Gaussian graphical models identified food intake networks and risk of type 2 diabetes, CVD, and cancer in the EPIC-Potsdam study

Dairy consumption and incident prediabetes: prospective associations and network models in the large population-based Lifelines Study

BACKGROUND

Evidence on associations between dairy consumption and incident prediabetes is inconsistent. One potential explanation for heterogeneity is that health behavior and food intake covary with the consumption of various high-fat and low-fat dairy types.

OBJECTIVE

The objective was to investigate the associations of total dairy and dairy types with incident prediabetes and to assess how dairy intake is linked with metabolic risk factors, lifestyle behaviors, and foods, as potential explanations for these associations.

METHODS

Overall, 74,132 participants from the prospective population-based Lifelines study were included (mean age, 45.5 ± 12.3 y; 59.7% female). Baseline dairy intake was measured using a validated food frequency questionnaire. Prediabetes at follow-up was defined based on the World Health Organization/International Expert Committee criteria as fasting plasma glucose of 110-125 mg/dL or glycated hemoglobin concentrations of 6.0%-6.5%. Associations were analyzed using Poisson regression models adjusted for social demographics, lifestyle behaviors, family history of diabetes, and food group intake. Interconnections were assessed with mixed graphical model networks.

RESULTS

At a mean follow-up of 4.1 ± 1.1 y, 2746 participants developed prediabetes (3.7%). In regression analyses, neutral associations were found for most dairy types. Intake of plain milk and low-fat milk were associated with a higher risk of prediabetes in the top compared with bottom quartiles (relative risk [RR]: 1.17; 95% confidence interval [CI]: 1.05, 1.30; P-trend = 0.04 and RR: 1.18; 95% CI: 1.06, 1.31; P-trend =0.01). Strong but nonsignificant effect estimates for high-fat yogurt in relation to prediabetes were found (RRservings/day: 0.80; 95% CI: 0.64, 1.01). The network analysis showed that low-fat milk clustered with energy-dense foods, including bread, meat, and high-fat cheese, whereas high-fat yogurt had no clear link with lifestyle risk factors and food intake.

CONCLUSIONS

In this large cohort of Dutch adults, low-fat milk intake was associated with higher prediabetes risk. Heterogeneous associations by dairy type and fat content might partly be attributed to confounding caused by behaviors and food intake related to dairy intake.

Association between a priori and a posteriori dietary patterns and the risk of type 2 diabetes: a representative cohort study in Taiwan

The present study aimed to investigate the relationship between dietary patterns and the risk of type 2 diabetes mellitus (T2DM) among Taiwanese individuals. Data were collected using a nationwide cohort study (2001-15) from the Triple-High Database. Dietary intake was assessed using the twenty-group food frequency questionnaire and used to calculate alternate Mediterranean diet (aMED) and Dietary Approaches to Stop Hypertension (DASH) scores. Principal component analysis (PCA) and partial least-squares (PLS) regression were used to derive dietary patterns, with incident T2DM as the outcome. Multivariable-adjusted hazard ratios and 95 % confidence intervals were calculated using time-dependent Cox proportional hazards (Cox PH) regression analysis, and subgroup analyses were performed. A total of 4705 participants were enrolled in the study, and 995 had newly developed T2DM during the median 5⋅28-year follow-up period (30⋅7 per 1000 person-years). Six dietary patterns were extracted (PCA: Western, prudent, dairy and plant-based; PLS: health-conscious, fish-vegetable and fruit-seafood). The highest aMED score quartile had a 25 % (hazard ratio 0⋅75; 95 % CI 0⋅61, 0⋅92; P = 0⋅039) lower risk of T2DM than the lowest quartile. This association remained significant after adjustment (adjusted hazard ratio 0⋅74; 95 % CI 0⋅60, 0⋅91; P = 0⋅010), and no effect modifier was found for aMED. The DASH scores, PCA and PLS dietary patterns were not significant after adjustment. In conclusion, high adherence to a MED-type dietary pattern by Taiwanese foods was associated with a lower risk of T2DM in the Taiwanese population, regardless of unhealthy lifestyle habits.

The Relationship between Dietary Patterns and High Blood Glucose among Adults Based on Structural Equation Modelling

The aim of this study was to examine the association between dietary patterns and high blood glucose in Jiangsu province of China by using structural equation modelling (SEqM). Methods: Participants in this cross-sectional study were recruited through the 2015 Chinese Adult Chronic Disease and Nutrition Surveillance Program in Jiangsu province using a multistage stratified cluster random sampling method. Dietary patterns were defined by exploratory factor analysis (EFA). Confirmatory factor analysis (CFA) was used to test the fitness of EFA. SEqM was used to investigate the association between dietary patterns and high blood glucose. Results: After exclusion, 3137 participants with complete information were analysed for this study. The prevalence of high blood glucose was 9.3% and 8.1% in males and females, respectively. Two dietary patterns: the modern dietary pattern (i.e., high in red meats and its products, vegetables, seafood, condiments, fungi and algae, main grains and poultry; low in other grains, tubers and preserves), and the fruit−milk dietary pattern (i.e., high in milk and its products, fruits, eggs, nuts and seeds and pastry snacks, but low in vegetable oils) were established. Modern dietary pattern was found to be positively associated with high blood glucose in adults in Jiangsu province (multivariate logistic regression: OR = 1.561, 95% CI: 1.025~2.379; SEqM: β = 0.127, p < 0.05). Conclusion: The modern dietary pattern—high intake of red meats—was significantly associated with high blood glucose among adults in Jiangsu province of China, while the fruit−milk dietary pattern was not significantly associated with high blood glucose.

Saturated fats network identified using Gaussian graphical models is associated with metabolic syndrome in a sample of Iranian adults

BACKGROUND

Gaussian graphical models (GGM) are an innovative method for deriving dietary networks which reflect dietary intake patterns and demonstrate how food groups are consuming in relation to each other, independently. The aim of this study was to derive dietary networks and assess their association with metabolic syndrome in a sample of the Iranian population.

METHODS

In this cross-sectional study, 850 apparently healthy adults were selected from referral health care centers. 168 food items food frequency questionnaire was used to assess dietary intakes. Food networks were driven by applying GGM to 40 food groups. Metabolic syndrome was defined based on the guidelines of the National Cholesterol Education Program Adult Treatment Panel III (ATP III).

RESULTS

Three GGM networks were identified: healthy, unhealthy and saturated fats. Results showed that adherence to saturated fats networks with the centrality of butter, was associated with higher odds of having metabolic syndrome after adjusting for potential confounders (OR = 1.81, 95% CI 1.61-2.82; P trend = 0.009) and higher odds of having hyperglycemia (P trend = 0.04). No significant association was observed between healthy and unhealthy dietary networks with metabolic syndrome, hypertension, hypertriglyceridemia and central obesity. Furthermore, metabolic syndrome components were not related to the identified networks.

CONCLUSION

Our findings suggested that greater adherence to the saturated fats network is associated with higher odds of having metabolic syndrome in Iranians. These findings highlight the effect of dietary intake patterns with metabolic syndrome.

Association between dietary intake networks identified through a Gaussian graphical model and the risk of cancer: a prospective cohort study

PURPOSE

In this study, we aimed to investigate the association between dietary communities identified by a Gaussian graphical model (GGM) and cancer risk.

METHODS

We performed GGM to identify the dietary communities in a Korean population. GGM-derived communities were then scored and investigated for their association with cancer incidence in the entire population as well as in the 1:1 age- and sex-matched subgroup using a Cox proportional hazards model. In the sensitivity analysis, GGM-derived communities were compared to dietary patterns (DPs) that were identified by principal component analysis (PCA) and reduced rank regression (RRR).

RESULTS

During a median time to follow-up of 6.6 years, 397 cancer cases were newly diagnosed. The GGM identified 17 and 16 dietary communities for the total and matched populations, respectively. For each one-unit increase in the standard deviation of the community-specific score of the community that was composed of dairy products and bread, there was a reduced risk of cancer according to the fully adjusted model (HR: 0.80, 95% CI: 0.66-0.96). In the matched population, the third tertile of the community-specific score of the community composed of poultry, seafood, bread, cakes and sweets, and meat by-products showed a significantly reduced risk of cancer compared to that of the lowest tertile in the fully adjusted model (HR: 0.66, 95% CI: 0.50-0.86, p-trend = 0.002).

CONCLUSION

We found that the GGM-identified community composed of dairy products and bread showed a reduced risk of cancer. Further population-based prospective studies should be conducted to examine possible associations of dietary intake and specific cancer types.

Nutrition-wide association study of microbiome diversity and composition in colorectal cancer patients

BACKGROUND

The effects of diet on the interaction between microbes and host health have been widely studied. However, its effects on the gut microbiota of patients with colorectal cancer (CRC) have not been elucidated. This study aimed to investigate the association between diet and the overall diversity and different taxa levels of the gut microbiota in CRC patients via the nutrition-wide association approach.

METHODS

This hospital-based study utilized data of 115 CRC patients who underwent CRC surgery in Department of Surgery, Seoul National University Hospital. Spearman correlation analyses were conducted for 216 dietary features and three alpha-diversity indices, Firmicutes/Bacteroidetes ratio, and relative abundance of 439 gut microbial taxonomy. To identify main enterotypes of the gut microbiota, we performed the principal coordinate analysis based on the β-diversity index. Finally, we performed linear regression to examine the association between dietary intake and main microbiome features, and linear discriminant analysis effect size (LEfSe) to identify bacterial taxa phylogenetically enriched in the low and high diet consumption groups.

RESULTS

Several bacteria were enriched in patients with higher consumption of mature pumpkin/pumpkin juice (ρ, 0.31 to 0.41) but lower intake of eggs (ρ, -0.32 to -0.26). We observed negative correlations between Bacteroides fragilis abundance and intake of pork (belly), beef soup with vegetables, animal fat, and fatty acids (ρ, -0.34 to -0.27); an inverse correlation was also observed between Clostridium symbiosum abundance and intake of some fatty acids, amines, and amino acids (ρ, -0.30 to -0.24). Furthermore, high intake of seaweed was associated with a 6% (95% CI, 2% to 11%) and 7% (95% CI, 2% to 11%) lower abundance of Rikenellaceae and Alistipes, respectively, whereas overall beverage consumption was associated with an 10% (95% CI, 2% to 18%) higher abundance of Bacteroidetes, Bacteroidia, and Bacteroidales, compared to that in the low intake group. LEfSe analysis identified phylogenetically enriched taxa associated with the intake of sugars and sweets, legumes, mushrooms, eggs, oils and fats, plant fat, carbohydrates, and monounsaturated fatty acids.

CONCLUSIONS

Our data elucidates the diet-microbe interactions in CRC patients. Additional research is needed to understand the significance of these results in CRC prognosis.

Dietary networks identified by Gaussian graphical model and general and abdominal obesity in adults

BACKGROUND

Gaussian graphical model (GGM) has been introduced as a new approach to identify patterns of dietary intake. We aimed to investigate the link between dietary networks derived through GGM and obesity in Iranian adults.

METHOD

A cross-sectional study was conducted on 850 men and women (age range: 20-59 years) who attended the local health centers in Tehran. Dietary intake was evaluated by using a validated food frequency questionnaire. GGM was applied to identify dietary networks. The odds ratios (ORs) and 95% confidence intervals (CIs) of general and abdominal adiposity across tertiles of dietary network scores were estimated using logistic regression analysis controlling for age, sex, physical activity, smoking status, marital status, education, energy intake and menopausal status.

RESULTS

GGM identified three dietary networks, where 30 foods were grouped into six communities. The identified networks were healthy, unhealthy and saturated fats networks, wherein cooked vegetables, processed meat and butter were, respectively, central to the networks. Being in the top tertile of saturated fats network score was associated with a higher likelihood of central obesity by waist-to-hip ratio (OR: 1.56, 95%CI: 1.08, 2.25; P for trend: 0.01). There was also a marginally significant positive association between higher unhealthy network score and odds of central obesity by waist circumference (OR: 1.37, 95%CI: 0.94, 2.37; P for trend: 0.09). Healthy network was not associated with central adiposity. There was no association between dietary network scores and general obesity.

CONCLUSIONS

Unhealthy and saturated fat dietary networks were associated with abdominal adiposity in adults. GGM-derived dietary networks represent dietary patterns and can be used to investigate diet-disease associations.

Network Analysis of Demographics, Dietary Intake, and Comorbidity Interactions

The aim of this study was to elucidate the complex interrelationships among dietary intake, demographics, and the risk of comorbidities. We applied a Gaussian graphical model to calculate the dietary scores of the participants. The network structure of dietary intake, demographics, and comorbidities was estimated in a mixed graphical model. The centrality indices of the nodes (strength (S), closeness (C), and betweenness (B)) were measured to identify the central node. Multinomial logistic regression was used to examine the association between the factors and comorbidities. Among 7423 participants, the strongest pairwise interactions were found between sex and smoking (1.56), sex and employment (0.66), sex and marital status (0.58), marital status and income (0.65), and age and employment (0.58). Among the factors in the network, sex played a central role (S = 4.63, C = 0.014, B = 41), followed by age (S = 2.81, C = 0.013, B = 18), smoking (S = 2.72, C = 0.013, B = 0), and employment (S = 2.17, C = 0.014, B = 22). While the odds of hypertension and diabetes were significantly higher among females than males, an inverse association was observed between high cholesterol and moderate chronic kidney disease. Among these factors, dietary intake was not a strongly interacting factor in the network, whereas age was consistently associated with the comorbidities of hypertension, high cholesterol, diabetes, and chronic kidney disease.

Effect of the Interaction between Dietary Patterns and the Gastric Microbiome on the Risk of Gastric Cancer

We aimed to observe the combined effects of Gaussian graphical model (GGM)-derived dietary patterns and the gastric microbiome on the risk of gastric cancer (GC) in a Korean population. The study included 268 patients with GC and 288 healthy controls. Food intake was assessed using a 106-item semiquantitative food frequency questionnaire. GGMs were applied to derive dietary pattern networks. 16S rRNA gene sequencing was performed using DNA extracted from gastric biopsy samples. The fruit pattern network was inversely associated with the risk of GC for the highest vs. lowest tertiles in the total population (odds ratio (OR): 0.47; 95% confidence interval (CI): 0.28-0.77; p for trend = 0.003) and in females (OR: 0.38; 95% CI: 0.17-0.83; p for trend = 0.021). Males who had a low microbial dysbiosis index (MDI) and high vegetable and seafood pattern score showed a significantly reduced risk of GC (OR: 0.44; 95% CI: 0.22-0.91; p-interaction = 0.021). Females who had a low MDI and high dairy pattern score showed a significantly reduced risk of GC (OR: 0.23; 95% CI: 0.07-0.76; p-interaction = 0.018). Our novel findings revealed that vegetable and seafood pattern might interact with dysbiosis to attenuate the risk of GC in males, whereas the dairy pattern might interact with dysbiosis to reduce the GC risk in females.

A posteriori dietary patterns and their association with systemic low-grade inflammation in adults: a systematic review and meta-analysis

CONTEXT

A posteriori dietary patterns are promising ways of uncovering potential public health strategies for the prevention of systemic, low-grade, inflammation-related, chronic noncommunicable diseases.

OBJECTIVE

To investigate and summarize the current evidence on the association between a posteriori dietary patterns and systemic, low-grade inflammation in adults.

DATA SOURCES

MEDLINE, EMBASE, Web of Science, and LILACS were searched.

DATA EXTRACTION

Data screening, extraction, and quality assessment were performed independently by 2 investigators. Meta-analysis with random effects was conducted. Differences and similarities between reduced rank regression-derived dietary patterns were assessed.

RESULTS

Healthy dietary patterns are inversely and the Western dietary pattern is positively associated with inflammation (r = -0.13, 95% confidence interval -0.20 to -0.06; and r = 0.11, 95% confidence interval, 0.09-0.12, respectively). Reduced rank regression-derived anti-inflammatory dietary patterns are consistently characterized by high intake of fresh fruits and inflammatory dietary patterns are consistently characterized by high intake of red and processed meat and low intake of vegetables.

CONCLUSION

Favoring the substitution of a Westernized diet for a healthy diet may lower inflammation, which might improve the prevention of some chronic noncommunicable diseases.

Differences in Dietary Patterns Identified by the Gaussian Graphical Model in Korean Adults With and Without a Self-Reported Cancer Diagnosis

BACKGROUND

The synergistic effect of food groups on health outcomes is better captured by examining dietary patterns (DPs) than single food groups. Regarding this issue, a Gaussian graphical model (GGM) can identify pairwise correlations between food groups and adjust for the remaining items. However, the application of GGMs in the nutritional field has not been widely investigated, especially in Korean adults.

OBJECTIVE

The aim of this study was to identify the major DPs of Korean adults by using a GGM and to examine the associations between the DP scores and prevalence of self-reported cancer.

DESIGN

This cross-sectional study used baseline data from the 2007-2019 Cancer Screenee Cohort of the National Cancer Center, Korea.

PARTICIPANTS/SETTING

In total, 10,777 Korean adults who completed a questionnaire regarding their general medical history, including clinical test results, and a validated food frequency questionnaire were included.

MAIN OUTCOME MEASURES

The main outcome measure was the prevalence of self-reported cancer at baseline.

STATISTICAL ANALYSIS

DP networks were identified using a GGM. The GGM-identified networks were scored and categorized into tertiles, and their association with the prevalence of self-reported cancer was investigated using a multivariable logistic regression model.

RESULTS

The GGM identified the following 4 DP networks: principal, oil-sweet, meat, and fruit. After adjusting for covariates, the odds of moderate and high consumption of foods in the oil-sweet DP for participants who self-reported cancer were 25% and 34% lower than those for participants who did not report a cancer diagnosis (odds ratio [OR] = 0.75, 95% confidence interval [CI] = 0.62-0.90 and OR = 0.66, 95% CI = 0.53-0.81, respectively). Additionally, the odds of meat DP consumption in the self-reported cancer group was 29% lower than in participants who did not report a cancer diagnosis (OR = 0.71 and 95% CI = 0.57-0.88). In contrast, an increase in the odds of fruit DP consumption was observed for self-reported cancer participants (OR = 1.34 and 95% CI = 1.09-1.65). Similar results were observed among the female but not the male subjects.

CONCLUSIONS

GGM is a novel method that can distinguish the direct pairwise correlation of food groups and control for the indirect effect of other foods. Future large-scale longitudinal population-based studies are needed to build on these findings in general populations.

Intake of dietary fats and fatty acids and the incidence of type 2 diabetes: A systematic review and dose-response meta-analysis of prospective observational studies

BACKGROUND

The role of fat quantity and quality in type 2 diabetes (T2D) prevention is controversial. Thus, this systematic review and meta-analysis aimed to investigate the associations between intake of dietary fat and fatty acids and T2D, and to evaluate the certainty of evidence.

METHODS AND FINDINGS

We systematically searched PubMed and Web of Science through 28 October 2019 for prospective observational studies in adults on the associations between intake of dietary fat and fatty acids and T2D incidence. The systematic literature search and data extraction were conducted independently by 2 researchers. We conducted linear and nonlinear random effects dose-response meta-analyses, calculated summary relative risks (SRRs) with their corresponding 95% confidence intervals (95% CIs), and assessed the certainty of evidence. In total, 15,070 publications were identified in the literature search after the removal of duplicates. Out of the 180 articles screened in full text, 23 studies (19 cohorts) met our inclusion criteria, with 11 studies (6 cohorts) conducted in the US, 7 studies (7 cohorts) in Europe, 4 studies (5 cohorts) in Asia, and 1 study (1 cohort) in Australia. We mainly observed no or weak linear associations between dietary fats and fatty acids and T2D incidence. In nonlinear dose-response meta-analyses, the protective association for vegetable fat and T2D was steeper at lower levels up to 13 g/d (SRR [95% CI]: 0.81 [0.76; 0.88], pnonlinearity = 0.012, n = 5 studies) than at higher levels. Saturated fatty acids showed an apparent protective association above intakes around 17 g/d with T2D (SRR [95% CI]: 0.95 [0.90; 1.00], pnonlinearity = 0.028, n = 11). There was a nonsignificant association of a decrease in T2D incidence for polyunsaturated fatty acid intakes up to 5 g/d (SRR [95% CI]: 0.96 [0.91; 1.01], pnonlinearity = 0.023, n = 8), and for alpha-linolenic acid consumption up to 560 mg/d (SRR [95% CI]: 0.95 [0.90; 1.00], pnonlinearity = 0.014, n = 11), after which the curve rose slightly, remaining close to no association. The association for long-chain omega-3 fatty acids and T2D was approximately linear for intakes up to 270 mg/d (SRR [95% CI]: 1.10 [1.06; 1.15], pnonlinearity < 0.001, n = 16), with a flattening curve thereafter. Certainty of evidence was very low to moderate. Limitations of the study are the high unexplained inconsistency between studies, the measurement of intake of dietary fats and fatty acids via self-report on a food group level, which is likely to lead to measurement errors, and the possible influence of unmeasured confounders on the findings.

CONCLUSIONS

There was no association between total fat intake and the incidence of T2D. However, for specific fats and fatty acids, dose-response curves provided insights for significant associations with T2D. In particular, a high intake of vegetable fat was inversely associated with T2D incidence. Thus, a diet including vegetable fat rather than animal fat might be beneficial regarding T2D prevention.

Gaussian Graphical Models Identified Food Intake Networks among Iranian Women with and without Breast Cancer: A Case-Control Study

BACKGROUND

Dietary patterns may be an important predictor of breast cancer risk. However, they cannot completely explain the pairwise correlations among foods. The purpose of this study is to compare food intake networks derived by Gaussian Graphical Models (GGMs) for women with and without breast cancer to better understand how foods are consumed in relation to each other according to disease status.

METHODS

A total of 134 women with breast cancer and 267 hospital controls were selected from referral hospitals of Tehran, Iran. Dietary intakes were evaluated by using a validated 168 food-items semi-quantitative food frequency questionnaire. GGMs were applied to log-transformed intakes of 28 food groups to construct outcome-specific food networks.

RESULTS

Among cases, a main network containing intakes of 12 central food groups (vegetables, fruits, nuts and seeds, olive oil and olive, processed meat, sweets, salt, soft drinks, fried potatoes, pickles, low-fat dairy, pizza) was detected. In controls, a main network including six central food groups (liquid oils, vegetables, fruits, sweets, fried potatoes and soft drinks) was identified.

CONCLUSIONS

The findings of this study revealed a difference in GGM-identified networks graphs between cases and controls. Overall, GGM may provide additional understanding of relationships between diet and health.

Comorbidity Risk Score in Association with Cancer Incidence: Results from a Cancer Screenee Cohort

The combined effects of comorbidities can cause cancer incidence, while the effects of individual conditions, alone, might not. This study was conducted to investigate the joint impact of comorbidities on cancer incidence. The dietary score for energy-adjusted intake was calculated by applying a Gaussian graphical model and was then categorized into tertiles representing light, normal, and heavy eating behaviors. The risk point for cancer, according to the statuses of blood pressure, total cholesterol, fasting glucose, and glomerular filtration rate was computed from a Cox proportional hazard model adjusted for demographics and eating behavior. The comorbidity risk score was defined as the sum of the risk points for four comorbidity markers. We finally quantified the hazard ratios (HRs) and 95% confidence intervals (CIs) for the association between the strata of the comorbidity risk score and cancer incidence. A total of 13,644 subjects were recruited from the Cancer Screenee Cohort from 2007–2014. The comorbidity risk score was associated with cancer incidence in a dose-dependent manner (HR = 2.15, 95% CI = 1.39, 3.31 for those scoring 16–30 vs. those scoring 0–8, P-trend < 0.001). Subgroup analysis still showed significant dose-dependent relationships (HR = 2.39, 95% CI = 1.18, 4.84 for males and HR = 1.99, 95% CI = 1.11, 3.59 for females, P-trend < 0.05). In summary, there was a dose-dependent impact of comorbidities on cancer incidence; Highlights: Previous studies have generally reported that hypertension, hypercholesterolemia, diabetes, and chronic kidney disease might predispose patients to cancer. Combining these chronic diseases into a single score, this study found a dose-dependent association between the data-driven comorbidity risk score and cancer incidence.

Identification of Dietary Pattern Networks Associated with Gastric Cancer Using Gaussian Graphical Models: A Case-Control Study

Gaussian graphical models (GGMs) are novel approaches to deriving dietary patterns that assess how foods are consumed in relation to one another. We aimed to apply GGMs to identify dietary patterns and to investigate the associations between dietary patterns and gastric cancer (GC) risk in a Korean population. In this case-control study of 415 GC cases and 830 controls, food intake was assessed using a 106-item semiquantitative food frequency questionnaire that captured 33 food groups. The dietary pattern networks corresponding to the total population contained a main network and four subnetworks. For the vegetable and seafood network, those who were in the highest tertile of the network-specific score showed a significantly reduced risk of GC both in the total population (OR = 0.66, 95% CI = 0.47-0.93, p for trend = 0.018) and in males (OR = 0.55, 95% CI = 0.34-0.89, p for trend = 0.012). Most importantly, the fruit pattern network was inversely associated with the risk of GC for the highest tertile (OR = 0.56, 95% CI = 0.38-0.81, p for trend = 0.002). The identified vegetable and seafood network and the fruit network showed a protective effect against GC development in Koreans.

Meal and habitual dietary networks identified through Semiparametric Gaussian Copula Graphical Models in a German adult population

Gaussian graphical models (GGMs) are exploratory methods that can be applied to construct networks of food intake. Such networks were constructed for meal-structured data, elucidating how foods are consumed in relation to each other at meal level. Meal-specific networks were compared with habitual dietary networks using data from an EPIC-Potsdam sub-cohort study. Three 24-hour dietary recalls were collected cross-sectionally from 815 adults in 2010-2012. Food intake was averaged to obtain the habitual intake. GGMs were applied to four main meals and habitual intakes of 39 food groups to generate meal-specific and habitual dietary networks, respectively. Communities and centrality were detected in the dietary networks to facilitate interpretation. The breakfast network revealed five communities of food groups with other vegetables, sauces, bread, margarine, and sugar & confectionery as central food groups. The lunch and afternoon snacks networks showed higher variability in food consumption and six communities were detected in each of these meal networks. Among the central food groups detected in both of these meal networks were potatoes, red meat, other vegetables, and bread. Two dinner networks were identified with five communities and other vegetables as a central food group. Partial correlations at meals were stronger than on the habitual level. The meal-specific dietary networks were only partly reflected in the habitual dietary network with a decreasing percentage: 64.3% for dinner, 50.0% for breakfast, 36.2% for lunch, and 33.3% for afternoon snack. The method of GGM yielded dietary networks that describe combinations of foods at the respective meals. Analysing food consumption on the habitual level did not exactly reflect meal level intake. Therefore, interpretation of habitual networks should be done carefully. Meal networks can help understand dietary habits, however, GGMs warrant validation in other populations.