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Original Article
- Correlation of bone mineral density with ankle fractures in older adults in Korea: a retrospective cohort study
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Seung Hyun Lee1
, Chae Hun Lee1, Seo Jin Park2, Jun Young Lee1 -
Journal of Musculoskeletal Trauma 2025;38(4):186-192.
DOI: https://doi.org/10.12671/jmt.2025.00150
Published online: October 24, 2025
1Department of Orthopedic Surgery, Chosun University Hospital, Gwangju, Korea
2Department of Nursing Science, Gwangju University, Gwangju, Korea
- Correspondence to: Jun Young Lee Department of Orthopedic Surgery, Chosun University Hospital, 365 Pilmun-daero, Dong-gu, Gwangju, Korea Tel: +82-62-220-3147 Email: leejy88@chosun.ac.kr
• Received: March 10, 2025 • Revised: August 1, 2025 • Accepted: August 8, 2025
© 2025 The Korean Orthopaedic Trauma Association
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Abstract
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Background Bone mineral density (BMD) is well-documented in relation to fractures of the spine, hip, distal radius, and proximal humerus; however, its correlations with other fracture types are less established. This study aimed to analyze BMD and associated risk factors in older adults (≥65 years of age) with osteoporotic ankle fractures. These fractures involve low-energy trauma, resulting from falls from a standing height or lower, and occur from impacts which typically do not cause fractures in individuals with normal bone.
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Methods This retrospective study analyzed data from 1,411 patients diagnosed with ankle fractures admitted to Chosun University Hospital between February 2012 and April 2023. After applying inclusion criteria (age ≥65 years; low-energy ankle fracture) and exclusion criteria (high-energy trauma, open/multiple fractures, missing dual-energy X-ray absorptiometry [DXA]), 73 of 1,411 patients were analyzed. Lumbar spine, femoral neck, and total hip T-scores were obtained with a Horizon Wi DXA scanner, and associations with age, sex, mechanism of injury, comorbidities, smoking status, alcohol consumption, body mass index (BMI), and history of fractures were tested by independent t-tests and one-way ANOVA with Scheffé post hoc tests.
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Results Significant differences in BMD were observed according to age, sex, and BMI. Age was significantly associated with the lowest, femoral neck, and total hip T-scores, sex with the lowest T-score, lumbar spine, and total hip T-scores, and BMI with the lowest and total hip T-scores (P<0.05). No significant associations were found with diabetes, hypertension, smoking, alcohol consumption, injury mechanism, or prior fractures; dementia was not tested because only one patient had dementia.
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Conclusions These results suggest that age, sex, and BMI are associated with BMD in older patients with ankle fractures, although the associations varied according to the measured site. These findings support the importance of osteoporosis screening and management in this population, particularly in women.
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Level of evidence IV.
Introduction
Osteoporosis is the most common metabolic bone disease in old age, and with the rapid increase in the elderly population, understanding and preventing the related factors is important. A previous history of osteoporotic fractures is a significant predictor of new fractures, making early and accurate diagnosis of osteoporotic fractures crucial [1].
Currently, bone mineral density (BMD) is primarily measured in the spine and hip, and a lower BMD value indicates a higher risk of fractures in the spine and hip [2]. Low BMD is a well-established risk factor for osteoporotic fractures of the spine, hip (including femoral neck and intertrochanteric fractures), distal radius, and proximal humerus; however, its correlation with other fracture types is less established [3,4]. In particular, while the incidence of ankle fractures increases in postmenopausal women [5,6], some studies suggest that ankle fractures may have a weaker association with osteoporosis [7]. This has led to ongoing research [8] to further investigate the relationship.
This study aims to analyze BMD and its associated risk factors in elderly patients (65 years and older) with osteoporotic ankle fractures. These fractures typically result from low-energy trauma, such as falls from a standing height or lower, which generally do not cause fractures in individuals with normal bone [9]. Various factors are known to influence BMD, including age, sex, physical activity level, smoking, alcohol consumption, and body weight [10]. Indeed, several studies have shown that higher body weight is correlated with higher BMD and may lower the incidence of hip fractures [10,11]. Building on these findings, the present study examines whether these factors also play a role in ankle fractures among the elderly and seeks to clarify the relationship between low BMD and the ankle fractures in this population.
Methods
It was approved by the institutional review board (IRB) of the Chosun University Hospital (IRB No. 2024-10-025).
It is a retrospective cohort study based on the electronic medical records of the hospital.
From February 2012 to April 2023, a total of 1,411 patients were admitted to Chosun University Hospital with ankle fractures. Their electronic medical records were analyzed.
The inclusion criteria for this study were: age ≥65 years, a single ankle fracture sustained from low-energy trauma (e.g., a fall from standing height or lower), and availability of BMD data. Therefore, we excluded 430 patients who had open fractures, multiple fractures, or experienced high-energy trauma (e.g., a fall from a height >1.5 m or traffic accidents). Among the remaining patients, 87 met the age criterion (≥65 years), but 14 had not undergone BMD testing. Consequently, a total of 73 patients were included in the final analysis.
Primary outcome variables were BMD of the lumbar spine, femoral neck, and total hip. Predictor/explanatory variables were age, sex, injury mechanism, comorbidities, lifestyle factors (smoking, alcohol drinking), BMI, and fracture history.
BMD was measured at lumbar spine, femoral neck, and total hip using a Horizon Wi dual-energy X-ray absorptiometry (DXA) scanner (Hologic Inc.), following the manufacturer’s recommended protocols.
Patients were categorized by age into three groups: ≤69 years (group a), 70–79 years (group b), and ≥80 years (group c). The mechanism of injury was classified into low-energy injuries corresponding to osteoporotic fractures, such as twisting injury, slip down, falls from a standing height or less, or other types of injuries. Comorbidities were classified based on the presence of diabetes, hypertension, and dementia, which are highly prevalent in the elderly. BMD was categorized based on the lowest T-score among the measured sites as follows: normal (T-score >−1.0), osteopenia (−2.5< T-score ≤−1.0), and osteoporosis (T-score ≤−2.5). Smoking status was divided into smokers and non-smokers, while alcohol consumption was classified as drinking if the intake was three units or more per day (with one unit representing 8–10 grams of alcohol, roughly equivalent to a standard glass of beer [285 mL], a single measure of spirits [30 mL], a medium-sized glass of wine [120 mL], or one measure of an aperitif [60 mL]) and non-drinking otherwise. Body mass index (BMI) was classified according to the World Health Organization Asia-Pacific criteria into normal weight (18.5–22.9 kg/m²), overweight (23–24.9 kg/m²), obesity (25–29.9 kg/m²), and severe obesity (≥30.0 kg/m²). No patients were underweight (<18.5 kg/m²). Lastly, patients were categorized based on a history of previous fractures.
Potential confounders such as medication use (e.g., corticosteroids, osteoporosis treatment), nutritional status, vitamin D levels, physical activity, and socioeconomic status were not controlled for. Their omission may impact associations between BMD and the studied risk factors. Sex distribution (61.6% women) may confound observed associations between age and BMD.
All included subjects were analyzed; therefore, no sample size calculation was performed.
Data were analyzed using IBM SPSS ver. 27.0 (IBM Corp.). Descriptive statistics were used to analyze general characteristics and fracture-related risk factors of the study subjects. For characteristics with two groups, independent-samples t-tests were used to compare T-scores. For characteristics with three or more groups, one-way ANOVA was performed, followed by Scheffé post hoc tests when the overall ANOVA was statistically significant. Statistical testing for dementia was not performed because only one patient had dementia. A two-sided P-value <0.05 was considered statistically significant.
Results
A total of 73 patients (mean age, 71.6±5.7 years) were included, of whom 28 (38.4%) were male and 45 (61.6%) were female. Most ankle fractures resulted from low-energy mechanisms such as twisting injuries (38.4%) or slip-down events (54.8%), and more than half of the patients (54.8%) had osteoporosis based on the lowest T-score. Detailed demographic and clinical characteristics, including comorbidities, smoking/alcohol status, and BMI categories, are presented in Table 1.
The mean lowest T-score of the total patients was –2.43±0.97. Specifically, –1.96±1.41 for the spine T-score, –1.80±0.99 for the hip-neck T-score, and –1.19±0.95 for the total hip T-score. The lowest T-score differed significantly among the age groups (P=0.037), although no pairwise difference reached statistical significance in the Scheffé post hoc test, whereas the spine T-score did not (P=0.086). Hip-neck T-score and total hip T-score also differed significantly across age groups (P=0.020 and P=0.025, respectively), with post hoc testing indicating significant differences between group a (≤69 years) and group c (≥80 years). By sex, the mean lowest T-score was −2.13±0.90 in males and −2.62±0.97 in females, indicating a statistically significant difference (P=0.032), and both spine and total hip T-scores also showed differences (P=0.038 and P=0.024, respectively). In BMI classification, the mean lowest T-score was −2.92±0.96 in the normal-weight group, −2.00±1.18 in the overweight group, −2.38±0.72 in the obese group, and −2.13±0.96 in the severely obese group. ANOVA analysis revealed a statistically significant difference among these groups (P=0.024), with post hoc testing indicating a significant difference between the normal-weight and overweight groups. Also, the total hip T-score differed significantly among the BMI groups (P=0.039), although no pairwise difference reached statistical significance in the Scheffé post hoc test. In injury mechanisms, comorbidities, smoking status, alcohol consumption and previous fracture history groups, none of these differences were statistically significant. Statistical testing was not performed for dementia because only one patient (n=1) had this condition; this variable was retained in Tables 1 and 2 for completeness.
Discussion
Our study demonstrated that age, sex, and BMI were significantly associated with BMD in elderly patients with ankle fractures, although the associations differed according to the measured site. Specifically, age was associated with the lowest, femoral neck, and total hip T-scores, female sex with the lowest T-score, spine T-score, and total hip T-score, and BMI with the lowest and total hip T-scores, whereas comorbidities (diabetes and hypertension), smoking, alcohol consumption, injury mechanism, and previous fracture history did not show a notable impact. Dementia was not statistically tested because only one patient had this condition.
Although our study did not directly measure peripheral BMD at the ankle joint, a previous study [12] analyzed the correlation between central BMD (spine and hip) and peripheral BMD (medial malleolus, distal tibia, lateral malleolus, and talus) using DXA, reporting a significant association across all measurement sites. Such findings imply that assessing BMD at or around the ankle may be useful for identifying high-risk patients. While our analysis focused on central BMD, these prior results suggest a potential linkage between central and peripheral bone measurements that warrants further direct evaluation in future studies.
Many studies indicate that higher body weight can provide a protective effect on bone through appropriate mechanical loading [13]. Also, a recent systematic review and meta-analysis found that obesity is generally associated with higher areal BMD, yet the overall fracture risk pattern is heterogeneous, weight-bearing sites may benefit from greater mechanical loading, whereas non-weight-bearing or peripheral sites can still experience increased fracture risk [14]. In our study, the lowest T-score differed significantly among BMI groups, and post hoc analysis showed that it was significantly lower in the normal-weight group than in the overweight group. However, obesity has been reported to have a negative impact on BMD due to systemic inflammatory responses, which may be harmful to bones. Furthermore, increased bone marrow adipogenesis in obese individuals may contribute to reduced bone mass [13]. A recent study in adults over 50 years of age suggested a nonlinear relationship between BMI and BMD, with a saturation effect occurring at approximately 26 kg/m² [15]. BMD increased with BMI up to this threshold, whereas the increase became substantially attenuated at higher BMI levels. This pattern may help explain the complex relationship between BMD and body weight. Although some ankle fracture studies have linked high BMI to fracture risk [16,17], our study focuses on BMD rather than fracture incidence, suggesting that BMI may modulate bone quality differently from its effect on fall mechanics. These findings highlight the complexity of the relationship between ankle fractures and BMD, which cannot be explained by a single factor. Given the ongoing debates and investigations in this area, further studies are needed to clarify these associations.
One might intuitively assume that patients with diabetes would exhibit lower BMD due to impaired bone metabolism. However, recent studies suggest that individuals with type 2 diabetes often have normal or even slightly higher BMD compared to non-diabetic controls, despite an overall increased risk of fractures [18,19]. This apparent paradox has been attributed to factors such as the accumulation of advanced glycation end-products, alterations in bone microarchitecture, and the potential confounding effect of higher BMI in many patients with type 2 diabetes [20]. In other words, even when BMD measurements appear preserved or elevated, qualitative changes in bone tissue and a heightened propensity for falls may collectively raise fracture risk. In our study, diabetic patients showed a numerically higher lowest T-score, but the difference did not reach statistical significance (P=0.136) and should therefore be interpreted cautiously.
A key limitation of this study is its retrospective design at a single institution, coupled with a relatively small sample size, which may restrict the generalizability of our findings. The small sample size also likely contributed to some unexpected observations. For example, significant age-group differences were observed in the lowest, femoral neck, and total hip T-scores, but not in the spine T-score. This age-related finding might be confounded by differences in sex distribution among the age groups. Postmenopausal women typically show a steeper decline in bone mass, suggesting that the observed age-related decrease may partly reflect sex differences rather than aging alone. Additionally, our data revealed that non-smokers and non-drinkers had slightly lower BMD contrary to most established reports [21,22]. Such discrepancies are likely influenced by limited statistical power and potential confounders like medication use, lifestyle factors and other unmeasured variables. Moreover, several subgroups included very few patients, including dementia (n=1), alcohol consumption (n=4), fall-down injury (n=3), other injury mechanisms (n=2), and severe obesity (n=4), limiting the precision of estimates and the assessment of distributional assumptions.
Future studies should therefore involve larger, more diverse populations and incorporate detailed individual factors to clarify these trends and strengthen the understanding of the relationship between BMD and ankle fractures.
In conclusion, in elderly patients (≥65 years) with low-energy ankle fractures, BMD was associated with age, sex, and BMI, although the associations varied according to the measured site. These findings highlight the importance of BMD assessment and osteoporosis screening, particularly in older patients with ankle fractures who may have clinical risk factors for low BMD.
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Author contribution
Conceptualization: SHL, CHL, JYL. Data curation: SHL, CHL, SJP. Funding acquisition: JYL. Formal analysis: SHL, JYL. Methodology: SHL, SJP, JYL. Project administration: JYL. Investigation: SHL, CHL. Supervision: JYL, SJP. Writing-original draft: SHL, CHL, SJP, JYL. Writing-review & editing: SHL, CHL, SJP, JYL. All authors read and approved the final manuscript.
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Conflict of interests
No potential conflict of interest relevant to this article was reported.
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Funding
This study was supported by research fund from Chosun University 2023.
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Data availability
Contact the corresponding author for data availability.
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Acknowledgments
None.
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Supplementary materials
None.
Article Information
Table 1.
Demographics of the patients (n=73)
Table 2.
Associations between bone mineral density and patients’ characteristics (n=73)
| Variable | Category | Lowest T-scorea) | Spine T-score | Hip-neck T-score | Total hip T-score | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Mean±SD | t/F | P-value | Mean±SD | t/F | P-value | Mean±SD | t/F | P-value | Mean±SD | t/F | P-value | ||
| Total | –2.43±0.97 | - | - | –1.96±1.41 | - | - | –1.80±0.99 | - | - | –1.19±0.95 | - | - | |
| Age (yr) | ≤69 (a) | –2.12±0.82 | 3.457 | 0.037 | –1.62±1.24 | 2.536 | 0.086 | –1.55±0.98 | 4.137 | 0.020 (a>c) | –0.92±0.93 | 3.872 | 0.025 (a>c) |
| 70–79 (b) | –2.58±1.06 | - | - | –2.36±1.29 | - | - | –1.82±1.00 | - | - | –1.26±0.95 | - | - | |
| ≥80 (c) | –2.94±0.83 | - | - | –1.66±2.08 | - | - | –2.58±0.45 | - | - | –1.87±0.71 | - | - | |
| Sex | Male | –2.13±0.90 | 2.193 | 0.032 | –1.53±1.51 | 2.117 | 0.038 | –1.59±0.98 | 1.474 | 0.145 | –0.87±1.00 | 2.299 | 0.024 |
| Female | –2.62±0.97 | - | - | –2.23±1.30 | - | - | –1.93±0.98 | - | - | –1.38±0.88 | - | - | |
| Injury mechanism | Twisting injury | –2.38±0.80 | 1.930 | 0.133 | –2.12±1.25 | 1.871 | 0.143 | –1.63±0.96 | 1.279 | 0.288 | –1.04±0.85 | 0.980 | 0.407 |
| Slip down | –2.35±1.01 | - | - | –1.69±1.44 | - | - | –1.82±1.02 | - | - | –1.22±1.02 | - | - | |
| Fall down | –3.40±0.56 | - | - | –3.37±0.60 | - | - | –2.53±0.32 | - | - | –1.80±0.30 | - | - | |
| Other | –3.45±2.19 | - | - | –2.80±3.11 | - | - | –2.60±0.99 | - | - | –1.85±1.63 | - | - | |
| Comorbidity | |||||||||||||
| Diabetes | No | –2.56±1.01 | 1.508 | 0.136 | –2.13±1.53 | 1.343 | 0.184 | –1.84±1.04 | 0.317 | 0.752 | –1.25±1.01 | 0.675 | 0.502 |
| Yes | –2.21±0.87 | - | - | –1.67±1.16 | - | - | –1.75±0.90 | - | - | –1.09±0.86 | - | - | |
| Hypertension | No | –2.41±1.09 | 0.228 | 0.820 | –2.11±1.35 | 0.883 | 0.380 | –1.63±1.14 | 1.384 | 0.171 | –1.03±1.09 | 1.376 | 0.173 |
| Yes | –2.46±0.86 | - | - | –1.82±1.48 | - | - | –1.95±0.81 | - | - | –1.33±0.79 | - | - | |
| Dementia | No | –2.43±0.98 | Not testedb) | –1.95±1.42 | Not testedb) | –1.79±0.99 | Not testedb) | –1.19±0.96 | Not testedb) | ||||
| Yes | –2.60 | - | - | –2.50 | - | - | –2.60 | - | - | –1.20 | - | - | |
| Health behavior | |||||||||||||
| Smoking | No | –2.44±0.98 | 0.348 | 0.729 | –1.95±1.44 | 0.104 | 0.918 | –1.83±0.99 | 0.731 | 0.467 | –1.21±0.96 | 0.634 | 0.528 |
| Yes | –2.30±0.92 | –2.02±1.24 | –1.52±0.93 | –0.95±0.88 | |||||||||
| Alcohol | No | –2.46±0.97 | 1.077 | 0.285 | –1.99±1.43 | 0.921 | 0.360 | –1.83±0.99 | 0.989 | 0.326 | –1.21±0.97 | 0.834 | 0.407 |
| Yes | –1.93±0.92 | –1.33±1.19 | –1.33±0.75 | –0.80±0.54 | |||||||||
| Body mass index | Normal (a) | –2.92±0.96 | 3.342 | 0.024 (a<b) | –2.50±1.54 | 2.498 | 0.067 | –2.25±0.87 | 2.441 | 0.072 | –1.66±0.76 | 2.932 | 0.039 |
| Overweight (b) | –2.00±1.18 | –1.48±1.44 | –1.56±1.22 | –1.05±1.14 | |||||||||
| Obese (c) | –2.38±0.72 | –1.98±1.01 | –1.69±0.88 | –1.02±0.90 | |||||||||
| Severely obese (d) | –2.13±0.96 | –0.93±2.47 | –1.28±0.69 | –0.58±0.62 | |||||||||
| Previous fracture history | No | –2.42±0.94 | 0.286 | 0.776 | –1.95±1.43 | 0.141 | 0.888 | –1.77±0.99 | 0.599 | 0.551 | –1.14±0.97 | 0.957 | 0.342 |
| Yes | –2.50±1.14 | –2.01±1.38 | –1.94±0.99 | –1.41±0.86 | |||||||||
F value for one-way analysis of variance among three or more groups. Letters in parentheses identify groups within each variable. Significant pairwise differences identified by Scheffé post hoc test are indicated as a>c or a<b (P<0.05).
SD, standard deviation; t/F, test statistic (t value for two-group comparisons, independent-samples t-test).
a)Lowest T-score indicates the lowest value among the lumbar spine, femoral neck, and total hip T-scores.
b)Statistical testing was not performed because only one patient had dementia.
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Correlation of bone mineral density with ankle fractures in older adults in Korea: a retrospective cohort study
Correlation of bone mineral density with ankle fractures in older adults in Korea: a retrospective cohort study
| Variable | Value |
|---|---|
| Age (yr) | 71.64±5.74 |
| ≤69 | 31 (42.5) |
| 70–79 | 33 (45.2) |
| ≥80 | 9 (12.3) |
| Sex (male:female) | 28 (38.4):45 (61.6) |
| Injury mechanism | |
| Twisting injury | 28 (38.4) |
| Slip down | 40 (54.8) |
| Fall down | 3 (4.1) |
| Other | 2 (2.7) |
| Comorbidity (no:yes) | |
| Diabetes | 46 (63.0):27 (37.0) |
| Hypertension | 35 (47.9):38 (52.1) |
| Dementia | 72 (98.6):1 (1.4) |
| Bone mineral density (lowest T-score) | |
| Normal | 7 (9.6) |
| Osteopenia | 26 (35.6) |
| Osteoporosis | 40 (54.8) |
| Health behavior (no:yes) | |
| Smoking | 67 (91.8):6 (8.2) |
| Alcohol | 69 (94.5):4 (5.5) |
| Body mass index (kg/m²) | 24.89±3.16 |
| Normal | 21 (28.8) |
| Overweight | 17 (23.3) |
| Obese | 31 (42.5) |
| Severely obese | 4 (5.5) |
| Previous fracture history (no:yes) | 59 (80.8):14 (19.2) |
| Variable | Category | Lowest T-score |
Spine T-score | Hip-neck T-score | Total hip T-score | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Mean±SD | t/F | P-value | Mean±SD | t/F | P-value | Mean±SD | t/F | P-value | Mean±SD | t/F | P-value | ||
| Total | –2.43±0.97 | - | - | –1.96±1.41 | - | - | –1.80±0.99 | - | - | –1.19±0.95 | - | - | |
| Age (yr) | ≤69 (a) | –2.12±0.82 | 3.457 | 0.037 | –1.62±1.24 | 2.536 | 0.086 | –1.55±0.98 | 4.137 | 0.020 (a>c) | –0.92±0.93 | 3.872 | 0.025 (a>c) |
| 70–79 (b) | –2.58±1.06 | - | - | –2.36±1.29 | - | - | –1.82±1.00 | - | - | –1.26±0.95 | - | - | |
| ≥80 (c) | –2.94±0.83 | - | - | –1.66±2.08 | - | - | –2.58±0.45 | - | - | –1.87±0.71 | - | - | |
| Sex | Male | –2.13±0.90 | 2.193 | 0.032 | –1.53±1.51 | 2.117 | 0.038 | –1.59±0.98 | 1.474 | 0.145 | –0.87±1.00 | 2.299 | 0.024 |
| Female | –2.62±0.97 | - | - | –2.23±1.30 | - | - | –1.93±0.98 | - | - | –1.38±0.88 | - | - | |
| Injury mechanism | Twisting injury | –2.38±0.80 | 1.930 | 0.133 | –2.12±1.25 | 1.871 | 0.143 | –1.63±0.96 | 1.279 | 0.288 | –1.04±0.85 | 0.980 | 0.407 |
| Slip down | –2.35±1.01 | - | - | –1.69±1.44 | - | - | –1.82±1.02 | - | - | –1.22±1.02 | - | - | |
| Fall down | –3.40±0.56 | - | - | –3.37±0.60 | - | - | –2.53±0.32 | - | - | –1.80±0.30 | - | - | |
| Other | –3.45±2.19 | - | - | –2.80±3.11 | - | - | –2.60±0.99 | - | - | –1.85±1.63 | - | - | |
| Comorbidity | |||||||||||||
| Diabetes | No | –2.56±1.01 | 1.508 | 0.136 | –2.13±1.53 | 1.343 | 0.184 | –1.84±1.04 | 0.317 | 0.752 | –1.25±1.01 | 0.675 | 0.502 |
| Yes | –2.21±0.87 | - | - | –1.67±1.16 | - | - | –1.75±0.90 | - | - | –1.09±0.86 | - | - | |
| Hypertension | No | –2.41±1.09 | 0.228 | 0.820 | –2.11±1.35 | 0.883 | 0.380 | –1.63±1.14 | 1.384 | 0.171 | –1.03±1.09 | 1.376 | 0.173 |
| Yes | –2.46±0.86 | - | - | –1.82±1.48 | - | - | –1.95±0.81 | - | - | –1.33±0.79 | - | - | |
| Dementia | No | –2.43±0.98 | Not tested |
–1.95±1.42 | Not tested |
–1.79±0.99 | Not tested |
–1.19±0.96 | Not tested |
||||
| Yes | –2.60 | - | - | –2.50 | - | - | –2.60 | - | - | –1.20 | - | - | |
| Health behavior | |||||||||||||
| Smoking | No | –2.44±0.98 | 0.348 | 0.729 | –1.95±1.44 | 0.104 | 0.918 | –1.83±0.99 | 0.731 | 0.467 | –1.21±0.96 | 0.634 | 0.528 |
| Yes | –2.30±0.92 | –2.02±1.24 | –1.52±0.93 | –0.95±0.88 | |||||||||
| Alcohol | No | –2.46±0.97 | 1.077 | 0.285 | –1.99±1.43 | 0.921 | 0.360 | –1.83±0.99 | 0.989 | 0.326 | –1.21±0.97 | 0.834 | 0.407 |
| Yes | –1.93±0.92 | –1.33±1.19 | –1.33±0.75 | –0.80±0.54 | |||||||||
| Body mass index | Normal (a) | –2.92±0.96 | 3.342 | 0.024 (a<b) | –2.50±1.54 | 2.498 | 0.067 | –2.25±0.87 | 2.441 | 0.072 | –1.66±0.76 | 2.932 | 0.039 |
| Overweight (b) | –2.00±1.18 | –1.48±1.44 | –1.56±1.22 | –1.05±1.14 | |||||||||
| Obese (c) | –2.38±0.72 | –1.98±1.01 | –1.69±0.88 | –1.02±0.90 | |||||||||
| Severely obese (d) | –2.13±0.96 | –0.93±2.47 | –1.28±0.69 | –0.58±0.62 | |||||||||
| Previous fracture history | No | –2.42±0.94 | 0.286 | 0.776 | –1.95±1.43 | 0.141 | 0.888 | –1.77±0.99 | 0.599 | 0.551 | –1.14±0.97 | 0.957 | 0.342 |
| Yes | –2.50±1.14 | –2.01±1.38 | –1.94±0.99 | –1.41±0.86 | |||||||||
Table 1. Demographics of the patients (n=73)
Values are presented as mean±standard deviation or number (%).
Table 2. Associations between bone mineral density and patients’ characteristics (n=73)
F value for one-way analysis of variance among three or more groups. Letters in parentheses identify groups within each variable. Significant pairwise differences identified by Scheffé post hoc test are indicated as a>c or a<b (P<0.05). SD, standard deviation; t/F, test statistic (t value for two-group comparisons, independent-samples t-test). Lowest T-score indicates the lowest value among the lumbar spine, femoral neck, and total hip T-scores. Statistical testing was not performed because only one patient had dementia.
