Integrative multiomics analysis of infant gut microbiome and serum metabolome reveals key molecular biomarkers of early onset childhood obesity

doi:10.1016/j.heliyon.2023.e16651

. 2023 May 30;9(6):e16651.

doi: 10.1016/j.heliyon.2023.e16651. eCollection 2023 Jun.

Integrative multiomics analysis of infant gut microbiome and serum metabolome reveals key molecular biomarkers of early onset childhood obesity

Talha Rafiq^{1

2}, Jennifer C Stearns^{3

4}, Meera Shanmuganathan⁵, Sandi M Azab^{3

6}, Sonia S Anand^{2

3

7}, Lehana Thabane^{7

8

9}, Joseph Beyene⁷, Natalie C Williams³, Katherine M Morrison^{10

11}, Koon K Teo^{2

3

7}, Philip Britz-McKibbin⁵, Russell J de Souza^{2

7}

Affiliations

¹ Medical Sciences Graduate Program, Faculty of Health Sciences, McMaster University, Hamilton, ON L8S 4L8, Canada.
² Population Health Research Institute, Hamilton Health Sciences, McMaster University, Hamilton, ON L8L 2X2, Canada.
³ Department of Medicine, McMaster University, Hamilton, ON L8S 4L8, Canada.
⁴ Farncombe Family Digestive Health Research Institute, McMaster University, Hamilton, ON L8S 4K1, Canada.
⁵ Department of Chemistry and Chemical Biology, McMaster University, Hamilton, ON L8S 4M1, Canada.
⁶ Department of Pharmacognosy, Alexandria University, Alexandria 21521, Egypt.
⁷ Department of Health Research Methods, Evidence & Impact, McMaster University, Hamilton, ON L8S 4L8, Canada.
⁸ Biostatistics Unit, Father Sean O'Sullivan Research Centre, The Research Institute, St Joseph's Healthcare Hamilton, Hamilton, ON L8N 4A6, Canada.
⁹ Faculty of Health Sciences, University of Johannesburg, Johannesburg 524, South Africa.
¹⁰ Department of Pediatrics, McMaster University, Hamilton, ON L8S 4L8, Canada.
¹¹ Centre for Metabolism, Obesity and Diabetes Research, McMaster University, Hamilton, ON L8S 4K1, Canada.

PMID: 37332914
PMCID: PMC10272340
DOI: 10.1016/j.heliyon.2023.e16651

Integrative multiomics analysis of infant gut microbiome and serum metabolome reveals key molecular biomarkers of early onset childhood obesity

Talha Rafiq et al. Heliyon. 2023.

. 2023 May 30;9(6):e16651.

doi: 10.1016/j.heliyon.2023.e16651. eCollection 2023 Jun.

Authors

Affiliations

¹ Medical Sciences Graduate Program, Faculty of Health Sciences, McMaster University, Hamilton, ON L8S 4L8, Canada.
² Population Health Research Institute, Hamilton Health Sciences, McMaster University, Hamilton, ON L8L 2X2, Canada.
³ Department of Medicine, McMaster University, Hamilton, ON L8S 4L8, Canada.
⁴ Farncombe Family Digestive Health Research Institute, McMaster University, Hamilton, ON L8S 4K1, Canada.
⁵ Department of Chemistry and Chemical Biology, McMaster University, Hamilton, ON L8S 4M1, Canada.
⁶ Department of Pharmacognosy, Alexandria University, Alexandria 21521, Egypt.
⁷ Department of Health Research Methods, Evidence & Impact, McMaster University, Hamilton, ON L8S 4L8, Canada.
⁸ Biostatistics Unit, Father Sean O'Sullivan Research Centre, The Research Institute, St Joseph's Healthcare Hamilton, Hamilton, ON L8N 4A6, Canada.
⁹ Faculty of Health Sciences, University of Johannesburg, Johannesburg 524, South Africa.
¹⁰ Department of Pediatrics, McMaster University, Hamilton, ON L8S 4L8, Canada.
¹¹ Centre for Metabolism, Obesity and Diabetes Research, McMaster University, Hamilton, ON L8S 4K1, Canada.

PMID: 37332914
PMCID: PMC10272340
DOI: 10.1016/j.heliyon.2023.e16651

Abstract

Evidence supports a complex interplay of gut microbiome and host metabolism as regulators of obesity. The metabolic phenotype and microbial metabolism of host diet may also contribute to greater obesity risk in children early in life. This study aimed to identify features that discriminated overweight/obese from normal weight infants by integrating gut microbiome and serum metabolome profiles. This prospective analysis included 50 South Asian children living in Canada, selected from the SouTh Asian biRth cohorT (START). Serum metabolites were measured by multisegment injection-capillary electrophoresis-mass spectrometry and the relative abundance of bacterial 16S rRNA gene amplicon sequence variant was evaluated at 1 year. Cumulative body mass index (BMI_AUC) and skinfold thickness (SSF_AUC) scores were calculated from birth to 3 years as the total area under the growth curve (AUC). BMI_AUC and/or SSF_AUC >85th percentile was used to define overweight/obesity. Data Integration Analysis for Biomarker discovery using Latent cOmponent (DIABLO) was used to identify discriminant features associated with childhood overweight/obesity. The associations between identified features and anthropometric measures were examined using logistic regression. Circulating metabolites including glutamic acid, acetylcarnitine, carnitine, and threonine were positively, whereas γ-aminobutyric acid (GABA), symmetric dimethylarginine (SDMA), and asymmetric dimethylarginine (ADMA) were negatively associated with childhood overweight/obesity. The abundance of the Pseudobutyrivibrio and Lactobacillus genera were positively, and Clostridium sensu stricto 1 and Akkermansia were negatively associated with childhood overweight/obesity. Integrative analysis revealed that Akkermansia was positively whereas Lactobacillus was inversely correlated with GABA and SDMA, and Pseudobutyrivibrio was inversely correlated with GABA. This study provides insights into metabolic and microbial signatures which may regulate satiety, energy metabolism, inflammatory processes, and/or gut barrier function, and therefore, obesity trajectories in childhood. Understanding the functional capacity of these molecular features and potentially modifiable risk factors such as dietary exposures early in life may offer a novel approach for preventing childhood obesity.

Keywords: Adiposity; Childhood obesity; Metabolomics; Microbiome; Multiomics; Nutrition.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. RJ de Souza has served as an external resource person to the World Health Organization’s Nutrition Guidelines Advisory Group on trans fats, saturated fats, and polyunsaturated fats. The WHO paid for his travel and accommodation to attend meetings from 2012-2017 to present and discuss this work. He has presented updates of this work to the WHO in 2022. He has also done contract research for the Canadian Institutes of Health Research’s Institute of Nutrition, Metabolism, and Diabetes, Health Canada, and the World Health Organization for which he received remuneration. He has received speaker’s fees from the University of Toronto, and McMaster Children’s Hospital. He has served as an independent director of the Helderleigh Foundation (Canada). He serves as a member of the Nutrition Science Advisory Committee to Health Canada (Government of Canada), co-chair of the Method working group of the ADA/EASD Precision Medicine in Diabetes group, and is a co-opted member of the Scientific Advisory Committee on Nutrition (SACN) Subgroup on the Framework for the Evaluation of Evidence (Public Health England). He has held grants from the 10.13039/501100000024Canadian Institutes of Health Research, 10.13039/501100000203Canadian Foundation for Dietetic Research, Population Health Research Institute, and Hamilton Health Sciences Corporation as a principal investigator, and is a co-investigator on several funded team grants from the 10.13039/501100000024Canadian Institutes of Health Research.

Figures

**Fig. 1**
DIABLO integrative analysis of metabolome and ASVs discriminatory between overweight/obese and normal weight groups. (A) Matrix scatter plot shows the clustering of samples based on the first component in each dataset and the correlation between the datasets. (B) Loading weights of the selected discriminant metabolites and ASVs. Colours indicate the group in which the median relative abundance is maximum, and values indicate the contribution to the first component. (C) Circos plot showing correlations between the most discriminatory metabolites and ASVs. Positive correlations are displayed using blue line-connectors. γ-aminobutyric acid (GABA); Symmetric dimethylarginine (SDMA); Asymmetric dimethylarginine (ADMA). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

**Fig. 2**
Correlation between most discriminatory metabolites in participants overall and by overweight/obese (O) and normal weight (N) groups. γ-aminobutyric acid (GABA); Symmetric dimethylarginine (SDMA); Asymmetric dimethylarginine (ADMA).

**Fig. 3**
Correlation between most discriminatory ASVs in participants overall and by overweight/obese (O) and normal weight (N) groups.

**Fig. 4**
(A) Receiver operating characteristic (ROC) curve and (B) boxplot for the serum Glu/GABA ratio illustrate the differentiation of overweight/obese (n = 11) from normal weight (n = 39) South Asian infants. Glutamic acid (Glu); γ-aminobutyric acid (GABA).

**Fig. 5**
Distribution of significantly different (A) metabolites (concentration) and (B) ASV between children who were overweight/obese (O) and normal weight (N). ASV counts were transformed using CLR-transformation.

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References

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[1] Di Cesare M., Sorić M., Bovet P., Miranda J.J., Bhutta Z., Stevens G.A., Laxmaiah A., Kengne A.-P., Bentham J. The epidemiological burden of obesity in childhood: a worldwide epidemic requiring urgent action. BMC Med. 2019;17:212. doi: 10.1186/s12916-019-1449-8. - DOI - PMC - PubMed

[2] Di Cesare M., Sorić M., Bovet P., Miranda J.J., Bhutta Z., Stevens G.A., Laxmaiah A., Kengne A.-P., Bentham J. The epidemiological burden of obesity in childhood: a worldwide epidemic requiring urgent action. BMC Med. 2019;17:212. doi: 10.1186/s12916-019-1449-8. - DOI - PMC - PubMed

[3] Townshend T., Lake A. Obesogenic environments: current evidence of the built and food environments. Persp. Pub. Heal. 2017;137:38–44. doi: 10.1177/1757913916679860. - DOI - PubMed

[4] Townshend T., Lake A. Obesogenic environments: current evidence of the built and food environments. Persp. Pub. Heal. 2017;137:38–44. doi: 10.1177/1757913916679860. - DOI - PubMed

[5] Geng J., Ni Q., Sun W., Li L., Feng X. The links between gut microbiota and obesity and obesity related diseases. Biomed. Pharmacother. 2022;147 doi: 10.1016/j.biopha.2022.112678. - DOI - PubMed

[6] Geng J., Ni Q., Sun W., Li L., Feng X. The links between gut microbiota and obesity and obesity related diseases. Biomed. Pharmacother. 2022;147 doi: 10.1016/j.biopha.2022.112678. - DOI - PubMed

[7] Hivert M.F., Perng W., Watkins S.M., Newgard C.S., Kenny L.C., Kristal B.S., Patti M.E., Isganaitis E., DeMeo D.L., Oken E., Gillman M.W. Metabolomics in the developmental origins of obesity and its cardiometabolic consequences. J. Dev. Orig. Heal. Dis. 2015;6:65–78. doi: 10.1017/s204017441500001x. - DOI - PMC - PubMed

[8] Hivert M.F., Perng W., Watkins S.M., Newgard C.S., Kenny L.C., Kristal B.S., Patti M.E., Isganaitis E., DeMeo D.L., Oken E., Gillman M.W. Metabolomics in the developmental origins of obesity and its cardiometabolic consequences. J. Dev. Orig. Heal. Dis. 2015;6:65–78. doi: 10.1017/s204017441500001x. - DOI - PMC - PubMed

[9] Chen X., Sun H., Jiang F., Shen Y., Li X., Hu X., Shen X., Wei P. Alteration of the gut microbiota associated with childhood obesity by 16S rRNA gene sequencing. PeerJ. 2020;8 doi: 10.7717/peerj.8317. - DOI - PMC - PubMed

[10] Chen X., Sun H., Jiang F., Shen Y., Li X., Hu X., Shen X., Wei P. Alteration of the gut microbiota associated with childhood obesity by 16S rRNA gene sequencing. PeerJ. 2020;8 doi: 10.7717/peerj.8317. - DOI - PMC - PubMed

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Integrative multiomics analysis of infant gut microbiome and serum metabolome reveals key molecular biomarkers of early onset childhood obesity

Affiliations

Integrative multiomics analysis of infant gut microbiome and serum metabolome reveals key molecular biomarkers of early onset childhood obesity

Authors

Affiliations

Abstract

Conflict of interest statement

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