Physical activity (PA) is linked to a decreased risk of type 2 diabetes mellitus (T2DM). However, the influence of circadian PA trajectories remains uncertain. This study aims to explore the optimal ...circadian PA trajectory pattern for reducing the risk of T2DM. Methods: A total of 502,400 participants were recruited from the UK Biobank between 2006 and 2010, and 102,323 participants provided valid accelerometer-captured acceleration data. After excluding individuals with prior T2DM, 99,532 participants were included in the final analysis. We initially investigated the association between PA intensity at 24 hourly time points and T2DM. Subsequently, PA trajectories were identified using K-means cluster analysis. Cox proportional hazard models were employed to estimate hazard ratios (HR). Four distinct PA trajectories were identified: consistently low, single peak, double peak, and intense trajectories. Compared to consistently low, single peak, double peak and intense PA trajectory reduced the risk of T2DM progressively. Sensitivity analyses, further excluding individuals with glycated hemoglobin (HbA1c) ≥ 6.5% or random glucose ≥ 11.1 mmol/L and adjusted for daily average acceleration, yielded consistent results. This confirms that the ideal circadian PA trajectory serves as a protective factor, independently of PA intensity. Subgroup analyses indicated that these effects were more pronounced in men and individuals with eGFR < 60 mL/(min*1.73 m
). In conclusion, ideal circadian PA trajectory patterns (especially intense and then double peak) reduced risk of T2DM.
There is a lack of relevant studies evaluating the long-term impact of cardiovascular health factor (CVH) metrics on chronic kidney disease (CKD).
This study investigates the long-term change in CVH ...metrics in older people and explores the relationship between CVH metrics trajectory and CKD.
In total, 27,635 older people aged over 60 from the community-based Tianjin Chronic Kidney Disease Cohort study were enrolled. The participants completed five annual physical examinations between January 01, 2014, and December 31, 2018, and a subsequent follow-up between January 01, 2019, and December 31, 2021. CVH metrics trajectories were established by the group-based trajectory model to predict CKD risk. The relationships between baseline CVH, CVH change (ΔCVH), and CKD risk were also explored by logistic regression and restricted cubic spline regression model. In addition, likelihood ratio tests were used to compare the goodness of fit of the different models.
Six distinct CVH metrics trajectories were identified among the participants: low-stable (11.19%), low-medium-stable (30.58%), medium-stable (30.54%), medium-high-decreased (5.46%), medium-high-stable (18.93%), and high-stable (3.25%). After adjustment for potential confounders, higher CVH metrics trajectory was associated with decreased risk of CKD (P for trend < 0.001). Comparing the high-stable with the low-stable group, the risk of CKD decreased by 46%. All sensitivity analyses, including adjusting for baseline CVH and removing each CVH component from the total CVH, produced consistent results. Furthermore, the likelihood ratio test revealed that the model established by the CVH trajectory fit better than the baseline CVH and Δ CVH.
The higher CVH metrics trajectory and improvement of CVH metrics were associated with decreased risk of CKD. This study emphasized the importance of improving CVH to achieve primary prevention of CKD in older people.
Variations in the red blood cell (RBC) lifespan can affect glycosylated hemoglobin (HbA1c) test values, but there is still a lack of evidence regarding how and to what degree the RBC lifespan ...influences HbA1c in the type 2 diabetes mellitus (T2DM) population owing to the restriction of traditional RBC lifespan detection means. In this study, we monitored 464 T2DM patients and 231 healthy control finger blood glucose levels at seven time points for three consecutive months. The HbA1c levels were assessed at the end of the third month as well as the RBC lifespan was measured through the CO breath test. T2DM patients were stratified into four quartile groups according to their RBC lifespans. There was no statistical significance in HbA1c among these four groups. However, the average blood glucose in the Q1 group was significantly higher than those in the other groups. Additionally, the contribution of RBC lifespan to HbA1c test value in the Q1 group was 14.07%, which was significantly higher than those in the other groups. Finally, we used multiple linear regression models to construct a mathematical formula to correct the HbA1c test value in the Q1 group, which would benefit the management of T2DM.
There is paucity of studies to investigate the association between combined and long-term exposure to air pollution and the risk of incident chronic kidney disease (CKD) in older adults.
A ...prospective cohort of 90,032 older adults who did not have CKD at baseline were followed up from January 1, 2017, to December 31, 2019. Various pollutant data, including particulate matter with diameters ≤ 2.5 mm (PM
), ≤ 10 mm (PM
), nitrogen dioxide (NO
), sulfur dioxide (SO
), Ozone (O
), and carbon monoxide (CO), from all monitoring stations in Binhai New Area, Tianjin were considered in calculating the mean exposure concentration of each pollutant over 2 years. By summing each pollutant concentration weighted by the regression coefficients, we developed an air pollution score that assesses the combined exposure of these air pollutants. Due to the strong correlation between air pollutants, Principal Component Analysis (PCA) score was also developed. The association between air pollutants and incident CKD in the elderly was analyzed.
A total of 90,032 subjects participated in this study with a median follow-up of 545 days. Among them, 22,336 (24.8%) developed CKD. The HR (95% CI) for air pollution score and incidence of CKD was 1.062 (1.060-1.063) and
<0.001 after adjusting for all confounders. The adjusted HRs for the quartile subgroups of combined air pollution score were: Q2: 1.064 (1.013-1.117); Q3: 1.141 (1.088-1.198); and Q4: 3.623 (3.482-3.770), respectively (
for trend <0.001). The adjusted HRs for the quartile subgroups of air quality index (AQI) were: Q2: 1.035 (0.985-1.086); Q3: 1.145 (1.091-1.201); and Q4: 3.603 (3.463-3.748), respectively (
for trend <0.001). When the risk score was over 86.9, it significantly rose in a steep curve. The subgroup analysis showed that male, younger or exercise were more likely to develop CKD.
Combined air pollution score, AQI, and PCA score were associated with an increased risk of CKD in an exposure-response relationship. Our current results might also provide evidence for developing environmental protection policies.
Physical activity (PA) is associated with mortality and cardiovascular disease (CVD). However, the effect of circadian PA trajectories remains ambiguous. This study aimed to explore ideal circadian ...PA patterns to reduce mortality and CVD, and potential mediators.
502,400 participants from UK Biobank were recruited between 2006 and 2010. Among them, 102,323 participants got valid continuously capturing acceleration data over 7 days by wrist-worn accelerometer. K-means cluster analysis was used to identify PA trajectories. The associations of PA with all-cause, cause-specific mortality and CVD were assessed by cox regression. A sensitivity test was also conducted, starting from the time of acceleration collection and excluding participants with corresponding disease prior to it. Furthermore, the mediation of aging and inflammation were explored.
During a median follow-up of 12.9 years, 3482 deaths were recorded (704 were due to CVD). Five distinct PA trajectories were identified: Persistently Low, Moderate and Stable, Single Increase, Double Increase, and Vigorous patterns. Ideal PA trajectory patterns offered progressively protective benefits against all-cause, CVD caused mortality and CVD, especially in Double Increase and Vigorous patterns. Other cause-specific mortality and renal failure incidence showed similar trend. The sensitivity result was consistent. The mediating effects of phenotypic age and inflammation markers were statistically significant.
Ideal PA trajectories offered protective benefits against all-cause, cause-specific mortality and CVD. The protection was associated with both intensity and circadian distribution. Double Increase and Vigorous activity patterns decreased these risks more significantly. Crucially, this protection was mediated by aging deceleration and inflammation regulation.
•Continuous acceleration data was captured from 102,323 participants.•Five distinct PA trajectories were identified by machine learning cluster analysis.•Ideal PA trajectories offer progressively protective benefit against mortality&CVD.•The protection was mediated by aging deceleration and inflammation regulation.
Physical activity (PA) has been linked with cancer incidence. However, the effects and mechanisms underpinning circadian PA trajectories on cancer remain elusive. This study aimed to explore the ...optimal PA patterns in reducing cancer incidence and the associated potential mediators.
Between 2006 and 2010, 502,400 participants were recruited from the UK Biobank. Out of these, 102,323 participants wore accelerometers, which allowed for collecting acceleration data continuously over 7 days. After excluding participants with previous cancer history, 96,687 participants were included in K-means cluster analysis to identify PA trajectories. The association between PA and cancer incidence was assessed using Cox regression analysis. Additionally, we investigated the mediating role of inflammation.
A total of 5995 cancer cases were recorded during a median follow-up of 7.1 years. Four distinct PA trajectories (persistent low, single peak, double peak, and vigorous) were identified. The ideal PA patterns reduced the risk of 7 out of 17 site-specific cancers, with the lowest hazard ratios and 95% confidence intervals of cancer for bladder (0.59, 0.40-0.86), breast (0.73, 0.60-0.89), kidney (0.45, 0.26-0.78), lung (0.59, 0.41-0.84), myeloma (0.49, 0.27-0.88), and oral & pharynx (0.51, 0.26-0.98) in the vigorous pattern and for colorectal (0.71, 0.54-0.93) in the double peak pattern. Moreover, the mediating effects of inflammation were significant.
Optimal PA trajectories reduced cancer incidence, especially in double peak and vigorous patterns. The protective effect was associated with both intensity and circadian rhythm. Crucially, this protection was mediated by inflammation regulation.
Diabetic kidney disease (DKD) is characterized by macrophage infiltration, which requires further investigation. This study aims to identify immune-related genes (IRGs) in macrophage and explore ...their potential as therapeutic targets. This study analyzed isolated glomerular cells from three diabetic mice and three control mice. A total of 59 glomeruli from normal kidney samples and 66 from DKD samples were acquired from four kidney transcriptomic profiling datasets. Bioinformatics analysis was conducted using both single-cell RNA (scRNA) and bulk RNA sequencing data to investigate inflammatory responses in DKD. Additionally, the “AUCell” function was used to investigate statistically different gene sets. The significance of each interaction pair was determined by assigning a probability using “CellChat.” The study also analyzed the biological diagnostic importance of immune hub genes for DKD and validated the expression of these immune genes in mice models. The top 2000 highly variable genes (HVGs) were identified after data normalization. Subsequently, a total of eight clusters were identified. It is worth mentioning that macrophages showed the highest percentage increase among all cell types in the DKD group. Furthermore, the present study observed significant differences in gene sets related to inflammatory responses and complement pathways. The study also identified several receptor-ligand pairs and co-stimulatory interactions between endothelial cells and macrophages. Notably, SYK, ITGB2, FCER1G, and VAV1 were identified as immunological markers of DKD with promising predictive ability. This study identified distinct cell clusters and four marker genes. SYK, ITGB2, FCER1G, and VAV1 may be important roles. Consequently, the present study extends our understanding regarding IRGs in DKD and provides a foundation for future investigations into the underlying mechanisms.
Graphical abstract
It shows the work flow of the study. The study comprised four sections. In the first section, the scRNA-seq data (GSE127235) from DKD were analyzed. The second section involved cell communication network analysis and AUCell scoring of cell clusters in DKD. The third section utilized bulk RNA-seq data (GSE 96804, GSE104948, GSE30122, and GSE30528) to validate and screen macrophage-specific IRGs and estimate immune cell infiltration. Finally, the fourth part involved
in vivo
experiments (RT-qPCR, western blot, and immunohistochemistry) to validate the expression of hub genes. Abbreviations: UMAP, uniform manifold approximation and projection; DKD, diabetic kidney disease; RNA-seq, RNA sequencing; DEGs, differentially expressed genes; ROC, receiver operating characteristic.
Despite abundant evidence indicating that higher triglyceride (TG) levels are associated with increased risks of hyperuricemia (HUA), it is unclear whether TG levels can independently predict the ...incidence of HUA.
The aim of the study was to investigate whether TG is an independent risk factor of HUA in a cohort study.
We explored the relationship between TG levels and HUA in a dynamic cohort established in 2009. During the 6 years of follow-up, 5442 subjects without HUA were studied. We divided subjects into 4 groups based on baseline TG levels and used the Cox hazard regression model to estimate HUA risk by TG quartile, after adjustment for potential confounding factors. Kaplan–Meier survival analysis compared the risk of HUA incidence among individuals in each TG quartile.
The incidence of HUA in this cohort was 25.9%. The hazard ratios (95% confidence intervals) for HUA in the second, third, and fourth TG quartiles, compared with the first quartile, were 1.19 (1.01–1.40), 1.33 (1.13–1.57), and 1.62 (1.37–1.92), respectively. The Kaplan–Meier survival analysis suggested that higher TG levels predicted higher incidences of HUA in a dose-dependent relationship. Stratification analyses showed that the association between TG levels and the presence of HUA was more pronounced in individuals aged <50 years, of obese, with normal estimated glomerular filtration rate, and with hypertension.
Our findings suggest that TG level is a significant and independent risk factor for HUA.
•Triglyceride (TG) is an independent predictor of hyperuricemia (HUA).•The relationship between TG and HUA was investigated in a dynamic cohort.•Elevated TG deserves attention as an emerging risk factor for HUA.
