Dietary Patterns Associated with Diabetes in an Older Population from Southern Italy Using an Unsupervised Learning Approach

Dietary patterns associated with the incidence of hypertension among adult Japanese males: application of machine learning to a cohort study

PURPOSE

The previous studies that examined the effectiveness of unsupervised machine learning methods versus traditional methods in assessing dietary patterns and their association with incident hypertension showed contradictory results. Consequently, our aim is to explore the correlation between the incidence of hypertension and overall dietary patterns that were extracted using unsupervised machine learning techniques.

METHODS

Data were obtained from Japanese male participants enrolled in a prospective cohort study between August 2008 and August 2010. A final dataset of 447 male participants was used for analysis. Dimension reduction using uniform manifold approximation and projection (UMAP) and subsequent K-means clustering was used to derive dietary patterns. In addition, multivariable logistic regression was used to evaluate the association between dietary patterns and the incidence of hypertension.

RESULTS

We identified four dietary patterns: 'Low-protein/fiber High-sugar,' 'Dairy/vegetable-based,' 'Meat-based,' and 'Seafood and Alcohol.' Compared with 'Seafood and Alcohol' as a reference, the protective dietary patterns for hypertension were 'Dairy/vegetable-based' (OR 0.39, 95% CI 0.19-0.80, P = 0.013) and the 'Meat-based' (OR 0.37, 95% CI 0.16-0.86, P = 0.022) after adjusting for potential confounding factors, including age, body mass index, smoking, education, physical activity, dyslipidemia, and diabetes. An age-matched sensitivity analysis confirmed this finding.

CONCLUSION

This study finds that relative to the 'Seafood and Alcohol' pattern, the 'Dairy/vegetable-based' and 'Meat-based' dietary patterns are associated with a lower risk of hypertension among men.

Are Major a Posteriori Dietary Patterns Reproducible in the Italian Population? A Systematic Review and Quantitative Assessment

Although a posteriori dietary patterns (DPs) naturally reflect actual dietary behavior in a population, their specificity limits generalizability. Among other issues, the absence of a standardized approach to analysis have further hindered discovery of genuinely reproducible DPs across studies from the same/similar populations. A systematic review on a posteriori DPs from principal component analysis or exploratory factor analysis (EFA) across study populations from Italy provides the basis to explore assessment and drivers of DP reproducibility in a case study of epidemiological interest. First to our knowledge, we carried out a qualitative (i.e., similarity plots built on text descriptions) and quantitative (i.e., congruence coefficients, CCs) assessment of DP reproducibility. The 52 selected articles were published in 2001-2022 and represented dietary habits in 1965-2022 from 70% of the Italian regions; children/adolescents, pregnancy/breastfeeding women, and elderly were considered in 15 articles. The included studies mainly derived EFA-based DPs on food groups from food frequency questionnaires and were of "good quality" according to standard scales. Based on text descriptions, the 186 identified DPs were collapsed into 113 (69 food-based and 44 nutrient-based) apparently different DPs (39.3% reduction), later summarized along with the 3 "Mixed-Salad/Vegetable-based Patterns," "Pasta-and-Meat-oriented/Starchy Patterns," and "Dairy Products" and "Sweets/Animal-based Patterns" groups, by matching similar food-based and nutrient-based groups of collapsed DPs. Based on CCs (215 CCs, 68 DPs, 18 articles using the same input lists), all pairs of DPs showing the same/similar names were at least "fairly similar" and ∼81% were "equivalent." The 30 "equivalent" DPs ended up into 6 genuinely different DPs (80% reduction) that targeted fruits and (raw) vegetables, pasta and meat combined, and cheese and deli meats. Such reduction reflects the same study design, list of input variables, and DP identification method followed across articles from the same groups. This review was registered at PROSPERO as CRD42022341037.

Relationship between Coffee, Tea, and Carbonated Beverages and Cardiovascular Risk Factors

We aimed to analyze the relationship between coffee, tea, and carbonated beverages and cardiovascular risk factors. We used data from the fourth to eighth Korea National Health and Nutrition Examination Surveys (2007-2016, 2019-2020). We categorized the frequency of intake into three groups (<1 time/week, 1 time/week to <1 time/day, and ≥1 time/day). Subsequently, logistic regression analyses by sex were performed to assess cardiovascular risk factors (hypertension (HTN), diabetes mellitus (DM), dyslipidemia (DL), or metabolic syndrome (MetS)) according to the frequency of coffee, tea, and carbonated beverage intake. For HTN, coffee intake showed an inverse relationship and tea intake showed a direct relationship. For DM, coffee intake showed an inverse relationship, and tea and carbonated beverage intake showed a direct relationship. For DL, coffee intake showed an inverse relationship, whereas tea intake demonstrated a direct relationship. In addition, carbonated beverage intake showed a direct relationship with MetS. Coffee intake showed an inverse relationship with HTN, DM, and DL. However, tea intake showed a direct relationship with HTN, DM, and DL, whereas carbonated beverage intake showed a direct relationship with DM and MetS.

A comparison of principal component analysis, partial least-squares, and reduced-rank regressions in the identification of dietary patterns associated with hypertension: YaHS-TAMYZ and Shahedieh cohort studies

Limited data exist on the advantage of data reduction hybrid methodologies for evaluating the relationship between dietary patterns (DPs) and chronic diseases and they have led to inconsistent results. This study aimed to investigate the association between DPs extracted using principal component analysis (PCA), partial least-squares (PLS), and reduced-rank regressions (RRRs) in identifying DPs associated with hypertension (HTN) risk. The current study was conducted in the context of two cohort studies accomplished in Iran. DPs were generated using PCA, PLS, and RRR methods. Log-binomial logistic regression test was used to assess the association between DPs and the risk of HTN. From a total of 12,403 included participants aged 20-70 years, 507 incident cases of confirmed HTN were identified. The PCA-DP2 was associated with HTN in the fully adjusted model (T3 vs. T1: RR: 0.737, 95% CI: 0.57-0.93, P _trend = 0.013). The PLS-DP2 and HTN risk were inversely associated in the multivariate model (T3 vs. T1: RR: 0.704, 95% CI: 0.54-0.91, P _trend = 0.013). The RRR-DP2 was associated with an increased risk of HTN (T3 vs. T1: RR: 1.412, 95% CI: 1.11-1.80, P _trend = 0.007). Our findings suggest that the RRR method reveals stronger results in association with HTN risk. However, further investigations are required to confirm the association between DPs derived by PLS and RRR methods by incorporating biomarkers related to HTN as the response variables.