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.

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.

Pneumonia and Related Conditions in Critically Ill Patients-Insights from Basic and Experimental Studies.

Pneumonia is an acute infectious disease with high morbidity and mortality rates. Pneumonia's development, severity and outcome depend on age, comorbidities and the host immune response. In this study, we combined theoretical and experimental investigations to characterize pneumonia and its comorbidities as well as to assess the host immune response measured by TREC/KREC levels in patients with pneumonia. The theoretical study was carried out using the Columbia Open Health Data (COHD) resource, which provides access to clinical concept prevalence and co-occurrence from electronic health records. The experimental study included TREC/KREC assays in young adults (18-40 years) with community-acquired (CAP) (n = 164) or nosocomial (NP) (n = 99) pneumonia and healthy controls (n = 170). Co-occurring rates between pneumonia, sepsis, acute respiratory distress syndrome (ARDS) and some other related conditions common in intensive care units were the top among 4170, 3382 and 963 comorbidities in pneumonia, sepsis and ARDS, respectively. CAP patients had higher TREC levels, while NP patients had lower TREC/KREC levels compared to controls. Low TREC and KREC levels were predictive for the development of NP, ARDS, sepsis and lethal outcome (AUCTREC in the range 0.71-0.82, AUCKREC in the range 0.67-0.74). TREC/KREC analysis can be considered as a potential prognostic test in patients with pneumonia.

An overview of germline variations in genes of primary immunodeficiences through integrative analysis of ClinVar, HGMD® and dbSNP databases.

Primary immunodeficiencies (PID) are a diverse group of genetic disorders caused by inadequate development and function of immune system. Identifying genetic etiology is important for genetic counselling and treatment decisions. Clinical relevance of genetic variants is a complex problem depending on gene-specific and variant specific genotype-phenotype interactions. To address this challenge, we aimed to characterize the pathogenic landscape of PID genes by combining the analysis of germline variations reported in ClinVar and HGMD® and identification of damaging variations available in dbSNP. We generated a joint ClinVar/HGMD database, which included 111,940 variants, among them 32,452 were classified as pathogenic/likely pathogenic. From a total of 5,415,794 bi- or multiallelic variants in PID genes recorded in dbSNP, we retrieved 38,291 high impact (HI) biallelic variants with presumably disruptive impact in the protein, of them 25,500 variants were not present in ClinVar/HGMD. Using a functional prediction algorithm, we additionally identified 28,507 deleterious and 56,016 neutral missense variants among dbSNP variants and created a collection of damaging and neutral variations in PID genes, not currently present in ClinVar/HGMD, with their allele frequencies and mappings to protein domains. The distribution of pathogenic variants from ClinVar/HGMD, HI variants and deleterious missense variants from dbSNP was analyzed in the context of hereditary pattern and gene specific metrics, such as pLI and haploinsufficiency. Our report summarized data on complex gene-specific variability in PID genes and might be useful for the identification of the most promising variants and gene regions for further study.

10 K: a large-scale prospective longitudinal study in Israel.

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.

Biological findings from the PheWAS catalog: focus on connective tissue-related disorders (pelvic floor dysfunction, abdominal hernia, varicose veins and hemorrhoids).

Pelvic floor dysfunction, specifically genital prolapse (GP) and stress urinary inconsistency (SUI) presumably co-occur with other connective tissue disorders such as hernia, hemorrhoids, and varicose veins. Observations on non-random coexistence of these disorders have never been summarized in a meta-analysis. The performed meta-analysis demonstrated that varicose veins and hernia are associated with GP. Disease connections on the molecular level may be partially based on shared genetic susceptibility. A unique opportunity to estimate shared genetic susceptibility to disorders is provided by a PheWAS (phenome-wide association study) designed to utilize GWAS data concurrently to many phenotypes. We searched the PheWAS Catalog, which includes the results of the PheWAS study with P value < 0.05, for genes associated with GP, SUI, abdominal hernia, varicose veins and hemorrhoids. We found pronounced signals for the associations of the SLC2A9 gene with SUI (P = 6.0e-05) and the MYH9 gene with varicose veins of lower extremity (P = 0.0001) and hemorrhoids (P = 0.0007). The comparison of the PheWAS Catalog and the NHGRI Catalog data revealed enrichment of genes associated with bone mineral density in GP and with activated partial thromboplastin time in varicose veins of lower extremity. In cross-phenotype associations, genes responsible for peripheral nerve functions seem to predominate. This study not only established novel biologically plausible associations that may warrant further studies but also exemplified an effective use of the PheWAS Catalog data.

Comparison of asymptotic confidence sets for regression in small samples.

In case of small samples, asymptotic confidence sets may be inaccurate, with their actual coverage probability far from a nominal confidence level. In a single framework, we consider four popular asymptotic methods of confidence estimation. These methods are based on model linearization, F-test, likelihood ratio test, and nonparametric bootstrapping procedure. Next, we apply each of these methods to derive three types of confidence sets: confidence intervals, confidence regions, and pointwise confidence bands. Finally, to estimate the actual coverage of these confidence sets, we conduct a simulation study on three regression problems. A linear model and nonlinear Hill and Gompertz models are tested in conditions of different sample size and experimental noise. The simulation study comprises calculation of the actual coverage of confidence sets over pseudo-experimental datasets for each model. For confidence intervals, such metrics as width and simultaneous coverage are also considered. Our comparison shows that the F-test and linearization methods are the most suitable for the construction of confidence intervals, the F-test - for confidence regions and the linearization - for pointwise confidence bands.

Efficacy of federated learning on genomic data: a study on the UK Biobank and the 1000 Genomes Project.

Combining training data from multiple sources increases sample size and reduces confounding, leading to more accurate and less biased machine learning models. In healthcare, however, direct pooling of data is often not allowed by data custodians who are accountable for minimizing the exposure of sensitive information. Federated learning offers a promising solution to this problem by training a model in a decentralized manner thus reducing the risks of data leakage. Although there is increasing utilization of federated learning on clinical data, its efficacy on individual-level genomic data has not been studied. This study lays the groundwork for the adoption of federated learning for genomic data by investigating its applicability in two scenarios: phenotype prediction on the UK Biobank data and ancestry prediction on the 1000 Genomes Project data. We show that federated models trained on data split into independent nodes achieve performance close to centralized models, even in the presence of significant inter-node heterogeneity. Additionally, we investigate how federated model accuracy is affected by communication frequency and suggest approaches to reduce computational complexity or communication costs.

Genome-wide association studies and polygenic risk score phenome-wide association studies across complex phenotypes in the human phenotype project.

BACKGROUND: Genome-wide association studies (GWASs) associate phenotypes and genetic variants across a study cohort. GWASs require large-scale cohorts with both phenotype and genetic sequencing data, limiting studied phenotypes. The Human Phenotype Project is a longitudinal study that has measured a wide range of clinical and biomolecular features from a self-assignment cohort over 5 years. The phenotypes collected are quantitative traits, providing higher-resolution insights into the genetics of complex phenotypes. METHODS: We present the results of GWASs and polygenic risk score phenome-wide association studies with 729 clinical phenotypes and 4,043 molecular features from the Human Phenotype Project. This includes clinical traits that have not been previously associated with genetics, including measures from continuous sleep monitoring, continuous glucose monitoring, liver ultrasound, hormonal status, and fundus imaging. FINDINGS: In GWAS of 8,706 individuals, we found significant associations between 169 clinical traits and 1,184 single-nucleotide polymorphisms. We found genes associated with both glycemic control and mental disorders, and we quantify the strength of genetic signals in serum metabolites. In polygenic risk score phenome-wide association studies for clinical traits, we found 16,047 significant associations. CONCLUSIONS: The entire set of findings, which we disseminate publicly, provides newfound resolution into the genetic architecture of complex human phenotypes. FUNDING: E.S. is supported by the Minerva foundation with funding from the Federal German Ministry for Education and Research and by the European Research Council and the Israel Science Foundation.

Rare Variants in Primary Immunodeficiency Genes and Their Functional Partners in Severe COVID-19.

The development of severe COVID-19, which is a complex multisystem disease, is thought to be associated with many genes whose action is modulated by numerous environmental and genetic factors. In this study, we focused on the ideas of the omnigenic model of heritability of complex traits, which assumes that a small number of core genes and a large pool of peripheral genes expressed in disease-relevant tissues contribute to the genetics of complex traits through interconnected networks. We hypothesized that primary immunodeficiency disease (PID) genes may be considered as core genes in severe COVID-19, and their functional partners (FPs) from protein-protein interaction networks may be considered as peripheral near-core genes. We used whole-exome sequencing data from patients aged ≤ 45 years with severe (n = 9) and non-severe COVID-19 (n = 11), and assessed the cumulative contribution of rare high-impact variants to disease severity. In patients with severe COVID-19, an excess of rare high-impact variants was observed at the whole-exome level, but maximal association signals were detected for PID + FP gene subsets among the genes intolerant to LoF variants, haploinsufficient and essential. Our exploratory study may serve as a model for new directions in the research of host genetics in severe COVID-19.

COVID-19 severity: does the genetic landscape of rare variants matter?

Rare variants affecting host defense against pathogens may be involved in COVID-19 severity, but most rare variants are not expected to have a major impact on the course of COVID-19. We hypothesized that the accumulation of weak effects of many rare functional variants throughout the exome may contribute to the overall risk in patients with severe disease. This assumption is consistent with the omnigenic model of the relationship between genetic and phenotypic variation in complex traits, according to which association signals tend to spread across most of the genome through gene regulatory networks from genes outside the major pathways to disease-related genes. We performed whole-exome sequencing and compared the burden of rare variants in 57 patients with severe and 29 patients with mild/moderate COVID-19. At the whole-exome level, we observed an excess of rare, predominantly high-impact (HI) variants in the group with severe COVID-19. Restriction to genes intolerant to HI or damaging missense variants increased enrichment for these classes of variants. Among various sets of genes, an increased signal of rare HI variants was demonstrated predominantly for primary immunodeficiency genes and the entire set of genes associated with immune diseases, as well as for genes associated with respiratory diseases. We advocate taking the ideas of the omnigenic model into account in COVID-19 studies.

Genes and Diseases: Insights from Transcriptomics Studies.

Results of expression studies can be useful to clarify the genotype-phenotype relationship. However, according to data from recent literature, there is a large group of genes that are revealed as differentially expressed (DE) in many studies, regardless of the biological context. Additional analyses could shed more light on the relationships between genes, their differential expression, and diseases. We generated a set of 9972 disease genes from five gene-phenotype databases (OMIM, ORPHANET, DDG2P, DisGeNet and MalaCards) and a report of the International Union of Immunological Societies. To study transcriptomics of disease and non-disease genes in healthy tissues, we obtained data from the Human Protein Atlas (HPA) website. We analyzed the dependency between expression in healthy tissues and gene occurrence in Gene Expression Omnibus series using tools within the Enrichr libraries. The results of expression studies were annotated with Gene Ontology (GO) and Human Phenotype Ontology (HPO) terms. Using transcriptomics analysis of healthy tissues, we validated the previous findings of higher expression levels of disease genes in pathologically linked tissues compared to other tissues. Preferentially DE genes were generally highly expressed in one or multiple tissues and were enriched for disease genes. According to the results of GO enrichment analyses, both down- and up-regulated DE genes most often took part in immune response, translation and tissue-specific processes. A connection between DE-related pathology and the diversity of HPO terms was found. Investigating a link between expression and phenotype contributes to understanding the mode of development and progression of human diseases.

BREAst Cancer Personalised NuTrition (BREACPNT): dietary intervention in breast cancer survivors treated with endocrine therapy - a protocol for a randomised clinical trial.

INTRODUCTION: Breast 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 population METHODS AND ANALYSIS: The 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 DISSEMINATION: The 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 NUMBER: NCT04079270.

GRAPE: genomic relatedness detection pipeline.

Classifying the degree of relatedness between pairs of individuals has both scientific and commercial applications. As an example, genome-wide association studies (GWAS) may suffer from high rates of false positive results due to unrecognized population structure. This problem becomes especially relevant with recent increases in large-cohort studies. Accurate relationship classification is also required for genetic linkage analysis to identify disease-associated loci. Additionally, DNA relatives matching service is one of the leading drivers for the direct-to-consumer genetic testing market. Despite the availability of scientific and research information on the methods for determining kinship and the accessibility of relevant tools, the assembly of the pipeline, which stably operates on a real-world genotypic data, requires significant research and development resources. Currently, there is no open source end-to-end solution for relatedness detection in genomic data, that is fast, reliable and accurate for both close and distant degrees of kinship, combines all the necessary processing steps to work on a real data, and is ready for production integration. To address this, we developed GRAPE: Genomic RelAtedness detection PipelinE. It combines data preprocessing, identity-by-descent (IBD) segments detection, and accurate relationship estimation. The project uses software development best practices, as well as Global Alliance for Genomics and Health (GA4GH) standards and tools. Pipeline efficiency is demonstrated on both simulated and real-world datasets. GRAPE is available from: https://github.com/genxnetwork/grape.

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

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.

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.

Personalized Postprandial Glucose Response-Targeting Diet Versus Mediterranean Diet for Glycemic Control in Prediabetes.

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.

A Prediction Model to Prioritize Individuals for a SARS-CoV-2 Test Built from National Symptom Surveys.

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 SARS-CoV-2 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) and 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 US, UK, 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. FUNDING: E.S. is supported by the Crown Human Genome Center, Larson Charitable Foundation New Scientist Fund, Else Kroener Fresenius Foundation, White Rose International Foundation, Ben B. and Joyce E. Eisenberg Foundation, Nissenbaum Family, Marcos Pinheiro de Andrade and Vanessa Buchheim, Lady Michelle Michels, and Aliza Moussaieff and grants funded by the Minerva foundation with funding from the Federal German Ministry for Education and Research and by the European Research Council and the Israel Science Foundation. H.R. is supported by the Israeli Council for Higher Education (CHE) via the Weizmann Data Science Research Center and by a research grant from Madame Olga Klein - Astrachan.

Cytokines mapping for tissue-specific expression, eQTLs and GWAS traits.

Dysregulation in cytokine production has been linked to the pathogenesis of various immune-mediated traits, in which genetic variability contributes to the etiopathogenesis. GWA studies have identified many genetic variants in or near cytokine genes, nonetheless, the translation of these findings into knowledge of functional determinants of complex traits remains a fundamental challenge. In this study we aimed at collection, analysis and interpretation of data on cytokines focused on their tissue-specific expression, eQTLs and GWAS traits. Using GO annotations, we generated a list of 314 cytokines and analyzed them with the GTEx resource. Cytokines were highly tissue-specific, 82.3% of cytokines had Tau expression metrics ≥ 0.8. In total, 3077 associations for 1760 unique SNPs in or near 244 cytokines were mapped in the NHGRI-EBI GWAS Catalog. According to the Experimental Factor Ontology resource, the largest numbers of disease associations were related to 'Inflammatory disease', 'Immune system disease' and 'Asthma'. The GTEx-based analysis revealed that among GWAS SNPs, 1142 SNPs had eQTL effects and influenced expression levels of 999 eGenes, among them 178 cytokines. Several types of enrichment analysis showed that it was cytokines expression variability that fundamentally contributed to the molecular origins of considered immune-mediated conditions.

Rationale and design of a randomised controlled trial testing the effect of personalised diet in individuals with pre-diabetes or type 2 diabetes mellitus treated with metformin.

INTRODUCTION: Metformin 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 ANALYSIS: PREDICT 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 <7.8 mmol/L and (2) glycaemic variability (continuous glucose monitoring), (3) body weight, (4) fat mass and (5) abdominal visceral fat volume (dual-energy X-ray absorptiometry), serum (6) low-density lipoprotein cholesterol (7) high-density lipoprotein cholesterol and (8) triglycerides concentrations, (9) blood pressure, and (10) liver fat (Fibroscan). ETHICS AND DISSEMINATION: The 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 NUMBER: NCT03558867; Pre-results.

Expression changes in pelvic organ prolapse: a systematic review and in silico study.

Pelvic organ prolapse (POP) is a highly disabling condition common for a vast number of women worldwide. To contribute to existing knowledge in POP pathogenesis, we performed a systematic review of expression studies on both specific gene and whole-genome/proteome levels and an in silico analysis of publicly available datasets related to POP development. The most extensively investigated genes in individual studies were related to extracellular matrix (ECM) organization. Three premenopausal and two postmenopausal sets from two Gene Expression Omnibus (GEO) studies (GSE53868 and GSE12852) were analyzed; Gene Ontology (GO) terms related to tissue repair (locomotion, biological adhesion, immune processes and other) were enriched in all five datasets. Co-expression was higher in cases than in controls in three premenopausal sets. The shared between two or more datasets up-regulated genes were enriched with those related to inflammatory bowel disease (IBD) in the NHGRI GWAS Catalog. ECM-related genes were not over-represented among differently expressed genes. Up-regulation of genes related to tissue renewal probably reflects compensatory mechanisms aimed at repair of damaged tissue. Inefficiency of this process may have different origins including age-related deregulation of gene expression.