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Influence of an intervention targeting a reduction in sugary beverage intake on the δ13C sugar intake biomarker in a predominantly obese, health-disparate sample

Published online by Cambridge University Press:  14 June 2016

Brenda M Davy
Affiliation:
Department of Human Nutrition, Foods and Exercise, Virginia Polytechnic Institute and State University, 295 West Campus Drive, 221 Wallace Hall (0430), Blacksburg, VA 24061, USA
A Hope Jahren
Affiliation:
School of Earth and Ocean Science and Technology, University of Hawai’i at Manoa, HI, USA
Valisa E Hedrick
Affiliation:
Department of Human Nutrition, Foods and Exercise, Virginia Polytechnic Institute and State University, 295 West Campus Drive, 221 Wallace Hall (0430), Blacksburg, VA 24061, USA
Wen You
Affiliation:
Department of Agricultural and Applied Economics, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA
Jamie M Zoellner
Affiliation:
Department of Human Nutrition, Foods and Exercise, Virginia Polytechnic Institute and State University, 295 West Campus Drive, 221 Wallace Hall (0430), Blacksburg, VA 24061, USA
Corresponding
E-mail address:
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Abstract

Objective

Controversy exists surrounding the health effects of added sugar (AS) and sugar-sweetened beverage (SSB) intakes, primarily due to a reliance on self-reported dietary intake. The purpose of the current investigation was to determine if a 6-month intervention targeting reduced SSB intake would impact δ13C AS intake biomarker values.

Design

A randomized controlled intervention trial. At baseline and at 6 months, participants underwent assessments of anthropometrics and dietary intake. Fasting fingerstick blood samples were obtained and analysed for δ13C value using natural abundance stable isotope MS. Statistical analysis included descriptive statistics, correlational analyses and multilevel mixed-effects linear regression analysis using an intention-to-treat approach.

Setting

Rural Southwest Virginia, USA.

Subjects

Adults aged ≥18 years who consumed ≥200 kcal SSB/d (≥837 kJ/d) were randomly assigned to either the intervention (n 155) or a matched-contact group (n 146). Participants (mean age 42·1 (sd 13·4) years) were primarily female and overweight (21·5 %) or obese (57·0 %).

Results

A significant group by time difference in δ13C value was detected (P<0·001), with mean (sd) δ13C value decreasing in the intervention group (pre: −18·92 (0·65) ‰, post: −18·97 (0·65) ‰) and no change in the comparison group (pre: −18·94 (0·72) ‰, post: −18·92 (0·73) ‰). Significant group differences in weight and BMI change were also detected. Changes in biomarker δ13C values were consistent with changes in self-reported AS and SSB intakes.

Conclusions

The δ13C sugar intake biomarker assessed using fingerstick blood samples shows promise as an objective indicator of AS and SSB intakes which could be feasibly included in community-based research trials.

Type
Short Communication
Copyright
Copyright © The Authors 2016 

In spite of a large body of evidence linking sugar-sweetened beverage (SSB) intake with adverse health outcomes, including increased risk of type 2 diabetes( 1 , Reference Basu, McKee and Galea 2 ), weight gain( 1 , Reference Malik, Pan and Willett 3 ) and obesity( 1 , Reference Basu, McKee and Galea 2 ), significant controversy exists surrounding the health effects of added sugar (AS) and SSB( 1 , Reference Bray and Popkin 4 , Reference Kahn and Sievenpiper 5 ) due primarily to a reliance on self-reported dietary intake methodologies( Reference Kahn and Sievenpiper 5 Reference Bingham, Luben and Welch 7 ). This controversy has recently extended into the health and economic policy arena (e.g. references Reference Archer, Pavela and Lavie8Reference Subar, Freedman and Tooze10). Public health guidelines have recommended that consumers limit AS intake and replace SSB with water( 1 ), yet critics of these guidelines cite the use of research utilizing memory-based dietary recall methods as a fatal flaw, with significant public health consequences( Reference Archer, Pavela and Lavie 8 ). The availability of an objective indicator of dietary intake, such as an AS intake biomarker, could overcome this research limitation( Reference Subar, Freedman and Tooze 10 ).

Because corn and cane plants employ the rare C4 photosynthetic pathway, their sugars naturally contain a high concentration of 13C (the heavy stable isotope of carbon) relative to 12C( Reference Jahren, Bostic and Davy 11 ). After digested food is absorbed from the intestine into the bloodstream, the carbon within food becomes carbon within tissues. Thus, a high 13C:12C (measured as ‘δ13C’) in human blood may reflect a high 13C:12C in the diet, with corn and cane sugars as important contributors( Reference Jahren, Bostic and Davy 11 ). Because approximately half of AS is consumed in the form of SSB( Reference Huth, Fulgoni and Keast 12 ) and the majority of the AS consumed is ultimately derived from corn and cane plants( Reference Jahren, Bostic and Davy 11 ), the δ13C value of human blood may be affected by SSB intake( Reference Jahren, Bostic and Davy 11 , Reference Hedrick, Davy and Wilburn 13 , Reference Davy, Jahren and Hedrick 14 ).

Talking Health is a randomized controlled trial which aimed to reduce SSB intake among residents of rural, health-disparate communities( Reference Zoellner, Chen and Davy 15 ). Participants were randomly assigned to either a 6-month intervention targeting a reduction in SSB intake (SIPsmartER) or a matched-contact comparison group (MoveMore). This trial represents the first randomized controlled trial directly targeting SSB intake reduction to include assessments of the δ13C biomarker, using fingerstick blood samples( Reference Hedrick, Davy and Wilburn 13 ). Our objective was to determine if group differences in biomarker δ13C values were evident over the 6-month intervention period.

Materials and methods

Detailed design and methods for the Talking Health trial have been reported previously( Reference Zoellner, Chen and Davy 15 ). Briefly, eligible individuals (English-speaking adults ≥18 years of age, who consumed ≥200 kcal SSB/d (≥837 kJ/d) and self-reported no contraindications for physical activity) were recruited and enrolled from eight counties/cohorts in Southwest Virginia, USA, each spaced approximately 3 months apart. Within each cohort, participants were randomly assigned to SIPsmartER (n 155) or MoveMore (n 146). For randomization, equal numbers of envelopes were prepared containing the name of each study condition; participants selected a sealed envelope to determine their assigned condition. SIPsmartER targeted decreasing SSB consumption, with the primary goal of ≤8 fluid ounces (≤237 ml) per day. MoveMore targeted physical activity promotion and did not contain content related to SSB or other dietary factors. Conditions were matched in terms of contact (i.e. three small-group classes, one live teach-back call, eleven interactive voice response calls) and structure( Reference Zoellner, Chen and Davy 15 ). Participants were compensated with a gift card for completing assessments (baseline, $US 25; month 6, $US 50).

At baseline and at 6 months, participants underwent assessments of height and weight, measured in light clothing without shoes (scale model 310GS; Tanita, Tokyo, Japan). AS sugar and SSB intakes were assessed using three 24 h recalls, obtained using the five-step multiple pass method( Reference Moshfegh, Rhodes and Baer 16 ). Recalls were collected within a two-week baseline testing period. Recalls were analysed using nutritional analysis software (Nutrition Data System for Research (NDS-R 2011), University of Minnesota, Minneapolis, MN, USA). The NDS-R database primarily utilizes the US Department of Agriculture’s Nutrient Data Laboratory for its food composition data, and is supplemented by information from food manufacturers and data available in the scientific literature. Imputation procedures are applied to minimize missing values. The database is 100 % complete for AS( 17 ).

Fasting blood samples were obtained via routine fingersticks (One Touch Fine Point Lancet; Johnson & Johnson Company, New Brunswick, NJ, USA). Blood samples were blotted on to sterilized binder-free glass microfibre filters (Whatman, type GF/D, 2·5 cm diameter; Whatman, Inc., Piscataway, NJ, USA), air-dried and then analysed for δ13C value using natural abundance stable isotope MS, as per our previous work( Reference Hedrick, Davy and Wilburn 13 , Reference Davy, Jahren and Hedrick 14 ). Samples were analysed in triplicate; the analytical error associated with each measurement in the current investigation was 0·039 ‰. Stable isotope values are reported using standard δ-notation in units of ‘per mil’ (‰) relative to international standards (Vienna Pee Dee Belemnite (VPDB)). The unit ‘per mil’ is standard within stable isotope reporting and refers to the number of units out of 1000; similar to how ‘per cent’ refers to the number of units out of 100. Human blood samples have lower δ13C values than the VPDB standard, therefore the δ13C values presented here are less than 0. A δ13C value of human blood that is closer to 0, representing a higher 13C:12C in the diet, corresponds to a higher AS or SSB intake( Reference Jahren, Bostic and Davy 11 , Reference Davy, Jahren and Hedrick 14 , Reference Jahren, Saudek and Yeung 18 ). Alanine was used as an internal laboratory standard for carbon. A more detailed description of this biomarker technique, which includes the δ13C values of several dietary sources of AS and SSB, as well as foods with ‘low’ δ13C values which may contribute to total sugar intake (e.g. fruit), has been previously published( Reference Jahren, Saudek and Yeung 18 ).

Five women who were pregnant were excluded from the present analysis (n 4, SIPsmartER; n 1, MoveMore). Descriptive statistics were used to summarize baseline demographic characteristics. The t test was used to compare group means; the χ 2 test was used to compare proportions across groups. Multilevel mixed-effects linear regression analyses were performed using the statistical software package Stata version 13 (2013) to account for clustering of individuals within cohorts. Results of intention-to-treat are presented. The models included controls for the following baseline covariates: age, gender, race/ethnicity, income, education level, health literacy level, employment status, number of children, smoking status and BMI. Correlational analyses were also performed.

Results

Demographic characteristics of participants (n 296) are presented in Table 1. Participants (mean age 42·1 (sd 13·4) years) were primarily female and Caucasian, and almost half of the sample (43 %) reported an annual household income of ≤$US 14 999. Most participants were overweight (21·5 %) or obese (57·0 %). There were no significant demographic differences between groups. At month 6, the overall retention rate was 74 %, which was not statistically different between conditions. Group differences over time (both P<0·05) were noted in BMI and weight, which decreased in SIPsmartER (−0·21 (95 % CI −0·35, −0·06) kg/m2, −0·5 (95 % CI −0·9, 0·0) kg) but did not change in MoveMore (0·10 (95 % CI −0·09, 0·30) kg/m2, 1·0 (95 % CI −0·2, 0·4) kg).

Table 1 Baseline participant characteristics in the full sample, and in the SIPsmartER intervention and MoveMore matched-contact comparison groups, rural Southwest Virginia, USA

The t test was used to compare group means; the χ 2 test was used to compare proportions across groups.

Changes in biomarker δ13C values and self-reported dietary intake over the 6-month intervention period are presented in Table 2. A significant group by time difference in δ13C value was detected, with mean δ13C value decreasing (i.e. reflecting a reduction in AS and SSB intakes) in the intervention group (Table 2). Group changes in biomarker δ13C values over time were largely consistent with changes in self-reported AS and SSB intakes. Total sugar intake declined in the intervention group and there was a significant group difference over time in total sugar intake. Significant correlations were noted between self-reported SSB intake and δ13C values at baseline (r=0·259, P<0·001) and at month 6 (r=0·280, P<0·001). Changes in self-reported SSB intake and δ13C values were not significantly associated (r=0·066, P=0·261), nor were changes in self-reported AS intake and δ13C values (r=0·041, P=0·487). The correlation between changes in percentage of total energy from AS and δ13C value was not statistically significant (r=0·101, P=0·084). As expected, changes in total sugar intake and δ13C values were not significantly associated (total sugar, percentage of total energy: r=0·099; total sugar, grams: r=0·040; both P≥0·05).

Table 2 δ13C sugar intake biomarker values and self-reported dietary intake (n 296) at baseline and after the 6-month intervention in the SIPsmartER intervention and MoveMore matched-contact comparison groups, rural Southwest Virginia, USA

SSB, sugar-sweetened beverages; AS, added sugar; TS, total sugar.

Means and standard deviations are not adjusted for covariates.

Within-group statistical significance indicated by asterisks: *P<0·05, ** P<0·01, *** P<0·001.

§ Models control for baseline covariates including age, gender, race/ethnicity, income, education level, health literacy level, employment status, number of children, smoking status and BMI.

|| 1 kcal=4·184 kJ.

1 fluid ounce=29·57 ml.

To determine if weight loss impacted our findings related to changes in biomarker values, we developed a separate model to predict change in δ13C value for each group and included changes in weight, AS and SSB intakes as predictors. The model for the MoveMore comparison group was not significant (P=0·4695). The model for the SIPsmartER group was significant (P=0·008) and the only significant predictor of change in δ13C value was change in SSB kcal (P=0·019). This suggests that weight loss does not impact change in δ13C values when holding SSB and AS intake changes at the same level.

Discussion

The Institute of Medicine and others have highlighted the need for novel methods to objectively assess dietary intake, including biomarkers of food and nutrient intakes( Reference Subar, Freedman and Tooze 10 , 19 ). Common limitations of existing biomarker techniques include cost, participant burden and degree of invasiveness( Reference Thompson, Subar and Loria 20 ). The current investigation describes results from the first randomized controlled trial evaluating an SSB intake reduction intervention to include assessment of the δ13C sugar intake biomarker with particular application to SSB. Group differences in biomarker δ13C values and in self-reported AS and SSB intakes were detected over the intervention period, and in weight and BMI. Importantly, this biomarker was evaluated using minimally invasive fingerstick blood samples obtained in a field research setting. These findings suggest that the δ13C value of fingerstick blood shows promise as a biomarker of AS – and by extension – SSB intake, which could be feasibly included in large-scale, community-based research trials.

Mean δ13C value in this sample was higher than that reported in a university community( Reference Davy, Jahren and Hedrick 14 ), which is expected given the high SSB consumption reported by participants (~400–500 kcal/d; 1674–2092 kJ/d). Although the significant correlations between biomarker values and self-reported SSB intake at baseline and at month 6 were considered modest (i.e. r~0·3)( Reference Dancey and Reidy 21 ), these correlations may underestimate biomarker validity due to dietary intake under-reporting( Reference Willett and Lenart 22 ), which is a particular problem when reporting intake of socially undesirable foods such as SSB( Reference Subar, Freedman and Tooze 10 , Reference Johnson, Yon and Hankin 23 ).

Strengths of the current investigation include a large sample size, the low degree of invasiveness for sample collection and the investigation of changes in biomarker values in response to an intervention targeting a reduction in SSB consumption. Limitations include the use of self-reported AS and SSB intakes as the method for biomarker comparison and the inability of the δ13C biomarker to detect some forms of AS such as beet sugar, honey and maple syrups. These represent minor contributors of AS to the US diet (i.e. 22 %) compared with corn- and cane-derived sweeteners (i.e. 78 %)( Reference Jahren, Bostic and Davy 11 ); however, we did not assess these forms of AS in this sample.

The availability of an objective indicator of AS and SSB intake could overcome a commonly cited methodological limitation of research investigating the health effects of AS and SSB consumption( Reference Kahn and Sievenpiper 5 Reference Bingham, Luben and Welch 7 , Reference Subar, Freedman and Tooze 10 ). Additional research is warranted to investigate the validity of the δ13C sugar intake biomarker in children and adolescents, who consume high amounts of AS, and in controlled feeding trials.

Acknowledgements

Financial support: This work was supported by the National Institutes of Heath (Principal Investigator J.M.Z., grant number 1R01CA154364). The National Institutes of Heath had no role in the design, analysis or writing of this article. Conflict of interest: The authors declare no conflicts of interest. Authorship: All authors formulated the research question and contributed to the study design. V.E.H. and J.M.Z. collected the data. A.H.J. performed the laboratory analyses. B.M.D., W.Y. and J.M.Z. conducted the data analysis. All authors contributed to manuscript drafting/revisions. Ethics of human subject participation: This study was conducted according to the guidelines laid down in the Declaration of Helsinki and all procedures involving human subjects were approved by the Virginia Tech Institutional Review Board. Written informed consent was obtained from all subjects.

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Figure 0

Table 1 Baseline participant characteristics in the full sample, and in the SIPsmartER intervention and MoveMore matched-contact comparison groups, rural Southwest Virginia, USA

Figure 1

Table 2 δ13C sugar intake biomarker values and self-reported dietary intake (n 296) at baseline and after the 6-month intervention in the SIPsmartER intervention and MoveMore matched-contact comparison groups, rural Southwest Virginia, USA

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