Incidence and Predictors of Acute Kidney Injury Among Type 2 Diabetes Mellitus Patients in Amhara Region Comprehensive Specialized Hospitals, 2021: Retrospective Follow-up Study

DOI: https://doi.org/10.21203/rs.3.rs-2267363/v1

Abstract

Background

- Acute kidney injury is an emerging global public health problem significantly associated with increased in morbidity, mortality, and extra cost incurred. Type 2 diabetes mellitus is an independent risk factor for acute kidney injury that is not well investigated in developing countries including Ethiopia.

Objective

- To assess the incidence and predictors of acute kidney injury among type 2 diabetes mellitus patients having follow-ups in Amhara region Comprehensive Specialized Hospitals.

Methods

- Institution-based retrospective follow-up study was conducted among 538 type 2 diabetes mellitus patients from January 1, 2014, to January 1, 2020, by systematic random sampling. Kaplan-Meier curve and Log-rank test were used to compare survival time between different categories of explanatory variables. Cox proportional hazard was used to determine significant predictors and proportional hazard assumptions were checked by plotting cox Snell’s residual and global test.

Results

- the incidence rate of acute kidney injury among type 2 diabetes mellitus patients was 38 per 10,000 person-months observations. The significant predictors were poor glycemic control [AHR (95% CI) 1.70(1.06, 2.74)], Hypertension [AHR (95% CI) 2.36(1.17, 4.79)], Congestive heart failure [AHR (95% CI) 1.79(1.11, 2.89)], Chronic kidney disease [AHR (95% CI) 2.02(1.23, 3.33)], Dyslipidemia [AHR (95% CI) 2.57(1.40, 4.70)], Diabetic nephropathy [AHR (95% CI) 2.08(1.24, 3.51)], Sepsis [AHR (95% CI) 2.96(1.87, 4.70)] and Body mass index((> 30 & 25-29.9 kg/m2); [AHR (95% CI) 4.24(1.98, 9.07) and 2.84(1.50, 5.38) respectively)].

Conclusion

- the incidence of acute kidney injury among type 2 diabetes mellitus patients was relatively higher in this study area than in previous studies. Implementing good glycemic control, close monitoring of comorbidities, infection prevention, and weight reduction were vital to reducing the incidence of acute kidney injury in type 2 diabetic patients.

Background

The rise in the burden of type 2 diabetes mellitus is a major concern in healthcare worldwide which accounts for approximately 462 million individual affected by type 2 diabetes mellitus with a prevalence rate of 6059 cases per 100,000 in 2017. The problem is rising globally and is continued rise across all regions of the world which can be projected to increase to 7079 individuals per 100,000 by 2030 [1]. Diabetes mellitus is a higher risk for acute kidney injury (AKI) typically associated with significant macro- and micro-vascular pathology on both atherosclerosis and arteriolosclerosis evolve over years, particularly if blood glucose levels are controlled poorly [2]. The advancement of kidney disease is related to poorly controlled Type 2 diabetes mellitus (T2DM) and other complications [3]. The rate of AKI was significantly higher in patients with diabetes mellitus patients than in those without diabetes mellitus patients [4]. T2DM patients were at higher risk for developing AKI compared with those without, with a 4.7-fold increase in AKI rate[5].

AKI is the sudden loss of kidney function characterized by a rapid decline in the glomerular filtration rate (GFR), increase metabolic waste products including urea and creatinine, and disturbance of fluid, electrolyte and acid-base homeostasis usually occurring over hours to days [6, 7]. The term AKI replaced the previous terms acute renal failure because a smaller change in kidney function without overt failure can result in significant clinical consequences and increased morbidity and mortality [8]. According to the Kidney Disease Improving Global Outcomes (KDIGO) criteria, AKI is defined as an increase in SCr by ≥ 0.3 mg/dl (≥ 26.5 µmol/l) within 48 hours; an increase in SCr greater than or equal to 1.5 times the baseline within previous 7 days or the urine volume < 0.5 ml/kg/hour for 6 hours [9].

AKI in diabetes patients may result from transient renal hypo perfusion or ischemia due to tubular cell dysfunction and damage as well as significant interstitial inflammation and structural damage of small peritubular and glomerular blood vessels [10]. It can also be resulting from oxidative stress response activated by inflammatory mediators produced from damaged epithelial cells and the release of various vasoconstrictor substances which aggravates ischemic damage especially proximal straight tubule and medullary thick ascending limb [11]. Previous studies have suggested that the risk of AKI for patients with Type 2 diabetes were advanced in age, obesity, hyperglycemia, diabetes duration, poor glycemic control, smoking, alcohol consumption, infection, medications such as angiotensin-converting enzyme inhibitors, aminoglycosides, statins, and non-steroidal anti-inflammatory drugs and specific comorbidities such as chronic kidney disease, diabetic nephropathy, hypertension, and congestive heart failure were [1215].

The potential burden of AKI becomes increased globally to an estimated 13.3 million cases of AKI per year, resulting in a potential 1.7 million deaths [16]. The study done in China showed that 1.3% of type 2 diabetes patients develop AKI [17]. A retrospective cohort study conducted in the United Kingdom showed that 48.6% of T2DM were at significantly higher risk to develop AKI than those without (17.2%) [5]. In Sub-Saharan African countries type 2 diabetes patients had a 50% significantly increased risk of impaired kidney function than those without type 2 diabetes mellitus [18]. A study conducted in Ethiopia indicated that the prevalence of AKI in patients who had renal function tests was approaching 20% [19]. Even though there is limited information about the incidence of AKI among type 2 diabetes, the study conducted in the Harari region of southeast Ethiopia showed that the incidence of AKI among type 2 diabetes was 14.5% [15].

AKI is significantly associated with morbidity, mortality, the extra cost incurred in the hospitalization process, longer stay in the hospital, and long-term consequences [20]. It is a powerful predictor for all causes of death including all major cerebrovascular, cardiovascular, and renal events in patients with type 2 diabetes [21]. A patient with AKI has a higher risk of developing chronic kidney disease (CKD), end-stage renal disease (ESRD), and mortality than those patients without AKI which requires greater attention [22]. Acute kidney injury is an emerging global public health problem that is not well investigated in low-income countries including Ethiopia. There is scarce data on the incidence of AKI and its predictors with changes in the rate of impaired kidney functions among type 2 diabetes mellitus patients in Ethiopia. Determining the incidence rate of AKI and early detection of the predictors among T2DM is very important to prevent its development and complications. Therefore the study will aim to assess the incidence of AKI and its predictors among type 2 diabetes mellitus patients.

OBJECTIVES

General Objective: - To assess incidence and predictors of acute kidney injury among type 2 diabetes mellitus patients having follow-up in Amhara region Comprehensive Specialized Hospitals, 2021.

Specific Objectives

Methods

3.1 Study Area And Study Period

The study was conducted in the Amhara region in four comprehensive specialized hospitals. Felege Hiwot Comprehensive Specialized Hospital has 400 beds and around 15 adult outpatient departments (OPD) serving over 7 million people from the surrounding area. The OPD serves around 900 patients per day [23]. Debre Markos Comprehensive Specialized Hospital serves over 5 million people living in Debre Markos city and its surrounding [24]. Dessie Comprehensive Specialized Hospital serves more than 5 million populations [25]. Debre Berhan Comprehensive Specialized Hospital serves approximately 3 million catchment populations [26].

3.2 Study Design And Study Periods

A retrospective follow-up study was conducted to assess the incidence and predictors of AKI among type 2 diabetes patients from April 1 to 28, 2021.

3.3 Source Of Population

The source populations were all newly diagnosed Type 2 diabetes mellitus patients having follow-ups in Amhara region comprehensive specialized hospitals from January 1, 2014, to January 1, 2020.

3.4 Study Population

The study populations were all newly diagnosed Type 2 diabetes mellitus patients who were enrolled in Amhara region comprehensive specialized hospitals from January 1, 2014, to January 1, 2020, during data collection time.

3.4 Inclusion And Exclusion Criteria

3.4.1 Inclusion criteria

All adult T2DM patients having follow-ups in Amhara region comprehensive specialized hospitals from January 1, 2014, to January 1, 2020

3.4.2 Exclusion Criteria

Patients’ charts incomplete with major variables (serum creatinine, fasting blood sugar), lost medical records, patients who had AKI at the time of the diagnosis for T2DM and patients have no baseline records were excluded from the study.

3.5. Sample Size Determination

The minimum sample size was 544 using the command (stpowerlogrank, hratio (1.29) power (0.8) wd prob (0.1)) in STATA version 4 software by having the following assumption statistical power of 80%, 95% confidence interval and withdrawal of probability of 10% and adjusted of 1.29 from the previous study [27, 28]. Predictors and their hazard ratios for AKI among T2DM taken from previous studies were history of heart failure, prior bolus insulin regimen, baseline eGFR, and baseline HbA1c > = 9%.

Sampling Technique And Sampling Procedures

The study participants were filtered from the registration book in each hospital. All registered newly diagnosed T2DM patients from January 1, 2014, to January 1, 2020, were listed based on their medical record number and proportionally allocated the total sample size for each hospital. Then the sampling interval (K) was determined by dividing the total number of T2DM patients in six-year by the desired sample size from each hospital proportionally. Finally, the patient’s charts were filtered for every K interval from each hospital by using systematic random sampling.

3.7 Operational And Term Definitions

Acute Kidney Injury:- is an abrupt loss of kidney functions characterized by an increase in Serum creatinine by 0.3 mg/dl or 1.5 to 2 times baseline value which was diagnosed and recorded by clinicians on the patient card [29]
Incidence of AKI: - numbers of new onset of acute kidney injury after diagnosis of T2DM within the follow-up time.

Time to acute kidney injury

- the time from the diagnosis of T2DM to the first episode of acute kidney injury.

Event

- development of acute kidney injury at any time during the follow-up after the diagnosis of T2DM

Censored

the Patients who did not develop AKI until the end of the study, transferred out, died or lost to follow-up before experiencing AKI within the follow-up period.

Body Mass Index (BMI)

dividing the weight in kilograms by the square of the height in meters that can be categorized as underweight, < 18.5 kg/m2, normal, 18.5–24.9 kg/m2, overweight, 25-29.9 kg/m2, and obesity, ≥ 30 kg/m2[30]

Sepsis:- was defined as a clinically recorded sepsis on the patient’s chart or a combination of evidence of infection and 2 out of 3 criteria in quick Sequential Organ Failure Assessment (qSOFA) score(respiratory rate > 22b/minute, Glasgow coma scale < 15 and systolic blood pressure < 100 mmHg) per the 3rd International consensus definition released in 2016 [31].

Incomplete patient charts

refers to patient cards which were not recorded including unknown date of DM and AKI diagnosis.

Obesity

the patient’s baseline body mass index exceeded 30 kg/m2.

Poor glycemic control:-the patient card was recorded as poor glycemic control or the patients who had average blood glucose measurements on three consecutive visits > 130 or < 70 mg/dl [32].

3.8 Variables

Dependent Variable: incidence of AKI

Independent Variables:

The independent factors of the study are socio-demographic factors (age, sex, residence, occupation, marital status, educational status, and family history of diabetes mellitus), behavioral factors (smoking history, history of alcohol intake, and glycemic control), clinical factors and laboratory factors includes fasting blood sugar, level of HgA1C, infection, Diabetic Keto Acidosis, Hyperglycemia Hyperosmolar State, hypoglycemia, sepsis, comorbid conditions (dyslipidemia, myocardial infarction, chronic kidney disease, congestive heart failure, diabetic foot ulcer, chronic liver disease, diabetic nephropathy, and hypertension), BMI, and treatment-related factors including insulin, oral hypoglycemic agent, non-steroidal anti-inflammatory drugs, statin, beta-blockers, diuretics, angiotensin-converting enzyme inhibitors, and other nephrotoxic antibiotics).

Result

Baseline Socio-demographic Characteristics

In this study, a total of 538 participants were included with a response rate of 98.8%. The median age of the participant at the time of diagnosis was 56 (IQR ± 13) with a minimum age of 25 and maximum age of 93 years. About half 271(50.4% of the study participants were female. Regarding their residential status, two-thirds 356(66.2%) of the participants were urban (Table 1). 

 
Table 1

The baseline socio-demographic characteristics of diabetic patients in Amhara region comprehensive specialized hospitals from January 1, 2014, to January 1, 2020(n = 538)

Covariates

Categories

AKI status

Frequency

Percentages (%)

Censored

Event

sex

Male

221

46

267

49.6

Female

226

45

271

50.4

Age groups

18–44 years

121

12

133

24.7

45–64 years

216

46

262

48.7

65–84 years

100

19

119

22.1

>=85 years

10

14

24

4.5

Residence

Urban

298

58

356

66.2

Rural

149

33

182

33.8

Occupational status

Gov’t Employee

167

24

191

35.5

Private Work

103

18

121

22.5

Farmer

151

35

186

34.6

Retired

26

14

40

7.4

Marital status

Married

339

63

402

74.7

Single

24

8

32

5.9

Widowed

47

11

58

10.8

Divorced

37

9

46

8.6

Educational status

unable to read and write

123

29

152

28.3

primary school

79

17

96

17.8

secondary school

103

21

124

23.0

diploma and above

142

24

166

30.9

Family history of DM

Yes

82

11

93

17.3

No

365

80

445

82.7

4.2 Baseline Behavioral Characteristics

In this study around one-third, 153(28.4%) of the participants have poor glycemic control. Around one-sixth, 90 (16.7%) of the participants have a history of alcohol intake and 5.6% of the participants have a history of smoking (Table 2). 

 
Table 2

Baseline behavioral characteristics of diabetic patients in Amhara region comprehensive specialized hospitals from January 1, 2014, to January 1, 2020(n = 538)

Covariates

Categories

AKI status

Frequency

Percentages (%)

Censored

Event

Smoking history

Yes

21

9

30

5.6

No

426

82

508

94.4

Alcohol intake history

Yes

80

10

90

16.7

No

367

81

448

83.3

Glycemic control

Poor

91

62

153

28.4

Good

356

29

385

71.6

4.3. Clinical And Laboratory Characteristics

According to this study, more than one-third of the study participants had hypertension 182(33.8%) and dyslipidemia 188(34.9%). More than one-fourth 150 (27.9%) of the participants develop hypoglycemia and 154(28.6%) of the participants develop DKA during the study follow-up time. From this study 63(11.7%) of participants were develop sepsis and around 34(6.3%) of the participant’s body mass index was greater than 30mg/dl. Around, 163(30.3%) of the participant’s level of Hemoglobin A1c was greater than 9%, and more than half, 311(57.8%) of them were low-density lipoprotein greater than 100mg/dl. On the other hand, more than two-thirds, 407 (75.7%) of the study participant’s fasting blood sugar level was greater than 126 mg/dl (Table 3). 

 
Table 3

Clinical and laboratory characteristics of diabetic patients in Amhara region comprehensive specialized hospitals from January 1, 2014, to January 1, 2020(n = 538)

Covariate

Category

AKI status

Frequency

Percentage (%)

Censored

Event

Hypertension

Yes

103

79

182

33.8

No

344

12

356

66.2

Congestive heart failure

Yes

11

41

52

9.7

No

436

50

486

90.3

Chronic kidney disease

Yes

16

56

72

13.4

No

431

35

466

86.6

Myocardial infarction

Yes

12

16

28

5.2

No

435

75

510

94.8

Chronic liver disease

Yes

23

8

31

5.8

No

424

83

507

94.2

Dyslipidemia

Yes

116

72

188

34.9

No

331

19

350

65.0

Sepsis

Yes

11

52

63

11.7

No

436

39

475

88.3

Diabetic nephropathy

Yes

50

67

117

21.7

No

397

24

421

78.3

Diabetic Ketoacidosis history

Yes

104

50

154

28.6

No

343

41

384

71.4

Hyperglycemic Hyperosmolar state

Yes

23

14

37

6.9

No

424

77

501

93.1

Diabetic foot ulcer

Yes

66

23

89

16.5

No

381

68

449

83.5

Stroke

Yes

47

18

65

12.1

No

400

73

473

87.9

Hypovolemia

Yes

67

13

80

14.9

No

380

78

458

85.1

Hypoglycemia

Yes

119

31

150

27.9

No

328

60

388

72.1

Infection

Yes

237

56

293

54.5

No

210

35

245

45.5

Body mass index

>=30 kg/m2

12

22

34

6.3

25-29.9 kg/m2

179

57

236

43.9

<=24.9 kg/m2

256

12

268

49.8

Fasting blood sugar

>=126 mg/dl

330

77

407

75.7

< 126mg/dl

117

14

131

24.3

Total cholesterol(TC)

>=200mg/dl

78

24

102

19.0

< 200 mg/dl

369

67

436

81.0

Total triglycerol (TG)

>=150 mg/dl

125

54

179

33.3

< 150 mg/dl

322

37

359

66.7

Hyperdensity lipoprotein (HDL)

< 40 mg/dl

43

18

61

11.3

>=40 mg/dl

404

73

477

88.7

Low-density lipoprotein (LDL)

>=100 mg/dl

< 100 mg/dl

240

207

71

20

311

227

57.8

42.2

Hemoglobin A 1C%

> 9

126

37

163

30.3

8-8.9

92

20

112

20.8

7-7.9

102

14

116

21.6

6-6.9

99

12

111

20.6

< 6

28

8

36

6.7

4.4 Treatment-related Factors Of Aki Among T2dm

Regarding treatment-related factors two-thirds, 348 (64.7%) of the study participants used oral hypoglycemic agents and also more than one-third, 208 (38.7%) of the participants used statin medications. More than half of the participants 277 (51.5%) were used anti-pain (NSAIDs) (Table 4). 

 
Table 4

Treatment-related factors of type 2 diabetes patients in Amhara region comprehensive specialized hospitals from January 1, 2014, to January 1, 2020(n = 538)

Covariates

Categories

AKI status

Frequency (%)

Percentages (%)

Censored

Event

Types of anti-diabetes

OHGA

297

51

348

64.7

Insulin

111

31

142

26.4

Combination of OHGA & insulin

39

9

48

8.9

Statin medication

Yes

129

79

208

38.7

No

318

12

330

61.3

Beta-blocker

Yes

21

27

48

8.9

No

426

64

490

91.1

Diuretics

Yes

42

51

93

17.3

No

405

40

445

82.7

ACEIs

Yes

79

52

131

24.3

No

368

39

407

75.7

NSAIDs

Yes

208

69

277

51.5

No

239

22

261

48.5

Antibiotics

Yes

236

64

300

55.8

No

211

27

238

44.2

NB: - OHGA; Oral Hypoglycemic Agents, ACEIs; Angiotensin Converting Enzyme Inhibitors, NSAIDs; None Steroidal Anti-Inflammatory Drugs 

4.5 Incidence Of Aki Among Type 2 Diabetes Mellitus

In this study of 538 participants, 91 of them developed AKI with a median follow-up time of 45.8 months as well as minimum and maximum follow-up times of 5.37 and 72.97 months respectively (Fig. 1). The overall restricted mean hazard time was used due to the largest observed analysis time was censored and the restricted mean was 64.69 months with 95% CI (63.21, 66.18). The cumulative incidence rate of AKI among type 2 diabetes mellitus patients was 16.91% with a 95% confidence interval (13.84–20.35). The overall incidence rate of acute kidney injury in the follow-up during the 23764.4person-month of observation (PMO) was found to be 38.2 per 10,000 person-months observation with 95% CI (31.18, 47.03).

4.6 Predictors of acute kidney injury among T2DM having follow-up in Amhara region comprehensive specialized hospitals

To determine the predictors of AKI the variance inflation factor was used to check the absence of multicollinearity and the Schoenfeld residual test was done to test the proportional hazards assumption (a global test; chi2 = 13.63, df = 9, and P-value of 0.1361) that indicates the proportional hazard assumption was satisfied. In addition, the goodness of fitness of the model was checked by Cox Snell residual plot which was satisfied because the cumulative hazard plot follows 45 degrees or a straight line through the origin with slope one (Fig. 3). The Kaplan-Meier curve of the hazard probability of AKI in different groups and log rank test was checked which interpreted as the follow-up time in the month increased there is an increased hazard probability to develop AKI within the groups (Fig. 2).

In the bivariate Cox proportional hazard regression model occupation, age, glycemic control, hypertension, congestive heart failure, chronic kidney disease, diabetic nephropathy, myocardial infarction, dyslipidemia, sepsis, diabetic ketoacidosis, hyperglycemic hyperosmolar state, infection, types of antidiabetic medication, statin drugs, beta-blockers, diuretics, angiotensin-converting enzyme inhibitors, ant-pain(NSAIDs), hemoglobin A1C, low-density lipoprotein, triglycerol and fasting blood sugar were significantly associated with AKI (P < 0.2) and included in multivariate analysis. From those variables included in multivariable cox regression analysis poor glycemic control, hypertension, congestive heart failure, diabetic nephropathy, chronic kidney disease, dyslipidemia, sepsis, and body mass index were statistically significant predictors of acute kidney injury among type 2 diabetes mellitus with p value < 0.05.

According to this study, the participants with poor glycemic control were to be 1.70 times more hazard to develop AKI as compared to those with good glycemic control (AHR: 95% CI: 1.70(1.06, 2.74)). On the other hand, the risk of developing AKI among the participants who had hypertension was found to be 2.36 higher hazard than those who had not hypertension (AHR: 95% (CI): 2.36 (1.17, 4.79)). The risk of developing AKI among the participants who had congestive heart failure was found to be 1.79 times higher hazard of developing AKI than those who had not congestive heart failure (AHR: 95% (CI): 1.79(1.11, 2.89). The risk of developing AKI among the participants who had chronic kidney disease was found to be 2.02 times higher hazard than those who had not chronic kidney disease (AHR: 95% (CI): 2.02(1.23, 3.33). The risk of developing AKI among the participants who had diabetic nephropathy was found to be 2.08 times higher hazard of developing AKI than those who had not diabetic nephropathy (AHR: 95% (CI): 2.08(1.24, 3.51). The hazard of developing AKI was 2.57 times higher among the participants who had dyslipidemia as compared to those who had not dyslipidemia (AHR: 95% (CI): 2.57(1.40, 4.70). The hazard of developing AKI was 2.96 times higher among the participants who had sepsis as compared to those who had not sepsis (AHR: 95% (CI): 2.96(1.87, 4.70). Those participants whose body mass index was greater than or equal to 30 kg/m2 were found to be 4.24 times higher hazard of developing AKI compared to those who had body max index < = 24.9kg/m2 (AHR: 95% (CI): 4.24 (1.98, 9.07) (Table 5).

 
Table 5

Results of the bivariate and multivariate cox regression analysis of diabetic patients having follow-up Amhara region comprehensive specialized hospitals from January 1, 2014, to January 1, 2020 (n = 538)

Variable

Category

AKI status

CHR (95% CI)

AHR (95% CI)

Censored

Event

Occupation

Gov’t Employee

167

24

1

1

Private Work

103

18

0.92(0.49, 1.71)

0.83(38, 1.78)

Farmer

151

35

1.57(0.93, 2.62)

0.93(0.50, 1.74)

Retired

26

14

3.24(1.67, 6.27)

0.84(0.30, 2.36)

Age groups

18–44 years

121

12

1

1

45–64 years

216

46

1.54(0.82, 2.92)

0.88(0.42, 1.83)

65–84 years

100

19

1.81(0.88, 3.74)

1.92(0.82, 4.45)

>=85 years

10

14

6.82(3.15, 14.77)

1.10(0.36, 3.42)

Glycemic control

poor

91

62

4.61(2.96, 7.17)

1.70(1.06, 2.74) *

good

356

29

1

1

Hypertension

Yes

103

79

12.93(7.04, 23.75)

2.36(1.17, 4.79) *

No

344

12

1

1

Congestive heart failure

Yes

11

41

9.87(6.46, 15.09)

1.79(1.11, 2.89) *

No

436

50

1

1

Chronic kidney disease

Yes

16

56

12.62(8.20, 19.43)

2.02(1.23, 3.33) *

No

431

35

1

1

Myocardial infarction

Yes

12

16

3.59(2.08, 6.17)

1.75(0.77, 3.99)

No

435

75

1

1

Dyslipidemia

Yes

116

72

7.14(4.30, 11.85)

2.57(1.40, 4.70) *

No

331

19

1

1

Sepsis

Yes

11

52

10.98(7.22, 16.70)

2.96(1.87, 4.70) *

No

436

39

1

1

Diabetic nephropathy

Yes

50

67

9.00(5.64, 14.36)

2.08(1.24, 3.51) *

No

397

24

1

1

Diabetic Ketoacidosis history

Yes

104

50

3.25(2.15, 4.91)

0.75(0.42, 1.36)

No

343

41

1

1

HHS

Yes

23

14

3.10 (1.74, 5.49)

1.74(0.83, 3.64)

No

424

77

1

1

Body mass index

>=30 kg/m2

12

22

20.27(9.97, 41.21)

4.24(1.98, 9.07) *

25-29.9kg/m2

179

57

6.05(3.24, 11.31)

2.84(1.50, 5.38) *

<=24.9kg/m2

256

12

1

1

Total triglycerol (TG)

>=150 mg/dl

125

54

3.04(2.00, 4.62)

0.59(0.32, 1.10)

< 150 mg/dl

322

37

1

1

Hyperdensity lipoprotein (HDL)

< 40 mg/dl

43

18

1.99(1.18, 3.34)

0.82(0.43, 1.57)

>=40 mg/dl

404

73

1

1

Low-density lipoprotein (LDL)

>=100 mg/dl

240

71

2.92(1.76, 4.83)

1.43(0.67, 3.06)

< 100 mg/dl

207

20

1

1

Antibiotics

Yes

236

64

1.86 (1.18, 2.93)

0.53(0.24, 1.13)

No

211

27

1

1

Statin medication

Yes

129

79

10.38(5.65, 19.07)

0.46(0.12, 1.68)

No

318

12

1

1

Beta-blocker

Yes

21

27

5.29(3.36, 8.33)

0.88(0.46, 1.71)

No

426

64

1

1

Diuretics

Yes

42

51

6.87(4.51, 10.45)

1.32(0.70, 2.48)

No

405

40

1

1

ACEIs

Yes

79

52

3.69(2.43, 5.59)

0.69(0.40, 1.16)

No

368

39

1

1

NSAIDs

Yes

208

69

2.58 (1.59, 4.17)

0.74(0.41, 1.36)

No

239

22

1

1

N.B; *p-value less than 0.05 at multivariate cox regression analysis; 1 shows the reference group of different variables, AHR; Adjusted hazard ratio; ACEIs; Angiotensin-converting enzyme inhibitors, CHR; crude hazard ratio, HHS; hyperglycemic hyperosmolar state.

Discussion

The study aimed to estimate the incidence of acute kidney injury and its predictors among type 2 diabetes mellitus patients. The finding of this study revealed that the incidence rate of AKI was found to be 38(95% CI; 31.1, 47.02) per 10,000 person-months observation among type 2 diabetic clients. This finding is higher than the studies conducted in the UK (198 per 100,000 person-years) [14], the university of Manitoba (117 AKI events in 7,745 patient-years) [33], and Scotland at Layside (39.0 cases/1000 person-year) [34]. The discrepancy may be due to screening and diagnostic modalities for early testing and detection formerly to disease progression, well-developed diabetic care setup, socioeconomic status, sample size, and follow-up periods. However, this finding is lower than a study conducted in the southeast Ethiopia Harari region (6 per 100 patients per year )[15]. The reason might be due to the methods of outcome identification, an increase in access and advancement of patient management, the difference in sample size, and increased patient awareness towards the disease’s progression, prevention, and management of its complications.

According to this study finding those participants who had sepsis was 3.4 times more likely to develop AKI than those patients who did not develop sepsis. This result was lower than the studies conducted in southwest Nigeria [35] and Sudan [36]. This might be due to the difference in infection prevention and management practice, socioeconomic status, study period, and sample size. Sepsis causes hypo perfusion of the renal tissue and subsequently induced ischemia and also probably due to the associated severe inflammatory state and hemodynamic instability. The study found that those participants who have poor glycemic control were 1.7 times higher hazard to develop AKI than those who have good glycemic control. This study was similar to the study conducted in the U.S.A and Swedish Routine Care [27]. This might be due to hyperglycemia causing a change in the glomerular hemodynamics modulated by local activation of the renin-angiotensin system, biochemical derangements, proteinuria, hypoxia, inducing pro-fibrotic and pro-inflammatory responses in the kidney [37].

Patients whose BMI ≥ 30kg/m2 were 4.24 times significantly at higher risk to develop AKI than those whose BMI < 25 kg/m2 with AHR; 95% CI, 4.24 (1.98, 9.07). The finding was higher than those of studies conducted in USA[14] and southwest Ethiopia [15]. The discrepancy could be due to the sedentary lifestyles and obesity becomes increased in developing countries including Ethiopia. An increased BMI promotes kidney damage through mechanisms like hemodynamic and hormonal effects. Obesity might cause some hemodynamic changes in the glomerulus such as glomerular hyperperfusion and hyperfiltration due to impaired natriuretic-associated activation of the renin and angiotensin system which may result in glomerular injury It may also increase the risk of production of activated inflammatory cytokines and oxidative stress that can damage the glomerulus [38].

The risk of developing AKI among the participants who had diabetic nephropathy was found to be 2.08 times higher hazard of developing AKI than those who had not diabetic nephropathy. the study was similar to a study conducted in Auckland hospitals in New Zealand [39], and Ethiopia [15]. The possible reason could be patients with diabetic nephropathy had greater degrees of glomerular sclerosis, interstitial fibrosis, and poorer capillary patency. This may be due to the decreased production of nitric oxide leading to vasoconstriction that causes vascular resistance and ischemic-reperfusion injury, the overproduction of reactive oxygen species, the activation of fibrotic pathway and mitochondrial dysfunction which may glomerular or tubercular injury [40]. The hazard of developing AKI among the participants who had hypertension was found to be 2.36 higher hazard than those who had not hypertension (AHR: 95% (CI): 2.36 (1.17, 4.79)). The finding was similar to the study conducted in the USA [14], and Ethiopia [15].

On the other hand, there is a hazard of developing AKI among the participants who had congestive heart failure 1.79 times the higher hazard of developing AKI than those who had not congestive heart failure (AHR: 95% (CI): 1.79(1.11, 2.89). The finding was similar to the study conducted in the USA [14], and Ethiopia [15]. The reason might be due to an increase in venous congestion and renal blood flow impairment which causes systemic and renal vasoconstriction and activation of neuro-hormonal pathways. Renal hypo perfusion results increase production of oxygen reactive species, overproduction of pro-inflammatory cytokines, and endothelial dysfunction that damages the kidney [41].

The risk of developing AKI among the participant who had chronic kidney disease was found to be 2.02 times higher hazard than those who had not chronic kidney disease (AHR: 95% (CI): 2.02(1.23, 3.33). The finding was similar to the study conducted in the USA [14], and Ethiopia [15]. the possible explanation might be due to the heavier proteinuria and diminished eGFR resulting in hemodynamic instability and failure of auto-regulation in CKD patients [42]. On the other hand, Participants who had dyslipidemia were at higher risk to develop AKI than those who had not. This might be due to dyslipidemia can increase the risk of cardiovascular disease associated with acute kidney injury due to inflammatory effect, oxidative stress, and formation of free radicals which affects the glomerulus [43].

LIMITATIONS OF THE STUDY

Due to the retrospective nature of the study, the source of data was only secondary data according to the physician’s diagnosis which might lead to misdiagnosis of AKI or other precipitants or predictors. Selection bias was also possibly introduced during secondary data collection because patients with incomplete records were excluded and patients lived at home undiagnosed during the time.

STRENGTHS OF THE STUDY

Even though there were the above possible limitations, the study has the following strengths: this gives an insight for researchers, especially for prospective follow-up study, the study was conducted slightly for a longer follow-up period, which increases the number of events and the study includes different variables including sepsis and behavior factors like glycemic control, smoking and alcohol consumption.

Conclusion

In this study, the incidence of acute kidney injury among type two diabetes mellitus patients was relatively high in the study area than the previous studies. Sepsis, poor glycemic control, body mass index, diabetic nephropathy, congestive heart failure, hypertension, chronic kidney disease, and dyslipidemia were the significant predictors of AKI among type 2 diabetes mellitus patients.

Abbreviations

AHR: - Adjusted Hazard Ratio

AKI: - Acute Kidney Injury

BMI: - Body Mass Index

CHR: - Crude Hazard Ratio

ESRD: - End Stage Renal Disease

eGFR: - Estimated Glomerular Filtration Rate

KDIGO: - Kidney Disease Improving Global Outcome

SCr: - Serum Creatinine

T2DM: - Type 2 Diabetes Mellitus

Declarations

We all have made significant contributions to this original research and agree to its submission to BMC Nephrology and if accepted to its publication in this journal. We warrant that this research article is original, does not infringe on any copyright or other proprietary right of any third party, is not under consideration by another journal, and has not been previously published.

ETHICS APPROVAL AND CONSENT TO PARTICIPATE

Ethical clearance was obtained from Debre Markos University College of Health Science from the institutional research ethics review committee (No: HSC/R/C/Ser/PG/Co/293/11/13) and it was sent to the regional public health institute. Permission letter was obtained from Amhara region public health institute and this was given to Felege Hiwot, Debre Markos, Desie, and Debre Brihan comprehensive specialized hospitals for their cooperation. All methods were performed in accordance with the relevant guidelines and regulations of this institutional research ethics review committee. The cooperative letter was obtained from the medical director of each hospital to access the medical records of diabetics. Informed consent of the participant was not applicable because the study was done retrospectively only by reviewing the participant’s card record. Confidentiality of the information was kept throughout the study by excluding names and patients’ medical record numbers as identification in the data extraction form and the data were kept confidential. 

CONSENT FOR PUBLICATION

 Not applicable

 AVAILABILITY OF DATA AND MATERIAL 

The data sets generated during this study are available from the corresponding authors for any reasonable request.

COMPETING INTERESTS

The authors declare that they have no competing interests.

FUNDING STATEMENT

The Debre Markos University has covered the costs of data collectors and supervisors. However, the University had no role in study design, data collection, analysis, manuscript writing, editing, approval, or decision to publish.

AUTHORS' CONTRIBUTIONS

All authors equally contributed to the work report on the design of the study, analysis, and interpretation, and drafting or revising the article, manuscript preparation, have agreed on the journal to which the article will be submitted, gave final approval of the version to be published, and agree to be accountable for all aspects of work.

AKNOWLEDGEMENT

We would like to express our gratitude to Debre Markos University for all the necessary service. Our deepest gratitude also goes to the data collectors and supervisors for their participation during the data collection.

References

  1. Khan, M.A.B., et al., Epidemiology of type 2 diabetes–global burden of disease and forecasted trends. Journal of epidemiology and global health, 2020. 10(1): p. 107.
  2. Patschan, D., et al., Acute Kidney Injury-Associated Systemic Inflammation Is Aggravated in Insulin-Dependent Diabetes Mellitus. J Clin Med Res, 2019. 11(10): p. 720-723.
  3. Weckmann, G.F., et al., Diagnosis and management of non-dialysis chronic kidney disease in ambulatory care: a systematic review of clinical practice guidelines. BMC nephrology, 2018. 19(1): p. 258.
  4. Kim, N.Y., et al., Effect of diabetes mellitus on acute kidney injury after minimally invasive partial nephrectomy: a case-matched retrospective analysis. Journal of clinical medicine, 2019. 8(4): p. 468.
  5. Hapca, S., et al., The relationship between AKI and CKD in patients with type 2 diabetes: an observational cohort study. Journal of the American Society of Nephrology, 2020. 32(1): p. 138-150.
  6. Duga, A.L., Acute kidney Injury: Prevalence, Diagnosis, Causes and treatment.
  7. Ostermann, M., Epidemiology, Incidence, Risk Factors, and Outcomes of Acute Kidney Injury, in Core Concepts in Acute Kidney Injury. 2018, Springer. p. 3-11.
  8. Awdishu, L. and S. Wu, Acute kidney injury. JQ Hudson, Pharm. D., FASN, FCCP, FNKF, & BCPS., Renal/Pulmonary Critical Care, 2017. 2: p. 7-26.
  9. Kellum, J.A., N. Lameire, and K.A.G.W. Group, Diagnosis, evaluation, and management of acute kidney injury: a KDIGO summary (Part 1). Critical care, 2013. 17(1): p. 204.
  10. Patschan, D. and G. Müller, Acute kidney injury in diabetes mellitus. International journal of nephrology, 2016. 2016.
  11. Liu, X., et al., Early predictors of acute kidney injury: a narrative review. Kidney and Blood Pressure Research, 2016. 41(5): p. 680-700.
  12. Fufaa, G.D., et al., Structural predictors of loss of renal function in American Indians with type 2 diabetes. Clinical Journal of the American Society of Nephrology, 2016. 11(2): p. 254-261.
  13. Nie, S., et al., Are there modifiable risk factors to improve AKI? BioMed research international, 2017. 2017.
  14. Girman, C., et al., Risk of acute renal failure in patients with type 2 diabetes mellitus. Diabetic Medicine, 2012. 29(5): p. 614-621.
  15. Regassa, L.D., Y.K. Gete, and F.A. Mekonnen, Time to acute kidney injury and its predictors among newly diagnosed Type 2 diabetic patients at government hospitals in Harari Region, East Ethiopia. PLoS One, 2019. 14(5): p. e0215967.
  16. Lewington, A.J., J. Cerdá, and R.L. Mehta, Raising awareness of acute kidney injury: a global perspective of a silent killer. Kidney international, 2013. 84(3): p. 457-467.
  17. Zhao, M., et al., Network meta-analysis of novel glucose-lowering drugs on risk of acute kidney injury. Clinical Journal of the American Society of Nephrology, 2021. 16(1): p. 70-78.
  18. Adebamowo SN, A.A., Tekola-Ayele F, Doumatey AP, Bentley AR, Chen G, Zhou J, Shriner D, Fasanmade OA, Okafor G, Eghan B Jr., Agyenim-Boateng K, Adeleye J, Balogun W, Amoah AG, Owusu S, Acheampong J, Johnson T, Oli J, Adebamowo CA and Rotimi CN, mpact of Type 2 Diabetes on Impaired Kidney Function in Sub-Saharan African Populations. Front. Endocrinol., 2016 May. 7(50).
  19. Riley, S., et al., Renal impairment among acute hospital admissions in a rural E thiopian hospital. Nephrology, 2013. 18(2): p. 92-96.
  20. Thongprayoon, C., et al., Diagnostics, risk factors, treatment and outcomes of acute kidney injury in a new paradigm. 2020, MDPI. p. 1104.
  21. Monseu, M., et al., Acute kidney injury predicts major adverse outcomes in diabetes: synergic impact with low glomerular filtration rate and albuminuria. Diabetes Care, 2015. 38(12): p. 2333-2340.
  22. See, E.J., et al., Risk factors for major adverse kidney events in the first year after acute kidney injury. Clinical kidney journal, 2021. 14(2): p. 556-563.
  23. Adem, A.M., et al., Incidence of Diabetic Foot Ulcer and Its Predictors Among Diabetes Mellitus Patients at Felege Hiwot Referral Hospital, Bahir Dar, Northwest Ethiopia: A Retrospective Follow-Up Study. Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy, 2020. 13: p. 3703.
  24. Aemro, A., A. Jember, and D.Z. Anlay, Incidence and predictors of tuberculosis occurrence among adults on antiretroviral therapy at Debre Markos referral hospital, Northwest Ethiopia: retrospective follow-up study. BMC infectious diseases, 2020. 20(1): p. 1-11.
  25. Girma, M., et al., Health-Related Quality of Life and Associated Factors Among Type Two Diabetic Patients on Follow-Up in Dessie Comprehensive Specialized Hospital, Dessie, North East Ethiopia, 2020 [Corrigendum]. Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy, 2021. 14: p. 751-752.
  26. Yosef, K., Magnitude Of Preterm Birth And Associated Factors Among Mothers Gave Birth In Debre Berhan Comprhensive Specialized Hospital, North Shoa, Amhara, Ethiopia. 2020.
  27. Xu, Y., et al., Glycemic Control and the Risk of Acute Kidney Injury in Patients With Type 2 Diabetes and Chronic Kidney Disease: Parallel Population-Based Cohort Studies in US and Swedish Routine Care. Diabetes care, 2020. 43(12): p. 2975-2982.
  28. Amod, A., et al., Risk factors for kidney disorders in patients with type 2 diabetes at high cardiovascular risk: An exploratory analysis (DEVOTE 12). Diabetes and Vascular Disease Research, 2020. 17(6): p. 1479164120970933.
  29. Waikar, S.S., P.T. Murray, and A.K. Singh, Core Concepts in Acute Kidney Injury. 2018: Springer.
  30. Marbou, W.J. and V. Kuete, Prevalence of metabolic syndrome and its components in Bamboutos Division’s adults, west region of Cameroon. BioMed research international, 2019. 2019.
  31. Singer, M., et al., The third international consensus definitions for sepsis and septic shock (Sepsis-3). Jama, 2016. 315(8): p. 801-810.
  32. Gebermariam, A.D., et al., Level of glycemic control and its associated factors among type II diabetic patients in debre tabor general hospital, northwest Ethiopia. Metabolism Open, 2020. 8: p. 100056.
  33. Rampersad, C., et al., Acute kidney injury events in patients with type 2 diabetes using SGLT2 inhibitors versus other glucose-lowering drugs: a retrospective cohort study. American Journal of Kidney Diseases, 2020. 76(4): p. 471-479. e1.
  34. Bell, S., et al., Risk of acute kidney injury and survival in patients treated with Metformin: an observational cohort study. BMC nephrology, 2017. 18(1): p. 1-8.
  35. Oluseyi, A., A. Ayodeji, and F. Ayodeji, Aetiologies and short-term outcomes of acute kidney injury in a tertiary centre in Southwest Nigeria. Ethiopian journal of health sciences, 2016. 26(1): p. 37-44.
  36. Magboul, S.M., B. Osman, and A.A. Elnour, The incidence, risk factors, and outcomes of acute kidney injury in the intensive care unit in Sudan. International Journal of Clinical Pharmacy, 2020. 42(6): p. 1447-1455.
  37. Abdallah, E., et al., Impact of Long-Standing Poor Glycemic Control on the Occurrence of Contrast-Induced Acute Kidney Injury in Patients with Type-II Diabetes Mellitus Undergoing Percutaneous Coronary Intervention. SM J Nephrol Therap. 2017; 2 (1): 1005. 2006.
  38. Ju, S., et al., Body mass index as a predictor of acute kidney injury in critically ill patients: a retrospective single-center study. Tuberculosis and Respiratory Diseases, 2018. 81(4): p. 311-318.
  39. Tan, J., et al., Presentation, pathology and prognosis of renal disease in type 2 diabetes. BMJ Open Diabetes Research and Care, 2017. 5(1): p. e000412.
  40. Yu, S.M.-W. and J.V. Bonventre, Acute kidney injury and progression of diabetic kidney disease. Advances in chronic kidney disease, 2018. 25(2): p. 166-180.
  41. Ronco, C., A. Bellasi, and L. Di Lullo, Implication of acute kidney injury in heart failure. Heart failure clinics, 2019. 15(4): p. 463-476.
  42. Chawla, L.S. and P.L. Kimmel, Acute kidney injury and chronic kidney disease: an integrated clinical syndrome. Kidney international, 2012. 82(5): p. 516-524.
  43. Munteanu, M., A. Apostol, and V. Ivan, New Considerations Regarding Chronic Kidney Disease, Cardiovascular Disease and Dyslipidemia in Diabetic Patients. Rev. Chim.(Bucharest), 2018. 69: p. 2064.