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ArticleEuropean journal of nutrition2024

Plasma metabolite profiles related to dietary patterns: exploring the association with colorectal tumor risk.

Jingjing Ni, Haojie Lu, Weiyi Chen, Yingying Zhao, Shuaishuai Yang, Jia Zhang, Zhen Wang, Yuting Shi, Jing Yi, Jia Li and 5 more

Abstract read
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In one paragraph

Article in European journal of nutrition, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
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4 · The record

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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

15 authors.

Jingjing NiDepartment of Epidemiology and Biostatistics, Ministry of Education Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, P.R. China.ORCID http://orcid.org/0000-0003-2217-3917
Haojie LuDepartment of Epidemiology and Biostatistics, Ministry of Education Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, P.R. China.
Weiyi ChenDepartment of Epidemiology and Biostatistics, Ministry of Education Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, P.R. China.
Yingying ZhaoDepartment of Epidemiology and Biostatistics, Ministry of Education Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, P.R. China.
Shuaishuai YangDepartment of Epidemiology and Biostatistics, Ministry of Education Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, P.R. China.
Jia ZhangDepartment of Epidemiology and Biostatistics, Ministry of Education Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, P.R. China.
Zhen WangDepartment of Epidemiology and Biostatistics, Ministry of Education Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, P.R. China.
Yuting ShiDepartment of Epidemiology and Biostatistics, Ministry of Education Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, P.R. China.
Jing YiDepartment of Epidemiology and Biostatistics, Ministry of Education Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, P.R. China.
Jia LiDepartment of Epidemiology and Biostatistics, Ministry of Education Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, P.R. China.
Xuemei SongDepartment of Epidemiology and Biostatistics, Ministry of Education Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, P.R. China.
Yuxin NiDepartment of Epidemiology and Biostatistics, Ministry of Education Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, P.R. China.
Sijia ZhuDepartment of Epidemiology and Biostatistics, Ministry of Education Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, P.R. China.
Zhihao ZhangDepartment of Epidemiology and Biostatistics, Ministry of Education Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, P.R. China.
Li LiuDepartment of Epidemiology and Biostatistics, Ministry of Education Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, P.R. China. liul2012@hust.edu.cn.ORCID http://orcid.org/0000-0001-7455-1735

Funding

National Natural Science Foundation of China 82173602
6 · The paper itself

Abstract

backgroundMultiple diet patterns play a crucial role in the development of colorectal cancer and its precursor, colorectal adenoma, but mediating effect of plasma metabolite profiles is unclear.

methodsA total of 95,275 participants from UK Biobank with plasma metabolomics and dietary information were analyzed. Metabolite profile scores for 14 dietary patterns were estimated through elastic net regression. Cox regression analysis assessed the associations of dietary patterns and their metabolite profile scores with colorectal tumor risk. Mediating effects of identified metabolite profile scores were estimated in the associations.

resultsFourteen metabolite profile scores, including a range of 28 to 205 signatures, were weak to moderate correlation with dietary patterns (all p < 0.001). Multivariable Cox regression analyses revealed that five dietary patterns were significantly correlated with a decreased risk of colorectal tumor after FDR correction and adjustment for covariates. HRs (95% CIs) per 1 SD for these diet patterns were as follows: WCRF (0.93, 0.90-0.96), CRC score (0.94, 0.92-0.97), AHEI-2010 (0.95, 0.92-0.97), DASH (0.94, 0.91-0.97), and hPDI (0.95, 0.93-0.98). Similarly, metabolite profile scores for these five dietary patterns were inversely associated with colorectal tumor risk, with HRs (95% CIs) per 1 SD as follows: WCRF (0.59, 0.49-0.70), CRC score (0.67, 0.58-0.77), AHEI-2010 (0.73, 0.65-0.80), DASH (0.75, 0.66-0.84), and hPDI (0.56, 0.47-0.67). The mediation proportions of five metabolite profile scores between dietary patterns and colorectal tumor risk ranged from 6.37 to 27.23% (all p < 0.001).

conclusionsFive dietary patterns and their metabolite profile scores, were inversely correlated with colorectal tumor risk. These findings highlight the potential of metabolite profiles as mediators in the association between dietary patterns and the risk of colorectal tumor, further contributing to the prevention of colorectal cancer or adenoma and providing new insights for future research.

Indexed as

Colorectal NeoplasmsDietAdultAgedFemaleHumansMaleMetabolomeMetabolomicsMiddle AgedRisk FactorsUnited KingdomColorectal tumorDietary patternMediating effectMetabolomicsProspective study

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