Apatinib Plus Toripalimab (Anti-PD1 Therapy) as Second-Line Therapy in Patients With Advanced Gastric or Esophagogastric Junction Cancer: Results From a Randomized, Open-Label Phase II Study.

BACKGROUND: This study aimed to assess the activity of apatinib plus toripalimab in the second line for patients with advanced gastric or esophagogastric junction cancer (GC/EGJC). METHODS: In this open-label, phase II, randomized trial, patients with advanced GC/EGJC who progressed after first-line chemotherapy were enrolled and received 250 mg apatinib per day plus 240 mg toripalimab on day 1 per 3 weeks (arm A) or physician's choice of chemotherapy (PC, arm B). The primary endpoint of this study was the 1-year survival rate. Progression-free survival (PFS), overall survival (OS), overall response rate (ORR), and safety were assessed as secondary endpoints. RESULTS: Twenty-five patients received apatinib plus toripalimab while 26 were enrolled in arm B. The 1-year survival rates of the 2 groups were 43.3% and 42.3%, respectively (P = .903). The PFS was 2.77 versus 2.33 months (P = .660). The OS was 8.30 versus 9.88 months (P = .539). An objective response was reported in 20.0% of patients in arm A compared to 26.9% in arm B (P = .368), respectively. A total of 6 (24.0%) patients experienced adverse events of grade ≥ 3 in arm A, while 9 (34.6%) patients suffered from adverse events of grade ≥ 3 in arm B. No drug-related deaths occurred in either group. CONCLUSION: Toripalimab plus apatinib treatment in second-line therapy of advanced GC/EGJC showed manageable toxicity but did not improve clinical outcomes relative to PC treatment (ClinicalTrials.gov Identifier: NCT04190745).

Visceral adiposity associated with incidence and development trajectory of cardiometabolic diseases: A prospective cohort study.

BACKGROUND AND AIMS: There is a lack of literature concerning the effects of visceral adipose on the development of first cardiometabolic disease (FCMD) and its subsequent progression to cardiometabolic multimorbidity (CMM) and mortality. METHODS AND RESULTS: 423,934 participants from the UK Biobank with different baseline disease conditions were included in the analysis. CMM was defined as the simultaneous presence of coronary heart disease, T2D, and stroke. Visceral adiposity was estimated by calculating the visceral adiposity index (VAI). Multistate models were used to assess the effect of visceral adiposity on the development of CMM. During a median follow-up of 13.5 years, 50,589 patients had at least one CMD, 6131 were diagnosed with CMM, whereas 24,634 patients died. We observed distinct roles of VAI with respect to different disease transitions of CMM. HRs (95 % CIs) of high VAI were 2.35 (2.29-2.42) and 1.64 (1.50-1.79) for transitions from healthy to FCMD and from FCMD to CMM, and 0.97 (0.93-1.02) for all-cause mortality risk from healthy, FCMD and CMM, respectively. CONCLUSIONS: Our study provides the first evidence that visceral adipose may contribute to the development of FCMD and CMM in healthy participants. However, visceral adipose may confer resistance to all-cause mortality in participants with existing CMD or CMM. A better understanding of the relationship between visceral adipose and CMM can focalize further investigations on patients with CMD with high levels of visceral fat and help take targeted preventive measures to reduce the medical burden on individual patients and society.

Trajectories of quality of life and cognition in different multimorbidity patterns: Evidence from SHARE.

OBJECTIVES: The study aimed to observe the trajectory of quality of life (QoL) and cognition, and to a analyze the bidirectional association between cognition and QoL for diverse multimorbidity patterns. METHODS: In total, 16,153 older participants age ≥50 years were included from the Survey of Health, Ageing and Retirement in Europe (SHARE). We used latent class analysis (LCA) to identify multimorbidity patterns in the baseline population. We used linear mixed models (LMM) to examine the trajectory of cognition and QoL in different multimorbidity patterns. A cross-lagged model was employed to analyze the bidirectional association between cognition and QoL in diverse multimorbidity patterns. RESULTS: Latent class analysis identified four multimorbidity patterns: high and low comorbidity burden (HC and LC), cardiometabolic (CA), and osteoarthrosis (OS). The HC group had the poorest cognitive function and QoL (p for trend < 0.001). Delayed and immediate episodic memory in the OS group declined at a highest rate (p for trend < 0.001). Additionally, a bidirectional association between cognition and QoL was observed. The effect of cognitive function on QoL was relatively stronger than the reverse in the CA and LC groups. CONCLUSIONS: The rate of decline in cognition and QoL over the time differs in diverse multimorbidity patterns, and patients with four or more chronic diseases should be specially considered. Notably, early monitoring of cognitive function and can help break the vicious cycle between cognitive deterioration and poor QoL in patients with OS or CA diseases.

Bayesian Approaches in Exploring Gene-environment and Gene-gene Interactions: A Comprehensive Review.

Rapid advancements in high-throughput biological techniques have facilitated the generation of high-dimensional omics datasets, which have provided a solid foundation for precision medicine and prognosis prediction. Nonetheless, the problem of missing heritability persists. To solve this problem, it is essential to explain the genetic structure of disease incidence risk and prognosis by incorporating interactions. The development of the Bayesian theory has provided new approaches for developing models for interaction identification and estimation. Several Bayesian models have been developed to improve the accuracy of model and identify the main effect, gene-environment (G×E) and gene-gene (G×G) interactions. Studies based on single-nucleotide polymorphisms (SNPs) are significant for the exploration of rare and common variants. Models based on the effect heredity principle and group-based models are relatively flexible and do not require strict constraints when dealing with the hierarchical structure between the main effect and interactions (M-I). These models have a good interpretability of biological mechanisms. Machine learning-based Bayesian approaches are highly competitive in improving prediction accuracy. These models provide insights into the mechanisms underlying the occurrence and progression of complex diseases, identify more reliable biomarkers, and develop higher predictive accuracy. In this paper, we provide a comprehensive review of these Bayesian approaches.

Mediation analysis method review of high throughput data.

High-throughput technologies have made high-dimensional settings increasingly common, providing opportunities for the development of high-dimensional mediation methods. We aimed to provide useful guidance for researchers using high-dimensional mediation analysis and ideas for biostatisticians to develop it by summarizing and discussing recent advances in high-dimensional mediation analysis. The method still faces many challenges when extended single and multiple mediation analyses to high-dimensional settings. The development of high-dimensional mediation methods attempts to address these issues, such as screening true mediators, estimating mediation effects by variable selection, reducing the mediation dimension to resolve correlations between variables, and utilizing composite null hypothesis testing to test them. Although these problems regarding high-dimensional mediation have been solved to some extent, some challenges remain. First, the correlation between mediators are rarely considered when the variables are selected for mediation. Second, downscaling without incorporating prior biological knowledge makes the results difficult to interpret. In addition, a method of sensitivity analysis for the strict sequential ignorability assumption in high-dimensional mediation analysis is still lacking. An analyst needs to consider the applicability of each method when utilizing them, while a biostatistician could consider extensions and improvements in the methodology.

Association between coffee and incident heart failure: A prospective cohort study from the UK Biobank.

BACKGROUND AND AIMS: The relationship between coffee consumption and heart failure (HF) incidence is inconclusive. This study aimed to explore the association between time-varying coffee consumption and incident HF using a longitudinal study design. METHODS AND RESULTS: Data were obtained from the UK Biobank, comprising 497,503 adults (age, 56.5 ± 8.1 years; 54.6% women) who were free from HF at baseline in 2006-2010. The median follow-up time for the HF incidence was 11.9 years. Marginal structural models (MSM) were employed to adjust for potential time-varying confounders and account for bias caused by loss of follow-up. Furthermore, we used a restricted cubic spline to test and describe the nonlinear relationship between coffee consumption and HF risk. At baseline, 70.5% of participants reported drinking ≥1 cups/d coffee and 2.7% participants developed HF. After adjusting for potential confounders, we identified a nonlinear J-shaped association between coffee consumption and HF risk (P < 0.001). Compared with drinking coffee <1 cups/d, 1-2 cups/d (HR = 0.878; 95% CI: 0.838-0.920), 3-4 cups/d (HR = 0.920; 95% CI: 0.869-0.974) may be associated with a reduced risk of HF, while >6 cups/d (HR = 1.209; 95% CI: 1.056-1.385) may be associated with a higher risk of HF. However, sensitive analyses stratified by gender and smoking status indicated that >6 cups/d does not significantly increase the risk of HF. Additionally, the type of coffee was found to significant impact on the incidence of HF (P < 0.05). CONCLUSION: In this large cohort of UK adults, moderate coffee consumption may reduce risk of HF incidence.

Association of ultra-processed food consumption with incident depression and anxiety: a population-based cohort study.

Background: Global ultra-processed food (UPF) consumption has risen rapidly. The development and prognosis of depression and anxiety remain unclarified. Herein, we aimed to examine the association between UPF consumption and the incidence and progression trajectory of depression and anxiety. Methods: In our study, participants were recruited between 2006 and 2010. UPF consumption was expressed as UPF servings, energy ratio, and weight ratio. The relationships between UPF consumption and depression or anxiety were assessed using the Cox proportional hazards model. Multi-state models were used to explore the association between UPF consumption and the risks of all transitions from a healthy state to depression or anxiety and then to all-cause mortality. Results: Among the 183 474 participants, 5453 were diagnosed with depression and 6763 with anxiety during the follow-up of 13.1 years. The participants in the highest quartile (Q4) of UPF servings, energy ratio, and weight ratio had an increased risk of depression compared to those in the lowest quartile (Q1), with hazard ratios (HRs) and 95% confidence intervals [CIs] of 1.22 (1.13-1.31), 1.13 (1.05-1.22), and 1.26 (1.17-1.36), respectively. Similarly, participants in Q4 of UPF consumption had a higher risk of anxiety, with HRs (95% CIs) of 1.13 (1.06-1.21), 1.13 (1.05-1.21), and 1.11 (1.04-1.19), compared to those in Q1. The study also found a significant association between UPF consumption and all-cause mortality, which disappeared for participants with depression or anxiety. Conclusions: Our findings revealed that UPF consumption is associated with depression or anxiety.

Bidirectional Association Between Multimorbidity and Frailty and the Role of Depression in Older Europeans.

BACKGROUND: Although previous studies have reported an association between multimorbidity and frailty, its direction and mechanism remain unclear. This study aimed to investigate the direction of this association, as well as the role of depression among older Europeans. METHODS: We used a cross-lagged panel design to evaluate the temporal relationship between multimorbidity and frailty and the role of depression. Multimorbidity status was assessed by the self-reporting of 14 chronic diseases. Frailty was assessed based on the frailty phenotype. The European-Depression Scale (EURO-D) was used to assess depression. RESULTS: There was a bidirectional relationship between frailty and multimorbidity. More severe multimorbidity predicted greater frailty (ß = 0.159; p < .001) and vice versa (ß = 0.107; p < .001). All paths from multimorbidity to frailty were stronger than the paths from frailty to multimorbidity (b1-a1: ß = 0.051; p < .001). Likewise, early multimorbidity change was a significant predictive factor for late frailty change (ß = 0.064; p < .001) and vice versa (ß = 0.048; p < .001). Depression in Wave 5 (T5) mediated the association between frailty in Wave 4 (T4) and multimorbidity in Wave 6 (T6; indirect effect: ß = 0.004; bootstrap 95% confidence interval: 0.003, 0.006). CONCLUSIONS: A positive, bidirectional association was observed between multimorbidity and frailty. Depression may be a potential cause of an increased risk of multimorbidity later in life in frail older adults. Early monitoring of frailty and depression may slow the progression of multimorbidity, thereby interrupting the vicious cycle.

Mechanism of the association between sleep quality and mortality in middle-aged and older adults: A prospective study analysis of the UK Biobank.

BACKGROUND: Although sleep quality is known to be associated with mortality, how poor sleep quality contributes to an increased risk of mortality is still unknown. We aimed to examine whether lifestyle, psychosocial and biological factors mediate the association. METHODS: 205,654 participants from UK Biobank were used for the analysis. The outcome was all-cause, cardiovascular disease (CVD) and cancer mortality by February 2022. Exposure was assessed by a sleep score consisting of five sleep behaviors at baseline. Lifestyle, psychosocial, and biological factors are regarded as potential mediators. Mediation analysis based on Cox proportional hazards models was performed. RESULTS: Poor sleep quality was associated with a higher risk of all-cause (Hazard Ratio [HR] = 1.098; 95% CI: 1.058-1.140), CVD (HR = 1.139; 95% CI: 1.045-1.243) and cancer mortality (HR = 1.095; 95% CI: 1.040-1.152). Lifestyle mediators (smoking, physical activity, sedentary, BMI and diet) could explain between 2.6% and 34.0% of the increased risk of all-cause mortality in individuals with poor sleep quality. Self-reported health, frailty, depression, and loneliness were significant psychosocial mediators of this association pathway. About one-fifth of the association can be explained by the biological role of CRP. Similar mediating patterns were observed for CVD and cancer mortality. LIMITATIONS: Both exposure and mediators were measured at baseline, so the possibility of reverse causality cannot be ruled out. CONCLUSIONS: Poor sleep quality is associated with an increased risk of death through a combination of lifestyle, psychosocial and biological pathways. Adopting healthy lifestyles and staying psychosocial well-being are cost-effective interventions to lower the risk of death.

Bidirectional Associations Between Sleep Quality and Grip Strength and the Mediating Role of Depression: Evidence From Two Nationally Representative Cohorts.

BACKGROUND: Although studies have demonstrated associations between sleep quality (SQ) and grip strength (GS) in older adults, the direction and underlying mechanisms of this relationship are yet to be better delineated. We aimed to longitudinally investigate the bidirectional association between SQ and GS and the mediating role of depression in this association. METHODS: Based on 2 nationally representative samples with people aged ≥50 years from the China Health and Retirement Longitudinal Study (CHARLS; 4 200 participants) and English Longitudinal Study of Ageing (ELSA; 5 922 participants), cross-lagged panel models were employed to examine the potential bidirectional relationships between objectively measured GS and self-reported SQ. RESULTS: We observed a GS-SQ bidirectional association dominated by GS. After adjusting for potential confounders, a higher GS at T1 predicted better SQ at T2 (ELSA: ß = 0.075; CHARLS: ß = 0.104, p < .001) and vice versa (ELSA: ß = 0.034; CHARLS: ß = 0.030, p < .01). Moreover, depression partially mediated the impact of GS on subsequent SQ (ELSA, indirect effect: 0.0057, 95% confidence interval [CI]: 0.0035-0.0084; CHARLS, indirect effect: 0.0086, 95% CI: 0.0051, 0.0131), but not vice versa. CONCLUSIONS: The results regarding data from both cohorts consistently supported a bidirectional association between GS and SQ and the mediating role of depression in the dominant pathway of this bidirectional relationship. Older adults with a low GS should be made aware of a potentially vicious cycle related to depression that can affect their sleep. Regular screening for depression may help to break this cycle.

The Longitudinal Association Between Frailty, Cognition, and Quality of Life in Older Europeans.

OBJECTIVES: Evidence on the association between frailty and quality of life (QoL) is mostly limited to cross-sectional studies. Thus, the temporal order and potential mechanisms of this association are largely unknown. Our study examines both the directionality of this association and the role of cognition in this association in longitudinal data. METHODS: Cross-lagged panel models were employed to examine the temporal relationship between frailty and QoL, as well as cognition's role among 19,649 older adults in Europe. Frailty, QoL, and cognition were assessed using the health deficit index, CASP-12, and 3 standard cognitive tests, respectively. RESULTS: We observed a bidirectional association between frailty and QoL and their dynamics. High initial levels of frailty predicted poorer QoL later and vice versa (ß = -0.151 and -0.052, p < .001). The early change in frailty predicted the late change in QoL, and vice versa (ß = -0.093 and -0.061, p < .001). Frailty or its early change drives this interrelationship. Cognition at Wave 5 partially mediated frailty's effect at Wave 4 on QoL at Wave 6 (indirect effect: ß = -0.005, 95% confidence interval = -0.006, -0.004). DISCUSSION: Our findings supported that early prevention of frailty and its risk factors may have more influential protective effects on later physical and mental health, as well as the need for ongoing screening for mental health in aging population. Also, the maintenance of good cognitive performance may help interrupt this possible vicious cycle linking frailty and QoL decline.

Moderated-mediation analysis of multimorbidity and health-related quality of life among the Chinese elderly: The role of functional status and cognitive function.

Objectives: To investigate the relationship between multimorbidity and health-related quality of life (HRQoL), and explore the effects of functional status and cognitive function on Chinses elderly behind this relationship. Methods: The Multivariate logistic regression and Tobit regression models were used to determine the influence of multimorbidity on HRQoL. Bootstrap analysis was used to probe the mediating effects of functional status and the moderating role of cognition on multimorbidity and HRQoL. Results: Results of the 2,887 participants age ≥ 60 years included in the analysis, 51.69% had chronic diseases. Stroke (ß = -0.190; 95% confidence interval [CI], -0.232, -0.149; p < 0.001) and the combination of hypertension and stroke (ß = -0.210; 95% CI, -0.259, -0.160; p < 0.001) had the greatest influence on HRQoL. Functional status partially mediated the relationship between the number of non-communicable diseases (No. of NCDs) and HRQoL, while cognitive function had a moderating effect not only in the A-path (No. of NCDs to functional status, ß = 0.143; t = 7.18; p < 0.001) and but also in the C-path (No. of NCDs to HRQoL, ß = 0.007; t = 6.08; p < 0.001). Conclusion: Functional status partially mediated the relationship between multimorbidity and HRQoL in older adults. And cognitive function, if declined, may strengthen this relationship. These findings suggested that improving cognitive function and functional status in those who developed multimorbidity could be a viable prevention or treatment strategy to improve HRQoL in elderly patients.

Joint effect of elevated-c-reactive protein level and hypertension on new-onset stroke: A nationwide prospective cohort study of CHARLS.

Background and aims: This study aimed to examine whether the combination of elevated-C-reactive protein (CRP) levels and hypertension increased the risk of stroke among middle-aged and elderly Chinese. Methods: This analysis included 9,821 Chinese participants aged ≥45 years in the China Health and Retirement Longitudinal Study (CHARLS). Data based on three waves of CHARLS were used (2011, 2013, and 2015). Multivariable Cox proportional hazards regression models were used to estimate hazard ratios (HRs) with a 95% confidence interval (95%CI) of new-onset stroke risk according to elevated-CRP level and hypertension. Moreover, the area under the curve (AUC), net reclassification index (NRI), and integrated discrimination improvement (IDI) were used to evaluate the incremental predictive value. Results: A total of 184 stroke events occurred during follow-up. The median follow-up time was 4 years. Compared with those with normal CRP levels (CRP ≤ 3 mg /L) and blood pressure, the adjusted HRs and 95%CI were 1.86 (0.90-3.85) for individuals with elevated-CRP levels alone, 2.70 (1.71-4.28) for those with hypertension alone, and 4.80 (2.83-8.12) for those with comorbid elevated-CRP levels and hypertension. People with the coexistence of elevated-CRP levels and hypertension had the highest risk of new-onset stroke among all subgroup analyses. Finally, adding the combination of elevated-CRP levels and hypertension to conventional factors significantly improved the risk prediction for new-onset stroke. Conclusion: Our findings indicate that the combined effect of elevated-CRP levels and hypertension increase the risk of new-onset stroke among the middle-aged and geriatric Chinese population.

Associations of ultra-processed food consumption with cardiovascular disease and all-cause mortality: UK Biobank.

BACKGROUND: This study aimed to investigate the associations between ultra-processed food (UPF) consumption and the risk of cardiovascular disease and all-cause mortality in the UK Biobank Cohort. METHODS: This observational prospective study evaluated 60 298 participants aged 40 years or older. We used the NOVA classification system to identify and categorize UPF. The associations among UPF consumption, cardiovascular disease (CVD) incidence and all-cause mortality were estimated using multivariable Cox proportional hazards models. Dose-response analysis of UPF consumption and CVD incidence and mortality was performed using a restricted cubic spline. RESULTS: After a median follow-up of 10.9 years, 6048 participants (10.0%) experienced CVD events, and 5327 (8.8%) and 1503 (2.5%) experienced coronary heart and cerebrovascular diseases, respectively. There were 2590 (4.3%) deaths, of which 384 (0.6%) deaths were caused by CVD. A higher intake of UPF was associated with a higher risk of CVD and all-cause mortality (all P < 0.001). A higher intake of UPF was associated with a higher risk of CVD [hazard ratio (HR) = 1.17, 95% confidence interval (CI): 1.09-1.26], coronary heart disease (HR = 1.16, 95% CI: 1.07-1.25), cerebrovascular disease (HR = 1.30, 95% CI: 1.13-1.50) and all-cause mortality (HR = 1.22, 95% CI: 1.09-1.36). The association of UPF consumption with a range of CVD incidents and all-cause mortality was monotonic (all P for non-linearity > 0.30). CONCLUSIONS: A higher proportion of UPF consumption was associated with CVD and all-cause mortality. Thus, actions to limit UPF consumption should be incorporated into the CVD and all-cause mortality prevention recommendations.

Association between the visceral adiposity index and risks of all-cause and cause-specific mortalities in a large cohort: Findings from the UK biobank.

BACKGROUND AND AIMS: The visceral adiposity index (VAI) has been recently established as a measure of visceral fat distribution and is shown to be associated with a wide range of adverse health events. However, the precise associations between the VAI score and all-cause and cause-specific mortalities in the general population remain undetermined. METHODS AND RESULTS: In this large-scale prospective epidemiological study, 357,457 participants (aged 38-73 years) were selected from the UK Biobank. We used Cox competing risk regression models to estimate the association between the VAI score and all-cause, cardiovascular disease (CVD), cancer, and other mortalities. The VAI score was significantly correlated with an increased risk of all-cause mortality (hazard ratio [HR], 1.200; 95% confidence interval [CI], 1.148-1.255; P < 0.0001), cancer mortality (HR, 1.224; 95% CI, 1.150-1.303; P < 0.0001), CVD mortality (HR, 1.459; 95% CI, 1.148-1.255; P < 0.0001), and other mortalities (HR, 1.200; 95% CI, 1.148-1.255; P < 0.0001) after adjusting for a series of confounders. In addition, the subgroup analyses showed that HRs were significantly higher in participants who were male, aged below 65 years, and body mass index less than 25. CONCLUSION: In summary, VAI was positively associated with an increased risk of all-cause and cause-specific mortalities in a nationwide, well-characterised population identified in a UK Biobank. The VAI score might be a complementary traditional predictive indicator for evaluating the risk of adverse health events in the population of Western adults aged 38 years and older.

Cohort profile: the Liyang cohort study on chronic diseases and risk factors monitoring in China (Liyang Study).

PURPOSE: The Liyang cohort study on chronic diseases and risk factors monitoring in China (Liyang Study) is a prospective population-based study which aims to investigate and identify the determinants of the most prevalent chronic non-communicable diseases (NCDs) and to evaluate the impact of demographic characteristics, lifestyle, dietary habits, cognition, disability and NCDs on the health-related quality of life. PARTICIPANTS: Between March 2019 and June 2020, 10 056 individuals aged ≥18 years were administered a baseline survey through a multistage cluster random sampling in Liyang City, southern Jiangsu Province, China. FINDINGS TO DATE: The Liyang Study included detailed sociodemographic, anthropometric and health-related behaviour, common NCDs and blood sample information. Moreover, the study gathered a series of data on specific scales including the activities of daily living, instrumental activities of daily living, abbreviated mental test, Food Frequency Questionnaire and EuroQol 5-Dimensions 5-Levels Scale. Of the 10 056 participants, 52.92% (n=5322) were female and 92.26% (n=9278) came from rural areas. The mean age was 49.9±16.2 years. Men were more likely to have a higher level of education, annual income and a paid job than women (p<0.05). The top three overall most prevalent NCDs in the study were hypertension (18.06%, n=1815), digestive diseases (7.88%, n=791), and arthritis or rheumatism (5.28%, n=530). Women had a significantly higher prevalence of diabetes (5.46%, n=290 vs 4.42%, n=209, p=0.016) and arthritis (6.04%, n=321 vs 4.42%, n=209, p<0.001) than men, while the opposite was true for chronic lung diseases such as chronic obstructive pulmonary disease (1.37%, n=65 vs 0.92%, n=49, p=0.032) and chronic hepatic diseases (0.80%, n=38 vs 0.47%, n=25, p=0.035). FUTURE PLANS: The current study will give valuable insights into the association between sociodemographic factors, health-related behaviour, diet, cognition, disability and genetic factors and the most prevalent NCDs among local community residents. Starting from 2022, a follow-up survey will be conducted every 3 years to further explore the causal relationship between the above factors and NCDs.

Estimating population norms for the health-related quality of life of adults in southern Jiangsu Province, China.

Although national health-related quality of life population norms had been published based on the EuroQol 5-Dimensions 5-levels scale, China is a vast country with diverse cultural and social development in various regions. Therefore, regional population norms may better reflect the health status of residents in a given area. The purpose of the study was to derive the HRQoL population norm for adult general population in southern Jiangsu Province using the EQ-5D-5L scale and explore potential influencing factors. The data were based on a cross-sectional survey conducted in Liyang City from March 2019 to July 2020. EQ-5D-5L utility scores based on Chinese value set and EQ-VAS scores were used to assess HRQoL. The Tobit regression model and generalized linear model were performed to identify the association among potential covariates and HRQoL. The means (95% confidence interval) of the EQ-5D-5L utility scores and EQ-VAS scores were 0.981(0.980-0.983) and 83.6(83.2-83.9), respectively. Younger people (≤ 40 years old) were more likely to experience problems with anxiety or depression. Additionally, women had lower HRQoL scores although multivariate analysis found no statistical difference between the sexes. Lower HRQoL was associated with advanced age, lower socioeconomic status, no spouse, lack of regular physical activities, smoking cessation, and chronic non-communicable diseases. Subjects who declared that they were afflicted by diseases presented significantly lower utility scores, ranging from 0.823 (0.766-0.880) for memory-related diseases to 0.978 (0.967-0.989) for hepatic diseases. Regional population norms of HRQoL are needed in the health economic study owing to the great socioeconomic differences across regions in China. The present study provides HRQoL population norms for adults in southern Jiangsu. These norm values could help policy makers better allocate limited health resources and prioritize service plans.

The role of depression and physical activity in the association of between sleep quality, and duration with and health-related quality of life among the elderly: a UK Biobank cross-sectional study.

BACKGROUND: Although studies have shown that sleep quality (duration) is associated with health-related quality of life (HRQoL), most of these studies have been small-sized and targeted at young and middle-aged adults. In addition, few studies have explored the path mechanism of sleep disorders leading to impaired HRQoL. OBJECTIVES: This study aimed to determine the association between sleep quality and duration and HRQoL among the elderly in the United Kingdom, assess whether depression mediated the association, and explore the role of physical activity (PA) in the path association. METHODS: Data were extracted from the baseline survey of the UK Biobank, a large prospective cohort study enrolling more than 500,000 participants, of which 52,551 older adults (aged ≥60 years) were included in the study. HRQoL was assessed using the European Quality of Life-5 Dimensions. Tobit and multivariate logistic regression models were used to determine the association between sleep quality and duration and HRQoL. The mediating and moderated mediation models were estimated using the PROCESS macro and MEDCURVE macro. RESULTS: The Tobit model showed that the elderly with short or long sleep duration (ß = - 0.062, 95% confidence interval [CI] = - 0.071 to - 0.053; ß = - 0.072, 95% CI = - 0.086 to - 0.058) had worse HRQoL after adjusting potential covariates. In the logistic regression models, we found an inverted U-shaped association between sleep duration and HRQoL. Moreover, a significant positive association was observed between sleep quality and HRQoL (all P < 0.05). The results also revealed that depression mediated the association between sleep disorders and HRQoL (sleep quality: ß = 0.008, 95% CI = 0.007-0.010; sleep duration: θ = 0.001 [mean], 95% CI = 0.001-0.002). Furthermore, PA moderated all paths among sleep quality and duration, depression, and HRQoL, and greater effects were observed in the elderly with lower PA levels. CONCLUSIONS: The findings show that poor sleep quality and duration were independently associated with worse HRQoL among the elderly in the United Kingdom. Furthermore, PA buffers the mediating effect of depression and adverse effects of sleep disorders on HRQoL. It is essential to properly increase PA and provide early intervention for depression in the elderly with sleep disorders to improve their HRQoL.

Bayesian hierarchical lasso Cox model: A 9-gene prognostic signature for overall survival in gastric cancer in an Asian population.

OBJECTIVE: Gastric cancer (GC) is one of the most common tumour diseases worldwide and has poor survival, especially in the Asian population. Exploration based on biomarkers would be efficient for better diagnosis, prediction, and targeted therapy. METHODS: Expression profiles were downloaded from the Gene Expression Omnibus (GEO) database. Survival-related genes were identified by gene set enrichment analysis (GSEA) and univariate Cox. Then, we applied a Bayesian hierarchical lasso Cox model for prognostic signature screening. Protein-protein interaction and Spearman analysis were performed. Kaplan-Meier and receiver operating characteristic (ROC) curve analysis were applied to evaluate the prediction performance. Multivariate Cox regression was used to identify prognostic factors, and a prognostic nomogram was constructed for clinical application. RESULTS: With the Bayesian lasso Cox model, a 9-gene signature included TNFRSF11A, NMNAT1, EIF5A, NOTCH3, TOR2A, E2F8, PSMA5, TPMT, and KIF11 was established to predict overall survival in GC. Protein-protein interaction analysis indicated that E2F8 was likely related to KIF11. Kaplan-Meier analysis showed a significant difference between the high-risk and low-risk groups (P<0.001). Multivariate analysis demonstrated that the 9-gene signature was an independent predictor (HR = 2.609, 95% CI 2.017-3.370), and the C-index of the integrative model reached 0.75. Function enrichment analysis for different risk groups revealed the most significant enrichment pathway/term, including pyrimidine metabolism and respiratory electron transport chain. CONCLUSION: Our findings suggested that a novel prognostic model based on a 9-gene signature was developed to predict GC patients in high-risk and improve prediction performance. We hope our model could provide a reference for risk classification and clinical decision-making.

A novel 14-gene signature for overall survival in lung adenocarcinoma based on the Bayesian hierarchical Cox proportional hazards model.

There have been few investigations of cancer prognosis models based on Bayesian hierarchical models. In this study, we used a novel Bayesian method to screen mRNAs and estimate the effects of mRNAs on the prognosis of patients with lung adenocarcinoma. Based on the identified mRNAs, we can build a prognostic model combining mRNAs and clinical features, allowing us to explore new molecules with the potential to predict the prognosis of lung adenocarcinoma. The mRNA data (n = 594) and clinical data (n = 470) for lung adenocarcinoma were obtained from the TCGA database. Gene set enrichment analysis (GSEA), univariate Cox proportional hazards regression, and the Bayesian hierarchical Cox proportional hazards model were used to explore the mRNAs related to the prognosis of lung adenocarcinoma. Multivariate Cox proportional hazard regression was used to identify independent markers. The prediction performance of the prognostic model was evaluated not only by the internal cross-validation but also by the external validation based on the GEO dataset (n = 437). With the Bayesian hierarchical Cox proportional hazards model, a 14-gene signature that included CPS1, CTPS2, DARS2, IGFBP3, MCM5, MCM7, NME4, NT5E, PLK1, POLR3G, PTTG1, SERPINB5, TXNRD1, and TYMS was established to predict overall survival in lung adenocarcinoma. Multivariate analysis demonstrated that the 14-gene signature (HR 3.960, 95% CI 2.710-5.786), T classification (T1, reference; T3, HR 1.925, 95% CI 1.104-3.355) and N classification (N0, reference; N1, HR 2.212, 95% CI 1.520-3.220; N2, HR 2.260, 95% CI 1.499-3.409) were independent predictors. The C-index of the model was 0.733 and 0.735, respectively, after performing cross-validation and external validation, a nomogram was provided for better prediction in clinical application. Bayesian hierarchical Cox proportional hazards models can be used to integrate high-dimensional omics information into a prediction model for lung adenocarcinoma to improve the prognostic prediction and discover potential targets. This approach may be a powerful predictive tool for clinicians treating malignant tumours.