DNA double-stranded breaks, a hallmark of aging, defined at the nucleotide resolution, are increased and associated with transcription in the cardiac myocytes in LMNA-cardiomyopathy.

AIMS: An intrinsic feature of gene transcription is the formation of DNA superhelices near the transcription bubble, which are resolved upon induction of transient double-stranded breaks (DSBs) by topoisomerases. Unrepaired DSBs are pathogenic as they lead to cell cycle arrest, senescence, inflammation, and organ dysfunction. We posit that DSBs would be more prevalent at the genomic sites that are associated with gene expression. The objectives were to identify and characterize genome-wide DSBs at the nucleotide resolution and determine the association of DSBs with transcription in cardiac myocytes. METHODS AND RESULTS: We identified the genome-wide DSBs in ∼1 million cardiac myocytes per heart in three wild-type and three myocyte-specific LMNA-deficient (Myh6-Cre:LmnaF/F) mice by END-Sequencing. The prevalence of DSBs was 0.8% and 2.2% in the wild-type and Myh6-Cre:LmnaF/F myocytes, respectively. The END-Seq signals were enriched for 8 and 6764 DSBs in the wild-type and Myh6-Cre:LmnaF/F myocytes, respectively (q < 0.05). The DSBs were preferentially localized to the gene regions, transcription initiation sites, cardiac transcription factor motifs, and the G quadruplex forming structures. Because LMNA regulates transcription through the lamin-associated domains (LADs), we defined the LADs in cardiac myocytes by a Cleavage Under Targets & Release Using Nuclease (CUT&RUN) assay (N = 5). On average there were 818 LADs per myocyte. Constitutive LADs (cLADs), defined as LADs that were shared by at least three genomes (N = 2572), comprised about a third of the mouse cardiac myocyte genomes. Transcript levels of the protein-coding genes located at the cLADs (N = 3975) were ∼16-fold lower than those at the non-LAD regions (N = ∼17 778). The prevalence of DSBs was higher in the non-LAD as compared to the cLAD regions. Likewise, DSBs were more common in the loss-of-LAD regions, defined as the genomic regions in the Myh6-Cre:LmnaF/F that were juxtaposed to the LAD regions in the wild-type myocytes. CONCLUSION: To our knowledge, this is the first identification of the DSBs, at the nucleotide resolution in the cardiovascular system. The prevalence of DSBs was higher in the genomic regions associated with transcription. Because transcription is pervasive, DSBs are expected to be common and pathogenic in various states and aging.

Immunomodulatory zymosan/ι-carrageenan/ agarose hydrogel for targeting M2 to M1 macrophages (antitumoral).

Several studies have been performed on the immunomodulatory effects of yeast ß-(1,3) glucan, but there is no proper evaluation of the thermal and immunomodulating properties of zymosan (ZM). Thermogravimetry analysis indicated a 54% weight loss of ZM at 270 °C. Circular dichroism showed absorption peaks in the region of 250 to 400 nm, suggesting a helical coil ß-sheet configuration. XRD showed a broad peak at 2θ of 20.38°, indicating the crystalline nature, and the size was found to be 23 nm. ZM is biocompatible and showed no toxicity against L929 and RAW 264.7 cell lines (cell viability > 90%). Immunomodulatory studies with PCR showed upregulation of M1 genes in human differentiated THP-1 macrophage cell lines, which were responsible for antitumor properties. The uptake of ZM particles inside the differentiated THP-1 macrophages and Raw 264.7 cells was confirmed (Video clip). ZM particle uptake via Dectin-1 was identified by competitive receptor blocking. Seaweed derived carrageenan/ZM/agarose hydrogel was successfully prepared (@5 : 5 wt%) and was seen to support the growth of L929 cells (1 × 105 cells per mL) and have a higher swelling (≈250-280%). This study indicates that ZM-based hydrogel could be a potential drug carrier (Rifampicin and Levofloxacin) for targeting tumour-associated macrophages (M2).

Montelukast as a repurposable additive drug for standard-efficacy multiple sclerosis treatment: Emulating clinical trials with retrospective administrative health claims data.

BACKGROUND: Effective and safe treatment options for multiple sclerosis (MS) are still needed. Montelukast, a leukotriene receptor antagonist (LTRA) currently indicated for asthma or allergic rhinitis, may provide an additional therapeutic approach. OBJECTIVE: The study aimed to evaluate the effects of montelukast on the relapses of people with MS (pwMS). METHODS: In this retrospective case-control study, two independent longitudinal claims datasets were used to emulate randomized clinical trials (RCTs). We identified pwMS aged 18-65 years, on MS disease-modifying therapies concomitantly, in de-identified claims from Optum's Clinformatics® Data Mart (CDM) and IQVIA PharMetrics® Plus for Academics. Cases included 483 pwMS on montelukast and with medication adherence in CDM and 208 in PharMetrics Plus for Academics. We randomly sampled controls from 35,330 pwMS without montelukast prescriptions in CDM and 10,128 in PharMetrics Plus for Academics. Relapses were measured over a 2-year period through inpatient hospitalization and corticosteroid claims. A doubly robust causal inference model estimated the effects of montelukast, adjusting for confounders and censored patients. RESULTS: pwMS treated with montelukast demonstrated a statistically significant 23.6% reduction in relapses compared to non-users in 67.3% of emulated RCTs. CONCLUSION: Real-world evidence suggested that montelukast reduces MS relapses, warranting future clinical trials and further research on LTRAs' potential mechanism in MS.

Unveiling Gene Interactions in Alzheimer's Disease by Integrating Genetic and Epigenetic Data with a Network-Based Approach.

Alzheimer's Disease (AD) is a complex disease and the leading cause of dementia in older people. We aimed to uncover aspects of AD's pathogenesis that may contribute to drug repurposing efforts by integrating DNA methylation and genetic data. Implementing the network-based tool, a dense module search of genome-wide association studies (dmGWAS), we integrated a large-scale GWAS dataset with DNA methylation data to identify gene network modules associated with AD. Our analysis yielded 286 significant gene network modules. Notably, the foremost module included the BIN1 gene, showing the largest GWAS signal, and the GNAS gene, the most significantly hypermethylated. We conducted Web-based Cell-type-Specific Enrichment Analysis (WebCSEA) on genes within the top 10% of dmGWAS modules, highlighting monocyte as the most significant cell type (p < 5 × 10-12). Functional enrichment analysis revealed Gene Ontology Biological Process terms relevant to AD pathology (adjusted p < 0.05). Additionally, drug target enrichment identified five FDA-approved targets (p-value = 0.03) for further research. In summary, dmGWAS integration of genetic and epigenetic signals unveiled new gene interactions related to AD, offering promising avenues for future studies.

Genetic overlap between Alzheimer's disease and immune-mediated diseases: an atlas of shared genetic determinants and biological convergence.

The occurrence of immune disease comorbidities in Alzheimer's disease (AD) has been observed in both epidemiological and molecular studies, suggesting a neuroinflammatory basis in AD. However, their shared genetic components have not been systematically studied. Here, we composed an atlas of the shared genetic associations between 11 immune-mediated diseases and AD by analyzing genome-wide association studies (GWAS) summary statistics. Our results unveiled a significant genetic overlap between AD and 11 individual immune-mediated diseases despite negligible genetic correlations, suggesting a complex shared genetic architecture distributed across the genome. The shared loci between AD and immune-mediated diseases implicated several genes, including GRAMD1B, FUT2, ADAMTS4, HBEGF, WNT3, TSPAN14, DHODH, ABCB9, and TNIP1, all of which are protein-coding genes and thus potential drug targets. Top biological pathways enriched with these identified shared genes were related to the immune system and cell adhesion. In addition, in silico single-cell analyses showed enrichment of immune and brain cells, including neurons and microglia. In summary, our results suggest a genetic relationship between AD and the 11 immune-mediated diseases, pinpointing the existence of a shared however non-causal genetic basis. These identified protein-coding genes have the potential to serve as a novel path to therapeutic interventions for both AD and immune-mediated diseases and their comorbidities.

Classification of Schizophrenia, Bipolar Disorder and Major Depressive Disorder with Comorbid Traits and Deep Learning Algorithms.

Recent GWASs have demonstrated that comorbid disorders share genetic liabilities. But whether and how these shared liabilities can be used for the classification and differentiation of comorbid disorders remains unclear. In this study, we use polygenic risk scores (PRSs) estimated from 42 comorbid traits and the deep neural networks (DNN) architecture to classify and differentiate schizophrenia (SCZ), bipolar disorder (BIP) and major depressive disorder (MDD). Multiple PRSs were obtained for individuals from the schizophrenia (SCZ) (cases = 6,317, controls = 7,240), bipolar disorder (BIP) (cases = 2,634, controls 4,425) and major depressive disorder (MDD) (cases = 1,704, controls = 3,357) datasets, and classification models were constructed with and without the inclusion of PRSs of the target (SCZ, BIP or MDD). Models with the inclusion of target PRSs performed well as expected. Surprisingly, we found that SCZ could be classified with only the PRSs from 35 comorbid traits (not including the target SCZ and directly related traits) (accuracy 0.760 ± 0.007, AUC 0.843 ± 0.005). Similar results were obtained for BIP (33 traits, accuracy 0.768 ± 0.007, AUC 0.848 ± 0.009), and MDD (36 traits, accuracy 0.794 ± 0.010, AUC 0.869 ± 0.004). Furthermore, these PRSs from comorbid traits alone could effectively differentiate unaffected controls, SCZ, BIP, and MDD patients (average categorical accuracy 0.861 ± 0.003, average AUC 0.961 ± 0.041). These results suggest that the shared liabilities from comorbid traits alone may be sufficient to classify SCZ, BIP and MDD. More importantly, these results imply that a data-driven and objective diagnosis and differentiation of SCZ, BIP and MDD may be feasible.

Large-Scale Fabrication of Freestanding Polymer Ultrathin Porous Membranes for Transparent Transwell Coculture Systems.

Advancements in cell coculture systems with porous membranes have facilitated the simulation of human-like in vitro microenvironments for diverse biomedical applications. However, conventional Transwell membranes face limitations in low porosity (ca. 6%) and optical opacity due to their large thickness (ca. 10 µm). In this study, we demonstrated a one-step, large-scale fabrication of freestanding polymer ultrathin porous (PUP) membranes with thicknesses of hundreds of nanometers. PUP membranes were produced by using a gap-controlled bar-coating process combined with polymer blend phase separation. They are 20 times thinner than Transwell membranes, possessing 3-fold higher porosity and exhibiting high transparency. These membranes demonstrate outstanding molecular permeability and significantly reduce the cell-cell distance, thereby facilitating efficient signal exchange pathways between cells. This research enables the establishment of a cutting-edge in vitro cell coculture system, enhancing optical transparency, and streamlining the large-scale manufacturing of porous membranes.

Genetic Deletion of Galectin-3 Inhibits Pancreatic Cancer Progression and Enhances the Efficacy of Immunotherapy.

BACKGROUND & AIMS: Pancreatic ductal adenocarcinoma (PDAC) has a desmoplastic tumor stroma and immunosuppressive microenvironment. Galectin-3 (GAL3) is enriched in PDAC, highly expressed by cancer cells and myeloid cells. However, the functional roles of GAL3 in the PDAC microenvironment remain elusive. METHODS: We generated a novel transgenic mouse model (LSL-KrasG12D/+;Trp53loxP/loxP;Pdx1-Cre;Lgals3-/- [KPPC;Lgals3-/-]) that allows the genetic depletion of GAL3 from both cancer cells and myeloid cells in spontaneous PDAC formation. Single-cell RNA-sequencing analysis was used to identify the alterations in the tumor microenvironment upon GAL3 depletion. We investigated both the cancer cell-intrinsic function and immunosuppressive function of GAL3. We also evaluated the therapeutic efficacy of GAL3 inhibition in combination with immunotherapy. RESULTS: Genetic deletion of GAL3 significantly inhibited the spontaneous pancreatic tumor progression and prolonged the survival of KPPC;Lgals3-/- mice. Single-cell analysis revealed that genetic deletion of GAL3 altered the phenotypes of immune cells, cancer cells, and other cell populations. GAL3 deletion significantly enriched the antitumor myeloid cell subpopulation with high major histocompatibility complex class II expression. We also identified that GAL3 depletion resulted in CXCL12 upregulation, which could act as a potential compensating mechanism on GAL3 deficiency. Combined inhibition of the CXCL12-CXCR4 axis and GAL3 enhanced the efficacy of anti-PD-1 immunotherapy, leading to significantly inhibited PDAC progression. In addition, deletion of GAL3 also inhibited the basal/mesenchymal-like phenotype of pancreatic cancer cells. CONCLUSIONS: GAL3 promotes PDAC progression and immunosuppression via both cancer cell-intrinsic and immune-related mechanisms. Combined treatment targeting GAL3, CXCL12-CXCR4 axis, and PD-1 represents a novel therapeutic strategy for PDAC.

The BMAL1/HIF2A heterodimer modulates circadian variations of myocardial injury.

Acute myocardial infarction stands as a prominent cause of morbidity and mortality worldwide1-6. Clinical studies have demonstrated that the severity of cardiac injury following myocardial infarction exhibits a circadian pattern, with larger infarct sizes and poorer outcomes in patients experiencing morning onset myocardial infarctions7-14. However, the molecular mechanisms that govern circadian variations of myocardial injury remain unclear. Here, we show that BMAL114-20, a core circadian transcription factor, orchestrates diurnal variability in myocardial injury. Unexpectedly, BMAL1 modulates circadian-dependent cardiac injury by forming a transcriptionally active heterodimer with a non-canonical partner, hypoxia-inducible factor 2 alpha (HIF2A)6,21-23, in a diurnal manner. Substantiating this finding, we determined the cryo-EM structure of the BMAL1/HIF2A/DNA complex, revealing a previously unknown capacity for structural rearrangement within BMAL1, which enables the crosstalk between circadian rhythms and hypoxia signaling. Furthermore, we identified amphiregulin (AREG) as a rhythmic transcriptional target of the BMAL1/HIF2A heterodimer, critical for regulating circadian variations of myocardial injury. Finally, pharmacologically targeting the BMAL1/HIF2A-AREG pathway provides effective cardioprotection, with maximum efficacy when aligned with the pathway's circadian trough. Our findings not only uncover a novel mechanism governing the circadian variations of myocardial injury but also pave the way for innovative circadian-based treatment strategies, potentially shifting current treatment paradigms for myocardial infarction.

Disentangling Accelerated Cognitive Decline from the Normal Aging Process and Unraveling Its Genetic Components: A Neuroimaging-Based Deep Learning Approach.

Background: The progressive cognitive decline, an integral component of Alzheimer's disease (AD), unfolds in tandem with the natural aging process. Neuroimaging features have demonstrated the capacity to distinguish cognitive decline changes stemming from typical brain aging and AD between different chronological points. Objective: To disentangle the normal aging effect from the AD-related accelerated cognitive decline and unravel its genetic components using a neuroimaging-based deep learning approach. Methods: We developed a deep-learning framework based on a dual-loss Siamese ResNet network to extract fine-grained information from the longitudinal structural magnetic resonance imaging (MRI) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. We then conducted genome-wide association studies (GWAS) and post-GWAS analyses to reveal the genetic basis of AD-related accelerated cognitive decline. Results: We used our model to process data from 1,313 individuals, training it on 414 cognitively normal people and predicting cognitive assessment for all participants. In our analysis of accelerated cognitive decline GWAS, we identified two genome-wide significant loci: APOE locus (chromosome 19 p13.32) and rs144614292 (chromosome 11 p15.1). Variant rs144614292 (G > T) has not been reported in previous AD GWA studies. It is within the intronic region of NELL1, which is expressed in neurons and plays a role in controlling cell growth and differentiation. The cell-type-specific enrichment analysis and functional enrichment of GWAS signals highlighted the microglia and immune-response pathways. Conclusions: Our deep learning model effectively extracted relevant neuroimaging features and predicted individual cognitive decline. We reported a novel variant (rs144614292) within the NELL1 gene.

Genome-wide CRISPR screen reveals the synthetic lethality between BCL2L1 inhibition and radiotherapy.

Radiation therapy (RT) is one of the most commonly used anticancer therapies. However, the landscape of cellular response to irradiation, especially to a single high-dose irradiation, remains largely unknown. In this study, we performed a whole-genome CRISPR loss-of-function screen and revealed temporal inherent and acquired responses to RT. Specifically, we found that loss of the IL1R1 pathway led to cellular resistance to RT. This is in part because of the involvement of radiation-induced IL1R1-dependent transcriptional regulation, which relies on the NF-κB pathway. Moreover, the mitochondrial anti-apoptotic pathway, particularly the BCL2L1 gene, is crucially important for cell survival after radiation. BCL2L1 inhibition combined with RT dramatically impeded tumor growth in several breast cancer cell lines and syngeneic models. Taken together, our results suggest that the combination of an apoptosis inhibitor such as a BCL2L1 inhibitor with RT may represent a promising anticancer strategy for solid cancers including breast cancer.

The HSP90-MYC-CDK9 network drives therapeutic resistance in mantle cell lymphoma.

Brexucabtagene autoleucel CAR-T therapy is highly efficacious in overcoming resistance to Bruton's tyrosine kinase inhibitors (BTKi) in mantle cell lymphoma. However, many patients relapse post CAR-T therapy with dismal outcomes. To dissect the underlying mechanisms of sequential resistance to BTKi and CAR-T therapy, we performed single-cell RNA sequencing analysis for 66 samples from 25 patients treated with BTKi and/or CAR-T therapy and conducted in-depth bioinformatics™ analysis. Our analysis revealed that MYC activity progressively increased with sequential resistance. HSP90AB1 (Heat shock protein 90 alpha family class B member 1), a MYC target, was identified as early driver of CAR-T resistance. CDK9 (Cyclin-dependent kinase 9), another MYC target, was significantly upregulated in Dual-R samples. Both HSP90AB1 and CDK9 expression were correlated with MYC activity levels. Pharmaceutical co-targeting of HSP90 and CDK9 synergistically diminished MYC activity, leading to potent anti-MCL activity. Collectively, our study revealed that HSP90-MYC-CDK9 network is the primary driving force of therapeutic resistance.

Unraveling the intercellular communication disruption and key pathways in Alzheimer's disease: an integrative study of single-nucleus transcriptomes and genetic association.

BACKGROUND: Recently, single-nucleus RNA-seq (snRNA-seq) analyses have revealed important cellular and functional features of Alzheimer's disease (AD), a prevalent neurodegenerative disease. However, our knowledge regarding intercellular communication mediated by dysregulated ligand-receptor (LR) interactions remains very limited in AD brains. METHODS: We systematically assessed the intercellular communication networks by using a discovery snRNA-seq dataset comprising 69,499 nuclei from 48 human postmortem prefrontal cortex (PFC) samples. We replicated the findings using an independent snRNA-seq dataset of 56,440 nuclei from 18 PFC samples. By integrating genetic signals from AD genome-wide association studies (GWAS) summary statistics and whole genome sequencing (WGS) data, we prioritized AD-associated Gene Ontology (GO) terms containing dysregulated LR interactions. We further explored drug repurposing for the prioritized LR pairs using the Therapeutic Targets Database. RESULTS: We identified 190 dysregulated LR interactions across six major cell types in AD PFC, of which 107 pairs were replicated. Among the replicated LR signals, we found globally downregulated communications in the astrocytes-to-neurons signaling axis, characterized, for instance, by the downregulation of APOE-related and Calmodulin (CALM)-related LR interactions and their potential regulatory connections to target genes. Pathway analyses revealed 44 GO terms significantly linked to AD, highlighting Biological Processes such as 'amyloid precursor protein processing' and 'ion transmembrane transport,' among others. We prioritized several drug repurposing candidates, such as cromoglicate, targeting the identified dysregulated LR pairs. CONCLUSIONS: Our integrative analysis identified key dysregulated LR interactions in a cell type-specific manner and the associated GO terms in AD, offering novel insights into potential therapeutic targets involved in disrupted cell-cell communication in AD.

Single-cell multiomics decodes regulatory programs for mouse secondary palate development.

Perturbations in gene regulation during palatogenesis can lead to cleft palate, which is among the most common congenital birth defects. Here, we perform single-cell multiome sequencing and profile chromatin accessibility and gene expression simultaneously within the same cells (n = 36,154) isolated from mouse secondary palate across embryonic days (E) 12.5, E13.5, E14.0, and E14.5. We construct five trajectories representing continuous differentiation of cranial neural crest-derived multipotent cells into distinct lineages. By linking open chromatin signals to gene expression changes, we characterize the underlying lineage-determining transcription factors. In silico perturbation analysis identifies transcription factors SHOX2 and MEOX2 as important regulators of the development of the anterior and posterior palate, respectively. In conclusion, our study charts epigenetic and transcriptional dynamics in palatogenesis, serving as a valuable resource for further cleft palate research.

Single-Cell Transcriptomics Reveals Pre-existing COVID-19 Vulnerability Factors in Lung Cancer Patients.

Coronavirus disease 2019 (COVID-19) and cancer are major health threats, and individuals may develop both simultaneously. Recent studies have indicated that patients with cancer are particularly vulnerable to COVID-19, but the molecular mechanisms underlying the associations remain poorly understood. To address this knowledge gap, we collected single-cell RNA-sequencing data from COVID-19, lung adenocarcinoma, small cell lung carcinoma patients, and normal lungs to perform an integrated analysis. We characterized altered cell populations, gene expression, and dysregulated intercellular communication in diseases. Our analysis identified pathologic conditions shared by COVID-19 and lung cancer, including upregulated TMPRSS2 expression in epithelial cells, stronger inflammatory responses mediated by macrophages, increased T-cell response suppression, and elevated fibrosis risk by pathologic fibroblasts. These pre-existing conditions in patients with lung cancer may lead to more severe inflammation, fibrosis, and weakened adaptive immune response upon COVID-19 infection. Our findings revealed potential molecular mechanisms driving an increased COVID-19 risk in patients with lung cancer and suggested preventive and therapeutic targets for COVID-19 in this population. IMPLICATIONS: Our work reveals the potential molecular mechanisms contributing to the vulnerability to COVID-19 in patients with lung cancer.

Enabling the clinical application of artificial intelligence in genomics: a perspective of the AMIA Genomics and Translational Bioinformatics Workgroup.

OBJECTIVE: Given the importance AI in genomics and its potential impact on human health, the American Medical Informatics Association-Genomics and Translational Biomedical Informatics (GenTBI) Workgroup developed this assessment of factors that can further enable the clinical application of AI in this space. PROCESS: A list of relevant factors was developed through GenTBI workgroup discussions in multiple in-person and online meetings, along with review of pertinent publications. This list was then summarized and reviewed to achieve consensus among the group members. CONCLUSIONS: Substantial informatics research and development are needed to fully realize the clinical potential of such technologies. The development of larger datasets is crucial to emulating the success AI is achieving in other domains. It is important that AI methods do not exacerbate existing socio-economic, racial, and ethnic disparities. Genomic data standards are critical to effectively scale such technologies across institutions. With so much uncertainty, complexity and novelty in genomics and medicine, and with an evolving regulatory environment, the current focus should be on using these technologies in an interface with clinicians that emphasizes the value each brings to clinical decision-making.

Injectable Asymmetric Adhesive-Antifouling Bifunctional Hydrogel for Peritoneal Adhesion Prevention.

Peritoneal adhesion is a common problem after abdominal surgery and can lead to various medical problems. In response to the lack of in situ retention and pro-wound healing properties of existing anti-adhesion barriers, this work reports an injectable adhesive-antifouling bifunctional hydrogel (AAB-hydrogel). This AAB-hydrogel can be constructed by "two-step" injection. The tissue adhesive hydrogel based on gallic acid-modified chitosan and aldehyde-modified dextran is prepared as the bottom hydrogel (B-hydrogel) by Schiff base reaction. The aldehyde-modified zwitterionic dextran/carboxymethyl chitosan-based hydrogel is formed on the B-hydrogel surface as the antifouling top hydrogel (T-hydrogel). The AAB-hydrogel exhibits good bilayer binding and asymmetric properties, including tissue adhesive, antifouling, and antimicrobial properties. To evaluate the anti-adhesion effect in vivo, the prepared hydrogels are injected onto the wound surface of a mouse abdominal wall abrasion-cecum defect model. Results suggest that the AAB-hydrogel has antioxidant capacity and can reduce the postoperative inflammatory response by modulating the macrophage phenotype. Moreover, the AAB-hydrogel could effectively inhibit the formation of postoperative adhesions by reducing protein deposition, and resisting fibroblast adhesions and bacteria attacking. Therefore, AAB-hydrogel is a promising candidate for the prevention of postoperative peritoneal adhesions.

Chronic kidney disease prediction using boosting techniques based on clinical parameters.

Chronic kidney disease (CKD) has become a major global health crisis, causing millions of yearly deaths. Predicting the possibility of a person being affected by the disease will allow timely diagnosis and precautionary measures leading to preventive strategies for health. Machine learning techniques have been popularly applied in various disease diagnoses and predictions. Ensemble learning approaches have become useful for predicting many complex diseases. In this paper, we utilise the boosting method, one of the popular ensemble learnings, to achieve a higher prediction accuracy for CKD. Five boosting algorithms are employed: XGBoost, CatBoost, LightGBM, AdaBoost, and gradient boosting. We experimented with the CKD data set from the UCI machine learning repository. Various preprocessing steps are employed to achieve better prediction performance, along with suitable hyperparameter tuning and feature selection. We assessed the degree of importance of each feature in the dataset leading to CKD. The performance of each model was evaluated with accuracy, precision, recall, F1-score, Area under the curve-receiving operator characteristic (AUC-ROC), and runtime. AdaBoost was found to have the overall best performance among the five algorithms, scoring the highest in almost all the performance measures. It attained 100% and 98.47% accuracy for training and testing sets. This model also exhibited better precision, recall, and AUC-ROC curve performance.