Profiling skin microbiota in an underrepresented population: Indonesian children with atopic dermatitis and controls.

Introduction: The skin microbiome plays a central role in the pathogenesis of atopic dermatitis (AD), but most studies have focused on high-income populations of European ancestry. Microbiome data from tropical and developing regions remains limited. In Asia, microbiome research has similarly centered around developed countries, leaving populous developing countries like Indonesia underrepresented. The aim was to profile the cutaneous bacterial microbiota of children with AD from Indonesia and compare it with controls. Methods: Skin swabs were collected from lesional sites of 111 children aged 4-18 years with AD and from the forearms of 107 controls, all attending Pediatric Dermatology Clinic of Dr. Hasan Sadikin General Hospital, an urban tertiary-care referral center in Bandung, West Java, Indonesia. AD was diagnosed using Hanifin-Rajka criteria, while controls had non-atopic, non-inflammatory dermatological conditions. Cutaneous bacterial microbiota was profiled using 16S rRNA sequencing with amplicon sequence variant (ASV) level analysis using DADA2 pipeline. Data was analyzed after quality control to estimate alpha and beta diversities, the later using, permutational multivariate analysis of variance (PERMANOVA) to assess contribution of individual variables to the variation in microbiota composition. Univariable differential abundance was done to analysis the composition of specific bacteria in cases versus controls. Analysis of core microbiota compositions and phylogenetic relationships were explored to identify key taxa associated with AD. Results: Most children came from families with higher household incomes, and children with AD were younger than controls (mean age 8.35 ± 3.51 vs. 9.91 ± 3.79 years, Conclusion: Our findings reveal distinct microbial profiles in children with AD from a tropical, underrepresented population with predominantly higher household incomes, and underscore the role of environmental and sociodemographic factors associated with skin microbiota. While generalizability to lower-income or rural populations may be limited, the value of ASV-level analysis lies in its ability to capture both known and less characterized microbial signals.