簡易檢索 / 詳目顯示

回結果列表
研究生： 方巧筑
Fang, Qiao-Zhu
論文名稱： 整合縱貫 eGFR 軌跡、臨床問卷與尿液代謝體學之多模態深度學習存活預測與風險分層用於糖尿腎臟病
Multimodal Deep Learning for Survival Prediction and Risk Stratification in Diabetic Kidney Disease: Integrating Longitudinal eGFR, Clinical Questionnaires, and Urinary Metabolomics
指導教授： 賀保羅
Horton, Paul
共同指導: 陳秀玲
Chen, Hsiu-Ling
學位類別： 碩士
Master
系所名稱： 電機資訊學院 - 資訊工程學系
Department of Computer Science and Information Engineering
論文出版年： 2026
畢業學年度： 114
語文別： 英文
論文頁數： 122
中文關鍵詞： 糖尿腎臟病 、非標靶代謝體學 、存活分析 、多模態深度學習 、跨模態注意力
外文關鍵詞： diabetic kidney disease, multimodal learning, survival analysis, crossmodal attention, untargeted metabolomics
相關次數： 點閱：7  下載：0 
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報

    • 糖尿腎臟病（DKD）是全球終末期腎臟病的主要成因，然而現有風險評估工具多仰賴單一模態臨床指標，難以捕捉疾病進展的多因子複雜性。本研究提出一個多模態存活分析框架，整合三類異質資料——縱向估算腎絲球過濾率(estimated Glomerular Filtration Rate,eGFR) 時序紀錄、飲食與環境暴露問卷、雙離子尿液代謝體學（ESI+/ESI− Thermo Scientific hybrid quadrupole-Orbitrap mass spectrometers）——針對100 位DKD 病人（事件發生率70%）預測疾病快速進展風險。
    各模態由專屬編碼器處理：eGFR 特徵經線性混合效應模型（LMM）提取後輸入 MLP-Cox 網路；問卷特徵透過一致性穩定選擇器篩選，以七種隨機種子深度學習集成模型訓練；代謝體資料則先進行單變量 Cox 篩選（FDR $<$ 0.10），將幾千個代謝物縮減至 200 個，再分離子模式進行主成分分析（每模式取 20 個主成分），並以自動編碼器預訓練初始化。每個編碼器輸出 64 維嵌入向量，依序通過跨模態注意力層與軟注意力門控融合模組整合，最終接入 Cox 比例風險頭進行存活預測。
    在五折交叉驗證下，融合模型的一致性指數（C-index）達0.8725 ± 0.0717，顯著優於所有單模態基準：eGFR（0.8365 ± 0.0791）、問卷（0.7015 ± 0.0524）及代謝體學（0.6999 ± 0.0363）。軟注意力門控權重分析顯示各模態貢獻互補（eGFR：46.4%，問卷：26.0%，代謝體：27.6%），且跨折未出現門控崩塌現象。SHAP 分析揭示：追蹤年數與eGFR 變異度為時序分支的主要驅動特徵；環境污染暴露評分與eGFR–HbA1c 交互項為問卷分支的領先指標；雙離子代謝體的判別性主成分則提供獨立的風險資訊。Kaplan–Meier三組風險分層曲線呈現清晰的存活差異，且Mann–Whitney 檢定確認預測風險分數在事件與設限病人之間具統計顯著區隔（p = 0.0046）。
    本研究結果證實，透過統一深度學習框架整合縱向臨床量測、生活暴露問卷與非標靶代謝體學，可大幅提升 DKD 進展預測效能，超越任何單一模態。模組化架構亦支援未來擴充影像資料與多中心驗證，為個人化腎臟病管理平台奠定可擴展的技術基礎。

    Diabetic kidney disease (DKD) is one of the leading causes of end-stage renal disease worldwide. Yet, existing risk stratification tools rely predominantly on single modality clinical markers and fail to capture the complex, multi-factorial nature of disease progression. This study presents a multimodal survival analysis framework that integrates three heterogeneous data sources—longitudinal eGFR trajectories, dietary and environmental exposure questionnaires, and dual-ion urinary metabolomics (ESI+/ESI− Thermo Scientific hybrid quadrupole-Orbitrap mass spectrometers)—to predict the risk of rapid DKD progression in a cohort of 100 patients (event rate: 70%).
    A dedicated encoder processes each modality: eGFR features are extracted via a linear mixed-effects model (LMM) and fed into an MLP-Cox network; questionnaire features are selected through a concordance-based stability selector and trained with a seven-seed deep learning ensemble; and metabolomics data undergo univariate Cox screening (FDR < 0.10), reducing a few thousand features to 200, followed by per-mode PCA (20 components per ion mode) and autoencoder re training. Each encoder produces a 64-dimensional embedding, which is fused through a Cross-Modal Attention layer and a soft-attention Gated Fusion module. The combined representation is passed to a Cox proportional hazards model for survival prediction.
    Under five-fold cross-validation, the proposed fusion model achieved a Concordance Index (C-index) of 0.8725 ± 0.0717, outperforming all unimodal baselines: eGFR (0.8365 ± 0.0791), questionnaire (0.7015 ± 0.0524), and metabolomics (0.6999 ± 0.0363). Soft-attention gate weights revealed complementary contributions from modalities (eGFR: 46.4%, questionnaire: 26.0%, metabolomics: 27.6%) and no gate collapse across folds. SHAP analysis identified follow-up duration and eGFR variability as dominant temporal signals, environmental pollution exposure and the eGFR–HbA1c interaction as leading clinical features, and discriminative PCA components from dual-ion metabolomics as independent risk contributors. Kaplan–Meier stratification into three risk groups demonstrated clear survival separation, and predicted risk scores showed statistically significant discrimination between event and censored patients (Mann–Whitney U, p = 0.0046).
    These findings demonstrate that integrating longitudinal clinical measurements, lifestyle exposure questionnaires, and untargeted metabolomics within a unified deep learning framework substantially improves DKD progression prediction beyond that of any single modality. The modular architecture supports future extensions to imaging data and multi-center validation, offering a scalable foundation for personalized kidney disease management.

    中文摘要 i Abstract iii Acknowledgements v Contents viii List of Tables xii List of Figures xiii Glossary xv 1 Introduction 1 1.1 Background and Motivation 1 1.2 Research Objectives 3 2 Related Works 5 2.1 Diabetic kidney disease 5 2.1.1 Pathogenesis of Diabetic Kidney Disease 5 2.1.2 Current methods for DKD risk assessment and diagnosis 7 2.1.3 New perspectives provided by metabolomics 8 2.1.4 Limitations of single-timepoint assessment and the value of longitudinal monitoring 10 2.2 Longitudinal eGFR Trajectory Modeling 11 2.2.1 Clinical significance of rapid progression and the definition challenge 11 2.2.2 Linear Mixed-Effect Models in renal research 13 2.3 Dietary and Environmental Risk Factors in DKD 14 2.3.1 Dietary patterns and progression 14 2.3.2 Environmental exposures and renal toxicity 15 2.3.3 Questionnaire-based assessment in clinical research 16 2.4 Metabolomics in DKD Research 17 2.4.1 Metabolomics Overview 17 2.4.2 Biological interpretation of metabolic labeling 19 2.5 Machine Learning and Deep Learning for DKD Risk Prediction 20 2.5.1 Traditional machine learning approaches 20 2.5.2 Deep learning for clinical time series 22 2.5.3 Multimodal learning in healthcare 23 2.5.4 Model interpretability 25 2.5.5 Model evaluation and feature importance analysis 26 2.6 Future Perspectives and Challenges 28 2.6.1 Technical challenges and development directions 28 2.6.2 Challenges in clinical translation and application 28 2.6.3 Multi-omics integration and personalized medicine applications 29 3 Proposed Scheme 31 3.1 Study Design 31 3.2 Data Collection and Ethical Statements 34 3.3 eGFR-Based Survival Analysis Module 34 3.3.1 Outcome Definition 34 3.3.2 Feature Extraction via Linear Mixed Effects Model 35 3.3.3 Model Architecture 37 3.4 Questionnaire-Based Survival Analysis Module 37 3.4.1 Feature Engineering 37 3.4.2 Feature Selection 39 3.4.3 Deep Learning Encoder Architecture 41 3.4.4 Loss Function and Training 41 3.4.5 Stacking Ensemble 42 3.5 Metabolomics-Based Survival Analysis Module 42 3.5.1 Dual-Ion Data Preprocessing 42 3.5.2 Univariate Cox Feature Screening 43 3.5.3 Dimensionality Reduction 44 3.5.4 Model Architecture and Two-Phase Training 45 3.5.5 Class-Imbalance Handling 45 3.5.6 Multi-Seed Training and Flip Detection 46 3.6 Multimodal Survival Fusion Model 46 3.6.1 Overview 46 3.6.2 Modality Dropout 46 3.6.3 Cross-Modal Attention 47 3.6.4 Gated Fusion 47 3.6.5 Fusion Cox Head and Combined Loss 48 3.7 Training Strategy and Evaluation 48 3.7.1 Loss Function 49 3.7.2 Cross-Validation and Optimization 49 3.7.3 Evaluation Metric 50 3.7.4 Survival Curve Estimation 50 3.7.5 Hyperparameter Selection 51 3.7.6 Reproducibility 51 4 Performance Evaluation 52 4.1 Experimental Setup 52 4.2 Unimodal Baseline Models 52 4.2.1 eGFR Longitudinal Model 52 4.2.2 Questionnaire / Clinical-Feature Model 53 4.2.3 Metabolomics Dual-Ion Model 55 4.3 Multimodal Fusion Model 55 4.4 Ablation Study and Modality Contribution Analysis 56 4.4.1 Cross-Model Comparison 56 4.4.2 Soft-Attention Gate Weight Analysis 59 4.5 Model Interpretability 60 4.5.1 SHAP Feature Importance 60 4.5.2 Fusion Embedding Projection 62 4.6 Clinical Risk Stratification 64 4.6.1 Kaplan–Meier Risk Group Curves 64 4.6.2 Risk Score Distribution 66 4.6.3 Individual Survival Curves 67 4.7 Summary 67 4.8 Limitations 69 4.9 Future Work 70 5 Conclusions 73 References 76

    [1] Radica Z. Alicic, Michele T. Rooney, and Katherine R. Tuttle. Diabetic kidney disease: Challenges, progress, and possibilities. Clinical Journal of the American Society of Nephrology, 12(12):2032–2045, May 2017.
    [2] Alyaa Hliel, Huda Ahmed, and Hiba Hasan. Assessment and prediction of diabetic kidney disease in patients with type 2 diabetes mellitus by using an advanced biomarkers. Nefrolog´ıa (English Edition), 45(6):101330, June 2025.
    [3] Petter Bjornstad, Phoom Narongkiatikhun, Thomas Benzing, Dana Y. Fuhrman, Lesley Inker, Bryan Kestenbaum, Jay Koyner, Kevin Lemley, Jesse Seegmiller, and Sushrut S. Waikar. A comprehensive framework for kidney function assessment: Summary of the national institute of diabetes and digestive and kidney diseases reimagining kidney function assessment workshop. Kidney International Reports, 10(9):2919–2936, September 2025.
    [4] Yuting Li, Changlin Li, Jiamin Duan, Qingli Yang, Jing Zhang, Duo Yu, Yuwei Chen, Jianing Ni, Xiaomeng Lin, and Xudong Cai. Global research landscape and trends of metabolomics in diabetic kidney disease: focus on immunometabolic interactions. Frontiers in Clinical Diabetes and Healthcare, 7, July 2026.
    [5] Fariborz Mollaie, Mohammad Hossein Shakoor, Majid Sepahvand, and Reza Boostani. Medical data types in machine learning and their challenges: A comprehensive review. Intelligence-Based Medicine, 15:100427, September 2026.
    [6] Hong Chen, Yuping Huang, and Lizhen Chen. Ensemble machine learning for predicting renal function decline in chronic kidney disease: development and external validation. Frontiers in Medicine, 12, October 2025.
    [7] Paul E. Stevens, Sofia B. Ahmed, Juan Jesus Carrero, Bethany Foster, Anna Francis, Rasheeda K. Hall, Will G. Herrington, Guy Hill, Lesley A. Inker, R¨umeyza Kazancıo˘glu, Edmund Lamb, Peter Lin, Magdalena Madero, Natasha McIntyre, Kelly Morrow, Glenda Roberts, Dharshana Sabanayagam, Elke Schaeffner, Michael Shlipak, Rukshana Shroff, Navdeep Tangri, Teerawat Thanachayanont, Ifeoma Ulasi, Germaine Wong, Chih-Wei Yang, Luxia Zhang, and Adeera Levin. Kdigo 2024 clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney International, 105(4):S117–S314, April 2024.
    [8] Marita Liebisch and Gunter Wolf. Role of epigenetic changes in the pathophysiology of diabetic kidney disease. Glomerular Diseases, 4:1–29, 11 2024.
    [9] Rajesh Jha, Sara Lopez-Trevino, Haritha Kankanamalage, and Jay Jha. Diabetes and renal complications: An overview on pathophysiology, biomarkers and therapeutic interventions. Biomedicines, 12:1098, 05 2024.
    [10] Amit Mukherjee, Tanusree Debbarman, Basu Banerjee, and Sheelu Siddiqi. The impact of epigenetics on the pathophysiology of type 2 diabetes and associated nephropathic complications. Indian Journal of Endocrinology and Metabolism, 28:569–578, 12 2024.
    [11] Moarij Qaz, Hanny Sawaf, Jeeda Ismail, Huma Qazi, and Tushar Vachharajani. Pathophysiology of diabetic kidney disease. Eur. Med. J., pages 102–113, June 2022.
    [12] Abiy Tereda. From pathophysiology to personalized care: A comprehensive review of diabetic kidney disease. Journal of Medical and Scientific Research, 12:246–252, 07 2024.
    [13] Wilhelmina Hauwanga, Tibyan Abdalhamed, Lynda Ezike, Ifeoma Chukwulebe, Aung Oo, Amal Wilfred, Abdul Khan, Johnny Chukwuwike, Edisond Florial, Habeebah Lawan, Asaju Felix, and Billy McBenedict. The pathophysiology and vascular complications of diabetes in chronic kidney disease: A comprehensive review. Cureus, 16, 12 2024.
    [14] Jiawei Yu, Yan Liu, Hongjie Li, and Peirong Zhang. Pathophysiology of diabetic kidney disease and autophagy: A review. Medicine, 102:e33965, 07 2023.
    [15] Stephanie Toth-Manikowski and Mohamed G. Atta. Diabetic kidney disease: Pathophysiology and therapeutic targets. Journal of Diabetes Research, 2015:1–16, 2015.
    [16] Tereda A. From pathophysiology to personalized care: A comprehensive review of diabetic kidney disease. Journal of Medical and Scientific Research, 12(3):246–252, July 2024.
    [17] Harini Ravi, Soumik Das, Ramprasad Elumalai, and Gnanasambandan Ramanathan. Obesity-induced diabetic kidney disease-review on pathophysiology and clinical manifestations. Journal of Renal Endocrinology, 10:e25073, January 2024.
    [18] Jia-Yu Duan, Guang-Cai Duan, Chong-Jian Wang, Dong-Wei Liu, Ying-Jin Qiao, Shao-Kang Pan, Deng-Ke Jiang, Yong Liu, Zi-Hao Zhao, Lu-Lu Liang, Fei Tian, and Zhang-Suo Liu. Prevalence and risk factors of chronic kidney disease and diabetic kidney disease in a central chinese urban population: a cross-sectional survey. BMC Nephrology, 21(1), April 2020.
    [19] Nuha A. and ElSayed, Grazia Aleppo, Raveendhara R. Bannuru, Dennis Bruemmer, Billy S. Collins, Laya Ekhlaspour, Marisa E. Hilliard, Eric L. Johnson, Kamlesh Khunti, Ildiko Lingvay, Glenn Matfin, Rozalina G. McCoy, Mary Lou Perry, Scott J. Pilla, Sarit Polsky, Priya Prahalad, Richard E. Pratley, Alissa R. Segal, Jane Jeffrie Seley, Robert C. Stanton, and Robert A. Gabbay. 11. chronic kidney disease and risk management: ¡i¿standards of care in diabetes—2024¡/i¿. Diabetes Care, 47(Supplement 1):S219–S230, December 2023.
    [20] David Y Gaitonde, David L Cook, and Ian M Rivera. Chronic kidney disease: Detection and evaluation. American family physician, 96 12:776–783, 2017.
    [21] Sarah F. Cook and Robert R. Bies. Disease progression modeling: Key concepts and recent developments. Current Pharmacology Reports, 2(5):221–230, August 2016.
    [22] Kathryn L McGrath and Rina Edi. Diabetic kidney disease: Diagnosis, treatment, and prevention. American family physician, 99 12:751–759, 2019.
    [23] Wenbin Xu, Yanfei Zhou, Qian Jiang, Yiqian Fang, and Qian Yang. Risk prediction models for diabetic nephropathy among type 2 diabetes patients in china: a systematic review and meta-analysis. Frontiers in Endocrinology, 15, July 2024.
    [24] Thorp ML. Diabetic nephropathy: common questions. American family physician, 72(1):96–9, 2005.
    [25] Robert C. Stanton. Clinical challenges in diagnosis and management of diabetic kidney disease. American Journal of Kidney Diseases, 63(2):S3–S21, February 2014.
    [26] Tianxiao Zhang, Xiaodan Wang, Yueying Zhang, Ying Yang, Congying Yang, Huiyi Wei, and Qingbin Zhao. Establishment of a potent weighted risk model for determining the progression of diabetic kidney disease. Journal of Translational Medicine, 21(1), June 2023.
    [27] Dongna Hui, Fang Zhang, Yuanyue Lu, Huiqiang Hao, Shuangshuang Tian, Xiuzhao Fan, Yanqin Liu, Xiaoshuang Zhou, and Rongshan Li. A multifactorial risk score system for the prediction of diabetic kidney disease in patients with type 2 diabetes mellitus. Diabetes, Metabolic Syndrome and Obesity, Volume 16:385–395, February 2023.
    [28] Guillermo Gervasini. Recent advances and remaining challenges in the management of diabetic kidney disease. Journal of Clinical Medicine, 12(8):2759, April 2023.
    [29] David S. Wishart. Metabolomics for investigating physiological and pathophysiological processes. Physiological Reviews, 99(4):1819–1875, October 2019.
    [30] Sofia Moco, Sebastiano Collino, Serge Rezzi, and François-Pierre J. Martin. Metabolomics perspectives in pediatric research. Pediatric Research, 73(2-4):570–576, January 2013.
    [31] Michele Mussap, Roberto Antonucci, Antonio Noto, and Vassilios Fanos. The role of metabolomics in neonatal and pediatric laboratory medicine. Clinica Chimica Acta, 426:127–138, November 2013.
    [32] Gary J. Patti, Oscar Yanes, and Gary Siuzdak. Metabolomics: the apogee of the omics trilogy. Nature Reviews Molecular Cell Biology, 13(4):263–269, March 2012.
    [33] Simone Rochfort. Metabolomics reviewed: a new “omics” platform technology for systems biology and implications for natural products research. Journal of Natural Products, 68(12):1813–1820, November 2005.
    [34] Elizabeth M.S. McNiven, J. Bruce German, and Carolyn M. Slupsky. Analytical metabolomics: nutritional opportunities for personalized health. The Journal of Nutritional Biochemistry, 22(11):995–1002, November 2011.
    [35] A. Koulman and D. A. Volmer. Perspectives for metabolomics in human nutrition: an overview. Nutrition Bulletin, 33(4):324–330, November 2008.
    [36] M. Oreˇsiˇc. Metabolomics, a novel tool for studies of nutrition, metabolism and lipid dysfunction. Nutrition, Metabolism and Cardiovascular Diseases, 19(11):816–824, December 2009.
    [37] Liang Cui, Haitao Lu, and Yie Hou Lee. Challenges and emergent solutions for lcms/ms based untargeted metabolomics in diseases. Mass Spectrometry Reviews, 37(6):772–792, February 2018.
    [38] Eli Riekeberg and Robert Powers. New frontiers in metabolomics: from measurement to insight. F1000Research, 6:1148, July 2017.
    [39] Nawaporn Vinayavekhin, Edwin A. Homan, and Alan Saghatelian. Exploring disease through metabolomics. ACS Chemical Biology, 5(1):91–103, December 2009.
    [40] Caroline H. Johnson, Julijana Ivanisevic, and Gary Siuzdak. Metabolomics: beyond biomarkers and towards mechanisms. Nature Reviews Molecular Cell Biology, 17(7):451–459, March 2016.
    [41] Arthur A. Stone, Joan E. Broderick, and Alan T. Kaell. Single momentary assessments are not reliable outcomes for clinical trials. Contemporary Clinical Trials, 31(5):466–472, September 2010.
    [42] Chapter 7. longitudinal studies. https://www.bmj.com/about-bmj/resources-readers/publications/epidemiology-uninitiated/7-longitudinal-studies.
    [43] Allison A Lewinski, Conor Walsh, Sharron Rushton, Diana Soliman, Scott M Carlson, Matthew W Luedke, David J Halpern, Matthew J Crowley, Ryan J Shaw, Jason A Sharpe, Anastasia-Stefania Alexopoulos, Amir Alishahi Tabriz, Jessica R Dietch, Diya M Uthappa, Soohyun Hwang, Katharine A Ball Ricks, Sarah Cantrell, Andrzej S Kosinski, Belinda Ear, Adelaide M Gordon, Jennifer M Gierisch, John W Williams Jr, and Karen M Goldstein. Telehealth for the longitudinal management of chronic conditions: Systematic review. Journal of Medical Internet Research, 24(8):e37100, August 2022.
    [44] Manu S Bala, Nisha Dhanabalsamy, and Bill Lucia. Impact of mental health on chronic conditions and cost implications- leveraging data to predict risk. Journal of Mental Health & Clinical Psychology, 9(1):21–24, February 2025.
    [45] Hiddo Heerspink, Stephen Nolan, Juan-Jesus Carrero, Matthew Arnold, Roberto Pecoits-Filho, Juan José García Sánchez, Eric Wittbrodt, Claudia Cabrera, Carolyn S. P. Lam, Hungta Chen, Eiichiro Kanda, Mitja Lainscak, Carol Pollock, and David C. Wheeler. Clinical outcomes in patients with ckd and rapid or nonrapid egfr decline: A report from the discover ckd retrospective cohort. Advances in Therapy, 41(8):3264–3277, July 2024.
    [46] Jixin Xing, Linxi Huang, Weifu Ren, and Xiaobin Mei. Risk factors for rapid kidney function decline in diabetes patients. Ren. Fail., 46(2):2398188, December 2024.
    [47] Guozhi Jiang, Andrea On Yan Luk, Claudia Ha Ting Tam, Fangying Xie, Bendix Carstensen, Eric Siu Him Lau, Cadmon King Poo Lim, Heung Man Lee, Alex Chi Wai Ng, Maggie Chor Yin Ng, Risa Ozaki, Alice Pik Shan Kong, Chun Chung Chow, Xilin Yang, Hui-Yao Lan, Stephen Kwok Wing Tsui, Xiaodan Fan, Cheuk Chun Szeto, Wing Yee So, Juliana Chung Ngor Chan, Ronald Ching Wan Ma, and Hong Kong Diabetes Register TRS Study Group. Progression of diabetic kidney disease and trajectory of kidney function decline in chinese patients with type 2 diabetes. Kidney Int., 95(1):178–187, January 2019.
    [48] Nuria Montero, Laia Oliveras, Alberto Martínez-Castelao, José Luis Gorriz, María José Soler, Beatriz Fernández-Fernández, Maria Quero, Clara García-Carro, Paula Garcia-Sancho, Marian Goicoechea, Juan Jose Gorgojo Martínez, Pablo Molina, María Jesús Puchades, Diana Rodríguez-Espinosa, Nery Sablón, Rafael Santamaría, Juan Francisco Navarro-González, and GEENDIAB (Spanish Diabetic Nephropathy Study Group). Clinical practice guideline for detection and management of diabetic kidney disease: A consensus report by the spanish society of nephrology. Nefrol. (Engl. Ed.), 45 Suppl 1:1–26, April 2025.
    [49] Miodrag Lovric. Linear Mixed Models: An Overview, page 1356–1374. Springer Berlin Heidelberg, 2025.
    [50] Cynthia J Janmaat, Merel van Diepen, Roula Tsonaka, Kitty J Jager, Carmine Zoccali, and Friedo W Dekker. Pitfalls of linear regression for estimating slopes over time and how to avoid them by using linear mixed-effects models. Nephrology Dialysis Transplantation, 34(4):561–566, May 2018.
    [51] Elke S. Schaeffner, Natalie Ebert, Martin K. Kuhlmann, Peter Martus, Nina Mielke, Alice Schneider, Markus van der Giet, and Dörte Huscher. Age and the course of gfr in persons aged 70 and above. Clinical Journal of the American Society of Nephrology, 17(8):1119–1128, August 2022.
    [52] Hiddo J.L. Heerspink, Dustin J. Little, Lars Frison, Samvel B. Gasparyan, Christoph Wanner, Niels Jongs, and Douwe Postmus. Clinical trial designs to assess treatment effects on glomerular filtration rate decline. Kidney International, 106(4):723–735, October 2024.
    [53] Karen Leffondre, Julie Boucquemont, Giovanni Tripepi, Vianda S. Stel, Georg Heinze, and Daniela Dunkler. Analysis of risk factors associated with renal function trajectory over time: a comparison of different statistical approaches. Nephrology Dialysis Transplantation, 30(8):1237–1243, October 2014.
    [54] Brian Kwan, Lin Liu, David Strong, H. Irene Su, and Loki Natarajan. Linear mixed model vs two-stage methods: Developing prognostic models of diabetic kidney disease progression, 2022.
    [55] Jian Yang, Noah A Zaitlen, Michael E Goddard, Peter M Visscher, and Alkes L Price. Advantages and pitfalls in the application of mixed-model association methods. Nature Genetics, 46(2):100–106, January 2014.
    [56] Hongtu Hu, Guohua Ding, and Wei Liang. Dietary therapy to halt the progression of diabetes to diabetic kidney disease. Food & Function, 16(7):2622–2636, 2025.
    [57] Huixing Huang, Cuiying Yu, Pengjie Sha, Leilei Chen, Rongrong Wang, and Yanli Wei. New developments in epidemiological research on dietary patterns associated with chronic kidney disease. Frontiers in Nutrition, 12, January 2026.
    [58] Gang Ko, Kamyar Kalantar-Zadeh, Jordi Goldstein-Fuchs, and Connie Rhee. Dietary approaches in the management of diabetic patients with kidney disease. Nutrients, 9(8):824, July 2017.
    [59] Ranjani N. Moorthi, Colby J. Vorland, and Kathleen M. Hill Gallant. Diet and diabetic kidney disease: Plant versus animal protein. Current Diabetes Reports, 17(3), March 2017.
    [60] Linli Cai, Yin Huang, Xingyuan Li, Dehong Cao, and Fang Liu. Effects of dietary intervention on diabetic nephropathy: an umbrella review of systematic reviews and meta-analyses of randomized controlled trials. Frontiers in Endocrinology, 15, April 2024.
    [61] Aiquan Jiao, Yingna Zhao, Laping Chu, Yueyue Yang, and Zhengyu Jin. A review on animal and plant proteins in regulating diabetic kidney disease: Mechanism of action and future perspectives. Journal of Functional Foods, 119:106353, August 2024.
    [62] Seung Eun Lee and Yong Seek Park. Environmental pollution and its impact on kidney diseases: A comprehensive review of current evidence. Life, 16(2):291, February 2026.
    [63] Benjamin A Vervaet, Patrick C D’Haese, and Anja Verhulst. Environmental toxin-induced acute kidney injury. Clinical Kidney Journal, 10(6):747–758, July 2017.
    [64] Abhijit V. Kshirsagar, Evan M. Zeitler, Anne Weaver, Nora Franceschini, and Lawrence S. Engel. Environmental exposures and kidney disease. Kidney360, 3(12):2174–2182, December 2022.
    [65] Chien-Ning Hsu and You-Lin Tain. Adverse impact of environmental chemicals on developmental origins of kidney disease and hypertension. Frontiers in Endocrinology, 12, October 2021.
    [66] Anglina Kataria, Leonardo Trasande, and Howard Trachtman. The effects of environmental chemicals on renal function. Nature Reviews Nephrology, 11(10):610–625, June 2015.
    [67] Mansee Arya, Priyanka Bansal, Bhavani Pentela, and Avijit Mazumder. Impact of environmental toxins on kidney health: A comprehensive review. Reviews on Recent Clinical Trials, 21, March 2026.
    [68] Lillie M A Barnett and Brian S Cummings. Nephrotoxicity and renal pathophysiology: A contemporary perspective. Toxicological Sciences, 164(2):379–390, June 2018.
    [69] Natalia Riva, Diego Grandi, Benjamín Cruzat, and Ruben Alvarado. Validation of questionnaires for the measurement of health variables: Fundamental concepts. Medwave, 24(01):e2746–e2746, January 2024.
    [70] Roger Tourangeau. Survey reliability: Models, methods, and findings. Journal of Survey Statistics and Methodology, 9(5):961–991, October 2020.
    [71] Priya Ranganathan and Carlo Caduff. Designing and validating a research questionnaire - part 1. Perspectives in Clinical Research, 14(3):152–155, July 2023.
    [72] Priya Ranganathan, Carlo Caduff, and Christopher M. A. Frampton. Designing and validating a research questionnaire - part 2. Perspectives in Clinical Research, 15(1):42–45, January 2024.
    [73] Phil Edwards. Questionnaires in clinical trials: guidelines for optimal design and administration. Trials, 11(1), January 2010.
    [74] Kamal Kishore, Vidushi Jaswal, Vinay Kulkarni, and Dipankar De. Practical guidelines to develop and evaluate a questionnaire. Indian Dermatology Online Journal, 12(2):266–275, March 2021.
    [75] Ilya Gertsman and Bruce A. Barshop. Promises and pitfalls of untargeted metabolomics. Journal of Inherited Metabolic Disease, 41(3):355–366, March 2018.
    [76] Lisa M. Labine and Myrna J. Simpson. The use of nuclear magnetic resonance (nmr) and mass spectrometry (ms)–based metabolomics in environmental exposure assessment. Current Opinion in Environmental Science & Health, 15:7–15, June 2020.
    [77] Julian Müller, Thomas Bertsch, Justus Volke, Alexander Schmid, Rebecca Klingbeil, Yulian Metodiev, Bican Karaca, Seung-Hyun Kim, Simon Lindner, Tobias Schupp, Maximilian Kittel, Gernot Poschet, Ibrahim Akin, and Michael Behnes. Narrative review of metabolomics in cardiovascular disease. Journal of Thoracic Disease, 13(4):2532–2550, April 2021.
    [78] Hanna de Sá, Emile dos Santos, Samara Carvalho, Rafaela Nunes, and Gisele Canuto. Fundamentals and analytical strategies for metabolomics workflow: An overview and microbial applications. Brazilian Journal of Analytical Chemistry, May 2023.
    [79] Hans Rolando Zamora Obando, Gustavo Henrique Bueno Duarte, and Ana Valéria Colnaghi Simionato. Metabolomics Data Treatment: Basic Directions of the Full Process, page 243–264. Springer International Publishing, 2021.
    [80] Clara Ibáñez, Alejandro Cifuentes, and Carolina Simó. Recent Advances and Applications of Metabolomics to Investigate Neurodegenerative Diseases, page 95–132. Elsevier, 2015.
    [81] S Rakusanova and T Cajka. Metabolomics and lipidomics for studying metabolic syndrome: Insights into cardiovascular diseases, type 1 & 2 diabetes, and metabolic dysfunction-associated steatotic liver disease. Physiological Research, (Suppl 1):S165–S183, August 2024.
    [82] MarkP Styczynski and KathleenA Vermeersch. Applications of metabolomics in cancer research. Journal of Carcinogenesis, 12(1):9, 2013.
    [83] Jordan Maximillian Wilkins and Eugenia Trushina. Application of metabolomics in alzheimer’s disease. Frontiers in Neurology, 8, January 2018.
    [84] Mingxiao Yang and Lixing Lao. Emerging applications of metabolomics in traditional chinese medicine treating hypertension: Biomarkers, pathways and more. Frontiers in Pharmacology, 10, March 2019.
    [85] Yumeng Zhang, Yingbo Yang, Lili Ding, Zhengtao Wang, Ying Xiao, and Wei Xiao. Emerging applications of metabolomics to assess the efficacy of traditional chinese medicines for treating type 2 diabetes mellitus. Frontiers in Pharmacology, 12, September 2021.
    [86] Antonia Garcia and Coral Barbas. Gas Chromatography-Mass Spectrometry (GCMS)-Based Metabolomics, page 191–204. Humana Press, December 2010.
    [87] Helen G. Gika, Georgios A. Theodoridis, Robert S. Plumb, and Ian D. Wilson. Current practice of liquid chromatography–mass spectrometry in metabolomics and metabonomics. Journal of Pharmaceutical and Biomedical Analysis, 87:12–25, January 2014.
    [88] Renuka Ranjan and Neeraj Sinha. Nuclear magnetic resonance (nmr)-based metabolomics for cancer research. NMR in Biomedicine, 32(10), May 2018.
    [89] Abdul-Hamid Emwas, Raja Roy, Ryan T. McKay, Leonardo Tenori, Edoardo Saccenti, G. A. Nagana Gowda, Daniel Raftery, Fatimah Alahmari, Lukasz Jaremko, Mariusz Jaremko, and David S. Wishart. Nmr spectroscopy for metabolomics research. Metabolites, 9(7):123, June 2019.
    [90] Marine P. M. Letertre, Gaud Dervilly, and Patrick Giraudeau. Combined nuclear magnetic resonance spectroscopy and mass spectrometry approaches for metabolomics. Analytical Chemistry, 93(1):500–518, November 2020.
    [91] Xueyan Bian, Chenwen Wang, Majie Wang, Ailing Yin, Jiayan Xu, Mijia Liu, Hui Wang, Yating Cao, Xin Huang, Chenxue Qin, Ye Zhang, and Heming Yu. Serum metabolic profiling of patients with diabetic kidney disease based on gas chromatography-mass spectrometry. Frontiers in Molecular Biosciences, 12, March 2025.
    [92] Qian Sha, Jinxiu Lyu, Meng Zhao, Haijuan Li, Mengzhe Guo, and Qiang Sun. Multi-omics analysis of diabetic nephropathy reveals potential new mechanisms and drug targets. Frontiers in Genetics, 11, December 2020.
    [93] Soumik Das, V Devi Rajeswari, Ganesh Venkatraman, Ramprasad Elumalai, Sivaraman Dhanasekaran, and Gnanasambandan Ramanathan. Current updates on metabolites and its interlinked pathways as biomarkers for diabetic kidney disease: A systematic review. Translational Research, 265:71–87, March 2024.
    [94] Claire Hill, Amy Jayne McKnight, and Laura J. Smyth. Integrated multiomic analyses: An approach to improve understanding of diabetic kidney disease. Diabetic Medicine, 42(2), October 2024.
    [95] Layana T. Takieddine and Joe M. El-Khoury. Laboratory anomalies in the basic metabolic panel: Core curriculum 2025. American Journal of Kidney Diseases, 85(1):104–114, January 2025.
    [96] Emily Wallace. Investigating the role of metabolic reprogramming in the pathogenesis of diabetic kidney disease using novel metabolic mouse models, 2023.
    [97] Lan Wei, Yuanyuan Han, and Chao Tu. Molecular pathways of diabetic kidney disease inferred from proteomics. Diabetes, Metabolic Syndrome and Obesity, Volume 16:117–128, January 2023.
    [98] Yuyun Hu, Xue Ni, Qinghuo Chen, Yihui Qu, Kanan Chen, Gaohui Zhu, Minqiao Zhang, Ningjie Xu, Xu Bai, Jing Wang, Yanhong Ma, Qun Luo, and Kedan Cai. Predicting diabetic kidney disease with serum metabolomics and gut microbiota. Scientific Reports, 15(1), April 2025.
    [99] V. Sharma, M. Khokhar, P. Panigrahi, A. Gadwal, P. Setia, and P. Purohit. Advancements, challenges, and clinical implications of integration of metabolomics technologies in diabetic nephropathy. Clinica Chimica Acta, 561:119842, July 2024.
    [100] Jialing Huang, Cornelia Huth, Marcela Covic, Martina Troll, Jonathan Adam, Sven Zukunft, Cornelia Prehn, Li Wang, Jana Nano, Markus F. Scheerer, Susanne Neschen, Gabi Kastenmüller, Karsten Suhre, Michael Laxy, Freimut Schliess, Christian Gieger, Jerzy Adamski, Martin Hrabe de Angelis, Annette Peters, and Rui Wang-Sattler. Machine learning approaches reveal metabolic signatures of incident chronic kidney disease in individuals with prediabetes and type 2 diabetes. Diabetes, 69(12):2756–2765, October 2020.
    [101] Midori Sakashita, Tetsuhiro Tanaka, and Reiko Inagi. Metabolic changes and oxidative stress in diabetic kidney disease. Antioxidants, 10(7):1143, July 2021.
    [102] Alison Callahan and Nigam H. Shah. Machine Learning in Healthcare, page 279–291. Elsevier, 2017.
    [103] Birendra Kumar Saraswat, Aditya Saxena, and P. C. Vashist. Prediction Model for the Healthcare Industry Using Machine Learning, page 33–47. Springer Nature Singapore, 2024.
    [104] Hema Sekhar Reddy Rajula, Giuseppe Verlato, Mirko Manchia, Nadia Antonucci, and Vassilios Fanos. Comparison of conventional statistical methods with machine learning in medicine: Diagnosis, drug development, and treatment. Medicina, 56(9):455, September 2020.
    [105] Gopi Battineni, Getu Gamo Sagaro, Nalini Chinatalapudi, and Francesco Amenta. Applications of machine learning predictive models in the chronic disease diagnosis. Journal of Personalized Medicine, 10(2):21, March 2020.
    [106] Shahadat Uddin, Arif Khan, Md Ekramul Hossain, and Mohammad Ali Moni. Comparing different supervised machine learning algorithms for disease prediction. BMC Medical Informatics and Decision Making, 19(1), December 2019.
    [107] Rahul C. Deo. Machine learning in medicine. Circulation, 132(20):1920–1930, November 2015.
    [108] TAE SUN PARK, YU JI KIM, and KYUNG AE LEE. 1788-lb: Machine learning–based prediction of diabetic kidney disease in patients with type 2 diabetes. Diabetes, 73(Supplement 1), June 2024.
    [109] Lu-Xi Zou, Xue Wang, Zhi-Li Hou, Ling Sun, and Jiang-Tao Lu. Machine learning algorithms for diabetic kidney disease risk predictive model of chinese patients with type 2 diabetes mellitus. Renal Failure, 47(1), April 2025.
    [110] Yosuke Hirakawa, Kentaro Yoshioka, Kensuke Kojima, Yasuho Yamashita, Takuma Shibahara, Takehiko Wada, Masaomi Nangaku, and Reiko Inagi. Potential progression biomarkers of diabetic kidney disease determined using comprehensive machine learning analysis of non-targeted metabolomics. Scientific Reports, 12(1), September 2022.
    [111] Jing Peng, Sha Yang, Chaomin Zhou, Chenguang Qin, Kaiyun Fang, Ying Tan, Jingjing Da, Jiqing Zhang, and Yan Zha. Identification of common biomarkers in diabetic kidney disease and cognitive dysfunction using machine learning algorithms. Scientific Reports, 14(1), September 2024.
    [112] Priyabrata Thatoi, Rohit Choudhary, Anto Lourdu Xavier Raj Arockia Selvarathinam, David Samuel Azariya S, and Kamarul Hasan. Towards intelligent nicu monitoring: Real-time anomaly detection in vital signs using deep learning models. In Proceedings of the 2024 7th Artificial Intelligence and Cloud Computing Conference, AICCC 2024, page 573–582. ACM, December 2024.
    [113] Mohammad Amin Morid, Olivia R. Liu Sheng, and Joseph Dunbar. Time series prediction using deep learning methods in healthcare, 2021.
    [114] Xi Xu, Jianqiang Li, Zhichao Zhu, Linna Zhao, Huina Wang, Changwei Song, Yining Chen, Qing Zhao, Jijiang Yang, and Yan Pei. A comprehensive review on synergy of multi-modal data and ai technologies in medical diagnosis. Bioengineering, 11(3):219, February 2024.
    [115] Yosri A. Fahim, Ibrahim W. Hasani, Samer Kabba, and Waleed Mahmoud Ragab. Artificial intelligence in healthcare and medicine: clinical applications, therapeutic advances, and future perspectives. European Journal of Medical Research, 30(1), September 2025.
    [116] Wenli Yang, Lilian Some, Michael Bain, and Byeong Kang. A comprehensive survey on integrating large language models with knowledge-based methods. Knowledge-Based Systems, 318:113503, June 2025.
    [117] Bassem Jandoubi and Moulay A Akhloufi. Multimodal artificial intelligence in medical diagnostics. Information (Basel), 16(7):591, July 2025.
    [118] N Ganeshan and G Magesh. Deep learning for cardiovascular disease: a comprehensive review of detection and risk forecasting. Front. Artif. Intell., 9(1840804), June 2026.
    [119] Zhe Guo, Xiang Li, Heng Huang, Ning Guo, and Quanzheng Li. Deep learning-based image segmentation on multimodal medical imaging. IEEE Trans. Radiat. Plasma Med. Sci., 3(2):162–169, March 2019.
    [120] Yi Gu, Kuniaki Saito, and Jiaxin Ma. Learning contrastive multimodal fusion with improved modality dropout for disease detection and prediction. September 2025.
    [121] Jinzhao Qian, Ankita Joshi, Hailong Li, Nehal A Parikh, Jonanthan R Dillman, and Lili He. Utility of deep learning to address missing modalities from multimodal medical imaging studies: A systematic review. Artif. Intell. Appl., October 2025.
    [122] Aya M Al-Zoghby, Ahmed Ismail Ebada, Aya S Saleh, Mohammed Abdelhay, and Wael A Awad. A comprehensive review of multimodal deep learning for enhanced medical diagnostics. Comput. Mater. Contin., 84(3):4155–4193, 2025.
    [123] Pantelis Linardatos, Vasilis Papastefanopoulos, and Sotiris Kotsiantis. Explainable ai: A review of machine learning interpretability methods. Entropy, 23(1):18, December 2020.
    [124] W. James Murdoch, Chandan Singh, Karl Kumbier, Reza Abbasi-Asl, and Bin Yu. Definitions, methods, and applications in interpretable machine learning. Proceedings of the National Academy of Sciences, 116(44):22071–22080, October 2019.
    [125] Cynthia Rudin. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5):206–215, May 2019.
    [126] Matteo Rizzo, Alberto Veneri, Matteo Marcuzzo, Alessandro Zangari, Andrea Albarelli, Claudio Lucchese, Marco Salvatore Nobile, and Cristina Conati. Machine learning models explanations as interpretations of evidence: a theoretical framework of explainability and its implications on high-stakes biomedical decision-making. BMC Medical Research Methodology, 25(S1), December 2025.
    [127] Tabea Ott and Peter Dabrock. Transparent human – (non-) transparent technology? the janus-faced call for transparency in ai-based health care technologies. Frontiers in Genetics, 13, August 2022.
    [128] Lianqin Chen, Xian Shao, and Pei Yu. Machine learning prediction models for diabetic kidney disease: systematic review and meta-analysis. Endocrine, 84(3):890–902, December 2023.
    [129] Xiaomeng Lin, Chao Liu, Huaiyu Wang, Xiaohui Fan, Linfeng Li, Jiming Xu, Changlin Li, Yao Wang, Xudong Cai, and Xin Peng. A superlearner approach for predicting diabetic kidney disease upon the initial diagnosis of t2dm in hospital. BMC Medical Informatics and Decision Making, 25(1), March 2025.
    [130] Oona Rainio, Jarmo Teuho, and Riku Klén. Evaluation metrics and statistical tests for machine learning. Scientific Reports, 14(1), March 2024.
    [131] Da Woon Kim and Sang Heon Song. A new journey to predict the prognosis of diabetic kidney disease. Kidney Research and Clinical Practice, 42(4):409–411, July 2023.
    [132] Charumathi Sabanayagam, Feng He, Simon Nusinovici, Jialiang Li, Cynthia Lim, Gavin Tan, and Ching Yu Cheng. Prediction of diabetic kidney disease risk using machine learning models: A population-based cohort study of asian adults. eLife, 12, September 2023.
    [133] Qiao Jin, Eric S. H. Lau, Andrea O. Luk, Claudia H. T. Tam, Risa Ozaki, Cadmon K. P. Lim, Hongjiang Wu, Elaine Y. K. Chow, Alice P. S. Kong, Heung Man Lee, Baoqi Fan, Alex C. W. Ng, Guozhi Jiang, Ka Fai Lee, Shing Chung Siu, Grace Hui, Chiu Chi Tsang, Kam Piu Lau, Jenny Y. Leung, Man-wo Tsang, Elaine Y. N. Cheung, Grace Kam, Ip Tim Lau, June K. Li, Vincent T. F. Yeung, Emmy Lau, Stanley Lo, Samuel Fung, Yuk Lun Cheng, Weichuan Yu, Stephen K. W. Tsui, Brian Tomlinson, Yu Huang, Hui-yao Lan, Cheuk Chun Szeto, Wing Yee So, Alicia J. Jenkins, Erik Fung, Mirthe Muilwijk, Marieke T. Blom, Leen M. ’t Hart, Juliana C. N. Chan, and Ronald C. W. and Ma. Circulating metabolomic markers linking diabetic kidney disease and incident cardiovascular disease in type 2 diabetes: analyses from the hong kong diabetes biobank. Diabetologia, 67(5):837–849, February 2024.
    [134] Shijia Liu, Yuan Gui, Mark S. Wang, Lu Zhang, Tingting Xu, Yuchen Pan, Ke Zhang, Ying Yu, Liangxiang Xiao, Yi Qiao, Christopher Bonin, Geneva Hargis, Tao Huan, Yanbao Yu, Jianling Tao, Rong Zhang, Donald L. Kreutzer, Yanjiao Zhou, Xiao-Jun Tian, Yanlin Wang, Haiyan Fu, Xiaofei An, Silvia Liu, and Dong Zhou. Serum integrative omics reveals the landscape of human diabetic kidney disease. Molecular Metabolism, 54:101367, December 2021.
    [135] Sandhya Nayak, Ashwini Amin, Swetha R. Reghunath, Girish Thunga, Dinesh Acharya U, K.N. Shivashankara, Ravindra Prabhu Attur, and Leelavathi D. Acharya. Development of a machine learning-based model for the prediction and progression of diabetic kidney disease: A single centred retrospective study. International Journal of Medical Informatics, 190:105546, October 2024.
    [136] Qiang Yang, Ai-hua Zhang, Jian-hua Miao, Hui Sun, Ying Han, Guang-li Yan, Fang-fang Wu, and Xi-jun Wang. Metabolomics biotechnology, applications, and future trends: a systematic review. RSC Advances, 9(64):37245–37257, 2019.
    [137] Xiaofeng Peng, Xiaoyi Wang, Xue Shao, Yucheng Wang, Shi Feng, Cuili Wang, Cunqi Ye, Jianghua Chen, and Hong Jiang. Serum metabolomics benefits discrimination kidney disease development in type 2 diabetes patients. Frontiers in Medicine, 9, May 2022.
    [138] Aya Galal, Marwa Talal, and Ahmed Moustafa. Applications of machine learning in metabolomics: Disease modeling and classification. Frontiers in Genetics, 13, November 2022.
    [139] Fengmei Qian, Li Zhao, Di Zhang, Mengjie Yu, Wei Zhou, and Juan Jin. Serum metabolomics detected by ¡scp¿ldi-tof-ms¡/scp¿ can be used to distinguish between diabetic patients with and without diabetic kidney disease. FEBS Open Bio, 13(10):1844–1858, August 2023.
    [140] Harald Hampel, Robert Nisticò, Nicholas T. Seyfried, Allan I. Levey, Erica Modeste, Pablo Lemercier, Filippo Baldacci, Nicola Toschi, Francesco Garaci, George Perry, Enzo Emanuele, Pedro L. Valenzuela, Alejandro Lucia, Andrea Urbani, Giulia M. Sancesario, Mark Mapstone, Massimo Corbo, Andrea Vergallo, and Simone Lista. Omics sciences for systems biology in alzheimer’s disease: State-of-the-art of the evidence. Ageing Research Reviews, 69:101346, August 2021.
    [141] August John, Bo Qin, Krishna R Kalari, Liewei Wang, and Jia Yu. Patient-specific multi-omics models and the application in personalized combination therapy. Future Oncology, 16(23):1737–1750, May 2020.
    [142] Paola Leon-Mimila, Jessica Wang, and Adriana Huertas-Vazquez. Relevance of multi-omics studies in cardiovascular diseases. Frontiers in Cardiovascular Medicine, 6, July 2019.
    [143] Alex E. Mohr, Carmen P. Ortega-Santos, Corrie M. Whisner, Judith Klein-Seetharaman, and Paniz Jasbi. Navigating challenges and opportunities in multiomics integration for personalized healthcare. Biomedicines, 12(7):1496, July 2024.
    [144] Indhupriya Subramanian, Srikant Verma, Shiva Kumar, Abhay Jere, and Krishanpal Anamika. Multi-omics data integration, interpretation, and its application. Bioinformatics and Biology Insights, 14:117793221989905, January 2020.
    [145] Chaoying Zhan, Tong Tang, Erman Wu, Yuxin Zhang, Mengqiao He, Rongrong Wu, Cheng Bi, Jiao Wang, Yingbo Zhang, and Bairong Shen. From multiomics approaches to personalized medicine in myocardial infarction. Frontiers in Cardiovascular Medicine, 10, October 2023.
    [146] Efi Athieniti and George M. Spyrou. A guide to multi-omics data collection and integration for translational medicine. Computational and Structural Biotechnology Journal, 21:134–149, 2023.
    [147] Emile Mardoc, Mamadou Dia Sow, Sébastien Déjean, and Jérôme Salse. Genomic data integration tutorial, a plant case study. BMC Genomics, 25(1), January 2024.
    [148] Robel Alemu, Nigussie T. Sharew, Yodit Y. Arsano, Muktar Ahmed, Fasil Tekola-Ayele, Tesfaye B. Mersha, and Azmeraw T. Amare. Multi-omics approaches for understanding gene-environment interactions in noncommunicable diseases: techniques, translation, and equity issues. Human Genomics, 19(1), January 2025.
    [149] Davide Chicco, Fabio Cumbo, and Claudio Angione. Ten quick tips for avoiding pitfalls in multi-omics data integration analyses. PLOS Computational Biology, 19(7):e1011224, July 2023.
    [150] Nasim Vahabi and George Michailidis. Unsupervised multi-omics data integration methods: A comprehensive review. Frontiers in Genetics, 13, March 2022.
    [151] Mingzhi Lin, Jiuqi Guo, Zhilin Gu, Wenyi Tang, Hongqian Tao, Shilong You, Dalin Jia, Yingxian Sun, and Pengyu Jia. Machine learning and multi-omics integration: advancing cardiovascular translational research and clinical practice. Journal of Translational Medicine, 23(1), April 2025.
    [152] Getnet Molla and Molalegne Bitew. Revolutionizing personalized medicine: Synergy with multi-omics data generation, main hurdles, and future perspectives. Biomedicines, 12(12):2750, November 2024.
    [153] Junwen Su, Lamei Yang, Ziran Sun, and Xianquan Zhan. Personalized drug therapy: Innovative concept guided with proteoformics. Molecular & Cellular Proteomics, 23(3):100737, March 2024.
    [154] Jared L Katzman, Uri Shaham, Alexander Cloninger, Jonathan Bates, Tingting Jiang, and Yuval Kluger. DeepSurv: personalized treatment recommender system using a cox proportional hazards deep neural network. BMC Med. Res. Methodol., 18(1):24, February 2018.
    [155] Hemant Ishwaran, Udaya B Kogalur, Eugene H Blackstone, and Michael S Lauer. Random survival forests. Ann. Appl. Stat., 2(3):841–860, September 2008.
    [156] Yichao Wu. Elastic net for cox’s proportional hazards model with a solution path algorithm. Stat. Sin., 22(1):27–294, 2012.
    [157] Yifei Chen, Zhenyu Jia, Dan Mercola, and Xiaohui Xie. A gradient boosting algorithm for survival analysis via direct optimization of concordance index. Comput. Math. Methods Med., 2013:873595, November 2013.
    [158] Jeffrey Lin and Sheng Luo. Deep learning for the dynamic prediction of multivariate longitudinal and survival data. Stat. Med., 41(15):2894–2907, July 2022.
    [159] Yvan Saeys, Inaki Inza, and Pedro Larrañaga. A review of feature selection techniques in bioinformatics. Bioinformatics, 23(19):2507–2517, October 2007.
    [160] Sebastian Pölsterl. scikit-survival: A library for time-to-event analysis built on top of scikit-learn. Journal of Machine Learning Research, 21(212):1–6, 2020.
    [161] Yoav Benjamini and Yosef Hochberg. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. Series B Stat. Methodol., 57(1):289–300, January 1995.
    [162] American Diabetes Association Professional Practice Committee. 11. chronic kidney disease and risk management: Standards of care in diabetes-2024. Diabetes Care, 47(Suppl 1):S219–S230, January 2024.
    [163] Hong Chen, Yuping Huang, and Lizhen Chen. Ensemble machine learning for predicting renal function decline in chronic kidney disease: development and external validation. Front. Med. (Lausanne), 12(1598065):1598065, October 2025.
    [164] Scott Lundberg and Su-In Lee. A unified approach to interpreting model predictions. May 2017.
    [165] Hui-Ju Tsai, Pei-Yu Wu, Jiun-Chi Huang, and Szu-Chia Chen. Environmental pollution and chronic kidney disease. Int. J. Med. Sci., 18(5):1121–1129, January 2021.
    [166] Hui-Ju Tsai, Chih-Hsing Hung, Chih-Wen Wang, Hung-Pin Tu, Chiu-Hui Li, Chun-Chi Tsai, Wen-Yi Lin, Szu-Chia Chen, and Chao-Hung Kuo. Associations among heavy metals and proteinuria and chronic kidney disease. Diagnostics (Basel), 11(2):282, February 2021.
    [167] Radica Z Alicic, Michele T Rooney, and Katherine R Tuttle. Diabetic kidney disease: Challenges, progress, and possibilities. Clin. J. Am. Soc. Nephrol., 12(12):2032–2045, December 2017.
    [168] Merlin C Thomas, Michael Brownlee, Katalin Susztak, Kumar Sharma, Karin A M Jandeleit-Dahm, Sophia Zoungas, Peter Rossing, Per-Henrik Groop, and Mark E Cooper. Diabetic kidney disease. Nat. Rev. Dis. Primers, 1(1):15018, July 2015.
    [169] Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group. KDIGO 2024 clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int., 105(4S):S117–S314, April 2024.
    [170] T Alp Ikizler, Jerrilynn D Burrowes, Laura D Byham-Gray, Katrina L Campbell, Juan-Jesus Carrero, Winnie Chan, Denis Fouque, Allon N Friedman, Sana Ghaddar, D Jordi Goldstein-Fuchs, George A Kaysen, Joel D Kopple, Daniel Teta, Angela Yee-Moon Wang, and Lilian Cuppari. KDOQI clinical practice guideline for nutrition in CKD: 2020 update. Am. J. Kidney Dis., 76(3 Suppl 1):S1–S107, September 2020.

    QR CODE