Imperial College London

DrKostasTsilidis

Faculty of MedicineSchool of Public Health

Reader in Cancer Epidemiology and Prevention
 
 
 
//

Contact

 

+44 (0)20 7594 2623k.tsilidis

 
 
//

Location

 

Praed StreetSt Mary's Campus

//

Summary

 

Publications

Publication Type
Year
to

359 results found

Papadimitriou N, Qu C, Harrison TA, Bever AM, Martin RM, Tsilidis KK, Newcomb PA, Thibodeau SN, Newton CC, Um CY, Obón-Santacana M, Moreno V, Brenner H, Mandic M, Chang-Claude J, Hoffmeister M, Pellatt AJ, Schoen RE, Harlid S, Ogino S, Ugai T, Buchanan DD, Lynch BM, Gruber SB, Cao Y, Hsu L, Huyghe JR, Lin Y, Steinfelder RS, Sun W, Van Guelpen B, Zaidi SH, Toland AE, Berndt SI, Huang W-Y, Aglago EK, Drew DA, French AJ, Georgeson P, Giannakis M, Hullar M, Nowak JA, Thomas CE, Le Marchand L, Cheng I, Gallinger S, Jenkins MA, Gunter MJ, Campbell PT, Peters U, Song M, Phipps AI, Murphy Net al., 2024, Body size and risk of colorectal cancer molecular defined subtypes and pathways: Mendelian randomization analyses., EBioMedicine, Vol: 101

BACKGROUND: Obesity has been positively associated with most molecular subtypes of colorectal cancer (CRC); however, the magnitude and the causality of these associations is uncertain. METHODS: We used Mendelian randomization (MR) to examine potential causal relationships between body size traits (body mass index [BMI], waist circumference, and body fat percentage) with risks of Jass classification types and individual subtypes of CRC (microsatellite instability [MSI] status, CpG island methylator phenotype [CIMP] status, BRAF and KRAS mutations). Summary data on tumour markers were obtained from two genetic consortia (CCFR, GECCO). FINDINGS: A 1-standard deviation (SD:5.1 kg/m2) increment in BMI levels was found to increase risks of Jass type 1MSI-high,CIMP-high,BRAF-mutated,KRAS-wildtype (odds ratio [OR]: 2.14, 95% confidence interval [CI]: 1.46, 3.13; p-value = 9 × 10-5) and Jass type 2non-MSI-high,CIMP-high,BRAF-mutated,KRAS-wildtype CRC (OR: 2.20, 95% CI: 1.26, 3.86; p-value = 0.005). The magnitude of these associations was stronger compared with Jass type 4non-MSI-high,CIMP-low/negative,BRAF-wildtype,KRAS-wildtype CRC (p-differences: 0.03 and 0.04, respectively). A 1-SD (SD:13.4 cm) increment in waist circumference increased risk of Jass type 3non-MSI-high,CIMP-low/negative,BRAF-wildtype,KRAS-mutated (OR 1.73, 95% CI: 1.34, 2.25; p-value = 9 × 10-5) that was stronger compared with Jass type 4 CRC (p-difference: 0.03). A higher body fat percentage (SD:8.5%) increased risk of Jass type 1 CRC (OR: 2.59, 95% CI: 1.49, 4.48; p-value = 0.001), which was greater than Jass type 4 CRC (p-difference: 0.03). INTERPRETATION: Body size was more strongly linked to the serrated (Jass types 1 and 2) and alternate (Jass type 3) pathways of colorectal carcinogenesis in comparison to the traditional pathway (Jass type 4). FUNDING: Cancer Research UK, National Institute for Health Research, Med

Journal article

Christakoudi S, Asimakopoulos A-G, Riboli E, Tsilidis KKet al., 2024, Links between the genetic determinants of morning plasma cortisol and body shape: a two-sample Mendelian randomisation study, Scientific Reports, ISSN: 2045-2322

High cortisol production in Cushing’s syndrome leads to fat centralisation. The influence of modest cortisol variations on body shape, however, is less clear. We examined potentially causal associations between morning plasma cortisol and body shape and obesity with inverse-variance weighted random-effects models in a two-sample Mendelian randomisation analysis. We used publicly available summary statistics from the CORtisol NETwork (CORNET) consortium, UK Biobank, and the Genetic Investigation of Anthropometric Traits (GIANT) consortium. Only in women, morning plasma cortisol (proxied by ten genetic polymorphisms) was associated positively with waist size reflected in waist-to-hip index (WHI, 0.035 standard deviation (SD) units change per one SD cortisol increase; 95% confidence interval (0.002−0.067); p=0.036) and “a body shape index” (ABSI; 0.039 (0.006−0.071); p=0.021). There was no evidence for associations with hip index (HI) or body mass index (BMI). Among individual polymorphisms, rs7450600 stood out (chromosome 6; Long Intergenic Non-Protein-Coding RNA 473 gene, LINC00473). Morning plasma cortisol proxied by rs7450600 was associated positively with WHI and inversely with HI and BMI in women and men. Our findings support a causal association of higher morning plasma cortisol with larger waist size in women and highlight LINC00473 as a genetic link between morning plasma cortisol and body shape.

Journal article

Rontogianni MO, Bouras E, Aglago EK, Freisling H, Murphy N, Cotterchio M, Hampe J, Lindblom A, Pai RK, Pharoah PDP, Phipps AI, van Duijnhoven FJB, Visvanathan K, van Guelpen B, Li CI, Brenner H, Pellatt AJ, Ogino S, Gunter MJ, Peters U, Christakoudi S, Tsilidis KKet al., 2024, Allometric versus traditional body-shape indices and risk of colorectal cancer: a Mendelian randomization analysis., Int J Obes (Lond)

BACKGROUND: Traditional body-shape indices such as Waist Circumference (WC), Hip Circumference (HC), and Waist-to-Hip Ratio (WHR) are associated with colorectal cancer (CRC) risk, but are correlated with Body Mass Index (BMI), and adjustment for BMI introduces a strong correlation with height. Thus, new allometric indices have been developed, namely A Body Shape Index (ABSI), Hip Index (HI), and Waist-to-Hip Index (WHI), which are uncorrelated with weight and height; these have also been associated with CRC risk in observational studies, but information from Mendelian randomization (MR) studies is missing. METHODS: We used two-sample MR to examine potential causal cancer site- and sex-specific associations of the genetically-predicted allometric body-shape indices with CRC risk, and compared them with BMI-adjusted traditional body-shape indices, and BMI. Data were obtained from UK Biobank and the GIANT consortium, and from GECCO, CORECT and CCFR consortia. RESULTS: WHI was positively associated with CRC in men (OR per SD: 1.20, 95% CI: 1.03-1.39) and in women (1.15, 1.06-1.24), and similarly for colon and rectal cancer. ABSI was positively associated with colon and rectal cancer in men (1.27, 1.03-1.57; and 1.40, 1.10-1.77, respectively), and with colon cancer in women (1.20, 1.07-1.35). There was little evidence for association between HI and colon or rectal cancer. The BMI-adjusted WHR and HC showed similar associations to WHI and HI, whereas WC showed similar associations to ABSI only in women. CONCLUSIONS: This large MR study provides strong evidence for a potential causal positive association of the allometric indices ABSI and WHI with CRC in both sexes, thus establishing the association between abdominal fat and CRC without the limitations of the traditional waist size indices and independently of BMI. Among the BMI-adjusted traditional indices, WHR and HC provided equivalent associations with WHI and HI, while differences were observed between WC and ABSI.

Journal article

Georgeson P, Steinfelder RS, Harrison TA, Pope BJ, Zaidi SH, Qu C, Lin Y, Joo JE, Mahmood K, Clendenning M, Walker R, Aglago EK, Berndt SI, Brenner H, Campbell PT, Cao Y, Chan AT, Chang-Claude J, Dimou N, Doheny KF, Drew DA, Figueiredo JC, French AJ, Gallinger S, Giannakis M, Giles GG, Goode EL, Gruber SB, Gsur A, Gunter MJ, Harlid S, Hoffmeister M, Hsu L, Huang W-Y, Huyghe JR, Manson JE, Moreno V, Murphy N, Nassir R, Newton CC, Nowak JA, Obón-Santacana M, Ogino S, Pai RK, Papadimitrou N, Potter JD, Schoen RE, Song M, Sun W, Toland AE, Trinh QM, Tsilidis K, Ugai T, Um CY, Macrae FA, Rosty C, Hudson TJ, Winship IM, Phipps AI, Jenkins MA, Peters U, Buchanan DDet al., 2024, Genotoxic colibactin mutational signature in colorectal cancer is associated with clinicopathological features, specific genomic alterations and better survival., medRxiv

BACKGROUND AND AIMS: The microbiome has long been suspected of a role in colorectal cancer (CRC) tumorigenesis. The mutational signature SBS88 mechanistically links CRC development with the strain of Escherichia coli harboring the pks island that produces the genotoxin colibactin, but the genomic, pathological and survival characteristics associated with SBS88-positive tumors are unknown. METHODS: SBS88-positive CRCs were identified from targeted sequencing data from 5,292 CRCs from 17 studies and tested for their association with clinico-pathological features, oncogenic pathways, genomic characteristics and survival. RESULTS: In total, 7.5% (398/5,292) of the CRCs were SBS88-positive, of which 98.7% (392/398) were microsatellite stable/microsatellite instability low (MSS/MSI-L), compared with 80% (3916/4894) of SBS88 negative tumors (p=1.5x10-28). Analysis of MSS/MSI-L CRCs demonstrated that SBS88 positive CRCs were associated with the distal colon (OR=1.84, 95% CI=1.40-2.42, p=1x10-5) and rectum (OR=1.90, 95% CI=1.44-2.51, p=6x10-6) tumor sites compared with the proximal colon. The top seven recurrent somatic mutations associated with SBS88-positive CRCs demonstrated mutational contexts associated with colibactin-induced DNA damage, the strongest of which was the APC:c.835-8A>G mutation (OR=65.5, 95%CI=39.0-110.0, p=3x10-80). Large copy number alterations (CNAs) including CNA loss on 14q and gains on 13q, 16q and 20p were significantly enriched in SBS88-positive CRCs. SBS88-positive CRCs were associated with better CRC-specific survival (p=0.007; hazard ratio of 0.69, 95% CI=0.52-0.90) when stratified by age, sex, study, and by stage. CONCLUSION: SBS88-positivity, a biomarker of colibactin-induced DNA damage, can identify a novel subtype of CRC characterized by recurrent somatic mutations, copy number alterations and better survival. These findings provide new insights for treatment and prevention strategies for this subtype of CRC.

Journal article

Christakoudi S, Tsilidis K, Evangelou E, Riboli Eet al., 2024, Interactions of obesity, body shape, diabetes, and sex steroids with respect to prostate cancer risk in the UK Biobank cohort, Cancer Medicine, ISSN: 2045-7634

Background:Obesity and diabetes are associated inversely with low-grade prostate cancer risk and affect steroid hormone synthesis but whether they modify each other’s impact on prostate cancer risk remains unknown.Methods:We examined the independent associations of diabetes, body mass index (BMI), “a body shape index” (ABSI), hip index (HI), circulating testosterone, sex hormone binding globulin (SHBG) (per one standard deviation increase), and oestradiol≥175 pmol/L with total prostate cancer risk using multivariable Cox proportional hazards models for UK Biobank men. We evaluated multiplicative interactions (pMI) and additive interactions (relative excess risk from interaction (pRERI), attributable proportion (pAR), synergy index (pSI)) with obese (BMI≥30 kg/m2) and diabetes.Results:During a mean follow-up of 10.3 years, 9417 incident prostate cancers were diagnosed in 195,813 men. Diabetes and BMI were associated more strongly inversely with prostate cancer risk when occurring together (pMI=0.0003, pRERI=0.032, pAP=0.020, pSI=0.002). ABSI was associated positively in obese men (HR=1.081; 95%CI=1.030−1.135) and men with diabetes (HR=1.114; 95%CI=1.021−1.216). The inverse associations with obesity and diabetes were attenuated for high-ABSI≥79.8 (pMI=0.022, pRERI=0.008, pAP=0.005, pSI<0.0001 obesity; pMI=0.017, pRERI=0.047, pAP=0.025, pSI=0.0005 diabetes). HI was associated inversely in men overall (HR=0.967; 95%CI=0.947−0.988). Free testosterone (FT) was associated most strongly positively in normal weight men (HR=1.098; 95%CI=1.045−1.153) and men with diabetes (HR=1.189; 95%CI=1.081−1.308). Oestradiol was associated inversely in obese men (HR=0.805; 95%CI=0.682−0.951). The inverse association with obesity was stronger for high-FT≥243 pmol/L (pRERI=0.040, pAP=0.031, pSI=0.002) and high-oestradiol (pRERI=0.030, pAP=0.012, pSI<0.0001). The inverse association with diabetes was attenuated for high-

Journal article

Tsilidis K, Psyhogiou M, Aretouli E, Tsilidis Ket al., 2024, Sleep quality and cognitive abilities in the greek cohort of epirus health study, Nature and Science of Sleep, Vol: 16, Pages: 33-42, ISSN: 1179-1608

Purpose: Sleep is essential to all human body functions as well as brain functions. Inadequate sleep quantity and poor sleep quality have been shown to directly affect cognitive functioning and especially memory. The primary aim of the present study was to investigate the association of sleep quality with cognitive abilities cross-sectionally in a middle-aged Greek population and secondarily to examine this association prospectively in a smaller group of these participants.Patients and Methods: A total of 2112 healthy adults aged 25– 70 years (mean: 46.7± 11.5) from the Epirus Health Study cohort were included in the analysis and 312 of them participated in secondary prospective analysis. Sleep quality was measured by the Pittsburgh Sleep Quality Index (PSQI) scale and cognition was assessed in primary cross-sectional analyses with three neuropsychological tests, namely the Verbal Fluency test, the Logical Memory test and the Trail Making test, and in secondary prospective analyses with online versions of Posner cueing task, an emotional recognition task, the Corsi block-tapping task and the Stroop task. Statistical analysis was performed using multivariable linear regression models adjusted for age, sex, education, body mass index and alcohol consumption.Results: Attention/processing speed was the only cognitive domain associated cross-sectionally with PSQI score. Specifically, participants with better self-reported sleep quality performed faster on the Trail Making Test - Part A (β= 0.272 seconds, 95% CI 0.052, 0.493).Conclusion: Further studies are needed to clarify the association of sleep quality with cognition, especially in middle-aged people that are still in productive working years.

Journal article

Emerging Risk Factors CollaborationEPIC-CVDVitamin D Studies Collaboration, 2024, Estimating dose-response relationships for vitamin D with coronary heart disease, stroke, and all-cause mortality: observational and Mendelian randomisation analyses, The Lancet Diabetes & Endocrinology, Vol: 12, Pages: e2-e11, ISSN: 2213-8587

BackgroundRandomised trials of vitamin D supplementation for cardiovascular disease and all-cause mortality have generally reported null findings. However, generalisability of results to individuals with low vitamin D status is unclear. We aimed to characterise dose-response relationships between 25-hydroxyvitamin D (25[OH]D) concentrations and risk of coronary heart disease, stroke, and all-cause mortality in observational and Mendelian randomisation frameworks.MethodsObservational analyses were undertaken using data from 33 prospective studies comprising 500 962 individuals with no known history of coronary heart disease or stroke at baseline. Mendelian randomisation analyses were performed in four population-based cohort studies (UK Biobank, EPIC-CVD, and two Copenhagen population-based studies) comprising 386 406 middle-aged individuals of European ancestries, including 33 546 people who developed coronary heart disease, 18 166 people who had a stroke, and 27 885 people who died. Primary outcomes were coronary heart disease, defined as fatal ischaemic heart disease (International Classification of Diseases 10th revision code I20-I25) or non-fatal myocardial infarction (I21-I23); stroke, defined as any cerebrovascular disease (I60-I69); and all-cause mortality.FindingsObservational analyses suggested inverse associations between incident coronary heart disease, stroke, and all-cause mortality outcomes with 25(OH)D concentration at low 25(OH)D concentrations. In population-wide genetic analyses, there were no associations of genetically predicted 25(OH)D with coronary heart disease (odds ratio [OR] per 10 nmol/L higher genetically-predicted 25(OH)D concentration 0·98, 95% CI 0·95–1·01), stroke (1·01, [0·97–1·05]), or all-cause mortality (0·99, 0·95–1·02). Null findings were also observed in genetic analyses for cause-specific mortality outcomes, and in stratified genetic analyses for

Journal article

Papadimitriou N, Kazmi N, Dimou N, Tsilidis KK, Martin RM, Lewis SJ, Lynch BM, Hoffmeister M, Kweon S-S, Li L, Milne RL, Sakoda LC, Schoen RE, Phipps AI, Figueiredo JC, Peters U, Dixon-Suen SC, Gunter MJ, Murphy Net al., 2023, Leisure time television watching, computer use and risks of breast, colorectal and prostate cancer: A Mendelian randomisation analysis., Cancer Med, Vol: 13

BACKGROUND: Sedentary behaviours have been associated with increased risks of some common cancers in epidemiological studies; however, it is unclear if these associations are causal. METHODS: We used univariable and multivariable two-sample Mendelian randomisation (MR) to examine potential causal relationships between sedentary behaviours and risks of breast, colorectal and prostate cancer. Genetic variants associated with self-reported leisure television watching and computer use were identified from a recent genome-wide association study (GWAS). Data related to cancer risk were obtained from cancer GWAS consortia. A series of sensitivity analyses were applied to examine the robustness of the results to the presence of confounding. RESULTS: A 1-standard deviation (SD: 1.5 h/day) increment in hours of television watching increased risk of breast cancer (OR per 1-SD: 1.15, 95% confidence interval [CI]: 1.05-1.26) and colorectal cancer (OR per 1-SD: 1.32, 95% CI: 1.16-1.49) while there was little evidence of an association for prostate cancer risk (OR per 1-SD: 0.94, 95% CI: 0.84-1.06). After adjusting for years of education, the effect estimates for television watching were attenuated (breast cancer, OR per 1-SD: 1.08, 95% CI: 0.92-1.27; colorectal cancer, OR per 1-SD: 1.08, 95% CI: 0.90-1.31). Post hoc analyses showed that years of education might have a possible confounding and mediating role in the association between television watching with breast and colorectal cancer. Consistent results were observed for each cancer site according to sex (colorectal cancer), anatomical subsites and cancer subtypes. There was little evidence of associations between genetically predicted computer use and cancer risk. CONCLUSIONS: Our univariable analysis identified some positive associations between hours of television watching and risks of breast and colorectal cancer. However, further adjustment for additional lifestyle factors especially years of education attenuated t

Journal article

Stern MC, Sanchez Mendez J, Kim AE, Obón-Santacana M, Moratalla-Navarro F, Martín V, Moreno V, Lin Y, Bien SA, Qu C, Su Y-R, White E, Harrison TA, Huyghe JR, Tangen CM, Newcomb PA, Phipps AI, Thomas CE, Kawaguchi ES, Lewinger JP, Morrison JL, Conti DV, Wang J, Thomas DC, Platz EA, Visvanathan K, Keku TO, Newton CC, Um CY, Kundaje A, Shcherbina A, Murphy N, Gunter MJ, Dimou N, Papadimitriou N, Bézieau S, van Duijnhoven FJ, Männistö S, Rennert G, Wolk A, Hoffmeister M, Brenner H, Chang-Claude J, Tian Y, Le Marchand L, Cotterchio M, Tsilidis KK, Bishop DTT, Melaku YA, Lynch BM, Buchanan DD, Ulrich CM, Ose J, Peoples AR, Pellatt AJ, Li L, Devall MA, Campbell PT, Albanes D, Weinstein SJ, Berndt SI, Gruber SB, Ruiz-Narvaez E, Song M, Joshi AD, Drew DA, Petrick JL, Chan AT, Giannakis M, Peters U, Hsu L, Gauderman WJet al., 2023, Genome-wide gene-environment interaction analyses to understand the relationship between red meat and processed meat intake and colorectal cancer risk., Cancer Epidemiol Biomarkers Prev

BACKGROUND: High red meat and/or processed meat consumption are established colorectal cancer (CRC) risk factors. We conducted a genome-wide gene-environment (GxE) interaction analysis to identify genetic variants that may modify these associations. METHODS: A pooled sample of 29,842 CRC cases and 39,635 controls of European ancestry from 27 studies were included. Quantiles for red meat and processed meat intake were constructed from harmonized questionnaire data. Genotyping arrays were imputed to the Haplotype Reference Consortium. Two-step EDGE and joint tests of GxE interaction were utilized in our genome-wide scan. RESULTS: Meta-analyses confirmed positive associations between increased consumption of red meat and processed meat with CRC risk (per quartile red meat OR = 1.30; 95%CI = 1.21-1.41; processed meat OR = 1.40; 95%CI = 1.20-1.63). Two significant genome-wide GxE interactions for red meat consumption were found. Joint GxE tests revealed the rs4871179 SNP in chromosome 8 (downstream of HAS2); greater than median of consumption ORs = 1.38 (95%CI = 1.29-1.46), 1.20 (95%CI = 1.12 -1.27), and 1.07 (95%CI = 0.95 - 1.19) for CC, CG and GG, respectively. The two-step EDGE method identified the rs35352860 SNP in chromosome 18 (SMAD7 intron); greater than median of consumption ORs = 1.18 (95%CI = 1.11-1.24), 1.35 (95%CI = 1.26-1.44), and 1.46 (95%CI = 1.26-1.69) for CC, CT, and TT, respectively. CONCLUSIONS: We propose two novel biomarkers that support the role of meat consumption with an increased risk of CRC. IMPACT: The reported GxE interactions may explain the increased risk of CRC in certain population subgroups.

Journal article

Cordova R, Viallon V, Fontvielle E, Peruchet-Noray L, Jansana A, Wagner K-H, Kyrø C, Tjønneland A, Katzke V, Bajracharya R, Schulze MB, Masala G, Sieri S, Panico S, Ricceri F, Tumino R, Boer JMA, Verschuren WMM, van der Schouw YT, Jakszyn P, Redondo-Sánchez D, Amiano P, Huerta JM, Guevara M, Borné Y, Sonestedt E, Tsilidis KK, Millett C, Heath AK, Aglago EK, Aune D, Gunter MJ, Ferrari P, Huybrechts I, Freisling Het al., 2023, Consumption of ultra-processed foods and risk of multimorbidity of cancer and cardiometabolic diseases: a multinational cohort study, The Lancet Regional Health. Europe, Vol: 35, Pages: 100771-100771, ISSN: 2666-7762

BackgroundIt is currently unknown whether ultra-processed foods (UPFs) consumption is associated with a higher incidence of multimorbidity. We examined the relationship of total and subgroup consumption of UPFs with the risk of multimorbidity defined as the co-occurrence of at least two chronic diseases in an individual among first cancer at any site, cardiovascular disease, and type 2 diabetes.MethodsThis was a prospective cohort study including 266,666 participants (60% women) free of cancer, cardiovascular disease, and type 2 diabetes at recruitment from seven European countries in the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Foods and drinks consumed over the previous 12 months were assessed at baseline by food-frequency questionnaires and classified according to their degree of processing using Nova classification. We used multistate modelling based on Cox regression to estimate cause-specific hazard ratios (HR) and their 95% confidence intervals (CI) for associations of total and subgroups of UPFs with the risk of multimorbidity of cancer and cardiometabolic diseases.FindingsAfter a median of 11.2 years of follow-up, 4461 participants (39% women) developed multimorbidity of cancer and cardiometabolic diseases. Higher UPF consumption (per 1 standard deviation increment, ∼260 g/day without alcoholic drinks) was associated with an increased risk of multimorbidity of cancer and cardiometabolic diseases (HR: 1.09, 95% CI: 1.05, 1.12). Among UPF subgroups, associations were most notable for animal-based products (HR: 1.09, 95% CI: 1.05, 1.12), and artificially and sugar-sweetened beverages (HR: 1.09, 95% CI: 1.06, 1.12). Other subgroups such as ultra-processed breads and cereals (HR: 0.97, 95% CI: 0.94, 1.00) or plant-based alternatives (HR: 0.97, 95% CI: 0.91, 1.02) were not associated with risk.InterpretationOur findings suggest that higher consumption of UPFs increases the risk of cancer and cardiometabolic multimorbidity.Fun

Journal article

Katsoulis M, Lai AG, Kipourou DK, Gomes M, Banerjee A, Denaxas S, Lumbers RT, Tsilidis K, Kostara M, Belot A, Dale C, Sofat R, Leyrat C, Hemingway H, Diaz-Ordaz Ket al., 2023, On the estimation of the effect of weight change on a health outcome using observational data, by utilising the target trial emulation framework., Int J Obes (Lond), Vol: 47, Pages: 1309-1317

BACKGROUND/OBJECTIVES: When studying the effect of weight change between two time points on a health outcome using observational data, two main problems arise initially (i) 'when is time zero?' and (ii) 'which confounders should we account for?' From the baseline date or the 1st follow-up (when the weight change can be measured)? Different methods have been previously used in the literature that carry different sources of bias and hence produce different results. METHODS: We utilised the target trial emulation framework and considered weight change as a hypothetical intervention. First, we used a simplified example from a hypothetical randomised trial where no modelling is required. Then we simulated data from an observational study where modelling is needed. We demonstrate the problems of each of these methods and suggest a strategy. INTERVENTIONS: weight loss/gain vs maintenance. RESULTS: The recommended method defines time-zero at enrolment, but adjustment for confounders (or exclusion of individuals based on levels of confounders) should be performed both at enrolment and the 1st follow-up. CONCLUSIONS: The implementation of our suggested method [adjusting for (or excluding based on) confounders measured both at baseline and the 1st follow-up] can help researchers attenuate bias by avoiding some common pitfalls. Other methods that have been widely used in the past to estimate the effect of weight change on a health outcome are more biased. However, two issues remain (i) the exposure is not well-defined as there are different ways of changing weight (however we tried to reduce this problem by excluding individuals who develop a chronic disease); and (ii) immortal time bias, which may be small if the time to first follow up is short.

Journal article

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, Männistö S, Moreno V, Murphy N, Newcomb PA, Obón-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 FJ, Van Guelpen B, Visvanathan K, Wang J, White E, Wolk A, Woods MO, Ulrich CM, Hsu L, Gauderman WJ, Peters U, Tsilidis KKet al., 2023, Genome-wide interaction analysis of folate for colorectal cancer risk., Am J Clin Nutr, Vol: 118, Pages: 881-891

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 ma

Journal article

Chalitsios CV, Meena D, Manou M, Papagiannopoulos C, Markozannes G, Gill D, Su B, Tsilidis KK, Evangelou E, Tzoulaki Iet al., 2023, Multiple long-term conditions in people with psoriasis: a latent class and bidirectional Mendelian randomisation analysis., Br J Dermatol

BACKGROUND: Co-existing long-term conditions (LTC) in psoriasis and their potential causal associations with the disease are not well-established. OBJECTIVES: This study aims to determine distinct clusters of LTC in people with psoriasis and the potential bi-directional causal association between these LTC and psoriasis. METHODS: Using latent class analysis, cross-sectional data of people with psoriasis from the UK Biobank were analysed to identify distinct psoriasis-related co-morbidity profiles. Linkage disequilibrium score regression (LDSR) was applied to compute the genetic correlation between psoriasis and LTC. Two-sample bidirectional Mendelian randomisation (MR) analysis assessed potential causal direction using independent genetic variants that reached genome-wide significance (P < 5 × 10-8). RESULTS: Five co-morbidity clusters were identified in a population of 10,873 people with psoriasis. LDSR revealed that psoriasis was positively genetically correlated with heart failure (rg = 0.23, p = 8.8 × 10-8), depression (rg = 0.12, p = 2.7 × 10-5), coronary artery disease (CAD) (rg = 0.15, p = 2 × 10-4), and type 2 diabetes (rg = 0.19, p = 3 × 10-3). Genetic liability to CAD was associated with an increased risk of psoriasis (ORIVW = 1.159; 95%CI 1.055-1.274; p = 2 × 10-3). The MR-PRESSO (ORMR-PRESSO = 1.13; 95%CI 1.042-1.228; p = 6 × 10-3) and the MR-RAPS (ORMR-RAPS = 1.149; 95%CI 1.062-1.242; p = 5 × 10-4) approaches corroborate the IVW findings. The weighted median generated similar and consistent effect estimates but was not statistically significant (ORWM = 1.076; 95%CI 0.949-1.221; p = 0.251). Evidence for a suggestive increased risk was detected for CAD (ORIVW&

Journal article

Georgiou AN, Zagkos L, Markozannes G, Chalitsios CV, Asimakopoulos AG, Xu W, Wang L, Mesa-Eguiagaray I, Zhou X, Loizidou EM, Kretsavos N, Theodoratou E, Gill D, Burgess S, Evangelou E, Tsilidis KK, Tzoulaki Iet al., 2023, Appraising the Causal Role of Risk Factors in Coronary Artery Disease and Stroke: A Systematic Review of Mendelian Randomization Studies., J Am Heart Assoc, Vol: 12

BACKGROUND Mendelian randomization (MR) offers a powerful approach to study potential causal associations between exposures and health outcomes by using genetic variants associated with an exposure as instrumental variables. In this systematic review, we aimed to summarize previous MR studies and to evaluate the evidence for causality for a broad range of exposures in relation to coronary artery disease and stroke. METHODS AND RESULTS MR studies investigating the association of any genetically predicted exposure with coronary artery disease or stroke were identified. Studies were classified into 4 categories built on the significance of the main MR analysis results and its concordance with sensitivity analyses, namely, robust, probable, suggestive, and insufficient. Studies reporting associations that did not perform any sensitivity analysis were classified as nonevaluable. We identified 2725 associations eligible for evaluation, examining 535 distinct exposures. Of them, 141 were classified as robust, 353 as probable, 110 as suggestive, and 926 had insufficient evidence. The most robust associations were observed for anthropometric traits, lipids, and lipoproteins and type 2 diabetes with coronary artery; disease and clinical measurements with coronary artery disease and stroke; and thrombotic factors with stroke. CONCLUSIONS Despite the large number of studies that have been conducted, only a limited number of associations were supported by robust evidence. Approximately half of the studies reporting associations presented an MR sensitivity analysis along with the main analysis that further supported the causality of associations. Future research should focus on more thorough assessments of sensitivity MR analyses and further assessments of mediation effects or nonlinearity of associations.

Journal article

Christakoudi S, Tsilidis K, Evangelou E, Riboli Eet al., 2023, Interactions of platelets with obesity in relation to lung cancer risk in the UK Biobank cohort, Respiratory Research, Vol: 24, ISSN: 1465-9921

Background:Platelet count (PLT) is associated positively with lung cancer risk but has a more complex association with body mass index (BMI), positive only in women (mainly never smokers) and inverse in men (mainly ever smokers), raising the question whether platelets interact with obesity in relation to lung cancer risk. Prospective associations of platelet size (an index of platelet maturity and activity) with lung cancer risk are unclear.Methods:We examined the associations of PLT, mean platelet volume (MPV), and platelet distribution width (PDW) (each individually, per one standard deviation increase) with lung cancer risk in UK Biobank men and women using multivariable Cox proportional hazards models adjusted for BMI and covariates. We calculated Relative Excess Risk from Interaction (RERI) with obese (BMI ≥30 kg/m2), dichotomising platelet parameters at ≥median (sex-specific), and multiplicative interactions with BMI (continuous scale). We examined heterogeneity according to smoking status (never, former, current smoker) and antiaggregant/anticoagulant use (no/yes).Results:During a mean follow-up of 10.4 years, 1620 lung cancers were ascertained in 192,355 men and 1495 lung cancers in 218,761 women. PLT was associated positively with lung cancer risk in men (hazard ratio HR=1.14; 95% confidence interval (CI): 1.09–1.20) and women (HR=1.09; 95%CI: 1.03–1.15) but interacted inversely with BMI only in men (RERI=-0.53; 95%CI: -0.80 to -0.26 for high-PLT-obese; HR=0.92; 95%CI=0.88–0.96 for PLTBMI). Only in men, MPV was associated inversely with lung cancer risk (HR=0.95; 95%CI: 0.90–0.99) and interacted positively with BMI (RERI=0.27; 95%CI=0.09–0.45 for high-MPV-obese; HR=1.08; 95%CI=1.04–1.13 for MPVBMI), while PDW was associated positively (HR=1.05; 95%CI: 1.00–1.10), with no evidence for interactions. The associations with PLT were consistent by smoking status, but MPV was associated inversely only in current sm

Journal article

Haycock PC, Borges MC, Burrows K, Lemaitre RN, Harrison S, Burgess S, Chang X, Westra J, Khankari NK, Tsilidis KK, Gaunt T, Hemani G, Zheng J, Truong T, O'Mara TA, Spurdle AB, Law MH, Slager SL, Birmann BM, Saberi Hosnijeh F, Mariosa D, Amos C, Hung RJ, Zheng W, Gunter MJ, Davey Smith G, Relton C, Martin RMet al., 2023, Design and quality control of large-scale two-sample Mendelian randomization studies, INTERNATIONAL JOURNAL OF EPIDEMIOLOGY, Vol: 52, Pages: 1498-1521, ISSN: 0300-5771

Journal article

Desai TA, Hedman ÅK, Dimitriou M, Koprulu M, Figiel S, Yin W, Johansson M, Watts EL, Atkins JR, Sokolov AV, Schiöth HB, Gunter MJ, Tsilidis KK, Martin RM, Pietzner M, Langenberg C, Mills IG, Lamb AD, Mälarstig A, Key TJ, PRACTICAL Consortium, Travis RC, Smith-Byrne Ket al., 2023, Identifying proteomic risk factors for overall, aggressive and early onset prostate cancer using Mendelian randomization and tumor spatial transcriptomics., medRxiv

BACKGROUND: Understanding the role of circulating proteins in prostate cancer risk can reveal key biological pathways and identify novel targets for cancer prevention. METHODS: We investigated the association of 2,002 genetically predicted circulating protein levels with risk of prostate cancer overall, and of aggressive and early onset disease, using cis-pQTL Mendelian randomization (MR) and colocalization. Findings for proteins with support from both MR, after correction for multiple-testing, and colocalization were replicated using two independent cancer GWAS, one of European and one of African ancestry. Proteins with evidence of prostate-specific tissue expression were additionally investigated using spatial transcriptomic data in prostate tumor tissue to assess their role in tumor aggressiveness. Finally, we mapped risk proteins to drug and ongoing clinical trials targets. RESULTS: We identified 20 proteins genetically linked to prostate cancer risk (14 for overall [8 specific], 7 for aggressive [3 specific], and 8 for early onset disease [2 specific]), of which a majority were novel and replicated. Among these were proteins associated with aggressive disease, such as PPA2 [Odds Ratio (OR) per 1 SD increment = 2.13, 95% CI: 1.54-2.93], PYY [OR = 1.87, 95% CI: 1.43-2.44] and PRSS3 [OR = 0.80, 95% CI: 0.73-0.89], and those associated with early onset disease, including EHPB1 [OR = 2.89, 95% CI: 1.99-4.21], POGLUT3 [OR = 0.76, 95% CI: 0.67-0.86] and TPM3 [OR = 0.47, 95% CI: 0.34-0.64]. We confirm an inverse association of MSMB with prostate cancer overall [OR = 0.81, 95% CI: 0.80-0.82], and also find an inverse association with both aggressive [OR = 0.84, 95% CI: 0.82-0.86] and early onset disease [OR = 0.71, 95% CI: 0.68-0.74]. Using spatial transcriptomics data, we identified MSMB as the genome-wide top-most predictive gene to distinguish benign regions from high grade cancer regions that had five-fold lower MSMB expression. Additionally, ten proteins that were

Journal article

Kanai M, Andrews SJ, Cordioli M, Stevens C, Neale BM, Daly M, Ganna A, Pathak GA, Iwasaki A, Karjalainen J, Mehtonen J, Pirinen M, Chwialkowska K, Trankiem A, Balaconis MK, Veerapen K, Wolford BN, Ahmad HF, Andrews S, von Hohenstaufen Puoti KA, Boer C, Boua PR, Butler-Laporte G, Cadilla CL, Chwiałkowska K, Colombo F, Douillard V, Dueker N, Dutta AK, El-Sherbiny YM, Eltoukhy MM, Esmaeeli S, Faucon A, Fave M-J, Cadenas IF, Francescatto M, Francioli L, Franke L, Fuentes M, Durán RG, Cabrero DG, Harry EN, Jansen P, Szentpéteri JL, Kaja E, Kanai M, Kirk C, Kousathanas A, Krieger JE, Patel SK, Lemaçon A, Limou S, Lió P, Marouli E, Marttila MM, Medina-Gómez C, Michaeli Y, Migeotte I, Mondal S, Moreno-Estrada A, Moya L, Nakanishi T, Nasir J, Pasko D, Pearson NM, Pereira AC, Priest J, Prijatelj V, Prokić I, Teumer A, Várnai R, Romero-Gómez M, Roos C, Rosenfeld J, Ruolin L, Schulte EC, Schurmann C, Sedaghati-khayat B, Shaheen D, Shivanathan I, Sipeky C, Sirui Z, Striano P, Tanigawa Y, Remesal AU, Vadgama N, Vallerga CL, van der Laan S, Verdugo RA, Wang QS, Wei Z, Zainulabid UA, Zárate RN, Auton A, Shelton JF, Shastri AJ, Weldon CH, Filshtein-Sonmez T, Coker D, Symons A, Aslibekyan S, OConnell J, Ye C, Hatoum AS, Agrawal A, Bogdan R, Colbert SMC, Thompson WK, Fan CC, Johnson EC, Niazyan L, Davidyants M, Arakelyan A, Avetyan D, Bekbossynova M, Tauekelova A, Tuleutayev M, Sailybayeva A, Ramankulov Y, Zholdybayeva E, Dzharmukhanov J, Kassymbek K, Tsechoeva T, Turebayeva G, Smagulova Z, Muratov T, Khamitov S, Kwong ASF, Timpson NJ, Niemi MEK, Rahmouni S, Guntz J, Beguin Y, Cordioli M, Pigazzini S, Nkambule L, Georges M, Moutschen M, Misset B, Darcis G, Gofflot S, Bouysran Y, Busson A, Peyrassol X, Wilkin F, Pichon B, Smits G, Vandernoot I, Goffard J-C, Tiembe N, Morrison DR, Afilalo J, Mooser V, Richards JB, Rousseau S, Durand M, Butler-Laporte G, Forgetta V, Laurent L, Afrasiabi Z, Bouab M, Tselios C, Xue X, Afilalo M, Oliveira M, St-Cyr J, Boisclair A, Ragoussis J, Auld D, Kaufet al., 2023, A second update on mapping the human genetic architecture of COVID-19, Nature, Vol: 621, Pages: E7-E26, ISSN: 0028-0836

Journal article

Dimou N, Kim AE, Flanagan O, Murphy N, Diez-Obrero V, Shcherbina A, Aglago EK, Bouras E, Campbell PT, Casey G, Gallinger S, Gruber SB, Jenkins MA, Lin Y, Moreno V, Ruiz-Narvaez E, Stern MC, Tian Y, Tsilidis KK, Arndt V, Barry EL, Baurley JW, Berndt SI, Bezieau S, Bien SA, Bishop DT, Brenner H, Budiarto A, Carreras-Torres R, Cenggoro TW, Chan AT, Chang-Claude J, Chanock SJ, Chen X, Conti DV, Dampier CH, Devall M, Drew DA, Figueiredo JC, Giles GG, Gsur A, Harrison TA, Hidaka A, Hoffmeister M, Huyghe JR, Jordahl K, Kawaguchi E, Keku TO, Larsson SC, Le Marchand L, Lewinger JP, Li L, Mahesworo B, Morrison J, Newcomb PA, Newton CC, Obon-Santacana M, Ose J, Pai RK, Palmer JR, Papadimitriou N, Pardamean B, Peoples AR, Pharoah PDP, Platz EA, Potter JD, Rennert G, Scacheri PC, Schoen RE, Su Y-R, Tangen CM, Thibodeau SN, Thomas DC, Ulrich CM, Um CY, van Duijnhoven FJB, Visvanathan K, Vodicka P, Vodickova L, White E, Wolk A, Woods MO, Qu C, Kundaje A, Hsu L, Gauderman WJ, Gunter MJ, Peters Uet al., 2023, Probing the diabetes and colorectal cancer relationship using gene - environment interaction analyses, Annual Meeting of the American-Association-for-Cancer-Research (AACR), Publisher: SPRINGERNATURE, Pages: 511-520, ISSN: 0007-0920

Conference paper

Christakoudi S, Tsilidis K, Evangelou E, Riboli Eet al., 2023, Associations of obesity and body shape with erythrocyte and reticulocyte parameters in the UK Biobank cohort, BMC Endocrine Disorders, Vol: 23, Pages: 1-12, ISSN: 1472-6823

Background: Obesity is associated with type 2 diabetes mellitus and chronic low-grade inflammation. Although chronic inflammatory conditions and diabetes are associated with anaemia, less is known about associations of obesity and body shape, independent of each other, with erythrocyte and reticulocyte parameters.Methods: We investigated the associations of body mass index (BMI) and the allometric body shape index (ABSI) and hip index (HI), which are uncorrelated with BMI, with erythrocyte and reticulocyte parameters (all continuous, on a standard deviation (SD) scale) in UK Biobank participants without known metabolic, endocrine, or major inflammatory conditions (glycated haemoglobin HbA1c<48 mmol/mol, C-reactive protein CRP<10 mg/L). We examined erythrocyte count, total reticulocyte count and percent, immature reticulocyte count and fraction (IRF), haemoglobin, haematocrit, mean corpuscular haemoglobin mass (MCH) and concentration (MCHC), mean corpuscular and reticulocyte volumes (MCV, MPV), and red cell distribution width (RDW) in multivariable linear regression models. We additionally defined body shape phenotypes with dichotomised ABSI (≥73 women; ≥80 men) and HI (≥64 women; ≥49 men), including “pear” (small-ABSI-large-HI) and “apple” (large-ABSI-small-HI), and examined these in groups according to BMI (18.5-25 normal weight; 25-30 overweight; 30-45 kg/m2 obese). Results: In 105,853 women and 100,854 men, BMI and ABSI were associated positively with haemoglobin, haematocrit, and erythrocyte count, and more strongly with total reticulocyte count and percent, immature reticulocyte count and IRF. HI was associated inversely with all, but least with IRF. Associations were comparable in women and men. In groups according to obesity and body shape, erythrocyte count was ~0.6 SD higher for obese-“apple” compared to normal-weight-“pear” phenotype (SD=0.31*1012/L women, SD=0.34*1012/L men), total reticulo

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 S, Bien SA, Bishop DT, Bouras E, Brenner H, Buchanan DD, Budiarto A, Carreras-Torres R, Casey G, Cenggoro TW, Chen AT, Chang-Claude J, Chen X, Conti D, 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, Le Marchand L, Lewinger JP, Li L, Lynch BM, Mahesworo B, Mandic M, Obon-Santacana M, Morento V, Murphy N, Men 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, A Genetic Locus within the FMN1/GREM1 Gene Region Interacts with Body Mass Index in Colorectal Cancer Risk, CANCER RESEARCH, Vol: 83, Pages: 2572-2583, ISSN: 0008-5472

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 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, 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 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, 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

Wang X, Tian R, Zong X, Jeon MS, Luo J, Colditz GA, Wang JS, Tsilidis KK, Ju Y-ES, Govindan R, Puri V, Cao Yet al., 2023, Sleep Behaviors, Genetic Predispositions, and Risk of Esophageal Cancer, CANCER EPIDEMIOLOGY BIOMARKERS & PREVENTION, Vol: 32, Pages: 1079-1086, ISSN: 1055-9965

Journal article

This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.

Request URL: http://wlsprd.imperial.ac.uk:80/respub/WEB-INF/jsp/search-html.jsp Request URI: /respub/WEB-INF/jsp/search-html.jsp Query String: respub-action=search.html&id=00838111&limit=30&person=true