Diet-Derived Antioxidants and Risk of Kidney Stone Disease: Results From the NHANES 2007–2018 and Mendelian Randomization Study

We aimed to explore the associations between diet-derived antioxidants and kidney stone disease (KSD) risk in this study. We performed weighted multivariable-adjusted logistic regression to assess the associations between the six main diet-derived antioxidants and the risk of KSD by using data from the National Health and Nutrition Examination Survey (NHANES) 2007–2018. Then, we used the Mendelian randomization (MR) approach to verify the causal relationships between circulating antioxidants levels and KSD risk. Genetic tools were extracted from published genome-wide association studies (GWAS). Summary data for KSD was from the FinnGen study and UK biobank. Inverse variance weighted (IVW) was the primary analysis. The 26,438 participants, including 2,543 stone formers, were included for analyses. There were no significant associations between retinol, vitamin B6, vitamin C, vitamin E, and lycopene intake with the risk of KSD across all the quartile categories. Similarly, pooled odds ratio (OR) for KSD risk in genetically predicted per unit change were 1.25 (95% CI: 0.39, 4.02; p = 0.712), 1.14 (95% CI: 0.84, 1.53; p = 0.400), 0.75 (95% CI: 0.52, 1.10; p = 0.141), 1.66 (95% CI: 0.80, 3.46; p = 0.178), 1.27 (95% CI: 0.29, 5.62; p = 0.756), and 0.92 (95% CI: 0.76, 1.12; p = 0.417) for retinol, β-carotene, vitamin B6, vitamin C, α-tocopherol, and lycopene, respectively. The above estimates were replicated in the secondary analyses using UK biobank data. Our study did not support a causal association between circulating antioxidants levels and KSD risk. However, these findings should be verified in larger sample-size MR due to the pleiotropy and other limitations.

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Genetic variants of calcium and vitamin D metabolism in kidney stone disease

Nature Communications ◽

10.1038/s41467-019-13145-x ◽

2019 ◽

Vol 10 (1) ◽

Cited By ~ 6

Author(s):

Sarah A. Howles ◽

Akira Wiberg ◽

Michelle Goldsworthy ◽

Asha L. Bayliss ◽

Anna K. Gluck ◽

...

Keyword(s):

Vitamin D ◽

Kidney Stone ◽

Association Studies ◽

Vitamin D Supplementation ◽

Stone Disease ◽

Calcium Excretion ◽

Genome Wide Association Studies ◽

Vitamin D Metabolism ◽

Kidney Stone Disease

AbstractKidney stone disease (nephrolithiasis) is a major clinical and economic health burden with a heritability of ~45–60%. We present genome-wide association studies in British and Japanese populations and a trans-ethnic meta-analysis that include 12,123 cases and 417,378 controls, and identify 20 nephrolithiasis-associated loci, seven of which are previously unreported. A CYP24A1 locus is predicted to affect vitamin D metabolism and five loci, DGKD, DGKH, WDR72, GPIC1, and BCR, are predicted to influence calcium-sensing receptor (CaSR) signaling. In a validation cohort of only nephrolithiasis patients, the CYP24A1-associated locus correlates with serum calcium concentration and a number of nephrolithiasis episodes while the DGKD-associated locus correlates with urinary calcium excretion. In vitro, DGKD knockdown impairs CaSR-signal transduction, an effect rectified with the calcimimetic cinacalcet. Our findings indicate that studies of genotype-guided precision-medicine approaches, including withholding vitamin D supplementation and targeting vitamin D activation or CaSR-signaling pathways in patients with recurrent kidney stones, are warranted.

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Genetic variants of calcium and vitamin D metabolism in kidney stone disease

10.1101/515882 ◽

2019 ◽

Author(s):

Sarah A. Howles ◽

Akira Wiberg ◽

Michelle Goldsworthy ◽

Asha L. Bayliss ◽

Emily Grout ◽

...

Keyword(s):

Vitamin D ◽

Genetic Variants ◽

Kidney Stone ◽

Association Studies ◽

Stone Disease ◽

Calcium Excretion ◽

Genome Wide Association Studies ◽

Vitamin D Metabolism ◽

Kidney Stone Disease

Kidney stone disease (nephrolithiasis) is a major clinical and economic health burden1,2 with a heritability of ~45-60%3. To identify genetic variants associated with nephrolithiasis we performed genome-wide association studies (GWAS) and meta-analysis in British and Japanese populations, including 12,123 nephrolithiasis cases and 416,928 controls. Twenty loci associated with nephrolithiasis were identified, ten of which are novel. A novel CYP24A1 locus is predicted to affect vitamin D metabolism and five loci, DGKD, DGKH, WDR72, GPIC1, and BCR, are predicted to influence calcium-sensing receptor (CaSR) signaling. In a validation cohort of nephrolithiasis patients the CYP24A1-associated locus correlated with serum calcium concentration and number of kidney stone episodes, and the DGKD-associated locus correlated with urinary calcium excretion. Moreover, DGKD knockdown impaired CaSR-signal transduction in vitro, an effect that was rectifiable with the calcimimetic cinacalcet. Our findings indicate that genotyping may inform risk of incident kidney stone disease prior to vitamin D supplementation and facilitate precision-medicine approaches, by targeting CaSR-signaling or vitamin D activation pathways in patients with recurrent kidney stones.

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Genetics of common complex kidney stone disease: insights from genome-wide association studies

Urolithiasis ◽

10.1007/s00240-018-1094-2 ◽

2018 ◽

Vol 47 (1) ◽

pp. 11-21 ◽

Cited By ~ 9

Author(s):

Runolfur Palsson ◽

Olafur S. Indridason ◽

Vidar O. Edvardsson ◽

Asmundur Oddsson

Keyword(s):

Kidney Stone ◽

Association Studies ◽

Stone Disease ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Kidney Stone Disease ◽

Genome Wide

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Association of Increased Body Mass Index and Waist to Hip Ratio with Kidney Stone Disease: a prospective analysis of 493,410 UK Biobank participants

Journal of the Nuffield Department of Surgical Sciences ◽

10.37707/jnds.v2i4.200 ◽

2021 ◽

Vol 2 (4) ◽

Author(s):

Catherine Lovegrove

Keyword(s):

Waist Circumference ◽

Kidney Stones ◽

Kidney Stone ◽

Stone Disease ◽

Uk Biobank ◽

Kidney Stone Disease ◽

University Of Oxford ◽

Increased Risk ◽

Nuffield Department ◽

The Uk

Catherine E Lovegrove1,2 – [email protected] Littlejohns3- [email protected] Allen3- [email protected] A Howles1,4- [email protected] W Turney 1,2- [email protected] 1 Department of Urology, Oxford University Hospitals NHS Trust, Oxford, Oxfordshire, UK2 University of Oxford Nuffield Department of Surgical Sciences, Oxford, Oxfordshire, UK3 University of Oxford Nuffield Department of Public Health, Oxford, Oxfordshire, UK4 Academic Endocrine Unit, Radcliffe Department of Medicine, University of Oxford, Oxford, UK Objectives To investigate the relationship between measures of adiposity and risk of incident kidney stone disease. Patients and methods The UK Biobank is a prospective cohort study of ~500,000 participants whose height, weight, BMI, waist circumference, hip circumference, waist:hip ratio (WHR), total fat mass, fat-free mass, body-fat percentage and percentage truncal fat were measured at enrolment with linkage to medical records. ICD-10 and OPCS codes were used to identify individuals with a new diagnosis of nephrolithiasis from 2006-2010. Individuals with a history of kidney stones or incomplete data were excluded. Multivariate Cox-proportional hazard models were used to assess associations between anthropometric measures and incident kidney stones. Results From the UK Biobank, 493,410 individuals were identified for inclusion; 3,466 developed a kidney stone during the study period. Increasing weight, BMI, waist and hip circumferences, WHR, and body and truncal fat were associated with increased risk of incident kidney stone disease. However, after adjustment for BMI, only waist circumference and WHR remained significantly associated with risk of nephrolithiasis. In overweight patients, high (men 94-102cm, women 80-88cm) waist circumference or WHR (men >0.9, women >0.85) conferred >40% increased risk of stone formation. Conclusion This study indicates that android fat distribution is independently associated with increased risk of developing nephrolithiasis. Kidney stone disease is known to be associated with hypertension, cardiovascular disease, and diabetes, all of which are linked to android body shape. Our findings provide insight into anthropometric risk factors for stone disease, will facilitate identification of patients at greatest risk of stone recurrence, and will inform prevention strategies.

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The impact of metaphylaxis of kidney stone disease in the renal function at long term in active kidney stone formers patients

Urological Research ◽

10.1007/s00240-011-0407-5 ◽

2011 ◽

Vol 40 (3) ◽

pp. 225-229 ◽

Cited By ~ 4

Author(s):

Jose A. Meneses ◽

Fernando M. Lucas ◽

Fernando C. Assunção ◽

Junia P. P. Castro ◽

Rogério B. Monteiro

Keyword(s):

Renal Function ◽

Kidney Stone ◽

Stone Disease ◽

Kidney Stone Disease ◽

Stone Formers ◽

The Impact

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Childhood obesity and multiple sclerosis: A Mendelian randomization study

Multiple Sclerosis Journal ◽

10.1177/13524585211001781 ◽

2021 ◽

pp. 135245852110017

Author(s):

Adil Harroud ◽

Ruth E Mitchell ◽

Tom G Richardson ◽

John A Morris ◽

Vincenzo Forgetta ◽

...

Keyword(s):

Multiple Sclerosis ◽

Childhood Obesity ◽

Mendelian Randomization ◽

Pubertal Timing ◽

Association Studies ◽

Sensitivity Analyses ◽

Genome Wide Association Studies ◽

Uk Biobank ◽

Increased Risk ◽

Age At Puberty

Background: Higher childhood body mass index (BMI) has been associated with an increased risk of multiple sclerosis (MS). Objective: To evaluate whether childhood BMI has a causal influence on MS, and whether this putative effect is independent from early adult obesity and pubertal timing. Methods: We performed Mendelian randomization (MR) using summary genetic data on 14,802 MS cases and 26,703 controls. Large-scale genome-wide association studies provided estimates for BMI in childhood ( n = 47,541) and adulthood ( n = 322,154). In multivariable MR, we examined the direct effects of each timepoint and further adjusted for age at puberty. Findings were replicated using the UK Biobank ( n = 453,169). Results: Higher genetically predicted childhood BMI was associated with increased odds of MS (odds ratio (OR) = 1.26/SD BMI increase, 95% confidence interval (CI): 1.07–1.50). However, there was little evidence of a direct effect after adjusting for adult BMI (OR = 1.03, 95% CI: 0.70–1.53). Conversely, the effect of adult BMI persisted independent of childhood BMI (OR = 1.43; 95% CI: 1.01–2.03). The addition of age at puberty did not alter the findings. UK Biobank analyses showed consistent results. Sensitivity analyses provided no evidence of pleiotropy. Conclusion: Genetic evidence supports an association between childhood obesity and MS susceptibility, mediated by persistence of obesity into early adulthood but independent of pubertal timing.

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Frequent Daytime Napping is Detrimental to Human Health: A phenotype-wide Mendelian Randomization Study

10.1101/2020.01.20.20017723 ◽

2020 ◽

Author(s):

Lanlan Chen ◽

Aowen Tian ◽

Zhipeng Liu ◽

Miaoran Zhang ◽

Xingchen Pan ◽

...

Keyword(s):

Health Outcomes ◽

Human Health ◽

Mendelian Randomization ◽

Wide Spectrum ◽

Association Studies ◽

Genome Wide Association Studies ◽

Uk Biobank ◽

Major Depressive ◽

Genome Wide ◽

The Uk

ABSTRACTBackgroundIt remains controversial whether daytime napping is beneficial for human health.ObjectiveTo examine the causal relationship between daytime napping and the risk for various human diseases.DesignPhenotype-wide Mendelian randomization study.SettingNon-UK Biobank cohorts reported in published genome-wide association studies (GWAS) provided the outcome phenotypes in the discovery stage. The UK Biobank cohort provided the outcome phenotypes in the validation stage.ParticipantsThe UK Biobank GWAS included 361,194 European-ancestry residents in the UK. Non-UKBB GWAS included various numbers of participants.ExposureSelf-reported daytime napping frequency.Main outcome measureA wide-spectrum of human health outcomes including obesity, major depressive disorder, and high cholesterol.MethodsWe examined the causal relationship between daytime napping frequency in the UK Biobank as exposure and a panel of 1,146 health outcomes reported in genome-wide association studies (GWAS), using a two-sample Mendelian randomization analysis. The significant findings were further validated in the UK Biobank health outcomes of 4,203 human traits and diseases. The causal effects were estimated using a fixed-effect inverse variance weighted model. MR-Egger intercept test was applied to detect horizontal pleiotropy, along with Cochran’s Q test to assess heterogeneity among the causal effects of IVs.FindingsThere were significant causal relationships between daytime napping frequency and a wide spectrum of human health outcomes. In particular, we validated that frequent daytime napping increased the risks of major depressive disorder, obesity and abnormal lipid profile.InterpretationThe current study showed that frequent daytime napping mainly had adverse impacts on physical and mental health. Cautions should be taken for health recommendations on daytime napping. Further studies are necessary to precisely define the best daytime napping strategies.

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Exploiting collider bias to apply two-sample summary data Mendelian randomization methods to one-sample individual level data

10.1101/2020.10.20.20216358 ◽

2020 ◽

Author(s):

Ciarrah Barry ◽

Junxi Liu ◽

Rebecca Richmond ◽

Martin K Rutter ◽

Deborah A Lawlor ◽

...

Keyword(s):

Mendelian Randomization ◽

Association Studies ◽

General Procedure ◽

Meta Analysis ◽

Genome Wide Association Studies ◽

Uk Biobank ◽

Individual Level ◽

Level Data ◽

Summary Data ◽

Collider Bias

AbstractOver the last decade the availability of SNP-trait associations from genome-wide association studies data has led to an array of methods for performing Mendelian randomization studies using only summary statistics. A common feature of these methods, besides their intuitive simplicity, is the ability to combine data from several sources, incorporate multiple variants and account for biases due to weak instruments and pleiotropy. With the advent of large and accessible fully-genotyped cohorts such as UK Biobank, there is now increasing interest in understanding how best to apply these well developed summary data methods to individual level data, and to explore the use of more sophisticated causal methods allowing for non-linearity and effect modification.In this paper we describe a general procedure for optimally applying any two sample summary data method using one sample data. Our procedure first performs a meta-analysis of summary data estimates that are intentionally contaminated by collider bias between the genetic instruments and unmeasured confounders, due to conditioning on the observed exposure. A weighted sum of these estimates is then used to correct the standard observational association between an exposure and outcome. Simulations are conducted to demonstrate the method’s performance against naive applications of two sample summary data MR. We apply the approach to the UK Biobank cohort to investigate the causal role of sleep disturbance on HbA1c levels, an important determinant of diabetes.Our approach is closely related to the work of Dudbridge et al. (Nat. Comm. 10: 1561), who developed a technique to adjust for index event bias when uncovering genetic predictors of disease progression based on case-only data. Our paper serves to clarify that in any one sample MR analysis, it can be advantageous to estimate causal relationships by artificially inducing and then correcting for collider bias.

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Exploiting collider bias to apply two-sample summary data Mendelian randomization methods to one-sample individual level data

PLoS Genetics ◽

10.1371/journal.pgen.1009703 ◽

2021 ◽

Vol 17 (8) ◽

pp. e1009703

Author(s):

Ciarrah Barry ◽

Junxi Liu ◽

Rebecca Richmond ◽

Martin K. Rutter ◽

Deborah A. Lawlor ◽

...

Keyword(s):

Mendelian Randomization ◽

Association Studies ◽

General Procedure ◽

Meta Analysis ◽

Genome Wide Association Studies ◽

Uk Biobank ◽

Individual Level ◽

Level Data ◽

Summary Data ◽

Collider Bias

Over the last decade the availability of SNP-trait associations from genome-wide association studies has led to an array of methods for performing Mendelian randomization studies using only summary statistics. A common feature of these methods, besides their intuitive simplicity, is the ability to combine data from several sources, incorporate multiple variants and account for biases due to weak instruments and pleiotropy. With the advent of large and accessible fully-genotyped cohorts such as UK Biobank, there is now increasing interest in understanding how best to apply these well developed summary data methods to individual level data, and to explore the use of more sophisticated causal methods allowing for non-linearity and effect modification. In this paper we describe a general procedure for optimally applying any two sample summary data method using one sample data. Our procedure first performs a meta-analysis of summary data estimates that are intentionally contaminated by collider bias between the genetic instruments and unmeasured confounders, due to conditioning on the observed exposure. These estimates are then used to correct the standard observational association between an exposure and outcome. Simulations are conducted to demonstrate the method’s performance against naive applications of two sample summary data MR. We apply the approach to the UK Biobank cohort to investigate the causal role of sleep disturbance on HbA1c levels, an important determinant of diabetes. Our approach can be viewed as a generalization of Dudbridge et al. (Nat. Comm. 10: 1561), who developed a technique to adjust for index event bias when uncovering genetic predictors of disease progression based on case-only data. Our work serves to clarify that in any one sample MR analysis, it can be advantageous to estimate causal relationships by artificially inducing and then correcting for collider bias.

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The consequences of adjustment, correction and selection in genome-wide association studies used for two-sample Mendelian randomization

10.1101/2020.07.13.20152413 ◽

2020 ◽

Cited By ~ 2

Author(s):

Venexia M Walker ◽

Sean Harrison ◽

Alice R Carter ◽

Dipender Gill ◽

Ioanna Tzoulaki ◽

...

Keyword(s):

Blood Pressure ◽

Systolic Blood Pressure ◽

Mendelian Randomization ◽

Association Studies ◽

Medication Use ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Uk Biobank ◽

Empirical Estimates ◽

Genome Wide

Introduction: Genome-wide association studies (GWASs) often adjust for covariates, correct for medication use, or select on medication users. If these summary statistics are used in two-sample Mendelian randomization analyses, estimates may be biased. We used simulations to investigate how GWAS adjustment, correction and selection affects these estimates and performed an analysis in UK Biobank to provide an empirical example. Methods: We simulated six GWASs: no adjustment for a covariate, correction for medication use, or selection on medication users; adjustment only; selection only; correction only; both adjustment and selection; and both adjustment and correction. We then ran two-sample Mendelian randomization analyses using these GWASs to evaluate bias. We also performed equivalent GWASs using empirical data from 318,147 participants in UK Biobank with systolic blood pressure as the exposure and body mass index as the covariate and ran two-sample Mendelian randomization with coronary heart disease as the outcome. Results: The simulation showed that estimates from GWASs with selection can produce biased two-sample Mendelian randomization estimates. Yet, we observed relatively little difference between empirical estimates of the effect of systolic blood pressure on coronary artery disease across the six scenarios. Conclusions: Given the potential for bias from using GWASs with selection on Mendelian randomization estimates demonstrated in our simulation, and the reduced sample size of these GWAS, this approach should be deprioritized. However, based on our empirical results, using adjusted, corrected or selected GWASs is unlikely to make a large difference to two-sample Mendelian randomization estimates in practice.

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