Autosomal dominant polycystic kidney disease (ADPKD) is the most common inherited renal disease, affecting approximately 1:500–1:1000 live births.1,2 It is characterized by the growth of numerous fluid-filled cysts which progressively destroy the adjacent renal parenchyma and massively enlarge the kidneys.3 Similar to the general population, body mass index (BMI) in individuals with ADPKD has been increasing over recent decades.4,5
Obesity is an established risk factor for the development and progression of CKD.6–9 However, surprisingly, the association of overweight and obesity with progression in patients with ADPKD has never been evaluated. Studies in mouse models of ADPKD have suggested that ADPKD is a state of defective glucose metabolism and metabolic reprogramming.10,11 Furthermore, mild-to-moderate food restriction slows disease progression in multiple mouse models of ADPKD,12,13 suggesting that changes in energy status may have a profound effect on ADPKD progression.
The Halt Progression of Polycystic Kidney Disease (HALT) Study A was a randomized, double-blind, placebo-controlled study in nondiabetic patients with early-stage ADPKD and is among the largest trials ever conducted in patients with ADPKD.14 In order to evaluate the association of overweight and obesity with ADPKD progression, we examined the longitudinal association of baseline BMI categories with the primary outcome in Study A (percent change in total kidney volume [TKV]). We hypothesized that categorization as overweight or obese would be independently associated with a greater rate of progression in early-stage ADPKD, as indicated by greater annual change in TKV. We also evaluated whether overweight and obesity were associated with decline in eGFR.
Participant Characteristics at Baseline
Four hundred forty-one participants with early ADPKD who participated in HALT Study A14 were included in the analysis of the association of overweight and obesity with change in TKV. Among these participants, the mean±SD age was 37±8 years, 93.9% (n=426) were white, and the mean±SD annual percent change in TKV was 7.4%±5.1%. Participants were categorized by baseline BMI (calculated from an adjusted weight removing the contribution of weight of the kidney and liver) as normal weight (18.5–24.9 kg/m2), overweight (25.0–29.9 kg/m2), or obese (≥30 kg/m2). The median BMI at baseline was 26.3±4.9 kg/m2. The median (interquartile range) baseline TKV was 1040 (808, 1552) ml. Individuals with a higher BMI were more likely to be men, had a higher systolic BP (SBP) and fasting serum glucose, and had a larger liver volume at baseline (Table 1).
Table 1. -
Baseline characteristics of study participants from HALT Study A according to BMI category
||Normal Weight (BMI 18.5–24.9 kg/m2) (n=192)
||Overweight (BMI 25–29.9 kg/m2) (n=168)
||Obese (BMI≥30 kg/m2) (n=81)
|White race, %
|Study BP Target Randomization Group (low, %)
|Study BP Medication Randomization Group (ACEi + ARB, %)
|CKD-EPI eGFR, ml/min per 1.73 m2
|SBP, mm Hg
|Urinary albumin, mg/24 h
|Fasting serum glucose, mg/dl
||1051 (763, 1689)
||1103 (762, 1394)
|Liver volume, ml
|Mutation class, %
| No mutation detected
Data are mean±SD, %, or median (IQR). BMI is calculated from body weight adjusted to remove the contribution of the liver and kidneys to total weight. Mutation class is unavailable in n=12. ARB, angiotensin receptor blocker; CKD-EPI, CKD-EPI equation.
Relation between Overweight and Obesity and Change in TKV
We first considered the initial and final TKV values from each participant in HALT Study A and calculated annual percent change in TKV. Using this approach, the annual percent change in TKV was greater with increasing BMI category (normal weight: 6.1%±4.7%, overweight: 7.9%±4.8%, obese: 9.4%±6.2%; P<0.001; Figure 1), with a mean±SD follow-up period of 4.7±0.8 years. In both unadjusted and adjusted analyses, obesity was associated with increased rate of change in TKV as compared with the normal weight group (Table 2). When BMI was considered as a continuous variable, higher BMI was also associated with greater annual percent change in TKV in the fully adjusted model (model 5: β=0.79; 95% confidence interval, 0.18 to 1.39, per 5-unit increase in BMI).
Table 2. -
-estimates [95% confidence intervals]) of BMI categories with annual percent change in TKV
||Normal Weight (BMI 18.5–24.9 kg/m2) (n=192)
||Overweight (BMI 25–29.9 kg/m2) (n=168)
||Obese (BMI>30 kg/m2) (n=81)
||1.84 (0.79 to 2.88)
||3.39 (2.08 to 4.71)
||1.36 (0.34 to 2.38)
||3.05 (1.80 to 4.29)
||1.33 (0.30 to 2.35)
||3.05 (1.81 to 4.29)
||0.93 (−0.11 to 1.96)
||2.71 (1.46 to 3.95)
||1.05 (0.02 to 2.07)
||2.84 (1.59 to 4.08)
||0.82 (−0.22 to 1.87)
||2.70 (1.45 to 3.95)
Model 1: adjusted for age, sex, and race/ethnicity. Model 2: adjusted for model 1+randomization group and SBP Model 3: model 2+eGFR (CKD-EPI equation), urinary albumin excretion, and serum glucose. Model 4: model 3+baseline TKV and liver volume. Model 5: model 4+mutation class. Mutation class is unavailable in n=12.
Because of a significant interaction term of BMI × sex in the linear regression model with an outcome of annual percent change TKV (P value for interaction=0.03) and previous analysis of baseline HALT Study A data which showed effect modification by sex,15 additional analyses were performed stratified by sex. In the final adjusted models, the association between BMI as a continuous variable and annual percent change in TKV was significant in both men and women, but quantitatively larger in men (men: β=1.71; 0.92 to 2.50; women: β=0.87; 0.34 to 1.39, per 5-unit increase in BMI).
Next, we considered three categories of annual percent change in TKV using the initial and final TKV measurements from each participant (<5% growth, 5%–7% growth, and ≥7% growth). In the fully adjusted model, compared with the normal weight group, the obese group had a 3.76 (1.81 to 7.80) greater odds of progressing at a rate of ≥7% compared with <5% TKV growth (Table 3). The odds of progressing at a rate ≥7% compared with <5% were also significantly greater in the overweight compared with normal weight group (odds ratio [OR], 2.02; 1.15 to 3.56). For every 5-unit increase in BMI, the odds of progressing at ≥7% was 1.89 (1.42 to 2.52) in the fully adjusted model.
Table 3. -
Associations (OR [95% confidence interval]) of BMI categories with categories of annual percent change in TKV
|End Point (Annual %∆ in htTKV)
||Normal Weight (BMI 18.5–24.9 kg/m2) (n=192)
||Overweight (BMI 25–29.9 kg/m2) (n=168)
||Obese (BMI ≥30 kg/m2) (n=81)
|5%–7% versus <5%
||2.10 (1.12 to 3.93)
||2.22 (0.95 to 5.20)
||1.92 (1.00 to 3.67)
||2.16 (0.92 to 5.12)
||2.00 (1.03 to 3.89)
||2.26 (0.94 to 5.44)
||1.55 (0.79 to 3.07)
||1.97 (0.81 to 4.81)
||1.62 (0.81 to 3.21)
||2.16 (0.87 to 5.38)
||1.66 (0.82 to 3.38)
||2.32 (0.91 to 5.91)
|≥7% versus <5%
||2.39 (1.50 to 3.83)
||3.54 (1.89 to 6.64)
||2.20 (1.33 to 3.64)
||3.45 (1.80 to 6.63)
||2.31 (1.38 to 3.85)
||3.72 (1.91 to 7.23)
||1.92 (1.11 to 3.07)
||3.29 (1.67 to 6.51)
||2.03 (1.19 to 3.48)
||3.70 (1.84 to 7.45)
||2.02 (1.15 to 3.56)
||3.76 (1.81 to 7.80)
Model 1: adjusted for age, sex, and race/ethnicity. Model 2: adjusted for model 1+randomization group and SBP. Model 3: model 2+eGFR (CKD-EPI equation), urinary albumin excretion, and serum glucose. Model 4: model 3+baseline TKV and liver volume. Model 5: model 4+mutation class. Mutation class is unavailable in n=12.
As a sensitivity analysis, we also considered a clinically meaningful final TKV end point of >1500 ml. In the fully adjusted model, the odds of reaching the >1500 ml end point were significantly greater in the overweight (OR, 3.33; 1.12 to 9.97) and obese (OR, 3.52; 1.06 to 11.69) compared with normal weight group. For every 5-unit increase in BMI, the odds of reaching a final TKV>1500 ml were 1.89 (1.41 to 4.04) in the fully adjusted model.
Last, in a sensitivity analysis utilizing a linear mixed model approach incorporating all available time points where TKV was measured, there was a significant BMI × time interaction in the fully adjusted model (P<0.01), consistent with the results of the primary analysis. For every 5-unit increase in BMI, TKV increased by 32.0 (12.2, 51.8) ml at month 24, 71.6 (33.3, 109.9) ml at month 48, and 101.8 (50.5, 153.0) ml at month 60.
Relation between Overweight and Obesity and eGFR Slope
Four hundred forty-eight participants with early ADPKD who participated in HALT Study A were included in the analysis of the association of overweight and obesity with slope of eGFR over the study duration. Baseline characteristics were very similar to the cohort included in the analysis of the TKV end point (Supplemental Table 1). The mean±SD annual decline in eGFR (long-term phase) was −3.2±3.1 ml/min per 1.73 m2 per year. In the fully adjusted linear regression model incorporating all available measurements >4 months (to eliminate short-term hemodynamic effects), obesity was associated with greater decline in eGFR as compared with the normal weight group (Table 4). Results were similar when BMI was considered as a continuous variable (model 4: β=−0.03; −0.05 to 0.00, per 5-unit increase in BMI).
Table 4. -
[95% confidence interval]) of BMI categories with eGFR slope
||Normal Weight (BMI 18.5–24.9 kg/m2) (n=206)
||Overweight (BMI 25–29.9 kg/m2) (n=168)
||Obese (BMI≥30 kg/m2) (n=81)
||−0.03 (−0.08 to 0.03)
||−0.08 (−0.15 to −0.02)
||−0.03 (−0.08 to 0.02)
||−0.09 (−0.15 to −0.02)
||−0.02 (−0.07 to 0.03)
||−0.08 (−0.14 to −0.01)
||−0.03 (−0.08 to 0.03)
||−0.08 (−0.14 to −0.02)
||−0.02 (−0.08 to 0.03)
||−0.08 (−0.15 to −0.02)
Model 1: adjusted for age, sex, race/ethnicity, and randomization group. Model 2: adjusted for model 1+randomization group and SBP. Model 3: Model 2+eGFR (CKD-EPI equation), urinary albumin excretion, and serum glucose. Model 4: model 3+mutation class. Mutation class is unavailable in n=11.
In a sensitivity analysis utilizing a linear mixed model approach incorporating all available time points after month 4 where eGFR was measured, there was a significant BMI × time interaction in the fully adjusted model (P=0.03), consistent with the results of the eGFR slope analysis. For every 5-unit increase in BMI, eGFR declined by −0.01 (−0.96, 0.93) ml/min per 1.73 m2 at month 24, −1.60 (−3.02, −0.19) ml/min per 1.73 m2 at month 48, and −1.71 (−3.17, −0.24) ml/min per 1.73 m2 at month 60.
We have demonstrated for the first time that overweight, and particularly obesity, are strongly associated with rate of progression of early-stage ADPKD, as measured by annual percent change in TKV and eGFR slope. In early-stage patients in HALT Study A, compared with normal weight individuals, obesity was associated with nearly four times greater adjusted odds of progressing at an annual rate of change in TKV of ≥7% compared with <5%. The annual percent increase in TKV in obese individuals was >50% greater than in normal weight individuals. These findings cannot be accounted for by baseline kidney and liver size, because BMI was calculated after removing the contribution of weight from these organs. Furthermore, baseline TKV and liver volume were included in all final adjusted models. In sensitivity analyses, overweight and obesity were also associated with achieving a clinically meaningful final TKV>1500 ml, a volume at which risk of subsequent decline in eGFR is increased.16 Importantly, obesity was also associated with greater decline in eGFR as compared with the normal weight group.
In a previous cross-sectional analysis of the baseline data from HALT, body surface area but not BMI was independently associated with baseline height-adjusted TKV and eGFR.15 Body surface area was thought to reflect genetic and environmental factors influencing both birth weight and postnatal growth velocities in a manner associated with, but distinct from, body size. In unadjusted analyses only, BMI was significantly associated with height-adjusted TKV and eGFR in men but not women. Similarly, in the current analysis, the association of BMI with annual percent change in TKV was slightly stronger in men than women. Notably, these associations were significant after adjustment for potential confounders, unlike in the analysis of baseline HALT Study A data. It is not unusual for longitudinal data to differ from cross-sectional data because the latter considers only a single time point. The current results suggest that overweight and obesity may indeed be important contributors to rate of progression in ADPKD.
It is biologically plausible that common pathways may be implicated in both ADPKD and obesity. Obesity can increase the mechanistic target of rapamycin (mTOR) activity via activation by PI3K/Akt and reduced AMP-activated protein kinase (AMPK) activity.17–19 Overnutrition and obesity activate mTOR complex 1 and its downstream target S6 kinase (S6K) via elevated cellular amino acid, glucose, and ATP concentrations,20 whereas caloric restriction represses mTOR via AMPK activation in the presence of low glucose, high AMP/ATP ratios, and decreased amino acids.21,22 Overactivation of mTOR/S6K is also central to the progression of ADPKD, playing a major role in mediating hyperproliferation of the cystic epithelium.23,24 Additionally, AMPK negatively regulates both the cystic fibrosis transmembrane conductance regulator (CFTR), which promotes secretion of cyst fluid,25,26 as well as mTOR signaling.27,28 Metformin, a pharmacologic activator of AMPK, has been shown to slow cyst growth in vitro and ex vivo in models of cystogenesis via inhibition of the mTOR pathway and CFTR.29
It has been proposed that obesity in the setting of a positive energy balance may increase cancer risk, in part due to inhibition of AMPK and activation of mTOR and downstream proliferative pathways.20 Additionally, mTOR inhibitors have been shown to block tumor-promoting effects of obesity in mouse models.18,30,31 Inflammation and oxidative stress are also increased in obesity, which may additionally promote tumorigenesis.20,32 Because ADPKD is characterized by many features that align with the hallmarks of cancer,33 it is tempting to speculate that obesity may contribute to cystogenesis. Consistent with this hypothesis, mild-to-moderate food restriction was recently shown to slow progression in multiple mouse models of ADPKD, concomitant with suppressed mTOR signaling and AMPK activation.12,13
Obesity is a well established independent risk factor for incident CKD,6,34,35 ESRD,7,8 and decline in eGFR in the general population.36 In individuals with prevalent CKD, obesity is associated with a decline in eGFR in some8,37 but not in other studies.38–40 The association of obesity with decline in renal function in an ADPKD cohort has not been evaluated previously. We found an independent association between categorization as obese, but not overweight, and decline in eGFR in early-stage ADPKD. This finding is notable, because in the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease study, an observational study of early-stage ADPKD, the decline in eGFR over a 3-year period was significant only in individuals with baseline TKV>1500 ml.16 Additionally, in HALT Study A, the low BP group had a significantly lower annual percent increase in TKV compared with the standard BP group with no differences in rate of change in eGFR,14 highlighting that HALT Study A participants were indeed in an early stage of a slowly progressing disease. Thus, a greater rate of decline in eGFR in patients with early-stage ADPKD with obesity is clinically significant.
There are several limitations to this study. We are only able to demonstrate an association of overweight and obesity with ADPKD progression, rather than causation, and there may be residual confounding. Although BMI is commonly used to classify overweight and obesity, it is unable to distinguish between fat and muscle mass. Additionally, only baseline BMI and covariates were used in the statistical models. The major strength of this study is that we demonstrated a strong association of overweight and obesity with ADPKD progression, which is a novel and clinically relevant finding. Our results were consistent across various statistical approaches and accounted for any contribution of baseline kidney and liver weight to BMI classification. Last, progression was evaluated longitudinally with approximately 5 years of follow-up, and covariates were well characterized in the setting of a clinical trial.
These results pose an interesting and clinically relevant question of whether weight loss may be an effective strategy to slow progression in patients with ADPKD. The prevalence of overweight and obesity in the HALT study was over half of participants, thus an effective intervention could affect a large number of individuals. Given well known difficulties with weight loss adherence and the life-long nature of ADPKD, targeting prevention of the development of overweight and obesity early in life could potentially be a novel approach. Future research should evaluate the association of overweight and obesity with ADPKD progression in other cohorts, including late-stage patients, and whether weight loss or prevention of weight gain may slow disease progression.
The design of the HALT PKD Study A has been described in detail previously.14,15,41 Briefly, the study was a prospective, randomized, double-blind, placebo-controlled, multicenter trial. Eligible participants were enrolled across seven clinical sites between February of 2006 and June of 2009. All participants provided written informed consent and the study adhered to the Declaration of Helsinki. Study A employed a 2×2 factorial design and evaluated the effect of (1) multilevel renin angiotensin aldosterone system blockade with an angiotensin converting enzyme inhibitor (ACEi) + angiotensin receptor blocker (ARB) compared with ACEi + placebo, and (2) low (95–110/60–75 mm Hg) compared with standard (120–130/70–80 mm Hg) BP control.
All participants had a known diagnosis of ADPKD and either hypertension or high-normal BP. All participants were free from diabetes. Participants in HALT Study A were 15–49 years of age with an eGFR>60 ml/min per 1.73 m2 using the four-variable Modification of Diet in Renal Disease equation. The primary outcome in HALT Study A was percent change in TKV assessed by magnetic resonance imaging. TKV was assessed at baseline, 24, 48, and 60 months.
Of the 558 participants randomized in Study A, 487 had at least two measurements of TKV. One was missing data for BMI, 14 were excluded due to classification of BMI as underweight (see below), and an additional 31 were missing covariates (described below), leaving 441 participants for the current analysis. Twenty-four months data were used as baseline for n=2 participants who were missing baseline TKV data. Of the 558 participants in HALT Study A, 529 also had at least two measurements of eGFR, 14 were excluded due to classification of BMI as underweight, and an additional 67 were missing covariates or TKV/liver volume for calculation of adjusted BMI, leaving 448 participants for the analysis of change in eGFR.
An adjusted body weight was calculated by subtracting out kidney and liver weight, assuming a tissue density equal to that of water (1 g/cm3),42 thus removing the contribution of kidney and liver size to BMI classification. BMI was then calculated using baseline adjusted body weight in kilograms divided by baseline height in meters squared (measured at clinical research clinics) and rounded to the nearest tenth. Participants were categorized on the basis of BMI as normal weight (18.5–24.9 kg/m2), overweight (25.0–29.9 kg/m2), or obese (≥30 kg/m2) using the National Heart, Lung, and Blood Institute’s criteria.43 Fourteen participants had an adjusted BMI<18.5 kg/m2 (i.e., underweight) and were thus excluded from analyses, because underweight individuals may differ physiologically from those of normal weight. Magnetic resonance imaging was performed at each study site using a protocol developed by the HALT PKD Imaging Subcommittee to determine TKV (as well as total liver volume).15,41 Following acquisition, images were reviewed locally and transferred electronically to the Image Analysis Center at the University of Pittsburgh for analysis.
Baseline and follow-up eGFR were calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation.14 Two blood samples were drawn a minimum of 1 hour apart and sent to the central laboratory (Cleveland Clinic Foundation Reference Laboratory) for measurement of serum creatinine.41 Consistency (<20% variation) was required, with a second set of samples drawn for repeat analysis if this requirement was not met.
Confounders related to BMI and the primary outcomes were selected a priori as potential covariates, and all were measured at baseline. Race was categorized as white or nonwhite, as determined by self-report. SBP was measured in the clinical research clinics with the participant seated quietly in a chair for at least 5 minutes, feet on the floor, and arm supported at heart level. Three measurements were taken with at least 30 seconds between each measurement and the last two readings were repeated if there was a >10 mm Hg difference. The last two readings were averaged and reported.41 Urinary albumin excretion was determined from 24-hour urine collections.41 Glucose level was measured in fasted serum samples during screening using standard methodology. Liver volume was measured as described above. Mutation analysis was performed previously, with mutation class categorized as PKD1 truncating mutations, PKD1 nontruncating mutations, PKD2 mutations, and no mutation detected.44
The association of overweight and obesity with change in TKV was assessed using linear regression and multinomial logistic regression models. Participants were classified into three categories according to BMI as described above (normal weight, overweight, and obese), with the normal weight category serving as the reference group in all analyses. In the linear regression models, the dependent variable was annual percent change in TKV calculated from the first and last available measurements. On the basis of analyses of baseline associations in HALT Study A,15 we tested for a statistical interaction between BMI and sex as a predictor of annual percent change in TKV. We performed stratified analyses on the basis of a significant interaction term (P<0.05) with BMI as a continuous variable only. There was no significant interaction with study randomization group (ACEi/ angiotensin receptor blocker versus ACEi/placebo or low versus standard BP target).
In the multinomial logistic regression models, the outcome was three categories of annual TKV growth (<5%, 5%–7%, and ≥7%), again calculated from the first and last available measurements. ORs were calculated with the <5% annual TKV growth, normal weight group serving at the reference. There were no significant interaction terms between BMI/BMI category and either sex or randomization group, thus stratified analyses were not performed.
In both approaches, the initial model was unadjusted, then multivariable adjusted models were performed to include age, sex, and race/ethnicity (model 1), model 1 plus randomization group and SBP (model 2), model 2 plus eGFR (CKD-EPI) and urinary albumin excretion (model 3), and model 3 plus baseline TKV, baseline liver volume, and serum glucose (model 4). Mutation class was added to model 4 (model 5) for those with the information available (n=429). We additionally considered BMI as a continuous predictor variable. The interaction term was included in model 2 for the linear regression model, considering BMI as a continuous variable. As a sensitivity analysis, we also evaluated achieving a final TKV>1500 ml as a clinically meaningful end point.16 The same covariates were included in these models as described previously.
Linear regression models were also used to evaluate the association of BMI categories with eGFR slope, which was obtained by fitting a linear regression model to all eGFR measurements from an individual participant obtained at >4 months (i.e., long-term phase14). The initial model was unadjusted, then multivariable adjusted models were performed to include age, sex, race/ethnicity, and randomization group (model 1), model 1 plus SBP (model 2), and model 2 plus eGFR (CKD-EPI) and urinary albumin excretion (model 3). Mutation class was added to model 3 (model 4) for those with the information available (n=437). Annual change in eGFR was calculated using the final and month 5 eGFR values. There were no significant interaction terms between BMI/BMI category and either sex or randomization group, thus stratified analyses were not performed.
Last, as sensitivity analyses, we performed linear mixed model analysis incorporating all available measurements (with the exception of month 90 and 96 eGFR, which caused model failure due to a low number of measurements), with BMI as a continuous predictor variable and (1) TKV and (2) eGFR as continuous end points. Results were similar when the variable TKV was log-transformed. The same covariates were used as described above.
In all analyses, baseline characteristics were summarized by BMI categories and presented as mean±SD or median (interquartile range) for continuous variables and n (%) for categoric variables. Comparisons across BMI categories were made using a chi-squared or Wilcoxon score test for categoric data and ANOVA for continuous variables.
Two-tailed values of P<0.05 were considered statistically significant for all analyses. All statistical analyses were performed using SAS version 9.4.
K.L.N. is supported by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), K01 DK103678. The Halt Progression of Polycystic Kidney Disease studies were supported by the NIDDK grants U01 DK062402, U01 DK062410, U01 CK082230, U01 DK062408, and U01 DK062401; the National Center for Research Resources General Clinical Research Centers (RR000039 to Emory University, RR000585 to the Mayo Clinic, RR000054 to Tufts Medical Center, RR000051 to the University of Colorado, RR023940 to the University of Kansas Medical Center, and RR001032 to Beth Israel Deaconess Medical Center); the National Center for Advancing Translational Sciences Clinical and Translational Science Awards (RR025008 and TR000454 to Emory University, RR024150 and TR00135 to the Mayo Clinic, RR025752 and TR001064 to Tufts University, RR025780 and TR001082 to the University of Colorado, RR025758 and TR001102 to Beth Israel Deaconess Medical Center, RR033179 and TR000001 to the University of Kansas Medical Center, and RR024989 and TR000439 to Cleveland Clinic); by funding from the Zell Family Foundation (to the University of Colorado); and by a grant from the Polycystic Kidney Disease Foundation.
The funding agencies had no direct role in the conduct of the study; the collection, management, analyses, and interpretation of the data; or preparation or approval of the manuscript.
1. Somlo S, Chapman AB: Autosomal dominant polycystic kidney disease. In: Schrier’s Diseases of the Kidney, 9th Ed., edited by Coffman TM, Falk RJ, Molitoris BA, Neilson EG, Schrier RW, Philadelphia, Lippincott Williams & Wilkins, 2012, pp 519–563
2. Torres VE, Harris PC, Pirson Y: Autosomal dominant polycystic kidney disease. Lancet 369: 1287–1301, 200717434405
3. Chapman AB, Guay-Woodford LM, Grantham JJ, Torres VE, Bae KT, Baumgarten DA, Kenney PJ, King BF Jr., Glockner JF, Wetzel LH, Brummer ME, O’Neill WC, Robbin ML, Bennett WM, Klahr S, Hirschman GH, Kimmel PL, Thompson PA, Miller JP; Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease cohort: Renal structure in early autosomal-dominant polycystic kidney disease (ADPKD
): The Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease (CRISP) cohort. Kidney Int 64: 1035–1045, 200312911554
4. Schrier RW, McFann KK, Johnson AM: Epidemiological study of kidney survival in autosomal dominant polycystic kidney disease. Kidney Int 63: 678–685, 200312631134
5. Helal I, McFann K, Reed B, Yan XD, Schrier RW: Changing referral characteristics of patients with autosomal dominant polycystic kidney disease. Am J Med 126: 832.e7–832.e11, 2013
6. Fox CS, Larson MG, Leip EP, Culleton B, Wilson PW, Levy D: Predictors of new-onset kidney disease in a community-based population. JAMA 291: 844–850, 200414970063
7. Iseki K, Ikemiya Y, Kinjo K, Inoue T, Iseki C, Takishita S: Body mass index and the risk of development of end-stage renal disease in a screened cohort. Kidney Int 65: 1870–1876, 200415086929
8. Hsu CY, McCulloch CE, Iribarren C, Darbinian J, Go AS: Body mass index and risk for end-stage renal disease. Ann Intern Med 144: 21–28, 200616389251
9. Wang Y, Chen X, Song Y, Caballero B, Cheskin LJ: Association between obesity
and kidney disease: A systematic review and meta-analysis. Kidney Int 73: 19–33, 200817928825
10. Rowe I, Chiaravalli M, Mannella V, Ulisse V, Quilici G, Pema M, Song XW, Xu H, Mari S, Qian F, Pei Y, Musco G, Boletta A: Defective glucose metabolism in polycystic kidney disease identifies a new therapeutic strategy. Nat Med 19: 488–493, 201323524344
11. Riwanto M, Kapoor S, Rodriguez D, Edenhofer I, Segerer S, Wüthrich RP: Inhibition of aerobic glycolysis attenuates disease progression in polycystic kidney Disease. PLoS One 11: e0146654, 201626752072
12. Warner G, Hein KZ, Nin V, Edwards M, Chini CC, Hopp K, Harris PC, Torres VE, Chini EN: Food restriction ameliorates the development of polycystic kidney disease. J Am Soc Nephrol 27: 1437–1447, 201626538633
13. Kipp KR, Rezaei M, Lin L, Dewey EC, Weimbs T: A mild reduction of food intake slows disease progression in an orthologous mouse model of polycystic kidney disease. Am J Physiol Renal Physiol 310: F726–F731, 201626764208
14. Schrier RW, Abebe KZ, Perrone RD, Torres VE, Braun WE, Steinman TI, Winklhofer FT, Brosnahan G, Czarnecki PG, Hogan MC, Miskulin DC, Rahbari-Oskoui FF, Grantham JJ, Harris PC, Flessner MF, Bae KT, Moore CG, Chapman AB; HALT-PKD Trial Investigators: Blood pressure in early autosomal dominant polycystic kidney disease. N Engl J Med 371: 2255–2266, 201425399733
15. Torres VE, Chapman AB, Perrone RD, Bae KT, Abebe KZ, Bost JE, Miskulin DC, Steinman TI, Braun WE, Winklhofer FT, Hogan MC, Oskoui FR, Kelleher C, Masoumi A, Glockner J, Halin NJ, Martin DR, Remer E, Patel N, Pedrosa I, Wetzel LH, Thompson PA, Miller JP, Meyers CM, Schrier RW; HALT PKD Study Group: Analysis of baseline parameters in the HALT polycystic kidney disease trials. Kidney Int 81: 577–585, 201222205355
16. Grantham JJ, Torres VE, Chapman AB, Guay-Woodford LM, Bae KT, King BF Jr., Wetzel LH, Baumgarten DA, Kenney PJ, Harris PC, Klahr S, Bennett WM, Hirschman GN, Meyers CM, Zhang X, Zhu F, Miller JP; CRISP Investigators: Volume progression in polycystic kidney disease. N Engl J Med 354: 2122–2130, 200616707749
17. Chen J: Multiple signal pathways in obesity
-associated cancer. Obes Rev 12: 1063–1070, 201122093240
18. Moore T, Beltran L, Carbajal S, Strom S, Traag J, Hursting SD, DiGiovanni J: Dietary energy balance modulates signaling through the Akt/mammalian target of rapamycin pathways in multiple epithelial tissues. Cancer Prev Res (Phila) 1: 65–76, 200819138937
19. Dann SG, Selvaraj A, Thomas G: mTOR Complex1-S6K1 signaling: At the crossroads of obesity
, diabetes and cancer. Trends Mol Med 13: 252–259, 200717452018
20. Hursting SD, Lashinger LM, Wheatley KW, Rogers CJ, Colbert LH, Nunez NP, Perkins SN: Reducing the weight of cancer: Mechanistic targets for breaking the obesity
-carcinogenesis link. Best Pract Res Clin Endocrinol Metab 22: 659–669, 200818971125
21. Cantó C, Auwerx J: Calorie restriction: Is AMPK a key sensor and effector? Physiology (Bethesda) 26: 214–224, 201121841070
22. Jia G, Aroor AR, Martinez-Lemus LA, Sowers JR: Overnutrition, mTOR signaling, and cardiovascular diseases. Am J Physiol Regul Integr Comp Physiol 307: R1198–R1206, 201425253086
23. Ibraghimov-Beskrovnaya O, Natoli TA: mTOR signaling in polycystic kidney disease. Trends Mol Med 17: 625–633, 201121775207
24. Laplante M, Sabatini DM: mTOR signaling in growth control and disease. Cell 149: 274–293, 201222500797
25. Hallows KR, Raghuram V, Kemp BE, Witters LA, Foskett JK: Inhibition of cystic fibrosis transmembrane conductance regulator by novel interaction with the metabolic sensor AMP-activated protein kinase. J Clin Invest 105: 1711–1721, 200010862786
26. King JD Jr., Fitch AC, Lee JK, McCane JE, Mak DO, Foskett JK, Hallows KR: AMP-activated protein kinase phosphorylation of the R domain inhibits PKA stimulation of CFTR. Am J Physiol Cell Physiol 297: C94–C101, 200919419994
27. Gwinn DM, Shackelford DB, Egan DF, Mihaylova MM, Mery A, Vasquez DS, Turk BE, Shaw RJ: AMPK phosphorylation of raptor mediates a metabolic checkpoint. Mol Cell 30: 214–226, 200818439900
28. Hall MN: mTOR-what does it do? Transplant Proc 40[Suppl 10]: S5–S8, 200819100909
29. Takiar V, Nishio S, Seo-Mayer P, King JD Jr., Li H, Zhang L, Karihaloo A, Hallows KR, Somlo S, Caplan MJ: Activating AMP-activated protein kinase (AMPK) slows renal cystogenesis. Proc Natl Acad Sci U S A 108: 2462–2467, 201121262823
30. Nogueira LM, Dunlap SM, Ford NA, Hursting SD: Calorie restriction and rapamycin inhibit MMTV-Wnt-1 mammary tumor growth in a mouse model of postmenopausal obesity
. Endocr Relat Cancer 19: 57–68, 201222143497
31. De Angel RE, Conti CJ, Wheatley KE, Brenner AJ, Otto G, Degraffenried LA, Hursting SD: The enhancing effects of obesity
on mammary tumor growth and Akt/mTOR pathway activation persist after weight loss and are reversed by RAD001. Mol Carcinog 52: 446–458, 201322290600
32. de Ferranti S, Mozaffarian D: The perfect storm: Obesity
, adipocyte dysfunction, and metabolic consequences. Clin Chem 54: 945–955, 200818436717
33. Seeger-Nukpezah T, Geynisman DM, Nikonova AS, Benzing T, Golemis EA: The hallmarks of cancer: Relevance to the pathogenesis of polycystic kidney disease. Nat Rev Nephrol 11: 515–534, 201525870008
34. Gelber RP, Kurth T, Kausz AT, Manson JE, Buring JE, Levey AS, Gaziano JM: Association between body mass index and CKD in apparently healthy men. Am J Kidney Dis 46: 871–880, 200516253727
35. Kramer H, Luke A, Bidani A, Cao G, Cooper R, McGee D: Obesity
and prevalent and incident CKD: The Hypertension Detection and Follow-Up Program. Am J Kidney Dis 46: 587–594, 200516183412
36. de Boer IH, Katz R, Fried LF, Ix JH, Luchsinger J, Sarnak MJ, Shlipak MG, Siscovick DS, Kestenbaum B: Obesity
and change in estimated GFR among older adults. Am J Kidney Dis 54: 1043–1051, 200919782454
37. Othman M, Kawar B, El Nahas AM: Influence of obesity
on progression of non-diabetic chronic kidney disease: A retrospective cohort study. Nephron Clin Pract 113: c16–c23, 200919590231
38. Khedr A, Khedr E, House AA: Body mass index and the risk of progression of chronic kidney disease. J Ren Nutr 21: 455–461, 201121454093
39. Brown RN, Mohsen A, Green D, Hoefield RA, Summers LK, Middleton RJ, O’Donoghue DJ, Kalra PA, New DI: Body mass index has no effect on rate of progression of chronic kidney disease in non-diabetic subjects. Nephrol Dial Transplant 27: 2776–2780, 201222442391
40. Mohsen A, Brown R, Hoefield R, Kalra PA, O’Donoghue D, Middleton R, New D: Body mass index has no effect on rate of progression of chronic kidney disease in subjects with type 2 diabetes mellitus. J Nephrol 25: 384–393, 201222241634
41. Chapman AB, Torres VE, Perrone RD, Steinman TI, Bae KT, Miller JP, Miskulin DC, Rahbari Oskoui F, Masoumi A, Hogan MC, Winklhofer FT, Braun W, Thompson PA, Meyers CM, Kelleher C, Schrier RW: The HALT polycystic kidney disease trials: Design and implementation. Clin J Am Soc Nephrol 5: 102–109, 201020089507
42. Wallace DP, Hou YP, Huang ZL, Nivens E, Savinkova L, Yamaguchi T, Bilgen M: Tracking kidney volume in mice with polycystic kidney disease by magnetic resonance imaging. Kidney Int 73: 778–781, 200818185504
43. National Heart, Lung, and Blood Institute: Clinical Guidelines on the Identification, Evaluation, and Treatment of Overweight and Obesity
in Adults: The Evidence Report, Bethesda, MD, National Institutes of Health, 1998. Available at: https://www.nhlbi.nih.gov/files/docs/guidelines/ob_gdlns.pdf
. Accessed April 11, 2017
44. Heyer CM, Sundsbak JL, Abebe KZ, Chapman AB, Torres VE, Grantham JJ, Bae KT, Schrier RW, Perrone RD, Braun WE, Steinman TI, Mrug M, Yu AS, Brosnahan G, Hopp K, Irazabal MV, Bennett WM, Flessner MF, Moore CG, Landsittel D, Harris PC, Halt PKD; HALT PKD and CRISP Investigators: Predicted mutation strength of nontruncating PKD1 mutations aids genotype-phenotype correlations in autosomal dominant polycystic kidney disease. J Am Soc Nephrol 27: 2872–2884, 201626823553