Skip to main content

Bone mineral density saturation as influenced by the visceral adiposity index in adults older than 20 years: a population-based study

Abstract

Objective

The goal of this research was to determine whether or not there is a saturation effect and whether or not the visceral adiposity index (VAI) correlates with bone mineral density (BMD) in adult Americans.

Methods

This study used multivariate logistic regression models to examine the association between VAI and total femur BMD, drawing on the most up-to-date data from the National Health and Nutrition Examination Survey (NHANES) between 2007 and 2018. Saturation levels and non-linear connections were calculated using a smooth curve-fitting algorithm and an investigation of saturation effects. Subgroup analyses and interaction tests were also conducted.

Results

This study ultimately recruited 6257 individuals aged 20 years or older. According to multivariate regression analysis, those with high VAI scores exhibited higher total femur BMD. Total femur BMD was greater in the highest VAI quartile (Q4: 0.060 g/cm2) after adjustment than in the lowest VAI quartile (Q1) (P < 0.05). After controlling for variables, subgroup analysis failed to reveal any significant interaction effects. Furthermore, the study determined that VAI and BMD exhibited a specific saturation effect through the investigation of the saturation effect and the fitting of smooth curves. Saturation effect investigation of total femur BMD using VAI revealed a saturation value of 3.3.

Conclusion

The present study uncovered a non-linear relationship between VAI and total femur BMD, which exhibited a saturation effect.

Background

Reduced bone mass and microstructural degradation in bone tissue define osteoporosis, a systemic skeletal condition that increases the risk of fracture [1]. Previous studies have demonstrated an increased prevalence of osteoporosis among middle-aged and elderly individuals each year [2, 3]. As the world’s population ages, osteoporosis significantly impacts the economy and public health [4]. Bone mineral density (BMD) is a reliable indicator of osteoporosis, and low BMD is related to a higher risk of fracture [5]. Thus, the search for novel risk factors for low BMD in osteoporosis is gaining more attention and is expected to lead to new preventative approaches.

By combining high-density lipoprotein (HDL), triglycerides (TG), body mass index (BMI), and waist circumference (WC), one may reliably predict visceral fat accumulation and adipose tissue dysfunction using the Visceral Adiposity Index (VAI) [6, 7]. It is a novel and one-of-a-kind biomarker for measuring visceral adipose function indirectly [8]. VAI is superior to conventional measures of adiposity, like BMI and WC, in differentiating between subcutaneous and visceral fat [9]. Visceral obesity may be detected with great accuracy using computed tomography (CT) and magnetic resonance imaging (MRI).

Obesity is a major global health issue affecting individuals worldwide [10]. Previous studies have shown that obesity enhances BMD due to increased mechanical stress, which may aid bone preservation [11, 12]. However, extreme obesity has significant negative impacts on various organs and systems, including type 2 diabetes [13], atherosclerosis [14], and non-alcoholic fatty liver disease. The current research tested the hypothesis that VAI reaches a saturation threshold and that sustaining VAI at this level results in a healthy compromise between obesity and BMD. Therefore, it is essential for public health to establish the VAI that would strike a happy medium between obesity and BMD.

Expanding on the theoretical framework outlined earlier, this study delved into the impact of VAI on total femur BMD, and explored the existence of a saturation point between the two variables. Drawing on extensive demographic data sourced from the National Health and Nutrition Examination study (NHANES) database, this research offers fresh perspectives on the underlying mechanisms at play.

Methods

Research subjects

The NHANES is a large, continuing cross-sectional study in the United States with the goal of collecting accurate data on health-related topics and addressing new public health challenges. To examine the relationship between nutrition and health in the United States, this research relied only on data collected from NHANES, laboratory components, and interviews. The present study collected data from NHANES 2007–2018, excluding NHANES 2011–2012 and NHANES 2015–2016, since BMD data were unavailable during those periods. The present study meticulously applied inclusion and exclusion criteria to arrive at a refined sample size of 6257 participants. Specifically, 16,624 participants under the age of 20, 8300 participants without BMD data, 8181 participants without VAI data, and 753 individuals with malignancy or cancer were excluded. This rigorous approach ensures the robustness and reliability of the study findings (Fig. 1).

Fig. 1
figure 1

Flowchart illustrating participant selection. Legend: VAI, visceral adiposity index; BMD, bone mineral density; NHANES, National Health and Nutrition Examination Survey

Outcome and exposure factors

The major outcome indicator of this study was the evaluation of total femur BMD using dual-energy X-ray absorptiometry (DXA). VAI was the primary risk factor, and it was calculated in the following ways depending on the person’s gender: VAI = WC/[39.68 + (BMI1.88)] * (TG/1.03) * (1.31/HDL) for men, and VAI = WC/[36.58 + (BMI1.89)] * (TG/0.81) * (1.52/HDL) for women. Calculations were made in mmol/L for TG and HDL, cm for WC, and kg/m2 for BMI.

Covariates

The following covariates were modified to strengthen the relationship between total femur BMD and VAI: smoking status, education level, race, gender, age, moderate activities, diabetes, creatinine, blood urea nitrogen, alkaline phosphatase (ALP), alanine transaminase (ALT), the ratio of family income to poverty, aspartate aminotransferase (AST), phosphorus, total cholesterol, total calcium, and total protein. This study also used pre-specified effect modifiers to assess the interaction impact and considered age (< 60/≥60 years), gender (male/female), and diabetes (yes/no) as stratified variables.

Statistical analysis

In NHANES, sampling weights are frequently utilized to consider more intricate research designs. Continuous variables were represented by means and standard deviations (SDs), whereas proportions were used to display categorical data. The data was analyzed using a chi-square test and a weighted t-test for significance. Using logistic regression models, we looked at the association between VAI and total femur BMD, both with and without controlling for potential confounding factors. Model 1 was not tweaked in any way. The second model took demographic factors into account, including age, gender, and race. Multiple factors i.e., age, gender, race, education, smoking, moderate activity, diabetes, family income-to-poverty ratio, alanine aminotransferase, aspartate aminotransferase, blood urea nitrogen, creatinine, phosphorus, total calcium, total protein, and total cholesterol were all included into Model 3. Multiple sensitivity analyses and propensity score matching were used to further examine the connection between VAI and total femur BMD. This research used the generalized additive model (GAM) and curve fitting to further examine whether or not VAI was associated with the risk of low total femur BMD. The upward trend in BMD was found to slow as VAI rises, with BMD eventually leveling off once VAI reaches a specific threshold, known as the saturation effect. Values for inflection points were established by likelihood ratio tests after it was shown that a non-linear relationship existed. Finally, subgroup analyses were stratified by sex, age, race, and diabetes status using hierarchical logistic regression models. Although power analysis was not employed in this work, based on our findings from other research, we considered the current sample size to be adequate [15,16,17]. All statistical analyses were conducted using R and Empower Stats. A two-tailed P-value of less than 0.05 was considered statistically significant.

Results

Baseline features

The research included a total of 6257 individuals that were eligible to participate. 49.46% of participants were women, while 50.54% were men overall. The VAI was considered both a continuous independent variable and a categorical variable (split into quartiles), with the lowest quartile acting as the benchmark. Among different groups of VAI (quartiles, Q1–Q4), age, race, education level, smoking status, moderate activities, diagnosed diabetes, family poverty ratio/median income, ALT, ALP, AST, phosphorus, blood urea nitrogen, total protein and total cholesterol, and total femur BMD are all significantly different (Table 1).

Table 1 Sample characteristics weighted for the research

Association between visceral adiposity index and total femur BMD

Model 1 [β(95%CI) = 0.002 (0.001, 0.003)], Model 2 [β(95%CI) = 0.003 (0.002, 0.004)], and Model 3 [β(95%CI) = 0.016 (0.014, 0.019)] of multivariate regression analysis revealed a positive correlation between VAI and total femur BMD. When VAI was transformed from a continuous to a categorical variable (quartiles), Model 1 [0.026(0.015,0.037)], Model 2 [0.054(0.044,0.064) < 0.00001], and Model 3 [0.060(0.049,0.071) < 0.00001] all found that Q4 participants had significantly higher total femur BMD than Q1 participants. There were no significant trends (P < 0.05) across any of the three models (Table 2). When employing smooth curve fitting, GAM, and piecewise linear regression, a connection between VAI and total femur BMD was investigated (Fig. 2and Table 3). After total adjustment, the smooth curve revealed a non-linear relationship between VAI and total femur BMD (Fig. 2). The total femur bone mineral density (BMD) exhibited a parabolic increase with an increase in visceral adiposity index (VAI), but eventually plateaued as VAI reached a certain threshold. To identify this turning point value of VAI, the present study utilized piecewise linear regression (Table 3). Total femur BMD increased by 0.025 g/cm2 for every unit increased in VAI when VAI reached < 3.3. The results of the study revealed a positive and non-linear correlation between VAI and total femur BMD. The saturation value was determined to be 3.3, as evidenced by a Log likelihood ratio test with a significance level of less than 0.05.

Table 2 Association of VAI with total femur BMD.
Fig. 2
figure 2

The relationship between the visceral adiposity index and the total bone mineral density of the femur. Legend: The smooth red line indicates the best possible fit of the curve between the variables. The blue shading indicates the 95% CI for the fit. All potential confounds were eliminated

Table 3 Analysis of the VAI saturation effect and the total BMD of the femur (g/cm2)

Subgroup analysis

The connection between VAI and total femur BMD was investigated utilizing subgroup analysis to determine whether it was stable across different demographic settings. Total femur BMD was not shown to be dependent on VAI in this study. Figure 3 illustrates that total femur BMD has a favorable relationship with VAI and was unaffected by any stratifications, including gender, age, race, and diabetes status (P values for interactions below 0.05 were consistent across the board). Among several subgroups, strong evidence of a favorable association were observed. For example, in diabetes, the present study found that each unit increases in VAI was associated with higher total femur BMD levels by 0.010 g/cm2. This association persisted in the absence of diabetes (β = 0.019, 95% CI: 0.016–0.022)

Fig. 3
figure 3

Analysis of VAI subgroups in relation to total femur BMD

Discussion

This cross-sectional analysis of 6,257 participants showed a positive relationship between VAI and total femur BMD. Notably, a VAI saturation value (3.3) in the total femur BMD for all subjects was found. As VAI increased beyond this point, the degree of the boost in total femur BMD naturally slowed, which is critical for maintaining BMD at an ideal level. Therefore, the present study considers VAI a practical indicator for clinically evaluating total femur BMD.

Osteoporosis is a biochemical condition that causes bones to weaken and become more brittle over time, increasing the likelihood that they may break [18]. A low BMD is a crucial diagnostic sign of osteoporosis. The World Health Organization (WHO) specifies a BMD of 2.5 SDs or less below the mean maximum BMD as indicative of osteoporosis [19]. The present study found a positive correlation between total femur BMD and visceral adiposity measured by VAI. Obesity is characterized by alterations in adipose tissue distribution and increased body mass, with two main types: visceral obesity, which refers to excess fat accumulation in the abdominal area, and subcutaneous obesity, which refers to fat accumulation beneath the skin [20]. However, several studies have shown that after controlling for other confounders, visceral adiposity evaluated by CT has a stronger association with BMD than subcutaneous adiposity [21,22,23]. VAI is a highly reliable and accurate tool for measuring the level of visceral fat in the body, making it a superior predictor when compared to other methods [8, 9, 24]. Visceral fat is closely linked to overall health in most individuals, and by providing a more precise measurement, VAI can offer valuable insights into an individual’s health status.

Both obesity and osteoporosis have become epidemics worldwide, although it is not yet clear if the two are linked. Previous research indicated that the lumbar spine and femoral neck bone densities of obese people were higher than those of normal-weight persons [12]. However, there is a variety of indicators used to evaluate obesity, particularly when it comes to the acknowledged harm of intra-abdominal fat. BMI, one of the most widely used anthropometric measures to assess obesity, was found to be positively correlated with BMD in several studies [25, 26]. However, for WC, an indicator for assessing abdominal obesity, according to certain research, WC is correlated negatively with BMD [17], supported by related studies [16, 27, 28]. Additionally, visceral adipose tissue has been demonstrated to negatively correlate with BMD in a number of investigations [29,30,31,32]. Despite the growing evidence that traditional anthropometric measurements are associated with BMD in several epidemiological studies, the obesity paradox still exists. The VAI, calculated using data from WC, TG, and HDL cholesterol, accurately reflects visceral fat distribution. Few studies have been conducted on VAI and BMD. DXA lumbar spine T-scores were positively correlated with VAI values, according to recent research [15]. Wung et al. also found a positive relationship between high VAI and high BMD, consistent with this study’s findings [33]. Although obesity benefits BMD, several studies have shown that obesity greatly increases an individual’s risk of developing conditions such as cancer and hypertension [34, 35]. This highlights the importance of maintaining VAI and BMD within an appropriate range.

Obesity and low BMD are linked, however, the underlying mechanism is still unclear. Several proposed mechanisms may contribute to this association. Firstly, excessive fat accumulation may increase the skeleton’s static mechanical compliance, leading to bone tissue alterations [36, 37]. Secondly, the growth of adipocytes in the bone marrow microenvironment may increase the production of chemicals that promote inflammation and regulate the immune system. These inflammatory chemicals may increase osteoclast formation and activation, reduce osteoblast differentiation, and stimulate osteoclasts [38]. Thirdly, overweight or obese individuals may synthesize and release higher amounts of insulin, estrogen, and other endocrine hormones, which help maintain BMD by preventing bone resorption and remodeling [39,40,41,42]. Fourthly, obesity may encourage bone mesenchymal stem cells (BMSCs) to differentiate into adipocytes, boosting the number of fat cells (adipocytes) and lowering the amount of bone-forming cells (osteoblasts) [43]. Finally, chronic inflammation in the fat tissue caused by obesity-related insulin resistance may be a factor in bone loss and decreased BMD, as adipose depots’ systemic production of inflammatory cytokines may contribute to this process [44, 45].

This study delved into the non-linear correlation compared to prior investigations, and the outcomes were just as surprising. A non-linear correlation was found between VAI and total femur BMD both before and after adjusting for confounding variables using a generalized additive model. This study is notable since it is the first to reveal a non-linear association between VAI and total femur BMD, including the discovery of threshold and saturation effects. This finding provides doctors with new tools to help people with obesity keep their VAI in a healthy range (about 3.3) and so preserve adequate BMD and lower their risk of obesity-related diseases and consequences. However, better explanation is needed as to the causes of the saturating effects of VAI on BMD. Why adult BMD does not rise after stunted growth may be due to the fact that bone development patterns and peak bone mass are set during early childhood [46, 47]. The presence of a separate bone-fat axis in vivo between adipose and bone tissue [48], coupled with numerous bioactive molecules that maintain bone homeostasis, is yet another factor in VAI saturation effects. According to the findings of researchers, bone and adipocytes have a common stem cell ancestor and compete with one another, with more fat leading to bone loss [49]. Experiments using animal models show that obesity, which is induced by high-fat diets, leads to a decline in BMD [50, 51].

Study strengths and limitations

This research has numerous benefits, starting with the fact that the sample size is both representative and substantial. In fact, this study utilized the most extensive sample size related to this particular subject matter. Additionally, the research made careful adjustments for various confounding factors, ensuring that the findings are trustworthy and relevant to a diverse range of individuals. Furthermore, due to the high prices and radiation concerns of CT and the high prices and lengthy procedures of MRI, they are not appropriate for use in broad populations [52]. Therefore, this study efficiently investigates how visceral adiposity affects clinical outcomes by utilizing VAI. This computational model for calculating VAI considers anthropometric and physiological data to analyze adipose tissue distribution. However, it is also important to note that there are several limitations to the current research. The primary issue is that it is not possible to determine whether or not VAI caused the decline in total femur BMD. In addition, even after adjusting for a few probable confounders, the present study is still unable to totally exclude confounding brought on by certain unknown variables. Furthermore, this study did not examine additional practical assessment tools like FRAX in further detail due to technological considerations. Finally, this research’s study populations were wide-ranging and the findings may not be applicable to specific populations such as cancer patients, as they were not included in the present study.

Conclusion

Ultimately, according to the results of the current research, there is a substantial positive association between VAI and BMD, with a saturation value determined for total femur BMD. The present study suggests that maintaining a moderate level of VAI (around 3.3) may let people over the age of 20 achieve the best possible VAI/BMD balance, promoting healthy bone growth. In times to come, VAI has the potential to assist individuals with obesity in upholding optimal BMD and reducing the risk of developing obesity-related diseases, thereby presenting a facile and cost-effective approach.

Data Availability

The datasets analyzed during the current study are available on the NHANES official website, https://wwwn.cdc.gov/Nchs/Nhanes/.

Abbreviations

VAI:

Visceral adiposity index

BMD:

Bone mineral density

NHANES:

National Health and Nutrition Examination Survey

WC:

Waist circumference

BMI:

Body mass index

TG:

Triglyceride

HDL:

High-density lipoprotein

ALT:

Alanine transaminase

ALP:

Alkaline phosphatase

AST:

Aspartate aminotransferase

SD:

Standard deviation

GAM:

Generalized additive model

WHO:

World Health Organization

DXA:

Dual-energy X-ray absorptiometry

BMSCs:

Bone mesenchymal stem cells

References

  1. Compston JE, McClung MR, Leslie WD, Osteoporosis. Lancet. 2019;393:364–76. https://doi.org/10.1016/s0140-6736(18)32112-3.

    Article  CAS  PubMed  Google Scholar 

  2. Kanis JA, Johnell O, Oden A, Sembo I, Redlund-Johnell I, Dawson A, De Laet C, Jonsson B. Long-term risk of osteoporotic fracture in Malmö. Osteoporos Int. 2000;11:669–74. https://doi.org/10.1007/s001980070064.

    Article  CAS  PubMed  Google Scholar 

  3. Melton LJ 3rd, Atkinson EJ, O’Connor MK, O’Fallon WM, Riggs BL. Bone density and fracture risk in men. J Bone Miner Res. 1998;13:1915–23. https://doi.org/10.1359/jbmr.1998.13.12.1915.

  4. Cummings SR, Melton LJ. Epidemiology and outcomes of osteoporotic fractures. Lancet. 2002;359:1761–7. https://doi.org/10.1016/s0140-6736(02)08657-9.

    Article  PubMed  Google Scholar 

  5. Cranney A, Horsley T, O’Donnell S, Weiler H, Puil L, Ooi D et al. Effectiveness and safety of vitamin D in relation to bone health. Evid Rep Technol Assess (Full Rep) 2007:1–235.

  6. Li R, Li Q, Cui M, Ying Z, Li L, Zhong T, Huo Y, Xie P. Visceral adiposity index, lipid accumulation product and intracranial atherosclerotic stenosis in middle-aged and elderly chinese. Sci Rep. 2017;7:7951. https://doi.org/10.1038/s41598-017-07811-7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Roriz AK, Passos LC, de Oliveira CC, Eickemberg M, Moreira Pde A, Sampaio LR. Evaluation of the accuracy of anthropometric clinical indicators of visceral fat in adults and elderly. PLoS ONE. 2014;9:e103499doi. https://doi.org/10.1371/journal.pone.0103499.

    Article  CAS  Google Scholar 

  8. Amato MC, Giordano C, Galia M, Criscimanna A, Vitabile S, Midiri M, Galluzzo A. Visceral Adiposity Index: a reliable indicator of visceral fat function associated with cardiometabolic risk. Diabetes Care. 2010;33:920–2. https://doi.org/10.2337/dc09-1825.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Dong Y, Bai L, Cai R, Zhou J, Ding W. Visceral adiposity index performed better than traditional adiposity indicators in predicting unhealthy metabolic phenotype among chinese children and adolescents. Sci Rep. 2021;11:23850. https://doi.org/10.1038/s41598-021-03311-x.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Jaacks LM, Vandevijvere S, Pan A, McGowan CJ, Wallace C, Imamura F, Mozaffarian D, Swinburn B, Ezzati M. The obesity transition: stages of the global epidemic. Lancet Diabetes Endocrinol. 2019;7:231–40. https://doi.org/10.1016/s2213-8587(19)30026-9.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Gkastaris K, Goulis DG, Potoupnis M, Anastasilakis AD, Kapetanos G. Obesity, osteoporosis and bone metabolism. J Musculoskelet Neuronal Interact. 2020;20:372–81.

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Qiao D, Li Y, Liu X, Zhang X, Qian X, Zhang H, Zhang G, Wang C. Association of obesity with bone mineral density and osteoporosis in adults: a systematic review and meta-analysis. Public Health. 2020;180:22–8. https://doi.org/10.1016/j.puhe.2019.11.001.

    Article  CAS  PubMed  Google Scholar 

  13. Rohm TV, Meier DT, Olefsky JM, Donath MY. Inflammation in obesity, diabetes, and related disorders. Immunity. 2022;55:31–55. https://doi.org/10.1016/j.immuni.2021.12.013.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Engelen SE, Robinson AJB, Zurke YX, Monaco C. Therapeutic strategies targeting inflammation and immunity in atherosclerosis: how to proceed? Nat Rev Cardiol. 2022;19:522–42. https://doi.org/10.1038/s41569-021-00668-4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Elmas H, Duran C, Can M, Tolu I, Guney I. The relationship between bone Mineral Densitometry and Visceral Adiposity Index in Postmenopausal Women. Rev Bras Ginecol Obstet. 2023;45:82–8. https://doi.org/10.1055/s-0043-1764497.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Chen L, Liang J, Wen J, Huang H, Li L, Lin W, et al. Is waist circumference a negative predictor of calcaneal bone mineral density in adult chinese men with normal weight? Ann Transl Med. 2019;7:201. https://doi.org/10.21037/atm.2019.04.71.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Hua Y, Fang J, Yao X, Zhu Z. Can waist circumference be a predictor of bone mineral density independent of BMI in middle-aged adults? Endocr Connect. 2021;10:1307–14. https://doi.org/10.1530/ec-21-0352.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Eastell R, O’Neill TW, Hofbauer LC, Langdahl B, Reid IR, Gold DT, Cummings SR. Postmenopausal osteoporosis. Nat Rev Dis Primers. 2016;2:16069. https://doi.org/10.1038/nrdp.2016.69.

    Article  PubMed  Google Scholar 

  19. Estell EG, Rosen CJ. Emerging insights into the comparative effectiveness of anabolic therapies for osteoporosis. Nat Rev Endocrinol. 2021;17:31–46. https://doi.org/10.1038/s41574-020-00426-5.

    Article  PubMed  Google Scholar 

  20. Calder PC, Ahluwalia N, Brouns F, Buetler T, Clement K, Cunningham K, et al. Dietary factors and low-grade inflammation in relation to overweight and obesity. Br J Nutr. 2011;106(Suppl 3):5–78. https://doi.org/10.1017/s0007114511005460.

    Article  Google Scholar 

  21. Choi HS, Kim KJ, Kim KM, Hur NW, Rhee Y, Han DS, Lee EJ, Lim SK. Relationship between visceral adiposity and bone mineral density in korean adults. Calcif Tissue Int. 2010;87:218–25. https://doi.org/10.1007/s00223-010-9398-4.

    Article  CAS  PubMed  Google Scholar 

  22. Zillikens MC, Uitterlinden AG, van Leeuwen JP, Berends AL, Henneman P, van Dijk KW, Oostra BA, van Duijn CM, Pols HA, Rivadeneira F. The role of body mass index, insulin, and adiponectin in the relation between fat distribution and bone mineral density. Calcif Tissue Int. 2010;86:116–25. https://doi.org/10.1007/s00223-009-9319-6.

    Article  CAS  PubMed  Google Scholar 

  23. Russell M, Mendes N, Miller KK, Rosen CJ, Lee H, Klibanski A, Misra M. Visceral fat is a negative predictor of bone density measures in obese adolescent girls. J Clin Endocrinol Metab. 2010;95:1247–55. https://doi.org/10.1210/jc.2009-1475.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Vizzuso S, Del Torto A, Dilillo D, Calcaterra V, Di Profio E, Leone A, Gilardini L, Bertoli S, Battezzati A, Zuccotti GV, Verduci E. Visceral Adiposity Index (VAI) in children and adolescents with obesity: no association with Daily Energy Intake but Promising Tool to identify metabolic syndrome (MetS). Nutrients. 2021;13. https://doi.org/10.3390/nu13020413.

  25. Khosla S, Atkinson EJ, Riggs BL, Melton LJ 3. Relationship between body composition and bone mass in women. J Bone Miner Res. 1996;11:857–63. https://doi.org/10.1002/jbmr.5650110618.

  26. Felson DT, Zhang Y, Hannan MT, Anderson JJ. Effects of weight and body mass index on bone mineral density in men and women: the Framingham study. J Bone Miner Res. 1993;8:567–73. https://doi.org/10.1002/jbmr.5650080507.

    Article  CAS  PubMed  Google Scholar 

  27. Kim YM, Kim S, Won YJ, Kim SH. Clinical manifestations and factors Associated with osteosarcopenic obesity syndrome: a cross-sectional study in Koreans with obesity. Calcif Tissue Int. 2019;105:77–88. https://doi.org/10.1007/s00223-019-00551-y.

    Article  CAS  PubMed  Google Scholar 

  28. Cui LH, Shin MH, Kweon SS, Choi JS, Rhee JA, Lee YH, Nam HS, Jeong SK, Park KS, Ryu SY, Choi SW. Sex-related differences in the association between waist circumference and bone mineral density in a korean population. BMC Musculoskelet Disord. 2014;15:326. https://doi.org/10.1186/1471-2474-15-326.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Gilsanz V, Chalfant J, Mo AO, Lee DC, Dorey FJ, Mittelman SD. Reciprocal relations of subcutaneous and visceral fat to bone structure and strength. J Clin Endocrinol Metab. 2009;94:3387–93. https://doi.org/10.1210/jc.2008-2422.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Huang JS, Rietschel P, Hadigan CM, Rosenthal DI, Grinspoon S. Increased abdominal visceral fat is associated with reduced bone density in HIV-infected men with lipodystrophy. Aids. 2001;15:975–82. https://doi.org/10.1097/00002030-200105250-00005.

    Article  CAS  PubMed  Google Scholar 

  31. Bredella MA, Torriani M, Ghomi RH, Thomas BJ, Brick DJ, Gerweck AV, Harrington LM, Breggia A, Rosen CJ, Miller KK. Determinants of bone mineral density in obese premenopausal women. Bone. 2011;48:748–54. https://doi.org/10.1016/j.bone.2010.12.011.

    Article  PubMed  Google Scholar 

  32. Yamaguchi T, Kanazawa I, Yamamoto M, Kurioka S, Yamauchi M, Yano S, Sugimoto T. Associations between components of the metabolic syndrome versus bone mineral density and vertebral fractures in patients with type 2 diabetes. Bone. 2009;45:174–9. https://doi.org/10.1016/j.bone.2009.05.003.

    Article  CAS  PubMed  Google Scholar 

  33. Wung CH, Chung CY, Wu PY, Huang JC, Tsai YC, Chen SC, Chiu YW, Chang JM. Associations between metabolic syndrome and obesity-related indices and bone Mineral density T-Score in Hemodialysis Patients. J Pers Med. 2021;11. https://doi.org/10.3390/jpm11080775.

  34. Gruber T, Pan C, Contreras RE, Wiedemann T, Morgan DA, Skowronski AA, et al. Obesity-associated hyperleptinemia alters the gliovascular interface of the hypothalamus to promote hypertension. Cell Metab. 2021;33:1155–1170e1110. https://doi.org/10.1016/j.cmet.2021.04.007.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Iyengar NM, Gucalp A, Dannenberg AJ, Hudis CA. Obesity and Cancer mechanisms: Tumor Microenvironment and inflammation. J Clin Oncol. 2016;34:4270–6. https://doi.org/10.1200/jco.2016.67.4283.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Lanyon LE. Control of bone architecture by functional load bearing. J Bone Miner Res. 1992;7(Suppl 2):369–75. https://doi.org/10.1002/jbmr.5650071403.

    Article  Google Scholar 

  37. Hla MM, Davis JW, Ross PD, Wasnich RD, Yates AJ, Ravn P, Hosking DJ, McClung MR. A multicenter study of the influence of fat and lean mass on bone mineral content: evidence for differences in their relative influence at major fracture sites. Early postmenopausal intervention cohort (EPIC) Study Group. Am J Clin Nutr. 1996;64:354–60. https://doi.org/10.1093/ajcn/64.3.345.

    Article  CAS  PubMed  Google Scholar 

  38. Segar AH, Fairbank JCT, Urban J. Leptin and the intervertebral disc: a biochemical link exists between obesity, intervertebral disc degeneration and low back pain-an in vitro study in a bovine model. Eur Spine J. 2019;28:214–23. https://doi.org/10.1007/s00586-018-5778-7.

    Article  PubMed  Google Scholar 

  39. Movérare-Skrtic S, Wu J, Henning P, Gustafsson KL, Sjögren K, Windahl SH, et al. The bone-sparing effects of estrogen and WNT16 are independent of each other. Proc Natl Acad Sci U S A. 2015;112:14972–7. https://doi.org/10.1073/pnas.1520408112.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Costantini S, Conte C. Bone health in diabetes and prediabetes. World J Diabetes. 2019;10:421–45. https://doi.org/10.4239/wjd.v10.i8.421.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Guo L, Chen K, Yuan J, Huang P, Xu X, Li C, et al. Estrogen inhibits osteoclasts formation and bone resorption via microRNA-27a targeting PPARγ and APC. J Cell Physiol. 2018;234:581–94. https://doi.org/10.1002/jcp.26788.

    Article  CAS  PubMed  Google Scholar 

  42. Krishnan A, Muthusami S. Hormonal alterations in PCOS and its influence on bone metabolism. J Endocrinol. 2017;232:R99–r113. https://doi.org/10.1530/joe-16-0405.

    Article  CAS  PubMed  Google Scholar 

  43. Khan AU, Qu R, Fan T, Ouyang J, Dai J. A glance on the role of actin in osteogenic and adipogenic differentiation of mesenchymal stem cells. Stem Cell Res Ther. 2020;11:283. https://doi.org/10.1186/s13287-020-01789-2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Xu H, Barnes GT, Yang Q, Tan G, Yang D, Chou CJ, Sole J, Nichols A, Ross JS, Tartaglia LA, Chen H. Chronic inflammation in fat plays a crucial role in the development of obesity-related insulin resistance. J Clin Invest. 2003;112:1821–30. https://doi.org/10.1172/jci19451.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Kern PA, Ranganathan S, Li C, Wood L, Ranganathan G. Adipose tissue tumor necrosis factor and interleukin-6 expression in human obesity and insulin resistance. Am J Physiol Endocrinol Metab. 2001;280:E745–751. https://doi.org/10.1152/ajpendo.2001.280.5.E745.

    Article  CAS  PubMed  Google Scholar 

  46. Jones G, Dwyer T. Birth weight, birth length, and bone density in prepubertal children: evidence for an association that may be mediated by genetic factors. Calcif Tissue Int. 2000;67:304–8. https://doi.org/10.1007/s002230001148.

    Article  CAS  PubMed  Google Scholar 

  47. Weiler HA, Yuen CK, Seshia MM. Growth and bone mineralization of young adults weighing less than 1500 g at birth. Early Hum Dev. 2002;67:101–12. https://doi.org/10.1016/s0378-3782(02)00003-8.

    Article  CAS  PubMed  Google Scholar 

  48. Gómez-Ambrosi J, Rodríguez A, Catalán V, Frühbeck G. The bone-adipose axis in obesity and weight loss. Obes Surg. 2008;18:1134–43. https://doi.org/10.1007/s11695-008-9548-1.

    Article  PubMed  Google Scholar 

  49. Akune T, Ohba S, Kamekura S, Yamaguchi M, Chung UI, Kubota N, et al. PPARgamma insufficiency enhances osteogenesis through osteoblast formation from bone marrow progenitors. J Clin Invest. 2004;113:846–55. https://doi.org/10.1172/jci19900.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Halade GV, Rahman MM, Williams PJ, Fernandes G. High fat diet-induced animal model of age-associated obesity and osteoporosis. J Nutr Biochem. 2010;21:1162–9. https://doi.org/10.1016/j.jnutbio.2009.10.002.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Halade GV, El Jamali A, Williams PJ, Fajardo RJ, Fernandes G. Obesity-mediated inflammatory microenvironment stimulates osteoclastogenesis and bone loss in mice. Exp Gerontol. 2011;46:43–52. https://doi.org/10.1016/j.exger.2010.09.014.

    Article  CAS  PubMed  Google Scholar 

  52. Li M, Hu L, Hu L, Huang X, Liu X, Zhou W, Wang T, Zhu L, Bao H, Cheng X. Visceral Adiposity Index is inversely Associated with renal function in normal-weight adults with hypertension: the China H-Type Hypertension Registry Study. J Nutr. 2021;151:1394–400. https://doi.org/10.1093/jn/nxab022.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

The authors thank the NCHS for their efforts in creating the data for the NHANES.

Funding

There needs to be funding to report.

Author information

Authors and Affiliations

Authors

Contributions

Zihao Chen: Methodology implementation, Formal analysis, Writing – original draft, Writing – review & editing. Tingfeng Zhou: Validation. Yitian Bu: Validation. Lei Yang: Methodology guidance, Project administration, Validation, Writing – review & editing. The author(s) read and approved the final manuscript.

Corresponding author

Correspondence to Lei Yang.

Ethics declarations

Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

The NHANES protocols were approved by the National Center for Health Statistics Ethics Review Board of the US CDC, and written informed consent from all the participants was provided during the survey.

Consent for publication

Not applicable.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, Zh., Zhou, Tf., Bu, Yt. et al. Bone mineral density saturation as influenced by the visceral adiposity index in adults older than 20 years: a population-based study. Lipids Health Dis 22, 170 (2023). https://doi.org/10.1186/s12944-023-01931-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12944-023-01931-y

Keywords