糖尿腎臟病 (Diabetic Kidney Disease, DKD) 是全球慢性腎臟病 (Chronic Kidney Disease, CKD) 的主要成因之一，影響超過 40%的糖尿病患者，並可能進一步發展為末期腎病 (End-Stage Kidney Disease, ESKD)。本研究運用非標靶代謝體學分析技術，發掘尿液中的潛在代謝生物標記，以預測 DKD 的進展。
研究方法包括第三至五期 (eGFR ≤60 mL/min /1.73 m2) 且尚未進行透析治療（血液透析、腹膜透析）、腎臟移植之 DKD 病患100位之飲食暴露問卷調查與尿液樣本收集與分析，並透過 UHPLC-Orbitrap-MS 技術進行代謝體學與農藥暴露分析，以識別農藥暴露與腎功能衰退相關的代謝物變化。
初步結果顯示，疾病惡化狀況快速下降組與穩定組在人口統計學、臨床特徵、生活方式及飲食習慣上均無顯著差異，可有效降低潛在混淆因子之干擾。在代謝體學分析中，監督式模型（PLS-DA/OPLS-DA）在正、負離子模式下皆成功分離出 DKD 進展者，並偵測到多種具顯著差異的代謝物特徵，反映出與疾病進展相關的獨特代謝轉變。關鍵病理驅動路徑分析顯示，五碳糖與醛糖酸鹽相互轉變顯著驅動早期代謝重塑（p<0.001）；抗壞血酸、硫與嘌呤代謝異常反映氧化壓力加劇與腎功能惡化；菸鹼酸/菸醯胺路徑紊亂則證實粒線體功能受損。
進一步結合多變量機器學習，本研究構建了基於支持向量機（Linear SVM）的預測模型。整合前 36 個權重特徵時，分類準確度達 72.9%，交叉驗證之聯合診斷效能達 AUC 0.823（95% CI: 0.720–0.896），顯著優於單變量標記物 Niacinamide（AUC = 0.748）或 2-Aminoadipic acid（AUC = 0.701）之獨立表現。在農藥暴露方面，雖然總暴露負荷與 DKD 快速惡化未達直接關聯，但兩組間農藥暴露之整體共變異結構具統計學顯著差異，並成功挖掘出 7 組不受腎功能與飲食型態混淆的「代謝物–農藥」相關性，其中胺基酸相關代謝物（Asp-Leu、DL-Arginine、HISTIDINOL）之關聯型態與既有文獻高度呼應。
透過比較代謝特徵，預期可發掘影響不同類型腎臟病的重要代謝途徑，進一步建立能預測 DKD 進展的生物標記模型。此外，本研究透過分析受測者之飲食型態，探討其與受測者代謝物變化之間的關聯，其結果可識別 DKD 腎功能衰退之生物標記，如Niacinamide、2-Aminoadipic acid，有助於提升早期診斷的準確性，並發展個人化醫療策略，以減緩腎功能衰退進展並降低 ESKD 風險。

Diabetic kidney disease (DKD) is a leading cause of chronic kidney disease (CKD) and may progress to end-stage kidney disease (ESKD). This study used untargeted metabolomics to identify urinary biomarkers associated with DKD progression. Urine samples and dietary exposure questionnaires were collected from 100 patients with stage 3–5 DKD (eGFR ≤60 mL/min/1.73 m²) without renal replacement therapy. UHPLC-Orbitrap-MS was used for untargeted metabolomic and pesticide exposure analyses. No significant differences in demographic, clinical, lifestyle, or dietary characteristics were observed between rapid-progressors and stable patients. PLS-DA/OPLS-DA identified distinct metabolic profiles between the groups. Key pathways included pentose and aldose interconversion (p<0.001), ascorbate, sulfur, purine, and nicotinate/nicotinamide metabolism, indicating altered oxidative stress and mitochondrial function. A Linear SVM model based on 36 weighted features achieved 72.9% classification accuracy and a cross-validated AUC of 0.823 (95% CI: 0.720–0.896), exceeding the performance of niacinamide (AUC = 0.748) and 2-aminadipic acid (AUC = 0.701). Seven metabolite–pesticide associations were also identified. These findings highlight potential urinary biomarkers and metabolic pathways for predicting DKD progression and supporting early risk stratification.

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