As obesity prevalence has risen around the world, research on dietary factors influencing BMI has proliferated(Reference Asghari, Mirmiran and Yuzbashian1). Of particular interest is the distribution of body fat, foremost the amount of visceral (VAT) v. subcutaneous adipose tissue, an important predictor of chronic disease beyond BMI(Reference Shuster, Patlas and Pinthus2,Reference Calle and Kaaks3) . VAT adipocytes are more metabolically active and insulin-resistant than those in subcutaneous adipose tissue, which may be among the mechanisms responsible for the higher incidence of type 2 diabetes(Reference Pearson-Stuttard, Zhou and Kontis4), postmenopausal breast cancer(5) and colorectal cancer(Reference Lathigara, Kaushal and Wilson6) associated with abdominal adiposity(Reference Fujimoto, Boyko and Hayashi7–Reference Nimptsch, Konigorski and Pischon9). Given that waist circumference cannot distinguish between subcutaneous adipose tissue and VAT(Reference Tchernof and Despres10), imaging techniques, that is, computed tomography (CT), MRI, dual-energy X-ray absorptiometry (DXA) or ultrasound need to be applied for their assessment(Reference Shuster, Patlas and Pinthus2).
Dietary patterns as a method of assessing overall dietary intake instead of individual food consumption have emerged as a useful approach to assess overall diet quality(Reference Hu11), which can be classified into a priori and a posteriori diet quality patterns (DQP). A priori DQP, also known as diet quality indices, are based on dietary recommendations and scientific evidence about diet and health-related outcomes(Reference Wirt and Collins12). They evaluate adherence to specific dietary guidelines, such as the Healthy Eating Index (HEI)-2010(Reference Guenther, Reedy and Krebs-Smith13,Reference Guenther, Casavale and Reedy14) , the Alternate HEI(Reference Chiuve, Fung and Rimm15), the Alternate Mediterranean Diet Score(Reference Fung, Hu and Wu16), the Dietary Approaches to Stop Hypertension(Reference Fung, Chiuve and McCullough17), the Dietary Inflammatory Index(Reference Shivappa, Steck and Hurley18) and several lesser-known ones(Reference Lassale, Fezeu and Andreeva19–Reference Ratjen, Morze and Enderle21). In contrast, a posteriori DQP are identified through exploratory data-driven approaches(Reference Hu11), for example, factor analysis, principal component analysis or cluster analysis(Reference Newby and Tucker22), which use observed correlations among dietary variables to create a pattern based on reported intakes and allow the discovery of new diet-disease associations(Reference Steffen and Hootman23). Despite differences in recommended food groups across indices, high-quality diets generally score high in fruits, vegetables, whole grains, dairy products, nuts and legumes, whereas meat products, processed foods, refined grains, Na and sugar-sweetened beverages lower the scores(Reference Liese, Krebs-Smith and Subar24).
Early on, a relation of a high Mediterranean diet with lower waist circumference as a measure of abdominal fat was described in a European study(Reference Romaguera, Norat and Mouw25). Starting 20 years ago, MRI and CT-based imaging studies reported significant inverse associations of DQP with VAT(Reference Liu, Hickson and Musani26,Reference Bertoli, Leone and Vignati27) with approximately 15 % lower values in the highest v. lowest tertile(Reference Hennein, Liu and McKeown28,Reference Panizza, Wong and Kelly29) . Given the rising number of reports exploring the association between DQP and VAT as new imaging methods were developed, it is important to understand the current state of knowledge. Therefore, the aims of the present project were to summarise findings for the association of a priori and a posteriori DQP with VAT assessed by an imaging technique and to evaluate differences according to design features, that is, the type of DQP, dietary assessment method, imaging technique, age, bias, location and adjustment for BMI or an alternate measure of body size.
Methods
Overview
This systematic review was carried out in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement(Reference Moher, Liberati and Tetzlaff30). The aim was operationalised according to the PI/ECO (Population, Intervention/Exposure, Comparison, Outcome) scheme after the inclusion and exclusion criteria had been determined a priori. We included studies conducted in disease-free children/adolescents or adults recruited from a general population, that is, not selected as patients with an underlying condition, using a priori or a posteriori DQP as exposure and VAT as outcome.
Search strategy and study selection
Based on pre-defined eligibility criteria, synonymous keywords for each search term were included in the search string (online Supplementary Table 1) after registering the protocol on PROSPERO (CRD42022366565). The systematic search was conducted on 22 October 2022 in PubMed and EMBASE via OVID using the same search string. The 628 hits on PubMed and 958 hits on EMBASE with 573 duplicates were imported into the CAMARADES Preclinical Systematic Review and Meta-analysis Facility for title–abstract screening (https://app.syrf.org.uk/) last accessed on 27 December 2022. To update the literature, a new search using the same strategy was performed in January 2024, which identified seventy-nine additional hits on PubMed and 142 on EMBASE with seventy-one duplicates.
As shown in the PRISMA flow diagram (Fig. 1), this review was limited to human observational studies written in the English language, which included adults or children older than 5 years. Studies with patients or pregnant women were excluded. Diet quality had to be assessed by a priori or a posteriori DQP. The original studies needed to allow a quantitative comparison of VAT, measured by one of four established imaging methods (MRI, CT, DXA or ultrasound)(Reference Shuster, Patlas and Pinthus2). CT uses X-rays to produce cross-sectional images of the body and differentiate between fat and other tissues, while MRI distinguishes tissue types based on their water and fat content in the abdominal region where VAT is concentrated(Reference Hemke, Buckless and Torriani31). In addition, a DXA method, which has been repeatedly validated against CT and MRI(Reference Kaul, Rothney and Peters32–Reference Maskarinec, Shvetsov and Wong34), analyses the differential attenuation of two X-ray beams with different energy levels to assess the amount of fat tissue and to estimate VAT based on fat distribution patterns through specific algorithms(Reference Hemke, Buckless and Torriani31). Ultrasound has been applied less commonly so far; body fat is measured 1 cm above the umbilicus as the distance between the anterior wall of the aorta and the posterior surface of the rectus abdominis muscle at the level of linea alba using an ultrasonographic probe(Reference Bertoli, Leone and Vignati27,Reference De Amicis, Galasso and Leone35,Reference Gradmark, Rydh and Renstrom36) .
Fig. 1. Flow chart of literature search and study selection.
Abstracts without a published full-text manuscript were excluded. The title–abstract screening on Systematic Review and Meta-analysis Facility was performed independently by two reviewers (AT and GM) to ensure that all relevant titles were retrieved after removing duplicates using a reference database. Each reviewer assigned a reason for exclusion, that is, animal study, intervention/randomised controlled trial, not an original study, for example, review/no original data, tissue/cell study, patients with chronic disease, no VAT measure or no DQP, and disagreements were solved by a third reviewer (KN). For the remaining papers, the full texts were screened independently by the two reviewers, and the third reviewer resolved conflicts. The references of all studies were screened to identify eligible reports to be added.
Data extraction
For studies that fulfilled the inclusion criteria, detailed information relevant to our research question was extracted: first author, year of publication, country, study design, sample size, ethnicity, mean age, sex, diet assessment method, DQP approach (a priori or a posteriori) and type, mean VAT value and VAT assessment method, for example, MRI, DXA, CT or ultrasound. For a posteriori DQP, we recorded the statistical technique, for example, principal component analysis, plus the number of food or nutrient items included. We summarised statistical methods, covariates, comparison groups (categorical or continuous), a unit of VAT measure and effect measures for VAT, that is, adjusted means, 95 % CI, standard error, P-value and type (e.g. tertiles or continuous DQP). If multiple models were applied, the adjusted mean for the model controlling for body size, that is, BMI, weight and total body fat, was extracted. If none of the models controlled for body size, the results for the model with the most comprehensive adjustment set were extracted. In studies reporting no exact mean values for age and sex, an approximate mean value was estimated. After finishing the extraction, the second reviewer checked the information.
Assessment of quality and risk of bias
With the help of the Risk of Bias in Non-randomised Studies of Interventions (ROBINS-I) tool(Reference Sterne, Hernan and Reeves37), the two reviewers independently assessed the methodological quality of the included studies (online Supplementary Table 2). This Cochrane assessment tool, when adapted for observational studies, consists of six bias domains: (1) confounding, (2) selection of participants, (3) exposure assessment, (4) missing data, (5) measurement of the outcome and (6) selective reporting of the results. The overall judgement of risk of bias was categorised as low, moderate or serious(Reference Sterne, Hernan and Reeves37). If at least one domain was rated as serious, then the overall judgement was serious. If a low or moderate risk of bias was found for all domains, then the overall judgement was classified as moderate.
Analysis
Tables showing the VAT-effect measures sorted by DQP and a graphical representation of the direction of the observed association were created from the data extraction tables. Also, a summary table presents the study results by design characteristics. As these varied across studies, a separate narrative synthesis was prepared for each DQP. VAT-effect measures were compared between high and low DQP scores either in quantiles or as continuous measures. When available, results adjusted for body size were selected to demonstrate the association of DQP with VAT beyond the influence of BMI.
Results
From a total of 1807 hits in both databases (Fig. 1), 644 duplicates were removed. Of the remaining 1163 publications, 1103 were excluded due to the following reasons (Fig. 1): interventions/clinical trials (n 412), animal studies (n 178), study population consisted of patients (n 184), no DQP (n 163) and other criteria (n 166). After screening the remaining sixty publications in full text (see the ‘Methods’ section), thirty-four publications plus one study found in the bibliography of a selected paper were included, thus, resulting in thirty-five population-based observational studies for this review.
Of the thirty-five publications, twenty-six examined a priori (Table 1) and nine a posteriori DQP (Table 2). All had a cross-sectional design and, with the exception of six primary analyses(Reference Bertoli, Leone and Vignati27,Reference Panizza, Wong and Kelly29,Reference De Amicis, Galasso and Leone35,Reference Correa, da Costa and Silva39,Reference Landry, Asigbee and Vandyousefi47,Reference de Paula Mancilha, Massarani and Vieira58,Reference Yin, Chen and Lu65) , were secondary analyses of prospective cohorts. The analytical sample sizes ranged from 59(Reference Correa, da Costa and Silva39) to 9640(Reference Liu, Steele and Li48) participants. Two studies included only women(Reference Makura-Kankwende, Gradidge and Crowther62,Reference Makura-Kankwende, Gradidge and Crowther63) , one included only men(Reference Correa, da Costa and Silva39) and three were performed on adolescents (one in Hispanic freshmen 18–19 years, one in Brazilian students 10–16 years and one in low-income US kids 10–16 years)(Reference Hu, Button and Tate44,Reference Landry, Asigbee and Vandyousefi47,Reference de Paula Mancilha, Massarani and Vieira58) , but no studies in children were identified. As to adults, four studies reported a mean age in the 20s, two in the 30s and five in the 40s, whereas the remaining ones were conducted in populations 50 years and older with twelve studies in the 50s and nine in the 60s. The majority of studies reported mean BMI in a range of 25–30 kg/m2; six studies showed values of 30 kg/m2 or higher and eight studies a mean below 25 kg/m2. As to location, sixteen were conducted in the USA(Reference Ratjen, Morze and Enderle21,Reference Liu, Hickson and Musani26,Reference Hennein, Liu and McKeown28,Reference Panizza, Wong and Kelly29,Reference Bellissimo, Bettermann and Tran38,Reference Holthaus, Sethi and Cannavale43–Reference Isanejad, Steffen and Terry45,Reference Landry, Asigbee and Vandyousefi47,Reference Liu, Steele and Li48,Reference Lozano, Wilkens and Shvetsov50–Reference Molenaar, Massaro and Jacques53,Reference Odegaard, Jacobs and Van Wagner55,Reference Shah, Murthy and Allison56) , four in South Africa(Reference Mtintsilana, Micklesfield and Chorell54,Reference Makura-Kankwende, Gradidge and Crowther62–Reference Ratshikombo, Goedecke and Soboyisi64) and three in Germany(Reference Jennings, Kuhn and Bondonno46,Reference di Giuseppe, Plachta-Danielzik and Koch59,Reference Fischer, Moewes and Koch60) , while the remaining came from Italy(Reference Bertoli, Leone and Vignati27,Reference De Amicis, Galasso and Leone35) , Spain(Reference Flor-Alemany, Marin-Jimenez and Nestares40,Reference Galmes-Panades, Konieczna and Abete42) , Brazil(Reference Correa, da Costa and Silva39,Reference de Paula Mancilha, Massarani and Vieira58) , the Netherlands(Reference van Eekelen, Geelen and Alssema20), Portugal(Reference Lopes, Fontes and Menezes49), Sweden(Reference Friden, Kullberg and Ahlstrom41), Japan(Reference Ito, Kawakami and Tanisawa61), Korea(Reference Shim, Ha and Kim57) or China(Reference Yin, Chen and Lu65). Twelve studies investigated multiple ethnic groups(Reference Panizza, Wong and Kelly29,Reference Bellissimo, Bettermann and Tran38,Reference Holthaus, Sethi and Cannavale43–Reference Isanejad, Steffen and Terry45,Reference Liu, Steele and Li48,Reference Lozano, Wilkens and Shvetsov50–Reference Maskarinec, Namatame and Kang52,Reference Odegaard, Jacobs and Van Wagner55,Reference Shah, Murthy and Allison56) , while the remaining studies focused on one ethnic group (n 18) or did not report any details (n 5). Dietary assessment tools included different types of FFQ (n 26), but short questionnaires (n 4), 24-h recalls (n 3), a 3-d food record (n 1) and a diet history (n 1) were also used. VAT was measured by DXA (n 14), MRI (n 13), CT (n 6) or ultrasound (n 2).
Table 1. Characteristics of studies using a priori diet quality patterns
* ISO 3166–1 alpha-2 codes.
† A, Asian; AA, African (American); AI, American Indian/Alaska Native; H, Hispanic; NH, Native Hawaiian; O, Other; W, white.
‡ Mean ± sd or median (IQR).
§ AHEI, Alternate HEI; aMED, Alternate Mediterranean Diet Score; DASH, Dietary Approach to Stop Hypertension; DGAI, Dietary Guidelines Adherence Index; DHD, Dutch Dietary Guidelines Index; DQP, diet quality pattern; E-DII, Energy-Adjusted Dietary Inflammatory Index; hPDI, healthy plant-based diet index; MED, Mediterranean Diet Score; MIND, Mediterranean Intervention for Neurodegenerative Delay; PDI, plant-based diet index; UPF, ultra-processed food; uPDI, unhealthy plant-based diet index.
|| Qx, questionnaire; (s)QFFQ, (semi-)quantitative FFQ.
¶ CT, computed tomography; DXA, dual-energy X-ray absorptiometry; VAT, visceral adipose tissue.
Table 2. Characteristics of studies using a posteriori diet quality patterns
* ISO 3166–1 alpha-2 codes.
† A, Asian; AA, African (American); H, Hispanic; O, Other; W, white.
‡ Mean ± s d or median (IQR).
§ PCA, principal component analysis; PLS, partial least squares regression; RRR, reduced-rank regression.
|| BDHQ, brief-type self-administered diet history questionnaire; (s)QFFQ, (semi-)quantitative FFQ.
¶ CT, computed tomography; DXA, dual-energy X-ray absorptiometry; VAT, visceral adipose tissue.
A priori DQP (Table 1) included Mediterranean Diet Score (MED) (n 10), several versions of the HEI (n 8), Alternate HEI (n 2), Dietary Approaches to Stop Hypertension (n 3) and one each of the Dietary Guidelines for Americans Adherence Index, Dutch Dietary Guidelines Index, plant-based diet index, Diet Quality Score and fast-food intake. The Energy-Adjusted Dietary Inflammatory Index (E-DII) (n 3) and the ultra-processed food (UPF) according to NOVA classification (n 3) were scored reversely; that is, a lower score represents better diet quality (Table 1). A posteriori DQP (Table 2) were mostly identified through principal component analysis with the exception of one study each using partial least squares regression or reduced-rank regression(Reference di Giuseppe, Plachta-Danielzik and Koch59). As expected, they usually derived specific patterns with different labels.
The results of all thirty-five investigations, classified as no, partial (mixed findings for a posteriori DQP) or significant association, that is, higher diet quality associated with lower VAT, indicated several differences according to design characteristics (Table 3). Of studies investigating a priori DQP, 81 % showed significant associations in the expected direction, but the findings for a posteriori DQP were more likely to be null. By geographic location, the proportion of significant results was higher for US studies than in other areas. By age, more significant findings were seen in younger than older participants (79 v. 52 %). Investigations in multi-ethnic populations were more likely to indicate significant associations than studies with one group only (82 v. 44 %). No major differences were seen by dietary assessment method, imaging method, year of publication and body-size adjustment. Studies with and without body-size adjustment showed a similar rate of significant findings (65 v. 60 %).
Table 3. Summary of included studies according to design characteristics
* DQP, diet quality pattern; VAT, visceral adipose tissue.
† CT, computed tomography; DXA, dual-energy X-ray absorptiometry.
‡ Significant association of higher diet quality with lower VAT.
Looking at the HEI and similar DQP (Table 4), all reported relations were significantly inverse. For example, of the seven studies investigating the HEI(Reference Panizza, Wong and Kelly29,Reference Holthaus, Sethi and Cannavale43–Reference Isanejad, Steffen and Terry45,Reference Maskarinec, Lim and Jacobs51,Reference Maskarinec, Namatame and Kang52) , the HEI-2015 was inversely associated with the Coronary Artery Risk Development in Young Adults (CARDIA) study with respective VAT values of 137 v. 117 cm3 in the lowest v. highest quantile(Reference Isanejad, Steffen and Terry45) as well as in the Multiethnic Cohort study with lower median VAT values (161 v. 175 cm2) in the highest than the lowest tertile(Reference Maskarinec, Namatame and Kang52). Among Hispanic adolescents, a one-point increase in HEI-score was associated with a 1·49 ml lower VAT(Reference Landry, Asigbee and Vandyousefi47). The Alternate HEI also demonstrated a significant inverse association with VAT(Reference Bellissimo, Bettermann and Tran38,Reference Maskarinec, Lim and Jacobs51) as did the Dietary Guidelines for Americans Adherence Index in a study not adjusted for body size(Reference Molenaar, Massaro and Jacques53) and the Dutch Dietary Guidelines Index after adjustment for body size(Reference van Eekelen, Geelen and Alssema20).
Table 4. Relation of a priori diet quality patterns with visceral adipose tissue (VAT) – part 1
* AHEI, Alternate HEI; DGAI, Dietary Guidelines Adherence Index; DHD, Dutch dietary Guidelines Index; HEI, Healthy Eating Index.
† Mean ± sd or median (IQR).
‡ C, continuous score, reported as ß (95 % CI) unless specified values; T, tertile; QT, quintile.
§ ↓, significant inverse association.
Of the eight studies investigating the original MED (Table 5), only two studies adjusted for body size; one found significant 50 cm3 less VAT accumulation for each 1-sd higher MED score(Reference Hennein, Liu and McKeown28), but the other one reported null findings(Reference De Amicis, Galasso and Leone35). Of the remaining six studies, four studies described an inverse association with VAT with significant lower values of –0·05 cm3(Reference Bertoli, Leone and Vignati27), –0·06 kg(Reference Bellissimo, Bettermann and Tran38) and –0·19 g(Reference Holthaus, Sethi and Cannavale43) per 1 unit MED score and a significant difference between a high v. low score with VAT values (106 v. 267 cm3)(Reference Lopes, Fontes and Menezes49); the remaining two studies reported null findings(Reference Galmes-Panades, Konieczna and Abete42,Reference Flor-Alemany, Acosta and Marin-Jimenez66) . The Alternate Mediterranean Diet Score and the Diet Quality Score were significantly inversely related to VAT adjusted for body size with respective values for extreme quantiles of 163 v. 173 cm2(Reference Maskarinec, Lim and Jacobs51), 3·8 v. 4·5 l(Reference Jennings, Kuhn and Bondonno46) and 460·5 v. 523·6 cm2/m(Reference Shah, Murthy and Allison56). Two Dietary Approaches to Stop Hypertension studies also showed significant inverse associations with VAT(Reference Bellissimo, Bettermann and Tran38,Reference Maskarinec, Lim and Jacobs51) . Regarding plant-based diet indices, adjusted for body size(Reference Ratjen, Morze and Enderle21), no association of VAT with plant-based diet index or unhealthy plant-based diet index but a –4·9 % significant lower VAT was seen. A higher E-DII was associated with higher VAT with(Reference Correa, da Costa and Silva39,Reference Lozano, Wilkens and Shvetsov50) and without(Reference Mtintsilana, Micklesfield and Chorell54) adjustment for body size. While VAT was 16·9 cm2 higher per 1 unit E-DII score without body-size adjustment(Reference Mtintsilana, Micklesfield and Chorell54), it differed only by 4·6 cm2 after body-size adjustment(Reference Lozano, Wilkens and Shvetsov50), but the comparability of results from two different studies is limited. In three UPF investigations, one showed no association(Reference Shim, Ha and Kim57), but the index was significantly associated with VAT per 1 % of total energy intake from UPF in a body-size-adjusted study(Reference Friden, Kullberg and Ahlstrom41) and in a non-adjusted one(Reference Liu, Steele and Li48). Higher levels of VAT with higher levels of fast-food intake were detected with(Reference Odegaard, Jacobs and Van Wagner55) and without adjusting for body size(Reference Odegaard, Jacobs and Van Wagner55).
Table 5. Relation of a priori diet quality patterns with visceral adipose tissue (VAT) – part 2
* aMED, Alternate Mediterranean Diet Score; DASH, Dietary Approach to Stop Hypertension; DQP, Diet Quality Pattern; E-DII, Energy-Adjusted Dietary Inflammatory Index; hPDI, healthy PDI; MED, Mediterranean Diet Score; PDI, plant-based diet index; UPF, ultra-processed food; uPDI, unhealthy PDI.
† Mean ± sd or median (IQR).
‡ C, continuous score, values reported as ß (95 % CI) unless specified; T, tertile; Q, quartile; QT, quintile; r, Pearson correlation coefficient.
§ ↑, significant positive association; ↓, significant inverse association; →, no association.
For the twenty-seven a posteriori DQP identified in nine reports (Table 6), six patterns were positively associated with VAT, and four patterns were inversely associated with VAT, whereas seventeen patterns indicated no relation to VAT. Generally, a higher score reflects a stronger adherence to the pattern indicated by the name of the pattern. Significant positive associations were seen for the following dietary patterns: a partial least squares regression-2 pattern high in nutrients found in meat and eggs, fish and beer and low in nutrients found in plants and dairy products in a German population(Reference Fischer, Moewes and Koch60), the southern US dietary pattern in African Americans(Reference Liu, Hickson and Musani26), an animal-driven nutrient pattern in South African women(Reference Makura-Kankwende, Gradidge and Crowther62,Reference Makura-Kankwende, Gradidge and Crowther63) , a retinol and vitamin B12-driven nutrient pattern in South Africans(Reference Ratshikombo, Goedecke and Soboyisi64), a sweet-fast pattern in Chinese men(Reference Yin, Chen and Lu65) and a in natura/minimally processed v. processed/ultra-processed pattern among Brazilian men(Reference de Paula Mancilha, Massarani and Vieira58). Inverse associations with VAT were identified for the following dietary patterns: a reduced-rank-regression-derived dietary pattern with high intakes of fruits and vegetables, which was linked to variation of selenoprotein P concentrations, in a German population(Reference di Giuseppe, Plachta-Danielzik and Koch59), the healthy Japanese dietary pattern in men but not women(Reference Ito, Kawakami and Tanisawa61), the vegetable-fruits pattern among Chinese individuals(Reference Yin, Chen and Lu65) and a principal component analysis-8 characterised by key nutrients in skimmed milk in a Germany study(Reference Fischer, Moewes and Koch60). Otherwise, null associations were seen for the remaining patterns based on foods(Reference Liu, Hickson and Musani26,Reference de Paula Mancilha, Massarani and Vieira58,Reference Ratshikombo, Goedecke and Soboyisi64) or nutrients(Reference Makura-Kankwende, Gradidge and Crowther62,Reference Makura-Kankwende, Gradidge and Crowther63) in different geographic locations.
Table 6. Relation of a posteriori diet quality patterns with visceral adipose tissue
* PCA, principal component analysis; PLS, partial least squares regression.
† Reported as mean ± sd or median (IQR).
‡ Unless specified, values reported as ß (95 % CI) or for tertiles, values are lowest (T1) and highest tertile (T3); C: continuous; r: Pearson coefficient.
§ ↑, significant positive association; ↓, significant inverse association; →, no association.
A serious risk of bias according to ROBINS-I was detected in twenty-eight reports (online Supplementary Table 3). Selection bias was deemed as a serious risk in thirteen studies due to the convenience sampling of participants. Missing data were rated as a serious threat in thirteen studies as less than half of the original participants were analysed. Nevertheless, it is important to note that in cohort studies, due to pragmatic reasons, only a subgroup can undergo imaging. The outcome assessment was judged as having a low risk of bias for all included studies, given that only studies with valid imaging techniques were included. Exposure assessment was considered as a moderate risk of bias for most studies; only ten studies using short-item questionnaires or 24-d recalls were rated as serious. The risk of bias due to selective reporting was deemed to be serious in six studies because the association between DQP and VAT was not the primary objective of the study, whereas this risk appeared moderate for the rest.
Discussion
The present systematic review provides a detailed evaluation of a priori and a posteriori DQP assessed in relation to VAT across different populations. The thirty-five studies revealed inverse associations between DQP and VAT, which were stronger for a priori than a posteriori DQP, younger v. older populations, studies in the USA as compared with elsewhere, multi-ethnic studies v. one group only and investigations with moderate v. serious risk of bias. No obvious differences were detected by dietary assessment method, imaging method, year of publication and body-size adjustment.
The majority of a priori indices representing dietary guidelines, that is, HEI, Alternate HEI, Dutch Dietary Guidelines Index and Dietary Guidelines for Americans Adherence Index, showed significant inverse associations with VAT. Apart from a few exceptions, indices based on specific diets, that is, Dietary Approaches to Stop Hypertension, healthy plant-based diet index and MED, were also inversely associated with VAT, while E-DII, UPF and fast-food intake were positively associated with higher VAT. In contrast, only about one in three a posteriori patterns was significantly associated with VAT, including a healthy Japanese dietary pattern, a vegetable-fruits pattern and the skimmed milk nutrient pattern and, positively, the southern US dietary pattern, the animal-driven nutrient pattern, the retinol and vitamin B12 pattern and the sweet-fast pattern.
The weaker associations for a posteriori patterns may be due to their data-driven nature, involving a subjective selection of specific food items and decision-making based on correlations among foods or nutrients consumed without a clear underlying hypothesis about the relation of diet to health(Reference Steffen and Hootman23) as compared with a priori DQP developed as guidelines based on the scientific literature(Reference Hu11). As higher scores of several a priori DQP have been linked to positive health effects(Reference Liese, Krebs-Smith and Subar24), it is possible that the beneficial health effects of high diet quality can be partly attributed to their potential lowering effect on VAT. Several a posteriori DQP, for example, the ‘prudent’ or ‘healthy pattern’, share an emphasis on vegetables and fruits with the a priori indices(Reference Newby and Tucker22), and higher scores in these patterns appear to be protective against different diseases(Reference Chandler, Balasubramanian and Paynter67). DQP that focus on nutrient density and/or low-energy foods may be effective through the same pathway(Reference Fischer, Pick and Moewes68). Higher scores of DQP describing a pro-inflammatory diet, that is, UPF, fast food and E-DII, were positively associated with VAT probably as a result of high total energy, saturated fat, cholesterol and sugar content(Reference Wolfson, Leung and Richardson69), which is linked to an increased chronic disease risk(Reference Shivappa, Hebert and Rietzschel70). Some a posteriori DQP reflect a diet often described as ‘western’ or ‘unhealthy’ diet pattern(Reference Newby and Tucker22) and linked to chronic inflammation(Reference Lathigara, Kaushal and Wilson6), which may mediate the association between poor quality diet and VAT(Reference Giugliano, Ceriello and Esposito71). Contrary to expectation, the lack of adjustment for body size(Reference Asghari, Mirmiran and Yuzbashian1) did not affect the ability of detecting associations between DQP and VAT, but the discrepant methods limited our ability to evaluate differences in the magnitude of effect measures, a necessary tool to identify the influence of DQP on VAT independent of BMI(Reference Asghari, Mirmiran and Yuzbashian1). However, individual studies support the hypothesis that the association weakens after adjustment for body size; for example, in a Dutch investigation, a higher DQP score showed lower VAT levels of –2·3 v. –3·2 cm2 with and without including body size into the model(Reference van Eekelen, Geelen and Alssema20).
Several limitations of the present review need to be considered, notably the relatively serious risk of bias inherent to observational studies(Reference Sterne, Hernan and Reeves37). Despite the relatively low bias for the outcome measure and the moderate bias for selective reporting and confounding, the high proportions of selection bias and overall serious bias introduce unavoidable uncertainty about the accuracy of the findings(Reference Sterne, Hernan and Reeves37). As to design features, the different body fat assessment methods are of concern as they result in units that are difficult to compare(Reference Shuster, Patlas and Pinthus2). However, the validity of DXA(Reference Kaul, Rothney and Peters32–Reference Maskarinec, Shvetsov and Wong34) as well as ultrasound(Reference Gradmark, Rydh and Renstrom36) in relation to MRI and CT has been demonstrated repeatedly(Reference Kaul, Rothney and Peters32), and the reliance on state-of-the-art methods for assessing adiposity represents a considerable strength(Reference Cheung, De and Hoermann72). The heterogeneity of statistical approaches, that is, continuous or categorical analysis, with or without body-size adjustment, made it impossible to estimate a quantitative summary of the findings as is typically done in meta-analyses. The challenges of different dietary assessments always need to be considered(Reference Freedman, Schatzkin and Midthune73). While most studies used FFQ covering the previous month or year, 24-h recalls or dietary records are even more prone to measurement error due to the short time covered, but under- and over-reporting as a result of self-report is common across methods. By limiting the studies to disease-free adolescents and adults, English language and full-text availability, some findings may have been missed and do not apply to chronically diseased individuals and children. Comparability of studies was limited by differences in dietary behaviour and intake across geographic regions and ethnic composition. Thus, the DQP may not capture diet quality to the same degree across populations; for example, the MED score was initially developed for the Greek population(Reference Trichopoulou, Costacou and Bamia74) and may not capture regular dietary intake as well among non-European ethnic groups(Reference Maskarinec, Lim and Jacobs51). This may explain the discrepant findings for the MED (Table 5). Also, a priori DQP are restricted by the current level of knowledge regarding the relation between diet and disease(Reference Wirt and Collins12). As recommendations can be met by making different decisions, for example, choosing raw v. processed fruits or using distinctive cooking methods, they may experience different health effects despite similar scores. As all findings were based on cross-sectional investigations, the causality of the relations cannot be ascertained, and the quality of included studies was judged to be at moderate or serious risk of bias (online Supplementary Table 3)(Reference Silverman75). Large cohorts, such as the German National Cohort (NAKO) and the UK Biobank, are now collecting imaging data for large numbers of individuals and will be able to conduct prospective analyses in the future.
Conclusions
This comprehensive review of thirty-five cross-sectional studies of diet quality in relation to VAT shows stronger evidence for an inverse association in studies with a priori than a posteriori DQP, possibly due to their development based on evidence-based food-disease relations(Reference Hu11). Yet, the heterogeneity across studies limits the ability to compute a quantitative summary measure of association. As the majority of DQP reflect high consumption of plant-based foods and low intake of animal products, the specific choice of DQP for public health interventions may be less important than the overall goal of achieving high diet quality, which can be achieved in different ways such as various combinations of food that are locally available and culturally acceptable. This review may serve as a basis for analyses in large prospective cohorts and for future interventions targeting VAT reduction at the population level.
Acknowledgements
This work was prepared by Annalena Thimm as part of a Master of Science in Public Health at the Berlin School of Public Health in 2023. Funding was provided by Stiftung Charité to support Gertraud Maskarinec as a visiting professor at the Berlin Institute of Health. The funders had no influence on the review and/or writing process. Cherie Guillermo was supported by T32 CA229110 Multidisciplinary Training in Ethnic Diversity and Cancer Disparities.
G. M., T. P., and K. N. designed the research; A. T. and G. M. registered the systematic review and obtained the references; A. T., K. N., and G. M. evaluated and categorized the abstracts; A. T. summarized the data and drafted the paper; C. G., G. M., K. N. updated the references and the text of the systematic review; A. T., C. G., K. N., G. M., and T. P. contributed to the interpretation of the data, the critical revision of the article for important intellectual content and to the final approval of the version to be published. G. M. had primary responsibility for the final content.
None of the authors has any conflicts of interest to declare.
Supplementary material
For supplementary material/s referred to in this article, please visit https://doi.org/10.1017/S000711452400179X.
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