Can sodium and potassium measured in timed voids be used as reference instruments for validating self-report instruments? Results from a urine calibration study

BACKGROUND

Sodium and potassium measured in 24-h urine collections are often used as reference measurements to validate self-reported dietary intake instruments.

OBJECTIVES

To evaluate whether collection and analysis of a limited number of urine voids at specified times during the day ("timed voids") can provide alternative reference measurements, and to identify their optimal number and timing.

METHODS

We used data from a urine calibration study among 441 adults aged 18-39 y. Participants collected each urine void in a separate container for 24 h and recorded the collection time. For the same day, they reported dietary intake using a 24-h recall. Urinary sodium and potassium were analyzed in a 24-h composite sample and in 4 timed voids (morning, afternoon, evening, and overnight). Linear regression models were used to develop equations predicting log-transformed 24-h urinary sodium or potassium levels using each of the 4 single timed voids, 6 pairs, and 4 triples. The equations also included age, sex, race, BMI (kg/m2), and log creatinine. Optimal combinations minimizing the mean squared prediction error were selected, and the observed and predicted 24-h levels were then used as reference measures to estimate the group bias and attenuation factors of the 24-h dietary recall. These estimates were compared.

RESULTS

Optimal combinations found were as follows: single voids-evening; paired voids-afternoon + overnight (sodium) and morning + evening (potassium); and triple voids-morning + evening + overnight (sodium) and morning + afternoon + evening (potassium). Predicted 24-h urinary levels estimated 24-h recall group biases and attenuation factors without apparent bias, but with less precision than observed 24-h urinary levels. To recover lost precision, it was estimated that sample sizes need to be increased by ∼2.6-2.7 times for a single void, 1.7-2.1 times for paired voids, and 1.5-1.6 times for triple voids.

CONCLUSIONS

Our results provide the basis for further development of new reference biomarkers based on timed voids.

CLINICAL TRIAL REGISTRY

clinicaltrials.gov as NCT01631240.

Developing the POTOMAC Model: A Novel Prediction Model to Study the Impact of Lymphopenia Kinetics on Survival Outcomes in Head and Neck Cancer Via an Ensemble Tree-Based Machine Learning Approach

PURPOSE

Lymphopenia is associated with poor survival outcomes in head and neck squamous cell carcinoma (HNSCC), yet there is no consensus on whether we should limit lymphopenia risks during treatment. To fully elucidate the prognostic role of baseline versus treatment-related lymphopenia, a robust analysis is necessary to investigate the relative importance of various lymphopenia metrics (LMs) in predicting survival outcomes.

METHODS

In this prospective cohort study, 363 patients were eligible for analysis (patients with newly diagnosed, nonmetastatic HNSCC treated with neck radiation with or without chemotherapy in 2015-2019). Data were acquired on 28 covariates: seven baseline, five disease, seven treatment, and nine LMs, including static and time-varying features for absolute lymphocyte count (ALC), neutrophil-to-lymphocyte ratio, and immature granulocytes (IGs). IGs were included, given their hypothesized role in inhibiting lymphocyte function. Overall, there were 4.0% missing data. Median follow-up was 2.9 years. We developed a model (POTOMAC) to predict survival outcomes using a random survival forest (RSF) procedure. RSF uses an ensemble approach to reduce the risk of overfitting and provides internal validation of the model using data that are not used in model development. The ability to predict survival risk was assessed using the AUC for the predicted risk score.

RESULTS

POTOMAC predicted 2-year survival with AUCs at 0.78 for overall survival (primary end point) and 0.73 for progression-free survival (secondary end point). Top modifiable risk factors included radiation dose and max ALC decrease. Top baseline risk factors included age, Charlson Comorbidity Index, Karnofsky Performance Score, and baseline IGs. Top-ranking LMs had superior prognostic performance when compared with human papillomavirus status, chemotherapy type, and dose (up to 2, 8, and 65 times higher in variable importance score).

CONCLUSION

POTOMAC provides important insights into potential approaches to reduce mortality in patients with HNSCC treated by chemoradiation but needs to be validated in future studies.

Issues in Implementing Regression Calibration Analyses

Regression calibration is a popular approach for correcting biases in estimated regression parameters when exposure variables are measured with error. This approach involves building a calibration equation to estimate the value of the unknown true exposure given the error-prone measurement and other covariates. The estimated, or calibrated, exposure is then substituted for the unknown true exposure in the health outcome regression model. When used properly, regression calibration can greatly reduce the bias induced by exposure measurement error. Here, we first provide an overview of the statistical framework for regression calibration, specifically discussing how a special type of error, called Berkson error, arises in the estimated exposure. We then present practical issues to consider when applying regression calibration, including: 1) how to develop the calibration equation and which covariates to include; 2) valid ways to calculate standard errors of estimated regression coefficients; and 3) problems arising if one of the covariates in the calibration model is a mediator of the relationship between the exposure and outcome. Throughout, we provide illustrative examples using data from the Hispanic Community Health Study/Study of Latinos (United States, 2008-2011) and simulations. We conclude with recommendations for how to perform regression calibration.

Urinary Sucrose and Fructose From Spot Urine May Be Used as a Predictive Biomarker of Total Sugar Intake-Findings From a Controlled Feeding Study

BACKGROUND

Recently, we confirmed 24-h urinary sucrose plus fructose (24 uSF) as a predictive biomarker of total sugar intake. However, the collection of 24-h urine samples has limited feasibility in population studies.

OBJECTIVE

We investigated the utility of the urinary sucrose plus fructose (uSF) biomarker measured in spot urine as a measure of 24 uSF biomarker and total sugar intake.

METHODS

Hundred participants, 18-70 y of age, from the Phoenix Metropolitan Area completed a 15-d feeding study. For 2 of the 8 collected 24-h urine samples, each spot urine sample was collected in a separate container. We considered 4 timed voids of the day [morning (AM) void: first void 08:30-12:30; afternoon (PM) void: first void 12:31-17:30; evening (EVE) void: first void 17:31-12:00; and next-day (ND) void: first void 04:00-12:00]. We investigated the performance of uSF from 1 void, and uSF combined from 2 and 3 voids as a measure of 24 uSF and sugar intake.

RESULTS

The biomarker averaged from PM/EVE void strongly correlated with 24 uSF (partial r = 0.75). The 24 uSF predicted from the PM/EVE combination was significantly associated with observed sugar intake and was selected for building the calibrated biomarker equation (marginal R2 = 0.36). Spot urine-based calibrated biomarker, ie, biomarker-estimated sugar intake was moderately correlated with the 15-d mean-observed sugar intake (r = 0.50).

CONCLUSIONS

uSF measured from a PM and EVE void may be used to generate biomarker-based sugar intake estimate when collecting 24-h urine samples is not feasible, pending external validation.

Evaluating a Model of Added Sugar Intake Based on Amino Acid Carbon Isotope Ratios in a Controlled Feeding Study of U.S. Adults

Previous studies suggest that amino acid carbon stable isotope ratios (CIRAAs) may serve as biomarkers of added sugar (AS) intake, but this has not been tested in a demographically diverse population. We conducted a 15-day feeding study of U.S. adults, recruited across sex, age, and BMI groups. Participants consumed personalized diets that resembled habitual intake, assessed using two consecutive 7-day food records. We measured serum (n = 99) CIRAAs collected at the end of the feeding period and determined correlations with diet. We used forward selection to model AS intake using participant characteristics and 15 CIRAAs. This model was internally validated using bootstrap optimism correction. Median (25th, 75th percentile) AS intake was 65.2 g/day (44.7, 81.4) and 9.5% (7.2%, 12.4%) of energy. The CIR of alanine had the highest, although modest, correlation with AS intake (r = 0.32, p = 0.001). Serum CIRAAs were more highly correlated with animal food intakes, especially the ratio of animal to total protein. The AS model included sex, body weight and 6 CIRAAs. This model had modest explanatory power (multiple R2 = 0.38), and the optimism-corrected R2 was lower (R2 = 0.15). Further investigations in populations with wider ranges of AS intake are warranted.

Sucrose and Fructose in Spot Urine as a Biomarker of Total Sugars Intake – Findings From a Controlled Feeding Study

Objectives

To investigate the utility of sucrose and fructose measured in spot urine (uSF) as a measure of 24-h urinary sucrose and fructose (24uSF) and a biomarker of total sugars (TS) intake.

Methods

A hundred participants, 18–70 years of age, recruited from the Phoenix Metropolitan Area completed a 15-d controlled feeding study, which simulated their usual dietary behavior. Participants collected eight nonconsecutive 24-h urines; for two of the urine collections, each spot urine void was collected in a separate container. In the analysis, we considered four voids out of all voids collected for the day [AM void – 1^st void after a meal or between 8:30 am to 12:30 pm; PM void - 1^st void after a meal and between 12:31 pm to 5:30 pm; EVE void - 1^st void after a meal and between 5:31 pm to 12:00 am; and Next-day (ND) void - 1^st next day morning void and between 4:00 am to 12:00 pm]. We investigated the performance of uSF measured in one void, and uSF combined from two and three voids as a measure of 1) 24uSF and 2) TS intake.

Results

Among the four selected voids, averaged over two collection days, uSF measured in the EVE void correlated best with 24uSF [partial r (adjusted for urinary creatinine) = 0.69]. For uSF biomarker combined from two voids, PM/EVE void produced the strongest correlation with 24uSF (r = 0.75). The correlation only marginally improved, when adding a 3^rd void (PM/EVE/ND: r = 0.78). Based on these findings, we developed prediction equations for log(24uSF) based on log(uSF) measured in EVE, PM/EVE or PM/EVE/ND voids, adjusted for gender, log(age), BMI and log(creatinine). The R² from the linear mixed model relating predicted 24uSF based on EVE, PM/EVE or PM/EVE/ND voids with observed TS, age and gender was 0.30, 0.46 and 0.48, respectively. Biomarker-estimated TS intake based on log(24uSF) predicted from PM/EVE voids had moderate model-based estimates of correlation with ‘usual’ TS intake (for uSF measured in PM/EVE voids from 1 day, r = 0.34; from 2 days, r = 0.45; and from 4 days, r = 0.52).

Conclusions

Our findings suggest that uSF measured in PM/EVE voids performs well as a measure of 24uSF, and may be used to generate biomarker-based TS intake estimate when collecting of 24-urine is not feasible. Collecting PM and EVE voids over at least 2 nonconsecutive days rather than one day will produce less biased results.

Funding Sources

NIH - National Cancer Institute.

Added Sugars Intake Explained by Amino Acid Carbon Isotope Ratio Profiles in a Controlled Feeding Study of U.S. Adults

Objectives

To evaluate an amino acid carbon stable isotope ratio (CIRAA) biomarker of added sugars (AS) intake in a controlled feeding study of men and women across age and BMI groups.

Methods

We conducted a 15-d feeding study in Phoenix, AZ, of men and women (N = 100, aged 18–70 y, BMI 17.9–35.0) who were recruited across sex, age, and BMI groups. Participants were provided personalized diets that resembled their habitual intakes, based on 2 consecutive 7-d food records. We measured CIRAAs in serum samples (N = 99) collected at the end of the feeding period and determined correlations with dietary intakes. We used forward selection to construct a model to explain AS intake using participant characteristics and 14 measured CIRAAs. This model was internally validated using a bootstrap optimism correction.

Results

Median (25th, 75th percentile) AS intake was 65.2 g/d (44.7, 81.4) and 9.5% (7.2%, 12.4%) of energy. The CIR of alanine had the highest, though still modest, correlation with AS intake (Pearson r = 0.32, P = 0.001). Serum CIRAAs were more highly correlated with animal food intakes, especially the ratio of animal to total protein intake (APR). The highest correlations were between the APR and the CIRs of phenylalanine (Pearson r = 0.85, P < 0.001) and leucine (Pearson r = 0.84, P < 0.001). The model of AS intake included participant sex and body weight and the CIRs of 6 AAs: alanine, valine, lysine, glutamic acid, serine, and glycine. This model had modest explanatory power (multiple R2 = 0.38), and the optimism-corrected R2 for the model was lower (R2 = 0.15).

Conclusions

The observed association between serum CIRAAs and AS intake in the U.S. diet is encouraging; however, further investigation in populations with wider ranges of AS intake is warranted.

Funding Sources

National Cancer Institute; Institutional Development Award (IDeA) from the National Institutes of General Medical Sciences.

Establishing 24-Hour Urinary Sucrose Plus Fructose as a Predictive Biomarker for Total Sugars Intake

BACKGROUND

Twenty-four-hour urinary sucrose and fructose (24uSF) has been studied as a biomarker of total sugars intake in two feeding studies conducted in the United Kingdom (UK) and Arizona (AZ). We compare the biomarker performance in these populations, testing whether it meets the criteria for a predictive biomarker.

METHODS

The UK and AZ feeding studies included 13 and 98 participants, respectively, aged 18 to 70 years, consuming their usual diet under controlled conditions. Linear mixed models relating 24uSF to total sugars and personal characteristics were developed in each study and compared. The AZ calibrated biomarker equation was applied to generate biomarker-estimated total sugars intake in UK participants. Stability of the model across AZ study subpopulations was also examined.

RESULTS

Model coefficients were similar between the two studies [e.g., log(total sugars): UK 0.99, AZ 1.03, P = 0.67], as was the ratio of calibrated biomarker person-specific bias to between-person variance (UK 0.32, AZ 0.25, P = 0.68). The AZ equation estimated UK log(total sugar intakes) with mean squared prediction error of 0.27, similar to the AZ study estimate (0.28). Within the AZ study, the regression coefficients of log(total sugars) were similar across age, gender, and body mass index subpopulations.

CONCLUSIONS

Similar model coefficients in the two studies and good prediction of UK sugar intakes by the AZ equation suggest that 24uSF meets the criteria for a predictive biomarker. Testing the biomarker performance in other populations is advisable.

IMPACT

Applications of the 24uSF biomarker will enable improved assessment of the role of sugars intake in risk of chronic disease, including cancer. See related commentary by Prentice, p. 1151.

Measurement Error Affecting Web- and Paper-Based Dietary Assessment Instruments: Insights From the Multi-Cohort Eating and Activity Study for Understanding Reporting Error

Few biomarker-based validation studies have examined error in online self-report dietary assessment instruments, and food records (FRs) have been considered less than food frequency questionnaires (FFQs) and 24-hour recalls (24HRs). We investigated measurement error in online and paper-based FFQs, online 24HRs, and paper-based FRs in 3 samples drawn primarily from 3 cohorts, comprising 1,393 women and 1,455 men aged 45-86 years. Data collection occurred from January 2011 to October 2013. Attenuation factors and correlation coefficients between reported and true usual intake for energy, protein, sodium, potassium, and respective densities were estimated using recovery biomarkers. Across studies, average attenuation factors for energy were 0.07, 0.07, and 0.19 for a single FFQ, 24HR, and FR, respectively. Correlation coefficients for energy were 0.24, 0.23, and 0.40, respectively. Excluding energy, the average attenuation factors across nutrients and studies were 0.22 for a single FFQ, 0.22 for a single 24HR, and 0.51 for a single FR. Corresponding correlation coefficients were 0.31, 0.34, and 0.53, respectively. For densities (nutrient expressed relative to energy), the average attenuation factors across studies were 0.37, 0.17, and 0.50, respectively. The findings support prior research suggesting different instruments have unique strengths that should be leveraged in epidemiologic research.

An evaluation of the serum carbon isotope ratio as a candidate predictive biomarker of the dietary animal protein ratio (animal protein/total protein) in a 15-day controlled feeding study of US adults

BACKGROUND

The serum natural abundance carbon isotope ratio (CIR) was recently identified as a candidate biomarker of animal protein intake in postmenopausal women. Such a biomarker would help clarify the relation between dietary protein source (plant or animal) and chronic disease risk.

OBJECTIVES

We aimed to evaluate the performance of the serum CIR as a biomarker of dietary protein source in a controlled feeding study of men and women of diverse age and BMI.

METHODS

We conducted a 15-d feeding study of 100 adults (age: 18-70 y, 55% women) in Phoenix, AZ. Participants were provided individualized diets that approximated habitual food intakes. Serum was collected at the end of the feeding period for biomarker measurements.

RESULTS

Median [IQR] animal protein intake was 67 g/d [55-88 g/d], which was 64% of total protein. The serum CIR was positively correlated with animal protein and inversely correlated with plant protein intake, leading to a strong correlation (r2 = 0.76) with the dietary animal protein ratio (APR; animal/total protein). Regressing serum CIR on the APR, serum nitrogen isotope ratio (NIR), gender, age, and body weight generated an R2 of 0.78. Following the measurement error model for predictive biomarkers, the resulting regression equation was then inverted to develop a calibrated biomarker equation for APR. Added sugars ratio (added/total sugars intake) and corn intakes also influenced the serum CIR but to a much lesser degree than the APR; variations in these intakes had only small effects on biomarker-estimated APR.

CONCLUSIONS

Based on our findings in this US cohort of mixed sex and age, we propose the serum CIR alongside NIR as a predictive dietary biomarker of the APR. We anticipate using this biomarker to generate calibrated estimates based on self-reported intake and ultimately to obtain more precise disease risk estimates according to dietary protein source.

Investigating the performance of 24-h urinary sucrose and fructose as a biomarker of total sugars intake in US participants - a controlled feeding study

BACKGROUND

Developing approaches for the objective assessment of sugars intake in population research is crucial for generating reliable disease risk estimates, and evidence-based dietary guidelines. Twenty-four-hour urinary sucrose and fructose (24uSF) was developed as a predictive biomarker of total sugars intake based on 3 UK feeding studies, yet its performance as a biomarker of total sugars among US participants is unknown.

OBJECTIVES

To investigate the performance of 24uSF as a biomarker of sugars intake among US participants, and to characterize its use.

METHODS

Ninety-eight participants, aged 18-70 y, consumed their usual diet under controlled conditions of a feeding study for 15 d, and collected 8 nonconsecutive 24-h urines measured for sucrose and fructose.

RESULTS

A linear mixed model regressing log 24uSF biomarker on log total sugars intake along with other covariates explained 56% of the biomarker variance. Total sugars intake was the strongest predictor in the model (Marginal R2 = 0.52; P <0.0001), followed by sex (P = 0.0002) and log age (P = 0.002). The equation was then inverted to solve for total sugars intake, thus generating a calibrated biomarker equation. Calibration of the biomarker produced mean biomarker-based log total sugars of 4.79 (SD = 0.59), which was similar to the observed log 15-d mean total sugars intake of 4.69 (0.35). The correlation between calibrated biomarker and usual total sugars intake was 0.59 for the calibrated biomarker based on a single biomarker measurement, and 0.76 based on 4 biomarker repeats spaced far apart.

CONCLUSIONS

In this controlled feeding study, total sugars intake was the main determinant of 24uSF confirming its utility as a biomarker of total sugars in this population. Next steps will include validation of stability assumptions of the biomarker calibration equation proposed here, which will allow its use as an instrument for dietary validation and measurement error correction in diet-disease association studies.

The Carbon Isotope Ratio of Breath Is Elevated by Short and Long-Term Added Sugar and Animal Protein Intake in a Controlled Feeding Study

Objectives

Objective biomarkers would help to clarify relationships between added sugar (AS) intake and chronic disease. A recent study identified the breath carbon isotope ratio (CIR) as a potential short-term AS biomarker. To further evaluate the biomarker potential of the breath CIR, we evaluate the effects of both short and longer-term intakes of AS in the context of normal dietary intake patterns, and also evaluate animal protein (AP), another dietary factor known to influence CIR.

Methods

We conducted a 15-d controlled feeding study of 100 adults (age 18–70, 55% women) in Phoenix, AZ. Participants were provided individualized diets that approximated habitual food intakes and recorded the time that all foods were consumed throughout each day. Three breath samples were collected on each of 3 nonconsecutive, randomly selected study days: one fasting sample, one “morning” sample (collected 10:00–14:00) and one “evening” sample (collected 14:00–20:00). We used a linear mixed model to evaluate the effects of AS and AP intake in each of 8 hours preceding collection of the breath sample (t1 = 0–1 hour prior, t2 = 1–2 hours prior, etc.). Besides daily intake, models also included 15-d mean AS and AP intake, as well as sex, age and BMI. Coefficients are presented as (β (SE), P).

Results

Mean (±SD) intakes of AS and AP in our study were 67 ± 34 and 73 ± 30 g/d, respectively. The breath CIR was increased by AS consumed 1–4 hours prior to sample collection (βt2 = 0.014 (0.005), P = 0.0025; βt3 = 0.0094 (0.004), P = 0.02; βt4 = 0.012 (0.005), P = 0.02) and AP consumed 3–6 hours prior to sample collection (βt4 = 0.012 (0.005), P = 0.03; βt5 = 0.0092 (0.004), P = 0.03; βt6 = 0.010 (0.006), P = 0.09). In addition, the breath CIR increased with higher 15-d intakes of both AS and AP (βAS = 0.012 (0.003), P &lt; 0.0001 and βAP = 0.014 (0.004), P = 0.0003, respectively).

Conclusions

Both short-term and longer-term intakes of AS and AP increased the breath CIR. Short-term AS intake had a more rapid effect on the breath CIR than short-term AP intake, although effects were of similar size. Furthermore, the size of short-term effects were similar to the size of long-term effects. Thus, breath CIR is influenced by both short and long-term intakes of AS and AP and could have potential for evaluating dietary patterns.

Funding Sources

This work was funded by NIH U01 CA197902.

Evaluating the Serum Carbon Isotope Ratio as a Biomarker for Animal Protein Ratio in a Controlled Feeding Study of US Adults

Objectives

Recent studies have identified the serum natural abundance carbon isotope ratio (CIR) as a candidate biomarker of animal protein intake in postmenopausal women. Such a biomarker would help clarify the contribution of dietary protein source (animal vs. vegetable) to chronic disease risk. Here we evaluate biomarker performance and develop a biomarker calibration equation in a mixed-age and – gender cohort.

Methods

We conducted a 15-d feeding study of 100 adults (age 18–70, 55% women) in Phoenix, AZ. Participants were provided individualized diets that approximated habitual food intakes. Total CIR and nitrogen isotope ratio (NIR) were measured in sera collected at the end of the feeding period. We expressed animal protein as a ratio of total protein intake (APratio). We evaluated a model of serum CIR based on APratio, the serum NIR, gender, age and body weight, and the resulting regression equation was inverted to develop an equation for the APratio that we call the calibrated biomarker. We evaluated the association of the calibrated biomarker with actual APratio using Pearson correlation and 5-fold cross validation.

Results

Animal protein intake in this study was 73 ± 30 g/d (mean ± SD) and the APratio was 0.63 ± 0.13. Our model explained a large proportion of the variation in serum CIR (R2 = 0.77) and APratio was the only significant model effect (coefficient = 6.22, SE = 0.44, P &lt; 0.0001). Inverting that model generated the following biomarker calibration equation: APratio = (CIR – 26.35 – 0.06 (gender) + 0.068 * In age – 0.215 * In body weight – 0.204 * serum NIR)/6.22, where gender = 1,0 (male, female). There was a strong correlation between model-predicted and actual APratio (rP = 0.85, P &lt; 0.0001), with the mean model-predicted APratio differing from mean actual APratio by 0.0015 (SE = 0.0077). The standard deviation of the prediction error was 0.076. The 5-fold cross validation procedure produced very similar model R2, effects, and prediction errors.

Conclusions

These data suggest that the serum CIR has potential as a predictive biomarker of APratio, providing a useful tool for objectively assessing dietary protein intake patterns. Such a tool could help resolve the contribution of dietary patterns favoring animal protein intake to chronic disease risk.

Funding Sources

This work was funded by NIH U01 CA197902.

Abstract PR04: Feasibility of assessing individual’s diet using a web-based dietary assessment tool, ASA24, in a longitudinal observational study

Background: Although diet is considered one of the major environmental risk factors related to chronic disease, many studies do not evaluate it not only because of practical and logistical issues, but also because of persistent concerns about error in self-reported diet. Web-based tools, however, make it feasible and affordable to collect high-quality, detailed dietary data in large observational studies. The Automated Self-Administered 24-hour recall (ASA24) is an easy-to-use, engaging, self-administered web-based dietary assessment tool. It is freely available from the National Cancer Institute and is available on all mobile devices. Studies can use ASA24 to collect single or multiday 24-hr recalls or food records. ASA24 automatically analyzes reported food, beverage, and dietary supplements intake, generating detailed data files of nutrients and food groups consumed at the person and food/beverage/supplement item level. The ASA24 System includes a researcher website where investigators register to use ASA24, schedule and track participant activities, and obtain data files. Self-reported diet using ASA24 was evaluated against recovery biomarkers (i.e., true intake) in the Interactive Diet and Activity Tracking in AARP (IDATA) Study. Absolute dietary intakes assessed by multiday ASA24 recalls were close to true intakes and outperformed a food frequency questionnaire (FFQ).

Aim: To assess the feasibility of using ASA24 (version 2011) in free living adults 50-74 years old.

Method: Over a 12-month period, men (n=530) and women (n=545) were contacted by email, every other month, to complete an ASA24-2011 (total, 6 ASA24s/year). If a participant did not complete ASA24 after the first contact, a reminder email was sent on a new randomly selected day. Up to three email notifications were sent to obtain each of six ASA24s. Participants also completed a web-based FFQ at months 1 and 12.

Results: Most men (92%) and women (87%) completed at least three ASA24s. 77% of participants completed at least five ASA24s. Completion rate for the 1st FFQ was 81% in men and 73% in women, dropping to 73% and 70%, respectively, for the 2nd FFQ. Most participants (men: 75%; women: 70%) completed ASA24 after the 1st email notification. Another 18% of men and 21% of women completed ASA24 after the 2nd email notification. Median time to complete ASA24-2011 for the 1st administration was 55 minutes in men and 58 minutes in women but declined to about 44 minutes by the 3rd ASA24. Participants &lt;60 years old had a shorter time to complete an ASA24 than those &gt;60 years old. A decline in completion time with each subsequent ASA24 did not appear to affect the quality of diet reporting as there were no systematic decreases in reported energy and nutrient intakes across ASA24 administrations.

Conclusion: It is feasible to collect high-quality diet data using multiday ASAS24s in longitudinal observational studies. New as well as ongoing epidemiologic studies should consider incorporating a detailed dietary assessment such as ASA24 in future studies.

This abstract is also being presented as Poster A35.

Citation Format: Yikyung Park, Kevin W. Dodd, Douglas Midthune, Victor Kipnis, Heather Bowles, Amy F. Subar. Feasibility of assessing individual’s diet using a web-based dietary assessment tool, ASA24, in a longitudinal observational study [abstract]. In: Proceedings of the AACR Special Conference on Modernizing Population Sciences in the Digital Age; 2019 Feb 19-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(9 Suppl):Abstract nr PR04.

STRATOS guidance document on measurement error and misclassification of variables in observational epidemiology: Part 2-More complex methods of adjustment and advanced topics

We continue our review of issues related to measurement error and misclassification in epidemiology. We further describe methods of adjusting for biased estimation caused by measurement error in continuous covariates, covering likelihood methods, Bayesian methods, moment reconstruction, moment-adjusted imputation, and multiple imputation. We then describe which methods can also be used with misclassification of categorical covariates. Methods of adjusting estimation of distributions of continuous variables for measurement error are then reviewed. Illustrative examples are provided throughout these sections. We provide lists of available software for implementing these methods and also provide the code for implementing our examples in the Supporting Information. Next, we present several advanced topics, including data subject to both classical and Berkson error, modeling continuous exposures with measurement error, and categorical exposures with misclassification in the same model, variable selection when some of the variables are measured with error, adjusting analyses or design for error in an outcome variable, and categorizing continuous variables measured with error. Finally, we provide some advice for the often met situations where variables are known to be measured with substantial error, but there is only an external reference standard or partial (or no) information about the type or magnitude of the error.

STRATOS guidance document on measurement error and misclassification of variables in observational epidemiology: Part 1-Basic theory and simple methods of adjustment

Measurement error and misclassification of variables frequently occur in epidemiology and involve variables important to public health. Their presence can impact strongly on results of statistical analyses involving such variables. However, investigators commonly fail to pay attention to biases resulting from such mismeasurement. We provide, in two parts, an overview of the types of error that occur, their impacts on analytic results, and statistical methods to mitigate the biases that they cause. In this first part, we review different types of measurement error and misclassification, emphasizing the classical, linear, and Berkson models, and on the concepts of nondifferential and differential error. We describe the impacts of these types of error in covariates and in outcome variables on various analyses, including estimation and testing in regression models and estimating distributions. We outline types of ancillary studies required to provide information about such errors and discuss the implications of covariate measurement error for study design. Methods for ascertaining sample size requirements are outlined, both for ancillary studies designed to provide information about measurement error and for main studies where the exposure of interest is measured with error. We describe two of the simpler methods, regression calibration and simulation extrapolation (SIMEX), that adjust for bias in regression coefficients caused by measurement error in continuous covariates, and illustrate their use through examples drawn from the Observing Protein and Energy (OPEN) dietary validation study. Finally, we review software available for implementing these methods. The second part of the article deals with more advanced topics.

Combining a Food Frequency Questionnaire With 24-Hour Recalls to Increase the Precision of Estimation of Usual Dietary Intakes-Evidence From the Validation Studies Pooling Project

Improving estimates of individuals' dietary intakes is key to obtaining more reliable evidence for diet-health relationships from nutritional cohort studies. One approach to improvement is combining information from different self-report instruments. Previous work evaluated the gains obtained from combining information from a food frequency questionnaire (FFQ) and multiple 24-hour recalls (24HRs), based on assuming that 24HRs provide unbiased measures of individual intakes. Here we evaluate the same approach of combining instruments but base it on the better assumption that recovery biomarkers provide unbiased measures of individual intakes. Our analysis uses data from the 5 large validation studies included in the Validation Studies Pooling Project: the Observing Protein and Energy Nutrition Study (1999-2000), the Automated Multiple-Pass Method validation study (2002-2004), the Energetics Study (2006-2009), the Nutrition Biomarker Study (2004-2005), and the Nutrition and Physical Activity Assessment Study (2007-2009). The data included intakes of energy, protein, potassium, and sodium. Under a time-varying usual-intake model analysis, the combination of an FFQ with 4 24HRs improved correlations with true intake for predicted protein density, potassium density, and sodium density (range, 0.39-0.61) in comparison with use of a single FFQ (range, 0.34-0.50). Absolute increases in correlation ranged from 0.02 to 0.26, depending on nutrient and sex, with an average increase of 0.14. Based on unbiased recovery biomarker evaluation for these nutrients, we confirm that combining an FFQ with multiple 24HRs modestly improves the accuracy of estimates of individual intakes.

Associations of Biomarker-Calibrated Intake of Total Sugars With the Risk of Type 2 Diabetes and Cardiovascular Disease in the Women's Health Initiative Observational Study

The inconsistent findings from epidemiologic studies relating total sugars (TS) consumption to cardiovascular disease (CVD) or type 2 diabetes (T2D) risk may be partly due to measurement error in self-reported intake. Using regression calibration equations developed based on the predictive biomarker for TS and recovery biomarker for energy, we examined the association of TS with T2D and CVD risk, before and after dietary calibration, in 82,254 postmenopausal women participating in the Women's Health Initiative Observational Study. After up to 16 years of follow-up (1993-2010), 6,621 T2D and 5,802 CVD incident cases were identified. The hazard ratio for T2D per 20% increase in calibrated TS was 0.94 (95% confidence interval (CI): 0.77, 1.15) in multivariable energy substitution, and 1.00 (95% CI: 0.85, 1.18) in energy partition models. Multivariable hazard ratios for total CVD were 0.97 (95% CI: 0.87, 1.09) from energy substitution, and 0.91 (95% CI: 0.80, 1.04) from energy partition models. Uncalibrated TS generated a statistically significant inverse association with T2D and total CVD risk in multivariable energy substitution and energy partition models. The lack of conclusive findings from our calibrated analyses may be due to the low explanatory power of the calibration equations for TS, which could have led to incomplete deattenuation of the risk estimates.

Comparison of self-reported dietary intakes from the Automated Self-Administered 24-h recall, 4-d food records, and food-frequency questionnaires against recovery biomarkers

Background

A limited number of studies have evaluated self-reported dietary intakes against objective recovery biomarkers.

Objective

The aim was to compare dietary intakes of multiple Automated Self-Administered 24-h recalls (ASA24s), 4-d food records (4DFRs), and food-frequency questionnaires (FFQs) against recovery biomarkers and to estimate the prevalence of under- and overreporting.

Design

Over 12 mo, 530 men and 545 women, aged 50-74 y, were asked to complete 6 ASA24s (2011 version), 2 unweighed 4DFRs, 2 FFQs, two 24-h urine collections (biomarkers for protein, potassium, and sodium intakes), and 1 administration of doubly labeled water (biomarker for energy intake). Absolute and density-based energy-adjusted nutrient intakes were calculated. The prevalence of under- and overreporting of self-report against biomarkers was estimated.

Results

Ninety-two percent of men and 87% of women completed ≥3 ASA24s (mean ASA24s completed: 5.4 and 5.1 for men and women, respectively). Absolute intakes of energy, protein, potassium, and sodium assessed by all self-reported instruments were systematically lower than those from recovery biomarkers, with underreporting greater for energy than for other nutrients. On average, compared with the energy biomarker, intake was underestimated by 15-17% on ASA24s, 18-21% on 4DFRs, and 29-34% on FFQs. Underreporting was more prevalent on FFQs than on ASA24s and 4DFRs and among obese individuals. Mean protein and sodium densities on ASA24s, 4DFRs, and FFQs were similar to biomarker values, but potassium density on FFQs was 26-40% higher, leading to a substantial increase in the prevalence of overreporting compared with absolute potassium intake.

Conclusions

Although misreporting is present in all self-report dietary assessment tools, multiple ASA24s and a 4DFR provided the best estimates of absolute dietary intakes for these few nutrients and outperformed FFQs. Energy adjustment improved estimates from FFQs for protein and sodium but not for potassium. The ASA24, which now can be used to collect both recalls and records, is a feasible means to collect dietary data for nutrition research.

Categorizing a continuous predictor subject to measurement error

Epidemiologists often categorize a continuous risk predictor, even when the true risk model is not a categorical one. Nonetheless, such categorization is thought to be more robust and interpretable, and thus their goal is to fit the categorical model and interpret the categorical parameters. We address the question: with measurement error and categorization, how can we do what epidemiologists want, namely to estimate the parameters of the categorical model that would have been estimated if the true predictor was observed? We develop a general methodology for such an analysis, and illustrate it in linear and logistic regression. Simulation studies are presented and the methodology is applied to a nutrition data set. Discussion of alternative approaches is also included.

Evaluation of the 24-Hour Recall as a Reference Instrument for Calibrating Other Self-Report Instruments in Nutritional Cohort Studies: Evidence From the Validation Studies Pooling Project

Calibrating dietary self-report instruments is recommended as a way to adjust for measurement error when estimating diet-disease associations. Because biomarkers available for calibration are limited, most investigators use self-reports (e.g., 24-hour recalls (24HRs)) as the reference instrument. We evaluated the performance of 24HRs as reference instruments for calibrating food frequency questionnaires (FFQs), using data from the Validation Studies Pooling Project, comprising 5 large validation studies using recovery biomarkers. Using 24HRs as reference instruments, we estimated attenuation factors, correlations with truth, and calibration equations for FFQ-reported intakes of energy and for protein, potassium, and sodium and their densities, and we compared them with values derived using biomarkers. Based on 24HRs, FFQ attenuation factors were substantially overestimated for energy and sodium intakes, less for protein and potassium, and minimally for nutrient densities. FFQ correlations with truth, based on 24HRs, were substantially overestimated for all dietary components. Calibration equations did not capture dependencies on body mass index. We also compared predicted bias in estimated relative risks adjusted using 24HRs as reference instruments with bias when making no adjustment. In disease models with energy and 1 or more nutrient intakes, predicted bias in estimated nutrient relative risks was reduced on average, but bias in the energy risk coefficient was unchanged.

Trajectories of anxiety among breast cancer patients treated with chemotherapy pre- and post-chemotherapy compared to healthy controls: A nationwide multicenter study

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Background: A diagnosis of breast cancer (BC) elicits a myriad of concerns for women. Treatments are complex, often with multiple components, including chemotherapy. This study describes the trajectory of anxiety among BC patients treated with chemotherapy compared to healthy controls. Methods: This prospective observational study of breast cancer and lymphoma patients is being conducted through the National Cancer Institute Community Oncology Research Program (NCORP). For this analysis, women diagnosed with stage I-III BC and age-matched healthy controls were assessed using the State Trait Anxiety Inventory (STAI) at baseline prior to chemotherapy, 1-month and 6-months post-treatment. Baseline characteristics were compared using Welch’s t-test and chi-squared tests, and proportions of participants reporting anxiety between groups were examined at each time point. Linear mixed models (LMM) were used to compare anxiety trajectories over time, accounting for relevant baseline clinical, medical and demographic factors. Results: Among 945 participants, including 581 BC patients and 364 controls, the average age was 53 years, 91% were White, and 98% were non-Hispanic. Compared to controls, more BC patients reported anxiety scores > 40 at baseline (35% vs. 13%), 1-month (31% vs. 16%), and 6-months (30% vs. 19%), indicating potentially clinically significant anxiety. While BC patients’ anxiety decreased after an apex at baseline, it remained significantly higher than controls’ anxiety at each time point (all p < 0.001), and LMM analysis identified group and group by time interactions for each follow-up (all p < 0.001). Among all participants, predictors of increased anxiety included younger age, post-menopausal status, lower baseline cognitive function and higher baseline depressive symptoms. Conclusions: BC patients reported higher anxiety than controls at all time points, providing further insight into the trajectory of anxiety pre- and post-chemotherapy. Research into optimal timing of assessment and appropriate interventions to minimize anxiety burden in this population is warranted. Clinical trial information: NCT01382082.

Longitudinal functional additive model with continuous proportional outcomes for physical activity data

Motivated by physical activity data obtained from the BodyMedia FIT device (www.bodymedia.com), we take a functional data approach for longitudinal studies with continuous proportional outcomes. The functional structure depends on three factors. In our three-factor model, the regression structures are specified as curves measured at various factor-points with random effects that have a correlation structure. The random curve for the continuous factor is summarized using a few important principal components. The difficulties in handling the continuous proportion variables are solved by using a quasilikelihood type approximation. We develop an efficient algorithm to fit the model, which involves the selection of the number of principal components. The method is evaluated empirically by a simulation study. This approach is applied to the BodyMedia data with 935 males and 84 consecutive days of observation, for a total of 78, 540 observations. We show that sleep efficiency increases with increasing physical activity, while its variance decreases at the same time.

The impact of stratification by implausible energy reporting status on estimates of diet-health relationships

The food frequency questionnaire (FFQ) is known to be prone to measurement error. Researchers have suggested excluding implausible energy reporters (IERs) of FFQ total energy when examining the relationship between a health outcome and FFQ-reported intake to obtain less biased estimates of the effect of the error-prone measure of exposure; however, the statistical properties of stratifying by IER status have not been studied. Under certain assumptions, including nondifferential error, we show that when stratifying by IER status, the attenuation of the estimated relative risk in the stratified models will be either greater or less in both strata (implausible and plausible reporters) than for the nonstratified model, contrary to the common belief that the attenuation will be less among plausible reporters and greater among IERs. Whether there is more or less attenuation depends on the pairwise correlations between true exposure, observed exposure, and the stratification variable. Thus exclusion of IERs is inadvisable but stratification by IER status can sometimes help. We also address the case of differential error. Examples from the Observing Protein and Energy Nutrition Study and simulations illustrate these results.

Methods to Assess Measurement Error in Questionnaires of Sedentary Behavior

Sedentary behavior has already been associated with mortality, cardiovascular disease, and cancer. Questionnaires are an affordable tool for measuring sedentary behavior in large epidemiological studies. Here, we introduce and evaluate two statistical methods for quantifying measurement error in questionnaires. Accurate estimates are needed for assessing questionnaire quality. The two methods would be applied to validation studies that measure a sedentary behavior by both questionnaire and accelerometer on multiple days. The first method fits a reduced model by assuming the accelerometer is without error, while the second method fits a more complete model that allows both measures to have error. Because accelerometers tend to be highly accurate, we show that ignoring the accelerometer's measurement error, can result in more accurate estimates of measurement error in some scenarios. In this manuscript, we derive asymptotic approximations for the Mean-Squared Error of the estimated parameters from both methods, evaluate their dependence on study design and behavior characteristics, and offer an R package so investigators can make an informed choice between the two methods. We demonstrate the difference between the two methods in a recent validation study comparing Previous Day Recalls (PDR) to an accelerometer-based ActivPal.

Moment reconstruction and moment-adjusted imputation when exposure is generated by a complex, nonlinear random effects modeling process

For the classical, homoscedastic measurement error model, moment reconstruction (Freedman et al., 2004, 2008) and moment-adjusted imputation (Thomas et al., 2011) are appealing, computationally simple imputation-like methods for general model fitting. Like classical regression calibration, the idea is to replace the unobserved variable subject to measurement error with a proxy that can be used in a variety of analyses. Moment reconstruction and moment-adjusted imputation differ from regression calibration in that they attempt to match multiple features of the latent variable, and also to match some of the latent variable's relationships with the response and additional covariates. In this note, we consider a problem where true exposure is generated by a complex, nonlinear random effects modeling process, and develop analogues of moment reconstruction and moment-adjusted imputation for this case. This general model includes classical measurement errors, Berkson measurement errors, mixtures of Berkson and classical errors and problems that are not measurement error problems, but also cases where the data-generating process for true exposure is a complex, nonlinear random effects modeling process. The methods are illustrated using the National Institutes of Health-AARP Diet and Health Study where the latent variable is a dietary pattern score called the Healthy Eating Index-2005. We also show how our general model includes methods used in radiation epidemiology as a special case. Simulations are used to illustrate the methods.

Measurement error models with interactions

An important use of measurement error models is to correct regression models for bias due to covariate measurement error. Most measurement error models assume that the observed error-prone covariate (WW ) is a linear function of the unobserved true covariate (X) plus other covariates (Z) in the regression model. In this paper, we consider models for W that include interactions between X and Z. We derive the conditional distribution of X given W and Z and use it to extend the method of regression calibration to this class of measurement error models. We apply the model to dietary data and test whether self-reported dietary intake includes an interaction between true intake and body mass index. We also perform simulations to compare the model to simpler approximate calibration models.

A bivariate measurement error model for semicontinuous and continuous variables: Application to nutritional epidemiology

Semicontinuous data in the form of a mixture of a large portion of zero values and continuously distributed positive values frequently arise in many areas of biostatistics. This article is motivated by the analysis of relationships between disease outcomes and intakes of episodically consumed dietary components. An important aspect of studies in nutritional epidemiology is that true diet is unobservable and commonly evaluated by food frequency questionnaires with substantial measurement error. Following the regression calibration approach for measurement error correction, unknown individual intakes in the risk model are replaced by their conditional expectations given mismeasured intakes and other model covariates. Those regression calibration predictors are estimated using short-term unbiased reference measurements in a calibration substudy. Since dietary intakes are often "energy-adjusted," e.g., by using ratios of the intake of interest to total energy intake, the correct estimation of the regression calibration predictor for each energy-adjusted episodically consumed dietary component requires modeling short-term reference measurements of the component (a semicontinuous variable), and energy (a continuous variable) simultaneously in a bivariate model. In this article, we develop such a bivariate model, together with its application to regression calibration. We illustrate the new methodology using data from the NIH-AARP Diet and Health Study (Schatzkin et al., 2001, American Journal of Epidemiology 154, 1119-1125), and also evaluate its performance in a simulation study.

Sugar-sweetened beverage intake and cardiovascular risk factor profile in youth with type 1 diabetes: application of measurement error methodology in the SEARCH Nutrition Ancillary Study

The SEARCH Nutrition Ancillary Study aims to investigate the role of dietary intake on the development of long-term complications of type 1 diabetes in youth, and capitalise on measurement error (ME) adjustment methodology. Using the National Cancer Institute (NCI) method for episodically consumed foods, we evaluated the relationship between sugar-sweetened beverage (SSB) intake and cardiovascular risk factor profile, with the application of ME adjustment methodology. The calibration sample included 166 youth with two FFQ and three 24 h dietary recall data within 1 month. The full sample included 2286 youth with type 1 diabetes. SSB intake was significantly associated with higher TAG, total and LDL-cholesterol concentrations, after adjusting for energy, age, diabetes duration, race/ethnicity, sex and education. The estimated effect size was larger (model coefficients increased approximately 3-fold) after the application of the NCI method than without adjustment for ME. Compared with individuals consuming one serving of SSB every 2 weeks, those who consumed one serving of SSB every 2 d had 3.7 mg/dl (0.04 mmol/l) higher TAG concentrations and 4.0 mg/dl (0.10 mmol/l) higher total cholesterol and LDL-cholesterol concentrations, after adjusting for ME and covariates. SSB intake was not associated with measures of adiposity and blood pressure. Our findings suggest that SSB intake is significantly related to increased lipid levels in youth with type 1 diabetes, and that estimates of the effect size of SSB on lipid levels are severely attenuated in the presence of ME. Future studies in youth with diabetes should consider a design that will allow for the adjustment for ME when studying the influence of diet on health status.

A statistical model for measurement error that incorporates variation over time in the target measure, with application to nutritional epidemiology

Most statistical methods that adjust analyses for measurement error assume that the target exposure T is a fixed quantity for each individual. However, in many applications, the value of T for an individual varies with time. We develop a model that accounts for such variation, describing the model within the framework of a meta-analysis of validation studies of dietary self-report instruments, where the reference instruments are biomarkers. We demonstrate that in this application, the estimates of the attenuation factor and correlation with true intake, key parameters quantifying the accuracy of the self-report instrument, are sometimes substantially modified under the time-varying exposure model compared with estimates obtained under a traditional fixed-exposure model. We conclude that accounting for the time element in measurement error problems is potentially important.

Pooled results from 5 validation studies of dietary self-report instruments using recovery biomarkers for potassium and sodium intake

We pooled data from 5 large validation studies (1999-2009) of dietary self-report instruments that used recovery biomarkers as referents, to assess food frequency questionnaires (FFQs) and 24-hour recalls (24HRs). Here we report on total potassium and sodium intakes, their densities, and their ratio. Results were similar by sex but were heterogeneous across studies. For potassium, potassium density, sodium, sodium density, and sodium:potassium ratio, average correlation coefficients for the correlation of reported intake with true intake on the FFQs were 0.37, 0.47, 0.16, 0.32, and 0.49, respectively. For the same nutrients measured with a single 24HR, they were 0.47, 0.46, 0.32, 0.31, and 0.46, respectively, rising to 0.56, 0.53, 0.41, 0.38, and 0.60 for the average of three 24HRs. Average underreporting was 5%-6% with an FFQ and 0%-4% with a single 24HR for potassium but was 28%-39% and 4%-13%, respectively, for sodium. Higher body mass index was related to underreporting of sodium. Calibration equations for true intake that included personal characteristics provided improved prediction, except for sodium density. In summary, self-reports capture potassium intake quite well but sodium intake less well. Using densities improves the measurement of potassium and sodium on an FFQ. Sodium:potassium ratio is measured much better than sodium itself on both FFQs and 24HRs.

Use of a urinary sugars biomarker to assess measurement error in self-reported sugars intake in the nutrition and physical activity assessment study (NPAAS)

BACKGROUND

Measurement error in self-reported sugars intake may be obscuring the association between sugars and cancer risk in nutritional epidemiologic studies.

METHODS

We used 24-hour urinary sucrose and fructose as a predictive biomarker for total sugars, to assess measurement error in self-reported sugars intake. The Nutrition and Physical Activity Assessment Study (NPAAS) is a biomarker study within the Women's Health Initiative (WHI) Observational Study that includes 450 postmenopausal women ages 60 to 91 years. Food Frequency Questionnaires (FFQ), four-day food records (4DFR), and three 24-hour dietary recalls (24HRs) were collected along with sugars and energy dietary biomarkers.

RESULTS

Using the biomarker, we found self-reported sugars to be substantially and roughly equally misreported across the FFQ, 4DFR, and 24HR. All instruments were associated with considerable intake- and person-specific bias. Three 24HRs would provide the least attenuated risk estimate for sugars (attenuation factor, AF = 0.57), followed by FFQ (AF = 0.48) and 4DFR (AF = 0.32), in studies of energy-adjusted sugars and disease risk. In calibration models, self-reports explained little variation in true intake (5%-6% for absolute sugars and 7%-18% for sugars density). Adding participants' characteristics somewhat improved the percentage variation explained (16%-18% for absolute sugars and 29%-40% for sugars density).

CONCLUSIONS

None of the self-report instruments provided a good estimate of sugars intake, although overall 24HRs seemed to perform the best.

IMPACT

Assuming the calibrated sugars biomarker is unbiased, this analysis suggests that measuring the biomarker in a subsample of the study population for calibration purposes may be necessary for obtaining unbiased risk estimates in cancer association studies.

Pooled results from 5 validation studies of dietary self-report instruments using recovery biomarkers for energy and protein intake

We pooled data from 5 large validation studies of dietary self-report instruments that used recovery biomarkers as references to clarify the measurement properties of food frequency questionnaires (FFQs) and 24-hour recalls. The studies were conducted in widely differing US adult populations from 1999 to 2009. We report on total energy, protein, and protein density intakes. Results were similar across sexes, but there was heterogeneity across studies. Using a FFQ, the average correlation coefficients for reported versus true intakes for energy, protein, and protein density were 0.21, 0.29, and 0.41, respectively. Using a single 24-hour recall, the coefficients were 0.26, 0.40, and 0.36, respectively, for the same nutrients and rose to 0.31, 0.49, and 0.46 when three 24-hour recalls were averaged. The average rate of under-reporting of energy intake was 28% with a FFQ and 15% with a single 24-hour recall, but the percentages were lower for protein. Personal characteristics related to under-reporting were body mass index, educational level, and age. Calibration equations for true intake that included personal characteristics provided improved prediction. This project establishes that FFQs have stronger correlations with truth for protein density than for absolute protein intake, that the use of multiple 24-hour recalls substantially increases the correlations when compared with a single 24-hour recall, and that body mass index strongly predicts under-reporting of energy and protein intakes.

Body mass and weight change in adults in relation to mortality risk

Using data from the National Institutes of Health-AARP Diet and Health Study, we evaluated the influence of adulthood weight history on mortality risk. The National Institutes of Health-AARP Diet and Health Study is an observational cohort study of US men and women who were aged 50-71 years at entry in 1995-1996. This analysis focused on 109,947 subjects who had never smoked and were younger than age 70 years. We estimated hazard ratios of total and cause-specific mortality for recalled body mass index (BMI; weight (kg)/height (m)(2)) at ages 18, 35, and 50 years; weight change across 3 adult age intervals; and the effect of first attaining an elevated BMI at 4 successive ages. During 12.5 years' follow-up through 2009, 12,017 deaths occurred. BMI at all ages was positively related to mortality. Weight gain was positively related to mortality, with stronger associations for gain between ages 18 and 35 years and ages 35 and 50 years than between ages 50 and 69 years. Mortality risks were higher in persons who attained or exceeded a BMI of 25.0 at a younger age than in persons who reached that threshold later in adulthood, and risks were lowest in persons who maintained a BMI below 25.0. Heavier initial BMI and weight gain in early to middle adulthood strongly predicted mortality risk in persons aged 50-69 years.

Estimating and testing interactions when explanatory variables are subject to non-classical measurement error

Assessing interactions in linear regression models when covariates have measurement error (ME) is complex.We previously described regression calibration (RC) methods that yield consistent estimators and standard errors for interaction coefficients of normally distributed covariates having classical ME. Here we extend normal based RC (NBRC) and linear RC (LRC) methods to a non-classical ME model, and describe more efficient versions that combine estimates from the main study and internal sub-study. We apply these methods to data from the Observing Protein and Energy Nutrition (OPEN) study. Using simulations we show that (i) for normally distributed covariates efficient NBRC and LRC were nearly unbiased and performed well with sub-study size ≥200; (ii) efficient NBRC had lower MSE than efficient LRC; (iii) the naïve test for a single interaction had type I error probability close to the nominal significance level, whereas efficient NBRC and LRC were slightly anti-conservative but more powerful; (iv) for markedly non-normal covariates, efficient LRC yielded less biased estimators with smaller variance than efficient NBRC. Our simulations suggest that it is preferable to use: (i) efficient NBRC for estimating and testing interaction effects of normally distributed covariates and (ii) efficient LRC for estimating and testing interactions for markedly non-normal covariates.

Checking for completeness of 24-h urine collection using para-amino benzoic acid not necessary in the Observing Protein and Energy Nutrition study

BACKGROUND/OBJECTIVES

The orally administered para-amino benzoic acid (PABA) is known to have near 100% excretion in urine and is used as a measure of 24-h urine collection completeness (referred to as PABAcheck). The purpose was to examine the effect of including urine collections deemed incomplete based on PABAcheck in a dietary measurement error study.

SUBJECTS/METHODS

The Observing Protein and Energy Nutrition (OPEN) study was conducted in 1999-2000 and included 484 men and women aged 40-69 years. A food frequency questionnaire and 24-h dietary recalls were evaluated using recovery biomarkers that included urinary nitrogen and potassium from two 24-h urine collections. Statistical modeling determined the measurement error properties of dietary assessment instruments. In the original analyses, PABAcheck was used as a measure of complete urine collection; incomplete collections were either excluded or adjusted to acceptable levels. The OPEN data were reanalyzed including all urine collections and by using criteria based on self-reported missing voids to assess the differences.

RESULTS

Means and coefficients of variation for biomarker-based protein and potassium intakes, and measurement error model-based correlations and attenuation factors were similar regardless of whether PABAcheck or missed voids were considered.

CONCLUSION

PABAcheck may not be required in large population-based biomarker studies. However, until there are more analyses evaluating the necessity of a PABAcheck, it is recommended that PABA be given to all participants, but not necessarily analyzed. Then, PABAcheck could be used selectively as a marker of completeness among the collections in which low levels of biomarker are detected or for which noncompliance is suspected.

Regression calibration with more surrogates than mismeasured variables

In a recent paper (Weller EA, Milton DK, Eisen EA, Spiegelman D. Regression calibration for logistic regression with multiple surrogates for one exposure. Journal of Statistical Planning and Inference 2007; 137: 449-461), the authors discussed fitting logistic regression models when a scalar main explanatory variable is measured with error by several surrogates, that is, a situation with more surrogates than variables measured with error. They compared two methods of adjusting for measurement error using a regression calibration approximate model as if it were exact. One is the standard regression calibration approach consisting of substituting an estimated conditional expectation of the true covariate given observed data in the logistic regression. The other is a novel two-stage approach when the logistic regression is fitted to multiple surrogates, and then a linear combination of estimated slopes is formed as the estimate of interest. Applying estimated asymptotic variances for both methods in a single data set with some sensitivity analysis, the authors asserted superiority of their two-stage approach. We investigate this claim in some detail. A troubling aspect of the proposed two-stage method is that, unlike standard regression calibration and a natural form of maximum likelihood, the resulting estimates are not invariant to reparameterization of nuisance parameters in the model. We show, however, that, under the regression calibration approximation, the two-stage method is asymptotically equivalent to a maximum likelihood formulation, and is therefore in theory superior to standard regression calibration. However, our extensive finite-sample simulations in the practically important parameter space where the regression calibration model provides a good approximation failed to uncover such superiority of the two-stage method. We also discuss extensions to different data structures.

Intake_epis_food(): An R Function for Fitting a Bivariate Nonlinear Measurement Error Model to Estimate Usual and Energy Intake for Episodically Consumed Foods

We consider a Bayesian analysis using WinBUGS to estimate the distribution of usual intake for episodically consumed foods and energy (calories). The model uses measures of nutrition and energy intakes via a food frequency questionnaire (FFQ) along with repeated 24 hour recalls and adjusting covariates. In order to estimate the usual intake of the food, we phrase usual intake in terms of person-specific random effects, along with day-to-day variability in food and energy consumption. Three levels are incorporated in the model. The first level incorporates information about whether an individual in fact reported consumption of a particular food item. The second level incorporates the amount of intake from those individuals who reported consumption of the food, and the third level incorporates the energy intake. Estimates of posterior means of parameters and distributions of usual intakes are obtained by using Markov chain Monte Carlo calculations. This R function reports to users point estimates and credible intervals for parameters in the model, samples from their posterior distribution, samples from the distribution of usual intake and usual energy intake, trace plots of parameters and summary statistics of usual intake, usual energy intake and energy adjusted usual intake.

Taking advantage of the strengths of 2 different dietary assessment instruments to improve intake estimates for nutritional epidemiology

With the advent of Internet-based 24-hour recall (24HR) instruments, it is now possible to envision their use in cohort studies investigating the relation between nutrition and disease. Understanding that all dietary assessment instruments are subject to measurement errors and correcting for them under the assumption that the 24HR is unbiased for usual intake, here the authors simultaneously address precision, power, and sample size under the following 3 conditions: 1) 1-12 24HRs; 2) a single calibrated food frequency questionnaire (FFQ); and 3) a combination of 24HR and FFQ data. Using data from the Eating at America's Table Study (1997-1998), the authors found that 4-6 administrations of the 24HR is optimal for most nutrients and food groups and that combined use of multiple 24HR and FFQ data sometimes provides data superior to use of either method alone, especially for foods that are not regularly consumed. For all food groups but the most rarely consumed, use of 2-4 recalls alone, with or without additional FFQ data, was superior to use of FFQ data alone. Thus, if self-administered automated 24HRs are to be used in cohort studies, 4-6 administrations of the 24HR should be considered along with administration of an FFQ.

Using regression calibration equations that combine self-reported intake and biomarker measures to obtain unbiased estimates and more powerful tests of dietary associations

The authors describe a statistical method of combining self-reports and biomarkers that, with adequate control for confounding, will provide nearly unbiased estimates of diet-disease associations and a valid test of the null hypothesis of no association. The method is based on regression calibration. In cases in which the diet-disease association is mediated by the biomarker, the association needs to be estimated as the total dietary effect in a mediation model. However, the hypothesis of no association is best tested through a marginal model that includes as the exposure the regression calibration-estimated intake but not the biomarker. The authors illustrate the method with data from the Carotenoids and Age-Related Eye Disease Study (2001--2004) and show that inclusion of the biomarker in the regression calibration-estimated intake increases the statistical power. This development sheds light on previous analyses of diet-disease associations reported in the literature.

Prostate-specific antigen/solvent interaction analysis: a preliminary evaluation of a new assay concept for detecting prostate cancer using urinary samples

OBJECTIVE

To provide preliminary clinical performance evaluation of a novel prostate cancer (CaP) assay, prostate-specific antigen/solvent interaction analysis (PSA/SIA) that focused on changes to the structure of PSA.

METHODS

Two-hundred twenty-two men undergoing prostate biopsy for accepted clinical criteria at 3 sites (University Hospitals Case Medical Center in Cleveland, Cleveland Clinic, and Veterans Administration Boston Healthcare System) were enrolled in institutional review board-approved study. Before transrectal ultrasound-guided biopsy, patients received digital rectal examination with systematic prostate massage followed by collection of urine. The PSA/SIA assay determined the relative partitioning of heterogeneous PSA isoform populations in urine between 2 aqueous phases. A structural index, K, whose numerical value is defined as the ratio of the concentration of all PSA isoforms, was determined by total PSA enzyme-linked immunosorbent assay and used to set a diagnostic threshold for CaP. Performance was assessed using receiver operating characteristic (ROC) analysis with biopsy as the gold standard.

RESULTS

Biopsies were pathologically classified as case (malignant, n=100) or control (benign, n=122). ROC performance demonstrated area under the curve=0.90 for PSA/SIA and 0.58 for serum total PSA. At a cutoff value of k=1.73, PSA/SIA displayed sensitivity=100%, specificity=80.3%, positive predictive value=80.6%, and negative predictive value=100%. No attempt was made in this preliminary study to further control patient population or selection criteria for biopsy, nor did we analytically investigate the type of structural differences in PSA that led to changes in k value.

CONCLUSION

PSA/SIA provides ratiometric information independently of PSA concentration. In this preliminary study, analysis of the overall structurally heterogeneous PSA isoform population using the SIA assay showed promising results to be further evaluated in future studies.

Dealing with dietary measurement error in nutritional cohort studies

Dietary measurement error creates serious challenges to reliably discovering new diet-disease associations in nutritional cohort studies. Such error causes substantial underestimation of relative risks and reduction of statistical power for detecting associations. On the basis of data from the Observing Protein and Energy Nutrition Study, we recommend the following approaches to deal with these problems. Regarding data analysis of cohort studies using food-frequency questionnaires, we recommend 1) using energy adjustment for relative risk estimation; 2) reporting estimates adjusted for measurement error along with the usual relative risk estimates, whenever possible (this requires data from a relevant, preferably internal, validation study in which participants report intakes using both the main instrument and a more detailed reference instrument such as a 24-hour recall or multiple-day food record); 3) performing statistical adjustment of relative risks, based on such validation data, if they exist, using univariate (only for energy-adjusted intakes such as densities or residuals) or multivariate regression calibration. We note that whereas unadjusted relative risk estimates are biased toward the null value, statistical significance tests of unadjusted relative risk estimates are approximately valid. Regarding study design, we recommend increasing the sample size to remedy loss of power; however, it is important to understand that this will often be an incomplete solution because the attenuated signal may be too small to distinguish from unmeasured confounding in the model relating disease to reported intake. Future work should be devoted to alleviating the problem of signal attenuation, possibly through the use of improved self-report instruments or by combining dietary biomarkers with self-report instruments.

A NEW MULTIVARIATE MEASUREMENT ERROR MODEL WITH ZERO-INFLATED DIETARY DATA, AND ITS APPLICATION TO DIETARY ASSESSMENT

In the United States the preferred method of obtaining dietary intake data is the 24-hour dietary recall, yet the measure of most interest is usual or long-term average daily intake, which is impossible to measure. Thus, usual dietary intake is assessed with considerable measurement error. Also, diet represents numerous foods, nutrients and other components, each of which have distinctive attributes. Sometimes, it is useful to examine intake of these components separately, but increasingly nutritionists are interested in exploring them collectively to capture overall dietary patterns. Consumption of these components varies widely: some are consumed daily by almost everyone on every day, while others are episodically consumed so that 24-hour recall data are zero-inflated. In addition, they are often correlated with each other. Finally, it is often preferable to analyze the amount of a dietary component relative to the amount of energy (calories) in a diet because dietary recommendations often vary with energy level. The quest to understand overall dietary patterns of usual intake has to this point reached a standstill. There are no statistical methods or models available to model such complex multivariate data with its measurement error and zero inflation. This paper proposes the first such model, and it proposes the first workable solution to fit such a model. After describing the model, we use survey-weighted MCMC computations to fit the model, with uncertainty estimation coming from balanced repeated replication.The methodology is illustrated through an application to estimating the population distribution of the Healthy Eating Index-2005 (HEI-2005), a multi-component dietary quality index involving ratios of interrelated dietary components to energy, among children aged 2-8 in the United States. We pose a number of interesting questions about the HEI-2005 and provide answers that were not previously within the realm of possibility, and we indicate ways that our approach can be used to answer other questions of importance to nutritional science and public health.

Sugars in diet and risk of cancer in the NIH-AARP Diet and Health Study

Prospective epidemiologic data on the effects of different types of dietary sugars on cancer incidence have been limited. In this report, we investigated the association of total sugars, sucrose, fructose, added sugars, added sucrose and added fructose in the diet with risk of 24 malignancies. Participants (n = 435,674) aged 50-71 years from the NIH-AARP Diet and Health Study were followed for 7.2 years. The intake of individual sugars was assessed using a 124-item food frequency questionnaire (FFQ). Cox proportional hazards regression was used to estimate hazard ratios (HR) and 95% confidence intervals (CI) in multivariable models adjusted for confounding factors pertinent to individual cancers. We identified 29,099 cancer cases in men and 13,355 cases in women. In gender-combined analyses, added sugars were positively associated with risk of esophageal adenocarcinoma (HR(Q5 vs. Q1) : 1.62, 95% CI: 1.07-2.45; p(trend) = 0.01), added fructose was associated with risk of small intestine cancer (HR(Q5 vs. Q1) : 2.20, 95% CI: 1.16-4.16; p(trend) = 0.009) and all investigated sugars were associated with increased risk of pleural cancer. In women, all investigated sugars were inversely associated with ovarian cancer. We found no association between dietary sugars and risk of colorectal or any other major cancer. Measurement error in FFQ-reported dietary sugars may have limited our ability to obtain more conclusive findings. Statistically significant associations observed for the rare cancers are of interest and warrant further investigation.