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      The effects of substituting red and processed meat for mycoprotein on biomarkers of cardiovascular risk in healthy volunteers: an analysis of secondary endpoints from Mycomeat

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          Abstract

          Purpose

          Mycoprotein is a relatively novel food source produced from the biomass of Fusarium venenatum. It has previously been shown to improve CVD risk markers in intervention trials when it is compared against total meat. It has not hitherto been assessed specifically for benefits relative to red and processed meat.

          Methods

          We leveraged samples from Mycomeat, an investigator-blind randomised crossover controlled trial in metabolically healthy male adults (n = 20), randomised to consume 240 g/day of red and processed meat for 14 days followed by mycoprotein, or vice versa. Blood biochemical indices were a priori defined secondary endpoints.

          Results

          Mycoprotein consumption led to a 6.74% reduction in total cholesterol ( P = 0.02) and 12.3% reduction in LDL cholesterol ( P = 0.02) from baseline values. Change in fasted triglycerides was not significantly different between diets (+ 0.19 ± 0.11 mmol/l with mycoprotein, P = 0.09). There was a small but significant reduction in waist circumference for mycoprotein relative to meat (− 0.95 ± 0.42 cm, P = 0.04). Following the mycoprotein diet, mean systolic (− 2.41 ± 1.89 mmHg, P = 0.23) and diastolic blood pressure (− 0.80 ± 1.23 mmHg, P = 0.43) were reduced from baseline. There were no statistically significant effects of the intervention on urinary sodium, nitrite or TMAO; while urinary potassium (+ 126.12 ± 50.30 mmol/l, P = 0.02) and nitrate (+ 2.12 ± 0.90 mmol/l, P = 0.04) were both significantly higher with mycoprotein relative to meat. The study population comprised metabolically healthy adults, therefore, changes in plasma lipids had little effect on cardiovascular risk scores (− 0.34% FRS for mycoprotein P = 0.24).

          Conclusions

          These results confirm potential cardiovascular benefits when displacing red and processed meat with mycoprotein in the diet. Longer trials in higher risk study populations are needed to fully elucidate suggested benefits for blood pressure and body composition.

          ClinicalTrials.gov Identifier: NCT03944421.

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          Most cited references59

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          General cardiovascular risk profile for use in primary care: the Framingham Heart Study.

          Separate multivariable risk algorithms are commonly used to assess risk of specific atherosclerotic cardiovascular disease (CVD) events, ie, coronary heart disease, cerebrovascular disease, peripheral vascular disease, and heart failure. The present report presents a single multivariable risk function that predicts risk of developing all CVD and of its constituents. We used Cox proportional-hazards regression to evaluate the risk of developing a first CVD event in 8491 Framingham study participants (mean age, 49 years; 4522 women) who attended a routine examination between 30 and 74 years of age and were free of CVD. Sex-specific multivariable risk functions ("general CVD" algorithms) were derived that incorporated age, total and high-density lipoprotein cholesterol, systolic blood pressure, treatment for hypertension, smoking, and diabetes status. We assessed the performance of the general CVD algorithms for predicting individual CVD events (coronary heart disease, stroke, peripheral artery disease, or heart failure). Over 12 years of follow-up, 1174 participants (456 women) developed a first CVD event. All traditional risk factors evaluated predicted CVD risk (multivariable-adjusted P<0.0001). The general CVD algorithm demonstrated good discrimination (C statistic, 0.763 [men] and 0.793 [women]) and calibration. Simple adjustments to the general CVD risk algorithms allowed estimation of the risks of each CVD component. Two simple risk scores are presented, 1 based on all traditional risk factors and the other based on non-laboratory-based predictors. A sex-specific multivariable risk factor algorithm can be conveniently used to assess general CVD risk and risk of individual CVD events (coronary, cerebrovascular, and peripheral arterial disease and heart failure). The estimated absolute CVD event rates can be used to quantify risk and to guide preventive care.
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            Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge.

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              Is Open Access

              Development and validation of QRISK3 risk prediction algorithms to estimate future risk of cardiovascular disease: prospective cohort study

              Objectives To develop and validate updated QRISK3 prediction algorithms to estimate the 10 year risk of cardiovascular disease in women and men accounting for potential new risk factors. Design Prospective open cohort study. Setting General practices in England providing data for the QResearch database. Participants 1309 QResearch general practices in England: 981 practices were used to develop the scores and a separate set of 328 practices were used to validate the scores. 7.89 million patients aged 25-84 years were in the derivation cohort and 2.67 million patients in the validation cohort. Patients were free of cardiovascular disease and not prescribed statins at baseline. Methods Cox proportional hazards models in the derivation cohort to derive separate risk equations in men and women for evaluation at 10 years. Risk factors considered included those already in QRISK2 (age, ethnicity, deprivation, systolic blood pressure, body mass index, total cholesterol: high density lipoprotein cholesterol ratio, smoking, family history of coronary heart disease in a first degree relative aged less than 60 years, type 1 diabetes, type 2 diabetes, treated hypertension, rheumatoid arthritis, atrial fibrillation, chronic kidney disease (stage 4 or 5)) and new risk factors (chronic kidney disease (stage 3, 4, or 5), a measure of systolic blood pressure variability (standard deviation of repeated measures), migraine, corticosteroids, systemic lupus erythematosus (SLE), atypical antipsychotics, severe mental illness, and HIV/AIDs). We also considered erectile dysfunction diagnosis or treatment in men. Measures of calibration and discrimination were determined in the validation cohort for men and women separately and for individual subgroups by age group, ethnicity, and baseline disease status. Main outcome measures Incident cardiovascular disease recorded on any of the following three linked data sources: general practice, mortality, or hospital admission records. Results 363 565 incident cases of cardiovascular disease were identified in the derivation cohort during follow-up arising from 50.8 million person years of observation. All new risk factors considered met the model inclusion criteria except for HIV/AIDS, which was not statistically significant. The models had good calibration and high levels of explained variation and discrimination. In women, the algorithm explained 59.6% of the variation in time to diagnosis of cardiovascular disease (R2, with higher values indicating more variation), and the D statistic was 2.48 and Harrell’s C statistic was 0.88 (both measures of discrimination, with higher values indicating better discrimination). The corresponding values for men were 54.8%, 2.26, and 0.86. Overall performance of the updated QRISK3 algorithms was similar to the QRISK2 algorithms. Conclusion Updated QRISK3 risk prediction models were developed and validated. The inclusion of additional clinical variables in QRISK3 (chronic kidney disease, a measure of systolic blood pressure variability (standard deviation of repeated measures), migraine, corticosteroids, SLE, atypical antipsychotics, severe mental illness, and erectile dysfunction) can help enable doctors to identify those at most risk of heart disease and stroke.
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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                European Journal of Nutrition
                Eur J Nutr
                Springer Science and Business Media LLC
                1436-6207
                1436-6215
                August 25 2023
                Article
                10.1007/s00394-023-03238-1
                d7763119-2d69-41b9-85bb-5e4ff607037d
                © 2023

                https://creativecommons.org/licenses/by/4.0

                https://creativecommons.org/licenses/by/4.0

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