Parkinson’s disease (PD) presents diverse symptoms and comorbidities, complicating its diagnosis and management. The primary objective of this cross-sectional, monocentric study was to assess digital gait sensor data’s utility for monitoring and diagnosis of motor and gait impairment in PD. As a secondary objective, for the more challenging tasks of detecting comorbidities, non-motor outcomes, and disease progression subgroups, we evaluated for the first time the integration of digital markers with metabolomics and clinical data. Using shoe-attached digital sensors, we collected gait measurements from 162 patients and 129 controls in a single visit. Machine learning models showed significant diagnostic power, with AUC scores of 83–92% for PD vs. control and up to 75% for motor severity classification. Integrating gait data with metabolomics and clinical data improved predictions for challenging-to-detect comorbidities such as hallucinations. Overall, this approach using digital biomarkers and multimodal data integration can assist in objective disease monitoring, diagnosis, and comorbidity detection.

Actinic keratoses (AKs) are sun-damaged skin areas that affect 20% of the European adult population and more than 50% of people aged 70 years and over. There are currently no clinical or histological features allowing us to identify to which clinical class (i.e., regression or progression) an AK belongs. A transcriptomic approach seems to be a robust tool for AK characterization, but there is a need for additional studies, including more patients and elucidating the molecular signature of an AK. In this context, the present study, including the largest number of patients to date, is the first aiming at identifying biological features to objectively distinguish different AK signatures. We highlight two distinct molecular profiles: AKs featuring a molecular profile similar to squamous cell carcinomas (SCCs), which are called “lesional AKs” (AK_Ls), and AKs featuring a molecular profile similar to normal skin tissue, which are called “non-lesional AKs” (AK_NLs). The molecular profiles of both AK subclasses were studied, and 316 differentially expressed genes (DEGs) were identified between the two classes. The 103 upregulated genes in AK_L were related to the inflammatory response. Interestingly, downregulated genes were associated with keratinization. Finally, based on a connectivity map approach, our data highlight that the VEGF pathway could be a promising therapeutic target for high-risk lesions.