DOI: https://doi.org/10.21203/rs.3.rs-1621955/v1
Objective: To investigate the association between different body fat distribution and different sites of BMD in male and female populations.
Methods: Use the National Health and Nutrition Examination Survey (NHANES) datasets to select participants. The weighted linear regression model investigated the difference in body fat and Bone Mineral Density (BMD) in different gender. Multivariate adjusted smoothing curve-fitting and multiple linear regression models were used to explore whether an association existed between body fat distribution and BMD. Last, a subgroup analysis was performed according to age and gender group.
Results: Overall, 2,881 participants were included in this study. Compared to males, female participants had lower BMD (P < 0.05) and higher Gynoid fat mass (P < 0.00001), while there was no difference between Android fat mass (P = 0.91). Android fat mass was positively associated with Total femur BMD (Males, β = 0.044, 95% CI = 0.037, 0.051, P < 0.00001; Females, β = 0.044, 95% CI = 0.039, 0.049, P < 0.00001), Total spine BMD (Males, β = 0.036, 95% CI = 0.029, 0.044, P < 0.00001; Females, β = 0.025, 95% CI = 0.019, 0.031, P < 0.00001), and other sites. Subgroup analysis of age and ethnicity reached similar results.
Conclusion: Body fat in different regions was positively associated with BMD in different sites, and this association persisted in subgroup analyses across age and race in different gender.
Obesity was one of the serious health concerns affecting the health of the global population 1, especially in the US 2. It had been shown that the adverse effects of obesity might be related to fat distribution 3. Android obesity (also known as abdominal obesity, apple-shaped obesity) was associated with increased cardiovascular risk 4, mortality 5, or hypertension 6. However, other studies suggested that Gynoid obesity (also known as pear-shaped obesity) may be related to a reduced cardiovascular disease risk7 and metabolic disease 8. So, what was the effect of fat distribution on BMD without considering body weight? This topic remained insufficiently researched.
Most previous studies used Body Mass Index (BMI) to assess obesity and explore the association between BMI and BMD 9, 10 and concluded a positive association. Nevertheless, BMI was widely used because it was easy to calculate, but it did not distinguish between fat, muscle, and fat distribution in different body sites. Furthermore, the extant studies that had examined the association between body fat and BMD reached controversial conclusions. In studies of Chinese populations, some studies had concluded that body fat mass was positively associated with BMD in both men and women 11–13, while other studies had concluded that increased fat had a negative effect on BMD 14. Differential findings across gender in studies of populations in Brazil 15, Japan 16, Australia 17, and elsewhere were also found.
Thus, this study aimed to investigate the association between body fat distribution (Android fat and Gynoid fat) and different sites of BMD (Femur and Lumbar spine) in different gender populations in the US
This cross-sectional research selected datasets from the NHANES project, a nationally representative project to evaluate the health and nutritional status in the US. In this study, we used the NHANES 2013–2014 and NHANES 2017–2018, as these were the only two datasets that had data on both BMD and body fat mass. The study was reviewed and approved by NCHS IRB/ERB, all participants signed informed consent forms, and all methods were performed in accordance with relevant guidelines and regulations.
Before the beginning of this study, the following people were not included: 1) Pregnant; 2) Received radiographic contrast agents in the past week; 3) Had body fat mass exceeding the device limits; 4) Had congenital malformations or degenerative diseases of the spine; 5) Had lumbar spinal surgery; 6) Had hip fractures or congenital malformations; 7) Had hip surgery; 8) Had implants in the spine, hip or body, or other problems affecting body measurements. From NHANES datasets, 20194 participants were initially included in this study, 14851 participants without femoral or lumbar spine BMD data, 2455 participants without body fat data, and 7 participants taking anti-osteoporosis or weight-loss pills were excluded. Eventually, a total of 2881 participants were included (Fig. 1).
The dual-energy X-ray absorptiometry (DXA) measured participants' body fat mass 18. The main measurements were Android/Gynoid fat mass, and the Hologic APEX software defined the Android/Gynoid regions 19. The android area was the area of the lower part of the trunk bounded by two lines: the horizontal cut line of the pelvis on its lower side and a line automatically placed above the pelvic line. Gynoid was defined by an upper line and a lower line, with the upper line being 1.5 times the height of the android area below the pelvic line and the lower line being twice the height of the android area. Finally, the Android/Gynoid ratio calculation was performed from the measured android and gynoid data.
Participants' BMD was also measured by DXA equipment. The femur and lumbar spine were scanned, including the Total femur, Femoral neck, Trochanter, Intertrochanter, Ward triangle, Total spine, L1, L2, L3, and L4 regions. Quality control of staff, scanning instruments, and scanning results were performed throughout the scanning process.
The following covariates were selected: demographics (age, race, education level, and poverty ratio), personal habits (physical activity, smoke, and alcohol use), comorbidities (osteoporosis, high blood pressure, and diabetes), and body measurements (Height, Weight, Body Mass Index).
All study models were analyzed in gender subgroups to explore whether a gender difference existed between body fat distribution and BMD. Continuous variables in participants' demographic information were expressed as Mean +/- standard deviations (SD), and P-values were calculated using a weighted linear regression model. Dichotomous variables were expressed as percentages, and weighted chi-square tests were used to calculate P-values.
Smoothing curve fitting models were used to assess whether there was an association between android fat mass, gynoid fat mass, and android to gynoid ratio and BMD. If the smoothing curve fitting were meaningful, the multiple regression analysis models were used to analyze the association between body fat mass and BMD, and the results were expressed in terms of β, 95% confidence intervals (CI), and P-values. Adjustments of covariates in the above models were based on the following criteria: 1) the addition or removal of the variable from the model had an effect of more than 10% on the coefficient value of body fat mass; 2) the covariate P < 0.1 in univariate model vs BMD (Supplementary File 1).
Finally, age and race analyses under different gender subgroups were performed with the same analytical models as above. All analyses were performed with R software (3.6.3 version) and EmpowerStats software (https://www.empowerstats.com). P < 0.05 was considered statistically significant.
The basic characteristics of the participants were shown in Table 1. A total of 2881 participants (1245 males, 1636 females; mean age: 49 years) were included in this study. Among male participants, 33.9% had physical activity, only 0.2% had osteoporosis, 43.3% had high blood pressure, and 21.9% had diabetes. While for female participants, 12.2% had osteoporosis, 48.5% had high blood pressure, and 15.7% had diabetes (All P-values < 0.05). For body examination data, female participants had higher BMI, Gynoid fat mass, and lower BMD (Except L2 and L3) when compared to the male participants (All P-values < 0.05).
Male (N = 1245) | Female (N = 1636) | P value | |
---|---|---|---|
Demographic Data | |||
Age (Years) | 49.344 ± 5.717 | 49.016 ± 5.681 | 0.12498 |
Race (%) | |||
Mexican American | 7.783 | 7.115 | 0.64412 |
Other Hispanic | 3.908 | 5.041 | |
Non-Hispanic White | 66.650 | 66.031 | |
Non-Hispanic Black | 13.719 | 13.926 | |
Other Race | 7.941 | 7.887 | |
Poverty ratio | 3.149 ± 1.723 | 2.858 ± 1.697 | 0.00001 |
Education level (%) | |||
< ninth grade | 3.789 | 3.302 | < 0.00001 |
ninth - eleven grade | 10.476 | 10.935 | |
High school | 23.181 | 21.038 | |
Some college | 25.322 | 38.829 | |
College graduate | 37.232 | 25.897 | |
Personal habits | |||
Physical activity (%) | |||
Yes | 33.920 | 27.527 | 0.00021 |
No | 66.080 | 72.473 | |
Smoke (%) | |||
Yes | 47.632 | 46.321 | 0.48372 |
No | 52.368 | 53.679 | |
Alcohol use days per year | 2.826 ± 2.134 | 2.197 ± 2.096 | < 0.00001 |
Comorbidities | |||
Osteoporosis (%) | |||
Yes | 0.210 | 12.223 | < 0.00001 |
No | 99.790 | 87.777 | |
High blood pressure (%) | |||
Yes | 43.423 | 48.587 | 0.00579 |
No | 56.577 | 51.413 | |
Diabetes (%) | |||
Yes | 21.900 | 15.749 | 0.00003 |
No | 78.100 | 84.251 | |
Body examination data | |||
Height (cm) | 176.766 ± 7.022 | 162.282 ± 6.906 | < 0.00001 |
Weight (kg) | 92.645 ± 20.987 | 80.028 ± 21.643 | < 0.00001 |
BMI (kg/m2) | 29.548 ± 6.049 | 30.285 ± 7.657 | 0.00513 |
Android fat mass (kg) | 2.843 ± 1.364 | 2.849 ± 1.466 | 0.91001 |
Gynoid fat mass (kg) | 4.204 ± 1.607 | 5.580 ± 2.029 | < 0.00001 |
Android to Gynoid ratio | 1.145 ± 0.185 | 0.927 ± 0.177 | < 0.00001 |
Total femur BMD (g/cm2) | 1.008 ± 0.146 | 0.949 ± 0.154 | < 0.00001 |
Femoral neck BMD (g/cm2) | 0.826 ± 0.140 | 0.795 ± 0.146 | < 0.00001 |
Trochanter BMD (g/cm2) | 0.752 ± 0.127 | 0.719 ± 0.131 | < 0.00001 |
Intertrochanter BMD (g/cm2) | 1.202 ± 0.169 | 1.130 ± 0.185 | < 0.00001 |
Wards triangle BMD (g/cm2) | 0.648 ± 0.172 | 0.664 ± 0.165 | 0.00938 |
Total spine BMD (g/cm2) | 1.049 ± 0.162 | 1.036 ± 0.157 | 0.03418 |
L1 BMD (g/cm2) | 0.994 ± 0.162 | 0.975 ± 0.155 | 0.00129 |
L2 BMD (g/cm2) | 1.050 ± 0.164 | 1.041 ± 0.164 | 0.12802 |
L3 BMD (g/cm2) | 1.073 ± 0.169 | 1.072 ± 0.167 | 0.90814 |
L4 BMD (g/cm2) | 1.070 ± 0.171 | 1.049 ± 0.168 | 0.00071 |
Mean +/- SD for continuous variables. Weighted linear regression model calculated P-value. | |||
Percentage (%) for continuous variables. Weighted chi-square test model calculated P-value. |
The multivariate-adjusted smoothed curve fitting models were used to investigate the association between android fat mass, gynoid fat mass and android to gynoid ratio and BMD in males and females. There was a linear positive association between android fat mass and BMD in each region, regardless of male or female (Fig. 2). Similarly, there was also a linear positive association between Gynoid fat mass and individual regional BMD in different gender participants (Fig. 3). However, there was no apparent curvilinear association between the android to gynoid ratio and BMD in each region in males or females (Fig. 4).
Furthermore, multiple linear regression models were used to assess the specific β values and 95% CI between body fat mass and BMD in different gender (Table 2). Android fat mass was positively associated with Total femur BMD (Males, β = 0.044, 95% CI = 0.037, 0.051, P < 0.00001; Females, β = 0.044, 95% CI = 0.039, 0.049, P < 0.00001), Femoral neck BMD (Males, β = 0.034, 95% CI = 0.027, 0.041, P < 0.00001; Females, β = 0.032, 95% CI = 0.027, 0.037, P < 0.00001), Intertrochanter BMD (Males, β = 0.049, 95% CI = 0.041, 0.058, P < 0.00001; Females, β = 0.051, 95% CI = 0.045, 0.057, P < 0.00001), Trochanter BMD (Males, β = 0.032, 95% CI = 0.026, 0.038, P < 0.00001; Females, β = 0.035, 95% CI = 0.030, 0.039, P < 0.00001), Wards triangle BMD (Males, β = 0.045, 95% CI = 0.037, 0.054, P < 0.00001; Females, β = 0.027, 95% CI = 0.021, 0.033, P < 0.00001), Total spine BMD (Males, β = 0.036, 95% CI = 0.029, 0.044, P < 0.00001; Females, β = 0.025, 95% CI = 0.019, 0.031, P < 0.00001), L1 BMD (Males, β = 0.039, 95% CI = 0.031, 0.047, P < 0.00001; Females, β = 0.026, 95% CI = 0.021, 0.032, P < 0.00001), L2 BMD (Males, β = 0.031, 95% CI = 0.023, 0.039, P < 0.00001; Females, β = 0.025, 95% CI = 0.019, 0.031, P < 0.00001), L3 BMD (Males, β = 0.031, 95% CI = 0.023, 0.039, P < 0.00001; Females, β = 0.020, 95% CI = 0.014, 0.027, P < 0.00001), and L4 BMD (Males, β = 0.031, 95% CI = 0.023, 0.039, P < 0.00001; Females, β = 0.020, 95% CI = 0.014, 0.027, P < 0.00001). Similarly, there was a similar positive association between gynoid fat mass and BMD in both males and females (Results are shown in Table 2).
Model | Android fat mass (kg) | Gynoid fat mass (kg) | |||
Male | Female | Male | Female | ||
Total femur BMD (g/cm2) | Model Ⅰ | 0.048 (0.043, 0.053) < 0.00001 | 0.059 (0.054, 0.063) < 0.00001 | 0.045 (0.041, 0.050) < 0.00001 | 0.042 (0.039, 0.045) < 0.00001 |
Model Ⅱ | 0.048 (0.042, 0.053) < 0.00001 | 0.050 (0.046, 0.054) < 0.00001 | 0.043 (0.038, 0.047) < 0.00001 | 0.035 (0.032, 0.038) < 0.00001 | |
Model Ⅲ | 0.044 (0.037, 0.051) < 0.00001 | 0.044 (0.039, 0.049) < 0.00001 | 0.039 (0.034, 0.045) < 0.00001 | 0.030 (0.026, 0.033) < 0.00001 | |
Femoral neck BMD (g/cm2) | Model Ⅰ | 0.035 (0.030, 0.041) < 0.00001 | 0.044 (0.040, 0.048) < 0.00001 | 0.036 (0.032, 0.041) < 0.00001 | 0.038 (0.035, 0.040) < 0.00001 |
Model Ⅱ | 0.035 (0.030, 0.040) < 0.00001 | 0.035 (0.031, 0.039) < 0.00001 | 0.034 (0.029, 0.038) < 0.00001 | 0.030 (0.027, 0.033) < 0.00001 | |
Model Ⅲ | 0.034 (0.027, 0.041) < 0.00001 | 0.032 (0.027, 0.037) < 0.00001 | 0.030 (0.025, 0.036) < 0.00001 | 0.028 (0.024, 0.031) < 0.00001 | |
Intertrochanter BMD (g/cm2) | Model Ⅰ | 0.056 (0.050, 0.062) < 0.00001 | 0.070 (0.065, 0.075) < 0.00001 | 0.052 (0.047, 0.058) < 0.00001 | 0.046 (0.042, 0.050) < 0.00001 |
Model Ⅱ | 0.056 (0.050, 0.062) < 0.00001 | 0.061 (0.056, 0.065) < 0.00001 | 0.050 (0.044, 0.055) < 0.00001 | 0.038 (0.034, 0.042) < 0.00001 | |
Model Ⅲ | 0.049 (0.041, 0.058) < 0.00001 | 0.051 (0.045, 0.057) < 0.00001 | 0.045 (0.038, 0.051) < 0.00001 | 0.031 (0.026, 0.035) < 0.00001 | |
Trochanter BMD (g/cm2) | Model Ⅰ | 0.035 (0.030, 0.039) < 0.00001 | 0.047 (0.043, 0.051) < 0.00001 | 0.035 (0.031, 0.039) < 0.00001 | 0.036 (0.033, 0.038) < 0.00001 |
Model Ⅱ | 0.035 (0.030, 0.040) < 0.00001 | 0.040 (0.037, 0.044) < 0.00001 | 0.033 (0.029, 0.037) < 0.00001 | 0.031 (0.028, 0.033) < 0.00001 | |
Model Ⅲ | 0.032 (0.026, 0.038) < 0.00001 | 0.035 (0.030, 0.039) < 0.00001 | 0.031 (0.026, 0.035) < 0.00001 | 0.026 (0.023, 0.029) < 0.00001 | |
Wards triangle BMD (g/cm2) | Model Ⅰ | 0.041 (0.035, 0.048) < 0.00001 | 0.040 (0.035, 0.045) < 0.00001 | 0.044 (0.039, 0.049) < 0.00001 | 0.035 (0.031, 0.038) < 0.00001 |
Model Ⅱ | 0.039 (0.033, 0.045) < 0.00001 | 0.029 (0.025, 0.034) < 0.00001 | 0.040 (0.035, 0.045) < 0.00001 | 0.026 (0.022, 0.029) < 0.00001 | |
Model Ⅲ | 0.045 (0.037, 0.054) < 0.00001 | 0.027 (0.021, 0.033) < 0.00001 | 0.043 (0.036, 0.050) < 0.00001 | 0.024 (0.019, 0.028) < 0.00001 | |
Total spine BMD (g/cm2) | Model Ⅰ | 0.047 (0.041, 0.053) < 0.00001 | 0.043 (0.038, 0.048) < 0.00001 | 0.044 (0.039, 0.049) < 0.00001 | 0.033 (0.030, 0.037) < 0.00001 |
Model Ⅱ | 0.048 (0.042, 0.054) < 0.00001 | 0.035 (0.031, 0.040) < 0.00001 | 0.043 (0.037, 0.048) < 0.00001 | 0.026 (0.023, 0.030) < 0.00001 | |
Model Ⅲ | 0.036 (0.029, 0.044) < 0.00001 | 0.025 (0.019, 0.031) < 0.00001 | 0.032 (0.026, 0.039) < 0.00001 | 0.020 (0.016, 0.025) < 0.00001 | |
L1 BMD (g/cm2) | Model Ⅰ | 0.047 (0.041, 0.053) < 0.00001 | 0.044 (0.040, 0.049) < 0.00001 | 0.045 (0.039, 0.050) < 0.00001 | 0.035 (0.031, 0.038) < 0.00001 |
Model Ⅱ | 0.049 (0.043, 0.056) < 0.00001 | 0.037 (0.032, 0.041) < 0.00001 | 0.044 (0.039, 0.050) < 0.00001 | 0.027 (0.024, 0.031) < 0.00001 | |
Model Ⅲ | 0.039 (0.031, 0.047) < 0.00001 | 0.026 (0.021, 0.032) < 0.00001 | 0.036 (0.029, 0.042) < 0.00001 | 0.022 (0.018, 0.026) < 0.00001 | |
L2 BMD (g/cm2) | Model Ⅰ | 0.044 (0.038, 0.050) < 0.00001 | 0.043 (0.038, 0.048) < 0.00001 | 0.042 (0.037, 0.047) < 0.00001 | 0.032 (0.029, 0.036) < 0.00001 |
Model Ⅱ | 0.044 (0.037, 0.050) < 0.00001 | 0.036 (0.032, 0.041) < 0.00001 | 0.040 (0.035, 0.045) < 0.00001 | 0.025 (0.022, 0.029) < 0.00001 | |
Model Ⅲ | 0.031 (0.023, 0.039) < 0.00001 | 0.025 (0.019, 0.031) < 0.00001 | 0.030 (0.023, 0.036) < 0.00001 | 0.019 (0.015, 0.023) < 0.00001 | |
L3 BMD (g/cm2) | Model Ⅰ | 0.045 (0.038, 0.051) < 0.00001 | 0.041 (0.036, 0.046) < 0.00001 | 0.041 (0.036, 0.047) < 0.00001 | 0.034 (0.030, 0.038) < 0.00001 |
Model Ⅱ | 0.045 (0.038, 0.052) < 0.00001 | 0.034 (0.029, 0.039) < 0.00001 | 0.039 (0.034, 0.045) < 0.00001 | 0.026 (0.023, 0.030) < 0.00001 | |
Model Ⅲ | 0.031 (0.023, 0.039) < 0.00001 | 0.020 (0.014, 0.027) < 0.00001 | 0.027 (0.020, 0.034) < 0.00001 | 0.019 (0.014, 0.023) < 0.00001 | |
L4 BMD (g/cm2) | Model Ⅰ | 0.045 (0.038, 0.051) < 0.00001 | 0.041 (0.036, 0.046) < 0.00001 | 0.047 (0.041, 0.052) < 0.00001 | 0.033 (0.030, 0.037) < 0.00001 |
Model Ⅱ | 0.045 (0.038, 0.052) < 0.00001 | 0.034 (0.029, 0.039) < 0.00001 | 0.046 (0.040, 0.051) < 0.00001 | 0.026 (0.022, 0.030) < 0.00001 | |
Model Ⅲ | 0.031 (0.023, 0.039) < 0.00001 | 0.020 (0.014, 0.027) < 0.00001 | 0.036 (0.029, 0.043) < 0.00001 | 0.022 (0.017, 0.027) < 0.00001 | |
All results were expressed as β (95% CI), P-value. | |||||
Model Ⅰ: No covariates were adjusted. | |||||
Model Ⅱ: Adjusted for Age and Race. | |||||
Model Ⅲ: Adjusted according to Supplementary File 1. |
In different age groups, android fat mass (Males, Supplementary Table 1, Supplementary Fig. 1; Females, Supplementary Table 2, Supplementary Fig. 2) and gynoid fat mass (Males, Supplementary Table 1, Supplementary Fig. 3; Females, Supplementary Table 2, Supplementary Fig. 4) were positively associated with BMD. In different race groups, android fat mass (Males, Supplementary Table 3, Supplementary Fig. 5; Females, Supplementary Table 2, Supplementary Fig. 6) and gynoid fat mass (Males, Supplementary Table 3, Supplementary Fig. 7; Females, Supplementary Table 1, Supplementary Fig. 8) were also positively associated with BMD.
In this US population-based cross-sectional research, we investigated the difference in body fat distribution in different gender and the association between body fat mass and BMD. There was a positive association between body fat distribution (Android and Gynoid) and BMD at each site (Femur and Lumbar spine) in both males and females. There was no difference in android fat between participants by gender (P = 0.91), while the female participant group had higher gynoid fat (P < 0.00001). Lastly, this association persisted when subgroup analyses for age and race were performed.
The main finding of this study was that body fat mass (Android or Gynoid) was positively associated with BMD, regardless of gender (Males or Females) and sites (Femur or Lumbar spine). This was similar to the conclusions reached by numerous previous studies, for example, in Asian regions 11, 16, 20 and European regions 21, 22. Also, some studies have concluded that there was no association or negative association between fat distribution and BMD 23–25. Possible reasons for the inconsistent conclusions drawn from the above studies were as follows: 1) the sample size was too small, with most studies including only tens or hundreds of samples; 2) differences in age, gender, and ethnicity of the included participants; 3) differences in adjusted covariates when performing correlation analyses; and 4) other unknown reasons.
Several possible explanations for the higher body fat mass associated with higher BMD. First, the more body fat there was, the greater the mechanical load on the bones. The mechanical load was very important for BMD maintenance 26, 27, and BMD would also decrease if one lost weight 28 or were in a weightless environment 29. Second, hormones in high body fat individuals were important for protecting BMD. Estrogen was an early discovery of adipocyte-derived hormone, where androgens in adipocytes were transformed into estrogen by the action of aromatase 30, 31. In addition, other hormones such as leptin 32 and insulin 33 were also involved in the adipose-bone mechanistic process. Finally, adipocytes and bone cells had a common origin from mesenchymal stem cells, and to some extent, adipocytes had the potential to differentiate into bone cells 34.
We also found gender differences in body fat distribution, consistent with the previous studies 35, 36. In males, fat was more likely to be concentrated in the abdomen (android fat), and in females, fat was more likely to be concentrated in the buttocks (gynoid fat) 37. On the one hand, fat distribution may be genetically determined. Genome-wide association studies from the UK Biobank suggested that specific loci might determine fat distribution 38. On the other hand, gene-environment-related effects were one of the possible mechanisms. Metabolomics 39, microbiomics 40, and the dietary lifestyle of individuals may all be involved.
Finally, the subgroup analysis led to the same conclusion. This indicated that the effect of body fat distribution on BMD was not significantly related to age and race.
The strengths of this study were the following: 1) a representative large sample study; 2) the association of fat distribution (Android and Gynoid) on BMD at different sites (Femur and Lumbar spine) was explored in different gender populations; 3) adjusted for multiple covariates; 4) subgroup analysis was performed. In fact, the limitations of this study were as follows: 1) although the final number of participants included in this study was 2881, subsequent studies with larger samples were needed to continue validation; 2) this study was a cross-sectional and more future research were needed; 3) due to the limitations of the database itself, the menstrual status and whether the female participants were menopausal were not known, which might have unpredictable results on the female population impact; and 4) although many covariates were adjusted, there were still unknowable covariates. Therefore, to the best of our knowledge, the results of this study needed to be interpreted with caution.
In this US population-based study, we found that android/gynoid fat mass was positively associated with femur/lumbar spine BMD in males and females. In addition, this positive correlation was also present in subgroups of age and race.
National Health and Nutrition Examination Survey
Bone mineral density
Body mass index
Dual-energy X-ray
Confidence Intervals
Standard Deviations
Ethics approval and consent to participate
The studies involving human participants were reviewed and approved by NCHS IRB/ERB. The participants provided their written informed consent to participate in this study. And all methods were performed in accordance with relevant guidelines and regulations. For detailed information, see the following URL:https:// www.cdc.gov/nchs/nhanes/irba98.htm
Consent for publication
Not applicable.
Competing interests
The authors declare no conflict of interest.
Funding
This work is supported by the the National Natural Science Foundation of China (81874017, 81960403 and 82060405); Lanzhou Science and Technology Plan Program (20JR5RA320); Cuiying Scientific and Technological Innovation Program of Lanzhou University Second Hospital (CY2017-ZD02, CY2021-MS-A07).
Authors' contributions
All authors read and approved the final manuscript. Ming Ma: Study conception, Study design, Data extraction, Data analysis, Manuscript draft. Xiaolong Liu and Gengxin Jia: Prepared the tables and figures. Bin Geng: Manuscript Review, Process Supervision. Yayi Xia: Manuscript Review, Process Supervision, Draft Revision.
Acknowledgements
We thank the NHANES Project for providing the data free of charge and all NHANES Project staff and anonymous participants.
Availability of data and materials
The datasets used and analyzed during the current study are available from the corresponding author on reasonable request. For detailed information, see the NHANES website: https://www.cdc.gov/nchs/nhanes.