The association between dynamic changes in vitamin D and frailty alterations: A prospective analysis of UK Biobank participants.

BACKGROUND: Frailty is a common geriatric syndrome associated with reduced reserves and increased vulnerability to stressors among older adults. Vitamin D deficiency has been implicated in frailty, as it is essential for maintaining musculoskeletal functions. The relationship between dynamic changes in vitamin D levels and frailty over time has not been extensively studied. METHODS: This study utilized data from the UK Biobank. Baseline and longitudinal changes in vitamin D levels were measured. Frailty status was assessed using both the frailty phenotype and frailty index approaches and classified as robust, pre-frail, or frail. Changes in frailty status were assessed by frailty phenotype at baseline (2006-2010) and the follow-up (2012-2013). Mixed effect model was performed to examine the association between vitamin D levels and frailty status. Using multistate transition models, we assessed the impact of increasing vitamin D levels on the probabilities of transitioning between robust, pre-frail, and frail states. RESULTS: Based on the frailty phenotype, 287 926 individuals (64.8%) were identified as having various degrees of frailty (median age 58.00 [51.00, 64.00] years, 55.9% females). Using the frailty index approach, 250 566 individuals (56%) were found to have different levels of frailty (median age 59.00 [51.00, 64.00] years, 55.3% females). Baseline vitamin D levels were found to be significantly associated with frailty status (frailty phenotype: ORfrail 0.78, 95% CI [0.76, 0.79], P < 0.001; frailty index: ORfrail 0.80, 95% CI [0.78, 0.81], P < 0.001). Dynamic changes in vitamin D levels were also found to be associated with changes in frailty over time. Furthermore, increasing vitamin D levels were associated with a transition from frailty to a healthier status. A higher degree of vitamin D (estimated at 1 nmol/L) was associated with a lower risk of transitioning from robust to prefrail (HR 0.997, 95% CI [0.995, 0.999]) and from prefrail to frail (HR 0.992, 95% CI [0.988, 0.995]). CONCLUSIONS: This study highlights the importance of vitamin D in the context of frailty. Low vitamin D levels are associated with increased frailty risk, while increasing vitamin D levels may contribute to improving frailty status. Recognizing the relationship between vitamin D levels and frailty can inform personalized management and early interventions for frail individuals. Further research is needed to explore the potential effects of vitamin D interventions on frailty and deepen our understanding of the biological connections between vitamin D and frailty.

Cardiovascular diseases consequences of type 1, type 2 diabetes mellitus and glycemic traits: A Mendelian randomization study.

OBJECTIVE: This Mendelian randomization (MR) study aimed to investigate the relationships between type 1 diabetes (T1D), type 2 diabetes (T2D), and glycemic traits, including fasting insulin, fasting glucose, and HbA1c, with cardiovascular diseases (CVDs). METHODS: We selected genetic instruments for predisposition to T1D, T2D, fasting insulin, fasting glucose, and HbA1c based on published genome-wide association studies. Using a 2-Sample MR approach, we assessed associations with 12 common CVDs sourced from the FinnGen and UK Biobank studies, along with stroke subtypes obtained from the GIGASTROKE and MEGASTROKE Consortium. RESULTS: T1D was associated with SVS. T2D showed associations with AIS, LAA, CES, SVS, coronary heart disease, myocardial infarction, pulmonary embolism, DVT of lower extremities, peripheral vascular diseases. Genetically predicted higher HbA1c levels were associated with eight CVDs. The results of MVMR aligned with the primary findings for T1D and T2D. CONCLUSIONS: T1D and T2D exhibit different genetic predisposition to CVDs. BMI, LDL, and HDL play intermediary roles in connecting TID and T2D to specific types of CVDs, providing insights into the potential underlying pathways and mechanisms involved in these relationships. Strategies aimed at achieving sustained reductions in HbA1c levels may offer potential for reducing the risk of various CVDs.

Assessing the impact of pine wilt disease on aboveground carbon storage in planted Pinus massoniana Lamb. forests via remote sensing.

The continuous spread of Bursaphelenchus xylophilus (Steiner and Buhrer) Nickle, commonly known as the organism that causes pine wilt disease (PWD), has become a notable threat to forest security in East Asia and southern Europe, and an assessment of the carbon loss caused by PWD damage is important to achieving carbon neutrality. This study used satellite remote sensing and 15-year ground monitoring data to measure the impact of PWD on the carbon storage of Pinus massoniana Lamb. (P. massoniana), the conifer with the largest planted area in southern China. This study showed that the occurrence of PWD had an impact on the increase in carbon storage of P. massoniana. The infected and dead P. massoniana trees accounted for only 1.46 % of the total number of trees but caused a carbon storage loss of 1.99 t/ha, which accounted for 6.23 % of the total carbon sink in healthy P. massoniana forests over the last 15 years. The most pronounced decline in carbon storage occurred in the first five years of PWD invasion. After 10 years of clearcutting and replanting of Schima superba Gardn. et Champ., the increase in carbon storage of the reformed forest far exceeded that of the healthy forest during the same period, which was 2.04 times (10 years) and 1.56 times (15 years) that of the healthy P. massoniana forest. In addition, our study found that during the 15-year period (from the forest age of 22 to the forest age of 37), the average carbon storage of P. massoniana forest was 31.9 t/ha. This study helps to evaluate the impact of PWD on the carbon sink of pine forests and provides methodological references for analyzing the impact of biological disturbances on the carbon cycle.

Serum uric acid and prognosis of ischemic stroke: Cohort study, meta-analysis and Mendelian randomization study.

INTRODUCTION: The role of serum uric acid (UA) levels in the functional recovery of ischemic stroke remains uncertain. To evaluate whether UA could predict clinical outcomes in patients with ischemic stroke. PATIENTS AND METHODS: A three-stage study design was employed, combining a large-scale prospective cohort study, a meta-analysis and a Mendelian randomization (MR) analysis. Firstly, we conducted a cohort study using data from the Nanjing Stroke Registry Program (NSRP) to assess the association between UA levels and 3-month functional outcomes in ischemic stroke patients. Secondly, the meta-analysis was conducted to integrate currently available cohort evidence. Lastly, MR analysis was utilized to explore whether genetically determined UA had a causal link to the functional outcomes of ischemic stroke using summary data from the CKDGen and GISCOME datasets. RESULTS: In the first stage, the cohort study included 5631 patients and found no significant association between UA levels and functional outcomes at 3 months after ischemic stroke. In the second stage, the meta-analysis, including 10 studies with 14,657 patients, also showed no significant association between UA levels and stroke prognosis. Finally, in the third stage, MR analysis using data from 6165 patients in the GISCOME study revealed no evidence of a causal relationship between genetically determined UA and stroke functional outcomes. DISCUSSION AND CONCLUSION: Our comprehensive triangulation approach found no significant association between UA levels and functional outcomes at 3 months after ischemic stroke.

Effect of INR on Outcomes of Endovascular Treatment for Acute Vertebrobasilar Artery Occlusion.

Endovascular treatment (EVT) has been proven to be the standard treatment for acute vertebrobasilar artery occlusion (VBAO). This study aimed to analyze the effects of international normalized ratio (INR) indicators on outcomes in patients with acute VBAO treated with EVT. Dynamic data on INR in patients with VBAO who received endovascular treatment (EVT) at 65 stroke centers in China were retrospectively enrolled. Outcome measures included the modified Rankin Scale (mRS) score at 90 days and 1 year and symptomatic intracranial hemorrhage (sICH). The associations between elevated INR (INR > 1.1), INR variability (time-weighted variance of INR changes), and various clinical outcomes were analyzed in all patients and subgroups stratified by oral anticoagulation (OAC) by mixed logistic regression analysis. A total of 1825 patients met the study criteria, of which 1384 had normal INR and 441 had elevated INR. Multivariate analysis showed that elevated INR was significantly associated with poor functional outcomes (mRS 4-6) at 90 days (odds ratio [OR] 1.36, 95% confidence interval [CI] 1.08-1.72) and 1 year (OR 1.32, 95% CI 1.05-1.66), but was not associated with an increased risk of sICH (OR 1.00, 95% CI 0.83-1.20). Similar associations exist between INR variability and poor functional outcomes at 90 days (OR 2.17, 95% CI 1.09-4.30), 1 year (OR 2.28, 95% CI 1.16-4.46), and sICH (OR 1.11, 95% CI 0.93-1.33). Subgroup analyses further revealed that elevated INR and INR variability remained associated with poor functional outcomes in patients not receiving oral anticoagulation (OAC) therapy, while no significant associations were observed in OAC-treated patients, regardless of whether they were on warfarin or direct oral anticoagulants. Elevated INR and INR variability in VBAO patients treated with EVT were associated with poor functional outcomes. The mechanism underlying the association between elevated INR and poor functional outcomes might be attributed to the fact that elevated INR indirectly reflects the burden of comorbidities, which could independently worsen outcomes. These findings underscore the importance of a comprehensive and dynamic evaluation of INR levels in the management of VBAO patients receiving EVT, providing valuable insights for optimizing patient outcomes.

Semantic Linear Genetic Programming for Symbolic Regression.

Symbolic regression (SR) is an important problem with many applications, such as automatic programming tasks and data mining. Genetic programming (GP) is a commonly used technique for SR. In the past decade, a branch of GP that utilizes the program behavior to guide the search, called semantic GP (SGP), has achieved great success in solving SR problems. However, existing SGP methods only focus on the tree-based chromosome representation and usually encounter the bloat issue and unsatisfactory generalization ability. To address these issues, we propose a new semantic linear GP (SLGP) algorithm. In SLGP, we design a new chromosome representation to encode the programs and semantic information in a linear fashion. To utilize the semantic information more effectively, we further propose a novel semantic genetic operator, namely, mutate-and-divide propagation, to recursively propagate the semantic error within the linear program. The empirical results show that the proposed method has better training and test errors than the state-of-the-art algorithms in solving SR problems and can achieve a much smaller program size.

Solving Generalized Vehicle Routing Problem With Occasional Drivers via Evolutionary Multitasking.

With the emergence of crowdshipping and sharing economy, vehicle routing problem with occasional drivers (VRPOD) has been recently proposed to involve occasional drivers with private vehicles for the delivery of goods. In this article, we present a generalized variant of VRPOD, namely, the vehicle routing problem with heterogeneous capacity, time window, and occasional driver (VRPHTO), by taking the capacity heterogeneity and time window of vehicles into consideration. Furthermore, to meet the requirement in today's cloud computing service, wherein multiple optimization tasks may need to be solved at the same time, we propose a novel evolutionary multitasking algorithm (EMA) to optimize multiple VRPHTOs simultaneously with a single population. Finally, 56 new VRPHTO instances are generated based on the existing common vehicle routing benchmarks. Comprehensive empirical studies are conducted to illustrate the benefits of the new VRPHTOs and to verify the efficacy of the proposed EMA for multitasking against a state-of-art single task evolutionary solver. The obtained results showed that the employment of occasional drivers could significantly reduce the routing cost, and the proposed EMA is not only able to solve multiple VRPHTOs simultaneously but also can achieve enhanced optimization performance via the knowledge transfer between tasks along the evolutionary search process.

Explicit Evolutionary Multitasking for Combinatorial Optimization: A Case Study on Capacitated Vehicle Routing Problem.

Recently, evolutionary multitasking (EMT) has been proposed in the field of evolutionary computation as a new search paradigm, for solving multiple optimization tasks simultaneously. By sharing useful traits found along the evolutionary search process across different optimization tasks, the optimization performance on each task could be enhanced. The autoencoding-based EMT is a recently proposed EMT algorithm. In contrast to most existing EMT algorithms, which conduct knowledge transfer across tasks implicitly via crossover, it intends to perform knowledge transfer explicitly among tasks in the form of task solutions, which enables the employment of task-specific search mechanisms for different optimization tasks in EMT. However, the autoencoding-based explicit EMT can only work on continuous optimization problems. It will fail on combinatorial optimization problems, which widely exist in real-world applications, such as scheduling problem, routing problem, and assignment problem. To the best of our knowledge, there is no existing effort working on explicit EMT for combinatorial optimization problems. Taking this cue, in this article, we thus embark on a study toward explicit EMT for combinatorial optimization. In particular, by using vehicle routing as an illustrative combinatorial optimization problem, the proposed explicit EMT algorithm (EEMTA) mainly contains a weighted l1 -norm-regularized learning process for capturing the transfer mapping, and a solution-based knowledge transfer process across vehicle routing problems (VRPs). To evaluate the efficacy of the proposed EEMTA, comprehensive empirical studies have been conducted with the commonly used vehicle routing benchmarks in multitasking environment, against both the state-of-the-art EMT algorithm and the traditional single-task evolutionary solvers. Finally, a real-world combinatorial optimization application, that is, the package delivery problem (PDP), is also presented to further confirm the efficacy of the proposed algorithm.

Toward Adaptive Knowledge Transfer in Multifactorial Evolutionary Computation.

A multifactorial evolutionary algorithm (MFEA) is a recently proposed algorithm for evolutionary multitasking, which optimizes multiple optimization tasks simultaneously. With the design of knowledge transfer among different tasks, MFEA has demonstrated the capability to outperform its single-task counterpart in terms of both convergence speed and solution quality. In MFEA, the knowledge transfer across tasks is realized via the crossover between solutions that possess different skill factors. This crossover is thus essential to the performance of MFEA. However, we note that the present MFEA and most of its existing variants only employ a single crossover for knowledge transfer, and fix it throughout the evolutionary search process. As different crossover operators have a unique bias in generating offspring, the appropriate configuration of crossover for knowledge transfer in MFEA is necessary toward robust search performance, for solving different problems. Nevertheless, to the best of our knowledge, there is no effort being conducted on the adaptive configuration of crossovers in MFEA for knowledge transfer, and this article thus presents an attempt to fill this gap. In particular, here, we first investigate how different types of crossover affect the knowledge transfer in MFEA on both single-objective (SO) and multiobjective (MO) continuous optimization problems. Furthermore, toward robust and efficient multitask optimization performance, we propose a new MFEA with adaptive knowledge transfer (MFEA-AKT), in which the crossover operator employed for knowledge transfer is self-adapted based on the information collected along the evolutionary search process. To verify the effectiveness of the proposed method, comprehensive empirical studies on both SO and MO multitask benchmarks have been conducted. The experimental results show that the proposed MFEA-AKT is able to identify the appropriate knowledge transfer crossover for different optimization problems and even at different optimization stages along the search, which thus leads to superior or competitive performances when compared to the MFEAs with fixed knowledge transfer crossover operators.

A Preliminary Study of Knowledge Transfer in Multi-Classification Using Gene Expression Programming.

Gene Expression Programming (GEP), a variant of Genetic Programming (GP), is a well established technique for automatic generation of computer programs. Due to the flexible representation, GEP has long been concerned as a classification algorithm for various applications. Whereas, GEP cannot be extended to multi-classification directly, and thus is only capable of treating an M-classification task as M separate binary classifications without considering the inter-relationship among classes. Consequently, GEP-based multi-classifier may suffer from output conflict of various class labels, and the underlying conflict can probably lead to the degraded performance in multi-classification. This paper employs evolutionary multitasking optimization paradigm in an existing GEP-based multi-classification framework, so as to alleviate the output conflict of each separate binary GEP classifier. Therefore, several knowledge transfer strategies are implemented to enable the interation among the population of each separate binary task. Experimental results on 10 high-dimensional datasets indicate that knowledge transfer among separate binary classifiers can enhance multi-classification performance within the same computational budget.

Evolutionary Multitasking via Explicit Autoencoding.

Evolutionary multitasking (EMT) is an emerging research topic in the field of evolutionary computation. In contrast to the traditional single-task evolutionary search, EMT conducts evolutionary search on multiple tasks simultaneously. It aims to improve convergence characteristics across multiple optimization problems at once by seamlessly transferring knowledge among them. Due to the efficacy of EMT, it has attracted lots of research attentions and several EMT algorithms have been proposed in the literature. However, existing EMT algorithms are usually based on a common mode of knowledge transfer in the form of implicit genetic transfer through chromosomal crossover. This mode cannot make use of multiple biases embedded in different evolutionary search operators, which could give better search performance when properly harnessed. Keeping this in mind, this paper proposes an EMT algorithm with explicit genetic transfer across tasks, namely EMT via autoencoding, which allows the incorporation of multiple search mechanisms with different biases in the EMT paradigm. To confirm the efficacy of the proposed EMT algorithm with explicit autoencoding, comprehensive empirical studies have been conducted on both the single- and multi-objective multitask optimization problems.