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."— Presentation transcript:
1A Tale of Two Challenges Conducting Longitudinal Studies in Children and Adolescents: Accurately Measuring Diet and Body Composition in ALSPACP. K. Newby, ScD, MPH, MSAssociate Professor of Pediatrics, Epidemiology, Nutrition, and Gastronomy & Research ScientistBoston UniversityUniversity of Bristol, UK19 October 2011
2American Diabetes Association AcknowledgmentsSabrina E. Noel, PhD, MS, RDSherman Bigornia, MAMichael LaValley, PhDLynn Moore, DScCarine Lenders, MD, ScDKate Northstone, PhD, MSPauline Emmett, PhDAndy Ness, PhD, DPH (etc.)Li Benfield, PhDCalum Mattocks, PhDChris Riddoch, PhDFunding SourcesAmerican Diabetes AssociationThe UK Medical Research Council, Wellcome Trust, and the University of Bristol provide core support for ALSPAC.
3Diet: 3-day dietary records (10 and 13 y) Challenge 1: Measuring DietHow to quantify dietary measurement errors?Research example: Flavored milk and body fatChallenge 2: Measuring Body CompositionAre we measuring what we think we’re measuring?Research example: SSBs and body fatALSPAC – n=2.300 for milk stuffOngoing prospective cohort study based out of Bristol, UKPregnant women with an expected due date between 1991 and 1992 were enrolledMeasuresDiet: 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.
4Challenge 1: Dietary Reporting Errors Significant misreporting of dietary intakes has been reported among childrenEspecially with increasing body weight and body fatnessr = -0.48, p < 0.001By 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 understandingdiet-obesity relationships (but it is often overlooked)Bandini et al, AJCN, 19904
5Methods Used for Capturing Implausible Energy Reporters Premise: reported energy intake = energy expenditure under weight-stable conditionsDirect measure of energy expenditure using doubly labeled water (DLW)Compare reported intake toenergy expenditureNot feasible for large populationstudiesEquations to estimate implausible and plausible reportingCompare reported intake to estimates of energy requirementsGoldberg et al, Eur J Clin Nutr, 1991; McCrory et al,Public Health Nutr, 2002; Huang et al, Obes Res 2004 & 2005DLW figure:
6Capturing Implausible Reporters Age- and sex-specific cut-off for the ratio of reported energy intake to predicted energy requirementsPredicted energy requirement equation (IOM)Includes coefficients for age, physical activity (PA), and weight and constants for sex and energy deposition during growthHuang 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 errors2 methods used physical activity data from accelerometers1 assumed a low-active level
7Three Variations of the PA Coefficient IOM PA CategoryIOM Description of CategoriesIOM PA CoefficientCategories based on mins of MVPABoysGirlsSedentaryTypical daily living activities1.00<30 minutes of MVPALow-activeSedentary min moderate activity1.131.1630 to <60 minutes of MVPAActiveSedentary + 60 min moderate activity1.261.31>60 minutes to<120 minutes of MVPAVery activeSedentary + 60 min moderate + 60 min vigorous or 120 min moderate activity1.421.56>120 minutes of MVPAWe 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.
8Percent Agreement between Methods 2. PAL Value Method3. MVPA Method1. Low-active MethodUR 51.8%PR37.9%OR 10.3%UR 37.1%PR 42.4%OR 20.4%UR, 51.5%88.015.597.436.1PR, 40.8%12.078.845.82.663.563.0OR, 7.7%5.754.20.437.0There 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 method8
9Body 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
10Comparison 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.
11Conclusions and Next Steps All three methods were associated with sociodemographic and body composition measures as expectedInclusion of objectively measured physical activity as MVPA may have resulted in more reasonable estimates of plausible and implausible reportersImproving measurement of dietary reporting errors will improve precision and accuracy of resultsFuture: Better quantification of MVPA using accelerometer data and direct comparisons with EE using DLWAll 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.
12Research example 1: Chocolate Milk, Body Fat, and Body Weight Serving Size 1 cup (240mL)Amount per ServingCalories 170Calories from Fat 25% Daily ValuesTotal Fat 3g4% Saturated Fat 2g9% Trans Fat 0gCholesterol 15mgSodium 170mg7%Total Carbohydrate 28gFiber <1g3%Sugar 26gProtein 9g17%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
131 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.
14Flavored 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.
15Conclusions 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 periodAssociations were strengthened when reporting errors were considered.These results limit recommendations that promote flavored milk consumption among children, especially those who are overweight or obeseFuture: Repeating study with greater variability in intakes and conducting an analysis looking at total dairy
16Challenge 2: How to Measure Body Fat Central adiposity is an important chronic disease risk factor in adultsStudies in children suggest correlations between central and total adiposity are high due to limited accrual of visceral fatLittle 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 ageCompare how measures of central and total adiposity were associated with SSBs and systolic blood pressure16
17Methods Body composition Sexual Maturity Total adiposity: BMI (kg/m2) and total body fat mass (TBFM, g) by DXACentral adiposity: waist circumference (WC, cm), trunk fat mass (TFM, g) by DXA, and intra-abdominal adipose tissue (IAAT, cm3) by MRISexual MaturitySelf-reported tanner stage (5 levels) collapsed to pre (1), early (2-3), and late (4-5).17
18Relationships between central and total adiposity measures among children at ages 9, 11, 13, and 15 y.*n= n= n= n=962n= n= n= n=192Results:Variance in TFM explained by TBFM (purple cross) was >=90% within all age, sex, and weight groupsBoys: 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 groupsGirls: 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=1173n= 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).
19Relationships 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
20ConclusionsCentral 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
21Research 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 yDetermine whether SSB consumption has similar and additional effects on measures of total and central adiposity (waist circumference)Adjust for dietary reporting errors21
22Methods 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 servingChange in SSB (∆SSB) = SSB 13 – SSB 11AdiposityBMI, waist circumference (WC), and total body fat mass (TBFM) at 13 y as previously described22
23∆SSBs (servings/d) and central and total adiposity at 13 y (n=2,455) Model1Change in adiposity per ∆SSB (servings/d)2StandardizedBetaP valueBMI, kg/m210.07 (0.03)0.0280.02520.09 (0.03)0.0390.00230.16 (0.04)0.074<0.001Waist, cm0.13 (0.10)0.0200.1880.22 (0.10)0.0340.55 (0.14)0.097Total body fat, kg0.10 (0.08)0.0170.2030.19 (0.08)0.0330.0110.33 (0.11)0.0650.003Should mention that mean change in SSB consumption from 10 to 13 years is 0.12 ± 1.4 servings per day, 180 g = 1 servingResults: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 energyNo 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 (median[inter-quartile range)], 23 , and 34  kg, respectively; for BMI they were 19 , 24 , and 29  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
24∆SSBs (servings/d) and central adiposity at 13 y (n=2,455) General adiposity at 13 adjustmentModelChange in adiposity per ∆SSB (servings/d)2StandardizedBetaP valueWaist, cmBMI, kg/m210.07 (0.07)0.0110.2920.06 (0.07)0.0100.3730.24 (0.10)0.0420.02Total body fat, kg0.11 (0.07)0.0180.100.08 (0.07)0.0130.220.27 (0.11)0.0480.01In simple (model 1), multivariable (model 2), and dietary reporting error adjusted models (model 3), SSB did not predict WC independent of BMI or TBFMAmong 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 findingsIt 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 amo1Model 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
25ConclusionsIncreased 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 gainSSBs have somewhat stronger and additional effects on WC independent of total adiposity but these are likely not clinically meaningfulAccounting for dietary reporting errors uniformly strengthened effect estimates, highlighting the importance of measuring and accounting for these errors.25
26Publications (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
28Thank you for your attention! P. K. Newby, ScD, MPH, MSAssociate Professor of Pediatrics, Epidemiology, Nutrition, and Gastronomy & Research ScientistBoston UniversityUniversity of Bristol, UK19 October 2011
29Supplemental SlidesAll 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.
30Sample characteristics by flavored milk consumption Flavored milk non-consumers, age 10 yFlavored milk consumers, age 10 yP valueGirls, %55.849.00.01Body fat, %11 y25.5 ± 9.125.5 ± 9.30.9813 y24.4 ± 10.124.8 ± 10.70.50Physical activity587.8 ± 171.9585.5 ± 165.60.80536.0 ± 193.5534.5 ± 177.90.89Dieting at age 13 y, %25.719.40.02Maternal body mass index, kg/m224.5 ± 4.424.6 ± 4.80.73
31Table 2. Adjusted means of daily total energy and selected nutrient & food intakes Energy, nutrient and food group intakeFlavored milk non-consumers, age 10 y (n=1890)Flavored milk consumers, age 10 y(n=380)P valueTotal energy, kcal1917 ± 112064 ± 24<0.001Fat, g75.6 ± 0.3277.5 ± 0.710.01Saturated fat, g29.2 ± 0.1630.6 ± 0.37Carbohydrate, g251.0 ± .86258.0 ± 1.90.001Fiber, g11.8 ± 0.0711.2 ± 0.160.002Added sugars, g89.1 ± 0.6785.9 ± 1.50.05Dietary calcium, g796.1 ± 5.7917.4 ± 12.8Sugar-sweetened beverages3, g106.8 ± 3.3192.6 ± 7.390.08Means 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.
32Flavored milk non-consumers, age 10 Flavored milk consumers, age 10 PvalueMean95% CINormal weight childrenChange in % body fat, (n=1,715)Model 1-0.83-1.42, -0.24-0.63-1.37, 0.120.48Model 2-0.86-1.44, -0.27-0.60-1.35, 0.140.40Overweight/obese childrenChange in % body fat, (n=449)-2.64-3.82, -1.45-1.09-2.60, 0.410.01-3.83, -1.45-1.11-2.62, 0.40Model 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.
33Pearson’s partial correlations between systolic blood pressure and BMI, WC, TBFM and TFM from 9 to 15 y adjusted for age and heightResults: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).