Predicting Cardiometabolic Risk From Visceral Abdominal Adiposity in Persons With Chronic Spinal Cord Injury

Christopher M. Cirnigliaro, Michael F. La Fountaine, Joshua C. Hobson, Steven C. Kirshblum, Donald R. Dengel, Ann M. Spungen, William A. Bauman

Research output: Contribution to journalArticlepeer-review

Abstract

Persons with spinal cord injury (SCI) have increased adiposity that may predispose to cardiovascular disease compared to those who are able-bodied (AB). The purpose of this study was to determine the relationships between dual energy X-ray absorptiometry (DXA)-derived visceral adipose tissue (VAT) and biomarkers of lipid metabolism and insulin resistance in persons with chronic SCI. A prospective observational study in participants with chronic SCI and age- and gender-matched AB controls. The study was conducted at a Department of Veterans Affairs Medical Center and Private Rehabilitation Hospital. The quantification of DXA-derived VAT volume (VATvol) and blood-derived markers of lipid and carbohydrate metabolism were determined in 100 SCI and 51 AB men. The VATvol was acquired from a total body DXA scan and analyzed using iDXA enCore CoreScan software (GE Lunar). Blood samples were collected for the serum lipid profile and plasma and glucose concentrations, with the latter two values used to calculate a measure of insulin resistance. In the SCI and AB groups, VAT% was significantly correlated with most cardiometabolic biomarkers. The results of the binary logistic regression analysis revealed that participants who had a VATvol above the cutoff value of 1630 cm3 were 3.1-, 4.8-, 5.6-, 19.2-, and 16.7-times more likely to have high serum triglycerides (R2N= 0.09, p = 0.014), low serum high density lipoprotein cholesterol (R2N = 0.16, p < 0.001), HOMA2-IR (R2N = 0.18, p < 0.001), metabolic syndrome (R2N = 0.25, p < 0.001), and a 10-yr Framingham Risk Score ≥ 10% (R2N = 0.16, p = 0.001), respectively, when compared to participants below this VATvol cutoff value. Our findings reveal that persons with chronic SCI have a higher VATvol than that of AB controls, and VATvol correlates directly with biomarkers of lipid and carbohydrate metabolism that are strong predictors of cardiometabolic disorders.

Original languageEnglish (US)
JournalJournal of Clinical Densitometry
Early online dateApr 8 2021
DOIs
StateE-pub ahead of print - Apr 8 2021

Bibliographical note

Funding Information:
The authors wish to thank the James J Peters VA Medical Center, Bronx, NY, the Department of Veterans Affairs Rehabilitation Research & Development Service, The Kessler Institute for Rehabilitation and the Kessler Foundation, West Orange, NJ, for their support. This material is from work supported by the Department of Veterans Affairs, Veterans Health Administration, Rehabilitation Research and Development Service National Center for the Medical Consequences of Spinal Cord Injury (# B9212-C and B2020-C ). This work does not represent the views of the US Department of Veterans Affairs or the US government.

Funding Information:
The authors wish to thank the James J Peters VA Medical Center, Bronx, NY, the Department of Veterans Affairs Rehabilitation Research & Development Service, The Kessler Institute for Rehabilitation and the Kessler Foundation, West Orange, NJ, for their support. This material is from work supported by the Department of Veterans Affairs, Veterans Health Administration, Rehabilitation Research and Development Service National Center for the Medical Consequences of Spinal Cord Injury (#B9212-C and B2020-C). This work does not represent the views of the US Department of Veterans Affairs or the US government.

Publisher Copyright:
© 2021

Keywords

  • Biomarkers
  • Dual energy X-ray absorptiometry
  • Spinal cord injury
  • Subcutaneous adipose tissue
  • Visceral adipose tissue

PubMed: MeSH publication types

  • Journal Article

Fingerprint

Dive into the research topics of 'Predicting Cardiometabolic Risk From Visceral Abdominal Adiposity in Persons With Chronic Spinal Cord Injury'. Together they form a unique fingerprint.

Cite this