A Simple Tool to Predict Genetic Focal Segmental Glomerulosclerosis

A Simple Tool to Predict Genetic Focal Segmental Glomerulosclerosis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article A Simple Tool to Predict Genetic Focal Segmental Glomerulosclerosis Jingyuan Xie, Xu Hao, Zhiying Liu, Shuwen Yu, Yafei Zhao, Zhengying Fang, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8630004/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction Genetic focal segmental glomerulosclerosis (GFSGS) is caused by pathogenic variant. In the present study, we aimed to develop and validate a predictive model for pathogenic variant in FSGS patients. Methods Patients with biopsy-proven FSGS from two independent cohorts were recruited. FSGS secondary to obesity, hypertension, etc. were excluded. All the enrolled patients underwent whole exome sequencing (WES). We developed a predictive model for pathogenic variants using multivariate logistic regression in the development cohort, and validated the performance of the model using ROC analysis and calibration curve analysis in the validation cohort. Results We recruited 197 FSGS patients for the development cohort and 155 patients for the validation cohort. In the development cohort, 70 patients had a family history and 127 patients did not have a family history; In the validation cohort, 70 patients had a family history and 85 patients did not. WES was performed on all the patients from the two cohorts. We identified 65 FSGS patients with pathogenic variants (33%) in the development cohort and 83 (53.5%) in the validation cohort. Using multivariate logistic regression, we established a predictive model for pathogenic variants, which included parameters such as male sex, lower eGFR at renal biopsy (≤ 30 ml/min/1.73 m 2 ), non-nephrotic syndrome and dominant inheritance [R 2 = 0.62; AUC (95%CI) = 0.91 (0.86–0.96)). We validated the model in the validation cohort, which demonstrated a good performance with an R 2 of 0.55 and AUC of 0.93 (95%CI: 0.89–0.98); In the calibration analysis, the predictive model showed a close alignment between predicted and observed risks of GFSGS in the validation cohort (R 2 = 0.96). Based on the predictive model, we established a simple tool that provided estimated risk of pathogenic variant. Conclusion We developed and validated a simple tool including four variables which had good performance for GFSGS prediction. Health sciences/Biomarkers Health sciences/Diseases Biological sciences/Genetics Health sciences/Medical research Health sciences/Nephrology Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Focal segmental glomerulosclerosis (FSGS) is a pathological lesion with complex etiology 1 – 3 . The clinical manifestations of FSGS vary in age of onset, degree of proteinuria, responsiveness to treatment and disease progression 4 . FSGS can be classified into primary, genetic, secondary forms and FSGS of undetermined cause (FSGS-UC) based on clinical and histological manifestations 5 . Primary FSGS (PFSGS) is presumably caused by circulating factors 6 and usually has a better response to glucocorticosteroids 7 . Secondary FSGS comprises maladaptive, viral and drug-induced FSGS 8 . Genetic FSGS (GFSGS) is often caused by a single pathogenic variant and usually has a poor response to glucocorticosteroids or immunosuppressive therapy. Proper classification and accurate identification GFSGS can help reduce unnecessary side effects of immunosuppressive therapy in FSGS patients. A causative genetic variant has been identified in 10% of adult-onset steroid-resistant nephrotic syndrome (SRNS) cases, and this rate is higher in familial cases 9 . When a causative mutation was identified, these patients should be classified as GFSGS. Most GFSGS patients could be explained by podocyte gene mutations 10 – 11 . Among these genes, ACTN4, INF2, TRPC6, and COL4A3/A4 12–18 et al. are associated with autosomal dominant GFSGS, and NPHS1, NPHS2 , and PLCE1 et al. can cause recessive GFSGS 19 – 21 . Since the different treatment strategies and prognosis, and there are no clear-cut clinical or histopathological findings to distinguish GFSGS from other types 22 – 23 , identifying pathogenetic mutations in FSGS patients by genetic testing is important for clinicians. But the high cost and slow result turnaround of genetic testing limit its widespread application in clinical practice. So, identifying the target population for genetic testing is crucial for improving the positive diagnostic rate of GFSGS. However, it remains unclear which FSGS patients should be subjected to genetic testing. The KDIGO guidelines have provided some recommendations for genetic testing populations, such as patients with a family history or SRNS, but the guidance for newly diagnosed patients is limited, and no risk probabilities are provided. Therefore, in our study we use data from 2 separate cohorts to develop and externally validate an accurate but simple prediction model for GFSGS. By establishing logistic regression models, we use variables routinely measured in patients to create a model for identifying a pathogenic variant, and provide a genetic testing strategy for FSGS patients. Materials and Methods Study Population The development cohort was derived from the department of nephrology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine. The validation cohort was derived from the renal division, Peking University First Hospital. Patients with biopsy-proven FSGS were enrolled, and FSGS secondary to systemic diseases, such as obesity or viral infection, were excluded. Enrolled FSGS patients were categorized into with family history group or without family history group in both the development cohort and the validation cohort, based on whether one or more family members had biopsy proven FSGS or unexplained proteinuria, hematuria, or renal failure. Ethical approval was obtained from the Human Research Ethics Committee of Shanghai Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, and Peking University First Hospital Ethics Committee (approval number 2018-Y-99). All participants or their legal proxies provided written informed consent, in accordance with the Declaration of Helsinki. Clinical definitions NS was defined as urinary protein excretion greater than 3.5 g per 24 hours and serum albumin level less than 3 g/dl. Complete remission and partial remission are defined in accordance with the KDIGO 2021 guidelines 5 . Family history was defined as one or more family members had biopsy proven FSGS or unexplained proteinuria, hematuria, or renal failure. Dominant inheritance (DI) was defined as one of the proband’s parents or children had biopsy proven FSGS or unexplained proteinuria, hematuria, or renal failure. Genetic testing Whole exome sequencing (WES)was performed on all the enrolled patients in both the development and validation cohorts. Determination of the pathogenicity of the detected variations was based on the American College of Medical Genetics and Genomics (ACMG) guidelines 24 . If the ACMG score indicated pathogenic (P) or likely pathogenic (LP), the variation was defined as pathogenic. Pathogenic variations were validated by Sanger sequencing in the index patient and all family members with available DNA samples. The flow chart of gene variant screening is shown in Supplementary Fig. 1. Statistics The distributions of quantitative variables were assessed for normality and summarized as the means and standard deviations (or medians and ranges for nonnormally distributed variables). For continuous variables, Student’s t test or the Mann‒Whitney U test was used to compare the two groups based on the distribution of variables. Categorical variables were expressed as frequencies and percentages, and the group proportions were compared with Pearson χ 2 tests. A logistic regression model was used to analyze the risk factors for positive gene testing. ROC analysis and calibration blot curve were used to validate the performance of the predictive model. Results Demographic and clinical data The flow diagram of the cohort study is shown in Fig. 1 . In the development cohort, 197 FSGS patients were recruited, including 70 with a family history and 127 without a family history; In the validation cohort, 155 FSGS patients were recruited, including 70 with a family history and 85 without a family history. WES was performed on all the enrolled patients. Finally, we identified 65 (33%) GFSGS patients in the development cohort and 83 (53.5%) GFSGS patients in the validation cohort (Fig. 1 ). As shown in Table 1 , there were 95 female patients (48.2%) in development cohort, and 56 female patients (36.1%) in validation cohort. The onset age of patients in development cohort was 34 ± 16 years, and 32.9 ± 10.5 years in validation cohort. Clinical and laboratory variables were extracted from medical records from the time of disease diagnosis, which were shown in Table 1 . The baseline eGFR of the development cohort was 82 (47–111) ml/min/1.73m 2 , and baseline eGFR of the validation cohort was 73 (39–116) ml/min/1.73m 2 . In the development cohort, 15.2% patients progressed to end stage kidney disease (ESKD), and 4.5% of patients in the validation cohort patients progressed to ESKD (Table 1 ). Table 1 Clinical Characteristics of FSGS Patients Development Cohort Validation Cohort All With Family history Without Family history All With Family history Without Family history Proband 197 70 127 155 70 85 Female (%) 95 (48.2) 39 (55.7) 56 (44.1) 56 (36.1) 32 (45.7) 24 (28.2) Onset age (year) 34 ± 16 31 ± 15 35 ± 16 32.9 ± 10.5 34.8 ± 10 31.4 ± 10.7 Biopsy age (year) 40 ± 17 36 ± 15 40 ± 16 36.6 ± 10.6 39.5 ± 10.8 34.3 ± 9.9 Scr (µmol/L) 83 (65–130) 94 (69–161) 83 (66–126) 111 (78–180) 116 (78–167) 105 (84–126) eGFR (ml/min/1.73m 2 ) 82 (47–111) 72 (43–115) 90 (47–118) 73 (39–116) 58 (42–99) 79 (39–124) UA (µmol/L) 372 ± 102 372 ± 107 374 ± 94 412 ± 114 405 ± 102 418 ± 123 UP (g/24 h) 2.6 (0.9–5.3) 2.2 (1.2-4) 3.6 (1.2–7.8) 2.8 (1.4-7) 2.2 (0.9–4.4) 5 (2-9.6) ESKD (%) 30 (15.2) 17 (24.3) 13 (10.2) 7 (4.5) 3 (4.3) 4 (4.7) Note: Scr, serum creatinine; eGFR, estimated glomerular filtration rate; UA, uric acid; UP, urinary protein; ESKD, end-stage kidney disease Prediction model in development cohort To investigate the risk factors for identifying pathogenic variants, we performed multivariate logistic regression analysis to establish predictive models for genetic FSGS in the development cohort. As shown in Supplementary Table 1, Model 1 including sex, onset age, Non-NS and eGFR, performed poorly [R 2 = 0.27; AUC (95%CI) = 0.83 (0.77–0.90)]; The inclusion of SRNS in Model 2 resulted in a slightly improvement in R 2 and AUC [R 2 = 0.28; AUC (95%CI) = 0.84 (0.78–0.91)]. The Model 3 including sex, eGFR, Non-NS and DI significantly improved the performance of the prediction model [R 2 = 0.62; AUC (95%CI) = 0.91 (0.86–0.96)]. Given the results, we selected Model 3 as the best model for predicting pathogenic variants based on the development cohort (Table 2 ). Moreover, we performed calibration curve to evaluate the performance of the predictive model, which depicted the predicted vs. observed genetic FSGS (Fig. 2 ). From the result of the calibration curve, we found that the predictive model had similar predicted and observed risks of genetic FSGS in development cohort (Fig. 2 a, R 2 = 0.97). Table 2 The predictive model for genetic FSGS based on the development cohort and the performance of this model in the validation cohort. R 2 AUC (95% CI) OR (95% CI) β Model in development cohort 0.62 0.91 (0.86–0.96) Male ( ref Female) 2.75 (1.07–7.04) 1.01 eGFR (ml/min/1.73 m 2 ) (≤ 30) 5.94 (1.81–19.51) 1.78 Non-NS 11.04 (2.95–41.26.59) 2.40 DI ( ref Non-DI) 17.61 (5.80-53.48) 2.87 Model in validation cohort 0.73 0.93 (0.89–0.98) Male ( ref Female) 6.85 (1.87–25.01) 1.92 eGFR (ml/min/1.73 m 2 ) (≤ 30) 5.33 (1.21–23.48) 1.67 Non-NS 102.8 (21.30-496.45) 4.63 DI ( ref Non-DI) 45.7 (10.50-198.88) 3.82 Note: NS, nephrotic syndrome; eGFR, estimated glomerular filtration rate; DI, dominant inheritance was defined as one of the proband’s parents or children had biopsy proven FSGS or unexplained proteinuria, hematuria, or renal failure. Prediction model performance in the validation cohort We validate the performance of the predictive model in predicting pathogenic variants in the validation cohort. As shown in Table 2 , the model demonstrated good performance in the validation cohort, with R 2 of 0.73 and AUC of 0.93 (95%CI: 0.89–0.98). In the calibration analysis, the predictive model showed a close alignment between predicted and observed risks of genetic FSGS in the validation cohort (Fig. 2b, R 2 = 0.96). Comparison to existing model Furthermore, we compared our predictive model with the 2021 KDIGO guideline on genetic testing recommendations. According to the 2021 KDIGO guideline, genetic testing is recommended in the following situations: patients with family history, resistant to treatment, determining risk of recurrence in kidney transplantation, assessing living-related kidney donor candidate, conducting prenatal diagnosis, et al. We did ROC curve analysis to compare our predictive model with the KDIGO model in both the development and validation cohorts (Fig. 3 ). Compared to the KDIGO model, our predictive model demonstrated improved predictive ability for identifying pathogenic variants [0.91 (95% CI, 0.86–0.96) vs. 0.85 (95%CI, 0.79–0.92) in the development cohort, and 0.93 (95% CI, 0.89–0.98) vs. 0.90 (95%CI, 0.86–0.96) in the validation cohort). Establishment of the simple tool for GFSGS prediction Based on the results above, we believe that the predictive model developed from the development cohort performed well in predicting genetic FSGS, and this performance has been validated in the validation cohort. Therefore, using the β value of the predictive model, we calculated the risk score for the independent risk factors (Fig. 4 a), with the calculation process outlined in Supplementary Table 2 25 . Furthermore, based on the risk score, we established a formula as a simple tool for GFSGS prediction and the formula is “Sex(Male = 1, Female = 0)+eGFR༈≤30ml/min/1.73m 2 =2, > 30ml/min/1.73m 2 =0༉་nephrotic syndrome༈Yes = 0, No = 3༉+dominant inheritance ( Yes = 4, No = 0) . According to the simple tool, we got a probability score for GFSGS, which ranged from 0 to 10. And the estimated risk based on the tool was shown in Fig. 4 b. Discussion It was reported that the ratio of GFSGS in the adult FSGS population was approximately 10%-43% based on who was chosen for genetic testing and which genes were tested 9 , 26 – 27 . GFSGS is caused by single gene mutations, which includes dozens of genes usually specifically expressed in podocytes. Just recently, we reported that GFSGS patients with INF2 variants exhibited poorer kidney prognosis and progressed to ESKD earlier than primary FSGS patients or patients with COL4A3/A4/A5-het variants 28 . Patients with GFSGS often response poorly to glucocorticosteroids and immunosuppressant treatment. So, it is very important to identify GFSGS in time while FSGS is dragonized. In addition, due to the high economic costs associated with genetic testing, it is not feasible to perform genetic tests on every patient in clinical practice. Therefore, developing a genetic testing strategy is essential, as it can both reduce costs and increase the positive detection rate. However, it is still not clear which FSGS patients should undergo genetic testing. In the present study, we established a predictive model for pathogenic variants in the development cohort, and external validate the performance of the model in the validation cohort. Using multivariate logistic regression analysis, we found that patients who were male, had lower eGFR, Non-NS, and DI were more likely to have a pathogenic variant. Based on these independent risk factors, we established a simple tool, which could predict GFSGS. Additionally, we validate the performance of the model by ROC and calibration curve analysis. The results confirms that our model exhibits excellent predictive capabilities for GFSGS, offering clinician an effective tool to identify patients who need genetic testing and improve the positive detection rate. Significantly, all the parameters included in our model are easily obtainable, which is important for the widespread application in clinical practice. It was widely accepted that FSGS patients with a family history are more likely to carry a pathogenic variant. In previous study, Martin R Pollak has reported that familial cases are more likely to be the result of single gene defects than nonfamilial cases 4 . Similarly, we also concluded that patients with a family history were more likely to carry pathogenic variants. Different from previous studies, we first included “dominant inheritance” in the predictive model, which was the independent risk factor for pathogenic variants. In another word, if one of the proband’s parents or children was diagnosed by FSGS or unexplained proteinuria, hematuria, or renal failure, the probability of carrying a pathogenic variant is much higher. This suggests that a detailed family history should be thoroughly investigated in clinical practice. Besides family history, we also found that patients who were male, had lower eGFR, Non-NS were also independent risk factor for GFSGS, which were similar with many previous studies. However, in our predictive model, we calculate the risk score for each risk factor, and based on these scores, we derived a simple tool for GFSGS prediction, which corresponds to varying risks of pathogenic variants. In the clinical practice, clinician could determine the risk of GFSGS according to the predictive tool. Previous studies suggested that individuals with primary FSGS who are resistant to immunosuppressive therapy should receive genetic testing 29 – 30 . However, unlike previous studies, we did not find steroid-resistant nephrotic syndrome (SRNS) to be an independent risk factor for pathogenetic variants in either the development cohort or validation cohort. We think the possible reason is that the majority of the patients enrolled in our study were newly diagnosed with FSGS and their clinical parameters were analyzed at baseline, which allowed us to assess the likelihood of pathogenic variants to avoid or minimize unnecessary steroid exposure. On the other hand, while our results further highlight the strong predictive capability of our model for patients with newly diagnosed FSGS, we also agree with the recommendations of previous studies, that during the disease progression, if a patient exhibits treatment resistance, genetic testing is essential. This not only helps clinician to identify the pathogenesis of the disease but also to avoid the potential side effects associated with immunosuppressive therapy. We have several limitations in this study. First, our study population consisted of 2 distinct nephrology department in China, meaning that these findings need to be validated in other ethnic groups, however, given that most previous genetic studies on FSGS were based on Caucasians, genetic studies on Chinese FSGS patients are highly necessary; Second, most of the patients enrolled in this study were adult-onset, which indicated that our predictive model may not be applicable to pediatric patients, and further investigations should be performed on pediatric population; Third, due to the limitations of whole-exome sequencing, we may have missed some other special genetic variants, such as chromosomal structural variants, alternative splicing, and mitochondrial mutations. In summary, we established a simple predictive model, which had good performance of GFSGS prediction. The model serves as a simple and effective predictive tool for predicting pathogenic variants. Our findings are important for the diagnosis and management of FSGS patients. Declarations Statement of Ethics Ethical approval was obtained from the Human Research Ethics Committee of Shanghai Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, and Peking University First Hospital Ethics Committee (approval number 2018-Y-99). All participants or their legal proxies provided written informed consent, in accordance with the Declaration of Helsinki. Conflict of Interest Statement The authors have no conflicts of interest to declare. Funding This work was supported by grants from the National Key Research and Development Program of China (2024YFC2511001), Major International (Regional) Joint Research Program of National Natural Science Foundation of China (No: 82120108007), National Natural Science Foundation of China (No.82370711), National Facility for Translational Medicine (Shanghai) Open subjects：(No:TMSK-2024-101, NRCTM(SH)-2025-10(Ruijin Base))，Program of Shanghai Academic/Technology Research Leader (No: 21XD1402000), Science and Technology Innovation Action Plan of Shanghai Science and Technology Commettee (No. 22140904000), Shanghai Municipal Education Commission Gaofeng Clinical Medicine Grant (No.20152207), Shanghai Shenkang Hospital Development Center “Three-year Action Plan for Promoting Clinical Skills and Clinical Innovation in Municipal Hospitals” (No:SHDC2020CR6017), and Shanghai Municipal Key Clinical Specialty (No. shslczdzk02502), Research Foundation of Ruijin Hospital (No. JZ202408), National Key Research and Development Program of China (2024YFC2511000); National Science Foundation of China (82370709). Author Contributions: Research idea and study design: JX and XZ; data acquisition: XH, ZL, SY, YZ, ZF and JL; statistical analysis: XH, ZL and SY; data interpretation: XH, ZL and SY. Investigation: CZ, JM, QZ, LY, XL, NC, HR, XZ and JX. Each author contributed important intellectual content during manuscript drafting or revision and accepts accountability for the overall work by ensuring that questions pertaining to the accuracy or integrity of any portion of the work are appropriately investigated and resolved. Data availability statement: All data relevant to the study are included in the article or uploaded as Supplementary Materials. More details are available upon reasonable request to the corresponding author. References Hao X, et al.: Increased risk of treatment failure and end-stage renal disease in familial focal segmental glomerular sclerosis. Contributions to nephrology. 181 : 101–108(2013). Rood IM, Deegens JK, Wetzels JF: Genetic causes of focal segmental glomerulosclerosis: implications for clinical practice. Nephrol Dial Transplant. 27 (3): 882–890(2012). Pollak MR: Familial FSGS. Adv Chronic Kidney Dis. 21 (5): 422–425(2014). Wang M, et al.: Contributions of Rare Gene Variants to Familial and Sporadic FSGS. Journal of the American Society of Nephrology: JASN. 30 (9): 1625–1640(2019). Rovin BH, et al.: KDIGO 2021 Clinical Practice Guideline for the Management of Glomerular Diseases. Kidney international. 100 (4): S1-S276(2021). Kachurina N, et al.: Novel unbiased assay for circulating podocyte-toxic factors associated with recurrent focal segmental glomerulosclerosis. American Journal of Physiology-Renal Physiology. 310 (10): F1148-F1156(2016). Allard L, et al.: Treatment by immunoadsorption for recurrent focal segmental glomerulosclerosis after paediatric kidney transplantation: a multicentre French cohort. Nephrology Dialysis Transplantation. 33 (6): 954–963(2018). Chandra P, Kopp JB: Viruses and collapsing glomerulopathy: a brief critical review: Table 1. Clinical kidney journal. 6 (1): 1–5(2013). Sheila S, Gemma B, Barbara T: Clinical utility of genetic testing in children and adults with steroid-resistant nephrotic syndrome. CJASN. 6 : 1139–1148(2011). Buscher AK, et al.: Mutations in podocyte genes are a rare cause of primary FSGS associated with ESRD in adult patients. Clin Nephrol. 78 (1): 47–53(2012). Gast C, et al.: Collagen (COL4A) mutations are the most frequent mutations underlying adult focal segmental glomerulosclerosis. Nephrol Dial Transplant. 31 (6): 961–970(2016). Brown EJ, et al.: Mutations in the formin gene INF2 cause focal segmental glomerulosclerosis. Nature genetics. 42 (1): 72–76(2010). Kaplan JM, et al.: Mutations in ACTN4, encoding alpha-actinin-4, cause familial focal segmental glomerulosclerosis. Nature genetics. 24 (3): 251–256(2000). Winn MP, et al.: A mutation in the TRPC6 cation channel causes familial focal segmental glomerulosclerosis. Science. 308 (5729): 1801–1804(2005). Lowik MM, et al.: Focal segmental glomerulosclerosis in a patient homozygous for a CD2AP mutation. Kidney international. 72 (10): 1198–1203(2007). Niaudet P, Gubler MC: WT1 and glomerular diseases. Pediatr Nephrol. 21 (11): 1653–1660(2006). Xie J, et al.: Novel mutations in the inverted formin 2 gene of Chinese families contribute to focal segmental glomerulosclerosis. Kidney international. 88 (3): 593–604(2015). Xie J, et al.: COL4A3 mutations cause focal segmental glomerulosclerosis. J Mol Cell Biol. 7 (2): 184(2015). Santin S, et al.: Nephrin mutations cause childhood- and adult-onset focal segmental glomerulosclerosis. Kidney international. 76 (12): 1268–1276(2009). Hinkes B, et al.: Specific podocin mutations correlate with age of onset in steroid-resistant nephrotic syndrome. Journal of the American Society of Nephrology: JASN. 19 (2): 365–371(2008). Hinkes B, et al.: Positional cloning uncovers mutations in PLCE1 responsible for a nephrotic syndrome variant that may be reversible. Nature genetics. 38 (12): 1397–1405(2006). De Vriese AS, et al.: Differentiating Primary, Genetic, and Secondary FSGS in Adults: A Clinicopathologic Approach. Journal of the American Society of Nephrology: JASN. 29 (3): 759–774(2018). Snoek R, et al.: Importance of Genetic Diagnostics in Adult-Onset Focal Segmental Glomerulosclerosis. Nephron. 142 (4): 351–358(2019). Richards S, et al.: Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genetics in Medicine. 17 (5): 405–424(2015). Wilson PW, et al.: Prediction of coronary heart disease using risk factor categories. Circulation. 97 (18): 1837–1847(1998). Sen ES, et al.: Clinical genetic testing using a custom-designed steroid-resistant nephrotic syndrome gene panel: analysis and recommendations. Journal of Medical Genetics. 54 (12): 795–804(2017). Sadowski CE, et al.: A Single-Gene Cause in 29.5% of Cases of Steroid-Resistant Nephrotic Syndrome. Journal of the American Society of Nephrology. 26 (6): 1279–1289(2015). Hao X, et al.: Clinical Characteristics and Prognosis of Genetic Focal Segment Glomerulosclerosis. Am J Kidney Dis. 84 (5): 660–662(2024). Yao T, et al.: Integration of Genetic Testing and Pathology for the Diagnosis of Adults with FSGS. Clin J Am Soc Nephrol. 14 (2): 213–223(2019). Maruyama K, et al.: NPHS2 mutations in sporadic steroid-resistant nephrotic syndrome in Japanese children. Pediatr Nephrol. 18 (5): 412–416(2003). Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterials.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8630004","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":583072654,"identity":"b03c075d-25ce-430f-b2a0-65e092024100","order_by":0,"name":"Jingyuan Xie","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvklEQVRIiWNgGAWjYHACNhDBw8De2PjwA2laeA43G0uQooWBQSK9TYCHGPXm7L3HHldUHJbhl3zYxiDBYCen20BAi2XPuXTDM2fSeCRnJ7Y9KGBINjY7QECLwY0cM8nGNhseg9uJ7QYSDAcStxHUcv8NSIsEj/3Ng0CSKC03eKC2SDASq+VMXppkA9AvEmcSgYFsQIxfjp89JtlQcdiev/34w4cfKuzkCGoBRSKyCQSVY2gZBaNgFIyCUYAFAABmND5F+aha0wAAAABJRU5ErkJggg==","orcid":"","institution":"Shanghai Jiao Tong University School of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Jingyuan","middleName":"","lastName":"Xie","suffix":""},{"id":583072655,"identity":"5625a652-2440-42de-80d4-398775addba9","order_by":1,"name":"Xu Hao","email":"","orcid":"","institution":"Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xu","middleName":"","lastName":"Hao","suffix":""},{"id":583072656,"identity":"540b305e-7c49-449e-8d27-b23927d74563","order_by":2,"name":"Zhiying Liu","email":"","orcid":"","institution":"Peking University First Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhiying","middleName":"","lastName":"Liu","suffix":""},{"id":583072657,"identity":"22d5bfa1-95e9-4686-9596-b288b0145966","order_by":3,"name":"Shuwen Yu","email":"","orcid":"","institution":"Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Shuwen","middleName":"","lastName":"Yu","suffix":""},{"id":583072658,"identity":"ffbf4e6e-bfa8-455d-8b90-9e463d6c648c","order_by":4,"name":"Yafei Zhao","email":"","orcid":"","institution":"Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yafei","middleName":"","lastName":"Zhao","suffix":""},{"id":583072659,"identity":"35fc3535-6bf5-4d30-ab32-2316d9afc252","order_by":5,"name":"Zhengying Fang","email":"","orcid":"","institution":"Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Zhengying","middleName":"","lastName":"Fang","suffix":""},{"id":583072660,"identity":"44f95e4d-43db-477f-b057-16b84568a808","order_by":6,"name":"Jian Liu","email":"","orcid":"","institution":"Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Liu","suffix":""},{"id":583072661,"identity":"bf069500-aa8a-491f-8c95-afba8454b406","order_by":7,"name":"Chunli Zhang","email":"","orcid":"","institution":"Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Chunli","middleName":"","lastName":"Zhang","suffix":""},{"id":583072662,"identity":"91a839c7-189d-4332-bfb7-ceae43c90f76","order_by":8,"name":"Jun Ma","email":"","orcid":"","institution":"Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Ma","suffix":""},{"id":583072663,"identity":"bc648be9-259e-4789-be42-e9903c863327","order_by":9,"name":"Qimin Zheng","email":"","orcid":"","institution":"Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Qimin","middleName":"","lastName":"Zheng","suffix":""},{"id":583072664,"identity":"aa8d68ff-4eb9-4b7c-99e6-42075afdef09","order_by":10,"name":"Li Yang","email":"","orcid":"","institution":"Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Yang","suffix":""},{"id":583072665,"identity":"8f45a34d-66c8-4515-a16d-ad52181bfd53","order_by":11,"name":"Xiaoling Lin","email":"","orcid":"","institution":"Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xiaoling","middleName":"","lastName":"Lin","suffix":""},{"id":583072666,"identity":"21249236-3f1a-4d3c-b404-7505e2b8e9a3","order_by":12,"name":"Nan Chen","email":"","orcid":"","institution":"Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Nan","middleName":"","lastName":"Chen","suffix":""},{"id":583072667,"identity":"8d206fe2-2fda-4014-bec3-55e85d13c849","order_by":13,"name":"Hong Ren","email":"","orcid":"","institution":"Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Hong","middleName":"","lastName":"Ren","suffix":""},{"id":583072668,"identity":"c3b0178f-ec33-44ac-baa4-4ca861c6d45e","order_by":14,"name":"Xujie Zhou","email":"","orcid":"","institution":"Peking University First Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xujie","middleName":"","lastName":"Zhou","suffix":""}],"badges":[],"createdAt":"2026-01-18 08:23:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8630004/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8630004/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101789006,"identity":"499fb504-337d-4975-b3ba-9598217ee5d2","added_by":"auto","created_at":"2026-02-03 15:55:53","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":98899,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of the study cohort.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8630004/v1/374ff8cd9edd13da8be350fd.jpg"},{"id":101788988,"identity":"1a25e6e1-a635-480a-a09c-ac4f3360f21b","added_by":"auto","created_at":"2026-02-03 15:55:47","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":59462,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curves depicting the predicted vs. observed genetic FSGS by the predictive model in development cohort (a) and validation cohort (b)\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8630004/v1/ceb15b1bf163bc2fc7f56a58.jpg"},{"id":101789031,"identity":"3fc2a2df-d5c9-4334-b814-b620bc9ad839","added_by":"auto","created_at":"2026-02-03 15:55:54","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":75219,"visible":true,"origin":"","legend":"\u003cp\u003eThe ROC curve analysis of predictive model based on the development cohort and validation cohort, compared with KDIGO model. (a) The AUC value of development cohort was 0.91 (95%CI, 0.86-0.96) by predictive model and 0.85 (95%CI, 0.79-0.92) by KDIGO model; (b) The AUC value of validation cohort was 0.93 (95%CI, 0.89-0.98) by predictive model and 0.9 (95%CI, 0.86-0.96) by KIDIGO model.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8630004/v1/03b9d8ba8382bfdf6fa74b0d.jpg"},{"id":101788998,"identity":"e323221b-6909-4f13-bdd9-58af480f5768","added_by":"auto","created_at":"2026-02-03 15:55:52","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":40743,"visible":true,"origin":"","legend":"\u003cp\u003eRisk score of predictive model and the estimate of risk. (a) Risk score of the risk factor was calculated based on the multivariate logistic analysis, and the total score ranged 0~10 score, and the “total score” was defined as probability score for GFSGS. The calculation process was shown in Supplementary Table 2. (b)The interpretation between probability score and estimate of risk, and the formulae was: \u003cem\u003eEstimate of risk=1/[1+exp(-4.233+B\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e*B\u003c/em\u003e\u003csub\u003e\u003cem\u003eref\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e+B*Probability Score)]\u003c/em\u003e\u003csup\u003e [25]\u003c/sup\u003e\u003cem\u003e.\u003c/em\u003e \u0026nbsp;\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8630004/v1/9f2f8bb010e19c4355a92c15.jpg"},{"id":105898065,"identity":"9877eb73-9a17-4f1e-bcdf-e91d5ad0792a","added_by":"auto","created_at":"2026-04-01 08:59:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1039275,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8630004/v1/a294cbd8-b600-4594-a138-bad7b3b804ec.pdf"},{"id":101789034,"identity":"e531afc1-7a88-428c-9839-0a5a46d65e44","added_by":"auto","created_at":"2026-02-03 15:55:55","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":30487,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-8630004/v1/9708e290cc6f32b9df78c890.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Simple Tool to Predict Genetic Focal Segmental Glomerulosclerosis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eFocal segmental glomerulosclerosis (FSGS) is a pathological lesion with complex etiology \u003csup\u003e\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. The clinical manifestations of FSGS vary in age of onset, degree of proteinuria, responsiveness to treatment and disease progression\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. FSGS can be classified into primary, genetic, secondary forms and FSGS of undetermined cause (FSGS-UC) based on clinical and histological manifestations\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Primary FSGS (PFSGS) is presumably caused by circulating factors\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e and usually has a better response to glucocorticosteroids\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Secondary FSGS comprises maladaptive, viral and drug-induced FSGS\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Genetic FSGS (GFSGS) is often caused by a single pathogenic variant and usually has a poor response to glucocorticosteroids or immunosuppressive therapy. Proper classification and accurate identification GFSGS can help reduce unnecessary side effects of immunosuppressive therapy in FSGS patients.\u003c/p\u003e \u003cp\u003eA causative genetic variant has been identified in 10% of adult-onset steroid-resistant nephrotic syndrome (SRNS) cases, and this rate is higher in familial cases\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. When a causative mutation was identified, these patients should be classified as GFSGS. Most GFSGS patients could be explained by podocyte gene mutations\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Among these genes, \u003cem\u003eACTN4, INF2, TRPC6, and COL4A3/A4\u003c/em\u003e\u003csup\u003e12\u0026ndash;18\u003c/sup\u003e et al. are associated with autosomal dominant GFSGS, and \u003cem\u003eNPHS1, NPHS2\u003c/em\u003e, and \u003cem\u003ePLCE1\u003c/em\u003e et al. can cause recessive GFSGS\u003csup\u003e\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Since the different treatment strategies and prognosis, and there are no clear-cut clinical or histopathological findings to distinguish GFSGS from other types\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, identifying pathogenetic mutations in FSGS patients by genetic testing is important for clinicians. But the high cost and slow result turnaround of genetic testing limit its widespread application in clinical practice. So, identifying the target population for genetic testing is crucial for improving the positive diagnostic rate of GFSGS. However, it remains unclear which FSGS patients should be subjected to genetic testing. The KDIGO guidelines have provided some recommendations for genetic testing populations, such as patients with a family history or SRNS, but the guidance for newly diagnosed patients is limited, and no risk probabilities are provided.\u003c/p\u003e \u003cp\u003eTherefore, in our study we use data from 2 separate cohorts to develop and externally validate an accurate but simple prediction model for GFSGS. By establishing logistic regression models, we use variables routinely measured in patients to create a model for identifying a pathogenic variant, and provide a genetic testing strategy for FSGS patients.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Population\u003c/h2\u003e \u003cp\u003eThe development cohort was derived from the department of nephrology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine. The validation cohort was derived from the renal division, Peking University First Hospital. Patients with biopsy-proven FSGS were enrolled, and FSGS secondary to systemic diseases, such as obesity or viral infection, were excluded. Enrolled FSGS patients were categorized into with family history group or without family history group in both the development cohort and the validation cohort, based on whether one or more family members had biopsy proven FSGS or unexplained proteinuria, hematuria, or renal failure.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthical approval\u003c/strong\u003e \u003cp\u003e was obtained from the Human Research Ethics Committee of Shanghai Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, and Peking University First Hospital Ethics Committee (approval number 2018-Y-99). All participants or their legal proxies provided written informed consent, in accordance with the Declaration of Helsinki.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eClinical definitions\u003c/h3\u003e\n\u003cp\u003eNS was defined as urinary protein excretion greater than 3.5 g per 24 hours and serum albumin level less than 3 g/dl. Complete remission and partial remission are defined in accordance with the KDIGO 2021 guidelines\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Family history was defined as one or more family members had biopsy proven FSGS or unexplained proteinuria, hematuria, or renal failure. Dominant inheritance (DI) was defined as one of the proband\u0026rsquo;s parents or children had biopsy proven FSGS or unexplained proteinuria, hematuria, or renal failure.\u003c/p\u003e\n\u003ch3\u003eGenetic testing\u003c/h3\u003e\n\u003cp\u003eWhole exome sequencing (WES)was performed on all the enrolled patients in both the development and validation cohorts. Determination of the pathogenicity of the detected variations was based on the American College of Medical Genetics and Genomics (ACMG) guidelines\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. If the ACMG score indicated pathogenic (P) or likely pathogenic (LP), the variation was defined as pathogenic. Pathogenic variations were validated by Sanger sequencing in the index patient and all family members with available DNA samples. The flow chart of gene variant screening is shown in Supplementary Fig.\u0026nbsp;1.\u003c/p\u003e\n\u003ch3\u003eStatistics\u003c/h3\u003e\n\u003cp\u003eThe distributions of quantitative variables were assessed for normality and summarized as the means and standard deviations (or medians and ranges for nonnormally distributed variables). For continuous variables, Student\u0026rsquo;s t test or the Mann‒Whitney U test was used to compare the two groups based on the distribution of variables. Categorical variables were expressed as frequencies and percentages, and the group proportions were compared with Pearson χ\u003csup\u003e2\u003c/sup\u003e tests. A logistic regression model was used to analyze the risk factors for positive gene testing. ROC analysis and calibration blot curve were used to validate the performance of the predictive model.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDemographic and clinical data\u003c/h2\u003e \u003cp\u003eThe flow diagram of the cohort study is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. In the development cohort, 197 FSGS patients were recruited, including 70 with a family history and 127 without a family history; In the validation cohort, 155 FSGS patients were recruited, including 70 with a family history and 85 without a family history. WES was performed on all the enrolled patients. Finally, we identified 65 (33%) GFSGS patients in the development cohort and 83 (53.5%) GFSGS patients in the validation cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, there were 95 female patients (48.2%) in development cohort, and 56 female patients (36.1%) in validation cohort. The onset age of patients in development cohort was 34\u0026thinsp;\u0026plusmn;\u0026thinsp;16 years, and 32.9\u0026thinsp;\u0026plusmn;\u0026thinsp;10.5 years in validation cohort. Clinical and laboratory variables were extracted from medical records from the time of disease diagnosis, which were shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The baseline eGFR of the development cohort was 82 (47\u0026ndash;111) ml/min/1.73m\u003csup\u003e2\u003c/sup\u003e, and baseline eGFR of the validation cohort was 73 (39\u0026ndash;116) ml/min/1.73m\u003csup\u003e2\u003c/sup\u003e. In the development cohort, 15.2% patients progressed to end stage kidney disease (ESKD), and 4.5% of patients in the validation cohort patients progressed to ESKD (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical Characteristics of FSGS Patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDevelopment Cohort\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eValidation Cohort\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWith Family history\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWithout Family history\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eWith Family history\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eWithout Family history\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProband\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFemale (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95 (48.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (55.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56 (44.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e56 (36.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e32 (45.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e24 (28.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOnset age (year)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34\u0026thinsp;\u0026plusmn;\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31\u0026thinsp;\u0026plusmn;\u0026thinsp;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35\u0026thinsp;\u0026plusmn;\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32.9\u0026thinsp;\u0026plusmn;\u0026thinsp;10.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e34.8\u0026thinsp;\u0026plusmn;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e31.4\u0026thinsp;\u0026plusmn;\u0026thinsp;10.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBiopsy age (year)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40\u0026thinsp;\u0026plusmn;\u0026thinsp;17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36\u0026thinsp;\u0026plusmn;\u0026thinsp;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40\u0026thinsp;\u0026plusmn;\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36.6\u0026thinsp;\u0026plusmn;\u0026thinsp;10.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e39.5\u0026thinsp;\u0026plusmn;\u0026thinsp;10.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e34.3\u0026thinsp;\u0026plusmn;\u0026thinsp;9.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eScr (\u0026micro;mol/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83 (65\u0026ndash;130)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94 (69\u0026ndash;161)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83 (66\u0026ndash;126)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e111 (78\u0026ndash;180)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e116 (78\u0026ndash;167)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e105 (84\u0026ndash;126)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eeGFR (ml/min/1.73m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82 (47\u0026ndash;111)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72 (43\u0026ndash;115)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90 (47\u0026ndash;118)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e73 (39\u0026ndash;116)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e58 (42\u0026ndash;99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e79 (39\u0026ndash;124)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUA (\u0026micro;mol/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e372\u0026thinsp;\u0026plusmn;\u0026thinsp;102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e372\u0026thinsp;\u0026plusmn;\u0026thinsp;107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e374\u0026thinsp;\u0026plusmn;\u0026thinsp;94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e412\u0026thinsp;\u0026plusmn;\u0026thinsp;114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e405\u0026thinsp;\u0026plusmn;\u0026thinsp;102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e418\u0026thinsp;\u0026plusmn;\u0026thinsp;123\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUP (g/24 h)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.6 (0.9\u0026ndash;5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.2 (1.2-4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.6 (1.2\u0026ndash;7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.8 (1.4-7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.2 (0.9\u0026ndash;4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5 (2-9.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eESKD (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (15.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (24.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7 (4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3 (4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4 (4.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cem\u003eNote: Scr, serum creatinine; eGFR, estimated glomerular filtration rate; UA, uric acid; UP, urinary protein; ESKD, end-stage kidney disease\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePrediction model in development cohort\u003c/h3\u003e\n\u003cp\u003eTo investigate the risk factors for identifying pathogenic variants, we performed multivariate logistic regression analysis to establish predictive models for genetic FSGS in the development cohort. As shown in Supplementary Table\u0026nbsp;1, Model 1 including sex, onset age, Non-NS and eGFR, performed poorly [R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.27; AUC (95%CI)\u0026thinsp;=\u0026thinsp;0.83 (0.77\u0026ndash;0.90)]; The inclusion of SRNS in Model 2 resulted in a slightly improvement in R\u003csup\u003e2\u003c/sup\u003e and AUC [R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.28; AUC (95%CI)\u0026thinsp;=\u0026thinsp;0.84 (0.78\u0026ndash;0.91)]. The Model 3 including sex, eGFR, Non-NS and DI significantly improved the performance of the prediction model [R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.62; AUC (95%CI)\u0026thinsp;=\u0026thinsp;0.91 (0.86\u0026ndash;0.96)]. Given the results, we selected Model 3 as the best model for predicting pathogenic variants based on the development cohort (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Moreover, we performed calibration curve to evaluate the performance of the predictive model, which depicted the predicted vs. observed genetic FSGS (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). From the result of the calibration curve, we found that the predictive model had similar predicted and observed risks of genetic FSGS in development cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.97).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe predictive model for genetic FSGS based on the development cohort and the performance of this model in the validation cohort.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAUC (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel in development cohort\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.91 (0.86\u0026ndash;0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale (\u003cem\u003eref\u003c/em\u003e Female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.75 (1.07\u0026ndash;7.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR (ml/min/1.73 m\u003csup\u003e2\u003c/sup\u003e) (\u0026le;\u0026thinsp;30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.94 (1.81\u0026ndash;19.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-NS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.04 (2.95\u0026ndash;41.26.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDI (\u003cem\u003eref\u003c/em\u003e Non-DI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.61 (5.80-53.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel in validation cohort\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.93 (0.89\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale (\u003cem\u003eref\u003c/em\u003e Female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.85 (1.87\u0026ndash;25.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR (ml/min/1.73 m\u003csup\u003e2\u003c/sup\u003e) (\u0026le;\u0026thinsp;30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.33 (1.21\u0026ndash;23.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-NS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e102.8 (21.30-496.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDI (\u003cem\u003eref\u003c/em\u003e Non-DI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45.7 (10.50-198.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNote: NS, nephrotic syndrome; eGFR, estimated glomerular filtration rate; DI, dominant inheritance was defined as one of the proband\u0026rsquo;s parents or children had biopsy proven FSGS or unexplained proteinuria, hematuria, or renal failure.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003ePrediction model performance in the validation cohort\u003c/h3\u003e\n\u003cp\u003eWe validate the performance of the predictive model in predicting pathogenic variants in the validation cohort. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the model demonstrated good performance in the validation cohort, with R\u003csup\u003e2\u003c/sup\u003e of 0.73 and AUC of 0.93 (95%CI: 0.89\u0026ndash;0.98). In the calibration analysis, the predictive model showed a close alignment between predicted and observed risks of genetic FSGS in the validation cohort (Fig.\u0026nbsp;2b, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.96).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eComparison to existing model\u003c/h2\u003e \u003cp\u003e Furthermore, we compared our predictive model with the 2021 KDIGO guideline on genetic testing recommendations. According to the 2021 KDIGO guideline, genetic testing is recommended in the following situations: patients with family history, resistant to treatment, determining risk of recurrence in kidney transplantation, assessing living-related kidney donor candidate, conducting prenatal diagnosis, et al. We did ROC curve analysis to compare our predictive model with the KDIGO model in both the development and validation cohorts (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Compared to the KDIGO model, our predictive model demonstrated improved predictive ability for identifying pathogenic variants [0.91 (95% CI, 0.86\u0026ndash;0.96) vs. 0.85 (95%CI, 0.79\u0026ndash;0.92) in the development cohort, and 0.93 (95% CI, 0.89\u0026ndash;0.98) vs. 0.90 (95%CI, 0.86\u0026ndash;0.96) in the validation cohort).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eEstablishment of the simple tool for GFSGS prediction\u003c/h2\u003e \u003cp\u003eBased on the results above, we believe that the predictive model developed from the development cohort performed well in predicting genetic FSGS, and this performance has been validated in the validation cohort. Therefore, using the β value of the predictive model, we calculated the risk score for the independent risk factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea), with the calculation process outlined in Supplementary Table\u0026nbsp;2\u003csup\u003e25\u003c/sup\u003e. Furthermore, based on the risk score, we established a formula as a simple tool for GFSGS prediction and the formula is \u003cem\u003e\u0026ldquo;Sex(Male\u0026thinsp;=\u0026thinsp;1, Female\u0026thinsp;=\u0026thinsp;0)+eGFR༈\u0026le;30ml/min/1.73m\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e=2, \u0026gt;\u0026thinsp;30ml/min/1.73m\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e=0༉་nephrotic syndrome༈Yes\u0026thinsp;=\u0026thinsp;0, No\u0026thinsp;=\u0026thinsp;3༉+dominant inheritance\u003c/em\u003e (\u003cem\u003eYes\u0026thinsp;=\u0026thinsp;4, No\u0026thinsp;=\u0026thinsp;0)\u003c/em\u003e. According to the simple tool, we got a probability score for GFSGS, which ranged from 0 to 10. And the estimated risk based on the tool was shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIt was reported that the ratio of GFSGS in the adult FSGS population was approximately 10%-43% based on who was chosen for genetic testing and which genes were tested\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. GFSGS is caused by single gene mutations, which includes dozens of genes usually specifically expressed in podocytes. Just recently, we reported that GFSGS patients with INF2 variants exhibited poorer kidney prognosis and progressed to ESKD earlier than primary FSGS patients or patients with \u003cem\u003eCOL4A3/A4/A5-het\u003c/em\u003e variants\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Patients with GFSGS often response poorly to glucocorticosteroids and immunosuppressant treatment. So, it is very important to identify GFSGS in time while FSGS is dragonized. In addition, due to the high economic costs associated with genetic testing, it is not feasible to perform genetic tests on every patient in clinical practice. Therefore, developing a genetic testing strategy is essential, as it can both reduce costs and increase the positive detection rate. However, it is still not clear which FSGS patients should undergo genetic testing.\u003c/p\u003e \u003cp\u003eIn the present study, we established a predictive model for pathogenic variants in the development cohort, and external validate the performance of the model in the validation cohort. Using multivariate logistic regression analysis, we found that patients who were male, had lower eGFR, Non-NS, and DI were more likely to have a pathogenic variant. Based on these independent risk factors, we established a simple tool, which could predict GFSGS. Additionally, we validate the performance of the model by ROC and calibration curve analysis. The results confirms that our model exhibits excellent predictive capabilities for GFSGS, offering clinician an effective tool to identify patients who need genetic testing and improve the positive detection rate. Significantly, all the parameters included in our model are easily obtainable, which is important for the widespread application in clinical practice.\u003c/p\u003e \u003cp\u003eIt was widely accepted that FSGS patients with a family history are more likely to carry a pathogenic variant. In previous study, Martin R Pollak has reported that familial cases are more likely to be the result of single gene defects than nonfamilial cases\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Similarly, we also concluded that patients with a family history were more likely to carry pathogenic variants. Different from previous studies, we first included \u0026ldquo;dominant inheritance\u0026rdquo; in the predictive model, which was the independent risk factor for pathogenic variants. In another word, if one of the proband\u0026rsquo;s parents or children was diagnosed by FSGS or unexplained proteinuria, hematuria, or renal failure, the probability of carrying a pathogenic variant is much higher. This suggests that a detailed family history should be thoroughly investigated in clinical practice.\u003c/p\u003e \u003cp\u003eBesides family history, we also found that patients who were male, had lower eGFR, Non-NS were also independent risk factor for GFSGS, which were similar with many previous studies. However, in our predictive model, we calculate the risk score for each risk factor, and based on these scores, we derived a simple tool for GFSGS prediction, which corresponds to varying risks of pathogenic variants. In the clinical practice, clinician could determine the risk of GFSGS according to the predictive tool.\u003c/p\u003e \u003cp\u003ePrevious studies suggested that individuals with primary FSGS who are resistant to immunosuppressive therapy should receive genetic testing\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. However, unlike previous studies, we did not find steroid-resistant nephrotic syndrome (SRNS) to be an independent risk factor for pathogenetic variants in either the development cohort or validation cohort. We think the possible reason is that the majority of the patients enrolled in our study were newly diagnosed with FSGS and their clinical parameters were analyzed at baseline, which allowed us to assess the likelihood of pathogenic variants to avoid or minimize unnecessary steroid exposure. On the other hand, while our results further highlight the strong predictive capability of our model for patients with newly diagnosed FSGS, we also agree with the recommendations of previous studies, that during the disease progression, if a patient exhibits treatment resistance, genetic testing is essential. This not only helps clinician to identify the pathogenesis of the disease but also to avoid the potential side effects associated with immunosuppressive therapy.\u003c/p\u003e \u003cp\u003eWe have several limitations in this study. First, our study population consisted of 2 distinct nephrology department in China, meaning that these findings need to be validated in other ethnic groups, however, given that most previous genetic studies on FSGS were based on Caucasians, genetic studies on Chinese FSGS patients are highly necessary; Second, most of the patients enrolled in this study were adult-onset, which indicated that our predictive model may not be applicable to pediatric patients, and further investigations should be performed on pediatric population; Third, due to the limitations of whole-exome sequencing, we may have missed some other special genetic variants, such as chromosomal structural variants, alternative splicing, and mitochondrial mutations.\u003c/p\u003e \u003cp\u003eIn summary, we established a simple predictive model, which had good performance of GFSGS prediction. The model serves as a simple and effective predictive tool for predicting pathogenic variants. Our findings are important for the diagnosis and management of FSGS patients.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eStatement of Ethics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was obtained from the Human Research Ethics Committee of Shanghai Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, and Peking University First Hospital Ethics Committee (approval number 2018-Y-99). All participants or their legal proxies provided written informed consent, in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants from the National Key Research and Development Program of China (2024YFC2511001), Major International (Regional) Joint Research Program of National Natural Science Foundation of China (No: 82120108007), National Natural Science Foundation of China (No.82370711), \u0026nbsp;National Facility for Translational Medicine (Shanghai) Open subjects：(No:TMSK-2024-101, NRCTM(SH)-2025-10(Ruijin Base))，Program of Shanghai Academic/Technology Research Leader (No: 21XD1402000), Science and Technology Innovation Action Plan of Shanghai Science and Technology Commettee (No. 22140904000), Shanghai Municipal Education Commission Gaofeng Clinical Medicine Grant (No.20152207), Shanghai Shenkang Hospital Development Center \u0026ldquo;Three-year Action Plan for Promoting Clinical Skills and Clinical Innovation in Municipal Hospitals\u0026rdquo;\u0026nbsp;(No:SHDC2020CR6017), and Shanghai Municipal Key Clinical Specialty (No. shslczdzk02502), Research Foundation of Ruijin Hospital (No. JZ202408), National Key Research and Development Program of China (2024YFC2511000); National Science Foundation of China (82370709).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResearch idea and study design: JX and XZ; data acquisition: XH, ZL, SY, YZ, ZF and JL; statistical analysis: XH, ZL and SY; data interpretation: XH, ZL and SY. Investigation: CZ, JM, QZ, LY, XL, NC, HR, XZ and JX. Each author contributed important intellectual content during manuscript drafting or revision and accepts accountability for the overall work by ensuring that questions pertaining to the accuracy or integrity of any portion of the work are appropriately investigated and resolved.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data relevant to the study are included in the article or uploaded as Supplementary Materials. More details are available upon reasonable request to the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHao X, et al.: Increased risk of treatment failure and end-stage renal disease in familial focal segmental glomerular sclerosis. Contributions to nephrology. \u003cem\u003e181\u003c/em\u003e: 101\u0026ndash;108(2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRood IM, Deegens JK, Wetzels JF: Genetic causes of focal segmental glomerulosclerosis: implications for clinical practice. Nephrol Dial Transplant. \u003cem\u003e27\u003c/em\u003e (3): 882\u0026ndash;890(2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePollak MR: Familial FSGS. Adv Chronic Kidney Dis. \u003cem\u003e21\u003c/em\u003e (5): 422\u0026ndash;425(2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang M, et al.: Contributions of Rare Gene Variants to Familial and Sporadic FSGS. Journal of the American Society of Nephrology: JASN. \u003cem\u003e30\u003c/em\u003e (9): 1625\u0026ndash;1640(2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRovin BH, et al.: KDIGO 2021 Clinical Practice Guideline for the Management of Glomerular Diseases. Kidney international. \u003cem\u003e100\u003c/em\u003e (4): S1-S276(2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKachurina N, et al.: Novel unbiased assay for circulating podocyte-toxic factors associated with recurrent focal segmental glomerulosclerosis. American Journal of Physiology-Renal Physiology. \u003cem\u003e310\u003c/em\u003e (10): F1148-F1156(2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAllard L, et al.: Treatment by immunoadsorption for recurrent focal segmental glomerulosclerosis after paediatric kidney transplantation: a multicentre French cohort. Nephrology Dialysis Transplantation. \u003cem\u003e33\u003c/em\u003e (6): 954\u0026ndash;963(2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChandra P, Kopp JB: Viruses and collapsing glomerulopathy: a brief critical review: Table 1. Clinical kidney journal. \u003cem\u003e6\u003c/em\u003e (1): 1\u0026ndash;5(2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSheila S, Gemma B, Barbara T: Clinical utility of genetic testing in children and adults with steroid-resistant nephrotic syndrome. CJASN. \u003cem\u003e6\u003c/em\u003e: 1139\u0026ndash;1148(2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBuscher AK, et al.: Mutations in podocyte genes are a rare cause of primary FSGS associated with ESRD in adult patients. Clin Nephrol. \u003cem\u003e78\u003c/em\u003e (1): 47\u0026ndash;53(2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGast C, et al.: Collagen (COL4A) mutations are the most frequent mutations underlying adult focal segmental glomerulosclerosis. Nephrol Dial Transplant. \u003cem\u003e31\u003c/em\u003e (6): 961\u0026ndash;970(2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrown EJ, et al.: Mutations in the formin gene INF2 cause focal segmental glomerulosclerosis. Nature genetics. \u003cem\u003e42\u003c/em\u003e (1): 72\u0026ndash;76(2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaplan JM, et al.: Mutations in ACTN4, encoding alpha-actinin-4, cause familial focal segmental glomerulosclerosis. Nature genetics. \u003cem\u003e24\u003c/em\u003e (3): 251\u0026ndash;256(2000).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWinn MP, et al.: A mutation in the TRPC6 cation channel causes familial focal segmental glomerulosclerosis. Science. \u003cem\u003e308\u003c/em\u003e (5729): 1801\u0026ndash;1804(2005).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLowik MM, et al.: Focal segmental glomerulosclerosis in a patient homozygous for a CD2AP mutation. Kidney international. \u003cem\u003e72\u003c/em\u003e (10): 1198\u0026ndash;1203(2007).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNiaudet P, Gubler MC: WT1 and glomerular diseases. Pediatr Nephrol. \u003cem\u003e21\u003c/em\u003e (11): 1653\u0026ndash;1660(2006).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXie J, et al.: Novel mutations in the inverted formin 2 gene of Chinese families contribute to focal segmental glomerulosclerosis. Kidney international. \u003cem\u003e88\u003c/em\u003e (3): 593\u0026ndash;604(2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXie J, et al.: COL4A3 mutations cause focal segmental glomerulosclerosis. J Mol Cell Biol. \u003cem\u003e7\u003c/em\u003e (2): 184(2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSantin S, et al.: Nephrin mutations cause childhood- and adult-onset focal segmental glomerulosclerosis. Kidney international. \u003cem\u003e76\u003c/em\u003e (12): 1268\u0026ndash;1276(2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHinkes B, et al.: Specific podocin mutations correlate with age of onset in steroid-resistant nephrotic syndrome. Journal of the American Society of Nephrology: JASN. \u003cem\u003e19\u003c/em\u003e (2): 365\u0026ndash;371(2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHinkes B, et al.: Positional cloning uncovers mutations in PLCE1 responsible for a nephrotic syndrome variant that may be reversible. Nature genetics. \u003cem\u003e38\u003c/em\u003e (12): 1397\u0026ndash;1405(2006).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Vriese AS, et al.: Differentiating Primary, Genetic, and Secondary FSGS in Adults: A Clinicopathologic Approach. Journal of the American Society of Nephrology: JASN. \u003cem\u003e29\u003c/em\u003e (3): 759\u0026ndash;774(2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSnoek R, et al.: Importance of Genetic Diagnostics in Adult-Onset Focal Segmental Glomerulosclerosis. Nephron. \u003cem\u003e142\u003c/em\u003e (4): 351\u0026ndash;358(2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRichards S, et al.: Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genetics in Medicine. \u003cem\u003e17\u003c/em\u003e (5): 405\u0026ndash;424(2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilson PW, et al.: Prediction of coronary heart disease using risk factor categories. Circulation. \u003cem\u003e97\u003c/em\u003e (18): 1837\u0026ndash;1847(1998).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSen ES, et al.: Clinical genetic testing using a custom-designed steroid-resistant nephrotic syndrome gene panel: analysis and recommendations. Journal of Medical Genetics. \u003cem\u003e54\u003c/em\u003e (12): 795\u0026ndash;804(2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSadowski CE, et al.: A Single-Gene Cause in 29.5% of Cases of Steroid-Resistant Nephrotic Syndrome. Journal of the American Society of Nephrology. \u003cem\u003e26\u003c/em\u003e (6): 1279\u0026ndash;1289(2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHao X, et al.: Clinical Characteristics and Prognosis of Genetic Focal Segment Glomerulosclerosis. Am J Kidney Dis. \u003cem\u003e84\u003c/em\u003e (5): 660\u0026ndash;662(2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYao T, et al.: Integration of Genetic Testing and Pathology for the Diagnosis of Adults with FSGS. Clin J Am Soc Nephrol. \u003cem\u003e14\u003c/em\u003e (2): 213\u0026ndash;223(2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaruyama K, et al.: NPHS2 mutations in sporadic steroid-resistant nephrotic syndrome in Japanese children. Pediatr Nephrol. \u003cem\u003e18\u003c/em\u003e (5): 412\u0026ndash;416(2003).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8630004/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8630004/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenetic focal segmental glomerulosclerosis (GFSGS) is caused by pathogenic variant. In the present study, we aimed to develop and validate a predictive model for pathogenic variant in FSGS patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients with biopsy-proven FSGS from two independent cohorts were recruited. FSGS secondary to obesity, hypertension, etc. were excluded. All the enrolled patients underwent whole exome sequencing (WES). We developed a predictive model for pathogenic variants using multivariate logistic regression in the development cohort, and validated the performance of the model using ROC analysis and calibration curve analysis in the validation cohort.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe recruited 197 FSGS patients for the development cohort and 155 patients for the validation cohort. In the development cohort, 70 patients had a family history and 127 patients did not have a family history; In the validation cohort, 70 patients had a family history and 85 patients did not. WES was performed on all the patients from the two cohorts. We identified 65 FSGS patients with pathogenic variants (33%) in the development cohort and 83 (53.5%) in the validation cohort. Using multivariate logistic regression, we established a predictive model for pathogenic variants, which included parameters such as male sex, lower eGFR at renal biopsy (≤ 30 ml/min/1.73 m\u003csup\u003e2\u003c/sup\u003e), non-nephrotic syndrome and dominant inheritance [R\u003csup\u003e2\u003c/sup\u003e = 0.62; AUC (95%CI) = 0.91 (0.86–0.96)). We validated the model in the validation cohort, which demonstrated a good performance with an R\u003csup\u003e2\u003c/sup\u003e of 0.55 and AUC of 0.93 (95%CI: 0.89–0.98); In the calibration analysis, the predictive model showed a close alignment between predicted and observed risks of GFSGS in the validation cohort (R\u003csup\u003e2\u003c/sup\u003e = 0.96). Based on the predictive model, we established a simple tool that provided estimated risk of pathogenic variant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe developed and validated a simple tool including four variables which had good performance for GFSGS prediction.\u003c/p\u003e","manuscriptTitle":"A Simple Tool to Predict Genetic Focal Segmental Glomerulosclerosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-03 15:54:00","doi":"10.21203/rs.3.rs-8630004/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"209c0900-d34a-49c1-b843-8eb2ffe8ced5","owner":[],"postedDate":"February 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":62034962,"name":"Health sciences/Biomarkers"},{"id":62034963,"name":"Health sciences/Diseases"},{"id":62034964,"name":"Biological sciences/Genetics"},{"id":62034965,"name":"Health sciences/Medical research"},{"id":62034966,"name":"Health sciences/Nephrology"}],"tags":[],"updatedAt":"2026-04-01T08:57:25+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-03 15:54:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8630004","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8630004","identity":"rs-8630004","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}