A Tale of Two Challenges Conducting Longitudinal Studies in Children and Adolescents: Accurately Measuring Diet and Body Composition in ALSPAC P. K.

A Tale of Two Challenges Conducting Longitudinal Studies in Children and Adolescents: Accurately Measuring Diet and Body Composition in ALSPAC P. K. Newby, ScD, MPH, MS Associate Professor of Pediatrics, Epidemiology, Nutrition, and Gastronomy & Research Scientist Boston University University of Bristol, UK 19 October 2011

American Diabetes Association
Acknowledgments Sabrina E. Noel, PhD, MS, RD Sherman Bigornia, MA Michael LaValley, PhD Lynn Moore, DSc Carine Lenders, MD, ScD Kate Northstone, PhD, MS Pauline Emmett, PhD Andy Ness, PhD, DPH (etc.) Li Benfield, PhD Calum Mattocks, PhD Chris Riddoch, PhD Funding Sources American Diabetes Association The UK Medical Research Council, Wellcome Trust, and the University of Bristol provide core support for ALSPAC.

Diet: 3-day dietary records (10 and 13 y)
Challenge 1: Measuring Diet How to quantify dietary measurement errors? Research example: Flavored milk and body fat Challenge 2: Measuring Body Composition Are we measuring what we think we’re measuring? Research example: SSBs and body fat ALSPAC – n=2.300 for milk stuff Ongoing prospective cohort study based out of Bristol, UK Pregnant women with an expected due date between 1991 and 1992 were enrolled Measures Diet: 3-day dietary records (10 and 13 y) Anthropometry: Weight, height, BMI (10, 11 and 13 y) Body composition: Dual-energy X-ray absorptiometry (DXA) (11 and 13 y) Physical activity: Uni-axial accelerometer (11 and 13 y) How to model question addressed somewhat in previous presentations so won’t discuss here but suffice to say that we just had two time points so went through lots of different models : baseline and follow up, change from two time points, average intakes, change in body fat, etc etc. Situations is easier for single dieteray exposures using mixed models and have heard some of the challenges in doing this for patterns but that’s all I’lll say about that.

Challenge 1: Dietary Reporting Errors
Significant misreporting of dietary intakes has been reported among children Especially with increasing body weight and body fatness r = -0.48, p < 0.001 By way of background- I would like to spend a few minutes talking about dietary reporting errors in general. Studies that have compared self-reported dietary intakes to energy expenditure measured using doubly labeled water have found significant misreporting of intakes among children and adolescents. It has noted that reporting errors are greater among overweight and obese individuals. This figure shows a significant inverse association between weight and reported energy intake as a percentage of total energy expenditure (measured from DLW) from a small classic study by Bandini and colleagues. Because reporting errors are associated with overweight or obese, accounting for these errors is key for understanding the relationship between dietary factors and obesity. Including implausible reporters in these analyses may lead to inconsistent results across studies. Accounting for reporting errors is key for understanding diet-obesity relationships (but it is often overlooked) Bandini et al, AJCN, 1990 4

Methods Used for Capturing Implausible Energy Reporters
Premise: reported energy intake = energy expenditure under weight-stable conditions Direct measure of energy expenditure using doubly labeled water (DLW) Compare reported intake to energy expenditure Not feasible for large population studies Equations to estimate implausible and plausible reporting Compare reported intake to estimates of energy requirements Goldberg et al, Eur J Clin Nutr, 1991; McCrory et al, Public Health Nutr, 2002; Huang et al, Obes Res 2004 & 2005 DLW figure:

Capturing Implausible Reporters
Age- and sex-specific cut-off for the ratio of reported energy intake to predicted energy requirements Predicted energy requirement equation (IOM) Includes coefficients for age, physical activity (PA), and weight and constants for sex and energy deposition during growth Huang et al, 2004 -As I mentioned earlier, we used methods by Huang et al to classify adolescents as plausible or implausible reporters. -This method creates age and sex-specific 1 SD cutoffs of reported energy intake as a percentage of predicted energy requirements. This takes into account variation in energy intake, energy expenditure and error in predicting energy requirements. Study Objective: Include objective measures of physical activity in equations used to predict energy requirements and quantify dietary reporting errors 2 methods used physical activity data from accelerometers 1 assumed a low-active level

Three Variations of the PA Coefficient
IOM PA Category IOM Description of Categories IOM PA Coefficient Categories based on mins of MVPA Boys Girls Sedentary Typical daily living activities 1.00 <30 minutes of MVPA Low-active Sedentary min moderate activity 1.13 1.16 30 to <60 minutes of MVPA Active Sedentary + 60 min moderate activity 1.26 1.31 >60 minutes to <120 minutes of MVPA Very active Sedentary + 60 min moderate + 60 min vigorous or 120 min moderate activity 1.42 1.56 >120 minutes of MVPA We created categories of minutes of MVPA to be able to assign participants an appropriate PA coefficient. The MVPA categories were based on the IOM description of the physical activity categories, which includes increasing the amount of time spent in moderate or vigorous physical activity to move to higher physical activity or PAL categories.

Percent Agreement between Methods
2. PAL Value Method 3. MVPA Method 1. Low-active Method UR 51.8% PR 37.9% OR 10.3% UR 37.1% PR 42.4% OR 20.4% UR, 51.5% 88.0 15.5 97.4 36.1 PR, 40.8% 12.0 78.8 45.8 2.6 63.5 63.0 OR, 7.7% 5.7 54.2 0.4 37.0 There was approximately 79% agreement between the low-active method and the PAL value method for plausible reporters and 64% between the Low active method and MVPA method. We classified more participants as over-reporters using both the PAL value and MVPA method compared with the low-active method. Interestingly, a large proportion of those classified as over-reporters using the 2 methods that included objectively measured physical activity were classified as plausible reporters using the low active method. к = 0.66 between the low-active and PAL value method; к = 0.53 between the low-active and MVPA method 8

Body Fatness Across Dietary Reporting Categories
All three methods showed that under-reporters had a higher % of body fat compared with plausible or over-reporters. Method for Capturing Reporting Errors

Comparison of Methods % Classified Our Methods
We wanted to see how the proportion of under-reporters and over-reporters classified using our three methods compared to other studies that used either DLW or similar prediction equations. We selected a few studies that included children of similar ages to our study. The first section of this chart shows the percentages in our study using the low-active, PAL value and MVPA method. We expected that our MVPA method would show percentages between those reported in DLW studies and studies using prediction equations. In general this is what we found. You will note that the last study in the chart by Lanctot and colleagues did not identify any participants and over-reporters.

Conclusions and Next Steps
All three methods were associated with sociodemographic and body composition measures as expected Inclusion of objectively measured physical activity as MVPA may have resulted in more reasonable estimates of plausible and implausible reporters Improving measurement of dietary reporting errors will improve precision and accuracy of results Future: Better quantification of MVPA using accelerometer data and direct comparisons with EE using DLW All three methods were associated with sociodemographic and body composition measures in the expected direction. However, including objectively measured physical activity as minutes of MVPA may have results in more reasonable estimates of plausible and implausible reporters.

Research example 1: Chocolate Milk, Body Fat, and Body Weight
Serving Size 1 cup (240mL) Amount per Serving Calories 170 Calories from Fat 25 % Daily Values Total Fat 3g 4% Saturated Fat 2g 9% Trans Fat 0g Cholesterol 15mg Sodium 170mg 7% Total Carbohydrate 28g Fiber <1g 3% Sugar 26g Protein 9g 17% Vitamin A 10% Vitamin C 0% Calcium 50% Iron 4% Vitamin D 25% Often serving size is 2 so sugar is 52 g – or 200 kcal of sugar. “In NYC, fat-free chocolate milk accounts for almost 60% of the 100 million cartons served each year.” Only two studies have looked at this, and only one prospective so wanted to look at it though consumption is lower in the UK th

1 Means were adjusted for pubertal status, maternal BMI and educational attainment as well as changes in age, height, height squared, physical activity, and dietary intakes (total fat intake, ready-to-eat cereal, 100% fruit juice, sugar-sweetened beverage, and plain milk). Analyses were conducted in plausible dietary reporters.

Flavored milk consumers had less favorable changes in body fat
Note small n here. Means were adjusted for pubertal status, maternal BMI and educational attainment, changes in age, height, height squared, physical activity, and intakes of total fat, ready-to-eat cereal, 100% fruit juice, sugar-sweetened beverage, and plain milk. Plausible reporters only.

Conclusions and Next Steps
Less favorable changes in body fat and weight were seen for overweight children consuming flavored milk compared with non-consumers over a 2 year period Associations were strengthened when reporting errors were considered. These results limit recommendations that promote flavored milk consumption among children, especially those who are overweight or obese Future: Repeating study with greater variability in intakes and conducting an analysis looking at total dairy

Challenge 2: How to Measure Body Fat
Central adiposity is an important chronic disease risk factor in adults Studies in children suggest correlations between central and total adiposity are high due to limited accrual of visceral fat Little is known how these relationships change as children move through puberty. Study Objectives: Examine relationships between central and total adiposity assessed by anthropometry, DXA and MRI (11 and 13 y only) at 9, 11, 13, and 15 y of age Compare how measures of central and total adiposity were associated with SSBs and systolic blood pressure 16

Methods Body composition Sexual Maturity
Total adiposity: BMI (kg/m2) and total body fat mass (TBFM, g) by DXA Central adiposity: waist circumference (WC, cm), trunk fat mass (TFM, g) by DXA, and intra-abdominal adipose tissue (IAAT, cm3) by MRI Sexual Maturity Self-reported tanner stage (5 levels) collapsed to pre (1), early (2-3), and late (4-5). 17

Relationships between central and total adiposity measures among children at ages 9, 11, 13, and 15 y.* n= n= n= n=962 n= n= n= n=192 Results: Variance in TFM explained by TBFM (purple cross) was >=90% within all age, sex, and weight groups Boys: WC (green triangle) and BMI (red square) explained comparable amounts of TBFM variances across ages in NWT and OWT groups. Suggest that in boys BMI and WC track TBFM similarly. Girls: BMI accounted for more TBFM variation (red square) compared to WC (green triangle) in NWT and OWT groups Girls: BMI (red square) accounted for a similar amount of TBFM variance across ages while the association between WC and TBFM (green triangle) attenuated after 11 y in NWT and OWT. Likely due to gain in lower body fat mass in girls with age. Inter-fat associations were generally stronger for OWT compared to NWT in boys and girls. Data not shown: Pubertal stage explained a small amount of variation in the various models (0% to 11.4%) n= n= n= n=1173 n= n= n= n=228 *WC, waist circumference; TBFM, total body fat mass, TFM, trunk fat mass † Values are the partial variances (%) accounted by select adiposity measures by multivariate linear regression with adjustment for age, height , and pubertal stage (pre-, early, and late).

Relationships between adiposity measures and intra-abdominal adipose tissue volume at ages 11 and 13 y* Results: IAAT volume was lower at 11 (median [inter-quartile range]; 72.9 [74.5] cm3 and [128.6] cm3 in boys and girls, respectively) compared to 13 y (135.4 [113.9] cm3 in boys and [67.5] cm3 in girls)(data not shown) BMI, WC, TBFM, and TFM correlations with IAAT were moderate to strong at all time-points (P<0.05, for all) Generally, correlations clustered around each other, particularly among boys. In girls WC was somewhat more strongly correlated with IAAT compared to BMI (P=0.02 and P=0.048 at 11 and 13) *Data are Pearson’s partial correlation coefficients adjusted for age and height. P < 0.05 for all values. †MRI data were collected at 11 and 13 on a subset of ALSPAC participants. 19

Conclusions Central and total fat measures were strongly correlated at all ages and modestly attenuated at age13 and 15 years. BMI, WC, TBFM, and TFM correlations with IAAT were comparable. Similar associations were observed with SBP (data not shown). Our findings have implications for the interpretation of epidemiological studies examining central adiposity on metabolic outcomes in late childhood and early adolescence, highlighting the need to also consider associations with total adiposity as they explain a large amount of variation in central adiposity

Research Example 2: SSBs and Body Composition
Examine the effect of change in SSB intake from 10 to 13 y (∆SSB) on total adiposity (BMI and total body fat) at 13 y Determine whether SSB consumption has similar and additional effects on measures of total and central adiposity (waist circumference) Adjust for dietary reporting errors 21

Methods Diet Adiposity 3 day diet records at 10 and 13 y
Sugar-sweetened beverages (SSB): fruit squashes, cordials and fizzy drinks (i.e. soda) with added sugar. 140 g water assumed for every 40 g of concentrate. 180 g = 1 serving Change in SSB (∆SSB) = SSB 13 – SSB 11 Adiposity BMI, waist circumference (WC), and total body fat mass (TBFM) at 13 y as previously described 22

∆SSBs (servings/d) and central and total adiposity at 13 y (n=2,455)
Model1 Change in adiposity per ∆SSB (servings/d)2 Standardized Beta P value BMI, kg/m2 1 0.07 (0.03) 0.028 0.025 2 0.09 (0.03) 0.039 0.002 3 0.16 (0.04) 0.074 <0.001 Waist, cm 0.13 (0.10) 0.020 0.188 0.22 (0.10) 0.034 0.55 (0.14) 0.097 Total body fat, kg 0.10 (0.08) 0.017 0.203 0.19 (0.08) 0.033 0.011 0.33 (0.11) 0.065 0.003 Should mention that mean change in SSB consumption from 10 to 13 years is 0.12 ± 1.4 servings per day, 180 g = 1 serving Results: Effect estimates strengthened after adjustment for dietary reporting errors (model 3) and further strengthened after exclusion of implausible dietary reporters (model 4) compared to simple and multivariable adjusted models (model 1 & 2). In primary models (model 4), ∆SSB positively predicted BMI, WC, and TBFM at 13 y. 1Model 1: ∆SSB (servings/d), SSB at 10 (servings/d), age at 10, sex, height at 10, height at 10 quadratic term, and adiposity at 10. For TBFM and TFM models BMI at 10 was used for baseline adiposity adjustment. Model 2: model 1 + covariates (physical activity at 13 [cpm], pubertal stage at 13 [pre-, early-, or late-pubertal], maternal overweight status [overweight or not], maternal education [none or Certificate of Secondary Education, vocational, O level, A level, and college degree], dieting at 13 [yes or no] and ∆fruit juice [g/d], ∆fruit and vegetables [g/d], and ∆total fat [% energy]). Model 3: model 2 + dietary reporting errors at 13 [under-, plausible-, and over-reporter]; Model 4: model 3 among plausible energy reporters at 13 only (n=1,059). Data not shown: Addition of total energy to models: standardized estimates for the effect of ∆SSB on adiposity at 13 were attenuated by 47% (β=0.035, P=0.05), 25% (β=0.058, P=0.004), and 22% (β=0.078, P=0.002) for weight, BMI and WC compared to models unadjusted for total energy No effect modification by sex or baseline SSB consumption in primary models (model 4). It is worth noting that effect estimates were quite modest. The 13 y TBFM for normal weight, overweight and obese adolescents was 10 [8](median[inter-quartile range)], 23 [6], and 34 [9] kg, respectively; for BMI they were 19 [3], 24 [2], and 29 [3] kg/m2, respectively. But a 180 g serving increase in SSB from ages 10 to 13 y was associated with a 0.33 kg and 0.16 kg/m2 change in body fat and BMI, respectively. 23

∆SSBs (servings/d) and central adiposity at 13 y (n=2,455)
General adiposity at 13 adjustment Model Change in adiposity per ∆SSB (servings/d)2 Standardized Beta P value Waist, cm BMI, kg/m2 1 0.07 (0.07) 0.011 0.29 2 0.06 (0.07) 0.010 0.37 3 0.24 (0.10) 0.042 0.02 Total body fat, kg 0.11 (0.07) 0.018 0.10 0.08 (0.07) 0.013 0.22 0.27 (0.11) 0.048 0.01 In simple (model 1), multivariable (model 2), and dietary reporting error adjusted models (model 3), SSB did not predict WC independent of BMI or TBFM Among plausible reporters only (model 4), SSB predicted WC independent of BMI or TBFM. Given that the independent effect was not seen in all models additional research is needed to confirm these findings It is worth noting that the effect of SSB on WC attenuated after adjustment for BMI by 57% and for TBFM by 50% suggesting that the additional effect on WC is likely not clinically important. E.g., after adjustment for concurrent BMI, an increase in SSB consumption of one 180 g serving over 3 y could result in a 0.24 cm higher WC at 13 y of age but WC at 13 y amo 1Model 1: ∆SSB (servings/d), SSB at 10 (servings/d), BMI or total body fat at 13, age at 10, sex, height at 10 and height quadratic term, waist at 10. Model 2: model 1 + covariates (physical activity at 13 [cpm], pubertal stage at 13 [pre-, early-, or late-pubertal], maternal overweight status [overweight or not], maternal education [none or Certificate of Secondary Education, vocational, O level, A level, and college degree], dieting at 13 [yes or no] and ∆fruit juice [g/d], ∆fruit and vegetables [g/d], and ∆total fat [% energy]). Model 3: model 2 + dietary reporting errors at 13 [under-, plausible-, and over-reporter]; Model 4: model 3 among plausible energy reporters only (n=1,059). ng normal weight (n=1,972) overweight (n=90), and obese (n=393) adolescents was 69 (7) (median inter-quartile range), 82 (9), and 95 (9) cm respectively. 24

Conclusions Increased SSB intakes over 3 y was associated with higher BMI and fat mass at 13 y supporting recommendations to limit SSB consumption to combat excess weight gain SSBs have somewhat stronger and additional effects on WC independent of total adiposity but these are likely not clinically meaningful Accounting for dietary reporting errors uniformly strengthened effect estimates, highlighting the importance of measuring and accounting for these errors. 25

Publications (Published and In Progress)
Noel SE, Ness AR, Northstone K, Emmett PE, Newby PK. Flavored milk consumption and changes in body fat in children: a prospective study. Journal of Nutrition. Submitted. Bigornia SJ, Noel SE, LaValley MP, Moore LL, Ness AR, Newby PK. Sugar-sweetened beverage intake among children from 10 to 13 years of age and central and total adiposity: a prospective population based cohort study. International Journal of Obesity. Submitted. Bigornia SJ, LaValley MP, Benfield LL, Ness AR, Newby PK. Relationships between direct and indirect measures of central and total adiposity in children at 9, 11, 13, and 15 years of age. American Journal of Clinical Nutrition. Submitted. Noel SE, Ness AR, Northstone K, Emmett P, Newby PK. Milk intakes are not associated with percent body fat in children from ages 10 to 13 years. Journal of Nutrition 2011; Sept 21. [Epub ahead of print] Noel SA, Mattocks C, Riddoch C, Emmett PE, Ness AR, Newby PK. Use of accelerometer data in prediction equations for capturing implausible dietary intakes among adolescents. American Journal of Clinical Nutrition 2010;92(6): 26

Thank you for your attention!
P. K. Newby, ScD, MPH, MS Associate Professor of Pediatrics, Epidemiology, Nutrition, and Gastronomy & Research Scientist Boston University University of Bristol, UK 19 October 2011

Supplemental Slides All three methods were associated with sociodemographic and body composition measures in the expected direction. However, including objectively measured physical activity as minutes of MVPA may have results in more reasonable estimates of plausible and implausible reporters.

Sample characteristics by flavored milk consumption
Flavored milk non-consumers, age 10 y Flavored milk consumers, age 10 y P value Girls, % 55.8 49.0 0.01 Body fat, % 11 y 25.5 ± 9.1 25.5 ± 9.3 0.98 13 y 24.4 ± 10.1 24.8 ± 10.7 0.50 Physical activity 587.8 ± 171.9 585.5 ± 165.6 0.80 536.0 ± 193.5 534.5 ± 177.9 0.89 Dieting at age 13 y, % 25.7 19.4 0.02 Maternal body mass index, kg/m2 24.5 ± 4.4 24.6 ± 4.8 0.73

Table 2. Adjusted means of daily total energy and selected nutrient & food intakes
Energy, nutrient and food group intake Flavored milk non-consumers, age 10 y (n=1890) Flavored milk consumers, age 10 y (n=380) P value Total energy, kcal 1917 ± 11 2064 ± 24 <0.001 Fat, g 75.6 ± 0.32 77.5 ± 0.71 0.01 Saturated fat, g 29.2 ± 0.16 30.6 ± 0.37 Carbohydrate, g 251.0 ± .86 258.0 ± 1.9 0.001 Fiber, g 11.8 ± 0.07 11.2 ± 0.16 0.002 Added sugars, g 89.1 ± 0.67 85.9 ± 1.5 0.05 Dietary calcium, g 796.1 ± 5.7 917.4 ± 12.8 Sugar-sweetened beverages3, g 106.8 ± 3.31 92.6 ± 7.39 0.08 Means for total energy intake were adjusted for sex only. Means for all other nutrients and food groups were adjusted for sex and total energy intake.

Flavored milk non-consumers, age 10 Flavored milk consumers, age 10
P value Mean 95% CI Normal weight children Change in % body fat, (n=1,715) Model 1 -0.83 -1.42, -0.24 -0.63 -1.37, 0.12 0.48 Model 2 -0.86 -1.44, -0.27 -0.60 -1.35, 0.14 0.40 Overweight/obese children Change in % body fat, (n=449) -2.64 -3.82, -1.45 -1.09 -2.60, 0.41 0.01 -3.83, -1.45 -1.11 -2.62, 0.40 Model 1 was adjusted for change in counts per minute, pubertal status, maternal BMI and educational attainment, change in total fat intake, and change in ready-to-eat cereal, 100% fruit juice and SSB intake. Model 2 also included change in total milk intake.

Pearson’s partial correlations between systolic blood pressure and BMI, WC, TBFM and TFM from 9 to 15 y adjusted for age and height Results: SBP correlations with BMI, WC, TBFM and TFM were similar within sex and weight groups at 9 and 11 y, similar patterns continued in girls (bottom 2 panels) whereas in boys SBP correlations became more varied at 13 and 15 (top 2 panels).

A Tale of Two Challenges Conducting Longitudinal Studies in Children and Adolescents: Accurately Measuring Diet and Body Composition in ALSPAC P. K.

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A Tale of Two Challenges Conducting Longitudinal Studies in Children and Adolescents: Accurately Measuring Diet and Body Composition in ALSPAC P. K.

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