Risk Factors for Ovarian Cancer : An Umbrella Review of the Literature

Simple Summary Ovarian cancer is the most lethal cancer of the female genital tract despite major advances in both surgical and oncological treatments. This is in part due to difficulties in identifying those most at risk of developing ovarian cancer, and that there are currently no effective screening strategies. Whilst 20% of cases have a genetic component, the majority have no obvious cause. Many risk factors have been associated with ovarian cancer, although the strength of this evidence remains unclear. This umbrella review attempts to review the validity of associations between non-genetic risk factors and the risk of developing or dying from ovarian cancer. There were six associations that were supported by strong evidence. Greater height, BMI and use of HRT increased the risk, whilst the use of oral contraceptive pill reduced that risk. This review will enable further research into these areas and may promote identification of individuals at high risk. Several non-genetic factors have been associated with ovarian cancer incidence or mortality. To evaluate the strength and validity of the evidence we conducted an umbrella review of the literature that included systematic reviews/meta-analyses that evaluated the link between non-genetic risk factors and ovarian cancer incidence and mortality. We searched PubMed, EMBASE, Cochrane Database of Systematic Reviews and performed a manual screening of references. Evidence was graded into strong, highly suggestive, suggestive or weak based on statistical significance of the random effects summary estimate and the largest study in a meta-analysis, the number of cases, between-study heterogeneity, 95% prediction intervals, small study effects, and presence of excess significance bias. We identified 212 meta-analyses, investigating 55 non-genetic risk factors for ovarian cancer. Risk factors were grouped in eight broad categories: anthropometric indices, dietary intake, physical activity, pre-existing medical conditions, past drug history, biochemical markers, past gynaecological history and smoking. Of the 174 meta-analyses of cohort studies assessing 44 factors, six associations were graded with strong evidence. Greater height (RR per 10 cm 1.16, 95% confidence interval (CI) 1.11-1.20), body mass index (BMI) (RR >= 30 kg/m(2) versus normal 1.27, 95% CI 1.17-1.38) and three exposures of varying preparations and usage related to hormone replacement therapy (HRT) use increased the risk of developing ovarian cancer. Use of oral contraceptive pill reduced the risk (RR 0.74, 95% CI 0.69-0.80). Refining the significance of genuine risk factors for the development of ovarian cancer may potentially increase awareness in women at risk, aid prevention and early detection.Peer reviewe

Identifying adults at high-risk for change in weight and BMI in England: a longitudinal, large-scale, population-based cohort study using electronic health records.

Funder: Department of HealthFunder: Medical Research CouncilBackgroundTargeted obesity prevention policies would benefit from the identification of population groups with the highest risk of weight gain. The relative importance of adult age, sex, ethnicity, geographical region, and degree of social deprivation on weight gain is not known. We aimed to identify high-risk groups for changes in weight and BMI using electronic health records (EHR).MethodsIn this longitudinal, population-based cohort study we used linked EHR data from 400 primary care practices (via the Clinical Practice Research Datalink) in England, accessed via the CALIBER programme. Eligible participants were aged 18-74 years, were registered at a general practice clinic, and had BMI and weight measurements recorded between Jan 1, 1998, and June 30, 2016, during the period when they had eligible linked data with at least 1 year of follow-up time. We calculated longitudinal changes in BMI over 1, 5, and 10 years, and investigated the absolute risk and odds ratios (ORs) of transitioning between BMI categories (underweight, normal weight, overweight, obesity class 1 and 2, and severe obesity [class 3]), as defined by WHO. The associations of demographic factors with BMI transitions were estimated by use of logistic regression analysis, adjusting for baseline BMI, family history of cardiovascular disease, use of diuretics, and prevalent chronic conditions.FindingsWe included 2 092 260 eligible individuals with more than 9 million BMI measurements in our study. Young adult age was the strongest risk factor for weight gain at 1, 5, and 10 years of follow-up. Compared with the oldest age group (65-74 years), adults in the youngest age group (18-24 years) had the highest OR (4·22 [95% CI 3·86-4·62]) and greatest absolute risk (37% vs 24%) of transitioning from normal weight to overweight or obesity at 10 years. Likewise, adults in the youngest age group with overweight or obesity at baseline were also at highest risk to transition to a higher BMI category; OR 4·60 (4·06-5·22) and absolute risk (42% vs 18%) of transitioning from overweight to class 1 and 2 obesity, and OR 5·87 (5·23-6·59) and absolute risk (22% vs 5%) of transitioning from class 1 and 2 obesity to class 3 obesity. Other demographic factors were consistently less strongly associated with these transitions; for example, the OR of transitioning from normal weight to overweight or obesity in people living in the most socially deprived versus least deprived areas was 1·23 (1·18-1·27), for men versus women was 1·12 (1·08-1·16), and for Black individuals versus White individuals was 1·13 (1·04-1·24). We provide an open access online risk calculator, and present high-resolution obesity risk charts over a 1-year, 5-year, and 10-year follow-up period.InterpretationA radical shift in policy is required to focus on individuals at the highest risk of weight gain (ie, young adults aged 18-24 years) for individual-level and population-level prevention of obesity and its long-term consequences for health and health care.FundingThe British Hearth Foundation, Health Data Research UK, the UK Medical Research Council, and the National Institute for Health Research

Separating the effects of early and later life adiposity on colorectal cancer risk:a Mendelian randomization study

BACKGROUND: Observational studies have linked childhood obesity with elevated risk of colorectal cancer; however, it is unclear if this association is causal or independent from the effects of obesity in adulthood on colorectal cancer risk. METHODS: We conducted Mendelian randomization (MR) analyses to investigate potential causal relationships between self-perceived body size (thinner, plumper, or about average) in early life (age 10) and measured body mass index in adulthood (mean age 56.5) with risk of colorectal cancer. The total and independent effects of body size exposures were estimated using univariable and multivariable MR, respectively. Summary data were obtained from a genome-wide association study of 453,169 participants in UK Biobank for body size and from a genome-wide association study meta-analysis of three colorectal cancer consortia of 125,478 participants. RESULTS: Genetically predicted early life body size was estimated to increase odds of colorectal cancer (odds ratio [OR] per category change: 1.12, 95% confidence interval [CI]: 0.98–1.27), with stronger results for colon cancer (OR: 1.16, 95% CI: 1.00–1.35), and distal colon cancer (OR: 1.25, 95% CI: 1.04–1.51). After accounting for adult body size using multivariable MR, effect estimates for early life body size were attenuated towards the null for colorectal cancer (OR: 0.97, 95% CI: 0.77–1.22) and colon cancer (OR: 0.97, 95% CI: 0.76–1.25), while the estimate for distal colon cancer was of similar magnitude but more imprecise (OR: 1.27, 95% CI: 0.90–1.77). Genetically predicted adult life body size was estimated to increase odds of colorectal (OR: 1.27, 95% CI: 1.03, 1.57), colon (OR: 1.32, 95% CI: 1.05, 1.67), and proximal colon (OR: 1.57, 95% CI: 1.21, 2.05). CONCLUSIONS: Our findings suggest that the positive association between early life body size and colorectal cancer risk is likely due to large body size retainment into adulthood. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12916-022-02702-9

Tissue-specific genetic variation suggests distinct molecular pathways between body shape phenotypes and colorectal cancer

It remains unknown whether adiposity subtypes are differentially associated with colorectal cancer (CRC). To move beyond single-trait anthropometric indicators, we derived four multi-trait body shape phenotypes reflecting adiposity subtypes from principal components analysis on body mass index, height, weight, waist-to-hip ratio, and waist and hip circumference. A generally obese (PC1) and a tall, centrally obese (PC3) body shape were both positively associated with CRC risk in observational analyses in 329,828 UK Biobank participants (3728 cases). In genome-wide association studies in 460,198 UK Biobank participants, we identified 3414 genetic variants across four body shapes and Mendelian randomization analyses confirmed positive associations of PC1 and PC3 with CRC risk (52,775 cases/45,940 controls from GECCO/CORECT/CCFR). Brain tissue-specific genetic instruments, mapped to PC1 through enrichment analysis, were responsible for the relationship between PC1 and CRC, while the relationship between PC3 and CRC was predominantly driven by adipose tissue-specific genetic instruments. This study suggests distinct putative causal pathways between adiposity subtypes and CRC

Body shape phenotypes of multiple anthropometric traits and cancer risk: a multi-national cohort study

Background - Classical anthropometric traits may fail to fully represent the relationship of weight, adiposity, and height with cancer risk. We investigated the associations of body shape phenotypes with the risk of overall and site-specific cancers. Methods - We derived four distinct body shape phenotypes from principal component (PC) analysis on height, weight, body mass index (BMI), waist (WC) and hip circumferences (HC), and waist-to-hip ratio (WHR). The study included 340,152 men and women from 9 European countries, aged mostly 35–65 years at recruitment (1990–2000) in the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Cox proportional hazards regression was used to estimate multivariable-adjusted hazard ratios (HRs) and 95% confidence intervals (CIs). Results - After a median follow-up of 15.3 years, 47,110 incident cancer cases were recorded. PC1 (overall adiposity) was positively associated with the risk of overall cancer, with a HR per 1 standard deviation (SD) increment equal to 1.07 (95% confidence interval 1.05 to 1.08). Positive associations were observed with 10 cancer types, with HRs (per 1 SD) ranging from 1.36 (1.30–1.42) for endometrial cancer to 1.08 (1.03–1.13) for rectal cancer. PC2 (tall stature with low WHR) was positively associated with the risk of overall cancer (1.03; 1.02–1.04) and five cancer types which were not associated with PC1. PC3 (tall stature with high WHR) was positively associated with the risk of overall cancer (1.04; 1.03–1.05) and 12 cancer types. PC4 (high BMI and weight with low WC and HC) was not associated with overall risk of cancer (1.00; 0.99–1.01). Conclusions - In this multi-national study, distinct body shape phenotypes were positively associated with the incidence of 17 different cancers and overall cancer

A Prospective Evaluation of Plasma Polyphenol Levels and Colon Cancer Risk

Polyphenols have been shown to exert biological activity in experimental models of colon cancer; however, human data linking specific polyphenols to colon cancer is limited. We assessed the relationship between pre-diagnostic plasma polyphenols and colon cancer risk in a case-control study nested within the European Prospective Investigation into Cancer and Nutrition study. Using high pressure liquid chromatography coupled to tandem mass spectrometry, we measured concentrations of 35 polyphenols in plasma from 809 incident colon cancer cases and 809 matched controls. We used multivariable adjusted conditional logistic regression models that included established colon cancer risk factors. The false discovery rate (qvalues ) was computed to control for multiple comparisons. All statistical tests were two-sided. After false discovery rate correction and in continuous log2 -transformed multivariable models, equol (odds ratio [OR] per log2 -value, 0.86, 95% confidence interval [95% CI] = 0.79-0.93; qvalue = 0.01) and homovanillic acid (OR per log2 -value, 1.46, 95% CI = 1.16-1.84; qvalue = 0.02) were associated with colon cancer risk. Comparing extreme fifths, equol concentrations were inversely associated with colon cancer risk (OR = 0.61, 95% CI = 0.41-0.91, ptrend = 0.003), while homovanillic acid concentrations were positively associated with colon cancer development (OR = 1.72, 95% CI = 1.17-2.53, ptrend < 0.0001). No heterogeneity for these associations was observed by sex and across other colon cancer risk factors. The remaining polyphenols were not associated with colon cancer risk. Higher equol concentrations were associated with lower risk, and higher homovanillic acid concentrations were associated with greater risk of colon cancer. These findings support a potential role for specific polyphenols in colon tumorigenesis

CA19-9 and apolipoprotein-A2 isoforms as detection markers for pancreatic cancer: a prospective evaluation.

Recently, we identified unique processing patterns of apolipoprotein A2 (ApoA2) in patients with pancreatic cancer. Our study provides a first prospective evaluation of an ApoA2 isoform ("ApoA2-ATQ/AT"), alone and in combination with carbohydrate antigen 19-9 (CA19-9), as an early detection biomarker for pancreatic cancer. We performed ELISA measurements of CA19-9 and ApoA2-ATQ/AT in 156 patients with pancreatic cancer and 217 matched controls within the European EPIC cohort, using plasma samples collected up to 60 months prior to diagnosis. The detection discrimination statistics were calculated for risk scores by strata of lag-time. For CA19-9, in univariate marker analyses, C-statistics to distinguish future pancreatic cancer patients from cancer-free individuals were 0.80 for plasma taken ≤6 months before diagnosis, and 0.71 for >6-18 months; for ApoA2-ATQ/AT, C-statistics were 0.62, and 0.65, respectively. Joint models based on ApoA2-ATQ/AT plus CA19-9 significantly improved discrimination within >6-18 months (C = 0.74 vs. 0.71 for CA19-9 alone, p = 0.022) and ≤ 18 months (C = 0.75 vs. 0.74, p = 0.022). At 98% specificity, and for lag times of ≤6, >6-18 or ≤ 18 months, sensitivities were 57%, 36% and 43% for CA19-9 combined with ApoA2-ATQ/AT, respectively, vs. 50%, 29% and 36% for CA19-9 alone. Compared to CA19-9 alone, the combination of CA19-9 and ApoA2-ATQ/AT may improve detection of pancreatic cancer up to 18 months prior to diagnosis under usual care, and may provide a useful first measure for pancreatic cancer detection prior to imaging

Association of Selenoprotein and Selenium Pathway Genotypes with Risk of Colorectal Cancer and Interaction with Selenium Status

Selenoprotein genetic variations and suboptimal selenium (Se) levels may contribute to the risk of colorectal cancer (CRC) development. We examined the association between CRC risk and genotype for single nucleotide polymorphisms (SNPs) in selenoprotein and Se metabolic pathway genes. Illumina Goldengate assays were designed and resulted in the genotyping of 1040 variants in 154 genes from 1420 cases and 1421 controls within the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Multivariable logistic regression revealed an association of 144 individual SNPs from 63 Se pathway genes with CRC risk. However, regarding the selenoprotein genes, only TXNRD1 rs11111979 retained borderline statistical significance after adjustment for correlated tests (P-ACT = 0.10; P-ACT significance threshold was P <0.1). SNPs in Wingless/Integrated (Wnt) and Transforming growth factor (TGF) beta-signaling genes (FRZB, SMAD3, SMAD7) from pathways affected by Se intake were also associated with CRC risk after multiple testing adjustments. Interactions with Se status (using existing serum Se and Selenoprotein P data) were tested at the SNP, gene, and pathway levels. Pathway analyses using the modified Adaptive Rank Truncated Product method suggested that genes and gene x Se status interactions in antioxidant, apoptosis, and TGF-beta signaling pathways may be associated with CRC risk. This study suggests that SNPs in the Se pathway alone or in combination with suboptimal Se status may contribute to CRC development.Peer reviewe

A Nested Case-Control Study of Metabolically Defined Body Size Phenotypes and Risk of Colorectal Cancer in the European Prospective Investigation into Cancer and Nutrition (EPIC).

BACKGROUND: Obesity is positively associated with colorectal cancer. Recently, body size subtypes categorised by the prevalence of hyperinsulinaemia have been defined, and metabolically healthy overweight/obese individuals (without hyperinsulinaemia) have been suggested to be at lower risk of cardiovascular disease than their metabolically unhealthy (hyperinsulinaemic) overweight/obese counterparts. Whether similarly variable relationships exist for metabolically defined body size phenotypes and colorectal cancer risk is unknown. METHODS AND FINDINGS: The association of metabolically defined body size phenotypes with colorectal cancer was investigated in a case-control study nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Metabolic health/body size phenotypes were defined according to hyperinsulinaemia status using serum concentrations of C-peptide, a marker of insulin secretion. A total of 737 incident colorectal cancer cases and 737 matched controls were divided into tertiles based on the distribution of C-peptide concentration amongst the control population, and participants were classified as metabolically healthy if below the first tertile of C-peptide and metabolically unhealthy if above the first tertile. These metabolic health definitions were then combined with body mass index (BMI) measurements to create four metabolic health/body size phenotype categories: (1) metabolically healthy/normal weight (BMI < 25 kg/m2), (2) metabolically healthy/overweight (BMI ≥ 25 kg/m2), (3) metabolically unhealthy/normal weight (BMI < 25 kg/m2), and (4) metabolically unhealthy/overweight (BMI ≥ 25 kg/m2). Additionally, in separate models, waist circumference measurements (using the International Diabetes Federation cut-points [≥80 cm for women and ≥94 cm for men]) were used (instead of BMI) to create the four metabolic health/body size phenotype categories. Statistical tests used in the analysis were all two-sided, and a p-value of <0.05 was considered statistically significant. In multivariable-adjusted conditional logistic regression models with BMI used to define adiposity, compared with metabolically healthy/normal weight individuals, we observed a higher colorectal cancer risk among metabolically unhealthy/normal weight (odds ratio [OR] = 1.59, 95% CI 1.10-2.28) and metabolically unhealthy/overweight (OR = 1.40, 95% CI 1.01-1.94) participants, but not among metabolically healthy/overweight individuals (OR = 0.96, 95% CI 0.65-1.42). Among the overweight individuals, lower colorectal cancer risk was observed for metabolically healthy/overweight individuals compared with metabolically unhealthy/overweight individuals (OR = 0.69, 95% CI 0.49-0.96). These associations were generally consistent when waist circumference was used as the measure of adiposity. To our knowledge, there is no universally accepted clinical definition for using C-peptide level as an indication of hyperinsulinaemia. Therefore, a possible limitation of our analysis was that the classification of individuals as being hyperinsulinaemic-based on their C-peptide level-was arbitrary. However, when we used quartiles or the median of C-peptide, instead of tertiles, as the cut-point of hyperinsulinaemia, a similar pattern of associations was observed. CONCLUSIONS: These results support the idea that individuals with the metabolically healthy/overweight phenotype (with normal insulin levels) are at lower colorectal cancer risk than those with hyperinsulinaemia. The combination of anthropometric measures with metabolic parameters, such as C-peptide, may be useful for defining strata of the population at greater risk of colorectal cancer