Very Low Prevalence and Incidence of Atrial Fibrillation among Bolivian Forager-Farmers

Abstract

Background: Atrial fibrillation is the most common arrhythmia in post-industrialized populations. Older age, hypertension, obesity, chronic inflammation, and diabetes are significant atrial fibrillation risk factors, suggesting that modern urban environments may promote atrial fibrillation. Objective: Here we assess atrial fibrillation prevalence and incidence among tropical horticulturalists of the Bolivian Amazon with high levels of physical activity, a lean diet, and minimal coronary atherosclerosis, but also high infectious disease burden and associated inflammation. Methods: Between 2005–2019, 1314 Tsimane aged 40–94 years (52% female) and 534 Moseten Amerindians aged 40–89 years (50% female) underwent resting 12-lead electrocardiograms to assess atrial fibrillation prevalence. For atrial fibrillation incidence assessment, 1059 (81% of original sample) Tsimane and 310 Moseten (58%) underwent additional ECGs (mean time to follow up 7.0, 1.8 years, respectively). Findings: Only one (male) of 1314 Tsimane (0.076%) and one (male) of 534 Moseten (0.187%) demonstrated atrial fibrillation at baseline. There was one new (female) Tsimane case in 7395 risk years for the 1059 participants with >1 ECG (incidence rate = 0.14 per 1,000 risk years). No new cases were detected among Moseten, based on 542 risk years. Conclusion: Tsimane and Moseten show the lowest levels of atrial fibrillation ever reported, 1/20 to ~1/6 of rates in high-income countries. These findings provide additional evidence that a subsistence lifestyle with high levels of physical activity, and a diet low in processed carbohydrates and fat is cardioprotective, despite frequent infection-induced inflammation. Findings suggest that atrial fibrillation is a modifiable lifestyle disease rather than an inevitable feature of cardiovascular aging.

A Causal Framework for Cross-Cultural Generalizability

“…For example, political scientists and sociologists apply demographic standardization (e.g., Kitagawa decomposition) to estimate effects of interventions for counterfactual populations (Acharya et al, 2016;Ciocca Eller & DiPrete, 2018;Kitagawa, 1955;Mize, 2016;Preston et al, 2000;Ross et al, 2021;Storer et al, 2020). Anthropologists calculate age-corrected values to standardize across populations (Borgerhoff Mulder et al, 2009;Jaeggi et al, 2021;Mattison et al, 2016;and Rowan et al, 2021). Economists calculate average treatment effects and marginal effects that can take into account effect modification by demographic variables (Asteriou & Hall, 2015;Athey & Imbens, 2016;Greene, 2000;Morgan & Winship, 2015), and the Heckman correction is applied to account for nonrandom sample selection (Heckman, 1976(Heckman, , 1979Puhani, 2000).…”

Prevalence of dementia and mild cognitive impairment in indigenous Bolivian forager‐horticulturalists

“…13 The antimicrobial protection hypothesis describes how neuroinflammatory pathways may help fight infection while contributing to AD pathology. 14 On the basis of this literature, we hypothesized a low prevalence of AD and related dementias among Tsimane and Moseten, due to their low prevalence of CAC 15 and atrial fibrillation 16 ; low rates of hypertension 17 , type 2 diabetes, 18 obesity, and smoking 15 ; high levels of physical activity 19 ; and a diet low in processed carbohydrates and fat. 20 In addition, as most dementia is found among individuals ≥ 75 years of age, 2 the pyramid-shaped age structure of the Tsimane and Moseten populations means that the crude prevalence would be expected to be low.…”

A Causal Framework for Cross-Cultural Generalizability

“…For example, political scientists and sociologists apply demographic standardization (e.g., Kitagawa decomposition) to estimate effects of interventions for counterfactual populations (Kitagawa 1955, Preston et al 2000, Acharya et al 2016, Ciocca Eller and DiPrete 2018, Mize 2016, Storer et al 2020, Ross et al 2021. Anthropologists calculate age-corrected values to standardize across populations (Borgerhoff Mulder et al 2009, Mattison et al 2016, Jaeggi et al 2021, Rowan et al 2021. Economists calculate average treatment effects (ATEs) and marginal effects that can take into account effect modification by demographic variables (Morgan and Winship 2015, Athey and Imbens 2016, Greene 2000, Asteriou and Hall 2015; and the Heckman correction is applied to account for non-random sample selection (Heckman 1976, 1979, Puhani 2000.…”