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Measurement error is a major issue in self-reported diet that can distort diet-disease relationships. Use of blood concentration biomarkers has the potential to mitigate the subjective bias inherent in self-reporting. As part of the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) baseline visit (2008-2011), self-reported information on diet was collected from all participants (n = 16,415). The HCHS/SOL also included annual telephone follow-up, as well as a second (2014-2017) and third (2020-2023) clinic visit. Blood concentration biomarkers for carotenoids, tocopherols, retinol, vitamin B12, and folate were measured in a subset of participants (n = 476) as part of the Study of Latinos: Nutrition and Physical Activity Assessment Study (SOLNAS) (2010-2012). We examined the relationships among biomarker levels, self-reported intake, Hispanic/Latino background (Central American, Cuban, Dominican, Mexican, Puerto Rican, or South American), and other participant characteristics in this diverse cohort. We built regression calibration-based prediction equations for 10 nutritional biomarkers and used a simulation to study the power of detecting a diet-disease association in a multivariable Cox model using a predicted concentration level. Good statistical power was observed for some nutrients with high prediction model R2 values, but further research is needed to understand how best to realize the potential of these dietary biomarkers. This study provides a comprehensive examination of several nutritional biomarkers within the HCHS/SOL, characterizing their associations with subject characteristics and the influence of the measurement characteristics on the power to detect associations with health outcomes., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

In large epidemiologic studies, it is typical for an inexpensive, non-invasive procedure to be used to record disease status during regular follow-up visits, with less frequent assessment by a gold standard test. Inexpensive outcome measures like self-reported disease status are practical to obtain, but can be error-prone. Association analysis reliant on error-prone outcomes may lead to biased results; however, restricting analyses to only data from the less frequently observed error-free outcome could be inefficient. We have developed an augmented likelihood that incorporates data from both error-prone outcomes and a gold standard assessment. We conduct a numerical study to show how we can improve statistical efficiency by using the proposed method over standard approaches for interval-censored survival data that do not leverage auxiliary data. We extend this method for the complex survey design setting so that it can be applied in our motivating data example. Our method is applied to data from the Hispanic Community Health Study/Study of Latinos to assess the association between energy and protein intake and the risk of incident diabetes. In our application, we demonstrate how our method can be used in combination with regression calibration to additionally address the covariate measurement error in self-reported diet.

Current U.S. dietary guidelines recommend a daily potassium intake of 3400 mg/day for men and 2600 mg/day for women. Sub-optimal access to nutrient-rich foods may limit potassium intake and increase cardiometabolic risk. We examined the association of neighborhood characteristics related to food availability with potassium intake in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). 13,835 participants completed a 24-h dietary recall assessment and had complete covariates. Self-reported potassium intake was calibrated with an objective 24-h urinary potassium biomarker, using equations developed in the SOL Nutrition & Physical Activity Assessment Study (SOLNAS, N = 440). Neighborhood population density, median household income, Hispanic/Latino diversity, and a retail food environment index by census tract were obtained. Linear regression assessed associations with 24-h potassium intake, adjusting for individual-level and neighborhood confounders. Mean 24-h potassium was 2629 mg/day based on the SOLNAS biomarker and 2702 mg/day using multiple imputation and HCHS/SOL biomarker calibration. Compared with the lowest quartile of neighborhood population density, living in the highest quartile was associated with a 26% lower potassium intake in SOLNAS (adjusted fold-change 0.74, 95% CI 0.59-0.94) and a 39% lower intake in HCHS/SOL (adjusted fold-change 0.61 95% CI 0.45-0.84). Results were only partially explained by the retail food environment. The mechanisms by which population density affects potassium intake should be further studied.

Regression calibration is the most widely used method to adjust regression parameter estimates for covariate measurement error. Yet its application in the context of a complex sampling design, for which the common bootstrap variance estimator can be less straightforward, has been less studied. We propose 2 variance estimators for a multistage probability-based sampling design, a parametric and a resampling-based multiple imputation approach, where a latent mean exposure needed for regression calibration is the target of imputation. This work was motivated by the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) data from 2008 to 2011, for which relationships between several outcomes and diet, an error-prone self-reported exposure, are of interest. We assessed the relative performance of these variance estimation strategies in an extensive simulation study built on the HCHS/SOL data. We further illustrate the proposed estimators with an analysis of the cross-sectional association of dietary sodium intake with hypertension-related outcomes in a subsample of the HCHS/SOL cohort. We have provided guidelines for the application of regression models with regression-calibrated exposures. Practical considerations for implementation of these 2 variance estimators in the setting of a large multicenter study are also discussed. Code to replicate the presented results is available online., (© The Author(s) 2021. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Hispanics/Latinos represent >15% of the United States (US) population and experience a high burden of cardiovascular disease (CVD) and diabetes. Dietary exposure, particularly to arsenic (As), may be associated with CVD and diabetes in Hispanics/Latinos. Rural populations in the US exposed to As in drinking water have increased risk of diabetes and CVD; however, little is known about the risk among urban populations with low As in water who are mostly exposed to As through food., Methods: To explore the levels of inorganic arsenic exposure (the sum of inorganic and methylated arsenic species in urine, ∑As, corrected by a residual-based method) in persons of Hispanic/Latino origin, we conducted a pilot study quantifying urinary arsenic levels among 45 participants in the Study of Latinos: Nutrition & Physical Activity Assessment Study (SOLNAS)., Results: The median (interquartile range) of the urinary arsenic species (µg/L) were as follows: inorganic As 0.6 (0.4, 1.0), monomethylarsonic acid 1.2 (0.7, 1.9), dimethylarsinic acid 7.2 (4.3, 15.3), and ∑As 6.0 (4.3, 10.5)., Conclusions: This study adds to the existing evidence that harmful forms of arsenic are present in this group of Hispanics/Latinos.

Using a cross-sectional probability sample with actigraphy data and two 24-h dietary recalls, we quantified the association between sleep duration, continuity, variability and timing with the Alternative Healthy Eating Index-2010 diet quality score and its components in 2140 Hispanic Community Health Study/Study of Latinos participants. The Alternative Healthy Eating Index diet quality-2010 score ranges from 0 to 110, with higher scores indicating greater adherence to the dietary guidelines and lower risk from major chronic disease. None of the sleep measures was associated with total caloric intake as assessed using dietary recalls. However, both an increase in sleep duration and sleep efficiency were associated with healthier diet quality. Each standard deviation increase in sleep duration (1.05 h) and sleep efficiency (4.99%) was associated with a 0.30 point increase and 0.28 point increase, respectively, in the total Alternative Healthy Eating Index-2010 score. The component of Alternative Healthy Eating Index-2010 most strongly associated with longer sleep duration was increased nuts and legumes intake. The components of Alternative Healthy Eating Index-2010 most strongly associated with higher sleep efficiency were increased whole fruit intake and decreased sodium intake. Both longer sleep duration and higher sleep efficiency were significantly associated with better diet quality among US Hispanic/Latino adults. The dietary components most strongly associated with sleep duration and sleep efficiency differed, suggesting potentially independent mechanisms by which each aspect of sleep impacts dietary choices. Longitudinal research is needed to understand the directionality of these identified relationships and the generalizability of these data across other ethnic groups., (© 2017 European Sleep Research Society.)

Objective: Measurement error in self-reported total sugars intake may obscure associations between sugars consumption and health outcomes, and the sum of 24 h urinary sucrose and fructose may serve as a predictive biomarker of total sugars intake., Design: The Study of Latinos: Nutrition & Physical Activity Assessment Study (SOLNAS) was an ancillary study to the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) cohort. Doubly labelled water and 24 h urinary sucrose and fructose were used as biomarkers of energy and sugars intake, respectively. Participants' diets were assessed by up to three 24 h recalls (88 % had two or more recalls). Procedures were repeated approximately 6 months after the initial visit among a subset of ninety-six participants., Setting: Four centres (Bronx, NY; Chicago, IL; Miami, FL; San Diego, CA) across the USA., Subjects: Men and women (n 477) aged 18-74 years., Results: The geometric mean of total sugars was 167·5 (95 % CI 154·4, 181·7) g/d for the biomarker-predicted and 90·6 (95 % CI 87·6, 93·6) g/d for the self-reported total sugars intake. Self-reported total sugars intake was not correlated with biomarker-predicted sugars intake (r=-0·06, P=0·20, n 450). Among the reliability sample (n 90), the reproducibility coefficient was 0·59 for biomarker-predicted and 0·20 for self-reported total sugars intake., Conclusions: Possible explanations for the lack of association between biomarker-predicted and self-reported sugars intake include measurement error in self-reported diet, high intra-individual variability in sugars intake, and/or urinary sucrose and fructose may not be a suitable proxy for total sugars intake in this study population.

We investigated measurement error in the self-reported diets of US Hispanics/Latinos, who are prone to obesity and related comorbidities, by background (Central American, Cuban, Dominican, Mexican, Puerto Rican, and South American) in 2010-2012. In 477 participants aged 18-74 years, doubly labeled water and urinary nitrogen were used as objective recovery biomarkers of energy and protein intakes. Self-report was captured from two 24-hour dietary recalls. All measures were repeated in a subsample of 98 individuals. We examined the bias of dietary recalls and their associations with participant characteristics using generalized estimating equations. Energy intake was underestimated by 25.3% (men, 21.8%; women, 27.3%), and protein intake was underestimated by 18.5% (men, 14.7%; women, 20.7%). Protein density was overestimated by 10.7% (men, 11.3%; women, 10.1%). Higher body mass index and Hispanic/Latino background were associated with underestimation of energy (P<0.05). For protein intake, higher body mass index, older age, nonsmoking, Spanish speaking, and Hispanic/Latino background were associated with underestimation (P<0.05). Systematic underreporting of energy and protein intakes and overreporting of protein density were found to vary significantly by Hispanic/Latino background. We developed calibration equations that correct for subject-specific error in reporting that can be used to reduce bias in diet-disease association studies., (© The Author 2015. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.)