For example, the proposed methodology for DKD diagnosis was only calibrated and validated using male samples from the same region, Australia. As many diseases, DKD presents gender and ethnic differences , so different global regions present not only distinct incidence but also varied presentations of the disease . Different health systems may have slightly different criteria for the diagnosis of the patients and their inclusion in the clinical study. Therefore, a new set of samples with different demographic characteristics may not generalize well, leading to errors that differ significantly from those observed in the original population. Furthermore, apart from biological variance, the use of different equipment is a key limitation in the transference of a method to a new laboratory, as changes introduced by manufacturers introduce unknown variables that may have significant impacts on method performance . In the case of FTIR, these non-biological variations could affect significantly the result, as usually spectral artifacts can appear under differing conditions . Furthermore, in our case study we have to add the variability of sample drying, as differences in air flow can strongly influence such a complex process . Beyond, as different filter materials may interact differently with the interest analyte and/or contaminants , it is a mandatory point to explore if this is also a factor affecting albumin preconcentration and hence, predictive capacities for DKD.