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Kim, Park, Yang, Shin, Park, Park, and Kim: Discordance in Secular Trends of Bone Mineral Density Measurements in Different Ages of Postmenopausal Women
Original Article
Endocrinology, Nutrition & Metabolism

Journal of Korean Medical Science 2023; 38(42): e364.

Published online: 19 October 2023

DOI: https://doi.org/10.3346/jkms.2023.38.e364

Discordance in Secular Trends of Bone Mineral Density Measurements in Different Ages of Postmenopausal Women

Kwang Yoon Kim1, *, Jaesun Park1, Sungwon Yang1, Junghwa Shin1, †, Ji Hyun Park2, Bumhee Park2, 3, Bom Taeck Kim1

1Department of Family Practice and Community Health, Ajou University School of Medicine, Suwon, Korea.

2Office of Biostatistics, Medical Research Collaborating Center, Ajou Research Institute for Innovative Medicine, Ajou University Hospital, Suwon, Korea.

3Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Korea.

Address for Correspondence: Bom Taeck Kim, MD, PhD. Department of Family Practice and Community Health, Ajou University School of Medicine, 164 World cup-ro, Yeongtong-gu, Suwon 16499, Republic of Korea. lovesong@ajou.ac.kr

*Current affiliation: Department of Family Medicine & Health Promotion Center, Myeongju Hospital, Yongin, Korea

†Current affiliation: Department of family medicine, Korea University Ansan Hospital, Ansan, Korea

Received 13 January 2023       Accepted 4 July 2023

© 2023 The Korean Academy of Medical Sciences.

The Korean Academy of Medical Sciences

https://creativecommons.org/licenses/by-nc/4.0/
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Background

Age-adjusted bone mineral density (BMD) in postmenopausal women decreases in developed countries whereas incidence of osteoporotic fracture decreases or remains stable. We investigated secular trends of bone density from 2008 to 2017 among different age groups of postmenopausal women.

Methods

We analyzed BMD data obtained from health check-ups of 4,905 postmenopausal women during three survey cycles from 2008 to 2017. We divided them into 3 groups by age (50–59 years, 60–69 years, and 70 years or more) and observed the transition of lumbar and femoral BMD in each group, before and after adjusting for variables that may affect BMD.

Results

Age-adjusted BMD, bone mineral content (BMC), and T-score demonstrated a declining trend over the survey period at lumbar spine (−2.8%), femur neck (−3.5%) and total femur (−4.3%), respectively. In the analysis for the age groups, the BMD, BMC, and T-score presented linear declining trend (−6.1%) in younger postmenopausal women while women aged over 70 or more showed linear increasing trends (+6.3%) at lumbar spine during the survey period. Femoral neck and total femur BMD demonstrated a declining linear trend only in the 50–59 and 60–69 years groups (−5.5%, −5.2%, respectively), but not in the 70 years or more group.

Conclusion

BMD in younger postmenopausal women has decreased considerably but has increased or plateaued in elderly women. This discordance of BMD trends among different age groups may contribute to decreased incidence of osteoporotic fracture despite a recent declining BMD trend in postmenopausal women.

Graphical Abstract

jkms-38-e364-abf001.jpg

Keywords: BMD, Fracture Risk, Secular Trend, Postmenopausal Osteoporosis

INTRODUCTION

Osteoporosis is a systemic skeletal disorder characterized by decreased bone mineral density (BMD), compromised bone strength, and increased vulnerability to fragility fractures.1 In general, BMD consistently shows an inverse relationship with osteoporotic fracture risk.23
The word secular is a descriptive word used to indicate certain activities that develop over the long period and remain consistent over time. Therefore, the secular trends are those the researchers expect to remain altering in the same direction over the long term.
Investigating the secular trend of BMD is important to predict the future trend of osteoporotic fractures. However, prior studies were mainly preceded to clarify the trend of osteoporotic fracture rather than the trend of BMD. These series of studies conducted in many countries including the United States, Canada, Australia, Italy, and Denmark,456789 revealed a decreasing or remaining osteoporotic fracture trend in recent years. Therefore, many researchers anticipated that the trend of BMD would demonstrate an improving trend. However, a study analyzing the National Health and Nutrition Examination Survey (NHANES) data raised an interesting question with the report of a declining secular trend of BMD at the femur in postmenopausal women10 despite published evidence that age-adjusted incidence of osteoporotic fracture decreases or remains stable lately in developed countries. It is not clear whether a decrease in age-adjusted BMD is an actual occurrence, and if it is, how this trend will affect the rate of osteoporotic fracture in the future. We investigated the trend of lumbar and femoral BMD in postmenopausal women from 2008 to 2017 to predict the trajectory of osteoporotic fractures in the future. Additionally, we explored the contributors for the discrepancy between the trend of BMD and the trend of osteoporotic fracture by investigating them in different age groups during the survey period.

METHODS

Study participants

The present study analyzed the BMD data of 5,628 postmenopausal subjects aged 50 years and older, who had health check-ups in three survey cycles 2008–2009, 2012–2013, and 2016–2017 in Ajou University Hospital, Suwon, South Korea. The exclusion criteria were: subjects diagnosed with osteoporotic fractures; with renal diseases or estimated glomerular filtration rate < 60 mL/min/1.73 m2; with impaired liver function defined as a serum alanine transaminase (ALT) or serum alanine aminotransferase level three times the upper limit of the normal range or more; presence of hepatitis or cirrhosis; with endocrine disease such as hyperthyroidism and hypothyroidism; history of gastric resection; subjects receiving chemotherapy for cancer treatment; conditions associated with the malabsorption of nutrients such as inflammatory bowel disease; subjects on medications that could affect bone mineral metabolism, such as anticonvulsants, thiazides, diuretics, selective serotonin reuptake inhibitor, proton pump inhibitors, oral steroids, and anti-osteoporotic medicines; and subjects with missing BMD values (Fig. 1). Consequently, 4,905 postmenopausal women were included in this study.
Fig. 1

Sample extraction.

eGFR = estimated glomerular filtration rate, SSRI = selective serotonin reuptake inhibitor, BMD = bone mineral density.
jkms-38-e364-g001

Study procedures

Body mass index (BMI) was calculated as body weight in kilograms divided by height in meters squared. Height and weight were measured with the Inbody® scale (BSM-330; Biospace®, Seoul, Korea). Laboratory tests such as serum creatinine, ALT, aspartate transaminase (AST), and fasting blood glucose were performed on the blood sample. Cigarette smoking habits and calcium supplementation were assessed with a survey questionnaire. BMD, bone mineral content (BMC), and T-score at the lumbar spine and proximal femur were measured by DXA scan (Lunar iDXA; General Electric®, Boston, MA, USA) performed in standard mode. Spine and hip scans were analyzed using enCORE (2007, Version 11.4; General Electric Company, Madison, WI, USA) in 2008–2009, and enCORE (Version 15.0) in 2012–2013 and 2016–2017. Quality control for calibration of DXA scan was conducted once a week using the aluminum spine phantom model.

Data analysis

Statistical analyses were performed by using R Statistical Software (version 4.0.2; R Foundation for Statistical Computing, Vienna, Austria). Continuous variables were presented as means ± standard deviation. We examined whether the change in secular BMD trend remained significant after adjusting for multiple BMD-associated factors such as BMI, serum creatinine, AST, ALT, fasting blood sugar, smoking habit, and calcium supplementation. Thus, the least-square means, which were adjusted for means of the aforementioned factors in the multiple regression model, and their 95% confidence intervals were calculated to plot our figures. Categorical variables were transformed into dummy variables so that they could be analyzed by multiple linear regression analysis. P values were obtained after Bonferroni adjustment, which are significant when < 0.05.

Ethics statement

All participants included in the current study have signed an informed consent. This study was approved by the Institutional Review Board of Ajou University Hospital (AJIRB-MED-MDB-18-049) and all procedures were carried out in accordance with relevant guidelines and regulations.

RESULTS

General characteristics

General characteristics of the subjects in each period are demonstrated in Table 1. The mean ages of the subjects and the mean serum creatinine values declined during the survey period, while the values of mean body weight, mean AST, mean ALT, and calcium supplementation rate increased. The proportion of subjects aged 50–59 years was higher in 2016–2017 (73.59%) compared with those in other periods, while the proportion of subjects aged 70 years or more was highest in 2008–2009.
Table 1

Demographic and clinical characteristics of the study group

jkms-38-e364-i001
Variables 2008–2009 (n = 1,445) 2012–2013 (n = 1,323) 2016–2017 (n = 2,137) P value
Agea 60.27 ± 7.53 59.33 ± 7.28 57.05 ± 6.54 < 0.001
Heighta 156.67 ± 5.27 156.32 ± 5.24 156.88 ± 5.26 0.005
Weighta 57.02 ± 7.62 57.24 ± 8.22 57.82 ± 7.88 0.003
BMIa 23.23 ± 2.93 23.42 ± 3.14 23.5 ± 3.03 0.065
Creatininea 0.78 ± 0.09 0.84 ± 0.07 0.69 ± 0.1 < 0.001
Fasting blood glucosea 95.88 ± 19.12 96.02 ± 19.28 96.16 ± 19.56 0.828
ASTa 22.09 ± 7.35 25.11 ± 9.27 23.24 ± 8.61 < 0.001
ALTa 19.96 ± 11.12 20.69 ± 11.74 20.78 ± 11.86 0.036
Calcium supplement statusb < 0.001
Yes 219 (15.16) 290 (21.92) 403 (18.86)
No 1,226 (84.84) 1,033 (78.08) 1,734 (81.14)
Smoking habitsb 0.595
Never 1,386 (95.92) 1,282 (96.9) 2,053 (96.07)
Former 19 (1.31) 14 (1.06) 32 (1.5)
Current 40 (2.77) 27 (2.04) 52 (2.43)
Data are presented as mean ± standard deviation and number (%) for the categorical variables.
Kruskal-Wallis rank-sum test was used for continuous variables and χ2 test was used for categorical variables, as appropriate.
BMI = body mass index, AST = aspartate transaminase, ALT = alanine transaminase.
aEvaluated by Kruskal-Wallis test; bEvaluated by χ2 test.

Trend of BMD, BMC and T-score across whole population

The BMD, BMC and T-score of the lumbar spine demonstrated a declining trend over the survey period. The BMD and T-score of the femur neck and femur total also showed a declining trend over the survey period. These declining trends became more apparent both for the lumbar spine and the femur after adjusting for age (Table 2, Fig. 2).
Table 2

Lumbar spine and femur bone density during the survey periods (before division into age groups)

jkms-38-e364-i002
Osteoporosis epidemiology 2008–2009 (n = 1,445) 2012–2013 (n = 1,323) 2016–2017 (n = 2,137) P value (linear) P value (quadratic)
L1–L4 BMD
Unadjusted 1.09 ± 0 1.06 ± 0 1.06 ± 0 < 0.001 0.028
Adjusted by age 1.1 ± 0 1.07 ± 0 1.05 ± 0 < 0.001 0.301
L1–L4 BMC
Unadjusted 58.52 ± 0.3 58.66 ± 0.31 56.88 ± 0.25 < 0.001 0.005
Adjusted by age 59.55 ± 0.28 57.11 ± 0.29 55.9 ± 0.23 < 0.001 0.065
L1–L4 T-score
Unadjusted −0.53 ± 0.04 −0.73 ± 0.04 −0.72 ± 0.03 < 0.001 0.028
Adjusted by age −0.39 ± 0.03 −0.66 ± 0.04 −0.86 ± 0.03 < 0.001 0.302
Femur neck BMD
Unadjusted 0.87 ± 0 0.85 ± 0 0.84 ± 0 < 0.001 0.312
Adjusted by age 0.88 ± 0 0.86 ± 0 0.83 ± 0 < 0.001 0.796
Femur neck T-score
Unadjusted −0.62 ± 0.03 −0.77 ± 0.03 −0.85 ± 0.02 < 0.001 0.312
Adjusted by age −0.52 ± 0.02 −0.72 ± 0.02 −0.94 ± 0.02 < 0.001 0.796
Total femur BMD
Unadjusted 0.94 ± 0 0.91 ± 0 0.9 ± 0 < 0.001 0.084
Adjusted by age 0.95 ± 0 0.92 ± 0 0.89 ± 0 < 0.001 0.519
Total femur T-score
Unadjusted −0.31 ± 0.03 −0.5 ± 0.03 −0.58 ± 0.02 < 0.001 0.083
Adjusted by age −0.21 ± 0.03 −0.46 ± 0.03 −0.67 ± 0.02 < 0.001 0.516
Data are presented as mean ± standard deviation.
P values are obtained by using Kruskal-Wallis rank sum test.
BMD = bone mineral density, BMC = bone mineral content.
Fig. 2

Secular trend of lumbar spine and femur bone density (calculated before division into age groups).

BMD = bone mineral density, BMC = bone mineral content.
jkms-38-e364-g002

Trend of BMD, BMC and T-score in different age groups

Subjects were divided into different age groups (50–59 years, 60–69 years, 70 years and more), and the outcomes of bone density analysis of the lumbar spine and the femur for each of the groups were calculated as demonstrated in Table 3. We found a declining linear trend of BMD, BMC, and T-scores in the 50–59 years age group and a quadratic declining trend in the 60–69 years age group during the survey periods. These declining trends of BMD, BMC and T-score in the 50–59 years and the 60–69 years age groups remained statistically significant even after adjusting for BMI, renal function, liver function, fasting blood glucose, cigarette smoking, and calcium supplementation. On the contrary, BMD and T-score of the lumbar spine demonstrated linear increasing trends during the survey periods in the 70 years or more age group. These linear increasing trends of BMD and T-scores changed to quadratic trends after applying the same adjustments but remained statistically significant (Table 3, Fig. 3A).
Table 3

Lumbar spine and femur bone density during the survey periods and for each age group

jkms-38-e364-i003
Osteoporosis epidemiology 2008–2009 2012–2013 2017–2018 P value (linear)b P value (quadratic)b
Lumbar spine (L1–L4) BMD
50–59 yr (n = 3,173)
Unadjusted 1.16 ± 0.01 1.12 ± 0.01 1.09 ± 0 < 0.001 > 0.999
Adjusteda 1.14 ± 0.01 1.1 ± 0.01 1.09 ± 0.01 < 0.001 0.097
60–69 yr (n = 1,226)
Unadjusted 1.03 ± 0.01 0.98 ± 0.01 0.99 ± 0.01 < 0.001 < 0.001
Adjusteda 1.04 ± 0.02 0.97 ± 0.02 1.01 ± 0.02 0.039 < 0.001
70 yr or more (n = 506)
Unadjusted 0.92 ± 0.01 0.93 ± 0.01 0.96 ± 0.01 0.127 > 0.999
Adjusteda 0.95 ± 0.03 0.94 ± 0.03 1.01 ± 0.03 0.002 0.066
Lumbar spine (L1–L4) BMC
50–59 yr (n = 3,173)
Unadjusted 62.84 ± 0.38 60.36 ± 0.37 58.65 ± 0.27 < 0.001 > 0.999
Adjusteda 62.09 ± 0.75 58.75 ± 0.76 59.04 ± 0.69 < 0.001 < 0.001
60–69 yr (n = 1,226)
Unadjusted 55.21 ± 0.48 51.49 ± 0.53 52.55 ± 0.50 < 0.001 < 0.001
Adjusteda 56.11± 1.20 51.67 ± 1.27 54.55 ± 1.28 0.112 < 0.001
70 yr or more (n = 506)
Unadjusted 49.07 ± 0.76 49.15 ± 0.88 50.3 ± 0.87 0.856 > 0.999
Adjusteda 51.41 ± 2.11 50.77 ± 2.16 53.97 ± 2.22 0.088 0.278
Lumbar spine (L1–L4) T-score
50–59 yr (n = 3,173)
Unadjusted 0.06 ± 0.05 −0.21 ± 0.05 −0.48 ± 0.03 < 0.001 > 0.999
Adjusteda −0.06 ± 0.09 −0.41 ± 0.09 −0.5 ± 0.08 < 0.001 0.097
60–69 yr (n = 1,226)
Unadjusted −0.97 ± 0.06 −1.44 ± 0.07 −1.33 ± 0.06 < 0.001 < 0.001
Adjusteda −0.93 ± 0.15 −1.48 ± 0.16 −1.16 ± 0.16 0.040 < 0.001
70 yr or more (n = 506)
Unadjusted −1.87 ± 0.09 −1.8 ± 0.11 −1.58 ± 0.11 0.127 > 0.999
Adjusteda −1.64 ± 0.26 −1.72 ± 0.26 −1.15 ± 0.27 0.002 0.066
Femur neck BMD
50–59 yr (n = 3,173)
Unadjusted 0.91 ± 0 0.89 ± 0 0.86 ± 0 < 0.001 0.780
Adjusteda 0.91 ± 0.01 0.88 ± 0.01 0.86 ± 0.01 < 0.001 > 0.999
60–69 yr (n = 1,226)
Unadjusted 0.84 ± 0 0.8 ± 0.01 0.79 ± 0.01 < 0.001 0.273
Adjusteda 0.83 ± 0.01 0.78 ± 0.01 0.78 ± 0.01 < 0.001 0.021
70 yr or more (n = 506)
Unadjusted 0.76 ± 0.01 0.74 ± 0.01 0.75 ± 0.01 > 0.999 0.212
Adjusteda 0.73 ± 0.01 0.7 ± 0.01 0.74 ± 0.01 > 0.999 0.007
Femur neck T-score
50–59 yr (n = 3,173)
Unadjusted −0.27 ± 0.03 −0.42 ± 0.03 −0.66 ± 0.02 < 0.001 0.780
Adjusteda −0.28 ± 0.07 −0.49 ± 0.07 −0.66 ± 0.06 < 0.001 > 0.999
60–69 yr (n = 1,226)
Unadjusted −0.84 ± 0.04 −1.15 ± 0.05 −1.28 ± 0.04 < 0.001 0.272
Adjusteda −0.96 ± 0.10 −1.32 ± 0.11 −1.33 ± 0.11 < 0.001 0.021
70 yr or more (n = 506)
Unadjusted −1.52 ± 0.06 −1.71 ± 0.07 −1.61 ± 0.07 > 0.999 0.212
Adjusteda −1.75 ± 0.17 −2.01 ± 0.18 −1.7 ± 0.18 > 0.999 0.007
Total femur BMD
50–59 yr (n = 3,173)
Unadjusted 0.98 ± 0 0.95 ± 0 0.93 ± 0 < 0.001 > 0.999
Adjusteda 0.97 ± 0.01 0.94 ± 0.01 0.92 ± 0.01 < 0.001 0.552
60–69 yr (n = 1,226)
Unadjusted 0.91 ± 0.01 0.87 ± 0.01 0.86 ± 0.01 < 0.001 0.137
Adjusteda 0.9 ± 0.01 0.85 ± 0.01 0.85 ± 0.01 < 0.001 0.009
70 yr or more (n = 506)
Unadjusted 0.83 ± 0.01 0.8 ± 0.01 0.81 ± 0.01 0.115 0.238
Adjusteda 0.81 ± 0.02 0.77 ± 0.02 0.79 ± 0.02 0.909 0.042
Total femur T-score
50–59 yr (n = 3,173)
Unadjusted 0.01 ± 0.04 −0.18 ± 0.04 −0.4 ± 0.03 < 0.001 > 0.999
Adjusteda −0.01 ± 0.07 −0.28 ± 0.07 −0.44 ± 0.06 < 0.001 0.552
60–69 yr (n= 1,226)
Unadjusted −0.5 ± 0.04 −0.85 ± 0.05 −0.97 ± 0.05 < 0.001 0.135
Adjusteda −0.62 ± 0.11 −1.02 ± 0.11 −1.02 ± 0.11 < 0.001 0.008
70 yr or more (n = 506)
Unadjusted −1.16 ± 0.06 −1.42 ± 0.07 −1.36 ± 0.07 0.115 0.238
Adjusteda −1.4 ± 0.18 −1.69 ± 0.18 −1.5 ± 0.19 0.909 0.042
Data are presented as mean ± standard deviation.
BMD = bone mineral density, BMC = bone mineral content.
aAdjusted for BMI, serum creatinine, AST, ALT, fasting blood glucose, smoking habit, calcium supplementation status.
bP values were obtained after Bonferroni adjustment, which are significant when < 0.05.
Fig. 3

Secular trend of lumbar spine and femur bone density of each age group.

BMD = bone mineral density, BMC = bone mineral content.
jkms-38-e364-g003
For the femoral neck and the total femur bone, we identified declining linear trends during the survey periods in the 50–59 years and the 60–69 years age group. The declining trends of BMD and T-score were persistently statistically significant even after adjusting for BMI, renal function, liver function, fasting blood glucose, cigarette smoking, and calcium supplementation in each age group. In women aged 70 years or more, U shape trends in BMD and T-score were seen but those trends were not statistically significant before or after the adjustment (Table 3, Fig. 3B and C).

DISCUSSION

In this study, we demonstrated that the BMD values of the lumbar spine and proximal femur in postmenopausal women have been decreasing from 2008 to 2017. However, the analysis conducted in different age groups revealed a discrepancy in the secular trend of BMD. The decrease in BMD values over the survey periods was more apparent in younger postmenopausal women, whereas women aged over 70 years showed increasing values of BMD at the lumbar spine and unchanged values for the proximal femur during the survey period.
Previous studies have reported that hip fracture incidence has decreased in developed countries.456789 This declining trend of fragility fracture was also observed among Asian countries,1112 including Korea.13 However, a recent study regarding the BMD trend in the United States reported an unexpected finding for hip fracture incidence. The prevalence of osteoporosis in the United States defined by the femur neck BMD and T score under −2.5 increased from 2007–2008 to 2013–2014 based on data from NHANES,10 and it also detailed a declining quadratic trend of femoral neck BMD during the same period.14 This finding is consistent with the present study. However, the background of the discordance between trends of BMD and fracture incidence reported in NHANES has not been clearly elucidated yet. The attempt to analyze BMD trends by dividing the study group into different age groups may shed light on the variations in BMD trends following age and may discover transition of BMD trends between those age groups as our analysis did.
Age is the most powerful and independent risk factor for osteoporotic fractures.1516 Fragility fracture, especially hip fracture, is more common in elderly postmenopausal women than that in young postmenopausal women with the same BMD.1718 The BMD trend in the younger ages may have a lesser impact on the incidence of osteoporotic fractures compared to that in the elderly. According to the National Osteoporosis Risk Assessment (NORA) study, the overall fracture incidence increases about 8 per 1,000 person-years as T-score changes from −1.0–−2.0 to less than −2.0 in ages of 50–69 years while it increases 13 per 1,000 person-years in ages of 70–79 years and even 15 per 1,000 person-year in ages over 80 years with the same T-score change.19 Accounting for the NORA study and our observation, the discordance between decreased fracture incidence and increasing BMD trend may have originated from the discrepancy of BMD trends across different age groups.
To figure out plausible causes for the observed differences in BMD trends among different age groups, we explored the changes in BMD-related factors such as BMI, renal function, liver function, fasting blood glucose, smoking habit, and calcium supplementation.202122 Surprisingly, these factors had shown a favorable change for BMD in the younger age group during the survey periods (Supplementary Table 1). However, a previous study revealed that changes in BMD-related factors may cause very little effect on the actual BMD change. Looker et al stated that adjustments for BMI and smoking habit failed to make a meaningful difference on BMD.10
This discordance in BMD trends may have originated from physical inactivity in younger women relative to the elderly.23 The sedentary lifestyle has become more pervasive in Korea lately, physical activity of adults has decreased by 13.1% as reported in Korea NHANES 2008 to 2014.24 And similarly, a study based on data from the 2020 National Health and Nutrition Survey, published in 2022, found that physical activity in the age group under 19–64 was significantly lower than those over 64.25 The level of physical activity is an important factor to maintain adequate bone mass. Especially, increased exercise or physical activity can reduce accelerated bone loss during the early menopausal period.2627 Another possible explanation is that in Korea, not like other countries, vitamin D deficiency is more prominent among young people. As is well known, vitamin D deficiency is significantly associated with decreased bone density.28
The increasing secular trend of BMD of the lumbar spine in the over 70 years age group shown in this study may contribute to the declining trend in vertebral compression fracture.29 In a study that reports the incidence of vertebral fracture in different age groups in Korea from 2012 to 2016, a substantial decline in vertebral fracture trend from 2013 to 2016 was observed only in the 70–74 age group. In other age groups, the incidence of vertebral fracture showed increasing trends, which supports our findings.
The strength of this study is that a substantial number of participants were included to probe the secular trends of BMD. The average number of subjects for each time period to find the secular trend of BMD in the United States was about 2,000 and 3,500 participants, respectively.1014 The population of the US is approximately 6 times larger than that of Korea, so an average of 1635 subjects for each time period in our study is relatively substantial, to analyze secular BMD trends. Moreover, the large number of subjects enabled us to perform an age-based analysis of participants, which revealed the discrepancy in BMD trends between younger and older postmenopausal women.
This study should be viewed in the light of its limitations. This research was conducted in a single institution in a single ethnic group. Due to the nature of these samples, some bias may occur in the sampling of participants between periods. In addition, since this study is a cross-sectional study and was not conducted on the same patients, there is a limitation that causality may not be evident. Another limitation is that the software version of the bone density measurement equipment used in this study is different between the data for 2008–2009 and the data for 2012–2013 and 2016-2017. Although this is a very subtle difference, it cannot be ruled out that it may have influenced the results of the study. And, this study was unable to completely address the causes of varying trends of BMD among the age groups due to lack of additional information such as serum vitamin D values.
In conclusion, our study indicated that BMD in postmenopausal women has declined significantly from 2008 to 2017 despite the increasing trends in elderly women. Our findings explain the recent discovery of the discrepancy between trends of BMD and fragility fracture. This data may help predict the trajectory of osteoporotic fractures in the future.

Notes

Disclosure: The authors have no potential conflicts of interest to disclose.

Data Availability Statement: The data that support the findings of this study are not publicly available due to limitations of ethical approval involving the patient data and anonymity but are available from the corresponding author on reasonable request.

Author Contributions:

  • Conceptualization: Kim KY, Yang S, Kim BT.

  • Data curation: Kim KY, Park J.

  • Formal analysis: Park JH, Park B.

  • Investigation: Shin J.

  • Methodology: Kim KY, Park J, Kim BT.

  • Project administration: Kim KY, Kim BT.

  • Supervision: Kim BT.

  • Validation: Kim BT.

  • Writing - original draft: Kim KY, Park J, Kim BT.

  • Writing - review & editing: Kim KY, Kim BT.

References

1. Osteoporosis prevention, diagnosis, and therapy. NIH Consens Statement. 2000; 17(1):1–45.
2. Seeley DG, Browner WS, Nevitt MC, Genant HK, Scott JC, Cummings SR. Which fractures are associated with low appendicular bone mass in elderly women? The Study of Osteoporotic Fractures Research Group. Ann Intern Med. 1991; 115(11):837–842. PMID: 1952469.
3. Ross PD, Davis JW, Epstein RS, Wasnich RD. Pre-existing fractures and bone mass predict vertebral fracture incidence in women. Ann Intern Med. 1991; 114(11):919–923. PMID: 2024857.
4. Lewiecki EM, Wright NC, Curtis JR, Siris E, Gagel RF, Saag KG, et al. Hip fracture trends in the United States, 2002 to 2015. Osteoporos Int. 2018; 29(3):717–722. PMID: 29282482.
5. Abtahi S, Driessen JH, Vestergaard P, van den Bergh J, Boonen A, de Vries F, et al. Secular trends in major osteoporotic fractures among 50+ adults in Denmark between 1995 and 2010. Osteoporos Int. 2019; 30(11):2217–2223. PMID: 31418061.
6. Leslie WD, O’Donnell S, Jean S, Lagacé C, Walsh P, Bancej C, et al. Trends in hip fracture rates in Canada. JAMA. 2009; 302(8):883–889. PMID: 19706862.
7. Crisp A, Dixon T, Jones G, Cumming RG, Laslett LL, Bhatia K, et al. Declining incidence of osteoporotic hip fracture in Australia. Arch Osteoporos. 2012; 7(1-2):179–185. PMID: 23225295.
8. Giannini S, Sella S, Rossini M, Braghin D, Gatti D, Vilei MT, et al. Declining trends in the incidence of hip fractures in people aged 65years or over in years 2000–2011. Eur J Intern Med. 2016; 35:60–65. PMID: 27363306.
9. Brauer CA, Coca-Perraillon M, Cutler DM, Rosen AB. Incidence and mortality of hip fractures in the United States. JAMA. 2009; 302(14):1573–1579. PMID: 19826027.
10. Looker AC, Sarafrazi Isfahani N, Fan B, Shepherd JA. Trends in osteoporosis and low bone mass in older US adults, 2005–2006 through 2013–2014. Osteoporos Int. 2017; 28(6):1979–1988. PMID: 28315954.
11. Imai N, Endo N, Shobugawa Y, Ibuchi S, Suzuki H, Miyasaka D, et al. A decrease in the number and incidence of osteoporotic hip fractures among elderly individuals in Niigata, Japan, from 2010 to 2015. J Bone Miner Metab. 2018; 36(5):573–579. PMID: 28884394.
12. Chen FP, Shyu YC, Fu TS, Sun CC, Chao AS, Tsai TL, et al. Secular trends in incidence and recurrence rates of hip fracture: a nationwide population-based study. Osteoporos Int. 2017; 28(3):811–818. PMID: 27832325.
13. Lee YK, Kim JW, Lee MH, Moon KH, Koo KH. Trend in the age-adjusted incidence of hip fractures in South Korea: systematic review. Clin Orthop Surg. 2017; 9(4):420–423. PMID: 29201294.
14. Xu Y, Wu Q. Decreasing trend of bone mineral density in US multiethnic population: analysis of continuous NHANES 2005–2014. Osteoporos Int. 2018; 29(11):2437–2446. PMID: 30091065.
15. Pinheiro MM, Reis Neto ET, Machado FS, Omura F, Yang JH, Szejnfeld J, et al. Risk factors for osteoporotic fractures and low bone density in pre and postmenopausal women. Rev Saude Publica. 2010; 44(3):479–485. PMID: 20549019.
16. Hui SL, Slemenda CW, Johnston CC Jr. Age and bone mass as predictors of fracture in a prospective study. J Clin Invest. 1988; 81(6):1804–1809. PMID: 3384952.
17. Amin S, Achenbach SJ, Atkinson EJ, Khosla S, Melton LJ 3rd. Trends in fracture incidence: a population-based study over 20 years. J Bone Miner Res. 2014; 29(3):581–589. PMID: 23959594.
18. Ensrud KE. Epidemiology of fracture risk with advancing age. J Gerontol A Biol Sci Med Sci. 2013; 68(10):1236–1242. PMID: 23833201.
19. Siris ES, Brenneman SK, Barrett-Connor E, Miller PD, Sajjan S, Berger ML, et al. The effect of age and bone mineral density on the absolute, excess, and relative risk of fracture in postmenopausal women aged 50–99: results from the National Osteoporosis Risk Assessment (NORA). Osteoporos Int. 2006; 17(4):565–574. PMID: 16392027.
20. Ho-Pham LT, Chau PM, Do AT, Nguyen HC, Nguyen TV. Type 2 diabetes is associated with higher trabecular bone density but lower cortical bone density: the Vietnam Osteoporosis Study. Osteoporos Int. 2018; 29(9):2059–2067. PMID: 29967929.
21. Huh JH, Choi SI, Lim JS, Chung CH, Shin JY, Lee MY. Lower serum creatinine is associated with low bone mineral density in subjects without overt nephropathy. PLoS One. 2015; 10(7):e0133062. PMID: 26207750.
22. Hinton PS, Rector RS, Linden MA, Warner SO, Dellsperger KC, Chockalingam A, et al. Weight-loss-associated changes in bone mineral density and bone turnover after partial weight regain with or without aerobic exercise in obese women. Eur J Clin Nutr. 2012; 66(5):606–612. PMID: 22190134.
23. Choi HS, Oh HJ, Choi H, Choi WH, Kim JG, Kim KM, et al. Vitamin D insufficiency in Korea--a greater threat to younger generation: the Korea National Health and Nutrition Examination Survey (KNHANES) 2008. J Clin Endocrinol Metab. 2011; 96(3):643–651. PMID: 21190984.
24. Kang YW, Ko YS, Kim KY, Sung C, Lee DH, Jeong E. Trends in health-related behaviors of Korean adults: study based on data from the 2008–2014 community health surveys. Epidemiol Health. 2015; 37:e2015042. PMID: 26493778.
25. Seo YB, Oh YH, Yang YJ. Current status of physical activity in South Korea. Korean J Fam Med. 2022; 43(4):209–219. PMID: 35903044.
26. Martin D, Notelovitz M. Effects of aerobic training on bone mineral density of postmenopausal women. J Bone Miner Res. 1993; 8(8):931–936. PMID: 8213255.
27. Wolff I, van Croonenborg JJ, Kemper HC, Kostense PJ, Twisk JW. The effect of exercise training programs on bone mass: a meta-analysis of published controlled trials in pre- and postmenopausal women. Osteoporos Int. 1999; 9(1):1–12.
28. Park JH, Hong IY, Chung JW, Choi HS. Vitamin D status in South Korean population: seven-year trend from the KNHANES. Medicine (Baltimore). 2018; 97(26):e11032. PMID: 29952942.
29. Choi SH, Kim DY, Koo JW, Lee SG, Jeong SY, Kang CN. Incidence and management trends of osteoporotic vertebral compression fractures in South Korea: a nationwide population-based study. Asian Spine J. 2020; 14(2):220–228. PMID: 31668050.

SUPPLEMENTARY MATERIAL

Supplementary Table 1

Demographic and clinical characteristics for 50 to 59 years age group
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