Abstract
Objective
We sought to examine the prospective association of vitamin K with radiographic progression of knee osteoarthritis.
Methods
In OAI, 1977 participants with radiographic knee OA and having dietary data at baseline were followed up to 12, 24, 36 and 48 months. Vitamin K was assessed with a Block Brief Food Frequency Questionnaire completed at baseline. To evaluate knee OA progression, we used quantitative medial tibiofemoral joint space width (JSW) based on plain radiographs. Progression was defined by measured Joints Space Width (JSW). The generalized linear mixed model was used to test the association of vitamin K and change in JSW over time, while adjusting for baseline KL grade and other potential confounding factors.
Results
We found a relationship between dietary Vitamin K with structural progression of knee osteoarthritis measured by quantitative JSW in a dose response manner. When stratified among KL groups, a significant trend was seen in the KL2 (p < 0.025)
Conclusion
Our results suggest that decreased vitamin K intake from food may be associated with increased progression of knee OA. Replication of these findings in other studies validating decreased vitamin K intake leading to increased knee OA progression are needed.
Significance
This study provides insight into a potential novel risk factor for the progression of knee OA. These findings have may have clinical implications given the potential for Vitamin K to be a simple therapy for knee OA.
Keywords: Knee osteoarthritis, OA, Diet, Vitamin k
1. Introduction
Osteoarthritis (OA) is the most common form of arthritis, with knee OA contributing to the most lower extremity disability among older adults in the US [1]. It is estimated that more than 20 million people within the United States suffer from knee OA and by 2030, 20% of Americans over the age of 65 years of age are at risk for OA [2]. Currently there are no effective treatments to prevent or slow down the process of OA [3]. A variety of risk factors including age, body mass index, joint injury, and musculoskeletal loading have been consistently associated with the increased risk for knee OA [4]. Knee osteoarthritis is a multifactorial process associated with inflammation, articular cartilage breakdown, and insufficient bone mineralization [5]. Diet has been extensively studied for prevention of many major chronic diseases including cardiovascular disease, diabetes and some cancers. Dietary factors associated with inflammation, obesity and other metabolic risk factors may also play a role in OA pathogenesis [[6], [7], [8], [9], [10]]. It follows that nutrition may play an important role in the prevention and management of OA. An approach that relies on dietary modification is clearly more attractive than medications in terms of risk/benefit and more likely to be implementable. There are limited data on the relationships between dietary factors and risk of OA [11].
Vitamin K has been recognized for its role in bone strength and mineralization. In adults aged 20 and older, the average daily vitamin K intake from foods is 122 mcg for women and 138 mcg for men. When both foods and supplements are considered, the average daily vitamin K intake increases to 164 mcg for women and 182 mcg for men. Insufficient Vitamin K leads to the reduction of chondrocyte differentiation and endochondral bone formation which affects musckulosketal development [12].There is limited preliminary evidence that suggests that low Vitamin K levels may be a predisposing risk factor for radiographic features of hand and knee OA. [13,14], Therefore we examined the relationship between Vitamin K in the diet and the progression of knee OA.
2. Methods
2.1. Cohort description
For detailed information about the Osteoarthritis Initiative (OAI) protocol see (OAI, http://oai.epi-ucsf.org) [15]. Briefly, the OAI is a multi-center, longitudinal, prospective observational study of knee OA. The overall aim of the OAI is to develop a public domain research resource to facilitate the scientific evaluation of biomarkers for OA as potential surrogate endpoints for disease onset and progression. From 2004 to 2006, the OAI collected baseline data from four study sites (i.e Baltimore, MD; Columbus, OH; Pittsburgh, PA; and Pawtucket, RI) totaling 4796 patients with 9592 knees established with OA or at risk for developing knee OA. Individuals with radiographic data of at least 1 knee based on OAI central radiograph reading at baseline were included in the analysis. Knees were excluded from this analysis if they had little to no evidence of radiographic OA, severe radiographic OA (Kellgren/Lawrence (K/L) grade equal to 0,1, or 4), and knees primarily with lateral joint disease at any point from baseline to 48 months. OAI participants with invalid FFQ data or on Warfarin were also excluded. Additionally, specific follow up knee data were excluded if there was unsatisfactory knee positioning on the follow up radiograph. Unsatisfactory knee positioning is indicated if the difference in rim distance (from the tibial plateau to the tibial rim closest to the femoral condyle) between that follow up visit and the baseline visit was >2 mm. This is done to minimize possible effects of knee position on measurement error of joint space width (JSW). Repeated radiographic measurements at 12, 24, 36, and 48 months were included in this analysis. For this analysis of progression, we excluded those who consumed ≤500 and ≥5000 calories per day as implausible dietary data. For this analysis, 1977 participants and 2801 knees with a KL grade of 2 or 3 and having valid dietary data at baseline comprised the eligible study sample (Fig. 1).
Fig. 1.
Derivation of vitamin K cohort.
2.2. Radiographic assessment for knees
In the OAI, current radiographic assessment techniques on plain radiographs involved quantitative assessment of JSW. To account for changes in beam angle and alignment at each visit, which introduces measurement error in serial JSW measurement, we also adjusted for changes of the beam angles and rim distances (from the tibial plateau to the tibial rim closest to the femoral condyle between follow-up visits and baseline). For these analyses, we used the publicly available quantitative JSW measurements (version 06/17/2013, online at http://oai.epi-ucsf.org).
A quantitative approach was used to provide a precise measure of JSW in millimeters between the adjacent bones of the knee [[16], [17], [18]]. Multiple JSWs were measured at fixed locations along the joint in the medial compartment, denoted as JSW(x), at intervals of 0.025 for x = 0.15–0.30. The reproducibility of this technique and the responsiveness to change have been documented elsewhere [[19], [20], [21]] including one study using OAI data that demonstrated a responsiveness that compared favorably to magnetic resonance imaging [22]. We used medial JSW at x = 0.25 with the best responsiveness of change to quantify the progression of OA [23]. We defined the repeated measures of the changes of JSW from baseline to 12, 24, 36, and 48 months as one of the outcome variables.
2.3. Assessment of dietary vitamin K
Dietary Vitamin K intake of all the participants were assessed at baseline using the Block Brief Food Frequency Questionnaire2000 [24,25]. This brief FFQ has been validated against three four-day records in a group of middle-aged women, and against two seven-day records in a group of older men [25]. The absolute value of macronutrients estimated by the reduced questionnaire were slightly lower than longer repeatedly measured food-record estimates, but most micronutrients were not underestimated. The amount of Vitamin K in supplements is zero to minimal and therefore was not assessed.
2.4. Covariates
Covariates that were used include age, KL grade, body mass index (BMI), physical activity, and self-reported NSAID use. BMI is calculated as weight (in kg)/[height (in m)] [2] and is categorized as normal, overweight, and obese <18.5–24.9, 25.0–29.9, and >30 kg/m2 respectively. Additionally, physical activity was based on Physical activity for the Elderly (PASE) scores, which have been validated in younger subjects [26,27].
2.5. Statistical analysis
The primary analysis was to assess the influence of dietary Vitamin K on knee OA progression as measured by changes in JSW. We first analyzed all the variables of interest including the exposure (Vitamin K intake). The primary outcomes were repeated measures of the JSW decreases from baseline to 12, 24, 36 and 48 months respectively. Energy adjusted Vitamin K intake was analyzed as a continuous variable and as quartiles. We used a generalized linear mixed models to account for within subject correlation and the correlation of repeated measures in knee level. The generalized linear mixed model adjusted for age, gender, race, and baseline KL grade, physical activity, saturated fat, and total calcium intake. When treating dietary Vitamin K as a continuous variable, beta estimated the difference in JSW change per standard deviation change in Vitamin K intake, for quartile analyses, the median value for each quartile was used to determine the beta coefficient. Data analyses were performed using SAS 9.4.
2.6. Results
In this study, we examined 1977 participants from OAI having a total of 2,801eligible knees. The mean age of the 1977 participants when enrolling in our study was 62.3 ± 8.9 y. Women and non-Hispanic whites accounted for 59.4% and 76.7% of the study population, respectively. The mean BMI was 29.7 ± 4.9 kg/m2. The baseline characteristics of participants are shown in Table 1 according to Vitamin k levels. Our data was divided into quartiles of dietary Vitamin K intake. Median energy adjusted quartiles were: quartile 1 (Q1)64.57 μg/day, quartile 2(Q2)100.59 μg/day, quartile 3 (Q3)167.81 μg/day, and quartile 4 (Q4)338.40 μg/day. Compared to low Vitamin K (Q1), higher Vitamin K(Q4) intake were more likely to be female and non-Hispanic African Americans (p < 0.001).
Table 1.
Sample characteristics.
| Total | Dietary Vitamin K (adjusted for total calories) |
p-values |
|||||
|---|---|---|---|---|---|---|---|
| Q1: 64.57 μg/day | Q2: 100.59 μg/day | Q3: 167.81ug/day | Q4: 338.40 μg/day | group | trend | ||
| Subject Characteristics | n=1977 | n=511 | n=473 | n=489 | n=504 | ||
| Gender | <0.001 | <0.001 | |||||
| Male | 803 (40.6) | 309 (60.5) | 190 (40.2) | 171 (35.0) | 133 (26.4) | ||
| Female | 1174 (59.4) | 202 (39.5) | 283 (59.8) | 318 (65.0) | 371 (73.6) | ||
| Age (mean, sd) | 62.3 (8.9) | 61.4 (9.3) | 62.5 (8.9) | 63.2 (9.0) | 62.3 (8.4) | 0.020 | 0.250 |
| Race/Ethnicity | <0.001 | ||||||
| Non-Hispanic White | 1517 (76.7) | 421 (82.4) | 378 (79.9) | 379 (77.5) | 339 (67.3) | ||
| Non-Hispanic Black | 397 (20.1) | 75 (14.7) | 82 (17.3) | 96 (19.6) | 144 (28.6) | ||
| Other | 63 (3.2) | 15 (2.9) | 13 (2.8) | 14 (2.9) | 21 (4.2) | ||
| Education | 0.031 | ||||||
| HS or less | 360 (18.2) | 113 (22.1) | 84 (17.8) | 77 (15.8) | 86 (17.1) | ||
| College (2 or 4 year) | 907 (45.9) | 244 (47.8) | 205 (43.4) | 228 (46.6) | 230 (45.6) | ||
| Graduate School | 709 (35.9) | 154 (30.1) | 183 (38.8) | 184 (37.6) | 188 (37.3) | ||
| Partnered | 1290 (65.3) | 330 (64.7) | 319 (67.4) | 332 (67.9) | 309 (61.4) | 0.121 | 0.328 |
| Annual Household Income | 0.099 | ||||||
| <$25,000 | 279 (15.1) | 87 (18.2) | 67 (14.9) | 57 (12.5) | 68 (14.6) | ||
| $25,000-$49,999 | 515 (27.8) | 122 (25.5) | 141 (31.3) | 126 (27.5) | 126 (27.1) | ||
| $50,000-$99,999 | 663 (35.86) | 174 (36.3) | 139 (30.8) | 182 (39.7) | 168 (36.1) | ||
| $100,000 or more | 396 (21.4) | 96 (20.0) | 104 (23.1) | 93 (20.3) | 103 (22.2) | ||
| Employed | 1161 (58.7) | 318 (62.2) | 275 (58.1) | 277 (56.7) | 291 (57.7) | 0.292 | 0.124 |
| Smoking Status | 0.072 | ||||||
| Never | 1068 (54.0) | 290 (56.8) | 265 (56.0) | 269 (55.0) | 244 (48.4) | ||
| Current | 125 (6.3) | 35 (6.9) | 27 (5.7) | 24 (4.9) | 39 (7.7) | ||
| Past | 784 (39.7) | 186 (36.4) | 181 (38.3) | 196 (40.1) | 221 (43.9) | ||
| BMI (mean, sd) | 29.8 (4.9) | 30.0 (4.7) | 29.5 (4.9) | 29.8 (5.1) | 29.8 (4.9) | 0.428 | 0.992 |
| BMI Group | 0.043 | 0.532 | |||||
| Normal (<25 kg/m∗∗2) | 314 (15.9) | 63 (12.3) | 81 (17.1) | 81 (16.6) | 89 (17.7) | ||
| Overweight (25–29.9 kg/m∗∗2) | 764 (38.6) | 209 (40.9) | 189 (40.0) | 197 (40.3) | 169 (33.5) | ||
| Obese (≥30 kg/m∗∗2) | 899 (45.5) | 239 (46.8) | 203 (42.9) | 211 (43.1) | 246 (48.8) | ||
| Physical Activity (PASE) (mean, sd) | 158.5 (82.0) | 161.1 (83.2) | 156.4 (83.1) | 152.8 (78.2) | 163.3 (83.3) | 0.183 | 0.484 |
| Alcohol consumption | 0.079 | 0.066 | |||||
| <5 g/day | 1215 (61.5) | 295 (57.7) | 298 (63.0) | 298 (60.9) | 324 (64.3) | ||
| 5 to < 10 g/day | 190 (9.6) | 44 (8.6) | 46 (9.7) | 58 (11.9) | 42 (8.3) | ||
| ≥10 g/day | 572 (28.9) | 172 (33.7) | 129 (27.3) | 133 (27.2) | 138 (27.4) | ||
| NSAID Use (more than half days in month) | 548 (27.7) | 147 (28.8) | 119 (25.2) | 139 (28.4) | 143 (28.4) | 0.561 | 0.835 |
| Any Medication used for knee pain | 819 (41.4) | 203 (39.7) | 202 (42.7) | 204 (41.7) | 210 (41.7) | 0.812 | 0.613 |
| Glucosamine Use | 716 (36.3) | 166 (32.5) | 164 (34.8) | 197 (40.3) | 189 (37.7) | 0.058 | 0.029 |
| Hyaluronic Acid Injection | 19 (1.0) | 6 (1.2) | 5 (1.1) | 6 (1.2) | 2 (0.4) | 0.505 | 0.264 |
| Daily Dietary Data (mean, sd) | |||||||
| Total calories (Kcal/day) | 1434 (578.6) | 1624 (630.7) | 1276 (488.8) | 1326 (501.2) | 1497 (601.7) | <0.001 | 0.435 |
| Total protein (g/day) | 61.2 (26.3) | 64.4 (28.8) | 53.0 (22.0) | 58.4 (23.1) | 68.7 (28.1) | <0.001 | <0.001 |
| Total carbohydrates (g/day) | 168.1 (72.8) | 185.9 (75.5) | 149.6 (59.0) | 157.6 (65.1) | 178.8 (81.1) | <0.001 | 0.379 |
| Total fat (g/day) | 55.9 (27.1) | 66.1 (30.5) | 50.0 (23.2) | 50.5 (23.6) | 56.1 (27.0) | <0.001 | 0.001 |
| Saturated fat (g/day) | 19.3 (9.7) | 23.6 (11.2) | 17.3 (8.3) | 17.2 (8.4) | 18.9 (9.3) | <0.001 | <0.001 |
| Monounsaturated fat (g/day) | 20.7 (10.6) | 25.3 (12.0) | 18.7 (9.1) | 18.6 (9.1) | 20.1 (10.2) | <0.001 | <0.001 |
| Polyunsaturated fat (g/day) | 11.1 (5.8) | 12.0 (6.1) | 9.9 (4.8) | 10.3 (5.0) | 12.2 (6.5) | <0.001 | 0.013 |
| Vegetable servings (#/day) | 3.4 (2.5) | 1.9 (1.3) | 2.4 (1.2) | 3.4 (1.4) | 5.9 (2.9) | <0.001 | <0.001 |
| Fruit servings (#/day) | 1.4 (0.9) | 1.2 (0.8) | 1.3 (0.8) | 1.5 (0.9) | 1.6 (1.0) | <0.001 | <0.001 |
| Dietary Vitamin K (ug/day) | 190.1 (175.9) | 72.0 (38.6) | 104.9 (39.0) | 172.8 (46.0) | 396.2 (213.6) | – | – |
| Dietary calcium (mg/day) | 676.2 (350.8) | 715.1 (372.1) | 583.7 (298.4) | 645.3 (320.6) | 767.3 (377.6) | <0.001 | <0.001 |
| Supplemental calcium (mg/day) | 472.1 (472.4) | 315.0 (410.0) | 490.3 (480.6) | 559.2 (486.8) | 535.6 (475.9) | <0.001 | <0.001 |
| Total calcium (mg/day) | 1148.3 (593.3) | 1030.1 (557.1) | 1074.0 (572.1) | 1204.4 (601.1) | 1302.8 (606.7) | <0.001 | <0.001 |
| Dietary Vitamin D (IU/day) | 143.6 (114.1) | 152.7 (117.3) | 129.1 (112.9) | 143.4 (107.1) | 150.1 (114.4) | 0.006 | 0.448 |
| Supplemental Vitamin D (IU/day) | 259.8 (220.9) | 219.4 (209.2) | 262.5 (228.5) | 283.3 (221.6) | 284.9 (219.9) | <0.001 | <0.001 |
| Total Vitamin D (IU/day) |
403.3 (253.8) |
372.1 (246.7) |
391.7 (257.0) |
426.7 (253.7) |
435.0 (257.4) |
<0.001 |
<0.001 |
| Knee Characteristics | n=2801 | n=708 | n=668 | n=702 | n=7233 | ||
| KL Grade | 0.011 | 0.004 | |||||
| KL 2 | 1896 (67.7) | 441 (62.3) | 461 (69.0) | 477 (68.0) | 517 (71.5) | ||
| KL 3 | 905 (32.3) | 267 (37.7) | 207 (31.0) | 225 (32.1) | 206 (28.5) | ||
| Knee Injury | 885 (31.9) | 249 (35.4) | 205 (31.1) | 215 (30.8) | 216 (30.4) | 0.187 | 0.112 |
Results of multivariable analyses are shown in Table 2. Four models were built to assess the changes in joint space width. Quartile 4 had the lowest change in JSW 0.27 mm in the crude with P for trend p = 0.011 which persisted across the other models. When stratified among KL groups, a significant trend was seen in KL2 (p < 0.025) and when both KL2 and KL3 groups were merged (p < 0.012). Additional, for every half a standard deviation increase in Vitamin K, there is a 0.014 mm decrease in the change of JSW. The average decrease in JSW by timepoint can be seen in Table 3. At 12, 24, 36, and 48 month time points, we found the average decrease in JSW was 0.145, 0.257, 0.405, and 0.483 respectively.
Table 2.
Change in JSW models.
| Model | p-values |
LS Means (95% CI) |
||||
|---|---|---|---|---|---|---|
| Group | trend | Q1 | Q2 | Q3 | Q4 | |
| Crude | 0.019 | 0.002 | 0.356 (0.317, 0.396) | 0.350 (0.309, 0.391) | 0.332 (0.292, 0.372) | 0.275 (0.236, 0.315) |
| Age adjusted | 0.019 | 0.002 | 0.356 (0.316, 0.396) | 0.350(0.309, 0.391) | 0.333 (0.293, 0.373) | 0.275 (0.235, 0.314) |
| Age + gender | 0.097 | 0.019 | 0.350 (0.310, 0.389) | 0.357 (0.316, 0.398) | 0.343 (0.302, 0.383) | 0.291(0.250, 0.332) |
| Model A | 0.061 | 0.014 | 0.347 (0.307, 0.387) | 0.361 (0.319, 0.402) | 0.343 (0.302, 0.383) | 0.287 (0.246, 0.328) |
| Model B | 0.063 | 0.011 | 0.367 (0.326, 0.408) | 0.378 (0.336, 0.419) | 0.355 (0.314, 0.395) | 0.305 (0.264, 0.346) |
| Model C | 0.088 | 0.024 | 0.393 (0.352, 0.433) | 0.412 (0.371, 0.454) | 0.388 (0.347, 0.428) | 0.342 (0.300, 0.383) |
| Model D |
0.076 |
0.027 |
0.368 (0.312, 0.423) |
0.390 (0.334, 0.445) |
0.374 (0.320, 0.428) |
0.316 (0.264, 0.369) |
| Model D |
0.093 |
0.048 |
0.3983 (0.3433, 0.4532) |
0.4276 (0.3724, 0.4828) |
0.4093 (0.3556, 0.4631) |
0.3564 (0.3042, 0.4087) |
| Stratified | ||||||
| KL2 | 0.126 | 0.025 | 0.3075 (0.2585, 0.3565) | 0.2869 (0.2385, 0.3353) | 0.2952 (0.2475, 0.3429) | 0.2329 (0.1851, 0.2807) |
| KL3 | 0.065 | 0.284 | 0.4685 (0.3989, 0.5381) | 0.5831 (0.5058, 0.6604) | 0.4773 (0.4037, 0.5510) | 0.4488 (0.3710, 0.5266) |
| KL Merged from stratified | ||||||
| 0.064 | 0.012 | 0.3734 (0.3326, 0.4142) | 0.3863 (0.3444, 0.4283) | 0.3603 (0.3194, 0.4011) | 0.3122 (0.2707, 0.3537) | |
Model A = age, gender, bmi, pase.
Model B = age, gender, bmi, pase, pain meds use, glucosamine use.
Model C = age, gender, bmi, pase, pain meds use, glucosamine use, KL grade.
Model D = age, gender, race, bmi, pase, dietary saturated fat intake, total calcium intake.
Table 3.
Average decrease in JSW by Timepoint.
| Time | Decrease JSW (se) |
|---|---|
| 12 M | 0.1453 (0.1126, 0.1780) |
| 24 M | 0.2573 (0.2245, 0.2901) |
| 36 M | 0.4057 (0.3728, 0.4385) |
| 48 M | 0.4831 (0.4501, 0.5162) |
2.7. Discussion
This study suggests that individuals with lower dietary Vitamin K have an increased risk of knee osteoarthritis progression measured by quantitative JSW. This is the first to our knowledge to demonstrate people with higher level of low vitamin k shows a reduced risk of progression in a dose repose manner. Previous results focusing on incident disease or present show similar findings [28,29]. The Multicenter Osteoarthritis (MOST) Study examined the relationship of subclinical vitamin K, defined clinically as bleeding, to new-onset radiographic knee OA and early OA changes on magnetic resonance imaging (MRI). This study found that participants (mean ± SD age = 62 ± 8 yrs) with subclinical vitamin K deficiency were 1.5–2 times more likely to develop radiographic knee osteoarthritis and cartilage damage over 30 months (risk ratio (95%CI) 1.56(1.08–2.25) and 2.39(1.05–5.40) respectively, compared to those without subclinical deficiency [29]. The Research on Osteoarthritis Against Disability (ROAD) study evaluating 719 elderly (>60 year of age) Japanese individuals with radiographic knee OA, found that low dietary vitamin K intake is risk factor for knee OA and is shown to be inversely associated with the prevalence of radiographic knee OA [30]. Additionally, the Framingham offspring cohort found that low Vitamin K concentrations were associated with higher prevalence of radiographic hand and knee OA [13].
The underline biological mechanisms that explain the results are the following. Knee OA progression have been thought to include other mechanisms besides cartilage loss such as bone changes over time as well as changes in bone shape. Vitamin k is associated with bone health which may be underlining biological mechanism which may explain our results. Vitamin K also acts as a co-factor for the process of gamma-carboxylation of Gla proteins [31,32]. These Gla proteins play an important role in regulation of mineralization in these tissues, and the lack of functioning protein's result in changes that are similar to what occurs osteoarthritis. The specific mechanism by which osteoarthritic chondrocytes produced uncarboxylated MGP was not discerned, but it seems that low levels of vitamin K concentrations could be potentially responsible, suggesting that vitamin K deficiency, leading to inadequate functional Gla proteins, may contribute to osteoarthritic changes.
These proteins such as osteocalcin play an important role of the mineralization of these tissues and in the absence of these regularity proteins, similar results are seen, such as in OA formation [33]. Lastly, Vitamin K has been verified to have anti-inflammatory properties, which may be attributed to why Vitamin K may influence OA [34].
The strengths of this study include the prospective design, large subset of participant with knee OA, state of the art quantitative measures of joint space width changes from high-technology imaging processors. In addition, we excluded knees in which the difference of rim distance between follow-up and baseline visits was ≥2 mm and adjusted for changes of rim distance and beam angle in the multivariate models to minimize the possible measurement error of radiographic data. The limitations of this study include its observational nature of the study, despite our best efforts residual confounding could explain our results. Although the quantitative approach provided a precise measure of JSW change the clinical relevance of these changes is unknown. Attempts were also made to overcome collider bias by adjusting for the most known risk factors in the analysis, which is thought to have been a limitation of previous research.
In conclusion, this study provides insight into a potential novel risk factor for the progression of knee OA. These findings have may have clinical implications given the potential for Vitamin K to be a simple therapy for knee OA. Replication of these novel findings in other prospective studies or clinical trials demonstrating the efficacy of Vitamin K as a preventative agent for progression of osteoarthritis appear warranted.
Role of the funding source
"This project has been funded in whole or in part with Federal funds from the National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Department of Health and Human Services, under Contract No. HHSN268201200032C".
Ethics approval
OAI was approved by the Institutional Review Board, the University of California, San Francisco (UCSF) and its affiliates. UCSF holds Office of Human Research Protections Federal wide assurance number FWA00000068.
Contributors
RH, CBE, JD, BL, MR and TM conceived the idea of the study and were responsible for the design of the study. RH and MR were responsible for undertaking the data analysis and produced the tables and graphs. The initial draft of the manuscript was prepared by RH and CBE and then circulated repeatedly among all authors for critical revision and all coauthors contributed to the interpretation of the results, read and approved the final manuscript.
Declaration of competing interest
None.
Acknowledgement
The OAI is a public-private partnership comprised of five contracts (N01-AR-2-2258; N01-AR-2-2259; N01-AR-2-2260; N01-AR-2-2261; N01-AR-2-2262) funded by the National Institutes of Health, a branch of the Department of Health and Human Services, and conducted by the OAI Study Investigators. Private funding partners include Merck Research Laboratories; Novartis Pharmaceuticals Corporation, GlaxoSmithKline; and Pfizer, Inc. Private sector funding for the OAI is managed by the Foundation for the National Institutes of Health. This manuscript was prepared using an OAI public use data set and does not necessarily reflect the opinions or views of the OAI investigators, the NIH, or the private funding partners.
Footnotes
Supplementary data to this article can be found online at https://doi.org/10.1016/j.ocarto.2021.100172.
Appendix A. Supplementary data
The following is the Supplementary data to this article:
References
- 1.Lawrence R.C., Felson D.T., Helmick C.G., Arnold L.M., Choi H., Deyo R.A., Gabriel S., Hirsch R., Hochberg M.C., Hunder G.G., Jordan J.M., Katz J.N., Kremers H.M., Wolfe F. National arthritis data workgroup. Arthritis Rheum. 2008 Jan;58(1):26–35. doi: 10.1002/art.23176. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Loeser R.F. The role of aging in the development of osteoarthritis. Trans. Am. Clin. Climatol. Assoc. 2017;128:44–54. [PMC free article] [PubMed] [Google Scholar]
- 3.Grässel S., Muschter D. Recent advances in the treatment of osteoarthritis. Faculty Rev. 2020;325 doi: 10.12688/f1000research.22115.1. F1000Research, 9, F1000. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Felson D.T., et al. Osteoarthritis: new Insights. Part 1: the disease and its risk factors. Ann. Intern. Med. 2000;133(8):635. doi: 10.7326/0003-4819-133-8-200010170-00016. [DOI] [PubMed] [Google Scholar]
- 5.Heidari B. Knee osteoarthritis prevalence, risk factors, pathogenesis and features: Part I. Caspian Journal of Internal Medicine. 2011;2(2):205–212. (Print) [PMC free article] [PubMed] [Google Scholar]
- 6.McAlindon T.E., Biggee B.A. Nutritional factors and osteoarthritis: recent developments. Curr. Opin. Rheumatol. 2005;17:647–652. doi: 10.1097/01.bor.0000175461.57749.46. [DOI] [PubMed] [Google Scholar]
- 7.Wojdasiewicz P., Poniatowski L.A., Szukiewicz D. The role of inflammatory and anti-inflammatory cytokines in the pathogenesis of osteoarthritis. Mediat. Inflamm. 2014;2014:561459. doi: 10.1155/2014/561459. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Lu B., Driban J.B., Duryea J., McAlindon T., Lapane K.L., Eaton C.B. Milk consumption and progression of medial tibiofemoral knee osteoarthritis: data from the Osteoarthritis Initiative. Arthritis Care Res. 2014;66:802. doi: 10.1002/acr.22297. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Lu B., Driban J.B., Xu C., Lapane K.L., McAlindon T.E., Eaton C.B. Dietary fat and progression of knee osteoarthritis dietary fat intake and radiographic progression of knee osteoarthritis: data from the Osteoarthritis Initiative. Arthritis Care Res. 2016 doi: 10.1002/acr.22952. in press. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.McAlindon T.E., Jacques P., Zhang Y., et al. Do antioxidant micronutrients protect against the development and progression of knee osteoarthritis? Arthritis Rheum. 1996;39:648–656. doi: 10.1002/art.1780390417. [DOI] [PubMed] [Google Scholar]
- 11.Melanson K.J. Nutrition Review: diet, nutrition, and osteoarthritis. Am. J. Lifestyle Med. 2016;1(4):260–263. [Google Scholar]
- 12.El-Brashy A.-E.W.S., El-Tanawy R.M., Hassan W.A., Shaban H.M., Bhnasawy M.M. Potential role of vitamin K in radiological progression of early knee osteoarthritis patients. The Egyptian Rheumatologist. 2016;38(3):217–223. [Google Scholar]
- 13.Neogi T., Booth S.L., Zhang Y.Q., et al. Low vitamin K status is associated with osteoarthritis in the hand and knee. Arthritis Rheum. 2006;54:1255–1261. doi: 10.1002/art.21735. [DOI] [PubMed] [Google Scholar]
- 14.Oka H, Akune T, Muraki S, En-yo Y, Yoshida M, Saika A, Nakamura K, Kawaguchi H, Yoshimura N. Association of low dietary vitamin K intake with radiographic knee osteoarthritis in the Japanese elderly population: dietary survey in a population-based cohort of the ROAD study. J Orthop Sci. 2009 Nov;14(6):92–687. doi: 10.1007/s00776-009-1395-y. Epub 2009 Dec 8. PMID: 19997813. [DOI] [PubMed] [Google Scholar]
- 15.The osteoarthritis initiative protocol for the cohort study. < http://oai.epi-.csf.org/datarelease/docs/StudyDesignProtocol.pdf>.
- 16.Felson D.T., Gale D.R., Elon Gale M., Niu J., Hunter D.J., Goggins J., Lavalley M.P. Rheumatology. 2005 Jan;44(1):100–104. doi: 10.1093/rheumatology/keh411. [DOI] [PubMed] [Google Scholar]
- 17.Sharp J.T., Angwin J., Boers M., Duryea J., von Ingersleben G., Hall J.R., Kauffman J.A., Landewé R., Langs G., Lukas C., Maillefert J.F., Bernelot Moens H.J., Peloschek P., Strand V., van der Heijde D.J. Rheumatol. 2007 Apr;34(4):874–883. [PubMed] [Google Scholar]
- 18.Duryea J., Neumann G., Niu J., Totterman S., Tamez J., Dabrowski C., et al. Comparison of radiographic joint space width with magnetic resonance imaging cartilage morphometry: analysis of longitudinal data from the Osteoarthritis Initiative. Arthritis Care Res. 2010;62:932–937. doi: 10.1002/acr.20148. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Duryea J., Li J., Peterfy C.G., Gordon C., Genant H.K. Trainable rule-based algorithm for the measurement of joint space width in digital radiographic images of the knee. Med. Phys. 2000;27:580–591. doi: 10.1118/1.598897. [DOI] [PubMed] [Google Scholar]
- 20.Neumann G., Hunter D., Nevitt M., Chibnik L.B., Kwoh K., Chen H., et al. Location specific radiographic joint space width for osteoarthritis progression. Osteoarthritis Cartilage. 2009;17:761–765. doi: 10.1016/j.joca.2008.11.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Duryea J., Neumann G., Niu J., Totterman S., Tamez J., Dabrowski C., Le Graverand M.P., Luchi M., Beals C.R., Hunter D. Arthritis Care Res. 2010 Jul;62(7):932–937. doi: 10.1002/acr.20148. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Duryea J., Zaim S., Genant H.K. New radiographic-based surrogate outcome measures for osteoarthritis of the knee. Osteoarthritis Cartilage. 2003;11:102–110. doi: 10.1053/joca.2002.0866. [DOI] [PubMed] [Google Scholar]
- 23.The Osteoarthritis Initiative protocol for the cohort study. URL: http://oai.epi-ucsf.org/datarelease/docs/StudyDesignProtocol.pdf.
- 24.Washburn R.A., Smith K.W., Jette A.M., Janney C.A. J. Clin. Epidemiol. 1993;46(2):153–162. doi: 10.1016/0895-4356(93)90053-4. Feb. [DOI] [PubMed] [Google Scholar]
- 25.Johansen K.L., Painter P., Kent-Braun J.A., Ng A.V., Carey S., Da Silva M. Chertow GM Kidney Int. 2001;59(3):1121–1127. doi: 10.1046/j.1523-1755.2001.0590031121.x. Mar. [DOI] [PubMed] [Google Scholar]
- 26.Shea M.K., Kritchevsky S.B., Hsu F.C., Nevitt M., Booth S.L., Kwoh C.K., Loeser R.F. The association between vitamin K status and knee osteoarthritis features in older adults: the Health, Aging and Body Composition Study. Osteoarthritis and Cartilage/OARS. Osteoarthritis Research Society. 2015;23(3):370–378. doi: 10.1016/j.joca.2014.12.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Misra D., Booth S.L., Tolstykh I., Felson D.T., Nevitt M.C., Lewis C.E., Torner J., Neogi T. Vitamin K deficiency is associated with incident knee osteoarthritis. Am. J. Med. 2013;126:243–248. doi: 10.1016/j.amjmed.2012.10.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Oka H., Akune T., Muraki S., et al. Association of low dietary vitamin K intake with radiographic knee osteoarthritis in the Japanese elderly population: dietary survey in a population-based cohort of the ROAD study. J. Orthop. Sci. 2009;14(6):687–692. doi: 10.1007/s00776-009-1395-y. [DOI] [PubMed] [Google Scholar]
- 29.Luo G., Ducy P., McKee M.D., Pinero G.J., Loyer E., Behringer R.R., Karsenty G. Nature. 1997;386(6620):78–81. doi: 10.1038/386078a0. Mar 6. [DOI] [PubMed] [Google Scholar]
- 30.Price P.A., Williamson M.K., Haba T., Dell R.B., Jee W.S. Proc. Natl. Acad. Sci. U. S. A. 1982;79(24):7734–7738. doi: 10.1073/pnas.79.24.7734. Dec. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Tsao Yu-Tzu, Huang Yi-Jeng, Wu Hao-Hsiang, Liu Yu-An, Liu Yi-Shiuan, Oscar L. Osteocalcin mediates biomineralization during osteogenic maturation in human mesenchymal stromal cells. Int. J. Mol. Sci. 2017;18(1):159. doi: 10.3390/ijms18010159. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Lian J.B., Mckee M.D., Todd A.M., Gerstenfeld L.C. Induction of bone-related proteins, osteocalcin and osteopontin, and their matrix ultrastructural localization with development of chondrocyte hypertrophy in vitro. J. Cell. Biochem. 1993;52(2):206–219. doi: 10.1002/jcb.240520212. [DOI] [PubMed] [Google Scholar]
- 33.Meury T., Akhouayri O., Jafarov T., Mandic V., St-Arnaud R. Nuclear alpha NAC influences bone matrix mineralization and osteoblast maturation in vivo. Mol. Cell Biol. 2010;30:43–53. doi: 10.1128/MCB.00378-09. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Harshman S.G., Shea M.K. The role of vitamin K in chronic aging diseases: inflammation, cardiovascular disease, and osteoarthritis. Current nutrition reports. 2016;5(2):90–98. doi: 10.1007/s13668-016-0162-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
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