Patients with chronic kidney disease (CKD) face an elevated risk of dementia, but multidomain determinants remain poorly defined. To identify modifiable psychosocial, lifestyle, and clinical factors associated with dementia risk in CKD and to examine subgroup-specific differences. We prospectively analyzed 16 083 CKD participants from the UK Biobank. A wide range of lifestyle, medical, social, electronic device use, environmental, and sensory factors were assessed. Cox proportional hazards models with inverse probability weighting (IPW) were applied, complemented by accelerated failure time models, phosphate-adjusted sensitivity analyses, Fine-Gray competing-risk models with death treated as a competing event, and model diagnostics. Stratified analyses were conducted by age, sex, kidney failure status, and genetic susceptibility in the genetic subcohort. Over a mean follow-up of 13.80 years, 806 dementia cases were identified. In nominal analyses, higher physical activity, greater educational attainment, and moderate computer use were associated with lower dementia risk, whereas hypertension, diabetes, depression, hearing loss, and limited social engagement were associated with higher risk. After FDR adjustment, moderate physical activity and moderate computer use remained associated with lower dementia risk, whereas hypertension, diabetes, depression, hearing loss, and lower leisure/social activity remained associated with higher risk. No nominally significant associations were detected for body mass index, low-density lipoprotein cholesterol, air pollution exposure, or visual impairment in this CKD cohort. Exploratory subgroup analyses, based on nominal P values, suggested that associations may differ by kidney function and genetic susceptibility. These findings support multidomain and individualized dementia-prevention strategies in CKD, while recognizing the need for further studies with non-CKD comparators and external validation.

YTHDC2, a unique YTH-domain-containing protein that recognizes N6-methyladenosine (m6A) on RNA, plays critical roles in diverse pathological processes and represents a promising therapeutic target. Despite its potential, no potent small-molecule inhibitors have been reported to date. To bridge this gap, we develop EPMolGen, a deep learning-based molecular generative model that explicitly incorporates the electrostatic features of receptor proteins. The model achieves state-of-the-art performance in dry-lab validations. Using EPMolGen, we identify H3, a YTHDC2 inhibitor with an IC50 of 16.84 μM. Subsequent structural optimization of H3 yields DC2-C1, a highly potent compound with an IC50 of 0.168 μM against YTHDC2 and selectivity over other YTH-domain proteins. In cellular assays, DC2-C1 effectively targets YTHDC2. Notably, DC2-C1 treatment substantially reduces the expression levels of multiple target mRNAs of YTHDC2, leading to phenotypic suppression of related cells. Overall, this study highlights the great potential of deep learning in drug discovery and provides a promising lead compound for drug development targeting YTHDC2. YTHDC2 is a promising therapeutic target, but lacks potent inhibitors. Yang et al. develop a deep learning-based molecule generator EPMolGen. Using this model, they discover a potent, selective, and cell-active small molecule inhibitor of YTHDC2.

Background:Gustave Roussy Immune Score (GRIm-Score), a new prognostic index based on nutritional and inflammatory status, acts as an adverse prognostic factor in patients diagnosed with esophageal cancer (EC). However, the clinical prognostic significance of the GRIm-Score in these patients after receiving neoadjuvant chemoradiotherapy (nCRT) remains unclear. The aim of the study was to evaluate the prognostic value of GRIm-Score in patients with EC following nCRT. Methods:A retrospective study was conducted involving 432 patients with EC who had undergone surgical resection. The GRIm-Score of each enrolled patient was calculated on the basis of three key parameters: lactate dehydrogenase (LDH), neutrophil-lymphocyte ratio (NLR), and albumin (ALB). Overall survival (OS) and disease-free survival (DFS) were set as the primary study endpoints, which were analyzed utilizing Cox proportional hazards regression analysis, the Kaplan-Meier method, and propensity score matching (PSM). Results:The study cohort comprised 359 male patients (83.1%) and 73 female patients (16.9%), with a mean age of 62.1±7.7 years and an age range of 39 to 80 years. Following the implementation of PSM, the matched research cohort was divided into a high GRIm-Score group and a low GRIm-Score group, with 55 patients in each group respectively. Patients with a high GRIm-Score exhibited inferior OS (cohort: P<0.001; PSM: P=0.009) and DFS (cohort: P<0.001; PSM: P=0.01). Before PSM, the GRIm-Score was confirmed as an independent prognostic factor for OS (P=0.02) in multivariate regression analyses, while none of the individual indicators of NLR, LDH, and ALB exhibited such prognostic significance. However, after PSM, the GRIm-Score acted as a powerful independent prognostic factor for both OS (P=0.03) and DFS (P=0.04) in these multivariate analyses. Further subgroup analyses demonstrated that the GRIm-Score could effectively identify pT3-4 stage EC patients with inferior OS or DFS, which suggests that the GRIm-Score plays a complementary role in the clinical decision-making for adjuvant therapy in EC patients. Conclusions:In patients with EC who underwent nCRT followed by surgical resection, the GRIm-Score was verified as an independent prognostic factor. Additionally, this study constitutes the first investigation to elucidate the prognostic significance of the GRIm-Score in EC patients after receiving nCRT.

Introduction: The identification of nondiabetic kidney disease (NDKD) in diabetic patients is critically important. Unlike diabetic nephropathy, NDKD often requires additional therapeutic interventions beyond standard diabetes care. There is a need to develop computational methods using electronic medical record data to identify NDKD in diabetic patients for whom kidney biopsy is not an option. Methods: The study included 1,136 diabetic patients who underwent kidney biopsy at a tertiary teaching hospital. We collected 103 parameters from electronic medical records, including demographic characteristics, physical examination results, laboratory tests, and the status of diabetic retinopathy. We developed seven models to detect NDKD, including k nearest neighbors, random forest, extreme gradient boosting (XGB), LASSO logistic regression, support vector machine, naïve Bayes, and multilayer perceptron (MLP), in the training set (n = 908) and compared their performances in the testing set (n = 228). The SHapley Additive exPlanations (SHAP) approach was used to analyze the importance of features. Results: Biopsy-confirmed NDKD was present in 53% of the 1,136 participants. In the testing set, the area under the receiver operating characteristic curve (AUC) for NDKD detection using XGB, LASSO regression, and MLP reached 0.8, with performances that were stable regardless of whether variable normalization was performed. Among them, XGB revealed the highest AUC (0.833; 95% CI: 0.800–0.864) without feature normalization, which was statistically superior to the other models according to DeLong’s tests. After feature normalization, SVM achieved the highest AUC of 0.841 (95% CI: 0.817–0.861) among all models. In addition to established predictive factors for NDKD (e.g., hematuria and absence of diabetic retinopathy), SHAP analysis identified several features, such as low IgG levels, that contributed significantly to the differentiation models. Conclusion: Despite performance variations in different modeling techniques, machine learning models may have the potential to facilitate the detection of NDKD for patients with contraindications for kidney biopsy. Further efforts are warranted to improve accuracy and facilitate their translation into clinical practice.

The rapid advancement of single-cell multi-omics technologies, typified by single-cell RNA sequencing (scRNA-seq), has provided systematic methodologies for dissecting gene expression heterogeneity within cell populations. However, long noncoding RNAs (lncRNAs) research is hindered by a lack of databases covering diverse tissues, disease contexts, and integrated multi-dimensional single-cell annotations. To address this gap, we updated NONCODE v7.0 (available at https://v7.noncode.org/) through the integration of scRNA-seq data, enabling systematic analysis of lncRNA expression. NONCODE v7.0 incorporates 2061 human scRNA-seq samples from 229 datasets, covering a spectrum of 6 categories, with 3 core ones including Physiological Control as an immune baseline, Physiological Development, and Pathological Disease. Following standardized quality control and processing, the database encompasses 14 million high-quality cells and annotates 94 843 lncRNAs (98.5% of human lncRNAs in NONCODE v6.0). Furthermore, we extended the database through the integration of single-cell-tailored functional modules, encompassing data query and retrieval, interactive visualization (including cell composition, UMAP/t-SNE clustering, marker gene heatmaps, and lncRNA expression profiles), and comparative analysis functionalities. The analytical module facilitates the identification of cell-type-specific differentially expressed (DE) lncRNAs across distinct disease states, thus establishing a foundational resource platform to facilitate the advancement of downstream functional investigations into lncRNAs.

Esophageal squamous cell carcinoma (ESCC) is a highly aggressive malignancy where the efficacy of anti-PD-1 immunotherapy varies among individuals, possibly influenced by the gut microbiota. Here, we analyze 122 fecal samples from ESCC patients undergoing neoadjuvant immunotherapy and identify an enrichment of Ligilactobacillus salivarius (L. salivarius) in non-responders. Humanized microbiome, orthotopic ESCC mouse models, and single-cell RNA sequencing confirm that L. salivarius-produced indole-3-lactic acid (ILA) suppresses tumor-infiltrating NKG7⁺CD8⁺ Tpex cells, impairing anti-tumor immunity. Moreover, ILA-deficient L. salivarius strains abolish ILA production and immune resistance. In vitro assays reveal that ILA targets the aryl hydrocarbon receptor and downregulates nuclear factor κB (NF-κB) signaling in Tpex cells. Pharmacological NF-κB activation restores Tpex function and reverses resistance. Two validation cohorts support the L. salivarius-ILA-NKG7⁺CD8⁺ Tpex axis as a resistance mechanism in ESCC patients. These findings highlight L. salivarius and ILA as key modulators of the tumor microenvironment, offering potential strategies for overcoming immunotherapy resistance in ESCC.

Chronic kidney disease (CKD), imposes a significant burden on both health and the economy. However, comprehensive analysis of the cross-talks with temporary order of disease trajectory following CKD diagnosis remains unclear. From 502,507 UK Biobank participants, 5,578 CKD patients without any other comorbidity preceding CKD diagnosis were identified. Disease trajectory after CKD was constructed with three interrelated steps. Firstly, CKD patients were individually matched with up to 5 participants without CKD by birth year, sex, and Townsend deprivation index (transformed to tertiles). Phenome-wide association analysis (PheWAS) using conditional Cox regression with age as underlying time-scale was performed to evaluate the association between CKD and subsequent risks of 468 medical conditions. Conditions occurred in at least 250 CKD patients, with a p value less than the Bonferroni correction threshold and hazard ratio (HR) >1, were considered in the second step of the analysis. Secondly, we conducted binomial tests to explore whether a later diagnosis (Disease 2, D2) occurring after another previous diagnosis (Disease 1, D1) was dominant (>50%) among CKD patients with both D1 and D2 diagnoses. All possible D1 and D2 pairs with a temporal order that occurred in at least 125 CKD patients, with binomial test p value 1, were considered as significant. The trajectory network was formed by combining disease pairs that share overlapping diseases. For example, the disease pairs D1→D2 and D2→D3, with D2 as the overlapping disease, were combined into the trajectory D1→D2→D3. Medical conditions were retrieved from UK Biobank inpatient records using 3-digit ICD-10 codes, and combined conditions with clinical or biological similarities. Only the first record and corresponding date were maintained for analysis. CKD significantly increased the risks of 43 subsequent medical conditions, including infectious disease, metabolic, circulatory, respiratory and musculoskeletal system diseases. There were two main clusters of affected diseases following CKD: circulatory and metabolic system diseases. Hypertensive disorder and diabetes served as important mediators linking a series of downstream health outcomes originating from CKD. For example, hypertensive disorder after CKD led to downstream sepsis, iron deficiency anemia, diabetes, obesity, hypothyroid conditions, heart failure, asthma, chronic obstructive pulmonary disease, inflammatory arthritis, as well as acute renal failure from ischemic heart disease (Fig. 1A). CKD preceding diabetes was associated with a range of downstream trajectories, including progression from anemia to disorders of fluid, electrolyte, and acid-base balance, as well as from disorders of lipoprotein metabolism and other lipid abnormalities to heart failure and hypotension (Fig. 1B). Furthermore, CKD was also associated with a subsequent trajectory leading from acute renal failure to pneumonia (Fig. 1C). Individuals with a primary diagnosis of CKD and no prior comorbidities exhibit increased risks of a variety of subsequent medical outcomes, which offer potential intervention targets for inhibiting adverse events in CKD patients.

Clinical trials and meta-analyses are considered high-level medical evidence with solid credibility. However, such clinical evidence for traditional Chinese medicine (TCM) is scattered, requiring a unified entrance to navigate all available evaluations on TCM therapies under modern standards. Besides, novel experimental evidence has continuously accumulated for TCM since the publication of HERB 1.0. Therefore, we updated the HERB database to integrate four types of evidence for TCM: (i) we curated 8558 clinical trials and 8032 meta-analyses information for TCM and extracted clear clinical conclusions for 1941 clinical trials and 593 meta-analyses with companion supporting papers. (ii) we updated experimental evidence for TCM, increased the number of high-throughput experiments to 2231, and curated references to 6 644. We newly added high-throughput experiments for 376 diseases and evaluated all pairwise similarities among TCM herbs/ingredients/formulae, modern drugs and diseases. (iii) we provide an automatic analyzing interface for users to upload their gene expression profiles and map them to our curated datasets. (iv) we built knowledge graph representations of HERB entities and relationships to retrieve TCM knowledge better. In summary, HERB 2.0 represents rich data type, content, utilization, and visualization improvements to support TCM research and guide modern drug discovery. It is accessible through http://herb.ac.cn/v2 or http://47.92.70.12.

Psychiatric disorders have been reported to influence many health outcomes, but evidence about their impact on chronic kidney disease (CKD) has not been fully explored, as well as possible mechanisms implicated are still unclear. Four hundred forty-one thousand eight hundred ninety-three participants from UK Biobank were included in this study. To assess the association between psychiatric disorders mainly including depression, anxiety, stress-related disorders, substance misuse as well as psychotic disorder, and CKD, a Cox regression model using age as the underlying time scale was employed. This approach considers the age progression of participants from the beginning to the end of the study as the elapsed time. Flexible nonparametric smoothing model was conducted to illustrate the temporal patterns. Subgroup analyses were performed by stratification of gender, genetic susceptibility to CKD, age at entry or exit the cohort, follow-up duration, and the number of psychiatric disorders at baseline. Mediation analysis was implemented to evaluate the roles of body mass index (BMI), hypertension, and diabetes. Compared with individuals without psychiatric disorders, an increased risk of CKD was observed in patients with psychiatric disorders (hazard ratios (HR) = 1.52, 95

Octacosanol (C₂₈H₅₈O) is a natural long-chain fatty alcohol that has attracted increasing attention in recent years due to its multiple biological activities, including anti-fatigue, anti-inflammatory, hypolipidemic, and antioxidant effects. However, its extremely low bioavailability limits the exertion of its biological activity in vivo and hinders its application potential. Therefore, enhancing its bioavailability has become a current research hotspot. This review analyzes the factors that restrict the bioavailability of octacosanol from the perspectives of bioaccessibility, tissue distribution, absorption, and metabolic transformation. Strategies for improving its bioavailability are systematically summarized, with a focus on nanocomplex, microcapsules, nanoemulsions, and micelles. Finally, the potential applications of modern technologies such as molecular dynamics simulations and artificial intelligence in improving its bioavailability are discussed. This review provides theoretical support for the efficient utilization of octacosanol in functional foods and pharmaceuticals and offers insights into delivery strategies for hydrophobic natural products.

Arrhythmia is a common and clinically relevant complication among patients with chronic kidney disease (CKD) undergoing hemodialysis (HD), contributing to adverse cardiovascular outcomes. We hypothesized that wearable electrocardiogram (ECG) monitors, being more patient-acceptable than standard ECG devices, could facilitate the detection of arrhythmias, including asymptomatic cases, in HD patients. This two-phase real-world observational study enrolled CKD patients undergoing maintenance HD. In Phase I, we assessed the wearable single-lead ECG monitors with comparison to standard 12-lead Holter monitoring for detecting arrhythmias and measuring heart rate variability (HRV). In Phase II, we used the wearable ECG monitors to investigate the prevalence of arrhythmias during HD. Additionally, LASSO logistic regression was conducted to identify subgroups of patients most likely to benefit from wearable devices. This study enrolled 538 patients from five hospitals. In Phase I (n = 185), we found that the wearable ECG enabled continuous monitoring of arrhythmias and HRV in patients undergoing HD. Linear regression revealed a significant correlation of parameters of HRV, including SDNN, SDANN, and pNN50 between the wearable ECG and the standard Holter monitoring (p < 0.001). LASSO logistic regression model identified the older patients (age ≥ 50) would benefit more from the usage of wearable devices than the younger. In Phase II, arrhythmias were detected in 34.0

Individuals with chronic kidney disease (CKD) are at an increased risk of cognitive impairment and dementia, which further exacerbates the disease burden of CKD. Identifying modifiable factors for the prevention of dementia in CKD patients is of great importance. This study aims to evaluate the associations between modifiable medical, behavioral, sociopsychological, and environmental factors and the development of dementia in CKD. This was a prospective cohort study based on the UK Biobank. We included 10700 Individuals with the diagnosis of CKD or estimated glomerular filtration rate (eGFR) < 60 ml/min/1.73 m2 at baseline. We collected information on medical (BMI, hearing loss, diabetes, hypertension, prevalent cardiovascular disease, albuminuria, GFR stages, and depression), behavioral (diet patterns derived by principal component analysis, sedentary lifestyle, physical activity, sleep duration, smoking, alcohol consumption, and playing computer games,), sociopsychological (educational qualification, living alone, friends visiting, and social activity), and environmental (exposure to nitrogen monoxide) factors at recruitment. The outcome was dementia as identified by the inpatient diagnoses during the follow-up. Cox proportional hazard regression models, using age as the time scale, were conducted to evaluate the associations between these potential risk factors with dementia, adjusting for sex, ethnicity, and Townsend deprivation index. The model was additionally adjusted for genetic risk which was estimated by polygenic risk score (PRS) of dementia and by the APOE genotype in 7561 participants with qualified genetic data. The mean age of this CKD cohort was 63.4 years, and 50.7% were male. Decreased eGFR was present in 64.0% of the participants. During a median follow-up duration of 13.7 years, 488 cases of incident dementia were identified, corresponding to an incidence of 3.3 per 1000 person-years. The results showed that, diabetes (HR 1.51, 95% CI, 1.21 to 1.89, P < 0.001), hearing loss (HR 1.33, 95% CI, 1.11 to 1.59, P = 0.002), prevalent cardiovascular disease (HR 1.52, 95% CI, 1.25 to 1.85, P < 0.001), eGFR less than 30 ml/min/1.73 m2 (HR 1.62, 95% CI, 1.04 to 2.51, P = 0.03), urine albumin/creatinine ratio > 30 mg/mmol (HR 1.67, 95% CI, 1.16 to 2.42, P = 0.006), high wholegrain diet (HR 1.40, 95% CI, 1.08 to 1.82, P = 0.01), and lack of social activity (HR 1.32, 95% CI, 1.09 to 1.59, P = 0.004) was independently associated with increased hazard of newly-onset dementia in CKD (Fig. 1). In 7,561 patients with qualified genetic data, where high genetic risk was associated with a markedly increased risk for dementia (HR 2.06, 95% CI: 1.67–2.58, P < 0.001), depression emerged as an associated risk factor for dementia, while the association between hearing loss and dementia was no longer statistically significant. Among individuals with CKD, modifiable medical, behavioral, and sociopsychological factors may contribute to the development of dementia. Interventions targeting the improvement of diabetes, cardiovascular disease, and social isolation, as well as delaying the progression of CKD, may reduce the risk of dementia in CKD.

BACKGROUND:Spatiotemporal disparities exist in the disease burden of non-communicable diseases (NCDs) attributable to kidney dysfunction, which has been poorly assessed. The present study aimed to evaluate the spatiotemporal trends of the global burden of NCDs attributable to kidney dysfunction and to predict future trends. METHODS:Data on NCDs attributable to kidney dysfunction, quantified using deaths and disability-adjusted life-years (DALYs), were extracted from the Global Burden of Diseases Injuries, and Risk Factors (GBD) Study in 2019. Estimated annual percentage change (EAPC) of age-standardized rate (ASR) was calculated with linear regression to assess the changing trend. Pearson's correlation analysis was used to determine the association between ASR and sociodemographic index (SDI) for 21 GBD regions. A Bayesian age-period-cohort (BAPC) model was used to predict future trends up to 2040. RESULTS:Between 1990 and 2019, the absolute number of deaths and DALYs from NCDs attributable to kidney dysfunction increased globally. The death cases increased from 1,571,720 (95% uncertainty interval [UI]: 1,344,420-1,805,598) in 1990 to 3,161,552 (95% UI: 2,723,363-3,623,814) in 2019 for both sexes combined. Both the ASR of death and DALYs increased in Andean Latin America, the Caribbean, Central Latin America, Southeast Asia, Oceania, and Southern Sub-Saharan Africa. In contrast, the age-standardized metrics decreased in the high-income Asia Pacific region. The relationship between SDI and ASR of death and DALYs was negatively correlated. The BAPC model indicated that there would be approximately 5,806,780 death cases and 119,013,659 DALY cases in 2040 that could be attributed to kidney dysfunction. Age-standardized death of cardiovascular diseases (CVDs) and CKD attributable to kidney dysfunction were predicted to decrease and increase from 2020 to 2040, respectively. CONCLUSION:NCDs attributable to kidney dysfunction remain a major public health concern worldwide. Efforts are required to attenuate the death and disability burden, particularly in low and low-to-middle SDI regions.

Kidney disease has long been a major public health concern, making it crucial to understand its causes, pathogenesis and progression for the development of effective prevention and treatment strategies. Alternative splicing (AS), an important post-transcriptional regulatory mechanism, has been increasingly recognized for its pivotal role in the pathogenesis and progression of kidney diseases. However, a dedicated database that systematically catalog AS events associated with kidney diseases is still lacking. In this study, we developed the Kidney Diseases Alternative Splicing Database (KDASDB, http://www.hxdsjzx.cn/KDASDB), which integrates 90,273 alternative splicing events (ASEs) derived from 2406 samples, encompassing 29 distinct kidney diseases and 126 projects across human and mouse. The database features 52,478 and 41,818 novel transcripts in human and mouse datasets, respectively, and identifies 3354 and 5638 novel ASEs. KDASDB offers intuitive query and visualization tools, enabling researchers to efficiently explore AS patterns and assess their biological significance. By providing comprehensive repository of ASEs and their associations with kidney diseases, KDASDB serves as a valuable platform for advancing our understanding of kidney disease pathogenesis and progression, and supporting the discovery of innovative therapeutic approaches.

Potential impact of psychological distress on the development of chronic kidney disease (CKD) has been implicated in recent research. A thorough investigation of the association between psychosocial factors and the incidence of CKD warrants further. The objective of this study was to evaluate the associations between various psychosocial factors and the risk of CKD. In this prospective cohort study, we included individuals who were free of CKD at baseline from the UK Biobank. A spectrum of psychosocial factors, encompassing social isolation, loneliness, mental health status, neuroticism, depressive symptoms, traumatic experiences, broad depression, and major depressive disorder, were assessed using validated self-report questionnaires. Cox proportional hazards regression models were conducted to evaluate the associations between these psychosocial factors and the incidence of CKD. In total, 485,324 participants (mean age: 56.9 ± 8.1 years; 54.5% female) were enrolled in this study. During a median follow-up period of 13.5 years, 20,247 participants (4.17%) were newly diagnosed with CKD. After adjustment for age, sex, and ethnicity, our findings revealed that social isolation (HR 1.14, 95% CI, 1.07–1.20, comparing the most isolated group to the least isolated group), loneliness (HR 1.47, 95% CI, 1.39–1.56) were significantly associated with an increased risk of incident CKD. Participants reporting poorer mental health (HR 1.04, 95% CI, 1.03–1.05), broad depression (HR 1.27, 95% CI, 1.24–1.31), severer depression symptom (HR 2.02, 95% CI, 1.91–2.12) or neuroticism scores ≥5 (HR 1.21, 95% CI, 1.17–1.25) exhibited a heightened risk of developing CKD, when compared to those with better psychological wellbeing. Notably, participants experiencing financial difficulties exhibited higher adjusted HRs (HR 1.51, 95% CI, 1.42–1.60) compared to those without such difficulties. Furthermore, participants who had experienced the death of a close relative (HR 1.41, 95% CI, 1.09–1.55) or a spouse/partner (HR 1.15, 95% CI, 1.02–1.31) displayed elevated HRs compared to those who had not (see Fig. 1). Psychosocial factors were independently associated with a higher hazard of incident CKD. It is imperative for healthcare professionals within the nephrology domain to possess an awareness of the potential linkage between psychosocial factors and CKD.

Chronic kidney disease (CKD), which affects approximately 10% of the global population, imposes a significant health and economic burden. Identifying comorbidity patterns that precede CKD could facilitate more effective prevention strategies. Our objective was to uncover the temporal relationships and the average time interval between comorbidity patterns and the subsequent onset of CKD in both males and females. From 502,507 UK Biobank participants 34,740 CKD patients were identified. Sequential pattern mining was performed to identify comorbidity patterns that precede the onset of CKD and to ascertain the average duration from comorbidity to the onset of CKD. Sequential pattern mining is a method employed to find frequent patterns of associated diseases in sequences of ordered events. Subsequently, case-control study was conducted in which each chronic kidney disease (CKD) patient was matched with up to 5 individuals without CKD, based on identical sex, birth year, and Townsend deprivation index (categorized into low, medium, and high tertiles). Conditional logistic regression was performed to evaluate the association between comorbidity patterns preceding CKD and the subsequent risk of developing CKD. Among the 34,740 patients with CKD in the UK Biobank, 31,852 patients (16,029 females and 15,823 males) had other comorbidities preceding their CKD diagnosis. For female patients, the most frequent comorbidity before CKD was hypertensive disorders, with 45.04% of female CKD patients being diagnosed with hypertension then diagnosed with CKD. The average time interval from the onset of hypertensive disorders to the development of CKD was 6.97 years. Compared to individuals without hypertensive disorders, patients with hypertensive disorders had a significantly higher risk of subsequent CKD, with an odds ratio (OR) of 3.67 (95% confidence interval (CI): 3.54–3.82) (in panel A of the Fig. 1). For male patients, the most frequent comorbidity prior to CKD was still hypertensive disorders, which accounted for 55.41% of male patients. The average time interval from the onset of hypertensive disorders to the diagnosis of CKD was 6.89 years (in panel B of the Fig. 1). Ischemic heart disease occurred more frequently before the onset of CKD in male patients compared to female patients. Specifically, 29.31% of males had ischemic heart disease prior to developing CKD, whereas the corresponding number for females was 12.43%. The time interval between the ischemic heart disease diagnosis and the subsequent development of CKD was 7.95 years for males and 6.42 years for females, respectively. A higher proportion of female patients were diagnosed with osteoarthritis (24.25%) before the onset of CKD compared to male patients (18.22%). Furthermore, the time interval from the diagnosis of osteoarthritis to the onset of CKD was shorter in females (6.74 years) than in males (7.10 years). All these comorbidity patterns before CKD diagnosis were significantly associated with following risk of CKD. Sex-specific comorbidity patterns prior to CKD and time intervals from diagnosis of comorbidity to the following development of CKD were identified. Our study highlights the significance of routine monitoring of kidney function, early screening for CKD, and personalized management strategies for patients with these frequent comorbidities as potential approaches to the precise prevention of CKD.