Cholesterol metabolic disorder is associated with worse spinal inflammation on MRI in axial spondyloarthritis

Cholesterol metabolic disorder is associated with worse spinal inflammation on MRI in axial spondyloarthritis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Cholesterol metabolic disorder is associated with worse spinal inflammation on MRI in axial spondyloarthritis Hexin Feng, Jiahong Zhu, Ying Zhang, Ziwei Fu, Yanbo Wu, Pengfei Li, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9389876/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Objective Axial spondyloarthritis is a chronic inflammatory disease characterized by bone destruction and pathological new bone formation, in which osteoclasts play a key role, and elevated cholesterol levels enhance osteoclast function. This study sought to investigate whether cholesterol metabolic disorder contributes to the severity of spinal inflammation in axial spondyloarthritis. Methods This prospective study enrolled 92 axSpA patients and collected clinical and laboratory data. MRI quantified acute spinal inflammation (SPARCC score), chronic spinal inflammation (vertebral fat fraction, VFF), as well as lipid metabolism-related imaging parameters, including liver fat fraction (LFF) and subcutaneous-to-visceral adipose ratio (SAT/VAT). To identify independent factors associated with imaging spinal inflammation and adjust for potential confounders, univariate linear regression and multivariable linear regression analyses were performed using three incrementally adjusted models: Model 1 adjusted for demographics; Model 2 additionally adjusted for inflammatory markers (CRP, ESR) and disease activity indices (BASDAI, ASDAS-CRP); Model 3 further adjusted for functional/structural impairment scores (BASMI, BASFI, mSASSS). Results In the fully adjusted multivariable regression model (Model 3), current smoking (β = 16.727, P = 0.001), Chol/HDL ratio (β = 4.253, P = 0.021), LDL/HDL ratio (β = 5.114, P = 0.038), and LFF (β = 1.014, P = 0.016) were independently associated with the SPARCC score; while current smoking (β = 3.646, P = 0.040), total cholesterol (β = 1.544, P = 0.050), Chol/HDL ratio (β = 1.399, P = 0.030), and LFF (β = 0.404, P = 0.005) were independently associated with VFF. Conclusion Elevated total Chol, Chol/HDL ratio, and LDL/HDL ratio—collectively reflecting a cholesterol metabolic disorder—were independently and positively associated with the severity of both acute and chronic spinal inflammation. These findings suggest that the cholesterol metabolic status of axSpA patients warrants greater attention in clinical evaluation. Trial Registration: Chinese Clinical Trial Registry, ChiCTR2500113832. Retrospectively registered on 03/12/2025. Axial spondyloarthritis Cholesterol spinal inflammation MRI Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Axial spondyloarthritis (axSpA) is a chronic systemic inflammatory disease primarily affecting the axial skeleton, leading to adverse outcomes including pain, fatigue, limited mobility, and postural changes[1]. Genetic factors, such as human leukocyte antigen-B27 (HLA-B27)[2], interact with various types of stress (including mechanical stress, endoplasmic reticulum stress, and microbial stress on body surfaces), resulting in the production of pro-inflammatory cytokines and danger signals[3, 4]. These subsequently activate inflammatory leukocytes and other cell populations at the entheses (the sites where tendons or ligaments attach to bone). Osteoclasts play a significant role in the pathological process of axSpA, not only causing inflammatory bone destruction but also indirectly promoting ligamentous ossification and bony ankylosis[5]. Bone metabolism is highly sensitive to environmental changes, and the relationship between lipid metabolism and bone metabolism is close. Previous studies have shown that hyperlipidemia can affect bones by influencing osteoblasts and osteoclasts[6]. Cholesterol (Chol) plays a crucial role in many cellular functions, such as participating in the formation of lipid rafts to provide a membrane signal transduction platform for osteoclastogenesis[7]. The regulation of cholesterol by lipoproteins also plays an important role in osteoclasts. Low-density lipoprotein (LDL) induced cholesterol delivery significantly enhances osteoclast viability, while LDL depletion inhibits osteoclast formation[8]. Cholesterol efflux to High-density lipoprotein (HDL) is critical for osteoclast apoptosis and fusion[9]. Furthermore, studies have shown that lipid ratios(Chol/LDL, HDL/LDL) more comprehensively reflect cholesterol metabolism status and disease risk than single lipid parameters[10]. It is now widely accepted that subcutaneous-to-visceral adipose ratio (SAT/VAT) are closely associated with lipid metabolism disorders [11] and liver fat fraction are positively correlated with long-term blood triglyceride and cholesterol levels[12], Studies have shown that visceral adipose tissue at the L4-L5 intervertebral disc level is significantly positively correlated with serum lipid and cholesterol levels in the Chinese population[13]. Therefore, to enhance the validity of this study, these two imaging parameters—LFF and SAT/VAT—were incorporated. Magnetic resonance imaging (MRI) can effectively display inflammatory lesions in the vertebrae of patients with spondyloarthritis. For acute inflammation, the STIR sequence, which is highly sensitive to edema, can be combined with the Spondyloarthritis Research Consortium of Canada (SPARCC) scoring system to semi-quantitatively evaluate acute inflammatory lesions across 23 vertebral units[14, 15]. For chronic inflammation, T1-weighted imaging (T1WI) can reveal fat deposition in the vertebral corner (fatty Romanus lesions) after inflammation subsides, and this phenomenon is a more important independent risk factor for osteogenesis than bone marrow edema[16]. However, there is currently no standardized semi-quantitative scoring system for such chronic lesions. Therefore, this study employed the multi-echo DIXON fat quantification technique(mDixon-Quant) to accurately quantify chronic spinal inflammatory lesions by measuring the fat fraction at multiple levels of the whole-spine vertebral corners[17]. Therefore, this study aimed to investigate the correlation between the severity of acute and chronic spinal inflammatory lesions and lipid metabolism abnormalities in patients with axial axSpA. Methods Study population We prospectively recruited 92 consecutive patients (65 males, 27 females) diagnosed with axSpA from the Department of Rheumatology and Immunology at Shengjing Hospital Of China Medical University between June 2024 and May 2025 and simultaneously obtain its imaging, clinical, and laboratory data. All patients fulfilled the 2009 ASAS classification criteria for axSpA, with diagnosis confirmed by board-certified rheumatologists. Clinical evaluation Baseline data collected from study participants included age, sex, disease duration, smoking status, alcohol consumption, International Physical Activity Questionnaire (IPAQ) score[18], and medication use (non-steroidal anti-inflammatory drugs [NSAIDs], conventional synthetic disease-modifying antirheumatic drugs [csDMARDs], and biological agents). Laboratory parameters encompassed C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), triglycerides (TG), total Chol, HDL, LDL, Chol/LDL and HDL/LDL. Disease activity was assessed using Ankylosing Spondylitis Disease Activity Score based on C-reactive protein (ASDAS-CRP)[19], while static and dynamic functional impairments were evaluated using the Bath Ankylosing Spondylitis Metrology Index (BASMI) [20]and the Bath Ankylosing Spondylitis Functional Index (BASFI)[21], respectively. Vertebral imaging assessment MRI examinations were performed using a PHILIPS Ingenia CX 3.0T MRI scanner, with detailed parameters listed in the Table 1 . Whole-spine sagittal STIR sequences were acquired to evaluate acute inflammatory lesions (acute Romanus lesions) and calculate the SPARCC score (Fig. 1 A). The mDixon-Quant sequence was applied to measure VFF (Fig. 1 B). The specific measurement method was as follows: from the inferior endplate of C2 to the superior endplate of S1, vertebral corner fat fraction was measured at three standardized slices per vertebra: midsagittal and bilateral planes (cervical: two slices medial to the pedicle; thoracic/lumbar: at the pedicle level) On each slice, the vertebral margin was equally trisected to define the corners, and regions of interest (ROIs) were placed at the four vertebral corners to calculate the corner fat fraction (Fig. 1 C). Vertebral corners with severe SPARCC bone marrow edema (i.e., those with signal intensity points) were excluded to avoid interference. Structural damage was assessed using lateral spine X-ray images according to the modified Stoke Ankylosing Spondylitis Spine Score (mSASSS)[22]. Both mSASSS scoring and vertebral corner fat fraction measurements were independently performed by two radiologists with five years of experience in imaging diagnosis of rheumatic bone diseases, and the final values were calculated as the mean of the two readers' measurements. Liver Fat Fraction The LFF was quantitatively assessed using the mDixon-Quant sequence. Two radiologists independently placed a total of eight regions of interest (ROIs, each with an area of 2.9–3.1 cm²) across the following hepatic segments at two standardized anatomical levels: the portal vein trunk level and the sub-hilar level. Specifically, ROIs were positioned in the left lateral lobe, left medial lobe, right anterior lobe, and right posterior lobe (Fig. 1 .D)[23]. The mean values from the two readers were calculated for final analysis. During ROI placement, careful attention was paid to avoid bile ducts, major vessels, focal lesions, liver margins, and imaging artifacts. Subcutaneous-Visceral adipose ratio Abdominal axial T1WI was performed to evaluate SAT/VAT at the L4-L5 level (Fig. 1 .E), with the scan range extending from the first lumbar vertebra to the first sacral vertebra. The corresponding single-slice DICOM files were imported into Mimics Research software (version 21.0, Materialise, Leuven, Belgium). Two radiologists independently performed adipose tissue segmentation to obtain SAT/VAT values, and the final results were calculated as the mean of the two readers' measurements. Table 1 Scanning parameters for different MRI sequences Sequence FOV(mm) Voxel(mm) Matrix Slices NSA TE(ms) TR(ms) Spine STIR 180×329×60 1.20×1.51×3.00 152×213 20 1 70.00 4142.00-4755.00 Spine mDixon-Quant 300×324×75 1.80×1.82×3.00 168×178 50 1 1.45 8.20 Liver mDixon-Quant 400×357×210 2.00×2.00×6.00 200×179 70 1 1.15 6.30 Abdominal axial T1WI 400×363×183 1.60×1.96×7.00 252×185 22 1 2.30 12.00 MRI examinations were performed using a PHILIPS Ingenia CX 3.0T scanner. Data are presented as field of view (FOV), voxel size, matrix, number of slices, number of signal averages (NSA), echo time (TE), and repetition time (TR). Statistical analysis Normality was tested using the Shapiro-Wilk test. Continuous variables with normal distribution were expressed as mean ± SD and compared using the independent samples t-test; otherwise, they were presented as median (IQR) and compared using the Mann-Whitney U test. Categorical variables were expressed as number (%) and compared using the χ² test. The intraclass correlation coefficient (ICC) with 95% confidence interval was calculated to assess inter-rater consistency for SAT/VAT ratio, LFF, mSASSS score, SPARCC score, and VFF.Univariate linear regression was performed to evaluate the associations of SPARCC score and vertebral fat fraction with clinical, imaging, and laboratory parameters. Three stepwise-adjusted multivariable regression models were constructed: Model 1 adjusted for age, sex, HLA-B27 status, and disease duration; Model 2 further adjusted for CRP, ESR, BASDAI, and ASDAS-CRP; Model 3 additionally adjusted for BASMI, mSASSS, and BASFI. Statistical significance was considered for a p-value < 0.05(two-sided). All analysis were performed with R 4.4.1 ® Results Reliability Analysis All five variables demonstrated good to excellent reliability. The ICC values were as follows: SAT/VAT (ICC = 0.990, 95% CI: 0.982–0.995), LFF(ICC = 0.977, 95% CI: 0.964–0.986), mSASSS score (ICC = 0.996, 95% CI: 0.993–0.998), SPARCC score(ICC = 0.985, 95% CI: 0.975–0.991), and VFF (ICC = 0.876, 95% CI: 0.823–0.916). All ICC values were statistically significant (all P < 0.001) (Fig. 2 ). The distribution of SPARCC scores and VFF across vertebral levels is shown in Fig. 3 . Baseline Characteristics by Disease Activity Status A total of 92 patients were categorized into non-high (n = 31) and high (n = 61) disease activity groups. Patients with high disease activity demonstrated significantly higher SPARCC scores and vertebral fat fraction (both P < 0.05) (Fig. 4 ). Additionally, the high-activity group had significantly elevated inflammatory markers (CRP and ESR), as well as higher disease activity indices (ASDAS-CRP, BASDAI), and functional impairment scores (BSAFI, BASMI) (all P < 0.05). No significant differences were observed between the two groups regarding age, sex, HLA-B27 status, disease duration, mSASSS score, smoking status, alcohol abuse, BMI, IPAQ score, lipid parameters, SAT/VAT ratio, LFF, or the use of NSAIDs, csDMARDs, or biologics (all P > 0.05), results are summarized in Table 2 . Table 2 Baseline characteristics by disease activity Disease activity Non-high activity High activity P -value n 31 61 SPARCC 10.00(4.50, 30.00) 37.5(21.00, 61.00) < 0.001 VFF (%) 44.06 ± 11.67 49.77 ± 9.39 0.013 Age 36.00 (32.00, 42.00) 38.00 (34.50, 44.00) 0.439 Gender 0.247 Male 20 (64.5%) 45 (73.8%) Female 11 (35.5%) 16 (26.2%) HLA-B27(+/-) 0.206 + 26 (83.9%) 45 (73.8%) - 5 (16.1%) 16 (26.2%) Disease duration 9.00 (2.00, 15.00) 12.00 (5.00, 18.00) 0.191 CRP (mg/L) 2.50 (2.50, 2.50) 4.55 (2.50, 8.90) < 0.001 ESR (mm/h) 6.00 (3.00, 13.00) 14.00 (4.00, 28.00) 0.014 BASDAI 2.25 ± 1.04 5.21 ± 1.53 < 0.001 BASFI 1.00 (1.00, 1.80) 2.50 (1.40, 5.00) < 0.001 BASMI 0 (0, 3.00) 1.00 (0, 4.50) 0.012 mSASSS Score 7.00 (0, 17.00) 9.05 (2.50, 28.58) 0.522 Current Smoking 0.333 Yes 12 (38.7%) 28 (45.9%) No 19 (61.3%) 33 (54.1%) Alcohol Abuse 0.221 Yes 7 (22.6%) 20 (32.8%) No 24 (77.4%) 41 (67.2%) BMI 25.51 ± 4.52 25.06 ± 3.90 0.623 IPAQ Score 693.00 (693.00, 1413.00) 693.00 (462.00, 1396.00) 0.153 TG (mmol/L) 1.12 (0.76, 1.90) 1.51 (0.91, 2.19) 0.120 Chol (mmol/L) 4.55 (3.83, 4.97) 4.35 (3.94, 5.06) 0.934 HDL (mmol/L) 1.16 ± 0.28 1.08 ± 0.24 0.147 LDL (mmol/L) 2.51 ± 0.65 2.60 ± 0.86 0.630 Chol/HDL 4.08 ± 1.25 4.37 ± 1.35 0.329 LDL/HDL 2.26 (1.60, 3.13) 2.36 (1.77, 3.08) 0.288 SAT/VAT 2.36 (1.50, 2.90) 1.95 (1.58, 2.94) 0.600 LFF (%) 3.34 (1.89, 9.12) 4.42 (2.86, 7.48) 0.290 NSAIDs Use 0.320 Yes 17 (54.8%) 38 (62.3%) No 14 (45.2%) 23 (37.7%) csDMARDs Use 0.379 Yes 4 (12.9%) 11 (18.0%) No 27 (87.1%) 50 (82.0%) Biologic s Use 0.391 Yes 21 (67.7%) 38 (62.3%) No 10 (32.3%) 23 (37.7%) Data are presented as mean ± SD, median (IQR), or n (%). P-values were derived using independent t-test, Mann-Whitney U test, or chi-square test, as appropriate, with disease activity status as the grouping variable. High disease activity was defined as ASDAS-CRP ≥ 2.1. Univariate Regression Analysis of SPARCC Score and Vertebral Fat Fraction Univariate linear regression analyses revealed distinct patterns of factors associated with SPARCC score and vertebral fat fraction. SPARCC score was significantly associated with a broad range of variables, including demographic factors (male), disease-related characteristics (disease duration, BASMI, mSASSS, BASDAI, ASDAS-CRP, BASFI), inflammatory markers (CRP, ESR), lifestyle factors (smoking, alcohol abuse, BMI), lipid parameters (Chol, HDL, Chol/HDL, LDL/HDL), and imaging parameters (SAT/VAT ratio, LFF) (all P 0.05). Notably, HLA-B27 status, IPAQ score, LDL, and the use of NSAIDs, csDMARDs, or biologics were not significantly associated with either outcome (all P > 0.05), results are summarized in Table 3 . Table 3 Univariate analysis: SPARCC score and VFF Variable SPARCC Vertebral Fat Fraction β B P -value β B P -value Age 0.264 0.101 0.337 0.536 0.486 < 0.001 Gender (Male) 17.752 0.326 0.001 6.690 0.292 0.005 HLA-B27(+) 5.950 0.101 0.339 -0.454 -0.018 0.863 Disease Duration (Years) 0.747 0.241 0.021 0.601 0.459 < 0.001 BASMI 0.972 0.363 < 0.001 0.203 0.180 0.086 mSASSS Total Score 0.614 0.391 < 0.001 0.161 0.243 0.019 CRP (mg/L) 3.376 0.267 0.010 0.733 0.138 0.191 ESR (mm/h) 11.621 0.433 < 0.001 3.521 0.311 0.003 BASDAI 3.078 0.269 0.009 2.480 0.514 < 0.001 ASDAS-CRP 3.669 0.371 < 0.001 2.267 0.543 < 0.001 BASFI 0.513 0.376 < 0.001 0.346 0.602 < 0.001 Current Smoking (Yes) 23.716 0.475 < 0.001 7.191 0.341 0.001 Alcohol Abuse (Yes) 11.552 0.212 0.042 4.187 0.182 0.082 BMI 1.286 0.212 0.043 0.718 0.281 0.007 IPAQ Score -0.001 -0.042 0.689 -0.001 -0.117 0.268 TG (mmol/L) 2.881 0.198 0.058 1.649 0.269 0.009 Chol (mmol/L) 6.379 0.251 0.016 2.914 0.272 0.009 HDL (mmol/L) -33.842 -0.348 0.001 -13.310 -0.324 0.002 LDL (mmol/L) 5.334 0.171 0.104 1.059 0.08 0.446 Chol/HDL 8.302 0.438 < 0.001 3.217 0.402 < 0.001 LDL/HDL 9.246 0.351 0.001 2.547 0.229 0.028 SAT/VAT -7.255 -0.385 < 0.001 -3.318 -0.417 < 0.001 LFF (%) 1.404 0.293 0.005 0.554 0.273 0.008 NSAIDs Use (Yes) -0.612 -0.012 0.909 2.212 0.104 0.325 csDMARDs Use (Yes) 8.106 0.121 0.251 4.112 0.145 0.167 Biologics Use (Yes) -5.159 -0.100 0.343 -1.461 -0.067 0.525 Univariate linear regression analysis was performed to evaluate the association of each variable with SPARCC score and vertebral fat fraction (VFF). Data are presented as β (unstandardized coefficient), B (standardized coefficient), and P-value. Bold values indicate statistical significance (P < 0.05). Multivariable Regression Analysis of SPARCC Score Multivariable linear regression analyses were performed to identify factors independently associated with SPARCC score. In Model 1 (adjusted for age, sex, HLA-B27, and disease duration), current smoking (β = 20.08, P = 0.001), HDL (β = -25.085, P = 0.018), Chol/HDL (β = 6.485, P = 0.002), LDL/HDL (β = 7.073, P = 0.008), SAT/VAT (β = -4.634, P = 0.037), and LFF (β = 1.115, P = 0.020) were significantly associated with SPARCC score. After additional adjustment for inflammatory markers and disease activity indices in Model 2, current smoking (β = 16.944, P = 0.001), Chol/HDL (β = 4.693, P = 0.014), and LFF (β = 1.259, P = 0.003) remained significant. In the fully adjusted Model 3 (further adjusted for functional impairment), current smoking (β = 16.727, P = 0.001), Chol/HDL (β = 4.253, P = 0.021), LDL/HDL (β = 5.114, P = 0.038), and LFF (β = 1.014, P = 0.016) remained independently associated with SPARCC score. Notably, current smoking and LFF demonstrated robust associations across all three models. Lipid parameters, particularly Chol/HDL and LDL/HDL, also showed independent positive associations with SPARCC score after full adjustment. Detailed results are summarized in Table 4 and Fig. 6 . Table 4 Multivariable analysis of factors associated with SPARCC score Variable Model 1 Model 2 Model 3 β B P -value β B P -value β B P -value Current Smoking (Yes) 20.08 0.402 0.001 16.944 0.339 0.001 16.727 0.335 0.001 Alcohol Abuse (Yes) 3.947 0.073 0.509 2.588 0.048 0.621 5.791 0.106 0.266 BMI 0.785 0.129 0.200 0.971 0.160 0.068 0.931 0.153 0.077 IPAQ Score -0.001 -0.059 0.558 < 0.001 0.012 0.893 0.001 0.028 0.744 TG (mmol/L) 1.944 0.134 0.181 1.717 0.118 0.189 0.744 0.051 0.568 Chol (mmol/L) 4.224 0.166 0.097 3.483 0.137 0.135 2.749 0.108 0.228 HDL (mmol/L) -25.085 -0.258 0.018 -15.742 -0.162 0.116 -14.945 -0.153 0.119 LDL (mmol/L) 4.508 0.144 0.141 2.438 0.078 0.367 2.873 0.092 0.280 Chol/HDL 6.485 0.342 0.002 4.693 0.248 0.014 4.253 0.225 0.021 LDL/HDL 7.073 0.268 0.008 4.563 0.173 0.073 5.114 0.194 0.038 SAT/VAT -4.634 -0.246 0.037 -3.758 -0.199 0.054 -2.724 -0.145 0.163 LFF (%) 1.115 0.232 0.020 1.259 0.262 0.003 1.014 0.211 0.016 NSAIDs Use (Yes) -2.218 -0.044 0.658 -5.300 -0.105 0.235 -5.050 -0.100 0.241 csDMARDs Use (Yes) 7.504 0.112 0.255 6.224 0.093 0.295 3.514 0.052 0.561 Biologics Use (Yes) -7.263 -0.141 0.162 -3.018 -0.058 0.519 -2.517 -0.049 0.577 Multivariable linear regression analysis with stepwise adjustment. Model 1 adjusted for age, sex, HLA-B27, and disease duration; Model 2 further adjusted for CRP, ESR, ASDAS-CRP, and BASDAI; Model 3 additionally adjusted for BASMI, BASFI, and mSASSS. Data are presented as β (unstandardized coefficient), B (standardized coefficient), and P-value. Bold values indicate statistical significance (P < 0.05). Multivariable Regression Analysis of Vertebral Fat Fraction Multivariable linear regression analyses were performed to identify factors independently associated with VFF. In Model 1 (adjusted for age, sex, HLA-B27, and disease duration), current smoking (β = 4.520, P = 0.029), TG (β = 1.201, P = 0.015), Chol/HDL ratio (β = 1.866, P = 0.009), and LFF (β = 0.509, P = 0.002) were significantly associated with VFF. After additional adjustment for inflammatory markers and disease activity indices in Model 2, TG (β = 1.074, P = 0.029), Chol/HDL (β = 1.473, P = 0.043), and LFF (β = 0.525, P = 0.001) remained significant, while current smoking lost significance (β = 3.730, P = 0.065). In the fully adjusted Model 3 (further adjusted for functional impairment), current smoking (β = 3.646, P = 0.040), total cholesterol (β = 1.544, P = 0.050), Chol/HDL ratio (β = 1.399, P = 0.030), and LFF (β = 0.404, P = 0.005) remained independently associated with VFF. Notably, LFF demonstrated robust associations across all three models, and lipid parameters, particularly total cholesterol and Chol/HDL, showed independent positive associations with VFF after full adjustment. Detailed results are summarized in Table 5 and Fig. 6 . Table 5 Multivariable analysis of factors associated with VFF Variable Model 1 Model 2 Model 3 β B P -value β B P -value β B P -value Current Smoking (Yes) 4.520 0.214 0.029 3.730 0.177 0.065 3.646 0.173 0.040 Alcohol Abuse (Yes) 0.070 0.003 0.973 -0.433 -0.019 0.827 2.097 0.091 0.247 BMI 0.404 0.158 0.054 0.419 0.164 0.037 0.298 0.116 0.105 IPAQ Score -0.001 -0.098 0.231 < 0.001 -0.055 0.492 < 0.001 -0.043 0.541 TG (mmol/L) 1.201 0.196 0.015 1.074 0.175 0.029 0.621 0.101 0.169 Chol (mmol/L) 1.626 0.152 0.063 1.551 0.145 0.078 1.544 0.144 0.050 HDL (mmol/L) -6.793 -0.165 0.065 -4.34 -0.106 0.254 -3.987 -0.097 0.234 LDL (mmol/L) 0.608 0.046 0.566 0.141 0.011 0.891 0.841 0.064 0.364 Chol/HDL 1.866 0.233 0.009 1.473 0.184 0.043 1.399 0.175 0.030 LDL/HDL 1.198 0.108 0.202 0.566 0.051 0.560 1.165 0.105 0.179 SAT/VAT -0.666 -0.084 0.389 -0.498 -0.063 0.505 -0.265 -0.033 0.698 LFF(%) 0.509 0.251 0.002 0.525 0.259 0.001 0.404 0.200 0.005 NSAIDs Use (Yes) 1.897 0.089 0.270 1.754 0.082 0.300 1.438 0.067 0.338 csDMARDs Use (Yes) 3.264 0.115 0.149 2.399 0.085 0.286 0.469 0.017 0.824 Biologics Use (Yes) -0.639 -0.029 0.722 0.610 0.028 0.731 0.353 0.016 0.822 Multivariable linear regression analysis with stepwise adjustment. Model 1 adjusted for age, sex, HLA-B27, and disease duration; Model 2 further adjusted for CRP, ESR, ASDAS-CRP, and BASDAI; Model 3 additionally adjusted for BASMI, BASFI, and mSASSS. Data are presented as β (unstandardized coefficient), B (standardized coefficient), and P-value. Bold values indicate statistical significance (P < 0.05). Discussion Axial spondyloarthritis (axSpA) is a chronic inflammatory disease primarily affecting the axial skeleton. During active phases, it manifests as pain, fatigue, and restricted mobility, while advanced structural vertebral damage leads to postural deformities. Poorly controlled disease with sustained high activity severely impairs quality of life and may compromise respiratory function due to thoracic cage deformities resulting from postural changes[24, 25]. Consequently, reducing disease activity and delaying structural progression represent critical therapeutic goals. However, despite the availability of biologic agents, treatment failure remains common, and the heterogeneity of therapeutic responses underscores the need to explore novel determinants of disease activity, including lipid metabolism[26, 27]. Magnetic resonance imaging was employed to comprehensively and accurately assess both acute and chronic spinal inflammatory lesions using semi-quantitative (SPARCC) and quantitative (VFF) methods. A previous study measuring lumbar VFF only at the mid-sagittal plane reported that VFF was highest in patients with high disease activity (ASDAS 2.1–3.5) but significantly lower in those with very high disease activity (ASDAS ≥ 3.5), a finding potentially confounded by bone marrow edema at inflammatory sites[28]. In contrast, the present study performed a more rigorous and comprehensive quantitative assessment by measuring VFF at three standardized anatomical levels throughout the entire spine (cervical, thoracic, and lumbar) while deliberately excluding vertebral corners with severe bone marrow edema. This approach ensured that the measured fat fraction accurately reflected chronic fatty deposition rather than acute inflammatory changes. Furthermore, the SPARCC scoring system was applied to semi-quantitatively evaluate acute inflammatory lesions across 23 vertebral units. Together, these methodologies provide a holistic and precise characterization of spinal inflammation in axSpA patients. In this study, after adjusting for age, sex, disease duration, disease activity, and structural/functional impairment, multivariable analysis revealed that current smoking, LFF, and cholesterol ratios (Chol/HDL, LDL/HDL) were independent determinants of acute spinal inflammation, while LFF, Chol, and the Chol/HDL ratio were independently associated with chronic spinal fat deposition. These findings indicate that axSpA patients with elevated circulating cholesterol levels exhibit more severe spinal inflammation in both extent and degree. Existing research has primarily focused on inflammatory cytokines and immune cells, whereas the role of osteoclasts in axSpA pathogenesis remains underexplored. Cholesterol plays a critical role in osteoclast function. As a component of lipid rafts, cholesterol facilitates RANK-RANKL signaling to drive osteoclastogenesis[7]. Cholesterol efflux from osteoclasts to high-density lipoprotein (HDL) is essential for osteoclast apoptosis and fusion, and cholesterol depletion induces osteoclast apoptosis, while low-density lipoprotein (LDL) depletion suppresses osteoclast formation[8]. Nuclear receptors such as ERRα mediate cholesterol-driven increases in osteoclast activity[29]. Clinical studies have confirmed that hypercholesterolemia is associated with lower bone mineral density and higher fracture risk[30]. High-fat diets promote osteoclastogenesis-induced bone loss and exacerbate joint damage[31, 32]. Collectively, these lines of evidence suggest that cholesterol metabolism disorders may contribute to bone inflammation and remodeling in axSpA by modulating osteoclast function. In the current study, the Chol/HDL and LDL/HDL ratios demonstrated stronger associations with spinal inflammation than individual cholesterol parameters. Total cholesterol measured in routine blood tests represents the sum of cholesterol carried by all lipoproteins, including LDL-C (60–70%), HDL-C (20–30%), and very-low-density lipoprotein cholesterol (VLDL-C) along with other minor lipoproteins (10–15%)[33]. therefore, it cannot accurately reflect the level of bioavailable cholesterol in peripheral tissues. Cholesterol ratios, by contrast, integrate information on cholesterol distribution and transport efficiency, thereby providing a more comprehensive assessment of overall cholesterol metabolism status. HDL serves as the key lipoprotein responsible for reverse cholesterol transport from peripheral tissues back to the liver. A relative deficiency in HDL, indicated by an elevated Chol/HDL ratio, leads to cholesterol excess in peripheral tissues, which may promote osteoclast generation and survival, thereby exacerbating spinal inflammation. Furthermore, elevated LFF, a marker of chronic dyslipidemia, was associated with high disease activity, potentially through inhibition of ApoA1 synthesis and HDL generation[34]. These findings suggest that cholesterol metabolism disorders, particularly abnormal cholesterol ratios and hepatic fat deposition, are independent risk factors for exacerbating spinal inflammatory lesions in axSpA. The present study identified current smoking as an independent risk factor for acute spinal inflammation. Smoking is closely associated with cholesterol metabolism. It induces oxidative stress and inflammatory responses, while simultaneously elevating serum total cholesterol and LDL levels and reducing HDL levels, leading to increased Chol/HDL and LDL/HDL ratios[35]. Furthermore, smoking promotes cholesterol accumulation in macrophages and accelerates foam cell formation, thereby amplifying inflammatory cascades[36]. Thus, smoking may exacerbate spinal inflammation through two pathways: a direct pro-inflammatory effect and an indirect effect mediated by cholesterol metabolism. These findings highlight the importance of smoking cessation counseling in improving spinal inflammatory lesions and lipid profiles. Conclusion This study demonstrates that cholesterol metabolism disorder, characterized by elevated total cholesterol, Chol/HDL ratio, and LDL/HDL ratio, is an independent risk factor for both acute and chronic spinal inflammation in axSpA patients. These findings directly link cholesterol metabolism to the pathophysiology of axSpA. The cholesterol metabolic status of axSpA patients warrants greater attention in clinical evaluation, and monitoring serum cholesterol and lipoprotein levels may help identify axSpA patients at high risk of spinal inflammation. Abbreviations ASAS Assessment of spondyloarthropathy international society ASDAS-CRP Ankylosing spondylitis disease activity score based on c-reactive protein axSpA Axial spondyloarthritis BASDAI Bath ankylosing spondylitis disease activity index BASFI Bath ankylosing spondylitis functional index BASMI Bath ankylosing spondylitis metrology index BMI Body mass index Chol Total cholesterol CRP C-reactive protein csDMARDs Conventional synthetic disease-modifying antirheumatic drugs ESR Erythrocyte sedimentation rate FOV Field of view HDL High density lipoprotein HLA-B27 Human leukocyte antigen-b27 ICC Intraclass correlation coefficient IPAQ International physical activity questionnaire LDL Low density lipoprotein LFF Liver fat fraction MRI Magnetic resonance imaging mSASSS Modified stoke ankylosing spondylitis spine score NSAIDs Non-steroidal anti-inflammatory drugs ROI Region of interest SAT/VAT Subcutaneous adipose tissue / visceral adipose ratio SPARCC Spondyloarthritis research consortium of canada STIR Short tau inversion recovery TE Echo time TG Triglycerides T1WI T1-weighted imaging TR Repetition time VFF Vertebral fat fraction Declarations Acknowledgements The authors thank Professor Yang Hou, Director of the Department of Radiology, Shengjing Hospital of China Medical University, for providing MRI equipment support for this study. We also thank Professor Lei Jin, Director of the Department of Rheumatology and Immunology, for recruiting axSpA patients. We are grateful to all the patients and their families for their participation and commitment to this study, as well as all the graduate students and their supervisors in the Musculoskeletal Imaging Research Group of the Department of Radiology for their dedication and contributions to this work. Author contributions Hexin Feng participated in MRI scanning and manuscript writing, and approved the final version for submission. Jiahong Zhu, Ying Zhang, Ziwei Fu, and Yanbo Wu contributed to data collection, aggregation, measurement, and analysis, critically reviewed the manuscript, and approved the final version. Pengfei Li and Shinong Pan are co-corresponding authors. Pengfei Li contributed to study design and manuscript revision, and approved the final version. Shinong Pan supervised the entire study, participated in its conception and design, interpreted the data, critically reviewed the manuscript, and approved the final version. All authors are members of the Musculoskeletal Imaging Research Group, Department of Radiology, Shengjing Hospital of China Medical University, with the exception of Pengfei Li, who is affiliated with the Musculoskeletal Imaging Research Group, Department of Radiology, Affiliated Hospital of Inner Mongolia Minzu University. Funding This study was supported by the Department of Education of Liaoning Province (Grant No. LJKMZ20221163). The funder had no role in the study design, data collection, analysis, interpretation, or manuscript writing. Shinong Pan is the grant recipient. Data availability The data that support the findings of this study are not publicly available due to ethical restrictions and to protect patient confidentiality. The Institutional Review Board of Shengjing Hospital of China Medical University has imposed restrictions on data sharing. De-identified data may be available from the corresponding author upon reasonable request, subject to approval by the Institutional Review Board. Ethics approval and consent to participate The study was approved by the Institutional Review Board of Shengjing Hospital of China Medical University (Approval No. 2024PS1182K). All procedures involving human participants were performed in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki Declaration and its later amendments, as well as other relevant guidelines and regulations. Written informed consent was obtained from all participants. Consent for publication Not applicable. Competing interests All authors declare that they have no competing interests References Victoria N-C, Alexandre S, Bassel E-Z, Désirée vdH. Axial spondyloarthritis. Ann Rheum Dis. 2021;80(12):1511-21.https://doi.org/10.1136/annrheumdis-2021-221035 David E, Luke J, Sarah L S, Adrian C, Jörn B, Buhm H, et al. Analysis of five chronic inflammatory diseases identifies 27 new associations and highlights disease-specific patterns at shared loci. Nat Genet. 2016;48(5):510-18.https://doi.org/10.1038/ng.3528 R C A, T S, N B-P, E M A H, H M-O, D M. The role of biomechanical factors in ankylosing spondylitis: the patient's perspective. Reumatismo. 2016;67(3):91-96.https://doi.org/10.4081/reumatismo.2015.853 Liesbet VP, Filip E VdB, Peggy J, Philippe C, Lennart J, Roos C, et al. Microscopic gut inflammation in axial spondyloarthritis: a multiparametric predictive model. Ann Rheum Dis. 2012;72(3):414-17.https://doi.org/10.1136/annrheumdis-2012-202135 Zhenhua L, Mingxi C, Haoteng K, Huazong D, Weijia Y, Tao W, et al. Fibroblast Insights into the Pathogenesis of Ankylosing Spondylitis. 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A new scoring system for the Bath Ankylosing Spondylitis Metrology Index (BASMI). J Rheumatol. 1995;22(8):1609 D B, A C, P C, L M B. An update on the Bath Ankylosing Spondylitis Disease Activity and Functional Indices (BASDAI, BASFI): excellent Cronbach's alpha scores. J Rheumatol. 1996;23(2):407-08 Désirée vdH, Jürgen B, Atul D, Xenofon B, Robert L, Hanno B R, et al. Modified stoke ankylosing spondylitis spinal score as an outcome measure to assess the impact of treatment on structural progression in ankylosing spondylitis. Rheumatology (Oxford). 2018;58(3):388-400.https://doi.org/10.1093/rheumatology/key128 Yonghong Z, Shengsheng Y, Xianyuan C, Jieqin L, Jiawei S, Shun Y. The Correlation between Type 2 Diabetes and Fat Fraction in Liver and Pancreas: A Study using MR Dixon Technique. Contrast Media Mol Imaging. 2023;2022(0):7073647.https://doi.org/10.1155/2022/7073647 Huifang Z, Min-Min L. Ankylosing spondylitis and psychiatric disorders in European population: a Mendelian randomization study. Front Immunol. 2023;14(0):1277959.https://doi.org/10.3389/fimmu.2023.1277959 Daniel S, Teresa S-S, Beata T, Beata S. Is there a connection between spine alignment, chest mobility, shoulder joint and respiratory parameters of patients with ankylosing spondylitis? Rheumatol Int. 2024;44(8):1481-86.https://doi.org/10.1007/s00296-024-05642-0 Abhijeet D, Atul D. Treatment of axial spondyloarthritis: an update. Nat Rev Rheumatol. 2022;18(4):205-16.https://doi.org/10.1038/s41584-022-00761-z Casper W, Augusta O, Alexandre S, Louise F, Xenofon B, Robert B M L, et al. Efficacy and safety of biological DMARDs: a systematic literature review informing the 2022 update of the ASAS-EULAR recommendations for the management of axial spondyloarthritis. Ann Rheum Dis. 2022;82(1):130-41.https://doi.org/10.1136/ard-2022-223298 Ga Young A, Bon San K, Kyung Bin J, Tae-Hwan K, Seunghun L. Use of Quantitative Vertebral Bone Marrow Fat Fraction to Assess Disease Activity and Chronicity in Patients with Ankylosing Spondylitis. Korean J Radiol. 2021;22(10):1671-79.https://doi.org/10.3348/kjr.2020.0953 Wei W, Adam G S, Xueqian W, Xunde W, Shili C, Qian C, et al. Ligand Activation of ERRα by Cholesterol Mediates Statin and Bisphosphonate Effects. Cell Metab. 2016;23(3):479-91.https://doi.org/10.1016/j.cmet.2015.12.010 Xi X, David T W L, Chengsheng J, Ziyi Z, Chao X, Paul W, et al. Associations of Serum Lipid Traits With Fracture and Osteoporosis: A Prospective Cohort Study From the UK Biobank. J Cachexia Sarcopenia Muscle. 2024;15(6):2669-83.https://doi.org/10.1002/jcsm.13611 Kristine P, Jaclynn K, Danese J, Michael R F, Steven A G, Keith R S. Hypercholesterolemia promotes an osteoporotic phenotype. Am J Pathol. 2012;181(3):928-36.https://doi.org/10.1016/j.ajpath.2012.05.034 I P-P, J A R-B, M J M-C, R G, R L, Gabriel H-B. Hypercholesterolemia boosts joint destruction in chronic arthritis. An experimental model aggravated by foam macrophage infiltration. Arthritis Res Ther. 2013;15(4):R81.https://doi.org/10.1186/ar4261 Kenneth R F. Lipid and Lipoprotein Metabolism. Endocrinol Metab Clin North Am. 2022;51(3):437-58.https://doi.org/10.1016/j.ecl.2022.02.008 Fabiana R, Claudia-Gabriela P, Teodor S, Petruța Violeta F, Corina P, Carmen F-B. The Link between NAFLD and Metabolic Syndrome. Diagnostics (Basel). 2023;13(4):614.https://doi.org/10.3390/diagnostics13040614 Mahmood M, Pedram E, Motahareh K, Mona M, Fatemeh M, Shamim M, et al. Association Between Smoking and Lipid Profile in Men Aged 35 to 70 Years: Dose-Response Analysis. Am J Mens Health. 2024;18(3).https://doi.org/10.1177/15579883241249655 Ming-Sheng Z, Kiranmai C, Armando J M, Edgar A J, Roy L S, Keith W, et al. Nicotine potentiates proatherogenic effects of oxLDL by stimulating and upregulating macrophage CD36 signaling. Am J Physiol Heart Circ Physiol. 2013;305(4).https://doi.org/10.1152/ajpheart.00042.2013 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 30 Apr, 2026 Reviewers invited by journal 19 Apr, 2026 Editor assigned by journal 16 Apr, 2026 Submission checks completed at journal 16 Apr, 2026 First submitted to journal 11 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9389876","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":629442909,"identity":"83ebe39f-6532-47ee-901f-7b11b654ce11","order_by":0,"name":"Hexin Feng","email":"","orcid":"","institution":"Sheng Jing Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hexin","middleName":"","lastName":"Feng","suffix":""},{"id":629442936,"identity":"af7c52ef-1b30-48e4-94c6-85f6763c4e5a","order_by":1,"name":"Jiahong Zhu","email":"","orcid":"","institution":"Sheng Jing Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jiahong","middleName":"","lastName":"Zhu","suffix":""},{"id":629442954,"identity":"987e596c-c5cc-4c53-a9e0-578b70cbb3cd","order_by":2,"name":"Ying Zhang","email":"","orcid":"","institution":"Sheng Jing Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Zhang","suffix":""},{"id":629442955,"identity":"af010e87-1f37-4ba0-88d7-38a13ecc1444","order_by":3,"name":"Ziwei Fu","email":"","orcid":"","institution":"Sheng Jing Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ziwei","middleName":"","lastName":"Fu","suffix":""},{"id":629442958,"identity":"d8c12c73-009a-4af4-a8f5-c582462007a7","order_by":4,"name":"Yanbo Wu","email":"","orcid":"","institution":"Sheng Jing Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yanbo","middleName":"","lastName":"Wu","suffix":""},{"id":629442959,"identity":"0eb23631-216e-43c4-bbaf-0c9a1af73822","order_by":5,"name":"Pengfei Li","email":"","orcid":"","institution":"Affiliated Hospital of Inner Mongolia Minzu","correspondingAuthor":false,"prefix":"","firstName":"Pengfei","middleName":"","lastName":"Li","suffix":""},{"id":629442960,"identity":"55b35413-2011-4d28-869b-536934bc8d8e","order_by":6,"name":"Shinong Pan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCElEQVRIie2RsUoDQRCG9zjYNMPZblAurzDhChsLH2UWxEokkubAJsfBbmNhGZ9DSL3HgTb7AEquiM3VaxdQgpvYCa6xE9yvmGKYj935h7FI5A/CB7VBKjf5QZpUK4bbjjZBJYMHmjhriqGua9wqGVgKKrm4wNc7ZSTaRyU+O6cY/hjY8T3wjvBJqmuYdIwLRmxdLgK7qGkB0F8OvbIE7Bk/rExyY5eBV9pFASKdZl55nmPL+JGhNFEBRdDxG2AqZ1652imC8AflDMdzauWtbRRzeyk+ZHTm3Idc1cJhDz4QakK7jLQ/pdyc+FMOXhy9d/lI62a1Lr9XvmJgV/ee/+1wJBKJ/BM+ALYWXzYNCsokAAAAAElFTkSuQmCC","orcid":"","institution":"Sheng Jing Hospital","correspondingAuthor":true,"prefix":"","firstName":"Shinong","middleName":"","lastName":"Pan","suffix":""}],"badges":[],"createdAt":"2026-04-11 17:53:58","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9389876/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9389876/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107948781,"identity":"3677ac55-a552-4bb9-b0b7-13607960b34d","added_by":"auto","created_at":"2026-04-28 00:23:22","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":6696783,"visible":true,"origin":"","legend":"\u003cp\u003eWhole-spine sagittal STIR sequence shows bone marrow edema (yellow arrows) representing acute inflammation (A), while whole-spine sagittal mDixon-Quant sequence reveals extensive short-T1 signal fat deposition indicating chronic inflammation (B). Pseudo-color images from mDixon-Quant sequence demonstrate the measurement of vertebral fat fraction with ROIs delineated at different levels of cervical and lumbar vertebral corners (C). Liver fat fraction is quantified by placing eight ROIs across the liver (D). Axial T1WI at L4-L5 level shows delineation of subcutaneous adipose tissue and visceral adipose tissue (E).\u003c/p\u003e","description":"","filename":"Fig.1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9389876/v1/b40af6aa555e408805ecb5b1.jpg"},{"id":107948766,"identity":"5afc3a47-c728-4061-811f-654118974d75","added_by":"auto","created_at":"2026-04-28 00:23:18","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":266634,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plots with ICC values for reliability analysis. Points are colored by density. Dashed red line: line of identity; solid red line: regression line.\u003c/p\u003e","description":"","filename":"Fig.2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9389876/v1/778e8f2ed8acdc7317ec11d6.jpeg"},{"id":107948769,"identity":"2360b8a3-ecdc-4d62-9aa3-de1908bed72c","added_by":"auto","created_at":"2026-04-28 00:23:19","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":173533,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of SPARCC scores and VFF across vertebral levels. Left: mean SPARCC scores for each vertebral unit (C2/3 to L5/S1). Right: mean VFF values for each vertebral level (C2 to S1). Colors indicate spinal regions: cervical (green), thoracic (blue), lumbar (red). SPARCC scores and VFF both showed a gradual increasing trend from cervical to lumbar levels.\u003c/p\u003e","description":"","filename":"Fig.3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9389876/v1/fdeb9fc07fcb61e4bbeee525.jpeg"},{"id":107948765,"identity":"760f469f-a436-4ca1-acf2-71d35b499764","added_by":"auto","created_at":"2026-04-28 00:23:18","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":167982,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of SPARCC scores and VFF between non-high and high disease activity groups. Violin plots show the distribution of SPARCC scores (left) and VFF values (right) in patients with non-high (n = 31) and high (n = 61) disease activity. Boxplots indicate median and interquartile range, with individual data points overlaid. Significance levels are indicated above each plot. SPARCC scores were significantly higher in the high disease activity group (P \u0026lt; 0.001), and VFF values also showed a significant difference between groups (P \u0026lt; 0.05). These indicate that both SPARCC score and VFF are closely associated with disease activity status in axSpA patients.\u003c/p\u003e","description":"","filename":"Fig.4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9389876/v1/acbb792450489599bc5d844b.jpeg"},{"id":107948771,"identity":"8dffc091-f1d9-4015-84cd-b8444b4172fa","added_by":"auto","created_at":"2026-04-28 00:23:20","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":525309,"visible":true,"origin":"","legend":"\u003cp\u003eUnivariate regression analysis of standardized lipid and imaging parameters with SPARCC and VFF. All variables were standardized using Z-score transformation (mean = 0, SD = 1). Red: SPARCC; Blue: VFF. Lines: linear regression fits with 95% confidence intervals. The substantial overlap between the two regression curves indicates that lipid and imaging parameters have comparable effects on SPARCC score and VFF, suggesting that lipid dysregulation similarly affects both acute vertebral inflammation and chronic fat deposition.\u003c/p\u003e","description":"","filename":"Fig.5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9389876/v1/e8cb0219bf4df02ec94391ee.jpeg"},{"id":107948772,"identity":"db5cf5d0-b2f6-4640-b00f-39cd4ae726cc","added_by":"auto","created_at":"2026-04-28 00:23:20","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":463051,"visible":true,"origin":"","legend":"\u003cp\u003eMultivariate regression analysis of standardized SPARCC score and VFF with lipid and imaging parameters. Six panels show β coefficients with 95% CI for three models. Model 1: adjusted for age, gender, HLA-B27, disease duration. Model 2: Model 1 + CRP, ESR, BASDAI, ASDAS-CRP. Model 3: Model 2 + BASFI, BASMI, mSASSS. Significance: *** p\u0026lt;0.001, ** p\u0026lt;0.01, * p\u0026lt;0.05, † p\u0026lt;0.1. Red (●): Model 1; Blue (▲): Model 2; Green (■): Model 3.\u003c/p\u003e","description":"","filename":"Fig.6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9389876/v1/7e0bc28aac20fdee572b7e38.jpeg"},{"id":108006217,"identity":"88f90364-9f6e-47f4-b064-4bd13e8d3b4d","added_by":"auto","created_at":"2026-04-28 12:54:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8960306,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9389876/v1/dce220ae-9d77-48f3-a6c8-32566a55eba2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Cholesterol metabolic disorder is associated with worse spinal inflammation on MRI in axial spondyloarthritis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAxial spondyloarthritis (axSpA) is a chronic systemic inflammatory disease primarily affecting the axial skeleton, leading to adverse outcomes including pain, fatigue, limited mobility, and postural changes[1]. Genetic factors, such as human leukocyte antigen-B27 (HLA-B27)[2], interact with various types of stress (including mechanical stress, endoplasmic reticulum stress, and microbial stress on body surfaces), resulting in the production of pro-inflammatory cytokines and danger signals[3, 4]. These subsequently activate inflammatory leukocytes and other cell populations at the entheses (the sites where tendons or ligaments attach to bone). Osteoclasts play a significant role in the pathological process of axSpA, not only causing inflammatory bone destruction but also indirectly promoting ligamentous ossification and bony ankylosis[5].\u003c/p\u003e \u003cp\u003eBone metabolism is highly sensitive to environmental changes, and the relationship between lipid metabolism and bone metabolism is close. Previous studies have shown that hyperlipidemia can affect bones by influencing osteoblasts and osteoclasts[6]. Cholesterol (Chol) plays a crucial role in many cellular functions, such as participating in the formation of lipid rafts to provide a membrane signal transduction platform for osteoclastogenesis[7]. The regulation of cholesterol by lipoproteins also plays an important role in osteoclasts. Low-density lipoprotein (LDL) induced cholesterol delivery significantly enhances osteoclast viability, while LDL depletion inhibits osteoclast formation[8]. Cholesterol efflux to High-density lipoprotein (HDL) is critical for osteoclast apoptosis and fusion[9]. Furthermore, studies have shown that lipid ratios(Chol/LDL, HDL/LDL) more comprehensively reflect cholesterol metabolism status and disease risk than single lipid parameters[10]. It is now widely accepted that subcutaneous-to-visceral adipose ratio (SAT/VAT) are closely associated with lipid metabolism disorders [11] and liver fat fraction are positively correlated with long-term blood triglyceride and cholesterol levels[12], Studies have shown that visceral adipose tissue at the L4-L5 intervertebral disc level is significantly positively correlated with serum lipid and cholesterol levels in the Chinese population[13]. Therefore, to enhance the validity of this study, these two imaging parameters\u0026mdash;LFF and SAT/VAT\u0026mdash;were incorporated.\u003c/p\u003e \u003cp\u003eMagnetic resonance imaging (MRI) can effectively display inflammatory lesions in the vertebrae of patients with spondyloarthritis. For acute inflammation, the STIR sequence, which is highly sensitive to edema, can be combined with the Spondyloarthritis Research Consortium of Canada (SPARCC) scoring system to semi-quantitatively evaluate acute inflammatory lesions across 23 vertebral units[14, 15]. For chronic inflammation, T1-weighted imaging (T1WI) can reveal fat deposition in the vertebral corner (fatty Romanus lesions) after inflammation subsides, and this phenomenon is a more important independent risk factor for osteogenesis than bone marrow edema[16]. However, there is currently no standardized semi-quantitative scoring system for such chronic lesions. Therefore, this study employed the multi-echo DIXON fat quantification technique(mDixon-Quant) to accurately quantify chronic spinal inflammatory lesions by measuring the fat fraction at multiple levels of the whole-spine vertebral corners[17].\u003c/p\u003e \u003cp\u003eTherefore, this study aimed to investigate the correlation between the severity of acute and chronic spinal inflammatory lesions and lipid metabolism abnormalities in patients with axial axSpA.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eWe prospectively recruited 92 consecutive patients (65 males, 27 females) diagnosed with axSpA from the Department of Rheumatology and Immunology at Shengjing Hospital Of China Medical University between June 2024 and May 2025 and simultaneously obtain its imaging, clinical, and laboratory data. All patients fulfilled the 2009 ASAS classification criteria for axSpA, with diagnosis confirmed by board-certified rheumatologists.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eClinical evaluation\u003c/h3\u003e\n\u003cp\u003eBaseline data collected from study participants included age, sex, disease duration, smoking status, alcohol consumption, International Physical Activity Questionnaire (IPAQ) score[18], and medication use (non-steroidal anti-inflammatory drugs [NSAIDs], conventional synthetic disease-modifying antirheumatic drugs [csDMARDs], and biological agents). Laboratory parameters encompassed C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), triglycerides (TG), total Chol, HDL, LDL, Chol/LDL and HDL/LDL. Disease activity was assessed using Ankylosing Spondylitis Disease Activity Score based on C-reactive protein (ASDAS-CRP)[19], while static and dynamic functional impairments were evaluated using the Bath Ankylosing Spondylitis Metrology Index (BASMI) [20]and the Bath Ankylosing Spondylitis Functional Index (BASFI)[21], respectively.\u003c/p\u003e\n\u003ch3\u003eVertebral imaging assessment\u003c/h3\u003e\n\u003cp\u003eMRI examinations were performed using a PHILIPS Ingenia CX 3.0T MRI scanner, with detailed parameters listed in the Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Whole-spine sagittal STIR sequences were acquired to evaluate acute inflammatory lesions (acute Romanus lesions) and calculate the SPARCC score (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). The mDixon-Quant sequence was applied to measure VFF (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). The specific measurement method was as follows: from the inferior endplate of C2 to the superior endplate of S1, vertebral corner fat fraction was measured at three standardized slices per vertebra: midsagittal and bilateral planes (cervical: two slices medial to the pedicle; thoracic/lumbar: at the pedicle level) On each slice, the vertebral margin was equally trisected to define the corners, and regions of interest (ROIs) were placed at the four vertebral corners to calculate the corner fat fraction (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Vertebral corners with severe SPARCC bone marrow edema (i.e., those with signal intensity points) were excluded to avoid interference. Structural damage was assessed using lateral spine X-ray images according to the modified Stoke Ankylosing Spondylitis Spine Score (mSASSS)[22]. Both mSASSS scoring and vertebral corner fat fraction measurements were independently performed by two radiologists with five years of experience in imaging diagnosis of rheumatic bone diseases, and the final values were calculated as the mean of the two readers' measurements.\u003c/p\u003e\n\u003ch3\u003eLiver Fat Fraction\u003c/h3\u003e\n\u003cp\u003eThe LFF was quantitatively assessed using the mDixon-Quant sequence. Two radiologists independently placed a total of eight regions of interest (ROIs, each with an area of 2.9\u0026ndash;3.1 cm\u0026sup2;) across the following hepatic segments at two standardized anatomical levels: the portal vein trunk level and the sub-hilar level. Specifically, ROIs were positioned in the left lateral lobe, left medial lobe, right anterior lobe, and right posterior lobe (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.D)[23]. The mean values from the two readers were calculated for final analysis. During ROI placement, careful attention was paid to avoid bile ducts, major vessels, focal lesions, liver margins, and imaging artifacts.\u003c/p\u003e\n\u003ch3\u003eSubcutaneous-Visceral adipose ratio\u003c/h3\u003e\n\u003cp\u003eAbdominal axial T1WI was performed to evaluate SAT/VAT at the L4-L5 level (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.E), with the scan range extending from the first lumbar vertebra to the first sacral vertebra. The corresponding single-slice DICOM files were imported into Mimics Research software (version 21.0, Materialise, Leuven, Belgium). Two radiologists independently performed adipose tissue segmentation to obtain SAT/VAT values, and the final results were calculated as the mean of the two readers' measurements.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eScanning parameters for different MRI sequences\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026times;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026times;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026times;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSequence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFOV(mm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVoxel(mm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMatrix\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSlices\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNSA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTE(ms)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTR(ms)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpine STIR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c2\"\u003e \u003cp\u003e180\u0026times;329\u0026times;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c3\"\u003e \u003cp\u003e1.20\u0026times;1.51\u0026times;3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cp\u003e152\u0026times;213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e70.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4142.00-4755.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpine mDixon-Quant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c2\"\u003e \u003cp\u003e300\u0026times;324\u0026times;75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c3\"\u003e \u003cp\u003e1.80\u0026times;1.82\u0026times;3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cp\u003e168\u0026times;178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver mDixon-Quant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c2\"\u003e \u003cp\u003e400\u0026times;357\u0026times;210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c3\"\u003e \u003cp\u003e2.00\u0026times;2.00\u0026times;6.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cp\u003e200\u0026times;179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbdominal axial T1WI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c2\"\u003e \u003cp\u003e400\u0026times;363\u0026times;183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c3\"\u003e \u003cp\u003e1.60\u0026times;1.96\u0026times;7.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c4\"\u003e \u003cp\u003e252\u0026times;185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMRI examinations were performed using a PHILIPS Ingenia CX 3.0T scanner. Data are presented as field of view (FOV), voxel size, matrix, number of slices, number of signal averages (NSA), echo time (TE), and repetition time (TR).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eNormality was tested using the Shapiro-Wilk test. Continuous variables with normal distribution were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD and compared using the independent samples t-test; otherwise, they were presented as median (IQR) and compared using the Mann-Whitney U test. Categorical variables were expressed as number (%) and compared using the χ\u0026sup2; test. The intraclass correlation coefficient (ICC) with 95% confidence interval was calculated to assess inter-rater consistency for SAT/VAT ratio, LFF, mSASSS score, SPARCC score, and VFF.Univariate linear regression was performed to evaluate the associations of SPARCC score and vertebral fat fraction with clinical, imaging, and laboratory parameters. Three stepwise-adjusted multivariable regression models were constructed: Model 1 adjusted for age, sex, HLA-B27 status, and disease duration; Model 2 further adjusted for CRP, ESR, BASDAI, and ASDAS-CRP; Model 3 additionally adjusted for BASMI, mSASSS, and BASFI. Statistical significance was considered for a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05(two-sided). All analysis were performed with R 4.4.1 \u0026reg;\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eReliability Analysis\u003c/h2\u003e \u003cp\u003eAll five variables demonstrated good to excellent reliability. The ICC values were as follows: SAT/VAT (ICC\u0026thinsp;=\u0026thinsp;0.990, 95% CI: 0.982\u0026ndash;0.995), LFF(ICC\u0026thinsp;=\u0026thinsp;0.977, 95% CI: 0.964\u0026ndash;0.986), mSASSS score (ICC\u0026thinsp;=\u0026thinsp;0.996, 95% CI: 0.993\u0026ndash;0.998), SPARCC score(ICC\u0026thinsp;=\u0026thinsp;0.985, 95% CI: 0.975\u0026ndash;0.991), and VFF (ICC\u0026thinsp;=\u0026thinsp;0.876, 95% CI: 0.823\u0026ndash;0.916). All ICC values were statistically significant (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The distribution of SPARCC scores and VFF across vertebral levels is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eBaseline Characteristics by Disease Activity Status\u003c/h2\u003e \u003cp\u003eA total of 92 patients were categorized into non-high (n\u0026thinsp;=\u0026thinsp;31) and high (n\u0026thinsp;=\u0026thinsp;61) disease activity groups. Patients with high disease activity demonstrated significantly higher SPARCC scores and vertebral fat fraction (both P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Additionally, the high-activity group had significantly elevated inflammatory markers (CRP and ESR), as well as higher disease activity indices (ASDAS-CRP, BASDAI), and functional impairment scores (BSAFI, BASMI) (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). No significant differences were observed between the two groups regarding age, sex, HLA-B27 status, disease duration, mSASSS score, smoking status, alcohol abuse, BMI, IPAQ score, lipid parameters, SAT/VAT ratio, LFF, or the use of NSAIDs, csDMARDs, or biologics (all P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), results are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics by disease activity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease activity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-high activity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh activity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSPARCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.00(4.50, 30.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.5(21.00, 61.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVFF (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44.06\u0026thinsp;\u0026plusmn;\u0026thinsp;11.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.77\u0026thinsp;\u0026plusmn;\u0026thinsp;9.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.013\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.00 (32.00, 42.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.00 (34.50, 44.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.439\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.247\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (64.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45 (73.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (35.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (26.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHLA-B27(+/-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.206\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (83.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45 (73.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (16.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (26.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease duration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.00 (2.00, 15.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.00 (5.00, 18.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.191\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.50 (2.50, 2.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.55 (2.50, 8.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eESR (mm/h)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.00 (3.00, 13.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.00 (4.00, 28.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBASDAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.25\u0026thinsp;\u0026plusmn;\u0026thinsp;1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.21\u0026thinsp;\u0026plusmn;\u0026thinsp;1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBASFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (1.00, 1.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.50 (1.40, 5.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBASMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0, 3.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (0, 4.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emSASSS Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.00 (0, 17.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.05 (2.50, 28.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.522\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent Smoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (38.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (45.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (61.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (54.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol Abuse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.221\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u0026nbsp;(22.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (32.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (77.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41 (67.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.51\u0026thinsp;\u0026plusmn;\u0026thinsp;4.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.06\u0026thinsp;\u0026plusmn;\u0026thinsp;3.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.623\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIPAQ Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e693.00 (693.00, 1413.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e693.00 (462.00, 1396.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.12 (0.76, 1.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.51 (0.91, 2.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.120\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChol (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.55 (3.83, 4.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.35 (3.94, 5.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.934\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.147\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.630\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChol/HDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.08\u0026thinsp;\u0026plusmn;\u0026thinsp;1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.37\u0026thinsp;\u0026plusmn;\u0026thinsp;1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.329\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL/HDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.26 (1.60, 3.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.36 (1.77, 3.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.288\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAT/VAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.36 (1.50, 2.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.95 (1.58, 2.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.600\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLFF (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.34 (1.89, 9.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.42 (2.86, 7.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.290\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNSAIDs Use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.320\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (54.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (62.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (45.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23\u0026nbsp;(37.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecsDMARDs Use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.379\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (12.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (18.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (87.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50 (82.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiologic\u003cb\u003es\u003c/b\u003e Use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.391\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (67.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (62.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (32.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (37.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eData are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, median (IQR), or n (%). P-values were derived using independent t-test, Mann-Whitney U test, or chi-square test, as appropriate, with disease activity status as the grouping variable. High disease activity was defined as ASDAS-CRP\u0026thinsp;\u0026ge;\u0026thinsp;2.1.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eUnivariate Regression Analysis of SPARCC Score and Vertebral Fat Fraction\u003c/h2\u003e \u003cp\u003eUnivariate linear regression analyses revealed distinct patterns of factors associated with SPARCC score and vertebral fat fraction. SPARCC score was significantly associated with a broad range of variables, including demographic factors (male), disease-related characteristics (disease duration, BASMI, mSASSS, BASDAI, ASDAS-CRP, BASFI), inflammatory markers (CRP, ESR), lifestyle factors (smoking, alcohol abuse, BMI), lipid parameters (Chol, HDL, Chol/HDL, LDL/HDL), and imaging parameters (SAT/VAT ratio, LFF) (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). VFF showed largely similar associations, with additional significance for age and TG, while CRP and alcohol abuse were not significantly associated (both P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Notably, HLA-B27 status, IPAQ score, LDL, and the use of NSAIDs, csDMARDs, or biologics were not significantly associated with either outcome (all P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), results are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate analysis: SPARCC score and VFF\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eSPARCC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eVertebral Fat Fraction\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (Male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17.752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHLA-B27(+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.863\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease Duration (Years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBASMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emSASSS Total Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.019\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.010\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.191\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eESR (mm/h)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBASDAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eASDAS-CRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBASFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent Smoking (Yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23.716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol Abuse (Yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.042\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.043\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.718\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIPAQ Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.268\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.881\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChol (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-33.842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-13.310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.446\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChol/HDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL/HDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.028\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAT/VAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-7.255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-3.318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLFF (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNSAIDs Use (Yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.325\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecsDMARDs Use (Yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.167\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiologics Use (Yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-5.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.525\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eUnivariate linear regression analysis was performed to evaluate the association of each variable with SPARCC score and vertebral fat fraction (VFF). Data are presented as β (unstandardized coefficient), B (standardized coefficient), and P-value. Bold values indicate statistical significance (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMultivariable Regression Analysis of SPARCC Score\u003c/h2\u003e \u003cp\u003eMultivariable linear regression analyses were performed to identify factors independently associated with SPARCC score. In Model 1 (adjusted for age, sex, HLA-B27, and disease duration), current smoking (β\u0026thinsp;=\u0026thinsp;20.08, P\u0026thinsp;=\u0026thinsp;0.001), HDL (β = -25.085, P\u0026thinsp;=\u0026thinsp;0.018), Chol/HDL (β\u0026thinsp;=\u0026thinsp;6.485, P\u0026thinsp;=\u0026thinsp;0.002), LDL/HDL (β\u0026thinsp;=\u0026thinsp;7.073, P\u0026thinsp;=\u0026thinsp;0.008), SAT/VAT (β = -4.634, P\u0026thinsp;=\u0026thinsp;0.037), and LFF (β\u0026thinsp;=\u0026thinsp;1.115, P\u0026thinsp;=\u0026thinsp;0.020) were significantly associated with SPARCC score. After additional adjustment for inflammatory markers and disease activity indices in Model 2, current smoking (β\u0026thinsp;=\u0026thinsp;16.944, P\u0026thinsp;=\u0026thinsp;0.001), Chol/HDL (β\u0026thinsp;=\u0026thinsp;4.693, P\u0026thinsp;=\u0026thinsp;0.014), and LFF (β\u0026thinsp;=\u0026thinsp;1.259, P\u0026thinsp;=\u0026thinsp;0.003) remained significant. In the fully adjusted Model 3 (further adjusted for functional impairment), current smoking (β\u0026thinsp;=\u0026thinsp;16.727, P\u0026thinsp;=\u0026thinsp;0.001), Chol/HDL (β\u0026thinsp;=\u0026thinsp;4.253, P\u0026thinsp;=\u0026thinsp;0.021), LDL/HDL (β\u0026thinsp;=\u0026thinsp;5.114, P\u0026thinsp;=\u0026thinsp;0.038), and LFF (β\u0026thinsp;=\u0026thinsp;1.014, P\u0026thinsp;=\u0026thinsp;0.016) remained independently associated with SPARCC score. Notably, current smoking and LFF demonstrated robust associations across all three models. Lipid parameters, particularly Chol/HDL and LDL/HDL, also showed independent positive associations with SPARCC score after full adjustment. Detailed results are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariable analysis of factors associated with SPARCC score\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent Smoking (Yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e16.727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol Abuse (Yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.266\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIPAQ Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.893\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.744\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.568\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChol (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.228\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-25.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.018\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-15.742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-14.945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.280\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChol/HDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL/HDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.038\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAT/VAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-4.634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.037\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-3.758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-2.724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.163\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLFF (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.020\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNSAIDs Use (Yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-5.300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-5.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.241\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecsDMARDs Use (Yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.561\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiologics Use (Yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-7.263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-3.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-2.517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.577\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMultivariable linear regression analysis with stepwise adjustment. Model 1 adjusted for age, sex, HLA-B27, and disease duration; Model 2 further adjusted for CRP, ESR, ASDAS-CRP, and BASDAI; Model 3 additionally adjusted for BASMI, BASFI, and mSASSS. Data are presented as β (unstandardized coefficient), B (standardized coefficient), and P-value. Bold values indicate statistical significance (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eMultivariable Regression Analysis of Vertebral Fat Fraction\u003c/h2\u003e \u003cp\u003eMultivariable linear regression analyses were performed to identify factors independently associated with VFF. In Model 1 (adjusted for age, sex, HLA-B27, and disease duration), current smoking (β\u0026thinsp;=\u0026thinsp;4.520, P\u0026thinsp;=\u0026thinsp;0.029), TG (β\u0026thinsp;=\u0026thinsp;1.201, P\u0026thinsp;=\u0026thinsp;0.015), Chol/HDL ratio (β\u0026thinsp;=\u0026thinsp;1.866, P\u0026thinsp;=\u0026thinsp;0.009), and LFF (β\u0026thinsp;=\u0026thinsp;0.509, P\u0026thinsp;=\u0026thinsp;0.002) were significantly associated with VFF. After additional adjustment for inflammatory markers and disease activity indices in Model 2, TG (β\u0026thinsp;=\u0026thinsp;1.074, P\u0026thinsp;=\u0026thinsp;0.029), Chol/HDL (β\u0026thinsp;=\u0026thinsp;1.473, P\u0026thinsp;=\u0026thinsp;0.043), and LFF (β\u0026thinsp;=\u0026thinsp;0.525, P\u0026thinsp;=\u0026thinsp;0.001) remained significant, while current smoking lost significance (β\u0026thinsp;=\u0026thinsp;3.730, P\u0026thinsp;=\u0026thinsp;0.065). In the fully adjusted Model 3 (further adjusted for functional impairment), current smoking (β\u0026thinsp;=\u0026thinsp;3.646, P\u0026thinsp;=\u0026thinsp;0.040), total cholesterol (β\u0026thinsp;=\u0026thinsp;1.544, P\u0026thinsp;=\u0026thinsp;0.050), Chol/HDL ratio (β\u0026thinsp;=\u0026thinsp;1.399, P\u0026thinsp;=\u0026thinsp;0.030), and LFF (β\u0026thinsp;=\u0026thinsp;0.404, P\u0026thinsp;=\u0026thinsp;0.005) remained independently associated with VFF. Notably, LFF demonstrated robust associations across all three models, and lipid parameters, particularly total cholesterol and Chol/HDL, showed independent positive associations with VFF after full adjustment. Detailed results are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariable analysis of factors associated with VFF\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent Smoking (Yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.029\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.730\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.040\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol Abuse (Yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.827\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.247\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.037\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIPAQ Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.541\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.015\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.029\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChol (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.050\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-6.793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-4.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-3.987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.566\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.364\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChol/HDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.866\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.043\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.030\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL/HDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.566\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.179\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAT/VAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.698\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLFF(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNSAIDs Use (Yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.338\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecsDMARDs Use (Yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.824\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiologics Use (Yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.822\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMultivariable linear regression analysis with stepwise adjustment. Model 1 adjusted for age, sex, HLA-B27, and disease duration; Model 2 further adjusted for CRP, ESR, ASDAS-CRP, and BASDAI; Model 3 additionally adjusted for BASMI, BASFI, and mSASSS. Data are presented as β (unstandardized coefficient), B (standardized coefficient), and P-value. Bold values indicate statistical significance (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAxial spondyloarthritis (axSpA) is a chronic inflammatory disease primarily affecting the axial skeleton. During active phases, it manifests as pain, fatigue, and restricted mobility, while advanced structural vertebral damage leads to postural deformities. Poorly controlled disease with sustained high activity severely impairs quality of life and may compromise respiratory function due to thoracic cage deformities resulting from postural changes[24, 25]. Consequently, reducing disease activity and delaying structural progression represent critical therapeutic goals. However, despite the availability of biologic agents, treatment failure remains common, and the heterogeneity of therapeutic responses underscores the need to explore novel determinants of disease activity, including lipid metabolism[26, 27].\u003c/p\u003e \u003cp\u003eMagnetic resonance imaging was employed to comprehensively and accurately assess both acute and chronic spinal inflammatory lesions using semi-quantitative (SPARCC) and quantitative (VFF) methods. A previous study measuring lumbar VFF only at the mid-sagittal plane reported that VFF was highest in patients with high disease activity (ASDAS 2.1\u0026ndash;3.5) but significantly lower in those with very high disease activity (ASDAS\u0026thinsp;\u0026ge;\u0026thinsp;3.5), a finding potentially confounded by bone marrow edema at inflammatory sites[28]. In contrast, the present study performed a more rigorous and comprehensive quantitative assessment by measuring VFF at three standardized anatomical levels throughout the entire spine (cervical, thoracic, and lumbar) while deliberately excluding vertebral corners with severe bone marrow edema. This approach ensured that the measured fat fraction accurately reflected chronic fatty deposition rather than acute inflammatory changes. Furthermore, the SPARCC scoring system was applied to semi-quantitatively evaluate acute inflammatory lesions across 23 vertebral units. Together, these methodologies provide a holistic and precise characterization of spinal inflammation in axSpA patients. In this study, after adjusting for age, sex, disease duration, disease activity, and structural/functional impairment, multivariable analysis revealed that current smoking, LFF, and cholesterol ratios (Chol/HDL, LDL/HDL) were independent determinants of acute spinal inflammation, while LFF, Chol, and the Chol/HDL ratio were independently associated with chronic spinal fat deposition. These findings indicate that axSpA patients with elevated circulating cholesterol levels exhibit more severe spinal inflammation in both extent and degree.\u003c/p\u003e \u003cp\u003eExisting research has primarily focused on inflammatory cytokines and immune cells, whereas the role of osteoclasts in axSpA pathogenesis remains underexplored. Cholesterol plays a critical role in osteoclast function. As a component of lipid rafts, cholesterol facilitates RANK-RANKL signaling to drive osteoclastogenesis[7]. Cholesterol efflux from osteoclasts to high-density lipoprotein (HDL) is essential for osteoclast apoptosis and fusion, and cholesterol depletion induces osteoclast apoptosis, while low-density lipoprotein (LDL) depletion suppresses osteoclast formation[8]. Nuclear receptors such as ERRα mediate cholesterol-driven increases in osteoclast activity[29]. Clinical studies have confirmed that hypercholesterolemia is associated with lower bone mineral density and higher fracture risk[30]. High-fat diets promote osteoclastogenesis-induced bone loss and exacerbate joint damage[31, 32]. Collectively, these lines of evidence suggest that cholesterol metabolism disorders may contribute to bone inflammation and remodeling in axSpA by modulating osteoclast function.\u003c/p\u003e \u003cp\u003eIn the current study, the Chol/HDL and LDL/HDL ratios demonstrated stronger associations with spinal inflammation than individual cholesterol parameters. Total cholesterol measured in routine blood tests represents the sum of cholesterol carried by all lipoproteins, including LDL-C (60\u0026ndash;70%), HDL-C (20\u0026ndash;30%), and very-low-density lipoprotein cholesterol (VLDL-C) along with other minor lipoproteins (10\u0026ndash;15%)[33]. therefore, it cannot accurately reflect the level of bioavailable cholesterol in peripheral tissues. Cholesterol ratios, by contrast, integrate information on cholesterol distribution and transport efficiency, thereby providing a more comprehensive assessment of overall cholesterol metabolism status. HDL serves as the key lipoprotein responsible for reverse cholesterol transport from peripheral tissues back to the liver. A relative deficiency in HDL, indicated by an elevated Chol/HDL ratio, leads to cholesterol excess in peripheral tissues, which may promote osteoclast generation and survival, thereby exacerbating spinal inflammation. Furthermore, elevated LFF, a marker of chronic dyslipidemia, was associated with high disease activity, potentially through inhibition of ApoA1 synthesis and HDL generation[34]. These findings suggest that cholesterol metabolism disorders, particularly abnormal cholesterol ratios and hepatic fat deposition, are independent risk factors for exacerbating spinal inflammatory lesions in axSpA.\u003c/p\u003e \u003cp\u003eThe present study identified current smoking as an independent risk factor for acute spinal inflammation. Smoking is closely associated with cholesterol metabolism. It induces oxidative stress and inflammatory responses, while simultaneously elevating serum total cholesterol and LDL levels and reducing HDL levels, leading to increased Chol/HDL and LDL/HDL ratios[35]. Furthermore, smoking promotes cholesterol accumulation in macrophages and accelerates foam cell formation, thereby amplifying inflammatory cascades[36]. Thus, smoking may exacerbate spinal inflammation through two pathways: a direct pro-inflammatory effect and an indirect effect mediated by cholesterol metabolism. These findings highlight the importance of smoking cessation counseling in improving spinal inflammatory lesions and lipid profiles.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrates that cholesterol metabolism disorder, characterized by elevated total cholesterol, Chol/HDL ratio, and LDL/HDL ratio, is an independent risk factor for both acute and chronic spinal inflammation in axSpA patients. These findings directly link cholesterol metabolism to the pathophysiology of axSpA. The cholesterol metabolic status of axSpA patients warrants greater attention in clinical evaluation, and monitoring serum cholesterol and lipoprotein levels may help identify axSpA patients at high risk of spinal inflammation.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eASAS Assessment of spondyloarthropathy international society\u003c/p\u003e \u003cp\u003eASDAS-CRP Ankylosing spondylitis disease activity score based on c-reactive protein\u003c/p\u003e \u003cp\u003eaxSpA Axial spondyloarthritis\u003c/p\u003e \u003cp\u003eBASDAI Bath ankylosing spondylitis disease activity index\u003c/p\u003e \u003cp\u003eBASFI Bath ankylosing spondylitis functional index\u003c/p\u003e \u003cp\u003eBASMI Bath ankylosing spondylitis metrology index\u003c/p\u003e \u003cp\u003eBMI Body mass index\u003c/p\u003e \u003cp\u003eChol Total cholesterol\u003c/p\u003e \u003cp\u003eCRP C-reactive protein\u003c/p\u003e \u003cp\u003ecsDMARDs Conventional synthetic disease-modifying antirheumatic drugs\u003c/p\u003e \u003cp\u003eESR Erythrocyte sedimentation rate\u003c/p\u003e \u003cp\u003eFOV Field of view\u003c/p\u003e \u003cp\u003eHDL High density lipoprotein\u003c/p\u003e \u003cp\u003eHLA-B27 Human leukocyte antigen-b27\u003c/p\u003e \u003cp\u003eICC Intraclass correlation coefficient\u003c/p\u003e \u003cp\u003eIPAQ International physical activity questionnaire\u003c/p\u003e \u003cp\u003eLDL Low density lipoprotein\u003c/p\u003e \u003cp\u003eLFF Liver fat fraction\u003c/p\u003e \u003cp\u003eMRI Magnetic resonance imaging\u003c/p\u003e \u003cp\u003emSASSS Modified stoke ankylosing spondylitis spine score\u003c/p\u003e \u003cp\u003eNSAIDs Non-steroidal anti-inflammatory drugs\u003c/p\u003e \u003cp\u003eROI Region of interest\u003c/p\u003e \u003cp\u003eSAT/VAT Subcutaneous adipose tissue / visceral adipose ratio\u003c/p\u003e \u003cp\u003eSPARCC Spondyloarthritis research consortium of canada\u003c/p\u003e \u003cp\u003eSTIR Short tau inversion recovery\u003c/p\u003e \u003cp\u003eTE Echo time\u003c/p\u003e \u003cp\u003eTG Triglycerides\u003c/p\u003e \u003cp\u003eT1WI T1-weighted imaging\u003c/p\u003e \u003cp\u003eTR Repetition time\u003c/p\u003e \u003cp\u003eVFF Vertebral fat fraction\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank Professor Yang Hou, Director of the Department of Radiology, Shengjing Hospital of China Medical University, for providing MRI equipment support for this study. We also thank Professor Lei Jin, Director of the Department of Rheumatology and Immunology, for recruiting axSpA patients. We are grateful to all the patients and their families for their participation and commitment to this study, as well as all the graduate students and their supervisors in the Musculoskeletal Imaging Research Group of the Department of Radiology for their dedication and contributions to this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHexin Feng participated in MRI scanning and manuscript writing, and approved the final version for submission. Jiahong Zhu, Ying Zhang, Ziwei Fu, and Yanbo Wu contributed to data collection, aggregation, measurement, and analysis, critically reviewed the manuscript, and approved the final version. Pengfei Li and Shinong Pan are co-corresponding authors. Pengfei Li contributed to study design and manuscript revision, and approved the final version. Shinong Pan supervised the entire study, participated in its conception and design, interpreted the data, critically reviewed the manuscript, and approved the final version. All authors are members of the Musculoskeletal Imaging Research Group, Department of Radiology, Shengjing Hospital of China Medical University, with the exception of Pengfei Li, who is affiliated with the Musculoskeletal Imaging Research Group, Department of Radiology, Affiliated Hospital of Inner Mongolia Minzu University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Department of Education of Liaoning Province (Grant No. LJKMZ20221163). The funder had no role in the study design, data collection, analysis, interpretation, or manuscript writing. Shinong Pan is the grant recipient.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are not publicly available due to ethical restrictions and to protect patient confidentiality. The Institutional Review Board of Shengjing Hospital of China Medical University has imposed restrictions on data sharing. De-identified data may be available from the corresponding author upon reasonable request, subject to approval by the Institutional Review Board.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Institutional Review Board of Shengjing Hospital of China Medical University (Approval No. 2024PS1182K). All procedures involving human participants were performed in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki Declaration and its later amendments, as well as other relevant guidelines and regulations. Written informed consent was obtained from all participants.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare that they have no competing interests\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eVictoria N-C, Alexandre S, Bassel E-Z, D\u0026eacute;sir\u0026eacute;e vdH. Axial spondyloarthritis. Ann Rheum Dis. 2021;80(12):1511-21.https://doi.org/10.1136/annrheumdis-2021-221035\u003c/li\u003e\n\u003cli\u003eDavid E, Luke J, Sarah L S, Adrian C, J\u0026ouml;rn B, Buhm H, et al. Analysis of five chronic inflammatory diseases identifies 27 new associations and highlights disease-specific patterns at shared loci. Nat Genet. 2016;48(5):510-18.https://doi.org/10.1038/ng.3528\u003c/li\u003e\n\u003cli\u003eR C A, T S, N B-P, E M A H, H M-O, D M. The role of biomechanical factors in ankylosing spondylitis: the patient\u0026apos;s perspective. Reumatismo. 2016;67(3):91-96.https://doi.org/10.4081/reumatismo.2015.853\u003c/li\u003e\n\u003cli\u003eLiesbet VP, Filip E VdB, Peggy J, Philippe C, Lennart J, Roos C, et al. Microscopic gut inflammation in axial spondyloarthritis: a multiparametric predictive model. Ann Rheum Dis. 2012;72(3):414-17.https://doi.org/10.1136/annrheumdis-2012-202135\u003c/li\u003e\n\u003cli\u003eZhenhua L, Mingxi C, Haoteng K, Huazong D, Weijia Y, Tao W, et al. Fibroblast Insights into the Pathogenesis of Ankylosing Spondylitis. 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Am J Physiol Heart Circ Physiol. 2013;305(4).https://doi.org/10.1152/ajpheart.00042.2013\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-rheumatology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"brhm","sideBox":"Learn more about [BMC Rheumatology](http://bmcrheumatol.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/brhm/default.aspx","title":"BMC Rheumatology","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Axial spondyloarthritis, Cholesterol, spinal inflammation, MRI","lastPublishedDoi":"10.21203/rs.3.rs-9389876/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9389876/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eAxial spondyloarthritis is a chronic inflammatory disease characterized by bone destruction and pathological new bone formation, in which osteoclasts play a key role, and elevated cholesterol levels enhance osteoclast function. This study sought to investigate whether cholesterol metabolic disorder contributes to the severity of spinal inflammation in axial spondyloarthritis.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis prospective study enrolled 92 axSpA patients and collected clinical and laboratory data. MRI quantified acute spinal inflammation (SPARCC score), chronic spinal inflammation (vertebral fat fraction, VFF), as well as lipid metabolism-related imaging parameters, including liver fat fraction (LFF) and subcutaneous-to-visceral adipose ratio (SAT/VAT). To identify independent factors associated with imaging spinal inflammation and adjust for potential confounders, univariate linear regression and multivariable linear regression analyses were performed using three incrementally adjusted models: Model 1 adjusted for demographics; Model 2 additionally adjusted for inflammatory markers (CRP, ESR) and disease activity indices (BASDAI, ASDAS-CRP); Model 3 further adjusted for functional/structural impairment scores (BASMI, BASFI, mSASSS).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn the fully adjusted multivariable regression model (Model 3), current smoking (β\u0026thinsp;=\u0026thinsp;16.727, P\u0026thinsp;=\u0026thinsp;0.001), Chol/HDL ratio (β\u0026thinsp;=\u0026thinsp;4.253, P\u0026thinsp;=\u0026thinsp;0.021), LDL/HDL ratio (β\u0026thinsp;=\u0026thinsp;5.114, P\u0026thinsp;=\u0026thinsp;0.038), and LFF (β\u0026thinsp;=\u0026thinsp;1.014, P\u0026thinsp;=\u0026thinsp;0.016) were independently associated with the SPARCC score; while current smoking (β\u0026thinsp;=\u0026thinsp;3.646, P\u0026thinsp;=\u0026thinsp;0.040), total cholesterol (β\u0026thinsp;=\u0026thinsp;1.544, P\u0026thinsp;=\u0026thinsp;0.050), Chol/HDL ratio (β\u0026thinsp;=\u0026thinsp;1.399, P\u0026thinsp;=\u0026thinsp;0.030), and LFF (β\u0026thinsp;=\u0026thinsp;0.404, P\u0026thinsp;=\u0026thinsp;0.005) were independently associated with VFF.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eElevated total Chol, Chol/HDL ratio, and LDL/HDL ratio\u0026mdash;collectively reflecting a cholesterol metabolic disorder\u0026mdash;were independently and positively associated with the severity of both acute and chronic spinal inflammation. These findings suggest that the cholesterol metabolic status of axSpA patients warrants greater attention in clinical evaluation.\u003c/p\u003e\u003ch2\u003eTrial Registration:\u003c/h2\u003e \u003cp\u003eChinese Clinical Trial Registry, ChiCTR2500113832. Retrospectively registered on 03/12/2025.\u003c/p\u003e","manuscriptTitle":"Cholesterol metabolic disorder is associated with worse spinal inflammation on MRI in axial spondyloarthritis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-28 00:23:04","doi":"10.21203/rs.3.rs-9389876/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"132533197075011938935831754340366110269","date":"2026-04-30T11:59:40+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-19T15:37:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-16T11:36:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-16T11:36:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Rheumatology","date":"2026-04-11T17:38:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-rheumatology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"brhm","sideBox":"Learn more about [BMC Rheumatology](http://bmcrheumatol.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/brhm/default.aspx","title":"BMC Rheumatology","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c6b07897-724f-4a04-b8dd-a6ca7b11e2f3","owner":[],"postedDate":"April 28th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"132533197075011938935831754340366110269","date":"2026-04-30T11:59:40+00:00","index":21,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-28T00:23:04+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-28 00:23:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9389876","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9389876","identity":"rs-9389876","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}