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Abstrak :
Diabetes melitus tipe 2 (DMT2) adalah penyakit gangguan metabolisme yang menyebabkan regulasi insulin terganggu dan dapat mengakibatkan komplikasi penyakit ginjal diabetes (PGD). Saat ini, gold standard penilaian fungsi ginjal dilakukan berdasarkan parameter eGFR dan albuminuria, tetapi kedua parameter tersebut memiliki beberapa keterbatasan. Oleh karena itu, metode baru untuk deteksi dini fungsi ginjal dapat bermanfaat dalam upaya pencarian target terapi yang lebih tepat, salah satunya melalui pendekatan metabolomik. Tujuan penelitian ini yaitu melihat perbedaan profil metabolit serum pasien DMT2 risiko rendah (n=16) dan tinggi (n=16) PGD berdasarkan klasifikasi KDIGO 2022 yang mengonsumsi metformin-glimepirid. Desain penelitian cross-sectional dengan teknik consecutive sampling dilakukan di Puskesmas Kecamatan Pasar Minggu dan RSUD Jatipadang. Sebanyak total 32 partisipan yang memenuhi kriteria inklusi dibandingkan profil metabolitnya berdasarkan kategori risiko PGD. Sampel darah, urin, serta data karakteristik dasar dan klinis dikumpulkan untuk analisis metabolomik. Analisis untargeted metabolomics dilakukan menggunakan LC/MS-QTOF dengan metode yang sudah tervalidasi. Pengolahan data dilakukan menggunakan MetaboAnalyst 5.0 dan SPSS versi 24.0. Seluruh metabolit yang terdeteksi diidentifikasi oleh database Metlin, HMDB, PubChem, dan KEGG. Tidak terdapat perbedaan bermakna pada seluruh karakteristik dasar dan klinis subjek penelitian. Terdapat perbedaan bermakna pada ekspresi metabolit antara dua kelompok sampel. Berdasarkan parameter VIP score >1; FC >1.2; p-value <0,05; AUC >0,65 yang ditetapkan, diperoleh tiga metabolit yang memiliki potensi sebagai senyawa biomarker dalam perkembangan PGD, yaitu acetyl-N-formyl-5-methoxykynurenamine (AFMK), phosphatydilinositol-4,5-bisphosphate (PIP2), dan cytidine diphosphate diacylglycerol (CDP-DAG). Berdasarkan ketiga metabolit tersebut, tiga jalur metabolisme berhasil terdeteksi dan berpotensi terlibat dalam perkembangan PGD yaitu metabolisme triptofan, metabolisme fosfatidilinositol, serta metabolisme gliserofosfolipid.  ......Type 2 diabetes mellitus (T2DM) is a metabolic disorder causing insulin regulation to be disrupted and may lead to diabetic kidney disease (DKD) complication. The current gold standard for assessing kidney function based on eGFR and albuminuria have some limitations. Therefore, a new method for assessing kidney function may be useful for a better therapeutic target discovery, such as through metabolomics approach. This study aims to compare the serum metabolite profiles of T2DM patients consuming metformin-glimepiride with low (n=16) and high (n=16) risk of DKD based on KDIGO 2022. A cross-sectional study with consecutive sampling method was carried out at Puskesmas Pasar Minggu and RSUD Jatipadang. A total of 32 participants fulfilled the inclusion criteria were compared for their metabolite profiles. Blood, urine, baseline and clinical characteristics data were collected to perform untargeted metabolomics analysis using a validated LC/MS-QTOF method. Data processing was performed using MetaboAnalyst 5.0 and SPSS 24.0. Metabolites were identified by Metlin, HMDB, PubChem, and KEGG databases. There were no significant differences among all basic and clinical characteristics of the participants. There were significant differences of metabolite expression between two sample groups. Based on the applied parameters VIP score >1; FC>1.2; p-value <0.05; AUC >0.65, three metabolites were found to have potential as biomarker in the development of DKD, namely acetyl-N-formyl-5-methoxykynurenamine (AFMK), phosphatydilinositol-4,5-bisphosphate (PIP2), and cytidine diphosphate diacylglycerol (CDP-DAG). Based on these metabolites, three metabolic pathways were detected and found to be potentially involved in the development of DKD, namely tryptophan metabolism, phosphatidylinositol metabolism, and glycerophospholipid metabolism.

Abstrak :
Background: Diabetic kidney disease (DKD), as a common cause of end-stage renal disease (ESRD), is a chronic complication of diabetes mellitus (DM). It has been established that vitamin D deficiency is one of DKD risk factors, which may be related to vitamin D receptor (VDR) polymorphisms. This study aimed to analyze the association between VDR polymorphisms and DKD in Indonesian population, also risk factors that influence it. Methods: a cross-sectional study was conducted in Type 2 DM patients who visited internal medicine outpatient clinic at Dr. Cipto Mangunkusumo Hospital, Jakarta, from November 2014 until March 2015. Data collection includes characteristics of subjects and laboratory examination, including BsmI polymorphisms in the vitamin D receptor gene. Patients with acute and severe disease were excluded from the study. Bivariate and multivariate analyses were done. Results: of 93 DM subjects, 42 (45.2%) subjects were without DKD and 51 (54.8%) subjects had DKD. Most of the subjects had the Bb genotype (89.2%), with no subject having the BB genotype. The proportions of the B and b alleles were 44.6% and 55.4%, respectively. There is no association between BsmI polymorphisms in the vitamin D receptor gene and DKD (OR = 1.243; CI 95% 0.334-4.621; p value = 0.751). Conclusion: the profile of BsmI polymorphisms in the vitamin D receptor gene in the Indonesian population were genotypes Bb (89.2%) and bb (10.8%). There was no association between BsmI polymorphisms in the vitamin D receptor gene and DKD. Duration of DM more than five years influenced the association between those variables.

Abstrak :
Penyakit Ginjal Diabetes (PGD) dapat menyebabkan albuminuria, yang berkembang menjadi insufisiensi ginjal. Namun, sekitar 20-40% kasus PGD merupakan PGD normoalbuminuria, yaitu gangguan fungsi ginjal dengan kadar albumin normal. Penelitian ini untuk membandingkan metabolit urin pada pasien penyakit ginjal diabetes dengan normoalbuminuria dan albuminuria yang mengonsumsi metformin-glimepirid. Desain penelitian potong lintang dengan metode consecutive sampling di Puskesmas Kecamatan Pasar Minggu dan RSUD Jati Padang. Sampel urin dan darah dikumpulkan untuk pengukuran HbA1c, UACR, dan analisis metabolit urin. Sebanyak masing-masing 16 pasien dibagi menjadi kelompok PGD normoalbuminuria dan PGD albuminuria, serta dianalisis metabolit urinnya menggunakan metabolomik tidak tertarget dengan Quadruple Time of Flight Liquid Chromatography-Mass Spectrometry. Metabolit yang berbeda signifikan divisualisasi dengan Projections to Latent Structures Discriminant Analysis (PLS-DA). Lalu, dianalisis nilai Variable Importance for the Projection (VIP) > 1.0; Fold Change (FC) >1,2 (p<0,05); dan Area Under the Receiver Operating Characteristic Curve (AUROC). Metabolit dengan nilai Area Under Curve (AUC) > 0,65 dinilai sebagai biomarker potensial. Tidak ada perbedaan bermakna pada karakteristik dasar dan klinis pada kedua kelompok, kecuali HbA1c (p<0,001). Terdapat 20 metabolit urin yang berbeda signifikan pada kelompok PGD normoalbuminuria dan albuminuria. Dari analisis jalur metabolisme pada metabolit tersebut ditemukan empat jalur metabolisme, yaitu metabolisme gliserofosfolipid, eter lipid, fenilalanin, dan triptofan. Dari keempat jalur metabolisme tersebut, ditemukan tiga metabolit biomarker potensial, yaitu glycerophosphocholine, hippuric acid, dan 2-aminobenzoic acid. Ketiga metabolit tersebut berkurang secara signifikan dari kondisi normoalbuminuria ke albuminuria. Oleh karena itu, diperlukan studi lanjut mengenai ketiga metabolit tersebut pada perkembangan PGD normoalbuminuria dan albuminuria. ......Diabetic Kidney Disease (DKD) leads to albuminuria and gradually progresses to renal insufficiency. However, about 20-40% of DKD are normoalbuminuric DKD, which has impaired kidney function with normal albumin levels. This study compared urine metabolites in patients consuming metformin-glimepiride with normoalbuminuric and albuminuria DKD. The research design was cross-sectional with consecutive sampling method at Pasar Minggu District Public Health Centre and Jati Padang Hospital. Urine and blood samples were collected for measurement of HbA1c, UACR, and metabolite analysis. There were each 16 samples divided into normoalbuminuric DKD group and albuminuria DKD group. All subjects were analysed using non-targeted metabolomics with Quadruple Time of Flight Liquid Chromatography-Mass Spectrometry. The signature metabolites were determined by Projections to Latent Structures Discriminant Analysis (PLS-DA) with Variable Importance for the Projection (VIP) > 1.0; Fold Change (FC) >1.2 (p<0.05); and Area Under the Receiver Operating Characteristic Curve (AUROC). Metabolites with an Area Under Curve (AUC) value > 0.65 are considered potential biomarkers. There were no significant differences in baseline and clinical characteristics of two groups, except for HbA1c (p<0.001). There were 20 metabolites identified between two groups. The metabolic pathway analysis of these metabolites found that four metabolic pathways were glycerophospholipid, ether lipid, phenylalanine, and tryptophan metabolism. There were three potential biomarkers, glycerophosphocholine, hippuric acid, and 2-aminobenzoic acid, enriched in these four metabolic pathways. Compared between normoalbuminuric and albuminuria groups these three metabolites were significantly reduced. Therefore, further studies are needed regarding these three metabolites in the development of normoalbuminuric and albuminuria DKD.