
==== Front
Adv Nutr
Adv Nutr
Advances in Nutrition
2161-8313
2156-5376
American Society for Nutrition

S2161-8313(24)00115-7
10.1016/j.advnut.2024.100281
100281
Review
The Carbon Isotope Ratio as an Objective Biomarker of Added Sugar Intake: A Scoping Review of Current Evidence in Human Nutrition
Tripicchio Gina L gina.tripicchio@temple.edu
12⁎
Smethers Alissa D 2
Johnson Jessica J 3
Olenginski Jordan A 14
O’Brien Diane M 3
Fisher Jennifer Orlet 12
Robinson Vitalina A 5
Nash Sarah H 6
1 Center for Obesity Research and Education, Temple University, PA, United States
2 Department of Social and Behavioral Sciences, Temple University, PA, United States
3 Institute of Arctic Biology, Department of Biology and Wildlife, University of Alaska Fairbanks, Fairbanks, AK, United States
4 Drexel University College of Medicine, PA, United States
5 Social Sciences and Clark Library, University of Michigan, Ann Arbor, MI, United States
6 Department of Epidemiology, University of Iowa, IA, United States
⁎ Corresponding author. gina.tripicchio@temple.edu
31 7 2024
9 2024
31 7 2024
15 9 1002816 3 2024
5 6 2024
29 7 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Objective biomarkers of dietary intake are needed to advance nutrition research. The carbon isotope ratio (C13/C12; CIR) holds promise as an objective biomarker of added sugar (AS) and sugar-sweetened beverage (SSB) intake. This systematic scoping review presents the current evidence on CIRs from human studies. Search results (through April 12, 2024) yielded 6297 studies and 24 final articles. Studies were observational (n = 12), controlled feeding (n = 10), or dietary interventions (n = 2). CIRs were sampled from blood (n = 23), hair (n = 5), breath (n = 2), and/or adipose tissue (n = 1). Most (n = 17) conducted whole tissue (that is, bulk) analysis, 8 used compound specific isotope analysis (CSIA), and/or 2 studies used methods appropriate for analyzing breath. Studies were conducted in 3 concentrated geographic regions of the United States (n = 7 Virginia; n = 5 Arizona; n = 4 Alaska), with only 2 studies conducted in other countries. Studies that used CSIA to examine the CIR from the amino acid alanine (CIR-Ala; n = 4) and CIR analyzed from breath (n = 2) provided the most robust evidence for CIR as an objective biomarker of AS and SSBs (R2 range 0.36–0.91). Studies using bulk analysis of hair or blood showed positive, but modest and more variable associations with AS and SSBs (R2 range 0.05–0.48). Few studies showed no association, particularly in non-United States populations and those with low AS and SSB intakes. Two studies provided evidence for CIR to detect changes in SSB intake in response to dietary interventions. Overall, the most compelling evidence supports CIR-Ala as an objective indicator of AS intake and breath CIR as an indicator of short-term AS intake. Considering how to adjust for underlying dietary patterns remains an important area of future work and emerging methods using breath and CSIA warrant additional investigation. More evidence is needed to refine the utility and specificity of CIRs to measure AS and SSB intake.

Keywords

added sugar
biomarker
stable isotopes
sugar-sweetened beverages
dietary assessment
Abbreviations

AA amino acid

AS added sugar

BEV-Q Beverage Intake Questionnaire

CI confidence interval

CIR carbon isotope ratio

CSIA compound specific isotope analysis

EAA essential amino acid

FFQ food-frequency questionnaire

HFCS high-fructose corn syrup

IQR interquartile range

kcal calories

NEAA nonessential amino acid

NLM National Libraries of Medicine

NIR nitrogen isotope ratio

NPAAS-FS Nutrition and Physical Activity Assessment Study Feeding Study

PRISMA-S Preferred Reporting Items for Systematic Reviews-Scoping Reviews

RBC red blood cell

RCT randomized controlled trial

SIR stable isotope ratio

SSB sugar-sweetened beverage

SoFAAS solid fats, alcohol, and added sugar

YAQ youth and adolescent FFQ
==== Body
pmc Statement of Significance

This is the first systematically conducted scoping review to evaluate current evidence for the carbon isotope ratio as an objective biomarker of added sugar and sugar-sweetened beverage intake in human nutrition research.

Introduction

Dietary intake is a key driver of chronic disease risk, making accurate assessment of dietary intake critically important to public health [1]. Bias and error in dietary self-report have led to much discourse and debate on diet–disease relationships, especially for nutrients of concern liked added sugar (AS) [[2], [3], [4], [5]]. The most widely used dietary assessment methods for human nutrition studies are 24-h dietary recalls and food-frequency questionnaires (FFQs), but limitations associated with these approaches are well documented [[6], [7], [8]]. In addition to concerns around accuracy of measurement, self-report methods can be costly, burdensome, and ill-suited for some populations, such as children, and those with lower literacy levels [6,7]. Thus, there is a pressing need to explore objective methodologies that can capture key aspects of dietary intake and support large-scale studies of dietary intake and chronic disease risk [[9], [10], [11]]. Such methods are particularly salient in the context of precision nutrition and personalized medicine that require nuanced capture of individual predictors of disease risk [12]. The stable carbon isotope ratio (13C/12C; hereafter: CIR) has emerged as a potential objective biomarker of AS and sugar-sweetened beverage (SSB) intake, presenting a novel avenue for addressing current limitations in dietary assessment [[13], [14], [15], [16]].

Naturally occurring variations in stable isotope ratios (SIRs) hold tremendous potential for enhancing the rigor and validity of human nutrition studies examining relationships between diet and disease [15]. Historically, SIRs have been widely used in fields such as ecology and archeology to assess animal and early human diets but remain underutilized as biomarkers in current nutrition research [17]. To date, several publications have provided informative overviews of the science of SIRs and their exciting potential in human nutrition [[13], [14], [15], [16],18]. All the common elements in human tissues (carbon, nitrogen, sulfur, hydrogen, and oxygen) exist as 1 or more stable isotopes, and naturally occurring variations in the ratio of heavy to light isotopes can be measured in human tissues, such as breath, hair, and blood. The SIRs of foods and beverages are incorporated into tissues throughout the body, so tissue SIRs reflect the food we eat (that is, providing a unique signature of dietary intake) and the time it takes to integrate into the tissue. The isotopic composition of a sample remains stable for long periods of time (that is, does not decay or change), meaning that SIRs can be measured in previously collected and stored biospecimens.

The CIR is a promising biomarker of dietary sugars because of the signature carbon metabolism pathways that occur in the food supply and in humans [19,20]. CIRs of plants vary on the basis of the underlying photosynthetic processes used to convert carbon dioxide into the precursor of plant organic molecules. The CIR is higher in plants like corn and sugarcane compared with most other types of fruits and vegetables, creating a unique CIR fingerprint for diets high in corn and cane sugar. CIRs are measured and reported as “delta values” (δ13C values), which describe the abundance of the heavy isotope relative to an international standard, as parts per mil (‰). Because plant and animal samples contain less 13C than the international standard, δ13C values representing dietary intake in humans are always negative [19]. Fruits and vegetables have average δ13C values of approximately −34 to −22‰, whereas corn and cane syrup have higher (that is, less negative) average δ13C values of −14 to −10‰ [15,19]. Given that high-fructose corn syrup (HFCS) and cane sugar constitute >70% of AS sources and almost all of the SSBs sold in the United States, a dietary pattern higher in AS and SSB has a higher CIR compared with a dietary pattern lower in AS and SSB [15,21,22]. However, it is also important to note that because carbon is transferred through the food web from plant to consumer, animal-based foods derived from livestock that are primarily corn-fed (that is, most United States livestock) also have elevated CIRs. Further, fish have elevated CIRs, with δ13C values up to about −17‰, because the source of carbon for aquatic photosynthesis is primarily bicarbonate, which also has an elevated CIR [23]. These factors warrant consideration and potentially impact the specificity of the CIR biomarker for assessing sugar intake.

Another appealing feature of the CIR is that it can be analyzed from multiple sample types (for example, hair, blood, breath), which reflect varying time frames of dietary intake on the basis of the rate of growth or turnover of the tissue or material sampled [19,20]. For example, breath CIR has been suggested as a short-term measure of intake (that is, hours) [24]. However, most tissue CIRs reflect longer-term intake on the basis of the rates of tissue growth or turnover. One study estimated ∼8 wk for blood plasma CIR turnover, and 19 wk for red blood cell (RBC) turnover [25]. These rates were longer than expected on the basis of tissue growth or replacement, likely because of carbon and nitrogen being recycled from other body tissues. In contrast, hair captures intake over time, with a growth rate of ∼1 cm/mo [15]. One can also analyze the CIR of different tissue sample components: whole tissue analysis (also known as bulk analysis) uses the entire sample to derive CIR values, whereas compound specific isotope analysis (CSIA) measures the CIR of specific molecules within the tissue [for example, amino acids (AAs) or fatty acids] and requires additional steps (that is, hydrolyzing and derivatizing AAs or fatty acids) before measuring the CIR [16,26]. CSIA has emerged as an approach to improve the specificity of the CIR because bulk analysis can include challenges related to confounding of other dietary components with similar CIR levels (that is, meat and fish, as noted above). For example, carbon in specific nonessential amino acids (NEAAs), like alanine, which are produced by the glucose-alanine cycle can be derived from sugar (as well as other sources), whereas essential amino acids (EAAs) must be derived exclusively from protein intake [27]. Therefore, examining CIR in AAs to determine specificity for AS and SSB intake is an important area of study. Moreover, newer studies have also begun to explore the utility of CIR captured in breath carbon dioxide, which can reflect AS and SSB intake given the proximal relationship between breath carbon dioxide and glucose metabolism [20]. The breath CIR has been used historically to examine dietary variation but remains understudied as a biomarker of AS and SSB intake [28,29].

Given the pressing public health concerns about AS and SSB consumption, and recent literature providing novel evidence for CIR as a biomarker of AS and SSB intake, there is a need for a comprehensive review that examines existing findings to advance our understanding of CIR biomarkers. Thus, the purpose of this systematically conducted scoping review is to summarize and synthesize the existing literature on the use of CIRs to objectively measure AS and SSB intake, identify outstanding gaps and key considerations, and describe future directions to enhance the application and utility of CIRs in nutrition research.

Methods

Search methods for identification of studies

To systematically identify studies for inclusion in this scoping review, the team worked with a research librarian (VAR) to develop a detailed search strategy for PubMed [National Libraries of Medicine (NLM)] that was subsequently adapted for other databases: Embase (Elsevier), Cochrane Central (Wiley), Web of Science (Clarivate Analytics), CINAHL (EBSCOhost), APA PsycInfo (EBSCOhost), and SPORTDiscus with Full Text (EBSCOhost) using a combination of keywords and subject headings (Supplemental File 1). The PubMed (NLM) search strategy was reviewed by the research team to check for accuracy and term relevancy, and all final searches were peer-reviewed by a second research librarian (TN) following the Peer Review of Electronic Search Strategies checklist [30]. To assist in mitigating publication bias, a gray literature search included 2 clinical trials registries, clinicaltrials.gov and the WHO International Clinical Trials Registry Platform (https://trialsearch.who.int/). The search was limited to human studies. All final searches were performed on July 20, 2022, by the research librarian and were fully reported (VAR). Updates to the search were performed by research librarians on March 29, 2023 (JP) and again on April 12, 2024 (TN). The full search strategies, as reported by the librarians, are provided in Supplemental Files 1–3, respectively. They are also archived in TUScholarShare (https://scholarshare.temple.edu/handle/20.500.12613/8172). The search followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews (PRISMA-ScR) [31,32].

The initial search resulted in 5335 studies from databases and 59 from registers. A total of 1007 duplicate studies were found and omitted by the librarian (VAR) using the EndNote 20 duplicate identification strategy. This resulted in 4387 records to screen from databases and registers and 0 records to screen from other methods, resulting in a total of 4387 records. These results are located in Supplemental File 1. A search update was performed in March 2023 by librarian (JP) and resulted in 388 studies from databases and 2 from registers. A total of 84 duplicate studies were found and omitted by the librarian using the EndNote 20. This resulted in 306 records to screen from databases and registers and 0 records to screen from other methods, resulting in a total of 306 records. These results are located in Supplemental File 2. A second search update was performed in April 2024 by librarian (TN) and resulted in 513 studies from databases and 0 from registers. A total of 114 duplicate studies were found and omitted by the librarian using the EndNote 20 duplicate identification strategy. This resulted in 399 records to screen from databases and registers. These results are located in Supplemental File 3. For clarity, the results of the initial and updated searches are combined and described below (Figure 1).FIGURE 1 PRISMA flow diagram for the systematically conducted scoping literature review search of databases and registers for studies on the CIR as a biomarker of AS and SSB intake. AS, added sugar; CIR, carbon isotope ratio; PRISMA, Preferred Reporting Items for Systematic Reviews; SSB; sugar-sweetened beverage.

FIGURE 1

Selection criteria

Following PRISMA-ScR guidelines, relevant articles that met the following criteria were included in the final review: 1) included a CIR measure sampled from modern humans (that is, nonarchaeological samples); 2) included a defined dietary outcome of AS and/or SSB intake; 3) reported a comparison between CIR and AS and/or SSB intake; and 4) were available in English. There were no constraints on publication year, study location, experimental design, or participant age. Eligible dietary outcomes for AS included categories defined as “Added Sugar” in calories (kcal) and/or grams (g) as well as categories that could clearly be categorized as AS (for example, HFCS, cane sugar). SSB categories, reported in measurable amounts (for example, kcal, g, servings) that were labeled to indicate “SSB” or similarly exclusive consumption of drinks with AS were considered eligible such as “soda,” “sugary drinks,” “sweetened beverages.” Dietary outcomes with labels that could not be clearly interpreted as exclusively AS or SSB (for example, refined carbohydrates, sweets, and market foods) were excluded. Review articles, conference abstracts, and commentaries were also excluded. Final full texts needed to be available in English (even if the primary publication was in another language) to ensure accurate screening by reviewers.

Data review and extraction

Guided by the PRISMA-ScR checklist, a data review and extraction protocol were developed [31,32]. For all searches, study titles and abstracts were initially screened by 2 independent reviewers (GLT, SHN). If a tiebreaker was needed, a third reviewer was called in to adjudicate (ADS). Full reports were screened using the same protocol as the initial screening (initial reviewers: GLT, SHN; tiebreak: ADS). Screening of titles and abstracts resulted in 59 articles in the initial search, 7 in the first updated search, and 9 in the second updated search that were included in the full report review. Full reports were sought for these 75 studies, and 15 were excluded because they were abstracts only or clinicaltrial.gov entries. A total of 60 full text articles were then reviewed for final inclusion. Of those, 17 studies were excluded because they were not a primary publication (for example, review articles, editorials), another 17 studies were excluded because they did not include a clearly defined AS or SSB outcome, or they did not report an association between CIRs and AS and/or SSB, and 2 articles were excluded because they appeared as duplicates in the updated searches but were already included in the initial search. Therefore, a total of 24 articles were included in the final review.

A data charting table was developed by the lead authors (GLT, SHN) to determine which key variables to extract. The table aimed to capture the main study features and included participant characteristics (for example, study location, sample size), study design (for example, observational, controlled feeding), dietary assessment metrics (for example, dietary recalls, AS/SSB measure), biomarker information (for example, CIR sampling method), and study outcomes (for example, CIR associations with AS/SSB). Each article was independently charted by 1 member of the review team using a standardized spreadsheet (GLT, ADS, JJJ, SHN). Results were discussed and the data chart was revised and refined to enhance clarity and interpretation of key study features using an iterative process across several meetings with all reviewers. Once finalized all extraction data was cross checked by a second reviewer (JAO). Given the scoping nature of this review, a risk of bias assessment was not conducted, per PRISMA-ScR guidelines [32,33].The final extracted variables are presented in TABLE 1, TABLE 2 [[24], [25], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48], [49], [50], [51], [52], [53], [54], [55]].TABLE 1 Descriptive characteristics of studies included in the scoping review.

TABLE 1Authors	Year	Study location	Study type	Sample size (n)	Sex (% female)	Age (M ± SD)	Race and ethnicity	Diet assessment method	Dietary exposure duration (experimental studies only)	AS outcome	SSB outcome	
Choy et al. [34]	2013	United States (Alaska)	Observational	68 [subset, = 30 hair]	49% [subset = 70%]	41 ± 18 y [subset = 37 ± 17 y]	Alaska Native (Yup’ik): 100%	Four 24HR	NA	AS (g/d)	SSB (servings/d; 237 ml (8 fl oz)/serving)	
Cook et al. [35]	2010	United States (Wisconsin)	Controlled feeding	5	40%	22 ± 2 y	NR	Experimental Diet (5, 16, 32% of total CHO/d) designed by RD	3 × 7 d diets with 2-wk washout period	Total daily CHO from C4 sugars – cane sugar and HFCS	No	
Davy et al. [36]	2011	United States (Virginia)	Observational	60	58%	43 ± 2 y	White: 90%
Asian: 6%
AA/Black: 2%
Other: 2%	4-d food intake record; BEV-Q	NA	AS (g, kcal)	SSB (g, kcal); regular soft drinks (g, kcal)	
Davy et al. [37]	2017	United States (Virginia)	Behavioral intervention	296	81%	42.1 ± 13.4 y	White: 93%
AA/Black: 4%
>1 race: 2.5%
Other: 0.5%
Hispanic: 1%	Three 24HR	NA	AS (%total energy; g/d)	SSB (kcal/d; fl oz/d)	
Fakhouri et al. [38]	2014	United States (Maryland)	Behavioral intervention	144	66%	50.7 ± 8.5 y	Black: 56.3%	Two 24HR	NA	No	SSB (servings/d; 12 fl oz/serving)	
Hedrick et al. [39]	2015	United States (Virginia)	Observational	257	77%	42 ± 15 y	White: 91%
AA/Black: 5%
Asian: 1.5%
>1 race: 1.5%
Hispanic: 1%
Other: 1%	Three 24HR OR one 4-d food intake record; BEVQ-15	NA	AS (g/d)	SSB (kcal/d)	
Hedrick et al. [40]	2016	United States (Virginia)	Observational	216	83%	41 ± 14 y	White: 94%AA/Black: 3.5%
>1 race: 2%
Other: 0.5%	Three 24HR; BEVQ-15	NA	AS (g/d)	SSB (kcal/d)	
Henze et al. [41]	2020	United States (Colorado)	Prospective cohort	39	49%	7.4 ± 1.1 y	White: 79%	FFQ	NA	AS (g/d)	SSB (servings/d; 12 fl oz/serving)	
Johnson et al. [42]	2021	United States (Arizona)	Controlled feeding	32	0% (100% male)	46.2 ± 10.5 y	White: 59.4%
American Indian: 31.3%
Hispanic:6.3%
AA/Black: 3.1%	Experimental diet designed using food processor (version 11.0.2; ESHA Research) to maintain body weight with the presence or absence of SSB (14% daily energy); meat (19% daily energy) and fish (6% daily energy)	12 wk	No	SSB (present or absent: 14% daily energy)	
Johnson et al. [43]	2022	United States (Arizona)	Controlled feeding	99	54%	38.0 [29.5, 52.5]y1	Non-Hispanic White: 78%
Hispanic/Latino:12%
Asian 4%AA/Black: 3%
Pacific Islander: 1%
American Indian:1%
Did not identify: 1%	Diet based on habitual intake using 2 7-d food records	15 d	AS (g/d)	No	
Liu et al. [44]	2018	United States (Virginia)	Controlled feeding	32	53%	15.3 ± 1.6 y	White: 97% Other/unknown: 3%	Experimental diet (low AS: 5%, or high AS: 25% of daily kcal) based on participant estimated energy requirements	2 × 7-d diets with a 4-wk washout period	AS (low AS: 5%, or high AS: 25% of daily kcal)	No	
MacDougall et al. [45]	2018	United States (Virginia)	Observational	326	51%	12 ± 3 y	White: 93%
Asian: 2%
AA/Black: 1%
Other: 1%	Four 24HR	NA	AS (g/d; kcal/d)	SSB (mL/d; kcal/d)	
Mitchell et al. [46]	2023	United States (Arizona)	Controlled feeding	32	0% (100% male)	46.2 ± 10.5 y	White: 59%
Indigenous American: 31%
Hispanic: 6%
AA/Black: 3%	Experimental diet designed using food processor (version 11.0.2; ESHA Research) to maintain body weight with the presence or absence of SSB (14% daily energy); meat (19% daily energy) and fish (6% daily energy)	12wk	No	SSB (present or absent: 14% daily energy)	
Nash et al. [47]	2013	United States (Alaska)	Observational	68	50%	40 ± 18 y2	Alaska Native (Yup’ik): 100%	Four 24HR	NA	AS (g/d)	SSB (servings/d)	
Nash et al. [48]	2014	United States (Alaska)	Observational	52	58%	40 ± 17 y2	Alaska Native (Yup’ik): 100%	Four 24HR	NA	AS (g/d)	SSB (servings/d)	
O'Brien et al. [24]	2021	United States (Alaska)	Controlled feeding	12	50%	30[[25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48], [49], [50], [51], [52], [53], [54], [55], [56], [57], [58], [59], [60]]y3	White: 92%
Alaska Native (Iñupiaq): 8%	Experimental diet (low AS (0 g/d), medium AS (75 g/d) and high AS (150 g/d)) designed by RD to meet daily energy requirements	Five 1-d dietary treatments with 1–2-wk washout periods	AS (low AS (0 g/d), medium AS (75 g/d) and high AS (150 g/d))	No	
O’Brien et al. [49]	2022	United States (Arizona)	Controlled feeding	100	55%	39.0 [29.5, 53.0]y1	White: 78%
Hispanic 12%
Asian: 4%
AA/Black: 3%	Diet based on habitual intake using 2 7-d food records	15 d	AS (g/d)	No	
Patel et al. [50]	2014	UK	Case-cohort	718	59%	57.7 ± 9.3 y	NR	FFQ	NA	No	SSBs (juice and fizzy drinks)	
Te Morenga et al. [51]	2021	New Zealand	Observational	36	28%	60.7 ± 16.7 y	Māori: 89%	FFQ	NA	AS (g/d)	SSBs (g/d)	
Valenzuela et al. [52]	2018	United States (Utah)	Observational	212	–	9–16y4	White: 54%
Hispanic: 46%	FFQ	NA	No	Sweetened beverages (servings per wk/kcal)	
Votruba et al. [25]	2019	United States (Arizona)	Controlled feeding	32	0% (100% male)	46.2 ± 10.5 y	White: 59.4%
American Indian: 31.3%
Hispanic: 6.3%
AA/Black: 3.1%	Experimental diet designed using food processor (version 11.0.2; ESHA Research) to maintain body weight with the presence or absence of SSB (14% daily energy); meat (19% daily energy) and fish (6% daily energy)	12 wk	No	SSB (present or absent: 14% daily energy)	
Yeung et al. [53]	2010	United States (Maryland)	Observational	186	47%	71 ± 5 y	White: 100%	FFQ	NA	No	Sweetened beverages (servings/d)	
Yun et al. [54]	2018	United States (Washington)	Controlled feeding	147	100%	75 ± 4 y	White: 95.2%
Non-White: 4.8%	Diet based on habitual intake using 4-d food record	2wk	AS (g/d)	SSB (kcal/d)	
Yun et al. [55]	2020	United States (Washington)	Controlled feeding	145	100%	75 [73, 78] y1	White: 95%
Non-White/ unknown: 5%	Diet based on habitual intake using 4d food record	2wk	AS (g/d)	SSB (g/d)	
Abbreviations: AA/Black, African-American; AS, added sugar; BEV-Q, beverage intake questionnaire; CHO, carbohydrate; FFQ, food-frequency questionnaire; HFCS, high-fructose corn syrup; NA, not applicable.; NR, not reported; RD, registered dietitian; SSB, sugar-sweetened beverage; UK, United Kingdom; United States, 24HR, 24-h dietary recall.

1 Age reported as Median [25th, 75th percentile].

2 Age range included children and adults 14–79 y.

3 Age reported as median age = 30 y [range 25–60 y].

4 Only age range is reported.

TABLE 2 Summary of CIR sampling and analysis methods with results of associations between CIR and AS/SSB intake

TABLE 2Authors	Year	CIR sampling method	CIR analysis method	AS association with CIR	SSB association with CIR	
Choy et al. [34]	2013	Hair (if length >2 cm), RBC (blood draw)	Compound specific/GC-C-IRMS (AA)	Positive (+), CIR-Ala only	Positive (+), CIR-Ala and CIR-Pro only	
Cook et al. [35]	2010	Plasma glucose (blood draw)	Compound Specific/GC-IRMS (plasma glucose)	Positive (+)	—	
Davy et al. [36]	2011	Whole blood (fingerstick)	Bulk analysis/EA-IRMS	Positive (+)	Positive (+)	
Davy et al. [37]	2017	Whole blood (fingerstick)	Bulk analysis/EA-IRMS	—	Positive (+)	
Fakhouri et al. [38]	2014	Serum (blood draw)	Bulk analysis/EA-IRMS	—	Positive (+)	
Hedrick et al. [39]	2015	Whole blood (fingerstick)	Bulk analysis/EA-IRMS	Positive (+)	Positive (+)	
Hedrick et al. [40]	2016	Whole blood (fingerstick)	Bulk analysis/EA-IRMS	Positive (+)	Positive (+)	
Henze et al. [41]	2020	RBCs (fingerstick)	Bulk analysis/EA-IRMS	Positive (+)	No association (x)	
Johnson et al. [42]	2021	Plasma, RBC (blood draw)	Compound specific (AA)	—	Positive (+), 4 plasmas; 5 RBC NEAAs	
Johnson et al. [43]	2022	Serum (blood draw)	Compound specific/GC-C-IRMS (AA)	Positive (+), CIR-Ala only	—	
Liu et al. [44]	2018	Whole blood (fingerstick)	Bulk analysis/EA-IRMS	Positive (+)	—	
MacDougall et al. [45]	2018	Whole blood (fingerstick)	Bulk analysis/EA-IRMS	Positive (+)	Positive (+)	
Mitchell et al. [46]	2023	Plasma and RBCs (blood draw), and adipose tissue (via subcutaneous tissue biopsies)	Compound specific/GC-C-IRMS (FA)	—	Positive (+), CIR-PA only	
Nash et al. [47]	2013	RBCs (blood draw)	Bulk analysis/EA-IRMS	Positive (+)	Positive (+)	
Nash et al. [48]	2014	Hair, RBCs, plasma, plasma glucose (blood draw)	Bulk analysis/EA-IRMS + Compound Specific/ GC-IRMS (plasma glucose)	Mixed results – Positive (+) associations for hair and RBC; no association with plasma or plasma glucose	Positive (+)	
O'Brien et al. [24]	2021	Breath	Cavity ring down spectroscopy	Positive (+)	Positive (+)	
O'Brien et al. [49]	2022	Serum (blood draw)	Bulk analysis/EA-IRMS	No association (x)	—	
Patel et al. [50]	2014	Serum (blood draw)	Bulk analysis/EA-IRMS	—	No association (x)	
Te Morenga et al. [51]	2021	RBCs (blood draw)	Bulk analysis/EA-IRMS + Compound specific/GC-C-IRMS (AA)	Positive (+), CIR-Ala only	No association (x)	
Valenzuela et al. [52]	2018	Hair, breath	Bulk analysis/EA-IRMS + GC-IRMS	—	Positive (+)	
Votruba et al. [25]	2019	Hair, RBC, plasma (blood draw)	Bulk analysis/EA-IRMS	—	Positive (+)	
Yeung et al. [53]	2010	Serum (blood draw)	Bulk analysis/EA-IRMS	—	Positive (+)	
Yun et al. [54]	2018	Serum (blood draw)	Bulk analysis/EA-IRMS	No association (x)	No association (x)	
Yun et al. [55]	2020	Serum (blood draw)	Compound specific/GC-C-IRMS (AA)	Positive (+), CIR-Ala only	No association (x)	
Abbreviations: CIR, carbon isotope ratio; AS, added sugar; SSB, sugar-sweetened beverages; RBC, red blood cells; CIR-Ala, carbon isotope ratio from the amino acid alanine; EA-IRMS, elemental analyzer isotope ratio mass spectrometry; GC-C-IRMS, gas chromatography combustion-linked isotope ratio mass spectrometry; GC-IRMS, gas chromatography isotope ratio mass spectrometry.

Results

Study characteristics

Design, setting, and participants

The sample sizes, sociodemographic characteristics of the participants, and key descriptive features of the included studies are shown in Table 1. Descriptive data reflect the samples that were used for the CIR analysis (for example, if the CIR analysis was conducted on a subsample, the characteristics of the subsample are described instead of the characteristics of the full study sample). The CIR sampling methods, analysis approach, and associations with AS and SSB intake are summarized in Table 2. Studies were based on 16 unique study populations (Supplemental Table 1) and used observational (n = 12 total; 6 unique studies), controlled feeding (n = 10 total; 8 unique studies), and intervention (n = 2) study designs. Of the studies that employed controlled feeding designs (n = 10), 6 used experimentally designed diets, and 4 used diets modeled on participants’ habitual intake. Most studies were conducted in 3 concentrated geographic regions in the United States (n = 7 Virginia; n = 5 Arizona; n = 4 Alaska), and only 2 studies were conducted outside the United States. Sample sizes varied from small (5 participants in a controlled feeding study; [35]) to large (>700 participants in a cohort study [50]) and most studies included predominantly White participants [[24], [25], [36], [37], [39], [40], [41], [42], [43], [44], [45], [46], [49], [52], [53], [54], [55]]. Five studies included diverse populations including predominantly Alaska Native participants [34,47,48], Black/African-American participants [38], and Māori participants from New Zealand [51]. Three studies included male participants only [25,42,46], and 2 studies included women only [54,55]. Only 6 studies included youth participants (≤18 y) and no studies included children younger than 5 y [41,44,45,47,48,52].

Dietary outcomes and assessment methods

A total of 14 studies examined associations of CIRs with both AS and SSB intake, 7 studies examined associations with SSBs only, and 4 studies examined associations with AS only. AS outcomes were reported as daily AS (kcal and/or g), and SSB outcomes included daily intake (kcal and/or g), daily frequency reported as drinks/day, and servings per week. Other dietary variables were also captured across studies including intake of total sugar, carbohydrates, corn, fish, meat (animal protein, animal fat intake), and dairy. For the sake of clarity and alignment with the key research questions of this investigation, associations between CIR and other dietary variables are not reported. A total of 10 studies used experimental diets and 14 assessed dietary intake using self-report methods. Most studies that used self-report measures derived dietary variables from 24-h dietary recalls (n = 8) or FFQs (n = 5). Other self-report methods included a 4-d food record (n = 2) and 3 studies included an additional beverage-specific FFQ to examine SSB intake.

CIR sample types

Blood was the most common CIR sample type analyzed (n = 22; whole/fingerstick blood, serum, plasma, RBCs, plasma glucose), but hair (n = 4), breath (n = 2), and/or adipose tissue (n = 1) were also collected. Most studies analyzed CIRs using bulk analysis (that is, analysis of whole tissue such as hair, whole blood, or blood fractions, n = 17), whereas 8 studies conducted CSIA (that is, analysis of CIR in a specific molecule within a tissue; n = 5 AA; n = 2 plasma glucose, n = 1 fatty acid), and 2 studies analyzed CIRs from breath samples. One study that conducted bulk analysis of hair and blood also conducted CSIA to examine the CIR in plasma glucose [48], 1 study examined the CIR in RBCs (bulk) and in the alanine fraction of RBCs (CSIA) [51], and 1 study that included breath analysis also conducted bulk analysis of blood [52].

CIR associations with AS and SSB intake

Results of the 24 articles are described below. To aid in interpretability of findings, study results are grouped by the CIR analysis method (whole tissue/bulk analysis, CSIA, breath analysis), and summarized within the analysis method by study design (observational, controlled feeding, intervention). Positive associations are described first, followed by studies that demonstrated mixed findings, and then studies that found no associations between CIR and AS or SSB intake.

Whole tissue/bulk analysis

Of the 17 studies that included whole tissue/bulk analysis, 11 used observational study designs (cross-sectional, case-cohort, prospective cohort), 4 used controlled feeding designs, and 2 used data from randomized controlled trials (RCTs).

Observational studies

Among the 11 studies that used observational designs, 7 showed positive associations between CIRs and AS/SSB intake, 2 demonstrated mixed findings, and 2 showed no association. The earliest work demonstrating a positive association was from Yeung et al. [53] in 2010. This study used FFQs to assess SSB intake among 186 adults and found that the blood serum CIR was positively associated with SSB intake (β: 0.18, 95% confidence interval [CI]: 0.08, 0.29, P ≤ 0.001). Additionally, CIRs were higher for those with high SSB intake compared with those with low SSB intake (−19.15‰ compared with −19.47‰, P ≤ 0.001), and blood serum CIRs increased 0.20‰ for every serving increase of SSB (P ≤ 0.01). In 2011, Davy et al. [36] conducted a beverage intake questionnaire (BEV-Q) validation study that involved 60 adults attending 3 visits over 2 wk. The goal of the study was to examine fingerstick blood CIR as a measure of AS and SSB intake and determine the validity of the BEV-Q as an FFQ for assessing beverage intake. The fingerstick blood CIR was positively correlated with total AS (g, r = 0.37, P ≤ 0.05; kcal, r = 0.37, P ≤ 0.05) derived from a 4-d food record and total SSB intake (g, r = 0.28, P ≤ 0.05; kcal, r = 0.35, P ≤ 0.05) derived from the BEV-Q.

A 2013 study by Nash et al. [47] (n = 68) used the RBC CIR to predict AS and SSB intake derived from 4 24-h dietary recalls in an Alaska Native population. This study found that a dual-isotope model [additionally adjusting for the nitrogen isotope ratio (NIR), a marker of fish intake in this population] was more robust in predicting AS and SSB intake than the single-isotope (CIR-only) model (single-isotope AS: β: 0.20, 95% CI: −0.08, 0.48, R2 = 0.03 compared with dual-isotope AS: β: 0.28, 95% CI: 0.04, 0.67, P ≤ 0.05, R2 = 0.33; single-isotope SSB: β: 0.21, 95% CI: 0.00, 0.42, P ≤ 0.05, R2 = 0.05 compared with dual-isotope SSB: β: 0.26, 95% CI: 0.08, 0.44, P ≤ 0.05, R2 = 0.31). In a related 2015 study by Hedrick et al. [39], data from 2 studies were combined to compare the use of a dual-isotope model (CIR and NIR) to a single-isotope (CIR-only) model (n = 257). AS and SSB intake were derived from 3 24-h dietary recalls or a 4-d food intake record plus the BEV-Q, depending on the initial study sample. Fingerstick blood CIR was positively associated with AS (single-isotope: β: 0.28, 95% CI: 0.07, 0.31, R2 = 0.09, P ≤ 0.05) and SSB intake (single-isotope: β: 0.35, 95% CI: 0.40, 1.12, R2 = 0.21, P ≤ 0.001), and the addition of NIR did not substantially increase R2 (AS dual-isotope R2 = 0.11; SSB dual-isotope R2 = 0.22), as the NIR was not associated with meat intake in this population. Finally, a 2016 study by Hedrick et al. [40] conducted a cross-sectional analysis of baseline data from 216 adults participating in “Talking Health,” a community-based RCT to reduce SSB consumption, and found that CIR from fingerstick blood was positively associated with AS (β: 0.25, P ≤ 0.001, R2 = 0.15) derived from 3 24-h dietary recalls and SSB (β: 0.26, P ≤ 0.001, R2 = 0.14) derived from the BEV-Q.

Two cross-sectional studies conducted bulk CIR analysis of samples from children and adolescents; both observed positive associations between CIR and AS and/or SSB intake. One study by MacDougall et al. [45] in 2018 included children 6–11 y and adolescents 12–18 y and collected 2 fingerstick blood samples over 4 testing sessions during a 3-wk period. The CIR was correlated with the intake of total AS (r = 0.23. P ≤ 0.001) and SSB intake (r = 0.35, P ≤ 0.001) derived from 4 24-h dietary recalls. Notably, CIRs were lower in children than those in adolescents, and the CIR was better able to discern between high and low SSB intake compared with AS intake (AUC SSB: 0.75 compared with AS: 0.61). In 2018, Valenzuela et al. [52] also examined hair and breath samples from children and adolescents 9–16 y (n = 212) and assessed diet via a youth and adolescent FFQ (YAQ) to derive servings per week of sweetened beverages as well as servings per week relative to total caloric intake (that is, spw/kcal). Among all participants, the hair CIR was correlated with SSB intake (spw/kcal; r = 0.19, P ≤ 0.05). This study also examined breath samples, described below.

Of the observational studies using whole tissue analysis with mixed findings, 1 cross-sectional study by Nash et al. [48] in 2014 sampled CIR from blood (RBC and plasma), as well as hair from a community-based sample of Alaska Native participants aged 14–79 (n = 52), and found that SSB intake was associated with the CIR in all sample types: RBCs (β: 33.8, 95% CI: 10.1, 62.7, P ≤ 0.05), plasma (β = 20.5, 95% CI: 1.6, 42.8, P ≤ 0.05), and hair (β = 27.5, 95% CI: 7.1, 51.7, P ≤ 0.05). However, AS intake was only associated with CIR in RBC (β = 31.4, 95% CI: 6.8, 61.6, P ≤ 0.05) and hair (β: 21.1, 95% CI: 0.49, 45.8, P ≤ 0.05), but not plasma (β: 10.3, 95% CI: −6.6, 30.3). Another study by Henze et al. [41], conducted in 2020 among 39 children 5–10 y at risk of type 1 diabetes (77% with islet autoimmunity), examined associations between prospective changes in CIRs and AS and SSB intake over 2 visits, ∼2 y apart. Change variables were created for CIR, AS, and SSB intake by subtracting values at the last visit from values at the first visit and associations were analyzed using linear regression models controlling for NIR. Changes in CIRs were associated with changes in AS intake (unadjusted: β: 0.0029, 95% CI: 0.0015, 0.0129, P ≤ 0.05) but not SSB intake (unadjusted β: 0.1798, 95% CI: −0.2356, 0.5952). This study also provided evidence of a dose response; for every 10 g increase in AS, the CIR increased 0.0029–0.0082‰.

Two observational studies reported no associations between CIR and AS and/or SSBs. One European study conducted by Patel et al. [50] in 2014 used a case-cohort design (n = 718 subcohort participants) and found no associations between the blood serum CIR and AS or SSB intake assessed via FFQ (data not shown). In 2021, Te Morenga et al. [51] conducted a cross-sectional analysis among predominately Māori adults in New Zealand (16 y and older, n = 36) to examine AS and SSB intake derived from a semi-quantitative FFQ with CIR from RBCs and found that CIR was not correlated with AS or SSB intake (AS R2 = 0.22; SSB intake model not estimable).

In summary, findings from observational studies of adults and children using whole tissue/bulk analysis showed positive, but modest associations between blood (blood serum, whole blood, RBC) and hair CIR with AS and SSB intake. Associations were generally stronger for SSB intake than those for AS intake and blood plasma CIR was not associated with AS intake in 1 study [48]. Two studies examined the inclusion of NIR as an important covariate and found that when the population had significant fish intake (as in Nash et al. [47]) adjusting for NIR improved model fit, but if NIR was not associated with animal protein intake (as in the case of Hedrick et al. [39]) adding NIR did not improve model fit. It is also worth highlighting that the 2 studies that showed no associations between CIR and AS or SSB intake were conducted in international samples (that is, a European sample [50] and a Māori sample in New Zealand [51]), where null findings could be attributed to the differing sugar content (for example, beet compared with cane sugar) and AS regulations that impact the food supply.

Controlled feeding studies

A total of 4 studies used controlled feeding designs and employed whole tissue/bulk analysis; of these, 2 found positive associations, and 2 found no associations. In 2018, Liu et al. [44] enrolled 33 adolescents 12–18 y to complete 2 randomly assigned 7-d controlled feeding diets with high AS (25% of total energy) or low AS (5% of total energy) and used fasting fingerstick blood samples to examine associations with the CIR across 7 d. In 32 completers, there was a significant interaction effect of diet with time, such that the average fingerstick CIR decreased on the low AS diet and increased on the high AS diet (P ≤ 0.001), and CIRs were 0.03 ± 0.083‰ higher on day 8 compared with day 1 in the high AS diet (P ≤ 0.05).

A study by Votruba et al. [25] in 2019, the Developing Biomarkers of Diet (DBD) study, used a 12-wk, randomized controlled inpatient experimental feeding protocol with a sample of 32 adult men to examine CIRs from plasma, RBCs, and hair with SSB intake. Participants were randomly assigned to 1 of 8 diets that varied in the presence or absence of SSBs (14% daily energy), meat (19% daily energy), and fish (6% daily energy). The plasma CIR was higher following the SSB diet (β = 0.48, 95% CI: 0.32, 0.64, P ≤ 0.001), and results were similar for RBCs (β = 0.33, 95% CI: 0.18, 0.47, P ≤ 0.001) and hair (β = 0.53, 95% CI: 0.17, 0.90, P ≤ 0.05); the level of accuracy for the SSB model, which included CIR and NIR, was modest (AUC = 0.78). Additionally, this study provided insights on turnover rates and estimated that the CIR from plasma required 8 wk to turn over completely, whereas RBCs required ∼19 wk.

Finally, 2 independent, 2-wk controlled feeding studies that provided diets on the basis of habitual food intake found no association between CIRs and AS or SSB intake [49,54]. In a 2018 study by Yun et al. [54], 153 postmenopausal women participating in the Nutrition and Physical Activity Assessment Study Feeding Study (NPAAS-FS) received a 2-wk controlled diet on the basis of habitual intake captured by a 4-d food record before the study. In a final sample of 147 women, fasting blood serum CIR was not associated with AS (r = 0.02) or SSB intake (r = 0.01). Similarly, a 2022 study by O’Brien et al. [49], conducted a 15-d feeding study in a sample of 100 adults using habitual diets on the basis of 2 7-d food records and found that fasting blood serum CIR was not associated with AS (r = 0.05, P = 0.60).

In summary, across the 4 studies that used controlled feeding designs and whole tissue/bulk analysis, studies that used experimental diets varying high and low AS/SSB intake showed positive associations with CIR and those based on habitual intake did not. Some factors warrant consideration in interpreting results. First, given the turnover rates reported by Votruba et al. [25], the effects observed in the Liu et al. [44] study are likely underestimated given the short duration of the feeding trial relative to RBC turnover. Additionally, the lack of associations observed in the Yun et al. [54] and O’Brien et al. [49] studies could be attributed to a lower AS and SSB intake observed in the habitual diets of participants.

Intervention studies

Two behavioral interventions used RCT study designs and whole tissue/bulk analysis and found positive associations between CIR and SSB intake. In 2017, Davy et al. [37] examined findings from a 6-mo RCT (“Talking Health”; described previously), designed to reduce SSB intake among 296 adult participants and compared fasting fingerstick blood CIR with dietary changes in AS and SSB intake assessed via 3 24-h dietary recalls at baseline and post-intervention. CIRs significantly decreased in the intervention group at post-intervention, whereas no changes were observed in the control group, and associations between CIRs and SSB intake were correlated at baseline (r = 0.259, P ≤ 0.001) and 6 mo (r = 0.280, P ≤ 0.001). Interestingly, changes in the CIR were not associated with changes in AS or SSB intake. One other RCT by Fakhouri et al. [38] from 2014, examined CIR changes from the PREMIER trial, an 18-mo RCT to reduce blood pressure. In a subsample of 144 adults, fasting blood serum samples were taken at baseline, 6 mo, and 18 mo along with 2 24-h dietary recalls. A reduction of 12 oz/d of SSB intake was associated with a 0.17‰ (95% CI: 0.08, 0.25, P ≤ 0.001) reduction in the blood serum CIR over the 18-mo intervention period. These 2 RCTs provide preliminary evidence that blood CIR can capture the change in SSB intake over periods of 6 mo and longer in adults.

Taken together, findings across studies that utilized whole tissue/bulk analysis to examine CIR as a measure of AS and SSBs found positive, but modest and variable associations (R2 range 0.05–0.48). Associations were generally stronger for SSB intake than those for AS intake and findings were impacted by underlying dietary patterns of participants; in international samples and samples with low AS or SSB intake, no associations were observed. Additionally, the extent to which studies accounted for the variation in animal protein intake impacted associations. Finally, 2 intervention studies and 1 prospective cohort provided preliminary evidence of CIR to capture the change in AS and SSB intake over time.

CSIA

A total of 8 studies conducted CSIA. CSIA is a type of analytic method that separates specific molecules in a sample before stable isotope analysis, allowing for the measurement of molecule-specific SIR [16,26]. Five studies examined the CIR of AAs from blood (n = 3, RBCs, n = 2, serum, and/or n = 1, plasma) and/or hair (n = 1) [34,42,43,51,55], 2 studies examined plasma glucose extracted from blood [35,48], and 1 study examined the CIR of specific fatty acids extracted from plasma, RBCs, and adipose tissue [46]. Three studies were observational [34,48,51], and 5 were controlled feeding studies [35,42,43,46,55].

Observational studies

In 2013, Choy et al. [34] conducted a cross-sectional investigation with 68 Alaska Native participants to examine associations between NEAA CIRs from hair and RBCs with AS and SSB intake derived from 4 24-h dietary recalls. In RBCs, the CIR from the amino acid alanine (CIR-Ala), showed the strongest associations with SSB intake (r = 0.70, P ≤ 0.001) and AS intake (r = 0.59, P ≤ 0.001), and associations were similar for the CIR-Ala from hair (R2 = 0.40) in a subset of the sample (n = 30). The CIR from proline was also moderately associated with SSB intake (r = 0.31, P ≤ 0.05) but not with AS, and no other NEAAs showed associations. In addition to the bulk analysis of hair and blood conducted by Nash et al. [48] in 2014 (described previously), CSIA was conducted to examine the CIR of fasting plasma glucose in 65 Alaska Native adults and was not associated with AS (β = 5.3, 95% CI: −1.7, 12.8, R2 = 0.03) or SSBs (β = 5.4, 95% CI: −0.75, 12.0, R2 = 0.05). In 2021, Te Morenga et al. [51] also conducted a CSIA analysis of RBCs (in addition to the bulk analysis of RBC described previously) to examine the CIR-Ala in a cross-sectional analysis among 36 predominately Māori adults in New Zealand. The CIR-Ala from RBCs was correlated with intakes of total AS (r = 0.40, P ≤ 0.05) but not SSB intake.

Controlled feeding studies

Of the 5 studies that used controlled feeding designs, the earliest work was from Cook et al. [35] in 2010 and tested 3 experimental 7-d diets with varying amounts of cane sugar and HFCS among a small sample of 5 adults. On day 7, the CIR from plasma glucose was elevated at all-time points compared with the morning fasting sample (P ≤ 0.05) and the average of daily plasma glucose CIRs were also associated with the total cane sugar and HFCS in the diet (R2 = 0.90, P ≤ 0.001). In 2020, Yun et al. [55] utilized the same sample of postmenopausal women participating in NPAAS-FS (described previously) to examine the CIR from 7 AAs (3 NEAAs: Ala, Gly, Pro; and 4 EAAs: Ile, Leu, Phe, and Val) using a 2-wk diet on the basis of habitual intake. Among the 145 participants in this study, only the CIR-Ala was associated with AS intake (ρ = 0.32, P ≤ 0.001, R2 = 0.37), and no associations were observed with SSB intake.

In 2021, Johnson et al. [42] used data from the 12-wk inpatient, randomized feeding study (DBD study, described previously) to examine SSB, meat, and fish intake on CIRs from 5 NEAAs (Ala, Asp/Asn, Glu/Gln, Pro, Ser) and 4 EAAs (Leu, Phe, Thr, Val) in a sample of 32 men. The CIR in 4 out of 5 NEAAs increased with SSB intake in plasma, and the CIR of all 5 NEAAs increased in RBCs. In both plasma and RBCs, the CIR-Ala was most sensitive to SSB intake (plasma: β (SE) = 2.81 (0.38), P ≤ 0.001; RBC: β (SE) = 1.66 (0.30), P ≤ 0.001). Importantly, this study also found that the CIR-Ala was not associated with meat or fish intake. In 2023, using the same study DBD study sample of 32 men on a 12-wk experimental diet, Mitchell et al. [46] examined CIRs from fatty acids and found that only the CIR of dihomo-γ-linolenic acid in plasma [β (SE) = 0.758 (0.252), P ≤ 0.05, AUC = 0.68, 95% CI (0.49, 0.88)] and the CIR of palmitic acid in abdominal adipose tissue significantly increased [β (SE) = 0.384 (0.133), P ≤ 0.05; AUC = 0.67, 95% CI (0.45, 0.89)] with SSB intake.

In 2022, another study by Johnson et al. [43] examined the effects of AS from a 15-d controlled feeding study, on the basis of habitual intake derived from 7-d food records, in a sample of 99 adults on serum CIRs from 15 AAs (6 NEAAs: Ala, Asp/Asn, Glu/Gln, Gly, Pro, and Ser; 9 EAAs: His, Ile, Leu, Lys, Met, Phe, Thr, Tyr, and Val). Again, the CIR-Ala was the only AA associated with AS intake (r = 0.32, P ≤ 0.001).

Taken together, evidence from CSIA studies to date suggests that the CIR-Ala is positively associated with AS intake. It is also interesting to note the differences in findings from studies that utilized both bulk analysis and CSIA. For example, in the studies by Yun et al. [54,55], as well as the study by Te Morenga et al. [51], results from the bulk analysis showed no associations between CIR and AS/SSB intake, whereas associations from CSIA between CIR-Ala from and AS intake were positive. Even more interesting to note is that in thse studies, the associations from CSIA were not observed between the CIR-Ala and SSB intake, only AS intake. Although most studies in United States samples also showed positive associations between CIR-Ala and SSB intake (and in some instances stronger associations), the inconsistent associations in some studies could be attributed to the lack of diversity in ranges of SSB, or in the case of the study from New Zealand, differences in the sources of SSB from the United States. Given the emerging nature of this approach, additional studies can help refine the utility of the CIR-Ala as an objective tool to capture AS and SSB intake.

Breath analysis

Two studies examined CIR derived from breath to assess AS and SSB intake: 1 observational study that demonstrated mixed results and 1 controlled feeding study that showed positive associations [24,52]. The observational study conducted by Valenzuela et al. [52] in 2018, collected breath samples from children and adolescents 9–16 y (n = 104 in the morning and n = 115 following lunch) and assessed diet using a YAQ (a 152-item FFQ). The breath CIR from the morning sample was positively correlated with SSB intake (r = 0.24, P ≤ 0.05), but the afternoon sample showed no association. In 2021, a controlled feeding study by O’Brien et al. [24] used a short-term dose response design among a small sample of 12 adults with breath samples collected every 2-h to examine CIR responses to varying amounts of AS (low 0 g/d, medium 75 g/d, or high 150 g/d). The breath CIR increased with AS intake at all-time points (all P ≤ 0.001) in a dose–response manner (0.30‰/g, 95% CI: 0.024, 0.037‰/g) with CIRs peaking at 2 h following AS exposure and remaining elevated for 4 h. These studies provide evidence that breath CIR is associated with AS and SSB intake, and evidence is currently most compelling for breath CIR as an indicator of short-term AS intake.

Discussion

To the best of our knowledge, this is the first systematically conducted scoping review to provide a comprehensive examination of the CIR as a biomarker of AS and SSB intake. Findings from 24 studies suggest that the CIR has strong potential as a biomarker of AS and SSB intake, but results reveal important nuances that indicate more evidence is needed to provide clear direction on the utility, specificity, and application in human nutrition research. Specifically, evidence suggests that the CIR-Ala from CSIA (n = 5) and the CIR from breath (n = 2) provide the most specific and sensitive biomarkers of AS and SSB intake to date. However, the majority of the evidence is from studies conducted using whole tissue/bulk analysis (n = 17), and findings from these studies showed modest and mixed associations. In general, studies analyzing CIRs via whole tissue/bulk analysis demonstrated stronger associations between CIRs and SSB intake compared with AS intake.

Results were impacted by various factors including the sample type (for example, blood compared with hair), the dietary assessment method used for comparison (for example, FFQ compared with 24-h dietary recall), the underlying dietary pattern of the study sample (for example, high compared with low AS intake, international compared with United States participants), and whether the NIR was considered as a covariate. Although it was not the focus of this review, it is important to note that many studies showed positive associations with AS and SSB, but also showed equally strong or stronger associations of the CIR with meat intake, raising concerns about lack of specificity as a biomarker. Additionally, some of the studies included participants that lacked variability in and/or had low AS/SSB intake (for example, O’Brien et al. [49]: 58% had <0.5 servings/d of SSB; Yun et al. [54]: median (IQR) AS intake: 48 [35,64] g/d; median (IQR) SSB intake: 17 [15,41] kcal/d)), making it difficult to detect associations with the CIR (that is, if SSB intake does not vary in the sample, correlations are undetectable). Moreover, in evaluations and applications of the CIR, it is important to consider the dietary pattern of the study sample, particularly with respect to varying meat, fish, and corn intake as this also impacts the ability to detect associations. In international contexts, it is important to consider the sources of AS for the population (for example, beet sugar in Europe, primarily sugarcane elsewhere). These factors are complex and are discussed in more detail below.

Key study characteristics

Overall, studies varied in their sample population selection and representativeness. Studies predominately included adults, and while 6 studies included child participants under 18 y [41,44,45,47,48,52], only 4 studies were conducted exclusively in youth populations [41,44,45,52], and no studies evaluated the validity of the CIR as a biomarker for AS or SSB intake in children under 5 y. Overall feasibility and acceptability among children remains understudied. Factors such as growth and underlying dietary trends by age (for example, breastmilk and dairy milk consumption, introduction of complementary foods, high SSB intake) might affect the validity of this biomarker across the pediatric age range and warrant investigation [56,57]. However, children and adolescents are a prime target population for the application of CIRs given that they have high intakes of AS and SSBs, and dietary self-report instruments are highly susceptible to error and bias when used among this group [58,59]. Moreover, capturing changes in child intake over time holds promise for rigorously mapping relationships between key overconsumed nutrients, like AS, and the development of associated chronic diseases like obesity and diabetes.

Extending the diversity of participants, particularly to include more representative groups (and with different underlying dietary patterns), across the United States should also be a priority for future work. Additionally, although participant characteristics (for example, age, race and ethnicity, body weight) do not have a strong biological basis for influencing CIR, they might represent differences in underlying dietary patterns that impact CIRs and warrant inclusion as key covariates in models [40,39,45,55]. Key covariates were not consistently examined or reported across studies, and this remains an important area of future work.

Consideration of habitual dietary intake

As noted above, an important and ongoing discussion that was evident among the studies included in this review is whether and how to adjust for key features of underlying dietary patterns, namely the intake of meat, fish, and/or corn. One approach that has been used, with mixed success, to address this issue is to use a dual-isotope marker, employing the CIR and the NIR together to predict AS intake. Rationale for including NIR as a covariate is appropriate when the NIR is associated with meat or fish intake in the population. For example, because the NIR is elevated particularly in marine food webs, this method has proven particularly effective in an Alaska Native population that consumes substantial energy from fish (that is, NIR was associated with fish intake in this population) [47]. However, the method is not as effective where dietary influences on the CIR, such as fish, meat, or animal protein, are not associated with the NIR [39].

To address challenges associated with bulk CIR analysis and confounding from meat and fish intake, analyzing the CIRs of specific NEAAs using CSIA has emerged as a potential approach. CSIA has identified specific AAs whose CIRs demonstrate higher correlations with AS intake (that is, higher sensitivity) and lower correlations with animal protein intakes (that is, higher specificity) than CIRs measured in whole/bulk tissue samples. The findings from the present review reveal that the CIR-Ala is the most promising AS biomarker thus far to emerge from CSIA [18]. This is likely because glucose can significantly contribute to the synthesis of alanine in humans, and therefore, CIR-Ala should be reflective of recent glucose intake [27,60]. However, CSIA has important differences from whole tissue/bulk analysis that should be considered [26]. In general, laboratory methods are more intensive and expensive, which may limit the utility of CSIA in large-scale epidemiologic research or trials with limited resources. CSIA can allow for the tandem measurement of CIRs in multiple compounds (for example, multiple AAs or fatty acids), but not all compounds will be informative regarding AS or SSB intake. Additionally, the use of multiple AA CIRs to better describe AS intake, by accounting for animal protein intake or measurement error, shows some promise and is an area for further research [55,61].

In addition to using CSIA to address confounding CIR effects of meat and corn intake with AS and SSB intake, using breath to derive the CIR has also emerged as a novel approach. Two studies of breath CIR were included in this review (conducted in 2018 and 2021 [24,52]), and 2 other recent studies, not included in this review due to non-specific AS/SSB outcome measures, have contributed to growing evidence for the breath CIR to reflect dietary sugars and carbohydrates. Of the studies that were not included, one controlled feeding study (n = 9) by Yazbeck et al. [62] in 2021 demonstrated that the breath CIR increased in a dose-dependent amount following 20 g, 40 g, and 60 g doses of sucrose, and CIRs remained elevated after 180 min following the 40 g and 60 g doses (P ≤ 0.05). The other study by Mancuso et al. [63] in 2022 explored changes in breath CIRs following meals and found that among adolescent participants (n = 31), breath CIRs reflected carbohydrate sources from prior meals, but they also found that daily CIR samples were not significantly different. This study aligns with findings from O’Brien et al. [24], which not only showed that the breath CIR was elevated in response to higher levels of AS, but also showed that daily CIR breath samples were correlated across timepoints. Moreover, fasting breath CIRs were correlated with self-reported intake of SSBs. Taken together, these findings not only support the postprandial breath CIR as an indicator of the short-term change in AS and SSB intake, but also suggest that the fasting breath CIR might hold promise as a measure of usual or longer-term dietary intake of AS (for example, fasting breath reflects energy stores). Therefore, the CIR could have utility as a marker of intervention adherence (for example, short-term change) as well as effectiveness/change over time. This is an important area of future inquiry.

Finally, despite limitations in interpretation because of the confounding effects of corn and meat, it is important to note that whole tissue/bulk analysis of CIRs (from whole tissues like hair and blood) are often easier and more cost effective to obtain and analyze than CSIA, and therefore, still hold potential for utility in reflecting dietary intake. Several studies, including some in this review, have found that the CIR is positively associated with meat intake and in some cases more strongly than with AS and SSB intake [25,46,49]. Although this presents challenges for elucidating the clear application of the CIR as an objective biomarker for sugars or meat intake separately, the findings suggest that bulk CIRs might be better suited as an objective indicator of broader dietary patterns or outcomes such as total diet quality or ultraprocessed food intake, which represents foods that are highly processed with ingredients including corn and AS (for example, snack foods and processed meats). The study by Hedrick et al. [40], included in this review, also examined the association between CIR and total diet quality using the Healthy Eating Index 2010 (HEI-2010) and a Solid Fats, Alcohol, and Added Sugars (SoFAAS) score and found that both were positively associated with the CIR (HEI-2010 R2 = 0.16, P ≤ 0.001; SoFAAS R2 = 0.19, P ≤ 0.001). Notably, model fit was higher for HEI-2010 and SoFAAS than that for AS (R2 = 0.15, P ≤ 0.001) and SSB intake (R2 = 0.14, P ≤ 0.001). Additionally, 2 other studies, which were not included in this review because they did not have an AS or SSB outcome, used the CIR as an indicator of processed food intake among young children 3–5 y and youth and adults 14–64 y [23,64]. Using the CIR as an indicator of overall diet quality or ultraprocessed food intake is an exciting area for future work that warrants investigation and could provide clarity on the application of the CIR from whole tissue/bulk analysis. Moreover, this could provide broader applications for using CIRs to examine associations with chronic disease risk and capturing changes in dietary trends.

Methodological considerations

A key benefit of using the CIR as a biomarker of AS and SSB intake is that it can be analyzed from different sample types. CIR sampling methods in this review included blood, hair, breath, and adipose tissue. Future studies should consider feasibility and participant burden when selecting sample types, but most methods for obtaining samples are low-burden, cost effective, and easily scalable. Additionally, some minimally invasive sample types that have been used to sample stable isotopes in other fields have yet to be explored in human nutrition. For example, fingernails have been commonly used to obtain stable isotopes in anthropological literature, and this holds potential given the low participant burden and stability of the sample over time [17,65]. Selecting a CIR sample type requires consideration of the underlying metabolic pathway and turnover rate of the tissue type; the sample selected for obtaining the CIR should match the timeframe of interest. For example, whole blood/RBCs or hair may be most appropriate for studies of long-term intake, whereas repeated serum samples and/or analyzing hair sections (<0.5 cm) may be more appropriate for studies evaluating dietary change over time. In this review, observational studies that compared the CIR from whole blood fingerstick samples with AS and SSB intake collected via 24-h dietary recalls were often positively correlated. Matching sample types to dietary time frames is an especially important consideration for future work that aims to explore the utility of the biomarker to assess intervention adherence or outcomes. In these designs, breath might be well-suited to assess adherence given its responsiveness to shorter-term intake as well as indicate longer-term dietary intake, but this has not yet been studied. The differential turnover rates of tissues also provide a potential explanation for mixed results in studies using various sample types; the timeline for the sample type might have been inconsistent with the timelines and methods for assessing dietary intake.

Finally, it is important to understand established laboratory best practices for stable isotope analysis and reporting guidelines for CIR analysis. Adherence to laboratory and reporting best practices ensures that readers of studies using the CIR are adequately able to assess methodologic rigor and ensures consistency between published works. Although these are not detailed in this review, internal and external standards are used for quality assurance during stable isotope analysis [66,67]. Studies should detail the standards and methods used and report the measurement error of quality assurance standards or samples that are handled with identical treatment to test samples to assess analytical (instrument) precision and reproducibility.

Considerations and future directions

Studies that included adjacent dietary outcomes (for example, ultraprocessed foods, carbohydrates) were not included in this systematic scoping review and could be explored in future work. Additionally, to clearly answer the research question and examine associations between CIRs and AS and/or SSB intake, other dietary outcomes (for example, meat, corn) were not included in this review. This is a limitation as many studies presented findings with other dietary outcomes and this precludes the ability to compare the strengths of associations between CIRs and AS/SSB intake with other dietary intake components and patterns. Our review also identified gaps in the literature: few studies examined changes in CIRs over time or in response to varying levels of AS intake (that is, dietary intervention); this warrants more robust examination, as current results do suggest that CIRs are sensitive to change over time. Short-term controlled feeding studies (ranging from 1-d treatment to several weeks) have demonstrated somewhat consistent associations of both bulk and AA CIRs with changes in AS and/or SSB intake, with some evidence of a dose–response relationship, but this remains underexplored in free living populations. Additionally, future studies should explore how to effectively account for meat intake when using bulk CIRs to estimate AS and SSB intake, or examine if bulk CIR analysis might be a better predictor of other metrics of dietary intake such as overall dietary quality or ultraprocessed food intake. However, this may be less important in the intervention setting where other dietary components like meat intake are assumed to be constant (at the individual level) and change in the CIR could be used as an indicator of intervention adherence and success in reducing AS and SSB intake. The sensitivity of CIRs to detect changes in this context remains unknown.

Further, newer methods using the CIR from breath and CSIA of the CIR-Ala from blood (for example, serum, plasma, RBCs) and hair show associations with AS and SSB intake and appear to overrepresent carbon from AS and SSB relative to bulk CIR analyses from blood and hair. Studies could consider using these approaches in lieu of bulk CIR analyses, recognizing their limitations concerning time frames of interest [the CIR-Ala represents the timeframe of the sample (RBCs, plasma) while evidence for breath supports use an indicator of short-term intake], and resource-/time-intensive processes (CSIA). Finally, few studies have examined the use of CIRs in pediatric and diverse populations. Findings suggest that CIRs are more reliable in populations with variable and higher levels of AS and SSB intake, and therefore, children and adolescents are a prime priority group for future directions and refining the potential of this method. Additionally, given disparities in diet-related chronic disease prevalence, more diverse groups should be represented in the building evidence base.

In conclusion, reducing AS intake is a public health priority and objective biomarkers could help advance the field of nutrition science [1]. Increased AS and SSB intake are associated with a myriad of diet-related chronic diseases such as diabetes, obesity, and cardiovascular disease and efforts to develop effective policies and interventions are greatly reliant on robust assessment tools. The CIR holds potential for addressing key limitations of dietary self-report and acting as an objective indicator of AS and SSB intake. In this systematic scoping review, studies that examined the CIR-Ala using CSIA and the CIR from breath were the most sensitive and specific stable isotope biomarkers of AS and SSB intake. Results from bulk analysis of CIR demonstrated potential utility in specific circumstances and warrant additional examination and refinement. More work is needed to build a robust evidence base for this approach, especially among populations with variable intake, like children, and in time-varying contexts, like interventions.

Acknowledgments

We thank Travis Nace, MSLIS, and Jenny Pierce, MS, at Temple University Libraries for their assistance conducting the scoping review search.

Author contributions

The authors’ contributions were as follows – GLT, SHN, JOF: conceptualized the study; GLT, SHN, VAR: conceptualized the search strategy; VAR: conducted the scoping review search; GLT, SHN: developed the research plan and provided study oversight; GLT, SHN, ADS: conducted the article screening; GLT, SHN, ADS, JJJ, JAO: analyzed and extracted the data; GLT, SHN, ADS, JJJ, JAO, DMO: wrote the article; GLT: had primary responsibility for final content; and all authors: read and approved the final manuscript.

Funding

Research reported in this publication was supported in part by the National Institutes of Health (NIH), National Heart, Lung, and Blood Institute under grant K01HL153783 (to GLT), from the NIH National Institute on Deafness and Other Communication Disorders under grant R01DC016616 (to JOF), and through an Institutional Development Award from the National Institute of General Medical Sciences of the NIH under grant number P20GM103395 (to JJJ). SHN is additionally support by the University of Iowa Holden Comprehensive Cancer Center (3P30CA086862). The content is solely the responsibility of the authors and does not necessarily reflect the official views of the NIH. The funding agencies did not have substantive input on the research questions, methods, results, interpretation of the data, or involvement regarding the submission of the report for publication.

Conflict of interest

Corresponding authors will be queried for their own and their co-authors’ conflict of interest disclosures during the manuscript submission process using the Declaration of Interests tool (https://declarations.elsevier.com/). The tool will generate a Word file for upload with your manuscript submission.

Data availability

No data or analytic code book were created to complete this scoping review.

Appendix A Supplementary data

The following is the Supplementary data to this article:multimedia component 1

multimedia component 1

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.advnut.2024.100281.
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