Introduction

Reduced bone density and compromised bone tissue microstructure are hallmarks of osteoporosis (OP), a prevalent systemic skeletal condition that increases bone fragility and fracture risk1. Bone mineral density (BMD) is currently used as an important indicator to assess osteoporosis and overall bone health2,3. It is usually measured using dual-energy X-ray bone densitometry (DXA)4. Although bone mass is also an important assessment of osteoporosis, bone density is more commonly used in clinical practice because of the complexity of its detection5. A BMD value of ≤−2.5 is often considered a marker of potential osteoporosis6.The global prevalence of OP is second only to hypertension, diabetes mellitus, and coronary heart disease and is accompanied by high rates of disability and mortality7,8. Surveys have shown that the prevalence of OP is 23.1% in women and 11.7% in men, making the United States one of the countries with a high prevalence of OP9,10. The occurrence of OP is influenced by a variety of factors, including smoking, aging, and sex hormone deficiency11,12,13,14. In addition, obesity and metabolic abnormalities are strongly associated with changes in bone mineral density15. Although some studies have pointed out that obesity may provide additional mechanical loading through increased adipose tissue, thus contributing to an increase in BMD, a growing number of studies have found that obesity, especially abdominal obesity and metabolic abnormality type of obesity, may negatively affect BMD through a variety of mechanisms16,17,18. However, the specific association between obesity levels and BMD needs to be further investigated.

Obesity and overweight are common health problems characterized by the accumulation of excess body fat, and the prevalence of obesity has risen dramatically in recent years, a trend that is expected to continue, making it a major challenge in global public health19. Risk factors for cardiovascular disease, type 2 diabetes, chronic inflammation, and dyslipidemia are all closely linked to obesity20,21. Although body mass index (BMI) is a traditional indicator for assessing obesity, it has limitations in reflecting differences in fat distribution and body size, especially in individuals with similar BMIs, which may be significantly different, and thus BMI may not accurately reflect the true extent of obesity22. To address this issue, researchers have developed a new obesity assessment index, the metabolic score for visceral fat (METS-VF)23. The METS-VF is calculated based on insulin resistance, waist-to-height ratio, age, and gender. Compared with traditional indicators such as BMI, METS-VF shows higher sensitivity and predictive value in measuring the degree of obesity and its relationship with other diseases (e.g., gallstones, diabetes, etc.)24,25.

Although studies have revealed the association of METS-VF with a variety of health conditions, its relationship with LS BMD has not been fully explored. Evaluating METS-VF’s positive and negative correlational involvement with osteoporosis requires an understanding of the possible link between METS-VF and BMD. The current study used cross-sectional analysis and NHANES data to further investigate the association between METS-VF and LS BMD. Our study aims to provide strong evidence for the interaction between visceral fat metabolism and osteoporosis, and to help healthcare practitioners more effectively monitor and manage patients with osteoporosis in their daily clinical practice, thereby improving their health prognosis.

Materials and methods

Study participants

Study participants were recruited from the NHANES, which utilizes a complex, multi-stage, stratified and clustered probability design to represent the U.S. population. Participants in this study reported their LS BMD values throughout the 2011–2018 NHANES cycle. Of the 39,156 participants initially considered, 5005 participants were ultimately included after excluding those with missing lumbar spine BMD data, those with missing METS-VF data, and those under 20 years of age. The National Center for Health Statistics’ Ethics Review Board authorized the study, and each participant gave their informed permission. (Fig. 1)

Fig. 1
Fig. 1
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NHANES 2011–2018 participant selection flowchart.

Study variables

DXA, a globally accepted screening method for determining the risk of fragility fractures, was used to measure the LS BMD. The measurements are the responsibility of certified and trained radiologic professionals in DXA screening. Prior research has validated the accuracy of these metrics26.

Covariates

Age, gender, race, education, marital status, poverty-to-income ratio (PIR), CA, smoking status, alcohol usage, diabetes, and hypertension were the variables. Additionally, whether a person had ever smoked 100 cigarettes or more was used to define their smoking status, and whether they had used alcohol at least 12 times in the previous year was used to evaluate their drinking status. Diabetes mellitus was diagnosed as follows: informed by a doctor or health professional. Hypertension was diagnosed as follows: informed by a doctor or health professional. To enhance the openness of the research process, this study referenced prior research to get comprehensive data on these factors16. The NHANES website offers composite assessments of every research variable. Please visit (www.cdc.gov/nchs/nhanes/).

Assessment of METS-VF

Together with the waist-to-height ratio (WHTR), age, gender, and the Index of Insulin Resistance (METS-IR), the METS-VF evaluates exposure to adipose tissue. Units of measurement were as follows: kg/m2 for BMI, years for age, mg/dL for FBG and HDL-C, and gender coded as male = 1 and female = 023.

$$\:WHTR=\frac{WC}{Height}$$
$$\:METS-IR=\frac{ln\left(\right(2\times\:FBG+TG)\times\:BMI)}{ln(HDL-C)}$$
$$\begin{gathered} {\text{METS}} - VF=4.466+0.01 \times {({\text{ln(METS}} - {\text{IR)}})^3}+3.329 \hfill \\ \quad \times {({\text{ln(WHTR)}})^3}+0.319 \times {\text{gender}}+0.594 \times {\text{ln(age)}} \hfill \\ \end{gathered}$$

Statistical analysis

This study used complicated sampling to weight all analyses in order to better reflect the whole population of the United States. The study used survey-weighted averages and SD for continuous variables and survey-weighted percentages for categorical factors to investigate the relationship between METS-VF and LS BMD. Every statistical analysis took into account the NHANES’s multi-stage design.

We employed three linear regression models—unadjusted, marginally adjusted, and completely adjusted for covariates—to investigate the relationship between METS-VF and LS BMD. Linear regression analyses were performed with METS-VF as a continuous and categorical variable (quartiles) to characterize the relationship between METS-VF and lumbar spine BMD.

Additionally, we used Restricted Cubic Splines(RCS)curve to examine the nonlinear association between METS-VF and LS BMD. A log-likelihood ratio test was employed to compute threshold effects and compare the two-segment linear regression model with a single linear model when a nonlinear connection was found. We then evaluated the possible confounders mentioned in the baseline table using subgroup analysis and interaction tests. To further validate the association between METS-VF and bone health, we examined the association of METS-VF with total femoral BMD, femoral neck BMD, and fragility fracture separately in sensitivity analyses. Femoral BMD data were analyzed by multiple linear regression, and fragility fractures were analyzed by multiple logistic regression as a dichotomous outcome variable. R version 4.4.2 and Empower software were used for the statistical analysis of the study, with a significance threshold of P < 0.05.

Results

Baseline characteristics of the study population

A total of 5005 people who were 20 years of age or older took part in our study, as indicated in Table 1. The participants’ weighted average age was 39.57 ± 11.71 years, and 53.11% of them were men. The mean BMD of the lumbar spine was 1.03 ± 0.15 g/cm2. Compared with other quartile groups, the population in the highest METS-VF quartile was predominantly male, non-Hispanic white, and those with a high school education or higher, with higher proportions of those who were married or living with a partner, had a BMI over 30, and had hypertension and diabetes.

Table 1 METS-VF quartile-based research population characteristics.
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Association between METS-VF and LS BMD

Table 2 presents the findings of the multivariate linear regression analysis that revealed a substantial negative connection between LS BMD and METS-VF levels. Specifically, when METS-VF was used as a continuous variable in Model 3, lumbar spine bone density decreased by 0.02 g/cm2 per unit increase (P < 0.0001). When the METS-VF was converted to a quartile variable, participants in the Q3 had a 0.03 g/cm2 lower BMD compared to the Q1 (P < 0.0001), while the Q4 decreased by 0.03 g/cm2 (P < 0.0001). In addition, there was a statistically significant trend toward lower lumbar spine bone density with increasing METS-VF quartiles (P < 0.001 for trend test).

Table 2 Association between METS-VF and LS BMD.
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Nonlinear analysis of METS-VF levels and LS BMD

To further explore whether there was a nonlinear association between METS-VF levels and LS BMD, we used a restricted cubic spline plot for fitting (Fig. 2). The RCS results showed nonlinear correlation between METS-VF levels and LS BMD (P for nonlinear < 0.001). For the saturation threshold effect study, we used a recursive technique to find an inflection point of 5.47 (p-value < 0.05 for the log-likelihood ratio test) (Table 3). There was no statistically significant relationship between METS-VF and LS BMD on the left side of the 5.47 inflection point (p-value = 0.9012). With a p-value of less than 0.0001, the LS BMD declined by 0.09 g/cm2 (β = −0.09, 95% CI: −0.11, −0.07) for every unit increase in METS-VF on the right side of inflection point 5.47.

Fig. 2
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The RCS curve of the association between METS-VF and LS BMD. RCS restricted cubic spline, METS-VF metabolic score for visceral fat. The solid line represents the fitted association between METS-VF and LS BMD calculated from the RCS model; the shaded area represents the 95% confidence interval for this estimated curve; the horizontal dashed line is used as a reference to indicate the zero-change baseline of BMD.

Table 3 Examination of the threshold influence between METS-VF and LS BMD.
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Subgroup analysis

To assess the consistency of the relationship between METS-VF levels and LS BMD across different populations, subgroup analysis was conducted. A significant interaction was observed with diabetes status (P for interaction = 0.0155). Among individuals without diabetes, a significant negative association was found between METS-VF and LS BMD (β= −0.02, P < 0.0001), whereas no significant association was observed in participants with diabetes (β = 0.03, P = 0.1008) (Fig. 3).

Fig. 3
Fig. 3
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Subgroup analysis of the association between METS-VF and LS BMD. Note 1: Age, gender, race, education level, marital status, PIR, CA, smoking, alcohol, diabetes, and hypertension were all taken into account while adjusting the aforementioned model. Note 2: The model does not account for the stratification variable in any of these situations.

Sensitivity analysis

In a sensitivity analysis, we further examined the association of METS-VF with total femoral BMD, femoral neck BMD, and fragility fracture. The results showed that there was a significant positive association between METS-VF levels and both. In Model 3, when METS-VF was used as a continuous variable, each one-unit increase in METS-VF increased total femoral BMD by 0.12 g/cm2 (P < 0.0001) and femoral neck BMD by 0.08 g/cm2 (P < 0.0001). When METS-VF was included in the analysis in quartiles, participants in the Q4 group had an increase in total femur BMD of 0.13 g/cm2 (P < 0.0001) and an increase in femoral neck BMD of 0.07 g/cm2 (P < 0.0001) compared with the Q1 group (Table S1).

Table S2 demonstrates that in the fragility fracture analysis, the unadjusted model showed a significantly higher risk of fracture in the high METS-VF group (OR = 4.80, P = 0.0003), but the association was significantly weaker and not statistically significant after adjustment for multiple confounders (OR = 1.01, P = 0.9871). The results of the quartile analysis were consistent.

Discussion

METS-VF is a novel metric that captures metabolically active visceral fat better than traditional measures such as BMI or waist circumference27. This is particularly valuable in identifying individuals at risk for hidden visceral obesity or metabolic abnormalities28. In terms of bone health, where metabolic and inflammatory factors play a key role, METS-VF provides a more sensitive and clinically relevant assessment of osteoporosis risk than traditional obesity metrics29.

We found a significant nonlinear association between METS-VF and LS BMD by RCS and threshold effect analysis, with a significant inflection point at METS-VF = 5.47. Suggesting that visceral adipose metabolic load negatively affects BMD after reaching a certain level. This phenomenon may be related to the protective effect of mechanical loading or fat-derived estrogens on bone tissue when fat levels are low30. However, once visceral fat accumulation exceeds physiological regulation, it may enhance bone resorption, inhibit bone formation, and thus accelerate bone loss through mechanisms such as induction of chronic low-grade inflammation, insulin resistance, and endocrine disruption31.

Studies on the mechanistic aspects of obesity combined with abnormalities of lipid metabolism and bone mineral density have suggested a number of potential causes, although the exact processes are not always clear. First of all, adipose tissue in obese individuals, as an active endocrine organ, secretes a variety of adipokines and pro-inflammatory factors, such as leptin, lipocalin, tumor necrosis factor α (TNF-α), and interleukin 6 (IL−6), which can have bidirectional regulatory effects on bone metabolism at different levels32,33. On the one hand, moderate amounts of leptin can exert a protective effect on bone metabolism by promoting osteoprotegerin (OPG) expression, inhibiting RANKL, and then inhibiting osteoclastogenesis32,34. On the other hand, in the obese state, excess leptin can activate the sympathetic nervous system through the hypothalamus, enhance bone resorption, and lead to decreased BMD35. In addition, obesity-associated chronic low-grade inflammation leads to elevated levels of TNF-α, IL−6, and other factors, which can promote osteoclast differentiation and inhibit osteoblast activity, further impairing bone structure and function36. Lipocalin, another important adipokine with anti-inflammatory and bone-promoting effects, often has decreased levels in obese individuals, thus weakening its protective effects on bone37. Secondly, obesity-induced insulin resistance inhibits its osteometabolic effects and increases bone resorption, thereby decreasing BMD38. Also, obesity-induced accumulation of fatty acids, especially elevated free fatty acids, may reduce bone mineralization by inhibiting osteoblast function, which in turn affects BMD39. Third, obesity affects sex hormone levels through several mechanisms, which in turn have complex effects on bone metabolism40. Aromatase, which is highly expressed in adipose tissue and converts androgens to estrogens, is particularly important in postmenopausal women, and after the decline of ovarian function, adipose tissue becomes the main peripheral source of estrogens, which helps to maintain bone mineral density and reduce the risk of osteoporosis41. However, obesity may also lead to sex hormone imbalance, and excessive accumulation of visceral fat may lead to excessive estrogen elevation, which in turn disrupts normal bone metabolism signaling pathways and reduces osteoclast function by down-regulating the expression of the estrogen receptor ERα/ERβ42. In addition, obesity-associated insulin resistance and chronic inflammatory states may also indirectly affect bone health by influencing sex hormone synthesis and metabolism43. Therefore, the effects of obesity on BMD are dual.

Diabetic status significantly influenced the association between METS-VF and LS BMD in this study.METS-VF, as a comprehensive metabolic indicator, reflects an individual’s risk of insulin resistance, visceral fat accumulation, and overall metabolic disorders25. Whereas diabetes itself is a chronic disease characterized by insulin dysfunction and abnormal glucose metabolism, its prolonged hyperglycemic state can impair the function of bone tissue and osteoblasts through mechanisms such as oxidative stress, chronic inflammation, and deposition of advanced glycosylation end-products (AGEs), which can diminish the effect of METS-VF on bone metabolism44,45,46. In addition, it has been shown that although patients with type 2 diabetes are often associated with high BMI and visceral fat levels, their bone mineral density is not necessarily elevated, but instead may be inhibited by inflammatory factors (e.g., IL−6, TNF-α) released from adipose tissue, which inhibit osteogenic activity and lead to decreased bone mass47.

In the present study, as the METS-VF quartiles increased, the age, BMI, waist circumference, and METS-IR levels of the study subjects increased significantly, while the lumbar spine BMD gradually declined to the lowest in the Q4 group. The average age of the Q4 group was close to 48 years old, which has entered the middle-aged and old-aged stage of bone loss susceptibility, especially in postmenopausal women with declining estrogen48. Secondly, central obesity was more obvious in the Q4 group, and abdominal fat could release pro-inflammatory factors and interfere with sex hormone metabolism, inhibiting osteogenesis and promoting bone resorption49. In addition, the highest level of METS-IR was found in Q4 group, suggesting higher insulin resistance, which may further affect bone metabolism by weakening the osteoprotective effects of insulin and IGF-150.

In the sensitivity analysis of the present study, we further explored the association of METS-VF with total femoral BMD and femoral neck BMD, and found that it showed an opposite trend to the results of lumbar spine BMD. This inconsistency has also been reported in previous studies, reflecting differences in the response of different bone positions to metabolic loads or physiologic stresses51,52. This inconsistency may stem from the different responses of BMD measurement sites to metabolic factors, especially in the lumbar spine region, which is more susceptible to age-related degenerative changes, resulting in different BMD measurements53. In contrast, femoral BMD is more influenced by mechanical stimuli such as body weight and muscle loading, and in some obese individuals, prolonged bone weight-bearing may promote bone formation, which manifests itself as a tendency to elevated BMD51,54. In addition, DXA measurements of BMD at different sites are also susceptible to interference by technical factors such as posture, positioning and scanning conditions, which in turn affect the results55.

Strengths and limitations

This study has several notable strengths. First, it employed RCS to uncover the complex, nonlinear relationship between METS-VF and BMD, offering a new perspective on their interaction. Second, the use of the nationally representative NHANES database ensures broad applicability and enhances the external validity of the findings. Third, the DXA measurement of lumbar spine BMD guarantees the accuracy and reliability of the data. Finally, METS-VF was not limited to fat measurements alone, but also incorporated metabolic status, providing a more comprehensive reflection of fat metabolism.

Despite these strengths, there are some limitations to consider. First, because the study is cross-sectional, it can only show a correlation rather than a cause-and-effect relationship between METS-VF and BMD. Second, although a variety of potential confounders were controlled for, there may still be unaccounted factors that could influence the results. Third, since BMD changes are part of an ongoing physiological process, the cross-sectional design does not capture dynamic changes in bone health over time. Fourth, although we conducted additional analyses to explore the association between METS-VF and fragility fractures, no significant association was observed after adjusting for confounding variables. This may be due to the small number of participants with fragility fractures in our sample, which limits the statistical power to detect meaningful relationships. Furthermore, fragility fractures are complex outcomes influenced not only by bone density, but also by muscle strength, balance, fall risk, and medication use. Therefore, future longitudinal studies with larger fracture case samples are needed to clarify the predictive value of METS-VF for fragility fractures.

Conclusion

In summary, this study demonstrated a significant inverse association between higher levels of visceral fat metabolism (METS-VF) and lumbar spine bone mineral density. These findings underscore the importance of managing central obesity as a potential strategy to preserve bone health. Moreover, individuals with diabetes may be particularly vulnerable to the dual burden of adiposity and low BMD, and thus warrant targeted prevention efforts.