Cheng Wu

Introduction: Previous studies have indicated a possible connection between Gut microbiota (GM) and dementia, however, the exact cause-and-effect relationships between GM, various types of dementia, and the potential influence of plasma lipidome as intermediaries are still unclear.Material and methods: We used genome-wide association study (GWAS) data to identify GM, plasma lipidome, and five types of dementia, including Alzheimer’s disease (AD), dementia with Lewy bodies (DLB), Parkinson's disease dementia (PDD), frontotemporal dementia (FTD) and vascular dementia (VD). We used Mendelian randomization (MR) to investigate the possible causal connections among GM, plasma lipidome, and dementias. The inverse variance weighting (IVW) method served as the primary statistical approach. We investigated the role of plasma lipidome as a potential mediating factor in this relationship.Results: A total of 41 positive and 39 negative causal relationships between genetic susceptibility in the GMs or bacterial pathway and dementia, as well as 14 negative causal relationships between plasma lipidome and dementias. Additionally, only 1 potential mediation pathway was identified as having a significant mediating effect.Conclusions: Our results suggest a link between GM and plasma lipidome with five distinct types of dementia, indicating that Phosphatidylcholine (O-16:1_18:2) level could play a role in the pathway from species Bacteroides coprocola to vascular dementia.

Objective: This study aimed to explore the association between base excess (BE) and the risk of 30-day mortality among patients with acute kidney injury (AKI) in the intensive care unit (ICU).Methods: This retrospective study included patients with AKI from the Medical Information Mart for Intensive Care (MIMIC)-IV database. We used a multivariate Cox proportional-hazards model to obtain the hazard ratio (HR) for the risk of 30-day mortality among patients with AKI. Furthermore, we utilized a Cox proportional-hazard model with restricted cubic splines (RCS) to explore the potential non-linear associations.Results: Among the 14,238 ICU patients with AKI, BE showed a U-shaped relationship with risk of 30-day mortality for patients with AKI, and higher or lower BE values could increase the risk. Compared with normal base excess (−3~3 mEq/L), patients in different groups (BE ≤ −9 mEq/L, −9 mEq/L < BE ≤ −3 mEq/L, 3 mEq/L < BE ≤ 9 mEq/L, and BE > 9 mEq/L) had different HRs for mortality: 1.57 (1.40, 1.76), 1.26 (1.14, 1.39), 0.97 (0.83, 1.12), 1.53 (1.17, 2.02), respectively. The RCS analyses also showed a U-shaped curve between BE and the 30-day mortality risk.Conclusion: Our results suggest that higher and lower BE in patients with AKI would increase the risk of 30-day mortality. BE measured at administration could be a critical prognostic indicator for ICU patients with AKI and provide guidance for clinicians.

Background: This study aimed to explore the association between diabetes mellitus and health-related quality of life (HRQoL) of older people in Shanghai, China, especially regarding the differences in each aspect of the EQ-5D and how large the score gaps are between older people with and without diabetes.Methods: A total of 11,103 people of either sex older than 60 years were enrolled from 17 districts of Shanghai. The EQ-5D-3L was used to assess the HRQoL of older people. The Wilcoxon rank sum test and t-test were used to compare the difference in HRQoL between people with or without diabetes. After univariate regression, multiple linear regression and ordinal logistic regression were conducted to evaluate the influence of diabetes mellitus and other confounding variables on the EQ VAS scores and on the five dimensions of the EQ-5D.Results: Twelve percent of all participants had diabetes mellitus, and the proportion was almost the same between men and women. The EQ VAS scores of people with diabetes mellitus were approximately 3.70 points lower than those of people without diabetes (95% CI = -4.40, -2.99,p < 0.001) after adjusting for confounding variables. People with diabetes mellitus had increased problems with mobility (OR = 1.57, 95% CI = 1.33, 1.85), self-care (OR = 1.65, 95% CI = 1.35, 2.01), usual activities (OR = 1.78, 95% CI = 1.51, 2.11), pain/discomfort (OR = 1.42, 95% CI = 1.24, 1.64), and anxiety/depression (OR = 1.33, 95% CI = 1.07, 1.64).Conclusions: This study showed that diabetes mellitus was associated with the HRQoL of older people and that older people with diabetes had poorer performance in every aspect of EQ-5D measurements.

Objective This study aimed to explore the association between base excess (BE) and risk of 30-day mortality among patients with acute kidney injury (AKI) in ICU.Methods This retrospective study including ICU patients with AKI from Medical Information Mart for Intensive Care (MIMIC)-IV database. We used multivariate Cox proportional-hazards model to calculate the hazard ratio (HR) for risk of 30-day mortality among patients with AKI. Furthermore, we utilized Cox proportional-hazard model with restrict cubic splines (RCS) to explore the potential no-linear association. Results Of all the 14238 ICU patients with AKI, BE showed U-shaped relationship with risk of 30-day mortality for patients with AKI, and higher or lower BE value could increase the risk. Compared with normal base excess (-3~3 mmol/L), patients with difference groups (BE ≤ -9mmol/L, -9 mmol/L Conclusion Our results suggest both higher and lower BE in patients with AKI would increase the risk of 30-day mortality. BE measured at administration could be a critical prognostic indicator for ICU patients with AIK and provide guidance for clinicians.

Background: Frailty is a common characteristic of older people with the ageing process. We aimed to develop and validate a dynamic statistical prediction model to calculate the risk of death in people aged ≥65 years, using a longitudinal frailty index (FI). Methods: One training dataset and three validation datasets from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) were used in our study. The training dataset and validation datasets 1 to 3 included data from 9,748, 7,459, 9,093 and 6,368 individuals, respectively. We used 35 health deficits to construct the FI and a longitudinal FI based on repeated measurement of FI at every wave of the CLHLS. A joint model was used to build a dynamic prediction model considering both baseline covariates and the longitudinal FI. Areas under time-dependent receiver operating characteristic curves (AUCs) and calibration curves were employed to assess the predictive performance of the model. Results: A linear mixed-effects model used time, sex, residence (city, town, or rural), living alone, smoking and alcohol consumption to calculate a subject-specific longitudinal FI. The dynamic prediction model was built using the longitudinal FI, age, residence, sex and an FI–age interaction term. The AUCs ranged from 0.64 to 0.84, and both the AUCs and the calibration curves showed good predictive ability. Conclusions: We developed a dynamic prediction model that was able to update predictions of the risk of death as updated measurements of FI became available. This model could be used to estimate the risk of death in individuals aged >65 years.

The aim of this study is to summarize the evidence on the dose–response relationship between body mass index (BMI) and mortality in patients with chronic obstructive pulmonary disease (COPD). We performed a systemic literature search in PubMed, Embase, and Web of Science for relevant studies that were published until June 2015. A random effects meta-analysis was used to estimate the pooled relative risks (RRs) of all-cause mortality in COPD patients with normal weight compared with those who were underweight, overweight, or obese. In addition, a dose–response meta-analysis was conducted to explore the dose–response relationship between BMI and all-cause mortality in COPD patients. A total of 17 observational studies involving 30,182 COPD patients among 285,960 participants were included. Compared with the reference category, the RRs of underweight, overweight, and obese individuals were 1.40 (95% confidence interval (CI), 1.20–1.63), 0.80 (95% CI, 0.67–0.96), and 0.77 (95% CI, 0.62–0.95), respectively. A significant nonlinear relationship between BMI and mortality of COPD patients was found by using a random effects model. COPD patients with BMI of 32 kg/m2. Our findings indicate that overweight is associated with a lower risk of all-cause mortality among patients with COPD whereas underweight is associated with a higher risk of all-cause mortality in these patients. However, there is limited evidence to support the association between obesity and the risk of all-cause mortality in patients with COPD.

In this study, we aimed to investigate the prevalence, awareness, treatment, and control of diabetes in Shanghai, China. A sample of 3600 residents aged from 18 to 80 years selected by a randomized stratified multiple-stage sampling method in Shanghai was investigated, with blood samples collected. Diabetes was defined as fasting plasma glucose (FPG) ≥ 7.0 mmol/L, or glycated haemoglobin (HbA1c) ≥ 6.5% (48 mmol/mol), or previous diagnosis by a physician. Adequate control of diabetes was taken as a level of HbA1c < 7.0% (53 mmol/mol) among people with treated diabetes. Multivariable regression analysis was used to explore associated factors for diabetes and prediabetes. In the 3136 participants suitable for analysis, the prevalences of diabetes, prediabetes, and previously diagnosed diabetes were 15.91%, 37.37%, and 4.46%, respectively. Among those with diabetes, only 28.06% were aware of their condition, 25.85% were currently undergoing medication treatment, and 12.42% achieved glycaemic control. Logistic regression showed that old age, preobesity, obesity, elevated triglyceride (TG), elevated C-reactive protein (CRP), and lower education level were associated with an increased risk of diabetes; old age, obesity, elevated TG, and elevated low-density lipoprotein (LDL) were associated with an increased risk of prediabetes, while male sex and rural residence were associated with a decreased risk of prediabetes. In summary, the state of diabetes in China is alarming; the rates of awareness, treatment, and control were relatively low. More efforts should be made to promote the prevention and control of diabetes in china.

Purpose: There has been much controversy over the use of angiotensin‐converting enzyme inhibitors/angiotensin receptor blockers (ACEIs/ARBs) on patients with renal dysfunction. The purpose of this study was to summarize the evidence regarding the effect of ACEIs/ARBs administration on mortality in patients with nondialysis‐dependent chronic kidney disease (CKD) by using a meta‐analytic approach.Methods: We searched the PubMed, Embase, and Web of Science databases for studies on the effect of ACEIs/ARBs administration on mortality in patients with nondialysis‐dependent CKD published before March 2015. Summary effect estimates with 95% confidence intervals were derived using the random‐effects model, no matter whether the heterogeneity between the included studies was of statistical significance or not. Subgroup analyses, sensitivity analyses, and publication bias tests were performed.Results: Up to 25 March 2015, 10 cohort studies were included in this meta‐analysis. The hazard risk of the association between ACEIs/ARBs administration and overall mortality was 0.83 (95% confidence interval 0.78–0.87) using a random‐effects model with no heterogeneity (heterogeneity test I² = 43.8%, p = 0.067) and publication bias (Egger's test, p = 0.763). The subgroup was divided according to estimated glomerular filtration rate, duration of follow‐up, Newcastle–Ottawa Scale star, and proportion of patients with common complications including heart failure, diabetes mellitus, and hypertension. Improved survival outcomes were observed in all subgroups analysis. Sensitivity analysis proved that overall estimated effect was robust.Conclusion: This meta‐analysis suggested that the use of ACEIs/ARBs in patients with nondialysis‐dependent CKD was associated with improved survival. However, randomized studies are needed to confirm these findings and further establish causal relationship. Copyright © 2016 John Wiley & Sons, Ltd.