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. 2017 Apr 11:7:46337.
doi: 10.1038/srep46337.

Indolepropionic acid and novel lipid metabolites are associated with a lower risk of type 2 diabetes in the Finnish Diabetes Prevention Study

Vanessa D de Mello  1 , Jussi Paananen  2 , Jaana Lindström  3 , Maria A Lankinen  1 , Lin Shi  4 , Johanna Kuusisto  5 , Jussi Pihlajamäki  1   6 , Seppo Auriola  7   8 , Marko Lehtonen  7   8 , Olov Rolandsson  9 , Ingvar A Bergdahl  10 , Elise Nordin  4 , Pirjo Ilanne-Parikka  11   12 , Sirkka Keinänen-Kiukaanniemi  13   14 , Rikard Landberg  4   15 , Johan G Eriksson  3   16   17   18   19 , Jaakko Tuomilehto  3   20   21 , Kati Hanhineva  1   8 , Matti Uusitupa  1   22
Affiliations

Indolepropionic acid and novel lipid metabolites are associated with a lower risk of type 2 diabetes in the Finnish Diabetes Prevention Study

Vanessa D de Mello et al. Sci Rep. .

Abstract

Wide-scale profiling technologies including metabolomics broaden the possibility of novel discoveries related to the pathogenesis of type 2 diabetes (T2D). By applying non-targeted metabolomics approach, we investigated here whether serum metabolite profile predicts T2D in a well-characterized study population with impaired glucose tolerance by examining two groups of individuals who took part in the Finnish Diabetes Prevention Study (DPS); those who either early developed T2D (n = 96) or did not convert to T2D within the 15-year follow-up (n = 104). Several novel metabolites were associated with lower likelihood of developing T2D, including indole and lipid related metabolites. Higher indolepropionic acid was associated with reduced likelihood of T2D in the DPS. Interestingly, in those who remained free of T2D, indolepropionic acid and various lipid species were associated with better insulin secretion and sensitivity, respectively. Furthermore, these metabolites were negatively correlated with low-grade inflammation. We replicated the association between indolepropionic acid and T2D risk in one Finnish and one Swedish population. We suggest that indolepropionic acid, a gut microbiota-produced metabolite, is a potential biomarker for the development of T2D that may mediate its protective effect by preservation of β-cell function. Novel lipid metabolites associated with T2D may exert their effects partly through enhancing insulin sensitivity.

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Conflict of interest statement

The authors declare no competing financial interests.

Figures

Figure 1
Figure 1. Identified metabolites and their association with the development of T2D in the DPS (N = 200).
Closed bars: FDR-P < 0.05 Opened bars: P < 0.05. Phe: phenylalanine GCA: Glycocholic acid TCDC: Taurochenodeoxycholic acid GCDC: Glycochenodeoxycholic acid GDC: Glycodeoxycholic DC: Deoxycholic acid CA: Cholic acid.
Figure 2
Figure 2. Association of indolepropionic acid with insulin secretion (DI30) in DPS.
Descriptive figure of the course of DI30 during the follow-up according to the median cut-off point in indolepropionic acid in non-T2D cases. FU1: first post follow-up. FU2: second post follow-up. Solid line = above median cut-off; broken lines = below median cut-off. P = 0.04 for the difference between cut-off point groups.
Figure 3
Figure 3. Correlation matrix (Pearson correlation coefficients) of identified top ranking metabolites with energy-adjusted dietary intake.
CHO: carbohydrates; SAFA: saturated fat; MUFA: monounsaturated fat; PUFA: polyunsaturated fat.
Figure 4
Figure 4
(A) Changes in indolepropionic acid (IPA) in cases and non-cases of T2D in METSIM study (n = 110). Changes are calculated as measurement at follow-up minus baseline and given as mean and 95% CI. The asterisk denotes P = 0.010 for the association between the changes in IPA and T2D (case or non-case at 5-year follow-up) after applying ANCOVA adjusted for baseline IPA. (B) Association of baseline IPA with T2D in BioDIVA study (P = 0.003, after applying conditional logistic regression, n = 1006).
Figure 5
Figure 5. Summary of the study set-up and major findings.
The non-targeted LC-MS based metabolite profiling was conducted within the Finnish Diabetes Prevention Study (DPS) by examining two groups of individuals who took part in the (DPS); those who either early (within five years) developed T2D (n = 96) or did not convert to T2D within the 15-year follow-up (n = 104). Key findings included the inverse association of indolepropionic acid with T2D risk. This finding was replicated in two additional cohorts, Biomarker Discovery and Validation (BioDIVA, 503 incident T2D cases and matched healthy controls), and Metabolic Syndrome in Men (METSIM, baseline and follow-up samples from 110 participants free of T2D at baseline from which 55 were diagnosed with T2D at the 5-year follow-up). Indolepropionic acid was associated with dietary fiber intake in the DPS and BioDIVA studies, whereas in METSIM such data was not available.
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References

    1. Wang T. J. et al.. Metabolite profiles and the risk of developing diabetes. Nat. Med. 17, 448–453 (2011). - PMC - PubMed
    1. Floegel A. et al.. Identification of serum metabolites associated with risk of type 2 diabetes using a targeted metabolomic approach. Diabetes 62, 639–648 (2013). - PMC - PubMed
    1. Menni C. et al.. Biomarkers for type 2 diabetes and impaired fasting glucose using a nontargeted metabolomics approach. Diabetes 62, 4270–4276 (2013). - PMC - PubMed
    1. Meikle P. J. et al.. Plasma lipid profiling shows similar associations with prediabetes and type 2 diabetes. PLoS One 8, e74341 (2013). - PMC - PubMed
    1. Hanhineva K. et al.. Nontargeted metabolite profiling discriminates diet-specific biomarkers for consumption of whole grains, fatty fish, and bilberries in a randomized controlled trial. J. Nutr. 145, 7–17 (2015). - PubMed

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