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Preview: American Journal of Epidemiology - current issue

American Journal of Epidemiology Current Issue

Published: Mon, 30 Oct 2017 00:00:00 GMT

Last Build Date: Wed, 01 Nov 2017 20:47:44 GMT


Blood Lead, Bone Turnover, and Survival in Amyotrophic Lateral Sclerosis


Blood lead and bone turnover may be associated with the risk of amyotrophic lateral sclerosis (ALS). We aimed to assess whether these factors were also associated with time from ALS diagnosis to death through a survival analysis of 145 ALS patients enrolled during 2007 in the National Registry of Veterans with ALS. Associations of survival time with blood lead and plasma biomarkers of bone resorption (C-terminal telopeptides of type I collagen (CTX)) and bone formation (procollagen type I amino-terminal peptide (PINP)) were estimated using Cox models adjusted for age at diagnosis, diagnostic certainty, diagnostic delay, site of onset, and score on the Revised ALS Functional Rating Scale. Hazard ratios were calculated for each doubling of biomarker concentration. Blood lead, plasma CTX, and plasma PINP were mutually adjusted for one another. Increased lead (hazard ratio (HR) = 1.38; 95% confidence interval (CI): 1.03, 1.84) and CTX (HR = 2.03; 95% CI: 1.42, 2.89) were both associated with shorter survival, whereas higher PINP was associated with longer survival (HR = 0.59; 95% CI: 0.42, 0.83), after ALS diagnosis. No interactions were observed between lead or bone turnover and other prognostic indicators. Lead toxicity and bone metabolism may be involved in ALS pathophysiology.

Comparison of Sociodemographic and Health-Related Characteristics of UK Biobank Participants With Those of the General Population


The UK Biobank cohort is a population-based cohort of 500,000 participants recruited in the United Kingdom (UK) between 2006 and 2010. Approximately 9.2 million individuals aged 40–69 years who lived within 25 miles (40 km) of one of 22 assessment centers in England, Wales, and Scotland were invited to enter the cohort, and 5.5% participated in the baseline assessment. The representativeness of the UK Biobank cohort was investigated by comparing demographic characteristics between nonresponders and responders. Sociodemographic, physical, lifestyle, and health-related characteristics of the cohort were compared with nationally representative data sources. UK Biobank participants were more likely to be older, to be female, and to live in less socioeconomically deprived areas than nonparticipants. Compared with the general population, participants were less likely to be obese, to smoke, and to drink alcohol on a daily basis and had fewer self-reported health conditions. At age 70–74 years, rates of all-cause mortality and total cancer incidence were 46.2% and 11.8% lower, respectively, in men and 55.5% and 18.1% lower, respectively, in women than in the general population of the same age. UK Biobank is not representative of the sampling population; there is evidence of a “healthy volunteer” selection bias. Nonetheless, valid assessment of exposure-disease relationships may be widely generalizable and does not require participants to be representative of the population at large.

Invited Commentary: Can Estimation of Sodium Intake Be Improved by Borrowing Information From Other Variables?


Estimation of dietary sodium intake is problematic. The most accurate measure is average sodium excretion from multiple 24-hour urine collections, but such an approach is impractical. Using data from the Women's Health Initiative, Prentice et al. (Am J Epidemiol. 2017;186(9):1035–1043) assessed the relationship of calibrated estimates of sodium and potassium excretion with cardiovascular outcomes. The calibrated estimates were a function of self-reported sodium-to-potassium ratio from a food frequency questionnaire, age, body mass index, race, supplement use, smoking status, educational level, income, and aspirin use. In general, associations with outcomes using the calibrated estimates were in the expected direction: direct for the sodium-to-potassium ratio and sodium intake and indirect for potassium. The unexpected associations were an increased risk of hemorrhagic stroke with lower sodium-to-potassium ratio and sodium intake and increased risk with higher potassium intake, along with a null relationship of sodium intake with ischemic stroke. Overall, our assessment is that the authors have improved the estimation of mean dietary sodium and potassium intakes. However, more work is needed to show that calibrated estimates actually improve estimation of future clinical events. If this methodological issue can be successfully addressed, their approach has the potential to improve estimation of dietary sodium and potassium intakes in observational studies.

Associations of Biomarker-Calibrated Sodium and Potassium Intakes With Cardiovascular Disease Risk Among Postmenopausal Women


Studies of the associations of sodium and potassium intakes with cardiovascular disease incidence often rely on self-reported dietary data. In the present study, self-reported intakes from postmenopausal women at 40 participating US clinical centers are calibrated using 24-hour urinary excretion measures in cohorts from the Women's Health Initiative, with follow-up from 1993 to 2010. The incidence of hypertension was positively related to (calibrated) sodium intake and to the ratio of sodium to potassium. The sodium-to-potassium ratio was associated with cardiovascular disease incidence during an average follow-up period of 12 years. The estimated hazard ratio for a 20% increase in the sodium-to-potassium ratio was 1.13 (95% confidence interval (CI): 1.04, 1.22) for coronary heart disease, 1.20 (95% CI: 1.01, 1.42) for heart failure, and 1.11 (95% CI: 1.04, 1.19) for a composite cardiovascular disease outcome. The association with total stroke was not significant, but it was positive for ischemic stroke and inverse for hemorrhagic stroke. Aside from hemorrhagic stroke, corresponding associations of cardiovascular disease with sodium and potassium jointly were positive for sodium and inverse for potassium, although some were not statistically significant. Specifically, for coronary heart disease, the hazard ratios for 20% increases were 1.11 (95% CI: 0.95, 1.30) for sodium and 0.85 (95% CI: 0.73, 0.99) for potassium; and corresponding values for heart failure were 1.36 (95% CI: 1.02, 1.82) for sodium and 0.90 (95% CI: 0.69, 1.18) for potassium.

Mammographic Density Reduction as a Prognostic Marker for Postmenopausal Breast Cancer: Results Using a Joint Longitudinal-Survival Modeling Approach


Previous studies have linked reductions in mammographic density after a breast cancer diagnosis to an improved prognosis. These studies focused on short-term change, using a 2-stage process, treating estimated change as a fixed covariate in a survival model. We propose the use of a joint longitudinal-survival model. This enables us to model long-term trends in density while accounting for dropout as well as for measurement error. We studied the change in mammographic density after a breast cancer diagnosis and its association with prognosis (measured by cause-specific mortality), overall and with respect to hormone replacement therapy and tamoxifen treatment. We included 1,740 women aged 50–74 years, diagnosed with breast cancer in Sweden during 1993–1995, with follow-up until 2008. They had a total of 6,317 mammographic density measures available from the first 5 years of follow-up, including baseline measures. We found that the impact of the withdrawal of hormone replacement therapy on density reduction was larger than that of tamoxifen treatment. Unlike previous studies, we found that there was an association between density reduction and survival, both for tamoxifen-treated women and women who were not treated with tamoxifen.

A Multinomial Regression Approach to Model Outcome Heterogeneity


When a risk factor affects certain categories of a multinomial outcome but not others, outcome heterogeneity is said to be present. A standard epidemiologic approach for modeling risk factors of a categorical outcome typically entails fitting a polytomous logistic regression via maximum likelihood estimation. In this paper, we show that standard polytomous regression is ill equipped to detect outcome heterogeneity and will generally understate the degree to which such heterogeneity may be present. Specifically, nonsaturated polytomous regression will often a priori rule out the possibility of outcome heterogeneity from its parameter space. As a remedy, we propose to model each category of the outcome as a separate binary regression. For full efficiency, we propose to estimate the collection of regression parameters jointly using a constrained Bayesian approach that ensures that one remains within the multinomial model. The approach is straightforward to implement in standard software for Bayesian estimation.

Correcting the Standard Errors of 2-Stage Residual Inclusion Estimators for Mendelian Randomization Studies


Mendelian randomization studies use genotypes as instrumental variables to test for and estimate the causal effects of modifiable risk factors on outcomes. Two-stage residual inclusion (TSRI) estimators have been used when researchers are willing to make parametric assumptions. However, researchers are currently reporting uncorrected or heteroscedasticity-robust standard errors for these estimates. We compared several different forms of the standard error for linear and logistic TSRI estimates in simulations and in real-data examples. Among others, we consider standard errors modified from the approach of Newey (1987), Terza (2016), and bootstrapping. In our simulations Newey, Terza, bootstrap, and corrected 2-stage least squares (in the linear case) standard errors gave the best results in terms of coverage and type I error. In the real-data examples, the Newey standard errors were 0.5% and 2% larger than the unadjusted standard errors for the linear and logistic TSRI estimators, respectively. We show that TSRI estimators with modified standard errors have correct type I error under the null. Researchers should report TSRI estimates with modified standard errors instead of reporting unadjusted or heteroscedasticity-robust standard errors.

Comprehensive Analysis of Prevalence, Epidemiologic Characteristics, and Clinical Characteristics of Monoinfection and Coinfection in Diarrheal Diseases in Children in Tanzania


The role of interactions between intestinal pathogens in diarrheal disease is uncertain. From August 2010 to July 2011, we collected stool samples from 723 children admitted with diarrhea (cases) to 3 major hospitals in Dar es Salaam, Tanzania, and from 564 nondiarrheic children (controls). We analyzed the samples for 17 pathogens and assessed interactions between coinfections in additive and multiplicative models. At least one pathogen was detected in 86.9% of the cases and 62.8%, of the controls. Prevalence of coinfections was 58.1% in cases and 40.4% in controls. Rotavirus, norovirus genogroup II, Cryptosporidium, and Shigella species/enteroinvasive Escherichia coli were significantly associated with diarrhea both as monoinfections and as coinfections. In the multiplicative interaction model, we found 2 significant positive interactions: rotavirus + Giardia (odds ratio (OR) = 23.91, 95% confidence interval (CI): 1.21, 470.14) and norovirus GII + enteroaggregative E. coli (OR = 3.06, 95% CI: 1.17, 7.98). One significant negative interaction was found between norovirus GII + typical enteropathogenic E. coli (OR = 0.09, 95% CI: 0.01, 0.95). In multivariate analysis, risk factors for death were presence of blood in stool and severe dehydration. In conclusion, coinfections are frequent, and the pathogenicity of each organism appears to be enhanced by some coinfections and weakened by others. Severity of diarrhea was not affected by coinfections.

Sex Differences in the Association Between Pain and Injurious Falls in Older Adults: A Population-Based Longitudinal Study


We investigated whether there are sex differences in the association between pain and incident injurious falls. A total of 2,934 people (ages ≥60 years) from the population-based Swedish National Study on Aging and Care in Kungsholmen (2001–2004) participated. Participants were followed up for 3 and 10 years for falls leading to hospitalization or outpatient care. Data were analyzed with flexible parametric survival models that adjusted for potential confounders. During the first 3 years of follow-up, 67 men and 194 women experienced an injurious fall, and over 10 years of follow up, 203 men and 548 women experienced such a fall. In men, the presence of pain, having pain that was at least mild, having pain that affected several daily activities, and having daily pain all significantly increased the likelihood of incurring an injurious fall during the 3-year follow-up period. The multivariate-adjusted hazard ratios ranged from 1.78 (95% confidence interval: 1.00, 3.15) for the presence of pain to 2.89 (95% confidence interval: 1.41, 5.93) for several daily activities’ being affected by pain. Results for the 10-year follow-up period were similar. No significant associations were detected in women. Although pain is less prevalent in men than in women, its impact on risk of injurious falls seems to be greater in men.

Quantitative Serum Nuclear Magnetic Resonance Metabolomics in Large-Scale Epidemiology: A Primer on -Omic Technologies


Detailed metabolic profiling in large-scale epidemiologic studies has uncovered novel biomarkers for cardiometabolic diseases and clarified the molecular associations of established risk factors. A quantitative metabolomics platform based on nuclear magnetic resonance spectroscopy has found widespread use, already profiling over 400,000 blood samples. Over 200 metabolic measures are quantified per sample; in addition to many biomarkers routinely used in epidemiology, the method simultaneously provides fine-grained lipoprotein subclass profiling and quantification of circulating fatty acids, amino acids, gluconeogenesis-related metabolites, and many other molecules from multiple metabolic pathways. Here we focus on applications of magnetic resonance metabolomics for quantifying circulating biomarkers in large-scale epidemiology. We highlight the molecular characterization of risk factors, use of Mendelian randomization, and the key issues of study design and analyses of metabolic profiling for epidemiology. We also detail how integration of metabolic profiling data with genetics can enhance drug development. We discuss why quantitative metabolic profiling is becoming widespread in epidemiology and biobanking. Although large-scale applications of metabolic profiling are still novel, it seems likely that comprehensive biomarker data will contribute to etiologic understanding of various diseases and abilities to predict disease risks, with the potential to translate into multiple clinical settings.