Using data mining technology to explore homocysteine at low levels.

Area of Science:

• Biochemistry
• Clinical Medicine
• Public Health

Background:

• High homocysteine levels are established cardiovascular disease risk factors.
• The health implications of low homocysteine levels remain under-researched.
• Acquiring healthy subject data for homocysteine studies presents challenges.

Purpose of the Study:

• To investigate the causal link between serum homocysteine levels and health status.
• To explore the relationship between homocysteine levels and lifestyle factors, especially low levels.
• To present a systematic platform for homocysteine data collection and analysis.

Main Methods:

• Cross-sectional analysis of 5864 health examination clients in Taipei, Taiwan (2017).
• Exclusion of personal identifying information.
• Statistical analysis using SPSS, including chi-square, t test, MANOVA, and logistic regression.

Main Results:

• Longer working hours (>12h/day) correlated with higher homocysteine levels (P < .001).
• Lower serum homocysteine levels (7.9 vs 8.6 μmol/L) were associated with poor sleep quality (P = .003).
• Subjects perceiving poor health status had lower homocysteine levels (≤6.3 μmol/L).

Conclusions:

• Homocysteine levels can serve as an effective health management indicator.
• A normal homocysteine level should be considered above 6.3 μmol/L.
• Fluctuations in homocysteine levels can provide insights into an individual's physical status.