Nephrology Madness: Meet the competitors for Glomerulus Region’s Mesangial Cell Group!
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(9) Whole-exome Sequencing vs (8) Epigenetics
Whole-exome sequencing has changed how we study rare Mendelian disorders. Although acquiring full genome sequences is a lot cheaper than it used to be, it’s not been practical to use whole genomes in genome-wide association studies, so researchers have tended to scan SNPs instead. But, since a lot of the action, mutation-wise, occurs in the coding regions of DNA (the exome), an alternative is to sequence this subset instead of the whole enchilada. Result? Sequence-level information on all genes for a (relatively) economical cost, giving way more information than even a half-million SNPs. Furthermore, smaller sample sizes can be utilized instead of larger numbers of family members using traditional linkage analysis. The speed at which whole-exome sequencing can be performed and the lower costs are two reasons that this technique is being utilized more and more. A recent paper by the Lifton group identifying mutations in kelch-like 3 and cullin 3 causing hypertension and electrolyte abnormalities is the latest to show the promise of this technique for nephrology.
If whole-exome sequencing is the Reader’s Digest book of life, epigenetics are the notes in the margins. Epigenetics refers to the processes by which cells can alter the expression of genes beyond the actual DNA sequences of exomes. The full implication of epigenetics on gene regulation yet to be uncovered. Examples of epigenetics include DNA methylation status, chromatin structure, imprinting, gene silencing, X chromosome inactivation just to name a few. Epigenetics has the potential to answer many important unanswered questions in nephrology, especially in complex diseases with possible environmental triggers. However, the application into kidney disease is only in its infancy. I think this is a true “diaper dandy” of the group.
(11) Cox Proportional Hazards vs (6) Propensity Scoring Matching
Cox Proportional Hazards
Nephrology operates in sea of observational and retrospective data. This data needs to be adjusted and controlled in order to make sense of it. Cox proportional hazards model allows multiple variables to be analyzed simultaneously to see which affect the outcome of interest. Hazard ratios over one, indicate the outcome is more likely and HR less than one indicate it is less likely.
One of the first uses of Cox proportional hazards was in nephrology. Burton and Walls study estimated the life expectancy of patient on hemodialysis, peritoneal dialysis and with a transplant. It was published in 1987.
A nice video review of Cox Proportional Hazards can be found here.
Propensity score matching
One of the largest problems in observational studies is bias by indication, i.e. patients who receive antibiotics are more likely to die of an infection, does this mean that antibiotics cause death by infection or rather it means people with infections are both more likely to get antibiotics and more likely to die of an infection than people without infections.
Propensity score matching is one way to adjust for this, every subject gets scored on how likely they are to receive an intervention (treatment). This is done with logistic regression, importantly the patient outcome is not considered at all here, just the variable that determine whether a patient get treated. In the antibiotic example, variables likely associated with antibiotics would be positive blood and urine cultures, elevated white count, fevers, etcetera. Patients with equivalent propensity scores for the treatment of interest, but who received different treatments are compared. So in the above example, all patients with fever and elevated white count are included and patients with antibiotics are compared against people who were not treated.
Propensity score matching is a sophisticated way to avoid a number of biases that contaminate observational studies. An early use of propensity scores in nephrology was Mehta’s article on diuretic use in acute kidney injury.
(13) Western Blot vs (4) Real-time PCR
The laboratory technique for determining protein abundance termed Western blotting finds its way into just about every basic science paper published. Western blotting was first described by 3 different labs back in 1979-1981. Renart et al published the first paper back in 1979, Towbin et al followed shortly therafter in the same year and described the technique as it is still performed today, and Burnette et al coined the name back in 1981. The name Western is a play on the related Southern blot method (for detecting DNA) named for its creator Dr. Edwin Southern (there are Northerns, too, but no “Easterns”). The technique relies on the separation of proteins by size using gel electrophoresis, transfer onto a nitrocellulose membrane, and subsequent “blotting” with primary and secondary antibodies. Western blotting is a mainstay in the field but has several limitations. Many antibodies lack requisite specificity, furthermore, the technique is frequently not quantitative, and knowledge about protein activity level and cellular localization is lacking.
Real-time PCR has emerged as another powerful tool used in basic science and clinical medicine. It is oftentimes used in conjunction with Western blotting to measure the expression level of a gene of interest. This technique was born out of the ability to replicate DNA using polymerase chain reaction (PCR), which was originally described in the mid 1980s and which earned Kary Mullis (later infamous for his flirtations with HIV/AIDS denialism) a Nobel Prize for this discovery in 1993. For gene expression analysis, real-time PCR relies on serial amplification of DNA that has been synthesized from mRNA transcripts (so-called complementary DNA, or cDNA). The technique, which relies upon fluorescent readout of reaction products, is relatively inexpensive and quantitative. Real-time PCR is used to monitor viral replication of many viruses such as polyoma, CMV, and HIV. In functional genomics, real-time PCR shows if a gene is being expressed in a cell/tissue of interest; however, since RNA levels don’t always correspond directly to protein amounts or activity, it’s an indirect measure of gene products.
(15) Meta-analysis vs (2) Randomized Clinical Trial
Meta-analysis is a popular technique to combine many smaller studies to try and understand trends that could be evident by using various statistical models. This differs from a classical literature review which only compiles the studies performed on a topic to date. This approach often combined with systematic reviews can help to provide very valuable information but often times is performed when there hasn’t been a definitive trial performed on a certain topic. The nephrology community has utilized such methodologies over the years. A recent example published in AJKD examines the use of induction and maintenance treatment of proliferative lupus nephritis.
Randomized Clinical Trial
The sine qua non for clinical trials are randomized double blinded clinical trials. This is where the rubber meets the road as far as evidence goes for evaluating treatment efficacy. Also, it has proven quite difficult in the field of nephrology to show efficacy of a particular treatment. These trials are very expensive and consume a considerable amount of up front time figuring out what the right patient population is, how to pick and measure the appropriate outcome and what the duration of the trial should be. This are critical to the success of a RCT. Recent failed examples include the BEACON trial. However, recently we have seen several positive studies including the TEMPO and FHN trials.
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