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Correlation between TCM Syndromes and Type 2 Diabetic Comorbidities Based on Fully Connected Neural Network

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Traditional Chinese Medicine (TCM) syndromes can accurately predict major type 2 diabetes comorbidities, including kidney and gastrointestinal issues. This research highlights the strong link between TCM syndrome characteristics and diabetic complications.

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Area of Science:

  • Integrative Medicine
  • Diabetology
  • Computational Biology

Background:

  • Type 2 diabetes (T2DM) is frequently accompanied by various comorbidities.
  • Traditional Chinese Medicine (TCM) offers a unique syndromic approach to disease patterns.
  • Understanding the relationship between TCM syndromes and T2DM comorbidities is crucial for holistic patient care.

Purpose of the Study:

  • To predict major comorbidities associated with type 2 diabetes.
  • To investigate the correlation between Traditional Chinese Medicine (TCM) syndromes and type 2 diabetes comorbidities.
  • To explore the diagnostic and predictive potential of TCM syndromes in managing diabetic complications.

Main Methods:

  • Analysis of electronic medical record data from 995 type 2 diabetes patients with comorbidities.
  • Application of descriptive statistical methods to analyze population characteristics, comorbidity distribution, and TCM syndromes.
  • Construction of a neural network model to predict T2DM comorbidities based on TCM syndromes.

Main Results:

  • High prediction sensitivity for specific comorbidities: renal insufficiency (95%) with 'blood amassment in the lower jiao' syndrome, gastrointestinal lesions (92%) with 'spleen deficiency' and 'ascending counterflow of stomach qi' patterns, and hypertension (91%) with 'spleen heat' and 'exuberance of heart fire' syndromes.
  • Prediction accuracy for neuropathy, heart disease, liver disease, and lipid metabolism disorders ranged from 70% to 90% based on TCM syndrome groups.
  • Identified common syndrome locations (heart, spleen, stomach, lower jiao, meridians) and patterns (deficiency, heat, phlegm, blood stasis) associated with diabetic comorbidities.

Conclusions:

  • TCM syndrome characteristics are significantly correlated with type 2 diabetes comorbidities.
  • A fully connected neural network model demonstrates the potential for accurate prediction of major diabetic comorbidities using TCM syndromes.
  • These findings support the early diagnosis, treatment, and prevention of diabetic comorbidities by leveraging TCM syndrome patterns.