Table 1 Characteristics of included studies in the 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 |