Introduction

Osteoporosis (OP) is a metabolic bone disease characterized by decreased bone mass density (BMD) and changes in bone tissue microarchitecture1,2. OP occurs when bone breakdown and formation processes lose balance3. BMD is influenced by the dynamic equilibrium between bone formation and resorption by osteoblasts (OB) and osteoclasts (OC)4. The global prevalence of OP ranges from 4% to 40% 5. Over 200 million people worldwide are affected by OP, with an estimated increase of up to 240% in women by 20506,7. OP is often called a silent disease because bone breakdown progresses so gradually that it is not detectable until the first fracture8. This condition is a major cause of disability and mortality in the elderly9. Only 33% of elderly women who experience hip fractures can regain independent living10. In Iranian women, primary OP prevalence based on T-scores for lumbar spine, femoral neck, and total is approximately 23.4%, 3.4%, and 24.5%, respectively. Primary osteopenia prevalence based on T-scores for lumbar spine, femoral neck, and total is approximately 42%, 35.5%, and 43.6%, respectively11.

Women exhibit a higher prevalence of OP and fracture rates compared to men12,13, a disparity largely attributed to estrogen deficiency following menopause, which accelerates bone resorption and density loss14. So, hormone therapy (HT) has been recognized as an effective intervention for preserving BMD in postmenopausal women15. Research indicates that advancing menopausal age is associated with reduced odds of OP11, though global variations in menopausal age persist, with median onset ranging from 42.1 to 52.8 years16. In Iran, a systematic review of 28 studies reported a mean menopausal age of 48.57 years, underscoring regional differences in reproductive aging patterns17. The most significant BMD decrease, approximately 5% per year, occurs in the first year after menopause, decreasing to 1% − 1.5% per year in subsequent years18.

Osteoporosis (OP) manifests as a critical public health crisis in Iran, characterized by multifactorial biopsychosocial determinants that interact synergistically across geographic, sociodemographic, and behavioral domains. Epidemiological surveillance documents a 17% national osteoporosis prevalence and 35% osteopenia rate among Iranian adults aged ≥ 30 years, with marked geographic stratification showing northern mountainous regions exhibiting 23% higher incidence than southern desert zones, a disparity mediated through environmental constraints on dermal vitamin D synthesis19. This spatial heterogeneity converges with pronounced socio-educational gradients, evidenced by an 18.0% OP prevalence among postmenopausal women with elementary education (4.7-fold higher than university-educated counterparts), a differential amplified by rural-urban healthcare access inequities20, while socioeconomic deprivation drives calcium deficiencies in 58 million citizens through suboptimal dairy intake (≤ 0.7 servings/day) and traditional dietary practices involving sodium bicarbonate in bread preparation19,21. Reproductive physiology further compounds susceptibility, with longitudinal studies identifying multiparity (≥ 3 gravidities) and extended lactation (> 18 cumulative months) as independent predictors of bone loss22. Regional epidemiological extremes reveal prevalence rates escalating to 42.2% overall and surpassing 50% in women > 45 years, a phenomenon underpinned by the intersectionality of nutritional inadequacies, health literacy deficits, and structural healthcare disparities, necessitating integrated interventions targeting bio-cultural determinants through nutrition policy reform, community-based education initiatives, and socio-economic disparities.

There are many factors related to OP, among which we can mention: lifestyle factors, genetic factors, hypogonadal state, digestive system disorders, hematological disorders, autoimmune and rheumatic diseases, drug therapy, and some diseases and miscellaneous conditions23,24. Body composition refers to the relative amounts of various components in the body, primarily consisting of water, protein, minerals, and fat. The body is generally divided into fat mass and fat-free mass25. Body composition can be measured using a body composition assessment device. International studies have shown the broad role of body composition in bone density, although these indices have not been fully examined in postmenopausal women with OP in Iran.

A study in 2019 conducted in Hungary on people aged 18 to 62 has demonstrated associations between metabolic health, bone mineral content, fat-free mass, mineral mass, skeletal fat-free mass, and skeletal muscle mass in individuals with osteopenia26. In the study of Coria Rodrigues et al.27, correlations have been demonstrated between height, weight, body mass index (BMI), fat-free mass, and fat mass, and bone quality index in Spanish young adults. Additionally, percentage of body fat (PBF) and visceral fat mass (VFM) have negative correlations with bone quality index28. High body fat levels are linked to decreased bone quality in sedentary women29,30. Interestingly, in a study on more than 6000 Italian women aged 30 to 80, obesity has been identified as a protective factor against OP31. High BMD is associated with increased index BMI and waist circumference32. Bruner et al.33 studied the German elderly population and found that fat-free mass has a positive impact on bone quality in the heel. Gjesdal34 reached similar results in the study of the relationship between lean body mass (LBM) and BMD in the femoral neck. Muscle mass is recognized as a protective factor for bone health due to its positive relationship with bone mineral density35.

Research on primary OP among Iran’s postmenopausal women reveals critical gaps in understanding the interplay between BMD and multifaceted personal-social characteristics. While age and menopause duration have been examined36,37, the synergistic effects of cultural factors (e.g., education level, urban/rural residence) and lifestyle behaviors (smoking, sun exposure, exercise habits) remain underexplored. Reproductive history, including gravida and breastfeeding duration, and their socioeconomic linkages are inadequately studied, particularly within cultural contexts such as traditional dress practices that exacerbate vitamin D deficiency risks. Against the backdrop of rising global OP prevalence and evolving lifestyle behaviors, this study addresses these gaps by analyzing the relationship between BMD and a comprehensive array of personal-social variables: age, marital status, occupation, income, education, residence, smoking, supplement use, sun exposure, exercise, physical activity levels, family fracture history, reproductive factors, menopausal elapsed time, and body composition metrics. By contextualizing these interactions within Iran’s unique sociocultural landscape, this research aims to clarify contradictions surrounding adipose tissue’s role in BMD and inform personal-social and lifestyle tailored prevention strategies for postmenopausal OP management.

Materials and methods

Patient selection and study design

The current study is an observational, cross-sectional study with analytical components aimed at determining the relationship between personal-social characteristics and bone density in postmenopausal women with primary OP. The research population consisted of all postmenopausal women aged 50 to 65 who had electronic records at healthcare centers in Tabriz, Iran. This study constitutes a component of a large-scale research initiative approved by Tabriz University of Medical Sciences, Iran, (No. 58943) titled “Assessment of primary osteoporosis status and effect of three intervention of Curcumin, Nigella Sativa, and Curcumin Nigella Sativa on cellular molecular and clinical outcomes in postmenopausal women of Tabriz.”

The sample size was determined using power analysis for correlation studies (Rahimi Petroudia et al., 2016)35. Calculations focused on two correlations: BMI vs. lumbar spine BMD (r = 0.34) and BMI vs. femoral neck BMD (r = 0.35). Applying the Pearson/Spearman formula:

n=(Zα/2+Zβ)2/C2 + 3 (Where: Zα/2=1.96 for α = 0.05; Zβ​=1.645 for β = 0.05, 95% power; C = 0.5×ln (1 + r/1 − r)), the required sample sizes were 102 (lumbar spine) and 96 (femoral neck). The larger estimate (102) was selected to ensure statistical power. Accounting for a 15% non-response rate, the final sample size was adjusted to 120 participants.

Inclusion criteria for the study included postmenopausal women aged 50 to 65, residents of Tabriz, cessation of menstruation for at least 12 consecutive months, no recent history of fractures in the past ten years, ability to communicate verbally to answer questions, no hormone therapy in the past year, and onset of menopause after the age of 40. Exclusion criteria included conditions such as bone diseases other than OP confirmed by an endocrinologist, metastatic bone diseases, malignancies, kidney diseases confirmed by an endocrinologist and laboratory tests, use of bone metabolism-affecting medications including intravenous bisphosphonates in the last 5 years, oral bisphosphonate use in the last 6 months, cumulative oral bisphosphonate use for more than 3 years or more than one month between 6 and 12 months prior to the study, use of parathyroid hormone analogs in the past 12 months, use of hormonal or corticosteroid medications during the study, anticoagulant medications (such as heparin and warfarin), excessive use of thyroxine hormone, cytotoxic medications, immune suppressants like cyclosporine’s, long-term use of certain antiepileptic drugs (e.g., phenytoin), inherited diseases (hemophilia, thalassemia, hemochromatosis) as reported by the patient, endocrine gland-related diseases (Cushing’s syndrome, hyperthyroidism, type 1 diabetes, primary hyperparathyroidism) confirmed by endocrine tests, chronic liver disease, gastrointestinal diseases (chronic liver diseases like primary biliary cirrhosis, celiac disease, Crohn’s disease, complete stomach removal, gastric surgery), BMI less than 18.5, and vitamin D levels below 20ng/ml 25-(OH) or current hypocalcemia.

After confirmation of this study by the ethics committee (IR.TBZMED.REC.1398.1104) and getting the required permissions, sampling was done randomly using the website www.random.org from 87 healthcare centers in Tabriz. The names of all eligible women within the relevant age groups from all mentioned centers were listed, and a simple random sampling method was used to select the required number of samples based on the calculated sample size. The sampling methodology involved a randomized selection of 850 postmenopausal women aged 50–65 years through the integrated health SIB system, followed by telephone-based screening for initial eligibility assessment. Participants subsequently underwent in-person biochemical testing to exclude secondary OP etiologies, coupled with clinical evaluation of inclusion/exclusion criteria. Primary OP diagnosis was confirmed via Dual-Energy X-ray Absorptiometry (DEXA), stratifying the participants (n = 445) into three BMD classifications: normal (n = 142), osteopenia (n = 194), and OP (n = 109). Following this diagnostic triage, 109 women meeting OP criteria were ultimately enrolled in the study.

Measurements

After obtaining written informed consent, questionnaire on personal-social characteristics, maternal, and medical information were completed. This questionnaire consisted of 16 items regarding age, menopausal age, marital status, education, occupation, family income, smoking status, passive smoking, sun exposure, supplement use (calcium & vitamin D), gravida, breastfeeding history, physical activity, exercise, housing situation, and history of fractures in close relatives. Physical activity was assessed using the culturally validated Iranian version of the International Physical Activity Questionnaire-Short Form (IPAQ-SF)36, a psychometrically robust tool from which total activity metrics were derived. This form consisted of 2 questions for mild physical activity, 2 questions for moderate physical activity, 2 questions for severe physical activity, and 1 question for sitting. Individuals mentioned their activity during the last 7 days in this study. Physical activity levels are introduced in the relevant metabolic equivalent. The reliability of this questionnaire has been described with a correlation coefficient of 0.86 in the Bashiri Moosavi et al. 38,39 study.

Additionally, a body composition analysis was conducted using a body composition assessment device, followed by DEXA at the bone density assessment section of Sina Hospital. The final diagnosis of OP was provided by an endocrinologist based on the DEXA results among 445 randomly selected postmenopausal women who were eligible and consented to participate in this study. The face validity of the questionnaires assessing personal-social characteristics and maternal/medical information was evaluated by a panel of 10 experts in obstetrics, public health, and epidemiology. Experts rated each item for clarity, relevance, and appropriateness using a 5-point Likert scale (1 = not clear/relevant to 5 = highly clear/relevant). Quantitative analysis revealed an mean score of 4.3 ± 0.6 for clarity and 4.5 ± 0.4 for relevance across all items, exceeding the pre-defined acceptable threshold of ≥ 3.5. Qualitative feedback highlighted the need to simplify terminology and remove redundant questions (e.g., duplicate items on socioeconomic status). After two rounds of revisions, the final questionnaires achieved a Face Validity Index (FVI) of 0.87, confirming their suitability for the target population.

In this study, data related to body composition were collected using the ZEUS 9.9 PLUS body composition measurement device manufactured in South Korea by the researcher between 8 and 10 am in a fasting state under standardized conditions for all individuals at the Physical Medicine Center. After recording the height and age of the individual, in a state without moving and without talking, with minimal covering, without shoes and socks and metal objects, body composition information including the PBF, fat tissue mass, soft non-fat tissue mass, non-fat tissue mass, VFM, total body water (TBW), body fat, and minerals was measured and documented on paper. This device is capable of measuring protein mass, mineral content, mineral tissue, fat tissue, fat-free mass, TBW weight, biological age, basic metabolism, waist-to-hip ratio, and subcutaneous fat level. For technical validity of body composition analyzer (ZEUS 9.9 PLUS), while the device claims multifactorial measurement capabilities (e.g., PBF, VFM), no peer-reviewed validation studies or manufacturer-reported accuracy metrics (e.g., correlation with gold-standard methods) were cited. Reliability of this device was enhanced by standardized protocols: fasting state, consistent measurement timing (8–10 AM), removal of confounding variables (shoes, metal objects), and controlled posture/communication. To assess the reliability of the device, body composition analysis was performed on 10 participants under standardized conditions with a 20-minute interval between measurements. The correlation coefficients for various parameters ranged from 0.95 to 1.0, indicating excellent test-retest reproducibility across measured component. To measure bone mineral density, DEXA method was utilized as the clinical gold standard for OP diagnosis, with interpretation by an endocrinologist, ensuring criterion validity. DEXA’s established validity for BMD quantification is well-documented in prior literature.

Statistical analysis

Analyses employed SPSS version 24, utilizing the Kolmogorov-Smirnov test to verify normality. Physical activity exhibited non-normal distribution, reported as medians (25th–75th percentiles). Descriptive statistics (counts, percentages, means ± SD, medians [percentiles], ranges) characterized personal, social, anthropometric, body composition, and BMD variables. Relationships between socio-demographic/fertility factors and lumbar spine/femoral neck BMD were assessed via one-way ANOVA, independent t-tests, Pearson correlation (for normal data), and Spearman’s correlation (non-parametric). Pearson correlations evaluated femoral neck BMD vs. body composition indices and lumbar spine BMD vs. body composition. A linear regression model analyzed femoral neck BMD-body composition associations adjusted for marital status (confounder). Lumbar spine regression assumptions were violated due to residual non-normality (Kolmogorov-Smirnov p < 0.05) and multicollinearity (VIF > 10), precluding parametric analysis; results were omitted to prevent bias. Significance threshold: p < 0.05.

Results

In this study, after reviewing the study criteria and getting a signed informed consent, serum samples were obtained from 536 individuals and sent for initial laboratory tests to rule out secondary causes of OP. Among these individuals, 79 (14.7%) were excluded due to test results and secondary causes of OP, and 12 (2.2%) were excluded due to unwillingness to continue the study. Ultimately, 445 (83%) individuals were referred for densitometry, and the necessary information was collected from them. Based on the densitometry results, 109 individuals (24.5%) were identified as having OP.

The mean ± standard deviation (SD) age of the study participants was 58.13 ± 3.74 years. The majority of the female participants were in the 56–60 age group, with a mean ± SD of 9.9 ± 5.2 years elapsed since menopause. Most study participants (74.3%) were married, and 39.5% reported having primary education. Two-thirds of participants (65.2%) mentioned their income to be somewhat sufficient. Most of them (60.5%) did not report supplement intake. Median (p25-p75) of total physical activity was 346.5 (346.5-625.5). Other demographic information is presented in Table 1.

Table 1 Sociodemographic characteristics of osteoporotic postmenopausal women.
Full size table

The mean ± SD of lumbar-sacral BMD in the study participants was 0.70 ± 0.073 g per square centimeter. The mean ± SD of the lumbar-sacral T-score in the study participants was − 3.07 ± 0.68, and the Z-score for the lumbar-sacral region was − 1.72 ± 0.69. The mean ± SD of femoral neck BMD in the study participants was 0.75 ± 0.09 g per square centimeter. The mean ± SD of the femoral neck T-score in the study participants was − 1.57 ± 0.77, and the Z-score for the femoral neck was − 0.67 ± 0.79 (Table 2).

Table 2 Body composition components and bone mineral density of lumbar spine and femoral neck of osteoporotic postmenopausal women.
Full size table

Additionally, the mean ± SD of PBF in the study participants was 35.81 ± 5.06%, mass of body fat (MBF) was 24.42 ± 6.56 kg (kg), soft lean mass (SLM) was 39.80 ± 5.26 kg, LBM was 43.67 ± 5.67 kg, VFM was 3.40 ± 1.34 kg, TBW amount was 31.39 ± 4.07 kg, mineral content was 3.83 ± 0.53 kg, weight was 67.55 ± 9.90 kg, and BMI was 28.43 ± 3.72 kg per square meter (Table 2).

Analysis of postmenopausal women with primary osteoporosis demonstrated significantly higher femoral neck bone density (± SD) in married versus unmarried individuals (p = 0.026). There was no significant difference in lumbar spine bone density between single and married individuals (p = 0.101). Additionally, the mean ± SD of lumbar spine bone density was lower in individuals who were smokers in their place of residence compared to non-smokers (p < 0.001). There was no significant difference in femoral neck bone density between the two groups (p = 0.969). Furthermore, a significant association was found between the number of pregnancies and femoral neck bone density (p = 0.038). No significant relationships were found in terms of other characteristics (Table 3).

Table 3 The correlation of socio-demographic characteristics of osteoporotic postmenopausal women with BMD.
Full size table

In the analysis of the relationship between femoral neck bone density and body composition indices, a significant positive correlation was found between femoral neck bone density and PBF (p = 0.018), MBF (p = 0.001), VFM (p = 0.002), mineral content (p = 0.001), TBW amount (p = 0.049), BMI (p = 0.004), and weight (p < 0.001). Statistical analysis to investigate the relationship between lumbar spine bone density and body composition indices did not reveal any significant correlations between lumbar spine bone density and any of the body components (p > 0.05) (Table 4). This relationship regarding the association of BMI, MBF, VFM, and mineral content with BMD is depicted in Fig. 1.

Table 4 The correlation of body composition with BMD of femoral neck and lumbar spine in osteoporotic postmenopausal women.
Full size table
Fig. 1
figure 1

Scatter plot of correlation between (a) body mass index (BMI), (b) (mass body fat (MBF), (c) visceral fat mass (VFM) and (d) Minerals with bone mineral density (BMD) (g/cm2) in the femoral neck and lumbar spine.

Full size image

In the linear regression analysis to determine the correlation of femoral neck bone density with body composition indices, a significant direct relationship was found between femoral neck bone density and PBF (p = 0.014), MBF (p = 0.001), LBM (p = 0.033), SLM (p = 0.043), VFM (p = 0.001), mineral content (p = 0.008), TBW amount (p = 0.027), BMI (p = 0.007), and weight (p < 0.001) after adjusting for confounding factor. The adjusted models demonstrated that each 1% increase in PBF corresponded to a 0.004 g/cm² elevation in BMD. Furthermore, 1 kg increments in MBF, SLM, LBM, VFM, TBW, mineral content, BMI, and body weight were associated with BMD increases of 0.005, 0.003, 0.003, 0.022, 0.005, 0.052, 0.054, and 0.004 g/cm², respectively (Table 5).

Table 5 The results of linear regression to determine the correlation between BMD of the femoral neck and body composition indices.
Full size table

Discussion

The current study focused on the relationship between personal-social characteristics and bone density among postmenopausal women with primary OP in Tabriz, Iran.

In examining the correlation of personal-social (qualitative) characteristics of postmenopausal women with primary OP and bone density in the lumbar spine and femoral neck, the mean of bone density in the femoral neck region was significantly higher in married individuals compared to single individuals. In women with the number of pregnancies more than 4, the BMD in femoral neck decreased significantly. Furthermore, the mean lumbar spine bone density was significantly lower in individuals who were smokers in their place of residence compared to non-smokers. No significant differences were found in lumbar spine and femoral neck bone density in terms of age groups, education levels, occupation, income, smoking status, sun exposure, supplement intake, exercise, and history of fractures in close relatives.

Body composition and bone density

This study identified BMI, VFM, TBW, MBF, PBF, mineral content, and body weight as risk factors for reduced femoral neck BMD in postmenopausal women. After adjusting for marital status, LBM and SLM also showed significant associations, highlighting quantifiable links between body composition and BMD.

Postmenopausal estrogen decline accelerates bone loss, increasing OP risk40. While studies report conflicting findings on MBF’s role, most agree that both fat and lean mass influence BMD. Ho-Pham et al.41 and Fighera et al.42 demonstrated positive correlations between LBM/fat mass and BMD, with appendicular fat-free mass emerging as a key predictor42. Hsu et al.43 identified basal metabolic rate (BMR) as a stronger BMD predictor than fat-free mass or fat mass. Genaro44, Ho-Pham45, and Gonnelli46 emphasized fat-free mass’s role, though Gonnelli noted MBF’s greater importance. Kapuš et al.47 found fat-free mass predictive of proximal femur BMD in early postmenopause (1–10 YSM), while fat mass dominated in later stages (11–30 YSM). Akkuş48 and Leslie et al.49 linked body weight and LBM to higher femoral neck BMD, though MBF had neutral effects49. Marin et al.50 and others51,52,53 corroborated positive fat/fat-free mass-BMD associations.

Discrepancies arise regarding fat mass’s impact. Sheng et al.54 and cross-ethnic studies55 reported positive LBM-BMD and negative fat-BMD correlations, whereas Rikkonen et al.56 and Sahin57 found OP patients had lower LBM but similar fat mass. These inconsistencies may stem from population heterogeneity (e.g., ethnicity, lifestyle) or inclusion of mixed genders.

In our cohort (aged 50–65 years), MBF and VFM positively correlated with femoral neck BMD, alongside BMI and weight. This aligns with evidence that adipose-derived estrogen58,59 and total body fat60 protect BMD, particularly in Caucasian/Japanese women. Longitudinal data further support MBF-BMD links61.

Mechanistic Insights: LBM consistently predicts BMD better than fat mass, likely due to mechanical loading and hormonal factors62,63. Estrogen, regulated by adipose tissue, enhances BMD via osteoclast inhibition64. While mechanical load from obesity may stimulate bone formation65, adipose-driven inflammation (via cytokines like IL-6) can suppress osteoblasts and promote resorption66,67,68. Adipokines (leptin, estrogen) further modulate bone metabolism, underscoring fat’s dual role as both protective (via estrogen) and detrimental (via inflammation).

Demographic factors and bone density

Marital status showed site-specific associations with BMD, with married individuals exhibiting higher femoral neck BMD than singles, potentially linked to lifestyle, social support, or economic factors69,70,71,72. No significant lumbar spine BMD differences were observed between marital groups, possibly due to bone composition: lumbar spine (trabecular bone, influenced by hormonal/metabolic factors) versus femoral neck (cortical bone, affected by weight-bearing activity)71,73,74,75. Marital history correlated more strongly with lumbar spine BMD, though data limitations on marital transitions warrant caution76. Gender differences emerged: men benefit from spousal social support69, while women gain economic security70, though social support within marriage, not economic factors, enhances female BMD71,72. Biological factors (e.g., hormonal changes) may overshadow psychosocial influences on women’s BMD71.

Passive smokers had lower lumbar spine BMD than non-smokers, aligning with smoking’s known detrimental effects on bone metabolism and estrogen levels. No femoral neck BMD differences were observed, suggesting site-specific susceptibility. Trevisan et al. (2020) highlighted smoking’s inhibition of vitamin D-parathyroid hormone function and estrogen reduction, accelerating femoral bone loss; former smokers showed no significant differences, underscoring cessation benefits77.

During pregnancy, bone turnover increases, with elevated bone resorption and formation markers78. Despite transient 2–5% BMD loss during pregnancy/lactation79,80, recovery mechanisms typically restore density, with parity/lactation posing no long-term fracture risks80. Pregnancy increased femoral neck BMD with higher parity (up to four pregnancies), beyond which BMD declined81. Adequate calcium intake mitigated postpartum BMD reductions82, while vitamin D deficiency adversely affected maternal and offspring bone health78. These findings emphasize the importance of nutritional support during pregnancy for maternal and child outcomes.

Recommendations

In this study, a random sampling method was used to prevent bias in selecting individuals. Since the study participants were menopausal women with electronic records in healthcare centers with various socioeconomic levels across the city of Tabriz, the results of this study can be generalized to the entire population of menopausal women.

We investigated the relationship between personal-social characteristics and BMD among a high-risk population of women aged 50–65 with OP. However, this study cannot be generalized to the entire population. Moreover, this study did not consider psychosocial factors, which may limit the comprehensiveness of the findings. It is recommended that future research include these components to provide a more thorough evaluation. Our study was cross-sectional and cannot accurately demonstrate causation; therefore, conducting cohort studies is recommended for confirming results and analyzing causation more robustly. As our study only examined individuals with OP whose BMD was at a certain level, the lack of significance in demographic and pregnancy variables could stem from this issue. So, we suggest doing a cohort or case-control study for this purpose.

Conclusion

In conclusion, based on the results, smoker roommate, the number of pregnancies, being unmarried, and low body composition were the risks of low bone density in primary OP. Increasing BMI, body weight, MBF, SLM, LBM, VFM, and mineral mass reduced the risk of decreased BMD in the femur. Future studies are advised to analyze and compare this relationship among different age groups and both genders. Furthermore, determining precise mechanisms and other risk factors is suggested.