In one paragraphArticle in Journal of the National Cancer Institute, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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 registryThe 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 literatureWho cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
4 · The recordCorrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what moneyAuthors and funding
19 authors.
Brian Z HuangDepartment of Population and Public Health Sciences, Keck School of Medicine of University of Southern California, Los Angeles, CA, United States.ORCID 0000-0001-8645-5142 Fei ChenDepartment of Population and Public Health Sciences, Keck School of Medicine of University of Southern California, Los Angeles, CA, United States.ORCID 0000-0002-1679-9932 David BogumilDepartment of Population and Public Health Sciences, Keck School of Medicine of University of Southern California, Los Angeles, CA, United States.ORCID 0000-0002-5001-1088 Sihao HanDepartment of Population and Public Health Sciences, Keck School of Medicine of University of Southern California, Los Angeles, CA, United States.ORCID 0000-0003-3433-3068 Adelynn PaikDepartment of Population and Public Health Sciences, Keck School of Medicine of University of Southern California, Los Angeles, CA, United States.ORCID 0009-0003-5175-665X Peggy WanDepartment of Population and Public Health Sciences, Keck School of Medicine of University of Southern California, Los Angeles, CA, United States.
Xin ShengDepartment of Population and Public Health Sciences, Keck School of Medicine of University of Southern California, Los Angeles, CA, United States.ORCID 0000-0002-6844-2740 Kimberly D SiegmundDepartment of Population and Public Health Sciences, Keck School of Medicine of University of Southern California, Los Angeles, CA, United States.ORCID 0000-0001-7599-9726 Ulrike PetersPublic Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, United States.ORCID 0000-0001-5666-9318 Trang VoPhamPublic Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, United States.ORCID 0000-0003-4787-1201 Tanya L AldereteDepartment of Environmental Health and Engineering, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States.ORCID 0000-0002-7751-9543 Bodour SalhiaNorris Comprehensive Cancer Center, University of Southern California (USC), Los Angeles, CA, United States.ORCID 0000-0003-3843-3621 Heinz-Josef LenzNorris Comprehensive Cancer Center, University of Southern California (USC), Los Angeles, CA, United States.
Daniel O StramDepartment of Population and Public Health Sciences, Keck School of Medicine of University of Southern California, Los Angeles, CA, United States.ORCID 0000-0001-7099-9022 Lynne R WilkensPopulation Sciences of the Pacific Program-Epidemiology, University of Hawaii Cancer Center, Honolulu, HI, United States.ORCID 0000-0001-8408-4536 Loïc Le MarchandPopulation Sciences of the Pacific Program-Epidemiology, University of Hawaii Cancer Center, Honolulu, HI, United States.ORCID 0000-0001-5013-980X David V ContiDepartment of Population and Public Health Sciences, Keck School of Medicine of University of Southern California, Los Angeles, CA, United States.ORCID 0000-0002-2941-7833 Christopher A HaimanDepartment of Population and Public Health Sciences, Keck School of Medicine of University of Southern California, Los Angeles, CA, United States.ORCID 0000-0002-0097-9971 Funding
Perfluoroalkyl Substances and the Gut Microbiome and Fecal Metabolome: Implications for Obesity Risk in Hispanic ChildrenR01ES035035 · NIEHS · UNIVERSITY OF COLORADO · PI Tanya Lynn Alderete · 2023 to 2026
$2.6MPre- and Postnatal Exposure to Air Pollutants and the Gut Microbiome: Implications for Brain Development in Early and Mid-ChildhoodR01ES035056 · NIEHS · UNIVERSITY OF COLORADO · PI Tanya Lynn Alderete, BRADLEY S PETERSON · 2024 to 2026
$2.1MAmerican Cancer SocietyAmerican Institute for Cancer Research 713270BLRD VA IK2 BX005891Clinical Research Professor CRP-24-1191392-01-PROFNational Cancer Institute at the National Institutes of Health K99CA256525National Cancer Institute at the National Institutes of Health R00CA256525National Cancer Institute at the National Institutes of Health T32CA229110National Cancer Institute at the National Institutes of Health U01CA164973National Cancer Institute at the National Institutes of Health U01CA261339NIEHS NIH HHS R01 ES035035NIEHS NIH HHS R01 ES035056
6 · The paper itselfAbstract
backgroundIntegrating genetic and lifestyle information has the potential to greatly improve the prediction of colorectal cancer (CRC) risk. However, racial and ethnic minorities are generally underrepresented in gene-environment studies of CRC risk.
methodsWe investigated the interplay of genetics and lifestyle on CRC risk in a prospective analysis of 68 397 African American, Japanese American, Latino, Native Hawaiian, and White individuals from the Multiethnic Cohort Study. Genetic predisposition was assessed using a 205-variant polygenic risk score. Lifestyle was assessed using a lifestyle risk score based on smoking, alcohol consumption, body mass index, physical activity, and diet. The independent and joint associations of the polygenic risk score and lifestyle risk score on CRC risk were evaluated using Cox regression.
resultsWe identified 1303 incident CRC cases (median 15.1-year follow-up). The highest quintile of the polygenic risk score was associated with a 2.4-fold increase in CRC risk compared with the lowest quintile (Q5 vs Q1: hazard ratio [HR] = 2.40, 95% confidence interval [CI] = 1.99 to 2.89). The highest quintile of the lifestyle risk score was associated with a 54% increased risk (Q5 vs Q1: HR = 1.54, 95% CI = 1.26 to 1.88). This association was stronger among those with high genetic risk (polygenic risk score ≥ 50%; Q5 vs Q1: HR = 1.82, 95% CI = 1.41 to 2.35) and statistically nonsignificant among those with low genetic risk (polygenic risk score < 50%; Q5 vs Q1: HR = 1.20, 95% CI = 0.88 to 1.64; P for interaction = 0.01). Results were similar across race and ethnicity.
conclusionsOur study suggests that lifestyle modification may offer greater risk reduction among those at higher genetic risk. Future research is warranted to enhance the integration of genetics and lifestyle in CRC risk stratification and screening approaches across populations.
Indexed as
Colorectal NeoplasmsEthnicityLife StyleAgedAlcohol DrinkingAsianBlack or African AmericanBody Mass IndexFemaleGene-Environment InteractionGenetic Predisposition to DiseaseGenetic Risk ScoreHumansMaleMiddle AgedProportional Hazards Models
Identifiers
PMID41697998
PMCPMC13339091
What OpenQuestion holds
Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390