Refined obesity, smoking exposure, and lipid metrics in mortality risk assessment: a nationwide cohort analysis

Obesity, smoking, and lipid imbalances are well-established predictors of all-cause mortality, but conventional definitions lead to misinterpretations commonly in observational studies. This study aims to evaluate three key refinements of obesity, smoking exposure, and lipid profiles in the context of all-cause mortality using two large-scale Korean cohorts.

This retrospective cohort study analyzed 659,494 participants from the Korean National Health Insurance Service-National Sample Cohort (NHIS-NSC), with external validation in 10,477 participants from the Korean National Health and Nutrition Examination Survey (KNHANES), both linked to mortality records. Obesity was classified by BMI and abdominal obesity criteria, smoking exposure was assessed using the pack-year to age ratio, and lipid abnormalities were measured using composite lipid ratios (total cholesterol/HDL≥5.0, triglyceride/HDL > 6.0, LDL/HDL≥5.0). Cox proportional hazards models were used for primary analyses, with time-dependent Cox models as sensitivity analyses.

In primary analyses using general Cox models, underweight individuals showed significantly elevated mortality risk across all age groups, with the combination of underweight and abdominal obesity showing a particularly high risk in those younger than 60 years (adjusted hazard ratio[AHR]=2.42, 95% confidence interval[CI]: 2.16-2.71 for underweight without abdominal obesity; AHR = 1.36, 95% CI: 0.19-9.67 for underweight with abdominal obesity). In those aged 60 years or older, being underweight without abdominal obesity was the strongest predictor (AHR = 1.79, 95% CI: 1.67-1.91). A pack-year to age ratio ≥1 was significantly associated with an increased risk of mortality (AHR = 1.65, 95% CI: 1.51-1.81). Individuals with one or more high-risk lipid profiles had an increased risk of death (AHR = 1.04, 95% CI: 1.01-1.07). Sensitivity analyses using time-dependent Cox models showed directionally consistent patterns with the primary analyses, and the findings were validated in an independent cohort (KNHANES).

Refining obesity, smoking exposure, and lipid profile definitions led to more interpretable and clinically consistent mortality risk estimates, avoiding paradoxical findings common in observational studies. Future research would explore whether these refined metrics improve predictive accuracy compared to conventional definitions and validate their applicability in diverse populations.