Randomised trial of coconut oil, olive oil or butter on blood lipids and other cardiovascular risk factors in healthy men and women.

INTRODUCTION: High dietary saturated fat intake is associated with higher blood concentrations of low-density lipoprotein cholesterol (LDL-C), an established risk factor for coronary heart disease. However, there is increasing interest in whether various dietary oils or fats with different fatty acid profiles such as extra virgin coconut oil may have different metabolic effects but trials have reported inconsistent results. We aimed to compare changes in blood lipid profile, weight, fat distribution and metabolic markers after four weeks consumption of 50 g daily of one of three different dietary fats, extra virgin coconut oil, butter or extra virgin olive oil, in healthy men and women in the general population. DESIGN: Randomised clinical trial conducted over June and July 2017. SETTING: General community in Cambridgeshire, UK. PARTICIPANTS: Volunteer adults were recruited by the British Broadcasting Corporation through their websites. Eligibility criteria were men and women aged 50-75 years, with no known history of cancer, cardiovascular disease or diabetes, not on lipid lowering medication, no contraindications to a high-fat diet and willingness to be randomised to consume one of the three dietary fats for 4 weeks. Of 160 individuals initially expressing an interest and assessed for eligibility, 96 were randomised to one of three interventions; 2 individuals subsequently withdrew and 94 men and women attended a baseline assessment. Their mean age was 60 years, 67% were women and 98% were European Caucasian. Of these, 91 men and women attended a follow-up assessment 4 weeks later. INTERVENTION: Participants were randomised to extra virgin coconut oil, extra virgin olive oil or unsalted butter and asked to consume 50 g daily of one of these fats for 4 weeks, which they could incorporate into their usual diet or consume as a supplement. MAIN OUTCOMES AND MEASURES: The primary outcome was change in serum LDL-C; secondary outcomes were change in total and high-density lipoprotein cholesterol (TC and HDL-C), TC/HDL-C ratio and non-HDL-C; change in weight, body mass index (BMI), waist circumference, per cent body fat, systolic and diastolic blood pressure, fasting plasma glucose and C reactive protein. RESULTS: LDL-C concentrations were significantly increased on butter compared with coconut oil (+0.42, 95% CI 0.19 to 0.65 mmol/L, P<0.0001) and with olive oil (+0.38, 95% CI 0.16 to 0.60 mmol/L, P<0.0001), with no differences in change of LDL-C in coconut oil compared with olive oil (-0.04, 95% CI -0.27 to 0.19 mmol/L, P=0.74). Coconut oil significantly increased HDL-C compared with butter (+0.18, 95% CI 0.06 to 0.30 mmol/L) or olive oil (+0.16, 95% CI 0.03 to 0.28 mmol/L). Butter significantly increased TC/HDL-C ratio and non-HDL-C compared with coconut oil but coconut oil did not significantly differ from olive oil for TC/HDL-C and non-HDL-C. There were no significant differences in changes in weight, BMI, central adiposity, fasting blood glucose, systolic or diastolic blood pressure among any of the three intervention groups. CONCLUSIONS AND RELEVANCE: Two different dietary fats (butter and coconut oil) which are predominantly saturated fats, appear to have different effects on blood lipids compared with olive oil, a predominantly monounsaturated fat with coconut oil more comparable to olive oil with respect to LDL-C. The effects of different dietary fats on lipid profiles, metabolic markers and health outcomes may vary not just according to the general classification of their main component fatty acids as saturated or unsaturated but possibly according to different profiles in individual fatty acids, processing methods as well as the foods in which they are consumed or dietary patterns. These findings do not alter current dietary recommendations to reduce saturated fat intake in general but highlight the need for further elucidation of the more nuanced relationships between different dietary fats and health. TRIAL REGISTRATION NUMBER: NCT03105947; Results

Communicating personalized risks from COVID-19: guidelines from an empirical study.

As increasing amounts of data accumulate on the effects of the novel coronavirus SARS-CoV-2 and the risk factors that lead to poor outcomes, it is possible to produce personalized estimates of the risks faced by groups of people with different characteristics. The challenge of how to communicate these then becomes apparent. Based on empirical work (total n = 5520, UK) supported by in-person interviews with the public and physicians, we make recommendations on the presentation of such information. These include: using predominantly percentages when communicating the absolute risk, but also providing, for balance, a format which conveys a contrasting (higher) perception of risk (expected frequency out of 10 000); using a visual linear scale cut at an appropriate point to illustrate the maximum risk, explained through an illustrative 'persona' who might face that highest level of risk; and providing context to the absolute risk through presenting a range of other 'personas' illustrating people who would face risks of a wide range of different levels. These 'personas' should have their major risk factors (age, existing health conditions) described. By contrast, giving people absolute likelihoods of other risks they face in an attempt to add context was considered less helpful. We note that observed effect sizes generally were small. However, even small effects are meaningful and relevant when scaled up to population levels

Communicating personalised risks from COVID-19: guidelines from an empirical study

As increasing amounts of data accumulate on the effects of the novel coronavirus Sars-CoV-2 and the risk factors that lead to poor outcomes, it is possible to produce personalised estimates of the risks faced by groups of people with different characteristics. The challenge of how to communicate these then becomes apparent. Based on empirical work (total n=5,520, UK) supported by in-person interviews with the public and physicians, we make recommendations on the presentation of such information. These include: using predominantly percentages when communicating the absolute risk, but also providing, for balance, a format which conveys a contrasting (higher) perception of risk (expected frequency out of 10,000); using a visual linear scale cut at an appropriate point to illustrate the maximum risk, explained through an illustrative ‘persona’ who might face that highest level of risk; and providing context to the absolute risk through presenting a range of other ‘personas’ illustrating people who would face risks of a wide range of different levels. These ‘personas’ should have their major risk factors (age, existing health conditions) described. By contrast, giving people absolute likelihoods of other risks they face in an attempt to add context was considered less helpful

Communicating personalized risks from COVID-19: guidelines from an empirical study.

As increasing amounts of data accumulate on the effects of the novel coronavirus SARS-CoV-2 and the risk factors that lead to poor outcomes, it is possible to produce personalized estimates of the risks faced by groups of people with different characteristics. The challenge of how to communicate these then becomes apparent. Based on empirical work (total n = 5520, UK) supported by in-person interviews with the public and physicians, we make recommendations on the presentation of such information. These include: using predominantly percentages when communicating the absolute risk, but also providing, for balance, a format which conveys a contrasting (higher) perception of risk (expected frequency out of 10 000); using a visual linear scale cut at an appropriate point to illustrate the maximum risk, explained through an illustrative 'persona' who might face that highest level of risk; and providing context to the absolute risk through presenting a range of other 'personas' illustrating people who would face risks of a wide range of different levels. These 'personas' should have their major risk factors (age, existing health conditions) described. By contrast, giving people absolute likelihoods of other risks they face in an attempt to add context was considered less helpful. We note that observed effect sizes generally were small. However, even small effects are meaningful and relevant when scaled up to population levels

The adverse effects of trastuzumab-containing regimes as a therapy in breast cancer: A piggy-back systematic review and meta-analysis.

BACKGROUND: Trastuzumab is a valuable therapy option for women with ERBB2(HER2)+ breast cancer tumours, often used in combination with chemotherapy and alongside other therapies. It is known to have adverse effects, but these have proved difficult to separate from the effects of other concurrent therapies patients are usually taking. This study aims to assess the adverse effects specifically attributable to trastuzumab, and whether they vary by patient subgroup or concurrent therapies. METHODS: As registered on PROSPERO (CRD42019146541), we used previous systematic reviews as well as the clinicaltrials.gov registry to identify randomised controlled trials in breast cancer which compared treatment regimes with and without trastuzumab. Neoadjuvant, adjuvant and metastatic settings were examined. Data was extracted from those which had, as of July 2022, reported adverse events. Risk of bias was assessed using ROB2. Primary outcomes were adverse events of any type or severity (excluding death). A standard random-effects meta-analysis was performed for each outcome independently. In order to ascertain whether adverse effects differed by individual factors such as age or tumour characteristics, or by use of trastuzumab concurrently with hormone therapy, we examined individual-level patient data for one large trial, HERA. RESULTS: 79 relevant trials were found, of which 20 contained comparable arms of trastuzumab-containing therapy and corresponding matched therapy without trastuzumab. This allowed a comparison of 8669 patients receiving trastuzumab versus 9556 receiving no trastuzumab, which gave a list of 25 statistically and clinically significant adverse effects related to trastuzumab alone: unspecified pain, asthenia, nasopharyngitis, skin disorders (mainly rash), dyspepsia, paraesthesia, infections (often respiratory), increased lacrimation, diarrhoea, myalgia, oedema (limb/peripheral), fever, nose bleeds, cardiac events, insomnia, cough, back pain, dyspnoea, chills, dizziness or vertigo, hypertension, congestive heart failure, increased levels of aspartate aminotransferase, gastrointestinal issues and dehydration. Analysis of individual patient-level data from 5102 patients suggested that nausea is slightly more likely for women taking trastuzumab who are ER+ /also taking hormone therapy than for those who are ER-/not taking hormone therapy; no other potential treatment-subgroup interactions were detected. We found no evidence for significantly increased rates of neutropenia, anaemia or lymphopenia in patients on trastuzumab-containing regimes compared to those on comparable regimes without trastuzumab. CONCLUSIONS: This meta-analysis should allow clinicians and patients to better identify and quantify the potential adverse effects of adding trastuzumab to their treatment regime for breast cancer, and hence inform their decision-making. However, limitations include serious risk of bias due to heterogeneity in reporting of the outcomes and the open-label nature of the trials.Medical Research Council, programme number MRC_MC_UU_00002/1

Communicating personalized risks from COVID-19: guidelines from an empirical study

As increasing amounts of data accumulate on the effects of the novel coronavirus SARS-CoV-2 and the risk factors that lead to poor outcomes, it is possible to produce personalized estimates of the risks faced by groups of people with different characteristics. The challenge of how to communicate these then becomes apparent. Based on empirical work (total n = 5520, UK) supported by in-person interviews with the public and physicians, we make recommendations on the presentation of such information. These include: using predominantly percentages when communicating the absolute risk, but also providing, for balance, a format which conveys a contrasting (higher) perception of risk (expected frequency out of 10 000); using a visual linear scale cut at an appropriate point to illustrate the maximum risk, explained through an illustrative ‘persona’ who might face that highest level of risk; and providing context to the absolute risk through presenting a range of other ‘personas’ illustrating people who would face risks of a wide range of different levels. These ‘personas’ should have their major risk factors (age, existing health conditions) described. By contrast, giving people absolute likelihoods of other risks they face in an attempt to add context was considered less helpful. We note that observed effect sizes generally were small. However, even small effects are meaningful and relevant when scaled up to population levels

Communicating personalized risks from COVID-19: guidelines from an empirical study

As increasing amounts of data accumulate on the effects of the novel coronavirus SARS-CoV-2 and the risk factors that lead to poor outcomes, it is possible to produce personalized estimates of the risks faced by groups of people with different characteristics. The challenge of how to communicate these then becomes apparent. Based on empirical work (total n = 5520, UK) supported by in-person interviews with the public and physicians, we make recommendations on the presentation of such information. These include: using predominantly percentages when communicating the absolute risk, but also providing, for balance, a format which conveys a contrasting (higher) perception of risk (expected frequency out of 10 000); using a visual linear scale cut at an appropriate point to illustrate the maximum risk, explained through an illustrative ‘persona’ who might face that highest level of risk; and providing context to the absolute risk through presenting a range of other ‘personas’ illustrating people who would face risks of a wide range of different levels. These ‘personas’ should have their major risk factors (age, existing health conditions) described. By contrast, giving people absolute likelihoods of other risks they face in an attempt to add context was considered less helpful. We note that observed effect sizes generally were small. However, even small effects are meaningful and relevant when scaled up to population levels