What will kidney medicine look like in 2046? Envision the following potential patient-physician scenario and at the end of the article, we discuss likely paradigm shifts that could begin in the next decade based on the recent scientific literature.
Entering the hospital, I am greeted by an Artificial Intelligence (AI) in hologram form, projected by a small, flashlight-like drone hovering above my head. The AI appears as a female physician in her 30s, with shoulder-length blonde hair and white coat. She has been communicating with my personal AI for nearly five seconds, and already knows everything about me.
“Hello and welcome,” she says, in a smooth, natural voice. “My name is Attending IV, and I will be your physician today. I understand you’re here for a consult?”
I nod my head.
She glances down at her chart. “Oh, and it’s your birthday today? Thirty years old?”
“That’s right.”
“Well, Happy Birthday.”
“Thanks very much,” I say. I gaze around the expansive space of the central atrium, kaleidoscopic advertisements projecting overhead, proclaiming the latest medical successes. “I guess things have changed a lot over the last 30 years.”
The AI raises her eyebrows. “Well, you would be right about that. Here’s an interesting fact for you to consider: 90% of the medical technology we use in this hospital today didn’t exist 30 years ago. And 90% of the technology we used 30 years ago has since been replaced by better medicine.”
I follow Attending IV to a small consult booth, where we both sit down.
“So, what brings you in today?” asks the hologram.
“It’s my signs,” I explain. “I feel fine. But for a couple of weeks now, my toilet keeps suggesting that I get a checkup.”
Attending IV smiles. “You know, those home Urinalyzers are great, but they’re no substitute for actually visiting a physician. Would you mind if I accessed your latest urinalysis records?”
“Of course not,” I say.
The hologram looks down at her chart, focuses for a few moments, and then looks up. “Well, I have an idea of what might be going on,” she says. “But to really be sure, I would like to get you checked out by our clinical Urinalyzer. This is a far more sophisticated machine than the one you have at home.”
My personal AI guides me down the hall to a private bathroom that houses the Urinalyzer, which looks like a commode with an overhead monitor. I follow the instructions. “Thank you,” says the toilet, “Your sample has been collected.” I then return to the consult booth, where Attending IV is waiting for me, looking excited.
“I’ve analyzed your results with those of 20 million other patients. It’s very interesting.”
“Anything to worry about?”
“Overall it’s good news. Your labs indicate that you are in excellent health, no signs of cancer or any serious illness.”
“That’s a relief,” I say.
“However, the Urinalyzer did confirm an unusual molecular signature in your urine. We’ve traced this to your kidneys. But to really understand it, we would need to take a closer look. Would you mind if I took a biopsy sample?”
“No problem,” I say. “It’s non-invasive, right?”
“Of course.” My chair reclines backwards, and a footrest extends, forming a small examination table beneath me. Attending IV takes to the air in flashlight form, hovering about 5 cm above my abdomen. The flashlight scans the area back and forth about ten times with a bluish-white beam. I flip over, and the AI repeats the scanning on my back.
“OK, all done,” she says. I sit up, and Attending IV returns to her chair. “Would you like to see what I’ve found?”
“Sure.”
She makes a sweeping gesture, and a pair of holographic kidneys appear in mid-air above her desk. “These are your kidneys,” she explains. Another sweep, and the kidneys are peeled back to reveal a volumetric cross-section. “This is their internal structure. This tube is the ureter, and at its far end, a drop of urine is forming.”
The AI opens her palm, and the holographic kidneys are magnified to focus on a small wedge of tissue, filled with microfluidic tubules. “See right here,” continues Attending IV. “These nephron tubules are filtering blood more slowly than the others. This currently affects about 4% of your kidney. It’s nothing to worry about right now, but might signal a rare condition that could cause problems down the road.”
“I see.”
“If it’s OK with you, I’m going to call in one of our non-AI physicians to talk with you about some of the treatment options available.” The AI takes flight again and leads me down the hall.
Through the door of the waiting room walks a man in his mid-60s. He is clean-shaven, with smooth, slick-backed brown hair, and greying temples.
“Hello, I’m Dr. Farsi. I’m a genomicist.” He extends his hand.
“Nice to meet you,” I reply, trying to act calm. A real live doctor!
Farsi sits me down in his office. “As Attending IV has explained, your kidneys are showing very early symptoms of disease. We used to call cases like yours ‘idiopathic,’ which means we don’t know what causes it. Nowadays, we have a much better understanding of how disease happens, thanks to advances in genomics.”
I recall the first time I had my genomics done. I was in middle school, and the nurse took a cheek swab from all the students, as part of the Universal Health Care and Security Act.
The doctor waves his hand, and a sequence of As, Ts, Gs, and Cs appears above his desk, floating in the air between us. “This is your DNA. I’ve superimposed it on the most common DNA sequences from millions of individuals. You see this small change, here – your DNA has an A, where most others have a G. It’s in a small stretch of DNA that doesn’t appear to be part of anything special, so when you first had your genomics done, we didn’t make much of it.
“Since that time, there have been a few cases of kidney disease we haven’t been able to clearly explain. These cases are very rare, affecting only a small handful around the planet. Exactly how the kidney is affected differs from individual to individual. But what brings all these cases together is that they all have changes in exactly this same part of the DNA.
“Thanks to you, we now have the strongest evidence yet supporting the role of this sequence in kidney health.”
“Doctor, all this is very interesting. But what does it mean for me practically? Should I be worried?”
Farsi folds his hands. “We’ve caught this early enough that we have several treatment options. I’m going to schedule you to come in for a follow-up visit, where we can evaluate you again. In the meantime, we will use the Urinalyzer sample you provided earlier to grow your own cells. We can perform clinical trials on those cells to determine which treatment will work best for you. The cells might also be used for regenerative therapy.
“Before your next visit, I would also like you to wear one of these.” Farsi brings out a panel of button-sized BioMods in colors ranging from electric blue to cool pink. I pick one that matches my skin tone, remove the adhesive, and fasten it to my waist just above the beltline. It vibrates, and I feel a bit of a tickle.
“Please try to wear this BioMod at all times – even when you are sleeping,” says Farsi. “It will keep track of your kidneys, but won’t intervene unless there’s an emergency. Based on its report, and our cell studies, we can decide what the best long-term treatment option is – and whether genetic correction might be right for you.”
“I got married last year,” I say. “Does this affect our plans to start a family?”
“That’s an excellent question,” says the doctor. He glances at his chart. “I’m happy to report you have nothing to worry about there. Your wife’s DNA makes it impossible for your future children to inherit your condition.”
“Great. That’s a relief,” I say.
“You are free to go and enjoy your birthday,” Farsi says. “Oh, and one last thing…”
“Yes?”
“As you know, this is a research hospital, which means that part of our mission is to improve mankind’s medical knowledge. Would it be OK with you if I shared the results of our discussions today with other physicians around the world as part of a scientific publication? Your identity will be completely protected, of course.”
“Sure, that sounds fine with me.”
The doctor makes a few quick motions on his chart. “There,” he says. “I’ve submitted the case study for publication. It will be reviewed by AI doctors and should be published within a few hours.”
He extends his hand. “It’s very nice to meet you, and thanks for coming in. I will see you in a few weeks.”
We can’t know how much of the above patient-physician visit will be real in 2046. But based on current trends in the field, we believe the treatment of renal disease will change in the following ways in the not-so-distant future.
Stem cell and bioengineering approaches will lead to new sources of autologous tissues for regenerative therapies and precision medicine.
Recent work has begun to unlock the potential of our own cells to generate new patient-matched identical kidney tissues. Although the body has no intrinsic ability to generate new nephrons after birth, it is now possible to derive nephron progenitor cells from patients through cellular reprogramming technologies.1-3 These stem cells can form rudimentary kidney tissues, which will become more architecturally complex and functional as research in this field moves forwards. Since stem cell-derived tissues can be produced from a patient’s own cells, they would be fully accepted by the body without the need for immunosuppressive medication since rejection of one’s own tissue will not occur. In addition, the replacement kidney can be manufactured on-demand, with gene-edited using methods, such as CRISPR/Cas9 and newer versions, to correct disease-causative mutations.3, 4 These major advantages over the existing therapeutic modalities of dialysis and allograft transplant are likely to propel this investigative field forward. Clinical trials for regenerative kidney grafts are being pursued by several groups internationally, and the first of these could happen within the next 10 years. There will be no waiting lists for donor organs since everyone could serve as his own donor by harvesting one’s own stem cells. In addition to transplant, as microphysiological models and organoids grow in sophistication, they will be used increasingly to model personalized drug responses, ultimately leading to ‘clinical trials in a dish’ for the kidney and other organs.3, 5, 6 High-throughput screening methodologies will be applied to gene-edited organoids with disease phenotypes to predict the effect of hundreds of thousands of chemicals or genes on the kidney, leading to the discovery of new therapeutics that target specific syndromes, such as polycystic kidney disease. 3
Genomics data will reveal virtually all of the genes that contribute directly to hereditary kidney disease or predispose them to develop CKD.
We still do not know many of the genes that cause kidney disease or how mutations in these genes affect phenotypes.7-9 This will change over the next decade, as large cohorts of patients have their genomes sequenced. Careful analysis of genetic mutations and inheritance patterns in these patients will reveal the major genes that contribute to hereditary kidney disease and the role of specific mutations in disease progression. We will hopefully understand the additive environmental influence on gene mutation and/or function. Candidate genes identified in genetic studies will be validated functionally using gene-editing techniques in organoids, kidneys-on-chips, and model organisms. 3, 4, 6 As targeted therapeutics and gene-editing approaches enter the clinic, society will move towards a system in which everyone’s disease genes will be sequenced non-invasively in utero to inform treatment strategies. Every several years our genome will be re-sequenced to detect the environmental influence on our genome, including alterations due to disease. We will carry with us the details of our sequenced genomes and medical histories, which will be accessed via credit card-like devices and thumbprints. A large proportion of kidney disease, such as that linked to type 2 diabetes and hypertension, may be more strongly associated with lifestyle choices and environment than with specific genes.10 At the population level, large-scale data sets that track personal habits will be correlated with electronic medical records to identify environmental risk factors for kidney disease.
Molecular diagnostic models for personalized and precision medicine will enter the clinic.
There is an increasing appreciation among physicians that disease is complex and patient-specific, with genetic background and environment playing an important role in the response to treatment. There can be a complete reclassification of kidney disease based upon integration of various “omics” platforms (genomics, transcriptomics, proteomics, metabolomics, lipidomics, glycomics). Over the next decade, transcriptome signatures will demarcate disease phenotypes and predict responsiveness. Patient testing will expand to include ‘big data’ metrics based on quantitative transcriptomic and proteomic assays. 11, 12
Computational analysis will provide a molecular blueprint of each patient’s medical state in unprecedented detail, enabling physicians to predict disease progression and responsiveness with precision. For the kidney, this will likely involve regular monitoring of urinary and serum biomarkers in a combinatorial way.11, 12 A baseline will be established in utero and periodically during periods of stable health. Episodic updates can be part of routine checkups or following a significant illness. The initial course of treatment will be calculated using a formula that adjusts for this baseline imprint together with gender and biometric data and a “second hit” of predicted environmental impact. Therapy will be further adjusted based on direct monitoring of biomarkers and biometrics during follow-up visits. Guesswork for which medication to use will be greatly reduced since metabolism of various medications is genetically determined and there will be automatic dispensing of the proper agent.
In the future, our care will be automated based on algorithms tested by evidence-based broad experience. An ongoing and constantly updated national data set for every specialty will be established, and our on-line record keeping will be automatically uploaded and analyzed for meaningful information. The physician/health care worker/AI will plug in measurable parameters, along with a patient’s symptoms, and a treatment plan will be generated at the visit. These “prescriptions” will be based on evidence-based, heuristical Bayesian algorithms that identify effective treatments based on tens of millions of EHRs. Accountability will be the watchword since the provider will have to use the standardized EHR to be paid.
Our increasing understanding of how the brain stem controls somatic function will lead to improved treatment of hypertension, obesity, heart failure, cardiac arrhythmias, and incontinence.
Many of the brain and brainstem circuits controlling renal excretion, vascular tone, appetite, metabolism, cardiac function, and the lower urinary tract are very similar in both mice and humans. Methods relying on highly selective expression of proteins in specific groups of neurons in specific mouse brain loci permit optogenetic or chemogenetic activation or inhibition of these neurons, direct monitoring of the timing of their activation, and precise mapping of neurons which synapse on them. Using these methods several laboratories are now mapping in fine detail the neural circuits controlling major somatic functions.13 As these maps are completed it will be possible to insert electrodes with “pacemaker” type generators into discrete sets of neurons in the human brain, and to use these to modulate the activities of these control circuits. These approaches are now being used effectively in treatment of patients with Parkinson’s disease and Tourette syndrome.14 Once we have worked out the circuit maps of the relevant controlling circuits, it should be possible to apply these techniques to modulation of blood pressure (by dialing up salt excretion or dialing down vascular tone), reduction of brainstem stimulation to arrhythmogenic centers of the heart, and, in obese patients, reducing appetite without the need for surgical alteration of the stomach.
Conventional dialysis will be replaced by mobile and bioartificial technologies.
The digital world is increasingly merging with the biological. We are becoming enhanced human beings, surrounded by sensors that monitor our activities and guide our choices with every step. As mobile technologies improve, dialysis as it is known at the turn of this century will become relegated to the History of Medicine section. Progressively miniaturized devices will be quickly employed when temporary metabolic support is needed. A wearable or implantable artificial kidney device will automatically sense the appropriate metabolic parameters to be achieved and needed fluid removal, adjustments based on muscle mass and bone density. Infused electrolytes will maximize tissue repletion.15, 16 Mobile sensors that constantly monitor patient food intake and personal habits will further inform and refine artificial kidney activity in real time. This 24-hour system will facilitate a “near normal” existence, with improved stability of blood volume and composition. Only minimal maintenance will be required, such as draining the reservoir episodically and replenishing the recycled fluid. Bioartificial kidney devices will increasingly integrate 3D-printed tissues and synthetic smart materials will mimic kidney design and function. Conventional dialysis with be a thing of the past, resulting in improved health and quality of life for patients on renal replacement therapy.
Health care reform in the U.S. will continue to change the practice of medicine.
As populations age, health care costs rise, and national debt balloons. Hence, public pressure, and subsequently political pressure, will steadily increase for more efficient and sustainable health care systems. In the United States, this will be a long and politically charged process, based on experiences to date. Nevertheless, 30 years from now a national single payer system will be such an integral part of health care financing, few will remember there was any previous system. Physicians will all be on salary, and this will have eliminated adverse incentives to do more for and to patients. Our healthcare system costs will be reduced because of the change in payment and financing, and legal reform that greatly limits the need for expensive malpractice insurance.
These financial changes will be coupled with incentives for the population to improve their own health. Funding for the societal impact on health (i.e., housing, nutrition) will be incorporated into the health care budget. Until a sustainable self-donation system is fully functional, there will be a push by public officials to increase donors. As an interim solution, organ donation will change from opt-in to opt-out. 17
Increasing awareness of the role that lifestyle and environment play in major causes of renal disease, such as diabetes, will result in more comprehensive warning labels appearing on popular food products. Enhanced use of biomedical devices, genetic data, and effective therapeutics will bring these topic into the forefront of public attention and discourse. This will provide an opportunity for society to expand federal budgets dedicated to productive and potentially life-saving research.
Simultaneously, privately held companies in the pharmaceutical and biotechnology industries will grow in size and influence, becoming household names and increasingly partnering with academic institutions to bring more cures to market.
Summary
The coming decades will see enormous changes in how kidney disease is diagnosed and treated. We can only predict a small proportion of the discoveries that will catalyze these changes. It is exhilarating to imagine how such discoveries might soon translate into improved medical care for millions of people.
References
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Takasato M, et al. Kidney organoids from human iPS cells contain multiple lineages and model human nephrogenesis. Nature 526, 564-568 (2015).
Freedman BS, et al. Modelling kidney disease with CRISPR-mutant kidney organoids derived from human pluripotent epiblast spheroids. Nat Commun 6, 8715 (2015).
Jinek M, et al. A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity. Science 337, 816-821 (2012).
Huang L, et al. Ductal pancreatic cancer modeling and drug screening using human pluripotent stem cell and patient-derived tumor organoids. Nature Medicine 21, 1364-1371 (2015).
Weber E, et al. Development of a microphysiological model of human kidney proximal tubule function. Kidney Int. (in revision) (2016).
Sadowski CE, et al. A single-gene cause in 29.5% of cases of steroid-resistant nephrotic syndrome. Journal of the American Society of Nephrology 26, 1279-1289 (2015).
Grgic I, et al. Discovery of new glomerular disease-relevant genes by translational profiling of podocytes in vivo. Kidney International 86, 1116-1129 (2014).
Heyer CM, et al. Predicted mutation strength of nontruncating PKD1 mutations aids genotype-phenotype correlations in autosomal dominant polycystic kidney disease. Journal of the American Society of Nephrology (2016).
Fuchsberger C, et al. The genetic architecture of type 2 diabetes. Nature 536, 41-47 (2016).
Craciun FL, et al. RNA sequencing identifies novel translational biomarkers of kidney fibrosis. Journal of the American Society of Nephrology 27, 1702-1713 (2016).
Ju W, et al. Tissue transcriptome-driven identification of epidermal growth factor as a chronic kidney disease biomarker. Science Translational Medicine 7, 316ra193 (2015).
Rajasethupathy P, Ferenczi E, Deisseroth K. Targeting neural circuits. Cell 165, 524-534 (2016).
Hickey P, Stacy M. Deep brain stimulation: A paradigm shifting approach to treat Parkinson’s Disease. Front Neurosci 10, 173 (2016).
Gura V, et al. A wearable artificial kidney for patients with end-stage renal disease. JCI Insight 1 (2016).
Fissell WH, et al. High-performance silicon nanopore hemofiltration membranes. Journal of Membrane Science 326, 58-63 (2009).
Shepherd L, O’Carroll RE, Ferguson E. An international comparison of deceased and living organ donation/transplant rates in opt-in and opt-out systems: a panel study. BMC Medicine 12, 131 (2014).
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