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Ultraprocessed beverages and processed meats increase the incidence of hypertension in Mexican women.

Adriana MongeDaniela Silva CanellaNancy López-OlmedoMartín LajousAdrian Cortés-ValenciaDalia Stern
Published in: The British journal of nutrition (2020)
Higher intake of ultraprocessed foods (UPF), which have undergone multiple processes and have poor nutrient quality, is associated with higher incidence of non-communicable diseases. Yet, its association with hypertension has scarcely been studied, especially in low- and middle-income countries (LMIC). We aimed to estimate the associations between consumption of UPF (total, liquid and solid) and UPF subgroups and incident hypertension in a prospective cohort study. We used data from the Mexican Teachers' Cohort including 64 934 disease-free women aged ≥25 years at baseline. We assessed baseline usual dietary intake using a validated FFQ, and each item was categorised according to NOVA, a degree of food processing classification system. UPF and UPF subgroups were categorised according to the distribution of their contribution to total energy intake. Hypertension was self-reported. We estimated incidence rate ratios (IRR) and their 95 % CI. During a median follow-up of 2·2 years, we identified 3752 incident cases of hypertension. Mean contribution of UPF to total energy intake was 29·8 (SD 9·4) % energy (23·4 (SD 8·9) % solid, 6·4 (SD 4·8) % liquid). Comparing extreme categories showed that higher total and solid UPF consumptions were not associated with incident hypertension (IRR 0·96, 95 % CI 0·79, 1·16; IRR 0·91, 95 % CI 0·82, 1·01, respectively). However, liquid UPF and processed meats were associated with increased hypertension (IRR 1·32, 95 % CI 1·10, 1·65; IRR 1·17, 95 % CI 1·01, 1·36, respectively). Addressing intake of liquid UPF and processed meats may help in managing hypertension in LMIC.
Keyphrases
  • blood pressure
  • cardiovascular disease
  • risk factors
  • ionic liquid
  • polycystic ovary syndrome
  • type diabetes
  • pregnant women
  • weight gain
  • machine learning
  • climate change
  • electronic health record
  • big data