Department of Physical Medicine and Rehabilitation, University of Miami Miller School of Medicine, Miami, Florida.
The Miami Project to Cure Paralysis, University of Miami Miller School of Medicine, Miami, Florida.
Top Spinal Cord Inj Rehabil. 2021;27(1):11-22. doi: 10.46292/sci20-00079.
Obesity is at epidemic proportions in the population with spinal cord injury (SCI), and adipose tissue (AT) is the mediator of the metabolic syndrome. Obesity, however, has been poorly appreciated in SCI because of the lack of sensitivity that body mass index (BMI) conveys for obesity risk in SCI without measuring AT. The specific objectives were to compare measures of body composition assessment for body fat with the criterion standard 4-compartment (4C) model in persons with SCI, to develop a regression equation that can be utilized in the clinical setting to estimate fat mass (FM), and to determine cardiometabolic risk using surrogates of obesity in a current model of metabolic syndrome. Seventy-two individuals with chronic (>1 year) motor complete (AIS A and B) C5-L2 SCI were recruited over 3 years. Subjects underwent assessment with 4C using hydrostatic (underwater) weighing (UWW), dual-energy x-ray absorptiometry (DXA), and total body water (TBW) assessment to determine percent body fat (%BF); fasting glucose and lipid profiles, and resting blood pressure were also obtained. BMI, DXA, bioelectrical impedance analyses (BIA), BodPod, circumferences, diameters, lengths, and nine-site skinfold (SF) were assessed and validated against 4C. A multiple linear regression model was used to fit %BF (dependent variable) using anthropometric and demographic data that had the greatest correlations with variables, followed by a combined forward/backward stepwise regression with Akaike information criterion (AIC) to identify the variables most predictive of the 4C %BF. To allow for a more practical model for use in the clinical setting, we further reduced the AIC model with minimal loss of predictability. Surrogate markers of obesity were employed with metabolic biomarkers of metabolic syndrome to determine prevalence in persons with SCI. Subject characteristics included age 44.4 ± 11.3 years, time since injury (TSI) 14.4 ± 11.0 years, BMI 27.3 ± 5.9 kg/m; 59 were men and 13 were women. Sitting waist circumference (WCSit ) was 95.5 ± 13.1 cm, supine waist circumference (WCSup) was 93.4 ± 12.7 cm, and abdominal skinfold (ABDSF) was 53.1 ± 19.6 mm. Findings showed 4C %BF 42.4 ± 8.6%, UWW %BF 37.3 ± 9.7%, DXA %BF 39.1 ± 9.4%, BodPod %BF 33.7 ± 11.4%, nine-site SF %BF 37.8 ± 9.3%, and BIA %BF 27.6 ± 8.6%. A regression equation using age, sex, weight, and ABDSF provided correlation of 0.57 with 4C %BF ( < .0001). Metabolic syndrome was identified in 59.4% of the sample. Body composition techniques to determine body fat are labor intensive and expensive for persons with SCI, and the regression equation developed against the criterion standard 4C model may allow clinicians to quickly estimate %BF and more accurately demonstrate obesity-induced cardiometabolic syndrome in this population.
肥胖在脊髓损伤（SCI）人群中已达到流行程度，脂肪组织（AT）是代谢综合征的中介。然而，由于缺乏对 SCI 中肥胖风险的敏感性，因为 BMI 未能测量 AT，因此对肥胖的认识不足。具体目标是比较身体成分评估身体脂肪的方法与脊髓损伤人群中标准的 4 区（4C）模型，制定可用于临床的回归方程，以估计脂肪量（FM），并使用代谢综合征当前模型中的肥胖替代物确定心脏代谢风险。在 3 年期间，招募了 72 名患有慢性（> 1 年）运动完全性（AIS A 和 B）C5-L2 SCI 的个体。受试者接受 4C 评估，使用静水称重（水下称重）（UWW），双能 X 射线吸收法（DXA）和全身水（TBW）评估来确定身体脂肪百分比（%BF）；还获得了空腹血糖和血脂谱以及静息血压。评估并验证了 BMI，DXA，生物电阻抗分析（BIA），BodPod，周长，直径，长度和 9 点皮褶（SF）与 4C。使用与变量相关性最大的人体测量学和人口统计学数据，使用多元线性回归模型拟合 %BF（因变量），然后使用 Akaike 信息量标准（AIC）进行向前/向后逐步回归，以识别最能预测 4C %BF 的变量。为了允许在临床环境中使用更实用的模型，我们进一步降低了 AIC 模型的预测能力，同时又最小程度地降低了可预测性。使用代谢综合征的代谢生物标志物来评估肥胖的替代标志物，以确定脊髓损伤人群中的患病率。受试者的特征包括年龄 44.4 ± 11.3 岁，受伤后时间（TSI）14.4 ± 11.0 年，BMI 27.3 ± 5.9 kg/m；59 名男性和 13 名女性。坐姿腰围（WCSit）为 95.5 ± 13.1 cm，仰卧位腰围（WCSup）为 93.4 ± 12.7 cm，腹部皮褶（ABDSF）为 53.1 ± 19.6 mm。研究结果显示，4C %BF 为 42.4 ± 8.6％，UWW %BF 为 37.3 ± 9.7％，DXA %BF 为 39.1 ± 9.4％，BodPod %BF 为 33.7 ± 11.4％，9 点 SF %BF 为 37.8 ± 9.3％，BIA %BF 为 27.6 ± 8.6％。使用年龄，性别，体重和 ABDSF 的回归方程与 4C %BF 具有 0.57 的相关性（<0.0001）。该样本中有 59.4％的人患有代谢综合征。确定身体脂肪的身体成分技术对于脊髓损伤患者来说既费力又昂贵，而针对标准 4C 模型开发的回归方程可能使临床医生能够快速估计％BF，并更准确地证明该人群中肥胖引起的心脏代谢综合征。
