Role of Gut Microbiota in Statin-Associated New-Onset Diabetes-A Cross-Sectional and Prospective Analysis of the FINRISK 2002 Cohort.

BACKGROUND: Dyslipidemia is treated effectively with statins, but treatment has the potential to induce new-onset type-2 diabetes. Gut microbiota may contribute to this outcome variability. We assessed the associations of gut microbiota diversity and composition with statins. Bacterial associations with statin-associated new-onset type-2 diabetes (T2D) risk were also prospectively evaluated. METHODS: We examined shallow-shotgun-sequenced fecal samples from 5755 individuals in the FINRISK-2002 population cohort with a 17+-year-long register-based follow-up. Alpha-diversity was quantified using Shannon index and beta-diversity with Aitchison distance. Species-specific differential abundances were analyzed using general multivariate regression. Prospective associations were assessed with Cox regression. Applicable results were validated using gradient boosting. RESULTS: Statin use associated with differing taxonomic composition (R2, 0.02%; q=0.02) and 13 differentially abundant species in fully adjusted models (MaAsLin; q<0.05). The strongest positive association was with Clostridium sartagoforme (ß=0.37; SE=0.13; q=0.02) and the strongest negative association with Bacteroides cellulosilyticus (ß=-0.31; SE=0.11; q=0.02). Twenty-five microbial features had significant associations with incident T2D in statin users, of which only Bacteroides vulgatus (HR, 1.286 [1.136-1.457]; q=0.03) was consistent regardless of model adjustment. Finally, higher statin-associated T2D risk was seen with [Ruminococcus] torques (ΔHRstatins, +0.11; q=0.03), Blautia obeum (ΔHRstatins, +0.06; q=0.01), Blautia sp. KLE 1732 (ΔHRstatins, +0.05; q=0.01), and beta-diversity principal component 1 (ΔHRstatin, +0.07; q=0.03) but only when adjusting for demographic covariates. CONCLUSIONS: Statin users have compositionally differing microbiotas from nonusers. The human gut microbiota is associated with incident T2D risk in statin users and possibly has additive effects on statin-associated new-onset T2D risk.

Efficient computation of Faith's phylogenetic diversity with applications in characterizing microbiomes.

The number of publicly available microbiome samples is continually growing. As data set size increases, bottlenecks arise in standard analytical pipelines. Faith's phylogenetic diversity (Faith's PD) is a highly utilized phylogenetic alpha diversity metric that has thus far failed to effectively scale to trees with millions of vertices. Stacked Faith's phylogenetic diversity (SFPhD) enables calculation of this widely adopted diversity metric at a much larger scale by implementing a computationally efficient algorithm. The algorithm reduces the amount of computational resources required, resulting in more accessible software with a reduced carbon footprint, as compared to previous approaches. The new algorithm produces identical results to the previous method. We further demonstrate that the phylogenetic aspect of Faith's PD provides increased power in detecting diversity differences between younger and older populations in the FINRISK study's metagenomic data.

Dealing with dimensionality: the application of machine learning to multi-omics data.

MOTIVATION: Machine learning (ML) methods are motivated by the need to automate information extraction from large datasets in order to support human users in data-driven tasks. This is an attractive approach for integrative joint analysis of vast amounts of omics data produced in next generation sequencing and other -omics assays. A systematic assessment of the current literature can help to identify key trends and potential gaps in methodology and applications. We surveyed the literature on ML multi-omic data integration and quantitatively explored the goals, techniques and data involved in this field. We were particularly interested in examining how researchers use ML to deal with the volume and complexity of these datasets. RESULTS: Our main finding is that the methods used are those that address the challenges of datasets with few samples and many features. Dimensionality reduction methods are used to reduce the feature count alongside models that can also appropriately handle relatively few samples. Popular techniques include autoencoders, random forests and support vector machines. We also found that the field is heavily influenced by the use of The Cancer Genome Atlas dataset, which is accessible and contains many diverse experiments. AVAILABILITY AND IMPLEMENTATION: All data and processing scripts are available at this GitLab repository: https://gitlab.com/polavieja_lab/ml_multi-omics_review/ or in Zenodo: https://doi.org/10.5281/zenodo.7361807. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Association of Long-Term Habitual Dietary Fiber Intake since Infancy with Gut Microbiota Composition in Young Adulthood.

BACKGROUND: Dietary fiber is an important health-promoting component of the diet, which is fermented by the gut microbes that produce metabolites beneficial for the host's health. OBJECTIVES: We studied the associations of habitual long-term fiber intake from infancy with gut microbiota composition in young adulthood by leveraging data from the Special Turku Coronary Risk Factor Intervention Project, an infancy-onset 20-y dietary counseling study. METHODS: Fiber intake was assessed annually using food diaries from infancy ≤ age 20 y. At age 26 y, the first postintervention follow-up study was conducted including food diaries and fecal sample collection (N = 357). Cumulative dietary fiber intake was assessed as the area under the curve for energy-adjusted fiber intake throughout the study (age 0-26 y). Gut microbiota was profiled using 16S ribosomal ribonucleic acid amplicon sequencing. The primary outcomes were 1) α diversity expressed as the observed richness and Shannon index, 2) ß diversity using Bray-Curtis dissimilarity scores, and 3) differential abundance of each microbial taxa with respect to the cumulative energy-adjusted dietary fiber intake. RESULTS: Higher cumulative dietary fiber intake was associated with decreased Shannon index (ß = -0.019 per unit change in cumulative fiber intake, P = 0.008). Overall microbial community composition was related to the amount of fiber consumed (permutational analysis of variation R2 = 0.005, P = 0.024). The only genus that was increased with higher cumulative fiber intake was butyrate-producing Butyrivibrio (log2 fold-change per unit change in cumulative fiber intake 0.40, adjusted P = 0.023), whereas some other known butyrate producers such as Faecalibacterium and Subdoligranulum were decreased with higher cumulative fiber intake. CONCLUSIONS: As early-life nutritional exposures may affect the lifetime microbiota composition and disease risk, this study adds novel information on the associations of long-term dietary fiber intake with the gut microbiota. This trial was registered at clinicaltrials.gov as NCT00223600.

Bone marrow metabolism is affected by body weight and response to exercise training varies according to anatomical location.

AIM: High body weight is a protective factor against osteoporosis, but obesity also suppresses bone metabolism and whole-body insulin sensitivity. However, the impact of body weight and regular training on bone marrow (BM) glucose metabolism is unclear. We studied the effects of regular exercise training on bone and BM metabolism in monozygotic twin pairs discordant for body weight. METHODS: We recruited 12 monozygotic twin pairs (mean ± SD age 40.4 ± 4.5 years; body mass index 32.9 ± 7.6, mean difference between co-twins 7.6 kg/m2 ; eight female pairs). Ten pairs completed the 6-month long training intervention. We measured lumbar vertebral and femoral BM insulin-stimulated glucose uptake (GU) using 18 F-FDG positron emission tomography, lumbar spine bone mineral density and bone turnover markers. RESULTS: At baseline, heavier co-twins had higher lumbar vertebral BM GU (p < .001) and lower bone turnover markers (all p < .01) compared with leaner co-twins but there was no significant difference in femoral BM GU, or bone mineral density. Training improved whole-body insulin sensitivity, aerobic capacity (both p < .05) and femoral BM GU (p = .008). The training response in lumbar vertebral BM GU was different between the groups (time × group, p = .02), as GU tended to decrease in heavier co-twins (p = .06) while there was no change in leaner co-twins. CONCLUSIONS: In this study, regular exercise training increases femoral BM GU regardless of weight and genetics. Interestingly, lumbar vertebral BM GU is higher in participants with higher body weight, and training counteracts this effect in heavier co-twins even without reduction in weight. These data suggest that BM metabolism is altered by physical activity.

Impacts of maternal microbiota and microbial metabolites on fetal intestine, brain, and placenta.

BACKGROUND: The maternal microbiota modulates fetal development, but the mechanisms of these earliest host-microbe interactions are unclear. To investigate the developmental impacts of maternal microbial metabolites, we compared full-term fetuses from germ-free and specific pathogen-free mouse dams by gene expression profiling and non-targeted metabolomics. RESULTS: In the fetal intestine, critical genes mediating host-microbe interactions, innate immunity, and epithelial barrier were differentially expressed. Interferon and inflammatory signaling genes were downregulated in the intestines and brains of the fetuses from germ-free dams. The expression of genes related to neural system development and function, translation and RNA metabolism, and regulation of energy metabolism were significantly affected. The gene coding for the insulin-degrading enzyme (Ide) was most significantly downregulated in all tissues. In the placenta, genes coding for prolactin and other essential regulators of pregnancy were downregulated in germ-free dams. These impacts on gene expression were strongly associated with microbially modulated metabolite concentrations in the fetal tissues. Aryl sulfates and other aryl hydrocarbon receptor ligands, the trimethylated compounds TMAO and 5-AVAB, Glu-Trp and other dipeptides, fatty acid derivatives, and the tRNA nucleobase queuine were among the compounds strongly associated with gene expression differences. A sex difference was observed in the fetal responses to maternal microbial status: more genes were differentially regulated in male fetuses than in females. CONCLUSIONS: The maternal microbiota has a major impact on the developing fetus, with male fetuses potentially more susceptible to microbial modulation. The expression of genes important for the immune system, neurophysiology, translation, and energy metabolism are strongly affected by the maternal microbial status already before birth. These impacts are associated with microbially modulated metabolites. We identified several microbial metabolites which have not been previously observed in this context. Many of the potentially important metabolites remain to be identified.

The gut microbiome is a significant risk factor for future chronic lung disease.

BACKGROUND: The gut-lung axis is generally recognized, but there are few large studies of the gut microbiome and incident respiratory disease in adults. OBJECTIVE: We sought to investigate the association and predictive capacity of the gut microbiome for incident asthma and chronic obstructive pulmonary disease (COPD). METHODS: Shallow metagenomic sequencing was performed for stool samples from a prospective, population-based cohort (FINRISK02; N = 7115 adults) with linked national administrative health register-derived classifications for incident asthma and COPD up to 15 years after baseline. Generalized linear models and Cox regressions were used to assess associations of microbial taxa and diversity with disease occurrence. Predictive models were constructed using machine learning with extreme gradient boosting. Models considered taxa abundances individually and in combination with other risk factors, including sex, age, body mass index, and smoking status. RESULTS: A total of 695 and 392 statistically significant associations were found between baseline taxonomic groups and incident asthma and COPD, respectively. Gradient boosting decision trees of baseline gut microbiome abundance predicted incident asthma and COPD in the validation data sets with mean area under the curves of 0.608 and 0.780, respectively. Cox analysis showed that the baseline gut microbiome achieved higher predictive performance than individual conventional risk factors, with C-indices of 0.623 for asthma and 0.817 for COPD. The integration of the gut microbiome and conventional risk factors further improved prediction capacities. CONCLUSIONS: The gut microbiome is a significant risk factor for incident asthma and incident COPD and is largely independent of conventional risk factors.

Aberrations in the early pregnancy serum metabolic profile in women with prediabetes at two years postpartum.

INTRODUCTION: Aberrations in circulating metabolites have been associated with diabetes and cardiovascular risk. OBJECTIVES: To investigate if early and late pregnancy serum metabolomic profiles differ in women who develop prediabetes by two years postpartum compared to those who remain normoglycemic. METHODS: An NMR metabolomics platform was used to measure 228 serum metabolite variables from women with pre-pregnancy overweight in early and late pregnancy. Co-abundant groups of metabolites were compared between the women who were (n = 40) or were not (n = 138) prediabetic at two years postpartum. Random Forests classifiers, based on the metabolic profiles, were used to predict the prediabetes status, and correlations of the metabolites to glycemic traits (fasting glucose and insulin, HOMA2-IR and HbA1c) and hsCRP at postpartum were evaluated. RESULTS: Women with prediabetes had higher concentrations of small HDL particles, total lipids in small HDL, phospholipids in small HDL and free cholesterol in small HDL in early pregnancy (p = 0.029; adj with pre-pregnancy BMI p = 0.094). The small HDL related metabolites also correlated positively with markers of insulin resistance at postpartum. Similar associations were not detected for metabolites in late pregnancy. A Random Forests classifier based on serum metabolites and clinical variables in early pregnancy displayed an acceptable predictive power for the prediabetes status at postpartum (AUROC 0.668). CONCLUSION: Elevated serum concentrations of small HDL particles in early pregnancy associate with prediabetes and insulin resistance at two years postpartum. The serum metabolic profile during pregnancy might be used to identify women at increased risk for type 2 diabetes.

The preterm gut microbiota and administration routes of different probiotics: a randomized controlled trial.

BACKGROUND: Preterm children with their aberrant gut microbiota and susceptibility to infections and inflammation constitute a considerable target group for probiotic therapy to generate the age-appropriate healthy microbiota. METHODS: 68 preterm neonates were randomized into five intervention groups: Beginning from the median age of 3 days, 13 children received Lactobacillus rhamnosus GG (LGG) directly orally, and 17 via the lactating mother. 14 children received LGG with Bifidobacterium lactis Bb-12 (Bb12) orally, and 10 via the lactating mother. 14 children received placebo. The children's faecal microbiota was assessed at the age of 7 days by 16S rRNA gene sequencing. RESULTS: The gut microbiota compositions of the children directly receiving the probiotic combination (LGG + Bb12) were significantly different from those of the children receiving the other intervention modes or placebo (p = 0.0012; PERMANOVA), the distinction being due to an increase in the relative abundance of Bifidobacterium animalis (P < 0.00010; ANCOM-BC), and the order Lactobacillales (P = 0.020; ANCOM-BC). CONCLUSION: The connection between aberrant primary gut microbiota and a heightened risk of infectious and non-communicable diseases invites effective microbiota modulation. We show that the direct, early, and brief probiotic intervention of LGG + Bb12 109 CFU each, is sufficient to modulate the gut microbiota of the preterm neonate. IMPACT: Preterm children have a higher risk of several health problems partly due to their aberrant gut microbiota. More research is needed to find a safe probiotic intervention to modify the gut microbiota of preterm children. The maternal administration route via breast milk might be safer for the newborn. In our study, the early and direct administration of the probiotic combination Lactobacillus rhamnosus GG with Bifidobacterium lactis Bb-12 increased the proportion of bifidobacteria in the preterm children's gut at the age of 7 days, but the maternal administration route was not as effective.

Quantifying the impact of ecological memory on the dynamics of interacting communities.

Ecological memory refers to the influence of past events on the response of an ecosystem to exogenous or endogenous changes. Memory has been widely recognized as a key contributor to the dynamics of ecosystems and other complex systems, yet quantitative community models often ignore memory and its implications. Recent modeling studies have shown how interactions between community members can lead to the emergence of resilience and multistability under environmental perturbations. We demonstrate how memory can be introduced in such models using the framework of fractional calculus. We study how the dynamics of a well-characterized interaction model is affected by gradual increases in ecological memory under varying initial conditions, perturbations, and stochasticity. Our results highlight the implications of memory on several key aspects of community dynamics. In general, memory introduces inertia into the dynamics. This favors species coexistence under perturbation, enhances system resistance to state shifts, mitigates hysteresis, and can affect system resilience both ways depending on the time scale considered. Memory also promotes long transient dynamics, such as long-standing oscillations and delayed regime shifts, and contributes to the emergence and persistence of alternative stable states. Our study highlights the fundamental role of memory in communities, and provides quantitative tools to introduce it in ecological models and analyse its impact under varying conditions.