Acetylcysteine has No Mechanistic Effect in Patients at Risk of Contrast-Induced Nephropathy: A Failure of Academic Clinical Science.

Contrast-induced nephropathy (CIN) is a major complication of imaging in patients with chronic kidney disease (CKD). The publication of an academic randomized controlled trial (RCT; n = 83) reporting oral (N)-acetylcysteine (NAC) to reduce CIN led to > 70 clinical trials, 23 systematic reviews, and 2 large RCTs showing no benefit. However, no mechanistic studies were conducted to determine how NAC might work; proposed mechanisms included renal artery vasodilatation and antioxidant boosting. We evaluated the proposed mechanisms of NAC action in participants with healthy and diseased kidneys. Four substudies were performed. Two randomized, double-blind, placebo-controlled, three-period crossover studies (n = 8) assessed the effect of oral and intravenous (i.v.) NAC in healthy kidneys in the presence/absence of iso-osmolar contrast (iodixanol). A third crossover study in patients with CKD stage III (CKD3) (n = 8) assessed the effect of oral and i.v. NAC without contrast. A three-arm randomized, double-blind, placebo-controlled parallel-group study, recruiting patients with CKD3 (n = 66) undergoing coronary angiography, assessed the effect of oral and i.v. NAC in the presence of contrast. We recorded systemic (blood pressure and heart rate) and renal (renal blood flow (RBF) and glomerular filtration rate (GFR)) hemodynamics, and antioxidant status, plus biomarkers of renal injury in patients with CKD3 undergoing angiography. Primary outcome for all studies was RBF over 8 hours after the start of i.v. NAC/placebo. NAC at doses used in previous trials of renal prophylaxis was essentially undetectable in plasma after oral administration. In healthy volunteers, i.v. NAC, but not oral NAC, increased blood pressure (mean area under the curve (AUC) mean arterial pressure (MAP): mean difference 29 h⋅mmHg, P = 0.019 vs. placebo), heart rate (28 h⋅bpm, P < 0.001), and RBF (714 h⋅mL/min, 8.0% increase, P = 0.006). Renal vasodilatation also occurred in the presence of contrast (RBF 917 h⋅mL/min, 12% increase, P = 0.005). In patients with CKD3 without contrast, only a rise in heart rate (34 h⋅bpm, P = 0.010) and RBF (288 h⋅mL/min, 6.0% increase, P = 0.001) occurred with i.v. NAC, with no significant effect on blood pressure (MAP rise 26 h⋅mmHg, P = 0.156). Oral NAC showed no effect. In patients with CKD3 receiving contrast, i.v. NAC increased blood pressure (MAP rise 52 h⋅mmHg, P = 0.008) but had no effect on RBF (151 h⋅mL/min, 3.0% increase, P = 0.470), GFR (29 h⋅mL/min/1.73m², P = 0.122), or markers of renal injury. Neither i.v. nor oral NAC affected plasma antioxidant status. We found oral NAC to be poorly absorbed and have no reno-protective effects. Intravenous, not oral, NAC caused renal artery vasodilatation in healthy volunteers but offered no protection to patients with CKD3 at risk of CIN. These findings emphasize the importance of mechanistic clinical studies before progressing to RCTs for novel interventions. Thousands were recruited to academic clinical trials without the necessary mechanistic studies being performed to confirm the approach had any chance of working.

Genetically predicted physical activity levels are associated with lower colorectal cancer risk: a Mendelian randomisation study.

BACKGROUND: We conducted a Mendelian randomisation (MR) study to investigate whether physical activity (PA) causes a reduction of colorectal cancer risk and to understand the contributions of effects mediated through changes in body fat. METHODS: Common genetic variants associated with self-reported moderate-to-vigorous PA (MVPA), acceleration vector magnitude PA (AMPA) and sedentary time were used as instrumental variables. To control for confounding effects of obesity, we included instrumental variables for body mass index (BMI), body fat percentage, waist circumference and arm, trunk and leg fat ratios. We analysed the effect of these instrumental variables in a colorectal cancer genome-wide association study comprising 31,197 cases and 61,770 controls of European ancestry by applying two-sample and multivariable MR study designs. RESULTS: We found decreased colorectal cancer risk for genetically represented measures of MVPA and AMPA that were additional to effects mediated through genetic measures of obesity. Odds ratio and 95% confidence interval (CI) per standard deviation increase in MVPA and AMPA was 0.56 (0.31, 1.01) and 0.60 (0.41, 0.88), respectively. No association has been found between sedentary time and colorectal cancer risk. The proportion of effect mediated through BMI was 2% (95% CI: 0, 14) and 32% (95% CI: 12, 46) for MVPA and AMPA, respectively. CONCLUSION: These findings provide strong evidence to reinforce public health measures on preventing colorectal cancer that promote PA at a population level regardless of body fatness.

Body mass index and body composition in relation to 14 cardiovascular conditions in UK Biobank: a Mendelian randomization study.

AIMS: The causal role of adiposity for several cardiovascular diseases (CVDs) is unclear. Our primary aim was to apply the Mendelian randomization design to investigate the associations of body mass index (BMI) with 13 CVDs and arterial hypertension. We also assessed the roles of fat mass and fat-free mass on the same outcomes. METHODS AND RESULTS: Single-nucleotide polymorphisms associated with BMI and fat mass and fat-free mass indices were used as instrumental variables to estimate the associations with the cardiovascular conditions among 367 703 UK Biobank participants. After correcting for multiple testing, genetically predicted BMI was significantly positively associated with eight outcomes, including and with decreasing magnitude of association: aortic valve stenosis, heart failure, deep vein thrombosis, arterial hypertension, peripheral artery disease, coronary artery disease, atrial fibrillation, and pulmonary embolism. The odds ratio (OR) per 1 kg/m2 increase in BMI ranged from 1.06 [95% confidence interval (CI) 1.02-1.11; P = 2.6 × 10-3] for pulmonary embolism to 1.13 (95% CI 1.05-1.21; P = 1.2 × 10-3) for aortic valve stenosis. There was suggestive evidence of positive associations of genetically predicted fat mass index with nine outcomes (P < 0.05). The strongest magnitude of association was with aortic valve stenosis (OR per 1 kg/m2 increase in fat mass index 1.46, 95% CI 1.13-1.88; P = 3.9 × 10-3). There was suggestive evidence of inverse associations of fat-free mass index with atrial fibrillation, ischaemic stroke, and abdominal aortic aneurysm. CONCLUSION: This study provides evidence that higher BMI and particularly fat mass index are associated with increased risk of aortic valve stenosis and most other cardiovascular conditions.

Factorial Mendelian randomization: using genetic variants to assess interactions.

BACKGROUND: Factorial Mendelian randomization is the use of genetic variants to answer questions about interactions. Although the approach has been used in applied investigations, little methodological advice is available on how to design or perform a factorial Mendelian randomization analysis. Previous analyses have employed a 2 × 2 approach, using dichotomized genetic scores to divide the population into four subgroups as in a factorial randomized trial. METHODS: We describe two distinct contexts for factorial Mendelian randomization: investigating interactions between risk factors, and investigating interactions between pharmacological interventions on risk factors. We propose two-stage least squares methods using all available genetic variants and their interactions as instrumental variables, and using continuous genetic scores as instrumental variables rather than dichotomized scores. We illustrate our methods using data from UK Biobank to investigate the interaction between body mass index and alcohol consumption on systolic blood pressure. RESULTS: Simulated and real data show that efficiency is maximized using the full set of interactions between genetic variants as instruments. In the applied example, between 4- and 10-fold improvement in efficiency is demonstrated over the 2 × 2 approach. Analyses using continuous genetic scores are more efficient than those using dichotomized scores. Efficiency is improved by finding genetic variants that divide the population at a natural break in the distribution of the risk factor, or else divide the population into more equal-sized groups. CONCLUSIONS: Previous factorial Mendelian randomization analyses may have been underpowered. Efficiency can be improved by using all genetic variants and their interactions as instrumental variables, rather than the 2 × 2 approach.

Shared mechanisms between coronary heart disease and depression: findings from a large UK general population-based cohort.

While comorbidity between coronary heart disease (CHD) and depression is evident, it is unclear whether the two diseases have shared underlying mechanisms. We performed a range of analyses in 367,703 unrelated middle-aged participants of European ancestry from UK Biobank, a population-based cohort study, to assess whether comorbidity is primarily due to genetic or environmental factors, and to test whether cardiovascular risk factors and CHD are likely to be causally related to depression using Mendelian randomization. We showed family history of heart disease was associated with a 20% increase in depression risk (95% confidence interval [CI] 16-24%, p < 0.0001), but a genetic risk score that is strongly associated with CHD risk was not associated with depression. An increase of 1 standard deviation in the CHD genetic risk score was associated with 71% higher CHD risk, but 1% higher depression risk (95% CI 0-3%; p = 0.11). Mendelian randomization analyses suggested that triglycerides, interleukin-6 (IL-6), and C-reactive protein (CRP) are likely causal risk factors for depression. The odds ratio for depression per standard deviation increase in genetically-predicted triglycerides was 1.18 (95% CI 1.09-1.27; p = 2 × 10-5); per unit increase in genetically-predicted log-transformed IL-6 was 0.74 (95% CI 0.62-0.89; p = 0.0012); and per unit increase in genetically-predicted log-transformed CRP was 1.18 (95% CI 1.07-1.29; p = 0.0009). Our analyses suggest that comorbidity between depression and CHD arises largely from shared environmental factors. IL-6, CRP and triglycerides are likely to be causally linked with depression, so could be targets for treatment and prevention of depression.

Genetic Determinants of Lipids and Cardiovascular Disease Outcomes: A Wide-Angled Mendelian Randomization Investigation.

BACKGROUND: Evidence from randomized trials has shown that therapies that lower LDL (low-density lipoprotein)-cholesterol and triglycerides reduce coronary artery disease (CAD) risk. However, there is still uncertainty about their effects on other cardiovascular outcomes. We therefore performed a systematic investigation of causal relationships between circulating lipids and cardiovascular outcomes using a Mendelian randomization approach. METHODS: In the primary analysis, we performed 2-sample multivariable Mendelian randomization using data from participants of European ancestry. We also conducted univariable analyses using inverse-variance weighted and robust methods, and gene-specific analyses using variants that can be considered as proxies for specific lipid-lowering medications. We obtained associations with lipid fractions from the Global Lipids Genetics Consortium, a meta-analysis of 188 577 participants, and genetic associations with cardiovascular outcomes from 367 703 participants in UK Biobank. RESULTS: For LDL-cholesterol, in addition to the expected positive associations with CAD risk (odds ratio [OR] per 1 SD increase, 1.45 [95% CI, 1.35-1.57]) and other atheromatous outcomes (ischemic cerebrovascular disease and peripheral vascular disease), we found independent associations of genetically predicted LDL-cholesterol with abdominal aortic aneurysm (OR, 1.75 [95% CI, 1.40-2.17]) and aortic valve stenosis (OR, 1.46 [95% CI, 1.25-1.70]). Genetically predicted triglyceride levels were positively associated with CAD (OR, 1.25 [95% CI, 1.12-1.40]), aortic valve stenosis (OR, 1.29 [95% CI, 1.04-1.61]), and hypertension (OR, 1.17 [95% CI, 1.07-1.27]), but inversely associated with venous thromboembolism (OR, 0.79 [95% CI, 0.67-0.93]) and hemorrhagic stroke (OR, 0.78 [95% CI, 0.62-0.98]). We also found positive associations of genetically predicted LDL-cholesterol and triglycerides with heart failure that appeared to be mediated by CAD. CONCLUSIONS: Lowering LDL-cholesterol is likely to prevent abdominal aortic aneurysm and aortic stenosis, in addition to CAD and other atheromatous cardiovascular outcomes. Lowering triglycerides is likely to prevent CAD and aortic valve stenosis but may increase thromboembolic risk.

Robust methods in Mendelian randomization via penalization of heterogeneous causal estimates.

Methods have been developed for Mendelian randomization that can obtain consistent causal estimates under weaker assumptions than the standard instrumental variable assumptions. The median-based estimator and MR-Egger are examples of such methods. However, these methods can be sensitive to genetic variants with heterogeneous causal estimates. Such heterogeneity may arise from over-dispersion in the causal estimates, or specific variants with outlying causal estimates. In this paper, we develop three extensions to robust methods for Mendelian randomization with summarized data: 1) robust regression (MM-estimation); 2) penalized weights; and 3) Lasso penalization. Methods using these approaches are considered in two applied examples: one where there is evidence of over-dispersion in the causal estimates (the causal effect of body mass index on schizophrenia risk), and the other containing outliers (the causal effect of low-density lipoprotein cholesterol on Alzheimer's disease risk). Through an extensive simulation study, we demonstrate that robust regression applied to the inverse-variance weighted method with penalized weights is a worthwhile additional sensitivity analysis for Mendelian randomization to provide robustness to variants with outlying causal estimates. The results from the applied examples and simulation study highlight the importance of using methods that make different assumptions to assess the robustness of findings from Mendelian randomization investigations with multiple genetic variants.

Extending the MR-Egger method for multivariable Mendelian randomization to correct for both measured and unmeasured pleiotropy.

Methods have been developed for Mendelian randomization that can obtain consistent causal estimates while relaxing the instrumental variable assumptions. These include multivariable Mendelian randomization, in which a genetic variant may be associated with multiple risk factors so long as any association with the outcome is via the measured risk factors (measured pleiotropy), and the MR-Egger (Mendelian randomization-Egger) method, in which a genetic variant may be directly associated with the outcome not via the risk factor of interest, so long as the direct effects of the variants on the outcome are uncorrelated with their associations with the risk factor (unmeasured pleiotropy). In this paper, we extend the MR-Egger method to a multivariable setting to correct for both measured and unmeasured pleiotropy. We show, through theoretical arguments and a simulation study, that the multivariable MR-Egger method has advantages over its univariable counterpart in terms of plausibility of the assumption needed for consistent causal estimation and power to detect a causal effect when this assumption is satisfied. The methods are compared in an applied analysis to investigate the causal effect of high-density lipoprotein cholesterol on coronary heart disease risk. The multivariable MR-Egger method will be useful to analyse high-dimensional data in situations where the risk factors are highly related and it is difficult to find genetic variants specifically associated with the risk factor of interest (multivariable by design), and as a sensitivity analysis when the genetic variants are known to have pleiotropic effects on measured risk factors.

Dissecting Causal Pathways Using Mendelian Randomization with Summarized Genetic Data: Application to Age at Menarche and Risk of Breast Cancer.

Mendelian randomization is the use of genetic variants as instrumental variables to estimate causal effects of risk factors on outcomes. The total causal effect of a risk factor is the change in the outcome resulting from intervening on the risk factor. This total causal effect may potentially encompass multiple mediating mechanisms. For a proposed mediator, the direct effect of the risk factor is the change in the outcome resulting from a change in the risk factor, keeping the mediator constant. A difference between the total effect and the direct effect indicates that the causal pathway from the risk factor to the outcome acts at least in part via the mediator (an indirect effect). Here, we show that Mendelian randomization estimates of total and direct effects can be obtained using summarized data on genetic associations with the risk factor, mediator, and outcome, potentially from different data sources. We perform simulations to test the validity of this approach when there is unmeasured confounding and/or bidirectional effects between the risk factor and mediator. We illustrate this method using the relationship between age at menarche and risk of breast cancer, with body mass index (BMI) as a potential mediator. We show an inverse direct causal effect of age at menarche on risk of breast cancer (independent of BMI), and a positive indirect effect via BMI. In conclusion, multivariable Mendelian randomization using summarized genetic data provides a rapid and accessible analytic strategy that can be undertaken using publicly available data to better understand causal mechanisms.