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Open Access

Peer-reviewed

Research Article

The effect of lipid accumulation product and its interaction with other factors on hypertension risk in Chinese Han population: A cross-sectional study

  • Jian Song,

    Roles Data curation, Methodology, Software, Writing – original draft

    Affiliation School of public health, Bengbu medical college, Bengbu, Anhui Province, China

    ⨯
  • Yingying Zhao,

    Roles Data curation, Validation

    Affiliation Bengbu health board, Bengbu, Anhui Province, China

    ⨯
  • Sumei Nie,

    Roles Formal analysis, Investigation

    Affiliation Bengbu health board, Bengbu, Anhui Province, China

    ⨯
  • Xue Chen,

    Roles Methodology, Resources, Validation

    Affiliation School of public health, Bengbu medical college, Bengbu, Anhui Province, China

    ⨯
  • Xuesen Wu ,

    Roles Software, Supervision, Writing – review & editing

    xuesenwu@163.com

    Affiliation School of public health, Bengbu medical college, Bengbu, Anhui Province, China

    ⨯
  • Jing Mi

    Roles Methodology, Supervision

    Affiliation School of public health, Bengbu medical college, Bengbu, Anhui Province, China

    ⨯

Abstract

Objectives

Lipid accumulation product (LAP) is a simple and effective indicator that reflects visceral obesity. This study aimed to compare the significance of LAP in predicting hypertension risk with other obesity indices, and to evaluate the interactive effects of LAP and smoking, family history of hypertension on hypertension risk in Chinese Han adults.

Methods

A community based cross-sectional study was performed in Bengbu, China. Participants received face-to-face questionnaire survey, anthropometric tests and laboratory examinations. Relevant indicators that reflect obesity including BMI (body mass index), waist-to-height ratio (WHtR) and LAP were calculated. Multivariate logistic regression analysis was applied to explore the association between LAP and hypertension risk. The area under the receiver-operating characteristics curves (AUC) of LAP, BMI, and WHtR were calculated and then compared. Interactive effect was evaluated by relative excess risk due to interaction (RERI), attributable proportion due to interaction (AP) and synergy index (SI).

Results

A total of 1777 participants were enrolled, and the prevalence of hypertension was 24.4% (n = 433). There was a significant increase in hypertension risk with LAP levels in the fourth quartile as compared with the bottom quartile (OR: 3.31, 95%CI: 1.76–6.25). The AUC of LAP was significantly different than that of BMI in males (Z = 2.158, p = 0.0309) and females (Z = 3.570, p = 0.0004), while only performed better in females as compared with that of WHtR (Z = 2.166, p = 0.0303). LAP was significantly interacted with family history of hypertension on hypertension risk both in males (RERI: 1.07, 95%CI: 0.09–2.05; AP: 0.33, 95%CI: 0.23–0.44; SI: 1.92, 95%CI: 1.53–2.41) and females (RERI: 0.80, 95%CI: 0.07–1.53; AP: 0.25, 95%CI: 0.11–0.39; SI: 1.59, 95%CI: 1.16–2.19). However, a significant interaction between LAP and smoking was only observed in males (RERI: 1.32, 95%CI: 0.15–2.75; AP: 0.40, 95% CI: 0.14–0.73).

Conclusion

Increased LAP was significantly associated with a higher risk of hypertension in Chinese Han adults. Moreover, the effect of LAP on predicting hypertension risk was better than that of other obesity indices. Our results also demonstrated interactive effects of LAP with smoking, family history of hypertension on hypertension risk.

Citation: Song J, Zhao Y, Nie S, Chen X, Wu X, Mi J (2018) The effect of lipid accumulation product and its interaction with other factors on hypertension risk in Chinese Han population: A cross-sectional study. PLoS ONE 13(6): e0198105. https://doi.org/10.1371/journal.pone.0198105

Editor: Ying-Mei Feng, Beijing Key Laboratory of Diabetes Prevention and Research, CHINA

Received: January 18, 2018; Accepted: May 14, 2018; Published: June 6, 2018

Copyright: © 2018 Song et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Due to ethical restriction imposed by the Ethics Committee of Bengbu medical college, the data supporting our findings will not be shared publicly. We have promised respondents not disclose any information in the questionnaire. Qualified researchers who meet the criteria for access to confidential data may request the data by contacting Dr. Fuyong Hu (Secretary of data management, Email: hufuyong@126.com). Additionally, data are available to qualified researchers by contacting the corresponding author on reasonable request (Prof. Xuesen Wu, Email: xuesenwu@163.com).

Funding: This study is supported by the National Natural Science Foundation of China (number: 81373100) and the Key Projects of Humanities and Social Sciences Research Bases of Universities in Anhui Province (SK2014A055). We are grateful to all professionals and participants of the study. This work is supported by Bengbu health board, China. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Competing interests: The authors have declared that no competing interests exist.

Abbreviations: BMI, body mass index; WHtR, waist-to-height ratio; WC, Waist circumference; FPG, fasting plasma glucose; TG, triglycerides; LAP, lipid accumulation product

Introduction

Hypertension is one of the most serious public health issues worldwide with an increased prevalence in recent years [1]. It was reported that 27.8% of Chinese adults were hypertensive [2]. Moreover, hypertension is also a predominant risk factor for cardiovascular diseases [3]. A prospective cohort study with 500223 adults in China indicated that hypertension accounted for about one-third of deaths due to cardiovascular diseases [4].

The prevalences of obesity and obesity-related diseases have dramatically increased both in developing and developed countries [5, 6]. From 2007 to 2013, the age-standardized prevalence of obesity in Northeastern China increased from 15.82% to 19.41% in males and 13.18% to 18.77% in females, respectively [7]. Traditionally, body mass index (BMI) and waist-to-height ratio (WHtR) are most frequently used indices to evaluate general obesity and abdominal obesity [8]. BMI can reflect the degree of overweight, but cannot reflect the individual fat distribution. Relevant studies have suggested that abdominal fat distribution may be more closely related to adverse outcomes than those of BMI, such as cardiovascular diseases [9, 10]. However, WHtR only reflects abdominal obesity accurately, but cannot distinguish between subcutaneous fat and visceral fat.

Recently, visceral obesity has attained increasing attention because of its higher value in predicting diseases risks [11, 12]. Also, emerging evidences have suggested that visceral fat may be more closely associated with hypertension risk. Compared with subcutaneous fat, visceral fat is the predominant cause of insulin resistance, dyslipidemia and cardiovascular diseases [13, 14]. Visceral adipose tissue can activate the renin-angiotensin system by releasing angiotensinogen, angiotensin converting enzyme and cathepsin [15]. Moreover, visceral fat expressed more angiotensinogen and more proinflammatory cytokines than that of subcutaneous adipose tissue [16]. Meanwhile, visceral fat was reported to be related with increased activity of sympathetic nervous system, which was also associated with hypertension [17]. Computer tomography(CT) and magnetic resonance imaging (MRI) are the gold standards to evaluate visceral fat in clinical application, their high costs and radiation exposure, however, significantly limit their widely use in practice. Therefore, searching for a simple and effective indicator reflecting visceral obesity is urgent.

LAP, as the product of waist circumference (WC) and triglycerides (TG), was proposed as a simple and effective index for lipid over accumulation among adults by Kahn et al [18]. LAP can better reflect the total fat body accumulation and visceral fat function, rather than simple high body weight. The third National Health and Nutrition Examination Survey III showed that LAP performed better than that of BMI for identifying higher total cholesterol, low-LDL-C, uric acid levels, higher total cholesterol/HDL-C and lower HDL-C levels among US adults [18]. Several studies have suggested that LAP can better predict metabolic syndrome, insulin resistance and diabetes risks [19–21]. In a cross-sectional study in Japan, LAP was suggested to be better for discriminating the risk of hypertension [22]. Gao et al [23] compared the ability of BMI and LAP in predicting hypertension risk among Mongolians, and the results showed that the performance of LAP performed was superior to that of BMI. As the ethnicity differences of body composition, the value of LAP in Han Chinese adults remains unclear. Chinese individuals have a greater amount of visceral adipose tissue than Europeans at a given BMI or WC [24]. Therefore, the relationship between LAP and hypertension risk in the Han of China needs to be further confirmed. Additionally, hypertension is regarded as a multifactorial disease that is associated with genetic and environmental factors. Besides, the interactions of gene-environment and environment- environment may aggravate the risk of hypertension. Previous studies have indicated that smoking and family history of hypertension were related with hypertension risk [25, 26]. To the best of our knowledge, there was no article exploring the interactive effects of LAP and smoking, family history of hypertension on hypertension risk.

In the present study, we first evaluated the association between LAP and hypertension risk in Chinese Han adults. Secondly, the abilities of BMI, WHtR and LAP in predicting hypertension risk were compared. Finally, we assessed the interactive effects between LAP and smoking, family history of hypertension on hypertension risk.

Materials and methods

Study participants

A community based cross-sectional survey was conducted in Longzihu, Bengbu, China. Firstly, seven communities were selected by a stratified sampling. Then, simple random sampling was used to identify participants. Inclusion criteria: (1) live in the selected communities for more than 6 months in the past year; (2) middle-aged and elderly adults; (3) willingness to participate in this project. Exclusion criteria: (1) have no abilities to normally communicate with investigators due to psychological or mental barriers; (2) cannot finish the overall survey independently because of inconvenience or serious illness. Written informed consent was obtained from each participant. The overall survey had three parts: face-to-face questionnaire, anthropometric tests and laboratory examinations. This study was approved by the Ethics Committee of Bengbu medical college.

Questionnaire survey

We used a self-designed questionnaire to investigate relevant information for each participant by sophisticated members through face-to-face interviews. Among them, smoking was defined as the status of pre-smoking or current-smoking. Educational level was classified as “elementary school or lower”, “middle school graduate” and “high school graduate or higher”. Marital status was categorized as “currently not married” and “currently married”. Family income was grouped as “0–2000”, “2000–4000” and “4000-”. Participants were required to answer the question “do you have a family history of hypertension (yes or no)”. Positive family history of hypertension was defined as at least one parent or sibling with hypertension.

Anthropometric tests and laboratory examinations

Height and weight were measured with the participants in lightweight clothing and without shoes. Waist circumference (WC) was measured at the level midway between the lower rib margin and the iliac crest. Blood pressure was detected using mercury sphygmomanometer by trained members according to standardized methods [27]. All subjects were required to have a rest for about 10 mins before measuring blood pressure in a quiet environment. Blood samples were collected from the antecubital vein after an overnight fast. Routine biochemical data including fasting plasma glucose (FPG), triglycerides (TG) and HbA1c were examined. FPG≥7.0 mmol/L, TG≥1.70mmol/l and HbA1c≥6.5% were defined as hyperglycemia, hypertriglyceridaemia and hyper-HbA1c, respectively [28, 29].

Definitions

  1. Hypertension was defined as systolic blood pressure (SBP)≥140 mmHg, or diastolic blood pressure (DBP)≥90 mmHg, or the subject reported with a medical history of anti-hypertensive medication [30].
  2. BMI was referred to weight (kg)/height (m)2. According to the Working Group on Obesity in China [31], BMI≥ 28 was defined as general obesity.
  3. WHtR was calculated by dividing WC by height, and ≥0.5 was defined as abdominal obesity [32].
  4. LAP was calculated as [WC (cm)-65]by height Obesity ales, and [WC (cm)-58]×[TG(mmol/L)] in females [18].

Statistical analysis

All data were entered into Epidata 3.1 software firstly by using double entry approach. Quantitative data were presented as meansentede data were into Epidata 3.1 software firstly by using double entry approach. bioparticipants were compared by t-test for normally distributed data or Wilcoxon rank sum test for non-normally distributed data. LAP was divided into four groups (Q1, Q2, Q3, and Q4) by quartiles. The differences of quantitative data across the LAP groups were compared by analysis of variance if the data were normally distributed and homogeneity of variance. Otherwise, Kruskal-Wallis H test was used. Categorical variables were expressed as percentages, and compared by Chi-squared test. Multivariate logistic regression model was performed when analyzing the relationship between LAP and hypertension risk. Optimal cut-off values of BMI, WHtR and LAP in predicting hypertension were identified according to best Youden index (YI, sensitivity+specificity−1). The area under the receiver-operating characteristics (ROC) curves (AUC) of LAP, BMI, and WHtR were calculated, and then compared by non-parametric significance test (statistic of Z). Finally, the interactive effects between LAP and family history of hypertension, smoking on risk of hypertension were examined by relevant indicators including the relative excess risk due to interaction (RERI = RR11-RR10-RR01+1), the attributable proportion due to interaction (AP = RERI/RR11), and the synergy index (SI = (RR11-1)/(RR01-1)+(RR10-1)). All of these indicators were calculated using the Excel table designed by Andersson et al [33, 34]. The interactive effect was considered as statistically significant if the corresponding 95% CI for RERI, AP, and SI did not overlap 0, 0 and 1, respectively. All p values were two-sided and p<0.05 was considered as statistically significant. Statistical calculations were performed using SPSS19.0 and Medcalc software.

Results

1. Basic characteristics

There were 1777 participants (748 men and 1029 women) with the average age of 60.82 years enrolled in this study. The overall prevalence of hypertension was 24.4% (n = 443). Male participants had a higher prevalence of hypertension than that of female (p<0.001). For the anthropometric measurements, there were statistically significant differences for BMI, WHtR and LAP between hypertension and non-hypertension members (p<0.001). TG (p<0.001), FPG (p<0.001) and HbA1c (p = 0.002) were significantly higher in hypertension participants. Significant differences in educational level (p = 0.017), family history of hypertension (p = 0.003) and smoking status (p = 0.012) between hypertension and non-hypertension members were also observed. However, the differences in marital status (p = 0.428) and family income (p = 0.673) were not statistically significant. The basic characteristics of participants were shown in Table 1.

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Table 1. Basic characteristic of the study participants.

https://doi.org/10.1371/journal.pone.0198105.t001

2. LAP and hypertension risk

LAP was grouped by quartiles in Table 2. Male had a relatively higher LAP than that of female (p<0.001). Participants with higher LAP quartiles had significantly higher BMI (p<0.001) and WHtR (p<0.001). The prevalence of hypertension (p<0.001), hyperglycemia (p<0.001), hyper-HbA1c (p<0.001), smoking (p = 0.043) progressively increased across LAP quartiles. However, family history of hypertension (p = 0.761), age (p = 0.347), marital status (p = 0.105), educational level (p = 0.157) and family income (p = 0.112) had no significant differences across LAP quartiles.

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Table 2. The comparisons of cardiovascular risk factors according to the quartiles of LAP.

https://doi.org/10.1371/journal.pone.0198105.t002

We then analyzed the relationship between LAP and hypertension risk by logistic regression model. The crude OR was 6.35 (95%CI: 4.39–9.12) of LAP levels in the fourth quartile as compared with the first quartile. A significant increase in hypertension risk with LAP levels in the fourth quartile as compared with the first quartile was also observed by multivariate analysis (adjust OR:3.31, 95% CI: 1.76–6.25). The results were presented in Table 3.

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Table 3. OR (95%CI) of LAP on risk of hypertension by logistic regression mode.

https://doi.org/10.1371/journal.pone.0198105.t003

The AUCs and cut-off values of LAP, BMI and WHtR were presented in Table 4. The best thresholds of LAP to predict hypertension were 40.60 in male and 29.14 in female respectively. In males, the AUC (95%CI) of LAP, BMI and WHtR were 0.66 (0.62–0.69), 0.61 (0.57–0.64) and 0.67 (0.63–0.70), respectively. The AUC of LAP was significantly different with BMI (Z = 2.158, p = 0.0309), while not different with WHtR (Z = 0.345, p = 0.7305). As for females, the AUC of LAP (0.70, 95%CI: 0.67–0.73) was significantly higher than that of BMI (0.63, 95%CI: 0.60–0.66) and WHtR 0.66 (95% CI: 0.63–0.69) with p value of 0.0004 and 0.0303 respectively. The ROC curves were shown in Fig 1 and Fig 2.

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Fig 1. The ROC curve of different obesity indices for prediction of hypertension in males.

https://doi.org/10.1371/journal.pone.0198105.g001

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Fig 2. The ROC curve of different obesity indices for prediction of hypertension in females.

https://doi.org/10.1371/journal.pone.0198105.g002

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Table 4. The comparisons of different obesity indices in predicting hypertension risk.

https://doi.org/10.1371/journal.pone.0198105.t004

3. Interactive effects analysis

Table 5 presented the results of interactive effects analysis. In males, the adjusted OR of hypertension was the highest in high-LAP and smoking subjects (3.32, 95%CI: 1.79–6.17) as compared with low-LAP and non-smoking subjects. There was a significant interaction between LAP and smoking (RERI: 1.32, 95%CI: 0.15–2.75; AP: 0.40, 95%CI: 0.14–0.73) on risk of hypertension. When analyzing the interaction between LAP and family history of history, RERI was 1.07 (95%CI: 0.09–2.05), suggesting that there would be 1.07 relative excess risk due to the interaction. AP was 0.33 (95%CI: 0.23–0.44), indicating that 33% of hypertension exposed to both risk factors was attributable to the interaction. Moreover, SI was 1.92 (95% CI: 1.53–2.41).

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Table 5. Interactions between LAP and family history of hypertension and smoking on risk of hypertension.

https://doi.org/10.1371/journal.pone.0198105.t005

In females, the adjusted OR of hypertension was also the highest in high-LAP and smoking subjects (2.47, 95%CI: 1.08–5.67) as compared with low-LAP and non-smoking subjects. However, no interactive effect between LAP and smoking was found by all three indicators. Specifically, RERI was 0.04 (95%CI:-2.22–2.30), AP was 0.02 (95%CI: -0.89–0.92) and SI was 1.03 (95%CI: 0.22–4.84). The values of RERI (0.80, 95%CI: 0.07–1.53), AP (0.25, 95%CI: 0.11–0.39) and SI (1.59, 95%CI: 1.16–2.19) indicated a significant interaction between LAP and family history of hypertension on hypertension risk.

Discussion

In this present study, we found a significant relationship between LAP and hypertension risk in Han Chinese adults. Similar results were found in Japanese [22] and Mongolians population [23]. Furthermore, we compared the predictive value of LAP, BMI and WHtR on hypertension risk, and the results suggested that LAP was substantially better than that of BMI in both males and females, but only better than that of WHtR in females. It is probably associated with the various patterns of lipid over accumulation in both males and females with aging [35]. For women, LAP was greater at older age or remained unchanged, while for men, the annual LAP changes were reduced at older age [35]. Compared with men, hypertriglyceridemia was a stronger risk factor for cardiovascular diseases in women [36]. A cohort study suggested that LAP was a better predictor of all-cause mortality in women than men [37]. The association between LAP and diabetes risk also tended to be stronger in women than in men [38]. Therefore, LAP may be more valuable in female.

With the changes of modern lifestyle and diet patterns, the prevalence of obesity has dramatically increased in China [7]. Accumulating evidences have shown that there is a significant association of blood pressure increase with weight gain [39, 40]. The mechanisms of obesity and obesity-related hypertension are complex. Overweight and obese people can secrete more leptin, TNF-α, IL-6 and other relevant adipocytokines, which may affect endothelial cells function, renin-angiotensin system, sympathetic nervous system and inflammatory response [41, 42]. It is a remarkable fact that the function of various adipose tissues is different and complex [43, 44]. A growing number of evidences have strongly suggested that the location of fat distribution was more harmful than the total amount of fat for obese people [45, 46]. The lipolytic activity of visceral adipose tissue cells was stronger than that of subcutaneous adipose tissue cells [47]. LAP, a combination of WC and TG, was proved to be a simple and inexpensive way to assess visceral fat [13]. WC is a commonly applied obesity index to evaluate central obesity, which is proved to be associated with insulin resistance [48], all cause/cardiovascular mortality [49] and hypertension risk [50, 51]. However, WC cannot sufficiently discriminate between visceral and subcutaneous fat [52]. TG concentrations are significantly related with visceral adipose tissue. Moreover, hypertriglyceridemia was associated with an increased risk for cardiovascular diseases [53]. Rotter et al [54] studied the relationship between LAP and metabolic syndrome and its components, the results showed that LAP was significantly positively correlated with serum total cholesterol, FPG, insulin, but negatively correlated with HDL in elderly men. Therefore, the LAP that derives from WC and TG is believed to be a better predictor of hypertension risk and suitable in clinical application.

In 2000, Lemieux et al [55] introduced an index named hypertriglyceridemic waist (HTGW) that was also combined by WC and TG. The HTGW phenotype was associated with metabolic alternations and visceral fat excess [56]. 82% of individuals with HTGW phenotype had more than three cardiovascular risk factors [56]. A meta-analysis confirmed that HTGW was closely associated with increased risk of type 2 diabetes mellitus in the general population [57]. HTGW was also a better simple marker than WHtR for identifying the risk of cardiometabolic disorders [58]. A cohort study with 95015 participants in China showed that HTGW was independently associated with hypertension and cardiovascular diseases risks [59]. In comparison, HTGW is a dichotomous indicator, while LAP is developed to express a continuous risk function by gender that can better reflect the lipid accumulation and the relationship between lipid toxicity and hypertension since obesity itself is a continuous process [18]. In a cohort study, LAP, rather than HTGW, showed an association with all-cause mortality [37]. On the other hand, the cut-off values of WC and TG are controversial, and the standard of positive HTGW are not uniform. One of the advantages of LAP is that it does not arbitrarily dichotomize. According to YI index, this article showed that the cut-points of LAP were higher in men than those in women, which was coherent with previous study [37].

According to our results, family history of hypertension was significantly interacted with LAP on hypertension risk both in males and females. As hypertension is a multifactorial disease that associated with genetic and environmental factors, the interaction between genes and the environment may aggravate the risk of hypertension. A cross-sectional survey showed that the prevalence of hypertension in adults with family history of hypertension was 29.3% and 24.4% in adults without family history of hypertension [60]. Also, several studies have shown that family history of hypertension was positively associated with the risk of overweight either in children or adults [61, 62].

Our results showed that smoking was interacted with LAP on hypertension risk in males while not in females. It is the fact that the smoking rate is very low in women but very high in men especially in middle-aged and elderly adults in China [63]. In this study, the smoking rate was 61.2% in males and 6.61% in females, respectively. A cohort study reported a significant interaction between smoking with abdominal obesity on diabetes risk in Chinese adults, but not with overall obesity [64]. Similarly, Cullen et al [65] reported a non-statistically significant interaction of smoking and BMI on diabetes risk in elderly women. Previous researches have indicated that smoking represented a major health hazard, which was an important risk factor for cardiovascular diseases. The mechanisms of interactive effect between visceral obesity and smoking on hypertension risk may be explained by the elevation of blood pressure levels via inhibiting vascular reflex vasodilation and damaging vascular endothelial function [66]. It should be noted that numerous Chinese non-smokers are exposed to second environmental tobacco smoke. A published meta-analysis has concluded that the pooled prevalence of passive smoking in the community population aged 15 years and older female in China were 47.8% [67]. Moreover, exposure to secondhand smoke frequently was significantly related with hypertension among nonsmoking female in China [68, 69]. A study included 5027731 females along with their husbands in 31 provinces in China confirmed that cumulative exposure of husband smoking was significantly associated with the risk of hypertension for females [70]. Unfortunately, the information about passive smoking of female was not investigated in this survey. Future research should be carried out to detect interactions between passive smoke of females and obesity, which may further light on the etiology of hypertension.

There are several published studies exploring the application value of LAP in diseases prediction. Dai et al [11] compared the ability of different obesity indices in predicting chronic kidney disease among the rural population in Northeast China, and the results showed that LAP performed better than that of BMI, WC and WHtR. Compared with BMI, LAP was proved to be a better predictor in the incidence of cardiovascular diseases [18]. LAP levels were independently associated with all-cause, cardiovascular and congestive heart failure mortality in normal weight postmenopausal women, whereas no significant associations were found in men [71]. In polycystic ovary syndrome women, the AUC of LAP was significantly higher than BMI and WC when compared the ability in predicting impaired glucose tolerance [20]. Meanwhile, LAP had a greater impact on the homeostasis model assessment of insulin resistance (HOMA-IR) than BMI and WC by multivariate analysis [72]. Chiang et al [73] tested the accuracy of LAP in predicting metabolic syndrome in middle-aged and elderly Taiwanese adults in China, and LAP was proved to be a simple index with significantly higher predictability. The similar results were found in Iran population [74]. A population based cohort study among Iran adults suggested that LAP was an independent predictor of cardiovascular events in normal BMI subjects [12].

Several limitations in this study needed to be pointed out. Firstly, it was a cross- sectional study, which cannot infer causality of our results. Secondly, the lack of information on the use of lipid-lowering drugs may influence the results. Thirdly, the participants in this study were all middle-aged and elderly. A study in Japan found that the association between LAP and diabetes risk were influenced by age [38]. The association between LAP and hypertension risk in younger groups may need to be further explored.

Conclusion

In conclusion, it is crucial to assess visceral fat accumulation in a convenient and cheap way for the prevention of cardiovascular diseases. Our study suggests that LAP is significantly associated with hypertension risk and performed better than that of other obesity indices. As traditional assessment methods of visceral fat evaluation are not available in daily clinical application, LAP can be extensively used in epidemiological studies and some large-scale clinical trials. Moreover, this is the first study that further demonstrates interactive effects of LAP and smoking, family history of hypertension on hypertension risk.

Supporting information

S1 File. Survey questionnaire in Chinese.

https://doi.org/10.1371/journal.pone.0198105.s001

(DOCX)

S2 File. Survey questionnaire in English.

https://doi.org/10.1371/journal.pone.0198105.s002

(DOCX)

References

  1. 1. Campbell NR, Lackland DT, Niebylski ML, World Hypertension League Committee, International Society of Hypertension Executive Committee. High blood pressure: why prevention and control are urgent and important: a 2014 fact sheet from the World Hypertension League and the International Society of Hypertension [J]. J Clin Hypertens (Greenwich).2014, 16(8):551–3.
  2. 2. Li Y, Yang L, Wang L, Zhang M, Huang Z, Deng Q. Burden of hypertension in China: A nationally representative survey of 174,621 adults [J]. Int J Cardiol. 2016, 227:516–23. pmid:27856040
  3. 3. Bundy JD, He J. Hypertension and Related Cardiovascular Disease Burden in China [J]. Ann Glob Health. 2016, 82(2):227–33. pmid:27372527
  4. 4. Lewington S, Lacey B, Clarke R, Guo Y, Kong XL, Yang L, et al. The Burden of Hypertension and Associated Risk for Cardiovascular Mortality in China [J]. JAMA Intern Med. 2016,176 (4): 524–32. pmid:26975032
  5. 5. Yatsuya H, Li Y, Hilawe EH, Ota A, Wang C, Chiang C, et al. Global trend in overweight and obesity and its association with cardiovascular disease incidence [J]. Circ J. 2014, 78(12):2807–18. pmid:25391910
  6. 6. Seravalle G, Grassi G. Obesity and hypertension [J]. Pharmacol Res. 2017, 122: 1–7. pmid:28532816
  7. 7. Wu J, Xu H, He X, Yuan Y, Wang C, Sun J, et al. Six-year changes in the prevalence of obesity and obesity- related diseases in Northeastern China from 2007 to 2013 [J]. Sci Rep.2017, 7:41518.
  8. 8. Carmienke S, Freitag MH, Pischon T, Schlattmann P, Fankhaenel T, Goebel H, et al. General and abdominal obesity parameters and their combination in relation to mortality: a systematic review and meta-regression analysis [J]. Eur J Clin Nutr. 2013, 67(6):573–85. pmid:23511854
  9. 9. Mi SQ, Yin P, Hu N, Li JH, Chen XR, Chen B, et al. BMI, WC, WHtR, VFI and BFI: which indictor is the most efficient screening index on type 2 diabetes in Chinese community population [J]. Biomed Environ Sci. 2013, 26(6):485–91. pmid:23816582
  10. 10. Caminha TC, Ferreira HS, Costa NS, Nakano RP, Carvalho RE, Xavier AF Jr, et al. Waist-to-height ratio is the best anthropometric predictor of hypertension: A population-based study with women from a state of northeast of Brazil [J]. Medicine (Baltimore). 2017, 96(2):e5874.
  11. 11. Dai D, Chang Y, Chen Y, Chen S, Yu S, Guo X et al. Visceral Adiposity Index and Lipid Accumulation Product Index: Two Alternate Body Indices to Identify Chronic Kidney Disease among the Rural Population in Northeast China [J]. Int J Environ Res Public Health. 2016, 13(12). pii:E1231.
  12. 12. Hosseinpanah F, Barzin M, Mirbolouk M, Abtahi H, Cheraghi L, Azizi F. Lipid accumulation product and incident cardiovascular events in a normal weight population: Tehran Lipid and Study [J]. Eur J Prev Cardiol. 2016.23 (2):187–93. pmid:25381336
  13. 13. Sandeep S, Gokulakrishnan K, Velmurugan K, Deepa M, Mohan V. Visceral & subcutaneous abdominal fat in relation to insulin resistance & metabolicsyndrome in non-diabetic south Indians [J]. Indian J Med Res. 2010, 131:629–35. pmid:20516533
  14. 14. Schlecht I, Gronwald W, Behrens G, Baumeister SE, Hertel J, Hochrein J, et al. Visceral adipose tissue but not subcutaneous adipose tissue is associated with urine and serum metabolites [J]. PLoS One. 2017, 12(4):e0175133. pmid:28403191
  15. 15. Giacchetti G, Faloia E, Mariniello B, Sardu C, Gatti C, Camilloni MA, et al. Overexpression of the renin- angiotensin system in human visceral adipose tissue in normal and overweight subjects [J]. Am J Hypertens. 2002, 15(5):381–8. pmid:12022238
  16. 16. Atzmon G, Yang XM, Muzumdar R, Ma XH,Gabriely I, Barzilai N. Differential gene expression between visceral and subcutaneous fat depots [J]. Horm Metab Res. 2002, 34:622–8. pmid:12660871
  17. 17. Alvarez GE, Beske SD, Ballard TP, Davy KP. Sympathetic neural activation in visceral obesity [J]. Circulation. 2002, 106:2533–6. pmid:12427647
  18. 18. Kahn HS. The "lipid accumulation product" performs better than the body mass index for recognizing cardiovascular risk: a population-based comparison [J]. BMC Cardiovasc Disord. 2005, 5:26. pmid:16150143
  19. 19. Xia C, Li R, Zhang S, Gong L, Ren W, Wang Z, et al. Lipid accumulation product is a powerful index for recognizing insulin resistance in non-diabetic individuals [J]. Eur J Clin Nutr.2012 66(9):1035–8. pmid:22781025
  20. 20. Wehr E, Gruber HJ, Giuliani A, Moller R, Pieber TR, Obermayer-Pietsch B. The lipid accumulation product is associated with impaired glucose tolerance in PCOS women [J]. J Clin Endocrinol Metab. 2011 96(6):E986–90. pmid:21470992
  21. 21. Kahn HS. The lipid accumulation product is better than BMI for identifying diabetes: a population-based comparison [J].Diabetes Care.2006, 29(1):151–3 pmid:16373916
  22. 22. Wakabayashi I. Associations of blood lipid-related indices with blood pressure and pulse pressure in middle-aged men [J]. Metab Syndr Relat Disord. 2015, 13 (1): 22–8. pmid:25321638
  23. 23. Gao X, Wang G, Wang A, Xu T, Tong W, Zhang Y. Comparison of lipid accumulation product with body mass index as an indicator of hypertension risk among Mongolians in China [J]. Obes Res Clin Pract. 2013, 7(4):e308–14 pmid:24306160
  24. 24. Nazare JA, Smith JD, Borel AL, Haffner SM, Balkau B, Ross R, et al. Ethnic influences on the relations between abdominal subcutaneous and visceral adiposity, liver fat, and cardiometabolic risk profile: the International Study of Prediction of IntraAbdominal Adiposity and Its Relationship with Cardiometabolic Risk/ IntraAbdominal Adiposity [J]. Am J Clin Nutr. 2012, 96:714–26. pmid:22932278
  25. 25. Virdis A, Giannarelli C, Neves MF, Taddei S, Ghiadoni L. Cigarette smoking and hypertension [J]. Curr Pharm Des. 2010, 16(23):2518–25. pmid:20550499
  26. 26. Igarashi R, Fujihara K, Heianza Y, Ishizawa M, Kodama S, Saito K, et al. Impact of individual components and their combinations within a family history of hypertension on the incidence of hypertension: Toranomon hospital health management center study 22 [J]. Medicine (Baltimore). 2016, 95(38):e4564.
  27. 27. Chobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, Izzo JL Jr, et al. The seventh report of the joint national committee on prevention, detection, evaluation, and treatment of high blood pressure: the JNC 7 report [J]. JAMA.2003, 289: 2560–72. pmid:12748199
  28. 28. Alberti KG, Zimmet PZ. Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation [J]. Diabet Med. 1998, 15:539–53. pmid:9686693
  29. 29. Joint Committee for Developing Chinese guidelines on Prevention and Treatment of Dyslipidemia in Adults. Chinese guidelines on prevention and treatment of dyslipidemia in adults [J]. Zhonghua Xin Xue Guan Bing Za Zhi. 2007, 35(5):390–419. (In Chinese) pmid:17711682
  30. 30. Writing Group of 2010 Chinese Guidelines for the Management of Hypertension. 2010 Chinese guidelines for the management of hypertension [J]. Chine J Cardiology 2011, 39:579–616. (In Chinese)
  31. 31. Chen C, Lu FC, Department of Disease Control Ministry of Health, PR China. The guidelines for prevention and control of overweight and obesity in Chinese adults [J]. Biomed EnvironSci. 2004, 17 Suppl:1–36.
  32. 32. Browning LM, Hsieh SD, Ashwell M. A systematic review of waist to-height ratio as a screening tool for the prediction of cardiovascular disease and diabetes: 0.5 could be a suitable global boundary value [J]. Nutr Res Rev.2010,23(2):247–69. pmid:20819243
  33. 33. Andersson T, Alfredsson L, Kallberg H, Zdravkovic S, Ahlbom A. Calculating measures of biological interaction [J]. Eur J Epidemiol. 2005, 20:575–9. pmid:16119429
  34. 34. Knol MJ, VanderWeele TJ, Groenwold RH, Klungel OH, Rovers MM, Grobbee DE. Estimating measures of interaction on an additive scale for preventive exposures [J]. Eur J Epidemiol. 2011, 26 (6): 433–38. pmid:21344323
  35. 35. Kahn HS, Cheng YJ. Longitudinal changes in BMI and in an index estimating excess lipids among white and black adults in the United States [J]. Int J Obes (Lond) 2008, 32:136–43.
  36. 36. Hokanson JE, Austin MA. Plasma triglyceride level is a risk factor for cardiovascular disease independent of high-density lipoprotein cholesterol level: a meta-analysis of population-based prospective studies [J]. J Cardiovasc Risk. 1996. 3: 213–19. pmid:8836866
  37. 37. Ioachimescu AG, Brennan DM, Hoar BM, Hoogwerf BJ. The Lipid Accumulation Product and All-cause Mortality in Patients at High Cardiovascular Risk: A PreCIS Database Study [J]. Obesity (Silver Spring). 2010, 18(9):1836–44.
  38. 38. Wakabayashi I. Influence of age and gender on lipid accumulation product and its relation to diabetes mellitus in Japanese [J]. Clin Chim Acta. 2014, 431:221–6. pmid:24530297
  39. 39. Zhou L, Li Y, Guo M, Wu Y, Zhao L. Relations of body weight status in early adulthood and weight changes until middle age with hypertension in the Chinese population [J]. Hypertens Res. 2016, 39(12):913–8. pmid:27357058
  40. 40. Ren Q, Su C, Wang H, Wang Z, Du W, Zhang B. Change in Body Mass Index and Its Impact on Incidence of Hypertension in 18-65-Year-Old Chinese Adults[J]. Int J Environ Res Public Health.2016, 13(3). pii: E257.
  41. 41. Seravalle G, Grassi G. Obesity and hypertension [J]. Pharmacol Res. 2017, 122:1–7. pmid:28532816
  42. 42. Seravalle G, Grassi G. Sympathetic Nervous System, Hypertension, Obesity and Metabolic Syndrome [J]. High Blood Press Cardiovasc Prev. 2016, 23(3):175–9. pmid:26942609
  43. 43. Frayn KN, Karpo F, Fielding BA, Macdonald IA, Coppack SW. Integrative physiology of human adipose tissue [J]. Int J Obes Relat Metab Disord. 2003, 27:875–88. pmid:12861227
  44. 44. Karelis AD, St Pierre DH, Conus F, Rabasa-Lhoret R, Poehlman ET. Metabolic and body composition factors in subgroups of obesity: what do we know? [J]. J Clin Endoerinol Metab.2004, 89: 2569–75.
  45. 45. Li Y, Bujo H, Takahashi K, Shibasaki M, Zhu Y, Yoshida Y, et al. Visceral fat: higher responsiveness of fat mass and gene expression to calorie restriction thansubcutaneous fat[J]. Exp Biol Med (Maywood).2003, 228(10):1118–23.
  46. 46. Hoffstedt J, Arner E, Wahrenberg H, Andersson DP, Qvisth V, Lofgren P, et al. Regional impact of adipose tissue morphology on the metabolic profile in morbid obesity[J]. Diabetologia.2010, 53 (12): 2496–2503. pmid:20830466
  47. 47. Lima-Martinez MM, Blandenier C, Iacobellis G. Epicardial adipose tissue: more than a simple fat deposit?[J]. Endocrinol Nutr. 2013, 60(6):320–28. pmid:23117053
  48. 48. Wolfgram PM, Connor EL, Rehm JL, Eickhoff JC, Zha W, Reeder SB, et al. In Nonobese Girls, Waist Circumference as a Predictor of Insulin Resistance Is Comparable to MRI Fat Measures and Superior to BMI [J]. Horm Res Paediatr. 2015, 84(4):258–65. pmid:26352642
  49. 49. David CN, Mello RB, Bruscato NM, Moriguchi EH. Overweight and Abdominal Obesity Association with All-Cause and Cardiovascular Mortality in the Elderly Aged 80 and Over: A Cohort Study [J]. J Nutr Health Aging. 2017.21(5):597–603. pmid:28448093
  50. 50. Nurdiantami Y, Watanabe K, Tanaka E, Pradono J, Anme T, et al. Association of general and central obesity with hypertension [J]. Clin Nutr. 2017. pii:S0261- 5614(17):30173–5.
  51. 51. Sun H, Zheng M, Wu S, Chen M, Cai J, Yang X. Waist circumference and incidence of hypertension in Chinese adults: Observations from the Kailuan Study[J]. Herz. 2016.
  52. 52. Du T, Sun X, Huo R, Yu X. Visceral adiposity index, hypertriglyceridemic waist and risk of diabetes: the China Health and Nutrition Survey 2009 [J]. Int J Obes (Lond). 2014.38(6):840–7.
  53. 53. Kim EH, Lee JB, Kim SH, Jo MW, Hwang JY, Bae SJ, et al. Serum Triglyceride Levels and Cardiovascular Disease Events in Koreans [J]. Cardiology. 2015, 131 (4): 228–35. pmid:25968991
  54. 54. Rotter I, Ryl A, Szylinska A, Pawlukowska W 1, Lubkowska A, Laszczynska M. Lipid Accumulation Product (LAP) as an Index of Metabolic and Hormonal Disorders in Aging Men[J].Exp Clin Endocrinol Diabetes. 2017.125(3):176–82. pmid:27898988
  55. 55. Lemieux I, Pascot A, Couillard C, Lamarche B, Tchernof A, Almeras N, et al. Hypertriglyceridemic waist: A marker of the atherogenic metabolic triad (hyperinsulinemia; hyperapolipoprotein B; small, dense LDL) in men? [J]. Circulation. 2000, 102(2):179–84. pmid:10889128
  56. 56. Cunha de Oliveira C, Carneiro Roriz AK, Eickemberg M, Barreto Medeiros JM, Barbosa Ramos L. Hypertriglyceridemic waist phenotype: association with metabolic disorders and visceral fat in adults [J]. Nutr Hosp. 2014, 30(1):25–31. pmid:25137258
  57. 57. Ren Y, Luo X, Wang C, Yin L, Pang C, Feng T, et al. Prevalence of hypertriglyceridemic waist and association with risk of type 2 diabetes mellitus: a meta-analysis [J]. Diabetes Metab Res Rev. 2016, 32(4):405–12. pmid:26417844
  58. 58. Bailey DP, Savory LA, Denton SJ, Davies BR, Kerr CJ. The hypertriglyceridemic waist, waist-to-height ratio, and cardiometabolic risk [J]. J Pediatr.2013, 162 (4):746–52 pmid:23140880
  59. 59. Wang A, Li Z, Zhou Y, Wang C, Luo Y, Liu X, et al. Hypertriglyceridemic waist phenotype and risk of cardiovascular diseases in China: results from the Kailuan Study [J]. Int J Cardiol. 2014, 174(1):106–9 pmid:24745860
  60. 60. Ranasinghe P, Cooray DN, Jayawardena R, Katulanda P. The influence of family history of Hypertension on disease prevalence and associated metabolic risk factors among Sri Lankan adults [J]. BMC Public Health. 2015.15:576. pmid:26092387
  61. 61. Liu J, Sekine M, Tatsuse T, Hamanishi S, Fujimura Y, Zheng X. Family history of hypertension and the risk of overweight in Japanese children: results from the Toyama Birth Cohort Study [J]. J Epidemiol. 2014, 24(4):304–11. pmid:24857956
  62. 62. Ranasinghe P, Cooray DN, Jayawardena R, Katulanda P. The influence of family history of hypertension on disease prevalence and associated metabolic risk factors among Sri Lankan adults [J]. BMC Public Health. 2015,15:576. pmid:26092387
  63. 63. Yang GH, Li Q, Wang CX, Hsia J, Yang Y, Xiao L, et al. Findings from 2010 Global Adult Tobacco Survey: implementation of MPOWER policy in China [J]. Biomed Environ Sci. 2010, 23:422–9.
  64. 64. Luo W, Guo Z, Wu M, Hao C, Zhou Z, Yao X, et al. Interaction of smoking and obesity on type 2 diabetes risk in a Chinese cohort [J]. Physiol Behav. 2015, 139: 240–3. pmid:25449404
  65. 65. Cullen MW, Ebbert JO, Vierkant RA, Wang AH, Cerhan JR. No interaction of body mass index and smoking on diabetes mellitus risk in elderly women [J]. Prev Med. 2009,48(1):74–8. pmid:19000710
  66. 66. Talukder MAH, Johnson WM, Varadharaj S, Lian J, Kearns PN, El-Mahdy MA, et al. Chronic cigarette smoking causes hypertension, increased oxidative stress, impaired NO bioavailability, endothelial dysfunction, and cardiac remodeling in mice [J]. Am J Physiol Heart Circ Physiol. 2011, 300(1):388–96
  67. 67. Zeng J, Yang S, Wu L, Wang J, Wang Y, Liu M, et al. Prevalence of passive smoking in the community population aged 15 years and older in China: a systematic review and meta-analysis [J]. BMJ Open. 2016,6:e009847. pmid:27059465
  68. 68. Li N, Li Z, Chen S, Yang N, Ren A, Ye R. Effects of passive smoking on hypertension in rural Chinese nonsmoking women[J]. J Hypertens. 2015,33(11): 2210–4. pmid:26259123
  69. 69. Wu L, Yang S, He Y, Liu M, Wang Y, Wang J, et al. Association between passive smoking and hypertension in Chinese non-smoking elderly women[J]. Hypertens Res. 2017,40(4):399–404. pmid:27928149
  70. 70. Yang Y, Liu F, Wang L, Li Q, Wang X, Chen JC, et al. Association of Husband Smoking With Wife’s Hypertension Status in Over 5 Million Chinese Females Aged 20 to 49 Years [J].J Am Heart Assoc. 2017,6(3). pii: e004924.
  71. 71. Wehr E, Pilz S, Boehm BO, Marz W, Obermayer-Pietsch B. The Lipid Accumulation Product Is Associated With Increased Mortality in Normal Weight Postmenopausal Women [J]. Obesity (Silver Spring). 2011, 19(9):1873–80.
  72. 72. Xia C, Li R, Zhang S, Gong L, Ren W, Wang Z, et al. Lipid accumulation product is a powerful index for recognizing insulin resistance in non-diabetic individuals [J]. Eur J Clin Nutr. 2012.66(9):1035–8. pmid:22781025
  73. 73. Chiang JK, Koo M. Lipid accumulation product: a simple and accurate index for predicting metabolic syndrome in Taiwanese people aged 50 and over [J].BMC Cardiovasc Disord. 2012, 24(12):78.
  74. 74. Motamed N, Razmjou S, Hemmasi G, Maadi , Zamani F. Lipid accumulation product and metabolic syndrome: a population-based study in northern Iran, Amol [J]. J Endocrinol Invest. 2016, 39(4):375–82. pmid:26319991