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10.1371/journal.pone.0308746
PONE-D-23-20551
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Work intensity and fat mass percentage are associated with asymptomatic morphometric vertebral fractures in knee osteoarthritis patients: A cross-sectional study
Work intensity and fat mass percentage in asymptomatic vertebral fractures in knee osteoarthritis
Zolkiply Izzatul Nadiah Data curation Investigation Methodology Project administration Resources Writing – original draft 1
Wong Kah Keng Data curation Formal analysis Resources Software Validation Visualization Writing – review & editing 2
Mohammad Sallehudin Hakimah Conceptualization Data curation Methodology Supervision Visualization Writing – review & editing 1
https://orcid.org/0000-0003-1019-0381
Bidin Mohammad Zulkarnain Data curation Formal analysis Methodology Resources Software 1
Che Hamzah Fahrudin Conceptualization Supervision Validation 3
Bahari Norafida Conceptualization Formal analysis Investigation Supervision Validation 4
https://orcid.org/0000-0002-7872-4581
Wan Ghazali Wan Syamimee Supervision Validation Visualization Writing – review & editing 1 5 *
1 Department of Medicine, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
2 Department of Immunology, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian, Kelantan, Malaysia
3 Department of Orthopedic, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
4 Department of Radiology, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
5 Department of Internal Medicine, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian, Kelantan, Malaysia
Yu Zhifeng Editor
Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, CHINA
Competing Interests: The authors have declared that no competing interests exist

* E-mail: syamimee@usm.my
16 9 2024
2024
19 9 e030874624 7 2023
29 7 2024
© 2024 Zolkiply et al
2024
Zolkiply et al
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Knee osteoarthritis (OA) is a common condition with a prevalence of 365 million individuals globally, and it is an independent risk factor for falls and fractures, notably asymptomatic morphometric vertebral fractures (AMVF). The high prevalence of knee OA, the severity of AMVF, and their combined impacts on quality of life underscore the need for early detection, appropriate treatment and management. To address this, our cross-sectional study aims to identify potential predictive factors associated with AMVF in knee OA patients. Our cohort consisted of 76 patients diagnosed with knee OA, predominantly female (84.2%), of Malay ethnicity (84.2%), and obese (55.3%). In univariable analysis, significant association was found between occupation (moderate or heavy work) and AMVF (p<0.001). Diabetes mellitus comorbidity (p = 0.016) and fat mass percentage (p = 0.027) also demonstrated a significant association with AMVF in knee OA patients. Multivariable logistic regression analysis revealed that an increase in fat mass percentage resulted in decreased AMVF incidence (HR: 0.83, 95% CI: 0.70–0.97; p = 0.018), while occupation (moderate or heavy work) remained a highly significant predictor (HR: 57.76, 95% CI: 4.23–788.57; p = 0.002). These findings support the potential importance of considering occupational activities and body fat composition in managing AMVF among knee OA patients, but further research is required to establish causal relationships.

The author(s) received no specific funding for this work. Data AvailabilityAll relevant data are within the paper.
Data Availability

All relevant data are within the paper.
==== Body
pmcIntroduction

Knee osteoarthritis (OA) is a prevalent condition affecting millions of people worldwide, particularly the elderly. In 2020, it was estimated that approximately 654.1 million individuals aged 40 and older had knee OA globally [1]. Knee OA is a complex and multifactorial condition involving not only the joint cartilage but also other joint structures, such as ligaments, menisci, and subchondral bone. It is also influenced by systemic factors such as inflammation, metabolic disorders, and hormonal changes [2, 3].

In addition to joint-related symptoms, knee OA is an independent risk factor of falls and fractures [3, 4], with vertebral fractures (VF) being the most common site for osteoporotic fractures (6). Approximately two-thirds of VF cases go unnoticed in a clinical setting, often referred to as asymptomatic morphometric vertebral fracture (AMVF) [5–7]. The ROAD study demonstrated a significant association between VF and knee OA with lower physical quality of life (QOL) scores in men over 40 years old. The impact of VF on physical QOL is greater than that of a cerebral stroke, highlighting the serious and potentially long-term consequences of this condition and underscoring the importance of early detection and treatment, particularly in older male populations [8].

Various factors contribute to the risk of VF in patients with knee OA. These include patient-specific factors such as age, sex, body mass index (BMI), smoking, and comorbidities, as well as disease-specific factors such as the severity and duration of joint damage, muscle weakness, and impaired balance [9–12].

The loss of skeletal muscle mass, or pre-sarcopenia, which mostly occurs with aging, can impair physical function and balance, thereby increasing the risk of falls and fractures [13, 14]. Obesity, which is common in knee OA, can increase mechanical stress on the joints and also affect bone metabolism, leading to reduced bone mineral density (BMD) and increased fracture risk [15, 16]. BMD, a measure of bone strength, is an important determinant of fracture risk, and low BMD is a well-established risk factor for VF in both men and women [17].

Despite the prevalence of knee OA and its associated increased risk of falls and fractures, there is the lack of a specific scoring system to predict fracture risk in knee OA patients. While commonly used validated scoring systems such as Knee Injury and Osteoarthritis Outcome Score, and Western Ontario and McMaster Universities Osteoarthritis Index assess joint pain, stiffness, and physical function, they do not directly evaluate fracture risk [18]. Furthermore, existing fracture risk assessment tools such as FRAX and Garvan Fracture Risk Calculator are not specific to knee OA patients and even include OA as a risk factor [19]. Overall, there is a pressing need for more specialized tools that can accurately evaluate fracture risk in knee OA patients and facilitate targeted preventive measures to reduce the risk of falls and fractures in this population.

In this study, we assessed and evaluated the relationship of potential predictive factors associated with AMVF among knee OA patients. The results of this study may aid healthcare professionals to identify knee OA patients at increased risk of fracture and to implement appropriate interventions for preventing fractures and improving patient outcomes.

Materials and methods

Study design and sampling procedures

This cross-sectional study was conducted at the orthopedics and rheumatology clinic at Hospital Pengajar Universiti Putra Malaysia (HPUPM). The study spanned from October 2022 to March 2023. The study population consisted of outpatient patients with specific knee osteoarthritis from the orthopedic and rheumatology department at HPUPM. A convenience sampling method was employed for this study. Potential candidates were selected from the list of patients at the orthopedics and rheumatology clinic, and only those who met the inclusion criteria were included in the study.

Participant eligibility criteria

The study’s inclusion criteria specified participants who were patients diagnosed with knee osteoarthritis using the Kellgren/Lawrence grading system (minimum grade of 2) by an orthopedic or rheumatologist, and who were aged over 50 years. The exclusion criteria were comprehensive to maintain the study’s integrity. Those excluded were patients diagnosed with osteoporosis or receiving osteoporosis medication, taking drugs affecting bone homeostasis such as high-dose corticosteroids, phenytoin, methotrexate, cyclosporine, or oral contraceptive pills [20], or having a known metabolic bone disorder. Also excluded were individuals with malabsorption issues [21], thyroid disease, malignancy [22], stage 3 and above chronic kidney disease (CKD) with an eGFR less than 60 ml/min using the Cockroft-Gault formula [23], and those with a history of trauma or surgical intervention on the spine. Additionally, patients unable to give consent or lie down during a whole-body dual-energy X-ray absorptiometry (DEXA) scan and pregnant women were not included in this study.

Data collection

Data collection for this study began with the acquisition of an initial patient list from the Orthopedics and Rheumatology departments at HPUPM, secured with the department head’s approval and the HPUPM’s director. Patient data were collected from both electronic patient records and questionnaires. Electronic records were used to collect data on patients’ socio-demographic profiles, comorbidities, comprehensive diagnoses, and examination results, including BMI. Additional information, such as detailed smoking history, occupational details, and menopause status for women, was collected through questionnaires during patients’ clinic visits. All collected data was then transcribed into the study’s proforma.

Upon the patients’ first visit to the clinic, the assigned physician in the orthopedic or rheumatology clinic undertook the task of assessing the patients’ conditions. Any necessary modifications on the medication were based on the physician’s clinical expertise and the specific needs of the individual patients. Following this, the patients were introduced to the research team, and their informed consent for participation in the study was obtained. A whole body DEXA scan, encompassing BMD and vertebral fracture assessment (VFA), was also scheduled for each patient during this initial visit. It is imperative to underscore that the research team neither added nor reduced any medication during this stage.

Outcome and risk factor definitions

The primary outcome of this study was the presence of asymptomatic morphometric vertebral fractures (AMVF). AMVF was assessed using vertebral fracture assessment (VFA) as part of the whole-body DEXA scan. Vertebral fractures were identified and graded according to semi-quantitative methods as described previously [24].

The variables of this study are categorized into independent and dependent variables. Independent variables include sociodemographic factors such as age, gender, race, smoking status, menopause status for women, duration of knee osteoarthritis, and whether the patient underwent knee arthroplasty. Comorbidities were also considered as independent variables including diabetes mellitus, hypertension, dyslipidemia, and ischemic heart disease (IHD). Furthermore, indicators of pre-sarcopenia, derived from the Appendicular Skeletal Muscle Mass from DEXA, lumbar BMD, and obesity status, determined through BMI or fat mass from DEXA, were also included as independent variables. Fat mass percentage was derived from the whole-body DEXA scan, with the scanner software calculating total body fat mass and providing this as a percentage of total body mass. BMI was calculated by dividing the patient’s weight in kilograms by the square of their height in meters (kg/m2). Lumbar BMD was measured directly by the DEXA scan, focusing on the L1-L4 vertebrae, and reported in g/cm2. On the other hand, the dependent variables in this study were the occurrence, location, type, severity, and number of AVMF.

Study ethics and patient confidentiality procedures

This study was approved by the Ethics Committee for Research involving human subjects of University Putra Malaysia (JKEUPM) on October 19, 2022, with the reference number UPM/TNCPI/RMC/JKEUPM/1.4.18.2 (JKEUPM) JKEUPM-2022-523. Prior to data collection, the necessary approvals were also secured from the orthopedic and rheumatology clinic involved. This study was strictly conducted in accordance with ethical principles, including the protection of subject vulnerability, absence of conflict of interest, privacy and confidentiality, sensitivity to community considerations, and ensuring benefits to the participants. To ensure patient confidentiality, a secure, password-protected database was used to store all names, linked only to a study identification number. This identification number replaced patient identifiers on subject data sheets. Data entry was completed on a password-protected computer. Both hard and soft copies of personal data, including medical records and study data, will be archived for a period of five years. After this period, all study data and documents will be properly disposed of, destroyed, or deleted in accordance with established protocols. In terms of publication policy, no personal information will be disclosed and subjects will not be identifiable in any published survey findings. The de-identified raw data of this study have been deposited to Zenodo: https://zenodo.org/records/12792011 and this record will be deleted after October 18, 2027, in accordance with the approved human ethics for this study.

Statistical analysis

The normality of variable distribution was assessed using Shapiro-Wilk test. The chi-squared test or Fisher’s exact test were utilized as appropriate for comparing categorical variables across multiple groups. For comparison between two groups of continuous variables, data with normal or non-normal distribution were tested using independent t-test or Mann-Whitney test, respectively. Multivariable logistic regression analysis was conducted by including multiple variables simultaneously to determine their individual associations with AMVF in knee OA patients after controlling for the effects of other variables. Our multivariable logistic regression model included variables that demonstrated significance in the univariable analysis, as well as variables that showed a trend towards significance. We also included other comorbidities (dyslipidemia, hypertension, IHD) to account for patients presenting with multiple comorbidities. All selected variables were included together in the multivariable analysis. A p-value below 0.05 was used to determine statistical significance. All statistical analyses were conducted using IBM SPSS Statistics (version 29.0).

Results

Patient’s demographic and clinical characteristics

Our study included a total of 76 patients diagnosed with knee OA. The demographic characteristics of the patients are presented in Table 1. The mean age of the patients was 66.39 ± 5.24 years, with 61.8% of them being 65 years or older. The majority of the patients were female (84.2%) and of Malay ethnicity (84.2%). Most of the patients were non-smokers (98.7%), with a mean height of 155.61±7.79 cm and a mean weight of 74.04±10.48 kg. The mean BMI was 30.65±4.83 kg/m2, with 55.3% of the patients being classified as obese.

10.1371/journal.pone.0308746.t001 Table 1 Clinico-demographic characteristics of knee OA patients (n = 76).

Variables	Mean ± SD or n (%)	
Age (years)	66.39 ± 5.24	
    <65	29 (38.2)	
    ≥65	47 (61.8)	
Gender		
    Male	12 (15.8)	
    Female	64 (84.2)	
Ethnicity		
    Malay	64 (84.2)	
    Chinese	3 (3.9)	
    Indian	9 (11.8)	
Smoking		
    No	75 (98.7)	
    Yes	1 (1.3)	
Measurements		
    Height (cm)	155.61 ± 7.79	
    Weight (kg)	74.04 ± 10.48	
BMI (kg/m 2 )	30.65 ± 4.83	
    Normal	8 (10.5)	
    Overweight	26 (34.2)	
    Obese	42 (55.3)	
Menopause status		
    No	1 (1.6)	
    Yes	63 (98.4)	
Occupation		
    Sedentary	3 (3.9)	
    Light work	25 (32.9)	
    Moderate work	35 (46.1)	
    Heavy work	13 (17.1)	
Comorbidities *		
    Diabetes mellitus	37 (48.7)	
    Hypertension	52 (68.4)	
    Dyslipidemia	55 (72.4)	
    IHD	6 (7.9)	
Duration of knee OA		
    <10 years	43 (56.6)	
    ≥10 years	33 (43.4)	
Knee arthroplasty		
    No	57 (75.0)	
    Yes	19 (25.0)	
Pre-sarcopenia status		
    Normal	60 (78.9)	
    Pre-sarcopenia	16 (21.1)	
Lumbar BMD category		
    Normal	31 (40.8)	
    Osteopenia	36 (47.4)	
    Osteoporosis	9 (11.8)	
*Certain patients presented with multiple comorbidities.

In terms of comorbidities, 48.7% of the patients had diabetes mellitus, 68.4% had hypertension, 72.4% had dyslipidemia, and 7.9% had IHD. The duration of knee OA was less than 10 years for 56.6% of the patients and 10 years or more for 43.4% of the patients. A quarter of the patients (25.0%) had undergone knee arthroplasty. Pre-sarcopenia was observed in 21.1% of the patients. The lumbar BMD was categorized as normal in 40.8% of the patients, osteopenia in 47.4%, and osteoporosis in 11.8%.

Association of categorical and continuous variables with AMVF in knee OA patients

The data was analyzed to determine the association of categorical variables with the presence of AMVF in knee OA patients (Table 2). The variables considered in this analysis included occupation, diabetes mellitus, hypertension, dyslipidemia, and IHD. The analysis revealed a significant association between occupation and AMVF. Specifically, patients engaged in moderate or heavy work had a higher incidence of AMVF (p<0.001). In addition to occupation, the presence of diabetes mellitus also showed a significant association with AMVF (p = 0.016). However, other comorbidities such as hypertension, dyslipidemia, and IHD did not show a significant association with AMVF, indicating that these conditions did not have a significant association with the presence of AMVF in knee OA patients.

10.1371/journal.pone.0308746.t002 Table 2 Association of categorical variables with AMVF status of knee OA patients (n = 76).

Variables	Without AMVF (n = 53); Mean ± SD or n (%)	With AMVF (n = 23); Mean ± SD or n (%)	p-value	
Age				
    <65 years old	22 (28.9)	7 (9.2)	0.361	
    ≥65 years old	31 (40.8)	16 (21.1)		
Gender				
    Male	6 (7.9)	6 (7.9)	0.105	
    Female	47 (61.8)	17 (22.4)		
Ethnicity				
    Malay	45 (61.8)	19 (25.0)	>0.999	
    Chinese	2 (2.6)	1 (1.3)		
    Indian	6 (7.9)	3 (3.9)		
Smoking				
    No	53 (69.7)	22 (28.9)	0.303	
    Yes	0 (0)	1 (1.3)		
BMI categories				
    Normal	4 (5.3)	4 (5.3)	0.288	
    Overweight	17 (22.4)	9 (11.8)		
    Obese	32 (42.1)	10 (13.2)		
Menopause				
    No	1 (1.6)	0 (0)	>0.999	
    Yes	46 (71.9)	17 (26.6)		
Occupation				
    Sedentary or light work	27 (35.5)	1 (1.3)	<0.001	
    Moderate or heavy work	26 (34.2)	22 (28.9)		
Comorbidities				
    Diabetes mellitus	21 (27.6)	16 (21.1)	0.016	
    No diabetes mellitus	32 (42.1)	7 (9.2)		
    Hypertension	34 (44.7)	18 (23.7)	0.224	
    No hypertension	19 (25.0)	5 (6.6)		
    Dyslipidemia	39 (51.3)	16 (21.1)	0.718	
    No dyslipidemia	14 (18.4)	7 (9.2)		
    IHD	4 (5.3)	2 (2.6)	>0.999	
    No IHD	49 (64.5)	21(27.6)		
Duration of Knee OA				
    ≥10 years	30 (39.5)	13 (17.1)	>0.999	
    <10 years	23(30.3)	10 (13.2)		
Knee Arthroplasty				
    Yes	15 (19.7)	4 (5.3)	0.313	
    No	38 (50.0)	19 (25.0)		
Pre-sarcopenia status				
    Normal	43 (56.6)	17 (22.4)	0.545	
    Pre-sarcopenia	10 (13.2)	6 (7.9)		
Lumbar BMD status				
    Normal	25 (32.9)	6 (7.9)	0.217	
    Osteopenia	22 (28.9)	14 (18.4)		
    Osteoporosis	6 (7.9)	3 (3.9)		
Significant p-value is in bold.

Our study also examined the association of continuous variables with AMVF in knee OA patients (Fig 1). The continuous variables considered in this analysis included fat mass percentage, BMI, lumbar BMD, age, height, and weight. The analysis demonstrated a significant association between fat mass percentage and AMVF (p = 0.027). BMI and lumbar BMD showed a non-significant trend towards AMVF, with p-values of 0.053 and 0.096, respectively. Other variables including age, height, and weight did not show a significant association with AMVF in knee OA patients.

10.1371/journal.pone.0308746.g001 Fig 1 Association of knee OA AMVF patients with continuous variables.

(A) Age (years); (B) Height (cm); (C) Weight (kg); (D) BMI (kg/m2); (E) Fat mass (%); (F) Lumbar BMD (g/cm2). Within each violin plot, the upper, middle (darker), and lower lines denote the third quartile, median, and first quartile, respectively.

Multivariable logistic regression analysis on the variables associated with AMVF in knee OA patients

A multivariable logistic regression analysis was performed to identify the variables associated with AMVF in knee OA patients. The variables included those that demonstrated significance in the univariable analysis (i.e. occupation, diabetes mellitus comorbidity, fat mass), and BMI and lumbar BMD due to both of these variables showed a trend towards significance in univariable analysis. Other comorbidities (i.e. dyslipidemia, hypertension, IHD) were also included in the multivariable model to account for patients presented with multiple comorbidities. The analysis showed that occupation, specifically moderate or heavy work, demonstrated a significant association with AMVF [Hazard ratio (HR): 57.76, 95% confidence interval (CI): 4.23–788.57; p = 0.002] (Table 3). Furthermore, an increase in fat mass percentage was significantly associated with a decrease in AMVF occurrence in knee OA patients (HR: 0.83, 95% CI: 0.70–0.97; p = 0.018). BMI (HR: 1.24, 95% CI: 1.00–1.54; p = 0.053) and lumbar BMD (HR: 0.56, 95% CI: 0.31–1.01; p = 0.054) showed trends towards significance. Other factors including comorbidities were not significantly associated with AMVF: dyslipidemia (HR: 0.21, 95% CI: 0.04–1.28; p = 0.091), diabetes mellitus (HR: 3.08, 95% CI: 0.62–15.40; p = 0.170), hypertension (HR: 2.56, 95% CI: 0.42–15.41; p = 0.306), and IHD (HR: 0.60, 95% CI: 0.05–7.40; p = 0.690) (Table 3).

10.1371/journal.pone.0308746.t003 Table 3 Multivariable logistic regression analysis on the variables associated with AMVF in knee OA patients (n = 76).

Variables	B coefficient	HR (95% confidence interval)	p-value	
Occupation (Moderate or heavy work)	4.06	57.76 (4.23–788.57)	0.002	
Fat mass (Increasing percentage)	-0.19	0.83 (0.70–0.97)	0.018	
BMI (Increasing kg/m 2 )	0.22	1.24 (1.00–1.54)	0.053	
Lumbar BMD (Increasing g/cm 2 )	-0.58	0.56 (0.31–1.01)	0.054	
Comorbidity (Dyslipidemia)	-1.55	0.21 (0.04–1.28)	0.091	
Comorbidity (Diabetes mellitus)	1.13	3.08 (0.62–15.40)	0.170	
Comorbidity (Hypertension)	0.94	2.56 (0.42–15.41)	0.306	
Comorbidity (IHD)	-0.51	0.60 (0.05–7.40)	0.690	
Significant p-value is in bold.

Discussion

In this study, further insights were provided pertaining to the potential factors associated with AMVF in knee OA patients. We identified a significant association of AMVF with moderate or heavy work occupation, and a significant inverse association with fat mass percentage.

In our cohort of knee OA patients, those engaged in moderate or heavy work demonstrated a higher incidence of AMVF. This is consistent with multiple past studies which have demonstrated that physically demanding occupations or heavy manual labor can increase the risk of musculoskeletal disorders including knee OA [25, 26]. Notably, a longitudinal, multiple-cohort study showed that individuals with heavy manual occupations demonstrated a two-fold increased risk of radiographic knee OA [26]. In a separate international participant-level cohort study, a two-fold increase risk of having radiographic knee OA, knee pain, and symptomatic radiographic knee OA was also observed in those with heavy manual occupations compared with sedentary occupations [25]. Likewise, our multivariable analysis indicates that physically demanding occupations may increase the risk of developing AMVF in knee OA patients. Further research is recommended to determine the mechanisms governing this association.

Interestingly, we observed that an increase in fat mass percentage, but not BMI, was associated with a decrease in AMVF occurrence in knee OA patients. This is comparable with the findings of another cross-sectional study of Malaysian knee OA patients whereby a low body fat percentage was inversely associated with AMVF prevalence in univariable analysis [7]. However, these observations are partially inconsistent with recent studies that demonstrated higher waist circumference was associated with increased risk of fracture in men [27]. Nonetheless, this study also found that BMI was not significantly associated with fracture risk, similar with another study which demonstrated that AMVF was not significantly associated with BMI categories [7]. Likewise, another study also concluded that BMI was not significantly associated with bone fracture [28]. These studies align with our findings that BMI was not significantly associated with fracture risk.

To the best of our knowledge, a direct mechanistic link between an increase in fat mass percentage and a decrease in AMVF occurrence in knee OA patients has not been demonstrated. It is noteworthy that our study examined fat mass percentage, which may provide a more precise measure of body composition compared with BMI or waist circumference. Findings of our study suggest that a higher body fat percentage may offer some protective benefits against AMVF in knee OA patients, potentially due to the capacity of fat tissues to absorb physical pressure. However, this remains unclear and warrants further investigation.

We acknowledge the limitations of our study as follows: 1) The cross-sectional design of this study does not allow for the determination of causal relationships between the factors examined and AMVF; 2) The small sample size of 76 patients confers limited statistical power, and our results should thus be considered exploratory and descriptive rather than definitive; 3) Our study showed an association between higher fat mass percentage and lower AMVF occurrence, but this finding should be interpreted cautiously. The cross-sectional design and small sample size limit causal inferences. Hence, the complex relationship between body composition and bone health requires further investigation.

Nevertheless, while DEXA scans are commonly used to assess BMD, they do not comprehensively evaluate the factors associated with fractures in knee OA patients. Therefore, it has been recommended to include a whole-body DEXA scan to assess sarcopenia and fat mass, as well as a VFA to detect vertebral fractures in addition to BMD by DEXA, in order to provide a more comprehensive assessment of fracture risk [29–31]. In this study, we utilized whole-body DEXA to assess BMD, muscle mass, and fat mass, providing a more extensive understanding of the relationship between these factors and vertebral fractures in this population. Furthermore, we used VFA instead of traditional X-rays to evaluate VF in order to improve sensitivity and specificity of the detection [31].

Overall, this study provides further insights into the factors associated with AMVF in knee OA patients, supporting the potential importance of considering patients’ occupational activities in the management and prevention of AMVF in knee OA patients. However, larger longitudinal studies are required to establish causal relationships and confirm these findings. Finally, our exploratory study found an unexpected association between higher fat mass percentage and lower AMVF occurrence in knee OA patients. This highlights the complex relationship between body composition and bone health, underscoring the need for longitudinal studies.

10.1371/journal.pone.0308746.r001
Decision Letter 0
Yu Zhifeng Academic Editor
© 2024 Zhifeng Yu
2024
Zhifeng Yu
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Submission Version0
4 Jul 2024

PONE-D-23-20551Work Intensity and Fat Mass Percentage are Risk Factors for Asymptomatic Morphologic Vertebral Fractures in Knee Osteoarthritis PatientsPLOS ONE

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3. We note you have included a table to which you do not refer in the text of your manuscript. Please ensure that you refer to Table 3 in your text; if accepted, production will need this reference to link the reader to the Table.

4. Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments:

The paper reads well and seeks to answer an important question of risk factors for asymptomatic morphological vertebral fractures in OA. Please see my comments below.

Please indicate the study design in the study title in accordance with study reporting guidelines e.g. STROBE

Report the prevalence of OA in this sentence instead of saying ‘millions’: “Knee osteoarthritis (OA) is a common condition affecting millions of individuals globally”.

A cross sectional study is not a suitable study design to assess risk factors for outcomes of interest (i.e., to derive causal relationships). Therefore, the authors of this work should refrain from claiming definitive conclusion regarding the association of work intensity and fat mass percentage on AMVF. Additionally, the sample size of 76 patients is very small for risk factor analysis in observational studies. Ideally, this should be a descriptive study.

If fat mass percentage associated with a decreased diagnosis of AMVF, how is it a risk factor?

Please tone down the postulated implication of this study. First, as I already mentioned, you cannot make inference from a cross-sectional study. Noted also that risk factor identification is a step towards producing a risk assessment tool which aids the identification of at-risk population.

Did you access electronic patient records or were data collected by questionnaires?

Please provide a sub-section on how the outcome was defined separate from that of how risk factors were defined., e.g. how was fat mass percentage derived? And was this already recorded in the data used or was it calculated by the research team?

Could you elaborate on the model building strategy you employed? You mention including variables in the model simultaneously, but it is unclear what this means.

Please be consistent in reporting OR (95% CI) in addition to P values in your results. Avoid reporting only P-values.

Could the authors not downplay the fact that sample size for this study was small while stating this as a limitation? I am surprised that this is the only limitation acknowledged.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

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Comments to the Author

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Reviewer #1: Yes

Reviewer #2: No

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

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Reviewer #1: Yes

Reviewer #2: No

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

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Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The document presents scientific relevance and the authors objectively describe what was proposed, and the results are clearly exposed. The discussion is well directed to the results found, as well as the conclusion.

Reviewer #2: Thank you for the opportunity to review Ghazali et al manuscript on “Work Intensity and Fat Mass Percentage are Risk Factors for Asymptomatic Morphologic Vertebral Fractures in Knee Osteoarthritis Patients”. The paper reads well and seeks to answer an important question of risk factors for asymptomatic morphological vertebral fractures in OA. Please see my comments below.

Please indicate the study design in the study title in accordance with study reporting guidelines e.g. STROBE

Report the prevalence of OA in this sentence instead of saying ‘millions’: “Knee osteoarthritis (OA) is a common condition affecting millions of individuals globally”.

A cross sectional study is not a suitable study design to assess risk factors for outcomes of interest (i.e., to derive causal relationships). Therefore, the authors of this work should refrain from claiming definitive conclusion regarding the association of work intensity and fat mass percentage on AMVF. Additionally, the sample size of 76 patients is very small for risk factor analysis in observational studies. Ideally, this should be a descriptive study.

If fat mass percentage associated with a decreased diagnosis of AMVF, how is it a risk factor?

Please tone down the postulated implication of this study. First, as I already mentioned, you cannot make inference from a cross-sectional study. Noted also that risk factor identification is a step towards producing a risk assessment tool which aids the identification of at-risk population.

Did you access electronic patient records or were data collected by questionnaires?

Please provide a sub-section on how the outcome was defined separate from that of how risk factors were defined., e.g. how was fat mass percentage derived? And was this already recorded in the data used or was it calculated by the research team?

Could you elaborate on the model building strategy you employed? You mention including variables in the model simultaneously, but it is unclear what this means.

Please be consistent in reporting OR (95% CI) in addition to P values in your results. Avoid reporting only P-values.

Could the authors not downplay the fact that sample size for this study was small while stating this as a limitation? I am surprised that this is the only limitation acknowledged.

**********

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Reviewer #1: No

Reviewer #2: No

**********

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While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

10.1371/journal.pone.0308746.r002
Author response to Decision Letter 0
Submission Version1
28 Jul 2024

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

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https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

Thank you for the comments. We have ensured that the manuscript meets PLOS ONE’s style requirements accordingly

2. We note that your Data Availability Statement is currently as follows: All relevant data are within the manuscript and its Supporting Information files.

Please confirm at this time whether or not your submission contains all raw data required to replicate the results of your study. Authors must share the “minimal data set” for their submission. PLOS defines the minimal data set to consist of the data required to replicate all study findings reported in the article, as well as related metadata and methods (https://journals.plos.org/plosone/s/data-availability#loc-minimal-data-set-definition).

For example, authors should submit the following data:

- The values behind the means, standard deviations and other measures reported;

- The values used to build graphs;

- The points extracted from images for analysis.

Authors do not need to submit their entire data set if only a portion of the data was used in the reported study.

If your submission does not contain these data, please either upload them as Supporting Information files or deposit them to a stable, public repository and provide us with the relevant URLs, DOIs, or accession numbers. For a list of recommended repositories, please see https://journals.plos.org/plosone/s/recommended-repositories.

If there are ethical or legal restrictions on sharing a de-identified data set, please explain them in detail (e.g., data contain potentially sensitive information, data are owned by a third-party organization, etc.) and who has imposed them (e.g., an ethics committee). Please also provide contact information for a data access committee, ethics committee, or other institutional body to which data requests may be sent. If data are owned by a third party, please indicate how others may request data access.

Thank you for the comments. We have deposited the raw data required to reproduce our study’s results including the clinical, demographical (without any personal identifiers and all patients have been anonymized) and other relevant characteristics, as well as the data and graphs for Figure 1, to Zenodo: https://zenodo.org/records/12792011

As stated in our manuscript: “This study was approved by the Ethics Committee for Research involving human subjects of University Putra Malaysia (JKEUPM) on October 19, 2022, with the reference number UPM/TNCPI/RMC/JKEUPM/1.4.18.2 (JKEUPM) JKEUPM-2022-523. Prior to data collection, the necessary approvals were also secured from the orthopedic and rheumatology clinic involved. This study was strictly conducted in accordance with ethical principles, including the protection of subject vulnerability, absence of conflict of interest, privacy and confidentiality, sensitivity to community considerations, and ensuring benefits to the participants.

To ensure patient confidentiality, a secure, password-protected database was used to store all names, linked only to a study identification number. This identification number replaced patient identifiers on subject data sheets. Data entry was completed on a password-protected computer. Both hard and soft copies of personal data, including medical records and study data, will be archived for a period of five years. After this period, all study data and documents will be properly disposed of, destroyed, or deleted in accordance with established protocols. In terms of publication policy, no personal information will be disclosed and subjects will not be identifiable in any published survey findings.”

Therefore, the raw data deposited to Zenodo will be deleted after October 18, 2027, in accordance with the approved human ethics for this study. We have added these new descriptions in the Study Ethics and Patient Confidentiality Procedures section:

**********

The de-identified raw data of this study have been deposited to Zenodo: https://zenodo.org/records/12792011 and this record will be deleted after October 18, 2027, in accordance with the approved human ethics for this study.

**********

3. We note you have included a table to which you do not refer in the text of your manuscript. Please ensure that you refer to Table 3 in your text; if accepted, production will need this reference to link the reader to the Table.

Thank you for the comments. Table 3 has now been referred in the text as follows (Results section):

**********

The analysis showed that occupation, specifically moderate or heavy work, demonstrated a significant association with AMVF [Hazard ratio (HR): 57.76, 95% confidence interval (CI): 4.23-788.57; p=0.002] (Table 3).

**********

4. Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Thank you for the comments. The reference list has been reviewed accordingly.

Additional Editor Comments:

The paper reads well and seeks to answer an important question of risk factors for asymptomatic morphological vertebral fractures in OA. Please see my comments below.

Please indicate the study design in the study title in accordance with study reporting guidelines e.g. STROBE

Thank you very much for the comments. The title has now been changed to:

**********

Work Intensity and Fat Mass Percentage are Associated with Asymptomatic Morphometric Vertebral Fractures in Knee Osteoarthritis Patients: A Cross-Sectional Study

**********

Report the prevalence of OA in this sentence instead of saying ‘millions’: “Knee osteoarthritis (OA) is a common condition affecting millions of individuals globally”.

Thank you for the comments. The sentence has now been revised as follows:

**********

Knee osteoarthritis (OA) is a common condition with a prevalence of 365 million individuals globally

**********

A cross sectional study is not a suitable study design to assess risk factors for outcomes of interest (i.e., to derive causal relationships). Therefore, the authors of this work should refrain from claiming definitive conclusion regarding the association of work intensity and fat mass percentage on AMVF. Additionally, the sample size of 76 patients is very small for risk factor analysis in observational studies. Ideally, this should be a descriptive study.

Thank you for the comments. We have now revised the required parts to tone down previous statements of the manuscript as follows:

**********

Abstract section: "These findings support the potential importance of considering occupational activities and body fat composition in managing AMVF among knee OA patients, but further research is required to establish causal relationships"

Revised limitations of the Discussion section: "1) The cross-sectional design of this study does not allow for the determination of causal relationships between the factors examined and AMVF; 2) The small sample size of 76 patients confers limited statistical power, and our results should thus be considered exploratory and descriptive rather than definitive."

Conclusion (the final paragraph of the manuscript): “Overall, this study provides further insights into the factors associated with AMVF in knee OA patients, supporting the potential importance of considering patients’ occupational activities in the management and prevention of AMVF in knee OA patients. However, larger longitudinal studies are required to establish causal relationships and confirm these findings.”

**********

If fat mass percentage associated with a decreased diagnosis of AMVF, how is it a risk factor?

Thank you for the comments. In the conclusion paragraph, we have removed the phrase “risk factors” and to simply state “factors” for the sentence “Overall, this study provides further insights into the factors associated with AMVF in knee OA patients”.

We have also added the following new limitation of the study in the Discussion section:

**********

3) Our study showed an association between higher fat mass percentage and lower AMVF occurrence, but this finding should be interpreted cautiously. The cross-sectional design and small sample size limit causal inferences. Hence, the complex relationship between body composition and bone health requires further investigation.

**********

The conclusion paragraph (the final paragraph of the manuscript) has also been revised as follows:

**********

Finally, our exploratory study found an unexpected association between higher fat mass percentage and lower AMVF occurrence in knee OA patients. This highlights the complex relationship between body composition and bone health, underscoring the need for longitudinal studies.

**********

Please tone down the postulated implication of this study. First, as I already mentioned, you cannot make inference from a cross-sectional study. Noted also that risk factor identification is a step towards producing a risk assessment tool which aids the identification of at-risk population.

Thank you for the comments.

The conclusion paragraph has been revised as follows:

**********

Previous version: Overall, this study provides further insights into the risk factors associated with AMVF in knee OA patients, highlighting the importance of considering patients’ occupational activities in the management and prevention of AMVF in knee OA patients. Moreover, our findings demonstrate the potential role of fat body composition in fracture risk, suggesting that this may be an important area of future research in knee OA patients.

Revised version: Overall, this study provides further insights into the factors associated with AMVF in knee OA patients, supporting the potential importance of considering patients’ occupational activities in the management and prevention of AMVF in knee OA patients. However, larger longitudinal studies are required to establish causal relationships and confirm these findings. Finally, our exploratory study found an unexpected association between higher fat mass percentage and lower AMVF occurrence in knee OA patients. This highlights the complex relationship between body composition and bone health, underscoring the need for longitudinal studies.

**********

In addition, when describing our results, the term "risk factors" have been removed from the title, discussion, and conclusion.

Did you access electronic patient records or were data collected by questionnaires?

Thank you for the comments. Patient’s data were collected from both electronic patients records as well as from questionnaires, and the follow descriptions have been added in the Materials and Methods section:

**********

Patient data were collected from both electronic patient records and questionnaires. Electronic records were used to collect data on patients’ socio-demographic profiles, comorbidities, comprehensive diagnoses, and examination results, including BMI. Additional information, such as detailed smoking history, occupational details, and menopause status for women, was collected through questionnaires during patients’ clinic visits. All collected data was then transcribed into the study’s proforma.

**********

Please provide a sub-section on how the outcome was defined separate from that of how risk factors were defined., e.g. how was fat mass percentage derived? And was this already recorded in the data used or was it calculated by the research team?

Thank you for the comments. The following descriptions have been added in the Materials and Methods section:

**********

Outcome and Risk Factor Definitions

The primary outcome of this study was the presence of asymptomatic morphometric vertebral fractures (AMVF). AMVF was assessed using vertebral fracture assessment (VFA) as part of the whole-body DEXA scan. Vertebral fractures were identified and graded according to semi-quantitative methods as described previously [24].

Fat mass percentage was derived from the whole-body DEXA scan, with the scanner software calculating total body fat mass and providing this as a percentage of total body mass. BMI was calculated by dividing the patient's weight in kilograms by the square of their height in meters (kg/m²). Lumbar BMD was measured directly by the DEXA scan, focusing on the L1-L4 vertebrae, and reported in g/cm².

**********

Could you elaborate on the model building strategy you employed? You mention including variables in the model simultaneously, but it is unclear what this means.

Thank you for the comments. The following descriptions have been added in the Statistical Analysis section:

**********

Our multivariable logistic regression model included variables that demonstrated significance in the univariable analysis (occupation, diabetes mellitus comorbidity, fat mass), as well as variables that showed a trend towards significance (BMI and lumbar BMD). We also included other comorbidities (dyslipidemia, hypertension, IHD) to account for patients presenting with multiple comorbidities. All selected variables were included together in the multivariable analysis.

**********

Please be consistent in reporting OR (95% CI) in addition to P values in your results. Avoid reporting only P-values.

Thank you for the comments. The following has now been added in the Results section:

**********

BMI (HR: 1.24, 95% CI: 1.00-1.54; p=0.053) and lumbar BMD (HR: 0.56, 95% CI: 0.31-1.01; p=0.054) showed trends towards significance. Other factors including comorbidities were not significantly associated with AMVF: dyslipidemia (HR: 0.21, 95% CI: 0.04-1.28; p=0.091), diabetes mellitus (HR: 3.08, 95% CI: 0.62-15.40; p=0.170), hypertension (HR: 2.56, 95% CI: 0.42-15.41; p=0.306), and IHD (HR: 0.60, 95% CI: 0.05-7.40; p=0.690) (Table 3).

**********

Could the authors not downplay the fact that sample size for this study was small while stating this as a limitation? I am surprised that this is the only limitation acknowledged.

Thank you for the comments. The limitations have been revised and expanded as follows:

**********

We acknowledge the limitations of our study as follows: 1) The cross-sectional design of this study does not allow for the determination of causal relationships between the factors examined and AMVF; 2) The small sample size of 76 patients confers limited statistical power, and our results should thus be considered exploratory and descriptive rather than definitive; 3) Our study showed an association between higher fat mass percentage and lower AMVF occurrence, but this finding should be interpreted cautiously. The cross-sectional design and small sample size limit causal inferences. Hence, the complex relationship between body composition and bone health requires further investigation.

**********

Thank you.

Attachment Submitted filename: Response to Reviewers_vFinal.docx

10.1371/journal.pone.0308746.r003
Decision Letter 1
Yu Zhifeng Academic Editor
© 2024 Zhifeng Yu
2024
Zhifeng Yu
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Submission Version1
30 Jul 2024

Work Intensity and Fat Mass Percentage are Associated with Asymptomatic Morphometric Vertebral Fractures in Knee Osteoarthritis Patients: A Cross-Sectional Study

PONE-D-23-20551R1

Dear Dr. Wan Ghazali,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Zhifeng Yu

Academic Editor

PLOS ONE

10.1371/journal.pone.0308746.r004
Acceptance letter
Yu Zhifeng Academic Editor
© 2024 Zhifeng Yu
2024
Zhifeng Yu
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
2 Aug 2024

PONE-D-23-20551R1

PLOS ONE

Dear Dr. Wan Ghazali,

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team.

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on behalf of

Dr. Zhifeng Yu

Academic Editor

PLOS ONE
==== Refs
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12 Bergink AP , van der Klift M , Hofman A , Verhaar JA , van Leeuwen JP , Uitterlinden AG , et al . Osteoarthritis of the knee is associated with vertebral and nonvertebral fractures in the elderly: the Rotterdam Study. Arthritis Rheum. 2003;49 (5 ):648–57. doi: 10.1002/art.11380 .14558050
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14 Visser AW , de Mutsert R , Loef M , le Cessie S , den Heijer M , Bloem JL , et al . The role of fat mass and skeletal muscle mass in knee osteoarthritis is different for men and women: the NEO study. Osteoarthritis Cartilage. 2014;22 (2 ):197–202. doi: 10.1016/j.joca.2013.12.002 .24333295
15 Lee S , Kim TN , Kim SH . Sarcopenic obesity is more closely associated with knee osteoarthritis than is nonsarcopenic obesity: a cross-sectional study. Arthritis Rheum. 2012;64 (12 ):3947–54. doi: 10.1002/art.37696 .23192792
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