirm these results.

Dogs have a key role in law enforcement and military work, and research with the goal of improving working dog performance is ongoing. While there have been intriguing studies from lab animal models showing a potential connection between the gut microbiome and behavior or mental health there is a dearth of studies investigating the microbiome-behavior relationship in working dogs. The overall objective of this study was to characterize the microbiota of working dogs and to determine if the composition of the microbiota is associated with behavioral and performance outcomes. Freshly passed stools from each working canine (Total n = 134) were collected and subject to shotgun metagenomic sequencing using Illumina technology. Behavior, performance, and demographic metadata were collected. Descriptive statistics and prediction models of behavioral/phenotypic outcomes using gradient boosting classification based on Xgboost were used to study associations between the microbiome and outcomes. Regarding machine learning methodology, only microbiome features were used for training and predictors were estimated in cross-validation. Microbiome markers were statistically associated with motivation, aggression, cowardice/hesitation, sociability, obedience to one trainer vs many, and body condition score (BCS). When prediction models were developed based on machine learning, moderate predictive power was observed for motivation, sociability, and gastrointestinal issues. Findings from this study suggest potential gut microbiome markers of performance and could potentially advance care for working canines.

IntroductionBreast cancer survivors treated with adjuvant endocrine therapy commonly experience weight gain, which has been associated with low adherence to therapy and worse breast cancer prognosis. We aim to assess whether a personalised postprandial glucose targeting diet will be beneficial for weight management as compared with the recommended Mediterranean diet in this patient populationMethods and analysisThe BREAst Cancer Personalised NuTrition study is a phase-2 randomised trial in hormone receptor positive patients with breast cancer, treated with adjuvant endocrine therapy. The study objective is to assess whether dietary intervention intended to improve postprandial glycaemic response to meals results in better weight and glycaemic control in this population as compared with the standard recommended Mediterranean diet. Consenting participants will be assigned in a single blinded fashion to either of two dietary arms (Mediterranean diet or an algorithm-based personalised diet). They will be asked to provide a stool sample for microbiome analysis and will undergo continuous glucose monitoring for 2 weeks, at the initiation and termination of the intervention period. Microbiome composition data will be used to tailor personal dietary recommendations. After randomisation and provision of dietary recommendations, participants will be asked to continuously log their diet and lifestyle activities on a designated smartphone application during the 6-month intervention period, during which they will be monthly monitored by a certified dietitian. Participants’ clinical records will be followed twice yearly for 5 years for treatment adherence, disease-free survival and recurrence.Ethics and disseminationThe study has been approved by the ethics committee in the Sheba medical centre (file 5725-18-SMC, Ramat Gan, Israel) and the Weizmann Institutional Review Board (file 693-2, Rehovot, Israel). The findings of this study will be published in a peer reviewed publication.Trial registration numberNCT04079270.

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.

Dietary modifications are crucial for the management of newly-diagnosed type-2 diabetes (T2D) and prevention of its health complications, but many patients fail to achieve clinical goals with diet alone. We previously developed a machine-learning algorithm for predicting personalized postprandial glucose responses (PPGR) to meals using clinical and gut microbiome features, and showed that dietary interventions based on this algorithm successfully lowered PPGRs in adults with prediabetes. Here, we sought to evaluate the clinical efficacy of our algorithm-based diet in subjects with newly-diagnosed T2D. We performed a short-term randomized controlled crossover trial and compared the effects of an algorithm-based personalized postprandial-targeting (PPT) diet, to those of a commonly used Mediterranean (MED) diet on glucose levels in 23 subjects with newly-diagnosed T2D. Average PPGR, glucose fluctuations, daily time of glucose levels >140 mg/dl and fructosamine levels decreased significantly more during the PPT intervention compared to the MED intervention. We further evaluated the long-term clinical effects of the PPT diet in 16 of the participants by an additional 6-month PPT intervention, and found significant improvements in multiple clinical parameters, including HbA1c (mean±SD, -0.39±0.48%, p Disclosure M. S. Rein: None. O. Ben-yacov: None. A. Godneva: None. S. Shilo: None. S. Zelber-sagi: None. E. Elinav: Consultant; Self; BiomX, DayTwo. E. Segal: Consultant; Self; DayTwo.

The 10K is a large-scale prospective longitudinal cohort and biobank that was established in Israel. The primary aims of the study include development of prediction models for disease onset and progression and identification of novel molecular markers with a diagnostic, prognostic and therapeutic value. The recruitment was initiated in 2018 and is expected to complete in 2021. Between 28/01/2019 and 13/12/2020, 4,629 from the expected 10,000 participants were recruited (46%). Follow-up visits are scheduled every year for a total of 25 years. The cohort includes individuals between the age of 40 and 70 years old. Predefined medical conditions were determined as exclusions. Information collected at baseline includes medical history, lifestyle and nutritional habits, vital signs, anthropometrics, blood tests results, Electrocardiography (ECG), Ankle–brachial pressure index (ABI), liver US and Dual-energy X-ray absorptiometry (DXA) tests. Molecular profiling includes transcriptome, proteome, gut and oral microbiome, metabolome and immune system profiling. Continuous measurements include glucose levels using a continuous glucose monitoring (CGM) device for 2 weeks and sleep monitoring by a home sleep apnea test (HSAT) device for 3 nights. Blood and stool samples are collected and stored at −80 °C in a storage facility for future research. Linkage is being established with national disease registries.

The 10 K is a large-scale prospective longitudinal cohort and biobank that was established in Israel. The primary aims of the study include development of prediction models for disease onset and progression and identification of novel molecular markers with a diagnostic, prognostic and therapeutic value. The recruitment was initiated in 2018 and is expected to complete in 2021. Between 28/01/2019 and 13/12/2020, 4,629 from the expected 10,000 participants were recruited (46 %). Follow-up visits are scheduled every year for a total of 25 years. The cohort includes individuals between the ages of 40 and 70 years. Predefined medical conditions were determined as exclusions. Information collected at baseline includes medical history, lifestyle and nutritional habits, vital signs, anthropometrics, blood tests results, Electrocardiography, Ankle-brachial pressure index (ABI), liver US and Dual-energy X-ray absorptiometry (DXA) tests. Molecular profiling includes transcriptome, proteome, gut and oral microbiome, metabolome and immune system profiling. Continuous measurements include glucose levels using a continuous glucose monitoring device for 2 weeks and sleep monitoring by a home sleep apnea test device for 3 nights. Blood and stool samples are collected and stored at - 80 °C in a storage facility for future research. Linkage is being established with national disease registries.

Background The gold standard for COVID-19 diagnosis is detection of viral RNA through PCR. Due to global limitations in testing capacity, effective prioritization of individuals for testing is essential. Methods We devised a model estimating the probability of an individual to test positive for COVID-19 based on answers to 9 simple questions that have been associated with COVID-19 infection. Our model was devised from a subsample of a national symptom survey that was answered over 2 million times in Israel in its first 2 months and a targeted survey distributed to all residents of several cities in Israel. Overall, 43,752 adults were included, from which 498 self-reported as being COVID-19 positive. Findings Our model was validated on a held-out set of individuals from Israel where it achieved an auROC of 0.737 (CI: 0.712-0.759), auPR of 0.144 (CI: 0.119-0.177) and demonstrated its applicability outside of Israel in an independently-collected symptom survey dataset from the U.S., U.K. and Sweden. Our analyses revealed interactions between several symptoms and age, suggesting variation in the clinical manifestation of the disease in different age groups. Conclusions our tool can be used online and without exposure to suspected patients, thus suggesting worldwide utility in combating COVID-19 by better directing the limited testing resources through prioritization of individuals for testing, thereby increasing the rate at which positive individuals can be identified. Moreover, individuals at high risk for a positive test result can be isolated prior to testing., Graphical Abstract

The spillover of animal coronaviruses (aCoVs) to humans has caused SARS, MERS, and COVID-19. While antibody responses displaying cross-reactivity between SARS-CoV-2 and seasonal/common cold human coronaviruses (hCoVs) have been reported, potential cross-reactivity with aCoVs and the diagnostic implications are incompletely understood. Here, we probed for antibody binding against all seven hCoVs and 49 aCoVs represented as 12,924 peptides within a phage-displayed antigen library. Antibody repertoires of 269 recovered COVID-19 patients showed distinct changes compared to 260 unexposed pre-pandemic controls, not limited to binding of SARS-CoV-2 antigens but including binding to antigens from hCoVs and aCoVs with shared motifs to SARS-CoV-2. We isolated broadly reactive monoclonal antibodies from recovered COVID-19 patients that bind a shared motif of SARS-CoV-2, hCoV-OC43, hCoV-HKU1, and several aCoVs, demonstrating that interspecies cross-reactivity can be mediated by a single immunoglobulin. Employing antibody binding data against the entire CoV antigen library allowed accurate discrimination of recovered COVID-19 patients from unexposed individuals by machine learning. Leaving out SARS-CoV-2 antigens and relying solely on antibody binding to other hCoVs and aCoVs achieved equally accurate detection of SARS-CoV-2 infection. The ability to detect SARS-CoV-2 infection without knowledge of its unique antigens solely from cross-reactive antibody responses against other hCoVs and aCoVs suggests a potential diagnostic strategy for the early stage of future pandemics. Creating regularly updated antigen libraries representing the animal coronavirome can provide the basis for a serological assay already poised to identify infected individuals following a future zoonotic transmission event.

IntroductionMetformin and diets aimed at promoting healthy body weight are the first line in treating type 2 diabetes mellitus (T2DM). Clinical practice, backed by clinical trials, suggests that many individuals do not reach glycaemic targets using this approach alone. The primary aim of the Personalised Medicine in Pre-diabetes—Towards Preventing Diabetes in Individuals at Risk (PREDICT) Study is to test the efficacy of personalised diet as adjuvant to metformin in improving glycaemic control in individuals with dysglycaemia.Methods and analysisPREDICT is a two-arm, parallel group, single-masked randomised controlled trial in adults with pre-diabetes or early-stage T2DM (with glycated haemoglobin (HbA1c) up to 8.0% (64 mmol/mol)), not treated with glucose-lowering medication. PREDICT is conducted at the Clinical Research Facility at the Garvan Institute of Medical Research (Sydney). Enrolment of participants commenced in December 2018 and expected to complete in December 2021. Participants are commenced on metformin (Extended Release, titrated to a target dose of 1500 mg/day) and randomised with equal allocation to either (1) the Personalised Nutrition Project algorithm-based diet or (2) low-fat high-dietary fibre diet, designed to provide caloric restriction (75%) in individuals with body mass index >25 kg/m2. Treatment duration is 6 months and participants visit the Clinical Research Facility five times over approximately 7 months. The primary outcome measure is HbA1c. The secondary outcomes are (1) time of interstitial glucose Ethics and disseminationThe study has been approved by the St Vincent’s Hospital Human Research Ethics Committee (File 17/080, Sydney, Australia) and the Weizmann Institutional Review Board (File 528-3, Rehovot, Israel). The findings will be published in peer-reviewed open access medical journals.Trial registration numberNCT03558867.

Beyond SARS-CoV-2, six more coronaviruses infect humans (hCoVs), four of which cause only mild symptoms (seasonal/common cold hCoVs) 12. Previous exposures to seasonal hCoVs may elicit immunological memory that could benefit the course of SARS-CoV-2 infections 3. While cross-reactive T cells epitopes of SARS-CoV-2 and seasonal hCoVs have been reported in individuals unexposed to SARS-CoV-2 4-6, potential antibody-based cross-reactivity is incompletely understood.Here, we have probed for high resolution antibody binding against all hCoVs represented as 1,539 peptides with a phage-displayed 7 antigen library. We detected broad serum antibody responses against peptides of seasonal hCoVs in up to 75% of individuals. Recovered COVID-19 patients exhibited distinct antibody repertoires targeting variable SARS-CoV-2 epitopes, and could be accurately classified from unexposed individuals (AUC=0.96). Up to 50% of recovered patients also mounted antibody responses against unique epitopes of seasonal hCoV-OC43, that were not detectable in unexposed individuals.These results indicate substantial interindividual variability and antibody cross-reactivity between hCoVs from the direction of SARS-CoV-2 infections towards seasonal hCoVs. Our accurate high throughput assay allows profiling preexisting antibody responses against seasonal hCoVs cost-effectively and could inform on their protective nature against SARS-CoV-2.

Prediabetes is a leading risk factor for the development of type 2 diabetes mellitus (T2DM) and its metabolic complications. Current practice for prevention of diabetes includes weight loss-targeting dietary and lifestyle modifications but has had limited success. Alternative approaches, such as dietary interventions that directly target blood glucose levels for prevention of T2DM, are not well established and have been much less studied. In particular, postprandial (post-meal) glucose responses (PPGR) are a major determinant of glycemic control, but dietary methods for controlling PPGRs are rare and show limited efficacy. We previously recruited a population-based cohort of 900 people in which we continuously tracked blood glucose and obtained clinical and gut microbiota measurements. Our data of ∼50,000 PPGRs revealed that different people have vastly different responses to the same meal. We devised a machine learning algorithm based on clinical and microbiome features that accurately predicted personalized PPGRs to any food combination. Here, we sought to evaluate the long-term clinical efficacy of a dietary intervention based on our algorithm in prediabetes, and test the hypothesis that a dietary treatment that targets meal PPGR can lead to long-term improvements in glycemic control and other metabolic outcomes, as compared to a standard Mediterranean-style diet. To this end, we conducted a 6-month randomized controlled diet intervention trial (RCT) followed by a 6-month post-intervention follow-up program, and randomly assigned 225 adults with prediabetes to receive a Mediterranean-style (Med) diet or a Personalized Postprandial-targeting (PPT) diet. At 6 months, mean daily time of glucose levels above 140 mg/dl changed by -29% and -65% in the Med diet and PPT diet, respectively (p=3.4x10-10). Mean HbA1c% levels changed by -0.08% and -0.16% in the Med diet and PPT diet, respectively (p=6.9x10-3). The favorable effects of the ’PPT diet’ on glycemic control persisted also at one year follow up. Disclosure O. Ben-Yacov: None. A. Godneva: None. E. Segal: Consultant; Self; DayTwo.

The gold standard for COVID-19 diagnosis is detection of viral RNA in a reverse transcription PCR test. Due to global limitations in testing capacity, effective prioritization of individuals for testing is essential. Here, we devised a model that estimates the probability of an individual to test positive for COVID-19 based on answers to 9 simple questions regarding age, gender, presence of prior medical conditions, general feeling, and the symptoms fever, cough, shortness of breath, sore throat and loss of taste or smell, all of which have been associated with COVID-19 infection. Our model was devised from a subsample of a national symptom survey that was answered over 2 million times in Israel over the past 2 months and a targeted survey distributed to all residents of several cities in Israel. Overall, 43,752 adults were included, from which 498 self-reported as being COVID-19 positive. We successfully validated the model on held-out individuals from Israel where it achieved a positive predictive value (PPV) of 46.3% at a 10% sensitivity and demonstrated its applicability outside of Israel by further validating it on an independently collected symptom survey dataset from the U.K., U.S. and Sweden, where it achieved a PPV of 34.7% at 10% sensitivity. Moreover, evaluating the model’s performance on this latter independent dataset on entries collected one week prior to the PCR test and up to the day of the test we found the highest performance on the day of the test. As our tool can be used online and without the need of exposure to suspected patients, it may have worldwide utility in combating COVID-19 by better directing the limited testing resources through prioritization of individuals for testing, thereby increasing the rate at which positive individuals can be identified and isolated.

The vast and rapid spread of COVID-19 calls for immediate action from policy-makers, and indeed various lockdown measures were implemented in many countries. Here, we utilized nationwide surveys that assess COVID-19 associated symptoms to analyze the effect of the lockdown policy in Israel on the prevalence of clinical symptoms in the population. Daily symptom surveys were distributed online and included fever, respiratory symptoms, gastrointestinal symptoms, anosmia and Ageusia. A total of 1,456,461 survey responses were analyzed. We defined a single measure of symptoms, Symptoms Average (SA), as the mean number of symptoms reported by responders. Data were collected between March 15th to May 11th, 2020. Notably, following severe lockdown measures, we found that between March 15th and April 20th, SA sharply declined by 83.8%, as did every individual symptom, including the most common symptoms reported by our responders, cough and rhinorrhea and\or nasal congestion, which decreased by 74.1% and 69.6%, respectively. Individual symptoms exhibit differences in reduction dynamics, suggesting differences in the medical conditions that they represent or in the nature of the symptoms themselves. The reduction in symptoms was observed in all the cities in Israel, and in several stratifications of demographic characteristics. Between April 20th and May 11th, following several subsequent lockdown relief measures, the decrease in SA and individual symptoms halted and they remain relatively stable with no significant change. Overall, these results demonstrate a profound decrease in a variety of clinical symptoms following the implementation of a lockdown in Israel. As our survey symptoms are not specific to COVID-19 infection, this effect likely represents an overall nationwide reduction in the prevalence of infectious diseases, including COVID-19. This quantification may be of major interest for COVID-19 pandemic, as many countries consider implementation of lockdown strategies.

Mining wild animal microbiomes We have only just begun to examine the multitude of bacteria associated with other organisms. Levin et al. sampled the feces of 184 wild animals, including fish, birds, and mammals, from four different continents to survey the diversity of gut bacteria (see the Perspective by Lind and Pollard). They discovered more than 1000 previously undescribed bacterial species and identified factors that correlate with the composition, diversity, and functional content of the microbiota. Supporting the association of specific bacteria with animal lifestyle, they identified proteases, some previously undescribed, from the gut of griffon vultures that can break down toxins that may be present in their carrion diet. Science , this issue p. eabb5352 ; see also p. 238

Human gut microbiome composition is shaped by multiple factors but the relative contribution of host genetics remains elusive. Here we examine genotype and microbiome data from 1,046 healthy individuals with several distinct ancestral origins who share a relatively common environment, and demonstrate that the gut microbiome is not significantly associated with genetic ancestry, and that host genetics have a minor role in determining microbiome composition. We show that, by contrast, there are significant similarities in the compositions of the microbiomes of genetically unrelated individuals who share a household, and that over 20% of the inter-person microbiome variability is associated with factors related to diet, drugs and anthropometric measurements. We further demonstrate that microbiome data significantly improve the prediction accuracy for many human traits, such as glucose and obesity measures, compared to models that use only host genetic and environmental data. These results suggest that microbiome alterations aimed at improving clinical outcomes may be carried out across diverse genetic backgrounds.

Differences in the presence of even a few genes between otherwise identical bacterial strains may result in critical phenotypic differences. Here we systematically identify microbial genomic structural variants (SVs) and find them to be prevalent in the human gut microbiome across phyla and to replicate in different cohorts. SVs are enriched for CRISPR-associated and antibiotic-producing functions and depleted from housekeeping genes, suggesting that they have a role in microbial adaptation. We find multiple associations between SVs and host disease risk factors, many of which replicate in an independent cohort. Exploring genes that are clustered in the same SV, we uncover several possible mechanistic links between the microbiome and its host, including a region in Anaerostipes hadrus that encodes a composite inositol catabolism-butyrate biosynthesis pathway, the presence of which is associated with lower host metabolic disease risk. Overall, our results uncover a nascent layer of variability in the microbiome that is associated with microbial adaptation and host health. The authors systematically characterize structural variation in the genomes of gut microbiota and show that they are associated with bacterial fitness and with host risk factors, and that examining genes coded in these regions facilitates investigation of mechanisms that may underlie these associations.

Human gut microbiome composition is shaped by multiple host intrinsic and extrinsic factors, but the relative contribution of host genetic compared to environmental factors remains elusive. Here, we genotyped a cohort of 696 healthy individuals from several distinct ancestral origins and a relatively common environment, and demonstrate that there is no statistically significant association between microbiome composition and ethnicity, single nucleotide polymorphisms (SNPs), or overall genetic similarity, and that only 5 of 211 (2.4%) previously reported microbiome-SNP associations replicate in our cohort. In contrast, we find similarities in the microbiome composition of genetically unrelated individuals who share a household. We define the termbiome-explainabilityas the variance of a host phenotype explained by the microbiome after accounting for the contribution of human genetics. Consistent with our finding that microbiome and host genetics are largely independent, we find significant biome-explainability levels of 16-33% for body mass index (BMI), fasting glucose, high-density lipoprotein (HDL) cholesterol, waist circumference, waist-hip ratio (WHR), and lactose consumption. We further show that several human phenotypes can be predicted substantially more accurately when adding microbiome data to host genetics data, and that the contribution of both data sources to prediction accuracy is largely additive. Overall, our results suggest that human microbiome composition is dominated by environmental factors rather than by host genetics.

Generalized dietary and lifestyle guidelines have been formulated and published for decades now from a variety of relevant agencies in an attempt to guide people towards healthy choices. As the pandemic rise in metabolic diseases continues to increase, it has become clear that the one-fit-for-all diet approach does not work and that there is a significant variation in inter-individual responses to diet and lifestyle interventions. Recent technological advances have given an unprecedented insight into the sources of this variation, pointing towards our genome and microbiome as potentially and previously under-explored culprits contributing to individually unique dietary responses. Variations in our genome influence the bioavailability and metabolism of nutrients between individuals, while inter-individual compositional variation of commensal gut microbiota leads to different microbe functional potential, metabolite production and metabolism modulation. Quantifying and incorporating these factors into a comprehensive personalized nutrition approach may enable practitioners to rationally incorporate individual nutritional recommendations in combating the metabolic syndrome pandemic.

Weight loss reduces the risk of type 2 diabetes mellitus (T2D) in overweight and obese individuals. Although the physiological response to food varies among individuals, standard dietary interventions use a "one-size-fits-all" approach. The Personal Diet Study aims to evaluate two dietary interventions targeting weight loss in people with prediabetes and T2D: (1) a low-fat diet, and (2) a personalized diet using a machine-learning algorithm that predicts glycemic response to meals. Changes in body weight, body composition, and resting energy expenditure will be compared over a 6-month intervention period and a subsequent 6-month observation period intended to assess maintenance effects. The behavioral intervention is delivered via mobile health technology using the Social Cognitive Theory. Here, we describe the design, interventions, and methods used.