Personalized Management of Bone and Mineral Disorders and Precision Medicine in End-Stage Kidney Disease

The arsenic-lowering effect of inulin-type prebiotics in end-stage renal disease: a randomized crossover trial

Background: Circulatory imbalance of trace elements is frequent in end-stage renal disease (ESRD), leading to a deficiency of essential elements and excess of toxic elements. The present study aimed to investigate whether inulin-type fructans (ITFs) could ameliorate the circulatory imbalance by modulating gut microbiota and regulating the absorption and elimination of trace elements. Methods: Peritoneal dialysis patients were enrolled in a randomized crossover trial, undergoing interventions with ITFs (10 g d-1) and maltodextrin (placebo) over a 9-month period (with a 3-month washout). The primary outcomes included essential elements Mn, Fe, Co, Cu, Zn, Se, Sr, and Mo and potential toxic elements V, Cr, Ni, As, Cd, Ba, Tl, Pb, Th, and U in plasma. Secondary outcomes included the gut microbiome, short chain fatty acids (SCFAs), bile acids (BAs), and daily removal of trace elements through urine, dialysate and feces. Results: Among the 44 participants initially randomized, 29 completed the prebiotic, placebo or both interventions. The daily dietary intake of macronutrients and trace elements remained consistent throughout the study. The administration of 10 g d-1 ITFs significantly reduced plasma arsenic (As) by 1.03 μg L-1 (95%CI: -1.74, -0.33) (FDR-adjusted P = 0.045) down from the baseline of 3.54 μg L-1 (IQRs: 2.61-4.40) and increased the As clearance rate by urine and dialysis (P = 0.033). Positive changes in gut microbiota were also observed, including an increase in the Firmicutes/Bacteroidetes ratio (P = 0.050), a trend towards higher fecal SCFAs (P = 0.082), and elevated excretion of primary BAs (P = 0.035). However, there were no significant changes in plasma concentrations of other trace elements or their daily removal by urine, dialysis and feces. Conclusions: The daily administration of 10 g d-1 ITFs proved to be effective in reducing the circulating retention of As but demonstrated to be ineffective for other trace elements in ESRD. These sentences are ok to include but as "The clinical trial registry number is ChiCTR-INR-17013739 (https://www.chictr.org.cn/showproj.aspx?proj=21228)".

AI-Powered Renal Diet Support: Performance of ChatGPT, Bard AI, and Bing Chat

Patients with chronic kidney disease (CKD) necessitate specialized renal diets to prevent complications such as hyperkalemia and hyperphosphatemia. A comprehensive assessment of food components is pivotal, yet burdensome for healthcare providers. With evolving artificial intelligence (AI) technology, models such as ChatGPT, Bard AI, and Bing Chat can be instrumental in educating patients and assisting professionals. To gauge the efficacy of different AI models in discerning potassium and phosphorus content in foods, four AI models-ChatGPT 3.5, ChatGPT 4, Bard AI, and Bing Chat-were evaluated. A total of 240 food items, curated from the Mayo Clinic Renal Diet Handbook for CKD patients, were input into each model. These items were characterized by their potassium (149 items) and phosphorus (91 items) content. Each model was tasked to categorize the items into high or low potassium and high phosphorus content. The results were juxtaposed with the Mayo Clinic Renal Diet Handbook's recommendations. The concordance between repeated sessions was also evaluated to assess model consistency. Among the models tested, ChatGPT 4 displayed superior performance in identifying potassium content, correctly classifying 81% of the foods. It accurately discerned 60% of low potassium and 99% of high potassium foods. In comparison, ChatGPT 3.5 exhibited a 66% accuracy rate. Bard AI and Bing Chat models had an accuracy rate of 79% and 81%, respectively. Regarding phosphorus content, Bard AI stood out with a flawless 100% accuracy rate. ChatGPT 3.5 and Bing Chat recognized 85% and 89% of the high phosphorus foods correctly, while ChatGPT 4 registered a 77% accuracy rate. Emerging AI models manifest a diverse range of accuracy in discerning potassium and phosphorus content in foods suitable for CKD patients. ChatGPT 4, in particular, showed a marked improvement over its predecessor, especially in detecting potassium content. The Bard AI model exhibited exceptional precision for phosphorus identification. This study underscores the potential of AI models as efficient tools in renal dietary planning, though refinements are warranted for optimal utility.

Mathematical Models of Parathyroid Gland Biology: Complexity and Clinical Use

Altered parathyroid gland biology is a major driver of chronic kidney disease-mineral bone disorder (CKD-MBD) in patients with chronic kidney disease. CKD-MBD is associated with a high risk of vascular calcification and cardiovascular events. A hallmark of CKD-MBD is secondary hyperparathyroidism with increased parathyroid hormone (PTH) synthesis and release and reduced expression of calcium-sensing receptors on the surface of parathyroid cells and eventually hyperplasia of parathyroid gland cells. The KDIGO guidelines strongly recommend the control of PTH in hemodialysis patients. Due to the complexity of parathyroid gland biology, mathematical models have been employed to study the interaction of PTH regulators and PTH plasma concentrations. Here, we present an overview of various model approaches and discuss the impact of different model structures and complexities on the clinical use of these models.

Use of Artificial Intelligence to Identify New Mechanisms and Approaches to Therapy of Bone Disorders Associated With Chronic Kidney Disease

Chronic kidney disease (CKD) leads to clinically severe bone loss, resulting from the deranged mineral metabolism that accompanies CKD. Each individual patient presents a unique combination of risk factors, pathologies, and complications of bone disease. The complexity of the disorder coupled with our incomplete understanding of the pathophysiology has significantly hampered the ability of nephrologists to prevent fractures, a leading comorbidity of CKD. Much has been learned from animal models; however, we propose in this review that application of multiple techniques of mathematical modeling and artificial intelligence can accelerate our ability to develop relevant and impactful clinical trials and can lead to better understanding of the osteoporosis of CKD. We highlight the foundational work that informed our current model development and discuss the potential applications of our approach combining principles of quantitative systems pharmacology, model predictive control, and reinforcement learning to deliver individualized precision medical therapy of this highly complex disorder.

Recent applications of quantitative systems pharmacology and machine learning models across diseases

Quantitative systems pharmacology (QSP) is a quantitative and mechanistic platform describing the phenotypic interaction between drugs, biological networks, and disease conditions to predict optimal therapeutic response. In this meta-analysis study, we review the utility of the QSP platform in drug development and therapeutic strategies based on recent publications (2019-2021). We gathered recent original QSP models and described the diversity of their applications based on therapeutic areas, methodologies, software platforms, and functionalities. The collection and investigation of these publications can assist in providing a repository of recent QSP studies to facilitate the discovery and further reusability of QSP models. Our review shows that the largest number of QSP efforts in recent years is in Immuno-Oncology. We also addressed the benefits of integrative approaches in this field by presenting the applications of Machine Learning methods for drug discovery and QSP models. Based on this meta-analysis, we discuss the advantages and limitations of QSP models and propose fields where the QSP approach constitutes a valuable interface for more investigations to tackle complex diseases and improve drug development.

Personalized Prediction of Short- and Long-Term PTH Changes in Maintenance Hemodialysis Patients

Background: Personalized management of secondary hyperparathyroidism is a critical part of hemodialysis patient care. We used a mathematical model of parathyroid gland (PTG) biology to predict (1) short-term peridialytic intact PTH (iPTH) changes in response to diffusive calcium (Ca) fluxes and (2) to predict long-term iPTH levels.

Methods: We dialyzed 26 maintenance hemodialysis patients on a single occasion with a dialysate Ca concentration of 1.75 mmol/l to attain a positive dialysate-to-blood ionized Ca (iCa) gradient and thus diffusive Ca loading. Intradialytic iCa kinetics, peridialytic iPTH change, and dialysate-sided iCa mass balance (iCaMB) were assessed. Patient-specific PTG model parameters were estimated using clinical, medication, and laboratory data. We then used the personalized PTG model to predict peridialytic and long-term (6-months) iPTH levels.

Results: At dialysis start, the median dialysate-to-blood iCa gradient was 0.3 mmol/l (IQR 0.11). The intradialytic iCa gain was 488 mg (IQR 268). Median iPTH decrease was 75% (IQR 15) from pre-dialysis 277 to post-dialysis 51 pg/ml. Neither iCa gradient nor iCaMB were significantly associated with peridialytic iPTH changes. The personalized PTG model accurately predicted both short-term, treatment-level peridialytic iPTH changes (r = 0.984, p < 0.001, n = 26) and patient-level 6-months iPTH levels (r = 0.848, p < 0.001, n = 13).

Conclusions: This is the first report showing that both short-term and long-term iPTH dynamics can be predicted using a personalized mathematical model of PTG biology. Prospective studies are warranted to explore further model applications, such as patient-level prediction of iPTH response to PTH-lowering treatment.

Current treatment options for secondary hyperparathyroidism in patients with stage 3 to 4 chronic kidney disease and vitamin D deficiency

Introduction: Secondary hyperparathyroidism (SHPT) represents a complication of chronic kidney disease (CKD). Vitamin D system is altered since early CKD, and vitamin D deficiency is an established trigger of SHPT. Although untreated SHPT may degenerate into tertiary hyperparathyroidism with detrimental consequences in advanced CKD, best treatments for counteracting SHPT from stage 3 CKD are still debated. Enthusiasm on prescription of vitamin D receptor activators (VDRA) in non-dialysis renal patients, has been mitigated by the risk of low bone turnover and positive calcium-phosphate balance. Nutritional vitamin D is now suggested as first-line therapy to treat SHPT with low 25(OH)D insufficiency. However, no high-grade evidence supports the best choice between ergocalciferol, cholecalciferol, and calcifediol (in its immediate or extended-release formulation).Areas covered: The review discusses available data on safety and efficacy of nutritional vitamin D, VDRA and nutritional therapy in replenishing 25(OH)D deficiency and counteracting SHPT in non-dialysis CKD patients.Expert opinion: Best treatment for low 25(OH)D and SHPT remains unknown, due to incomplete understanding of the best homeostatic, as mutable, adaptation of mineral metabolism to CKD progression. Nutritional vitamin D and nutritional therapy appear safest interventions, whenever contextualized with single-patient characteristics. VDRA should be restricted to uncontrolled SHPT by first-line therapy.

Development of a quantitative systems pharmacology model of chronic kidney disease: metabolic bone disorder

Chronic kidney disease mineral bone disorder (CKD-MBD) is a virtually universal complication of kidney diseases, starting early in the course of disease and resulting in devastating clinical consequences ranging from bone fragility to accelerated atherosclerosis and early cardiovascular death. Guidelines for therapeutic goals for CKD-MBD have been published, and achievement of these guidelines is associated with improved survival. However, the incomplete understanding of CKD-MBD and the individual variability in the manifestations of CKD-MBD have made it difficult to achieve these guidelines. We hypothesized that the progression of MBD through all stages of CKD, including end-stage kidney disease, could be represented by a quantitative systems pharmacology/systems biology (QSP) model. To address this hypothesis, we constructed a QSP model of CKD-MBD, building on an open-source model of calcium and phosphorus metabolism. Specifically, we estimated and validated the model using data from 5,496 patients with CKD enrolled in the Chronic Renal Insufficiency Cohort study. Our model accurately predicted changes in markers of mineral metabolism related to progressing CKD. We demonstrated that the incorporation of fibroblast growth factor 23 and the soft tissue compartment is essential for accurate modeling of the changes in calcium, phosphorus, intact parathyroid hormone, and calcitriol in CKD-MBD. We conclude that our systems biology model accurately represents CKD-MBD disease progression and can be used as a test bench for improving therapeutic interventions.

Nursing Personnel in the Era of Personalized Healthcare in Clinical Practice

Personalized, stratified, or precision medicine (PM) introduces a new era in healthcare that tries to identify and predict optimum treatment outcomes for a patient or a cohort. It also introduces new scientific terminologies regarding therapeutic approaches and the need of their adoption from healthcare providers. Till today, evidence-based practice (EBP) was focusing on population averages and their variances among cohorts for clinical values that are essential for optimizing healthcare outcome. It can be stated that EBP and PM are complementary approaches for a modern healthcare system. Healthcare providers through EBP often see the forest (population averages) but miss the trees (individual patients), whereas utilization of PM may not see the forest for the trees. Nursing personnel (NP) play an important role in modern healthcare since they are consulting, educating, and providing care to patients whose needs often needs to be individualized (personalized nursing care, PNC). Based on the clinical issues earlier addressed from clinical pharmacology, EBP, and now encompassed in PM, this review tries to describe the challenges that NP have to face in order to meet the requisites of the new era in healthcare. It presents the demands that should be met for upgrading the provided education and expertise of NP toward an updated role in a modern healthcare system.

Biomarkers of vascular calcification in serum

Over the last decades, the association between vascular calcification (VC) and all-cause/cardiovascular mortality, especially in patients with high atherogenic status, such as those with diabetes and/or chronic kidney disease, has been repeatedly highlighted. For over a century, VC has been noted as a passive, degenerative, aging process without any treatment options. However, during the past decades, studies confirmed that mineralization of the arteries is an active, complex process, similar to bone genesis and formation. The main purpose of this review is to provide an update of the existing biomarkers of VC in serum and develop the various pathogenetic mechanisms underlying the calcification process, including the pivotal roles of matrix Gla protein, osteoprotegerin, bone morphogenetic proteins, fetuin-a, fibroblast growth-factor-23, osteocalcin, osteopontin, osteonectin, sclerostin, pyrophosphate, Smads, fibrillin-1 and carbonic anhydrase II.

New Intravenous Calcimimetic Agents: New Options, New Problems. An Example on How Clinical, Economical and Ethical Considerations Affect Choice of Treatment

BACKGROUND

Dialysis treatment is improving, but several long-term problems remain unsolved, including metabolic bone disease linked to chronic kidney disease (CKD-MBD). The availability of new, efficacious but expensive drugs (intravenous calcimimetic agents) poses ethical problems, especially in the setting of budget limitations.

METHODS

Reasons of choice, side effects, biochemical trends were discussed in a cohort of 15 patients (13% of the dialysis population) who stared treatment with intravenous calcimimetics in a single center. All patients had previously been treated with oral calcimimetic agents; dialysis efficacy was at target in 14/15; hemodiafiltration was employed in 10/15. Median Charlson Comorbidity Index was 8. The indications were discussed according to the principlist ethics (beneficience, non maleficience, justice and autonomy). Biochemical results were analyzed to support the clinical-ethical choices.

RESULTS

In the context of a strict clinical and biochemical surveillance, the lack of side effects ensured "non-maleficence"; efficacy was at least similar to oral calcimimetic agents, but tolerance was better. Autonomy was respected through a shared decision-making model; all patients appreciated the reduction of the drug burden, and most acknowledged better control of their biochemical data. The ethical conflict resides in the balance between the clinical "beneficience, non-maleficience" advantage and "justice" (economic impact of treatment, potentially in attrition with other resources, since the drug is expensive and included in the dialysis bundle). The dilemma is more relevant when a patient's life expectancy is short (economic impact without clear clinical advantages), or when non-compliance is an issue (unclear advantage if the whole treatment is not correctly taken).

CONCLUSIONS

In a context of person-centered medicine, autonomy, beneficence and non-maleficence should weight more than economic justice. While ethical discussions are not aimed at finding "the right answer" but asking "the right questions", this example can raise awareness of the importance of including an ethical analysis in the choice of "economically relevant" drugs.

Mineral Bone Abnormalities and Vascular Calcifications

Vascular calcification (VC) is common in chronic kidney disease, increases in prevalence as patients progress to end-stage renal disease, and is significantly associated with mortality. VC is a complex and highly regulated process similar to bone formation whereby hydroxyapatite crystals deposit in the intimal or medial layer of arteries. Mineral bone abnormalities are common in chronic kidney disease; reduction in glomerular filtration rate and changes in vitamin D, parathyroid hormone, and fibroblast growth factor 23 result in the dysregulation of phosphorus and calcium metabolism. Cell culture studies, animal models, and observational and clinical studies all suggest this abnormal mineral metabolism plays a role in the initiation and progression of VC in kidney disease. This review will focus on these mineral bone abnormalities and how they may contribute to mechanisms that induce VC in kidney disease.