Table 3 Methodological factors contributing to inconsistent findings across studies
Methodological Factor | Studies Affected | Impact on Results | Recommendations for Standardization |
|---|---|---|---|
CT measurement location | Umbilicus (Shibata, Higuchi, Okuno) vs. L3 (McInerney, Pekar) vs. L4 (Foldyna) vs. L4/L5 (Mancio) | Different anatomical levels yield different fat volumes and compositions | Standardize to L3 level (best correlation with total body composition) |
Effect reporting direction | HR < 1 (Shibata, Higuchi, Guler) vs. HR > 1 (Okuno, Foldyna) for same parameter | Reverse comparison groups create apparent contradictions | Report all results as high vs. low for consistency |
Patient population differences | Japanese cohorts (Shibata, Higuchi) vs. European/American (McInerney, Foldyna) | Ethnic differences in body composition affect outcomes | Stratify analyses by ethnicity or region |
Statistical approaches | Different cutoff determination methods (median, quartiles, CART analysis) | Arbitrary thresholds yield different risk groups | Use standardized statistical approaches for threshold determination |
Follow-up duration | Short-term (Okuno: 665Â days) vs. long-term (Pekar: 5.89Â years) | Short follow-up may miss late outcomes, leading to divergent conclusions | Report both short and long-term outcomes |
Obesity-specific effects | Mancio found opposite effects in obese vs. non-obese patients | Non-linear, U-shaped relationships obscured by whole-population analysis | Analyze by BMI categories to detect non-linear relationships |
CT scan quality and parameters | Slice thickness variations (1.0–10 mm) | Thicker slices may reduce measurement precision | Standardize scan parameters and quality control |
Software and HU thresholds | Various software packages with different HU ranges for tissue classification | Different tissue classification leads to measurement inconsistencies | Adopt standardized HU ranges for tissue classification |
Sex-specific differences | Foldyna found VAT density significant in men but not women | Sex-based differences in fat metabolism and distribution | Always perform sex-stratified analyses |
Adjustment for confounders | Varying degrees of multivariate adjustment | Unadjusted or insufficiently adjusted analyses may show spurious associations | Define minimum set of covariates for adjustment |