mb-PHENIX: diffusion and supervised uniform manifold approximation for denoizing microbiota data.

MOTIVATION: Microbiota data encounters challenges arising from technical noise and the curse of dimensionality, which affect the reliability of scientific findings. Furthermore, abundance matrices exhibit a zero-inflated distribution due to biological and technical influences. Consequently, there is a growing demand for advanced algorithms that can effectively recover missing taxa while also considering the preservation of data structure. RESULTS: We present mb-PHENIX, an open-source algorithm developed in Python that recovers taxa abundances from the noisy and sparse microbiota data. Our method infers the missing information of count matrix (in 16S microbiota and shotgun studies) by applying imputation via diffusion with supervised Uniform Manifold Approximation Projection (sUMAP) space as initialization. Our hybrid machine learning approach allows to denoise microbiota data, revealing differential abundance microbes among study groups where traditional abundance analysis fails. AVAILABILITY AND IMPLEMENTATION: The mb-PHENIX algorithm is available at https://github.com/resendislab/mb-PHENIX. An easy-to-use implementation is available on Google Colab (see GitHub).

Effect of Insulin Resistance on Abdominal Obesity, Liver Fat Infiltration, and Body Mass Index in Youngsters.

AIM: Evaluate insulin resistance (IR) as a mediator of the effect of body fat distribution on liver fat infiltration and stiffness (LSt) in young adults using structural equation modeling (SEM). METHODS: We invited 500 first year students from two universities and evaluated their family history to determine the risk for cardiometabolic disease. Of these, 174 students (age 19 ± 1 years) were assessed for total body fat percentage (BF%), LSt, fat infiltration (Coefficient attenuated parameter CAP), and serum biochemical analysis. We performed a mediation analysis using two different structural equation models to determine the relationship between BMI, BF%, abdominal obesity (AO), IR, LSt, and fat infiltration using standardized ß coefficients. The symbol "->" means "explains/causes". RESULTS: Model#1 supported that mediation analysis and had a better fit than the direct effect. AO->IR (b = 0.62, p = 0.005), AO->CAP (b = 0.63, p <0.001), and CAP->IR (b = 0.23, p = 0.007), with negligible effect of BMI on CAP and IR. Model#2 showed direct effect of BMI on LSt was a better fit than mediation. BMI->LSt (b = 0.17, p = 0.05) but no effect AO->LSt. Interestingly, LSt->IR (b = 0.18, p = 0.001), but bi-directional IR->LSt (b = 0.23, p = 0.001). CONCLUSIONS: AO and BMI in young adults have differential phenotypic effects on liver CAP and LSt. Visceral fat had a direct effect on IR and CAP. Meanwhile, BMI was associated with LSt. Our findings shed light on the complex interplay of factors influencing liver stiffness, particularly in young individuals. Further research is needed to elucidate the precise mechanisms underlying these associations and their implications for liver health.

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.

A network perspective on the ecology of gut microbiota and progression of type 2 diabetes: Linkages to keystone taxa in a Mexican cohort.

Introduction: The human gut microbiota (GM) is a dynamic system which ecological interactions among the community members affect the host metabolism. Understanding the principles that rule the bidirectional communication between GM and its host, is one of the most valuable enterprise for uncovering how bacterial ecology influences the clinical variables in the host. Methods: Here, we used SparCC to infer association networks in 16S rRNA gene amplicon data from the GM of a cohort of Mexican patients with type 2 diabetes (T2D) in different stages: NG (normoglycemic), IFG (impaired fasting glucose), IGT (impaired glucose tolerance), IFG + IGT (impaired fasting glucose plus impaired glucose tolerance), T2D and T2D treated (T2D with a 5-year ongoing treatment). Results: By exploring the network topology from the different stages of T2D, we observed that, as the disease progress, the networks lose the association between bacteria. It suggests that the microbial community becomes highly sensitive to perturbations in individuals with T2D. With the purpose to identify those genera that guide this transition, we computationally found keystone taxa (driver nodes) and core genera for a Mexican T2D cohort. Altogether, we suggest a set of genera driving the progress of the T2D in a Mexican cohort, among them Ruminococcaceae NK4A214 group, Ruminococcaceae UCG-010, Ruminococcaceae UCG-002, Ruminococcaceae UCG-005, Alistipes, Anaerostipes, and Terrisporobacter. Discussion: Based on a network approach, this study suggests a set of genera that can serve as a potential biomarker to distinguish the distinct degree of advances in T2D for a Mexican cohort of patients. Beyond limiting our conclusion to one population, we present a computational pipeline to link ecological networks and clinical stages in T2D, and desirable aim to advance in the field of precision medicine.

Functional improvement in individuals with chronic spinal cord injury treated with 4-aminopyridine: A systematic review.

Study design: Systematic review. Objective: To provide current evidence on the efficacy of 4-aminopyridine (4-AP) to bring about functional improvement in individuals with chronic traumatic spinal cord injury (SCI). Methods: The Medline (PubMed), Web of Science and SCOPUS databases were systematically searched for relevant articles on the efficacy of 4-AP to treat SCI, from the dates such articles were first published until May 2022. Full-text versions of all the articles selected were examined independently by two reviewers. Methodological quality was rated using the Modified Jadad Scale, and risk of bias was assessed with the RoB-2 test. Data extracted included human models/types, PRISMA assessment protocols, and the results of each study. Descriptive syntheses are provided. Results: In total, 28 articles were initially identified, 10 of which were included after screening. Most of the studies reviewed reported some degree of patient improvement in one or more of the following parameters: motor, sensitivity and sexual function, sphincter control, spasticity, ability to function independently, quality of life, central motor conduction, pain, and pulmonary function. Conclusions: This review confirms the efficacy of 4-AP in improving several conditions resulting from SCI but further research on this topic is warranted. Additional randomized clinical trials with 4-AP involving larger sample sizes are needed, as are consistent outcome measures in order to obtain adequate data for analysis with a view to enhance treatment benefits. Systematic review registration: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=334835, PROSPERO CRD42022334835.

Type 2 diabetes, gut microbiome, and systems biology: A novel perspective for a new era.

The association between the physio-pathological variables of type 2 diabetes (T2D) and gut microbiota composition suggests a new avenue to track the disease and improve the outcomes of pharmacological and non-pharmacological treatments. This enterprise requires new strategies to elucidate the metabolic disturbances occurring in the gut microbiome as the disease progresses. To this end, physiological knowledge and systems biology pave the way for characterizing microbiota and identifying strategies in a move toward healthy compositions. Here, we dissect the recent associations between gut microbiota and T2D. In addition, we discuss recent advances in how drugs, diet, and exercise modulate the microbiome to favor healthy stages. Finally, we present computational approaches for disentangling the metabolic activity underlying host-microbiota codependence. Altogether, we envision that the combination of physiology and computational modeling of microbiota metabolism will drive us to optimize the diagnosis and treatment of T2D patients in a personalized way.

The combination of linagliptin, metformin and lifestyle modification to prevent type 2 diabetes (PRELLIM). A randomized clinical trial.

BACKGROUND: Prediabetes is a highly prevalent health problem with a high risk of complications and progression to type 2 diabetes (T2D). The goals of this study were to evaluate the effect of the combination of lingaliptin + metformin + lifestyle on glucose tolerance, pancreatic ß-cell function and T2D incidence in patients with prediabetes. METHODS: A single center parallel double-blind randomized clinical trial with 24 months of follow-up in patients with impaired glucose tolerance plus two T2D risk factors which were randomized to linagliptin 5 mg + metformin 1700 mg daily + lifestyle (LM group) or metformin 1700 mg daily + lifestyle (M group). Primary outcomes were regression to normoglycemia and T2D incidence; glucose levels and pancreatic ß-cell function were secondary outcomes. RESULTS: Subjects were screened for eligibility by OGTT and 144 patients with prediabetes were randomized to LM group (n = 74) or M group (n = 70); 52 and 36 participants in the LM group and 52 and 27 participants in the M group, completed the 12 and 24 months of treatment, respectively; average follow-up was 17 ±â€¯6 and 18 ±â€¯7 months in M and LM group, respectively. Glucose levels during OGTT improved more in LM group. OGTT disposition index (DI) improved significantly better during the first months in LM group, increasing from 1·31 (95% CI: 1·14-1·49) to 2·41 (95% CI: 2.10-2.72) and to 2.07 (95% CI: 1.82-2.31) at 6 and 24 months in LM group vs from 1.21 (95% CI: 0.98-1.34) to 1.56 (95% CI: 1.17-1.95) and to 1.72 (95% CI: 1.45-1.98) at 6 and 24 months in M group (p < .05). T2D incidence was higher in M group in comparison to LM group (HR 4.0, 95% CI: 1.24-13.04, p = .020). The probability of achieving normoglycemia was higher in LM group (OR 3.26 CI 95% 1.55-6.84). No major side effects were observed during the study. CONCLUSIONS: The combination of linagliptin, metformin and lifestyle improved significantly glucose metabolism and pancreatic ß-cell function, and reduced T2D incidence in subjects with prediabetes as compared to metformin and lifestyle.