Association Between Adherence to EAT-Lancet Diet and Risk of Hypertension: An 18-Year National Cohort Study in China

Our findings suggest that a higher adherence to ELD is associated with a lower risk of hypertension. These results emphasize that ELD may serve as a potential strategy to prevent hypertension.

Authors

Institutions

Publication Details

Journal
Journal of the American Nutrition Association
Published
2024-09-05
DOI
https://doi.org/10.1080/27697061.2024.2399826
Citations
4
Primary Topic
Nutritional Studies and Diet
Type
article
Field-Weighted Citation Impact
2.17
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Association Between Adherence to EAT-Lancet Diet and Risk of Hypertension: An 18-Year National Cohort Study in China

Wenwei Yin, Lifu Lei, Yu Ling Sun, Haixia Qin et al.
4 citations
Journal of the American Nutrition Association
Nutritional Studies and Diet
2.17
article

Association Between Adherence to EAT-Lancet Diet and Risk of Hypertension: An 18-Year National Cohort Study in China

Wenwei Yin, Lifu Lei, Yu Ling Sun, Haixia Qin, Yushi Chen, Shiwen Tong
article en
4 citations

Abstract

Our findings suggest that a higher adherence to ELD is associated with a lower risk of hypertension. These results emphasize that ELD may serve as a potential strategy to prevent hypertension.

Journal of the American Nutrition AssociationVol. 44(1)
Dalian Medical University (CN), Second Affiliated Hospital of Chongqing Medical University (CN), Chongqing Medical University (CN)
Zero hunger
Openalex Percentile: Top 12%
Nutritional Studies and Diet
2.17
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.