Which factors are associated with bone marrow oedema suspicious of axial spondyloarthritis as detected by MRI in the sacroiliac joints and the spine in the general population?

OBJECTIVE: Identify factors associated with presence and extension of spinal and sacroiliac joints (SIJ)-MRI lesions suggestive of axial spondyloarthritis (axSpA) in a population-based cohort (Study of Health in Pomerania) aged <45 years. METHODS: Spinal (sagittal T1/T2) and SIJ (semicoronal STIR sequences) MRIs were evaluated by two trained blinded readers. The presence (yes/no) and extension (Berlin MRI Score) of bone marrow oedema (BME) were captured. Degenerative spinal lesions were excluded and discrepancies resolved by consensus. Cross-sectional associations between clinical factors and presence/extension of BME were analysed by logistic/negative binomial regression. Record linkage of claims data was applied to identify participants with axSpA. RESULTS: MRIs of 793 volunteers were evaluated. The presence of SIJ-BME (odds ratio) was strongly associated delivery during the last year (4.47, 1.49-13.41). For SIJ-BME extension, associations (incidence rate ratios, 95% CI) were found for delivery ((during last year) 4.52, 1.48-13.84), human leucocyte antigen (HLA)-B27+ (2.32, 1.30-4.14), body mass index (25-30 vs <25 kg/m²; 1.86 (1.19-2.89)) and back pain ((last 3 months) 1.55, 1.04-2.31), while for spinal BME, associations were found for age per decade (1.46, 1.13-1.90) and physically demanding work (1.46, 1.06-2.00). Record linkage was available for 694 (87.5%) participants and 9/694 (1.3%) had a record of axSpA (ICD M45.09). CONCLUSION: These population-based data support the hypothesis of mechanic strain contributing to BME in the general population aged <45 years and the role of HLA-B27+ as a severity rather than a susceptibility factor for SIJ-BME.

Frequency of MRI changes suggestive of axial spondyloarthritis in the axial skeleton in a large population-based cohort of individuals aged <45 years.

OBJECTIVE: To investigate the frequency of bone marrow oedema (BME) and fatty lesions (FL) suggestive of axial spondyloarthritis (axSpA) on MRI of the spine and sacroiliac joints (SIJ) in a general population sample. METHODS: As part of a community-based cohort project (Study of Health in Pomerania), volunteers underwent spinal (sagittal T1/T2) and SIJ (semicoronal short tau inversion recovery) MRI examinations. Two calibrated readers evaluated the images to detect BME in SIJ and vertebral corners (VC) and FL in VC suggestive of axSpA using Assessment of SpondyloArthritis international Society definitions. RESULTS: MRIs of 793 volunteers (49.4% males, mean age 37.3±6.3 years, 8.4% human leucocyte antigen-B27+) aged <45 years were evaluated. SIJ BME was seen in 136 (17.2%), VC BME in 218 (27.5%) and FL in 645 (81.4%) volunteers. SIJ BME in ≥1, ≥3 and ≥5 SIJ quadrants was seen in 136 (17.2%), 7 (0.9%) and 1 (0.1%) volunteers, respectively. In VC, BME≥1, ≥3 and ≥5 lesions were seen in 218 (27.5%), 38 (4.8%) and 6 (0.8%) volunteers, respectively, while FL≥1, ≥3 and ≥5 were seen in 645 (81.3%), 351 (44.3%) and 185 (23.3%) volunteers, respectively. Logistic regression analysis showed that BME and FL in VC were related to increasing age: OR 1.33, 95% CI 1.02 to 1.72, and OR 1.73, 95% CI 1.32 to 2.27, per decade increase, respectively. CONCLUSIONS: In this large population-based study, a high frequency of inflammatory and fatty MRI lesions suggestive of axSpA was found, especially in the spine. This indicates a limited value of such MRI findings for diagnosis and classification of axSpA. The increasing frequency with age suggests that mechanical factors could play a role.

Associated factors of white matter hyperintensity volume: a machine-learning approach.

To identify the most important parameters associated with cerebral white matter hyperintensities (WMH), in consideration of potential collinearity, we used a data-driven machine-learning approach. We analysed two independent cohorts (KORA and SHIP). WMH volumes were derived from cMRI-images (FLAIR). 90 (KORA) and 34 (SHIP) potential determinants of WMH including measures of diabetes, blood-pressure, medication-intake, sociodemographics, life-style factors, somatic/depressive-symptoms and sleep were collected. Elastic net regression was used to identify relevant predictor covariates associated with WMH volume. The ten most frequently selected variables in KORA were subsequently examined for robustness in SHIP. The final KORA sample consisted of 370 participants (58% male; age 55.7 ± 9.1 years), the SHIP sample comprised 854 participants (38% male; age 53.9 ± 9.3 years). The most often selected and highly replicable parameters associated with WMH volume were in descending order age, hypertension, components of the social environment (i.e. widowed, living alone) and prediabetes. A systematic machine-learning based analysis of two independent, population-based cohorts showed, that besides age and hypertension, prediabetes and components of the social environment might play important roles in the development of WMH. Our results enable personal risk assessment for the development of WMH and inform prevention strategies tailored to the individual patient.