Thrilled to share our new paper in @NatureHumBehav! https://t.co/pcNX1FCvZJ We show that combining biological 🧬 and psychosocial 📋 data offers a much stronger, more reliable path to predicting pain and diagnosis. 🧵

Phenotyping is the vast, minimally-explored frontier in genome-wide association studies. Important thread🧵 Briefly, phenotyping is how you measure people's traits. Measure poorly, get bad results; measure well, get good results. Example? Janky knees.

Benchmarking methods for mapping functional connectivity in the brain | https://t.co/N2XmnTJXsA What is the best FC metric? led by @liuzhenqi0303 in @naturemethods ⤵️

Beautiful paper on #biomarkers, #neuromarkers, and psychosocial predictors of chronic pain and associated #chronicdiseases. A tour de force. @FillingimMatt @evp82 https://t.co/tifVpzpnDc

What a tremendous paper here 🤯🤯🤯 ⬇️⬇️⬇️

🔥Most important paper of 2025🔥 👉Injury/disease don't reliably predict pain 👉Biomarkers alone can't explain pain 👉PSYCHOSOCIAL factors reliably predict chronic pain 👉Painful conditions can be predicted from the SYNERGY btwn bio + psychosocial factors https://t.co/vny3M93POi

Excited to share our new publication! See the thread and article below 👇

If you're trying to predict pain, worth a look

another Nature (this time @NatureHumBehav) paper from the @evp82 lab, this time from @FillingimMatt

Great paper demonstrating how taking into account the social context of your participants can improve your understanding and accuracy of your biomarkers. An essential read! congrats @FillingimMatt on a great paper!

Open access!

Our new @NatureHumBehav paper shows why we need a holistic pain biomarker framework. ML on blood tests, brain/bone imaging & genetics predicts clinical diagnoses but falls short on subjective pain. Adding psychosocial (mood, sleep, stress) boosts both.

Fillingim et al. show that biological and psychosocial factors jointly predict chronic pain associated conditions https://t.co/0TltCPOMfm

Huge thanks to our incredible team and collaborators. Read the full paper here: https://t.co/pcNX1FCvZJ Grateful to @NatureHumBehav for publishing this work. #ChronicPain #PrecisionMedicine #Biomarkers #Psychosocial #MachineLearning

Adding 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.

We 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.

Biomarkers 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.

We then compared the biological models to psychosocial models spanning mental health, physical well-being, and sociodemographic factors.

We 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.

We asked: Can combining biological and psychosocial information improve prediction of chronic pain conditions? Spoiler: Yes, significantly.