- 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.",
