Fatty Acid–Binding Protein 4 (FABP4) Is Associated with Cartilage Thickness in End-Stage Knee Osteoarthritis

Abstract

Background

There is no single blood biomarker for the staging of knee osteoarthritis (KOA). The purpose of this study was to assess the relationship of obesity, serum biomarkers, the hip-knee-ankle angle (HKAA) with sonographic cartilage thickness.

Methods

We conducted a cross-sectional study of n = 33 patients undergoing knee arthroplasty. Body mass index (BMI) was recorded, and patients were grouped based on BMI. Serum blood samples were collected, and the following biomarkers were measured using the ELISA technique (subgroup of n = 23): oxidized low-density lipoprotein (oxLDL), soluble receptor for advanced glycation end-products (sRAGE), fatty acid–binding protein 4 (FABP4), membrane-bound phospholipase A2 (PLA2G2A). The HKAA was analyzed on full-length limb standing x-ray images. Cartilage thickness was assessed on ultrasound images. Multivariable regression analysis was performed to account for confounding.

Results

After adjusting for age, gender, and HKAA, obese patients had thicker medial femoral cartilage (β = 0.165, P = 0.041). Furthermore, lateral cartilage thickness was negatively correlated with FABP4 level after adjusting for of age, gender, BMI, and HKAA (β = −0.006, P = 0.001). Confirming previous studies, after adjustment, FABP4 level was associated with a higher BMI group (β = 42.99, P < 0.001). None of the other markers (oxLDL, PLA2G2A, and sRAGE) was associated with BMI or cartilage thickness.

Discussion

Our results indicate that BMI has a weak, positive association with cartilage thickness in end-stage KOA patients. FABP4 levels were negatively associated with cartilage thickness. While our study is limited by a small sample size, these results further highlight the role of FABP4 as promising biomarkers of burden of disease in KOA.

Introduction

Knee osteoarthritis (KOA) is a leading cause of disability worldwide. ¹ For the prevention and treatment, early diagnosis, disease staging, and assessment of treatment response are key factors. While currently diagnosis is based on symptoms and signs, several imaging modalities can aid in diagnosis. Several studies have explored and validated the role of ultrasound in KOA. Ultrasound findings have a moderate to strong agreement with measurements in magnetic resonance imaging (MRI) studies and are also associated with pain and function. ² This was also shown in a cadaveric study, where agreement of sonographic and anatomic cartilage thickness was strong on the medial femoral condyle and weak on the lateral condyle and intercondylar notch. ³

While there are several established risk factors, such as gender, age, and trauma, obesity also plays an important role in the pathogenesis of KOA. ⁴ It not only causes an increased load on weightbearing joints but is also associated with systemic and local joint inflammation and is a key component of metabolic syndrome. In metabolic syndrome, a combination of arterial hypertension, dyslipidemia, insulin resistance, and obesity cause a pro-inflammatory state that is associated with cartilage destruction. Both KOA and metabolic syndrome have been associated with systemic mediators of inflammation, such as C-reactive protein and interleukin-1. Oxidative stress, commonly observed in metabolic syndrome, is a known contributor to accelerated cellular aging and apoptosis in cartilage. Atherosclerosis and endothelial dysfunction have been associated with knee and hand osteoarthritis severity. ⁵

Despite a huge ongoing effort in the detection of blood-based diagnostic markers, no single blood biomarker has emerged so far, however. ⁶ Previous studies have suggested a potential role of fatty acid–binding protein 4 (FABP4), oxidized low-density lipoprotein (oxLDL), membrane-bound phospholipase A2 (PLA2G2A) and soluble receptor for advanced glycation end-products (sRAGE) in the pathogenesis of osteoarthritis (OA).^7-10 Cytoplasmic FABP4, a member of the FABP superfamily and mostly expressed in adipocytes, regulates intracellular fatty acid transportation by increasing fatty acid solubility and facilitating transport to cellular compartments and specific enzymes. ¹¹ FABP4 also acts as a regulator of energy homeostasis and high circulating levels have been associated with obesity, increased cardiovascular risk, cancer and also KOA severity.^8,12 FABP4 levels were found to be higher in KOA patients compared to non-KOA controls and increased with KOA stage based on the radiographice Kellgren-Lawrence scale in a previous study. ⁸ OxLDL is involved in the formation of atherosclerotic plaques, and a known regulator of cartilage degeneration via induction of chondrocyte cell death. ¹³ PLA2G2A, member of the phospholipase A2 family (PLA2), is the membrane-bound form of phospholipase A2, a lipolytic enzyme that catalyses the hydrolysis of membrane phospholipids. ⁹ Several phospholipase A2 isoforms have been implicated in the pathogenesis of OA via regulation of inflammation. Intra-articular injection of human phospholipase A2 in rats was found to induce acute inflammation and cause chronic degenerative changes. Furthermore, the expression of multiple PLA2 isoforms could be induced in response to stimulation with pro-inflammatory cytokines in OA chondrocytes in another study.^14,15 Finally, the interaction of soluble receptors for advanced-glycation endproducts (sRAGE) and advanced glycation endproducts (AGE), commonly seen in hyperglycemic states, inhibits pro-inflammatory responses. ¹⁶ Advanced glycation endproducts might be helpful in the early detection of OA. ¹⁷ To the best of our knowledge, no other study has examined the relationship of obesity and aforementioned serum biomarkers with sonographic cartilage thickness in KOA.

In this study, we investigated the relationship of obesity and aforementioned serum biomarkers with sonographic cartilage thickness in end-stage KOA. The aim of the study was to identify a possible relationship between BMI, serum biomarkers, and sonographic cartilage thickness in end-stage KOA.

In order to answer these questions, we conducted a cross-sectional study in end-stage KOA patients, using knee ultrasound examination, aforementioned blood-based markers measured using the ELISA (enzyme-linked immunosorbent assay) technique, and anthropometric variables in multivariable analysis.

Methods

Study Population

To answer our research questions, we conducted a cross-sectional clinical/laboratory study at the Department of Orthopedics and Trauma of the Medical University of Graz, Austria, from January 2019 to April 2020. This study was conducted in accordance with the ethical standards of the Ethics Committee of the Medical University of Graz (IRB#31-133 ex18/19) and with the Declaration of Helsinki.

This study population (n = 33) was enrolled at random from patients undergoing total knee arthroplasty. Adult patients older than 18 years undergoing knee arthroplasty were eligible. All patients were enrolled after informed consent. BMI was recorded and patients were grouped based on BMI (group 1: BMI < 25 kg/m² [n = 6], group 2: BMI 25-35 kg/m² [n = 21], group 3: BMI > 35 kg/m² [n = 6]). The preoperative ASA (American Society of Anesthesiologists) physical status classification score was recorded for every patient.

Enzyme-Linked Immunosorbent Assay

Serum blood samples were collected on the day before surgery, centrifuged within an hour, and stored at −20 °C. We measured the following biomarkers in a subgroup of 23 patients (group 1, n = 6; group 2, n = 11; group 3, n = 6): oxLDL (Mercodia, Uppsala, Sweden), soluble receptor for advanced glycation end-products (sRAGE; BioVendor, Brno, Czech Republic), fatty acid–binding protein 4 (FABP4; BioVendor, Brno, Czech Republic), membrane phospholipase A2 (PLA2G2A; RayBiotech Life, Peachtree Corners, GA, USA) using ready-to-use kits according to the manufacturer’s recommendation. Measurements were performed at 450 nm on a microplate reader (Infinite F50, Tecan, Austria) in duplicates. For quality reasons, the controls (high and low) included in the kits (oxLDL, sRAGE, FABP4) were also used. An intra-assay coefficient of variance (CV) <10% and an inter-assay CV <15% were used as run approval criteria. All CV values were within aforementioned limits. All samples measured were within detection limits of the respective ELISA kit. For detailed description on the performance of assays used in this study, please see Supplementary Appendix A.

X-Ray Measurments

Full-length limb standing x-ray images were taken preoperatively. The hip-knee-ankle-angle (HKAA) was assessed using imageJ (National Institutes of Health, version 1.52j) by PS and by MF in a subset of 16 patients. ¹⁸ Interrater agreement on HKAA was assessed using intraclass correlation coefficient 3,A was found to be excellent (ICC3,A = 0.99, P < 0.001).

Ultrasound Measurements

Ultrasound measurements were performed on a Siemens Sonoline G50 (Siemens, Germany) using a linear 8 MHz transducer. B-Mode examination of the knee were performed by PS under supervision of RH. The grayscale settings of the machine were kept the same for every patient. Medial, intercondylar notch and lateral femoral cartilage thickness was measured on a suprapatellar transverse view in millimeter.^3,19,20 ( Fig. 1 ). Images were taken in supine position on the evening before surgery. Images were measured twice with a break of 2 weeks using imageJ (see above). Intrarater agreement was assessed using ICC3A,1. Intrarater reliability of cartilage thickness was moderate to good (medial: ICC = 0.609, P = 0.001; lateral: ICC = 0.411, P = 0.006; intercondylar notch: ICC = 0.447,P = 0.004).

Figure 1.

Measurement of cartilage thickness using ultrasound on suprapatellar transverse image on the medial (right), intercondylar notch, and lateral femoral condyle. This figure shows a standard transverse suprapatellar view of the knee. The hyperechoic (white) band at the bottom shows the femoral bone. Sitting on it, the cartilage is depicted by a hypoechoic (dark) band with a small hyperechoic (white) line right on top of it. The yellow markers 1-3 indicate the distances measured medially, laterally, and on the intercondylar notch (center), respectively.

Statistical Analysis

All statistical analyses were performed using the statistical programming language R version 4.0.3 (2020-10-10) on Manjaro Linux 5.6.15-1. ²¹ The following R packages were used: tidyverse, visreg, irr.^22-24 Continuous variables are expressed as mean (standard deviation), categorical data as counts (percentage). Normal distribution was assessed using Shapiro-Wilk test and Q-Q plots. If data were normally distributed, groups were compared using Student t test or univariate analysis of variance (ANOVA), otherwise Wilcoxon rank sum test or Kruskal-Wallis test. Categorical variables were tested using the chi-square test. Correlations were assessed using the Spearman’s rank correlation coefficient. To adjust for age, gender, BMI, and HKAA as confounders, multivariable regression analysis was performed. Sonographic cartilage thickness was used as the outcome variable for multivariable analysis. P values were adjusted using the Benjamini-Hochberg adjustment and considered significant if <0.05. There were no missing data.

Results

A total of 33 patients (23 women, 69.7%), were enrolled ( Table 1 ). The mean age was 70.06 ± 7.91, mean BMI was 29.93 ± 4.94 kg/m². Of the enrolled patients, only 1 patient had an ASA score of 1 and 1 patient had an ASA score of 4.

Table 1.

Study Population by Body Mass Index (BMI) Groups. ^a

	BMI < 25 kg/m2 (n = 6)	BMI 25-35 kg/m2 (n = 21)	BMI >35 kg/m2 (n = 6)	Total (n = 33)
BMI, kg/m2, mean (SD)	22.81 (2.05)	29.95 (2.54)	36.99 (2.38)	29.93 (4.94)
Gender, n (%)
Male	0 (0.0)	10 (47.6)	0 (0.0)	10 (30.3)
Female	6 (100.0)	11 (52.4)	6 (100.0)	23 (69.7)
Age, years, median (range)	76.00 (60.00-79.00)	73.00 (50.00-82.00)	61.50 (55.00-69.00)	72.00 (50.00-82.00)
HKAA, deg, mean (SD)	5.61 (5.85)	6.90 (4.21)	7.57 (3.69)	6.79 (4.35)
ASA, n (%)
1	0 (0.0)	1 (4.8)	0 (0.0)	1 (3.0)
2	4 (66.7)	8 (38.1)	5 (83.3)	17 (51.5)
3	2 (33.3)	11 (52.4)	1 (16.7)	14 (42.4)
4	0 (0.0)	1 (4.8)	0 (0.0)	1 (3.0)
CT, mm, mean (SD)
Intercondylar notch	0.31 (0.05)	0.33 (0.06)	0.45 (0.15)	0.35 (0.09)
Lateral	0.25 (0.04)	0.25 (0.06)	0.34 (0.16)	0.26 (0.09)
Medial	0.24 (0.08)	0.26 (0.07)	0.38 (0.14)	0.28 (0.10)

CT = cartilage thickness; HKAA = hip-knee-ankle angle. ASA = American Society of Anesthesiologists physical status classification system.

^aContinuous variables are expressed as mean (SD) and categorical data as counts (percentage).

Correlation of BMI and Cartilage Thickness

Multivariable linear regression was used to test the association of BMI and cartilage thickness, adjusting for age, gender, and HKAA.

In univariate analysis, medial cartilage thickness was higher in BMI group 3 (BMI > 35 kg/m²) compared with BMI groups 1 (BMI < 25 kg/m²) and 2 (BMI 25-35 kg/m²) (P = 0.033 and P = 0.013). After adjusting for age, gender, and HKAA, BMI group 3 (BMI > 35 kg/m²) compared with BMI group 1 (BMI < 25 kg/m²) was still positively associated with medial cartilage thickness ( Table 2 , Fig. 2 ). Intercondylar notch and lateral cartilage thickness showed a positive association and trend toward a positive association with BMI group 3 (P = 0.013 and P = 0.068), respectively, but not after adjusting (P = 0.692 and P = 0.692, respectively). Thus, these results indicate that patients with a higher BMI had thicker medial cartilage.

Table 2.

Results of Linear Regression with Medial Cartilage Thickness as the Dependent Variable. ^a

Variable	Crude Estimate	Adjusted Estimate	2.5%	97.5%	Adjusted P Value
Age	0.00	0.003	−0.002	0.007	0.437
BMI group 2	0.01	−0.018	−0.108	0.073	0.843
BMI group 3	0.13	0.165	0.050	0.281	0.041*
Gender (male)	0.02	0.066	−0.012	0.145	0.285
HKAA	0.00	0.001	−0.007	0.008	0.843

BMI = body mass index; HKAA = hip-knee-ankle angle.

^aThe model was adjusted for age, gender, and HKAA; 2.5% and 97.5% indicate 2.5% and 97.5% confidence interval.

Figure 2.

The relationship of body mass index (BMI) group and medial cartilage thickness as predicted by linear regression analysis, adjusted for age and gender. Patients in BMI group 3 had a significantly higher cartilage thickness on the medial femoral condyle.

FABP4 Level Is Associated with Cartilage Thickness Independent of BMI

Multivariable linear regression was used to test the association of ELISA biomarkers with cartilage thickness. We were interested to see if any of the blood biomarkers was associated with cartilage thickness. Several of the biomarkers measured in this study, are associated with BMI. In order to understand, if biomarkers are related to cartilage thickness independently, the model was adjusted for BMI. In addition, to account for the effects of gender, age and HKAA, the model was also adjusted for these covariates.

We found that the FABP4 level was associated with lateral cartilage thickness independent of age, gender, BMI, and HKAA ( Table 3 , Fig. 3 ). This relationship was not significant in the medial or intercondylar area (not shown). In our model, there was no association of cartilage thickness and HKAA ( Table 3 ). In this model, there was also a significant association of BMI group and lateral cartilage thickness ( Table 3 ).

Table 3.

Results of Linear Regression with Lateral Cartilage Thickness as the Dependent Variable.

Variable	Crude Estimate	Adjusted Estimate a	2.5%	97.5%	Adjusted P Value a
Age	0.00	0.002	−0.003	0.007	0.477
BMI group 2	−0.01	0.069	−0.023	0.162	0.308
BMI group 3	0.13	0.280	0.150	0.409	0.001**
FABP4	0.00	−0.006	−0.008	−0.003	0.001**
Gender (male)	−0.01	−0.022	−0.121	0.077	0.649
HKAA	0.00	0.003	−0.003	0.010	0.438

BMI = body mass index; FABP4 = fatty acid–binding protein 4; HKAA = hip-knee-ankle angle; ** = p-value < 0.01.

^aThe model was adjusted for age, gender, and HKAA; 2.5% and 97.5% indicate 2.5% and 97.5% confidence interval.

Figure 3.

The relationship of body mass index (BMI) group, fatty acid–binding protein 4 (FABP4) level, and lateral cartilage thickness (CT, lateral) as predicted by linear regression analysis, adjusted for age, gender, hip-knee-ankle angle (HKAA). Figure A shows that a higher BMI group is associated with higher lateral cartilage thickness. Figure B shows the linear regression line of lateral cartilage thickness, predicted by FABP4, holding all other model covariates constant. There is a negative correlation of lateral cartilage thickness and FABP4.

As expected from previous studies, in univariate analysis and after adjusting for age, gender and HKAA, FABP4 was associated with BMI group 3 (ANOVA: P = 0.002, Regression: β = 42.99, P < 0.001, respectively).

These results indicate that higher BMI was associated with greater cartilage thickness on the lateral and medial (see above) condyle. We also found that higher FABP4 levels were independently associated with lower cartilage thickness on the lateral condyle.

Other Markers Were Not Associated with BMI or Ultrasound Findings

None of the other markers (oxLDL, PLA2G2A, and sRAGE) was associated with cartilage thickness using multivariable regression (data not shown). Thus, these markers did not show a relationship with cartilage thickness and cannot be considered candidate biomarkers of burden of disease.

Discussion

The purpose of this study was to assess the relationship of obesity, serum biomarkers and sonographic cartilage thickness. We conducted a cross-sectional study and assessed associations using multivariable regression analysis. Our results indicate that patients with a higher BMI had a weak, positive association with cartilage thickness on the medial and lateral femoral condyle. In addition, we found that FABP4 was negatively associated with lateral cartilage thickness even after adjustment for BMI, gender, age, and HKAA.

The relationship of obesity and cartilage thickness or volume is complex and there are contradictory findings. Several MRI studies have detected no association of BMI and cartilage thickness or cartilage loss. However, in these studies there was a positive trend in multivariable analysis or a positive association in univariate analysis.^25-30 In children, there seems to be no obesity-dependent difference in cartilage volume. ³¹ Similarly, one study found a higher cartilage loss in KOA patients but not obese patients. ³² There is also evidence suggesting fat-free body mass and muscle mass or muscle strength, but not BMI, is positiviely associated with cartilage thickness, while the opposite might be true for fat mass.^29,30,33,34

Contrary to this, in one study, there was a positive association of BMI and cartilage thickness in the femoral groove. ³⁵ On the other hand, there are also studies that have found a BMI-dependent reduction of cartilage volume.^36,37

Other important factors that influence cartilage thickness are sex (males have higher cartilage volume), physical activity, and age.^33,38,39 Obesity and age also seem to impact gait and alter the positive relationship of cartilage thickness and ambulatory loads. ⁴⁰ Systemic inflammation also affects cartilage: High-sensitivity C-reactive protein levels (hsCRP) were higher in patients with lower cartilage thickness. ²⁶ Metabolic syndrome is a complex systemic disease that is associated with systemic low-grade inflammation, and other metabolic derangements, such as hyperglycemia, hyperlipidemia, and arterial hypertension. ⁵ Thus, not surprisingly, metabolic syndrome also affects cartilage volume significantly: In an MRI study, metabolic syndrome was associated with medial compartment cartilage loss even after adjustment for BMI and central obesity. ⁴¹

While there was no significant association of BMI and femoral cartilage thickness in a sonographic study comparing obese with nonobese patients, obese patients tended to have higher cartilage thickness values. ⁴² We found a significant, weakly positive association of obesity with cartilage thickness. The variations in literature above and our findings can be explained by differences in study portocol, site (femoral vs. tibial), technique of cartilage measurement, and imaging modality.

FABP4 is mainly expressed in adipocytes and acts as a lipid chaperon in intracellular fatty acid transportation. As a member of the FABP superfamily, it facilitates the transport of fatty acids to specific enzymes and cellular compartments. ⁴³ Several studies have shown that FABP4 plays an important role in the development of atherosclerosis, insulin resistance and other findings of metabolic syndrome. FABP4 deficiency is known to protect against atherosclerosis in apolipoprotein E–deficient mice. In this study, we could confirm that FABP4 is correlated with obesity, and expression is higher in females.^43-45 Pharmaceutical inhibition of FABP4 has been shown to improve insulin resistance, diabetes mellitus, and atherosclerosis in mice. ⁴⁶ Similarly, FABP4 knock-out or pharmaceutical inhibition mitigated cartilage degeneration by induced obesity in mouse models. ⁴⁷ A previous study suggested FABP4 as a promising biomarker as FABP4 levels are higher in KOA patients and are associated with obesity. ⁸ In this study, we not only found an association of FABP4 with obesity but also with cartilage thickness measured on ultrasound images. This further supports the role of FABP4 as a biomarker candidate for KOA burden of disease.

Limitations

This study has several limitations. Only a small sample size was enrolled in this study and because of the exploratory nature, no sample size calculation could be performed. Blood samples were taken only in a subpopulation due to resource limitations. Blood samples were taken at random times with unknown fasting status and this might influence ELISA results. Intake of sugar or high-fat meal can significantly reduce FABP4 levels consistently by 20% from baseline over a 4- to 6-hour period. ⁴⁸ It is also known that FABP4 levels are higher in obese patients and women.^43-45 While we cannot exclude changes of FABP4 levels due to meal intake, we believe this effect is negligible due to a consistent decrease over 4 to 6 hours and given the strong agreement (higher FABP4 levels in obesity and women) with previous studies. oxLDL levels are not altered by meal intake in overweight and obese patients. ⁴⁹ We are not aware of an effect of food intake on isoform PLA2G2A. sRAGE levels seem to be dynamically regulated postprandially, and we therefore might have missed a possible association. ⁵⁰

Furthermore, we also adjusted for age, gender, BMI, and HKAA, in multivariable analysis. However, we cannot exclude any confounding effects of covariates not observed in this study. Further research in a larger study population is necessary to confirm our findings. Our results are only applicable to end-stage KOA patients.

Conclusion

Our results indicate that BMI has a weak, positive association with cartilage thickness in end-stage KOA patients. FABP4 levels were negatively associated with cartilage thickness. While our study is limited by a small sample size, these results further highlight the role of FABP4 as a promising biomarker of KOA burden of disease.