Dietary pattern in autism increases the need for probiotic supplementation: A comprehensive narrative and systematic review on oxidative stress hypothesis.

Autism spectrum disorders (ASDs) are associated with specific dietary habits, including limited food selection and gastrointestinal problems, resulting in an altered gut microbiota. Autistic patients have an elevated abundance of certain gut bacteria associated with increased oxidative stress in the gastrointestinal tract. Probiotic supplementation has been shown to decrease oxidative stress in a simulated gut model, but the antioxidant effects of probiotics on the oxidative stress of the gut in autistic patients have not been directly studied. However, it is speculated that probiotic supplementation may help decrease oxidative stress in the gastrointestinal tract of autistic patients due to their specific dietary habits altering the microbiota. PubMed, Scopus and Web of Science databases and Google Scholar were searched up to May 2023. This systematic-narrative review aims to present the latest evidence regarding the changes in eating habits of autistic children which may further increase the gut microbiota induced oxidative stress. Additionally, this review will assess the available literature on the effects of probiotic supplementation on oxidative stress parameters.

Modulation of gene expression profile following consumption of high-dairy products in subjects with hyperinsulinemia.

BACKGROUND AND AIM: Dysregulation of gene expression is associated to a higher risk of type 2 diabetes (T2D). Further, research indicates that dairy consumption may potentially affect gene expression. The aim of this study was to examine if genes and pathways associated with T2D are differentially changed in subjects with hyperinsulinemia after high dairy (HD) diet. METHODS AND RESULTS: Ten obese patients with hyperinsulinemia who consumed HD (4 servings/day according to the Canadian Food Guide (2007)) for six weeks participated in this study. Before and after HD consumption, fasting blood samples were collected. Blood was taken in PAX-gene tubes and RNA was extracted and analyzed using Clariom S microarrays. Results indicated that 236 genes (137 up-regulated and 99 down-regulated; fold change (FC) ≥ ±1.2; p < 0.05) were expressed differentially between before and after HD intake. Genes related to pathways associated with insulin signaling and inflammation, such as olfactory receptor activity, G-protein-coupled receptors (GPCR), phosphatidylinositol-3-OHKinase (PI3K)/AKT2 (PI3K-AKT2), Ras signaling, Mitogen-Activated Protein Kinase (MAPK) were altered following HD. CONCLUSION: Overall, results suggest a potential protective effect of HD intake in individuals at risk of T2D through modification of gene expression profiles. REGISTRATION NUMBER FOR CLINICAL STUDIES: NCT02961179.

Dairy Product Intake Modifies MicroRNA Expression among Individuals with Hyperinsulinemia: A Post-Intervention Cross-Sectional Study.

INTRODUCTION: MicroRNA (miRNA) profiles have been shown to change after intake of dairy products. Dysregulation of miRNA is associated with the changes in the level of glycemic parameters. The objectives are: (1) to investigate miRNA expression after consumption of dairy products and (2) to study the association between miRNAs and glycemic profile among individuals with hyperinsulinemia. METHODS: In crossover design, 24 participants were randomized into 2 phases: high dairy (HD) (≥4 servings/day according to the Canadian food guide [2007]) and adequate dairy (AD) (≤2 servings/day) over 6 weeks. First, miRNAs were extracted from a pooled plasma sample of 10 subjects after HD and AD intervention which analyzed in duplicate by array hybridization (Affymetrix Gene Chip miRNA Array v. 4.0). Second, 6 miRNAs related to type 2 diabetes (T2D) were validated by quantitative reverse transcription polymerase chain reaction (qRT-PCR) from plasma of 24 participants. RESULTS: Microarray analysis indicated that 297 miRNAs expressed differentially (FC ≥ ±1.2; p value <0.05) in a pooled plasma sample of 10 subjects. Among pooled miRNAs, the level of selected miRNAs, including miR-652-3p, miR-106b-5p, miR-93-5p, and miR-107 were downregulated; conversely, miR-223-3p and miR-122-5p were upregulated. After qRT-PCR validation, only the expression level of miR-106-5p tended to be increased after HD compared to AD (p = 0.06). After AD intervention, the level of fasting plasma glucose (FPG) and insulin and homeostatic model assessment of insulin resistance were negatively correlated with miR-122-5p. Similarly, negative correlation was found between miR-106-5p and FPG. CONCLUSION: The miRNAs profile was modified after HD compared to AD, and this may have role in modifying the risk of T2D (registration No. NCT02961179).

Impact of Dairy Intake on Plasma F2-IsoProstane Profiles in Overweight Subjects with Hyperinsulinemia: A Randomized Crossover Trial.

F2-IsoProstanes (F2-IsoPs) are major biomarkers of oxidative stress and are associated with type 2 diabetes (T2D). Further, plasma levels of F2-IsoPs may be modified by dairy products. The aim is to investigate the effect of high dairy product (HD) consumption compared to an adequate dairy product (AD) consumption on the level of F2-IsoPs among hyperinsulinemic subjects. In this crossover study, participants were randomized in two groups: HD (≥4 servings/day), or AD (≤2 servings/day) for six weeks. Fasting blood glucose and insulin were measured. The homeostatic model assessment of insulin resistance (HOMA-IR) was calculated. Six isomers of F2-IsoPs were quantified by HPLC-MS/MS. Twenty-seven subjects with hyperinsulinemia (mean age; 55 ± 13 years, BMI; 31.4 ± 3.3 kg/m2) were included. Fasting glucose, insulin and HOMA-IR were unchanged after HD or AD intervention. After HD intake, the total level of F2-IsoPs (p = 0.03), 5-F2t-IsoP (p = 0.002), and 8-F2t-IsoP (p = 0.004) decreased compared to AD. The 15-F2t-IsoP tended to be positively correlated with fasting blood glucose (r = 0.39, p = 0.08). Generally, F2-IsoPs levels were higher among men compared to women regardless of the dairy intake. Overall, intake of HD decreased plasma levels of F2-IsoPs compared to AD without modifying glycemic parameters.

Effect of Yogurt Consumption on Metabolic Syndrome Risk Factors: a Narrative Review.

PURPOSE OF REVIEW: Metabolic syndrome (MetS) comprises risk factors such as obesity, hypertriglyceridemia, hypertension, and hyperglycemia. Here we described the outcome of various yogurt consumption, either conventional, low-fat, high-fat, Greek, or enriched with nutrients or probiotics: (1) on the parameters of MetS risk factors and (2) on the mechanisms of action of the MetS risk factors. RECENT FINDING: The majority (25 studies) of clinical trials and meta-analyses of clinical trials reported a beneficial effect of yogurt consumption in the prevention of MetS risk. Yogurt components, such as calcium, vitamin D, proteins, and probiotics, were associated with the multiple beneficial effects on the prevention of MetS. In general, yogurt consumption may be promoted within healthy dietary patterns to prevent MetS. More studies are needed to determine what type of yogurt has the greatest benefits for specific MetS risk factor prevention.

Dairy product intake modifies gut microbiota composition among hyperinsulinemic individuals.

PURPOSES: The objectives of this study were to investigate differences in gut microbiota (GM) composition after high dairy intake (HD) compared to adequate dairy intake (AD) and to correlate GM composition variations with the change in glycemic parameters in hyperinsulinemic subjects. METHODS: In this crossover study, 10 hyperinsulinemic adults were randomized to HD (≥ 4 servings/day) or AD (≤ 2 servings/day) for 6 weeks, separated by a 6-week washout period. Fasting insulin and glucose levels were measured after each intervention. Insulin resistance was calculated with the homeostasis model assessment of insulin resistance (HOMA-IR). GM was determined with 16S rRNA-based high-throughput sequencing at the end of each intervention. Paired t test, correlations and machine learning analyses were performed. RESULTS: Endpoint glycemic parameters were not different between HD and AD intake. After HD compared with AD intake, there was a decrease in the abundance of bacteria in Roseburia and Verrucomicrobia (p = 0.04 and p = 0.02, respectively) and a trend for an increase abundance in Faecalibacteria and Flavonifractor (p = 0.05 and p = 0.06, respectively). The changes in abundance of Coriobacteriia, Erysipelotrichia, and Flavonifractor were negatively correlated with the change in HOMA-IR between the AD and HD phases. Furthermore, a predictive GM signature, including Anaerotruncus, Flavonifractor, Ruminococcaceae, and Subdoligranulum, was related to HOMA-IR. CONCLUSION: Overall, these results suggest that HD modifies the abundance of specific butyrate-producing bacteria in Firmicutes and of bacteria in Verrucomicrobia in hyperinsulinemic individuals. In addition, the butyrate producing bacteria in Firmicutes phylum correlate negatively with insulin resistance.

Statistical and Machine-Learning Analyses in Nutritional Genomics Studies.

Nutritional compounds may have an influence on different OMICs levels, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, and metagenomics. The integration of OMICs data is challenging but may provide new knowledge to explain the mechanisms involved in the metabolism of nutrients and diseases. Traditional statistical analyses play an important role in description and data association; however, these statistical procedures are not sufficiently enough powered to interpret the large integrated multiple OMICs (multi-OMICS) datasets. Machine learning (ML) approaches can play a major role in the interpretation of multi-OMICS in nutrition research. Specifically, ML can be used for data mining, sample clustering, and classification to produce predictive models and algorithms for integration of multi-OMICs in response to dietary intake. The objective of this review was to investigate the strategies used for the analysis of multi-OMICs data in nutrition studies. Sixteen recent studies aimed to understand the association between dietary intake and multi-OMICs data are summarized. Multivariate analysis in multi-OMICs nutrition studies is used more commonly for analyses. Overall, as nutrition research incorporated multi-OMICs data, the use of novel approaches of analysis such as ML needs to complement the traditional statistical analyses to fully explain the impact of nutrition on health and disease.