Augusto Vargas Pessi, Vivian de Lima Spode Coutinho, Bruna Bento Dos Santos, Ida Vanessa Doederlein Schwartz, Liane Nanci Rotta
Galactosemias (GALAC) are rare inherited metabolic disorders with highly variable prevalence worldwide. Differences in newborn screening (NBS) implementation, diagnostic methodologies, and population-specific genetic factors contribute to heterogeneity in epidemiological estimates. We aimed to map the estimated global GALAC birth prevalence and explore the role of NBS in shaping epidemiological patterns. A scoping review was conducted following PRISMA-ScR guidelines. Studies reporting prevalence or incidence of GALAC were included regardless of country or screening context. Data were extracted on study design, population characteristics, screening strategies, diagnostic methods, and reported prevalence, standardized to cases per 100,000 live births when possible. Twenty-eight studies were included, predominantly from Europe, Asia, and North America. Birth prevalence estimates ranged widely, from undetected cases in some Asian populations to over 100 cases per 100,000 live births in isolated populations. Most studies reported one-to-five cases per 100,000 live births in regions with established NBS programs. Higher birth prevalence in specific populations was associated with founder effects, genetic isolation, and sociocultural factors including endogamy and consanguinity. Variability was also influenced by screening coverage, study design, and diagnostic approaches. Programs relying solely on biochemical methods showed limitations in detecting non-classical subtypes, while combined or multi-tier strategies improved diagnostic accuracy but affected the birth prevalence estimates through inclusion of variants. GALAC estimated global epidemiology reflects a complex interplay of genetic, demographic, and methodological factors. Standardization of screening strategies and NBS expansion, particularly in underrepresented regions, are essential to improve comparability of epidemiological data and accurately estimate disease burden.