Article Type: Original Article

Title:  A study of modifiable and non-modifiable risk factors associated with of diabetic nephropathy – A preliminary observational study in Eastern Odisha, India

 Year: 2021; Volume: 1; Issue: 1; Page No: 21 – 25

Authors:  Suchanda Sahu1, Manish Taywade2, Sujata Devi3, Saurav Nayak4, Dipti Sudha M5

Affiliations: 1Associate Professor, Department of Biochemistry, 2Assistant Professor, Department of Community Medicine and Family Medicine, 3Assistant Professor, Department of Medicine, 4Second year Junior Resident, Department of Biochemistry, 5First year Junior Resident, Department of Biochemistry, All India Institute of Medical Sciences, Bhubaneswar, India.

Paper Summary:  
Submitted:  31-March-2021
Revised    :  15-May-2021
Accepted :  31-May-2021
Published:  10-June-2021


Background:  One of the commonest complications of poorly controlled Type 2 diabetes mellitus (T2DM) is Diabetic nephropathy (DN), which occurs in 30-40% of DM cases.  It is important to identify the high-risk group who are likely to develop DN with the modifiable and non-modifiable risk factors.  This study had the objectives to estimate and correlate the levels of the urine albumin creatinine ratio (UACR) with age, anthropometric measures, glycaemic control markers, lipids and renal function. To estimate each variable as independent and multivariate risk factors.

Materials and Methods:  It was an observational and cross-sectional study conducted in a tertiary care centre in Eastern India.  Totally, 221 consecutive ambulatory T2DM subjects were recruited after obtaining their written consent.

Results: The diabetics were classified as having diabetic nephropathy by the urine albumin creatinine ratio (ACR) of >30 mg/gm. 53.4% of our study group had DN. There was a significant risk associated with PPBS with p=0.043 (<0.05), serum creatinine with p=0.032 (<0.05), and urine albumin with p=0.0001 (<0.001).  The multivariate regression analysis of all these variables there was a highly significant likelihood ratio for predicting DN with p=0.0001 (<0.001) with a predictive value of 74.5% in females and 75% in males.

Conclusion: The additive factors contributed by the risk factors in prediction of DN will benefit the DM in prevention of DN. 

Key words: diabetic nephropathy, risk factors, diabetic kidney disease, Asian Indian

Corresponding Author: 
Dr. Suchanda Sahu,
Associate Professor, Department of Biochemistry,
All India Institute of Medical Sciences,
Sijua, Dumduma (Post),
Bhubaneswar, Odisha, India.
 Email ID:


Diabetic nephropathy (DN) affects approximately 40% of the type 2 diabetes mellitus (T2DM) patients. [1] DN is diagnosed by the presence of albumin in urine. They are classified as microalbuminuria and macroalbuminuria with urine albumin: creatinine ratio of 30 – 300 mg/gm in the former and > 300 mg/gm in the latter.  Microalbuminuria stage of renal involvement was termed as incipient nephropathy which may already be present in T2DM at the time of diagnosis. [2]   Progression of normo-albuminuria to micro and macroalbuminuria can occur silently and faster with associated risk factors like dyslipidaemia, smoking habit, hypertension and poor glycaemic control. [3]   In the South-Asian population, there is an increased predisposition to DN irrespective of the central obesity, [4] hence the need to point causal factors to body fat distribution initiating insulin resistance and inflammation. In routine management of diabetic patients, their blood and urine tests are done annually to monitor the disease control and to screen for DN.  Microalbuminuria is also associated with increased risk for cardiovascular diseases and death. [1] Hence, it is imperative to adopt strategies for preventing the development of microalbuminuria and in delaying the progression to advanced stages of DN.  That can be achieved by good glycaemic control by maintaining glycated haemoglobin (HbA1C) at 7%, treating comorbidities like hypertension and dyslipidaemia.

Though microalbuminuria is the gold standard for screening and detection of DN, its determination in clinical laboratories are inconsistent because of the immunoassay techniques used [5, 6].   In diabetics, the albumin in urine can be modified by non- enzymatic glycation and hydrolysis during its passage in the renal tubules. These modifications can underestimate the albumin by the antibodies used for assay [7, 8], thereby delay the detection of DN and its treatment.  In this study we assessed the correlation of ACR with non-modifiable risk factors like age and gender of patient and with modifiable risk factors like body mass index (BMI), waist hip ratio (WHR), atherogenic index (AI) calculated from fasting blood lipid levels and HbA1C.

Material and Methods:

It was a cross-sectional comparative study on 221 ambulatory T2DM subjects conducted in a tertiary care centre in Eastern India after the approval of the Institutional Ethical Committee (IEC- T/IMF/18-19/32). The cases were from our Non – Communicable Diseases (NCD) Out- patient’s clinic (OPD), All India Institute of Medical Sciences, Bhubaneswar, India, who attended for routine follow-up clinic during the month of March 2020.  Convenient sampling was done due to the COVID-19 pandemic. Their clinical and anthropometric data were noted after obtaining their written consent.  5ml of blood in fasting state and 10 ml of midstream spot urine was collected in different vacutainers and urine vials for estimation of the following: serum creatinine, glycated haemoglobin fasting (FBS) and post prandial blood sugar (PPBS), urine creatinine and albumin. The lipid profile included total cholesterol (TC), triglycerides (TG), high density lipoprotein (HDL) and low-density lipoproteins (LDL). All the estimations were done the same day using the Beckman Coulter Chemistry Analyzer AU5800 (Beckman Coulter, Brea, USA).

The calculated parameters were body mass index (BMI), waist hip ratio (WHR), atherogenic index of plasma (AIP) [9], urinary albumin: creatinine ratio (UACR) and estimated glomerular filtration rate (eGFR). [10] The statistical analysis was done to compare the diabetics with and without DN. The comparison was done by Mann Whitney U test.

The relative risk estimates were calculated and the correlation was estimated for the independent variables by Spearman’s correlation as the data were not normally distributed which was seen by the Kolmogorov-Smirnov test. The comparisons between gender groups and those with and without DN were by Kruskal Wallis Test and post hoc test by Tukey’s test. The multivariate regression was estimated for the independent variables. All these were done using SPSS 19.0 version [IBM, Armonk, NY, USA].  p-value <0.05 was considered as statistically significant.


Clinical characteristics: There were 221 diabetic patients who consented to participate in the study. There were 143 males and 78 females. Table-1 shows the general clinical characteristics of the study participants. Considering the anthropometric cut-off levels for Asian Indians, 72.4% were overweight and obese; 94.4 % of males and 98.7 % females had high WHR. 79.1% had poor glycaemic control as seen by HbA1C and 90.9% had a high-risk dyslipidaemia as seen by the AIP.  Though 53.4 % of our study group had DN, a larger proportion of the total that is 72.8% had decreased eGFR.  The study participants were classified as having diabetic nephropathy by the UACR.

Table 1: Distribution of general characteristics of the study population (N=221)

Covariates No. of Patients Percentage
Age (in years) < 60 172 77.8
≥ 60 49 22.2
Gender Male 143 64.7
Female 78 35.3
BMI <23 61 27.6
≥23 160 72.4
WHR M, <0.90 8 5.6
M ≥0.90 135 94.4
F < 0.80 1 1.3
F ≥0.80 77 98.7
HbA1C <7.0% 46 20.8
≥7.0% 175 79.1
AIP <0.24 20 9.1
≥0.24 201 90.9
ACR < 30 mg/gm 94 42.5
≥ 30 mg/gm 127 57.5
eGFR > 90 ml/min 60 27.2
< 90ml/min 161 72.8
DN Absent 103 46.6
Present 118 53.4

 The comparison of the two groups by Mann Whitney U test. There was a significant difference in PPBS (p=0.013), HbA1C (p=0.041) and UACR (p<0.001). There was no significant difference in the age, gender, BMI, WHR, FBS, lipid profile, AIP, serum creatinine and eGFR between the groups as shown in Table-2.

Association of risk factors with DN:

 The risk estimate calculated using the cut off values for each variable relevant to our population and as per gender wherever applicable. Though there was a 10–20% increased risk for age >60 years, male gender, BMI, WHR, FBS, HbA1C, AIP and LDL among the serum lipids and a 20% decrease in HDL had increased risk of DN, none were statistically significant as shown in Table-3.  This may be because of the consecutive convenient sampling of one month evaluated as a preliminary study here. There was a significant risk associated with PPBS (p=0.043), serum creatinine (p=0.032), and urine albumin (p=0.0001).

Table 2: Differences Between Diabetic Nephropathy Group for Various Risk Factors


Parameters Diabetic Nephropathy




(n = 94)


(n = 127)

Age 52 (43 – 60) 50 (43 – 58) 0.636
Sex Male 62 (65.96%) 76 (59.84%) 0.353
Female 32 (34.04%) 51 (40.16%)
BMI (kg/m2)


(23.46 – 27.69)


(22.22 – 28.06)

WHR 0.948 (0.91 – 0.99) 0.946 (0.91 – 0.98) 0.439
FBS (mg/dl) 141.5 (121 – 179) 152 (122 – 221) 0.187
PPBS (mg/dl) 208.5 (185 – 290) 245 (189 – 321) 0.013
HbA1C (%) 7.875 (7.08 – 8.8) 8.3 (7.2 – 10.3) 0.041
S. Creatinine (mg/dl) 1 (0.8 – 1.1) 1 (0.8 – 1.2) 0.393
eGFR (ml/min)


(66.67 – 94.31)


(64.94 – 91.08)

TC (mg/dl) 187 (161 – 210) 190 (159 – 227) 0.613
TG (mg/dl) 153 (119 – 204) 149 (108 – 204) 0.648
HDL (mg/dl) 44 (38 – 51) 46 (39 – 53) 0.160
LDL (mg/dl) 110 (91 – 135) 114 (92 – 136) 0.740
AIP 0.557 (0.43 – 0.69) 0.529 (0.37 – 0.69) 0.280
UACR (mg/gm)


(6.53 – 22.23)


(46.95 – 190.43)


            Bolded p-value < 0.05  Statistically Significant

The Spearman’s correlation (Table 4) of ACR with FBS, PPBS, HbA1C and u. albumin were positive with (p=0.028, <0.001, 0.001 and <0.001 respectively) on overall estimation of all cases (not shown here). But on estimating the correlation in females, only PPBS correlated significantly and in males there was significant positive correlation with FBS, PPBS, HbA1C, serum creatinine and spot urine albumin was negative with eGFR with p=0.028. The other individual independent factors like age, BMI, WHR, serum lipids and AIP did not correlate significantly with ACR in both sexes.  On doing a multivariate logistic regression analysis of all these variables [Table 5a and 5b] there was a significant likelihood ratio for predicting DN (p=0.014 and 0.001 in females and males respectively) with a substantial predictive value of 74.5% in females and 75% in males by Cox and Snell R square.


Diabetic nephropathy is a common complication of T2DM. It is usually detected in the late stages from where it rapidly progresses to end-stage renal disease (ESRD). Early detection, good glycaemic control, and neuroprotective treatment can prevent ESRD. [11] Apart from this, it is important to identify the subgroup among T2DM, likely to develop DN considering the modifiable risk factors like body fat, serum lipids, kidney function, and glycaemic control.  From our preliminary, it is evident that there is a risk associated with biomarkers of glycaemic control (PPBS and HbA1C), more so in males as compared to females.

Table 3: Risk Factors (Modifiable & Non-Modifiable) for Diabetic Nephropathy


Factors Parameters Classifications Diabetic Nephropathy Relative Risk p-Value
   Absent Present
Non – Modifiable Age (in years) <60 74 108 1.218 0.284
≥60 20 19
Sex Male 62 76 1.165 0.400
Female 32 51
Modifiable BMI (kg/m2) <23 20 41 1.250 0.048
≥23 74 86
WHR <0.9(M)/<0.85(F 1 1 1.177 1.000
≥0.9(M)/ ≥0.85(F) 93 126
FBS (mg/dl) <110 9 22 1.284 0.119
≥110 85 105
PPBS (mg/dl) <140 3 0 2.396 0.043
≥140 91 127
HbA1C (%) <7 23 23 1.232 0.315
≥7 71 104
Serum Creatinine (mg/dl) ≤1.2(M)/ ≤1.1(F) 87 105 1.877 0.032
>1.2(M)/>1.1(F) 7 22
eGFR (ml/min) <90 67 94 1.062 0.650
≥90 27 33
TC (mg/dl) <200 60 80 1.021 1.000
≥200 34 47
TG (mg/dl) <150 44 65 1.077 0.587
≥150 50 62
HDL (mg/dl) >35(M)/>39(F) 77 112 0.767 0.246
<35(M)/<39(F) 17 15
LDL (mg/dl) <100 36 42 1.138 0.477
≥100 58 85
AIP ≤0.24 6 14 1.245 0.343
>0.24 88 113
Urine Albumin (mg/L) <30 70 33 3.341    0.0001

Bolded p-value < 0.05  Statistically Significant

Similar results have been reported by authors in Asia [12] among Asians in Europe [4], and from heterogenous populations from 20 cohorts. [13] Though strict glycaemic control decreased the risk for DN b 40%, it alone cannot prevent the initiation and progression of DN. [1] Majority of our patients were overweight or obese and had higher WHR than normal.  Though BMI associated significantly as a risk factor for DN with UACR, WHR didn’t. Neither of them was different in the two groups. There was no difference in gender as a risk factor for DN. However, studies among Asians have shown that women who have DN rapidly progress to ESRD as compared to their male counterparts. [14, 15]

Table 4: Correlation of ACR with independent factors in the diabetics grouped according to gender (N=221)




Females Males
Spearman’s rho p  value Spearman’s rho p  value
Age (years) -0.137 0.228 0.032 0.703
BMI (kg/m2) 0.05 0.661 -0.095 0.262
WHR 0.008 0.947 -0.083 0.329
FBS (mg/dl) 0.085 0.455 0.176* 0.036
PPBS (mg/dl) 0.267* 0.017 0.213* 0.011
HbA1C (%) 0.161 0.156 0.248** 0.003
S. Creatinine (mg/dl) 0.076 0.508 0.191* 0.022
eGFR (ml/min) 0.043 0.705 -0.185* 0.028
TC (mg/dl) 0.172 0.129 -0.005 0.956
TG (mg/dl) 0.060 0.601 -0.070 0.406
HDL (mg/dl) 0.111 0.332 0.030 0.724
LDL (mg/dl) 0.176 0.120 -0.011 0.901
AIP -0.015 0.895 -0.073 0.387
U. Albumin (mg/L) 0.752** 0.001 0.658** 0.001

         ** Correlation is significant p<0.01; * Correlation is significant p<0.05

Table 5a: Multivariate Regression of the independent variables as risk factors for DN

Model Fitting Information
Sex Model Model Fitting Criteria Likelihood Ratio Tests  
-2 Log Likelihood Chi-Square df Sig.
Female Intercept Only 107.981      
  Final 0 107.981 78 0.014
Male Intercept Only 196.741      
  Final 0 196.741 141 0.001

Bolded p-value < 0.05  Statistically Significant

Table 5b: Predictive value of the multivariate analysis


Pseudo R-Square  
Female Cox and Snell 0.745
  Nagelkerke 1
  McFadden 1
Male Cox and Snell 0.75
  Nagelkerke 1
  McFadden 1


The baseline eGFR and kidney function are important factors in DN risk and progression. [1] In our study though eGFR was not significantly different in the two groups; with and without DN, serum creatinine levels were a risk in both sexes and eGFR negatively correlated with UACR in males as shown in Table-4.  As eGFR is closely related to age, baseline eGFR at the time of diagnosis of T2DM and further monitoring to see the rate of decline [16] is imperative for initiating treatment with antidiabetic agents which will protect the kidneys also. [12]

In our study, the serum lipids were not statistically different in the two groups, yet studies have shown that HDL and TG are independent risk factors for cardiovascular disease, systolic blood pressure (SBP) being the measure. [13] As we have not considered the treatment naïve T2DM patients and we have not taken in account the drug history of each participant, our statement for or against the association of serum lipids in DN will not be exact.

As T2DM involves multiple organs and its complications can coexist to varying degrees in individuals, multiple factors affect the course of the disease. The multivariate regression analysis showed significant predictive value of the risk factors considered in our study. Studies on identification of risk factors have identified similar factors and others like SBP, duration of disease, rate of decline in eGFR, age and presence of diabetic retinopathy. [3, 12]

The strength of our study is that, though it is a preliminary study conducted during the lockdown for pandemic, the conjoined effect of the modifiable and non-modifiable risk factors showed substantial predictive value.  Our study is limited by the sample number and the lack of drug history such as lipid lowering agents, antihypertensives, insulin or oral hypoglycaemic agents.


In conlusion, the findings of our study have implications in the clinical scenario of diabetes.  As the disease, T2DM is not only about current glycaemic control, but involves constant clinical and lab monitoring to evade complications. Patient education about disease and empowering them with the knowledge and ability is more important that medications alone.  An overall change in diet, physical activity, and other lifestyle modifications should benefit each patient. Larger cohort studies are suggested to understand the additive effects of risk factors.

Acknwledgment: We acknowledge the consent and cooperation of our study participants and the help of other department staff and colleagues.

Authors’ Contributions: SS, MT and SDM conceived the study design and initial draft of the manuscript. SD, SN and DSM collected clinical data. SS and SN analysed the data. All the authors edited and approved of the final draft of the manuscript.

Here, SS – Suchanda Sahu, MT – Manish Taywade, SD – Sujata Devi, SN – Saurav Nayak and, Dipti Sudha M – DSM

Source of funding: This study was done as intramural research grant for Faculty from my Institute; All India Institute of Medical Sciences (AIIMS), Bhubaneswar, India.

Conflict of Interest: The authors declare that there was no conflict of interest.


  1. Gross JL, de Azevedo MJ, Silveiro SP, Canani LH, Caramori ML, Zelmanovitz T. Diabetic Nephropathy: Diagnosis, Prevention, and Treatment. Diabetes Care 2005;28(1):164–176. Available from:
  2. Van JAD, Scholey JW, Konvalinka A. Insights into Diabetic Kidney Disease Using Urinary Proteomics and Bioinformatics. Journal of the American Society of Nephrology 2017;28(4):1050–1061. DOI: 1681/ASN.2016091018
  3. Viswanathan V, Tilak P, Kumpatla S. Risk factors associated with the development of overt nephropathy in type 2 diabetes patients: A 12 years observational study.  Indian Journal of Medical Research 2012;136:46–53.
  4. Chandie Shaw PK, Baboe F, van Es LA, van der Vijver JC, van de Ree MA, de Jonge N, et al. South-Asian type 2 diabetic patients have higher incidence and faster progression of renal disease compared with Dutch-European diabetic patients. Diabetes Care 2006;29(6):1383–1385.
  5. Greive KA, Eppel GA, Reeve S, Smith AI, Jerums G, Comper WD. Immuno–unreactive albumin excretion increases in streptozotoin diabetic rats. AM J Kidney Dis 2001;38:144–152. PMID: 11431194
  6. Qsicka TM, Comper WD. Characterization of Immunochemically Non-reactive urinary albumin. Clin Chem 2004;50(12):2286–2291. PMID: 15388637
  7. Burne MJ, Panagiotopoulus S, Jerums G, Comper WD. Alterations in the renal degradation of albumin in early experimental diabetes in the rat: a new factor in the mechanism of albuminuria. Clin Sci (Lond) 1998;95(1):67–72.  PMID: 9662487
  8. Qsicka TM, Honlihas CA, Chan JG, Jerums G, Comper WD. Albuminuria in patients with type 1 diabetes is directly linked to changes in the lysosome-mediated degradation of albumin during renal passage. Diabetes 2000;49:1579–1584.  PMID: 10969843
  9. Dobiásová M, Frohlich J. The plasma parameter log (TG/HDL-C) as an atherogenic index: correlation with lipoprotein particle size and esterification rate in apoB-lipoprotein-depleted plasma (FER(HDL)). Clin Biochem 2001;34(7):583–588.  PMID: 11738396
  10. Cockcroft DW, Gault MH. Prediction of creatinine clearance from serum creatinine. Nephron 1976;16(1):31–41. PMID: 1244564
  11. Żyłka A, Dumnicka P, Kuśnierz-Cabala B, Gala-Błądzińska A, Ceranowicz P, Kucharz J, et al. Markers of Glomerular and Tubular Damage in the Early Stage of Kidney Disease in Type 2 Diabetic Patients. Mediators Inflamm 2018;2018:7659243.  DOI: 1155/2018/7659243
  12. Huang CH, Chen CP, Huang YY, Hsu BRS. Modifiable factors related to 7-year renal outcomes in subjects with type 2 diabetes and chronic kidney disease stage 3.  J Postgrad Med 2020;66(4):187–193.
  13. Jiang W, Wang J, Shen X, Lu W, Wang Y, Li W, et al. Establishment and Validation of a Risk Prediction Model for Early Diabetic Kidney Disease Based on a Systematic Review and Meta-Analysis of 20 Cohorts.  Diabetes Care. 2020;43(4):925–933.  PMID: 32198286
  14. Shen Y, Cai R, Sun J, Dong X, Huang R, Tian S, et al. Diabetes mellitus as a risk factor for incident chronic kidney disease and end-stage renal disease in women compared with men: a systematic review and meta-analysis.  Endocrine 2017;55(1):66–76.   PMID: 27477292
  15. Liu J-J, Liu S, Choo RWM, Wee SL, Xu A, Lim SC. Sex modulates the association of fibroblast growth factor 21 with end-stage renal disease in Asian people with Type 2 diabetes: a 6.3-year prospective cohort study. Diabet Med 2018;35(7):880–886.  PMID: 29653030
  16. Kerschbaum J, Rudnicki M, Dzien A, Dzien-Bischinger C, Winner H, Heerspink HL, et al. Intra-individual variability of eGFR trajectories in early diabetic kidney disease and lack of performance of prognostic biomarkers.  Sci Rep 2020;10(1):19743.  DOI: 1038/s41598-020-76773-0

This is an open-access journal, and articles are distributed under the terms of the Creative Commons Attribution‑Non-Commercial‑ShareAlike 4.0 International License, which allows others to remix, tweak, and build upon the work non‑commercially, as long as appropriate credit is given and the new creations are licensed under the identical terms