Exploring the contribution of lifestyle to the impact of education on the risk of cancer through Mendelian randomization analysis.

Educational attainment (EA) has been linked to the risk of several types of cancer, despite having no expected direct biological connection. In this paper, we investigate the mediating role of alcohol consumption, smoking, vegetable consumption, fruit consumption and body mass index (BMI) in explaining the effect of EA on 7 cancer groupings. Large-scale genome wide association study (GWAS) results were used to construct the genetic instrument for EA and the lifestyle factors. We conducted GWAS in the UK Biobank sample in up to 335,024 individuals to obtain genetic association data for the cancer outcomes. Univariable and multivariable two-sample Mendelian randomization (MR) analyses and mediation analyses were then conducted to explore the causal effect and mediating proportions of these relations. MR mediation analysis revealed that reduced lifetime smoking index accounted for 81.7% (49.1% to 100%) of the protective effect of higher EA on lower respiratory cancer. Moreover, the effect of higher EA on lower respiratory cancer was mediated through vegetable consumption by 10.2% (4.4% to 15.9%). We found genetic evidence that the effect of EA on groups of cancer is due to behavioural changes in avoiding well established risk factors such as smoking and vegetable consuming.

Using genetics to investigate the association between lanosterol and cataract.

Background: Cataract is one of the most prevalent causes of blindness worldwide. Whilst surgery is the primary treatment for cataracts, it is not always an available option, particularly in developing countries. Non-surgical methods of treatment would increase treatment availability for more patients. Several studies have investigated how topical application of oxysterols, such as lanosterol, may break down aggregated proteins and restore lens transparency. However, the results are conflicting and inconclusive. Aim: In this study, we focus on combining genetic evidence for associations between lanosterol related genetic variation and cataract to explore whether lanosterol is a potentially suitable drug treatment option. Method: Using data from 45,449 available cataract cases from the UK Biobank, with participant ages ranging from 40-69, we conducted a genetic association study (GWAS) to assess the risk of cataract. Cataract cases were defined using diagnostic and operation codes. We focused on genetic variants in the lanosterol synthase gene region. We also compared our results with previously published genetic associations of phytosterol-to-lanosterol ratios. Finally, we performed a genetic risk score analysis to test the association between lanosterol within the cholesterol synthesis pathway and the risk of cataract. Results: No statistically significant single nucleotide polymorphisms (SNPs) associations with cataract were observed in the gene region of lanosterol synthase at a multiple testing adjusted significance threshold of p < 0.05/13. The comparison between cataract risk and genetic association of 8 phytosterol-to-lanosterol GWAS results also showed no evidence to support lanosterol's protective properties for cataract risk. No statistically significant association was found between the lanosterol within the cholesterol synthesis pathway genetic risk score and cataract outcomes (OR = 1.002 p = 0.568). Conclusion: There was no evidence observed for genetic associations between lanosterol and cataract risk. Our results do not support lanosterol's potential role in treating cataracts. Further research may be needed to address the effect of lanosterol on specific cataract subtypes.

The citrate transporter SLC13A5 as a therapeutic target for kidney disease: evidence from Mendelian randomization to inform drug development.

BACKGROUND: Solute carrier family 13 member 5 (SLC13A5) is a Na+-coupled citrate co-transporter that mediates entry of extracellular citrate into the cytosol. SLC13A5 inhibition has been proposed as a target for reducing progression of kidney disease. The aim of this study was to leverage the Mendelian randomization paradigm to gain insight into the effects of SLC13A5 inhibition in humans, towards prioritizing and informing clinical development efforts. METHODS: The primary Mendelian randomization analyses investigated the effect of SLC13A5 inhibition on measures of kidney function, including creatinine and cystatin C-based measures of estimated glomerular filtration rate (creatinine-eGFR and cystatin C-eGFR), blood urea nitrogen (BUN), urine albumin-creatinine ratio (uACR), and risk of chronic kidney disease and microalbuminuria. Secondary analyses included a paired plasma and urine metabolome-wide association study, investigation of secondary traits related to SLC13A5 biology, a phenome-wide association study (PheWAS), and a proteome-wide association study. All analyses were compared to the effect of genetically predicted plasma citrate levels using variants selected from across the genome, and statistical sensitivity analyses robust to the inclusion of pleiotropic variants were also performed. Data were obtained from large-scale genetic consortia and biobanks, with sample sizes ranging from 5023 to 1,320,016 individuals. RESULTS: We found evidence of associations between genetically proxied SLC13A5 inhibition and higher creatinine-eGFR (p = 0.002), cystatin C-eGFR (p = 0.005), and lower BUN (p = 3 × 10-4). Statistical sensitivity analyses robust to the inclusion of pleiotropic variants suggested that these effects may be a consequence of higher plasma citrate levels. There was no strong evidence of associations of genetically proxied SLC13A5 inhibition with uACR or risk of CKD or microalbuminuria. Secondary analyses identified evidence of associations with higher plasma calcium levels (p = 6 × 10-13) and lower fasting glucose (p = 0.02). PheWAS did not identify any safety concerns. CONCLUSIONS: This Mendelian randomization analysis provides human-centric insight to guide clinical development of an SLC13A5 inhibitor. We identify plasma calcium and citrate as biologically plausible biomarkers of target engagement, and plasma citrate as a potential biomarker of mechanism of action. Our human genetic evidence corroborates evidence from various animal models to support effects of SLC13A5 inhibition on improving kidney function.

Caffeine Intake, Plasma Caffeine Level, and Kidney Function: A Mendelian Randomization Study.

Caffeine is a psychoactive substance widely consumed worldwide, mainly via sources such as coffee and tea. The effects of caffeine on kidney function remain unclear. We leveraged the genetic variants in the CYP1A2 and AHR genes via the two-sample Mendelian randomization (MR) framework to estimate the association of genetically predicted plasma caffeine and caffeine intake on kidney traits. Genetic association summary statistics on plasma caffeine levels and caffeine intake were taken from genome-wide association study (GWAS) meta-analyses of 9876 and of >47,000 European ancestry individuals, respectively. Genetically predicted plasma caffeine levels were associated with a decrease in estimated glomerular filtration rate (eGFR) measured using either creatinine or cystatin C. In contrast, genetically predicted caffeine intake was associated with an increase in eGFR and a low risk of chronic kidney disease. The discrepancy is likely attributable to faster metabolizers of caffeine consuming more caffeine-containing beverages to achieve the same pharmacological effect. Further research is needed to distinguish whether the observed effects on kidney function are driven by the harmful effects of higher plasma caffeine levels or the protective effects of greater intake of caffeine-containing beverages, particularly given the widespread use of drinks containing caffeine and the increasing burden of kidney disease.

A mathematical model which examines age-related stochastic fluctuations in DNA maintenance methylation.

Due to its complexity and its ubiquitous nature the ageing process remains an enduring biological puzzle. Many molecular mechanisms and biochemical process have become synonymous with ageing. However, recent findings have pinpointed epigenetics as having a key role in ageing and healthspan. In particular age related changes to DNA methylation offer the possibility of monitoring the trajectory of biological ageing and could even be used to predict the onset of diseases such as cancer, Alzheimer's disease and cardiovascular disease. At the molecular level emerging evidence strongly suggests the regulatory processes which govern DNA methylation are subject to intracellular stochasticity. It is challenging to fully understand the impact of stochasticity on DNA methylation levels at the molecular level experimentally. An ideal solution is to use mathematical models to capture the essence of the stochasticity and its outcomes. In this paper we present a novel stochastic model which accounts for specific methylation levels within a gene promoter. Uncertainty of the eventual site-specific methylation levels for different values of methylation age, depending on the initial methylation levels were analysed. Our model predicts the observed bistable levels in CpG islands. In addition, simulations with various levels of noise indicate that uncertainty predominantly spreads through the hypermethylated region of stability, especially for large values of input noise. A key outcome of the model is that CpG islands with high to intermediate methylation levels tend to be more susceptible to dramatic DNA methylation changes due to increasing methylation age.

Data-driven selection and parameter estimation for DNA methylation mathematical models.

Epigenetics is coming to the fore as a key process which underpins health. In particular emerging experimental evidence has associated alterations to DNA methylation status with healthspan and aging. Mammalian DNA methylation status is maintained by an intricate array of biochemical and molecular processes. It can be argued changes to these fundamental cellular processes ultimately drive the formation of aberrant DNA methylation patterns, which are a hallmark of diseases, such as cancer, Alzheimer's disease and cardiovascular disease. In recent years mathematical models have been used as effective tools to help advance our understanding of the dynamics which underpin DNA methylation. In this paper we present linear and nonlinear models which encapsulate the dynamics of the molecular mechanisms which define DNA methylation. Applying a recently developed Bayesian algorithm for parameter estimation and model selection, we are able to estimate distributions of parameters which include nominal parameter values. Using limited noisy observations, the method also identified which methylation model the observations originated from, signaling that our method has practical applications in identifying what models best match the biological data for DNA methylation.

Mathematical models of DNA methylation dynamics: Implications for health and ageing.

DNA methylation is a key epigenetic process which has been intimately associated with gene regulation. In recent years growing evidence has associated DNA methylation status with a variety of diseases including cancer, Alzheimer's disease and cardiovascular disease. Moreover, changes to DNA methylation have also recently been implicated in the ageing process. The factors which underpin DNA methylation are complex, and remain to be fully elucidated. Over the years mathematical modelling has helped to shed light on the dynamics of this important molecular system. Although the existing models have contributed significantly to our overall understanding of DNA methylation, they fall short of fully capturing the dynamics of this process. In this paper we develop a linear and nonlinear model which captures more fully the dynamics of the key intracellular events which characterise DNA methylation. In particular the outcomes of our linear model result in gene promoter specific methylation levels which are more biologically plausible than those revealed by previous mathematical models. In addition, our nonlinear model predicts DNA methylation promoter bistability which is commonly observed experimentally. The findings from our models have implications for our current understanding of how changes to the dynamics which underpin DNA methylation affect ageing and health. We also propose how our ideas can be tested in the lab.