Predictive Ability of Hypertriglyceridemic Waist, Hypertriglyceridemic Waist-to-Height Ratio, and Waist-to-Hip Ratio for Cardiometabolic Risk Factors Clustering Screening among Chinese Children and Adolescents

XIAO Tian Li YUAN Shu Qian GAO Jing Yu Julien S. Baker YANG Yi De WANG Xi Jie ZHENG Chan Juan DONG Yan Hui ZOU Zhi Yong

XIAO Tian Li, YUAN Shu Qian, GAO Jing Yu, Julien S. Baker, YANG Yi De, WANG Xi Jie, ZHENG Chan Juan, DONG Yan Hui, ZOU Zhi Yong. Predictive Ability of Hypertriglyceridemic Waist, Hypertriglyceridemic Waist-to-Height Ratio, and Waist-to-Hip Ratio for Cardiometabolic Risk Factors Clustering Screening among Chinese Children and Adolescents[J]. Biomedical and Environmental Sciences, 2024, 37(3): 233-241. doi: 10.3967/bes2024.027
Citation: XIAO Tian Li, YUAN Shu Qian, GAO Jing Yu, Julien S. Baker, YANG Yi De, WANG Xi Jie, ZHENG Chan Juan, DONG Yan Hui, ZOU Zhi Yong. Predictive Ability of Hypertriglyceridemic Waist, Hypertriglyceridemic Waist-to-Height Ratio, and Waist-to-Hip Ratio for Cardiometabolic Risk Factors Clustering Screening among Chinese Children and Adolescents[J]. Biomedical and Environmental Sciences, 2024, 37(3): 233-241. doi: 10.3967/bes2024.027

doi: 10.3967/bes2024.027

Predictive Ability of Hypertriglyceridemic Waist, Hypertriglyceridemic Waist-to-Height Ratio, and Waist-to-Hip Ratio for Cardiometabolic Risk Factors Clustering Screening among Chinese Children and Adolescents

Funds: This study was supported by the National Natural Science Foundation of China [no. 81903336, Yi-de Yang]; the Health Research Project of Hunan Provincial Health Commission [no. 202112031516, Yi-de Yang]; Scientific Research Fund of Hunan Provincial Education Department [no. 22B0038, Yi-de Yang]; the Research Team for Reproduction Health and Translational Medicine of Hunan Normal University [2023JC101]; Key Project of Developmental Biology and Breeding from Hunan Province [no. 2022XKQ0205]; and Open Project for Postgraduates of Hunan Normal University [no. KF2022019, Tian-li Xiao]. The funders had no role in the design, analysis, or writing of this article.
More Information
    Author Bio:

    XIAO Tian Li, female, born in 1998, Bachelor Degree, majoring in growth and development of children and adolescents

    YUAN Shu Qian, female, born in 1997, Master Degree, majoring in growth and development of children and adolescents

    Corresponding author: YANG Yi De, PhD, Associate Professor, Tel: 86-10-16607315363, E-mail: yangyide2007@126.comWANG Xi Jie, PhD, Tel: 86-10-16601126810, E-mail: xijie_wang@126.comZOU Zhi Yong, Professor, PhD, Supervisor, PhD, Tel: 86-10-13581525193, E-mail: harveyzou2002@bjmu.edu.cn
  • XIAO Tian Li and YUAN Shu Qian drafted the manuscript and performed the statistical analyses. ZHENG Chan Juan, WANG Xi Jie, and YANG Yi De conceived of the study. YANG Yi De, DONG Yan Hui, and ZOU Zhi Yong participated in its design and coordination and helped to draft the manuscript. GAO Jing Yu and Julien S. Baker contributed to the discussion. All authors participated in critically revising and approving the final manuscript.
  • The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
  • &These authors contributed equally to this work.
    • 关键词:
    •  / 
    •  / 
    •  / 
    •  / 
    •  / 
    •  
    XIAO Tian Li and YUAN Shu Qian drafted the manuscript and performed the statistical analyses. ZHENG Chan Juan, WANG Xi Jie, and YANG Yi De conceived of the study. YANG Yi De, DONG Yan Hui, and ZOU Zhi Yong participated in its design and coordination and helped to draft the manuscript. GAO Jing Yu and Julien S. Baker contributed to the discussion. All authors participated in critically revising and approving the final manuscript.
    The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
    &These authors contributed equally to this work.
    注释:
    1) AUTHOR CONTRIBUTIONS: 2) CONFLICT OF INTEREST:
  • Figure  1.  Forest plot of cardiometabolic health risk factors for HW (A), HWHtR (B), and WHR (C) phenotypes stratified by sex. BP, blood pressure; FPG, fasting plasma glucose; HDL, high-density lipoprotein; LDL, low-density lipoprotein; TG, triglyceride; TC, total cholesterol; NA, data not available. CHRF clustering: cardiometabolic health risk factors clustering. Model: adjusted for age (years), BMI, area and family income. HW, hypertriglyceridemic waist; HWHtR, hypertriglyceridemic waist-to-height ratio; WHR, waist-to-hip ratio.

    Figure  2.  Forest plot of cardiometabolic health risk factors for HW (A), HWHtR (B), and WHR (C) phenotypes stratified by age. BP, blood pressure; FPG, fasting plasma glucose; HDL, high-density lipoprotein; LDL, low-density lipoprotein; TG, triglyceride; TC, total cholesterol; NA, data not available. CHRF clustering: cardiometabolic health risk factors clustering. Model: adjusted for sex, BMI, area and family income. HW, hypertriglyceridemic waist; HWHtR, hypertriglyceridemic waist-to-height ratio; WHR, waist-to-hip ratio.

    Table  1.   General characteristics of the study population by sex

    Variables Total Boy (n = 32,064) Girl (n = 30,104) P
    N x ± s n x ± s n x ± s
    Age (years) 62,168 10.80 ± 3.30 32,064 10.72 ± 3.26 30,104 10.94 ± 3.34 < 0.001
    BMI (kg/m2) 62,168 18.55 ± 3.75 32,064 18.84 ± 3.92 30,104 18.24 ± 3.54 < 0.001
    WC (cm) 61,802 64.75 ± 10.79 31,858 65.85 ± 11.50 29,944 63.59 ± 9.86 < 0.001
    Hipline (cm) 61,815 77.18 ± 12.00 31,882 77.07 ± 12.07 29,933 77.30 ± 11.92 0.018
    CHRF clustering, n (%)
     < 2 13,195 82.5 6,675 81.9 6,520 83.1 0.056
     ≥ 2 2,800 17.5 1,472 18.1 1,328 16.9
    HW, n (%)
     0 13,736 91.7 7,050 91.8 6,686 91.5 0.479
     1 1,249 8.3 628 8.2 621 8.5
    HWHtR, n (%)
     0 9,079 93.4 4,587 92.0 4,492 94.8 < 0.001
     1 644 6.6 397 8.0 247 5.2
    WHR, n (%)
     0 40,480 65.7 21,373 67.2 19,107 60.0 < 0.001
     1 21,153 34.3 10,410 32.8 10,743 40.0
      Note. BMI, body mass index; WC, waist circumference; HW, hypertriglyceridemic waist; HWHtR, hypertriglyceridemic waist-to-height ratio; WHR, waist-to-hip ratio; CHRF clustering, cardiometabolic health risk factors clustering.
    下载: 导出CSV

    S2.   Comparisons of cardiometabolic health risk factors between subjects with and without HW phenotype

    Variables Category non-HW HW P
    N Freq. (%) N Freq. (%)
    Sex boy 7,050 51.3 628 50.3 0.479
    girl 6,686 48.7 621 49.7
    Area rural 4,946 41.7 385 36.4 < 0.001
    urban 6,905 58.3 672 63.6
    Family income
    (CNY)
    < 5,000 3,756 64.3 357 62.9 0.494
    ≥ 5,000 2,086 35.7 211 37.1
    BP (mmHg) < P90 10,632 77.4 669 53.6 < 0.001
    P90 3,104 22.6 580 46.4
    FPG (mmol/L) < 6.1 13,696 99.7 1,242 99.7 0.739
    ≥ 6.1 37 0.3 4 0.3
    HDL (mmol/L) ≥ 1.03 12,235 89.1 839 67.2 < 0.001
    < 1.03 1,501 10.9 410 32.8
    LDL (mmol/L) < 3.37 13,384 97.4 1,159 92.8 < 0.001
    ≥ 3.37 352 2.6 90 7.2
    TG (mmol/L) < 1.24 10,845 79.0 0 0.0 < 0.001
    ≥ 1.24 2,891 21.0 1,249 100.0
    TC (mmol/L) < 5.17 13,075 95.2 1,098 87.9 < 0.001
    ≥ 5.17 661 4.8 151 12.1
    CHRF clustering < 2 11,851 86.3 395 31.6 < 0.001
    ≥ 2 1,885 13.7 854 68.4
      Note. BP, blood pressure; FPG, fasting plasma glucose; HDL, high-density lipoprotein; LDL, low-density lipoprotein; TG, triglyceride; TC, total cholesterol; CHRF clustering, cardiometabolic health risk factors clustering; HW, hypertriglyceridemic waist.
    下载: 导出CSV

    S4.   Comparisons of cardiometabolic health risk factors between subjects with and without WHR phenotype

    VariablesCategorynon-WHRWHRP
    NFreq. (%)NFreq. (%)
    Sexboy21,37352.810,41049.2< 0.001
    girl19,10747.210,74350.8
    Arearural12,75637.76,87736.90.081
    urban21,12462.311,77063.1
    Family income (CNY)< 5,00010,91463.55,61263.90.494
    ≥ 5,0006,28536.53,17236.1
    BP (mmHg)< P9027,95077.113,99969.3< 0.001
    P908,31622.96,20630.7
    FPG (mmol/L)< 6.110,16699.75,64199.70.683
    ≥ 6.1270.3170.3
    HDL (mmol/L)≥ 1.039,13189.64,74483.8< 0.001
    < 1.031,06410.492016.2
    LDL (mmol/L)< 3.379,97997.95,40895.5< 0.001
    ≥ 3.372162.12564.5
    TG (mmol/L)< 1.247,59274.54,01370.9< 0.001
    ≥ 1.242,60225.51,64929.1
    TC (mmol/L)< 5.179,74595.65,25492.8< 0.001
    ≥ 5.174504.44107.2
    CHRF clustering< 28,71585.54,37277.2< 0.001
    ≥ 21,47914.51,29022.8
      Note. BP, blood pressure; FPG, fasting plasma glucose; HDL, high-density lipoprotein; LDL, low-density lipoprotein; TG, triglyceride; TC, total cholesterol; CHRF clustering, cardiometabolic health risk factors clustering; WHR, waist-to-hip ratio.
    下载: 导出CSV

    Table  2.   OR (95% CI) of cardiometabolic health risk factors for the HW, HWHtR, and WHR phenotypes

    Models HW HWHtR WHR
    OR 95% CI P OR 95% CI P OR 95% CI P
    Model 1
    Elevated TG NA NA NA NA NA NA 0.82 0.75–0.89 < 0.001
    Low HDL 2.83 2.42–3.32 < 0.001 2.63 2.13–3.24 < 0.001 1.18 1.05–1.31 0.004
    Elevated LDL 2.08 1.55–2.78 < 0.001 3.18 2.13–4.74 < 0.001 1.94 1.59–2.38 < 0.001
    Elevated TC 2.58 2.05–3.25 < 0.001 2.85 2.04–3.98 < 0.001 1.64 1.41–1.91 < 0.001
    Elevated BP 1.42 1.23–1.63 0.007 1.31 1.08–1.58 0.007 0.92 0.88–0.96 < 0.001
    FPG 1.07 0.32–3.53 0.540 0.60 0.12–3.02 0.538 1.14 0.57–2.26 0.720
    CHRF clustering 10.79 9.28–12.55 < 0.001 9.21 7.45–11.39 < 0.001 1.05 0.95–1.15 0.350
    Model 2
    Elevated TG NA NA NA NA NA NA 1.09 0.95–1.25 0.234
    Low HDL 2.45 1.88–3.19 < 0.001 2.71 1.93–3.80 < 0.001 1.30 1.08–1.55 0.005
    Elevated LDL 2.96 1.84–4.75 < 0.001 6.87 3.58–13.19 < 0.001 1.64 1.15–2.33 < 0.001
    Elevated TC 3.89 2.65–5.73 < 0.001 4.94 2.88–8.47 < 0.001 1.51 1.15–1.98 0.003
    Elevated BP 1.32 1.06–1.66 0.015 1.08 0.80–1.47 0.614 1.00 0.93–1.07 0.905
    FPG 0.98 0.10–9.63 0.990 1.59 0.13–19.23 0.716 1.89 0.64–5.54 0.247
    CHRF clustering 12.22 9.54–15.67 < 0.001 9.70 6.93–13.58 < 0.001 1.14 0.97–1.34 0.115
      Note. BP, blood pressure; FPG, fasting plasma glucose; HDL, high-density lipoprotein; LDL, low-density lipoprotein; TG, triglyceride; TC, total cholesterol; CHRF clustering, cardiometabolic health risk factors clustering; HW, hypertriglyceridemic waist; HWHtR, hypertriglyceridemic waist-to-height ratio; WHR, waist-to-hip ratio; NA, data not available. Model 1: adjusted for sex (boy or girl), age (years) and BMI. Model 2: adjusted for variables from model 1, and additionally adjusted by area and family income.
    下载: 导出CSV

    S5.   OR (95% CI) of cardiometabolic health risk factors for HW, HWHtR and WHR phenotype stratified by sex

    Models HW HWHtR WHR
    OR 95% CI P OR 95% CI P OR 95% CI P
    Boys Model 1
    Elevated TG NA NA NA NA NA NA 0.94 0.83−1.07 0.330
    Low HDL 2.93 2.35−3.67 < 0.001 3.05 2.32−4.01 < 0.001 1.39 1.19−1.62 < 0.001
    Elevated LDL 2.22 1.47−3.35 < 0.001 3.02 1.77−5.15 < 0.001 2.01 1.46−2.75 < 0.001
    Elevated TC 2.48 1.78−3.46 < 0.001 2.59 1.67−4.02 < 0.001 1.54 1.22−1.96 < 0.001
    Elevated BP 1.46 1.19−1.78 0.007 1.48 1.14−1.91 0.003 0.86 0.81−0.92 < 0.001
    FPG NA NA NA NA NA NA 1.15 0.49−2.69 0.752
    CHRF clustering 14.11 11.23−17.72 < 0.001 15.19 11.23−20.55 < 0.001 1.15 1.00−1.33 0.053
    Model 2
    Elevated TG NA NA NA NA NA NA 1.12 0.90−1.41 0.304
    Low HDL 2.83 1.94−4.15 < 0.001 3.49 2.22−5.48 < 0.001 1.62 1.24−2.11 < 0.001
    Elevated LDL 4.58 2.14−9.80 < 0.001 7.47 2.67−20.91 < 0.001 1.78 0.98−3.26 0.060
    Elevated TC 3.38 1.88−6.07 < 0.001 3.97 1.85−8.52 < 0.001 1.33 0.86−2.05 0.197
    Elevated BP 1.13 0.81−1.58 0.476 1.20 0.78−1.83 0.407 0.93 0.83−1.03 0.152
    FPG NA NA NA NA NA NA 3.06 0.89−10.06 0.077
    CHRF clustering 15.28 10.44−22.36 < 0.001 16.18 9.91−26.42 < 0.001 1.31 1.02−1.67 0.034
    Girls Model 1
    Elevated TG NA NA NA NA NA NA 0.73 0.65−0.82 < 0.001
    Low HDL 2.68 2.14−3.36 < 0.001 2.16 1.54−3.02 < 0.001 1.04 0.89−1.21 0.646
    Elevated LDL 2.00 1.32−3.04 0.001 3.37 1.86−6.14 < 0.001 1.84 1.41−2.41 < 0.001
    Elevated TC 2.82 2.05−3.89 < 0.001 3.11 1.87−5.19 < 0.001 1.66 1.36−2.03 < 0.001
    Elevated BP 1.34 1.10−1.64 0.004 1.10 0.81−1.49 0.531 0.96 0.90−1.023 0.205
    FPG 2.95 0.70−12.47 0.140 2.18 0.29−16.15 0.447 1.43 0.46−4.47 0.542
    CHRF clustering 8.84 7.20−10.85 < 0.001 5.33 3.91−7.26 < 0.001 0.96 0.84−1.09 0.498
    Model 2
    Elevated TG NA NA NA NA NA NA 1.05 0.88−1.26 0.568
    Low HDL 2.10 1.45−3.03 < 0.001 2.07 1.22−3.49 0.007 1.11 0.87−1.42 0.413
    Elevated LDL 2.26 1.21−4.22 0.010 6.44 2.74−15.15 < 0.001 1.56 1.01−2.42 0.045
    Elevated TC 4.55 2.74−7.57 < 0.001 6.25 2.95−13.25 < 0.001 1.64 1.16−2.31 0.005
    Elevated BP 1.46 1.07−1.98 0.017 0.98 0.78−1.23 0.858 1.03 0.94−1.14 0.506
    FPG 1.43 0.11−19.29 0.786 1.63 0.22−11.96 0.631 0.78 0.12−5.28 0.799
    CHRF clustering 10.26 7.38−14.27 < 0.001 5.76 3.57−9.29 < 0.001 1.02 0.83−1.27 0.825
     Note. BP, blood pressure; FPG, fasting plasma glucose; HDL, high-density lipoprotein; LDL, low−density lipoprotein; TG, triglyceride; TC, total cholesterol. CHRF clustering, cardiometabolic health risk factors clustering; HW, hypertriglyceridemic waist; HWHtR, hypertriglyceridemic waist-to-height ratio; WHR, waist−to−hip ratio; NA, data not available. Model 1: adjusted for age (years) and BMI. Model 2: adjusted for variables from model 1, and additionally adjusted by area and family income.
    下载: 导出CSV

    S1.   General characteristics of the study population by sex

    Variables Total Boy (n = 32,064) Girl (n = 30,104) P
    N x ± s (%) n x ± s (%) n x ± s (%)
    Height 62,168 145.86 ± 17.08 32,064 146.93 ± 18.44 30,104 144.72 ± 15.43 < 0.001
    Weight 62,168 40.97 ± 15.53 32,064 42.35 ± 16.88 30,104 39.50 ± 13.79 < 0.001
    SBP (mmHg) 62,168 104.33 ± 12.17 32,064 105.72 ± 12.5 30,104 102.85 ± 11.62 < 0.001
    DBP (mmHg) 62,168 66.27 ± 8.84 32,064 66.8 ± 8.98 30,104 65.71 ± 8.64 < 0.001
    TG (mmol/L) 15,995 1.14 ± 0.81 8,147 1.09 ± 0.80 7,848 1.19 ± 0.83 < 0.001
    HDL (mmol/L) 15,998 1.89 ± 1.35 8,149 1.90 ± 1.38 7,849 1.88 ± 1.31 0.476
    LDL (mmol/L) 15,998 1.97 ± 0.71 8,149 1.94 ± 0.70 7,849 1.99 ± 0.72 < 0.001
    TC (mmol/L) 15,998 3.86 ± 0.90 8,149 3.81 ± 0.90 7,849 3.92 ± 0.90 < 0.001
    FPG (mmol/L) 15,990 4.14 ± 1.28 8,143 4.19 ± 1.31 7,847 4.09 ± 1.25 < 0.001
    Area rural 19,753 37.3 10,158 37.5 9,595 37.1 0.359
    urban 33,221 62.7 16,947 62.5 16,274 62.9
    Family income < 5,000 16,646 63.6 8,234 63.0 8,412 64.2 0.042
    (CNY) ≥ 5,000 9529 36.4 4,838 37.0 4,691 35.8
    BP (mmHg) < P90 42,292 74.3 20,716 70.6 21,576 78.2 < 0.001
    P90 14,642 25.7 8,611 29.4 6,031 21.8
    FPG (mmol/L) < 6.1 15,945 99.7 8,111 99.6 7,834 99.8 0.007
    ≥ 6.1 45 0.3 32 0.4 13 0.2
    HDL (mmol/L) ≥ 1.03 13,987 87.4 7,035 86.3 6,952 88.6 < 0.001
    < 1.03 2,011 12.6 1,114 13.7 897 11.4
    LDL (mmol/L) < 3.37 15,520 97.0 7,928 97.3 7,592 96.7 0.037
    ≥ 3.37 478 3.0 221 2.7 257 3.3
    TG (mmol/L) < 1.24 11,702 73.2 6,149 75.5 5,553 70.8 < 0.001
    ≥ 1.24 4,293 26.8 1,998 24.5 2,295 29.2
    TC (mmol/L) < 5.17 15,128 94.6 7,745 95.0 7,383 94.1 < 0.001
    ≥ 5.17 870 5.4 404 5.0 466 5.9
      Note. BMI, body mass index; WC, waist circumference; SBP, systolic blood pressure; DBP, diastolic blood pressure; BP, blood pressure; FPG, fasting plasma glucose; HDL, high-density lipoprotein; LDL, low-density lipoprotein; TG, triglyceride; TC, total cholesterol; HW, hypertriglyceridemic waist; HWHtR, hypertriglyceridemic waist-to-height ratio; WHR, waist-to-hip ratio; CHRF clustering, cardiometabolic health risk factors clustering.
    下载: 导出CSV

    S3.   Comparisons of cardiometabolic health risk factors between subjects with and without HWHtR phenotype

    VariablesCategorynon-HWHtRHWHtRP
    NFreq. (%)NFreq. (%)
    Sexboy4,58750.539761.6< 0.001
    girl4,49249.524738.4
    Arearural3,23241.021436.70.041
    urban4,64659.036963.3
    Family income (CNY)< 5,0002,53465.418264.3< 0.001
    ≥ 5,0001,33834.610135.7
    BP (mmHg)< P906,65473.328944.9< 0.004
    P902,42526.735555.1
    FPG (mmol/L)< 6.19,05199.763999.70.907
    ≥ 6.1260.320.3
    HDL (mmol/L)≥ 1.037,85786.539861.8< 0.001
    < 1.031,22213.524638.2
    LDL (mmol/L)< 3.378,88397.858691.0< 0.001
    ≥ 3.371962.2589.0
    TG (mmol/L)< 1.246,61772.900.0< 0.001
    ≥ 1.242,46227.1644100.0
    TC (mmol/L)< 5.178,70095.856788.0< 0.001
    ≥ 5.173794.27712.0
    CHRF clustering< 27,48182.416525.6< 0.001
    ≥ 21,59817.647974.4
      Note. BP, blood pressure; FPG, fasting plasma glucose; HDL, high-density lipoprotein; LDL, low-density lipoprotein; TG, triglyceride; TC, total cholesterol; CHRF clustering, cardiometabolic health risk factors clustering; HWHtR, hypertriglyceridemic waist-to-height ratio.
    下载: 导出CSV

    S6.   OR (95% CI) of cardiometabolic health risk factors for HW, HWHtR and WHR phenotype stratified by age

    Models HW HWHtR WHR
    OR 95% CI P OR 95% CI P OR 95% CI P
    6–9 years Model 1
    Elevated TG NA NA NA NA NA NA 0.70 0.59–0.82 < 0.001
    Low HDL 2.97 2.14–4.14 < 0.001 NA NA NA 1.45 1.18–1.78 < 0.001
    Elevated LDL 1.38 0.81–2.34 0.233 NA NA NA 1.94 1.44–2.62 < 0.001
    Elevated TC 2.08 1.40–3.07 < 0.001 NA NA NA 1.48 1.17–1.86 0.001
    Elevated BP 1.54 1.16–2.04 0.003 NA NA NA 0.97 0.89–1.05 0.428
    FPG 3.11 0.28–34.78 0.356 NA NA NA 1.81 0.58–5.65 0.310
    CHRF clustering 10.04 7.53–13.38 < 0.001 NA NA NA 1.38 1.15–1.66 0.001
    Model 2
    Elevated TG NA NA NA NA NA NA 1.10 0.83–1.47 0.508
    Low HDL 3.23 1.79–5.85 < 0.001 NA NA NA 1.43 1.00–2.04 0.052
    Elevated LDL 1.73 0.70–4.28 0.236 NA NA NA 1.49 0.86–2.57 0.155
    Elevated TC 2.88 1.42–5.82 0.003 NA NA NA 1.31 0.87–1.98 0.192
    Elevated BP 1.40 0.86–2.28 0.172 NA NA NA 1.17 1.03–1.34 0.017
    FPG NA NA NA NA NA NA 0.94 0.10–8.86 0.955
    CHRF clustering 11.77 7.13−19.44 < 0.001 NA NA NA 1.33 0.96–1.85 0.091
    10–12 years Model 1
    Elevated TG NA NA NA NA NA NA 0.63 0.54−0.73 < 0.001
    Low HDL 3.11 2.32−4.16 < 0.001 3.28 2.37−4.55 < 0.001 1.17 0.93−1.46 0.174
    Elevated LDL 2.83 1.73−4.62 < 0.001 3.82 2.25−6.50 < 0.001 2.07 1.42−3.02 < 0.001
    Elevated TC 2.91 1.94−4.38 < 0.001 3.56 2.28−5.56 < 0.001 1.83 1.37−2.44 < 0.001
    Elevated BP 1.54 1.20−1.98 0.001 1.51 1.14−2.01 0.004 0.87 0.79−0.94 0.001
    FPG 1.10 0.11−11.32 0.937 NA NA NA 1.59 0.51−4.96 0.428
    CHRF clustering 13.00 9.81−17.22 < 0.001 12.36 8.98−17.03 < 0.001 0.01 0.68−0.97 0.022
    Model 2
    Elevated TG NA NA NA NA NA NA 0.81 0.64−1.04 0.104
    Low HDL 2.85 1.78−4.57 < 0.001 3.50 2.08−5.88 < 0.001 1.57 1.09−2.26 0.016
    Elevated LDL 3.71 1.69−8.18 0.001 5.87 2.45−14.02 < 0.001 1.70 0.89−3.25 0.110
    Elevated TC 3.37 1.74−6.52 < 0.001 4.84 2.34−10.01 < 0.001 1.87 1.10−3.18 0.021
    Elevated BP 1.59 1.07−2.36 0.022 1.12 0.71−1.76 0.634 0.86 0.75−0.99 0.037
    FPG NA NA NA NA NA NA 9.05 0.95−85.90 0.055
    CHRF clustering 16.73 10.54−26.55 < 0.001 12.69 7.61−21.16 < 0.001 0.96 0.71−1.29 0.776
    13−15 years
    Model 1
    Elevated TG NA NA NA NA NA NA 0.88 0.75−1.03 0.109
    Low HDL 2.55 1.92−3.38 < 0.001 2.19 1.55−3.10 < 0.001 1.03 0.85−1.25 0.746
    Elevated LDL 1.48 0.73−3.01 0.275 1.95 0.86−4.40 0.109 1.28 0.75−2.18 0.361
    Elevated TC 2.64 1.58−4.41 < 0.001 1.57 0.80−3.09 0.195 1.57 1.09−2.26 0.016
    Elevated BP 1.30 0.99−1.69 0.057 1.47 1.05−2.05 0.025 0.90 0.83−0.98 0.019
    FPG NA NA NA NA NA NA 0.31 0.05−2.04 0.222
    CHRF clustering 10.06 7.55−13.39 < 0.001 8.07 5.62−11.58 < 0.001 0.86 0.72−1.03 0.095
    Model 2
    Elevated TG NA NA NA NA NA NA 1.22 0.94−1.57 0.132
    Low HDL 1.72 1.08−2.75 0.023 2.29 1.31−4.01 0.004 1.10 0.80−1.51 0.550
    Elevated LDL 2.04 0.66−6.35 0.217 4.14 1.07−16.08 0.040 2.46 0.99−6.07 0.052
    Elevated TC 5.64 2.45−12.99 < 0.001 3.01 1.02−8.85 0.045 1.94 1.00−3.77 0.050
    Elevated BP 1.06 0.69−1.62 0.789 1.03 0.61−1.77 0.903 0.97 0.84−1.11 0.628
    FPG NA NA NA NA NA NA NA NA NA
    CHRF clustering 9.79 6.21−15.44 < 0.001 8.17 4.60−14.53 < 0.001 1.07 0.80−1.42 0.654
    16−17 years
    Model 1
    Elevated TG NA NA NA NA NA NA 0.98 0.77−1.24 0.839
    Low HDL 2.57 1.76−3.75 < 0.001 2.52 1.61−3.93 < 0.001 1.22 0.91−1.63 0.184
    Elevated LDL 4.50 1.97−10.28 < 0.001 3.83 1.48−9.92 0.006 1.81 0.82−3.97 0.140
    Elevated TC 4.26 2.17−8.38 < 0.001 4.01 1.79−9.00 0.001 1.37 0.76−2.44 0.295
    Elevated BP 1.20 0.84−1.73 0.322 0.94 0.60−1.45 0.768 0.95 0.83−1.08 0.428
    FPG 1.39 0.16−11.81 0.763 1.85 0.19−18.26 0.601 0.59 0.06−5.64 0.644
    CHRF clustering 10.02 6.79−14.78 < 0.001 8.77 5.45−14.11 < 0.001 1.23 0.94−1.61 0.124
    Model 2
    Elevated TG NA NA NA NA NA NA 0.93 0.63−1.38 0.712
    Low HDL 2.56 1.36−4.81 0.004 2.48 1.19−5.14 0.012 1.32 0.80−2.16 0.275
    Elevated LDL 8.62 2.17−34.23 0.002 20.95 4.12−106.44 < 0.001 0.70 0.19−2.64 0.597
    Elevated TC 7.85 2.45−25.12 0.001 12.90 3.26−51.15 < 0.001 0.83 0.29−2.35 0.723
    Elevated BP 1.25 0.70−2.23 0.453 1.34 0.67−2.68 0.408 0.96 0.78−1.19 0.694
    FPG 2.56 0.11−60.52 0.561 3.24 0.11−96.18 0.497 NA NA NA
    CHRF clustering 12.22 6.48−23.04 < 0.001 11.23 5.31−23.76 < 0.001 1.15 0.74−1.81 0.533
      Note. BP, blood pressure; FPG, fasting plasma glucose; HDL, high−density lipoprotein; LDL, low−density lipoprotein; TG, triglyceride; TC, total cholesterol; CHRF clustering, cardiometabolic health risk factors clustering; HW, hypertriglyceridemic waist; HWHtR, hypertriglyceridemic waist−to−height ratio; WHR, waist−to−hip ratio. Model 1: adjusted for sex (boy or girl) and BMI. Model 2: adjusted for variables from model 1, and additionally adjusted by area and family income.
    下载: 导出CSV
  • [1] Sacco RL, Roth GA, Reddy KS, et al. The heart of 25 by 25: achieving the goal of reducing global and regional premature deaths from cardiovascular diseases and stroke: a modeling study from the american heart association and world heart federation. Circulation, 2016; 133, e674−90.
    [2] Liu SW, Li YC, Zeng XY, et al. Burden of cardiovascular diseases in China, 1990-2016: findings from the 2016 global burden of disease study. JAMA Cardiol, 2019; 4, 342−52. doi:  10.1001/jamacardio.2019.0295
    [3] Yan YK, Liu JT, Zhao XY, et al. Regional adipose compartments confer different cardiometabolic risk in children and adolescents: the China Child and adolescent cardiovascular health study. Mayo Clin Proc, 2019; 94, 1974−82. doi:  10.1016/j.mayocp.2019.05.026
    [4] Dou YL, Jiang Y, Yan YK, et al. Waist-to-height ratio as a screening tool for cardiometabolic risk in children and adolescents: a nationwide cross-sectional study in China. BMJ Open, 2020; 10, e037040. doi:  10.1136/bmjopen-2020-037040
    [5] Cornier MA, Després JP, Davis N, et al. Assessing adiposity: a scientific statement from the American Heart Association. Circulation, 2011; 124, 1996−2019. doi:  10.1161/CIR.0b013e318233bc6a
    [6] The InterAct Consortium. Long-term risk of incident type 2 diabetes and measures of overall and regional obesity: the EPIC-InterAct case-cohort study. PLoS Med, 2012; 9, e1001230. doi:  10.1371/journal.pmed.1001230
    [7] The Decoda Study Group, Nyamdorj R. BMI compared with central obesity indicators in relation to diabetes and hypertension in Asians. Obesity, 2008; 16, 1622−35. doi:  10.1038/oby.2008.73
    [8] Yang XL, Ouyang YF, Zhang XF, et al. Waist circumference of the elderly over 65 years old in China increased gradually from 1993 to 2015: a cohort study. Biomed Environ Sci, 2022; 35, 604−12.
    [9] Wang AX, Li ZX, Zhou Y, et al. Hypertriglyceridemic waist phenotype and risk of cardiovascular diseases in China: results from the Kailuan Study. Int J Cardiol, 2014; 174, 106−9. doi:  10.1016/j.ijcard.2014.03.177
    [10] Després JP, Lemieux I, Bergeron J, et al. Abdominal obesity and the metabolic syndrome: contribution to global cardiometabolic risk. Arterioscler Thromb Vasc Biol, 2008; 28, 1039−49. doi:  10.1161/ATVBAHA.107.159228
    [11] Nicklas BJ, Penninx BWJH, Ryan AS, et al. Visceral adipose tissue cutoffs associated with metabolic risk factors for coronary heart disease in women. Diabetes Care, 2003; 26, 1413−20. doi:  10.2337/diacare.26.5.1413
    [12] Sam S, Haffner S, Davidson MH, et al. Relationship of abdominal visceral and subcutaneous adipose tissue with lipoprotein particle number and size in type 2 diabetes. Diabetes, 2008; 57, 2022−7. doi:  10.2337/db08-0157
    [13] Sironi AM, Gastaldelli A, Mari A, et al. Visceral fat in hypertension: influence on insulin resistance and beta-cell function. Hypertension, 2004; 44, 127−33. doi:  10.1161/01.HYP.0000137982.10191.0a
    [14] Arsenault BJ, Lachance D, Lemieux I, et al. Visceral adipose tissue accumulation, cardiorespiratory fitness, and features of the metabolic syndrome. Arch Intern Med, 2007; 167, 1518−25. doi:  10.1001/archinte.167.14.1518
    [15] Ebbert JO, Jensen MD. Fat depots, free fatty acids, and dyslipidemia. Nutrients, 2013; 5, 498−508. doi:  10.3390/nu5020498
    [16] Bailey DP, Savory LA, Denton SJ, et al. The hypertriglyceridemic waist, waist-to-height ratio, and cardiometabolic risk. J Pediatr, 2013; 162, 746−52. doi:  10.1016/j.jpeds.2012.09.051
    [17] Hobkirk JP, King RF, Gately P, et al. The predictive ability of triglycerides and waist (hypertriglyceridemic waist) in assessing metabolic triad change in obese children and adolescents. Metab Syndr Relat Disord, 2013; 11, 336−42. doi:  10.1089/met.2012.0152
    [18] Ma CM, Wang R, Liu XL, et al. The relationship between hypertriglyceridemic waist phenotype and early diabetic nephropathy in type 2 diabetes. Cardiorenal Med, 2017; 7, 295−300. doi:  10.1159/000477828
    [19] Lam BCC, Koh GCH, Chen C, et al. Comparison of Body Mass Index (BMI), Body Adiposity Index (BAI), Waist Circumference (WC), Waist-To-Hip Ratio (WHR) and Waist-To-Height Ratio (WHtR) as predictors of cardiovascular disease risk factors in an adult population in Singapore. PLoS One, 2015; 10, e0122985. doi:  10.1371/journal.pone.0122985
    [20] Yang YD, Dong B, Zou ZY, et al. Association between Vegetable consumption and blood pressure, stratified by BMI, among Chinese adolescents aged 13-17 years: a national cross-sectional study. Nutrients, 2018; 10, 451. doi:  10.3390/nu10040451
    [21] Chen YJ, Ma L, Ma YH, et al. A national school-based health lifestyles interventions among Chinese children and adolescents against obesity: rationale, design and methodology of a randomized controlled trial in China. BMC Public Health, 2015; 15, 210. doi:  10.1186/s12889-015-1516-9
    [22] Alberti KGMM, Eckel RH, Grundy SM, et al. Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation, 2009; 120, 1640−5. doi:  10.1161/CIRCULATIONAHA.109.192644
    [23] US Department of Health and Human Services, Public Health Service, National Institutes of Health, et al. National Cholesterol Education Program (NCEP): highlights of the report of the Expert Panel on Blood Cholesterol Levels in Children and Adolescents. Pediatrics, 1992; 89, 495-501.
    [24] Flynn JT, Kaelber DC, Baker-Smith CM, et al. Clinical practice guideline for screening and management of high blood pressure in children and adolescents. Pediatrics, 2017; 140, e20171904. doi:  10.1542/peds.2017-1904
    [25] Jennings A, Cassidy A, Van Sluijs EMF, et al. Associations between eating frequency, adiposity, diet, and activity in 9-10 year old healthy-weight and centrally obese children. Obesity, 2012; 20, 1462−8. doi:  10.1038/oby.2012.72
    [26] McCarthy HD, Jarrett KV, Crawley HF. The development of waist circumference percentiles in British children aged 5.0-16.9 y. Eur J Clin Nutr, 2001; 55, 902−7. doi:  10.1038/sj.ejcn.1601240
    [27] Lv RR, Xu Z, Sun Y, et al. Normal waist-hip ratio of children and adolescents in Beijing. Chin J Sch Health, 2012; 33, 750−1. (In Chinese
    [28] Ford ES, Ajani UA, Mokdad AH. The metabolic syndrome and concentrations of C-reactive protein among U. S. youth. Diabetes Care, 2005; 28, 878−81. doi:  10.2337/diacare.28.4.878
    [29] Camhi SM, Katzmarzyk PT. Prevalence of cardiometabolic risk factor clustering and body mass index in adolescents. J Pediatr, 2011; 159, 303−7. doi:  10.1016/j.jpeds.2011.01.059
    [30] Dong YH, Zou ZY, Wang HJ, et al. National school-based health lifestyles intervention in Chinese Children and adolescents on obesity and hypertension. Front Pediatr, 2021; 9, 615283. doi:  10.3389/fped.2021.615283
    [31] Sobczak AIS, Blindauer CA, Stewart AJ. Changes in plasma free fatty acids associated with type-2 diabetes. Nutrients, 2019; 11, 2022. doi:  10.3390/nu11092022
    [32] Hirano T. Pathophysiology of diabetic dyslipidemia. J Atheroscler Thromb, 2018; 25, 771−82. doi:  10.5551/jat.RV17023
    [33] Zhang MH, Cao YX, Wu LG, et al. Association of plasma free fatty acids levels with the presence and severity of coronary and carotid atherosclerotic plaque in patients with type 2 diabetes mellitus. BMC Endocr Disord, 2020; 20, 156. doi:  10.1186/s12902-020-00636-y
    [34] Zhang FL, Ren JX, Zhang P, et al. Strong Association of Waist Circumference (WC), Body Mass Index (BMI), Waist-to-Height Ratio (WHtR), and Waist-to-Hip Ratio (WHR) with diabetes: a population-based cross-sectional study in Jilin Province, China. J Diabetes Res, 2021; 2021, 8812431.
    [35] Gamboa Delgado EM, Domínguez Urrego CL, Quintero Lesmes DC. Waist-to-height ratio and its relation with cardiometabolic risk factors in children from Bucaramanga, Colombia. Nutr Hosp, 2017; 34, 1338−44.
    [36] Yan YK, Cheng H, Zhao XE, et al. Change in the prevalence of obesity phenotypes and cardiometabolic disorders among children aged 6-17 in Beijing during 2004-2013. Chin J Prev Med, 2016; 50, 34−9. (In Chinese
    [37] Jiang Y, Dou YL, Chen HY, et al. Performance of waist-to-height ratio as a screening tool for identifying cardiometabolic risk in children: a meta-analysis. Diabetol Metab Syndr, 2021; 13, 66. doi:  10.1186/s13098-021-00688-7
    [38] Cabral Rocha AL, Feliciano Pereira P, Cristine Pessoa M, et al. Hypertriglyceridemic waist phenotype and cardiometabolic alterations in brazilian adults. Nutr Hosp, 2015; 32, 1099−106.
    [39] Kelishadi R, Jamshidi F, Qorbani M, et al. Association of hypertriglyceridemic-waist phenotype with liver enzymes and cardiometabolic risk factors in adolescents: the CASPIAN-III study. J Pediatr, 2016; 92, 512−20. doi:  10.1016/j.jped.2015.12.009
    [40] de Farias Costa PR, Assis AMO, de Magalhães Cunha C, et al. Hypertriglyceridemic waist phenotype and changes in the fasting glycemia and blood pressure in children and adolescents over one-year follow-up period. Arq Bras Cardiol, 2017; 109, 47−53.
    [41] Ma CM, Liu XL, Yin FZ, et al. Hypertriglyceridemic waist-to-height ratio phenotype: association with atherogenic lipid profile in Han adolescents. Eur J Pediatr, 2015; 174, 1175−81. doi:  10.1007/s00431-015-2522-8
    [42] Yusuf S, Hawken S, Ôunpuu S, et al. Obesity and the risk of myocardial infarction in 27 000 participants from 52 countries: a case-control study. Lancet, 2005; 366, 1640−9. doi:  10.1016/S0140-6736(05)67663-5
    [43] Emdin CA, Khera AV, Natarajan P, et al. Genetic association of waist-to-hip ratio with cardiometabolic traits, type 2 diabetes, and coronary heart disease. JAMA, 2017; 317, 626−34. doi:  10.1001/jama.2016.21042
    [44] Szabo L, McCracken C, Cooper J, et al. The role of obesity-related cardiovascular remodelling in mediating incident cardiovascular outcomes: a population-based observational study. Eur Heart J Cardiovasc Imaging, 2023; 24, 921−9. doi:  10.1093/ehjci/jeac270
    [45] Lemieux I, Després JP. Metabolic syndrome: past, present and future. Nutrients, 2020; 12, 3501. doi:  10.3390/nu12113501
    [46] Zhu YY, Zheng RZ, Wang GX, et al. Inverted U-shaped associations between glycemic indices and serum uric acid levels in the general chinese population: findings from the China cardiometabolic disease and cancer cohort (4C) study. Biomed Environ Sci, 2021; 34, 9−18.
    [47] Sanchez-Lozada LG, Rodriguez-Iturbe B, Kelley EE, et al. Uric acid and hypertension: an update with recommendations. Am J Hypertens, 2020; 33, 583−94. doi:  10.1093/ajh/hpaa044
    [48] Mukhopadhyay P, Ghosh S, Pandit K, et al. Uric acid and its correlation with various metabolic parameters: a population-based study. Indian J Endocrinol Metab, 2019; 23, 134−9. doi:  10.4103/ijem.IJEM_18_19
    [49] Wu L, He Y, Jiang B, et al. Association between serum uric acid level and hypertension in a Chinese elderly rural population. Clin Exp Hypertens, 2017; 39, 505−12. doi:  10.1080/10641963.2016.1259325
    [50] Dong JK, Yang HJ, Zhang YP, et al. Triglyceride-glucose index is a predictive index of hyperuricemia events in elderly patients with hypertension: a cross-sectional study. Clin Exp Hypertens, 2022; 44, 34−9. doi:  10.1080/10641963.2021.1984499
  • [1] DU Min, ZHANG Shi Mo, SHANG Wei Jing, YAN Wen Xin, LIU Qiao, QIN Chen Yuan, LIU Min, LIU Jue.  2022 Multiple-country Monkeypox Outbreak and Its Importation Risk into China: An Assessment Based on the Risk Matrix Method . Biomedical and Environmental Sciences, 2022, 35(10): 878-887. doi: 10.3967/bes2022.115
    [2] YANG Xin Li, OUYANG Yi Fei, ZHANG Xiao Fan, SU Chang, BAI Jing, ZHANG Bing, HONG Zhong Xin, DU Shu Fa, WANG Hui Jun.  Waist Circumference of the Elderly over 65 Years Old in China Increased Gradually from 1993 to 2015: A Cohort Study . Biomedical and Environmental Sciences, 2022, 35(7): 604-612. doi: 10.3967/bes2022.080
    [3] WANG Wei, ZHANG Mei, XU Cheng Dong, YE Peng Peng, LIU Yun Ning, HUANG Zheng Jing, HU Cai Hong, ZHANG Xiao, ZHAO Zhen Ping, LI Chun, CHEN Xiao Rong, WANG Li Min, ZHOU Mai Geng.  Hypertension Prevalence, Awareness, Treatment, and Control and Their Associated Socioeconomic Factors in China: A Spatial Analysis of A National Representative Survey . Biomedical and Environmental Sciences, 2021, 34(12): 937-951. doi: 10.3967/bes2021.130
    [4] HU Yue Hua, WANG Xin Yu, YAO Hong Yan, ZHANG Jian, YIN Da Peng, FENG Guo Shuang.  Increases in Height among Chinese Children and Adolescents by Gender: An Age-Period-Cohort Analysis . Biomedical and Environmental Sciences, 2021, 34(5): 348-355. doi: 10.3967/bes2021.046
    [5] GAO Huai Quan, WANG Bang Xuan, SUN Li Li, LI Ting, WU Lu, FU Lian Guo, MA Jun.  The Mediating Effect of Body Dissatisfaction in Association between Obesity and Dietary Behavior Changes for Weight Loss in Chinese Children . Biomedical and Environmental Sciences, 2019, 32(9): 639-646. doi: 10.3967/bes2019.083
    [6] LIU Dan, HAO Yun Xia, ZHAO Ting Zhi, SONG Peng Kun, ZHAI Yi, PANG Shao Jie, ZHAO Yan Fang, ZHANG Mei, WANG Zhuo Qun, MI Sheng Quan, WANG Yu Ying, ZHANG Jian, ZHAO Wen Hua.  Childhood BMI and Adult Obesity in a Chinese Sample: A 13-Year Follow-up Study . Biomedical and Environmental Sciences, 2019, 32(3): 162-168. doi: 10.3967/bes2019.023
    [7] XU Pei Pei, YANG Ti Ti, XU Juan, LI Li, CAO Wei, GAN Qian, HU Xiao Qi, PAN Hui, ZHAO Wen Hua, ZHANG Qian.  Dairy Consumption and Associations with Nutritional Status of Chinese Children and Adolescents . Biomedical and Environmental Sciences, 2019, 32(6): 393-405. doi: 10.3967/bes2019.054
    [8] WANG Ting Ting, LIN Bo, CUI Wen Xiu, ZHANG Ming Zhi, ZHANG Yong Hong, ZHANG Shao Yan.  Clustering of Cardiovascular Risk Factors and Diabetes: A Prospective Cohort Study on the Inner Mongolian Population in China . Biomedical and Environmental Sciences, 2018, 31(10): 749-756. doi: 10.3967/bes2018.100
    [9] ZHAI Yi, FANG Hong Yun, YU Wen Tao, YU Dong Mei, ZHAO Li Yun, LIANG Xiao Feng, ZHAO Wen Hua.  Changes in Waist Circumference and Abdominal Obesity among Chinese Adults over a Ten-year Period . Biomedical and Environmental Sciences, 2017, 30(5): 315-322. doi: 10.3967/bes2017.042
    [10] WANG Zhuo, DENG Ying, LIU Shi Wei, HE Jun, JI Kui, ZENG Xin Ying, YANG Shu Juan, XU Xin Yin, LUO Yu, ZHOU Mai Geng, ZHANG Jian Xin.  Prevalence and Years of Life Lost due to Disability from Dental Caries among Children and Adolescents in Western China, 1990-2015 . Biomedical and Environmental Sciences, 2017, 30(10): 701-707. doi: 10.3967/bes2017.095
    [11] ZHONG Chong Ke, ZHONG Xiao Yan, XU Tan, ZHANG Yong Hong.  Measures of Abdominal Adiposity and Risk of Stroke:A Dose-Response Meta-analysis of Prospective Studies . Biomedical and Environmental Sciences, 2016, 29(1): 12-23. doi: 10.3967/bes2016.002
    [12] FU Lian Guo, WANG Hai Jun, LI Xiao Hui, WANG Zhi Qiang, Patrick WC Lau, YANG Yi De, MENG Xiang Kun, MA Jun.  An Objective Measure to Evaluate Actual Body Shape among Children and Adolescents in China . Biomedical and Environmental Sciences, 2014, 27(8): 582-593. doi: 10.3967/bes2014.033
    [13] CHEN Tian Jiao, JI Cheng Ye.  Secular Change in Stature of Urban Chinese Children and Adolescents, 1985-2010 . Biomedical and Environmental Sciences, 2013, 26(1): 13-22. doi: 10.3967/0895-3988.2013.01.002
    [14] Reduced Arterial Compliance Associated with Metabolic Syndrome in Chinese Children and Adolescents . Biomedical and Environmental Sciences, 2010, 23(2): 102-107.
    [15] GUAN-SHENG MA, CHENG-YE JI, JUN MA, JIE MI, RITA YT SUNG, FENG XIONG, WEI-LI YAN, XIAO-QI HU, YAN-PING LI, SONG-MING DU, HONG-YUN FANG, JING-XIONG JIANG.  Waist Circumference Reference Values for Screening Cardiovascular Risk Factors in Chinese Children and Adolescents . Biomedical and Environmental Sciences, 2010, 23(1): 21-31.
    [16] YAN-PING LI, XIAO-QI HU, JING-ZHAO, XIAO-GUANG YANG, GUAN-SHENG MA.  Application of the WHO Growth Reference (2007) to Assess the Nutritional Status of Children in China . Biomedical and Environmental Sciences, 2009, 22(2): 130-135.
    [17] HAI-JUN WANG, HENG ZHANG, SHI-WEI ZHANG, YONG-PING PAN, JUN MA.  Association of the Common Genetic Variant Upstream of INSIG2 Gene with Obesity Related Phenotypes in Chinese Children and Adolescents . Biomedical and Environmental Sciences, 2008, 21(6): 528-536.
    [18] SHAO-JUN MA, JUN-FANG WANG, CUI-ZHU MEI, XUE-FANG XU, GONG-HUAN YANG.  Passive Smoking in China: Contributing Factors and Areas for Future Interventions . Biomedical and Environmental Sciences, 2007, 20(5): 420-425.
    [19] GUAN-SHENG MA, YAN-PING LI, XIAO-QI HU, ZHAO-HUI CUI, XIAO-GUANG YANG, CHUN-MING CHEN.  Report on Childhood Obesity in China (2)Verification of BMI Classification Reference for Overweight and Obesity in Chinese Children and Adolescents . Biomedical and Environmental Sciences, 2006, 19(1): 1-7.
    [20] XIN ZHANG, CHENG-YE JI.  Autism and Mental Retardation of Young Children in China . Biomedical and Environmental Sciences, 2005, 18(5): 334-340.
  • 23144+Supplementary Materials.pdf
  • 加载中
图(2) / 表ll (8)
计量
  • 文章访问数:  135
  • HTML全文浏览量:  64
  • PDF下载量:  54
  • 被引次数: 0
出版历程
  • 收稿日期:  2023-05-04
  • 录用日期:  2023-10-09
  • 网络出版日期:  2024-04-07
  • 刊出日期:  2024-03-20

Predictive Ability of Hypertriglyceridemic Waist, Hypertriglyceridemic Waist-to-Height Ratio, and Waist-to-Hip Ratio for Cardiometabolic Risk Factors Clustering Screening among Chinese Children and Adolescents

doi: 10.3967/bes2024.027
    基金项目:  This study was supported by the National Natural Science Foundation of China [no. 81903336, Yi-de Yang]; the Health Research Project of Hunan Provincial Health Commission [no. 202112031516, Yi-de Yang]; Scientific Research Fund of Hunan Provincial Education Department [no. 22B0038, Yi-de Yang]; the Research Team for Reproduction Health and Translational Medicine of Hunan Normal University [2023JC101]; Key Project of Developmental Biology and Breeding from Hunan Province [no. 2022XKQ0205]; and Open Project for Postgraduates of Hunan Normal University [no. KF2022019, Tian-li Xiao]. The funders had no role in the design, analysis, or writing of this article.
    作者简介:

    XIAO Tian Li, female, born in 1998, Bachelor Degree, majoring in growth and development of children and adolescents

    YUAN Shu Qian, female, born in 1997, Master Degree, majoring in growth and development of children and adolescents

    通讯作者: YANG Yi De, PhD, Associate Professor, Tel: 86-10-16607315363, E-mail: yangyide2007@126.comWANG Xi Jie, PhD, Tel: 86-10-16601126810, E-mail: xijie_wang@126.comZOU Zhi Yong, Professor, PhD, Supervisor, PhD, Tel: 86-10-13581525193, E-mail: harveyzou2002@bjmu.edu.cn

English Abstract

XIAO Tian Li, YUAN Shu Qian, GAO Jing Yu, Julien S. Baker, YANG Yi De, WANG Xi Jie, ZHENG Chan Juan, DONG Yan Hui, ZOU Zhi Yong. Predictive Ability of Hypertriglyceridemic Waist, Hypertriglyceridemic Waist-to-Height Ratio, and Waist-to-Hip Ratio for Cardiometabolic Risk Factors Clustering Screening among Chinese Children and Adolescents[J]. Biomedical and Environmental Sciences, 2024, 37(3): 233-241. doi: 10.3967/bes2024.027
Citation: XIAO Tian Li, YUAN Shu Qian, GAO Jing Yu, Julien S. Baker, YANG Yi De, WANG Xi Jie, ZHENG Chan Juan, DONG Yan Hui, ZOU Zhi Yong. Predictive Ability of Hypertriglyceridemic Waist, Hypertriglyceridemic Waist-to-Height Ratio, and Waist-to-Hip Ratio for Cardiometabolic Risk Factors Clustering Screening among Chinese Children and Adolescents[J]. Biomedical and Environmental Sciences, 2024, 37(3): 233-241. doi: 10.3967/bes2024.027
    • Cardiovascular disease (CVD) is one of the leading causes of mortality worldwide[1,2], especially in China, where prevalent cases of cardiovascular diseases reached nearly 94 million in 2016[2], and accounts for more than 40% of all deaths in the country[1]. In the last few decades, the prevalence of cardiometabolic risk factors among Chinese children and adolescents has increased significantly, posing a threat to the health of children and adolescents in China[3,4]. In addition, cardiometabolic risk factors may be more significantly related to visceral adiposity compared with overall adiposity[5].

      Some indicators which consider body fat distribution, such as waist-to-height ratio (WHtR), waist circumference (WC), and waist-to-hip ratio (WHR), have been shown to be associated with increased cardiovascular morbidity and mortality[6,7]. A cohort study in China found that the WC of elderly people aged over 65 years showed a significant increasing trend[8]. However, these measures of WC can only identify subcutaneous abdominal fat[9]. Some studies have demonstrated that visceral fat is more strongly associated with CVDs as well as metabolic risk factors than subcutaneous abdominal fat[10,11]. Compared with those without excess visceral fat, patients with excess visceral adiposity face significantly greater risks of metabolic abnormalities, coronary artery disease, and type 2 diabetes[12-14]. However, due to radiation exposure and high cost, measurements of visceral fat, such as Electronic Computed Tomography Instrument (CT) or Magnetic Resonance Imaging (MRI), are not widely available tools for health screening[9]. However, a previous study showed that triglyceride is significantly correlated with visceral fat, suggesting that it could be a good predictor of cardiovascular disease[15].

      Several studies have shown that the hypertriglyceridemic waist (HW) phenotype is a cost-effective tool to identity visceral fat for people of all ages, and is associated with an elevated risk of coronary heart disease in youth[16-18]. However, compared with adults, the WC cutoffs are age- and sex-specific for children, and are less available or challenging to use non-professionally[19]. Therefore, a cross-sectional study of Han adolescents indicated that the hypertriglyceridemic waist-to-height ratio (HWHtR) phenotype could be used as a marker to identify the lipid profile of adolescent atherosclerosis[19]. However, it is not known whether the HWHtR phenotype can be used as an alternative to the HW phenotype for children in China. Previous studies on the associations between the WHR phenotype and cardiovascular risk factors clustering are limited, especially in children and adolescents. In addition, a previous study in adults indicated that WHR has a weaker association with cardiovascular risk factors than HW and HWHtR[19]. However, it is not clear whether WHR is a better marker than others among children and adolescents.

      Therefore, the purpose of this study was to investigate the relationship between HW, HWHtR, and WHR and cardiovascular risk factors, as well as their potential or predictive ability as screening tools for cardiometabolic risk factor clustering in children and adolescents.

    • The study was a cross-sectional study which was embedded in the baseline survey of a national multicentered cluster randomized controlled trial (Trial registration date: January 22, 2015; Registration number: NCT02343588) involving about 70,000 Chinese participants aged 6–18 years from seven provinces (including Shanghai, Liaoning, Tianjin, Guangdong, Hunan, Chongqing, and Ningxia)[20]. The trial was approved by the Ethics Committee of Peking University (No. IRB0000105213034). With consent obtained from students, their parents, and the principals of their educational institutions, participants completed the questionnaires, physical examinations, and biochemical assessments. The details of the trial are available in the previously published protocol[21]. Participants who did not have complete baseline data and who were over the age of 18 years were excluded from this analysis. Finally, 62,168 children aged 6–17 years were selected for analysis.

    • Anthropometric parameters included weight, height, hip circumference (HC), WC, and body mass index (calculated using the formula of weight divided by square of height, kg/m2). Weight was measured to the nearest 0.1 kg on a calibrated level type weight scale (model RGT-140, China). Height was measured to an accuracy of 1 mm with Portable stadiometer (model TZG, China). Each participant was measured without shoes and in light clothing. HC and WC were measured using steel tape. HC was defined as the maximum extension of the hip, and WC was defined as the horizontal line between the upper border of the iliac crest and the lowest rib. WHtR was calculated by dividing WC by height, whereas WHR was calculated by dividing WC by HC. Blood pressure (BP) was measured using a mercury sphygmomanometer, (model XJ1ID, China) and a TZ-1 stethophone. Diastolic blood pressure (DBP) and systolic blood pressure (SBP) were recorded twice on the right arm. Potential covariates were investigated by questionnaires, including a children questionnaire and parent questionnaire. Age, sex, area (rural or urban), and family income (< 5,000 CNY, or ≥ 5,000 CNY) were included in the present study as covariates.

    • Biochemical variables included Triglycerides (TG), fasting plasma glucose (FPG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C). Venous blood samples were taken and collected after a 12-h overnight fast. All biochemical analyses was performed at a biomedical analysis laboratory.

    • WC cut-off values ≥ 90th percentile for age and sex for children and adolescents were classified as high WC[22]. HC was defined as the dimension at the level of the maximum extension of the buttocks. WHR was calculated as WC (cm) divided by HC (cm)[19]. WHtR was calculated as WC (cm) divided by height (cm)[19]. According the National Cholesterol Education Program’s (NCEP) Pediatric Panel Report, elevated TG was defined as ≥ 1.24 mmol/L[23]. High SBP and DBP were classified as ≥ 90th percentile for age, sex according to published reference values[24]. Hypercholesterolemia was defined as TC ≥ 200 mg/dL[23]. Low HDL-C was defined as ≤ 1.03 mmol/L and high LDL-C as ≥ 130 mg/dL[25]. The HWHtR phenotype was defined as serum TG concentrations ≥ 1.24 mmol/L and WHtR ≥ 0.50[16]. The HW phenotype was defined as serum TG concentrations ≥ 1.24 mmol/L and WC ≥ 90th percentile for age and sex[23,26]. The WHR phenotype was defined as WHR threshold for age and sex according to lambda-mu-sigma (LMS) curve[27]. Cardiometabolic health risk factor clustering was defined as having at least two of the following cardiometabolic health risk factors: elevated TG, low HDL, elevated LDL, hypercholesterolemia, elevated BP, and impaired FPG[28,29].

    • We used SPSS version 26.0 for all statistical analyses, considering P < 0.05 as statistically significant. Comparisons were conducted based on sex using the ANOVA. Data are described as means ± standard deviation (SD). Chi-square analysis based on sex and category of the phenotype was conducted for categorical variables. Multiple logistic regression models were conducted for modeling relationships among HW, HWHtR, and WHR, and elevated TG, low HDL, elevated LDL, hypercholesterolemia, elevated BP, and impaired FPG. Logistic regression was also used to determine the risk (OR, 95% CI) of cardiometabolic health risk factor clustering. Participants without the HW and HWHtR phenotypes were considered as the reference groups. Two multivariable logistic regression models with different covariates were fitted for the analysis. Model 1 was adjusted for sex, age, and BMI. In addition to adjusting for the covariates in Model 1, Model 2 was adjusted for area and family income. The stratified analyses were performed by sex (boy and girl) and age (6–9, 10–12, 13–15, and 16–17 years)[30].

    • Of the 62,168 study participants, 17.5% individuals fulfilled the diagnosis for the cardiometabolic risk factors clustering (18.1% boys and 16.9% girls), and 8.3% were defined as having the HW phenotype, with 8.2% in boys and 8.5% in girls. A total of 6.6% of the participants were classified as having the HWHtR phenotype (8.0% in boys and 5.2% in girls), 34.3% of the participants had high WHR (32.8% in boys and 40.0% in girls). For different cardiometabolic risk factors, 25.7% had elevated BP, 0.3% had impaired FPG, 12.6% had low HDL, 3.0% had elevated LDL, 26.8% had elevated TG, and 5.4% had hypercholesterolemia. Boys had significantly higher SBP, DBP, and FPG than girls (P < 0.05). Girls had significantly higher TG, LDL, and TC than boys (P < 0.05) (Table 1 and Supplementary Table S1, available in www.besjournal.com).

      Table 1.  General characteristics of the study population by sex

      Variables Total Boy (n = 32,064) Girl (n = 30,104) P
      N x ± s n x ± s n x ± s
      Age (years) 62,168 10.80 ± 3.30 32,064 10.72 ± 3.26 30,104 10.94 ± 3.34 < 0.001
      BMI (kg/m2) 62,168 18.55 ± 3.75 32,064 18.84 ± 3.92 30,104 18.24 ± 3.54 < 0.001
      WC (cm) 61,802 64.75 ± 10.79 31,858 65.85 ± 11.50 29,944 63.59 ± 9.86 < 0.001
      Hipline (cm) 61,815 77.18 ± 12.00 31,882 77.07 ± 12.07 29,933 77.30 ± 11.92 0.018
      CHRF clustering, n (%)
       < 2 13,195 82.5 6,675 81.9 6,520 83.1 0.056
       ≥ 2 2,800 17.5 1,472 18.1 1,328 16.9
      HW, n (%)
       0 13,736 91.7 7,050 91.8 6,686 91.5 0.479
       1 1,249 8.3 628 8.2 621 8.5
      HWHtR, n (%)
       0 9,079 93.4 4,587 92.0 4,492 94.8 < 0.001
       1 644 6.6 397 8.0 247 5.2
      WHR, n (%)
       0 40,480 65.7 21,373 67.2 19,107 60.0 < 0.001
       1 21,153 34.3 10,410 32.8 10,743 40.0
        Note. BMI, body mass index; WC, waist circumference; HW, hypertriglyceridemic waist; HWHtR, hypertriglyceridemic waist-to-height ratio; WHR, waist-to-hip ratio; CHRF clustering, cardiometabolic health risk factors clustering.

      Comparisons were conducted between the participants with and without the HW, HWHtR, and WHR phenotypes. As the results in Supplementary Tables S2S4 (available in www.besjournal.com) showed, the participants with the HW, HWHtR or WHR phenotypes had significantly higher levels of LDL, TG, TC, and BP, as well as higher cardiometabolic health risk. Moreover, the participants had lower HDL levels than those without these phenotypes. Additionally, the difference of the level of FPG between the participants with and without those phenotypes was insignificant.

      Table S2.  Comparisons of cardiometabolic health risk factors between subjects with and without HW phenotype

      Variables Category non-HW HW P
      N Freq. (%) N Freq. (%)
      Sex boy 7,050 51.3 628 50.3 0.479
      girl 6,686 48.7 621 49.7
      Area rural 4,946 41.7 385 36.4 < 0.001
      urban 6,905 58.3 672 63.6
      Family income
      (CNY)
      < 5,000 3,756 64.3 357 62.9 0.494
      ≥ 5,000 2,086 35.7 211 37.1
      BP (mmHg) < P90 10,632 77.4 669 53.6 < 0.001
      P90 3,104 22.6 580 46.4
      FPG (mmol/L) < 6.1 13,696 99.7 1,242 99.7 0.739
      ≥ 6.1 37 0.3 4 0.3
      HDL (mmol/L) ≥ 1.03 12,235 89.1 839 67.2 < 0.001
      < 1.03 1,501 10.9 410 32.8
      LDL (mmol/L) < 3.37 13,384 97.4 1,159 92.8 < 0.001
      ≥ 3.37 352 2.6 90 7.2
      TG (mmol/L) < 1.24 10,845 79.0 0 0.0 < 0.001
      ≥ 1.24 2,891 21.0 1,249 100.0
      TC (mmol/L) < 5.17 13,075 95.2 1,098 87.9 < 0.001
      ≥ 5.17 661 4.8 151 12.1
      CHRF clustering < 2 11,851 86.3 395 31.6 < 0.001
      ≥ 2 1,885 13.7 854 68.4
        Note. BP, blood pressure; FPG, fasting plasma glucose; HDL, high-density lipoprotein; LDL, low-density lipoprotein; TG, triglyceride; TC, total cholesterol; CHRF clustering, cardiometabolic health risk factors clustering; HW, hypertriglyceridemic waist.

      Table S4.  Comparisons of cardiometabolic health risk factors between subjects with and without WHR phenotype

      VariablesCategorynon-WHRWHRP
      NFreq. (%)NFreq. (%)
      Sexboy21,37352.810,41049.2< 0.001
      girl19,10747.210,74350.8
      Arearural12,75637.76,87736.90.081
      urban21,12462.311,77063.1
      Family income (CNY)< 5,00010,91463.55,61263.90.494
      ≥ 5,0006,28536.53,17236.1
      BP (mmHg)< P9027,95077.113,99969.3< 0.001
      P908,31622.96,20630.7
      FPG (mmol/L)< 6.110,16699.75,64199.70.683
      ≥ 6.1270.3170.3
      HDL (mmol/L)≥ 1.039,13189.64,74483.8< 0.001
      < 1.031,06410.492016.2
      LDL (mmol/L)< 3.379,97997.95,40895.5< 0.001
      ≥ 3.372162.12564.5
      TG (mmol/L)< 1.247,59274.54,01370.9< 0.001
      ≥ 1.242,60225.51,64929.1
      TC (mmol/L)< 5.179,74595.65,25492.8< 0.001
      ≥ 5.174504.44107.2
      CHRF clustering< 28,71585.54,37277.2< 0.001
      ≥ 21,47914.51,29022.8
        Note. BP, blood pressure; FPG, fasting plasma glucose; HDL, high-density lipoprotein; LDL, low-density lipoprotein; TG, triglyceride; TC, total cholesterol; CHRF clustering, cardiometabolic health risk factors clustering; WHR, waist-to-hip ratio.

      Multivariate adjusted ORs (95% CIs) for cardiometabolic health risk factors among the HW, HWHtR, and WHR phenotypes are presented in Table 2. With adjustment of age and sex, and BMI, the risk of cardiometabolic health risk factors in the HW or HWHtR phenotype was significantly higher than those without the phenotype among children (HW: OR = 10.79, 95% CI: 9.28–12.55; HWHtR: OR = 9.21, 95% CI: 7.45–11.39). The risks of low HDL, elevated LDL, elevated TC, and high BP were also significantly higher for children with these phenotypes compared with those for children without the HW, HWHtR or WHR phenotype. After adjusting for age, sex, BMI, area, and family income by multivariate logistic regression, the risk of cardiometabolic health risk factors clustering in the HW phenotype was 11.22-fold higher than that in the non-HW group (OR = 12.22, 95% CI: 9.54–15.67). The risk of cardiometabolic health risk factors clustering in the HWHtR phenotype was also higher that than in the non-HWHtR group (HW: OR = 9.70, 95% CI: 6.93–13.58). Those with the HW phenotype were more likely to have low HDL, elevated LDL, elevated TC, and high BP compared with non-HW group. Additionally, except for LDL and TC, there was no significant difference between the HWHtR and HW phenotypes in the OR of risk factors for cardiometabolic risk factors clustering after adjustment of the covariates. The risk of elevation of LDL with the HWHtR phenotype was significantly higher than that with the HW phenotype (HW: OR = 2.96, 95% CI: 1.84–4.75; HWHtR: OR = 6.87, 95% CI: 3.58–13.19). The OR(95%CI) for risk of elevated TC is 3.89 (95%CI: 2.65-5.73) and 4.94 (95%CI: 2.88-8.47) for HW and HWHtR phenotype, respectively. Compared with children with the HW or HWHtR phenotype, the risk of cardiometabolic risk factors clustering in the WHR phenotype was much lower and insignificant.

      Table 2.  OR (95% CI) of cardiometabolic health risk factors for the HW, HWHtR, and WHR phenotypes

      Models HW HWHtR WHR
      OR 95% CI P OR 95% CI P OR 95% CI P
      Model 1
      Elevated TG NA NA NA NA NA NA 0.82 0.75–0.89 < 0.001
      Low HDL 2.83 2.42–3.32 < 0.001 2.63 2.13–3.24 < 0.001 1.18 1.05–1.31 0.004
      Elevated LDL 2.08 1.55–2.78 < 0.001 3.18 2.13–4.74 < 0.001 1.94 1.59–2.38 < 0.001
      Elevated TC 2.58 2.05–3.25 < 0.001 2.85 2.04–3.98 < 0.001 1.64 1.41–1.91 < 0.001
      Elevated BP 1.42 1.23–1.63 0.007 1.31 1.08–1.58 0.007 0.92 0.88–0.96 < 0.001
      FPG 1.07 0.32–3.53 0.540 0.60 0.12–3.02 0.538 1.14 0.57–2.26 0.720
      CHRF clustering 10.79 9.28–12.55 < 0.001 9.21 7.45–11.39 < 0.001 1.05 0.95–1.15 0.350
      Model 2
      Elevated TG NA NA NA NA NA NA 1.09 0.95–1.25 0.234
      Low HDL 2.45 1.88–3.19 < 0.001 2.71 1.93–3.80 < 0.001 1.30 1.08–1.55 0.005
      Elevated LDL 2.96 1.84–4.75 < 0.001 6.87 3.58–13.19 < 0.001 1.64 1.15–2.33 < 0.001
      Elevated TC 3.89 2.65–5.73 < 0.001 4.94 2.88–8.47 < 0.001 1.51 1.15–1.98 0.003
      Elevated BP 1.32 1.06–1.66 0.015 1.08 0.80–1.47 0.614 1.00 0.93–1.07 0.905
      FPG 0.98 0.10–9.63 0.990 1.59 0.13–19.23 0.716 1.89 0.64–5.54 0.247
      CHRF clustering 12.22 9.54–15.67 < 0.001 9.70 6.93–13.58 < 0.001 1.14 0.97–1.34 0.115
        Note. BP, blood pressure; FPG, fasting plasma glucose; HDL, high-density lipoprotein; LDL, low-density lipoprotein; TG, triglyceride; TC, total cholesterol; CHRF clustering, cardiometabolic health risk factors clustering; HW, hypertriglyceridemic waist; HWHtR, hypertriglyceridemic waist-to-height ratio; WHR, waist-to-hip ratio; NA, data not available. Model 1: adjusted for sex (boy or girl), age (years) and BMI. Model 2: adjusted for variables from model 1, and additionally adjusted by area and family income.

      The results of multivariate logistic regression analysis of cardiometabolic health risk factors of the HW, HWHtR, and WHR phenotypes by sex and age stratified according to model 2 are shown in Supplementary Tables S5S6 (available in www.besjournal.com) and Figures 12. The results after stratification by sex and age were similar to those without stratification. The WHR phenotype had a significantly lower and more insignificant risk of cardiometabolic risk factor aggregation than children with the HW or HWHtR phenotypes.

      Figure 1.  Forest plot of cardiometabolic health risk factors for HW (A), HWHtR (B), and WHR (C) phenotypes stratified by sex. BP, blood pressure; FPG, fasting plasma glucose; HDL, high-density lipoprotein; LDL, low-density lipoprotein; TG, triglyceride; TC, total cholesterol; NA, data not available. CHRF clustering: cardiometabolic health risk factors clustering. Model: adjusted for age (years), BMI, area and family income. HW, hypertriglyceridemic waist; HWHtR, hypertriglyceridemic waist-to-height ratio; WHR, waist-to-hip ratio.

      Figure 2.  Forest plot of cardiometabolic health risk factors for HW (A), HWHtR (B), and WHR (C) phenotypes stratified by age. BP, blood pressure; FPG, fasting plasma glucose; HDL, high-density lipoprotein; LDL, low-density lipoprotein; TG, triglyceride; TC, total cholesterol; NA, data not available. CHRF clustering: cardiometabolic health risk factors clustering. Model: adjusted for sex, BMI, area and family income. HW, hypertriglyceridemic waist; HWHtR, hypertriglyceridemic waist-to-height ratio; WHR, waist-to-hip ratio.

      Table S5.  OR (95% CI) of cardiometabolic health risk factors for HW, HWHtR and WHR phenotype stratified by sex

      Models HW HWHtR WHR
      OR 95% CI P OR 95% CI P OR 95% CI P
      Boys Model 1
      Elevated TG NA NA NA NA NA NA 0.94 0.83−1.07 0.330
      Low HDL 2.93 2.35−3.67 < 0.001 3.05 2.32−4.01 < 0.001 1.39 1.19−1.62 < 0.001
      Elevated LDL 2.22 1.47−3.35 < 0.001 3.02 1.77−5.15 < 0.001 2.01 1.46−2.75 < 0.001
      Elevated TC 2.48 1.78−3.46 < 0.001 2.59 1.67−4.02 < 0.001 1.54 1.22−1.96 < 0.001
      Elevated BP 1.46 1.19−1.78 0.007 1.48 1.14−1.91 0.003 0.86 0.81−0.92 < 0.001
      FPG NA NA NA NA NA NA 1.15 0.49−2.69 0.752
      CHRF clustering 14.11 11.23−17.72 < 0.001 15.19 11.23−20.55 < 0.001 1.15 1.00−1.33 0.053
      Model 2
      Elevated TG NA NA NA NA NA NA 1.12 0.90−1.41 0.304
      Low HDL 2.83 1.94−4.15 < 0.001 3.49 2.22−5.48 < 0.001 1.62 1.24−2.11 < 0.001
      Elevated LDL 4.58 2.14−9.80 < 0.001 7.47 2.67−20.91 < 0.001 1.78 0.98−3.26 0.060
      Elevated TC 3.38 1.88−6.07 < 0.001 3.97 1.85−8.52 < 0.001 1.33 0.86−2.05 0.197
      Elevated BP 1.13 0.81−1.58 0.476 1.20 0.78−1.83 0.407 0.93 0.83−1.03 0.152
      FPG NA NA NA NA NA NA 3.06 0.89−10.06 0.077
      CHRF clustering 15.28 10.44−22.36 < 0.001 16.18 9.91−26.42 < 0.001 1.31 1.02−1.67 0.034
      Girls Model 1
      Elevated TG NA NA NA NA NA NA 0.73 0.65−0.82 < 0.001
      Low HDL 2.68 2.14−3.36 < 0.001 2.16 1.54−3.02 < 0.001 1.04 0.89−1.21 0.646
      Elevated LDL 2.00 1.32−3.04 0.001 3.37 1.86−6.14 < 0.001 1.84 1.41−2.41 < 0.001
      Elevated TC 2.82 2.05−3.89 < 0.001 3.11 1.87−5.19 < 0.001 1.66 1.36−2.03 < 0.001
      Elevated BP 1.34 1.10−1.64 0.004 1.10 0.81−1.49 0.531 0.96 0.90−1.023 0.205
      FPG 2.95 0.70−12.47 0.140 2.18 0.29−16.15 0.447 1.43 0.46−4.47 0.542
      CHRF clustering 8.84 7.20−10.85 < 0.001 5.33 3.91−7.26 < 0.001 0.96 0.84−1.09 0.498
      Model 2
      Elevated TG NA NA NA NA NA NA 1.05 0.88−1.26 0.568
      Low HDL 2.10 1.45−3.03 < 0.001 2.07 1.22−3.49 0.007 1.11 0.87−1.42 0.413
      Elevated LDL 2.26 1.21−4.22 0.010 6.44 2.74−15.15 < 0.001 1.56 1.01−2.42 0.045
      Elevated TC 4.55 2.74−7.57 < 0.001 6.25 2.95−13.25 < 0.001 1.64 1.16−2.31 0.005
      Elevated BP 1.46 1.07−1.98 0.017 0.98 0.78−1.23 0.858 1.03 0.94−1.14 0.506
      FPG 1.43 0.11−19.29 0.786 1.63 0.22−11.96 0.631 0.78 0.12−5.28 0.799
      CHRF clustering 10.26 7.38−14.27 < 0.001 5.76 3.57−9.29 < 0.001 1.02 0.83−1.27 0.825
       Note. BP, blood pressure; FPG, fasting plasma glucose; HDL, high-density lipoprotein; LDL, low−density lipoprotein; TG, triglyceride; TC, total cholesterol. CHRF clustering, cardiometabolic health risk factors clustering; HW, hypertriglyceridemic waist; HWHtR, hypertriglyceridemic waist-to-height ratio; WHR, waist−to−hip ratio; NA, data not available. Model 1: adjusted for age (years) and BMI. Model 2: adjusted for variables from model 1, and additionally adjusted by area and family income.
    • In this cross-sectional study of 62,168 participants, the HW, HWHtR, and WHR phenotypes were used as a marker for cardiometabolic risk factors and their clustering. We found that the HW phenotype was associated with a significantly increased risk of cardiometabolic risk factors clustering among children and adolescents. The increased risk of cardiometabolic risk factors clustering also exists in children and adolescents with the HWHtR phenotype compared with children and adolescents without an HWHtR phenotype. However, compared with the WHR phenotype, the HW and HWHtR phenotypes were superior as screening indicators for cardiometabolic health risk factor clustering.

      When body fat capacity exceeds the normal load, the storage capacity of subcutaneous fat will significantly decrease and release excess free fatty acids in the body[15,31]. High concentrations of fatty acids will lead to excess visceral fat, accumulation of TG, and infiltration of inflammatory cells and fat tissues, leading to increased risk of brain disease[32,33]. Therefore, it is important to find cost-effective, simple, and convenient indexes that can measure subcutaneous and visceral fats for the prevention and control of clustering of cardiometabolic risk factors.

      There is a known advantage in adults using the WHtR as a predictor of cardiometabolic risk factors clustering[34]. A previous study conducted in Singapore indicated that a combination of WHtR and BMI could be the best clinical marker in identifying adults with CVD risk factors[19]. Also, a study conducted in Columbia showed that high WHtR was a risk factor for dyslipidemia for children aged 6–10 years[35]. Similar results have also been reported in a study conducted in Chinese children and adolescents aged 6–17 years[36]. A meta-analysis suggests that WHtR has good and robust performance as a screening tool for identifying cardiometabolic risk in children[37].

      The HW phenotype has also been used as a convenient tool to screen the population at high risk of cardiometabolic risk factors clustering. A transversal study including 976 middle-aged Brazilian adults reported that the HW phenotype predicted the incidence of cardiometabolic health risk factors[38]. Similarly, a cohort study including 95,015 Asian adults (18–98 years of age) found that the HW phenotype might be a simple and useful clinical tool to screen individuals who are at risk for future cardiometabolic health risk factors[9]. Among children and adolescents, the HW phenotype was also regarded as a significant marker for cardiometabolic health risk factors. A national study of adolescents in Iran showed that the HW phenotype was associated with cardiometabolic risk factors, particularly elevated cholesterol[39]. A cohort study conducted in Brazil indicated that the HW phenotype could be a good predictor for SBP and glycemia in children and adolescents[40]. Additionally, a study conducted among Chinese adolescents indicated that the HW phenotype was a useful marker for screening adolescents who are at high risk of metabolic syndrome[41]. Overall, the HW phenotype is a convenient and useful tool to identify groups with high risks of cardiometabolic risk factors clustering among both in adults and adolescents.

      However, the HWHtR phenotype in children and adolescents has received limited attention. A previous study that enrolled 3,136 Han adolescents indicated that compared with the HW phenotype, the HWHtR phenotype is a better marker of atherogenic lipid profile for adolescents[41]. In addition, the results of the study showed that the HWHtR phenotype is a non-age-dependent index (P = 0.042). Therefore, this study supposed that the HWHtR phenotype is much more applicable for screening adolescents with higher cardiometabolic risk. However, whether the HWHtR phenotype applies to other ethnic groups is unclear[41]. In our study, except for LDL showing a stronger correlation with the HWHtR phenotype than the HW phenotype with the adjustment of potential covariates, there was no significant difference between the two phenotypes (HWHtR and HW) in the OR of other risk factors for cardiometabolic health risk factors clustering. In addition, the measurement of WC is more readily available than WHtR[16], and the HW phenotype showed a weaker association with sex than the HWHtR phenotype. Therefore, the HW phenotype may be a better marker than the HWHtR phenotype. Previous studies have indicated that the HW phenotype may be a better marker than WHtR for identifying children and adolescents who are at risk for cardiometabolic disorders[16,40], which is similar to the results of this study.

      The majority of adults with high WHR have disproportionate amounts of visceral fat[15]. In a case-control study on acute myocardial infarction, elevated WHR was associated with a higher risk of myocardial infarction[42]. In Europe, it was confirmed that elevated WHR causes higher SBP, higher triglycerides, and two-hour glucose levels[43]. However, a previous study indicated that WHR is more weakly associated with cardiovascular risk factors than HW and HWHtR[19]. Moreover, a large representative observational study in the UK showed that a combination of BMI and the WHR phenotype is more effective in assessing the cardiovascular risk that is associated with obesity[44]. In our study, the WHR phenotype also showed a weak ability to predict cardiometabolic risk factors clustering. The present study did not find an association between the three phenotypes and the risk of hyperglycemia. Hyperglycemia and dyslipidemia are important components of metabolic syndrome and are closely related[45]. A cross-sectional study of a large sample in China (n = 105,922) showed an inverted U-shaped association between the primary glycemic indices and uric acid levels in adults[46]. However, elevated serum uric acid may be associated with the risk of hypertension and dyslipidemia, and the intermediate regulatory mechanism may involve the regulation of FPG[47-50].

      Our study has significant strengths. Although it is not the first study to explore indicators for cardiometabolic risk factors clustering among Chinese children and adolescents, the sample size of this study was large and more than 60,000 children and adolescents from different provinces and ethnic groups in China, ranging in age from 6 to 17 years old. A previous study was conducted among Chinese children and adolescents, but only among Han Chinese aged 13 to 17, and the sample size was only approximately 3,000[41]. However, there were some limitations of this study. For example, it was a cross-sectional study, which limited our abilities to infer causality.

    • In conclusion, the HW phenotype is a better simple marker for identifying children and adolescents with cardiometabolic risk factors clustering. Compared with HWHtR and WHR, the HW phenotype is a non-sex-dependent indicator with higher applicability to screen children and adolescents for cardiovascular risk factors.

    • The study protocol was approved by the Medical Ethical Committee of Peking University (Number: IRB0000105213034). Informed consent was obtained from all participants and their parents involved in the study.

    • Table S1.  General characteristics of the study population by sex

      Variables Total Boy (n = 32,064) Girl (n = 30,104) P
      N x ± s (%) n x ± s (%) n x ± s (%)
      Height 62,168 145.86 ± 17.08 32,064 146.93 ± 18.44 30,104 144.72 ± 15.43 < 0.001
      Weight 62,168 40.97 ± 15.53 32,064 42.35 ± 16.88 30,104 39.50 ± 13.79 < 0.001
      SBP (mmHg) 62,168 104.33 ± 12.17 32,064 105.72 ± 12.5 30,104 102.85 ± 11.62 < 0.001
      DBP (mmHg) 62,168 66.27 ± 8.84 32,064 66.8 ± 8.98 30,104 65.71 ± 8.64 < 0.001
      TG (mmol/L) 15,995 1.14 ± 0.81 8,147 1.09 ± 0.80 7,848 1.19 ± 0.83 < 0.001
      HDL (mmol/L) 15,998 1.89 ± 1.35 8,149 1.90 ± 1.38 7,849 1.88 ± 1.31 0.476
      LDL (mmol/L) 15,998 1.97 ± 0.71 8,149 1.94 ± 0.70 7,849 1.99 ± 0.72 < 0.001
      TC (mmol/L) 15,998 3.86 ± 0.90 8,149 3.81 ± 0.90 7,849 3.92 ± 0.90 < 0.001
      FPG (mmol/L) 15,990 4.14 ± 1.28 8,143 4.19 ± 1.31 7,847 4.09 ± 1.25 < 0.001
      Area rural 19,753 37.3 10,158 37.5 9,595 37.1 0.359
      urban 33,221 62.7 16,947 62.5 16,274 62.9
      Family income < 5,000 16,646 63.6 8,234 63.0 8,412 64.2 0.042
      (CNY) ≥ 5,000 9529 36.4 4,838 37.0 4,691 35.8
      BP (mmHg) < P90 42,292 74.3 20,716 70.6 21,576 78.2 < 0.001
      P90 14,642 25.7 8,611 29.4 6,031 21.8
      FPG (mmol/L) < 6.1 15,945 99.7 8,111 99.6 7,834 99.8 0.007
      ≥ 6.1 45 0.3 32 0.4 13 0.2
      HDL (mmol/L) ≥ 1.03 13,987 87.4 7,035 86.3 6,952 88.6 < 0.001
      < 1.03 2,011 12.6 1,114 13.7 897 11.4
      LDL (mmol/L) < 3.37 15,520 97.0 7,928 97.3 7,592 96.7 0.037
      ≥ 3.37 478 3.0 221 2.7 257 3.3
      TG (mmol/L) < 1.24 11,702 73.2 6,149 75.5 5,553 70.8 < 0.001
      ≥ 1.24 4,293 26.8 1,998 24.5 2,295 29.2
      TC (mmol/L) < 5.17 15,128 94.6 7,745 95.0 7,383 94.1 < 0.001
      ≥ 5.17 870 5.4 404 5.0 466 5.9
        Note. BMI, body mass index; WC, waist circumference; SBP, systolic blood pressure; DBP, diastolic blood pressure; BP, blood pressure; FPG, fasting plasma glucose; HDL, high-density lipoprotein; LDL, low-density lipoprotein; TG, triglyceride; TC, total cholesterol; HW, hypertriglyceridemic waist; HWHtR, hypertriglyceridemic waist-to-height ratio; WHR, waist-to-hip ratio; CHRF clustering, cardiometabolic health risk factors clustering.

      Table S3.  Comparisons of cardiometabolic health risk factors between subjects with and without HWHtR phenotype

      VariablesCategorynon-HWHtRHWHtRP
      NFreq. (%)NFreq. (%)
      Sexboy4,58750.539761.6< 0.001
      girl4,49249.524738.4
      Arearural3,23241.021436.70.041
      urban4,64659.036963.3
      Family income (CNY)< 5,0002,53465.418264.3< 0.001
      ≥ 5,0001,33834.610135.7
      BP (mmHg)< P906,65473.328944.9< 0.004
      P902,42526.735555.1
      FPG (mmol/L)< 6.19,05199.763999.70.907
      ≥ 6.1260.320.3
      HDL (mmol/L)≥ 1.037,85786.539861.8< 0.001
      < 1.031,22213.524638.2
      LDL (mmol/L)< 3.378,88397.858691.0< 0.001
      ≥ 3.371962.2589.0
      TG (mmol/L)< 1.246,61772.900.0< 0.001
      ≥ 1.242,46227.1644100.0
      TC (mmol/L)< 5.178,70095.856788.0< 0.001
      ≥ 5.173794.27712.0
      CHRF clustering< 27,48182.416525.6< 0.001
      ≥ 21,59817.647974.4
        Note. BP, blood pressure; FPG, fasting plasma glucose; HDL, high-density lipoprotein; LDL, low-density lipoprotein; TG, triglyceride; TC, total cholesterol; CHRF clustering, cardiometabolic health risk factors clustering; HWHtR, hypertriglyceridemic waist-to-height ratio.

      Table S6.  OR (95% CI) of cardiometabolic health risk factors for HW, HWHtR and WHR phenotype stratified by age

      Models HW HWHtR WHR
      OR 95% CI P OR 95% CI P OR 95% CI P
      6–9 years Model 1
      Elevated TG NA NA NA NA NA NA 0.70 0.59–0.82 < 0.001
      Low HDL 2.97 2.14–4.14 < 0.001 NA NA NA 1.45 1.18–1.78 < 0.001
      Elevated LDL 1.38 0.81–2.34 0.233 NA NA NA 1.94 1.44–2.62 < 0.001
      Elevated TC 2.08 1.40–3.07 < 0.001 NA NA NA 1.48 1.17–1.86 0.001
      Elevated BP 1.54 1.16–2.04 0.003 NA NA NA 0.97 0.89–1.05 0.428
      FPG 3.11 0.28–34.78 0.356 NA NA NA 1.81 0.58–5.65 0.310
      CHRF clustering 10.04 7.53–13.38 < 0.001 NA NA NA 1.38 1.15–1.66 0.001
      Model 2
      Elevated TG NA NA NA NA NA NA 1.10 0.83–1.47 0.508
      Low HDL 3.23 1.79–5.85 < 0.001 NA NA NA 1.43 1.00–2.04 0.052
      Elevated LDL 1.73 0.70–4.28 0.236 NA NA NA 1.49 0.86–2.57 0.155
      Elevated TC 2.88 1.42–5.82 0.003 NA NA NA 1.31 0.87–1.98 0.192
      Elevated BP 1.40 0.86–2.28 0.172 NA NA NA 1.17 1.03–1.34 0.017
      FPG NA NA NA NA NA NA 0.94 0.10–8.86 0.955
      CHRF clustering 11.77 7.13−19.44 < 0.001 NA NA NA 1.33 0.96–1.85 0.091
      10–12 years Model 1
      Elevated TG NA NA NA NA NA NA 0.63 0.54−0.73 < 0.001
      Low HDL 3.11 2.32−4.16 < 0.001 3.28 2.37−4.55 < 0.001 1.17 0.93−1.46 0.174
      Elevated LDL 2.83 1.73−4.62 < 0.001 3.82 2.25−6.50 < 0.001 2.07 1.42−3.02 < 0.001
      Elevated TC 2.91 1.94−4.38 < 0.001 3.56 2.28−5.56 < 0.001 1.83 1.37−2.44 < 0.001
      Elevated BP 1.54 1.20−1.98 0.001 1.51 1.14−2.01 0.004 0.87 0.79−0.94 0.001
      FPG 1.10 0.11−11.32 0.937 NA NA NA 1.59 0.51−4.96 0.428
      CHRF clustering 13.00 9.81−17.22 < 0.001 12.36 8.98−17.03 < 0.001 0.01 0.68−0.97 0.022
      Model 2
      Elevated TG NA NA NA NA NA NA 0.81 0.64−1.04 0.104
      Low HDL 2.85 1.78−4.57 < 0.001 3.50 2.08−5.88 < 0.001 1.57 1.09−2.26 0.016
      Elevated LDL 3.71 1.69−8.18 0.001 5.87 2.45−14.02 < 0.001 1.70 0.89−3.25 0.110
      Elevated TC 3.37 1.74−6.52 < 0.001 4.84 2.34−10.01 < 0.001 1.87 1.10−3.18 0.021
      Elevated BP 1.59 1.07−2.36 0.022 1.12 0.71−1.76 0.634 0.86 0.75−0.99 0.037
      FPG NA NA NA NA NA NA 9.05 0.95−85.90 0.055
      CHRF clustering 16.73 10.54−26.55 < 0.001 12.69 7.61−21.16 < 0.001 0.96 0.71−1.29 0.776
      13−15 years
      Model 1
      Elevated TG NA NA NA NA NA NA 0.88 0.75−1.03 0.109
      Low HDL 2.55 1.92−3.38 < 0.001 2.19 1.55−3.10 < 0.001 1.03 0.85−1.25 0.746
      Elevated LDL 1.48 0.73−3.01 0.275 1.95 0.86−4.40 0.109 1.28 0.75−2.18 0.361
      Elevated TC 2.64 1.58−4.41 < 0.001 1.57 0.80−3.09 0.195 1.57 1.09−2.26 0.016
      Elevated BP 1.30 0.99−1.69 0.057 1.47 1.05−2.05 0.025 0.90 0.83−0.98 0.019
      FPG NA NA NA NA NA NA 0.31 0.05−2.04 0.222
      CHRF clustering 10.06 7.55−13.39 < 0.001 8.07 5.62−11.58 < 0.001 0.86 0.72−1.03 0.095
      Model 2
      Elevated TG NA NA NA NA NA NA 1.22 0.94−1.57 0.132
      Low HDL 1.72 1.08−2.75 0.023 2.29 1.31−4.01 0.004 1.10 0.80−1.51 0.550
      Elevated LDL 2.04 0.66−6.35 0.217 4.14 1.07−16.08 0.040 2.46 0.99−6.07 0.052
      Elevated TC 5.64 2.45−12.99 < 0.001 3.01 1.02−8.85 0.045 1.94 1.00−3.77 0.050
      Elevated BP 1.06 0.69−1.62 0.789 1.03 0.61−1.77 0.903 0.97 0.84−1.11 0.628
      FPG NA NA NA NA NA NA NA NA NA
      CHRF clustering 9.79 6.21−15.44 < 0.001 8.17 4.60−14.53 < 0.001 1.07 0.80−1.42 0.654
      16−17 years
      Model 1
      Elevated TG NA NA NA NA NA NA 0.98 0.77−1.24 0.839
      Low HDL 2.57 1.76−3.75 < 0.001 2.52 1.61−3.93 < 0.001 1.22 0.91−1.63 0.184
      Elevated LDL 4.50 1.97−10.28 < 0.001 3.83 1.48−9.92 0.006 1.81 0.82−3.97 0.140
      Elevated TC 4.26 2.17−8.38 < 0.001 4.01 1.79−9.00 0.001 1.37 0.76−2.44 0.295
      Elevated BP 1.20 0.84−1.73 0.322 0.94 0.60−1.45 0.768 0.95 0.83−1.08 0.428
      FPG 1.39 0.16−11.81 0.763 1.85 0.19−18.26 0.601 0.59 0.06−5.64 0.644
      CHRF clustering 10.02 6.79−14.78 < 0.001 8.77 5.45−14.11 < 0.001 1.23 0.94−1.61 0.124
      Model 2
      Elevated TG NA NA NA NA NA NA 0.93 0.63−1.38 0.712
      Low HDL 2.56 1.36−4.81 0.004 2.48 1.19−5.14 0.012 1.32 0.80−2.16 0.275
      Elevated LDL 8.62 2.17−34.23 0.002 20.95 4.12−106.44 < 0.001 0.70 0.19−2.64 0.597
      Elevated TC 7.85 2.45−25.12 0.001 12.90 3.26−51.15 < 0.001 0.83 0.29−2.35 0.723
      Elevated BP 1.25 0.70−2.23 0.453 1.34 0.67−2.68 0.408 0.96 0.78−1.19 0.694
      FPG 2.56 0.11−60.52 0.561 3.24 0.11−96.18 0.497 NA NA NA
      CHRF clustering 12.22 6.48−23.04 < 0.001 11.23 5.31−23.76 < 0.001 1.15 0.74−1.81 0.533
        Note. BP, blood pressure; FPG, fasting plasma glucose; HDL, high−density lipoprotein; LDL, low−density lipoprotein; TG, triglyceride; TC, total cholesterol; CHRF clustering, cardiometabolic health risk factors clustering; HW, hypertriglyceridemic waist; HWHtR, hypertriglyceridemic waist−to−height ratio; WHR, waist−to−hip ratio. Model 1: adjusted for sex (boy or girl) and BMI. Model 2: adjusted for variables from model 1, and additionally adjusted by area and family income.
参考文献 (50)
补充材料:
23144+Supplementary Materials.pdf

目录

    /

    返回文章
    返回