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 RISK METABOLIC AND CARDIOVASCULAR 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 the tion-based cohorts having metabolomics results as hazard ratios (HR) and 95% CIs. data available are needed to obtain re- Laboratory Measurements We tested Cox proportionality assump- liable results. Therefore, we performed a We measured plasma glucose using an tion for the metabolites using survival study including 5,169 participants of the enzymatic hexokinase photometric assay and survminer packages in R, and we Metabolic Syndrome in Men (METSIM) (Konelab Systems Reagents; Thermo found that a fitted Cox regression model study having a follow-up of 7.4 years to Fisher Scientific, Vantaa, Finland). Insulin adequately described the data. P , 5.8 3 investigate the role of microbiota-based was determined by immunoassay (ADVIA 1025 (corrected for 857 metabolites) was metabolites as risk factors for type 2 Centaur Insulin IRI no. 02230141; Sie- considered as statistically significant and diabetes. mens Medical Solutions Diagnostics, P , 0.05 as nominally significant. We care.diabetesjournals.org Vangipurapu and Associates 1321

used the area under the receiver oper- other metabolites were nominally asso- CONCLUSIONS ating characteristic (ROC) curve to assess ciated (P , 0.05) with incident diabetes Recent studies have indicated that the the accuracy of the clinical risk factors after adjustment for batch effect and composition of the microbiota affects (age, BMI, smoking, and physical activity) other confounding factors. systemic metabolism. Changes in the and metabolites in detecting incident composition of the microbiota, referred type 2 diabetes. We examined the asso- Associations With Decreased Risk for to as microbiota dysbiosis, have been ciation of the metabolites with Matsuda Type 2 Diabetes linked to several diseases, including obe- ISI, DI, and glucose levels with linear Ten metabolites decreased the risk of diabe- sity, diabetes, and cardiovascular disease regression adjusted for batch effect, fol- tes either significantly or nominally. We (11). Our large population-based MET- low-up time, and corresponding baseline found that 1-linoleoyl-glycerophosphocholine SIM study gives further evidence that values.Wepresenttheresultsasstan- (GPC) (18:2) lowered the risk of type 2 microbiota metabolites increase the risk fi b dardized regression coef cients ( and SE). diabetes significantly and 1-lignoceroyl- of incident type 2 diabetes. GPC (24:0), 1-linolenoyl-GPC (18:3), RESULTS 1-stearoyl-GPC (18:0), and indolepro- Metabolites Increasing Significantly We investigated the association of 86 mi- pionate nominally after the adjustment for the Risk of Type 2 Diabetes crobiome-based metabolites (Microbiome all confounding factors. We also found that We found that among the metabolites panel; Metabolon Inc.), representing es- 3-phenylproprionate, Hippurate, and sper- investigated, creatine was most strongly tablished microbiome metabolites, novel mide lowered the risk of type 2 diabetes microbially derived metabolites, host me- associated with the risk of type 2 di- nominally (Fig. 1 and Supplementary Table 2). fi tabolites, and xenobiotics/dietary metab- abetes, which is a new nding. Creatine olites) with incident type 2 diabetes increases muscle mass, and as a dietary Associations With FG and 2HG Levels supplement combined with exercise, it (Supplementary Table 1). fi All metabolites signi cantly associated improves insulin sensitivity (12). How- with an increase in incident type 2 di- ever, long-term effects of creatine on the Associations With Increased Risk of fi abetes were signi cantly or nominally risk of type 2 diabetes in middle-aged or Type 2 Diabetes associated with increases in FG and/or Figure 1 and Supplementary Table 2 show elderly individuals have not been pre- 2HG except for 2-hydroxyhippurate. Me- the associations of the metabolites with viously reported. In our study, creatine tabolites significantly decreasing the risk incident diabetes. Creatine had the most significantly reduced insulin sensitivity of incident type 2 diabetes had decreases significant association with the risk of type 2 but only nominally reduced insulin se- in FG and/or 2HG levels (Table 1). diabetes (HR 1.43, 95% CI 1.30–1.56). Mono- cretion. In agreement with our findings, acylglycerols, 1-palmitoleoylglycerol animal studies have reported that pro- (16:1) (HR 1.41, 95% CI 1.28–1.55), Associations With DI and Matsuda ISI longed creatine supplementation de- To investigate the mechanisms behind 1-oleoylglycerol (18:1) (HR 1.29, 95% creases insulin sensitivity (13). Bacteria the risk of type 2 diabetes and hyper- CI 1.29–1.41), and 1-myristoylglycerol and fungi capable of degrading creatine glycemia, we calculated DI (insulin se- (HR 1.23, 95% CI 1.13–1.34) were among have been identified in the human colon cretion adjusted for insulin sensitivity) the seven metabolites significantly as- (14,15). and Matsuda ISI (Table 1). We found sociated with incident type 2 diabetes We found that three monoacylgly- significant or nominally significant de- after adjustment for batch effect, base- cerols, 1-palmitoleoylglycerol (16:1), creases in DI for 11 metabolites that were line age, BMI, smoking, and physical 1-oleoylglycerol (18:1), and 1-myristoyl- significantly associated with incident activity. Other metabolites significantly glycerol (14:0), indirectly controlled by type 2 diabetes, whereas insulin sensi- associated with incident type 2 diabetes gut microbiota, increased significantly tivity was decreased only for 5 metabo- after adjustment for confounding factors the risk of incident type 2 diabetes by lites who developed incident diabetes were urate (HR 1.39, 95% CI 1.27–1.52), 41%, 29%, and 24%, respectively, as well during the follow-up. Correspondingly, 2-hydroxybuturate/2-hydroxyisobutyrate as FG and 2HG levels. Bile and enzymes insulin sensitivity and insulin secretion (HR 1.33, 95% CI 1.21–1.46), kynurenate emulsify triacylglycerides in the intesti- were increased in individuals whose risk (HR 1.31, 95% CI 1.20–1.42), xanthine nal lumen to form free fatty acids and of type 2 diabetes was decreased during (HR 1.31, 95% CI 1.21–1.42), xanthuren- monoacylglycerols that are taken up by the follow-up. ate (HR 1.31, 95% CI 1.18–1.44), 3-(4- intestine. No previous studies have re- hydroxyphelyl)lactate (HR 1.30, 95% CI ROC Curves Predicting of Incident Type 2 ported that monoacylglycerols, except 1.20–1.42), dimethylglycine (HR 1.20, Diabetes for monoacylglyceride 18:2 (16), increase 95% CI 1.12–1.28), and 2-hydroxyhippurate Figure 2 shows for the ROC curve for a the risk of type 2 diabetes or glucose (HR1.19, 95% CI 1.10–1.30). Kynurenate, model including known risk factors for levels. Also, 1-palmitoleoylglycerol (16:1), N-acetyltryptophan, taurochenodeoxy- type 2 diabetes (age, BMI, smoking, and 1-oleoylglycerol (18:1), and 1-myristoyl- cholate, glycocholate, and glycocheno- physical activity) giving the AUC of glycerol (14:0) significantly decreased deoxycholate were significantly associated 0.686.Whenthreemetabolites(creatine, insulin secretion but not insulin sensitivity. with incident diabetes after adjustment 1-palmitoleoylglycerol [16:1], and urate) Uric acid was associated with a 39% for batch effect, but after further ad- were included in the model the AUC increased risk of type 2 diabetes in our justment for age, BMI, smoking, and increased up to 0.723 (95% CI 0.698– study confirming the results of a previous physical activity, they were onlynominally 0.748). However, this increase was not study (17). Similarly, xanthine, a precur- associated with incident diabetes. Fifteen statistically significant (P 5 0.051). sorofuricacid,was foundtobeassociated 1322 Diabetes and Microbiota-Related Metabolites Diabetes Care Volume 43, June 2020

in insulin secretion and insulin sensitivity. Kynurenine is involved in tryptophan metabolism, which is in direct or indirect control of microbiota. Moreover, indole- amine 2,3-dioxygenase 1, the enzyme responsible for conversion of tryptophan to kynurenine has been shown to be regulated by microbiota (24). Trypto- phan metabolites have been shown to inhibit both proinsulin synthesis and glucose- and leucine-induced insulin re- lease from rat pancreatic islets in agree- ment with our findings (25). Tryptophan is also a precursor for serotonin synthesis in the gut mucosa (26). Interestingly, in our study, serotine nominally decreased the risk of type 2 diabetes, FG, and 2HG and increased insulin secretion, in agree- ment with a previous study in human and mouse islets (27). The 2-hydroxyhippurate (salicylurate) microbial metabolite is an aryl glycine conjugate formed by the body to elim- inate excess salicylates, including aspirin. This metabolite was significantly associ- ated with a 20% increase in the risk of type 2 diabetes but did not have signif- icant effects on FG or 2HG levels, insulin secretion, or insulin sensitivity. A pre- vious study demonstrated that aspirin reduces the risk of developing diabetes Figure 1—Metabolites significantly (bold and underlining) or nominally (bold) associated with probably attributable to inhibition of increased or decreased risk of type 2 diabetes (HR and their 95% CIs) adjusted for confounding inflammation (28). factors (age, BMI, smoking, and physical activity) are marked by bold and underlining. Dimethylglycine, a derivative of gly- cine, increased the risk of type 2 diabetes with an increased risk of type 2 diabetes in effects on insulin sensitivity. No pre- by 20% in our study confirming the our study, as previously reported (18). A vious studies are available on the effects results of a previous study (29). Addi- novel finding in our study was that xan- of 3-(4-hydroxyphenyl) lactate on inci- tionally, dimethylglycine increased 2HG thine was also associated with impaired dent diabetes, glucose levels, insulin level and reduced insulin sensitivity. High insulin secretion. secretion, or insulin sensitivity. levels of dimethylglycine have been pre- Also, 2-hydroxybutyrate, produced by Our study is the first to show that viously associated with an increase in threonine and methionine, significantly kynurenate and xanthurenate, products HbA1c in a prospective study (30). increased the risk of type 2 diabetes by of the metabolism of amino acid tryp- Bile acids (taurochenodeoxycholate, gly- 32%. It is a good marker of early stage tophan, significantly increased the risk of cocholate, and glycochenodeoxy-cholate) hyperglycemia and insulin resistance and incident type 2 diabetes by 30%. A pre- were nominally associated with an in- the risk of type 2 diabetes, as previ- vious study showed that kynurenate and creased risk of type 2 diabetes after ously published (19,20). In our study, xanthurenate levels were increased in the adjustment for confounding fac- 2-hydroxybutyrate was significantly as- type 2 diabetes (22). Another study re- tors, and they also increased both FG sociated with the risk of incident type 2 ported that kynurenate increased insulin and 2HG levels, and decreased insulin diabetes and insulin resistance. Our novel resistance but did not increase the risk of secretion (except for glycochenodeox- finding was that 2-hydroxybutyrate was type 2 diabetes (23). Our study showed ycholate). Bile acids are metabolized also significantly associated with reduced that kynurenate significantly decreased by the microbiota in the lower part insulin secretion. insulin secretion but only nominally, of the small intestine and colon, and We found that 3-(4-hydroxyphenyl)- whereas xanthurenate significantly de- they facilitate the absorption of dietary lactate, a lactobacillus breakdown pro- creased both insulin secretion and insulin fat-soluble molecules (4,31). Impaired duct of phenylalanine (21), increased sensitivity. Kynurenine, an upstream me- bile acid signaling contributes to hyper- significantly the risk of type 2 diabetes tabolite of kynurenate in the tryptophan glycemia and progression to type 2 di- by 32% and increased 2HG and, nom- pathway, was nominally associated with abetes through multiple mechanisms, inally, FG. It also decreased insulin se- increases in incident type 2 diabetes and including farnesoid X receptor and G pro- cretion significantly but did not have FG but significantly, with decreases both tein-coupled bile acid receptor TGR5 (32). care.diabetesjournals.org Vangipurapu and Associates 1323

Table 1—Association of the metabolites with FG, 2HG, DI, and ISI during 7.4-year N-acetyltryptophan, a derivative of follow-up visit tryptophan, was nominally associated Metabolite* FG 2HG DI ISI with type 2 diabetes but significantly with increases in FG and 2HG. It was Creatine ↑↑ ↑↑ ↓ ↓↓ associated with a decrease in insulin 1-palmitoleoylglycerol (16:1) ↑↑ ↑↑ ↓↓ 6 secretion, whereas insulin sensitiv- ↑↑ ↑↑ ↓↓ ↓ Urate ity was only nominally decreased. No ↑↑ ↑ ↓↓ ↓↓ 2-hydroxybutyrate/2-hydroxyisobutyrate previous population-based studies are N ↑↑ ↑↑ ↓↓ ↓ -acetyltryptophan available investigating the role of N- Xanthine ↑↑ ↑↑ ↓↓ ↓↓ acetyltryptophan with respect to the Xanthurenate ↑↑ ↑↑ ↓↓ ↓ risk of type 2 diabetes. Kynurenate ↑↑ ↑↑ ↓↓ ↓ Lactate was nominally associated with 3-(4-hydroxyphenyl)lactate ↑↑↑↓↓6 incident type 2 diabetes, and increases in 1-oleoylglycerol (18:1) ↑↑↓↓6 FG and 2HG, and a decrease in insulin Kynurenine ↑↑↑↓↓↓↓secretion. Previously the Atherosclerosis 1-myristoylglycerol (14:0) ↑↑↓↓6 Risk in Communities (ARIC) Study has Taurochenodeoxycholate ↑↑↑↓↓shown that lactate levels are associated Glycocholate ↑↑↓6 with a 20% increased risk of type 2 di- 2-oleoylglycerol (18:1) 66↓ 6 abetes (33). Thirteen other metabolites Dimethylglycine 6 ↑ 6 ↓ had nominally significant associations Glycochenodeoxycholate 6 ↑ 66with type 2 diabetes without and after 2-hydroxyhippurate (salicylurate) 666↑ adjustments for confounding factors. We fi fi N-acetylputrescine ↑↑↓6 did not nd signi cant association of Lactate ↑↑6 ↑↑ trimethylamine N-oxide with type 2 di- Glycocholenate sulfate ↑ 6 ↓ 6 abetes, although a recent meta-analysis foundthat association (34).However, the Isovalerate (i5:0) ↑ 6 ↓↓ causality of this association needs further Uridine ↑↑ 6 ↓↓ ↓ studies (35). Indolelactate ↑↑↓6 The ROC curve analysis indicated that 3-hydroxyisobutyrate ↑↑ ↑ ↓ ↓ the three best metabolites predicting 6666 4-hydroxyphenylacetate incident type 2 diabetes improved slightly 6 ↑ 66 Phenyllactate but not significantly (P 5 0.051) the 6666 Imidazole propionate prediction of incident type 2 diabetes Glycodeoxycholate ↑↑66compared with the model including 1-linolenoylglycerol (18:3) 666↑ known risk factors for type 2 diabetes 1-palmitoylglycerol (16:0) 66↓ 6 (age, BMI, smoking, and physical activity). Phenol sulfate 6666 1-arachidonoyl-GPE (20:4n6) 666↑ Metabolites Decreasing the Risk of 1-dihomo-linolenylglycerol (20:3) 66↓ 6 Type 2 Diabetes Spermidine 66661-Linoleoylglycerophosphocholine had Hippurate ↓↓↑↑↑a preventive effect of 38% on the risk 3-phenylpropionate (hydrocinnamate) ↓ ↓ ↑↑ ↑ of type 2 diabetes after the adjustment 1-palmitoyl-GPC (16:0) 666↑↑ for confounding factors, in agreement 1-stearoyl-GPC (18:0)** 6 ↓↑↑↑with a previous publication (21). Simi- Indolepropionate** ↓↓ ↓ ↑ ↑↑ larly, 1-oleoylglycerophosphocholine was 1-lignoceroyl-GPC (24:0)** ↓↓↓↑↑↑↑associated with a 22% increased risk of fi 1-linolenoyl-GPC (18:3)** ↓ ↓ ↑↑ ↑↑ type 2 diabetes con rming a previous 1-oleoyl-GPC (18:1)** ↓↓ ↓↓ ↑↑ ↑↑ study (36), but after the adjustment 1-linoleoyl-GPC (18:2)** ↓↓ ↓↓ ↑↑ ↑↑ for confounding factors, the associa- tion was only nominally significant. Our GPE, glycerophosphoethanolamine. *Bold and underlining, metabolites having statistically 2 novel finding was that lignoceroyl- significant (P , 5.8 3 10 5) association with incident diabetes after adjustment for batch effect,baselineage,BMI,smoking,andphysical activity. Bold, metabolites were nominally glycerophosphocholine decreased the risk associated withincident type 2 diabetesafter adjustment for confounding factors (qFig. 1).The of type 2 diabetes by 23%. All the me- linear regression analysis for FG, 2HG, DI, and ISI adjusted for follow-up time and baseline tabolitesmentioned previously werealso measurements. ↑↑, statistically significant increase (P , 5.8 3 1025); ↑, nominally significant (P , 0.05) increase; ↓↓, statistically significant decrease (P , 5.8 3 1025); and ↓, nominally associated with the elevation of FG and significant (P , 0.05) decrease. **Metabolites having preventive effect on incident type 2 2HG levels, and increases in insulin se- diabetes. cretion and insulin sensitivity. Lysophos- pholipids have a role in lipid signaling by acting on lysophospholipidreceptors,and they can stimulate glucose-dependent 1324 Diabetes and Microbiota-Related Metabolites Diabetes Care Volume 43, June 2020

findings. The limitations of the study are that only middle-aged and elderly Finnish men were included in our study, and therefore, we do not know whether the results are valid for women, different age groups, and other ethnic and racial groups. We are not aware of any un- selected population sample where a large set of microbiota metabolites had been measured and where insulin secre- tion and insulin sensitivity were eva- luated using similar validated insulin secretion and insulin sensitivity markers. Therefore, we could not replicate our findings in other populations. Other lim- itations of our study are that information regarding the diet and medication affect- ing microbiota was not available and that we did not have microbiota sam- ples. Finally, the exact location of the source of the metabolites requires fu- ture studies, especially with respect to the contribution of oral versus gut microbiota. In conclusion, we have identified sev- eral new microbiota metabolites increas- ing the risk of type 2 diabetes. We also demonstrated that the conversion to Figure 2—Area under the ROC curve in the prediction of incident type 2 diabetes in the METSIM diabetes was associated with increased study. The first ROC curve (blue) includes age, BMI, smoking, and physical activity as predictors for FG and 2HG levels and decreased insulin type 2 diabetes (model1, AUC 5 0.686)and the second ROC curve (red) includes model 1 and three secretion during the follow-up, but the metabolites, creatine, 1-palmitoleoylglycerol (16:1), and urate as predictors of type 2 diabetes association with decreased insulin sen- (AUC 5 0.723 [95% CI 0.698–0.748]). Number of participants in statistical analyses: with type 2 diabetes (n 5 420) and without type 2 diabetes (n 5 3,751). Change in AUC among the two models sitivity was not as strong. This dem- was not significant (P 5 0.051). onstrates the crucial role of insulin secretion in the conversion to type 2 diabetes. We also demonstrated that the insulin release (37) through lysophospho- sensitivity. Correspondingly, metabolites metabolites that were associated with lipid receptors such as G-protein–coupled associated with a reduced risk of incident a reduced risk of type 2 diabetes had receptor 119, localized in pancreatic diabetes had improvements both in increased insulin sensitivity and quite b-cells. insulin secretion and insulin sensitivity. often increased insulin secretion. How- As previously published indolepro- However, there were also metabolites ever, not all metabolites increasing the pionate (38), a microbial metabolite of increasing the risk of incident diabetes risk of type 2 diabetes were associated tryptophan, reduced the risk of type 2 without having a significant effect on with insulin secretion and insulin sensi- diabetes in our study by 18%, but after insulin secretion or insulin sensitivity. tivity, indicating that there may be other the adjustment for confounding factors, Dimethylglycine and 2-hydroxyhippurate less known mechanisms increasing the this association was only nominally sig- increased the risk of incident diabetes risk of type 2 diabetes. nificant. The mechanism for the preven- significantly, but their effects on insulin tive effect of indolepropionate is the secretion and insulin sensitivity were stimulation of glucagon-like peptide 1 only modest. This may indicate that there from intestinal enteroendocrine L cells are other less known mechanisms lead- Acknowledgments. The authors thank all par- which stimulate insulin secretion and ing to type 2 diabetes. ticipants of the METSIM study. increase insulin sensitivity as demon- Funding. The research leading to these results strated also in our study (39). has received support from the Innovative Strengths and Limitations Medicines Initiative Joint Undertaking under Euro- The strengths of this study include the pean Medical Information Framework grant agree- Metabolites Affecting Insulin Secretion large and homogeneous METSIM study ment no. 115372 (to M.L. and U.S.). The METSIM and Insulin Sensitivity population. We applied validated meth- study was supported by grants from the Academy of As expected, metabolites strongly asso- ods to measure insulin sensitivity and Finland (321428), Sigrid Juselius Foundation, Finnish Foundation for Cardiovascular Research, Kuopio ciated with incident diabetes and ele- secretion (10). Additionally, we used a University Hospital, and Centre of Excellence vated levels of FG and 2HG exhibited very conservative threshold for statistical of Cardiovascular and Metabolic Diseases, sup- decreases in insulin secretion and insulin significance to increase credibility of our ported by the Academy of Finland (to M.L.). care.diabetesjournals.org Vangipurapu and Associates 1325

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