Higher Longitudinal Milk Intakes Are Associated with Increased Height in a Birth Cohort Followed for 17 Years

Abstract

Background

Height is an indicator of nutritional status; linear growth faltering has recognized consequences for cognitive, emotional, and chronic disease risk. Although height is routinely studied in developing countries, less attention is given to height in the United States.

Objective

The objective of this study was to identify longitudinal associations between childhood and adolescent beverage intakes, nutrient adequacy, or energy intake and height in a birth cohort.

Methods

Data for participants through ages 2–17 y (n = 717; 353 males, 364 females) recruited at birth for the longitudinal Iowa Fluoride Study (IFS) were used in the current cohort analyses. Beverage intakes (n = 708) were collected by beverage-frequency questionnaires at 3- to 6-mo intervals, whereas nutrient data (n = 652) were obtained from 3-d food diaries completed at 3- to 6-mo intervals through age 8.5 y and from Block Kids’ food-frequency questionnaires at 2-y intervals after age 8.5 y. Nutrient adequacy ratios were calculated with the use of age- and sex-specific Estimated Average Requirements. Height was measured at clinic visits when the participants were approximately ages 5, 9, 11, 13, 15, and 17 y. Linear mixed models were used to identify longitudinal associations between dietary variables and height. A baseline model that adjusted for changing growth patterns during adolescence was established. Dietary and potential confounding variables were added to this baseline model.

Results

Milk intake adjusted for mean adequacy ratio, energy intake, and baseline socioeconomic status was associated with height; for each additional 8 ounces (236 mL) of milk consumed per day throughout childhood and adolescence, height increased, on average, by 0.39 cm (95% CI: 0.18, 0.60 cm; P < 0.001).

Conclusions

IFS participants’ height increased by 0.39 cm for each additional 8 ounces (236 mL) of milk consumed throughout childhood and adolescence. The clinical implications of the mild linear growth faltering observed in healthy youth are unknown. This trial was registered at www.clinicaltrials.gov as 199112665.

Introduction

The achievement of optimal growth potential is associated with a healthy physique, mental acuity, emotional health, and reduced chronic disease burden. Numerous insults, including exposure to severe malnutrition, infectious disease, toxic metals, inadequate sleep, and prematurity, have deleterious consequences for physical, cognitive, and emotional growth (1–5).

Anthropometric measures are used to monitor physical growth, and deviations from normal are recognized markers of cognitive, emotional, and chronic disease risk (1). Protein-energy malnutrition characterized by linear growth faltering (i.e., low height for age and sex) represents an insult to physical growth and is due to inadequate intake or impaired utilization of food energy, nutrients, or both, including protein (6). In contrast, obesity, characterized by excess adipose mass, is due to excessive energy intake. Obesity is also associated with reduced cognitive capacity, emotional issues, and increased risk of chronic disease (7, 8).

Obesity has been identified as the primary nutritional disorder among both children and adults in the United States, with numerous academic and public health initiatives addressing related chronic diseases. However, the achievement of linear growth potential by healthy children within the United States has received less attention. Obese children are reported to have more rapid linear growth than their normal-weight peers during childhood; however, adult heights do not differ (9, 10).

We previously reported that heights at age 17 y differed according to adolescent beverage consumption patterns (11). The objective of the current study was to identify longitudinal associations between beverage intakes, nutrient adequacy, or energy intake and height in a cohort followed from birth.

Methods

Data collection

Data collected as part of the longitudinal Iowa Fluoride Study (IFS) and Iowa Bone Development Study (IBDS; a substudy of the IFS) were used in these analyses (Figure 1) (11–15). Clinical Trial Registry number: 199112665. All components of the IFS and IBDS were approved by the Institutional Review Board at the University of Iowa. Written informed consent was provided by mothers at their children's births and by parents at clinic visits, and written assent was provided by participants beginning at age 13 y.

FIGURE 1.

Flow chart showing dietary intakes and height in the Iowa Fluoride Study participants. ¹Enrollment was defined as having returned ≥1 questionnaire at age 6 wk, 3 mo, or 6 mo. ²Participants with height measurements.

Beverage intakes

Validated beverage frequency questionnaires were used to estimate the previous week's beverage intakes (13, 14). Beverages were collapsed into 4 categories: water and other sugar-free beverages, 100% juice, milk, and sugar-sweetened beverages (SSBs). Mean daily beverage intakes were averaged from all available questionnaires for the time periods preceding clinic exams (i.e., 2–4.7, 5–8.5, 9–10.5, 11–12.5, 13–14.5, and 15–17 y).

Nutrient intakes

Nutrient and energy intakes were obtained from 3-d food and beverage diaries completed at 3- to 6-mo intervals before age 8.5 y and from Block Kids’ FFQs (Nutrition Quest) completed for the previous week at ages 9, 11, 13, 15, and 17 y (14, 15).

Participants

Inclusion in the beverage intake analyses required completion of ≥2 questionnaires between ages 2 and 4.7 y or 5 and 8.5 y, or completion of ≥1 questionnaire between ages 9 and 10.5, 11 and 12.5, 13 and 14.5, or 15 and 17 y, and height measurement at the corresponding age 5-, 9-, 11-, 13-, 15-, or 17-y exams. For the nutrient intake analyses, inclusion required the return of ≥2 dietary diaries between ages 2 and 4.7 y or 5 and 8.5 y or the completion of Block Kids’ FFQs at age 9, 11, 13, 15, or 17 y and height measurement at the corresponding age 5-, 9-, 11-, 13-, 15-, or 17-y exams.

DRIs were used to determine the adequacy of individual nutrient intakes. The nutrient adequacy ratio (NAR) is the ratio of one's mean daily intake to the age- and sex-specific Estimated Average Requirement for that nutrient (16). The mean adequacy ratio (MAR) is the average of the individual's NARs and offers an estimate of overall diet quality. NARs are truncated at 1 when calculating the MAR to prevent an excess intake of one nutrient from compensating for low intakes of other nutrients. The MAR was calculated from NARs for protein, 9 vitamins (vitamin A, vitamin C, thiamin, riboflavin, niacin, folate, vitamin B-6, vitamin D, and vitamin E), and 5 minerals (calcium, iron, magnesium, phosphorus, and zinc). Mean daily NARs and the MAR were averaged from all available 3-d diaries for ages 2–4.7 and 5–8.5 y and from Block Kids’ FFQs for ages 9, 11, 13, 15, and 17 y.

Anthropometric measures

Height was measured without shoes with the use of a stadiometer during clinic visits at ages 5, 9, 11, 13, 15, and 17 y.

Statistical analyses

Longitudinal analyses were conducted to identify associations between beverage intakes, NARs, MAR, or energy intake and height. A linear mixed model was fit for each dietary variable under study, with the dietary variable as a predictor and height as the outcome. Because height is approximately normally distributed, traditional Gaussian linear mixed-effects models were used, with random slopes and intercepts used to account for the correlation between repeated height measurements on a single subject. Linear mixed models allow for participants to be included in the model if the subject has full data available (dietary intake information and height outcome information) for ≥1 time point. The models were fit by applying maximum likelihood estimation. All of the modeling results were obtained by using PROC MIXED in SAS (version 9.4; SAS Institute, Inc.).

We were interested in the associations between dietary intakes and height throughout childhood and adolescence. To improve statistical power, for each particular dietary variable, we included all participants and all available time points in a single model examining the effect of that dietary intake on height. In order to accommodate changing growth patterns starting in early adolescence and the differences in growth patterns for female and male participants, a change point was incorporated into the model at age 15.5 y for males and 13.5 y for females (17). The change point allowed us to model 4 different slopes for the association between height and time: males before 15.5 y, males after 15.5 y, females before 13.5 y, and females after 13.5 y.

A baseline model was developed that allowed for 4 slopes for the association between height and time for the 4 different groups described above by using indicators to represent slopes for 3 groups and defining a reference intercept and slope for the fourth group. This modeling framework allowed for different slope and intercept variables for the 4 relevant partitions of the data. On the basis of a visual inspection of scatterplots, age 13.5 y was a clear change point for female subjects, whereas the deceleration in growth appeared to be more gradual for males. Therefore, 4 different change points were considered for male participants: ages 13.5, 15, 15.5, and 16 y. After comparison of the log-likelihoods for the baseline models with the 4 change-point options for male participants (and the change point for female participants held constant at age 13.5 y), the change point at age 15.5 y for males was shown to have substantially better fit than the other 3 change-point options for the male participants.

In the initial set of models, a covariate representing intake of a beverage, NAR, MAR, or energy intake was added to the baseline model to identify if the beverage, NAR, MAR, or energy intake was associated with a difference in height over time. In a second set of models, each covariate representing a dietary intake and the interaction between that intake and the indicator for sex was used to determine if such an association differed between females and males. In order to decide between the 2 sets of models, we examined the P value for the interaction between the intake covariate and the indicator for sex. In addition, for each intake variable under study, we compared the Bayesian Information Criterion (BIC) of the model with and without the interaction to determine which exhibited better penalized fit. Models were then adjusted for potential confounders, including MAR, energy intake, and baseline socioeconomic status (SES; i.e., low, middle, and high based on household income and mother's educational attainment). The effect of beverage, the selected NAR, MAR, or energy intake on height for a particular model was summarized, whereas the fixed effects estimates from the baseline covariates were omitted for clarity.

Results

Fifty-one percent of the 717 participants were female. At baseline, 45% of mothers had a 4-y college degree and 19% of households had an annual income of ≥$60,000, whereas in 2007, 50% of mothers had a college degree and 67% of households had an annual income >$60,000. Most participants (94%) were non-Hispanic white. Mean ± SD participant weights and heights at approximately ages 5, 9, 11, 13, 15, and 17 y are presented in Table 1.

TABLE 1.

Weights and heights of Iowa Fluoride Study participants at approximate ages of 5, 9, 11, 13, 15, and 17 y¹

	Age
Anthropometric measure and sex	5 y	9 y	11 y	13 y	15 y	17 y
Males, n	317	285	229	252	200	207
Females, n	334	288	246	263	213	232
Weight, kg
Males	20.2 ± 3.2	35.5 ± 9.6	45.4 ± 13.0	60.7 ± 17.3	70.7 ± 16.2	80.3 ± 18.0
Females	19.7 ± 3.6	33.6 ± 8.6	44.2 ± 12.3	57.2 ± 15.0	61.9 ± 14.6	66.6 ± 16.6
Height, cm
Males	111.8 ± 5.6	137.9 ± 7.3	149.1 ± 7.6	164.9 ± 9.3	175.3 ± 7.9	179.2 ± 7.4
Females	110.9 ± 5.4	135.7 ± 6.9	148.9 ± 7.5	161.2 ± 6.5	164.5 ± 6.6	165.6 ± 6.7

¹Values are means ± SDs unless otherwise indicated, n = 717 (353 males and 364 females).

Median (25th, 75th percentile) daily beverage intakes are presented in Table 2. Water and sugar-free beverage and SSB intakes increased with age, whereas 100% juice intakes declined with age and milk intakes remained relatively stable.

TABLE 2.

Daily beverage intakes of Iowa Fluoride Study participants¹

	Age
Beverage and sex	2–4.7 y	5–8.5 y	9–10.5 y	11–12.5 y	13–14.5 y	15–17 y
Males, n	317	285	156	186	158	193
Females, n	333	288	187	220	177	209
Water/other sugar-free beverages, ounces
Males	4.7 (2.7, 7.6)	8.9 (5.0, 13.6)	10.1 (6.8, 18.1)	13.5 (9.0, 20.8)	15.5 (10.1, 24.0)	17.7 (10.7, 30.6)
Females	4.7 (2.8, 7.6)	8.1 (5.1, 11.7)	8.9 (6.3, 15.4)	12.6 (7.5, 18.9)	14.4 (9.0, 24.2)	17.3 (10.9, 27.6)
Milk, ounces
Males	10.9 (7.3, 15.7)	12.7 (8.9, 17.2)	11.3 (7.8, 16.0)	13.7 (8.1, 18.5)	12.1 (7.2, 19.9)	11.9 (6.8, 20.9)
Females	10.9 (7.6, 14.1)	11.9 (8.5, 15.6)	10.3 (6.4, 15.4)	10.8 (5.9, 15.7)	10.2 (5.7, 14.4)	8.6 (4.3, 15.4)
100% juice, ounces
Males	7.2 (4.4, 10.7)	5.6 (3.4, 7.7)	1.5 (0.1, 3.9)	1.1 (0.0, 3.3)	0.7 (0.0, 3.4)	0.9 (0.0, 3.8)
Females	6.0 (3.7, 9.6)	4.2 (2.5, 6.6)	1.5 (0.0, 3.7)	1.7 (0.0, 3.7)	1.3 (0.0, 3.4)	1.1 (0.0, 3.6)
Sugar-sweetened beverages, ounces
Males	2.7 (1.1, 5.1)	4.4 (2.7, 7.5)	8.4 (5.0, 13.7)	9.7 (6.5, 16.6)	12.1 (7.0, 18.4)	12.6 (7.0, 20.9)
Females	2.6 (1.2, 5.1)	3.2 (1.6, 5.8)	6.4 (4.0, 10.0)	7.5 (4.7, 13.1)	7.5 (4.6, 12.5)	8.0 (4.1, 14.3)

¹Values are median (25th, 75th percentile) beverage intakes averaged for age ranges 2–4.7, 5–8.5, 9–10.5, 11–12.5, 13–14.5, and 15–17 y; n = 708 (349 males and 359 females). One ounce = 29.57 mL.

For all dietary intake variables, the BIC was >2 units lower in the model without the interaction compared with the model with the interaction between the intake variable and sex. A difference of ≥2 units in BIC values is considered meaningful, with the model corresponding to the smaller BIC value being preferred. The P value for the interaction between intake and sex was not significant (P > 0.05) in any model, except for calcium where it was marginally significant (P = 0.037). Therefore, the simpler set of models without the interactions between dietary intake and sex is reported here.

After adjustment for MARs, energy intakes, and baseline SES, neither 100% juice nor SSB intakes were associated with height in longitudinal models (Table 3). Milk intake had the largest effect size of beverages and was significantly associated with height in the adjusted longitudinal model. The model indicates that each 8 ounces (236 mL) of additional daily milk intake throughout childhood and adolescence increases expected height by 0.39 cm (95% CI: 0.18, 0.60 cm) after adjusting for age, sex, MAR, energy intakes, and baseline SES, whereas each additional 16 ounces (473 mL) of daily milk intake increases expected height by 0.78 cm.

TABLE 3.

Summary of effects of beverage intakes on height during childhood and adolescence in Iowa Fluoride Study participants

	Estimated (95% CI)
Beverage variable	Effect of beverage intake on height1	P	Adjusted effect of beverage intake on height2	P
Water/other	0.10 (−0.02, 0.23)	0.11	0.16 (0.01, 0.30)	0.032
sugar-free
beverages
Milk	0.34 (0.16, 0.51)	<0.001	0.39 (0.18, 0.60)	<0.001
100% juice	0.21 (−0.11, 0.54)	0.20	0.32 (−0.08, 0.73)	0.12
Sugar-sweetened	0.15 (−0.03, 0.33)	0.10	0.15 (−0.06, 0.36)	0.16
beverages

¹Values are the estimated effects (95% CIs) of 8 ounces of additional daily beverage intake on mean height (centimeters) from linear mixed models with no interaction between beverage intake and indicator for participant sex; n = 708 (349 males, 359 females).

²Values are the estimated effects (95% CIs) of 8 ounces of additional daily beverage intake on mean height (centimeters) from linear mixed models with no interaction between beverage intake and indicator for participant sex, adjusted for mean adequacy ratio, energy intake, and baseline socioeconomic status; n = 571 (281 males, 290 females).

Mean NARs were >0.95 for most vitamins and minerals (data not shown). Median (25th, 75th percentile) calcium and vitamin D NARs, MARs, and energy intakes at each age are presented in Supplemental Table 1. Calcium NAR was significantly associated with height in the longitudinal model adjusted for energy intakes and baseline SES; each additional 0.1 calcium NAR throughout childhood and adolescence increases expected height by 0.06 cm (95% CI: 0.01, 0.11 cm) (Table 4). Because milk intakes and calcium NARs were each associated with height and milk is a primary source of calcium, we examined the cross-sectional association between milk intakes and calcium NARs. Spearman correlation coefficients for the association between milk intakes and calcium NARs were significant at 2–4.7 (ρ = 0.76), 5–8.5 (ρ = 0.64), 9–10.5 (ρ = 0.47), 11–12.5 (ρ = 0.49), 13–14.5 (ρ = 0.57), and 15–17 (ρ = 0.55) y (all P < 0.001). Neither MAR adjusted for energy intake and baseline SES nor energy intakes adjusted for baseline SES were associated with height.

TABLE 4.

Summary of effects of NAR, MAR, and energy intakes on height during childhood and adolescence in Iowa Fluoride Study participants¹

	Estimated (95% CI)
Nutrient variable	Effect of NAR, MAR, or energy on height2	P	Adjusted effect of NAR, MAR, or energy on height3	P
Vitamin D NAR	0.04 (−0.02, 0.10)	0.20	0.03 (−0.03, 0.10)	0.35
Calcium NAR	0.05 (0.01, 0.10)	0.010	0.06 (0.01, 0.11)	0.022
MAR	−0.01 (−0.16, 0.15)	0.93	−0.11 (−0.32, 0.10)	0.32
Energy	0.02 (−0.01, 0.05)	0.28	0.02 (−0.01, 0.05)	0.23

¹The NAR is the ratio of the mean daily intake to the age- and sex-specific Estimated Average Requirement. The MAR is the average of the individual's NARs and estimates overall diet quality. MAR, mean adequacy ratio; NAR, nutrient adequacy ratio.

²Values are the estimated effects (95% CIs) of each additional 0.1 NAR or MAR or 100-kcal/d intake on mean height (centimeters) from linear mixed models with no interaction between nutrient intake and indicator for participant sex; n = 652 (324 males and 328 females).

³Values are the estimated effects (95% CIs) of each additional 0.1 NAR or MAR or 100-kcal/d intake on mean height (centimeters) from linear mixed models with no interaction between nutrient intake and indicator for subject sex, with vitamin D and calcium NAR and MAR models adjusted for energy intake and baseline socioeconomic status and with the energy model adjusted for baseline socioeconomic status; n = 629 (312 males and 317 females).

Discussion

The achievement of one's genetic height potential requires an enriching environment including access to appropriate and adequate nutrition. Our results support the hypothesis that milk intake and perhaps calcium adequacy are associated with achievement of height. Other beverage intakes, adequacy of other nutrient intakes, and energy intakes were not associated with height in our participants.

Our results are consistent with previous investigations reporting associations between higher milk intakes and greater height in young children (18–20). Black et al. (18) reported that New Zealand children aged 3–10 y who avoided milk were shorter and had smaller skeletons than did their milk-consuming peers. deBoer et al. (19) reported that children who consumed ≥3 servings of milk/d at age 4 y were taller, on average, at ages 4 and 5 y than their peers who consumed ≤1 serving in a nationally representative US sample. Morency et al. (20) reported that healthy Canadian children aged 24–72 mo who consumed non–cow milk were, on average, 0.4 cm (95% CI: 0.2, 0.8 cm) shorter for each cup consumed than their peers who consumed cow milk. The magnitude of height difference reported by Morency et al. is similar to the height difference observed in our IFS participants (0.34 cm/8 ounces) (20). However, our results extend previous observations, suggesting that milk intake supports linear growth throughout childhood and adolescence and is important for the attainment of adult height.

The adequacy of calcium intake was significantly associated with height in our IFS participants; however, the effect size is of minimal clinical relevance. Calcium is a structural component of bone, and supplementation is associated with small changes in bone mineral density (21). Our results are consistent with previous investigations of calcium intake, bone mineral content, and height (22–24). Prentice et al. (22) reported that boys who received 12 mo of calcium supplementation at 16–18 y of age had higher whole-body bone mineral content and were 0.41 cm taller than peers who received placebo at 15 mo posttreatment. Ward et al. (23) reported that calcium supplementation of Gambian prepubertal boys led to earlier bone mineral deposition and growth spurt; however, bone mineral, bone size, and height did not differ between boys who received calcium supplements or placebo at a 12-y follow-up. Fang et al. (24) reported that very low calcium intakes (<327 mg/d) were associated with shorter adult heights and that calcium supplementation supported a faster growth spurt in Chinese boys; however, supplementation did not affect adult height. Because our participants generally had adequate calcium intakes through age 9 y, we might not have been able to identify a larger effect on height. Calcium NARs were strongly associated with milk intakes in our participants. Thus, the observed association between calcium adequacy and height supports, but does not explain, the observed association between milk intake and height.

Although milk is also the predominant source of dietary vitamin D, our participants generally had inadequate intakes of vitamin D, suggesting that vitamin D was not responsible for the observed association between milk and height in our participants. Although inadequate intakes of energy and protein are associated with stunting in developing countries that lack access to adequate food resources, energy intakes were not associated with height in our participants. Energy intakes of our participants were generally adequate, suggesting that we could not identify an association, if one existed, due to restricted variability in the covariate.

Mean heights of IFS participants generally tracked at the 75th percentile of the CDC’s growth chart (25), whereas median milk intakes did not meet ChooseMyPlate recommendations (26), suggesting that milk is not essential for linear growth. The mechanism explaining the association observed between milk intakes and height in IFS participants is unclear; milk intakes might be a marker of a more enriching environment. Karra et al. (27) reported that children living in poverty and with mothers with lower educations or limited access to safe water and sanitation were shorter than their peers living in healthier environments in low- and middle-income countries. Although the majority of IFS participants, particularly those who remained in the study through age 17 y, were raised in moderate-income households with highly educated mothers, less obvious environmental factors associated with height faltering could have affected their height. Expectations for milk consumption by children and adolescents could be a marker of engaged parental involvement, including expectations for regular and adequate sleep, family meal patterns, or reduced tolerance for high-risk behaviors contributing to infectious or toxic exposures. Alternatively, aging infrastructures are associated with increased water lead concentrations in Iowa (28). Dietary calcium is associated with reduced lead absorption, and milk calcium could reduce the risk of lead poisoning (29).

Although the underlying mechanisms for the associations between milk intake and height are unknown, the identification of an association between diet and height in a moderate-income and educated population is a public health concern. Stunting is clearly associated with adverse cognitive, emotional, and physical outcomes, whereas the clinical implications of mild linear growth faltering are unknown. Additional investigation is necessary to understand both the mechanism and implications of mild linear growth faltering at the individual and societal levels.

The current study is not without limitations. Beverage and dietary intakes were caregiver or self-reported and might not reflect actual intakes. The sample was small and self-selected. The population was mostly white, of moderate income, and reasonably well educated, and not representative of other US or international populations. Strengths of the study include a loyal, long-standing cohort. Beverage and dietary intakes were queried with the use of validated instruments at multiple time points throughout life. Longitudinal analyses enabled us to examine childhood and adolescent associations between beverages and nutrient adequacy and height without the concern of a cohort effect.

We identified associations between milk intake and height throughout childhood and adolescence. On average, heights of children increased by 0.39 cm for each additional 8 ounces (236 mL) of milk consumed per day. The associations are consistent with the literature, which reports associations between milk intakes and height in young children.

Notes

Supported by the NIH (R03-DE023784, R01-DE12101, R01-DE09551, UL1-RR024979, UL1-TR000442, UL1-TR001013, and M01-RR00059), The Roy J Carver Charitable Trust, and Delta Dental of Iowa Foundation.

Author disclosures: TAM, AMC, JEC, JJW, and SML, no conflicts of interest.

Supplemental Table 1 is available from the “Supplementary data” link in the online posting of the article and from the same link in the online table of contents at https://academic.oup.com/jn/.

Abbreviations used:

BIC

Bayesian Information Criterion

IFS

Iowa Fluoride Study

MAR

mean adequacy ratio

NAR

nutrient adequacy ratio

SES

socioeconomic status

SSB

sugar-sweetened beverage