Clinical trial designs of emerging therapies for diabetic kidney disease (DKD).

Current evidence for medical therapies for diabetic kidney disease (DKD) is largely based on large-scale clinical trials. These trials, however, often exhibit heterogeneity in participant characteristics and baseline kidney function. These differences may lead to misinterpretation in clinical practice, such that treatment effects from different trials are directly compared and generalized to broader populations beyond the population in which each trial was conducted. This is particularly relevant if comparisons on efficacy and safety are made when the underlying study populations are distinctly different. Indeed, key clinical trials evaluating sodium-glucose transport protein-2 inhibitors (SGLT2i), non-steroidal mineralocorticoid receptor antagonist (nsMRA), and glucagon-like peptide-1 receptor agonist (GLP-1RA) differed in recruitment requirements (inclusion/exclusion criteria), resulting in differences in the severity of the underlying kidney disease as well as risk factor profiles. Moreover, these trials defined their primary and secondary outcomes differently. Collectively, these factors lead to distinct study populations with different baseline risks for DKD progression in the placebo arm in each clinical trial. Consequently, a direct head-to-head comparison of the treatment effect between treatments using relative risk measures from placebo-controlled clinical trials alone is not recommended. In addition, healthcare professionals should be equipped to understand the specific target population of clinical trials to avoid over-generalization when drawing conclusions from these trials.

The medicines approved to help people with compromised kidney function were developed based on clinical trials that differed in many ways. There is a risk that clinical trials may be incorrectly compared and generalized by healthcare providers. In this review, the authors highlight the importance of interpreting clinical trial results cautiously while being mindful of the study population features. Key clinical trials for the treatment of diabetic kidney disease had different recruitment requirements for participants, a wide range of kidney disease severity, and different risks of disease progression in the comparison arm that did not receive the treatment during the trial. The conclusion of this review is to highlight the inappropriateness of comparing these medicines with each other using the results of clinical trials alone. It is important for the medical community to understand the specific types of patients that were involved in the clinical trials, to avoid unjustified conclusions.

Validation of the Klinrisk chronic kidney disease progression model in the FIDELITY population.

Background: Chronic kidney disease (CKD) affects >800 million individuals worldwide and is often underrecognized. Early detection, identification and treatment can delay disease progression. Klinrisk is a proprietary CKD progression risk prediction model based on common laboratory data to predict CKD progression. We aimed to externally validate the Klinrisk model for prediction of CKD progression in FIDELITY (a prespecified pooled analysis of two finerenone phase III trials in patients with CKD and type 2 diabetes). In addition, we sought to identify evidence of an interaction between treatment and risk. Methods: The validation cohort included all participants in FIDELITY up to 4 years. The primary and secondary composite outcomes included a ≥40% decrease in estimated glomerular filtration rate (eGFR) or kidney failure, and a ≥57% decrease in eGFR or kidney failure. Prediction discrimination was calculated using area under the receiver operating characteristic curve (AUC). Calibration plots were calculated by decile comparing observed with predicted risk. Results: At time horizons of 2 and 4 years, 993 and 1795 patients experienced a primary outcome event, respectively. The model predicted the primary outcome accurately with an AUC of 0.81 for 2 years and 0.86 for 4 years. Calibration was appropriate at both 2 and 4 years, with Brier scores of 0.067 and 0.115, respectively. No evidence of interaction between treatment and risk was identified for the primary composite outcome (P = .31). Conclusions: Our findings demonstrate the accuracy and utility of a laboratory-based prediction model for early identification of patients at the highest risk of CKD progression.

Impact of Preoperative Weight Loss on 30-Day Complication Rate after Bariatric Surgery.

BACKGROUND: The aim of this study is to evaluate the impact of preoperative weight loss on surgical outcomes and operating room (OR) times after primary bariatric procedures, including laparoscopic sleeve gastrectomy (LSG) and laparoscopic Roux-en-Y gastric bypass (RYGB). STUDY DESIGN: A retrospective cohort study uses the 2021 MBSAQIP dataset. Preoperative total weight loss (TWL)% was calculated. Patients were then divided in to 4 groups: those with no weight loss, lost <0 to <5%, lost ≥5% to <10%, or lost ≥10% TWL preoperatively. These groups were then stratified into those with BMI less than 50 kg/m 2 and those with BMI 50 kg/m 2 or more and 30-day outcomes and OR times were compared. RESULTS: Analysis included 171,010 patients. For BMI less than 50 kg/m 2 , preoperative weight loss led to no consistent improvement in surgical outcomes. Although >0% to <5% TWL led to a decrease in intra- and postoperative occurrences after RYGB and a decrease in reoperation rates after LSG, these observations were not seen in those with higher degree of weight loss. In patients with BMI 50 kg/m 2 or more, preoperative weight loss showed a consistent improvement in reintervention rates after LSG, and readmission rates after RYGB. There was no improvement in other outcomes, however, irrespective of degree of preoperative weight loss. CONCLUSIONS: In patients undergoing primary bariatric surgery, preoperative weight loss does not lead to a consistent improvement in outcomes or OR times. In those with BMI 50 kg/m 2 or more, there may be improvement in select outcomes that is procedure-specific. Overall, these data do not support a uniform policy of preoperative weight loss, although selective use in some high-risk patients may be appropriate.

Albuminuria: An Underappreciated Risk Factor for Cardiovascular Disease.

Albuminuria, an established biomarker of the progression of chronic kidney disease, is also recognized as a biomarker for the risk of cardiovascular disease. Elevated urinary albumin excretion indicates kidney damage and systemic vascular disease, including myocardial capillary disease and arterial stiffness. Albuminuria is associated with an increased risk of coronary artery disease, stroke, heart failure, arrhythmias, and microvascular disease. There are now several therapeutic agents that can lead to albuminuria lowering and a reduction in cardiovascular risk. However, screening for albuminuria is still low. Considering the importance of multidisciplinary management of patients with cardiovascular disease, it is crucial that health care professionals managing such patients are aware of the benefits of albuminuria surveillance and management.

Plasma Ferritin Levels, Incident Heart Failure, and Cardiac Structure and Function: The ARIC Study.

BACKGROUND: Whether iron deficiency contributes to incident heart failure (HF) and cardiac dysfunction has important implications given the prevalence of iron deficiency and the availability of several therapeutics for iron repletion. OBJECTIVES: The aim of this study was to estimate the associations of plasma ferritin level with incident HF overall, HF phenotypes, and cardiac structure and function measures in older adults. METHODS: Participants in the ongoing, longitudinal ARIC (Atherosclerosis Risk In Communities) study who were free of prevalent HF and anemia were studied. The associations of plasma ferritin levels with incident HF overall and heart failure with preserved ejection fraction (HFpEF) and heart failure with reduced ejection fraction (HFrEF) were estimated using Cox proportional hazards regression models. Linear regression models estimated the cross-sectional associations of plasma ferritin with echocardiographic measures of cardiac structure and function. RESULTS: The cohort included 3,472 individuals with a mean age of 75 ± 5 years (56% women, 14% Black individuals). In fully adjusted models, lower ferritin was associated with higher risk for incident HF overall (HR: 1.20 [95% CI: 1.08-1.34] per 50% lower ferritin level) and higher risk for incident HFpEF (HR: 1.28 [95% CI: 1.09-1.50]). Associations with incident HFrEF were not statistically significant. Lower ferritin levels were associated with higher E/e' ratio and higher pulmonary artery systolic pressure after adjustment for demographics and HF risk factors but not with measures of left ventricular structure or systolic function. CONCLUSIONS: Among older adults without prevalent HF or anemia, lower plasma ferritin level is associated with a higher risk for incident HF, HFpEF, and higher measures of left ventricular filling pressure.