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Relative children’s lipid accumulation with hypertension in Chinese children and adolescents

BMC Public Health volume 21, Article number: 1884 (2021) Cite this article

Abstract

Background

This study aimed to develop a novel indicator associated with hypertension in Chinese children and adolescents, the relative children’s lipid accumulation product (RCLAP).

Methods

A cross-sectional study was conducted in 2018. A total of 683 students aged 8–15 years were recruited via a stratified cluster sampling Methods. Anthropometric indexes (waist circumference (WC), Body mass index (BMI), Waist-height ratio (WHtR), logarithm children LAP (LnCLAP), RCLAP per height (RCLAP-H)) were standardized using a z-score method (standardized variables: SWC, SBMI, SWHtR, SLnCLAP, SRCLAP-H). A logistic regression model was performed to evaluate the association of the above indicators with the outcome of hypertension.

Results

The overall prevalence of hypertension was 5.7% (5.5% in boys, 6.0% in girls). SWC ≥ P75, SBMI ≥ P75, SWHtR ≥ P75, SlnCLAP ≥ P75 and SRCLAP-H ≥ P75 significantly increased risk of hypertension, with odds ratios (OR) of 2.21 (95% confidence interval (CI): 1.13, 4.30), 2.30 (1.18, 4.49), 2.64 (1.35, 5.14), 4.43 (2.28, 8.61), and 4.49 (2.31, 8.71), respectively.

Conclusion

RCLAP is a novel indicator associated with hypertension in Chinese children and adolescents, and it performs better than WC, BMI, WHtR and children LAP.

Peer Review reports

Introduction

Hypertension is the most common risk factor for cardiovascular disease (CVD) morbidity and mortality, and it has been found to be seriously harmful to human health worldwide [1, 2]. Childhood hypertension was once considered a rare disease, but it is now a major public health problem worldwide [3,4,5,6]. A meta-analysis of 25 studies showed that the prevalence of hypertension among 943,128 17-year-olds increased steadily from 6.3% in 1995 to 19.2% in 2014 [7]. The Health and Nutrition Survey of Chinese residents reported that the prevalence of childhood hypertension increased significantly from 7.6% in 1993 to 13.8% in 2009 [8].

There is evidence that the root causes of hypertension can be traced back to childhood [9]. Childhood hypertension not only increases the prevalence of adulthood hypertension but also increases the morbidity and mortality from cardiovascular disease [9,10,11,12,13]. The occurrence of hypertension in children and adolescents is asymptomatic, and it is easy to ignore. Therefore, the early detection of elevated blood pressure in children is an important measure to control and prevent adulthood hypertension.

It is well known that many chronic diseases, such as CVD, diabetes and hypertension, are associated with increased body fat and lipid accumulation [14]. Traditionally, the body mass index (BMI), waist-to-height ratio (WHtR) and triglycerides (TG) are the most commonly used indicators to evaluate overall obesity and abdominal obesity in children [15]. However, BMI can only reflect the degree of overweight, but not the fat distribution of the individual. Abdominal fat is more closely related to hypertension than BMI [16, 17]. Although the waist circumference (WC) and WHtR can accurately reflect the degree of abdominal obesity, they cannot distinguish between subcutaneous fat and visceral fat. In addition, a prospective study reported a significant relationship between TG and hypertension [18], but triglycerides were limited to showing adipose tissue accumulation.

In 2005, Kahn et al. [19] put forward the concept of the lipid accumulation product (LAP) for the first time and combined the WC and TG to estimate excessive lipid accumulation in adults. It was found that association between LAP and diabetes was stronger than that between BMI and diabetes in adults [19]. Increasing numbers of studies have shown that LAP has a stronger association with hypertension than other obesity indicators in adults [20,21,22].

Due to their characteristics of rapid growth and development, LAP cannot be directly used to reflect the lipid accumulation of children and adolescents. Our previous study developed a new indicator, the children lipid accumulation product (children LAP), and confirmed that children LAP was significantly associated with metabolic syndrome and hypertension in children [23, 24]. The children LAP is the product of WC, TG and abdominal skinfold thickness (AST) (children LAP = WC (cm) × AST (mm) × TG (mmol/L) / 100). However, children LAP is limited to reflecting lipid accumulation per unit density of the body. The relative children LAP per unit body height may better reflect the density of lipid accumulation among children and adolescents. This study aimed to evaluate the association of relative children’s lipid accumulation product (RCLAP) with hypertension in Chinese children and adolescents.

Methods

Study participants

In this study, a cross-sectional survey among students aged 8–15 years from two nine-year-system schools in Bengbu was conducted in 2018. A total of 683 students (366 boys (53.6%) and 317 girls (46.4%)) were selected by stratified cluster sampling. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The present study was approved by the Medical Ethics Committee of Bengbu Medical College [2015 No. 003] and informed consent was taken from all the participants (or their parents) before the survey.

Data collection and measurements

Anthropometric indices

The medical staff who received standardized training measured the participants’ body weight, height, sitting height (SH), WC, and AST [23]. The participants were required to have an empty stomach, wear light clothes, and be barefoot when measured. Body height and SH were measured using a mechanical height gauge with an accuracy of 0.1 cm. Weight was measured using an electronic weight scale with an accuracy of 0.1 kg. The WC that was the perimeter of the WC at 1 cm above the belly button was measured using nylon tape with an accuracy of 0.1 cm. The AST, which was the skinfold thickness at the junction of the right collarbone midline and belly button horizontal line, was measured using a skinfold thickness gauge with an accuracy of 0.1 mm.

Dietary behaviors and physical activity indices

A self-administered food frequency questionnaire was used to investigate dietary behaviors, including breakfast frequency, eggs, milk, fresh vegetables, fruits, nuts, eating out, fried food, carbonated drinks, western fast food, high-energy snacks [23, 24]. The scores of each dietary behavior were as follows: 7 points (7 times / week), 5 points (4 times / week), 2 points (1 time / week), 0.5 points (2 times / month), 0.25 points (1 time / month) and 0 points (never). Eating behaviors were divided into healthy and unhealthy dietary behaviors (healthy dietary behaviors including breakfast, eggs, milk, fresh vegetables, fruits, nuts; unhealthy dietary behaviors including eating out, fried foods, carbonated drinks, Western fast food, high-energy snacks). The total scores of healthy eating behaviors and unhealthy eating behaviors were calculated. Cronbach’s alpha coefficients of healthy dietary behaviors and unhealthy dietary behaviors scores were 0.705 and 0.700, respectively. According to the 75th percentile (P75) of participants with healthy eating behaviors were divided into ≥ P75 group and < P75 group. According to the 75th percentile (P75) of participants with unhealthy eating behaviors were divided into ≥ P75 group and < P75 group. The Chinese Children’s Leisure Activities Study Survey questionnaire (CLASS-C) [25] was used to investigate the time of moderate to severe physical activity and sedentary activity. The time of moderate or strenuous exercise was divided into two grades: ≥ 60 min and < 60 min [26]. The time of sedentary activity was divided into two levels: ≥ 120 min and < 120 min [27].

Blood lipid measurements

Three milliliters of fasting venous blood were collected from each student at the physical examination center of a grade III, class A hospital. The collected blood samples were centrifuged within 2 h and TG (mmol/L) was detected by an enzymatic method using an automated biochemical analyzer.

Blood pressure measurement

After having a rest for at least 10 min, appropriate cuffs were selected according to age and arm circumference of the subjects, and the systolic blood pressure (SBP) and diastolic blood pressure (DBP) on the right arm were measured using a mercury sphygmomanometer. SBP was defined as the onset of the Korotkoff sound (K1), and DBP was defined as the fifth Korotkoff sound (K5). The blood pressure was measured at an interval of 2 min, and the average of the two measurements was recorded as the final blood pressure value.

Definition of hypertension

In this study, hypertension was diagnosed according to “Reference of screening for elevated blood pressure among Chinese children and adolescents aged 7–18 years” (Health industry standard of the People’s Republic of China: WS/T 610–2018) [28].

Calculation of derivative indicators

BMI = weight (kg) / height2 (m2); WHtR = WC (cm) / height (cm); Children LAP = WC (cm) × AST (mm) × TG (mmol/L) / 100. Relative children’s lipid accumulation product per height (RCLAP-H) = WC (cm) × AST (mm) × TG (mmol/L) / height (cm).

Statistical analysis

The sample size was determined according to the cross-sectional design of the current situation. The calculation formula was \( n=\frac{\mu_{\alpha /2}^2p\left(1-p\right)}{\delta^2} \) (where α = 0.05, δ = 0.03, and p is the prevalence of hypertension). A study showed that the prevalence of hypertension in children aged 6–13 years in China was 18.4% [29]. According to the formula of sample size, the sample size should be 641(641 = 1.96 × 1.96 × 0.184 × (1–0.184)/0.03/0.03), however, the 5% of sample size needed to be increased for sampling error. Therefore, the minimum sample size was 673 (673 = 641 × (1 + 1.05)). In fact, the effective sample size was 683. SPSS 23.0 software was used for statistical analysis. The measurement and counting data are described by average with 95% confidence interval (CI) and ratio or proportion, respectively. Logarithm Children LAP (LnCLAP), RCLAP-H, height, weight, SH, WC, WHtR, BMI, AST and TG were standardized by sex and age using z-scores (standardized variables: SLnCLAP, SRCLAP-H, Sheight, Sweight, SSH, SWC, SWHtR, SBMI, SAST and STG). T-tests was used to compare the differences in above standardized indices between children with hypertension and non-hypertension. Chi-square tests were used to analyze associations between above standardized indices and hypertension. Furthermore, after adjusting for moderate-to-vigorous physical activity time, sedentary activity time, healthy dietary behaviors and risky dietary behaviors, the logistic regression models were used to analyse the relations between anthropometric indexes and hypertension.

Results

The overall prevalence of hypertension was 5.7% (5.5% for boys and 6.0% for girls). From Table 1, we see that SSH, Sweight, SWC, SAST, STG, SBMI, SWHtR, SlnCLAP and SRCLAP-H were significantly lower in non-hypertensive children than they were in hypertensive children (P < 0.05). The mean difference in standardized units of two groups with 95%CI of Sheight, SSH, Sweight, SWC, SAST, STG, SBMI, SWHtR, SlnCLAP and SRCLAP-H were − 0.17 (− 0.49, 0.15), − 0.33 (− 0.65, − 0.01), − 0.32 (− 0.64, − 0.01), − 0.59 (− 0.98, − 0.2), − 0.69 (− 1.00, − 0.37), − 0.43 (− 0.85, − 0.02), − 0.32 (− 0.64, 0.00), − 0.59 (− 1.02, − 0.15), − 0.64 (− 0.96, − 0.33) and − 0.83 (− 1.29, − 0.37), respectively.

Table 1 Differences in standardized anthropometric indexes in children with hypertension and non-hypertension. (n = 683)
Full size table

As shown in Table 2. The moderate-to-vigorous physical activity time, sedentary activity time, healthy dietary behaviors and unhealthy dietary behaviors were not significantly related to hypertension in these children (P > 0.05). There was no significant difference in Sheight and SSH between hypertensive and non-hypertensive children (P > 0.05). The results of chi-square test showed that Sweight, SWC, SAST, STG, SBMI, SWHtR, SlnCLAP and SRCLAP-H were significantly associated with hypertension in sample children (P < 0.05).

Table 2 Standardized differences in anthropmetric indexes, dietary behaviors and physical activity in children with hypertension and non-hypertension. (n = 683)
Full size table

As shown in Table 3, Logistic models of the relationship between anthropometric indices and hypertension were setted up. After adjusting for moderate-to-vigorous physical activity time, sedentary activity time, healthy dietary behaviors and risky dietary behaviors, then factors Sweight, SWC, SAST, STG, SBMI, SWHtR, SlnCLAP and SRCLAP-H were separately entered into the logistic regression models as covariates. The results of the logistic regression models showed that the z-sores ≥P75 groups of Sweight, SWC, SAST, STG, SBMI, SWHtR, SlnCLAP and SRCLAP-H were significantly associated with an increased risk of hypertension in children (P < 0.05), with odds ratios (OR) of 1.99 (95%CI: 1.02, 3.91), 2.21 (1.13, 4.30), 3.61 (1.86, 6.99), 2.43 (1.25, 4.71), 2.30 (1.18, 4.49), 2.64 (1.35, 5.14), 4.43 (2.28, 8.61), and 4.49 (2.31, 8.71), respectively. In addition, Sweight, SWC, SAST, STG, SBMI, SWHtR, SlnCLAP and SRCLAP-H as independent variables were put into logistic model, after adjusting for moderate-to-vigorous physical activity time, sedentary activity time, healthy dietary behaviors and risky dietary behaviors, the result of stepwise logistic model (forward conditional method) showed only SRCLAP-H ≥ P75 significantly increased risk of hypertension (OR (95%CI) = 4.49 (2.31, 8.71)).

Table 3 The associations between different groups of anthropometric indexes and hypertension using logistic regressions
Full size table

Discussion

The prevalence of childhood hypertension has been increasing worldwide [30]. The current research showed that the overall prevalence of childhood hypertension was 5.7% (5.5% for boys and 6.0% for girls), which was generally lower than that reported at the national level. According to a national survey, the prevalence of hypertension among 35,657 Chinese teenagers aged 17 years old was 12.4% (12.4% in boys and 12.3% in girls) [31]. Reports from the Chinese National Survey on Students’ Constitution and Health (CNSSCH) showed that the prevalence of elevated blood pressure among children in 2010 was 16.1% for boys and 12.9% for girls [32]. The reasons for the discrepancies may be regional and population differences in children’s blood pressure distribution; alternatively, the diagnostic criteria, children’s ages, and sample size in this study were different from other studies. A meta-analysis also found that the overall prevalence of hypertension among preschool children in China was only 5% (95% CI: 4%, 9%) [33]. In addition, this study found that there were no significant differences in moderate to strenuous physical activity time, sedentary activity time, healthy eating behaviors and unhealthy eating behaviors between children with hypertension and non-hypertensive children. Qu et al. [34] reported the consistent findings that physical activity intensity and dietary behavior management had nothing to do with the occurrence of hypertension in children. However, some studies also reported that an increase of physical activity intensity and time decreased the incidence of hypertension and that an unhealthy diet increased the risk of hypertension [35,36,37].

There is growing evidence that an increase of hypertension in children is closely related to weight gain [38]. BMI is an internationally recognized index for screening obesity. Previous studies showed that BMI was also associated with hypertension in children and adolescents [39,40,41]. In this study, it was found that the body weight and BMI in hypertensive children were significantly higher than those in non-hypertensive children. Although BMI provides a simple and convenient measure of obesity, it does not measure the distribution of body fat [42]. The mechanism of childhood obesity leading to hypertension is very complex [43]. It is worth noting that the functions of different adipose tissues are very different [44]. Some studies have pointed out that abdominal fat is more closely related to hypertension in children than BMI [45].

WC and WHtR can accurately reflect the degree of abdominal obesity. One study showed that an increase of WC in children with a normal weight was associated with an increase in blood pressure [46]. A study by Cruz et al. [47] showed that waistline was a good predictor of elevated blood pressure in children aged 8 to 10 years old. This study also found that the WC in hypertensive children was significantly higher than that in non-hypertensive children, and SWC ≥ P75 significantly increased the risk of hypertension (OR (95% CI): 2.21 (1.13, 4.30). Lo K et al. [48] reported that the WC was more closely associated with cardiac metabolism and hypertension in children compared with BMI, WC, and WHtR. Some researchers believe that WHtR is an important indicator of obesity and is more likely to predict cardiovascular risk than BMI [49]. In this study, we also found that there was a significant correlation between WHtR and hypertension in children, with an OR (95% CI) of 2.64 (1.35, 5.14). Previous studies [23, 24] reported that children LAP was significant associated with hypertension or metabolic syndrome in Chinese children and adolescents as compared with BMI and WHtR.

This study found that BMI, WC, WHtR, and children LAP were significantly associated with hypertension in children, which is consistent with previous studies. However, BMI is limited to distinguishing the ratio of body fat to muscle tissue and not the distribution of body fat [19]. WC and WHtR can only show the accumulation of fat in the blood circulation [50]. Children LAP only reflects the accumulation of adipose tissue and lipids in the blood circulation, while neglecting the accumulation of lipids per unit density of the human body.

The present study showed that SRCLAP-H in hypertensive children was significantly higher than it was in non-hypertensive children; SRCLAP-H ≥ P75 was significantly associated with an increased risk of hypertension (OR (95% CI) was 4.49 (2.31, 8.71), which were higher than those of WC, BMI, WHtR, and children LAP, respectively). Therefore, compared with WC, BMI, WHtR, and children LAP, RCLAP-H was more strongly associated with hypertension on the basis of the same critical values. In addition, after adjusting for physical activity time and dietary behavior factors, the stepwise logistic model (forward conditional method) showed SRCLAP-H significantly increased risk of hypertension, while SWC, SBMI, SWHtR and SlnCLAP did not, which means RCLAP-H may be more strongly associated with hypertension than other indicators. We speculate that RCLAP may be more suitable for differential diagnosis of hypertension in children and adolescents. RCLAP-H reflects the accumulation of lipids per unit height. Children with the same children LAP but with a shorter height may have a higher risk of developing high blood pressure.

However, this study has some limitations. First, this was a cross-sectional study. Therefore, the causal relationship between RCLAP and hypertension cannot be inferred. Second, there is no biological or rationale for the calculation of RCLAP. In addition, this study used a self-administered food frequency questionnaire which has not been validated elsewhere. Blood pressure was measured only twice in this study, which could generate misclassification bias. 3 measurements and taking the average of the last 2 measurements would be ideal. Finally, we only studied Chinese children and adolescents, and the generalizability of our findings to other countries is limited and needs additional data for confirmation.

Conclusion

In conclusion, it is crucial to assess visceral fat accumulation in a convenient and cheap way for the prevention of early CVD in children and adolescents. Compared with traditional obesity indicators such as BMI, WC and WHtR, RCLAP-H can better reflect the density of lipid accumulation per unit height among children and adolescents, rather than simple high body weight or abdominal fat thickness. Our study suggests that the relative children’s lipid accumulation product (RCLAP-H) is associated with hypertension. As traditional assessment methods of visceral fat evaluation are not available in daily clinical application, RCLAP can be extensively used in epidemiological studies and some large-scale clinical trials. Due to the sample size in our study is limited, RCLAP also requires extensive population data screening to determine criteria for wider guidance in clinical practice.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

SH:

Standardized height

SSH:

Standardized sitting height

Sweight:

Standardized weight

WC:

Waist circumference

SWC:

Standardized waist circumference

AST:

Abdominal skinfold thickness

SAST:

Standardized abdominal skinfold thickness

TG:

Triglycerides

STG:

Standardized triglycerides

BMI:

Body mass index

SBMI:

Standardized body mass index

WHtR:

Waist-height ratio

SWHtR:

Standardized Waist-height ratio

CVD:

Cardiovascular disease

SBP:

Systolic blood pressure

DBP:

Diastolic blood pressure

LAP:

Lipid accumulation product

Children LAP:

Children’s lipid accumulation product

SlnCLAP:

Standardized logarithmic children’s lipid accumulation product

RCLAP-H:

Relative children’s lipid accumulation product per height

SRCLAP-H:

Standardized relative children’s lipid accumulation product per height

References

  1. Brody AM, Kumar VA, Levy PD. Hot topic: global burden of treating hypertension-what is the role of the emergency department? Curr Hypertens Rep. 2017;19(1):8. https://doi.org/10.1007/s11906-017-0707-4.

    Article  PubMed  Google Scholar 

  2. Tadic M, Cuspidi C, Hering D. Hypertension and cognitive dysfunction in elderly: blood pressure management for this global burden. BMC Cardiovasc Disord. 2016;16(1):208. https://doi.org/10.1186/s12872-016-0386-0.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Ingelfinger JR. The child or adolescent with elevated blood pressure. N Engl J Med. 2014;371(11):1075. https://doi.org/10.1056/NEJMc1408238.

    Article  PubMed  Google Scholar 

  4. Okpokowuruk FS, Akpan MU, Ikpeme EE. Prevalence of hypertension and prehypertension among children and adolescents in a semi-urban area of Uyo Metropolis, Nigeria. Pan Afr Med J. 2017;28:303. https://doi.org/10.11604/pamj.2017.28.303.14396.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Samuels JA, Zavala AS, Kinney JM, Bell CS. Hypertension in children and adolescents. Adv Chronic Kidney Dis. 2019;26(2):146–50. https://doi.org/10.1053/j.ackd.2019.02.003.

    Article  PubMed  Google Scholar 

  6. Zou Y, Xia N, Zou Y, Chen Z, Wen Y. Smartphone addiction may be associated with adolescent hypertension: a cross-sectional study among junior school students in China. BMC Pediatr. 2019;19(1):310. https://doi.org/10.1186/s12887-019-1699-9.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Dong Y, Ma J, Song Y, Ma Y, Dong B, Zou Z, et al. Secular trends in blood pressure and overweight and obesity in Chinese boys and girls aged 7 to 17 years from 1995 to 2014. Hypertension. 2018;72(2):298–305. https://doi.org/10.1161/HYPERTENSIONAHA.118.11291.

    Article  CAS  PubMed  Google Scholar 

  8. Xi B, Liang Y, Mi J. Hypertension trends in Chinese children in the national surveys, 1993 to 2009. Int J Cardiol. 2013;165(3):577–9. https://doi.org/10.1016/j.ijcard.2012.09.032.

    Article  PubMed  Google Scholar 

  9. Chrysaidou K, Chainoglou A, Karava V, Dotis J, Printza N, Stabouli S. Secondary hypertension in children and adolescents: novel insights. Curr Hypertens Rev. 2020;16(1):37–44. https://doi.org/10.2174/1573402115666190416152820.

    Article  CAS  PubMed  Google Scholar 

  10. Marcon D, Tagetti A, Fava C. Subclinical organ damage in children and adolescents with hypertension: current guidelines and beyond. High Blood Press Cardiovasc Prev. 2019;26(5):361–73. https://doi.org/10.1007/s40292-019-00345-1.

    Article  PubMed  Google Scholar 

  11. Patel A, Bharani A, Sharma M, Bhagwat A, Ganguli N, Chouhan DS. Reply: prevalence of hypertension and prehypertension in schoolchildren from Central India. Ann Pediatr Cardiol. 2019;12(3):349. https://doi.org/10.4103/apc.APC_90_19.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Hope KD, Zachariah JP. Predictors and consequences of pediatric hypertension: have advanced echocardiography and vascular testing arrived? Curr Hypertens Rep. 2019;21(7):54. https://doi.org/10.1007/s11906-019-0958-3.

    Article  PubMed  Google Scholar 

  13. Urbina EM, Khoury PR, Bazzano L, Burns TL, Daniels S, Dwyer T, et al. Relation of blood pressure in childhood to self-reported hypertension in adulthood. Hypertension. 2019;73(6):1224–30. https://doi.org/10.1161/HYPERTENSIONAHA.118.12334.

    Article  CAS  PubMed  Google Scholar 

  14. McCracken E, Monaghan M, Sreenivasan S. Pathophysiology of the metabolic syndrome. Clin Dermatol. 2018;36(1):14–20. https://doi.org/10.1016/j.clindermatol.2017.09.004.

    Article  PubMed  Google Scholar 

  15. 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. Eur J Clin Nutr. 2013;67(6):573–85. https://doi.org/10.1038/ejcn.2013.61.

    Article  CAS  PubMed  Google Scholar 

  16. Peer N, Lombard C, Steyn K, Levitt N. Waist-to-height ratio is a useful indicator of cardio-metabolic risk in South Africa. Fam Pract. 2020;37(1):36–42. https://doi.org/10.1093/fampra/cmz044.

    Article  PubMed  Google Scholar 

  17. 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. Biomed Environ Sci. 2013;26(6):485–91. https://doi.org/10.3967/0895-3988.2013.06.009.

    Article  PubMed  Google Scholar 

  18. Halperin RO, Sesso HD, Ma J, Buring JE, Stampfer MJ, Gaziano JM. Dyslipidemia and the risk of incident hypertension in men. Hypertension. 2006;47(1):45–50. https://doi.org/10.1161/01.HYP.0000196306.42418.0e.

    Article  CAS  PubMed  Google Scholar 

  19. Kahn HS. The “lipid accumulation product” performs better than the body mass index for recognizing cardiovascular risk: a population-based comparison. BMC Cardiovasc Disord. 2005;5(1):26. https://doi.org/10.1186/1471-2261-5-26.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Wakabayashi I. Associations of blood lipid-related indices with blood pressure and pulse pressure in middle-aged men. Metab Syndr Relat Disord. 2015;13(1):22–8. https://doi.org/10.1089/met.2014.0093.

    Article  CAS  PubMed  Google Scholar 

  21. Song J, Zhao Y, Nie S, Chen X, Wu X, Mi J. 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. 2018;13(6):e0198105. https://doi.org/10.1371/journal.pone.0198105.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. 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. Obes Res Clin Pract. 2013;7(4):e308–14. https://doi.org/10.1016/j.orcp.2012.02.002.

    Article  PubMed  Google Scholar 

  23. Wang Y, Liu W, Sun L, Zhang Y, Wang B, Yuan Y, et al. A novel indicator, childhood lipid accumulation product, is associated with hypertension in Chinese children and adolescents. Hypertens Res. 2020;43(4):305–12. https://doi.org/10.1038/s41440-019-0366-8.

    Article  CAS  PubMed  Google Scholar 

  24. Zhang Y, Hu J, Li Z, Li T, Chen M, Wu L, et al. A novel Indicator of lipid accumulation product associated with metabolic syndrome in Chinese children and adolescents. Diabetes Metab Syndr Obes. 2019;12:2075–83. https://doi.org/10.2147/DMSO.S221786.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Huang YJ, Wong SH, Salmon J. Reliability and validity of the modified Chinese version of the Children's leisure activities study survey (CLASS) questionnaire in assessing physical activity among Hong Kong children. Pediatr Exerc Sci. 2009;21(3):339–53. https://doi.org/10.1123/pes.21.3.339.

    Article  CAS  PubMed  Google Scholar 

  26. Santos R, Zhang Z, Pereira JR, Sousa-Sa E, Cliff DP, Okely AD. Compliance with the Australian 24-hour movement guidelines for the early years: associations with weight status. BMC Public Health. 2017;17(Suppl 5):867. https://doi.org/10.1186/s12889-017-4857-8.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Villagran Perez S, Novalbos-Ruiz JP, Rodriguez-Martin A, Martinez-Nieto JM, Lechuga-Sancho AM. Implications of family socioeconomic level on risk behaviors in child-youth obesity. Nutr Hosp. 2013;28(6):1951–60. https://doi.org/10.3305/nutrhosp.v28in06.6848.

    Article  PubMed  Google Scholar 

  28. Huo JK, Zhou LJ, Wen B, Wang XJ, Dong B, Ma J. The method of blood pressure evaluation among children and adolescents aged 7 to 17 years old in China. Zhonghua Yu Fang Yi Xue Za Zhi. 2019;53(12):1299–304. https://doi.org/10.3760/cma.j.issn.0253-9624.2019.12.018.

    Article  CAS  PubMed  Google Scholar 

  29. Zhai Y, Li WR, Shen C, Qian F, Shi XM. Prevalence and correlates of elevated blood pressure in Chinese children aged 6-13 years: a Nationwide School-based survey. Biomed Environ Sci. 2015;28(6):401–9. https://doi.org/10.3967/bes2015.057.

    Article  PubMed  Google Scholar 

  30. Flynn J. The changing face of pediatric hypertension in the era of the childhood obesity epidemic. Pediatr Nephrol. 2013;28(7):1059–66. https://doi.org/10.1007/s00467-012-2344-0.

    Article  PubMed  Google Scholar 

  31. Yu D, Xu X, Gao X, Fang H, Ju L, Guo H, et al. Status of blood pressure and prevalence of hypertension among 6-17 years old children and adolescents in 2010-2012 in China. Wei Sheng Yan Jiu. 2018;47(1):1–6.

    PubMed  Google Scholar 

  32. Dong B, Ma J, Wang HJ, Wang ZQ. The association of overweight and obesity with blood pressure among Chinese children and adolescents. Biomed Environ Sci. 2013;26(6):437–44. https://doi.org/10.3967/0895-3988.2013.06.004.

    Article  PubMed  Google Scholar 

  33. Zhang T, Xu R, Hu Y, Liu Q. A meta-analysis of prevalence and trend of hypertension in preschool children in China. Occup Health. 2019;35(04):541–5.

    Google Scholar 

  34. Qu Z, Parry M, Liu F, Wen X, Li J, Zhang Y, et al. Self-management and blood pressure control in China: a community-based multicentre cross-sectional study. BMJ Open. 2019;9(3):e025819. https://doi.org/10.1136/bmjopen-2018-025819.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Ruiz LD, Zuelch ML, Dimitratos SM, Scherr RE. Adolescent Obesity: Diet Quality, Psychosocial Health, and Cardiometabolic Risk Factors. Nutrients. 2019;12(1). https://doi.org/10.3390/nu12010043.

  36. Keung V, Lo K, Cheung C, Tam W, Lee A. Changes in dietary habits and prevalence of cardiovascular risk factors among school students in Macao, China. Obes Res Clin Pract. 2019;13(6):541–7. https://doi.org/10.1016/j.orcp.2019.10.007.

    Article  PubMed  Google Scholar 

  37. Morales Suarez-Varela M, Mf MC, Soler Quiles C, Llopis-Morales A, Peraita-Costa I, Llopis-Gonzalez A. Prevalence of arterial hypertension and its association with anthropometry and diet in children (6 to 9 years old): ANIVA study. Nutr Hosp. 2019;36(1):133–41. https://doi.org/10.20960/nh.02105.

    Article  PubMed  Google Scholar 

  38. Zhang Q, Yang L, Zhang Y, Zhao M, Liang Y, Xi B. Hypertension prevalence based on three separate visits and its association with obesity among Chinese children and adolescents. Front Pediatr. 2019;7:307. https://doi.org/10.3389/fped.2019.00307.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Wang J, Zhu Y, Jing J, Chen Y, Mai J, Wong SH, et al. Relationship of BMI to the incidence of hypertension: a 4 years’ cohort study among children in Guangzhou, 2007-2011. BMC Public Health. 2015;15(1):782. https://doi.org/10.1186/s12889-015-1997-6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Hu J, Chu GP, Huang FF, Zhou YK, Teng CG, Yang HB, et al. Relation of body mass index (BMI) to the prevalence of hypertension in children: a 3years’ school-based prospective study in Suzhou, China. Int J Cardiol. 2016;222:270–4. https://doi.org/10.1016/j.ijcard.2016.07.217.

    Article  PubMed  Google Scholar 

  41. Kuciene R, Dulskiene V. Associations between body mass index, waist circumference, waist-to-height ratio, and high blood pressure among adolescents: a cross-sectional study. Sci Rep. 2019;9(1):9493. https://doi.org/10.1038/s41598-019-45956-9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Rivera-Soto WT, Rodriguez-Figueroa L. Is waist-to-height ratio a better obesity risk-factor Indicator for Puerto Rican children than is BMI or waist circumference? P R Health Sci J. 2016;35(1):20–5.

    PubMed  Google Scholar 

  43. Raj M, Krishnakumar R. Hypertension in children and adolescents: epidemiology and pathogenesis. Indian J Pediatr. 2013;80(Suppl 1):S71–6. https://doi.org/10.1007/s12098-012-0851-4.

    Article  PubMed  Google Scholar 

  44. 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. Diabetologia. 2010;53(12):2496–503. https://doi.org/10.1007/s00125-010-1889-3.

    Article  CAS  PubMed  Google Scholar 

  45. Pirimoglu B, Sade R, Islek A, Kantarci M. The liver fat fraction and abdominal subcutaneous and visceral fat volume distribution in Normal-weight, overweight, and obese children using a new magnetic resonance imaging technique. J Comput Assist Tomogr. 2019;43(2):194–9. https://doi.org/10.1097/RCT.0000000000000803.

    Article  PubMed  Google Scholar 

  46. Pazin DC, Rosaneli CF, Olandoski M, Oliveira ERN, Baena CP, Figueredo AS, et al. Waist circumference is associated with blood pressure in children with Normal body mass index: a cross-sectional analysis of 3,417 school children. Arq Bras Cardiol. 2017;109(6):509–15. https://doi.org/10.5935/abc.20170162.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Cruz NRC, Cardoso PC, Frossard T, Ferreira FO, Brener S, Gomides AFF, et al. Waist circumference as high blood pressure predictor in school age children. Cien Saude Colet. 2019;24(5):1885–93. https://doi.org/10.1590/1413-81232018245.18012017.

    Article  PubMed  Google Scholar 

  48. Lo K, Wong M, Khalechelvam P, Tam W. Waist-to-height ratio, body mass index and waist circumference for screening paediatric cardio-metabolic risk factors: a meta-analysis. Obes Rev. 2016;17(12):1258–75. https://doi.org/10.1111/obr.12456.

    Article  CAS  PubMed  Google Scholar 

  49. Mehta SK. Waist circumference to height ratio and left ventricular mass in children and adolescents. Cardiol Young. 2016;26(4):658–62. https://doi.org/10.1017/S1047951115000803.

    Article  PubMed  Google Scholar 

  50. Liu C, Zhang L. Research progress of new type body fat indexes of lipid accumulation product index and visceral adiposity index. Chin J Diabetes. 2016;24(11):1032–5.

    Google Scholar 

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Acknowledgments

The authors would like to thank the students who participated in the current study.

Funding

This project was supported by grants from the National Natural Science Foundation of China (No.81502823), Excellent Young Talents Fund Program of Higher Education Institutions of Anhui Province (No.gxyqZD2017063), 512 Talent Cultivation Plan of Bengbu Medical College (by51201204), 2020 Outstanding Young Backbone Talents in Colleges and Universities Overseas Visiting and Training Project (by15200053) and Innovative Team Project of Natural Science Category of Bengbu Medical College (BYKC201901).

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Authors and Affiliations

  1. Department of child and adolescent health, School of public health, Bengbu Medical College, No. 2600 east sea avenue, Room 207, Bengbu, 233030, Anhui, China

    Zizhe Zhang, Li Zhang, Lili Sun, Bangxuan Wang, Yongting Yuan, Huaiquan Gao & Lianguo Fu

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  1. Zizhe Zhang
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  2. Li Zhang
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  3. Lili Sun
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  4. Bangxuan Wang
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  7. Lianguo Fu
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Contributions

ZZ collected and analyzed the data and prepared the first draft of the manuscript. LZ and LS analyzed and collected the data. BW and YY collected the data and revised the manuscript. HG and LF conceived and designed the research and revised the manuscript. All authors reviewed the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Huaiquan Gao or Lianguo Fu.

Ethics declarations

Ethics approval and consent to participate

The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The present study was approved by the Medical Ethics Committee of Bengbu Medical College [2015 No. 003] and informed consent was taken from all the participants (or their parents) before the survey.

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Not applicable.

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The authors declare that they have no competing interests.

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Zhang, Z., Zhang, L., Sun, L. et al. Relative children’s lipid accumulation with hypertension in Chinese children and adolescents. BMC Public Health 21, 1884 (2021). https://doi.org/10.1186/s12889-021-11868-5

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BMC Public Health

ISSN: 1471-2458

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