=Paper=
{{Paper
|id=Vol-2790/paper19
|storemode=property
|title=
Renal Impairment Risk Factors in Patients With Type 2 Diabetes (short paper)
|pdfUrl=https://ceur-ws.org/Vol-2790/paper19.pdf
|volume=Vol-2790
|authors=Darya A. Shipilova,Oleg A. Nagibovich
|dblpUrl=https://dblp.org/rec/conf/rcdl/ShipilovaN20
}}
==
Renal Impairment Risk Factors in Patients With Type 2 Diabetes (short paper)
==
https://ceur-ws.org/Vol-2790/paper19.pdf
Renal Impairment Risk Factors in Patients with Type 2
Diabetes
Darya A. Shipilova[0000-0003-4862-6208], Oleg A. Nagibovich[0000-0002-1520-0860]
Military Medical Academy named after S.M. Kirov,
Saint-Petersburg, Akademika Lebedeva street, 6G, Russia
dashuta_shipilova@mail.ru
Abstract. The article presents the Kaplan-Meier analysis, which estimates the
cumulative survival function at the time of occurrence of each outcome. In order
to predict the risk of an event for the object under consideration and assess the
influence of independent variables on this risk, a Cox regression was built, which
is also called the Cox proportional hazards model. The capabilities of these
methods have been demonstrated using a real example of the analysis of risk
factors affecting renal outcome in patients with type 2 diabetes mellitus (DM). A
retrospective study of 82 patients with type 2 DM complicated by chronic kidney
disease (CKD) stage 1-3 with the level of albumin excretion in urine A1-A2 was
conducted. During the observation period, 26.8% of patients showed a decrease
in glomerular filtration rate (GFR) below 60 ml / min / 1,73 m2 and an increase
in the albumin-creatinine ratio (A/Cr) above 3 mg / mmol. In this regard, patients
were divided into two groups depending on the presence of signs of CKD. We
studied the main clinical and laboratory parameters: anthropometric,
hemodynamic, the level of compensation for carbohydrate metabolism, serum
creatinine, urine creatinine, urine albumin, and lipid metabolism. The indicator
of intrarenal vascular resistance: resistive index (RI) of the right segmental artery
was determined. It has been established that risk factors of impaired renal
function in patients with type 2 diabetes are age, duration of diabetes, obesity,
level of carbohydrate metabolism compensation and resistance index. The most
informative indicator is the resistance index of 0,70 and higher. This indicator
can be taken as a criterion for renal prognosis.
Keywords. Diabetes mellitus type 2, chronic kidney disease, glomerular
filtration rate, renal circulatory, doppler ultrasound, resistive index.
1 Introduction
Chronic kidney disease (CKD) is the leading microvascular complication in patients
with type 2 diabetes mellitus (DM) [1, 2]. Currently, the diagnosis of diabetic kidney
damage is mainly based on clinical and laboratory parameters. According to the
recommendations of national and foreign expert groups to clarify the stage of kidney
damage in diabetes, it is necessary to determine the glomerular filtration rate (GFR)
and the level of albumin excretion in the urine [3, 4]. These indicators characterize both
Copyright © 2020 for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0).
205
structural and functional changes in the kidneys, but they do not predict the rate of
progression of diabetic kidney damage. It is known that the risk factors for the
development and progression of deterioration of renal function are unsatisfactory
compensation of diabetes mellitus, long duration of the disease, impaired intrarenal
hemodynamics, arterial hypertension, hyperlipidemia [5]. However, it is still difficult
to predict the renal prognosis, since there are additional causes of the loss of renal
filtration capacity. So, according to some authors, Doppler ultrasound, namely, the
determination of the index of intrarenal vascular resistance, can detect hemodynamic
disturbances in the early stages of renal disease and prevent the onset or progression of
renal failure in patients with diabetes [6-8]. In addition, R. Ikee et al found a pronounced
relationship between histopathological parameters and the level of the resistance index
(RI) in patients with type 2 diabetes [9]. H. Xu et al., in 2017, conducted a study in
which, using renal Doppler sonography, revealed that microvascular disorders are an
early marker of nephrosclerosis, even before the detection of morphological changes in
mice with induced type 2 diabetes [10]. Thus, the search for additional risk factors will
expand the understanding of the pathogenesis of diabetic kidney damage, and will allow
developing effective approaches to prevent the progression of CKD.
Purpose of the study is to identify risk factors for deteriorating renal function and
determine the possibility of using RI as a criterion for predicting renal outcome in
patients with type 2 diabetes.
2 Materials and Methods
From October 2015 to December 2019, 82 patients with type 2 diabetes were under
observation. During the observation period, 26,8% of patients showed a decrease in
GFR below 60 ml/min/1,73 m2 and an increase in the albumin-creatinine ratio (A/Cr)
above 3 mg/mmol. In this regard, the patients were divided into two groups: 1st - 60
patients (39 men and 21 women), who had a GFR higher or equal to 60 ml/min/1,73
m2 and A/Cr from 0,2 to 1,8 mg/mmol. 2nd - 22 patients (18 men and 4 women), who
had GFR below 60 ml/min/1,73 m2 and A/Cr from 5 to 16,4 mg/mmol. The main
clinical and laboratory parameters were studied: anthropometric, hemodynamic, level
of carbohydrate metabolism compensation, serum creatinine, urine creatinine, urine
albumin, lipid metabolism indicators. All patients underwent a Doppler study of one of
the segmental arteries of the right kidney. The index RI of intrarenal vascular resistance
was determined. The diagnosis of CKD was established based on the determination of
GFR using the CKD-EPI formula and the calculation of A/Cr in accordance with the
recommendations of the International Society of Nephrology (KDIGO). Statistical
processing of the experimental data was carried out using the STATISTICA 10
software and included methods of variation statistics, correlation analysis, and
nonparametric tests (Mann-Whitney, Ro-Spearman) [11]. The analysis of factors
affecting renal outcome was carried out in several stages. At the first stage, a
statistically significant difference in the studied parameters was revealed. Threshold
values were generated for each selected factor. At the second stage, a one-way analysis
of renal outcome according to Kaplan-Meier using a long-rank test was performed [12].
206
At the final stage, multivariate analysis was carried out using the Cox regression model.
The magnitude of the relative risk was determined with the indication of 95%
сonfidence interval (CI). Sample data are presented in the form Me [Xmin; Xmax],
where Me is the median of the sample data, [Xmin; Xmax] is the range of the sample.
In paired comparisons, the level of significance α = 0.05 is accepted.
3 Results and Discussion
The study revealed that patients with type 2 diabetes with signs of CKD (group 2) were
older compared to group 1 (65 [60;70] vs 56[50;63] years, р2,1=5,5*10-8,
respectively). In addition, they differed in a longer duration of the disease (15[10;23]
vs 6[5;8] years, р2,1=0,0001, respectively). The presence of obesity (31[29;33] vs
29[26;32] kg/m2, р2,1=0,009, respectively); a higher level of glycated hemoglobin
(HbA1c) (9,0[7,8;10,0] vs 7,9[6,9;8,9] %, р2,1=0,002, respectively) and a higher RI
value (0,71[0,67;0,73] vs 0,66[0,61;0,69], р2,1=0,000001, respectively) (tabl. 1).
Table 1. Clinical and laboratory characteristics of the examined groups, Me [xmin; xmax].
1 group 2 group
DM without CKD DM with CKD
Index р
(n=60) (n=22)
Duration of diabetes,
years
6 [5; 8] 15 [10; 23] 0,0001*
5,5*10 *
-8
Age, years 56 [50; 63] 65 [60; 70]
BMI, kg/m2 29 [26; 31] 31,0 [29; 33] 0,009*
BP(S), mm Hg 140 [130; 145] 140 [135; 145] 0,125
BP(D), mm Hg 90 [80; 100] 90 [80; 100] 0,420
Heart rate, blows/minute 72 [70; 75] 73 [72; 77] 0,187
HbA1с, % 7,9 [6,9; 8,9] 9,0 [7,8; 10,0] 0,002*
Fasting glycemia,
7,4 [6,4; 8,6] 7,6 [6,6; 9,0] 0,123
mmol/l
Cholesterol, mmol/l 4,1 [3,4; 5,2] 4,5 [3,7; 5,7] 0,125
Triglycerides, mmol/l 1,4 [1,1;2,7] 1,5 [1,2; 2,2] 0,745
Albumin/creatinine ratio, -6
mg/mmol
0,4 [0,2; 1,8] 2,3 [5,0; 16,4] 1,20*10 *
Blood creatinine, μmol/l 70 [64;80] 109 [84; 127 4,5*10 *
-9
GFR (CKD-EPI),
2,52*10 *
-10
92 [81; 104] 48 [37; 59]
ml/min/1,732
RI 0,66 [0,61; 0,69] 0,71 [0,67; 0,73] 0,00002*
Legend: Ме – sample median; [xmin; xmax] – sample span; * – difference with the 2
group, р<0,05.
207
In the general group of the surveyed, a relationship was found between serum creatinine
and: age (ρ=0,38, р=0,0001), the duration of diabetes (ρ=0,35, р=0,0004), BMI
(ρ=0,28, р=0,005), RI (ρ=0,41, р=0,0001). And also between GFR and: age (ρ=-0,55,
р=0,002), BMI (ρ=-0,30, р=0,0001), HbA1c (ρ=-0,32, р=0,001), RI (ρ=-0,38,
р=0,001), relationship between А/Cr and: RI (ρ=0,30, р=0,001), HbA1c (ρ=0,31,
р=0,001) (see Fig. 1).
Fig. 1. Correlation pleiad of relationships between renal parameters and characteristics of
patients with diabetes.
For univariate analysis of renal outcome using the Kaplan-Meier method, patients were
divided into two groups depending on the duration of diabetes mellitus: duration of
diabetes <10 years and duration of diabetes ≥10 years. This threshold value was chosen
because it is known that the development of irreversible morphological changes begins
10 years after the onset of DM [13]. Deterioration of renal function during the follow-
up period was found equal to 40% in the group with DM duration ≥10 years (p =
0,0001) (see Fig. 2).
208
Fig. 2. Survival depending on the duration DM.
For BMI, the upper limit of 30 kg/m2 is taken as the threshold value in accordance with
the standards of the World Health Organization (WHO). Two groups of patients were
identified. In the group with BMI ≥30 kg/m2, the deterioration of renal function was
45% (p = 0,0001) (see Fig. 3).
Fig. 3. Survival by BMI.
For HbA1c, the threshold was 8% based on algorithms for specialized medical care for
patients with diabetes [3]. Two groups of patients were identified. Deterioration of renal
209
function during the observation period was detected in the group with HbA1c ≥8% and
amounted to 45% (р = 0,001) (see Fig. 4).
Fig. 4. Survival based on HbA1c.
For age, the upper limit of the norm of 60 years (elderly age) was taken as a threshold
value and 2 groups of patients were identified. In the group of patients aged ≥60 years,
the deterioration of renal function was 50% (p = 0.0001) (see Fig. 5).
Fig. 5. Survival by age.
It is known that an increase in the resistance index values above 0,70 indicates a
decrease in renal function in patients with type 2 DM [6]. Patients by RI were divided
into two groups: RI <0,70 and RI ≥ 0,70. Deterioration of renal function over the
210
observation period was found in the group with RI ≥ 0,70 and amounted to 60% (p =
0,001) (see Fig. 6).
Fig. 6. Survival depending on RI.
At the final stage, a Cox model was formed, which was based on all factors affecting
the renal outcome identified at the previous stages: age, duration of the disease, BMI,
HbA1c, RI. The analysis showed that a significant risk factor for deterioration of renal
function is RI≥0,70 (Er=1,9; CI=1,6–2,3; р=0,001) (see Fig.7). In particular, patients
with a high RI had a 1,9 times greater risk of death than patients with normal values.
Fig. 7. RI ≥0,70 the most significant risk factor for deteriorating kidney function.
In conclusion, would like to note that the complexity of the problem we are solving
required a more sophisticated method of statistical analysis. Along with analysis of
variance, logistic regression, survival analysis takes a significant place. This is a
method by which, over a certain period of time, the patterns of the appearance of a
211
certain outcome in representatives of the observed sample are studied. There are several
mathematical and statistical methods that can be used to analyze survival, in cases
where there is incomplete information about the sample: using life tables, the Kaplan-
Meier method, Cox regression and Cox regression with time-dependent predictors [14].
In this article, we examined the possibility of using the Kaplan-Meier method and Cox
regression on a real example of analyzing risk factors affecting renal outcome in
patients with type 2 DM.
4 Conclusions
Thus, using the Kaplan-Meier method and Cox regression, we confirmed that the risk
factors for deteriorating renal function in patients with type 2 diabetes are:
age,
duration of diabetes,
obesity,
level of compensation of carbohydrate metabolism,
index of resistivity.
According to the results of multivariate analysis in the Cox model, the most significant
factor for predicting renal outcome is a resistance index equal to 0,70 and higher. The
definition of this indicator can be used for non-invasive diagnosis and assessment of
kidney damage in patients with type 2 diabetes.
References
1. Shamkhalova, M.S.: Trends in the epidemiology of chronic kidney disease in Russian
Federation according to the Federal Diabetes Register (2013–2016). Diabetes Mellitus
21(3), 160-169 (2018).
2. Klimontov, V.V.: Cystatin C and collagen type IV in diagnostics of chronic kidney
disease in type 2 diabetic patients. Diabetes Mellitus 18(1), 87-93 (2015).
3. Dedov, I.I.: Standards of specialized diabetes care. Diabetes Mellitus 22(S1), 68–79
(2019).
4. Shilov, E.M., Smirnov, A.V., Kozlovskaya, N.L.: Nephrology. Clinical guidelines.
GEOTAR-MEDIA, Moscow (2020).
5. Shestakova, M.V., Martynov, S.A.: Diabetic Nephropathy: Do We Consider All Risk
Factors? Diabetes Mellitus 4, 29-33 (2006).
6. Mancini, M.: Renal duplex sonographic evaluation of type 2 diabetic patients. Journal
of Ultrasound in Medicine 32(6), 1033–1040 (2013).
7. Kopel, J., Pena-Hernandez, C, Nugent, K.: Evolving spectrum of diabetic nephropathy.
World journal of diabetes 10(5), 269 (2019).
8. Kim, J.H.: Resistive index as a predictor of renal progression in patients with moderate
renal dysfunction regardless of angiotensin converting enzyme inhibitor or angiotensin
receptor antagonist medication. Kidney research and clinical practice 36(1), 58–67 (2017).
9. Ikee, R.: Correlation between the resistive index by Doppler ultrasound and kidney
function and histology. American journal of kidney diseases 46(4), 603–609 (2005).
10. Xu, H.: Renal resistive index as a novel indicator for renal complications in high-fat
diet-fed mice. Kidney and Blood Pressure Research 42(6), 1128–1140 (2017).
212
11. Trukhacheva, N.V.: Mathematical statistics in biomedical research using the Statistica
package. GEOTAR-MEDIA, Moscow (2013).
12. Glantz, S.: Medical and biological statistics. Practice, Moscow (1999).
13. Nadeeva, R.A., Sigitova, O.N.: Clinical guidelines for the treatment of diabetic
nephropathy. Archive of internal diseases 5, 3-8 (2015).
14. Sharashova, E.E.: Survival analysis in health sciences using SPSS software. Science &
Healthcare 5-28 (2017).
213