# Delineating markers of disease-disease interaction: a systematic methodology and its application to multiple diabetes-helminth cohorts

## TL;DR

This paper introduces a new method to study how two diseases interact in the body, using diabetes and helminth infections as examples to identify immune markers affected by both conditions.

## Contribution

The study presents a systematic methodology to quantify disease-disease interaction effects on immune markers using multi-cohort data.

## Key findings

- Cytokines like IFN-gamma and TNF-alpha showed significant disease-disease interaction effects before helminth treatment.
- The contribution of disease-disease interaction to cytokine variation dropped significantly after helminth treatment.
- Signaling pathways such as IL-10 and IL-4/IL-13 were enriched for genes targeted by DDI markers.

## Abstract

Understanding how the molecules in our bodyrespond to the co-occurrence of two diseases in an individual (comorbidity) could lead tomechanistic insights into novel treatments for comorbid conditions. Studies have shown forinstance, that responses of our immune system to comorbid conditions could be more complexthan the union of immune responses to each disease occurring separately, but a data-drivenquantification of this complexity is lacking.

In this study, we present a systematicmethodology to quantify the interaction effect of two diseases on marker variables of interest(using a chronic inflammatory disease diabetes and parasitic infection helminth as illustrativedisease pairs to identify cytokines or other immune markers that respond distinctively under acomorbid condition). To perform this systematic comorbidity analysis, we (i) collected andpreprocessed data measurements from multiple single- and double-disease cohorts, (ii)extended differential expression analysis of such data to identify disease-disease interaction(DDI) markers (such as cytokines that respond antagonistically or synergistically to the double-disease condition relative to single-disease states), and (iii) interpreted the resulting DDImarkers in the context of prior cytokine/immune-cell knowledgebases. We applied this three-step DDI methodology to multiple cohorts of helminth and diabetes (specifically, helminth-infected and helminth-treated individuals in diabetic and non-diabetic conditions, and non-disease control individuals), and identified cytokines such as IFN-gamma, TNF-alpha, and IL-2 tobe DDI markers acting at the interface of both diseases in data collected prior to helminthtreatment. Validating our expectations, for these cytokines and other T helper Th-2 cytokineslike IL-13 and IL-4, their DDI statuses were lost after treatment for helminth infection. Forinstance, the relative contribution of the DDI term in explaining the individual-to-individualvariation of IFN-gamma and TNF-alpha cytokines was 67.68% and 48.88%, respectively, beforeanthelmintic treatment, and dropped to 6.09% and 14.56%, respectively, after treatment.Furthermore, signaling pathways like IL-10 and IL-4/IL-13 were found to be significantlyenriched for genes targeted by certain DDI markers, thereby suggesting mechanistic hypotheses on how these DDI markers influence both diseases. Future experimental validation is necessaryto support these proposed hypotheses.

Our results quantified the extent ofhelminth-diabetes DDI exhibited by various tested cytokine markers, and thereby delineatedtheir role in the pathogenesis of both diseases. These results are promising and encourage theapplication of our DDI methodology (https://github.com/BIRDSgroup/DDI) to dissect theinteraction between any two diseases, provided multi-cohort measurements of markers areavailable.

The online version of this article (10.1186/s12859-025-06226-w) contains supplementary material, which is available to authorized users.