Abstract
Introduction
Preeclampsia increases the risk for future chronic kidney disease (CKD). Among those diagnosed with CKD, it is unclear whether a prior history of preeclampsia, or other complications in pregnancy, negatively impact kidney disease progression. In this longitudinal analysis, we assessed kidney disease progression among women with glomerular disease with and without a history of a complicated pregnancy.
Methods
Adult women enrolled in the Cure Glomerulonephropathy study (CureGN) were classified based on a history of a complicated pregnancy (defined by presence of worsening kidney function, proteinuria, or blood pressure; or a diagnosis of preeclampsia, eclampsia, or hemolysis, elevated liver enzymes, and low platelets [HELLP] syndrome), pregnancy without these complications, or no pregnancy history at CureGN enrollment. Linear mixed models were used to assess estimated glomerular filtration rate (eGFR) trajectories and urine protein-to-creatinine ratios (UPCRs) from enrollment.
Results
Over a median follow-up period of 36 months, the adjusted decline in eGFR was greater in women with a history of a complicated pregnancy compared to those with uncomplicated or no pregnancies (−1.96 [−2.67, −1.26] vs. −0.80 [−1.19, −0.42] and −0.64 [−1.17, −0.11] ml/min per 1.73 m2 per year, P = 0.007). Proteinuria did not differ significantly over time. Among those with a complicated pregnancy history, eGFR slope did not differ by timing of first complicated pregnancy relative to glomerular disease diagnosis.
Conclusions
A history of complicated pregnancy was associated with greater eGFR decline in the years following glomerulonephropathy (GN) diagnosis. A detailed obstetric history may inform counseling regarding disease progression in women with glomerular disease. Continued research is necessary to better understand pathophysiologic mechanisms by which complicated pregnancies contribute to glomerular disease progression.
Keywords: Chronic kidney disease, Glomerular disease, Pregnancy
Graphical abstract
See Commentary on Page 696
Preeclampsia is known to cause glomerular endotheliosis that is generally thought to reverse following delivery.1 However, population-based studies have shown that a history of preeclampsia is associated with higher incidence of future kidney biopsy, CKD and end-stage kidney disease.2, 3, 4 Glomerular injury, particularly focal segmental glomerulosclerosis (FSGS), is more common in pregnancy-related biopsies compared to age-matched controls.5 Endothelial dysfunction is a common pathophysiologic mechanism that may be activated or unmasked in preeclampsia, leading to glomerulosclerosis. Likewise, altered angiogenesis, activation of complement and the renin-angiotensin system, as well as podocyturia have been hypothesized to contribute to future CKD.6 It is particularly relevant to examine CKD progression among women with glomerular disease given the overlapping pathophysiology on the endothelium and podocyte.
CureGN is a prospective longitudinal cohort study of over 2400 individuals with biopsy-proven minimal change disease (MCD), membranous nephropathy, FSGS and IgA nephropathy or vasculitis. Parous women in CureGN who self-reported previous complications in their first pregnancy (including increased proteinuria, worsening kidney function, and/or preeclampsia) were found to have a shorter latency time between that pregnancy and subsequent glomerular disease diagnosis when compared to women with an uncomplicated first pregnancy.7 Building on this finding, we aimed to assess whether a history of a complicated pregnancy before CureGN enrollment negatively impacts kidney disease progression. We hypothesized that a history of a complicated pregnancy would associate with accelerated decline in eGFR and greater proteinuria in the years following enrollment. We further postulated that women with a complicated pregnancy in proximity to GN diagnosis would experience a greater eGFR decline compared to those with complicated pregnancy distantly preceding the GN diagnosis. Finally, we explored whether kidney biopsy histopathology associated with pregnancy history.
Methods
Patient Population
CureGN is a prospective, multicenter, longitudinal cohort study of children and adults with biopsy-proven MCD, membranous nephropathy, FSGS or IgA nephropathy or vasculitis.8 The initial diagnostic kidney biopsy must have been performed within 5 years before enrollment. Eligible participants were enrolled from 72 participating clinical sites throughout the United States, Canada, Italy, and Poland. The study protocol was approved by the institutional review board at each participating center and was conducted in adherence with the Declaration of Helsinki. All participants provided written informed consent or assent, if appropriate, at enrollment. This analysis included all adult women (18 years or older at study enrollment) who joined CureGN between December 2014 and August 2022 and had at least 1 subsequent study visit. This analysis was deemed nonhuman subject research because of the utilization of a completely deidentified data set.
Data Collection
Demographics and clinical characteristics, including obstetric history and comorbid health conditions, were collected at CureGN study enrollment. Race and Hispanic ethnicity were self-reported, and reporting race and ethnicity in the CureGN study was mandated by the US National Institutes of Health, consistent with the Inclusion of Women, Minorities, and Children policy. The Chronic Kidney Disease in Children under 25 equations were used to calculate eGFR for all adult subjects younger than 25 years and the race-free 2021 CKD-epidemiology collaboration equation was used for subjects aged 25 years and above, both based on serum creatinine only.9,10 UPCR was obtained from a first morning void, random spot urine, or an aliquot of a 24-hour urine collection, depending on availability. The central CureGN laboratory used a Randox RX Daytona chemistry analyzer (Randox Laboratories Ltd, Antrim, United Kingdom) to measure serum and urine creatinine, using the Jaffe method, and measured the urine protein using a colorimetric method. When central measurements were not available, measurements from clinical site laboratories were used. Pathology features were assessed by the study pathologists who reviewed digital biopsy materials from each study participant’s first clinically-indicated kidney biopsy. Degree of global sclerosis, interstitial fibrosis, and sclerotic vascular disease (defined as any arteriosclerosis, arteriolar hyalinosis, or arteriolosclerosis) were scored in semiquantitative scales. For this analysis, each histologic variable was dichotomized to present versus absent. Updated clinical data were collected approximately every 4 to 6 months at in-person or remote study visits and biospecimens were obtained at all in-person visits, or at least yearly. Obstetric history was obtained via self-report at enrollment and updated at subsequent study visits (see Supplementary Materials for CureGN obstetric questionnaire).
Definitions
Women were classified into pregnancy history groups at the time of enrollment based on their self-reported history of at least 1 complicated pregnancy, only uncomplicated pregnancies, or no history of pregnancy. A complicated pregnancy was defined by self-reporting at least 1 of the following: worsening blood pressure, worsening kidney function, increased proteinuria, preeclampsia, eclampsia, or HELLP. In secondary analyses, complicated pregnancies were further stratified into severe versus mild categories. Severely complicated pregnancies were considered those with preeclampsia, eclampsia, HELLP, and presumed preeclampsia (i.e., both worsening blood pressure and worsening kidney function, or both worsening blood pressure and increased proteinuria); all other complicated pregnancies were considered mildly complicated. Preterm birth was defined as less than 37 weeks’ gestation and low birth weight was considered less than 2500 g.
Statistical Analysis
Normally distributed continuous measures were described using mean and SD, non-normally distributed continuous measures were described using median and interquartile range (IQR), and categorical measures were described using frequency and percent. To assess the association between pregnancy history and longitudinal eGFR and UPCR outcomes, we used linear mixed models with a random intercept for each individual to account for repeated measures over time. To assess whether change in eGFR differed across pregnancy history groups, we included an interaction between pregnancy history and time. We considered flexible forms of time to allow for nonlinear eGFR trajectories within each group. Models were adjusted sequentially to control for demographics (age, race, and ethnicity), then additionally for likely confounders (i.e., exposures that were present at CureGN enrollment and plausibly at time of pregnancy, including body mass index and previous or current tobacco use), and finally for factors that may be confounders or mediators (i.e., exposures that may have occurred following pregnancy, including specific GN diagnosis and any history of hypertension, immunosuppressant use, or renin-angiotensin-aldosterone system inhibitor use at any time before enrollment). Models were not adjusted for factors that would clearly be mediators (e.g., eGFR or UPCR at study enrollment) because they would be on the causal pathway between pregnancy history and longitudinal outcomes. UPCR was log-transformed to better fit model assumptions, and model coefficients were exponentiated to estimate mean ratios. If there was a significant difference in outcomes across pregnancy history groups, we performed posthoc pairwise tests with Tukey adjustment of P-values for multiple comparisons to assess which specific groups were different. Primary analyses grouped all complicated pregnancies together, whereas secondary analyses differentiated between severe versus mild complications, and a history of multiple versus 1 complicated pregnancy. Two sensitivity analyses were conducted, first excluding women with any pregnancies after GN diagnosis and a second excluding those with a diagnosis of MCD (because this group was hypothesized to experience less CKD progression than the other glomerular diseases).
We also utilized the fully adjusted linear mixed model above to evaluate whether change in eGFR differed by the timing of the first complicated pregnancy in relation to GN diagnosis among women with a history of complicated pregnancy. Finally, among women with a pregnancy before GN diagnosis and with histopathology data available, we used a logistic regression model adjusted for the same covariates as above to explore the association between pregnancy history and odds of specific histopathology findings (interstitial fibrosis, glomerulosclerosis, and sclerotic vascular disease). All models were based on complete-case analysis. Statistical significance was defined as P < 0.05, and statistical analyses were completed using R software, version 4.1.1 (R Development Core Team, Vienna).
Results
A total of 804 women were included in our analysis. Over half (58%) reported at least 1 previous pregnancy and 16% reported at least 1 complicated pregnancy. At the time of CureGN study enrollment, eGFR and UPCR were similar across groups (Table 1). Age at enrollment was older among those with a history of uncomplicated pregnancies compared to those with complicated pregnancies or no pregnancy (mean 52 vs. 41 or 40 years old, respectively) (Supplementary Figure S1). Among those with pregnancy history, those with membranous nephropathy were the least likely to report a complicated pregnancy (22/134, 16%) and those with FSGS reported the most complications (46/136, 34%). Nearly half (66/141, 48%) of the women with MCD reported no pregnancy history.
Table 1.
Demographics and clinical characteristics at enrollment by pregnancy history
| Demographics and clinical characteristics | Any complicated pregnancies | Only uncomplicated pregnancies | No pregnancies |
|---|---|---|---|
| N | 128 | 339 | 337 |
| # Pregnancies, median (IQR) | 2 (1–3) | 2 (2–3) | -- |
| # Complicated Pregnancies, median (IQR) | 1 (1–2) | -- | -- |
| Months from Biopsy, median (IQR) | 15 (6–41) | 11 (4–31) | 13 (4–34) |
| Age at first pregnancy, yr, mean (SD) | 25 (6) | 24 (5) | -- |
| Age at enrollment, yr, mean (SD) | 41 (13) | 52 (14) | 40 (17) |
| Hispanic a | 23 (18%) | 45 (13%) | 44 (13%) |
| Racea | |||
| Asian or Pacific Islander | 10 (8%) | 38 (12%) | 33 (10%) |
| Black and/or African American | 36 (30%) | 72 (22%) | 55 (17%) |
| Native American or Multiracial | 3 (3%) | 12 (4%) | 9 (3%) |
| White | 70 (59%) | 208 (63%) | 221 (69%) |
| Diagnosisa | |||
| MCD (including IgM nephropathy) | 18 (14%) | 55 (16%) | 68 (20%) |
| FSGS | 46 (36%) | 90 (27%) | 87 (26%) |
| MN | 22 (17%) | 112 (33%) | 89 (26%) |
| IgAN/IgAV | 42 (33%) | 82 (24%) | 93 (28%) |
| Family history of kidney diseasea | 47 (38%) | 114 (34%) | 94 (34%) |
| Hypertension ever | 77 (60%) | 191 (56%) | 133 (39%) |
| BMI, median (IQR)a | 30 (25–37) | 28 (24–34) | 26 (22–31) |
| Previous or current tobacco usea | 38 (30%) | 121 (36%) | 75 (27%) |
| eGFR, ml/min/1.73 m2, mean (SD) | 70 (34) | 68 (31) | 74 (35) |
| UPCR, mg/mg, median (IQR)b | 1 (1–4) | 2 (0–5) | 2 (0–4) |
| Hematuriab | |||
| Negative/trace | 49 (49%) | 124 (55%) | 105 (51%) |
| 1+ | 18 (18%) | 38 (17%) | 34 (17%) |
| 2+ or 3+ | 32 (32%) | 65 (29%) | 65 (32%) |
| Immunosuppressant at enrollmenta | 53 (42%) | 156 (46%) | 135 (48%) |
| Immunosuppressant prior to enrollment | 90 (71%) | 241 (71%) | 206 (74%) |
| RAASi at enrollmenta | 79 (62%) | 202 (60%) | 166 (59%) |
| RAASi prior to enrollmenta | 114 (90%) | 275 (81%) | 226 (81%) |
| Global sclerosis on first biopsyc | 21 (45%) | 60 (38%) | 26 (37%) |
| Interstitial fibrosis on first biopsyc | 33 (72%) | 105 (66%) | 41 (58%) |
| Arterial vascular disease on first biopsyc | 37 (80%) | 102 (68%) | 37 (55%) |
BMI, body mass index; eGFR, estimated glomerular filtration rate; FSGS, focal segmental glomerulosclerosis; IgAN, IgA nephropathy; IgAV, IgA vasculitis; MCD, minimal change disease; MN, membranous nephropathy; RAASi, renin-angiotensin-aldosterone system inhibitors; UPCR urine protein-to-creatinine ratio.
Values are mean (SD), median (interquartile range), or N (%)
Missingness:
<11%.
25%–35%.
65%–68%.
Among all 181 complicated pregnancies, 58% were complicated by worsening blood pressure, 63% by worsening proteinuria, and 28% by worsening kidney function; 31% reported presumed preeclampsia (worsening blood pressure with either worsening proteinuria or worsening kidney function). In addition, 32% reported diagnosis of preeclampsia, 3% reported eclampsia and 6% reported diagnosis of HELLP syndrome. Out of 128 women who reported a complicated pregnancy history, 89 (69%) had only 1 complicated pregnancy and 39 (30%) had 2 or more. Complicated pregnancies were more common in women ≥35 years old at pregnancy and had higher rates of preterm delivery (26% vs. 12%) and low birth weight (25% vs. 12%) when compared to those in the uncomplicated pregnancy group.
Timing of pregnancies relative to GN diagnosis are shown in Figure 1. Among complicated pregnancies, 1 in 7 (14%) began within 15 months and an additional 9% began within 30 months before GN diagnosis. Following GN diagnosis, there were 52 total pregnancies among 39 women. The mean (SD) age at these pregnancies was 29.4 (5.5) and the median time from GN diagnosis to pregnancy was 2.3 years (IQR 0.8–5.5). Almost half (25) of these pregnancies were complicated and made up 12.4% of all reported complicated pregnancies. The live birth rate among these postdiagnosis pregnancies was 90% (47/52) with 25% born preterm and 22% with low birth weight. Based on an unadjusted linear mixed model, the estimated mean (95% confidence interval [CI]) eGFR slope was −4.19 ml/min per 1.73 m2 per year (−5.70, −2.69) and mean (95% CI) UPCR level during follow-up was 2.09 (1.38, 2.79) among this group (n = 39).
Figure 1.
Timing of pregnancy start relative to GN diagnosis by pregnancy complication status. P denotes the number of pregnancies and N the number of unique women.
There were 4763 eGFR measurements in our cohort with a median (IQR) of 6 (4–10) per participant with complicated pregnancy history, 7 (4–10) per participant with uncomplicated pregnancy history and 4 (2–8) per participant with no pregnancy history. Over a median (IQR) follow-up period of 36 (12–60) months, eGFR had a steeper decline in women with a history of complicated pregnancy compared to those with uncomplicated pregnancy or no pregnancy (−2.41 [−2.93, −1.90] vs. −0.90 [−1.20, −0.60] and −1.00 [−1.40, −0.59] ml/min per 1.73 m2 per year, P < 0.001). As shown in Table 2 and Figure 2, this difference remained significant after adjusting for age, race, ethnicity, body mass index, tobacco use, GN diagnosis, hypertension history, and any previous use of immunosuppression or renin-angiotensin-aldosterone system inhibitor (−1.96 [−2.67, −1.26] vs. −0.80 [−1.19, −0.42] and −0.64 [−1.17, −0.11] ml/min per 1.73 m2 per year, P = 0.012 for complicated vs. uncomplicated, P = 0.009 for complicated vs. no pregnancies, P = 0.881 for uncomplicated vs. no pregnancies). In similarly adjusted models that stratified complications by severity, women with history of a severely complicated pregnancy had a similar decline compared to those with history of only mildly complicated pregnancy (−1.91 [−2.75, −1.07] vs. −2.11 [−3.40, −0.81], P = 0.995). Women with history of multiple complicated pregnancies also had a similar decline compared to those with only 1 complicated pregnancy (−1.41 [−2.44, −0.38] vs. −2.44 [−3.40, −1.48], P = 0.475). These results were similar in sensitivity analyses that excluded women with any pregnancies occurring after GN diagnosis (Table 3) and women with a diagnosis of MCD (Supplementary Table S1A). Among 113 participants with MCD with at least 2 eGFRs, 15 (13%) had an eGFR decline of at least 30%; of which 4 had a complicated pregnancy before enrollment, 4 had only uncomplicated pregnancies before enrollment, and 7 reported no pregnancies before enrollment.
Table 2.
eGFR Slope (ml/min per 1.73 m2 per year) by pregnancy history model results with progressive levels of adjustment
| Unadjusted (N = 732) | Model 1: age, race, ethnicity (N = 704) | Model 2: Model 1 + BMI, tobacco (N = 686) | Model 3: Model 2 + Diagnosis, HTN, Medsa (N = 686) | |
|---|---|---|---|---|
| P-value for interaction | <0.001 | <0.001 | 0.007 | 0.007 |
| Complicated vs. uncomplicated | <0.001 | <0.001 | 0.011 | 0.012 |
| Complicated vs. no pregnancies | <0.001 | 0.001 | 0.009 | 0.009 |
| Uncomplicated vs. no pregnancies | 0.922 | 0.963 | 0.897 | 0.881 |
| Had any complicated pregnancies | −2.41 (−2.93, −1.90) | −2.22 (−2.75, −1.68) | −1.99 (−2.69, −1.28) | −1.96 (−2.67, −1.26) |
| Had only uncomplicated pregnancies | −0.90 (−1.20, −0.60) | −0.94 (−1.24, −0.64) | −0.81 (−1.20, −0.43) | −0.80 (−1.19, −0.42) |
| Had no pregnancies | −1.00 (−1.40, −0.59) | −1.00 (−1.41, −0.60) | −0.66 (−1.19, −0.13) | −0.64 (−1.17, −0.11) |
BMI, body mass index; HTN, hypertension
Each linear mixed model used eGFR as the outcome, pregnancy history category as the primary exposure of interest, visit month, and the interaction between pregnancy history and visit month. The table shows the P-value for the test of whether the interaction coefficients were different from 0 and the estimated eGFR slope (95% confidence interval) for each pregnancy history group.
Medications: renin-angiotensin-aldosterone system inhibitors and immunosuppressive use prior to enrollment.
Figure 2.
Predicted values of eGFR by pregnancy history group from adjusted linear mixed model. Each line starts at the average estimated glomerular filtration rate (eGFR) for that pregnancy history group at enrollment and slopes reflect adjusted values from a linear mixed model. Colored bands represent 95% confidence intervals. The model used eGFR as the outcome, pregnancy history category as the primary exposure of interest, visit month, and the interaction between pregnancy history and visit month, and adjusted for age, race, ethnicity, body mass index, previous or current tobacco use, glomerulonephropathy (GN) diagnosis, history of hypertension, and any previous use of immunosuppression or renin-angiotensin aldosterone system inhibitor (RAASi).
Table 3.
Sensitivity analyses excluding women with pregnancies after GN diagnosis and prior to CureGN enrollment
| Unadjusted (N = 694) | Model 1: age, race, ethnicity (N = 667) | Model 2: Model 1 + BMI, tobacco (N = 651) | Model 3: Model 2 + diagnosis, HTN, Medsa (N = 651) | |
|---|---|---|---|---|
| P-value for interaction | <0.001 | <0.001 | 0.002 | 0.002 |
| Complicated vs. uncomplicated | <0.001 | <0.001 | 0.003 | 0.003 |
| Complicated vs. no pregnancies | <0.001 | <0.001 | 0.004 | 0.004 |
| Uncomplicated vs. no pregnancies | 0.637 | 0.704 | 0.975 | 0.963 |
| Had any complicated pregnancies | −2.51 (−3.04, −1.97) | −2.30 (−2.86, −1.74) | −2.15 (−2.90, −1.41) | −2.14 (−2.88, −1.40) |
| Had only uncomplicated pregnancies | −0.76 (−1.07, −0.46) | −0.80 (−1.11, −0.50) | −0.73 (−1.13, −0.34) | −0.73 (−1.12, −0.34) |
| Had no pregnancies | −1.00 (−1.40, −0.59) | −1.00 (−1.40, −0.61) | −0.66 (−1.19, −0.14) | −0.64 (−1.17, −0.11) |
BMI body mass index; eGFR, estimated glomerular filtration rate; HTN, hypertension.
Medications: renin-angiotensin-aldosterone system inhibitors and immunosuppressive use prior to enrollment.
We subsequently utilized the fully adjusted linear mixed model to evaluate women with a history of complicated pregnancy (n = 128) and whether eGFR slope differed by the timing of the first complicated pregnancy (greater than 30 months before GN diagnosis, 15 to 30 months before GN diagnosis, less than 15 months before GN diagnosis, or after GN diagnosis). As shown in Figure 3, the adjusted eGFR slopes ranged between −0.43 and −2.12 ml/min per 1.73 m2 per year and were not significantly different among these groups (P = 0.463).
Figure 3.
Predicted values of eGFR by timing of first complicated pregnancy relative to GN diagnosis, among women with a complicated pregnancy. Each line starts at the average estimated glomerular filtration rate (eGFR) at enrollment for that subgroup and slopes reflect adjusted values from a linear mixed model. The model used eGFR as the outcome, an indicator of whether the first complicated pregnancy started before or after glomerulonephropathy (GN) diagnosis, visit month, and adjusted for age, race, ethnicity, body mass index, previous or current tobacco use, GN diagnosis, history of hypertension, and any previous use of immunosuppression or renin-angiotensin aldosterone system inhibitor (RAASi).
There were 3501 UPCR measurements with a median (IQR) of 5 (3–7) per participant with complicated pregnancy history, 5 (2–8) per participant with uncomplicated pregnancy history and 3 (2–6) per participant with no pregnancy history. Over the follow-up period, the UPCR levels were not significantly different among women with a history of complicated or uncomplicated pregnancy compared to those with no pregnancy history (Table 4). Similarly, our stratified analyses did not result in significantly different UPCR levels between groups (data not shown). Results were similar in sensitivity analyses excluding women with pregnancies after GN diagnosis (Table 5) and excluding women with a diagnosis of MCD (Supplementary Table S1b).
Table 4.
Mean UPCR (g/g) by pregnancy history model results with progressive levels of adjustment.
| Unadjusted (N = 689) | Model 1: age, race, ethnicity (N = 664) | Model 2: Model 1 + BMI, tobacco (N = 633) | Model 3: Model 2 + Diagnosis, HTN, Medsa (N = 633) | |
|---|---|---|---|---|
| P-value for joint test | 0.045 | 0.073 | 0.284 | 0.243 |
| Had any complicated pregnancies | 1.27 (1.01, 1.61) | 1.22 (0.96, 1.55) | 1.07 (0.82, 1.39) | 1.07 (0.83, 1.38) |
| Had only uncomplicated pregnancies | 0.98 (0.81, 1.17) | 0.97 (0.80, 1.19) | 0.92 (0.75, 1.14) | 0.96 (0.78, 1.17) |
| Had no pregnancies (reference) | 1 | 1 | 1 | 1 |
BMI, body mass index; HTN, hypertension.
Each linear mixed model used log-transformed UPCR as the outcome, pregnancy history category as the primary exposure of interest, and controlled for visit month. The table shows the P-value for the joint test of whether the pregnancy history category coefficients were different from 0 and the exponentiated estimated coefficients (95% confidence interval) for each pregnancy history group, representing the mean ratios of UPCR over follow-up relative to having no pregnancies (reference group).
Table 5.
Sensitivity analyses excluding women with pregnancies after GN diagnosis and prior to CureGN enrollment
| Unadjusted (N = 651) | Model 1: age, race, ethnicity (N = 627) | Model 2: Model 1 + BMI, tobacco (N = 598) | Model 3: Model 2 + diagnosis, HTN, Medsa (N = 598) | |
|---|---|---|---|---|
| P-value for joint test | 0.135 | 0.208 | 0.516 | 0.471 |
| Had any complicated pregnancies | 1.24 (0.96, 1.58) | 1.18 (0.91, 1.53) | 1.01 (0.77, 1.34) | 1.02 (0.78, 1.34) |
| Had only uncomplicated pregnancies | 0.98 (0.82, 1.18) | 0.98 (0.80, 1.20) | 0.92 (0.74, 1.14) | 0.96 (0.78, 1.19) |
| Had no pregnancies (reference) | 1 | 1 | 1 | 1 |
BMI, body mass index; HTN, hypertension.
Abbreviations: GN, glomerulonephropathy; eGFR, estimated glomerular filtration rate; UPCR urine protein-to-creatinine ratio; BMI, body mass index; HTN, hypertension
Medications: renin-angiotensin-aldosterone system inhibitors and immunosuppressive use before enrollment
Histopathologic data were available for 277 (34%) participants, with demographics and enrollment time characteristics shown in Supplementary Table S2. Women with histopathologic data available were older at study enrollment (48 vs. 44, P = 0.002), had more hypertension (57% vs. 46%, P < 0.001), and had pregnancies that occurred closer to kidney biopsy (9 vs. 15 months, P < 0.001) than those without data available. The mean eGFR for women with histopathologic data at enrollment was 66 ml/min per 1.73 m2 versus 74 ml/min per 1.73 m2 in those without data (P = 0.004). Of those with histopathology available, 47 had a history of a complicated pregnancy. Among women with a pregnancy before GN diagnosis and histopathologic data available, complicated pregnancy history was not statistically significantly associated with any histopathologic variables (P = 0.580 for global sclerosis, P = 0.451 for interstitial fibrosis, and P = 0.089 for sclerotic vascular disease). Those with only uncomplicated or no pregnancies had reduced odds of sclerotic vascular disease (odds ratio 0.32 [95% CI 0.10–1.02] and 0.29 [95% CI 0.08–1.02], respectively) compared to those with complicated pregnancies, although this was not statistically significant. In addition, histopathologic variables did not alter eGFR slope when added to our fully adjusted model (data not shown).
Discussion
Among a geographically and racially diverse population of women with glomerular diseases, our results indicate that history of complicated pregnancy was associated with a greater eGFR decline in the years following GN diagnosis. The eGFR loss in those with a complicated pregnancy history occurred at a rate of 1.96 ml/min per 1.73 m2 per year compared to 0.80 ml/min per 1.73 m2 per year and 0.64 ml/min per 1.73 m2 per year in those with uncomplicated and no pregnancies, respectively. Interestingly, among women with a complicated pregnancy history, eGFR slope did not differ by the timing of the first complicated pregnancy relative to GN diagnosis. Previously reported predictors of disease progression in GN include clinical markers such as proteinuria, hypoalbuminemia, and elevated blood pressure, as well as histopathology at initial biopsy (e.g., FSGS, interstitial fibrosis, and tubular atrophy).11, 12, 13, 14 Our findings suggest that obtaining a detailed obstetric history, including history of worsening kidney function, proteinuria, or blood pressure, or diagnosis of preeclampsia, eclampsia, or HELLP syndrome, may inform counseling of patients with GN about disease progression as well.
Pregnancy after kidney disease diagnosis has been shown to be associated with CKD progression. For example, preeclampsia after the diagnosis of IgA nephropathy has been shown to be a major risk factor for kidney function decline, particularly in those with pregestational eGFR <60 ml/min per 1.73 m2, hypertension, or proteinuria >1 g/d.15 In addition, in CKD stages 3 to 5, pregnancy is associated with a loss of GFR equivalent to advancing toward end-stage kidney disease by an average of 2.5 years.16 However, evidence for the impact of preeclampsia preceding the diagnosis of kidney disease on GFR and proteinuria over time, is conflicting. In otherwise healthy women in Norway, preeclampsia in first pregnancy was not associated with an increased risk of microalbuminuria 10 years later.17 A recent systematic review and meta-analysis also found no statistically significant difference in albuminuria between those with and those without a prior episode of preeclampsia, although data from the individual studies was heterogenous (meta-analytical risk ratio 4.31, 95% CI 0.95−19.58).18 In contrast, a Danish national registry cohort study found associations between preeclampsia and both CKD and proteinuric kidney disease were stronger within 5 years of pregnancy (hazard ratio 6.11 [3.84–9.72] and 4.77 [3.88–5.86], respectively) and still present 5 years or longer after pregnancy.3
In those with biopsy-proven kidney disease, similar to CureGN, the association of preeclampsia with disease progression is also mixed. An analysis of 173 biopsies performed antenatally or within 1 year of pregnancy compared to matched controls found a more rapid decline in eGFR during the follow-up period among pregnancy-related biopsies (−1.33 ml/min per 1.73 m2 per year vs. −0.56 ml/min per 1.73 m2 per year), perhaps because of higher rates of FSGS in the pregnancy-related cases.5 However, a Norwegian kidney biopsy registry of 582 women who were biopsied after their last pregnancy found that a prior preterm birth, but not preeclampsia, was a risk factor for progression to end-stage kidney disease.19 Although we did find that women with a history of complicated pregnancy had a steeper decline in eGFR after GN diagnosis, we did not find a significant difference in proteinuria. In these women with established GN, this may be due to the use of renin-angiotensin aldosterone system blockade and/or escalation of immunosuppression in response to proteinuria among all individuals. Further research is needed to better understand the mechanisms of CKD progression, both including and apart from proteinuria, in those with prior pregnancy complications.
Nearly one-fourth (23%) of the complicated pregnancies among our cohort occurred within 30 months before GN diagnosis. It is conceivable that underlying GN may have been present in these pregnancies but misdiagnosed (e.g., as preeclampsia) or that complications in pregnancy served as a “second hit” to trigger or unmask GN. A recent interrogation of biopsies performed within 5 years of preeclampsia showed that 8 of 11 cases had a clinical history suggesting preexisting kidney disease.20 We hypothesized that those with a complicated pregnancy closest to GN diagnosis might experience the greatest eGFR decline, potentially because of the compounding effects of both GN and preeclampsia-induced glomerular endothelial dysfunction and podocyte depletion. Although we did not find significant differences in eGFR slope based on the timing of a complicated pregnancy, this deserves further evaluation in larger populations because our small numbers limited the power of this analysis. Long-term follow-up is also necessary to understand if changes in eGFR slope attenuate or amplify over time and continued longitudinal observation in CureGN will facilitate these analyses.
Glomerular endotheliosis is commonly found in preeclamptic pregnancies but has also been described in hypertensive and normal pregnancies.21 Podocyte dysfunction, including increased turnover, shedding, and podocyturia, have also been demonstrated in preeclampsia.1,22, 23, 24, 25, 26 Sustained endothelial dysfunction and podocyte depletion above a certain threshold may contribute to glomerulosclerosis.27 Specifically in FSGS, increased glomerular endothelial cell dysfunction associates with lower rates of remission and poor prognosis.28 Furthermore, in patients undergoing postpartum biopsy after preeclampsia, those with persistent CKD had increased endocapillary duplication and interstitial fibrosis compared with those whose kidney function and proteinuria improved.29 Kattah et al.30 assessed kidney histology in 33 prepregnancy and 21 postpregnancy biopsies in women with pregnancies following kidney transplant. After adjusting for time from transplant to kidney biopsy, there was significantly more vascular fibrous intimal thickening in postpregnancy biopsies compared to prepregnancy biopsies (55% vs. 15.2%, P = 0.04), potentially related to the high incidence of preeclampsia in the cohort.30 We hypothesized that histopathologic variables could serve as mediators between complicated pregnancy and eGFR decline; however, our exploratory models did not support this. Although our study observed that the odds of sclerotic vascular disease found on kidney biopsy were greater after a complicated pregnancy, this was not statistically significant and did not ultimately alter eGFR slope when added to our fully adjusted model. However, these models were limited by smaller sample size and incomplete data. We were not able to assess whether histopathologic changes were present prior to or at pregnancy, potentially contributing to the likelihood for both pregnancy complications and the subsequent decline in eGFR.
This study is one of few longitudinal analyses examining the association of pregnancy complications with eGFR trajectory in women with GN, but we acknowledge several limitations. Although we recognize that epidemiologic trends of eGFR decline differ among these glomerular diseases (and within disease subtypes), our limited number of complicated pregnancy events precluded our ability to analyze by specific GN diagnosis. Our participants’ pregnancy data were self-reported and thus subject to recall bias and possibly misclassification. Little is known regarding the accuracy and reliability of maternal recall of kidney-relevant complications in pregnancy, such as worsening creatinine, eGFR, or proteinuria. Maternal recall of hypertensive disorders of pregnancy has been found to have high specificity, though sensitivity is low.31 More severe hypertensive disorders of pregnancy, such as preterm preeclampsia, can be accurately recalled.32 In addition, fetal outcomes such as small for gestational age were not able to be reported because of the predefined categorical responses offered on the obstetric questionnaire (see Supplementary Material). In our cohort, complicated pregnancies were more common in older women and resulted in more preterm birth and low birth weight infants. Surprisingly, analyses which stratified more severe pregnancy complications from more mild complications did not have statistically significant differences in eGFR trajectory. There are potential explanations for this. First, any kidney-relevant complication in pregnancy, even those we categorized as “mild,” may be a harbinger of future CKD progression; gestational hypertension alone was found to be associated with increased risk of CKD (aRR, 1.49; 95% CI 1.11–2.01) and end-stage kidney disease (aRR , 3.64; 95% CI 2.34–5.66) in a recent meta-analysis.4 Second, defining the nuances of hypertensive disorders of pregnancy can be challenging, which could result in contamination of our stratified groups and biasing the results of the stratified analyses to the null. Finally, some relevant data were not available to refine our analyses. Chart extraction of eGFR and UPCR data from the time of pregnancy to CureGN enrollment was not feasible because of the typical time between pregnancy and enrollment, number of clinical sites for data extraction, and lack of colocalized obstetric and nephrology care. Similarly, it was not possible to determine if factors such as obesity, tobacco use, and hypertension were true confounders present at time of pregnancy or whether they were mediators introduced after pregnancy, nor were we able to control for dose of renin-angiotensin aldosterone system inhibition or immunosuppression. However, using sequential adjustment, we observed a significantly greater decline in eGFR among those with complicated pregnancies in all models.
In this large sample of women with glomerular disease, those with a history of complicated pregnancy, occurring at any length of time from GN diagnosis, experienced a greater decline in eGFR over the first several years following enrollment in CureGN. This information identifies a history of complicated pregnancy as a potential important predictor of adverse outcomes following GN diagnosis and may help guide expectations for disease progression. Likewise, it reinforces the importance of obtaining a comprehensive obstetric history at the time of disease diagnosis. Continued research is needed to understand how complicated pregnancies contribute to both glomerular and nonglomerular kidney disease as well as disease progression. Identifying biologically linked pathways, particularly focused on glomerular endothelial cells and podocyte dysfunction, may serve as targets of future therapeutic interventions.
Appendix
CureGN Collaborators
The CureGN Consortium members listed below, from within the 4 Participating Clinical Center networks and Data Coordinating Center, are acknowledged by the authors as Collaborators.
∗∗CureGN Principal Investigators; ∗CureGN Site Principal Investigators; #CureGN Lead Coordinators.
CureGN Participating Clinical Centers (PCC) through Columbia University:
Columbia University, New York, NY, US: Wooin Ahn, Gerald Appel, Paul Appelbaum, Revekka Babayev, Andrew Bomback, Pietro Canetta, Brenda Chan, Vivette Denise D'Agati, Samitri Dogra, Hilda Fernandez, Ali Gharavi∗∗, William Hines, Syed Ali Husain, Namrata Jain, Krzysztof Kiryluk, Fangming Lin, Maddalena Marasa#, Glen Markowitz, Hila Milo Rasouly, Sumit Mohan, Nicola Mongera, Jordan Nestor, Thomas Nickolas, Jai Radhakrishnan, Maya Rao, Simone Sanna-Cherchi, Shayan Shirazian, Michael Barry Stokes, Natalie Uy, Anthony Valeri, Natalie Vena
University of Warsaw, Warszawa, Poland: Bartosz Foroncewicz, Barbara Moszczuk, Krzysztof Mucha∗, Agnieszka Perkowska-Ptasińska
Gaslini Children’s Hospital, Genoa, Italy: Gian Marco Ghiggeri∗, Francesca Lugani
CureGN Participating Clinical Centers (PCC) through the Pediatric Nephrology Research Consortium:
Arkana Laboratories, Little Rock, AR, USA: Josephine Ambruzs, Helen Liapis
Children’s Hospital of Michigan, Detroit, MI, USA: Rossana Baracco, Amrish Jain∗
Children’s Hospital of New Orleans/ LSU Health, New Orleans, LA, USA: Isa Ashoor, Diego Aviles∗
Children’s Mercy Hospital, Kansas City, MO, USA: Tarak Srivastava∗
Children’s National Medical Center, Washington DC, USA: Sun-Young Ahn∗
Cincinnati Children’s Hospital Cincinnati, OH, USA: Prasad Devarajan, Elif Erkan∗, Donna Claes, Hillarey Stone
Connecticut Children’s Medical Center, Hartford, CT, USA: Sherene Mason∗
Duke Children’s Hospital Medical Center, Durham, NC, USA: Rasheed Gbadegesin∗
East Carolina University Brody School of Medicine, Greenville, NC, USA: Liliana Gomez-Mendez∗
Emory University, Atlanta, GA, USA: Larry Greenbaum∗∗, Chia-shi Wang, Hong (Julie) Yin
Helen DeVos Children’s Hospital, Grand Rapids, MI, USA: Yi Cai∗, Goebel Jens, Julia Steinke
Levine Children’s Hospital/Atrium Health, Charlotte, NC, USA: Donald Weaver∗
Lurie Children’s Hospital, Chicago IL, USA: Jerome Lane∗
Mayo Clinic, Rochester, MN, USA: Carl Cramer∗
Medical College of Wisconsin, Milwaukee, WI, USA: Cindy Pan, Neil Paloian, Rajasree Sreedharan∗
Medical University of South Carolina, Charleston SC, USA: David Selewski, Katherine Twombley∗
Nationwide Children’s Hospital, Columbus, OH, USA: Corinna Bowers#, Mary Dreher# Mahmoud Kallash∗, John Mahan, Samantha Sharpe#, William Smoyer∗∗
Oregon Health and Science University, Portland, OR, USA: Amira Al-Uzri∗, Sandra Iragorri
Riley Children’s Hospital, Indianapolis, IN, USA: Myda Khalid∗
Cardinal Glennon Children’s Medical Center/ St. Louis University, St. Louis, MO, USA: Craig Belsha∗
Texas Children’s Hospital, Houston, TX, USA: Joseph Alge∗, Michael Braun, AC Gomez, Scott Wenderfer∗
Texas Tech Health Sciences Center, Amarillo, TX, USA: Tetyana Vasylyeva∗
Children’s of Alabama, University of Alabama, Birmingham, AL, USA: Daniel Feig∗
University of Colorado Children’s Hospital, Colorado, Aurora, CO, USA: Gabriel Cara Fuentes, Melisha Hannah∗
University of Iowa Children’s Hospital, Iowa City, IA, USA: Carla Nester∗
University of Kentucky, Lexington, KY, USA: Aftab Chishti∗
University of Louisville, Louisville, KY, USA: Jon Klein∗∗
Holtz Medical Center, University of Miami, Miami, FL, USA: Chryso Katsoufis, Wacharee Seeherunvong∗
University of Minnesota Children’s Hospital, Minneapolis, MN, USA: Michelle Rheault∗
University of New Mexico Health Sciences Center, Albuquerque, NM, USA: Craig Wong∗
University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA: Nisha Mathews∗
University of Virginia, Charlottesville, VA, USA: John Barcia∗, Agnes Swiatecka-Urban
University of Wisconsin, Madison, WI, USA: Sharon Bartosh∗
Vanderbilt Children’s Hospital, Nashville TN, USA: Tracy Hunley∗
Washington University in St. Louis, St. Louis, MO, USA: Vikas Dharnidharka∗, Joseph, Gaut
CureGN Participating Clinical Centers (PCC) through the University of North Carolina:
Hôpital Maisonneuve-Rosemont, Montreal, Canada: Louis-Philippe Laurin∗, Virginie Royal
Medical University of South Carolina, Charleston, SC, USA: Anand Achanti, Milos Budisavljevic∗, Sally Self
Northwestern University, Chicago, IL, USA: Cybele Ghossein, Yonatan Peleg, Shikha Wadhwani∗
Ohio State University, Columbus, OH, USA: Salem Almaani, Isabelle Ayoub, Tibor Nadasdy, Samir, Parikh, Brad Rovin∗
University of Chicago, Chicago, IL, USA: Anthony Chang
University of Alabama at Birmingham, Birmingham, AL, USA: Huma Fatima, Bruce Julian, Jan Novak, Matthew Renfrow, Dana Rizk∗
University of North Carolina Kidney Center, Chapel Hill, NC, USA: Dhruti Chen, Vimal Derebail, Ronald Falk∗∗, Keisha Gibson, Dorey Glenn, Susan Hogan, Koyal Jain, J. Charles Jennette, Amy Mottl∗, Caroline Poulton#, Manish Kanti Saha
Vanderbilt University, Nashville, TN, USA: Agnes Fogo, Neil Sanghani∗
Virginia Commonwealth University, Richmond, VA, USA: Jason Kidd∗, Selvaraj Muthusamy
CureGN Participating Clinical Centers (PCC) through the University of Pennsylvania:
MetroHealth Medical Center/Case Western Reserve University, Cleveland, OH, USA: Jeffrey Schelling∗
Cedars-Sinai Health System, Los Angeles, CA, USA: Jean Hou
Children’s Hospital of LA, Los Angeles, CA, USA: Kevin Lemley∗, Warren Mika, Pierre Russo
Children’s Hospital of Philadelphia, Philadelphia, PA, USA: Michelle Denburg, Amy Kogon, Kevin Meyers∗, Madhura Pradhan
Cleveland Clinic, Cleveland, OH, CA: Raed Bou Matar∗, John O'Toole∗, John Sedor∗
Cohen Children’s Medical Center, New Hyde Park, NY, USA: Christine Sethna∗, Suzanne Vento #
Johns Hopkins University, Baltimore, MD, USA: Mohamed Atta, Serena Bagnasco, Alicia Neu, John Sperati∗
Lundquist Institute at Harbor-UCLA Medical Center, Torrance, CA, USA: Sharon Adler∗, Tiane Dai, Ram Dukkipati
Mayo Clinic, Rochester, MN, USA: Fernando Fervenza∗, Sanjeev Sethi
Montefiore Medical Center, The Bronx, New York, NY, USA: Frederick Kaskel, Kaye Brathwaite, Kimberly Reidy∗
New York University, New York, NY, USA: Joseph Weisstuch, Ming Wu, Olga Zhdanova
NIDDK, Bethesda, MD, USA: Jurgen Heymann, Jeffrey Kopp∗, Meryl Waldman, Cheryl Winkler
Spokane Providence Medical Center, Spokane, WA, USA: Katherine Tuttle∗
Stanford University, Palo Alto, CA, USA: Jill Krissberg, Richard Lafayette∗, Kamal Fahmeedah, Elizabeth Talley
Sunnybrook Health Sciences Center, Toronto, Canada: Michelle Hladunewich∗
The Hospital for Sick Children, Toronto, Canada: Rulan Parekh∗
University Health Network, Toronto, Canada: Carmen Avila-Casado, Daniel Cattran∗, Reich Heather, Philip Boll
University of Miami, Miami, FL, USA: Yelena Drexler, Alessia Fornoni∗
University of Michigan, Ann Arbor, MI, USA: Patrick Gipson∗, Jeffrey Hodgin, Andrea Oliverio
University of Pennsylvania, Philadelphia, PA, USA: Jon Hogan, Lawrence Holzman∗∗, Matthew Palmer, Gaia Coppock
University of Pittsburgh School of Medicine, Pittsburgh, PA, USA: Blaise Abromovitz∗, Michael Mortiz∗
University of Washington, Seattle, WA, USA: Charles Alpers, J. Ashley Jefferson∗
UT Southwestern, Dallas, TX, USA: Elizabeth Brown, Kamal Sambandam∗, Bethany Roehm
Data Coordinating Center (DCC):
Arbor Research Collaborative for Health, Ann Arbor, MI, USA: Bruce Robinson∗∗, Abigail Smith
Cedar Sinai Medical Center, Los Angeles, CA, USA: Cynthia Nast
Duke University, Durham, NC, USA: Laura Barisoni
University of Michigan, Ann Arbor, MI, USA: Brenda Gillespie∗∗, Debbie Gipson∗∗, Matthias Kretzler, Laura Mariani∗∗
Steering Committee Chair: Lisa M. Guay-Woodford, Children’s National Hospital, Washington DC, USA
Disclosure
Please see the disclosure forms for all authors. All the authors declared no competing interests.
Acknowledgments
Funding
Funding for the CureGN consortium is provided by U24DK100845 (formerly UM1DK100845), U01DK100846 (formerly UM1DK100846), U01DK100876 (formerly UM1DK100876), U01DK100866 (formerly UM1DK100866), and U01DK100867 (formerly UM1DK100867) from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). Patient recruitment is supported by NephCure Kidney International. Dates of funding for first phase of CureGN was September 16, 2013 to May 31, 2019. ALO is supported by K23DK123413.
Footnotes
Figure S1. Age at CureGN study enrollment by pregnancy history group and GN diagnosis.
Table S1. Sensitivity analyses excluding women with diagnosis of MCD for (a) eGFR Slope (ml/min per 1.73 m2 per year) and (b) UPCR (g/g), by pregnancy history model results with progressive levels of adjustment.
Table S2. Demographics and enrollment time clinical characteristics by pathology data availability.
CureGN pregnancy case report form.
STROBE Checklist.
Supplementary Material
Figure S1. Age at CureGN study enrollment by pregnancy history group and GN diagnosis.
Table S1. Sensitivity analyses excluding women with diagnosis of MCD for (a) eGFR Slope (ml/min per 1.73 m2 per year) and (b) UPCR (g/g), by pregnancy history model results with progressive levels of adjustment.
Table S2. Demographics and Enrollment Time Clinical Characteristics by Pathology Data Availability
CureGN Pregnancy Case Report Form.
STROBE Checklist.
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