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 Table of Contents  
Year : 2014  |  Volume : 1  |  Issue : 1  |  Page : 29-32

Ambulatory blood pressure as a predictor of diabetic nephropathy

Department of Nephrology-Rheumatology, The Sixth People's Hospital Affiliated to Shanghai Jiao Tong University, Shanghai 200233, China

Date of Web Publication25-Jul-2014

Correspondence Address:
Guihua Jian
Department of Nephrology-Rheumatology, The Sixth People's Hospital Affiliated to Shanghai Jiao Tong University, Shanghai 200233
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/2225-1243.137551

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Objective: The aim was to study the ambulatory blood pressure as a predictor of diabetic nephropathy (DN). Materials and Methods: A total of 73 patients with DN at Stage III were selected as DN group and 73 cases with 5-10 years of diabetes were as diabetes group. The results of blood routine, biochemical indexes, dynamic blood pressure and the diversity index, which as the predictors of DN, analyzed by multivariate logistic regression analysis and compared between two groups. Results: Body mass index, the dose of oral glucose (GLU)-lowering drugs, the levels of fasting plasma GLU, HbA1c, total cholesterol, triglyceride, high-density lipoprotein-cholesterol, low-density lipoprotein-cholesterol, and high-sensitivity C-reactive protein index had no statistically significant difference in two groups in the course of the disease. There were significant differences in peripheral blood leukocyte count, platelet count, erythrocyte sedimentation rate, uric acid, estimated glomerular filtration rate, average daytime systolic blood pressure (SBP), average nighttime SBP, average daytime diastolic blood pressure (DBP), average night DBP, 24 h SBP variability, daytime SBP variability and nighttime SBP variability between the two groups. In univariate analysis, we found that platelet count (P = 0.03), average night DBP (P = 0.01), nighttime SBP variability (P < 0.001) were the independent predictor of DN. Conclusion: Platelet count, average diastolic pressure and SBP at night are the independent predictors of DN.

Keywords: Ambulatory blood pressure, diabetes, diabetic nephropathy, predictors

How to cite this article:
Jian G, Yan Y, Li J, Wang N. Ambulatory blood pressure as a predictor of diabetic nephropathy. J Integr Nephrol Androl 2014;1:29-32

How to cite this URL:
Jian G, Yan Y, Li J, Wang N. Ambulatory blood pressure as a predictor of diabetic nephropathy. J Integr Nephrol Androl [serial online] 2014 [cited 2023 Jun 4];1:29-32. Available from: http://www.journal-ina.com/text.asp?2014/1/1/29/137551

  Introduction Top

Diabetic nephropathy (DN) is a common diabetic microvascular complication, but diabetes may also be complicated by cardiovascular and cerebrovascular diseases and hypertension. Epidemiological data show there is a certain incidence for type II diabetes to progress to DN, generally not >40% for patients with long-term poorly controlled blood glucose (GLU). [1]

In recent times, the relationship between diabetes, DN and ambulatory blood pressure has attracted attention. Past studies have shown that abnormal change in ambulatory blood pressure is related with DN microalbumin count and renal function deterioration. However, early detection and prediction of DN need more concern. In this paper, we screened the risk factors of type II diabetic DN, including ambulatory blood pressure, to identify, which factors are really related with type II diabetic DN and to provide a basis for early prevention and treatment of DN in future.

  Materials and methods Top


We continuously included 73 patients with DN (Stage III) meeting the inclusion criteria and 73 patients with type II diabetes meeting WHO 1999 diagnostic criteria for diabetes and the inclusion criteria in the same period at the outpatient department in our hospital from July 2010 to June 2012.


Basic data

We recorded the personal data and drug use conditions, measured body height and weight, and calculated body mass index (BMI).

Grouping method

Inclusion criteria of DN group:

  1. Duration of diabetes was 5-10 years;
  2. Meeting DN diagnostic and Stage III criteria;
  3. Patients without refractory hypertension, hyperuricemia or hyperlipidemia;
  4. Patients without diabetes complicated by cardiovascular disease, cerebrovascular disease, retinopathy or diabetic foot;
  5. Patients without moderate or severe renal dysfunction who had other diseases causing proteinuria and renal injury were excluded. All the included patients had signed the informed consent forms.

Diabetic nephropathy diagnosis and staging

We consecutively measured 24 h urinary albumin for 3 times (Nephelometry, BN-100 Automatic Special Protein Analyzer, Dade Behring INC., USA) and calculated the mean value. Referring to Mogensen DN staging method, [2],[3] Patients at DN Stage III appeared microalbuminuria, (30 mg/24 h ≤ urinary albumin excretion rate <300 mg/24 h). Those at DN Stage I and II was identified by kidney volume increase by >20% and estimated glomerular filtration rate (eGFR) increase by >130 (mL/min/1.73 m 2 ).

Biochemical test

We extracted venous blood in the morning after 10 h of fasting, used Hitachi 7600-020 automatic biochemical analyzer to measure GLU, creatinine, total cholesterol (TC), triglyceride (TG), low-density lipoprotein-cholesterol (LDL-C), high-density lipoprotein-cholesterol (HDL-C), blood routine and HbAlc (HLC-723G7 automatic HbAlc analyzer, Tosoh Corporation, Japan), and calculated eGFR by the MDRD.

Blood pressure and blood pressure variability test

We used Welch Allyn Cardioperfect ambulatory blood pressure meter 6100 to monitor 24 h blood pressure for all patients and recorded the day and night mean systolic blood pressure (SBP) and mean diastolic blood pressure (DBP).

By the method described by Parati et al., [4] blood pressure variability (BPV) took 24 h blood pressure standard deviation as the indicator. BPV parameters included:

  1. 24 h systolic BPV (24 h SS);
  2. 24 h diastolic BPV (24 h DS);
  3. Daytime systolic BPV (dSS);
  4. Daytime diastolic BPV (dDS);
  5. Nighttime systolic BPV (nSS);
  6. Nighttime diastolic BPV (nDS).

Statistical analysis

SPSS 19.0 (IBM Corporation, USA) statistical software was used for processing. Measurement data were expressed as mean ± standard deviation, unpaired t-test was made for normal distribution data, Mann-Whitney U-test was made for abnormal distribution data, and Chi-square test or corrected Chi-square test was made for enumeration data. In univariate analysis, P < 0.10 would be introduced to multi-factor stepwise regression analysis to verify whether the factors were independent predictors for DN. P < 0.05 was the standard for statistically significant difference.

  Results Top

Basic clinical characteristics of the patients

A total of 146 patients were included in this study, including 68 males and 78 females (aged: 43-82 years, mean: 62 years).The duration of diabetes was 5-10 years (mean: 8.1 ± 1.3 years). Seventy-three cases were in DN group and 73 cases in diabetes group. The differences in age, gender, disease duration, BMI, doses of hypoglycemic and fasting blood GLU drugs, levels of HbA1c, TC, TG, HDL-C, LDL-C and high-sensitivity C-reactive protein (hs-CRP), and other indicators between the two groups were not statistically significant. Compared with the diabetic group, the DN group showed higher peripheral white blood cell (WBC) count (P = 0.0038), platelet count (P = 0.0011), erythrocyte sedimentation rate (ESR) (P = 0.0085), uric acid (P = 0.0082), and eGFR (P = 0.0086) [Table 1].
Table 1: Comparison in clinical characteristics between the two groups (n = 73)

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Comparison in ambulatory blood pressure

Compared with the diabetic group, the DN group appeared higher daytime mean SBP (P = 0.0293), daytime mean DBP (P = 0.0183), nighttime mean SBP (P = 0.0017), nighttime mean DBP (P = 0.0003). Day and night systolic BPV in 24 h was significantly increased [Table 2].
Table 2: Comparison in ambulatory blood pressure between the two groups (n = 73)

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Diabetic nephropathy prediction

We made the multiple stepwise regression analysis with DN occurrence as the dependent variable and peripheral WBC and platelet counts, ESR, uric acid, eGFR, day and night mean SBP, day and night mean DBP and 24 h, day and night systolic BPV as the independent variables. The results showed that increased platelet count, night mean DBP and night systolic BPV were independent predictors for diabetic patients to eventually progress to DN [Table 3].
Table 3: Results of multiple logistic regression analysis for DN prediction

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  Discussion Top

The epidemiological data on diabetes showed that when patients are at the hyperglycemic state long enough, some of them progressed to DN. [5] The result also suggested that high blood GLU might develop to DN combined with other factors. This case-control study included the patients diagnosed as type II diabetes, who were divided into two groups by whether DN occurred or not. We compared the exposure ratio of the various risk factors between these two groups, thus we could better screen the factors statistically related with DN occurrence and development than previous studies. The results showed that in the DN group and the diabetes group, diabetes duration, levels of HbAlc and hs-CRP were similar, which reconfirmed the hypothesis came from diabetes, that is, other factors might be required to promote DN occurrence. In this study, we found that DN was independently correlated with nighttime diastolic hypertension, increased nighttime systolic BPV and platelet count, which suggested that there might be some mechanisms promote DN.

It is known that hypertension can affect renal blood vessels by progression of inflammation and atherosclerosis. Nelson et al's. [6] early study has shown that the mean blood pressure level at an early stage of diabetes can predict the occurrence of proteinuria after diabetes, believing that hypertension is not only be the result of DN, but also an initiating factor to promote its occurrence. The studies of Bordier et al. [7] suggested that the night mean blood pressure of patient with albuminuria was significantly higher than that of the population with normal albuminuria, because hypertension was not only cause of DN, but also the result of DN, resulting in cycles of renal injury.

Blood pressure variability is a research hotspot in the field hypertension in recent years, ASCOT-BPLA [8] study has shown that BPV is a strong predictor of stroke and coronary heart disease, and its predictive value is even higher than mean blood pressure level. However, the relationship between BPV and renal injury has been rarely reported in China and foreign countries, and one study involving 803 patients with hypertension has shown that 24 h systolic BPV and degree of renal injury are independently correlated. [9] In this study, we proved that in the population with type II diabetes mellitus (DM), increased night systolic BPV might contribute to the occurrence and development of DN. The underlying mechanism has not yet clear, foreign animal studies have given some explanations, and a study has shown that in aortic sinus denervated rats (an animal model with normal blood pressure but increased BPV), compared with blood pressure level, increased BPV is a more critical contributing factor causing myocardial damage, kidney disease and aortic hypertrophy. Saita et al. [10] demonstrated that aortic sinus denervation reduced endothelium-dependent vasodilation and enhanced neointimal formation after balloon injury, and these results suggested that increased BPV as independent of blood pressure itself, could contribute to atherothrombosis, and ultimately affected the renal vessels. In addition, increased night BPV may lead to too big tissue perfusion changes, thus cell metabolism may be disturbed. In addition, too high BPV can directly lead to arterial endothelial cell injury, and we all know that angiotensin II may cause vasoconstriction, cell growth and proliferation, cardiac hypertrophy and sympathetic activation, the vast majority of which will result in the target organ damage.

This study also showed that compared with the control group, the platelet count in the DN group increased significantly, and higher platelet count could increase the chance of microthrombosis and ultimately affect the renal microcirculation. Sterner et al. [11] also observed significantly increased platelet count in patients with DN, and Tarnow et al. [12] believed that the total platelet count and aggregation ability would affect DM microcirculation and play an important role in progression of atherosclerotic plaques.

  Conclusion Top

Our preliminary studies found that diabetes duration and poor glycemic control could not provide a sufficient condition for DN occurrence, and increased night blood pressure, night systolic BPV and i platelet level may accelerate DN occurrence and development in patients with type II DM. Of course, this is a single-center study, the number of cases is relatively small, and there are certain limitations and hence the results need to be confirmed by large-scale prospective studies.

  Acknowledgment Top

Guihua Jian and Yan Yan have made equivalent contributions to this paper, so they were the joint first authors.

We thank the staff of the Medical Records Room and Biochemical Room for their assistance, and we thank our patients and their families for their participation. The research is supported by foundation of Shanghai Sixth People's Hospital (1459).

  References Top

1.Gross JL, de Azevedo MJ, Silveiro SP, Canani LH, Caramori ML, Zelmanovitz T. Diabetic nephropathy: Diagnosis, prevention, and treatment. Diabetes Care 2005;28:164-76.  Back to cited text no. 1
2.Mogensen CE. Natural history of renal functional abnormalities in human diabetes mellitus. In: Brenner BM, Stein JH, editors. The Kidney in Diabetes Mellitus. New York: Livingstone; 1989. p. 9.  Back to cited text no. 2
3.Niu J, Gu Y. Diabetic nephropathy. In: Chen H, Lin G, editors. Practical Internal Medicine. 13 th ed., Vol. 2. Beijing: People′s Medical Publishing House; 2009. p. 2303-12.  Back to cited text no. 3
4.Parati G, Pomidossi G, Albini F, Malaspina D, Mancia G. Relationship of 24-hour blood pressure mean and variability to severity of target-organ damage in hypertension. J Hypertens 1987;5:93-8.  Back to cited text no. 4
5.Klein R, Klein BE, Moss SE, Cruickshanks KJ. The Wisconsin epidemiologic study of diabetic retinopathy: XVII. The 14-year incidence and progression of diabetic retinopathy and associated risk factors in type 1 diabetes. Ophthalmology 1998;105:1801-15.  Back to cited text no. 5
6.Nelson RG, Pettitt DJ, Knowler WC, Bennett PH. Prediabetic blood pressure and familial predisposition to renal disease in Pima Indians with non-insulin-dependent diabetes mellitus. J Diabetes Complications 1995;9:212-4.  Back to cited text no. 6
7.Bordier L, Le Berre JP, Garcia C, Dupuy O, Mayaudon H, Bauduceau B. Influence of blood pressure level on urinary albumin excretion rate and erythropoietin production in diabetic patients. Arch Mal Coeur Vaiss 2006;99:701-4.  Back to cited text no. 7
8.Rothwell PM, Howard SC, Dolan E, O′Brien E, Dobson JE, Dahlöf B, et al. Prognostic significance of visit-to-visit variability, maximum systolic blood pressure, and episodic hypertension. Lancet 2010;375:895-905.  Back to cited text no. 8
9.Manios E, Tsagalis G, Tsivgoulis G, Barlas G, Koroboki E, Michas F, et al. Time rate of blood pressure variation is associated with impaired renal function in hypertensive patients. J Hypertens 2009;27:2244-8.  Back to cited text no. 9
10.Saita M, Shimokawa A, Kunitake T, Kato K, Hanamori T, Kitamura K, et al. Central actions of adrenomedullin on cardiovascular parameters and sympathetic outflow in conscious rats. Am J Physiol 1998;274:R979-84.  Back to cited text no. 10
11.Sterner G, Carlson J, Ekberg G. Raised platelet levels in diabetes mellitus complicated with nephropathy. J Intern Med 1998;244:437-41.  Back to cited text no. 11
12.Tarnow I, Michelson AD, Barnard MR, Frelinger AL 3 rd , Aasted B, Jensen BR, et al. Nephropathy in type 1 diabetes is associated with increased circulating activated platelets and platelet hyperreactivity. Platelets 2009;20:513-9.  Back to cited text no. 12


  [Table 1], [Table 2], [Table 3]


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