Search

Abstract Background Aging is a major risk factor for type 2 diabetes (T2D), but individuals of the same chronological age may vary in their biological aging rate. The associations of Phenotypic Age Acceleration (PhenoAgeAccel), a new accelerated biological aging indicator based on clinical chemistry biomarkers, with the risk of dynamic progression remain unclear. We aimed to assess these associations and examine whether these associations varied by genetic risk and lifestyle. Methods We conducted a prospective cohort study that included 376,083 adults free of T2D and diabetes-related events at baseline in UK Biobank. PhenoAgeAccel > 0 and ≤ 0 were defined as biologically older and younger than chronological age. The outcomes of interest were incident T2D, diabetic complications, and mortality. Hazard ratios (HRs) with 95% confidence intervals (CIs) and population attributable fractions (PAFs) for these associations were calculated using multi-state model. Results During a median follow-up of 13.7 years, 17,615 participants developed T2D, of whom, 4,524 subsequently developed complications, and 28,373 died. Being biologically older was associated with increased risks of transitions from baseline to T2D (HR 1.77, 95% CI 1.71–1.82; PAF 24.8 [95% CI 23.5–26.2]), from T2D to diabetic complications (1.10, 1.04–1.17; 4.4 [1.4–7.4]), from baseline to all-cause death (1.53, 1.49–1.57; 17.6 [16.6–18.6]), from T2D to all-cause death (1.14, 1.03–1.26; 7.4 [1.8–13.0]), and from diabetic complications to all-cause death (1.32, 1.15–1.51; 15.4 [7.5–23.2]) than being biologically younger. Additionally, participants with older biological age and high genetic risk had a higher risk of incident T2D (4.76,4.43–5.12;18.2 [17.5–19.0]) than those with younger biological age and low genetic risk. Compared with participants with younger biological age and healthy lifestyle, those with older biological age and unhealthy lifestyle had higher risks of transitions in the T2D trajectory, with HRs and PAFs ranging from 1.34 (1.16–1.55; 3.7 [1.8–5.6]) to 5.39 (5.01–5.79; 13.0 [12.4–13.6]). Conclusions PhenoAgeAccel was consistently associated with an increased risk of all transitions in T2D progression. It has the potential to be combined with genetic risk to identify early T2D incidence risk and may guide interventions throughout T2D progression while tracking their effectiveness.

Transfer learning aims to integrate useful information from multi-source datasets to improve the learning performance of target data. This can be effectively applied in genomics when we learn the gene associations in a target tissue, and data from other tissues can be integrated. However, heavy-tail distribution and outliers are common in genomics data, which poses challenges to the effectiveness of current transfer learning approaches. In this paper, we study the transfer learning problem under high-dimensional linear models with t-distributed error (Trans-PtLR), which aims to improve the estimation and prediction of target data by borrowing information from useful source data and offering robustness to accommodate complex data with heavy tails and outliers. In the oracle case with known transferable source datasets, a transfer learning algorithm based on penalized maximum likelihood and expectation-maximization algorithm is established. To avoid including non-informative sources, we propose to select the transferable sources based on cross-validation. Extensive simulation experiments as well as an application demonstrate that Trans-PtLR demonstrates robustness and better performance of estimation and prediction when heavy-tail and outliers exist compared to transfer learning for linear regression model with normal error distribution. Data integration, Variable selection, T distribution, Expectation maximization algorithm, Genotype-Tissue Expression, Cross validation.

Objectives: Diabetes was associated with increased serum urate levels and a higher risk of dementia. However, current evidence regarding the association between serum urate and dementia is controversial.The research gap on how to effectively control urate levels in the population with diabetes still remains. We aim to examine the association of diabetes status and serum urate with dementia incidence, and the differences in this association among participants with different diabetes statuses. Methods: A total of 321,896 participants was recruited from the UK Biobank and followed up until 2022. Diabetes status was classified into diabetes, prediabetes and normoglycaemia according to the American Diabetes Association 2023 guideline. Serum urate levels were stratified using gender-specific quartiles of concentrations. All-cause dementia, Alzheimer’s disease and vascular dementia were ascertained using the International Classification of Diseases-10th revision (ICD-10). Cox proportional hazards regression models were used to examine the association between serum urate, diabetes status, and dementia incidence. Results: Of the 321,896 participants (mean age, 57 years old; 43.5% males), 7,087 (2.20%) individuals were diagnosed with dementia during the follow-up period. Diabetes was associated with a 70% 58%, and 134% increased risk for all-cause dementia, Alzheimer’s disease, and vascular dementia respectively. Elevated serum urate levels were associated with a lower risk of all-cause and cause-specific dementia regardless of the status of diabetes. Each standard deviation increase in urate concentration was related to a 11% reduced risk for all-cause dementia (HR, 0.89; 95% CI, 0.86 to 0.91), 7% for Alzheimer’s disease (HR, 0.93; 95% CI, 0.88 to 0.98), and 12% for vascular dementia (HR, 0.88; 95% CI, 0.81 to 0.95). Conclusion: : Appropriately higher urate levels within the threshold of hyperuricemia can reduce the adverse health effects of excessively high urate levels and better protect the cognitive health of people with varying diabetes status.

Abstract Background Obesity may increase perioperative mortality of acute Stanford type A aortic dissection (ATAAD). However, the available evidence was limited. This study aimed to systematically review published literatures about body mass index (BMI) and perioperative mortality of ATAAD. Methods Electronic literature search was conducted in PubMed, Medline, Embase and Cochrane Library databases. All observational studies that investigated BMI and perioperative mortality of ATAAD were included. Pooled odds ratio (OR) and 95% confidence interval (CI) were calculated using a random-effects model. Meta-regression analysis was performed to assess the effects of different clinical variables on BMI and perioperative mortality of ATAAD. Sensitivity analysis was performed to determine the sources of heterogeneity. Egger’s linear regression method and funnel plot were used to determine the publication bias. Results A total of 12 studies with 5,522 patients were eligible and included in this meta-analysis. Pooled analysis showed that perioperative mortality of ATAAD increased by 22% for each 1 kg/m2 increase in BMI (OR = 1.22, 95% CI: 1.10–1.35). Univariable meta-regression analysis indicated that age and female gender significantly modified the association between BMI and perioperative mortality of ATAAD in a positive manner (meta-regression on age: coefficient = 0.04, P = 0.04; meta-regression on female gender: coefficient = 0.02, P = 0.03). Neither significant heterogeneity nor publication bias were found among included studies. Conclusions BMI is closely associated with perioperative mortality of ATAAD. Optimal perioperative management needs to be further explored and individualized for obese patient with ATAAD, especially in elderly and female populations. Trial registration PROSPERO (CRD42022358619). Graphical Abstract BMI and perioperative mortality of ATAAD.

Abstract Magnetization promoted activity of magnetic catalysts towards the oxygen evolution reaction (OER) has attracted great attention, but remains a puzzle where the increment comes from. Magnetization of a ferromagnetic material only changes its magnetic domain structure. It does not directly change the spin orientation of unpaired electrons in the material. The confusion is that each magnetic domain is a small magnet and theoretically the spin-polarization promoted OER already occurs on these magnetic domains, and thus the enhancement should have been achieved without magnetization. Here, we demonstrate that the enhancement comes from the disappeared domain wall upon magnetization. Magnetization leads to the evolution of the magnetic domain structure, from a multi-domain one to a single domain one, in which the domain wall disappears. The surface occupied by the domain wall is reformatted into one by a single domain, on which the OER follows the spin-facilitated pathways and thus the overall increment on the electrode occurs. This study fills the missing gap for understanding the spin-polarized OER and it further explains the type of ferromagnetic catalysts which can give increment by magnetization.

Bistable morphing structures are widely used as actuation mechanisms in soft actuators, soft robotics, energy absorbers, mechanical computers, optical lenses, meta materials, and flexible electronics. However, untethered actuators, repetitive actuators, and hybrid-assembly (containing in-plane-assembly and out-of-plane-assembly) actuators remain challenging to realize using existing bistable structures, which hinders the novel application of such actuators in research, engineering, and daily life. This problem is solved by fabricating a magnet-driven soft bistable actuator (MSBA) unit. The self-buckling of the circular polydimethylsiloxane (PDMS) sheet ensures the bistability of the actuator and allows it to operate as an independent unit, free of external constraints. The reorientation of neodymium-iron-boron (NdFeB) microparticles embedded in the PDMS sheet enables the dome-shaped actuators to exhibit repetitive snapping under the stimulus of a direction-switching magnetic field. The potential of this MSBA unit in bionics, electronics, and biomechanics applications is demonstrated in systematic studies involving modification of the buckling deflection and magnetic moment density. The MSBA unit exhibits excellent performance in hybrid designs and intelligent systems. [ABSTRACT FROM AUTHOR]