Diabetes-associated thigh muscle degeneration mediates knee osteoarthritis-related outcomes: results from a longitudinal cohort study

Erosive hand osteoarthritis and sarcopenia: data from Osteoarthritis Initiative cohort

OBJECTIVES

There is no evidence linking specific osteoarthritis (OA) types, such as erosive hand OA (EHOA), with distant generalised changes in muscle composition (sarcopenia), which can potentially be modified. This study pioneers the exploration of the association between EHOA and sarcopenia, both of which are predominantly observed in the older adults.

METHODS

Using the Osteoarthritis Initiative cohort, we selected hand OA (modified Kellgren and Lawrence (grade ≥2 in ≥1 hand joint) participants with radiographic central erosions in ≥1 joints (EHOA group) and propensity score-matched hand OA participants with no erosion (non-EHOA group). MRI biomarkers of thigh muscles were measured at baseline, year 2 and year 4 using a validated deep-learning algorithm. To adjust for 'local' effects of coexisting knee OA (KOA), participants were further stratified according to presence of radiographic KOA. The outcomes were the differences between EHOA and non-EHOA groups in the 4-year rate of change for both intramuscular adipose tissue (intra-MAT) deposition and contractile (non-fat) area of thigh muscles.

RESULTS

After adjusting for potential confounders, 844 thighs were included (211 EHOA:633 non-EHOA; 67.1±7.5 years, female/male:2.9). Multilevel mixed-effect regression models showed that EHOA is associated a different 4-year rate of change in intra-MAT deposition (estimate, 95% CI: 71.5 mm2/4 years, 27.9 to 115.1) and contractile area (estimate, 95% CI: -1.8%/4 years, -2.6 to -1.0) of the Quadriceps. Stratified analyses showed that EHOA presence is associated with adverse changes in thigh muscle quality only in participants without KOA.

CONCLUSIONS

EHOA is associated with longitudinal worsening of thigh muscle composition only in participants without concomitant KOA. Further research is needed to understand the systemic factors linking EHOA and sarcopenia, which unlike EHOA is modifiable through specific interventions.

Osteoarthritis year in review 2023: Imaging

PURPOSE

This narrative review summarizes the original research in the field of in vivo osteoarthritis (OA) imaging between 1 January 2022 and 1 April 2023.

METHODS

A PubMed search was conducted using the following several terms pertaining to OA imaging, including but not limited to "Osteoarthritis / OA", "Magnetic resonance imaging / MRI", "X-ray" "Computed tomography / CT", "artificial intelligence /AI", "deep learning", "machine learning". This review is organized by topics including the anatomical structure of interest and modality, AI, challenges of OA imaging in the context of clinical trials, and imaging biomarkers in clinical trials and interventional studies. Ex vivo and animal studies were excluded from this review.

RESULTS

Two hundred and forty-nine publications were relevant to in vivo human OA imaging. Among the articles included, the knee joint (61%) and MRI (42%) were the predominant anatomical area and imaging modalities studied. Marked heterogeneity of structural tissue damage in OA knees was reported, a finding of potential relevance to clinical trial inclusion. The use of AI continues to rise rapidly to be applied in various aspect of OA imaging research but a lack of generalizability beyond highly standardized datasets limit interpretation and wide-spread application. No pharmacologic clinical trials using imaging data as outcome measures have been published in the period of interest.

CONCLUSIONS

Recent advances in OA imaging continue to heavily weigh on the use of AI. MRI remains the most important modality with a growing role in outcome prediction and classification.

Subchondral bone in knee osteoarthritis: bystander or treatment target?

The subchondral bone is an important structural component of the knee joint relevant for osteoarthritis (OA) incidence and progression once disease is established. Experimental studies have demonstrated that subchondral bone changes are not simply the result of altered biomechanics, i.e., pathologic loading. In fact, subchondral bone alterations have an impact on joint homeostasis leading to articular cartilage loss already early in the disease process. This narrative review aims to summarize the available and emerging imaging techniques used to evaluate knee OA-related subchondral bone changes and their potential role in clinical trials of disease-modifying OA drugs (DMOADs). Radiographic fractal signature analysis has been used to quantify OA-associated changes in subchondral texture and integrity. Cross-sectional modalities such as cone-beam computed tomography (CT), contrast-enhanced cone beam CT, and micro-CT can also provide high-resolution imaging of the subchondral trabecular morphometry. Magnetic resonance imaging (MRI) has been the most commonly used advanced imaging modality to evaluate OA-related subchondral bone changes such as bone marrow lesions and altered trabecular bone texture. Dual-energy X-ray absorptiometry can provide insight into OA-related changes in periarticular subchondral bone mineral density. Positron emission tomography, using physiological biomarkers of subchondral bone regeneration, has provided additional insight into OA pathogenesis. Finally, artificial intelligence algorithms have been developed to automate some of the above subchondral bone measurements. This paper will particularly focus on semiquantitative methods for assessing bone marrow lesions and their utility in identifying subjects at risk of symptomatic and structural OA progression, and evaluating treatment responses in DMOAD clinical trials.

Predictive Value of Deep Learning-derived CT Pectoralis Muscle and Adipose Measurements for Incident Heart Failure: Multi-Ethnic Study of Atherosclerosis

Purpose

To develop a deep learning algorithm capable of extracting pectoralis muscle and adipose measurements and to longitudinally investigate associations between these measurements and incident heart failure (HF) in participants from the Multi-Ethnic Study of Atherosclerosis (MESA).

Materials and Methods

MESA is a prospective study of subclinical cardiovascular disease characteristics and risk factors for progression to clinically overt disease approved by institutional review boards of six participating centers (ClinicalTrials.gov identifier: NCT00005487). All participants with adequate imaging and clinical data from the fifth examination of MESA were included in this study. Hence, in this secondary analysis, manual segmentations of 600 chest CT examinations (between the years 2010 and 2012) were used to train and validate a convolutional neural network, which subsequently extracted pectoralis muscle and adipose (intermuscular adipose tissue (IMAT), perimuscular adipose tissue (PAT), extramyocellular lipids and subcutaneous adipose tissue) area measurements from 3031 CT examinations using individualized thresholds for adipose segmentation. Next, 1781 participants without baseline HF were longitudinally investigated for associations between baseline pectoralis muscle and adipose measurements and incident HF using crude and adjusted Cox proportional hazards models. The full models were adjusted for variables in categories of demographic (age, race, sex, income), clinical/laboratory (including physical activity, BMI, and smoking), CT (coronary artery calcium score), and cardiac MRI (left ventricular ejection fraction and mass (% of predicted)) data.

Results

In 1781 participants (median age, 68 (IQR,61, 75) years; 907 [51%] females), 41 incident HF events occurred over a median 6.5-year follow-up. IMAT predicted incident HF in unadjusted (hazard ratio [HR]:1.14; 95% CI: 1.03-1.26) and fully adjusted (HR:1.16, 95% CI: 1.03-1.31) models. PAT also predicted incident HF in crude (HR:1.19; 95% CI: 1.06-1.35) and fully adjusted (HR:1.25; 95% CI: 1.07-1.46) models.

Conclusion

The study demonstrates that fast and reliable deep learning-derived pectoralis muscle and adipose measurements are obtainable from conventional chest CT, which may be predictive of incident HF.©RSNA, 2023.