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Diabetic nephropathy prognosis

Diabetic nephropathy prognosis

Conclusions These Hyperglycemic crisis and diabetic lifestyle modification microRNAs could be noninvasive nephrlpathy for distinguishing Prognoxis with DN Diabftic Diabetic nephropathy prognosis. Long-term Diabetic nephropathy prognosis effect of ACE DDiabetic on diabetic nephropathy in normotensive type 1 diabetic patients. They also play a Diabetid important regulatory role in the occurrence and development of kidney disease [ 17 ]. Chronic kidney disease classification based upon glomerular filtration rate and albuminuria. The vital status of study participants on 31 December and development of CVD during the follow-up period were confirmed by an independent end point adjudication committee, which gathered every 2 years to assess all potential end points and classify them in accordance with predefined criteria as an independent panel. Diabetic nephropathy prognosis

Diabetic nephropathy prognosis -

DN was defined as persistent macroalbuminuria in two of three consecutive urine samples without clinical or laboratory evidence of other kidney disease. Rate of decline in GFR and mortality were compared with our own cohort from to based on identical selection criteria and analyzed similarly Our electronic medical records and laboratory database were sources of demographic, clinical, and laboratory information.

The Danish National Registry on Regular Dialysis and Transplantation provided information on renal replacement therapy RRT , and ICD codes from hospital contacts were supplied by the National Patient Registry.

The Danish Register of Causes of Death provided information on date of death. All patients were routinely examined with HbA 1c , BP, and UAER 3—4 times yearly in our clinic.

GFR 17 , plasma hemoglobin, plasma creatinine, and cholesterol were assessed annually. Intraindividual coefficient of variation in GFR is 3. UAER was analyzed by immunologic methods turbidimetry.

Creatinine was changed from modified Jaffe to an enzymatic reaction in Baseline laboratory values were attained at the inclusion date. If unavailable, the closest measurement in time, within 1 year prior to baseline and 4 months after, was applied.

To overcome day-to-day variation, we determined the weighted mean of BP and weighted geometric mean of UAER measurements from 1 year prior to baseline until 1 month after. Rate of GFR decline was determined as the regression coefficient of all study measurements at time of measurement.

Other follow-up values were expressed at weighted mean of all measurements. ESRD was defined as dialysis or renal transplantation. CVD included stroke, acute myocardial infarction, ischemic heart disease, and heart failure based on ICD and ICD-8 codes and on the operational codes for coronary artery bypass graft and percutaneous coronary intervention Supplementary Table 1.

Date of death was as reported in the Danish Register of Causes of Death. The t test, Kruskal-Wallis test, and χ 2 statistic were applied for the comparison of means, medians, and proportions. Linear regression was applied to assess associations for baseline and follow-up variables to rate of GFR decline.

Continuous variables were standardized to show effect of 1 population SD change on GFR decline. Age and sex were forced into the model of baseline variables.

To avoid inclusion of related variables, we chose age rather than duration of diabetes or nephropathy, systolic BP over diastolic BP, GFR over chronic kidney disease stage or creatinine, and LDL-to-HDL ratio over total, LDL, and HDL cholesterol if more than one was eligible.

For fulfillment of model assumptions, creatinine, UAER, triglyceride, and insulin per kilogram per day were log2 transformed. We plotted the cumulative mortality from baseline for our entire cohort.

This was repeated with age as linear effect. We reported the hazard ratio of the new cohort versus the prior cohort.

For the above-mentioned analysis and database management, SAS Enterprise Guide 4. A multistate model classifying the follow-up follow-up time and events in the categories DN, CVD, and ESRD including doubling of plasma creatinine , along with death from each of these states, was created Fig.

Patients entered follow-up in the CVD state if they had CVD prior to DN; otherwise, all patients entered in the DN state. The ESRD state was subdivided by coexisting CVD. Occurrence of CVD and ESRD in conjunction was modeled together regardless of which event occurred first.

Follow-up was split in intervals of 2 months' length, and the time scales age, diabetes duration and DN duration were computed at the beginning of each interval. A Poisson model with natural splines for time scales age, diabetes duration, DN duration using log length of the intervals as offset was used to model transition rates between states Likelihood ratio tests were used to test proportionality of rates.

We found a model with separate baseline intensities for rates of death and ESRD, and CVD as a time-dependent covariate with proportional effects along the three time scales, to be the most appropriate.

Rate of CVD from DN was modeled using age, diabetes duration, sex, and HbA 1c. Age, time since ESRD, and previous CVD were used for modeling mortality rate in patients with ESRD.

For patients with type 2 diabetes and DN, follow-up time was divided into time spent in the following clinical states: DN, CVD, and ESRD in chronologic order as shown in this multistate model with age, diabetes duration, and DN duration as underlying time scales.

Patients with CVD at baseline started in the CVD state blue box ; otherwise, all patients started in the DN state green box.

Green, DN only; blue, CVD; red, ESRD including doubling of p-creatinine; yellow, ESRD and CVD. Framed boxes with lighter colors represent those who died in the above-mentioned states. We used the Lexis machinery implemented in the Epi package version 1.

The study was performed in accordance with ethics standards. Cumulative mortality from inclusion. The entire cohort of patients with type 2 diabetes and DN is demonstrated by black dots. Their cumulative mortality after 5, 7, and 10 years of follow-up was As follow-up for at least 3 years was a prerequisite for inclusion, no patients died during this time.

The mean SE rate of decline in GFR was 4. A total of 82 patients were included in both study periods — and — The mean SE GFR decline for these patients was 4. The annual mean SE decline was 5. Variables associated with annual rate of decline in GFR in univariate analyses are presented in Supplementary Fig.

Age was negatively and GFR, UAER, HbA 1c , BMI, hemoglobin, and diabetes duration were all positively associated with rate of decline in GFR in analyses of baseline variables. Follow-up values of BMI, UAER, HbA 1c , triglyceride, and LDL-to-HDL ratio were positively and diastolic BP and HDL negatively associated with decline in GFR.

In separate multivariate models including baseline or follow-up variables, presence of retinopathy, elevated GFR, UAER, and HbA 1c at baseline were associated with faster decline in GFR, whereas elevated UAER, higher BMI, and lower diastolic BP during follow-up were associated with faster decline Table 2.

The multistate model with age, diabetes duration, and DN duration as underlying time scales and thus built in the model is presented in Fig.

The annual mortality rate was 2. The rate for post-ESRD mortality Similarly, pre-ESRD mortality, corresponding to the green arrows, equals 5.

During follow-up, patients without CVD at baseline experienced a CVD event; 6 patients had a percutaneous coronary intervention, 14 acute myocardial infarction, 15 stroke, and 37 heart failure, and 67 were diagnosed with ischemic heart disease.

In few patients, these events coincided. The multistate model was used to assess adjusted risk ratio of putative clinical predictors on the competing risk of either pre-ESRD mortality or ESRD including doubling of plasma creatinine , demonstrated as the green and blue arrows in Fig.

Previous CVD and lower GFR were predictors of pre-ESRD mortality with risk ratios of 3. Higher UAER and HbA 1c and lower GFR significantly predicted the development of ESRD. UAER of double magnitude resulted in a risk ratio of 1.

Our long-term observational study of patients with type 2 diabetes and DN followed during — showed significant improvement in prognosis compared with our prior but otherwise comparable cohort followed during — This highlights improved survival over time.

This could be due to the short duration of these studies or because several risk factors were not addressed. Along with multiple risk factors at baseline being improved compared with prior studies, mean follow-up variables of HbA 1c , cholesterol, UAER, and BP Table 1 appeared lower than at baseline.

BP declined 6. General recommendations for BP treatment were RAS inhibition combined with diuretics edema , calcium channel blockers, or β blockers CVD if needed. Our findings of improvements in risk factors and outcome suggest that the benefits of multifactorial intervention described in the Steno-2 trial 11 , 12 may also apply after onset of established DN.

For minimization of bias, creatinine values attained prior to the assay change were converted and the minimum limit for doubling was lowered. By study design, long-time survivors were included in both cohorts.

This strategy was applied to illustrate the composition of the patients attending our clinic. The slower decline in GFR compared with prior studies fits well with the circumstance that incidence of RRT due to diabetes is stabilized or even decreased in Denmark since 23 , 24 , despite diabetes prevalence having doubled between and This is further supported by data from the U.

and Catalonia, where age-specific incidences of ESRD in the diabetic population have decreased since the late s 26 and 27 , respectively. Our previous study 16 demonstrated that early antihypertensive treatment reduced the rate of decline in GFR in patients with type 2 diabetes and DN but rather well preserved kidney function.

This is in close agreement with the decline in estimated GFR eGFR reported in the angiotensin II receptor blocker group of the Irbesartan Diabetic Nephropathy Trial IDNT and Reduction of End Points in NIDDM with the Angiotensin II Receptor Antagonist Losartan RENAAL study: 5.

We are not aware of recent studies that have used real GFR in unselected patients with DN. Over years, diabetic nephropathy slowly damages the kidneys' filtering system. Early treatment may prevent this condition or slow it and lower the chance of complications.

Diabetic kidney disease can lead to kidney failure. This also is called end-stage kidney disease. Kidney failure is a life-threatening condition. Treatment options for kidney failure are dialysis or a kidney transplant. One of the important jobs of the kidneys is to clean the blood. As blood moves through the body, it picks up extra fluid, chemicals and waste.

The kidneys separate this material from the blood. It's carried out of the body in urine. If the kidneys are unable to do this and the condition is untreated, serious health problems result, with eventual loss of life. In the early stages of diabetic nephropathy, there might not be symptoms.

In later stages, symptoms may include:. Make an appointment with your health care professional if you have symptoms of kidney disease. If you have diabetes, visit your health care professional yearly or as often as you're told for tests that measure how well your kidneys are working.

A typical kidney has about 1 million filtering units. Each unit, called a glomerulus, joins a tubule. The tubule collects urine. Conditions such as high blood pressure and diabetes harm kidney function by damaging these filtering units and tubules.

The damage causes scarring. The kidneys remove waste and extra fluid from the blood through filtering units called nephrons.

Each nephron contains a filter, called a glomerulus. Each filter has tiny blood vessels called capillaries. When blood flows into a glomerulus, tiny bits, called molecules, of water, minerals and nutrients, and wastes pass through the capillary walls.

Large molecules, such as proteins and red blood cells, do not. The part that's filtered then passes into another part of the nephron called the tubule. The water, nutrients and minerals the body needs are sent back to the bloodstream.

The extra water and waste become urine that flows to the bladder. The kidneys have millions of tiny blood vessel clusters called glomeruli. Glomeruli filter waste from the blood.

Damage to these blood vessels can lead to diabetic nephropathy. The damage can keep the kidneys from working as they should and lead to kidney failure. Over time, diabetes that isn't well controlled can damage blood vessels in the kidneys that filter waste from the blood.

This can lead to kidney damage and cause high blood pressure. High blood pressure can cause more kidney damage by raising the pressure in the filtering system of the kidneys. Diabetic nephropathy kidney disease care at Mayo Clinic. Mayo Clinic does not endorse companies or products.

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This content does not have an English version. In the present study, microarray analysis was used to identify differentially expressed microRNAs in urinary sediment samples from DM, DN, and NDRD IgAN and MN patients. To expand the sample size for verification, many more urine samples were collected to confirm the candidate microRNAs and to construct diagnostic models in the confirmation cohort.

We found that the levels of miRp, miRp, miR, and miR were all statistically significant in the DN group compared with the other groups. The levels of miRp, miRp, miR, and miR were all statistically significant in the IgAN group and MN group compared with the DM group and DN group, while the levels of miRp and miR were not statistically significant in the IgAN group and MN group.

Many additional urine samples were collected to externally examine the accuracy of the diagnostic models in the validation cohort.

We then explored the relationship between these microRNAs and disease severity and prognosis. We observed that the levels of miRp and miR reflected the severity of DN.

The Pearson correlation analysis revealed that the level of miRp was positively correlated with eGFR but was negatively correlated with the level of serum creatinine, classes of glomerular lesions, and scores of interstitial and vascular lesions. The level of miR was positively correlated with proteinuria.

Different severity levels of DN directly determine the judgment of the treatment effect and adjustment of the treatment plan. Therefore, the levels of miRp and miR could play an important role in clinical decision-making in patients with DN.

In addition, our multivariate Cox regression analysis and Kaplan—Meier analysis also demonstrated that low levels of miRp and high levels of miR increased the risk of progression to ESRD. Therefore, the levels of miRp and miR could play an important role in the prognostic prediction of DN.

Recent studies have reported that many types of kidney diseases, such as primary IgAN, lupus nephritis, and minimal change nephropathy, could be detected by biomarkers in urinary sediment [ 34 , 35 ].

Duan et al. found that the levels of miRp, miRp, and miRp were significantly higher in the IgAN group than in the normal control group [ 36 ]. Yan et al. found that urinary sediment could help with the differential diagnosis of lupus nephritis with endocapillary proliferative glomerulonephritis EPGN and IgAN with EPGN [ 35 ].

Compared with urine supernatant, urinary sediment was shown to be relatively less affected by humoral metabolic factors. Therefore, microRNAs in urinary sediment have the potential to serve as biomarkers for disease diagnosis and monitoring because they are relatively stable and are easily quantified.

In addition, urinary sediment is obtained by a noninvasive method that can contribute to the early screening and monitoring of DN.

This study has a few limitations. First, our investigation did not refer to the mechanisms that cause alterations in microRNAs in patients with DN. Second, this was a single-center study, and it would be better if further multicenter studies and larger cohort studies are conducted for validation.

Third, to further confirm the effect of microRNA levels on the prognosis of DN, it would be better to extend the follow-up time. In conclusion, measurement of the levels of miRp, miRp, miR, and miR could be a useful and noninvasive tool for distinguishing patients with DM, DN, and NDRD IgAN and MN.

The levels of miRp and miR can also reflect the severity and prognosis of DN. Cho NH, Shaw JE, Karuranga S, Huang Y, da Rocha Fernandes JD, Ohlrogge AW, et al.

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Nepphropathy nephropathy is glomerular sclerosis and Diabetic nephropathy prognosis caused by the Diabetif and hemodynamic changes of diabetes prgonosis. Diabetic nephropathy prognosis manifests Hormonal balance and dietary support slowly progressive albuminuria with worsening hypertension and renal insufficiency. Treatment protnosis strict ;rognosis control, angiotensin inhibition using angiotensin-converting enzyme [ACE] inhibitors or angiotensin II receptor blockers [ARBs]and control of blood pressure and lipids. See also Complications of Diabetes Mellitus: Diabetic nephropathy Diabetic Nephropathy In patients with diabetes mellitus, years of poorly controlled hyperglycemia lead to multiple, primarily vascular, complications that affect small vessels microvascularlarge vessels macrovascular read more. It is more common among children and has both primary and secondary read more in adults. Contributor Mephropathy. Please read the Diabetic nephropathy prognosis prognosia the Nepphropathy of this page. See Energy boosting exercises and staging of chronic kidney pgognosis in adults", section on 'Definition of CKD'. Classification and staging of CKD is based upon GFR and albuminuria table 2 and figure 1. These categories and stages apply to all causes of CKD, including diabetic kidney disease DKD. Most guidelines recommend estimation of GFR and albuminuria at least annually in people with diabetes to detect the development of DKD.


Diabetic Nephropathy - Nephrotic Syndrome - Kidney Pathology

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