Skip to main content

Table 1 Characteristics of included studies in the meta-analysis

From: Performance of waist-to-height ratio as a screening tool for identifying cardiometabolic risk in children: a meta-analysis

Author, year Study year Country Designa N(boys/girls) Age (range or mean ± SD) CMRs Categoriesb
Dou [20], 2020 2012–2014 China 1 8130(4325/3805) 7 ~ 18 Elevated FBG; HDL-C; LDL-C; TC; TG; dyslipidaemia; elevated blood pressure; central obesity; CMR1; CMR2; CMR3 1, 2, 3, 4, 5
Nan [42], 2013 NA China 1 1095 18 ± 0.95 FBG; pre-hypertension; HDL-C; TG; mets (CMR3) 1, 2, 3, 4
Hou [43], 2018 2012–2014 China 1 1170 6 ~ 17 FBG; hypertension; HDL-C; TG; 1 RF; 2rfs 1, 2, 3, 4
Perona [44], 2017 NA Spain 1 1001(468/533) 13.2 ± 1.2 Glucose; SBP hypertension; DBP hypertension; HDL-C; tgs; LDL-C; mets criteria ≥ 3 risks 1, 2, 3, 4
Quadros [45], 2016 2011 Brazil 1 1139 6 ~ 17 Glucose 3
López-González [46], 2016 2011–2015 Mexico 1 365 10 ~ 18 FBG; pre-hypertension; low HDL-C; TG; ≥ 2 rfs 1, 2, 3, 4
Kruger [47], 2013 2003 South African 1 178 14 ~ 18 Glucose; pre-hypertension 2, 3
Xue [48], 2014 2011– 2014 China 1 8378(4245/4133) 6 ~ 17 SBP; DBP; hypertension 3
Motswagole [49], 2011 2000– 2001 South African 1 688(321/367) 9 ~ 15 High BP (95th percentiles) 3
Kromeyer-Hauschild [50], 2013 2003– 2006 Germany 1 6813(3492/3321) 11 ~ 17 Hypertension 3
Chiolero [51], 2013 2005 Switzerland 1 5207 12.3 ± 0.5 Elevated BP 3
Cheah [52], 2018 2015 Malaysia 1 2461(1033/1428) 13 ~ 17 Hypertension 3
Meng [53], 2008 2004 China 2 4939 6 ~ 18 High BP; dyslipidaemia; 1 RF; ≥ 2 rfs; 3rfs 1, 3, 4
Christofaro [54], 2018 2011 Brazil 1 8295 10 ~ 17 Hypertension 3
Ma [55], 2016 1993– 2011 China 1 10,163(5346/4817) 7 ~ 17 Elevated BP 3
Beck [56], 2011 2006 Brazil 1 660(317/343) 14 ~ 19 High BP 3
Wariri [57], 2018 2015 Nigeria 1 367 10 ~ 18 Elevated BP 3
Mishra [58], 2015 2011–2013 India 1 1913 6 ~ 16 High SBP (pre-hypertension); high DBP (pre-hypertension) 3
Liu [59], 2007 2004 China 1 962 5 ~ 19 Dyslipidaemia 4
Zheng [60], 2016 2011–2012 China 1 399(boy only) 9.3 ± 1.7 Dyslipidaemia 4
Chen [61], 2019 2015–2017 China 1 452(255/197) 6 ~ 9 Abdominal fat 5
Ejtahed [62], 2019 2015 Iran 1 14,233(7019/7214) 7 ~ 18 Central obesity 5
Dong [63], 2016 2010 China 1 105,245(60,435/60590) 7 ~ 18 Abdominally overweight 5
Fujita [64], 2011 2008–2010 Japan 1 422(226/196) 10 Abdominal fat 5
Zhou [16], 2014 2010 China 1 16,914(8843/8071) 7 ~ 17 Central obesity; meeting 3 criteria of mets 1, 5
Dai [65], 2014 2009–2010 China 1 18,529(9771/8758) 6 ~ 15  ≥ 2 rfs 1
Matsha [66], 2013 2007–2008 South African 1 1272(496/776) 10 ~ 16 2 components of mets 1
Bauer [26], 2015 2006–2009 the United States 1 6052 10 ~ 13  ≥ 1 RF; ≥ 2 rfs; ≥ 3 rfs 1
Liu [67], 2015 2006 China 1 3136(1601/1535) 13 ~ 17 Hypertriglyceridemia waist phenotype 1
Seo [21], 2017 2011–2014 Korea 1 2935 10 ~ 19 Mets (CMR2) 1
Aguirre [68], 2017 NA Ecuador 1 395(186/209) 10 ~ 15 Meeting 3 criteria of mets; meeting 4 criteria of mets 1
Adegboye [69], 2010 Denmark 1997–1998, Estonia 1998–1999, Portugal 1999–2000 Denmark, Estonia, Portugal 1 2835(1385/1452) 8.2 ~ 17.3 3 rfs 1
Ma [70], 2017 2006 China 1 3136(1601/1535) 13 ~ 17 Mets (CMR3) 1
Zhao [71], 2017 1999–2012 The United States 1 3621(1868/1753) 12 ~ 17  ≥ 3 criteria of mets 1
Xu [72], 2017 2007–2011 China 1 11,174(6170/5004) 10 ~ 17 Mets (CMR3) 1
Oliveira [73], 2018 2014 Brazil 1 1035(470/565) 12 ~ 20 Mets (CMR3) 1
LIU [74], 2017 2010–2011 China 1 928(492/436) 11 ~ 16 Mets (CMR3) 1
Arsang-Jang [75], 2019 2003–2016 Iran 1 14,286(7235/7051)  > 10 Mets (CMR3) 1
Vasquez [76], 2019 NA Chile 1 678(354/324) 16 Mets (CMR3) 1
Graves [77], 2014 1998–2005 The United Kingdom 3 2856(1368/1488) 7 ~ 13  ≥ 3 rfs 1
Tompuri [22], 2019 2007–2009 Finland 1 482(249/233) 6 ~ 8 Meeting 3 criteria of mets 1
Benmohammed [38], 2015 2007 Algeria 1 1088(528/560) 15.5 ± 1.8 Meeting 3 criteria of mets 1
Zhang [78], 2019 NA China 1 683(366/317) 8–15 Mets (CMR3) 1
Yuan [79], 2020 NA China 1 683(366/317) 8–15 FBG 2
Wang [80], 2019 NA China 1 683(366/317) 8–15 Hypertension 3
Tee [81], 2020 NA Malaysia 1 513(211/302) 12–16 Hypertension (90th percentiles, 95th percentiles) 3
Vaquero-Álvarez [82], 2020 2018 Spain 1 265(144/121) 6–16 Hypertension 3
Cristine Silva [24], 2019 NA Brazil 1 548(238/310) 12–17 Mets (CMR3) 1
Li [83], 2020 2013 China 1 15,698(8004/7694) 6–17 Dyslipidaemia, hypertension, CMR3 1,3,4
Mai [84], 2020 2014–2015 Vietnam 1 10,936(5537,5399) 6–18 Elevated BP, dyslipidaemia, CMR3 1,3,4
Yazdi [85], 2020 2015 Iran 1 14,008(7091,6917) 7–18 Elevated BP, hypertension 3
Kilinc [86], 2019 2011 Turkey 1 2718(1467/1251) 6–17 Abnormality obesity 5
Arellano‐Ruiz [23], 2020 2010 Spain 1 848(408/440) 8–11 HDL-C, TG, elevated BP (95th percentiles), mets 1,3,4
  1. a1. Cross-sectional study; 2. Case–control study; 3. Cohort study
  2. b1.Clustering of cardiometabolic risk factors; 2. Elevated fasting blood glucose; 3. Elevated blood pressure; 4. Dyslipidaemia; 5. Central obesity;
  3. MetS, metabolic syndrome; CMR: cardiometabolic risk factor; CMR1: presenting with least one of CMRs; CMR2: presenting with at least two CMRs; CMR3: presenting with at least three CMRs; RF, risk factor; FBG: fasting blood glucose; BP, blood pressure; SBP, systolic blood pressure; DBP, diastolic blood pressure; TC, total cholesterol; TG, triglyceride; HDL-C, High-density leptin cholesterol; LDL-C, Low-density leptin cholesterol