Large-scale epidemiological analysis of common skin diseases to identify shared and unique comorbidities and demographic factors

Introduction

The utilization of large-scale claims databases has greatly improved the management, accessibility, and integration of extensive medical data. However, its potential for systematically identifying comorbidities in the context of skin diseases remains unexplored.

Methods

This study aims to assess the capability of a comprehensive claims database in identifying comorbidities linked to 14 specific skin and skin-related conditions and examining temporal changes in their association patterns. This study employed a retrospective case-control cohort design utilizing 13 million skin/skin-related patients and 2 million randomly sampled controls from Optum's de-identified Clinformatics® Data Mart Database spanning the period from 2001 to 2018. A broad spectrum of comorbidities encompassing cancer, diabetes, respiratory, mental, immunity, gastrointestinal, and cardiovascular conditions were examined for each of the 14 skin and skin-related disorders in the study.

Results

Using the established type-2 diabetes (T2D) and psoriasis comorbidity as example, we demonstrated the association is significant (P-values<1x10-15) and stable across years (OR=1.15-1.31). Analysis of the 2014-2018 data reveals that celiac disease, Crohn's disease, and ulcerative colitis exhibit the strongest associations with the 14 skin/skin-related conditions. Systemic lupus erythematosus (SLE), leprosy, and hidradenitis suppurativa show the strongest associations with 30 different comorbidities. Particularly notable associations include Crohn's disease with leprosy (odds ratio [OR]=6.60, 95% confidence interval [CI]: 3.09-14.08), primary biliary cirrhosis with SLE (OR=6.07, 95% CI: 4.93-7.46), and celiac disease with SLE (OR=6.06, 95% CI: 5.49-6.69). In addition, changes in associations were observed over time. For instance, the association between atopic dermatitis and lung cancer demonstrates a marked decrease over the past decade, with the odds ratio decreasing from 1.75 (95% CI: 1.47-2.07) to 1.02 (95% CI: 0.97-1.07). The identification of skin-associated comorbidities contributes to individualized healthcare and improved clinical management, while also enhancing our understanding of shared pathophysiology. Moreover, tracking these associations over time aids in evaluating the progression of clinical diagnosis and treatment.

Discussion

The findings highlight the potential of utilizing comprehensive claims databases in advancing research and improving patient care in dermatology.

Large-scale functional inference for skin-expressing lncRNAs using expression and sequence information

Long noncoding RNAs (lncRNAs) regulate the expression of protein-coding genes and have been shown to play important roles in inflammatory skin diseases. However, we still have limited understanding of the functional impact of lncRNAs in skin, partly due to their tissue specificity and lower expression levels compared with protein-coding genes. We compiled a comprehensive list of 18,517 lncRNAs from different sources and studied their expression profiles in 834 RNA-Seq samples from multiple inflammatory skin conditions and cytokine-stimulated keratinocytes. Applying a balanced random forest to predict involvement in biological functions, we achieved a median AUROC of 0.79 in 10-fold cross-validation, identifying significant DNA binding domains (DBDs) for 39 lncRNAs. G18244, a skin-expressing lncRNA predicted for IL-4/IL-13 signaling in keratinocytes, was highly correlated in expression with F13A1, a protein-coding gene involved in macrophage regulation, and we further identified a significant DBD in F13A1 for G18244. Reflecting clinical implications, AC090198.1 (predicted for IL-17 pathway) and AC005332.6 (predicted for IFN-γ pathway) had significant negative correlation with the SCORAD metric for atopic dermatitis. We also utilized single-cell RNA and spatial sequencing data to validate cell type specificity. Our research demonstrates lncRNAs have important immunological roles and can help prioritize their impact on inflammatory skin diseases.

Roles Played by Stress-Induced Pathways in Driving Ethnic Heterogeneity for Inflammatory Skin Diseases

Immune-mediated skin conditions (IMSCs) are a diverse group of autoimmune diseases associated with significant disease burden. Atopic dermatitis and psoriasis are among the most common IMSCs in the United States and have disproportionate impact on racial and ethnic minorities. African American patients are more likely to develop atopic dermatitis compared to their European American counterparts; and despite lower prevalence of psoriasis among this group, African American patients can suffer from more extensive disease involvement, significant post-inflammatory changes, and a decreased quality of life. While recent studies have been focused on understanding the heterogeneity underlying disease mechanisms and genetic factors at play, little emphasis has been put on the effect of psychosocial or psychological stress on immune pathways, and how these factors contribute to differences in clinical severity, prevalence, and treatment response across ethnic groups. In this review, we explore the heterogeneity of atopic dermatitis and psoriasis between African American and European American patients by summarizing epidemiological studies, addressing potential molecular and environmental factors, with a focus on the intersection between stress and inflammatory pathways.

Transcriptome Profiling of Embryonic Retinal Pigment Epithelium Reprogramming

The plasticity of human retinal pigment epithelium (RPE) has been observed during proliferative vitreoretinopathy, a defective repair process during which injured RPE gives rise to fibrosis. In contrast, following injury, the RPE of the embryonic chicken can be reprogrammed to regenerate neural retina in a fibroblast growth factor 2 (FGF2)-dependent manner. To better explore the mechanisms underlying embryonic RPE reprogramming, we used laser capture microdissection to isolate RNA from (1) intact RPE, (2) transiently reprogrammed RPE (t-rRPE) 6 h post-retinectomy, and (3) reprogrammed RPE (rRPE) 6 h post-retinectomy with FGF2 treatment. Using RNA-seq, we observed the acute repression of genes related to cell cycle progression in the injured t-rRPE, as well as up-regulation of genes associated with injury. In contrast, the rRPE was strongly enriched for mitogen-activated protein kinase (MAPK)-responsive genes and retina development factors, confirming that FGF2 and the downstream MAPK cascade are the main drivers of embryonic RPE reprogramming. Clustering and pathway enrichment analysis was used to create an integrated network of the core processes associated with RPE reprogramming, including key terms pertaining to injury response, migration, actin dynamics, and cell cycle progression. Finally, we employed gene set enrichment analysis to suggest a previously uncovered role for epithelial-mesenchymal transition (EMT) machinery in the initiation of embryonic chick RPE reprogramming. The EMT program is accompanied by extensive, coordinated regulation of extracellular matrix (ECM) associated factors, and these observations together suggest an early role for ECM and EMT-like dynamics during reprogramming. Our study provides for the first time an in-depth transcriptomic analysis of embryonic RPE reprogramming and will prove useful in guiding future efforts to understand proliferative disorders of the RPE and to promote retinal regeneration.

A MATLAB-based tool for accurate detection of perfect overlapping and nested inverted repeats in DNA sequences

SUMMARY

Palindromic sequences, or inverted repeats (IRs), in DNA sequences involve important biological processes such as DNA-protein binding, DNA replication and DNA transposition. Development of bioinformatics tools that are capable of accurately detecting perfect IRs can enable genome-wide studies of IR patterns in both prokaryotes and eukaryotes. Different from conventional string-comparison approaches, we propose a novel algorithm that uses a cumulative score system based on a prime number representation of nucleotide bases. We then implemented this algorithm as a MATLAB-based program for perfect IR detection. In comparison with other existing tools, our program demonstrates a high accuracy in detecting nested and overlapping IRs.

AVAILABILITY AND IMPLEMENTATION

The source code is freely available on (http://bioinfolab.miamioh.edu/bioinfolab/palindrome.php)

CONTACT

liangc@miamioh.edu or karroje@miamioh.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.