Muscle Function Tests as Supportive Outcome Measures for Performance-Based and Self-Reported Physical Function in Patients With Knee Osteoarthritis: Exploratory Analysis of Baseline Data From a Randomized Trial.

ABSTRACT: Holm, PM, Kemnitz, J, Bandholm, T, Wernbom, M, Schrøder, HM, and Skou, ST. Muscle function tests as supportive outcome measures for performance-based and self-reported physical function in patients with knee osteoarthritis: Exploratory analysis of baseline data from a randomized trial. J Strength Cond Res 36(9): 2635-2642, 2022-Uncertainty on the role of muscle function in relation to physical function in knee osteoarthritis (KOA) persists. This study aimed to assess the associations between muscle function and performance-based and self-reported physical function in patients with KOA. Physical function in 80 subjects with symptomatic and radiographic KOA was assessed using 40-m fast-paced walk, 30-second chair stand, 9-step stair climb tests, and the subscale activities of daily living from the Knee injury and Osteoarthritis Outcome Score (KOOS-ADL). Measurements of muscle function included leg extension (LE) power, knee extension (KE) torque, and estimated leg press one repetition maximum (LP RM). Associations were investigated using multivariable hierarchical linear regressions adjusted for age, sex, body mass index, self-reported physical activity, and thigh muscle lean area. Leg extension power was significantly associated with 40-m walk, stair climb, and 30-second chair stand, explaining 18, 8, and 3% of additional variance, respectively. Knee extension torque explained 13, 7, 17, and 7% of additional variance in the 40-m walk, stair climb, 30-second chair stand, and KOOS-ADL, respectively. Leg press one repetition maximum explained 11% of additional variance in the 30-second chair stand. In conclusion, LE power was the best explanatory variable for performance on the 40-m walk and stair climb tests, whereas KE torque best explained chair stand performance. Only KE torque was associated with KOOS-ADL. Our results highlight the importance of selecting supportive muscle function tests based on the specific physical function and suggest that other factors may be more important for certain physical function outcomes. Level of significance p < 0.05. Trial identifier: NCT03215602.

Accuracy and longitudinal reproducibility of quantitative femorotibial cartilage measures derived from automated U-Net-based segmentation of two different MRI contrasts: data from the osteoarthritis initiative healthy reference cohort.

OBJECTIVE: To evaluate the agreement, accuracy, and longitudinal reproducibility of quantitative cartilage morphometry from 2D U-Net-based automated segmentations for 3T coronal fast low angle shot (corFLASH) and sagittal double echo at steady-state (sagDESS) MRI. METHODS: 2D U-Nets were trained using manual, quality-controlled femorotibial cartilage segmentations available for 92 Osteoarthritis Initiative healthy reference cohort participants from both corFLASH and sagDESS (n = 50/21/21 training/validation/test-set). Cartilage morphometry was computed from automated and manual segmentations for knees from the test-set. Agreement and accuracy were evaluated from baseline visits (dice similarity coefficient: DSC, correlation analysis, systematic offset). The longitudinal reproducibility was assessed from year-1 and -2 follow-up visits (root-mean-squared coefficient of variation, RMSCV%). RESULTS: Automated segmentations showed high agreement (DSC 0.89-0.92) and high correlations (r ≥ 0.92) with manual ground truth for both corFLASH and sagDESS and only small systematic offsets (≤ 10.1%). The automated measurements showed a similar test-retest reproducibility over 1 year (RMSCV% 1.0-4.5%) as manual measurements (RMSCV% 0.5-2.5%). DISCUSSION: The 2D U-Net-based automated segmentation method yielded high agreement compared with manual segmentation and also demonstrated high accuracy and longitudinal test-retest reproducibility for morphometric analysis of articular cartilage derived from it, using both corFLASH and sagDESS.

Mesenchymal iron deposition is associated with adverse long-term outcome in non-alcoholic fatty liver disease.

BACKGROUND & AIMS: Approximately one-third of patients with non-alcoholic fatty liver disease (NAFLD) show signs of mild-to-moderate iron overload. The impact of histological iron deposition on the clinical course of patients with NAFLD has not been established. METHODS & RESULTS: For this retrospective study, 299 consecutive patients with biopsy-proven NAFLD and a mean follow-up of 8.4 (±4.1; range: 0.3-18.0) years were allocated to one of four groups according to presence of hepatic iron in the reticuloendothelial system (RES) and/or hepatocytes (HC): 156 subjects (52%) showed no stainable iron (NONE), 58 (19%) exclusively reticuloendothelial (xRES), 19 (6%) exclusively hepatocellular (xHC) and 66 (22%) showed a mixed (HC/RES) pattern of iron deposition. A long-term analysis for overall survival, hepatic, cardiovascular or extrahepatic-malignant events was conducted. Based on multivariate Cox proportional hazards models any reticuloendothelial iron was associated with fatal and non-fatal hepatic events. Specifically, xRES showed a cause-specific hazard ratio (csHR) of 2.4 (95%-CI, 1.0-5.8; P = .048) for hepatic as well as cardiovascular fatal and non-fatal events combined (csHR 3.2; 95%-CI, 1.2-8.2; P = .015). Furthermore, the mixed HC/RES iron pattern showed a higher rate of combined hepatic fatal and non-fatal events (csHR 3.6; 95%-CI, 1.4-9.5; P = .010), while xHC iron deposition was not associated with any defined events. CONCLUSIONS: The presence of reticuloendothelial-accentuated hepatic iron distribution patterns is associated with detrimental long-term outcomes reflected in a higher rate of both liver-related and cardiovascular fatal and non-fatal events.

Clinical evaluation of fully automated thigh muscle and adipose tissue segmentation using a U-Net deep learning architecture in context of osteoarthritic knee pain.

OBJECTIVE: Segmentation of thigh muscle and adipose tissue is important for the understanding of musculoskeletal diseases such as osteoarthritis. Therefore, the purpose of this work is (a) to evaluate whether a fully automated approach provides accurate segmentation of muscles and adipose tissue cross-sectional areas (CSA) compared with manual segmentation and (b) to evaluate the validity of this method based on a previous clinical study. MATERIALS AND METHODS: The segmentation method is based on U-Net architecture trained on 250 manually segmented thighs from the Osteoarthritis Initiative (OAI). The clinical evaluation is performed on a hold-out test set bilateral thighs of 48 subjects with unilateral knee pain. RESULTS: The segmentation time of the method is < 1 s and demonstrated high agreement with the manual method (dice similarity coeffcient: 0.96 ± 0.01). In the clinical study, the automated method shows that similar to manual segmentation (- 5.7 ± 7.9%, p < 0.001, effect size: 0.69), painful knees display significantly lower quadriceps CSAs than contralateral painless knees (- 5.6 ± 7.6%, p < 0.001, effect size: 0.73). DISCUSSION: Automated segmentation of thigh muscle and adipose tissues has high agreement with manual segmentations and can replicate the effect size seen in a clinical study on osteoarthritic pain.

Validation of an active shape model-based semi-automated segmentation algorithm for the analysis of thigh muscle and adipose tissue cross-sectional areas.

OBJECTIVE: To validate a semi-automated method for thigh muscle and adipose tissue cross-sectional area (CSA) segmentation from MRI. MATERIALS AND METHODS: An active shape model (ASM) was trained using 113 MRI CSAs from the Osteoarthritis Initiative (OAI) and combined with an active contour model and thresholding-based post-processing steps. This method was applied to 20 other MRIs from the OAI and to baseline and follow-up MRIs from a 12-week lower-limb strengthening or endurance training intervention (n = 35 females). The agreement of semi-automated vs. previous manual segmentation was assessed using the Dice similarity coefficient and Bland-Altman analyses. Longitudinal changes observed in the training intervention were compared between semi-automated and manual segmentations. RESULTS: High agreement was observed between manual and semi-automated segmentations for subcutaneous fat, quadriceps and hamstring CSAs. With strength training, both the semi-automated and manual segmentation method detected a significant reduction in adipose tissue CSA and a significant gain in quadriceps, hamstring and adductor CSAs. With endurance training, a significant reduction in adipose tissue CSAs was observed with both methods. CONCLUSION: The semi-automated approach showed high agreement with manual segmentation of thigh muscle and adipose tissue CSAs and showed longitudinal training effects similar to that observed using manual segmentation.

Effects of neuromuscular control and strengthening exercises on MRI-measured thigh tissue composition and muscle properties in people with knee osteoarthritis - an exploratory secondary analysis from a randomized controlled trial.

OBJECTIVE: To investigate the effects of adding strength training to neuromuscular control exercises on thigh tissue composition and muscle properties in people with radiographic-symptomatic knee osteoarthritis (KOA). METHODS: In this exploratory secondary analysis of a randomized controlled trial, using a complete-case approach, participants performed 12 weeks of twice-weekly neuromuscular control exercise and patient education (NEMEX, n = 34) or NEMEX plus quadriceps strength training (NEMEX+ST, n = 29). Outcomes were MRI-measured inter- and intramuscular adipose tissue (InterMAT, IntraMAT), quadriceps muscle cross-sectional area (CSA), knee-extensor strength, specific strength (strength/lean CSA) and 30 s chair-stands. Between-group effects were compared using a mixed model analysis of variance. RESULTS: At 12 weeks, responses to NEMEX+ST overlapped with NEMEX for all outcomes. Both groups reduced InterMAT (NEMEX+ST=25 %, NEMEX=21 %); between-group difference: 0.8cm2 (95 % CI: -0.1, 1.7). NEMEX+ST decreased IntraMAT (2 %) and NEMEX increased IntraMAT (4 %); between-group difference 0.1 %-points (-0.3, 0.5). Both groups increased quadriceps CSA and lean CSA (CSA minus IntraMAT), improved knee-extensor strength and specific strength, and improved chair-stand performance with a trend towards greater effects in NEMEX+ST. CONCLUSION: Adding strength training to 12 weeks of neuromuscular control exercises provided largely similar effects to neuromuscular control exercises alone in decreasing InterMAT and IntraMAT, in improving knee-extensor strength, CSA and in improving performance-based function in KOA persons, with a trend towards greater effects with additional strength training. Notably, both groups substantially reduced InterMAT and improved specific strength (an index of muscle quality). Our hypothesis-generating work warrants exploration of the roles played by InterMAT and IntraMAT in exercise effects in KOA.

Local MRI-based measures of thigh adipose tissue derived from fully automated deep convolutional neural network-based segmentation show a comparable responsiveness to bidirectional change in body weight as from quality controlled manual segmentation.

BACKGROUND: Thigh intermuscular (IMF) and subcutaneous (SCF) fat are associated with joint function, inflammation and knee osteoarthritis. Fully automated segmentation from MRI is important to study the above relationship in larger cohorts. However, such algorithms are not clinically evaluated for longitudinal studies. Our aim was to evaluate a fully automated U-Net segmentation approach and its ability to detect longitudinal changes in thigh IMF and SCF during weight changes compared to manual segmentation. METHODS: 103 Osteoarthritis Initiative subjects, were studied, 52 with> 10% weight loss, and 51 with> 10% weight gain over 2-years. Longitudinal change in IMF and SCF were determined from baseline and year-2 axial thigh MRIs using U-Net segmentation. The standardised response mean (SRM) was used as measure of sensitivity to change. RESULTS: The U-Net took substantially less time (single-slice MRI:< 1 s) and IMF and SCF showed very similar sensitivity to change as manual segmentation: With an average weight gain of + 14%, we observed an + 12% /+ 26% increase in IMF / SCF (SRM=0.99 /1.03) using the U-Net, compared with + 21% /+ 27% (SRM=0.60 /1.07) for manual segmentation. During an average weight loss of - 18%, we observed an - 14% /- 22% reduction in IMF /SCF (SRM = - 1.04 /-1.20) using the U-Net, compared with - 16% /- 22% (SRM = - 0.70 /-1.23) for manual segmentation. CONCLUSION: U-Net segmentation replicates longitudinal changes of IMF and SCF associated with weight changes with a similar sensitivity to change as manual segmentation. This method is applicable to large databases for studying relationships between IMF and SCF and various disease conditions.

Detection of Differences in Longitudinal Cartilage Thickness Loss Using a Deep-Learning Automated Segmentation Algorithm: Data From the Foundation for the National Institutes of Health Biomarkers Study of the Osteoarthritis Initiative.

OBJECTIVE: To study the longitudinal performance of fully automated cartilage segmentation in knees with radiographic osteoarthritis (OA), we evaluated the sensitivity to change in progressor knees from the Foundation for the National Institutes of Health OA Biomarkers Consortium between the automated and previously reported manual expert segmentation, and we determined whether differences in progression rates between predefined cohorts can be detected by the fully automated approach. METHODS: The OA Initiative Biomarker Consortium was a nested case-control study. Progressor knees had both medial tibiofemoral radiographic joint space width loss (≥0.7 mm) and a persistent increase in Western Ontario and McMaster Universities Osteoarthritis Index pain scores (≥9 on a 0-100 scale) after 2 years from baseline (n = 194), whereas non-progressor knees did not have either of both (n = 200). Deep-learning automated algorithms trained on radiographic OA knees or knees of a healthy reference cohort (HRC) were used to automatically segment medial femorotibial compartment (MFTC) and lateral femorotibial cartilage on baseline and 2-year follow-up magnetic resonance imaging. Findings were compared with previously published manual expert segmentation. RESULTS: The mean ± SD MFTC cartilage loss in the progressor cohort was -181 ± 245 µm by manual segmentation (standardized response mean [SRM] -0.74), -144 ± 200 µm by the radiographic OA-based model (SRM -0.72), and -69 ± 231 µm by HRC-based model segmentation (SRM -0.30). Cohen's d for rates of progression between progressor versus the non-progressor cohort was -0.84 (P < 0.001) for manual, -0.68 (P < 0.001) for the automated radiographic OA model, and -0.14 (P = 0.18) for automated HRC model segmentation. CONCLUSION: A fully automated deep-learning segmentation approach not only displays similar sensitivity to change of longitudinal cartilage thickness loss in knee OA as did manual expert segmentation but also effectively differentiates longitudinal rates of loss of cartilage thickness between cohorts with different progression profiles.

Moderate Physical Activity and Prevention of Cartilage Loss in People With Knee Osteoarthritis: Data From the Osteoarthritis Initiative.

OBJECTIVE: To examine the impact of physical activity on cartilage thickness loss in knee osteoarthritis (OA). METHODS: A total of 689 participants with radiographic knee OA at baseline (Kellgren/Lawrence grade ≥2) from the Osteoarthritis Initiative completed the Physical Activity Scale for the Elderly (PASE) questionnaires at annual intervals over 4 years. Magnetic resonance imaging-based cartilage thickness change in the medial femorotibial compartment (MFTC) over 4 years was the main outcome. The impact of PASE tertiles (low, moderate, or high) on changes in MFTC cartilage thickness was estimated using a mixed-effects model adjusted for baseline characteristics. Furthermore, stratification by sex was performed for secondary analyses. RESULTS: Structural progression of MFTC cartilage loss of -0.20 mm (95% confidence interval [95% CI] -0.22, -0.17) was observed in the entire cohort, with no significant difference between physical activity levels after adjustment for baseline characteristics. An interaction between sex and physical activity was observed in the adjusted analysis (P = 0.02). Stratification by sex showed that women with low physical activity had a statistically greater cartilage loss than women with moderate physical activity (adjusted between-group difference -0.09 mm [95% CI -0.16, 0.02]), whereas no significant differences were observed in men. CONCLUSION: While physical activity was not associated with cartilage thickness loss in the whole cohort, this relationship significantly differed between sexes. In women, but not in men, moderate physical activity may slow down structural disease progression compared to low physical activity levels. For both men and women, high physical activity levels do not appear to be more detrimental than lower physical activity levels for cartilage thickness loss.

Brief Report: Loss of Muscle Strength Prior to Knee Replacement: A Question of Anatomic Cross-Sectional Area or Specific Strength?

OBJECTIVE: To determine whether loss in thigh muscle strength prior to knee replacement is caused by reductions of muscle strength in the anatomic cross-sectional area or by reductions of specific strength. METHODS: All 100 of the participants in the Osteoarthritis Initiative who underwent knee replacement and whose medical records included data on thigh isometric muscle strength and magnetic resonance imaging (MRI) (58 women, and 42 men, mean ± SD age 65 ± 8 years, mean ± SD body mass index [BMI] 29 ± 5 kg/m2 ) were matched with a control (no knee replacement) for age, sex, height, BMI, and radiographic severity. Thigh muscle anatomic cross-sectional area was determined by MRI at the research visit before knee replacement (time 0) and 2 years before time 0 (time -2). Specific strength (strength/anatomic cross-sectional area) was calculated, and the measures were compared by conditional logistic regression (i.e., odds ratio [OR] per standard deviation). ORs adjusted for pain (ORadj ) and 95% confidence intervals (95% CIs) were also calculated. RESULTS: Knee replacement cases had significantly smaller extensor (but not flexor) anatomic cross-sectional areas than controls at time 0 (women, ORadj 1.89 [95% CI 1.05-3.90]; men, ORadj 2.22 [95% CI 1.04-4.76]), whereas no significant differences were found at time -2. Women who had knee replacement showed lower levels of extensor specific strength than controls at time 0 (OR 1.59 [95% CI 1.02-2.50]), although this difference was not observed in men and did not maintain significance after adjustment for pain (ORadj 1.22 [95% CI 0.71-2.08]). Female cases lost significantly more extensor specific strength between time -2 and time 0 than controls (ORadj 3.76 [95% CI 1.04-13.60]), whereas no significant differences were noted at time -2, or in men. CONCLUSION: Prior to knee replacement, a significant reduction in knee extensor strength appears to occur in women through 2 mechanisms: one driven by pain (loss of specific strength) and one independent of pain (loss of muscle anatomic cross-sectional area). Men who underwent knee replacement showed significantly reduced levels of extensor anatomic cross-sectional area, but not significantly lower strength or specific strength.