Presentation on theme: "A Tale of Two Challenges Conducting Longitudinal Studies in Children and Adolescents: Accurately Measuring Diet and Body Composition in ALSPAC P. K. Newby,"— Presentation transcript:
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
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.
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 were measuring? –Research example: SSBs and body fat
Challenge 1: Dietary Reporting Errors Significant misreporting of dietary intakes has been reported among children –Especially with increasing body weight and body fatness Bandini et al, AJCN, 1990 r = -0.48, p < Accounting for reporting errors is key for understanding diet-obesity relationships (but it is often overlooked)
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 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 BoysGirls SedentaryTypical daily living activities 1.00 <30 minutes of MVPA Low-activeSedentary min moderate activity to <60 minutes of MVPA ActiveSedentary + 60 min moderate activity >60 minutes to <120 minutes of MVPA Very activeSedentary + 60 min moderate + 60 min vigorous or 120 min moderate activity >120 minutes of MVPA
Percent Agreement between Methods 2. PAL Value Method3. 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% PR, 40.8% OR, 7.7% к = 0.66 between the low-active and PAL value method; к = 0.53 between the low-active and MVPA method
Body Fatness Across Dietary Reporting Categories Body Fat (%) Method for Capturing Reporting Errors a b c a b c a b c
Comparison of Methods % Classified Our Methods
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
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 3g4% Saturated Fat 2g9% Trans Fat 0g Cholesterol 15mg4% Sodium 170mg7% Total Carbohydrate 28g 9% Fiber <1g3% Sugar 26g Protein 9g17% Vitamin A 10%Vitamin C 0% Calcium 50%Iron 4% Vitamin D 25%
Flavored milk consumers had less favorable changes in body fat 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: 1.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 2.Compare how measures of central and total adiposity were associated with SSBs and systolic blood pressure
Methods Body composition Total adiposity: BMI (kg/m 2 ) 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, cm 3 ) by MRI Sexual Maturity Self-reported tanner stage (5 levels) collapsed to pre (1), early (2-3), and late (4-5).
Relationships between central and total adiposity measures among children at ages 9, 11, 13, and 15 y.* *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). n=2031 n=1816 n=1616 n=962 n=437 n=505 n=370 n=192 n=672 n=646 n=486 n=228 n=2183 n=2079 n=1824 n=1173
Relationships between adiposity measures and intra- abdominal adipose tissue volume at ages 11 and 13 y* *Data are Pearsons 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.
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 1)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 1)Determine whether SSB consumption has similar and additional effects on measures of total and central adiposity (waist circumference) 2)Adjust for dietary reporting errors
Methods Diet 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
SSBs (servings/d) and central and total adiposity at 13 y (n=2,455) Adiposity at 13 Model 1 Change in adiposity per SSB (servings/d) 2 Standardized Beta P value BMI, kg/m (0.03) (0.03) (0.04)0.074<0.001 Waist, cm (0.10) (0.10) (0.14)0.097<0.001 Total body fat, kg (0.08) (0.08) (0.11)
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/m (0.07) (0.07) (0.10) Waist, cm Total body fat, kg (0.07) (0.07) (0.11)
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.
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):
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
Sample characteristics by flavored milk consumption Sample Characteristics Flavored milk non-consumers, age 10 y Flavored milk consumers, age 10 y P value Girls, % Body fat, % 11 y 25.5 ± ± y 24.4 ± ± Physical activity 11 y587.8 ± ± y536.0 ± ± Dieting at age 13 y, % Maternal body mass index, kg/m ± ±
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, kcal1917 ± ± 24<0.001 Fat, g75.6 ± ± Saturated fat, g29.2 ± ± 0.37<0.001 Carbohydrate, g251.0 ± ± Fiber, g11.8 ± ± Added sugars, g89.1 ± ± Dietary calcium, g796.1 ± ± 12.8<0.001 Sugar-sweetened beverages 3, g ± ± 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 (n=1890) Flavored milk consumers, age 10 (n=380) P value Mean95% CIMean95% CI Normal weight children Change in % body fat, (n=1,715) Model , , Model , , Overweight/obese children Change in % body fat, (n=449) Model , , Model , , 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.
Pearsons partial correlations between systolic blood pressure and BMI, WC, TBFM and TFM from 9 to 15 y adjusted for age and height