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Abstract

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. SUBJECTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. ROLE OF THE STUDY SPONSOR
  9. Acknowledgements
  10. REFERENCES

Objective

To examine the association of concurrent low back pain (LBP), and other musculoskeletal pain comorbidity, with knee pain severity in symptomatic knee osteoarthritis (OA).

Methods

Individuals from the Progression Cohort of the Osteoarthritis Initiative (n = 1,389, ages 45–79 years) with symptomatic tibiofemoral knee OA were studied. Participants identified pain in the low back, neck, shoulder, elbow, wrist, hand, hip, knee, ankle, or foot. The primary outcome was the pain subscale of the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) applied to the more symptomatic knee. We examined WOMAC pain score in persons with and without LBP, before and after adjusting for other musculoskeletal symptoms.

Results

Of the participants, 57.4% reported LBP. The mean ± SD WOMAC pain score (possible range 0–20) was 6.5 ± 4.1 in participants with and 5.2 ± 3.4 in participants without LBP (P < 0.0001). In multivariate analyses, LBP was significantly associated with increased WOMAC knee pain score (β [SE] = 1.00 [0.21], P < 0.0001). However, pain in all other individual musculoskeletal locations demonstrated similar associations with knee pain score. In models including all pain locations simultaneously, only LBP (β [SE] = 0.65 [0.21], P = 0.002), ipsilateral elbow pain (β [SE] = 0.98 [0.40], P = 0.02), and ipsilateral foot pain (β [SE] = 1.03 [0.45], P = 0.02) were significantly associated with knee pain score. Having >1 pain location was associated with greater WOMAC knee pain; this relationship was strongest for individuals having 4 (β [SE] = 1.83 [0.42], P < 0.0001) or ≥5 pain locations (β [SE] = 1.86 [0.36], P < 0.0001).

Conclusion

LBP, foot pain, and elbow pain are significantly associated with WOMAC knee pain score, as are a higher total number of pain locations. This may have implications for clinical trial planning.


INTRODUCTION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. SUBJECTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. ROLE OF THE STUDY SPONSOR
  9. Acknowledgements
  10. REFERENCES

Osteoarthritis (OA) of the knee is the leading cause of disability in the US (1). Low back pain (LBP) is the most common cause of time lost from work among individuals <45 years of age and the third most common cause among individuals between 45 and 65 years of age (2, 3). The coexistence of LBP in individuals with knee pain may predispose such individuals to symptom severity well beyond the situation of isolated knee pain. Cross-sectional studies of individuals with knee OA have suggested that concurrent LBP may be associated with greater knee OA symptoms (4, 5). Wolfe reported an association between concurrent LBP and higher scores on the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). However, this study used a version of the WOMAC applied globally without referring to the knee joint specifically (4). Longitudinal studies have demonstrated that the presence of preoperative LBP is one of the most important factors associated with poor pain outcomes after total knee arthroplasty (TKA) (6) and revision TKA (7). This small body of evidence supports a relationship between concurrent LBP and knee-specific functional limitations, but the nature of this relationship, and other factors that may affect this relationship, remain unexplored. Pain in other musculoskeletal locations, including the hip and the foot, may also be associated with symptoms in the knee (8, 9).

There are multiple mechanisms by which pain in locations external to the knee may be associated with increased knee pain. First, LBP and other joint pains may directly cause increased knee pain due to the biomechanical interrelationship of joints in the kinetic chain (10, 11). Second, other pain locations may be associated with factors that themselves cause knee-related functional limitations and pain. For example, the presence of LBP may simply be a marker for individuals prone to pain states who may intrinsically experience higher levels of knee-related symptoms (12, 13). In this situation, we might expect any musculoskeletal pain location to be associated with knee pain, regardless of proximity or biomechanical relationship to the knee joint. Furthermore, an increased number of pain locations may be a marker of widespread pain comorbidity and may be associated with still higher levels of knee-related symptoms (14). Third, problems of pain attribution and localization may create a situation where an individual is unable to discriminate between different joint-specific sources of pain (15, 16). This may cause the WOMAC to include pain reporting external to the knee, and effectively act as a global measure even when asked in a joint-specific manner.

An understanding of the mechanisms by which LBP and other musculoskeletal pain are associated with knee pain may help to identify patients who are at risk for poor outcomes following TKA, and patients who may benefit from cointerventions to treat musculoskeletal pain in other locations. Furthermore, the effects of other musculoskeletal pain on knee pain may have implications for studies that use the WOMAC as an outcome but do not account for pain external to the knee.

This study seeks to evaluate the association between concurrent LBP and other musculoskeletal pain comorbidity and WOMAC knee pain in a population of individuals with symptomatic knee OA. We hypothesized that LBP would be associated with higher knee-specific WOMAC pain scores. However, we expected that other musculoskeletal pain sites would also be similarly associated with higher knee-specific WOMAC pain scores.

SUBJECTS AND METHODS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. SUBJECTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. ROLE OF THE STUDY SPONSOR
  9. Acknowledgements
  10. REFERENCES

Participants.

Data used in the preparation of this article were obtained from the Osteoarthritis Initiative (OAI) database AllClinical00, V0.2.2 (available for public access at http://www.oai.ucsf.edu). The OAI is a publicly available multicenter population-based observational cohort study of knee OA that is comprised of 3 groups, the Progression subcohort (n = 1,389), the Incidence subcohort (n = 3,285), and the Non-exposed Control group (n = 122). The current study included individuals from the Progression subcohort of the OAI, consisting of individuals ages 45–79 years with symptomatic tibiofemoral knee OA in at least 1 knee at baseline. Symptomatic tibiofemoral knee OA was defined by 1) participant report of frequent knee symptoms defined as “pain, aching, or stiffness in and around the knee on most days” for ≥1 month during the past 12 months, and 2) radiographic evidence of tibiofemoral knee OA defined as the presence of an Osteoarthritis Research Society International (OARSI) atlas osteophyte grade 1–3 (equivalent to a Kellgren/Lawrence [K/L] grade ≥2) on a fixed flexion radiograph based on the individual clinic readings. Exclusion criteria included participant report of rheumatoid arthritis or inflammatory arthritis, having severe joint space narrowing (JSN) in both knees or unilateral TKA and severe JSN in the contralateral knee, inability to undergo 3.0T magnetic resonance imaging examination of the knee, a positive pregnancy test, inability to provide a blood sample, requirement of ambulatory aids aside from the use of a single straight cane ≤50% of ambulation time, comorbid conditions that might interfere with the ability to participate in a study with a 4-year followup time, unlikelihood to reside in the clinic area for ≥3 years, current participation in a double-blind randomized controlled trial, and unwillingness to sign informed consent.

Demographic information.

All of the participants in the Progression cohort received a standard battery of questions during the initial OAI eligibility interview, the OAI screening visit, or the OAI enrollment visit. This baseline assessment of participant demographics included participant self-report of age, sex, race, ethnicity, employment status, yearly income, educational attainment, and health insurance status. Employment status was defined as being currently employed for pay at the enrollment visit. Participants who were on leave but expecting to return to work within 6 months were considered to be employed. Yearly income was identified as less than $50,000 or as $50,000 or greater. Health insurance coverage status was identified as “currently having any kind of health coverage.”

Medical history and mental health comorbidity.

Comorbid medical conditions were assessed by a self-reported version of the Charlson comorbidity index. The Charlson comorbidity index is a commonly used measure of comorbidity burden that has demonstrated validity and reliability (17–19). Smoking history was assessed by the question “Do you smoke cigarettes now?” Depression was measured by participant self-report using the Center for Epidemiologic Studies Depression Scale, which has been shown to have valid and reliable psychometric properties (20, 21). Anxiety was measured by item 9 of the Short Form 12 (SF-12) health survey, in which the participant answers the question “How much of the time during the past 4 weeks have you felt calm and peaceful?” using a 5-point Likert scale ranging from 1 to 5, where 1 = all of the time and 5 = none of the time (22). Higher scores indicated greater anxiety. Fatigue was measured by item 10 of the SF-12 using an identical 5-point Likert scale response to the question “How much of the time during the past 4 weeks did you have a lot of energy?” Higher scores indicated greater fatigue. The SF-12 is a widely used, validated, and reliable measure of health-related quality of life (22). Although items 9 and 10 of the SF-12 have not been validated for item-specific use as measures of anxiety or fatigue, they have been used in this manner previously (23).

Anthropometric characteristics.

Examination measures were obtained at the enrollment visit of the OAI. Body mass index (BMI) was calculated as weight (kg) divided by the square of height (m2). Abdominal circumference was assessed with the participant standing, using a tape measure over bare skin (24).

Radiographic assessment of knee OA.

Fixed flexion knee radiographs for assessment of each tibiofemoral joint were obtained at the enrollment visit for each participant using a Synaflexer (Synarc). Knee radiographs were interpreted by readers at the OAI Clinical Centers who were specifically trained to assess the baseline fixed flexion knee radiographs using a classification based on the OARSI atlas grading system (25). A simulated K/L grade was used for assessment of JSN and osteophytosis (26).

Musculoskeletal pain comorbidity.

The presence of musculoskeletal pain was identified by participant self-report of back pain, neck pain, shoulder pain, elbow pain, wrist pain, hand pain, ankle pain, hip pain, knee pain, and foot pain. Back pain was defined by the question “During the past 30 days, have you had any back pain?” Back pain was characterized by severity as mild, moderate, or severe on self-report. The location of back pain was identified by the patient on a pain diagram, which allowed the selection of pain in the upper back, middle back, lower back, and buttocks. LBP was defined as pain in the lower back or buttocks as per recent consensus criteria on optimal definitions of LBP location (27). Neck pain, shoulder pain, elbow pain, wrist pain, hand pain, knee pain, ankle pain, and foot pain were defined by the question “During the past 30 days, which of these joints have had pain, aching, or stiffness on most days?” The locations of pain were then identified by the participant on a pain diagram, which permitted the identification of site-specific pain locations on either side of the body. The presence of right hip pain or left hip pain was defined by the question “During the past 12 months, have you had any pain, aching, or stiffness in your hip?” Laterality of pain was specified by the patient for all pain locations, with the exception of LBP and neck pain.

Outcomes.

Participants completed separate knee-specific WOMAC scores for both the right and left knee at the enrollment visit. The WOMAC is a widely used outcome measure for lower extremity OA, and has demonstrated reliability and validity in the context of knee OA (28). The WOMAC consists of 3 knee-specific subscales: the WOMAC function subscale, the WOMAC pain subscale, and the WOMAC stiffness subscale. Because the WOMAC subscales are known to have substantial intercorrelations, the knee-specific WOMAC pain score was used as the primary outcome for the current study (29, 30).

Statistical analysis.

We began by characterizing the prevalence of each musculoskeletal pain location in the entire cohort. The potential confounding variables of participant demographics, medical and psychiatric comorbidity, anthropometric features, musculoskeletal pain, and WOMAC scores were compared between the subgroups of patients with and without LBP. Educational attainment was dichotomized as less than college level education versus college level or higher educational attainment. To estimate the association between the presence of LBP and knee-specific WOMAC pain score, we used linear regression to examine the subgroup of the entire Progression cohort that had no missing values for any of the variables initially examined. In the first stage of the analysis, we constructed a model examining the relationship between the independent variables of demographic features, medical and psychiatric comorbidity, anthropometric characteristics, and OA grading, using the dependent variable of WOMAC pain score for the most symptomatic (painful) knee. We initially included all variables in the model, and employed a backward selection algorithm with a significance threshold of P values of 0.10 for variable removal. The variables age, sex, race, and ethnicity were believed to have particular clinical importance and were forced into the model.

In the second stage of the analysis, we examined the effects of specific musculoskeletal pain locations added to the model created in the first stage of the analysis. For extremity pain locations, we examined locations ipsilateral and contralateral to the more symptomatic knee separately. For axial pain in the neck and back, we did not have specific information on laterality of pain. First, each pain location was added separately to the first stage model, and associations between the pain location and WOMAC pain score were observed. Contralateral knee pain was included as an independent variable, although ipsilateral knee pain was not. Second, all pain locations were added simultaneously to the variables included in the first-stage model, and the independent effects of each pain location were observed. We then conducted a series of analyses to compare the proportion of WOMAC pain score variance explained by different combinations of independent variables. We first removed from the full model all pain locations that were not significantly and independently associated with WOMAC pain score (P values less than 0.05). We next constructed a model incorporating all pain locations and variables from the first-stage model, but using 3 different levels of LBP severity: mild, moderate, and severe. Last, we constructed a model including the total number of pain comorbidities, irrespective of location, using the categories of 0, 1, 2, 3, 4, and ≥5 pain locations.

RESULTS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. SUBJECTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. ROLE OF THE STUDY SPONSOR
  9. Acknowledgements
  10. REFERENCES

The study sample was 57.1% female, with a mean ± SD age of 61.4 ± 9.1 years and a mean ± SD BMI of 30.2 ± 4.9 kg/m2. The sample was 70.1% white, 26.8% African American, <1.0% Asian, and 2.2% of other races. Of these, 1.5% reported Hispanic or Latino ethnicity. Of the sample, 51.5% had received a college education, 54.9% reported an annual income of more than $50,000, 95.5% had health insurance, and 59.6% were currently employed.

The prevalence of musculoskeletal pain by location in the study sample is demonstrated in Table 1. The majority of participants (57.4%) reported having LBP in the past 30 days, and 20% reported neck pain. The prevalence of extremity pain at a joint on either side of the body that was present for more than half of the days during the past 30 days was highest in the knee and lowest in the elbow. Not all participants reported pain in a knee due to the fact that the definition of knee pain used to identify symptomatic knee OA in the parent study was more broad (symptoms during the past 12 months) than the definition used in this subanalysis (symptoms during the past 30 days). Hip aching or stiffness during the past 12 months was present in 59.6% of participants. A comparison of pain laterality for each joint demonstrated that a substantial percentage of individuals with pain at 1 joint also experienced pain in the same joint on the opposite side of the body.

Table 1. Prevalence of musculoskeletal pain (pain, aching, or stiffness on more than half the days in the past 30 days) by location*
Pain locationEither sideRightLeft
  • *

    Values are the number (percentage) of participants.

  • Any low back or buttock pain in the past 30 days.

  • Any pain, aching, or stiffness in the past 12 months.

Spine pain   
 Low back pain798 (57.4)
 Neck pain278 (20.0)
Extremity pain   
 Shoulder387 (27.8)285 (20.5)246 (17.7)
 Elbow172 (12.4)110 (7.9)112 (8.1)
 Wrist214 (15.4)167 (12.0)152 (10.9)
 Hand469 (33.7)397 (28.6)366 (26.3)
 Hip828 (59.6)670 (48.3)561 (40.4)
 Knee1,216 (87.9)923 (66.7)879 (63.5)
 Ankle219 (15.8)175 (12.6)154 (11.1)
 Foot230 (16.6)192 (13.8)175 (12.6)

The characteristics of individuals in the Progression cohort with respect to LBP status are demonstrated in Table 2. Age, race, and ethnicity were comparable in participants with and without LBP. Participants with LBP were less likely to be male, be a college graduate, be currently employed, and have an annual income greater than $50,000. BMI and abdominal circumference were slightly higher in participants with LBP. Participants with LBP had higher scores on measures of psychiatric comorbidity, a higher prevalence of depression, greater overall comorbidity burden as reflected by the Charlson comorbidity index, and a higher prevalence of specific conditions likely to impact lower extremity function, including peripheral vascular disease, diabetes mellitus, and past stroke. Participants with LBP had higher frequencies of self-reported pain at every non-LBP pain location. Grade 2 and 3 knee OA was more common in participants with LBP. All WOMAC subscale scores were higher in participants with LBP (representing greater knee-related symptoms), with a mean ± SD WOMAC pain score of 6.5 ± 4.1 in participants with LBP and of 5.2 ± 3.8 in participants without LBP.

Table 2. Relationships between LBP status and baseline characteristics in the progression cohort of the Osteoarthritis Initiative*
VariableLBP (n = 798)No LBP (n = 592)P
  • *

    Values are the number (percentage) of participants unless otherwise indicated. LBP = low back pain; BMI = body mass index; CES-D = Center for Epidemiologic Studies Depression Scale; PAD = peripheral artery disease; OA = osteoarthritis; WOMAC = Western Ontario and McMaster Universities Osteoarthritis Index.

  • Taken from the Short Form 12, which grades symptoms on a 5-point Likert-type scale from 1 to 5, where 1 = no symptoms and 5 = severe symptoms.

  • Pain, aching, or stiffness on more than half the days in the past 30 days.

  • §

    Any pain, aching, or stiffness in the past 12 months.

  • Values listed are for most painful knee as evidenced by WOMAC pain score.

  • #

    Simulated Kellgren/Lawrence grade.

Demographics   
 Age, mean ± SD years61.2 ± 9.061.6 ± 9.30.52
 Sex, women476 (59.7)317 (53.6)0.02
 Race  0.96
  White559 (70.1)415 (70.1) 
  African American214 (26.9)158 (26.7) 
  Asian6 (0.8)6 (1.0) 
  Other nonwhite18 (2.3)13 (2.2) 
  Hispanic11 (1.4)10 (1.7)0.64
 Education, college graduate383 (48.6)324 (55.4)0.01
 Health insurance745 (94.8)561 (96.4)0.16
 Yearly income > $50,000395 (52.5)327 (58.2)0.04
 Currently employed460 (57.6)369 (62.3)0.08
Anthropometric features   
 BMI, mean ± SD kg/m230.4 ± 5.129.6 ± 4.60.10
 Abdominal circumference, mean ± SD cm106.3 ± 13.3104.9 ± 12.50.04
Medical and psychosocial comorbidities   
 Depression, CES-D score ≥16121 (15.4)68 (11.7)0.05
 Anxiety, mean ± SD score2.31 ± 0.802.21 ± 0.760.02
 Fatigue, mean ± SD score2.75 ± 0.892.59 ± 0.890.001
 Smoking, current73 (9.2)31 (5.2)0.006
 History of stroke26 (3.3)19 (3.3)0.003
 Diabetes mellitus90 (11.7)61 (10.6)0.54
 PAD, previous bypass surgery13 (1.7)7 (1.2)0.50
 Charlson comorbidity index score  0.001
  0516 (66.1)433 (74.7) 
  ≥1265 (33.9)147 (24.8) 
Musculoskeletal pain comorbidities   
 Neck pain208 (26.1)70 (11.8)< 0.0001
 Shoulder pain278 (34.8)109 (18.4)< 0.0001
 Elbow pain128 (16.0)44 (7.4)< 0.0001
 Wrist pain157 (19.7)57 (9.6)< 0.0001
 Hand pain299 (37.5)170 (28.7)< 0.0001
 Hip pain§553 (69.3)275 (46.5)< 0.0001
 Knee pain713 (89.6)503 (85.5)0.02
 Ankle pain161 (20.2)58 (9.8)< 0.0001
 Foot pain157 (19.7)73 (12.3)< 0.0003
Knee OA and knee symptoms   
 Radiographic tibiofemoral knee OA#  0.05
  Grade 2233 (29.5)162 (27.6) 
  Grade 3326 (41.2)227 (38.7) 
  Grade 4160 (20.2)157 (26.8) 
 WOMAC pain score, mean ± SD6.5 ± 4.15.2 ± 3.8< 0.0001
 WOMAC total score, mean ± SD29.5 ± 18.124.4 ± 17.0< 0.0001

In the first stage of the linear regression analysis, we used the outcome of the most symptomatic WOMAC pain score and considered all variables from Table 2 (except for pain locations) for inclusion in the model. In order to facilitate comparisons between models, we limited the sample to participants with no missing values (n = 1,219). Age, sex, race, ethnicity, college education, income >$50,000, BMI, abdominal circumference, depression, fatigue, current smoking, Charlson comorbidity score ≥1, and OA grade were included in the final model after the backward selection algorithm. The first stage regression model had a total variance (R2) of 0.223, and an adjusted R2 of 0.213.

In the second stage of the analysis, we added each pain location separately as an independent variable to the first-stage model. The results of linear regression analyses, including pain locations while adjusting for all factors from the first-stage model, are depicted in Table 3. The leftmost columns demonstrate the association between each individual pain location and the WOMAC pain score when only 1 pain location was added to the model. The rightmost columns allow the reader to see the effects of individual pain locations in the full model, which includes all pain locations added to the model simultaneously. Although all single pain locations demonstrated statistically significant associations with WOMAC pain score and comparable β coefficients when added to the model individually, most pain locations were not significantly and independently associated with WOMAC pain score when all pain locations were included in the model simultaneously. When adjusting for all pain locations, only LBP, ipsilateral elbow pain, and ipsilateral foot pain were significantly associated with WOMAC pain score (P < 0.05). The second-stage linear regression including all pain locations had a variance of R2 of 0.271, and an adjusted R2 of 0.252. A reduced model including only the statistically significant pain locations of LBP, ipsilateral elbow pain, and ipsilateral foot pain had a variance of R2 of 0.258, and an adjusted R2 of 0.247. A statistical comparison to detect a difference in model fit between the reduced model and the all-locations model approached significance (P = 0.08).

Table 3. Associations between other musculoskeletal pain locations and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) knee pain scores*
Pain locationFor individual musculoskeletal pain locationsIncluding all musculoskeletal pain locations
β (SE)Pβ (SE)P
  • *

    WOMAC pain score for the most symptomatic knee, adjusting for age, sex, race, ethnicity, education, income, body mass index, abdominal circumference, depression, fatigue, smoking, comorbidity burden, and osteoarthritis grade.

  • Pain, aching, or stiffness on more than half of the days in the past 30 days.

  • Any low back or buttock pain in past 30 days.

  • §

    Any pain, aching, or stiffness in the past 12 months.

Low back pain1.00 (0.21)< 0.00010.65 (0.21)0.002
Neck pain0.88 (0.26)0.00070.38 (0.27)0.16
Shoulder pain    
 Ipsilateral0.78 (0.26)0.0020.08 (0.29)0.79
 Contralateral0.84 (0.27)0.0020.21 (0.48)0.48
Elbow pain    
 Ipsilateral1.59 (0.36)< 0.00010.98 (0.40)0.02
 Contralateral1.14 (0.40)0.005−0.24 (0.45)0.60
Wrist pain    
 Ipsilateral1.01 (0.32)0.001−0.02 (0.41)0.97
 Contralateral1.28 (0.34)0.00020.38 (0.44)0.39
Hand pain    
 Ipsilateral0.70 (0.23)0.0020.09 (0.32)0.77
 Contralateral0.77 (0.24)0.0010.14 (0.33)0.68
Hip pain§    
 Ipsilateral0.57 (0.21)0.0060.09 (0.22)0.69
 Contralateral0.76 (0.21)0.00030.38 (0.23)0.09
Knee pain, contralateral0.59 (0.21)0.0050.36 (0.21)0.09
Ankle pain    
 Ipsilateral1.25 (0.31)< 0.00010.06 (0.41)0.89
 Contralateral1.64 (0.35)< 0.00010.71 (0.46)0.13
Foot pain    
 Ipsilateral1.46 (0.31)< 0.00011.03 (0.45)0.02
 Contralateral1.16 (0.31)0.0002−0.32 (0.45)0.48

In secondary analyses intended to account for individuals with substantial pain in both knees, and for factors specific to the laterality of knee pain, we examined associations between all variables included in the second stage model and the outcome of unilateral WOMAC pain score (right and left). LBP was independently and significantly associated with both right-sided (β [SE] = 0.57 [0.20], P = 0.005) and left-sided (β [SE] = 0.67 [0.22], P = 0.0001) WOMAC knee pain scores; however, ipsilateral foot pain and elbow pain were not. Ipsilateral ankle pain was the only other pain location that was associated with both right-sided (β [SE] = 1.10 [0.39], P = 0.005) and left-sided (β [SE] = 1.07 [0.45], P = 0.02) WOMAC knee pain scores.

Table 4 depicts the results of linear regression analysis with the full model including all pain locations, but with stratification by LBP severity using “no LBP” as the reference group. When controlling for all other factors, including other musculoskeletal pain, mild LBP had no effect on WOMAC pain score. Moderate LBP and severe LBP, however, demonstrated significant and independent associations with WOMAC pain score, with β coefficients of 1.08 and 1.93, respectively. The linear regression including all pain locations and LBP severity had a total variance of R2 of 0.288, and an adjusted R2 of 0.268.

Table 4. Associations between low back pain severity and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) knee pain scores*
Low back painPrevalence, no. (%)β (SE)P
  • *

    WOMAC pain score for the most symptomatic knee, adjusting for age, sex, race, ethnicity, education, income, body mass index, abdominal circumference, depression, fatigue, smoking, comorbidity burden, osteoarthritis grade, and all other pain locations.

None592 (42.6)(reference)(reference)
Mild341 (24.5)−0.06 (0.25)0.79
Moderate379 (27.3)1.21 (0.25)< 0.0001
Severe76 (5.5)2.09 (0.52)< 0.0001

We conducted further analyses in order to examine whether specific pain locations were less important in explaining the variance in WOMAC pain score than the total number of pain locations. In a linear regression model including all variables from the first-stage model, and including the total number of pain locations in the categories of 0, 1, 2, 3, 4, and ≥5 pain locations, we found that having 2, 4, or ≥5 pain locations was associated with higher WOMAC pain scores. Table 5 depicts the independent association between the number of pain locations and the outcome of WOMAC pain score in a linear regression model including all variables from the first-stage model, and including the total number of pain locations in the categories of 0, 1, 2, 3, 4, and ≥5 pain locations. We found that having 2 or 3 pain locations showed an association with higher average WOMAC pain scores of borderline statistical significance (β [SE] = 0.78 and 0.71, respectively), whereas having 4 or ≥5 pain locations was associated with substantially higher average WOMAC pain scores (β = 1.83 and 1.86, respectively). The model including a continuous number of pain locations had a total variance of R2 of 0.250, and an adjusted R2 of 0.238.

Table 5. Associations between number of pain locations and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) knee pain scores*
Pain locations, no.Prevalence, no. (%)β (SE)P
  • *

    WOMAC pain score for the most symptomatic knee, adjusting for age, sex, race, ethnicity, education, income, body mass index, abdominal circumference, depression, fatigue, smoking, comorbidity burden, and osteoarthritis grade.

0159 (11.5)(reference)(reference)
1217 (15.6)0.38 (0.39)0.32
2220 (15.9)0.78 (0.38)0.04
3227 (16.4)0.71 (0.38)0.06
4151 (10.9)1.83 (0.42)< 0.0001
≥5414 (29.8)1.86 (0.36)< 0.0001

DISCUSSION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. SUBJECTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. ROLE OF THE STUDY SPONSOR
  9. Acknowledgements
  10. REFERENCES

In this study of individuals with symptomatic knee OA, we found that any single musculoskeletal pain location external to the knee was associated with higher WOMAC knee pain scores. However, when all pain locations were taken into account, only the associations of LBP, ipsilateral foot pain, and ipsilateral elbow pain were significant. Although mild LBP was not associated with WOMAC knee pain score, moderate and severe LBP were each associated with substantially higher WOMAC knee pain scores. In contrast, regression models accounting for the number of pain locations found an association between having 2, 4, or ≥5 pain locations at any site and a higher WOMAC knee pain score. These models explained a comparable, although slightly smaller, amount of the variance in WOMAC knee pain scores as compared with a parsimonious model including only the pain locations of LBP, ipsilateral elbow pain, and ipsilateral foot pain.

Our finding that LBP and ipsilateral foot pain were significantly and independently associated with higher WOMAC knee pain scores supports the commonsense clinical view that pain and function in any joint affects nearby joints both above and below in the kinetic chain, but would not be directly related to pain in a more distant location. The idea that LBP is biomechanically linked to knee pain via the so-called knee-spine syndrome has been proposed in the spine literature (10, 11). It is noteworthy that pain at locations immediately adjacent to the knee (i.e., the hip and ankle) was not independently associated with knee pain intensity, whereas pain localized one joint removed from the knee in the kinetic chain (i.e., the low back and foot) was independently associated with knee pain intensity. This lack of an association between immediately adjacent joint pain (at the hip and ankle) and knee pain in this study suggests that individuals are able to accurately localize knee pain to a specific joint. However, true knee pain may also be confused with referred pain from the spine such as in the case of nerve root impingement and radiculopathy. A prior study by Wood et al has drawn attention to radicular pain from spinal pathology as a possible cause of nonarticular knee pain (31). Foot pain was also found to be associated with greater knee symptoms in one prior study that did not account for non–lower extremity pain (9).

In contrast to the associations with foot and LBP, our finding that ipsilateral elbow pain is related to knee pain severity is neither intuitive nor easy to explain from either a biomechanical or biologic perspective. Although use of an assistive device causing upper extremity overuse seemed to be a possible mechanism, adjustment for the use of an assistive device during walking performance tasks in the baseline OAI assessment did not materially change the magnitude or significance of the relationship between ipsilateral elbow pain and ipsilateral knee pain (data not shown). Given the many pain locations examined in this study, this association may have been a consequence of multiple statistical comparisons.

The prevalence of LBP in our study sample was 57.4%. This estimate is quite similar to the prevalence of 54.6% reported by Wolfe et al in an outpatient rheumatology clinic population of patients with clinical knee OA (5). This extraordinarily high concomitant frequency is not well recognized, nor is the finding of LBP commonly documented in the clinic. Our findings provide a potential explanation for why prior studies have found that preoperative LBP is a risk factor for poor outcomes following joint replacement (6, 7). Furthermore, our findings suggest that the severity of LBP is an important correlate of increased knee symptoms. When accounting for LBP intensity, severe LBP was associated with a 2-point increase in knee-specific WOMAC pain score, an amount that is comparable with previously reported relative thresholds for the minimum clinically important difference in WOMAC pain score following rehabilitation interventions (32). Conversely, mild LBP was not associated with WOMAC scores. These findings build upon evidence from the geriatric mobility literature, which suggest associations between LBP severity and function in older adults (33, 34).

Our finding that any single pain location (regardless of proximity or biomechanical relatedness to the symptomatic knee) is associated with higher WOMAC pain scores, and that a greater number of pain locations is associated with still higher WOMAC pain scores, provides further insights into the relationship between concurrent musculoskeletal pain burden and WOMAC pain score. The presence of pain in multiple locations may identify individuals with a greater propensity to pain states (12, 13). Croft et al reported that pain elsewhere was common in individuals with knee pain, and individuals with other musculoskeletal pain reported more severe knee pain (8). Peat et al reported that a higher number of pain locations in the hip and foot was associated with greater pain as measured by the validated WOMAC scale applied specifically to the knee, highlighting the importance of concurrent lower extremity pain in particular (9). In contrast, our current analysis accounts for all common sites of concurrent musculoskeletal pain (with the exception of headache/facial pain) and demonstrates that a model limited to specific pain locations (LBP, foot pain, and elbow pain) explains only a slightly greater proportion of the variance in WOMAC knee pain scores than a model including total pain comorbidity (i.e., number of pain locations). Total pain comorbidity and specific pain locations may therefore represent important separate but interrelated factors associated with higher WOMAC pain score. That is, although certain pain locations are more strongly associated with higher WOMAC pain scores, progressively higher pain comorbidity burden irrespective of location captures information that is also related to higher WOMAC pain scores. Accounting for pain intensity as part of the assessment of musculoskeletal pain comorbidity may reveal even stronger associations with WOMAC pain scores, and is an area worthy of future study.

Taken together, our findings suggest that pain external to the knee may exert small but clinically significant effects on WOMAC pain score, even when the WOMAC is applied in a knee-specific manner. Given that success versus failure in clinical trials is often decided by a difference of 1 or 2 points on the WOMAC pain score, stratification by musculoskeletal pain comorbidity may be a factor worth considering in trial design. Furthermore, the demonstrated relationship of non–knee pain to knee-specific pain suggests that cointervention for musculoskeletal pain comorbidity may augment treatment for the knee and improve overall knee-related outcomes. Future studies of this or similar questions will benefit from a longitudinal design, and may be incorporated into existing cohort studies.

The current study does have limitations. First, subjects in our study represent a sample of patients with knee OA. Although we believe this sample to be representative of patients with symptomatic knee OA, in theory these findings may not be generalizable to other populations. Second, although the case definitions for pain were uniform for most pain locations (“aching or stiffness on more than half the days in past 30 days”), a different definition was used for LBP (“any low back or buttock pain in past 30 days”). This may explain both the higher prevalence of LBP in our sample and the effect of moderate or severe LBP (but not mild LBP) on WOMAC pain scores. It is possible that using a more permissive definition for LBP may have contributed to our findings. However, we believe the results of stratification by LBP severity show that, if anything, the more permissive definition would have biased toward the null. Third, the assessment of pain locations used in this study may have led to recall bias. Although such bias would be expected to affect measurement of pain in all locations equally, hip pain was an exception, and would have been most susceptible to differential bias. Last, multiple statistical comparisons were made in this analysis. Our intent was to identify important relationships, and pre hoc statistical adjustments to account for multiple comparisons were not planned or performed.

The results of this study draw further attention to the fact that symptomatic knee OA rarely occurs in isolation. Pain in the low back and in the ipsilateral foot and elbow may be associated with greater knee pain. In addition, the total number of pain comorbidities also appears to be associated with more severe symptoms. Future studies are needed to determine whether treatment of musculoskeletal pain comorbidity may improve the outcomes of treatment for knee OA.

AUTHOR CONTRIBUTIONS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. SUBJECTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. ROLE OF THE STUDY SPONSOR
  9. Acknowledgements
  10. REFERENCES

All authors were involved in drafting the article or revising it critically for important intellectual content, and all authors approved the final version to be submitted for publication. Dr. Suri had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study conception and design. Suri, Morgenroth, Kwoh, Kalichman.

Acquisition of data. Kwoh.

Analysis and interpretation of data. Suri, Morgenroth, Kwoh, Bean, Hunter.

ROLE OF THE STUDY SPONSOR

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. SUBJECTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. ROLE OF THE STUDY SPONSOR
  9. Acknowledgements
  10. REFERENCES

Private funding partners of the Osteoarthritis Initiative (OAI) include Merck Research Laboratories, Novartis Pharmaceuticals Corporation, GlaxoSmithKline, and Pfizer. Private sector funding for the OAI is managed by the Foundation for the NIH. This manuscript has received the approval of the OAI Publications Committee based on a review of its scientific content and data interpretation. The private funding partners listed above had no role in the design, data collection, data analysis, or writing of this manuscript. The publication of this article was not contingent on the approval of Merck Research Laboratories, Novartis Pharmaceuticals Corporation, GlaxoSmithKline, and Pfizer.

Acknowledgements

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. SUBJECTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. ROLE OF THE STUDY SPONSOR
  9. Acknowledgements
  10. REFERENCES

The authors would like to thank the participants, Principal Investigators (Michael Nevitt, Charles B. Eaton, Rebecca Jackson, Marc Hochberg, Joan Bathon), Co-Investigators, and staff of the OAI.

REFERENCES

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. SUBJECTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. ROLE OF THE STUDY SPONSOR
  9. Acknowledgements
  10. REFERENCES