PDF(Pubmed)
Abstract：
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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).
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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).
UNASSIGNED: 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.
摘要：
■在慢性脊髓损伤（SCI）和健康的非SCI人群中，使用主成分分析（PCA），确定可导致心血管疾病（CVD）风险增加的心脏代谢（CM）测量值。
■对98名患有慢性SCI的非卧床男性和51名健康非SCI个体（非卧床对照组）进行了一项横断面研究。获得以下CM生物标志物的空腹血样：脂质，脂蛋白颗粒，空腹血糖和胰岛素浓度，瘦素,脂联素,和炎症的标志。总脂肪和中心脂肪[总脂肪（TBF）百分比和内脏脂肪组织（VAT）百分比，分别]通过双X射线吸收法（DXA）获得。PCA用于识别CM结局测量，这些测量聚集在SCI和非SCI队列中赋予CVD风险。
■使用PCA，提取了六个因子成分(FC)，解释SCI和非SCI队列总方差的77%和82%，分别。在这两组中,FC-1主要由脂蛋白颗粒浓度变量组成。SCI组的FC-2包括TBF和增值税，但不是非SCI组。在SCI队列中，logistic回归分析结果显示,在PCA过程中,统计软件产生的FC-1标准化分数每增加一个单位,MetS的风险增加216%(P=0.001)，10年风险增加209%。FRS≥10%(P=0.001)，HOMA2-IR≥2.05的风险增加92%(P=0.01)。
■PCA的应用确定了SCI和非SCI组的6-FC模型。将变量聚类到各自的模型中，队列之间的差异很大，表明在慢性SCI患者中，CM结局可能对其赋予CVD风险起不同作用。
■对98名患有慢性SCI的非卧床男性和51名健康非SCI个体（非卧床对照组）进行了一项横断面研究。获得以下CM生物标志物的空腹血样：脂质，脂蛋白颗粒，空腹血糖和胰岛素浓度，瘦素,脂联素,和炎症的标志。总脂肪和中心脂肪[总脂肪（TBF）百分比和内脏脂肪组织（VAT）百分比，分别]通过双X射线吸收法（DXA）获得。PCA用于识别CM结局测量，这些测量聚集在SCI和非SCI队列中赋予CVD风险。
■使用PCA，提取了六个因子成分(FC)，解释SCI和非SCI队列总方差的77%和82%，分别。在这两组中,FC-1主要由脂蛋白颗粒浓度变量组成。SCI组的FC-2包括TBF和增值税，但不是非SCI组。在SCI队列中，logistic回归分析结果显示,在PCA过程中,统计软件产生的FC-1标准化分数每增加一个单位,MetS的风险增加216%(P=0.001)，10年风险增加209%。FRS≥10%(P=0.001)，HOMA2-IR≥2.05的风险增加92%(P=0.01)。
■PCA的应用确定了SCI和非SCI组的6-FC模型。将变量聚类到各自的模型中，队列之间的差异很大，表明在慢性SCI患者中，CM结局可能对其赋予CVD风险起不同作用。
