ECE2023 Rapid Communications Rapid Communications 1: Diabetes, Obesity, Metabolism and Nutrition 1 (6 abstracts)
Utility of urinary metabolomic signatures to differentiate biopsy confirmed diabetic and non-diabetic kidney disease
Madhurima Basu 1,2 , Sk Ramiz Islam 3 , Pradip Mukhopadhyay 1 , Sanghamitra Sengupta 4 , Nitai Pada Bhattacharyya 1 , Arpita Raychoudhury 5 , Soumen Kanti Manna 3 & Sujoy Ghosh 1
1Institute of Post Graduate Medical Education and Research, Department of Endocrinology and Metabolism, Kolkata, India; 2IPGME&R, Endocrinology, Kolkata, India; 3Saha Institute of Nuclear Physics (HBNI), Biophysics& Structural Genomics Division, Kolkata, India; 4University of Calcutta, Department of Biochemistry, Kolkata, India; 5Institute of Post Graduate Medical Education and Research, Department of Nephrology, Kolkata, India
Background: Renal involvement in T2DM can be due to diabetes per se (Diabetic Kidney Disease) or causes other than diabetes (non-diabetic kidney disease). Currently available clinical, biochemical, and radiological markers fail to differentiate DKD from NDKD. Renal biopsy remains gold standard for diagnosis. Urinary signatures may provide a non-invasive alternative to that end. In cell culture and in animal models with diabetes and kidney disease, dysregulation of several metabolites has been reported. This has not been explored in humans with biopsy-proven kidney disease DKD and NDKD subjects.
Aims: To identify the expression of urinary metabolic signatures as putative marker for differentiation of biopsy-confirmed DKD and NDKD subjects.
Methods: Consecutive patient with renal involvement (eGFR >30 ml/min/m2 to <60 ml/min/m2 and/or ACR>300 mg/ mg) were subjected to biopsy and classified as per ISN/RPS Classification. We also recruited subjects with T2DM without nephropathy (eGFR >60 ml/min/m2 and ACR<30 mg/ mg) for comparison. Morning urine sample were collected for analysis by Gas Chromatography and mass spectrometry. Features were extracted and analysed using appropriate statistical method.
Results: We recruited first 110 subjects who agreed to undergo renal biopsy. Among those 110 subjects, 73 (66.4%) had DKD; 20 (18.2 %) had NDKD; and 17 (15.4 %) had mixed kidney disease. We included only those with pure DKD or NDKD and T2DM subjects without nephropathy for analysis of urinary metabolomics. Urinary metabolites (M1, M2, M3, M4) were found to be depleted in patients with kidney disease (both in DKD and NDKD as compared to T2DM (P< 0.004, P<0.006, and P<0.01, <0.03 respectively). M5 was exclusively depleted in NDKD. Level of M6(P<0.01), M7(P<0.03) was increased and M8(P<0.05), M9(P<0.03), M10(P<0.02) was decreased in DKD subjects. Analysis of fragmentation pattern indicated that theSemetabolites putatively belong to central carbon metabolism, fatty acid metabolism, purine metabolism in addition to metabolites exclusively of non-endogenous (microbial) origin.
| Variable | All Patients(n= 53075) | SGLT2i Only (n= 11712) | TRT Only (n= 40769) | Both (n= 594) | Effect Size |
| Age, yrs (Mean±SD) | 62.54 (9.26) | 65.51 (8.67) | 61.69 (9.25) | 61.77 (8.80) | 0.01 |
| BMI, kg/m | 34.15 (6.59) | 34.03 (6.19) | 34.16 (6.70) | 35.86 (6.16) | 0.00 |
| White Race,% (n) | 74.2 (39379) | 78.5 (9195) | 72.9 (29728) | 76.8 (456) | 0.05 |
| Black Race,% (n) | 15.2 (8068) | 12.1 (1413) | 16.1 (6569) | 14.5 (88) | 0.05 |
| Hematocrit,% (Mean±SD) | 41.37 (4.02) | 41.81 (3.78) | 41.22 (4.08) | 42.64 (3.61) | 0.08 |
| Hemoglobin A1c,% (Mean±SD) | 7.49 (1.51) | 8.54 (1.22) | 7.18 (1.45) | 8.31 (1.20) | 0.04 |
| OSA,% (n) | 25.6 (13564) | 37.3 (4371) | 21.8 (8869) | 54.5 (324) | 0.16 |
| Diuretic use,% (n) | 39.5 (20945) | 38.1 (4468) | 39.8 (16243) | 39.8 (234) | 0.01 |
Conclusion: Urinary metabolomics analysis may help to distinguish DKD and NDKD subjects. Our results warrant validation in another cohort.
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Endocrine Abstracts
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