International Journal of Bone and Rheumatology Research(IJBRR)  /  IJBRR-2470-4520-02-001e

Recent Efforts to Find Disease - Modifying Treatments of Osteoarthritis

Kawaguchi H

The Chief of the Spine Center, Japan Community Health Care Organization (JCHO), Tsukudo 5-1, Tokyo, Japan.

*Corresponding Author

Hiroshi Kawaguchi,
The Chief of the Spine Center,
Japan Community Health Care Organization (JCHO),
Tsukudo 5-1, Tokyo 162-8543, Japan.
Tel: 03-3269-8111
Fax: 03-3260-7840
Article Type: Editorial
Received: August 06, 2015; Published: August 12, 2015

Citation: Kawaguchi H (2015) Recent Efforts to Find Disease-Modifying Treatments of Osteoarthritis. Int J Bone Rheumatol Res, 2(1e), 01-03. doi:

Copyright: Kawaguchi H© 2015. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited.

1.Key Words
3.Stringency of Disease Definition
4.Selection of appropriate controls
5.Adjustment for Confounding Environmental / Clinical Factors

Key Words

Genetic, Genome, Osteoarthritis.


Despite high prevalence and social impact, osteoarthritis (OA) is far behind other skeletal diseases like osteoporosis in the developmentof disease-modifying treatments. This is mainly because little is known about the underlying molecular mechanism that could be the therapeutic target. Since OA is a multifactorial disease caused by complex interplay between environmental and genetic factors with estimates of around 50% heritability depending on the site [1], numerous efforts and great expense have been spent on human genetic studies on OA worldwide. Although linkage studies have shown large areas of chromosomes associated with the disease, they have failed to detect the susceptible genes. Candidate gene studies have proposed over 100 genes as being responsible; however, most of them have not later been reproduced in larger meta-analysis studies. Recently, while genome-wide association studies (GWAS) have led to the discovery of over 600 gene loci in over 50 common multifactorial diseases, most of the gene variants are of only minimal individual effect. Even though the identified genes with such small effect sizes could possibly be therapeutic targets or at least prognostic markers, it is questionable whether or not these conventional OA genetic studies are worthy of such enormous investment. Aiming at a well-powered approach for this highly polygenic disease with multiple risk loci conferring small effects, consortium studies like Treat-OA and arc OGEN have been developed to enlarge the sample size. Considering the disease characteristics and prevalence, however, it is our opinion that not only the quantity but also the quality of studies is critical for identification of the genetic architecture. In this sense, the conventional OA genetic studies do not seem to us who are clinicians, although not genetic experts, to have been performed with sufficient scientific strictness, even as compared to those on other common diseases.

Several studies indicate that inconsistent and ambiguous definition of OA is a critical limitation of conventional genetic studies [2]. In addition to the stringency of disease definition raised by them, here we propose two other capital issues in the conventional studies: selection of appropriate controls and adjustment for environmental/clinical factors, from a clinician’s point of view.

Stringency of Disease Definition

Although most conventional genetic studies determine OA on radiographs as Kellgren-Lawrence (KL) score=2 or higher (Table I) [3-7], the KL grading is limited in reproducibility and sensitivity due to the subjective judgment of observers and the categorical classification into only a five-grade scale [8]. In the ROAD (Research on Osteoarthritis Against Disability) study with a highquality population-based cohort database of detailed environmental and genetic information of more than 3,000 participants [9], we delete the intermediate and ambiguous KL=2 subgroup for the case-control analysis to increase the detection power. For example, our association analysis of the EPAS1 gene which was identified to be crucial for OA development in mice was able to detect a significant difference of the minor allelic frequency (mAF) of a SNP in the gene between KL=3 & 4 (case; mAF=11.1%) and KL=0 & 1 (control; mAF=15.2%) [10]. The mAF of the omitted KL=2 subgroup was 12.3%, confirming an inverse relationship between mAF of the SNP and KL scores. This clearly indicates that inclusion of the KL=2 subjects in the case group had caused a decrease in the detection power. In fact, this association was not reproduced by conventional Japanese and Chinese studies that include KL=2 in the case group [11]. Considering that prevalence of the KL=2 subgroup is shown to be fairly high in representative epidemiologic studies (17.3-41.3%; difference between KL ≥ 2 and KL ≥ 3 in Table II), removal of this subgroup may inevitably cause a decrease in the total sample size.

Table 1. Source of subjects, definition, and adjustment for confounders in representative knee OA genetic studies.

Generally, a lack of objective and quantitative measure for the disease definition remains a fatal limitation of clinical OA studies. The ROAD study has recently established the fully automatic program KOACAD (knee OA computer-aided diagnosis) to quantify the major OA parameters (joint space, osteophyte, etc.) on plain radiographs [8]. We believe that the KOACAD system as well as magnetic resonance image systems [12] will serve as optimal measures for the definition of OA in the near future, just as bone mineral density does in osteoporosis.

Selection of appropriate controls

In genetic studies on common diseases with a high prevalence, selection of disease-free controls is essential to avoid the poten tial bias due to contamination of affected subjects in the control. In representative epidemiologic studies worldwide, the prevalence of radiographic knee OA (KL ≥ 2) in the elderly was ≥ 30% in all populations and > 60% in Asian populations like Japan (ROAD study) and China (Shanghai) (Table II) [13]. Furthermore, the prevalence of asymptomatic knee OA was 24-36% in all populations. Hence, if so-called healthy subjects without knee symptoms were collected as controls, a considerable number of OA subjects would be included in the control group. Even in a series of genetic studies in Japan with a high OA prevalence [13], the control subjects are miscellaneous mixtures of various populations including considerable numbers of so-called healthy subjects and other disease patients without radiographic diagnosis (Table I) [3, 5, 7], indicating that a substantial percentage in the control groups are affected subjects. A recent analysis of the effect of controls selected with different levels of stringency on the association of known knee OA susceptibility genes demonstrates that a control with poor selection or without selection cannot be compensated by increase of the sample size [14]. Hence, selection of appropriate controls confirmed to be disease-free may be crucial to achieve a high detection power.

Table 2. Prevalence of radiographic knee OA in representative population-based cohorts.

Adjustment for Confounding Environmental/ Clinical Factors

Lastly, we should again note that OA is a multifactorial disease with environmental and genetic backgrounds and that the genetic contribution is less than half in knee OA [1]. A report by Takahashi et al. constructed knee OA prediction models based on genotype (combination of three risk alleles of ASPN, GDF5 and DVWA) and environmental/clinical information (age, gender and BMI), and evaluated the predictive power by area under the curve (AUC; range, 0.5 [worst] to 1 [best]) on a receiver operating characteristic (ROC) curve in a case-control association study [15]. The result was that the power by the genotype information was very small (AUC=0.554), implicating uselessness of the three famous genotypes as a prognostic marker. Contrarily, the environmental/clinical information was a much better predictor (AUC=0.678), but was little improved by the combination with the genotype information (AUC=0.685), again confirming its uselessness. Hence, to achieve a high detection power for the susceptibility gene, all efforts should be made to exclude the influence of environmental/clinical factors.

Surprisingly, however, there are big differences in age and gender between case and control groups in previous representative studies (Table I). Even a sole difference in age of about 20 years between case and control groups that is seen in the Japanese studies [3, 5, 7] is calculated to cause an increase of odds ratio for OA to 2.65 (=1.0520), according to the authors’ own estimation (1.05/year) [15]. Indeed, we are not opposed to recent activities of OA consortiums to pool subjects worldwide; however, we should note that the pooled subjects are miscellaneous mixtures of various populations with different backgrounds. Selection of case and control subjects with similar backgrounds is essential to minimize selection bias which strongly influences the results in genetic studies with small effect sizes of the risk alleles. Hence, at least for the initial screening, case and control groups should be selected from a single population-based cohort to adjust the living environment and stratified by confounding environmental/ clinical factors which have been identified in preparatory epidemiologic analysis in the cohort. The reproducibility may then be examined in other replication cohorts of the worldwide consortiums, after adjustment for the specific confounding factors in the respective cohorts.

Taken together, conventional OA genetic studies appear to compare a case group containing a substantial number of subjects with ambiguous definition versus a control group containing a substantial number of affected subjects, plus without adjustment for confounding environmental/clinical factors. Contrary to the genetic studies, studies of clinical trial and observational epidemiology are performed under a sound scientific rigidity in compliance with very strict rules to examine the accurate effect sizes of interventions and environmental/clinical factors, respectively. Introduction of strict regulation in the genetic field, just like CONSORT guidelines in the clinical trial field [16], might improve the scientific rigidity.


  1. Spector TD, MacGregor AJ (2004) Risk factors for osteoarthritis: genetics. Osteoarthritis Cartilage 12: S39-44.
  2. Kerkhof HJ, Meulenbelt I, Akune T, Arden NK, Aromaa A, et al. (2011) Recommendations for standardization and phenotype definitions in genetic studies of osteoarthritis: the TREAT-OA consortium. Osteoarthritis Cartilage 19(3): 254-264.
  3. Miyamoto Y, Mabuchi A, Shi D, Kubo T, Takatori Y, et al. (2007) A functional polymorphism in the 5' UTR of GDF5 is associated with susceptibility to osteoarthritis. Nat Genet 39(4): 529-533.
  4. Valdes AM, Loughlin J, Timms KM, van Meurs JJ, Southam L, et al. (2008) Genome-wide association scan identifies a prostaglandin-endoperoxide synthase 2 variant involved in risk of knee osteoarthritis. Am J Hum Genet 82(6): 1231-1240.
  5. Miyamoto Y, Shi D, Nakajima M, Ozaki K, Sudo A, et al. (2008) Common variants in DVWA on chromosome 3p24.3 are associated with susceptibility to knee osteoarthritis. Nat Genet 40(8): 994-998.
  6. Kerkhof HJ, Lories RJ, Meulenbelt I, Jonsdottir I, Valdes AM, et al. (2010) A genome-wide association study identifies an osteoarthritis susceptibility locus on chromosome 7q22. Arthritis Rheum 62(2): 499-510.
  7. Nakajima M, Takahashi A, Kou I, Rodriguez-Fontenla C, Gomez-Reino JJ, et al. (2010) New sequence variants in HLA class II/III region associated with susceptibility to knee osteoarthritis identified by genome-wide association study. PLoS One 5(3): e9723.
  8. Oka H, Muraki S, Akune T, Mabuchi A, Suzuki T, et al. (2008) Fully automatic quantification of knee osteoarthritis severity on plain radiographs. Osteoarthritis Cartilage 16(11): 1300-1306.
  9. Yoshimura N, Muraki S, Oka H, Kawaguchi H, Nakamura K, et al. (2010) Cohort Profile: Research on Osteoarthritis/Osteoporosis Against Disability study. Int J Epidemiol 39(4): 988-995.
  10. Saito T, Fukai A, Mabuchi A, Ikeda T, Yano F, et al. (2010) Transcriptional regulation of endochondral ossification by HIF-2α during skeletal growth and osteoarthritis development. Nat Med 16(6): 678-686.
  11. Nakajima N, Shi D, Dai J, Tsezou A, Zheng M, Norman PE, et al. (2011) Replication studies in various ethnic populations do not support the association of the HIF-2α SNP rs17039192 with knee osteoarthritis. Nat Med 17(1): 26-27.
  12. Eckstein F, Guermazi A, Roemer FW (2009) Quantitative MR imaging of cartilage and trabecular bone in osteoarthritis. Radiol Clin North Am 47(4): 655-673.
  13. Muraki S, Oka H, Akune T, Mabuchi A, En-yo Y, et al. (2009) Prevalence of radiographic knee osteoarthritis and its association with knee pain in the elderly of Japanese population-based cohorts: The ROAD study. Osteoarthritis Cartilage 17(9): 1137-1143.
  14. Rodriguez-Fontenla C, López-Golán Y, Calaza M, Pombo-Suarez M, Gómez-Reino JJ, et al. (2012) Genetic risk load and age at symptom onset of knee osteoarthritis. J Orthop Res 30(6): 905-909.
  15. Takahashi H, Nakajima M, Ozaki K, Tanaka T, Kamatani N, et al. (2010) Prediction model for knee osteoarthritis based on genetic and clinical information. Arthritis Res Ther 12(5): R187.
  16. Schulz KF, Altman DG, Moher D (2010) CONSORT 2010 Statement: updated guidelines for reporting parallel group randomised trials. BMC Med 8(1): 18-26.

         Indexed in

pubhub   CGS   indexcoop   j-gate   DOAJ   Google_Scholar_logo

       Total Visitors

SciDoc Counter

Get in Touch

SciDoc Publishers
16192 Coastal Highway
Lewes, Delaware 19958
Tel :+1-(302)-703-1005
Fax :+1-(302)-351-7355

Creative Commons License
SciDoc Publishers is licensed under a Creative Commons Attribution 4.0 International License.