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    Genetic risk impacts the association of menopausal hormone therapy with colorectal cancer risk
    Tian, Y ; Lin, Y ; Qu, C ; Arndt, V ; Baurley, JW ; Berndt, SI ; Bien, SA ; Bishop, DT ; Brenner, H ; Buchanan, DD ; Budiarto, A ; Campbell, PT ; Carreras-Torres, R ; Casey, G ; Chan, AT ; Chen, R ; Chen, X ; Conti, DV ; Diez-Obrero, V ; Dimou, N ; Drew, DA ; Figueiredo, JC ; Gallinger, S ; Giles, GG ; Gruber, SB ; Gunter, MJ ; Harlid, S ; Harrison, TA ; Hidaka, A ; Hoffmeister, M ; Huyghe, JR ; Jenkins, MA ; Jordahl, KM ; Joshi, AD ; Keku, TO ; Kawaguchi, E ; Kim, AE ; Kundaje, A ; Larsson, SC ; Marchand, LL ; Lewinger, JP ; Li, L ; Moreno, V ; Morrison, J ; Murphy, N ; Nan, H ; Nassir, R ; Newcomb, PA ; Obon-Santacana, M ; Ogino, S ; Ose, J ; Pardamean, B ; Pellatt, AJ ; Peoples, AR ; Platz, EA ; Potter, JD ; Prentice, RL ; Rennert, G ; Ruiz-Narvaez, EA ; Sakoda, LC ; Schoen, RE ; Shcherbina, A ; Stern, MC ; Su, Y-R ; Thibodeau, SN ; Thomas, DC ; Tsilidis, KK ; van Duijnhoven, FJB ; Van Guelpen, B ; Visvanathan, K ; White, E ; Wolk, A ; Woods, MO ; Wu, AH ; Peters, U ; Gauderman, WJ ; Hsu, L ; Chang-Claude, J (SPRINGERNATURE, 2024-06-01)
    BACKGROUND: Menopausal hormone therapy (MHT), a common treatment to relieve symptoms of menopause, is associated with a lower risk of colorectal cancer (CRC). To inform CRC risk prediction and MHT risk-benefit assessment, we aimed to evaluate the joint association of a polygenic risk score (PRS) for CRC and MHT on CRC risk. METHODS: We used data from 28,486 postmenopausal women (11,519 cases and 16,967 controls) of European descent. A PRS based on 141 CRC-associated genetic variants was modeled as a categorical variable in quartiles. Multiplicative interaction between PRS and MHT use was evaluated using logistic regression. Additive interaction was measured using the relative excess risk due to interaction (RERI). 30-year cumulative risks of CRC for 50-year-old women according to MHT use and PRS were calculated. RESULTS: The reduction in odds ratios by MHT use was larger in women within the highest quartile of PRS compared to that in women within the lowest quartile of PRS (p-value = 2.7 × 10-8). At the highest quartile of PRS, the 30-year CRC risk was statistically significantly lower for women taking any MHT than for women not taking any MHT, 3.7% (3.3%-4.0%) vs 6.1% (5.7%-6.5%) (difference 2.4%, P-value = 1.83 × 10-14); these differences were also statistically significant but smaller in magnitude in the lowest PRS quartile, 1.6% (1.4%-1.8%) vs 2.2% (1.9%-2.4%) (difference 0.6%, P-value = 1.01 × 10-3), indicating 4 times greater reduction in absolute risk associated with any MHT use in the highest compared to the lowest quartile of genetic CRC risk. CONCLUSIONS: MHT use has a greater impact on the reduction of CRC risk for women at higher genetic risk. These findings have implications for the development of risk prediction models for CRC and potentially for the consideration of genetic information in the risk-benefit assessment of MHT use.
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    Causation and familial confounding as explanations for the associations of polygenic risk scores with breast cancer: Evidence from innovative ICE FALCON and ICE CRISTAL analyses
    Li, S ; Dite, GS ; Macinnis, RJ ; Bui, M ; Nguyen, TL ; Esser, VFC ; Ye, Z ; Dowty, JG ; Makalic, E ; Sung, J ; Giles, GG ; Southey, MC ; Hopper, JL (WILEY, 2024-03-12)
    A polygenic risk score (PRS) combines the associations of multiple genetic variants that could be due to direct causal effects, indirect genetic effects, or other sources of familial confounding. We have developed new approaches to assess evidence for and against causation by using family data for pairs of relatives (Inference about Causation from Examination of FAmiliaL CONfounding [ICE FALCON]) or measures of family history (Inference about Causation from Examining Changes in Regression coefficients and Innovative STatistical AnaLyses [ICE CRISTAL]). Inference is made from the changes in regression coefficients of relatives' PRSs or PRS and family history before and after adjusting for each other. We applied these approaches to two breast cancer PRSs and multiple studies and found that (a) for breast cancer diagnosed at a young age, for example, <50 years, there was no evidence that the PRSs were causal, while (b) for breast cancer diagnosed at later ages, there was consistent evidence for causation explaining increasing amounts of the PRS-disease association. The genetic variants in the PRS might be in linkage disequilibrium with truly causal variants and not causal themselves. These PRSs cause minimal heritability of breast cancer at younger ages. There is also evidence for nongenetic factors shared by first-degree relatives that explain breast cancer familial aggregation. Familial associations are not necessarily due to genes, and genetic associations are not necessarily causal.
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    Breast and bowel cancers diagnosed in people 'too young to have cancer': A blueprint for research using family and twin studies
    Hopper, JL ; Li, S ; MacInnis, RJ ; Dowty, JG ; Nguyen, TL ; Bui, M ; Dite, GS ; Esser, VFC ; Ye, Z ; Makalic, E ; Schmidt, DF ; Goudey, B ; Alpen, K ; Kapuscinski, M ; Win, AK ; Dugue, P-A ; Milne, RL ; Jayasekara, H ; Brooks, JD ; Malta, S ; Calais-Ferreira, L ; Campbell, AC ; Young, JT ; Nguyen-Dumont, T ; Sung, J ; Giles, GG ; Buchanan, D ; Winship, I ; Terry, MB ; Southey, MC ; Jenkins, MA (WILEY, 2024-03-19)
    Young breast and bowel cancers (e.g., those diagnosed before age 40 or 50 years) have far greater morbidity and mortality in terms of years of life lost, and are increasing in incidence, but have been less studied. For breast and bowel cancers, the familial relative risks, and therefore the familial variances in age-specific log(incidence), are much greater at younger ages, but little of these familial variances has been explained. Studies of families and twins can address questions not easily answered by studies of unrelated individuals alone. We describe existing and emerging family and twin data that can provide special opportunities for discovery. We present designs and statistical analyses, including novel ideas such as the VALID (Variance in Age-specific Log Incidence Decomposition) model for causes of variation in risk, the DEPTH (DEPendency of association on the number of Top Hits) and other approaches to analyse genome-wide association study data, and the within-pair, ICE FALCON (Inference about Causation from Examining FAmiliaL CONfounding) and ICE CRISTAL (Inference about Causation from Examining Changes in Regression coefficients and Innovative STatistical AnaLysis) approaches to causation and familial confounding. Example applications to breast and colorectal cancer are presented. Motivated by the availability of the resources of the Breast and Colon Cancer Family Registries, we also present some ideas for future studies that could be applied to, and compared with, cancers diagnosed at older ages and address the challenges posed by young breast and bowel cancers.
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    Genetically proxied glucose-lowering drug target perturbation and risk of cancer: a Mendelian randomisation analysis.
    Yarmolinsky, J ; Bouras, E ; Constantinescu, A ; Burrows, K ; Bull, CJ ; Vincent, EE ; Martin, RM ; Dimopoulou, O ; Lewis, SJ ; Moreno, V ; Vujkovic, M ; Chang, K-M ; Voight, BF ; Tsao, PS ; Gunter, MJ ; Hampe, J ; Pellatt, AJ ; Pharoah, PDP ; Schoen, RE ; Gallinger, S ; Jenkins, MA ; Pai, RK ; PRACTICAL consortium, ; VA Million Veteran Program, ; Gill, D ; Tsilidis, KK (Springer Science and Business Media LLC, 2023-08)
    AIMS/HYPOTHESIS: Epidemiological studies have generated conflicting findings on the relationship between glucose-lowering medication use and cancer risk. Naturally occurring variation in genes encoding glucose-lowering drug targets can be used to investigate the effect of their pharmacological perturbation on cancer risk. METHODS: We developed genetic instruments for three glucose-lowering drug targets (peroxisome proliferator activated receptor γ [PPARG]; sulfonylurea receptor 1 [ATP binding cassette subfamily C member 8 (ABCC8)]; glucagon-like peptide 1 receptor [GLP1R]) using summary genetic association data from a genome-wide association study of type 2 diabetes in 148,726 cases and 965,732 controls in the Million Veteran Program. Genetic instruments were constructed using cis-acting genome-wide significant (p<5×10-8) SNPs permitted to be in weak linkage disequilibrium (r2<0.20). Summary genetic association estimates for these SNPs were obtained from genome-wide association study (GWAS) consortia for the following cancers: breast (122,977 cases, 105,974 controls); colorectal (58,221 cases, 67,694 controls); prostate (79,148 cases, 61,106 controls); and overall (i.e. site-combined) cancer (27,483 cases, 372,016 controls). Inverse-variance weighted random-effects models adjusting for linkage disequilibrium were employed to estimate causal associations between genetically proxied drug target perturbation and cancer risk. Co-localisation analysis was employed to examine robustness of findings to violations of Mendelian randomisation (MR) assumptions. A Bonferroni correction was employed as a heuristic to define associations from MR analyses as 'strong' and 'weak' evidence. RESULTS: In MR analysis, genetically proxied PPARG perturbation was weakly associated with higher risk of prostate cancer (for PPARG perturbation equivalent to a 1 unit decrease in inverse rank normal transformed HbA1c: OR 1.75 [95% CI 1.07, 2.85], p=0.02). In histological subtype-stratified analyses, genetically proxied PPARG perturbation was weakly associated with lower risk of oestrogen receptor-positive breast cancer (OR 0.57 [95% CI 0.38, 0.85], p=6.45×10-3). In co-localisation analysis, however, there was little evidence of shared causal variants for type 2 diabetes liability and cancer endpoints in the PPARG locus, although these analyses were likely underpowered. There was little evidence to support associations between genetically proxied PPARG perturbation and colorectal or overall cancer risk or between genetically proxied ABCC8 or GLP1R perturbation with risk across cancer endpoints. CONCLUSIONS/INTERPRETATION: Our drug target MR analyses did not find consistent evidence to support an association of genetically proxied PPARG, ABCC8 or GLP1R perturbation with breast, colorectal, prostate or overall cancer risk. Further evaluation of these drug targets using alternative molecular epidemiological approaches may help to further corroborate the findings presented in this analysis. DATA AVAILABILITY: Summary genetic association data for select cancer endpoints were obtained from the public domain: breast cancer ( https://bcac.ccge.medschl.cam.ac.uk/bcacdata/ ); and overall prostate cancer ( http://practical.icr.ac.uk/blog/ ). Summary genetic association data for colorectal cancer can be accessed by contacting GECCO (kafdem at fredhutch.org). Summary genetic association data on advanced prostate cancer can be accessed by contacting PRACTICAL (practical at icr.ac.uk). Summary genetic association data on type 2 diabetes from Vujkovic et al (Nat Genet, 2020) can be accessed through dbGAP under accession number phs001672.v3.p1 (pha004945.1 refers to the European-specific summary statistics). UK Biobank data can be accessed by registering with UK Biobank and completing the registration form in the Access Management System (AMS) ( https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access ).
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    A genome-wide gene-environment interaction study of breast cancer risk for women of European ancestry
    Middha, PK ; Wang, X ; Behrens, S ; Bolla, MK ; Wang, Q ; Dennis, J ; Michailidou, K ; Ahearn, TU ; Andrulis, IL ; Anton-Culver, H ; Arndt, V ; Aronson, KJ ; Auer, PL ; Augustinsson, A ; Baert, T ; Freeman, LEB ; Becher, H ; Beckmann, MW ; Benitez, J ; Bojesen, SE ; Brauch, H ; Brenner, H ; Brooks-Wilson, A ; Campa, D ; Canzian, F ; Carracedo, A ; Castelao, JE ; Chanock, SJ ; Chenevix-Trench, G ; Cordina-Duverger, E ; Couch, FJ ; Cox, A ; Cross, SS ; Czene, K ; Dossus, L ; Dugue, P-A ; Eliassen, AH ; Eriksson, M ; Evans, DG ; Fasching, PA ; Figueroa, J ; Fletcher, O ; Flyger, H ; Gabrielson, M ; Gago-Dominguez, M ; Giles, GG ; Gonzalez-Neira, A ; Grassmann, F ; Grundy, A ; Guenel, P ; Haiman, CA ; Hakansson, N ; Hall, P ; Hamann, U ; Hankinson, SE ; Harkness, EF ; Holleczek, B ; Hoppe, R ; Hopper, JL ; Houlston, RS ; Howell, A ; Hunter, DJ ; Ingvar, C ; Isaksson, K ; Jernstroem, H ; John, EM ; Jones, ME ; Kaaks, R ; Keeman, R ; Kitahara, CM ; Ko, Y-D ; Koutros, S ; Kurian, AW ; Lacey, JV ; Lambrechts, D ; Larson, NL ; Larsson, S ; Le Marchand, L ; Lejbkowicz, F ; Li, S ; Linet, M ; Lissowska, J ; Martinez, ME ; Maurer, T ; Mulligan, AM ; Mulot, C ; Murphy, RA ; Newman, WG ; Nielsen, SF ; Nordestgaard, BG ; Norman, A ; O'Brien, KM ; Olson, JE ; Patel, AV ; Prentice, R ; Rees-Punia, E ; Rennert, G ; Rhenius, V ; Ruddy, KJ ; Sandler, DP ; Scott, CG ; Shah, MT ; Shu, X-O ; Smeets, A ; Southey, MC ; Stone, J ; Tamimi, RM ; Taylor, JA ; Teras, LR ; Tomczyk, K ; Troester, MA ; Truong, T ; Vachon, CM ; Wang, SS ; Weinberg, CR ; Wildiers, H ; Willett, W ; Winham, SJ ; Wolk, A ; Yang, X ; Zamora, MP ; Zheng, W ; Ziogas, A ; Dunning, AM ; Pharoah, PDP ; Garcia-Closas, M ; Schmidt, MK ; Kraft, P ; Milne, RL ; Lindstroem, S ; Easton, DF ; Chang-Claude, J (BMC, 2023-08-09)
    BACKGROUND: Genome-wide studies of gene-environment interactions (G×E) may identify variants associated with disease risk in conjunction with lifestyle/environmental exposures. We conducted a genome-wide G×E analysis of ~ 7.6 million common variants and seven lifestyle/environmental risk factors for breast cancer risk overall and for estrogen receptor positive (ER +) breast cancer. METHODS: Analyses were conducted using 72,285 breast cancer cases and 80,354 controls of European ancestry from the Breast Cancer Association Consortium. Gene-environment interactions were evaluated using standard unconditional logistic regression models and likelihood ratio tests for breast cancer risk overall and for ER + breast cancer. Bayesian False Discovery Probability was employed to assess the noteworthiness of each SNP-risk factor pairs. RESULTS: Assuming a 1 × 10-5 prior probability of a true association for each SNP-risk factor pairs and a Bayesian False Discovery Probability < 15%, we identified two independent SNP-risk factor pairs: rs80018847(9p13)-LINGO2 and adult height in association with overall breast cancer risk (ORint = 0.94, 95% CI 0.92-0.96), and rs4770552(13q12)-SPATA13 and age at menarche for ER + breast cancer risk (ORint = 0.91, 95% CI 0.88-0.94). CONCLUSIONS: Overall, the contribution of G×E interactions to the heritability of breast cancer is very small. At the population level, multiplicative G×E interactions do not make an important contribution to risk prediction in breast cancer.
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    Body Size, Diet Quality, and Epigenetic Aging: Cross-Sectional and Longitudinal Analyses
    Li, DL ; Hodge, AM ; Cribb, L ; Southey, MC ; Giles, GG ; Milne, RL ; Dugue, P-A ; Duque, G (OXFORD UNIV PRESS INC, 2024-04-01)
    Epigenetic age is an emerging marker of health that is highly predictive of disease and mortality risk. There is a lack of evidence on whether lifestyle changes are associated with changes in epigenetic aging. We used data from 1 041 participants in the Melbourne Collaborative Cohort Study with blood DNA methylation measures at baseline (1990-1994, mean age: 57.4 years) and follow-up (2003-2007, mean age: 68.8 years). The Alternative Healthy Eating Index-2010 (AHEI-2010), the Mediterranean Dietary Score, and the Dietary Inflammatory Index were used as measures of diet quality, and weight, waist circumference, and waist-to-hip ratio as measures of body size. Five age-adjusted epigenetic aging measures were considered: GrimAge, PhenoAge, PCGrimAge, PCPhenoAge, and DunedinPACE. Multivariable linear regression models including restricted cubic splines were used to assess the cross-sectional and longitudinal associations of body size and diet quality with epigenetic aging. Associations between weight and epigenetic aging cross-sectionally at both time points were positive and appeared greater for DunedinPACE (per SD: β ~0.24) than for GrimAge and PhenoAge (β ~0.10). The longitudinal associations with weight change were markedly nonlinear (U-shaped) with stable weight being associated with the lowest epigenetic aging at follow-up, except for DunedinPACE, for which only weight gain showed a positive association. We found negative, linear associations for AHEI-2010 both cross-sectionally and longitudinally. Other adiposity measures and dietary scores showed similar results. In middle-aged to older adults, declining diet quality and weight gain may increase epigenetic age, while the association for weight loss may require further investigation. Our study sheds light on the potential of weight management and dietary improvement in slowing aging processes.
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    Genome-wide interaction analysis of folate for colorectal cancer risk
    Bouras, E ; Kim, AE ; Lin, Y ; Morrison, J ; Du, M ; Albanes, D ; Barry, EL ; Baurley, JW ; Berndt, SI ; Bien, SA ; Bishop, TD ; Brenner, H ; Budiarto, A ; Burnett-Hartman, A ; Campbell, PT ; Carreras-Torres, R ; Casey, G ; Cenggoro, TW ; Chan, AT ; Chang-Claude, J ; Conti, DV ; Cotterchio, M ; Devall, M ; Diez-Obrero, V ; Dimou, N ; Drew, DA ; Figueiredo, JC ; Giles, GG ; Gruber, SB ; Gunter, MJ ; Harrison, TA ; Hidaka, A ; Hoffmeister, M ; Huyghe, JR ; Joshi, AD ; Kawaguchi, ES ; Keku, TO ; Kundaje, A ; Le Marchand, L ; Lewinger, JP ; Li, L ; Lynch, BM ; Mahesworo, B ; Mannisto, S ; Moreno, V ; Murphy, N ; Newcomb, PA ; Obon-Santacana, M ; Ose, J ; Palmer, JR ; Papadimitriou, N ; Pardamean, B ; Pellatt, AJ ; Peoples, AR ; Platz, EA ; Potter, JD ; Qi, L ; Qu, C ; Rennert, G ; Ruiz-Narvaez, E ; Sakoda, LC ; Schmit, SL ; Shcherbina, A ; Stern, MC ; Su, Y-R ; Tangen, CM ; Thomas, DC ; Tian, Y ; Um, CY ; van Duijnhoven, FJB ; Van Guelpen, B ; Visvanathan, K ; Wang, J ; White, E ; Wolk, A ; Woods, MO ; Ulrich, CM ; Hsu, L ; Gauderman, WJ ; Peters, U ; Tsilidis, KK (Elsevier, 2023-11)
    BACKGROUND: Epidemiological and experimental evidence suggests that higher folate intake is associated with decreased colorectal cancer (CRC) risk; however, the mechanisms underlying this relationship are not fully understood. Genetic variation that may have a direct or indirect impact on folate metabolism can provide insights into folate's role in CRC. OBJECTIVES: Our aim was to perform a genome-wide interaction analysis to identify genetic variants that may modify the association of folate on CRC risk. METHODS: We applied traditional case-control logistic regression, joint 3-degree of freedom, and a 2-step weighted hypothesis approach to test the interactions of common variants (allele frequency >1%) across the genome and dietary folate, folic acid supplement use, and total folate in relation to risk of CRC in 30,550 cases and 42,336 controls from 51 studies from 3 genetic consortia (CCFR, CORECT, GECCO). RESULTS: Inverse associations of dietary, total folate, and folic acid supplement with CRC were found (odds ratio [OR]: 0.93; 95% confidence interval [CI]: 0.90, 0.96; and 0.91; 95% CI: 0.89, 0.94 per quartile higher intake, and 0.82 (95% CI: 0.78, 0.88) for users compared with nonusers, respectively). Interactions (P-interaction < 5×10-8) of folic acid supplement and variants in the 3p25.2 locus (in the region of Synapsin II [SYN2]/tissue inhibitor of metalloproteinase 4 [TIMP4]) were found using traditional interaction analysis, with variant rs150924902 (located upstream to SYN2) showing the strongest interaction. In stratified analyses by rs150924902 genotypes, folate supplementation was associated with decreased CRC risk among those carrying the TT genotype (OR: 0.82; 95% CI: 0.79, 0.86) but increased CRC risk among those carrying the TA genotype (OR: 1.63; 95% CI: 1.29, 2.05), suggesting a qualitative interaction (P-interaction = 1.4×10-8). No interactions were observed for dietary and total folate. CONCLUSIONS: Variation in 3p25.2 locus may modify the association of folate supplement with CRC risk. Experimental studies and studies incorporating other relevant omics data are warranted to validate this finding.
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    A Population-Based Family Case-Control Study of Sun Exposure and Follicular Lymphoma Risk
    Odutola, MK ; van Leeuwen, MT ; Bruinsma, F ; Turner, J ; Hertzberg, M ; Seymour, JF ; Prince, HM ; Trotman, J ; Verner, E ; Roncolato, F ; Opat, S ; Lindeman, R ; Tiley, C ; Milliken, ST ; Underhill, CR ; Benke, G ; Giles, GG ; Vajdic, CM (AMER ASSOC CANCER RESEARCH, 2024-01-09)
    BACKGROUND: Epidemiologic evidence suggests an inverse association between sun exposure and follicular lymphoma risk. METHODS: We conducted an Australian population-based family case-control study based on 666 cases and 459 controls (288 related, 171 unrelated). Participants completed a lifetime residence and work calendar and recalled outdoor hours on weekdays, weekends, and holidays in the warmer and cooler months at ages 10, 20, 30, and 40 years, and clothing types worn in the warmer months. We used a group-based trajectory modeling approach to identify outdoor hour trajectories over time and examined associations with follicular lymphoma risk using logistic regression. RESULTS: We observed an inverse association between follicular lymphoma risk and several measures of high lifetime sun exposure, particularly intermittent exposure (weekends, holidays). Associations included reduced risk with increasing time outdoors on holidays in the warmer months [highest category OR = 0.56; 95% confidence interval (CI), 0.42-0.76; Ptrend < 0.01], high outdoor hours on weekends in the warmer months (highest category OR = 0.71; 95% CI, 0.52-0.96), and increasing time outdoors in the warmer and cooler months combined (highest category OR = 0.66; 95% CI, 0.50-0.91; Ptrend 0.01). Risk was reduced for high outdoor hour maintainers in the warmer months across the decade years (OR = 0.71; 95% CI, 0.53-0.96). CONCLUSIONS: High total and intermittent sun exposure, particularly in the warmer months, may be protective against the development of follicular lymphoma. IMPACT: Although sun exposure is not recommended as a cancer control policy, confirming this association may provide insights regarding the future control of this intractable malignancy.
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    Estimated dietary intake of polyphenols from cereal foods and associated lifestyle and demographic factors in the Melbourne Collaborative Cohort Study.
    Vingrys, K ; Mathai, ML ; Apostolopoulos, V ; Bassett, JK ; de Courten, M ; Stojanovska, L ; Millar, L ; Giles, GG ; Milne, RL ; Hodge, AM ; McAinch, AJ (Nature Portfolio, 2023-05-26)
    Cereal foods are consumed globally and are important sources of polyphenols with potential health benefits, yet dietary intakes are unclear. We aimed to calculate the dietary intakes of polyphenols from cereal foods in the Melbourne Collaborative Cohort Study (MCCS), and describe intakes by demographic and lifestyle factors. We estimated intakes of alkylresorcinols, lignans and phenolic acids in n = 39,892 eligible MCCS participants, using baseline dietary data (1990-1994) from a 121-item FFQ containing 17 cereal foods, matched to a polyphenol database developed from published literature and Phenol-Explorer Database. Intakes were estimated within groups according to lifestyle and demographic factors. The median (25th-75th percentile) intake of total polyphenols from cereal foods was 86.9 mg/day (51.4-155.8). The most consumed compounds were phenolic acids, with a median intake of 67.1 mg (39.5-118.8), followed by alkylresorcinols of 19.7 mg (10.8-34.6). Lignans made the smallest contribution of 0.50 mg (0.13-0.87). Higher polyphenol intakes were associated with higher relative socio-economic advantage and prudent lifestyles, including lower body mass index (BMI), non-smoking and higher physical activity scores. The findings based on polyphenol data specifically matched to the FFQ provide new information on intakes of cereal polyphenols, and how they might vary according to lifestyle and demographic factors.
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    Association between circulating inflammatory markers and adult cancer risk: a Mendelian randomization analysis.
    Yarmolinsky, J ; Robinson, JW ; Mariosa, D ; Karhunen, V ; Huang, J ; Dimou, N ; Murphy, N ; Burrows, K ; Bouras, E ; Smith-Byrne, K ; Lewis, SJ ; Galesloot, TE ; Kiemeney, LA ; Vermeulen, S ; Martin, P ; Albanes, D ; Hou, L ; Newcomb, PA ; White, E ; Wolk, A ; Wu, AH ; Le Marchand, L ; Phipps, AI ; Buchanan, DD ; International Lung Cancer Consortium, ; PRACTICAL Consortium, ; Zhao, SS ; Gill, D ; Chanock, SJ ; Purdue, MP ; Davey Smith, G ; Brennan, P ; Herzig, K-H ; Järvelin, M-R ; Amos, CI ; Hung, RJ ; Dehghan, A ; Johansson, M ; Gunter, MJ ; Tsilidis, KK ; Martin, RM (Elsevier BV, 2024-02)
    BACKGROUND: Tumour-promoting inflammation is a "hallmark" of cancer and conventional epidemiological studies have reported links between various inflammatory markers and cancer risk. The causal nature of these relationships and, thus, the suitability of these markers as intervention targets for cancer prevention is unclear. METHODS: We meta-analysed 6 genome-wide association studies of circulating inflammatory markers comprising 59,969 participants of European ancestry. We then used combined cis-Mendelian randomization and colocalisation analysis to evaluate the causal role of 66 circulating inflammatory markers in risk of 30 adult cancers in 338,294 cancer cases and up to 1,238,345 controls. Genetic instruments for inflammatory markers were constructed using genome-wide significant (P < 5.0 × 10-8) cis-acting SNPs (i.e., in or ±250 kb from the gene encoding the relevant protein) in weak linkage disequilibrium (LD, r2 < 0.10). Effect estimates were generated using inverse-variance weighted random-effects models and standard errors were inflated to account for weak LD between variants with reference to the 1000 Genomes Phase 3 CEU panel. A false discovery rate (FDR)-corrected P-value ("q-value") <0.05 was used as a threshold to define "strong evidence" to support associations and 0.05 ≤ q-value < 0.20 to define "suggestive evidence". A colocalisation posterior probability (PPH4) >70% was employed to indicate support for shared causal variants across inflammatory markers and cancer outcomes. Findings were replicated in the FinnGen study and then pooled using meta-analysis. FINDINGS: We found strong evidence to support an association of genetically-proxied circulating pro-adrenomedullin concentrations with increased breast cancer risk (OR: 1.19, 95% CI: 1.10-1.29, q-value = 0.033, PPH4 = 84.3%) and suggestive evidence to support associations of interleukin-23 receptor concentrations with increased pancreatic cancer risk (OR: 1.42, 95% CI: 1.20-1.69, q-value = 0.055, PPH4 = 73.9%), prothrombin concentrations with decreased basal cell carcinoma risk (OR: 0.66, 95% CI: 0.53-0.81, q-value = 0.067, PPH4 = 81.8%), and interleukin-1 receptor-like 1 concentrations with decreased triple-negative breast cancer risk (OR: 0.92, 95% CI: 0.88-0.97, q-value = 0.15, PPH4 = 85.6%). These findings were replicated in pooled analyses with the FinnGen study. Though suggestive evidence was found to support an association of macrophage migration inhibitory factor concentrations with increased bladder cancer risk (OR: 2.46, 95% CI: 1.48-4.10, q-value = 0.072, PPH4 = 76.1%), this finding was not replicated when pooled with the FinnGen study. For 22 of 30 cancer outcomes examined, there was little evidence (q-value ≥0.20) that any of the 66 circulating inflammatory markers examined were associated with cancer risk. INTERPRETATION: Our comprehensive joint Mendelian randomization and colocalisation analysis of the role of circulating inflammatory markers in cancer risk identified potential roles for 4 circulating inflammatory markers in risk of 4 site-specific cancers. Contrary to reports from some prior conventional epidemiological studies, we found little evidence of association of circulating inflammatory markers with the majority of site-specific cancers evaluated. FUNDING: Cancer Research UK (C68933/A28534, C18281/A29019, PPRCPJT∖100005), World Cancer Research Fund (IIG_FULL_2020_022), National Institute for Health Research (NIHR202411, BRC-1215-20011), Medical Research Council (MC_UU_00011/1, MC_UU_00011/3, MC_UU_00011/6, and MC_UU_00011/4), Academy of Finland Project 326291, European Union's Horizon 2020 grant agreement no. 848158 (EarlyCause), French National Cancer Institute (INCa SHSESP20, 2020-076), Versus Arthritis (21173, 21754, 21755), National Institutes of Health (U19 CA203654), National Cancer Institute (U19CA203654).