Federated learning for medical image analysis: A survey.

Machine learning in medical imaging often faces a fundamental dilemma, namely, the small sample size problem. Many recent studies suggest using multi-domain data pooled from different acquisition sites/centers to improve statistical power. However, medical images from different sites cannot be easily shared to build large datasets for model training due to privacy protection reasons. As a promising solution, federated learning, which enables collaborative training of machine learning models based on data from different sites without cross-site data sharing, has attracted considerable attention recently. In this paper, we conduct a comprehensive survey of the recent development of federated learning methods in medical image analysis. We have systematically gathered research papers on federated learning and its applications in medical image analysis published between 2017 and 2023. Our search and compilation were conducted using databases from IEEE Xplore, ACM Digital Library, Science Direct, Springer Link, Web of Science, Google Scholar, and PubMed. In this survey, we first introduce the background of federated learning for dealing with privacy protection and collaborative learning issues. We then present a comprehensive review of recent advances in federated learning methods for medical image analysis. Specifically, existing methods are categorized based on three critical aspects of a federated learning system, including client end, server end, and communication techniques. In each category, we summarize the existing federated learning methods according to specific research problems in medical image analysis and also provide insights into the motivations of different approaches. In addition, we provide a review of existing benchmark medical imaging datasets and software platforms for current federated learning research. We also conduct an experimental study to empirically evaluate typical federated learning methods for medical image analysis. This survey can help to better understand the current research status, challenges, and potential research opportunities in this promising research field.

Hybrid representation learning for cognitive diagnosis in late-life depression over 5 years with structural MRI.

Late-life depression (LLD) is a highly prevalent mood disorder occurring in older adults and is frequently accompanied by cognitive impairment (CI). Studies have shown that LLD may increase the risk of Alzheimer's disease (AD). However, the heterogeneity of presentation of geriatric depression suggests that multiple biological mechanisms may underlie it. Current biological research on LLD progression incorporates machine learning that combines neuroimaging data with clinical observations. There are few studies on incident cognitive diagnostic outcomes in LLD based on structural MRI (sMRI). In this paper, we describe the development of a hybrid representation learning (HRL) framework for predicting cognitive diagnosis over 5 years based on T1-weighted sMRI data. Specifically, we first extract prediction-oriented MRI features via a deep neural network, and then integrate them with handcrafted MRI features via a Transformer encoder for cognitive diagnosis prediction. Two tasks are investigated in this work, including (1) identifying cognitively normal subjects with LLD and never-depressed older healthy subjects, and (2) identifying LLD subjects who developed CI (or even AD) and those who stayed cognitively normal over five years. We validate the proposed HRL on 294 subjects with T1-weighted MRIs from two clinically harmonized studies. Experimental results suggest that the HRL outperforms several classical machine learning and state-of-the-art deep learning methods in LLD identification and prediction tasks.

Recent Advances on Small-Molecule Inhibitors of Lipocalin-like Proteins.

Lipid transfer proteins (LTPs) are crucial players in nonvesicular lipid trafficking. LTPs sharing a lipocalin lipid transfer domain (lipocalin-like proteins) have a wide range of biological functions, such as regulating immune responses and cell proliferation, differentiation, and death as well as participating in the pathogenesis of inflammatory, metabolic, and neurological disorders and cancer. Therefore, the development of small-molecule inhibitors targeting these LTPs is important and has potential clinical applications. Herein, we summarize the structure and function of lipocalin-like proteins, mainly including retinol-binding proteins, lipocalins, and fatty acid-binding proteins and discuss the recent advances on small-molecule inhibitors for these protein families and their applications in disease treatment. The findings of our Perspective can provide guidance for the development of inhibitors of these LTPs and highlight the challenges that might be faced during the procedures.

Prevalence and factors associated with overweight, obesity and central obesity among adults in Shenmu City, Shaanxi Province, China.

Objectives: This research aimed to explore the prevalence and determinants of overweight, obesity, and central obesity in Shenmu City, Shaanxi Province, China and to offer guidance for preventative health measures. Methods: We conducted a multi-stage, stratified random sampling survey among 4,565 residents of Shenmu City. Data collection included questionnaires and anthropometric assessments to gather socio-demographic data and to identify cases of overweight, obesity, and central obesity. Multivariable logistic regression analysis was utilized to assess the association between various factors and these conditions. Results: The observed prevalence rates for overweight, obesity, central obesity, and the combination of overweight/obesity with central obesity were 39.9%, 18.2%, 48.0%, 32.8%, and 22.8%, respectively. Notably, the incidence of these conditions was significantly higher in men compared to women. The prevalence of overweight and obesity initially increased and then decreased with age, whereas the prevalence of central obesity consistently rose. Furthermore, a higher educational level correlated with lower prevalence rates. Additionally, our analysis indicated that hypertension, dyslipidemia, and hyperuricemia are risk factors for these conditions. Conclusions: The findings of this study offer crucial insights for formulating effective strategies to prevent and manage obesity in Shenmu City.

The prevalence of obesity and relationship between obesity indicators and chronic diseases in Northern Shaanxi, China.

INTRODUCTION: Obesity not only affects human health but also is an important risk factor for a variety of chronic diseases. Therefore, it is particularly important to analyse the epidemic trend of obesity and actively carry out the prevention and control of obesity in the population. MATERIAL AND METHODS: A total of 4565 adults were selected by multi-stage stratified random sampling in Shenmu, Shaanxi Province, China. Univariate analysis was used to explore the epidemic characteristics of obesity in this region. Multivariate logistic regression was used to analyse the relationship between obesity and chronic diseases. Finally, the prediction efficiency of different obesity indexes was analysed by drawing receiver operator characteristic curves (ROC). All statistical analysis was completed by SPSS 26.0 software. RESULTS: The prevalence rates of overweight, obesity, and central obesity were 39.9%, 18.2%, and 48.0%, respectively. After adjusting for other confounding factors, multivariate logistic regression analysis showed that overweight and obesity were risk factors for hypertension, dyslipidaemia, and hyperuricaemia. Central obesity is a risk factor for dyslipidaemia and hyperuricaemia. High level of waist-to-height ratio (WHtR) was a risk factor for dyslipidaemia and hyperuricaemia (p < 0.05). Obesity-related indicators: body mass index (BMI), waist circumference (WC), and WHtR, are strongly correlated with the increased risk of chronic diseases in northern Shaanxi, China. The optimal BMI cut-off values for predicting hypertension, dyslipidaemia, and hyperuricaemia were 24.27, 24.04, and 25.54, respectively. The optimal WC cut-off values for predicting dyslipidaemia and hyperuricaemia were 84.5 and 90.5, and WHtR cut-off values were 0.52 and 0.54, respectively. CONCLUSION: The problem of overweight, obesity, and central obesity in adults is serious in northern Shaanxi, China. Obesity of all types will increase the risk of chronic diseases. Therefore, a variety of preventive and therapeutic measures should be adopted to curb obesity and reduce the incidence of related chronic diseases.

Source-free unsupervised domain adaptation: A survey.

Unsupervised domain adaptation (UDA) via deep learning has attracted appealing attention for tackling domain-shift problems caused by distribution discrepancy across different domains. Existing UDA approaches highly depend on the accessibility of source domain data, which is usually limited in practical scenarios due to privacy protection, data storage and transmission cost, and computation burden. To tackle this issue, many source-free unsupervised domain adaptation (SFUDA) methods have been proposed recently, which perform knowledge transfer from a pre-trained source model to the unlabeled target domain with source data inaccessible. A comprehensive review of these works on SFUDA is of great significance. In this paper, we provide a timely and systematic literature review of existing SFUDA approaches from a technical perspective. Specifically, we categorize current SFUDA studies into two groups, i.e., white-box SFUDA and black-box SFUDA, and further divide them into finer subcategories based on different learning strategies they use. We also investigate the challenges of methods in each subcategory, discuss the advantages/disadvantages of white-box and black-box SFUDA methods, conclude the commonly used benchmark datasets, and summarize the popular techniques for improved generalizability of models learned without using source data. We finally discuss several promising future directions in this field.

Triplet-constrained deep hashing for chest X-ray image retrieval in COVID-19 assessment.

Radiology images of the chest, such as computer tomography scans and X-rays, have been prominently used in computer-aided COVID-19 analysis. Learning-based radiology image retrieval has attracted increasing attention recently, which generally involves image feature extraction and finding matches in extensive image databases based on query images. Many deep hashing methods have been developed for chest radiology image search due to the high efficiency of retrieval using hash codes. However, they often overlook the complex triple associations between images; that is, images belonging to the same category tend to share similar characteristics and vice versa. To this end, we develop a triplet-constrained deep hashing (TCDH) framework for chest radiology image retrieval to facilitate automated analysis of COVID-19. The TCDH consists of two phases, including (a) feature extraction and (b) image retrieval. For feature extraction, we have introduced a triplet constraint and an image reconstruction task to enhance discriminative ability of learned features, and these features are then converted into binary hash codes to capture semantic information. Specifically, the triplet constraint is designed to pull closer samples within the same category and push apart samples from different categories. Additionally, an auxiliary image reconstruction task is employed during feature extraction to help effectively capture anatomical structures of images. For image retrieval, we utilize learned hash codes to conduct searches for medical images. Extensive experiments on 30,386 chest X-ray images demonstrate the superiority of the proposed method over several state-of-the-art approaches in automated image search. The code is now available online.

Leveraging Brain Modularity Prior for Interpretable Representation Learning of fMRI.

Resting-state functional magnetic resonance imaging (rs-fMRI) can reflect spontaneous neural activities in the brain and is widely used for brain disorder analysis. Previous studies focus on extracting fMRI representations using machine/deep learning methods, but these features typically lack biological interpretability. The human brain exhibits a remarkable modular structure in spontaneous brain functional networks, with each module comprised of functionally interconnected brain regions-of-interest (ROIs). However, existing learning-based methods cannot adequately utilize such brain modularity prior. In this paper, we propose a brain modularity-constrained dynamic representation learning framework for interpretable fMRI analysis, consisting of dynamic graph construction, dynamic graph learning via a novel modularity-constrained graph neural network (MGNN), and prediction and biomarker detection. The designed MGNN is constrained by three core neurocognitive modules (i.e., salience network, central executive network, and default mode network), encouraging ROIs within the same module to share similar representations. To further enhance discriminative ability of learned features, we encourage the MGNN to preserve network topology of input graphs via a graph topology reconstruction constraint. Experimental results on 534 subjects with rs-fMRI scans from two datasets validate the effectiveness of the proposed method. The identified discriminative brain ROIs and functional connectivities can be regarded as potential fMRI biomarkers to aid in clinical diagnosis.

Vascular injury activates the ELK1/SND1/SRF pathway to promote vascular smooth muscle cell proliferative phenotype and neointimal hyperplasia.

BACKGROUND: Vascular smooth muscle cell (VSMC) proliferation is the leading cause of vascular stenosis or restenosis. Therefore, investigating the molecular mechanisms and pivotal regulators of the proliferative VSMC phenotype is imperative for precisely preventing neointimal hyperplasia in vascular disease. METHODS: Wire-induced vascular injury and aortic culture models were used to detect the expression of staphylococcal nuclease domain-containing protein 1 (SND1). SMC-specific Snd1 knockout mice were used to assess the potential roles of SND1 after vascular injury. Primary VSMCs were cultured to evaluate SND1 function on VSMC phenotype switching, as well as to investigate the mechanism by which SND1 regulates the VSMC proliferative phenotype. RESULTS: Phenotype-switched proliferative VSMCs exhibited higher SND1 protein expression compared to the differentiated VSMCs. This result was replicated in primary VSMCs treated with platelet-derived growth factor (PDGF). In the injury model, specific knockout of Snd1 in mouse VSMCs reduced neointimal hyperplasia. We then revealed that ETS transcription factor ELK1 (ELK1) exhibited upregulation and activation in proliferative VSMCs, and acted as a novel transcription factor to induce the gene transcriptional activation of Snd1. Subsequently, the upregulated SND1 is associated with serum response factor (SRF) by competing with myocardin (MYOCD). As a co-activator of SRF, SND1 recruited the lysine acetyltransferase 2B (KAT2B) to the promoter regions leading to the histone acetylation, consequently promoted SRF to recognize the specific CArG motif, and enhanced the proliferation- and migration-related gene transcriptional activation. CONCLUSIONS: The present study identifies ELK1/SND1/SRF as a novel pathway in promoting the proliferative VSMC phenotype and neointimal hyperplasia in vascular injury, predisposing the vessels to pathological remodeling. This provides a potential therapeutic target for vascular stenosis.

Preserving specificity in federated graph learning for fMRI-based neurological disorder identification.

Resting-state functional magnetic resonance imaging (rs-fMRI) offers a non-invasive approach to examining abnormal brain connectivity associated with brain disorders. Graph neural network (GNN) gains popularity in fMRI representation learning and brain disorder analysis with powerful graph representation capabilities. Training a general GNN often necessitates a large-scale dataset from multiple imaging centers/sites, but centralizing multi-site data generally faces inherent challenges related to data privacy, security, and storage burden. Federated Learning (FL) enables collaborative model training without centralized multi-site fMRI data. Unfortunately, previous FL approaches for fMRI analysis often ignore site-specificity, including demographic factors such as age, gender, and education level. To this end, we propose a specificity-aware federated graph learning (SFGL) framework for rs-fMRI analysis and automated brain disorder identification, with a server and multiple clients/sites for federated model aggregation and prediction. At each client, our model consists of a shared and a personalized branch, where parameters of the shared branch are sent to the server while those of the personalized branch remain local. This can facilitate knowledge sharing among sites and also helps preserve site specificity. In the shared branch, we employ a spatio-temporal attention graph isomorphism network to learn dynamic fMRI representations. In the personalized branch, we integrate vectorized demographic information (i.e., age, gender, and education years) and functional connectivity networks to preserve site-specific characteristics. Representations generated by the two branches are then fused for classification. Experimental results on two fMRI datasets with a total of 1218 subjects suggest that SFGL outperforms several state-of-the-art approaches.

Recent Progress in Mass Spectrometry-Based Metabolomics in Major Depressive Disorder Research.

Major depressive disorder (MDD) is a serious mental illness with a heavy social burden, but its underlying molecular mechanisms remain unclear. Mass spectrometry (MS)-based metabolomics is providing new insights into the heterogeneous pathophysiology, diagnosis, treatment, and prognosis of MDD by revealing multi-parametric biomarker signatures at the metabolite level. In this comprehensive review, recent developments of MS-based metabolomics in MDD research are summarized from the perspective of analytical platforms (liquid chromatography-MS, gas chromatography-MS, supercritical fluid chromatography-MS, etc.), strategies (untargeted, targeted, and pseudotargeted metabolomics), key metabolite changes (monoamine neurotransmitters, amino acids, lipids, etc.), and antidepressant treatments (both western and traditional Chinese medicines). Depression sub-phenotypes, comorbid depression, and multi-omics approaches are also highlighted to stimulate further advances in MS-based metabolomics in the field of MDD research.

Risk Factors for Hypocoagulability After Cardiac Surgery: A Retrospective Study.

Hemostatic disturbances after cardiac surgery can lead to excessive postoperative bleeding. Thromboelastography (TEG) was employed to evaluate perioperative coagulative alterations in patients undergoing cardiac surgery with cardiopulmonary bypass (CPB), investigating the correlation between factors concomitant with cardiac surgery and modifications in coagulation. Coagulation index as determined by TEG correlated significantly with postoperative bleeding at 24-72 h after cardiac surgery (P < .001). Among patients with a normal preoperative coagulation index, those with postoperative hypocoagulability showed significantly lower nadir temperature (P = .003), larger infused fluid volume (P = .003), and longer CPB duration (P = .033) than those with normal coagulation index. Multivariate logistic regression showed that nadir intraoperative temperature was an independent predictor of postoperative hypocoagulability (adjusted OR: 0.772, 95% CI: 0.624-0.954, P = .017). Multivariate linear regression demonstrated linear associations of nadir intraoperative temperature (P = .017) and infused fluid volume (P = .005) with change in coagulation index as a result of cardiac surgery. Patients are susceptible to hypocoagulability after cardiac surgery, which can lead to increased postoperative bleeding. Ensuring appropriate temperature and fluid volume during cardiac surgery involving CPB may reduce risk of postoperative hypocoagulability and bleeding.

Nadir Hemoglobin Concentration After Spinal Tumor Surgery: Association With Risk of Composite Adverse Events.

STUDY DESIGN: Retrospective case-control study. OBJECTIVE: To explore the association of early postoperative nadir hemoglobin with risk of a composite outcome of anemia-related and other adverse events. METHODS: We retrospectively analyzed data from spinal tumor patients who received intraoperative blood transfusion between September 1, 2013 and December 31, 2020. Uni- and multivariate logistic regression was used to explore relationships of clinicodemographic and surgical factors with risk of composite in-hospital adverse events, including death. Subgroup analysis explored the relationship between early postoperative nadir hemoglobin and composite adverse events. RESULTS: Among the 345 patients, 331 (95.9%) experienced early postoperative anemia and 69 (20%) experienced postoperative composite adverse events. Multivariate logistic regression analysis showed that postoperative nadir Hb (OR = .818, 95% CI: .672-.995, P = .044), ASA ≥3 (OR = 2.007, 95% CI: 1.086-3.707, P = .026), intraoperative RBC infusion volume (OR = 1.133, 95% CI: 1.009-1.272, P = .035), abnormal hypertension (OR = 2.199, 95% CI: 1.085-4.457, P = .029) were correlated with composite adverse events. The lumbar spinal tumor was associated with composite adverse events with a decreased odds compared to thoracic spinal tumors (OR = .444, 95% CI: .226-.876, P = .019). Compared to patients with postoperative nadir hemoglobin ≥11.0 g/dL, those with nadir <9.0 g/dL were at significantly higher risk of postoperative composite adverse events (OR = 2.709, 95% CI: 1.087-6.754, P = .032). CONCLUSION: Nadir hemoglobin <9.0 g/dL after spinal tumor surgery is associated with greater risk of postoperative composite adverse events in patients who receive intraoperative blood transfusion.

Unsupervised contrastive graph learning for resting-state functional MRI analysis and brain disorder detection.

Resting-state functional magnetic resonance imaging (rs-fMRI) helps characterize regional interactions that occur in the human brain at a resting state. Existing research often attempts to explore fMRI biomarkers that best predict brain disease progression using machine/deep learning techniques. Previous fMRI studies have shown that learning-based methods usually require a large amount of labeled training data, limiting their utility in clinical practice where annotating data is often time-consuming and labor-intensive. To this end, we propose an unsupervised contrastive graph learning (UCGL) framework for fMRI-based brain disease analysis, in which a pretext model is designed to generate informative fMRI representations using unlabeled training data, followed by model fine-tuning to perform downstream disease identification tasks. Specifically, in the pretext model, we first design a bi-level fMRI augmentation strategy to increase the sample size by augmenting blood-oxygen-level-dependent (BOLD) signals, and then employ two parallel graph convolutional networks for fMRI feature extraction in an unsupervised contrastive learning manner. This pretext model can be optimized on large-scale fMRI datasets, without requiring labeled training data. This model is further fine-tuned on to-be-analyzed fMRI data for downstream disease detection in a task-oriented learning manner. We evaluate the proposed method on three rs-fMRI datasets for cross-site and cross-dataset learning tasks. Experimental results suggest that the UCGL outperforms several state-of-the-art approaches in automated diagnosis of three brain diseases (i.e., major depressive disorder, autism spectrum disorder, and Alzheimer's disease) with rs-fMRI data.

Brain morphometric features predict depression symptom phenotypes in late-life depression using a deep learning model.

Objectives: Our objective was to use deep learning models to identify underlying brain regions associated with depression symptom phenotypes in late-life depression (LLD). Participants: Diagnosed with LLD (N = 116) and enrolled in a prospective treatment study. Design: Cross-sectional. Measurements: Structural magnetic resonance imaging (sMRI) was used to predict five depression symptom phenotypes from the Hamilton and MADRS depression scales previously derived from factor analysis: (1) Anhedonia, (2) Suicidality, (3) Appetite, (4) Sleep Disturbance, and (5) Anxiety. Our deep learning model was deployed to predict each factor score via learning deep feature representations from 3D sMRI patches in 34 a priori regions-of-interests (ROIs). ROI-level prediction accuracy was used to identify the most discriminative brain regions associated with prediction of factor scores representing each of the five symptom phenotypes. Results: Factor-level results found significant predictive models for Anxiety and Suicidality factors. ROI-level results suggest the most LLD-associated discriminative regions in predicting all five symptom factors were located in the anterior cingulate and orbital frontal cortex. Conclusions: We validated the effectiveness of using deep learning approaches on sMRI for predicting depression symptom phenotypes in LLD. We were able to identify deep embedded local morphological differences in symptom phenotypes in the brains of those with LLD, which is promising for symptom-targeted treatment of LLD. Future research with machine learning models integrating multimodal imaging and clinical data can provide additional discriminative information.

Self-supervised learning with application for infant cerebellum segmentation and analysis.

Accurate tissue segmentation is critical to characterize early cerebellar development in the first two postnatal years. However, challenges in tissue segmentation arising from tightly-folded cortex, low and dynamic tissue contrast, and large inter-site data heterogeneity have limited our understanding of early cerebellar development. In this paper, we propose an accurate self-supervised learning framework for infant cerebellum segmentation. We validate its accuracy using 358 subjects from three datasets. Our results suggest the first six months exhibit the most rapid and dynamic changes, with gray matter (GM) playing a dominant role in cerebellar growth over white matter (WM). We also find both GM and WM volumes are larger in males than females, and GM and WM volumes are larger in autistic males than neurotypical males. Application of our method to a larger population will fuel more cerebellar studies, ultimately advancing our comprehension of its structure and function in neurotypical and disordered development.

Hepatocyte ABCA1 deficiency is associated with reduced HDL sphingolipids.

ATP binding cassette transporter A1 (ABCA1) limits the formation of high density lipoproteins (HDL) as genetic loss of ABCA1 function causes virtual HDL deficiency in patients with Tangier disease. Mice with a hepatocyte-specific ABCA1 knockout (Abca1 HSKO) have 20% of wild type (WT) plasma HDL-cholesterol levels, suggesting a major contribution of hepatic ABCA1 to the HDL phenotype. Whether plasma sphingolipids are reduced in Tangier disease and to what extent hepatic ABCA1 contributes to plasma sphingolipid (SL) levels is unknown. Here, we report a drastic reduction of total SL levels in plasma of a Tangier patient with compound heterozygosity for mutations in ABCA1. Compared to mutation-free controls, heterozygous mutations in ABCA1 had no significant effect on total SLs in plasma; however, apoB-depleted plasma showed a reduction in total SL also in het carriers. Similarly, liver specific Abca1 KO mice (Abca1 HSKO) showed reduced total sphingolipids in plasma and liver. In parallel, apoM and sphingosine-1-phosphate (S1P) levels were reduced in plasma of Abca1 HSKO mice. Primary hepatocytes from Abca1 HSKO mice showed a modest, but significant reduction in total SLs concentration compared to WT hepatocytes, although SL de novo synthesis and secretion were slightly increased in Abca1 HSKO hepatocytes. We conclude that hepatic ABCA1 is a signficant contributor to maintaining total plasma pool of HDL sphingolipids, including sphingomyelins and S1P.

Multi-Scale Dynamic Graph Learning for Brain Disorder Detection With Functional MRI.

Resting-state functional magnetic resonance imaging (rs-fMRI) has been widely used in the detection of brain disorders such as autism spectrum disorder based on various machine/deep learning techniques. Learning-based methods typically rely on functional connectivity networks (FCNs) derived from blood-oxygen-level-dependent time series of rs-fMRI data to capture interactions between brain regions-of-interest (ROIs). Graph neural networks have been recently used to extract fMRI features from graph-structured FCNs, but cannot effectively characterize spatiotemporal dynamics of FCNs, e.g., the functional connectivity of brain ROIs is dynamically changing in a short period of time. Also, many studies usually focus on single-scale topology of FCN, thereby ignoring the potential complementary topological information of FCN at different spatial resolutions. To this end, in this paper, we propose a multi-scale dynamic graph learning (MDGL) framework to capture multi-scale spatiotemporal dynamic representations of rs-fMRI data for automated brain disorder diagnosis. The MDGL framework consists of three major components: 1) multi-scale dynamic FCN construction using multiple brain atlases to model multi-scale topological information, 2) multi-scale dynamic graph representation learning to capture spatiotemporal information conveyed in fMRI data, and 3) multi-scale feature fusion and classification. Experimental results on two datasets show that MDGL outperforms several state-of-the-art methods.

Causal relationship between obesity and iron deficiency anemia: a two-sample Mendelian randomization study.

Background: Observational studies have suggested an association between obesity and iron deficiency anemia, but such studies are susceptible to reverse causation and residual confounding. Here we used Mendelian randomization to assess whether the association might be causal. Methods: Data on single-nucleotide polymorphisms that might be associated with various anthropometric indicators of obesity were extracted as instrumental variables from genome-wide association studies in the UK Biobank. Data on genetic variants in iron deficiency anemia were extracted from a genome-wide association study dataset within the Biobank. Heterogeneity in the data was assessed using inverse variance-weighted regression, Mendelian randomization Egger regression, and Cochran's Q statistic. Potential causality was assessed using inverse variance-weighted, Mendelian randomization Egger, weighted median, maximum likelihood and penalized weighted median methods. Outlier SNPs were identified using Mendelian randomization PRESSO analysis and "leave-one-out" analysis. Results: Inverse variance-weighted regression associated iron deficiency anemia with body mass index, waist circumference, trunk fat mass, body fat mass, trunk fat percentage, and body fat percentage (all odds ratios 1.003-1.004, P ≤ 0.001). Heterogeneity was minimal and no evidence of horizontal pleiotropy was found. Conclusion: Our Mendelian randomization analysis suggests that obesity can cause iron deficiency anemia.

Imidazolium-based mass tags for protein biomarker detection using laser desorption ionization mass spectrometry.

Novel imidazolium-based mass tags (IMTs) were designed, synthesized and applied to simultaneous in situ analysis of multiple biomarkers on less than 10 cells. The high sensitivity, flexible extensibility and excellent distinguishability of IMTs open new avenues for designing common mass tag templates suitable for mass spectrometric immunoassay and provide an ideal option for multiplex-sensitive detection at the cellular scale.