Context/Objective

To 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.

Results

Using 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 (P = 0.001), a 209% increased risk of a 10-yr. FRS ≥ 10% (P = 0.001), and a 92% increase in the risk of HOMA2-IR ≥ 2.05 (P = 0.01).

Conclusion

Application 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.

中文翻译：

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脊髓损伤患者的心脏代谢危险因素聚类：主成分分析方法

背景/目标

使用主成分分析 (PCA) 在慢性脊髓损伤 (SCI) 和健康非 SCI 个体队列中识别导致心血管疾病 (CVD) 风险增加的心脏代谢 (CM) 测量值。

方法

对 98 名患有慢性 SCI 的非卧床男性和 51 名健康非 SCI 个体（卧床对照组）进行了一项横断面研究。获得空腹血样以检测以下 CM 生物标志物：脂质、脂蛋白颗粒、空腹血糖和胰岛素浓度、瘦素、脂联素和炎症标志物。总脂肪率和中心脂肪率[分别为全身脂肪 (TBF) 百分比和内脏脂肪组织 (VAT) 百分比] 通过双 X 射线吸收测定法 (DXA) 获得。PCA 用于确定 CM 结果测量，这些测量结果聚类可赋予 SCI 和非 SCI 队列中的 CVD 风险。

结果

使用 PCA，提取了六个因子成分 (FC)，分别解释了 SCI 和非 SCI 队列中总方差的 77% 和 82%。在两组中，FC-1 主要由脂蛋白颗粒浓度变量组成。SCI 组的 TBF 和 VAT 包含在 FC-2 中，但非 SCI 组则不包含在 FC-2 中。在 SCI 队列中，逻辑回归分析结果显示，PCA 期间统计软件生成的 FC-1 标准化评分每增加一个单位，MetS 风险就会增加 216%（P = 0.001），增加209  % 10年的风险。FRS ≥ 10% ( P = 0.001)，HOMA2-IR ≥ 2.05 ( P  = 0.01) 的风险增加 92% 。

结论

应用 PCA 确定了 SCI 和非 SCI 组的 6-FC 模型。各个模型中的变量聚类在队列之间差异很大，表明 CM 结果可能在慢性 SCI 个体的 CVD 风险中发挥不同的作用。