Ultra-processed food (UPF) consumption have increased in the world during the last decades since they are hyper-palatable, cheap and ready-to-consume products. However, uncertainty exists on their impact on health. We conducted a systematic review and meta-analysis evaluating the association of UPF consumption with the all-cause mortality risk. Five bibliographic databases were searched for relevant studies. Random effects models were used to calculate pooled relative risks (RR) and 95% confidence intervals (CI). Of 6,951 unique citations, 40 unique prospective cohort studies comprising 5,750,133 individuals were included. Publication date of the included studies ranged from 1984 to2021. Compared to low consumption, highest consumption of UPF (RR=1.29, 95% CI 1.17-1.42), sugar-sweetened beverages (SSB) (RR=1.11, 95% CI, 1.04-1.18), artificially sweetened beverages (ASB) (RR=1.14, 95% CI, 1.05-1.22) and processed meat/red meat (RR=1.15, 95% CI, 1.10-1.21) were significantly associated with increased risk of mortality. On the contrary, breakfast cereals were associated with a lower mortality risk (RR=0.85, 95% CI, 0.79-0.92). Conclusion: This meta-analysis suggests that high consumption of UPF, SSB, ASB, processed meat and processed red meat might increase all-cause mortality, while breakfast cereals might decrease it. Future studies are needed to address lack of standardized methods in UPF categorization.
Cardiovascular disease (CVD) and type 2 diabetes (T2D) remain the top disease and mortality burdens worldwide. Oats have been shown to benefit cardiovascular health and improve insulin resistance. However, the evidence linking oat consumption with CVD, T2D and all-cause mortality remains inconclusive. We conducted a comprehensive systematic review and meta-analysis of prospective cohort studies to evaluate the associations between oat consumption and risks of T2D, CVD and all-cause mortality in the general population. Five electronic databases were searched until September, 2020. Study specific relative risks (RR) were meta-analyzed using random effect models. Of 4686 relevant references, we included 9 articles, based on 8 unique studies and 471,157 participants. Comparing oat consumers versus non-consumers, RRs were 0.86 (95% CI 0.72–1.03) for T2D incidence and 0.73 (95% CI 0.5–1.07) for combined CVD incidence. Comparing participants with highest versus lowest oat intake, RRs were 0.78 (95% CI 0.74–0.82) for T2D incidence, 0.81 (95% CI 0.61–1.08) for CHD incidence and 0.79 (95% CI 0.59–1.07) for stroke. For all-cause mortality one study based on three cohorts found RR for men and women were 0.76 (95% CI 0.69–0.85) and 0.78 (95% CI 0.70–0.87), respectively. Most studies (n = 6) were of fair to good quality. This meta-analysis suggests that consumption of oat could reduce the risk for T2D and all-cause mortality, while no significant association was found for CVD. Future studies should address a lack of standardized methods in assessing overall oat intake and type of oat products, and investigate a dose-dependent response of oat products on cardiometabolic outcomes in order to introduce oat as preventive and treatment options for the public.
Current evidence on COVID-19 prognostic models is inconsistent and clinical applicability remains controversial. We performed a systematic review to summarize and critically appraise the available studies that have developed, assessed and/or validated prognostic models of COVID-19 predicting health outcomes. We searched six bibliographic databases to identify published articles that investigated univariable and multivariable prognostic models predicting adverse outcomes in adult COVID-19 patients, including intensive care unit (ICU) admission, intubation, high-flow nasal therapy (HFNT), extracorporeal membrane oxygenation (ECMO) and mortality. We identified and assessed 314 eligible articles from more than 40 countries, with 152 of these studies presenting mortality, 66 progression to severe or critical illness, 35 mortality and ICU admission combined, 17 ICU admission only, while the remaining 44 studies reported prediction models for mechanical ventilation (MV) or a combination of multiple outcomes. The sample size of included studies varied from 11 to 7,704,171 participants, with a mean age ranging from 18 to 93 years. There were 353 prognostic models investigated, with area under the curve (AUC) ranging from 0.44 to 0.99. A great proportion of studies (61.5%, 193 out of 314) performed internal or external validation or replication. In 312 (99.4%) studies, prognostic models were reported to be at high risk of bias due to uncertainties and challenges surrounding methodological rigor, sampling, handling of missing data, failure to deal with overfitting and heterogeneous definitions of COVID-19 and severity outcomes. While several clinical prognostic models for COVID-19 have been described in the literature, they are limited in generalizability and/or applicability due to deficiencies in addressing fundamental statistical and methodological concerns. Future large, multi-centric and well-designed prognostic prospective studies are needed to clarify remaining uncertainties.
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