From Proteomic Signatures to Candidate Endotypes in Obesity, Type 2 Diabetes, MASLD/MASH, and MetALD: Study Designs, Bioinformatics, and Biostatistical Strategies

Obesity, type 2 diabetes (T2D), metabolic dysfunction-associated steatotic liver disease (MASLD), metabolic dysfunction-associated steatohepatitis (MASH), and dual-etiology metabolic dysfunction-associated alcohol-related liver disease (MetALD) form an overlapping metabolic dysfunction spectrum, rather than a single linear disease sequence. Proteomics offers a functional readout of this spectrum by measuring proteins, proteoforms, and protein species involved in tissue injury, inflammation, metabolic stress, and inter-organ communication. This review asks how proteomic data can support mechanism-based stratification, rather than simply generate disease-associated signatures. We summarize advances in circulating and tissue-based proteomics across obesity, T2D, MASLD/MASH, and MetALD, highlighting shared and disease-specific pathways such as mitochondrial dysfunction, extracellular matrix remodeling, immune activation, proteostasis stress, and endocrine crosstalk. We emphasize that proteomic clusters should be considered candidate endotypes only when they are reproducible, mechanistically coherent, linked to tissue or causal evidence, and clinically informative. We also evaluate bioinformatics and biostatistical strategies needed for reliable interpretation, including preprocessing, missing-data handling, normalization, longitudinal modeling, multi-omics integration, protein quantitative trait locus (pQTL) analysis, colocalization, and Mendelian randomization. Finally, we discuss how proteoforms, post-translational modifications (PTMs), and platform-dependent proteome complexity shape interpretation. Together, these concepts provide practical guidance for moving from proteomic signatures to candidate endotypes and for prioritizing clinically useful biomarkers and therapeutic targets.