Previous case–control studies have reported aberrations of the gut microbiota in individuals with prediabetes. The primary objective of the present study was to explore the dynamics of the gut microbiota of individuals with prediabetes over 4 years with a secondary aim of relating microbiota dynamics to temporal changes of metabolic phenotypes. The study included 486 European patients with prediabetes. Gut microbiota profiling was conducted using shotgun metagenomic sequencing and the same bioinformatics pipelines at study baseline and after 4 years. The same phenotyping protocols and core laboratory analyses were applied at the two timepoints. Phenotyping included anthropometrics and measurement of fasting plasma glucose and insulin levels, mean plasma glucose and insulin under an oral glucose tolerance test (OGTT), 2-h plasma glucose after an OGTT, oral glucose insulin sensitivity index, Matsuda insulin sensitivity index, body mass index, waist circumference, and systolic and diastolic blood pressure. Measures of the dynamics of bacterial microbiota were related to concomitant changes in markers of host metabolism. Over 4 years, significant declines in richness were observed in gut bacterial and viral species and microbial pathways accompanied by significant changes in the relative abundance and the genetic composition of multiple bacterial species. Additionally, bacterial-viral interactions diminished over time. Despite the overall reduction in bacterial richness and microbial pathway richness, 80 dominant core bacterial species and 78 core microbial pathways were identified at both timepoints in 99

The application of multiple omics technologies in biomedical cohorts has the potential to reveal patient-level disease characteristics and individualized response to treatment. However, the scale and heterogeneous nature of multi-modal data makes integration and inference a non-trivial task. We developed a deep-learning-based framework, multi-omics variational autoencoders (MOVE), to integrate such data and applied it to a cohort of 789 people with newly diagnosed type 2 diabetes with deep multi-omics phenotyping from the DIRECT consortium. Using in silico perturbations, we identified drug-omics associations across the multi-modal datasets for the 20 most prevalent drugs given to people with type 2 diabetes with substantially higher sensitivity than univariate statistical tests. From these, we among others, identified novel associations between metformin and the gut microbiota as well as opposite molecular responses for the two statins, simvastatin and atorvastatin. We used the associations to quantify drug-drug similarities, assess the degree of polypharmacy and conclude that drug effects are distributed across the multi-omics modalities.

Periodic revisions of the international classification of diseases (ICD) ensure that the classification reflects new practices and knowledge; however, this complicates retrospective research as diagnoses are coded in different versions. For longitudinal disease trajectory studies, a crosswalk is an essential tool and a comprehensive mapping between ICD-8 and ICD-10 has until now been lacking. In this study, we map all ICD-8 morbidity codes to ICD-10 in the expanded Danish ICD version. We mapped ICD-8 codes to ICD-10, using a many-to-one system inspired by general equivalence mappings such that each ICD-8 code maps to a single ICD-10 code. Each ICD-8 code was manually and unidirectionally mapped to a single ICD-10 code based on medical setting and context. Each match was assigned a score (1 of 4 levels) reflecting the quality of the match and, if applicable, a “flag” signalling choices made in the mapping. We provide the first complete mapping of the 8596 ICD-8 morbidity codes to ICD-10 codes. All Danish ICD-8 codes representing diseases were mapped and 5106 (59.4%) achieved the highest consistency score. Only 334 (3.9%) of the ICD-8 codes received the lowest mapping consistency score. The mapping provides a scaffold for translation of ICD-8 to ICD-10, which enable longitudinal disease studies back to and 1969 in Denmark and to 1965 internationally with further adaption.

Cow’s milk protein allergy (CMPA) is a prevalent food allergy among infants and young children. We conducted a randomized, multicenter intervention study involving 194 non-breastfed infants with CMPA until 12 months of age (clinical trial registration: NCT03085134). One exploratory objective was to assess the effects of a whey-based extensively hydrolyzed formula (EHF) supplemented with 2′-fucosyllactose (2′-FL) and lacto-N-neotetraose (LNnT) on the fecal microbiome and metabolome in this population. Thus, fecal samples were collected at baseline, 1 and 3 months from enrollment, as well as at 12 months of age. Human milk oligosaccharides (HMO) supplementation led to the enrichment of bifidobacteria in the gut microbiome and delayed the shift of the microbiome composition toward an adult-like pattern. We identified specific HMO-mediated changes in fecal amino acid degradation and bile acid conjugation, particularly in infants commencing the HMO-supplemented formula before the age of three months. Thus, HMO supplementation partially corrected the dysbiosis commonly observed in infants with CMPA. Further investigation is necessary to determine the clinical significance of these findings in terms of a reduced incidence of respiratory infections and other potential health benefits.

Background Human milk oligosaccharides (HMOs) have important biological functions for a healthy development in early life. Objective This study aimed to investigate gut maturation effects of an infant formula containing five HMOs (2′-fucosyllactose, 2′,3-di-fucosyllactose, lacto-N-tetraose, 3′-sialyllactose, and 6′-sialyllactose). Methods In a multicenter study, healthy infants (7–21 days old) were randomly assigned to a standard cow’s milk-based infant formula (control group, CG); the same formula with 1.5 g/L HMOs (test group 1, TG1); or with 2.5 g/L HMOs (test group 2, TG2). A human milk-fed group (HMG) was enrolled as a reference. Fecal samples collected at baseline (n∼150/formula group; HMG n = 60), age 3 (n∼140/formula group; HMG n = 65) and 6 (n∼115/formula group; HMG n = 60) months were analyzed for microbiome (shotgun metagenomics), metabolism, and biomarkers. Results At both post-baseline visits, weighted UniFrac analysis indicated different microbiota compositions in the two test groups (TGs) compared to CG (P < 0.01) with coordinates closer to that of HMG. The relative abundance of Bifidobacterium longum subsp. infantis (B. infantis) was higher in TGs vs. CG (P < 0.05; except at 6 months: TG2 vs. CG P = 0.083). Bifidobacterium abundance was higher by ∼45% in TGs vs. CG at 6-month approaching HMG. At both post-baseline visits, toxigenic Clostridioides difficile abundance was 75–85% lower in TGs vs. CG (P < 0.05) and comparable with HMG. Fecal pH was significantly lower in TGs vs. CG, and the overall organic acid profile was different in TGs vs. CG, approaching HMG. At 3 months, TGs (vs. CG) had higher secretory immunoglobulin A (sIgA) and lower alpha-1-antitrypsin (P < 0.05). At 6 months, sIgA in TG2 vs. CG remained higher (P < 0.05), and calprotectin was lower in TG1 (P < 0.05) vs. CG. Conclusion Infant formula with a specific blend of five HMOs supports the development of the intestinal immune system and gut barrier function and shifts the gut microbiome closer to that of breastfed infants with higher bifidobacteria, particularly B. infantis, and lower toxigenic Clostridioides difficile. Clinical Trial Registration [https://clinicaltrials.gov/ct2/show/], identifier [NCT03722550].

Previous microbiome and metabolome analyses exploring non-communicable diseases have paid scant attention to major confounders of study outcomes, such as common, pre-morbid and co-morbid conditions, or polypharmacy. Here, in the context of ischemic heart disease (IHD), we used a study design that recapitulates disease initiation, escalation and response to treatment over time, mirroring a longitudinal study that would otherwise be difficult to perform given the protracted nature of IHD pathogenesis. We recruited 1,241 middle-aged Europeans, including healthy individuals, individuals with dysmetabolic morbidities (obesity and type 2 diabetes) but lacking overt IHD diagnosis and individuals with IHD at three distinct clinical stages—acute coronary syndrome, chronic IHD and IHD with heart failure—and characterized their phenome, gut metagenome and serum and urine metabolome. We found that about 75% of microbiome and metabolome features that distinguish individuals with IHD from healthy individuals after adjustment for effects of medication and lifestyle are present in individuals exhibiting dysmetabolism, suggesting that major alterations of the gut microbiome and metabolome might begin long before clinical onset of IHD. We further categorized microbiome and metabolome signatures related to prodromal dysmetabolism, specific to IHD in general or to each of its three subtypes or related to escalation or de-escalation of IHD. Discriminant analysis based on specific IHD microbiome and metabolome features could better differentiate individuals with IHD from healthy individuals or metabolically matched individuals as compared to the conventional risk markers, pointing to a pathophysiological relevance of these features.

ObjectivesGut microbiota is a key component in obesity and type 2 diabetes, yet mechanisms and metabolites central to this interaction remain unclear. We examined the human gut microbiome’s functional composition in healthy metabolic state and the most severe states of obesity and type 2 diabetes within the MetaCardis cohort. We focused on the role of B vitamins and B7/B8 biotin for regulation of host metabolic state, as these vitamins influence both microbial function and host metabolism and inflammation.DesignWe performed metagenomic analyses in 1545 subjects from the MetaCardis cohorts and different murine experiments, including germ-free and antibiotic treated animals, faecal microbiota transfer, bariatric surgery and supplementation with biotin and prebiotics in mice.ResultsSevere obesity is associated with an absolute deficiency in bacterial biotin producers and transporters, whose abundances correlate with host metabolic and inflammatory phenotypes. We found suboptimal circulating biotin levels in severe obesity and altered expression of biotin-associated genes in human adipose tissue. In mice, the absence or depletion of gut microbiota by antibiotics confirmed the microbial contribution to host biotin levels. Bariatric surgery, which improves metabolism and inflammation, associates with increased bacterial biotin producers and improved host systemic biotin in humans and mice. Finally, supplementing high-fat diet-fed mice with fructo-oligosaccharides and biotin improves not only the microbiome diversity, but also the potential of bacterial production of biotin and B vitamins, while limiting weight gain and glycaemic deterioration.ConclusionStrategies combining biotin and prebiotic supplementation could help prevent the deterioration of metabolic states in severe obesity.Trial registration numberNCT02059538.

Human milk oligosaccharides (HMOs) are structurally diverse oligosaccharides present in breast milk, supporting the development of the gut microbiota and immune system. Previously, 2-HMO (2'fucosyllactose, lacto-N-neotetraose) compared to control formula feeding was associated with reduced risk of lower respiratory tract infections (LRTIs), in part linked to lower acetate and higher bifidobacteria proportions. Here, our objective was to gain further insight into additional molecular pathways linking the 2-HMO formula feeding and LRTI mitigation. From the same trial, we measured the microbiota composition and 743 known biochemical species in infant stool at 3 months of age using shotgun metagenomic sequencing and untargeted mass spectrometry metabolomics. We used multivariate analysis to identify biochemicals associated to 2-HMO formula feeding and LRTI and integrated those findings with the microbiota compositional data. Three molecular pathways stood out: increased gamma-glutamylation and N-acetylation of amino acids and decreased inflammatory signaling lipids. Integration of stool metagenomic data revealed some Bifidobacterium and Bacteroides species to be implicated. These findings deepen our understanding of the infant gut/microbiome co-metabolism in early life and provide evidence for how such metabolic changes may influence immune competence at distant mucosal sites such as the airways.

The presentation and underlying pathophysiology of type 2 diabetes (T2D) is complex and heterogeneous. Recent studies attempted to stratify T2D into distinct subgroups using data-driven approaches, but their clinical utility may be limited if categorical representations of complex phenotypes are suboptimal. We apply a soft-clustering (archetype) method to characterize newly diagnosed T2D based on 32 clinical variables. We assign quantitative clustering scores for individuals and investigate the associations with glycemic deterioration, genetic risk scores, circulating omics biomarkers, and phenotypic stability over 36 months. Four archetype profiles represent dysfunction patterns across combinations of T2D etiological processes and correlate with multiple circulating biomarkers. One archetype associated with obesity, insulin resistance, dyslipidemia, and impaired β cell glucose sensitivity corresponds with the fastest disease progression and highest demand for anti-diabetic treatment. We demonstrate that clinical heterogeneity in T2D can be mapped to heterogeneity in individual etiological processes, providing a potential route to personalized treatments.

Abstract Purpose: Cachexia is a multifactorial syndrome, associated with poor survival in patients with cancer, and is influenced by the gut microbiota. We investigated the effects of fecal microbiota transplantation (FMT) on cachexia and treatment response in patients with advanced gastroesophageal cancer. Experimental Design: In a double-blind randomized placebo-controlled trial performed in the Amsterdam University Medical Center, we assigned 24 cachectic patients with metastatic HER2-negative gastroesophageal cancer to either allogenic FMT (healthy obese donor) or autologous FMT, prior to palliative chemotherapy (capecitabine and oxaliplatin). Primary objective was to assess the effect of allogenic FMT on satiety. Secondary outcomes were other features of cachexia, along with disease control rate (DCR), overall survival (OS), progression-free survival (PFS), and toxicity. Finally, exploratory analyses were performed on the effect of FMT on gut microbiota composition (metagenomic sequencing) and metabolites (untargeted metabolomics). Results: Allogenic FMT did not improve any of the cachexia outcomes. Patients in the allogenic group (n = 12) had a higher DCR at 12 weeks (P = 0.035) compared with the autologous group (n = 12), longer median OS of 365 versus 227 days [HR = 0.38; 95% confidence interval (CI), 0.14–1.05; P = 0.057] and PFS of 204 versus 93 days (HR = 0.50; 95% CI, 0.21–1.20; P = 0.092). Patients in the allogenic group showed a significant shift in fecal microbiota composition after FMT (P = 0.010) indicating proper engraftment of the donor microbiota. Conclusions: FMT from a healthy obese donor prior to first-line chemotherapy did not affect cachexia, but may have improved response and survival in patients with metastatic gastroesophageal cancer. These results provide a rational for larger FMT trials.

During the transition from a healthy state to cardiometabolic disease, patients become heavily medicated, which leads to an increasingly aberrant gut microbiome and serum metabolome, and complicates biomarker discovery1-5. Here, through integrated multi-omics analyses of 2,173 European residents from the MetaCardis cohort, we show that the explanatory power of drugs for the variability in both host and gut microbiome features exceeds that of disease. We quantify inferred effects of single medications, their combinations as well as additive effects, and show that the latter shift the metabolome and microbiome towards a healthier state, exemplified in synergistic reduction in serum atherogenic lipoproteins by statins combined with aspirin, or enrichment of intestinal Roseburia by diuretic agents combined with beta-blockers. Several antibiotics exhibit a quantitative relationship between the number of courses prescribed and progression towards a microbiome state that is associated with the severity of cardiometabolic disease. We also report a relationship between cardiometabolic drug dosage, improvement in clinical markers and microbiome composition, supporting direct drug effects. Taken together, our computational framework and resulting resources enable the disentanglement of the effects of drugs and disease on host and microbiome features in multimedicated individuals. Furthermore, the robust signatures identified using our framework provide new hypotheses for drug-host-microbiome interactions in cardiometabolic disease.

Knowledge about in vivo effects of human circulating C-6 hydroxylated bile acids (BAs), also called muricholic acids, is sparse. It is unsettled if the gut microbiome might contribute to their biosynthesis. Here, we measured a range of serum BAs and related them to markers of human metabolic health and the gut microbiome. We examined 283 non-obese and obese Danish adults from the MetaHit study. Fasting concentrations of serum BAs were quantified using ultra-performance liquid chromatography-tandem mass-spectrometry. The gut microbiome was characterized with shotgun metagenomic sequencing and genome-scale metabolic modeling. We find that tauro- and glycohyocholic acid correlated inversely with body mass index (P = 4.1e-03, P = 1.9e-05, respectively), waist circumference (P = 0.017, P = 1.1e-04, respectively), body fat percentage (P = 2.5e-03, P = 2.3e-06, respectively), insulin resistance (P = 0.051, P = 4.6e-4, respectively), fasting concentrations of triglycerides (P = 0.06, P = 9.2e-4, respectively) and leptin (P = 0.067, P = 9.2e-4). Tauro- and glycohyocholic acids, and tauro-a-muricholic acid were directly linked with a distinct gut microbial community primarily composed of Clostridia species (P = 0.037, P = 0.013, P = 0.027, respectively). We conclude that serum conjugated C-6-hydroxylated BAs associate with measures of human metabolic health and gut communities of Clostridia species. The findings merit preclinical interventions and human feasibility studies to explore the therapeutic potential of these BAs in obesity and type 2 diabetes.

Background Non-alcoholic fatty liver disease (NAFLD) is highly prevalent and causes serious health complications in individuals with and without type 2 diabetes (T2D). Early diagnosis of NAFLD is important, as this can help prevent irreversible damage to the liver and, ultimately, hepatocellular carcinomas. We sought to expand etiological understanding and develop a diagnostic tool for NAFLD using machine learning. Methods and findings We utilized the baseline data from IMI DIRECT, a multicenter prospective cohort study of 3,029 European-ancestry adults recently diagnosed with T2D (n = 795) or at high risk of developing the disease (n = 2,234). Multi-omics (genetic, transcriptomic, proteomic, and metabolomic) and clinical (liver enzymes and other serological biomarkers, anthropometry, measures of beta-cell function, insulin sensitivity, and lifestyle) data comprised the key input variables. The models were trained on MRI-image-derived liver fat content (<5% or ≥5%) available for 1,514 participants. We applied LASSO (least absolute shrinkage and selection operator) to select features from the different layers of omics data and random forest analysis to develop the models. The prediction models included clinical and omics variables separately or in combination. A model including all omics and clinical variables yielded a cross-validated receiver operating characteristic area under the curve (ROCAUC) of 0.84 (95% CI 0.82, 0.86; p < 0.001), which compared with a ROCAUC of 0.82 (95% CI 0.81, 0.83; p < 0.001) for a model including 9 clinically accessible variables. The IMI DIRECT prediction models outperformed existing noninvasive NAFLD prediction tools. One limitation is that these analyses were performed in adults of European ancestry residing in northern Europe, and it is unknown how well these findings will translate to people of other ancestries and exposed to environmental risk factors that differ from those of the present cohort. Another key limitation of this study is that the prediction was done on a binary outcome of liver fat quantity (<5% or ≥5%) rather than a continuous one. Conclusions In this study, we developed several models with different combinations of clinical and omics data and identified biological features that appear to be associated with liver fat accumulation. In general, the clinical variables showed better prediction ability than the complex omics variables. However, the combination of omics and clinical variables yielded the highest accuracy. We have incorporated the developed clinical models into a web interface (see: https://www.predictliverfat.org/) and made it available to the community. Trial registration ClinicalTrials.gov NCT03814915.

Background The rising prevalence of type 2 diabetes (T2D) poses a major global challenge. It remains unresolved to what extent transcriptomic signatures of metabolic dysregulation and T2D can be observed in easily accessible tissues such as blood. Additionally, large-scale human studies are required to further our understanding of the putative inflammatory component of insulin resistance and T2D. Here we used transcriptomics data from individuals with (n = 789) and without (n = 2127) T2D from the IMI-DIRECT cohorts to describe the co-expression structure of whole blood that mainly reflects processes and cell types of the immune system, and how it relates to metabolically relevant clinical traits and T2D. Methods Clusters of co-expressed genes were identified in the non-diabetic IMI-DIRECT cohort and evaluated with regard to stability, as well as preservation and rewiring in the cohort of individuals with T2D. We performed functional and immune cell signature enrichment analyses, and a genome-wide association study to describe the genetic regulation of the modules. Phenotypic and trans-omics associations of the transcriptomic modules were investigated across both IMI-DIRECT cohorts. Results We identified 55 whole blood co-expression modules, some of which clustered in larger super-modules. We identified a large number of associations between these transcriptomic modules and measures of insulin action and glucose tolerance. Some of the metabolically linked modules reflect neutrophil-lymphocyte ratio in blood while others are independent of white blood cell estimates, including a module of genes encoding neutrophil granule proteins with antibacterial properties for which the strongest associations with clinical traits and T2D status were observed. Through the integration of genetic and multi-omics data, we provide a holistic view of the regulation and molecular context of whole blood transcriptomic modules. We furthermore identified an overlap between genetic signals for T2D and co-expression modules involved in type II interferon signaling. Conclusions Our results offer a large-scale map of whole blood transcriptomic modules in the context of metabolic disease and point to novel biological candidates for future studies related to T2D.

ABSTRACTBackgroundNon-alcoholic fatty liver disease (NAFLD) is highly prevalent and causes serious health complications in type 2 diabetes (T2D) and beyond. Early diagnosis of NAFLD is important, as this can help prevent irreversible damage to the liver and ultimately hepatocellular carcinomas.Methods and FindingsUtilizing the baseline data from the IMI DIRECT participants (n=1514) we sought to expand etiological understanding and develop a diagnostic tool for NAFLD using machine learning. Multi-omic (genetic, transcriptomic, proteomic, and metabolomic) and clinical (liver enzymes and other serological biomarkers, anthropometry, and measures of beta-cell function, insulin sensitivity, and lifestyle) data comprised the key input variables. The models were trained on MRI image-derived liver fat content (<5% or ≥5%). We applied LASSO (least absolute shrinkage and selection operator) to select features from the different layers of omics data and Random Forest analysis to develop the models. The prediction models included clinical and omics variables separately or in combination. A model including all omics and clinical variables yielded a cross-validated receiver operator characteristic area under the curve (ROCAUC) of 0.84 (95% confidence interval (CI)=0.82, 0.86), which compared with a ROCAUC of 0.82 (95% CI=0.81, 0.83) for a model including nine clinically-accessible variables. The IMI DIRECT prediction models out-performed existing non-invasive NAFLD prediction tools.ConclusionsWe have developed clinically useful liver fat prediction models (see:www.predictliverfat.org) and identified biological features that appear to affect liver fat accumulation.

Microbiome community typing analyses have recently identified the Bacteroides 2 (Bact2) enterotype, an intestinal microbiota configuration that is associated with systemic inflammation and has a high prevalence in loose stools in humans 1 , 2 . Bact2 is characterized by a high proportion of Bacteroides , a low proportion of Faecalibacterium and low microbial cell densities 1 , 2 , and its prevalence varies from 13% in a general population cohort to as high as 78% in patients with inflammatory bowel disease 2 . Reported changes in stool consistency 3 and inflammation status 4 during the progression towards obesity and metabolic comorbidities led us to propose that these developments might similarly correlate with an increased prevalence of the potentially dysbiotic Bact2 enterotype. Here, by exploring obesity-associated microbiota alterations in the quantitative faecal metagenomes of the cross-sectional MetaCardis Body Mass Index Spectrum cohort ( n = 888), we identify statin therapy as a key covariate of microbiome diversification. By focusing on a subcohort of participants that are not medicated with statins, we find that the prevalence of Bact2 correlates with body mass index, increasing from 3.90% in lean or overweight participants to 17.73% in obese participants. Systemic inflammation levels in Bact2-enterotyped individuals are higher than predicted on the basis of their obesity status, indicative of Bact2 as a dysbiotic microbiome constellation. We also observe that obesity-associated microbiota dysbiosis is negatively associated with statin treatment, resulting in a lower Bact2 prevalence of 5.88% in statin-medicated obese participants. This finding is validated in both the accompanying MetaCardis cardiovascular disease dataset ( n = 282) and the independent Flemish Gut Flora Project population cohort ( n = 2,345). The potential benefits of statins in this context will require further evaluation in a prospective clinical trial to ascertain whether the effect is reproducible in a randomized population and before considering their application as microbiota-modulating therapeutics.

A Correction to this paper has been published: https://doi.org/10.1038/s41467-020-20412-9.

Aims/hypothesis Circulating metabolites have been shown to reflect metabolic changes during the development of type 2 diabetes. In this study we examined the association of metabolite levels and pairwise metabolite ratios with insulin responses after glucose, glucagon-like peptide-1 (GLP-1) and arginine stimulation. glucose, GLP-1 and arginine stimulation. We validated our results in cohorts with OGTT data ( n = 340) and epidemiological case – control studies of prevalent ( n = 4925) and incident ( n = 4277) diabetes. The data were analysed using regression models with adjustment for potential confounders. Results There were dynamic changes in metabolite levels in response to the different secretagogues. Furthermore, several fasting pairwise metabolite ratios were associated with one or multiple clamp-derived measures of insulin secretion (all p < 9.2 × 10 − 7 ). These associations were significantly stronger compared with the individual metabolite components. One of the ratios, valine to phosphatidylcholine acyl-alkyl C32:2 (PC ae C32:2), in addition showed a directionally consistent positive association with OGTT-derived measures of insulin secretion and resistance ( p ≤ 5.4 × 10 − 3 ) and prevalent type 2 diabetes (OR Val_PC ae C32:2 2.64 [ β 0.97 ± 0.09], p = 1.0 × 10 − 27 ). Furthermore, Val_PC ae C32:2 predicted incident diabetes independent of established risk factors in two epidemiological cohort studies (HR Val_PC ae C32:2 1.57 [ β 0.45 ± 0.06]; p = 1.3 × 10 − 15 ), leading to modest improve-ments in the receiver operating characteristics when added to a model containing a set of established risk factors in both cohorts (increases from 0.780 to 0.801 and from 0.862 to 0.865 respectively, when added to the model containing traditional risk factors + glucose). Conclusions/interpretation In this study we have shown that the Val_PC ae C32:2 metabolite ratio is associated with an increased risk of type 2 diabetes and measures of insulin secretion and resistance. The observed effects were stronger than that of the individual metabolites and independent of known risk factors.

We recently presented a three-pronged association study that integrated human intestinal microbiome data derived from shotgun-based sequencing with untargeted serum metabolome data and measures of host physiology. Metabolome and microbiome data are high dimensional, posing a major challenge for data integration. Here, we present a step-by-step computational protocol that details and discusses the dimensionality-reduction techniques used and methods for subsequent integration and interpretation of such heterogeneous types of data. Dimensionality reduction was achieved through a combination of data normalization approaches, binning of co-abundant genes and metabolites, and integration of prior biological knowledge. The use of prior knowledge to overcome functional redundancy across microbiome species is one central advance of our method over available alternative approaches. Applying this framework, other investigators can integrate various '-omics' readouts with variables of host physiology or any other phenotype of interest (e.g., connecting host and microbiome readouts to disease severity or treatment outcome in a clinical cohort) in a three-pronged association analysis to identify potential mechanistic links to be tested in experimental settings. Although we originally developed the framework for a human metabolome-microbiome study, it is generalizable to other organisms and environmental metagenomes, as well as to studies including other -omics domains such as transcriptomics and proteomics. The provided R code runs in ~1 h on a standard PC.