Nat Struct Mol Biol. 2025 Mar 4. doi: 10.1038/s41594-025-01512-w. Online ahead of print.

ABSTRACT

Differential high-order chromatin interactions between homologous chromosomes affect many biological processes. Traditional chromatin conformation capture genome analysis methods mainly identify two-way interactions and cannot provide comprehensive haplotype information, especially for low-heterozygosity organisms such as human. Here, we present a pipeline of methods to delineate diploid high-order chromatin interactions from noisy Pore-C outputs. We trained a previously published single-nucleotide variant (SNV)-calling deep learning model, Clair3, on Pore-C data to achieve superior SNV calling, applied a filtering strategy to tag reads for haplotypes and established a haplotype imputation strategy for high-order concatemers. Learning the haplotype characteristics of high-order concatemers from high-heterozygosity mouse allowed us to devise a progressive haplotype imputation strategy, which improved the haplotype-informative Pore-C contact rate 14.1-fold to 76% in the HG001 cell line. Overall, the diploid three-dimensional (3D) genome interactions we derived using Dip3D surpassed conventional methods in noise reduction and contact distribution uniformity, with better haplotype-informative contact density and genomic coverage rates. Dip3D identified previously unresolved haplotype high-order interactions, in addition to an understanding of their relationship with allele-specific expression, such as in X-chromosome inactivation. These results lead us to conclude that Dip3D is a robust pipeline for the high-quality reconstruction of diploid high-order 3D genome interactions.

PMID:40038455 | DOI:10.1038/s41594-025-01512-w

Sci Rep. 2025 Mar 4;15(1):7604. doi: 10.1038/s41598-025-92096-4.

ABSTRACT

Burns represents a serious clinical problem because the diagnosis and assessment are very complex. This paper proposes a methodology that combines the use of advanced medical imaging with predictive modeling for the improvement of burn injury assessment. The proposed framework makes use of the Adaptive Complex Independent Components Analysis (ACICA) and Reference Region (TBSA) methods in conjunction with deep learning techniques for the precise estimation of burn depth and Total Body Surface Area analysis. It also allows for the estimation of the depth of burns with high accuracy, calculation of TBSA, and non-invasive analysis with 96.7% accuracy using an RNN model. Extensive experimentation on DCE-LUV samples validates enhanced diagnostic precision and detailed texture analysis. These technologies provide nuanced insights into burn severity, improving diagnostic accuracy and treatment planning. Our results demonstrate the potential of these methods to revolutionize burn care and optimize patient outcomes.

PMID:40038450 | DOI:10.1038/s41598-025-92096-4

Sci Rep. 2025 Mar 4;15(1):7540. doi: 10.1038/s41598-025-91622-8.

ABSTRACT

In the rapidly evolving field of healthcare, Artificial Intelligence (AI) is increasingly driving the promotion of the transformation of traditional healthcare and improving medical diagnostic decisions. The overall goal is to uncover emerging trends and potential future paths of AI in healthcare by applying text mining to collect scientific papers and patent information. This study, using advanced text mining and multiple deep learning algorithms, utilized the Web of Science for scientific papers (1587) and the Derwent innovations index for patents (1314) from 2018 to 2022 to study future trends of emerging AI in healthcare. A novel self-supervised text mining approach, leveraging bidirectional encoder representations from transformers (BERT), is introduced to explore AI trends in healthcare. The findings point out the market trends of the Internet of Things, data security and image processing. This study not only reveals current research hotspots and technological trends in AI for healthcare but also proposes an advanced research method. Moreover, by analysing patent data, this study provides an empirical basis for exploring the commercialisation of AI technology, indicating the potential transformation directions for future healthcare services. Early technology trend analysis relied heavily on expert judgment. This study is the first to introduce a deep learning self-supervised model to the field of AI in healthcare, effectively improving the accuracy and efficiency of the analysis. These findings provide valuable guidance for researchers, policymakers and industry professionals, enabling more informed decisions.

PMID:40038367 | DOI:10.1038/s41598-025-91622-8

Sci Rep. 2025 Mar 4;15(1):7644. doi: 10.1038/s41598-025-90511-4.

ABSTRACT

Loop closure detection is a key module in visual SLAM. During the robot's movement, the cumulative error of the robot is reduced by the loop closure detection method, which can provide constraints for the back-end pose optimization, and the SLAM system can build an accurate map. Traditional loop closure detection algorithms rely on the bag-of-words model, which involves a complex process, has slow loading speeds, and is sensitive to changes in illumination or viewing angles. Therefore, aiming at the problems of traditional methods, this paper proposes an algorithm based on the Siamese capsule neural network by using the deep learning method. We have designed a new feature extractor for capsule networks, and in order to further reduce the parameter count, we have performed pruning based on the characteristics of the capsule layer. The algorithm was tested on the CityCentre dataset and the New College dataset. Our experimental results show that the proposed algorithm in this paper has higher accuracy and robustness compared to traditional methods and other deep learning methods. Our algorithm demonstrates good robustness under changes in illumination and viewing angles. Finally, we evaluated the performance of the complete SLAM system on the KITTI dataset.

PMID:40038350 | DOI:10.1038/s41598-025-90511-4

Sci Rep. 2025 Mar 4;15(1):7552. doi: 10.1038/s41598-025-90616-w.

ABSTRACT

The process of image formulation uses semantic analysis to extract influential vectors from image components. The proposed approach integrates DenseNet with ResNet-50, VGG-19, and GoogLeNet using an innovative bonding process that establishes algorithmic channeling between these models. The goal targets compact efficient image feature vectors that process data in parallel regardless of input color or grayscale consistency and work across different datasets and semantic categories. Image patching techniques with corner straddling and isolated responses help detect peaks and junctions while addressing anisotropic noise through curvature-based computations and auto-correlation calculations. An integrated channeled algorithm processes the refined features by uniting local-global features with primitive-parameterized features and regioned feature vectors. Using K-nearest neighbor indexing methods analyze and retrieve images from the harmonized signature collection effectively. Extensive experimentation is performed on the state-of-the-art datasets including Caltech-101, Cifar-10, Caltech-256, Cifar-100, Corel-10000, 17-Flowers, COIL-100, FTVL Tropical Fruits, Corel-1000, and Zubud. This contribution finally endorses its standing at the peak of deep and complex image sensing analysis. A state-of-the-art deep image sensing analysis method delivers optimal channeling accuracy together with robust dataset harmonization performance.

PMID:40038324 | DOI:10.1038/s41598-025-90616-w

Sci Rep. 2025 Mar 4;15(1):7558. doi: 10.1038/s41598-025-88184-0.

ABSTRACT

Traffic signs are pivotal components of traffic management, ensuring the regulation and safety of road traffic. However, existing detection methods often suffer from low accuracy and poor real-time performance in dynamic road environments. This paper reviews traditional traffic sign detection methods and introduces an enhanced detection algorithm (YOLO-BS) based on YOLOv8 (You Only Look Once version 8). This algorithm addresses the challenges of complex backgrounds and small-sized detection targets in traffic sign images. A small object detection layer was incorporated into the YOLOv8 framework to enrich feature extraction. Additionally, a bidirectional feature pyramid network (BiFPN) was integrated into the detection framework to enhance the handling of multi-scale objects and improve the performance in detecting small objects. Experiments were conducted on the TT100K dataset to evaluate key metrics such as model size, recall, mean average precision (mAP), and frames per second (FPS), demonstrating that YOLO-BS surpasses current mainstream models with mAP50 of 90.1% and FPS of 78. Future work will refine YOLO-BS to explore broader applications within intelligent transportation systems.

PMID:40038318 | DOI:10.1038/s41598-025-88184-0

Jpn J Radiol. 2025 Mar 5. doi: 10.1007/s11604-025-01756-y. Online ahead of print.

ABSTRACT

PURPOSE: To assess the utility of dual-type deep learning (DL)-based image reconstruction with DL-based image denoising and super-resolution processing by comparing images reconstructed with the conventional method in head and neck fat-suppressed (Fs) T2-weighted imaging (T2WI).

MATERIALS AND METHODS: We retrospectively analyzed the cases of 43 patients who underwent head/neck Fs-T2WI for the assessment of their head and neck lesions. All patients underwent two sets of Fs-T2WI scans with conventional- and DL-based reconstruction. The Fs-T2WI with DL-based reconstruction was acquired based on a 30% reduction of its spatial resolution in both the x- and y-axes with a shortened scan time. Qualitative and quantitative assessments were performed with both the conventional method- and DL-based reconstructions. For the qualitative assessment, we visually evaluated the overall image quality, visibility of anatomical structures, degree of artifact(s), lesion conspicuity, and lesion edge sharpness based on five-point grading. In the quantitative assessment, we measured the signal-to-noise ratio (SNR) of the lesion and the contrast-to-noise ratio (CNR) between the lesion and the adjacent or nearest muscle.

RESULTS: In the qualitative analysis, significant differences were observed between the Fs-T2WI with the conventional- and DL-based reconstruction in all of the evaluation items except the degree of the artifact(s) (p < 0.001). In the quantitative analysis, significant differences were observed in the SNR between the Fs-T2WI with conventional- (21.4 ± 14.7) and DL-based reconstructions (26.2 ± 13.5) (p < 0.001). In the CNR assessment, the CNR between the lesion and adjacent or nearest muscle in the DL-based Fs-T2WI (16.8 ± 11.6) was significantly higher than that in the conventional Fs-T2WI (14.2 ± 12.9) (p < 0.001).

CONCLUSION: Dual-type DL-based image reconstruction by an effective denoising and super-resolution process successfully provided high image quality in head and neck Fs-T2WI with a shortened scan time compared to the conventional imaging method.

PMID:40038217 | DOI:10.1007/s11604-025-01756-y

Biochem Genet. 2025 Mar 4. doi: 10.1007/s10528-025-11071-w. Online ahead of print.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a progressive interstitial lung disease linked to aging. Mitochondrial dysfunction in circulating T cells, often caused by disruption of mitochondrial DNA (mtDNA), may play a role in age-related conditions like IPF. In our previous study, we found high mtDNA mutational loads in blood lymphocytes from IPF patients, especially in regions critical for mtDNA expression. Since Complex I of the electron transport chain, partly encoded by mtDNA, is essential for energy production, we conducted a preliminary study on its activity. We found significantly reduced Complex I activity (p < 0.001) in lymphocytes from 40 IPF patients compared to 40 controls, which was positively correlated with lung function decline, specifically in functional vital capacity and diffusing capacity for carbon monoxide. These findings indicate that T cell mitochondrial dysfunction is associated with disease progression in IPF. Future work will explore the mechanisms linking T cell mitochondrial disruption with fibrosis, potentially uncovering new therapeutic targets.

PMID:40038177 | DOI:10.1007/s10528-025-11071-w

Intern Emerg Med. 2025 Mar 4. doi: 10.1007/s11739-025-03908-4. Online ahead of print.

ABSTRACT

Aortitis and periaortitis refer to the inflammation of the aortic wall and the surrounding tissues. Both conditions are associated with various diseases and express nonspecific manifestations. Early diagnosis and treatment are crucial to improve the prognosis of the disease. This study aimed to assess the causes and main clinical features of aortitis and periaortitis in patients from a single centre in Spain. Observational, retrospective study of patients diagnosed with aortitis or periaortitis at a Spanish referral center over the last decade. 134 patients (87 female; mean age of 55.1 ± 9.1 years) were recruited, 132 of which had aortitis and two periaortitis. Aortitis was associated with giant cell arteritis (n = 102), Takayasu's arteritis (n = 6), IgG4-related disease (n = 6), infectious diseases (n = 3), malignancy (n = 1), drugs (n = 1), isolated aortitis (n = 1), and other immune-mediated inflammatory diseases (IMIDs) (n = 12). IMIDs included were Sjögren's syndrome (n = 2), sarcoidosis (n = 2), rheumatoid arthritis (n = 2), axial spondyloarthritis (n = 2), inflammatory bowel disease (n = 1), primary biliary cirrhosis (n = 1), idiopathic pulmonary fibrosis (n = 1), and polyarteritis nodosa (n = 1). Periaortitis was due to idiopathic retroperitoneal fibrosis in both cases. Imaging techniques used for diagnosis included 18F-FDG PET/CT scan (n = 133), CT-angiography (n = 44), and/or MRI-angiography (n = 33). Polymyalgia rheumatica (52.2%) and asthenia (53.7%) were the most common manifestations, followed by limb claudication (23.9%) and inflammatory back pain (26.9%). Acute-phase reactants were typically increased. Aortitis is a common condition and may be associated with multiple non-infectious diseases. Its clinical presentation is often unspecific, requiring a high level of suspicion to get an early diagnosis and treatment.

PMID:40038164 | DOI:10.1007/s11739-025-03908-4

J Ethnopharmacol. 2025 Mar 2:119568. doi: 10.1016/j.jep.2025.119568. Online ahead of print.

ABSTRACT

ETHNOPHARMACOLOGICAL RELEVANCE: Maimendong decoction (MMDD) originates from the ancient Chinese medical text Synopsis of the Golden Chamber and is a well-established remedy for treating lung diseases. It has demonstrated efficacy in the long-term clinical management of idiopathic pulmonary fibrosis (IPF); however, its underlying mechanisms remain unclear.

AIM OF THE STUDY: This study investigates whether MMDD alleviates IPF by reducing type 2 alveolar epithelial cell (AEC2) senescence and enhancing mitochondrial autophagy. It also explores whether these effects are mediated through the PTEN-induced putative kinase 1 (PINK1)/Parkinson juvenile disease protein 2 (Parkin) pathway.

MATERIALS AND METHODS: An IPF mouse model was established with bleomycin (BLM). Mice were administered MMDD, pirfenidone (PFD), or saline for 7 or 28 days. Body weight, lung coefficient, and lung appearance were monitored, and lung tissue pathology was assessed. The expression levels of p53, p21, p16, SA-β-gal activity, and senescence-associated secretory phenotype (SASP) markers were measured. Ultrastructural changes in AEC2 mitochondria were analyzed using transmission electron microscopy. Protein levels of autophagy markers sequestosome-1 and light chain 3 were assessed. The protein levels of PINK1, Parkin, and phosphorylated Parkin were further assessed using network pharmacology analysis and molecular docking technology.

RESULTS: MMDD alleviated BLM-induced IPF by improving body weight, lung appearance, and histopathological features. It reduced AEC2 senescence markers, including p53, p21, p16, SA-β-gal, and SASP, while enhancing mitochondrial autophagy and repairing mitochondrial damage. Network pharmacology and molecular docking identified PINK1 as a major target, and Western blot (WB) analysis confirmed that MMDD regulates the PINK1/Parkin signaling pathway in the treatment of IPF.

CONCLUSIONS: MMDD regulates the PINK1/Parkin signaling pathway, alleviates AEC2 senescence, and enhances mitochondrial autophagy, providing significant therapeutic potential for IPF treatment.

PMID:40037475 | DOI:10.1016/j.jep.2025.119568

BMC Genomics. 2025 Mar 4;26(1):215. doi: 10.1186/s12864-025-11250-4.

ABSTRACT

BACKGROUND: Integrating germplasm populations genotyped by different genotyping platforms via genotype imputation is a way to utilize accumulated genetic resources. In this study, we used 278 canola samples genotyped via whole-genome sequencing (WGS) at 10× coverage to evaluate the imputation accuracy of three imputation approaches. The optimal imputation methods were used to impute and integrate two Canola genotype datasets: a diverse canola collection genotyped by genotyping-by-sequencing via transcriptome (GBS-t) and a double haploid (DH) line collection genotyped with low-coverage WGS (skim-WGS). The genomic predictive ability (GP) and detection power of marker‒trait association (GWAS) of the combined population for blackleg resistance were evaluated.

RESULTS: The empirical imputation accuracy (r2) measured as the squared correlation between observed and imputed genotypes was moderate for Minimac3 when imputing from the GBS-t density to the WGS. The accuracy dramatically improved from 0.64 to 0.82 by removing SNPs with poor Minimac3-reported Rsq (Rsq < 0.2) quality statistics. The r2 for GLIMPSE was higher than that for Beagle when imputing from different low-coverage to full-coverage WGS. We imputed and integrated the diverse canola collection and the DH lines, and the combined population showed similar or slightly greater predictive ability (PA) for blackleg resistance traits than did each of the single populations with ~ 921 K SNPs. Higher marker-trait association (MTA) detection powers were indicated with the combined population; however, similar numbers of MTAs were discovered when each single population was combined in a meta-GWAS.

CONCLUSION: It is feasible to impute and integrate germplasms from different sequencing platforms for downstream analyses. However, genetic heterogeneity across populations could add complexity to the analysis. Increasing the sample size by combining datasets showed slightly greater predictive ability and greater detection power in GWASs in the present study.

PMID:40038585 | DOI:10.1186/s12864-025-11250-4

Commun Biol. 2025 Mar 4;8(1):361. doi: 10.1038/s42003-025-07796-4.

ABSTRACT

Chromosomal instability (CIN) is a hallmark of cancer that drives metastasis, immune evasion and treatment resistance. CIN may result from chromosome mis-segregation errors and excessive chromatin is frequently packaged in micronuclei (MN), which can be enumerated to quantify CIN. The assessment of CIN remains a predominantly manual and time-consuming task. Here, we present micronuclAI, a pipeline for automated and reliable quantification of MN of varying size and morphology in cells stained only for DNA. micronuclAI can achieve close to human-level performance on various human and murine cancer cell line datasets. The pipeline achieved a Pearson's correlation of 0.9278 on images obtained at 10X magnification. We tested the approach in otherwise isogenic cell lines in which we genetically dialed up or down CIN rates, and on several publicly available image datasets where we achieved a Pearson's correlation of 0.9620. Given the increasing interest in developing therapies for CIN-driven cancers, this method provides an important, scalable, and rapid approach to quantifying CIN on images that are routinely obtained for research purposes. We release a GUI-implementation for easy access and utilization of the pipeline.

PMID:40038430 | DOI:10.1038/s42003-025-07796-4

Geroscience. 2025 Mar 4. doi: 10.1007/s11357-025-01571-3. Online ahead of print.

ABSTRACT

A significant challenge in multi-omic geroscience research is the collection of high quality, fit-for-purpose biospecimens from a diverse and well-characterized study population with sufficient sample size to detect age-related changes in physiological biomarkers. The Dog Aging Project designed the precision cohort to study the mechanisms underlying age-related change in the metabolome, microbiome, and epigenome in companion dogs, an emerging model system for translational geroscience research. One thousand dog-owner pairs were recruited into cohort strata based on life stage, sex, size, and geography. We designed and built a novel implementation of the REDCap electronic data capture system to manage study participants, logistics, and biospecimen and survey data collection in a secure online platform. In collaboration with primary care veterinarians, we collected and processed blood, urine, fecal, and hair samples from 976 dogs. The resulting data include complete blood count, chemistry profile, immunophenotyping by flow cytometry, metabolite quantification, fecal microbiome characterization, epigenomic profile, urinalysis, and associated metadata characterizing sample conditions at collection and during lab processing. The project, which has already begun collecting second- and third-year samples from precision cohort dogs, demonstrates that scientifically useful biospecimens can be collected from a geographically dispersed population through collaboration with private veterinary clinics and downstream labs. The data collection infrastructure developed for the precision cohort can be leveraged for future studies. Most important, the Dog Aging Project is an open data project. We encourage researchers around the world to apply for data access and utilize this rich, constantly growing dataset in their own work.

PMID:40038157 | DOI:10.1007/s11357-025-01571-3

Plant Cell. 2025 Mar 4:koaf041. doi: 10.1093/plcell/koaf041. Online ahead of print.

ABSTRACT

Powdery mildews are obligate biotrophic fungi that manipulate plant metabolism to supply lipids to the fungus, particularly during fungal asexual reproduction when lipid demand is high. We found levels of leaf storage lipids (triacylglycerols, TAGs) are 3.5-fold higher in whole Arabidopsis (Arabidopsis thaliana) leaves with a 15-fold increase in storage lipids at the infection site during fungal asexual reproduction. Lipid bodies, not observable in uninfected mature leaves, were found in and external to chloroplasts in mesophyll cells underlying the fungal feeding structure. Concomitantly, thylakoid disassembly occurred and thylakoid membrane lipid levels decreased. Genetic analyses showed that canonical endoplasmic reticulum TAG biosynthesis does not support powdery mildew spore production. Instead, Arabidopsis chloroplast-localized DIACYLGLYCEROL ACYLTRANSFERASE 3 (DGAT3) promoted fungal asexual reproduction. Consistent with the reported AtDGAT3 preference for 18:3 and 18:2 acyl substrates, which are dominant in thylakoid membrane lipids, dgat3 mutants exhibited a dramatic reduction in powdery mildew-induced chloroplast TAGs, attributable to decreases in TAG species largely comprised of 18:3 and 18:2 acyl substrates. This pathway for TAG biosynthesis in the chloroplast at the expense of thylakoids provides insights into obligate biotrophy and plant lipid metabolism, plasticity and function. By understanding how photosynthetically active leaves can be converted into TAG producers, more sustainable and environmentally friendly plant oil production may be developed.

PMID:40037697 | DOI:10.1093/plcell/koaf041

Plant Physiol. 2025 Mar 4:kiaf066. doi: 10.1093/plphys/kiaf066. Online ahead of print.

NO ABSTRACT

PMID:40037614 | DOI:10.1093/plphys/kiaf066

G3 (Bethesda). 2025 Mar 3:jkaf047. doi: 10.1093/g3journal/jkaf047. Online ahead of print.

ABSTRACT

Genetic suppression occurs when the phenotypic defects caused by a deleterious mutation are rescued by another mutation. Suppression interactions are of particular interest for genetic diseases, as they identify ways to reduce disease severity, thereby potentially highlighting avenues for therapeutic intervention. To what extent suppression interactions are influenced by the genetic background in which they operate remains largely unknown. However, a high degree of suppression conservation would be crucial for developing therapeutic strategies that target suppressors. To gain an understanding of the effect of the genetic context on suppression, we isolated spontaneous suppressor mutations of temperature sensitive alleles of SEC17, TAO3, and GLN1 in three genetically diverse natural isolates of the budding yeast Saccharomyces cerevisiae. After identifying and validating the genomic variants responsible for suppression, we introduced the suppressors in all three genetic backgrounds, as well as in a laboratory strain, to assess their specificity. Ten out of eleven tested suppression interactions were conserved in the four yeast strains, although the extent to which a suppressor could rescue the temperature sensitive mutant varied across genetic backgrounds. These results suggest that suppression mechanisms are highly conserved across genetic contexts, a finding that is potentially reassuring for the development of therapeutics that mimic genetic suppressors.

PMID:40037589 | DOI:10.1093/g3journal/jkaf047

Plant Physiol. 2025 Mar 4:kiaf064. doi: 10.1093/plphys/kiaf064. Online ahead of print.

NO ABSTRACT

PMID:40037583 | DOI:10.1093/plphys/kiaf064

Open Biol. 2025 Mar;15(3):240231. doi: 10.1098/rsob.240231. Epub 2025 Mar 5.

ABSTRACT

The lung microbiome has recently gained attention for potentially affecting respiratory viral infections, including influenza A virus, respiratory syncytial virus (RSV) and SARS-CoV-2. We will discuss the complexities of the lung microenvironment in the context of viral infections and the use of organ-on-chip (OoC) models in replicating the respiratory tract milieu to aid in understanding the role of temporary microbial colonization. Leveraging the innovative capabilities of OoC, particularly through integrating gut and lung models, opens new avenues to understand the mechanisms linking inter-organ crosstalk and respiratory infections. We will discuss technical aspects of OoC lung models, ranging from the selection of cell substrates for extracellular matrix mimicry, mechanical strain, breathing mechanisms and air-liquid interface to the integration of immune cells and use of microscopy tools for algorithm-based image analysis and systems biology to study viral infection in vitro. OoC offers exciting new options to study viral infections across host species and to investigate human cellular physiology at a personalized level. This review bridges the gap between complex biological phenomena and the technical prowess of OoC models, providing a comprehensive roadmap for researchers in the field.

PMID:40037530 | DOI:10.1098/rsob.240231

J Clin Endocrinol Metab. 2025 Mar 3:dgaf131. doi: 10.1210/clinem/dgaf131. Online ahead of print.

ABSTRACT

CONTEXT: Tangier disease (TD) is a rare, autosomal recessive genetic disorder associated with a deficiency in cellular cholesterol export leading to cholesterol accumulation in peripheral tissues. With approximately 150 described cases, the disease is significantly understudied, and the clinical presentation appears to be heterogenous.

OBJECTIVE: To investigate the phenotype and lipid metabolism in TD.

DESIGN: Multicenter cohort study.

PATIENTS: Four patients with TD.

MAIN OUTCOME MEASURES: Nuclear magnetic resonance (NMR)-based lipidomic and metabolomic analyses were performed in patients with TD and healthy controls.

RESULTS: While showing similar laboratory patterns with respect to high-density lipoprotein depletion, the clinical phenotypes of four TD patients were heterogenous with two patients diagnosed at 47 and 72 years having predominantly gastrointestinal and neurological phenotypes. Two previously undescribed variants (c.2418G>A, c.5055.del) were reported.Apart from pathognomonic changes in HDL composition, NMR spectroscopy revealed an increased abundance of VLDL with higher total lipid and cholesterol concentrations, pointing towards an impaired clearance of triglyceride-rich lipoproteins. Increased triglyceride-rich IDL supports impaired hepatic lipase activity, together with a CETP-mediated increase in LDL-triglycerides at higher abundance of large LDL subtypes and decreased small dense LDL.The lipid composition of HDL particles and LDL-1/LDL-4 remained the strongest differentiating factors as compared to healthy controls.

CONCLUSIONS: Clinical phenotypes of TD can be heterogeneous including gastrointestinal and neurological manifestations. Impaired triglyceride-rich lipoprotein clearance and hepatic lipase activity could be a pathophysiological hallmark of TD.

PMID:40037526 | DOI:10.1210/clinem/dgaf131

Prog Neuropsychopharmacol Biol Psychiatry. 2025 Mar 2:111309. doi: 10.1016/j.pnpbp.2025.111309. Online ahead of print.

ABSTRACT

BACKGROUND: The diagnosis and treatment of Alzheimer's disease (AD) is challenging due to the complexity of its pathogenesis. Although research suggests a link between circulating metabolites and AD, their causal relationship is not fully understood.

METHODS: Based on publicly available genome-wide association study data, we investigated the causative relationship between AD (7759 cases and 334,740 controls) and 233 traits describing circulating metabolites (136,016 participants) using a two-sample Mendelian randomization (MR) method. We adopted the inverse variance weighted approach as the priority and performed sensitivity analyses with MR-Egger intercept method and Cochran's Q test.

RESULTS: The overall causal effect of circulating metabolic traits on AD was significantly higher than the inverse effect (beta: 0.15 ± 0.42 vs. 0.04 ± 0.07; p < 0.05). A total of 72 circulating metabolic traits (odd ratio (OR): 1.16-2.48) had a significant positive causal effect on AD, while a total of 16 circulating metabolic traits with significant negative causal effects on AD were detected (OR: 0.38-0.88). AD had a significant positive causal effect (OR: 1.02-1.17) on 142 circulating metabolic traits and a negative causal effect (OR: 0.87-0.99) on 43 circulating metabolic traits. Circulating metabolites that have a bi-directional causative relationship with AD mainly include apolipoprotein B levels, total cholesterol levels, total triglycerides levels, and omega-6 fatty acids levels.

CONCLUSION: The causative relationship between AD and the circulating metabolic traits is complex and bidirectional. Analyzing metabolites causally involved in the development of AD may provide clues for identifying preventive and therapeutic targets for this disorder.

PMID:40037511 | DOI:10.1016/j.pnpbp.2025.111309