Imperial College London

DrNeilMurphy

Faculty of MedicineSchool of Public Health

Honorary Senior Research Fellow
 
 
 
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Contact

 

neil.murphy

 
 
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Location

 

Norfolk PlaceSt Mary's Campus

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Summary

 

Publications

Publication Type
Year
to

182 results found

King SD, Veliginti S, Brouwers MCGJ, Ren Z, Zheng W, Setiawan VW, Wilkens LR, Shu X-O, Arslan AA, Beane Freeman LE, Bracci PM, Canzian F, Du M, Gallinger SJ, Giles GG, Goodman PJ, Haiman CA, Kogevinas M, Kooperberg C, LeMarchand L, Neale RE, Visvanathan K, White E, Albanes D, Andreotti G, Babic A, Berndt SI, Brais LK, Brennan P, Buring JE, Rabe KG, Bamlet WR, Chanock SJ, Fuchs CS, Gaziano JM, Giovannucci EL, Hackert T, Hassan MM, Katzke V, Kurtz RC, Lee I-M, Malats N, Murphy N, Oberg AL, Orlow I, Porta M, Real FX, Rothman N, Sesso HD, Silverman DT, Thompson IM, Wactawski-Wende J, Wang X, Wentzensen N, Yu H, Zeleniuch-Jacquotte A, Yu K, Wolpin BM, Duell EJ, Li D, Hung RJ, Perdomo S, McCullough ML, Freedman ND, Patel AV, Peters U, Riboli E, Sund M, Tjønneland A, Zhong J, Van Den Eeden SK, Kraft P, Risch HA, Amundadottir LT, Klein AP, Stolzenberg-Solomon RZ, Antwi SOet al., 2023, Genetic Susceptibility to Nonalcoholic Fatty Liver Disease and Risk for Pancreatic Cancer: Mendelian Randomization., Cancer Epidemiol Biomarkers Prev, Vol: 32, Pages: 1265-1269

BACKGROUND: There are conflicting data on whether nonalcoholic fatty liver disease (NAFLD) is associated with susceptibility to pancreatic cancer. Using Mendelian randomization (MR), we investigated the relationship between genetic predisposition to NAFLD and risk for pancreatic cancer. METHODS: Data from genome-wide association studies (GWAS) within the Pancreatic Cancer Cohort Consortium (PanScan; cases n = 5,090, controls n = 8,733) and the Pancreatic Cancer Case Control Consortium (PanC4; cases n = 4,163, controls n = 3,792) were analyzed. We used data on 68 genetic variants with four different MR methods [inverse variance weighting (IVW), MR-Egger, simple median, and penalized weighted median] separately to predict genetic heritability of NAFLD. We then assessed the relationship between each of the four MR methods and pancreatic cancer risk, using logistic regression to calculate ORs and 95% confidence intervals (CI), adjusting for PC risk factors, including obesity and diabetes. RESULTS: No association was found between genetically predicted NAFLD and pancreatic cancer risk in the PanScan or PanC4 samples [e.g., PanScan, IVW OR, 1.04; 95% confidence interval (CI), 0.88-1.22; MR-Egger OR, 0.89; 95% CI, 0.65-1.21; PanC4, IVW OR, 1.07; 95% CI, 0.90-1.27; MR-Egger OR, 0.93; 95% CI, 0.67-1.28]. None of the four MR methods indicated an association between genetically predicted NAFLD and pancreatic cancer risk in either sample. CONCLUSIONS: Genetic predisposition to NAFLD is not associated with pancreatic cancer risk. IMPACT: Given the close relationship between NAFLD and metabolic conditions, it is plausible that any association between NAFLD and pancreatic cancer might reflect host metabolic perturbations (e.g., obesity, diabetes, or metabolic syndrome) and does not necessarily reflect a causal relationship between NAFLD and pancreatic cancer.

Journal article

Constantinescu A-E, Bull CJ, Jones N, Mitchell R, Burrows K, Dimou N, Bézieau S, Brenner H, Buchanan DD, D'Amato M, Jenkins MA, Moreno V, Pai RK, Um CY, White E, Murphy N, Gunter M, Timpson NJ, Huyghe JR, Vincent EEet al., 2023, Circulating white blood cell traits and colorectal cancer risk: A Mendelian randomisation study., Int J Cancer

Observational studies have suggested a protective role for eosinophils in colorectal cancer (CRC) development and implicated neutrophils, but the causal relationships remain unclear. Here, we aimed to estimate the causal effect of circulating white blood cell (WBC) counts (N = ~550 000) for basophils, eosinophils, monocytes, lymphocytes and neutrophils on CRC risk (N = 52 775 cases and 45 940 controls) using Mendelian randomisation (MR). For comparison, we also examined this relationship using individual-level data from UK Biobank (4043 incident CRC cases and 332 773 controls) in a longitudinal cohort analysis. The inverse-variance weighted (IVW) MR analysis suggested a protective effect of increased basophil count and eosinophil count on CRC risk [OR per 1-SD increase: 0.88, 95% CI: 0.78-0.99, P = .04; OR: 0.93, 95% CI: 0.88-0.98, P = .01]. The protective effect of eosinophils remained [OR per 1-SD increase: 0.88, 95% CI: 0.80-0.97, P = .01] following adjustments for all other WBC subtypes, to account for genetic correlation between the traits, using multivariable MR. A protective effect of increased lymphocyte count on CRC risk was also found [OR: 0.84, 95% CI: 0.76-0.93, P = 6.70e-4] following adjustment. Consistent with MR results, a protective effect for eosinophils in the cohort analysis in the fully adjusted model [RR per 1-SD increase: 0.96, 95% CI: 0.93-0.99, P = .02] and following adjustment for the other WBC subtypes [RR: 0.96, 95% CI: 0.93-0.99, P = .001] was observed. Our study implicates peripheral blood immune cells, in particular eosinophils and lymphocytes, in CRC development, highlighting a need for mechanistic studies to interrogate these relationships.

Journal article

Aglago EK, Kim A, Lin Y, Qu C, Evangelou M, Ren Y, Morrison J, Albanes D, Arndt V, Barry EL, Baurley JW, Berndt SI, Bien SA, Bishop DT, Bouras E, Brenner H, Buchanan DD, Budiarto A, Carreras-Torres R, Casey G, Cenggoro TW, Chan AT, Chang-Claude J, Chen X, Conti DV, Devall M, Diez-Obrero V, Dimou N, Drew D, Figueiredo JC, Gallinger S, Giles GG, Gruber SB, Gsur A, Gunter MJ, Hampel H, Harlid S, Hidaka A, Harrison TA, Hoffmeister M, Huyghe JR, Jenkins MA, Jordahl K, Joshi AD, Kawaguchi ES, Keku TO, Kundaje A, Larsson SC, Marchand LL, Lewinger JP, Li L, Lynch BM, Mahesworo B, Mandic M, Obón-Santacana M, Moreno V, Murphy N, Nan H, Nassir R, Newcomb PA, Ogino S, Ose J, Pai RK, Palmer JR, Papadimitriou N, Pardamean B, Peoples AR, Platz EA, Potter JD, Prentice RL, Rennert G, Ruiz-Narvaez E, Sakoda LC, Scacheri PC, Schmit SL, Schoen RE, Shcherbina A, Slattery ML, Stern MC, Su Y-R, Tangen CM, Thibodeau SN, Thomas DC, Tian Y, Ulrich CM, van Duijnhoven FJB, Van Guelpen B, Visvanathan K, Vodicka P, Wang J, White E, Wolk A, Woods MO, Wu AH, Zemlianskaia N, Hsu L, Gauderman WJ, Peters U, Tsilidis KK, Campbell PTet al., 2023, Supplementary Data from A Genetic Locus within the FMN1/GREM1 Gene Region Interacts with Body Mass Index in Colorectal Cancer Risk

<jats:p>&lt;p&gt;supplementary materials&lt;/p&gt;</jats:p>

Other

Aglago EK, Kim A, Lin Y, Qu C, Evangelou M, Ren Y, Morrison J, Albanes D, Arndt V, Barry EL, Baurley JW, Berndt SI, Bien SA, Bishop DT, Bouras E, Brenner H, Buchanan DD, Budiarto A, Carreras-Torres R, Casey G, Cenggoro TW, Chan AT, Chang-Claude J, Chen X, Conti DV, Devall M, Diez-Obrero V, Dimou N, Drew D, Figueiredo JC, Gallinger S, Giles GG, Gruber SB, Gsur A, Gunter MJ, Hampel H, Harlid S, Hidaka A, Harrison TA, Hoffmeister M, Huyghe JR, Jenkins MA, Jordahl K, Joshi AD, Kawaguchi ES, Keku TO, Kundaje A, Larsson SC, Marchand LL, Lewinger JP, Li L, Lynch BM, Mahesworo B, Mandic M, Obón-Santacana M, Moreno V, Murphy N, Nan H, Nassir R, Newcomb PA, Ogino S, Ose J, Pai RK, Palmer JR, Papadimitriou N, Pardamean B, Peoples AR, Platz EA, Potter JD, Prentice RL, Rennert G, Ruiz-Narvaez E, Sakoda LC, Scacheri PC, Schmit SL, Schoen RE, Shcherbina A, Slattery ML, Stern MC, Su Y-R, Tangen CM, Thibodeau SN, Thomas DC, Tian Y, Ulrich CM, van Duijnhoven FJB, Van Guelpen B, Visvanathan K, Vodicka P, Wang J, White E, Wolk A, Woods MO, Wu AH, Zemlianskaia N, Hsu L, Gauderman WJ, Peters U, Tsilidis KK, Campbell PTet al., 2023, Table 2 from A Genetic Locus within the FMN1/GREM1 Gene Region Interacts with Body Mass Index in Colorectal Cancer Risk

<jats:p>&lt;p&gt;Summary of G × BMI analyses using 1DF, two-step, and 3DF analyses.&lt;/p&gt;</jats:p>

Other

Aglago EK, Kim A, Lin Y, Qu C, Evangelou M, Ren Y, Morrison J, Albanes D, Arndt V, Barry EL, Baurley JW, Berndt SI, Bien SA, Bishop DT, Bouras E, Brenner H, Buchanan DD, Budiarto A, Carreras-Torres R, Casey G, Cenggoro TW, Chan AT, Chang-Claude J, Chen X, Conti DV, Devall M, Diez-Obrero V, Dimou N, Drew D, Figueiredo JC, Gallinger S, Giles GG, Gruber SB, Gsur A, Gunter MJ, Hampel H, Harlid S, Hidaka A, Harrison TA, Hoffmeister M, Huyghe JR, Jenkins MA, Jordahl K, Joshi AD, Kawaguchi ES, Keku TO, Kundaje A, Larsson SC, Marchand LL, Lewinger JP, Li L, Lynch BM, Mahesworo B, Mandic M, Obón-Santacana M, Moreno V, Murphy N, Nan H, Nassir R, Newcomb PA, Ogino S, Ose J, Pai RK, Palmer JR, Papadimitriou N, Pardamean B, Peoples AR, Platz EA, Potter JD, Prentice RL, Rennert G, Ruiz-Narvaez E, Sakoda LC, Scacheri PC, Schmit SL, Schoen RE, Shcherbina A, Slattery ML, Stern MC, Su Y-R, Tangen CM, Thibodeau SN, Thomas DC, Tian Y, Ulrich CM, van Duijnhoven FJB, Van Guelpen B, Visvanathan K, Vodicka P, Wang J, White E, Wolk A, Woods MO, Wu AH, Zemlianskaia N, Hsu L, Gauderman WJ, Peters U, Tsilidis KK, Campbell PTet al., 2023, Table 2 from A Genetic Locus within the FMN1/GREM1 Gene Region Interacts with Body Mass Index in Colorectal Cancer Risk

<jats:p>&lt;p&gt;Summary of G × BMI analyses using 1DF, two-step, and 3DF analyses.&lt;/p&gt;</jats:p>

Other

Aglago EK, Kim A, Lin Y, Qu C, Evangelou M, Ren Y, Morrison J, Albanes D, Arndt V, Barry EL, Baurley JW, Berndt SI, Bien SA, Bishop DT, Bouras E, Brenner H, Buchanan DD, Budiarto A, Carreras-Torres R, Casey G, Cenggoro TW, Chan AT, Chang-Claude J, Chen X, Conti DV, Devall M, Diez-Obrero V, Dimou N, Drew D, Figueiredo JC, Gallinger S, Giles GG, Gruber SB, Gsur A, Gunter MJ, Hampel H, Harlid S, Hidaka A, Harrison TA, Hoffmeister M, Huyghe JR, Jenkins MA, Jordahl K, Joshi AD, Kawaguchi ES, Keku TO, Kundaje A, Larsson SC, Marchand LL, Lewinger JP, Li L, Lynch BM, Mahesworo B, Mandic M, Obón-Santacana M, Moreno V, Murphy N, Nan H, Nassir R, Newcomb PA, Ogino S, Ose J, Pai RK, Palmer JR, Papadimitriou N, Pardamean B, Peoples AR, Platz EA, Potter JD, Prentice RL, Rennert G, Ruiz-Narvaez E, Sakoda LC, Scacheri PC, Schmit SL, Schoen RE, Shcherbina A, Slattery ML, Stern MC, Su Y-R, Tangen CM, Thibodeau SN, Thomas DC, Tian Y, Ulrich CM, van Duijnhoven FJ, Van Guelpen B, Visvanathan K, Vodicka P, Wang J, White E, Wolk A, Woods MO, Wu AH, Zemlianskaia N, Hsu L, Gauderman WJ, Peters U, Tsilidis KK, Campbell PTet al., 2023, A Genetic Locus within the FMN1/GREM1 Gene Region Interacts with Body Mass Index in Colorectal Cancer Risk., Cancer Res, Vol: 83, Pages: 2572-2583

UNLABELLED: Colorectal cancer risk can be impacted by genetic, environmental, and lifestyle factors, including diet and obesity. Gene-environment interactions (G × E) can provide biological insights into the effects of obesity on colorectal cancer risk. Here, we assessed potential genome-wide G × E interactions between body mass index (BMI) and common SNPs for colorectal cancer risk using data from 36,415 colorectal cancer cases and 48,451 controls from three international colorectal cancer consortia (CCFR, CORECT, and GECCO). The G × E tests included the conventional logistic regression using multiplicative terms (one degree of freedom, 1DF test), the two-step EDGE method, and the joint 3DF test, each of which is powerful for detecting G × E interactions under specific conditions. BMI was associated with higher colorectal cancer risk. The two-step approach revealed a statistically significant G×BMI interaction located within the Formin 1/Gremlin 1 (FMN1/GREM1) gene region (rs58349661). This SNP was also identified by the 3DF test, with a suggestive statistical significance in the 1DF test. Among participants with the CC genotype of rs58349661, overweight and obesity categories were associated with higher colorectal cancer risk, whereas null associations were observed across BMI categories in those with the TT genotype. Using data from three large international consortia, this study discovered a locus in the FMN1/GREM1 gene region that interacts with BMI on the association with colorectal cancer risk. Further studies should examine the potential mechanisms through which this locus modifies the etiologic link between obesity and colorectal cancer. SIGNIFICANCE: This gene-environment interaction analysis revealed a genetic locus in FMN1/GREM1 that interacts with body mass index in colorectal cancer risk, suggesting potential implications for precision prevention strategies.

Journal article

Aglago EK, Kim A, Lin Y, Qu C, Evangelou M, Ren Y, Morrison J, Albanes D, Arndt V, Barry EL, Baurley JW, Berndt SI, Bien SA, Bishop DT, Bouras E, Brenner H, Buchanan DD, Budiarto A, Carreras-Torres R, Casey G, Cenggoro TW, Chan AT, Chang-Claude J, Chen X, Conti DV, Devall M, Diez-Obrero V, Dimou N, Drew D, Figueiredo JC, Gallinger S, Giles GG, Gruber SB, Gsur A, Gunter MJ, Hampel H, Harlid S, Hidaka A, Harrison TA, Hoffmeister M, Huyghe JR, Jenkins MA, Jordahl K, Joshi AD, Kawaguchi ES, Keku TO, Kundaje A, Larsson SC, Marchand LL, Lewinger JP, Li L, Lynch BM, Mahesworo B, Mandic M, Obón-Santacana M, Moreno V, Murphy N, Nan H, Nassir R, Newcomb PA, Ogino S, Ose J, Pai RK, Palmer JR, Papadimitriou N, Pardamean B, Peoples AR, Platz EA, Potter JD, Prentice RL, Rennert G, Ruiz-Narvaez E, Sakoda LC, Scacheri PC, Schmit SL, Schoen RE, Shcherbina A, Slattery ML, Stern MC, Su Y-R, Tangen CM, Thibodeau SN, Thomas DC, Tian Y, Ulrich CM, van Duijnhoven FJB, Van Guelpen B, Visvanathan K, Vodicka P, Wang J, White E, Wolk A, Woods MO, Wu AH, Zemlianskaia N, Hsu L, Gauderman WJ, Peters U, Tsilidis KK, Campbell PTet al., 2023, Table 1 from A Genetic Locus within the FMN1/GREM1 Gene Region Interacts with Body Mass Index in Colorectal Cancer Risk

<jats:p>&lt;p&gt;Selected characteristics of the participants.&lt;/p&gt;</jats:p>

Other

Aglago EK, Kim A, Lin Y, Qu C, Evangelou M, Ren Y, Morrison J, Albanes D, Arndt V, Barry EL, Baurley JW, Berndt SI, Bien SA, Bishop DT, Bouras E, Brenner H, Buchanan DD, Budiarto A, Carreras-Torres R, Casey G, Cenggoro TW, Chan AT, Chang-Claude J, Chen X, Conti DV, Devall M, Diez-Obrero V, Dimou N, Drew D, Figueiredo JC, Gallinger S, Giles GG, Gruber SB, Gsur A, Gunter MJ, Hampel H, Harlid S, Hidaka A, Harrison TA, Hoffmeister M, Huyghe JR, Jenkins MA, Jordahl K, Joshi AD, Kawaguchi ES, Keku TO, Kundaje A, Larsson SC, Marchand LL, Lewinger JP, Li L, Lynch BM, Mahesworo B, Mandic M, Obón-Santacana M, Moreno V, Murphy N, Nan H, Nassir R, Newcomb PA, Ogino S, Ose J, Pai RK, Palmer JR, Papadimitriou N, Pardamean B, Peoples AR, Platz EA, Potter JD, Prentice RL, Rennert G, Ruiz-Narvaez E, Sakoda LC, Scacheri PC, Schmit SL, Schoen RE, Shcherbina A, Slattery ML, Stern MC, Su Y-R, Tangen CM, Thibodeau SN, Thomas DC, Tian Y, Ulrich CM, van Duijnhoven FJB, Van Guelpen B, Visvanathan K, Vodicka P, Wang J, White E, Wolk A, Woods MO, Wu AH, Zemlianskaia N, Hsu L, Gauderman WJ, Peters U, Tsilidis KK, Campbell PTet al., 2023, Data from A Genetic Locus within the FMN1/GREM1 Gene Region Interacts with Body Mass Index in Colorectal Cancer Risk

<jats:p>&lt;div&gt;Abstract&lt;p&gt;Colorectal cancer risk can be impacted by genetic, environmental, and lifestyle factors, including diet and obesity. Gene-environment interactions (G × E) can provide biological insights into the effects of obesity on colorectal cancer risk. Here, we assessed potential genome-wide G × E interactions between body mass index (BMI) and common SNPs for colorectal cancer risk using data from 36,415 colorectal cancer cases and 48,451 controls from three international colorectal cancer consortia (CCFR, CORECT, and GECCO). The G × E tests included the conventional logistic regression using multiplicative terms (one degree of freedom, 1DF test), the two-step EDGE method, and the joint 3DF test, each of which is powerful for detecting G × E interactions under specific conditions. BMI was associated with higher colorectal cancer risk. The two-step approach revealed a statistically significant G×BMI interaction located within the Formin 1/Gremlin 1 (&lt;i&gt;FMN1/GREM1&lt;/i&gt;) gene region (rs58349661). This SNP was also identified by the 3DF test, with a suggestive statistical significance in the 1DF test. Among participants with the CC genotype of rs58349661, overweight and obesity categories were associated with higher colorectal cancer risk, whereas null associations were observed across BMI categories in those with the TT genotype. Using data from three large international consortia, this study discovered a locus in the &lt;i&gt;FMN1/GREM1&lt;/i&gt; gene region that interacts with BMI on the association with colorectal cancer risk. Further studies should examine the potential mechanisms through which this locus modifies the etiologic link between obesity and colorectal cancer.&lt;/p&gt;Significance:&lt;p&gt;This gene-environment interaction analysis revealed a genetic locus in FMN1/GREM1 that interacts with body mass index in colorectal

Other

Aglago EK, Kim A, Lin Y, Qu C, Evangelou M, Ren Y, Morrison J, Albanes D, Arndt V, Barry EL, Baurley JW, Berndt SI, Bien SA, Bishop DT, Bouras E, Brenner H, Buchanan DD, Budiarto A, Carreras-Torres R, Casey G, Cenggoro TW, Chan AT, Chang-Claude J, Chen X, Conti DV, Devall M, Diez-Obrero V, Dimou N, Drew D, Figueiredo JC, Gallinger S, Giles GG, Gruber SB, Gsur A, Gunter MJ, Hampel H, Harlid S, Hidaka A, Harrison TA, Hoffmeister M, Huyghe JR, Jenkins MA, Jordahl K, Joshi AD, Kawaguchi ES, Keku TO, Kundaje A, Larsson SC, Marchand LL, Lewinger JP, Li L, Lynch BM, Mahesworo B, Mandic M, Obón-Santacana M, Moreno V, Murphy N, Nan H, Nassir R, Newcomb PA, Ogino S, Ose J, Pai RK, Palmer JR, Papadimitriou N, Pardamean B, Peoples AR, Platz EA, Potter JD, Prentice RL, Rennert G, Ruiz-Narvaez E, Sakoda LC, Scacheri PC, Schmit SL, Schoen RE, Shcherbina A, Slattery ML, Stern MC, Su Y-R, Tangen CM, Thibodeau SN, Thomas DC, Tian Y, Ulrich CM, van Duijnhoven FJB, Van Guelpen B, Visvanathan K, Vodicka P, Wang J, White E, Wolk A, Woods MO, Wu AH, Zemlianskaia N, Hsu L, Gauderman WJ, Peters U, Tsilidis KK, Campbell PTet al., 2023, Table 1 from A Genetic Locus within the FMN1/GREM1 Gene Region Interacts with Body Mass Index in Colorectal Cancer Risk

<jats:p>&lt;p&gt;Selected characteristics of the participants.&lt;/p&gt;</jats:p>

Other

Bull CJ, Hazelwood E, Bell JA, Tan VY, Constantinescu A-E, Borges MC, Legge DN, Burrows K, Huyghe JR, Brenner H, Castellvi-Bel S, Chan AT, Kweon S-S, Le Marchand L, Li L, Cheng I, Pai RK, Figueiredo JC, Murphy N, Gunter MJ, Timpson NJ, Vincent EEet al., 2023, Identifying metabolic features of colorectal cancer liability using Mendelian randomization., medRxiv

Recognizing the early signs of cancer risk is vital for informing prevention, early detection, and survival. To investigate whether changes in circulating metabolites characterise the early stages of colorectal cancer (CRC) development, we examined associations between a genetic risk score (GRS) associated with CRC liability (72 single nucleotide polymorphisms) and 231 circulating metabolites measured by nuclear magnetic resonance spectroscopy in the Avon Longitudinal Study of Parents and Children (N=6,221). Linear regression models were applied to examine associations between genetic liability to colorectal cancer and circulating metabolites measured in the same individuals at age 8, 16, 18 and 25 years. The GRS for CRC was associated with up to 28% of the circulating metabolites at FDR-P<0.05 across all time points, particularly with higher fatty acids and very-low- and low-density lipoprotein subclass lipids. Two-sample reverse Mendelian randomization (MR) analyses investigating CRC liability (52,775 cases, 45,940 controls) and metabolites measured in a random subset of UK Biobank participants (N=118,466, median age 58y) revealed broadly consistent effect estimates with the GRS analysis. In conventional (forward) MR analyses, genetically predicted polyunsaturated fatty acid concentrations were most strongly associated with higher CRC risk. These analyses suggest that higher genetic liability to CRC can cause early alterations in systemic metabolism, and suggest that fatty acids may play an important role in CRC development.

Journal article

Aglago EK, Kim A, Lin Y, Qu C, Evangelou M, Ren Y, Morrison J, Albanes D, Arndt V, Barry EL, Baurley JW, Berndt SI, Bien SA, Bishop DT, Bouras E, Brenner H, Buchanan DD, Budiarto A, Carreras-Torres R, Casey G, Cenggoro TW, Chan AT, Chang-Claude J, Chen X, Conti DV, Devall M, Diez-Obrero V, Dimou N, Drew D, Figueiredo JC, Gallinger S, Giles GG, Gruber SB, Gsur A, Gunter MJ, Hampel H, Harlid S, Hidaka A, Harrison TA, Hoffmeister M, Huyghe JR, Jenkins MA, Jordahl K, Joshi AD, Kawaguchi ES, Keku TO, Kundaje A, Larsson SC, Marchand LL, Lewinger JP, Li L, Lynch BM, Mahesworo B, Mandic M, Obón-Santacana M, Moreno V, Murphy N, Nan H, Nassir R, Newcomb PA, Ogino S, Ose J, Pai RK, Palmer JR, Papadimitriou N, Pardamean B, Peoples AR, Platz EA, Potter JD, Prentice RL, Rennert G, Ruiz-Narvaez E, Sakoda LC, Scacheri PC, Schmit SL, Schoen RE, Shcherbina A, Slattery ML, Stern MC, Su Y-R, Tangen CM, Thibodeau SN, Thomas DC, Tian Y, Ulrich CM, van Duijnhoven FJB, Van Guelpen B, Visvanathan K, Vodicka P, Wang J, White E, Wolk A, Woods MO, Wu AH, Zemlianskaia N, Hsu L, Gauderman WJ, Peters U, Tsilidis KK, Campbell PTet al., 2023, Data from A Genetic Locus within the FMN1/GREM1 Gene Region Interacts with Body Mass Index in Colorectal Cancer Risk

<jats:p>&lt;div&gt;Abstract&lt;p&gt;Colorectal cancer risk can be impacted by genetic, environmental, and lifestyle factors, including diet and obesity. Gene-environment interactions (G × E) can provide biological insights into the effects of obesity on colorectal cancer risk. Here, we assessed potential genome-wide G × E interactions between body mass index (BMI) and common SNPs for colorectal cancer risk using data from 36,415 colorectal cancer cases and 48,451 controls from three international colorectal cancer consortia (CCFR, CORECT, and GECCO). The G × E tests included the conventional logistic regression using multiplicative terms (one degree of freedom, 1DF test), the two-step EDGE method, and the joint 3DF test, each of which is powerful for detecting G × E interactions under specific conditions. BMI was associated with higher colorectal cancer risk. The two-step approach revealed a statistically significant G×BMI interaction located within the Formin 1/Gremlin 1 (&lt;i&gt;FMN1/GREM1&lt;/i&gt;) gene region (rs58349661). This SNP was also identified by the 3DF test, with a suggestive statistical significance in the 1DF test. Among participants with the CC genotype of rs58349661, overweight and obesity categories were associated with higher colorectal cancer risk, whereas null associations were observed across BMI categories in those with the TT genotype. Using data from three large international consortia, this study discovered a locus in the &lt;i&gt;FMN1/GREM1&lt;/i&gt; gene region that interacts with BMI on the association with colorectal cancer risk. Further studies should examine the potential mechanisms through which this locus modifies the etiologic link between obesity and colorectal cancer.&lt;/p&gt;Significance:&lt;p&gt;This gene-environment interaction analysis revealed a genetic locus in FMN1/GREM1 that interacts with body mass index in colorectal

Other

Aglago EK, Kim A, Lin Y, Qu C, Evangelou M, Ren Y, Morrison J, Albanes D, Arndt V, Barry EL, Baurley JW, Berndt SI, Bien SA, Bishop DT, Bouras E, Brenner H, Buchanan DD, Budiarto A, Carreras-Torres R, Casey G, Cenggoro TW, Chan AT, Chang-Claude J, Chen X, Conti DV, Devall M, Diez-Obrero V, Dimou N, Drew D, Figueiredo JC, Gallinger S, Giles GG, Gruber SB, Gsur A, Gunter MJ, Hampel H, Harlid S, Hidaka A, Harrison TA, Hoffmeister M, Huyghe JR, Jenkins MA, Jordahl K, Joshi AD, Kawaguchi ES, Keku TO, Kundaje A, Larsson SC, Marchand LL, Lewinger JP, Li L, Lynch BM, Mahesworo B, Mandic M, Obón-Santacana M, Moreno V, Murphy N, Nan H, Nassir R, Newcomb PA, Ogino S, Ose J, Pai RK, Palmer JR, Papadimitriou N, Pardamean B, Peoples AR, Platz EA, Potter JD, Prentice RL, Rennert G, Ruiz-Narvaez E, Sakoda LC, Scacheri PC, Schmit SL, Schoen RE, Shcherbina A, Slattery ML, Stern MC, Su Y-R, Tangen CM, Thibodeau SN, Thomas DC, Tian Y, Ulrich CM, van Duijnhoven FJB, Van Guelpen B, Visvanathan K, Vodicka P, Wang J, White E, Wolk A, Woods MO, Wu AH, Zemlianskaia N, Hsu L, Gauderman WJ, Peters U, Tsilidis KK, Campbell PTet al., 2023, Supplementary Data from A Genetic Locus within the FMN1/GREM1 Gene Region Interacts with Body Mass Index in Colorectal Cancer Risk

<jats:p>&lt;p&gt;supplementary materials&lt;/p&gt;</jats:p>

Other

Watling CZ, Kelly RK, Murphy N, Gunter M, Piernas C, Bradbury KE, Schmidt JA, Key TJ, Perez-Cornago Aet al., 2023, Supplementary Material from Prospective Analysis Reveals Associations between Carbohydrate Intakes, Genetic Predictors of Short-Chain Fatty Acid Synthesis, and Colorectal Cancer Risk

<jats:p>&lt;p&gt;Supplementary Material&lt;/p&gt;</jats:p>

Other

Watling CZ, Kelly RK, Murphy N, Gunter M, Piernas C, Bradbury KE, Schmidt JA, Key TJ, Perez-Cornago Aet al., 2023, Figure 3 from Prospective Analysis Reveals Associations between Carbohydrate Intakes, Genetic Predictors of Short-Chain Fatty Acid Synthesis, and Colorectal Cancer Risk

<jats:p>&lt;p&gt;Multivariable-adjusted hazard ratios (95% CI) for intake of carbohydrates and fiber and colorectal cancer risk separated by genetically predicted host short-chain fatty acid production for butyrate (&lt;b&gt;A&lt;/b&gt;) and propionate (&lt;i&gt;n&lt;/i&gt; = 87,417; &lt;b&gt;B&lt;/b&gt;). All models are stratified by sex, age at recruitment, adjusted for region of recruitment, first 10 genetic principal components, body mass index, height, physical activity, Townsend deprivation index, education, smoking, alcohol consumption, diabetes status, nonsteroidal anti-inflammatory drug use, red and processed meat intake, fruit and vegetable intake (except when fiber from vegetables and/or fruits, and non-free sugar intake was the exposure), energy intake, and female-specific covariates: menopausal hormone therapy use and menopausal status. Analyses are restricted to white British participants. χ&lt;sup&gt;2&lt;/sup&gt; and &lt;i&gt;P&lt;/i&gt; value represents improvement of fit obtained from likelihood ratio tests for including an interaction term between butyrate or propionate polygenic score and carbohydrate type/source (modeled as a 5% energy increment) or fiber source (modeled as a 5 gram/day increment) into the model.&lt;/p&gt;</jats:p>

Other

Watling CZ, Kelly RK, Murphy N, Gunter M, Piernas C, Bradbury KE, Schmidt JA, Key TJ, Perez-Cornago Aet al., 2023, Figure 3 from Prospective Analysis Reveals Associations between Carbohydrate Intakes, Genetic Predictors of Short-Chain Fatty Acid Synthesis, and Colorectal Cancer Risk

<jats:p>&lt;p&gt;Multivariable-adjusted hazard ratios (95% CI) for intake of carbohydrates and fiber and colorectal cancer risk separated by genetically predicted host short-chain fatty acid production for butyrate (&lt;b&gt;A&lt;/b&gt;) and propionate (&lt;i&gt;n&lt;/i&gt; = 87,417; &lt;b&gt;B&lt;/b&gt;). All models are stratified by sex, age at recruitment, adjusted for region of recruitment, first 10 genetic principal components, body mass index, height, physical activity, Townsend deprivation index, education, smoking, alcohol consumption, diabetes status, nonsteroidal anti-inflammatory drug use, red and processed meat intake, fruit and vegetable intake (except when fiber from vegetables and/or fruits, and non-free sugar intake was the exposure), energy intake, and female-specific covariates: menopausal hormone therapy use and menopausal status. Analyses are restricted to white British participants. χ&lt;sup&gt;2&lt;/sup&gt; and &lt;i&gt;P&lt;/i&gt; value represents improvement of fit obtained from likelihood ratio tests for including an interaction term between butyrate or propionate polygenic score and carbohydrate type/source (modeled as a 5% energy increment) or fiber source (modeled as a 5 gram/day increment) into the model.&lt;/p&gt;</jats:p>

Other

Watling CZ, Kelly RK, Murphy N, Gunter M, Piernas C, Bradbury KE, Schmidt JA, Key TJ, Perez-Cornago Aet al., 2023, Table 1 from Prospective Analysis Reveals Associations between Carbohydrate Intakes, Genetic Predictors of Short-Chain Fatty Acid Synthesis, and Colorectal Cancer Risk

<jats:p>&lt;p&gt;Baseline characteristics by lowest and highest quartile of intake of whole grain starch, refined grain starch, and fiber in 114,217 UK Biobank participants.&lt;/p&gt;</jats:p>

Other

Watling CZ, Kelly RK, Murphy N, Gunter M, Piernas C, Bradbury KE, Schmidt JA, Key TJ, Perez-Cornago Aet al., 2023, Supplementary Material from Prospective Analysis Reveals Associations between Carbohydrate Intakes, Genetic Predictors of Short-Chain Fatty Acid Synthesis, and Colorectal Cancer Risk

<jats:p>&lt;p&gt;Supplementary Material&lt;/p&gt;</jats:p>

Other

Watling CZ, Kelly RK, Murphy N, Gunter M, Piernas C, Bradbury KE, Schmidt JA, Key TJ, Perez-Cornago Aet al., 2023, Prospective Analysis Reveals Associations between Carbohydrate Intakes, Genetic Predictors of Short-Chain Fatty Acid Synthesis, and Colorectal Cancer Risk., Cancer Res, Vol: 83, Pages: 2066-2076

UNLABELLED: Whole grain and fiber intakes may decrease the risk of colorectal cancer. The interplay between host genetic factors, colonization of specific bacteria, production of short-chain fatty acids (SCFA), and intake of whole grains and fiber could alter the protective role of carbohydrates against colorectal cancer. Here, we assessed intakes of types and sources of carbohydrates in 114,217 UK Biobank participants with detailed dietary data (2-5 24-hour dietary assessments), and a host polygenic score (PGS) was applied to categorize participants as high or low for intraluminal microbial SCFA production, namely, butyrate and propionate. Multivariable Cox proportional hazards models were used to determine the associations of carbohydrates and SCFA with colorectal cancer incidence. During a median follow-up of 9.4 years, 1,193 participants were diagnosed with colorectal cancer. Risk was inversely associated with intakes of non-free sugar and whole grain fiber. Evidence of heterogeneity was observed by the butyrate PGS; consuming higher amounts of whole grain starch was only associated with a lower risk of colorectal cancer in those with predicted high SCFA production. Similarly, in additional analyses utilizing the larger UK Biobank cohort (N = 343,621) with less detailed dietary assessment, only individuals with a high genetically predicted butyrate production had a lower risk of colorectal cancer per 5 g/day intake of bread and cereal fiber. This study suggests that colorectal cancer risk varies by intake of carbohydrate types and sources, and the impact of whole grain intake may be modified by SCFA production. SIGNIFICANCE: Prospective population-level analyses provide evidence supporting the importance of butyrate production in reduction of colorectal cancer risk by whole grain consumption.

Journal article

Watling CZ, Kelly RK, Murphy N, Gunter M, Piernas C, Bradbury KE, Schmidt JA, Key TJ, Perez-Cornago Aet al., 2023, Data from Prospective Analysis Reveals Associations between Carbohydrate Intakes, Genetic Predictors of Short-Chain Fatty Acid Synthesis, and Colorectal Cancer Risk

<jats:p>&lt;div&gt;Abstract&lt;p&gt;Whole grain and fiber intakes may decrease the risk of colorectal cancer. The interplay between host genetic factors, colonization of specific bacteria, production of short-chain fatty acids (SCFA), and intake of whole grains and fiber could alter the protective role of carbohydrates against colorectal cancer. Here, we assessed intakes of types and sources of carbohydrates in 114,217 UK Biobank participants with detailed dietary data (2–5 24-hour dietary assessments), and a host polygenic score (PGS) was applied to categorize participants as high or low for intraluminal microbial SCFA production, namely, butyrate and propionate. Multivariable Cox proportional hazards models were used to determine the associations of carbohydrates and SCFA with colorectal cancer incidence. During a median follow-up of 9.4 years, 1,193 participants were diagnosed with colorectal cancer. Risk was inversely associated with intakes of non-free sugar and whole grain fiber. Evidence of heterogeneity was observed by the butyrate PGS; consuming higher amounts of whole grain starch was only associated with a lower risk of colorectal cancer in those with predicted high SCFA production. Similarly, in additional analyses utilizing the larger UK Biobank cohort (&lt;i&gt;N&lt;/i&gt; = 343,621) with less detailed dietary assessment, only individuals with a high genetically predicted butyrate production had a lower risk of colorectal cancer per 5 g/day intake of bread and cereal fiber. This study suggests that colorectal cancer risk varies by intake of carbohydrate types and sources, and the impact of whole grain intake may be modified by SCFA production.&lt;/p&gt;Significance:&lt;p&gt;Prospective population-level analyses provide evidence supporting the importance of butyrate production in reduction of colorectal cancer risk by whole grain consumption.&lt;/p&gt;&lt;/div&gt;</jats:p>

Other

Watling CZ, Kelly RK, Murphy N, Gunter M, Piernas C, Bradbury KE, Schmidt JA, Key TJ, Perez-Cornago Aet al., 2023, Figure 4 from Prospective Analysis Reveals Associations between Carbohydrate Intakes, Genetic Predictors of Short-Chain Fatty Acid Synthesis, and Colorectal Cancer Risk

<jats:p>&lt;p&gt;Multivariable-adjusted hazard ratios (95% CI) for intake of fiber from breads and cereals from the touchscreen questionnaire and colorectal cancer risk by genetically predicted host short-chain fatty acid production for butyrate (&lt;b&gt;A&lt;/b&gt;) and propionate (&lt;i&gt;n&lt;/i&gt; = 343,621; &lt;b&gt;B&lt;/b&gt;). Models stratified for sex and age at recruitment, and further adjusted for region, first 10 principal components, height, physical activity, Townsend deprivation index, education, employment, smoking, alcohol consumption measured at recruitment, diabetes status, nonsteroidal anti-inflammatory drug use, body mass index, processed and red meat intake, and female-specific covariates: menopausal hormone therapy use and menopausal status. Analyses are restricted to white British participants. χ&lt;sup&gt;2&lt;/sup&gt; and &lt;i&gt;P&lt;/i&gt; value represents improvement of fit obtained from likelihood ratio tests for including an interaction term between butyrate or propionate polygenic score and fiber from breads and cereals (modeled as a 5 gram/day increment) into the model. g/day, grams per day; &lt;i&gt;N&lt;/i&gt;, number of participants; Q, quintile; ref, reference group.&lt;/p&gt;</jats:p>

Other

Watling CZ, Kelly RK, Murphy N, Gunter M, Piernas C, Bradbury KE, Schmidt JA, Key TJ, Perez-Cornago Aet al., 2023, Data from Prospective Analysis Reveals Associations between Carbohydrate Intakes, Genetic Predictors of Short-Chain Fatty Acid Synthesis, and Colorectal Cancer Risk

<jats:p>&lt;div&gt;Abstract&lt;p&gt;Whole grain and fiber intakes may decrease the risk of colorectal cancer. The interplay between host genetic factors, colonization of specific bacteria, production of short-chain fatty acids (SCFA), and intake of whole grains and fiber could alter the protective role of carbohydrates against colorectal cancer. Here, we assessed intakes of types and sources of carbohydrates in 114,217 UK Biobank participants with detailed dietary data (2–5 24-hour dietary assessments), and a host polygenic score (PGS) was applied to categorize participants as high or low for intraluminal microbial SCFA production, namely, butyrate and propionate. Multivariable Cox proportional hazards models were used to determine the associations of carbohydrates and SCFA with colorectal cancer incidence. During a median follow-up of 9.4 years, 1,193 participants were diagnosed with colorectal cancer. Risk was inversely associated with intakes of non-free sugar and whole grain fiber. Evidence of heterogeneity was observed by the butyrate PGS; consuming higher amounts of whole grain starch was only associated with a lower risk of colorectal cancer in those with predicted high SCFA production. Similarly, in additional analyses utilizing the larger UK Biobank cohort (&lt;i&gt;N&lt;/i&gt; = 343,621) with less detailed dietary assessment, only individuals with a high genetically predicted butyrate production had a lower risk of colorectal cancer per 5 g/day intake of bread and cereal fiber. This study suggests that colorectal cancer risk varies by intake of carbohydrate types and sources, and the impact of whole grain intake may be modified by SCFA production.&lt;/p&gt;Significance:&lt;p&gt;Prospective population-level analyses provide evidence supporting the importance of butyrate production in reduction of colorectal cancer risk by whole grain consumption.&lt;/p&gt;&lt;/div&gt;</jats:p>

Other

Watling CZ, Kelly RK, Murphy N, Gunter M, Piernas C, Bradbury KE, Schmidt JA, Key TJ, Perez-Cornago Aet al., 2023, Figure 4 from Prospective Analysis Reveals Associations between Carbohydrate Intakes, Genetic Predictors of Short-Chain Fatty Acid Synthesis, and Colorectal Cancer Risk

<jats:p>&lt;p&gt;Multivariable-adjusted hazard ratios (95% CI) for intake of fiber from breads and cereals from the touchscreen questionnaire and colorectal cancer risk by genetically predicted host short-chain fatty acid production for butyrate (&lt;b&gt;A&lt;/b&gt;) and propionate (&lt;i&gt;n&lt;/i&gt; = 343,621; &lt;b&gt;B&lt;/b&gt;). Models stratified for sex and age at recruitment, and further adjusted for region, first 10 principal components, height, physical activity, Townsend deprivation index, education, employment, smoking, alcohol consumption measured at recruitment, diabetes status, nonsteroidal anti-inflammatory drug use, body mass index, processed and red meat intake, and female-specific covariates: menopausal hormone therapy use and menopausal status. Analyses are restricted to white British participants. χ&lt;sup&gt;2&lt;/sup&gt; and &lt;i&gt;P&lt;/i&gt; value represents improvement of fit obtained from likelihood ratio tests for including an interaction term between butyrate or propionate polygenic score and fiber from breads and cereals (modeled as a 5 gram/day increment) into the model. g/day, grams per day; &lt;i&gt;N&lt;/i&gt;, number of participants; Q, quintile; ref, reference group.&lt;/p&gt;</jats:p>

Other

Watling CZ, Kelly RK, Murphy N, Gunter M, Piernas C, Bradbury KE, Schmidt JA, Key TJ, Perez-Cornago Aet al., 2023, Table 1 from Prospective Analysis Reveals Associations between Carbohydrate Intakes, Genetic Predictors of Short-Chain Fatty Acid Synthesis, and Colorectal Cancer Risk

<jats:p>&lt;p&gt;Baseline characteristics by lowest and highest quartile of intake of whole grain starch, refined grain starch, and fiber in 114,217 UK Biobank participants.&lt;/p&gt;</jats:p>

Other

Watling CZ, Kelly RK, Murphy N, Gunter M, Piernas C, Bradbury KE, Schmidt JA, Key TJ, Perez-Cornago Aet al., 2023, Supplementary Material from Prospective Analysis Reveals Associations between Carbohydrate Intakes, Genetic Predictors of Short-Chain Fatty Acid Synthesis, and Colorectal Cancer Risk

<jats:p>&lt;p&gt;Supplementary Material&lt;/p&gt;</jats:p>

Other

Watling CZ, Kelly RK, Murphy N, Gunter M, Piernas C, Bradbury KE, Schmidt JA, Key TJ, Perez-Cornago Aet al., 2023, Data from Prospective Analysis Reveals Associations between Carbohydrate Intakes, Genetic Predictors of Short-Chain Fatty Acid Synthesis, and Colorectal Cancer Risk

<jats:p>&lt;div&gt;Abstract&lt;p&gt;Whole grain and fiber intakes may decrease the risk of colorectal cancer. The interplay between host genetic factors, colonization of specific bacteria, production of short-chain fatty acids (SCFA), and intake of whole grains and fiber could alter the protective role of carbohydrates against colorectal cancer. Here, we assessed intakes of types and sources of carbohydrates in 114,217 UK Biobank participants with detailed dietary data (2–5 24-hour dietary assessments), and a host polygenic score (PGS) was applied to categorize participants as high or low for intraluminal microbial SCFA production, namely, butyrate and propionate. Multivariable Cox proportional hazards models were used to determine the associations of carbohydrates and SCFA with colorectal cancer incidence. During a median follow-up of 9.4 years, 1,193 participants were diagnosed with colorectal cancer. Risk was inversely associated with intakes of non-free sugar and whole grain fiber. Evidence of heterogeneity was observed by the butyrate PGS; consuming higher amounts of whole grain starch was only associated with a lower risk of colorectal cancer in those with predicted high SCFA production. Similarly, in additional analyses utilizing the larger UK Biobank cohort (&lt;i&gt;N&lt;/i&gt; = 343,621) with less detailed dietary assessment, only individuals with a high genetically predicted butyrate production had a lower risk of colorectal cancer per 5 g/day intake of bread and cereal fiber. This study suggests that colorectal cancer risk varies by intake of carbohydrate types and sources, and the impact of whole grain intake may be modified by SCFA production.&lt;/p&gt;Significance:&lt;p&gt;Prospective population-level analyses provide evidence supporting the importance of butyrate production in reduction of colorectal cancer risk by whole grain consumption.&lt;/p&gt;&lt;/div&gt;</jats:p>

Other

Watling CZ, Kelly RK, Murphy N, Gunter M, Piernas C, Bradbury KE, Schmidt JA, Key TJ, Perez-Cornago Aet al., 2023, Supplementary Material from Prospective Analysis Reveals Associations between Carbohydrate Intakes, Genetic Predictors of Short-Chain Fatty Acid Synthesis, and Colorectal Cancer Risk

<jats:p>&lt;p&gt;Supplementary Material&lt;/p&gt;</jats:p>

Other

Campbell PT, Newton CC, Jacobs EJ, McCullough ML, Wang Y, Rees-Punia E, Guinter MA, Murphy N, Koshiol J, Dehal AN, Rohan T, Strickler H, Petrick J, Gunter M, Zhang X, McGlynn KA, Pollak M, Patel AV, Gapstur SMet al., 2023, Data from Prospective Associations of Hemoglobin A&lt;sub&gt;1c&lt;/sub&gt; and c-peptide with Risk of Diabetes-related Cancers in the Cancer Prevention Study-II Nutrition Cohort

<jats:p>&lt;div&gt;&lt;p&gt;Self-reported type 2 diabetes mellitus (T2DM) is a risk factor for many cancers, suggesting its pathology relates to carcinogenesis. We conducted a case-cohort study to examine associations of hemoglobin A&lt;sub&gt;1c&lt;/sub&gt; (HbA&lt;sub&gt;1c&lt;/sub&gt;) and c-peptide with cancers associated with self-reported T2DM. This study was drawn from a prospective cohort of 32,383 women and men who provided blood specimens at baseline: c-peptide and HbA&lt;sub&gt;1c&lt;/sub&gt; were assessed in 3,000 randomly selected participants who were cancer-free-at-baseline and an additional 2,281 participants who were cancer-free-at-baseline and subsequently diagnosed with incident colorectal, liver, pancreatic, female breast, endometrial, ovarian, bladder, or kidney cancers. Weighted Cox regression models estimated HRs and 95% confidence intervals (CI), adjusted for covariates. c-peptide was associated with higher risk of liver cancer [per SD HR: 1.80; 95% CI: 1.32–2.46]. HbA&lt;sub&gt;1c&lt;/sub&gt; was associated with higher risk of pancreatic cancer (per SD HR: 1.21; 95% CI: 1.05–1.40) and with some suggestion of higher risks for all-cancers-of-interest (per SD HR: 1.05; 95% CI: 0.99–1.11) and colorectal (per SD HR: 1.09; 95% CI: 0.98–1.20), ovarian (per SD HR: 1.18; 95% CI: 0.96–1.45) and bladder (per SD HR: 1.08; 95% CI: 0.96–1.21) cancers. Compared with no self-reported T2DM and HbA&lt;sub&gt;1c&lt;/sub&gt; &lt; 6.5% (reference group), self-reported T2DM and HbA&lt;sub&gt;1c&lt;/sub&gt; &lt; 6.5% (i.e., T2DM in good glycemic control) was not associated with risk of colorectal cancer, whereas it was associated with higher risks of all-cancers-of-interest combined (HR: 1.28; 95% CI: 1.01–1.62), especially for breast and endometrial cancers. Additional large, prospective studies ar

Other

Campbell PT, Newton CC, Jacobs EJ, McCullough ML, Wang Y, Rees-Punia E, Guinter MA, Murphy N, Koshiol J, Dehal AN, Rohan T, Strickler H, Petrick J, Gunter M, Zhang X, McGlynn KA, Pollak M, Patel AV, Gapstur SMet al., 2023, Data from Prospective Associations of Hemoglobin A&lt;sub&gt;1c&lt;/sub&gt; and c-peptide with Risk of Diabetes-related Cancers in the Cancer Prevention Study-II Nutrition Cohort

<jats:p>&lt;div&gt;&lt;p&gt;Self-reported type 2 diabetes mellitus (T2DM) is a risk factor for many cancers, suggesting its pathology relates to carcinogenesis. We conducted a case-cohort study to examine associations of hemoglobin A&lt;sub&gt;1c&lt;/sub&gt; (HbA&lt;sub&gt;1c&lt;/sub&gt;) and c-peptide with cancers associated with self-reported T2DM. This study was drawn from a prospective cohort of 32,383 women and men who provided blood specimens at baseline: c-peptide and HbA&lt;sub&gt;1c&lt;/sub&gt; were assessed in 3,000 randomly selected participants who were cancer-free-at-baseline and an additional 2,281 participants who were cancer-free-at-baseline and subsequently diagnosed with incident colorectal, liver, pancreatic, female breast, endometrial, ovarian, bladder, or kidney cancers. Weighted Cox regression models estimated HRs and 95% confidence intervals (CI), adjusted for covariates. c-peptide was associated with higher risk of liver cancer [per SD HR: 1.80; 95% CI: 1.32–2.46]. HbA&lt;sub&gt;1c&lt;/sub&gt; was associated with higher risk of pancreatic cancer (per SD HR: 1.21; 95% CI: 1.05–1.40) and with some suggestion of higher risks for all-cancers-of-interest (per SD HR: 1.05; 95% CI: 0.99–1.11) and colorectal (per SD HR: 1.09; 95% CI: 0.98–1.20), ovarian (per SD HR: 1.18; 95% CI: 0.96–1.45) and bladder (per SD HR: 1.08; 95% CI: 0.96–1.21) cancers. Compared with no self-reported T2DM and HbA&lt;sub&gt;1c&lt;/sub&gt; &lt; 6.5% (reference group), self-reported T2DM and HbA&lt;sub&gt;1c&lt;/sub&gt; &lt; 6.5% (i.e., T2DM in good glycemic control) was not associated with risk of colorectal cancer, whereas it was associated with higher risks of all-cancers-of-interest combined (HR: 1.28; 95% CI: 1.01–1.62), especially for breast and endometrial cancers. Additional large, prospective studies ar

Other

Campbell PT, Newton CC, Jacobs EJ, McCullough ML, Wang Y, Rees-Punia E, Guinter MA, Murphy N, Koshiol J, Dehal AN, Rohan T, Strickler H, Petrick J, Gunter M, Zhang X, McGlynn KA, Pollak M, Patel AV, Gapstur SMet al., 2023, Appendix 1 from Prospective Associations of Hemoglobin A&lt;sub&gt;1c&lt;/sub&gt; and c-peptide with Risk of Diabetes-related Cancers in the Cancer Prevention Study-II Nutrition Cohort

<jats:p>&lt;p&gt;lab methods and qc&lt;/p&gt;</jats:p>

Other

Campbell PT, Newton CC, Jacobs EJ, McCullough ML, Wang Y, Rees-Punia E, Guinter MA, Murphy N, Koshiol J, Dehal AN, Rohan T, Strickler H, Petrick J, Gunter M, Zhang X, McGlynn KA, Pollak M, Patel AV, Gapstur SMet al., 2023, Appendix 1 from Prospective Associations of Hemoglobin A&lt;sub&gt;1c&lt;/sub&gt; and c-peptide with Risk of Diabetes-related Cancers in the Cancer Prevention Study-II Nutrition Cohort

<jats:p>&lt;p&gt;lab methods and qc&lt;/p&gt;</jats:p>

Other

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