Metabolomic pattern of ultraprocessed food intake and its association with colorectal cancer risk
Du M, Wang X, Hang D, Wang F, Lu Y, Wang K, Bever AM, Nogal A, Haslam D, Ogino S, Meyerhardt JA, Liang L, Sun Q, Huttenhower C, Chan AT, Hu FB, Song M
Gut · 3 citas
Etiquetas de revisión
Cuestionario de frecuencia de consumoSin ajuste declarado por estilo de vidaDatos neutrales que nuestra revisión registró sobre cómo se hizo este estudio. Describen el método, nunca si nos gusta el resultado.
Cómo se estudió
- Diseño
- Estudio de casos y controles (clasificado por nuestro filtro de IA)
- Estudiado en
- Personas
- Resultado principal
- Eventos clínicos como enfermedad o muerte
- Consumo medido con
- Cuestionario de frecuencia de consumo
Quién lo pagó
- Financiación
- Financiación independiente
- Organización sin fines de lucro
- American Cancer Society
- Gobierno
- National Cancer Institute
- Gobierno
- National Institute of General Medical Sciences
- Gobierno
- National Institute of Diabetes and Digestive and Kidney Diseases
- Gobierno
- NIDDK NIH HHS
- Gobierno
- NCI NIH HHS
- Gobierno
- NIGMS NIH HHS
- Subvenciones
- National Cancer Institute (U01 CA167552); National Institute of General Medical Sciences (T32GM144273); National Cancer Institute (R01 CA263776); National Cancer Institute (R35CA253185); National Institute of Diabetes and Digestive and Kidney Diseases (U2C DK129670); National Cancer Institute (P01 CA087969); National Cancer Institute (UM1 CA186107); American Cancer Society (MRSG-17-220-01-NEC); National Cancer Institute (K00 CA274714); National Cancer Institute (R01 CA285851); National Institute of Diabetes and Digestive and Kidney Diseases (K01 DK136968); National Cancer Institute (U01CA261961); American Cancer Society (CRP-24-1185864-01-PROF); National Cancer Institute ([P01 CA87969)
Según 7 financiadores declarados.
Publicación
- Publicado
- 2025-12-24 · Gut · vol. 75 · número 3 · págs. 538–547
- Editorial
- BMJ
- Citado
- 3 citas · más que el 77% de estudios similares · 1.0× el promedio de su campo
- Referencias
- 57 trabajos
- Acceso
- Acceso abierto (copia en repositorio)
- Áreas de investigación
- Metabolomics and Mass Spectrometry Studies · Nutritional Studies and Diet · Nutrition, Genetics, and Disease
- Palabras clave
- Metabolomics, Colorectal cancer, Food intake, Colonic disease, Cancer, Association (psychology)
17 autores
- Mengxi Du🇺🇸Harvard University; Massachusetts General Hospital
- Xinyu Wang🇺🇸Harvard University
- Dong Hang🇨🇳Nanjing Medical University
- F. Wang🇺🇸Harvard University
- Y Lu🇺🇸Harvard University
- Kai Wang🇺🇸Harvard University; Massachusetts General Hospital
- Alaina M. Bever🇺🇸Harvard University; Harvard–MIT Division of Health Sciences and Technology
- Ana Nogal🇺🇸Harvard University
- Danielle E. Haslam🇺🇸Brigham and Women's Hospital; Harvard University
- Shuji Ogino🇺🇸🇯🇵Broad Institute; Tokyo Medical and Dental University; Brigham and Women's Hospital; Harvard University
- Jeffrey A. Meyerhardt🇺🇸Dana-Farber Cancer Institute
- Liming Liang🇺🇸Harvard University
- Qi Sun🇺🇸Brigham and Women's Hospital; Harvard University
- Curtis Huttenhower🇺🇸Broad Institute; Harvard University
- Andrew T Chan🇺🇸Harvard University; Massachusetts General Hospital
- FB Hu🇺🇸Brigham and Women's Hospital; Harvard University
- Mingyang Song🇺🇸 · autor de correspondenciaHarvard University; Massachusetts General Hospital
Resumen
El texto original del estudio, en el idioma en que se publicó.
Background
High ultra-processed food (UPF) intake has been linked to colorectal cancer (CRC), but underlying mechanisms remain unclear.
Objective
To evaluate a metabolomic pattern of UPF intake and its association with CRC risk.
Design
Integrating food frequency questionnaire data and high-throughput metabolomic profiling in 1740 participants (mean age at blood draw: 59.9 years; >95% non-Hispanic white participants) from nested case-control studies within the Nurses' Health Study and Health Professionals Follow-up Study, we derived and validated a UPF-related metabolomic pattern as a weighted sum of metabolites selected via elastic net regression with 10-fold cross-validation. We evaluated prospective associations of this pattern and individual metabolites with CRC risk using multivariable conditional logistic regression in 686 pairs of incident CRC cases and matched controls.
Results
Among 222 metabolites, we constructed a UPF metabolomic pattern comprising 50 metabolites, primarily lipids and amino acids, with 22 positively and 28 inversely associated with total UPF intake (pattern vs intake: Spearman rho=0.35). The pattern was associated with higher CRC risk (highest vs lowest quintile: OR (95% CI) 1.71 (1.15 to 2.53), p value trend=0.002). Correlations of individual metabolites with UPF intake were moderately aligned with their associations with CRC risk (rho=0.50). N2, N2-dimethylguanosine, a marker of meat/poultry intake, was positively associated with CRC risk (1.96 (1.27 to 3.03)), while 21-deoxycortisol, related to cortisol biosynthesis, was inversely associated (0.59 (0.41 to 0.86)).
Conclusion
We developed a UPF metabolomic pattern. The pattern and several metabolites were associated with CRC risk, providing biological insights into potential pathways underlying the UPF-CRC relationship.
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