Molecular programs associated with glomerular hyperfiltration in early diabetic kidney disease

Hyperfiltration is a state of high glomerular filtration rate (GFR) observed in early diabetes that damages glomeruli, resulting in an iterative process of increasing filtration load on fewer and fewer remaining functional glomeruli. To delineate underlying cellular mechanisms of damage associated with hyperfiltration, transcriptional profiles of kidney biopsies from Pima Indians with type 2 diabetes with or without early-stage diabetic kidney disease were grouped into two hyperfiltration categories based on annual iothalamate GFR measurements. Twenty-six participants with a peak GFR measurement within two years of biopsy were categorized as the hyperfiltration group, and 26 in whom biopsy preceded peak GFR by over two years were considered pre-hyperfiltration. The hyperfiltration group had higher hemoglobin A1c, higher urine albumin-to-creatinine ratio, increased glomerular basement membrane width and lower podocyte density compared to the pre-hyperfiltration group. A glomerular 1240-gene transcriptional signature identified in the hyperfiltration group was enriched for endothelial stress response signaling genes, including endothelin-1, tec-kinase and transforming growth factor-β1 pathways, with the majority of the transcripts mapped to endothelial and inflammatory cell clusters in kidney single cell transcriptional data. Thus, our analysis reveals molecular pathomechanisms associated with hyperfiltration in early diabetic kidney disease involving putative ligand-receptor pairs with downstream intracellular targets linked to cellular crosstalk between endothelial and mesangial cells. Hyperfiltration is a state of high glomerular filtration rate (GFR) observed in early diabetes that damages glomeruli, resulting in an iterative process of increasing filtration load on fewer and fewer remaining functional glomeruli. To delineate underlying cellular mechanisms of damage associated with hyperfiltration, transcriptional profiles of kidney biopsies from Pima Indians with type 2 diabetes with or without early-stage diabetic kidney disease were grouped into two hyperfiltration categories based on annual iothalamate GFR measurements. Twenty-six participants with a peak GFR measurement within two years of biopsy were categorized as the hyperfiltration group, and 26 in whom biopsy preceded peak GFR by over two years were considered pre-hyperfiltration. The hyperfiltration group had higher hemoglobin A1c, higher urine albumin-to-creatinine ratio, increased glomerular basement membrane width and lower podocyte density compared to the pre-hyperfiltration group. A glomerular 1240-gene transcriptional signature identified in the hyperfiltration group was enriched for endothelial stress response signaling genes, including endothelin-1, tec-kinase and transforming growth factor-β1 pathways, with the majority of the transcripts mapped to endothelial and inflammatory cell clusters in kidney single cell transcriptional data. Thus, our analysis reveals molecular pathomechanisms associated with hyperfiltration in early diabetic kidney disease involving putative ligand-receptor pairs with downstream intracellular targets linked to cellular crosstalk between endothelial and mesangial cells. see commentary on on page 1217 see commentary on on page 1217 Diabetic kidney disease (DKD) is a common cause of kidney failure with increasing prevalence worldwide.1United States Renal Data SystemUSRDS Annual Data Report: Epidemiology of Kidney Disease in the United States. National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, 2020Google Scholar,2Ng M. Fleming T. Robinson M. et al.Global, regional, and national prevalence of overweight and obesity in children and adults during 1980-2013: a systematic analysis for the Global Burden of Disease Study 2013.Lancet. 2014; 384: 766-781Abstract Full Text Full Text PDF PubMed Scopus (8271) Google Scholar Alterations in glomerular hemodynamic function at the onset of diabetes often lead to sustained increases in the glomerular filtration rate (GFR), commonly referred to as hyperfiltration (HF). The presence of HF is considered a key early driver of DKD as HF is associated with development and progression of DKD3Brenner B.M. Hostetter T.H. Olson J.L. et al.The role of glomerular hyperfiltration in the initiation and progression of diabetic nephropathy.Acta Endocrinol Suppl (Copenh). 1981; 242: 7-10PubMed Google Scholar, 4Ruggenenti P. Porrini E.L. Gaspari F. et al.Glomerular hyperfiltration and renal disease progression in type 2 diabetes.Diabetes Care. 2012; 35: 2061-2068Crossref PubMed Scopus (225) Google Scholar, 5Nelson R.G. Bennett P.H. Beck G.J. et al.Diabetic Renal Disease Study Group. Development and progression of renal disease in Pima Indians with non-insulin-dependent diabetes mellitus.N Engl J Med. 1996; 335: 1636-1642Crossref PubMed Scopus (408) Google Scholar and with increased mortality6Park M. Yoon E. Lim Y.H. et al.Renal hyperfiltration as a novel marker of all-cause mortality.J Am Soc Nephrol. 2015; 26: 1426-1433Crossref PubMed Scopus (84) Google Scholar and is considered a prime therapeutic target in DKD. HF may also be part of the etiology of some nondiabetic chronic kidney diseases, including obesity-related glomerulopathy.7Helal I. Fick-Brosnahan G.M. Reed-Gitomer B. et al.Glomerular hyperfiltration: definitions, mechanisms and clinical implications.Nat Rev Nephrol. 2012; 8: 293-300Crossref PubMed Scopus (439) Google Scholar,8D'Agati V.D. Chagnac A. de Vries A.P. et al.Obesity-related glomerulopathy: clinical and pathologic characteristics and pathogenesis.Nat Rev Nephrol. 2016; 12: 453-471Crossref PubMed Scopus (371) Google Scholar The pathomechanisms triggered in HF are not fully understood. Proposed mechanisms include increased nitric oxide signaling, tubular sodium and glucose reabsorption, and intraglomerular mechanical stress in kidneys from glomerular hypertension.7Helal I. Fick-Brosnahan G.M. Reed-Gitomer B. et al.Glomerular hyperfiltration: definitions, mechanisms and clinical implications.Nat Rev Nephrol. 2012; 8: 293-300Crossref PubMed Scopus (439) Google Scholar,9Lewko B. Stepinski J. Hyperglycemia and mechanical stress: targeting the renal podocyte.J Cell Physiol. 2009; 221: 288-295Crossref PubMed Scopus (68) Google Scholar, 10Veelken R. Hilgers K.F. Hartner A. et al.Nitric oxide synthase isoforms and glomerular hyperfiltration in early diabetic nephropathy.J Am Soc Nephrol. 2000; 11: 71-79Crossref PubMed Google Scholar, 11Fioretto P. Zambon A. Rossato M. et al.SGLT2 inhibitors and the diabetic kidney.Diabetes Care. 2016; 39: S165-S171Crossref PubMed Scopus (238) Google Scholar, 12Kriz W. Lemley K.V. A potential role for mechanical forces in the detachment of podocytes and the progression of CKD.J Am Soc Nephrol. 2015; 26: 258-269Crossref PubMed Scopus (184) Google Scholar However compelling evidence on how these and other pathways are regulated in the kidneys in HF is yet to be established. We explored pathomechanisms associated with HF in Pima Indians from the Gila River Indian Community in Arizona who have a high prevalence of obesity, type 2 diabetes (T2D), and DKD. Individuals from this community have participated in decades-long prospective studies of early DKD that included clinical data and serial measurements of GFR by iothalamate clearance. A subset of these participants also underwent research kidney biopsies. The structural lesions observed in kidney biopsies in the Pima Indians are attributable to diabetes, without evidence of obesity-related or hypertensive glomerulopathy,13Fufaa G.D. Weil E.J. Lemley K.V. et al.Structural predictors of loss of renal function in American Indians with type 2 diabetes.Clin J Am Soc Nephrol. 2016; 11: 254-261Crossref PubMed Scopus (63) Google Scholar which could also induce HF. Previous studies in this cohort documented the presence of HF and established a temporal link to the onset of diabetes.5Nelson R.G. Bennett P.H. Beck G.J. et al.Diabetic Renal Disease Study Group. Development and progression of renal disease in Pima Indians with non-insulin-dependent diabetes mellitus.N Engl J Med. 1996; 335: 1636-1642Crossref PubMed Scopus (408) Google Scholar,14Nelson R.G. Tan M. Beck G.J. et al.Changing glomerular filtration with progression from impaired glucose tolerance to type II diabetes mellitus.Diabetologia. 1999; 42: 90-93Crossref PubMed Scopus (23) Google Scholar Although the diagnosis of HF typically relies on measures of whole kidney GFR, without accounting for individual differences in nephron numbers,15Pagtalunan M.E. Miller P.L. Jumping-Eagle S. et al.Podocyte loss and progressive glomerular injury in type II diabetes.J Clin Invest. 1997; 99: 342-348Crossref PubMed Scopus (907) Google Scholar,16Looker H.C. Mauer M. Saulnier P.-J. et al.Changes in albuminuria but not GFR are associated with early changes in kidney structure in type 2 diabetes.J Am Soc Nephrol. 2019; 30: 1049-1059Crossref PubMed Scopus (28) Google Scholar a previous study of kidney donors found similar levels of single-nephron GFR across a range of whole-kidney GFR, underlining the problem with absolute whole-kidney GFR HF thresholds.17Denic A. Mathew J. Lerman L.O. et al.Single-nephron glomerular filtration rate in healthy adults.N Engl J Med. 2017; 376: 2349-2357Crossref PubMed Scopus (190) Google Scholar To more accurately reflect HF at a single nephron level, we defined HF as peak GFR within each individual based on observed trends during long-term follow-up. We then examined transcriptional differences in kidney tissue obtained from individuals who reached peak GFR around the time of kidney biopsy and in those who reached peak GFR well after biopsy to identify signatures associated with HF that may contribute to progression of DKD. The aim of the present study was to integrate clinical measurements, structural morphometry, and gene expression analyses of kidney tissue, including single-cell RNA-sequencing (scRNAseq) analyses, to identify pathways associated with HF that may promote progressive DKD and be amenable to targeted therapies. An overview of the analytical strategy used in this study is shown in Figure 1. Pima Indians from the Gila River Indian Community participated in a longitudinal study of diabetes and its complications between 1965 and 2007. In 1996, 169 Pima adults with T2D participated in a prospective, randomized, placebo-controlled, double-blinded intervention trial (Renoprotection in Early Diabetic Nephropathy in Pima Indians trial; clinicaltrials.gov, NCT00340678).18Weil E.J. Fufaa G. Jones L.I. et al.Effect of losartan on prevention and progression of early diabetic nephropathy in American Indians with type 2 diabetes.Diabetes. 2013; 62: 3224-3231Crossref PubMed Scopus (71) Google Scholar At the end of the 6-year trial, 111 of the participants underwent a kidney biopsy. Subsequently, all trial participants were followed up annually. In 2015, first-degree relatives of clinical trial participants (N = 42) enrolled in the follow-up study, and clinical trial participants not previously biopsied (N = 2), provided kidney biopsies that were used for the scRNAseq analysis described below. Follow-up continued through 2019. All participants provided informed consent. Because of privacy protection concerns, individual-level genotype and gene expression data from this study cannot be made publicly available. Individuals who had a mean GFR <60 ml/min and/or a mean urine albumin–to–creatinine ratio (ACR) >300 mg/g within 1 year of their kidney biopsy were considered to have advanced DKD and were excluded, leaving 84 participants considered for the present study. GFR measurements performed in all kidney study protocols were reviewed, and these participants were classified into 3 groups based on the date they had their highest recorded GFR measurement (peak GFR), regardless of its absolute value. The absolute GFR was used because the study involved overweight and obese participants and indexing for body surface area may significantly underestimate their actual GFR and mask HF.19Delanaye P. Radermecker R.P. Rorive M. et al.Indexing glomerular filtration rate for body surface area in obese patients is misleading: concept and example.Nephrol Dial Transplant. 2005; 20: 2024-2028Crossref PubMed Scopus (168) Google Scholar Those who achieved peak GFR <2 years from the time of biopsy (N = 26) were categorized as the “HF group.” The remaining 58 participants were split into 2 groups based on whether their peak was >2 years before (N = 32) or >2 years after (N = 26) the biopsy. Those whose GFR peak was >2 years before the biopsy were considered likely to have more advanced DKD because of their declining GFR and were excluded from subsequent analysis, whereas the 26 participants with a peak >2 years after biopsy were included in the analyses and were referred to as the “pre-HF group.” A flowchart of participant selection for this study is summarized in Figure 2. Seven participants reached the identical GFR peak twice. Of these, 2 had their peaks within the same time interval category, whereas 3 participants had their initial peak >2 years before biopsy and the second peak within 2 years of biopsy, and 2 had their first peak within 2 years of biopsy and then a second peak >2 years after biopsy. In these cases, the first GFR peak was used for the purpose of categorization. Individual time-course plots of GFR for study participants by timing of peak GFR are provided in Supplementary Figure S1. As a sensitivity analysis, peak GFR was also categorized on the basis of creatinine-based estimated GFR computed using the Chronic Kidney Disease (CKD) Epidemiology Collaboration equation.20Levey A.S. Stevens L.A. Schmid C.H. et al.A new equation to estimate glomerular filtration rate.Ann Intern Med. 2009; 150: 604-612Crossref PubMed Scopus (16634) Google Scholar Structural parameters were measured by unbiased random sampling. Biopsy tissue was processed and embedded in epoxy resin (LX112; Ladd Research Industries). Measurements were made from digital micrographs, and stereological methods were used to account for 2-dimensional sampling of 3-dimensional objects.18Weil E.J. Fufaa G. Jones L.I. et al.Effect of losartan on prevention and progression of early diabetic nephropathy in American Indians with type 2 diabetes.Diabetes. 2013; 62: 3224-3231Crossref PubMed Scopus (71) Google Scholar Tissue was prepared for light and electron microscopy studies, according to standard procedures.21Fioretto P. Kim Y. Mauer M. Diabetic nephropathy as a model of reversibility of established renal lesions.Curr Opin Nephrol Hypertens. 1998; 7: 489-494Crossref PubMed Scopus (24) Google Scholar, 22Mauer M. Zinman B. Gardiner R. et al.Renal and retinal effects of enalapril and losartan in type 1 diabetes.N Engl J Med. 2009; 361: 40-51Crossref PubMed Scopus (621) Google Scholar, 23Ibrahim H.N. Jackson S. Connaire J. et al.Angiotensin II blockade in kidney transplant recipients.J Am Soc Nephrol. 2013; 24: 320-327Crossref PubMed Scopus (77) Google Scholar The following glomerular structural parameters were measured on electron microscopy images, as described elsewhere21Fioretto P. Kim Y. Mauer M. Diabetic nephropathy as a model of reversibility of established renal lesions.Curr Opin Nephrol Hypertens. 1998; 7: 489-494Crossref PubMed Scopus (24) Google Scholar,22Mauer M. Zinman B. Gardiner R. et al.Renal and retinal effects of enalapril and losartan in type 1 diabetes.N Engl J Med. 2009; 361: 40-51Crossref PubMed Scopus (621) Google Scholar,24Mauer M. Caramori M.L. Fioretto P. et al.Glomerular structural-functional relationship models of diabetic nephropathy are robust in type 1 diabetic patients.Nephrol Dial Transplant. 2015; 30: 918-923Crossref PubMed Scopus (29) Google Scholar: glomerular basement membrane width,25Caramori M.L. Kim Y. Huang C. et al.Cellular basis of diabetic nephropathy: 1. study design and renal structural-functional relationships in patients with long-standing type 1 diabetes.Diabetes. 2002; 51: 506-513Crossref PubMed Scopus (161) Google Scholar,26Klein R. Zinman B. Gardiner R. et al.The relationship of diabetic retinopathy to preclinical diabetic glomerulopathy lesions in type 1 diabetic patients: the Renin-Angiotensin System Study.Diabetes. 2005; 54: 527-533Crossref PubMed Scopus (119) Google Scholar mesangial fractional volume (including mesangial cell and mesangial matrix fractional volumes),25Caramori M.L. Kim Y. Huang C. et al.Cellular basis of diabetic nephropathy: 1. study design and renal structural-functional relationships in patients with long-standing type 1 diabetes.Diabetes. 2002; 51: 506-513Crossref PubMed Scopus (161) Google Scholar,26Klein R. Zinman B. Gardiner R. et al.The relationship of diabetic retinopathy to preclinical diabetic glomerulopathy lesions in type 1 diabetic patients: the Renin-Angiotensin System Study.Diabetes. 2005; 54: 527-533Crossref PubMed Scopus (119) Google Scholar glomerular filtration surface density,25Caramori M.L. Kim Y. Huang C. et al.Cellular basis of diabetic nephropathy: 1. study design and renal structural-functional relationships in patients with long-standing type 1 diabetes.Diabetes. 2002; 51: 506-513Crossref PubMed Scopus (161) Google Scholar,26Klein R. Zinman B. Gardiner R. et al.The relationship of diabetic retinopathy to preclinical diabetic glomerulopathy lesions in type 1 diabetic patients: the Renin-Angiotensin System Study.Diabetes. 2005; 54: 527-533Crossref PubMed Scopus (119) Google Scholar foot process width,27Najafian B. Mauer M. Quantitating glomerular endothelial fenestration: an unbiased stereological approach.Am J Nephrol. 2011; 33: 34-39Crossref PubMed Scopus (9) Google Scholar percentage of endothelial fenestrations,27Najafian B. Mauer M. Quantitating glomerular endothelial fenestration: an unbiased stereological approach.Am J Nephrol. 2011; 33: 34-39Crossref PubMed Scopus (9) Google Scholar and the glomerular podocyte fractional volume per glomerulus.28Najafian B. Tondel C. Svarstad E. et al.One year of enzyme replacement therapy reduces globotriaosylceramide inclusions in podocytes in male adult patients with Fabry disease.PLoS One. 2016; 11e0152812Crossref Scopus (31) Google Scholar Cortical interstitial fractional volume23Ibrahim H.N. Jackson S. Connaire J. et al.Angiotensin II blockade in kidney transplant recipients.J Am Soc Nephrol. 2013; 24: 320-327Crossref PubMed Scopus (77) Google Scholar and mean glomerular volume29Weibel E. Stereological Methods: Practical Methods for Biological Morphometry. Academic Press, 1979Google Scholar,30Lane P.H. Steffes M.W. Mauer S.M. Estimation of glomerular volume: a comparison of four methods.Kidney Int. 1992; 41: 1085-1089Abstract Full Text PDF PubMed Scopus (152) Google Scholar were estimated using light microscopy. RNA sequencing data obtained from microdissected glomerular and tubulointerstitial compartments from kidney biopsy tissue were analyzed (Supplementary Methods). Eigengene-based weighted gene coexpression network analysis modules were constructed using Weighted Gene Co-Expression Network Analysis31Langfelder P. Horvath S. WGCNA: an R package for weighted correlation network analysis.BMC Bioinformatics. 2008; 9: 559Crossref PubMed Scopus (11063) Google Scholar (Supplementary Methods). Eigen genes were then correlated (Pearson correlation) with the HF categories and the morphometric parameters. Transcripts contained in modules with statistically significant (P ≤ 0.05) associations to these traits were used for downstream functional analysis. All statistical analysis were done in R statistical software (www.r-project.org) and Stata MP 15 (Stata Corp.; www.stata.com). Ingenuity Pathway System (Qiagen) was used to reveal associated functional pathways. Statistical significance was set at a Bonferroni-adjusted P < 0.05. Pathway analysis was performed using the Ingenuity Pathway System software querying the HF-associated transcripts. Cytoscape32Shannon P. Markiel A. Ozier O. et al.Cytoscape: a software environment for integrated models of biomolecular interaction networks.Genome Res. 2003; 13: 2498-2504Crossref PubMed Scopus (26683) Google Scholar visualization platform and ggplot2 package in R statistical platform were used to render the network images from the pathway network. An in-house custom python script was used to parse the Ingenuity Pathway System output for the Cytoscape subnetwork generations. Pathways with <5 shared genes were filtered out to reduce the number of nodes in the pathway network. Major cancer pathways were also removed. The resulting network yielded 175 nodes and 5288 edges. Default parameters in the Cytoscape plugin, MCODE,33Bader G.D. Hogue C.W.V. An automated method for finding molecular complexes in large protein interaction networks.BMC Bioinformatics. 2003; 4: 2Crossref PubMed Scopus (3953) Google Scholar were used to construct subnetworks. scRNAseq data were generated from CryoStor (Stemcell Technologies) preserved DKD (N = 44) and control (living donor; N = 18) biopsies,34Menon R. Otto E.A. Sealfon R. et al.SARS-CoV-2 receptor networks in diabetic and COVID-19-associated kidney disease.Kidney Int. 2020; 98: 1502-1518Abstract Full Text Full Text PDF PubMed Scopus (48) Google Scholar as previously published,34Menon R. Otto E.A. Sealfon R. et al.SARS-CoV-2 receptor networks in diabetic and COVID-19-associated kidney disease.Kidney Int. 2020; 98: 1502-1518Abstract Full Text Full Text PDF PubMed Scopus (48) Google Scholar,35Menon R. Otto E.A. Hoover P. et al.Single cell transcriptomics identifies focal segmental glomerulosclerosis remission endothelial biomarker.JCI Insight. 2020; 5e133267Crossref Scopus (69) Google Scholar Individual cell barcoding, reverse RNA transcription, library generation, and single-cell sequencing using Illumina were all performed using the 10X Genomics protocol.34Menon R. Otto E.A. Sealfon R. et al.SARS-CoV-2 receptor networks in diabetic and COVID-19-associated kidney disease.Kidney Int. 2020; 98: 1502-1518Abstract Full Text Full Text PDF PubMed Scopus (48) Google Scholar The output from the sequencer was first processed by CellRanger (https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/what-is-cell-ranger). CellRanger output data files were analyzed using the Seurat 3 R package (https://cran.r-project.org/web/packages/Seurat/index.html).34Menon R. Otto E.A. Sealfon R. et al.SARS-CoV-2 receptor networks in diabetic and COVID-19-associated kidney disease.Kidney Int. 2020; 98: 1502-1518Abstract Full Text Full Text PDF PubMed Scopus (48) Google Scholar Hub genes from the HF signature modules were compared with an external scRNAseq data set obtained from protocol biopsies of patients with youth onset T2D.36Schaub J.A. AlAkwaa F.M. McCown P.J. et al.SGLT2 inhibition mitigates perturbations in nephron segment-specific metabolic transcripts and mTOR pathway activity in kidneys of young persons with type 2 diabetes. Preprint. MedRxiv. 22277943.https://doi.org/10.1101/2022.07.23.22277943Google Scholar This data set included healthy controls (n = 6) and participants ranging from 12 to 21 years with T2D (n = 6) from the Renal Hemodynamics, Energetics and Insulin Resistance in Youth Onset Type 2 Diabetes Study (NCT03584217) and the Impact of Metabolic Surgery on Pancreatic, Renal and Cardiovascular Health in Youth With Type 2 Diabetes (NCT03620773). Participants with T2D were obese (body mass index, 38.0 ± 4.9 kg/m2) and had elevated measured iohexol GFR (208 ± 53 ml/min), but normal to borderline elevated ACR (10 ± 10 mg/g). Morphometric evaluation of the kidney biopsies showed higher mesangial and glomerular volumes in participants with T2D compared with healthy controls, consistent with early kidney dysfunction, but no features indicative of advanced DKD.36Schaub J.A. AlAkwaa F.M. McCown P.J. et al.SGLT2 inhibition mitigates perturbations in nephron segment-specific metabolic transcripts and mTOR pathway activity in kidneys of young persons with type 2 diabetes. Preprint. MedRxiv. 22277943.https://doi.org/10.1101/2022.07.23.22277943Google Scholar scRNAseq followed the same protocol described above. NicheNetR37Browaeys R. Saelens W. Saeys Y. NicheNet: modeling intercellular communication by linking ligands to target genes.Nat Methods. 2020; 17: 159-162Crossref PubMed Scopus (398) Google Scholar (https://github.com/saeyslab/nichenetr) was used to identify ligand-receptor (LR) interactions that drive the observed expression changes in the target cell population in the single-cell transcriptome. NichNetR compiles literature-based LR interactions, signal transductions, and regulatory networks to prioritize the LR–target gene identification. On the basis of cell type enrichment of HF genes, the LR interactions and downstream target genes were identified from the cell-cell communication between endothelial and mesangial cells. Clinical and demographic characteristics for the HF and pre-HF groups (Table 1) showed a mean GFR in the HF group of 173 ± 48 ml/min, 21 ml/min higher, on average, than in the pre-HF group (152 ± 38 ml/min; P = 0.08). The HF group also had higher mean hemoglobin A1c (P = 0.03) and median ACR (P = 0.007). Although not statistically significant, there was also a trend toward longer duration of diabetes. There were no statistically significant differences in the initial study GFR or the maximal GFR between groups. Comparison of histopathologic structural parameters from the kidney biopsies (Table 2) showed wider glomerular basement membrane (P = 0.02) and higher mesangial fractional volume (P = 0.004) among those in the HF group, reflecting greater structural changes near peak GFR, compared with pre-HF. The difference in mesangial fractional volume was due predominantly to differences in the mesangial matrix fractional volume (P = 0.0001) and not to differences in mesangial cell fractional volume (P = 0.25). Podocyte density (P = 0.03) was also slightly lower among those in the HF group, but podocyte fractional volume of the glomerulus was maintained, indicating that podocytes were fewer in number but larger. Clinical characteristics and structural parameters in the 32 participants not included in the primary analyses, because their peak GFR occurred >2 years before the kidney biopsy, compared with the 52 participants included in the study are provided in Supplementary Table S1. These patients had longer diabetes duration (P < 0.001), lower GFR (P = 0.007), and tissue morphometric measurements, including increased cortical interstitial fractional volume and decreased glomerular podocyte fractional volume (P = 0.032), reflective of their more advanced stage of DKD.Table 1Clinical characteristics at time of kidney biopsy by peak mGFR groupCharacteristicPre-HF (n = 26)HF (n = 26)P valueaP value for male sex is from a χ2 test; P values for continuous variables are based on t tests.Male sex7 (26.9)3 (11.5)0.16Age, yr44.8 ± 8.644.6 ± 12.00.95Diabetes duration, yr12.4 ± 3.414.4 ± 4.30.07BMI, kg/m237.0 ± 8.037.2 ± 9.20.91Systolic blood pressure, mm Hg120 ± 8121 ± 80.70Diastolic blood pressure, mm Hg75 ± 577 ± 50.41HbA1c, %8.7 ± 2.09.9 ± 1.80.03mGFR, ml/min152 ± 38173 ± 480.08Mean mGFR, ml/minbMean mGFR is the mean of all available mGFRs.154 ± 30149 ± 380.57Peak mGFR, ml/mincPeak mGFR indicates the highest mGFR.221 ± 48236 ± 470.25ACR, mg/gdACR was log transformed before analysis.18 (13–31)58 (34–74)0.007RAS blocker use21 (80.8)17 (65.4)0.21ACR, albumin-to-creatinine ratio; BMI, body mass index; HbA1c, hemoglobin A1c; HF, hyperfiltration; mGFR, measured glomerular filtration rate; RAS, renin-angiotensin system.Values are mean ± SD, n (%), or median (interquartile range).a P value for male sex is from a χ2 test; P values for continuous variables are based on t tests.b Mean mGFR is the mean of all available mGFRs.c Peak mGFR indicates the highest mGFR.d ACR was log transformed before analysis. Open table in a new tab Table 2Morphometric measures by peak measured GFR groupStructural parameterPre-HF (n = 26)HF (n = 26)P valueaP values are based on t tests.Mean glomerular volume, ×106 μm32.25 ± 0.68bn = 22.2.51 ± 0.75cn = 24.0.22Glomerular basement membrane width, nm428 ± 75484 ± 880.02Mesangial fractional volume per glomerulus0.23 ± 0.050.27 ± 0.050.004Mesangial cell fractional volume per glomerulus0.08 ± 0.020.09 ± 0.020.25Mesangial matrix fractional volume per glomerulus0.11 ± 0.030.14 ± 0.040.0001Cortical interstitial fractional volume0.17 ± 0.03dn = 25.0.19 ± 0.04cn = 24.0.06Glomerular filtration surface density, μm2/μm30.12 ± 0.040.10 ± 0.040.21Foot process width, nmeFoot process width was log transformed before analysis.726 (637–952)821 (631–1175)0.53Glomerular podocyte fractional volume0.18 ± 0.060.16 ± 0.050.19Podocyte number density per glomerulus, ×106 μm3161 ± 81116 ± 540.03Fenestrated endothelium, %44.5 ± 16.443.5 ± 21.80.85GFR, glomerular filtration rate; HF, hyperfiltration.Values are mean ± SD or median (interquartile range).a P values are based on t tests.b n = 22.c n = 24.d n = 25.e Foot process width was log transformed before analysis. Open table in a new tab ACR, albumin-to-creatinine ratio; BMI, body mass index; HbA1c, hemoglobin A1c; HF, hyperfiltration; mGFR, measured glomerular filtration rate; RAS, renin-angiotensin system. Values are mean ± SD, n (%), or median (interquartile range). GFR, glomerular filtration rate; HF, hyperfiltration. Values are mean ± SD or median (interquartile range). To determine whether HF categorization based on measured GFR could be replicated using estimated GFR (eGFR), we repeated the analyses using the serum creatinine–based CKD Epidemiology Collaboration equation20Levey A.S. Stevens L.A. Schmid C.H. et al.A new equation to estimate glomerular filtration rate.An