Case–control study of multiple chemical sensitivity, comparing haematology, biochemistry, vitamins and serum volatile organic compound measures

Abstract

BACKGROUND: Multiple chemical sensitivity (MCS), although poorly understood, is associated with considerable morbidity. AIM: To investigate potential biological mechanisms underlying MCS in a case-control study. METHODS: Two hundred and twenty-three MCS cases and 194 controls (urban females, aged 30-64 years) fulfilled reproducible eligibility criteria with discriminant validity. Routine laboratory results and serum levels of volatile organic compounds (VOCs) were compared. Dose-response relationships, a criterion for causality, were examined linking exposures to likelihood of case status. RESULTS: Routine laboratory investigations revealed clinically unimportant case-control differences in means. Confounder-adjusted odds ratios (OR) showed MCS was negatively associated with lymphocyte count and total plasma homocysteine, positively associated with mean cell haemoglobin concentration, alanine aminotransferase and serum vitamin B6, and not associated with thyroid stimulating hormone, folate or serum vitamin B12. More cases than controls had detectable serum chloroform (P = 0.001) with the OR for detectability 2.78 (95% confidence interval = 1.73-4.48, P < 0.001). Chloroform levels were higher in cases. However, cases had significantly lower means of detectable serum levels of ethylbenzene, m&p-xylene, 3-methylpentane and hexane, and means of all serum levels of 1,3,5- and 1,2,3-trimethylbenzene, 2- and 3-methylpentane, and m&p-xylene. CONCLUSIONS: Our findings are inconsistent with proposals that MCS is associated with vitamin deficiency or thyroid dysfunction, but the association of lower lymphocyte counts with an increased likelihood of MCS is consistent with theories of immune dysfunction in MCS. Whether avoidance of exposures or different metabolic pathways in cases explain the observed lower VOC levels or the higher chloroform levels should be investigated.

Key Biomarkers

Symptom Clusters