Predicting the risk of lung cancer using machine learning: A large study based on UK Biobank.

In response to the high incidence and poor prognosis of lung cancer, this study tends to develop a generalizable lung-cancer prediction model by using machine learning to define high-risk groups and realize the early identification and prevention of lung cancer. We included 467,888 participants from UK Biobank, using lung cancer incidence as an outcome variable, including 49 previously known high-risk factors and less studied or unstudied predictors. We developed multivariate prediction models using multiple machine learning models, namely logistic regression, naïve Bayes, random forest, and extreme gradient boosting models. The performance of the models was evaluated by calculating the areas under their receiver operating characteristic curves, Brier loss, log loss, precision, recall, and F1 scores. The Shapley additive explanations interpreter was used to visualize the models. Three were ultimately 4299 cases of lung cancer that were diagnosed in our sample. The model containing all the predictors had good predictive power, and the extreme gradient boosting model had the best performance with an area under curve of 0.998. New important predictive factors for lung cancer were also identified, namely hip circumference, waist circumference, number of cigarettes previously smoked daily, neuroticism score, age, and forced expiratory volume in 1 second. The predictive model established by incorporating novel predictive factors can be of value in the early identification of lung cancer. It may be helpful in stratifying individuals and selecting those at higher risk for inclusion in screening programs.

Branched-chain amino acids and the risks of dementia, Alzheimer's disease, and Parkinson's disease.

Background: We aimed to examine the association between blood levels of Branched-chain amino acids (BCAAs) - specifically isoleucine, leucine, and valine - and the susceptibility to three neurodegenerative disorders: dementia, Alzheimer's disease (AD), and Parkinson's disease (PD). Methods: Based on data from the UK Biobank, a Cox proportional hazard regression model and a dose-response relationship were used to analyze the association between BCAAs and the risks of dementia, AD, and PD. We also generated a healthy lifestyle score and a polygenic risk score. Besides, we conducted a sensitivity analysis to ensure the robustness of our findings. Results: After adjusting for multiple covariates, blood concentrations of isoleucine, leucine, and valine were significantly associated with a reduced risk of dementia and AD. This association remained robust even in sensitivity analyses. Similarly, higher levels of isoleucine and leucine in the blood were found to be associated with an increased risk of PD, but this positive correlation could potentially be explained by the presence of covariates. Further analysis using a dose-response approach revealed that a blood leucine concentration of 2.14 mmol/L was associated with the lowest risk of dementia. Conclusion: BCAAs have the potential to serve as a biomarker for dementia and AD. However, the specific mechanism through which BCAAs are linked to the development of dementia, AD, and PD remains unclear and necessitates additional investigation.

Improving Sedum plumbizincicola genetic transformation with the SpGRF4-SpGIF1 gene and the self-excision CRE/LoxP system.

MAIN CONCLUSION: S. plumbizincicola genetic transformation was optimized using a self-excision molecular-assisted transformation system by integrating the SpGRF4/SpGIF1 gene with XVE and Cre/loxP. Sedum plumbizincicola, despite being an excellent hyperaccumulator of cadmium and zinc with significant potential for soil pollution phytoremediation on farmland, has nonetheless trailed behind other major model plants in genetic transformation technology. In this study, different explants and SpGRF4-SpGIF1 genes were used to optimize the genetic transformation of S. plumbizincicola. We found that petiole and stem segments had higher genetic transformation efficiency than cluster buds. Overexpression of SpGRF4-SpGIF1 could significantly improve the genetic transformation efficiency and shorten the period of obtaining regenerated buds. However, molecular assistance with overexpression of SpGRF4-SpGIF1 leads to abnormal morphology, resulting in plant tissue enlargement and abnormal growth. Therefore, we combined SpGRF4-SpGIF1 with XVE and Cre/loxP to obtain DNA autocleavage transgenic plants induced by estradiol, thereby ensuring normal growth in transgenic plants. This study optimized the S. plumbizincicola genetic transformation system, improved the efficiency of genetic transformation, and established a self-excision molecular-assisted transformation system. This work also established the basis for studying S. plumbizincicola gene function, and for S. plumbizincicola breeding and germplasm innovation.

Doping with KBr to Achieve High-Performance CsPbBr3 Semitransparent Perovskite Solar Cells.

Wide-bandgap semitransparent perovskite photovoltaics are emerging as one of the ideal candidates for building-integrated photovoltaics (BIPV). However, surface defects in inorganic CsPbBr3 perovskite prepared by vapor deposition severely limit the optoelectronic performance of perovskite solar cells. To address this issue, a strategy of doping a trace amount of KBr into perovskite by vapor deposition is adopted, effectively improving the quality of the film, reducing surface defect concentration, and enhancing the transportation and extraction of charge carriers. Simultaneously, fully physical vapor deposition technology is employed to fabricate perovskite solar cells with an average visible light transmittance of 44%. These devices exhibited an ultrahigh open-circuit voltage of 1.55 V and a superior power conversion efficiency (PCE) of 7.28%, demonstrating excellent moisture and heat resistance. Moreover, the corresponding 5 cm × 5 cm modules achieve a PCE of 5.35% with great thermal insulation capability. This work provides an approach for fabricating highly efficient all-inorganic perovskite solar cells with high average visible light transmittance, demonstrating new insights into their application in building-integrated photovoltaics.

A machine learning algorithm based on circulating metabolic biomarkers offers improved predictions of neurological diseases.

BACKGROUND AND AIMS: A machine learning algorithm based on circulating metabolic biomarkers for the predictions of neurological diseases (NLDs) is lacking. To develop a machine learning algorithm to compare the performance of a metabolic biomarker-based model with that of a clinical model based on conventional risk factors for predicting three NLDs: dementia, Parkinson's disease (PD), and Alzheimer's disease (AD). MATERIALS AND METHODS: The eXtreme Gradient Boosting (XGBoost) algorithm was used to construct a metabolic biomarker-based model (metabolic model), a clinical risk factor-based model (clinical model), and a combined model for the prediction of the three NLDs. Risk discrimination (c-statistic), net reclassification improvement (NRI) index, and integrated discrimination improvement (IDI) index values were determined for each model. RESULTS: The results indicate that incorporation of metabolic biomarkers into the clinical model afforded a model with improved performance in the prediction of dementia, AD, and PD, as demonstrated by NRI values of 0.159 (0.039-0.279), 0.113 (0.005-0.176), and 0.201 (-0.021-0.423), respectively; and IDI values of 0.098 (0.073-0.122), 0.070 (0.049-0.090), and 0.085 (0.068-0.101), respectively. CONCLUSION: The performance of the model based on circulating NMR spectroscopy-detected metabolic biomarkers was better than that of the clinical model in the prediction of dementia, AD, and PD.

Effects of exogenous glycine betaine on growth and development of tomato seedlings under cold stress.

Low temperature is a type of abiotic stress affecting the tomato (Solanum lycopersicum) growth. Understanding the mechanisms and utilization of exogenous substances underlying plant tolerance to cold stress would lay the foundation for improving temperature resilience in this important crop. Our study is aiming to investigate the effect of exogenous glycine betaine (GB) on tomato seedlings to increase tolerance to low temperatures. By treating tomato seedlings with exogenous GB under low temperature stress, we found that 30 mmol/L exogenous GB can significantly improve the cold tolerance of tomato seedlings. Exogenous GB can influence the enzyme activity of antioxidant defense system and ROS levels in tomato leaves. The seedlings with GB treatment presented higher Fv/Fm value and photochemical activity under cold stress compared with the control. Moreover, analysis of high-throughput plant phenotyping of tomato seedlings also supported that exogenous GB can protect the photosynthetic system of tomato seedlings under cold stress. In addition, we proved that exogenous GB significantly increased the content of endogenous abscisic acid (ABA) and decreased endogenous gibberellin (GA) levels, which protected tomatoes from low temperatures. Meanwhile, transcriptional analysis showed that GB regulated the expression of genes involved in antioxidant capacity, calcium signaling, photosynthesis activity, energy metabolism-related and low temperature pathway-related genes in tomato plants. In conclusion, our findings indicated that exogenous GB, as a cryoprotectant, can enhance plant tolerance to low temperature by improving the antioxidant system, photosynthetic system, hormone signaling, and cold response pathway and so on.

Causal association of blood lipids with all-cause and cause-specific mortality risk: a Mendelian randomization study.

Dyslipidemia has long been implicated in elevating mortality risk; yet, the precise associations between lipid traits and mortality remained undisclosed. Our study aimed to explore the causal effects of lipid traits on both all-cause and cause-specific mortality. One-sample Mendelian randomization (MR) with linear and nonlinear assumptions was conducted in a cohort of 407,951 European participants from the UK Biobank. Six lipid traits, consisting of low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), triglycerides, apolipoprotein A1 (ApoA1), apolipoprotein B (ApoB), and lipoprotein(a), were included to investigate the causal associations with mortality. Two-sample MR was performed to replicate the association between each lipid trait and all-cause mortality. Univariable MR results showed that genetically predicted higher ApoA1 was significantly associated with a decreased all-cause mortality risk (HR[95% CI]:0.93 [0.89-0.97], P value = 0.001), which was validated by the two-sample MR analysis. Higher lipoprotein(a) was associated with an increased risk of all-cause mortality (1.03 [1.01-1.04], P value = 0.002). Multivariable MR confirmed the direct causal effects of ApoA1 and lipoprotein(a) on all-cause mortality. Meanwhile, nonlinear MR found no evidence for nonlinearity between lipids and all-cause mortality. Our examination into cause-specific mortality revealed a suggestive inverse association between ApoA1 and cancer mortality, a significant positive association between lipoprotein(a) and cardiovascular disease mortality, and a suggestive positive association between lipoprotein(a) and digestive disease mortality. High LDL-C was associated with an increased risk of cardiovascular disease mortality but a decreased risk of neurodegenerative disease mortality. The findings suggest that implementing interventions to raise ApoA1 and decrease lipoprotein(a) levels may improve overall health outcomes and mitigate cancer and digestive disease mortality.

Reinforcing self-assembly of hole transport molecules for stable inverted perovskite solar cells.

Power conversion efficiencies (PCEs) of inverted perovskite solar cells (PSCs) have been improved by the use of a self-assembled monolayer (SAM) hole transport layer. Long-term stability of PSCs requires keeping the SAM compact under the perovskite layer during operation. We found that strong polar solvents in the perovskite precursor desorb the SAM if it is anchored on substrates by hydrogen-bonded, rather than covalently bonded, hydroxyl groups. We used atomic-layer deposition to create an indium tin oxide substrate with a fully covalent hydroxyl-covered surface for SAM anchoring, as well as a SAM with a trimethoxysilane group that exhibited strong tridentate anchoring to the substrate. The resulting PSCs achieved PCEs of 24.8 (certified 24.6) and 23.2% with aperture areas of 0.08 and 1.01 square centimeters, respectively. The devices retained 98.9 and 98.2% of the initial PCE after 1000 hours damp-heat test and operation in maximum power point tracking at 85°C for 1200 hours under standard illumination, respectively.

Serum Olink Proteomics-Based Identification of Protein Biomarkers Associated with the Immune Response in Ischemic Stroke.

The immune response is considered essential for pathology of ischemic stroke (IS), but it remains unclear which immune response-related proteins exhibit altered expression in IS patients. Here, we used Olink proteomics to examine the expression levels of 92 immune response-related proteins in the sera of IS patients (n = 88) and controls (n = 88), and we found that 59 of these proteins were differentially expressed. Feature variables were screened from the differentially expressed proteins by the least absolute shrinkage and selection operator (LASSO) and the random forest and by determining whether their proteins had an area under the curve (AUC) greater than 0.8. Ultimately, we identified six potential protein biomarkers of IS, namely, MASP1, STC1, HCLS1, CLEC4D, PTH1R, and PIK3AP1, and established a logistic regression model that used these proteins to diagnose IS. The AUCs of the models in the internal validation and the test set were 0.962 (95% confidence interval (CI): 0.895-1.000) and 0.954 (95% CI: 0.884-1.000), respectively, and the same protein detection method was performed in an external independent validation set (AUC: 0.857 (95% CI: 0.801-0.913)). These proteins may play a role in immune regulation via the C-type lectin receptor signaling pathway, the PI3K-AKT signaling pathway, and the B-cell receptor signaling pathway.

Development and validation of a prediction model for early screening of people at high risk for colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) is a serious threat worldwide. Although early screening is suggested to be the most effective method to prevent and control CRC, the current situation of early screening for CRC is still not optimistic. In China, the incidence of CRC in the Yangtze River Delta region is increasing dramatically, but few studies have been conducted. Therefore, it is necessary to develop a simple and efficient early screening model for CRC. AIM: To develop and validate an early-screening nomogram model to identify individuals at high risk of CRC. METHODS: Data of 64448 participants obtained from Ningbo Hospital, China between 2014 and 2017 were retrospectively analyzed. The cohort comprised 64448 individuals, of which, 530 were excluded due to missing or incorrect data. Of 63918, 7607 (11.9%) individuals were considered to be high risk for CRC, and 56311 (88.1%) were not. The participants were randomly allocated to a training set (44743) or validation set (19175). The discriminatory ability, predictive accuracy, and clinical utility of the model were evaluated by constructing and analyzing receiver operating characteristic (ROC) curves and calibration curves and by decision curve analysis. Finally, the model was validated internally using a bootstrap resampling technique. RESULTS: Seven variables, including demographic, lifestyle, and family history information, were examined. Multifactorial logistic regression analysis revealed that age [odds ratio (OR): 1.03, 95% confidence interval (CI): 1.02-1.03, P < 0.001], body mass index (BMI) (OR: 1.07, 95%CI: 1.06-1.08, P < 0.001), waist circumference (WC) (OR: 1.03, 95%CI: 1.02-1.03 P < 0.001), lifestyle (OR: 0.45, 95%CI: 0.42-0.48, P < 0.001), and family history (OR: 4.28, 95%CI: 4.04-4.54, P < 0.001) were the most significant predictors of high-risk CRC. Healthy lifestyle was a protective factor, whereas family history was the most significant risk factor. The area under the curve was 0.734 (95%CI: 0.723-0.745) for the final validation set ROC curve and 0.735 (95%CI: 0.728-0.742) for the training set ROC curve. The calibration curve demonstrated a high correlation between the CRC high-risk population predicted by the nomogram model and the actual CRC high-risk population. CONCLUSION: The early-screening nomogram model for CRC prediction in high-risk populations developed in this study based on age, BMI, WC, lifestyle, and family history exhibited high accuracy.

Corrigendum to "Differences in primary and secondary stroke prevention strategies for Chinese men and women" [Prev. Med. Rep. 33 (2023) 102219].

[This corrects the article DOI: 10.1016/j.pmedr.2023.102219.].

Dietary protein sources, genetics, and cardiovascular disease incidence.

BACKGROUND: To explore the potential correlation between the amount and source of dietary protein and cardiovascular disease (CVD), as well as the potential impact of genetic susceptibility on these connections. METHODS: We performed a prospective analysis of 98,224 participants from the UK. We measured dietary protein intake using two 24-hour dietary recall interviews. To analyze the data, we used multivariable-adjusted Cox regression models and restricted cubic spline models. Additionally, we calculated weighted genetic risk scores. RESULTS: A total of 8818 new cases of CVD were documented, which included 4076 cases of coronary artery disease (CAD) and 1126 cases of stroke. The study found a J-shaped association (p nonlinearity = 0.005) between CVD risk and the percentage of energy obtained from consuming plant protein. Higher intake of plant protein and whole protein was associated with a decreased risk of CVD. On the other hand, larger intakes of animal protein was linked to a higher occurrence of CAD. Additionally, increased intake of plant protein was also linked to a lower incidence of stroke. Replacing 5 % of animal protein-based energy intake with plant protein-based energy intake resulted in a 5 % decrease in CVD risk. LIMITATIONS: There remains an effect of residual confounders. CONCLUSION: The consumption of larger amounts of plant protein, whole protein, and nut protein was found to be associated with a lower risk of CVD events. Conversely, higher intakes of animal protein was associated with an increased risk of CAD events. Furthermore, replacing 5 % of energy intake from animal protein with energy intake from plant protein was found to reduce the risk of CVD by 5 %.

Learning and Controlling Multiscale Dynamics in Spiking Neural Networks Using Recursive Least Square Modifications.

Invasive brain-computer interfaces (BCIs) have the capability to simultaneously record discrete signals across multiple scales, but how to effectively process and analyze these potentially related signals remains an open challenge. This article introduces an innovative approach that merges modern control theory with spiking neural networks (SNNs) to bridge the gap among multiscale discrete information. Specifically, the macroscopic point-to-point trajectory is formulated as an optimal control problem with fixed terminal time and state, and it is iteratively solved using the direct dynamic programming (DDP) algorithm. Additionally, SNN is utilized to simulate microscale neural activities in the premotor cortex, employing the product of the weighted adjacency matrix and the mesoscale firing rate to approximate the macroscopic trajectory. The error between actual macroscale behavior and the preceding approximation is then used to update the weighted adjacency matrix through the recursive least square (RLS) method. Analysis and simulation of various tasks, including low-dimensional point-to-point tasks, high-dimensional complex Lorenz systems, and center-out-and-back tasks, verify the feasibility and interpretability of our method in processing multiscale signals ranging from spiking neurons to motion trajectory through the integration of SNN and control theory.

Plasma lipidomic profiling reveals six candidate biomarkers for the prediction of incident stroke in patients with hypertension.

INTRODUCTION: The burden of stroke in patients with hypertension is very high, and its prediction is critical. OBJECTIVES: We aimed to use plasma lipidomics profiling to identify lipid biomarkers for predicting incident stroke in patients with hypertension. METHODS: This was a nested case-control study. Baseline plasma samples were collected from 30 hypertensive patients with newly developed stroke, 30 matched patients with hypertension, 30 matched patients at high risk of stroke, and 30 matched healthy controls. Lipidomics analysis was performed by ultrahigh-performance liquid chromatography-tandem mass spectrometry, and differential lipid metabolites were screened using multivariate and univariate statistical methods. Machine learning methods (least absolute shrinkage and selection operator, random forest) were used to identify candidate biomarkers for predicting stroke in patients with hypertension. RESULTS: Co-expression network analysis revealed that the key molecular alterations of the lipid network in stroke implicate glycerophospholipid metabolism and choline metabolism. Six lipid metabolites were identified as candidate biomarkers by multivariate statistical and machine learning methods, namely phosphatidyl choline(40:3p)(rep), cholesteryl ester(20:5), monoglyceride(29:5), triglyceride(18:0p/18:1/18:1), triglyceride(18:1/18:2/21:0) and coenzyme(q9). The combination of these six lipid biomarkers exhibited good diagnostic and predictive ability, as it could indicate a risk of stroke at an early stage in patients with hypertension (area under the curve = 0.870; 95% confidence interval: 0.783-0.957). CONCLUSIONS: We determined lipidomic signatures associated with future stroke development and identified new lipid biomarkers for predicting stroke in patients with hypertension. The biomarkers have translational potential and thus may serve as blood-based biomarkers for predicting hypertensive stroke.

Association between sleep pattern and incidence of hypertension: A prospective cohort study of older adult participants in the Chinese longitudinal healthy longevity survey.

BACKGROUND: The relationship between sleep duration or sleep quality and the risk of hypertension has been previously examined. However, little is known regarding the association between sleep duration and quality and the risk of developing hypertension in the older adult Chinese population. METHODS: The sleep patterns of 5683 participants without hypertension at baseline from the Chinese Longitudinal Healthy Longevity Survey were analyzed. Cox proportional hazard models were used to study the associations between sleep patterns and hypertension. RESULTS: It was found that 1712 (30.12%) of the 5683 participants had an unhealthy sleep pattern. After an average follow-up of 3.31 years, 1350 of the participants had hypertension. Compared with participants with an unhealthy sleep pattern, those with a healthy sleep pattern had a 20% (hazard ratio = 0.80, 95% confidence interval = 0.67-0.94, P = = 0.008) lower risk of incident hypertension in the fully adjusted models. In addition, an approximately linear dose-response association was observed between sleep duration and the incidence of hypertension (P for non-linear =0.43). Subgroup analyses demonstrated significant interactions between age and sleep pattern concerning hypertension (P for interaction <0.05). Several sensitivity analyses were conducted, and the obtained findings were similar to the main results. CONCLUSIONS: A healthy sleep pattern, comprising an adequate sleep duration and good sleep quality, can help reduce hypertension risk. Thus, a healthy sleep pattern is crucial to decreasing hypertension in older Chinese adults in a rapidly aging society.

Ternary-Metal Sn-Pb-Zn Perovskite to Reconstruct Top Surface for Efficient and Stable Less-Pb Perovskite Solar Cells.

Though Sn-Pb alloyed perovskite solar cells (PSCs) achieved great progress, there is a dilemma to further increase Sn for less-Pb requirement. High Sn ratio (>70%) perovskite exhibits nonstoichiometric Sn:Pb:I at film surface to aggravate Sn2+ oxidation and interface energy mismatch. Here, ternary metal alloyed (FASnI3 )0.7 (MAPb1- x Znx I3 )0.3 (x = 0-3%) is constructed for Pb% < 30% perovskite. Zn with smaller ionic size and stronger ionic interaction than Sn/Pb assists forming high-quality perovskite film with ZnI6 4- enriched at surface to balance Sn:Pb:I ratio. Differing from uniform bulk doping, surface-rich Zn with lower lying orbits pushes down the energy band of perovskite and adjusts the interface energy for efficient charge transfer. The alloyed PSC realizes efficiency of 19.4% at AM1.5 (one of the highest values reported for Pb% < 30% PSCs). Moreover, stronger bonding of Zn─I and Sn─I contributes to better durability of ternary perovskite than binary perovskite. This work highlights a novel alloy method for efficient and stable less-Pb PSCs.

Functional analysis of transgenic cry1Ah-1 maize.

Maize is an important food crop in the world, but the yield and quality of maize have been significantly reduced due to the impact of insect pests. In order to address this issue, the cry1Ah gene was subjected to error-prone PCR for mutagenesis, and subsequently, the mutant cry1Ah-1 gene was introduced into maize inbred line GSH9901 callus using the Agrobacterium-mediated method. The T2 generation transformed plants were obtained by subculture, and 9 transgenic positive plants were obtained by molecular detection which was carried out by PCR, qRT-PCR, Bt gold-labeled immunoassay test strips, Western blot and ELISA. It was found that the Cry1Ah-1 gene could be transcribed normally in maize leaves, of which OE1 and OE3 had higher relative expression levels and could successfully express proteins of 71.94 KD size. They were expressed in different tissues at the 6-leaf stage, heading stage and grain-filling stage, and could ensure the protection of maize from corn borer throughout the growth period. The biological activities of OE1 and OE3 were tested indoors and in the field, and the results showed that in indoors, the corn borer that fed on OE1 and OE3 corn leaves had a mortality rate of 100 % after 3 days; in the field, OE1 and OE3 had strong insecticidal activity against corn borer, reaching a high resistance level. In conclusion, the transgenic cry1Ah-1 maize has a strong insecticidal effect on corn borer, and has a good prospect of commercialization.

Investigating linear and nonlinear associations of LDL cholesterol with incident chronic kidney disease, atherosclerotic cardiovascular disease and all-cause mortality: A prospective and Mendelian randomization study.

BACKGROUND AND AIMS: Observational studies suggest potential nonlinear associations of low-density lipoprotein cholesterol (LDL-C) with cardio-renal diseases and mortality, but the causal nature of these associations is unclear. We aimed to determine the shape of causal relationships of LDL-C with incident chronic kidney disease (CKD), atherosclerotic cardiovascular disease (ASCVD) and all-cause mortality, and to evaluate the absolute risk of adverse outcomes contributed by LDL-C itself. METHODS: Observational analysis and one-sample Mendelian randomization (MR) with linear and nonlinear assumptions were performed using the UK Biobank of >0.3 million participants with no reported prescription of lipid-lowering drugs. Two-sample MR on summary-level data from the Global Lipid Genetics Consortium (N = 296,680) and the CKDGen (N = 625,219) was employed to replicate the relationship for kidney traits. The 10-year probabilities of the outcomes was estimated by integrating the MR and Cox models. RESULTS: Observationally, participants with low LDL-C were significantly associated with a decreased risk of ASCVD, but an increased risk of CKD and all-cause mortality. Univariable MR showed an inverse total effect of LDL-C on incident CKD (HR [95% CI]:0.84 [0.73-0.96]; p = 0.011), a positive effect on ASCVD (1.41 [1.29-1.53]; p<0.001), and no significant causal effect on all-cause mortality. Multivariable MR, controlling for high-density lipoprotein cholesterol (HDL-C) and triglycerides, identified a positive direct effect on ASCVD (1.32 [1.18-1.47]; p<0.001), but not on CKD and all-cause mortality. These results indicated that genetically predicted low LDL-C had an inverse indirect effect on CKD mediated by HDL-C and triglycerides, which was validated by a two-sample MR analysis using summary-level data from the Global Lipid Genetics Consortium (N = 296,680) and the CKDGen consortium (N = 625,219). Suggestive evidence of a nonlinear causal association between LDL-C and CKD was found. The 10-year probability curve showed that LDL-C concentrations below 3.5 mmol/L were associated with an increased risk of CKD. CONCLUSIONS: In the general population, lower LDL-C was causally associated with lower risk of ASCVD, but appeared to have a trade-off for an increased risk of CKD, with not much effect on all-cause mortality. LDL-C concentration below 3.5 mmol/L may increase the risk of CKD.

Global, regional, and national burden of neurological disorders in 204 countries and territories worldwide.

Background: We aimed to determine the incidence and disability-adjusted life-years (DALYs) of neurological disorders worldwide from 1990 to 2019. Methods: We obtained age-standardised incidence and DALY rates of neurological disorders in 204 countries and territories from 1990 to 2019 from the Global Burden of Disease (GBD) database. We determined trends stratified by age, sex, region, country, and Social Development Index (SDI) and the risk factors contributing to DALYs associated with these neurological disorders. Results: The largest increases in the age-standardised incidence rates of neurological disorders in 1990-2019 occurred in four regions (East Asia: estimated annual percentage change (EAPC) = 0.19, tropical Latin America: EAPC = 0.07, Southern Latin America: EAPC = 0.03, Western Europe: EAPC = 0.03) and three countries (China: EAPC = 0.20, Ecuador: EAPC = 0.13, Italy: EAPC = 0.13). We observed the largest increases in age-standardised incidence rates for Parkinson disease, idiopathic epilepsy, and bipolar disorder, and in age-standardised DALY rates for Alzheimer disease and other dementias. High-SDI regions showed the highest EAPC for age-standardised incidence rates of Parkinson disease, depression, and motor neuron disease, and age-standardised DALY rates of neurological disorders. Conclusions: There is a need to control the increase in age-standardised incidence rates of neurological disorders in East Asia, tropical Latin America, Southern Latin America, and Western Europe, particularly in China, Ecuador, and Italy.

Identification of Serum Biomarkers of Ischemic Stroke in a Hypertensive Population Based on Metabolomics and Lipidomics.

Hypertensive individuals are at a high risk of stroke, and thus, prevention of stroke in hypertensive patients is essential. Metabolomics and lipidomics can be used to identify diagnostic biomarkers and conduct early assessments of stroke risk in hypertensive populations. In this study, serum samples were collected from 30 hypertensive ischemic stroke (IS), 30 matched hypertensive and 30 matched healthy participants. Metabolomics and lipidomics analyses were conducted via liquid chromatography-tandem mass spectrometry, and the data were analyzed using multivariate and univariate statistical methods. A random forest algorithm and binary logistic regression were used to screen the biomarkers and establish diagnostic model. We detected 21 differential metabolites and 38 differential lipids between the hypertensive IS and healthy group. Moreover, we found 18 differential metabolites and 31 differential lipids between the hypertensive IS and hypertension group. In particular, the following seven metabolites or lipids distinguished the hypertensive IS from the healthy group: 4-hydroxyphenylpyruvic acid, cafestol, phosphatidylethanolamine (PE) (18:0p/18:2), PE (16:0e/20:4), (O-acyI)-1-hydroxy fatty acid (36:3), PE (16:0p/20:3) and PE (18:1p/18:2) (rep). The following seven biomarkers distinguished the hypertensive IS from the hypertension group: diglyceride (DG) (20:1/18:2), PE (18:0p/18:2), PE (16:0e/22:5), phosphatidylcholine (40:7), dimethylphosphatidylethanolamine (50:3), DG (18:1/18:2), and 4-hydroxyphenylpyruvic acid. The aforementioned panels had good diagnostic and predictive ability for hypertensive IS. Our study determines the metabolomic and lipidomic profiles of hypertensive IS patients and thereby identifies potential biomarkers of the presence of IS in hypertensive populations.