In general, global models proved to outperform those calibrated using only samples from one dataset, showing the compatibility of the spectral markers obtained in the different regions. Overall, PLS was outperformed by non-linear machine learning algorithms, although in the global models the differences between models were reduced. The only condition under which SVM and RF struggled was the domain transfer DKD classification, where the non-linear models lose performance due to unbalanced classes in the training dataset . Thus, strategies to compensate for the unbalanced set of samples should be considered before choosing a ML algorithm. Classification results indicated that spectral markers were robust and comparable across datasets, and inter-batch differences were not considerably enough to profoundly compromise their use. We aimed to study the aetiology of these spectral markers, which represented quantitative and qualitative changes in protein composition associated to DKD and microalbuminuria. In general, both classifications were expected to be correlated, as patients diagnosed with DKD are expected to have high levels of albuminuria. However, they should also show differences, because DKD diagnostic criteria also consider the estimated glomerular filtration rate (eGFR). In our datasets there were several normoalbuminuric samples from DKD patients, as there is a substantial number of cases diagnosed with this renal disease that present normal albuminuria levels . Results described in sections 3.1 and 3.2 showed that the DKD classification models were able to discriminate between normoalbuminuric samples from DKD patients and those from controls.