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American Journal of  Medical 
Science and Innovation (AJMSI) 

Evaluating Osteoporosis Risk Patterns and Prevalence in Adult of  Bangladesh: 
Calcaneus Quantitative Ultrasound Measurements

Md. Alahi Khandaker1*, Mukul Chandra Paul2, Shanta Saha3, Rumana Yasmin Ferdausi4

Volume 4 Issue 1, Year 2025
ISSN: 2836-8509 (Online)

DOI: https://doi.org/10.54536/ajmsi.v4i1.5200
https://journals.e-palli.com/home/index.php/ajmsi

Article Information ABSTRACT

Received: April 21, 2025
Accepted: May 29, 2025
Published: June 19, 2025

Osteoporosis affects the health of  more than 200 million individuals in the world. As the 
population is aging in Bangladesh, there is not enough information about bone health to 
guide public health strategies. The objective of  the research is to determine the prevalence 
and patterns of  reduced bone mineral density in Bangladeshi adult using calcaneal 
quantitative ultrasound (QUS) and identify associated risk factors. The cross-sectional study 
conducted in Community based health centre (Momen medical store) and   collected data 
from 190 adult (76 males, 114 females) who were chosen by systematic random sampling. 
Bone mineral density was measured using QUS at the calcaneus (heel bone). Individuals 
were assigned T-scores and information about their age group, gender, weight, height 
and presence of  comorbidities was examined using multivariate regression. An alarming 
84.2% of  participants demonstrated compromised bone health, with 55.8% presenting 
osteopenia and 28.5% showing reduced ossification. Women exhibited significantly lower 
mean T-scores (-2.0 ± 0.8) than men (-1.5 ± 0.9, p<0.001). All underweight participants 
showed compromised bone health, while 57.7% of  obese participants maintained normal 
bone status. Multiple regression identified age (β=-0.220, p=0.013), female gender (β=-
0.170, p=0.021), diabetes mellitus (β=-0.190, p=0.013), obesity (β=-0.180, p=0.027), and 
multimorbidity (β=-0.200, p=0.012) as significant predictors of  poor bone health, while 
moderate physical activity (β=0.160, p=0.047) and higher education (β=0.170, p=0.030) 
showed protective effects. Bangladeshis of  all ages often have compromised bone health, 
including a higher risk in females, elderly people, diabetics and those with several health 
problems. These findings stress that all people should regularly have their bones checked and 
that special effort should be made to prevent problems by focusing on exercise, controlling 
their weight and diabetes management. 

Keywords
Bone Mineral Density, 
Osteoporosis, Prevalence, 
Quantitative Ultrasound, Risk 
Factors

1 Bangladesh Center for Health Studies, Dhaka, Bangladesh
2 Sylhet MAG Osmani Medical College Hospital, Sylhet, Bangladesh
3 College of  Health and Human Sciences Purdue University (West Lafayette Campus), USA
4 Ministry of  Law, Justice and Parliamentary Affairs, Government of  the People’s Republic of  Bangladesh, Bangladesh
* Corresponding author’s e-mail: dralahi@gmail.com

INTRODUCTION
Osteoporosis is an important health issue throughout the 
world due to issues with bone strength and the higher 
risk of  fractures. WHO considers osteoporosis the 
most common metabolic bone disease and has found 
that more than 200 million people around the globe 
already have it, with cases expected to climb, mainly in 
countries experiencing faster rates of  population aging 
(Cleveland Clinic, 2023).  Since Bangladesh is seeing 
more older citizens and people are living longer, there 
are greater challenges from bone-related aging. At 
present, scant epidemiological reports on bone condition 
exist for the Bangladeshi population (Ali et al., 2021). 
Lacking the full picture of  information creates real 
difficulties in planning and developing effective measures 
for public health. Dualenergy Xray absorptiometry 
(DXA) is traditionally used to determine bone mineral 
density (BMD), but it is limited for use in lowresource 
environments such as Bangladesh where the remoteness 
of  rural and semiurban areas make sophisticated 
diagnostic equipment inaccessible (Krugh & Langaker, 
2024). In this context, quantitative ultrasound (QUS) 
of  the calcaneus has become an accepted practical 
alternative screening tool. QUS has many advantages: 

the methodology is portable; costs are down; there is 
no radiation exposure to the subject and, based on their 
own studies, QUS measurements have been shown to 
correlate with fracture risk at all BMD (Nieuwkamer et 
al., 2023). Bone health deterioration pathophysiology 
consists of  complicated interactions between genetic 
predisposition, hormonal factors, metabolic conditions 
and life style determinants. Ethnicity specific patterns 
of  bone metabolism and loss are present and therefore, 
population specific data is more useful than extrapolating 
data from different ethnic contexts (Marini & Brandi, 
2018). Such South Asian populations have distinctive risk 
profiles and different progression patterns of  bone loss 
than found in Western populations and warrant focused 
elucidation (Darling et al., 2017). Osteoporosis advances 
silently until fracture occurs which puts people at risk for 
considerable morbidity, mortality and high health care 
burden. Appropriate screening leads to early detection 
and timely intervention of  the disease which can change 
disease trajectory and improves outcomes (LeBoff  et 
al., 2022). This information is important for identifying 
population specific risk factors to then develop tailored 
screening and prevention strategies. Multiple factors may 
be contributing towards poor bone health in Bangladesh: 



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nutritional deficiencies (low calcium and vitamin D), but 
also cultural practices that reduce sun exposure, food 
patterns that modify due to socioeconomic constraints 
and rising prevalence of  chronic metabolic diseases such 
as diabetes mellitus (Ahmed et al., 2020). These factors 
all operate from within a healthcare system that is under 
resourced for bone health management and not aware of  
it. BMD has been associated with several demographic, 
anthropometric and clinical parameters e.g. age, sex, 
BMI, physical activity and comorbidity such as diabetes 
mellitus and cardiovascular disease (Kong et al., 2024). 
Nevertheless, the characteristics of  the interdependent 
relationships among them and the combined effect on 
bone health in particular in the Bangladeshi population 
are poorly understood.
The resolution of  this critical knowledge gap is attempted 
in this study by screening for the prevalence and patterns 
of  reduced bone mineral density among specimens 
of  the Bangladeshi adult population, as measured by 
calcaneal QUS and by determining associated risk factors. 
The results can be used to guide the development of  
context appropriate screening protocols, preventive and 
therapeutic interventions designed to address the distinct 
circumstances and traits among this population.

MATERIALS AND METHODS
Study Design and Participants
This cross - sectional study conducted in Community 
based health centre (Momen medical store) Sunamganj, 
Bangladesh. Systematic random sampling was used to 
recruit 190 adult (76 males, 114 females) patients and their 
accompanying relatives attending outpatient department. 
Adult (≥ 18 years old) who consented to participate were 
our inclusion criteria. Pregnancy, history of  metabolic 
bone diseases other than osteoporosis, current (or recent; 
up to 6 months) use of  medications known to affect bone 
metabolism, history of  fractures or surgeries involving 
calcaneus, were all exclusion criteria.

Data Collection Instruments
Sociodemographic information such as age, sex, 
marital status and educational level were obtained by 
using a structured questionnaire. A diabetes mellitus, 
cardiovascular disease or other chronic condition medical 
history focused on was. Anthropometric data included 
height and weight for calculation of  BMI. Self-reported 
regular activity levels were categorized by physical activity 
as sedentary, light or moderate.

Bone Mineral Density Assessment
All subjects were measured by calcaneal quantitative 
ultrasound (QUS) using a calibrated portable ultrasound 
bone densitometer (model details withheld for 
anonymity). The assessment was of  the dominant heel, 
after removal of  footwear and cleaning. However, 

broadband ultrasound attenuation (BUA) and speed of  
sound (SOS) were measured by the device and integrated 
to form a composite parameter where T-scores were 
derived. Daily calibration against a phantom and duplicate 
measurements on 10% of  participants were performed 
quality control procedures.

Bone Status Classification
Based on WHO criteria adapted for QUS measurements, 
participants were classified into four categories according 
to T-scores:

• Normal: T-score ≥ -1.0
• Osteopenia: T-score between -1.0 and -2.5
• Reduced Ossification (RO): T-score between -2.5 and -3.5
• Severe Reduced Ossification: T-score < -3.5

The term “Reduced Ossification” (RO) was used instead 
of  “osteoporosis” since DXA remains the gold standard 
for definitive osteoporosis diagnosis.

Statistical Analysis
Analysis of  data was done using SPSS version 25.0. 
Frequencies, percentages, means and standard deviations 
were used as descriptive statistics. Associations between 
categorical variables were evaluated by Chi-square tests. 
Mean T-scores were compared between groups, using 
independent sample t-tests. Linear multiple regression 
analysis was conducted on T-scores for significant 
predictors; model included age, sex, BMI category, 
comorbidities, physical activity and sociodemographic 
factors. All data were presented as mean ± standard error. 
Statistical significance was established at p<0.05.

Ethical Considerations
Written informed consent was obtained from all 
participants after explaining the study objectives and 
procedures. Confidentiality of  personal information was 
maintained throughout the study.

RESULTS AND DISCUSSION
The study included 190 participants with a mean age of  
43.3 ± 15.6 years, ranging from under 20 to over 70 years. 
The largest age cohort was 31-40 years (23.2%), followed 
by 21-30 years (18.9%). Female participants constituted 
60% of  the sample. Regarding comorbidities, 27% of  
participants had diabetes mellitus, 15% had cardiovascular 
disease, and 33% had obesity. Multimorbidity was present 
in 27% of  the sample. Educational status assessment 
revealed that 35% were illiterate, 55% had elementary 
education, and 10% were graduates. Based on BMI 
classification, 5% were underweight, 45% had normal 
weight, 22% were overweight, and 28% were obese. Most 
participants (55%) reported sedentary lifestyle patterns, 
while 25% engaged in light physical activity and 20% in 
moderate physical activity (Table 1).
Analysis of  bone mineral density using calcaneal QUS 



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Table 1: Baseline Demographic and Clinical Characteristics of  Study Participants
Variable Frequency (n) Percentage (%)
Age Group
≤20 years 11 5.8
21–30 years 36 18.9
31–40 years 44 23.2
41–50 years 33 17.4
51–60 years 33 17.4
61–70 years 27 14.2
>70 years 6 3.2
Mean ± SD 43.3 ± 15.6 years
Sex
Male 76 40
Female 114 60
DM
Yes 51 27.0
No 139 73.0
Marital Status
Married 161 85.0
Unmarried 29 15.0
Education Status
Illiterate 66 35.0
Elementary 105 55.0
Graduate 19 10.0
BMI
Underweight 10 5.0
Normal weight 86 45.0
Overweight 42 22.0
Obese 53 28.0
Physical Activity
Sedentary 105 55.0
Light 48 25.0
Moderate 37 20.0
Cardiovascular Disease
Yes 29 15.0
No 161 85.0
Obesity
Yes 63 33.0
No 127 67.0
Multimorbidity Status
Present 51 27.0
Absent 139 73.0

revealed concerning patterns of  bone health in the study 
population. Only 15.8% of  participants demonstrated 
normal bone status (T-score ≥ -1.0). The majority 
(55.8%) presented with osteopenia (T-score between -1.0 
and -2.5), while 25.3% showed reduced ossification (RO) 
with T-scores between -2.5 and -3.5. Severe RO (T-score 

< -3.5) was identified in 3.2% of  participants (Table 2).
Significant gender-based disparities in bone health were 
observed. Female participants exhibited significantly 
lower mean T-scores (-2.0 ± 0.8) compared to males (-1.5 
± 0.9), with a statistically significant difference (p<0.001). 
The 95% confidence intervals for these means were -1.92 



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Figure 1: Column Chart & Pie Chart Baseline Demographic and Clinical Characteristics of  Study Participants



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Table 2: Distribution of  Bone Mineral Density Status Based on T-score Ranges
Bone Status T-score Range Frequency Percentage (%)
Severe RO < -3.5 6 3.2
RO < -2.5 to -3.5 48 25.3
Osteopenia < -2.5 106 55.8
Normal < -1 30 15.8
Total 190 100

Table 3: Sex-Specific Differences in Mean T-score Values Indicating Bone Health Disparities
Gender Mean T-score ± SD 95% CI p-value
Male -1.5 ± 0.9 (-1.31 to -1.70) <.001 *
Female -2.0 ± 0.8 (-1.92 to -2.25)

Table 4: Bone Health Patterns by Age Group in the Study Population
Age Group Bone Status

Severe RO Osteopenia Normal Total
N % N % N % N % N %

≤20 years 0 0.0 1 0.5 6 3.2 3 1.6 10 5.3
21–30 years 0 0.0 5 2.6 27 14.2 11 5.8 43 22.6
31–40 years 0 0.0 13 6.8 25 13.2 10 5.3 48 25.3
41–50 years 2 1.1 12 6.3 20 10.5 3 1.6 37 19.5
51–60 years 2 1.1 10 5.3 17 8.9 1 0.5 30 15.8
61–70 years 0 0.0 6 3.2 10 5.3 0 0.0 16 8.4
>70 years 2 1.1 1 0.5 1 0.5 2 1.1 6 3.2

Figure 2: Distribution of  Bone Health Status by T-score Classification Among Participants

to -2.25 for females and -1.31 to -1.70 for males, indicating 
minimal overlap and confirming the robustness of  this 

gender difference (Table 3).
Age-stratified analysis demonstrated progressive 

deterioration in bone health with advancing age. The 
prevalence of  normal bone status declined from 30% in 
the youngest age group (≤20 years) to 0% in the 61-70 
years group, with an unexpected 33.3% in the >70 years 
group likely due to the small sample size in this cohort 

(n=6). Conversely, the prevalence of  RO increased from 
10% in the youngest age group to 37.5% in the oldest 
participants. Severe RO was predominantly observed in 
the 41-60 and >70 age groups (Table 4).
BMI demonstrated a significant association with bone 

health status (p<0.001). All underweight participants 
exhibited compromised bone health, with 60% showing 
severe RO and 40% showing RO. In the normal weight 
category, 51.2% had RO and 48.8% had osteopenia. All 

overweight participants (100%) demonstrated osteopenia. 
Among obese participants, 57.7% maintained normal 
bone status while 42.3% presented with osteopenia 
(Table 5).



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Table 5: Relationship Between BMI and Bone Status in Study Participants
BMI Bone Status p-value

Severe RO Osteopenia Normal Total

<.001

N % N % N % N % N %
Underweight 6 60.0 4 40.0 0 0.0 0 0.0 10 100.0
Normal weight 0 0.0 44 51.2 42 48.8 0 0.0 86 100.0
Overweight 0 0.0 0 0.0 42 100.0 0 0.0 42 100.0
Obese 0 0.0 0 0.0 22 42.3 30 57.7 52 100.0

Multiple regression analysis identified several significant 
predictors of  lower T-scores (poorer bone health). Age 
emerged as a significant negative predictor (β=-0.220, 
p=0.013), indicating progressive bone health deterioration 
with advancing age. Female gender was associated 
with lower T-scores (β=-0.170, p=0.021), confirming 
gender-based vulnerability. Among comorbidities, 

diabetes mellitus (β=-0.190, p=0.013), obesity (β=-
0.180, p=0.027), and multimorbidity (β=-0.200, p=0.012) 
significantly predicted poorer bone health. The obese 
BMI category showed strong negative association with 
T-scores (β=-0.240, p=0.004). Moderate physical activity 
demonstrated protective effects (β=0.160, p=0.047), as 
did graduate-level education (β=0.170, p=0.030) (Table 6).

Table 6: Association Between Sociodemographic and Health Characteristics and Outcome Variable
Variable Coefficient (B) Std. Error Beta (β) t-statistic p-value
Demographics
Age -0.015 0.006 -0.220 -2.500 0.013*
Male (vs. Female) -0.280 0.120 -0.170 -2.333 0.021*
Health Conditions
Diabetes Mellitus -0.350 0.140 -0.190 -2.500 0.013*
Obesity -0.300 0.135 -0.180 -2.222 0.027*
Cardiovascular 
Disease

-0.260 0.145 -0.140 -1.793 0.075

Multimorbidity -0.370 0.145 -0.200 -2.552 0.012*
Lifestyle
Physical Activity: 
Light

0.180 0.140 0.100 1.286 0.200

Physical Activity: 
Moderate

0.290 0.145 0.160 2.000 0.047*

BMI Category (Ref: Normal)
Underweight -0.320 0.190 -0.110 -1.684 0.095
Overweight -0.210 0.130 -0.130 -1.615 0.108
Obese -0.410 0.140 -0.240 -2.929 0.004**
Sociodemographic
Married -0.090 0.160 -0.040 -0.562 0.575
Education: 
Elementary

0.210 0.130 0.130 1.615 0.108

Education: 
Graduate

0.350 0.160 0.170 2.188 0.030*

Line chart showed, ‘Age-specific prevalence of  
osteoporosis risk between men and women’ shows a 
clear age-related increase in osteoporosis risk, with a 
significant gender gap. Although both men and women 
show an increasing prevalence with advancing age, the 
risk of  multiple sclerosis is higher among women. The 
difference is most pronounced after 50 years of  age and 

coincides with postmenopausal hormonal changes that 
accelerate bone loss in women. By age >70, prevalence in 
women is almost twice that of  men. This trend underlines 
the critical role of  oestrogen deficiency in the health of  
female bones and highlights the need for age and gender 
specific screening and prevention strategies to address 
osteoporosis, especially in older women.



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Figure 3: Line chart showing the Age-Specific Prevalence of  Osteoporosis Risk Among Male and Female

Discussion
Finally, this study presents a unique insight into the 
bone health status and related risk factors in the adult 
Bangladeshi population which currently represents a 
knowledge gap in the least represented region. The results 
show an alarming high prevalence of  compromised 
bone health – 84.2% of  the participants had T-scores 
below normal values, 28.5% of  which were reduced 
ossification or severe reduced ossification. We found that 
the prevalence of  suboptimal bone health is greater than 
many international studies have reported and is similar to 
data from South Asian nations nearby. While Khadilkar 
et al. (2018) found that as many as 37% of  Indian adults 
may have osteoporosis, Fatima et al. (2009) suggest that 
of  29.8% of  Pakistani adults have osteoporosis. The 
findings indicate that people of  South Asian background 
could share certain genetic, environmental or lifestyle 
traits affecting their bone strength. Like in previous 
studies, our analysis revealed that females had inferior 
bone health status compared to men in our population. 
A decrease in estrogen causes women to lose bone at a 
quicker rate (Cauley, 2015). However, the fact that 72% 
of  the men in our sample had unhealthy bones means 
we need to look beyond the group of  high-risk women 
in Bangladesh. Such research emphasizes that adding 
male osteoporosis to public consciousness matters as it is 
frequently missed and overlooked in handling (Gielen et 
al., 2011). This progressive deterioration of  bone health 
as age increases in our study agrees with the commonly 
known negative relationship between age and BMD in 
different population (Demontiero et al., 2012). However, 
with 70% of  participants under 20 years showing T 
scores < 0 such early compromise of  bone health in our 
study population is a cause for concern with regard to 

peak bone mass in this population. This can be caused by 
nutritional deficiencies during the bone building period 
usually during childhood such as inadequate calcium 
and Vitamin D, both which are known to occur in the 
Bangladeshi population (Islam et al., 2022). We found that 
BMI affects bone health in a complex manner. Although 
having a low body weight is a well-known risk factor for 
osteoporosis, we also found that obesity seems to lower 
T-scores independently. It could be that the relationship 
between bone metabolism and adiposity is changing as 
we understand it better. Konstantinos Gkastaris et al. 
(2020) suggested that being overweight might strengthen 
bones by putting pressure on them; on the other hand, 
factors such as chronic inflammation, unusual adipokine 
production and adipose sequestration of  vitamin D could 
weaken bones. Our research agrees with this nuance and 
suggests we should revisit the idea that greater BMI 
is always good for warding off  osteoporosis. Results 
from our research link diabetes mellitus with worsening 
bone health which is supported by evidence from other 
reports. Chen et al. (2022) found that diabetes increases 
fracture risk in patients, even when their DXA-measured 
bone density was in the normal range or higher. Among 
these are extra glycation on collagen, problems with small 
blood vessels and changes in bone rebuilding (Singh et 
al., 2014). It is notable that our findings support treating 
diabetes as a main reason for worrying about weak bones 
in Bangladesh, where diabetes cases are increasing at a fast 
rate. Being physically active in moderation is connected 
to strong bones, showing once more that being active is 
good for your bones. According to Hong and Kim (2018), 
going through combined weight-bearing and resistance 
exercises preserves and may boost bone mineral density. 
The fact that a large majority of  our participants reported 



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being mostly sedentary indicates a risk factor that is easily 
manageable by public health initiatives. We found that 
having more education was linked to better bone health 
in our group. The relationship is likely supported by 
several processes, including eating better, gaining health 
knowledge, accessing medical services and undertaking 
activities beneficial for bones. 
Our results should be interpreted with a few limitations in 
mind. The way this study was set up makes it impossible 
to determine if  one thing caused another. While it was 
useful to rely on QUS in this study setting, not using 
DXA makes it hard to compare results with those from 
digital x-ray analysis. However, using this approach might 
make it difficult to apply the findings to all Bangladeshis. 
Even so, the data presented here offer an important first 
step in studying this group and helps set up further, more 
advanced studies.

CONCLUSION
According to this research, a large number of  
Bangladeshis of  all ages, but especially females, older 
individuals, diabetics and those with numerous illnesses, 
suffer from bone health concerns. Putting a name to 
modifiable and non-modifiable risk factors allows for 
the design of  effective screening and treatment plans. 
This suggests that screening for bone health should be 
organized routinely in Bangladesh, starting with high-
risk populations. Strategies aimed at prevention ought to 
highlight making people active, helping them maintain 
their weight and controlling their diabetes. It would also 
help to add public health messages on bone health and 
osteoporosis to conventional healthcare programs. It is 
important to conduct future long-term studies to link the 
risk factors identified here to outcomes related to bone 
health in the population. Checking nutritional elements 
such as vitamin D and calcium levels would shed more 
light on the causes of  bone health issues in Bangladesh. 
How much bone health is negatively affected as shown 
here emphasizes the urgent need for efforts to tackle 
osteoporosis in Bangladesh.

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