Associations between Atopic Dermatitis and Risk of Gastric Cancer: A Nationwide Population-based Study

Atopic dermatitis and risk of 14 site-specific cancers: A Mendelian randomization study

BACKGROUND

Atopic dermatitis (AD) accounts for a large proportion of the burden of skin disease, with a prevalence of around 10% among adults worldwide. In addition, systematic reviews and meta-analyses have found that AD is associated with cancer risk at several sites; if found to be causal this could highlight potential treatment targets to reduce cancer risk.

OBJECTIVES

To assess the potential causative link between AD and 14 site-specific cancers in a two-sample randomization study.

METHODS

From the largest genome-wide association study (GWAS) of AD (10,788 cases and 30,047 non-cases), genetic variants highly associated (p < 5 × 10-8 ) with AD in the European population were selected as instrumental variables (IVs). Data from large cancer consortia, as well as the UK Biobank study (n = 442,239) and the FinnGen study (n = 218,792), were employed to assess genetic associations with 14 site-specific cancers and overall cancer. A set of complementary approaches and sensitivity analyses were carried out to examine the robustness of our results. In addition, associations for the same cancer site from different data sources were combined using meta-analyses.

RESULTS

We discovered no strong causal evidence of AD on the risk of overall cancer, with effect estimates close to zero. After the Benjamini-Hochberg correction, the inverse-variance weighted method indicated no association between AD and overall cancer risk in both the UK Biobank (OR, 1.00; 95% CI, 0.94-1.06; FDR, 0.98) and FinnGen studies (OR, 0.96; 95% CI, 0.92-1.02; FDR, 0.68). No strong evidence of an association was found between genetically predicted AD and the risk of any site-specific cancers.

CONCLUSIONS

Our MR investigation does not support a causal effect of AD on cancer risk. This finding has important implications for the prevention and management of both AD and cancer, as it reduces the concern of potential adverse effects of AD on cancer outcomes.

Risk of type 2 diabetes mellitus in adult patients with atopic dermatitis

AIMS

This study aimed to investigate the subsequent risk of type 2 diabetes mellitus (T2D) in adults newly diagnosed with atopic dermatitis (AD).

METHODS

This propensity score-matching cohort study used data from the National Health Insurance Service-National Sample Cohort (NHIS-NSC) 2.0 database in South Korea from 2002 to 2015. The adjusted hazard ratios (HRs) with 95% confidence intervals (CIs) were estimated using a Cox proportional hazards model, for the new onset of T2D (ICD-10 code, E11) in AD patients compared to the matched controls. Subgroup and sensitivity analyses were also conducted.

RESULTS

Each of the 36,692 individuals in the AD group and matched control group was included in the analysis. The risk of T2D in the AD group was significantly higher than that of the matched controls in the adjusted model (adjusted HR 1.44; 95% CI 1.27-1.63, P <.001). The results of subgroup analysis by sex, age, and body mass index were consistent with the results of the primary analysis. Sensitivity analyses using different T2D and/or AD definitions also showed consistent results.

CONCLUSIONS

The significant risk of subsequent T2D in adult AD patients suggested the necessity for efforts to prevent T2D in AD patients.

Increased Regulatory T Cells and Decreased Myeloid-Derived Suppressor Cells Induced by High CCL17 Levels May Account for Normal Incidence of Cancers among Patients with Atopic Dermatitis

The incidence of cancers in atopic dermatitis (AD) is not increased, although the Th2-dominant environment is known to downregulate tumor immunity. To gain mechanistic insights regarding tumor immunity in AD, we utilized CCL17 transgenic (TG) mice overexpressing CCL17, which is a key chemokine in AD. Tumor formation and lung metastasis were accelerated in CCL17 TG mice when melanoma cells were injected subcutaneously or intravenously. Flow cytometric analysis showed increases in regulatory T cells (Tregs) in lymph nodes in CCL17 TG mice with high mRNA levels of IL-10 and Foxp3 in tumors, suggesting that Tregs attenuated tumor immunity. The frequency of myeloid-derived suppressor cells (MDSCs), however, was significantly decreased in tumors of CCL17 TG mice, suggesting that decreased MDSCs might promote tumor immunity. Expression of CXCL17, a chemoattractant of MDSCs, was decreased in tumors of CCL17 TG mice. Depletion of Tregs by the anti-CD25 antibody markedly reduced tumor volumes in CCL17 TG mice, suggesting that tumor immunity was accelerated by the decrease in MDSCs in the absence of Tregs. Thus, CCL17 attenuates tumor immunity by increasing Tregs and Th2 cells, while it decreases MDSCs through reductions in CXCL17, which may work as a “safety-net” to reduce the risk of malignant tumors in the Th2-dominant environment.

A Medical Decision Support System to Assess Risk Factors for Gastric Cancer Based on Fuzzy Cognitive Map

Gastric cancer (GC), one of the most common cancers around the world, is a multifactorial disease and there are many risk factors for this disease. Assessing the risk of GC is essential for choosing an appropriate healthcare strategy. There have been very few studies conducted on the development of risk assessment systems for GC. This study is aimed at providing a medical decision support system based on soft computing using fuzzy cognitive maps (FCMs) which will help healthcare professionals to decide on an appropriate individual healthcare strategy based on the risk level of the disease. FCMs are considered as one of the strongest artificial intelligence techniques for complex system modeling. In this system, an FCM based on Nonlinear Hebbian Learning (NHL) algorithm is used. The data used in this study are collected from the medical records of 560 patients referring to Imam Reza Hospital in Tabriz City. 27 effective features in gastric cancer were selected using the opinions of three experts. The prediction accuracy of the proposed method is 95.83%. The results show that the proposed method is more accurate than other decision-making algorithms, such as decision trees, Naïve Bayes, and ANN. From the perspective of healthcare professionals, the proposed medical decision support system is simple, comprehensive, and more effective than previous models for assessing the risk of GC and can help them to predict the risk factors for GC in the clinical setting.

Current status and perspectives regarding the association between allergic disorders and cancer

While activation of immune system may lead to a lower risk of some diseases, it has been shown that a history of atopic allergic disorders such as asthma, hay fever, eczema, and food allergies could be related to several types of cancer. However, the evidence is not entirely conclusive. Two proposals suggest a possible mechanism for the association between allergic disorders and cancers: immune surveillance and the antigenic stimulation. The association of allergy and cancer may vary by cancer site and the type of exposure. The aim of current review was to summarize the current knowledge of the association between allergic diseases and the risk of cancers with particular emphasis on case-controls and cohort studies to estimate the cancer risk associated with allergy.