Morphometry Predicts Early GFR Change in Primary Proteinuric Glomerulopathies: A Longitudinal Cohort Study Using Generalized Estimating Equations

<div><p>Objective</p><p>Most predictive models of kidney disease progression have not incorporated structural data. If structural variables have been used in models, they have generally been only semi-quantitative.</p><p>Methods</p><p>We examined the predictive utility of quantitative structural parameters measured on the digital images of baseline kidney biopsies from the NEPTUNE study of primary proteinuric glomerulopathies. These variables were included in longitudinal statistical models predicting the change in estimated glomerular filtration rate (eGFR) over up to 55 months of follow-up.</p><p>Results</p><p>The participants were fifty-six pediatric and adult subjects from the NEPTUNE longitudinal cohort study who had measurements made on their digital biopsy images; 25% were African-American, 70% were male and 39% were children; 25 had focal segmental glomerular sclerosis, 19 had minimal change disease, and 12 had membranous nephropathy. We considered four different sets of candidate predictors, each including four quantitative structural variables (for example, mean glomerular tuft area, cortical density of patent glomeruli and two of the principal components from the correlation matrix of six fractional cortical areas–interstitium, atrophic tubule, intact tubule, blood vessel, sclerotic glomerulus, and patent glomerulus) along with 13 potentially confounding demographic and clinical variables (such as race, age, diagnosis, and baseline eGFR, quantitative proteinuria and BMI). We used longitudinal linear models based on these 17 variables to predict the change in eGFR over up to 55 months. All 4 models had a leave-one-out cross-validated R² of about 62%.</p><p>Conclusions</p><p>Several combinations of quantitative structural variables were significantly and strongly associated with changes in eGFR. The structural variables were generally stronger than any of the confounding variables, other than baseline eGFR. Our findings suggest that quantitative assessment of diagnostic renal biopsies may play a role in estimating the baseline risk of succeeding loss of renal function in future clinical studies, and possibly in clinical practice.</p></div

Pearson correlations among fractional cortical areas.

<p>Pearson correlations among fractional cortical areas.</p

Observed eGFR progression patterns over follow-up stratified by quartiles of FATA.

<p>Linear fits to observed eGFR values stratified by FATA from lowest (red) to highest (purple). Initial eGFR is highest for the lowest FATA quartile and decreases with each quartile. Compared to the reference category of the lowest FATA quartile, testing for the differential eGFR slopes of the second, third and fourth quartiles yielded P values of 0.15, 0.25 and <0.001, respectively. The apparent more negative slope of the lowest quartile may be due to a smaller number of long follow-up points.</p

Illustration of the point counting method on a section of cortex (PAS stain).

<p>Illustration of the point counting principle for assessing cortical compartments using SlidePath software. The hand-written numbers (10, 11, 12, 13 and part of 20) were added by the slide annotator, to keep track of individual glomeruli. Cross points of the red grid box were used other than the upper and right-side lines (equivalently, using the bottom-left corner point of each grid sub-box). Starting at the top-left, the first point ‘hits’ an intact glomerulus (#12). Moving right, the second point hits an intact tubule. The third hits an intact glomerulus (#11). Twenty-five points per grid are evaluated.</p

Adverse events, insulin therapy-related only.

<p>Adverse events, insulin therapy-related only.</p

Insulin infusion rates.

<p>Mean CSII basal rate ± standard deviation after dose titration over 24 hours.</p

Enrollment and randomization of the SAPT-NODAT and ITP-NODAT trial participants at the Medical University of Vienna.

<p>Enrollment and randomization of the SAPT-NODAT and ITP-NODAT trial participants at the Medical University of Vienna.</p

Baseline patient characteristics.

<p>Baseline patient characteristics.</p