Combined associations of visceral adipose tissue and adherence to a Mediterranean lifestyle with T2D and diabetic microvascular complications among individuals with prediabetes.

BACKGROUND: It's unclear if excess visceral adipose tissue (VAT) mass in individuals with prediabetes can be countered by adherence to a Mediterranean lifestyle (MEDLIFE). We aimed to examine VAT mass, MEDLIFE adherence, and their impact on type 2 diabetes (T2D) and diabetic microvascular complications (DMC) in individuals with prediabetes. METHODS: 11,267 individuals with prediabetes from the UK Biobank cohort were included. VAT mass was predicted using a non-linear model, and adherence to the MEDLIFE was evaluated using the 25-item MEDLIFE index, encompassing categories such as "Mediterranean food consumption," "Mediterranean dietary habits," and "Physical activity, rest, social habits, and conviviality." Both VAT and MEDLIFE were categorized into quartiles, resulting in 16 combinations. Incident cases of T2D and related DMC were identified through clinical records. Cox proportional-hazards regression models were employed to examine associations, adjusting for potential confounding factors. RESULTS: Over a median follow-up of 13.77 years, we observed 1408 incident cases of T2D and 714 cases of any DMC. High adherence to the MEDLIFE, compared to the lowest quartile, reduced a 16% risk of incident T2D (HR: 0.84, 95% CI: 0.71-0.98) and 31% for incident DMC (0.69, 0.56-0.86). Conversely, compared to the lowest quartile of VAT, the highest quartile increased the risk of T2D (5.95, 4.72-7.49) and incident any DMC (1.79, 1.36-2.35). We observed an inverse dose-response relationship between MEDLIFE and T2D/DMC, and a dose-response relationship between VAT and all outcomes (P for trend < 0.05). Restricted cubic spline analysis confirmed a nearly linear dose-response pattern across all associations. Compared to individuals with the lowest MEDLIFE quartile and highest VAT quartile, those with the lowest T2D risk had the lowest VAT and highest MEDLIFE (0.12, 0.08-0.19). High MEDLIFE was linked to reduced T2D risk across all VAT categories, except in those with the highest VAT quartile. Similar trends were seen for DMC. CONCLUSION: High adherence to MEDLIFE reduced T2D and MDC risk in individuals with prediabetes, while high VAT mass increases it, but MEDLIFE adherence may offset VAT's risk partly. The Mediterranean lifestyle's adaptability to diverse populations suggests promise for preventing T2D.

Impact of healthy lifestyle on the incidence and progression trajectory of mental disorders: A prospective study in the UK Biobank.

BACKGROUND: Healthier lifestyle decreased the risk of mental disorders (MDs) such as depression and anxiety. However, research on the effects of a comprehensive healthy lifestyle on their progression is lacking. METHODS: 385,704 individuals without baseline MDs from the UK Biobank cohort were included. A composite healthy lifestyle score was computed by assessing alcohol intake, smoking status, television viewing time, physical activity, sleep duration, fruit and vegetable intake, oily fish intake, red meat intake, and processed meat intake. Follow-up utilized hospital and death register records. Multistate model was used to examine the role of healthy lifestyle on the progression of specific MDs, while a piecewise Cox regression model was utilized to assess the influence of healthy lifestyle across various phases of disease progression. RESULTS: Higher lifestyle score reduced risks of transitions from baseline to anxiety and depression, as well as from anxiety and depression to comorbidity, with corresponding hazard ratios (HR) and 95 % confidence intervals (CI) of 0.94 (0.93, 0.95), 0.90 (0.89, 0.91), 0.94 (0.91, 0.98), and 0.95 (0.92, 0.98), respectively. Healthier lifestyle decreased the risk of transitioning from anxiety to comorbidity within 2 years post-diagnosis, with HR 0.93 (0.88, 0.98). Higher lifestyle scores at 2-4 years and 4-6 years post-depression onset were associated with reduced risk of comorbidity, with HR 0.93 (0.87, 0.99) and 0.92 (0.86, 0.99), respectively. LIMITATION: The generalizability to other ethnic groups is limited. CONCLUSION: This study observed a protective role of holistic healthy lifestyle in the trajectory of MDs and contributed to identifying critical progression windows.

A muti-modal feature fusion method based on deep learning for predicting immunotherapy response.

Immune checkpoint therapy (ICT) has greatly improved the survival of cancer patients in the past few years, but only a small number of patients respond to ICT. To predict ICT response, we developed a multi-modal feature fusion model based on deep learning (MFMDL). This model utilizes graph neural networks to map gene-gene relationships in gene networks to low dimensional vector spaces, and then fuses biological pathway features and immune cell infiltration features to make robust predictions of ICT. We used five datasets to validate the predictive performance of the MFMDL. These five datasets span multiple types of cancer, including melanoma, lung cancer, and gastric cancer. We found that the prediction performance of multi-modal feature fusion model based on deep learning is superior to other traditional ICT biomarkers, such as ICT targets or tumor microenvironment-associated markers. In addition, we also conducted ablation experiments to demonstrate the necessity of fusing different modal features, which can improve the prediction accuracy of the model.

Attentive Learning Facilitates Generalization of Neural Networks.

This article studies the generalization of neural networks (NNs) by examining how a network changes when trained on a training sample with or without out-of-distribution (OoD) examples. If the network's predictions are less influenced by fitting OoD examples, then the network learns attentively from the clean training set. A new notion, dataset-distraction stability, is proposed to measure the influence. Extensive CIFAR-10/100 experiments on the different VGG, ResNet, WideResNet, ViT architectures, and optimizers show a negative correlation between the dataset-distraction stability and generalizability. With the distraction stability, we decompose the learning process on the training set S into multiple learning processes on the subsets of S drawn from simpler distributions, i.e., distributions of smaller intrinsic dimensions (IDs), and furthermore, a tighter generalization bound is derived. Through attentive learning, miraculous generalization in deep learning can be explained and novel algorithms can also be designed.

A Discrete, Boron-Containing Triangular Triradical.

A neutral boron-containing triangular triradical based on a triptycene derivative has been designed and synthesized. Its structure, bonding and physical property have been studied by EPR spectroscopy, SQUID magnetometry and single crystal X-ray diffraction, as well as theoretical calculations. The triradical has a series of isosceles triangle conformations in the solution due to the Jahn-Teller distortion, leading to the splitting of the two low-lying doublet states. This factor together with negligible spin-orbit coupling (SOC) of composing light atoms quenches the spin frustration. The work represents a rare example of a neutral through-space triangular triradical.

Dual-View Learning Based on Images and Sequences for Molecular Property Prediction.

The prediction of molecular properties remains a challenging task in the field of drug design and development. Recently, there has been a growing interest in the analysis of biological images. Molecular images, as a novel representation, have proven to be competitive, yet they lack explicit information and detailed semantic richness. Conversely, semantic information in SMILES sequences is explicit but lacks spatial structural details. Therefore, in this study, we focus on and explore the relationship between these two types of representations, proposing a novel multimodal architecture named ISMol. ISMol relies on a cross-attention mechanism to extract information representations of molecules from both images and SMILES strings, thereby predicting molecular properties. Evaluation results on 14 small molecule ADMET datasets indicate that ISMol outperforms machine learning (ML) and deep learning (DL) models based on single-modal representations. In addition, we analyze our method through a large number of experiments to test the superiority, interpretability and generalizability of the method. In summary, ISMol offers a powerful deep learning toolbox for drug discovery in a variety of molecular properties.

The predictive, preventive, and personalized medicine of insomnia: gut microbiota and inflammation.

Background: The human gut microbiota (GM) has been recognized as a significant factor in the development of insomnia, primarily through inflammatory pathways, making it a promising target for therapeutic interventions. Considering the principles of primary prediction, targeted prevention, and personalized treatment medicine (PPPM), identifying specific gut microbiota associated with insomnia and exploring the underlying mechanisms comprehensively are crucial steps towards achieving primary prediction, targeted prevention, and personalized treatment of insomnia. Working hypothesis and methodology: We hypothesized that alterations in the composition of specific GM could induce insomnia through an inflammatory response, which postulates the existence of a GM-inflammation-insomnia pathway. Mendelian randomization (MR) analyses were employed to examine this pathway and explore the mediative effects of inflammation. We utilized genetic proxies representing GM, insomnia, and inflammatory indicators (including 41 circulating cytokines and C-reactive protein (CRP)), specifically identified from European ancestry. The primary method used to identify insomnia-related GM and examine the medicative effect of inflammation was the inverse variance weighted method, supplemented by the MR-Egger and weighted median methods. Our findings have the potential to identify individuals at risk of insomnia through screening for GM imbalances, leading to the development of targeted prevention and personalized treatment strategies for the condition. Results: Nine genera and three circulating cytokines were identified to be associated with insomnia; only the associations of Clostridium (innocuum group) and ß-NGF on insomnia remained significant after the FDR test, OR = 1.08 (95% CI = 1.04-1.12, P = 1.45 × 10-4, q = 0.02) and OR = 1.06 (95% CI = 1.02-1.10, P = 1.06 × 10-3, q = 0.04), respectively. CRP was associated with an increased risk of insomnia, OR = 1.05 (95% CI = 1.01-1.10, P = 6.42 × 10-3). CRP mediated the association of Coprococcus 1, Holdemania, and Rikenellaceae (RC9gut group) with insomnia. No heterogeneity or pleiotropy were detected. Conclusions: Our study highlights the role of specific GM alterations in the development of insomnia and provides insights into the mediating effects of inflammation. Targeting these specific GM alterations presents a promising avenue for advancing the transition from reactive medicine to PPPM in managing insomnia, potentially leading to significant clinical benefits. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-023-00345-1.

AMGDTI: drug-target interaction prediction based on adaptive meta-graph learning in heterogeneous network.

Prediction of drug-target interactions (DTIs) is essential in medicine field, since it benefits the identification of molecular structures potentially interacting with drugs and facilitates the discovery and reposition of drugs. Recently, much attention has been attracted to network representation learning to learn rich information from heterogeneous data. Although network representation learning algorithms have achieved success in predicting DTI, several manually designed meta-graphs limit the capability of extracting complex semantic information. To address the problem, we introduce an adaptive meta-graph-based method, termed AMGDTI, for DTI prediction. In the proposed AMGDTI, the semantic information is automatically aggregated from a heterogeneous network by training an adaptive meta-graph, thereby achieving efficient information integration without requiring domain knowledge. The effectiveness of the proposed AMGDTI is verified on two benchmark datasets. Experimental results demonstrate that the AMGDTI method overall outperforms eight state-of-the-art methods in predicting DTI and achieves the accurate identification of novel DTIs. It is also verified that the adaptive meta-graph exhibits flexibility and effectively captures complex fine-grained semantic information, enabling the learning of intricate heterogeneous network topology and the inference of potential drug-target relationship.

Differentiating Oxygen Exchange Reaction Mechanisms across Phase Boundaries.

Triggering phase transitions by controlling the anion stoichiometry is an effective method of tuning the electrocatalytic activity of the functional oxides. However, understanding the potential differences in the reaction mechanism(s) of different phases requires the accurate mapping of phase boundaries during the electrochemical reactions, which can be quite challenging. In this work, we have established a feasible electrochemical method based on the measurement of chemical capacitance to resolve the critical stoichiometry at phase boundaries under operando conditions. We select a simple binary oxide PrOx as a proof-of-principle model system, which shows excellent activity for high-temperature oxygen incorporation and evolution reactions (OIR/OER). We show that the phase transition can be sensitively probed by quantifying the chemical capacitance, which can be further used for differentiating the OIR/OER mechanisms across the phase boundary of PrOx. Therefore, our findings provide a new framework for exploring phase engineering as a tool for the design of electrocatalysts.

Dynamically tunable terahertz quadruple-function absorber based on a hybrid configuration of graphene and vanadium dioxide.

A quadruple-function dynamically tunable terahertz absorber that uses a hybrid configuration of graphene and vanadium dioxide is proposed in this paper. The absorber achieves dynamic conversion of four functions in one structure: ultra-broadband, broadband, single-frequency narrowband and dual-frequency narrowband, by utilizing the electrical control properties of graphene and the phase-shifting properties of vanadium dioxide. Furthermore, the paper also reveals the physical mechanism of the proposed absorber through the electric field distribution and impedance matching theory. In addition, the influences of the Fermi energy level of graphene and the electrical conductivity of vanadium dioxide on the absorption spectra are investigated, demonstrating the structure's dynamic tunability. Due to the above features, the designed absorber is expected to have potential applications in terahertz imaging, modulation and filtering.

Total and Regional Fat/Muscle Mass Ratio and Risks of Incident Cardiovascular Disease and Mortality.

Background To evaluate the sex-specific associations of total and regional fat/muscle mass ratio (FMR) with cardiovascular disease (CVD) incidence and mortality, and to explore the underlying mechanisms driven by cardiometabolites and inflammatory cells. We compared the predictive value of FMRs to body mass index. Methods and Results This population-based, prospective cohort study included 468 885 UK Biobank participants free of CVD at baseline. Fat mass and muscle mass were estimated using a bioelectrical impedance assessment device. FMR was calculated as fat mass divided by muscle mass in corresponding body parts (total body, trunk, arm, and leg). Multivariable Cox proportional hazards models and mediation analyses were used. During 12.5 years of follow-up, we documented 49 936 CVD cases and 4158 CVD deaths. Higher total FMR was associated with an increased risk of incident CVD (hazard ratios [HRs] were 1.63 and 1.83 for men and women, respectively), ischemic heart disease (men: HR, 1.61; women: HR, 1.81), myocardial infarction (men: HR, 1.72; women: HR, 1.49), and congestive heart failure (men: HR, 2.25; women: HR, 2.57). The positive associations of FMRs with mortality from total CVD or its subtypes were significant mainly in trunk and arm for male patients (P for trend <0.05). We also identified 8 cardiometabolites and 5 inflammatory cells that partially mediated FMR-CVD associations. FMRs were modestly better at discriminating cardiovascular mortality risk. Conclusions Higher total and regional FMRs were associated with an increased risk of CVD and mortality, partly mediated through cardiometabolites and inflammatory cells. Early monitoring of FMR should be considered to alleviate CVD risk. FMRs were superior to body mass index in predicting CVD mortality.

Comprehensive evaluation of deep and graph learning on drug-drug interactions prediction.

Recent advances and achievements of artificial intelligence (AI) as well as deep and graph learning models have established their usefulness in biomedical applications, especially in drug-drug interactions (DDIs). DDIs refer to a change in the effect of one drug to the presence of another drug in the human body, which plays an essential role in drug discovery and clinical research. DDIs prediction through traditional clinical trials and experiments is an expensive and time-consuming process. To correctly apply the advanced AI and deep learning, the developer and user meet various challenges such as the availability and encoding of data resources, and the design of computational methods. This review summarizes chemical structure based, network based, natural language processing based and hybrid methods, providing an updated and accessible guide to the broad researchers and development community with different domain knowledge. We introduce widely used molecular representation and describe the theoretical frameworks of graph neural network models for representing molecular structures. We present the advantages and disadvantages of deep and graph learning methods by performing comparative experiments. We discuss the potential technical challenges and highlight future directions of deep and graph learning models for accelerating DDIs prediction.

Associations of blood biomarkers with arterial stiffness in patients with diabetes mellitus: A population-based study.

BACKGROUND: Arterial stiffness contributes to additional cardiovascular risks in diabetic patients by triggering the loss of vascular and myocardial compliance and promoting endothelial dysfunction. Thus, prevention of arterial stiffness is a public health priority, and the identification of potential biomarkers may provide benefits for early prevention. This study investigates the relationships between serum laboratory tests and pulse wave velocity (PWV) tests. We also investigated the associations between PWV and all-cause mortality. METHODS: We examined a panel of 33 blood biomarkers among diabetic populations in the Atherosclerosis Risk in Communities Study. The carotid-femoral (cfPWV) and femoral-ankle PWV (faPWV) were measured using an automated cardiovascular screening device. The aortic-femoral arterial stiffness gradient (afSG) was calculated as faPWV divided by cfPWV. Biomarker levels were log-transformed and correlated with PWV. Cox proportional hazard models were employed for survival analysis. RESULTS: Among 1079 diabetic patients, biomarkers including high-density lipoprotein cholesterol, glycated hemoglobin, high-sensitivity troponin T, cystatin C, creatinine, and albuminuria were significantly correlated with afSG (R = 0.078, -0.193, -0.155, -0.153, -0.116, and -0.137, respectively) and cfPWV (R = -0.068, 0.175, 0.128, 0.066, 0.202, and 0.062, respectively). Compared with the lowest tertile of afSG, the risk of all-cause mortality was lower in the highest tertile (hazard ratio 0.543; 95% confidence interval 0.328-0.900). CONCLUSION: Certain biomarkers related to blood glucose monitoring, myocardial injury, and renal function significantly correlated with PWV, suggesting that these putative risk factors are likely to be important atherosclerosis mechanisms in diabetic patients. AfSG may be an independent predictor of mortality among diabetic populations.

"Life's Essential 8" Cardiovascular Health and Dementia Risk, Cognition, and Neuroimaging Markers of Brain Health.

OBJECTIVE: To evaluate associations of Life's Essential 8 (LE8) score, the recently updated metric for promoting cardiovascular health (CVH), with the risk of incident dementia and its subtypes, cognition, and neuroimaging outcomes and to determine whether these associations differ among apolipoprotein E (APOE)-ε4 genotypes. DESIGN: Prospective cohort study. SETTING AND PARTICIPANTS: A total of 316,669 participants [mean (SD) age, 56.3 (8.1) years] without prior cardiovascular disease or dementia from the UK Biobank study at baseline survey (2006-2010) were enrolled. METHODS: A modified version of the LE8 score was created (range: 0-100) and categorized into poor (0-49), intermediate (50-79), and optimal (80-100) CVH. Cox proportional hazard and multivariable linear regression models were used. RESULTS: During a median 12.6 years of follow-up, 4238 all-cause dementia cases including 1797 Alzheimer's disease and 939 vascular dementia (VaD) occurred. Individuals with optimal CVH had 44% (95% CI, 0.48-0.64) lower incident all-cause dementia risk and 71% (95% CI, 0.22-0.38) lower VaD risk compared with those who had poor CVH. A 10-point increment in LE8 was associated with higher fluid intelligence scores (ß, 0.088; 95% CI, 0.073-0.102) and numeric memory scores (ß, 0.054; 95% CI, 0.043-0.065), and was also associated with lower white matter hyperintensity volume (ß, -0.673; 95% CI, -0.751 to -0.596), larger total brain volume (ß, 77.93; 95% CI, 62.03-93.84), and hippocampal volume (ß, 0.197; 95% CI, 0.106-0.288). In addition, the association between LE8 profiles and dementia diagnosis differed by APOE genotype (all P for interaction ≤ .001), and was more evident among APOE-ε4 noncarriers. CONCLUSIONS AND IMPLICATIONS: Individuals with a higher LE8 score experienced fewer dementia events (driven especially by incident VaD) and were associated with better neurocognitive brain health profiles. CVH optimization may be beneficial to the maintenance of brain health.

Full-Color Sterically Shielded Boron Difluoride Emitters with Efficient and Ultrapure Electroluminescence via Sensitized Fluorescence.

Emitters with narrowband emissions are essential to improve the color purity of organic light-emitting diodes (OLEDs). Boron difluoride (BF) derivatives have preliminarily exhibited small full width at half-maximum (FWHM) values in electroluminescent devices, which, however, still face formidable challenges in recycling triplet excitons and realizing full-color emissions covering the whole visible spectra. Here, a systematic molecular engineering on the aza-fused aromatic emitting core and peripheral substitutions is made, affording a family of full-color BF emitters spanning from blue (461 nm) to red (635 nm), with high photoluminescence quantum yields of >90% and a small FWHM of 0.12 eV. The device architectures are delicately manipulated to form effective thermally activated sensitizing emissions, first affording the highest maximum external quantum efficiency of >20% for BF-based OLEDs with negligible efficiency roll-off.

Epidural Steroid Injections and the Risk of Osteoporosis in Lumbar Spondylosis Patients: A Nationwide Population-Based Cohort Study.

BACKGROUND: Epidural steroid injections (ESIs) involve the administration of steroids and local anesthetics into the spinal epidural space, and they are performed by inserting a needle between the ligamentum flavum and dura. This procedure is suitable for patients with lumbosacral radiculopathy secondary to disc herniation or postsurgical radicular pain. The relief period of the analgesic medications may be prolonged by > 6 weeks, resulting in nonsurgical management becoming a suitable option. However, the negative effect of ESIs on bone mineral density has been reported. OBJECTIVES: We aimed to clarify the association between ESIs and osteoporosis risk by analyzing a nationwide population database. STUDY DESIGN: This study is a nationwide retrospective cohort study. SETTING: Data on 1 million cases randomly selected from the 2000 Registry for Beneficiaries of the National Health Insurance Research Database (NHIRD) were collected. METHODS: In total, 4,957 patients who were diagnosed with lumbar spondylosis and received ESIs between 2000 and 2013 were identified from the NHIRD. Subsequently, another 4,957 patients with lumbar spondylosis were randomly selected from the same database and frequency matched by age, gender, and index year with the patients who received ESIs. RESULTS: The mean age of the patients were 50.3 ± 17.1 years. The incident rates of osteoporosis in the ESI and non-ESI groups were 7.95 and 7.01 per 1,000 person-years, respectively. Osteoporosis risk was significantly higher in the ESI cohort than in the non-ESI cohort (absolute standardized hazard ratio = 1.23, 95% confidence interval = 1.05-1.45, P = 0.01). The risk factors for osteoporosis were old age, being female, and undergoing ESIs. Osteoporosis risk was significantly higher in the ESI cohort than in the non-ESI cohort in the male, lowest-urbanization-level (fourth level), other-occupations, and comorbidity-free subgroups. LIMITATIONS: The NHIRD did not provide information on osteoporosis-related scales, renal function, blood pressure, smoking habit, pulmonary function, daily activities, and dosage of injected steroids. CONCLUSIONS: For patients diagnosed with lumbar spondylosis, ESIs are associated with a high osteoporosis risk. Thus, this therapy should be recommended with caution, especially for patients with correlated risk factors (e.g., high risk of osteoporotic fracture, low socioeconomic status, and retired or unemployed status).

Association of serum uric acid and fasting plasma glucose with cognitive function: a cross-sectional study.

BACKGROUND: The combined effect of serum uric acid (SUA) and blood glucose on cognition has not been explored. This study aimed to examine the separate and combined association of SUA and fasting plasma glucose (FPG) or diabetes mellitus (DM) with cognition in a sample of Chinese middle-aged and elderly population. METHODS: A total of 6,509 participants aged 45 years or older who participated in the China Health and Retirement Longitudinal Study (CHARLS, 2011) were included. The three cognitive domains assessed were episodic memory, mental status, and global cognition (the sum of the first two terms). Higher scores indicated better cognition. SUA and FPG were measured. The participants were grouped based on SUA and FPG quartiles to evaluate their combined associations of cognition with SUA Q1-Q3 only (Low SUA), with FPG Q4 only (High FPG), without low SUA and high FPG levels (Non), and with low SUA and high FPG levels (Both), multivariate linear regression models were used to analyze their association. RESULTS: Lower SUA quartiles were associated with poorer performance in global cognition and episodic memory compared with the highest quartile. Although no association was found between FPG or DM and cognition, high FPG or DM combined with low SUA levels in women (ßFPG = -0.983, 95% CI: -1.563--0.402; ßDM = -0.800, 95% CI: -1.369--0.232) had poorer cognition than those with low SUA level only (ßFPG = -0.469, 95% CI: -0.926--0.013; ßDM = -0.667, 95% CI: -1.060--0.275). CONCLUSION: Maintaining an appropriate level of SUA may be important to prevent cognitive impairment in women with high FPG.

The independent and joint association of accelerometer-measured physical activity and sedentary time with dementia: a cohort study in the UK Biobank.

BACKGROUND: Research on the association of physical activity and sedentary time with dementia is accumulating, though elusive, and the interaction effects of the two remain unclear. We analysed the joint associations of accelerometer-measured physical activity and sedentary time with risk of incident dementia (all-cause dementia, Alzheimer's disease and vascular dementia). METHODS: A total of 90,320 individuals from the UK Biobank were included. Accelerometer-measured total volume of physical activity (TPA) and sedentary time were measured at baseline and dichotomised by median (low TPA [< 27 milli-gravity (milli-g)], high TPA [≥ 27 milli-g]; low sedentary time [< 10.7 h/day], high sedentary time [≥ 10.7 h/day]). Cox proportional hazards models were used to evaluate the joint associations with incident dementia on both additive and multiplicative scales. RESULTS: During a median follow-up of 6.9 years, 501 cases of all-cause dementia were identified. Higher TPA was associated with a lower risk of all-cause dementia, Alzheimer's disease and vascular dementia; the multivariate adjusted hazard ratios (HRs) (95% CI) per 10 milli-g increase were 0.63 (0.55-0.71), 0.74 (0.60-0.90) and 0.69 (0.51-0.93), respectively. Sedentary time was only found to be linked to all-cause dementia, and the HR for high sedentary time was 1.03 (1.01-1.06) compared with that for low sedentary time. No additive and multiplicative relationship of TPA and sedentary time to incident dementia was found (all P values > 0.05). CONCLUSION: Higher TPA level was related to a lower risk of incident dementia irrespective of sedentary time, which highlighted the implication of promoting physical activity participation to counteract the potential detrimental effect of sedentary time on dementia.

Role of uric acid as a biomarker of cognitive function in schizophrenia during maintenance period.

Background: Previous studies involving uric acid (UA) in some specialized disease populations have found that high UA is associated with enhanced patient function. The mechanism to explain this association may be that UA, an important antioxidant, exerts neuroprotective effects. Patients with schizophrenia (SCZ) have severe oxidative stress abnormalities, and cognitive impairment is a major obstacle to their rehabilitation. Only few studies have been conducted on UA and cognitive impairment in SCZ. This study aims to clarify the relationship between UA and cognitive impairment and explore whether UA could be used as a potential biological marker of cognition in SCZ during maintenance period. Methods: A total of 752 cases of SCZ during maintenance period from Baiyun Jingkang Hospital were included. Cognition was measured using the Mini-Mental State Examination scale. UA was measured using the Plus method. The participants were grouped on the basis of UA to evaluate the association of cognition with low-normal (3.50-5.07 mg/dL for men, 2.50-4.19 mg/dL for women), middle-normal (5.07-6.39 mg/dL for men, 4.19-5.18 mg/dL for women), high-normal (6.39-7.00 mg/dL for men, 5.18-6.00 mg/dL for women), and high (>7.00 mg/dL for men, >6.00 mg/dL for women) levels of UA. Multiple logistic regression and linear regression models and restricted cubic spline (RCS) were utilized to evaluate the relationship. Results: Uric acid was positively associated with cognitive function. Subgroup analyses showed that high UA was associated with enhanced cognition in participants with low anticholinergic cognitive burden (ACB). Conclusion: Uric acid may be used as a simple objective biological indicator to assess cognition in SCZ during maintenance period.

The association between daytime napping and risk of type 2 diabetes is modulated by inflammation and adiposity: Evidence from 435 342 UK-Biobank participants.

BACKGROUND: Existing evidence concerning the relationship between daytime napping and type 2 diabetes (T2D) is inconsistent, and whether the effects of napping differ by body fat percentage (BFP) and C-reactive protein (CRP) is unclear. We aimed to investigate the association between daytime napping frequency and T2D risk and whether such an association was modified by BFP and CRP. METHODS: We included 435 342 participants free of diabetes from the UK Biobank. Participants were categorized as nonnappers, occasional nappers, and frequent nappers based on napping frequency, and BFP/CRP was divided into quartiles. Cox proportional hazards models were used. RESULTS: During a median follow-up of 9.2 years, 17 592 T2D cases occurred. Higher frequency of daytime napping was significantly associated with an increased risk of T2D. Compared with nonnappers, the adjusted hazard ratios (HRs) for occasional nappers and habitual nappers were 1.28 (95% confidence interval [CI]: 1.24-1.32) and 1.49 (95% CI: 1.41-1.57), respectively. There was a significant additive and multiplicative interaction (relative excess risk due to interaction [RERI] = 0.490, 95% CI 0.307-0.673; p for multiplicative interaction <.001) between napping and BFP, whereby a higher hazard of T2D associated with more frequent napping was greatest among participants in the highest BFP quartile (HR = 4.45, 95% CI: 3.92-5.06). The results for CRP were similar (RERI = 0.266, 95% CI: 0.094-0.439; p for multiplicative interaction <.001). CONCLUSIONS: Higher daytime napping frequency is associated with an increased T2D risk, and such relationships are modified by BFP and CRP. These findings underscore the importance of adiposity and inflammation control to mitigate diabetes risk.