Matt Fillingim @mfilling.bsky.social · 12/05/2025Adding psychosocial context dramatically improved prediction accuracy across all pain phenotypes. 🧬 + 📋 = 🔍 This synergy paints a richer picture of pain vulnerability and brings us closer to personalized pain care. 111
Matt Fillingim @mfilling.bsky.social · 12/05/2025We created biomarker and psychosocial risk scores and grouped participants into quintiles. Those high on both risks had over 2× higher incidence of painful conditions over 15 years, while those high on just one showed little to no added risk. 100
Matt Fillingim @mfilling.bsky.social · 12/05/2025Biomarkers alone accurately predicted many painful medical conditions, often outperforming psychosocial models. But for self-reported pain, biology wasn’t enough, psychosocial models performed significantly better. 100
Matt Fillingim @mfilling.bsky.social · 12/05/2025We applied machine learning to four biological data types:🩸blood assays, 🦴bone scans, 🧠brain imaging, and 🧬genetics, to develop biomarkers for conditions like arthritis or migraine, as well as self-reported bodily pain. 100
Matt Fillingim @mfilling.bsky.social · 12/05/2025We asked: Can combining biological and psychosocial information improve prediction of chronic pain conditions? Spoiler: Yes, significantly. 100
