Skip to main content

Identification of hypertriglyceridemia based on bone density, body fat mass, and anthropometry in a Korean population

Abstract

Background

Hypertriglyceridemia is strongly associated with the risks of cardiovascular disease, coronary heart disease, and metabolic syndrome. The relationship between hypertriglyceridemia or high triglyceride levels and bone mineral density remains controversial. Furthermore, to date, no study has simultaneously examined the association among hypertriglyceridemia, bone area, bone mineral content, bone mineral density, body fat mass, and anthropometrics. The present study aimed to evaluate the association among hypertriglyceridemia, anthropometrics and various bone density and body fat composition variables to identify the best indicator of hypertriglyceridemia in a Korean population.

Methods

The data were obtained from the fifth Korea National Health and Nutrition Examination Survey. In total, 3918 subjects aged 20–80 years participated in this study. In the variable analysis of the waist circumference (WC), trunk fat mass (Trk-Ft), body mass index, etc., a binary logistic regression analysis was performed to examine the significance of the differences between the normal group and hypertriglyceridemia groups.

Results

In both men and women, the WC showed the strongest association with hypertriglyceridemia in the crude analysis (odds ratio (OR) = 1.738 [confidence interval = 1.529–1.976] and OR = 2.075 [1.797–2.397]), but the Trk-Ft was the most strongly associated with the disease after adjusting for age and body mass index (adjusted OR = 1.565 [1.262–1.941] and adjusted OR = 1.730 [1.291–2.319]). In particular, the Pelvis area (Plv-A) was the most significant among the bone variables in women (adjusted OR = 0.641 [0.515–0.796]). In the predictive power analysis, the best indicator of hypertriglyceridemia was WC in women (the area under the receiver operating characteristic curve (AUC) = 0.718 [0.685–0.751]) and Trk-Ft in men (AUC = 0.672 [0.643–0.702]). The WC was also the most predictive among the anthropometric variables in men (AUC = 0.670 [0.641–0.700]). The strength of the association and predictive power was stronger in women than in men.

Conclusions

The WC in women and Trk-Ft in men exhibited the best predictive power for hypertriglyceridemia. Our findings support the use of basic information for the identification of hypertriglyceridemia or high triglyceride levels in initial health screening efforts.

Peer Review reports

Background

Hypertriglyceridemia is a well-known vascular risk factor that is strongly correlated with the risks of cardiovascular disease (CVD) [1,2,3,4] and coronary heart disease (CHD) [5,6,7]. High triglyceride (TG) levels are also associated with insulin resistance syndrome and metabolic syndrome, as they represent a vascular risk factor [8]. Numerous studies have reported correlations between TG levels and metabolic syndrome [9,10,11], insulin resistance syndrome [12,13,14], and abdominal obesity [9, 12, 15,16,17].

Hypertriglyceridemia is related to many chronic diseases and is a relatively common disorder; 33.2% of the general population in the 2007 Korean National Survey [18] and 33% of adults in the United States [19] have TG levels above 150 mg/dL. For decades, numerous studies have investigated the best indicators of hypertriglyceridemia, and high TG levels, i.e., hypertriglyceridemia, are strongly associated with anthropometric measures, such as waist circumference (WC) [20,21,22], the waist-to-hip ratio (WHR) [20], the waist-to-height ratio (WHtR) [23, 24], and the rib-to-forehead circumference ratio (RFcR) [24].

Several recent studies have used dual energy X-ray absorptiometry (DXA) to measure body composition and investigate the association between hypertriglyceridemia and bone mineral density and body fat mass. In particular, the association between body fat distribution and TG levels differs according to ethnicity and race [13, 25]. For example, the relationship between body fat distribution variables and TG levels differed among black, white and Hispanic women [25] and between black and white South African women [13]. Some studies have also reported an association between TG levels and the amount of body fat in the upper body [12, 15, 26], particularly trunk fat [4, 27]. Many studies also report a relationship between hypertriglyceridemia or TG levels and bone mineral density (BMD) [16, 28,29,30,31,32,33,34]. TG levels are associated with BMD at the trochanter site [32], lumbar spine [33,34,35], total femoral region [33, 36], and hip region [16]. However, the relationship between hypertriglyceridemia or high TG levels and BMD remains controversial. Some studies have not reported an association between TG levels and BMD at any skeletal sites [37,38,39,40,41].

Many studies have attempted to identify the best indicators of hypertriglyceridemia, but these studies were based only on partial information, such as anthropometrics, BMD, and body fat mass. Most studies investigating bone density consider only BMD, which is calculated as the ratio of the bone mineral content (BMC) and bone area (BA). An accurate indicator must be identified using measurements based on more detailed variables, such as BMC and BA, which affect BMD at all body sites. The primary hypothesis of this study was that anthropometric measures, body fat mas, and BMD are associated with hypertriglyceridemia or TG levels. In the present study, our objective is to comparatively evaluate anthropometric measures, bone density and body fat mass indices as discriminators of hypertriglyceridemia in Korean adults to identify the best indicator of hypertriglyceridemia. Our study simultaneously examined the association among hypertriglyceridemia, BMD, body fat mass, and anthropometrics. The results of this study may aid in the identification of hypertriglyceridemia in initial health screening efforts and the establishment of a model for more precise identification based on a combination of BMD, anthropometrics, and body fat mass data. To the best of our knowledge, no previous studies have analyzed the associations between detailed bone density and body composition variables measured using DXA with hypertriglyceridemia in Korean adults.

Methods

Study population and data source

The data used in this study were obtained from the fifth Korea National Health and Nutrition Examination Survey (KNHANES V-1) conducted in 2010, which is a prospective, cross-sectional, nationally representative survey study conducted by the Korea Centers for Disease Control and Prevention [42]. The KNHANES V-1 was approved by the Korea Ministry of Health and Welfare (2010-02CON-21-C). The Institutional Review Boards of Konkuk University and the Korea Institute of Oriental Medicine also approved the access and analysis of open source data from the KNHANES in the present study with a waiver of documentation of informed consent (IRB No. 7001355–201,802-E-063 and I-1805/003–001).

The KNHANES V-1 included 7043 subjects over the age of 10 years who underwent blood, bone densitometry and body fat composition tests. The National Cholesterol Education Program (NCEP) recommends a measurement of the fasting lipid panel in adults over the age of 20 to evaluate hypertriglyceridemia [43]. We followed the NCEP recommendation. The sample selection procedure included 5915 subjects aged 20–80 years and excluded 1412 subjects who did not fast for 12 h before the health survey. In total, 585 subjects with missing values for the WC, bone density and body fat composition variables were excluded, and data from 3918 subjects were ultimately collected. The final data set consisted of 2285 females (normal: 2080, hypertriglyceridemia: 205) and 1633 males (normal: 1281, hypertriglyceridemia: 352). Figure 1 shows a detailed schematic of the data preprocessing procedure.

Fig. 1
figure1

Sample selection procedure. HTG, Hypertriglyceridemia

Definition

Hypertriglyceridemia is defined as abnormal TG levels in the blood and is associated with other lipid and metabolic derangements [43]. Hypertriglyceridemia is defined as fasting TG levels ≥200 mg/dL according to the recommendation of the NCEP and previous studies [24, 44, 45]. Therefore, in this study, hypertriglyceridemia was defined as fasting TG levels ≥200 mg/dL.

Measurement

All anthropometric measurements, such as height, weight, and WC, were recorded using standard methods. Weight was measured with an accuracy of 0.1 kg using an electronic scale (GL-6000-20; Caskorea, Seoul, Korea), and height was measured to the nearest 0.1 cm using a portable stadiometer (Seca 225; Seca, Hamburg, Germany). WC was measured at the midline between the lower rib margin and iliac chest to the nearest 0.1 cm. The body mass index (BMI) was calculated as the weight (kg)/square of height (m2). Blood samples were collected from all participants after a 12-h fast. The total cholesterol (TC), high density lipoprotein cholesterol (HDL-C), low density lipoprotein cholesterol (LDL-C), and triglyceride (TG) levels were analyzed using enzymatic methods (Hitachi Automatic Analyzer 7600, Hitachi, Tokyo, Japan). The bone area, BMC, and BMD of the total femur, trochanter, intertrochanter, femoral neck, ward’s triangle, lumbar spine, left arm, right arm, left rib, right rib, thoracic spin, pelvis, left leg, right leg and whole body excluding the head were measured using DXA (DISCOVERY QDR-4500 W fan-beam densitometer, Hologic, Inc., Bedford, MA, USA). The body fat composition was measured using the same equipment and methods used to measure the BMD. The body fat mass, lean body mass, weight (mass) and body fat percentage were measured in the head, left arm, right arm, trunk, left leg, and right leg.

Statistical analysis

The statistical analyses were performed using SPSS 21 for Windows (SPSS Inc., Chicago, IL, USA). A binary logistic regression analysis was performed in the crude analyses, and the analyses were adjusted for age and BMI to identify the differences between the normal and hypertriglyceridemia groups after applying standardized transformations to the data sets. Independent two-sample t-tests were performed to statistically assess the gender differences in characteristics. Table 1 provides a detailed description of the demographic characteristics and values of all study variables per group. The area under the receiver operating characteristic curve (AUC) is a major criterion for comparisons of the predictive ability of individual measures.

Table 1 Demographic characteristics and values of all study variables in the two groups

Results

Associations among hypertriglyceridemia, bone density and body fat mass

Tables 2 and 3 list the associations between hypertriglyceridemia and the anthropometric, bone density and body fat composition measurements in women and men. Among all variables examined in this study, WC displayed the strongest association with hypertriglyceridemia among women in the crude analysis (odds ratio (OR) = 2.075 [confidence interval = 1.797–2.397]), and the association remained highly significant after adjusting for age and BMI (adjusted OR = 1.615 [1.202–2.171]). The trunk fat mass (Trk-Ft) was highly associated with hypertriglyceridemia in the crude analysis (OR = 1.940 [1.691–1.226]) and remained the variable most strongly associated with hypertriglyceridemia after adjusting for confounders (adjusted OR = 1.730 [1.291–2.319]). Of the bone density variables, the pelvis area (Plv-A) displayed the greatest negative association with hypertriglyceridemia in both the crude (OR = 0.487 [0.415–0.571]) and adjusted analyses (adjusted OR = 0.641 (0.515–.796)). Among the body fat variables, Trk-Ft displayed the most significant association with hypertriglyceridemia in both the crude and adjusted analyses.

Table 2 Associations between hypertriglyceridemia and bone density and body fat mass in women
Table 3 Associations between hypertriglyceridemia and bone density and body fat mass in men

In men, among all variables, WC exhibited the strongest association with hypertriglyceridemia in the crude analysis (OR = 1.738 [1.529–1.976]), but after adjusting for age and BMI, Trk-Ft exhibited the strongest association with hypertriglyceridemia (adjusted OR = 1.565 [1.262–1.941]). Of the bone density variables, the left rib area (LRb-A) displayed the strongest association with hypertriglyceridemia (OR = 1.566 [1.388–1.767]) in the crude analysis, and this association remained highly significant after adjusting for confounders (OR = 1.332 [1.144–1.550]). Plv-A displayed a strong negative association with hypertriglyceridemia (adjusted OR = 0.661[0.573–0.763]). In the present study, Trk-Ft exhibited the strongest associations with hypertriglyceridemia in both men (OR = 1.565 [1.262–1.941]) and women (OR = 1.730 [1.291–2.319]) in the adjusted analysis.

Power of bone density and body fat mass in the identification of hypertriglyceridemia

Table 4 lists the predictive power of all variables in identifying hypertriglyceridemia. WC exhibited the highest AUC value (AUC = 0.718 [0.685–0.751]) in women. Among the bone density variables, Plv-A exhibited a strong predictive power (AUC = 0.696 [0.660–0.731]), and among the body fat variables, Trk-Ft exhibited substantial predictive power (AUC = 0.715 [0.684–0.747]). In men, Trk-Ft exhibited the highest AUC value among all body fat variables (AUC = 0.672 [0.643–0.702]). Of the bone density variables, LRb-A exhibited a strong predictive power (AUC = 0.633 [0.601–0.665]). These results clearly revealed gender differences. Among all variables, WC was the highest overall indicator of hypertriglyceridemia in women, and Trk-Ft was the highest overall indicator in men. The bone density variable Plv-A exhibited the strongest predictive power in women, and LRb-A was the strongest indicator in men. The predictive power of these variables in women was stronger than that in men. Figures 2 and 3 show a comparison of the predictive power of several variables based on the AUCs in men and women.

Table 4 Analysis of the predictive power of the individual measures using AUCs
Fig. 2
figure2

Comparison of the predictive power based on AUCs (the area under a receiver operating characteristic curve) among men. WC, Waist circumference; BMI, Body mass index; Trk-Ft, Trunk fat mass

Fig. 3
figure3

Comparison of the predictive power based on AUCs (the area under a receiver operating characteristic curve) among women. WC, Waist circumference; BMI, Body mass index; Trk-Ft, Trunk fat mass

Discussion

High TG levels are clearly associated with various diseases, such as CVD [2,3,4], CHD [5,6,7], insulin resistance syndrome [12, 13], metabolic syndrome [8,9,10,11], and abdominal obesity [9, 12, 15,16,17].

Numerous studies have investigated the best indicators of hypertriglyceridemia. Hypertriglyceridemia or TG levels are associated with anthropometric measures. As shown in a study conducted by Ghosh et al. [20], WC and the WHR are significantly and positively correlated with TG levels in middle-aged Bengalee Hindu men. According to Sharp et al. [21], WC is the single best indicator of the risk of disease, including CVD, in Hispanic and Caucasian adolescents. In a study conducted by Lee et al. [22], WC exhibited the best predictive power for hypertriglyceridemia in Korean adults. Lee et al. [23] also reported that the WHtR was the best discriminator of dyslipidemia in both men and women. Moreover, women in whom dyslipidemia was identified tended to have higher AUCs than men, which is consistent with the results of the present study. Based on the results reported by Lee et al. [24], age is the highest risk factor in women, and the anthropometric measures of WHtR in women and the RFcR in men are the strongest indicators of hypertriglyceridemia. Most previous studies using anthropometric measures have revealed that the WC or WHtR is an important indicator of hypertriglyceridemia or CVD. Our findings are consistent with the results of some previous studies [20,21,22] indicating that WC is an important risk factor for hypertriglyceridemia in women.

Many studies have also revealed a strong correlation between BMD and TG levels. According to Muhlen et al. [28], men and women with metabolic syndrome exhibit higher total hip BMDs than subjects without metabolic syndrome in an age-adjusted analysis. Men with metabolic syndrome also exhibit higher femoral neck BMDs. Akira et al. [29] revealed a correlation between osteoporosis and hypertriglyceridemia in postmenopausal women. As reported in a study conducted by Lawlor et al. [30], TG levels are positively associated with BMD, BMC, and BA in male but not female adolescents. Son et al. [31] reported that TG levels are positively correlated with bone density T-values, and a significant positive correlation was observed in healthy Korean men after correcting for age and BMI. In a study conducted by Cui et al. [32], the TG levels were significantly and positively correlated with the BMD at the trochanter site in postmenopausal women, and premenopausal women with TG levels in the higher quartile exhibited lower lumbar BMD values. Dennison et al. [33] revealed correlations between BMDs measured at the lumbar spine and total femoral region and the serum TG levels. Yujie et al. [35] showed that the TG levels were directly correlated with the BMD at the lumbar spine in type 2 diabetes patients. Saoji et al. [36] reported that the BMD at the spine and femur site was associated with TG in women from Northeast India of Tibeto-Burman origin. Mirzababaei et al. [16] postulated that high serum TG levels and low serum HDL-C levels exerted mediating effects on the relationship between obesity and high BMD at the hip region in metabolically unhealthy obese subjects in Iran. Yoldemir et al. [34] reported weak negative correlations between TG levels and BMD at the lumbar spine in healthy postmenopausal Turkish women. However, the relationship between hypertriglyceridemia or high TG levels and BMD remains controversial. According to Yamaguchi et al. [37], plasma TG levels are not correlated with BMD values at any skeletal site. Kim et al. [39] failed to observe correlations between TG levels and BMDs measured at any site in postmenopausal Korean women. Sung et al. [38] reported that TG levels were not correlated with BMDs in elderly Korean men. Lilianne et al. [40] failed to observe significant correlations between TG levels and BMDs measured at various skeletal sites, such as the lumbar spine, femoral neck, and total hip. Li et al. [41] did not identify an association between TG levels and BMD in postmenopausal Chinese women. In the present study, the area and BMC of the pelvis in women and the area and BMC of the left rib in men were the most important indicators of hypertriglyceridemia. These results are similar to those reported in previous studies [16, 28,29,30,31,32,33,34,35,36], indicating that BMD is correlated with TG levels.

The TG levels are associated with the fat mass in the abdominal region. In a study conducted by Kissebah et al. [12], high plasma TG levels were correlated with upper body obesity. Despres et al. [26] reported that abdominal fat was associated with low serum HDL-C concentrations. Additionally, obesity and abdominal fat accumulation were associated with hypertriglyceridemia, and high plasma TG levels were associated with TGs enriched in LDL and HDL in another study [15]. As reported by Lipsky et al. [4], the trunk fat mass was positively correlated with the TG levels. According to a simple correlation analysis reported by Takeuchi et al. [14], the trunk/leg fat ratio is strongly and positively correlated with TG levels and postprandial triglyceridemia in young Japanese women, and leg and trunk fat are negatively and positively correlated, respectively, with TG levels and postprandial triglyceridemia after mutual adjustment. Lee et al. [27] showed that the TG level was significantly correlated with trunk fat in an obese group of patients with gastric neoplasms over 1 year of follow-up after laparoscopic gastrectomy. The present results are consistent with some previous studies [4, 14, 15, 26] and indicate strong associations between upper body fat mass, particularly trunk fat mass, and TG levels.

TG levels, bone mineral density, and body fat mass have been reported to differ among ethnic and race groups [14, 46,47,48,49,49,51]. Sharma et al. [46] reported lower TG concentrations in African-Americans than Whites or Hispanics diagnosed with type 2 diabetes mellitus. Marcus et al. [47] examined the correlations with BMD in a postmenopausal estrogen/progestin intervention trial and reported that black women exhibited the highest 2nd-4th lumbar spine BMD, and Hispanic women exhibited the highest femoral neck BMD. Araujo et al. [48] reported that the BMC and BMD values in black men were greater than those in Hispanic or white men. These authors proposed that the differences in BMC and BMD potentially explain the variations in the fracture rates among black, Hispanic, and white men. Lu et al. [49] reported that ethnicity exerted the strongest effect on most regional body BMD values among Chinese, white, and black subjects across both men and women. George et al. [50] reported that the whole body, hip, femoral neck and lumbar spine BMD values in black African subjects were significantly higher than those in Indian subjects in South Africa. Keswell et al. [13] described differences in body fat composition according to ethnicity. Based on their findings, black women are significantly shorter and heavier and present a higher BMI and greater fat mass than white women, and black women exhibit greater absolute trunk, leg and arm fat mass measurement values than white women. Moreover, black women exhibit lower TG concentrations and higher trunk fat masses than white women. Additionally, the associations between the body fat composition and TG levels differ by ethnicity and race [13, 25]. Hosain et al. [25] reported ethnicity- and race-specific differences in correlations between body fat distribution variables and serum lipid profiles, including TG levels, among reproductive-age black, white and Hispanic women. According to Keswell et al. [13], a higher trunk fat mass in black women and a higher visceral adipose tissue mass in white women are associated with TG concentrations.

Several studies have described gender differences in correlations between BMDs and TG levels and metabolic syndrome. As shown in a study conducted by Lawlor et al. [30], the TG concentrations are positively correlated with BMD, BMC, and BA in adolescent men but not in women. Kim et al. [10] indicated that the BMD is negatively correlated with the TG levels in men but not in women. In a study conducted by Muka et al. [52], WC was inversely correlated with the FNk-BMD in men, and the HDL-C concentrations were positively correlated with the FNk-BMD in women but not in men. Loke et al. [11] reported that metabolic syndrome was positively correlated with BMD in men and negatively correlated with BMD in women in Taiwanese elderly populations. In the present study, WC in women and Trk-Ft in men were the best indicators of hypertriglyceridemia. Our results support some previous studies identifying gender differences in correlations between TG levels and BMDs in metabolic syndrome patients [10, 11, 30, 52]. The present study has several limitations. First, cause-effect associations are difficult to determine because of the cross-sectional design. Second, our results were limited to Korean adults because we used data from the fifth Korea National Health and Nutrition Examination Survey in this study. Despite these limitations, the results of this and previous studies support that anthropometric indices, such as WC, WHtR, and WHR, are associated with TG levels. Therefore, anthropometric indices may be used for the identification of hypertriglyceridemia or TG levels in initial health screening efforts. However, although the BMD was associated with the TG levels in our results, this association remains controversial because there are conflicting arguments in many studies.

Conclusion

The present study examined anthropometric variables, bone density and body fat composition (bone area, BMC, BMD, body fat mass, and lean body mass) in Korean adults and showed that WC in women and Trk-Ft in men exhibited the best predictive power for hypertriglyceridemia. WC and Trk-Ft exhibited similar predictive powers for hypertriglyceridemia in both women and men. Moreover, WC and Trk-Ft exhibited greater predictive power in women than in men. Our findings provide clinical information that may be useful for the identification of hypertriglyceridemia or high TG levels during initial screening steps. Further studies are needed to build a model for accurate identification based on a combination of BMD, anthropometric, and fat mass data.

Abbreviations

AUC:

Area under the receiver operating characteristic curve

BMC:

Bone mineral content

BMD:

Bone mineral density

DXA:

Dual energy X-ray absorptiometry

HDL-C:

High density lipoprotein cholesterol

LDL-C:

Low density lipoprotein cholesterol

TC:

Total cholesterol

TG:

Triglyceride

Trk-Ft:

Trunk fat mass

WC:

Waist circumference

WHR:

Waist-to-hip ratio

WHtR:

Waist-to-height ratio

References

  1. 1.

    Han SH, Nicholls SJ, Sakuma I, Zhao D, Koh KK. Hypertriglyceridemia and cardiovascular diseases: revisited. Korean Circ J. 2016;46(2):135–44.

    PubMed  PubMed Central  Article  Google Scholar 

  2. 2.

    Melissa AA, John EH, Karen LE. Hypertriglyceridemia as a cardiovascular risk factor. Am J Cardiol. 1998;81(4A):7–12.

    Google Scholar 

  3. 3.

    Bansal S, Buring JE, Rifai N, Mora S, Sacks FM, Ridker PM. Fasting compared with nonfasting triglycerides and risk of cardiovascular events in women. JAMA. 2007;298:309–16.

    CAS  PubMed  Article  Google Scholar 

  4. 4.

    Lipsky LM, Gee B, Liu A, Nansel TR. Body mass index and adiposity indicators associated with cardiovascular biomarkers in youth with type 1 diabetes followed prospectively. Pediatr Obes. 2017;12:468–76.

    CAS  PubMed  Article  Google Scholar 

  5. 5.

    Castelli WP. The triglyceride issue: a view from Framingham. Am Heart J. 1986;112(2):432–7.

    CAS  PubMed  Article  Google Scholar 

  6. 6.

    Sarwar N, Danesh J, Eiriksdottir G, Sigurdsson G, Wareham N, Bingham S, et al. Triglycerides and the risk of coronary heart disease 10,158 incident cases among 262,525 participants in 29 western prospective studies. Circulation. 2007;115:450–8.

    CAS  PubMed  Article  Google Scholar 

  7. 7.

    Ren J, Grundy SM, Liu J, Wang W, Wang M, Sun J, et al. Long-term coronary heart disease risk associated with very-low-density lipoprotein cholesterol in Chinese: the results of a 15-year Chinese multi-provincial cohort study (CMCS). Atherosclerosis. 2010;211:327–32.

    CAS  PubMed  Article  Google Scholar 

  8. 8.

    Park YW, Zhu S, Palaniappan L, Heshka S, Carnethon MR, Heymsfield SB. The metabolic syndrome: prevalence and associated risk factor findings in the US population from the third National Health and nutrition examination survey, 1988-1994. Arch Intern Med. 2003;163(4):427–36.

    PubMed  PubMed Central  Article  Google Scholar 

  9. 9.

    Szulc P, Varennes A, Delmas PD, Goudable J, Chapurlat R. Men with metabolic syndrome have lower bone mineral density but lower fracture risk—the MINOS study. J Bone Miner Res. 2010;25:1446–54.

    PubMed  Article  Google Scholar 

  10. 10.

    Kim YH, Cho KH, Choi YS, Kim SM, Nam GE, Lee SH, et al. Low bone mineral density is associated with metabolic syndrome in south Korean men but not in women: the 2008–2010 Korean National Health and nutrition examination survey. Arch Osteoporos. 2013;8:142.

    PubMed  Article  Google Scholar 

  11. 11.

    Loke SS, Chang HW, Li WC. Association between metabolic syndrome and bone mineral density in a Taiwanese elderly population. J Bone Miner Metab. 2018;36(2):200–8.

    PubMed  Article  Google Scholar 

  12. 12.

    Klssebah AH, Pelrls AN. Biology of regional body fat distribution: relationship to non-insulin-dependent diabetes mellitus. Diabetes Metab Rev. 1989;5:83–109.

    Article  Google Scholar 

  13. 13.

    Keswell D, Tootla M, Goedecke JH. Associations between body fat distribution, insulin resistance and dyslipidaemia in black and white south African women. Cardiovasc J Afr. 2016;27(3):177–83.

    PubMed  PubMed Central  Article  Google Scholar 

  14. 14.

    Takeuchi M, Tsuboi A, Kurata M, Kazumi T, Fukuo K. Associations of postprandial lipemia with trunk/leg fat ratio in young normal weight women independently of fat mass and insulin resistance. Asia Pac J Clin Nutr. 2018;27(2):293–9.

    CAS  PubMed  Google Scholar 

  15. 15.

    Després JP, Moorjani S, Tremblay A, Ferland M, Lupien PJ, Nadeau A, et al. Relation of high plasma triglyceride levels associated with obesity and regional adipose tissue distribution to plasma lipoprotein lipid composition in premenopausal women. Clin Invest Med. 1989;12:374–80.

    PubMed  Google Scholar 

  16. 16.

    Mirzababaei A, Mirzaei K, Khorrami-Nezhad L, Maghbooli Z, Keshavarz SA. Metabolically healthy/unhealthy components may modify bone mineral density in obese people. Arch Osteoporos. 2017;12(1):95.

    PubMed  Article  Google Scholar 

  17. 17.

    Shen SW, Lu Y, Li F, Yang CJ, Feng YB, Li HW, et al. Atherogenic index of plasma is an effective index for estimating abdominal obesity. Lipids Health Dis. 2018;17(1):11.

    PubMed  PubMed Central  Article  Google Scholar 

  18. 18.

    Lim S, Shin H, Song JH, Kwak SH, Kang SM, Won Yoon J, et al. Increasing prevalence of metabolic syndrome in Korea: the Korean National Health and nutrition examination survey for 1998-2007. Diabetes Care. 2011;34:1323–8.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  19. 19.

    Ford ES, Li C, Zhao G, Pearson WS, Mokdad AH. Hypertriglyceridemia and its pharmacologic treatment among US adults. Arch Intern Med. 2009;169:572–8.

    CAS  PubMed  Article  Google Scholar 

  20. 20.

    Ghosh A, Bose K, Das Chaudhuri AB. Association of food patterns, central obesity measures and metabolic risk factors for coronary heart disease (CHD) in middle aged Bengalee Hindu men, Calcutta. India Asia Pac J Clin Nutr. 2003;12(2):166–71.

    PubMed  Google Scholar 

  21. 21.

    Sharp TA, Grunwald GK, Giltinan KE, King DL, Jatkauskas CJ, Hill JO. Association of anthropometric measures with risk of diabetes and cardiovascular disease in Hispanic and Caucasian adolescents. Prev Med. 2003;37(6):611–6.

    PubMed  Article  Google Scholar 

  22. 22.

    Lee HH, Lee HJ, Cho JI, Stampfer MJ, Willett WC, Kim CI, et al. Overall and abdominal adiposity and hypertriglyceridemia among Korean adults: the Korea National Health and nutrition examination survey 2007–2008. Eur J Clin Nutr. 2013;67(1):83–90.

    CAS  PubMed  Article  Google Scholar 

  23. 23.

    Lee CM, Huxley RR, Wildman RP, Woodward M. Indices of abdominal obesity are better discriminators of cardiovascular risk factors than BMI: a meta-analysis. J Clin Epidemiol. 2008;61(7):646–53.

    PubMed  Article  Google Scholar 

  24. 24.

    Lee BJ, Kim JY. Indicators of hypertriglyceridemia from anthropometric measures based on data mining. Comput Biol Med. 2015;57:201–11.

    PubMed  Article  Google Scholar 

  25. 25.

    Hosain GMM, Rahman M, Williams KJ, Berenson AB. Racial differences in the association between body fat distribution and lipid profiles among reproductive-aged women. Diabetes Metab. 2010;36(4):278–85.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  26. 26.

    Despres JP, Allard C, Tremblay A, Talbot J, Bouchard C. Evidence for a regional component of body fatness in the association with serum lipids in men and women. Metabolism. 1985;34:967–73.

    CAS  PubMed  Article  Google Scholar 

  27. 27.

    Lee SJ, Kim JY, Ha TK, Choi YY. Changes in lipid indices and body composition one year after laparoscopic gastrectomy: a prospective study. Lipids Health Dis. 2018;17(1):113.

    PubMed  PubMed Central  Article  Google Scholar 

  28. 28.

    von Muhlen D, Safii S, Jassal SK, Svartberg J, Barrett-Connor E. Associations between the metabolic syndrome and bone health in older men and women: the rancho Bernardo study. Osteoporos Int. 2007;18:1337–44.

    Article  Google Scholar 

  29. 29.

    Hirasawa A, Makita K, Akahane T, Yamagami W, Makabe T, Yokota M, et al. Osteoporosis is less frequent in endometrial cancer survivors with hypertriglyceridemia. Jpn J Clin Oncol. 2015;45(1):127–31.

    PubMed  Article  Google Scholar 

  30. 30.

    Lawlor DA, Sattar N, Sayers A, Tobias JH. The association of fasting insulin, glucose, and lipids with bone mass in adolescents: findings from a cross-sectional study. J Clin Endocrinol Metab. 2012;97(6):2068–76.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  31. 31.

    Son JS, Koh HM, Park JK. Relationship between triglyceride and bone mineral density in healthy Korean men. Korean J Health Promot. 2015;15(3):115–20.

    Article  Google Scholar 

  32. 32.

    Cui LH, Shin MH, Chung EK, Lee YH, Kweon SS, Park KS, et al. Association between bone mineral densities and serum lipid profiles of pre- and post-menopausal rural women in South Korea. Osteoporosis Int. 2005;16:1975–81.

    CAS  Article  Google Scholar 

  33. 33.

    Dennison EM, Syddall HE, Aihie Sayer A, Martin HJ, Cooper C. Hertfordshire cohort study group. Lipid profile, obesity and bone mineral density: the Hertfordshire cohort study. QJM. 2007;100(5):297–303.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  34. 34.

    Yoldemir T, Erenus M. The impact of metabolic syndrome on bone mineral density in postmenopausal women. Gynecol Endocrinol. 2012;28(5):391–5.

    CAS  PubMed  Article  Google Scholar 

  35. 35.

    Wu Y, Xing X, Ye S, Chen C, Wang J. Lipid level related with osteoporosis in type 2 diabetes patients. Exp Clin Endocrinol Diabetes. 2018. https://doi.org/10.1055/a-0735-9361.

  36. 36.

    Saoji R, Das RS, Desai M, Pasi A, Sachdeva G, Das TK, Khatkhatay MI. Association of high-density lipoprotein, triglycerides, and homocysteine with bone mineral density in young Indian tribal women. Arch Osteoporos. 2018;13(1):108.

    PubMed  Article  Google Scholar 

  37. 37.

    Yamaguchi T, Sugimoto T, Yano S, Yamauchi M, Sowa H, Chen Q, et al. Plasma lipids and osteoporosis in postmenopausal women. Endocr J. 2002;49:211–7.

    CAS  PubMed  Article  Google Scholar 

  38. 38.

    Sung DJ, So WY. Negative association of plasma cholesterol and low-density lipoprotein cholesterol, but not testosterone or growth hormone, with bone mineral density in elderly Korean men. Iran J Public Health. 2016;45(2):255–6.

    PubMed  PubMed Central  Google Scholar 

  39. 39.

    Kim KC, Shin DH, Lee SY, Im JA, Lee DC. Relation between obesity and bone mineral density and vertebral fractures in Korean postmenopausal women. Yonsei Med J. 2010;51(6):857–63.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  40. 40.

    Lilianne HH, Zaynab A. Serum lipids effect on bone mineral density: a pilot study in apparently healthy Syrians. Intern Med. 2014;4(179):2.

    Google Scholar 

  41. 41.

    Li S, Guo H, Liu Y, Wu F, Zhang H, Zhang Z, et al. Relationships of serum lipid profiles and bone mineral density in postmenopausal Chinese women. Clin Endocrinol. 2015;82(1):53–8.

    CAS  Article  Google Scholar 

  42. 42.

    Korea Centers for Disease Control and Prevention. The Fifth Korea National Health and Nutrition Examination Survey (KNHANES V), 2010–2012. Republic of Korea, 2010. https://knhanes.cdc.go.kr/knhanes/main.do.

  43. 43.

    Pejic RN, Lee DT. Hypertriglyceridemia. J Am Board Fam Med. 2006;19(3):310–6.

    PubMed  Article  Google Scholar 

  44. 44.

    Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults. Executive summary of the third report of the National Cholesterol Education Program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III). JAMA. 2001;285(19):2486–97.

    Article  Google Scholar 

  45. 45.

    Cho YM. Fish consumption, mercury exposure, and the risk of cholesterol profiles: findings from the Korea National Health and nutrition examination survey 2010-2011. Environ Health Toxicol. 2017;32:e2017014.

    PubMed  PubMed Central  Article  Google Scholar 

  46. 46.

    Sharma MD, Pavlik VN. Dyslipidemia in African Americans, Hispanics and whites with type 2 diabetes mellitus and hypertension. Diabetes Obes Metab. 2001;3(1):41–5.

    CAS  PubMed  Article  Google Scholar 

  47. 47.

    Marcus R, Greendale G, Blunt BA, Bush TL, Sherman S, Sherwin R, et al. Correlates of bone mineral density in the postmenopausal estrogen/progestin interventions trial. J Bone Miner Res. 1994;9:1467–76.

    CAS  PubMed  Article  Google Scholar 

  48. 48.

    Araujo AB, Travison TG, Harris SS, Holick MF, Turner AK, McKinlay JB. Race/ethnic differences in bone mineral density in men. Osteoporos Int. 2007;18(7):943–53.

    CAS  PubMed  Article  Google Scholar 

  49. 49.

    Lu H, Fu X, Ma X, Wu Z, He W, Wang Z, et al. Relationships of percent body fat and percent trunk fat with bone mineral density among Chinese, black, and white subjects. Osteoporos Int. 2011;22(12):3029–35.

    CAS  PubMed  Article  Google Scholar 

  50. 50.

    George JA, Micklesfield LK, Norris SA, Crowther NJ. The association between body composition, 25(OH)D and PTH, and bone mineral density in black African and Asian Indian population groups. J Clin Endocrinol Metab. 2014;99(6):2146–54.

    CAS  PubMed  Article  Google Scholar 

  51. 51.

    Bagger YZ, Rasmussen HB, Alexandersen P, Werge T, Christiansen C, Tankó LB, et al. Links between cardiovascular disease and osteoporosis in postmenopausal women: serum lipids or atherosclerosis per se? Osteoporos Int. 2007;18:505–12.

    CAS  PubMed  Article  Google Scholar 

  52. 52.

    Muka T, Trajanoska K, Kiefte-de Jong JC, Oei L, Uitterlinden AG, Hofman A, et al. The association between metabolic syndrome, bone mineral density, hip bone geometry and fracture risk: the Rotterdam study. PLoS One. 2015;10(6):e0129116.

    PubMed  PubMed Central  Article  Google Scholar 

  53. 53.

    Cardadeiro G, Baptista F, Zymbal V, Rodrigues LA, Sardinha LB. Ward's area location, physical activity, and body composition in 8-and 9-year-old boys and girls. J Bone Miner Res. 2010;25(11):2304–12.

    PubMed  Article  Google Scholar 

Download references

Acknowledgments

The authors are grateful for the collaboration with the Korea Centers for Disease Control and Prevention, which provided the data for the present study.

Funding

This research was supported by the Bio and Medical Technology Development Program of the National Research Foundation of Korea (NRF) funded by the Korean government, MSIP (NRF-2015M3A9B6027139). The funder had no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.

Availability of data and materials

The data are available from the fifth Korea National Health and Nutrition Examination Survey (KNHANES V-1), which was conducted by the Korea Centers for Disease Control and Prevention (KCDCP), and are freely available from KCDCP (https://knhanes.cdc.go.kr).

Author information

Affiliations

Authors

Contributions

JHC and BJL contributed to the conceptualization, formal analysis, interpretation of the results, validation, and writing of the manuscript. BJL contributed to the funding acquisition. MSS performed the data collection and preprocessing, formal analysis, and revision of the manuscript. All authors reviewed the subsequent versions and read and approved the final manuscript.

Corresponding author

Correspondence to Bum Ju Lee.

Ethics declarations

Ethics approval and consent to participate

The fifth Korea National Health and Nutrition Examination Survey (KNHANES V-1) was approved by the Korea Ministry of Health and Welfare (2010-02CON-21-C). The Institutional Review Boards of Konkuk University and the Korea Institute of Oriental Medicine also approved the access and analysis of open source data from the KNHANES in the present study with a waiver of documentation of informed consent (IRB No. 7001355–201,802-E-063 and I-1805/003–001).

All procedures in studies involving human participants were performed in accordance with the ethical standards of the institutional and/or national research committee and the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

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 distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Chi, J.H., Shin, M.S. & Lee, B.J. Identification of hypertriglyceridemia based on bone density, body fat mass, and anthropometry in a Korean population. BMC Cardiovasc Disord 19, 66 (2019). https://doi.org/10.1186/s12872-019-1050-2

Download citation

Keywords

  • Hypertriglyceridemia
  • Bone mineral density
  • Anthropometric characteristics
  • Triglyceride
  • Body fat mass
  • Public health