OBJECTIVES: To further explore the relationship between @entity5815 ( @entity5815 ) and @entity9420 ( @entity9420 ), we conducted a computational analysis involving 43 @entity1 with @entity5815 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 @entity9420 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 @entity5815 symptoms based on the individual components of the @entity9420 ( @entity9420 ). METHODS: An @entity9420 was computed for all study @entity1 using available laboratory and clinical data on metabolic, cardiovascular and @entity2339 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 SI summary score. RESULTS: Among @entity5815 @entity1 , but not controls, a high level of @entity9420 was significantly associated with lower median values (indicating worse health) of @entity158 , physical functioning and general symptom frequency/intensity. Using genetic programming, the @entity9420 was determined to be a better predictor of these three health measures than any subcombination of @entity9420 components among cases, but not controls.

Allostatic load is associated with symptoms in @entity5815 XXXX .

@entity1 :: ('9606', 'Species') :: ['participants', 'patients'] @entity9420 :: ('MESH:C536761', 'Disease') :: ['allostatic load', 'allostatic load index', 'AL', 'ALI'] @entity2339 :: ('MESH:D007029', 'Disease') :: ['hypothalamic-pituitary-adrenal (HPA) axis'] @entity158 :: ('MESH:D010146', 'Disease') :: ['bodily pain'] @entity5815 :: ('MESH:D015673', 'Disease') :: ['chronic fatigue syndrome', 'CFS']
@entity1 :: (9606,Species) :: ['patients']
