Cardiometabolic Risk Clustering and Atherosclerosis: Is There a Link in Spinal Cord Injury?

2011 ◽  
Vol 16 (3) ◽  
pp. 1-13 ◽  
Author(s):  
Suzanne Groah ◽  
Hillary Hosier ◽  
Emily Ward ◽  
Mark Nash ◽  
Alexander Libin ◽  
...  
Keyword(s):  
Spinal Cord ◽  
Spinal Cord Injury ◽  
Cardiometabolic Risk ◽  
Cord Injury

scholarly journals Relationship Between Gonadal Function and Cardiometabolic Risk in Young Men With Chronic Spinal Cord Injury

PM&R ◽  
2017 ◽  
Vol 10 (4) ◽  
pp. 373-381 ◽  
Author(s):  
Shannon D. Sullivan ◽  
Mark S. Nash ◽  
Eshetu Tefara ◽  
Emily Tinsley ◽  
Suzanne Groah
Keyword(s):  
Spinal Cord ◽  
Spinal Cord Injury ◽  
Cardiometabolic Risk ◽  
Young Men ◽  
Gonadal Function ◽  
Chronic Spinal Cord Injury ◽  
Cord Injury

Cardiometabolic Risk in Community-Dwelling Persons With Chronic Spinal Cord Injury

2011 ◽  
Vol 31 (2) ◽  
pp. 73-80 ◽  
Author(s):  
Suzanne L. Groah ◽  
Mark S. Nash ◽  
Emily A. Ward ◽  
Alexander Libin ◽  
Armando J. Mendez ◽  
...  
Keyword(s):  
Spinal Cord ◽  
Spinal Cord Injury ◽  
Cardiometabolic Risk ◽  
Community Dwelling ◽  
Chronic Spinal Cord Injury ◽  
Cord Injury

Cardiometabolic Risk Factors in Experimental Spinal Cord Injury

2010 ◽  
Vol 27 (1) ◽  
pp. 275-285 ◽  
Author(s):  
Jessica Inskip ◽  
Ward Plunet ◽  
Leanne Ramer ◽  
John Byron Ramsey ◽  
Andrew Yung ◽  
...  
Keyword(s):  
Risk Factors ◽  
Spinal Cord ◽  
Spinal Cord Injury ◽  
Cardiometabolic Risk ◽  
Cardiometabolic Risk Factors ◽  
Experimental Spinal Cord Injury ◽  
Cord Injury

scholarly journals Cardiometabolic Risk Clustering in Spinal Cord Injury: Results of Exploratory Factor Analysis

2013 ◽  
Vol 19 (3) ◽  
pp. 183-194 ◽  
Author(s):  
Alexander Libin ◽  
Emily Tinsley ◽  
Mark Nash ◽  
Armando Mendez ◽  
Patricia Burns ◽  
...  
Keyword(s):  
Spinal Cord ◽  
Spinal Cord Injury ◽  
Factor Analysis ◽  
Exploratory Factor Analysis ◽  
Cardiometabolic Risk ◽  
Cord Injury

scholarly journals Cardiometabolic Risk Profiles in Pre-Versus Postmenopausal Women With Spinal Cord Injury: Preliminary Findings

2012 ◽  
Vol 18 (4) ◽  
pp. 322-330 ◽  
Author(s):  
Hillary Hosier ◽  
Suzanne Groah ◽  
Alex Libin ◽  
Emily Tinsley ◽  
Patricia Burns ◽  
...  
Keyword(s):  
Spinal Cord ◽  
Spinal Cord Injury ◽  
Postmenopausal Women ◽  
Cardiometabolic Risk ◽  
Risk Profiles ◽  
Cord Injury

scholarly journals Cardiometabolic risk factors in Iranians with spinal cord injury: Analysis by injury-related variables

2013 ◽  
Vol 50 (5) ◽  
pp. 635 ◽  
Author(s):  
Hadis Sabour ◽  
Abbas Norouzi Javidan ◽  
Neda Ranjbarnovin ◽  
Mohammad Reza Vafa ◽  
Zahra Khazaeipour ◽  
...  
Keyword(s):  
Risk Factors ◽  
Spinal Cord ◽  
Spinal Cord Injury ◽  
Cardiometabolic Risk ◽  
Cardiometabolic Risk Factors ◽  
Cord Injury ◽  
Injury Analysis

scholarly journals Changes in cardiometabolic risk factors during inpatient rehabilitation of traumatic spinal cord injury from a multicenter Swiss Spinal Cord Injury Cohort (SwiSCI)

2021 ◽  
Vol 42 (Supplement_1) ◽  
Author(s):  
P F Raguindin ◽  
J Stoyanov ◽  
I Eriks ◽  
G Stucki ◽  
X Jordan ◽  
...  
Keyword(s):  
Spinal Cord ◽  
Spinal Cord Injury ◽  
Risk Factor ◽  
Lipid Profile ◽  
Longitudinal Analysis ◽  
Cardiometabolic Risk ◽  
Inpatient Rehabilitation ◽  
Sensitivity Analyses ◽  
Injury Characteristics ◽  
Cord Injury

Abstract Introduction Cardiometabolic health has a growing relevance in spinal cord injury (SCI) considering the increasing survival and aging population. We explored longitudinal changes in cardiometabolic risk profile and examined whether injury characteristics could be a non-modifiable risk factor for individuals with SCI in subacute phase of the injury. Methods We used the data from a multicenter Swiss Spinal Cord Injury Cohort (SwiSCI) study and included adults with traumatic SCI (TSCI) without cardiometabolic diseases and diabetes at baseline. We included individuals with available data on admission and prior to discharge from first SCI rehabilitation. Blood pressure, lipid profile, fasting glucose, waist circumference (WC), weight, and body mass index (BMI) were compared according to the injury level (tetraplegia-TP vs paraplegia-PP) and completeness (motor complete-COM vs incomplete-INC). We used multivariable linear regression for cross-sectional analysis and linear mixed models for longitudinal analysis, adjusting for age, sex, lifestyle factors, medication-use, and injury characteristics. We performed age- (above and below median age) and sex-stratified analyses. Sensitivity analyses were also performed by removing systemic steroid-use (proxy for acute injury), adjusting for opioid-use (medication side-effect) and adjusting further for BMI and WC. Results We analyzed 258 individuals with TSCI (110 TP and 148 PP, 122 COM and 136 INC). Median age was 50 years (IQR 32–60), with 76.36% (n=197) of the population being male. The median rehabilitation duration was 5.5 months (IQR 3.2–7.1). On admission, the fully-adjusted models showed higher baseline weight, systolic BP, diastolic BP and triglycerides in PP than TP. Systolic BP, diastolic BP, HDL, HDL/LDL were higher in INC than COM. In the overall population, we observed increases in cholesterol, HDL, and HDL/LDL ratio over rehabilitation period. Individuals with PP had a higher increase in BMI as compared to TT, while no differences were detected when comparing INC and COM injury (Table 1–3). Results from sensitivity analyses were generally in line with the overall findings; however, at baseline, there was some indication that lipid profile may be different in COM and INC injury. In sex-stratified longitudinal analysis, triglycerides were higher in females PP than TP, and diastolic BP was higher in females with INC than COM. For age-stratified longitudinal analysis, elderly with PP have higher triglycerides than younger adults. Conclusion We reported changes in BMI and lipid profile during the inpatient rehabilitation of individuals with traumatic SCI and without history of cardiovascular diseases and diabetes. Injury characteristics may not be an independent risk factor for subacute phase, but maybe important in specific subgroups, like in women and in the elderly (>50 years old). FUNDunding Acknowledgement Type of funding sources: None. Methods Results

scholarly journals The Relationship Between Healthy Eating Index-2015 and Cardiometabolic Risk Factors in People with Long-Standing Spinal Cord Injury

2020 ◽  
Vol 4 (Supplement_2) ◽  
pp. 540-540
Author(s):  
Jia Li ◽  
Aynur Demirel ◽  
Andres Azuero ◽  
Amie McLain ◽  
Ceren Yarar-Fisher
Keyword(s):  
Risk Factors ◽  
Spinal Cord ◽  
Spinal Cord Injury ◽  
Healthy Eating ◽  
Diet Quality ◽  
Cardiometabolic Risk ◽  
Healthy Eating Index ◽  
Effect Sizes ◽  
Cord Injury ◽  
The Relationship

Abstract Objectives The Healthy Eating Index (HEI)-2015 is a measure of diet quality in reference to the 2015 Dietary Guidelines for Americans. We examined the relationship between HEI-2015 and several indices of cardiometabolic risk factors among individuals with chronic spinal cord injury (SCI) (>3 years after injury). Methods Twenty-four participants without type 2 diabetes were included (45 ± 12 y, 8F/16 M, 9 Tetraplegia/15 Paraplegia, time since injury: 20 ± 13 y). All participants underwent a 2-hour oral glucose tolerance test (OGTT), a Dual-energy X-ray absorptiometry scan (DXA), and 3 24-hour multiple-pass dietary recalls. HEI-2015 was calculated using the dietary recall data. To build the regression model, firstly, principal component (PC) analysis was used to reduce the number of covariates from 3 (level of injury, gender, percentage body fat estimated from the DXA) to 2 PCs. Multiple linear regression analyses were run to predict indices of lipid metabolism and glucose homeostasis as well as C-reactive protein (CRP) from the HEI and the 2 PC scores. Results The average HEI-2015 score was lower for participants with SCI compared to the general American population (48 ± 11 vs. 59, P < 0.05). The regression models for fasting glucose (FG), Cholesterol, HDL, LDL, and CRP had moderate to large effect sizes (adjusted R2 > 13%), indicating good explanatory ability of the predictors. Small or limited effect sizes were observed for other models (glucose tolerance, fasting insulin, triaglycerides, and Matsuda index, adjusted R2 < 13% for all). Furthermore, the HEI accounted for a moderate amount of variation in FG as evidenced by partial Omega-squared of 13%. Each 10-point increase of the HEI was associated with a 3.3 mg/dL decrease in FG concentrations. However, HEI accounted for a limited amount of variations in other indices (ωP2 < 5% for all). Conclusions Our exploratory analyses suggest that HEI-2015 has limited effects on blood lipids and CRP but may be associated with lower FG concentrations in our sample group. Future larger studies are warranted to delineate the relationship between diet quality and cardiometabolic health outcomes in individuals with SCI. Funding Sources The National Institute on Disability, Independent Living, and Rehabilitation Research.

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