#NephMadness 2020: Transplantation Region

Submit your picks! | NephMadness 2020 | #NephMadness | #TransplantRegion

Selection Committee Member: Vinay Nair @vinaynairdo

Vinay Nair is an Associate Professor of Medicine at the Donald and Barbara Zucker School of Medicine at Hofstra and the medical director of kidney transplantation for Northwell Health. His clinical and research interests include transplanting highly sensitized patients, immunosuppressive protocols, immune monitoring, and infectious and malignant complications after transplantation.


Writer: Adrian Whelan @AdrianWhelan6

Adrian Whelan is a nephrology fellow at the University of California, San Francisco. He was one of the inaugural class of AJKD Editorial Interns in 2018 and is interested in studying access to kidney transplantation.

Competitors for the Transplantation Region

Biomarkers in Rejection vs Biopsy in Rejection

Marijuana is OK vs Marijuana is Not OK in Transplantation

After a one-year hiatus, kidney transplantation is back in the tournament and hoping for better luck than in 2018 (but don’t forget that kidney transplantation was the inaugural NephMadness champion in 2013). This year, we present four teams with two common goals: to improve outcomes for patients who receive a kidney transplant, and to once again wear that NephMadness crown. 

Biomarkers in Rejection vs Biopsy in Rejection

Current clinical practice guidelines recommend monitoring kidney allograft function with serum creatinine and proteinuria measurements at regular intervals following transplantation. When kidney function declines, an allograft biopsy is typically performed to diagnose or rule out rejection and other causes of allograft dysfunction. However, monitoring specifically for rejection with serum creatinine and proteinuria can be problematic. Serum creatinine has both suboptimal sensitivity and specificity for allograft rejection. The poor sensitivity is evidenced by the phenomenon of subclinical rejection, whereby rejection is detected on protocol biopsy but without an increase in serum creatinine. Proteinuria is also sub-optimal for detecting rejection as it has been reported that proteinuria levels do not differ statistically between patients with normal histology and those with acute rejection on one-year protocol biopsy. Even the current gold standard of allograft biopsy can suffer from sampling error and is considered imperfect for detecting rejection. 

Adapted Figure 1 from Amer et al


 

Biomarkers in Rejection

There are big issues with the current tests and gaps to be filled in terms of how we monitor for and diagnose rejection. We greatly need a non-invasive biomarker that can detect rejection at an early stage with both high sensitivity and specificity. There are several candidates that are currently in several stages of development. What are the most promising biomarkers? Are they close to prime time? Team Biomarkers has been training hard for years and is ready to make its tournament debut. Let’s see if the players below are up for the challenge.

Urine Chemokines
Chemokines are a family of chemoattractant cytokines, or small proteins that induce cell movement, typically of immune cells, through the process of chemotaxis. Two urinary chemokines,(C-X-C motif ligand 9) CXCL9 and CXCL10, have been evaluated as biomarkers for kidney allograft rejection.

Hricik et al reported the performance of urinary CXCL9 protein as a non-invasive marker of acute rejection in a multi-center observational study of 280 adult and pediatric patients following first kidney transplant. Urinary levels of CXCL9 protein were associated with rejection when checked at the time of for-cause biopsies (Area Under the Receiver Operating Characteristic, AUROC, curve value of 0.856 for discriminating acute rejection). Furthermore, among those who had an episode of acute rejection, CXCL9 levels were elevated up to 30 days prior to clinical recognition of graft dysfunction, and so CXCL9 may be useful as a marker even before allograft function declines. The authors also demonstrated the utility of measuring urinary CXCL9 protein level during clinical quiescence to risk stratify patients in terms of future rejection. In fact, none of the patients in the study with low urinary CXCL9 values at 6 months developed rejection between 6 and 24 months.

Visual Abstract by @krithicism on Hricik et al

A study by Jackson et al showed similar results. Urinary levels of CXCL9 and CXCL10 were markedly elevated in patients with either clinical or subclinical acute rejection or BK infection, but not in recipients with stable allograft function, CNI toxicity, or interstitial fibrosis and tubular atrophy (IFTA).

In both the Hricik et al and Jackson et al studies, CXCL9 performed modestly better than CXCL10. One issue, however, is that levels of both chemokines were elevated in the urine when there was BK virus infection. In the Hricik et al study, there was no significant difference in CXCL9 levels between the patients with infection and those with suspicious biopsy findings, although there was a difference when compared to those with acute rejection. In the Jackson et al study, neither chemokine could distinguish between acute rejection and BK infection.  

These urinary chemokines have great potential to detect allograft inflammation but are limited in terms of distinguishing whether this is due to rejection, BK virus, or other viral infections leading to kidney inflammation. To use them as a single test for rejection may be problematic, especially as rejection and BK virus infection are managed in conflicting manners (increasing or decreasing immunosuppression). However, a simple BK virus PCR test in the serum can rule out polyomavirus nephropathy leading to rejection as the most likely etiology. Combining these chemokines with other immunologic markers, such as donor-specific antibodies, may also improve their ability to diagnose rejection. Overall, they could also direct us to patients that we should follow more closely and in whom we should consider a diagnosis of rejection or BK infection.

Donor-Derived Cell-Free DNA (dd-cf DNA)
Donor-derived cell-free DNA (dd-cf DNA), DNA of donor origin in the blood of the transplant recipient, has been proposed as a biomarker indicating damage to the allograft and rejection. dd-cf DNA is typically less than 1% of the total cell-free DNA when there is no active damage to the allograft. The test quantifies the proportion of total cell-free DNA that is derived from the donor and the recipient using targeted amplification and sequencing of single-nucleotide polymorphisms, and does not require prior genotyping of either the donor or recipient.

Bloom et al reported the test characteristics of dd-cf DNA to detect rejection using 107 clinically indicated kidney biopsies with paired blood samples for dd-cf DNA. The median dd-cf DNA levels were higher in antibody mediated rejection (ABMR) (2.9%) and T cell-mediated types ≥1B (1.2%) compared to no rejection (0.3%). The AUROC curve for discriminating between any rejection and no rejection was 0.74 (95% CI 0.61-0.86). One very important caveat to this study is that it only included a small number of protocol biopsies. These reported test characteristics therefore do not apply to detecting subclinical rejection (SCR) in patients with stable allograft function.

Visual Abstract by @Errantnephron on Bloom et al

Additional studies have shown mixed results. Huang et al found that for ABMR, the AUC was 0.82 (95% CI: 0.71-0.93) and a dd-cf DNA ≥0.74% yielded a sensitivity of 100%, specificity 71.8%. However the dd-cf DNA test did not discriminate cell-mediated rejection from no rejection among kidney transplant recipients. Gielis et al assessed 107 kidney transplant recipients and collected blood for dd-cf DNA at 10 timepoints over the first 3 months after transplantation, and at times of creatinine rise, or protocol or indication biopsy. Increases in dd-cf DNA were associated with episodes of acute rejection, acute tubular necrosis, and acute pyelonephritis. However, it performed no better than serum creatinine in diagnosing acute rejection.

The Bloom et al and Gielis et al studies had different findings with regard to the effect of BK virus infection on dd-cf DNA levels. In the Bloom et al study, BK virus was present on biopsy in 2 cases, both of which had elevated dd-cf DNA (serum BK viral load was >2 million copies/ml in both cases). The Gielis et al study had 12 cases of BK infection (which they defined as a progressive rise in BK viral load requiring an adjustment of maintenance immunosuppressive treatment) and did not find an association between BK infection and dd-cf DNA levels. These observations seem to suggest that the inflammation associated with severe BK infection can elevate dd-cf DNA levels, but this does not seem to occur in milder BK infections. 

Finally, Sigdel et al reported an alternative dd-cf DNA method that uses a single nucleotide polymorphism-based massively multiplex polymerase chain reaction (mmPCR). They looked at a total of 300 plasma samples (217 biopsy-matched: 38 with active rejection, 72 borderline rejection, 82 with stable allografts, and 25 with other injury) that were collected from 193 kidney transplant patients including those receiving protocol biopsies.  dd-cf DNA was processed by mmPCR targeting 13,392 SNPs. The test was able to discriminate rejection from non-rejection with an AUROC curve of 0.87. Unlike the other dd-cf DNA technology, the test was able to distinguish cell- and antibody-mediated rejection from no rejection.  

dd-cf DNA is clearly a marker of allograft damage and rejection. Both tests have been approved for monitoring patients with a functional kidney transplant for rejection in routine clinical settings and are reimbursed by Medicare. Whether and how best to incorporate dd-cf DNA as a non-invasive marker in the care of patients following transplantation remains an area of active debate. 

Blood and Urine mRNA
Blood and urine messenger ribonucleic acid (mRNA) testing is another potential avenue for non-invasive markers of rejection. The presence of specific mRNA in blood or urine can indicate genes that are active during an episode of rejection that would not otherwise be active. Many of these individual mRNA tests, such as granzyme B or perforin mRNA, have performed inconsistently in previous studies. An additional limitation of the urine mRNA tests is that RNA is not stable in urine. Blood multi-gene mRNA signatures that combine several mRNA tests have been developed and perform better than tests of single-gene mRNA products, while also circumventing the stability issues of urine mRNA tests. These multi-gene mRNA signatures can be considered as representing a profile of active genes that allow patients to be classified as having rejection or not.

Kurian et al studied 148 patients with biopsy-proven graft phenotypes including 63 with biopsy-proven acute rejection, 39 with graft dysfunction without rejection, and 46 with excellent function with normal histology. Notably, patients with BK virus, other infections, or recurrent kidney disease were excluded from the study as it included only biopsy-proven, unequivocal phenotypes. They used global RNA expression of peripheral blood to identify 200 high-value probesets (short nucleic acid sequences corresponding to the nucleotide sequences) associated with each of the phenotypes. In their validation cohort, they reported an AUROC curve of 0.885 for distinguishing between biopsy-proven acute rejection and normal histology.

The non-invasive Kidney Solid Organ Response Test (kSORT) also uses gene expression to detect kidney transplant patients at high risk of acute rejection. It was developed by analyzing blood samples from a large cohort of patients with kidney transplants to identify a set of 17 genes that could be used to classify acute rejection. Based on the patient’s gene expression for the 17 genes, a novel algorithm assigns an acute rejection risk score corresponding to high, intermediate, or low risk. kSORT indicated the risk and prevalence of acute rejection with an AUROC curve of 0.92 (95% CI 0.86-0.98). The score is also predictive of acute rejection when graft function is stable. Among patients who had biopsy proven acute rejection, a high-risk score occurred up to 3 months prior to the biopsy diagnosis and preceded any decline in graft function.

More recently, Murphy et al reported the utility of peripheral blood RNA sequencing at 3-months to diagnose subclinical or borderline acute rejection among a cohort of patients undergoing protocol biopsy at the same time. They identified a set of 17 genes that could be used to detect ongoing subclinical rejection. They also validated the test in an independent cohort of 110 transplant recipients where it had a positive predictive value of 0.73, and a negative predictive value of 0.89 for detecting subclinical or borderline acute rejection. 

Interferon-𝝲 ELISPOT
The last NephMadness appearance of Interferon-𝝲 ELISPOT was 2016. What progress have we seen in 4 years? The alloreactive T-cell interferon-𝝲 enzyme-linked immunosorbent spot (IFN-𝝲 ELISPOT) assay has also been proposed as a method to non-invasively detect subclinical rejection. The test quantifies post transplant donor-specific alloreactive T-cells. Crespo et al showed that in patients with stable allograft function, IFN-𝝲 ELISPOT could be used to non-invasively differentiate patients with subclinical T-cell mediated rejection at 6-months post-transplantation. A negative IFN-𝝲 ELISPOT had an odds ratio of 0.07 (95% CI 0.01 to 0.65) for the presence of subclinical T-cell mediated rejection. Furthermore, a positive IFN-𝝲 ELISPOT was associated with subsequent donor-specific antibodies and worse allograft function at 2-years post-transplant. Therefore, the IFN-𝝲 ELISPOT assay could be useful in stratification of patients with stable function in terms of their risk of sub-clinical rejection and future allograft function. 

These biomarkers explore novel ways to detect allograft rejection non-invasively. There are a host of other candidate biomarkers under development, and warming the bench, beyond those mentioned here. The appeal of a biomarker that could detect rejection non-invasively months before it causes allograft dysfunction is clear. Though they have the potential to improve allograft outcomes, patient safety, and lower healthcare costs, questions remain concerning how to best use them clinically and whether they can take Team Biopsy out of the running. 


 

Biopsy in Rejection

Though it doesn’t show off as much razzle dazzle as its opponent Team Biomarker, there are few diagnostic tests that transplant nephrologists rely on more heavily than the allograft biopsy. Let’s take a closer look at this established, powerhouse of a team. 

The allograft biopsy is an invasive test associated with both complications and significant healthcare costs. However, the rate of serious complications is low and information gained can be invaluable. We should consider kidney biopsy and any techniques to optimize its interpretation as valuable tools to help us maximize graft survival, which is of particular importance in the current climate of scarce organ availability. Maximizing graft survival will reduce the number of patients requiring subsequent transplants and will thereby help reduce the excessive demand for transplants while also improving quality of life for patients. 

The Achilles’ heels that have plagued this team are interpretation and sampling. Currently, diagnosis of rejection relies on the interpretation of kidney biopsy histology by expert pathologists and the application of Banff consensus criteria. However, this can be a labor-intensive process and disagreement between pathologists in terms of pathological score and the presence of rejection have been well-described, in particular in ambiguous or non-specific cases. Furthermore, samples occasionally may be of insufficient  size to allow adequate assessment by current standard techniques. Consequently, methods that require less tissue may be advantageous.

Let’s look at a few energetic, young Team Biopsy recruits that have been training hard to push our evaluation of the kidney allograft biopsy into the 21st century. They might even land this team a spot in the coveted Filtered Four!

Microarray
Microarray (also known as the “molecular microscope”) relies on molecular assessment of the kidney allograft biopsy sample. The molecular assessment can be of RNA, proteins, or other metabolites. Microarrays which assess tissue RNA are of particular utility, as RNA can be amplified and can provide more mechanistic insights than the other molecules. 

Using an RNA microarray technique, Sarwal et al demonstrated that allografts with acute rejection that were indistinguishable on standard histological evaluation could have extensive differences in gene expression, and that these differences in gene expression were associated with clinical course and prognosis. Subsequently, Halloran et al, Reeve et al, and colleagues developed molecular patterns or “classifiers” within the allograft specimen for both antibody- and T-cell-mediated rejection. Halloran et al went on to develop the Molecular Microscope Diagnostic System (MMDx) which uses an RNA microarray method to diagnose rejection within an allograft specimen. The technique involves extracting RNA from the allograft tissue, processing and generating raw RNA expression data output, and then comparing this output to a reference set of samples to generate a report. The report-generating software uses machine learning-derived algorithms to maximize the predictive ability to classify the disease state. 

Potential benefits of microarray in the diagnosis and management of allograft rejection include diagnosing rejection in the absence of typical histological criteria, diagnosing ABMR in the absence of C4d positivity, and improving reproducibility and prognostication. Furthermore, there may be fewer issues with inadequate sampling of the allograft, as microarray may require less tissue, does not require glomeruli or arteries to diagnose rejection, and it can “read the medulla”(meaning it performs similarly in the cortex and medulla).  

Aside from rejection, the microarray approach can also give information about interstitial fibrosis/tubular atrophy (IFTA) and the future survival of the allograft. The MMDx report provides probabilities of IFTA and progression to graft failure. Separately, O’Connell et al also showed that microarray techniques can be used to identify patients at risk of developing allograft fibrosis in a prospective cohort. They used microarray on biopsy samples of stable allografts at 3 months following transplantation to identify a 13-gene set that was associated with the development of fibrosis at 12 months. Among the subgroup with normal histology at 3 months, the gene set could discriminate between transplants at high and low risk for later fibrosis. Accurate identification of patients who are at high risk for the development of fibrosis but in whom fibrosis has not yet developed could be a target for future therapeutic interventions to prevent irreversible graft damage and fibrosis. 

Visual Abstract by @whatsthegfr on O’Connell et al

Computer-Assisted and Machine Learning Techniques
As mentioned above, current methods for allograft biopsy interpretation are both labor intensive and have interobserver disagreement. There have been several recent publications reporting how computerized techniques can be used to interpret the allograft specimen, which may have the potential to mitigate these issues. 

Aguado-Dominguez et al used a computer-assisted technique to identify and quantify immune cells within the kidney allograft biopsy during rejection. However, the ability of this technique to classify patients based on the Banff criteria was limited, as the cellular composition varied significantly among individuals within the same Banff diagnostic category. 

Sicard et al used a computerized technique termed “Computer-assisted Analysis of Graft Inflammation” (CAGI) to improve the classification of allograft inflammation in antibody-mediated rejection. The technique involves the digitization of stained biopsy specimen, image processing, and algorithm-driven analysis to quantify the various immune cell types in the interstitium, capillaries, and glomeruli. When combined with the C3d binding ability of donor-specific antibodies, CAGI improved risk stratification of graft outcomes including graft failure in patients with antibody-medication rejection. 

Hermsen et al used neural networks, a machine learning technique, to classify kidney biopsies. Their main goal was to use an algorithm that could segment a PAS-stained kidney biopsy image into the various tissue types including glomeruli, tubules, and interstitium. The algorithm was trained using allograft biopsies and then tested in both allograft and nephrectomy samples. The neural network was able to distinguish between the various tissue types and performed best for detecting glomeruli. It detected 93% of glomeruli in the biopsies analyzed and could differentiate between healthy, segmentally sclerotic, and globally sclerotic glomeruli, as well as between proximal and distal tubules. They also compared the algorithm’s quantification of tissue types to components of the Banff classification scored by pathologists using whole slide images. There was a correlation between the algorithm assessment of the interstitium and the ci and ti scores assigned by three pathologists (the Spearman correlation coefficients were 0.55 and 0.71, respectively). Overall, the algorithm could distinguish between various tissue types on PAS-stained allograft biopsy images, and its quantifications correlated with those of pathologists using Banff classification. The benefits of the algorithm include reproducibility of biopsy interpretation and aid for pathologists in tedious tasks such as counting glomeruli and visual estimation of interstitial fibrosis and tubular atrophy. 

Visual Abstract by @Errantnephron on Hermsen et al

These techniques to enhance evaluation of the allograft biopsy are exciting and could potentially have a great impact in the management of kidney transplant recipients with the ultimate aim of improving allograft and patient survival. They are a force to be reckoned with in this year’s NephMadness!

 

Marijuana is OK vs Marijuana is Not OK in Transplantation

Marijuana is not just common; it is now a multi-billion dollar industry in the United States. Thirty-three states permit medicinal use, while 11 states and Washington, DC, have legalized recreational use of marijuana, and that number is growing. The industry is huge, with legal and illegal sales estimated to be over $50 billion in North America in 2016. That’s more than sales of wine and many other common commodities!

With public acceptance and support for marijuana legalization at an all-time high, we are going to see more and more marijuana use, both medicinal and recreational, in our transplant clinics. Marijuana’s ubiquity presents specific challenges and questions with regard to the field of transplantation and how we assess and manage both recipients and donors who use marijuana. Making its tournament debut, is marijuana this year’s dark horse? 

For those of us who do not know the difference between a blunt, bong, or bowl, now is a good time to highlight some of the background and specific terminology with regard to marijuana. Marijuana refers to the dried flowers of the Cannabis sativa plant, and has a large number of colloquial terms including weed, herb, pot, grass, bud, ganja, and Mary Jane (to name just a few). The primary method of use is smoking, which can be achieved with a variety of equipment including joints (cigarette papers), bowls (glass pipes), bongs (water pipes), blunts (tobacco-leaf wrapping from cigars), and vaporizers (similar to nicotine-oil containing e-cigarettes, though marijuana vaporizers heat dried cannabis or concentrates to a temperature just below its combustion point). However, there are alternative methods of consumption involving the extraction of chemicals to make edible products (such as baked goods and candies), topical creams, sublingual tinctures, etc.

The main psychoactive chemical in marijuana is delta-9-(trans)tetrahydrocannabinol (THC). THC exerts its effects through cannabinoid (CB) receptors in the brain that are normally activated by endogenous neurotransmitters (such as anandamide) as part of the endocannabinoid system. CB receptors are membrane bound G-protein coupled receptors (GPCR). Marijuana can produce a variety of effects on mood, perception, and cognitive/psychomotor performance. The main effect is to produce euphoria with decreased anxiety, alertness, and depression, although it also has the potential to produce dysphoria with severe anxiety, panic, paranoia, and psychosis. The effects on perception can include colors and music seeming more vivid, disruption of time perception (where perceived time goes faster than reality), and even hallucinations. Apart from THC, the Cannabis plant contains over 500 other chemicals, including more than 100 that are chemically related to THC (termed cannabinoids). These chemicals can vary widely in terms of their psychoactive effects (some having no psychoactive effect) as well as their likelihood to interact with medications.

It is worth noting that while marijuana for medicinal indications has been legalized in many states, “medical marijuana” has not been approved by the US Food and Drug Administration (FDA) for any indication. However, several cannabinoid medications have been approved. For example, dronabinol, an orally active cannabinoid, is used to treat chemotherapy-induced nausea and vomiting, as well as to stimulate appetite for anorexia in patients with AIDS. Cannabidiol (commonly called CBD), a cannabinoid without psychoactive effects, was recently approved by the FDA to treat two rare and severe forms of epilepsy (Lennox-Gastaut syndrome and Dravet syndrome), and is the first approved drug to be derived directly from the Cannabis sativa plant. Outside of the United States, nabiximols, an oromucosal spray preparation containing a standardized extract of THC in addition to CBD and other minor cannabinoids, has been approved for treatment of pain and muscle spasticity due to multiple sclerosis.

As in the kidney transplantation world, major US sports organizations also have not reached a consensus on marijuana use policies. Let’s take a look at the pros and cons in kidney transplantation. 


 

Marijuana is OK in Transplantation

We should all relax about this. Marijuana is now legal in many states and many of our patients with kidney disease are already using it, both before and after transplantation. Marijuana may not only be reasonably safe but could provide benefits to our patients with advanced kidney disease. The prevalence of anxiety and depression in patients on dialysis can be as high as 53% and 42%, respectively, symptoms which may improve during the euphoria of marijuana use. Marijuana could help some patients cope with the burden of kidney disease. Furthermore, anxiety and pain experienced by patients are likely to increase around the time of transplantation, a period when the effects of marijuana could offer greater benefit. Clinical studies have shown efficacy of cannabinoids in the treatment of symptoms including insomnia, anorexia, pruritus, nausea, and vomiting. 

Aside from the effects on mood and other symptoms, studies have demonstrated that cannabinoids could have a direct mechanistic role to prevent organ rejection. While CB1 receptors are expressed primarily in the brain and mediate the neurological effects of marijuana, CB2 receptors are expressed at high density in immune cells. CB receptor agonism is an active area of immunological research with the potential to be applied to transplant immunology. Activation of CB receptors reduces T-cell proliferation and activation and has been shown to have an anti-inflammatory role. CB receptor activation is effective in attenuating the severity of autoimmune illnesses in animal models, including models of multiple sclerosis, autoimmune hepatitis, and rheumatoid arthritis. Additionally, activation of CB receptors reduces host versus graft responses in animal models of allograft rejection.

In the clinical setting, overall consensus is currently lacking with regard to screening for marijuana use and whether patients who use marijuana should be approved for transplantation. This was highlighted in a recent survey of members of the American Society of Transplantation (AST) that included providers involved in all types of transplantation, not just kidney. With regard to screening of transplant candidates, 55% of survey respondents reported that their center screens all transplant candidates for marijuana use, while 20% reported that screening depended on the organ being transplanted, and 20% reported not screening. There were also significant variations in approval policies. 28% of respondents reported rejection of all active marijuana-using candidates regardless of the type of organ transplant and 52% reported that approval varied depending on the type of organ transplant planned.

Acute intoxication with marijuana could affect adherence to the crucial immunosuppressant medication regimen following transplantation through impairing alertness, attention, and perception, including perception of time. However, we should also consider that the risks associated with chronic, non-intoxicated use are likely to be a lot lower, as tolerance develops through changes in the associated biological pathways. Assessing the pattern of marijuana use and appropriate risk stratification may be more pragmatic than excluding all marijuana users from transplantation.

While there is somewhat limited evidence examining any association between marijuana use and outcomes following transplantation, Greenan et al reported the results of a retrospective review of 1,225 kidney transplant recipients at a single center in the United States. The investigators identified marijuana use prior to transplantation through review of the health record, defining use based on patient report or positive urine drug screen for marijuana. There were 56 marijuana users in the study. Marijuana use was not associated with allograft failure or death at one year and allograft function was similar at one year post-transplantation.

Fabbri et al reported a further single-center study of 919 transplant recipients that included 48 marijuana users. Marijuana use was not associated with biopsy-proven rejection at one year in adjusted analyses compared to the reference group of non-users. There was also no difference in graft loss between marijuana users and non-users over follow up of 10 years.

Adapted Figure 1 from Fabbri et al

Alhamad et al recently published the results of a study examining the association between pre-transplant cannabis dependence or abuse (CDOA) and post-transplantation outcomes using Medicare data. Of 52,689 patients undergoing kidney transplantation, 254 were diagnosed with CDOA in the year before transplantation. They reported that CDOA in the year before transplantation was not associated with graft failure or death in the year following transplantation, although it was associated with psychosocial problems, use of other substances, and non-adherence. 

Another single-center study by Ruckle et al reported that long-term kidney allograft function in 230 recipients of living donor transplant was no different between the 27 patients that received a kidney from a marijuana-using donor compared to the 203 from a non-marijuana-using donor. Furthermore, we may expect that the safety of marijuana, including its potential to interact with medications such as transplant immunosuppression, is likely to improve with legalization as the preparations become more standardized as a result of regulation of the industry.

Visual Abstract by @whatsthegfr on Ruckle et al

We should also be aware of the legal protections that users of medical marijuana have with regard to access to transplantation. Seven states that permit medical marijuana have laws that specifically prohibit transplant centers from denying transplantation to medical marijuana users solely based on their use of the drug.

The question also arises of whether marijuana-using kidney donor candidates should be approved to proceed to living donation. With the current shortage of organs available for transplantation, we need to consider donations from all available sources, including marijuana-users. The Organ Procurement and Transplantation Network (OPTN) policy and KDIGO living kidney donor guideline recommend assessing for substance abuse or dependence in living donor candidates but do not specifically advise whether candidates that use marijuana should be approved to donate or not. The above manuscript by Ruckle et al also reported the outcomes of the marijuana-using donors. There was no difference in post-donation kidney function between 31 marijuana-using donors and 263 non-marijuana-using donors.

Overall, we can consider that marijuana may offer patients with kidney disease symptomatic benefits, is safe in terms of allograft function (provided acute intoxication is avoided and medication adherence is maintained), and that we should not exclude patients from transplantation provided we incorporate their marijuana use into our overall assessment of suitability for kidney transplantation. We should also consider approving marijuana-users as kidney donors to expand the donor pool.


 

Marijuana is Not OK in Transplantation

Wait a minute, this sounds like a technical foul and may even merit an ejection! Kidney disease is undoubtedly a huge burden for patients and comes with many severe and troubling symptoms, some of which may improve with marijuana. Despite this, we need to accept that there are many other more widely studied and approved medical treatments for symptoms such as anxiety, depression, or pain.

If a patient “self-medicates” with recreational marijuana rather than seeking out more conventional therapies with their care team, then shouldn’t this raise some concerns about the risk of non-adherence with the complicated medication regimen and the patient’s likelihood to seek medical advice following transplantation? We should also consider the differences between marijuana use as a coping mechanism for mental health issues or pain and purely “recreational” use.

Marijuana potency has steadily increased over the last 20 years, reflecting changes in cultivation techniques, and consequently poses greater risks than ever before. The average percentage of THC of illicit cannabis products seized by the Drug Enforcement Agency has increased three-fold from 4% in 1995 to 12% in 2014. Marijuana use can also lead to “cannabis use disorder” and in some cases even addiction. Cannabis use disorder is diagnosed when cannabis use leads to clinically significant impairment or distress as manifested by at least two of the following:

  • Cannabis is taken in larger amounts or over a longer period than was intended.
  • Persistent desire or unsuccessful efforts to cut down or control cannabis use.
  • A great deal of time is spent in activities necessary to obtain cannabis, use cannabis, or recover from its effects.
  • Craving, or a strong desire or urge to use cannabis.
  • Recurrent cannabis use resulting in failure to fulfill major role obligations at work, school, or home. 
  • Continued cannabis use despite having persistent or recurrent social or interpersonal problems caused or exacerbated by the effects of cannabis.
  • Important social, occupational, or recreational activities are given up or reduced because of cannabis use. 
  • Recurrent cannabis use in situations in which it is physically hazardous.
  • Cannabis use is continued despite knowledge of having a persistent or recurrent physical or psychological problem that is likely to have been caused or exacerbated by cannabis.
  • Tolerance (need for markedly increased amounts of cannabis to achieve intoxication or desired effect, or markedly diminished effect with continued use of the same amount of cannabis).
  • Withdrawal (withdrawal syndrome, or cannabis is taken to relieve or avoid withdrawal symptoms). 

It has been estimated that approximately 30% of marijuana users in the US have a prevalent “cannabis use disorder”. A common myth is that there is no withdrawal syndrome after the cessation of cannabis use. However, cannabis withdrawal can develop if there is a cessation or substantial reduction in marijuana intake, and may be of particular concern in the setting of kidney transplantation when marijuana intake is likely to be interrupted. Typical symptoms include irritability, aggression, anxiety, sleep difficulty, depressed mood, and even tremors, sweating, and headaches. Withdrawal symptoms typically have onset within 24-72 hours and can last as long as 2 weeks. Also concerning is marijuana’s role as a “gateway drug”, where its use may precede use of illicit substances. While this is an area of some controversy, one study estimated a cumulative probability of illicit drug use 10 years after the onset of cannabis use to be 36% in the United States.

Could the munchies slow this team down? Data on dietary intake and nutritional status from the Third National Health and Nutrition Examination Survey (NHANES III) found that dietary intake was higher among current marijuana uses, though body mass index (BMI) was slightly lower. The study also reported higher cigarette-smoking rates and consumption of sodas and alcohol (specifically beer) among marijuana users, as well as fewer fruits and more sodium, pork, cheese, and salty snacks. Serum carotenoid levels were lower among marijuana users,  which may be associated with increased cardiovascular risk. Further, smoking marijuana carries some of the same cardiovascular risk as smoking tobacco        

Concern about medication non-adherence following kidney transplantation is particularly important as it is strongly associated with development of de novo donor-specific antibodies, as well as late rejection, graft dysfunction, and loss. Marijuana use has been described to impair executive function, attention, and concentration, even after several weeks of abstinence. We therefore can anticipate that medication non-adherence would be more likely to occur in marijuana users than non-users, even at times of abstinence. A further concern is “cannabinoid hyperemesis syndrome”, a cyclical vomiting syndrome associated with regular cannabis use, which could interrupt the vital immunosuppression regimen.

With regard to maintaining appropriate immunosuppression levels following kidney transplantation, we also need to consider the potential for interactions between marijuana and immunosuppressant medication. Cannabidiol is an inhibitor of the cytochrome P450 3A and could result in reduced calcineurin-inhibitor metabolism and consequently elevated levels. A recent case report demonstrated a three-fold increase in tacrolimus levels following administration of cannabidiol in a clinical trial setting. Also concerning are the many other chemicals in marijuana that have varying effects on cytochromes. This is further compounded by reports that only 17% of commercially available marijuana products are accurately labelled in terms of their THC and CBD content. Therefore, even despite legalization the content of marijuana products and their expected interactions with immunosuppression and other medications are unpredictable.

Marijuana use may also increase infection risk. In the survey of AST members, 43% of respondents reported observing fungal infections in transplant recipients in clinical practice that they associated with marijuana use. Pulmonary aspergillosis in the early post-transplant period attributed to marijuana use has been case reported. However, the overall evidence regarding risk of fungal pneumonia due to marijuana is limited. The Fabbri et al study described above reported no episodes of fungal pneumonia within one year of transplantation among marijuana users, although it occurred in 1.5% of recipients using tobacco and 0.6% of recipients using neither marijuana nor tobacco.

The evidence for an association between marijuana use and outcomes following transplantation is far from definitive. The studies by Greenan et al, Fabbri et al, and Ruckle et al described above provide some evidence, but have limitations in that they are all single-center with relatively small numbers of marijuana users, used retrospective reviews of medical records to collect data and, with the exception of the Fabbri et al study, had relatively short follow up (1 year for Greenan et al, and 5.2 years for Ruckle et al).

While the study by Alhamad et al was multicenter and showed that CDOA in the year before transplantation was not associated with an increase in death or graft failure in the first-year post-transplantation, it did not report longer term follow up of these outcomes. Furthermore, CDOA in the year before transplantation was associated with alcohol abuse, other drug abuse, non-compliance, schizophrenia, and depression in the first-year post-transplantation. Another worrying signal from the study by Alhamad et al is that CDOA in the first-year post-transplantation was associated with an approximately two-fold increased risk of graft loss (both all-cause and death-censored), and death in the subsequent two years. Post-transplant CDOA was also associated with cardiovascular, pulmonary, and psychosocial problems, as well as with accidents and fractures. We therefore need to be cautious in assuming that marijuana use pre-transplant is safe.

Many of the above considerations also apply to donors, raising concerns about their suitability to donate. Risk of non-adherence, reduced likelihood to attend follow-up appointments, underlying mental illness, cannabis use disorder, and addiction may all make marijuana-using donors less optimal in terms of their suitability to donate. Furthermore, smoking has been identified as a factor associated with the development of end-stage kidney disease following kidney donation, which raises questions about whether smokers of marijuana may also be at increased risk. While Ruckle et al found no significant difference in kidney function between marijuana-using and non-marijuana-using donors at one year post-donation, there were only 31 marijuana-using donors and mean follow up time was only 2.1 years. In short, we simply do not know the long-term risks of donation in marijuana-using kidney donors. And what about cardiovascular risk? An entity known as “cannabis arteritis” has been described, and THC (in addition to smoke inhalation) may have increase platelet aggregation and vasospasm. 

In summary, we need to be aware of the potential risks and the paucity of long-term outcomes data in marijuana-using recipients and donor candidates. We should work to expand the evidence base with limited grounds for reassurance at this time. Let’s take a timeout before we encourage marijuana use in the kidney transplantation and donor populations. 

– Executive Team Member for this region: Samira Farouk, AJKD Editorial Board Member. Follow her @ssfarouk.

 

How to Claim CME and MOC
US-based physicians can earn 1.0 CME credit and 1.0 MOC point for reading this region.

  1. Register/log in to the NKF’s Professional Education Resource Center (PERC). If you select “Physician” in the drop-down menu during registration, the ABIM ID will pop up – make sure to complete this during registration to receive MOC points after course completion.
  2. Review the activity, disclosure, and accreditation information.
  3. Click “Continue” and review Course Instructions.
  4. Complete Post-Test. Please note: By selecting “Yes” to the participation questions for each region, the corresponding Post-Test questions will appear. Click “Save Draft” to save your responses and finish later. When you are ready to submit your answers, click “Preview” to review all responses, then click “Submit.”
  5. Click “Next” to complete the Evaluation form, then click“Submit.”
  6. Claim 1.0 CME credit and 1.0 MOC point per region (up to 8.0 total for 8 regions of NephMadness).
  7. Save/print your certificate.

The CME and MOC activity will expire on June 13th, 2020.

Submit your picks! | #NephMadness | @NephMadness | #TransplantRegion

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out /  Change )

Google photo

You are commenting using your Google account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s

This site uses Akismet to reduce spam. Learn how your comment data is processed.