Allostatic load is associated with symptoms in chronic fatigue syndrome patients
Allostatic load is associated with symptoms in chronic fatigue syndrome patients
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DOI：
10.2217/14622416.7.3.485
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发表时间：
2006-04-01
期刊：
影响因子：
2.1
通讯作者：
Gurbaxani, B
中科院分区：
文献类型：
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作者：
Goertzel, BN;Pennachin, C;Gurbaxani, B
目的：为了进一步探讨慢性疲劳综合征 (CFS) 与非稳态负荷 (AL) 之间的关系，我们对参加威奇托（美国堪萨斯州）一项基于人群的病例对照研究的 43 名 CFS 患者和 60 名非疲劳健康对照 (NF) 进行了计算分析。我们使用传统的生物统计学方法来衡量高 AL 与身体和心理功能、残疾、疲劳和一般症状严重程度的标准化测量的关联。我们还使用嵌入机器学习算法中的非线性回归技术来学习基于非稳态负荷指数 (ALI) 的各个组成部分预测各种 CFS 症状的方程。方法：利用代谢、心血管和下丘脑-垂体-肾上腺 (HPA) 轴因素的现有实验室和临床数据，计算所有研究参与者的 ALI。使用医疗结果研究 36 项简短健康调查 (SF-36) 测量身体和精神功能/损伤；使用 20 项多维疲劳量表 (MFI) 测量当前疲劳程度；使用 19 项症状清单 (SI) 测量症状的频率和强度。遗传编程是一种非线性回归技术，用于学习不同预测方程的集合，而不仅仅是单个方程。统计分析基于计算集合中利用每个输入变量的方程的百分比，从而产生变量对当前预测问题的“效用”的度量。传统的生物统计学方法包括中位数和 Wilcoxon 检验，用于比较 SF-36、MFI 和 51 汇总分数获得的子量表分数的中位数水平。结果：在 CFS 患者中，高水平的 AL 与身体疼痛、身体功能和一般症状频率/强度的中值较低（表明健康状况较差）显着相关，但对照组则不然。使用遗传编程，确定 ALI 比病例中 ALI 成分的任何子组合都能更好地预测这三种健康指标，但对照组则不然。
Objectives: To further explore the relationship between chronic fatigue syndrome (CFS) and allostatic load (AL), we conducted a computational analysis involving 43 patients with CFS and 60 nonfatigued, healthy controls (NF) enrolled in a population-based case-control study in Wichita (KS, USA). We used traditional biostatistical methods to measure the association of high AL to standardized measures of physical and mental functioning, disability, fatigue and general symptom severity. We also used nonlinear regression technology embedded in machine learning algorithms to learn equations predicting various CFS symptoms based on the individual components of the allostatic load index (ALI). Methods: An ALI was computed for all study participants using available laboratory and clinical data on metabolic, cardiovascular and hypothalamic-pituitary-adrenal (HPA) axis factors. Physical and mental functioning/impairment was measured using the Medical Outcomes Study 36-item Short Form Health Survey (SF-36); current fatigue was measured using the 20-item multidimensional fatigue inventory (MFI); frequency and intensity of symptoms was measured using the 19-item symptom inventory (SI). Genetic programming, a nonlinear regression technique, was used to learn an ensemble of different predictive equations rather just than a single one. Statistical analysis was based on the calculation of the percentage of equations in the ensemble that utilized each input variable, producing a measure of the 'utility' of the variable for the predictive problem at hand. Traditional biostatistics methods include the median and Wilcoxon tests for comparing the median levels of subscale scores obtained on the SF-36, the MFI and the 51 summary score. Results: Among CFS patients, but not controls, a high level of AL was significantly associated with lower median values (indicating worse health) of bodily pain, physical functioning and general symptom frequency/intensity. Using genetic programming, the ALI was determined to be a better predictor of these three health measures than any subcombination of ALI components among cases, but not controls.