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Using Multiple Statistical Methods to Derive Dietary Patterns Associated with Cardiovascular Disease in Patients with Type 2 Diabetes: Results from a Multiethnic Population-Based Study
BACKGROUND: There are few reports on the relationship between dietary patterns and cardiovascular disease (CVD) risk in patients with type 2 diabetes (T2D). This study aimed to explore relationships between dietary patterns and CVD risk in the T2D population using multiple statistical analysis metho...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9381206/ https://www.ncbi.nlm.nih.gov/pubmed/35983006 http://dx.doi.org/10.1155/2022/2802828 |
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author | Qiao, Tingting Zhao, Hui Luo, Tao Wang, Duolao Mu, Kaili Aimudula, Aliya Pei, Hualian Zhang, Guozhen Dai, Jianghong |
author_facet | Qiao, Tingting Zhao, Hui Luo, Tao Wang, Duolao Mu, Kaili Aimudula, Aliya Pei, Hualian Zhang, Guozhen Dai, Jianghong |
author_sort | Qiao, Tingting |
collection | PubMed |
description | BACKGROUND: There are few reports on the relationship between dietary patterns and cardiovascular disease (CVD) risk in patients with type 2 diabetes (T2D). This study aimed to explore relationships between dietary patterns and CVD risk in the T2D population using multiple statistical analysis methods. METHODS: A total of 2,984 patients with T2D from the Xinjiang Multi-Ethnic Cohort, 555 of whom were suffering from CVD, were enrolled in this study. Participants' dietary intake was measured by the semiquantitative food frequency questionnaire (FFQ). Three statistical methods were used to construct dietary patterns, including principal component analysis (PCA) method, reduced-rank regressions (RRR) method, and partial least-squares regression (PLS) method. Then, the association between dietary patterns and CVD risk in T2D patients was analyzed by logistic regression. After excluding participants with CVD, the associations between dietary patterns and 10-year CVD risk scores were subsequently evaluated to reduce reverse causality. RESULTS: In this study, four dietary patterns were identified by three methods. Adjustment for confounding factors, subjects with the highest scores on the “high-protein and high-carbohydrate” patterns derived from PCA, RRR, and PLS had higher odds of CVD than those with the lowest scores (OR: 2.89, 95% CI: 2.11–3.96, P(trend) < 0.001; OR: 2.96, 95% CI: 2.17–4.03, P(trend) < 0.001; OR: 2.01, 95% CI: 1.50–2.70, P(trend) < 0.001, respectively). However, the dietary pattern of PCA-prudent was not significantly related to the odds of having CVD in T2D patients (adjusted OR(Q4vsQ1): 0.93, 95% CI: 0.70–1.24, P(trend)=0.474). Interestingly, we also found significant associations between “high-protein and high-carbohydrate” patterns and the elevated predicted 10-year CVD risk in T2D patients (all P(trend) < 0.05). CONCLUSION: The positive correlation between “high-protein and high-carbohydrate” patterns and CVD risk in T2D patients was robust across all three data-driven approaches. These findings may have public health significance, encouraging an emphasis on food choices in the usual diet and promoting nutritional interventions for patients with T2D to prevent CVD. |
format | Online Article Text |
id | pubmed-9381206 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-93812062022-08-17 Using Multiple Statistical Methods to Derive Dietary Patterns Associated with Cardiovascular Disease in Patients with Type 2 Diabetes: Results from a Multiethnic Population-Based Study Qiao, Tingting Zhao, Hui Luo, Tao Wang, Duolao Mu, Kaili Aimudula, Aliya Pei, Hualian Zhang, Guozhen Dai, Jianghong Evid Based Complement Alternat Med Research Article BACKGROUND: There are few reports on the relationship between dietary patterns and cardiovascular disease (CVD) risk in patients with type 2 diabetes (T2D). This study aimed to explore relationships between dietary patterns and CVD risk in the T2D population using multiple statistical analysis methods. METHODS: A total of 2,984 patients with T2D from the Xinjiang Multi-Ethnic Cohort, 555 of whom were suffering from CVD, were enrolled in this study. Participants' dietary intake was measured by the semiquantitative food frequency questionnaire (FFQ). Three statistical methods were used to construct dietary patterns, including principal component analysis (PCA) method, reduced-rank regressions (RRR) method, and partial least-squares regression (PLS) method. Then, the association between dietary patterns and CVD risk in T2D patients was analyzed by logistic regression. After excluding participants with CVD, the associations between dietary patterns and 10-year CVD risk scores were subsequently evaluated to reduce reverse causality. RESULTS: In this study, four dietary patterns were identified by three methods. Adjustment for confounding factors, subjects with the highest scores on the “high-protein and high-carbohydrate” patterns derived from PCA, RRR, and PLS had higher odds of CVD than those with the lowest scores (OR: 2.89, 95% CI: 2.11–3.96, P(trend) < 0.001; OR: 2.96, 95% CI: 2.17–4.03, P(trend) < 0.001; OR: 2.01, 95% CI: 1.50–2.70, P(trend) < 0.001, respectively). However, the dietary pattern of PCA-prudent was not significantly related to the odds of having CVD in T2D patients (adjusted OR(Q4vsQ1): 0.93, 95% CI: 0.70–1.24, P(trend)=0.474). Interestingly, we also found significant associations between “high-protein and high-carbohydrate” patterns and the elevated predicted 10-year CVD risk in T2D patients (all P(trend) < 0.05). CONCLUSION: The positive correlation between “high-protein and high-carbohydrate” patterns and CVD risk in T2D patients was robust across all three data-driven approaches. These findings may have public health significance, encouraging an emphasis on food choices in the usual diet and promoting nutritional interventions for patients with T2D to prevent CVD. Hindawi 2022-08-09 /pmc/articles/PMC9381206/ /pubmed/35983006 http://dx.doi.org/10.1155/2022/2802828 Text en Copyright © 2022 Tingting Qiao et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Qiao, Tingting Zhao, Hui Luo, Tao Wang, Duolao Mu, Kaili Aimudula, Aliya Pei, Hualian Zhang, Guozhen Dai, Jianghong Using Multiple Statistical Methods to Derive Dietary Patterns Associated with Cardiovascular Disease in Patients with Type 2 Diabetes: Results from a Multiethnic Population-Based Study |
title | Using Multiple Statistical Methods to Derive Dietary Patterns Associated with Cardiovascular Disease in Patients with Type 2 Diabetes: Results from a Multiethnic Population-Based Study |
title_full | Using Multiple Statistical Methods to Derive Dietary Patterns Associated with Cardiovascular Disease in Patients with Type 2 Diabetes: Results from a Multiethnic Population-Based Study |
title_fullStr | Using Multiple Statistical Methods to Derive Dietary Patterns Associated with Cardiovascular Disease in Patients with Type 2 Diabetes: Results from a Multiethnic Population-Based Study |
title_full_unstemmed | Using Multiple Statistical Methods to Derive Dietary Patterns Associated with Cardiovascular Disease in Patients with Type 2 Diabetes: Results from a Multiethnic Population-Based Study |
title_short | Using Multiple Statistical Methods to Derive Dietary Patterns Associated with Cardiovascular Disease in Patients with Type 2 Diabetes: Results from a Multiethnic Population-Based Study |
title_sort | using multiple statistical methods to derive dietary patterns associated with cardiovascular disease in patients with type 2 diabetes: results from a multiethnic population-based study |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9381206/ https://www.ncbi.nlm.nih.gov/pubmed/35983006 http://dx.doi.org/10.1155/2022/2802828 |
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