Abstract
Objective
To investigate the relationship between LCS, energy intake, and weight in United States youth.
Methods
Data were collected from individuals aged 2 to 19 years, who participated in NHANES in 2009–2010 (n=3,296), 2011–2012 (n=3,139), and 2013–2014 (n=3,034). Logistic regression, unadjusted and adjusted for age, sex, race/ethnicity, income, energy intake, and physical activity, was used to estimate the odds of obesity in LCS consumers vs. non-consumers, overall, and across product categories (foods vs. beverages) and socio-demographic subgroups.
Results
Among adolescents, odds of obesity were 55% and 70% higher in LCS beverage consumers compared to non-consumers, in unadjusted and adjusted models, respectively. Energy intakes did not differ based on LCS consumption. In contrast, associations between LCS consumption and obesity risk were not statistically significant among children (2–11 years old), except in boys and those who self-identified as Hispanic.
Conclusions
LCS consumption is associated with increased odds of obesity among adolescents. This relationship is strikingly independent of total energy intake. While findings should be interpreted cautiously due to limitations of self-report dietary intake and the cross-sectional nature of this analysis, our observational analysis supports the need to investigate mechanisms by which LCS may influence body weight, independently of changes in energy intake.
Introduction
Low-calorie sweetener (LCS) consumption is increasingly prevalent among youth1. Beverages are the predominant contributors to LCS intake in children, although LCS are also found widely in foods, condiments, and sweetener packets1. While LCS offer a lower-calorie alternative to added sugars, their role in weight management and metabolic health is unclear2.
Epidemiologic studies report positive associations between LCS and body weight in children and adults3. However, little is known about whether LCS consumption correlates with energy intake, especially in youth. In adults, LCS consumers have higher discretionary calorie intake4, purchase more snack foods, and consume more calories5, compared to sugar-sweetened beverage (SSB) consumers. A recent analysis also reported that overweight and obese adults who consume LCS beverages have higher calorie intake compared to similar weight individuals who consume SSB6. Despite the growing body of epidemiologic literature connecting LCS intake to higher body weight in adults7, causality cannot be inferred from observational analyses, and the majority of randomized controlled trials in adults demonstrate that LCS may be a useful tool for modest weight loss in the context of intensive lifestyle interventions8,9. Meanwhile, population-level relationships between LCS consumption, energy intake, and obesity in youth have not been evaluated. We investigated this relationship in youth, using data from three cycles (2009–2014) of the National Health and Nutrition Examination Survey (NHANES).
Methods
Data source
NHANES is a continuous, cross-sectional study of the US population, with data released in 2-year cycles. NHANES sampling and data collection methods are described elsewhere17. The current analyses used data collected from individuals aged 2 to 19 years, who participated in NHANES 2009–2010, 2011–2012, and 2013–2014, providing a sample of 9,469 individuals. NHANES response rates were ≥75%, for the age-groups studied, in all three survey cycles10. Demographic and anthropometric were collected, categorized, and analyzed as detailed previously1. Consistent with prior analyses1, those with missing weight (n=177) or implausible energy intake (n=31) were excluded, providing a final sample of 9,261. Participants with missing data for any characteristic were excluded only from the subgroup comparison for which information was missing. Because assessment of physical activity in NHANES (described below) differs for younger children (2–11 years) and adolescents (12–19 years), all analyses were conducted separately for children (2–5 years, 6–11 years) and adolescents (12–19 years).
LCS Consumption
LCS use was also identified and categorized in accordance with our prior publications1,11. Briefly, food and beverage items containing LCSs reported during the 24-hour recalls were identified using food descriptions provided in the Food and Nutrient Database for Dietary Studies (FNDDS) version 5.021 and version 11–1222, in NHANES 2009–2010 and NHANES 2011–2014, respectively. Food codes containing the terms “diet,” “dietetic,” “low-calorie,” “no sugar added”, “light”, “sugar-free”, “sugar substitute,” “low-calorie sweetener,” or “no-calorie sweetener” were extracted. After confirming that food codes extracted did indeed reflect the presence of LCS (e.g. a food such as light mayonnaise does not contain LCS despite being labeled ‘light’) using publicly available ingredient information, each code was then categorized as an LCS beverage, or LCS food. Any participant who completed one (n= 1,299) or both (n= 8,170) dietary recalls was included in the analysis. Children who reported consuming ≥1 food or beverage containing LCSs during at least one of their two dietary recalls were defined as LCS consumers.
Obesity
Body mass index percentile was then calculated based on measured height and weight and weight status subgroups (underweight, normal weight, overweight, or obese) were determined using standard cut-offs. Obesity was defined as BMI at or above the sex-specific 95th percentile of BMI for age, based on the 2000 CDC growth charts12,13.
Physical Activity
For children (2–11y), physical activity was assessed as number of days physically active at least 60 min per week. This was assessed based on the question, ‘During the past 7 days, on how many days was participant physically active for a total of at least 60 minutes per day?’ For adolescents (12–19), physical activity was assessed as MET-min of moderate and vigorous activity per week, which was derived using NHANES recommended MET score. This was assessed based on the question, ‘In a typical week, on how many days do you do moderate or vigorous-intensity sports, fitness or recreational activities, and how much time {do you/does SP} spend doing vigorous-intensity sports, fitness or recreational activities on a typical day?
Covariates
Covariates included the participant’s age (categorized as child or adolescent), sex, socioeconomic status (coded as low, middle, or high, determined using tertiles of family income to poverty ratio), and self-reported race-ethnicity (coded as non-Hispanic white, non-Hispanic black, Hispanic, or other), energy intake, and physical activity.
Statistical Analysis
SAS 9.4 (SAS Institute, 2013) was used to account for the complex sampling design. Sample weights were used to generate national-level estimates of consumption. Differences in sociodemographic characteristics across weight categories were examined by F-test. Logistic regression, unadjusted and adjusted for age, sex, race/ethnicity, income, energy intake, and physical activity, was used to estimate the obesity odds in LCS consumers vs. non-consumers. All p-values were 2-sided and p< 0.05 was considered statistically significant. Values are presented as odds ratios (OR) with 95% confidence intervals (CI) or percentages, as appropriate.
Results
Sociodemographic characteristics by weight status and reported LCS consumption are presented in Table 1 and Table 2, respectively. Odds ratios of obesity overall and by product category are shown in Table 3. LCS packet use was not separately analyzed, due to low prevalence of LCS packet use in youth.
Table 1.
CHILDREN (2–11 YEARS) | ||||||
---|---|---|---|---|---|---|
Characteristic | All (n=5590) | Underweight (n=219) | Healthy (n=3609) | Overweight (n=845) | Obese (n=922) | p-value |
Total | 100% | 3.8% | 66.2% | 15.3% | 14.7% | |
Gender, n (%) | 0.5132 | |||||
Male | 2842 (51.1) | 110 (48.3) | 1823 (50.6) | 422 (51.1) | 487 (54.0) | |
Female | 2748 (48.9) | 104 (51.7) | 1786 (49.4) | 423 (48.9) | 435 (46.0) | |
Race/Ethnicityc, n (%) | <.0001 | |||||
White | 1552 (53.2) | 56 (53.0) | 1096 (56.6) | 215 (51.6) | 185 (39.7) | |
Black | 1376 (13.8) | 53 (12.6) | 877 (13.2) | 195 (12.6) | 251 (18.3) | |
Hispanic | 1949 (24.0) | 58 (19.5) | 1130 (20.9) | 352 (28.7) | 409 (34.3) | |
Age, n (%) | <0.0001 | |||||
2–5 years | 2270 (39.3) | 100 (34.5) | 1593 (42.6) | 310 (38.0) | 267 (27.2) | |
6–11 years | 3320 (60.7) | 114 (65.5) | 2016 (57.4) | 535 (62.0) | 655 (72.8) | |
Incomed, n (%) | 0.0011 | |||||
1st tertile | 2491 (35.58) | 80 (26.06) | 1586 (34.32) | 377 (36.79) | 448 (42.42) | |
2nd tertile | 1587 (33.0) | 63 (34.4) | 1000 (32.7) | 251 (33.6) | 273 (33.2) | |
3rd tertile | 1134 (31.4) | 55 (39.5) | 795 (32.9) | 155 (29.6) | 129 (24.4) | |
LCS Consumer, n (%) | 1785 (34.6) | 59 (26.7) | 1108 (34.2) | 274 (34.9) | 344 (37.8) | 0.0684 |
LCS Beverage Use, n (%) | 1451 (28.3) | 45 (20.1) | 895 (27.9) | 223 (28.2) | 288 (32.3) | 0.0558 |
LCS Food User, n (%) | 484 (10.0) | 18 (7.6)g | 309 (10.2) | 76 (10.1) | 81 (9.3) | 0.7168 |
Physical activitye (days per week) | 6.1 ± 0.0 | 6.1 ± 0.2 | 6.3 ± 0.0 | 6.1 ± 0.1 | 5.7 ± 0.1 | <0.001 |
Energy intake (kcal) | 1756.3 ± 9.4 | 1674.6 ± 40.7 | 1743.5 ± 10.9 | 1753.5 ± 25.7 | 1837.5 ± 28.3 | 0.005 |
ADOLESCENTS (12–19 YEARS) | ||||||
Characteristic | All (n=3671) | Underweight (n=115) | Healthy (n=2193) | Overweight (n=611) | Obese (n=752) | p-value |
Total | 100% | 3.1% | 61.7% | 15.6% | 19.7% | |
Gender, n (%) | 0.1462 | |||||
Male | 1889 (51.1) | 66 (65.3) | 1131 (50.1) | 306 (52.1) | 386 (51.3) | |
Female | 1782 (48.9) | 49 (34.7) | 1062 (49.9) | 305 (47.9) | 366 (48.7) | |
Race/Ethnicity, n (%) | 0.0054 | |||||
White | 1008 (56.4) | 34 (59.0) | 644 (59.3) | 141 (50.1) | 189 (52.2) | |
Black | 927 (14.4) | 23 (10.8) | 526 (13.2) | 164 (16.4) | 214 (16.8) | |
Hispanic | 1240 (20.9) | 35 (20.2) | 696 (19.0) | 233 (24.8) | 276 (23.7) | |
Incomeb, n (%) | 0.0001 | |||||
1st tertile | 1421 (30.3) | 43 (28.8) | 804 (27.6) | 242 (33.6) | 332 (36.4) | |
2nd tertile | 1113 (33.8) | 39 (30.0) | 646 (32.1) | 188 (35.5) | 240 (38.2) | |
3rd tertile | 836 (35.9) | 27 (41.3)g | 566 (40.2) | 129 (30.9) | 114 (25.4) | |
LCS Consumer, n (%) | 872 (28.2) | 23 (21.0)g | 496 (26.9) | 149 (26.6) | 204 (34.5) | 0.0125 |
LCS Beverage Use, n (%) | 744 (24.2) | 20 (19.7)g | 412 (22.2) | 131 (24.5) | 181 (31.1) | 0.0065 |
LCS Food Use, n (%) | 187 (5.4) | 4 (2.0)g | 114 (5.9) | 26 (3.8) | 43 (5.9) | 0.1981 |
Physical activityf (MET-min per week) | 2229.8 ± 58.1 | 1705.3 ± 337.6 | 2342.4 ± 77.6 | 2405.8 ± 176.3 | 1819.1 ± 136.3 | 0.009 |
Energy intake (kcal) | 2046.1 ± 20.7 | 2239.2 ± 103.8 | 2097.9 ± 27.1 | 1992.4 ± 36.6 | 1895.6 ± 41.5 | <0.001 |
N reflects the number of participants in the sample, while percentages are weighted to account for the complex NHANES survey design.
Respondents reporting ‘other’ race (n=713 children, n=496 adolescents), including multi-racial, are included in overall estimates but are not shown separately.
Defined based on tertiles of poverty-to-income ratio (PIR). N=378 children and N=301 adolescents were missing data for income.
In children 2–11 years, physical activity was assessed as number of days physically active at least 60 min per week. N=11 children were missing data for physical activity.
In adolescents 12–19 years, physical activity was assessed as MET-min of moderate and vigorous activity per week, which was derived using NHANES recommended MET score. N=110 adolescents were missing data for physical activity.
Due to small sample size (n<30), these estimates may be unreliable and should be interpreted with caution.
Table 2.
Children 2–11 years | Adolescents 12–19 years | |||||
---|---|---|---|---|---|---|
Consumers | Non-consumers | p-value | Consumers | Non-consumers | p-value | |
Gender, n (%) | 0.0157 | 0.0031 | ||||
Male | 876 (48.7) | 1966 (52.4) | 435 (46.2) | 1454 (53.1) | ||
Female | 909 (51.3) | 1839 (47.6) | 437 (53.8) | 1345 (46.9) | ||
Race/Ethnicityb, n (%) | 0.0011 | <0.0001 | ||||
White | 550 (58.3) | 1002 (50.6) | 312 (66.6) | 696 (52.5) | ||
Black | 409 (12.2) | 967 (14.7) | 198 (10.7) | 729 (15.8) | ||
Hispanic | 623 (22.1) | 1326 (25.0) | 269 (16.6) | 971 (22.5) | ||
Age, n (%) | <0.001 | N/A | ||||
2–5y | 627 (33.5) | 1643 (42.4) | N/Ad | N/A | ||
6–11y | 1158 (66.5) | 2162 (57.6) | N/A | N/A | ||
Income, n (%)c | 0.0066 | 0.0009 | ||||
1st tertile | 730 (31.4) | 1761 (37.8) | 302 (24.3) | 1119 (32.7) | ||
2nd tertile | 524 (33.6) | 1063 (32.7) | 276 (34.4) | 837 (33.5) | ||
3rd tertile | 431 (34.9) | 703 (29.5) | 234 (41.3) | 602 (33.8) | ||
Weight statusd, n (%) | 0.0684 | 0.0125 | ||||
Underweight | 59 (2.9) | 155 (4.2) | 23 (2.3)h | 92 (3.4) | ||
Healthy | 1108 (65.5) | 2501 (66.5) | 496 (58.9) | 1697 (62.8) | ||
Overweight | 274 (15.5) | 571 (15.2) | 149 (14.7) | 462 (15.9) | ||
Obese | 344 (16.1) | 578 (14.0) | 204 (24.1) | 548 (17.9) | ||
Physical activityf,g | 6.1 (0.1) | 6.2 (0.1) | 0.6318 | 2192 (88.7) | 2244 (66.7) | 0.6064 |
Energy intake (kcal) | 1789 (15.5) | 1739 (9.9) | 0.0046 | 2032 (33.3) | 2051 (24.8) | 0.6338 |
N reflects the number of participants in the sample, while percentages are weighted to account for the complex NHANES survey design.
Respondents reporting ‘other’ race (n=713 children, n=496 adolescents), including multi-racial, are included in overall estimates but are not shown separately.
Defined based on tertiles of poverty-to-income ratio (PIR)
N/A indicates that age sub-group comparisons were not performed for adolescents
In children 2–11 years, physical activity was assessed as number of days physically active at least 60 min per week.
In adolescents 12–19 years, physical activity was assessed as MET-min of moderate and vigorous activity per week, which was derived using NHANES recommended MET score.
Due to small sample size (n<30), these estimates may be unreliable and should be interpreted with caution.
Table 3.
Any LCS | LCS Beverages | LCS Foods | ||||
---|---|---|---|---|---|---|
OR (95% CI) | OR (95% CI) | OR (95% CI) | ||||
Unadjusted | Adjusted1 | Unadjusted | Adjusted1 | Unadjusted | Adjusted1 | |
Children (2–11 years) | ||||||
All (n=5, 590) | 1.18 (0.97, 1.44) | 1.19 (0.97, 1.46) | 1.25 (1.01, 1.56) | 1.24 (0.98, 1.56) | 0.91 (0.68, 1.23) | 0.99 (0.73, 1.34) |
Sex | ||||||
Male (n=2, 842) | 1.21 (0.93,1.59) | 1.24 (0.94, 1.63) | 1.42 (1.08, 1.88) | 1.45 (1.09, 1.93) | 0.79 (0.50, 1.23) | 0.87 (0.55, 1.37) |
Female (n=2, 748) | 1.16 (0.89, 1.51) | 1.13 (0.84, 1.52) | 1.09 (0.81, 1.47) | 1.02 (0.74, 1.42) | 1.06 (0.70, 1.63) | 1.13 (0.75, 1.72) |
Age Group | ||||||
2–5 years (n=2270) | 1.33 (0.96, 1.87) | 1.28 (0.92, 1.79) | 1.48 (1.00, 2.18) | 1.35 (0.89, 2.05) | 0.84 (0.47, 1.52) | 0.87 (0.47, 1.61) |
6–11 years (n=3320) | 1.05 (0.85, 1.28) | 1.13 (0.91, 1.41) | 1.09 (0.87, 1.37) | 1.17 (0.91, 1.50) | 0.94 (0.65, 1.37) | 1.03 (0.72, 1.49) |
Race/Ethnicity | ||||||
White (n=1,552) | 1.04 (0.72, 1.50) | 0.99 (0.68, 1.44) | 1.16 (0.77, 1.76) | 1.09 (0.71, 1.67) | 0.93 (0.56, 1.53) | 0.93 (0.57, 1.52) |
Black (n=1,376) | 1.25 (0.84, 1.87) | 1.21 (0.82, 1.80) | 1.11 (0.73, 1.71) | 1.06 (0.69, 1.61) | 1.35 (0.81, 2.25) | 1.46 (0.86, 2.49) |
Hispanic (n=1,949) | 1.57 (1.19, 2.08) | 1.59 (1.20, 2.10) | 1.68 (1.26, 2.26) | 1.66 (1.23, 2.24) | 0.87 (0.58, 1.31) | 0.86 (0.60, 1.25) |
Income | ||||||
1st tertile (n=2,491) | 1.23 (0.91, 1.65) | 1.23 (0.92, 1.64) | 1.18 (0.89, 1.56) | 1.16 (0.88, 1.53) | 1.13 (0.64, 1.99) | 1.19 (0.68, 2.11) |
2nd tertile (n= 1,587) | 1.27(0.79, 2.04) | 1.28 (0.78, 2.07) | 1.43 (0.90, 2.28) | 1.44 (0.88, 2.36) | 0.84 (0.47, 1.52) | 0.89 (0.50, 1.60) |
3rd tertile (n= 1,134) | 1.11 (0.67, 1.82) | 1.06 (0.62, 1.82) | 1.23 (0.76, 1.99) | 1.14 (0.67, 1.92) | 0.89 (0.46, 1.74) | 0.94 (0.48, 1.84) |
Adolescents (12–19 years) | ||||||
All (n= 3, 671) | 1.45 (1.15, 1.83) | 1.57 (1.23, 2.01) | 1.55 (1.17, 2.06) | 1.71 (1.31, 2.23) | 1.12 (0.64, 1.97) | 1.10 (0.61, 1.96) |
Sex | ||||||
Male (n= 1,889) | 1.86 (1.32, 2.64) | 1.92 (1.35, 2.72) | 1.94 (1.28, 2.95) | 2.03 (1.33, 3.08) | 1.74 (0.98, 3.09) | 1.66 (0.90, 3.05) |
Female (n= 1,782) | 1.14 (0.88, 1.49) | 1.32 (1.01, 1.73) | 1.25 (0.95, 1.66) | 1.46 (1.13, 1.89) | 0.73 (0.34, 1.57) | 0.73 (0.31, 1.71) |
Race/Ethnicity2 | ||||||
White (n= 1,008) | 1.81 (1.26, 2.60) | 1.95 (1.35, 2.82) | 1.99 (1.33, 2.99) | 2.17 (1.50, 3.15) | 1.14 (0.50, 2.60) | 1.12 (0.47, 2.67) |
Black (n= 927) | 0.99 (0.68, 1.43) | 1.01 (0.68, 1.49) | 1.06 (0.70, 1.62) | 1.17 (0.78, 1.75) | 0.81 (0.42, 1.58) | 0.69 (0.37, 1.28) |
Hispanic (n= 1,240) | 1.27 (0.85, 1.89) | 1.34 (0.88, 2.06) | 1.17 (0.80, 1.72) | 1.23 (0.82, 1.85) | 1.53 (0.65, 3.60) | 1.69 (0.69, 4.10) |
Income | ||||||
1st tertile (n= 1,421) | 1.48 (1.03, 2.12) | 1.39 (0.97, 2.01) | 1.46 (1.05, 2.04) | 1.35 (0.96, 1.89) | 1.42 (0.59, 3.41) | 1.35 (0.61, 3.00) |
2nd tertile (n= 1,113) | 1.61 (0.96, 2.70) | 1.77 (1.08, 2.91) | 1.83 (1.04, 3.24) | 1.99 (1.13, 3.49) | 0.93 (0.44, 1.98) | 0.99 (0.47, 2.06) |
3rd tertile (n= 836) | 1.46 (0.86, 2.47) | 1.71 (0.99, 2.93) | 1.69 (1.00, 2.87) | 1.95 (1.14, 3.33) | 0.96 (0.31, 2.96) | 1.13 (0.36, 3.50) |
Adjusted model included the following covariates: age, sex, race/ethnicity, income, total energy intake, and physical activity.
Respondents reporting ‘other’ race (n=713 children, n=496 adolescents), including multi-racial, are included in overall estimates but are not shown separately.
Values in bold are statistically significant
Values were rounded to the nearest hundredth and thus, the lower bound of the 95% confidence interval was below 1.00 prior to rounding.
Among adolescents, obesity odds were 55% and 70% higher in LCS beverage consumers compared to non-consumers, in unadjusted and adjusted models, respectively. This pattern was observed across sex and income strata. In contrast, LCS food consumption was not associated with obesity odds and daily energy intakes did not differ based on LCS consumption (Table 2). Associations between LCS consumption and obesity were not consistently observed in children 2–11 years, except in males and Hispanics, before and after adjustment. Energy intakes did not differ with LCS consumption in any subgroup (Supplemental Table).
Discussion
LCS beverage consumption is associated with obesity in US adolescents, even after adjustment for relevant covariates, including energy intake. This finding is also supported by recent data in adults, where BMI was consistently higher with increasing diet beverage consumption, despite similar reported daily energy intakes14. While the observed associations do not imply causation, these results underscore the need to investigate mechanisms by which LCS may independently influence weight. LCS have been shown to upregulate adipogenesis and inhibit lipolysis in vitro, and alter gut microbiota in rodents15. Augmentation of insulin is reported in humans16 and is particularly relevant for adolescents, given the physiological insulin resistance of puberty17.
The lack of an association observed between consumption of LCS foods and obesity risk across sociodemographic subgroups is noteworthy. While likely explained by the low prevalence of LCS foods among children and adolescents, it is also possible that LCS foods are used differently in the diet compared to LCS beverages and thus may be associated with different dietary patterns or lifestyle habits.
Lack of consistent associations in younger children is likely multifactorial. Since obesity is much more prevalent in children above the age of 6 years compared to 2–5 year olds18, combining data from young and school-aged children may be misleading. In addition, if LCS are determined to be causally related to the development of obesity, it may occur gradually and will thus be observable only in older children. Also, LCS consumption is much more common in adolescents and thus, greater exposure may be necessary to observe an association1. Heightened susceptibility to LCS’s effects on insulin secretion (e.g. insulin resistance of puberty) may also be necessary17.
Limitations of the current investigation include analysis of self-reported dietary recall data, which is subject to systematic bias and susceptible to misreporting of energy intake, specifically among individuals with obesity19,20. In addition, as the observational nature of our study is not sufficient to establish causation, the observed effects may be in part explained by reverse causality and residual confounding. Furthermore, it is not possible to distinguish between different LCS using NHANES dietary data and potential misclassification of consumers is possible with self-report dietary assessment.
Conclusion
Taken together, our observational findings emphasize the need to determine whether chronic LCS ingestion is causally related to the development of obesity. It is also important to consider race/ethnicity, gender, age, and other factors when evaluating potential effects of LCS on body weight, as heterogeneity in associations was observed across socio-demographic subgroups.
Supplementary Material
What is already known about this subject?
Low-calorie sweetener use has increased markedly in children and adolescents over the past decade
Low-calorie sweetener use is associated with obesity and metabolic disease in adults.
Low-calorie sweetener use is associated with higher energy intake in adults but this has not been examined in children
What does your study add?
Adolescents who consume beverages containing low-calorie sweeteners have significantly higher odds of obesity, even when adjusted for total calorie intake.
Daily energy intakes do not differ with low-calorie sweetener consumption in children or adolescents.
Our findings support the need to elucidate the physiologic mechanisms through which low-calorie sweetener may paradoxically increase body weight, independently of changes in energy intake.
Acknowledgments
Funding: This work was funded in part by the Department of Exercise and Nutrition Sciences at The George Washington University and in part by the intramural research program of the National Institutes of Health.
Footnotes
Clinical Trial Registration: Not applicable.
Disclosure: None of the authors have conflicts of interest to report.
Author Contributions: ACS and SAT designed the research. YJ and KM performed the statistical analyses. ACS, LD, KIR, and SAT interpreted the data. ACS wrote the first draft of the manuscript. All authors were involved in editing the manuscript and approved of the final version.
References
- 1.Sylvetsky AC, Jin Y, Clark EJ, Welsh JA, Rother KI, Talegawkar SA. Consumption of Low-Calorie Sweeteners among Children and Adults in the United States. J Acad Nutr Diet. 2017 doi: 10.1016/j.jand.2016.11.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Mattes RD. Low calorie sweeteners: Science and controversy: Conference proceedings. Physiol Behav. 2016;164(Pt B):429–431. doi: 10.1016/j.physbeh.2016.01.005. [DOI] [PubMed] [Google Scholar]
- 3.Sylvetsky A, Rother KI, Brown R. Artificial sweetener use among children: epidemiology, recommendations, metabolic outcomes, and future directions. Pediatr Clin North Am. 2011;58(6):1467–1480. xi. doi: 10.1016/j.pcl.2011.09.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.An R. Beverage Consumption in Relation to Discretionary Food Intake and Diet Quality among US Adults, 2003 to 2012. J Acad Nutr Diet. 2016;116(1):28–37. doi: 10.1016/j.jand.2015.08.009. [DOI] [PubMed] [Google Scholar]
- 5.Binkley J, Golub A. Comparison of grocery purchase patterns of diet soda buyers to those of regular soda buyers. Appetite. 2007;49(3):561–571. doi: 10.1016/j.appet.2007.03.225. [DOI] [PubMed] [Google Scholar]
- 6.Bleich SN, Wolfson JA, Vine S, Wang YC. Diet-beverage consumption and caloric intake among US adults, overall and by body weight. Am J Public Health. 2014;104(3):e72–78. doi: 10.2105/AJPH.2013.301556. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Fowler SP. Low-calorie sweetener use and energy balance: Results from experimental studies in animals, and large-scale prospective studies in humans. Physiol Behav. 2016 doi: 10.1016/j.physbeh.2016.04.047. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Peters JC, Beck J. Low Calorie Sweetener (LCS) use and energy balance. Physiol Behav. 2016 doi: 10.1016/j.physbeh.2016.03.024. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Peters JC, Wyatt HR, Foster GD, et al. The effects of water and non-nutritive sweetened beverages on weight loss during a 12-week weight loss treatment program. Obesity (Silver Spring) 2014;22(6):1415–1421. doi: 10.1002/oby.20737. [DOI] [PubMed] [Google Scholar]
- 10.Centers for Disease Control and Prevention. [Accessed February 10th, 2015];National Health and Nutrition Examination Survey. 2015 http://www.cdc.gov/nchs/nhanes.htm.
- 11.Sylvetsky AC, Welsh JA, Brown RJ, Vos MB. Low-calorie sweetener consumption is increasing in the United States. Am J Clin Nutr. 2012;96(3):640–646. doi: 10.3945/ajcn.112.034751. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Kuczmarski RJ, Ogden CL, Guo SS, et al. 2000 CDC Growth Charts for the United States: methods and development. Vital Health Stat. 2002;11(246):1–190. [PubMed] [Google Scholar]
- 13.Barlow SE, Expert C. Expert committee recommendations regarding the prevention, assessment, and treatment of child and adolescent overweight and obesity: summary report. Pediatrics. 2007;120(Suppl 4):S164–192. doi: 10.1542/peds.2007-2329C. [DOI] [PubMed] [Google Scholar]
- 14.Pase MP, Himali JJ, Beiser AS, et al. Sugar- and Artificially Sweetened Beverages and the Risks of Incident Stroke and Dementia: A Prospective Cohort Study. Stroke. 2017;48(5):1139–1146. doi: 10.1161/STROKEAHA.116.016027. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Suez J, Korem T, Zilberman-Schapira G, Segal E, Elinav E. Non-caloric artificial sweeteners and the microbiome: findings and challenges. Gut Microbes. 2015;6(2):149–155. doi: 10.1080/19490976.2015.1017700. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Sylvetsky AC, Brown RJ, Blau JE, Walter M, Rother KI. Hormonal responses to non-nutritive sweeteners in water and diet soda. Nutr Metab (Lond) 2016;13:71. doi: 10.1186/s12986-016-0129-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Hannon TS, Janosky J, Arslanian SA. Longitudinal study of physiologic insulin resistance and metabolic changes of puberty. Pediatr Res. 2006;60(6):759–763. doi: 10.1203/01.pdr.0000246097.73031.27. [DOI] [PubMed] [Google Scholar]
- 18.Ogden CL, Carroll MD, Fryar CD, Flegal KM. Prevalence of Obesity Among Adults and Youth: United States, 2011–2014. NCHS Data Brief. 2015;(219):1–8. [PubMed] [Google Scholar]
- 19.Subar AF, Freedman LS, Tooze JA, et al. Addressing Current Criticism Regarding the Value of Self-Report Dietary Data. J Nutr. 2015;145(12):2639–2645. doi: 10.3945/jn.115.219634. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Lopes TS, Luiz RR, Hoffman DJ, et al. Misreport of energy intake assessed with food records and 24-h recalls compared with total energy expenditure estimated with DLW. Eur J Clin Nutr. 2016 doi: 10.1038/ejcn.2016.85. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.