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Updated: Jul 17, 2026

Dried Blood Spots - Preparing and Processing for Use in Immunoassays and in Molecular Techniques
Published on: March 13, 2015
Establishment of DBS-based reference intervals for vitamins, essential elements, and potentially toxic elements in
Mengtong Yang1, Zhenghua Huang1, Limiao Jiang2
1BYHEALTH Institute of Nutrition & Health, Guangzhou, China.
Objectives:
This study aimed to establish a comprehensive panel of dried blood spot (DBS)-specific reference intervals (RIs) for vitamins, essential elements, and potentially toxic elements (PTEs) in Chinese adults, and to introduce a hierarchical decision framework for selecting between healthy- and general-population-derived intervals.
Methods:
In a cohort of 857 adults (aged 20-80 years) recruited in Shiyan, Hubei Province, central China, a healthy reference subpopulation (n=217) was defined based on the CLSI C28-A3c criteria. RIs were established for 17 analytes - vitamins (A, D, E, B1, B2, B3, B5, B6, and B9), essential macro- and trace elements (Mg, Fe, Cu, Zn, and Se), and PTEs (Pb, Hg, As) - quantified from venous whole-blood-derived DBSs using LC-MS/MS and ICP-MS. Bootstrap-based nonparametric estimation was applied following CLSI guidelines. A hierarchical decision framework was applied to select the optimal RI source.
Results:
Sex-specific partitioning was supported for seven analytes, yielding 14 sex-specific RIs and 10 combined-sex RIs. Application of the hierarchical decision framework identified significant distributional differences for VA, VD, VB5, VB6, VB9, Cu, and Hg, warranting retention of healthy-subpopulation-derived RIs. Healthy-population RIs were retained for all female sex-specific intervals to prioritize biological specificity, whereas general-population RIs were adopted for 6 of 7 male intervals given sub-threshold healthy male sample sizes.
Conclusions:
This study established a comprehensive DBS-specific RI panel for 17 analytes in Chinese adults, with primary applicability to individuals aged 20-59 years. The hierarchical decision framework provides a transparent, evidence-driven approach to balancing biological specificity against statistical precision in RI establishment.
