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Serum metabolomics profile of type 2 diabetes mellitus in a Brazilian rural population

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Abstract

Introduction

The development of common forms of diabetes comes from the interaction between environmental and genetic factors, and the consequences of poor glycemic control in these patients could result in several complications. Metabolomic studies for type 2 diabetes mellitus in serum/plasma have reported changes in numerous metabolites, which might be considered possible targets for future mechanistic research. However, the specific role of a particular metabolite as cause or consequence of diabetes derangements is difficult to establish.

Objectives

As type 2 diabetes is a disease that changes the metabolic profile in several levels, this work aimed to compare the metabolomic profiles of type 2 diabetes mellitus and non-diabetic participants. In addition, we exploited our family-based study design to bring a better understanding of the causal relationship of identified metabolites and diabetes.

Methods

In the current study, population based metabolomics was applied in 939 subjects from the Baependi Heart Study. Participants were separated into two groups: diabetic (77 individuals) and non-diabetic (862 individuals), and the metabolic profile was performed by GC/MS technique.

Results

We have identified differentially concentrated metabolites in serum of diabetic and non-diabetic individuals. We identified 72 metabolites up-regulated in type 2 diabetes mellitus compared to non-diabetics. It was possible to recapitulate the main pathways that the literature shows as changed in diabetes. Also, based on metabolomic profile, we separated pre-diabetic individuals (with glucose concentration between 100–125 mg/dL) from non-diabetics and diabetics. Finally, using heritability analysis, we were able to suggest metabolites in which altered levels may precede diabetic development.

Conclusion

Our data can be used to derive a better understanding of the causal relationship of the observed associations and help to prioritize diabetes-associated metabolites for further work.

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Acknowledgments

We acknowledge the Agilent Technologies Brasil Ltda, Life Sciences & Chemical Analysis for the use of the GC/MS system (7890B gas chromatograph coupled to a mass selective detector model 5977A-Agilent) for support data collection.

Funding information

This work was supported by Fundação de Amparo a Pesquisa do Estado de São Paulo (FAPESP 2012/05447-0, 2012/12042-7, 2013-17368-0, PROADI_ SUS Project nº 25000.180664/2011-35).

Author information

Authors and Affiliations

  1. Laboratory of Genetics and Molecular Cardiology/LIM 13, Heart Institute (InCor), Faculty of Medicine, University of São Paulo, Av. Dr. Enéas C. Aguiar 44, São Paulo, SP, 05403-000, Brazil

    Kallyandra Padilha, Gabriela Venturini, Thiago de Farias Pires, Andréa R. V. R. Horimoto, Pamella Araujo Malagrino, Tamiris Carneiro Gois, Bianca Kiers, Camila Maciel Oliveira, Rafael de Oliveira Alvim, José Eduardo Krieger & Alexandre Costa Pereira

  2. Life Sciences & Chemical Analysis, Agilent Technologies Brasil Ltda, Barueri, SP, Brazil

    Celso Blatt

Authors
  1. Kallyandra Padilha
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  2. Gabriela Venturini
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  3. Thiago de Farias Pires
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  4. Andréa R. V. R. Horimoto
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  5. Pamella Araujo Malagrino
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  6. Tamiris Carneiro Gois
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  7. Bianca Kiers
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  8. Camila Maciel Oliveira
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  9. Rafael de Oliveira Alvim
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  10. Celso Blatt
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  11. José Eduardo Krieger
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  12. Alexandre Costa Pereira
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Corresponding author

Correspondence to Alexandre Costa Pereira.

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Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All experiments were carried out in accordance with the ethics committee of the Hospital das Clinicas, University of São Paulo, Brazil (protocol number 3759/12/015), approved the study protocol.

Informed consent

All participants signed a written informed consent.

Electronic supplementary material

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Figure S1

PCA scores plot based on the GC/MS spectra of serum samples obtained from diabetic patients treated and diabetic patients not treated with hypoglycemic medication. (PDF 20 kb)

Figure S2

Samples first principal component scores distributed by the day of analysis. The red points represent the diabetic samples subject. (TIFF 1346 kb)

Figure S3

Metabolites concentration before (A) and after (B) LOESS normalization according to injection order and day of analysis. (TIFF 2194 kb)

Figure S4

Representativity of significant metabolites found by polygenic analysis. The blue side represent diabetic group, and the red side represent the non-diabetic group. (TIFF 1426 kb)

Table S1

Main biological functions disturbed relate with metabolites found by PLS-DA to diabetic, pre-diabetic and non diabetic participants. In #molecules column is the number of metabolites present in each pathway. (DOCX 15 kb)

Table S2

Heritability (h2) estimates (in percentage) for each metabolite, unadjusted (Model 1), adjusted for age and sex (Model 2), and adjusted for age, sex and T2DM (Model 3). (XLSX 15 kb)

Table S3

Main biological functions disturbed by proximal metabolites. In #molecules column is the number of metabolites present in each pathway. (DOCX 15 kb)

Table S4

Main biological functions related to distal metabolites. In #molecules column is the number of metabolites present in each pathway. (DOCX 15 kb)

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Padilha, K., Venturini, G., de Farias Pires, T. et al. Serum metabolomics profile of type 2 diabetes mellitus in a Brazilian rural population. Metabolomics 12, 156 (2016). https://doi.org/10.1007/s11306-016-1107-5

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