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Our interpretation / GoldDec 2026
Not yet verifiedThis paper was discovered by the mouse-research search pipeline, but mouse involvement has not been confirmed.

FTIR spectroscopy classifies kidney spectra of diabetic mice with 97% accuracy

In a whole‑organism study of diabetic db/db mice, kidney and urine samples were examined with ATR‑FTIR spectroscopy and machine‑learning analysis. Partial Least Squares Discriminant Analysis achieved 97.5% accuracy for classifying renal spectra and 92.5% for urinary spectra, and urinary FTIR spectra showed a strong association with histological renal injury scores (R²CV = 0.846).

Editorial projection

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Source

PubMed / NCBI

Mouse involvement

Not yet verifiedThis paper was discovered by the mouse-research search pipeline, but mouse involvement has not been confirmed.

Our semantic representation

What happened

  • exhibited the highest classification accuracy across time pointsClassification accuracy of PLS‑DA for renal spectraPartial Least Squares Discriminant Analysis (PLS‑DA) achieved 97.50% classification accuracy for renal spectra.
  • exhibited the highest classification accuracy across time pointsClassification accuracy of PLS‑DA for urinary spectraPartial Least Squares Discriminant Analysis (PLS‑DA) achieved 92.50% classification accuracy for urinary spectra.
  • strong associationAssociation between urinary FTIR spectra and histologically derived renal injury scoresUrinary FTIR spectra showed a strong association with renal injury scores (R²CV = 0.846).
  • strong linear correlation observedCorrelation between predicted and reference renal H&E scores using urinary FTIR spectraUrinary FTIR spectra were used to predict renal H&E scores, and a strong linear correlation was observed between predicted and reference values.

The experiment

What they did

whole-organism studyDiabetic db/db mouse model with kidney and urine sampling for ATR‑FTIR spectroscopy and downstream machine‑learning analysis
  • Kidney samplingApplication details not reported
  • Urine samplingApplication details not reported
  • ATR‑FTIR spectroscopyApplication details not reported
  • Partial Least Squares Discriminant Analysis (PLS‑DA) classificationApplication details not reported
  • Partial Least Squares Regression (PLSR) model for renal injury scoringApplication details not reported

The subjects

Who / what was studied

whole-organism study
  • mice
  • mice

The researchers' voice

Original research

Non-invasive urinary FTIR spectroscopy for early diagnosis and monitoring of diabetic nephropathy.

Original abstract
Diabetic nephropathy (DN) is a progressive and life-threatening complication of diabetes mellitus (DM). However, developing sensitive and specific non-invasive diagnostic approaches remains a critical clinical challenge. In this study, we characterized the biochemical and metabolic alterations in the kidney and urine using attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy in a type 2 diabetic db/db mouse model at 7, 12, and 21 weeks. Spectral analysis revealed distinct, time-dependent disruptions in the vibrational bands associated with protein, lipid, and carbohydrate components in both kidney and urine samples. Principal component analysis (PCA) effectively distinguished diabetic mice from controls, capturing spectral alterations associated with the progression of DN. Based on these discriminative features, we established two independent machine learning frameworks for kidney and urine data to classify temporal groups and to evaluate whether urinary spectral profiles could non-invasively reflect renal pathology. Among all evaluated classifiers, Partial Least Squares Discriminant Analysis (PLS-DA) exhibited the highest classification accuracy across time points, achieving 97.50% for renal spectra and 92.50% for urinary spectra. To quantitatively assess renal injury, we developed a partial least squares regression (PLSR) model using renal spectra to predict hematoxylin and eosin (H&E)-derived scores. Furthermore, urinary FTIR spectra were used to predict renal H&E scores, and a strong linear correlation was observed between predicted and reference values. This analysis revealed a strong association between urinary FTIR spectra and histologically derived renal injury scores (R2CV = 0.846), indicating that urinary spectral signatures are significantly associated with renal pathological changes in this model. Collectively, these findings support urinary FTIR profiling as a rapid, cost-effective, and non-invasive complementary approach to traditional tissue-based assessments of DN, enabling early detection and longitudinal monitoring of the disease. Future studies should evaluate the clinical validity and translational applicability of this technique in independent cohorts.