Araf Reshad^1*, Md. Moklesur Rahman², Nafisur Rahman³, Jannatul Ferdaus⁴

¹Faculty of Health Sciences, Queen’s University, Kingston, Ontario, Canada

²Division of Orthopedics, Department of Surgery, Square Hospitals Ltd., Dhaka, Bangladesh

^3,4Department of Radiology, Square Hospitals Ltd., Dhaka, Bangladesh

^*Corresponding Author: Araf Reshad, Faculty of Health Sciences, Queen’s University, Kingston, Ontario, Canada

Received: 09 February 2026; Accepted: 16 February 2026; Published: 19 February 2026

Article Information

Citation: Araf Reshad, Md. Moklesur Rahman, Nafisur Rahman, Jannatul Ferdaus. Frequency and Clinical Correlates of Low Bone Mineral Density among Adults Attending a Tertiary Care Diagnostic Center. Journal of Surgery and Research. 9 (2026): 92-97.

DOI: 10.26502/jsr.10020492

Abstract

Keywords

Low bone mineral density, Osteoporosis, Skeletal disorder

Article Details

Introduction

Reduced bone mineral density (BMD), microarchitectural deterioration, and weakened bones are the hallmarks of osteoporosis, a progressive systemic skeletal disorder that puts people at risk for fragility fractures [1]. Due mostly to population aging and longer life expectancies, the prevalence of osteoporosis is estimated to be over 200 million worldwide [2]. Due to fracture-related complications that primarily affect the spine, hip, and distal radius, the condition imposes significant morbidity, mortality, and economic costs [3]. Because estrogen deficiency after menopause accelerates bone resorption and upsets the balance of bone remodeling, postmenopausal women are the demographic group most at risk for osteoporosis [4]. Research shows that the prevalence of osteopenia and osteoporosis rises sharply with age, especially in postmenopausal women compared to men, and that BMD has a major impact on the risk of long-term complications [5]. Osteoporosis does, however, also affect men, particularly those over 70, though it is often underdiagnosed in this group. Measurements are usually taken at the lumbar spine, total hip, and femoral neck using dual-energy X-ray absorptiometry (DXA), which is still the gold standard diagnostic technique for BMD assessment [6]. T-scores are used by the World Health Organization to determine diagnostic thresholds: normal (T-score ≥ -1.0), osteopenia (-2.5 < T-score < -1.0), and osteoporosis (T-score ≤ -2.5) [7]. Clinical management decisions about pharmacological interventions are guided by these standardized criteria, which also make international comparisons easier. Osteoporosis is caused by several risk factors, such as advanced age, female sex, low body mass index (BMI), hormonal deficiencies, insufficient intake of calcium and vitamin D, sedentary lifestyle, smoking, excessive alcohol consumption, and certain medications like glucocorticoids [8]. The relationship between body mass index and bone mineral density (BMD) is complicated; research indicates that higher BMI categories may offer protective effects against bone loss, although this association seems to be site-dependent and age-specific [9]. Developing focused prevention and treatment plans requires an understanding of these modifiable and non-modifiable risk factors. Osteoporosis is still largely underdiagnosed and undertreated worldwide, despite increased awareness; many people have their first fracture without a diagnosis or treatment [10]. Tertiary care diagnostic centers, which offer specialized BMD evaluation services for symptomatic individuals and those with identified risk factors, are essential in case finding and risk assessment. However, little is known about the clinical correlates and prevalence patterns of low BMD in these contexts, especially in diverse geographic areas with different dietary habits and access to healthcare. This study assessed the prevalence of osteoporosis and low BMD in adults who visited a tertiary care diagnostic facility. Characterizing the demographic profile of those impacted, looking at site-specific patterns of bone loss, assessing the connections between BMD and clinical factors like age and BMI, and using multivariable analysis to find independent predictors of osteoporosis were secondary goals. These results will support evidence-based screening guidelines and guide public health initiatives for managing and preventing osteoporosis in tertiary care settings.

Methods

This cross-sectional observational study was conducted at Square Hospitals Ltd., Dhaka, Bangladesh, from June 2022 to February 2023. The study population comprised 826 adults who were referred for BMD testing due to clinical indications, established risk factors, or routine screening. All individuals 30 years of age and older who had complete, technically adequate DXA scans and had access to demographic data, such as age, sex, height, weight, and BMI, were included. Patients under 30 years old, those with incomplete or subpar scans (poor quality/below standard), those with conditions that could skew the interpretation of BMD (such as severe osteoarthritis, fractures at measurement sites, or metallic implants), those taking drugs that significantly affect bone metabolism (such as bisphosphonates, denosumab, teriparatide, or systemic corticosteroids), and those with secondary causes of osteoporosis (such as endocrine disorders, chronic kidney disease, or malabsorption syndromes) were also disqualified. Standardized dual-energy X-ray absorptiometry (DXA) protocols were used to measure BMD at the femoral neck (left and right), total hip (left and right), and lumbar spine (L1-L4). All scans were carried out by qualified technicians following daily calibration protocols, and BMI was computed using recorded height and weight. The lowest T-score from the femoral neck or lumbar spine was used to classify bone mineral density according to World Health Organization criteria [11], classifying people as having normal bone density (T-score ≥ -1.0), osteopenia (-2.5 < T-score < -1.0), or osteoporosis (T-score ≤ -2.5). Statistical analysis was performed on SPSS version 26, including descriptive summaries of demographic and clinical characteristics, with continuous variables expressed as means and standard deviations and categorical variables presented as frequencies and percentages. Chi-square tests evaluated associations between BMD categories and demographic factors, Pearson correlation coefficients assessed relationships between age, BMI, and site-specific BMD values, and multivariable logistic regression identified independent predictors of osteoporosis, with adjusted odds ratios and 95% confidence intervals. Statistical significance was set at p < 0.05.

Results

Table 1 represents the baseline characteristics of the study population. The mean age of the study was 63.2±9.4 years. There were only 76 men (9.2%), and the great majority (90.8%) were women. The age distribution revealed that the majority of participants were between the ages of 60-69 (41.9%), followed by those between the ages of 50-59 (28.1%) and ≥70 (24.7%). In terms of body mass index, just 1.7% of people were underweight, 29.9% were obese, and 45.2% were overweight. The majority of the population was overweight, as indicated by the mean BMI of 28.0±5.9 kg/m².

Table 1: Baseline characteristics of the study population (n = 826)

Table 2 shows the bone mineral density assessment of the study population. The femoral neck (78.3% on the left side) and total hip (79.3% on the left side) were the most common locations for BMD measurements. However, all participants had lumbar spine BMD measurements (100%).

Table 2: Bone mineral density assessment profile (n = 826)

Table 3 depicts the BMD classification based on WHO criteria and site-specific low BMD prevalence. Only 18.4% of participants had normal bone density in the combined measurements of the lumbar spine and femoral neck. Over 80% of the study population had compromised bone health, as evidenced by the 50.6% prevalence of osteopenia and the 30.9% who met the criteria for osteoporosis. Only 15.5% of patients had low BMD at the lumbar spine, compared to 66.1% at the femoral neck, while 33.9% had normal BMD at the femoral neck and 84.5% had normal BMD at the lumbar spine.

Table 3: Bone mineral density classification and prevalence.

Table 4 reveals the BDM classification across age, sex, and BMI. Age-stratified analysis demonstrated progressive deterioration in bone health with advancing age. The percentage of people over 70 who had osteoporosis rose from 16.3% in the 40-49 age group to 40.9%. Males had a higher percentage of normal BMD (35.5%) than females (16.6%), but they also had a significant prevalence of osteoporosis (35.5%). Higher body weight has a protective effect, according to BMI analysis, with 85.7% of underweight participants having osteoporosis and only 17.8% of obese participants. On the other hand, 24.7% of obese participants and 0% of underweight participants had normal BMD.

Table 4: BMD classification across age, sex, and BMI.

The mean BMD and T-score values at the main skeletal sites are shown in table 5. The lumbar spine had the highest mean BMD values (0.962 g/cm²), while the femoral neck had the lowest (0.791 g/cm² on the left and 0.810 g/cm² on the right). The BMD values for the entire hip were intermediate (0.889 g/cm² on the left and 0.878 g/cm² on the right). All measured sites showed osteopenia, as indicated by corresponding T-scores, lumbar spine (-1.87), femoral neck left (-1.82), and femoral neck right (-1.69).

Table 5: Mean BMD and T-score values at major skeletal sites.

• • A T-score between -1.0 and -2.5 indicates Osteopenia (WHO)
• • A T-score of <-2.5 indicates Osteoporosis (WHO)

Correlation analysis in Table 6 showed weak linear correlations between BMD, age, and BMI at various locations. Age did not significantly correlate with lumbar spine BMD (r = +0.055) or femoral neck BMD (r=+0.027), indicating that age-related bone loss in this clinical population does not follow a straightforward linear pattern. BMI showed almost no correlation with femoral neck BMD (r = -0.001) but a weak positive correlation with lumbar spine BMD (r = +0.148).

Table 6: Correlation Matrix (Age, BMI vs BMD at FN & LS).

Table 7 demonstrates a multivariable logistic regression model predicting osteoporosis. Age was found to be a significant independent predictor after controlling for potential confounders. The odds of developing osteoporosis increased by about 5% for every extra year (AOR=1.05, 95% CI: 1.03-1.07, p<0.001). The odds of osteoporosis decreased by about 12% for every unit increase in BMI (AOR=0.88, 95% CI: 0.85-0.92, p<0.001). It's interesting to note that after controlling for age and BMI, sex was not a statistically significant predictor (AOR=0.64, 95% CI: 0.37-1.11, p=0.115). This suggests that age and body composition differences, rather than sex per se, may account for a large portion of the higher osteoporosis rates in females.

Table 7: Multivariable Logistic Regression Model Predicting Osteoporosis (Outcome: Osteoporosis vs non-osteoporosis vs Predictors).

Discussion

This cross-sectional study revealed a substantial burden of low bone mineral density, with 81.5% of participants exhibiting either osteopenia or osteoporosis. Our study population's 30.9% osteoporosis prevalence is significantly higher than rates found in a number of recent studies [12-14]. Only 10.4% of postmenopausal women had osteoporosis, according to Pinar et al. [12], and even lower rates have been reported in general populations. This disparity probably reflects the clinical features of our tertiary care population, which is a higher-risk cohort than community-based screening programs since patients are specifically referred for BMD evaluation based on risk factors or symptoms. Because osteopenia and osteoporosis are significantly more common in women than in men, especially among postmenopausal women, the study's 90.8% female predominance is consistent with a study by Khurmah et al. [13]. The critical postmenopausal period, when estrogen deficiency causes accelerated bone loss, is represented by the mean age of 63.2 years. Our age-stratified analysis showed that the prevalence of osteoporosis increased with age, rising from 16.3% in people aged 40-49 to 40.9% in people aged ≥70. The established pathophysiology of bone remodeling imbalance and the cumulative effects of risk factors over time are consistent with Zhang et al. [14]. Our study's positive correlation between BMI and BMD supports results from nationally representative samples that indicate an increase in femoral neck BMD is correlated with every unit increase in BMI [15]. The framework of mechanical loading effects on bone metabolism was supported by our multivariable analysis, which showed that every unit increase in BMI decreased the odds of osteoporosis by 12%. Although adipose tissue endocrine functions through leptin and other adipokines may also contribute to bone-fat interactions, the protective mechanism most likely involves increased mechanical strain from higher body weight [16]. The lumbar spine's weak linear correlations (r=+0.148) indicate that this relationship is complicated and influenced by a variety of factors, such as muscle mass, metabolic status, and fat distribution. With 66.1% showing low BMD at the femoral neck and only 15.5% at the lumbar spine, the site-specific patterns provided significant clinical insights. This disparity is a result of both real biological variation in bone loss patterns and variations in measurement frequency. Because the femoral neck is primarily composed of cortical bone, which reacts differently to aging and hormonal changes than the trabecular-rich lumbar spine, it is a notable site for fracture prediction and diagnosis [17]. Furthermore, degenerative changes, osteophytes, and vascular calcifications may cause lumbar spine measurements in older populations to be artificially elevated, potentially underestimating actual trabecular bone loss [18]. Conventional wisdom regarding sex-specific risk is challenged by our analysis, that sex was not an independent predictor of osteoporosis after controlling for age and BMI. Although osteoporosis rates were higher in females (30.5% vs. 35.5% in males) in crude comparisons, this difference was not significant in multivariable models. This implies that, rather than sex, age-related factors and body composition may play a major role in mediating the well-documented female predominance in osteoporosis. One of the best indicators of future fractures is past fracture history, with a study by Singer et al. showing a five-fold increased risk in the year after an initial fracture [19]. Despite increased awareness over the past 20 years, there are still gaps in osteoporosis screening, diagnosis, and treatment, as our study's significant burden highlights [20]. There is a great deal of potential for focused interventions, such as lifestyle changes, calcium and vitamin D supplementation, fall prevention techniques, and pharmaceutical therapy, when necessary, given that more than 80% of our tertiary care population has impaired bone health. Many at-risk individuals do not get screened until after their first fracture, despite current guidelines recommending BMD testing for all women ≥65 years and men ≥70 years [21]. Despite being statistically significant, the protective effect of higher BMI poses a clinical conundrum because obesity is known to have negative health effects, such as diabetes, cardiovascular disease, and some types of cancer. To maximize both bone and general health, clinical focus should prioritize adequate nutrition, muscle-strengthening exercises, and weight-bearing physical activity rather than advocating weight gain as an osteoporosis prevention strategy. Lifestyle changes, medication, and new technologies, such as artificial intelligence applications for fracture risk assessment, are all part of modern management protocols [22]. This study provides valuable observational data from a tertiary care setting, where specialized knowledge of bone health assessment enables thorough evaluation and management planning. The results highlight the urgent need for systematic screening programs, especially for older people with additional risk factors and postmenopausal women. The significant morbidity and mortality linked to osteoporotic fractures may be avoided if standardized protocols are implemented at the primary care level.

Limitations of the study

The results may not be as broadly applicable as they could be because this study was limited to a single tertiary care diagnostic center and relied on a referral-based population. Furthermore, the lack of comprehensive clinical histories, such as menopausal status, medication use, and comorbidities, may have affected the evaluation of osteoporosis risk, and the cross-sectional design makes it impossible to establish causal relationships.

Conclusion

According to this study, over four-fifths of adults who visit a tertiary care diagnostic center have either osteopenia or osteoporosis, indicating an alarmingly high prevalence of low bone mineral density. Higher body mass index provided protective effects, while advanced age emerged as a significant independent risk factor. These results highlight the critical need for improved screening techniques, early detection initiatives, and all-encompassing management strategies to address the significant burden of impaired bone health in this population.

Recommendations

To determine temporal relationships and assess the efficacy of interventions, longitudinal cohort studies should monitor bone density trajectories and fracture outcomes. Comprehensive risk factor assessments, including lifestyle factors, nutritional status, comorbidities, and genetic predisposition, should be included in future studies.

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