The role of collaborative, multistakeholder partnerships in reshaping the health management of patients with noncommunicable diseases during and after the COVID-19 pandemic

Background: Policies to combat the COVID-19 pandemic have disrupted the screening, diagnosis, treatment, and monitoring of noncommunicable (NCD) patients while affecting NCD prevention and risk factor control. Aims: To discuss how the first wave of the COVID-19 pandemic affected the health management of NCD patients, identify which aspects should be carried forward into future NCD management, and propose collaborative efforts among public–private institutions to effectively shape NCD care models. Methods: The NCD Partnership, a collaboration between Upjohn and the European Innovation Partnership on Active and Healthy Ageing, held a virtual Advisory Board in July 2020 with multiple stakeholders; healthcare professionals (HCPs), policymakers, researchers, patient and informal carer advocacy groups, patient empowerment organizations, and industry experts. Results: The Advisory Board identified barriers to NCD care during the COVID-19 pandemic in four areas: lack of NCD management guidelines; disruption to integrated care and shift from hospital-based NCD care to more community and primary level care; infodemics and a lack of reliable health information for patients and HCPs on how to manage NCDs; lack of availability, training, standardization, and regulation of digital health tools. Conclusions: Multistakeholder partnerships can promote swift changes to NCD prevention and patient care. Intra- and inter-communication between all stakeholders should be facilitated involving all players in the development of clinical guidelines and digital health tools, health and social care restructuring, and patient support in the short-, medium- and long-term future. A comprehensive response to NCDs should be delivered to improve patient outcomes by providing strategic, scientific, and economic support

Lipoprotein Particle Profiles Mark Familial and Sporadic Human Longevity

BACKGROUND: Genetic and biochemical studies have indicated an important role for lipid metabolism in human longevity. Ashkenazi Jewish centenarians and their offspring have large low-density lipoprotein (LDL) and high-density lipoprotein (HDL) particles as compared with control individuals. This profile also coincided with a lower prevalence of disease. Here, we investigate whether this observation can be confirmed for familial longevity in an outbred European population and whether it can be extended to sporadic longevity in the general population. METHODS AND FINDINGS: NMR-measured lipoprotein profiles were analyzed in 165 families from the Leiden Longevity Study, consisting of 340 long-lived siblings (females >91 y, males >89 y), 511 of their offspring, and 243 partners of the offspring. Offspring had larger (21.3 versus 21.1 nm; p = 0.020) and fewer (1,470 versus 1,561 nmol/l; p = 0.011) LDL particles than their same-aged partners. This effect was even more prominent in the long-lived siblings (p < 10(−3)) and could be pinpointed to a reduction specifically in the concentration of small LDL particles. No differences were observed for HDL particle phenotypes. The mean LDL particle sizes in 259 90-y-old singletons from a population-based study were similar to those in the long-lived siblings and thus significantly larger than in partners of the offspring, suggesting that the relevance of this phenotype extends beyond familial longevity. A low concentration of small LDL particles was associated with better overall health among both long-lived siblings (p = 0.003) and 90-y-old singletons (p = 0.007). CONCLUSIONS: Our study indicates that LDL particle profiles mark both familial and sporadic human longevity already in middle age

Dimethyl fumarate in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial

Dimethyl fumarate (DMF) inhibits inflammasome-mediated inflammation and has been proposed as a treatment for patients hospitalised with COVID-19. This randomised, controlled, open-label platform trial (Randomised Evaluation of COVID-19 Therapy [RECOVERY]), is assessing multiple treatments in patients hospitalised for COVID-19 (NCT04381936, ISRCTN50189673). In this assessment of DMF performed at 27 UK hospitals, adults were randomly allocated (1:1) to either usual standard of care alone or usual standard of care plus DMF. The primary outcome was clinical status on day 5 measured on a seven-point ordinal scale. Secondary outcomes were time to sustained improvement in clinical status, time to discharge, day 5 peripheral blood oxygenation, day 5 C-reactive protein, and improvement in day 10 clinical status. Between 2 March 2021 and 18 November 2021, 713 patients were enroled in the DMF evaluation, of whom 356 were randomly allocated to receive usual care plus DMF, and 357 to usual care alone. 95% of patients received corticosteroids as part of routine care. There was no evidence of a beneficial effect of DMF on clinical status at day 5 (common odds ratio of unfavourable outcome 1.12; 95% CI 0.86-1.47; p = 0.40). There was no significant effect of DMF on any secondary outcome

The Development and Evaluation of “Life Age”—a Primary Prevention and Population-Focused Risk Communication Tool: Feasibility Study With a Single-Arm Repeated Measures Design

BackgroundCommunicating cardiovascular risk to the general population requires forms of communication that can enhance risk perception and stimulate lifestyle changes associated with reduced cardiovascular risk. ObjectiveThe aim of this study was to evaluate the motivational potential of a novel lifestyle risk assessment (“Life Age”) based on factors predictive of both premature mortality and psychosocial well-being. MethodsA feasibility study with a single-arm repeated measures design was conducted to evaluate the potential efficacy of Life Age on motivating lifestyle changes. Participants were recruited via social media, completed a web-based version of the Life Age questionnaire at baseline and at follow-up (8 weeks), and received 23 e-newsletters based on their Life Age results along with a mobile tracker. Participants’ estimated Life Age scores were analyzed for evidence of lifestyle changes made. Quantitative feedback of participants was also assessed. ResultsIn total, 18 of 27 participants completed the two Life Age tests. The median baseline Life Age was 1 year older than chronological age, which was reduced to –1.9 years at follow-up, representing an improvement of 2.9 years (P=.02). There were also accompanying improvements in Mediterranean diet score (P=.001), life satisfaction (P=.003), and sleep (P=.05). Quantitative feedback assessment indicated that the Life Age tool was easy to understand, helpful, and motivating. ConclusionsThis study demonstrated the potential benefit of a novel Life Age tool in generating a broad set of lifestyle changes known to be associated with clinical risk factors, similar to “Heart Age.” This was achieved without the recourse to expensive biomarker tests. However, the results from this study suggest that the motivated lifestyle changes improved both healthy lifestyle risks and psychosocial well-being, consistent with the approach of Life Age in merging the importance of a healthy lifestyle and psychosocial well-being. Further evaluation using a larger randomized controlled trial is required to fully evaluate the impact of the Life Age tool on lifestyle changes, cardiovascular disease prevention, and overall psychosocial well-being

Trajectories of entering the metabolic syndrome: the framingham heart study

Background-We evaluated the progression of the metabolic syndrome (MetS) and its components, the trajectories followed by individuals entering MetS, and the manner in which different trajectories predict cardiovascular disease and mortality. Methods and Results-Using data from 3078 participants from the Framingham Offspring Study (a cohort study) who attended examinations 4 (1987), 5 (1991), and 6 (1995), we evaluated the progression of MetS and its components. MetS was defined according to the Adult Treatment Panel III criteria. Using logistic regression, we evaluated the predictive ability of the presence of each component of the MetS on the subsequent development of MetS. Additionally, we examined the probability of developing cardiovascular disease or mortality (until 2007) by having specific combinations of 3 that diagnose MetS. The prevalence of MetS almost doubled in 10 years of follow-up. Hyperglycemia and central obesity experienced the highest increase. High blood pressure was most frequently present when a diagnosis of MetS occurred (77.3%), and the presence of central obesity conferred the highest risk of developing MetS (odds ratio, 4.75; 95% confidence interval, 3.78 to 5.98). Participants who entered the MetS having a combination of central obesity, high blood pressure, and hyperglycemia had a 2.36-fold (hazard ratio, 2.36; 95% confidence interval, 1.54 to 3.61) increase of incident cardiovascular events and a 3-fold (hazard ratio, 3.09, 95% confidence interval, 1.93 to 4.94) increased risk of mortality. Conclusions-Particular trajectories and combinations of factors on entering the MetS confer higher risks of incident cardiovascular disease and mortality in the general population and among those with MetS. Intense efforts are required to identify populations with these particular combinations and to provide them with adequate treatment at early stages of disease. (Circulation. 2009; 120: 1943-1950.

Risk prediction tools in cardiovascular disease prevention: A report from the ESC Prevention of CVD Programme led by the European Association of Preventive Cardiology (EAPC) in collaboration with the Acute Cardiovascular Care Association (ACCA) and the Association of Cardiovascular Nursing and Allied Professions (ACNAP)

Risk assessment and risk prediction have become essential in the prevention of cardiovascular disease. Even though risk prediction tools are recommended in the European guidelines, they are not adequately implemented in clinical practice. Risk prediction tools are meant to estimate prognosis in an unbiased and reliable way and to provide objective information on outcome probabilities. They support informed treatment decisions about the initiation or adjustment of preventive medication. Risk prediction tools facilitate risk communication to the patient and their family, and this may increase commitment and motivation to improve their health. Over the years many risk algorithms have been developed to predict 10-year cardiovascular mortality or lifetime risk in different populations, such as in healthy individuals, patients with established cardiovascular disease and patients with diabetes mellitus. Each risk algorithm has its own limitations, so different algorithms should be used in different patient populations. Risk algorithms are made available for use in clinical practice by means of - usually interactive and online available - tools. To help the clinician to choose the right tool for the right patient, a summary of available tools is provided. When choosing a tool, physicians should consider medical history, geographical region, clinical guidelines and additional risk measures among other things. Currently, the U-prevent.com website is the only risk prediction tool providing prediction algorithms for all patient categories, and its implementation in clinical practice is suggested/advised by the European Association of Preventive Cardiology