Fast and automated reconstruction of retinal hyperreflective foci (HRF) is of great importance for many eye-related disease understanding. In this paper, we introduced a new automated framework, driven by recent advances in deep learning to automatically extract 12 three-dimensional parameters from the segmented hyperreflective foci in optical coherence tomography (OCT).Unlike traditional convolutional neural networks, which struggle with long-range feature correlations, we introduce a spatial and channel attention module within the bottleneck layer, integrated into the nnU-Net architecture. Spatial Attention Block aggregates features across spatial locations to capture related features, while Channel Attention Block heightens channel feature contrasts. The proposed model was trained and tested on 162 retinal OCT volumes of patients with diabetic macular edema (DME), yielding robust segmentation outcomes. We further investigate HRF’s potential as a biomarker of DME.Results unveil notable discrepancies in the amount and volume of HRF subtypes. In the whole retinal layer (WR), the mean distance from HRF to the retinal pigmented epithelium was significantly reduced after treatment. In WR, the improvement in central macular thickness resulting from intravitreal injection treatment was positively correlated with the mean distance from HRF subtypes to the fovea.Our study demonstrates the applicability of OCT for automated quantification of retinal HRF in DME patients, offering an objective, quantitative approach for clinical and research applications. With abIn recent decades, advances in high-resolution fundus imaging techniques have led to the discovery of specific imaging features of retinal diseases, which may serve as diagnostic, predictive, and prognostic biomarkers for this disease . OpticalHowever, manual annotation of HRF in OCT is time-consuming, and sometimes excessively subjective. With the rapid development of computer science, there is great potential for automatic segmentation and quantification of HRF in OCT images, with benefits for clinical practice. The segmentation algorithms for HRF can be categorized into two primary groups: traditional segmentation algorithms and deep learning-based segmentation methods. Traditional HRF segmentation approaches usually require manual parameter tuning and extensive prior knowledge. Okuwobi et al.  employedIn this study, we presented a deep learning-based framework for the quantitative analysis of HRF in OCT images. Specifically, the main contributions of our article can be summarized as follows:We achieve excellent HRF segmentation performance by combining nnU-Net  adaptabiUsing the proposed method, we extracted 12 parameters to characterize HRF morphology and distribution, showing significant differences in volume and amount among the three HRF sub-types in retinal OCT images.Using the extracted 12 HRF parameters, we evaluated changes in HRF before and after treatment and their correlation with central macular thickness (CMT) improvement.2.This is a retrospective, longitudinal study conducted at the Affiliated Ningbo Eye Hospital of Wenzhou Medical University  from November 2020 to July 2022. This study was approved by the ethics committee of the Affiliated Ningbo Eye Hospital of Wenzhou Medical University (ID: 20210327A), and informed written consent was obtained from each participant involved in our study according to the Declaration of Helsinki.2.1.Type 2 diabetes mellitus (DM) patients were recruited and diagnosed by an endocrine specialist. Demographic and clinical information from all patients such as age, gender, duration of DM, and systolic/diastolic blood pressure were recorded. All patients had an extensive ophthalmic examination, involving slit-lamp biomicroscopy, and assessment of intraocular pressure, axial length, and visual acuity. The inclusion criteria of our patients are as follows: 1. Diagnosed with type 2 DM; 2. Age > 18 years; 3. Macular edema, defined clinically and by a retinal thickness of >250 μm in the central subfield ; 4. Coul2.2.2, with 384 B-scans, and 6 × 6 mm2, with 512 B-scans. OCT images showing retinal abnormalities such as age macular degeneration (AMD), severe cataract, and glaucoma; images with signal quality less than 7; or with OCT artifacts present, were excluded. OCT data displayed in our study followed the OSCAR-IB quality criteria . This imaging equipment has a scanning protocol of 40,000 A-scans/s , with ancriteria  and APOScriteria . Patient2.3.We introduced an automatic tool for HRF analysis in OCT images. A deep learning-based approach was employed for precise segmentation of HRF, boundaries of inner retina (IR) and outer retina (OR) in OCT images. The resulting segmentations are then used to calculate HRF parameters.2.3.1.HRF was defined as discrete and well-defined lesions distributed between the in-ternal limiting membrane (ILM) and retinal pigmented epithelium (RPE), with similar reflectivity to the RPE layer . Conside2.3.2.The distribution of HRF in the IR and OR, and their downward shift, have been previously studied . The IR 2.4.2.4.1.In this research, we modified the nnU-Net, to, respectively, perform two segmentation tasks: hyperreflective foci segmentation and retinal layer segmentation. The framework comprises a basic U-Net architecture library that includes 2D and 3D version.For the retinal layer segmentation task, we modified the 2D version of nnU-Net as the underlying network topology. The network architecture is shown in Convolutional neural networks with U-Net structure have higher inductive bias, but lack the ability to capture long-distance dependent features. Inspired by CS2-Net , we embewhere where For the HRF segmentation task, we have extended the modified nnU-Net from 2D to 3D. To achieve this, we have replaced all the 2D operations in both the encoder and decoder modules with 3D ones. Additionally, we have incorporated the 3D version of the SCA module into the bottleneck layer of the network. The detailed network architecture is illustrated in All convolutions in the encoder and decoder adopt the form of Convolution-InstanceNorm-LeakyReLU, which are different from that in the vanilla architecture. Specifically, LeakyReLU (negative slope = 0.01) is used instead of ReLU, and instance normalization  is used The dice loss formula used here is a variant of that used in Drozdzal et al. , and it I × K denotes the softmax output of the network, v ∈ RI × K denotes the one-hot encoding of the ground truth, I represent the number of pixels in a training batch and K represents the number of categories.where u ∈ R2.5.In this study, we analyzed changes in HRF’s morphology and distribution in OCT images before and after IVI treatment. A previous study limited the maximum diameter range of HRF to 20–50 μm, which excludes the other two signals , refers 2.5.1.Noise Signal Quantity : Number of NS within the analyzed region.- Hyperreflective Dots Quantity : Number of HD within the analyzed region.- Hyperreflective Clumps Quantity : Number of HC within the analyzed region.- Noise Signal Volume : Volume of NS within the analyzed region in μm3.- Hyperreflective Dots Volume : Volume of HD within the analyzed region in μm3.- Hyperreflective Clumps Volume : Volume of HC within the analyzed region in μm3.- 2.5.2.Distance between noise signal and fovea : Distance between NS and fovea, indicating average distance of NS pixels from the foveal center in μm.- Distance between hyperreflective dots and fovea : Distance between HD and fovea, indicating average distance of HD pixels from the foveal center in μm.- Distance between hyperreflective clumps and fovea : Distance between HC and fovea, indicating average distance of HC pixels from the foveal center in μm.- Distance between noise signal and RPE : Distance between NS and RPE, indicating average distance of NS pixels from RPE in μm.- Distance between hyperreflective dots and RPE : Distance between HD and RPE, indicating average distance of HD pixels from RPE in μm.- Distance between hyperreflective clumps and RPE : Distance between HC and RPE, indicating average distance of HC pixels from RPE in μm.- 2.6.p < 0.05 (two-sided test) was adopted to express statistical significance.All statistical analysis was performed using version 18.0 of SPSS software . Continuous variables were expressed as mean ± standard deviation (SD) for normal data; and median and interquartile ranges (IQR) for skewed data. Categorical variables were presented as frequencies. To compare the differences among different subtypes of HRF and the differences in HRF parameters before and after treatment, the Wilcoxon signed-rank test was used, and the results were expressed as the median (IQR). To investigate the correlation between the improvement in CMT and given parameters of HRF, Spearman’s rank correlation coefficients were calculated using a non-parametric test for linear correlation. A significance level of 3.3.1.3.1.1.The proposed model was implemented in PyTorch using an NVIDIA GeForce 3,090 GPU with 24GB memory. The training process involved 500 epochs, and employed the following settings: Adam optimization, with an initial learning rate of 0.01; a batch size of 2 for HRF segmentation; and a batch size of 1 for retinal layer segmentation. To enhance training stability, we adopted a poly learning rate policy, with a momentum of 0.9.3.1.2.To quantitatively assess the proposed network’s segmentation performance, we employ the following metrics. The Dice Similarity Coefficient (DSC) quantifies the agreement between HRF manually annotated by expert ophthalmologists and those automatically segmented by the proposed network, which can be defined as:We also assess our method using Intersection over Union (IOU), precision, recall, and F1-Score, defined as:where TP indicates true positives, FP indicates false positives, TN indicates true negatives, and FN indicates false negatives.3.1.3.In order to evaluate the effectiveness of the proposed network, we selected several state-of-the-art neural networks for comparison, including FCN , U-Net , U-Net++As seen in 3.1.4.To demonstrate the effectiveness of the channel attention and spatial attention modules, we compared our proposed method with the baseline method and two variants.Baseline + SAB: We removed the CAB from this variant to assess its contribution.Baseline + CAB: We removed the SAB from this variant to assess its contribution.Baseline: We removed both SAB and CAB to evaluate their combined contribution.3.2.We enrolled 47 eyes from 26 patients with DME, acquired with OCT , and a total of 11 eyes from 8 patients were included in this study. We excluded 36 eyes from 18 patients from the analysis. One eye of one patient was excluded due to poor OCT image quality (motion artifacts on OCT images): 22 eyes of 11 patients were excluded due to lack of follow-up records; and 13 eyes from 7 patients were excluded due to no improvement in CMT after anti-VEGF or dexamethasone IVI treatment. The characteristics and clinical information of our study participants are displayed in 3.2.1.p = 0.003). No significant differences were found between p = 0.131). Among the distance-related parameters, the results showed no significant differences between the HRF classified according to their diameter size.3.2.2.Due to the retrospective design of the study, OCT examinations were not performed at regular intervals. To avoid bias related to the duration of follow-up, only two consecutive follow-up visits with improvement in CMT were selected for each eye. The longitudinal study included 11 eyes from 8 patients, with a follow-up of 1.9 ± 1.6 months . During study period, all eyes were treated with intravitreal injections: 91% (10/11) of eyes received anti-VEGF injections and 9% (1/11) of eyes received dexamethasone injections. The number of intravitreal injections was 1.4 ± 0.7 .p = 0.003, p = 0.033, p = 0.003, p = 0.026, p = 0.008, p = 0.004, p = 0.003, respectively). There were no significant changes in the other six quantitative parameters between the two phases. In IR, p = 0.016, p = 0.016, respectively). There were no significant changes in the other 10 quantitative parameters between the two phases. In OR, p = 0.006, p = 0.047, p = 0.006, p = 0.004, p = 0.004, respectively). There were no significant changes in the other seven quantitative parameters between the two phases.We assessed whether changes in HRF were significant at two consecutive follow-up visits in the presence of improved CMT. 3.2.3.p = 0.015, p = 0.016, p < 0.001, respectively). There was no significant correlation between the other nine baseline quantitative parameters and p = 0.006, p = 0.019, p < 0.001, respectively). There was no significant correlation between the other nine baseline quantitative parameters and We assessed whether there was a significant correlation between improvement in CMT at two consecutive follow-up visits and baseline HRF. 4.The given study aims to quantify HRF in OCT images as part of a retrospective study on patients with DME at baseline and follow-up. Previous studies relied on manual counting methods to quantify HRF, which is time-consuming and less reliable. In examining HRF as a potential biomarker, the existing body of literature has been inconsistent , 35–42, 4.1.Statistical analysis indicated significant disparities in both the number and volume parameters of baseline HRF subtypes. Our study validated HRF discrimination based on diameter range by analyzing baseline HRF morphological parameters. A previous study used 20 μm and 50 μm diameters to differentiate HRF subtypes , found a4.2.We showed the mean distance from HRF to RPE was significantly reduced after IVI treatment. By studying the distribution parameters of HRF in a longitudinal analysis of two consecutive follow-ups, our study indicated the tendency of HRF to migrate from the inner retina to the outer retina after IVI treatment; similar to our findings, Pemp et al. showed that DME uptake triggered the downward migration of HRF  into the4.3.p < 0.001), which could explain why there was no correlation between the concentration of baseline HRF in the fovea in IR and the reduction in CMT, since the convergence of smaller HRF subtypes to larger HRF subtypes mainly occurs in OR according to the discussion above. We speculate that future studies on the differential distribution of HRF aggregated in the fovea may be able to verify whether it produces some physiological changes that affect the outcome of IVI treatment.We also showed that improvement in CMT resulting from IVI treatment was positively correlated with the mean distance from HRF to the fovea in OR and WR, and not significantly correlated with other parameters. We explored the correlation between the therapeutic effect of IVI on foveal edema and the quantitative parameters of HRF at baseline by examining the quantitative parameters of HRF at baseline and the percentage improvement in CMT at two consecutive follow-up visits. A previous study  performe4.4.Our study has certain limitations. Firstly, all participants were Chinese, and enrolled from a single medical center: larger and more various samples would be an advantage. More multi-center studies should therefore be conducted on larger cohorts to confirm the reproducibility of analysis on these parameters of HRF. Secondly, the enrollment criteria for this study only included cases with a positive response to injection therapy, rather than including refractory cases. It would be more convincing to recruit subjects with definitive treatment and make a long-term follow-up comparison. Thirdly, the study design lacked untreated blank controls to derive reasons for changes in parameters before and after treatment, while the small sample size and high homogeneity made the study findings more indicative of a pilot.5.We introduced a deep learning-based approach to quantify hyperreflective foci in OCT images of DME patients. Our retrospective analysis of 11 eyes using this method showed that it effectively quantified baseline and follow-up changes in hyperreflective foci by extracting relevant geometric parameters. In this study, we were able to validate certain findings reported in prior research and uncover novel insights: for instance, our investigation revealed that the concentration of HRF in the fovea region may influence the efficacy of IVI treatment. We believe that accurate quantification and follow-up of HRF in OCT images at baseline and during treatment may enable clinicians to monitor DME disease progression, assess treatment response and identify patients who may benefit from a personalized approach to treatment.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving humans were approved by the ethics committee of the Affiliated Ningbo Eye Hospital of Wenzhou Medical University (ID: 20210327A). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.XW: Conceptualization, Methodology, Software, Writing – original draft, Writing – review & editing, Formal analysis. YaZ: Conceptualization, Formal analysis, Resources, Writing – review & editing. YM: Writing – review & editing. WK: Conceptualization, Formal analysis, Writing – review & editing. JY: Investigation, Writing – review & editing. JL: Software, Writing – review & editing. SM: Conceptualization, Writing – review & editing. QiY: Conceptualization, Validation, Writing – review & editing. QuY: Conceptualization, Resources, Writing – review & editing. YiZ: Conceptualization, Data curation, Funding acquisition, Investigation, Methodology, Resources, Software, Supervision, Visualization, Writing – original draft, Writing – review & editing.


Metabolic dysfunction-associated fatty liver disease (MAFLD) is a newly defined condition encompassing hepatic steatosis and metabolic dysfunction. However, the relationship between MAFLD and multi-system diseases remains unclear, and the time-dependent sequence of these diseases requires further clarification.After propensity score matching, 163,303 MAFLD subjects and 163,303 matched subjects were included in the community-based UK Biobank study. The International Classification of Diseases, Tenth Revision (ICD-10), was used to reclassify medical conditions into 490 and 16 specific causes of death. We conducted a disease trajectory analysis to map the key pathways linking MAFLD to various health conditions, providing an overview of their interconnections.Participants aged 59 (51–64) years, predominantly males (62.5%), were included in the study. During the 12.9-year follow-up period, MAFLD participants were found to have a higher risk of 113 medical conditions and eight causes of death, determined through phenome-wide association analysis using Cox regression models. Temporal disease trajectories of MAFLD were established using disease pairing, revealing intermediary diseases such as asthma, diabetes, hypertension, hypothyroid conditions, tobacco abuse, diverticulosis, chronic ischemic heart disease, obesity, benign tumors, and inflammatory arthritis. These trajectories primarily resulted in acute myocardial infarction, disorders of fluid, electrolyte, and acid–base balance, infectious gastroenteritis and colitis, and functional intestinal disorders. Regarding death trajectories of MAFLD, malignant neoplasms, cardiovascular diseases, and respiratory system deaths were the main causes, and organ failure, infective disease, and internal environment disorder were the primary end-stage conditions. Disease trajectory analysis based on the level of genetic susceptibility to MAFLD yielded consistent results.Individuals with MAFLD have a risk of a number of different medical conditions and causes of death. Notably, these diseases and potential causes of death constitute many pathways that may be promising targets for preventing general health decline in patients with MAFLD.The online version contains supplementary material available at 10.1186/s12916-023-03080-6. Nonalcoholic fatty liver disease (NAFLD) is characterized by the presence of excessive hepatic steatosis (> 5%) in individuals without heavy drinking or viral hepatitis . It is tNAFLD-related adverse outcomes have been well documented in a previous study . In receDisease trajectory analysis, proposed by Jensen et al., is a new visual method to explore the development of diseases over the course of a population’s lifespan . By visuThe UK Biobank cohort study, with a population of half a million, has fully evaluated MAFLD using a non-invasive approach and collected data on disease diagnosis and causes of death records using International Classification of Diseases, Tenth Revision (ICD-10) codes since 2006. Thus, it provides ideal data resources and sufficient follow-up time to analyze the disease trajectory network of MAFLD. Therefore, the purpose of this study was to investigate the key pathway of MAFLD leading to subsequent multi-system diseases and cause-specific death and to draw a panorama of the disease tree diagram through disease trajectory analysis based on the UK Biobank community cohort.http://www.ukbiobank.ac.uk. Data and materials were obtained from the website https://ukbiobank.dnanexus.com/panx/projects. The National Research Ethics Committee approved the experimental protocol. All participants signed written informed consent forms; the research was conducted according to the ethical guidelines of the Helsinki Declaration, and the protocol was approved by each institutional review board. Data usage was approved by the Human Ethical Committee of the West China Hospital of Sichuan University.The UK Biobank study design was presented in a previous study . From 20n = 242) were missing the Townsend deprivation index (n = 623), or missing data to identify MAFLD  were excluded. Thus, 466,288 participants were included in this study. To reduce confounding biases in this observational study, we conducted propensity score matching (1:1) according to age, sex, and the Townsend deprivation index  for participants with and without MAFLD. In total, 326,606 participants were included in the analysis of MAFLD-related trajectories  and dead  individuals were included in the MAFLD group , or (3) metabolic abnormality including any of the two: insulin resistance (not collected in the UK Biobank study), prediabetes (fasting glucose ≥ 100 mg/dl or Hemoglobin A1c ≥ 5.7%), low high-density lipoprotein cholesterol , hypertriglyceridemia (≥ 1.7 mmol/L), hypertension (≥ 130/85 mmHg or use of antihypertensive medication), and increased waist circumference .MAFLD was defined based on international expert consensus . MAFLD iHepatic steatosis was assessed using the fatty liver index (FLI) according to the formula for waist circumference, gamma-glutamyl transferase, triglycerides, and BMI . FLI ≥ 62)] + [1.13 × T2D ] + [0.99 × AST/ALT ratio] − [0.013 × platelet count (109/L)] − [0.66 × albumin (g/dL)]. For predicting advanced fibrosis, the cutoff of the NAFLD fibrosis score is − 1.455, which achieved a negative predictive value of 93% according to previous reports [Advanced liver fibrosis was assessed via NAFLD fibrosis score using the following formulas: − 1.675 + [0.037 × age (years)] + [0.094 × BMI (kg/m reports .https://icd.who.int/browse10/2019/en).The diagnosis of each medical disease for the population in the UK Biobank study originated from the main and secondary diagnostic data during hospitalization, which were recorded using ICD-10 codes. ICD-10 codes for pregnancy, childbirth, and unclassified symptoms or signs were excluded. All medical diagnoses were made using the three-digit ICD-10 codes. We combined diagnoses with high clinical or biological similarity, reducing the number of diagnostic categories from 1645 to 490. For example, we considered all cases of typhoid and paratyphoid fever, cholera, and shigellosis, which are defined as bacterial intestinal infections. For patients with multiple disease records, only the first record and the date of the first hospital visit were used as the diagnosis dates. The discharge coding accuracy of medical conditions of inpatients in the UK was verified, with a primary diagnosis accuracy of 96% and an average diagnostic accuracy of 80.3% . OriginaThe cause of death of participants in the UK Biobank was defined as the primary or secondary cause of death recorded in the mortality data. According to ICD-10, there were 16 causes of death.Genotyping and imputation originated from the UK BiLEVE and UK Biobank Axiom arrays in the UK Biobank study; other detailed information has been provided elsewhere . Single-http://dtb.cpr.ku.dk/). Because no ICD-10 code exists for MAFLD, the ICD-10 codes K76  and K70  were used to validate the reproducibility of the disease trajectory of MAFLD.The database for the Danish Disease Trajectory Browser is the Danish National Patient Register, which includes 7 million individuals from 1994 to 2018, and covers 122 million hospital admissions with 1777 unique ICD-10 codes recorded . The addn = 1633). According to the results of the Cox regression analysis, diseases with a hazard ratio (HR) > 1 and p < 0.05/n  were retained. Second, we analyzed all possible disease 1 (D1) and disease 2 (D2) pairs calculated by n*(n − 1), and pairs that occurred in > 0.5% of MAFLD participants (n = 816) were included. To ensure logical temporal order, we conducted binomial tests to determine whether more MAFLD individuals (> 50%) had D2 diagnosed later than D1 among those with both D1 and D2 diagnoses. Disease pairs with P < the Bonferroni-corrected threshold were considered statistically significant. Third, we set D1 as the exposure and D2 as the outcome, and a logistic regression model was used to ensure an association between D1 and D2 in all disease pairs. Fourth, disease pairs with a significantly increased risk of D2 after D1  were confirmed.Referring to methods used in previous studies –16, we pn × (n − 1) for each cause of death. Finally, binomial tests and logistic regression analyses were conducted to confirm the D1 and D2 pairs with a significantly increased risk of D2 after diagnosis of D1. The detailed steps of the trajectory analysis are described in Additional file https://github.com/youyialex/MAFLD.To analyze the disease trajectory of MAFLD leading to death, we performed similar steps , daily consuming excessive alcohol (≥ 20 g for women and ≥ 30 g for men), carrying risk alleles, or had liver fibrosis.To analyze whether the disease phenotype of MAFLD was consistent in different sexes, males and females were separated. Subsequently, to investigate the impact of alcohol, obesity, liver fibrosis, and genotyping on the disease phenotype of MAFLD, subgroups were established based on whether individuals were overweight/obese  from the baseline in the subcohort. For example, to investigate the relationship between MAFLD and heart failure, participants with any cardiovascular disease at baseline were excluded.A similar study flow path was implemented for the trajectory analysis of genetic susceptibility to MAFLD , chronic ischemic heart disease (n = 25), obesity (n = 20), benign tumors (n = 19), and inflammatory arthritis (n = 17). Moreover, circulatory system diseases  and digestive system diseases  were the primary last layer diseases in the tree diagram  and other alcoholic liver disease (ICD-10 K76) using the Danish Disease Trajectories Browser were drawn in Additional file The present study provides a panoramic view of temporal disease and death trajectories of MAFLD in the community-dwelling population of the UK Biobank cohort study. MAFLD was proved to be related to 113 medical conditions and eight causes of death when limiting disease incidence to ≥ 1%. The network of these diseases originates from asthma, diabetes, hypothyroid conditions, and tobacco abuse, with mediation primarily through diverticular diseases of intestine, chronic ischemic heart disease, obesity, benign tumors, and inflammatory arthritis. These pathways predominantly culminated in acute myocardial infarction, disorders of fluid, electrolyte, and acid–base balance, bacterial infectious agents, infectious gastroenteritis and colitis, and functional intestinal disorders. Regarding the death trajectories of MAFLD, the main causes of death included malignant neoplasm, cardiovascular, and respiratory system deaths. The prevalent lethal medical conditions associated with MAFLD were acute renal failure, heart failure, sepsis, pneumonia, and disorders of fluid, electrolyte, and acid–base balance. These maps provide a series of key pathways that link MAFLD to a broad range of health conditions. Considering the huge disease burden caused by MAFLD worldwide, these findings suggest potential key intervention targets for inhibiting the progression of MAFLD-related health events.2) had a higher risk of multi-system diseases than did those who were overweight/obese. However, Liu et al. demonstrated that this effect remained significant only for hepatic outcomes after fully adjusting for confounding factors [In the subgroup analysis, the association of MAFLD with medical conditions and death in different subgroups of sex, alcohol consumption, body weight, and liver fibrosis was largely significant. Although males had a higher prevalence of MAFLD, females had a higher risk of developing subsequent medical conditions. These sex-related differences may be explained by estrogen levels . Overwei factors . Based o factors –31. Base factors , 33. In Consistent with a previous study’s findings, the pathophysiological mechanism linking MAFLD and cardiovascular disease could be explained by diabetes, obesity, hypertension, and dyslipidemia –36. We fA series of medical conditions involved key nodes in MAFLD disease trajectories. Among these, hypertension, diabetes, obesity, and ischemic heart disease are widely considered adverse outcomes , 40, 41.MAFLD-induced malignant tumors and related deaths have always been a concern. Similar to the findings of Hwang et al., malignant neoplasms were the main cause of death (51.5%) in patients with MAFLD . Our resAlthough the results of the subgroup and sensitivity analyses were consistent, the observed disease phenotypes of MAFLD may also be explained by common etiologies, such as comorbidities, environmental factors, and lifestyle. The PRS can be used to assess the genetic risk of individual-specific diseases or disease characteristics and is not affected by other confounding factors . TherefoThe repeatability of the UK Biobank results was partially verified by the Danish National Patient Register data, but there was heterogeneity in cardiovascular diseases, such as hypertension, coronary heart disease, angina pectoris, myocardial infarction, and heart failure. However, previous studies have validated that cardiovascular diseases and cardiovascular death are important adverse events and outcomes of MAFLD . TherefoUnlike the previous definition of NAFLD, which excluded other potential causes of liver diseases such as viral hepatitis or alcohol consumption, the MAFLD definition takes into account the presence of metabolic risk factors, including obesity, diabetes, dyslipidemia, and insulin resistance, in addition to evidence of hepatic steatosis . It enabThe major strength of the present study is the large sample community-based cohort that prospectively collected complete data on disease diagnosis and was validated by a large sample from an electronic medical record database. Importantly, the disease trajectory analysis was used to draw a panoramic picture of the time-dependent disease occurrence and development pattern. By using data-driven methods instead of traditional analysis methods, we aimed to overcome the limitation of verifying single disease pairings based on specific hypotheses. Thus, the disease trajectory of MAFLD provides novel information that strengthens our understanding of its pathological effects. Moreover, identifying key nodes in the MAFLD disease network can provide potential intervention targets to mitigate the overall decline in health caused by MAFLD.We acknowledge some limitations of this study. First, although liver biopsy is the gold standard for diagnosing hepatic steatosis, it is infeasible for large-scale cohort studies; thus, FLI with relatively high sensitivity and specificity is an acceptable alternative . MoreoveIn conclusion, the disease trajectory analysis of MAFLD identified a range of increased risks for medical conditions and causes of death. Based on the trajectory panorama, we observed that malignant tumors, organ failure, infectious disease, and internal environment disorders were the main end-stage conditions for malignant neoplasm and cardiovascular and respiratory system death, while asthma, diabetes, hypertension, hypothyroid conditions, tobacco abuse, diverticulosis, chronic ischemic heart disease, obesity, benign tumors, and inflammatory arthritis were the primary intermediary medical conditions. Therefore, developing relevant intervention measures that target these key pathways may benefit the MAFLD population by preventing a decline in general health.Additional file 1. Supplementary data.Additional file 2: Fig. S1. Research flowchart for disease trajectory. Fig. S2. Research flowchart for death trajectory. Fig. S3. Research flow path. Fig. S4. Research flowchart disease trajectory of genetic susceptibility to MAFLD. Fig. S5. Research flowchart for death trajectory of genetic susceptibility to MAFLD. Fig. S6. Tree diagram of disease trajectory for MAFLD to (A) genitourinary system disease death, (B) unnatural cause death, (C) digestive system disease death, and (D) endocrine system disease death. Fig. S7. Overview map of the disease trajectories of genetic susceptibility to MAFLD. Fig. S8. Disease trajectories of genetic susceptibility to MAFLD leading to (A) malignant neoplasm death, (B) endocrine system disease death, and (C) cardiovascular disease death. Fig. S9. Disease trajectory of genetic susceptibility to MAFLD leading to (A) genitourinary system disease death, (B) digestive system disease death, and (C) endocrine system disease death. Fig. S10. Disease trajectory of alcoholic liver disease leading to death. Fig. S11. Disease trajectory of other alcoholic liver diseases leading to death.Additional file 3. Supplementary methods.Additional file 4: Table S1. PheWAS of MAFLD and 490 medical conditions. Table S2. Temporal disease pairs in MAFLD individuals. Table S3. Basic characteristics of the dead and surviving participants. Table S4. PheWAS of medical conditions and 7 causes of death. Table S5. Temporal disease pairs in dead individuals with MAFLD. Table S6. PheWAS of MAFLD and 490 disease conditions in heavy drinkers and nonheavy drinkers. Table S7. PheWAS of MAFLD and 490 disease conditions in normal weight and overweight/obese individuals. Table S8. PheWAS of MAFLD and cause of death in normal weight and overweight/obese individuals. Table S9. PheWAS of MAFLD and cause of death in heavy drinkers and nonheavy drinkers. Table S10. Sensitivity analysis of the relationship between MAFLD and 490 medical conditions. Table S11. Sensitivity analysis of the relationship between MAFLD and cause of death. Table S12. PheWAS of nonfibrotic MAFLD and fibrotic MAFLD with 490 medical conditions. Table S13. PheWAS of nonfibrotic and fibrotic MAFLD with causes of death.Table S14. PheWAS of MAFLD with or without MBOAT7 rs641738 (C > T) and 490 medical conditions. Table S15. PheWAS of MAFLD with or without MBOAT7 rs641738 (C > T) and causes of death. Table S16. PheWAS of MAFLD with or without GCKR rs1260326 (C > T) and 490 medical conditions. Table S17. PheWAS of MAFLD with or without GCKR rs1260326 (C > T) and causes of death. Table S18. PheWAS of MAFLD with or without TM6SF2 rs58542926 (C > T) and 490 medical conditions. Table S19. PheWAS of MAFLD with or without TM6SF2 rs58542926 (C > T) and causes of death. Table S20. PheWAS of MAFLD with or without PNPLA3 rs738409 (C > G) and 490 medical conditions. Table S21. PheWAS of MAFLD with or without PNPLA3 rs738409 (C > G) and causes of death. Table S22. Basic characteristics of the participants with and without genetic susceptibility to MAFLD. Table 23. Basic characteristics of the dead and surviving participants with genetic susceptibility to MAFLD. Table S24. PheWAS of genetic susceptibility to MAFLD and 490 medical conditions in males and females. Table S25. PheWAS of genetic susceptibility to MAFLD and cause of death in males and females. Table S26. Temporal disease pairs in individuals with high genetic susceptibility to MAFLD. Table S27. PheWAS of medical conditions and 7 causes of death in individuals with genetic susceptibility to MAFLD. Table S28. Temporal disease pairs in dead individuals with genetic susceptibility to MAFLD. Table S29. Temporal disease pairs in individuals with alcoholic liver disease. Table S30. Temporal disease pairs in individuals with other alcoholic liver diseases.


Furthermore, we revealed that widespread genetic complementarity contributed to indica–japonica intersubspecific heterosis in yield traits, with dominance effect loci making a greater contribution to phenotypic variance than overdominance effect loci. On the basis of the comprehensive dataset, a genomic model applicable to diverse rice varieties was developed and optimized to predict the performance of hybrid combinations. Our data offer a valuable resource for advancing the understanding and facilitating the utilization of heterosis in rice.Exploitation of crop heterosis is crucial for increasing global agriculture production. However, the quantitative genomic analysis of heterosis was lacking, and there is currently no effective prediction tool to optimize cross-combinations. Here 2,839 rice hybrid cultivars and 9,839 segregation individuals were resequenced and phenotyped. Our findings demonstrated that  2 individuals from 18 elite crosses is used to characterize the genetics underlying a range of grain yield-related traits, providing insights into heterosis during breeding and a predictive model.Resequencing of 2,839 hybrid rice cultivars and 9,839 F With the development of the first commercial hybrid maize variety in the 1930s (ref. 1), and the development of rice hybrid varieties in the early 1970s in China2, exploitation of heterosis in crop plants has achieved remarkable yield advantages over inbred lines, and remains a crucial approach to increase agricultural production for global food demand in response to rapidly increasing global population and changing climate4.Heterosis or hybrid vigor refers to the phenomenon that the heterozygous first filial generation  is observed to contribute to heterosis in Arabidopsis, maize and rice13. Large-scale genomic analysis based on commercial rice hybrids and associated populations, provides the support for partial dominance of heterozygous loci for yield-related traits, and the phenomenon of heterosis in rice agricultural production emerges from the nonlinear effects of multiple heterozygous genomic loci combined15. The observation provides evidence that there is a dosage-sensitive component to heterosis16. Genomic studies have identified a number of quantitative trait loci (QTLs) that contribute to rice heterosis18. However, comprehensive analysis that scans genome-wide breeding footprints in rice hybrids to identify and evaluate heterotic loci in a dynamic perspective is lacking.Three non-mutually exclusive hypotheses—dominance, overdominance and epistasis—have been proposed to explain the genetic basis of crop heterosis2 individuals derived from 18 elite hybrids, which represents the majority of commercially used rice hybrids in China over past 50 years. We systematically investigated the genetic basis of phenotypic changes for grain yield-related traits, flowering time (heading date) and grain quality, and explored how breeding history applied heterotic loci and shaped the genomic architecture in rice hybrids. We further investigated the genetic basis underlying strong heterosis in intersubspecific rice hybrids. Moreover, a genomic selection model was developed on the basis of the comprehensive dataset, to facilitate selection of hybrid combinations with high yield potential and design of crossing plans. The model has been validated with respect to its accuracy and broad applicability, including testing in intersubspecific rice hybrids.In this Article, we performed a comprehensive genome analysis of 2,839 rice hybrid cultivars and 9,839 FOryzasativa cv. Nipponbare IRGSP 1.0 reference genome19 to identify polymorphisms. When compared with 100-fold sequencing data, 35-fold sequencing data could capture 96.70% of high-quality single-nucleotide polymorphisms (SNPs), with accuracy estimated at 95.21% and 94.72% for all and heterozygous loci  for 964 representative hybrids.The experimental design of this study is shown in Extended Data Fig. 2 progenies . Users can rapidly access the sample, phenotype, variant and genome-wide association analysis (GWAS) information of interest. The tool also demonstrates the allele frequency and potential impact of variants (according to annotation by SnpEff22), and provides haplotype analysis.To facilitate access to the resource, we constructed a web-based tool , Boro II (BT) and Honglian (HL) cytoplasms from three-line breeding system, as well as Twoline-Jap (TJ) and Twoline-Ind (TI) cytoplasms from two-line breeding system. Phylogenetic analysis based on nuclear polymorphisms implied that WA, TJ and BT hybrids were clearly separated with only a few exceptions and that TI hybrids were genetically mixed with WA and TJ hybrids .According to previous report, hybrids hold five types of cytoplasmPhenotypic comparison of hybrids from three breeding periods was conducted. Of involved traits, heading date, source and sink organ traits, and grain quality changed significantly Fig. . CorrelaPhenotypic changes demonstrated that the achievements of hybrid breeding in the past five decades were reflected in the substantial enlargement of source and sink organs, slight reduction of flowering time and improvement of grain quality.indica–indica hybrids but that TJ hybrids possessed japonica cytoplasms23, we further surveyed the japonica introgression level. Across the whole genome, unequal levels of both heterozygous and homozygous introgression between WA and TJ hybrids were observed (the details for definition of heterozygous and homozygous introgression in NAL1 (Os04g0615000) controlling leaf size and spikelet numbers25, Waxy (Os06g0133000) associated with grain quality28, GW3p6 (Os03g0215400) (refs. 30) and GS6 (Os06g0127800) (ref. 31) controlling grain size and weight  or large (≥100) count of alleles leading to short heading date was less than that in Y1 and Y2 periods  (ref. 32), Hd1 (Os06g0275000) (ref. 33) and Ghd7 (Os07g0261200) (ref. 34), increased from periods Y1 to Y3   respectively explained 12.71% and 8.11% phenotypic variance for leaf width and grain number per panicle in hybrids, with d/a value estimated at 0.61 and 0.79. The number of WA and TI hybrids holding heterozygous genotypes slightly increased during improvement breeding. A newly identified locus GNP3p1 explained 1.89% of phenotypic variance for full grain number per panicle in hybrids, and had its d/a value estimated at −0.14, indicating a negative dominance effect   , ALK (Os06g0229800) (ref. 26), GW5 (Os05g0187500) (refs. 38), GS3 (Os03g0407400) (ref. 39) and GW7 (Os07g0603300) (refs. 41), were identified as master loci controlling grain quality by GWAS. For those genes, frequencies of the breeding-favorable alleles increased during improvement breeding , Ghd7, Ghd8 (Os08g0174500) and Hd1, which were differentiated between indica and japonica subspecies according to previous report46. These results indicated genetic complementation was prevalent in intersubspecific hybrids. We further evaluated the effects of genetic complementation by F2 individuals derived from an indica–japonica hybrid Quanjingyou No.1. In the hybrid, 0.98%, 59.22% and 39.80% genomic segments were indica/indica, indica/japonica and japonica/japonica genotypes, respectively  located nearby) and bin1687 (Hd1 located nearby), breeding-favorable alleles were contributed by maternal line and their d/a were evaluated at 1.40 and 0.24, respectively. Loci at bin0796 and bin2547 (DEP1 (Os09g0441900) located nearby) were from paternal line, with d/a evaluated at 0.09 and 0.01  method. The prediction accuracies for yield per plant, valid panicle number, full grain number per panicle, seed setting rate, plant height, heading date and grain shape were 0.518, 0.559, 0.689, 0.728, 0.790, 0.799 and 0.945, respectively  (ref. 49) and rf4 (Os10g0495200) (ref. 50), both of which were responsible for wild-abortive sterility. Moreover, there was no high-scoring TJ combination containing the homozygous genotype of tms5 (Os02g0214300) (refs. 52), which contributed to environment-sensitive genic male sterility  the magnitude of dominance effects varied across different loci, posing a challenge for reliably estimating the dominance effects; (2) including dominance in model elevated complexity, hindering precise estimation of marker weights. Current models were inadequate in handling the non-additive effect, and the community will need more time to construct a new model to address this issue. The breeding potential predicted by our selection model could help identify core collections from extensive germplasm resources, thereby serving as backbone materials for further improvement. In the future, with the characterization of more quantitative trait genes and better understanding of genetic frameworks, molecular design can be implemented to fully develop the potential of heterosis.The genomic selection model developed in this study presented a valuable tool for breeders to search efficiently for optimal crossbreeding combinations and accelerate breeding cycles while reducing costs. Given the significance of non-additive effects in rice hybrid heterosisEthics approval was not required in this article.13. According to information recorded by the China Rice Data Center (https://www.ricedata.cn/), 2,715 out of 2,839 hybrids had registration information and were released from year 1985 to 2017. We performed Y1 to Y3 classification for 2,715 hybrids based on the following: (1) Policy steering in late 1990s and 2000s. China Ministry of Agriculture established a national mega project entitled ‘Breeding and cultivation system of super rice in China’ in 1996. The project encouraged combined use of heterosis and ideal plant type to increase yield, and they set standards for ideal plant type of super rice: erect panicle model, long erect leaves with delayed senescence model, early vigorous growth and heavy panicle model4. (2) Boom of two-line hybrids in 2000s . With the reference sequence available and development of high-throughput sequencing and genotyping methods, rice molecular biology and functional genomics made great progress, which further accelerated the rational design and molecular breeding of hybrid super rice by marker assisted selection in the past decade62.All 2,839 rice hybrids were from the collections preserved at the China National Rice Research Institute in Hangzhou, China, comprising 1,495 rice hybrid accessions reported previously00s Fig. . (3) Pro1 lines were grown in the consecutive farmland with well-distributed soil status. For each accession, 15 samples were germinated and planted in three rows with row and column spacing setting to 20 and 26.7 cm, respectively. Plants in the middle of the plot were selected to investigate phenotype, involving morphological characteristics , yield components and relevant factors , grain quality-related traits  and physiological feature (heading date). Among them, heading date and morphological characteristics were measured in the field. The heading date was recorded as the duration in days from the date of sowing to the emergence of first inflorescences above flag leaf sheath of five plants for each accession. The other field traits were investigated after heading, and three individuals were selected for phenotyping: the tallest panicle was selected to investigate plant height, flag leaf length and width; tiller angle was measured between tiller and the ground level. The collection of observations for grain-related traits was carried out in the laboratory after harvest, and they were investigated with three biological replicates for each sample. Full/total grain number per plant was counted manually; valid panicle number was directly counted after removing ineffective panicles; full grain number per panicle was estimated by dividing the full grain number per plant by the valid panicle number; seed setting rate was estimated by calculating the ratio of full grain number to total grain number per plant; kilo-grain weight was obtained by weighing 400 fully filled grains and then converting the value to 1,000-grain weight; yield per plant was recorded as the weight of all full grains per plant. Grain quality traits were investigated according to the Chinese national standard . Grain shape was measured as length-to-width ratio of polished grain, which was the grain having its husk and outer brown layers removed. Grains from mixed harvest of the same accession were randomly selected to investigate grain quality-related traits, with two replicates.All hybrid accessions were germinated and planted in an experimental field in Hangzhou, China in summer of 2020. All the F2 populations, generating a total of 9,839 F2 lines. The 18 hybrids were carefully selected to encompass a comprehensive range of major hybrid rice types. Specifically, they consisted of ten intrasubspecific and eight intersubspecific combinations; additionally, they were also categorized into five WA, three TJ, three TI and seven BT hybrids according to cytoplasmic types, providing a diverse representation within the chosen set. The planting standards utilized for F2 individuals were as those employed for F1s. Similar to the procedures employed for F1s, the investigation of physiological feature, morphological characteristics, yield components and relevant factors was also carried out for the F2 lines. No replication was prepared for F2 lines because F2 individuals have different genotypes.Eighteen hybrid accessions were chosen to construct FLeaves were collected from plants in vegetative growth phase in the field for each accession, and genomic DNA was then extracted from the fresh leaf tissue using DNeasy Plant Mini Kit (Qiagen). A sequencing library with ~400 bp insert size was constructed by Trueprep Tagment 3 Enzyme . The amplification-free method was used for library construction to accurately judge heterozygous sites by reducing duplicate sequences. Sequencing was performed on the NovaSeq 6000 platform, generating 150 bp paired-end reads.63), and bases of low quality or from adapter contamination were removed by the tools with parameters ‘ILLUMINACLIP:TruSeq3-PE.fa:2:30:10:2:true MAXINFO:50:0.6’. The clean reads were then aligned against the rice reference genome IRGSP 1.0 by software BWA (version 0.7.1) (ref. 64) with parameters of ‘-R ‘@RG\tID:SampleID\tPL:Illumina\tSM:SampleName’ -M’. Read alignment results were sorted according to their coordination by ‘SortSam’ functions in Genome Analysis Toolkit v4.1.4.1 (GATK) (ref. 65) with default parameters. Variations were called using ‘HaplotypeCaller’, ‘GenomicsDBImport’ and ‘GenotypeGVCFs’ functions in GATK. High-quality variations were contained by the ‘VariantFiltration’ function in GATK with parameters of ‘–cluster-size 3 –cluster-window-size 10 QD < 10.00 FS > 15.000 AC < 3 DP > 200||DP < 5’ for SNPs and ‘QD < 10.00 FS > 30.000 DP > 200||DP < 5’ for InDels.Quality control was conducted using the software Trimmomatic (version 0.38) (ref. 66) using 5,222,902 high-quality SNPs. Kinship analysis was conducted to identify hybrid pairs sharing close relationships in the same breeding period, allowing for further assessment of genomic diversity of rice hybrids. Kinship coefficients were estimated using the ‘emmax-kin’ program in EMMAX software (version emmaxbeta-07Mar2010) (ref. 67) with default parameters. The cutoff value of kinship coefficient was set as 0.9. The result was visualized using Cytoscape desktop software version 3.8.2 (ref. 68). A dot represented a hybrid accession and was colored according to its cytoplasm type. Pairs of hybrids with kinship coefficient greater than 0.9 were connected by a beeline. Principal component analysis (PCA) and admixture analysis were based on 223,533 fourfold degenerate (4DTv) sites. SNPs were annotated by a gene model (snpEff_v4_3_ENSEMBL_BFMPP_32_268.zip) in software SnpEff (v 4.3t) (ref. 22), and the 4DTv sites were identified using customized Python script from the annotated SNPs. PCA was performed by software GCTA  (ref. 69) with default parameters. The highest-ranked three principal components were chosen for visualizing the population structure. Ancestral components for hybrids were inferred by the ADMIXTURE program (version 1.3.0) (ref. 70) with K values from 2 to 5, and the population admixture result was visualized using the R package pophelper (version 2.3.1) (ref. 71).Nucleotide diversity was calculated with VCFtools (version 0.1.15)  ≥21 japonica varieties held the same genotype; (3) the major alleles in indica and japonica populations were different. We identified 830,245 indica–japonica differential SNPs in total.Zhao et al. have reported sequence variants for 66 divergent rice accessions that included 19 japonica fragments were detected across the whole genome: First, we analyzed the genotypes  at the 830,245 differentiated loci in the samples to be tested. Then, a 199-SNP sliding window with 1-SNP step was applied to identify introgressive fragments. Within the 199-SNP length fragment, if there were at least 120 SNPs with homozygous genotypes of japonica origin, the segment was labeled as homozygous japonica introgression; if there were at least 120 SNPs with heterozygous genotypes (indica/japonica), the segment was deemed as heterozygous japonica-introgressive fragments. Adjacent fragments in the same state were then combined for further analysis.On the basis of the differentiated SNPs, introgressive indica- or japonica-origin sequences, representative O. sativa japonica cv. Nipponbare and O. sativa indica cv. Shuhui498 (ref. 73) were chosen to scan for the distribution of indica- or japonica-origin segments across the whole genome, respectively. Out of 830,245 loci, 99.97% SNPs from cv. Nipponbare were judged as of japonica origin and all the 199-SNP length fragments across the whole genome were considered to be of japonica lineage. For cv. Shuhui498, 95.39% SNPs were judged as of indica origin and all the 199-SNP length fragments were identified as of indica lineage 74 for all three cohorts. For rice hybrid cohort, the high-quality SNP data were further filtered by software PLINK (v.1.90b6.12 64-bit)75 to keep variants with a missing rate ≤10% and minor allele frequency ≥5%. For F2 progenies, every individual was genotyped on the basis of high-density SNPs of both parental lines, as described previously21, and further imputed by the genotyping results and SNP information in both parental lines using a customized Perl script. In total, 1,296,386 and 1,059,427 SNPs were supplied to perform GWAS analysis for indica–indica and indica–japonica F2 progenies, respectively. PCA was performed using the input genetic markers by the software GCTA69, and the first two principal components were incorporated as the covariates to effectively account for population structure. Kinship matrix was generated based on the input genetic markers by ‘Kinship’ function in TASSEL. The significant threshold was set as 1 × 10−6 for all three cohorts.GWAS was performed for three cohorts separately, including 2,716 d/a) for association signal was calculated based on genotype effect estimated by TASSEL. The genotype effects of peak SNP in target association signal were chosen to calculate d/a index:A and C respectively represented the genotype effects of two homozygous genotypes, and M represented the genotype effect of heterozygous genotype. For traits with high observation preferred by breeding practice , the degree of dominance was quantified by calculating the ratio of d/a; for traits with low observation preferred by breeding practice , the degree of dominance was quantified by calculating the ratio of −d/a. If the number of observations of any homozygous genotype was less than 5, the estimated effect could be influenced by outliers and might be unreliable. Thus, such locus was excluded in the calculation of d/a index.The dominance-effect/additive-effect  (ref. 76).Furthermore, for QTL mapping by F77. The candidate regions were the 1 Mb region surrounding the genome-wide association signals. A mixed linear model with multiple random effect was applied to estimate the variance components using the R package sommer (version 4.2.0.1) (refs. 79). The model can be written as:y is a vector of the phenotypic value, b is a vector of fixed effects, and ua1, ud1, ua2 and ud2 are vectors of random effect. L, T1, T2, T3 and T4 are incidence matrices for b, ua1, ud1, ua2 and ud2, respectively. e represents a matrix of residual effects and e ~ N and outside (i = 2) the candidate region, and udi ~ N and outside (i = 2) the candidate region, and The PVE by the candidate region surrounding the association signal was estimated according to previously report80. With respect to others, the genotype effects of leading SNP in significant association signal were used to define the breeding-favorable genotype. If a locus associated with multiple phenotypes, only the leading SNP in the association signal with the largest −log10(P) value was selected for analysis. Genotyping was conducted by the leading SNP, and the average phenotypes of both homozygous genotypes were calculated to help determine the breeding-favorable genotype. For grain yield-related traits, the genotype associated with higher grain yield was considered to be the breeding-favorable genotype. For morphological traits, the genotype associated with larger leaf size or tiller angle (erect plant architecture) was defined as the breeding-favorable genotype. With respect to heading date, short heading date resulted in a yield penalty but long heading date was impractical for agricultural production in most rice planting areas; thus, an appropriate heading date was chosen for hybrid breeding. According to the performance of hybrid cultivars in our collection, almost all accessions completed their life cycle when planting in Hangzhou, and a slightly shortened heading date was observed in Y3 hybrids. Thus, the genotype with shorter heading date was defined as the breeding-favorable genotype in our work. Definition of the breeding-favorable genotype for grain quality referred to the Chinese national standard . According to the standard, the value of chalkiness and grain translucency (level) were lower for O. sativa varieties with better grain quality, and the value of gel consistency was higher for O. sativa varieties with better grain quality. Thus, the genotype leading to lower chalkiness, lower grain translucency (level) or higher gel consistency was deemed as the breeding-favorable genotype in our work. With respect to amylose content, the value range was 13.0–18.0 for first-class O. sativa indica and japonica rice varieties, was 13.0–20.0 and 13.0–19.0 for second-class O. sativa indica and japonica rice varieties, and was 13.0–22.0 and 13.0–20.0 for third-class O. sativa indica and japonica rice varieties, respectively. The value range was narrower for first-class varieties, with a lower upper limit. Thus, for convenience, the genotype associated with lower amylose content within limit (13.0–22.0) was determined as the breeding-favorable genotype.For loci with definite QTNs reported previously, the definition of breeding-favorable genotype was in accord with previous reporty is a vector of the phenotypic value, β is the vector of fixed effects, ua and ud are vectors for additive and dominant effects, X, Z1 and Z2 are incidence matrices for fixed and random effects, and ε is the residual error. The additive relationship matrix A was constructed with the A.mat function in sommer, and markers were coded −1, 0 and 1 with respect to the homozygous reference genotype, heterozygous genotype and homozygous alternative genotype, respectively. The dominance relationship matrix D was calculated with the D.mat function in sommer, and markers were coded as 0 and 1 for homozygotes and heterozygote respectively. The equations for the additive relationship matrix A and the dominance relationship matrix D are as follows:W and H being the scaled marker matrix for A and D, respectively, and p and q being the allelic frequencies for the jth marker (j = 1…m).We used the GBLUP method in R package sommer to train the model. The basic equation for our model was as follows:We used sixfold cross-validation to test the accuracy of the model. The accuracy was evaluated through calculating the Pearson correlation coefficient between the GEBV and observed value. Three iterations were performed and averaged to determine the final accuracy.All individuals were sorted by their GEBV, and a high score was given to individuals in the best rank. The score of seven traits was comprehensively incorporated by calculating the selection index. For yield per plant, seed setting rate, full grain number per panicle and valid panicle number, the following percentile GEBV ranges and associated scores were respectively assigned: >80%: 40; 60–80%: 30; 40–60%: 20; 20–40%: 10; <20%: 0. With respect to the rest of three traits, the optimal value ranges were 80–90 days for heading date, 115–125 cm for plant height, and 3.0–3.4 for grain shape. A score of 40 was assigned to the GEBV falling within the optimal range, while a score of 20 was allocated to the GEBV falling outside the optimal range.A validation population was derived from a random combination of 58 inbred lines and 19 commercial male sterile lines. Among all possible combinations, we randomly selected 67 combinations and planted them in the field conditions of Shanghai, China in the summer of 2021. On the basis of the selection index, 13 and 10 individuals were scored in the top 20% and bottom 20%, respectively. We investigated grain yield-related traits . Two-sided Wilcoxon test was conducted to discern significant differences between high-scoring and low-scoring subsets.indica–indica hybrids, to better identify loci associated with phenotypic variation in indica–indica rice hybrids. The field work and phenotypic investigation of randomly selected 67 pseudo-combinations out of all 1,102 combinations were parallel to the model construction and selection index calculation, and the analysis was under double-blind experimental control.No statistical method was used to predetermine sample size. No data were excluded from the phenotypic comparison analysis. Considering the TASSEL software was sensitive to outliers, the outliers were identified by ‘boxplot$out’ script in R-4.1.0 and removed for yield per plant, full grain number per plant, seed setting rate, valid panicle number and chalkiness in P ≤ 0.05) for data with distributions conforming to the assumption of homoscedasticity and data with a heteroscedastic distribution. ‘LSD.test’ in R package agricolae (version 1.3–5) and ‘kwAllPairsNemenyiTest’ in R package PMCMRplus (version 1.9.6) with parameter of ‘dist = Chisquare’ were further used to determine multiple comparison in data with distributions of homoscedasticity and heteroscedasticity, respectively. Box plot was used to demonstrate the distribution of data. For each box plot, the upper and lower boundaries represent the 25th and 75th percentile, respectively; the middle horizontal lines represent the median; the whiskers represent 1.5× the interquartile range; and the dots beyond the whiskers represent outliers.The function of ‘aov’ and ‘Kruskal.test’ in R 4.1.0 were used to identify significant phenotypic change (Further information on research design is available in the Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41588-023-01495-8.Supplementary InformationSupplementary Fig. 1.Reporting SummaryPeer Review FileSupplementary DataSupplementary Table 1. Information of 2,839 rice hybrids. Supplementary Table 2. Resequencing and alignment information of 2,839 rice hybrids. Supplementary Table 3. Estimation of SNP capture rate and calling accuracy for resequencing data with graded depth. Supplementary Table 4. Phenotype of 2,839 rice hybrids. Supplementary Table 5. Association signals underlying grain quality and explaining phenotypic variance larger than 1%. Supplementary Table 6. The list of 58 inbred lines and 19 commercial male sterile lines applied for model verification.


In critically ill patients, measured creatinine clearance (CrCl) is the most reliable method to evaluate glomerular filtration rate in routine clinical practice and may vary subsequently on a day-to-day basis. We developed and externally validated models to predict CrCl one day ahead and compared them with a reference reflecting current clinical practice.A gradient boosting method (GBM) machine-learning algorithm was used to develop the models on data from 2825 patients from the EPaNIC multicenter randomized controlled trial database. We externally validated the models on 9576 patients from the University Hospitals Leuven, included in the M@tric database. Three models were developed: a “Core” model based on demographic, admission diagnosis, and daily laboratory results; a “Core + BGA” model adding blood gas analysis results; and a “Core + BGA + Monitoring” model also including high-resolution monitoring data. Model performance was evaluated against the actual CrCl by mean absolute error (MAE) and root-mean-square error (RMSE).All three developed models showed smaller prediction errors than the reference. Assuming the same CrCl of the day of prediction showed 20.6 (95% CI 20.3–20.9) ml/min MAE and 40.1 (95% CI 37.9–42.3) ml/min RMSE in the external validation cohort, while the developed model having the smallest RMSE (the Core + BGA + Monitoring model) had 18.1 (95% CI 17.9–18.3) ml/min MAE and 28.9 (95% CI 28–29.7) ml/min RMSE.Prediction models based on routinely collected clinical data in the ICU were able to accurately predict next-day CrCl. These models could be useful for hydrophilic drug dosage adjustment or stratification of patients at risk.Trial registration. Not applicable.The online version contains supplementary material available at 10.1186/s13054-023-04553-z. Critical illness often affects kidney function. Epidemiologic studies have shown that 40–60% of intensive care unit (ICU) patients have episodes of acute kidney injury (AKI) , 2, wher2 and has significant consequences concerning the pharmacokinetics of hydrophilic drugs [Existing machine-learning predictions for kidney function have focused on predicting the onset of AKI –22, becaic drugs –30. Studic drugs , have moic drugs , and havic drugs . AKI andDespite the importance and need for continuous kidney function prediction, to the best of our knowledge, no prediction models for daily prediction of CrCl in critically ill patients exist. Hence, this study aims to develop and validate prediction models that apply machine learning algorithms to routinely collected patient data to predict CrCl one day ahead.This study aims to predict the CrCl of the next patient day. CrCl was calculated by daily 24-h urine output (UO), urinary creatinine (UCr), and serum creatinine (SCr) without correction for an average body surface area: CrCl (ml/min) = UCr(mg/dL) × 24-h UO(ml/day) /SCr(mg/dL)/1440 (min/day). In an additional analysis, the same methodology was applied to develop models to predict the average CrCl over the next two days ahead  database , where tExternal validation was performed on a dataset of 20,930 patients of the University Hospitals Leuven included in the large multicenter M@tric database between 2013 and 2018 . The M@tOnly data up to the day of predicting CrCl were used as input to the models. The considered data included: 1) admission data: demographics, diagnosis, and comorbidities, 2) time-series data such as minute-by-minute monitoring data and daily or hourly laboratory results, 3) medication-related data, 4) time-related data: day of the week, and day from ICU admission. Data were retrieved from both the EPaNIC study database  and the patient data management system (PDMS) database .The minimum, maximum, mean, standard deviation, linear regression slope, fast Fourier transform (FFT), cepstrum analysis, autoregressive analyses, and first-order derivative were applied to derive characteristics from the timestamped data. All the features with more than 10% missing values were excluded. For the remaining features, missing values were imputed with the mean and the mode from the development cohort for continuous data and categorical data, respectively. Finally, continuous data were standardized to zero mean and unit variance, and categorical data without order relation were converted into a form with binary data for each category.The prediction models were trained with the gradient-boosting regressor method , with feCore model” using only admission data and daily routine laboratory results.A “Core + BGA model” that adds to the above, blood gas analysis data.A “Core + BGA + Monitoring model” that adds to the above, monitoring data .A “For each prediction task, three models with progressively more features were developed which are meant to be utilized sequentially, based on the data availability at the bedside.Prediction day’s CrCl. To assess daily fluctuations in CrCl, we calculated the difference between the CrCl of each pair of two consecutive days. It was labeled as stable if the absolute difference was less than 20 ml/min and unstable if more than 20 ml/min, as this is a meaningful difference for drug dosing and because the CrCl variability in healthy volunteers has been reported with mean differences of 21.7 ml/min/1.73m2 [Models were developed and internally validated on the EPaNIC database with tenfold cross-validation. At the external validation stage, models trained on the entire EPaNIC database were applied to the previously unseen external validation cohort to assess generalizability. To examine the model usefulness, model performance was further compared against a reference reflecting the current clinical practice: using as prediction for one-day ahead the same CrCl value of the day of prediction. This reference CrCl was henceforth referred to as n/1.73m2 .Mean absolute error (MAE) and root-mean-square error (RMSE) were computed for all available patient-days, stable days, and unstable days for each model in both cohorts. Both MAE and RMSE measured the errors between the model predictions and the target CrCl values, with RMSE more sensitive to large errors. Predictive performance was also evaluated visually with scatter plots and plots of daily MAE and RMSE for all available patient-days, stable days, and unstable days during the first week of ICU stay. As multiple patient-days were available in many patients, no overall p-value can be calculated as this may be biased by repeated measures, but we compared the MAE on a day-by-day basis with the Diebold–Mariano test . Count-bhttp://www.python.org), SciPy version 1.3.1 (SciPy.org), and Scikit-learn library 0.24.2 (scikit-learn.org) were used for all analyses. The study population was described using descriptive statistics, with continuous data presented as medians and interquartile ranges (IQR) and categorical data expressed as counts and percentages (%). Mann–Whitney U test and Fisher's exact test were used to evaluate the statistical significance of differences for continuous and categorical data, respectively. Significance levels were set at the 5% level.Python 3.7.4  65.6 (54.6–75) years, p < 0.01), emergency admission was less frequently , APACHE II score was lower (median (IQR) 17 (13–21), p < 0.01), cardiac surgery was still the major admission diagnosis but occurred less often , and average CrCl over the entire ICU stay was similar (median (IQR) 93.1 (56.2–133.6) ml/min, p = 0.7)  Fig. . For theThe developed models performed well in both the internal validation  ml/min MAE and 13.9 (95% CI 13.7–14.1) ml/min RMSE, while the prediction day’s CrCl showed 7.6 (95% CI 7.5–7.6) ml/min MAE and 9.3 (95% CI 9.3–9.4) ml/min RMSE. The model had a larger RMSE of 4 ml/min on average than the prediction day’s CrCl.However, on the days when kidney function was unstable, the model having the smallest RMSE was the Core model exhibiting 34.8 (95% CI 34.3–35.2) ml/min MAE and 47.3 (95% CI 45.6–49) ml/min RMSE, whereas the prediction day’s CrCl showed 50.1 (95% CI 49.3–50.9) ml/min MAE and 71.1 (95% CI 67–75.1) ml/min RMSE. The model had a smaller RMSE of 23 ml/min on average than the prediction day’s CrCl.These differences in prediction performance for unstable days remained when analyzing the daily predictions during the first week of ICU stay only as evidenced in Fig. In this study, we presented three models to predict daily CrCl in critically ill adults, based on information derived from routinely collected clinical data. The predictive performance remained similar when adding high-resolution data. The developed models were externally validated on previously unseen patients with good performance. Finally, the models demonstrated smaller prediction errors than using the CrCl of the day of prediction , whereas the model performed similarly as the reference of the previous days on the days that the CrCl remained stable (‘stable days’). To the best of our knowledge, this study presents the first machine-learning algorithm for daily CrCl prediction in the ICU.There are many reasons why there is a need for such daily prediction of CrCl during the entire ICU stay. First, measured urinary CrCl is currently considered the most suitable method to estimate the GFR in clinical practice , as manyHaving a reference to compare against helps to understand whether the models could have clinical usefulness. Compared to the current clinical practice of assuming the same CrCl as the day of prediction, our developed models reduced the RMSE from 40.1 to 28.9 ml/min. Importantly, in the subgroup of patient-days with stable kidney function, the developed models demonstrated a clinically insignificant larger RMSE, around 4 ml/min on average, than the reference. This difference, however, is too small to be of clinical relevance and unlikely to cause treatment failure or drug toxicity due to altered renal clearance. Noticeably, in the subgroup of patient-days with unstable kidney function , the developed models had clinically relevant smaller RMSEs, around 23 ml/min on average. This subgroup analysis of days with high CrCl instability clearly exhibited our models’ capability of better capturing the dynamics of kidney function. Nevertheless, despite the large reduction in prediction errors during unstable days, whether or not the models help improve drug dosing and patient outcomes still needs to be investigated prospectively.Our study has many strengths. First, the use of a general ICU population instead of a specific subset of patients made it more generally applicable, and the daily prediction truthfully reflected the fluctuating kidney function on each patient day, allowing for risk stratification and drug dose adjustment. Second, the reporting of this study was performed following the Transparent Reporting of a Multivariate Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) guidelines . Third, There are several limitations in our study. First, the development cohort was based on a RCT database in Belgium dating back to 2010, which might limit its generalizability in other settings. However, model performance remained unchanged when externally validated on a very large database with patient data collected up to 2018. Second, the use of high-resolution data might be difficult to implement in hospitals with limited resources, and some settings might even struggle to have the necessary data for the lower-resolution Core model. Third, there might be a selection bias resulting from the exclusion from the analyses of patient-days with KRT on the day of prediction and in previous week, or of patient-days when less than 2 consecutive CrCls were available, or patient-days after the first 90 days in ICU. These exclusion criteria were necessary to ensure reliable CrCl prediction models could be developed. Fourth, this study was based on retrospective data, and the developed models still need prospective validation in independent cohorts. Fifth, the model performance was not compared against novel biomarkers such as cystatin C and proenkephalin that may be less biased, and urea clearance that may add valuable information especially in those with low CrCl. However, measured CrCl is a fast and cheap test, which are important characteristics as the measurements were taken on a daily basis. Sixth, the measurement of creatinine changed from the Jaffe method in the development cohort to the enzymatic method in the validation cohort, and it was found that the Jaffe method yielded higher creatinine values . HoweverWe have shown that CrCl can be accurately predicted one day in advance during ICU stay. We have also demonstrated the robustness of the developed models on previously unseen patients in external validation. The developed models’ usefulness has been shown in comparison with a reference reflecting current clinical practice, mainly on the patient-days with high kidney function instability. Despite the promising performance, these findings should be prospectively validated in independent patient populations, before these prediction models can be further used for risk stratification or incorporated into a pharmacokinetic model to support a more optimized dose regimen.Additional file 1: Supplementary methods and results.


This study aims to describe the characteristics of the brain network attributes in children diagnosed with Infantile Epileptic Spasms Syndrome (IESS) and to determine the influence exerted by adrenocorticotrophic hormone (ACTH) or methylprednisolone (MP) on network attributes.In this retrospective cohort study, we recruited 19 infants diagnosed with IESS and 10 healthy subjects as the control from the Pediatric Neurology Department at the Third Affiliated Hospital of Zhengzhou University between October 2019 and December 2020. The first thirty-minute processed electroencephalograms (EEGs) were clipped and filtered into EEG frequency bands (2 s each). A comparative assessment was conducted between the IESS group and the controls as well as the pre- and post-treatment in the IESS group. Mutual information values for each EEG channel were collected and compared including characteristic path length (CPL), node degree (ND), clustering coefficient (CC), and betweenness centrality (BC), based on graph theory.P < 0.05), a decreased in CPL of the fast waves and an increase in ND and CC (P < 0.05) in the sleep-wake cycle, and a decrease in ND and CC of the Theta band in the waking phase. Post-treatment compared with the pre-treatment, during the waking ictal phase, there was a noted decrease in CPL in the Delta band and fast waves, while an increase was observed in ND and CC (P < 0.05).Comparing the control group, in the IESS group, there was an increase in CPL of the Delta band, and a decrease in ND and CC of the Delta band during the waking period, contrary to those during the sleeping period (The Delta band and fast waves are crucial components of the network attributes in IESS.This investigation provides a precise characterization of the brain network in children afflicted with IESS, and lays the groundwork for predicting the prognosis using graph theory. The synBFN stands as a pivotal approach in the treatment of epilepsy, extensively employed to probe functional alterations within the brain –10. PrevThis study aims to provide a fresh perspective to comprehend the idiosyncrasies of IESS by analyzing the transformations of brain networks in children with IESS and healthy controls, as well as IESS patients between pre- and post-ACTH or MP treatment. This could potentially yield invaluable insights for future clinical intervention and timely prognostic evaluation.2.2.1.This study enrolled 19 hospitalized participants , all diagnosed with IESS and 10 healthy controls from the Pediatric Neurology Department at the Third Affiliated Hospital of Zhengzhou University, between October 2019 and December 2020. IESS diagnosis was made in accordance with the International League Against Epilepsy (ILAE) guidelines. The inclusion criteria entailed: (1) display of epileptic spasms as the sole seizure type during infancy; (2) interictal EEG indicative of hypsarrhythmia; (3) significant developmental delay or regression; (4) availability of an EEG recorded before and after the initial treatment with ACTH or MP; (5) a follow-up period exceeding 12 months after hormonal therapy; and (6) comprehensive clinical data. The exclusion criteria included: (1) concurrent severe illnesses; (2) termination of treatment; (3) loss to follow-up or withdrawal from clinical observation, as per parental request. Ten control subjects with normal EEG were comparable in age and sex to the IESS patient. The study was approved by the Research Ethics Committees at the Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China (reference: 2021-042-01). Written informed consent from the legal guardian or next of kin was waived by the committee, given the retrospective nature of the study and the minimal risk to participants.The enrolled children with IESS were followed up until July 1, 2022. A structured clinical information form was utilized to gather the demographic and medical history data, including the age at the first onset of IESS, the administration of ACTH/MP and anti-seizure medications (ASMs), and results of video electroencephalogram (VEEG), cranial magnetic resonance imaging (MRI). The results of Griffiths Mental Development Scales-Chinese (GDS-C) were also collected which measures the rate of development of motor, personal-social, language, eye-hand coordination, performance and practical reasoning for children from birth to eight years of age, and any requisite genetic testing.2.2.Scalp 32-Channel VEEG (16 h/24 h) data were collected using electrodes placed according to the international 10/20 system, sampled at 500 Hz . EEG data was examined by two clinical neurophysiologists certified by the China Association Against Epilepsy. Discrepancies were resolved by a third one. The 2021 BASED score system was employed to compute pretreatment and posttreatment scores  using a 2.3.Ictal and interictal EEG clips lasting the first 30 min from the processed EEGs of different phases, encompassing isolated or clustered epileptic spasms, were filtered into EEG frequency bands (2s each)—Delta (0.3–3.5 Hz), Theta (4–7.5 Hz), Alpha (8–13 Hz), Beta (14–30 Hz), and Gamma (30–70 Hz). These selected EEGs were then divided into 900 epochs (1 epoch per 2 s). Mutual information values for each channel of EEG, such as characteristic path length (CPL), node degree (ND), clustering coefficient (CC), and betweenness centrality (BC), were directly compared between the IESS patients and controls, as well as before and after ACTH or MP treatment, as shown in Detailed introduction to the BFN of patients with IESS, based on graph theory combined with Weighted Phase Lag Index (wPLI), can be found in our previous study  and Supphttps://sccn.ucsd.edu/eeglab/index.php). The filtered signal was then subjected to independent component analysis. Artifact data were removed using the SASICA toolkit (https://eeglab.org/others/EEGLAB_Extensions.html) to obtain the pre-processed EEG signal. Finally, the BCT toolkit (https://www.nitrc.org/projects/bct) was used to determine CPL, ND, CC and BC.For the raw EEG data, an average reference was first applied to the band-pass filter (0.5–45 Hz) using the EEGLAB toolkit , and network attributes were compared using a two-sided non-parametric Wilcoxon rank sum test.t-test employed for normally distributed data. Median (interquartile range) values were reported for non-normally distributed data, analyzed via Wilcoxon's rank-sum test. P < 0.05 was considered statistically significant.Demographic data and BASED scores were analyzed using SPSS 26.0 software . Count data were presented as proportions and analyzed via the chi-square test. Measured clinical data were displayed as mean ± standard deviation, with Student's 3.3.1.P > 0.05). All patients in the IESS group exhibited developmental delays, with GDS-C Z-scores below −2.The average age at enrollment for the IESS group was 6.93 (5.77–10.77) months, while that was 7.00 ± 3.36 months in the control group. There was no significant difference in age or sex between the two groups (de novo heterozygous IRF2BP variation). There were no instances of infectious or immunological etiology.In the IESS group, structural etiology was the most prevalent, accounting for 47.37% (9 cases), among which 77.78% (7 cases) were due to encephalomalacia  . Six casP > 0.05). Seventeen patients (89.47%) showed varying degrees of developmental delay.In the IESS group, fourteen patients received MP pulse treatment, while the remaining five were treated with ACTH pulse therapy. In our cohort, the long-term control rate of IESS with structural etiology was lower than that of IESS with an unknown etiology, although the difference was not statistically significant (3.2.P < 0.01). After more than a year of post-treatment follow-up, no spasms were detected in six cases, eight cases reported a decreased frequency of spasms, and five cases showed no significant reduction in spasms.Interictal phase VEEG displayed hypsarrhythmia, with spasm attacks observed in the IESS children. The mean BASED score was 4.83 ± 0.38 before hormonal therapy and 3.44 ± 0.82 after therapy, with a statistically significant reduction (3.3.3.3.1.3.3.1.1.P < 0.05) in the IESS group. The ND and CC of the Delta and Theta bands were decreased, though they were increased in the fast wave band (P < 0.05) in the IESS group. The BC in the temporal region of the Delta band increased but decreased in the fast wave band (P < 0.05) in the IESS group, as shown in Compared the brain network properties with the control group, the CPL of the Delta band increased, and the CPL of the Beta and Gamma bands decreased (3.3.1.2.P < 0.05) in the IESS group      from IS, is a common intractable epilepsy disorder . The cliγ band optimize performance in identifying responses to treatment (Chu et al. found that total EEG complexity was associated with spasm freedom . In our reatment . Also, treatment . Douw etreatment . Partialreatment .Lastly, we presented evidence that the local connectivity and defense capability of the slow waves were enhanced in patients with IESS during ictal onset . NotablyMoreover, HFOs have been identified as potential triggers for spasms . Despiteβ and γ frequency bands have been noted as indicators of seizure cycle changes in IESS (Changes in  in IESS . In addi in IESS . Our stuOur research forms a new foundation for the development of diagnosis and treatment methods for IESS from a network attributes perspective. It offers resources to identify new candidates for a timely prognosis. However, this study is not without limitations. We sought to investigate and summarize the standard features of IESS, but overlooked the metabolic and infectious etiologies of IESS, which are infrequent in our practical setting. In future research, we aim to encompass and separately study patients with differing etiologies. Additionally, due to the sample size, the cases were not further subdivided according to treatment effect. Instead, we examined the global impact of hormone therapy on network attributes in children with IESS. By doing so, we aim to predict the effects of hormonal therapy on prognosis determination and timely adjustment of treatment regimens.5.In conclusion, graph theory-based EEG analyses in IESS patients may serve as a promising avenue for biomarker identification. Our findings suggest that the fast waves and Delta band play a significant role in the brain network attributes of IESS. Concurrently, MP and ACTH can remediate the dysfunctions leading to the emergence of the fast waves and Theta band during the interictal period, and the Delta band during the ictal state. Collectively, these results offer a unique insight into the EEG biomarkers of IESS patients and elucidate the modes of action of hormones on network attributes.