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Objective To establish and verify a postoperative delirium risk prediction model for elderly patients after individualized hip fracture surgery. Methods Clinical data of 393 elderly patients with hip fracture who underwent surgical treatment in the Department of Orthopedics, Xi'an Central Hospital from December 2016 to January 2021 were collected retrospectively in this study. The data from 333 patients during December 2016 and July 2020 were used as the model training set, and postoperative delirium was regarded as the research endpoint. Based on the model training set, uniuariate and multivariate logistic regression were adopted to screen out independent related factors of postoperative delirium. The model was established by regression coefficient, the nomogram was drawn combined with the rms package of R language, and the discrimination, calibration and clinical applicability of the model were tested. Bootstrap was used to repeatedly sample 1 000 times in the training set for internal verification of the model, and the data of 60 patients treated from August 2020 to January 2021 were employed as the verification set for external verification of the model. Results There were 393 elderly patients with hip fracture, including 333 cases in the training set, and 60 cases in the validation set. Univariate and multivariate logistic analysis showed that dementia, preoperative albumin level and intraoperative hypotension were independent related factors of postoperative delirium in elderly patients with hip fracture. The prediction model is obtained according to the relevant factors and regression coefficients: P=1/(1+e-Z), Z=1.396×dementia or not -0.275× preoperative albumin level +1.420×intraoperative hypotension or not +6.875. The cut-off value of the model in the training set is 0.235, and the specificity and sensitivity is 0.875 and 0.622, respectively. The cut-off value of the model in the validation set is 0.211, and the specificity and sensitivity is 0.800 and 0.800, respectively. The area under curve (AUC) of the receiver operating characteristic curve (ROC) in the training set and the validation set is 0.827 (95%CI: 0.762~0.892) and 0.836 (95%CI: 0.716~0.956) respectively. After 1 000 repeated sampling by bootstrap, the AUC of the internal verification of the model is 0.831. The slopes of the calibration curves of the training set and the validation set is 1 and 1.066, respectively. The P values of the Hosmer-Lemeshow goodness-of-fit test are 0.124 and 0.743, respectively. The clinical decision curve showed that the threshold probability interval of the maximum net benefit value of the postoperative delirium predicted by the model in the training set and the validation set was about 5%~90% and 3%~90% respectively. Conclusion Our established model can predict the risk of postoperative delirium in elderly patients after hip fracture surgery.

Long non-coding RNAs (lncRNAs) serve as vital candidates to mediate cancer risk. Here, we aimed to identify the risk single-nucleotide polymorphisms (SNPs)-induced lncRNAs and to investigate their roles in gastric cancer (GC) development. Through integrating the differential expression analysis of lncRNAs in GC tissues and expression quantitative trait loci analysis in normal stomach tissues and GC tissues, as well as genetic association analysis based on GC genome-wide association studies and an independent validation study, we identified four lncRNA-related SNPs consistently associated with GC risk, including SNHG7 [odds ratio (OR) = 1.16, 95% confidence interval (CI): 1.09–1.23], NRAV (OR = 1.11, 95% CI: 1.05–1.17), LINC01082 (OR = 1.16, 95% CI: 1.08–1.22) and FENDRR (OR = 1.16, 95% CI: 1.07–1.25). We further found that a functional SNP rs6489786 at 12q24.31 increases binding of MEOX1 or MEOX2 at a distal enhancer and results in up-regulation of NRAV. The functional assays revealed that NRAV accelerates GC cell proliferation while inhibits GC cell apoptosis. Mechanistically, NRAV decreases the expression of key subunit genes through the electron transport chain, thereby driving the glucose metabolism reprogramming from aerobic respiration to glycolysis. These findings suggest that regulating lncRNA expression is a crucial mechanism for risk-associated variants in promoting GC development. [ABSTRACT FROM AUTHOR]

Background: Gastrointestinal (GI) cancer risk has been associated with metabolic syndrome (MetS), a surrogate indicator for unhealthy lifestyles, and a number of genetic loci, but the combined effect of MetS and genetic variants on GI cancer risk is uncertain. Methods: We included 430,036 participants with available MetS and genotype data from UK Biobank. During the follow‐up time, 5494 incident GI cancer cases, including esophageal cancer, gastric cancer, and colorectal cancer, were identified. We created a GI polygenic risk score (GI‐PRS) for overall GI cancer derived from three site‐specific cancer PRSs. Cox proportional hazards regression was used to estimate the associations of MetS and GI‐PRS with the risk of GI cancer. Results: MetS was significantly associated with 28% increment in GI cancer risk (hazard ratio [HR]MetS vs. non‐MetS: 1.28, 95% confidence interval [CI]: 1.21–1.35, p < 0.0001), whereas a high GI‐PRS (top quintile) was associated with 2.28‐fold increase in risk (HRhigh vs. low: 2.28, 95% CI: 2.09–2.49, p < 0.0001). Compared with participants without MetS and at low genetic risk (bottom quintile of GI‐PRS), those with MetS and at high genetic risk had 2.75‐fold increase in GI cancer risk (HR: 2.75, 95% CI: 2.43–3.12, p < 0.0001). Additionally, MetS in comparison with no MetS had 1.49‰, 2.75‰, and 3.37‰ absolute risk increases in 5 years among participants at low, intermediate (quintiles 2–4 of GI‐PRS) and high genetic risk, respectively, representing the number of subjects diagnosed as MetS causing a new GI cancer case in 5 years were 669, 364, and 296, respectively. Conclusions: Metabolic and genetic factors may jointly contribute to GI cancer risk and may serve as predictors by quantitative measurements to identify high‐risk populations of GI cancer for precise prevention. [ABSTRACT FROM AUTHOR]

Background and Aim: Helicobacter pylori (H. pylori) infection rates have been changing with different populations and geographic areas. We systematically evaluated the longitudinal trends in H. pylori prevalence in China over the past decades. Methods: We performed a systematic review of literature reporting the prevalence of H. pylori infection in mainland China from 1990 to 2019 in the PubMed and China National Knowledge Infrastructure databases. We conducted a meta‐analysis of qualified studies using a random effects model to estimate the pooled prevalence with a 95% confidence interval (95%CI). Results: A total of 412 eligible studies with 1 377 349 subjects were included. The pooled H. pylori prevalence was 44.2% (95%CI: 43.0–45.5%) in mainland China, with an estimated 589 million individuals infected with H. pylori. The prevalence was relatively high in the Northwest (51.8%, 95%CI: 47.5–56.1%), East (47.7%, 95%CI: 45.4–50.0%), and Southwest China (46.6%, 95%CI: 42.1–51.1%). The prevalence significantly decreased from 58.3% (95%CI: 50.7–65.5%) in the period 1983–1994 to 40.0% (95%CI: 38.2–41.8%) in the period 2015–2019. The prevalence increased with age, ranging from 28.0% (95%CI: 23.9–32.5%) in children and adolescents to 46.1% (95%CI: 44.5–47.6%) in adults. Conclusion: Although the burden of H. pylori infections is still huge in China, the infection rate has been decreasing over the past decades. Targeted H. pylori eradication strategies may be considered in areas or populations with a high incidence of gastric cancer. [ABSTRACT FROM AUTHOR]

Background: Age is the most important risk factor for cancer, but aging rates are heterogeneous across individuals. We explored a new measure of aging-Phenotypic Age (PhenoAge)-in the risk prediction of site-specific and overall cancer., Methods: Using Cox regression models, we examined the association of Phenotypic Age Acceleration (PhenoAgeAccel) with cancer incidence by genetic risk group among 374,463 participants from the UK Biobank. We generated PhenoAge using chronological age and nine biomarkers, PhenoAgeAccel after subtracting the effect of chronological age by regression residual, and an incidence-weighted overall cancer polygenic risk score (CPRS) based on 20 cancer site-specific polygenic risk scores (PRSs)., Results: Compared with biologically younger participants, those older had a significantly higher risk of overall cancer, with hazard ratios (HRs) of 1.22 (95% confidence interval, 1.18-1.27) in men, and 1.26 (1.22-1.31) in women, respectively. A joint effect of genetic risk and PhenoAgeAccel was observed on overall cancer risk, with HRs of 2.29 (2.10-2.51) for men and 1.94 (1.78-2.11) for women with high genetic risk and older PhenoAge compared with those with low genetic risk and younger PhenoAge. PhenoAgeAccel was negatively associated with the number of healthy lifestyle factors (Beta = -1.01 in men, p<0.001; Beta = -0.98 in women, p<0.001)., Conclusions: Within and across genetic risk groups, older PhenoAge was consistently related to an increased risk of incident cancer with adjustment for chronological age and the aging process could be retarded by adherence to a healthy lifestyle., Funding: This work was supported by the National Natural Science Foundation of China (82230110, 82125033, 82388102 to GJ; 82273714 to MZ); and the Excellent Youth Foundation of Jiangsu Province (BK20220100 to MZ)., Competing Interests: LB, ZM, XF, CJ, TW, CY, JD, HM, ZH, HS, LW, MZ, GJ No competing interests declared, (© 2024, Bian, Ma et al.)