Accurate population stratification of colorectal cancer risk enables identification of individuals who would benefit from screening and risk-reducing interventions. We conducted a population-based cohort study using almost 400,000 unaffected UK Biobank participants who were aged 40-69 years at their baseline assessment and who had genetically determined UK ancestry. For women and men separately, we developed (i) a multivariable risk prediction model using family history, a polygenic risk score (PRS) and clinical risk factors, and (ii) a simple model comprising family history and a PRS. We then compared their performance to that of existing models. The models were developed using Cox regression with age as the time axis in a 70% training dataset. The performance of the 10-year risk of colorectal cancer was assessed in a 30% testing dataset using Cox regression to estimate the hazard ratio per standard deviation of risk, Harrell's C-index to assess discrimination and logistic regression to assess calibration. There were 214,183 women and 181,889 men in the dataset with 1,913 women and 2,598 men diagnosed with colorectal cancer during the follow-up period. The mean age at diagnosis was 66.4 years (standard deviation = 7.3 years) for women and 67.3 years (standard deviation = 6.7 years) for men. In the 30% testing dataset, the new multivariable models discriminated better (Harrell's C-index = 0.690, 95% CI = 0.669 to 0.712 for women; 0.699, 95% CI = 0.681 to 0.717 for men) than the new family history and PRS models (Harrell's C-index = 0.683, 95% CI = 0.663 to 0.704 for women; 0.692, 95% CI = 0.673 to 0.710 for men; change in discrimination P = 0.02 for women and P = 0.01 for men). Our models identify individuals who are at increased risk of colorectal cancer and who would benefit from personalised screening and risk-reduction options.

While metabolic pathway alterations are linked to colorectal cancer (CRC), the predictive value of pre-diagnostic metabolomic profiling in CRC risk assessment remains to be clarified. This study evaluated the predictive performance of a metabolomics risk panel (MRP) both independently and in combination with established risk factors.

We derived, internally validated (IV), and externally validated (EV) a metabolomics risk panel (MRP) for CRC from data of the UK Biobank (UKB) and the German ESTHER cohort. Baseline blood samples were assessed for 249 metabolites using nuclear magnetic resonance spectroscopy analysis. We applied LASSO Cox proportional hazards regression to identify metabolites for inclusion in the MRP and evaluated the model performance using the concordance index (C-index). We compared the performance of the MRP to an environmental risk panel (ERP; sex, age, body mass index, smoking status, and alcohol consumption) and a genetic risk panel (GRP; polygenic risk score).

The study included 154,892 participants of the UKB cohort (mean age at baseline 54.5 years; 55.5% female) with 1879 incident CRC and 3242 participants of the ESTHER cohort (mean age 61.5 years; 52.2% female) with 103 CRC cases. Twenty-three metabolites, primarily amino acid and lipid-related metabolites, were selected for the MRP, showing moderate predictive performance (C-index 0.60 [IV] and 0.54 [EV]). The ERP and GRP showed superior performance, with C-index values of 0.73 (IV) and 0.69 (EV). Adding the MRP to these risk models did not change the C-indices in both cohorts.

Genetic and environmental risk information provided strong predictive accuracy for CRC risk, with no improvements from adding metabolomics data. These findings suggest that metabolomics data may have limited impact on enhancing established CRC risk models in clinical practice.

Cancer is a life-threatening disease and a leading cause of death worldwide, with an estimated 611,000 deaths and over 2 million new cases in the United States in 2024. The rising incidence of major cancers, including among younger individuals, highlights the need for early screening and monitoring of risk factors to manage and decrease cancer risk.

This study aimed to leverage explainable machine learning models to identify and analyze the key risk factors associated with breast, colorectal, lung, and prostate cancers. By uncovering significant associations between risk factors and these major cancer types, we sought to enhance the understanding of cancer diagnosis risk profiles. Our goal was to facilitate more precise screening, early detection, and personalized prevention strategies, ultimately contributing to better patient outcomes and promoting health equity.

Deidentified electronic health record data from Medical Information Mart for Intensive Care (MIMIC)-III was used to identify patients with 4 types of cancer who had longitudinal hospital visits prior to their diagnosis presence. Their records were matched and combined with those of patients without cancer diagnoses using propensity scores based on demographic factors. Three advanced models, penalized logistic regression, random forest, and multilayer perceptron (MLP), were conducted to identify the rank of risk factors for each cancer type, with feature importance analysis for random forest and MLP models. The rank biased overlap was adopted to compare the similarity of ranked risk factors across cancer types.

Our framework evaluated the prediction performance of explainable machine learning models, with the MLP model demonstrating the best performance. It achieved an area under the receiver operating characteristic curve of 0.78 for breast cancer (n=58), 0.76 for colorectal cancer (n=140), 0.84 for lung cancer (n=398), and 0.78 for prostate cancer (n=104), outperforming other baseline models (P<.001). In addition to demographic risk factors, the most prominent nontraditional risk factors overlapped across models and cancer types, including hyperlipidemia (odds ratio [OR] 1.14, 95% CI 1.11-1.17; P<.01), diabetes (OR 1.34, 95% CI 1.29-1.39; P<.01), depressive disorders (OR 1.11, 95% CI 1.06-1.16; P<.01), heart diseases (OR 1.42, 95% CI 1.32-1.52; P<.01), and anemia (OR 1.22, 95% CI 1.14-1.30; P<.01). The similarity analysis indicated the unique risk factor pattern for lung cancer from other cancer types.

The study's findings demonstrated the effectiveness of explainable ML models in assessing nontraditional risk factors for major cancers and highlighted the importance of considering unique risk profiles for different cancer types. Moreover, this research served as a hypothesis-generating foundation, providing preliminary results for future investigation into cancer diagnosis risk analysis and management. Furthermore, expanding collaboration with clinical experts for external validation would be essential to refine model outputs, integrate findings into practice, and enhance their impact on patient care and cancer prevention efforts.

Several single nucleotide polymorphisms (SNPs) identified through genome-wide association studies (GWASs) on colorectal cancer (CRC) incidence are also shown as promising predictors of clinical outcomes in CRC patients. These genetic variants might help inform precision prognostic strategies by predicting disease progression, treatment response, and overall survival, thereby guiding more personalized treatment plans. However, conflicting evidence exists regarding their clinical relevance.

A systematic review and meta-analysis was performed to assess the potential of GWAS-identified SNPs in predicting CRC outcomes.

We conducted a comprehensive search of PubMed, Web of Science, Embase, and Cochrane databases up to 18 October 2024 for prospective studies that investigated the associations of CRC-related SNPs and polygenic risk scores (PRSs) built based on multiple SNPs with clinical outcomes. Quality of the included studies was assessed using the Newcastle-Ottawa Scale, and the heterogeneity was assessed by I2 index and Cochran's Q test. The final analysis included 22 studies with overall high quality and heterogeneity in several aspects (e.g., genetic models, ethnic background, and genetic signatures of CRC types). Among over 100 CRC risk-related loci, 12 SNPs were statistically associated with CRC clinical outcomes (mainly survival outcomes), which were replicated in multiple studies. Notably, rs9929218 and rs6983267, located in genes involved in processes of tumorigenesis, were linked to poor survival with hazard ratios (95% CIs) of 1.26 (1.12-1.42) under a recessive model and 1.33 (1.10-1.61) under an additive model, respectively, in the stratified analysis by genetic models. Besides, PRSs built based on survival-related SNPs were moderately associated with overall survival in CRC patients with hazard ratios exceeding 2.6 for each one-point increase in PRS.

Individual genetic variants and PRSs are predictive of CRC survival, and might serve as potential factors for risk stratification, which could help develop personalized treatment and surveillance strategies for CRC patients. However, the potential for false positives and the significant heterogeneity among studies that cannot be fully addressed in the current analysis due to limited data require a cautious interpretation of these findings. Large-scale studies are warranted to further explore and validate GWAS-identified SNPs for promising prognostic biomarkers in CRC patients while accounting for factors such as ethnic differences and tumor subtypes.

Effective screening is essential to reducing CRC incidence and mortality by detecting the disease at early stages and identifying non-invasive precursors. While colonoscopy remains the most sensitive modality to visualize and remove neoplastic lesions thereby reducing CRC and the related death, its high cost and invasive nature limit its widespread use. The fecal immunochemical test (FIT), which offers a non-invasive alternative with higher public acceptance and comparable cost-effectiveness to colonoscopy, has become the preferred screening method in many regions. Newer non-invasive tests, such as multitarget stool DNA or RNA tests, have shown improved sensitivity for CRC and advanced adenomas, although their high costs and lower specificity present challenges for large-scale implementation. Blood-based circulating cell-free DNA test also offer promise but still require optimization to be cost-effective. The heterogeneity of the screening population further complicates the effectiveness of CRC screening programs. Variations in non-communicable disease risk factors, such as metabolic syndrome, lifestyle habits, and comorbidities, can significantly influence CRC risk and screening outcomes. Moreover, diverse screening behaviors, including inconsistent adherence to recommended screening intervals and the interchangeable use of different screening modalities, add complexity to achieving uniform effectiveness across populations. This variability underscores the need for personalized screening strategies that consider individual risk profiles and screening behaviors, as well as the application of cutting-edge technologies such as big data analytics, artificial intelligence, and digital twin approaches to evaluate its effectiveness. This article reviews the current CRC screening strategies, the advantages of non-invasive methods, and the potential of fecal hemoglobin concentration, to tailor screening intervals and improve risk stratification. It also discusses the emerging role of real-world data and advanced technologies in enhancing CRC screening accuracy and effectiveness, particularly in complex real-world scenarios where traditional methods may fall short. Before novel non-invasive approaches, such as ctDNA tests or polygenic risk scores, are validated and proven cost-effective, exploring the clinical utility of FIT and its quantitative measurement in both screening and surveillance by integrating real-world clinical big data seems a feasible direction for achieving sustained development in population screening.

Diet and genetic risk are risk factors for colorectal cancer (CRC). The interaction between the EAT (Lancet Commission on healthy diets from sustainable food systems)-Lancet diet and genetic variants on CRC risk remains unclear.

We aim to investigate the association between EAT-Lancet diet and CRC risk and to evaluate its combined effect with genetic risk on CRC risk.

We conducted a prospective cohort study involving 177,441 participants from the UK Biobank who completed 24-h food recall questionnaires at least once. The EAT-Lancet Diet Index (ELD-I) was calculated using the dietary recall data to assess EAT-Lancet diet, and a polygenic risk score (PRS) was constructed by using 197 single-nucleotide polymorphisms to evaluate genetic risk. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated using Cox proportional hazards regression models to assess the associations.

During a median follow-up of 13.05 y, 2,016 participants developed CRC. Higher ELD-I was significantly associated with a reduced CRC risk (the highest compared with the lowest HR: 0.87, 95% CI: 0.76-0.99). A significant additive interaction between PRS and ELD-I was identified on CRC risk (relative excess risk due to interaction: 0.142, 95% CI: 0.058, 0.225). The ELD-I was significantly associated with reduced CRC risk in individuals with moderate but not low and high genetic risk, with HRs (95% CIs) of 0.76 (0.63, 0.92), 0.84 (0.53, 1.33), and 0.96 (0.76, 1.20), respectively. Compared with participants with higher PRS and lower ELD-I, those with lower PRS and higher ELD-I showed a 75% reduction in CRC risk (HR: 0.25; 95% CI: 0.17, 0.36).

The ELD-I could reduce 13% of CRC risk, especially in individuals with a moderate genetic risk. Individuals with high ELD-I and low PRS had the lowest CRC risk than those with low ELD-I and high PRS. These findings underscore the potential role of EAT-Lancet Diet in CRC prevention.

Despite the worrisome rise of early-onset colorectal cancer (EOCRC), risk factors have not been definitively established. We use a large, granular database to evaluate risk factors for EOCRC, investigate differences in associations with EOCRC and later-onset CRC (LOCRC), and compare metrics of accelerated aging, a hypothesized driver of EOCRC.

This was a case-control analysis within the UK Biobank. Risk factors for each cancer were identified and compared, including aging measures (chronological age, telomere length, PhenoAge, and homeostatic dysregulation).

A total of 31,164 persons were matched. We found an increased risk of EOCRC with PRS (OR 1.53; 95% CI 1.19-1.97; p < 0.001). LOCRC was associated with increasing PRS (OR 1.48; 95% CI 1.44 - 1.53; p < 0.001), increasing waist-to-hip ratio (OR 5.81; 95% CI 3.25 - 10.38; p < 0.001), family history of CRC (OR 1.27; 95% CI 1.16 - 1.40; p < 0.001), and history of smoking (OR 1.11; 95% CI 1.03 - 1.19; p = 0.01). Male sex and prior CRC screening were associated with reduced risk of LOCRC. The inclusion of PhenoAge as the measure of aging demonstrated the best model fit for both EOCRC and LOCRC. For each year that PhenoAge exceeded chronological age, the odds of EOCRC increased by 7%, while odds of LOCRC only increased by 1%.

Within this study, we find that genetic risk variants are a significant driver of EOCRC risk. Accelerated aging appears to be associated with increased risk of both EOCRC and LOCRC, and measures such as PhenoAge warrant continued study.

One major topic in colorectal cancer (CRC) research is the role of immune cells against cancer cells. The association of single-nucleotide polymorphisms (SNPs) and polygenic risk scores (PRS) with CRC was examined and their functional properties were identified using a gene-gene interaction network. 960 CRC patients at Seoul National University Hospital (SNUH, discovery) and 6,627 CRC patients at Chonnam National University Hospital (CNNUH, validation) were enrolled. SNPs were genotyped using the Korean Biobank Array. 2,729 immune-related genes were selected from the Ensembl, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes, and 37,398 SNPs were mapped. PRS were categorized into tertiles. Cox proportional hazard models were fitted for overall survival (OS) and progression-free survival (PFS). A gene-gene interaction network was analyzed. Among CRC patients from SNUH, 154 (16.0%) died, while 245 (25.5%) had progression. In CNNUH, 3,537 (53.4%) died. For OS, the most significant association was observed for rs117322760 (8q23.1, PKHD1L1, hazard ratio (HR) = 4.58, p-value = 1.40 × 10- 6). For PFS, it was observed in rs143531681 (7q36.1, NOS3, HR = 4.67, p-value = 9.72 × 10- 8). For PRS, the highest tertile group showed an increased risk for OS (HR = 59.58, p-value = 9.20 × 10-48) and PFS (HR = 9.81, p-value = 1.69 × 10-23). Significant interactions were observed between PIK3R2 and PIK3CA for OS and ALOX5 and COTL1 for PFS. This study presented novel genetic variants associated with OS and PFS in CRC patients, and notable findings from the analysis of PRS and the gene-gene interaction.

Excess alcohol consumption is associated with increased risk of colorectal cancer, but the evidence on the individual and joint effects of alcohol consumption and genetic risk on the occurrence of various stages of colorectal carcinogenesis is limited.

We evaluated the associations of alcohol consumption and a polygenic risk score based on 140 colorectal cancer related loci with findings of colorectal neoplasia among 4662 participants in the German screening colonoscopy program. Analyses were conducted by multiple logistic regression. We determined genetic risk equivalents to quantify the effect of alcohol consumption in terms of the difference in polygenic risk score conveying equivalent risk.

Moderate and high (12 to <25 g/d and ≥25 g/d) alcohol consumption was associated with increased risk of advanced colorectal neoplasia (adjusted odds ratio = 1.28, 95% CI = 1.03 to 1.58, and adjusted odds ratio = 1.44, 95% CI = 1.14 to 1.81, respectively), while associations with any colorectal neoplasia were weaker. No significant interactions between alcohol consumption and polygenic risk score were observed. Participants with high alcohol consumption in the highest polygenic risk score tertile had a 3.4-fold increased risk of advanced neoplasia compared with individuals with low or no alcohol consumption in the lowest polygenic risk score tertile. The estimated impact of high alcohol consumption on the risk of advanced neoplasia was equivalent to the risk increase by a 26-percentile-higher polygenic risk score (genetic risk equivalent = 26, 95% CI = 9 to 44).

High alcohol consumption and polygenic risk score have a major impact on the risk of advanced colorectal neoplasia. The estimated preventive impact of avoiding high alcohol consumption is as strong as the impact of having a substantially lower polygenic risk.

Polygenic risk scores (PRS) hold prognostic value for identifying individuals at higher risk of type 2 diabetes (T2D). However, further characterization is needed to understand the generalizability of T2D PRS in diverse populations across various contexts. We characterized a multi-ancestry T2D PRS among 244,637 cases and 637,891 controls across eight populations from the Population Architecture Genomics and Epidemiology (PAGE) Study and 13 additional biobanks and cohorts. PRS performance was context dependent, with better performance in those who were younger, male, with a family history of T2D, without hypertension, and not obese or overweight. Additionally, the PRS was associated with various diabetes-related cardiometabolic traits and T2D complications, suggesting its utility for stratifying risk of complications and identifying shared genetic architecture between T2D and other diseases. These findings highlight the need to account for context when evaluating PRS as a tool for T2D risk prognostication and potentially generalizable associations of T2D PRS with diabetes-related traits despite differential performance in T2D prediction across diverse populations.

Both genetic factors and lifestyle play a critical role in colorectal cancer, but the extent to which an increased genetic risk can be offset by a healthy lifestyle remains unclear.

We included 51,171 participants from the Prostate, Lung, Colorectal, and Ovarian Cancer cohort. A polygenic risk score was created based on 205 genetic variants associated with colorectal cancer, and a healthy lifestyle score was constructed based on six lifestyle factors. Cox regression models were used to evaluate the association of genetic and lifestyle factors with colorectal cancer incidence.

Compared with individuals at low genetic risk (the lowest 20%), those with intermediate genetic risk (20%-80%) and high genetic risk (the highest 20%) had a significantly increased risk of colorectal cancer (HR = 1.71 and 2.52, respectively). Compared with participants with a favorable lifestyle (scoring 4-6), those with an unfavorable lifestyle (scoring 0 or 1) had a 47% higher risk of colorectal cancer. Moreover, participants with a high genetic risk and a favorable lifestyle had a 45% lower risk of colorectal cancer than those with a high genetic risk and an unfavorable lifestyle, with their 10-year absolute risks of 1.29% and 2.07%, respectively.

Our findings suggest that adherence to a healthy lifestyle holds promise to reduce the genetic impact on colorectal cancer risk.

This study indicates that modifiable lifestyle factors play an important role in colorectal cancer prevention, providing new insights for personalized prevention strategies.

Glaucoma polygenic risk scores could guide glaucoma public health screening initiatives. We investigated how age influences the relationship between a multitrait glaucoma polygenic risk score (mtGPRS) and primary open-angle glaucoma indicators, including intraocular pressure (IOP), retinal structure, and glaucoma prevalence.

We analyzed UK Biobank participants with demographic and genetic data, assessing IOP (n = 118,153), macular retinal nerve fiber layer thickness (mRNFL; n = 42,132), macular ganglion cell inner plexiform layer thickness (mGCIPL; n = 42,042), and prevalent glaucoma status (8982 cases among 192,283 participants). An mtGPRS was constructed using 2673 genetic variants. We used multivariable linear regression to assess how age modifies the relationship between mtGPRS and glaucoma traits (IOP, mRFNL, and mGCIPL) and multivariable logistic regression for prevalent glaucoma risk. We analyzed age quartiles (Q1 = <51, Q2 = 51-57, Q3 = 58-62, and Q4 = ≥63 years) - glaucoma trait interaction tests with the Wald test. All analyses were adjusted for confounders, including nonlinear age effects.

Age significantly modified the relationship between the mtGPRS and IOP (Pinteraction = 2.7e-27). Mean IOP differences (millimeters of mercury [mm Hg]) per standard deviation (SD) of mtGPRS were 0.95, 1.02, 1.18, and 1.24 across age quartiles. Similar trends were observed for glaucoma risk (odds ratio per SD of mtGPRS = 2.38, 2.57, 2.80, and 2.75; Pinteraction = 1.0e-06). Relationships between mtGPRS and inner retinal thickness (mRNFL and mGCIPL) across age strata were inconsistently modified by age (Pinteraction ≥ 0.01).

With increasing age, an mtGPRS was a better predictor of higher IOP and glaucoma prevalence. It is useful to consider chronological age with genetic information in designing glaucoma screening strategies.

Family history (FH) of cancer and polygenic risk scores (PRS) are pivotal for cancer risk assessment, yet their combined impact remains unclear. Participants in the UK Biobank (UKB) were recruited between 2006 and 2010, with complete follow-up data updated until February 2020 for Scotland and January 2021 for England and Wales. Using UKB data (N = 442,399), we constructed PRS and incidence-weighted overall cancer PRS (CPRS). FH was assessed through self-reported standardized questions. Among 202,801 men (34.6% with FH) and 239,598 women (42.0% with FH), Cox regression was used to examine the associations between FH, PRS, and cancer risk. We found a significant dose-response relationship between FH of cancer and corresponding cancer risk (Ptrend < .05), with over 10 significant pairs of cross-cancer effects of FH. FH and PRS are positively correlated and independent. Joint effects of FH of cancer (multiple cancers) and PRS (CPRS) on corresponding cancer risk were observed: for instance, compared with participants with no FH of cancer and low PRS, men with FH of cancer and high PRS had the highest risk of colorectal cancer (hazard ratio [HR]: 3.69, 95% confidence interval [CI]: 3.01-4.52). Additive interactions were observed in prostate and overall cancer risk for men and breast cancer for women, with the most significant result being a relative excess risk of interaction (RERI) of 2.98, accounting for ~34% of the prostate cancer risk. In conclusion, FH and PRS collectively contribute to cancer risk, supporting their combined application in personalized risk assessment and early intervention strategies.

Colorectal cancer is increasingly diagnosed in people aged <50 years. New U.S. guidelines recommend screening initiation at age 45 years. Providing personalized risk for colorectal cancer using polygenic risk scores may be an opportunity to engage this younger population in colorectal cancer screening. There is limited research on patient understanding of polygenic risk scores results and use of polygenic risk scores to inform colorectal cancer screening decisions.

From May 2022 to June 2023, 20 Kaiser Permanente Colorado members aged 46-51 years who had been offered colorectal cancer screening but had never completed it signed consent to provide a saliva sample for colorectal cancer polygenic risk score analysis. After receiving personalized polygenic risk scores for colorectal cancer, participants completed a semistructured interview regarding the understanding of their polygenic risk scores, perceived colorectal cancer risk, and intention to screen. Thematic analysis was conducted using Atlas.ti, Version 8.

Of the 19 participants who successfully completed polygenic risk score-related testing and a semistructured interview, 13 were female, 14 never smoked cigarettes, 6 were Hispanic, and 13 were non-Hispanic White. One participant had high risk for colorectal cancer on the basis of polygenic risk score results. Qualitative interviews showed participants' understanding of their results, trust in polygenic risk scores, perception of risk for colorectal cancer, plans to complete colorectal cancer screening, intent to share polygenic risk scores with healthcare providers, and concerns about genetic results impacting health care.

Qualitative analyses suggest that participants were interested in and understood their polygenic risk score results. Further study is needed to develop guidelines, effective calls to action, provider engagement, and health education materials on use of polygenic risk scores for health decision making.

Colorectal cancer (CRC) is a complex disease with monogenic, polygenic and environmental risk factors. Polygenic risk scores (PRSs) aim to identify high polygenic risk individuals. Due to differences in genetic background, PRS distributions vary by ancestry, necessitating standardization. We compared four post-hoc methods using the All of Us Research Program Whole Genome Sequence data for a transancestry CRC PRS. We contrasted results from linear models trained on A. the entire data or an ancestrally diverse subset AND B. covariates including principal components of ancestry or admixture. Standardization with the training subset also adjusted the variance. All methods performed similarly within ancestry, OR (95% C.I.) per s.d. change in PRS: African 1.5 (1.02, 2.08), Admixed American 2.2 (1.27, 3.85), European 1.6 (1.43, 1.89), and Middle Eastern 1.1 (0.71, 1.63). Using admixture and an ancestrally diverse training set provided distributions closest to standard Normal. Training a model on ancestrally diverse participants, adjusting both the mean and variance using admixture as covariates, created standard Normal z-scores, which can be used to identify patients at high polygenic risk. These scores can be incorporated into comprehensive risk calculation including other known risk factors, allowing for more precise risk estimates.

The advent of genome-wide sequencing and large-scale genetic epidemiological studies has led to numerous opportunities for the application of genetics in clinical medicine. Leveraging this information toward the formation of clinically useful tools has been an ongoing research goal in this area. A genetic risk score (GRS) is a measure that attempts to estimate the cumulative contribution of established genetic risk factors toward an outcome of interest, taking into account the cumulative risk that each of these individual genetic risk factors conveys. The purpose of this perspective is to provide a systematic framework to evaluate a GRS for clinical application.

Since the initial polygenic risk score methodology in 2007, there has been increasing GRS application across the medical literature. In rheumatology, this has included application to rheumatoid arthritis, gout, spondyloarthritis, lupus, and inflammatory arthritis.

GRSs are particularly relevant to rheumatology, where common diseases have many complex genetic factors contributing to risk. Despite this, there is no widely accepted method for the critical application of a GRS, which can be a particular challenge for the clinical rheumatologist seeking to clinically apply GRSs. This review provides a framework by which the clinician may systematically evaluate a GRS.

As genotyping becomes more accessible and cost-effective, it will become increasingly important to recognize the clinical applicability of GRSs and identify those of the highest utility for patient care. This framework for the evaluation of a GRS will also help ensure reliability among GRS research in rheumatology, thereby helping to advance the field.

The feasibility of population screening for colorectal cancer has been demonstrated in several studies. Most of these studies have considered individual characteristics, diagnostic approaches, epidemiological data, and socioeconomic factors. In this article, we comment on an editorial by Metaxas et al published in the recent issue of the journal. The authors emphasized the need to raise public awareness through health education programs and the possibility of using easily accessible non-invasive screening methods. Here, we focus on non-invasive molecular genetic approaches that can aid in colorectal cancer screening. On the one hand, we highlighted the use of tumor DNA/RNA markers directly for screening and, on the other hand, underline the use of polygenic risk assessment and hereditary predisposition to select individuals for more thorough cancer screening.

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.

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.

Osteoporosis is a complex metabolic bone disease that severely undermines the quality of life and overall health of the elderly. While previous studies have established a close relationship between gut microbiome and host bone metabolism, the role of genetic factors has received less scrutiny. This research aims to identify potential taxa associated with various bone mineral density states, incorporating assessments of genetic factors. Fecal microbiome profiles from 605 individuals (334 females and 271 males) aged 55-65 from the Taizhou Imaging Study with osteopenia (n = 270, 170 women) or osteoporosis (n = 94, 85 women) or normal (n = 241, 79 women) were determined using shotgun metagenomic sequencing. The linear discriminant analysis was employed to identify differentially enriched taxa. Utilizing the Kyoto Encyclopedia of Genes and Genomes for annotation, functional pathway analysis was conducted to identify differentially metabolic pathways. Polygenic risk score for osteoporosis was estimated to represent genetic susceptibility to osteoporosis, followed by stratification and interaction analyses. Gut flora diversity did not show significant differences among various bone mineral groups. After multivariable adjustment, certain species, such as Clostridium leptum, Fusicatenibacter saccharivorans and Roseburia hominis, were enriched in osteoporosis patients. Statistically significant interactions between the polygenic risk score and taxa Roseburia faecis, Megasphaera elsdenii were observed (P for interaction = 0.005, 0.018, respectively). Stratified analyses revealed a significantly negative association between Roseburia faecis and bone mineral density in the low-genetic-risk group (β = -0.045, P < 0.05), while Turicimonas muris was positively associated with bone mineral density in the high-genetic-risk group (β = 4.177, P < 0.05) after multivariable adjustments. Functional predictions of the gut microbiome indicated an increase in pathways related to structural proteins in high-genetic-risk patients, while low-genetic-risk patients exhibited enrichment in enzyme-related pathways. This study emphasizes the association between gut microbes and bone mass, offering new insights into the interaction between genetic background and gut microbiome.

Colorectal cancer (CRC) is a leading cause of cancer mortality worldwide. Accurate cancer risk assessment approaches could increase rates of early CRC diagnosis, improve health outcomes for patients and reduce pressure on diagnostic services. The faecal immunochemical test (FIT) for blood in stool is widely used in primary care to identify symptomatic patients with likely CRC. However, there is a 6-16% noncompliance rate with FIT in clinic and ~90% of patients over the symptomatic 10 µg/g test threshold do not have CRC. A polygenic risk score (PRS) quantifies an individual's genetic risk of a condition based on many common variants. Existing PRS for CRC have so far been used to stratify asymptomatic populations. We conducted a retrospective cohort study of 50,387 UK Biobank participants with a CRC symptom in their primary care record at age 40+. A PRS based on 201 variants, 5 genetic principal components and 22 other risk factors and markers for CRC were assessed for association with CRC diagnosis within 2 years of first symptom presentation using logistic regression. Associated variables were included in an integrated risk model and trained in 80% of the cohort to predict CRC diagnosis within 2 years. An integrated risk model combining PRS, age, sex, and patient-reported symptoms was predictive of CRC development in a testing cohort (receiver operating characteristic area under the curve, ROCAUC: 0.76, 95% confidence interval: 0.71-0.81). This model has the potential to improve early diagnosis of CRC, particularly in cases of patient noncompliance with FIT.

The occurrence of colorectal cancer (CRC) is increasing among young adults, but the etiology is still largely unknown. In addition to germline monogenetic variants also polygenic risk scores (PRS) have been proven to correctly estimate the risk of CRC.

We present a 24-year-old male with disseminated colon cancer who carried a germline duplication on chromosome 1 spanning 200 kb and covering CD101, TTF2, MIR942, TRIM45, and parts of PTGFRN and VTCN1. The duplication was located in tandem. A similar duplication was previously reported in a family with CRC among two brothers aged 52 and 61 years old at diagnosis. Particularly, MIR942 was an interesting finding as it is involved in the regulation of the Wnt signaling pathway. Disruption of the Wnt pathway is known to cause CRC. However, in our case the duplication did not segregate with disease in the family. Calculation of a PRS in our patient found an average PRS for CRC.

Our findings do not support that this duplication is a monogenetic cause of CRC, nor did a PRS point towards an increased risk in this 24-year-old male. Whether the duplication is a risk factor in combination with other genetic and non-genetic risk factors requires further studies.

Polygenic risk scores (PRS) aggregate the contribution of many risk variants to provide a personalized genetic susceptibility profile. Since sample sizes of glioma genome-wide association studies (GWAS) remain modest, there is a need to efficiently capture genetic risk using available data.

We applied a method based on continuous shrinkage priors (PRS-CS) to model the joint effects of over 1 million common variants on disease risk and compared this to an approach (PRS-CT) that only selects a limited set of independent variants that reach genome-wide significance (P < 5 × 10-8). PRS models were trained using GWAS stratified by histological (10 346 cases and 14 687 controls) and molecular subtype (2632 cases and 2445 controls), and validated in 2 independent cohorts.

PRS-CS was generally more predictive than PRS-CT with a median increase in explained variance (R2) of 24% (interquartile range = 11-30%) across glioma subtypes. Improvements were pronounced for glioblastoma (GBM), with PRS-CS yielding larger odds ratios (OR) per standard deviation (SD) (OR = 1.93, P = 2.0 × 10-54 vs. OR = 1.83, P = 9.4 × 10-50) and higher explained variance (R2 = 2.82% vs. R2 = 2.56%). Individuals in the 80th percentile of the PRS-CS distribution had a significantly higher risk of GBM (0.107%) at age 60 compared to those with average PRS (0.046%, P = 2.4 × 10-12). Lifetime absolute risk reached 1.18% for glioma and 0.76% for IDH wildtype tumors for individuals in the 95th PRS percentile. PRS-CS augmented the classification of IDH mutation status in cases when added to demographic factors (AUC = 0.839 vs. AUC = 0.895, PΔAUC = 6.8 × 10-9).

Genome-wide PRS has the potential to enhance the detection of high-risk individuals and help distinguish between prognostic glioma subtypes.

Low-dose computed tomography (LDCT) can help reducing lung cancer mortality. In Taiwan, the existing screening criteria revolve around smoking habits and family history of lung cancer. The role of genetic variation in non-small cell lung cancer (NSCLC) development is increasingly recognized. In this study, we aimed to investigate the potential benefits of polygenic risk scores (PRSs) in predicting NSCLC and enhancing the effectiveness of screening programs.

We conducted a retrospective cohort study that included participants without prior diagnosis of lung cancer and later received LDCT for lung cancer screening. Genetic data for these participants were obtained from the project of Taiwan Precision Medicine Initiative. We adopted the model of genome-wide association study-derived PRS calculation using 19 susceptibility loci associated with the risk of NSCLC as reported by Dai et al.

We studied a total of 2,287 participants (1,197 male, 1,090 female). More female participants developed NSCLC during the follow-up period (4.4% v 2.5%, P = .015). The only risk factor of NSCLC diagnosis among male participants was age. Among female participants, independent risk factors of NSCLC diagnosis were age (adjusted hazard ratio [aHR], 1.08 [95% CI, 1.04 to 1.11]), a family history of lung cancer (aHR, 3.21 [95% CI, 1.78 to 5.77]), and PRS fourth quartile (aHR, 2.97 [95% CI, 1.25 to 7.07]). We used the receiver operating characteristics to show an AUC value of 0.741 for the conventional model. With the further incorporation of PRS, the AUC rose to 0.778.

The evaluation of PRS for NSCLC prediction holds promise for enhancing the effectiveness of lung cancer screening in Taiwan especially in women. By incorporating genetic information, screening criteria can be tailored to identify individuals at higher risks of NSCLC.

Since the nineties, the incidence of sporadic early-onset (EO) cancers has been rising worldwide. The underlying reasons are still unknown. However, identifying them is vital for advancing both prevention and intervention. Here, we exploit available knowledge derived from clinical observations to formulate testable hypotheses aimed at defining the causal factors of this epidemic and discuss how to experimentally test them. We explore the potential impact of exposome changes from the millennials to contemporary young generations, considering both environmental exposures and enhanced susceptibilities to EO-cancer development. We emphasize how establishing the time required for an EO cancer to develop is relevant to defining future screening strategies. Finally, we discuss the importance of integrating multi-dimensional data from international collaborations to generate comprehensive knowledge and translate these findings back into clinical practice.

Phenotypic misclassification in genetic association analyses can impact the accuracy of PRS-based prediction models. The bias reduction method proposed by Tong et al. (2019) has demonstrated its efficacy in reducing the effects of bias on the estimation of association parameters between genotype and phenotype while minimizing variance by employing chart reviews on a subset of the data for validating phenotypes, however its improvement of subsequent PRS prediction accuracy remains unclear. Our study aims to fill this gap by assessing the performance of simulated PRS models and estimating the optimal number of chart reviews needed for validation.

To comprehensively assess the efficacy of the bias reduction method proposed by Tong et al. in enhancing the accuracy of PRS-based prediction models, we simulated each phenotype under different correlation structures (an independent model, a weakly correlated model, a strongly correlated model) and introduced error-prone phenotypes using two distinct error mechanisms (differential and non-differential phenotyping errors). To facilitate this, we used genotype and phenotype data from 12 case-control datasets in the Alzheimer's Disease Genetics Consortium (ADGC) to produce simulated phenotypes. The evaluation included analyses across various misclassification rates of original phenotypes as well as quantities of validation set. Additionally, we determined the median threshold, identifying the minimal validation size required for a meaningful improvement in the accuracy of PRS-based predictions across a broad spectrum.

This simulation study demonstrated that incorporating chart review does not universally guarantee enhanced performance of PRS-based prediction models. Specifically, in scenarios with minimal misclassification rates and limited validation sizes, PRS models utilizing debiased regression coefficients demonstrated inferior predictive capabilities compared to models using error-prone phenotypes. Put differently, the effectiveness of the bias reduction method is contingent upon the misclassification rates of phenotypes and the size of the validation set employed during chart reviews. Notably, when dealing with datasets featuring higher misclassification rates, the advantages of utilizing this bias reduction method become more evident, requiring a smaller validation set to achieve better performance.

This study highlights the importance of choosing an appropriate validation set size to balance between the efforts of chart review and the gain in PRS prediction accuracy. Consequently, our study establishes a valuable guidance for validation planning, across a diverse array of sensitivity and specificity combinations.

Risk prediction and disease prevention are the innovative care challenges of the 21st century. Apart from freeing the individual from the pain of disease, it will lead to low medical costs for society. Until very recently, risk assessments have ushered in a new era with the emergence of omics technologies, including genomics, transcriptomics, epigenomics, proteomics, and so on, which potentially advance the ability of biomarkers to aid prediction models. While risk prediction has achieved great success, there are still some challenges and limitations. We reviewed the general process of omics-based disease risk model construction and the applications in four typical diseases. Meanwhile, we highlighted the problems in current studies and explored the potential opportunities and challenges for future clinical practice.

Excess weight is an established risk factor of colorectal cancer (CRC). However, evidence is lacking on how its impact varies by polygenic risk at different stages of colorectal carcinogenesis.

We assessed the individual and joint associations of body mass index (BMI) and polygenic risk scores (PRSs) with findings of colorectal neoplasms among 4,784 participants of screening colonoscopy. Adjusted odds ratios (aORs) for excess weight derived by multiple logistic regression were converted to genetic risk equivalents (GREs) to quantify the impact of excess weight compared with genetic predisposition.

Overweight and obesity (BMI 25-<30 and ≥30 kg/m 2 ) were associated with increased risk of any colorectal neoplasm (aOR [95% confidence interval, CI] 1.26 [1.09-1.45] and 1.47 [1.24-1.75]). Obesity was associated with increased risk of advanced colorectal neoplasm (aOR [95% CI] 1.46 [1.16-1.84]). Dose-response relationships were seen for the PRS (stronger for advanced neoplasms than any neoplasms), with no interaction with BMI, suggesting multiplicative effects of both factors. Obese participants with a PRS in the highest tertile had a 2.3-fold (95% CI 1.7-3.1) and 2.9-fold (95% CI 1.9-4.3) increased risk of any colorectal neoplasm and advanced colorectal neoplasm, respectively. The aOR of obesity translated into a GRE of 38, meaning that its impact was estimated to be equivalent to the risk caused by 38 percentiles higher PRS for colorectal neoplasm.

Excess weight and polygenic risk are associated with increased risk of colorectal neoplasms in a multiplicative manner. Maintaining normal weight is estimated to have an equivalent effect as having 38 percentiles lower PRS.

High red and processed meat intake and genetic predisposition are risk factors of colorectal cancer (CRC). However, evidence of their independent and joint associations on the risk of colorectal neoplasms is limited. We assessed these associations among 4774 men and women undergoing screening colonoscopy. Polygenic risk scores (PRSs) were calculated based on 140 loci related to CRC. We used multiple logistic regression models to evaluate the associations of red and processed meat intake and PRS with the risk of colorectal neoplasms. Adjusted odds ratios (aORs) were translated to genetic risk equivalents (GREs) to compare the strength of the associations with colorectal neoplasm risk of both factors. Compared to ≤1 time/week, processed meat intake >1 time/week was associated with a significantly increased risk of colorectal neoplasm [aOR (95% CI): 1.28 (1.12-1.46)]. This risk increase was equivalent to the risk increase associated with a 19 percentile higher PRS. The association of red meat intake with colorectal neoplasm was weaker and did not reach statistical significance. High processed meat intake and PRS contribute to colorectal neoplasm risk independently. Limiting processed meat intake may offset a substantial proportion of the genetically increased risk of colorectal neoplasms.

Colorectal cancer (CRC) is the third cancer among the known causes of cancer that impact people. Although CRC drug options are imperfect, primary detection of CRC can play a key role in treating the disease and reducing mortality. Cancer tissues show many molecular markers that can be used as a new way to advance therapeutic methods. Nanotechnology includes a wide range of nanomaterials with high diagnostic and therapeutic power. Several nanomaterials and nanoformulations can be used to treat cancer, especially CRC. In this review, we discuss recent insights into nanotechnology in colorectal cancer.

Colorectal cancer (CRC) is one of the leading causes of cancer-related deaths, and improving the prognosis of CRC patients is an urgent concern. The aim of this study was to explore new immunotherapy targets to improve survival in CRC patients.

We analyzed CRC-related single-cell data GSE201348 from the Gene Expression Omnibus (GEO) database, and identified differentially expressed genes (DEGs). Subsequently, we performed differential analysis on the rectum adenocarcinoma (READ) and colon adenocarcinoma (COAD) transcriptome sequencing data [The Cancer Genome Atlas (TCGA)-CRC queue] and clinical data downloaded from TCGA database. Subgroup analysis was performed using CIBERSORTx and cluster analysis. Finally, biomarkers were identified by one-way cox regression as well as least absolute shrinkage and selection operator (LASSO) analysis.

In this study, we analyzed CRC-related single-cell data GSE201348, and identified 5,210 DEGs. Subsequently, we performed differential analysis on the TCGA-CRC queue database, and obtained 4,408 DEGs. Then, we categorized the cancer samples in the sequencing data into three groups (k1, k2, and k3), with significant differences observed between the k1 and k2 groups via survival analysis. Further differential analysis on the samples in the k1 and k2 groups identified 1,899 DEGs. A total of 77 DEGs were selected among those DEGs obtained from three differential analyses. Through subsequent Cox univariate analysis and LASSO analysis, seven biomarkers (RETNLB, CLCA4, UGT2A3, SULT1B1, CCL24, BMP5, and ATOH1) were identified and selected to establish a risk score (RS).

To sum up, this study demonstrates the potential of the seven-gene prognostic risk model as instrumental variables for predicting the prognosis of CRC.