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Background Missing data are common in longitudinal studies. Multiple imputation (MI) is widely used to handle missing data. However, most of the MI methods assume various missing data types as missing at random (MAR) in imputation. Two-stage MI is a flexible method that accounts for two types of missing data in a two-step process, allowing researchers to employ diverse assumptions regarding the mechanisms underlying the missing data. This method has immense potential yet limited application and extension within the field. Methods We evaluated the performance of two-stage MI in a novel context, imputing a composite variable constructed from several continuous and binary components in the longitudinal setting while handling missing data due to MAR and missing not at random (MNAR). Additionally, we compared three fully conditional specification (FCS) methods within the two-stage MI framework. Simulation studies were conducted using a longitudinal dataset that mimicked a cohort study. Sensitivity analysis was performed with various ignorability assumptions. Results In simulation studies, the imputation models within two-stage MI, assuming appropriate ignorability assumptions, exhibited the smallest bias and achieved optimal coverage probabilities for the means, slopes across different time points, and hazard ratios for mortality related to the composite variable. The FCS methods that incorporated longitudinal information yielded the best performance in most scenarios. Conclusions In the context of a longitudinal composite variable with missing values due to various missing mechanisms, the selection of imputation methods and ignorability assumptions plays an important role within the two-stage MI framework.

Background As an important foodborne bacterium, non-typhoidal Salmonella (NTS) is estimated to cause approximately 215,000 deaths worldwide each year. Globally, S. Montevideo has been reported to cause several outbreaks, with low frequencies of antimicrobial resistance (AMR). In China, the information on S. Montevideo isolates was limited, especially the genomic data. This study aimed to characterize the phenotypic and genomic features of 26 S. Montevideo isolates collected in China between 2018 and 2024. During this period, 26 S. Montevideo isolates were collected from different sources in five provinces or municipalities in China. Minimum inhibitory concentrations (MICs) of nine antimicrobial agents were determined using the broth microdilution method. Isolates were sequenced on the Illumina platform to identify antimicrobial resistance genes, virulence genes, mobile genetic elements (MGEs), and phylogenetic relationships with global isolates. Results The whole length of the 26 assembled genome sequences ranged from 4,612,761 bp to 5,071,299 bp. There were three sequence types (STs), among which ST-4 (61.5%, 16/26) was predominant, followed by ST-10844 (34.6%, 9/26) and ST-316 (3.8%, 1/26). A total of 21 isolates (80.8%) harbored multidrug resistance (MDR). They showed AMR phenotypes to ampicillin (76.9%, 20/26), cefotaxime (26.9%, 7/26), and ciprofloxacin (69.2%, 18/26; including three ciprofloxacin-intermediate isolates), corresponding to AMR genes and/or chromosomal point mutations of β-lactams (76.9%, 20/26) and fluoroquinolones (100%, 26/26). All ST-10844 isolates possessed MDR, with higher proportions of resistance to cefotaxime and tetracycline. The corresponding AMR genes were mainly carried by two plasmids, IncHI2 and pKPC-CAV1321. The number of virulence genes among the S. Montevideo genomes ranged from 151 to 153, with type III secretion system genes (T3SS; n  = 86) predominant. The ranges of the number of prophages, Salmonella Pathogenic Islands (SPIs), plasmid replicons, and MGEs were 2–4, 8–10, 0–5, 17–30, respectively. Phylogenetic analysis showed that global S. Montevideo strains can be divided into five clades, including a novel clade, clade V. Most Chinese isolates (96.6%, 28/29) were assigned to clade II. All the ST-10,844 isolates were from mainland China and assigned to clade II, constituting a distinct cluster. Conclusion This study is the first report to characterize S. Montevideo genomes from China. The phenotypic and genomic characteristics of Chinese S. Montevideo isolates were distinct from isolates of other countries, standing out with MDR and tending to be waterborne. A ST-10844 cluster was identified, with higher frequencies of resistance to cefotaxime and tetracycline.

Background Clear cell renal cell carcinoma (ccRCC) is the most common renal malignancy, although newly developing targeted therapy and immunotherapy have been showing promising effects in clinical treatment, the effective biomarkers for immune response prediction are still lacking. The study is to construct a gene signature according to ccRCC immune cells infiltration landscape, thus aiding clinical prediction of patients response to immunotherapy. Methods Firstly, ccRCC transcriptome expression profiles from Gene Expression Omnibus (GEO) database as well as immune related genes information from IMMPORT database were combine applied to identify the differently expressed meanwhile immune related candidate genes in ccRCC comparing to normal control samples. Then, based on protein–protein interaction network (PPI) and following module analysis of the candidate genes, a hub gene cluster was further identified for survival analysis. Further, LASSO analysis was applied to construct a signature which was in succession assessed with Kaplan–Meier survival, Cox regression and ROC curve analysis. Moreover, ccRCC patients were divided as high and low-risk groups based on the gene signature followed by the difference estimation of immune treatment response and exploration of related immune cells infiltration by TIDE and Cibersort analysis respectively among the two groups of patients. Results Based on GEO and IMMPORT databases, a total of 269 differently expressed meanwhile immune related genes in ccRCC were identified, further PPI network and module analysis of the 269 genes highlighted a 46 genes cluster. Next step, Kaplan–Meier and Cox regression analysis of the 46 genes identified 4 genes that were supported to be independent prognosis indicators, and a gene signature was constructed based on the 4 genes. Furthermore, after assessing its prognosis indicating ability by both Kaplan–Meier and Cox regression analysis, immune relation of the signature was evaluated including its association with environment immune score, Immune checkpoint inhibitors expression as well as immune cells infiltration. Together, immune predicting ability of the signature was preliminary explored. Conclusions Based on ccRCC genes expression profiles and multiple bioinformatic analysis, a 4 genes containing signature was constructed and the immune regulation of the signature was preliminary explored. Although more detailed experiments and clinical trials are needed before potential clinical use of the signature, the results shall provide meaningful insight into further ccRCC immune researches.

The current research has aimed to investigate and develop machine-learning approaches by using the data in the dataset to be applied to classify location-based social network data and predict user activities based on the nature of various locations (such as entertainment). The analysis of user activities and behavior from location-based social network data is often based on venue types, which require the input of data into various categories. This has previously been done through a tedious and time-consuming manual method. Therefore, we proposed a novel approach of using machine-learning models to extract these venue categories. In this study, we used a Weibo dataset as the main source of research and analyzed machine-learning methods for more efficient implementation. We proposed four models based on well-known machine-learning techniques, including the generalized linear model, logistic regression, deep learning, and gradient-boosted trees. We designed, tested, and evaluated these models. We then used various assessment metrics, such as the Receiver Operating Characteristic or Area Under the Curve, Accuracy, Recall, Precision, F-score, and Sensitivity, to show how well these methods performed. We discovered that the proposed machine-learning models are capable of accurately classifying the data, with deep learning outperforming the other models with 99% accuracy, followed by gradient-boosted tree with 98% and 93%, generalized linear model with 90% and 85%, and logistic regression with 86% and 91%, for multiclass distributions and single class predictions, respectively. We classified the data using our machine-learning models into the 10 classes we used in our previous study and predicted tourist destinations among the data to demonstrate the effectiveness of using machine learning for location-based social network data analysis, which is vital for the development of smart city environments in the current technological era.

Survey fatigue is a common challenge in longitudinal studies, particularly when using smartphone apps to collect survey data. Evidence-based strategies are needed to maintain longitudinal response rates. This study aims to evaluate the effect of a more frequent smartphone-administered survey deployment strategy with smaller survey batches on participant response rates over an extended period. We conducted a randomized controlled trial (NCT04752657) embedded in the electronic Framingham Heart Study cohorts between June 2021 and December 2023. Participants were randomly allocated to receive a full set of surveys every 4 weeks (control group) or half of the survey set biweekly, such that the full set is completed every 4 weeks (experimental group). Randomization was stratified by age (≤75 y vs >75 y) and phone type (Android vs iPhone). Married couples were assigned to the same group using a blocked randomization approach. The primary outcome was the proportion of surveys returned per participant assessed longitudinally across four periods (baseline to wk 8, wk 8-16, wk 16-24, and wk 24-32), with 19, 17, 16, and 15 unique surveys deployed, respectively. We used mixed-effects regression models with random intercepts to compare the repeated outcome between groups. Stratified analyses by age and sex were performed. Among 492 participants (mean age 74, SD 6.3 y; 58%, n=284 women, 84%, n=413 non-Hispanic White), there was evidence that the experimental group had higher response rates over time compared to the control group (P=.003 for interaction between deployment pattern and time). Both groups showed similar proportions of surveys returned during the first period (75% vs 76%). The experimental group had higher response rates than the control group in subsequent periods (70% vs 67% in wk 8-16, 64% vs 59% in wk 16-24, and 58% vs 50% in wk 24-32). The proportion of participants not returning any surveys increased from 3% to 38% in the control group compared to 1% to 28% in the experimental group across the four time periods. Stratified analyses revealed that among younger participants (≤75 y), the experimental group showed 12% higher survey response rates compared to the control group in the final period, while the difference was minimal among older participants (>75 years). The effect of the deployment pattern was similar for men and women. Three-way interaction analyses revealed no significant differences in the deployment pattern effect over time by age group (P=.95) or sex (P=.65). Administering half of the surveys every 2 weeks, as compared to all surveys every 4 weeks, was associated with higher maintained longitudinal survey response rates. This strategy may help mitigate survey fatigue and improve data quality in digital health studies. ClinicalTrials.gov NCT04752657; https://clinicaltrials.gov/study/NCT04752657.

Resting heart rate (HR) and routine physical activity are associated with cardiorespiratory fitness levels. Commercial smartwatches permit remote HR monitoring and step count recording in real-world settings over long periods of time, but the relationship between smartwatch-measured HR and daily steps to cardiorespiratory fitness remains incompletely characterized in the community. This study aimed to examine the association of nonactive HR and daily steps measured by a smartwatch with a multidimensional fitness assessment via cardiopulmonary exercise testing (CPET) among participants in the electronic Framingham Heart Study. Electronic Framingham Heart Study participants were enrolled in a research examination (2016-2019) and provided with a study smartwatch that collected longitudinal HR and physical activity data for up to 3 years. At the same examination, the participants underwent CPET on a cycle ergometer. Multivariable linear models were used to test the association of CPET indices with nonactive HR and daily steps from the smartwatch. We included 662 participants (mean age 53, SD 9 years; n=391, 59% women, n=599, 91% White; mean nonactive HR 73, SD 6 beats per minute) with a median of 1836 (IQR 889-3559) HR records and a median of 128 (IQR 65-227) watch-wearing days for each individual. In multivariable-adjusted models, lower nonactive HR and higher daily steps were associated with higher peak oxygen uptake (VO ), % predicted peak VO , and VO at the ventilatory anaerobic threshold, with false discovery rate (FDR)-adjusted P values <.001 for all. Reductions of 2.4 beats per minute in nonactive HR, or increases of nearly 1000 daily steps, corresponded to a 1.3 mL/kg/min higher peak VO . In addition, ventilatory efficiency (V /VCO ; FDR-adjusted P=.009), % predicted maximum HR (FDR-adjusted P<.001), and systolic blood pressure-to-workload slope (FDR-adjusted P=.01) were associated with nonactive HR but not associated with daily steps. Our findings suggest that smartwatch-based assessments are associated with a broad array of cardiorespiratory fitness responses in the community, including measures of global fitness (peak VO ), ventilatory efficiency, and blood pressure response to exercise. Metrics captured by wearable devices offer a valuable opportunity to use extensive data on health factors and behaviors to provide a window into individual cardiovascular fitness levels.

Physical inactivity is a known risk factor for atrial fibrillation (AF). Wearable devices, such as smartwatches, present an opportunity to investigate the relation between daily step count and AF risk. The objective of this study was to investigate the association between daily step count and the predicted 5-year risk of AF. Participants from the electronic Framingham Heart Study used an Apple smartwatch. Individuals with diagnosed AF were excluded. Daily step count, watch wear time (hours and days), and self-reported physical activity data were collected. Individuals' 5-year risk of AF was estimated, using the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE)-AF score. The relation between daily step count and predicted 5-year AF risk was examined via linear regression, adjusting for age, sex, and wear time. Secondary analyses examined effect modification by sex and obesity (BMI≥30 kg/m ), as well as the relation between self-reported physical activity and predicted 5-year AF risk. We examined 923 electronic Framingham Heart Study participants (age: mean 53, SD 9 years; female: n=563, 61%) who had a median daily step count of 7227 (IQR 5699-8970). Most participants (n=823, 89.2%) had a <2.5% CHARGE-AF risk. Every 1000 steps were associated with a 0.08% lower CHARGE-AF risk (P<.001). A stronger association was observed in men and individuals with obesity. In contrast, self-reported physical activity was not associated with CHARGE-AF risk. Higher daily step counts were associated with a lower predicted 5-year risk of AF, and this relation was stronger in men and participants with obesity. The utility of a wearable daily step counter for AF risk reduction merits further investigation.

Smartphone apps can be used to monitor chronic conditions and offer opportunities for self-assessment conveniently at home. However, few digital studies include older adults. We aim to describe a new electronic cohort of older adults embedded in the Framingham Heart Study including baseline smartphone survey return rates and survey completion rates by smartphone type (iPhone [Apple Inc] and Android [Google LLC] users). We also aim to report survey results for selected baseline surveys and participant experience with this study's app. Framingham Heart Study Offspring and Omni (multiethnic cohort) participants who owned a smartphone were invited to download this study's app that contained a range of survey types to report on different aspects of health including self-reported measures from the Patient-Reported Outcomes Measurement Information System (PROMIS). iPhone users also completed 4 tasks including 2 cognitive and 2 physical function testing tasks. Baseline survey return and completion rates were calculated for 12 surveys and compared between iPhone and Android users. We calculated standardized scores for the PROMIS surveys. The Mobile App Rating Scale (MARS) was deployed 30 days after enrollment to obtain participant feedback on app functionality and aesthetics. We enrolled 611 smartphone users (average age 73.6, SD 6.3 y; n=346, 56.6% women; n=88, 14.4% Omni participants; 478, 78.2% iPhone users) and 596 (97.5%) returned at least 1 baseline survey. iPhone users had higher app survey return rates than Android users for each survey (range 85.5% to 98.3% vs 73.8% to 95.2%, respectively), but survey completion rates did not differ in the 2 smartphone groups. The return rate for the 4 iPhone tasks ranged from 80.9% (380/470) for the gait task to 88.9% (418/470) for the Trail Making Test task. The Electronic Framingham Heart Study participants had better standardized t scores in 6 of 7 PROMIS surveys compared to the general population mean (t score=50) including higher cognitive function (n=55.6) and lower fatigue (n=45.5). Among 469 participants who returned the MARS survey, app functionality and aesthetics was rated high (total MARS score=8.6 on a 1-10 scale). We effectively engaged community-dwelling older adults to use a smartphone app designed to collect health information relevant to older adults. High app survey return rates and very high app survey completion rates were observed along with high participant rating of this study's app.

Long-term use of digital devices is critical for successful clinical or research use, but digital health studies are challenged by a rapid drop-off in participation. A nested e-cohort (eFHS) is embedded in the Framingham Heart Study and uses three system components: a new smartphone app, a digital blood pressure (BP) cuff, and a smartwatch. This study aims to identify factors associated with the use of individual eFHS system components over 1-year. Among 1948 eFHS enrollees, we examine participants who returned surveys within 90 days ( n  = 1918), and those who chose to use the smartwatch ( n  = 1243) and BP cuff ( n  = 1115). For each component, we investigate the same set of candidate predictors for usage and use generalized linear mixed models to select predictors ( P  < 0.1, P value from Z test statistic), adjusting for age, sex, and time (app use: 3-month period, device use: weekly). A multivariable model with the predictors selected from initial testing is used to identify factors associated with use of components ( P  < 0.05, P value from Z test statistic) adjusting for age, sex, and time. In multivariable models, older age is associated with higher use of all system components. Female sex and higher education levels are associated with higher completion of app-based surveys whereas higher scores for depressive symptoms, and lower than excellent self-rated health are associated with lower use of the smartwatch over the 12-month follow-up. Our findings show that sociodemographic and health related factors are significantly associated with long-term use of digital devices. Future research is needed to test interventional strategies focusing on these factors to evaluate improvement in long-term engagement.

Circulating lipid concentrations are clinically associated with cardiometabolic diseases. The phenotypic variance explained by identified genetic variants remains limited, highlighting the importance of searching for additional factors beyond genetic sequence variants. DNA methylation has been linked to lipid concentrations in previous studies, although most of the studies harbored moderate sample sizes and exhibited underrepresentation of non-European ancestry populations. In addition, knowledge of nongenetic factors on lipid profiles is extremely limited. In the Population Architecture Using Genomics and Epidemiology (PAGE) Study, we performed methylome-wide association analysis on 9,561 participants from diverse race and ethnicity backgrounds for HDL-c, LDL-c, TC, and TG levels, and also tested interactions between smoking or alcohol intake and methylation in their association with lipid levels. We identified novel CpG sites at 16 loci ( P  < 1.18E-7) with successful replication on 3,215 participants. One additional novel locus was identified in the self-reported White participants ( P  = 4.66E-8). Although no additional CpG sites were identified in the genome-wide interaction analysis, 13 reported CpG sites showed significant heterogeneous association across smoking or alcohol intake strata. By mapping novel and reported CpG sites to genes, we identified enriched pathways directly linked to lipid metabolism as well as ones spanning various biological functions. These findings provide new insights into the regulation of lipid concentrations.