A Genetic Risk Score Improves the Prediction of Type 2 Diabetes Mellitus in Mexican Youths but Has Lower Predictive Utility Compared With Non-Genetic Factors

被引:10
|
作者
Miranda-Lora, America Liliana [1 ]
Vilchis-Gil, Jenny [1 ]
Juarez-Comboni, Daniel B. [2 ]
Cruz, Miguel [3 ]
Klunder-Klunder, Miguel [4 ]
机构
[1] Hosp Infantil Mexico Dr Federico Gomez, Epidemiol Res Unit Endocrinol & Nutr, Mexico City, DF, Mexico
[2] Hosp Infantil Mexico Dr Federico Gomez, Pediat Med Residency, Mexico City, DF, Mexico
[3] Inst Mexicano Seguro Social, Med Res Unit Biochem, Hosp Especialidades, Ctr Med Nacl SXXI, Mexico City, DF, Mexico
[4] Hosp Infantil Mexico Dr Federico Gomez, Res Subdirectorate, Mexico City, DF, Mexico
来源
FRONTIERS IN ENDOCRINOLOGY | 2021年 / 12卷
关键词
type; 2; diabetes; children; youth; genetic risk score; risk factors; obesity; body mass index; IMPAIRED GLUCOSE-TOLERANCE; LIFE-STYLE; FASTING GLUCOSE; PLASMA-GLUCOSE; ASSOCIATION; VARIANTS; ARCHITECTURE; METAANALYSIS; HISTORY;
D O I
10.3389/fendo.2021.647864
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Background Type 2 diabetes (T2D) is a multifactorial disease caused by a complex interplay between environmental risk factors and genetic predisposition. To date, a total of 10 single nucleotide polymorphism (SNPs) have been associated with pediatric-onset T2D in Mexicans, with a small individual effect size. A genetic risk score (GRS) that combines these SNPs could serve as a predictor of the risk for pediatric-onset T2D. Objective To assess the clinical utility of a GRS that combines 10 SNPs to improve risk prediction of pediatric-onset T2D in Mexicans. Methods This case-control study included 97 individuals with pediatric-onset T2D and 84 controls below 18 years old without T2D. Information regarding family history of T2D, demographics, perinatal risk factors, anthropometric measurements, biochemical variables, lifestyle, and fitness scores were then obtained. Moreover, 10 single nucleotide polymorphisms (SNPs) previously associated with pediatric-onset T2D in Mexicans were genotyped. The GRS was calculated by summing the 10 risk alleles. Pediatric-onset T2D risk variance was assessed using multivariable logistic regression models and the area under the receiver operating characteristic curve (AUC). Results The body mass index Z-score (Z-BMI) [odds ratio (OR) = 1.7; p = 0.009] and maternal history of T2D (OR = 7.1; p < 0.001) were found to be independently associated with pediatric-onset T2D. No association with other clinical risk factors was observed. The GRS also showed a significant association with pediatric-onset T2D (OR = 1.3 per risk allele; p = 0.006). The GRS, clinical risk factors, and GRS plus clinical risk factors had an AUC of 0.66 (95% CI 0.56-0.75), 0.72 (95% CI 0.62-0.81), and 0.78 (95% CI 0.70-0.87), respectively (p < 0.01). Conclusion The GRS based on 10 SNPs was associated with pediatric-onset T2D in Mexicans and improved its prediction with modest significance. However, clinical factors, such the Z-BMI and family history of T2D, continue to have the highest predictive utility in this population.
引用
收藏
页数:10
相关论文
共 33 条
  • [1] Utility of genetic and non-genetic risk factors in prediction of type 2 diabetes: Whitehall II prospective cohort study
    Talmud, Philippa J.
    Hingorani, Aroon D.
    Cooper, Jackie A.
    Marmot, Michael G.
    Brunner, Eric J.
    Kumari, Meena
    Kivimaeki, Mika
    Humphries, Steve E.
    BMJ-BRITISH MEDICAL JOURNAL, 2010, 340 : 192
  • [2] The Play of Genes and Non-genetic Factors on Type 2 Diabetes
    Mambiya, Michael
    Shang, Mengke
    Wang, Yue
    Li, Qian
    Liu, Shan
    Yang, Luping
    Zhang, Qian
    Zhang, Kaili
    Liu, Mengwei
    Nie, Fangfang
    Zeng, Fanxin
    Liu, Wanyang
    FRONTIERS IN PUBLIC HEALTH, 2019, 7
  • [3] Prediction of type 2 diabetes in women with a history of gestational diabetes using a genetic risk score
    Kwak, Soo Heon
    Choi, Sung Hee
    Kim, Kyunga
    Jung, Hye Seung
    Cho, Young Min
    Lim, Soo
    Cho, Nam H.
    Kim, Seong Yeon
    Park, Kyong Soo
    Jang, Hak C.
    DIABETOLOGIA, 2013, 56 (12) : 2556 - 2563
  • [4] SNP-Based Genetic Risk Score Modeling Suggests No Increased Genetic Susceptibility of the Roma Population to Type 2 Diabetes Mellitus
    Werissa, Nardos Abebe
    Piko, Peter
    Fiatal, Szilvia
    Kosa, Zsigmond
    Sandor, Janos
    Adany, Roza
    GENES, 2019, 10 (11)
  • [5] Early Prediction for Prediabetes and Type 2 Diabetes Using the Genetic Risk Score and Oxidative Stress Score
    Huang, Ximei
    Han, Youngmin
    Jang, Kyunghye
    Kim, Minjoo
    ANTIOXIDANTS, 2022, 11 (06)
  • [6] Genetic and biochemical risk factors for type 2 diabetes mellitus
    Prabhu, Sukumaran
    INDIAN JOURNAL OF BIOTECHNOLOGY, 2013, 12 (04): : 447 - 450
  • [7] Genetic Risk Score Increased Discriminant Efficiency of Predictive Models for Type 2 Diabetes Mellitus Using Machine Learning: Cohort Study
    Wang, Yikang
    Zhang, Liying
    Niu, Miaomiao
    Li, Ruiying
    Tu, Runqi
    Liu, Xiaotian
    Hou, Jian
    Mao, Zhenxing
    Wang, Zhenfei
    Wang, Chongjian
    FRONTIERS IN PUBLIC HEALTH, 2021, 9
  • [8] The Search for Genetic Risk Factors of Type 2 Diabetes Mellitus
    Park, Kyong Soo
    DIABETES & METABOLISM JOURNAL, 2011, 35 (01) : 12 - 22
  • [9] Genetic Risk Score in Diabetes Associated With Chronic Pancreatitis Versus Type 2 Diabetes Mellitus
    Goodarzi, Mark O.
    Nagpal, Tanvi
    Greer, Phil
    Cui, Jinrui
    Chen, Yii-Der, I
    Guo, Xiuqing
    Pankow, James S.
    Rotter, Jerome, I
    Alkaade, Samer
    Amann, Stephen T.
    Baillie, John
    Banks, Peter A.
    Brand, Randall E.
    Conwell, Darwin L.
    Cote, Gregory A.
    Forsmark, Christopher E.
    Gardner, Tmothy B.
    Gelrud, Andres
    Guda, Nalini
    LaRusch, Jessica
    Lewis, Michele D.
    Money, Mary E.
    Muniraj, Thiruvengadam
    Papachristou, Georgios, I
    Romagnuolo, Joseph
    Sandhu, Bimaljit S.
    Sherman, Stuart
    Singh, Vikesh K.
    Wilcox, C. Mel
    Pandol, Stephen J.
    Park, Walter G.
    Andersen, Dana K.
    Bellin, Melena D.
    Hart, Phil A.
    Yadav, Dhiraj
    Whitcomb, David C.
    CLINICAL AND TRANSLATIONAL GASTROENTEROLOGY, 2019, 10
  • [10] Early prediction for newly-diagnosed prediabetes and type 2 diabetes using the genetic risk score and oxidative stress score
    Huang, Ximei
    Cho, Da Hyun
    Han, Youngmin
    Kim, Minjoo
    ANNALS OF NUTRITION AND METABOLISM, 2023, 79 : 657 - 657