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Record W3211105588 · doi:10.1101/2020.09.04.20188284

Trans-ethnic Mendelian randomization study reveals causal relationships between cardio-metabolic factors and chronic kidney disease

2020· preprint· en· W3211105588 on OpenAlexaff
Jie Zheng, YueMiao Zhang, Humaira Rasheed, Venexia Walker, Yuka Sugawara, Jiachen Li, Yue Leng, Benjamin Elsworth, Robyn E. Wootton, Si Fang, Qian Yang, Stephen Burgess, Philip Haycock, Maria Carolina Borges, Yoonsu Cho, Rebecca Carnegie, Amy Howell, Jamie Robinson, Laurent F. Thomas, Ben Brumpton, Kristian Hveem, Stein Hallan, Nora Franceschini, Andrew P. Morris, Anna Köttgen, Cristian Pattaro, Matthias Wuttke, Masayuki Yamamoto, Naoki Kashihara, Masato Akiyama, Masahiro Kanai, Koichi Matsuda, Yukinori Okada, Min Xu, Yufang Bi, Guang Ning, George Davey Smith, Sean Barbour, Canqing Yu, Bjørn Olav Åsvold, Hong Zhang, Tom R. Gaunt

Bibliographic record

VenuemedRxiv · 2020
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsUniversity of British Columbia
FundersMajor Research PlanPeking University Health Science CenterMedical Research CouncilNational Natural Science Foundation of ChinaPeking UniversityNational Institute for Health and Care ResearchNational Institutes of HealthUniversity Hospitals Bristol NHS Foundation TrustUniversity of BristolDepartment of Health and Social CareWellcome TrustUniversity of Michigan
KeywordsMendelian randomizationMedicineKidney diseaseInternal medicineRenal functionBody mass indexDiabetes mellitusBiobankBlood pressureType 2 diabetesDiseaseEndocrinologyPhysiologyBioinformaticsBiologyGeneticsGenotype

Abstract

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Abstract BACKGROUND The chronic kidney disease (CKD) public health burden is substantial and has not declined as expected with current interventions on disease treatments. A large number of clinical, biological, and behavioural risk factors have been associated with CKD. However, it is unclear which of them are causal. OBJECTIVE To systematically test whether previously reported risk factors for CKD are causally related to the disease in European and East Asian ancestries. DESIGN Two-sample Mendelian randomization (MR) and non-linear MR analyses. PARTICIPANTS 53,703 CKD cases and 960,624 controls of European ancestry from CKDGen, UK Biobank and HUNT, and 13,480 CKD cases and 238,118 controls of East Asian ancestry from Biobank Japan, China Kadoorie Biobank and Japan-Kidney-Biobank/ToMMo. MEASURES Systematic literature mining of PubMed studies identified 45 clinical risk factors and biomarkers with robustly associated genetic variants, including phenotypes related to blood pressure, diabetes, glucose, insulin, lipids, obesity, smoking, sleep disorders, nephrolithiasis, uric acid, coronary artery disease, bone mineral density, homocysteine, C-reactive protein, micro-nutrients and thyroid function, which were selected as exposures. The outcome was CKD (defined by clinical diagnosis or by estimated glomerular filtration rate (eGFR) < 60 ml/min/1.73m 2 ). RESULTS Eight risk factors showed evidence of causal effects on CKD in European ancestry, including body mass index (BMI), hypertension, systolic blood pressure, high density lipoprotein cholesterol, apolipoprotein A-I, lipoprotein A, type 2 diabetes (T2D) and nephrolithiasis. In East Asian ancestry, BMI, T2D and nephrolithiasis showed evidence of causal effects on CKD. Hypertension showed reliable evidence of a strong causal effect on CKD in Europeans but in contrast appeared to show a null effect in East Asians, suggesting the possibility of different causal risk factors in Europeans and East Asians. Although liability to T2D showed consistent effects on CKD, the effect of glycemic traits on CKD was weak, suggesting T2D may have glucose-independent mechanisms to influence CKD. Nonlinear MR indicated a threshold relationship between genetically predicted BMI and CKD, with increased risk at BMI above 25 kg/m 2 . LIMITATION Due to the unbalanced distribution of data between ancestries, we could only test 17 of the 45 risk factors in East Asian participants. CONCLUSIONS Eight CKD-associated risk factors showed evidence of causal effects on the disease in over 1.2 million European and East Asian ancestries. These risk factors were predominantly related to cardio-metabolic health, which supports the shared causal link between cardio-metabolic health and kidney function. This study provides evidence of potential intervention targets for primary prevention of CKD, which could help reduce the global burden of CKD and its cardio-metabolic co-morbidities. Research in context Evidence before this study Chronic kidney disease (CKD) has a major effect on global health, both as a direct cause of morbidity and mortality, and as an important complication for cardio-metabolic diseases. However, even with the existing interventions, the burden of CKD has not declined as expected over the last 30 years. Existing epidemiological studies of CKD have mainly focused on disease treatment in patients from specific populations and estimated association rather than causality. A systematic assessment of the causal determinants of CKD in different populations is urgently needed, to help promote a shift from treatment of CKD patients to prevention of the disease in high-risk groups. The use of genetic data and the latest Mendelian randomization (MR) methodologies offers a cost-effective way to evaluate the potential intervention targets for prevention of CKD in high-risk groups. Added value of this study In this study, we systematically constructed a causal atlas of 45 risk factors on CKD in European and East Asian ancestries using MR. To maximise power of these analyses and accuracy of the findings, we collected and harmonised CKD genetic association data from six large-scale biobanks (in over 1.1 million Europeans and 250,000 East Asians). By applying a comprehensive MR framework, including linear two-sample MR, bidirectional MR, multivariable MR and non-linear MR approaches, we identified eight risk factors with reliable evidence of causal effects on CKD in European ancestry studies, including body mass index (BMI), hypertension, systolic blood pressure, high density lipoprotein cholesterol, apolipoprotein A-I, lipoprotein A, type 2 diabetes (T2D) and nephrolithiasis. In East Asian studies, BMI, T2D and nephrolithiasis also showed causal effects on CKD. Among other factors, hypertension showed reliable evidence of a strong causal effect on CKD in Europeans but in contrast appeared to show a null effect in East Asians. This MR finding together with previous literature evidence opens up the possibility that hypertension could play different causal roles on CKD across ancestries. For diabetes and glycemic phenotypes, our MR and sensitivity analyses suggested the causal role of liability of T2D on CKD but suggested weak effects of glycemic phenotypes on CKD. This aligns with the recent trial of SGLT2 inhibitors on kidney disease, which implies T2D may have glucose-independent mechanisms to influence CKD. For lipids phenotypes, we found good evidence to support the role of high-density lipoprotein cholesterol on CKD and further suggested the effects of two lipids targets: circulating CETP level and lipoprotein A concentration. For body weight, our study quantified a threshold relationship between BMI and CKD, with increased risk at BMI above 25 kg/m 2 . The causal relationship between nephrolithiasis and CKD were reported in previous studies, but our study confirmed the causal links between the two for the first time. Implication of all the available evidence This study makes a significant advance in comprehensively prioritising intervention targets for CKD in over 1.2 million participants. Our study presents causal evidence from both European and East Asian population samples, widening the generalisability of the causal atlas. Importantly, the prioritised risk factors are predominantly related to cardio-metabolic health, which supports the shared causal link between cardio-metabolic health and kidney function. Clinically, the high-quality evidence from this study highlights the value of exploring these causal factors in the general population and prioritizes drug targets and life-style interventions for CKD primary prevention, which could help reduce the global burden of CKD and its cardio-metabolic co-morbidities.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.024
metaresearch head score (Gemma)0.055
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.126

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.055
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.005
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.052
GPT teacher head0.305
Teacher spread0.253 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations2
Published2020
Admission routes1
Has abstractyes

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