Evidence map›Paper›PMID 39346654›Full record

ArticleFrontiers in nutrition2024

Analysis of dietary inflammatory potential and mortality in cancer survivors using NHANES data.

Yemei Wu, Jing Yi, Qu Zhang

Abstract read
In one paragraph

Article in Frontiers in nutrition, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Yemei WuHubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Jing YiSchool of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Qu ZhangDepartment of Radiotherapy Center, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: In the United States, cancer is a leading cause of mortality, with inflammation playing a crucial role in cancer progression and prognosis. Diet, with its capacity to modulate inflammatory responses, represents a potentially modifiable risk factor in cancer outcomes. Methods: This study utilized data from the National Health and Nutrition Examination Survey (NHANES, 1999-2018) to investigate the association between the Dietary Inflammatory Index (DII), which reflects dietary-induced inflammation, and mortality among cancer survivors. A total of 3,011 participants diagnosed with cancer were included, with DII scores derived from dietary recall data. All-cause and cancer-related mortalities served as primary endpoints. Results: The study identified a significant linear positive correlation between higher DII scores and all-cause mortality among cancer survivors. Each unit increase in DII was associated with a 10% higher risk of all-cause mortality (hazard ratio [HR] per 1-unit increase, 1.10; 95% confidence interval [CI], 1.04-1.15). Similarly, a unit increase in DII was associated with a 13% higher risk of cancer-related mortality (HR per 1-unit increase, 1.13; 95% CI, 1.02-1.25). Kaplan-Meier analyses demonstrated higher all-cause mortality rates in individuals with elevated DII scores. Sensitivity analyses confirmed the robustness of these findings. Conclusion: Adoption of an anti-inflammatory diet, characterized by lower DII scores, may improve survival outcomes in cancer survivors. These results emphasize the critical role of dietary interventions in post-cancer care.

Indexed as

cancer survivorsdietary inflammatory indexdietary interventionsinflammationmortality

Identifiers

PMID39346654
PMCPMC11427406

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.