Senescent preosteoclast secretome promotes metabolic syndrome associated osteoarthritis through cyclooxygenase 2

  1. Weiping Su
  2. Guanqiao Liu
  3. Bahram Mohajer
  4. Jiekang Wang
  5. Alena Shen
  6. Weixin Zhang
  7. Bin Liu
  8. Ali Guermazi
  9. Peisong Gao
  10. Xu Cao
  11. Shadpour Demehri  Is a corresponding author
  12. Mei Wan  Is a corresponding author
  1. Department of Orthopaedic Surgery, Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, United States
  2. Department of Orthopaedic Surgery, The Third Xiangya Hospital of Central South University, China
  3. Division of Orthopaedics & Traumatology, Department of Orthopaedics, Southern Medical University Nanfang Hospital, China
  4. Musculoskeletal Radiology, Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, United States
  5. University of Southern California, Dornsife College of Letters, Arts and Sciences, United States
  6. Department of Radiology, Boston University School of Medicine, United States
  7. Johns Hopkins Asthma & Allergy Center, Johns Hopkins University School of Medicine, United States

Abstract

Background:

Metabolic syndrome–associated osteoarthritis (MetS-OA) is a distinct osteoarthritis phenotype defined by the coexistence of MetS or its individual components. Despite the high prevalence of MetS-OA, its pathogenic mechanisms are unclear. The aim of this study was to determine the role of cellular senescence in the development of MetS-OA.

Methods:

Analysis of the human osteoarthritis initiative (OAI) dataset was conducted to investigate the MRI subchondral bone features of MetS-human OA participants. Joint phenotype and senescent cells were evaluated in two MetS-OA mouse models: high-fat diet (HFD)-challenged mice and STR/Ort mice. In addition, the molecular mechanisms by which preosteoclasts become senescent as well as how the senescent preosteoclasts impair subchondral bone microenvironment were characterized using in vitro preosteoclast culture system.

Results:

Humans and mice with MetS are more likely to develop osteoarthritis-related subchondral bone alterations than those without MetS. MetS-OA mice exhibited a rapid increase in joint subchondral bone plate and trabecular thickness before articular cartilage degeneration. Subchondral preosteoclasts undergo senescence at the pre- or early-osteoarthritis stage and acquire a unique secretome to stimulate osteoblast differentiation and inhibit osteoclast differentiation. Antagonizing preosteoclast senescence markedly mitigates pathological subchondral alterations and osteoarthritis progression in MetS-OA mice. At the molecular level, preosteoclast secretome activates COX2-PGE2, resulting in stimulated differentiation of osteoblast progenitors for subchondral bone formation. Administration of a selective COX2 inhibitor attenuated subchondral bone alteration and osteoarthritis progression in MetS-OA mice. Longitudinal analyses of the human Osteoarthritis Initiative (OAI) cohort dataset also revealed that COX2 inhibitor use, relative to non-selective nonsteroidal antiinflammatory drug use, is associated with less progression of osteoarthritis and subchondral bone marrow lesion worsening in participants with MetS-OA.

Conclusions:

Our findings suggest a central role of a senescent preosteoclast secretome-COX2/PGE2 axis in the pathogenesis of MetS-OA, in which selective COX2 inhibitors may have disease-modifying potential.

Funding:

This work was supported by the National Institutes of Health grant R01AG068226 and R01AG072090 to MW, R01AR079620 to SD, and P01AG066603 to XC.

Editor's evaluation

The manuscript presents novel findings that link, in mechanistic terms, metabolic syndrome with osteoarthritis. A central role of a senescent preosteoclast secretome-COX2/PGE2 axis has been established. The translational significance relates to the future use of selective COX2 inhibitors as disease-modifying agents in the osteoarthritis that accompanies metabolic syndrome.

https://doi.org/10.7554/eLife.79773.sa0

Introduction

During the past two decades, major advancements have been made in understanding the pathogenesis of osteoarthritis-the most common chronic articular disease associated with pain and disability. However, effective disease-modifying osteoarthritis therapies are still unavailable (Misra et al., 2015; Veronese et al., 2017; Chen et al., 2020a). The heterogeneous causes of osteoarthritis make developing such therapies challenging. Osteoarthritis has long been considered the consequence of a ‘wear and tear’ process that leads to cartilage degradation, which is initiated and/or accelerated by direct joint trauma and excessive mechanical overloading (Heijink et al., 2012; Zhang and Jordan, 2010). However, only 12% cases of symptomatic osteoarthritis are attributable to post-traumatic osteoarthritis (PTOA) of the hip, knee, or ankle (Brown et al., 2006), indicating that mechanisms other than biomechanical factors are involved in osteoarthritis development.

While the causal relationship between metabolic syndrome (MetS) and OA is still under debate, epidemiological and prospective clinical studies have clearly showed that osteoarthritis is strongly associated with metabolic diseases, including obesity, diabetes, dyslipidemia, and hypertension, which are the individual components of MetS (Collins et al., 2018; Francisco et al., 2018; Misra et al., 2019; Mohajer et al., 2021; Chen et al., 2020b). In particular, 59% of participants with osteoarthritis had MetS compared with 23% of the general population (Puenpatom and Victor, 2009). The middle-aged population with osteoarthritis has more than five times the risk of MetS compared with the age-matched population without osteoarthritis (Puenpatom and Victor, 2009). Participants with osteoarthritis have a higher prevalence of cardiovascular disease risk factors, including dyslipidemia, hypertension, and diabetes mellitus, independent of weight (Saleh et al., 2007; Haara et al., 2003; Cerhan et al., 1995; Philbin et al., 1996). Moreover, up to 81% of the elderly population have radiographic signs of hand osteoarthritis (Banks and Lindau, 2013), non-weight bearing joints closely associated with MetS. MetS-associated osteoarthritis (MetS-OA) is now considered a distinct osteoarthritis phenotype defined by the presence of MetS—both individual MetS components and MetS as a whole (Zhuo et al., 2012). Therefore, it is imperative to understand the pathogenic mechanisms for MetS-OA development and progression.

Rather than being a primarily cartilage-based disease, osteoarthritis involves changes in the subchondral bone microarchitecture that might precede articular cartilage damage (Walsh et al., 2010; Suri et al., 2007). Furthermore, increasing evidence suggests that pathological alterations in subchondral bone are not merely a secondary manifestation of osteoarthritis but are critical contributors to early osteoarthritis progression and its severity (Su et al., 2020; Mazur et al., 2019; Burr, 1998; Burr and Gallant, 2012; Findlay and Atkins, 2014; Muratovic et al., 2019). Subchondral bone includes trabecular bone and the subchondral bone plate, which is corticalized bone similar to that found in other locations. Human osteoarthritis studies using histological and imaging analyses showed that subchondral bone changes are generally characterized by increased bone sclerosis with thickening of the cortical plate, loss of subchondral trabecular rods with thickening of the remaining trabecular bone, alteration in subchondral bone three-dimensional morphology (Haj-Mirzaian et al., 2018), and formation of new bone at the joint margins (i.e. osteophytes) (Li et al., 2013). These changes were detected in late-stage human osteoarthritis. Subchondral bone changes during early-stage osteoarthritis remain unclear because of the lack of histological assessment of subchondral bone in human osteoarthritis. Animal studies have found increased osteoclast number and activity, with a high bone turnover rate in early-stage PTOA mice, rats, and rabbits (Kwan Tat et al., 2010; Zhen et al., 2013). As osteoarthritis progressed in PTOA mice, increased formation of osteroid islets and trabecular irregularity were observed (Kwan Tat et al., 2010). Studies of the subchondral bone changes in MetS-OA are limited, especially during the early stage of this distinct osteoarthritis phenotype.

Cellular senescence has been viewed as a series of diverse and dynamic cellular states with irreversible cell-cycle arrest and the senescence‐associated secretory phenotype (SASP) (Campisi, 2013; Coppé et al., 2010). Senescent cells (SnCs) exhibit stable cell-cycle arrest through the actions of tumor suppressors, such as Cdkn2a, Cdkn2b, Trp53, Cdkn1a (Campisi, 2013; McHugh and Gil, 2018). SnCs communicate with neighboring cells and influence the tissue microenvironment through SASP. Recently, it was found that SnCs increased in joint cartilage and synovium in mice after PTOA, and the selective elimination of SnCs led to attenuated osteoarthritis progression (Jeon et al., 2017). It remains unclear whether and how cellular senescence is involved in the pathogenesis of MetS-OA.

In the present study, we conducted analysis of the human osteoarthritis initiative (OAI) dataset to investigate the MRI subchondral bone features of MetS-human OA participants. We also characterized the joint phenotype of two MetS mouse models: high-fat diet (HFD)-challenged mice and STR/Ort mice. C57BL/6 mice fed a HFD are known to present key components of MetS relative to the mice fed a chow-food diet (CHD) (Gallou-Kabani et al., 2007) and to develop articular cartilage degeneration (Sansone et al., 2019). The STR/Ort mouse strain, an inbred substrain of STR/N mice (Staines et al., 2017), is a well-recognized model of spontaneous OA characterized by subchondral bone sclerosis, osteophyte formation, and articular cartilage degeneration. STR/Ort mice also develop hypercholesterolemia and hyperlipidemia (Staines et al., 2017; Mason et al., 2001), and therefore are a promising model for studying the pathogenic mechanisms of MetS-OA. We uncovered a unique, early, structural joint alteration of MetS-OA (i.e. subchondral bone thickening), which distinguishes MetS-OA from the joint changes of PTOA. We further elucidated a SASP-stimulated cyclooxygenase 2 (COX2)/prostaglandin E2 (PGE2) (COX2-PGE2) pathway that mediates the paracrine effect of senescent preosteoclasts on osteoblast lineage to promote subchondral bone formation. Finally, we examined the potential disease-modifying properties of selective COX2 inhibitors for MetS-OA in mice and humans.

Materials and methods

Mice and treatment

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All experimental procedures were approved by and conducted in accordance with the Institutional Animal Care and Use Committee guidelines of The Johns Hopkins University. C57BL/6 J mice (stock no. 000664), CBA/J mice (stock no. 000656), and ROSA26lsl-EYFP mice (stock no. 006148) were purchased from The Jackson Laboratory (Bar Harbor, Maine). STR/Ort mice were purchased from Harlan Laboratories (Frederick, MD). Cdkn2aflox/flox mice were generated by Dr. Gloria H. Su’s laboratory from the Department of Pathology, Columbia University Medical Center (Qiu et al., 2011). Cdkn2atdTom reporter mice (C57BL/6 background) were generated by Dr. Norman E. Sharpless’s laboratory from University of North Carolina School of Medicine (Chapel Hill, NC) (Liu, 2019). The Tnfrsf11aCre/+ mouse strain was generously provided by Yasuhiro Kobayashi (Matsumoto Dental University, Japan) (Maeda et al., 2012). Tnfrsf11aCre/+; ROSA26lsl-EYFP mice (RANK-EYFP mice) were generated by crossing Tnfrsf11aCre/+ with ROSA26lsl-EYFP mice. We crossed the Tnfrsf11aCre/+ mice with Cdkn2aflox/flox mice. The offspring were intercrossed to generate Tnfrsf11aCre/+; Cdkn2aflox/flox (p16cko) and Cdkn2aflox/flox (WT) mice. The genotypes of the mice were determined by PCR analyses of genomic DNA using the following primers: Tnfrsf11aCre/+ allele forward, 5′-GCAATCCCCAGAAATGCCAGATTAC-3′and reverse,5′-GCAAGAACCTGATGATGGACATGTTCAG-3′; Cdkn2aflox/flox allele (Note: Cdkn2a is the approved gene name for p16) P1, 5′-AGCAGCTTCTAATCCCAGCA-3′ P2, 5′-CCACTCCTGGAACTCAGCAT-3′ P3, 5′-AGGAGTCCTGGCCCTAGAAA-3′ and P4, 5′-CCAAAGGCAAACTTCTCAGC-3′; Cdkn2atdTom allele (Note: Cdkn2a is the approved gene name for p16) forward, 5′-ACCTCCCACAACGA-GGACTA-3′ and reverse, 5′-CTTGTACAGCTCGTCCATGC-3′; ROSA26lsl-EYFP allele forward, 5′-AGGGCGAGGAGCTGTTCA-3′ and reverse,5′-TGAAGTCGATGCCCTTCAG-3′.

Mice were housed in a 12 hr light/12 hr dark cycle with ad libitum water and food access. At 10–12 weeks of age, mice were placed on a Western HFD (21% fat by weight) (TD 88137, Harlan Laboratories, Madison, WI) or a normal CHD for periods ranging from 2 weeks to 5 months. At the time of euthenasia, body weight was measured. The knee joints and serum were collected. For celecoxib treatment, mice were gavage-fed celecoxib at a dose of 16 mg/kg b.w for 2 months.

Body composition and metabolic studies

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Whole-body fat and lean body mass were assessed by quantitative nuclear magnetic resonance (echo MRI), as previously described (Hu et al., 2020; Kim et al., 2019; Kim et al., 2017). Plasma triglycerides (Sigma-Aldrich, St. Louis, MO), cholesterol (BioAssay Systems, Hayward, CA), and glycerol (Sigma-Aldrich) were measured colorimetrically. Glucose levels were measured using a hand-held OneTouch Ultra glucose monitor (LifeScan Inc, Milpitas, CA).

MicroCT analysis

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MicroCT analysis of the tibial subchondral bone was performed as previously described (Su et al., 2020; Zhen et al., 2013). The knee joint was analyzed by μCT (voltage, 65 kVp; current, 153 μA; resolution, 9 μm/pixel) (Skyscan 1174, Bruker MicroCT, Kontich, Belgium). The parameters of the tibia subchondral bone image were analyzed using reconstruction software (NRecon v1.6, Bruker), data analysis software (CTAn v1.9, Bruker), and three-dimensional model visualization software (μCTVol v2.0, Bruker). Three-dimensional structural parameters analyzed were BV/TV, Tb.Pf, Tb.Th, Tb.N, and SBP.Th. Ten consecutive images from the whole subchondral bone medial compartment were used to do three-dimensional reconstruction and analysis.

Histology and Immunofluorescence staining

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Mouse knee joints were harvested after euthanasia. For frozen sections, the bones were fixed in 4% formaldehyde overnight, decalcified in 1.5 M EDTA (PH = 7.4) for 14 days, and embedded in optimal cutting temperature compound. We used 30-μm-thick sagittal-oriented sections for immunofluorescent staining using a standard protocol. We incubated the sections with primary antibodies to mouse VPP3 (1:100, Abcam, Cambridge, UK), HMGB1 (1:500, Novus Biologicals, Littleton, CO), lamin B1 (1:100, Santa Cruz Biotechnology, Dallas, TX), F4/80 (Abcam, 1:50), OCN (1:200, Takara Bio Inc, Shiga, Japan) overnight at 4 °C followed by corresponding fluorescence-linked secondary antibodies (Jackson ImmunoResearch Laboratories, West Grove, PA) for 1 hr while avoiding light. The sections were then co-stained with 4′,6-diamidino-2-phenylindole (H-1200, DAPI, Vector Laboratories, Burlingame, CA). The sample images were captured by a confocal microscope (Zeiss LSM 780). SA-βgal staining was conducted as previously described (Li et al., 2017; Liu et al., 2021), and the sample images were observed and captured by a microscope camera (DP71, Olympus BX51, Tokyo, Japan). For paraffin sections, bones were fixed in 4% formaldehyde overnight, decalcified in 1.5 M EDTA (PH = 7.4) for 21 days, and embedded in paraffin. We used 4-μm-thick sagittal-oriented sections for Safranin O (Sigma-Aldrich, S2255) and fast green staining (Sigma-Aldrich, 473 F7252). OARSI scores were calculated according to Safranin O–fast green staining. Osteoclasts were stained for TRAP. Osteoblasts were stained by OCN immunohistochemistry. Quantitative histomorphometry analyses were performed in a blinded fashion using OsteoMeasure Software (OsteoMetrics, Inc, Decatur, GA). Number of osteoblasts per bone marrow area (Ob.N/Bm. Ar.), osteocytes per bone area (Osteocytes N/B. Ar.), number of osteoclasts per bone perimeter (Oc.N/B. Pm) in the whole joint subchondral bone area per specimen. For each treatment group, 5–10 mice were used. For each sample, 3 tissue sections were used, and the whole joint subchondral bone area was analyzed.

Cytokine array analysis of subchondral bone/bone marrow extracts

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Knee joints from the mice were dissected, and tibia subchondral bone tissue was homogenized in lysis buffer containing 1% Triton X-100 and protease inhibitors. Protein extracts were collected and protein concentration were measured. The antibody array for secreted factors was performed using Mouse XL Cytokine Array Kit (ARY028, R&D Systems, Minneapolis, MN) according to the manufacturer’s instructions.

Quantitative real-time PCR

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Total RNA for qRT-PCR was extracted from the cultured cells using RNeasy Mini Kit (74104, QIAGEN, Hilden, Germany) according to the manufacturer’s protocol. For qRT-PCR, cDNA was prepared with random primers using the SuperScript First-Strand Synthesis System (Invitrogen, Waltham, MA). Then qRT-PCR was performed with SYBR Green Master Mix (QIAGEN) using C1000 Thermal Cycler (Bio-Rad Laboratories, Hercules, CA). Relative expression was calculated by the 2−CT method with GAPDH for normalization.

CFU-F, CFU-Ob, and in vitro differentiation assays of mouse BMSCs

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For CFU-F assays, freshly isolated single-cell suspensions from the long bone of 12-week-old male mice were plated in 6-well plates with a density of 5×105 cells per well. CFU-F colonies were counted after 10 days of culture with Crystal violet staining. We measured the colonies that had 50 cells or more. For CFU-Ob assays, freshly isolated bone marrow single-cell suspensions were seeded at a density of 5×105 cells per well in six-well plates. After 21 days of culture with StemPro Osteogenesis Differentiation Kits (Invitrogen), osteogenic differentiation was detected by Alizarin red staining. The colony-forming efficiency was determined by counting the number of colonies per 5×105 marrow cells plated.

Generation of senescent preosteoclasts and preparation of CM

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Preosteoclasts were generated in vitro as previously described (Xie et al., 2014). Briefly, monocytes/macrophages were isolated from the bone marrow of 3-month-old WT male mice by flushing cells from the bone marrow of femora and tibiae. The flushed bone marrow cells were cultured overnight on Petri dishes in α-MEM containing 10% fetal bovine serum, 1% penicillin-streptomycin solution, 40 ng/mL M-CSF (Amizona Scientific LLC, AM10003-600). The cells were then incubated with osteoclastogenesis medium, containing αMEM with 10% fetal bovine serum, 1% penicillin-streptomycin solution, 40 ng/mL M-CSF and 60 ng/mL RANKL (Amizona Scientific LLC, AM10003-500) for 3 days, when most cells became mononuclear TRAP+ preosteoclasts (Xie et al., 2014). The formation of preosteoclasts was validated using TRAP staining (Sigma-Aldrich, 837) according to the manufacturer’s protocol. The cells were then challenged with 80 μg/mL oxLDL (Alfa Aesar, Cat# J64164) or vehicle (control) for 24 hr. Cellular senescence was confirmed using SA-βgal staining, Lamin B1 immunocytochemical staining, and qRT-PCR of Cdkn2a, Cdkn1a, Mki67 in the cells. CM was prepared by incubating the cells with serum-free medium for another 48 hr. After centrifugation (2500 rpm for 10 min at 4 °C), the CM was aliquoted and stored at –80 °C for different in vitro assays.

RNA-sequencing analysis

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Preosteoclasts were challenged with H2O2 (200 µM for 2 hr, then 20 µM for 1 day) or vehicle (control). Total RNA of pre-osteoclasts was isolated using TRIzol (Life Technologies, USA). RNA-Seq library construction and RNA high-throughput sequencing were performed. We analyzed aging/senescence-induced genes (ASIGs) from publicly available mouse RNA-seq data (Aging Atlas database; KEGG pathway database; GO database; MSigD database). GO, KEGG pathway, GSEA analysis were analyzed based on the comparison of our data with ASIGs.

Statistical analysis of animal studies and in vitro cell culture studies

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Data are presented as means ± standard errors of the mean. Unpaired, two-tailed Student t-tests were used for comparisons between 2 groups. For multiple comparisons, one-way analysis of variance with post hoc Tukey test was used. All data were normally distributed and had similar variation between groups. Statistical analysis was performed using SAS, version 9.3, software (SAS Institute Inc, Cary, NC). p<0.05 was deemed significant. All representative images of bones or cells were selected from at least three independent experiments with similar results unless indicated differently in the figure legend.

Human participant selection criteria

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We selected OAI participants according to tailored stepwise criteria. (Supplementary file 1D) Knees with previous joint replacement surgery as shown in baseline radiographs were excluded (N=64, Exclusion #1, Supplementary Flowchart 1). From three OAI cohorts of knee osteoarthritis incidence, progression, and cohort non-exposed to osteoarthritis risk factors, we excluded participants from the non-exposed cohort of OAI (N=233, Exclusion #2, Supplementary file 1D) because of minimal risk of osteoarthritis incidence and progression. In order to assess OA-related subchondral BML damage, we included participants with available BML MOAKS scoring on the baseline and follow-up MRIs. We collected and pooled all previously conducted MRI-based measurements of participants from nested ancillary studies performed inside OAI. These studies' design and selection criteria are specially tailored to assess MRI-based OA structural damage worsening in a specific subset from all OAI participants (details of these studies are explained in the OAI online repository) (Overview and Description of Central Image Assessments, 2016). Following deletion of duplicate measurements (753 cases between different projects), MRI Osteoarthritis Knee Score (MOAKS) measurements for 1671 knees were included from the following OAI ancillary studies: (1) Foundation for the National Institute of Health (FNIH) Consortium Osteoarthritis Biomarkers Project (473 knees, project no. 22), (2) project no. 30 (125 knees) (3) projects no. 63 A-63F (328 knees) (4) Pivotal OAI MRI Analyses (POMA) study (751 knees). The same OAI team centrally performed all measurements according to the validated semi-quantitative MOAKS (Hunter et al., 2011). Participants without available MRI reads were excluded (N=7614, Exclusion #3, Supplementary file 1D). To further homogenize our selection criteria and delineate the role of subchondral bone damage worsening in the MetS-OA, we included participants with MetS-OA without a history of knee trauma (to exclude PTOA cases) versus participants with PTOA without MetS (to exclude MetS-OA cases). MetS-OA participants with a history of knee trauma and PTOA participants with MetS were excluded (N=1041, Exclusion #4, Supplementary file 1D). These selection criteria are specifically aimed to investigate the unique pathophysiology (e.g. prominent subchondral bone damage) in the MetS-OA phenotype compared to PTOA, an OA phenotype with the central role of cartilage degeneration.

Assessment of human knee osteoarthritis outcomes using Osteoarthritis Initiative (OAI) dataset

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We conducted observational studies by analyzing the human OAI dataset, which consists of annual clinical and radiographic data for the 9,572 knees of 4796 participants’ (Peterfy et al., 2008) with a follow-up of 8 years at annual time points (2004–2015, clinicaltrials.gov identifier: NCT00080171, https://nda.nih.gov/oai/). We selected 1671 participants who had available MRI Osteoarthritis Knee Score (MOAKS) scoring from OAI ancillary studies. We defined MetS presence and its components according to the International Diabetes Federation (IDF) criteria (Alberti et al., 2006). A history of the knee was assessed with the answer to the question "have you ever injured your knee badly enough to limit your ability to walk for at least two days?" and participants with a positive history of knee injury were considered as having PTOA. We further used a detailed selection criteria and propensity score (PS)-matching method (Haj-Mirzaian et al., 2019; D’Agostino, 1998).

Definition of PTOA phenotype

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Knees of OAI participants who responded positive to the question "ever injured badly enough your knee to limit the ability to walk for at least two days?" were regarded as having PTOA phenotype. To further homogenize selection criteria, we excluded participants with PTOA who had criteria for MetS (Exclusion #3, Supplementary file 1D).

Definition of MetS-OA phenotype

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No unified criteria for the definition of MetS exist; therefore, we defined MetS presence and components at the baseline visit according to the most widely accepted criteria defined by the IDF: (1) Hypertension was defined as systolic blood pressure (BP) of ≥130 mm Hg or diastolic BP of ≥85 mm Hg at baseline physical examination or as the use of BP-lowering medication indicated in the participants’ medication inventory form. (2) Diabetes mellitus was indicated by self-report or the presence of oral or injectable anti-diabetic medications on the medication inventory form. (3) Dyslipidemia was defined as the use of lipid-lowering medications indicated in the participants’ medication inventory form at baseline. (4) Abdominal obesity was defined as a waist circumference of ≥94 cm in men and ≥80 cm in women on physical examination. Participants with abdominal obesity and at least two of the three other components (dyslipidemia, diabetes mellitus, hypertension) were regarded as having MetS. Participants with MetS were regarded as MetS-OA phenotype, whereas participants who did not meet the IDF criteria for MetS were not. Participants with missing medication data on medication inventory forms were excluded (4 knees).

Definition of COX2 inhibitor and non-selective NSAID use

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At the baseline visit, participants who reported regular COX2 inhibitor use on the OAI medication inventory forms were categorized as COX2 inhibitor users, and the remaining participants were categorized as non-users. Among COX2 inhibitor users, participants with a≤1 year history of regular COX2 inhibitor use before the baseline time point were categorized as incident users (initiators). Participants with >1 year of regular COX2 inhibitor use were regarded as prevalent users and were excluded from the study (Johnson et al., 2013). Prevalent users of COX2 inhibitors were excluded from the study (150 knees), and only COX2 inhibitor incident users were included because the differences in duration of COX2 inhibitor use before participation in the study (1 month to 10 years) can lead to ‘Neyman’ bias (Danaei et al., 2012), in which very sick and very well participants are excluded from enrollment (Danaei et al., 2012). It is possible that chronic and long-lasting conditions associated with COX2 inhibitor use led to rapid deterioration in knee osteoarthritis, which resulted in exclusion from OAI enrollment. Use of cohort studies and new-exposure (i.e. ‘incident use’ of COX2 inhibitor) designs are recommended to avoid this bias (Johnson et al., 2013; Danaei et al., 2012; Ray, 2003).

Treatment groups, per-protocol follow-up design

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To assess COX2 inhibitor use, at each visit, participants reporting regular use of COX2 inhibitor during the last 12 months were coded as COX2 inhibitor users for that year, whereas negative reports of COX2 inhibitor use at each annual visit were considered as non-users for that year. Included participants were followed annually for 8 years (median, 6 years; interquartile range, 5 years). The same approach was used to assess non-selective NSAID use. Using propensity-score- (PS) matching method, COX2 inhibitor user were matched with non-selective NSAID users considering potential confounders. Following a ‘per-protocol’ design in our longitudinal analysis, which is associated with less selection bias in observational designs than in ‘intention to treat’ design (Danaei et al., 2013), participants who did not adhere to their assigned treatment (COX2 inhibitor vs. non-selective NSAID use) in each visit were right-censored from that visit (Danaei et al., 2013). Details of participant selection, PS-matching, and per-protocol design is explained in detail in Materials and methods section.

Imputation

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The pattern of missing data in covariates used in PS matching was assessed using the test of missing completely at random (Little’s test), visual representation, and logistic regression models, which resulted in a ‘missing not at random’ pattern (Sterne et al., 2009) in the OAI dataset, with <1.5% of values missing for all matching variables. Despite the ‘missing not at random’ pattern of data, multiple imputation models were used according to previous studies to try to reduce possible bias (Resseguier et al., 2011).

PS matching method

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We matched study groups according to baseline characteristics to minimize confounding by indication bias. We first used PS matching to assess the presence of MetS on knee osteoarthritis outcomes. Next, we separately used PS matching to select participants for the two assessments of knee osteoarthritis and subchondral BML worsening outcomes in this study (1) comparison between MetS-OA and PTOA (2) comparison between COX2 inhibitor users and non-selective NSAID users. Possible confounders were investigated using a directed acyclic graph to assess causal inference (Schisterman et al., 2009). The selected covariates for PS matching were age (years), sex (male/female), body mass index (categorized in pentiles for ease of matching of kg/m2), and race (Caucasian or non-Caucasian). For assessment of MetS presence, smoking (current smoker, yes/no), use of alcohol (<1 or≥1 unit/week), Physical Activity Score for the Elderly (PASE), baseline KL grade, and medial JSN OARSI grade were also included as covariates. Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain score and history of knee trauma were also included as covariates in the PS-matching model for COX2 inhibitor use. For every knee in the exposure group (presence of MetS or COX2 inhibitor use), one best-matched knee in the non-exposure group (PTOA or non-selective NSAID users, respectively) was selected according to the confounding above variables (1:1 match). We used the nearest-neighbor method with a caliper distance of 0.2 on the imputed dataset (using multiple imputation models, all variables had <1.5% missing). The best matches were defined as knees with the highest-level match on their PS, calculated using logistic regression. We calculated standardized mean difference (SMD) to confirm the balanced outcome of matching between groups. Variables that did not optimally match in the PS matching method (SMD ≥0.1, baseline KL and JSN grades in the PS-matching for MetS vs. PTOA participants) were included in all subsequent statistical models as covariates of adjustment.

Assessment of standard osteoarthritis outcomes

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Longitudinal risk of knee osteoarthritis outcomes was assessed. Radiographic osteoarthritis incidence was defined as a Kellgren-Lawrence (KL) grade of ≥2 in follow-up assessment of knees with KL grades <2 at baseline (Felson et al., 2011). Progression was defined as an increase of 1 grade or more in medial Osteoarthritis Research Society International (OARSI) medial joint space narrowing (JSN) score ≥1 during follow-up assessments (Felson et al., 2011; Reijman et al., 2007). Knee radiographs were read centrally, and all KL grading and medial JSN OARSI scorings at baseline and follow-up points (1–8 years) are publicly available. For symptom assessment, participants’ self-reported pain was assessed using Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores for pain and disability at baseline and follow-up points (1–9 years). WOMAC pain and disability scores were standardized to a range of 0–100 and were summed, resulting in a standardized combined WOMAC score. A standardized combined WOMAC pain/disability score of ≥80 in 2 consecutive years was used as the definition for non-acceptable symptom state (Tubach et al., 2005; Gandek, 2015; Angst et al., 2005) incidence. Time of standard osteoarthritis outcomes in the study was defined as the earliest year with each outcome (Christensen, 1987). For participants with no event, the last available follow-up was used as the time in the study (Christensen, 1987). We adjusted all analyses for participants’ PS to minimize ‘confounding by indication’.

Assessment of subchondral BML structural damage worsening

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Three Tesla MRI systems (Trio, Siemens Healthcare) were used for OAI MRI acquisition. Parameters and pulse sequence protocol of OAI MRIs have been previously reported (Hunter et al., 2011). For comparison of MetS-OA and PTOA participants (i.e. MetS-OA+(PTOA) versus PTOA+(MetS-OA)), we used available data on MOAKS scorings of the OAI ancillary studies. These include the available 1671 MOAKS measurements for the knees that we pooled from all previously conducted from nested ancillary studies performed inside OAI to assess OA-related subchondral bone damage (Hunter et al., 2011) [explained in detail in Materials and methods section and the OAI online repository (Overview and Description of Central Image Assessments, 2016)]. Next, to compare BML worsening between COX-2 inhibitor users and non-selective NSAID users, MOAKS measurements of the included participants were not available on OAI ancillary studies. Therefore, a musculoskeletal radiologist with 12 years of experience (S.D.) read and scored baseline and 24-month follow-up T2 knee MRIs, using the same validated MOAKS scoring method (Hunter et al., 2011). The reader was blind to the participants' group (COX2 inhibitor users versus non-selective NSAID users). A 24-month BML score worsening was defined as a whole- or within-grade change, where within-grade was defined as a definite visual change while not fulfilling a whole-grade change definition. Previously validated MOAKS BML measures (Collins et al., 2016) were used: (1) worsening in the number of affected subregions with BML (improvement, no change, worsening in 1 subregion, and worsening in ≥2 subregions), (2) maximum worsening in BML score (no change, within-grade worsening, worsening by 1 grade, and by ≥2 grades), (3) improvement in the number of affected subregions (yes/no), and (4) improvement in subregions BML score (yes/no) (Collins et al., 2016).

Statistics

We used Cox proportional hazards regression to determine associations between either MetS presence or COX2 inhibitor use with outcomes. HRs and 95% CIs are reported. All modeling was conducted using complex sample analysis, in which matched users/non-users, and same-participant knees were included in the specific clusters with equal weight. Logistic mixed-effect regression models were used to assess 24 month worsening in the BML MOAKS scores while considering random intercept for each cluster of matched COX2 inhibitor: non-selective NSAID user and within-subject similarities (due to the inclusion of both knees of included knees). Analyses were performed using the R platform (version 4.3.3). Two-tailed p-values <0.05 were considered significant.

Study approval

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The animal protocol (MO20M127) was reviewed and approved by the Institutional Animal Care and Use Committee of The Johns Hopkins University. We used data from the longitudinal multi-center OAI study (2004–2015 clinicaltrials.gov identifier: NCT00080171). All 4796 enrolled patients gave written informed consent. Institutional review boards of four OAI collaborating centers have approved the OAI study’s Health Insurance Portability and Accountability Act-compliant protocol (approval number: FWA00000068).

Results

Human participants with MetS-OA have a higher risk of subchondral bone marrow lesion worsening compared with those with PTOA

To delineate the specific pathophysiology of MetS-OA and further similarize our study sample with non-traumatic animal model models of MetS-OA, we excluded participants with MetS-OA who had a history of knee trauma (MetS+ PTOA), and we selected participants with PTOA and without MetS as the control group (PTOA+ MetS). A total of 630 knees met the inclusion criteria (explained in detail in Materials and methods section). After 1:1 PS matching, 338 matched knees were included, of which 169 were MetS-OA and 169 were PTOA. MetS-OA participants had a mean ± standard deviation (SD) age of 63.7±8.0 years and were 59.8% female. Similarly, PTOA participants had a mean ± SD age of 63.4±8.3 years and were 56.2% female. Supplementary file 1A shows the baseline characteristics of MetS+ and PTOA participants before and after matching. There was no imbalance in either of the potential confounding variables included in the matching between MetS-OA and PTOA groups (standardized mean difference or SMD <0.1) except for their baseline KL and JSN grades (SMDs of 0.159 and 0.132) which both were included as covariates of adjustment in all further statistical analyses. The mean follow-up duration for standard knee OA outcomes assessment was 6.9 years (Median and 1st and 3rd quartiles of 8 years). Participants with MetS-OA, had similar risk of radiographic osteoarthritis incidence (radiographic hazard ratio [HR], 0.89; 95% CI, 0.56–1.41, and symptomatic HR, 0.95; 95% CI, 0.54–1.67) and progression (assessed by OARSI JSN, HR, 1.04; 95% confidence interval (95% CI), 0.73–1.49) compared to matched PTOA participants (Table 1). However, despite the similar risk of knee OA incidence and radiographic progression, participants with MetS-OA had increased odds of 24 month worsening in subchondral bone marrow lesions (BML) damage compared to participants with PTOA. This finding was evident as the higher odds of increased knee joint subregions with subchondral BMLs (odds ratio [OR], 1.37; 95% CI, 1.06–1.77). Despite MetS-OA participants display a similar risk of knee OA incidence and progression compared to PTOA participants, they have more subchondral bone damage worsening compared to the matched PTOA participants. Therefore, MetS-OA participants have unique subchondral bone damage worsening, distinguishable from that of PTOA.

Table 1
Longitudinal comparison of the standard knee OA outcomes and subchondral BML worsening between human participants with MetS-OA and their matched PTOA participants (MetS+ PTOA versus PTOA+ MetS).
PS-matched MetS-OA versus PTOA participants
Knee OA standard outcomesHazard ratio (95% Confidence Interval), p-value, Sample size, Number of events* [MetS-OA: PTOA]
Knee OA incidence0.89 (0.56–1.41), p:0.609, N:184 (92:92), Event [34:36]
Knee OA progression1.04 (0.73–1.49), p:0.822, N:316 (158:158), Event [65:62]
Symptomatic incidence (NASS)0.95 (0.54–1.67), p:0.859, N:338 (169:169), Event [28:30]
Subchondral BML Worsening (MOAKS)Odds ratio (95% Confidence Interval), P-value, N:338 (169:169), Number of events* [MetS-OA: PTOA]
Worsening in number of affected subregions with BML1.37 (1.06–1.77), p:0.015
Improvement, [16:31]
No change, [92:84]
Worsening, [61:54]
Worsening in the number of affected subregions0.44 (0.22–0.87), p:0.018
Yes, [31:16]
Maximum worsening in BML score1.17 (0.85–1.6), p:0.337
No change, [83:91]
Worsening by ≤1 grade, [59:54] by ≥2 grades, [27:24]
Improvement in the subregions' BML score0.82 (0.51–1.30), p:0.389
Yes, [90:98]
  1. Standard OA outcomes (baseline to 8th year) and validated MOAKS measures of subchondral BML worsening (between baseline and 24 month visit) were assessed between knees of participants with MetS-OA and their PS-matched knees of participants with PTOA. Cox proportional hazards were used for standard OA outcomes, and participants had a mean follow-up duration of 6.9 years (median and 1st and 3rd quartiles of 8 years). and logistic mixed-effect regression models were used for subchondral BML assessments. Knees of participants were matched for confounders using the 1:1 PS matching method. All analyses were adjusted for the baseline Kellgren-Lawrence (KL) and Osteoarthritis Research Society International medial joint space narrowing (OARSI JSN) grades of knees. Standard OA outcomes included knee OA incidence defined by KL grade ≥2 in participants with KL equal to 0–1, knee OA progression defined by partial or whole grade progression in OARSI JSN grade, and knee OA symptomatic incidence measured by NASS. Subchondral BML worsening was assessed using standard MOAKS measures. N corresponds to the total number of knees included in each analysis and the number of matched knees of MetS-OA and PTOA participants in the parenthesis.

  2. *

    Number of events for each outcome has been shown separately for participants with MetS-OA and PTOA in the brackets.

  3. BML: Bone marrow lesion, COX2I: Cyclooxygenase 2 inhibitor, MetS: metabolic syndrome, MOAKS: MRI Osteoarthritis Knee Score, NASS: non-acceptable symptomatic state, PS: propensity score, OA: osteoarthritis.

MetS mice develop rapid osteoarthritis-related subchondral bone changes in knee joints

To investigate subchondral bone changes during the progression of MetS-OA, we studied two MetS mouse models: high-fat diet (HFD)-challenged and STR/Ort mice. Mice fed a HFD for 3 months had higher body weight, body fat mass, and serum glucose relative to CHD mice (Figure 1—figure supplement 1A-D). Serum lipid oxidation products assessed by malondialdehyde (MDA) level were also markedly higher in the HFD mice than in the CHD mice (Figure 1—figure supplement 1E). Therefore, HFD mice developed key components of MetS. We investigated the changes in osteoarthritic joints of HFD mice. Three-month-old mice were placed on a CHD or HFD for various periods, and subchondral bone changes were assessed. Mice fed a HFD for 3 and 5 months had different degrees of cartilage degeneration, indicated by proteoglycan loss and OARSI scoring (Figure 1A and B). Cartilage degeneration was not found in mice fed a HFD for 0.5 month or 1 month. Notably, three-dimensional micro-computed tomography (μCT) analysis showed a high subchondral bone mass phenotype in HFD-challenged male mice (Figure 1C and Figure 1—figure supplement 2). Tibial subchondral bone volume (BV)/tissue volume (TV) ratio, subchondral bone plate thickness (SBP.Th), and subchondral trabecular bone thickness (Tb.Th) increased dramatically in mice fed a HFD (vs. CHD) for all time periods tested (Figure 1D-F), indicating an early and severe thickening of the subchondral plate and trabecular bone. Subchondral trabecular number (Tb.N) was reduced in mice fed a HFD for 1, 3, or 5 months (Figure 1G), likely because of the fusion of the trabeculae. HFD-challenged female mice had a same high subchondral bone mass phenotype (Figure 1—figure supplement 3A), with increased BV/TV, SBP.Th, and Tb.Th but decreased Tb.N (Figure 1—figure supplement 3B-E). Moreover, osteophyte formation, another characteristic of osteoarthritis (Dieppe and Lohmander, 2005), was also identified in mice fed a HFD for 5 months (Figure 1—figure supplement 2). However, unlike PTOA mice that have apparent trabecular bone irregularity, as indicated by an increase in trabecular pattern factor (Tb.Pf) (Su et al., 2020; Zhen et al., 2013), HFD mice had similar subchondral Tb.Pf relative to CHD mice (Figure 1H). Thus, HFD-challenged mice exhibited a unique subchondral bone phenotype distinguishable from that of PTOA mice.

Figure 1 with 4 supplements see all
High-fat-diet (HFD) challenge leads to rapid subchondral bone thickening before cartilage damage occurs.

Three-month-old C57BL/6 mice were fed a standard chow-food diet (CHD) or HFD for 0.5, 1, 3, or 5 months. n=6 mice per group. (A) Safranin O-fast green staining of the tibia subchondral bone medial compartment (sagittal view). Scale bar, 200 μm. (B) Calculation of Osteoarthritis Research Society International (OARSI) scores. (C–H) Three-dimensional micro-computed tomography (μCT) images (C) and quantitative analysis of structural parameters of knee joint subchondral bone: bone volume/tissue volume (BV/TV, %) (D), subchondral bone plate thickness (SBP. Th, mm) (E), trabecular thickness (Tb.Th, mm) (F), trabecular number (Tb.N, mm–1) (G), and trabecular pattern factor (Tb. Pf, mm–1) (H). All data are shown as means ± standard deviations. *p<0.05, **p<0.01, and ***p<0.001. Statistical significance was determined by unpaired, two-tailed Student’s t-test.

We also investigated the joint changes in STR/Ort mice, which have been shown to develop osteoarthritis spontaneously early in life with concomitant hypercholesterolemia and hyperlipidemia (Staines et al., 2017; Mason et al., 2001). Male STR/Ort and CBA control mice were used because male mice are known to have a higher incidence of OA than female mice (Staines et al., 2017). Higher body weight (Figure 1—figure supplement 4A) and greatly increased serum triglyceride and cholesterol levels were detected in STR/Ort mice relative to those of CBA control mice (Figure 1—figure supplement 4B, C). Significant proteoglycan loss in joint cartilage (Figure 1—figure supplement 4D) and increased OARSI scores (Figure 1—figure supplement 4E) were detected only in 4-month-old mice but not in 2-month-old STR/Ort mice. However, an increase in subchondral bone mass was detected in STR/Ort mice as early as 2 months of age, with higher BV/TV ratio (Figure 1—figure supplement 4F), SBP.Th (Figure 1—figure supplement 4G), and Tb.Th (Figure 1—figure supplement 4), as well as unchanged Tb.N (Figure 1—figure supplement 4J). Collectively, the MetS-OA mice, similar to our findings in human MetS-OA participants, developed progressive subchondral bone damage and, importantly, exhibited rapid increases in subchondral bone plate and trabecular thickness before the occurrence of cartilage degeneration.

Osteoblast and osteoclast lineage cells in subchondral bone change rapidly in response to HFD challenge

We then attempted to elucidate the cellular changes that underlie subchondral bone thickening in MetS. Immunostaining analysis showed that osteocalcin (OCN)+ osteoblasts were localized primarily on the subchondral bone surface in mice fed a CHD (Figure 2A). Although OCN+ cell number per bone surface was unchanged in HFD mice relative to CHD mice (Figure 2B), OCN+ cell number per bone marrow area markedly increased and formed clusters (Figure 2A and C) in mice at 0.5 month and 1 month after a HFD challenge. The results suggest that osteoblasts are located on subchondral bone surface in normal healthy joints but aberrantly accumulate in bone marrow cavity in the joints of MetS-OA mice. A higher number of osteocytes in subchondral bone was also detected in HFD mice relative to CHD mice (Figure 2D). Tartrate-resistant acid phosphatase (TRAP) staining showed reduced bone surface TRAP+ osteoclasts in HFD mice relative to CHD mice (Figure 2E and F). Consistently, HFD challenge induced a significant decrease in VPP3+ osteoclasts on the bone surface (Figure 2G and H). We studied Tnfrsf11aCre/+; ROSA26lsl-EYFP (RANK-EYFP) mice, in which RANK+ cells and their progeny are labeled with EYFP. Although the EYFP+ cells on the bone surface were reduced (Figure 2I and J), the EYFP+ cell number within the bone marrow cavity were elevated in HFD mice compared to those in CHD mice (Figure 2I and K). These results suggest that bone marrow RANK+ preosteoclasts may be unable to mature into bone surface osteoclasts.

Subchondral osteoblast and osteoclast lineage cells change rapidly in response to a high-fat-diet (HFD) challenge.

(A–E) Three-month-old C57BL/6 mice were fed a standard chow-food diet (CHD) or HFD for 0.5 month or 1 month. n=6 mice per group. Immunohistochemical staining of knee joint tissue sections with antibody against osteocalcin (OCN) (A). Quantification of OCN+ cells within bone marrow (BM) cavity (B) and on bone surface (BS) (C). Calculation of the number of osteocytes embedded in bone matrix (D). Scale bar, 100 μm(up), 50 μm(down). TRAP staining (E) and quantification of TRAP+ cells at the bone surface (F). Scale bar, 100 μm(up), 50 μm(down). (G–K) Three-month-old Tnfrsf11aCre/+; ROSA26lsl-EYFP mice were fed with CHD or HFD for 1 month. Immunofluorescence staining of knee joint tissue sections with antibody against VPP3 (red) (G). Quantification of the number of VPP3+ cells at the bone surface (H). Scale bar, 100 μm. Immunofluorescence staining of knee joint tissue sections with antibody against GFP (green) (I). Quantification of the number of EYFP+ cells at the bone surface (J) or in the BM area (K). Scale bar, 100 μm. Ar, area; BM., bone marrow. B.S., bone surface. All data are shown as means ± standard deviations. *p<0.05, **p<0.01, and ***p<0.001. Statistical significance was determined by unpaired, two-tailed Student’s t-test.

Subchondral preosteoclasts exhibit senescence-like phenotype in MetS mice

We reasoned that the reserved preosteoclasts in subchondral bone may undergo cellular senescence, causing an inability to migrate to the bone surface and fuse together into mature osteoclasts. We investigated SnCs in the joints of a senescence reporter transgenic mouse line, p16tdTom (Cdkn2a is the approved gene name for p16 protein, and Cdkn2atdTom is used hereafter). Intriguingly, 1-month and 3-month HFD challenge led to the accumulation of tdTom+ SnCs exclusively in the subchondral bone (Figure 3A and C). tdTom+ SnCs were not seen in articular cartilage until later 5 months after HFD challenge (Figure 3A and B) when significant cartilage degeneration occurs (Figure 1A, B). Detection of other cellular senescence markers showed consistently increased SA-βGal+ cell number at the subchondral bone/bone marrow (Figure 3D and E) but not in articular cartilage in mice fed a HFD for 1 month or 3 months (Figure 3D and F). Conversely, fewer SA-βGal+ cells were found in the same region of CHD mice. We also performed immunostaining for HMGB1, the redistribution of which from nucleus to extracellular as a secretory protein initiates the process of cellular senescence (Davalos et al., 2013). Although most cells had strong nuclear staining of HMGB1 in CHD mice, many cells lost nuclear HMGB1 in the subchondral bone of the mice fed a HFD for 1 month or 3 months (Figure 3—figure supplement 1A B). Downregulation/loss of Lamin B1 has been recognized as a biomarker and crucial step for the development of cellular senescence (Freund et al., 2012). Unlike the well-preserved Lamin B1 expression in the cell membrane of CHD mice, many bone marrow cells lost Lamin B1 expression in the subchondral bone of HFD mice (Figure 3—figure supplement 1C D). We also investigated cellular senescence in the subchondral bone of STR/Ort mice. Similar to the HFD-challenged mice, the number of SA-βGal+ cells in the subchondral bone marrow was higher in STR/Ort mice relative to CBA control mice at 2 months of age, when joint cartilage degeneration was not yet developed (Figure 3—figure supplement 2A B). Consistently, more subchondral bone marrow cells lost HMGB1 expression in STR/Ort mice relative to CBA mice (Figure 3—figure supplement 2C D), indicating that cellular senescence occurs at the pre- or early-osteoarthritis stage.

Figure 3 with 2 supplements see all
Senescent cells accumulate in subchondral bone of high-fat-diet (HFD)-challenged mice.

(A–B) Three-month-old Cdkn2atdTom mice were fed a standard chow-food diet (CHD) or HFD for different time periods as indicated, n=6 mice per group. Fluorescence images showing tdTom+ cells (red) (A) with quantification of the number of tdTom+ cells at articular cartilage (B) or subchondral bone/bone marrow (C) of knee joints. Scale bar, 100 μm. (D–F) Three-month-old C57BL/6 mice were fed a CHD or HFD for 1 or 3 months. SA-βGal staining (D) and quantification of SA-βGal+ cells at subchondral bone/bone marrow (E) or articular cartilage (F) of knee joints. Scale bar, 100 μm.

We then investigated whether osteoclast lineage cells undergo cellular senescence in HFD-challenged Cdkn2atdTom mice. Consistent with Figure 2G–2H, fewer VPP3+ osteoclasts at the bone surface (Figure 4A and B) and more bone marrow tdTom+ SnCs (Figure 4A and C) were detected in HFD mice than in CHD mice. However, the percentage of tdTom+ cells out of the bone surface VPP3+ cell population was not different in HFD mice compared with CHD mice (Figure 4A and D), indicating that mature osteoclasts did not undergo senescence in the subchondral bone of HFD-challenged mice. On the contrary, a markedly higher percentage of tdTom+ SnCs in RANK+ preosteoclasts was found in the subchondral bone marrow of HFD mice relative to CHD mice (Figure 4E and F). Approximately 49.2% and 56.5% of bone marrow RANK+ cells expressed tdTom after HFD challenge of 0.5 and 1 month, respectively. These results suggest that bone marrow preosteoclasts, but not bone surface mature osteoclasts, exhibit senescence-like changes in mice with HFD challenge.

Subchondral marrow preosteoclasts exhibit senescence-like feature in high-fat-diet (HFD)-challenged mice.

Three-month-old Cdkn2atdTom mice were fed a CHD or HFD for 0.5 month or 1 month, n=6 mice per group. Immunofluorescence staining of knee joint tissue sections with antibody against VPP3. Double fluorescence imaging of tdTom (red) and VPP3 (green) are shown in (A). Quantification of VPP3+ cell numbers at the bone surface (B); tdTom+ cell numbers in subchondral bone marrow area (C); percentage of tdTom+ cells out of total VPP3+ cells (D). Immunofluorescence staining of knee joint tissue sections with antibody against RANK. Double fluorescence imaging of tdTom (red) and RANK (green) were shown in (E). Percentage of tdTom+ cells out of total RANK+ cells were shown in (F). Scale bar, 100 μm. All data are shown as means ± standard deviations. ***p<0.001. Statistical significance was determined by unpaired, two-tailed Student’s t test.

Deletion of Cdkn2a in osteoclast lineage attenuates subchondral bone alterations and osteoarthritis progression in HFD mice

To determine whether cellular senescence plays a role in osteoarthritis development, we generated conditional Tnfrsf11aCre/+; Cdkn2aflox/flox mice, in which the senescence gene Cdkn2a is deleted in RANK+ osteoclast lineage cells (Maeda et al., 2012; Zou et al., 2016). Consistent with Figure 3D, SA-βGal+ cells accumulated in subchondral bone marrow in WT mice with 1 month HFD treatment, and importantly this increase was greatly dampened in the p16cKO mice relative to WT mice (Figure 5—figure supplement 1A B). The results suggest that deletion of Cdkn2a in RANK+ cells efficiently prevents/blocks subchondral cellular senescence. We then investigated subchondral bone changes in the mice. Consistent with Figure 1, μCT analysis showed that tibial subchondral BV/TV ratio, SBP.Th, and Tb.Th were all higher in wild-type (WT) mice fed a HFD (vs. CHD) (Figure 5A-D). However, these subchondral bone alterations induced by 1 month HFD treatment were not significant in the p16cKO mice. Moreover, the reduction in the number of bone surface osteoclasts (Figure 5E and F) and the increase in the osteoblast clusters in the bone marrow cavity (Figure 5G and H) induced by HFD treatment were both alleviated after Cdkn2a deletion in preosteoclasts. Of note, the μCT measurements (Figure 5A-D) and the histological parameters (Figure 5E-H) were not significantly different in the p16cKO mice compared with the WT mice fed a normal CHD, suggesting that subchondral bone remodeling, as well as the activity of osteoclast and osteoblast lineage were not affected by Cdkn2a deletion in osteoclast lineage cells at baseline. Finally, we investigated joint cartilage changes and found that articular cartilage was well preserved in WT and p16cKO mice fed a CHD. WT mice treated with 5 months of HFD exhibited obvious proteoglycan loss in the joint cartilage and an increased OARSI score, which were not observed in p16cKO mice (Figure 5I and J). Consistently, HFD-induced increase in the percentage of MMP13+ chondrocytes, another feature of articular cartilage degeneration, was dramatically reduced in p16cKO mice relative to WT mice (Figure 5K and L). Therefore, deletion of Cdkn2a in osteoclast lineage cells attenuated HFD-induced osteoarthritis progression.

Figure 5 with 1 supplement see all
Deletion of Cdkn2a in preosteoclasts attenuates subchondral bone thickness and cartilage damage in high-fat-diet (HFD) mice.

(A–H) Three-month-old Tnfrsf11aCre/+; Cdkn2aflox/flox mice (p16cko) and Cdkn2aflox/flox littermates (wild-type [WT]) were fed a standard chow-food diet (CHD) or HFD for 1 months, n=6 mice per group. Three-dimensional micro-computed tomography (μCT) images (A) and quantitative analysis of structural parameters of knee joint subchondral bone: bone volume/tissue volume (BV/TV, %) (B), subchondral bone plate thickness (SBP. Th, mm) (C), and trabecular thickness (Tb.Th, mm) (D). TRAP staining of knee joint tissue sections (E) and quantification of TRAP+ cells at the bone surface (F). Scale bar, 100 μm. Immunofluorescence staining of knee joint tissue sections with antibody against osteocalcin (OCN) (green) (G). Quantification of the number of OCN+ cells per bone marrow area (H). (I–L) Three-month-old Tnfrsf11aCre/+; Cdkn2aflox/flox mice (p16cko) and Cdkn2aflox/flox littermates (wild-type [WT]) were fed a standard chow-food diet (CHD) or HFD for 5 months, n=6 mice per group. Safranin O-fast green staining of the tibia subchondral bone medial compartment (sagittal view) (I). Scale bar, 200 μm. Calculation of Osteoarthritis Research Society International (OARSI) scores (J). Scale bar, 100 μm. Immunostaining of knee joint tissue sections with antibody against MMP13 (brown) (K) and quantification of MMP13+ cells on cartilage (L). Red arrows: MMP13+ cells. Results are expressed as mean ± standard deviations, ***p<0.001. Statistical significance was determined by unpaired, two-tailed Student’s t test.

Senescent preosteoclasts acquire a secretory phenotype

One of the characteristics of SnCs is the SASP. We investigated whether subchondral bone preosteoclasts in HFD mice acquire SASP by performing proteomic profiling. We assessed the differentially expressed proteins in tibial plateau subchondral bone from p16 flox/flox (WT) and p16cKO mice with HFD challenge (Figure 6A). Overall, 12 of 111 cytokines/growth factors were increased in the subchondral bone of HFD-challenged WT mice relative to that of those fed a normal CHD (Figure 6B). Of note, the levels of 9 of these 12 factors were restored in the subchondral bone of p16cKO mice, suggesting that these factors are secreted from the p16 +senescent preosteoclasts.

Figure 6 with 4 supplements see all
Subchondral preosteoclasts acquire a unique secretory phenotype in high-fat-diet (HFD) mice.

Three-month-old Cdkn2aflox/flox mice were fed a standard chow-food diet (CHD). Three-month-old Tnfrsf11aCre/+; Cdkn2aflox/flox mice(p16cko) and Cdkn2aflox/flox littermates (wild-type [WT]) were fed with the HFD. (A) The subchondral bone protein extracts were harvested after 1 month. n=6 mice per group. (B) Differentially expressed proteins are shown on a heat map. (C) Isolated bone marrow monocytes/macrophages were treated with M-CSF and receptor activator of nuclear factor kappa-B ligand (RANKL) to obtain mononuclear preosteoclasts. Cellular senescence of the preosteoclasts was induced by challenging the cells with oxidized low-density lipoprotein (oxLDL) or vehicle. (D) In vitro senescence-associated changes in normalized mRNA expression of three senescence effectors. (Cdkn2a, Cdkn1a, and Mki67) (E, F) SA-βGal staining (E) and percentage quantification of SA-βGal+ cells (F). Scale bar, 100 μm. (G–H) Immunofluorescence staining of Lamin B1 (green) (G) and percentage quantification of Lamin B1+ cells (H). Scale bar, 50 μm. (I) In vitro senescence-associated changes in normalized mRNA expression of established SASP components are shown. Results are expressed as mean ± standard deviations, ***p<0.001. Statistical significance was determined by unpaired, two-tailed Student’s t-test.

We then used an in vitro osteoclast-based cell culture system (Xie et al., 2014), in which isolated bone marrow monocytes/macrophages were treated with M-CSF and RANKL for different durations to obtain mononuclear preosteoclasts and multinuclear mature osteoclasts. Cellular senescence of the preosteoclasts was induced by challenging the cells with oxidized low-density lipoprotein (oxLDL) (Figure 6C), a key mediator of MetS-associated abnormalities in multiple tissues (Holvoet et al., 2008; Hurtado-Roca et al., 2017). The mRNA expression of senescence genes Cdkn2a and Cdkn1a was upregulated, and proliferation marker Mki67 was downregulated in oxLDL-treated cells compared to those of vehicle-treated cells (Figure 6D). Moreover, number of SA-β-gal+ cells increased (Figure 6E and F) and the number of Lamin-B1+ cells decreased (Figure 6G and H) in response to oxLDL treatment. Therefore, oxLDL induces in vitro preosteoclast senescence efficiently. We then used quantitative real-time polymerase chain reaction (qRT-PCR) to evaluate whether the same secreted factors identified in the subchondral bone of HFD mice were also elevated in oxLDL-challenged preosteoclasts. oxLDL-treated cells had much higher expression of interleukin-1β (IL-1β) and IL-6, which are two common SASP factors. Moreover, seven factors identified in the subchondral bone of HFD mice, including osteopontin (OPN), Lipocalin-2, Cystatin C, IL-33, vascular endothelial growth factor (VEGF), cellular communication network factor 4 (CCN4), and platelet-derived growth factor BB (PDGF-BB), were also markedly increased in oxLDL-treated cells relative to vehicle-treated cells (Figure 6I), indicating that these are important SASP factors involved in osteoarthritis pathogenesis.

Senescent preosteoclasts have diminished differentiation capacity and secrete SASP to inhibit differentiation of non-senescent osteoclast precursors

We tested whether the factors secreted by senescent preosteoclasts act on surrounding cells to regulate their activity in a paracrine manner. To do this, we prepared conditioned medium (CM) from control non-senescent preosteoclasts (Con-CM) and senescent preosteoclasts (SnC-CM) (Figure 6C). We first examined the functional changes of osteoclast lineage cells by incubating bone marrow Mo/Mac, osteoclast precursor cells, with the SnC-CM. SnC-CM inhibited the osteoclastogenesis ability of the non-adherent bone marrow Mo/Mac compared with Con-CM (Figure 6—figure supplement 1), indicating an inhibitory effect of preosteoclast SASP on osteoclastogenesis. We then test whether preosteoclasts lose their capacity to further differentiate into mature osteoclasts when become senescent. Cellular senescence of in vitro differentiated mononuclear preosteoclasts was induced by challenging the cells with hydrogen peroxide (H2O2) (Figure 6—figure supplement 2A), which has been widely used to achieve oxidative stress-induced cellular senescence (Toussaint et al., 2000). Senescence of preosteoclasts was successfully induced as indicated by upregulated expression of senescence genes Cdkn2a (Figure 6—figure supplement 2B) and nuclear loss of HMGB1 (Hernandez-Segura et al., 2018; Figure 6—figure supplement 2C) relative to vehicle-treated control cells. Moreover, bulk RNA-seq analysis revealed 4,056 differentially expressed genes in the senescent vs. control preosteoclasts (p<0.05). Comparison of our data with previously defined aging/senescence-induced genes (ASIGs) from publicly available mouse RNA-seq data sets (Aging Atlas database; KEGG pathway database; GO database; MSigD database) identified a total of 150 ASIGs in the senescent preosteoclasts (vs. control non-senescent preosteoclasts) (Figure 6—figure supplement 3A). Among these ASIGs, 31 genes were upregulated, and 119 genes were downregulated in the senescent preosteoclasts relative to control cells (Figure 6—figure supplement 3B). In the main biological process and molecular function genes, alterations in those involved in ‘Aging’, ‘Damaged DNA binding’, ‘chromatin DNA binding’, and ‘NF-kappaB binding’ were notable for their known links to senescence and SASP triggering (Tilstra et al., 2012; Di Micco et al., 2021; Figure 6—figure supplement 3C). Of note, osteoclast differentiation- and bone resorption-associated genes are among the most significantly downregulated genes (Figure 6—figure supplement 4), indicating a diminished osteoclast differentiation capacity of the preosteoclasts after becoming senescent. Therefore, the senescent preosteoclasts, on one hand, have declined differentiation capacity toward mature osteoclasts; on the other hand, secrete SASP factors that inhibit the differentiation of non-senescent osteoclast precursors to osteoclasts in a paracrine manner.

Preosteoclast secretome promotes osteoblast differentiation via COX2-PGE2 signaling

We then tested whether SnC-CM affects the activity of osteoblast lineage cells. Human bone marrow stromal cells (BMSCs), precursors of osteoblasts, incubated with SnC-CM, had greater osteoblast differentiation capacity than Con-CM, as detected by colony forming unit–osteoblasts (CFU-OB) (Figure 7C and D). SnC-CM did not affect the colony-forming capacity of the BMSCs (Figure 7A and B). Consistently, the expression of osteoblast differentiation markers alkaline phosphatase (Alpl), Bglap, and collagen type 1 A (Col1a1) were all upregulated in BMSCs incubated with SnC-CM (vs. Con-CM) (Figure 7E). The expression of RUNX2, the master regulator for the commitment of undifferentiated mesenchymal stem cells toward the osteoblast lineage, was unchanged in the cells treated with SnC-CM compared with Con-CM. These results suggest that the SASP factors produced by preosteoclasts promote late-stage osteoblast differentiation without affecting the lineage commitment of BMSCs.

Secreted factors from preosteoclasts stimulate osteoblast differentiation through COX2-PGE2 signaling.

(A–D) Representative images (A, C) and the quantified CFU-F frequency (B), and CFU-OB frequency (D) of bone marrow stromal cells treated with control conditioned medium (Con-CM) or senescent conditioned medium (SnC-CM). (E) qRT-PCR analysis of the relative levels of Alpl, Col1a1, Bglap, Runx2 mRNA expression in bone marrow stromal cells cultured in the mixture of osteoblast differentiation medium (DM) and Con-CM or SnC-CM (DM:CM = 1:1). **p<0.01, and ***p<0.001. Statistical significance was determined by unpaired, two-tailed Student’s t-test. (F) Relative levels of Cox2 mRNA expression in BMSCs cultured in growth medium (GM), osteoblast DM, Con-CM, or SnC-CM. (G) PGE2 protein levels from GM, DM, Con-CM, or SnC-CM were calculated. Results are expressed as mean ± standard deviations. (H) qRT-PCR analysis of the relative levels of Alpl, Col1a1, Bglap mRNA expression in BMSCs cultured in the mixture of osteoblast DM and Con-CM or SnC-CM (DM:CM = 1:1), or together with celecoxib (40 μM). (I) Schematic model for the role of preosteoclast secretome-COX2/PGE2 axis in mediating subchondral bone formation during metabolic syndrome (MetS). Under MetS, preosteoclasts in subchondral bone marrow undergo cellular senescence and secrete SASP factors, which acts on both osteoclast precursors to suppress osteoclast differentiation and osteoblast precursors to activate COX2-PGE2 signaling to promote osteoblast differentiation for bone formation.

We explored the mechanisms by which preosteoclast-secreted factors affect osteoblast differentiation. Most of these SASP factors, such as IL-1β, IL-6, VEGF, PDGF-BB, OPN, Lipocalin-2, myeloperoxidase (MPO), Cystatin C, resistin, and IL-33, are direct COX2 gene-activating factors (Hurtado-Roca et al., 2017; Chien et al., 2009; Zhang et al., 2010; Su et al., 2017; Li et al., 2018; Hamzic et al., 2013; Panagopoulos et al., 2017; Samad et al., 2001). COX2 is the inducible enzyme in the production of PGE2, which acts on osteoblastic precursors to stimulate osteoblast differentiation (Pilbeam, 2020). We detected the mRNA expression of COX2 in BMSCs using various culture media and measured PGE2 produced by the cells. COX2 gene expression was upregulated in the cells incubated in osteoblast differentiation medium (DM) compared with those incubated in growth medium (GM). Importantly, the addition of SnC-CM, but not Con-CM, stimulated much higher COX2 expression in the cells compared to those incubated with DM alone (Figure 7F). PGE2 protein level was also higher in the culture medium from the cells incubated with SnC-CM relative to that of the cells incubated with DM alone (Figure 7G). Further, treating the cells with selective COX2 inhibitor celecoxib significantly downregulated all 3 osteoblast differentiation marker genes stimulated by SnC-CM (Figure 7H), suggesting a requirement of the COX2-PGE2 pathway in the osteoblast differentiation of the BMSCs. Our results suggest that preosteoclast-derived SASP factors have a paracrine effect on surrounding osteoblast lineage cells for COX2-PGE2 activation, which consequently promotes osteoblast differentiation in a cell-autonomous fashion (Figure 7I).

Selective COX2 inhibitor attenuates HFD-induced subchondral bone changes and osteoarthritis progression in mice

To further define the role of COX2-PGE2 pathway in stimulating subchondral bone formation in vivo, we assessed COX2 expression in the subchondral bone of HFD-challenged mice using immunostaining analysis. Our results showed that there were very few COX2+ cells at cartilage in mice fed HFD for shorter periods (0.5, 1, 3, and 4 months) relative to mice fed CHD, whereas there was a much higher number of COX2+ cell number on bone surface (osteoblasts) and in mineralized bone (osteocytes) at these time points (Figure 8A, C and D). A higher number of COX2+ chondrocytes in cartilage was found in mice only after 5 months of HFD treatment (Figure 8A and B), during which severe cartilage degeneration occurs. Therefore, the majority of COX2+ cells were primarily subchondral osteoblasts and osteocytes during the progression of MetS-OA.

High-fat diet (HFD) mice have increased COX2+ cells in subchondral bone.

Three-month-old C57BL/6 mice were fed a standard chow-food diet (CHD) or HFD for different time periods as indicated, n=6 mice per group. Immunofluorescence staining of knee joint tissue sections with antibody against COX2 (green) (A). Green arrows, COX2+ cells in cartilage; Blue arrows, COX2+ cells in bone surface osteoblasts; Black arrows, COX2+ cells in osteocytes. Quantification of the number of COX2+ cells per cartilage area (B), COX2+ cells per bone surface (C), and COX2+ cells per bone area (D). Results are expressed as mean ± standard deviation. n=5, *** p<0.001.

To investigate whether inhibiting COX2-PGE2 signaling could alleviate subchondral bone changes, such as subchondral plate thickening, and thus slow osteoarthritis progression in MetS-OA mice, we gavage-fed a selective COX2 inhibitor, celecoxib, to HFD mice at a dose of 16  mg/kg–1 daily for 2 months. Subchondral bone alterations induced by the HFD were alleviated by celecoxib treatment relative to vehicle treatment (Figure 9A), as evidenced by significantly reduced subchondral BV/TV, SBP.Th, and Tb.Th. (Figure 9B-D). The reduction of Tb.N induced by a HFD was also normalized by celecoxib treatment (Figure 9E). Importantly, the degeneration of articular cartilage was also ameliorated after celecoxib treatment (Figure 9F and G). Celecoxib treatment in CHD mice did not change the architecture of the subchondral bone or articular cartilage. This result, consistent with the in vitro data, suggests that the elevation of COX2 levels in subchondral bone is a key mediator of HFD-induced subchondral bone alteration and osteoarthritis progression.

Cyclooxygenase 2 (COX2) inhibitor alleviates high-fat diet (HFD)-induced joint degeneration in mice.

Three-month-old C57BL/6 mice were fed a standard chow-food diet (CHD) or HFD for 5 months. During the last 2 months of the HFD challenge, the mice also received celecoxib (16  mg/kg–1 daily) or vehicle. n=6 mice per group. (A–E) Three-dimensional micro-computed tomography (μCT) images (A) and quantitative analysis of structural parameters of subchondral bone: bone volume/tissue volume (BV/TV, %) (B), trabecular thickness (Tb.Th, mm) (C), subchondral bone plate thickness (SBP. Th, mm) (D) and trabecular number (Tb.N, mm–1) (E). (F) Safranin O-fast green staining of the tibia subchondral bone medial compartment (sagittal view). Scale bar, 200 μm (G) Calculation of Osteoarthritis Research Society International (OARSI) scores. *p<0.05, **p<0.01 and ***p<0.001. Statistical significance was determined by multifactorial ANOVA. All data are shown as means ± standard deviations. (H) COX2 inhibitor user (i–iv) and nonselective nonsteroidal anti-inflammatory drug (NSAID) user (v-viii) baseline and follow-up radiographs and magnetic resonance images of patients with metabolic syndrome (MetS)-associated osteoarthritis phenotype (MetS+). Weightbearing posteroanterior radiograph of the right knee using fixed flexion protocol of a 64-year-old MetS+ woman with COX2 inhibitor use at baseline (i) and at 24-month follow-up (ii). Sagittal intermediate-weighted fat-suppressed MRI sequences in the same knee of COX2 inhibitor user patient at baseline (iii) and at 24-month follow-up (iv). Weightbearing posteroanterior radiograph of the left knee using fixed flexion protocol of a 62-year-old MetS+woman with nonselective NSAID use at baseline (v) and at 24-month follow-up (vi). Sagittal intermediate-weighted fat-suppressed MRI sequences in the same knee of nonselective NSAID use patient at baseline (vii) and at 24-month follow-up (viii).

Selective COX2 inhibitor use is associated with reduced risk of osteoarthritis progression and odds of BML structural damage worsening in humans with MetS-OA

To determine whether selective COX2 inhibitor also alleviates joint structural alterations in humans with MetS-OA, we conducted a longitudinal comparison of standard knee osteoarthritis outcomes between PS-matched COX2 inhibitor users and non-selective nonsteroidal anti-inflammatory drug (NSAID) users. Of 315 knees of COX2 inhibitor users and 488 knees of non-selective NSAID users, we selected 239 COX2 inhibitor users along with pair-matched 239 non-selective NSAID users using a 1:1 PS-matching method for potential confounders (Supplementary file 1E). The mean follow-up duration for standard knee OA outcomes was 4.4 years (median: 4 years, 1st and 3rd quartiles of 1 and 8 years, respectively). All analyses were further stratified according to the presence or absence of the MetS-OA phenotype (i.e., MetS-OA and no MetS-OA). Supplementary file 1B summarizes the baseline characteristics of included participants before and after applying PS matching. The mean (± standard deviation) age of the matched participants was 61 years±9 (61.3 years±8.8 in COX2 inhibitor users and 60.9 years±9.2 in non-selective NSAID users) and both groups were 73% female. The hazard of medial JSN OARSI osteoarthritis progression was significantly lower in COX2 inhibitor users compared with non-selective NSAID users, in participants with the MetS-OA phenotype (HR, 0.18; 95% CI, 0.04–0.85) but not in participants without MetS-OA (HR, 0.96; 95% CI, 0.53–1.74) (Table 2). No significant difference was found between COX2-inhibitor users and non-selective NSAID users in knee osteoarthritis incidence or symptoms of knee osteoarthritis in either study strata. (Table 2). We further compared 24 month worsening in subchondral BML structural damage between COX2 inhibitor users and non-selective NSAID users in participants with the MetS-OA phenotype. Our results showed that MetS-OA COX2 Inhibitor users, compared to non-selective NSIAD users, had lower odds of subchondral damage worsening. This was evident as lower odds of worsening in the number of affected knee subregions with BMLs (OR, 0.35; 95% CI, 0.13–0.93) and lower odds of worsening in the BML scores (OR, 0.45; 95% CI, 0.20–0.99). (Table 3).

Table 2
Longitudinal comparison of standard knee OA outcomes between human COX2 inhibitor users vs. non-selective NSAID users according to the presence of MetS.
COX2I users vs. non-selective NSAID usersHazard ratio (95% Confidence Interval), p-value, Sample size,Number of events* [COX2I: NSAID]
MetS-OA+MetS-OA
Knee OA incidence0.42 (0.04–4.77), p:0.487, N: 30 (15:15), Events* [2:3]0.62 (0.13–2.84), p:0.537, N: 118 (59:59), Events* [5:9]
Knee OA progression0.18 (0.04–0.85), p:0.030, N: 94 (47:47), Events* [2:8]0.96 (0.53–1.74), p:0.886, N: 266 (133:133), Events* [21:23]
Symptomatic incidence (NASS)0.8 (0.27–2.4), p:0.689, N: 108 (54:54), Events* [2:3]1.56 (0.63–3.89), p:0.337, N: 324 (162:162), Events* [18:12]
  1. COX2I: cyclooxygenase 2 inhibitor, MetS: metabolic syndrome, NASS: non-acceptable symptomatic state, NSAID: Non-Steroidal Anti-Inflammatory Drug, OA: Osteoarthritis. Standard OA outcomes were compared between knees of COX2 inhibitor users vs. matched non-selective NSAID users. Analysis was stratified analysis for the presence of MetS-associated OA (MetS-OA+ vs. MetS-OA). Cox proportional hazards were used. Knees of participants were matched for confounders using the 1:1 propensity-score matching method. Events are knee OA incidence defined by Kellgren-Lawrence (KL) grade ≥2 in participants with KL equal to 0–1, knee OA progression defined by partial or whole grade progression in Osteoarthritis Research Society International medial joint space narrowing grade, and knee OA symptomatic incidence measured by NASS. The mean follow-up duration for standard knee OA outcomes was 4.4 years (median: 4 years, 1st and 3rd quartiles of 1 and 8 years). N corresponds to the total number of knees included in each analysis and the number of matched knees of COX2 inhibitor users vs. non-selective NSAID users in the parentheses.

  2. *

    Number of events for each outcome has been shown separately in the brackets for COX2 inhibitor users and non-selective NSAID users.

Table 3
Longitudinal comparison of subchondral BML worsening between human COX2 inhibitor users vs. non-selective NSAID users with MetS-OA.
Subchondral BML Worsening (MOAKS)COX2 inhibitor users vs. non-selective NSAID users with MetS-OA
Odds ratio (95% Confidence Interval), p-value, Sample sizeNumber of events in each group[COX2 inhibitor: NSAID]*
Worsening in number of affected subregions with BML0.35 (0.13–0.93), p:0.035, N: 88 (44:44)Improvement, [3:2]
No change, [14:9]
Worsening, [27:33]
Improvement in number of affected subregions1.21 (0.19–7.72), p:0.839, N: 88 (44:44)Yes, [3:2]
Maximum worsening in BML score0.45 (0.2–0.99), p:0.046, N: 88 (44:44)No change, [18:9]
Worsening by ≤1 grade, [22:27] by ≥2 grades, 4:8
Improvement in the subregions' BML score1.28 (0.33–5.00), p:0.722, N: 88 (44:44)Yes, [9:6]
  1. Among the PS-matched COX2 inhibitor users and non-selective NSAID users with MetS-OA, participants with available baseline and 24-month follow-up MRIs were included. A musculoskeletal radiologist read and scored MRIs according to validated MOAKS measures of subchondral BML worsening. Logistic mixed-effect regression models were used for subchondral BML assessments. All analyses were adjusted for the baseline Kellgren-Lawrence (KL) and Osteoarthritis Research Society International medial joint space narrowing (OARSI JSN) grades of knees. Subchondral BML worsening was assessed using standard MOAKS measures. N corresponds to the total number of knees included in each analysis and the number of matched knees of MetS-OA and PTOA participants in the parenthesis.

  2. *

    Number of events for each outcome has been shown separately in the brackets for COX2 inhibitor users and non-selective NSAID users.

  3. BML: Bone marrow lesion, MetS: metabolic syndrome, MOAKS: MRI Osteoarthritis Knee Score, NSAID: Non-Steroidal Anti-Inflammatory Drug, OA: osteoarthritis.

A representative knee radiograph showed grade 2 medial JSN (using the OARSI grading method) with 3.5 mm of joint space width in a 64-year-old woman with MetS-OA phenotype (MetS+) at baseline (Figure 9Hi). After 24 months of COX2 inhibitor use, follow-up radiographs of the same knee showed minimal progression of medial JSN, as evidenced by no interval change in OARSI grading and minimal narrowing of the joint space width to 2.9 mm (Figure 9Hii). Conversely, a knee radiograph of a 62-year-old MetS+ woman showed grade 2 JSN with minimal (3.7 mm) joint space width at baseline (Figure 9Hv); however, a follow-up radiograph of the same knee showed progression of JSN, as evidenced by interval change in OARSI grading to grade 3 with a joint space width of 1.0 mm (Figure 9Hvi). Consistently, magnetic resonance imaging (MRI) showed a significant increase in the size and number of subchondral bone marrow lesions in the knee of a non-selective NSAID user (Figure 9Hviii vs. vii) but unchanged subchondral bone marrow lesion size and number in the knee of a selective COX2 inhibitor user (Figure 9Hiv vs. iii). The decreased risk of medial JSN OARSI osteoarthritis progression in COX2 inhibitor users with MetS-OA phenotype is consistent with our findings in animal models of MetS-OA.

Discussion

In clinical practice, osteoarthritis has various causes (Misra et al., 2015) (e.g. PTOA, non-traumatic age-associated, and MetS-OA) and presents at different stages (early vs. late). In animal studies, increasing evidence supports an important role of subchondral bone changes during PTOA progression. However, almost nothing is known about progressive changes in subchondral bone in nontraumatic osteoarthritis. Our findings show that humans and mice with MetS-OA have a subchondral bone phenotype distinct from that of PTOA and have a greater likelihood of developing osteoarthritis-related subchondral bone damage. Structurally, contrary to the increased osteoclast bone resorption in subchondral bone in early-stage PTOA (Zhen et al., 2013; Sun et al., 2021; Zhu et al., 2019), there is rapid thickening of subchondral bone plate and trabecular bone in HFD-challenged mice and STR/Ort mice, both of which are MetS-OA mouse models. These subchondral alterations appear much earlier than the occurrence of cartilage degradation. Cellularly, unlike the accumulated SnCs in cartilage and synovium in PTOA (Jeon et al., 2017; Jeon et al., 2018), increased SnCs are located almost exclusively in the subchondral bone in MetS-OA mice. We also found that many of the SnCs were RANK+TRAP+ preosteoclasts in bone marrow. Mechanistically, the senescent preosteoclasts acquire a unique secretome, which acts on both osteoclast and osteoblast lineages in a paracrine manner, leading to inhibited osteoclast bone resorption and increased osteoblastic bone formation for rapid subchondral bone thickening at the onset of osteoarthritis.

Accumulated senescent chondrocytes in articular cartilage have been found in PTOA (Jeon et al., 2017; Diekman, 2018). We observed significantly increased senescent preosteoclasts in subchondral bone but not in articular cartilage in HFD or STR/Ort mice. Therefore, the joint locations of the SnCs and the major SnC type are distinct in MetS-OA and PTOA. We also show that deletion of senescence gene Cdkn2a specifically in osteoclast lineage attenuated short term HFD challenge-induced pathological subchondral bone alterations. As a result, HFD-induced cartilage degeneration was also greatly alleviated. Therefore, preosteoclast senescence in subchondral bone is a key mediator for the progression of MetS-OA. Our data showing that accumulation of senescent preosteoclasts and subchondral bone architectural change occur rapidly (0.5 months) after HFD treatment but cartilage degeneration occurred much later (5 months) after HFD treatment (Figures 1 and 3) suggests that the eventual cartilage damage may be caused by progressive subchondral bone architectural changes rather than a direct effect from the SASP of preosteoclasts. The contribution of aberrant subchondral bone alteration to articular cartilage degeneration has been well-recognized. Accumulating evidence suggests that normal subchondral bone structure is essential for the homeostasis of articular cartilage (Zhen et al., 2021; Goldring and Goldring, 2016), and the incremental increase of either subchondral bone/plate thickness or subchondral plate stiffness modulus subsequently increases stress and creates uneven stress distributions in the overlaying articular cartilage for its degradation (Zhen et al., 2021; DeFrate et al., 2019; Lories and Luyten, 2011). Moreover, strategies reducing/normalizing subchondral bone alterations during the early stage of PTOA effectively prevented cartilage degeneration (Su et al., 2020; Zhen et al., 2013). It is of interest to define whether increases in subchondral bone volume and subchondral plate/trabecular thickness result in incremental increase in mechanical stress on cartilage for its degeneration in MetS-OA.

Our in vivo results from HFD-challenged mice and in vitro evidence from cultured preosteoclasts showed that senescent preosteoclasts secrete previously identified common SASP factors, such as IL-1β, IL-6, VEGF, and OPN (Coppé et al., 2010; Tchkonia et al., 2013; Shang et al., 2020; Gómez-Santos et al., 2020), as well as several factors that were not previously recognized in SnCs, including Lipocalin-2, Resistin, Cystatin C, IL-33, CCN4, MPO, and PDGF-BB. Importantly, the abnormally high production of these factors in the subchondral bone of HFD-challenged mice is fully or partially rectified by deletion of Cdkn2a from preosteoclasts. It is well-accepted that the SASP is largely distinct in composition, highly cell type–specific, and also dynamic depending on the senescence inducers (Basisty et al., 2020). The SASP secretome promotes, via autocrine/paracrine pathways, the reprogramming of neighboring cells and modifies the microenvironment (Mosteiro et al., 2016). Here, we show that the secretome of preosteoclasts negatively regulates the differentiation of osteoclasts but promotes osteoblast differentiation in the metabolic dysregulation–associated microenvironment of subchondral bone/bone marrow. These effects of the SASP factors appear opposite of what have been found in bone in the context of aging. Particularly, it has been reported that the SASP in bone microenvironment impair osteoblastic bone formation and enhance osteoclastic bone resorption during aging (Farr et al., 2017; Farr et al., 2016). The contradicting results is likely attributed to the different composition and activity of subchondral preosteoclast SASP in the setting of MetS-OA than in the setting of aging. Of note, among the preosteoclast-derived factors that we identified, there are both osteoclast differentiation stimulators, such as IL-1β (Ruscitti et al., 2015) and IL-6 (Rose-John, 2018), and inhibitors, such as MPO (Zhao et al., 2021), IL-33 (Amarasekara et al., 2018), and CCN4 (Chang et al., 2018). The net result of these 2 opposite effects is likely an inhibition of osteoclastic bone resorption given our in vivo observation of the reduction in osteoclast numbers in subchondral bone of HFD mice and in vitro finding of the partial inhibition of osteoclastogenesis by the SnC-CM. Therefore, the SASP factors from senescent preosteoclasts have a unique paracrine effect on the nearby non-senescent osteoclast precursors to inhibit their osteoclastogenic ability. Moreover, our RNA-seq data also showed many downregulated osteoclast differentiation genes in the senescent preosteoclasts relative to non-senescent cells, suggesting that the senescent preosteoclasts have decreased ability to further differentiate into mature osteoclasts. An unexpected effect of the preosteoclast secretome is to stimulate osteoblast differentiation of the BMSCs. Of note, the CFU-F and gene expression of RUNX2, a transcription factor controlling the early commitment of BMSCs to osteoblast lineage, were unchanged in cells treated with SnC-CM relative to those treated with Con-CM. Thus, the SASP secretome specifically promotes osteoblast differentiation/maturation without affecting the colony forming capacity and lineage commitment of BMSCs. Importantly, our in vivo data demonstrate that unlike bone surface localization in normal physiological conditions, osteoblasts were aberrantly accumulated and formed clusters in the marrow cavity of HFD mice. This in vivo finding is consistent with the increased osteoblast differentiation of the BMSCs in response to SnC-CM. The formation of osteoblast clusters within the bone marrow may represent a unique subchondral bone feature of MetS-OA, contributing to the rapid development of subchondral plate thickening and sclerosis.

Our work shows that COX2-PGE2 activation is a key mediator of the SASP secretome–induced osteoblast differentiation and subchondral bone thickening that occurs in MetS. Of note, all of the identified SASP factors produced by senescent preosteoclasts, including IL-1β, IL-6, OPN (Jain et al., 2006), IL-33 (Li et al., 2018), Lipocalin-2 (Hamzic et al., 2013), MPO (Panagopoulos et al., 2017), VEGF (Chien et al., 2009), Resistin (Su et al., 2017), and PDGF-BB (Englesbe et al., 2004), were identified previously as positive COX2-PGE2 stimulators. Indeed, both COX2 gene expression in BMSCs and PGE2 level in the culture medium were upregulated when the cells were incubated with SnC-CM relative to Con-CM. Addition of selective COX2 inhibitor celecoxib in the BMSC culture medium significantly downregulated the osteoblast differentiation marker genes stimulated by SnC-CM, confirming the requirement of COX2-PGE2 activation for SASP-induced osteoblast differentiation of the BMSCs. Further, our in vivo data also show that the majority of COX2+ cells were subchondral osteoblasts and osteocytes during the progression of MetS-OA, and selective COX2 celecoxib treatment markedly alleviated pathological subchondral bone thickening and osteoarthritis progression. Thus, COX2-PGE2 is a critical mediator of subchondral bone alteration and disease progression of MetS-OA. Clinically, COX2 inhibitors have been used widely to relieve arthritis-associated pain at lower doses and to inhibit inflammation at higher doses (Geba et al., 2002). However, there are contradictory reports regarding their direct disease-modifying function (Zweers et al., 2011). It is unclear from multiple clinical trials and preclinical studies during the past two decades whether selective COX2 inhibitors protect cartilage and slow osteoarthritis progression. One of the main reasons for the inconclusive results of these studies could be the heterogeneous causes of osteoarthritis. Our findings from the human OAI dataset analysis confirmed the central role of subchondral bone damage in MetS-OA worsening. We showed that participants with MetS-OA with similar longitudinal worsening to their matched PTOA participants had increased subchondral bone damage. Intriguingly, among selective COX2-inhibitor users, only those with MetS-OA have alleviated joint structural alterations, and use of COX2-inhibitor is associated with reduced the odds subchondral bone damage. More importantly, this effect is seen compared with participants who use other analgesics, even after matching for pain status (which can be a source for confounding by indication bias, as the most widely indication of both non-selective NSAID and selective COX2 inhibitor prescription). These findings suggest that selective COX2 inhibitors but not the non-selective NSAIDs may have disease-modifying properties for MetS-OA.

Data availability

The data that support the findings of this study are available within the article and Supplementary file. Sequencing data have been deposited in Dryad and can be acquired through online portal at https://doi.org/10.5061/dryad.q2bvq83n6. The naming and version of OAI dataset files used in our study are listed in Supplementary file 1C and can be acquired through OAI online portal at https://nda.nih.gov/oai.

The following data sets were generated
    1. Su W
    (2022) Dryad Digital Repository
    Senescent preosteoclast secretome promotes metabolic syndrome-associated osteoarthritis through COX2-PGE2.
    https://doi.org/10.5061/dryad.q2bvq83n6

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Author response

[Editors' note: we include below the reviews that the authors received from another journal, along with the authors’ responses.]

Point-by-point response to reviewers’ comments

We would like to thank the reviewers for their thoughtful and constructive comments regarding our manuscript. We have addressed all of the questions and concerns brought forth through additional experimentation and clarification. Specifically, in the mouse study, the reviewers (Reviewer A and B) mainly concerned about the changes (i.e. cellular senescence) of articular cartilage during metabolic syndrome associated osteoarthritis (MetS-OA) and how the subchondral preosteoclast senescence and bone alterations lead to cartilage degeneration. We have conducted more immunostaining analyses to demonstrate the change in senescent cells in both subchondral bone and articular cartilage during the progression of MetS-OA. More importantly, we have demonstrated that senescent preosteoclasts contribute to the progression of cartilage degeneration using a new genetic mouse model to delete the key senescence gene specifically in osteoclast lineage. In the human study, the major concern raised by the reviewers (Reviewer B and C) is that the results of the human participants with MetS-OA are insufficient to prove the role of subchondral bone alterations in OA progression as shown in the MetS-OA mouse models. To further similarize our human OA participants with corresponding mouse models of OA, using propensity score matching, we have further compared subchondral bone marrow lesions worsening between MetS-OA participants (without PTOA) to PTOA participants (without MetS-OA) akin to the mice models to better delineate the unique role of subchondral bone damage in MetS-OA.

To aid in readability, we have made the modified text in blue font to distinguish from the black original text. The following responses have been prepared to address all of the reviewers’ comments in a point-by-point fashion.

Response to comments from Reviewer A:

In this study, Su et al. examine the pathogenesis of metabolic syndrome-associated OA (MetS-OA) using a combination of human and mouse/in vitro data. Their findings implicate senescent pre-osteoclastic cells and their SASP in inhibiting osteoclasts and stimulating osteoblasts in the subchondral bone, leading to changes of OA.

Overall, the studies are done well and the results support the conclusions. To the authors’ credit, they use multiple approaches to establish the presence of senescent pre-osteoclasts (increased p16 expression, SA-βGal staining, redistribution of HMGB1 from the nucleus to the cytoplasm, loss of lamin B1). I have the following points for the authors to address:

1. The authors focus exclusively on subchondral bone changes in their mouse models. It would be important to define what is happening in the cartilage during the time that the bone changes are occurring in their models. Are cartilage cells becoming senescent? Is there evidence for damage to the articular cartilage from the SASP? Although OA is associated with sub-chondral bone changes, the ultimate problem is the deterioration of the articular cartilage, and that seems to be ignored here.

We recognize the importance of this question and have conducted a panel of new experiments to define the cartilage changes when the alterations of subchondral bone occurred in our model system. We have first evaluated the senescent cells on the joint cartilage tissue in our HFD-induced osteoarthritis (OA) mouse model. Fluorescence imaging of the knee joint tissue sections from p16tdTom reporter mice shows that tdTom+ cells were exclusively localized at subchondral bone/bone marrow at earlier time points (1 and 3 months) after the mice were fed HFD (Figure 3, A and C). tdTom+ SnCs were not seen in articular cartilage until later (5 months) after HFD challenge (Figure 3, A and B), when significant cartilage degeneration occurs as indicated by a markedly increased OARSI score (Figure 1, A and B). Consistently, increased SA-βGal+ cell number were detected at the subchondral bone/bone marrow (Figure 3, D and F) but not in articular cartilage (Figure 3, D and E) in mice fed a HFD for 1 month or 3 months. Our results suggest that senescent cells primarily accumulate in subchondral bone at the pre- or early-OA stage and appear in cartilage only at more advanced stages.

We then examined whether cellular senescence in subchondral bone may be a major pathogenic culprit of the eventual cartilage degeneration induced by metabolic syndrome by characterizing joint phenotype of a conditional RANK-Cre; p16 flox/flox (p16cKO) mouse line, in which senescence gene p16INK4a is deleted in RANK+ cells. RANK-Cre (kindly provided by Dr. Y. Kobayashi at Matsumoto Dental University, Japan) targets osteoclast lineage cells (Nat Med. 2012; 18: 405–412; Nat Med. 2016; 22: 1203–1205). Consistent with Figure 3, D-F, SA-βGal+ cells accumulated only in subchondral bone marrow in WT mice with 1-month HFD treatment, and this increase in subchondral SA-βGal+ cells was greatly dampened in the p16cKO mice relative to WT mice (Figure 5—figure supplement 7). Therefore, deletion of p16INK4a in RANK+ cells efficiently prevents/blocks subchondral cellular senescence. Importantly, deletion of senescent preosteoclasts in subchondral bone indeed prevents the increase of subchondral thickness (Figure 5, A-H) and greatly attenuated cartilage degeneration (Figure 5, I-L) induced by HFD treatment. Collectively, the results from the new panel of experiments suggest that preosteoclast senescence in subchondral bone is a key initiating factor for the pathological subchondral bone architectural alterations, which may eventually lead to cartilage degeneration.

Whether bone preosteoclast SASP directly causes pathological chondrocyte changes for articular cartilage degeneration is an interesting, unresolved question. Our data showing that accumulation of senescent preosteoclasts and subchondral bone architectural change occur rapidly (0.5 months) after HFD treatment but cartilage degeneration occurred much later (5 months) after HFD treatment (Figure 1 and 3) suggests that the eventual cartilage damage may be caused by progressive subchondral bone architectural changes rather than a direct effect from the SASP of preosteoclasts. The contribution of aberrant subchondral bone alteration to articular cartilage degeneration has been well-recognized in the OA field. Accumulating evidence suggest that normal subchondral bone structure is essential for the homeostasis of articular cartilage (Nat Commun. 2021. 12: 1706; Nat Rev Rheumatol. 2016. 12: 632-644), and the incremental increase of either subchondral bone/plate thickness or subchondral plate stiffness modulus subsequently increases stress and creates uneven stress distributions in the overlaying articular cartilage for its degradation (Osteoarthritis Cartilage 2019. 27: 392-400; Nat Commun. 2021. 12: 1706). Moreover, strategies reducing/normalizing subchondral bone alterations during the early stage of post-traumatic OA (PTOA) effectively prevented cartilage degeneration (JCI Insight. 2020. 5:e135446; Nat Med 2013;19:704-712). It is of interest in the future to define whether increases in subchondral bone volume and subchondral plate/trabecular thickness result in incremental increase in mechanical stress on cartilage for its degeneration in MetS-OA. The discussion on this point has also been added in the revised manuscript (Line 425-442).

2. Their demonstration that the SASP of senescent pre-osteoclastic cells inhibits osteoclasts and promotes osteoblasts is the opposite of what other studies have found, specifically in the context of aging, where the SASP from senescent cells stimulates osteoclasts and inhibits bone formation. The most obvious explanation is that the SASP in the setting of MetS-OA is different than that in the setting of aging. But this point should be explicitly addressed in the Discussion, as otherwise this discrepancy may lead to confusion in the literature.

We appreciate the valuable comment and agree with the reviewer that it is important to clarify the discrepancy of our results and previous findings regarding the SASP effects on osteoclast and osteoblast lineage cells. Our results show that the senescent preosteoclast secretome negatively regulates the differentiation of osteoclasts but promotes osteoblast differentiation in the metabolic dysregulation–associated microenvironment of subchondral bone/bone marrow. These effects of the SASP factors appear opposite of what have been found in bone in the context of aging. Particularly, it has been reported that the SASP in bone microenvironment impair osteoblastic bone formation and enhance osteoclastic bone resorption during aging (Nat Med 23, 1072-1079; J Bone Miner Res 31, 1920-1929). We fully agree with the reviewer that the contradicting results is likely attributed to the different composition and activity of subchondral preosteoclast SASP in the setting of MetS-OA than in the setting of aging. Indeed, we have identified several new SASP factors, including Lipocalin-2, Resistin, Cystatin C, IL-33, CCN4, MPO, and PDGF-BB (Figure 6), that were not previously recognized in SnCs in bone in the setting of aging. Some of these factors, such as IL-33 and CCN4, have potent inhibitory effect on osteoclast differentiation (J Immunol 2011;186:6097-6105; J Biol Chem 2015;290:14004-14018). All of them are positive activators of COX2-PGE2 signaling, which acts on osteoblastic precursors to stimulate osteoblast differentiation (J Cell Biochem 2006;99:824-834; Biochem Biophys Res Commun 2007;360:199-204; Immune Netw 2018;18:e8; Int J Oncol. 2017;50(4): 1191-1200; J Clin Invest 2019;129:2578-2594; J Neuroendocrinol. 2013;25: 271-280; J Bone Miner Res 2011;26:193-208). Importantly, the results from our in vitro cell culture (Figure 7 and Figure 6—figure supplement 8) and in vivo mouse studies (Figure 5) consistently demonstrate that the SASP of senescent preosteoclasts inhibits osteoclast differentiation and promotes osteoblast differentiation and bone formation in subchondral bone in MetS-OA. Moreover, the new RNA-seq data revealed many downregulated osteoclast differentiation genes in the senescent preosteoclasts relative to non-senescent cells (Figure 6—figure supplement 10 and 11), suggesting that the senescent preosteoclasts also have decreased ability to further differentiate into mature osteoclasts. The discussion on this point has been added in the revised manuscript (Line 449-481).

Aging in mice contributes to the development of spontaneous OA, similar to what occurs in humans. For example, C57BL/6 mice usually develop knee OA in the absence of surgical knee operation at about 17 months of age. Significant ongoing effort in the lab is to determine the subchondral bone phenotypic changes in the setting of aging. We found that unlike increased subchondral bone volume and subchondral plate thickness in MetS-associated OA, there is a decrease in subchondral bone volume and trabecular thickness in aged mice relative to young mice (see Author response image 1), indicating increased subchondral osteoclast bone resorption and/or reduced osteoblast bone formation in age-associated OA joints. We will further characterize the senescent cells and the SASP in subchondral bone/bone marrow in the setting of aging. These experiments will be of great interest in understanding the distinct role of SASP in subchondral bone changes in different OA subtypes and will be an important area of future focus in the laboratory.

Author response image 1
Subchondral bone changes in aged mice relative to young mice.

3. A brief introduction to the STR/Ort mice should be provided in the Introduction for readers who may not be familiar with this model.

As suggested, an introduction of the STR/Ort mice have been included in the Introduction section (Line 78-82).

4. In each of the studies, the sex of the mice should be indicated. Were the mice in the different groups matched for sex, as MetS-OA may be different in male vs female mice (or humans)?

In the animal studies, we used male mice in most of the experiments as we were characterizing a new subtype of OA mouse models and wished to first address the effects of MetS while excluding biological sex as a confounding variable. Now, however, we have included additional data in the subchondral bone assessments using female HFD mice. As noted in the updated figures, female mice fed HFD for 3 months had the same subchondral bone phenotype (i.e. dramatically increased subchondral bone volume, subchondral plate thickness, and trabecular thickness) (Figure 1—figure supplement 3) as did in the male mice fed HFD relative to the chow diet mice (Figure 1). The results suggest that HFD challenge induces similar alterations in subchondral bone in male and female mice. As for the STR/Ort mice, it has been recognized that male mice have a higher incidence of OA than female mice (Osteoarthritis Cartilage 2017. 25: 802-808). We therefore used male STR/Ort and CBA control mice in this study. We look forward to future studies examining in more detail on the comparison of the female and male mice regarding the role of cellular senescence in the development of MetS-OA. Indeed, we are currently testing whether subchondral preosteoclasts become senescent and have the same SASP in female MetS mice as those from the male MetS mice.

In the human participants assessments, both female and male human subjects were included, and the sex of the participants is propensity-score matched between comparison groups and are presented in Supplementary Tables 1 and 2 (information on sex of the participants is in blue font in these 2 tables in the manuscript). In addition, we also added a description of participants' sex in the manuscript text Results section (Line 99-102 and line 376).

Response to comments from Reviewer B:

In this paper the authors investigate the pathogenic mechanisms of metabolic syndrome-associated osteoarthritis (MetS-OA), which is a distinct type of OA. By using two different mouse models of metabolic syndrome/OA (high fat diet and STR/Ort mice), they found that a rapid increase in joint subchondral bone plate and trabecular thickness occurs before cartilage degeneration. This increase in bone mass is attributed to increased osteoblast number in the subchondral bone. They show that the increase in osteoblasts is due to senescent pre-osteoclasts that secrete senescence- associate secretory phenotype (SASP) factors which activate Cyclooxygenase 2 transcription and prostaglandin 2 (PGE2) production in osteoblasts. PGE2 stimulates the differentiation of osteoblasts progenitors and bone formation in the subchondral bone. Celecoxib, a COX 2 inhibitor, attenuates subchondral bone formation and progression of OA associated with metabolic syndrome in both mice models.

The studies in humans indicate that patients with metabolic syndrome have increased progression of OA during longitudinal follow up, compared to matched patients without metabolic syndrome; more importantly, the use of COX2 inhibitors reduced OA progression in patients with metabolic syndrome but not in matched patient without metabolic syndrome. MRI imaging, shown in just 1 patient/group, shows the presence of significant increase in size of the subchondral bone marrow lesions in the knee of a non–selective NSAIDS user with metabolic syndrome compared to unchanged subchondral bone marrow lesions in the knee of a selective COX2 inhibitor user with metabolic syndrome.

The authors conclude that senescent pre-osteoclasts, by affecting COX2/PGE2 axis in osteoblasts, play a central pathogenic role in metabolic syndrome- induced OA.

This paper is very well written and clear. The topic studied is very clinically relevant and has an immediate translational application. The strengths of the paper are the use of two different mouse models and the longitudinal analysis of the human osteoarthritis initiative cohort dataset.

There are, however, issues that need to be addressed.

Major issues:

– Mouse models:

– The role of subchondral bone changes is osteoarthritis progression is not new. Others have shown that in OA subchondral bone thickness is increased while trabecular bone thickness is reduced. Overall, previous models have shown increased subchondral bone remodeling with increase in osteoclasts. The changes in subchondral bone are associated with reduced osteochondral integrity and disruption of the barrier between the intra-articular and sub compartmental joint which causes vascular and nerve invasion and endochondral ossification.

In this paper, however, the author report the new finding that trabecular bone is increased in the subchondral space and the osteoclast number is decreased in both mouse models of OA- associated metabolic syndrome.

It remains unclear how those changes, not previously reported in subchondral bone, are pathogenic in the progression of OA and not just an associated phenomenon. To prove that senescent osteoclasts are the pathogenic culprit of the progression of metabolic syndrome-induced OA, the authors should measure with microCT, the subchondral bone changes and the OA progression (OARSI score) in mice with deletion of senescent osteoclast progenitors fed a high fat diet for 1 month (the mice shown in Figure 4 A), compared to the appropriate controls. If deletion of senescent pre osteoclasts in subchondral bone prevents the increase of subchondral bone thickness in mice fed a high fat diet and the increase of the HFD-induced increase in the OARSI score, this would be a compelling evidence that they could be the culprit of those changes. It would be crucial to use a short term high fat diet as the authors show in Figure 3b that the senescent cells are only present in subchondral bone and not in the articular space with this HFD duration.

We appreciate the valuable comments and constructive suggestion. As mentioned by the reviewer, MetS-associated OA mice used in this study (high fat diet and STR/Ort mice) have distinct subchondral bone phenotype from post-traumatic OA (PTOA) mice, which have been much more intensively investigated in the field. Unlike increased subchondral bone remodeling (increased osteoclast activity) at early-stage OA in the PTOA mice (ACLT and DMM mice), there is a rapid and persistent increase in joint subchondral plate and trabecular thickness during the progression of the MetS-OA mice.

As suggested, we have examined whether senescent osteoclasts are the pathogenic culprit of the progression of MetS-OA by conducting additional in vivo experiment using our established conditional RANK-Cre; p16 flox/flox mice (named “p16cKO” thereafter). In this mouse line, a key senescence gene p16INK4a is deleted in RANK+ cells. RANK-Cre (kindly provided by Dr. Yasuhiro Kobayashi at Matsumoto Dental University, Japan) targets osteoclast lineage cells (Nat Med. 2012; 18: 405–412; Nat Med. 2016; 22: 1203–1205). In the past several months, we have conducted a systemic characterization on the changes in knee joint subchondral bone and cartilage in the p16cKO mice and the p16flox/loxf littermate control mice (named “WT” thereafter), especially under HFD challenge for 1 month. Consistent with Figure 3, D-F, SA-βGal+ cells accumulated only in subchondral bone marrow in WT mice with 1-month HFD treatment, and this increase in subchondral SA-βGal+ cells was greatly dampened in the p16cKO mice relative to WT mice (Figure 5—figure supplement 7). The results suggest that deletion of p16INK4a in RANK+ cells efficiently prevents/blocks subchondral cellular senescence. Importantly, microCT analyses showed that tibial subchondral BV/TV ratio, SBP.Th, and Tb.Th were all higher in WT mice fed a HFD (vs. CHD) (Figure 5, A-D); however, these subchondral bone alterations induced by 1-month HFD treatment were not significant in p16cKO mice. Moreover, the reduction in the number of bone surface osteoclasts (Figure 5, E and F) and the increase in the osteoblast clusters in bone marrow (Figure 5, G and H) induced by HFD were both alleviated in p16cKO mice. Therefore, deletion of senescent preosteoclasts in subchondral bone indeed prevents the increase of subchondral thickness induced by a short-term (1 month) HFD treatment.

We also assessed whether deletion of p16INK4a in preosteoclasts could ultimately lead to attenuated cartilage degeneration induced by HFD using the p16cKO mice. To reach the purpose, the mice have to be fed HFD for a much longer period of time than 1 month to see cartilage degeneration in our mouse model because we found that the MetS-OA mice started to show subchondral thickening phenotype as early as 2 weeks after HFD treatment, but cartilage degeneration did not occur until 3-5 months after HFD treatment (Figure 1). Because of this reason, we fed the p16cKO mice and WT littermate mice with HFD for 5 months. Our data show that HFD-challenged WT mice exhibited obvious proteoglycan loss in the joint cartilage and an increased OARSI score, which were not observed in p16cKO mice (Figure 5, I and J). Consistently, HFD-induced increase in the percentage of MMP13+ chondrocytes, another feature of articular cartilage degeneration, was dramatically reduced in p16cKO mice relative to WT mice (Figure 5, K and L). Therefore, blockage of preosteoclast senescence led to a greatly attenuated cartilage degeneration induced by HFD. Of note, p16INK4a is specifically deleted in osteoclast lineage in bone in the conditional p16cKO mice, and chondrocytes in cartilage were not targeted. Moreover, we did not detect any senescent cells in cartilage in our MetS-OA mouse model until 5 months HFD treatment (Figure 3). Thus, the attenuated cartilage degeneration in the p16cKO mice should be primarily through blockage of subchondral preosteoclast senescence. Collectively, the results from the new panel of experiments suggest that preosteoclast senescence drives pathological increase in subchondral bone volume and subchondral plate thickness, eventually leading to cartilage degeneration.

We agree with the reviewer that the mechanisms of how increases in subchondral bone volume and subchondral plate/trabecular thickness lead to cartilage degeneration were not explored in this study. In fact, the mechanisms by which subchondral bone alteration contribute to OA development/progression is a long-standing question in the OA field in general. There is significant evidence in the literature, however, to suggest that normal subchondral bone structure is essential for the homeostasis of articular cartilage (Nat. Rev. Rheumatol. 2011;7:43–49; Trends Pharmacol. Sci. 2014;35:227–236) and that the incremental increase of either subchondral bone/plate thickness or subchondral plate stiffness modulus subsequently increases stress and creates uneven stress distributions in the overlaying articular cartilage for its degradation (Osteoarthritis Cartilage 2019. 27: 392-400; Nat Commun. 2021. 12: 1706). Moreover, strategies reducing/ normalizing subchondral bone alterations during the early stage of OA effectively prevented cartilage degeneration (JCI Insight. 2020. 5:e135446; Nat Med 2013;19:704-712). Significant ongoing effort in the lab is focused on the functional relationship between subchondral bone and articular cartilage in the MetS-OA models. The discussion on this point has also been added in the revised manuscript (Line 425-442).

– Also the protective effect of Celecoxib in the experiment in Figure 6 does not clearly prove an effect of Cox2 derived from osteoblasts as the beneficial effect of this drug on OA could be mediated by other cell types.

This is an intriguing question. We wished to provide evidence to prove that the beneficial effect of Celecoxib is due to osteoblast/osteocyte-specific COX2 inhibition using conditional Osteocalcin-Cre; COX2flox/flox or Dmp1-Cre; COX2flox/flox mouse lines. However, COX2flox/flox mice were not obtained until this month. We are now breeding this mouse strain with Osteocalcin-Cre to generate mice with conditional COX2 deletion in osteoblasts. We look forward to the exciting results from the mice, but it typically takes over a year to generate/validate the mouse line, apply HFD treatment, and conduct analyses of the joint phenotype. Instead, in this present study, we have carefully re-calculated the number of COX2+ cells at both articular cartilage and subchondral bone regions in mice at different time points after HFD treatment. Our results showed that there were very few COX2+ cells at cartilage in mice fed HFD for shorter periods (0.5, 1, 3, and 4 months) relative to mice fed CHD, whereas there was a much higher number of COX2+ cell number on bone surface (osteoblasts) and in mineralized bone (osteocytes) at these time points (Figure 8, A, C, and D). A higher number of COX2+ chondrocytes in cartilage was found in mice only after 5 months of HFD treatment (Figure 8, A and B), during which severe cartilage degeneration occurs. Therefore, the majority of COX2+ cells were primarily subchondral osteoblasts and osteocytes during the progression of MetS-OA, and the major cellular target of celecoxib treatment should be subchondral bone osteoblast lineage cells but not cartilage cells. Discussion on this point has also been added in our revised manuscript (Line 491-494).

Histology:

– Figure 2: The authors indicate that the increase in subchondral bone is due exclusively secondary to increased osteoblasts, however the osteoclasts are also reduced on the bone surface. This reduction could be also causing increased bone mass in the subchondral bone. This aspect should be discussed.

This is an important point, and we fully agree with this reviewer that both increased osteoblastic bone formation and decreased osteoclastic bone resorption contribute to the increases in subchondral plate thickening and trabecular bone volume. We have included three new figures (Figure 6—figure supplement 8-10) to demonstrate the effects of preosteoclast senescence and the corresponding SASP on osteoclast differentiation. Our new results suggest that the senescent preosteoclasts, on one hand, have declined differentiation capacity toward mature osteoclasts; on the other hand, secrete SASP factors to inhibit the differentiation of non-senescent osteoclast precursors to osteoclasts in a paracrine manner. The new results have been describe in the Result section (Line 278-307). Discussion on the involvement of reduced osteoclast bone resorption in this process has been added in the revised Discussion section (Line 465-470).

– Figure 2: the number of osteoblasts /bone surface should also be calculated to prove that they are increased. Alternatively, measurements of dynamic indices of bone histomorphometry should be reported (i.e. quantification of bone formation after calcein injection).

As suggested, we have re-evaluated the number of osteoblasts in subchondral bone. We found that, unlike subchondral bone surface localization of the osteocalcin (OCN)+ osteoblasts in chow diet control mice, OCN+ osteoblasts accumulate and form clusters within marrow cavity in HFD challenged mice. Osteoblast number per bone marrow area (OCN+ N/BM.Ar) significantly increased but OCN+ osteoblasts per bone surface (OCN+ N/BS) remained unchanged in subchondral bone of HFD-challenged mice relative to CHD control mice (Figure 2, A-C). The results suggest that osteoblasts are located on subchondral bone surface in normal healthy joints but aberrantly accumulate in bone marrow cavity in the joints of MetS-OA mice. The formation of aberrant osteoblast clusters within the bone marrow may represent a unique subchondral bone feature of MetS-OA, contributing to the rapid development of subchondral sclerosis and increased subchondral bone mass. The point has been included in the revised Discussion (Line 475-481).

Human longitudinal analysis:

– In supplementary table 1 it looks like that OA is less severe in patients with metabolic syndrome (based on KL grade) compared to matched patients without metabolic syndrome. This is the opposite of what it should be expected, as the premise of this research is that OA is more severe in patients with metabolic syndrome. The authors should discuss this contradictory finding.

We appreciate this very constructive comment of the reviewer. We have responded to this comment in two different sections:

1) "OA is less severe in participants with metabolic syndrome (based on KL grade) compared to matched participants without metabolic syndrome": We have re-assessed our propensity score (PS) matching method. A high body-mass index (BMI) is a well-known risk factor for OA (International Journal of Obesity 2001; 25: 622-627). On the other hand, while high BMI itself is not included in the definition of MetS (as defined by IDF [Diabetic Medicine 2006; 23: 469-480]), it is strongly correlated with abdominal (i.e., central) obesity, which is among MetS criteria. Furthermore, BMI is both associated with knee OA and MetS and can confound the relationship between MetS and OA. Thus, BMI has to be included in the PS-matching model. Since, by MetS definition, all participants with MetS had abdominal obesity (and therefore had high BMIs), PS-matched participants without MetS (MetS) also had high BMIs (see Author response table 1). To address this comment, we have speculated that, given the inclusion of BMI in the PS-matching, MetS participants may have a relative high BMI after PS-matching and consequently have higher radiographic KL-grades compared to matched MetS+ participants. Therefore, we tested this hypothesis with matching the participants once with BMI in the matching model and once without it. We observed that the exclusion of BMI from PS-matching resulted in MetS+ participants with higher KL grades compared to PS-matched MetS participants (Marked as blue in Author response table 1).

Author response table 1
Result of PS-matching MetS+ and MetS- participants once with BMI inclusion in the PS-matching covariates and once without including BMI.

With BMI in the matching model, MetS+ participants have lower mean KL grades, and without its inclusion, MetS+ participants have higher mean KL grades.

All OAI subjectsMatched subjects without BMI in matchingMatched subjects with BMI in matching
MetSMetS+MetSMetS+MetSMetS+
74591810SMD18031803SMDN: 1800N: 1800SMD
Variables included in the PS matching model
Age (year) [mean (SD)]60.38 (9.09)64.94 (8.49)0.5265.22 (8.95)64.90 (8.48)0.0465.12 (8.47)64.87 (8.46)0.03
Sex, Female, N (%)4426 (59.3)985 (54.4)0.101043 (57.8)985 (54.6)0.071014 (56.3)983 (54.6)0.04
Non-white race [N (%)]1468 (19.7)494 (27.3)0.18439 (24.4)487 (27.0)0.06481 (26.8)488 (27.1)0.01
BMI (kg/m2) [mean (SD)]28.23 (4.78)30.71 (4.43)0.5428.09 (4.52)30.72 (4.43)0.5930.51 (4.53)30.69 (4.43)0.04
Smoking, current smoker [N (%)]517 (6.9)120 (6.6)0.01113 (6.3)120 (6.7)0.02107 (5.9)120 (6.7)0.03
Alcohol use, ≥1/week [N (%)]3250 (43.6)698 (38.6)0.10689 (38.2)698 (38.7)0.01733 (40.7)696 (38.7)0.04
PASE score [mean (SD)]165.42 (83.48)141.00 (76.02)0.31137.63 (72.17)141.25 (76.03)0.05143.72 (78.12)141.42 (75.89)0.03
Variables not included in the PS matching model
KL grade, N (%)0.210.120.11
Grade 02875 (38.5)540 (29.8)634 (35.2)538 (29.8)511 (28.4)539 (29.9)
Grade 11318 (17.7)345 (19.1)317 (17.6)342 (19.0)283 (15.7)342 (19.0)
Grade 22019 (27.1)515 (28.5)496 (27.5)514 (28.5)549 (30.5)511 (28.4)
Grade 31005 (13.5)332 (18.3)296 (16.4)331 (18.4)375 (20.8)331 (18.4)
Grade 4242 (3.2)78 (4.3)60 (3.3)78 (4.3)82 (4.6)77 (4.3)
Medial JSN score, N (%)0.220.170.04
Grade 04625 (65.9)937 (55.9)1068 (63.4)933 (55.8)959 (57.1)936 (56.0)
Grade 11503 (21.4)431 (25.7)379 (22.5)430 (25.7)409 (24.4)427 (25.6)
Grade 2742 (10.6)255 (15.2)205 (12.2)254 (15.2)265 (15.8)254 (15.2)
Grade 3148 (2.1)54 (3.2)32 (1.9)54 (3.2)46 (2.7)54 (3.2)
WOMAC pain score (mean (SD))2.35 (3.28)2.96 (3.56)0.182.43 (3.27)2.96 (3.56)0.162.75 (3.62)2.95 (3.56)0.06
Cardio/Cerebrovascular diseases, N (%)239 (3.3)196 (11.4)0.3179 (4.5)196 (11.4)0.2682 (4.7)192 (11.2)0.24
Hypertension [N (%)]2704 (36.3)1734 (95.8)1.62750 (41.6)1728 (95.8)1.44829 (46.1)1724 (95.8)1.31
Diabetes Mellitus [N (%)]86 (1.2)646 (36.7)1.0218 (1.0)641 (36.5)1.0213 (0.7)636 (36.3)1.03
Dyslipidemia [N (%)]1010 (13.5)1599 (88.3)2.26257 (14.3)1594 (88.4)2.21272 (15.1)1591 (88.4)2.16
Abdominal obesity [N (%)]6293 (84.6)1810 (100.0)0.601535 (85.4)1803 (100.0)0.581659 (92.6)1800 (100.0)0.40

2) Modifying the subject selection criteria to assess the primary role of pathological subchondral bone structural alterations in the MetS-OA: Following this comment, your other comment (#4), and also similar comments from other reviewers (e.g., Reviewer C, comment #1), to assess the role of subchondral bone structural changes and to better similarize the human participants and mice models of OA design, we tailored our selection criteria. We compared KOA progression in participants with MetS-OA without a history of knee trauma (to exclude PTOA cases) versus participants with PTOA without MetS (to exclude MetS-OA cases). These selection criteria helped us delineate the unique pathophysiology (e.g., prominent and rapid subchondral bone marrow changes) in the MetS-OA phenotype. As we have shown in the animal model, while both PTOA and MetS-OA have degrees of cartilage degradation, prominent and rapid subchondral bone change is a feature of MetS-OA. We gathered data of OAI participants with MRI reads for baseline and 24-month follow-up visits and carefully selected and matched participants. Following your constructive comments, we added KL and medial JSN grades to the PS-matching and adjusted the Cox models for these variables to address the inhomogeneity in the baseline knee OA status and grade. (Supplementary Flowchart 1 and Supplementary Table 1). We compared both standard knee OA outcomes (including JSN progression) and subchondral bone marrow lesion (BML) worsening between these two groups of MetS-OA+(PTOA) versus PTOA+(MetS-OA). For the assessment of BMLs, we used MRI Osteoarthritis Knee Score (MOAKS) measures, which have been shown to have excellent reliability (Osteoarthritis and cartilage 2011; 19: 990-1002; Arthritis Rheumatol 2016; 68: 2422-31). MOAKS is a validated and the most commonly used semi-quantitative scoring tool to assess longitudinal change in subchondral BMLs within knee subregions in terms of time points (Osteoarthritis and cartilage 2011; 19: 990-1002; Arthritis Rheumatol 2016; 68: 2422-31). While we observed that the risk of knee OA radiographic progression in participants with MetS-OA is similar to participants with PTOA during the follow-up period, we found that participants with MetS-OA have more odds of subchondral bone marrow lesion (BML) worsening compared to participants with PTOA (even though they had similar risk of radiographic knee OA progression). This finding was evident because of the higher odds of worsening in the number of knee joint subregions with subchondral BMLs (Table 1). The changes have been reflected in the revised Methods section (Line 552-570 and Line 576-579), Results section (Line 92-119), Table 1, Discussion section (Line 409-411 and Line 501-506), Supplementary material section (Line 116-122, Line 138-164 and Line 181-184, Supplementary Table 1, Supplementary Flowchart 1).

– In the longitudinal analysis of table 1 and 2 more details are needed. The detailed data for the matched patients should be reported in addition to the average length of follow up. In addition, the detailed analysis of the OA scores should be reported and not just the Hazard ratio of progression. Those data would give a better idea on the degree of progression in every group (i.e patient with metabolic syndrome vs matched patients without metabolic syndrome; patients with metabolic syndrome chronically using Cox2 inhibitors vs matched patients not using this drug).

As requested, we have added details of the analysis, the follow-up duration, and the number of events to the manuscript text and tables (see changed Table 1 and 2 in the manuscript). In all BML assessments, we used 24-month worsening in BML MOAKS measures between baseline and 24-month follow-up MRI assessments. Therefore, the follow-up duration for BML assessments was 24 months. Regarding the comparison of knee OA standard outcomes between participants with MetS-OA and PTOA, we have added to the Results section (Line 107) and also footnote of Table 1. Regarding the comparisons of knee OA standard outcomes between COX2 inhibitor users and non-selective NSAID users, we have added to the Results section (Line 370-371) and also footnote of Table 2.

– Based on the data presented in table 1 and 2 metabolic syndrome does not affect the incidence of OA, but worsens the progression of OA. Therefore, in the abstract, the sentence in line 5 should be changed. As reported above the data in table 1 suggest the opposite, as patients without metabolic syndrome have worsen KL score at baseline (grade 2-3-4).

We have changed the mentioned sentence in the Abstract.

– The data presented in the longitudinal studies in humans do not prove the role of subchondral bone thickening and subchondral Cox2 activation on the progression of osteoarthritis. The effects of celecoxib could be due to Cox2 inhibitions in other cells types. The authors present the MRI imaging of just 1 patient where they show subchondral bone changes, however these data, although suggestive, are not sufficient to prove a clear pathogenic mechanism. If possible the data on the subchondral bone in these patients should be reported.

Following your precise and relevant comment, we assessed subchondral BML changes between COX2 inhibitor users and non-selective NSAID users using validated MOAKS measures (Osteoarthritis and cartilage 2011; 19: 990-1002; Arthritis Rheumatol 2016; 68: 2422-31). Of a total of 94 (47:47) COX2 inhibitor users: non-selective NSAID users with MetS-OA in the Knee OA progression analysis, 88 (44:44) had available baseline and follow-up MRIs. A musculoskeletal radiologist with 12 years of experience read and scored these images according to MOAKS measures. We showed that COX2 inhibitor users have lower odds of 24-month worsening in subchondral BMLs than non-selective NSAID users. New results have been added in the revised manuscript text, Results section (Line 381-387), Methods section (Line 551-570) and tables (Table 3). Details of the manuscript change following this, and your first comment has been presented earlier in response to Comment #1.

Minor issues:

– Abstract line 9. Change “undergo” to “undergoing”

– Page 12 line 219: the experiment indicates that senescent preosteoclasts secrete SASP factors, not that they are involved in OA pathogenesis. This sentence should be changed

– Please add the number of mice used in the experiments for Figure 3, supplementary Figures 3, 4, 5, 7

– The authors should indicate the source of OxLDL in the methods

The errors/typos have been changed. The number of mice in all figures have been added in Figure Legends. The source of oxLDL has been added in the Methods section (Supplemental material Line 48).

Response to comments from Reviewer C:

This is a highly interesting paper that provides important insights into metabolic syndrome (MetS) associated OA. The authors suggest that in MetS OA subchondral bone changes (as induced by senescent pre-osteoclasts and their specific secretory phenotype) precede cartilage damage and that selective inhibition of COX-2 may have beneficial effects in patients with MetS OA that got beyond the effects seen in non-MetS patients. Generally, this is a very well performed study that may contribute important novel aspects to the field. I have three major comments

– The human data as obtained from the OAI dataset are not completely convincing. This is because they largely focus in JSN as a readout for progression, which actually reflects cartilage loss rather than subchondral bone thickness. If the hypothesis of the authors holds true, differences in subchondral bone thickness should be visible particularly in early OA patients with MetS (vs non- MetS patients) and it should be possible to retrieve those data. The same is true for the COX-2 data. Also here, the authors may want to show differences in subchondral bone thickness between (part early) OA patients with/without MetS that had taken COX-2 inhibitors.

We appreciate the constructive comment of the reviewer. Following this comment and similar comments of other reviewers (Reviewer B, comments #1 & #4), we tailored our selection criteria to assess the role of subchondral bone structural changes and to better similarize the human participants and mice models of OA design, we tailored our selection criteria. We compared KOA progression in participants with MetS-OA without a history of knee trauma (to exclude PTOA cases) versus participants with PTOA without MetS (to exclude MetS-OA cases). These selection criteria helped us delineate the unique pathophysiology (e.g., prominent and rapid subchondral bone changes) in the MetS-OA phenotype. As we have shown in the animal model, while both PTOA and MetS-OA have degrees of cartilage degradation, prominent and early subchondral bone alteration is a feature of MetS-OA compared to PTOA. We gathered data of OAI participants with MRI reads for baseline and 24-month follow-up visits and carefully selected and matched participants. Following your constructive comments, we compared both standard knee OA outcomes (including JSN progression) and subchondral bone marrow lesion (BML) worsening between these two groups of MetS-OA+(PTOA) versus PTOA+(MetS-OA). For the assessment of BMLs, we used MRI Osteoarthritis Knee Score (MOAKS) measures, which have been shown to have excellent reliability (Osteoarthritis and cartilage 2011; 19: 990-1002; Arthritis Rheumatol 2016; 68: 2422-31). MOAKS is a validated and most commonly used semi-quantitative scoring tool to assess longitudinal change in subchondral BMLs within knee subregions in terms of time points (Osteoarthritis and cartilage 2011; 19: 990-1002; Arthritis Rheumatol 2016; 68: 2422-31). While we observed that the risk of knee OA radiographic progression in participants with MetS-OA is similar participants with PTOA during the follow-up period, we found that participants with MetS-OA have more odds of subchondral bone marrow lesion (BML) changes compared to participants with PTOA (even though they had similar risk of radiographic knee OA progression). This finding was evident as the higher odds of worsening in the number of knee joint subregions with subchondral BMLs. (Table 1) Further, following your precise and relevant comment, we assessed subchondral BML worsening between COX2 inhibitor users and non-selective NSAID users using validated MOAKS measures (Osteoarthritis and cartilage 2011; 19: 990-1002; Arthritis Rheumatol 2016; 68: 2422-31). Of a total of 94 (47:47) COX2 inhibitor users: non-selective NSAID users with MetS-OA in the Knee OA progression analysis, 88 (44:44) had available baseline and follow-up MRIs. A musculoskeletal radiologist with 12 years of experience read and scored these images according to MOAKS measures. We showed that COX2 inhibitor users have lower odds of 24-month worsening in subchondral BMLs than non-selective NSAID users. Results have been added to the manuscript text and tables (Table 3). The changes have been reflected in the revised Methods section (Line 552-570 and Line 576-579), Results section (Line 92-119), Table 1, Discussion section (Line 409-411 and Line 501-506), Supplementary material section (Line 116-122, Line 138-164 and Line 181-184, Supplementary Table 1, Supplementary Flowchart 1).

– As one experimental point, the authors used cytokine protein arrays to characterize the specific phenotype of the pre-osteoclasts. This is a useful though somewhat limited approach that isn't entirely up-to-date. A more holistic and unbiased approach (e.g. RNAseq) would have been far more informative.

As suggested, we have performed bulk RNA-sequencing of control and senescent preosteoclasts. The RNA-seq analysis revealed 4,056 differentially expressed genes in the senescent vs. control preosteoclasts (p < 0.05). Comparing our data with previously defined aging/senescence-induced genes (ASIGs) from publicly available mouse RNA-seq data sets (Aging Atlas database; KEGG pathway database; GO database; MSigD database) identified a total of 150 ASIGs in the senescent preosteoclasts (vs. control non-senescent preosteoclasts) (Supplemental Figure 10A). Among these ASIGs, 31 genes were upregulated, and 119 genes were downregulated in the senescent preosteoclasts relative to control cells (Supplemental Figure 10B). In the main biological process and molecular function genes, alterations in “Aging,” “Damaged DNA binding,” “chromatin DNA binding,” and “NF-kappaB binding” are notable for their known links to senescence and SASP triggering (Supplemental Figure 10C). Of note, osteoclast differentiation- and bone resorption-associated genes are among the most significantly downregulated genes (Supplemental Figure 11), indicating a diminished osteoclast differentiation capacity of the preosteoclasts after becoming senescent. These results further validate that preosteoclasts become senescent and exhibit SASP under pathological conditions. In addition, significantly downregulated osteoclast differentiation-associated genes in the senescent preosteoclasts, in combination with the results from the in vitro osteoclast differentiation assay (Supplemental Figure 8, A and B), suggest that the senescent preosteoclasts, on one hand, have declined differentiation capacity toward mature osteoclasts; on the other hand, secrete SASP factors to inhibit the differentiation of non-senescent osteoclast precursors to osteoclasts in a paracrine manner. An important future focus in the laboratory is on single-cell RNA-seq, which will give more insight into gene expression profile of individual cells and offer important understanding on the interplay between senescent cells and their neighboring cells in joint subchondral bone in the context of MetS-OA.

https://doi.org/10.7554/eLife.79773.sa2

Article and author information

Author details

  1. Weiping Su

    1. Department of Orthopaedic Surgery, Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, United States
    2. Department of Orthopaedic Surgery, The Third Xiangya Hospital of Central South University, Changsha, China
    Contribution
    Data curation, Software, Formal analysis, Investigation, Methodology
    Contributed equally with
    Guanqiao Liu and Bahram Mohajer
    Competing interests
    No competing interests declared
  2. Guanqiao Liu

    1. Department of Orthopaedic Surgery, Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, United States
    2. Division of Orthopaedics & Traumatology, Department of Orthopaedics, Southern Medical University Nanfang Hospital, Guangzhou, China
    Contribution
    Data curation, Software, Formal analysis, Investigation, Methodology
    Contributed equally with
    Weiping Su and Bahram Mohajer
    Competing interests
    No competing interests declared
  3. Bahram Mohajer

    Musculoskeletal Radiology, Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, United States
    Contribution
    Data curation, Software, Formal analysis, Validation, Investigation, Methodology, Writing – original draft
    Contributed equally with
    Weiping Su and Guanqiao Liu
    Competing interests
    No competing interests declared
  4. Jiekang Wang

    Department of Orthopaedic Surgery, Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, United States
    Contribution
    Data curation, Software, Methodology
    Competing interests
    No competing interests declared
  5. Alena Shen

    University of Southern California, Dornsife College of Letters, Arts and Sciences, Los Angeles, United States
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  6. Weixin Zhang

    Department of Orthopaedic Surgery, Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, United States
    Contribution
    Data curation, Methodology
    Competing interests
    No competing interests declared
  7. Bin Liu

    Department of Orthopaedic Surgery, Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, United States
    Contribution
    Software, Formal analysis
    Competing interests
    No competing interests declared
  8. Ali Guermazi

    Department of Radiology, Boston University School of Medicine, Boston, United States
    Contribution
    Conceptualization, Writing – review and editing
    Competing interests
    received consultancy fees from Pfizer, Novartis, MerckSerono, TissueGene, AstraZeneca, and Regeneron. The author has no other competing interests to declare
  9. Peisong Gao

    Johns Hopkins Asthma & Allergy Center, Johns Hopkins University School of Medicine, Baltimore, United States
    Contribution
    Conceptualization, Writing – review and editing
    Competing interests
    No competing interests declared
  10. Xu Cao

    Department of Orthopaedic Surgery, Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, United States
    Contribution
    Conceptualization, Funding acquisition, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8614-6059
  11. Shadpour Demehri

    Musculoskeletal Radiology, Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, United States
    Contribution
    Conceptualization, Resources, Supervision, Funding acquisition, Investigation, Writing – original draft, Project administration, Writing – review and editing
    For correspondence
    sdemehr1@jh.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5991-5924
  12. Mei Wan

    Department of Orthopaedic Surgery, Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, United States
    Contribution
    Conceptualization, Resources, Supervision, Funding acquisition, Validation, Visualization, Writing – original draft, Project administration, Writing – review and editing
    For correspondence
    mwan4@jhmi.edu
    Competing interests
    Reviewing editor, eLife
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9404-540X

Funding

National Institutes of Health (R01AG068226)

  • Mei Wan

National Institutes of Health (R01AG072090)

  • Mei Wan

National Institutes of Health (R01AR079620)

  • Shadpour Demehri

National Institutes of Health (P01AG066603)

  • Xu Cao

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

The authors thank Yasuhiro Kobayashi (Matsumoto Dental University, Japan) and Gloria H Su (Columbia University Medical Center) for kindly providing the Tnfrsf11aCre/+ (RANK-Cre) mice and the Cdkn2aflox/flox (p16flox/flox) mice, respectively. We acknowledge the assistance of The Johns Hopkins School of Medicine Microscope Facility. The authors also acknowledge the assistance of Rachel Box, Jenni Weems, and Kerry Kennedy at The Johns Hopkins Department of Orthopaedic Surgery Editorial Services for editing the manuscript. This work was supported by the National Institutes of Health grant R01AG068226 and R01AG072090 to MW, R01AR079620 to SD, and P01AG066603 to XC.

Ethics

Human subjects: We used data from the longitudinal multi-center OAI study (2004-2015 clinicaltrials.gov identifier: NCT00080171). All 4,796 enrolled patients gave written informed consent. Institutional review boards of four OAI collaborating centers have approved the OAI study's Health Insurance Portability and Accountability Act-compliant protocol (approval number: FWA00000068).

Senior and Reviewing Editor

  1. Mone Zaidi, Icahn School of Medicine at Mount Sinai, United States

Publication history

  1. Received: April 27, 2022
  2. Preprint posted: May 5, 2022 (view preprint)
  3. Accepted: May 6, 2022
  4. Accepted Manuscript published: July 26, 2022 (version 1)
  5. Version of Record published: August 10, 2022 (version 2)

Copyright

© 2022, Su, Liu, Mohajer et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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  1. Weiping Su
  2. Guanqiao Liu
  3. Bahram Mohajer
  4. Jiekang Wang
  5. Alena Shen
  6. Weixin Zhang
  7. Bin Liu
  8. Ali Guermazi
  9. Peisong Gao
  10. Xu Cao
  11. Shadpour Demehri
  12. Mei Wan
(2022)
Senescent preosteoclast secretome promotes metabolic syndrome associated osteoarthritis through cyclooxygenase 2
eLife 11:e79773.
https://doi.org/10.7554/eLife.79773

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