Introduction

Obesity has become a major global epidemic with substantial health implications for children and adolescents worldwide [1, 2]. Weight problems and obesity are increasing in most of the EU Member States [3]. Overall, in the 33 countries of the WHO European region that collected data in the fifth round of the Childhood Obesity Surveillance Initiative (COSI), 29% of children aged 7–9 years old were affected by having overweight and/or obesity. The prevalence ranged from 6% in Tajikistan to 43% in Cyprus (3). In addition, obesity among children and adolescents tends to persist into later life, thus increasing the risk of obesity during adulthood [3, 4].

It is well known that eating habits and dietary patterns acquired during childhood are likely to be maintained into adulthood [5]. Obesity is considered as a risk factor in the development of Metabolic Syndrome (MetS) [2, 4, 5]. MetS is a complex disorder affecting individuals across all age groups, including children and adolescents [1, 2]. Having MetS increases the risk of developing type 2 diabetes mellitus and cardiovascular disease [6]. In 2020, the worldwide prevalence of MetS was estimated to be 3% in children and 5% in adolescents [7].

The concept of diet quality has recently gained considerable attention in nutritional research. Despite its widespread use, it is often poorly defined and remains difficult to measure [8]. Diet quality describes the individual´s compliance to dietary recommendations that are often reflected in food-based dietary guidelines [8]. Moreover, in children, it can also refer to both the amount of nutrients and the uptake of specific nutrients from foods to support growth and maintenance [9]. Hence, a high diet quality reflects improved food intake [10].

Dietary Quality Scores or Indices (DQSs) are tools aiming to evaluate an individual’s overall diet and categorize individuals according to the extent to which their dietary habits are healthy [11]. The three major categories of DQSs are nutrients-based, food frequencies/food groups-based, or a combination of both [11]. Moreover, some DQSs are designed for specific countries or age groups and may not be applicable to other populations [12]. The DQI (Diet Quality Index), MDS (Mediterranean Diet Score), and HDI (Healthy Diet Index) are three internationally recognized and widely used DQSs [11,12,13].

Although low diet quality has been linked to obesity, the relationship remains unclear. The association between dietary components reflected in DQSs and the weight status of children and adolescents has not been extensively studied [14]. A cross-sectional study on children assessing three predefined DQSs found that higher scores in the DQI and HDI were significantly associated with lower weight status and waist circumference (WC), while no significant associations were observed with the MDS [13]. A cross-sectional study on diet quality and adiposity in children found that consumption of a low diet quality during early childhood is linked to obesity at the age of 6 years old [14]. Similarly, a longitudinal study assessing adherence to the MD in children found that high DQIs at 3 years old were associated with a lower risk of obesity, while adherence to the MD itself did not show any association [15]. Furthermore, an intervention program involving adolescents, which assessed the associations between adherence to the DQI and changes in body composition, found a significant association between higher DQI scores and improved z-score body mass index (BMI) and fat-free mass index (FFMI) [16].

Although only a few studies have documented the association between DQSs and MetS. A cross-sectional study showed a low prevalence of MetS in those adolescents with a low Healthy Eating Index (HEI). The study found that lower scores of HEI were associated with several adverse components of MetS (i.e., low high-density lipoprotein (HDL) concentrations, high triglycerides concentrations and high blood pressure levels) [17]. Similarly, another cross-sectional study of 4,450 US adolescents found that the prevalence of MetS decreased with higher adherence to the overall HEI [15]. Another study also showed that MetS prevalence was 16 times higher in adolescents with overweight [18]. A systematic review of DQSs, adiposity and MetS among children showed that improvements in diet quality could be associated with lower adiposity levels, and lower risk of developing MetS or its individual components [19]. Moreover, a cross-sectional study of 135 participants aged 6–17 years old in Turkey suggested that higher DQSs were associated with a lower proportion of children with insulin resistance [20], which is considered a common trigger of MetS [21]. Similarly, a longitudinal study in Australian children found that those adhering to the least healthy diet showed impaired functional cardiovascular phenotypes and an increased risk of MetS [22].

Given the current importance of obesity and MetS and the limited number of studies investigating the association between dietary quality and these disorders in children and adolescents, this systematic review and meta-analysis aims to analyse various diet quality scores and their association with obesity and MetS in children and adolescents.

Materials and Methods

This systematic review was conducted following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) recommendations [23]. This systematic review was preregistered on the international prospective register of systematic reviews (PROSPERO: CRD42021236079).

Two independent researchers (AL-G and LM) conducted a comprehensive search for scientific articles in the following electronic databases: Medline/PubMed (National Library of Medicine of the USA), Cochrane, Scopus (Elsevier), Embase (Elsevier) and SciELO Science Citation Index (Clarivate Analytics), without restrictions on period and location. The research was structured and organized according to the Population, Intervention, Comparison, Outcomes and Study type (PICOS) model. The population of interest or health problem (P) consisted of participants aged 2 to 20 years old; intervention (I) referred to any DQS related to obesity or MetS; comparison (C) did not involve a specific comparator; outcomes (O) included obesity and MetS; and the types of studies (S) included were cross-sectional, cohort studies and clinical trials.

Search Strategy

The descriptors were selected from the Health Sciences Descriptors (DeCS) dictionary and Medical Subject Heading Terms (MeSH), considering their widespread use by the scientific community for indexing articles in the PubMed database. Their suitability for the other databases used in the search was also considered.

The keywords and MeSH terms included in the search strategy were: “diet quality”, “diet patterns”, “diet score”, “Healthy Eating Indices, “diet (quality) index”, “diet indices”, “Dietary Approaches To Stop Hypertension”Mesh, “DASH Diet”, “Mediterranean Diet”, “Metabolic Syndrome”MeSH Terms, “Insulin Resistance”MeSH Terms, “insulin sensitive”, “Obesity”MeSH Terms, “Pediatric Obesity”MeSH Terms, “Body Composition”MeSH Terms, “Skinfold Thickness”MeSH Terms, “Body fat distribution”MeSH Terms, “Adiposity”MeSH Terms, “Body weight”MeSH Terms, “Body weight and measures”, “Body Mass Index”MeSH Terms, “Body fat distribution”MeSH Terms, “Fat mass index”, “Quetelet Index”, “Waist circumference”MeSH Terms, “Waist-hip ratio”MeSH Terms, “Waist-height ratio”MeSH Terms, “child”MeSH Terms, “Child, Preschool*”MeSH Terms, “adolescent”MeSH Terms, “Youth”, “Teen”, “Young people”. The search strategies applied were included in the Supplementary Material (Table S1).

A total of 3,519 articles were retrieved, and their titles were checked for duplications and relevance to the research topic. Subsequently, the retrieved articles were screened using the inclusion and exclusion criteria to identify their eligibility.

Eligibility Criteria

The review question was defined as follows: What is the association between diet quality scores (DQSs) and obesity and MetS in children and adolescents?

Thus, in the present report, articles were considered eligible if they met these inclusion criteria: (a) studies based on human subjects between 2 to 20 years old, (b) studies published in English and/or Spanish language, (c) studies using quantitative methods that examine DQSs and their association with body composition characteristics such as BMI, z-score BMI, WC, skinfolds and percentage of body fat or fat mass index, and metabolic syndrome indicators such as blood pressure, HDL-cholesterol, triglycerides and homeostasis model assessment (HOMA)-Index, in children and adolescents, (d) peer-reviewed observational and intervention studies (i.e., cross-sectional studies…).

Exclusion Criteria

Exclusion criteria comprised: (1) studies that they did not meet the above-mentioned inclusion criteria, (2) studies conducted in infants (< 2 years old) and among adults (> 20 years old), (3) meta-analyses, systematic reviews, literature reviews, narrative reviews, letters to the editor, and conference abstracts, (4) studies with missing information, unclear data, or unavailable in full text or in languages other than English or Spanish.

Risk of Bias

The methodological quality of the included studies was independently assessed by the reviewers (AL-G and LM) according to the Cochrane risk of bias guidelines. This assessment was conducted blindly, with the names of the authors and journals masked, to avoid potential bias and conflicts of interest.

Data Extraction

Data extraction for the study eligibility process was performed using a specific form for the systematic review, prepared by the researchers using an Excel file. Both researchers (AL-G and LM) independently added the extracted data to the file, which was then compared to ensure accuracy. Finally, another researcher (PM-E) reviewed the extracted data for verification. The first step of the screening process involved selecting articles based on their titles. Both researchers (AL-G and LM) independently reviewed all the titles and then reached a consensus on whether to include them or not. The next step was abstract selection and eligibility assessment. Articles selected based on the abstracts screening underwent full text review, and only those meeting all eligibility criteria were included. In cases of disagreement between the researchers, a third researcher (PM-E) made the final decision.

Collected Data

After the initial screening, the full text of the selected articles underwent a standardized review and data extraction process conducted by two researchers (AL-G and LM), under the supervision of a third researcher (PM-E). The following information was extracted: author names, publication year, study year, country where the research was conducted, study design, sample size (N), sex of the subjects split into female and male groups (N, %), age of participants, DQS used, measures of obesity, measures of MetS, study results, and quality control assessment.

Quality Assessment

The quality of the included studies was independently assessed by two of the authors (AL-G and LM) using the following tools: 1) for cross-sectional studies, the BSA Medical Sociology Group quality evaluation tool [24]; 2) for cohort studies, the Newcastle–Ottawa Scale [25]; 3) for intervention studies, the National Heart, Lung, and Blood Institute quality assessment tool for controlled intervention studies [26]. The results of the quality assessment are presented in supplementary Table Q1. Quality was rate as high, moderate, low, or very low according to the Grading of Recommendations Assessment, Development and Evaluation (GRADE) criteria [27].

Statistical Analysis

Articles using the same DQS and similar obesity outcomes were considered for meta-analysis. Only the Mediterranean diet (MD) scores were included in the meta-analysis, as they were the only ones that provided the necessary data to perform the analysis.

For continuous data (BMI, kg/m2 and WC, cm), the mean difference with 95% confidence intervals (95% CI) was calculated to compare individuals with low adherence to the MD score versus those with high adherence. DerSimonian and Laird estimators using random-effects models were applied for continuous data. Effect sizes were calculated for each outcome.

Sources of heterogeneity were investigated through subgroup analyses comparing results by sex (boys vs. girls) and age (< 12 years old vs. > 12 years old). All analyses were performed by AL-G and MM-B using Open Meta [Analyst] software.

The heterogeneity of the studies was tested using the I2 statistic, with values of 50% to 75% indicating high heterogeneity and values > 75% indicating very high heterogeneity. The associated p-value of the heterogeneity of the studies was also calculated, with a nonsignificant result indicating absence of heterogeneity.

To explore the influence of potential sources of heterogeneity on the high MD score adherence, a meta-regression analysis assuming a random-effects model was performed considering BMI, z-score BMI and WC as predictors, grouped by sex and age.

Results

A flow chart summarizing the study selection procedure is presented in Fig. 1 The screening process involved searching five relevant electronic databases (PubMed, Cochrane, Scopus, Embase and SciELO), resulting in the retrieval of 3,519 articles. After removing duplicates, a total of 2,284 articles were screened. Following the initial screening based on article titles, 262 articles underwent abstract screening, and 99 articles were assessed based on full text to decide their final inclusion. The main reasons for study exclusion were the absence of diet quality scores (DQSs) (n = 1,604), inappropriate publication type (n = 59), unsuitable age range (n = 372), unrelated disease (n = 17) or non-English/Spanish language (n = 1). A total of 73 articles met the inclusion criteria and were included in this systematic review and meta-analysis.

Fig. 1
figure 1

PRISMA flowchart of study selection

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Data extraction revealed 36 different DQSs assessing the association with obesity and/or MetS in children and adolescents, with various outcomes related to obesity and MetS indicators.

The characteristics of the selected studies are reported in Tables 1 and 2, divided by type of DQSs: “Mediterranean diet-based scores” and “different dietary quality scores”, respectively.

Table 1 Summary of the findings for different Mediterranean diet adherence indices and body composition and metabolic syndrome measures
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Table 2 Summary of the findings for different diet quality indices and body composition and metabolic syndrome measures
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Overall, thirty eight studies (52%) were conducted in European countries [15, 16, 28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63], ten studies (14%) were conducted in Asian countries [64,65,66,67,68,69,70,71,72,73], two studies (3%) were conducted in Australia [74] and New Zealand [75], and one study was conducted in Morocco [76]. The remaining studies were carried out in the Americas, with 29% conducted in U.S and Canada [18, 77,78,79,80,81,82,83,84,85,86,87,88,89], and Latin America (including Caribbean) [17, 90,91,92,93,94,95]. Additionally, one study was conducted in Africa [96]

The studies were evaluated as cross sectional (n = 53, 72%), cohort (n = 15, 22%), and clinical trials (n = 4, 6%).

Tools Used to Assess Dietary Quality Scores in the Studies

Mediterranean Diet Scores

A total of 37 studies examined the Mediterranean dietary pattern. Among these, 25 used the KIDMED score, with one study considering the Italian version [38]. Of these studies, 11 were conducted in Spain [40,41,42, 45,46,47, 50,51,52, 54, 57], 3 in Greece [29, 49, 58], 3 in Turkey [67, 69, 70], 2 in Italy [33, 60], and 5 in other European countries [39, 48, 59, 62, 63]. Additionally, other DQSs related to the Mediterranean dietary pattern were identified. Three articles used the Mediterranean Diet Score (MDS) [34, 35, 55], while the Relative Mediterranean Diet Score (rMDS) and the Frequency-Based Mediterranean Diet Score (fMDS) were each used in one study [37, 43]. One study used both the fMDS and a Diet Quality Index (DQI) [97], while another study used three scores: aMedDiet, Dietary Approaches to Stop Hypertension (DASH) and Childrens’s Dietary Inflammatory Index (C-DII) [92]. One study used both the KIDMED score and the MedLIFE-index [30]. Further, one study used the KIDMED score alongside with two other dietary scores (DQI-A and Healthy Lifestyle Diet-Index (HDL-I)) [44]. Another study used the KIDMED and an Index of a healthy Alimentation diet for the Spanish population (IAES) [53]. Moreover, one study used a combination of the Healthy Eating Index-2015 (HEI), Alternative HEI-2010 (AHEI) and MedDiet [81], while another study used the Mediterranean-Style Dietary Pattern Score (MSDPS) [73].

Other Diet Quality Scores

Different versions of the HEI were used in 17 studies: one study used the HEI [18], one study used the HEI-2005 [90], 4 studies used the HEI-2010 [32, 77, 85, 91], and 4 studies used the HEI-2015 [72, 78, 80, 83]. Moreover, adaptations of the HEI were also employed: one study utilized the Modified HEI [66], another used the Finland Children Healthy Eating Index (FCHEI) [31], and one study employed the Brazilian Healthy Eating Index Revised (BHEI-R) [17]. Additionally, one study used both HEI-2005, HEI-2010, along with the Dietary Guidelines for American Adherence Index (DGAI) [68]. Another study used the AHEI-2010 and the C-DII [88], while another study used the AHEI-2010 and DASH [89]. One study combined the AHEI-2010 and the BHEI-R [5].

DQI was employed in 7 studies. Among these, 4 studies used the DQI for Adolescents (DQI-A). Specifically, one used the DQI-A [16], another study also used the DQI including Physical Activity (DQI-PA) [28], and a third study used the New Zealand DQI-A (NZDQI-A) [75]. Additionally, one study used three types of DQSs: KIDMED, DQI-A and HDL-I [44]. The other DQI studies used two different versions of the score: the DQI based on Australian Dietary Guidelines [74] and the DQI-International (DQII) [86]. Finally, one study used the Prime Dietary Quality Score (PDQS) [96].

Regarding the DASH score, 5 studies used this score: 3 studies employed the DASH score [64, 65, 93], one study used the Modified DASH score [82] and another study used both the DASH score and the Canada’s Food Guidelines (CFG) [84].

Seven additional studies using different DQSs were identified. One study used the validated Dietary Quality Score (DQS) [36] while another considered the Children’s Food Trust (CFT) and the Nutrition and Physical Activity Self-Assessment for Child Care UK Nutrition Best Practice Standards (NAP SACC UK) [61]. Furthermore, the Mediterranean-DASH Intervention for Neurodegenerative Delay (MIND) was employed in another study [71]. The Lifelines Diet Score (LLDS) [56], the Krece Plus Test [94], and the modified version of the Chinese Children Dietary Index (mCCDI) [79] were each used in separate studies. Finally, one study combined the use of Dietary Diversity Score (DDS) and food variety score (DVS) [76].

Obesity Assessment

Most of the studies found were assessing associations with obesity outcomes (n = 52; 73%). All the studies used the measures of weight, height and BMI as obesity related measures. Furthermore, 22 out of 52 also included WC as an obesity-related outcome. Some studies combined BMI and WC with other body composition measurements, such as skinfolds [16, 37, 40, 41, 55, 85] or body fat percentage [30, 39, 42, 58, 59, 62, 63, 65, 73, 75, 80, 84, 86].

Metabolic Syndrome Assessment

A total of 21 studies (29%) measured MetS. All the studies used outcomes such as abdominal obesity, insulin resistance, hypertension, and dyslipidemia, except for 3 studies [28, 31, 47]. Additionally, total cholesterol and triglycerides were measured in some studies [17, 18, 32, 44, 47, 52, 64, 66, 68, 70, 78, 79, 88, 89, 92, 95], and six studies considered the HOMA index [28, 31, 52, 80, 92, 95].

Meta-Analysis Results

The meta-analysis was comprised of data from six original articles that conducted analyses with 5 separate cohort studies: GRECO (Greece); EYZHN (Greece); INMA (Spain); AdolesHealth (Estonia, Iceland and Spain); and an additional cross-sectional sample of school children from Spain. However, it is important to note that the AdolesHealth study involved participants from Estonia in one study and participants from Estonia, Iceland and Spain in another study. These studies provided comparable information to perform a meta-analysis [30, 43, 47, 58, 59, 63]. Figures 2 and 3 shows the individual study results and plot the global effect of adherence to the Mediterranean Diet and BMI and WC.

Fig. 2
figure 2

Random-effects meta-analysis of the effects of low adherence versus high adherence to the MD scores on BMI. A BMI differences between low and high adherence to the MD scores. B Subgroup analyses by sex. C Subgroup analyses by age. Abbreviations: MD, Mediterranean diet; BMI, Body Mass Index

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Fig. 3
figure 3

Random-effects meta-analysis of the effects of low adherence versus high adherence to the MD scores on WC. A WC differences between low and high adherence to the MD scores. B Subgroup analyses by sex. C Subgroup analyses by age. Abbreviations: MD, Mediterranean diet; WC, Waist Circumference

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As shown in Fig. 2A, there are no statistically significant differences in the BMI of children with a high adherence to the MD score compared to those with a low adherence score (MD = 0.22 kg/m2, 95% CI: -0.06, 0.50). However, due to high variability among the included studies (96% heterogeneity), a trend is observed suggesting that children with a high MD score adherence tend to have a lower BMI than those with a low MD score adherence. In subgroup analysis by sex (Fig. 2B), no statistically significant differences are shown in the BMI of children with a high MD score adherence compared to those with a low MD score adherence (MD = 0.19 kg/m2, 95% CI: -0.16, 0.55). Regarding age, (Fig. 2C), statistically significant differences in BMI are observed among children younger than 12 years old, favouring those with a high MD score adherence over those with a low MD score adherence (MD = 0.33 kg/m2, 95% CI: 0.01, 0.64). However, in children older than 12 years old, no statistically significant differences are observed in BMI between those with a high MD score adherence and those with a low MD score adherence (MD = -0.13 kg/m2, 95% CI: -0.09, 0.68). Nevertheless, considering the high proportion of heterogeneity among the studies included in the meta-analysis (97% heterogeneity), these differences may not be clinically relevant.

For WC, as shown in Fig. 3A, there are statistically significant differences favouring children with a high MD score adherence compared to those with a low adherence to the MD score (MD = 0.87 cm, 95% CI: 0.26, 1.48). Subgroup analysis by sex (Fig. 3B), does not reveal statistically significant differences in WC between children with high MD score adherence and those with a low adherence score (MD = 1.00 cm, 95% CI: -0.13, 2.13). However, subgroup analysis by age (Fig. 3C), shows statistically significant differences in WC among children younger than 12 years old, favouring those with high MD score adherence over those with low adherence scores (MD = 1.22 cm, 95% CI: 0.50, 1.94). No statistically significant differences are found among children older than 12 years old between those with high adherence scores and those with low adherence scores (MD = 0.03 cm, 95% CI: -1.25, 1.31).

A meta-regression analysis was performed to assess the potential effect of sex and age on the association between adherence to the MD score and BMI, z-score BMI, and WC (Table 3). In children older than 12 years old, the association between adherence to the MD score and BMI, as well as WC, is stronger (β = 21.77, 95% CI: 18.79, 20.67 and β = 72.02, 95% CI: 70.50, 73.54, respectively), compared to younger children. In addition, boys show stronger associations between MD score adherence and BMI, z-score BMI and WC (β = 19.82, 95% CI: 17.62, 22.03, β = 0.64, 95% CI: 0.32, 0.95 and β = 67.03, 95% CI: 57.29, 76.77, respectively) compared to girls.

Table 3 Meta-regression analysis of the predictors of having a high MD adherence score
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Discussion

The current systematic review and meta-analysis offers a comprehensive summary of the relationship between diet quality scores (DQSs), which have been developed to assess the overall diet quality, and their relationship with obesity and MetS during childhood and adolescence. The MD scores were the most widely used scores to assess the overall diet quality and its association with obesity in children. The main findings of the meta-analysis indicate that high adherence to the MD was associated with low obesity measures but only in adolescents. While we did not perform a meta-analysis for MetS outcome, the published literature suggests that high adherence to a DQS has beneficial effects on MetS and its components in children and adolescents.

Our systematic review identified 25 studies assessing the association between MD adherence using the KIDMED index and obesity measures such as BMI, z-score BMI, and WC. Among these, six articles did not found a significant association between MD adherence and various obesity measures, such as height, weight, BMI and WC. This lack of association could be due to the inclusion of young children. It is more common for associations between MD scores and obesity outcomes tend to emerge more frequently at older ages, as proper adherence to the MD, and consequently its effectiveness, requires time to manifest. Hence, monitoring changes in children´s adiposity is essential for understanding the development of metabolic diseases [98]. These results are consistent with findings in adults, which suggest that greater adherence to the MD may have a protective effect against cardiovascular disease and the development of obesity [99].

Regarding the 5 studies using adapted versions of the MDS, the study using the fMDS found an inverse association between the total score and measures of overweight, obesity and fat mass percentage. An increased fMDS score was found to be a protective factor against high BMI, WC and waist-to-hip-ratio. The study using the rMDS showed an association between high adherence to the MD score at the age of four years old and a low incidence of overweight, obesity or abdominal obesity [43]. This significant association may be attributed to the modified nature of the score used, adapted to the dietary habits of children and adolescents, and calculated based on sex and age [100]. Moreover, the food frequency questionnaires employed in the study covered a longer period of time than other dietary assessment methods, as they involved 24-h dietary recalls. These findings align with those observed in adults, further strengthening the evidence for the MD in preventing overweight and obesity in early childhood.

In this systematic review, only 4 studies investigated the association between MD scores and MetS [33, 44, 47, 49]. This finding may be due to the broader use of the DASH dietary recommendations are more commonly used in preventing the onset of MetS compared to MD recommendations [101]. Concerning the DASH diet, three studies included in this systematic review found an association between the DASH diet and obesity; participants adhering to the diet showed lower obesity measures [64, 65, 84]. One of the studies using the DASH score [64] suggested that the risk of MetS decreases with increasing adherence to the DASH diet.

In relation to the HEI, we found several articles that investigated its association with obesity and MetS. Of the six articles that focused on both diseases, four of them reported that as the HEI score, and its versions, increase, the risk of developing MetS decreases. This finding may be due to the HEI being based on the recommendations of the Dietary Guidelines for Americans, which provide guidance on preventing diet-related chronic diseases, such as cardiovascular disease [102]. In the case of articles associating different versions of HEI with obesity, there are less favourable results for weight status, although three of them found inverse significant associations between HEI scores and the prevalence of overweight or obesity.

Of the six included versions of the DQI included in this systematic review, only one article, which utilized the DQI-A and DQI-PA [28] investigated the association with MetS. The remaining five studies focused on obesity; four of them reported that higher adherence to the DQI was associated with reduced obesity outcomes, such as z-score BMI or body fat percentage. Moreover, The DQI-I, has been adapted for use in a range of countries with different dietary patterns [103]. However, the mentioned study that used an adapted version of the DQI-I according to the Australian dietary guidelines did not find the expected associations between the adapted score and obesity measures. As suggested in a recent systematic review, the construction of population-based DQSs in Australia and New Zealand should be adjusted to align with the original scoring systems [104]. This is particularly important given that nutritional provision in Australian child care settings often fails to comply with national nutritional guidelines for children in early childhood settings [105].

The meta-analysis conducted in this investigation identified significant associations between higher adherence to MD scores and lower obesity measures. Specifically, for both BMI and WC, statistically significant associations favouring higher MD scores were observed among children younger than 12 years old. This finding may suggest that the effects of consuming foods traditionally included in the MD may require more time to manifest during childhood [106]. In addition, it is important to consider the interplay between MD and other lifestyle behaviours (i.e., eating behaviors, physical activity) in terms of obesity development [55].

This systematic review and meta-analysis present some limitations. First, there is a variety of DQSs used to assess diet in the same population, which may introduce heterogeneity into the analyses. Second, some of the studies included in this systematic review did not investigate the association between all obesity measurements, thus limiting the results presented in the meta-analysis and making it challenging to draw conclusions regarding body composition and its association with MD, because the most frequently used obesity measures were BMI and WC. Third, this review did not include grey literature such as conference proceedings, doctoral theses, and technical reports, which could also prove valuable insights. Additionally, although many studies showed high quality (44%), a substantial proportion of them had medium to low quality (48%). Nevertheless, the studies included in the meta-analysis were of high and moderate quality. Most of the included studies were cross-sectional, making it difficult to establish causality. However, our systematic review included a few prospective studies and clinical trials which may reduce the risk of reverse causation. Finally, it is worth noting that the majority of the reviewed studies were conducted among European samples, therefore, the generalizability of these findings to other populations should be evaluated.

A major strength of this evidence synthesis is the comprehensive assessment of studies examining the association between DQSs and obesity and MetS, providing an updated overview of the literature. This review followed strict procedures to ensure the validity of the results. It was registered in the PROSPERO database, followed the PRISMA protocol, involved two reviewers in the development process, conducted quality evaluations of the included studies, and performed a meta-analysis.

Based on the findings of this investigation, an important consideration for future research in this area is the standardization of DQSs. Unifying DQSs would allow for a more uniform assessment of the relationship between diet quality and various parameters used to assess obesity and MetS. This could improve comparability between studies and facilitate a better understanding of the impact of diet quality on health outcomes in children and adolescents.

Conclusions

This systematic review and meta-analysis suggest that high adherence to diet quality scores has a positive effect on obesity measures and MetS in children and adolescents, although a few studies found no associations between diet quality and the studied outcomes. Specifically, adherence to MD aligns with existing evidence in adults, reinforcing its protective effect against obesity and cardiovascular disease.

The use of DQSs in pediatric populations is becoming more widespread in epidemiology. While DQSs play a role in observational studies, they are also emerging as valuable tools in clinical and interventional studies. However, more research is needed to draw stronger conclusions about the risk of developing health problems in the pediatric population in relation to the different DQSs. Efforts to standardize these scores are necessary to identify those most useful for effectively screening obesity risk.

Future research should focus on prospective study designs and randomized controlled trials to establish causality and provide stronger evidence. Moreover, expanding research beyond European populations would enhance the generalizability of findings to broader demographic groups.

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