BACKGROUND:Deep learning (DL)-based image enhancement is widely used to improve suboptimal medical imaging. Whether it also benefits diagnostic-quality MRI in downstream task performance and data-efficiency remains unclear. PURPOSE:To investigate the impact of DL-based enhancement applied to diagnostic quality structural MRI for Alzheimer's disease (AD) classification. STUDY TYPE:Retrospective. POPULATION:A total of 2293 brain MRI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were split into training (n = 1605), validation (n = 229), and internal test (n = 459) sets. Two hundred and seventy scans from the National Alzheimer's Coordinating Center (NACC) served as an external test set. FIELD STRENGTH/SEQUENCE:1.5 T; 3D T1-weighted gradient-echo. ASSESSMENT:Each scan was enhanced by SubtleHD (SHD), an FDA-cleared DL-based MR enhancement tool. ResNet34 and DenseNet121 were trained on standard-of-care (SOC) and SHD-enhanced images to classify subjects as cognitively normal, mild cognitive impairment, or AD and evaluated by accuracy and macro-area under the receiver operating characteristic curve (macro-AUC). Data efficiency was assessed by retraining on stratified training subsets (50%-100%). STATISTICAL TESTS:McNemar test for accuracy and DeLong test for macro-AUC in three-class one-versus-rest setting (p < 0.05). RESULTS:SHD enhancement increased ResNet34 accuracy from 85.2% to 88.7% and macro-AUC from 0.951 to 0.968 (both significant), and DenseNet121 accuracy from 90.2% to 92.2% (p = 0.18) and macro-AUC from 0.978 to 0.982 (p = 0.29). Models trained on 70% of SHD-enhanced dataset matched those trained on the full SOC dataset (accuracy: 85.9%, macro-AUC: 0.942), indicating improved data efficiency with enhancement. In NACC, the SOC-trained model achieved accuracy of 49.2% and macro-AUC of 0.679 versus 63.0% and 0.819 for the SHD-trained model (both significant); the SHD-trained model retained an advantage on unenhanced NACC images (macro-AUC: 0.772). DATA CONCLUSION:DL-based enhancement of diagnostic-quality MRI improves downstream Alzheimer's disease classification performance and reduces the amount of training data required. This suggests that conventional definitions of image quality may underestimate the information content available for machine learning. EVIDENCE LEVEL:3. TECHNICAL EFFICACY STAGE:2.

BACKGROUND:It has been shown that rupture of vulnerable plaques of atherosclerosis (AS) is one of the main causes of ischemic stroke. Increasing evidence suggests that the inflammatory changes of perivascular adipose tissue (PVAT) were independently associated with both vulnerable plaque characteristics and cerebrovascular symptoms. Therefore, it is essential to conduct non-invasive joint assessment of carotid AS plaques and PVAT characteristics for better stratifying the risk of ischemic cerebrovascular events. PURPOSE:To develop a simultaneous magnetic resonance (MR) imaging technique for carotid artery PVAT and vessel wall, and determine its feasibility and repeatability. METHODS:This study developed an MR sequence for simultaneous imaging carotid vessel wall and PVAT. Seventeen healthy subjects and nine patients with carotid AS were recruited for MR imaging experiments, of whom five healthy subjects were selected for the repeatability test. All participants underwent bilateral carotid three-dimensional MR imaging by acquiring the proposed iMSDE-mDIXON, MERGE, and mDIXON sequences. To evaluate the reliability of the proposed iMSDE-mDIXON sequence, we analyzed its agreement in measuring morphology (lumen area, wall area, mean wall thickness, and normalized wall index) of carotid wall with the reference sequence of MERGE, as well as its agreement in quantifying carotid PVAT (PVAT area, proton density fat fraction [PDFF], area index, and volume index) with the reference sequence of mDIXON. The interclass correlation coefficient (ICC) and Bland-Altman plots were conducted in statistical analysis. RESULTS:The proposed iMSDE-mDIXON technique demonstrated high reliability in quantifying carotid vessel wall morphology (healthy subjects: ICC = 0.903-0.997; patients: ICC = 0.928-0.999) and PVAT morphology (healthy subjects: ICC = 0.906-0.988; patients: ICC = 0.957-0.996). Although iMSDE-mDIXON sequence showed potential in assessing carotid AS, there was a substantial bias (>20%) in PDFF quantification. Nevertheless, moderate to excellent agreement was maintained between iMSDE-mDIXON and mDIXON in measuring PVAT PDFF both in healthy subjects (ICC: left, 0.782; right, 0.740) and AS patients (ICC: left, 0.790; right, 0.628). In addition, the proposed sequence showed excellent agreement in quantifying carotid vessel wall (ICC = 0.845-0.999) and PVAT morphology (ICC = 0.841-0.989) between the repeated scans. CONCLUSIONS:This study proposed an iMSDE-mDIXON sequence that enables simultaneous imaging of the carotid vessel wall and PVAT in a single scan with high efficiency, reliability, and repeatability. This technique has considerable potential for jointly characterizing the changes of PVAT and pathology of the vessel wall in carotid artery.

Growing evidence reveals that indirect genetic effects (IGEs) contribute substantially to human complex traits. However, the genomic architecture and underlying mechanistic pathways of IGEs remain poorly understood. In this study, we employed a two-stage family study design to investigate IGEs and direct genetic effects (DGEs) on blood lipid profiles in the Fangshan Family-based Ischemic Stroke Study in China (FISSIC) cohort. We identified 16 IGE loci resulting in 14 candidate IGE genes, and 20 DGE loci resulting in 22 DGE genes, by an integrative functional mapping approach. The identified IGE genes were predominantly related to behavioral and neuropsychiatric phenotypes, while DGE genes were primarily involved in lipid metabolic pathways. To examine whether IGE genes are associated with lipids through behavioral factors in IGEs, we compared IGE estimates before and after adjusting for behavioral covariates, including healthy food score, drinking status, educational attainment, obesity, physical activity, sleep duration, and smoking status. Adjustment for behavioral factors attenuated the IGE associations, and mediation analysis further indicated that healthy food score partially mediated the association between IGE-GRS and HDL-C (mediation proportion: 17.9%), whereas the proportion mediated by behavioral mediators in the DGE pathway was negligible. Our results suggest that lifestyle behaviors may substantially mediate IGE effects, indicating that they could serve as modifiable factors to mitigate IGE-related genetic susceptibility for lipid or other complex traits.

BACKGROUND AND PURPOSE: Accelerated and blood-suppressed postcontrast 3D intracranial vessel wall MRI (IVW) enables high-resolution rapid scanning but is associated with low SNR. We hypothesized that a deep-learning (DL) denoising algorithm applied to accelerated, blood-suppressed postcontrast IVW can yield high-quality images with reduced artifacts and higher SNR in shorter scan times. MATERIALS AND METHODS: Sixty-four consecutive patients underwent IVW, including conventional postcontrast 3D T1-sampling perfection with application-optimized contrasts by using different flip angle evolution (SPACE) and delay alternating with nutation for tailored excitation (DANTE) blood-suppressed and CAIPIRINHIA-accelerated (CAIPI) 3D T1-weighted TSE postcontrast sequences (DANTE-CAIPI-SPACE). DANTE-CAIPI-SPACE acquisitions were then denoised by using an unrolled deep convolutional network (DANTE-CAIPI-SPACE+DL). SPACE, DANTE-CAIPI-SPACE, and DANTE-CAIPI-SPACE+DL images were compared for overall image quality, SNR, severity of artifacts, arterial and venous suppression, and lesion assessment by using 4-point or 5-point Likert scales. Quantitative evaluation of SNR and contrast-to-noise ratio (CNR) was performed. RESULTS: DANTE-CAIPI-SPACE+DL showed significantly reduced arterial (1 [1?1.75] versus 3 [3?4], P < .001) and venous flow artifacts (1 [1?2] versus 3 [3?4], P < .001) compared with SPACE. There was no significant difference between DANTE-CAIPI-SPACE+DL and SPACE in terms of image quality, SNR, artifact ratings, and lesion assessment. For SNR ratings, DANTE-CAIPI-SPACE+DL was significantly better compared with DANTE-CAIPI-SPACE (2 [1?2], versus 3 [2?3], P < .001). No statistically significant differences were found between DANTE-CAIPI-SPACE and DANTE-CAIPI-SPACE+DL for image quality, artifact, arterial blood and venous blood flow artifacts, and lesion assessment. Quantitative vessel wall SNR and CNR median values were significantly higher for DANTE-CAIPI-SPACE+DL (SNR: 9.71, CNR: 4.24) compared with DANTE-CAIPI-SPACE (SNR: 5.50, CNR: 2.64) (P < .001 for each), but there was no significant difference between SPACE (SNR: 10.82, CNR: 5.21) and DANTE-CAIPI-SPACE+DL. CONCLUSIONS: DL denoised postcontrast T1-weighted DANTE-CAIPI-SPACE accelerated and blood-suppressed IVW showed improved flow suppression with a shorter scan time and equivalent qualitative and quantitative SNR measures relative to conventional postcontrast IVW. It also improved SNR metrics relative to postcontrast DANTE-CAIPI-SPACE IVW. Implementing DL denoised DANTE-CAIPI-SPACE IVW has the potential to shorten protocol time while maintaining or improving the image quality of IVW.

Within-family genome-wide association studies (GWAS) can separate direct genetic effects from non-direct genetic biases introduced by analyses based on unrelated individuals, yet evidence regarding metabolic phenotypes remains sparse. Here, we aim to uncover non-direct genetic effects for metabolic traits and the role of diet in the non-direct genetic mechanism. We conducted family-based GWAS studies on six metabolic traits using data from full siblings (N = 777) and parent–offspring trios (N = 386). We calculated and compared within-family and population-based polygenic score (PGS) associations to identify non-direct genetic effects. Additionally, we assessed the parental indirect genetic effects of diet on offspring's metabolic traits. Within-sibship GWAS analyses were also conducted to evaluate the impact of non-direct genetic effects at the individual variant level. On average, the magnitudes of within-family PGS associations for metabolic traits showed a 35.2 β : 0.44, 95

Introduction: Sleep irregularity is increasingly recognized as a modifiable factor for cardiovascular health. This study aims to investigate relationships between short- and long-term sleep irregularity with blood pressure (BP) dynamics among older adults. Methods: We used data from a prospective cohort involving community-dwelling older adults based on a mobile health (mHealth) app from 2018 to 2022. Short-term exposure was defined as sleep irregularity for one week. Cumulative sleep irregularity, calculated as the area under the curve over 12 months, was regarded as long-term exposure. Outcomes included short-term deviations in BP, longitudinal changes in BP, and cumulative BP over one year. Linear mixed models and generalized additive mixed models were conducted to investigate the associations between sleep irregularity and BP. Results: A total of 1611 participants with a median age of 73.0 years were included. Short-term and long-term cumulative sleep irregularities were associated with increased SBP, DBP, and global BP Z-score. For instance, each SD increment in cumulative sleep onset timing SD was associated with a 0.42 mmHg increase in SBP (95 % CI, 0.25 to 0.60), a 0.31 mmHg increase in DBP (95 % CI, 0.17 to 0.45), respectively. Subgroup analyses indicated stronger associations among males and those with normotension. Strong linear dose-response relationships were further observed between cumulative sleep irregularity and cumulative BP. Conclusions: Sleep irregularity, in both short-term and long-term exposure, is a risk factor for poor blood pressure control among older adults, highlighting the importance of implementing interventions promoting healthy sleep habits to mitigate cardiovascular risks.

Magnetic resonance imaging (MRI) is a non-invasive, radiation-free imaging modality widely used in clinical diagnosis. While 3D MRI offers higher spatial resolution for improved delineation of small lesions compared to 2D MRI, its acquisition is often time-consuming. To address this limitation, we propose a novel Multi-Contrast Volumetric Super-Resolution (MCVSR) method that synthesizes high-resolution (HR) 3D MRI images from a low-resolution (LR) 2D MRI scan and an auxiliary HR 3D MRI acquired with a different contrast as guidance. Our approach introduces two key innovations: a Discrete Wavelet Transform (DWT) module and a multi-scale Simple Attention Module (MS-SimAM). The DWT module decomposes the image features into frequency sub-bands, enabling the model to capture both global structures and fine details such as edges and textures. In addition, MS-SimAM enhances feature selection across varying receptive fields, facilitating the restoration of high-frequency details. Extensive experiments demonstrate that our method consistently outperforms existing single-/multi-contrast slice-/volume-based super-resolution methods. These results highlight the significant advantages of leveraging multi-contrast information to enhance the quality of clinical 2D MRI scans, offering a promising solution for accelerating 3D MRI acquisition without compromising diagnostic accuracy.

Background/Objectives: Genes and environments were transmitted across generations. Parents’ genetics influence the environments of their offspring; these two modes of inheritance can produce a genetic nurture effect, also known as indirect genetic effects. Such indirect effects may partly account for estimated genetic variance in T2D. However, the well-established specific genetic risk factors about genetic nurture effect for T2D are not fully understood. This study aimed to investigate the genetic nurture effect on type 2 diabetes and reveal the potential underlying mechanism using publicly available data. Methods: Whole-genome genotyping data of 881 offspring and/or their parents were collected. We assessed SNP-level, gene-based, and pathway-based associations for different types of genetic effects. Results: Rs3805116 (β: 0.54, p = 4.39 × 10−8) was significant for paternal genetic nurture effects. MRPS33 (p = 1.58 × 10−6), PIH1D2 (p = 6.76 × 10−7), and SD1HD (p = 2.67 × 10−6) revealed significantly positive paternal genetic nurture effects. Five ontologies were identified as enrichment in both direct and indirect genetic effects, including flavonoid metabolic process and antigen processing and presentation via the MHC class Ib pathway. Two pathways were only enriched in paternal genetic nurture effects, including the transforming growth factor beta pathway. Tissue enrichment of type 2 diabetes-associated genes on different genetic effect types was performed using publicly available gene expression data from the Human Protein Atlas database. We observed significant gene enrichment in paternal genetic nurture effects in the gallbladder, smooth muscle, and adrenal gland tissues. Conclusions: MRPS33, PIH1D2, and SD1HD are associated with increased T2D risk through the environment influenced by paternal genotype, suggesting a novel perspective on paternal contributions to the T2D predisposition.

Coronary artery disease (CAD) is a common comorbidity of type 2 diabetes mellitus (T2DM). However, the pathophysiology connecting these two phenotypes remains to be further understood. Combined analysis in multi-ethnic populations can help contribute to deepening our understanding of biological mechanisms caused by shared genetic loci. We applied genetic correlation analysis and then performed conditional and joint association analyses in Chinese, Japanese, and European populations to identify the genetic variants jointly associated with CAD and T2DM. Next, the associations between genes and the two traits were also explored. Finally, fine-mapping and functional enrichment analysis were employed to identify the potential causal variants and pathways. Genetic correlation results indicated significant genetic overlap between CAD and T2DM in the three populations. Over 10,000 shared signals were identified, and 587 were shared by East Asian and European populations. Fifty-six novel shared genes were found to have significant effects on both CAD and T2DM. Most loci were fine-mapped to plausible causal variant sets. Several similarities and differences of the involved genes in GO terms and KEGG pathways were revealed across East Asian and European populations. These findings highlight the importance of immunoregulation, neuroregulation, heart development, and the regulation of glucose metabolism in shared etiological mechanisms between CAD and T2DM.

OBJECTIVE:To explore the robust relationship between insomnia and type 2 diabetes mellitus by two-sample Mendelian randomization analysis to overcome confounding factors and reverse causality in observational studies.METHODS:We identified strong, independent single nucleotide polymorphisms (SNPs) of insomnia from the most up to date genome wide association studies (GWAS) within European ancestors and applied them as instrumental variable to GWAS of type 2 diabetes mellitus. After excluding SNPs that were significantly associated with smoking, physical activity, alcohol consumption, educational attainment, obesity, or type 2 diabetes mellitus, we assessed the impact of insomnia on type 2 diabetes mellitus using inverse variance weighting (IVW) method. Weighted median and MR-Egger regression analysis were also conducted to test the robustness of the association. We calculated the F statistic of the selected SNPs to test the applicability of instrumental variable and F statistic over than ten indicated that there was little possibility of bias of weak instrumental variables. We further examined the existence of pleiotropy by testing whether the intercept term in MR-Egger regression was significantly different from zero. In addition, the leave-one-out method was used for sensitivity analysis to verify the stability and reliability of the results.RESULTS:We selected 248 SNPs independently associated with insomnia at the genome-wide level (P<5×10-8) as a preliminary candidate set of instrumental variables. After clumping based on the reference panel from 1000 Genome Project and removing the potential pleiotropic SNPs, a total of 167 SNPs associated with insomnia were included as final instrumental variables. The F statistic of this study was 39. 74, which was in line with the relevance assumption of Mendelian randomization. IVW method showed insomnia was associated with higher risk of type 2 diabetes mellitus that po-pulation with insomnia were 1. 14 times more likely to develop type 2 diabetes mellitus than those without insomnia (95% CI: 1.09-1.21, P<0.001). The weighted median estimator (WME) method and MR-Egger regression showed similar causal effect of insomnia on type 2 diabetes mellitus. And MR-Egger regression also showed that the effect was less likely to be triggered by pleiotropy. Sensitivity analyses produced directionally similar estimates.CONCLUSION:Insomnia is a risk factor of type 2 diabetes mellitus, which has positively effects on type 2 diabetes mellitus. Our study provides further rationale for indivi-duals at risk for diabetes to keep healthy lifestyle.

BackgroundThe paucity of evidence on longitudinal and consecutive recordings of physical activity (PA) and blood pressure (BP) under real-life conditions and their relationships is a vital research gap that needs to be addressed. ObjectiveThis study aims to (1) investigate the short-term relationship between device-measured step volume and BP; (2) explore the joint effects of step volume and variability on BP; and (3) examine whether the association patterns between PA and BP varied across sex, hypertension status, and chronic condition status. MethodsThis study used PA data of a prospective cohort of 3070 community-dwelling older adults derived from a mobile health app. Daily step counts, as a proxy of step volume, were derived from wearable devices between 2018 and 2022 and categorized into tertiles (low, medium, and high). Step variability was assessed using the SD of daily step counts. Consecutive daily step count recordings within 0 to 6 days preceding each BP measurement were analyzed. Generalized estimation equation models were used to estimate the individual and joint associations of daily step volume and variability with BP. Stratified analyses by sex, the presence of hypertension, and the number of morbidities were further conducted. ResultsA total of 3070 participants, with a median age of 72 (IQR 67-77) years and 71.37% (2191/3070) women, were included. Participants walked a median of 7580 (IQR 4972-10,653) steps and 5523 (IQR 3590-7820) meters per day for a total of 592,597 person-days of PA monitoring. Our results showed that higher levels of daily step volume were associated with lower BP (systolic BP, diastolic BP, mean arterial pressure, and pulse pressure). Compared with participants with low step volume (daily step counts <6000/d) and irregular steps, participants with high step volume (≥9500/d) and regular steps showed the strongest decrease in systolic BP (–1.69 mm Hg, 95% CI –2.2 to –1.18), while participants with medium step volume (6000/d to <9500/d) and regular steps were associated with the lowest diastolic BP (–1.067 mm Hg, 95% CI –1.379 to –0.755). Subgroup analyses indicated generally greater effects on women, individuals with normal BP, and those with only 1 chronic disease, but the effect pattern was varied and heterogeneous between participants with different characteristics. ConclusionsIncreased step volume demonstrated a substantial protective effect on BP among older adults with chronic conditions. Furthermore, the beneficial association between step volume and BP was enhanced by regular steps, suggesting potential synergistic protective effects of both increased step volume and step regularity. Targeting both step volume and variability through PA interventions may yield greater benefits in BP control, particularly among participants with hypertension and a higher chronic disease burden.

Abstract Background and objective Excessive daytime sleepiness (EDS) has been far back reported as the most disabling symptom in the pediatric narcoleptic patients. However, there is a lack of studies to examine the circadian rhythms of EDS in pediatric narcoleptic population. Therefore, we aim to investigate the circadian rhythm of EDS in pediatric narcolepsy patients. Methods We identified 50 pediatric narcoleptic patients (36 males and 14 females, mean age 13.68 ± 2.75 years). Data were collected through interviews and the relevant questionnaires (children depression inventory [CDI] and the pediatric quality of life inventory [PedsQL]). Result The frequencies of sleep attacks during different intervals of the day differed significantly, with higher frequency in the morning (p < .001). The times of sleep attacks in the morning and in the afternoon were significantly associated with the degree of impairment on class and the severity of worry about sleepiness, with spearman correlation coefficient ranging from .289 to .496 (p < .05). The total scores of PedsQL and CDI differed significantly among morning sleepiness dominant, afternoon sleepiness dominant, and evening sleepiness dominant groups (p = .042, p = .040). The severity scores of the narcoleptic patients’ sleepiness had two peaks, one of which occurred at 16:00, and the other peaks occurred at about 11:00. Conclusion These results suggest that changes based on the circadian rhythm of sleepiness of the pediatric narcoleptic patients should be made in the treatment strategy. In addition, regulating the secretion of melatonin could serve as a promising treatment to relieve sleepiness in the future.

Background: Atrial fibrillation (AF) is the most common cardiac arrhythmia, with uncovered genetic etiology and pathogenesis. We aimed to screen out AF susceptibility genes with potential pathogenesis significance in the Chinese population. Methods: Differentially expressed genes (DEGs) were screened by the Limma package in three GEO data sets of atrial tissue. AF-related genes were identified by combination of DEGs and public GWAS susceptibility genes. Potential drug target genes were selected using the DrugBank, STITCH and TCMSP databases. Pathway enrichment analyses of AF-related genes were performed using the databases GO and KEGG databases. The pathway gene network was visualized by Cytoscape software to identify gene–gene interactions and hub genes. GWAS analysis of 110 cases of AF and 1201 controls was carried out through a genome-wide efficient mixed model in the Fangshan population to verify the results of bioinformatic analysis. Results: A total of 3173 DEGs were identified, 57 of which were found to be significantly associated with of AF in public GWAS results. A total of 75 AF-related genes were found to be potential therapeutic targets. Pathway enrichment analysis selected 79 significant pathways and classified them into 7 major pathway networks. A total of 35 hub genes were selected from the pathway networks. GWAS analysis identified 126 AF-associated loci. PDE3A and GSK3B were found to be overlapping genes between bioinformatic analysis and GWAS analysis. Conclusions: We screened out several pivotal genes and pathways involved in AF pathogenesis. Among them, PDE3A and GSK3B were significantly associated with the risk of AF in the Chinese population. Our study provided new insights into the mechanisms of action of AF.

The aggregation and interaction of metabolic risk factors leads to highly heterogeneous pathogeneses, manifestations, and outcomes, hindering risk stratification and targeted management. To deconstruct the heterogeneity, we used baseline data from phase II of the Fangshan Family-Based Ischemic Stroke Study (FISSIC), and a total of 4632 participants were included. A total of 732 individuals who did not have any component of metabolic syndrome (MetS) were set as a reference group, while 3900 individuals with metabolic abnormalities were clustered into subtypes using multi-trait limited mixed regression (MFMR). Four metabolic subtypes were identified with the dominant characteristics of abdominal obesity, hypertension, hyperglycemia, and dyslipidemia. Multivariate logistic regression showed that the hyperglycemia-dominant subtype had the highest coronary heart disease (CHD) risk (OR: 6.440, 95% CI: 3.177–13.977) and that the dyslipidemia-dominant subtype had the highest stroke risk (OR: 2.450, 95% CI: 1.250–5.265). Exome-wide association studies (EWASs) identified eight SNPs related to the dyslipidemia-dominant subtype with genome-wide significance, which were located in the genes APOA5, BUD13, ZNF259, and WNT4. Functional analysis revealed an enrichment of top genes in metabolism-related biological pathways and expression in the heart, brain, arteries, and kidneys. Our findings provide directions for future attempts at risk stratification and evidence-based management in populations with metabolic abnormalities from a systematic perspective.

Objective Hyperlipidemia is traditionally considered a risk factor for diabetes. The effect of low-density lipoprotein cholesterol (LDL-C) is counterintuitive to diabetes. We sought to investigate the relationship between LDL-C and diabetes for better lipid management. Methods We tested the shape of association between LDL-C and diabetes and created polygenic risk scores of LDL-C and generated linear Mendelian randomization (MR) estimates for the effect of LDL-C and diabetes. We evaluated for nonlinearity in the observational and genetic relationship between LDL-C and diabetes. Results Traditional observational analysis suggested a complex non-linear association between LDL-C and diabetes while nonlinear MR analyses found no evidence for a non-linear association. Under the assumption of linear association, we found a consistently protective effect of LDL-C against diabetes among the females without lipid-lowering drugs use. The ORs were 0.84 (95% CI, 0.72–0.97, P=0.0168 ) in an observational analysis which was more prominent in MR analysis and suggested increasing the overall distribution of LDL-C in females led to an overall decrease in the risk of diabetes ( P=0.0258 ). Conclusions We verified the liner protective effect of LDL-C against diabetes among the females without lipid-lowering drug use. Non-linear associations between LDL-C against diabetes in observational analysis are not causal.

BACKGROUND:Post-vaccination safety is a major public health concern. The genetic predisposition on immune response has not been clearly identified. Clarifying whether individual genetic predisposition plays a role on adverse events (AEs) is critical for the prevention of AEs. METHODS:From July 2019 to June 2020, we performed a case-control study among children aged 3-24 months in seven Chinese provinces. Each child received a combination vaccination against diphtheria, tetanus, acellular pertussis, and Haemophilus influenzae type b (DTaP-Hib). Through daily telephone follow-up, we collected AEs within seven days. Oral swab samples were collected to investigate the effects of single nucleotide polymorphisms (SNPs) on the risk of AEs. RESULTS:304 participants were included in the study. In univariate analysis, we discovered three protective SNPs (rs452204, OR = 0.67, P = 0.0352; rs9282763 and rs839, OR = 0.64, P = 0.0256) and one risk SNP (rs9610, OR = 2.20, P = 0.0397). In multivariate analysis, the effects of rs452204 and rs839 were found to be stable. The interaction between rs452204 and rs9610 was observed (OR = 7.25, 95% CI: 1.44-36.58, P = 0.0165). CONCLUSION:Genetic predisposition was associated with the risk of AEs after DTaP-Hib vaccination, emphasizing the potential application in the prevention of AEs.

Genome-wide association studies (GWAS) have identified several common variants associated with polycystic ovary syndrome (PCOS). However, the etiology behind PCOS remains incomplete. Available evidence suggests a potential genetic correlation between PCOS and type 2 diabetes (T2D). The publicly available data may provide an opportunity to enhance the understanding of the PCOS etiology. Here, we quantified the polygenic overlap between PCOS and T2D using summary statistics of PCOS and T2D and then identified the novel genetic variants associated with PCOS behind this phenotypic association. A bivariate causal mixture model (MiXeR model) found a moderate genetic overlap between PCOS and T2D (Dice coefficient = 44.1% and after adjusting for body mass index, 32.1%). The conditional/conjunctional false discovery rate method identified 11 potential risk variants of PCOS conditional on associations with T2D, 9 of which were novel and 6 of which were jointly associated with two phenotypes. The functional annotation of these genetic variants supports a significant role for genes involved in lipid metabolism, immune response, and the insulin signaling pathway. An expression quantitative trait locus functionality analysis successfully repeated that 5 loci were significantly associated with the expression of candidate genes in many tissues, including the whole blood, subcutaneous adipose, adrenal gland, and cerebellum. We found that SCN2A gene is co-localized with PCOS in subcutaneous adipose using GWAS-eQTL co-localization analyses. A total of 11 candidate genes were differentially expressed in multiple tissues of the PCOS samples. These findings provide a new understanding of the shared genetic architecture between PCOS and T2D and the underlying molecular genetic mechanism of PCOS.

Abstract Background Coronary heart disease (CHD) and type 2 diabetes (T2D) are two complex diseases with complex interrelationships. However, the genetic architecture of the two diseases is often studied independently by the individual single-nucleotide polymorphism (SNP) approach. Here, we presented a genotypic-phenotypic framework for deciphering the genetic architecture underlying the disease patterns of CHD and T2D. Method A data-driven SNP-set approach was performed in a genome-wide association study consisting of subpopulations with different disease patterns of CHD and T2D (comorbidity, CHD without T2D, T2D without CHD and all none). We applied nonsmooth nonnegative matrix factorization (nsNMF) clustering to generate SNP sets interacting the information of SNP and subject. Relationships between SNP sets and phenotype sets harboring different disease patterns were then assessed, and we further co-clustered the SNP sets into a genetic network to topologically elucidate the genetic architecture composed of SNP sets. Results We identified 23 non-identical SNP sets with significant association with CHD or T2D (SNP-set based association test, P < 3.70 × $${10}^{-4}$$ 10 - 4 ). Among them, disease patterns involving CHD and T2D were related to distinct SNP sets (Hypergeometric test, P < 2.17 × $${10}^{-3}$$ 10 - 3 ). Accordingly, numerous genes (e.g., KLKs, GRM8, SHANK2) and pathways (e.g., fatty acid metabolism) were diversely implicated in different subtypes and related pathophysiological processes. Finally, we showed that the genetic architecture for disease patterns of CHD and T2D was composed of disjoint genetic networks (heterogeneity), with common genes contributing to it (pleiotropy). Conclusion The SNP-set approach deciphered the complexity of both genotype and phenotype as well as their complex relationships. Different disease patterns of CHD and T2D share distinct genetic architectures, for which lipid metabolism related to fibrosis may be an atherogenic pathway that is specifically activated by diabetes. Our findings provide new insights for exploring new biological pathways.