Lifestyle Patterns and Incidence of Cardiovascular Diseases, Cancer, Respiratory Diseases, and Type 2 Diabetes: A Large-Scale Prospective Cohort Study

<b>Background</b>: Lifestyle factors often interact in complex ways when influencing chronic disease risk. We aimed to examine the prospective associations between empirically derived real-life lifestyle patterns (LPs) and the incidence of major chronic diseases, and to explore the linearity of the relationships between lifestyle summation scores and disease risk. <b>Methods</b>: We included adults free of cardiovascular diseases (CVDs), cancer, chronic respiratory diseases (CRDs), or type 2 diabetes (T2D) at baseline (2006-2013) from the Dutch Lifelines cohort. LPs and lifestyle summation scores were derived from baseline self-reported data on diet, physical activity, substance use, sleep, stress, and social connectedness, each categorised as healthy, moderately healthy, or unhealthy. Fine-Gray sub-hazard regression models assessed associations between LPs and disease incidence, with natural spline functions used to evaluate linearity in summation scores. <b>Results</b>: Among 114,919 T2D-free, 131,248 cancer-free, 91,777 CRD-free, and 77,645 CVD-free participants, we observed 3114 T2D, 4685 cancer, 4133 CRDs, and 2850 CVD incident cases (median follow-up time: 8 years). Compared to the "Unhealthy" pattern, both the "Healthy-in-a-balanced-way" and "Healthy-but-physically-inactive" patterns were broadly significantly protective. The "Unhealthy-but-no-substance-use" pattern was associated with increased T2D risk (Sub-Hazard Ratio (SHR) = 1.27, 95% Confidence Interval (CI): 1.11-1.47) but reduced cancer risk (SHR = 0.85, 95%CI: 0.74-0.97). The "Unhealthy-but-light-drinking-and-never-smoked" pattern was protective for T2D (SHR = 0.89, 95%CI: 0.79-0.99). Linear associations were observed between lifestyle summation scores and disease risk, except for "healthy lifestyle" scores with T2D and "unhealthy lifestyle" scores with CRDs (non-linear <i>p</i>-value < 0.05). <b>Conclusions</b>: There are potential protective effects of healthy lifestyles on T2D, cancer, CRDs, and CVDs. However, the "Unhealthy but no substance use" demonstrated increased risk on T2D, protective effect on cancer and no significant effect on CRDs or CVDs. The relationship between combined lifestyle factors and NCD risk is complex and partly non-linear, showing diminishing benefits beyond certain thresholds, especially T2D and CRDs.

Authors

Institutions

Publication Details

Journal
Nutrients
Published
2025-12-12
DOI
https://doi.org/10.3390/nu17243883
Primary Topic
Cardiovascular Health and Risk Factors
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Lifestyle Patterns and Incidence of Cardiovascular Diseases, Cancer, Respiratory Diseases, and Type 2 Diabetes: A Large-Scale Prospective Cohort Study

Rafael Ogaz-González, Eva Corpeleijn, Gerton A. Lunter, Qian Zou
Nutrients
Cardiovascular Health and Risk Factors
article

Lifestyle Patterns and Incidence of Cardiovascular Diseases, Cancer, Respiratory Diseases, and Type 2 Diabetes: A Large-Scale Prospective Cohort Study

Rafael Ogaz-González, Eva Corpeleijn, Gerton A. Lunter, Qian Zou
article en

Abstract

<b>Background</b>: Lifestyle factors often interact in complex ways when influencing chronic disease risk. We aimed to examine the prospective associations between empirically derived real-life lifestyle patterns (LPs) and the incidence of major chronic diseases, and to explore the linearity of the relationships between lifestyle summation scores and disease risk. <b>Methods</b>: We included adults free of cardiovascular diseases (CVDs), cancer, chronic respiratory diseases (CRDs), or type 2 diabetes (T2D) at baseline (2006-2013) from the Dutch Lifelines cohort. LPs and lifestyle summation scores were derived from baseline self-reported data on diet, physical activity, substance use, sleep, stress, and social connectedness, each categorised as healthy, moderately healthy, or unhealthy. Fine-Gray sub-hazard regression models assessed associations between LPs and disease incidence, with natural spline functions used to evaluate linearity in summation scores. <b>Results</b>: Among 114,919 T2D-free, 131,248 cancer-free, 91,777 CRD-free, and 77,645 CVD-free participants, we observed 3114 T2D, 4685 cancer, 4133 CRDs, and 2850 CVD incident cases (median follow-up time: 8 years). Compared to the "Unhealthy" pattern, both the "Healthy-in-a-balanced-way" and "Healthy-but-physically-inactive" patterns were broadly significantly protective. The "Unhealthy-but-no-substance-use" pattern was associated with increased T2D risk (Sub-Hazard Ratio (SHR) = 1.27, 95% Confidence Interval (CI): 1.11-1.47) but reduced cancer risk (SHR = 0.85, 95%CI: 0.74-0.97). The "Unhealthy-but-light-drinking-and-never-smoked" pattern was protective for T2D (SHR = 0.89, 95%CI: 0.79-0.99). Linear associations were observed between lifestyle summation scores and disease risk, except for "healthy lifestyle" scores with T2D and "unhealthy lifestyle" scores with CRDs (non-linear <i>p</i>-value < 0.05). <b>Conclusions</b>: There are potential protective effects of healthy lifestyles on T2D, cancer, CRDs, and CVDs. However, the "Unhealthy but no substance use" demonstrated increased risk on T2D, protective effect on cancer and no significant effect on CRDs or CVDs. The relationship between combined lifestyle factors and NCD risk is complex and partly non-linear, showing diminishing benefits beyond certain thresholds, especially T2D and CRDs.

NutrientsVol. 17(24)
University Medical Center Groningen (NL)
Openalex Percentile: Top 43%
Cardiovascular Health and Risk Factors
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.