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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.

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.

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 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.