Application of machine learning tools: Potential and useful approach for the prediction of type 2 diabetes mellitus based on the gut microbiome profile

Gut microbiome research: Revealing the pathological mechanisms and treatment strategies of type 2 diabetes

The high prevalence and disability rate of type 2 diabetes (T2D) caused a huge social burden to the world. Currently, new mechanisms and therapeutic approaches that may affect this disease are being sought. With in-depth research on the pathogenesis of T2D and growing advances in microbiome sequencing technology, the association between T2D and gut microbiota has been confirmed. The gut microbiota participates in the regulation of inflammation, intestinal permeability, short-chain fatty acid metabolism, branched-chain amino acid metabolism and bile acid metabolism, thereby affecting host glucose and lipid metabolism. Interventions focusing on the gut microbiota are gaining traction as a promising approach to T2D management. For example, dietary intervention, prebiotics and probiotics, faecal microbiota transplant and phage therapy. Meticulous experimental design and choice of analytical methods are crucial for obtaining accurate and meaningful results from microbiome studies. How to design gut microbiome research in T2D and choose different machine learning methods for data analysis are extremely critical to achieve personalized precision medicine.

Leveraging innovative diagnostics as a tool to contain superbugs

The evolutionary adaptation of pathogens to biological materials has led to an upsurge in drug-resistant superbugs that significantly threaten public health. Treating most infections is an uphill task, especially those associated with multi-drug-resistant pathogens, biofilm formation, persister cells, and pathogens that have acquired robust colonization and immune evasion mechanisms. Innovative diagnostic solutions are crucial for identifying and understanding these pathogens, initiating efficient treatment regimens, and curtailing their spread. While next-generation sequencing has proven invaluable in diagnosis over the years, the most glaring drawbacks must be addressed quickly. Many promising pathogen-associated and host biomarkers hold promise, but their sensitivity and specificity remain questionable. The integration of CRISPR-Cas9 enrichment with nanopore sequencing shows promise in rapid bacterial diagnosis from blood samples. Moreover, machine learning and artificial intelligence are proving indispensable in diagnosing pathogens. However, despite renewed efforts from all quarters to improve diagnosis, accelerated bacterial diagnosis, especially in Africa, remains a mystery to this day. In this review, we discuss current and emerging diagnostic approaches, pinpointing the limitations and challenges associated with each technique and their potential to help address drug-resistant bacterial threats. We further critically delve into the need for accelerated diagnosis in low- and middle-income countries, which harbor more infectious disease threats. Overall, this review provides an up-to-date overview of the diagnostic approaches needed for a prompt response to imminent or possible bacterial infectious disease outbreaks.

Gut microbiota in relationship to diabetes mellitus and its late complications with a focus on diabetic foot syndrome: A review

Diabetes mellitus is a chronic disease affecting glucose metabolism. The pathophysiological reactions underpinning the disease can lead to the development of late diabetes complications. The gut microbiota plays important roles in weight regulation and the maintenance of a healthy digestive system. Obesity, diabetes mellitus, diabetic retinopathy, diabetic nephropathy and diabetic neuropathy are all associated with a microbial imbalance in the gut. Modern technical equipment and advanced diagnostic procedures, including xmolecular methods, are commonly used to detect both quantitative and qualitative changes in the gut microbiota. This review summarises collective knowledge on the role of the gut microbiota in both types of diabetes mellitus and their late complications, with a particular focus on diabetic foot syndrome.

Multi-omics approaches to studying gastrointestinal microbiome in the context of precision medicine and machine learning

The human gastrointestinal (gut) microbiome plays a critical role in maintaining host health and has been increasingly recognized as an important factor in precision medicine. High-throughput sequencing technologies have revolutionized -omics data generation, facilitating the characterization of the human gut microbiome with exceptional resolution. The analysis of various -omics data, including metatranscriptomics, metagenomics, glycomics, and metabolomics, holds potential for personalized therapies by revealing information about functional genes, microbial composition, glycans, and metabolites. This multi-omics approach has not only provided insights into the role of the gut microbiome in various diseases but has also facilitated the identification of microbial biomarkers for diagnosis, prognosis, and treatment. Machine learning algorithms have emerged as powerful tools for extracting meaningful insights from complex datasets, and more recently have been applied to metagenomics data via efficiently identifying microbial signatures, predicting disease states, and determining potential therapeutic targets. Despite these rapid advancements, several challenges remain, such as key knowledge gaps, algorithm selection, and bioinformatics software parametrization. In this mini-review, our primary focus is metagenomics, while recognizing that other -omics can enhance our understanding of the functional diversity of organisms and how they interact with the host. We aim to explore the current intersection of multi-omics, precision medicine, and machine learning in advancing our understanding of the gut microbiome. A multidisciplinary approach holds promise for improving patient outcomes in the era of precision medicine, as we unravel the intricate interactions between the microbiome and human health.

Methodology for biomarker discovery with reproducibility in microbiome data using machine learning

BACKGROUND

In recent years, human microbiome studies have received increasing attention as this field is considered a potential source for clinical applications. With the advancements in omics technologies and AI, research focused on the discovery for potential biomarkers in the human microbiome using machine learning tools has produced positive outcomes. Despite the promising results, several issues can still be found in these studies such as datasets with small number of samples, inconsistent results, lack of uniform processing and methodologies, and other additional factors lead to lack of reproducibility in biomedical research. In this work, we propose a methodology that combines the DADA2 pipeline for 16s rRNA sequences processing and the Recursive Ensemble Feature Selection (REFS) in multiple datasets to increase reproducibility and obtain robust and reliable results in biomedical research.

RESULTS

Three experiments were performed analyzing microbiome data from patients/cases in Inflammatory Bowel Disease (IBD), Autism Spectrum Disorder (ASD), and Type 2 Diabetes (T2D). In each experiment, we found a biomarker signature in one dataset and applied to 2 other as further validation. The effectiveness of the proposed methodology was compared with other feature selection methods such as K-Best with F-score and random selection as a base line. The Area Under the Curve (AUC) was employed as a measure of diagnostic accuracy and used as a metric for comparing the results of the proposed methodology with other feature selection methods. Additionally, we use the Matthews Correlation Coefficient (MCC) as a metric to evaluate the performance of the methodology as well as for comparison with other feature selection methods.

CONCLUSIONS

We developed a methodology for reproducible biomarker discovery for 16s rRNA microbiome sequence analysis, addressing the issues related with data dimensionality, inconsistent results and validation across independent datasets. The findings from the three experiments, across 9 different datasets, show that the proposed methodology achieved higher accuracy compared to other feature selection methods. This methodology is a first approach to increase reproducibility, to provide robust and reliable results.

From-Toilet-to-Freezer: A Review on Requirements for an Automatic Protocol to Collect and Store Human Fecal Samples for Research Purposes

The composition, viability and metabolic functionality of intestinal microbiota play an important role in human health and disease. Studies on intestinal microbiota are often based on fecal samples, because these can be sampled in a non-invasive way, although procedures for sampling, processing and storage vary. This review presents factors to consider when developing an automated protocol for sampling, processing and storing fecal samples: donor inclusion criteria, urine-feces separation in smart toilets, homogenization, aliquoting, usage or type of buffer to dissolve and store fecal material, temperature and time for processing and storage and quality control. The lack of standardization and low-throughput of state-of-the-art fecal collection procedures promote a more automated protocol. Based on this review, an automated protocol is proposed. Fecal samples should be collected and immediately processed under anaerobic conditions at either room temperature (RT) for a maximum of 4 h or at 4 °C for no more than 24 h. Upon homogenization, preferably in the absence of added solvent to allow addition of a buffer of choice at a later stage, aliquots obtained should be stored at either -20 °C for up to a few months or -80 °C for a longer period-up to 2 years. Protocols for quality control should characterize microbial composition and viability as well as metabolic functionality.

Dysbiosis signatures of gut microbiota and the progression of type 2 diabetes: a machine learning approach in a Mexican cohort

Introduction

The gut microbiota (GM) dysbiosis is one of the causal factors for the progression of different chronic metabolic diseases, including type 2 diabetes mellitus (T2D). Understanding the basis that laid this association may lead to developing new therapeutic strategies for preventing and treating T2D, such as probiotics, prebiotics, and fecal microbiota transplants. It may also help identify potential early detection biomarkers and develop personalized interventions based on an individual's gut microbiota profile. Here, we explore how supervised Machine Learning (ML) methods help to distinguish taxa for individuals with prediabetes (prediabetes) or T2D.

Methods

To this aim, we analyzed the GM profile (16s rRNA gene sequencing) in a cohort of 410 Mexican naïve patients stratified into normoglycemic, prediabetes, and T2D individuals. Then, we compared six different ML algorithms and found that Random Forest had the highest predictive performance in classifying T2D and prediabetes patients versus controls.

Results

We identified a set of taxa for predicting patients with T2D compared to normoglycemic individuals, including Allisonella, Slackia, Ruminococus_2, Megaspgaera, Escherichia/Shigella, and Prevotella, among them. Besides, we concluded that Anaerostipes, Intestinibacter, Prevotella_9, Blautia, Granulicatella, and Veillonella were the relevant genus in patients with prediabetes compared to normoglycemic subjects.

Discussion

These findings allow us to postulate that GM is a distinctive signature in prediabetes and T2D patients during the development and progression of the disease. Our study highlights the role of GM and opens a window toward the rational design of new preventive and personalized strategies against the control of this disease.

Black Rice Anthocyanidins Regulates Gut Microbiota and Alleviates Related Symptoms through PI3K/AKT Pathway in Type 2 Diabetic Rats

Black rice anthocyanins (BRAs) have extremely high nutritional value and health care effects. This study investigated the intervention effect of BRAs on type 2 diabetes mellitus (T2DM) and the regulation effect on intestinal microbiota imbalance in T2DM rats. This study established successfully a T2DM model in a high-fat and high-glucose diet combined with streptozotocin (STZ). BRAs intervention reduced significantly the fasting blood glucose level of T2DM rats, improved the glucose tolerance of rats, reduced the blood lipid level and inflammation state, and repaired liver, oxidative stress, and other injuries. In addition, BRAs’s intervention enhanced the expression of phosphoinositol 3-kinase (PI3K)/protein kinase B (AKT), activated the expression of adenosine 5’-monophosphate-activated protein kinase(AMPK), and the downstream acetyl-CoA carboxylase (ACC) and carnitine palmitoyl transferase (CPT1) in the liver. 16S rRNA sequencing showed that BRAs significantly decreased the abundances of Bifidobacterium and Clostridiaceae_Clostridium, and promoted the abundances of Akkermansia and Lactobacillus. Accelerate the recovery of gut microbiota diversity. BRAs play an antidiabetic role by regulating the PI3K/AKT signaling pathway and intestinal microbiota in T2MD rats.

Pharmacomicrobiomics and type 2 diabetes mellitus: A novel perspective towards possible treatment

Type 2 diabetes mellitus (T2DM), a major driver of mortality worldwide, is more likely to develop other cardiometabolic risk factors, ultimately leading to diabetes-related mortality. Although a set of measures including lifestyle intervention and antidiabetic drugs have been proposed to manage T2DM, problems associated with potential side-effects and drug resistance are still unresolved. Pharmacomicrobiomics is an emerging field that investigates the interactions between the gut microbiome and drug response variability or drug toxicity. In recent years, increasing evidence supports that the gut microbiome, as the second genome, can serve as an attractive target for improving drug efficacy and safety by manipulating its composition. In this review, we outline the different composition of gut microbiome in T2DM and highlight how these microbiomes actually play a vital role in its development. Furthermore, we also investigate current state-of-the-art knowledge on pharmacomicrobiomics and microbiome's role in modulating the response to antidiabetic drugs, as well as provide innovative potential personalized treatments, including approaches for predicting response to treatment and for modulating the microbiome to improve drug efficacy or reduce drug toxicity.