Establishment and Temporal Validation of Next-Generation Reference Intervals for Routine Hematological Parameters Using Large-Scale Data

<b>Background</b>: Conventional reference intervals (RIs) are typically expressed as fixed limits and may not adequately reflect continuous biological variation across age and sex. Next-generation reference intervals (NGRIs) allow dynamic modeling of laboratory parameters across the lifespan. This study aimed to establish age- and sex-specific NGRIs for routine hematological parameters using large-scale health examination data and to evaluate their temporal stability. <b>Methods</b>: Health examination records were linked with laboratory data, and a relatively healthy reference population was defined based on age (18-80 years), normal body mass index, normal blood pressure, and absence of documented disease history. NGRIs were constructed using generalized additive models for location, scale, and shape (GAMLSS) with the Box-Cox Cole and Green distribution. Age-dependent percentile curves (2.5th-97.5th) were generated using bootstrap resampling (100 iterations). Temporal external validation was conducted in five independent annual cohorts (2019-2023). <b>Results</b>: Age- and sex-dependent distributional patterns were observed across multiple hematological parameters, particularly RBC, HGB, and HCT. Continuous percentile curves demonstrated gradual age-related trends, with more pronounced changes in females after midlife. In temporal validation cohorts, the proportion of individuals classified outside the reference intervals remained consistently below 10% across years and sexes, indicating stable performance. <b>Conclusions</b>: Using large-scale real-world health examination data and a flexible distributional modeling framework, we established stable age-continuous next-generation reference intervals for routine hematological parameters. The proposed approach provides a reproducible strategy for modernizing laboratory reference interval construction and supports broader implementation in population-based laboratory medicine.