The Journal of cardiovascular nursing · 2026
Association of a Diet Index for the Gut Microbiome With Hypertension and Obesity-Related Hypertension: A Cross-Sectional Analysis of NHANES 2007-2020.
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Zhang Jingyi, Teng Yubo, Xue Guocheng, Jiang Shengwen, Yang Boxiang, Zhang Miao
Аннотация
BACKGROUND: Dietary patterns that support gut-microbiome diversity may influence hypertension, but phenotype-specific evidence is limited.
OBJECTIVE: Assess associations of a diet index for the gut microbiome (DI-GM) with prevalent hypertension and obesity-related hypertension in U.S. adults.
METHODS: The National Health and Nutrition Examination Survey 2007-2020 included 22,784 adults aged ≥20 years. Multivariable logistic models related DI-GM (continuous; quartiles) to outcomes; restricted cubic splines characterized dose-response. Effect modification was tested across demographic and clinical subgroups. Variable importance of DI-GM components was estimated using the Boruta algorithm. Discrimination was assessed by the area under the receiver operating characteristic curve.
RESULTS: Higher DI-GM was inversely associated with hypertension-per 1-unit higher DI-GM: odds ratio ( OR ) = 0.97 (95% confidence interval [CI], 0.96-0.99; P <.001)-and more strongly with obesity-related hypertension: OR = 0.95 (95% CI, 0.92-0.98; P <.01). Spline analyses suggested approximately linear inverse associations. There was no significant interaction between DI-GM and hypertension, as well as between DI-GM and obesity-related hypertension, in any of the subgroups. Refined grains, fermented dairy, and dietary fiber ranked highest for hypertension, while high-fat diet, processed meats, and avocados were most influential for obesity-related hypertension. The areas under the receiver operating characteristic curves were 0.695 for hypertension and 0.767 for obesity-related hypertension.
CONCLUSIONS: In this cross-sectional analysis, higher DI-GM was related to lower odds of hypertension, particularly obesity-related hypertension. Diet index for the gut microbiome may aid hypothesis generation and risk stratification for microbiome-supportive dietary guidance; causal inference is not possible. Prospective cohorts and randomized dietary interventions integrating microbiome profiling are needed.
OBJECTIVE: Assess associations of a diet index for the gut microbiome (DI-GM) with prevalent hypertension and obesity-related hypertension in U.S. adults.
METHODS: The National Health and Nutrition Examination Survey 2007-2020 included 22,784 adults aged ≥20 years. Multivariable logistic models related DI-GM (continuous; quartiles) to outcomes; restricted cubic splines characterized dose-response. Effect modification was tested across demographic and clinical subgroups. Variable importance of DI-GM components was estimated using the Boruta algorithm. Discrimination was assessed by the area under the receiver operating characteristic curve.
RESULTS: Higher DI-GM was inversely associated with hypertension-per 1-unit higher DI-GM: odds ratio ( OR ) = 0.97 (95% confidence interval [CI], 0.96-0.99; P <.001)-and more strongly with obesity-related hypertension: OR = 0.95 (95% CI, 0.92-0.98; P <.01). Spline analyses suggested approximately linear inverse associations. There was no significant interaction between DI-GM and hypertension, as well as between DI-GM and obesity-related hypertension, in any of the subgroups. Refined grains, fermented dairy, and dietary fiber ranked highest for hypertension, while high-fat diet, processed meats, and avocados were most influential for obesity-related hypertension. The areas under the receiver operating characteristic curves were 0.695 for hypertension and 0.767 for obesity-related hypertension.
CONCLUSIONS: In this cross-sectional analysis, higher DI-GM was related to lower odds of hypertension, particularly obesity-related hypertension. Diet index for the gut microbiome may aid hypothesis generation and risk stratification for microbiome-supportive dietary guidance; causal inference is not possible. Prospective cohorts and randomized dietary interventions integrating microbiome profiling are needed.