Including measures of chronic kidney disease to improve cardiovascular risk prediction by SCORE2 and SCORE2-OP.

AIMS: The 2021 European Society of Cardiology (ESC) guideline on cardiovascular disease (CVD) prevention categorizes moderate and severe chronic kidney disease (CKD) as high and very-high CVD risk status regardless of other factors like age and does not include estimated glomerular filtration rate (eGFR) and albuminuria in its algorithms, systemic coronary risk estimation 2 (SCORE2) and systemic coronary risk estimation 2 in older persons (SCORE2-OP), to predict CVD risk. We developed and validated an 'Add-on' to incorporate CKD measures into these algorithms, using a validated approach. METHODS: In 3,054 840 participants from 34 datasets, we developed three Add-ons [eGFR only, eGFR + urinary albumin-to-creatinine ratio (ACR) (the primary Add-on), and eGFR + dipstick proteinuria] for SCORE2 and SCORE2-OP. We validated C-statistics and net reclassification improvement (NRI), accounting for competing risk of non-CVD death, in 5,997 719 participants from 34 different datasets. RESULTS: In the target population of SCORE2 and SCORE2-OP without diabetes, the CKD Add-on (eGFR only) and CKD Add-on (eGFR + ACR) improved C-statistic by 0.006 (95%CI 0.004-0.008) and 0.016 (0.010-0.023), respectively, for SCORE2 and 0.012 (0.009-0.015) and 0.024 (0.014-0.035), respectively, for SCORE2-OP. Similar results were seen when we included individuals with diabetes and tested the CKD Add-on (eGFR + dipstick). In 57 485 European participants with CKD, SCORE2 or SCORE2-OP with a CKD Add-on showed a significant NRI [e.g. 0.100 (0.062-0.138) for SCORE2] compared to the qualitative approach in the ESC guideline. CONCLUSION: Our Add-ons with CKD measures improved CVD risk prediction beyond SCORE2 and SCORE2-OP. This approach will help clinicians and patients with CKD refine risk prediction and further personalize preventive therapies for CVD.

Kidney Age Index (KAI): A novel age-related biomarker to estimate kidney function in patients with diabetic kidney disease using machine learning.

BACKGROUND AND OBJECTIVE: With aging, patients with diabetic kidney disease (DKD) show progressive decrease in kidney function. We investigated whether the deviation of biological age (BA) from the chronological age (CA) due to DKD can be used (denoted as Kidney Age Index; KAI) to quantify kidney function using machine learning algorithms. METHODS: Three large datasets were used in this study to develop KAI. The machine learning algorithms were trained on PREVEND dataset with healthy subjects (N = 7963) using 13 clinical markers to predict the CA. The trained model was then used to predict the BA of patients with DKD using RENAAL (N = 1451) and IDNT (N = 1706). The performance of four traditional machine learning algorithms were evaluated and the KAI = BA-CA was estimated for each patient. RESULTS: The neural network model achieved the best performance and predicted the CA of healthy subjects in PREVEND dataset with a mean absolute deviation (MAD) = 6.5 ± 3.5 years and pearson correlation = 0.62. Patients with DKD showed a significant higher KAI of 15.4 ± 11.8 years and 13.6 ± 12.3 years in RENAAL and IDNT datasets, respectively. CONCLUSIONS: Our findings suggest that for a given CA, patients with DKD shows excess BA when compared to their healthy counterparts due to disease severity. With further improvement, the proposed KAI can be used as a complementary easy-to-interpret tool to give a more inclusive idea into disease state.

Perspectives on a Way Forward to Implementation of Precision Medicine in Patients With Diabetic Kidney Disease; Results of a Stakeholder Consensus-Building Meeting.

Aim: This study aimed to identify from different stakeholders the benefits and obstacles of implementing precision medicine in diabetic kidney disease (DKD) and to build consensus about a way forward in order to treat, prevent, or even reverse this disease. Methods: As part of an ongoing effort of moving implementation of precision medicine in DKD forward, a two-day consensus-building meeting was organized with different stakeholders involved in drug development and patient care in DKD, including patients, patient representatives, pharmaceutical industry, regulatory agencies representatives, health technology assessors, healthcare professionals, basic scientists, and clinical academic researchers. The meeting consisted of plenary presentations and discussions, and small group break-out sessions. Discussion topics were based on a symposium, focus groups and literature search. Benefits, obstacles and potential solutions toward implementing precision medicine were discussed. Results from the break-out sessions were presented in plenary and formed the basis of a broad consensus discussion to reach final conclusions. Throughout the meeting, participants answered several statement and open-ended questions on their mobile device, using a real-time online survey tool. Answers to the statement questions were analyzed descriptively. Results of the open-ended survey questions, the break-out sessions and the consensus discussion were analyzed qualitatively. Results and conclusion: Seventy-one participants from 26 countries attended the consensus-building meeting in Amsterdam, April 2019. During the opening plenary on the first day, the participants agreed with the statement that precision medicine is the way forward in DKD (n = 57, median 90, IQR [75-100]). Lack of efficient tools for implementation in practice and generating robust data were identified as significant obstacles. The identified benefits, e.g., improvement of the benefit-risk ratio of treatment, offer substantive incentives to find solutions for the identified obstacles. Earlier and increased multi-stakeholder collaboration and specific training may provide solutions to alter clinical and regulatory guidelines that lie at the basis of both obstacles and solutions. At the end of the second day, the opinion of the participants toward precision medicine in DKD was somewhat more nuanced (n = 45, median 83, IQR [70-92]) and they concluded that precision medicine is an important way forward in improving the treatment of patients with DKD.

Thiazide diuretics and the rate of disease progression in autosomal dominant polycystic kidney disease: an observational study.

BACKGROUND: In autosomal dominant polycystic kidney disease (ADPKD), hypertension is prevalent and cardiovascular events are the main cause of death. Thiazide diuretics are often prescribed as second-line antihypertensives, on top of renin-angiotensin-aldosterone system (RAAS) blockade. There is a concern, however, that diuretics may increase vasopressin concentration and RAAS activity, thereby worsening disease progression in ADPKD. We aimed to investigate the validity of these suggestions. METHODS: We analysed an observational cohort of 533 ADPKD patients. Plasma copeptin (surrogate for vasopressin), aldosterone and renin were measured by enzyme-linked immunosorbent assay and radioimmunoassay, respectively. Linear mixed models were used to assess the association of thiazide use with estimated glomerular filtration rate (eGFR) decline and Cox proportional hazards models for the association with the composite kidney endpoint of incident end-stage kidney disease, 40% eGFR decline or death. RESULTS: A total of 23% of participants (n = 125) used thiazide diuretics at baseline. Compared with non-users, thiazide users were older, a larger proportion was male, they had lower eGFRs and similar blood pressure under more antihypertensives. Plasma copeptin was higher, but this difference disappeared after adjustment for age and sex. Both renin and aldosterone were higher in thiazide users. There was no difference between thiazide users and non-users in the rate of eGFR decline {difference -0.35 mL/min/1.73 m2 per year [95% confidence interval (CI) -0.83 to -0.14], P = 0.2} during 3.9 years of follow-up (interquartile range 2.5-4.9). This did not change after adjustment for potential confounders [difference final model: 0.08 mL/min/1.73 m2 per year [95% CI -0.46 to -0.62], P = 0.8). In the crude model, thiazide use was associated with a higher incidence of the composite kidney endpoint [hazard ratio (HR) 1.53 (95% CI 1.05-2.23), P = 0.03]. However, this association lost significance after adjustment for age and sex and remained unassociated after adjustment for additional confounders [final model: HR 0.80 (95% CI 0.50-1.29), P = 0.4]. CONCLUSIONS: These data do not show that thiazide diuretics have a detrimental effect on the rate of disease progression in ADPKD and suggest that these drugs can be prescribed as second-line antihypertensives.

Incorporating kidney disease measures into cardiovascular risk prediction: Development and validation in 9 million adults from 72 datasets.

BACKGROUND: Chronic kidney disease (CKD) measures (estimated glomerular filtration rate [eGFR] and albuminuria) are frequently assessed in clinical practice and improve the prediction of incident cardiovascular disease (CVD), yet most major clinical guidelines do not have a standardized approach for incorporating these measures into CVD risk prediction. "CKD Patch" is a validated method to calibrate and improve the predicted risk from established equations according to CKD measures. METHODS: Utilizing data from 4,143,535 adults from 35 datasets, we developed several "CKD Patches" incorporating eGFR and albuminuria, to enhance prediction of risk of atherosclerotic CVD (ASCVD) by the Pooled Cohort Equation (PCE) and CVD mortality by Systematic COronary Risk Evaluation (SCORE). The risk enhancement by CKD Patch was determined by the deviation between individual CKD measures and the values expected from their traditional CVD risk factors and the hazard ratios for eGFR and albuminuria. We then validated this approach among 4,932,824 adults from 37 independent datasets, comparing the original PCE and SCORE equations (recalibrated in each dataset) to those with addition of CKD Patch. FINDINGS: We confirmed the prediction improvement with the CKD Patch for CVD mortality beyond SCORE and ASCVD beyond PCE in validation datasets (Δc-statistic 0.027 [95% CI 0.018-0.036] and 0.010 [0.007-0.013] and categorical net reclassification improvement 0.080 [0.032-0.127] and 0.056 [0.044-0.067], respectively). The median (IQI) of the ratio of predicted risk for CVD mortality with CKD Patch vs. the original prediction with SCORE was 2.64 (1.89-3.40) in very high-risk CKD (e.g., eGFR 30-44 ml/min/1.73m2 with albuminuria ≥30 mg/g), 1.86 (1.48-2.44) in high-risk CKD (e.g., eGFR 45-59 ml/min/1.73m2 with albuminuria 30-299 mg/g), and 1.37 (1.14-1.69) in moderate risk CKD (e.g., eGFR 60-89 ml/min/1.73m2 with albuminuria 30-299 mg/g), indicating considerable risk underestimation in CKD with SCORE. The corresponding estimates for ASCVD with PCE were 1.55 (1.37-1.81), 1.24 (1.10-1.54), and 1.21 (0.98-1.46). INTERPRETATION: The "CKD Patch" can be used to quantitatively enhance ASCVD and CVD mortality risk prediction equations recommended in major US and European guidelines according to CKD measures, when available. FUNDING: US National Kidney Foundation and the NIDDK.

COVID-19-related mortality in kidney transplant and dialysis patients: results of the ERACODA collaboration.

BACKGROUND: Patients on kidney replacement therapy comprise a vulnerable population and may be at increased risk of death from coronavirus disease 2019 (COVID-19). Currently, only limited data are available on outcomes in this patient population. METHODS: We set up the ERACODA (European Renal Association COVID-19 Database) database, which is specifically designed to prospectively collect detailed data on kidney transplant and dialysis patients with COVID-19. For this analysis, patients were included who presented between 1 February and 1 May 2020 and had complete information available on the primary outcome parameter, 28-day mortality. RESULTS: Of the 1073 patients enrolled, 305 (28%) were kidney transplant and 768 (72%) dialysis patients with a mean age of 60 ± 13 and 67 ± 14 years, respectively. The 28-day probability of death was 21.3% [95% confidence interval (95% CI) 14.3-30.2%] in kidney transplant and 25.0% (95% CI 20.2-30.0%) in dialysis patients. Mortality was primarily associated with advanced age in kidney transplant patients, and with age and frailty in dialysis patients. After adjusting for sex, age and frailty, in-hospital mortality did not significantly differ between transplant and dialysis patients [hazard ratio (HR) 0.81, 95% CI 0.59-1.10, P = 0.18]. In the subset of dialysis patients who were a candidate for transplantation (n = 148), 8 patients died within 28 days, as compared with 7 deaths in 23 patients who underwent a kidney transplantation <1 year before presentation (HR adjusted for sex, age and frailty 0.20, 95% CI 0.07-0.56, P < 0.01). CONCLUSIONS: The 28-day case-fatality rate is high in patients on kidney replacement therapy with COVID-19 and is primarily driven by the risk factors age and frailty. Furthermore, in the first year after kidney transplantation, patients may be at increased risk of COVID-19-related mortality as compared with dialysis patients on the waiting list for transplantation. This information is important in guiding clinical decision-making, and for informing the public and healthcare authorities on the COVID-19-related mortality risk in kidney transplant and dialysis patients.

Machine-learning-based early prediction of end-stage renal disease in patients with diabetic kidney disease using clinical trials data.

AIM: To predict end-stage renal disease (ESRD) in patients with type 2 diabetes by using machine-learning models with multiple baseline demographic and clinical characteristics. MATERIALS AND METHODS: In total, 11 789 patients with type 2 diabetes and nephropathy from three clinical trials, RENAAL (n = 1513), IDNT (n = 1715) and ALTITUDE (n = 8561), were used in this study. Eighteen baseline demographic and clinical characteristics were used as predictors to train machine-learning models to predict ESRD (doubling of serum creatinine and/or ESRD). We used the area under the receiver operator curve (AUC) to assess the prediction performance of models and compared this with traditional Cox proportional hazard regression and kidney failure risk equation models. RESULTS: The feed forward neural network model predicted ESRD with an AUC of 0.82 (0.76-0.87), 0.81 (0.75-0.86) and 0.84 (0.79-0.90) in the RENAAL, IDNT and ALTITUDE trials, respectively. The feed forward neural network model selected urinary albumin to creatinine ratio, serum albumin, uric acid and serum creatinine as important predictors and obtained a state-of-the-art performance for predicting long-term ESRD. CONCLUSIONS: Despite large inter-patient variability, non-linear machine-learning models can be used to predict long-term ESRD in patients with type 2 diabetes and nephropathy using baseline demographic and clinical characteristics. The proposed method has the potential to create accurate and multiple outcome prediction automated models to identify high-risk patients who could benefit from therapy in clinical practice.

Precision Nephrology Is a Non-Negligible State of Mind in Clinical Research: Remember the Past to Face the Future.

CKD is a major public health problem. It is characterized by a multitude of risk factors that, when aggregated, can strongly modify outcome. While major risk factors, namely, albuminuria and low estimated glomerular filtration rate (eGFR) have been well analyzed, a large variability in disease progression still remains. This happens because (1) the weight of each risk factor varies between populations (general population or CKD cohort), countries, and single individuals and (2) response to nephroprotective drugs is so heterogeneous that a non-negligible part of patients maintains a high cardiorenal risk despite optimal treatment. Precision nephrology aims at individualizing cardiorenal prognosis and therapy. The purpose of this review is to focus on the risk stratification in different areas, such as clinical practice, population research, and interventional trials, and to describe the strategies used in observational or experimental studies to afford individual-level evidence. The future of precision nephrology is also addressed. Observational studies can in fact provide more adequate findings by collecting more information on risk factors and building risk prediction models that can be applied to each individual in a reliable fashion. Similarly, new clinical trial designs can reduce the individual variability in response to treatment and improve individual outcomes.

Baseline urinary metabolites predict albuminuria response to spironolactone in type 2 diabetes.

The mineralocorticoid receptor antagonist spironolactone significantly reduces albuminuria in subjects with diabetic kidney disease, albeit with a large variability between individuals. Identifying novel biomarkers that predict response to therapy may help to tailor spironolactone therapy. We aimed to identify a set of metabolites for prediction of albuminuria response to spironolactone in subjects with type 2 diabetes. Systems biology molecular process analysis was performed a priori to identify metabolites linked to molecular disease processes and drug mechanism of action. Individual subject data and urine samples were used from 2 randomized placebo controlled double blind clinical trials (NCT01062763, NCT00381134). A urinary metabolite score was developed to predict albuminuria response to spironolactone therapy using penalized ridge regression with leave-one-out cross validation. Bioinformatic analysis identified a set of 18 metabolites linked to a diabetic kidney disease molecular model and potentially affected by spironolactone mechanism of action. Spironolactone reduced UACR relative to placebo by median -42% (25th to 75% percentile -65 to 6) and -29% (25th to 75% percentile -37 to -1) in the test and replication cohorts, respectively. In the test cohort, UACR reduction was higher in the lowest tertile of the baseline urinary metabolite score compared with middle and upper tertiles -58% (25th to 75% percentile -78 to 33), -28% (25th to 75% percentile -46 to 8), -40% (25th to 75% percentile -52% to 31), respectively, P = 0.001 for trend). In the replication cohort, UACR reduction was -54% (25th to 75% percentile -65 to -50), -41 (25th to 75% percentile -46% to 30), and -17% (25th to 75% percentile -36 to 5), respectively, P = 0.010 for trend). We identified a set of 18 urinary metabolites through systems biology to predict albuminuria response to spironolactone in type 2 diabetes. These data suggest that urinary metabolites may be used as a tool to tailor optimal therapy and move in the direction of personalized medicine.

Prediction of the effect of dapagliflozin on kidney and heart failure outcomes based on short-term changes in multiple risk markers.

BACKGROUND: Besides improving glucose control, sodium-glucose co-transporter 2 inhibition with dapagliflozin reduces blood pressure, body weight and urinary albumin:creatinine ratio (UACR) in patients with type 2 diabetes (T2DM). The parameter response efficacy (PRE) score was developed to predict how short-term drug effects on cardiovascular risk markers translate into long-term changes in clinical outcomes. We applied the PRE score to clinical trials of dapagliflozin to model the effect of the drug on kidney and heart failure (HF) outcomes in patients with T2DM and impaired kidney function. METHODS: The relationships between multiple risk markers and long-term outcome were determined in a background population of patients with T2DM with a multivariable Cox model. These relationships were then applied to short-term changes in risk markers observed in a pooled database of dapagliflozin trials (n = 7) that recruited patients with albuminuria to predict the drug-induced changes to kidney and HF outcomes. RESULTS: A total of 132 and 350 patients had UACR >200 mg/g and >30 mg/g at baseline, respectively, and were selected for analysis. The PRE score predicted a risk change for kidney events of -40.8% [95% confidence interval (CI) -51.7 to -29.4) and -40.4% (95% CI -48.4 to -31.1) with dapagliflozin 10 mg compared with placebo for the UACR >200 mg/g and >30 mg/g subgroups. The predicted change in risk for HF events was -27.3% (95% CI -47.7 to -5.1) and -21.2% (95% CI -35.0 to -7.8), respectively. Simulation analyses showed that even with a smaller albuminuria-lowering effect of dapagliflozin (10% instead of the observed 35% in both groups), the estimated kidney risk reduction was still 26.5 and 26.8%, respectively. CONCLUSIONS: The PRE score predicted clinically meaningful reductions in kidney and HF events associated with dapagliflozin therapy in patients with diabetic kidney disease. These results support a large long-term outcome trial in this population to confirm the benefits of the drug on these endpoints.

Effects of dapagliflozin on urinary metabolites in people with type 2 diabetes.

AIM: To assess the effects of the sodium-glucose co-transporter-2 (SGLT2) inhibitor dapagliflozin on a pre-specified panel of 13 urinary metabolites linked to mitochondrial metabolism in people with type 2 diabetes and elevated urine albumin levels. MATERIALS AND METHODS: Urine and plasma samples were used from a double-blind, randomized, placebo-controlled crossover trial in 31 people with type 2 diabetes, with an albumin:creatinine ratio >100 mg/g, and who were on a stable dose of an angiotensin-converting enzyme inhibitor or an angiotensin receptor blocker. Dapagliflozin or placebo treatment periods each lasted 6 weeks, with a 6-week washout period in between. Urinary and plasma metabolites were quantified by gas-chromatography mass spectrometry, corrected for creatinine level, and then combined into a single-valued urinary metabolite index. Fractional excretion of the metabolites was calculated. RESULTS: All 13 urinary metabolites were detectable. After 6 weeks of dapagliflozin therapy, nine of the 13 metabolites were significantly increased from baseline. The urinary metabolite index increased by 42% (95% confidence interval [CI] 8.5 to 85.6; P = .01) with placebo versus 121% (95% CI 69 to 189; P < .001) with dapaglifozin. The placebo-adjusted effect was 56% (95% CI 11 to 118; P = .012). In plasma, seven of the 13 metabolites were detectable, and none was modified by dapagliflozin. CONCLUSIONS: Dapagliflozin significantly increased a panel of urinary metabolites previously linked to mitochondrial metabolism. These data support the hypothesis that SGLT2 inhibitors improve mitochondrial function, and improvements in mitochondrial function could be a mechanism for kidney protection. Future studies with longer treatment duration and clinical outcomes are needed to confirm the clinical impact of these findings.

Evaluating Glomerular Filtration Rate Slope as a Surrogate End Point for ESKD in Clinical Trials: An Individual Participant Meta-Analysis of Observational Data.

BACKGROUND: Decline in eGFR is a biologically plausible surrogate end point for the progression of CKD in clinical trials. However, it must first be tested to ensure strong associations with clinical outcomes in diverse populations, including patients with higher eGFR. METHODS: To investigate the association between 1-, 2-, and 3-year changes in eGFR (slope) with clinical outcomes over the long term, we conducted a random effects meta-analysis of 3,758,551 participants with baseline eGFR≥60 ml/min per 1.73 m2 and 122,664 participants with eGFR<60 ml/min per 1.73 m2 from 14 cohorts followed for an average of 4.2 years. RESULTS: Slower eGFR decline by 0.75 ml/min per 1.73 m2 per year over 2 years was associated with lower risk of ESKD in participants with baseline eGFR≥60 ml/min per 1.73 m2 (adjusted hazard ratio, 0.70; 95% CI, 0.68 to 0.72) and eGFR<60 ml/min per 1.73 m2 (0.71; 95% CI, 0.68 to 0.74). The relationship was stronger with 3-year slope. For a rapidly progressing population with predicted 5-year risk of ESKD of 8.3%, an intervention that reduced eGFR decline by 0.75 ml/min per 1.73 m2 per year over 2 years would reduce the ESKD risk by 1.6%. For a hypothetical low-risk population with a predicted 5-year ESKD risk of 0.58%, the same intervention would reduce the risk by only 0.13%. CONCLUSIONS: Slower decline in eGFR was associated with lower risk of subsequent ESKD, even in participants with eGFR≥60 ml/min per 1.73 m2, but those with the highest risk would be expected to benefit the most.

Lanreotide Reduces Liver Growth In Patients With Autosomal Dominant Polycystic Liver and Kidney Disease.

BACKGROUND & AIMS: Polycystic liver disease is the most common extrarenal manifestation of autosomal dominant polycystic kidney disease (ADPKD). There is need for robust long-term evidence for the volume-reducing effect of somatostatin analogues. We made use of data from an open-label, randomized trial to determine the effects of lanreotide on height-adjusted liver volume (hTLV) and combined height-adjusted liver and kidney volume (hTLKV) in patients with ADPKD. METHODS: We performed a 120-week study comparing the reno-protective effects of lanreotide vs standard care in 305 patients with ADPKD (the DIPAK-1 study). For this analysis, we studied the 175 patients with polycystic liver disease with hepatic cysts identified by magnetic resonance imaging and liver volume ≥2000 mL. Of these, 93 patients were assigned to a group that received lanreotide (120 mg subcutaneously every 4 weeks) and 82 to a group that received standard care (blood pressure control, a sodium-restricted diet, and antihypertensive agents). The primary endpoint was percent change in hTLV between baseline and end of treatment (week 120). A secondary endpoint was change in hTLKV. RESULTS: At 120 weeks, hTLV decreased by 1.99% in the lanreotide group (95% confidence interval [CI], -4.21 to 0.24) and increased by 3.92% in the control group (95% CI, 1.56-6.28). Compared with the control group, lanreotide reduced the growth of hTLV by 5.91% (95% CI, -9.18 to -2.63; P < .001). Growth of hTLV was still reduced by 3.87% at 4 months after the last injection of lanreotide compared with baseline (95% CI, -7.55 to -0.18; P = .04). Lanreotide reduced growth of hTLKV by 7.18% compared with the control group (95% CI, -10.25 to -4.12; P < .001). CONCLUSIONS: In this subanalysis of a randomized trial of patients with polycystic liver disease due to ADPKD, lanreotide for 120 weeks reduced the growth of liver and combined liver and kidney volume. This effect was still present 4 months after the last injection of lanreotide. ClinicalTrials.gov, Number: NCT01616927.

Diabetes mellitus comorbidity in patients enrolled in tuberculosis drug efficacy trials around the world: A systematic review.

AIMS: With a prevalence of 16%, diabetes mellitus (DM) is one of the most frequent non-communicable comorbidities of tuberculosis (TB). DM is a major risk factor for adverse TB outcomes and may require personalized TB drug dosing regimens. However, information on the inclusion of DM in TB drug trials is lacking. We aimed to assess the percentage of recent TB drug efficacy trials that included DM patients. METHODS: A systematic review was performed and reported according to PRISMA guidelines. PubMed, Science Direct, and ClinicalTrials.gov databases were systematically searched for TB drug trials published between 1 January 2012 and 12 September 2017. Primary outcome was the percentage of TB drug trials performed around the world that included DM patients. RESULTS: Out of the included 41 TB drug trials, 12 (29.3%) reported DM comorbidity among the study participants. Nine trials (21.9%) excluded all patients with DM comorbidity, ten (24.4%) excluded only insulin-dependent or uncontrolled DM, and 10 (24.4%) did not mention whether DM was included or excluded. Of the 12 trials that included DM comorbidity, the majority did not report the diagnostic criteria for DM and none reported outcomes in the DM subpopulation. Inclusion of DM was higher in drug-resistant-TB trials (67%, P = .003, vs drug-susceptible) and trials performed in Asia (60%, P = .006, vs Africa). CONCLUSIONS: Fewer than 1/3 recent TB drug trials reported the inclusion of DM. To better reflect real-world DM prevalence and differential TB drug effectiveness, inclusion of DM patients requires increased attention for future TB drug trials.

Proteinuria and cholesterol reduction are independently associated with less renal function decline in statin-treated patients; a post hoc analysis of the PLANET trials.

BACKGROUND: Statins have shown multiple effects on different renal risk factors such as lowering of total cholesterol (TC) and lowering of urine protein:creatinine ratio (UPCR). We assessed whether these effects of statins vary between individuals, the extent of discordance of treatment effects on both TC and UPCR within an individual, and the association of responses in TC and UPCR with estimated glomerular filtration rate (eGFR) decline. METHODS: The PLANET I and II (Renal effects of Rosuvastatin and Atorvastatin in Patients Who Have Progressive Renal Disease) trials examined effects of atorvastatin and rosuvastatin on proteinuria and renal function in patients with proteinuria. We post hoc analysed 471 therapy-adherent proteinuric patients from the two trials and assessed the individual variability in UPCR and TC response from 0 to 14 weeks and whether these responses were predictive of eGFR decline during the subsequent 9 months of follow-up. RESULTS: UPCR and TC response varied between individuals: mean UPCR response was -1.3% (5th-95th percentile -59.9 to 141.8) and mean TC response was -93.9 mg/dL (-169.1 to -26.9). Out of 471 patients, 123 (26.1%) showed a response in UPCR but not in TC, and 96 (20.4%) showed a response in TC but not in UPCR. eGFR (mL/min/1.73 m2) did not decrease significantly from baseline in both UPCR responders [0.4; 95% confidence interval (CI) -1.6 to 0.9; P = 0.54] and TC responders (0.3; 95% CI -1.8 to 1.1; P = 0.64), whereas UPCR and TC non-responders showed a significant decline in eGFR from baseline (1.8; 95% CI 0.6-3.0; P = 0.004 and 1.7; 95% CI 0.5-2.9; P = 0.007, respectively). A lack of response in both parameters resulted in the fastest rate of eGFR decline (2.1; 95% CI 0.5-3.7; P = 0.01). These findings were not different for rosuvastatin or atorvastatin. CONCLUSIONS: Statin-induced changes in cholesterol and proteinuria vary between individuals and do not run in parallel within an individual. The initial fall in cholesterol and proteinuria is independently associated with a reduction in eGFR decline. This highlights the importance of monitoring both cholesterol and proteinuria after initiating statin therapy.

Effect of Lanreotide on Kidney Function in Patients With Autosomal Dominant Polycystic Kidney Disease: The DIPAK 1 Randomized Clinical Trial.

Importance: Autosomal dominant polycystic kidney disease (ADPKD) is characterized by progressive cyst formation in both kidneys and loss of renal function, eventually leading to a need for kidney replacement therapy. There are limited therapeutic management options. Objective: To examine the effect of the somatostatin analogue lanreotide on the rate of kidney function loss in patients with later-stage ADPKD. Design, Setting, and Participants: An open-label randomized clinical trial with blinded end point assessment that included 309 patients with ADPKD from July 2012 to March 2015 at 4 nephrology outpatient clinics in the Netherlands. Eligible patients were 18 to 60 years of age and had an estimated glomerular filtration rate (eGFR) of 30 to 60 mL/min/1.73 m2. Follow-up of the 2.5-year trial ended in August 2017. Interventions: Patients were randomized to receive either lanreotide (120 mg subcutaneously once every 4 weeks) in addition to standard care (n = 153) or standard care only (target blood pressure <140/90 mm Hg; n = 152). Main Outcomes and Measures: Primary outcome was annual change in eGFR assessed as slope through eGFR values during the 2.5-year treatment phase. Secondary outcomes included change in eGFR before vs after treatment, incidence of worsening kidney function (start of dialysis or 30% decrease in eGFR), change in total kidney volume and change in quality of life (range: 1 [not bothered] to 5 [extremely bothered]). Results: Among the 309 patients who were randomized (mean [SD] age, 48.4 [7.3] years; 53.4% women), 261 (85.6%) completed the trial. Annual rate of eGFR decline for the lanreotide vs the control group was -3.53 vs -3.46 mL/min/1.73 m2 per year (difference, -0.08 [95% CI, -0.71 to 0.56]; P = .81). There were no significant differences for incidence of worsening kidney function (hazard ratio, 0.87 [95% CI, 0.49 to 1.52]; P = .87), change in eGFR (-3.58 vs -3.45; difference, -0.13 mL/min/1.73 m2 per year [95% CI, -1.76 to 1.50]; P = .88), and change in quality of life (0.05 vs 0.07; difference, -0.03 units per year [95% CI, -0.13 to 0.08]; P = .67). The rate of growth in total kidney volume was lower in the lanreotide group than the control group (4.15% vs 5.56%; difference, -1.33% per year [95% CI, -2.41% to -0.24%]; P = .02). Adverse events in the lanreotide vs control group included injection site discomfort (32% vs 0.7%), injection site papule (5.9% vs 0%), loose stools (91% vs 6.6%), abdominal discomfort (79% vs 20%), and hepatic cyst infections (5.2% vs 0%). Conclusions and Relevance: Among patients with later-stage autosomal dominant polycystic kidney disease, treatment with lanreotide compared with standard care did not slow the decline in kidney function over 2.5 years of follow-up. These findings do not support the use of lanreotide for treatment of later-stage autosomal dominant polycystic kidney disease. Trial Registration: ClinicalTrials.gov Identifier: NCT01616927.

Implementing personalized medicine in diabetic kidney disease: Stakeholders' perspectives.

The promise of personalized medicine to deliver "the right treatments at the right time to the right person" is the next frontier in healthcare. However, to implement personalized medicine in chronic diseases such as diabetes mellitus and diabetic kidney disease (DKD), a number of different aspects need to be taken into account. Better risk stratification and more precise options for treatment need to be developed and included in clinical practice guidelines. A patient's unique psychological, social and environmental situation also drive disease progression and outcomes. Appraising the cost effectiveness of precision medicines is necessary, not just as the cost of new therapies, but also the cost of diagnosis with novel methodologies and averted complications. As the prevalence of DKD grows worldwide to epidemic proportions, challenges such as global disparities in resources, access to healthcare and prevalence need to be addressed. This review considers these issues to achieve the short and longer-term goals of implementing personalized medicine in clinical practice.

Personalized medicine in diabetic kidney disease: a novel approach to improve trial design and patient outcomes.

PURPOSE OF REVIEW: In the last decade many attempts have been made to reduce the high residual risk of end-stage kidney disease and cardiovascular disease in patients with diabetic kidney disease by targeting a variety of risk markers. Subsequent analyses revealed that the variation in individual drug response to the tested interventions partly explains why these trials did not result in additional kidney or cardiovascular protection. This review summarizes recent insights regarding individual variation in drug response. Additionally, we explore novel approaches to incorporate this drug response variability in the design of new clinical trials. RECENT FINDINGS: Recent studies suggest that a plausible explanation for individual therapy resistance emanates from intrinsic individual characteristics such as genetic make-up or volume status and is likely only partially explained by drug characteristics such as the dose or type of intervention. Biomarker-based enrichment strategies to identify high-risk individuals and/or those who are more likely to respond to interventions offer opportunities to tailor therapies to individual patients. SUMMARY: Individual drug response variability is a recognized phenomenon in clinical practice. It is time to implement novel approaches that take into account this response variability in the design of new trials in diabetic kidney disease in order to define optimal therapies for individual patients.