SEARCH

SEARCH BY CITATION

Abstract

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

Objective

To evaluate the association of metabolic risk factors with severity and 2-year progression of early degenerative cartilage changes at the knee, measured with T2 relaxation times in middle-aged subjects from the Osteoarthritis Initiative.

Methods

Cartilage segmentation and T2 map generation were performed in knee 3T magnetic resonance images from 403 subjects ages 45–60 years without radiographic osteoarthritis (OA). The influence of risk factors on baseline T2 and longitudinal progression of T2 was analyzed using linear regression, adjusting for age, sex, and other OA risk factors.

Results

Four metabolic risk factors, i.e., high abdominal circumference (P < 0.001), hypertension (P = 0.041), high fat consumption (P = 0.023), and self-reported diabetes mellitus (P = 0.010), were individually associated with higher baseline T2. When the 4 metabolic risk factors were considered in a multivariate regression model, higher T2 remained significantly associated with abdominal circumference (P < 0.001) and diabetes mellitus (P = 0.026), and there was a trend for high fat consumption (P = 0.096). For the individual risk factors, only diabetes mellitus remained associated with higher baseline T2 after adjustment for body mass index (BMI). After adjustment for BMI, baseline T2 increased in a dose-response manner with the number of metabolic risk factors present (P = 0.032 for linear trend), and subjects with ≥3 metabolic factors (versus <3) had significantly higher baseline T2 (mean difference 1.2 msec [95% confidence interval 0.3, 2.1]; P = 0.011). Metabolic risk factors were not significantly associated with increases in T2 during followup.

Conclusion

Metabolic risk factors are associated with higher T2, suggesting that increased cartilage degeneration may be caused by modifiable metabolic disorders.


INTRODUCTION

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

Osteoarthritis (OA) is the most common musculoskeletal disorder, affecting millions of elderly individuals ([1]). Apart from relieving debilitating symptoms with analgesia, currently there is no treatment that targets and inhibits the progressive degenerative structural changes. This adds weight to the importance of modifiable factors that may contribute to an increased risk for developing OA ([2, 3]) and to the ability to detect OA early before irreversible damage to the joint has occurred.

The progressive loss of hyaline articular cartilage in OA ([1, 4]) can be detected and monitored by magnetic resonance imaging (MRI) ([5, 6]). Recent studies have demonstrated the potential of MRI for detecting early biochemical shifts in the cartilage matrix prior to irreversible morphologic damage or clinical symptoms. T2 relaxation time mapping has been used as a biomarker to noninvasively detect early cartilage degeneration quantitatively ([7]) by virtue of its correlation with the water content and deterioration of the collagen network ([8-11]). T2 has been shown to be a sensitive indicator of the effects of knee OA risk factors on knee cartilage ([11-15]) and to predict disease progression.

OA increasingly is understood as a systemic disease, especially in terms of a possible relationship to metabolic disorders linked to obesity ([16-19]). Several studies have found an increased risk of OA of the knee and other joints associated with both individual, and the accumulation of, metabolic risk factors that are considered part of the metabolic syndrome ([17, 18, 20, 21]). To our knowledge, no studies have examined the association of metabolic risk factors with MRI measures of cartilage degradation, and T2 mapping specifically, in knees without radiographic OA.

The purpose of this study was to evaluate the association of metabolic risk factors with baseline knee cartilage T2 and with 2-year changes in these measurements. We hypothesized that metabolic risk factors would be associated with higher baseline T2 and with greater increases in T2 over 2 years, and that an increasing number of metabolic risk factors present would be associated with higher T2 and greater progression of T2.

Box 1. Significance & Innovations

  • This study demonstrated a significant association of metabolic risk factors with higher cartilage T2 relaxation times in a large cohort, suggesting an abnormal biochemical composition of cartilage in these individuals.
  • The individual metabolic risk factors (large abdominal circumference, hypertension, high fat consumption, and diabetes mellitus) as well as the number of these risk factors that were present in a subject were associated with significantly higher baseline T2, suggesting more severe cartilage degradation. The difference between the group with ≥3 metabolic factors and the group with <3 metabolic factors (P < 0.001) remained significant after adjustment for body mass index (P = 0.011).
  • Since all of these risk factors are modifiable, our results suggest potential ways to prevent or delay knee cartilage degradation and possibly the development of knee OA.

PATIENTS AND METHODS

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

Subjects

The Osteoarthritis Initiative (OAI) is a National Institutes of Health–funded multicenter, longitudinal, observational cohort study focusing primarily on knee OA. The study enrolled 4,796 subjects ages 45–79 years, and at annual followup visits obtains clinical assessments and knee joint imaging, including MRI with T2 mapping sequences of the knee ([22]).

The OAI protocol, amendments, and informed consent documentation were approved by the local institutional review boards. Data used in the preparation of this article were obtained from the OAI public database (http://www.oai.ucsf.edu/). Specific data sets used are baseline clinical data set 0.2.2 as well as baseline and 2-year followup image data sets 0.E.1 and 3.E.1.

Individuals included in the present study were from the OAI incidence cohort, which did not have symptomatic radiographic knee OA (defined as a Kellgren/Lawrence [K/L] grade ≥2 and frequent pain in the same knee) at baseline but had ≥1 risk factor for developing knee OA. In order to focus on early knee degenerative changes in a middle-aged cohort, we selected the younger half of the cohort (ages 45–60 years) who had baseline Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain scores of 0 ([23]) and a K/L grade <2 in the study knee. In addition to the OAI exclusion criteria, we excluded those in whom the study knee had knee surgery with hardware implantation or poor MR quality and missing MR sequences. Followup T2 data were available for 381 of 403 individuals who met all of the study criteria at baseline (Figure 1).

image

Figure 1. Flow chart of the selected subjects from the Osteoarthritis Initiative (OAI). WOMAC = Western Ontario and McMaster Universities Osteoarthritis Index; KL = Kellgren/Lawrence; MR = magnetic resonance.

Download figure to PowerPoint

Metabolic risk factors

Based on the data available from the OAI, 4 metabolic risk factors corresponding to the components of the metabolic syndrome (central obesity, hypertension, impaired glucose tolerance, and dyslipidemia [[24-26]]) were assessed for their association with T2 relaxation times. We used abdominal circumference instead of body mass index (BMI) as the measure of central obesity because it has been found to have stronger associations with visceral adiposity, insulin resistance, and cardiovascular disease risk than BMI ([27, 28]), and current consensus definitions ([24, 29, 30]) use abdominal circumference as one of the risk factors comprising the metabolic syndrome. Abdominal circumference (cm) was measured by a clinical examiner using a tape measure over bare skin with the subject standing. Central obesity was defined as a waist circumference ≥102 cm in men and ≥88 cm in women using the American Heart Association/National Heart, Lung, and Blood Institute (Adult Treatment Panel III) cut points ([24]). Blood pressure was assessed in a sitting position, and high blood pressure was defined as systolic blood pressure >130 mm Hg and/or diastolic blood pressure >85 mm Hg using the International Diabetes Federation consensus definition ([25]). For impaired glucose tolerance and dyslipidemia, we were unable to replicate consensus criteria for metabolic syndrome, since these require blood specimens not available to the authors. Instead, we used factors related to these 2 components. For a factor related to glucose tolerance, we used self-report of diabetes mellitus. Participants who answered yes to the question “Do you have diabetes (high blood sugar)?” were classified as having diabetes mellitus. For a factor related to dyslipidemia, we relied on dietary fat consumption, since this has been linked to metabolic syndrome in some studies ([31, 32]), and a recent study has suggested that greater consumption of ω-6 polyunsaturated fatty acids is related to an increased risk of subchondral bone marrow lesions in the knee ([33]). Fat consumption (gm/day) was calculated from the Block Brief 2000 Food Frequency Questionnaire administered at the baseline examination (http://www.nutritionquest.com/). Since the recommended fat consumption is <78 gm/day ([34]), this threshold was used to define high fat consumption. Because of the use of these imperfect proxy measures, we did not classify subjects on the presence of metabolic syndrome.

Imaging

MRI acquisition

MRI knee examinations were obtained with 1 of 4 identical 3T MRI systems (Trio; Siemens) using identical standard knee coils and protocols specifically obtained for the OAI. In the right knee, or in the left knee if the right knee had contraindications for MRI, a sagittal 2-dimensional multislice multiecho (MSME) spin-echo sequence for T2 mapping (repetition time 2,700 msec; 7 echo times 10 msec, 20 msec, 30 msec, 40 msec, 50 msec, 60 msec, and 70 msec; field of view 12 cm; slice thickness 3 mm with a 0.5-mm gap; in-plane spatial resolution 0.313 × 0.446 mm2; and bandwidth 250 Hz/pixel) was performed for quantitative T2 relaxation time assessment ([22]). Further details regarding MRI techniques and protocols have been published previously ([13, 35]).

T2 relaxation time measurements

The MSME spin-echo sequences were transferred to a remote workstation (SPARC; Sun Microsystems). Images were analyzed by using software developed at our institution with an interactive display language environment (Research Systems). Segmentation of artifact-free cartilage areas of the patella, medial and lateral femoral condyle, and medial and lateral tibia in every section was performed by a single observer (MSK) and supervised by 2 radiologists (PMJ, TML). Due to pulsation artifacts from the popliteal artery resulting in significant artifacts, the trochlea was excluded. Mean T2 of the baseline and 2-year followup time points was calculated individually (for each compartment) and globally (mean of all compartments) from the segmented regions of interest, skipping the first echo and using a noise-corrected exponential fitting as previously described ([36]). To demonstrate T2 progression over time, the individual longitudinal increase over time was calculated as an absolute value (T2followup − T2baseline).

Reproducibility of T2 measurements

Averaged over all compartments, interobserver agreement for T2 measurements in our group was described previously, with an interreader reproducibility error for mean T2 of 1.57% (0.53 msec) ([37]). The mean intrareader reproducibility for T2 measurements was 1.17% ([38]).

Measurement of covariates

Participants were asked about a history of knee injury that resulted in difficulty walking for ≥2 days (yes/no) and about a history of any surgery of the knee (yes/no). Familial predisposition for knee OA was defined as a total knee replacement for OA in a biologic parent or sibling (yes/no). Hands were examined by the OAI examiner at the baseline visit according to a protocol available on the OAI web site (http://oai.epi-ucsf.org/datarelease/forms.asp). Heberden's nodes were considered present if bony enlargements were found in ≥3 distal interphalangeal joints of either hand. Isometric strength measurements were performed using a Good Strength Chair (Metitur; www.oai.ucsf.edu/datarelease/OperationsManuals.asp) for knee flexion and extension. Two submaximal practice trials were completed before force was measured 3 times for 3 seconds, each separated by 30 seconds; the highest value is used for maximum strength reported (N) ([39]).

Statistical analysis

Statistical analysis was performed with JMP software, version 7 (SAS Institute). For analysis of the association of potential risk factors with baseline T2 and with change in T2, descriptive statistics were obtained, applying a 2-sided t-test and a one-way analysis of variance. Multivariate linear regression analyses of risk factors with T2 were adjusted for the effects of other OA risk factors, including age, sex, history of knee injury, history of knee surgery, family history of knee replacement, and Heberden's nodes in the hands. The effect of the accumulation of metabolic risk factors was evaluated by including a variable in the regression model for the count (range 0–4) of risk factors present, and in a separate analysis, a dichotomous variable for the number of risk factors (≥3 versus <3). Because of the importance of BMI as a well-established risk factor for incident knee OA ([2]), and to control for possible residual confounding by obesity in multivariate models that include high abdominal circumference, analyses were repeated using continuous measures of BMI. In a sensitivity analysis, we adjusted for isometric knee strength because there is some evidence that fatty infiltration of muscle is a cause of both glycemic dysregulation and muscle weakness that leads to cartilage degradation. Conversely, muscle weakness may be on the causal pathway from metabolic obesity to cartilage degradation and would not be included as a covariate. Mean ± SD or mean ± SEM of T2 and 95% confidence intervals (95% CIs) around adjusted differences in T2 are shown, as indicated. Results were considered as significant if P values were less than 0.05.

RESULTS

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

Subject characteristics

The mean ± SD age of the subjects in this study (n = 403) was 52.1 ± 3.9 years and the mean ± SD BMI was 28.5 ± 4.9 kg/m2. The correlation of abdominal circumference with BMI, considered as a continuous variable, was 0.87 (P < 0.001). There were no significant differences between men and women for age, BMI, abdominal circumference, or blood pressure (Table 1). Mean abdominal circumference in both men and women was above the threshold used for central obesity. Mean ± SD dietary fat consumption was significantly lower in women (52.0 ± 27.0 gm/day) than in men (62.0 ± 32.3 gm/day; P = 0.002). With respect to metabolic risk factors, high abdominal circumference was present in 298 subjects (73.9%), hypertension was present in 113 subjects (28.0%), self-reported diabetes mellitus was found in 9 subjects (2.2%), and high fat consumption was found in 74 subjects (18.4%). One of these 4 metabolic risk factors was present in 164 subjects (40.7%), 2 risk factors were present in 89 subjects (22.1%), 3 risk factors were present in 24 subjects (6.0%), and all 4 risk factors were present in just 2 subjects (0.5%); consequently, ≥3 metabolic risk factors were present in 26 subjects.

Table 1. Baseline characteristics of the study sample*
ParameterWomen (n = 199)Men (n = 204)
  1. Values are the mean ± SD. BMI = body mass index.

Age, years52.2 ± 4.052.0 ± 3.8
BMI, kg/m228.1 ± 5.628.9 ± 4.2
Abdominal circumference, cm101.1 ± 15.2102.5 ± 12.3
Systolic blood pressure, mm Hg116.7 ± 13.8120.2 ± 13.0
Diastolic blood pressure, mm Hg74.6 ± 9.379.1 ± 9.2
Fat consumption, gm/day52.0 ± 27.062.0 ± 32.3
T2 at baseline, msec33.6 ± 2.433.6 ± 2.2
T2 change, %3.2 ± 5.63.8 ± 5.1
T2 change, msec1.0 ± 1.91.2 ± 1.7

Abdominal circumference, hypertension, fat consumption, and diabetes mellitus and baseline T2.

When each metabolic factor was considered individually and adjusted for other OA risk factors, baseline global T2 was higher in subjects with high abdominal circumference (mean difference 1.3 msec [95% CI 0.8, 1.8]; P < 0.001), hypertension (0.5 msec [95% CI 0.0, 1.0]; P = 0.041), diabetes mellitus (2.1 msec [95% CI 0.5, 3.8]; P = 0.010), and fat consumption (0.7 msec [95% CI 0.1, 1.3]; P = 0.023) compared to those without these risk factors (Table 2). For hypertension, systolic blood pressure had a significant influence (P = 0.046), whereas diastolic blood pressure did not (P = 0.753). Examining T2 in the individual knee compartments (data not shown), the most significant influence of abdominal circumference (P < 0.001) and hypertension (medial tibia, P = 0.036; lateral tibia, P = 0.030) was seen for tibial T2, and the most significant influence of fat consumption was seen for the medial femoral condyle (P = 0.015). If adjusted for continuous BMI, the association of the individual parameter diabetes mellitus remained significant (P = 0.046), whereas fat consumption (P = 0.210) and hypertension (P = 0.477) were not significant.

Table 2. Differences describing the influence of individual metabolic risk factors on baseline global T2 and T2 progression, with metabolic factors considered one at time adjusted for other OA risk factors, metabolic factors considered one at time adjusted for BMI and other OA risk factors, and all 4 metabolic factors included in the multivariate regression model*
 Individual risk factorsIndividual risk factors additionally adjusted for BMIAll 4 metabolic factors in the same model
  1. Values are the mean (95% confidence interval). All analyses include other osteoarthritis (OA) risk factors as covariates: age, sex, Heberden's nodes in the hands, family history of joint replacement, previous knee surgery, and previous knee injury. BMI = body mass index.

Baseline T2, msec   
Abdominal circumference1.3 (0.8, 1.8)0.4 (−0.2, 1.0)1.2 (0.7, 1.7)
P< 0.0010.164< 0.001
Blood pressure0.5 (0.0, 1.0)0.2 (−0.3, 0.7)0.2 (−0.3, 0.7)
P0.0410.4770.362
Diabetes mellitus2.1 (0.5, 3.8)1.6 (0.0, 3.1)1.8 (0.2, 2.4)
P0.0100.0460.026
Fat consumption0.7 (0.1, 1.3)0.4 (−0.2, 0.9)0.5 (−0.1, 1.0)
P0.0230.2100.096
T2 progression, msec   
Abdominal circumference0.3 (−0.1, 0.7)0.6 (−1.0, 2.3)0.2 (−0.2, 0.7)
P0.1550.4390.307
Blood pressure0.2 (−0.2, 0.7)0.5 (−0.9, 1.8)0.1 (−0.3, 0.6)
P0.3670.5070.541
Diabetes mellitus1.3 (−0.1, 2.6)3.0 (−1.0, 7.0)1.2 (−0.1, 2.6)
P0.0600.1450.080
Fat consumption0.2 (−0.4, 0.7)0.3 (−1.2, 1.9)0.1 (−0.4, 0.6)
P0.5660.6710.732

In a multivariate regression model including all 4 metabolic factors as well as other OA risk factors, abdominal circumference (P < 0.001) and diabetes mellitus (P = 0.026) were significantly associated with global T2, and fat consumption had a nonsignificant trend (P = 0.096) (Table 2).

Number of metabolic risk factors present and baseline T2.

Since all of the individual metabolic factors are putative measures of the same underlying concept, they are expected to be interrelated and to explain some of the variation in the other factors. Therefore, we evaluated whether the accumulation of individual risk factors was associated with baseline T2, also adjusting for BMI. Baseline T2 was significantly higher in individuals with a higher number of individual metabolic risk factors present (P < 0.001 for the number of metabolic risk factors adjusted for the other baseline risk factors and P = 0.032 when additionally adjusted for continuous BMI) (Figure 2A). Mean ± SEM adjusted (also for continuous BMI) global baseline T2 increased stepwise from 32.8 ± 0.2 msec for 0 risk factors to 36.3 ± 2.0 msec when all 4 risk factors were present. This increase was seen for all individual knee compartments, but it was only significant for the medial and lateral tibial compartments (P < 0.001).

image

Figure 2. A, Adjusted mean ± SEM baseline global T2 relaxation times (msec) for subgroups with increasing numbers of metabolic risk factors (range 0–4), adjusted for other osteoarthritis (OA) risk factors and a continuous measure of body mass index (BMI). Mean ± SEM T2 increased stepwise with the number of metabolic risk factors increasing, from 32.8 ± 0.2 msec for no risk factors to 36.3 ± 2.0 msec for 4 risk factors. For the number of metabolic risk factors present (range 0–4), P < 0.001 without adjustment for BMI and P = 0.032 with adjustment for BMI. Other OA risk factors include age, sex, Heberden's nodes, family history of joint replacement, previous knee surgery, and previous knee injury. Underneath A, representative cartilage T2 color maps are overlaid on the first-echo images of the multislice multiecho sequence of each group. Blue indicates low cartilage T2 and red indicates high cartilage T2. Subjects without any metabolic risk factors showed lower T2 than subjects with metabolic risk factors in an increasing manner. B, Progression of mean ± SEM T2 relaxation times (%) over 2 years for subgroups with increasing numbers of metabolic risk factors. Analyses are the same as described for A. For the number of metabolic risk factors present (range 0–4), P < 0.071 without adjustment for BMI and P = 0.191 with adjustment for BMI.

Download figure to PowerPoint

Individuals with ≥3 metabolic risk factors had significantly higher baseline T2 (mean ± SEM 35.5 ± 0.5 msec) compared to individuals with ≤2 metabolic risk factors (mean ± SEM 33.5 ± 0.1 msec; P < 0.001) (Table 3). If additionally adjusted for continuous BMI, the P value for differences in T2 was 0.011 (mean difference 1.2 msec; 95% CI 0.3, 2.1). The most significant differences were found for the medial and lateral tibia (P < 0.001) and for the lateral femoral condyle (P = 0.014).

Table 3. Adjusted differences of baseline global T2 and adjusted differences of T2 progression for subjects with ≤2 metabolic factors (n = 377) compared with subjects with ≥3 metabolic factors (n = 26)*
CompartmentBaseline T2, msecT2 progression, msec
Adjusted for other OA risk factorsAdditionally adjusted for continuous BMIAdjusted for other OA risk factorsAdditionally adjusted for continuous BMI
  1. Values are the mean (95% confidence interval). All analyses include other osteoarthritis (OA) risk factors as covariates: Heberden's nodes in the hands, family history of joint replacement, previous knee surgery, previous knee injury, age, and sex. BMI = body mass index; MFC = medial femoral condyle; LFC = lateral femoral condyle; MT = medial tibia; LT = lateral tibia.

Global2.0 (1.0, 2.9)1.2 (0.3, 2.1)0.1 (−0.7, 0.9)0.1 (−0.8, 0.9)
P< 0.0010.0110.8280.871
Patella1.0 (−0.6, 2.6)0.5 (−1.2, 2.2)1.3 (−0.3, 2.9)1.3 (−0.3, 3.0)
P0.2280.5700.1150.112
MFC1.4 (0.3, 2.4)1.3 (0.1, 2.4)0.0 (−1.0, 1.1)0.3 (−0.8, 1.4)
P0.0140.0290.9250.578
LFC0.9 (−0.1, 1.9)0.9 (−0.2, 1.9)0.6 (−0.4, 1.6)0.5 (−0.5, 1.6)
P0.0760.1050.2560.308
MT3.8 (2.5, 5.1)2.4 (1.1, 3.6)1.4 (0.1, 2.8)0.7 (−0.7, 2.0)
P< 0.001< 0.0010.0400.354
LT2.3 (0.9, 3.7)0.6 (−0.7, 1.8)0.1 (−1.0, 1.2)0.1 (−1.0, 1.3)
P0.0010.3870.8430.813

In a sensitivity analysis, we additionally adjusted the analysis of ≥3 versus ≤2 risk factors for knee flexion and extension isometric strength. This essentially had no effect on our results. For example, subjects with ≥3 risk factors had higher baseline global T2 (+1.8 msec [95% CI 0.8, 2.9]; P < 0.001) and higher medial femoral condyle T2 (+1.5 msec [95% CI 0.3, 2.7]; P = 0.012) after this adjustment.

Progression analysis

Mean ± SD longitudinal change of global T2 in all subjects over time was 3.5% ± 5.3% (1.1 ± 1.8 msec). None of the individual metabolic factors was significantly associated with change in global T2 (P = 0.130 to P = 0.977; data not shown). There was a statistical trend for the association of global T2 progression with the number of metabolic risk factors present (P < 0.071 without and P = 0.191 with adjustment for BMI) (Figure 2B). Although individuals with ≥3 metabolic risk factors had slightly greater increases in global and compartment-specific mean T2 than subjects with ≤2 metabolic risk factors (Table 3), especially in the medial tibia, none of these differences was significant after adjustment for BMI.

DISCUSSION

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

The present study demonstrated a significant association of metabolic risk factors with higher baseline T2 relaxation times. The individual factors (high abdominal circumference, hypertension, high fat consumption, and diabetes mellitus) were associated with significantly higher baseline T2, although of the individual factors, only the association with diabetes mellitus remained significant after adjustment for BMI. However, the number of risk factors present in an individual was associated with higher baseline T2 values independently of BMI. These results suggest that subjects with an accumulation of metabolic risk factors have more severe cartilage degradation. Since all of these risk factors are modifiable, our results suggest potential avenues to prevent or delay knee cartilage degradation and possibly the development of knee OA.

Cartilage T2 relaxation mapping is a noninvasive biomarker to detect early cartilage matrix degeneration, mainly collagen disarrangement and increases in water content ([16]). A correlation with the severity of OA consistently has been shown ([6, 40-43]). High T2 was associated with increased severity of cartilage defects and was able to predict cartilage loss ([11, 15, 41, 44]). The difference in adjusted global T2 between those with ≤2 risk factors and those with ≥3 risk factors was 2.0 msec (95% CI 1.0, 2.9), and was 1.2 msec (95% CI 0.3, 2.1) after further adjustment for BMI as a continuous measure. In previous longitudinal analyses, we found that differences of 1.0 SD in baseline T2 were significantly associated with a 40–70% increase in the risk of compartment-specific cartilage loss and worsening bone marrow lesions in the knee ([15, 41]). In a cross-sectional analysis, knees with WOMAC pain scores ≥5 had T2 values that were approximately 2.0 msec higher than knees with WOMAC pain scores of 0 ([41]).

In contrast, in both the present and previous studies, there is less evidence supporting the relevance of progression of T2 changes over time. In knees from the OAI normal control cohort, we observed a significant increase in T2 values over 2 years that was moderately correlated with increases in cartilage damage over the same period ([12, 14]). However, we also recently reported that while obese individuals had significantly greater baseline T2 than nonobese individuals, we did not find greater increases in T2 over 36 months ([41]). The results of the present study mirror these previous findings, with stronger and more consistent cross-sectional associations of the obesity-related risk factors studied with baseline T2 than associations with T2 change over 24 months. There are several possible reasons for the less robust associations seen with T2 change over time: 1) the followup time of 24 months was relatively short and the changes observed were relatively small compared to the variability in T2 progression; 2) differences in baseline T2 may be larger and reflect cumulative damage over time, and T2 progression therefore may be a less sensitive outcome for measuring association; and 3) there is also more recent evidence suggesting that T2 progression occurs more slowly when significant cartilage degradation is already present and T2 is elevated ([44-46]). The upper 95% CIs for effects on T2 progression are approximately 0.7 msec, close to what may be clinically relevant differences. Therefore, we cannot rule out clinically important differences in T2 progression that are, nevertheless, not statistically significant.

There is growing evidence that OA is a multisystemic disease with interrelated risk factors and metabolic disorders. OA has been linked to obesity, but also to other cardiovascular risk factors, such as dyslipidemia, hypertension, and insulin resistance, that characterize the metabolic syndrome ([18, 47]). This is in agreement with our findings, since T2 continuously increased with the number of metabolic risk factors present. Large longitudinal studies have confirmed that being overweight precedes the development of knee OA ([2]). OA is also moderately associated with obesity in non–weight-bearing joints such as the hand joints ([48]). We found a correlation of central obesity with increased T2, indicating more advanced cartilage matrix degeneration. Abdominal circumference has been found to be more highly correlated with cardiovascular disease than BMI. There is limited evidence on whether this remains true for OA ([49]). We primarily concentrated on the parameter abdominal circumference, since it is implied in definitions of metabolic syndrome. Replacing abdominal circumference with BMI showed similar results. Considering the high correlation of 0.87 of these 2 parameters, it is challenging to assess whether one parameter has a higher impact. Although other studies like the Japanese Research on Osteoarthritis Against Disability study ([18]) did not adjust for BMI, we additionally presented results for BMI to account for its clinical relevance. Interestingly, the group with ≥3 metabolic risk factors showed significantly higher T2 despite adjustment for BMI. This supports the hypothesis that an accumulation of risk factors increases the risk for OA.

Self-reported diabetes mellitus had a significant effect on early degenerative changes, but not on their progression. These may be chance findings given the small numbers with self-reported diabetes mellitus, or may be due to our relatively short followup interval. An equally plausible explanation is that once diabetes mellitus is diagnosed and presumably glycemia is controlled, diabetes mellitus–correlated risk for OA is reduced, as seen for cardiovascular complications, diabetic retinopathy, or chronic kidney disease ([50, 51]). Previous studies have revealed mixed results with respect to any association of hypertension with knee OA. An association of hypertension and knee OA, independent of body weight, was reported in one study ([52]), but other groups have not been able to verify this finding ([53]). Our study also revealed an influence of hypertension on T2 relaxation times that, however, was attenuated by adjustment for other metabolic factors. Greater absolute fat intake was associated with increased baseline T2. This is consistent with other studies showing that fatty acid intake influences adipose tissue expression of leptin, which may play a role in OA by promoting nitric oxide synthesis in chondrocytes ([54]). Increased saturated fatty acid consumption may increase the risk of developing bone marrow lesions, and dietary modification of fatty acid intake may be one strategy in the prevention of knee OA ([33, 55, 56]).

In a model that includes all 4 metabolic risk factors, the effect of each risk factor is attenuated, and only abdominal circumference and diabetes mellitus remain significantly associated with T2. Since all 4 factors are putative measures of a single concept, they are expected to be interrelated and to explain some of the variation in the other factors. High abdominal circumference and BMI are both measures of obesity and are highly interrelated, and neither is significantly associated with T2 when both are included in the same model (data not shown).

There are several limitations of our study. First, this was an observational study. Clinical trials are needed to determine if modifying risk factors can protect against the development of cartilage degradation and OA. Second, although the metabolic risk factors for high T2 correspond to the consensus components of the metabolic syndrome, the latter require the use of blood samples to determine impaired glucose tolerance and dyslipidemia. We used imperfect proxies for these factors, relying on self-report of diabetes mellitus and dietary fat consumption. Although there is evidence supporting the association of these proxy measures with metabolic abnormalities, using them does not allow us to classify individuals in our study for the presence of metabolic syndrome. Rather, our focus is on the association of the individual metabolic factors for which we have data, and the accumulation of these factors in an individual subject, with our outcome of T2.

In conclusion, we found that the metabolic risk factors high abdominal circumference, hypertension, high fat consumption, and diabetes mellitus were associated with increased baseline T2. When more of these factors were present, higher T2 was found, suggesting an abnormal biochemical composition of cartilage in these individuals. These results underline the importance of public health initiatives targeting the growing prevalence of these risk factors in the modern Western society.

AUTHOR CONTRIBUTIONS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. PATIENTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. ROLE OF THE STUDY SPONSORS
  9. 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 published. Dr. Jungmann 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. Jungmann, Kraus, Alizai, Nardo, Nevitt, McCulloch, Link.

Acquisition of data. Jungmann, Kraus, Alizai, Nardo, Nevitt, Lynch, Link.

Analysis and interpretation of data. Jungmann, Kraus, Alizai, Nardo, Baum, Nevitt, McCulloch, Joseph, Link.

ROLE OF THE STUDY SPONSORS

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

The Osteoarthritis Initiative (OAI) private funding partners (Merck Research Laboratories, Novartis Pharmaceuticals Corporation, GlaxoSmithKline, and Pfizer Inc.) had no role in the study design, data collection, data analysis, or writing of this manuscript. Publication of this article was not contingent on the approval of these sponsors. This manuscript has received the approval of the OAI Publications Committee based on a review of its scientific content and data interpretation.

REFERENCES

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. PATIENTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. ROLE OF THE STUDY SPONSORS
  9. REFERENCES
  • 1
    Lawrence RC, Felson DT, Helmick CG, Arnold LM, Choi H, Deyo RA, et al, for the National Arthritis Data Workgroup.Estimates of the prevalence of arthritis and other rheumatic conditions in the United States: part II.Arthritis Rheum2008;58:2635.
  • 2
    Felson DT, Lawrence RC, Dieppe PA, Hirsch R, Helmick CG, Jordan JM, et al.Osteoarthritis: new insights. Part 1: the disease and its risk factors.Ann Intern Med2000;133:63546.
  • 3
    Reijman M, Pols HA, Bergink AP, Hazes JM, Belo JN, Lievense AM, et al.Body mass index associated with onset and progression of osteoarthritis of the knee but not of the hip: the Rotterdam study.Ann Rheum Dis2007;66:15862.
  • 4
    Oliveria SA, Felson DT, Reed JI, Cirillo PA, Walker AM.Incidence of symptomatic hand, hip, and knee osteoarthritis among patients in a health maintenance organization.Arthritis Rheum1995;38:113441.
  • 5
    Burstein D, Gray M.New MRI techniques for imaging cartilage.J Bone Joint Surg Am2003;85-A Suppl:707.
  • 6
    Burstein D, Gray M, Mosher T, Dardzinski B.Measures of molecular composition and structure in osteoarthritis.Radiol Clin North Am2009;47:67586.
  • 7
    Link TM, Steinbach LS, Ghosh S, Ries M, Lu Y, Lane N, et al.Osteoarthritis: MR imaging findings in different stages of disease and correlation with clinical findings.Radiology2003;226:37381.
  • 8
    Ding C, Cicuttini F, Scott F, Cooley H, Jones G.Knee structural alteration and BMI: a cross-sectional study.Obes Res2005;13:35061.
  • 9
    Dunn TC, Lu Y, Jin H, Ries MD, Majumdar S.T2 relaxation time of cartilage at MR imaging: comparison with severity of knee osteoarthritis.Radiology2004;232:5928.
  • 10
    Eckstein F, Cicuttini F, Raynauld JP, Waterton JC, Peterfy C.Magnetic resonance imaging (MRI) of articular cartilage in knee osteoarthritis (OA): morphological assessment.Osteoarthritis Cartilage2006;14 Suppl:A4675.
  • 11
    Joseph GB, Baum T, Alizai H, Carballido-Gamio J, Nardo L, Virayavanich W, et al.Baseline mean and heterogeneity of MR cartilage T2 are associated with morphologic degeneration of cartilage, meniscus, and bone marrow over 3 years: data from the Osteoarthritis Initiative.Osteoarthritis Cartilage2012;20:72735.
  • 12
    Baum T, Stehling C, Joseph GB, Carballido-Gamio J, Schwaiger BJ, Muller-Hocker C, et al.Changes in knee cartilage T2 values over 24 months in subjects with and without risk factors for knee osteoarthritis and their association with focal knee lesions at baseline: data from the Osteoarthritis Initiative.J Magn Reson Imaging2012;35:3708.
  • 13
    Hovis KK, Stehling C, Souza RB, Haughom BD, Baum T, Nevitt M, et al.Physical activity is associated with magnetic resonance imaging–based knee cartilage T2 measurements in asymptomatic subjects with and without osteoarthritis risk factors.Arthritis Rheum2011;63:224856.
  • 14
    Pan J, Pialat JB, Joseph T, Kuo D, Joseph GB, Nevitt MC, et al.Knee cartilage T2 characteristics and evolution in relation to morphologic abnormalities detected at 3-T MR imaging: a longitudinal study of the normal control cohort from the Osteoarthritis Initiative.Radiology2011;261:50715.
  • 15
    Prasad AP, Nardo L, Schooler J, Joseph GB, Link TM.T(1)ρ and T(2) relaxation times predict progression of knee osteoarthritis.Osteoarthritis Cartilage2013;21:6976.
  • 16
    Katz JD, Agrawal S, Velasquez M.Getting to the heart of the matter: osteoarthritis takes its place as part of the metabolic syndrome.Curr Opin Rheumatol2010;22:5129.
  • 17
    Sowers M, Karvonen-Gutierrez CA, Palmieri-Smith R, Jacobson JA, Jiang Y, Ashton-Miller JA.Knee osteoarthritis in obese women with cardiometabolic clustering.Arthritis Rheum2009;61:132836.
  • 18
    Yoshimura N, Muraki S, Oka H, Kawaguchi H, Nakamura K, Akune T.Association of knee osteoarthritis with the accumulation of metabolic risk factors such as overweight, hypertension, dyslipidemia, and impaired glucose tolerance in Japanese men and women: the ROAD study.J Rheumatol2011;38:92130.
  • 19
    Zhuo Q, Yang W, Chen J, Wang Y.Metabolic syndrome meets osteoarthritis.Nat Rev Rheumatol2012;8:72937.
  • 20
    Engstrom G, Gerhardsson de Verdier M, Rollof J, Nilsson PM, Lohmander LS.C-reactive protein, metabolic syndrome and incidence of severe hip and knee osteoarthritis: a population-based cohort study.Osteoarthritis Cartilage2009;17:16873.
  • 21
    Puenpatom RA, Victor TW.Increased prevalence of metabolic syndrome in individuals with osteoarthritis: an analysis of NHANES III data.Postgrad Med2009;121:920.
  • 22
    Peterfy CG, Schneider E, Nevitt M.The Osteoarthritis Initiative: report on the design rationale for the magnetic resonance imaging protocol for the knee.Osteoarthritis Cartilage2008;16:143341.
  • 23
    Bellamy N, Buchanan WW, Goldsmith CH, Campbell J, Stitt LW.Validation study of WOMAC: a health status instrument for measuring clinically important patient relevant outcomes to antirheumatic drug therapy in patients with osteoarthritis of the hip or knee.J Rheumatol1988;15:183340.
  • 24
    Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, et al.Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity.Circulation2009;120:16405.
  • 25
    Alberti KG, Zimmet P, Shaw J.The metabolic syndrome: a new worldwide definition.Lancet2005;366:105962.
  • 26
    Freiberg MS, Cabral HJ, Heeren TC, Vasan RS, Curtis Ellison R.Alcohol consumption and the prevalence of the metabolic syndrome in the US: a cross-sectional analysis of data from the Third National Health and Nutrition Examination Survey.Diabetes Care2004;27:29549.
  • 27
    Pouliot MC, Despres JP, Lemieux S, Moorjani S, Bouchard C, Tremblay A, et al.Waist circumference and abdominal sagittal diameter: best simple anthropometric indexes of abdominal visceral adipose tissue accumulation and related cardiovascular risk in men and women.Am J Cardiol1994;73:4608.
  • 28
    Van Dijk SJ, Feskens EJ, Bos MB, de Groot LC, de Vries JH, Muller M, et al.Consumption of a high monounsaturated fat diet reduces oxidative phosphorylation gene expression in peripheral blood mononuclear cells of abdominally overweight men and women.J Nutr2012;142:121925.
  • 29
    Physical status: the use and interpretation of anthropometry. Report of a WHO Expert Committee.World Health Organ Tech Rep Ser1995;854:1452.
  • 30
    Obesity: preventing and managing the global epidemic. Report of a WHO consultation.World Health Organ Tech Rep Ser2000;894:1253.
  • 31
    Freire RD, Cardoso MA, Gimeno SG, Ferreira SR.Dietary fat is associated with metabolic syndrome in Japanese Brazilians.Diabetes Care2005;28:177985.
  • 32
    Lottenberg AM, Afonso Mda S, Lavrador MS, Machado RM, Nakandakare ER.The role of dietary fatty acids in the pathology of metabolic syndrome.J Nutr Biochem2012;23:102740.
  • 33
    Wang Y, Davies-Tuck ML, Wluka AE, Forbes A, English DR, Giles GG, et al.Dietary fatty acid intake affects the risk of developing bone marrow lesions in healthy middle-aged adults without clinical knee osteoarthritis: a prospective cohort study.Arthritis Res Ther2009;11:R63.
  • 34
    Sharma S, Murphy SP, Wilkens LR, Shen L, Hankin JH, Henderson B, et al.Adherence to the Food Guide Pyramid recommendations among Japanese Americans, Native Hawaiians, and whites: results from the Multiethnic Cohort Study.J Am Diet Assoc2003;103:11958.
  • 35
    Carballido-Gamio J, Joseph GB, Lynch JA, Link TM, Majumdar S.Longitudinal analysis of MRI T2 knee cartilage laminar organization in a subset of patients from the Osteoarthritis Initiative: a texture approach.Magn Reson Med2011;65:118494.
  • 36
    Souza RB, Stehling C, Wyman BT, Hellio Le Graverand MP, Li X, Link TM, et al.The effects of acute loading on T1ρ and T2 relaxation times of tibiofemoral articular cartilage.Osteoarthritis Cartilage2010;18:155763.
  • 37
    Stehling C, Baum T, Mueller-Hoecker C, Liebl H, Carballido-Gamio J, Joseph GB, et al.A novel fast knee cartilage segmentation technique for T(2) measurements at MR imaging: data from the Osteoarthritis Initiative.Osteoarthritis Cartilage2011;19:9849.
  • 38
    Pan J, Stehling C, Muller-Hocker C, Schwaiger BJ, Lynch J, McCulloch CE, et al.Vastus lateralis/vastus medialis cross-sectional area ratio impacts presence and degree of knee joint abnormalities and cartilage T2 determined with 3T MRI: an analysis from the incidence cohort of the Osteoarthritis Initiative.Osteoarthritis Cartilage2011;19:6573.
  • 39
    Berger MJ, Kean CO, Goela A, Doherty TJ.Disease severity and knee extensor force in knee osteoarthritis: data from the Osteoarthritis Initiative.Arthritis Care Res (Hoboken)2012;64:72934.
  • 40
    Baum T, Joseph GB, Arulanandan A, Nardo L, Virayavanich W, Carballido-Gamio J, et al.Association of magnetic resonance imaging–based knee cartilage T2 measurements and focal knee lesions with knee pain: data from the Osteoarthritis Initiative.Arthritis Care Res (Hoboken)2012;64:24855.
  • 41
    Baum T, Joseph GB, Nardo L, Virayavanich W, Arulanandan A, Alizai H, et al.Correlation of magnetic resonance imaging–based knee cartilage T2 measurements and focal knee lesions with body mass index: thirty-six–month followup data from a longitudinal, observational multicenter study.Arthritis Care Res (Hoboken)2013;65:2333.
  • 42
    Li X, Pai A, Blumenkrantz G, Carballido-Gamio J, Link T, Ma B, et al.Spatial distribution and relationship of T1ρ and T2 relaxation times in knee cartilage with osteoarthritis.Magn Reson Med2009;61:13108.
  • 43
    Mosher TJ, Dardzinski BJ.Cartilage MRI T2 relaxation time mapping: overview and applications.Semin Musculoskelet Radiol2004;8:35568.
  • 44
    Jungmann P, Kraus M, Nardo L, Liebl H, Alizai G, Joseph G, et al.T2 relaxation time measurements are limited in monitoring progression, once advanced cartilage defects at the knee occur: longitudinal data from the Osteoarthritis Initiative.J Magn Reson Imaging. In press.
  • 45
    Crema MD, Roemer FW, Marra MD, Burstein D, Gold GE, Eckstein F, et al.Articular cartilage in the knee: current MR imaging techniques and applications in clinical practice and research.Radiographics2011;31:3761.
  • 46
    Koff MF, Amrami KK, Kaufman KR.Clinical evaluation of T2 values of patellar cartilage in patients with osteoarthritis.Osteoarthritis Cartilage2007;15:198204.
  • 47
    National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III).Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) final report.Circulation2002;106:3143421.
  • 48
    Carman WJ, Sowers M, Hawthorne VM, Weissfeld LA.Obesity as a risk factor for osteoarthritis of the hand and wrist: a prospective study.Am J Epidemiol1994;139:11929.
  • 49
    Ghroubi S, Elleuch H, Guermazi M, Kaffel N, Feki H, Abid M, et al.Abdominal obesity and knee osteoarthritis.Ann Readapt Med Phys2007;50:6616. In French.
  • 50
    Laakso M.Hyperglycemia as a risk factor for cardiovascular disease in type 2 diabetes.Prim Care1999;26:82939.
  • 51
    Mattila TK, de Boer A.Influence of intensive versus conventional glucose control on microvascular and macrovascular complications in type 1 and 2 diabetes mellitus.Drugs2010;70:222945.
  • 52
    Hart DJ, Doyle DV, Spector TD.Association between metabolic factors and knee osteoarthritis in women: the Chingford study.J Rheumatol1995;22:111823.
  • 53
    Davis MA, Ettinger WH, Neuhaus JM.The role of metabolic factors and blood pressure in the association of obesity with osteoarthritis of the knee.J Rheumatol1988;15:182732.
  • 54
    Hynes GR, Jones PJ.Leptin and its role in lipid metabolism.Curr Opin Lipidol2001;12:3217.
  • 55
    Knoops KT, de Groot LC, Kromhout D, Perrin AE, Moreiras-Varela O, Menotti A, et al.Mediterranean diet, lifestyle factors, and 10-year mortality in elderly European men and women: the HALE project.JAMA2004;292:14339.
  • 56
    Salto LM, Cordero-MacIntyre Z, Beeson L, Schulz E, Firek A, De Leon M.En Balance participants decrease dietary fat and cholesterol intake as part of a culturally sensitive Hispanic diabetes education program.Diabetes Educ2011;37:23953.