Circulating metabolites in progression to islet autoimmunity and type 1 diabetes

Aims/hypothesis: Metabolic dysregulation may precede the onset of type 1 diabetes. However, these metabolic disturbances and their specific role in disease initiation remain poorly understood. In this study, we examined whether children who progress to type 1 diabetes have a circulatory polar metabolite profile distinct from that of children who later progress to islet autoimmunity but not type 1 diabetes and a matched control group.Methods: We analysed polar metabolites from 415 longitudinal plasma samples in a prospective cohort of children in three study groups: those who progressed to type 1 diabetes; those who seroconverted to one islet autoantibody but not to type 1 diabetes; and an antibody-negative control group. Metabolites were measured using two-dimensional GC high-speed time of flight MS.Results: In early infancy, progression to type 1 diabetes was associated with downregulated amino acids, sugar derivatives and fatty acids, including catabolites of microbial origin, compared with the control group. Methionine remained persistently upregulated in those progressing to type 1 diabetes compared with the control group and those who seroconverted to one islet autoantibody. The appearance of islet autoantibodies was associated with decreased glutamic and aspartic acids.Conclusions/interpretation: Our findings suggest that children who progress to type 1 diabetes have a unique metabolic profile, which is, however, altered with the appearance of islet autoantibodies. Our findings may assist with early prediction of the disease.</p

Differential diagnosis of parkinsonian syndromes: a comparison of clinical and automated - metabolic brain patterns’ based approach

© 2020, Springer-Verlag GmbH Germany, part of Springer Nature. Purpose: Differentiation among parkinsonian syndromes may be clinically challenging, especially at early disease stages. In this study, we used 18F-FDG-PET brain imaging combined with an automated image classification algorithm to classify parkinsonian patients as Parkinson’s disease (PD) or as an atypical parkinsonian syndrome (APS) at the time when the clinical diagnosis was still uncertain. In addition to validating the algorithm, we assessed its utility in a “real-life” clinical setting. Methods: One hundred thirty-seven parkinsonian patients with uncertain clinical diagnosis underwent 18F-FDG-PET and were classified using an automated image-based algorithm. For 66 patients in cohort A, the algorithm-based diagnoses were compared with their final clinical diagnoses, which were the gold standard for cohort A and were made 2.2 ± 1.1 years (mean ± SD) later by a movement disorder specialist. Seventy-one patients in cohort B were diagnosed by general neurologists, not strictly following diagnostic criteria, 2.5 ± 1.6 years after imaging. The clinical diagnoses were compared with the algorithm-based ones, which were considered the gold standard for cohort B. Results: Image-based automated classification of cohort A resulted in 86.0% sensitivity, 92.3% specificity, 97.4% positive predictive value (PPV), and 66.7% negative predictive value (NPV) for PD, and 84.6% sensitivity, 97.7% specificity, 91.7% PPV, and 95.5% NPV for APS. In cohort B, general neurologists achieved 94.7% sensitivity, 83.3% specificity, 81.8% PPV, and 95.2% NPV for PD, while 88.2%, 76.9%, 71.4%, and 90.9% for APS. Conclusion: The image-based algorithm had a high specificity and the predictive values in classifying patients before a final clinical diagnosis was reached by a specialist. Our data suggest that it may improve the diagnostic accuracy by 10–15% in PD and 20% in APS when a movement disorder specialist is not easily available

Atypical clinical presentation of pathologically proven Parkinson\u27s disease: The role of Parkinson\u27s disease related metabolic pattern

© 2020 Regional changes in brain metabolism upgraded with measurements of specific metabolic brain patterns and automated diagnostic algorithms can help to differentiate among neurodegenerative parkinsonisms, but with few reports on pathological confirmation. Here we describe a parkinsonian patient with atypical presentation and 18F-FDG-PET imaging consistent with idiopathic Parkinson\u27s disease. The latter was confirmed at the pathohistological examination

Impact of proanthocyanidin-rich apple intake on gut microbiota composition and polyphenol metabolomic activity in healthy mildly hypercholesterolemic subjects

Apples are a rich source of polyphenols and fiber. Proanthocyanidins (PAs), the largest polyphenolic class in apples, can reach the colon almost intact where they interact with the gut microbiota producing simple phenolic acids. These metabolites have the potential to modulate gut microbiota composition and activity and impact on host physiology. A randomized, controlled, crossover, dietary intervention study was performed to determine the broad effects of whole apple intake on fecal gut microbiota composition and activity. Forty heathy mildly hypercholesterolemic volunteers (23 women, 17 men), with a mean BMI (± SD) 25.3 ± 3.7 kg/m2 and age 51 ± 11 years, consumed 2 apples/day (Renetta Canada, rich in PAs), or a sugar matched control apple beverage, for 8 weeks separated by a 4-week washout period in a random order. Fecal and 24-h urine samples were collected before and after each treatment. The broad effects of apple intake on fecal gut microbiota composition were explored by the high throughput sequencing (HTS) of 16S rRNA gene lllumina MiSeq sequencing (V3-V4 region). Sequencing data analysis was performed using the Quantitative Insight Into Microbial Ecology (QIIME) open-source pipeline version 1.9.1. Specific bacterial groups were also enumerated using the quantitative Fluorescence In Situ Hybridization (FISH). Furthermore, the potential formation of microbial polyphenol metabolites, after apple intake, was explored in urine using Liquid Chromatography (LC) High-Resolution Mass Spectrometry (HRMS) metabolomics. Preliminary analysis showed no changes in gut microbiota abundances measured by Illumina MiSeq, after correction for multiple testing. Apple intake significantly decreased Enterobacteriaceae population (P=0.04) compared to the control beverage, as determined with FISH. Twenty-four polyphenol microbial metabolites were identified in higher concentrations in the apple group (P<0.05) compared to the control, including valerolactones, valeric and phenolic acids. In conclusion, preliminary data suggest that the daily intake of 2 Renetta Canada apples significantly decreased Enterobacteriaceae population, a family known for its pathogenic members, in healthy mildly hypercholesterolemic subjects. Moreover, several polyphenol microbial metabolites were identified, suggesting that microbial activity is crucial and a prerequisite for the absorption of apple polyphenols, producing active metabolites with potential health benefits

Human gut microbes impact host serum metabolome and insulin sensitivity

Insulin resistance is a forerunner state of ischaemic cardiovascular disease and type 2 diabetes. Here we show how the human gut microbiome impacts the serum metabolome and associates with insulin resistance in 277 non-diabetic Danish individuals. The serum metabolome of insulin-resistant individuals is characterized by increased levels of branched-chain amino acids (BCAAs), which correlate with a gut microbiome that has an enriched biosynthetic potential for BCAAs and is deprived of genes encoding bacterial inward transporters for these amino acids. Prevotella copri and Bacteroides vulgatus are identified as the main species driving the association between biosynthesis of BCAAs and insulin resistance, and in mice we demonstrate that P. copri can induce insulin resistance, aggravate glucose intolerance and augment circulating levels of BCAAs. Our findings suggest that microbial targets may have the potential to diminish insulin resistance and reduce the incidence of common metabolic and cardiovascular disorders