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Microbiota-Related Metabolites and the Risk of Type 2 Diabetes

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Microbiota-Related Metabolites and the Risk of Type 2 Diabetes Diabetes Care Volume 43, June 2020 1319 Microbiota-Related Metabolites Jagadish Vangipurapu,1 Lilian Fernandes Silva,1 Teemu Kuulasmaa,2 and the Risk of Type 2 Diabetes Ulf Smith,3 and Markku Laakso1,4 Diabetes Care 2020;43:1319–1325 | https://doi.org/10.2337/dc19-2533 OBJECTIVE Recent studies have highlighted the significance of the microbiome in human health and disease. Changes in the metabolites produced by microbiota have been implicated in several diseases. Our objective was to identify microbiome metab- olites that are associated with type 2 diabetes. RESEARCH DESIGN AND METHODS Our study included 5,181 participants from the cross-sectional Metabolic Syndrome in Men (METSIM) study that included Finnish men (age 57 6 7 years, BMI 26.5 6 3.5 kg/m2) having metabolomics data available. Metabolomics analysis was performed based on fasting plasma samples. On the basis of an oral glucose tolerance test, Matsuda ISI and disposition index values were calculated as markers ofinsulinsensitivity and insulinsecretion.A totalof 4,851participants had a7.4-year follow-up visit, and 522 participants developed type 2 diabetes. RESULTS Creatine, 1-palmitoleoylglycerol (16:1), urate, 2-hydroxybutyrate/2-hydroxyisobu- tyrate, xanthine, xanthurenate, kynurenate, 3-(4-hydroxyphenyl)lactate, 1-oleoylglycerol (18:1), 1-myristoylglycerol (14:0), dimethylglycine, and 2-hydroxyhippurate (salicylurate) were significantly associated with an increased risk of type 2 diabetes. These metabolites CARDIOVASCULAR AND METABOLIC RISK were associated with decreased insulin secretion or insulin sensitivity or both. Among the metabolites that were associated with a decreased risk of type 2 1Institute of Clinical Medicine, University of East- diabetes,1-linoleoylglycerophosphocholine (18:2) significantly reduced the riskof ern Finland, Kuopio, Finland type 2 diabetes. 2Institute of Biomedicine, Bioinformatics Center, University of Eastern Finland, Kuopio, Finland 3 CONCLUSIONS Lundberg Laboratory for Diabetes Research, Department of Molecular and Clinical Medicine, Several novel and previously reported microbial metabolites related to the gut Sahlgrenska Academy, University of Gothen- microbiota were associated with an increased risk of incident type 2 diabetes, and burg, Gothenburg, Sweden they were also associated with decreased insulin secretion and insulin sensitivity. 4Department of Medicine, Kuopio University Microbial metabolites are important biomarkers for the risk of type 2 diabetes. Hospital, Kuopio, Finland Corresponding author: Markku Laakso, markku .laakso@uef.fi Type 2 diabetes is a major global health concern. It is caused by genetic risk variants in Received 18 December 2019 and accepted 18 interplay with environmental and lifestyle factors. The two main pathophysiological March 2020 disturbances in this disease are impaired insulin secretion and insulin resistance (1). This article contains Supplementary Data Understanding the pathophysiology of type 2 diabetes is crucial for its prevention and online at https://doi.org/10.2337/dc20-1234/ treatment. It is especially important to identify early biomarkers for the risk of suppl.12009102. type 2 diabetes. Advances in metabolomics allowing studies of small molecules, © 2020 by the American Diabetes Association. metabolites, have opened an emerging technology for biomarker studies and Readers may use this article as long as the work is properly cited, the use is educational and not for precision medicine (2). profit, and the work is not altered. More infor- A growing number of studies have shown that the microbiota is likely to play an mation is available at https://www.diabetesjournals important role in human health and disease (3). The microbiota consists of various .org/content/license. 1320 Diabetes and Microbiota-Related Metabolites Diabetes Care Volume 43, June 2020 microorganisms, mainly bacteria, but RESEARCH DESIGN AND METHODS Tarrytown, NY). We measured metab- also viruses, protozoa, and fungi. It is Study Population olites as part of Metabolon’s untar- responsible for food digestion, modula- The METSIM study comprises 10,197 geted Discovery HD4 platform using tion of immune responses, and the gen- Finnish men, aged from 45 to 73 years, ultra-high-performance liquid chroma- eration of different metabolites resulting randomly selected from the population tography–tandem mass spectroscopy, as from microbial metabolic activities (4). register of Kuopio town, Eastern Finland. previously described in detail (9). The Imbalances or disturbances in the ho- The cross-sectional and follow-up studies determination of metabolites was per- meostasis between the microbiota and of the METSIM cohort were performed formed in three batches. Batch one in- host environment may play an important using identical protocols and similar clin- cluded 999 samples with 717metabolites role in the pathogenesis of many disor- ical and laboratory measurements. Height identified; batch two included 1,231 ders, such as liver, gastrointestinal, and was measured to the nearest 0.5 cm, and samples with 778 metabolites; and batch metabolic diseases, including obesity, weight was measured using a calibrated three included 3,000 samples with fi type 2 diabetes, lipid disorders, and digital scale (Seca 877; Seca, Hamburg, 843 metabolites identi ed. All metabo- . cardiovascular disease (5). Moreover, the Germany) to the nearest 0.1 kg. BMI was lites having 50% missing values, in- microbiome produces metabolites that calculated as the weight in kilograms cluding mainly those with data available exclusively from a single batch, were could be pathogenic or beneficial to the divided by the height in meters squared. omitted from the statistical analyses. host. These metabolites and their end Glucose tolerance was evaluated with a Among a total of 857 unique metabo- products may play crucial roles in host 2-h oral glucose tolerance test (75 g lites, 86 metabolites related to gut mi- biosynthetic and metabolic networks as glucose). We measured glucose and in- crobiota were included in current statistical well as various immunological and neu- sulin levels at 0, 30, and 120 min, and we analysis (Supplementary Table 1). robiological processes (4). determined glucose tolerance status ac- The microbiota regulates several im- cording to the American Diabetes Asso- Calculations portant metabolic mechanisms of the ciation criteria at both baseline and follow-up studies (8). Altogether 1,412 We calculated the Matsuda ISI as pre- body, and is associated with metabolic viously published (9), and insulin secre- diseases and other disorders. Previous participants were diagnosed with type 2 diabetes at the baseline study, and they tion index (area under the curve [AUC]; studies have suggested that patients were excluded from all statistical anal- InsAUC0–30/GluAUC0–30) as follows: (in- with type 2 diabetes show evidence of yses. A total of 5,169 men without di- sulin at 0 min 1 insulin at 30 min)/ changes in the intestinal microbiota com- abetes at baseline (age 57 6 7 years, BMI (glucose at 0 min 1 glucose at 30 min). We position although the results have been 26.5 6 3.5 kg/m2, mean 6 SD) having the have previously validated the Matsuda ISI as conflicting (6). The gut microbiota is able metabolomics data were included in the the best index for ISI as compared with to ferment indigestible carbohydrates current study. Their plasma fasting glu- the M value of the euglycemic hyperinsu- resulting in the release of metabolites, cose (FG) was 5.6 6 0.4 mmol/L, and 2-h linemic clamp, and InsAUC0–30/GluAUC0–30 such as short chain fatty acids, acetate, glucose (2HG) was 5.9 6 1.6 mmol/L. A as the best marker of insulin secretion propionate, and butyrate. These metab- total of 4,851 men participated in the as compared with insulin secretion during fi olites may have a bene cial effect on follow-up study (mean follow-up time 7.4 6 a frequently sampled intravenous glucose fl weight control, in ammation, insulin 2.9 years). We have previously shown tolerance test) (10). Disposition index (DI), a sensitivity, and glucose homeostasis (7). that this subset of men had clinical measure of insulin secretion adjusted for Recent advances in high-throughput and laboratory characteristics similar to prevailing insulin sensitivity, was calculated 3 technologies have facilitated large-scale those of the entire METSIM population as Matsuda ISI (InsAUC0–30/GluAUC0–30) biomarker studies applying a metabolo- without diabetes, and therefore, this (10). mics approach. This has made it possible subsample can be considered to repre- to investigate the association of all sent the entire METSIM cohort (9). Statistical Analysis known metabolites from the gut micro- During the follow-up, 522 participants We conducted statistical analyses using biota with the risk of type 2 diabetes developed incident type 2 diabetes. The IBM SPSS Statistics, version 25. We log- compared with previous studies that study was approved by the Ethics Com- transformed all continuous traits with included only selected metabolites. Fur- mittee of the University of Kuopio and the exception of age and follow-up time thermore, previous studies have often Kuopio University Hospital, Kuopio, Fin- to correct for their skewed distribution. been cross-sectional and small in size land. All study participants gave written We applied Cox regression to associate and, therefore, underpowered. Large informed consent. the levels of metabolites with incident prospective randomly selected popula- type 2 diabetes, and we present
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