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Page 8 of 12 Cao et al. J Transl Genet Genom 2019;3:4. I https://doi.org/10.20517/jtgg.2018.16
[78]
different populations . Genotyping of 406 type 2 diabetes patients and 214 controls from the Chinese Han
[79]
population by Li et al. revealed rs10789038 and rs2796498 polymorphisms in adenosine monophosphate-
activated protein kinase subunit alpha 2 (PRKAA2), which were related to susceptibility to type 2 diabetes,
whereas rs2796498 might be associated with DN [Table 2].

However, a large, comprehensive meta-GWAS effort was unable to identify clear loci associated with
[74]
DN and many of the previously identified candidate signals were not validated . Such apparent lack of
reproducibility may be explained by differences in study design, populations, outcome ascertainment, and/or
[80]
false-positive results between different studies . Overall, these results indicate that the genetic landscape of
DN is more complex than expected. An increasing number of large, diverse population-based studies on DN
are required to provide conclusive genomic evidence.

TOWARDS PERSONALIZED MEDICINE: FUTURE PROSPECTS AND CHALLENGES
Personalized genomic medicine promises to combine genomic data with clinical phenotypes to develop
novel clinical biomarkers for predicting CKD risk, drug selection, and for accurate monitoring of patient
prognosis. To achieve this goal, numerous obstacles must be overcome, including determination of the most
significant genetic markers, limiting the off-target effects of gene-based therapies, and conducting clinical
[7]
studies to confirm genetic variants associated with drug response .

[81]
Questions regarding personalized medicine have been summarized by Joyner and Paneth . One of the
most important issues regarding what types of studies should be performed for personalized medicine
because convenient samples have often been used without considering how selection bias and other factors
[81]
could influence the outcome . The Secretary’s Advisory Committee on Genetic Testing had proposed four
criteria to guide the assessment of benefits and risks with a genetic test: (1) analytical validity; (2) clinical
validity; (3) clinical utility; and (4) social consequences. Strategies complying with these recommendations
are required to obtain a panel of genomic biomarkers for diagnosis, prognostic evaluation, and genotype-
[82]
guided counseling .
Even though mRNA expression levels are not necessarily a functional read-out, we previously reported
that urinary podocalyxin, CD2-associated protein, α-actin4, and podocin mRNAs correlated with serum
[83]
creatinine in DN patients . Using targeted microarrays, we identified urinary vimentin mRNA as a
[84]
biomarker to predict renal fibrosis and verified its predictive ability in CKD patients . Upon iterative
random forest analysis of a targeted microarray, four fibrosis-associated mRNAs (tumor growth factor β1,
matrix metallopeptidase 9, tissue inhibitor of metalloproteinases 2, and vimentin) in urinary sediments were
[85]
identified as sensitive predictors of tubulointerstitial fibrosis .
Despite compelling examples of the use of genomics to support personalized medicine, genomics alone is
unlikely to provide sufficient information regarding disease pathophysiology and prognosis. Indeed, in spite
of other omics approaches, including transcriptomics and metabolomics, have emerged as powerful tools
for developing novel biomarkers for CKD in recent years [86-88] , and proteomics remains the classic realm for
biomarker discovery. In this respect, transcriptional, translational, and post-translational modifications,
which cause functional changes to proteins and their function, represent another unexplored area. To
maximize the information obtained by these various approaches, integrative personal omics profiling
(iPOP) is increasingly regarded as a promising strategy that combines genomic, transcriptomic, proteomic
(including autoantibodies), and metabolomic profiles from the same individual for long-term follow-up
[89]
of their genomic/transcriptomic composition . Longitudinal iPOP is extremely powerful in interpreting
healthy and diseased states as it associates genomic information with other dynamic omics activity.