Assessing the Fitness-for-Purpose of Published Breath Analysis Data: A Quality Assessment Framework for Diabetes Biomarker Research

Abstract Background Exhaled breath analysis is a promising field for non-invasive diabetes diagnostics, but its clinical translation is hindered by contradictory findings across studies. We argue that this inconsistency stems from significant methodological heterogeneity and the lack of appropriate criteria for screening published data for secondary analysis. Existing tools, such as QUADAS-2, assess the quality of clinical study design but are not equipped to evaluate the technical comparability and fitness-for-purpose of the quantitative data itself. This study aimed to develop and validate a novel, data-driven framework to systematically assess the fitness-for-purpose of published data, thereby addressing this critical gap. Methods We developed the National Institute of Metrology - Diabetes Breath Assessment (NIM-DBA) framework, a multi-domain quality and fitness-for-purpose assessment tool. Its theoretical basis is derived from the stringent specifications for Standard Reference Data in metrology and aligned with international ISO data quality standards. A systematic literature search was conducted in PubMed, Scopus, Embase, and Web of Science (up to April 2025) to identify studies reporting quantitative data on breath volatile organic compounds (VOCs) in diabetic patients. Using breath acetone as a case study, we applied the NIM-DBA framework to the resulting literature pool. A parallel assessment using the QUADAS-2 tool was also performed on the same pool to compare the data-driven (NIM-DBA) and hypothesis-driven (QUADAS-2) evaluation paradigms. Results The systematic search identified an initial pool of 38 eligible studies. Application of the multi-stage NIM-DBA screening process filtered this heterogeneous pool down to a core subset of only six studies (15.8%) that met all criteria for high data quality and fitness-for-purpose. In contrast, the parallel QUADAS-2 assessment of the same 38 studies revealed widespread high or unclear risk of bias, particularly in the domains of Patient Selection (79% high risk) and Index Test reporting (82% unclear risk). The six studies that passed the NIM-DBA framework demonstrated a highly consistent biological pattern—elevated breath acetone concentrations in diabetic patients—and shared common methodological best practices, such as standardized alveolar gas collection and the use of high-sensitivity analytical instruments. Conclusion The prevalent contradictory conclusions in breath analysis literature are likely attributable to differences in methodological rigor rather than biomarker instability. The proposed NIM-DBA framework is an effective tool for systematically managing data heterogeneity, filtering literature for secondary analysis, and identifying methodologically robust studies. This data-driven approach provides a necessary complement to classic clinical evaluation tools, offering a new perspective on research quality assessment and providing valuable guidance for future study design in the field.