| ORIGINAL ARTICLE |
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| Year : 2021 | Volume
: 6
| Issue : 4 | Page : 134-141 |
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Disease comorbidities associated with chemical intolerance
Raymond F Palmer, Tatjana Walker, Roger B Perales, Rodolfo Rincon, Carlos Roberto Jaén, Claudia S Miller
Department of Family and Community Medicine, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
Correspondence Address:
Raymond F Palmer
7703 Floyd Curl Drive, San Antonio, TX
USA
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/ed.ed_18_21
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Background: Chemical intolerance (CI) is characterized by multisystem symptoms initiated by a one-time high-dose or a persistent low-dose exposure to environmental toxicants. Prior studies have investigated symptom clusters rather than defined comorbid disease clusters. We use a latent class modeling approach to determine the number and type of comorbid disease clusters associated with CI.
Methods: Two hundred respondents with and without CI were recruited to complete the Quick Environmental Exposure and Sensitivity Inventory (QEESI), and a 17-item comorbid disease checklist. A logistic regression model was used to predict the odds of comorbid disease conditions between groups. A latent class analysis was used to inspect the pattern of dichotomous item responses from the 17 comorbid diseases.
Results: Those with the highest QEESI scores had significantly greater probability of each comorbid disease compared to the lowest scoring individuals (P < 0.0001). Three latent class disease clusters were found. Class 1 (17% of the sample) was characterized by a cluster consisting of irritable bowel syndrome (IBS), arthritis, depression, anxiety, fibromyalgia, and chronic fatigue. The second class (53% of the sample) was characterized by a low probability of any of the co-morbid diseases. The third class (30% of the sample) was characterized only by allergy.
Discussion: We have demonstrated that several salient comorbid diseases form a unique statistical cluster among a subset of individuals with CI. Understanding these disease clusters may help physicians and other health care workers to gain a better understanding of individuals with CI. As such, assessing their patients for CI may help identify the salient initiators and triggers of their CI symptoms—therefore guide potential treatment efforts.
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