Polycystic liver disease, characterized by the presence of multiple cysts causing progressive liver enlargement, is one of the most common extrarenal manifestations of autosomal dominant polycystic kidney disease (ADPKD). Some patients experience severe complications and disability (1,2), requiring interventions including cyst aspiration, sclerosis, fenestration, partial resection, or even liver transplantation (3–7). Given the considerable variability in disease severity and availability of potential treatments (6,8–12), it is important to identify patients at high risk for developing massive polycystic liver disease.
The increasing prevalence of liver cysts with age and the increased severity in women with ADPKD are well established (13–16); however, only a few longitudinal studies have investigated progression of polycystic liver disease (17,18), partly due to the lack of an adequate imaging biomarker to characterize polycystic liver disease.
Total kidney volume (TKV) at a given age is currently the most valuable predictor of kidney disease progression in ADPKD (19–21). Compared with the kidney, the volumetric characterization of liver disease is more difficult because the spectrum of liver cyst burden is more heterogeneous. Simply applying liver volume as an index for the severity of polycystic liver disease would misclassify patients with large livers but no or limited burden of liver cysts. Liver cyst volume may be a more sensitive and specific imaging biomarker than liver volume to assess progression of polycystic liver disease.
To our knowledge, no longitudinal study has utilized liver cyst volume to characterize progression of polycystic liver disease. Whether progression of liver cyst volume is associated with the polycystic kidney disease (PKD) genotype, TKV, and Mayo imaging class (22) is also unknown. Thus, the purpose of the study is to evaluate height-adjusted liver cyst volume (htLCV) as a potential imaging biomarker in association with other ADPKD progression variables and to classify polycystic liver progression on the basis of htLCV at patient's age.
Materials and Methods
The study participants consisted of all patients recruited into the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease (CRISP) study and the HALT Progression of Polycystic Kidney Disease (HALT PKD) study. The study protocols and baseline characteristics of these two study populations have been reported in detail elsewhere (23,24). The baseline phenotypic data collected as part of these studies included baseline height-adjusted total kidney volume (htTKV), genotype, and Mayo imaging class of each participant.
Liver, Cyst, and Parenchymal Volume Measurement
The magnetic resonance (MR) images for the liver were acquired on 1.5-T MR scanners using coronal T2-weighted single-shot fast-spin echo sequence with fat saturation. The details of the magnetic resonance imaging (MRI) protocol are described in previous publications (23–25). For liver volume measurement, a radiologist reviewed and manually delineated the liver boundary slice by slice from the bounded abdominal MR images using commercially available software (Analyze 12.0; Mayo Clinic, Rochester, MN). Liver volume was computed from the product of the slice thickness and areas of the segmented liver regions. Following the segmentation of the liver, the radiologist adjusted the intensity threshold in each image slice to obtain a binary image that separated the cysts (bright region) from the liver parenchyma (dark region). Liver cyst volume was calculated from each set of contiguous images by summing the products of the bright cyst areas measured and the slice thickness. Liver cyst volume can be easily determined as a “by-product” in the process of estimating liver volume because after whole-liver regions are determined on T2-weighted MRI, liver cyst regions can readily be segmented from the background parenchyma due to their uniformly bright signal intensity compared with the gray signal intensity of liver parenchyma. After the liver volume and liver cyst volume were estimated, the liver parenchyma volume was calculated by subtracting liver cyst volume from liver volume.
Annualized Growth Calculation for Measurements with Only Two Time Points
When there are volume measurements from only two time points, the volumetric progression of the liver, liver cyst, and liver parenchyma for each participant can be annualized and expressed using the following formula:
However, when there are multiple follow-up volume measurements of more than two time points, we need a generalized approach, such as a linear mixed model method, to compute the growth trajectories.
Minimum Baseline Volume to Calculate Growth
To calculate a growth rate, the baseline volume should not be zero as evident from the aforementioned growth formula. Therefore, participants without a liver cyst were excluded from the cyst growth rate calculation. In addition, given the intrinsic variability in MRI volume measurement, participants with small liver cyst volume are subjected to high instability in the computation of the annualized progression rate. Therefore, we arbitrarily defined a liver cyst volume (i.e., the combined volume of all liver cysts) of 50 ml as the baseline threshold to divide the participants into two groups: minimal cyst burden ≤50 ml and substantial cyst burden >50 ml. The cyst growth calculation was applied only to the substantial cyst burden group.
Analysis was restricted to participants without clinical history of liver surgery or intervention. Descriptive statistics are presented as mean ± SD or median and interquartile range for continuous variables and frequency (percentage) for categorical variables. Differences in baseline distributions between cyst burden groups were assessed using chi-squared tests for categorical variables, two-sample t tests for continuous variables, or their nonparametric equivalents. Liver cyst volume, liver volume, liver parenchyma volume, and TKV were adjusted for height. Given the skewed nature of these height-adjusted volume measurements, htLCV, height-adjusted liver volume (htLV), height-adjusted liver parenchyma volume (htLPV), and baseline htTKV were log transformed for normality. For the substantial cyst burden group, the linear mixed model method was used to estimate the annual growth rate of htLCV, htLV, and htLPV. The primary predictor was participant age, with random effects for participant’s intercept and slope. Annual growth rates were calculated using model-based slope coefficients and participant-specific random effects. Sensitivity analyses were conducted by restricting the cohort to participants with net positive growth in htLCV. Additionally, the effect of sex, body mass index (BMI), genotype (PKD1, PKD2, and no mutations detected groups), Mayo imaging class, and baseline htTKV on each of htLCV, htLV, and htLPV was assessed by including each covariate separately as well as an interaction with participant age. Additionally, we evaluated the effect of hormonal birth control use among women in the same way. Similar linear mixed models were used to assess the association between the trajectories of htTKV and htLCV. Because liver cyst growth has been reported to decrease after menopause (9,16), the differences in htLCV and htLV trajectories between women younger than 48 years of age and women at least 48 years of age at baseline as well as for men between those age groups were investigated.
The participants in the substantial cyst burden group were stratified into five subclasses on the basis of estimated htLCV growth rates. A scheme similar to the Mayo imaging classification for the htTKV approach was applied (22). We need a nonzero theoretical starting htLCV to calculate htLCV ranges for each specific age. We used the age of 15 years (the youngest recruitment age for the CRISP study) as the theoretical starting htLCV. The mean baseline age of the substantial cyst burden group was 40 years. The mean baseline htLCV and the annual growth rate of htLCV in this group were 109 ml/m and 12%, respectively. Using these values, we interpolated backward in a compound annual growth that would return to the htLCV value at age of 15 years, resulting in 6.3 ml/m as the theoretical starting htLCV. Because our goal was to identify patients with slow, intermediate, and rapid progression, polycystic liver disease was stratified into five subclasses centered around the mean annual rate: class A: <5%; B: 5%−10%; C: 10%−15%; D: 15%−20%; and E: ≥20%. All tests were done with a significance level of 0.05, and SAS version 9.4 (SAS Institute, Cary, NC) was used for all statistical analyses.
The flow chart
describing the inclusion and exclusion of study participants is shown in Figure 1. Of 695 participants who had htLCV and htLV measurements, some were excluded because of documented clinical history of liver surgery or intervention (n=7; men/women=1/6) or clear MRI evidence (n=6; men/women: 0/6) of interval liver surgery. Participants who had no liver cysts (n=103; men/women: 63/40) or liver cyst volume ≤50 ml (n=322; men/women: 160/162) were assigned to the minimal cyst burden group (n=425; men/women: 223/202). For the growth analysis of htLCV and htLV, participants who had just a one–time point measurement of htLCV or htLV and no follow-up MRI were excluded (n=12; men/women: 4/8). The remaining 245 with liver cyst volume >50 ml at baseline were assigned to the substantial cyst burden group. Of note, repeated measurements of liver cyst volumes showed an intraobserver raw correlation of 0.99, whereas the intraclass correlation coefficient was 0.99, indicating very high measurement repeatability. The mean/median and distribution of the follow-up time and number of liver/cyst volume measurements are shown in Supplemental Table 1. The 425 participants with minimal cyst burden had minimal growth in htLCV (Supplemental Table 2).
Comparison between the Minimal Cyst Burden and Substantial Cyst Burden Groups
At baseline, sex and age distribution differed between the minimal and substantial cyst burden groups (Table 1). Participants in the substantial cyst burden group were older and more often women. By definition, median baseline htLCV and htLV were higher in the substantial cyst burden group; htTKV was also higher. Baseline characteristics of the substantial cyst burden group by sex are presented in Table 1. Median baseline htLCV was higher in women than men (109 versus 54.8 ml/m).
Table 1. -
Baseline characteristics of participants with autosomal dominant polycystic kidney disease in the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease and the HALT Progression of Polycystic Kidney Disease studies
||Minimal Cyst Burden (Height-Adjusted Liver Cyst Volume ≤50 ml), n=425
||Substantial Cyst Burden (Height-Adjusted Liver Cyst Volume >50 ml), n=245
||Substantial Cyst Burden (Women), n=152
||Substantial Cyst Burden (Men), n=93
Sex, n (%)
|Baseline age, yr
Baseline age ≥48 yr, n (%)
|Baseline htLCV, ml/m
|Baseline htLV, ml/m
|Baseline htLPV, ml/m
|Baseline htTKV, ml/m
Baseline Mayo htTKV imaging classification, n (%)
Genotype, n (%)
| No mutations detected
| PKD1 mutation
| PKD2 mutation
|Baseline BMI, ml/m
Median (interquartile range) is reported for baseline liver
and kidney measurements. Mean ± SD is reported for BMI. More about missingness is in Supplemental Table 3
. N/A, not applicable; htLCV, height-adjusted liver
cyst volume; htLV, height-adjusted liver
volume; htLPV, height-adjusted liver
parenchyma volume; htTKV, height-adjusted total kidney volume; PKD1, polycystic kidney disease 1; PKD2, polycystic kidney disease 2; BMI, body mass index.
aMissing data: Mayo imaging classification (n=52).
bMissing data: genotype (n=54).
Annualized Liver Cyst and Liver Volume Growth of the Substantial Cyst Burden Group
For 245 participants included in this analysis, the mean annualized rate of growth was 12% for htLCV (95% confidence interval [95% CI], 11.1% to 13.1%; P<0.001), 2% for htLV (95% CI, 1.9% to 2.6%; P<0.001), and 0.3% for htLPV (95% CI, 0.1% to 0.6%; P=0.01) (Figures 2 and 3). Although 225 (92%; men/women: 88/137, mean age 40 years) participants showed a net increase in htLCV, 20 (8%; men/women: 5/15, mean age 44 years) experienced a net decrease in htLCV. Serial follow-up MRI examples are shown in Figure 4. A qualitative review of follow-up images from the 20 cases of decline in htLCV demonstrated interval reductions of dominant liver cysts (n=13) or shrinkage of complex hemorrhagic cysts (n=5), whereas two cases were without discernible interval changes in MRI.
Height-Adjusted Liver Cyst Volume Progression on the Basis of Sex, Body Mass Index, Genotype, Mayo Imaging Class, Height-Adjusted Total Kidney Volume, and Hormonal Birth Control Use
On multivariable linear regression analysis of the 245 substantial cyst burden participants, there was a significant association between sex and htLCV progression (Table 2), with men having a higher estimated htLCV growth rate than women by 2% (95% CI, 0.4% to 4.5%; P=0.02). Among women, those younger than 48 years at baseline had a mean rate of increase in htLCV approximately twice as high as those aged 48 years or older (13%; 95% CI, 11.7% to 14.6% versus 7%; 95% CI, 4.2% to 8.8% per year; difference of −7%; 95% CI, −9.1% to −4.3%; P<0.001). For men, however, the annual htLCV growth rate was not different between those less than and at least 48 years of age (14%; 95% CI, 12.4% to 15.3% versus 13%; 95% CI, 9.6% to 15.3%; P=0.35). The rate of change in htLCV was not significantly associated with BMI (95% CI, −0.4% to 0.03%; P=0.09).
Table 2. -
Association of sex, genotype, and Mayo imaging class with the growth rate of liver
cyst volume and liver
||Log Height-Adjusted Liver Cyst Volume
||Log Height-Adjusted Liver Volume
|Annual Growth Rate, % (95% Confidence Interval)
||Estimated Difference, %
||Annual Growth Rate, % (95% Confidence Interval)
||Estimated Difference, %
||11.2 (9.9 to 12.4)
||2.5 (2.0 to 3.0)
||13.6 (12.0 to 15.2)
||2.4 (0.4 to 4.5)
||1.8 (1.2 to 2.4)
||−0.7 (–1.4 to 0.1)
| No mutations detected
||7.6 (–1.6 to 16.9)
||−1.2 (–5.6 to 3.1)
| PKD1 mutation
||12.4 (11.3 to 13.6)
||4.8 (–4.5 to 14.1)
||2.3 (1.9 to 2.7)
||3.5 (–0.8 to 7.9)
| PKD2 mutation
||10.8 (8.3 to 13.2)
||3.1 (–6.5 to 12.7)
||2.3 (1.4 to 3.1)
||3.5 (–0.9 to 7.9)
Mayo imaging class
||10.5 (6.3 to 14.7)
||2.9 (1.3 to 4.5)
||10.7 (8.7 to 12.6)
||0.1 (–4.5 to 4.7)
||2.4 (1.7 to 3.1)
||−0.5 (–2.2 to 1.2)
||11.5 (9.8 to 13.2)
||0.98 (–3.6 to 5.5)
||1.8 (1.2 to 2.5)
||−1.0 (–2.7 to 0.7)
||13.7 (11.8 to 15.7)
||3.2 (–1.4 to 7.8)
||2.4 (1.7 to 3.2)
||−0.4 (–2.2 to 1.3)
||15.4 (11.9 to 18.9)
||4.9 (–0.6 to 10.4)
||2.5 (1.3 to 3.8)
||−0.4 (–2.4 to 1.7)
PKD1, polycystic kidney disease 1; PKD2, polycystic kidney disease 2.
There was no significant association between the rate of change in htLCV (log transformed) and the type of ADPKD mutations (PKD1, PKD2, or no mutations detected) (Table 2), but there was a borderline significant (P=0.05) positive association with Mayo imaging class (Table 2). In addition, for every 1-unit increase in ln(htTKV) trajectory, there was a 17% increase in htLCV (P=0.02). There was no significant difference in the htLCV growth rate between hormonal birth control users and nonusers (Supplemental Table 4).
Height-Adjusted Liver Volume Progression on the Basis of Sex, Body Mass Index, Genotype, Mayo Imaging Class, Height-Adjusted Total Kidney Volume, and Hormonal Birth Control Use
On multivariable linear regression analysis, there was no significant association between sex and htLV progression (P=0.09) (Table 2). The annual growth rate in htLV was 3% for women and 2% for men without difference between those less than and at least 48 years of age or on the basis of BMI. The rate of change in htLV (log transformed) was not associated with type of ADPKD mutations, Mayo imaging class, or baseline htTKV (log transformed). There was no significant difference in the htLV growth rate between hormonal birth control users and nonusers (Supplemental Table 4).
Proposed Imaging Classification of Liver Cyst and Liver Volume Growth
According to the annual growth rate of htLCV, five classes (A: <5%; B: 5%–<10%; C: 10%–<15%; D: 15%–<20%; and E: ≥20%) were defined. The numbers for women and men in each class were A: 24 and six; B: 44 and 13; C: 43 and 48; D: 28 and 17; and E: 13 and nine, respectively. Figure 5 shows the distribution of htLCV profiles and age of the participants in the five different liver imaging classes. The distribution of htLV profiles is superimposed over the htLCV profiles. The range of the distribution of htLCV is much broader than that of htLV, reflecting higher growth rates of htLCV over the corresponding htLV. Most patients maintained the same imaging class during follow-up, but 25% fell into a different class at last follow-up (Supplemental Table 5).
Volumetric progression of polycystic liver disease has been investigated previously in several studies (8,9,11,12,18). However, these studies focused on the progression of liver volume, not liver cyst volume, and were limited in the number of patients and follow-up durations. Our study took advantage of a large cohort of well-controlled, longitudinal MRI data from the CRISP and HALT PKD studies with long follow-up (CRISP: up to 14 years; HALT PKD: 5 years) using htLCV, a new imaging biomarker, to evaluate the progression of polycystic liver disease. We showed that liver volume, the traditional imaging biomarker for polycystic liver disease, is limited because of a wide spectrum of liver cyst burden, particularly at younger age (15). Given this variable distribution of liver cysts and the fact that extensive cyst growth is the cause of clinically significant hepatomegaly, we believe it is essential to use a cyst-specific measure, such as htLCV, as the preferred imaging biomarker.
To our knowledge, this is the first study reporting the growth rates and patterns of ADPKD-associated liver cysts from a large cohort with preserved kidney function. The annual growth rate for htLCV was approximately 5.5 times that of htLV in participants with substantial cyst burden (>50 ml). This differential rate between htLCV and htLV is expected from the fact that the baseline volume of the liver cysts was much smaller than that of the liver as a whole. Although no data for the growth rate of htLCV were published in previous studies, the mean annual growth of htLV of 2% in our study appears comparable with htLV (median growth rates of 1%–2%) reported previously (18).
The preponderance of women with polycystic liver disease was confirmed in our study. Women were 12 of 13 participants who were excluded because of a documented history of liver surgery or intervention or clear MRI evidence of interval liver surgery. More women were present in the substantial cyst burden group than the minimal cyst burden group. In the substantial cyst burden group, the median baseline htLCV of women was twice as high as that of men, whereas htLPV was marginally higher in men. Rather unexpectedly, the mean annual htLCV growth rate in premenopausal (<48 years) women (13%) was not higher than in men (14%), likely reflecting the twice larger baseline htLCV in women than men in the growth rate calculation. It may also be related to the fact that the early growth of numerous tiny liver cysts cannot be differentiated from liver parenchyma with currently available MRI, as indirectly shown in a previous study in which liver parenchyma volumes in HALT PKD participants were larger than predicted by normative data from the general population (14).
The presence of an inflection point in the rate of liver growth at approximately 48 years of age in women, likely related to declining estrogens during menopause, was suggested in previous studies (9,18). Chebib et al. (18) reported that some women >48 years of age with severe polycystic liver disease experienced significant regression in liver volume. Similarly, our study found that the annual rate of growth in htLCV in women <48 years of age (13%) was almost twice that of women ≥48 years (7%). In contrast, this differential htLCV growth rate before or after age 48 years was not observed in men in either our study or the previous study (18).
Unlike the htLCV growth rate, the htLV growth rate showed no association with sex and no difference before or after 48 years of age in either women or men, suggesting that htLV is a less sensitive biomarker for liver cyst growth. Obesity had no discernible effect on polycystic liver disease progression, as there was no significant association between BMI and the growth rate in htLCV or htLV.
Although there were more PKD1 mutations in the substantial cyst burden than the minimal cyst burden group, genotype was not associated with the growth rate of htLCV or htLV, which is in agreement with a previous study (18). Baseline htTKV also was not associated with the htLCV or htLV growth rate, but the growth trajectories of htTKV and htLCV were positively correlated (i.e., htLCV increased positively with htTKV).
No significant association was found between hormonal birth control use and htLCV or htLV growth. However, this finding is limited and rather inconclusive because our study is a post hoc analysis of a completed observational study and a clinical trial, and information on hormonal birth control use was not sufficiently detailed.
Previous classifications of polycystic liver disease were usually on the basis of the liver volume. For instance, in a previous HALT PKD study, Hogan et al. (14) classified the severity of polycystic liver disease into mild (htLV<1000 ml/m), moderate (1000–1800 ml/m), and severe (>1800 ml/m); however, this did not factor in the presence or absence of liver cysts or the patient’s age. The Mayo imaging classification of kidney volume is a widely accepted tool that uses htTKV and age to identify patients at the highest risk for disease progression (22,26). In analogy, for polycystic liver disease classification, we adopted a similar scheme but with a modification in the annual growth rates and the initial starting volume appropriate to polycystic liver disease. Our proposed scheme may be used to stratify patients with polycystic liver disease. For instance, assuming a constant htLPV volume of approximately 800 ml/m, the htLCV growth rates represented by the five classes may be used to predict whether a patient is likely to reach htLV of 1600 ml/m, which has previously been reported to reflect high risk for clinical complications due to polycystic liver disease (27).
The study has limitations. First, we arbitrarily defined 50 ml to divide our study cohort into the minimal cyst burden and substantial cyst burden groups to perform the growth analysis on the latter group. We repeated the analyses using the threshold values of 30 and 100 ml and observed the same general trends (not shown). We chose 50 ml because 30 ml introduced greater imaging and measurement variability, whereas 100 ml led to a smaller sample size, limiting information. Second, we excluded 13 participants (12 women) who had surgery or other interventions to reduce liver cyst volume. Although this number is relatively small, the excluded subset likely represents patients with the most severe polycystic liver involvement. Third, although the Mayo imaging classification model was found to be a strong predictor of subsequent eGFR decrease, our proposed polycystic liver disease imaging classification lacks proven clinical correlations. However, massive polycystic livers are a significant clinical problem for many patients due to pain and mass effects (1–7,9,11,12,15,16,28–33). Further, a previous cross-sectional study of HALT PKD demonstrated a positive correlation between increases in htLV and reductions in quality of life (14). Fourth, investigating associations between htLCV trajectories and clinical variables was hampered in our study because HALT PKD (interventional trial) and CRISP (observational study) differed in collecting clinical variables, such as estrogen use or abdominal symptoms. Fifth, our longitudinal study sample of 245 participants was too small to create a validation set.
In conclusion, we report the growth rates of liver cysts and liver volumes from the CRISP and HALT PKD cohorts, both studies of early-stage ADPKD. We propose age-specific htLCV as a novel, more sensitive and specific biomarker than liver volume that would constitute a framework to stratify risk for developing polycystic liver disease. Given the reported association of estrogens with massive polycystic liver disease, young women in imaging classes C, D, and E may be counseled about minimizing exposure to estrogens (14,15,18,32,34). Likewise, the proposed classification may facilitate selection for clinical trials of patients with high liver cyst growth rates who might benefit from treatments the most (9,10,12,33,35).
K.T. Bae reports consultancy agreements with Kadmon, Otsuka, and Sanofi-Genzyme and honoraria from Kadmon and Otsuka. W.M. Bennett reports honoraria from the American Society of Nephrology and serving as an Editor-in-Chief, Emeritus of CJASN. A.B. Chapman reports consultancy agreements with Guidepoint Global, Janssen, NovusMed, Otsuka Pharmaceuticals, Pfizer, Inc., Pfizer Pharmaceuticals, Reata, and Sanofi-Genzyme; research funding from Boston Scientific, the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), Otsuka, Reata, and Sanofi-Genzyme; honoraria from Guidepoint Global, Otsuka, Reata, and UpToDate; serving as an external advisor to the O’Brien Center, Northwestern University; speakers bureau for Otsuka; and other interests/relationships with the Department of Defense Review Committee and the Special Emphasis Panel and Review Panel, the National Institutes of Health/NIDDK and Small Business Innovation Research. P.C. Harris reports consultancy agreements with Mitobridge, Otsuka, Regulus, and Vertex; research funding from Acceleron, Jemincare, Navitor, and Otsuka Pharmaceuticals; and patents and inventions with Amgen, Bayer, Genzyme, GlaxoSmithKline, Millipore, Mitobridge, and Vertex. D.P. Landsittel reports consultancy agreements with the Society for Critical Care Medicine (not related to this project); research funding from Eli Lilly & Company Pharmaceuticals and GlaxoSmithKline Pharmaceuticals to his university; and serving as chair of the Centers for Disease Control and Prevention/National Institute For Occupational Safety and Health Safety and Occupational Study Section, a member of the external expert panel for the “Prevention of Lower Urinary Tract Symptoms in Women” project funded through NIDDK, and a member of the data safety monitoring board for the Improving Chronic Disease Management with Pieces trial funded through NIDDK. M. Mrug reports employment with the Department of Veterans Affairs Medical Center (Birmingham, AL); consultancy agreements with Caraway Therapeutics, Chinook, Goldilocks Therapeutics, Natera, Otsuka Corp., Reata, and Sanofi-Genzyme; research funding from Chinook, Goldilocks Therapeutics, Otsuka Corp., and Sanofi-Genzyme; honoraria from Chinook, Otsuka Corp., Natera, Reata, and Sanofi; serving as a scientific advisor or member of the Polycystic Kidney Disease Foundation and the Medical Research Study Designed to Determine if Venglustat Can be a Future Treatment for ADPKD Patients steering committee (Sanofi); and serving on the advisory boards of Carraway Therapeutics, Goldilocks Therapeutics, and Santa Barbara Nutrients. R.D. Perrone reports consultancy agreements with Caraway, Navitor, Otsuka, Palladiobio, Reata, and Sanofi-Genzyme; research funding from Kadmon, Otsuka, Palladiobio, Reata, and Sanofi-Genzyme; honoraria from Otsuka and Sanofi-Genzyme; serving as a scientific advisor or member of Otsuka, PalladioBio, Sanofi-Genzyme, and UpToDate; and other interests/relationships with the PKD Foundation and UpToDate. T.I. Steinman reports research funding from Kadmon, Reata, Regulus, and Travere; honoraria from Mallinkrodt and Otsuka; serving on the Nephrology News and Issues editorial board; serving on the medical advisory board of National Kidney Foundation; serving as a committee member for transforming dialysis safety, International Society of Nephrology; and other interests or relationships with the National Kidney Foundation and the Polycystic Kidney Foundation. V.E. Torres reports consultancy agreements with Blueprint Medicines, Mironid, Otsuka Pharmaceuticals, Palladio, Reata, Regulus, and Sanofi; research funding from Blueprint Medicines, Mironid, Otsuka Pharmaceuticals, Palladio Biosciences, Reata, Regulus (all preclinical trial, preclinical research, or clinical trials), and Sanofi-Genzyme; honoraria from Otsuka Pharmaceuticals (to institution); and serving as a scientific advisor or member of Mironid, Otsuka Pharmaceuticals, Palladio, Reata, and Sanofi-Genzyme. A.S.L. Yu reports consultancy agreements with Calico, Otsuka, Navitor, Palladio, and Regulus Therapeutics; ownership interest in Amgen Corp., Gilead Sciences, Pfizer, and Prothena; research funding from Regulus and Sanofi; honoraria from Elsevier, Otsuka, and Wolters Kluwer; and served on an advisory board for Otsuka. All remaining authors have nothing to disclose.
This work was supported by National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health grants DK056943, DK056956, DK056957, DK056961, and R01 DK113111. This study was also supported in part by the National Institute of Diabetes and Digestive and Kidney Diseases; PKD grant DK106912; Mayo Translational PKD Center grant DK090728; Emory University; National Center for Research Resources grant RR000039; Kansas University Medical Center grants RR033179, RR23940, and TR000001; National Center for Advancing Translational Sciences grants RR025008 and TR000454; Mayo College of Medicine grants RR00585, RR024150, and TR000135; University of Alabama at Birmingham grants RR000032, RR025777, TR000165, and TR001417; and University of Pittsburgh School of Medicine grants RR024153 and TR000005.
We thank the patients involved in the study for their participation and contribution. The investigators are indebted to the study coordinators in CRISP and HALT PKD. Some of the data were presented at the American Society of Nephrology Kidney Week in November 2020.
K.T. Bae conceptualized the study; W.E. Braun, G. Brosnahan, A.B. Chapman, M. Mrug, R.D. Perrone, T.I. Steinman, V.E. Torres, and A.S.L. Yu were responsible for data curation; K.Z. Abebe, K.T. Bae, R. Feldman, P.C. Harris, D.P. Landsittel, A. Srivastava, and C. Tao were responsible for formal analysis; K.T. Bae, R. Feldman, and C. Tao were responsible for visualization; K.Z. Abebe and K.T. Bae wrote the original draft; and K.Z. Abebe, K.T. Bae, W.M. Bennett, W.E. Braun, G. Brosnahan, A.B. Chapman, R. Feldman, P.C. Harris, D.P. Landsittel, M. Mrug, R.D. Perrone, A. Srivastava, T.I. Steinman, C. Tao, V.E. Torres, and A.S.L. Yu reviewed and edited the manuscript.
This article contains the following supplemental material online at http://cjasn.asnjournals.org/lookup/suppl/doi:10.2215/CJN.08660621/-/DCSupplemental.
Supplemental Table 1. Distribution of follow-up time and number of liver/cyst volume measurements.
Supplemental Table 2. Characteristics at baseline and the last follow-up for the 425 participants with minimal cyst burden.
Supplemental Table 3. Information on missingness of genotype and BMI measurements.
Supplemental Table 4. Growth rate for height-adjusted liver cyst volume and height-adjusted liver volume between hormonal birth control use and no use groups.
Supplemental Table 5. Number of patients at each baseline class who remained the same or changed to different follow-up growth classes for liver cyst volume growth.
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