Only a few developed countries have reported the incidence of ESRD among patients with diabetes 15 , Two large multinational renal registries, the European Renal Association—European Dialysis and Transplant Association ERA-EDTA Registry and the United States Renal Data System USRDS , reported percentages of incident ESRD patients due to diabetes that ranged from Notably, this wide gap could not be explained by the difference in diabetes prevalence between these two countries Comparisons of the proportions of patients with diabetes who develop ESRD are needed to better understand the diabetes-related renal failure.

We hypothesize that these proportions varied substantially across the geographic regions and that a nonrandom variation implied a fundamental difference in factors such as appropriateness of disease care, environmental risk exposure, and genetic susceptibility.

A better understanding of the relative global importance of diabetes in ESRD pathogenesis may also advance our knowledge about the underlying mechanisms, enable the more efficient allocation of limited health care resources, and allow the development of better prevention and treatment strategies.

Five parameters are required to calculate the incidence of ESRD among patients with diabetes i. The number of incident ESRD patients with diabetes was calculated as the number of incident ESRD patients multiplied by the percentage of incident ESRD patients with diabetes.

The number of prevalent ESRD patients with diabetes was calculated as the number of prevalent ESRD patients multiplied by the percentage of prevalent ESRD patients with diabetes. The resulting values were converted to counts per million population pmp for comparability among countries.

Both type 1 and type 2 diabetes were included in this study. Linear regression models based on the data from to were used to estimate the data in The other four parameters were acquired from regional Europe and Latin America and national renal registries and from reliable literature. All renal registries, reviews, book chapters, clinical or epidemiological studies on kidney diseases or ESRD, and news articles by journalists were eligible for data extraction.

The reported data were defined as those extracted from regional or national renal registries or from peer-reviewed journal articles or book chapters. The following methods were used for estimation of data for countries that did not report ESRD prevalence or incidence. First, we estimated the prevalence or incidence by the reported values of other years using either a linear regression or exponential curve model if multiple data points were available.

The model type was selected based on face validity no negative values or extreme values , R 2 values, incidence or prevalence trend, and trends in neighboring countries.

If the year s to be estimated were surrounded by years with available data, at least four data points were included into the model whenever possible. Otherwise, at least six data points were included whenever possible.

The estimates were based on years close to the targeted years. Second, if incidence data were available in the literature or by estimation and only one prevalence data point was available, the prevalence in other years was estimated according to the incidence trend.

This was supported by an observed linear correlation between reported prevalence and incidence data see Supplementary Material for European countries, U.

Third, if no incidence data and only one prevalence data point were available, the prevalence in other years was estimated according to the trend of an adjacent or nearby country in the same region. Selection of the index country was based on the following criteria in the specified order: geographic proximity, availability of reliable data from renal registry, literature, or news articles, in this order , comparability of economic status such as gross national income per capita , and similarity of nephrology care e.

The same rules were applied to estimate the incidence from the reported prevalence. Nearly all sub-Saharan African countries had no data of ESRD incidence available.

It is defined as the percentage of ESRD patients who required but did not receive RRT. The percentage of prevalent ESRD patients with diabetes was defined as the percentage of prevalent ESRD patients diagnosed with diabetes before or after entry to ESRD.

The percentage of incident ESRD patients with diabetes was defined as the percentage of incident ESRD patients diagnosed with diabetes before reaching ESRD. If the percentage data for ESRD patients were not available in any way, we adopted the frequency of diabetes among patients with chronic kidney disease CKD , using the stage as late as possible, as a less stringent but close estimate of the percentage of diabetes among incident ESRD patients.

These estimates might have been less than the true proportion of incident ESRD patients with diabetes because CKD patients with diabetes are more likely to progress to ESRD. First, they were estimated from a linear regression or exponential curve model established with use of available data points if more than two were available.

Fourth, the percentage of prevalent ESRD patients with diabetes directly adopted the percentage of incident patients, or vice versa, if no reliable reference data from another country were available.

Fifth, the percentage adopted the data directly from an adjacent country if no reported data were available. We retrieved the literature that reported the data of the proportion of patients with diabetes who progress to ESRD.

We also compared the estimates of the ESRD prevalence with the data in , , and provided by Fresenius Medical Care, Germany. We used SPSS, version 18 Chicago, IL , and the built-in statistical tools in Microsoft Excel to perform the linear regression model, the exponential curve fitting, and statistical analyses including calculation of the R 2 of the models, the Pearson correlation coefficient, and one-way ANOVA and the Scheffe post hoc analysis.

We obtained data from countries as indicated in Supplementary Table 1 and completed tabulation for countries in the years , , , , , , and These data represented The reported data revealed a strong correlation between the ESRD prevalence and incidence as well as between the percentage of prevalent ESRD in patients with diabetes and of incident ESRD in patients caused by diabetes Supplementary Tables 2 and 3.

These findings supported the estimation of either the prevalence or incidence, or either percentages, from its counterpart if data were lacking for one parameter. The global percentage of prevalent ESRD patients with diabetes increased from However, significant variation was observed among geographic regions Supplementary Table 5.

The most rapid increase rates occurred in the Western Pacific and Eastern Mediterranean Regions Table 1 and Supplementary Table 6. In contrast, the slowest increases were observed in Europe, where Within any given year, the percentages of prevalent ESRD patients with diabetes did not differ statistically across the four income groups Supplementary Table 5.

This percentage increased at a significantly slower rate in high-income countries relative to upper-middle-income and lower-middle-income countries Supplementary Table 7. Percentage of prevalent ESRD patients or incident ESRD patients with diabetes worldwide from years to The yearly change rate is the slope calculated by linear regression model.

The percentage of incident ESRD patients due to diabetes increased steadily worldwide, from Increasing trends were observed in most countries Countries in these regions had already reported high percentages of incident diabetes-related ESRD in the early s and much more rapid increases relative to other countries Supplementary Table 9.

Both African and European countries had the lowest percentages of incident ESRD patients caused by diabetes in the early s and reported slower rates of increase over time Supplementary Table Consequently, the percentages in these regions remained the lowest throughout the study period.

Furthermore, the income level had a differential effect on the percentages each year, and this difference was most pronounced between high- and low-income countries Table 1 and Supplementary Table 5 , with increasing trends observed in The ESRD incidence data in three African countries, namely, Algeria, South Africa, and Zimbabwe, were derived from patients receiving treatment, and the African region that included only these three countries had the lowest incidence over time.

For the rest of 34 WHO-defined African countries and Somalia defined as an Eastern Mediterranean country , the ESRD incidence rates were derived from patients requiring RRT. As a result, the African Region, the low-income group, and the lower-middle-income group posted substantially higher ESRD incidence than all other countries Table 2 , Supplementary Table 11 , and Supplementary Fig.

A dramatic increase in the ESRD incidence was observed in Southeast Asia during the study period Taiwan, Thailand, and Nepal were among the highest , leading to the second highest nearly the highest incidence of ESRD in Table 2.

The yearly change rate was the slope calculated by linear regression model. Data excluding the countries whose ESRD incidence rates were estimated by the number of new patients in need of RRT instead of those being treated.

Between and , the global annual incidence of ESRD among patients with diabetes increased from This incidence was modest in the European Region, ranging from approximately half of that in the Western Pacific Region in to one-third in The Western Pacific Region and Europe also exhibited the fastest and slowest annual increases in the ESRD incidence among patients with diabetes, respectively; the latter was significantly slower than the global rate of change.

From to , the highest average annual rate of increase was observed in the Western Pacific Region. The incidence of ESRD among patients with diabetes was remarkably high in the low-income and lower-middle-income countries, which also reported high annual rates of increase in this incidence.

A sensitivity analysis, which included only the three African countries Algeria, South African, and Zimbabwe whose estimation of the ESRD incidence was based on new patients under treatment, instead revealed the lowest incidence of diabetes-related ESRD in the African Region Table 3.

This incidence was similar among all four income groups over time Table 3 and Supplementary Table 5. However, a significantly slower annual rate of increase in this incidence was observed in the low-income group relative to the other groups Table 3 and Supplementary Table Similarly, the annual rate of increase in this incidence in the Eastern Mediterranean Region slowed considerably when Somalia was not included Table 3 and Supplementary Table Annual incidence of ESRD among patients with diabetes worldwide and by the WHO regions or the World Bank income groups from years to The numbers are people per million patients with diabetes.

The yearly change rate was the slope of annual rate against year calculated by the linear regression model. The annual incidence of ESRD among patients with diabetes in and , in people pmp.

The inset maps show the results. ND, no data. The incidence of ESRD from the population with diabetes was reported from 19 countries or territories during — Supplementary Table Twenty-eight data points were comparable plus 16 studies focusing on type 1 diabetes or special subgroups.

From to , the global ESRD prevalence doubled from In , 72 of 85 countries Similar patterns were observed for the differences in counts or percentages in the data from and Supplementary Figs. In , based on the model, 40 of 44 countries Moreover, the estimates of ESRD prevalence in 31 countries This study yielded three major findings.

First, the proportion of prevalent ESRD patients with diabetes continued to rise worldwide. The slowest annual increase in this proportion was observed in Europe, but nearly threefold increase was reported in the Eastern Mediterranean and Western Pacific regions. Second, the importance of diabetes as a risk factor for ESRD was observed both in high-income countries and in increasing numbers of developing and underdeveloped countries.

Third, substantial geographic variation was observed in the incidence of ESRD among patients with diabetes. Remarkably, the incidence in Western Pacific countries was twice the world average and thrice that of the lowest incidence observed in Europe.

Our findings reveal that the expansion of populations with diabetes among the ESRD patients is a global phenomenon, and it does not appear to be stoppable anytime soon.

Special consideration should be given to the challenges of providing care to an ESRD population with a higher proportion of patients with preexisting diabetes.

We should appreciate the fact that diabetes is becoming the dominant risk factor for ESRD in developing and underdeveloped countries—not just as seen in the developed countries. Interestingly, decrease of the percentage of incident ESRD patients due to diabetes was seen in five European countries and three African countries Table 1 , even though their diabetes prevalence kept increasing as in all other countries.

Risk stratification based on geographic origins may be needed to identify populations with diabetes that should be targeted more aggressively to prevent the initiation of renal complications or halt further deterioration. Our survey for the incidence of ESRD among populations with diabetes with a global perspective may help disclose a mechanism in determining the progression of diabetic kidney disease.

The international variation was enormous, yet the pattern was nonrandom. One potential explanation is the competing risks between death and kidney failure in patients with diabetes Our equation included only patients treated with RRT and did not consider those who died of cardiovascular or renal complications before reaching ESRD.

This incidence may be deceptively lower in countries where a higher proportion of patients with diabetes died before reaching ESRD due to lack of appropriate care or delayed initiation of RRT or soon after reaching ESRD due to lack of RRT or voluntary choice of conservative treatment.

Proper care including blood pressure control, blockade of renin-angiotensin system, awareness of CKD itself, and timely referral to a nephrologist can halt the progression of diabetic kidney disease, but they were inadequate in many underdeveloped countries As the aging population is more vulnerable to the progression to ESRD, the developed countries with a higher proportion of older patients with diabetes should have more patients with diabetes entering ESRD.

However, our analysis of data from countries in Western Europe versus those in industrialized area of Asia Japan, Taiwan, South Korea basically excluded the possible effects of age, sex, and RRT access. Tobacco smoking is known to increase the risks of mortality and vascular complications in patients with diabetes This small discrepancy is far less than the fold difference in the annual rate of incident ESRD among patients with diabetes between these countries.

The effects of climate 26 or air pollution 27 are said to be important but inconclusive. The existence of an unknown protective environmental factor is supported by the finding that the annual incidence of ESRD among patients with diabetes in Northwestern Europe Denmark, Finland, Iceland, Norway, Sweden, U.

ranged from to pmp in compared with 1, pmp among people in the U. Food choice such as high meat intake is another risk factor for the progression of diabetes complications Interestingly, Fuller and Rowlands proposed a long-lasting difference in food selection and preparation between eastern and western Asia since BC: grinding, roasting, and bread baking in western Eurasia, including the Mediterranean region, versus whole grain boiling and steaming in China and the Far East Asian people with diabetes have a greater risk of developing related complications than their counterparts in Western countries; consequently, the former population also faces higher risks of all-cause and cause-specific mortality 30 , Data from the USRDS indicated that from to , the age- and sex-adjusted incidence of ESRD due to diabetes was 3.

A longitudinal observational study of 62, patients with diabetes conducted at Kaiser Permanente of Northern California reported adjusted hazard ratios for ESRD of 2.

In a sample of patients with diabetes with advanced CKD estimated glomerular filtration rate of We used two approaches to validate our model. First, we compared our data regarding the annual incidence of ESRD among patients with diabetes with data from a limited number of literature reports.

The data in the latter sources were generally higher because the study populations had been carefully followed and all recruited case subjects reached ESRD.

In contrast, our model had a larger denominator because it included the entire population at risk i. Second, we compared the ESRD prevalence in this study with the data provided by Fresenius Medical Care and determined a high level of similarity, with few exceptions.

Accordingly, the Fresenius data set is validated as an accurate reference. Furthermore, the similarities between our estimates and the Fresenius data vindicate the model-building concept in our study in terms of estimating the global ESRD prevalence.

Only six countries, namely, India, Myanmar, Sri Lanka, China, Vietnam, and Bangladesh, showed a twofold difference between the reported or estimated ESRD prevalence and the Fresenius data. For the first three countries and Yemen , we estimated the prevalence of ESRD patients requiring RRT, which was the prevalence of treated ESRD multiplied by a ratio between the ESRD patients who required RRT and those who received it.

Apparently, this correction made the estimates remarkably high. Take India, for example. The sample to report the prevalence in China was presumably overrepresenting because the subjects were urban residents and insured The ESRD prevalence for Vietnam was derived from the total number of dialysis patients and, hence, supposed to be more accurate than the Fresenius data.

The data of ESRD prevalence in Bangladesh were obtained from the USRDS and were considered authorized. This study had a few limitations. First, even in a patient with diabetes, ESRD may or may not be caused by diabetic nephropathy; diabetes might simply be a comorbidity with ESRD The annual incidence of diabetes-related ESRD may have been overestimated.

Although some progress has been made in reducing diabetes-related mortality and delaying the development of kidney disease from DM, the percentage of DN patients who progress to ESRD has not substantially declined [ 5 ].

Disappointingly, there has been an impasse in the development of new drugs for DN, with no success in Phase 3 clinical trials [ 15 ].

One reason is the lack of accurate understanding of the underlying pathophysiological mechanisms of human DN development and progression. On one hand, mechanisms underlying DN development and progression are complicated with many interacting molecules and a number of crosstalk pathways.

In addition, patients who strictly complied with treatment recommendations can still develop overt DN whereas patients with similar or poor compliance may not.

Likewise, not all DM patients with microalbuminuria progress to macroalbuminuria or ESRD some patients even revert and the microalbuminuria disappears. Therefore, more broad-based approaches including systems biology and multiple omics are being applied to understanding DN pathological mechanisms today [ 17 , 18 , 19 ].

Regarding this situation, we collected all DN prognostic markers risk factors for DN progression from both routine and high-throughput research based on human samples in the past two decades and performed additional bioinformatics analyses, hoping to offer some insights into the mechanism of DN progression, which might help DN research and the discovery of new therapeutic targets for DN.

We constructed a database dbPKD [ 20 ], for prognostic markers of DN, as well as other CKDs including IgA nephropathy IgAN , idiopathic membranous nephropathy IMN , primary focal segmental glomerulosclerosis pFSGS and Lupus nephritis LN.

There have been no previously focused databases for risk factors of kidney diseases. dbPKD may provide a resource for searching reported prognostic factors for common CKDs.

All DN prognostic markers risk factors for DN progression were collected by screening through related literature. We searched the PubMed database using 32 keywords, e.

Additional file 1 : Table S1. Reviews and non-English literature were excluded first. Initial screening of literature was based on title and abstract. Four hundred and three papers were retained for further filtration. Their contents were checked for information in detail. Besides DN prognostic markers, we also collected prognostic markers of other four CKDs IgAN, IMN, pFSGS and LN.

The collection guidelines were basically the same as that for DN data. The workflow for data processing is shown in Additional file 1 : Figure S1.

All this work was done using the g:Profiler platform [ 21 ]. In order to analyze the connectivity and co-regulation among the DN prognostic molecules, we constructed a network according to the main enriched pathways in DN progression based on KEGG [ 22 ] using Edraw Version 9. We also manually constructed a signal-transduction diagram by extracting the regulatory relationship from the enriched signal transduction pathways to illustrate the speculated role of prognostic molecules in DN progression more clearly.

To establish the expression and location of prognostic molecules in normal kidney tissues, we searched all prognostic genes and proteins in the HPA [ 24 ]. glomeruli, tubules, etc. related data. Finally, we obtained the expression levels and location data of prognostic genes and proteins in kidney tissues by molecule ID mapping.

To avoid duplication and to unify the naming of markers across different studies, genes were mapped to Entrez Gene IDs, and proteins were mapped to UniProt IDs.

Mixed clinical indicators were given unified names if these are widely used. All the collected data were incorporated into the database after collation and normalization, and each entry included five types of information: reference, research parameters, marker annotation, prognostic effect s and the supportive public data.

The web interface for dbPKD was developed using PHP Version 5. JavaScript and jQuery were also used to enable dynamic web services. The database was implemented in MySQL Server 5. Data analyses were mainly developed using R script.

The web interface mainly provides four types of application service: Browse, Search, Analysis and Download. Most DN prognosis studies were multi-centered, and were mainly located in Europe, North America and East Asia. According to the primary DM subtypes, the DN study population could be divided into three subgroups: T1DN, T2DN and undefined DN.

Specially, the undefined DN subgroup indicates that the study population did not include an independent, well-defined T1DN secondary to T1DM cohort or T2DN secondary to T2DM cohort. The prognostic markers could also be divided into three groups based on the DN population Additional file 1 : Figure S2.

Only one gene ACE and six proteins ADIPOQ, CST3, TNNT2, TNFRSF1A, FABP1, HBB were verified as potentially prognostic in both T1DN and T2DN Table 1.

Without distinguishing amongst DN subtypes, almost all prognostic genes were verified using human blood specimens, while prognostic proteins were verified mainly based on blood and urine specimens Additional file 1 : Figure S3. Additionally, four molecules, ADIPOQ, CCL2, CTGF and HP, were verified as potentially prognostic for DN progression in both gene and protein levels Additional file 1 : Figure S4.

Blue arrow represents protein change in blood, green arrow is for urine specimen, and orange arrow for kidney tissue. Based on the DN classification [ 25 ] in and a preliminary analysis of all defined end point events in the collected papers Fig.

Among them, two groups were of particular interest: the ESRD group, and the overt DN group referring to a group of molecules that were prognostic for GFR decline not reaching ESRD. Grouping based on the end point events and corresponding clinical parameters. a End point events and corresponding clinical parameters.

b Grouping of DN prognostic genes and proteins according to the end point events involved in different studies. We performed GO and KEGG enrichment analysis. Interestingly, as shown in Fig. In addition, referring to the adipocytokine signaling pathway enriched in ESRD group, there have been several adipocytokines reported to participate in DN development and progression in recent years.

One of them was adiponectin ADIPOQ , besides being verified as a prognostic molecule in DN prognosis studies [ 29 , 30 , 31 ], it was observed increased in the serum of DN patients, protected the kidney by reducing inflammatory response and ameliorating glomerular hypertrophy and albuminuria, as an anti-inflammatory adipokine and insulin sensitizer mainly secreted by adipocytes [ 32 ].

There were also some other adipocytokines reported, such as visfatin and apelin. Visfatin, or pre-B cell colony-enhancing factor, is synthesized in adipocytes, had an important paracrine role in the development of DN through inducing tyrosine phosphorylation of the insulin receptor, activating downstream insulin signaling pathways and increasing the levels of TGF beta1, PAI-1, type I collagen, and MCP-1 CCL2 [ 33 ].

Apelin contributed to DN progression by inhibiting autophagy in podocytes [ 34 ]. KEGG enrichment analysis of DN prognostic genes and proteins corresponding to different end point events.

Although there are many biological processes BPs involved in DN progression, we only focused on the top 15 BPs significantly enriched for all the DN prognostic genes and proteins Additional file 1 : Figure S5. According to the three clusters of DN prognostic molecules, based on different end point events Fig.

There were very few overlapping risk molecules between the ESRD group and the overt DN group, which indicated that there might be different key molecules promoting DN progression at different DN stages. For example, CTGF was verified as a risk gene for albuminuria progression [ 35 ] and a risk protein for progressing to ESRD [ 36 ].

In podocytes, its overexpression could damage podocytes and exacerbate proteinuria and mesangial expansion [ 39 ]. Considering all the above observations, it is speculated that CTGF should exert a very weak or no effect on the promotion of DN progression in the early albuminuria stage of DN, although it was a risk gene for albuminuria progression, while in the middle and late DN stages, CTGF should act as a key molecule promoting the development of ESRD and play an very important role in DN progression.

We constructed a network according to the aforementioned KEGG pathways Fig. To illustrate the role of DN prognostic molecules in the mechanism of DN progression more clearly, we also drew a signal-transduction diagram by extracting the regulatory relationship from the enriched signal transduction pathways Fig.

For the integrity of the regulation loop, AGE-RAGE signaling pathway in diabetic complications is also included in the diagram. As shown in Fig. Actually, the role of some of the DN prognostic molecules in the mechanism of DN development and progression and their regulatory relationship have been studied in the past two decades using animal and cell culture models Additional file 1 : Figure S7 [ 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 ].

For example, TNF could cause cholesterol-dependent podocyte apoptosis and albuminuria, which was mediated by nuclear factor of activated T cells 1 NFATc1 [ 52 ].

Blockade of macrophage-derived TNF could protect kidney and reduce albuminuria and plasma creatinine in a diabetic mouse model [ 53 ]. CRP transgenic mice developed more severe DN with increased albuminuria and enhanced renal inflammation compared to wild-type mice [ 41 ].

In addition, PEDF SERPINF1 could inhibit tubular cell injury by suppressing RAGE AGER expression in streptozotocin-induced diabetic rats [ 45 ], while EGF could prevent podocyte apoptosis induced by high glucose [ 54 ]. Overview of regulatory relationships among DN prognostic molecules in enriched signal transduction pathways.

Solid line represents molecular interaction or relation. Dotted line represents indirect link, state change or unknown reaction. Red line represents link in the cytoplasm. Molecule in the rectangle represents gene product, mostly protein but including RNA.

Some of them have high protein expression in normal kidneys, for example, ICAM1 and NPHS1 are high expressed in normal glomeruli, while UMOD, RBP4, CST3, TNFRSF1B, TNFRSF11B, ACE, COX5A, ITGA2, PON2, TKT, UQCRC1 are high expressed in tubules. And several molecules are expressed in normal kidneys but not in other human normal tissues: NPHS1, UMOD, and SLC12A3.

Moreover, most of the prognostic genes expressed in normal kidneys could be found in both glomeruli and tubules. Interestingly, almost all of the prognostic proteins verified only through urine specimens are expressed in normal renal tubules, except C4A, CLU and HP with C8A and EGF unknown , which suggests that DN progression might be closely related to the dysregulation of protein expression that originally existed in normal kidneys.

Protein expression and location of DN prognostic molecules in renal tissues using the HPA [ 24 ]. Bold indicates high protein expression, and proteins expressed in both glomeruli and tubules are in red. In total, 69 genes, 72 proteins, 4 microRNAs, and 92 mixed clinical indicators were extracted from qualified papers, without distinguishing specimen sources.

And 46 genes, 42 proteins, 3 microRNAs, and 60 mixed clinical indicators were extracted from qualified papers for DN progression. In addition, 30 genes, 43 proteins, 1 microRNA, and 41 mixed clinical indicators were extracted from qualified papers for IgAN, IMN, pFSGS and LN.

The browse interface provides data exploration to users across several features, such as specimen sources, marker types and prognosis effects etc.

Users can also search for one marker or a group of possible prognostic genes in the Search interface Fig. Analysis interface provides users with three types of analysis service: survival analysis, enrichment analysis and Venn diagram analysis.

Analysis results will be shown when the calculation is completed, including univariate and multivariate analysis tables, Kaplan—Meier survival curves, and prognosis models.

Venn diagram analysis focuses on screening for common or specific markers in PKD research. Finally, users can download data in Download interface Fig. And dbPKD is free for non-commercial activities. Web interfaces of the dbPKD.

a The browse interface of dbPKD for prognostic markers in blood. b The search interface for a gene symbol. c The analysis interface which includes three modules: survival analysis, enrichment analysis and Venn analysis.

d The download page of dbPKD with url and description. Theoretically, proper genetic intervention to DM patient might prevent DN from happening. However, resolving the genetics of DN remains complex with little progress. In the past decades, only a few molecules were identified as DN genetic factors through genome-wide association studies GWAS , such as ACE, AKR1B1, APOE, PPARG, etc.

At present, in addition to strict management of diabetic patients, there seems to be no precautions for DN development.

The main therapeutic strategy for DN patients is to inhibit or retard the disease progression. The prognostic markers collected here were all verified as risk factors for DN progression in DN prognosis studies. They were all directly related to the end point events of DN patients regardless of the complex interactions among molecules and Epigenetics.

Analyzing these prognostic markers might offer some insights in understanding the mechanism of DN progression. MicroRNAs are small non-coding RNA molecules that usually function in RNA silencing and post-transcriptional regulation by affecting their target mRNAs.

Here we only collected three microRNAs that were verified as risk factors of DN progression. Interestingly, their target molecules included more DN prognostic genes and proteins [ 56 ] Additional file 1 : Figure S9 , indicating that microRNAs should play an important role in DN progression.

In some other related works, we confirmed the clinical application value of miRa for several types of kidney diseases [ 57 , 58 ]. The regulation details between microRNAs and their targets as well as the possible associations among these three microRNAs need further research, which might help to understand the mechanism of DN progression.

In addition, there were also some clinical indicators including metabolites, biochemical indicators, pathological parameters, etc.

that could be used as DN prognostic markers. In fact, serum creatinine has been widely reported and clinically used as an important parameter in assessing and monitoring renal functions of kidney diseases for decades [ 59 , 60 ].

Vitamin D has been discussed to be a treatment option in DN for many years [ 61 , 62 ]. Both of these suggest that DN prognostic markers have potential important applications in the clinical diagnosis and treatment of DN.

Although we attempted to collect all the DN prognostic markers and analyze them as accurately as possible, there were still some limitations in our study. First, due to the limited prognosis studies, the number of DN prognostic molecules collected was small.

Second, because of the fuzzy definitions of end point events, it was difficult to judge the accurate DN stages for which some prognostic markers were used. This also hindered subsequent further analysis. Lastly, specimen sources of risk factors for DN progression were variable, including urine, blood and kidney tissue, which posed difficulties for further mechanistic studies of DN progression.

The work on prognostic markers will be continued and the data is scheduled to be updated every 2 years. In the meantime, we will keep trying to improve the efficiency of data extraction by adopting some machine learning methods and endeavor to optimize the workflows.

In addition, other types of related data, such as data from single cell sequencing studies, may also be collected in the subsequent work for further analysis. We hope that more prognostic markers of kidney diseases and valuable insights could be provided to clinicians and researchers.

In conclusion, we collected human DN prognostic markers that were verified as independent risk factors of DN progression mostly through multivariate analysis in the past two decades and constructed a database.

To our knowledge, this is the first systematic summary of DN prognostic markers. Also, we demonstrated the connections and regulation among these molecules and emphasized some related GO terms and KEGG pathways by bioinformatics analysis.

The in-depth study of these molecules and related pathways will help to further understand the mechanism of human DN progression, discover new therapeutic targets and explore new DN drugs.

In addition, some prognostic markers mixed clinical indicators might contribute to the improvement of the managements of DN patients. In the future, we will expand the data content and improve the functional modules for dbPKD, and strive to provide some more valuable insights for the research and treatment of related kidney diseases by adopting more and better analytical methods.

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