Exploring Patient Data from `sugar_data_split.xlsx`: Overview and Insights

Aug 4, 2025, 2:19 PM

sugar_data_split.xlsx

Missing file

describe the contents of this file to me!

Overview of sugar_data_split.xlsx

The workbook contains 3 worksheets, each holding 264 patient records identified by a common ID column:
Sheet# Rows# ColumnsKey columns (first few)
Demographics2644ID, Gender, AGE, HasDiabetes
Biometrics2645ID, BMI, Urea, Cr, HbA1c
Lipids2646ID, Chol, TG, HDL, LDL, …
A quick graphic comparison of row/column counts is shown in the bar chart above. Every sheet is already row-aligned (equal row count), so they can be safely merged on the ID key for a complete patient-level view.
Highlights
  • Demographics: basic patient characteristics and a diabetes flag.
  • Biometrics: key renal and glycaemic biomarkers (HbA1c).
  • Lipids: full lipid panel ready for cardiovascular-risk analyses.
Let me know if you’d like to:
  • join the sheets together,
  • explore any particular variable (e.g., average HbA1c by diabetes status), or
  • create further visualisations.
I’m ready when you are! 😊

Relationship Between BMI and HbA1c by Diabetes Status

BMI vs. HbA1c by Diabetes Status

The scatter-plot above shows 264 patients, grouped by whether they carry a diabetes diagnosis. A few take-aways:
  1. Visual pattern
    • Among patients without diabetes (blue points) there is an obvious upward trend: larger BMI is generally accompanied by higher HbA1c.
    • For patients with diabetes (red points) the cloud is more diffuse and the slope appears flat to slightly negative.
  2. Within-group Pearson correlations
    ρNo Diabetes=0.62\rho_{\text{No Diabetes}} = 0.62 → moderate positive relationship.
    ρDiabetes=−0.18\rho_{\text{Diabetes}} = -0.18 → weak, slightly negative relationship.
    In other words, for people who have not (yet) developed diabetes, weight-gain (higher BMI) is fairly strongly associated with worsening glycaemic control (HbA1c). Once diabetes is present, that simple relationship disappears—likely because glucose control is driven by many other factors (medications, disease duration, lifestyle).
  3. Practical implication
    • Weight management may be a key prevention lever before diabetes onset.
    • After diagnosis, BMI alone is not a good predictor of HbA1c; a multifactorial model would be needed.
Would you like to:
  • run a formal regression model (e.g., include Age, Gender)?
  • compare mean HbA1c between BMI categories?
  • explore any other biomarker relationships?
Let me know! 😊
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