Smoking, sedentary behavior, alcohol use among factors tied to PAD events
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In a machine-learning analysis presented at the Society for Vascular Surgery Vascular Annual Meeting, smoking, lack of physical activity and alcohol use were the lifestyle factors most strongly correlated with events related to peripheral artery disease.
Elsie Ross, MD, assistant professor of surgery and medicine at Stanford University Medical Center, and colleagues analyzed data from the UK Biobank cohort of more than 500,000 adults aged 40 to 69 years.
The researchers age-matched patients with PAD to a random sample of adults without PAD. The final cohort included 13,309 adults, of whom 4,327 had PAD events.
Ross and colleagues then performed machine-learning analysis to identify 20 lifestyle variables with PAD events, defined as PAD-related death, hospitalization or surgical events.
After Ross and colleagues conducted a multivariable analysis, they determined the following lifestyle factors most associated with PAD events were the following:
- age of smoking cessation (OR = 1.05; 95% CI, 1.04-1.05);
- number of days per week in which someone walked for at least 10 minutes (OR = 0.9; 95% CI, 0.9-0.94);
- never consuming alcohol (OR = 1.7; 95% CI, 1.5-2);
- average weekly beer and cider intake (OR = 1.03; 95% CI, 1.02-1.03);
- maternal smoking around birth (OR = 1.2; 95% CI, 1.1-1.3);
- time spent watching television (OR = 1.06; 95% CI, 1.05-1.08); and
- time spent driving (OR = 1.01; 95% CI, 1-1.02).
“We were surprised by the association between never consuming alcohol and increased risk of PAD events, though other studies have found a positive association between moderate wine intake and improved cardiovascular health,” Ross told Cardiology Today’s Intervention.
Other dietary and nutrition factors played less of a role in determining the risk for PAD in this analysis, Ross said during a presentation.
“Currently, a majority of patients with PAD go undiagnosed before a significant clinical event,” Ross said in an interview. “Using big data and machine learning, we can automate screening and improve early diagnosis and treatment, which can help reduce limb loss and premature death in the long run.” – by Erik Swain
Reference:
Ross E, et al. Abstract VESS04. Presented at: Society for Vascular Surgery Vascular Annual Meeting; June 12-15, 2019; National Harbor, Md.
Disclosure: Ross reports no relevant financial disclosures.