Front Mol Biosci. 2023 Oct 12;10:1242943. doi: 10.3389/fmolb.2023.1242943. eCollection 2023.

ABSTRACT

The holm oak (Quercus ilex subsp. ballota) is the most representative species of the Mediterranean Basin and the agrosylvopastoral Spanish "dehesa" ecosystem. Being part of our life, culture, and subsistence since ancient times, it has significant environmental and economic importance. More recently, there has been a renewed interest in using the Q. ilex acorn as a functional food due to its nutritional and nutraceutical properties. However, the holm oak and its related ecosystems are threatened by different factors, with oak decline syndrome and climate change being the most worrying in the short and medium term. Breeding programs informed by the selection of elite genotypes seem to be the most plausible biotechnological solution to rescue populations under threat. To achieve this and other downstream analyses, we need a high-quality and well-annotated Q. ilex reference genome. Here, we introduce the first draft genome assembly of Q. ilex using long-read sequencing (PacBio). The assembled nuclear haploid genome had 530 contigs totaling 842.2 Mbp (N50 = 3.3 Mbp), of which 448.7 Mb (53%) were repetitive sequences. We annotated 39,443 protein-coding genes of which 94.80% were complete and single-copy genes. Phylogenetic analyses showed no evidence of a recent whole-genome duplication, and high synteny of the 12 chromosomes between Q. ilex and Quercus lobata and between Q. ilex and Quercus robur. The chloroplast genome size was 142.3 Kbp with 149 protein-coding genes successfully annotated. This first draft should allow for the validation of omics data as well as the identification and functional annotation of genes related to phenotypes of interest such as those associated with resilience against oak decline syndrome and climate change and higher acorn productivity and nutraceutical value.

PMID:37905231 | PMC:PMC10613499 | DOI:10.3389/fmolb.2023.1242943

bioRxiv. 2023 Oct 17:2023.10.17.562707. doi: 10.1101/2023.10.17.562707. Preprint.

ABSTRACT

Multidrug-resistant tuberculosis (MDR-TB) is a growing source of global mortality and threatens global control of tuberculosis (TB) disease. The diarylquinoline bedaquiline (BDQ) recently emerged as a highly efficacious drug against MDR-TB, defined as resistance to the first-line drugs isoniazid (INH) and rifampin. INH resistance is primarily caused by loss-of-function mutations in the catalase KatG, but mechanisms underlying BDQ's efficacy against MDR-TB remain unknown. Here we employ a systems biology approach to investigate BDQ hyper-susceptibility in INH-resistant Mycobacterium tuberculosis . We found hyper-susceptibility to BDQ in INH-resistant cells is due to several physiological changes induced by KatG deficiency, including increased susceptibility to reactive oxygen species and DNA damage, remodeling of transcriptional programs, and metabolic repression of folate biosynthesis. We demonstrate BDQ hyper-susceptibility is common in INH-resistant clinical isolates. Collectively, these results highlight how altered bacterial physiology can impact drug efficacy in drug-resistant bacteria.

PMID:37905073 | PMC:PMC10614911 | DOI:10.1101/2023.10.17.562707

Nat Cancer. 2023 Oct 30. doi: 10.1038/s43018-023-00652-6. Online ahead of print.

ABSTRACT

Safely expanding indications for cellular therapies has been challenging given a lack of highly cancer-specific surface markers. Here we explore the hypothesis that tumor cells express cancer-specific surface protein conformations that are invisible to standard target discovery pipelines evaluating gene or protein expression, and these conformations can be identified and immunotherapeutically targeted. We term this strategy integrating cross-linking mass spectrometry with glycoprotein surface capture 'structural surfaceomics'. As a proof of principle, we apply this technology to acute myeloid leukemia (AML), a hematologic malignancy with dismal outcomes and no known optimal immunotherapy target. We identify the activated conformation of integrin β2 as a structurally defined, widely expressed AML-specific target. We develop and characterize recombinant antibodies to this protein conformation and show that chimeric antigen receptor T cells eliminate AML cells and patient-derived xenografts without notable toxicity toward normal hematopoietic cells. Our findings validate an AML conformation-specific target antigen and demonstrate a tool kit for applying these strategies more broadly.

PMID:37904046 | DOI:10.1038/s43018-023-00652-6

Sci Rep. 2023 Oct 30;13(1):18601. doi: 10.1038/s41598-023-45863-0.

ABSTRACT

Bacterial extracellular vesicles (EVs) are generally formed by pinching off outer membrane leaflets while simultaneously releasing multiple active molecules into the external environment. In this study, we aimed to identify the protein cargo of leptospiral EVs released from intact leptospires grown under three different conditions: EMJH medium at 30 °C, temperature shifted to 37 °C, and physiologic osmolarity (EMJH medium with 120 mM NaCl). The naturally released EVs observed under transmission electron microscopy were spherical in shape with an approximate diameter of 80-100 nm. Quantitative proteomics and bioinformatic analysis indicated that the EVs were formed primarily from the outer membrane and the cytoplasm. The main functional COG categories of proteins carried in leptospiral EVs might be involved in cell growth, survival and adaptation, and pathogenicity. Relative to their abundance in EVs grown in EMJH medium at 30 °C, 39 and 69 proteins exhibited significant changes in response to the temperature shift and the osmotic change, respectively. During exposure to both stresses, Leptospira secreted several multifunctional proteins via EVs, while preserving certain virulence proteins within whole cells. Therefore, leptospiral EVs may serve as a decoy structure for host responses, whereas some virulence factors necessary for direct interaction with the host environment are reserved in leptospiral cells. This knowledge will be useful for understanding the pathogenesis of leptospirosis and developing as one of vaccine platforms against leptospirosis in the future.

PMID:37903905 | DOI:10.1038/s41598-023-45863-0

Nat Commun. 2023 Oct 30;14(1):6903. doi: 10.1038/s41467-023-42682-9.

ABSTRACT

Sensitive and reliable protein biomarkers are needed to predict disease trajectory and personalize treatment strategies for multiple sclerosis (MS). Here, we use the highly sensitive proximity-extension assay combined with next-generation sequencing (Olink Explore) to quantify 1463 proteins in cerebrospinal fluid (CSF) and plasma from 143 people with early-stage MS and 43 healthy controls. With longitudinally followed discovery and replication cohorts, we identify CSF proteins that consistently predicted both short- and long-term disease progression. Lower levels of neurofilament light chain (NfL) in CSF is superior in predicting the absence of disease activity two years after sampling (replication AUC = 0.77) compared to all other tested proteins. Importantly, we also identify a combination of 11 CSF proteins (CXCL13, LTA, FCN2, ICAM3, LY9, SLAMF7, TYMP, CHI3L1, FYB1, TNFRSF1B and NfL) that predict the severity of disability worsening according to the normalized age-related MS severity score (replication AUC = 0.90). The identification of these proteins may help elucidate pathogenetic processes and might aid decisions on treatment strategies for persons with MS.

PMID:37903821 | DOI:10.1038/s41467-023-42682-9

Nat Commun. 2023 Oct 30;14(1):6928. doi: 10.1038/s41467-023-42560-4.

ABSTRACT

The impact of genetic regulatory variation active in early pancreatic development on adult pancreatic disease and traits is not well understood. Here, we generate a panel of 107 fetal-like iPSC-derived pancreatic progenitor cells (iPSC-PPCs) from whole genome-sequenced individuals and identify 4065 genes and 4016 isoforms whose expression and/or alternative splicing are affected by regulatory variation. We integrate eQTLs identified in adult islets and whole pancreas samples, which reveal 1805 eQTL associations that are unique to the fetal-like iPSC-PPCs and 1043 eQTLs that exhibit regulatory plasticity across the fetal-like and adult pancreas tissues. Colocalization with GWAS risk loci for pancreatic diseases and traits show that some putative causal regulatory variants are active only in the fetal-like iPSC-PPCs and likely influence disease by modulating expression of disease-associated genes in early development, while others with regulatory plasticity likely exert their effects in both the fetal and adult pancreas by modulating expression of different disease genes in the two developmental stages.

PMID:37903777 | DOI:10.1038/s41467-023-42560-4

NPJ Syst Biol Appl. 2023 Oct 30;9(1):54. doi: 10.1038/s41540-023-00314-4.

ABSTRACT

In systems biology, mathematical models and simulations play a crucial role in understanding complex biological systems. Different modelling frameworks are employed depending on the nature and scales of the system under study. For instance, signalling and regulatory networks can be simulated using Boolean modelling, whereas multicellular systems can be studied using agent-based modelling. Herein, we present PhysiBoSS 2.0, a hybrid agent-based modelling framework that allows simulating signalling and regulatory networks within individual cell agents. PhysiBoSS 2.0 is a redesign and reimplementation of PhysiBoSS 1.0 and was conceived as an add-on that expands the PhysiCell functionalities by enabling the simulation of intracellular cell signalling using MaBoSS while keeping a decoupled, maintainable and model-agnostic design. PhysiBoSS 2.0 also expands the set of functionalities offered to the users, including custom models and cell specifications, mechanistic submodels of substrate internalisation and detailed control over simulation parameters. Together with PhysiBoSS 2.0, we introduce PCTK, a Python package developed for handling and processing simulation outputs, and generating summary plots and 3D renders. PhysiBoSS 2.0 allows studying the interplay between the microenvironment, the signalling pathways that control cellular processes and population dynamics, suitable for modelling cancer. We show different approaches for integrating Boolean networks into multi-scale simulations using strategies to study the drug effects and synergies in models of cancer cell lines and validate them using experimental data. PhysiBoSS 2.0 is open-source and publicly available on GitHub with several repositories of accompanying interoperable tools.

PMID:37903760 | DOI:10.1038/s41540-023-00314-4

J Chem Inf Model. 2023 Oct 30. doi: 10.1021/acs.jcim.3c01245. Online ahead of print.

ABSTRACT

Conformer generation, the assignment of realistic 3D coordinates to a small molecule, is fundamental to structure-based drug design. Conformational ensembles are required for rigid-body matching algorithms, such as shape-based or pharmacophore approaches, and even methods that treat the ligand flexibly, such as docking, are dependent on the quality of the provided conformations due to not sampling all degrees of freedom (e.g., only sampling torsions). Here, we empirically elucidate some general principles about the size, diversity, and quality of the conformational ensembles needed to get the best performance in common structure-based drug discovery tasks. In many cases, our findings may parallel "common knowledge" well-known to practitioners of the field. Nonetheless, we feel that it is valuable to quantify these conformational effects while reproducing and expanding upon previous studies. Specifically, we investigate the performance of a state-of-the-art generative deep learning approach versus a more classical geometry-based approach, the effect of energy minimization as a postprocessing step, the effect of ensemble size (maximum number of conformers), and construction (filtering by root-mean-square deviation for diversity) and how these choices influence the ability to recapitulate bioactive conformations and perform pharmacophore screening and molecular docking.

PMID:37903507 | DOI:10.1021/acs.jcim.3c01245

Genome Res. 2023 Oct 30. doi: 10.1101/gr.277891.123. Online ahead of print.

ABSTRACT

Rapid advances in spatial transcriptomics (ST) have revolutionized the interrogation of spatial heterogeneity and increase the demand for comprehensive methods to effectively characterize spatial domains. As a prerequisite for ST data analysis, spatial domain characterization is a crucial step for downstream analyses and biological implications. Here we propose a prior-based self-attention framework for spatial transcriptomics (PAST), a variational graph convolutional autoencoder for ST, which effectively integrates prior information via a Bayesian neural network, captures spatial patterns via a self-attention mechanism, and enables scalable application via a ripple walk sampler strategy. Through comprehensive experiments on data sets generated by different technologies, we show that PAST can effectively characterize spatial domains and facilitate various downstream analyses, including ST visualization, spatial trajectory inference and pseudotime analysis. Also, we highlight the advantages of PAST for multislice joint embedding and automatic annotation of spatial domains in newly sequenced ST data. Compared with existing methods, PAST is the first ST method that integrates reference data to analyze ST data. We anticipate that PAST will open up new avenues for researchers to decipher ST data with customized reference data, which expands the applicability of ST technology.

PMID:37903634 | DOI:10.1101/gr.277891.123

Proc Natl Acad Sci U S A. 2023 Nov 7;120(45):e2317677120. doi: 10.1073/pnas.2317677120. Epub 2023 Oct 30.

NO ABSTRACT

PMID:37903253 | DOI:10.1073/pnas.2317677120

Mol Biol Cell. 2023 Oct 30:mbcE23060246. doi: 10.1091/mbc.E23-06-0246. Online ahead of print.

ABSTRACT

The Hippo pathway is an evolutionarily conserved regulator of tissue growth. Multiple Hippo signaling components are regulated via proteolytic degradation. However, how these degradation mechanisms are themselves modulated remains unexplored. Kibra is a key upstream pathway activator that promotes its own ubiquitin-mediated degradation upon assembling a Hippo signaling complex. Here, we demonstrate that Hippo complex-dependent Kibra degradation is modulated by cortical tension. Using classical genetic, osmotic, and pharmacological manipulations of myosin activity and cortical tension, we show that increasing cortical tension leads to Kibra degradation, whereas decreasing cortical tension increases Kibra abundance. Our study also implicates Par-1 in regulating Kib abundance downstream of cortical tension. We demonstrate that Par-1 promotes ubiquitin-mediated Kib degradation in a Hippo complex-dependent manner and is required for tension-induced Kib degradation. Collectively, our results reveal a previously unknown molecular mechanism by which cortical tension affects Hippo signaling and provide novel insights into the role of mechanical forces in growth control. [Media: see text] [Media: see text] [Media: see text].

PMID:37903240 | DOI:10.1091/mbc.E23-06-0246

Mol Biol Rep. 2023 Oct 30. doi: 10.1007/s11033-023-08877-5. Online ahead of print.

ABSTRACT

BACKGROUND: Bioactive polysaccharides are a promising way for bone disease prevention with high efficiency. Schizophyllan (SPG) is a polysaccharide derived from a species of fungus with anticancer, antitumor, and anti-inflammatory effects. In the present study, for the first time, the cell proliferation, osteogenic markers, mineral deposition, and osteogenic gene expression of human adipose tissue-derived mesenchymal stem cells (hADMSCs) grown on SPG were evaluated by in vitro assays.

METHODS AND RESULTS: The cytotoxicity of SPG was measured using the MTT assay and acridine orange staining. Differentiation of hADMSCs was assessed using alkaline phosphatase (ALP) activity test, cellular calcium content assay, and mineralized matrix staining. To this end, Alizarin red S, von Kossa staining, and the expression of bone-specific markers, including ALP, Runx2, and osteonectin, were used by real-time RT-PCR over a 2-week period. According to the results, SPG at 10 µg/ml concentration was determined as the optimal dosage for differentiation studies. The results of osteogenic differentiation tests showed that compared to the control groups in vitro, SPG enhanced the osteogenic markers and mineralization as well as upregulation of the expression of bone specific genes in differentiated hADMSCs during differentiation.

CONCLUSIONS: The results revealed that SPG could be applied as effective factor for osteogenic differentiation in the future. The current study provides insights into the hADMSC-based treatment and introduces promising therapeutic material for individuals who suffer from bone defects and injuries.

PMID:37902909 | DOI:10.1007/s11033-023-08877-5

J Am Soc Nephrol. 2023 Oct 30. doi: 10.1681/ASN.0000000000000255. Online ahead of print.

NO ABSTRACT

PMID:37902619 | DOI:10.1681/ASN.0000000000000255

Protein Sci. 2023 Oct 30:e4822. doi: 10.1002/pro.4822. Online ahead of print.

ABSTRACT

Post-translational modification (PTM) of a protein occurs after it has been synthesized from its genetic template, and involves chemical modifications of the protein's specific amino acid residues. Despite of the central role played by PTM in regulating molecular interactions, particularly those driven by reversible redox reactions, it remains challenging to interpret PTMs in terms of protein dynamics and function because there are numerous combinatorially enormous means for modifying amino acids in response to changes in protein environment. In this study, we provide a workflow that allows users to interpret how perturbations caused by PTMs affect a protein's properties, dynamics, and interactions with its binding partners based on inferred or experimentally determined protein structure. This Python-based workflow, called PTM-Psi, integrates several established open-source software packages, thereby enabling the user to infer protein structure from sequence, develop force fields for non-standard amino acids using quantum mechanics, calculate free energy perturbations through molecular dynamics simulations, and score the bound complexes via docking algorithms. Using the S-nitrosylation of several cysteines on the GAP2 protein as an example, we demonstrated the utility of PTM-Psi for interpreting sequence-structure-function relationships derived from thiol redox proteomics data. We demonstrate that the S-nitrosylated cysteine that is exposed to the solvent indirectly affects the catalytic reaction of another buried cysteine over distance in GAP2 protein through the movement of the two ligands. Our workflow tracks the PTMs on residues that are responsive to changes in redox environment and lays the foundation for the automation of molecular and systems biology modelling. This article is protected by copyright. All rights reserved.

PMID:37902126 | DOI:10.1002/pro.4822

Expert Rev Mol Diagn. 2023 Oct 30. doi: 10.1080/14737159.2023.2277371. Online ahead of print.

NO ABSTRACT

PMID:37902050 | DOI:10.1080/14737159.2023.2277371

ACS Omega. 2023 Oct 13;8(42):39662-39672. doi: 10.1021/acsomega.3c05571. eCollection 2023 Oct 24.

ABSTRACT

The mining of antidiabetic dipeptidyl peptidase IV (DPP-IV) inhibitory peptides (DPP-IV-IPs) is currently a costly and laborious process. Due to the absence of rational peptide design rules, it relies on cumbersome screening of unknown enzyme hydrolysates. Here, we present an enhanced deep learning model called bidirectional encoder representation (BERT)-DPPIV, specifically designed to classify DPP-IV-IPs and explore their design rules to discover potent candidates. The end-to-end model utilizes a fine-tuned BERT architecture to extract structural/functional information from input peptides and accurately identify DPP-IV-Ips from input peptides. Experimental results in the benchmark data set showed BERT-DPPIV yielded state-of-the-art accuracy and MCC of 0.894 and 0.790, surpassing the 0.797 and 0.594 obtained by the sequence-feature model. Furthermore, we leveraged the attention mechanism to uncover that our model could recognize the restriction enzyme cutting site and specific residues that contribute to the inhibition of DPP-IV. Moreover, guided by BERT-DPPIV, proposed design rules for DPP-IV inhibitory tripeptides and pentapeptides were validated, and they can be used to screen potent DPP-IV-IPs.

PMID:37901493 | PMC:PMC10601436 | DOI:10.1021/acsomega.3c05571

Brain Commun. 2023 Oct 18;5(5):fcad272. doi: 10.1093/braincomms/fcad272. eCollection 2023.

ABSTRACT

Idiopathic intracranial hypertension, a disease classically occurring in women with obesity, is characterized by raised intracranial pressure. Weight loss leads to the reduction in intracranial pressure. Additionally, pharmacological glucagon-like peptide-1 agonism reduces cerebrospinal fluid secretion and intracranial pressure. The potential mechanisms by which weight loss reduces intracranial pressure are unknown and were the focus of this study. Meal stimulation tests (fasted plasma sample, then samples at 15, 30, 60, 90 and 120 min following a standardized meal) were conducted pre- and post-bariatric surgery [early (2 weeks) and late (12 months)] in patients with active idiopathic intracranial hypertension. Dynamic changes in gut neuropeptides (glucagon-like peptide-1, gastric inhibitory polypeptide and ghrelin) and metabolites (untargeted ultra-high performance liquid chromatography-mass spectrometry) were evaluated. We determined the relationship between gut neuropeptides, metabolites and intracranial pressure. Eighteen idiopathic intracranial hypertension patients were included [Roux-en-Y gastric bypass (RYGB) n = 7, gastric banding n = 6 or sleeve gastrectomy n = 5]. At 2 weeks post-bariatric surgery, despite similar weight loss, RYGB had a 2-fold (50%) greater reduction in intracranial pressure compared to sleeve. Increased meal-stimulated glucagon-like peptide-1 secretion was observed after RYGB (+600%) compared to sleeve (+319%). There was no change in gastric inhibitory polypeptide and ghrelin. Dynamic changes in meal-stimulated metabolites after bariatric surgery consistently identified changes in lipid metabolites, predominantly ceramides, glycerophospholipids and lysoglycerophospholipids, which correlated with intracranial pressure. A greater number of differential lipid metabolites were observed in the RYGB cohort at 2 weeks, and these also correlated with intracranial pressure. In idiopathic intracranial hypertension, we identified novel changes in lipid metabolites and meal-stimulated glucagon-like peptide-1 levels following bariatric surgery which were associated with changes in intracranial pressure. RYGB was most effective at reducing intracranial pressure despite analogous weight loss to gastric sleeve at 2 weeks post-surgery and was associated with more pronounced changes in these metabolite pathways. We suggest that these novel perturbations in lipid metabolism and glucagon-like peptide-1 secretion are mechanistically important in driving a reduction in intracranial pressure following weight loss in patients with idiopathic intracranial hypertension. Therapeutic targeting of these pathways, for example with glucagon-like peptide-1 agonist infusion, could represent a therapeutic strategy.

PMID:37901040 | PMC:PMC10608960 | DOI:10.1093/braincomms/fcad272

JAMIA Open. 2023 Oct 27;6(4):ooad090. doi: 10.1093/jamiaopen/ooad090. eCollection 2023 Dec.

ABSTRACT

OBJECTIVE: Anaphylaxis is a severe life-threatening allergic reaction, and its accurate identification in healthcare databases can harness the potential of "Big Data" for healthcare or public health purposes.

METHODS: This study used claims data obtained between October 1, 2015 and February 28, 2019 from the CMS database to examine the utility of machine learning in identifying incident anaphylaxis cases. We created a feature selection pipeline to identify critical features between different datasets. Then a variety of unsupervised and supervised methods were used (eg, Sammon mapping and eXtreme Gradient Boosting) to train models on datasets of differing data quality, which reflects the varying availability and potential rarity of ground truth data in medical databases.

RESULTS: Resulting machine learning model accuracies ranged between 47.7% and 94.4% when tested on ground truth data. Finally, we found new features to help experts enhance existing case-finding algorithms.

DISCUSSION: Developing precise algorithms to detect medical outcomes in claims can be a laborious and expensive process, particularly for conditions presented and coded diversely. We found it beneficial to filter out highly potent codes used for data curation to identify underlying patterns and features. To improve rule-based algorithms where necessary, researchers could use model explainers to determine noteworthy features, which could then be shared with experts and included in the algorithm.

CONCLUSION: Our work suggests machine learning models can perform at similar levels as a previously published expert case-finding algorithm, while also having the potential to improve performance or streamline algorithm construction processes by identifying new relevant features for algorithm construction.

PMID:37900974 | PMC:PMC10611436 | DOI:10.1093/jamiaopen/ooad090

J Clin Transl Sci. 2023 Sep 14;7(1):e214. doi: 10.1017/cts.2023.619. eCollection 2023.

ABSTRACT

Knowledge graphs have become a common approach for knowledge representation. Yet, the application of graph methodology is elusive due to the sheer number and complexity of knowledge sources. In addition, semantic incompatibilities hinder efforts to harmonize and integrate across these diverse sources. As part of The Biomedical Translator Consortium, we have developed a knowledge graph-based question-answering system designed to augment human reasoning and accelerate translational scientific discovery: the Translator system. We have applied the Translator system to answer biomedical questions in the context of a broad array of diseases and syndromes, including Fanconi anemia, primary ciliary dyskinesia, multiple sclerosis, and others. A variety of collaborative approaches have been used to research and develop the Translator system. One recent approach involved the establishment of a monthly "Question-of-the-Month (QotM) Challenge" series. Herein, we describe the structure of the QotM Challenge; the six challenges that have been conducted to date on drug-induced liver injury, cannabidiol toxicity, coronavirus infection, diabetes, psoriatic arthritis, and ATP1A3-related phenotypes; the scientific insights that have been gleaned during the challenges; and the technical issues that were identified over the course of the challenges and that can now be addressed to foster further development of the prototype Translator system. We close with a discussion on Large Language Models such as ChatGPT and highlight differences between those models and the Translator system.

PMID:37900350 | PMC:PMC10603356 | DOI:10.1017/cts.2023.619

Iran J Public Health. 2023 Oct;52(10):2169-2178. doi: 10.18502/ijph.v52i10.13855.

ABSTRACT

BACKGROUND: Professional driving is associated with overworking, lack of physical activity, and high stress, which are susceptible to cardiovascular diseases (CVDs). We aimed to determine the prevalence of hypertension and obesity in Iranian professional drivers.

METHODS: Overall, 132,452 drivers were included by census sampling methods and those who did not pass periodic examinations were excluded. Demographics and anthropometric data, including height and weight and the driver's blood pressure, were recorded. The criteria for hypertension assumed as the systolic blood pressure ≥ 130 mm and/or diastolic blood pressure ≥ 80 mm, and the criteria for prehypertension assumed as 120-129 systolic and < 80 mm Hg. In addition, body mass index (BMI) ≥ 25 is assumed as overweight, and BMI ≥ 30 is assumed as obesity.

RESULTS: Overall, 113,856 male drivers were included in the final analysis. The prevalence of HTN, pre-HTN, and abnormal blood pressure (HTN + pre-HTN) was calculated to be 14.2%, 57.4%, and 71.6%, respectively. Khuzestan, West Azerbaijan, and Yazd had the most prevalence of abnormal blood pressure. The prevalence of overweight, obesity, and abnormal weight (overweight + obesity) was calculated to be 50.9%, 22.6%, and 73.5%, respectively, and the northwest provinces had the highest prevalence of abnormal weight.

CONCLUSION: Professional Iranian drivers have a high prevalence of abnormal blood pressure and weight associated with job-related risk factors. Preventive measures should be taken to confront a possible outbreak of CVDs in this population.

PMID:37899925 | PMC:PMC10612542 | DOI:10.18502/ijph.v52i10.13855