Overview

This report documents the cleaning and exploratory review of the DOLORisk phenotype table (GWASPheno), the clinical dataset underpinning the m.C2639T mitochondrial-variant analysis. This is first step of the pipeline: cleaning, quality control and exploratory analysis. The downstream carrier vs non-carrier comparison and the nociceptor-vs-non-nociceptor contrast build on the dataset produced here.

Data cleaning

Six data-quality issues were identified and corrected in a single pass. The raw table held 2,741 records; after de-duplication 2,740 remain.

Cleaning operations applied
Issue Detail Action
Inconsistent ID casing Sample IDs mixed DOL-/dol- prefixes Upper-cased & trimmed
Duplicate record 1 conflicting duplicate ID (PROPENG047) Kept first occurrence
Numeric columns stored as text 1992 cells with 'No bloods', '#N/A' etc. Coerced to numeric → NA
Mixed-type diabetes field Values 1 / 2 / 'LADA' / blank Factor: T1DM / T2DM / LADA
Medication flags 9 columns coded 1 / blank Recoded to 0 / 1 indicators
Untyped variables Sex, batch, grades read as numbers Cast to labelled factors

Cohort at a glance

Pain burden is summarised by the Brief Pain Inventory (BPI) average score (0 = no pain, 10 = worst), the cohort’s core pain-severity measure.

Pain phenotype: nociceptor vs non-nociceptor

Provisional axis (pending QST). The nociceptor / non-nociceptor contrast is operationalised here from the NeuPSIG grade (NPalgo) as a first approximation: grades 3–4 (probable/definite neuropathic pain) = neuropathic / non-nociceptor; grades 0–1 = non-neuropathic / nociceptor. A definitive split requires quantitative sensory testing (QST), which will be incorporated when available.

The DN4 screening score cleanly separates the two provisional groups, as expected, since higher DN4 reflects neuropathic features, supporting the axis as a sensible first approximation.

Missing data

Missingness is substantial and highly variable — from near-complete demographics to pain-diary fields that are >90% empty. Variables are triaged into four tiers that drive how each is handled downstream.

Handling strategy by missingness
Missingness tier Variables
<5% — near-complete 25
5–40% — impute 5
40–80% — describe only 19
≥80% missing — drop 5
Free text (not modelled) 5

Strategy. Free-text fields are retained for reference but excluded from modelling. Variables ≥80% missing are dropped from the analysis set. Fields missing 5–40% (key clinical measures such as HbA1c, BMI, sensory scores) are candidates for multiple imputation; those 40–80% missing are used descriptively only. The existing carrier comparison uses pairwise deletion, so the cleaned dataset is exported with missing values preserved.

Next steps

  • Step 2: Carrier comparison: carrier vs non-carrier phenotype analysis
  • Nociceptor axis: refine the provisional NPalgo-based split with QST data once located, to define nociceptor vs non-nociceptor on a mechanistic basis.