ArticleThe journal of spinal cord medicine2024
Cardiometabolic risk factor clustering in persons with spinal cord injury: A principal component analysis approach.
Article in The journal of spinal cord medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 2 citations in OpenAlex.
- Nutritional Status, Body Composition and Cardiometabolic Profile in Individuals with Tetraplegia: A Pilot Cross-Sectional Study.Medicina (Kaunas, Lithuania) · 2025Article
- Screening, Diagnosis, and Treatment of Cardiometabolic Risk in Persons With Spinal Cord Injury/Disease: A Scoping Review.Archives of rehabilitation research and clinical translation · 2025Review
- Analysis of Influencing Factors Related to Health Literacy of Diabetic Patients: A Survey Based on DHLEIS.Journal of diabetes research · 2024Article
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Authors and funding
7 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
CONTEXT/
objectiveTo identify cardiometabolic (CM) measurements that cluster to confer increased cardiovascular disease (CVD) risk using principal component analysis (PCA) in a cohort of chronic spinal cord injury (SCI) and healthy non-SCI individuals. APPROACH: A cross-sectional study was performed in ninety-eight non-ambulatory men with chronic SCI and fifty-one healthy non-SCI individuals (ambulatory comparison group). Fasting blood samples were obtained for the following CM biomarkers: lipid, lipoprotein particle, fasting glucose and insulin concentrations, leptin, adiponectin, and markers of inflammation. Total and central adiposity [total body fat (TBF) percent and visceral adipose tissue (VAT) percent, respectively] were obtained by dual x-ray absorptiometry (DXA). A PCA was used to identify the CM outcome measurements that cluster to confer CVD risk in SCI and non-SCI cohorts.
resultsUsing PCA, six factor-components (FC) were extracted, explaining 77% and 82% of the total variance in the SCI and non-SCI cohorts, respectively. In both groups, FC-1 was primarily composed of lipoprotein particle concentration variables. TBF and VAT were included in FC-2 in the SCI group, but not the non-SCI group. In the SCI cohort, logistic regression analysis results revealed that for every unit increase in the FC-1 standardized score generated from the statistical software during the PCA, there is a 216% increased risk of MetS (
conclusionApplication of PCA identified 6-FC models for the SCI and non-SCI groups. The clustering of variables into the respective models varied considerably between the cohorts, indicating that CM outcomes may play a differential role on their conferring CVD-risk in individuals with chronic SCI.
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