# The association between dietary approaches to stop hypertension diet and bone mineral density in US adults: evidence from the National Health and Nutrition Examination Survey (2011–2018)

## Abstract

Xiang‑Long Zhai 1,3, Mo‑Yao Tan 2,3, Gao‑Peng Wang 2, Si‑Xuan Zhu 2 & Qi‑Chen Shu 1*
This study aimed to investigate the relationship between the dietary approaches to stop hypertension (DASH) dietary patterns and bone mineral density (BMD) in adults residing in the United States. To achieve this, data from the National Health and Nutrition Examination Survey (NHANES) database for 2011–2018 were utilized. This study utilized the NHANES database from 2011 to 2018, with a sample size of 8,486 US adults, to investigate the relationship between the DASH diet and BMD. The DASH diet was assessed based on nine target nutrients: total fat, saturated fat, protein, fiber, cholesterol, calcium, magnesium, sodium and potassium. The primary outcome measures were BMD values at the total BMD, thoracic spine, lumbar spine, and pelvis. Multivariable linear models were employed to analyze the association between the DASH diet and BMD. Interaction tests, subgroup, and sensitivity analysis were also followed. A negative correlation was observed between the DASH diet and total BMD (OR: − 0.003 [95%CI: − 0.005, − 0.001), pelvic (OR: − 0.005 [95%CI: − 0.007, − 0.002]), and thoracic BMD (OR: − 0.003 [95%CI: − 0.005, − 0.001]). However, the DASH diet does not appear to have a particular effect on lumbar spine BMD (OR: − 0.002 [95%CI: − 0.004, 0.001]). Similarly, when the DASH diet was categorized into tertiles groups, the relationship with total BMD, pelvic BMD, thoracic BMD, and lumbar spine BMD remained consistent. Furthermore, we performed a sensitivity analysis by converting BMD to Z-scores, and the results remained unchanged. Subgroup analyses and interaction tests indicated no significant dependence of BMI, gender, smoking, hypertension, and diabetes on the observed association (all p for interactions > 0.05). The DASH diet has been identified as potentially reducing total BMD, while specifically impacting thoracic and pelvic BMD. However, it appears to have no significant effect on lumbar spine BMD.
Bone mineral density (BMD) is a crucial determinant of bone fragility as it represents the amount of bone mineral within bone ­tissue1. Healthy adult BMD typically falls within the range of approximately 1.045 ± 0.135 g/ cm2 for males and 0.991 ± 0.107 g/cm2 for f­emales2. The NHANES website also provides assessed BMD status for the U.S. population, for example, with BMD quartiles ranging from 1.143–1.280 g/cm2 for 20–29 years males and 1.060–1.171 g/cm2 for females of the same a­ ge3. When BMD drops below a certain threshold, osteoporosis is t­riggered4. In the United States, it is estimated that nearly half of individuals aged 46 and older have low BMD, with projections indicating a rise to over 3 million fractures and an annual cost of $25.3 billion due to osteoporosis by ­20255. Moreover, it is anticipated that by 2030, over 70 million Americans will be diagnosed with ­osteoporosis6. Given the aging global population, this condition is recognized as a significant public health c­ oncern7. Numerous studies have consistently established a strong association between dietary patterns and bone ­health8–10. Adopting a healthy dietary pattern has the potential to impact BMD positively. In their investigation,

## Introduction

1Chengdu Integrated TCM and Western Medicine Hospital, Chengdu, Sichuan, China. 2Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China. 3These authors contributed equally: Xiang-Long Zhai and
Mo-Yao Tan. *email: qichenshu2023@163.com

Scientific Reports | (2023) 13:23043

| https://doi.org/10.1038/s41598-023-50423-7

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Hsu E. et al. explored the correlation between plant-based diets and BMD, proposing mechanisms that promote bone h­ ealth11. Recent studies have also indicated that adherence to the Mediterranean diet may be a preventive measure against ­osteoporosis12. Furthermore, a meta-analysis has proved that incorporating soy isoflavones, enriched with omega-3 fatty acids, into dietary supplementation effectively improves women’s bone health during ­menopause13. This intervention not only mitigates bone loss caused by menopause but also enhances bone formation while reducing bone r­ esorption14.
The Dietary Approaches to Stop Hypertension (DASH) diet, which encompasses reduced sodium and calorie intake along with a diet abundant in fruits, vegetables, low-fat dairy products, whole grains, poultry, fish, nuts, and unsaturated vegetable oils, has received endorsement from the United States Department of Agriculture’s Dietary Guidelines for Americans (2020–2025)15,16. Additionally, the DASH diet has been found to have various other applications. For instance, Zhang et al.17 discovered that the DASH diet can effectively decrease both blood pressure and the incidence of osteoarthritis. Additionally, the DASH diet has been observed to have a glycemic control effect in diabetic ­patients18. Furthermore, a study investigating the relationship between the DASH diet and serum uric acid levels over time revealed that adherence to the DASH diet can reduce serum uric acid l­evels19. Therefore, research on the uses of the DASH diet should include more than just lowering high blood pressure. Considering the rising prevalence of the DASH diet in special populations everyday lives, even the smallest effects that accrue over time could have a substantial impact on our bodies. Thus, it is essential to investigate and give attention to the potential impact of this diet on BMD.
Previous research suggests that the DASH diet may decrease BMD. Recent findings indicated that moderate increases in total fat, fiber intake, and magnesium intake might improve B­ MD20–22. However, a study discovered that the individuals in their study who followed the DASH diet did not meet the recommended values for total fat, fiber intake, and magnesium i­ntake23. Additionally, certain studies have shown that adhering to the DASH diet is associated with lower levels of dietary triglycerides (TG) and low-density lipoprotein cholesterol (LDLC)24, both of which are positively associated with B­ MD25,26. Therefore, there may be a risk of indirectly decreasing BMD by implementing the DASH diet.
Given the extensive acceptance of this dietary pattern among the general population, it is crucial to investigate its influence on BMD. We conducted a comprehensive study using NHANES data from 2011 to 2018 to further explore the relationship between the DASH diet and BMD. Our approach involved multivariate linear regression, subgroup analysis, sensitivity analyses, and interaction tests.

## Methods

Data available
The National Health and Nutrition Examination Survey (NHANES), sponsored by the National Center for Health Statistics (NCHS) within the Centers for Disease Control and Prevention (CDC), is a recurring, nationwide crosssectional survey. It has been conducted periodically since the 1960s with the primary objective of evaluating the health and nutritional status of both children and adults in the United States. Annually, approximately 5,000 participants are recruited using a multistage stratified sampling method, ensuring a nationally representative sample from counties across the United States. Demographic information and lifestyle data including dietary habits are gathered through surveys and physical examinations. The results of this comprehensive survey are published biennially. Details about survey design and data files can be accessed publicly at https://​www.​cdc.​gov/​ nchs/​nhanes/. The ethics protocol has been formally approved by the Research Ethics Review Board of NCHS, and informed consent was signed by all recruited p­ articipants27. Notably, this study diligently adheres to the principles delineated by Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) regarding cross-sectional s­ tudies28.
Study population This study utilized data from four survey cycles conducted by NHANES (2011–2012, 2013–2014, 2015–2016, and 2017–2018). These specific cycles were selected due to their inclusion of data on total BMD, spanning 2011 to 2018. After a rigorous selection process, the study incorporated a total of 39,156 participants over four biennial periods. The distribution was as follows: 9756 participants from 2011–2012, 10,175 from 2013–2014, 9971 from 2015–2016, and 9254 from 2017–2018. Individuals under the age of 20 were subsequently excluded, which accounted for 16,539 of the initial cohort. Furthermore, we eliminated records that lacked DASH diet scores or BMD data, amounting to 12,776 participants. Additional data pruning included the exclusion of entries missing essential covariates such as education (n = 2), body mass index (BMI) (n = 20), smoking status (n = 4), alcohol consumption (n = 658), and poverty income ratio (PIR) (n = 671). Ultimately, this rigorous process yielded a final sample of 8,486 subjects eligible for analysis, as depicted in Fig. 1.
Definition of DASH The DASH diet is a commonly followed dietary regimen incorporating a range of vital nutrients, including total fat, saturated fat, protein, fiber, cholesterol, calcium, magnesium, sodium and potassium. A detailed elucidation on the calculation of the DASH diet has been previously ­recorded23 and can be accessed in Supplementary Material 2 for additional information.
BMD measurement Dual-energy X-ray absorptiometry (DXA), a widely acknowledged and extensively employed bone densitometry technique in contemporary clinical practice, offers significant merits, including expeditiousness, ease of use, and limited radiation exposure. The Hologic Discovery model A densitometer manufactured by Hologic, Inc., based in Bedford, MA, USA, was utilized for conducting the scans. The BMD measurements were performed

Figure 1.  Flowchart of the sample selection from the National Health and Nutrition Examination Survey (NHANES).

meticulously and professionally by radiologists with training and certification. To maintain result accuracy, individuals who were pregnant, had recently used contrast media, or were overweight were excluded from the study. For more comprehensive information on the BMD measurements and the protocols employed, the NHANES website provides detailed documentation.
Covariates We utilized multivariable adjustment models to address the possibility of confounding variables in the correlation between DASH and BMD, as previously employed in related s­ tudies29,30. The demographic variables examined in our study encompass gender (male/female), age (in years), ethnicity (Mexican American/Non-Hispanic white/ Non-Hispanic black/Other races), educational level (less Than 9th grade/9-11th grade (includes 12th grade without diploma)/High School Graduate/GED or Equivalent/Some College or AA Degree/College Graduate or above), marital status (married/widowed/divorced/separated/never married/living with partner) and PIR (lowincome/middle-income/high-income)31. Additionally, the study considers smoking habits (never/former/current) and patterns of alcohol consumption (never/former/heavy/mild/moderate) as outlined in an earlier ­report32. In addition, the research incorporates anthropometric and laboratory covariates, namely BMI (kg/m2), which is determined by dividing weight in kilograms by the square of height in meters. Health status variables encompass hypertension (Yes/No) and diabetes (Yes/No). Diabetes is defined as: (1) doctor told you have diabetes, (2) glycohemoglobin HbA1c(%) >  = 6.5, (3) fasting glucose (mmol/l) >  = 7.0, (4) random blood glucose (mmol/l) >  = 11.1, (5) two-hour OGTT blood glucose (mmol/l) >  = 11.1, or (6) use of diabetes medication or i­nsulin33. Hypertension is defined as: taking antihypertensive medication, a doctor’s diagnosis of hypertension, or having systolic blood pressure ≥ 140 mmHg or diastolic blood pressure ≥ 90 mmHg on three consecutive r­ eadings34.
Statistical analysis In our study, we adhered rigorously to the statistical analysis protocols endorsed by the CDC. Additionally, we considered the intricacies inherent in a complex multistage cluster survey design during our analytical p­ rocedures35,36. Mean values accompanied by standard errors were used to represent continuous variables, while percentages were employed for categorical variables. Subsequently, we employed a Student’s t-test (for continuous variables) or a chi-square test (for categorical variables) to evaluate group disparities. Linear regression was

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applied to estimate the relationship between DASH diet and BMD. DASH diet served as the dependent variable and was modeled both categorically (in three categories) and continuously (scores), respectively. Meanwhile, BMD, as the independent variable, was modeled continuously, both in its raw scale and Z-score form. Estimated effects size was presented as betas (βs) along with their respective 95% confidence intervals (CIs). On one hand, our primary objective was to assess the impact of DASH dietary differences among participants on BMD. To achieve this, we categorized DASH scores into tertiles, allowing us to compare the BMD of populations in the highest tertile with those in the lowest tertile. On the other hand, we also conducted analyses using its continuous scale of the DASH scores, enabling us to explore its potential linear relationships with BMD. We accounted for potential confounding variables. Model 1 did not incorporate any additional factors. Model 2, on the other hand, included adjustments for age, sex, and race. Model 3 entailed adjustments for sex, age, race, education level, PIR, marital status, BMI, smoking status, diabetes, alcohol consumption, and hypertension. Subgroup analyses were conducted based on age, sex, BMI, smoking, hypertension, and diabetes status. Additionally, subgroup analyses as well as interaction tests were conducted to assess potential modifying effects of these variables on the relationship between DASH and BMD, and potential interactions between these variables and DASH. A significance level of less than 0.05 is commonly considered to indicate statistical significance in statistical analysis. The statistical analyses conducted in this study were executed utilizing R software (version 4.1.2; http://​www.R-​proje​ct.​org, R Foundation for Statistical Computing, Vienna, Austria).
Ethics approval and consent to participate The ethics review board of the National Center for Health Statistics approved all NHANES protocols.

## Results

Essential characteristics of the included participants
Table 1 presents the pertinent details regarding the inclusion of 8,486 participants. The mean age of the included population was 39.07 ± 0.28 years, with males accounting for 51.82% and females accounting for 48.18%. Measurements of total BMD, thoracic spine BMD, lumbar spine BMD, and pelvic BMD yielded values of 1.11 ± 0.01 g/ cm2, 0.82 ± 0.01 g/cm2, 1.04 ± 0.01 g/cm2, and 1.25 ± 0.01 g/cm2, respectively. The clinical characteristics of the participants, stratified by DASH tertiles, are presented in Table 2. This table demonstrates notable disparities in variables including age, gender, race, BMI, hypertension, education, pelvic BMD, thoracic spine BMD, and total BMD among the three DASH tertiles (all p-values < 0.05, refer to Table 2 for specific data). Our study revealed that individuals with higher DASH levels exhibited a higher likelihood of being female, older, and of Mexican American or other racial backgrounds, with these associations achieving statistical significance at p-value < 0.05 (refer to Table 2 for specific data).
The association between DASH and BMD Table 3 presents the results of a linear regression analysis examining the correlation between DASH and BMD. The fully adjusted models revealed significant negative associations between DASH and total BMD (β = − 0.003, 95% CI: − 0.005, − 0.001), pelvic BMD (β = − 0.005, 95% CI: − 0.007, − 0.002), and thoracic spine BMD (β = − 0.003, 95% CI: − 0.005, − 0.001). Taking the effect size of DASH on total BMD (β = − 0.003) as an example, it suggested that for every one-point increase in DASH score, there is a corresponding decrease of 0.003 g/cm2 in total BMD. However, the relationship between the DASH diet and lumbar spine BMD did not achieve statistical significance (β = − 0.002, 95% CI: − 0.004, 0.001). We also performed a sensitivity analysis by transforming DASH from a continuous to a categorical variable (tertiles), and the outcomes remained consistent.
Subgroup analysis To conduct a more comprehensive investigation, we performed subgroup analyses and interaction tests to examine the potential impact of a population stratification variable on the observed relationship between DASH and BMD (as shown in Supplementary Material 3). The negative correlations between DASH and BMD remained consistent across specific subgroups. An interaction effect with age was noted in total BMD (p for interaction = 0.04), manifesting a more pronounced negative relationship between DASH and BMD in the older age groups. Among the stratification variables considered, including BMI, gender, diabetes, hypertension, and alcohol, the interaction test was not found to be significant (p for interaction > 0.05, refer to Supplementary Material 3 for specific data). This suggests that the association between DASH and BMD is not influenced by the stratification mentioned above variables.
Sensitivity analysis The sensitivity analysis transformed BMD into a Z-score format to facilitate a more comprehensive exploration of its potential linear relationship with DASH diet (Table 4). In the full adjusted model, the β for BMD across the tertiles of DASH was were as follows: total BMD (β = − 0.026, 95% CI: − 0.044, − 0.008), pelvic BMD (β = − 0.028, 95% CI: − 0.043, − 0.013), thoracic spine BMD (β = − 0.038, 95% CI: − 0.057, − 0.020), and lumbar spine BMD (β = − 0.012, 95% CI: − 0.029, 0.004). Overall, the results demonstrated the robustness of the observed correlation between DASH and BMD.

## Discussion

This cross-sectional study encompassed a sample size of 8,486 individuals and aimed to investigate the correlation between the DASH diet and BMD in adults in the United States. The primary objective of this research was to ascertain whether the DASH diet was linked to BMD levels. The findings of this study suggest a negative

### Table 1
*Baseline characteristics of participants. Notes: All values are presented as proportion (%), or mean(standard error). PIR, ratio of family income to poverty; BMD, bone mineral density.*

<table>
  <thead>
    <tr>
      <th>Characteristics</th>
      <th>Means (standard error) or percentage</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Age (year)</td>
      <td>39.07 (0.28)</td>
    </tr>
    <tr>
      <td>Body mass index (kg/m2)</td>
      <td>28.78 (0.16)</td>
    </tr>
    <tr>
      <td>DASH</td>
      <td>2.30 (0.03)</td>
    </tr>
    <tr>
      <td>Lumbar<br>Spine<br>BMD (g/cm2)</td>
      <td>1.04 (0.01)</td>
    </tr>
    <tr>
      <td>Pelvic<br>BMD (g/cm2)</td>
      <td>1.25 (0.01)</td>
    </tr>
    <tr>
      <td>Thoracic<br>Spine<br>BMD (g/cm2)</td>
      <td>0.82 (0.01)</td>
    </tr>
    <tr>
      <td>Total BMD (g/cm2)</td>
      <td>1.11 (0.01)</td>
    </tr>
    <tr>
      <td>Sex (%)</td>
      <td></td>
    </tr>
    <tr>
      <td>Male</td>
      <td>51.82</td>
    </tr>
    <tr>
      <td>Female</td>
      <td>48.18</td>
    </tr>
    <tr>
      <td>Race (%)</td>
      <td></td>
    </tr>
    <tr>
      <td>Mexican American</td>
      <td>9.85</td>
    </tr>
    <tr>
      <td>Non-Hispanic White</td>
      <td>63.40</td>
    </tr>
    <tr>
      <td>Non-Hispanic Black</td>
      <td>10.79</td>
    </tr>
    <tr>
      <td>Other Race</td>
      <td>15.97</td>
    </tr>
    <tr>
      <td>Education level (%)</td>
      <td></td>
    </tr>
    <tr>
      <td>Less than 9th grade</td>
      <td>3.16</td>
    </tr>
    <tr>
      <td>9–11th grade (Includes 12th grade with no diploma)</td>
      <td>8.54</td>
    </tr>
    <tr>
      <td>High school graduate /GED or equivalent</td>
      <td>21.66</td>
    </tr>
    <tr>
      <td>Some college or AA degree</td>
      <td>33.13</td>
    </tr>
    <tr>
      <td>College graduate or above</td>
      <td>33.51</td>
    </tr>
    <tr>
      <td>Marital status (%)</td>
      <td></td>
    </tr>
    <tr>
      <td>Married</td>
      <td>51.28</td>
    </tr>
    <tr>
      <td>Widowed</td>
      <td>1.12</td>
    </tr>
    <tr>
      <td>Divorced</td>
      <td>9.20</td>
    </tr>
    <tr>
      <td>Separated</td>
      <td>2.55</td>
    </tr>
    <tr>
      <td>Never married</td>
      <td>25.18</td>
    </tr>
    <tr>
      <td>Living with partner</td>
      <td>10.67</td>
    </tr>
    <tr>
      <td>Smoking status (%)</td>
      <td></td>
    </tr>
    <tr>
      <td>Never</td>
      <td>60.23</td>
    </tr>
    <tr>
      <td>Former</td>
      <td>19.26</td>
    </tr>
    <tr>
      <td>Now</td>
      <td>20.50</td>
    </tr>
    <tr>
      <td>PIR (%)</td>
      <td></td>
    </tr>
    <tr>
      <td>Low-income</td>
      <td>15.55</td>
    </tr>
    <tr>
      <td>Middle-income</td>
      <td>48.20</td>
    </tr>
    <tr>
      <td>High-income</td>
      <td>36.25</td>
    </tr>
    <tr>
      <td>Hypertension (%)</td>
      <td></td>
    </tr>
    <tr>
      <td>Yes</td>
      <td>25.90</td>
    </tr>
    <tr>
      <td>No</td>
      <td>74.10</td>
    </tr>
    <tr>
      <td>Diabetes (%)</td>
      <td></td>
    </tr>
    <tr>
      <td>Yes</td>
      <td>26.33</td>
    </tr>
    <tr>
      <td>No</td>
      <td>73.67</td>
    </tr>
    <tr>
      <td>Alcohol (%)</td>
      <td></td>
    </tr>
    <tr>
      <td>Former</td>
      <td>8.11</td>
    </tr>
    <tr>
      <td>Heavy</td>
      <td>27.21</td>
    </tr>
    <tr>
      <td>Mild</td>
      <td>34.61</td>
    </tr>
    <tr>
      <td>Moderate</td>
      <td>20.76</td>
    </tr>
    <tr>
      <td>Never</td>
      <td>9.31</td>
    </tr>
  </tbody>
</table>

association between the DASH diet and various aspects of BMD, including total BMD, thoracic BMD, and pelvic BMD. Sensitivity analyses were performed to confirm the findings’ robustness. Furthermore, subgroup analyses and interaction tests were conducted, demonstrating that the observed correlation remained unaffected. A previous study reported that changes of about 0.050 g/cm2 (equivalent to 4–7% change, depending on

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### Table 2
*Baseline characteristics of participants based on DASH diet tertiles. Significant values are in [bold]. Notes: All values are presented as proportion (%), or mean(standard error). PIR, ratio of family income to poverty; BMD, bone mineral density.*

<table>
  <thead>
    <tr>
      <th>Dietary approaches to stop hypertension</th>
      <th>Tertile 1</th>
      <th>Tertile 2</th>
      <th>Tertile 3</th>
      <th>p for trend</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Age (year)</td>
      <td>38.68 (0.30)</td>
      <td>39.65 (0.36)</td>
      <td>42.33 (0.99)</td>
      <td>&lt; 0.001</td>
    </tr>
    <tr>
      <td>Body mass index (kg/m2)</td>
      <td>29.15 (0.17)</td>
      <td>28.09 (0.21)</td>
      <td>27.70 (0.60)</td>
      <td>&lt; 0.001</td>
    </tr>
    <tr>
      <td>Lumbar<br>Spine<br>BMD (g/cm2)</td>
      <td>1.04 (0.01)</td>
      <td>1.03 (0.01)</td>
      <td>1.03 (0.01)</td>
      <td>0.120</td>
    </tr>
    <tr>
      <td>Pelvic<br>BMD (g/cm2)</td>
      <td>1.26 (0.01)</td>
      <td>1.24 (0.01)</td>
      <td>1.20 (0.01)</td>
      <td>&lt; 0.001</td>
    </tr>
    <tr>
      <td>Thoracic<br>Spine<br>BMD (g/cm2)</td>
      <td>0.83 (0.01)</td>
      <td>0.81 (0.01)</td>
      <td>0.80 (0.01)</td>
      <td>&lt; 0.001</td>
    </tr>
    <tr>
      <td>Total BMD (g/cm2)</td>
      <td>1.12 (0.01)</td>
      <td>1.11 (0.01)</td>
      <td>1.10 (0.01)</td>
      <td>&lt; 0.001</td>
    </tr>
    <tr>
      <td>Sex (%)</td>
      <td></td>
      <td></td>
      <td></td>
      <td>&lt; 0.001</td>
    </tr>
    <tr>
      <td>Male</td>
      <td>53.96</td>
      <td>48.28</td>
      <td>38.17</td>
      <td></td>
    </tr>
    <tr>
      <td>Female</td>
      <td>46.04</td>
      <td>51.72</td>
      <td>61.83</td>
      <td></td>
    </tr>
    <tr>
      <td>Race (%)</td>
      <td></td>
      <td></td>
      <td></td>
      <td>&lt; 0.001</td>
    </tr>
    <tr>
      <td>Mexican American</td>
      <td>9.50</td>
      <td>10.29</td>
      <td>13.62</td>
      <td></td>
    </tr>
    <tr>
      <td>Non-Hispanic White</td>
      <td>64.04</td>
      <td>62.44</td>
      <td>57.98</td>
      <td></td>
    </tr>
    <tr>
      <td>Non-Hispanic Black</td>
      <td>12.22</td>
      <td>7.88</td>
      <td>8.46</td>
      <td></td>
    </tr>
    <tr>
      <td>Other Race</td>
      <td>14.24</td>
      <td>19.39</td>
      <td>19.94</td>
      <td></td>
    </tr>
    <tr>
      <td>Education level (%)</td>
      <td></td>
      <td></td>
      <td></td>
      <td>&lt; 0.001</td>
    </tr>
    <tr>
      <td>Less than 9th grade</td>
      <td>2.70</td>
      <td>4.02</td>
      <td>4.64</td>
      <td></td>
    </tr>
    <tr>
      <td>9–11th grade (Includes 12th grade with no diploma) 9.23</td>
      <td></td>
      <td>7.44</td>
      <td>3.81</td>
      <td></td>
    </tr>
    <tr>
      <td>High school graduate/GED or equivalent</td>
      <td>22.82</td>
      <td>19.78</td>
      <td>14.03</td>
      <td></td>
    </tr>
    <tr>
      <td>Some college or AA degree</td>
      <td>34.44</td>
      <td>30.83</td>
      <td>26.57</td>
      <td></td>
    </tr>
    <tr>
      <td>College graduate or above</td>
      <td>30.82</td>
      <td>37.93</td>
      <td>50.95</td>
      <td></td>
    </tr>
    <tr>
      <td>Marital status (%)</td>
      <td></td>
      <td></td>
      <td></td>
      <td>0.250</td>
    </tr>
    <tr>
      <td>Married</td>
      <td>50.73</td>
      <td>52.37</td>
      <td>52.47</td>
      <td></td>
    </tr>
    <tr>
      <td>Widowed</td>
      <td>1.27</td>
      <td>0.75</td>
      <td>1.61</td>
      <td></td>
    </tr>
    <tr>
      <td>Divorced</td>
      <td>9.14</td>
      <td>9.19</td>
      <td>11.07</td>
      <td></td>
    </tr>
    <tr>
      <td>Separated</td>
      <td>2.29</td>
      <td>3.24</td>
      <td>1.02</td>
      <td></td>
    </tr>
    <tr>
      <td>Never married</td>
      <td>25.69</td>
      <td>24.02</td>
      <td>25.92</td>
      <td></td>
    </tr>
    <tr>
      <td>Living with partner</td>
      <td>10.88</td>
      <td>10.44</td>
      <td>7.91</td>
      <td></td>
    </tr>
    <tr>
      <td>Smoking status (%)</td>
      <td></td>
      <td></td>
      <td></td>
      <td>0.110</td>
    </tr>
    <tr>
      <td>Never</td>
      <td>58.74</td>
      <td>59.11</td>
      <td>70.42</td>
      <td></td>
    </tr>
    <tr>
      <td>Former</td>
      <td>19.51</td>
      <td>20.04</td>
      <td>18.01</td>
      <td></td>
    </tr>
    <tr>
      <td>Now</td>
      <td>21.75</td>
      <td>20.86</td>
      <td>11.57</td>
      <td></td>
    </tr>
    <tr>
      <td>PIR (%)</td>
      <td></td>
      <td></td>
      <td></td>
      <td>0.010</td>
    </tr>
    <tr>
      <td>Low-income</td>
      <td>15.83</td>
      <td>15.18</td>
      <td>12.47</td>
      <td></td>
    </tr>
    <tr>
      <td>Middle-income</td>
      <td>49.58</td>
      <td>45.46</td>
      <td>45.27</td>
      <td></td>
    </tr>
    <tr>
      <td>High-income</td>
      <td>34.59</td>
      <td>39.36</td>
      <td>42.26</td>
      <td></td>
    </tr>
    <tr>
      <td>Hypertension (%)</td>
      <td></td>
      <td></td>
      <td></td>
      <td>0.040</td>
    </tr>
    <tr>
      <td>Yes</td>
      <td>27.28</td>
      <td>24.18</td>
      <td>27.82</td>
      <td></td>
    </tr>
    <tr>
      <td>No</td>
      <td>72.72</td>
      <td>75.82</td>
      <td>72.18</td>
      <td></td>
    </tr>
    <tr>
      <td>Diabetes (%)</td>
      <td></td>
      <td></td>
      <td>0.070</td>
      <td></td>
    </tr>
    <tr>
      <td>Yes</td>
      <td>9.00</td>
      <td>7.67</td>
      <td>6.63</td>
      <td></td>
    </tr>
    <tr>
      <td>No</td>
      <td>91.00</td>
      <td>92.33</td>
      <td>93.37</td>
      <td></td>
    </tr>
    <tr>
      <td>Alcohol (%)</td>
      <td></td>
      <td></td>
      <td></td>
      <td>0.01</td>
    </tr>
    <tr>
      <td>Former</td>
      <td>8.10</td>
      <td>8.03</td>
      <td>9.12</td>
      <td></td>
    </tr>
    <tr>
      <td>Heavy</td>
      <td>27.59</td>
      <td>26.98</td>
      <td>19.59</td>
      <td></td>
    </tr>
    <tr>
      <td>Mild</td>
      <td>25.28</td>
      <td>33.14</td>
      <td>35.02</td>
      <td></td>
    </tr>
    <tr>
      <td>Moderate</td>
      <td>20.83</td>
      <td>20.72</td>
      <td>19.33</td>
      <td></td>
    </tr>
    <tr>
      <td>Never</td>
      <td>8.20</td>
      <td>11.13</td>
      <td>16.94</td>
      <td></td>
    </tr>
  </tbody>
</table>

the baseline BMD value) are likely to be associated with clinically significant BMD ­changes37. In the context of our findings, the impact of the DASH diet may not be substantial for individuals with healthy BMD. However, for those with borderline BMD, a significant adherence to the DASH diet could potentially push them into the low BMD category.

### Table 3
*Multivariate linear regression analysis of the association between the DASH diet and bone mineral density. Significant values are in [bold]. Note: BMD values are presented as raw variables without any transformation.*

<table>
  <thead>
    <tr>
      <th>www.nature.com/scientificreports/<br>Total bone mineral density (g/cm2)</th>
      <th>Model  1a</th>
      <th>Model  2b</th>
      <th>Model  3c</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Dietary approaches to stop hypertension</td>
      <td>-0.006 (− 0.007, − 0.004) &lt; 0.001</td>
      <td>− 0.002 (− 0.004, − 0.000) &lt; 0.001</td>
      <td>− 0.003 (− 0.005, − 0.001) 0.004</td>
    </tr>
    <tr>
      <td>Q1</td>
      <td>Reference</td>
      <td>Reference</td>
      <td>Reference</td>
    </tr>
    <tr>
      <td>Q2</td>
      <td>− 0.011 (− 0.018, − 0.005) &lt; 0.001</td>
      <td>− 0.003 (− 0.009, 0.003) 0.320</td>
      <td>− 0.004 (− 0.011, 0.002) 0.170</td>
    </tr>
    <tr>
      <td>Q3</td>
      <td>− 0.020 (− 0.037, − 0.003) 0.020</td>
      <td>− 0.002 (− 0.020,0.015) 0.770</td>
      <td>− 0.009 (− 0.024, 0.007) 0.270</td>
    </tr>
    <tr>
      <td>Lumbar spine-BMD (g/cm2)</td>
      <td>Model  1a</td>
      <td>Model  2b</td>
      <td>Model  3c</td>
    </tr>
    <tr>
      <td>Dietary approaches to stop hypertension</td>
      <td>− 0.004 (-0.006, -0.002) 0.002</td>
      <td>-0.002 (-0.004, 0.000) 0.100</td>
      <td>-0.002 (-0.004, 0.001) 0.136</td>
    </tr>
    <tr>
      <td>Q1</td>
      <td>Reference</td>
      <td>Reference</td>
      <td>Reference</td>
    </tr>
    <tr>
      <td>Q2</td>
      <td>− 0.008 (− 0.017, 0.000) 0.046</td>
      <td>− 0.003 (− 0.012, 0.005) 0.424</td>
      <td>− 0.003 (− 0.012, 0.006) 0.471</td>
    </tr>
    <tr>
      <td>Q3</td>
      <td>− 0.006 (− 0.032, 0.020) 0.646</td>
      <td>0.002 (− 0.023, 0.027) 0.851</td>
      <td>0.003 (− 0.022, 0.027) 0.831</td>
    </tr>
    <tr>
      <td>Thoracic spine-BMD (g/cm2)</td>
      <td>Model  1a</td>
      <td>Model  2b</td>
      <td>Model  3c</td>
    </tr>
    <tr>
      <td>Dietary approaches to stop hypertension</td>
      <td>− 0.007 (− 0.009, − 0.005) &lt; 0.001</td>
      <td>− 0.005 (− 0.007, − 0.003) &lt; 0.001</td>
      <td>− 0.003 (− 0.005, − 0.001) 0.003</td>
    </tr>
    <tr>
      <td>Q1</td>
      <td>Reference</td>
      <td>Reference</td>
      <td>Reference</td>
    </tr>
    <tr>
      <td>Q2</td>
      <td>− 0.015 (− 0.021, − 0.009) &lt; 0.001</td>
      <td>− 0.010 (− 0.016, − 0.004) 0.001</td>
      <td>− 0.008 (− 0.014, − 0.001) 0.018</td>
    </tr>
    <tr>
      <td>Q3</td>
      <td>− 0.022 (− 0.042, − 0.003) 0.027</td>
      <td>− 0.015 (− 0.034, 0.004) 0.123</td>
      <td>− 0.014 (− 0.031, 0.003) 0.100</td>
    </tr>
    <tr>
      <td>Pelvic-BMD (g/cm2)</td>
      <td>Model  1a</td>
      <td>Model  2b</td>
      <td>Model  3c</td>
    </tr>
    <tr>
      <td>Dietary approaches to stop hypertension</td>
      <td>− 0.010 (− 0.013, − 0.008) &lt; 0.001</td>
      <td>− 0.007 (− 0.009, − 0.004) &lt; 0.001</td>
      <td>− 0.005 (− 0.007, − 0.002) &lt; 0.001</td>
    </tr>
    <tr>
      <td>Q1</td>
      <td>Reference</td>
      <td>Reference</td>
      <td>Reference</td>
    </tr>
    <tr>
      <td>Q2</td>
      <td>− 0.018 (− 0.028, 0.008) &lt; 0.001</td>
      <td>− 0.008 (− 0.018, 0.001) 0.090</td>
      <td>− 0.004 (− 0.014, 0.006) 0.426</td>
    </tr>
    <tr>
      <td>Q3</td>
      <td>− 0.048 (− 0.041, − 0.001) 0.037</td>
      <td>− 0.029 (− 0.050, − 0.007) 0.011</td>
      <td>− 0.021 (− 0.040, − 0.001) 0.037</td>
    </tr>
  </tbody>
</table>

Table 3.  Multivariate linear regression analysis of the association between the DASH diet and bone mineral density. Significant values are in [bold]. Note: BMD values are presented as raw variables without any transformation. Model ­1a: no covariates were adjusted; Model ­2b: adjusted for sex, age, and race; Model 3­ c: adjusted for age, race, sex, education, ratio of family income to poverty, marital status, body mass index, alcohol intake, smoking status, diabetes, and hypertension. BMD, bone mineral density; 95% CI, 95% confidence interval.

Previous studies have also suggested a possible negative correlation between the DASH diet and B­ MD38. First, the DASH diet is calcium-rich, but consuming too much calcium can also lead to soft tissue calcification and loss of bone ­mineral39. An experimental study by Doyle and Cashman found that continually feeding a DASHtype diet to rats inhibited bone formation and bone resorption, decreasing ­BMD40. Secondly, a reduced intake of lipids is one of the characteristics of the DASH diet, which leads to lower levels of total cholesterol (TC) and TG in the b­ ody41. Several studies have shown a positive correlation between TC and TG levels and ­BMD42–45. A cross-sectional study in China showed a positive association between LDL-C and BMD in ­women46. Also, a study from Spain found that BMD was positively associated with total cholesterol and LDL-C47. Insights from Hassoon’s trial shed light on the connection between DASH, blood osteotriol concentrations, and BMD. Their findings suggest that the impact of the DASH diet on blood osteotriol concentrations could be attributed to the lower fat content, particularly saturated f­at48. This insight underscores the importance of prudently managing saturated fat intake, particularly for individuals with very high DASH scores. Notably, in the DASH diet, it has been suggested that inorganic nitrate-rich foods can generate nitric oxide (NO) through a non-enzymatic p­ rocess49. The effects of NO on osteoblast (OB) and osteoclast (OC) activity in vivo may vary. Specifically, the induction of proinflammatory cytokines, such as tumor necrosis factor-α, interleukin-1β, and interferon-gamma, can promote bone resorption by activating NOS, decreasing ­BMD50.
The precise mechanism underlying the association between the DASH diet and BMD remains uncertain; however, specific evidence suggests a potential negative correlation between the two. The DASH diet has been shown to have a fat-reducing ­effect51. Numerous studies have confirmed the impact of fat on BMD. Specifically, obesity, often accompanied by elevated fat levels, exerts a weight-bearing effect on the skeleton, potentially leading to increased BMD due to this mechanical ­stimulus52. Additionally, it is essential to note that fat is the primary source of aromatase, an enzyme responsible for the synthesis of e­ strogen53. Numerous studies have consistently demonstrated a positive association between estrogen and B­ MD54, indicating that a decrease in fat content will likely result in a decline in BMD.
Consequently, this comprehensive analysis supports the conclusion that implementing the DASH diet is also likely to reduce BMD through its impact on fat reduction. Previous research has indicated that the DASH diet is associated with insufficient fiber c­ onsumption23. Adequate fiber intake is crucial for maintaining B­ MD55. Investigative studies have demonstrated that fiber intake is protective in preserving B­ MD21 and mitigating bone loss, among other beneficial e­ ffects56. This phenomenon may be attributed to the alteration of gastrointestinal microorganisms by fiber i­ntake57, which in turn influences BMD through the production of short-chain fatty acids (SCFA)58. Increased fiber consumption has been found to elevate SCFA levels, thereby promoting calcium a­ bsorption59. Insufficient dietary fiber consumption resulting from adherence to the DASH diet may contribute to a reduction in BMD via alterations in gastrointestinal microbiota. The DASH diet has resulted in insufficient magnesium ­intake23. Likewise, magnesium intake plays a significant role in maintaining ­BMD60. Inadequate magnesium intake has been linked to a decline in systemic ­BMD61. Animal experiments have demonstrated

7 Vol.:(0123456789)

### Table 4
*DASH diet and bone mineral density correlationa Z-score linear regression analysis. Significant values are in [bold].*

<table>
  <thead>
    <tr>
      <th>www.nature.com/scientificreports/<br>Total bone mineral density (g/cm2)</th>
      <th>Model  1a</th>
      <th>Model  2b</th>
      <th>Model  3c</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Dietary approaches to stop hypertension</td>
      <td>− 0.05 (− 0.068, − 0.035) &lt; 0.001</td>
      <td>− 0.018 (− 0.033, − 0.003) 0.020</td>
      <td>− 0.026 (− 0.044, − 0.008)  0.005</td>
    </tr>
    <tr>
      <td>Q1</td>
      <td>Reference</td>
      <td>Reference</td>
      <td>Reference</td>
    </tr>
    <tr>
      <td>Q2</td>
      <td>− 0.103 (− 0.162, − 0.045) &lt; 0.001</td>
      <td>− 0.028 (− 0.083, 0.028) 0.320</td>
      <td>− 0.04 (− 0.090, 0.020) 0.100</td>
    </tr>
    <tr>
      <td>Q3</td>
      <td>− 0.039 (− 0.200, 0.120) 0.640</td>
      <td>− 0.023 (− 0.181, 0.136) 0.770</td>
      <td>− 0.07 (− 0.221, 0.067) 0.120</td>
    </tr>
    <tr>
      <td>Lumbar spine-BMD (g/cm2)</td>
      <td>Model  1a</td>
      <td>Model  2b</td>
      <td>Model  3c</td>
    </tr>
    <tr>
      <td>Dietary approaches to stop hypertension</td>
      <td>− 0.025 (− 0.040, − 0.010) 0.002</td>
      <td>− 0.013 (− 0.029, − 0.003) 0.106</td>
      <td>− 0.012 (− 0.029, 0.004) 0.136</td>
    </tr>
    <tr>
      <td>Q1</td>
      <td>Reference</td>
      <td>Reference</td>
      <td>Reference</td>
    </tr>
    <tr>
      <td>Q2</td>
      <td>− 0.050 (− 0.110, − 0.001) 0.046</td>
      <td>− 0.023 (− 0.079, 0.032) 0.408</td>
      <td>− 0.020 (− 0.078, 0.037) 0.478</td>
    </tr>
    <tr>
      <td>Q3</td>
      <td>− 0.039 (− 0.200, 0.120) 0.640</td>
      <td>0.017 (− 0.140, 0.180) 0.838</td>
      <td>0.020 (− 0.142, 0.183) 0.796</td>
    </tr>
    <tr>
      <td>Thoracic spine-BMD (g/cm2)</td>
      <td>Model  1a</td>
      <td>Model  2b</td>
      <td>Model  3c</td>
    </tr>
    <tr>
      <td>Dietary approaches to stop hypertension</td>
      <td>− 0.060 (− 0.078, − 0.044) &lt; 0.001</td>
      <td>− 0.044 (− 0.060, − 0.027) &lt; 0.001</td>
      <td>− 0.038 (− 0.057, − 0.020) &lt; 0.001</td>
    </tr>
    <tr>
      <td>Q1</td>
      <td>Reference</td>
      <td>Reference</td>
      <td>Reference</td>
    </tr>
    <tr>
      <td>Q2</td>
      <td>− 0.130 (− 0.180, − 0.070) &lt; 0.001</td>
      <td>− 0.090 (− 0.143, − 0.040) 0.001</td>
      <td>− 0.04 (− 0.105, 0.010) 0.110</td>
    </tr>
    <tr>
      <td>Q3</td>
      <td>− 0.200 (− 0.360, − 0.020) 0.027</td>
      <td>− 0.132 (− 0.300, 0.030) 0.124</td>
      <td>− 0.06 (− 0.200, 0.080) 0.407</td>
    </tr>
    <tr>
      <td>Pelvic-BMD (g/cm2)</td>
      <td>Model  1a</td>
      <td>Model  2b</td>
      <td>Model  3c</td>
    </tr>
    <tr>
      <td>Dietary approaches to stop hypertension</td>
      <td>− 0.060 (− 0.079, − 0.048) &lt; 0.001</td>
      <td>− 0.040 (− 0.055, − 0.025) &lt; 0.001</td>
      <td>− 0.028 (− 0.043, − 0.013) &lt; 0.001</td>
    </tr>
    <tr>
      <td>Q1</td>
      <td>Reference</td>
      <td>Reference</td>
      <td>Reference</td>
    </tr>
    <tr>
      <td>Q2</td>
      <td>− 0.110 (− 0.160, 0.040) &lt; 0.001</td>
      <td>− 0.050 (− 0.110, 0.010) 0.090</td>
      <td>− 0.024 (− 0.080, 0.030) 0.420</td>
    </tr>
    <tr>
      <td>Q3</td>
      <td>− 0.300 (− 0.420, − 0.160) &lt; 0.001</td>
      <td>− 0.170 (− 0.310, 0.040) 0.010</td>
      <td>− 0.130 (− 0.290, − 0.010) 0.030</td>
    </tr>
  </tbody>
</table>

Table 4.  DASH diet and bone mineral density correlationa Z-score linear regression analysis. Significant values are in [bold]. Model ­1b: no covariates were adjusted; Model ­2c: adjusted for sex, age, and race; Model ­3d: adjusted for age, race, sex, education, ratio of family income to poverty, marital status, body mass index, alcohol intake, smoking status, diabetes, and hypertension. BMD, bone mineral density; 95% CI, 95% confidence interval.

that animals with magnesium deficiency exhibit delayed development and reduced bone mineral ­content62,63. In both animals and humans, magnesium deficiency leads to reduced secretion of parathyroid h­ ormone64 and decreased levels of serum 1,25(OH)2-vitamin ­D65. Insufficiency of these two hormones contributes to impaired bone f­ ormation66.
Our study’s subgroup analysis and interaction test revealed a significant interaction between total BMD in relation to age. Specifically, individuals aged 50 and above exhibited a heightened vulnerability to decreased BMD when adhering to the same DASH diet. One potential factor that may impact BMD is the alteration of nutritional requirements as individuals age. This can be attributed to several reasons. Firstly, individuals over 50 tend to experience a diminished capacity to absorb calcium and vitamin D compared to their younger c­ ounterparts67. It is worth noting that both calcium and vitamin D have been found to influence BMD ­positively68.
Furthermore, empirical research has demonstrated that supplementing calcium and vitamin D among older adults fails to alter the prevailing pattern of age-related decline in B­ MD69. Consequently, there appears to be a discernible decline in BMD with age among individuals adhering to the DASH diet. Notably, protein consumption is a component of the DASH d­ iet15; however, it is observed that digestion tends to be less efficient in older ­individuals70, and the overall protein intake tends to decline with age. A research investigation on sarcopenia elucidated a gradual reduction in muscle mass as individuals ­age71, which was found to have a positive correlation with protein ­consumption72. Additionally, another study demonstrated that muscle mass positively influences the maintenance and enhancement of B­ MD73. Therefore, as the need for protein increases with age, adhering to a long-term DASH diet may contribute to muscle loss, thereby impacting BMD. Hormonal factors were identified as significant contributors in the conducted investigations, wherein it was observed that both testosterone and estrogen levels decrease as individuals age, regardless of g­ ender74,75. The insufficiency of testosterone and estrogen is the primary catalyst for reducing BMD among men and ­women74,76. Notably, the DASH diet does not prioritize hormone intake, exacerbating the decline in BMD as individuals age. The DASH diet, which emphasizes the daily consumption of dairy products for their calcium and protein content, may not be suitable for older ­people15. With advancing age, there is a decline in lactase production in the body, resulting in diminished digestion and absorption of lactose among older adults. This leads to lactose intolerance and necessitates reducing dairy i­ntake77.
Consequently, as individuals age, the calcium and protein derived from dairy products, essential for maintaining BMD, become less accessible within the DASH population, potentially exacerbating the loss of BMD with increasing age. The reasons mentioned above may contribute to the significant age interaction. Nonetheless, in the case of other variables, including hypertension, diabetes mellitus, BMI, smoking, and gender, the interaction does not exhibit significance. This implies that the relationship between the DASH diet and BMD remains unaffected.
Our study has several noteworthy strengths. Firstly, it uses data from the esteemed NHANES, which has been meticulously weighted to accurately depict the association between the DASH diet and BMD in US adults. Additionally, we have incorporated strategies to address confounding covariates, selecting them based on prior research to ensure the reliability of our results. However, it is important to acknowledge the inherent limitations

of our study. Firstly, it is crucial to acknowledge that the study was limited to a cross-sectional design, which unfortunately hinders our ability to definitively establish causality. Secondly, this constraint also restricts our ability to evaluate participants’ adherence to the DASH diet and its long-term influence on BMD. Understanding the effects of prolonged adherence to the diet could provide valuable insights into the correlation between the DASH diet and BMD. Additionally, this study found a negative correlation between the DASH diet and BMD. However, determining the specific constituents of the DASH diet that contributed to this correlation proved to be a challenging task. This complexity highlights the need for further refinement in this area. Resolving this issue and achieving improved accuracy in the field of nutrition undoubtedly require additional efforts in future research. Finally, it is of great concern that this study relied on only two 24-h dietary data to make an assessment of DASH, and that a simple average intake over one or two days may not accurately capture usual intake. Future studies, particularly longitudinal cohort or intervention trials, are urgently needed to overcome this limitation.

## Conclusions

This study demonstrates a significant negative correlation between the DASH diet and BMD in various skeletal regions, encompassing total BMD, thoracic spine, and pelvic BMD within the adult population of the United States. Notably, the relationship between the DASH diet and lumbar spine BMD was not found to be significant. Further research is imperative to substantiate these findings.

## Data availability

The survey data are publicly available on the Internet for data users and researchers throughout the world (www.​ cdc.​gov/​nchs/​nhanes/).
Received: 27 September 2023; Accepted: 19 December 2023

## References

1. Whiting, S. J. et al. Factors that affect bone mineral accrual in the adolescent growth spurt. J. Nutr. 134(3), 696S-700S. https://​doi.​ org/​10.​1093/​jn/​134.3.​696S (2004).
2. Lloyd, T. & Eggli, D. F. Measurement of bone mineral content and bone density in healthy twelve-year-old white females. J. Nucl. Med. 33, 1143–1145 (1992).
3. National Center for Health Statistics. NHANES 1999–2006 DXA Multiple Imputation Data Files. Centers for Disease Control and Prevention (2023, accessed Nov 2023). https://​wwwn.​cdc.​gov/​Nchs/​Nhanes/​Dxa/​Dxa.​aspx.
4. Kanis, J. A. & Kanis, J. A. Assessment of fracture risk and its application to screening for postmenopausal osteoporosis: Synopsis of a WHO report. Osteoporos. Int. 4, 368–381. https://​doi.​org/​10.​1007/​BF016​22200 (1994).
5. Burge, R. et al. Incidence and economic burden of osteoporosis-related fractures in the United States, 2005–2025. J. Bone Miner. Res. 22, 465–475. https://​doi.​org/​10.​1359/​jbmr.​061113 (2007).
6. Clynes, M. A. et al. Bone densitometry worldwide: A global survey by the ISCD and IOF. Osteoporos. Int. 31, 1779–1786. https://​ doi.​org/​10.​1007/​s00198-​020-​05435-8 (2020).
7. Wang, L. et al. Polyunsaturated fatty acids level and bone mineral density: A two-sample mendelian randomization study. Front. Endocrinol. 13, 858851. https://​doi.​org/​10.​3389/​fendo.​2022.​858851 (2022).
8. Neufingerl, N. & Eilander, A. Nutrient intake and status in adults consuming plant-based diets compared to meat-eaters: A systematic review. Nutrients 14, 29. https://​doi.​org/​10.​3390/​nu140​10029 (2022).
9. Veronese, N. & Reginster, J. Y. The effects of calorie restriction, intermittent fasting and vegetarian diets on bone health. Aging Clin. Exp. Res. 31, 753–758. https://​doi.​org/​10.​1007/​s40520-​019-​01174-x (2019).
10. El-Shebini, S. M., Ahmed, N. H., Rasheed, E. A. & Kamal, A. N. Dietary pattern and bone health in pre and post-menopausal obese women. Pak. J. Biolog. Sci. 23, 602–611. https://​doi.​org/​10.​3923/​pjbs.​2020.​602.​611 (2020).
11. Hsu, E. Plant-based diets and bone health: Sorting through the evidence. Curr. Opin. Endocrinol. Diabetes Obes. 27, 248–252. https://​doi.​org/​10.​1097/​MED.​00000​00000​000552 (2020).
12. Noori, M., Jayedi, A., Khan, T., Moradi, S. & Shab-Bidar, S. Mediterranean dietary pattern and bone mineral density: A systematic review and dose-response meta-analysis of observational studies. Eur. J. Clin. Nutr. 76, 1657–1664. https://d​ oi.o​ rg/1​ 0.1​ 038/s​ 41430-​ 022-​01093-7 (2022).
13. Shen, D., Zhang, X., Li, Z., Bai, H. & Chen, L. Effects of omega-3 fatty acids on bone turnover markers in postmenopausal women: Systematic review and meta-analysis. Climacteric 20, 522–527. https://​doi.​org/​10.​1080/​13697​137.​2017.​13849​52 (2017).
14. Zheng, X., Lee, S. K. & Chun, O. K. Soy isoflavones and osteoporotic bone loss: A review with an emphasis on modulation of bone remodeling. J. Med. Food 19, 1–14. https://​doi.​org/​10.​1089/​jmf.​2015.​0045 (2016).
15. Sacks, F. M. & Campos, H. Dietary therapy in hypertension. N. Engl. J. Med. 362(22), 2102–2112. https://​doi.​org/​10.​1056/​NEJMc​ t0911​013 (2010).
16. U.S. Department of Agriculture, U.S. Department of Health and Human Services. Dietary Guidelines for Americans, 2020–2025, (9th ed) Washington, DC, (2020).
17. Zhang, Y. et al. Adherence to DASH dietary pattern is inversely associated with osteoarthritis in Americans. Int. J. Food Sci. Nutr. 71(6), 750–756. https://​doi.​org/​10.​1080/​09637​486.​2020.​17220​75 (2020).
18. Wang, J. S., Liu, W. J. & Lee, C. L. Associations of adherence to the DASH diet and the mediterranean diet with all-cause mortality in subjects with various glucose regulation states. Front. Nutr. 9, 828792. https://​doi.​org/​10.​3389/​fnut.​2022.​828792 (2022).
19. Tang, O. et al. DASH diet and change in serum uric acid over time. Clin. Rheumatol. 36(6), 1413–1417. https://​doi.​org/​10.​1007/​ s10067-​017-​3613-x (2017).
20. Li, Y. Association between obesity and bone mineral density in middle-aged adults. J. Orthop. Surg. Res. 17(1), 268. https://​doi.​ org/​10.​1186/​s13018-​022-​03161-x (2022).
21. Lee, T. & Suh, H. S. Associations between dietary fiber intake and bone mineral density in adult Korean population: Analysis of national health and nutrition examination survey in 2011. J. Bone Metab. 26(3), 151–160. https://​doi.​org/​10.​11005/​jbm.​2019.​26.3.​ 151 (2019).
22. Carpenter, T. O. et al. A randomized controlled study of effects of dietary magnesium oxide supplementation on bone mineral content in healthy girls. J. Clin. Endocrinol. Metab. 91(12), 4866–4872. https://​doi.​org/​10.​1210/​jc.​2006-​1391 (2006).
23. Mellen, P. B., Gao, S. K., Vitolins, M. Z. & Goff, D. C. Jr. Deteriorating dietary habits among adults with hypertension: DASH dietary accordance, NHANES 1988–1994 and 1999–2004. Arch. Intern. Med. 168(3), 308–314. https://​doi.​org/​10.​1001/​archi​ntern​ med.​2007.​119 (2008).

9 Vol.:(0123456789)

24. Akhlaghi, M. Dietary Approaches to Stop Hypertension (DASH): Potential mechanisms of action against risk factors of the metabolic syndrome. Nutr. Res. Rev. 33(1), 1–18. https://​doi.​org/​10.​1017/​S0954​42241​90001​55 (2020).
25. Jeong, I. K. et al. Lipid profiles and bone mineral density in pre- and postmenopausal women in Korea. Calcif. Tissue Int. 87(6), 507–512. https://​doi.​org/​10.​1007/​s00223-​010-​9427-3 (2010).
26. Wang, P. et al. High cholesterol and low triglycerides are associated with total lumbar bone mineral density among adults aged 50 years and over: The NHANES 2017–2020. Front. Med. (Lausanne) 9, 923730. https://​doi.​org/​10.​3389/​fmed.​2022.​923730 (2022).
27. Centers for Disease Control and Prevention (CDC). National Center for Health Statistics. NCHS Research Ethics Review Board (ERB). Approval (2023, 7 Jun 2023). https://​www.​cdc.​gov/​nchs/​nhanes/​irba98.​htm.
28. von Elm, E. et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: Guidelines for reporting observational studies. Lancet 370, 1453–1457. https://​doi.​org/​10.​1016/​s0140-​6736(07)​61602-x (2007).
29. Li, Q. & Zhou, J. Influence of dietary patterns and physical activity on bone mineral content and density, osteoporosis among children with stimulant use. Front. Pediat. 10, 976258. https://​doi.​org/​10.​3389/​fped.​2022.​976258/​full (2022).
30. Fulay, A. P., Rifas-Shiman, S. L., Oken, E. & Perng, W. Associations of the dietary approaches to stop hypertension (DASH) diet with pregnancy complications in Project Viva. Europ. J. Clin. Nutrit. 72(10), 1385–1395. https://d​ oi.o​ rg/1​ 0.1​ 038/s​ 41430-0​ 17-0​ 068-8 (2018).
31. Li, S. et al. The role of hypertension in bone mineral density among males older than 50 years and postmenopausal females: Evidence from the US National Health and Nutrition Examination Survey, 2005–2010. Front. Public Health 11, 156 (2023).
32. Rattan, P. et al. Inverse association of telomere length with liver disease and mortality in the US population. Hepatol. Commun. 6, 399–410. https://​doi.​org/​10.​1002/​hep4.​1803 (2022).
33. Serum selenium concentrations and risk of all-cause and heart disease mortality among individuals with type 2 diabetes—PubMed (2023, accessed 25 Oct 2023). https://​pubmed.​ncbi.​nlm.​nih.​gov/​34664​061/.
34. Chen, L. et al. Association of different obesity patterns with hypertension in US male adults: A cross-sectional study. Sci. Rep. 13, 10551. https://​doi.​org/​10.​1038/​s41598-​023-​37302-x (2023).
35. Johnson, C. L., Dohrmann, S. M., Burt, V. L. & Mohadjer, L. K. National health and nutrition examination survey: Sample design, 2011–2014. Vital Health Stat. 2(162), 1–33 (2014).
36. Chen, T. C., Clark, J., Riddles, M. K., Mohadjer, L. K. & Fakhouri, T. H. I. National health and nutrition examination survey, 2015–2018: Sample design and estimation procedures. Vital Health Stat. 184, 1–35 (2020).
37. Phillips, P. J. & Phillipov, G. Bone mineral density—frequently asked questions. Austr. Family Phys. 35, 145 (2006). 38. Monjardino, T., Lucas, R., Ramos, E. & Barros, H. Associations between apriori-defined dietary patterns and longitudinal changes
in bone mineral density in adolescents. Public Health Nutr. 17(1), 195–205. https://​doi.​org/​10.​1017/​S1368​98001​20048​79 (2014). 39. Raggi, P. et al. Decrease in thoracic vertebral bone attenuation with calcium-based phosphate binders in hemodialysis. J. Bone
Miner. Res. 20(5), 764–772 (2005). 40. Doyle, L. & Cashman, K. D. The effect of nutrient profiles of the dietary approaches to stop hypertension (DASH) diets on blood
pressure and bone metabolism and composition in normotensive and hypertensive rats. Br. J. Nutr. 89, 713–724. https://​doi.​org/​ 10.​1359/​JBMR.​041221 (2003). 41. Feng, J. et al. Association between adherence to the Dietary Approaches to Stop Hypertension diet and serum uric acid. Sci. Rep. 13, 6347. https://​doi.​org/​10.​1038/​s41598-​023-​31762-x (2023). 42. Makovey, J., Chen, J. S., Hayward, C., Williams, F. M. & Sambrook, P. N. Association between serum cholesterol and bone mineral density. Bone 44(2), 208–213. https://​doi.​org/​10.​1016/j.​bone.​2008.​09.​020 (2009). 43. Kan, B. et al. Association between lipid biomarkers and osteoporosis: A cross-sectional study. BMC Musculoskel. Dis. 22, 1–8. https://​doi.​org/​10.​1186/​s12891-​021-​04643-5 (2021). 44. Buizert, P. J., van Schoor, N. M., Lips, P., Deeg, D. J. & Eekhoff, E. M. Lipid levels: A link between cardiovascular disease and osteoporosis?. J. Bone Miner. Res. 24(6), 1103–1109. https://​doi.​org/​10.​1359/​jbmr.​081262 (2009). 45. Garg, M. K. et al. Relationship of lipid parameters with bone mineral density in Indian population. Indian J. Endocrinol. Metabol. 18(3), 325. https://​doi.​org/​10.​4103/​2230-​8210.​131165 (2014). 46. Zhang, Q. et al. Association between bone mineral density and lipid profile in Chinese women. Clin. Interv. Aging 15, 1649–1664. https://​doi.​org/​10.​2147/​CIA.​S2667​22 (2020). 47. Martín-González, C., González-Reimers, E. & Quintero-Platt, G. Lipid profile and bone mineral density in heavy alcoholics. Clin. Nutr. 37, 2137–2143. https://​doi.​org/​10.​1016/j.​clnu.​2017.​10.​008 (2018). 48. Hassoon, A., Michos, E. D., Miller, E. R., Crisp, Z. & Appel, L. J. Effects of different dietary interventions on calcitriol, parathyroid hormone, calcium, and phosphorus: Results from the DASH trial. Nutrients 10, 367 (2018). 49. Chiavaroli, L. et al. DASH dietary pattern and cardiometabolic outcomes: An umbrella review of systematic reviews and metaanalyses. Nutrients 11(2), 338. https://​doi.​org/​10.​3390/​nu110​20338 (2019). 50. Li, A., Xiao, J. & Xue, Y. Osteoporosis and nitric oxide. Chin. J. Pathophysiol. 17(2), 174–179 (2001). 51. Chiu, S. et al. Comparison of the DASH (Dietary Approaches to Stop Hypertension) diet and a higher-fat DASH diet on blood pressure and lipids and lipoproteins: A randomized controlled trial. Am. J. Clin. Nutr. 103(2), 341–347. https://​doi.​org/​10.​3945/​ ajcn.​115.​123281 (2016). 52. Etherington, J. et al. The effect of weight-bearing exercise on bone mineral density: A study of female ex-elite athletes and the general population. J. Bone Miner. Res. 11, 1333–1338. https://​doi.​org/​10.​1002/​jbmr.​56501​10918 (1996). 53. Leeners, B., Geary, N., Tobler, P. & Asarian, L. Ovarian hormones and obesity. Hum. Reprod. Update 23, 300–321. https://​doi.​org/​ 10.​1093/​humupd/​dmw045 (2017). 54. Riggs, B. L. The mechanisms of estrogen regulation of bone resorption. J. Clin. Invest. 106(10), 1203–1204. https://d​ oi.o​ rg/1​ 0.1​ 172/​ JCI11​468 (2000). 55. Tang, Y., Liu, J., Zhang, X. & Geng, B. Dietary fiber intake and femoral bone mineral density in middle-aged and older us adults: A cross-sectional study of national health and nutrition examination survey 2013–2014. Front. Nutr. 9, 851820. https://​doi.​org/​ 10.​3389/​fnut.​2022.​851820 (2022). 56. Dai, Z. et al. Association between dietary fiber intake and bone loss in the framingham offspring study. J. Bone Miner. Res. 33(2), 241–249. https://​doi.​org/​10.​1002/​jbmr.​3308 (2018). 57. Makki, K., Deehan, E. C., Walter, J. & Bäckhed, F. The impact of dietary fiber on gut microbiota in host health and disease. Cell Host Microbe 23(6), 705–715. https://​doi.​org/​10.​1016/j.​chom.​2018.​05.​012 (2018). 58. So, D. et al. Dietary fiber intervention on gut microbiota composition in healthy adults: A systematic review and meta-analysis. Am. J. Clin. Nutr. 107(6), 965–983. https://​doi.​org/​10.​1093/​ajcn/​nqy041 (2018). 59. Zhou, T. et al. Genetically determined SCFA concentration modifies the association of dietary fiber intake with changes in bone mineral density during weight loss: The Preventing Overweight Using Novel Dietary Strategies (POUNDS LOST) trial. Am. J. Clin. Nutr. 114(1), 42–48. https://​doi.​org/​10.​1093/​ajcn/​nqab0​37 (2021). 60. Farsinejad-Marj, M., Saneei, P. & Esmaillzadeh, A. Dietary magnesium intake, bone mineral density and risk of fracture: A systematic review and meta-analysis. Osteoporos. Int. 27(4), 1389–1399. https://​doi.​org/​10.​1007/​s00198-​015-​3400-y (2016). 61. Orchard, T. S. et al. Magnesium intake, bone mineral density, and fractures: Results from the Women’s Health Initiative Observational Study. Am. J. Clin. Nutr. 99(4), 926–933. https://​doi.​org/​10.​3945/​ajcn.​113.​067488 (2014). 62. Kenney, M. A., McCoy, H. & Williams, L. Effects of magnesium deficiency on strength, mass, and composition of rat femur. Calcif. Tissue Int. 54(1), 44–49. https://​doi.​org/​10.​1007/​BF003​16289 (1994).

63. Rude, R. K. et al. Reduction of dietary magnesium by only 50% in the rat disrupts bone and mineral metabolism. Osteoporos. Int. 17(7), 1022–1032. https://​doi.​org/​10.​1007/​s00198-​006-​0104-3 (2006).
64. Rude, R. K., Oldham, S. B., Sharp, C. F. & Singer, F. R. Parathyroid hormone secretion in magnesium deficiency. J. Clin. Endocrinol. Metab. 47(4), 800–806. https://​doi.​org/​10.​1210/​jcem-​47-4-​800 (1978).
65. Fatemi, S., Ryzen, E., Flores, J., Endres, D. B. & Rude, R. K. Effect of experimental human magnesium depletion on parathyroid hormone secretion and 1,25- dihydroxyvitamin D metabolism. J. Clin. Endocrinol. Metab. 73(5), 1067–1072. https://​doi.​org/​10.​ 1210/​jcem-​73-5-​1067 (1991).
66. Rude, R. K. et al. Dietary magnesium reduction to 25% of nutrient requirement disrupts bone and mineral metabolism in the rat. Bone 2, 211–219. https://​doi.​org/​10.​1016/j.​bone.​2005.​04.​005 (2005).
67. Felicetta, J. V. Age-related changes in calcium metabolism. Why they occur and what can be done. Postgrad. Med. 85(4), 85–94. https://​doi.​org/​10.​1080/​00325​481.​1989.​11700​616 (1989).
68. Méndez-Sánchez, L. et al. Calcium and vitamin D for increasing bone mineral density in premenopausal women. Cochrane Database Syst. Rev. 1(1), 012664. https://​doi.​org/​10.​1002/​14651​858.​CD012​664.​pub2 (2023).
69. Bronner, F. Calcium and osteoporosis. Am. J. Clin0 Nutr. 60(6), 831–836. https://​doi.​org/​10.​1093/​ajcn/​60.6.​831 (1994). 70. Rémond, D. et al. Understanding the gastrointestinal tract of the elderly to develop dietary solutions that prevent malnutrition.
Oncotarget. 6(16), 13858–13898. https://​doi.​org/​10.​18632/​oncot​arget.​4030 (2015). 71. Proctor, D. N., O’Brien, P. C., Atkinson, E. J. & Nair, K. S. Comparison of techniques to estimate total body skeletal muscle mass
in people of different age groups. Am. J. Physiol. 277(3), E489–E495. https://​doi.​org/​10.​1152/​ajpen​do.​1999.​277.3.​E489 (1999). 72. Baumgartner, R. N., Koehler, K. M., Romero, L. & Garry, P. J. Serum albumin is associated with skeletal muscle in elderly men and
women. Am. J. Clin. Nutr. 64(4), 552–558. https://​doi.​org/​10.​1093/​ajcn/​64.4.​552 (1996). 73. Capato, L. L. et al. Contribution of hip abductors muscles on bone mineral density and functionality in older women. J. Clin.
Densitom. 26(1), 97–103. https://​doi.​org/​10.​1016/j.​jocd.​2022.​12.​007 (2023). 74. Cauley, J. A. Estrogen and bone health in men and women. Steroids 99(Pt A), 11–15. https://​doi.​org/​10.​1016/j.​stero​ids.​2014.​12.​
010 (2015). 75. Fink, H. A. et al. Association of testosterone and estradiol deficiency with osteoporosis and rapid bone loss in older men. J. Clin.
Endocrinol. Metab. 91(10), 3908–3915. https://​doi.​org/​10.​1210/​jc.​2006-​0173 (2006). 76. Khosla, S., Melton, L. J. 3rd. & Riggs, B. L. The unitary model for estrogen deficiency and the pathogenesis of osteoporosis: Is a
revision needed?. J. Bone Miner. Res. 26(3), 441–451. https://​doi.​org/​10.​1002/​jbmr.​262 (2011). 77. Wilt, T. J. et al. Lactose intolerance and health. Evid. Rep. Technol. Assess. (Full Rep). 192, 1–410 (2010).

## Author contributions

X.-L.Z., M.-Y.T., and Q.-C.S. contributed to the study conception and design. Material preparation, data collection, and analysis were performed by XX.-L.Z., M.-Y.T., Q.-C.S., G.-P.W., and S.-X.Z. The first draft of the manuscript was written by Xiang-Long Zhai, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

## Competing interests

The authors declare no competing interests.
Additional information

## Supplementary Information

The online version contains supplementary material available at https://​doi.​org/​ 10.​1038/​s41598-0​ 23-5​ 0423-7.
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## Figures

*Figure 1. Flowchart of the sample selection from the National Health and Nutrition Examination Survey (NHANES).*
