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Introduction: Altered serum levels of growth hormones, adipokines, and exocrine pancreas enzymes have been individually linked with type 1 diabetes (T1D). We collectively evaluated seven such biomarkers, combined with islet autoantibodies (AAb) and genetic risk score (GRS2), for their utility in predicting AAb/T1D status., Research Design and Methods: Cross-sectional serum samples (n=154 T1D, n=56 1AAb+, n=77 ≥2AAb+, n=256 AAb-) were assessed for IGF1, IGF2, adiponectin, leptin, amylase, lipase, and trypsinogen (n=543, age range 2.7-30.0 years) using random forest modeling., Results: GRS2, age, lipase, trypsinogen, and AAb against ZnT8, GAD65, and insulin were the most informative markers. Notably, these variables were differentially informative according to AAb/T1D status. Higher GRS2 (p<0.001) and lower lipase levels (p=0.002) favored ≥2AAb+ versus AAb- classification. AAb against ZnT8 (p<0.01), GAD65 (p=0.021), or insulin (p=0.01) each independently favored ≥2AAb+ versus 1AAb+ classification. Reduced trypsinogen (p<0.001) and increased lipase levels (p<0.001) favored recent-onset T1D versus ≥2AAb+ classification., Conclusions: Among the serological markers tested, lipase and trypsinogen levels were the most informative for differentiating among clinical groups, with the utility of each enzyme varying according to GRS2 and AAb/T1D status. These data support exocrine pancreas enzymes as candidates for longitudinal follow-up., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ Group.)

Spatially resolved omics (SRO) technologies enable the identification of cell types while preserving their organization within tissues. Application of such technologies offers the opportunity to delineate cell-type spatial relationships, particularly across different length scales, and enhance our understanding of tissue organization and function. To quantify such multi-scale cell-type spatial relationships, we present CRAWDAD, Cell-type Relationship Analysis Workflow Done Across Distances, as an open-source R package. To demonstrate the utility of such multi-scale characterization, recapitulate expected cell-type spatial relationships, and evaluate against other cell-type spatial analyses, we apply CRAWDAD to various simulated and real SRO datasets of diverse tissues assayed by diverse SRO technologies. We further demonstrate how such multi-scale characterization enabled by CRAWDAD can be used to compare cell-type spatial relationships across multiple samples. Finally, we apply CRAWDAD to SRO datasets of the human spleen to identify consistent as well as patient and sample-specific cell-type spatial relationships. In general, we anticipate such multi-scale analysis of SRO data enabled by CRAWDAD will provide useful quantitative metrics to facilitate the identification, characterization, and comparison of cell-type spatial relationships across axes of interest., Competing Interests: Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)