Literature DB >> 29795366

Associations of serum indolepropionic acid, a gut microbiota metabolite, with type 2 diabetes and low-grade inflammation in high-risk individuals.

Marjo Tuomainen1, Jaana Lindström2, Marko Lehtonen3,4, Seppo Auriola3,4, Jussi Pihlajamäki1,5, Markku Peltonen2, Jaakko Tuomilehto2,6, Matti Uusitupa1, Vanessa D de Mello7, Kati Hanhineva8,9.   

Abstract

We recently reported using non-targeted metabolic profiling that serum indolepropionic acid (IPA), a microbial metabolite of tryptophan, was associated with a lower likelihood of developing type 2 diabetes (T2D). In the present study, we established a targeted quantitative method using liquid chromatography with mass spectrometric detection (HPLC-QQQ-MS/MS) and measured the serum concentrations of IPA in all the participants from the Finnish Diabetes Prevention Study (DPS), who had fasting serum samples available from the 1-year study follow-up (n = 209 lifestyle intervention and n = 206 control group). Higher IPA at 1-year study was inversely associated with the incidence of T2D (OR [CI]: 0.86 [0.73-0.99], P = 0.04) and tended to be directly associated with insulin secretion (β = 0.10, P = 0.06) during the mean 7-year follow-up. Moreover, IPA correlated positively with dietary fiber intake (g/day: r = 0.24, P = 1 × 10-6) and negatively with hsCRP concentrations at both sampling (r = - 0.22, P = 0.0001) and study follow-up (β = - 0.19, P = 0.001). Thus, we suggest that the putative effect of IPA on lowering T2D risk might be mediated by the interplay between dietary fiber intake and inflammation or by direct effect of IPA on β-cell function.

Entities:  

Mesh:

Substances:

Year:  2018        PMID: 29795366      PMCID: PMC5968030          DOI: 10.1038/s41387-018-0046-9

Source DB:  PubMed          Journal:  Nutr Diabetes        ISSN: 2044-4052            Impact factor:   5.097


Introduction

Well-established lifestyle, and metabolic and genetic factors are currently used for stratifying people at high risk of developing type 2 diabetes (T2D). Even though physical inactivity, overweight, and obesity are generally accepted major risk factors contributing to the T2D incidence[1], the quality of the diet seems also to have a role. We recently performed a non-targeted metabolite profiling study in pre-selected participants with impaired glucose tolerance (IGT) from the Finnish Diabetes Prevention Study (DPS) and reported that serum indolepropionic acid (IPA) was associated with a lower likelihood of developing T2D[2]. Furthermore, we replicated this association in two other independent cohorts[2]. In DPS, IPA was the only metabolite linked with preservation of β-cell function in those who did not develop T2D[2]. IPA is a specific microbial product from dietary tryptophan absorbed from the gut into the blood stream and is also found in cerebrospinal fluid[3,4]. In animal models, IPA exerts antioxidant and anti-inflammatory effects and possibly ameliorates glucose metabolism[5,6]. Because of the putative link of gut microbiota and T2D[7], we aimed at getting a more accurate picture of the interplay between IPA, T2D, glucose metabolism, inflammation, and diet. Therefore, we established a targeted quantitative method using liquid chromatography with triple quadrupole mass spectrometric detection (HPLC-QQQ-MS/MS) to measure the precise concentrations of IPA in serum samples from the DPS study.

Research design and methods

Study participants

The DPS was a randomized, controlled, multicenter study carried out in Finland between the years 1993 and 2001 (ClinicalTrials.gov NCT00518167). A total of 522 individuals with IGT were randomly allocated into either a lifestyle intervention or control group. After a mean 4-year intervention (active study) period, the post-intervention follow-up was carried out with annual examinations. The DPS study design and methods have been reported in detail elsewhere[8] and are described in the Supplementary Information (SI) material. The present study included all the participants who had fasting serum samples available for IPA analysis from the one-year follow-up. Altogether, IPA was measured in serum of 415 participants (n = 209 lifestyle and n = 206 control groups, respectively).

Laboratory determinations and genotyping

Glucose and insulin levels were determined as previously described[9] and as surrogate index of the first/early-phase insulin secretion we used the disposition index30 (DI30)[9] (details in SI material). High sensitive C-reactive protein (hsCRP) was measured in fasting serum at IPA sampling (1-year follow-up) and yearly during the mean 4-year intervention (active study) period using an IMMULITE® 2000 Systems Analyzer (Siemens Healthcare Diagnostics, Inc. Tarrytown, NY)[2]. Genotyping of TCF7L2 rs7903146 and rs12255372 was performed as reported[10].

Quantitation with HPLC-MS/MS

IPA was quantified by HPLC-QQQ-MS/MS using reversed-phase separation technique. Commercial IPA and IPA-d2 were used as a standard and internal standard, respectively. Details on materials, sample preparation, HPLC-QQQ-MS/MS system, and method validation are described in the SI material.

Statistical analyses

The data were analyzed using IBM SPSS Statistics 23 software (IBM, Inc., Armonk, NY). After data normalization, Cox proportional hazards regression models assessed the association of IPA with the risk of incident T2D during a mean follow-up of 7 years (range 1–14 years) after IPA sampling. In addition, analysis of variance models adjusted for study group tested the associations of IPA with TCF7L2 genotypes and with insulin secretion (DI30) during the long-term follow-up. For testing correlations, we applied Pearson’s correlation test. A value of P < 0.05 was considered significant.

Results

IPA concentration and diabetes incidence

After quality control (detailed in SI material), 403 samples (202 lifestyle intervention and 201 control) were included in the final analyses (Table 1). IPA concentration was not different between the study groups (P = 0.14, Table 1). When participants with incident T2D at IPA sampling (1-year examination) were excluded (n = 4, intervention and n = 13, control), the results remained the same (P = 0.12).
Table 1

Characteristics of the participants at serum IPA sampling (1-year examination study) (n = 403)

Intervention (202)Control (201) P *
Age (years)56.2 ± 7.055.0 ± 7.00.11
Sex (male/female)70/13258/1430.24
Body weight (kg)82.0 ± 13.484.7 ± 14.50.05
BMI (kg/m2)29.6 ± 4.330.8 ± 4.60.006
Plasma glucose (mmol/l)
  Fasting5.9 ± 0.76.2 ± 0.90.0001
  120 min8.1 ± 1.98.5 ± 2.10.01
Serum insulin (pmol/l)
  Fasting76 (63; 104) (196)90 (63; 118) (193)0.02
  120 min358 (236; 606) (190)438 (313; 705) (189)0.02
  IPA (ng/ml)192 (118; 291)174 (104; 276)0.14

ANOVA analysis of variance, BMI body mass index, IPA indolepropionic acid

Data are mean ± SD, median (interquartile range) or (n). *P for the difference between groups at 1-year study using one-way ANOVA for continuous variables or Fisher’s exact test for sex variables.

Characteristics of the participants at serum IPA sampling (1-year examination study) (n = 403) ANOVA analysis of variance, BMI body mass index, IPA indolepropionic acid Data are mean ± SD, median (interquartile range) or (n). *P for the difference between groups at 1-year study using one-way ANOVA for continuous variables or Fisher’s exact test for sex variables. During a mean follow-up of 7 years since IPA sampling, the number of diabetes cases was 95 in the control and 71 in the intervention groups. Participants who progressed from IGT to diabetes compared with those who did not had reduced levels of IPA at 1-year follow-up (169 [104-264] vs. 207 [118-304] ng/ml, respectively; P = 0.05). We observed that higher IPA concentrations were inversely associated with the incidence of diabetes during the mean 7-year follow-up (odds ratio [confidence interval]: 0.86 [0.73–0.99], P = 0.04). A 1 SD increase in IPA was associated with a 14% decrease in the risk of developing diabetes. However, the association lost its significance when body mass index (BMI) (P = 0.15), fasting (P = 0.07), or 2 h (P = 0.26) plasma glucose at IPA sampling were also taken into account. In a model where DPS study group, age and sex were included the association of IPA with diabetes incidence was boderline (P = 0.09).

IPA and insulin secretion

The IPA concentrations tended to be directly associated with insulin secretion (DI30) during the mean 7-year follow-up (β = 0.10, P = 0.06; Fig. 1a). Models adjusted for sex, age, and DPS group retrieved similar results (P = 0.07 for the effect of IPA on DI30 during the follow-up).
Fig. 1

Scattered plot of the relationship of IPA concentrations (natural log transformed; ng/ml) measured from 1-year examination samples and a. average insulin secretion (DI30) during the mean seven years of follow-up in the DPS (β = 0.10, P = 0.06) and b. fiber intake (g/day) at IPA sampling (r = 0.24, P = 1 × 10−6). c. Association of indolepropionic acid (IPA) and hsCRP serum concentrations in DPS (n = 291). Descriptive figure of the course of serum hsCRP (natural log transformed; mg/L) during the active study follow-up since IPA sampling (yr 1) according to the median cut-off point in IPA. Solid line = above median cut-off; broken lines = below median cut-off. P = 0.008 for the difference between cut-off point groups

Scattered plot of the relationship of IPA concentrations (natural log transformed; ng/ml) measured from 1-year examination samples and a. average insulin secretion (DI30) during the mean seven years of follow-up in the DPS (β = 0.10, P = 0.06) and b. fiber intake (g/day) at IPA sampling (r = 0.24, P = 1 × 10−6). c. Association of indolepropionic acid (IPA) and hsCRP serum concentrations in DPS (n = 291). Descriptive figure of the course of serum hsCRP (natural log transformed; mg/L) during the active study follow-up since IPA sampling (yr 1) according to the median cut-off point in IPA. Solid line = above median cut-off; broken lines = below median cut-off. P = 0.008 for the difference between cut-off point groups

IPA and TCF7L2

Because of the strong relationship of TCF7L2 genotype with T2D and insulin secretion[10,11], we tested whether specific related genotypes could interfere in the relationship of IPA with insulin secretion. Overall, these genotypes did not influence IPA concentrations (STable 1) or its association with DI30 (P > 0.30 for each variant at each respective model).

IPA correlates with dietary fiber and low-grade inflammation

Our previous results suggested a correlation between IPA and dietary fiber[2], which was confirmed in the current study (Fig. 1b. r = 0.24, P = 1 × 10−6). There was only a mild correlation between saturated fat intake and IPA, which was no longer significant after controlling for fiber intake (STable 2). We found a negative correlation of IPA and serum hsCRP levels (r = − 0.22, P = 0.0001), even after controlling for study group (P = 0.0002) or BMI (P = 0.001). One-year serum hsCRP was inversely associated with DI30 during the long-term follow-up independently of study group (β = − 0.14, P = 0.01), but not after controlling for BMI (P = 0.30). Serum hsCRP also correlated negatively with fiber intake (r = − 0.22, P = 6.6 × 10−5) after controlling for study group (P = 6.6 × 10−5) or BMI (P = 0.001). When controlled for fiber intake, the correlation between serum IPA and hsCRP concentrations remained significant (P = 0.003). We also observed an impact of serum IPA at 1-year study on the average of circulating levels of hsCRP during the 4 years of the study (β = − 0.19, P = 0.001), independently of the study group (Fig. 1c).

Discussion

We established the quantitative HPLC-QQQ-MS/MS method for measuring serum IPA in the Finnish DPS. We showed a relationship of IPA with the incidence of T2D using the original design of the DPS and demonstrated a trend for an association with insulin secretion during a longer follow-up time of 7 years. Moreover, we demonstrated that higher serum IPA was associated with lower low-grade inflammation and higher dietary fiber intake. In our study, the predictive value of IPA on T2D incidence weakened after controlling for confounding factors. This suggests that healthy lifestyle changes resulting in higher fiber intake that protected from T2D may modify IPA concentrations and consequently diminish any associations in the whole cohort. IPA levels correlated negatively with hsCRP concentrations, which has been previously linked with an increased risk of T2D in the DPS population[12]. The gut microbiota seem to have a role in T2D[7]. Therefore, the beneficial effect of increasing dietary fiber and concomitant weight loss on gut microbiota could be linked to the production of IPA, which by enhancing intestinal barrier integrity and lowering inflammation[13-15], ultimately leads to improved insulin secretion as seen in our study, thereby lowering the risk of T2D. In addition, due to IPA modulation of incretin hormones[5] this could lead to enhanced insulin secretion. It has been suggested that higher IPA could ameliorate inflammation[16] and cell oxidative damage[17,18], thereby resulting on better insulin secretion due to preservation of β-cells, and consequently lowering the risk of T2D. Accordingly, lower hsCRP concentrations at IPA sampling was associated with a better insulin secretion during the follow-up years. However, it is known that weight loss has an impact on ameliorating beta-cell function and lowering inflammation[9,19,20]. Therefore, it is not surprising that in our study obesity modified the association of insulin secretion with both IPA and inflammation. Importantly, neither the strongest common T2D-associated variant of TCF7L2 rs7903146[11] nor the previously reported T2D-associated variant rs7903146 in DPS[10] modified the association of IPA levels and insulin secretion, confirming that the effect of TCF7L2 is probably not mediated by IPA. Strengths of the present study include the well-characterized and homogenous study population and yearly measurements of insulin secretion estimates during a long follow-up. Moreover, we developed a method for quantification of IPA in serum using all samples available from the DPS. Our study has limitations. The surrogate for insulin secretion (DI30) was based on indexes that were not measured by either the hyperinsulinemic-euglycemic clamp or the intravenous glucose tolerance test (IVGTT). Instead, we used an IVGTT for validation[9]. In conclusion, we propose that the putative beneficial effects of IPA on lowering T2D risk relate to the interplay between high dietary fiber intake and decreased inflammation, or by the direct effect of IPA on β-cell function. Overall, our study further highlights the importance of the gut microbiota as a mediator for the development of metabolic disorders like T2D. Online supplementary information
  19 in total

Review 1.  Islet inflammation in type 2 diabetes and physiology.

Authors:  Kosei Eguchi; Ryozo Nagai
Journal:  J Clin Invest       Date:  2017-01-03       Impact factor: 14.808

2.  Symbiotic bacterial metabolites regulate gastrointestinal barrier function via the xenobiotic sensor PXR and Toll-like receptor 4.

Authors:  Madhukumar Venkatesh; Subhajit Mukherjee; Hongwei Wang; Hao Li; Katherine Sun; Alexandre P Benechet; Zhijuan Qiu; Leigh Maher; Matthew R Redinbo; Robert S Phillips; James C Fleet; Sandhya Kortagere; Paromita Mukherjee; Alessio Fasano; Jessica Le Ven; Jeremy K Nicholson; Marc E Dumas; Kamal M Khanna; Sridhar Mani
Journal:  Immunity       Date:  2014-07-24       Impact factor: 31.745

3.  The origin of indoleacetic acid and indolepropionic acid in rat and human cerebrospinal fluid.

Authors:  S N Young; G M Anderson; S Gauthier; W C Purdy
Journal:  J Neurochem       Date:  1980-05       Impact factor: 5.372

4.  Microbiota-derived tryptophan indoles increase after gastric bypass surgery and reduce intestinal permeability in vitro and in vivo.

Authors:  M Jennis; C R Cavanaugh; G C Leo; J R Mabus; J Lenhard; P J Hornby
Journal:  Neurogastroenterol Motil       Date:  2017-08-07       Impact factor: 3.598

5.  Indole-3-propionic acid, a melatonin-related molecule, protects hepatic microsomal membranes from iron-induced oxidative damage: relevance to cancer reduction.

Authors:  M Karbownik; R J Reiter; J J Garcia; J Cabrera; S Burkhardt; C Osuna; A Lewiński
Journal:  J Cell Biochem       Date:  2001       Impact factor: 4.429

6.  Improved lifestyle and decreased diabetes risk over 13 years: long-term follow-up of the randomised Finnish Diabetes Prevention Study (DPS).

Authors:  J Lindström; M Peltonen; J G Eriksson; P Ilanne-Parikka; S Aunola; S Keinänen-Kiukaanniemi; M Uusitupa; J Tuomilehto
Journal:  Diabetologia       Date:  2012-10-24       Impact factor: 10.122

7.  Bacterial metabolite indole modulates incretin secretion from intestinal enteroendocrine L cells.

Authors:  Catalin Chimerel; Edward Emery; David K Summers; Ulrich Keyser; Fiona M Gribble; Frank Reimann
Journal:  Cell Rep       Date:  2014-11-13       Impact factor: 9.423

8.  A gut microbiota-targeted dietary intervention for amelioration of chronic inflammation underlying metabolic syndrome.

Authors:  Shuiming Xiao; Na Fei; Xiaoyan Pang; Jian Shen; Linghua Wang; Baorang Zhang; Menghui Zhang; Xiaojun Zhang; Chenhong Zhang; Min Li; Lifeng Sun; Zhengsheng Xue; Jingjing Wang; Jie Feng; Feiyan Yan; Naisi Zhao; Jiaqi Liu; Wenmin Long; Liping Zhao
Journal:  FEMS Microbiol Ecol       Date:  2013-10-21       Impact factor: 4.194

Review 9.  Microbial metabolism of dietary components to bioactive metabolites: opportunities for new therapeutic interventions.

Authors:  Linda S Zhang; Sean S Davies
Journal:  Genome Med       Date:  2016-04-21       Impact factor: 11.117

Review 10.  Gut Microbiota Dysbiosis Drives and Implies Novel Therapeutic Strategies for Diabetes Mellitus and Related Metabolic Diseases.

Authors:  Xuan Li; Keita Watanabe; Ikuo Kimura
Journal:  Front Immunol       Date:  2017-12-20       Impact factor: 7.561

View more
  49 in total

1.  Microbial metabolite indole-3-propionic acid supplementation does not protect mice from the cardiometabolic consequences of a Western diet.

Authors:  Dustin M Lee; Kayl E Ecton; S Raj J Trikha; Scott D Wrigley; Keely N Thomas; Micah L Battson; Yuren Wei; Sarah A Johnson; Tiffany L Weir; Christopher L Gentile
Journal:  Am J Physiol Gastrointest Liver Physiol       Date:  2020-05-18       Impact factor: 4.052

2.  Metabolic Profiling of Blood and Urine for Exploring the Functional Role of the Microbiota in Human Health.

Authors:  Ana F Diallo; Mark B Lockwood; Katherine A Maki; Alexis T Franks; Abhrarup Roy; Rosario Jaime-Lara; Paule V Joseph; Wendy A Henderson; Seon Yoon Chung; Jacqueline McGrath; Stefan J Green; Anne M Fink
Journal:  Biol Res Nurs       Date:  2020-07-29       Impact factor: 2.522

3.  Ammonia generation by tryptophan synthase drives a key genetic difference between genital and ocular Chlamydia trachomatis isolates.

Authors:  Shardulendra P Sherchand; Ashok Aiyar
Journal:  Proc Natl Acad Sci U S A       Date:  2019-05-16       Impact factor: 11.205

4.  Daily Rice Bran Consumption for 6 Months Influences Serum Glucagon-Like Peptide 2 and Metabolite Profiles without Differences in Trace Elements and Heavy Metals in Weaning Nicaraguan Infants at 12 Months of Age.

Authors:  Luis E Zambrana; Annika M Weber; Erica C Borresen; Iman Zarei; Johann Perez; Claudia Perez; Iker Rodríguez; Sylvia Becker-Dreps; Lijuan Yuan; Samuel Vilchez; Elizabeth P Ryan
Journal:  Curr Dev Nutr       Date:  2021-07-21

Review 5.  Gut microbiota and chronic kidney disease: evidences and mechanisms that mediate a new communication in the gastrointestinal-renal axis.

Authors:  Natalia Lucía Rukavina Mikusic; Nicolás Martín Kouyoumdzian; Marcelo Roberto Choi
Journal:  Pflugers Arch       Date:  2020-02-17       Impact factor: 3.657

Review 6.  Metabolites Linking the Gut Microbiome with Risk for Type 2 Diabetes.

Authors:  Tiantian Zhu; Mark O Goodarzi
Journal:  Curr Nutr Rep       Date:  2020-06

Review 7.  Gut microbiome and cardiometabolic risk.

Authors:  Ben Arpad Kappel; Massimo Federici
Journal:  Rev Endocr Metab Disord       Date:  2019-12       Impact factor: 6.514

8.  Metabolomic Analysis of the Improvements in Insulin Secretion and Resistance After Sleeve Gastrectomy: Implications of the Novel Biomarkers.

Authors:  Yeongkeun Kwon; Mi Jang; Youngsun Lee; Jane Ha; Sungsoo Park
Journal:  Obes Surg       Date:  2020-08-19       Impact factor: 4.129

9.  Metabolomic Markers of Southern Dietary Patterns in the Jackson Heart Study.

Authors:  Casey M Rebholz; Yan Gao; Sameera Talegawkar; Katherine L Tucker; Lisandro D Colantonio; Paul Muntner; Debby Ngo; Zsu Zsu Chen; Daniel Cruz; Daniel H Katz; Usman A Tahir; Clary Clish; Robert E Gerszten; James G Wilson
Journal:  Mol Nutr Food Res       Date:  2021-03-11       Impact factor: 5.914

Review 10.  Gut microbiota-derived metabolites in the regulation of host immune responses and immune-related inflammatory diseases.

Authors:  Wenjing Yang; Yingzi Cong
Journal:  Cell Mol Immunol       Date:  2021-03-11       Impact factor: 11.530

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.