Disease comorbidities associated with chemical intolerance

• Published in: Environmental disease 2021-10, Vol.6 (4), p.134-141
• Main Authors: Palmer, Raymond, Walker, Tatjana, Perales, Roger, Rincon, Rodolfo, Jaén, Carlos, Miller, Claudia
• Format: Article
• Language: English
• Subjects: chemical intolerance; comorbid disease; idiopathic environmental intolerance; latent class; multiple chemical sensitivity; quick environmental exposure and sensitivity inventory
• ISSN: 2468-5690; 2468-5704; 2468-5704
• DOI: 10.4103/ed.ed_18_21

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.