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Identifying risk factors for early onset colorectal cancer (EOCRC) could help reverse its rising incidence through risk factor reduction and/or early screening. We sought to identify EOCRC risk factors that could be used for decisions about early screening. Using electronic databases and medical record review, we compared male veterans aged 35-49 years diagnosed with sporadic EOCRC (2008-2015) matched 1:4 to clinic and colonoscopy controls without CRC, excluding those with established inflammatory bowel disease, high-risk polyposis and non-polyposis syndromes, prior bowel resection, and high-risk family history. We ascertained sociodemographic and lifestyle factors, family and personal medical history, physical measures, vital signs, medications, and laboratory values 6-18 months prior to case diagnosis. In the derivation cohort (75% of the total sample), univariate and multivariate logistic regression models were used to derive a full model and a more parsimonious model. Both models were tested using a validation cohort. Among 600 cases of sporadic EOCRC (mean [SD] age 45.2 [3.5] years; 66% White), 1200 primary care clinic controls (43.4 [4.2] years; 68% White), and 1200 colonoscopy controls (44.7 [3.8] years; 63% White), independent risk factors included age, cohabitation and employment status, BMI, comorbidity, CRC or other visceral cancer in a first- or second-degree relative; alcohol use; exercise; hyperlipidemia; use of statins, NSAIDs, and multivitamins. Validation c-statistics were 0.75-0.76 for the full model and 0.74-0.75 for the parsimonious model, respectively. These independent risk factors for EOCRC may identify veterans for whom CRC screening prior to age 45 or 50 years should be considered.

Colorectal cancer incidence has continually fallen among those 50 years old and over. However, the incidence has increased in those under 50. Even with the recent screening guidelines recommending that screening begins at age 45, nearly half of all early-onset colorectal cancer will be missed. Methods are needed to identify high-risk individuals in this age group for targeted screening. Colorectal cancer studies, as with other clinical studies, have required labor intensive chart review for the identification of those affected and risk factors. Natural language processing and machine learning can be used to automate the process and enable the screening of large numbers of patients. This study developed and compared four machine learning and statistical models: logistic regression, support vector machine, random forest, and deep neural network, in their performance in classifying colorectal cancer patients. Excellent classification performance is achieved with AUCs over 97%.

ImportancePostcolonoscopy colorectal cancer (PCCRC) refers to colorectal cancer (CRC) diagnosed after a colonoscopy in which no cancer was found and is reflective of colonoscopy quality at the individual and system levels. Colonoscopy is widely performed in the Veterans Affairs (VA) health care system, but the prevalence of PCCRC and its associated mortality are unknown.ObjectiveTo examine PCCRC prevalence and its all-cause mortality (ACM) and CRC-specific mortality (CSM) within the VA health care system.Design, Setting, and ParticipantsThis retrospective cohort study used VA-Medicare administrative data to identify 29 877 veterans aged 50 to 85 years with newly diagnosed CRC between January 1, 2003, and December 31, 2013. Patients whose colonoscopy occurred less than 6 months before CRC diagnosis with no other colonoscopy within the previous 36 months were categorized as having detected CRC (DCRC). Those who had a colonoscopy that did not detect CRC between 6 and 36 months before CRC diagnosis were categorized as having postcolonoscopy CRC (PCCRC-3y). A third group included patients with CRC and no colonoscopy within the prior 36 months. The final analysis of the data was performed in September 2022.ExposuresPrior receipt of colonoscopy.Main Outcomes and MeasuresCox proportional hazards regression (with censoring, last follow-up December 31, 2018) analyses were conducted to compare PCCRC-3y and DCRC for 5-year ACM and CSM after CRC diagnosis.ResultsOf 29 877 patients with CRC (median [IQR] age, 67 [60-75] years; 29 353 [98%] male; 5284 [18%] Black, 23 971 [80%] White, and 622 [2%] other), 1785 (6%) were classified as having PCCRC-3y and 21 811 (73%) as having DCRC. The 5-year ACM rates were 46% vs 42% for patients with PCCRC-3y vs patients with DCRC. The 5-year CSM rates were 26% vs 25% for patients with PCCRC-3y vs patients with DCRC. In multivariable Cox proportional hazards regression analysis, there was no significant difference in ACM and CSM between patients with PCCRC-3y (adjusted hazard ratio [aHR], 1.04; 95% CI, 0.98-1.11; P = .18) and patients with DCRC (aHR, 1.04; 95% CI, 0.95-1.13; P = .42). However, compared with patients with DCRC, patients with no prior colonoscopy had significantly higher ACM (aHR, 1.76; 95% CI, 1.70-1.82; P P P Conclusions and RelevanceThis study found that PCCRC-3y constituted 6% of CRCs in the VA system, which is similar to other settings. Compared with patients with CRC detected by colonoscopy, those with PCCRC-3y have comparable ACM and CSM.

Similarity is a well-known contributor to attraction, but aesthetic preferences similarity in particular has not received much research attention. Study 1 was a correlational study with 54 couples from South Carolina and North Carolina. In these existing relationships, higher perceived relationship quality was found to be positively correlated with some measures of partner aesthetic preferences similarity. Study 2 was a “phantom other” experimental investigation of the effect of aesthetic preferences similarity on attraction to strangers involving 129 undergraduates at Indiana University East. It was found that manipulating music and movie rating similarity caused both platonic and romantic attraction to vary. Compared to a control condition, aesthetic preferences similarity enhanced attraction and dissimilarity decreased attraction, but the impact of dissimilarity was stronger. The results are discussed in terms of hypotheses about the relative importance of similarity and dissimilarity, and how aesthetic preferences similarity information could serve as a shortcut estimate of relationship compatibility and potential quality.