Jaro Karppinen

Background: Intervertebral disc degeneration (IVDD) encompasses structural changes of the spine that are commonly studied using clinically defined diagnoses or symptom-based phenotypes, which reflect a mixture of anatomical degeneration and pain-related processes. Magnetic resonance imaging (MRI) provides direct assessment of spinal structure, but genetic studies using imaging-derived traits have been limited by challenges in large-scale phenotyping. Methods: We applied SpineNet, a deep learning framework for automated MRI grading, to derive lumbar spine degeneration phenotypes in 7,414 individuals from 3 cohorts. GWAS was conducted within each cohort and results meta-analysed. Phenotypic and genetic relationships were assessed using exploratory factor analysis and LD score regression. genomicSEM modelled shared genetic liability across MRI traits and with GWAS of chronic pain and spinal disorders, followed by a common factor GWAS. Results: GenomicSEM identified a latent genetic dimension representing structural spinal degeneration, with strong contributions from disc narrowing, endplate defect, herniation, foraminal stenosis, and marrow signal change. When integrated with pain and clinical phenotypes, structural MRI traits and chronic pain had distinct genetic dimensions, while clinically defined spinal diagnoses showed contributions from both. The common factor GWAS identified several suggestive loci enriched in regulatory genomic regions, mapping to genes previously implicated in musculoskeletal traits. Phenotypic and genetic analyses revealed a consistent factor structure in which MRI-derived structural traits clustered together and were distinct from alignment-related abnormalities. Conclusions: Automated MRI phenotyping combined with multivariate genetic modelling identified a genetic dimension of structural spinal degeneration that is partially distinct from pain-related liability, providing a scalable framework for future spine studies.

Purpose: Engagement in moderate levels of physical activity appears to be beneficial for low back pain (LBP) prevention and treatment. In this 15-year prospective follow-up study, we aimed to examine whether certain sports and their performance levels are associated with lower odds of experiencing repeatedly observed LBP across a 15-year follow-up. Methods: Study sample composed of participants of the Northern Finland Birth Cohort 1966 ( n = 3352) who had data available on the engagement frequency in the following sports and other physical activities: (a) cross-country skiing; (b) swimming; (c) running; (d) aerobics and gymnastics; (e) gym workouts; (f) floorball, ice hockey, football, rinkball, and basketball; and (h) badminton, volleyball, tennis, and squash at 31 years, and LBP occurrence between 31 and 46 years. Multinomial logistic regression models were used to produce unadjusted and adjusted odds ratios (ORs) and 95% confidence intervals (CIs). Results: Cross-country skiing more than once a month or at least twice a week, running at least twice a week and gym training up to once a month were associated with lower odds of experiencing repeatedly observed LBP than exercising up to once a month. After adjustments, only the cross-country skiing models remained (for up to once a week: OR 0.77, 95% CI 0.60–0.98; for at least twice a week: 0.71, 0.51–1.00). No association was found between sports and incident or resolved LBP. Conclusion: Engagement in cross-country skiing in early adulthood appears to be a protective factor against repeatedly observed LBP between early and mid-adulthood.

Background: Movement control impairment (MCI) tests of patients with chronic low back pain (LBP) have shown good validity. However, the association between MCI test results and LBP has not been evaluated in a population-based cohort. Methods: The participants of this cross-sectional study consisted of 1907 members of the Northern Finland Birth Cohort 1966. Their assessment included the MCI tests of sitting knee extension (SKE), prone knee bend (PKB), one leg stance (OLS), rocking backwards (RB) and waiter’s bow (WB). The tests were classified as positive or negative. The SKE, PKB and OLS tests were performed on the left and right sides separately and then summed up to include both sides. Each MCI test evaluates at least one movement direction; flexion (SKE, RB, WB), extension (PKB), rotation (SKE, PKB, OLS), flexion-rotation (SKE, OLS) and extension-rotation (PKB, OLS). The participants’ pain level was categorized as mild, moderate, or severe according to the frequency and perceived bothersomeness of LBP. The outcomes were LBP, sciatica and prolonged sciatica (duration over three months). Multinomial regression analysis was used to analyse the association between each MCI direction and the symptoms, and adjusted for body mass index, sex, socioeconomic status, psychological symptoms, physical activity, and smoking. The reference categories were mild symptoms and negative MCI test results of the respective direction. Results: Three positive left-side rotation MCI test results were associated with moderate (odds ratio [OR] 2.81; 95% confidence interval (CI) 1.22–6.50) and severe (2.98; 1.20–7.40) LBP. Two and three positive rotation tests (both sides) were also associated with severe LBP (2.31; 1.02–5.24 and 3.03; 1.27–7.24, respectively). Two positive left-side flexion-rotation test results were associated with moderate LBP (1.83; 1.01–3.29) and one positive flexion-rotation test result (both sides) with severe LBP (1.84; 1.08–3.10). One positive right-side flexion-rotation test result was negatively associated with moderate prolonged sciatica (0.28; 0.10–0.77). Conclusions: Positive rotational MCI test results were associated with moderate and severe LBP in this large-scale population-based cohort. The reason for the association between positive rotational test results and LBP, especially left-sided, requires further research.

Background: We designed an educational simulation, ‘MediCampus AI’, using a large language model with Retrieval-Augmented Generation to replicate realistic clinical scenarios aligned with evidence-based Current Care Guideline (CCG). We piloted the simulation to assess its usability for supporting clinical reasoning, patient interaction, and CCG implementation in medical and physiotherapy education. Methods: We used Educational Design Research method to develop artificial intelligence (AI)-based simulation prototype. Medical and physiotherapy students (n = 11) were randomized into simulation (SG) and traditional case-based learning groups (TG). User experiences and implementation determinants were measured with study-specific questions, the Determinants of Implementation Behavior Questionnaire (DIBQ-mp) rated using a 7-point Likert scale, and game-generated data. Results were reported as change in median scores before and after educational interventions. Open-ended questions were analysed using inductive content analysis. Results: A change in median scores in the DIBQ-mp items was observed in “knowledge” (+2 in SG vs. +1 in TG); “skills” (+3 vs. +1.5); “beliefs about capabilities” (+4 vs. +0.5); “beliefs about consequences” (+2 vs. +0.5); “social influences” (+1 vs. +0); “organization” (+3 vs. +1); “behavioral regulation” (+2 vs. +1); “intentions” (+0 vs. +0.5) and “innovation” (+0 vs. +1), “patient” (+1 vs. +1) and “innovation strategy” (+1 vs. +1). Four categories were identified from qualitative data: A) individually supportive game design; B) safe, scalable environment for future needs; C) content enhancements and D) usability/accessibility development needs. Conclusions: Virtual simulation seemed feasible for embedding CCGs into education. Compared with traditional case-based learning, the AI-based simulation showed greater improvements in most implementation determinants and supported competence and guideline-concordant behaviors.

Introduction: Chronic back pain (CBP) is a leading cause of work disability worldwide, yet identifying individuals at risk remains difficult due to its multifactorial aetiology. This population‐based cohort study investigated whether integrating a polygenic risk score (PRS) for CBP with the STarT Back Tool (SBT)—a widely used psychosocial screening instrument—could improve the prediction of work disability, measured as disability leave days over a 2‐year follow‐up. Methods: We analysed data from 1938 participants in the Northern Finland Birth Cohort 1966 with complete genotyping, SBT responses and registry‐linked disability records. A zero‐inflated negative binomial regression model was applied to account for the highly skewed distribution of work disability days. Results: Results showed that both SBT and CBP genetic risk independently predicted the cumulative number of disability leave days. While SBT was also associated with the likelihood of having no disability leave, CBP genetic risk was not, suggesting that polygenic risk contributes specifically to the burden of disability among affected individuals. When participants split into 4 risk groups, those in the highest CBP genetic risk quartile experienced significantly more work disability days. Conclusion: The two tools captured complementary domains: SBT reflected modifiable biopsychosocial risks, while the PRS represented fixed genetic liability. This distinction supports the value of integrating a CBP PRS into existing screening frameworks, with potential in early CBP management. Significance Statement: This study is the first to combine a polygenic risk score for chronic back pain with a clinical screening tool to assess work disability outcomes. It demonstrates that genetic and psychosocial risk capture distinct aspects of vulnerability, and that their integration improves risk stratification. These findings add to the growing evidence supporting personalized approaches in pain management and highlight the potential utility of genetic data in early assessment of disabling back pain.

Background: The temporal sequence of lumbar spine degeneration (trajectory) is difficult to characterize due to limited availability of longitudinal imaging data. The aim of this cross-sectional study was to discover degeneration trajectories and their clinical relevance by applying an innovative computational approach to an extensive dataset from chronic low back pain patients. Methods: Clinical MRI exams from 423 patients in the comeBACK study were graded for disc degeneration, facet osteoarthritis, and other pathoanatomical features. We then trained an event-based model, which is specifically designed to infer longitudinal trajectories from cross-sectional data, to model spine degeneration trajectory subtypes. The clinical significance of the identified trajectories was assessed using propensity score matching of trajectory subtypes and subsequent generalized linear mixed-effects modeling. Pain characteristics included the Fear-Avoidance Beliefs Questionnaire, neuropathic pain (painDETECT), pain impact score, and chronic widespread pain (CWP). Results: Two distinct trajectories were identified. A “disc-first” subtype ( n = 260, 61%) was characterized by a high prevalence of disc herniation and disc degeneration that was more severe than facet osteoarthritis. Conversely, a “facet-first” subtype ( n = 146, 39%) was characterized by a greater severity of facet osteoarthritis than disc degeneration. The disc-first subtype was associated with more CWP ( p = 0.030), while the facet-first subtype had higher neuropathic pain (painDETECT scores) ( p = 0.039). Conclusion: We identified two distinct trajectories of lumbar spine degeneration related to differing clinical presentations. After replication with other large datasets, this method could be used to characterize and stage spinal degeneration of individual patients. Ultimately, this new information would help clarify degeneration mechanisms and risk factors and support treatment optimization.

Objectives Chronic low back pain is a common symptom in axial spondyloarthritis (axSpA) and in patients with degenerative spinal disease associated with Modic changes (MC) on MRI. A clear distinction between axSpA and MC might be difficult in early disease, particularly in the absence of axSpA sacroiliac changes. Therefore, a diagnostic serum biomarker for ambiguous cases would be useful. To evaluate if the potential diagnostic biomarker anti-CD74 for axSpA is elevated in patients with MC as well, we measured this antibody in patients with MC and healthy controls. We presumed that a negative anti-CD74 antibody in MC would be helpful to distinguish it from axSpA. Method Participants from the NFBC1966 with MC and matched participants without MC were included in this study. Anti-CD74 immunoglobulin A (IgA) was measured with a commercially available kit. An association between continuous log-transformed anti-CD74 IgA was evaluated with a linear regression model adjusted for body mass index (BMI), sex, C-reactive protein (CRP), and presence of low back pain. Results The study involved 307 participants, 148 with MC and 159 without MC. There was no association between MC and continuous anti-CD74 IgA after adjustment for potential confounders (Exp(B) = 1.3, 95% CI 0.8 to 2.1). None of the participants with MC had elevated anti-CD74 IgA above the cut-off of 15 U/ml. Conclusions Anti-CD74 IgA is not elevated in patients with MC. Further studies comparing directly patients with axSpA and MC are required to evaluate if it is a diagnostic tool to differentiate axSpA and MC in unclear cases. Key Points • Anti-CD74 IgA are not elevated in patients with Modic changes on MRI.