Fast and scalable ensemble learning method for versatile polygenic risk prediction.

Polygenic risk scores (PRS) enhance population risk stratification and advance personalized medicine, but existing methods face several limitations, encompassing issues related to computational burden, predictive accuracy, and adaptability to a wide range of genetic architectures. To address these issues, we propose Aggregated L0Learn using Summary-level data (ALL-Sum), a fast and scalable ensemble learning method for computing PRS using summary statistics from genome-wide association studies (GWAS). ALL-Sum leverages a L0L2 penalized regression and ensemble learning across tuning parameters to flexibly model traits with diverse genetic architectures. In extensive large-scale simulations across a wide range of polygenicity and GWAS sample sizes, ALL-Sum consistently outperformed popular alternative methods in terms of prediction accuracy, runtime, and memory usage by 10%, 20-fold, and threefold, respectively, and demonstrated robustness to diverse genetic architectures. We validated the performance of ALL-Sum in real data analysis of 11 complex traits using GWAS summary statistics from nine data sources, including the Global Lipids Genetics Consortium, Breast Cancer Association Consortium, and FinnGen Biobank, with validation in the UK Biobank. Our results show that on average, ALL-Sum obtained PRS with 25% higher accuracy on average, with 15 times faster computation and half the memory than the current state-of-the-art methods, and had robust performance across a wide range of traits and diseases. Furthermore, our method demonstrates stable prediction when using linkage disequilibrium computed from different data sources. ALL-Sum is available as a user-friendly R software package with publicly available reference data for streamlined analysis.

Socioeconomic area deprivation index is not associated with postoperative complications following revision total hip and knee joint arthroplasty.

Introduction: Revision hip and knee total joint arthroplasty (TJA) carries a high burden of postoperative complications, including surgical site infections (SSI), venous thromboembolism (VTE), reoperation, and readmission, which negatively affect postoperative outcomes and patient satisfaction. Socioeconomic area-level composite indices such as the area deprivation index (ADI) are increasingly important measures of social determinants of health (SDoH). This study aims to determine the potential association between ADI and SSI, VTE, reoperation, and readmission occurrence 90 days following revision TJA. Methods: 1047 consecutive revision TJA patients were retrospectively reviewed. Complications, including SSI, VTE, reoperation, and readmission, were combined into one dependent variable. ADI rankings were extracted using residential zip codes and categorized into quartiles. Univariate and multivariate logistic regressions were performed to analyze the association of ADI as an independent factor for complication following revision TJA. Results: Depression (p = 0.034) and high ASA score (p < 0.001) were associated with higher odds of a combined complication postoperatively on univariate logistic regression. ADI was not associated with the occurrence of any of the complications recorded following surgery (p = 0.092). ASA remained an independent risk factor for developing postoperative complications on multivariate analysis. Conclusion: An ASA score of 3 or higher was significantly associated with higher odds of developing postoperative complications. Our findings suggest that ADI alone may not be a sufficient tool for predicting postoperative outcomes following revision TJA, and other area-level indices should be further investigated as potential markers of social determinants of health.

Are social determinants of health associated with an increased length of hospitalization after revision total hip and knee arthroplasty? A comparison study of social deprivation indices.

INTRODUCTION: Length of stay (LOS) has been extensively assessed as a marker for healthcare utilization, functional outcomes, and cost of care for patients undergoing arthroplasty. The notable patient-to-patient variation in LOS following revision hip and knee total joint arthroplasty (TJA) suggests a potential opportunity to reduce preventable discharge delays. Previous studies investigated the impact of social determinants of health (SDoH) on orthopaedic conditions and outcomes using deprivation indices with inconsistent findings. The aim of the study is to compare the association of three publicly available national indices of social deprivation with prolonged LOS in revision TJA patients. MATERIALS AND METHODS: 1,047 consecutive patients who underwent a revision TJA were included in this retrospective study. Patient demographics, comorbidities, and behavioral characteristics were extracted. Area deprivation index (ADI), social deprivation index (SDI), and social vulnerability index (SVI) were recorded for each patient, following which univariate and multivariate logistic regression analyses were performed to determine the relationship between deprivation measures and prolonged LOS (greater than five days postoperatively). RESULTS: 193 patients had a prolonged LOS following surgery. Categorical ADI was significantly associated with prolonged LOS following surgery (OR = 2.14; 95% CI = 1.30-3.54; p = 0.003). No association with LOS was found using SDI and SVI. When accounting for other covariates, only ASA scores (ORrange=3.43-3.45; p < 0.001) and age (ORrange=1.00-1.03; prange=0.025-0.049) were independently associated with prolonged LOS. CONCLUSION: The varying relationship observed between the length of stay and socioeconomic markers in this study indicates that the selection of a deprivation index could significantly impact the outcomes when investigating the association between socioeconomic deprivation and clinical outcomes. These results suggest that ADI is a potential metric of social determinants of health that is applicable both clinically and in future policies related to hospital stays including bundled payment plan following revision TJA.

Effects of Laccase and Transglutaminase on the Physicochemical and Functional Properties of Hybrid Lupin and Whey Protein Powder.

Plant-based protein is considered a sustainable protein source and has increased in demand recently. However, products containing plant-based proteins require further modification to achieve the desired functionalities akin to those present in animal protein products. This study aimed to investigate the effects of enzymes as cross-linking reagents on the physicochemical and functional properties of hybrid plant- and animal-based proteins in which lupin and whey proteins were chosen as representatives, respectively. They were hybridised through enzymatic cross-linking using two laccases (laccase R, derived from Rhus vernicifera and laccase T, derived from Trametes versicolor) and transglutaminase (TG). The cross-linking experiments were conducted by mixing aqueous solutions of lupin flour and whey protein concentrate powder in a ratio of 1:1 of protein content under the conditions of pH 7, 40 °C for 20 h and in the presence of laccase T, laccase R, or TG. The cross-linked mixtures were freeze-dried, and the powders obtained were assessed for their cross-linking pattern, colour, charge distribution (ζ-potential), particle size, thermal stability, morphology, solubility, foaming and emulsifying properties, and total amino acid content. The findings showed that cross-linking with laccase R significantly improved the protein solubility, emulsion stability and foaming ability of the mixture, whereas these functionalities were lower in the TG-treated mixture due to extensive cross-linking. Furthermore, the mixture treated with laccase T turned brownish in colour and showed a decrease in total amino acid content which could be due to the enzyme's oxidative cross-linking mechanism. Also, the occurrence of cross-linking in the lupin and whey mixture was indicated by changes in other investigated parameters such as particle size, ζ-potential, etc., as compared to the control samples. The obtained results suggested that enzymatic cross-linking, depending on the type of enzyme used, could impact the physicochemical and functional properties of hybrid plant- and animal-based proteins, potentially influencing their applications in food.

Assessing Excess Mortality of Baby Boomers from the COVID-19 Pandemic: Taiwan Omicron-naïve Cohort.

BACKGROUND: Asia's elderly Baby Boomer demographic (born between 1946 and 1964) faced a huge problem during the COVID-19 pandemic due to increased all-cause mortality. We aimed to provide a unique Taiwan situation regarding the impact of Baby Boomers on excess mortalities from all causes relative to non-Baby Boomers throughout distinct times of SARS-CoV-2 mutations during the COVID-19 pandemic. METHODS: We used the Poisson time series design with a Bayesian directed acyclic graphic approach to build the background mortality prior to the COVID-19 pandemic between 2015 and 2019. It was then used for predicting the expected all-cause deaths compared to the reported figures during the COVID-19 pandemic period based on Taiwan residents, an Omicron-naïve cohort. RESULTS: Baby Boomers experienced a 2% negative excess mortality in 2020 (Wuhan/D614G) and a 4% excess mortality in 2021 (Alpha/Delta) with a rising background mortality trend whereas non-Baby Boomers showed the corresponding figures of 4% negative excess and 1% excess with a stable trend. Baby Boomer and non-Baby Boomer excess mortality soared to 9% (95% CI: 7-10%) and 10% (95% CI: 9-11%), respectively, during the epidemic Omicron period from January to June 2022. Surprisingly, Baby Boomers aged 58-76 experienced the same 9% excess mortality as non-Baby Boomers aged 77 and beyond. Non-COVID-19 deaths were more prevalent among Baby Boomers than non-Baby Boomers (33% vs. 29%). CONCLUSION: Baby Boomers were more likely to die from COVID-19 in early pandemic and had more non-COVID-19 deaths in late pandemic than older non-Baby Boomers demonstrated in Taiwan Omicron-naïve cohort. For this vulnerable population, adequate access to medical care and medical capacity require more consideration.

Neighborhood socioeconomic disadvantages associated with prolonged length of stay and non-home discharge following revision total hip and knee joint arthroplasty.

Background: Discharge disposition and length of stay (LOS) are widely recognized markers of healthcare utilization patterns of total hip and knee joint arthroplasty (TJA). These markers are commonly associated with increased postoperative complications, patient dissatisfaction, and higher costs. Area deprivation index (ADI) has been validated as a composite metric of neighborhood-level disadvantage. This study aims to determine the potential association between ADI and discharge disposition or extended LOS following revision TJA. Methods: This study conducted a retrospective analysis of a consecutive series of revision hip and knee TJA patients from a single tertiary institution. Univariate and multivariate regression analysis was used to determine the association between ADI and discharge disposition or LOS, adjusting for patient demographics and comorbidities. Results: 1047 consecutive revision TJA patients were identified across 463 different neighborhoods. 193 (18.4 %) had an extended LOS, and 334 (31.9 %) were discharged to non-home facilities. Compared with Q1 (least deprived cohort), Q2 (odds ratio [OR] = 1.63; p = 0.030) and Q4 (most deprived cohort: OR = 2.04; p = 0.002) cohorts demonstrated higher odds of non-home discharge. Patients in the highest ADI quartile (most deprived cohort) were associated with increased odds of prolonged LOS following revision TJA compared to those in the lowest ADI quartile (OR = 2.63; p < 0.001). Conclusion: This study suggests that higher levels of neighborhood-level disadvantage may be associated with higher odds of non-home discharge and prolonged LOS following revision TJA. Development of interventions based on the area deprivation index may improve discharge planning and reduce unnecessary non-home discharges in patients living in areas of socioeconomic deprivation.

Prediction of 30-Day Mortality Following Revision Total Hip and Knee Arthroplasty: Machine Learning Algorithms Outperform CARDE-B, 5-Item, and 6-Item Modified Frailty Index Risk Scores.

BACKGROUND: Although risk calculators are used to prognosticate postoperative outcomes following revision total hip and knee arthroplasty (total joint arthroplasty [TJA]), machine learning (ML) based predictive tools have emerged as a promising alternative for improved risk stratification. This study aimed to compare the predictive ability of ML models for 30-day mortality following revision TJA to that of traditional risk-assessment indices such as the CARDE-B score (congestive heart failure, albumin (< 3.5 mg/dL), renal failure on dialysis, dependence for daily living, elderly (> 65 years of age), and body mass index (BMI) of < 25 kg/m2), 5-item modified frailty index (5MFI), and 6MFI. METHODS: Adult patients undergoing revision TJA between 2013 and 2020 were selected from the American College of Surgeons National Surgical Quality Improvement Program database and randomly split 80:20 to compose the training and validation cohorts. There were 3 ML models - extreme gradient boosting, random forest, and elastic-net penalized logistic regression (NEPLR) - that were developed and evaluated using discrimination, calibration metrics, and accuracy. The discrimination of CARDE-B, 5MFI, and 6MFI scores was assessed individually and compared to that of ML models. RESULTS: All models were equally accurate (Brier score = 0.005) and demonstrated outstanding discrimination with similar areas under the receiver operating characteristic curve (AUCs, extreme gradient boosting = 0.94, random forest = NEPLR = 0.93). The NEPLR was the best-calibrated model overall (slope = 0.54, intercept = -0.004). The CARDE-B had the highest discrimination among the scores (AUC = 0.89), followed by 6MFI (AUC = 0.80), and 5MFI (AUC = 0.68). Albumin < 3.5 mg/dL and BMI (< 30.15) were the most important predictors of 30-day mortality following revision TJA. CONCLUSIONS: The ML models outperform traditional risk-assessment indices in predicting postoperative 30-day mortality after revision TJA. Our findings highlight the utility of ML for risk stratification in a clinical setting. The identification of hypoalbuminemia and BMI as prognostic markers may allow patient-specific perioperative optimization strategies to improve outcomes following revision TJA.

Precision Colorectal Cancer Fecal Immunological Test Screening With Fecal-Hemoglobin-Concentration-Guided Interscreening Intervals.

Importance: Given a gradient relationship between fecal hemoglobin (f-Hb) concentration and colorectal neoplasia demonstrated previously, using f-Hb-guided interscreening interval has increasingly gained attention in population-based fecal immunological test (FIT), but it is very rare to address how to implement such a precision strategy and whether it can economize the use of FIT and colonoscopy. Objective: To demonstrate the applicability of personalized colorectal cancer (CRC) screening with f-Hb-guided screening intervals to reduce the number of FITs and colonoscopy with as equivalent efficacy as universal biennial screening. Design, Setting, and Participants: A retrospective cohort study for developing f-Hb-guided precision interscreening interval was conducted using data on a Taiwanese biennial nationwide FIT screening program that enrolled more than 3 million participants aged 50 to 74 years between 2004 and 2014. The cohort was followed up over time until 2019 to ascertain colorectal neoplasia and causes of death. A comparative study was further designed to compare the use of FIT and colonoscopy between the personalized f-Hb-guided group and the universal biennial screening group given the equivalent efficacy of reducing CRC-related outcomes. Main Outcomes and Measurements: A spectrum of f-Hb-guided intervals was determined by using the Poisson regression model given the equivalent efficacy of a universal biennial screening. The use of FIT and colonoscopy for the pragmatic f-Hb-guided interval group was measured compared with the universal biennial screening group. Data analysis was performed from September 2022 to October 2023. Results: Using data from the 3 500 250 participants (mean [SD] age, 57.8 [6.0] years) enrolled in the Taiwanese biennial nationwide FIT screening program, an incremental increase in baseline f-Hb associated with colorectal neoplasia and CRC mortality consistently was observed. Participants with different f-Hb levels were classified into distinct risk categories. Various screening intervals by different f-Hb levels were recommended. Using the proposed f-Hb-guided screening intervals, it was found that the personalized method was imputed to reduce the number of FIT tests and colonoscopies by 49% and 28%, respectively, compared with the universal biennial screening. Conclusion and Relevance: The gradient relationship between f-Hb and colorectal neoplasia and CRC mortality was used to develop personalized FIT screening with f-Hb-guided screening intervals. Such a precision interscreening interval led to the reduced use of FIT test and colonoscopy without compromising the effectiveness of universal biennial screening.

Locomotion as manipulation with ReachBot.

Caves and lava tubes on the Moon and Mars are sites of geological and astrobiological interest but consist of terrain that is inaccessible with traditional robot locomotion. To support the exploration of these sites, we present ReachBot, a robot that uses extendable booms as appendages to manipulate itself with respect to irregular rock surfaces. The booms terminate in grippers equipped with microspines and provide ReachBot with a large workspace, allowing it to achieve force closure in enclosed spaces, such as the walls of a lava tube. To propel ReachBot, we present a contact-before-motion planner for nongaited legged locomotion that uses internal force control, similar to a multifingered hand, to keep its long, slender booms in tension. Motion planning also depends on finding and executing secure grips on rock features. We used a Monte Carlo simulation to inform gripper design and predict grasp strength and variability. In addition, we used a two-step perception system to identify possible grasp locations. To validate our approach and mechanisms under realistic conditions, we deployed a single ReachBot arm and gripper in a lava tube in the Mojave Desert. The field test confirmed that ReachBot will find many targets for secure grasps with the proposed kinematic design.

Heterogeneous associations between interleukin-6 receptor variants and phenotypes across ancestries and implications for therapy.

The Phenome-Wide Association Study (PheWAS) is increasingly used to broadly screen for potential treatment effects, e.g., IL6R variant as a proxy for IL6R antagonists. This approach offers an opportunity to address the limited power in clinical trials to study differential treatment effects across patient subgroups. However, limited methods exist to efficiently test for differences across subgroups in the thousands of multiple comparisons generated as part of a PheWAS. In this study, we developed an approach that maximizes the power to test for heterogeneous genotype-phenotype associations and applied this approach to an IL6R PheWAS among individuals of African (AFR) and European (EUR) ancestries. We identified 29 traits with differences in IL6R variant-phenotype associations, including a lower risk of type 2 diabetes in AFR (OR 0.96) vs EUR (OR 1.0, p-value for heterogeneity = 8.5 × 10-3), and higher white blood cell count (p-value for heterogeneity = 8.5 × 10-131). These data suggest a more salutary effect of IL6R blockade for T2D among individuals of AFR vs EUR ancestry and provide data to inform ongoing clinical trials targeting IL6 for an expanding number of conditions. Moreover, the method to test for heterogeneity of associations can be applied broadly to other large-scale genotype-phenotype screens in diverse populations.

Genomic Insights for Personalized Care: Motivating At-Risk Individuals Toward Evidence-Based Health Practices.

Background: Lung cancer and tobacco use pose significant global health challenges, necessitating a comprehensive translational roadmap for improved prevention strategies. Polygenic risk scores (PRSs) are powerful tools for patient risk stratification but have not yet been widely used in primary care for lung cancer, particularly in diverse patient populations. Methods: We propose the GREAT care paradigm, which employs PRSs to stratify disease risk and personalize interventions. We developed PRSs using large-scale multi-ancestry genome-wide association studies and standardized PRS distributions across all ancestries. We applied our PRSs to 796 individuals from the GISC Trial, 350,154 from UK Biobank (UKBB), and 210,826 from All of Us Research Program (AoU), totaling 561,776 individuals of diverse ancestry. Results: Significant odds ratios (ORs) for lung cancer and difficulty quitting smoking were observed in both UKBB and AoU. For lung cancer, the ORs for individuals in the highest risk group (top 20% versus bottom 20%) were 1.85 (95% CI: 1.58 - 2.18) in UKBB and 2.39 (95% CI: 1.93 - 2.97) in AoU. For difficulty quitting smoking, the ORs (top 33% versus bottom 33%) were 1.36 (95% CI: 1.32 - 1.41) in UKBB and 1.32 (95% CI: 1.28 - 1.36) in AoU. Conclusion: Our PRS-based intervention model leverages large-scale genetic data for robust risk assessment across populations. This model will be evaluated in two cluster-randomized clinical trials aimed at motivating health behavior changes in high-risk patients of diverse ancestry. This pioneering approach integrates genomic insights into primary care, promising improved outcomes in cancer prevention and tobacco treatment.

Generalizability of machine learning models predicting 30-day unplanned readmission after primary total knee arthroplasty using a nationally representative database.

Unplanned readmission after primary total knee arthroplasty (TKA) costs an average of US $39,000 per episode and negatively impacts patient outcomes. Although predictive machine learning (ML) models show promise for risk stratification in specific populations, existing studies do not address model generalizability. This study aimed to establish the generalizability of previous institutionally developed ML models to predict 30-day readmission following primary TKA using a national database. Data from 424,354 patients from the ACS-NSQIP database was used to develop and validate four ML models to predict 30-day readmission risk after primary TKA. Individual model performance was assessed and compared based on discrimination, accuracy, calibration, and clinical utility. Length of stay (> 2.5 days), body mass index (BMI) (> 33.21 kg/m2), and operation time (> 93 min) were important determinants of 30-day readmission. All ML models demonstrated equally good accuracy, calibration, and discriminatory ability (Brier score, ANN = RF = HGB = NEPLR = 0.03; ANN, slope = 0.90, intercept = - 0.11; RF, slope = 0.93, intercept = - 0.12; HGB, slope = 0.90, intercept = - 0.12; NEPLR, slope = 0.77, intercept = 0.01; AUCANN = AUCRF = AUCHGB = AUCNEPLR = 0.78). This study validates the generalizability of four previously developed ML algorithms in predicting readmission risk in patients undergoing TKA and offers surgeons an opportunity to reduce readmissions by optimizing discharge planning, BMI, and surgical efficiency.

New insights into three trajectories of omicron-related all-cause death reduced by COVID-19 booster vaccination.

BACKGROUND: The trajectories of all-cause deaths linked to omicron infections are rarely studied, especially in relation to the efficacy of booster shots. For assessing three epidemiological death trajectories, including dying from COVID-19, dying with COVID-19, and non-COVID-19 death, we offer a new COVID-19-and-death competing risk model that deals with the primary pathway (e.g., dying from COVID-19) competing with two other pathways. METHODS: We applied this model to track three trajectories: deaths directly from COVID-19, deaths with COVID-19 as a contributing factor, and indirect non-COVID-19 deaths. The study used data from a Taiwanese cohort, covering periods of Omicron subvariants BA.2, BA.5, and BA.2.75. It focused on the effectiveness of monovalent and bivalent booster vaccines against these death trajectories. RESULTS: The highest mortality was observed during the BA.2 phase, which decreased in the BA.5 period and increased again in the BA.2.75 period. Analyzing each trajectory, we noted similar trends in deaths directly from and with COVID-19, while non-COVID-19 deaths remained stable across subvariants. Booster vaccines reduced all-cause mortality by 58% (52%-62%) for BA.2, 70% (65%-75%) for BA.5%, and 75% (70%-80%) for BA.2.75, compared to incomplete vaccination. The reduction in deaths directly from COVID-19 was 66% (61%-72%) for BA.2, 78% (72%-84%) for BA.5%, and 85% (76%-93%) for BA.2.75. For deaths with COVID-19, the figures were 46% (36%-55%), 76% (68%-84%), and 90% (86%-95%). Additionally, the booster shots decreased non-COVID-19 deaths by 64% (63%-66%) for BA.2, 38% (36%-40%) for BA.5, and 19% (17%-21%) for BA.2.75. CONCLUSION: Our competing risk analysis is effective for monitoring all-cause death trajectories amidst various Omicron infections. It provides insights into the impact of booster vaccines, especially bivalent ones, and highlights the consequences of inadequate healthcare for vulnerable groups.

Predicting 30-day unplanned hospital readmission after revision total knee arthroplasty: machine learning model analysis of a national patient cohort.

Revision total knee arthroplasty (TKA) is associated with a higher risk of readmission than primary TKA. Identifying individual patients predisposed to readmission can facilitate proactive optimization and increase care efficiency. This study developed machine learning (ML) models to predict unplanned readmission following revision TKA using a national-scale patient dataset. A total of 17,443 revision TKA cases (2013-2020) were acquired from the ACS NSQIP database. Four ML models (artificial neural networks, random forest, histogram-based gradient boosting, and k-nearest neighbor) were developed on relevant patient variables to predict readmission following revision TKA. The length of stay, operation time, body mass index (BMI), and laboratory test results were the strongest predictors of readmission. Histogram-based gradient boosting was the best performer in distinguishing readmission (AUC: 0.95) and estimating the readmission probability for individual patients (calibration slope: 1.13; calibration intercept: -0.00; Brier score: 0.064). All models produced higher net benefit than the default strategies of treating all or no patients, supporting the clinical utility of the models. ML demonstrated excellent performance for the prediction of readmission following revision TKA. Optimization of important predictors highlighted by our model may decrease preventable hospital readmission following surgery, thereby leading to reduced financial burden and improved patient satisfaction.

Impact of individual level uncertainty of lung cancer polygenic risk score (PRS) on risk stratification.

BACKGROUND: Although polygenic risk score (PRS) has emerged as a promising tool for predicting cancer risk from genome-wide association studies (GWAS), the individual-level accuracy of lung cancer PRS and the extent to which its impact on subsequent clinical applications remains largely unexplored. METHODS: Lung cancer PRSs and confidence/credible interval (CI) were constructed using two statistical approaches for each individual: (1) the weighted sum of 16 GWAS-derived significant SNP loci and the CI through the bootstrapping method (PRS-16-CV) and (2) LDpred2 and the CI through posteriors sampling (PRS-Bayes), among 17,166 lung cancer cases and 12,894 controls with European ancestry from the International Lung Cancer Consortium. Individuals were classified into different genetic risk subgroups based on the relationship between their own PRS mean/PRS CI and the population level threshold. RESULTS: Considerable variances in PRS point estimates at the individual level were observed for both methods, with an average standard deviation (s.d.) of 0.12 for PRS-16-CV and a much larger s.d. of 0.88 for PRS-Bayes. Using PRS-16-CV, only 25.0% of individuals with PRS point estimates in the lowest decile of PRS and 16.8% in the highest decile have their entire 95% CI fully contained in the lowest and highest decile, respectively, while PRS-Bayes was unable to find any eligible individuals. Only 19% of the individuals were concordantly identified as having high genetic risk (> 90th percentile) using the two PRS estimators. An increased relative risk of lung cancer comparing the highest PRS percentile to the lowest was observed when taking the CI into account (OR = 2.73, 95% CI: 2.12-3.50, P-value = 4.13 × 10-15) compared to using PRS-16-CV mean (OR = 2.23, 95% CI: 1.99-2.49, P-value = 5.70 × 10-46). Improved risk prediction performance with higher AUC was consistently observed in individuals identified by PRS-16-CV CI, and the best performance was achieved by incorporating age, gender, and detailed smoking pack-years (AUC: 0.73, 95% CI = 0.72-0.74). CONCLUSIONS: Lung cancer PRS estimates using different methods have modest correlations at the individual level, highlighting the importance of considering individual-level uncertainty when evaluating the practical utility of PRS.

Storage Stability of Plant-Based Drinks Related to Proteolysis and Generation of Free Amino Acids.

The market for plant-based drinks (PBDs) is experiencing a surge in consumer demand, especially in Western societies. PBDs are a highly processed food product, and little is known about this relatively new food product category when compared to bovine milk. In the present study, the storage stability, proteolysis and generation of free amino acids were investigated in commercially available PBDs over the course of a one-year storage period. Generally, pH, color and protein solubility were found to be stable in the PBDs during storage, except for the pea-based product, which showed less protein solubility after storage. The pea-based drinks also had higher initial levels of free N-terminals prior to storage compared with levels for the other plant-based drinks, as well as significantly increasing levels of total free, and especially bitter free, amino acids. The development of free amino acids in the oat-based drink indicated that the released amino acids could be involved in various reactions such as the Maillard reaction during the storage period.

Risk of Gastric Adenocarcinoma in a Multiethnic Population Undergoing Routine Care: An Electronic Health Records Cohort Study.

BACKGROUND: Gastric adenocarcinoma (GAC) is often diagnosed at advanced stages and portends a poor prognosis. We hypothesized that electronic health records (EHR) could be leveraged to identify individuals at highest risk for GAC from the population seeking routine care. METHODS: This was a retrospective cohort study, with endpoint of GAC incidence as ascertained through linkage to an institutional tumor registry. We utilized 2010 to 2020 data from the Palo Alto Medical Foundation, a large multispecialty practice serving Northern California. The analytic cohort comprised individuals ages 40-75 receiving regular ambulatory care. Variables collected included demographic, medical, pharmaceutical, social, and familial data. Electronic phenotyping was based on rule-based methods. RESULTS: The cohort comprised 316,044 individuals and approximately 2 million person-years (p-y) of observation. 157 incident GACs occurred (incidence 7.9 per 100,000 p-y), of which 102 were non-cardia GACs (incidence 5.1 per 100,000 p-y). In multivariable analysis, male sex [HR: 2.2, 95% confidence interval (CI): 1.6-3.1], older age, Asian race (HR: 2.5, 95% CI: 1.7-3.7), Hispanic ethnicity (HR: 1.9, 95% CI: 1.1-3.3), atrophic gastritis (HR: 4.6, 95% CI: 2.2-9.3), and anemia (HR: 1.9, 95% CI: 1.3-2.6) were associated with GAC risk; use of NSAID was inversely associated (HR: 0.3, 95% CI: 0.2-0.5). Older age, Asian race, Hispanic ethnicity, atrophic gastritis, and anemia were associated with non-cardia GAC. CONCLUSIONS: Routine EHR data can stratify the general population for GAC risk. IMPACT: Such methods may help triage populations for targeted screening efforts, such as upper endoscopy.

Do abnormal screening results for chronic diseases motivate inactive people to start exercising? A community-based prospective cohort study in Changhua, Taiwan.

OBJECTIVE: Exercise improves health, but illnesses can cause changes in exercise behavior, including starting or stopping. This study investigated the effects of chronic disease screening on inactive individuals' exercise behavior and analyzed the impact of age and chronic disease history on this relationship using stratified analysis. METHODS: Using a community-based prospective observational cohort design and data from the Changhua Community-Based Integrated Screening (CHCIS) dataset from 2005 to 2020, we examined 12,038 people who were screened at least twice and self-reported having never exercised at their first screening. Changes in exercise behavior were classified as "initiating exercise" and "remaining inactive." We obtained chronic disease screening results from CHCIS records, which included measurements of waist circumference, blood glucose, blood pressure, triglycerides, and high-density lipoproteins. SAS version 9.4 was used for COX proportional hazards regression. RESULTS: The findings indicated that abnormal waist circumference and blood pressure increased the likelihood of initiating exercise compared to normal results. Age stratification showed that those aged 40-49 with abnormal results were more likely to start exercising than normal participants, but not those under 40 or over 65. When stratified by chronic disease history, abnormal screening results correlated with exercise initiation only in groups without chronic disease history, except for those with a history of hyperlipidemia. CONCLUSIONS: This is the first study to demonstrate that abnormal screening results may influence exercise initiation in individuals who have never exercised, and this association varies by screening item, age, and disease history.

Performance measures of 8,169,869 examinations in the National Breast Cancer Screening Program in Taiwan, 2004-2020.

BACKGROUND: The benefits of mammographic screening have been shown to include a decrease in mortality due to breast cancer. Taiwan's Breast Cancer Screening Program is a national screening program that has offered biennial mammographic breast cancer screening for women aged 50-69 years since 2004 and for those aged 45-69 years since 2009, with the implementation of mobile units in 2010. The purpose of this study was to compare the performance results of the program with changes in the previous (2004-2009) and latter (2010-2020) periods. METHODS: A cohort of 3,665,078 women who underwent biennial breast cancer mammography screenings from 2004 to 2020 was conducted, and data were obtained from the Health Promotion Administration, Ministry of Health and Welfare of Taiwan. We compared the participation of screened women and survival rates from breast cancer in the earlier and latter periods across national breast cancer screening programs. RESULTS: Among 3,665,078 women who underwent 8,169,869 examinations in the study population, the screened population increased from 3.9% in 2004 to 40% in 2019. The mean cancer detection rate was 4.76 and 4.08 cancers per 1000 screening mammograms in the earlier (2004-2009) and latter (2010-2020) periods, respectively. The 10-year survival rate increased from 89.68% in the early period to 97.33% in the latter period. The mean recall rate was 9.90% (95% CI: 9.83-9.97%) in the early period and decreased to 8.15% (95%CI, 8.13-8.17%) in the latter period. CONCLUSIONS: The evolution of breast cancer screening in Taiwan has yielded favorable outcomes by increasing the screening population, increasing the 10-year survival rate, and reducing the recall rate through the participation of young women, the implementation of a mobile unit service and quality assurance program, thereby providing historical evidence to policy makers to plan future needs.