Genetic architecture of 11 organ traits derived from abdominal MRI using deep learning

Polygenic risk scores for eGFR are associated with age at kidney failure

BACKGROUND

The genetic architecture of chronic kidney disease (CKD) is complex, including monogenic and polygenic contributions. CKD progression to kidney failure is influenced by factors including male sex, baseline estimated glomerular filtration rate (eGFR), hypertension, diabetes, proteinuria, and the underlying kidney disease. These traits all have strong genetic components, which can be partially quantified using polygenic risk scores. This paper examines the association between polygenic risk scores for CKD-related traits and age at kidney failure development.

METHODS

Genome-wide genotype data from 10,586 patients with kidney failure were compiled from 12 cohorts. Polygenic risk scores for hypertension, albuminuria, rapid decline in eGFR, decreased total kidney volume, and decreased eGFR were calculated using weights from published independent population-scale genome-wide association studies. The association between each polygenic risk score and age at kidney failure was investigated using logistic regression models. The association between polygenic risk score and age at kidney failure was also investigated separately for each primary kidney disease.

RESULTS

Individuals in the highest 10% of polygenic risk score for decreased eGFR developed kidney failure 2 years earlier than those in the bottom 90% (49.9 years and 47.9 years, P = 5e-5). A standard deviation increase in decreased eGFR polygenic risk score was associated with increased odds of developing kidney failure before the age of 60 years (Odds ratio (OR) = 1.05; 95% CI 1.01-1.10; P = 0.01), as was high decreased eGFR polygenic risk score (OR = 1.26; 95% CI 1.08-1.46; P = 0.003).

CONCLUSIONS

We conclude that decreased eGFR polygenic risk score explains a portion of the variation in age at development of kidney failure.

Spleen volume in relation to ulcerative colitis and Crohn's disease: a Mendelian randomization study

Prior research has established the significance of spleen volume (SV) in the pathogenesis and advancement of ulcerative colitis (UC) and Crohn's disease (CD). Nevertheless, these investigations are predominantly observational, thereby leaving their causal associations ambiguous. Moreover, the breadth of existing research is constrained by various uncontrollable variables in clinical settings. This study aims to deduce the causal link between SV and the susceptibility to UC and CD through a genetic perspective. The objective of this study was to investigate the genetic association between SV and inflammatory bowel disease (IBD) risk using Mendelian randomization (MR) analysis. Single nucleotide polymorphisms (SNPs) associated with SV were used as instrumental variables. Genetic associations for UC and CD were extracted from the International Inflammatory Bowel Disease Genetics Consortium (IIBDGC), the FinnGen study, and other publicly available genome-wide association studies (GWAS). Methods such as inverse variance weighted, Bayesian weighted Mendelian randomization (BWMR), contamination mixture (ConMix), along with sensitivity analyses and the Steiger test were used in the study. A meta-analysis was conducted to synthesize the results. The study found that genetically predicted SV was associated with an increased risk of UC in the IIBDGC dataset (OR = 1. 223, 95% CI: 1. 055-1. 417, P = 0. 008), FinnGen (OR = 1. 169, 95% CI: 1. 003-1. 363, P = 0. 045), the GWAS study by Sakaue S (OR = 1. 188, 95% CI: 1. 008-1. 399, P = 0. 040), and in the meta-analysis (OR = 1. 115, 95% CI: 1. 014-1. 227, P = 0. 025). Similarly, genetically predicted SV was associated with an increased risk of CD in the IIBDGC dataset (OR = 1. 235, 95% CI: 1. 026-1. 488, P = 0. 026), FinnGen (OR = 1. 308, 95% CI: 1. 026-1. 667, P = 0. 030), the GWAS study by Zorina-Lichtenwalter K (OR = 1. 316, 95% CI: 1. 037-1. 670, P = 0. 024), and in the meta-analysis (OR = 1. 272, 95% CI: 1. 133-1. 428, P < 0. 001). According to the meta-analysis results, for each standard unit increase in SV, the risk of developing UC increases by 11. 5%, and the risk of developing CD increases by 27. 2%. This study presents findings that suggest a positive causal association between SV and the onset of IBD.

Deep Learning Derived Adipocyte Size Reveals Adipocyte Hypertrophy is under Genetic Control

Fat distribution and macro structure of white adipose tissue are important factors in predicting obesity-associated diseases, but cellular microstructure of white adipose tissue has been less explored. To investigate the relationship between adipocyte size and obesity-related traits, and their underlying disease-driving genetic associations, we performed the largest study of automatic adipocyte phenotyping linking histological measurements and genetics to date. We introduce deep learning based methods for scalable and accurate semantic segmentation of subcutaneous and visceral adipose tissue histology samples (N=2,667) across 5 independent cohorts, including data from 9,000 whole slide images, with over 27 million adipocytes. Estimates of mean size of adipocytes were validated against Glastonbury et al. 2020. We show that adipocyte hypertrophy correlates with an adverse metabolic profile with increased levels of leptin, fasting plasma glucose, glycated hemoglobin and triglycerides, and decreased levels of adiponectin and HDL cholesterol. We performed the largest GWAS (N Subcutaneous = 2066, N Visceral = 1878) and subsequent meta-analysis of mean adipocyte area, and find two genome-wide significant loci (rs73184721, rs200047724) associated with increased 95%-quantile adipocyte size in respectively visceral and subcutaneous adipose tissue. Stratifying by sex, in females we find two genome-wide significant loci, with one variant (rs140503338) associated with increased mean adipocyte size in subcutaneous adipose tissue, and the other (rs11656704) is associated with decreased 95%-quantile adipocyte size in visceral adipose tissue.

PennPRS: a centralized cloud computing platform for efficient polygenic risk score training in precision medicine

Polygenic risk scores (PRS) are becoming increasingly vital for risk prediction and stratification in precision medicine. However, PRS model training presents significant challenges for broader adoption of PRS, including limited access to computational resources, difficulties in implementing advanced PRS methods, and availability and privacy concerns over individual-level genetic data. Cloud computing provides a promising solution with centralized computing and data resources. Here we introduce PennPRS (https://pennprs.org), a scalable cloud computing platform for online PRS model training in precision medicine. We developed novel pseudo-training algorithms for multiple PRS methods and ensemble approaches, enabling model training without requiring individual-level data. These methods were rigorously validated through extensive simulations and large-scale real data analyses involving over 6,000 phenotypes across various data sources. PennPRS supports online single- and multi-ancestry PRS training with seven methods, allowing users to upload their own data or query from more than 27,000 datasets in the GWAS Catalog, submit jobs, and download trained PRS models. Additionally, we applied our pseudo-training pipeline to train PRS models for over 8,000 phenotypes and made their PRS weights publicly accessible. In summary, PennPRS provides a novel cloud computing solution to improve the accessibility of PRS applications and reduce disparities in computational resources for the global PRS research community.

Genetic Markers of Postmortem Brain Iron

Brain iron (Fe) dyshomeostasis is implicated in neurodegenerative diseases. Genome-wide association studies (GWAS) have identified plausible loci correlated with peripheral levels of Fe. Systemic organs and the brain share several Fe regulatory proteins but there likely exist different homeostatic pathways. We performed the first GWAS of inductively coupled plasma mass spectrometry measures of postmortem brain Fe from 635 Rush Memory and Aging Project (MAP) participants. Sixteen single nucleotide polymorphisms (SNPs) associated with Fe in at least one of four brain regions were measured (p < 5 × 10-8). Promising SNPs (p < 5 × 10-6) were followed up for replication in published GWAS of blood, spleen, and brain imaging Fe traits and mapped to candidate genes for targeted cortical transcriptomic and epigenetic analysis of postmortem Fe in MAP. Results for SNPs previously associated with other Fe traits were also examined. Ninety-eight SNPs associated with postmortem brain Fe were at least nominally (p < 0.05) associated with one or more related Fe traits. Most novel loci identified had no direct links to Fe regulatory pathways but rather endoplasmic reticulum-Golgi trafficking (SORL1, SORCS2, MARCH1, CLTC), heparan sulfate (HS3ST4, HS3ST1), and coenzyme A (SLC5A6, PANK3); supported by nearest gene function and omic analyses. We replicated (p < 0.05) several previously published Fe loci mapping to candidate genes in cellular and systemic Fe regulation. Finally, novel loci (BMAL, COQ5, SLC25A11) and replication of prior loci (PINK1, PPIF, LONP1) lend support to the role of circadian rhythms and mitochondria function in Fe regulation more generally. In summary, we provide support for novel loci linked to pathways that may have greater relevance to brain Fe accumulation; some of which are implicated in neurodegeneration. However, replication of a subset of prior loci for blood Fe suggests that genetic determinants or biological pathways underlying Fe accumulation in the brain are not completely distinct from those of Fe circulating in the periphery.

Effect of smoking behaviour and related blood DNA methylation on visceral adipose tissues

BACKGROUND

Recent studies have found that tobacco smoking is associated with fat distribution, yet limited research has focused on its relationship with visceral adipose tissues (VATs). Furthermore, the cellular and molecular mechanisms underlying the interactions among smoking, epigenetic modifications, and VATs remain unknown.

METHOD

We performed univariable Mendelian randomization (MR) analysis to elucidate the causal relationship between smoking behaviours and VATs, including epicardial and pericardial adipose tissue (EPAT), liver fat (LF), and pancreas fat (PF). This approach could minimize the impact of confounders and reverse causality through utilizing genetic variants to proxy the smoking behaviours. Mediation MR analysis were conducted to detect potential mediators. Additionally, summary-data-based MR (SMR) and colocalization analysis were performed to explore the association between smoking-related DNA methylation and VATs.

RESULTS

We identified a convincing association between smoking initiation and increased EPAT (beta: 0.15, 95% CI: 0.06, 0.23, p = 7.01 × 10-4) and LF area (beta: 0.15, 95% CI = 0.05, 0.24, p = 2.85 × 10-3), respectively. Further mediation analysis suggested type 2 diabetes mellitus (T2DM) as a potential mediator within these co-relationships. When further exploring the associations between the smoking related DNA methylation and VATs, we identified that WT1 methylation at cg05222924 was significantly linked to a lower EPAT area (beta: -0.12, 95% CI: -0.16, -0.06, PFDR = 2.24 × 10-3), while GPX1 methylation at cg18642234 facilitated the deposition of EPAT (beta: 0.15, 95% CI: 0.10, 0.20, PFDR = 1.66 × 10-4).

CONCLUSION

Our study uncovered a significant causal effect between smoking and VATs, with T2DM identified as a potential mediator. Further investigation into DNA methylation yielded novel insights into the pathogenic role of smoking on EPAT.

Association of organ iron levels with type 2 diabetes mellitus and glycemic traits: A bidirectional two-sample Mendelian randomization study

INTRODUCTION

Observational studies have found that higher iron levels are associated with an increased risk of diabetes mellitus. Given the limitations of causal inferences from observational studies and the expensive and time-consuming nature of randomized controlled trials, Mendelian randomization analysis presents a reasonable alternative to study causal relationships. Previous MR analyses studying iron levels and diabetes have used indirect markers of iron levels, such as serum ferritin, and found conflicting results. In this study, we performed bidirectional Mendelian Randomization analyses using organ iron (liver, spleen, and pancreas) levels, which are more direct markers of iron status, to study the causal association of iron levels with type 2 diabetes mellitus and glycaemic traits.

METHODS

Two sample MR analyses were employed bi-directionally to study the causal effect of liver, spleen, and pancreas iron levels on type 2 diabetes and glycaemic traits and the causal effect of type 2 diabetes on organ iron levels, using summary data from genome-wide association studies (UK-Biobank, DIAGRAM, and MAGIC consortia). SNPs associated with organ iron levels with a cut-off of P < 5 × 10-7 were used as instrumental variables for the MR analyses of the effect of organ iron levels on type 2 diabetes/glycaemic traits, and SNPs associated with diabetes mellitus with a cut-off of P < 5 × 10-8 were used as instrumental variables for the MR analyses of the causal effect of type 2 diabetes on organ iron levels. Serum ferritin (GWAS meta-analysis of deCODE, UK INTERVAL, and Denmark studies) and haemoglobin (Blood Cell consortium) were used as positive controls for the MR analysis with liver iron as the exposure. Primary analyses used the inverse variance weighted means of Wald's ratio. Sensitivity analyses included inverse variance weighted median, weighted mode, and MR-Egger methods.

RESULTS

Our findings reveal no causal association between liver and pancreas iron levels with type 2 diabetes (Liver iron: OR = 1.02, P = 0.1, Pancreas iron: OR = 1.11, P = 0.5). This also holds for glycaemic traits, except for the negative causal effect of liver iron levels on HbA1c (OR = 0.93, P = 0.001). Spleen iron levels had a negative causal effect on type 2 diabetes (OR = 0.94, P = 0.049). However, these exceptions are likely due to possible pleiotropy, as these associations can be explained by the effect of the genetic variants on factors that falsely decrease HbA1c levels. No causal association was found for the effect of type 2 diabetes on organ iron levels.

CONCLUSION

Organ iron levels, which are relatively more direct indicators of iron status, showed no causal association with type 2 diabetes in the European population.

Association between different obesity metrics and risk of inguinal hernia

PURPOSE

Obesity is closely associated with a lower risk of inguinal hernia, but the association between different obesity metrics and the risk of inguinal hernia is still unclear.

METHODS

In our study, we categorized obesity measurement indicators into three groups based on the difficulty of measurement: (1) indicators easily available, such as body mass index (BMI), waist circumference (WC), hip circumference (HC), and waist-to-hip ratio (WHR); (2) indicators accessible with moderate difficulty, such as body fat percentage and body fat mass; (3) indicators difficultly accessible, such as the volume of visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT). Mendelian randomization (MR) analysis was used to investigate the causal relationship between various adiposity measures and the risk of inguinal hernia in both European ancestry and East Asians.

RESULTS

We identified a total of 17,096 patients with inguinal hernia in the FinnGen cohort and 1664 cases in the Japan Biobank cohort. For European ancestry, MR analysis reported a significant causal association between one standard deviation increase of BMI, WC, HC, body fat percentage, and body fat mass and the lower risk of inguinal hernia, rather than WHR, VAT, and SAT. After the adjustment of BMI, increased WC is still causally associated with a lower risk of inguinal hernia (OR: 0.52; 95% CI: 0.33-0.80; P < 0.01). Among East Asians, only body fat mass is causally associated with a reduced risk of inguinal hernia, rather than BMI, WC, and HC.

CONCLUSION

Obesity is causally associated with a relatively lower risk of inguinal hernia. The association between different obesity measures and the risk of inguinal hernia has ethnic specificity. These findings help us deepen our understanding of the intrinsic causal relationship between fat distribution and the risk of inguinal hernias at the genetic level.

Genome-wide analysis in northern Chinese twins identifies twelve new susceptibility loci for pulmonary function

BACKGROUND

Previous genome-wide association studies (GWAS) have established association between genetic variants and pulmonary function across various ethnics, whereas such associations are scarcely reported in Chinese adults. Therefore, we conducted an GWAS to explore relationships between genetic variants and pulmonary function among middle-aged Chinese dizygotic twins and further validated the top variants using data from the UK Biobank (UKB).

METHODS

In the discovery phase, 139 dizygotic twin pairs were drawn from the Qingdao Twin Registry. Pulmonary function was assessed using three parameters: forced expiratory volume the first second (FEV1), forced vital capacity (FVC), and FEV1/FVC ratio. GWAS was performed using GEMMA, Gene-based analysis was conducted by VEGAS2. And pathway enrichment analysis was performed using PASCAL. In the validation phase, Single-nucleotide polymorphisms (SNPs) with suggestive significance were examined through linear regression analysis of the additive effect model among 1573 Chinese ethnic participants from UKB.

RESULTS

The median age of twin pairs in the study was 49 years. 3 SNPs (rs80345886, rs117883876, and 75139439) related to FEV1 achieved genome-wide significance. Moreover, 222, 150, and 73 SNPs surpassed suggestive evidence level (p < 1 × 10- 5) for FEV1, FVC, and FEV1/FVC, respectively. Among them, 16 SNPs located in TBC1D16 for FEV1, 25 SNPs located in GPR126 for FVC, and 2 SNPs located in CCDC110 for FEV1/FVC, the three genes were also revealed by gene-based analysis. Moreover, 12 novel SNPs related to pulmonary function were validated to reach the nominal significance level (p < 0.05) in the UKB, with some located in the TBC1D16, TAFA5, and MTHFD1L genes.

CONCLUSION

Our GWAS results on Chinese dizygotic twins provide new references for the genetic regulation on pulmonary function. Twelve novel susceptibility loci are considered as possible crucial to pulmonary function.

Metabolic dysfunction-associated steatotic liver disease and gastroesophageal reflux disease: a mendelian randomization study in European and East Asian populations

Background

Numerous observational studies have shown a potential association between metabolic dysfunction-associated steatotic liver disease (MASLD) and gastroesophageal reflux disease (GERD). However, causality is unclear. This study utilized genome-wide association study (GWAS) genetic data to explore the causal relationship between MASLD and GERD in European and East Asian populations.

Methods

This study utilized a bidirectional, two-sample Mendelian randomization (MR) approach. All disease data were obtained from the GWAS database, and single nucleotide polymorphisms strongly associated with exposure were selected as instrumental variables. The inverse variance weighted (IVW) method is primarily utilized to evaluate the causal relationship between exposure and outcome. Finally, sensitivity analyses were performed to ensure the robustness of the results.

Results

The IVW estimates indicated that non-alcoholic fatty liver disease (NAFLD) (odds ratio (OR) = 1.054, 95% confidence interval (CI), 0.966-1.150, p = 0.236) and percent liver fat (OR = 0.977, 95% CI, 0.937-1.018, p = 0.258) in European population were not linked to a higher risk of GERD. However, GERD in European population was associated with an increased risk of NAFLD (OR = 1.485, 95% CI, 1.274-1.729, p < 0.001) and percent liver fat (OR = 1.244, 95% CI, 1.171-1.321, p < 0.001). In addition, the IVW analysis in East Asian population showed that alanine aminotransferase (ALT) was associated with an increased risk of GERD (OR = 2.305, 95% CI, 1.241-4.281, p = 0.008), whereas aspartate aminotransferase (AST) had no causal effects on GERD risk (OR = 0.973, 95% CI, 0.541-1.749, p = 0.926). Furthermore, the associations between GERD and ALT (OR = 1.007, 95% CI, 0.998-1.015, p = 0.123) or AST (OR = 1.004, 95% CI, 0.997-1.012, p = 0.246) were not significant. After removing outliers, a significant correlation between GERD and ALT was observed (OR = 1.009, 95% CI, 1.001-1.016, p = 0.020).

Conclusion

There was reverse causality between MASLD and GERD in European population, while there was bidirectional causality between a proxie for MASLD (ALT) and GERD in East Asian population. This study can provide novel insights into cross-ethnic genetic research on MASLD and GERD.

Syndrome-informed phenotyping identifies a polygenic background for achondroplasia-like facial variation in the general population

Human craniofacial shape is highly variable yet highly heritable with numerous genetic variants interacting through multiple layers of development. Here, we hypothesize that Mendelian phenotypes represent the extremes of a phenotypic spectrum and, using achondroplasia as an example, we introduce a syndrome-informed phenotyping approach to identify genomic loci associated with achondroplasia-like facial variation in the general population. We compare three-dimensional facial scans from 43 individuals with achondroplasia and 8246 controls to calculate achondroplasia-like facial scores. Multivariate GWAS of the control scores reveals a polygenic basis for facial variation along an achondroplasia-specific shape axis, identifying genes primarily involved in skeletal development. Jointly modeling these genes in two independent control samples, both human and mouse, shows craniofacial effects approximating the characteristic achondroplasia phenotype. These findings suggest that both complex and Mendelian genetic variation act on the same developmentally determined axes of facial variation, providing insights into the genetic intersection of complex traits and Mendelian disorders.

A novel deep learning method for large-scale analysis of bone marrow adiposity using UK Biobank Dixon MRI data

Background

Bone marrow adipose tissue (BMAT) represents > 10% fat mass in healthy humans and can be measured by magnetic resonance imaging (MRI) as the bone marrow fat fraction (BMFF). Human MRI studies have identified several diseases associated with BMFF but have been relatively small scale. Population-scale studies therefore have huge potential to reveal BMAT's true clinical relevance. The UK Biobank (UKBB) is undertaking MRI of 100,000 participants, providing the ideal opportunity for such advances.

Objective

To establish deep learning for high-throughput multi-site BMFF analysis from UKBB MRI data.

Materials and methods

We studied males and females aged 60-69. Bone marrow (BM) segmentation was automated using a new lightweight attention-based 3D U-Net convolutional neural network that improved segmentation of small structures from large volumetric data. Using manual segmentations from 61-64 subjects, the models were trained to segment four BM regions of interest: the spine (thoracic and lumbar vertebrae), femoral head, total hip and femoral diaphysis. Models were tested using a further 10-12 datasets per region and validated using datasets from 729 UKBB participants. BMFF was then quantified and pathophysiological characteristics assessed, including site- and sex-dependent differences and the relationships with age, BMI, bone mineral density, peripheral adiposity, and osteoporosis.

Results

Model accuracy matched or exceeded that for conventional U-Nets, yielding Dice scores of 91.2% (spine), 94.5% (femoral head), 91.2% (total hip) and 86.6% (femoral diaphysis). One case of severe scoliosis prevented segmentation of the spine, while one case of Non-Hodgkin Lymphoma prevented segmentation of the spine, femoral head and total hip because of T2 signal depletion; however, successful segmentation was not disrupted by any other pathophysiological variables. The resulting BMFF measurements confirmed expected relationships between BMFF and age, sex and bone density, and identified new site- and sex-specific characteristics.

Conclusions

We have established a new deep learning method for accurate segmentation of small structures from large volumetric data, allowing high-throughput multi-site BMFF measurement in the UKBB. Our findings reveal new pathophysiological insights, highlighting the potential of BMFF as a novel clinical biomarker. Applying our method across the full UKBB cohort will help to reveal the impact of BMAT on human health and disease.

The Causal Effect of Intrapancreatic Fat Deposition on Acute and Chronic Pancreatitis: A Mendelian Randomization Study

INTRODUCTION

Recent associative studies have linked intrapancreatic fat deposition (IPFD) with risk of pancreatitis, but the causal relationship remains unclear.

METHODS

Using Mendelian randomization, we evaluated the causal association between genetically predicted IPFD and pancreatitis. This approach used genetic variants from genomewide association studies of IPFD (n = 25,617), acute pancreatitis (n = 6,787 cases/361,641 controls), and chronic pancreatitis (n = 3,875 cases/361,641 controls).

RESULTS

Genetically predicted IPFD was significantly associated with acute pancreatitis (odds ratio per 1-SD increase: 1.40 [95% CI: 1.12-1.76], P = 0.0032) and chronic pancreatitis (odds ratio: 1.64 [95% CI: 1.13-2.39], P = 0.0097).

DISCUSSION

Our findings support a causal role of IPFD in pancreatitis, suggesting that reducing IPFD could lower the risk of pancreatitis.

The development of an attention mechanism enhanced deep learning model and its application for body composition assessment with L3 CT images

Body composition assessment is very useful for evaluating a patient's status in the clinic, but recognizing, labeling, and calculating the body compositions would be burdensome. This study aims to develop a web-based service that could automate calculating the areas of skeleton muscle (SM), visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT) according to L3 computed tomography (CT) images. 1500 L3 CT images were gathered from Xuzhou Central Hospital. Of these, 70% were used as the training dataset, while the remaining 30% were used as the validating dataset. The UNet framework was combined with attention gate (AG), Squeeze and Excitation block (SEblock), and Atrous Spatial Pyramid Pooling (ASSP) modules to construct the segmentation deep learning model. The model's efficacy was externally validated using two other test datasets with multiple metrics, the consistency test and manual result checking. A graphic user interface was also created and deployed using the Streamlit Python package. The custom deep learning model named L3 Body Composition Segmentation Model (L3BCSM) was constructed. The model's Median Dice is 0.954(0.930, 0.963)(SATA), 0.849(0.774,0.901)(VATA), and 0.920(0.901, 0.936)(SMA), which is equal to or better than classic models, including UNETR and AHNet. L3BCSM also achieved satisfactory metrics in two external test datasets, consistent with the qualified label. An internet-based application was developed using L3BCSM, which has four functional modules: population analysis, time series analysis, consistency analysis, and manual result checking. The body composition assessment application was well developed, which would benefit the clinical practice and related research.

Mapping rare protein-coding variants on multi-organ imaging traits

Human organ structure and function are important endophenotypes for clinical outcomes. Genome-wide association studies (GWAS) have identified numerous common variants associated with phenotypes derived from magnetic resonance imaging (MRI) of the brain and body. However, the role of rare protein-coding variations affecting organ size and function is largely unknown. Here we present an exome-wide association study that evaluates 596 multi-organ MRI traits across over 50,000 individuals from the UK Biobank. We identified 107 variant-level associations and 224 gene-based burden associations (67 unique gene-trait pairs) across all MRI modalities, including PTEN with total brain volume, TTN with regional peak circumferential strain in the heart left ventricle, and TNFRSF13B with spleen volume. The singleton burden model and AlphaMissense annotations contributed 8 unique gene-trait pairs including the association between an approved drug target gene of KCNA5 and brain functional activity. The identified rare coding signals elucidate some shared genetic regulation across organs, prioritize previously identified GWAS loci, and are enriched for drug targets. Overall, we demonstrate how rare variants enhance our understanding of genetic effects on human organ morphology and function and their connections to complex diseases.

A bidirectional Mendelian randomization study of spleen volume and Crohn disease

In observational studies, there has been an association found between spleen volume and Crohn disease. We conducted a two-way, two-sample Mendelian randomization analysis to determine whether these associations have a causal relationship. Single nucleotide polymorphisms (P < 5 × 10-8) were used as instrumental variables for spleen volume and Crohn disease. Estimates of the genetic associations between spleen volume and Crohn disease were obtained from the Integrative Epidemiology Unit, European Bioinformatics Institute, UK Biobank, and FinnGen databases. Analysis was performed using MR-Egger regression, weighted median estimator, inverse variance weighted, simple model, and weighted model. Genetically predicted spleen volume was found to be associated with Crohn disease. In the IEU database, the odds ratios (ORs) for Crohn disease caused by spleen volume were 1.237 (95% CI, 1.056-1.417, P = .021), and the ORs for spleen volume caused by Crohn disease were 1.015 (95% CI, 0.985-1.044; P = .049). In the EBI database, the ORs for Crohn disease caused by spleen volume were 1.292 (95% CI, 1.120-1.463, P = .003), and the ORs for spleen volume caused by Crohn disease were 1.026 (95% CI, 1.005-1.046; P = .013). Results from the UKB and FinnGen databases showed no causal relationship between the two. The summary results showed that Crohn disease caused an increase in spleen volume, with ORs of 1.009 (95% CI, 1.000-1.018; P = .047). This study provides evidence for a mutual causal relationship between spleen volume and an increased risk of Crohn disease.

Association Between Low Sex Hormone-Binding Globulin and Increased Risk of Type 2 Diabetes Is Mediated by Increased Visceral and Liver Fat: Results From Observational and Mendelian Randomization Analyses

The aim of this study was to investigate the associations among sex hormone-binding globulin (SHBG), visceral adipose tissue (VAT), liver fat content, and risk of type 2 diabetes (T2D). In the Netherlands Epidemiology of Obesity study, 5,690 women (53%) and men (47%) without preexisting diabetes were included and followed for incident T2D. SHBG concentrations were measured in all participants, VAT was measured using MRI, and liver fat content was measured using proton magnetic resonance spectroscopy in a random subset of 1,822 participants. We examined associations between SHBG and liver fat using linear regression and bidirectional Mendelian randomization analyses and between SHBG and T2D using Cox regression adjusted for confounding and additionally for VAT and liver fat to examine mediation. Mean age was 56 (SD 6) years, mean BMI was 30 (SD 4) kg/m2, median SHBG was 47 (interquartile range [IQR] 34-65) nmol/L in women and 34 (26-43) nmol/L in men, and median liver fat was 3.4% (IQR 1.6-8.2%) in women and 6.0% (2.9-13.5%) in men. Compared with the highest SHBG quartile, liver fat was 2.9-fold (95% CI 2.4, 3.4) increased in women and 1.6-fold (95% CI 1.3, 1.8) increased in men, and the hazard ratio of T2D was 4.9 (95% CI 2.4, 9.9) in women and 1.8 (1.1, 2.9) in men. Genetically predicted SHBG was associated with liver fat content (women: SD -0.45 [95% CI -0.55, -0.35]; men: natural logarithm, -0.25 [95% CI -0.34, -0.16]). VAT and liver fat together mediated 43% (women) and 60% (men) of the SHBG-T2D association. To conclude, in a middle-aged population with overweight, the association between low SHBG and increased risk of T2D was, for a large part, mediated by increased VAT and liver fat.

ARTICLE HIGHLIGHTS

The genetically predicted causal associations between circulating 3-hydroxybutyrate levels and malignant neoplasms: A pan-cancer Mendelian randomization study

OBJECTIVE

The ketogenic diet or exogenous supplementation with 3-hydroxybutyrate (3HB) is progressively gaining recognition as a valuable therapeutic or health intervention strategy. However, the effects of 3HB on cancers have been inconsistent in previous studies. This study aimed to comprehensively investigate the causal effects of circulating 3HB levels on 120 cancer phenotypes, and explore the 3HB mediation effect between liver fat accumulation and cancers.

METHODS

Univariate Mendelian randomization (UVMR) was used in this study to investigate the causal impact of circulating 3HB levels on cancers. We conducted meta-analyses for 3HB-cancer associations sourced from different exposure data. In multivariate MR(MVMR), the body mass index, alcohol frequency and diabetes were included as covariates to investigate the independent effect of 3HB on cancer risk. Additionally, utilizing mediation MR analysis, we checked the potential mediating role of 3HB in the association between liver fat and cancer.

RESULTS

Integrating findings from UVMR and MVMR, we observed that elevated circulating 3HB levels were associated with reduced risk of developing diffuse large B-cell lymphoma(DLBCL) (OR[95%CI] = 0.28[0.14-0.57] p = 3.92e-04), biliary malignancies (OR[95%CI] = 0.30[0.15-0.60], p = 7.67e-04), hepatocellular carcinoma(HCC) (OR[95%CI] = 0.25[0.09-0.71], p = 9.33e-03), primary lymphoid and hematopoietic malignancies (OR[95%CI] = 0.76[0.58-0.99], p = 0.045). Further UVMR analysis revealed that an increase in the percent liver fat was associated with reduced 3HB levels (Beta[95%CI] = -0.073[-0.122∼-0.024], p = 0.0034) and enhanced susceptibility to HCC (OR[95%CI] = 13.9[9.76-19.79], p = 3.14e-48), biliary malignancies (OR[95%CI] = 4.04[3.22-5.07], p = 1.64e-33), nasopharyngeal cancer (OR[95%CI] = 3.26[1.10-9.67], p = 0.03), and primary lymphoid and hematopoietic malignancies (OR[95%CI] = 1.27[1.13-1.44], p = 1.04e-4). Furthermore, 3HB fully mediated the effect of liver fat on susceptibility to DLBCL (OR[95%CI] = 1.076[1.01-1.15], p = 0.034).

CONCLUSIONS

Circulating 3HB is associated with a reduced susceptibility to developing DLBCL, HCC, biliary malignancies, and primary lymphoid and hematopoietic malignancies. The impaired ketogenesis induced by metabolic-dysfunction associated fatty liver disease (MAFLD) contributes to risk of DLBCL.

Obstructive Sleep Apnea Syndrome Exacerbates NASH Progression via Selective Autophagy-Mediated Eepd1 Degradation

Obstructive sleep apnea syndrome (OSAS), characterized by chronic intermittent hypoxia (CIH), is an independent risk factor for aggravating non-alcoholic steatohepatitis (NASH). The prevailing mouse model employed in CIH research is inadequate for the comprehensive exploration of the impact of CIH on NASH development due to reduced food intake observed in CIH-exposed mice, which deviates from human responses. To address this issue, a pair-feeding investigation with CIH-exposed and normoxia-exposed mice is conducted. It is revealed that CIH exposure aggravates DNA damage, leading to hepatic fibrosis and inflammation. The analysis of genome-wide association study (GWAS) data also discloses the association between Eepd1, a DNA repair enzyme, and OSAS. Furthermore, it is revealed that CIH triggered selective autophagy, leading to the autophagic degradation of Eepd1, thereby exacerbating DNA damage in hepatocytes. Notably, Eepd1 liver-specific knockout mice exhibit aggravated hepatic DNA damage and further progression of NASH. To identify a therapeutic approach for CIH-induced NASH, a drug screening is conducted and it is found that Retigabine dihydrochloride suppresses CIH-mediated Eepd1 degradation, leading to alleviated DNA damage in hepatocytes. These findings imply that targeting CIH-mediated Eepd1 degradation can be an adjunctive approach in the treatment of NASH exacerbated by OSAS.

Causal effects of female reproductive features on nonalcoholic fatty liver disease: A mendelian randomization study

BACKGROUND AND AIMS

Epidemiological evidence on the associations between female reproductive features and nonalcoholic fatty liver disease (NAFLD) is conflicting. To explore their causalities, we conducted a Mendelian randomization (MR) study.

METHODS

Summary-level data were obtained, and univariable MR was performed to explore the causalities between female reproductive features and NAFLD. And we performed multivariable MR and MR mediation analysis to explore the mediation effects of educational attainment (EA) and body mass index (BMI) for these associations. Sensitivity analyses were performed to evaluate pleiotropy and heterogeneity.

RESULTS

There were causal effects of age at menarche (AAMA) (odds ratio [OR]: 0.817, 95% confidence interval [CI]: 0.736-0.907, per year-increase), age at first birth (AFB) (OR: 0.851, 95%CI: 0.791-0.926, per year-increase) and age at first sexual intercourse (AFS) (OR: 0.676, 95%CI: 0.511-0.896, per standard deviation-increase) on NAFLD risk. Besides, the causal effects were also observed on NAFLD phenotypes including liver fat content (LFC) and alanine aminotransferase (ALT). Further mediation analysis showed that BMI mediated partial proportion of effects of AAMA and AFS on NAFLD/ALT, AFB on NAFLD/LFC/ALT, while EA mediated partial proportion of effects of AFB on NAFLD/LFC/ALT, and AFS on NAFLD/ALT.

CONCLUSIONS

This study provided convincing evidence that early AAMA, AFB, and AFS were risk factors for NAFLD. Reproductive health education, obesity management, and education spread might be the beneficial strategies for NAFLD prevention.

The genetic architecture of biological age in nine human organ systems

Investigating the genetic underpinnings of human aging is essential for unraveling the etiology of and developing actionable therapies for chronic diseases. Here, we characterize the genetic architecture of the biological age gap (BAG; the difference between machine learning-predicted age and chronological age) across nine human organ systems in 377,028 participants of European ancestry from the UK Biobank. The BAGs were computed using cross-validated support vector machines, incorporating imaging, physical traits and physiological measures. We identify 393 genomic loci-BAG pairs (P < 5 × 10-8) linked to the brain, eye, cardiovascular, hepatic, immune, metabolic, musculoskeletal, pulmonary and renal systems. Genetic variants associated with the nine BAGs are predominantly specific to the respective organ system (organ specificity) while exerting pleiotropic links with other organ systems (interorgan cross-talk). We find that genetic correlation between the nine BAGs mirrors their phenotypic correlation. Further, a multiorgan causal network established from two-sample Mendelian randomization and latent causal variance models revealed potential causality between chronic diseases (for example, Alzheimer's disease and diabetes), modifiable lifestyle factors (for example, sleep duration and body weight) and multiple BAGs. Our results illustrate the potential for improving human organ health via a multiorgan network, including lifestyle interventions and drug repurposing strategies.

Integrating Multi-Organ Imaging-Derived Phenotypes and Genomic Information for Predicting the Occurrence of Common Diseases

As medical imaging technologies advance, these tools are playing a more and more important role in assisting clinical disease diagnosis. The fusion of biomedical imaging and multi-modal information is profound, as it significantly enhances diagnostic precision and comprehensiveness. Integrating multi-organ imaging with genomic information can significantly enhance the accuracy of disease prediction because many diseases involve both environmental and genetic determinants. In the present study, we focused on the fusion of imaging-derived phenotypes (IDPs) and polygenic risk score (PRS) of diseases from different organs including the brain, heart, lung, liver, spleen, pancreas, and kidney for the prediction of the occurrence of nine common diseases, namely atrial fibrillation, heart failure (HF), hypertension, myocardial infarction, asthma, type 2 diabetes, chronic kidney disease, coronary artery disease (CAD), and chronic obstructive pulmonary disease, in the UK Biobank (UKBB) dataset. For each disease, three prediction models were developed utilizing imaging features, genomic data, and a fusion of both, respectively, and their performances were compared. The results indicated that for seven diseases, the model integrating both imaging and genomic data achieved superior predictive performance compared to models that used only imaging features or only genomic data. For instance, the Area Under Curve (AUC) of HF risk prediction was increased from 0.68 ± 0.15 to 0.79 ± 0.12, and the AUC of CAD diagnosis was increased from 0.76 ± 0.05 to 0.81 ± 0.06.

Causal Relationship Between Micronutrient and Sleep Disorder: A Mendelian Randomization Study

Background

Sleep played an important part in human health, and COVID-19 led to a continuous deterioration of sleep. However, the causal relationship between micronutrient and sleep disorder was not yet fully understood.

Methods

In this research, the genetic causal relationship between micronutrient and sleep disorder was analyzed utilizing a two-sample Mendelian randomization (MR). Single nucleotide polymorphisms (SNPs) were used as instrumental variables. The analyses were conducted using the MR-Egger, inverse variance weighted, weighted mode, weighted median, simple mode, Cochran's Q test and leave-one-out.

Results

Our results suggested that 8 genetically predicted micronutrients participated in sleep disorders, including liver iron (L-iron) and iron in sleeping too much, spleen iron (S-iron) in sleeplessness/insomnia, trouble falling or staying asleep, sleep duration (undersleepers) and nonorganic sleeping disorders, iron metabolism disorder (IMD) and vitamin B12 deficiency anaemia (VB12DA) in narcolepsy, urine sodium (uNa) in narcolepsy, sleep apnea syndrome and sleep disorder, vitamin D (VD) in sleep duration (oversleepers), 25-Hydroxyvitamin D (25(OH)D) in trouble falling or staying asleep.

Conclusion

Our study used Mendelian randomization methods at the SNP level to explore the potential causal relationship among L-iron, iron, S-iron, IMD, uNa, 25(OH)D, VD, VB12DA with certain sleep disorder subtypes. Our results uncovered a micronutrient-based strategy for alleviating sleep disorder symptoms.

Deep learning-derived splenic radiomics, genomics, and coronary artery disease

Background

Despite advances in managing traditional risk factors, coronary artery disease (CAD) remains the leading cause of mortality. Circulating hematopoietic cells influence risk for CAD, but the role of a key regulating organ, spleen, is unknown. The understudied spleen is a 3-dimensional structure of the hematopoietic system optimally suited for unbiased radiologic investigations toward novel mechanistic insights.

Methods

Deep learning-based image segmentation and radiomics techniques were utilized to extract splenic radiomic features from abdominal MRIs of 42,059 UK Biobank participants. Regression analysis was used to identify splenic radiomics features associated with CAD. Genome-wide association analyses were applied to identify loci associated with these radiomics features. Overlap between loci associated with CAD and the splenic radiomics features was explored to understand the underlying genetic mechanisms of the role of the spleen in CAD.

Results

We extracted 107 splenic radiomics features from abdominal MRIs, and of these, 10 features were associated with CAD. Genome-wide association analysis of CAD-associated features identified 219 loci, including 35 previously reported CAD loci, 7 of which were not associated with conventional CAD risk factors. Notably, variants at 9p21 were associated with splenic features such as run length non-uniformity.

Conclusions

Our study, combining deep learning with genomics, presents a new framework to uncover the splenic axis of CAD. Notably, our study provides evidence for the underlying genetic connection between the spleen as a candidate causal tissue-type and CAD with insight into the mechanisms of 9p21, whose mechanism is still elusive despite its initial discovery in 2007. More broadly, our study provides a unique application of deep learning radiomics to non-invasively find associations between imaging, genetics, and clinical outcomes.

Circulating pancreatic enzyme levels are a causal biomarker of type 1 diabetes

Novel biomarkers of type 1 diabetes (T1D) are needed for earlier detection of disease and identifying therapeutic targets. We identified biomarkers of T1D by combining plasma cis and trans protein QTLs (pQTLs) for 2,922 proteins in the UK Biobank with a T1D genome-wide association study (GWAS) in 157k samples. T1D risk variants at over 20% of known loci colocalized with cis or trans pQTLs, and distinct sets of T1D loci colocalized with immune, pancreatic secretion, or gut-related proteins. We identified 23 proteins with evidence for a causal role in using pQTLs as genetic instruments in Mendelian Randomization which included multiple sensitivity analyses. Proteins increasing T1D risk were involved in immune processes (e.g. HLA-DRA) and, more surprisingly, T1D protective proteins were enriched in pancreatic secretions (e.g. CPA1), cholesterol metabolism (e.g. APOA1), and gut homeostasis. Genetic variants associated with plasma levels of T1D-protective pancreatic enzymes such as CPA1 were enriched in cis-regulatory elements in pancreatic exocrine and gut enteroendocrine cells, and the protective effects of CPA1 and other enzymes on T1D were consistent when using instruments specific to acinar cells. Finally, pancreatic enzymes had decreased acinar expression in T1D, including CPA1 which was altered prior to onset. Together, these results reveal causal biomarkers and highlight processes in the exocrine pancreas, immune system, and gut that modulate T1D risk.

Associations of genetically predicted iron status with 24 gastrointestinal diseases and gut microbiota: a Mendelian randomization study

Background

Iron status has been implicated in gastrointestinal diseases and gut microbiota, however, confounding factors may influence these associations.

Objective

We performed Mendelian randomization (MR) to investigate the associations of iron status, including blood iron content, visceral iron content, and iron deficiency anemia with the incidence of 24 gastrointestinal diseases and alterations in gut microbiota.

Methods

Independent genetic instruments linked with iron status were selected using a genome-wide threshold of p = 5 × 10-6 from corresponding genome-wide association studies. Genetic associations related to gastrointestinal diseases and gut microbiota were derived from the UK Biobank, the FinnGen study, and other consortia.

Results

Genetically predicted higher levels of iron and ferritin were associated with a higher risk of liver cancer. Higher levels of transferrin saturation were linked to a decreased risk of celiac disease, but a higher risk of non-alcoholic fatty liver disease (NAFLD) and liver cancer. Higher spleen iron content was linked to a lower risk of pancreatic cancer. Additionally, higher levels of liver iron content were linked to a higher risk of NAFLD and liver cancer. However, certain associations lost their statistical significance upon accounting for the genetically predicted usage of cigarettes and alcohol. Then, higher levels of iron and ferritin were associated with 11 gut microbiota abundance, respectively. In a secondary analysis, higher iron levels were associated with lower diverticular disease risk and higher ferritin levels with increased liver cancer risk. Higher levels of transferrin saturation were proven to increase the risk of NAFLD, alcoholic liver disease, and liver cancer, but decrease the risk of esophageal cancer. MR analysis showed no mediating relationship among iron status, gut microbiota, and gastrointestinal diseases.

Conclusion

This study provides evidence suggesting potential causal associations of iron status with gastrointestinal diseases and gut microbiota, especially liver disease.

The interaction of genetics and physical activity in the pathogenesis of metabolic dysfunction associated liver disease

Genetic variants associated with increased liver fat and volume have been reported, but whether physical activity (PA) can attenuate the impact of genetic susceptibility to these traits is poorly understood. We aimed to investigate whether higher PA modify genetic impact on liver-related traits in the UK Biobank cohort. PA was self-reported, while magnetic resonance images were used to estimate liver fat (n = 27,243) and liver volume (n = 24,752). Metabolic dysfunction-associated liver disease (MASLD) and chronic liver disease (CLD) were diagnosed using ICD-9 and ICD-10 codes. Ten liver fat and eleven liver volume-associated genetic variants were selected and unweighted genetic-risk scores for liver fat (GRSLF) and liver volume (GRSLV) were computed. Linear regression analyses were performed to explore interactions between GRSLF/ GRSLV and PA in relation to liver-related traits. Association between GRSLF and liver fat was not different among lower (β = 0.063, 95% CI 0.041-0.084) versus higher PA individuals (β = 0.065, 95% CI 0.054-0.077, pinteraction = 0.62). The association between the GRSLV and liver volume was not different across different PA groups (pinteraction = 0.71). Similarly, PA did not modify the effect of GRSLF and GRSLV on MASLD or CLD. Our findings show that physical activity and genetic susceptibility to liver-related phenotypes seem to act independently, benefiting all individuals regardless of genetic risk.

Multi-omics characterization of type 2 diabetes associated genetic variation

Discerning the mechanisms driving type 2 diabetes (T2D) pathophysiology from genome-wide association studies (GWAS) remains a challenge. To this end, we integrated omics information from 16 multi-tissue and multi-ancestry expression, protein, and metabolite quantitative trait loci (QTL) studies and 46 multi-ancestry GWAS for T2D-related traits with the largest, most ancestrally diverse T2D GWAS to date. Of the 1,289 T2D GWAS index variants, 716 (56%) demonstrated strong evidence of colocalization with a molecular or T2D-related trait, implicating 657 cis-effector genes, 1,691 distal-effector genes, 731 metabolites, and 43 T2D-related traits. We identified 773 of these cis- and distal-effector genes using either expression QTL data from understudied ancestry groups or inclusion of T2D index variants enriched in underrepresented populations, emphasizing the value of increasing population diversity in functional mapping. Linking these variants, genes, metabolites, and traits into a network, we elucidated mechanisms through which T2D-associated variation may impact disease risk. Finally, we showed that drugs targeting effector proteins were enriched in those approved to treat T2D, highlighting the potential of these results to prioritize drug targets for T2D. These results represent a leap in the molecular characterization of T2D-associated genetic variation and will aid in translating genetic findings into novel therapeutic strategies.

Genome-wide Studies Reveal Genetic Risk Factors for Hepatic Fat Content

Genetic susceptibility to metabolic associated fatty liver disease (MAFLD) is complex and poorly characterized. Accurate characterization of the genetic background of hepatic fat content would provide insights into disease etiology and causality of risk factors. We performed genome-wide association study (GWAS) on two noninvasive definitions of hepatic fat content: magnetic resonance imaging proton density fat fraction (MRI-PDFF) in 16,050 participants and fatty liver index (FLI) in 388,701 participants from the United Kingdom (UK) Biobank (UKBB). Heritability, genetic overlap, and similarity between hepatic fat content phenotypes were analyzed, and replicated in 10,398 participants from the University Medical Center Groningen (UMCG) Genetics Lifelines Initiative (UGLI). Meta-analysis of GWASs of MRI-PDFF in UKBB revealed five statistically significant loci, including two novel genomic loci harboring CREB3L1 (rs72910057-T, P = 5.40E-09) and GCM1 (rs1491489378-T, P = 3.16E-09), respectively, as well as three previously reported loci: PNPLA3, TM6SF2, and APOE. GWAS of FLI in UKBB identified 196 genome-wide significant loci, of which 49 were replicated in UGLI, with top signals in ZPR1 (P = 3.35E-13) and FTO (P = 2.11E-09). Statistically significant genetic correlation (rg) between MRI-PDFF (UKBB) and FLI (UGLI) GWAS results was found (rg = 0.5276, P = 1.45E-03). Novel MRI-PDFF genetic signals (CREB3L1 and GCM1) were replicated in the FLI GWAS. We identified two novel genes for MRI-PDFF and 49 replicable loci for FLI. Despite a difference in hepatic fat content assessment between MRI-PDFF and FLI, a substantial similar genetic architecture was found. FLI is identified as an easy and reliable approach to study hepatic fat content at the population level.

Do iron homeostasis biomarkers mediate the associations of liability to type 2 diabetes and glycemic traits in liver steatosis and cirrhosis: a two-step Mendelian randomization study

BACKGROUND

Previous studies, including Mendelian randomization (MR), have demonstrated type 2 diabetes (T2D) and glycemic traits are associated with increased risk of metabolic dysfunction-associated steatotic liver disease (MASLD). However, few studies have explored the underlying pathway, such as the role of iron homeostasis.

METHODS

We used a two-step MR approach to investigate the associations of genetic liability to T2D, glycemic traits, iron biomarkers, and liver diseases. We analyzed summary statistics from various genome-wide association studies of T2D (n = 933,970), glycemic traits (n ≤ 209,605), iron biomarkers (n ≤ 246,139), MASLD (n ≤ 972,707), and related biomarkers (alanine aminotransferase (ALT) and proton density fat fraction (PDFF)). Our primary analysis was based on inverse-variance weighting, followed by several sensitivity analyses. We also conducted mediation analyses and explored the role of liver iron in post hoc analysis.

RESULTS

Genetic liability to T2D and elevated fasting insulin (FI) likely increased risk of liver steatosis (ORliability to T2D: 1.14 per doubling in the prevalence, 95% CI: 1.10, 1.19; ORFI: 3.31 per log pmol/l, 95% CI: 1.92, 5.72) and related biomarkers. Liability to T2D also likely increased the risk of developing liver cirrhosis. Genetically elevated ferritin, serum iron, and liver iron were associated with higher risk of liver steatosis (ORferritin: 1.25 per SD, 95% CI 1.07, 1.46; ORliver iron: 1.15 per SD, 95% CI: 1.05, 1.26) and liver cirrhosis (ORserum iron: 1.31, 95% CI: 1.06, 1.63; ORliver iron: 1.34, 95% CI: 1.07, 1.68). Ferritin partially mediated the association between FI and liver steatosis (proportion mediated: 7%, 95% CI: 2-12%).

CONCLUSIONS

Our study provides credible evidence on the causal role of T2D and elevated insulin in liver steatosis and cirrhosis risk and indicates ferritin may play a mediating role in this association.

A causal relationship between particulate matter 2.5 and obesity and its related indicators: a Mendelian randomization study of European ancestry

Background

In recent years, the prevalence of obesity has continued to increase as a global health concern. Numerous epidemiological studies have confirmed the long-term effects of exposure to ambient air pollutant particulate matter 2.5 (PM2.5) on obesity, but their relationship remains ambiguous.

Methods

Utilizing large-scale publicly available genome-wide association studies (GWAS), we conducted univariate and multivariate Mendelian randomization (MR) analyses to assess the causal effect of PM2.5 exposure on obesity and its related indicators. The primary outcome given for both univariate MR (UVMR) and multivariate MR (MVMR) is the estimation utilizing the inverse variance weighted (IVW) method. The weighted median, MR-Egger, and maximum likelihood techniques were employed for UVMR, while the MVMR-Lasso method was applied for MVMR in the supplementary analyses. In addition, we conducted a series of thorough sensitivity studies to determine the accuracy of our MR findings.

Results

The UVMR analysis demonstrated a significant association between PM2.5 exposure and an increased risk of obesity, as indicated by the IVW model (odds ratio [OR]: 6.427; 95% confidence interval [CI]: 1.881-21.968; P FDR = 0.005). Additionally, PM2.5 concentrations were positively associated with fat distribution metrics, including visceral adipose tissue (VAT) (OR: 1.861; 95% CI: 1.244-2.776; P FDR = 0.004), particularly pancreatic fat (OR: 3.499; 95% CI: 2.092-5.855; PFDR =1.28E-05), and abdominal subcutaneous adipose tissue (ASAT) volume (OR: 1.773; 95% CI: 1.106-2.841; P FDR = 0.019). Furthermore, PM2.5 exposure correlated positively with markers of glucose and lipid metabolism, specifically triglycerides (TG) (OR: 19.959; 95% CI: 1.269-3.022; P FDR = 0.004) and glycated hemoglobin (HbA1c) (OR: 2.462; 95% CI: 1.34-4.649; P FDR = 0.007). Finally, a significant negative association was observed between PM2.5 concentrations and levels of the novel obesity-related biomarker fibroblast growth factor 21 (FGF-21) (OR: 0.148; 95% CI: 0.025-0.89; P FDR = 0.037). After adjusting for confounding factors, including external smoke exposure, physical activity, educational attainment (EA), participation in sports clubs or gym leisure activities, and Townsend deprivation index at recruitment (TDI), the MVMR analysis revealed that PM2.5 levels maintained significant associations with pancreatic fat, HbA1c, and FGF-21.

Conclusion

Our MR study demonstrates conclusively that higher PM2.5 concentrations are associated with an increased risk of obesity-related indicators such as pancreatic fat content, HbA1c, and FGF-21. The potential mechanisms require additional investigation.

The Genetic Architecture of Biological Age in Nine Human Organ Systems

Understanding the genetic basis of biological aging in multi-organ systems is vital for elucidating age-related disease mechanisms and identifying therapeutic interventions. This study characterized the genetic architecture of the biological age gap (BAG) across nine human organ systems in 377,028 individuals of European ancestry from the UK Biobank. We discovered 393 genomic loci-BAG pairs (P-value<5×10-8) linked to the brain, eye, cardiovascular, hepatic, immune, metabolic, musculoskeletal, pulmonary, and renal systems. We observed BAG-organ specificity and inter-organ connections. Genetic variants associated with the nine BAGs are predominantly specific to the respective organ system while exerting pleiotropic effects on traits linked to multiple organ systems. A gene-drug-disease network confirmed the involvement of the metabolic BAG-associated genes in drugs targeting various metabolic disorders. Genetic correlation analyses supported Cheverud's Conjecture1 - the genetic correlation between BAGs mirrors their phenotypic correlation. A causal network revealed potential causal effects linking chronic diseases (e.g., Alzheimer's disease), body weight, and sleep duration to the BAG of multiple organ systems. Our findings shed light on promising therapeutic interventions to enhance human organ health within a complex multi-organ network, including lifestyle modifications and potential drug repositioning strategies for treating chronic diseases. All results are publicly available at https://labs-laboratory.com/medicine.

Kidney Volume and Risk of Incident Kidney Outcomes

BACKGROUND

Low total kidney volume (TKV) is a risk factor for chronic kidney disease (CKD). However, evaluations of nonlinear relationships, incident events, causal inference, and prognostic utility beyond traditional biomarkers are lacking.

METHODS

TKV, height-adjusted TKV, and body surface area-adjusted TKV (BSA-TKV) of 34,595 White British ancestry participants were derived from the UK Biobank. Association with incident CKD, acute kidney injury (AKI), and cardiovascular events were assessed with Cox proportional hazard models. Prognostic thresholds for CKD risk stratification were identified using a modified Mazumdar method with bootstrap resampling. Two-sample Mendelian randomization was performed to assess the bidirectional association of genetically predicted TKV with kidney and cardiovascular traits.

RESULTS

Adjusted for eGFR and albuminuria, a lower TKV of 10 mL was associated with a 6% higher risk of incident CKD (hazard ratio [HR] 1.06, 95% confidence interval [CI] 1.03 to 1.08, P = 5.8 x 10-6) in contrast to no association with incident AKI (HR 1.00, 95% CI 0.98 to 1.02, P = 0.66). Comparison of nested models demonstrated improved accuracy over the CKD Prognosis Consortium Incident CKD Risk Score with the addition of BSA-TKV or prognostic thresholds at 119 (10th percentile) and 145 mL/m2 (50th percentile). In Mendelian randomization, a lower genetically predicted TKV by 10 mL was associated with 10% higher CKD risk (odds ratio [OR] 1.10, 95% CI 1.06 to 1.14, P = 1.3 x 10-7). Reciprocally, an elevated risk of genetically predicted CKD by 2-fold was associated with a lower TKV by 7.88 mL (95% CI -9.81 to -5.95, P = 1.2 x 10-15). There were no significant observational or Mendelian randomization associations of TKV with cardiovascular complications.

CONCLUSIONS

Kidney volume was associated with incident CKD independent of traditional risk factors including baseline eGFR and albuminuria. Mendelian randomization demonstrated a bidirectional relationship between kidney volume and CKD.

Genetic Evidence for a Causal Link between Intra-Pancreatic Fat Deposition and Pancreatitis: a Mendelian Randomization Study

Introduction

Recent associative studies have linked intra-pancreatic fat deposition (IPFD) with risk of pancreatitis, but the causal relationship remains unclear.

Methods

Utilizing Mendelian randomization, we evaluated the causal association between genetically predicted IPFD and pancreatitis. This approach utilized genetic variants from genome-wide association studies of IPFD (n=25,617), acute pancreatitis (n=6,787 cases/361,641 controls), and chronic pancreatitis (n=3,875 cases/361,641 controls).

Results

Genetically predicted IPFD was significantly associated with acute pancreatitis (OR per 1-SD increase: 1.40[95%CI:1.12-1.76], p=0.0032) and chronic pancreatitis (OR:1.64[95%CI:1.13-2.39], p=0.0097).

Discussion

Our findings support a causal role of IPFD in pancreatitis, suggesting that reducing IPFD could lower the risk of pancreatitis.

Multi-organ imaging-derived polygenic indexes for brain and body health

The UK Biobank (UKB) imaging project is a crucial resource for biomedical research, but is limited to 100,000 participants due to cost and accessibility barriers. Here we used genetic data to predict heritable imaging-derived phenotypes (IDPs) for a larger cohort. We developed and evaluated 4,375 IDP genetic scores (IGS) derived from UKB brain and body images. When applied to UKB participants who were not imaged, IGS revealed links to numerous phenotypes and stratified participants at increased risk for both brain and somatic diseases. For example, IGS identified individuals at higher risk for Alzheimer's disease and multiple sclerosis, offering additional insights beyond traditional polygenic risk scores of these diseases. When applied to independent external cohorts, IGS also stratified those at high disease risk in the All of Us Research Program and the Alzheimer's Disease Neuroimaging Initiative study. Our results demonstrate that, while the UKB imaging cohort is largely healthy and may not be the most enriched for disease risk management, it holds immense potential for stratifying the risk of various brain and body diseases in broader external genetic cohorts.

Cross-sectional imaging of the pancreas in diabetes

Diabetes mellitus presents a global health challenge characterized by dysregulated glucose metabolism and insulin resistance. Pancreas dysfunction contributes to the development and progression of diabetes. Cross-sectional imaging modalities have provided new insight into the structural and functional alterations of the pancreas in individuals with diabetes. This review summarizes MRI and CT studies that characterize pancreas alterations in both type 1 and type 2 diabetes and discusses future applications of these techniques.

Noninvasive assessment of organ-specific and shared pathways in multi-organ fibrosis using T1 mapping

Fibrotic diseases affect multiple organs and are associated with morbidity and mortality. To examine organ-specific and shared biologic mechanisms that underlie fibrosis in different organs, we developed machine learning models to quantify T1 time, a marker of interstitial fibrosis, in the liver, pancreas, heart and kidney among 43,881 UK Biobank participants who underwent magnetic resonance imaging. In phenome-wide association analyses, we demonstrate the association of increased organ-specific T1 time, reflecting increased interstitial fibrosis, with prevalent diseases across multiple organ systems. In genome-wide association analyses, we identified 27, 18, 11 and 10 independent genetic loci associated with liver, pancreas, myocardial and renal cortex T1 time, respectively. There was a modest genetic correlation between the examined organs. Several loci overlapped across the examined organs implicating genes involved in a myriad of biologic pathways including metal ion transport (SLC39A8, HFE and TMPRSS6), glucose metabolism (PCK2), blood group antigens (ABO and FUT2), immune function (BANK1 and PPP3CA), inflammation (NFKB1) and mitosis (CENPE). Finally, we found that an increasing number of organs with T1 time falling in the top quintile was associated with increased mortality in the population. Individuals with a high burden of fibrosis in ≥3 organs had a 3-fold increase in mortality compared to those with a low burden of fibrosis across all examined organs in multivariable-adjusted analysis (hazard ratio = 3.31, 95% confidence interval 1.77-6.19; P = 1.78 × 10-4). By leveraging machine learning to quantify T1 time across multiple organs at scale, we uncovered new organ-specific and shared biologic pathways underlying fibrosis that may provide therapeutic targets.

Liver Iron Overload Drives COVID-19 Mortality: a Two-Sample Mendelian Randomization Study

Iron overload has been associated with an increased risk of COVID-19 severity and mortality in observational studies, but it remains unclear whether these associations represent causal effects. We performed a two-sample Mendelian randomization (MR) to determine associations between genetic liability to iron overload and the risk of COVID-19 severity and mortality. From genome-wide association studies of European ancestry, single-nucleotide polymorphisms associated with liver iron (n = 32,858) and ferritin (n = 23,986) were selected as exposure instruments, and summary statistics of the hospitalization (n = 16,551) and mortality (n = 15,815) of COVID-19 were utilized as the outcome. We used the inverse-variance weighted (IVW) method as the primary analysis to estimate causal effects, and other alternative approaches as well as comprehensive sensitivity analysis were conducted for estimating the robustness of identified associations. Genetically predicted high liver iron levels were associated with an increased risk of COVID-19 mortality based on the results of IVW analysis (OR = 1.38, 95% CI: 1.05-1.82, P = 0.02). Likewise, sensitivity analyses showed consistent and robust results in general (all P > 0.05). A higher risk of COVID-19 hospitalization trend was also observed in patients with high liver iron levels without statistical significance. This study suggests that COVID-19 mortality might be partially driven by the iron accumulation in the liver, supporting the classification of iron overload as one of the independent death risk factors. Therefore, avoiding iron overload and maintaining normal iron levels may be a powerful measure to reduce COVID-19 mortality.

Impact of loss-of-function in angiopoietin-like 4 on the human phenome

BACKGROUND

Carriers of the E40K loss-of-function variant in Angiopoietin-like 4 (ANGPTL4), have lower plasma triglyceride levels as well as lower rates of coronary artery disease (CAD) and type 2 diabetes (T2D). These genetic data suggest ANGPTL4 inhibition as a potential therapeutic target for cardiometabolic diseases. However, it is unknown whether the association between E40K and human diseases is due to linkage disequilibrium confounding. The broader impact of genetic ANGPTL4 inhibition is also unknown, raising uncertainties about the safety and validity of this target.

METHODS

To assess the impact of ANGPLT4 inhibition, we evaluated whether E40K and other loss-of-function variants in ANGPTL4 influenced a wide range of health markers and diseases using 29 publicly available genome-wide association meta-analyses of cardiometabolic traits and diseases, as well as 1589 diseases assessed in electronic health records within FinnGen (n = 309,154). To determine whether these relationships were likely causal, and not driven by other correlated variants, we used the Bayesian fine mapping algorithm CoPheScan.

RESULTS

The CoPheScan posterior probability of E40K being the causal variant for triglyceride levels was 99.99 %, validating the E40K to proxy lifelong lower activity of ANGPTL4. The E40K variant was associated with lower risk of CAD (odds ratio [OR] = 0.84, 95 % CI = 0.81 to 0.87, p=3.6e-21) and T2D (OR = 0.91, 95 % CI = 0.87 to 0.95, p=2.8e-05) in GWAS meta-analyses, with results replicated in FinnGen. These significant results were also replicated using other rare loss-of-function variants identified through whole exome sequencing in 488,278 participants of the UK Biobank. Using a Mendelian randomization study design, the E40K variant effect on cardiometabolic diseases was concordant with lipoprotein lipase enhancement (r = 0.82), but not hepatic lipase enhancement (r = -0.10), suggesting that ANGPTL4 effects on cardiometabolic diseases are potentially mainly mediated through lipoprotein lipase. After correction for multiple testing, the E40K variant did not significantly increase the risk of any of the 1589 diseases tested in FinnGen.

CONCLUSIONS

ANGPTL4 inhibition may represent a potentially safe and effective target for cardiometabolic diseases prevention or treatment.

Phenome-Wide Investigation of the Causal Associations Between Pre-Pregnancy Obesity Traits and Gestational Diabetes: A Two-Sample Mendelian Randomization Analyses

Pre-pregnancy obesity was associated with gestational diabetes in observational studies, but whether this relationship is causal remains to be determined. To evaluate whether pre-pregnancy obesity traits causally affect gestational diabetes risk, a two-sample Mendelian randomization (MR) analysis was performed utilizing summary-level statistics from published genome-wide association studies (GWAS). Obesity-related traits included body mass index (BMI), overweight, obesity, obesity class 1, obesity class 2, obesity class 3, childhood obesity, waist circumference (WC), hip circumference (HC), waist-to-hip ratio (WHR), percent liver fat, visceral adipose tissue volume, abdominal subcutaneous adipose tissue volume. Effect estimates were evaluated using the inverse-variance weighting method. Weighted median, MR-Egger, simple mode, and weighted mode were performed as sensitivity analyses. Genetically predicted pre-pregnancy BMI [odds ratio (OR) = 1.68; 95% confidence interval (CI): 1.45-1.95; P = 9.13 × 10-12], overweight (OR = 1.49; 95% CI: 1.21-1.85; P = 2.06 × 10-4), obesity (OR = 1.25; 95% CI: 1.18-1.33; P = 8.01 × 10-13), obesity class 1 (OR = 1.31; 95% CI: 1.17-1.46; P = 1.49 × 10-6), obesity class 2 (OR = 1.26; 95% CI: 1.16-1.37; P = 5.23 × 10-8), childhood obesity (OR = 1.33; 95% CI: 1.23-1.44; P = 4.06 × 10-12), and WHR (OR = 2.35; 95% CI: 1.44-3.83; P = 5.89 × 10-4) were associated with increased risk of gestational diabetes. No significant association was observed with obesity class 3, WC, HC, percent liver fat, visceral adipose tissue volume, or abdominal subcutaneous adipose tissue volume. Similar results were observed in sensitivity analyses. Therefore, genetically predicted pre-pregnancy obesity traits may increase the risk of gestational diabetes. Weight control before pregnancy may be beneficial to prevent gestational diabetes.

Causal relationship of obesity and adiposity distribution on risk of ventral hernia

PURPOSE

Ventral hernia (VH) is a common surgical disease. Previous studies suggested that obesity is an important risk factor for VH. However, the causal relationship between fat distribution and the risk of VH is still unclear. This study used Mendelian randomization (MR) to evaluate their causal relationship.

METHODS

We used the body mass index (BMI), body fat percentage, and body fat mass to represent general obesity and utilized the volume of abdominal subcutaneous adiposity tissue, visceral adiposity tissue, waist circumference, hip circumference, and waist-to-hip ratio to represent abdominal adiposity. The data were extracted from the large-scale genome-wide association study of European ancestry. We used two-sample MR to infer causality, using multivariate MR to correct the effects of confounding factors.

RESULTS

Increased BMI, body fat percentage, body fat mass, visceral adiposity tissue, waist circumference, and hip circumference rather than subcutaneous adiposity tissue or waist-to-hip ratio, were causally associated with a higher risk of VH. The results of multivariate MR suggested that body fat percentage was causally associated with a higher risk of VH after adjusting for body mass index, diabetes, and smoking.

CONCLUSION

General obesity, increased visceral adiposity tissue, waist circumference, and hip circumference rather than subcutaneous adiposity tissue or the waist-to-hip ratio were causally associated with a higher risk of VH. These findings provided a deeper understanding of the role that the distribution of adiposity plays in the mechanism of VH.

Spleen volume is independently associated with non-alcoholic fatty liver disease, liver volume and liver fibrosis

Non-alcoholic fatty liver disease (NAFLD) can lead to irreversible liver damage manifesting in systemic effects (e.g., elevated portal vein pressure and splenomegaly) with increased risk of deadly outcomes. However, the association of spleen volume with NAFLD and related type 2-diabetes (T2D) is not fully understood. The UK Biobank contains comprehensive health-data of 500,000 participants, including clinical data and MR images of >40,000 individuals. The present study estimated the spleen volume of 37,066 participants through automated deep learning-based image segmentation of neck-to-knee MR images. The aim was to investigate the associations of spleen volume with NAFLD, T2D and liver fibrosis, while adjusting for natural confounders. The recent redefinition and new designation of NAFLD to metabolic dysfunction-associated steatotic liver disease (MASLD), promoted by major organisations of studies on liver disease, was not employed as introduced after the conduct of this study. The results showed that spleen volume decreased with age, correlated positively with body size and was smaller in females compared to males. Larger spleens were observed in subjects with NAFLD and T2D compared to controls. Spleen volume was also positively and independently associated with liver fat fraction, liver volume and the fibrosis-4 score, with notable volumetric increases already at low liver fat fractions and volumes, but not independently associated with T2D. These results suggest a link between spleen volume and NAFLD already at an early stage of the disease, potentially due to initial rise in portal vein pressure.

Clinically relevant plasma proteome for adiposity depots: evidence from systematic mendelian randomization and colocalization analyses

BACKGROUND

The accumulation of visceral and ectopic fat comprise a major cause of cardiometabolic diseases. However, novel drug targets for reducing unnecessary visceral and ectopic fat are still limited. Our study aims to provide a comprehensive investigation of the causal effects of the plasma proteome on visceral and ectopic fat using Mendelian randomization (MR) approach.

METHODS

We performed two-sample MR analyses based on five large genome-wide association study (GWAS) summary statistics of 2656 plasma proteins, to screen for causal associations of these proteins with traits of visceral and ectopic fat in over 30,000 participants of European ancestry, as well as to assess mediation effects by risk factors of outcomes. The colocalization analysis was conducted to examine whether the identified proteins and outcomes shared casual variants.

RESULTS

Genetically predicted levels of 14 circulating proteins were associated with visceral and ectopic fat (P < 4.99 × 10- 5, at a Bonferroni-corrected threshold). Colocalization analysis prioritized ten protein targets that showed effect on outcomes, including FST, SIRT2, DNAJB9, IL6R, CTSA, RGMB, PNLIPRP1, FLT4, PPY and IL6ST. MR analyses revealed seven risk factors for visceral and ectopic fat (P < 0.0024). Furthermore, the associations of CTSA, DNAJB9 and IGFBP1 with primary outcomes were mediated by HDL-C and SHBG. Sensitivity analyses showed little evidence of pleiotropy.

CONCLUSIONS

Our study identified candidate proteins showing putative causal effects as potential therapeutic targets for visceral and ectopic fat accumulation and outlined causal pathways for further prevention of downstream cardiometabolic diseases.

Associations of MRI-derived kidney volume, kidney function, body composition and physical performance in ≈38 000 UK Biobank participants: a population-based observational study

Background

Kidney volume is used as a predictive and therapeutic marker for several clinical conditions. However, there is a lack of large-scale studies examining the relationship between kidney volume and various clinicodemographic factors, including kidney function, body composition and physical performance.

Methods

In this observational study, MRI-derived kidney volume measurements from 38 526 UK Biobank participants were analysed. Major kidney volume-related measures included body surface area (BSA)-adjusted total kidney volume (TKV) and the difference in bilateral kidneys. Multivariable-adjusted linear regression and cubic spline analyses were used to explore the association between kidney volume-related measures and clinicodemographic factors. Cox or logistic regression was used to identify the risks of death, non-kidney cancer, myocardial infarction, ischaemic stroke and chronic kidney disease (CKD).

Results

The median of BSA-adjusted TKV and the difference in kidney volume were 141.9 ml/m2 [interquartile range (IQR) 128.1-156.9] and 1.08-fold (IQR 1.04-1.15), respectively. Higher BSA-adjusted TKV was significantly associated with higher estimated glomerular filtration rate {eGFR; β = 0.43 [95% confidence interval (CI) 0.42-0.44]; P < .001}, greater muscle volume [β = 0.50 (95% CI 0.48-0.51); P < .001] and greater mean handgrip strength [β = 0.15 (95% CI 0.13-0.16); P < .001] but lower visceral adipose tissue volume [VAT; β = -0.09 (95% CI -0.11 to -0.07); P < .001] in adjusted models. A greater difference in bilateral kidney volumes was associated with lower eGFR, muscle volume and physical performance but with higher proteinuria and VAT. Higher BSA-adjusted TKV was significantly associated with a reduced risk of CKD [odds ratio (OR) 0.7 (95% CI 0.63-0.77); P < .001], while a greater difference in kidney volume was significantly associated with an increased risk of CKD [OR 1.13 (95% CI 1.07-1.20); P < .001].

Conclusion

Higher BSA-adjusted TKV and lower differences in bilateral kidney volumes are associated with higher kidney function, muscle volume and physical performance and a reduced risk of CKD.

Relationship between educational attainment and non-alcoholic fatty liver disease: A two-sample Mendelian randomization study

BACKGROUND

Observational studies have identified an inverse association between education and non-alcoholic fatty liver disease (NAFLD). However, it is not possible to establish causality for this relationship.

AIMS

To gain more insight into the causal nature of the relationship between education and NAFLD.

METHODS

We performed two-sample Mendelian randomization (MR) analyses using summary-level, large-scale datasets to study the association of genetically predicted educational attainment (n = 1271 genetic instruments, obtained from 1,131,881 participants) with risk of NAFLD (i.e., liver fat [n = 32,858 participants] and electronic health record (EHR)-based NAFLD [n = 778,614 participants]). In sensitivity analyses, educational attainment was replaced by three education-related traits (i.e., genetically predicted cognition, math ability and highest math).

RESULTS

Inverse-variance weighted method showed a statistically significant association between genetically predicted educational attainment and liver fat (beta: -0.251, 95%CI: -0.305; -0.198) and EHR-based NAFLD (OR: 0.609, 95%CI: 0.547; 0.677). MR-Egger regression did not show statistically significant intercepts. Similar findings were obtained when other MR tests were used or when educational attainment was replaced by education-related traits.

CONCLUSIONS

This study suggests a causal, protective effect of higher education on NAFLD risk. Societal interventions targeted at people with low education are needed to alleviate the burden of NAFLD.

Causality between allergic diseases and kidney diseases: a two-sample Mendelian randomization study

Background

Evidence from observational studies and clinical trials suggests that the allergic diseases (ADs) are associated with kidney diseases (KDs). However, the causal association between them remains to be determined. We used bidirectional two-sample Mendelian randomization (MR) analysis to evaluate the potential causality between them.

Methods

Mendelian randomization (MR) was performed using publicly available genome-wide association study (GWAS) summary datasets. Inverse variance weighted (IVW), weighted median, MR-Egger regression, simple mode, and weighted mode methods are used to evaluate the causality between ADs and KDs. Sensitivity and heterogeneity analyses were used to ensure the stability of the results.

Results

The MR results indicated that genetic susceptibility to ADs was associated with a higher risk of CKD [odds ratio (OR) = 1.124, 95% CI = 1.020-1.239, p = 0.019] and unspecified kidney failure (OR = 1.170, 95% CI = 1.004-1.363, p = 0.045) but not with kidney stone, ureter stone or bladder stone (OR = 1.001, 95% CI = 1.000-1.002, p = 0.216), other renal or kidney problem (OR = 1.000, 95% CI = 1.000-1.001, p = 0.339), urinary tract or kidney infection (OR = 1.000, 95% CI = 0.999-1.001, p = 0.604), kidney volume (OR = 0.996, 95% CI = 0.960-1.033, p = 0.812) and cyst of kidney (OR = 0.914, 95% CI = 0.756-1.105, p = 0.354). No causal evidence of KDs on ADs was found in present study.

Conclusion

Results from MR analysis indicate a causal association between ADs and CKD and unspecified kidney failure. These findings partly suggest that early monitoring of CKD risk in patients with ADs is intentional.

Genetic drivers of heterogeneity in type 2 diabetes pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P < 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.

Causal relationship between sleep apnea and non-alcoholic fatty liver disease: A Mendelian randomization study

BACKGROUND

Observational studies indicate that sleep apnea is associated with non-alcoholic fatty liver disease (NAFLD) and its related metabolic features, independent of confounding factors including obesity. However, the causal relationships remain to be determined.

METHODS

Univariable and multivariable Mendelian randomization (MR) analyses were performed to investigate the causal relationship between sleep apnea and NAFLD, along with its typical features including liver function, glycemic traits and lipid profiles. Summary-level data for sleep apnea were obtained from the Finngen consortium (33,423 cases and 307,648 controls). Summary-level data for NAFLD were available from a GWAS meta-analysis (8434 cases and 770,180 controls), and data for 12 NAFLD-related features from corresponding published GWASs. The inverse variance weighted (IVW) analysis was employed as the primary statistical method. Bidirectional MR and CAUSE analysis were conducted to avoid reverse causality and false positive findings.

RESULTS

In univariable MR analyses, we found evidence to support a causal effect of genetically predicted sleep apnea on NAFLD (OR = 1.50, 95% CI = 1.18-1.91) and HDL-C (β = -0.045, 95% CI = -0.090 to -0.001). In reverse MR, genetically predicted serum TG was associated with an increased risk of sleep apnea (OR = 1.07, 95% CI = 1.02-1.12), while genetically predicted HDL-C was associated with a decreased risk of sleep apnea (OR = 0.93, 95% CI = 0.89-0.98). After adjusting body mass index or educational attainment, none of these causal associations were retained. However, CAUSE method and MR analyses focusing on lipoprotein subfractions supported a causal effect of sleep apnea on HDL-C and HDL subfractions.

CONCLUSION

This MR study indicated that sleep apnea has no direct causal association with NAFLD, elevated liver enzymes and insulin resistance. Our results showed suggestive inverse associations of genetically predicted sleep apnea on HDL-C and HDL subfractions, indicating that both HDL-C levels and HDL function may be causally implicated in sleep apnea.

Bidirectional two-sample mendelian randomization analysis identifies causal associations of MRI-based cortical thickness and surface area relation to NAFLD

BACKGROUND

Non-alcoholic fatty liver disease (NAFLD) patients have exhibited extra-hepatic neurological changes, but the causes and mechanisms remain unclear. This study investigates the causal effect of NAFLD on cortical structure through bidirectional two-sample Mendelian randomization analysis.

METHODS

Genetic data from 778,614 European individuals across four NAFLD studies were used to determine genetically predicted NAFLD. Abdominal MRI scans from 32,860 UK Biobank participants were utilized to evaluate genetically predicted liver fat and volume. Data from the ENIGMA Consortium, comprising 51,665 patients, were used to evaluate the associations between genetic susceptibility, NAFLD risk, liver fat, liver volume, and alterations in cortical thickness (TH) and surface area (SA). Inverse-variance weighted (IVW) estimation, Cochran Q, and MR-Egger were employed to assess heterogeneity and pleiotropy.

RESULTS

Overall, NAFLD did not significantly affect cortical SA or TH. However, potential associations were noted under global weighting, relating heightened NAFLD risk to reduced parahippocampal SA and decreased cortical TH in the caudal middle frontal, cuneus, lingual, and parstriangularis regions. Liver fat and volume also influenced the cortical structure of certain regions, although no Bonferroni-adjusted p-values reached significance. Two-step MR analysis revealed that liver fat, AST, and LDL levels mediated the impact of NAFLD on cortical structure. Multivariable MR analysis suggested that the impact of NAFLD on the cortical TH of lingual and parstriangularis was independent of BMI, obesity, hyperlipidemia, and diabetes.

CONCLUSION

This study provides evidence that NAFLD causally influences the cortical structure of the brain, suggesting the existence of a liver-brain axis in the development of NAFLD.

Evidence for a causal link between intra-pancreatic fat deposition and pancreatic cancer: A prospective cohort and Mendelian randomization study

Prior observational studies suggest an association between intra-pancreatic fat deposition (IPFD) and pancreatic ductal adenocarcinoma (PDAC); however, the causal relationship is unclear. To elucidate causality, we conduct a prospective observational study using magnetic resonance imaging (MRI)-measured IPFD data and also perform a Mendelian randomization study using genetic instruments for IPFD. In the observational study, we use UK Biobank data (N = 29,463, median follow-up: 4.5 years) and find that high IPFD (>10%) is associated with PDAC risk (adjusted hazard ratio [HR]: 3.35, 95% confidence interval [95% CI]: 1.60-7.00). In the Mendelian randomization study, we leverage eight out of nine IPFD-associated genetic variants (p < 5 × 10-8) from a genome-wide association study in the UK Biobank (N = 25,617) and find that genetically determined IPFD is associated with PDAC (odds ratio [OR] per 1-standard deviation [SD] increase in IPFD: 2.46, 95% CI: 1.38-4.40) in the Pancreatic Cancer Cohort Consortium I, II, III (PanScan I-III)/Pancreatic Cancer Case-Control Consortium (PanC4) dataset (8,275 PDAC cases and 6,723 non-cases). This study provides evidence for a potential causal role of IPFD in the pathogenesis of PDAC. Thus, reducing IPFD may lower PDAC risk.