Allostatic load is associated with symptoms in chronic fatigue syndrome patients

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Goertzel:2006:P,
  author =       "Benjamin N Goertzel and Cassio Pennachin and 
                 Lucio {de Souza Coelho} and Elizabeth M Maloney and 
                 James F Jones and Brian Gurbaxani",
  title =        "Allostatic load is associated with symptoms in chronic
                 fatigue syndrome patients",
  journal =      "Pharmacogenomics",
  year =         "2006",
  volume =       "7",
  number =       "3",
  pages =        "485--494",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.2217/14622416.7.3.485",
  URL =          "http://www.futuremedicine.com/doi/abs/10.2217/14622416.7.3.485",
  abstract =     "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 used 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 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.",
}

Genetic Programming entries for Ben Goertzel Cassio Pennachin Lucio de Souza Coelho Elizabeth M Maloney James F Jones Brian Gurbaxani

Citations