Early Prediction of New-Onset Diabetes Mellitus by Fifth-Day Fasting Plasma Glucose, Pulse Pressure, and Proteinuria

doi:10.1016/j.transproceed.2011.05.005

Transplantation Proceedings

Volume 43, Issue 6, July–August 2011, Pages 2208-2210

https://doi.org/10.1016/j.transproceed.2011.05.005 Get rights and content

Abstract

Renal transplant recipients are at high risk of cardiovascular disease (CVD). New-onset diabetes mellitus after transplantation (NODAT) contributes to the risk of CVD, reducing graft and patient survival. To improve outcome of kidney transplant recipients, it is of great interest to identify those patients who will develop NODAT. The aim of our study was to explore the predictive value of fifth-day fasting plasma glucose (FPG), third-month proteinuria, and pulse pressure (PP) for NODAT development. We analyzed 282 non-previously-diabetic kidney transplants in our center. Fifth-day FPG, PP, and third-month 24-hour proteinuria were collected. NODAT was defined at month 12 according to the “consensus guidelines”: symptoms of diabetes plus casual glucose concentrations ≥ 200 mg/dL or FPG ≥ 126 mg/dL. Some 46 patients (16.3%) developed NODAT at month 12. Fifth-day FPG (133 ± 35 vs 108 ± 16 mg/dL, P < .001) and PP (57 ± 17 vs 49 ± 15 mm Hg, P = .007) were significantly higher in patients at risk for NODAT, but there was no difference in third-month proteinuria (652 ± 959 vs 472 ± 1336 mg, P = .390). A multivariate regression model showed an increased risk for NODAT associated with recipient age, body mass index, smoking habit, and a fifth-day FPG ≥ 126 mg/dL (relative risk 4.784, 95% confidence interval 2.121–10.788, P = .0002). The negative predictive value of a fifth-day FPG ≥ 126 mg/dL for predicting 1-year NODAT was 89.4%. Fifth-day FPG was independently related to NODAT development. The detection of a fifth-day FPG ≥ 126 mg/dL increases the risk of suffering NODAT more than 4 times. Fifth-day FPG < 126 mg/dL allows us to identify a transplant population with a low risk (near 10%) for NODAT.

Section snippets

Methods

We retrospectively analyzed 282 non-previously-diabetic kidney transplants between 2002 and 2008 in our center. Only grafts with a survival higher than 1 year were selected. Available data included donor and recipient ages, recipient gender, family history of type 2 diabetes mellitus, dialysis type, pretransplant hepatitis C seropositivity, previous smoking habit, immunosuppressive therapy, biopsy-proven acute rejection, and body mass index. Different immunosuppressive drug regimens were used

Results

Some 46 patients (16.3%) developed NODAT at month 12. In univariate analysis, patients who developed NODAT were significantly older, received a graft from an older donor, had a higher body mass index, and more frequently showed a positive family history of diabetes and smoking habit compared with recipients who remained free of NODAT. Fifth-day FPG and PP were significantly higher in patients at risk for NODAT, but there was no difference in third-month proteinuria (Table 1).

Discussion

This single-center study demonstrated that fifth-day FPG, but neither PP nor proteinuria, can independently identify patients at risk for NODAT. By contrast, Roland et al reported that both proteinuria lower than 1 g/d and PP were independent risk factors for long-term NODAT (hazard ratio [HR] 2.04, P = .004; HR 1.26, P = .0002, respectively). The association between proteinuria and NODAT may be due to the fact that patients with metabolic syndrome are more prone to having proteinuria. The

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Cited by (19)

Management of metabolic alterations in adult kidney transplant recipients: A joint position statement of the Italian Society of Nephrology (SIN), the Italian Society for Organ Transplantation (SITO) and the Italian Diabetes Society (SID)
2020, Nutrition, Metabolism and Cardiovascular Diseases
Citation Excerpt :
In the first 6 weeks after KT, afternoon capillary blood glucose testing might be more sensitive than other tests in detecting patients at risk for PTDM [26]. In addition, bedside capillary glucose ≥200 and fasting plasma glucose (FPG) measured early after KT identify patients at risk for PTDM [27,28], prompting the need for closer monitoring during follow-up. However, many patients with hyperglycaemia in the early postoperative period show significant improvements both in glycaemic control and variability in the following months [25].
Chronic metabolic alterations such as post-transplant diabetes mellitus (PTDM), dyslipidaemias and overweight/obesity significantly impact on kidney transplant (KT) outcomes. This joint position statement is based on the evidence on the management of metabolic alterations in KT recipients (KTRs) published after the release of the 2009 KDIGO clinical practice guideline for the care of KTRs. Members of the Italian Society of Nephrology (SIN), the Italian Society for Organ Transplantation (SITO) and the Italian Diabetes Society (SID) selected to represent professionals involved in the management of KTRs undertook a systematic review of the published evidence for the management of PTDM, dyslipidaemias and obesity in this setting. The aim of this work is to provide an updated review of the evidence on the prevention, diagnosis and treatment of metabolic alterations in KTRs, in order to support physicians, patients and the Healthcare System in the decision-making process when choosing among the various available options.
Association of Body Mass Index and the Risk of New-Onset Diabetes After Kidney Transplantation: A Meta-analysis
2018, Transplantation Proceedings
Citation Excerpt :
Then 46 studies (including 15 reviews, 8 letters/editorials, 9 case series/reports, and 8 studies from which relevant data could not be extracted) were further removed via full-text review. Finally, 55 eligible studies were included in the meta-analysis [7,12,19–71]; 15,458 kidney transplantation cases were included, among which 3,007 cases had NODAT. The general characteristics of the 55 eligible studies are listed in Table 1.
To comprehensively examine the correlation between body mass index (BMI) and the risk of new-onset diabetes after kidney transplantation (NODAT).
The electronic databases Pubmed, Embase, and Cochrane Library, updated in December 2016, were searched, and a literature review was conducted as well to identify relevant research studies. With the use of R 3.12 software, the association between BMI and NODAT risk was analyzed by means of a meta-analysis, with the mean differences (MDs) and their 95% confidence intervals (CIs) as effect indexes. Publication bias was assessed with the use of the Egger test. A sensitivity analysis was performed by excluding 1 study at a time. And the overall morbidity of NODAT was calculated.
In the meta-analysis, 55 eligible studies involving 15,458 kidney transplantation cases were included. After the heterogeneity test, the random-effects model was used to calculate the pooled results of the effect indexes. The results of the meta-analysis showed that BMI was an independent risk factor of NODAT (MD, 1.88; 95% CI, 1.48–2.27). No publication bias was found among the included studies (t = 0.3417; P = 0.7339). The sensitivity analysis revealed that the pooled MD did not reverse after ignoring 1 study at a time. In addition, the overall morbidity of NODAT was 21% (95% CI, 21%–23%).
Our results suggest that BMI is an independent risk factor for NODAT.
Association between use of FK506 and prevalence of post-transplantation diabetes mellitus in kidney transplant patients
2014, Transplantation Proceedings
Citation Excerpt :
There is consistent evidence that PTDM is more common in older patients [8,9,13]. Therefore, early detection and prevention of PTDM is needed for older transplant recipients [15,16]. The prevalence of DM may increase with the increase of survival times; therefore, long-term supervision of DM is key to improving long-term survival and patients' well-being [6].
Tacrolimus (FK506) use has been suggested as a risk factor for post-transplantation diabetes mellitus (PTDM) because it can impair insulin secretion. This association warrants further investigation. This study aimed to examine the prevalence of PTDM and its association with FK506 use in kidney transplant recipients. The study also aimed to examine the relationship of FK506 use and diabetes-related biologic markers.
A retrospective chart review was used to collect data at a medical center in northern Taiwan from September 2003 to February 2012. PTDM was defined with the use of the criteria of the American Diabetes Association.
Among 166 patients included in the analysis, PTDM was reported in 49 patients (29.5%). A total of 93 patients used the FK506 regimen, of whom 34 (36.6%) were PTDM cases. Logistic regression showed that FK506 use (odds ratio [OR], 2.71; 95% confidence interval [CI], 1.20–6.11; P = .016) and older age (OR,1.08; 95% CI, 1.03–1.13; P = .001) were significant risk factors for PTDM. In addition, FK506 use in PTDM cases was associated with a significantly higher hemoglobin A1c level (7.55 vs 5.81; P = .01) and a borderline significantly higher insulin resistance index (3.24 vs 1.92; P = .053) than was FK506 use without the presence of PTDM.
Older age and an FK506 regimen were important predictors of the prevalence of PTDM. Greater early detection and prevention efforts for PTDM are needed for older transplant recipients. PTDM patients with an FK506 regimen had higher hemoglobin A1c levels and insulin resistance index than did patients who did not use FK506. The association of serum indicators with FK506 use in the prevalence of PTDM warrants further investigation.
Is pulse pressure a predictor of diabetes in Chinese Han nationality population? 15-year prospective study in Chengdu community
2014, International Journal of Cardiology
Metabolic Disorders Following Kidney Transplantation
2012, Journal of Renal Nutrition
Citation Excerpt :
Patients with blood glucose levels at 126 mg/dL or greater are diagnosed with diabetes. Early indications of abnormal fasting plasma glucose increase the risk of new-onset diabetes.23,30,44 Glycated hemoglobin levels should be tested 3 months after transplantation and then routinely with fasting plasma glucose to ensure controlled blood sugar levels.
Kidney transplantation in patients suffering from end-stage renal disease, although beneficial, may result in potential complications increasing cardiovascular risk of mortality. Common metabolic problems after surgery are weight gain, hypertension, hyperlipidemia, and insulin resistance. Immunosuppressant therapy can enhance comorbidity progression. Early identification and treatment of these abnormalities can promote transplant function. Lifestyle modifications have shown to be promising in the reduction of the metabolic syndrome symptoms, but there remain limited trials focusing on this area. This article reflects a comprehensive review of the available research of each of the potential metabolic complications within the renal transplant population. Immunosuppressant medication effects, biochemical values, and medical nutrition therapy intervention are also included with regard to their influence with these metabolic disorders. Methods for review completion included a MEDLINE search for peer-reviewed research, using the following keywords: transplant, chronic kidney disease, nutrition, metabolic syndrome, and diet after transplantation.
Prediction of diabetes mellitus after kidney transplantation using patient-specific induced pluripotent stem cells
2024, Kidney Research and Clinical Practice

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KidneyComplication: Metabolic problemEarly Prediction of New-Onset Diabetes Mellitus by Fifth-Day Fasting Plasma Glucose, Pulse Pressure, and Proteinuria

Abstract

Section snippets

Methods

Results

Discussion

Am J Transplant

New onset diabetes after kidney transplantation: risk factors

J Am Soc Nephrol

Posttransplantation diabetesA systematic review of the literature

Diabetes Care

Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance

N Engl J Med

Kidney
Complication: Metabolic problem
Early Prediction of New-Onset Diabetes Mellitus by Fifth-Day Fasting Plasma Glucose, Pulse Pressure, and Proteinuria