Direct supplementation with Urolithin A overcomes limitations of dietary exposure and gut microbiome. . .
of the MGS. However, an MGS was considered detected only if read pairs were mapped to at least three of its 100 signature genes; counts for MGSs that did not satisfy this criterion were set to zero. MGS counts was normalized according to effective gene length (accounting for read length) and then normalized to sum to 100%, resulting in relative abundance estimates of each MGS. Statistical com- parisons between groups were performed using the two-sided Mann – Whitney U test. To assess differences in the overall species community composition between the study groups (beta diversity), we used permutational multivariate analysis of variance (PERMANOVA) with 1000 permutations. When performing statistical testing on multiple hypotheses, the Benjamini – Hochberg (BH) method was used to control the false discovery rate (FDR) at a level of 10%.
with a limit of quanti fi cation of 1.17 ng/ml. The quanti fi - cation was performed by column separation with reversed phase liquid chromatography followed by detection with a diode array detector.
Shotgun metagenomic sequencing and analysis of microbiome
DNA was extracted from ~0.1 g aliquots of the fecal samples using the NucleoSpin ® 96 Soil kit (Macherey Nagel). A minimum of one negative control was included per batch of samples from the DNA extraction and throughout the laboratory process (including sequencing). A Zymo- BIOMICS ™ Microbial Community Standard (Zymo Research) was also included in the analysis as a positive (mock) control. Before sequencing, the quality of the DNA samples was evaluated using agarose gel electrophoresis, and the quantity of the DNA was evaluated by Qubit 2.0 fl uo- rometer quantitation. The prepared DNA libraries were evaluated using Qubit 2.0 fl uorometer quantitation and Agilent 2100 Bioanalyzer for the fragment size distribution. Quantitative real-time PCR was used to determine the con- centration of the fi nal library before sequencing. The library was sequenced using 2 × 150 bp paired-end sequencing on an Illumina platform. A total of 99 fecal samples were sequenced to an average depth of 19.9 M read pairs (Illumina 2 × 150 PE) per sample. On average, 96.5% of the high- quality microbiome reads from a sample were mapped to a reference human gut gene catalog, and on average 200 metagenomic species (MGS) were detected per sample. Shotgun sequencing was successful for all samples, and metagenomic analysis was performed to provide taxonomical and functional abundance pro fi les. For MGS abundance pro fi ling, a set of 1273 MGS, which have highly coherent abundance and base composition in a set of 1776 indepen- dent reference human gut samples, was detected. The ana- lysis is based on the MGS concept . To taxonomically annotate the MGSs, all the catalog genes were blasted to the NCBI RefSeq genome database (October 1, 2018). To annotate at the various taxonomic ranks, different levels of similarity were required (95, 95, 85, 75, 65, 55, 50, and 45% for subspecies, species, genus, family, order, class, phylum, super kingdom, respectively) and a minimum of 80% sequence coverage. The percentage of genes of each MGS that mapped to each species was calculated, and species level taxonomy were assigned to an MGS if >75% of genes could be annotated to a single species. For genus, family, order, class, and phylum, 60, 50, 40, 30, and 25% consistency levels were used, respectively. Furthermore, at species and at genus level, the MGS was not assigned if another set of more than 10% of the genes belonged to a single alternative spe- cies/genus. For each MGS, a signature gene set was de fi ned as the 100 genes optimized for accurate abundance pro fi ling
During the study, all adverse events (AEs) reported by the subject were documented with respect to onset, severity, resolution, and relatedness to the investigational products by a quali fi ed medical investigator. All AEs occurring during the conduct of the clinical study were recorded in the electronic Case Report Form. All AEs coded via MedDRA and recorded during the study are reported in Supplemen- tary Table 3. Blood pressure, heart rate, and temperature were measured at the screening visit and at all following visits. Safety labs (blood chemistry, hematology, lipid pro fi le, and HbA1c) were measured at the screening visit only.
An independent statistician performed sample size esti- mates. The study was powered on the primary objective of showing superiority of UA supplementation to PJ con- sumption to increase UA levels from baseline. With a sample size of 100, the estimated power to detect a 3.5 times higher UA concentration after UA supplementation as compared with PJ consumption was set at 80%. Following the ICH guideline E9, a blind data review meeting was held prior to locking the study database to de fi ne the Intent-to- treat (ITT) and Per-Protocol (PP) statistical populations. ITT population was de fi ned as all randomized participants with at least one consumption of either placebo or treatment product. PP population was de fi ned as all randomized par- ticipants with complete data for the primary outcome and at least 95% compliance in terms of amount of product con- sumed with no major protocol deviations as agreed during the blind data review. Participants who dropped out prior to receiving any treatment were excluded. The primary end- point was the absolute change in UA glucuronide plasma levels from T0 to T24 in the UA group as compared with
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