Eating patterns associated with overweight and obesity in children and adolescents: methodological considerations Megan A. McCrory, PhD Dept of Foods and Nutrition Dept of Psychological Sciences Ingestive Behavior Research Center Purdue University

Trends in Overweight in US Children and Adolescents *Sex-and age-specific BMI > 95th percentile based on the CDC growth charts. Source: CDC

How to Study Causes and Treatment of Overweight? DesignAdvantagesDisadvantages Clinical studies/controlled interventions Precisely control conditions and monitor effects not like “home” not like “home” non-compliance may occur non-compliance may occur possible inconsistency between short-term vs. long term; wt gain vs. wt loss possible inconsistency between short-term vs. long term; wt gain vs. wt loss Outpatient/ behavioral interventions Shows what is possible goals usually modest goals usually modest non-compliance is major issue non-compliance is major issue effects may be protocol- specific effects may be protocol- specific Observational/epidemiology Observe “usual life” conditions does not show cause and effect does not show cause and effect inaccurate (implausible) dietary reporting can lead to spurious conclusions inaccurate (implausible) dietary reporting can lead to spurious conclusions Modified from: Susan Roberts

How to Assess Compliance in Human Feeding Studies Checklist Checklist Container Return Container Return Innocuous Marker – eg PABA Innocuous Marker – eg PABA Why isn’t the latter done more often? Why isn’t the latter done more often?

Assessment of Dietary Compliance with Para-amino benzoic acid (PABA) – use in controlled feeding studies Supplementation of provided foods with PABA and recovery of PABA in complete urine collections can help determine whether provided foods are actually consumed in clinical studies. Supplementation of provided foods with PABA and recovery of PABA in complete urine collections can help determine whether provided foods are actually consumed in clinical studies. In a validation study in 10 adults, PABA recovery was 98.7±3.7% over 3 days In a validation study in 10 adults, PABA recovery was 98.7±3.7% over 3 days Expected minimum PABA excretion is 92.6% (5th percentile) Expected minimum PABA excretion is 92.6% (5th percentile) Roberts et al. Am J Clin Nutr 1990;51:485

Assessment of Dietary Compliance by Measurement of Urinary Osmolar Excretion Rate (OER) - use in controlled feeding studies Urinary OER can be used to predict whether subjects in clinical studies are eating food other than that provided by the research center. Urinary OER can be used to predict whether subjects in clinical studies are eating food other than that provided by the research center. This is because the urinary osmolar load is 95% due to N, Na and K salts, which come from the diet This is because the urinary osmolar load is 95% due to N, Na and K salts, which come from the diet In 34 subjects in 3 closely- supervised metabolic studies, mean OER over 6 days was 100±7% of expected In 34 subjects in 3 closely- supervised metabolic studies, mean OER over 6 days was 100±7% of expected Study 1 Study 2Study 3 Roberts et al. Am J Clin Nutr 1991;54:774

Assess dietary intake Assess dietary intake Intake-balance method Intake-balance method How to Assess Compliance in Controlled and Behavioral Interventions and Observational Studies

Traditional Dietary Assessment Methods in Free-Living Persons Food intake record (3-7 days) Food intake record (3-7 days) Weighed or measured Weighed or measured Estimated with visual aids Estimated with visual aids 24-h recall 24-h recall Multiple-pass technique Multiple-pass technique Several (≥3) Several (≥3) Unscheduled Unscheduled Food Frequency Questionnaire (not in kids) Food Frequency Questionnaire (not in kids) Diet History Questionnaire (not in kids) Diet History Questionnaire (not in kids)

Problem: Underreporting of Energy Intake Greater amount and prevalence of underreporting with increasing weight status Greater amount and prevalence of underreporting with increasing weight status In adults, selective underreporting occurs for foods high in energy density, and selective underreporting occurs for vegetables (fruits?) In adults, selective underreporting occurs for foods high in energy density, and selective underreporting occurs for vegetables (fruits?) Related to social desirability Related to social desirability

Total Energy Expenditure: Biomarker for Energy Intake If total energy expenditure and energy balance are measured accurately, energy intake can be validated because: If total energy expenditure and energy balance are measured accurately, energy intake can be validated because: Energy Intake = Total Energy Expenditure +∆ Energy Balance Energy Intake = Total Energy Expenditure +∆ Energy Balance

Validation of Reported Energy Intake (rEI) rEI can be validated by comparing it with total energy expenditure (TEE), since during body weight stability Total Energy Expenditure = Total Energy Intake (TEE = TEI) If rEI does not agree with TEE, it could be considered “implausible” for representing usual EI

Validating Reported Energy Intake (rEI) kcal/d rEI can be validated by comparing it with total energy expenditure (TEE), since during body weight and composition stability over the long-term, ENERGY INTAKE ENERGY EXPENDITURE = Plausible rEI Implausible rEI kcal/d

1.Usual Energy Intake over the long term is represented by Energy Requirements, since when an individual is in energy balance Energy Intake = Energy Expenditure (Requirement) 2. Energy Requirement determined  Measured precisely by using doubly-labeled water method  calculated using equations based on age, weight, height, sex and physical activity level (Dietary Reference Intakes, 2003)  Others? Steps for Determining Plausibility of Reported Energy Intake

3.Reporting “plausibility” = reported energy intake (rEI) expressed as a percentage of predicted energy requirement (pER), i.e. (rEI/pER)*100% 4.Determine acceptable cutoffs for EI reporting plausibility based on day-to-day biological and methodological variation associated with EI and ER measurement (McCrory et al, 2002; Huang et al, 2005) Steps for Determining Plausibility of Reported Energy Intake

Measurement of Total Energy Expenditure with Doubly Labeled Water Two stable isotopes of water are given by mouth ( 2 H 2 O and H 2 18 O) Two stable isotopes of water are given by mouth ( 2 H 2 O and H 2 18 O) They mix in body water and then disappear over time as they are diluted out of the body They mix in body water and then disappear over time as they are diluted out of the body Rate of 2 H reflects water turnover, rate of 18 O reflects water plus CO 2 turnover Rate of 2 H reflects water turnover, rate of 18 O reflects water plus CO 2 turnover Difference between two disappearance rates equals CO 2 production, from which TEE can be calculated Difference between two disappearance rates equals CO 2 production, from which TEE can be calculated

Dose Time 2 H H 2 O output 18 O H 2 O + CO 2 output Isotopic abundances Isotopic backgrounds

DLW is a Practical Method Measures TEE over long periods of time (7-14 days) Measures TEE over long periods of time (7-14 days) Shown to be accurate in wide range of subjects Shown to be accurate in wide range of subjects Easy on subjects - just a dose of isotope to drink and collection of few urine specimens Easy on subjects - just a dose of isotope to drink and collection of few urine specimens Hard on investigators - very expensive, needs careful analysis (mass spectrometer), but nothing else is as good Hard on investigators - very expensive, needs careful analysis (mass spectrometer), but nothing else is as good

Predicting Energy Requirements (IOM, 2002) Predicted from: Predicted from: Age Age Weight Weight Height Height PAL estimate (sedentary, low active*, moderately active, highly active) PAL estimate (sedentary, low active*, moderately active, highly active) Separate equations for: Separate equations for: Normal weight (BMI kg/m 2 ) vs. Overweight/Obese (BMI ≥25 kg/m 2 ) adults Normal weight (BMI kg/m 2 ) vs. Overweight/Obese (BMI ≥25 kg/m 2 ) adults Women vs. Men Women vs. Men Children and adolescents (girls vs boys) Children and adolescents (girls vs boys) Infants Infants Lactating women Lactating women * PAL = , where PAL is TEE/REE

√(CV 2 mTEE + CV 2 pTEE + (CV 2 wEI /d)) = “± 1 SD cut-off” Calculating Cut-offs for the Agreement Between rEI and pTEE [(rEI/pTEE)*100%] ± error CV wEI is the within-person CV for rEI (20-28% among BMI-age-sex strata) d is the number of days of intake (2) CV mTEE is the within-person CV for TEE measured by doubly-labeled water and includes measurement error and biological variation (8.2%) Error Propagation: The sum of the errors of the different components that constitute an estimate, measurement or calculation CV pTEE is the within-person CV for predicted TEE, calculated by dividing the SD of prediction equation residuals by mean doubly labeled water-measured TEE (8-15% among BMI-age-sex strata) ± 1 SD cut-offs ranged from 20-24%; we therefore used the average, 22%

Characteristics of Sample (CSFII ‘94-96, NPNL adults age 21-45, reported weight and height, and 2 d of intake) Men (n=1969)Women (n=1786) White, %8077 Income >130% of78 74 poverty threshold, % Current smokers, %3227 Education, years (mean) Body Mass Index (mean)

Cut-off Ranges Tested for Agreement between rEI and pER Cut-offValueRange of (rEI/pER)*100% ± 1 SD± 22%78% - 122% ± 1.5 SD± 33%67% - 133% ± 2 SD *± 44%56% - 144% * 95% confidence interval Huang et al 2005, Obes Res How close do rEI and pER need to be in order for rEI to be considered plausible? Adults aged y, CSFII (not dieting, not pregnant or lactating)

(rEI/pER)*100% in Men and Women Huang et al 2005, Obes Res

Regression of rEI on pER (-----) Line of identity ( ) Regression line differs from line of identity in total sample but not the ±2 and ±1.5 SD samples. Slope in ±1 SD sample does not differ from 1.0, but intercept differs from 0. Residuals are smaller and R 2 improves with progressively narrower cut-offs.

Regressions of rEI on weight in CSFII and mTEE on weight in DRI data Note increasing approximation of the two slopes with progressively narrower cut-offs Only in the ±1 SD sample are the two slopes not significantly different

Underrecording vs. Undereating Underrecording – not reporting everything actually consumed Undereating – reporting everything consumed, but eating less than required to maintain current body weight Question: How would you tell the difference? Use a 7-day weighed food record as an example

Characteristics Associated with Under-reporting in Adults Obesity Dietary Restraint Sex Age Ethnicity Race and culture Physical Activity Smoking Status Education level Literacy Social class Living arrangements Depression Past Dieting Frequency Reviewed in McCrory et al 2002

Major Factors Related to Dietary Misreporting in Children Age Age Weight Status Weight Status Weight Concern (girls) Weight Concern (girls) Dietary Method (?) Dietary Method (?)

Ratio of rEI:TEE in Studies in Children and Adolescents aged 1-18 yrs * Data from Table 1 in Livingstone et al Br J Nutr 2004;92:S * Studies , where TEE was measured by the “gold standard” doubly-labeled water Range ~ 0.6 to 1.6

White, non-Hispanic Age 11.3±0.3 y (SD) 3 x 24-h multi-pass recalls over 2 wks Mothers present but only assisted when necessary Pre-adolescent Girls (n=171) Ventura et al 2006 Obes 14:

Pre-adolescent Girls (n=171) Ventura et al 2006 Obes 14: Under (n=57) Plausible (n=86) Over (n=28) BMI (kg/m 2 ) 21.6±4.2 a 19.5±3.9 b 18.5±3.0 b BMI z-score 0.9±0.9 a 0.3±1.0 b 0.1±0.9 b BMI percentile 88.0±23.2 a 72.0±28.0 b 58.0±27.2 b Fat mass (kg) 15.0±61.8 a 11.6±62.0 b 10.4±48.2 b Body Fat (%) 30.6±6.9 a 26.5±6.9 b 25.2±6.8 b

Reported Pyramid Food Group Intake Ventura et al

Reported Grain Intake Ventura et al

Reported Vegetable Intake Ventura et al

Reported Dairy Intake Ventura et al

Reported Beverage Intake Ventura et al

Reported Meal and Snack Frequency Over 3 Days Ventura et al

Pre-adolescent Girls (n=171) Fiorito et al JADA 2006;106: Dairy <3 svgs/d Dairy ≥ 3 svgs/d Energy Intake (kcal/d) Total sample Total sample1706± ±419 *** Plausible Plausible1861± ±171 Under Under1418± ±194 Over Over2654± ±302 BMI Percentile † Total sample Total sample66.9± ±28.0 * Plausible Plausible58.7± ±27.5 Under Under76.0± ±21.9 Over Over59.7± ±28.2 Mean±SD ***p<0.001; * p<0.05 † † Same results using BMI z-score and % body fat

EI Reporting Plausibility in Boys: CSFII Huang et al 2004 Obes Res 2004;12:

Huang et al 2004 Obes Res EI Reporting Plausibility in Girls: CSFII

Eating Patterns Associations with BMI in Boys Age (y) N Sig variable Total Meal portion Snack portion Total eating freq. (-), %energy FAFH, % energy CHO (-) Plausible Nothing Energy intake; meal portion; meal energy Energy intake; meal portion; meal energy; %energy FAFH; %energy from fat; %energy CHO (-)

Future Goals Test validity of objective biomarkers for specific macronutrients or other components of diet Test validity of objective biomarkers for specific macronutrients or other components of diet In conditions where dietary intake is known In conditions where dietary intake is known Reflection of actual intake Reflection of actual intake Eg urinary sugars (Sheila Bingham group) Eg urinary sugars (Sheila Bingham group) Method to statistically adjust reported dietary data for specificity of misreporting Method to statistically adjust reported dietary data for specificity of misreporting

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