First mitochondrial genome-wide association study with metabolomics.

In the era of personalized medicine with more and more patient-specific targeted therapies being used, we need reliable, dynamic, faster and sensitive biomarkers both to track the causes of disease and to develop and evolve therapies during the course of treatment. Metabolomics recently has shown substantial evidence to support its emerging role in disease diagnosis and prognosis. Aside from biomarkers and development of therapies, it is also an important goal to understand the involvement of mitochondrial DNA (mtDNA) in metabolic regulation, aging and disease development. Somatic mutations of the mitochondrial genome are also heavily implicated in age-related disease and aging. The general hypothesis is that an alteration in the concentration of metabolite profiles (possibly conveyed by lifestyle and environmental factors) influences the increase of mutation rate in the mtDNA and thereby contributes to a range of pathophysiological alterations observed in complex diseases. We performed an inverted mitochondrial genome-wide association analysis between mitochondrial nucleotide variants (mtSNVs) and concentration of metabolites. We used 151 metabolites and the whole sequenced mitochondrial genome from 2718 individuals to identify the genetic variants associated with metabolite profiles. Because of the high coverage, next-generation sequencing-based analysis of the mitochondrial genome allows for an accurate detection of mitochondrial heteroplasmy and for the identification of variants associated with the metabolome. The strongest association was found for mt715G > A located in the MT-12SrRNA with the metabolite ratio of C2/C10:1 (P-value = 6.82*10-09, ß = 0.909). The second most significant mtSNV was found for mt3714A > G located in the MT-ND1 with the metabolite ratio of phosphatidylcholine (PC) ae C42:5/PC ae C44:5 (P-value = 1.02*10-08, ß = 3.631). A large number of significant metabolite ratios were observed involving PC aa C36:6 and the variant mt10689G > A, located in the MT-ND4L gene. These results show an important interconnection between mitochondria and metabolite concentrations. Considering that some of the significant metabolites found in this study have been previously related to complex diseases, such as neurological disorders and metabolic conditions, these associations found here might play a crucial role for further investigations of such complex diseases. Understanding the mechanisms that control human health and disease, in particular, the role of genetic predispositions and their interaction with environmental factors is a prerequisite for the development of safe and efficient therapies for complex disorders.

TIGER: technical variation elimination for metabolomics data using ensemble learning architecture.

Large metabolomics datasets inevitably contain unwanted technical variations which can obscure meaningful biological signals and affect how this information is applied to personalized healthcare. Many methods have been developed to handle unwanted variations. However, the underlying assumptions of many existing methods only hold for a few specific scenarios. Some tools remove technical variations with models trained on quality control (QC) samples which may not generalize well on subject samples. Additionally, almost none of the existing methods supports datasets with multiple types of QC samples, which greatly limits their performance and flexibility. To address these issues, a non-parametric method TIGER (Technical variation elImination with ensemble learninG architEctuRe) is developed in this study and released as an R package (https://CRAN.R-project.org/package=TIGERr). TIGER integrates the random forest algorithm into an adaptable ensemble learning architecture. Evaluation results show that TIGER outperforms four popular methods with respect to robustness and reliability on three human cohort datasets constructed with targeted or untargeted metabolomics data. Additionally, a case study aiming to identify age-associated metabolites is performed to illustrate how TIGER can be used for cross-kit adjustment in a longitudinal analysis with experimental data of three time-points generated by different analytical kits. A dynamic website is developed to help evaluate the performance of TIGER and examine the patterns revealed in our longitudinal analysis (https://han-siyu.github.io/TIGER_web/). Overall, TIGER is expected to be a powerful tool for metabolomics data analysis.

Bidirectional modulation of TCA cycle metabolites and anaplerosis by metformin and its combination with SGLT2i.

BACKGROUND: Metformin and sodium-glucose-cotransporter-2 inhibitors (SGLT2i) are cornerstone therapies for managing hyperglycemia in diabetes. However, their detailed impacts on metabolic processes, particularly within the citric acid (TCA) cycle and its anaplerotic pathways, remain unclear. This study investigates the tissue-specific metabolic effects of metformin, both as a monotherapy and in combination with SGLT2i, on the TCA cycle and associated anaplerotic reactions in both mice and humans. METHODS: Metformin-specific metabolic changes were initially identified by comparing metformin-treated diabetic mice (MET) with vehicle-treated db/db mice (VG). These findings were then assessed in two human cohorts (KORA and QBB) and a longitudinal KORA study of metformin-naïve patients with Type 2 Diabetes (T2D). We also compared MET with db/db mice on combination therapy (SGLT2i + MET). Metabolic profiling analyzed 716 metabolites from plasma, liver, and kidney tissues post-treatment, using linear regression and Bonferroni correction for statistical analysis, complemented by pathway analyses to explore the pathophysiological implications. RESULTS: Metformin monotherapy significantly upregulated TCA cycle intermediates such as malate, fumarate, and α-ketoglutarate (α-KG) in plasma, and anaplerotic substrates including hepatic glutamate and renal 2-hydroxyglutarate (2-HG) in diabetic mice. Downregulated hepatic taurine was also observed. The addition of SGLT2i, however, reversed these effects, such as downregulating circulating malate and α-KG, and hepatic glutamate and renal 2-HG, but upregulated hepatic taurine. In human T2D patients on metformin therapy, significant systemic alterations in metabolites were observed, including increased malate but decreased citrulline. The bidirectional modulation of TCA cycle intermediates in mice influenced key anaplerotic pathways linked to glutaminolysis, tumorigenesis, immune regulation, and antioxidative responses. CONCLUSION: This study elucidates the specific metabolic consequences of metformin and SGLT2i on the TCA cycle, reflecting potential impacts on the immune system. Metformin shows promise for its anti-inflammatory properties, while the addition of SGLT2i may provide liver protection in conditions like metabolic dysfunction-associated steatotic liver disease (MASLD). These observations underscore the importance of personalized treatment strategies.

Stool and blood metabolomics in the metabolic syndrome: a cross-sectional study.

INTRODUCTION/OBJECTIVES: Changes in the stool metabolome have been poorly studied in the metabolic syndrome (MetS). Moreover, few studies have explored the relationship of stool metabolites with circulating metabolites. Here, we investigated the associations between stool and blood metabolites, the MetS and systemic inflammation. METHODS: We analyzed data from 1,370 participants of the KORA FF4 study (Germany). Metabolites were measured by Metabolon, Inc. (untargeted) in stool, and using the AbsoluteIDQ® p180 kit (targeted) in blood. Multiple linear regression models, adjusted for dietary pattern, age, sex, physical activity, smoking status and alcohol intake, were used to estimate the associations of metabolites with the MetS, its components and high-sensitivity C-reactive protein (hsCRP) levels. Partial correlation and Multi-Omics Factor Analysis (MOFA) were used to investigate the relationship between stool and blood metabolites. RESULTS: The MetS was significantly associated with 170 stool and 82 blood metabolites. The MetS components with the highest number of associations were triglyceride levels (stool) and HDL levels (blood). Additionally, 107 and 27 MetS-associated metabolites (in stool and blood, respectively) showed significant associations with hsCRP levels. We found low partial correlation coefficients between stool and blood metabolites. MOFA did not detect shared variation across the two datasets. CONCLUSIONS: The MetS, particularly dyslipidemia, is associated with multiple stool and blood metabolites that are also associated with systemic inflammation. Further studies are necessary to validate our findings and to characterize metabolic alterations in the MetS. Although our analyses point to weak correlations between stool and blood metabolites, additional studies using integrative approaches are warranted.

Identification of candidate metabolite biomarkers for metabolic syndrome and its five components in population-based human cohorts.

BACKGROUND: Metabolic Syndrome (MetS) is characterized by risk factors such as abdominal obesity, hypertriglyceridemia, low high-density lipoprotein cholesterol (HDL-C), hypertension, and hyperglycemia, which contribute to the development of cardiovascular disease and type 2 diabetes. Here, we aim to identify candidate metabolite biomarkers of MetS and its associated risk factors to better understand the complex interplay of underlying signaling pathways. METHODS: We quantified serum samples of the KORA F4 study participants (N = 2815) and analyzed 121 metabolites. Multiple regression models adjusted for clinical and lifestyle covariates were used to identify metabolites that were Bonferroni significantly associated with MetS. These findings were replicated in the SHIP-TREND-0 study (N = 988) and further analyzed for the association of replicated metabolites with the five components of MetS. Database-driven networks of the identified metabolites and their interacting enzymes were also constructed. RESULTS: We identified and replicated 56 MetS-specific metabolites: 13 were positively associated (e.g., Val, Leu/Ile, Phe, and Tyr), and 43 were negatively associated (e.g., Gly, Ser, and 40 lipids). Moreover, the majority (89%) and minority (23%) of MetS-specific metabolites were associated with low HDL-C and hypertension, respectively. One lipid, lysoPC a C18:2, was negatively associated with MetS and all of its five components, indicating that individuals with MetS and each of the risk factors had lower concentrations of lysoPC a C18:2 compared to corresponding controls. Our metabolic networks elucidated these observations by revealing impaired catabolism of branched-chain and aromatic amino acids, as well as accelerated Gly catabolism. CONCLUSION: Our identified candidate metabolite biomarkers are associated with the pathophysiology of MetS and its risk factors. They could facilitate the development of therapeutic strategies to prevent type 2 diabetes and cardiovascular disease. For instance, elevated levels of lysoPC a C18:2 may protect MetS and its five risk components. More in-depth studies are necessary to determine the mechanism of key metabolites in the MetS pathophysiology.

Identification of proliferative and mature ß-cells in the islets of Langerhans.

Insulin-dependent diabetes is a complex multifactorial disorder characterized by loss or dysfunction of ß-cells. Pancreatic ß-cells differ in size, glucose responsiveness, insulin secretion and precursor cell potential; understanding the mechanisms that underlie this functional heterogeneity might make it possible to develop new regenerative approaches. Here we show that Fltp (also known as Flattop and Cfap126), a Wnt/planar cell polarity (PCP) effector and reporter gene acts as a marker gene that subdivides endocrine cells into two subpopulations and distinguishes proliferation-competent from mature ß-cells with distinct molecular, physiological and ultrastructural features. Genetic lineage tracing revealed that endocrine subpopulations from Fltp-negative and -positive lineages react differently to physiological and pathological changes. The expression of Fltp increases when endocrine cells cluster together to form polarized and mature 3D islet mini-organs. We show that 3D architecture and Wnt/PCP ligands are sufficient to trigger ß-cell maturation. By contrast, the Wnt/PCP effector Fltp is not necessary for ß-cell development, proliferation or maturation. We conclude that 3D architecture and Wnt/PCP signalling underlie functional ß-cell heterogeneity and induce ß-cell maturation. The identification of Fltp as a marker for endocrine subpopulations sheds light on the molecular underpinnings of islet cell heterogeneity and plasticity and might enable targeting of endocrine subpopulations for the regeneration of functional ß-cell mass in diabetic patients.

Circulating Metabolites Differentiate Acute Ischemic Stroke from Stroke Mimics.

OBJECTIVE: Early discrimination of patients with ischemic stroke (IS) from stroke mimics (SMs) poses a diagnostic challenge. The circulating metabolome might reflect pathophysiological events related to acute IS. Here, we investigated the utility of early metabolic changes for differentiating IS from SM. METHODS: We performed untargeted metabolomics on serum samples obtained from patients with IS (N = 508) and SM (N = 349; defined by absence of a diffusion weighted imaging [DWI] positive lesion on magnetic resonance imaging [MRI]) who presented to the hospital within 24 hours after symptom onset (median time from symptom onset to blood sampling = 3.3 hours; interquartile range [IQR] = 1.6-6.7 hours) and from neurologically normal controls (NCs; N = 112). We compared diagnostic groups in a discovery-validation approach by applying multivariable linear regression models, machine learning techniques, and propensity score matching. We further performed a targeted look-up of published metabolite sets. RESULTS: Levels of 41 metabolites were significantly associated with IS compared to NCs. The top metabolites showing the highest value in separating IS from SMs were asymmetrical and symmetrical dimethylarginine, pregnenolone sulfate, and adenosine. Together, these 4 metabolites differentiated patients with IS from SMs with an area under the curve (AUC) of 0.90 in the replication sample, which was superior to multimodal cranial computed tomography (CT; AUC = 0.80) obtained for routine diagnostics. They were further superior to previously published metabolite sets detected in our samples. All 4 metabolites returned to control levels by day 90. INTERPRETATION: A set of 4 metabolites with known biological effects relevant to stroke pathophysiology shows unprecedented utility to identify patients with IS upon hospital arrival, thus encouraging further investigation, including multicenter studies. ANN NEUROL 2020;88:736-746.

Correction: Effect of Insulin Resistance on Monounsaturated Fatty Acid Levels: A Multi-cohort Non-targeted Metabolomics and Mendelian Randomization Study.

[This corrects the article DOI: 10.1371/journal.pgen.1006379.].

Effect of Insulin Resistance on Monounsaturated Fatty Acid Levels: A Multi-cohort Non-targeted Metabolomics and Mendelian Randomization Study.

Insulin resistance (IR) and impaired insulin secretion contribute to type 2 diabetes and cardiovascular disease. Both are associated with changes in the circulating metabolome, but causal directions have been difficult to disentangle. We combined untargeted plasma metabolomics by liquid chromatography/mass spectrometry in three non-diabetic cohorts with Mendelian Randomization (MR) analysis to obtain new insights into early metabolic alterations in IR and impaired insulin secretion. In up to 910 elderly men we found associations of 52 metabolites with hyperinsulinemic-euglycemic clamp-measured IR and/or ß-cell responsiveness (disposition index) during an oral glucose tolerance test. These implicated bile acid, glycerophospholipid and caffeine metabolism for IR and fatty acid biosynthesis for impaired insulin secretion. In MR analysis in two separate cohorts (n = 2,613) followed by replication in three independent studies profiled on different metabolomics platforms (n = 7,824 / 8,961 / 8,330), we discovered and replicated causal effects of IR on lower levels of palmitoleic acid and oleic acid. A trend for a causal effect of IR on higher levels of tyrosine reached significance only in meta-analysis. In one of the largest studies combining "gold standard" measures for insulin responsiveness with non-targeted metabolomics, we found distinct metabolic profiles related to IR or impaired insulin secretion. We speculate that the causal effects on monounsaturated fatty acid levels could explain parts of the raised cardiovascular disease risk in IR that is independent of diabetes development.

Metabolite ratios as potential biomarkers for type 2 diabetes: a DIRECT study.

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. We then investigated if the identified metabolite ratios were associated with measures of OGTT-derived beta cell function and with prevalent and incident type 2 diabetes. METHODS: We measured the levels of 188 metabolites in plasma samples from 130 healthy members of twin families (from the Netherlands Twin Register) at five time points during a modified 3 h hyperglycaemic clamp with 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 (ORVal_PC ae C32:2 2.64 [ß 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 (HRVal_PC ae C32:2 1.57 [ß 0.45 ± 0.06]; p = 1.3 × 10-15), leading to modest improvements 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.