Что едят австра利йцы? Систематический обзор диетических паттернов и нежелательных здоровых последствий.

INTRODUCTION

According to the 2024 Australian Institute of Health and Welfare brief, noncommunicable diseases, including coronary heart disease, cerebrovascular disease, dementia, and cancer, were the leading causes of disease burden in Australia. 1 Almost half of Australians across all age groups (47%) were affected by 1 or more chronic conditions, while nearly 1 in 5 (19%) had 2 or more chronic conditions, a state commonly referred to as multimorbidity (multiple long-term conditions). 2 In addition, mental health disorders, such as depression, were among the leading contributors to disease burden in Australia. 3 According to the 2015 Burden of Diseases Study in Australia, poor dietary choices accounted for one-fifth of chronic diseases. 4

Dietary behaviors are important risk factors for chronic conditions 5 and mental health disorders. 6 However, this insight has traditionally relied on the analysis of individual nutrients or specific food items. 7 There has been a growing emphasis in the literature on exploring dietary consumption patterns to assess their overall impact on health outcomes. 8 , 9 Dietary patterns may provide a more accurate representation of dietary risks associated with adverse health outcomes compared to approaches that focus on specific foods or nutrients. This is because they provide a complete picture of dietary behavior as well as the relative contribution of distinct food items within the dietary patterns. 8 , 10

Based on existing scientific evidence, adopting healthy dietary patterns—characterized by increased consumption of fruits, vegetables, whole grains, and legumes—is linked to a lower risk of cardiovascular disease (CVD), 11 diabetes, 12 cancer, 13 and overweight/obesity. 14 Conversely, unhealthy dietary patterns characterized by a higher intake of red and processed meat, fast and processed foods, refined grains, and alcohol have been associated with a broad spectrum of adverse health outcomes. 12 , 14 , 15 Recognizing these associations, the Australian Dietary Guidelines (ADG) advocate for the adoption of healthy dietary patterns. 16

As our understanding of the complex interplay between diet and health outcomes continues to evolve in Australia, 4 providing a comprehensive summary of the existing evidence on dietary patterns and health outcomes is crucial for designing interventions to mitigate the impact of unhealthy dietary behaviors and promote optimal health and well-being among the Australian population. However, systematically generated evidence that comprehensively summarizes the dietary patterns of Australian adults and their associations with chronic health conditions is lacking.

The aim of this systematic review was to characterize dietary patterns identified by a posteriori (data-driven) and hybrid (a combination of data-driven and theory-based/predefined indices) methods and explore their longitudinal associations with chronic health conditions in the Australian context. This review focuses on a posteriori and hybrid methods to reflect the food intake patterns of the Australian population, including all consumed food items in the data analysis. As the a priori (based on predefined indices) methods mainly depend on selected parts of the dietary components (eg, Healthy Eating Index) 17 and do not examine the interaction of all food components, 18 these were not the focus of the current review.

METHODS

Protocol Registration

The protocol for this systematic review was registered in the International Prospective Register of Systematic Reviews (PROSPERO) with a registration ID CRD42023452960 (registered on August 17, 2023). The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines was used to structure the search, conduct the review, and report the findings. 19

Inclusion and Exclusion Criteria

The inclusion and exclusion criteria were developed using the Population, Exposure, Comparator, and Outcome (PECO) criteria. 20 No limitations were imposed on publication date and study designs, and the following exclusion criteria were applied: (1) non-original research (ie, reviews, editorials, non-research letters); (2) case reports or case series; (3) ecological studies; (4) studies without health outcome(s) as the endpoint; (5) studies published in a non-English language; and (6) studies involving non-adult populations (<18 years) ( Table 1 ).

Search Strategy

One of the authors (Y.A.M.) developed the search strategy with an expert librarian, L.M., with input from K.D. and K.M.L. A combination of keywords and Medical Subject Headings (MeSH) terms was used to search articles including dietary patterns, health outcomes, dietary pattern analysis, principal components analysis (PCA), cluster analysis (CA), partial least squares (PLS), and reduced rank regression (RRR). The articles were searched in 6 electronic databases (Scopus, Medline, CINAHL, EmCare, Web of Science, and Cochrane Library) first in December 2020 and updated on August 24, 2023. A detailed search strategy is provided in Table S1 .

Data Extraction

All studies retrieved through the search strategy were imported into EndNote (Clarivate, Philadelphia, PA, USA), and duplicates were removed. Between December 2020 and April 2021, studies were screened by Y.A.M. and K.D. based on titles, abstracts, and full texts to identify those that met the inclusion criteria. All potentially eligible articles were read in full, and relevant studies were selected. Between August and November 2023, additional screening of newly identified articles was conducted by Z.A.A. and Y.A.M. Any disagreements during the initial screening and full-text review were resolved through discussion.

A data-extraction sheet was prepared in Excel (Microsoft Corporation, Redmond, WA, USA) and tested on a subsample of studies for necessary modifications before starting the extraction of all included studies for the final analysis. Data extracted from each relevant study included author identification, publication year, name of the study (if applicable), the state in which a study was conducted, study population (sex and age), study design, sample size, dietary intake assessment methods (number of food items/group, frequency of assessment, and reference period), identified dietary patterns (explained variance and food items), classification (categorization) of dietary pattern scores, analysis methods for dietary pattern derivation (including response variables for RRR and PLS), outcomes, effect (association) estimates and their confidence intervals, covariates used in models as confounders, and principal conclusions. In cases where studies reported different associations between dietary patterns and health outcomes, with varying degrees of adjustment, the effect estimates from models with the greatest number of covariates were extracted. Z.A.A. extracted the data and Y.A.M. cross-checked approximately 14% of the studies, which were selected at random.

Health Outcomes

The risk of developing chronic conditions or cause-specific and all-cause mortality among an adult population with specific dietary patterns derived using an a posteriori or a hybrid method was determined. The associations between dietary patterns and these conditions were described and summarized from longitudinal studies; however, all study designs were used to characterize dietary patterns.

Risk-of-Bias Assessment

The Newcastle-Ottawa Quality Assessment Scale (NOS) 21 and its adapted version by Zou et al 22 and Modesti et al 23 were used to assess the methodological quality and risk of bias of included studies. This scale evaluates the quality of studies across 3 dimensions: selection of the study groups, comparability of the groups, and verification of either exposure or outcome of interest. An adapted version was used for cross-sectional studies as the original scale has a limitation, allowing a maximum of 10 stars. 22 , 23 The adaptations included assessing the general representativeness of the entire sample rather than just the exposure and control groups separately and omitting the criterion “Demonstration that the outcome of interest was not present at the start of the study” as it was not applicable for cross-sectional designs. 23 The quality of studies were rated as follows: high (8–10), medium (5–7), or low (0–4) for cross-sectional studies and as high (7–9), medium (4–6), or low (0–3) for cohort/case-control studies. 22 Two authors (Z.A.A. and T.C.M.) independently evaluated the quality of the included studies. Any discrepancies between their assessments were resolved through mutual discussion, and no third party was involved, as all conflicts were resolved between the 2 authors.

Dietary Pattern Identification

Studies used a wide variety of nomenclatures and lacked standardization in reporting dietary patterns. 24 To mitigate this challenge, the reported dietary patterns were categorized into 2 groups (healthy and unhealthy) for both characterizing dietary patterns and summarizing effect estimates for longitudinal studies. To classify a dietary pattern into either of these categories, the following approaches were used. First, if the authors gave defined names such as Mediterranean, healthy and prudent (healthy), and unhealthy and Western (unhealthy), the classification was maintained. Second, some authors assigned names based on specific food items and contents. In this case, the name and factor loadings of food items were evaluated to classify them either to healthy or unhealthy dietary patterns. For instance, fruit, vegetables, prudent, low-fat dairy, and Mediterranean-style patterns were defined as healthy, as they encompass substantial quantities of health-promoting foods like fruits, vegetables, fiber, and whole grains. Conversely, patterns characterized by high consumption of meat, saturated fat, sugar, alcohol, and processed foods were classified as unhealthy dietary patterns, given their significant inclusion of food items generally considered detrimental to health ( Table S2 ). This classification was guided by the ADG, 16 which emphasizes the importance of consuming adequate amounts of vegetables, fruits, grains, cereals, dairy, and lean meats, while limiting the intake of saturated fat, added salt, added sugar, and alcohol. 16

Data Synthesis

A systematic narrative synthesis was used to describe the available studies separately for each health outcome to summarize and synthesize all findings. The following steps were applied to synthesize the data: (1) describing the characteristics of included studies and grouping findings by dietary patterns and health outcomes, and (2) summarizing the magnitude and direction of the association between dietary patterns and health outcomes. The studies were categorized into 5 themes based on the health outcomes they reported: (1) cardiometabolic health, cardiometabolic risk factors, or CVD mortality; (2) cancer incidence or mortality; (3) pregnancy-related metabolic conditions and birth outcomes; (4) mental health and cognitive status; and (5) other health outcomes ( Table S3 ). The findings were then summarized and presented using figures, tables, and text.

An attempt was made to pool effect estimates using meta-analysis 25 , 26 if the studies (1) were longitudinal and (2) assessed the same outcome categories as described above. However, 3 prospective studies that utilized relative risks (RRs) to measure the association between 11 dietary patterns and cardiometabolic risk factors were identified. One study examined multiple dietary patterns or/and health outcomes. Additionally, 2 studies on pregnancy-related metabolic conditions (gestational diabetes mellitus [GDM] or hypertensive disorder during pregnancy [HDP] outcomes) were found from the same dataset and authors. Similarly, 3 prospective studies utilized odds ratios (ORs) to measure the association with 8 dietary patterns and cardiometabolic risk factors. Pooling together these dietary patterns and health outcomes would violate independence assumptions of meta-analysis. Therefore, forest plots were used to display the association of each dietary pattern with health outcomes (cardiometabolic risk factors or CVD mortality, pregnancy-related metabolic conditions and birth outcomes, cancer, mental health, cognitive status, and other health outcomes) without pooling effect estimates.

The most adjusted effect estimates, comparing the highest quantile, quartile, or tertile with the lowest for both healthy and unhealthy dietary patterns, were summarized and plotted using R version 4.3.1 (R Foundation for Statistical Computing, Vienna, Austria). 27 Since ORs and RRs have different interpretations, separate forest plots were used for each.

RESULTS

Search Results

A total of 2068 articles were identified and 943 remained after removing duplicates. In the title and abstract screening phase, 831 articles were excluded. After applying the inclusion and exclusion criteria, 59 articles published between 2001 and 2023, with a total of 362 263 participants, were included ( Figure 1 ). 28–86

Characteristics of the Included Studies

Three case-control, 29 , 30 , 52 3 both cross-sectional and longitudinal, 71 , 76 , 86 31 cross-sectional, 28 , 32 , 35–39 , 42 , 44 , 49 , 50 , 54–68 , 70 , 72 , 78 , 84 , 85 and 22 longitudinal 31 , 33 , 34 , 40 , 41 , 43 , 45 , 47 , 48 , 51 , 53 , 69 , 73–75 , 77 , 79–83 studies were included in the current review. While more than one-quarter (28.8%) of studies used national samples, 28 , 32 , 34 , 35 , 37 , 45 , 52 , 54 , 55 , 58 , 59 , 66 , 67 , 71 , 73–75 15.3% were conducted in South Australia, 38 , 42 , 49 , 50 , 64 , 65 , 69 , 76 , 83 15.3% in Victoria, 31 , 43 , 44 , 46–48 , 60 , 62 , 63 and 6.78% in New South Wales 36 , 39 , 68 , 72 ( Table S4 ). The study sample sizes ranged from 73 to 156 035 with participant ages between 18 and 90 years. Two studies reported CVD mortality as an outcome, 43 , 69 3 reported on cognitive status, 34 , 39 , 50 1 study reported on GDM, 74 1 reported on esophageal cancer, 52 1 study reported on aging, 48 4 reported on body mass index, 33 , 60 , 79 , 83 2 reported on diabetes, 47 , 75 and 4 studies reported on body mineral content. 36 , 64 , 65 , 84

Risk of Bias

Of the 25 prospective studies included, only 4 (16%) 69 , 73 , 76 , 86 were rated as medium quality, while the remaining 21 (84%) were considered high-quality studies ( Table S5 ). All case-control studies 29 , 30 , 52 were rated as high quality. Less than one-third (10 studies; 29.4%) 28 , 32 , 36 , 44 , 55 , 66 , 67 , 71 , 72 , 76 of the 34 cross-sectional studies were rated as medium quality, while the remaining were rated as high quality.

Characteristics of Dietary Patterns

To evaluate dietary intake, 50 used food-frequency questionnaires (FFQs), 5 studies used 24-hour dietary recalls, 28 , 35 , 58 , 59 , 62 2 used dietary history, 32 , 63 and 2 studies used weighted food record methods. 60 , 68 Most of these studies, except for 8, 28 , 35 , 49 , 51 , 58 , 59 , 63 , 68 measured dietary exposure at a single time point. The range of food items covered by FFQs varied from 12 72 to 212. 42 The time frame of the dietary assessment instruments in the studies varied, evaluating dietary intake ranging from just the previous day 35 to lifetime dietary exposure. 50 Most of the studies measured the frequency of consuming specific food items per week and the quantity in grams per day ( Table S2 ).

Two studies used PCA, RRR, and PLS 65 , 76 ; 1 study used both factor analysis and CA 56 ; 4 studies used RRR 58–61 ; and the remaining studies used either factor analysis or PCA. The number of unique dietary patterns generated by the studies was 88, the most common being “Western” ( n = 22) and “prudent” ( n = 17) ( Figure 2 ).

Based on the characteristics of the food items, the reported dietary patterns were categorized into 2 dietary patterns: healthy and unhealthy ( Table S2 ). Of the 192 total dietary patterns generated by the studies, 107 (55.7%) were classified as healthy. Dietary patterns identified by terms such as “healthy,” “fruits,” “vegetables,” “plant-based,” “prudent,” “vegetarian,” “dairy,” and “fruit” were classified as healthy dietary patterns. These healthy dietary patterns included high loadings for foods such as dark-yellow vegetables, green leafy vegetables, fruits, cruciferous vegetables, other vegetables, nuts, whole grains, tomatoes, fish, and low-fat dairy.

On the other hand, dietary patterns labeled as “unhealthy,” “Western,” “meat,” “alcohol,” “snacks,” “refined and processed,” “meat and fat,” “processed-high sugar,” and “high-fat” were considered unhealthy dietary patterns. These dietary patterns were characterized by high positive loadings for processed meat, high-fat potatoes, discretionary fat, takeaway foods, jam, Vegemite, beer, soft drinks, red meat, high-fat dairy, poultry with skin on, white bread, sweet snacks, and fat spreads, and very low factor loadings for fruits, vegetables, and fish ( Table S2 ).

Longitudinal Association of Dietary Patterns and Health Outcomes

In the longitudinal studies, dietary patterns had different levels of associations with health outcomes. In this review, 25 longitudinal studies 31 , 33 , 34 , 40 , 41 , 43 , 45 , 47 , 48 , 51 , 53 , 69 , 71 , 73–77 , 79–83 , 86 were included to determine the relationship between 95 dietary patterns and 90 health outcomes, as described in Figures 3 and 4 and Table 2 . 31 , 33 , 34 , 40 , 41 , 43 , 45–48 , 51 , 53 , 69 , 71 , 73–77 , 79–83 , 86

Cardiometabolic Health and CVD Mortality

Ten studies explored cardiometabolic health ( n = 45 079) 33 , 47 , 75 , 79–83 and CVD mortality ( n = 41 687). 43 , 69 A longitudinal study from Victoria assessed CVD and ischemic heart disease (IHD) mortality using 4 dietary patterns: Mediterranean, vegetables, meat, and fresh fruit. 43 The findings from this study showed that higher adherence to Mediterranean (risk ratio = 0.66; 95% CI: 0.45, 0.98), vegetable pattern (RR = 0.66; 95% CI: 0.48, 0.92), and fresh fruit dietary patterns (RR = 0.69; 95% CI: 0.52, 0.93) was associated with a 31%–34% reduced risk of mortality from CVD. However, only a Mediterranean dietary pattern was associated with a 49% reduced risk of mortality from IHD (RR = 0.51; 95% CI: 0.30, 0.88) ( Table S6 ).

A nationwide study by Schoenaker et al 75 showed that higher adherence to Western dietary patterns was associated with a 73% increased odds of diabetes incidence (OR = 1.73; 95% CI: 1.12, 2.67). Similarly, a study in Queensland 80 showed that higher adherence to a Western dietary pattern was associated with an increased risk of metabolic syndrome (RR = 2.32; 95% CI: 1.34, 4.0), insulin resistance (RR = 1.69; 95% CI: 1.07, 2.65), impaired beta-cell function (RR = 1.60; 95% CI: 1.10, 2.31), and decreased insulin sensitivity (RR = 0.57; 95% CI: 0.39, 0.84). In contrast, higher adherence to prudent dietary patterns was associated with a decreased risk of metabolic syndrome (RR = 0.47; 95% CI: 0.29, 0.75), insulin resistance (RR = 0.57; 95% CI: 0.39, 0.82), and impaired beta-cell function (RR = 0.69; 95% CI: 0.50, 0.93) and increased insulin sensitivity (RR = 1.84; 95% CI: 1.30, 2.60). 80

Three studies 80 , 81 , 83 investigated the association between 11 healthy dietary patterns—namely, healthy, prudent, vegetables, and fruit—and cardiometabolic health risk factors. Except for prediabetes, all other associations remained uniform across studies, suggesting a decreased risk of cardiometabolic risk factors. The adjusted RRs ranged from 0.38 to 2.17 for the highest category of dietary pattern score compared with the lowest category ( Figure 3 ).

Similarly, 11 unhealthy dietary patterns were examined for their association with cardiometabolic risk factors, yielding inconsistent results. The corresponding RRs ranged from 0.57 to 3.60. The estimates in Figure 4 suggested that unhealthy dietary patterns significantly increase the risk of cardiometabolic conditions.

Cancer

Two studies reported cancer outcomes ( n = 2394). 53 , 69 A study by Nobbs et al. 69 in South Australia identified 5 dietary patterns: discretionary choices and breads and cereals; vegetables and fruit; white meat and milk products; breads and cereals, sweet bakery goods, and milk products; and red meat and protein alternatives. The findings showed that only a red meat and protein alternatives pattern was associated with increased risk of cancer incidence or mortality (lowest tertile vs. second tertile: OR = 1.46; 95% CI: 1.03, 2.07; and third tertile: OR = 1.28; 95% CI: 0.87, 1.90). Another study conducted in Queensland by Ibiebele et al. 53 found that a meat and fat dietary pattern was associated with increased risk of basal and squamous cell carcinoma of the skin (RR = 1.83; 95% CI: 1.00, 3.37). Additionally, a study by Hughes et al. 51 examined the link between precancerous lesions and dietary patterns. They identified 3 main dietary patterns: vegetable-fruit, potato-sweets-meat, and alcohol-snack. Only the vegetable-fruit dietary pattern showed a negative association with actinic keratoses of the skin ( Figures 3 and 4 ).

Pregnancy-Related Metabolic Conditions (GDM and HDP) and Birth Outcomes

Only 2 studies were available on the association between dietary patterns and GDM and HDP. 73 , 74 Findings from the Australian Longitudinal Study on Women’s Health (ALSWH) showed that a Mediterranean-style dietary pattern reduced the risk of both GDM (RR = 0.56; 95% CI: 0.41, 0.77) and HDP (RR = 0.58; 95% CI: 0.42, 0.81). 73 , 74 On the other hand, “Western” dietary patterns had no association with HDP (RR = 0.94; 95% CI: 0.57; 1.55) or GDM (RR = 1.23; 95% CI: 0.76, 1.97). Similarly, a single study examined the association between dietary patterns and birth outcomes, and the findings showed that neither healthy nor unhealthy dietary patterns were associated with low birth weight or preterm birth 41 ( Figures 3 and 4 ).

Mental Health Conditions and Cognitive Function

Five studies reported on dietary patterns and either mental health conditions or cognitive function ( n = 18 211) ( Table 2 ). 34 , 40 , 46 , 71 , 76 Healthy, fruit, traditional, prudent, and typical Australian dietary patterns were inversely associated with mental health and cognitive function, 46 , 71 , 76 whereas unhealthy dietary patterns were positively associated with depression and cognitive function impairment. 40 , 76 A longitudinal study by Ashby-Mitchell et al 34 reported on 3 dietary patterns: variety, Western and dairy, grains, and alcohol patterns. These dietary patterns had no significant association with cognitive status. However, findings from a study conducted by Gardener et al 40 showed that a Western dietary pattern was associated with greater cognitive decline after 36 months of follow-up.

Moreover, 2 studies 71 , 76 investigated the correlation between dietary patterns and symptoms of depression. The findings from these studies indicate that adhering to healthy dietary patterns was linked to a reduced risk of depression symptoms. 76 Conversely, in the other study, 76 it was observed that adhering to Western dietary patterns was associated with an elevated risk of depression symptoms.

Other Health Outcomes

Seven studies considered other health outcomes related to dietary patterns ( n = 37 280) ( Figures 3 and 4 and Table 2 ). 31 , 41 , 45 , 48 , 51 , 77 , 86 A study conducted in the Melbourne Collaborative Cohort Study (MCCS) identified 6 dietary patterns 31 : fruit, vegetables, grains and fish, meat, processed foods, and salad. None of the patterns were significantly associated with earlier stages of age-related macular degeneration.

A study conducted by Herber-Gast and Mishra, 45 as part of the ALSWH, examined various dietary patterns and their association with vasomotor menopausal symptoms. The study identified 6 dietary patterns: vegetables, fruit, Mediterranean-style, meat, dairy, and high-fat and high-sugar patterns. The fruit and Mediterranean-style patterns were found to have a negative association with vasomotor menopausal symptoms, whereas the high-fat and -sugar pattern showed a positive association.

One study explored the association between dietary patterns and outcomes during childbirth. 41 The study identified 4 distinct dietary patterns: meat and high-fat; prudent; traditional vegetables; and sugar, refined grain, and processed foods. The findings suggested that none of these dietary patterns was associated with preterm birth or low birth weight ( Table 2 ).

DISCUSSION

Summary of the Findings

This comprehensive systematic review investigated the association between dietary patterns identified through a posteriori and hybrid methods, and their relationship with health outcomes in Australian studies. The review identified 59 eligible studies (of which 25 were longitudinal in design) that examined associations between dietary patterns and their associations with health outcomes. The main findings are that healthy dietary patterns are associated with a reduced risk of cardiometabolic conditions, GDM, and HDP. Furthermore, unhealthy dietary patterns were associated with an increased risk of overweight/obesity, diabetes, insulin resistance, mental health conditions, and cognitive function impairment.

Even though this review supports the current ADG recommendation, the ADG were developed based on findings from a single nutrient or food group. 16 It is critical that the current revision of the ADG 87 considers dietary patterns in addition to individual food items or groups when making recommendations. Most of the prospective studies (17 out of 25) included in this systematic review were published after the release of the 2013 ADG. 16 Therefore, the findings from these studies could be considered when updating the guidelines.

Comparison of Findings

In this review, healthy dietary patterns were associated with reduced risk of cardiometabolic conditions (such as obesity, insulin resistance, triglycerides, low-density-lipoprotein [LDL] cholesterol, total cholesterol, and prediabetes), GDM, and HDP. Additionally, a healthy dietary pattern may reduce the risk of mental health conditions (stress, depression, anxiety), cognitive function decline, and pregnancy complications (low birth weight and preterm birth). The healthy dietary patterns were characterized by a higher consumption of fruits and vegetables, whole grains, milk and dairy products. The findings of the current review align with previous systematic reviews of observational studies that reported a protective effect of healthy dietary patterns against multiple chronic conditions such as colorectal cancer, 88 , 89 CVD, 90 breast cancer, 91 and overweight/obesity. 92 , 93

Adopting a healthy dietary pattern offers a multifaceted approach to reduce the risk of diet-related health outcomes by influencing various interconnected mechanisms. These include the regulation of inflammation, 94 blood sugar, 95 weight, 14 gut microbiota, 96 antioxidant defenses, 97 and immune function. 98 Chronic inflammation, a common denominator in many diet-related diseases, can be mitigated by a healthy dietary pattern that is high in fruits, vegetables, and whole grains, as these contain anti-inflammatory components. 94 , 99 Moreover, incorporating dietary components, such as fiber-rich foods, contributes to the regulation of blood sugar levels. 100–102 This is particularly crucial to prevent and manage diabetes, where unstable blood sugar can lead to insulin resistance and other metabolic disorders. 103 Additionally, high-fiber diets foster the growth of beneficial gut bacteria. 104 , 105 A balanced and diverse gut microbiota is linked to improved digestion and nutrient absorption, and a diminished risk of gastrointestinal diseases. 106 Furthermore, fruits and vegetables, rich in antioxidants, play a pivotal role in neutralizing free radicals and alleviating oxidative stress. 107 Finally, maintaining a diet low in saturated and trans fats while increasing the intake of omega-3 fatty acids from sources like fish and nuts plays an important role in preventing chronic diseases. 108

In this review, an unhealthy dietary pattern was associated with an increased risk of cardiometabolic diseases and related metabolic risks, consistent with other studies demonstrating a strong link to the development of chronic conditions. 109 Unhealthy dietary patterns are characterized by high consumption of meat, fried and processed foods, saturated fat, added salt, added sugar, alcohol, and high-fat dairy products. Excess consumption of these dietary components is considered as a risk factor for chronic conditions in dietary guidelines including the ADG, 16 World Health Organization, 110 and the World Cancer Research Fund. 111 Contrary to previous systematic reviews, 112 , 113 findings of the current review on unhealthy dietary patterns and GDM/HDP are inconsistent. Despite contradictions, these studies are either global 112 and include a large number of observational studies or focus on a larger number of cohort studies. 113 In addition, it is important to note that the above 2 reviews included both a posteriori and a priori dietary analysis approaches. Furthermore, only 2 studies, 73 , 74 which were part of the above two reviews, were included in the current review, and both had the same study population.

This review suggests an association between dietary patterns and mental health conditions, cognitive function, birth outcomes, and other health-related conditions. However, a definitive conclusion cannot be drawn because of the limited number of studies, confounding, and other methodological challenges. Most of the studies included in this review only measured dietary data at a single point in time. Dietary behavior is dynamic; therefore, it is important to assess it over time to establish associations with health outcomes. For instance, both early childhood and adulthood dietary exposures have been associated with overweight/obesity, which is a risk factor for many other health outcomes. 114 Additionally, studies reported different exposure assessment time frames, ranging from 1 day to a lifetime recall period. This variability could potentially bias the results, and interpretation of the results is challenging. 115

Implications for the ADG and beyond

Despite inconsistencies, overall, the findings highlight the positive effects of healthy dietary patterns and the detrimental effects of unhealthy dietary patterns on health outcomes in Australia. Although the 2013 ADG's dietary recommendations are mostly based on evidence that was derived from isolated food items, 116 our findings are generally in line with the recommendations. 117 However, the message in the ADG diet can focus on overall dietary patterns alongside individual dietary components. While recommendations based on individual foods are important for providing clear messages and encouraging measurable behavioral changes, people typically do not consume food items in isolation. 118 Although difficult to define measurably, recommendations based on dietary patterns offer a more realistic and practical approach. 119 Furthermore, evidence originating from studies that focus on dietary patterns and health outcomes rather than isolated food items has 3 main advantages. First, the methods account for the interactions and confounding effects of other food items to some extent. 17 , 120 Second, analyzing dietary patterns is beneficial for evaluating dietary guidelines and can help identify the complex relationships between diet and health outcomes, as the combined effect of an entire diet may be greater than that of its individual components. 17 Third, recommendations based on the whole diet may be easier for consumers to understand and follow. 121

Despite the limited evidence on certain health outcomes, the available data on diet–disease relationships among Australian adults have been systematically gathered, which could help inform the current revision of the ADG. This review aligns with 1 of 4 priority research questions (“dietary patterns [combinations and amounts of foods regularly consumed]”) set for the revision of the 2013 ADG. 87 Therefore, it is recommended that the ongoing revision of the ADG consider the comprehensive evidence on dietary patterns presented in this work. In recent years, countries other than Australia have emphasized using evidence on dietary patterns and health outcomes, in addition to isolated food items, to inform dietary guidelines. 121 This approach considers not only the effect of ‘one-in-many’ but also ‘many-in-one’ dietary exposures on health outcomes. Overall, while it is important to continue using evidence that focuses on individual dietary components, these complex interactions between the components suggest that incorporating evidence on dietary patterns should also be a complementary approach when reviewing and developing dietary guidelines.

Limitations

Despite being conducted using a predefined set of criteria and protocol, this review has the following limitations. First, only observational studies were included. Therefore, confounding, including residual confounding from the original studies, cannot be ruled out. However, conducting randomized controlled trials to assess the long-term effects of dietary patterns on health outcomes is often infeasible or unethical. 122 Second, the variation in dietary assessment tools, methods for deriving dietary patterns, naming conventions of dietary patterns, measurements of associations, and health outcomes introduces heterogeneity, making it difficult to compare results across studies. Third, while many studies used validated dietary assessment tools, such as FFQs, the dietary data in these studies often relied on self-reporting with varying recall periods (as shown in Table S2 ). Additionally, most of the data were collected at a single time point. This variability across studies could influence the reported relationship between dietary patterns and health outcomes. Fourth, different types of dietary patterns were derived from various methods. These patterns were classified into 2 commonly reported categories—healthy and unhealthy dietary patterns—based on the characteristics of the food items within each pattern. This approach helps reduce the risk of bias, although there remains the potential for misclassification. Finally, this systematic review does not include dietary patterns identified by standardized index-based approaches determined a priori , as these methods do not consider all dietary components consumed and only depend on selected food items a priori . However, this is an important area that could provide valuable evidence for dietary guidelines and should be explored in future reviews.

CONCLUSION

This review highlights the scarcity of longitudinal studies examining dietary patterns and health outcomes in Australian adults. Moreover, widespread inconsistencies in reporting dietary patterns and their effects on adverse health outcomes underscore the need for standardized approaches in analysis, nomenclature, and reporting of data-driven dietary patterns, as well as the need for further longitudinal studies. Despite these limitations, the overall synthesis of available evidence shows that healthy dietary patterns are associated with a reduced risk of chronic conditions, including cardiometabolic risks and outcomes, GDM, and HDP among Australian adults. Conversely, unhealthy dietary patterns are associated with an increased risk of cardiometabolic issues, mental health conditions, and cognitive decline. These findings align with the 2013 ADG, 117 although the guidelines’ recommendations are largely based on individual food items. The ongoing revision of the ADG, 87 threfore, should incorporate the findings from this review, alongside evidence on individual dietary components.

Supplementary Material

Contributor Information

Zegeye Abebe, Flinders Health and Medical Research Institute, Flinders University, Bedford Park, Adelaide, SA 5042, Australia.

Kacie Dickinson, Caring Futures Institute, College of Nursing and Health Sciences, Flinders University, Bedford Park, Adelaide, SA 5042, Australia.

Tefera Chane Mekonnen, Adelaide Medical School, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA 5000, Australia.

Amy Reynolds, Flinders Health and Medical Research Institute, Flinders University, Bedford Park, Adelaide, SA 5042, Australia.

Sarah Appleton, Flinders Health and Medical Research Institute, Flinders University, Bedford Park, Adelaide, SA 5042, Australia.

Leila Mohammadi, Central Coast Local Health District, Directorate of Clinical Safety, Quality & Governance Library, Gosford, NSW, Australia.

Danny J Eckert, Flinders Health and Medical Research Institute, Flinders University, Bedford Park, Adelaide, SA 5042, Australia.

Robert Adams, Flinders Health and Medical Research Institute, Flinders University, Bedford Park, Adelaide, SA 5042, Australia.

Katherine M Livingstone, Institute for Physical Activity and Nutrition (IPAN), School of Exercise and Nutrition Sciences, Deakin University, Geelong, VIC 3220, Australia.

Yohannes Adama Melaku, Flinders Health and Medical Research Institute, Flinders University, Bedford Park, Adelaide, SA 5042, Australia.

Author Contributions

Z.A.A.: Screened articles, extracted data, performed critical appraisal, organized and analyzed data, assessed quality of evidence, co-wrote the first draft, incorporated feedback from co-authors; K.D.: conceived the study, screened articles, extracted data, critically reviewed the draft, and read and approved the version of the manuscript being submitted; T.M.C.: performed critical appraisal, critically reviewed the draft, and read and approved the version of the manuscript being submitted; A.R., S.A., L.M., D.J.E., and R.A.: data interpretation, critically reviewed the draft, and approved the version of the manuscript being submitted; K.M.L.: conceived the study, data interpretation, critically reviewed the draft, and read and approved the version of the manuscript being submitted; Y.A.M.: conceived the study, screened articles, extracted data, assessed quality of evidence, co-wrote the first draft, incorporated feedback from co-authors, and supervised the overall project.

Supplementary Material

Supplementary Material is available at Nutrition Reviews online.

Funding

This research was funded by the National Health and Medical Research Council (NHMRC) (2009776). K.M.L. and Y.A.M. are supported by a NHMRC Emerging Leadership Fellowship (2009776 and 1173803). D.J.E. is supported by an NHMRC Leadership Fellowship (1196261). The findings of this review are solely the responsibility of the authors and do not represent the views of the funding institution.

Conflicts of Interest

Outside the submitted work, D.J.E. has received research grants from Bayer, Takeda, Invicta Medical, Apnimed, Eli Lilly, and Withings and has served on Scientific Advisory Boards or as a consultant for Apnimed, Invicta, Mosanna, Takeda, and Bayer. All other authors report no potential conflicts.

Data Availability

All data generated or analyzed during this study are included in this published article and its supplementary information files.

REFERENCES

1. Australian Institute of Health and Welfare. Australia's Health 2024: In Brief . Canberra, Australia: Australian Institute of Health and Welfare; 2024.

2. Australian Bureau of Statistics. Health conditions prevalence. 2020-2021. Accessed November 21, 2023. https://www.abs.gov.au/statistics/health/health-conditions-and-risks/health-conditions-prevalence/latest-release

3. Ciobanu LG, Ferrari AJ, Erskine HE, et al. The prevalence and burden of mental and substance use disorders in Australia: findings from the Global Burden of Disease Study 2015. Aust N Z J Psychiatry. 2018;52:483-490. 10.1177/0004867417751641

4. Melaku YA, Renzaho A, Gill TK, et al. Burden and trend of diet-related non-communicable diseases in Australia and comparison with 34 OECD countries, 1990-2015: findings from the Global Burden of Disease Study 2015. Eur J Nutr. 2019;58:1299-1313. 10.1007/s00394-018-1656-7

5. Afshin A, Sur PJ, Fay KA, et al. Health effects of dietary risks in 195 countries, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2019;393:1958-1972.

6. Phillips CM, Shivappa N, Hébert JR, Perry IJ. Dietary Inflammatory Index and mental health: a cross-sectional analysis of the relationship with depressive symptoms, anxiety and well-being in adults. Clin Nutr. 2018;37:1485-1491. 10.1016/j.clnu.2017.08.029

7. Agnoli C, Pounis G, Krogh V. Dietary pattern analysis. In: Barrios SP, Samaniego-Vaesken ML, eds. Analysis in Nutrition Research. Elsevier; 2019:75-101.

8. Schneeman B , ed. Part D, Chapter 8: Dietary patterns. In: Scientific Report of the 2020 Dietary Guidelines Advisory Committee. U.S. Department of Agriculture, U.S. Department of Health and Human Services; 2020:1-83.

9. Hu FB. Dietary pattern analysis: a new direction in nutritional epidemiology. Curr Opin Lipidol. 2002;13:3-9.

10. Zhao J, Li Z, Gao Q, et al. A review of statistical methods for dietary pattern analysis. Nutr J. 2021;20:37-18.

11. Rodríguez-Monforte M, Flores-Mateo G, Sánchez E. Dietary patterns and CVD: a systematic review and meta-analysis of observational studies. Br J Nutr. 2015;114:1341-1359.

12. Jannasch F, Kröger J, Schulze MB. Dietary patterns and type 2 diabetes: a systematic literature review and meta-analysis of prospective studies. J Nutr. 2017;147:1174-1182.

13. Grosso G, Bella F, Godos J, et al. Possible role of diet in cancer: systematic review and multiple meta-analyses of dietary patterns, lifestyle factors, and cancer risk. Nutr Rev. 2017;75:405-419.

14. Min M, Li-Fa X, Dong H, Jing W, Ming-Jie B. Dietary patterns and overweight/obesity: a review article. Iran J Public Health. 2017;46:869.

15. Jannasch F, Kröger J, Schulze M. Dietary patterns and type 2 diabetes: a systematic literature review and meta-analysis of prospective studies. J Nutr . 2017;147:1174-1192.

16. National Health and Medical Research Council. Australian Dietary Guidelines. National Health and Medical Research Council; 2013. Accessed November 27, 2023. https://www.health.gov.au/resources/publications/the-australian-dietary-guidelines

17. Willett W. Issues in analysis and presentation of dietary data. In: Willett W, ed. Nutritional Epidemiology. 3rd ed. Oxford University Press; 2012:305-333.

18. Yusof AS, Isa ZM, Shah SA. Dietary patterns and risk of colorectal cancer: a systematic review of cohort studies (2000-2011). Asian Pac J Cancer Prev. 2012;13:4713-4717.

19. Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71. 10.1136/bmj.n71

20. McKenzie JE, Brennan SE, Ryan RE, Thomson HJ, Johnston RV, Thomas J. Chapter 3: Defining the criteria for including studies and how they will be grouped for the synthesis. In: Higgins JPT, Thomas J, Chandler J, et al, eds. Cochrane Handbook for Systematic Reviews of Interventions, Version 6.4 . Updated August 2023. Cochrane; 2023. Accessed April 16, 2023. https://training.cochrane.org/handbook/current/chapter-03

21. Stang A. Critical evaluation of the Newcastle-Ottawa Scale for the assessment of the quality of nonrandomized studies in meta-analyses. Eur J Epidemiol. 2010;25:603-605. 10.1007/s10654-010-9491-z

22. Zou M, Northstone K, Perry R, Johnson L, Leary S. The association between later eating rhythm and adiposity in children and adolescents: a systematic review and meta-analysis. Nutr Rev. 2022;80:1459-1479. 10.1093/nutrit/nuab079

23. Modesti PA, Reboldi G, Cappuccio FP, et al. ; ESH Working Group on CV Risk in Low Resource Settings. Panethnic differences in blood pressure in Europe: a systematic review and meta-analysis. PLoS One. 2016;11:e0147601. 10.1371/journal.pone.0147601

24. Wingrove K, Lawrence MA, McNaughton SA. A systematic review of the methods used to assess and report dietary patterns. Front Nutr. 2022;9:892351. 10.3389/fnut.2022.892351

25. Higgins JPT, Green S, eds. Cochrane Handbook for Systematic Reviews of Interventions . Version 5.0.2. The Cochrane Collaboration; 2009:Section 9.5.2, pages 9-23. Accessed April 16, 2023. https://handbook-5-1.cochrane.org/chapter_9/9_5_2_identifying_and_measuring_heterogeneity.htm

26. Harrer M, Cuijpers P, Furukawa TA, Ebert DD. Doing Meta-Analysis with R: A Hands-On Guide. 1st ed. Chapman & Hall/CRC Press; 2021.

27. Posit Team. RStudio: Integrated Development Environment for R. Posit Software, PBC. Accessed April 12, 2023. http://www.posit.co/

28. Alston L, Nichols M, Allender S, et al. Dietary patterns in rural and metropolitan Australia: a cross-sectional study exploring dietary patterns, inflammation and association with cardiovascular disease risk factors. BMJ Open. 2023;13:e069475.

29. Ambrosini GL, De Klerk NH, Mackerras D, Leavy J, Fritschi L. Dietary patterns and surgically treated benign prostatic hyperplasia: a case control study in Western Australia. BJU Int. 2008;101:853-860. 10.1111/j.1464-410X.2007.07345.x

30. Ambrosini GL, Fritschi L, de Klerk NH, MacKerras D, Leavy J. Dietary patterns identified using factor analysis and prostate cancer risk: a case control study in Western Australia. Ann Epidemiol. 2008;18:364-370.

31. Amirul Islam FM, Chong EW, Hodge AM, et al. Dietary patterns and their associations with age-related macular degeneration: the Melbourne Collaborative Cohort Study. Ophthalmology. 2014;121:1428-1434.e2.

32. Anil S, Charlton KE, Tapsell LC, Probst Y, Ndanuko R, Batterham MJ. Identification of dietary patterns associated with blood pressure in a sample of overweight Australian adults. J Hum Hypertens. 2016;30:672-678. 10.1038/jhh.2016.10

33. Arabshahi S, Ibiebele TI, Hughes MCB, Lahmann PH, Williams GM, van der Pols JC. Dietary patterns and weight change: 15-year longitudinal study in Australian adults. Eur J Nutr. 2017;56:1455-1465.

34. Ashby-Mitchell K, Peeters A, Anstey KJ. Role of dietary pattern analysis in determining cognitive status in elderly Australian adults. Nutrients. 2015;7:1052-1067.

35. Bell LK, Edwards S, Grieger JA. The relationship between dietary patterns and metabolic health in a representative sample of adult Australians. Nutrients. 2015;7:6491-6505. 10.3390/nu7085295

36. Berg J, Seyedsadjadi N, Grant R. Increased consumption of plant foods is associated with increased bone mineral density. J Nutr Health Aging. 2020;24:388-397. 10.1007/s12603-020-1339-y

37. Cao Y, Dunstan DW, Sethi P, Owen N. The association of TV viewing time with 2-hour plasma glucose is modified by a prudent dietary pattern. J Diabetes. 2021;13:661-671.

38. Cao Y, Taylor AW, Wittert G, Adams R, Shi Z. Dietary patterns and sleep parameters in a cohort of community dwelling Australian men. Asia Pac J Clin Nutr. 2017;26:1158-1169. 10.6133/apjcn.122016.03

39. Chen X, Liu Z, Sachdev PS, Kochan NA, O'Leary F, Brodaty H. Dietary patterns and cognitive health in older adults: findings from the Sydney Memory and Ageing Study. J Nutr Health Aging. 2021;25:255-262. 10.1007/s12603-020-1536-8

40. Gardener SL, Rainey-Smith SR, Barnes MB, et al. Dietary patterns and cognitive decline in an Australian study of ageing. Mol Psychiatry. 2015;20:860-866. 10.1038/mp.2014.79

41. Gete DG, Waller M, Mishra GD. Prepregnancy dietary patterns and risk of preterm birth and low birth weight: findings from the Australian Longitudinal Study on Women's Health. Am J Clin Nutr. 2020;111:1048-1058. 10.1093/ajcn/nqaa057

42. Grieger JA, Grzeskowiak LE, Wood LG, Clifton VL. Asthma control in pregnancy is associated with pre-conception dietary patterns. Public Health Nutr. 2016;19:332-338. 10.1017/s1368980015001226

43. Harriss LR, English DR, Powles J, et al. Dietary patterns and cardiovascular mortality in the Melbourne Collaborative Cohort Study. Am J Clin Nutr. 2007;86:221-229.

44. Hart MJ, Milte CM, Torres SJ, Thorpe MG, McNaughton SA. Dietary patterns are associated with depressive symptoms in older Australian women but not men. Br J Nutr. 2019;122:1424-1431. 10.1017/s0007114519002435

45. Herber-Gast GCM, Mishra GD. Fruit, Mediterranean-style, and high-fat and -sugar diets are associated with the risk of night sweats and hot flushes in midlife: results from a prospective cohort study. Am J Clin Nutr. 2013;97:1092-1099. 10.3945/ajcn.112.049965

46. Hodge A, Almeida OP, English DR, Giles GG, Flicker L. Patterns of dietary intake and psychological distress in older Australians: benefits not just from a Mediterranean diet. Int Psychogeriatr. 2013;25:456-466.

47. Hodge AM, English DR, O'Dea K, Giles GG. Dietary patterns and diabetes incidence in the Melbourne Collaborative Cohort Study. Am J Epidemiol. 2007;165:603-610.

48. Hodge AM, O'Dea K, English DR, Giles GG, Flicker L. Dietary patterns as predictors of successful ageing. J Nutr Health Aging. 2014;18:221-227. 10.1007/s12603-013-0405-0

49. Hosking D, Danthiir V. Retrospective lifetime dietary patterns are associated with demographic and cardiovascular health variables in an older community-dwelling Australian population. Br J Nutr. 2013;110:2069-2083.

50. Hosking DE, Nettelbeck T, Wilson C, Danthiir V. Retrospective lifetime dietary patterns predict cognitive performance in community-dwelling older Australians. Br J Nutr. 2014;112:228-237. 10.1017/s0007114514000646

51. Hughes MC, Williams GM, Fourtanier A, Green AC. Food intake, dietary patterns, and actinic keratoses of the skin: a longitudinal study. Am J Clin Nutr. 2009;89:1246-1255.

52. Ibiebele TI, Hughes MC, Whiteman DC, Webb PM; Australian Cancer Study. Dietary patterns and risk of oesophageal cancers: a population-based case–control study. Br J Nutr. 2012;107:1207-1216. 10.1017/S0007114511004247

53. Ibiebele TI, van der Pols JC, Hughes MC, Marks GC, Williams GM, Green AC. Dietary pattern in association with squamous cell carcinoma of the skin: a prospective study. Am J Clin Nutr. 2007;85:1401-1408.

54. Jacka FN, Pasco JA, Mykletun A, et al. Association of Western and traditional diets with depression and anxiety in women. Am J Psychiatry. 2010;167:305-311.

55. Jacka FN, Pasco JA, Mykletun A, et al. Diet quality in bipolar disorder in a population-based-sample of women. J Affect Disord. 2011;129:332-337. 10.1016/j.jad.2010.09.004

56. Khalesi S, Irwin C, Sun J. Dietary patterns, nutrition knowledge, lifestyle, and health-related quality of life: associations with anti-hypertension medication adherence in a sample of Australian adults. High Blood Press Cardiovasc Prev. 2017;24:453-462. 10.1007/s40292-017-0229-9

57. Khalesi S, Sharma S, Irwin C, Sun J. Dietary patterns, nutrition knowledge and lifestyle: associations with blood pressure in a sample of Australian adults (the Food BP study). J Hum Hypertens. 2016;30:581-590. 10.1038/jhh.2016.22

58. Livingstone KM, McNaughton SA. Dietary patterns by reduced rank regression are associated with obesity and hypertension in Australian adults. Br J Nutr. 2017;117:248-259. 10.1017/S0007114516004505

59. Livingstone KM, McNaughton SA. Association between diet quality, dietary patterns and cardiometabolic health in Australian adults: a cross-sectional study. Nutr J. 2018;17:19. 10.1186/s12937-018-0326-1

60. Livingstone KM, Sexton-Dhamu MJ, Pendergast FJ, Worsley A, Brayner B, McNaughton SA. Energy-dense dietary patterns high in free sugars and saturated fat and associations with obesity in young adults. Eur J Nutr. 2022;61:1595-1607. 10.1007/s00394-021-02758-y

61. Mahamat-Saleh Y, Hughes MCB, Miura K, et al. Patterns of omega-3 and omega-6 fatty acid dietary intake and melanoma thickness at diagnosis. Cancer Epidemiol Biomarkers Prev. 2020;29:1647-1653. 10.1158/1055-9965.EPI-20-0319

62. Margerison C, Riddell LJ, McNaughton SA, Nowson CA. Associations between dietary patterns and blood pressure in a sample of Australian adults. Nutr J. 2020;19:5.

63. McNaughton SA, Wattanapenpaiboon N, Wark JD, Nowson CA. An energy-dense, nutrient-poor dietary pattern is inversely associated with bone health in women. J Nutr. 2011;141:1516-1523. 10.3945/jn.111.138271

64. Melaku YA, Gill TK, Adams R, Shi Z. Association between dietary patterns and low bone mineral density among adults aged 50 years and above: findings from the North West Adelaide Health Study (NWAHS). Br J Nutr. 2016;116:1437-1446. 10.1017/S0007114516003366

65. Melaku YA, Gill TK, Taylor AW, Adams R, Shi Z. A comparison of principal component analysis, partial least-squares and reduced-rank regressions in the identification of dietary patterns associated with bone mass in ageing Australians. Eur J Nutr. 2018;57:1969-1983. 10.1007/s00394-017-1478-z

66. Mondin TC, Stuart AL, Williams LJ, Jacka FN, Pasco JA, Ruusunen A. Diet quality, dietary patterns and short sleep duration: a cross-sectional population-based study. Eur J Nutr. 2019;58:641-651.

67. Moran LJ, Grieger JA, Mishra GD, Teede HJ. The association of a Mediterranean-style diet pattern with polycystic ovary syndrome status in a community cohort study. Nutrients. 2015;7:8553-8564. 10.3390/nu7105419

68. Ndanuko RN, Tapsell LC, Charlton KE, Neale EP, Batterham MJ. Associations between dietary patterns and blood pressure in a clinical sample of overweight adults. J Acad Nutr Diet. 2017;117:228-239.

69. Nobbs HM, Yaxley A, Thomas J, et al. Do dietary patterns in older age influence the development of cancer and cardiovascular disease: a longitudinal study of ageing. Clin Nutr. 2016;35:528-535. 10.1016/j.clnu.2015.04.003

70. Ognjenovic M, Ambrosini GL, Malacova E, et al. Associations between major dietary patterns and testicular function in a population-based cohort of young men: results from the Western Australian Pregnancy Cohort (Raine) Study. Andrology. 2019;7:273-280. 10.1111/andr.12598

71. Rienks J, Dobson AJ, Mishra GD. Mediterranean dietary pattern and prevalence and incidence of depressive symptoms in mid-aged women: results from a large community-based prospective study. Eur J Clin Nutr. 2013;67:75-82. 10.1038/ejcn.2012.193

72. Rosenkranz RR, Rosenkranz SK, Neessen KJ. Dietary factors associated with lifetime asthma or hayfever diagnosis in Australian middle-aged and older adults: a cross-sectional study. Nutr J. 2012;11:84. 10.1186/1475-2891-11-84

73. Schoenaker D, Soedamah-Muthu SS, Callaway LK, Mishra GD. Prepregnancy dietary patterns and risk of developing hypertensive disorders of pregnancy: results from the Australian Longitudinal Study on Women's Health. Am J Clin Nutr. 2015;102:94-101. 10.3945/ajcn.114.102475

74. Schoenaker D, Soedamah-Muthu SS, Callaway LK, Mishra GD. Pre-pregnancy dietary patterns and risk of gestational diabetes mellitus: results from an Australian population-based prospective cohort study. Diabetologia. 2015;58:2726-2735. 10.1007/s00125-015-3742-1

75. Schoenaker DAJM, Dobson AJ, Soedamah-Muthu SS, Mishra GD. Factor analysis is more appropriate to identify overall dietary patterns associated with diabetes when compared with treelet transform analysis. J Nutr. 2013;143:392-398. 10.3945/jn.112.169011

76. Shakya PR, Melaku YA, Page A, Gill TK. Association between dietary patterns and adult depression symptoms based on principal component analysis, reduced-rank regression and partial least-squares. Clin Nutr. 2019;14:14.

77. Simpson-Yap S, Oddy WH, Taylor B, et al. ; Ausimmune/AusLong Investigators Group. High prudent diet factor score predicts lower relapse hazard in early multiple sclerosis. Mult Scler. 2021;27:1112-1124. 10.1177/1352458520943087

78. Torres SJ, Lautenschlager NT, Wattanapenpaiboon N, et al. Dietary patterns are associated with cognition among older people with mild cognitive impairment. Nutrients. 2012;4:1542-1551.

79. Ushula TW, Mamun A, Darssan D, et al. Dietary patterns and young adult body mass change: a 9-year longitudinal study. Eur J Nutr. 2023;62:1657-1666. 10.1007/s00394-023-03101-3

80. Ushula TW, Mamun A, Darssan D, et al. Dietary patterns and the risks of metabolic syndrome and insulin resistance among young adults: evidence from a longitudinal study. Clin Nutr. 2022;41:1523-1531.

81. Ushula TW, Mamun A, Darssan D, et al. Dietary patterns and the risk of abnormal blood lipids among young adults: a prospective cohort study. Nutr Metab Cardiovasc Dis. 2022;32:1165-1174.

82. Ushula TW, Mamun A, Darssan D, et al. Dietary patterns explaining variations in blood biomarkers in young adults are associated with the 30-year predicted cardiovascular disease risks in midlife: a follow-up study. Nutr Metab Cardiovasc Dis. 2023;33:1007-1018.

83. Wang YB, Shivappa N, Hebert JR, Page AJ, Gill TK, Melaku YA. Association between Dietary Inflammatory Index, dietary patterns, plant-based dietary index and the risk of obesity. Nutrients. 2021;13:02.

84. Wu F, Wills K, Laslett LL, Oldenburg B, Jones G, Winzenberg T. Associations of dietary patterns with bone mass, muscle strength and balance in a cohort of Australian middle-aged women. Br J Nutr. 2017;118:598-606.

85. Zabetian-Targhi F, Srikanth VK, Smith KJ, et al. Dietary patterns are not associated with brain atrophy or cerebral small vessel disease in older adults with and without type 2 diabetes. J Nutr. 2019;149:1805-1811. 10.1093/jn/nxz139

86. Zheng S, Wu F, Winzenberg T, et al. The cross-sectional and longitudinal associations of dietary patterns with knee symptoms and MRI detected structure in patients with knee osteoarthritis. Osteoarthritis Cartilage. 2021;29:527-535.

87. National Health and Medical Research Council (NHMRC). The revision of the 2013 Australian Dietary Guidelines. Accessed March 2024. https://www.nhmrc.gov.au/health-advice/nutrition/australian-dietary-guidelines-review/guideline-development

88. Veettil SK, Wong TY, Loo YS, et al. Role of diet in colorectal cancer incidence: umbrella review of meta-analyses of prospective observational studies. JAMA Netw Open. 2021;4:e2037341-e2037341.

89. Feng Y-L, Shu L, Zheng P-F, et al. Dietary patterns and colorectal cancer risk. Eur J Cancer Prev. 2017;26:201-211.

90. Gan ZH, Cheong HC, Tu Y-K, Kuo P-H. Association between plant-based dietary patterns and risk of cardiovascular disease: a systematic review and meta-analysis of prospective cohort studies. Nutrients. 2021;13:3952.

91. Dandamudi A, Tommie J, Nommsen-Rivers L, Couch S. Dietary patterns and breast cancer risk: a systematic review. Anticancer Res. 2018;38:3209-3222.

92. Seifu CN, Fahey PP, Hailemariam TG, Frost SA, Atlantis E. Dietary patterns associated with obesity outcomes in adults: an umbrella review of systematic reviews. Public Health Nutr. 2021;24:6390-6414.

93. Vilela DL, Fonseca PG, Pinto SL, Bressan J. Influence of dietary patterns on the metabolically healthy obesity phenotype: a systematic review. Nutr Metab Cardiovasc Dis. 2021;31:2779-2791.

94. Hart MJ, Torres SJ, McNaughton SA, Milte CM. Dietary patterns and associations with biomarkers of inflammation in adults: a systematic review of observational studies. Nutr J. 2021;20:24-14.

95. Evert AB, Dennison M, Gardner CD, et al. Nutrition therapy for adults with diabetes or prediabetes: a consensus report. Diabetes Care. 2019;42:731-754.

96. Attaye I, Warmbrunn MV, Boot AN, et al. A systematic review and meta-analysis of dietary interventions modulating gut microbiota and cardiometabolic diseases—striving for new standards in microbiome studies. Gastroenterology. 2022;162:1911-1932.

97. Aleksandrova K, Koelman L, Rodrigues CE. Dietary patterns and biomarkers of oxidative stress and inflammation: a systematic review of observational and intervention studies. Redox Biol. 2021;42:101869.

98. Liu K, Ding X, Zhao W. Influence of dietary pattern on human immunity. Zhonghua Yi Xue Za Zhi. 2020;100:3890-3896.

99. Furman D, Campisi J, Verdin E, et al. Chronic inflammation in the etiology of disease across the life span. Nat Med. 2019;25:1822-1832.

100. Tsitsou S, Athanasaki C, Dimitriadis G, Papakonstantinou E. Acute effects of dietary fiber in starchy foods on glycemic and insulinemic responses: a systematic review of randomized controlled crossover trials. Nutrients. 2023;15:2383.

101. Xie Y, Gou L, Peng M, Zheng J, Chen L. Effects of soluble fiber supplementation on glycemic control in adults with type 2 diabetes mellitus: a systematic review and meta-analysis of randomized controlled trials. Clin Nutr. 2021;40:1800-1810.

102. Mao T, Huang F, Zhu X, Wei D, Chen L. Effects of dietary fiber on glycemic control and insulin sensitivity in patients with type 2 diabetes: a systematic review and meta-analysis. J Funct Foods. 2021;82:104500.

103. Lattimer JM, Haub MD. Effects of dietary fiber and its components on metabolic health. Nutrients. 2010;2:1266-1289.

104. Fu J, Zheng Y, Gao Y, Xu W. Dietary fiber intake and gut microbiota in human health. Microorganisms. 2022;10:2507.

105. Zhang F, Fan D, Huang J-l, Zuo T. The gut microbiome: linking dietary fiber to inflammatory diseases. Med Microecol. 2022;14:100070.

106. Hills RD, Pontefract BA, Mishcon HR, Black CA, Sutton SC, Theberge CR. Gut microbiome: profound implications for diet and disease. Nutrients. 2019;11:1613.

107. Lobo V, Patil A, Phatak A, Chandra N. Free radicals, antioxidants and functional foods: impact on human health. Pharmacogn Rev. 2010;4:118-126.

108. Khan SU, Lone AN, Khan MS, et al. Effect of omega-3 fatty acids on cardiovascular outcomes: a systematic review and meta-analysis. EClinicalMedicine. 2021;38:100997.

109. Karagiannidi A, Kanellopoulou A, Panagiotakos D. Dietary patterns and cardiovascular disease: a systematic review of 29 prospective studies with 1,249,644 participants, published in the last 20 years. J Atheroscler Prev Treat . 2022;13:7-16.

110. World Health Organization. Healthy diet. 2019. Accessed July 21, 2023. https://www.who.int/news-room/fact-sheets/detail/healthy-diet

111. World Cancer Research Fund/American Institute for Cancer Research. Diet, nutrition, physical activity and cancer: a global perspective. Continuous Update Project Expert Report. 2018. Accessed April 14, 2022. https://www.wcrf.org/wp-content/uploads/2021/02/Summary-of-Third-Expert-Report-2018.pdf

112. Lambert V, Muñoz SE, Gil C, Román MD. Maternal dietary components in the development of gestational diabetes mellitus: a systematic review of observational studies to timely promotion of health. Nutr J. 2023;22:15-17.

113. Abdollahi S, Soltani S, de Souza RJ, Forbes SC, Toupchian O, Salehi-Abargouei A. Associations between maternal dietary patterns and perinatal outcomes: a systematic review and meta-analysis of cohort studies. Adv Nutr. 2021;12:1332-1352.

114. Sahoo K, Sahoo B, Choudhury AK, Sofi NY, Kumar R, Bhadoria AS. Childhood obesity: causes and consequences. J Family Med Prim Care. 2015;4:187-192.

115. Kipnis V, Midthune D, Freedman L, et al. Bias in dietary-report instruments and its implications for nutritional epidemiology. Public Health Nutr. 2002;5:915-923. 10.1079/phn2002383

116. National Health and Medical Research Council. A review of the evidence to address targeted questions to inform the revision of the Australian Dietary Guidelines 2011. Accessed April 19, 2024. https://www.eatforhealth.gov.au/sites/default/files/2022-11/n55d_dietary_guidelines_evidence_report_2011_0.pdf

117. National Health and Medical Research Council. Australian Dietary Guidelines. National Health and Medical Research Council; 2013. Accessed November 27, 2023. https://www.health.gov.au/resources/publications/the-australian-dietary-guidelines

118. Tapsell LC, Neale EP, Satija A, Hu FB. Foods, nutrients, and dietary patterns: interconnections and implications for dietary guidelines. Adv Nutr. 2016;7:445-454. 10.3945/an.115.011718

119. Schulze MB, Martínez-González MA, Fung TT, Lichtenstein AH, Forouhi NG. Food based dietary patterns and chronic disease prevention. BMJ. 2018;361:k2396. 10.1136/bmj.k2396

120. Mozaffarian D, Rosenberg I, Uauy R. History of modern nutrition science—implications for current research, dietary guidelines, and food policy. BMJ. 2018;361:k2392. 10.1136/bmj.k2392

121. Vepsäläinen H, Lindström J. Dietary patterns—a scoping review for nordic nutrition recommendations 2023. Food Nutr Res. 2024;02:/68. 10.29219/fnr.v68.10541

122. Satija A, Yu E, Willett WC, Hu FB. Understanding nutritional epidemiology and its role in policy. Adv Nutr. 2015;6:5-18. 10.3945/an.114.007492

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Supplementary Materials

Data Availability Statement

All data generated or analyzed during this study are included in this published article and its supplementary information files.