Biomedicines | Free Full-Text | Performance of Radiological and Biochemical Biomarkers in Predicting Radio-Symptomatic Knee Osteoarthritis Progression

Bone is among the key factors in the pathological process of OA, as illustrated by the numerous features depicted by several imaging modalities, such as Magnetic Resonance Imaging (MRI)-based bone marrow lesions [1], dual-energy X-ray absorptiometry (DXA)-based tibial subchondral bone mineral density [2], X-ray-based subchondral bone thickening, osteophytes [3], and the trabecular bone texture (TBT) of subchondral tibial bone [4].

TBT is a promising imaging biomarker for the prediction of radiographic knee osteoarthritis (KOA) outcomes (incidence, progression, and total knee arthroplasty) [5,6,7,8,9,10]. TBT-based prediction models have, furthermore, demonstrated to be robust and flexible, as they have been trained and validated on different cohorts [11].

Searching for tools for predicting KOA progression is not limited to imaging biomarkers, as KOA is also characterized by an imbalance between pro-inflammatory (procatabolic) and anti-inflammatory cytokines and growth factors. Thus, the corresponding imbalance between tissue degradation and formation can be assessed and monitored by both synovial and blood-based biomarkers. Biochemical trial enrichment biomarkers have been suggested to improve successful therapy development for KOA [8,12].

In the present study, we included biochemical data obtained from the Foundation for the National Institutes of Health (FNIH) OA Biomarkers Consortium, Bethesda, MD, USA, namely urinary C-terminal crosslinked telopeptide type II collagen (uCTX-II), crosslinked N-telo peptide of type I collagen (sNTXI) serum, and hyaluronic acid (sHA) serum, which have previously been evaluated for their role in the progression of the disease [8,13,14]. The restriction to utilizing these three biochemical biomarkers stemmed from their demonstrated superior predictive capacity within the FNIH dataset [8]. Combining imaging biomarkers and molecular biomarkers has received limited attention [15] despite its potential to improve the prediction of KOA progression, and to stratify therapeutic interventions [16,17]. Thus, in addition to the baseline parameters employed by the reference model (age, sex, Body Mass Index (BMI), Kellgren–Lawrence (KL), joint space narrowing in the medial tibial plateau (JSNM), and TBT at baseline), we examined the use of longitudinal TBT variations associated with the aforementioned biochemical markers (uCTX-II, sNTXI, and sHA) for the prediction of the radiographic, symptomatic, and radio-symptomatic progression of KOA in the FNIH dataset.

KOA progression is often considered to be slow; 12% to 23% of knees with radiographic KOA experience radiographic progression over 5 years [26]. Hence, the findings in the current study would help in better selecting participants in future structure-modifying KOA trials.

The use of baseline, 12-month, and 24-month TBT parameters was previously evaluated [7] for the prediction of 48-month radiographic and symptomatic progression in the FNIH cohort. In that study, the medial subchondral tibial region only was investigated to extract TBT parameters computed using the fractal signature analysis (FSA) method [20]. Introducing the time-integrated values (TIVs) of the TBT parameters over 24 months provided a benefit to the prediction of KOA radio-symptomatic progression (primary analysis), with an AUC of 0.649, compared to the use of clinical covariates alone (AUC = 0.608) [7]. While the authors in [7,27] investigated the use of the 24-month TIVs of TBT parameters, equivalent to the area under the curve defined by the baseline and 24-month TBT values, they did not investigate the use of time-longitudinal changes, quantified as the difference between the baseline and 24-month TBT values. In addition, in their study [7,27], six TBT parameters were employed, extracted from the medial tibial plateau only.

This study also highlights the importance of exploiting the whole subchondral bone of the tibia, rather than only the medial plateau or a limited part of the medial and lateral plateaus [5,7], to extract radiographic TBT parameters as the performance of the prediction models was lower in all the different scenarios using TBT parameters extracted from the medial, lateral or central tibial plateaus alone (Tables S1–S5 of the Supplementary File).

The best AUC score was obtained for the prediction of radiographic progression (AUC = 0.783). It is more difficult to predict symptomatic (pain) progression since it is related to changes in pain scores, which are subjective due to differences in patient tolerance. In addition, it has been demonstrated that pain scores can only be considered modest markers in the prediction of KOA-related outcomes [6,11].

The results obtained by the present study confirm the interest in using both baseline TBT parameters and their variations over 24 months, allowing a better prediction of radio-symptomatic, radiographic, or symptomatic progression. When TBT descriptors were excluded, all assessed models had failed (0.5 ≤ AUC 28,29].

In the present study, adding molecular biomarkers into the model to the core set of radiographic and clinical markers did not improve the performance of the reference model. The AUC scores of the reference model (AUC = 0.709 and 0.779 using TBT and TBT + ∆TBT, respectively) were similar to those obtained by including molecular biomarkers (AUC = 0.708 and 0.779 using TBT and TBT + ∆TBT, respectively) for the prediction of radiological progression. This observation could be because the information given by biochemical markers is already captured by data from subchondral bone (its texture), osteophytes, and joint space width, which are known as strong predictors of KOA progression [4,10,11,28,30]. Similar results have been found for the other scenarios (Table 1). The three molecular biomarkers used in the present study were selected based on their formerly successful use in the literature concerning the prediction of KOA progression [8,13]. Other relevant parameters might be used to improve KOA prediction models such as type II collagen KOA formation [12] or inter-alpha trypsin inhibitor heavy chain 1 [31].

Our study has several strengths. The proposed prediction models are based on TBT descriptors extracted from plain radiographs, widely used in clinical routine, while biochemical parameters are not yet included in daily clinical routines and need much more time to be extracted from blood or urine samples. Furthermore, data were selected in accordance with each specific type of progression evaluated in the current study. For radiographic-only progression, knees with symptomatic progression were not included, and vice versa; for symptomatic-only progression, knees with radiographic progression were not included. In addition, for radio-symptomatic progression, knees with symptomatic-only or radiographic-only progression were not included. To avoid possible correlation between the TBT parameters of both the baseline and 24-month variations, which can lead to problems with traditional logistic regression with respect to overfitting and convergence, the LASSO method was used as an alternative regularization method [32].

A growing number of researchers are interested in evaluating the potential of imaging biomarkers to enhance patient screening in phase III studies for KOA and determining under which conditions they provide such enhancements [4,8]. From a clinical standpoint, our model showed great precision in predicting false progressors. Incorporating such progressors in a disease-modifying osteoarthritis drug (DMOAD) randomized clinical trial could have counterproductive consequences.