The considerable growing body of knowledge and gaps about sugar intake in children and genetics has led to multiple lines of investigations. Importantly, this narrative review focuses on preschool-aged children where there is limited, but growing research highlighting the importance of this age group. This paper provides an overview of the many different areas where dietary sugar intake and genetics independently and in combination influence cardiometabolic health in preschool-aged children. In the first section, we summarize research examining dietary sugar intake, its associations with cardiometabolic health, the impact of genetics on sweet taste perception and intake, and how genetics and select single nucleotide polymorphisms moderate the association of dietary intake and CMR in preschool-aged children (1.5–5 year of age). Given the research evidence is limited for this specific age group, the PubMed, Omni, and Ovid databases were reviewed for literature to date. The following keywords (alone and/or in combination) were used: preschool, children, sugar intake, cardiometabolic, dental caries, genetics, heritability, single nucleotide polymorphism, solute carrier family 2 member 2 (SLC2A2) , dopamine receptor D2 (DRD2), taste 1 receptor member 2 (TAS1R2), taste 1 receptor member 3 (TAS1R3), G protein subunit alpha transducin 3 (GNAT3), cannabinoid receptor 1 (CNR1), proprotein convertase subtilisin/kexin type 1 (PCSK1), insulin receptor (INSR), insulin receptor substrate 1 (IRS-1), and insulin receptor substrate 2 (IRS-2). In the second section, methods used to assess dietary sugar intake, genetic information, and markers of cardiometabolic health; as well as important considerations for their use in preschool-aged children are provided. Finally, key methodological recommendations and gaps in knowledge in this field are summarized in the third section. Together, this review highlights the state of the literature pertaining to sugar intake and CMR in preschool-aged children and the methodological, genetic and cardiometabolic considerations crucial for research in this field. Aligning future research with our recommendations will support a robust research body, which can credibly inform healthy eating policies and recommendations designed to reduce cardiometabolic risk in young children.

Consistent definitions related to sugar intake are important to develop dietary recommendations and to assess compliance to these recommendations. However, leading health authorities such as the World Health Organization (WHO) and United States Department of Agriculture (USDA) have published recommendations based on different definitions of sugar, i.e., free and added sugars respectively (Table 1, Ref. [8, 14, 15] and Table 2, Ref. [7, 8, 9, 15, 16, 17, 18, 19, 20, 21]). Importantly, the USDA added sugar and WHO free sugar definitions include the same sugars except for those from fruit juice, thus the WHO has a broader definition for free sugars.

The WHO recommends that free sugar be limited to 10% energy intake (EI) to reduce risk of cardiovascular disease and dental caries and 5% EI for a further reduction of risk of dental caries [8]. The USDA recommends that added sugar be limited to 10% EI each day (~12 teaspoons per day) [22]. Finally, the American Heart Association (AHA) recommends that children consume $\le$25 grams (100 kcal or ~6 teaspoons) of added sugar per day [9].

Inconsistent definitions of sugar exist within the research literature making it difficult to compare values across databases and causing variation in guidelines, data gathered, and how dietary intakes are compared against guidelines [23]. Differences in guidelines and recommendations such as the use of free or added sugars can lead to lack of clarity and differing opinions. Table 2 provides a snapshot of differing recommendations from various national and international organizations [8, 9, 15, 16, 17, 18, 19, 20, 21, 22, 23].

Overconsumption of free and added sugars increases the risk of developing chronic diseases such as type 2 diabetes (T2D) and cardiovascular disease in all population groups [8, 16]. Thus, excessive intakes of these sugars is a public health concern for all children [8, 16]. Dietary patterns are well-established as young as 3 years of age and can extend into adulthood [4]. It is known that added sugar intake increases with age and can be significantly higher in older boys [4, 24]. Currently, free and added sugar intake from all food sources in preschool-aged children (6 years of age) and their association with the development of CMR factors have not been well-researched, although there has been significant attention focused on sugar-sweetened beverage (SSB) intake in children [24]. To our knowledge, studies have examined sugar intake in older children, and do not examine all food sources of free and added sugar intake. Thus, the development of CMR in preschool-aged children needs to be further examined to implement early life interventions and inform policies for this age group. Improved health outcomes in adolescence and adulthood will follow. The next section reviews cross-sectional studies on free and added sugar intake along with the different food sources of sugar that preschool-aged children (inclusion criteria: includes children 1.5–5 years of age) are consuming (Table 3, Ref. [4, 5, 6, 7, 25, 26, 27]).

One study assessed the diets of preschool-aged children (n = 5437) between 2 to 5 years old from the National Health and Nutrition Examination Survey (NHANES) (1988–1992). This study collected two non-consecutive 24-hour recalls from children and found that 11% of participants (2 to 3 years) and 12% of participants (4 to 5 years) exceeded 25% EI per day from added sugar [4]. Furthermore, 72% of participants aged 2 to 3 years and 79% of those aged 4 to 5 years, exceeded 10% EI per day from added sugar [4]. Intake of added sugars in this population exceeded recommendations from the Institute of Medicine and the WHO [4]. Additionally, the primary sources of added sugar in the children’s diets were fruit drinks, high-fat desserts (such as ice cream, pies, cookies, cakes), and regular soft drinks [4]. Furthermore, as added sugar levels increased in the dietary pattern, intake of all other macro and micronutrients (except for carbohydrates and vitamin C) decreased. Finally, children with added sugar intake $>$25% EI had lower intakes of fibre, protein and fat and higher intake of carbohydrates [4].

Similar findings of preschoolers exceeding sugar recommendations have also been observed in the Growing Healthy Study, an obesity prevention trial in the US [6]. This secondary data analysis included n = 524 preschool-aged children of low socioeconomic status, with a mean $\pm$ SD age of 4.1 $\pm$ 0.5 years [6]. Three 24-hour diet recalls were completed, including two weekdays and one weekend day and diet-related study measures included %EI from added sugar. Results showed that the mean added sugar intake (mean $\pm$ SD) was 12.9 $\pm$ 5.9 % EI per day for boys and 12.8 $\pm$ 6.3% EI per day for girls. Finally, similar to the previous study [4], a significant proportion of children exceeded WHO recommendations with 69% of boys and 65% of girls exceeding 10% EI per day from added sugars [6].

The Dortmund Nutritional and Anthropometric Longitudinally Designed Study in Germany (n = 288 participants) examined added sugar intake in infancy (through commercial complementary foods) and its relationship with added sugar intake in later years, i.e., preschool-aged (3 to 4 years) and primary school-aged (6 to 7 years) [5]. For each participant, a weighed food record was used to assess diets from three consecutive days and %EI per day was determined. Mean added sugar was reported to be 34.2 g per day and 11.9 %EI per day for preschool-aged children, and 49.7 g per day and 13.4% EI per day for primary school-aged children [5]. It was concluded that a higher intake of added sugar from commercial foods in infancy may predispose children to higher added sugar intakes in preschool-aged (p 0.041) and primary school-aged children (p 0.039). Preschool- and primary-aged children in this population exceeded the recommended intake of added sugar by the WHO and the American Heart Association (AHA) [5].

The effect of skipping the first meal, breakfast, on daily sugar intake has also been examined through a cross-sectional analysis of participants (n = 3443), 2 to 5 years old from the US NHANES 2005-2012 cohort [25]. Two 24-hour diet recalls were used to determine daily added sugar intake and its relationship with breakfast consumption. Mean added sugar intake for preschool-aged children who skipped breakfast was 62 g/day [25] and lower for those who did not skip breakfast, only 51 g/day [25]. It was concluded that preschool-aged children in this population exceeded the recommended intake of added sugar from the AHA regardless of breakfast consumption, but those who skipped breakfast had an even higher intake of added sugar.

Examination of food sources of free sugar has revealed important trends and insights into sugar intake in children. An Irish study of free sugar intake in 3-year-old children including two national data sets (Growing Up in Ireland n = 9793, 51% male; National Preschool Nutrition Survey n = 126, 48% male) [26] found that 75% of the preschool-aged children had free sugar above the 10% EI recommended by the WHO and less than 4% of the study sample attained the 5% EI WHO recommendation. It was noted that the median frequency of free sugar consumption was 4.0 (range of 3.0–5.0) times per day. The main food sources of free sugar were fruit juice and smoothies, dairy products, confectionery, and soft drinks [26].

The SMILE study, using dietary data from 2-year old Australian children (n = 1043) found that 71% and 38% of the children exceeded the WHO recommendations of 5% EI and 10% EI from free sugars, respectively [27]. This study found the primary food sources of free sugar were fruit juices, biscuits, cakes, desserts; and confectionery, with yogurt and non-dairy milk alternatives [27]. In a Canadian study of preschool-aged children, the Guelph Family Health Study found that 80% of 109 preschool-aged participants (aged 1.5–5 years) had free sugar intake above the 5% EI daily recommendation set by the WHO [7]. The most common food sources of free and added sugar were bakery products, sugars and sweets, and cereals and grain products [7].

Overall, these cross-sectional studies across multiple countries reveal that young children exceed global and national recommendations in different countries for free and added sugar intake and are consuming sugars from a variety of food sources.

The following section describes cross-sectional and longitudinal studies investigating the associations between sugars from SSBs and CMR factors among preschool-aged children (Table 4, Ref. [28, 29, 30, 31, 32, 33, 34, 35]).

A cross-sectional study linking SSB intake with CMR factors in n = 1778 participants between the ages of 3 to 6 years [28] highlighted that SSB consumption was positively associated with CMR scores, specifically lower high-density lipoprotein (HDL-c) and higher triglycerides (TG). This study utilized the CMR scores as described by the American Academy of Pediatrics and included systolic blood pressure, TG, waist circumference (WC), glucose, and HDL-c [28]. The authors demonstrated that with every 1 cup of SSB intake, there was a significant increase in the CMR score by 0.05 SD units [28]. It was also noted that 100% fruit juice did not have a significant association with the CMR factors except lower HDL-c was reported [28].

Similarly, cross-sectional analyses of NHANES (1994 to 2004) data including children (n = 4880) aged 3 to 11 years found that the highest tertile of SSB consumption (4.39 $\pm$ 1.71 SSB servings (or 35 oz daily)) was positively associated with the C- reactive protein (CRP) and WC and negatively associated with HDL-c [29]. A subgroup analysis of children aged 3 to 5 years found a positive association between SSB intake and low density lipoprotein cholesterol (LDL-c) [9]. In 3- to 11-year-old children, SSB intake was positively associated with CRP in both sexes; and inversely associated with HDL-c and positively associated with LDL-c in girls [29]. Furthermore, in both non-Hispanic Black and White groups, a significant inverse association was noted between SSB intake and HDL-c. In the non-Hispanic Black group, SSB intake was significantly positively associated with TG and CRP levels [29].

A follow-up study conducted in Amsterdam, further highlighted the longitudinal associations between SSB intake and BP in children aged 5 to 6 years or 11 to 12 years [30]. The study found an average intake of 2.6 servings of SSB per day. There were no associations found between SSB and BP for children aged 5 to 6 years [21]. However, as the number of servings increased in older children (11 to 12 years old), the association between SSB and BP became positive and significant [30].

Similar results were seen in the Early Childhood Longitudinal Birth-Cohort study in the US, where SSB intake in children aged 2 to 5 years (n = 9600) were examined [31]. Study participants with higher SSB intake had higher BMI Z-scores at age 4 years and 5 years but not at 2 years [31]. By 5 years of age, regular SSB drinkers had increased odds of obesity [31]. Another longitudinal study investigating Dutch children with a median age of 5.9 years (n = 2045) found that high SSB consumption in boys was significantly associated with CMR at age 6 years [32]. In yet another longitudinal study from Québec, Canada, children ages 2.5 to 3.5 years (n = 2103) who consumed higher SSBs had more than three times the odds of being overweight at 4.5 years [33].

Studies examining SSB intake over time from infancy onward have revealed similar outcomes. For example, a longitudinal study of SSB intake in children (n = 1189) in the US from infancy to 6 years of age showed that infants exposed to SSBs were twice as likely to be obese compared with infants who were not [34]. Furthermore, the likelihood of consuming SSBs at age 6 years was 71% higher for children who consumed SSBs at infancy and 92% higher for those children who consumed SSBs before 6 months of age [34]. Another study found that excess intake of added sugars (from dairy products, baked goods, sweets and spreads) consumed in the first two years of life were associated with an increased BMI at age 7 years in a subset of the longitudinal Dortmund Nutritional and Anthropometric Longitudinally Designed study (n = 216; 51% male) [35].

Thus, these cross-sectional and longitudinal studies suggest that excessive SSB intake starting from the first few years of life can predispose preschool-aged children towards higher CMR. The next section will discuss genetic research investigating dietary sugar intake, CMR, and their relationship in preschool-aged children.

Dietary sugar intake, genetic variation, and CMR have complex interactions. Studies investigating heritability suggest that genetics plays an important role in dietary sugar intake and CMR in preschool-aged children. However, how genetic variation and gene-diet interactions may impact dietary sugar intake and moderate the association between dietary sugar intake and CMR phenotypes is not well understood in this population.

Heritability is defined as the proportion of a traits’ variability that can be attributed to genetic variation. Twin and adoption studies provide estimates of the proportion of variability in a trait that can be attributed to heritability, i.e., additive genetic factors; common environmental factors shared by family members, and unique environmental factors. Heritability of dietary sugar intake is not well studied in adults and even more rare in preschool-aged children. One study investigated 1216 twin pairs born in England and Wales in 2007 [11]. Dietary data collected at a mean age of 21 months revealed that the shared environment predicted 66%, 84%, 91%, and 97% of the variation in dietary intake of milk-based desserts, sweet cereal products, added sugar and confectionary, and juice, respectively. Conversely, genetic factors were estimated to predict 15%, 5%, 5%, and 1% of the same food groups, respectively. These data indicate that a small but significant proportion of dietary sugar intake can be attributed to genetic influences in very young children. However, it is unknown how the proportion of dietary sugar intake attributable to shared environment, genetic factors, and nonshared environment, may change with age.

Twin and adoption studies in adults have established that many components of CMR are highly heritable. One twin study using variable-aged cohorts (age 13–92 years old) from Sweden, Australia, and the Netherlands found that between 48% to 83% of lipid levels, i.e., LDL-c, HDL-c and triglycerides, are heritable [36]. However, twin studies investigating heritability of CMR in children, and how this relates with heritability estimates in adults are rare. In one systematic review of twin and adoption studies of children (largely Caucasian, aged 1 to 18 years old), BMI was found to have moderate to high heritability [12]. This heritability was high in children 1.5 years of age and decreased by approximately age 5 years, before increasing during adolescence. This suggests that environmental factors further influence CMR at different life stages.

In adults, SNP variation has only been able to explain a limited amount, i.e., 1.5%–12% of the CMR variation predicted by twin and family studies, and data are even more sparse in children [37]. In 1,169 children from the Tracking Adolescents’ Individual Lives Survey Cohort at 7 years of age, a genetic risk score calculated from 25 genome wide significant SNPs explained 3.6% of the variance of childhood BMI [13]. This cohort was part of a larger genome wide association study (GWAS) meta-analysis of 61,111 children between the ages of 2 and 10 years, which found that genetic background of childhood BMI overlaps with that of adult anthropometric and CMR factors, i.e., BMI, waist-to-hip-ratio, body fat percentage diastolic BP, and T2D [13]. Thus, observed correlations between childhood obesity and adult CMR may be partially explained by shared genetics but may also be explained by strong tracking of childhood BMI into adulthood.

In children, the most important single gene mutations linked to monogenic obesity include those in the leptin receptor (LEPR), melanocortin 4 receptor (MC4R), pro-opiomelanocortin (POMC) and prohormone convertase 1 (PCSK1) genes [38]. Glucokinase Maturity Onset Diabetes of the Young is characterized by juvenile onset hyperglycemia caused by a mutation in the glucokinase gene [39]. The majority of obesity and hyperglycemia cases are polygenic in nature. Monogenic forms of obesity have been found to account for less than 5% of all severe obesity cases and has not been quantified in children [40]. Monogenic forms of hyperglycemia account for 1% to 4% of pediatric diabetes cases [41, 42].

The FTO is the most well-known gene influencing obesity in children and adults. FTO polymorphisms that have been associated with overweight/obesity and BMI in preschool aged children include rs9939609 and rs17817449 [13, 43]. In adults, rs55872725 was significantly associated with SSB consumption [44]. Further investigation may reveal more pathways by which FTO, sugar intake, and CMR are linked.

CMR is complex and while key genes have been identified, it is also important to consider potential additive effects across a range of processes that may contribute to overall risk. Since more than 93% of obesity cases have complex polygenic causes, investigations into common genetic risk loci are warranted [40]. This section will discuss SNP variation in sugar-related pathways that have been investigated for associations with dietary intake of sugar and/or CMR factors in preschool-aged children.

In particular, the following selected genes involved in glucose sensing (SLC2A2), the reward system (DRD2), sweet taste receptors (TAS1R2, TAS1R3, GNAT3), the endocannabinoid system (CNR1), and insulin mediated glucose uptake (PCSK1, INSR, IRS-1, IRS-2) are reviewed for potential relationship with sugar intake and CMR. Fig. 1 [45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61] provides background information of these genes, which have been investigated in young children and may be of interest for future research. Furthermore, studies investigating the relationship between select genetic polymorphisms, dietary sugar intake, and cardiometabolic risk factors in preschool-aged children (1.5–5 years) are reviewed in Table 5 (Ref. [45, 46, 47, 48, 49, 50, 51, 52]). The dietary sugar outcomes reviewed included sugar preference and consumption as well as the presence of dental caries. The CMR outcomes reviewed included BMI Z-scores, overweight/obesity, and TG.

Fig. 1.

Genes involved in glucose sensing, the reward system, sweet taste receptors, the endocannabinoid system, and insulin mediated glucose uptake found to have SNP associations with dietary sugar intake and/or cardiometabolic risk factors in children. (A) The solute carrier family 2 (SLC2A2) gene encodes GLUT2†, a facilitated glucose transporter, which has both transport and receptor functions. DRD2†encodes dopamine receptor D2. (B) TAS1R2* and TAS1R3* code for proteins (T1R2 and T1R3) that heterodimerize to form sweet taste receptors. GNAT3* encodes the alpha subunit of gustducin, the G protein responsible for transmitting sweet, bitter and umami tastes from taste receptors. Endocannabinoids bind with G-protein coupled cannabinoid receptors, i.e., CB1 and CB2 encoded by CNR1†and CNR2 respectively. (C) Proprotein convertase subtilisin/kexin type 1 and 2 (PCSK1*¤, PCSK2¤) encode proprotein convertase 1/3 and 2 (PC1/3, PC2) respectively. INSR*¤ encodes preprotein precursors cleaved into alpha and beta subunits of the heterotetrameric insulin receptor. Binding of ligands including insulin and insulin like growth factor 1 (IGF-1) along with insulin receptor substrate 1 (IRS1¤) or insulin receptor substrate 2 (IRS2) to the insulin receptor results in activation of the insulin signaling pathway. *Genes that have been studied in preschool-aged children (includes 18 months–5 years) for a relationship with dietary sugar intake and/or cardiometabolic risk [45, 46, 47, 48, 49, 50, 51, 52]. †Genes that have been studied in children aged 6–13 years old for a relationship between SNPs with dietary sugar intake and/or cardiometabolic risk and are potentially of interest for studies in preschool-aged children [53, 54, 55, 56]. ¤Genes that have been studied in children aged 4–18 years old for a relationship between SNPs with dietary sugar intake and/or cardiometabolic risk and are potentially of interest for studies in preschool-aged children [57, 58, 59, 60, 61]. BMI, body mass index; CB1, cannabinoid receptor type 1; CB2, cannabinoid receptor type 2; CNR1, cannabinoid receptor 1; CNR2, cannabinoid receptor 2; DRD2, dopamine receptor D2; GLUT2, glucose transporter 2; GLUT4, glucose transporter 4; GNAT3, G protein subunit alpha transducin 3; GSV, GLUT4 storage vesicle; INSR, insulin receptor; IRS1, insulin receptor substrate 1; IRS2, insulin receptor substrate; SLC2A2, solute carrier family 2 member 2, SNP, single nucleotide polymorphism; TAS1R2, taste 1 receptor member 2; TAS1R3, taste 1 receptor member 3; T1R2, taste receptor type 1 member 2; T1R3, taste receptor type 1 member 3; PCSK1, proprotein convertase subtilisin/kexin type 1; PCSK2, proprotein convertase subtilisin/kexin type 2; PC1/3, proprotein convertase 1/3; PC2, proprotein convertase 2; PI3K, phosphatidylinositol 3-kinase; T2D, type 2 diabetes; D2, opamine receptor D2.

As seen in Fig. 1, variation in the brain reward system may impact the hypothalamic control of downstream appetite and T2D pathways. The solute carrier family 2 (SLC2A2) gene encodes GLUT2, a facilitated glucose transporter, which has both transport and receptor functions. Due to its low affinity for glucose, GLUT2 has been proposed to be a glucose sensor involved in regulating blood glucose concentration. DRD2 encodes dopamine receptor D2 and the binding of dopaminergic agents such as bromocriptine to dopamine receptors is used to treat T2D symptoms including hyperglycemia, insulin resistance, free fatty acids, and TG [62]. Variation in sweet taste perception may impact dietary sugar intake and CMR. TAS1R2 and TAS1R3 code for proteins (T1R2 and T1R3) that heterodimerize to form sweet taste receptors. GNAT3 encodes the alpha subunit of gustducin, the G protein responsible for transmitting sweet, bitter and umami tastes from taste receptors. In preschool-aged children, SNP variants in TAS1R2 have been associated with dietary sugar intake [45, 46] and SNP variants in GNAT3 have been nominally associated with dental caries [47]. Bidirectional relationships between sweet taste perception and response with the endocannabinoid system have been identified [62, 63]. Endocannabinoids bind with G-protein coupled cannabinoid receptors, i.e., CB1 and CB2 encoded by CNR1 and CNR2, respectively [64]. In mice, administration of cannabinoids enhanced sweet gustatory nerve response and increased sugar seeking behaviour via CB1 receptors, which are concurrently expressed with T1R3 in taste cells [62, 64]. Variation in insulin-mediated glucose transport may impact CMR. Proprotein convertase subtilisin/kexin type 1 and 2 (PCSK1, PCSK2) encode proprotein convertase 1/3 and 2 (PC1/3, PC2) respectively. These enzymes are involved in the cleavage of proinsulin to insulin and proglucagon to glucagon. A recent study demonstrated that the PC1/3 gene processes proinsulin in healthy human $\beta$-cells while the PC2 gene was only detectable in $\beta$-cells from donors with T2D [65]. Therefore, variation in PC1/3 may negatively impact regular insulin processing. INSR encodes preprotein precursors cleaved into alpha and beta subunits of the heterotetrameric insulin receptor and SNP variants in INSR have been associated with CMR factors in preschool-aged children [48]. Binding of ligands including insulin and insulin like growth factor 1 (IGF-1) along with insulin receptor substrate 1 (IRS1) or insulin receptor substrate 2 (IRS2) to the insulin receptor results in activation of the insulin signalling pathway. Under basal conditions the GLUT4 receptor is sequestered in GLUT 4 storage vesicles and released to the plasma membrane in response to elevated blood sugar (insulin-dependent) or increased energy needs [66, 67].

Preschool-aged children are underrepresented in genetic research. However, transitioning from candidate gene approaches to GWAS and epigenetic methods will likely expedite the discovery of genetic patterns underlying CMR as larger samples become available in this population. Healthful habits introduced by families may be able to reprogram epigenetics and establish healthy lives for children, but the actual epigenetic effect of excess sugar intake remains unclear. SSB intake is associated with adiposity and evidence suggests that hypermethylation of peroxisome proliferator-activated receptor Alpha (PPAR$\alpha$) and CPT1A by fructose consumption is one mechanism driving this effect [68]. The specifics of this pathway have not been elucidated and other genetic pathways by which fructose influences adiposity may exist. Future research should continue to investigate the biological basis of SNP associations with dietary sugar intake and CMR in young children as well as how dietary sugar intake impacts epigenetic programming of CMR over a lifespan.

Monitoring dietary sugar consumption in preschool-aged children is essential to assess adherence to national sugar reduction guidelines. Given the varying definitions and descriptions of free and added sugars in the literature, together with the lack of consensus of recommended daily intakes globally, evaluations of both added and free sugar intakes are challenging. Currently, there are various methods used to determine the amount of total sugars present in foods or in the diet of preschool-aged children. Some approaches commonly used to evaluate EI include food frequency questionnaires, food records, direct observation, dietary screening questionnaires, 24-hour dietary recalls and the doubly labeled water method [69, 70]. However, assessing added or free sugar intake is especially challenging because there are limited nutrient databases that include added or free sugar content of foods. Furthermore, there is no universal system that is used in research to determine added sugar intake since its definition varies. Nonetheless, there are some existing approaches in the literature that may be used for estimating total and added sugar intake in preschool-aged children. In particular, the four methodologies that will be examined in this review include, the Automated Self-Administered 24-hour Recall (ASA24®), Nutrition Data System for Research (NDSR), ESHA’s Food Processor® Nutrition Analysis Software, version 11.11 (ESHA Research, Salem, OR, USA) and the disaggregation method. The ASA24, NDSR and ESHA are commonly used methodologies, whereas the disaggregation method is a newer approach used to assess added sugar intake. An overview of the strengths and limitations of these methodologies in estimating sugar intake is provided in Table 6 (Ref. [71, 72, 73, 74, 75, 76]).

3.1.4 Disaggregation Method

Introduced by Amoutzopoulos et al. [76] the disaggregation method is a novel UK-based system designed to measure dietary intake of added and free sugars and can also be applied to preschool-aged children. Unlike the above approaches, a unique and important feature of this method is that it can be adapted to different sugar definitions across jurisdictions and is responsive to changes in definitions [76]. The disaggregation method pre-assigns total sugars into seven sub-categories including table sugar, sugar-based sweeteners, honey, fruit and vegetable juice, fruit puree, stewed fruit and dried fruit [76]. The summation of these individual ‘sugar components’ are subsequently used to estimate the added sugar contents of the food item according to the different sugar definitions [76]. The disaggregation method involves five steps, as illustrated in Fig. 2 (Ref. [76]).

Fig. 2.

Five steps for estimating the added or free sugar contents in food products according to the disaggregation method. Figure is reproduced with permission from, [76].

First, the proportion of each ingredient present in a food item is identified from the food label. Second, a total sugar value, obtained from a food composition database, is assigned to each ingredient per 100 g. Next, 100% of the total sugar value of each ingredient is assigned to one of the above seven sugar components. Following this third step, using the predetermined formula (Fig. 2), the proportion of each ingredient in the food item is used to calculate the amount of sugar for each sugar component. Finally, these sugar component values (in 100 g of foods) are summed according to the specific sugar definition, to provide an estimate of the added or free sugar content of 100 g of food.

The step-by-step breakdown of the disaggregation method illustrates the flexibility in this approach in determining the added sugar contents of food products for different sugar definitions (i.e., added and free sugars). However, this system may introduce bias, as users are required to subjectively decide which sugar component is appropriate for the food product and accordingly, assign the sugar value. Furthermore, the accuracy of the total and added sugar values assigned to the ingredients are dependent on the quality of the food composition database. Another limitation is that users are required to have detailed information regarding the recipes for the food items, along with the total sugar data, which may not be feasible in some situations. Further, the calculated added sugar value of a particular food item may not be consistent across jurisdictions, due to the differing sugar definitions and fortification methods, and therefore, would necessitate recalculation. Despite these drawbacks, the disaggregation method can be adapted using country-specific food composition databases and therefore, flexibly applied to different definitions of added and free sugars. This method may also be used to assess intake of different populations, including preschool-aged children.

Measuring meaningful CMR in preschool-aged children is an important and challenging task, which may track into adulthood [89]. Measurements such as anthropometrics, insulin resistance, BP, and dyslipidemia are used to assess pediatric CMR. Anthropometrics including body weight, WC, and BMI are the most investigated CMR factors in preschool-aged children as these variables are non-invasive and relatively simple and inexpensive to collect. A strong link between obesity and CMR, e.g., insulin resistance is well established in pediatric populations [90]. Furthermore, given that visceral adipose tissue (VAT) is well known to have a greater impact on metabolic dysregulation and controversial evidence of metabolically healthy obese children, VAT-specific adiposity measurements such as WC may be preferrable [91]. There is also evidence for insulin resistance in children, however, the lack of accepted reference ranges is a barrier to the interpretation of these findings [90] A recent review addresses the validity of current methods of measurement and diagnosis of pediatric insulin resistance [92].