Dietary patterns generated by the Treelet Transform and risk of stroke: a Danish cohort study

Association of ultra-processed foods consumption with risk of cardio-cerebrovascular disease: A systematic review and meta-analysis of cohort studies

AIMS

The epidemiological evidence regarding the impact of ultra-processed foods (UPFs) on the risk of cardio-cerebrovascular diseases (CCVDs) is controversial. The aim of this systematic review and meta-analysis is to examine the association between UPF consumption and the risk of CCVDs within cohort studies.

DATA SYNTHESIS

A systematic literature search was conducted across multiple databases, including PubMed/Medline, Embase, Web of Science, Scopus, and the Cochrane Library databases, covering the inception of these databases up until January 1st, 2023. A total of 39 cohort studies involving 63,573,312 human participants were deemed eligible according to the inclusion criteria. Utilizing random-effects models, risk ratios (RRs) were estimated to determine the pooled results. Our findings indicate a significant association between a higher consumption of UPF and an increased likelihood of CCVDs (RR: 1.08, 95% CI: 1.01-1.16, I2 = 89%; p < 0.01) compared to individuals who either abstain from or consume lesser amounts of UPF. Nonlinear dose-response meta-analyses showed that a consistent high intake of UPFs was associated with an elevated risk of developing CCVDs (p non-linearity <0.001). Notably, the risk of CCVDs escalated by approximately 7% with an UPF intake of up to 1 serving per day. Subgroup analysis further revealed a significant augmentation in the risk of total CVD and hypertension with increased UPF consumption.

CONCLUSIONS

A higher intake of UPF significantly increases the risk of developing CCVDs. Prospective studies controlling for confounding factors are needed to validate the relationship between UPF intake and the development of CCVDs.

Different statistical methods identify similar population-specific dietary patterns: an analysis of Longitudinal Study of Adult Health (ELSA-Brasil)

In recent decades, different data-driven approaches have emerged to identify dietary patterns (DP) and little is discussed about how these methods are able to capture diet complexity within the same population. This study aimed to apply three statistical methods to identify the DP of the Longitudinal Study of Adult Health (ELSA-Brasil) population and evaluate the similarities and differences between them. Dietary data were assessed at baseline in the ELSA-Brasil study using a FFQ. DP were identified by applying three statistical methods: (1) factor analysis (FA), (2) treelet transform (TT) and (3) reduced rank regression (RRR). The characteristics of individuals classified in the last tertile of each DP were compared. Cross-classification and Pearson's correlation coefficients were assessed to evaluate the agreement between individuals' adherence to DP of the three methods. A similar convenience DP was identified for all three methods. FA and TT also identified a similar prudent DP and a DP highly loaded for the food groups rice and beans. Individuals classified in the third tertile of similar DP of each method presented similar socio-demographic and nutrient intake characteristics. Regarding the cross-classification, prudent DP from FA and TT presented a higher level of agreement (75 %), while convenience DP from TT and RRR presented the lowest agreement (44·8 %). The different statistical methods were able to capture the populations' DP in a similar way while highlighting the particularities of each method.

Association between dietary patterns and stroke in patients with type 2 diabetes mellitus in China: a propensity score-matched analysis

OBJECTIVE

This study aimed to examine the impact of different dietary patterns on stroke outcomes among type 2 diabetes mellitus (T2DM) patients in China.

DESIGN

Participants were enrolled by a stratified random cluster sampling method in the study. After collecting dietary data using a quantified food frequency questionnaire, latent class analysis was used to identify dietary patterns, and propensity score matching was used to reduce confounding effects between different dietary patterns. Binary logistic regression and conditional logistic regression were used to analyze the relationship between dietary patterns and stroke in patients with T2DM.

SETTING

A cross-sectional survey available from December 2013 to January 2014.

PARTICIPANTS

A total of 13731 Chinese residents aged 18 years or over.

RESULTS

Two dietary patterns were identified: 61.2% of T2DM patients were categorized in the High-fat dietary pattern while 38.8% of patients were characterized by the Balanced dietary pattern. Compared to the High-fat dietary pattern, the Balanced dietary pattern was associated with reduced stroke risk (OR=0.63, 95%CI: 0.52-0.76, P<0.001) after adjusting for confounding factors. The protective effect of the balanced model did not differ significantly (interaction P>0.05).

CONCLUSION

This study provides sufficient evidence to support the dietary intervention strategies to prevent stroke effectively. Maintaining a Balanced dietary pattern, especially with moderate consumption of foods rich in quality protein and fresh vegetables in T2DM patients, might decrease the risk of stroke in China.

Development and validation of the Health Education Adherence Scale for Stroke Patients: a cross-sectional study

Background

Due to the lack of health education adherence assessment tools for stroke patients, the assessment of health education adherence in this population is insufficient, which hinders the prevention and rehabilitation of stroke. This study aims to develop and validate a Health Education Adherence Scale for Stroke Patients (HEAS-SP).

Methods

A cross-sectional design with a purposive sampling method was used for this study. Six hundred and fifty-four eligible participants completed the demographic questionnaire and the HEAS-SP. The data collection lasted for 7 months, from March 1stto September 30th in 2019. Item analysis and exploratory and confirmatory factor analysis were employed to develop and validate the HEAS-SP.

Results

The item analysis, exploratory and confirmatory factor analysis resulted in a 20-item HEAS-SP with 4 domains: medication adherence, diet adherence, rehabilitation exercise adherence, and healthy lifestyle adherence. The four-domain model demonstrated acceptable model fit indexes and the 20-item HEAS-SP demonstrated acceptable reliability and validity.

Conclusion

The 20-item HEAS-SP was shown to have acceptable reliability and validity for assessing health education adherence with respect to diet, medication, rehabilitation exercise and healthy lifestyle in stroke patients, making it a potential basis for developing targeted interventions for stroke patients.