Causal Relationship Between Intelligence, Noncognitive Education, Cognition and Urinary Tract or Kidney Infection: A Mendelian Randomization Study

被引：0

作者：

Fu, Shuai ^{[1
]}

Li, Qiang ^{[1
]}

Cheng, Li ^{[1
]}

Wan, Sheng ^{[1
]}

Wang, Quan ^{[1
]}

Min, Yonglong ^{[1
]}

Xie, Yanghao ^{[1
]}

Liu, Huizhen ^{[1
]}

Hu, Taotao ^{[1
]}

Liu, Hong ^{[1
]}

Chen, Weidong ^{[1
]}

Zhang, Yanmin ^{[1
]}

Xiong, Fei ^{[1
]}

机构：

[1] Wuhan 1 Hosp, Dept Nephrol, 215 Zhongshan Ave, Wuhan 430022, Hubei, Peoples R China

来源：

INTERNATIONAL JOURNAL OF NEPHROLOGY AND RENOVASCULAR DISEASE | 2025年 / 18卷

基金：

中国国家自然科学基金;

关键词：

cognition; intelligence; kidney infection; mediation analyses; Mendelian randomization; noncognitive education; urinary tract infection; GENETIC-VARIANTS; EPIDEMIOLOGY; ASSOCIATION; INSTRUMENTS; PATHWAYS; ATLAS; LOCI;

D O I：

10.2147/IJNRD.S511736

中图分类号：

R5 [内科学]; R69 [泌尿科学（泌尿生殖系疾病）];

学科分类号：

1002 ; 100201 ;

摘要：

Background: The occurrence of urinary tract or kidney infection is correlated with intelligence, noncognitive education and cognition, but the causal relationship between them remains uncertain, and which risk factors mediate this causal relationship remains unknown. Methods: The intelligence (n=269,867), noncognitive education (n=510,795) and cognition data (n=257,700) were obtained from genome-wide association studies (GWAS) conducted in individuals of European ethnicities. The genetic associations between these factors and urinary tract or kidney infection (UK Biobank, n=397,867) were analyzed using linkage disequilibrium score regression. We employed a two-sample univariate and multivariate Mendelian randomization to evaluate the causal relationship, and utilized a two-step Mendelian randomization to examine the involvement of 28 potential mediators and their respective mediating proportions. Results: The genetic correlation coefficients of intelligence, noncognitive education, cognition, and urinary tract or kidney infection were -0.338, -0.218, and -0.330. The Mendelian randomization using the inverse variance weighted method revealed each 1-SD increase in intelligence, the risk of infection decreased by 15.9%, while after adjusting for noncognitive education, the risk decreased by 20%. For each 1-SD increase in noncognitive education, the risk of infection decreased by 8%, which further reduced to 7.1% after adjusting for intelligence and to 6.7% after adjusting for cognition. For each 1-SD increase in cognition, the risk of infection decreased by 10.8%, increasing to 11.9% after adjusting for noncognitive education. The effects of intelligence and cognition are interdependent. 2 out of 28 potential mediating factors exhibited significant mediation effects in the causal relationship between noncognitive education and urinary tract or kidney infection, with body mass index accounting for 12.1% of the mediation effect and smoking initiation accounting for 14.7%. Conclusion: Enhancing intelligence, noncognitive education, and cognition can mitigate the susceptibility to urinary tract or kidney infection. Noncognitive education exhibited independent effect, while body mass index and smoking initiation assuming a mediating role.

引用

页码：71 / 85

页数：15

共 57 条

[11] Demange PA, Malanchini M, Mallard TT., Investigating the genetic architecture of noncognitive skills using GWAS-by-subtraction, Nat genetics, 53, pp. 35-44, (2021)
[12] Lawlor DA, Harbord RM, Sterne JA, Timpson N, Davey Smith G., Mendelian randomization: using genes as instruments for making causal inferences in epidemiology, Stat Med, 27, pp. 1133-1163, (2008)
[13] Sanderson E, Davey Smith G, Windmeijer F, Bowden J., An examination of multivariable Mendelian randomization in the single-sample and two-sample summary data settings, Int J Epidemiol, 48, pp. 713-727, (2019)
[14] Carter AR, Sanderson E, Hammerton G., Mendelian randomisation for mediation analysis: current methods and challenges for implementation, Eur j Epidemiol, 36, pp. 465-478, (2021)
[15] Skrivankova VW, Richmond RC, Woolf BAR., Strengthening the reporting of observational studies in epidemiology using Mendelian randomiza-tion: the STROBE-MR statement, JAMA, 326, pp. 1614-1621, (2021)
[16] Burgess S, Davies NM, Thompson SG., Bias due to participant overlap in two-sample Mendelian randomization, Genetic Epidemiology, 40, pp. 597-608, (2016)
[17] Bulik-Sullivan BK, Loh PR, Finucane HK., LD score regression distinguishes confounding from polygenicity in genome-wide association studies, Nature genetics, 47, pp. 291-295, (2015)
[18] Bulik-Sullivan B, Finucane HK, Anttila V., An atlas of genetic correlations across human diseases and traits, Nat genetics, 47, pp. 1236-1241, (2015)
[19] Computationally efficient whole-genome regression for quantitative and binary traits, Nature genetics, 53, pp. 1097-1103, (2021)
[20] Yengo L, Sidorenko J, Kemper KE., Meta-analysis of genome-wide association studies for height and body mass index in ~700000 individuals of European ancestry, Human Molecular Genetics, 27, pp. 3641-3649, (2018)

← 1 2 3 4 5 6 →