ArticlePloS one2024
Effects of lifestyle-related risk factors on life expectancy: A comprehensive model for use in early prevention of premature mortality from noncommunicable diseases.
Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 4 citations in OpenAlex.
- Tianjin Health and Chronic Disease Study (THCDS): study design and baseline characteristics of the cohort - cohort profile.BMJ open · 2026Article
- Designing and implementing a self-learning game with a problem-solving approach to improve students' personal health and healthy nutrition behavior.BMC research notes · 2025Article
- Associations of cardiovascular-kidney-metabolic syndrome stages with premature mortality and the role of social determinants of health.The journal of nutrition, health & aging · 2025Article
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Morbidity and premature mortality from noncommunicable diseases can be largely prevented by adopting a healthy lifestyle at the earliest possible age. However, tools designed for the early identification of those at risk among young adults are lacking. We developed and validated a multivariable model for the prediction of life expectancy, allowing the early identification of apparently healthy adults at risk of lifestyle-related diseases. We used a cross-sectional approach to calculate life expectancy using data from 38,481 participants of the National Health and Nutrition Examination Survey (1999-2014), aged ≥20 years. A multivariable logistic model was used to quantify the impact of risk factors on mortality. The model included the following lifestyle-related mortality risk factors as predictors: smoking, diet, physical activity, and body mass index. The presence of the following chronic diseases was considered: diabetes, arrhythmia, coronary artery disease, myocardial infarction, stroke, and malignant neoplasms. The model showed a good predictive ability; the area under the receiver operating characteristic curve measure was 0.846 (95% uncertainty interval 0.838-0.859). Life expectancy was determined using the life table method and the period life tables for the US population as the baseline. The results of this model underscore the importance of lifestyle-related risk factors in life expectancy. The difference between life expectancy for 30-year-old individuals with lifestyle characteristics ranked in 90% and 10% of their gender and age groups was 23 years for males and 18 years for females, whereas in 75% and 25%, it was 14 years for males and 10 years for females. In addition to early risk identification, the model estimates the deferred effect of lifestyle and the impact of lifestyle changes on life expectancy. Thus, it can be used in early prevention to demonstrate the potential risks and benefits of complex lifestyle modifications for educational purposes or to motivate behavioral changes.
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