Noninvasive Urinary Biomarkers for Obesity-Related Metabolic Diseases: Diagnostic Applications and Future Directions
Abstract
:1. Introduction
1.1. Obesity-Related Metabolic Diseases and Current Diagnostic Challenges
1.2. Noninvasive Biomarkers: A Shift from Blood to Urine
2. Advances in Urinary Biomarker Detection Technologies
3. Disease-Specific Biomarkers and Applications
3.1. Type 2 Diabetes
3.2. Hyperlipidemia and MASLD
3.3. Hyperuricemia
3.4. Hypertension
3.5. Polycystic Ovarian Syndrome
3.6. Metabolic Syndrome
4. Challenges and Future Perspectives
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Technology | Abbreviation | Comparative Cost Tier | Advantages | Limitations | Primary Applications |
---|---|---|---|---|---|
Nuclear Magnetic Resonance | NMR | $$ | - Provides detailed information on molecular structures - Can analyze complex mixtures - Enables dynamic tracking of chemical reactions | - Low sensitivity for low-abundance metabolites - Requires expensive equipment - Requires skilled operators - Requires isotope enrichment (e.g., carbon-13) to improve sensitivity - Long analysis time for multi-dimensional experiments - High maintenance costs (e.g., liquid helium cooling) | - Structural biology - Metabolomics - Drug discovery - Material science |
Gas Chromatography–Mass Spectrometry | GC-MS | $$$ | - High sensitivity and specificity - Suitable for volatile compounds - Reliable - Compatible with standard compound databases for rapid identification - Ideal for environmental pollutants | - Requires sample derivatization - High operational cost - Complex sample preparation needed - Unsuitable for large or thermally unstable molecules (e.g., proteins) - Limited column lifespan - Time-consuming derivatization for polar/nonvolatile compounds | - Environmental monitoring - Forensic analysis - Food safety testing - Petrochemical analysis |
Liquid Chromatography–Mass Spectrometry | LC-MS | $$$ | - Excellent for analyzing complex mixtures - High sensitivity and accuracy - Suitable for both targeted and untargeted analysis - Widely used in metabolomics and drug discovery | - Expensive - Requires skilled operators - Ion suppression may be observed in complex samples - High maintenance costs for hyphenated systems - Time-consuming method development - Reliant on MS compatibility | - Pharmaceutical R&D - Clinical toxicology - Metabolomics - Proteomics |
Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry | MALDI-TOF | $$$$ | - High sensitivity for large biomolecules - Fast analysis time - Suitable for high-throughput applications - Minimal sample preparation for intact protein analysis - Allows direct tissue section analysis | - Limited ability to analyze small molecules - Sample preparation can be challenging - Requires expensive instruments - Requires matrix optimization for reproducibility - Limited quantitative accuracy - Matrix interference in low-mass regions (<500 Da) | - Clinical microbiology (pathogen identification) - Proteomics - Biomarker discovery - Tissue imaging |
Ultra Performance Liquid Chromatography | UPLC | $$ | - High resolution and precision - Faster than traditional HPLC - Suitable for complex mixtures - Reduced solvent consumption (eco-friendly) | - Requires expensive instruments - Requires highly trained personnel - Durable components are required for high-pressure systems - Strict sample filtration requirements - Limited qualitative analysis capability - Often paired with MS for qualitative analysis | - Pharmaceutical analysis - Environmental analysis - Food and beverage testing - Metabolomics |
Proteomics (Mass-Spectrometry-based) | Proteomics (MS-based) | $$$$ | - Provides in-depth insights into protein expression and modifications - High throughput - Quantifies low-abundance proteins - Detects post-translational modifications (e.g., phosphorylation) - Supports multiplexed quantification (e.g., TMT and iTRAQ) | - High equipment and operational costs - Complex sample preparation - Requires specialized expertise - Limited dynamic range (high-abundance proteins may mask low-abundance signals) - Massive data storage/processing demands | - Biomarker discovery - Drug target identification - Clinical proteomics - Systems biology |
Microfluidic Chips | Microfluidic Chips | $$$ | - Miniaturized, portable, and cost-effective - Potential for point-of-care diagnostics - Requires smaller sample volumes - Enables integrated workflows (separation + detection on-chip) - High-throughput parallel processing | - Limited sensitivity compared to larger instruments - Still under development for widespread clinical use - Custom chip designs limit versatility - Long-term stability issues (e.g., clogging) - Complex manufacturing processes | - Point-of-care diagnostics - Single-cell analysis - Lab-on-a-chip applications - Environmental monitoring |
Portable Devices (point-of-care diagnostics) | POCT | $/$$ | - Fast, real-time results - User-friendly and easy to operate - Suitable for remote or emergency settings - Supports wireless data transfer (e.g., smartphone integration) | - Limited sensitivity and accuracy - Often less comprehensive than lab-based technologies - Frequent calibration needed (sensitive to environmental conditions) - Limited battery life - Narrow target scope (single/few analytes) | - Emergency medicine - Remote health monitoring - Field diagnostics - Wearable health devices |
Year | Disease | Population | Results | Reference |
---|---|---|---|---|
2023 | T2D | 52 T2D, 53 controls |
| [22] |
2018 | T2D | 73 T2D, 67 controls |
| [23] |
2024 | Renal injury in diabetes mellitus | 285 with renal injury, 122 without renal injury |
| [27] |
2020 | T2D | 22 T2D, 93 normal |
| [25] |
2013 | T2D | 28 T2D, 29 controls |
| [26] |
2024 | Diabetic kidney disease | 585 T2D, 152 with DKD |
| [28] |
2022 | MASLD | 10 controls, 10 mild hepatic steatosis patients, and 10 severe hepatic steatosis patients | For distinguishing between mild steatosis from controls:
| [36] |
2017 | MASLD | 33 MASLD, 45 MASH, and 30 controls | For distinguishing MASH from controls:
| [35] |
2022 | MASLD | Hepatic steatosis and fibrosis in 68 men and 65 women |
| [38] |
2019 | MASLD | 121 with biopsy-proven MASLD, 48 with alcohol-related cirrhosis, and 106 controls | For distinguishing advanced (F3-F4) from early (F0-F2) fibrosis:
| [37] |
2024 | Hyperuricemia | 26 HUA patients, 25 controls |
| [41] |
2016 | Hypertension | 118 patients, 30 non-hypertensive subjects | For predicting albuminuria development:
| [43] |
2024 | Hypertension | 72 pre-hypertensive participants, 72 controls |
| [42] |
2018 | PCOS | 21 PCOS, 16 controls |
| [48] |
2021 | PCOS | 18 with less insulin secretion, 24 with more insulin secretion |
| [50] |
2021 | MetS | 11,754 individuals, 4–5% with developed MetS |
| [53] |
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Zhan, S.; Zhou, X.; Fu, J. Noninvasive Urinary Biomarkers for Obesity-Related Metabolic Diseases: Diagnostic Applications and Future Directions. Biomolecules 2025, 15, 633. https://doi.org/10.3390/biom15050633
Zhan S, Zhou X, Fu J. Noninvasive Urinary Biomarkers for Obesity-Related Metabolic Diseases: Diagnostic Applications and Future Directions. Biomolecules. 2025; 15(5):633. https://doi.org/10.3390/biom15050633
Chicago/Turabian StyleZhan, Shumin, Xuelian Zhou, and Junfen Fu. 2025. "Noninvasive Urinary Biomarkers for Obesity-Related Metabolic Diseases: Diagnostic Applications and Future Directions" Biomolecules 15, no. 5: 633. https://doi.org/10.3390/biom15050633
APA StyleZhan, S., Zhou, X., & Fu, J. (2025). Noninvasive Urinary Biomarkers for Obesity-Related Metabolic Diseases: Diagnostic Applications and Future Directions. Biomolecules, 15(5), 633. https://doi.org/10.3390/biom15050633