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"Wheeler, David C."
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Reconstructing value : leadership skills for a sustainable world
\"Reconstructing Value prepares contemporary business leaders for the increasingly important task of developing a sustainability vision and translating it across levels in an organization. The book is based on insights gained over the past decade from research involving hundreds of practitioners, front line managers to senior executives, who have been working to integrate sustainability within their organizations. It illustrates how building capacity for managing the complex issues of sustainability requires key process skills that leaders need to develop.
Book
Characterization of Weighted Quantile Sum Regression for Highly Correlated Data in a Risk Analysis Setting
In risk evaluation, the effect of mixtures of environmental chemicals on a common adverse outcome is of interest. However, due to the high dimensionality and inherent correlations among chemicals that occur together, the traditional methods (e.g. ordinary or logistic regression) suffer from collinearity and variance inflation, and shrinkage methods have limitations in selecting among correlated components. We propose a weighted quantile sum (WQS) approach to estimating a body burden index, which identifies \"bad actors\" in a set of highly correlated environmental chemicals. We evaluate and characterize the accuracy of WQS regression in variable selection through extensive simulation studies through sensitivity and specificity (i.e., ability of the WQS method to select the bad actors correctly and not incorrect ones). We demonstrate the improvement in accuracy this method provides over traditional ordinary regression and shrinkage methods (lasso, adaptive lasso, and elastic net). Results from simulations demonstrate that WQS regression is accurate under some environmentally relevant conditions, but its accuracy decreases for a fixed correlation pattern as the association with a response variable diminishes. Nonzero weights (i.e., weights exceeding a selection threshold parameter) may be used to identify bad actors; however, components within a cluster of highly correlated active components tend to have lower weights, with the sum of their weights representative of the set. Supplementary materials accompanying this paper appear on-line.
Journal Article
Comparative efficacy and safety of blood pressure-lowering agents in adults with diabetes and kidney disease: a network meta-analysis
The comparative efficacy and safety of pharmacological agents to lower blood pressure in adults with diabetes and kidney disease remains controversial. We aimed to investigate the benefits and harms of blood pressure-lowering drugs in this population of patients.
We did a network meta-analysis of randomised trials from around the world comparing blood pressure-lowering agents in adults with diabetic kidney disease. Electronic databases (the Cochrane Collaboration, Medline, and Embase) were searched systematically up to January, 2014, for trials in adults with diabetes and kidney disease comparing orally administered blood pressure-lowering drugs. Primary outcomes were all-cause mortality and end-stage kidney disease. We also assessed secondary safety and cardiovascular outcomes. We did random-effects network meta-analysis to obtain estimates for primary and secondary outcomes and we presented these estimates as odds ratios or standardised mean differences with 95% CIs. We ranked the comparative effects of all drugs against placebo with surface under the cumulative ranking (SUCRA) probabilities.
157 studies comprising 43 256 participants, mostly with type 2 diabetes and chronic kidney disease, were included in the network meta-analysis. No drug regimen was more effective than placebo for reducing all-cause mortality. However, compared with placebo, end-stage renal disease was significantly less likely after dual treatment with an angiotensin-receptor blocker (ARB) and an angiotensin-converting-enzyme (ACE) inhibitor (odds ratio 0·62, 95% CI 0·43–0·90) and after ARB monotherapy (0·77, 0·65–0·92). No regimen significantly increased hyperkalaemia or acute kidney injury, although combined ACE inhibitor and ARB treatment had the lowest rank among all interventions because of borderline increases in estimated risks of these harms (odds ratio 2·69, 95% CI 0·97–7·47 for hyperkalaemia; 2·69, 0·98–7·38 for acute kidney injury).
No blood pressure-lowering strategy prolonged survival in adults with diabetes and kidney disease. ACE inhibitors and ARBs, alone or in combination, were the most effective strategies against end-stage kidney disease. Any benefits of combined ACE inhibitor and ARB treatment need to be balanced against potential harms of hyperkalaemia and acute kidney injury.
Canterbury Medical Research Foundation, Italian Medicines Agency.
Journal Article
Daprodustat for the Treatment of Anemia in Patients Undergoing Dialysis
This trial compared the oral HIF-PHI daprodustat with conventional erythropoiesis-stimulating agents for the treatment of anemia in patients with chronic kidney disease receiving dialysis. Daprodustat was noninferior to ESAs regarding the change in the hemoglobin level from baseline and cardiovascular outcomes.
Journal Article
Assessment of Weighted Quantile Sum Regression for Modeling Chemical Mixtures and Cancer Risk
In evaluation of cancer risk related to environmental chemical exposures, the effect of many chemicals on disease is ultimately of interest. However, because of potentially strong correlations among chemicals that occur together, traditional regression methods suffer from collinearity effects, including regression coefficient sign reversal and variance inflation. In addition, penalized regression methods designed to remediate collinearity may have limitations in selecting the truly bad actors among many correlated components. The recently proposed method of weighted quantile sum (WQS) regression attempts to overcome these problems by estimating a body burden index, which identifies important chemicals in a mixture of correlated environmental chemicals. Our focus was on assessing through simulation studies the accuracy of WQS regression in detecting subsets of chemicals associated with health outcomes (binary and continuous) in site-specific analyses and in non-site-specific analyses. We also evaluated the performance of the penalized regression methods of lasso, adaptive lasso, and elastic net in correctly classifying chemicals as bad actors or unrelated to the outcome. We based the simulation study on data from the National Cancer Institute Surveillance Epidemiology and End Results Program (NCI-SEER) case-control study of non-Hodgkin lymphoma (NHL) to achieve realistic exposure situations. Our results showed that WQS regression had good sensitivity and specificity across a variety of conditions considered in this study. The shrinkage methods had a tendency to incorrectly identify a large number of components, especially in the case of strong association with the outcome.
Journal Article
New strategies to improve clinical outcomes for diabetic kidney disease
Background
Diabetic kidney disease (DKD), the most common cause of kidney failure and end-stage kidney disease worldwide, will develop in almost half of all people with type 2 diabetes. With the incidence of type 2 diabetes continuing to increase, early detection and management of DKD is of great clinical importance.
Main body
This review provides a comprehensive clinical update for DKD in people with type 2 diabetes, with a special focus on new treatment modalities. The traditional strategies for prevention and treatment of DKD, i.e., glycemic control and blood pressure management, have only modest effects on minimizing glomerular filtration rate decline or progression to end-stage kidney disease. While cardiovascular outcome trials of SGLT-2i show a positive effect of SGLT-2i on several kidney disease-related endpoints, the effect of GLP-1 RA on kidney-disease endpoints other than reduced albuminuria remain to be established. Non-steroidal mineralocorticoid receptor antagonists also evoke cardiovascular and kidney protective effects.
Conclusion
With these new agents and the promise of additional agents under clinical development, clinicians will be more able to personalize treatment of DKD in patients with type 2 diabetes.
Journal Article
Diabetic kidney disease: a clinical update from Kidney Disease: Improving Global Outcomes
The incidence and prevalence of diabetes mellitus (DM) continue to grow markedly throughout the world, due primarily to the increase in type 2 DM (T2DM). Although improvements in DM and hypertension management have reduced the proportion of diabetic individuals who develop chronic kidney disease (CKD) and progress to end-stage renal disease (ESRD), the sheer increase in people developing DM will have a major impact on dialysis and transplant needs. This KDIGO conference addressed a number of controversial areas in the management of DM patients with CKD, including aspects of screening for CKD with measurements of albuminuria and estimated glomerular filtration rate (eGFR); defining treatment outcomes; glycemic management in both those developing CKD and those with ESRD; hypertension goals and management, including blockers of the renin–angiotensin–aldosterone system; and lipid management.
Journal Article
Assessment of Grouped Weighted Quantile Sum Regression for Modeling Chemical Mixtures and Cancer Risk
Individuals are exposed to a large number of diverse environmental chemicals simultaneously and the evaluation of multiple chemical exposures is important for identifying cancer risk factors. The measurement of a large number of chemicals (the exposome) in epidemiologic studies is allowing for a more comprehensive assessment of cancer risk factors than was done in earlier studies that focused on only a few chemicals. Empirical evidence from epidemiologic studies shows that chemicals from different chemical classes have different magnitudes and directions of association with cancers. Given increasing data availability, there is a need for the development and assessment of statistical methods to model environmental cancer risk that considers a large number of diverse chemicals with different effects for different chemical classes. The method of grouped weighted quantile sum (GWQS) regression allows for multiple groups of chemicals to be considered in the model such that different magnitudes and directions of associations are possible for each group of chemicals. In this paper, we assessed the ability of GWQS regression to estimate exposure effects for multiple chemical groups and correctly identify important chemicals in each group using a simulation study. We compared the performance of GWQS regression with WQS regression, the least absolute shrinkage and selection operator (lasso), and the group lasso in estimating exposure effects and identifying important chemicals. The simulation study results demonstrate that GWQS is an effective method for modeling exposure to multiple groups of chemicals and compares favorably with other methods used in mixture analysis. As an application, we used GWQS regression in the California Childhood Leukemia Study (CCLS), a population-based case-control study of childhood leukemia in California to estimate exposure effects for many chemical classes while also adjusting for demographic factors. The CCLS analysis found evidence of a positive association between exposure to the herbicide dacthal and an increased risk of childhood leukemia.
Journal Article
Analysis of Environmental Chemical Mixtures and Non-Hodgkin Lymphoma Risk in the NCI-SEER NHL Study
There are several suspected environmental risk factors for non-Hodgkin lymphoma (NHL). The associations between NHL and environmental chemical exposures have typically been evaluated for individual chemicals (i.e., one-by-one).
We determined the association between a mixture of 27 correlated chemicals measured in house dust and NHL risk.
We conducted a population-based case-control study of NHL in four National Cancer Institute-Surveillance, Epidemiology, and End Results centers--Detroit, Michigan; Iowa; Los Angeles County, California; and Seattle, Washington--from 1998 to 2000. We used weighted quantile sum (WQS) regression to model the association of a mixture of chemicals and risk of NHL. The WQS index was a sum of weighted quartiles for 5 polychlorinated biphenyls (PCBs), 7 polycyclic aromatic hydrocarbons (PAHs), and 15 pesticides. We estimated chemical mixture weights and effects for study sites combined and for each site individually, and also for histologic subtypes of NHL.
The WQS index was statistically significantly associated with NHL overall [odds ratio (OR) = 1.30; 95% CI: 1.08, 1.56; p = 0.006; for one quartile increase] and in the study sites of Detroit (OR = 1.71; 95% CI: 1.02, 2.92; p = 0.045), Los Angeles (OR = 1.44; 95% CI: 1.00, 2.08; p = 0.049), and Iowa (OR = 1.76; 95% CI: 1.23, 2.53; p = 0.002). The index was marginally statistically significant in Seattle (OR = 1.39; 95% CI: 0.97, 1.99; p = 0.071). The most highly weighted chemicals for predicting risk overall were PCB congener 180 and propoxur. Highly weighted chemicals varied by study site; PCBs were more highly weighted in Detroit, and pesticides were more highly weighted in Iowa.
An index of chemical mixtures was significantly associated with NHL. Our results show the importance of evaluating chemical mixtures when studying cancer risk.
Journal Article
Estimating an area-level socioeconomic status index and its association with colonoscopy screening adherence
Socioeconomic status (SES) is often considered a risk factor for health outcomes. SES is typically measured using individual variables of educational attainment, income, housing, and employment variables or a composite of these variables. Approaches to building the composite variable include using equal weights for each variable or estimating the weights with principal components analysis or factor analysis. However, these methods do not consider the relationship between the outcome and the SES variables when constructing the index. In this project, we used weighted quantile sum (WQS) regression to estimate an area-level SES index and its effect in a model of colonoscopy screening adherence in the Minnesota-Wisconsin Metropolitan Statistical Area. We considered several specifications of the SES index including using different spatial scales (e.g., census block group-level, tract-level) for the SES variables. We found a significant positive association (odds ratio = 1.17, 95% CI: 1.15-1.19) between the SES index and colonoscopy adherence in the best fitting model. The model with the best goodness-of-fit included a multi-scale SES index with 10 variables at the block group-level and one at the tract-level, with home ownership, race, and income among the most important variables. Contrary to previous index construction, our results were not consistent with an assumption of equal importance of variables in the SES index when explaining colonoscopy screening adherence. Our approach is applicable in any study where an SES index is considered as a variable in a regression model and the weights for the SES variables are not known in advance.
Journal Article