You are here

  • Home
  • Archive
  • Volume 8, Issue 1
  • Hepatic fat is a stronger correlate of key clinical and molecular abnormalities than visceral and abdominal subcutaneous fat in youth

Article Text

Article menu

Download PDFPDF

Genetics/Genomes/Proteomics/Metabolomics
Hepatic fat is a stronger correlate of key clinical and molecular abnormalities than visceral and abdominal subcutaneous fat in youth
  1. Catherine E Cioffi1,
  2. K M Venkat Narayan2,
  3. Ken Liu3,
  4. Karan Uppal3,
  5. Dean P Jones3,
  6. ViLinh Tran3,
  7. Tianwei Yu4,
  8. Jessica A Alvarez3,
  9. Moriah P Bellissimo1,
  10. Kristal M Maner-Smith5,
  11. Bridget Pierpoint6,
  12. Sonia Caprio6,
  13. Nicola Santoro6,7,
  14. Miriam B Vos8
  1. 1Nutrition and Health Sciences, Emory University Laney Graduate School, Atlanta, Georgia, USA
  2. 2Hubert Department of Global Health, Rollins School of Public Health, Atlanta, Georgia, USA
  3. 3Department of Medicine, Emory University School of Medicine, Atlanta, Georgia, USA
  4. 4Department of Biostatistics, Rollins School of Public Health, Atlanta, Georgia, USA
  5. 5Emory Integrated Lipidomics Core, Emory University School of Medicine, Atlanta, Georgia, USA
  6. 6Department of Pediatrics, Yale School of Medicine, New Haven, Connecticut, USA
  7. 7Department of Medicine and Health Sciences "V. Tiberio", University of Molise, Campobasso, Molise, Italy
  8. 8Department of Pediatrics, Emory University School of Medicine, Atlanta, Georgia, USA
  1. Correspondence to Dr Catherine E Cioffi; catherine.cioffi{at}emory.edu

Abstract

Introduction Body fat distribution is strongly associated with cardiometabolic disease (CMD), but the relative importance of hepatic fat as an underlying driver remains unclear. Here, we applied a systems biology approach to compare the clinical and molecular subnetworks that correlate with hepatic fat, visceral fat, and abdominal subcutaneous fat distribution.

Research design and methods This was a cross-sectional sub-study of 283 children/adolescents (7–19 years) from the Yale Pediatric NAFLD Cohort. Untargeted, high-resolution metabolomics (HRM) was performed on plasma and combined with existing clinical variables including hepatic and abdominal fat measured by MRI. Integrative network analysis was coupled with pathway enrichment analysis and multivariable linear regression (MLR) to examine which metabolites and clinical variables associated with each fat depot.

Results The data divided into four communities of correlated variables (|r|>0.15, p<0.05) after integrative network analysis. In the largest community, hepatic fat was associated with eight clinical biomarkers, including measures of insulin resistance and dyslipidemia, and 878 metabolite features that were enriched predominantly in amino acid (AA) and lipid pathways in pathway enrichment analysis (p<0.05). Key metabolites associated with hepatic fat included branched-chain AAs (valine and isoleucine/leucine), aromatic AAs (tyrosine and tryptophan), serine, glycine, alanine, and pyruvate, as well as several acylcarnitines and glycerophospholipids (all q<0.05 in MLR adjusted for covariates). The other communities detected in integrative network analysis consisted of abdominal visceral, superficial subcutaneous, and deep subcutaneous fats, but no clinical variables, fewer metabolite features (280, 312, and 74, respectively), and limited findings in pathway analysis.

Conclusions These data-driven findings show a stronger association of hepatic fat with key CMD risk factors compared with abdominal fats. The molecular network identified using HRM that associated with hepatic fat provides insight into potential mechanisms underlying the hepatic fat–insulin resistance interface in youth.

  • insulin resistance
  • NAFLD
  • dyslipidemia
  • visceral fat
http://creativecommons.org/licenses/by-nc/4.0/

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

https://doi.org/10.1136/bmjdrc-2019-001126

Statistics from Altmetric.com

    Request Permissions

    If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.

    View Full Text

    Footnotes

    • Presented at This work was presented at the American Diabetes Association’s 79th Annual Scientific Sessions in 2019.

    • Contributors CEC conducted the metabolomics analyses and wrote the manuscript. NS and SC provided data from the parent study, and BP performed hepatic and abdominal fat calculations. VT performed the untargeted high-resolution metabolomics assay, and KL and KMM-S assisted with metabolite annotation and interpretation. KU, TY, and DPJ provided guidance on the analysis and interpretation of the metabolomics data. JAA and MPB reviewed the body composition and metabolomics methods and assisted with the interpretation of findings. MBV and NS guided the overall design of the study and provided funding for this substudy and the parent study, respectively. All authors reviewed and contributed scientific expertise to the final manuscript. CEC and MBV are the guarantors of this work and as such had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

    • Funding This manuscript was in part supported by the National Institutes of Health’s National Institute of Environmental Health Sciences (NIEHS) (award/grant numbers P30-ES019776 and U2C-ES026560), the National Institute of Diabetes and Digestive and Kidney Disease (NIDDK) (award/grant numbers R01-DK111038, R01-DK114504, and P30DK111024), the NIH Eunice Kennedy Shriver National Institute of Child Health and Human Development (award/grant numbers R01-HD028016 and R21-HD089056), and the National Institutes of Health Office of the Director (S10 OD018006).

    • Competing interests MBV has consulting arrangements with Boehringer Ingelheim, Bristol Myers Squibb, Intercept, Mallinckrodt, Novo Nordisk, and Target Pharmasolutions, and research funding from Target Pharmasolutions and Bristol Myers Squibb, which are all outside of the submitted work.

    • Patient consent for publication Not required.

    • Ethics approval The parent study was approved by Yale University IRB (IRB# 1112009408), and the secondary analysis performed in this study was determined to be exempt from IRB review and approved by Emory University IRB (IRB# 00091260).

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Data availability statement The data that support the findings of this study are available from the corresponding author, upon reasonable request.