We have suspected for some time that the correlation of systemic biomarkers with disease has been hampered by the inability to fully phenotype the burden of OA in a patient, and in a sense, giving systemic biomarkers a ‘bad name’. The results presented here illustrate the strong correlation of total body burden of disease and several biomarkers, revealed upon adequate patient phenotyping. These results clearly show that all three biomarkers, sHA, sCOMP and uCTX2 are quantitative traits of the radiographic feature of OST while sCOMP is a negative indicator of joint space narrowing affected joint faces in the body. This paper may serve some in the relevant medical communities as an introduction to some novel methods for assessing biomarkers in combination, joint systems in combination, and OA features in combination. For instance, evaluated in combination using multimodel inference with linear mixed-effects regression, it becomes evident that sCOMP adds little information for predicting total OST burden when sHA and uCTX2 are available, although sCOMP is a marker of osteophytes.
There have been several previous studies that used OA-related biomarkers in combination to quantify and characterize the OA process 
. Four notable examples 
used principal components analysis (PCA). Meulenbelt et. al.
identified three components that reflected radiographic OA at different joint sites. The components were comprised of 1) structural markers of cartilage (uCTX2, uTINE, uGlc-Gal-PYD) and bone turnover (uCTX-1 and total sOsteocalcin) associated with hip radiographic OA; 2) a marker of inflammation (serum hsCRP) associated with knee OA, high Western Ontario and McMaster Universities (WOMAC) scores and BMI; and 3) markers of cartilage turnover (sPIIANP, sCOMP) associated with radiographic OA of the hands and lumbar spine as well as age. PCA by Davis et. al.
has also identified several factors based on biomarkers that correlated with OA features: 1) a factor representing osteophytes that overlapped, as expected, with a factor that correlated with Kellgren Lawrence score; 2) a separate factor that correlated with subchondral bone mineral density; and 3) a factor that correlated with joint space width that overlapped with biomarkers associated with both osteophytes and bone mineral density 
. In work by Garnero et al 
, 10 markers segregated into 5 factors: Factor 1 comprised markers of bone (S-PINP, U-CTX-I) and cartilage (U-CTX2) turnover; factor 2 comprised of SCOMP, S-HA, and S-PIIINP; factor 3 comprised of markers of systemic inflammation S-CRP and S-YKL-40; and factors 4 and 5 comprised of MMP-1 and MMP-3 respectively. Prior work by Otterness et al 
showed that 14 biomarkers segregated into 5 rational groups based on inflammation, bone turnover, cartilage anabolism, cartilage catabolism and transforming growth factor beta.
These studies support our finding that different biomarkers report on different aspects of joint pathology. Specifically, different biomarkers may reflect different molecular pathobiologic mechanisms and different joint contributions to the systemic biomarker concentrations. The interpretation of biomarker levels may be further complicated by the complex biology represented by a biomarker. For instance, decreased levels may reflect reduced matrix degradation, decreased synthesis, or impaired release from the tissue or origin 
. Osteophyte formation can be considered an anabolic phenomenon and all three biomarkers reported independently on this feature of joint pathology. In contrast, joint space narrowing can be considered a phenomenon of catabolism in excess of anabolism, or a net failure of repair. The negative correlation of COMP and joint space narrowing could represent the failed repair phenomenon (waning of an anabolic epitope) with increased disease severity, or depletion of a catabolic epitope with increased disease severity. In this cohort, neither HA nor CTX2 reported independently on JSN. These findings only came to light with comprehensive phenotyping that demonstrated the utility of total body measures of OA when qualifying a systemic OA-related biomarker for a specific purpose. Moreover, these results also highlight the importance of accounting for both OST and JSN. Failure to account for both is problematic and may lead to spurious conclusions as demonstrated here through comparing and contrasting the results obtained from adjusted versus unadjusted analyses ( versus Figure S3
, and versus Figure S5
The biomarker uCTX2 was independent of age while sHA and sCOMP increased with age; uCTX2 was also independent of height and weight, as was sCOMP while sHA increased with weight and decreased with height. The biomarkers increased the variance explained over simple demographic factors for predicting total body burden of disease. The different proportions of variance explained by biomarkers for the different joint systems may be related to genetic variation or to measurement variation, i.e. adequacy of phenotyping. In this regard, it is interesting that biomarkers added most to the prediction of knee OA, for which a standardized radiographic phenotyping procedure was used. This specialized x-ray may more accurately represent the disease. Thus, the biomarkers may not be so marginal but rather, true correlations of systemic biomarkers with disease burden may be higher, and only demonstrable with better gold standards for measuring the disease.
The coefficients of a linear model estimate the average contribution per affected joint to the serum concentration of a biomarker. OST and JSN had very similar coefficients for s
HA and uCTX2
, however, there were cases where the difference among OST and JSN were significant and may have real consequences for the use of these biomarkers. One example of the potential complex interrelationship of OST and JSN was demonstrated by the increase of u
CTX2 with increasing number of CMC joint faces affected by JSN. When the number of joint faces affected by JSN was held constant, however, u
CTX2 declined with increasing number of CMC joint faces affected by OST. Another point, convincingly demonstrated herein by the large contribution of CMC-OST to both s
HA and u
CTX2 concentrations, is that joint size does not necessarily determine the contribution of a joint to the systemic biomarker concentration. One could truly say, based on these data that the CMC “sticks out like a sore thumb”. We do not know why the CMC joint, despite its small size, disproportionally impacted the concentration of all three systemic biomarkers. It is possible that the stage of disease accounted for this phenomenon. For instance, relative to other joints, increasing JSN in the CMC represented increasingly active matrix turnover while increasing CMC osteophyte severity, an anabolic phenomenon, represented waning activity with respect to biomarker production. Another possible explanation relates to relative turnover and clearance of the biomarkers from the CMC joint. Strong evidence exists for differences in joint tissue turnover for different joint systems, best exemplified by differences between knees and ankles 
Radiographic features of OST and JSN are interrelated and this correlation can prove to be a potential confounder in attempts to characterize the independent contributors to the systemic level of a biomarker. There are just two examples of OST and JSN features from a single joint system contributing independently to the concentration of a systemic biomarker: MCP on sCOMP, and CMC on uCTX2 (). In both cases, JSN and OST features had opposing effects on biomarker concentration. This study was limited to women, so these results will need to be validated in other cohorts, including men, selected by other means. Further, because the study participants were selected on the basis of familial hand OA, they may have had greater correlation of OA among joint sites than individuals without so clearly a familial etiology of OA. For this reason, the generalizability of these results would need to be evaluated. Finally, these analyses were limited to the association of a discrete set of biomarkers and structural changes. The field would benefit from similar analyses of a broader range of biomarkers, and qualification studies related to clinical and symptomatic patient-reported outcomes to complement those presented here related to structural endpoints.