Isha Shaw
Data analyst intern
Healthy lifestyle in late-life, longevity genes, and life expectancy among older adults: a 20-year, population-based, prospective cohort study.
Published on: April 17, 2024
Original author: Jun Wang, et al. (2023) (DOI: 10.1016/S2666-7568(23)00140-X)
The number of people aged 65 years or older is increasing globally, with this group making up 13.5% of China's population. Promoting healthy aging is crucial for individual and societal health. Studies show that following a healthy lifestyle, including not smoking, limiting alcohol, being active, and eating well, reduces the risk of chronic diseases and death. This lifestyle also correlates with a longer life, especially if adopted early. Modifying these habits in midlife can lead to a longer life without major diseases like diabetes, cardiovascular disease, and cancer. Genetics also play a role in life expectancy, with studies suggesting that 20–30% of longevity is genetic, rising up to 40% for those over 85 years of age. Genome studies have found genes affecting lifespan. Previous research on genetics and lifestyle mostly focused on younger adults, leaving questions about how lifestyle impacts life expectancy in older adults. This study aims to fill that gap using data from a 20-year study of Chinese individuals aged 65 years and older. They want to see how a healthy lifestyle affects mortality and if this differs based on genetic risk. Procedures: For the analysis of lifestyle and genetic risk combined, 9,633 participants with genetic data were included. Lifestyle factors like smoking, alcohol consumption, physical activity, and diet were assessed at the beginning of the study. A healthy lifestyle score was created based on these factors. Each factor was given a score of 0 or 1, with 1 indicating a healthy behavior. A total score ranging from 0 to 4 was calculated, with a higher score indicating a healthier lifestyle. Participants were categorized into unhealthy, intermediate, or healthy lifestyle groups based on their scores. Genetic data from a subset of participants were analyzed for single nucleotide polymorphisms (SNPs) associated with longevity. A genetic risk score was calculated based on the number of effect alleles at each SNP, with a higher score indicating a higher genetic risk for lifespan. Participants were divided into low and high-genetic-risk groups based on the median genetic risk score. (β1 x factor1 + β2 x factor2 + β3 × factor3 + β4 x factor4) ---------------------------------------------------------------------------------- (sum of the β coefficient) Outcomes: The study looked at mortality rates among participants based on their lifestyle and genetic risk factors. Mortality data was collected from death certificates, family reports, and follow-up interviews. They used statistical models to analyze the data, adjusting for factors like age, sex, education, income, and health status. They found that a healthier lifestyle was associated with lower mortality rates. They also looked at the combined effects of lifestyle and genetic risk on mortality. The study concluded that adhering to a healthy lifestyle could reduce the risk of death and increase life expectancy. They used various statistical methods to ensure the robustness of their findings. Results: Between January 13, 1998, and December 31, 2018, a total of 27,462 deaths among 36,164 people were documented, with a median follow-up of 3.12 years. The genetic association analysis included 9,633 participants, among whom 5,618 deaths were recorded during a median follow-up of 5.57 years. Participants with a healthier lifestyle were more likely to be younger, male, married, with an independent income source, living in an urban area, and have higher educational attainment. A healthier lifestyle was associated with a lower risk of all-cause mortality, while a high genetic risk was associated with a higher mortality risk. Participants with both a high genetic risk and an unhealthy lifestyle had the highest mortality risk. Adherence to a healthy lifestyle was associated with lower mortality rates and longer life expectancy, even among those with a high genetic risk. Sensitivity analyses confirmed the robustness of these findings. Conclusion: In this study of Chinese older adults, maintaining a healthy lifestyle was linked to a lower risk of death and a moderate increase in life expectancy. These benefits were more pronounced among those with a high genetic risk of shorter lifespan. Ideal physical activity was found to have the most significant impact on reducing mortality. While previous studies have shown that a healthy lifestyle can reduce the risk of major health issues in older individuals, its effect on lifespan has been less explored. The findings suggest that following a healthy lifestyle can reduce mortality risk and increase life expectancy, even up to the age of 100 years. Genetic factors play a significant role in mortality risk. The present study found that adhering to a healthy lifestyle had a greater impact on mortality risk for those with a high genetic risk. The mechanisms behind these findings require further investigation. Previous studies have shown mixed results regarding the interaction between lifestyle and socioeconomic status on mortality risk. The present study found that the effect of a healthy lifestyle on reducing mortality risk was more significant among urban residents compared to rural residents. These differences could be due to variations in how socioeconomic status and lifestyle factors are defined, as well as differences in population characteristics. Efforts to promote healthy aging should be prioritized at both the national and individual levels. Policies should be tailored to the health needs of older adults, considering their individual characteristics and nutritional status. Health education programs should also emphasize the importance of a healthy lifestyle and encourage active aging and self-health management. Interventions to improve subjective well-being could also promote the maintenance of a healthy lifestyle and reduce mortality risk.
Social networks, social support, and life expectancy in older adults: The cardiovascular health study.
Published on: December 13, 2023
Original author: Roma Bhatia, et al. (2023) (DOI: 10.1016/j.archger.2023.104981)
Since the 1970s, researchers have suggested that social networks, the connections people have in their daily lives, can impact health. The size, frequency of contact, and diversity of social networks have been linked to health effects. Smaller social networks are associated with more hospital visits and higher healthcare costs. On the other hand, stronger social networks are believed to reduce the risk of mortality and protect against disability in older adults. However, some challenges persist in understanding this connection. Many studies provided unadjusted or unclear results. Additionally, no prior studies have measured the absolute years of life gained or lost due to social networks in older adults. The relationship between physical health, comorbidities, and social isolation is complex, making it hard to establish the directionality of the connection, especially in older adults. It is also unclear if robust social networks can influence the duration of disability, or the proportion of life spent with difficulties in activities of daily living. To address these gaps, the researchers studied participants from the Cardiovascular Health Study (CHS), a long-term study of adults aged 65 years and older. This study aims to quantify how social networks impact crucial health outcomes, such as total and disability-free years of life lived. Methodology: As part of the Cardiovascular Health Study, 5,749 adults aged 65 years and older were followed for 25 years across four U.S. field centers. The researchers looked at their social networks using scores that measure connections and support. These scores were assessed for two years, starting when they enrolled for the study. To estimate how long they might live, the remaining years were calculated from the time they enrolled for the study until they passed away. They also counted the years they lived without facing difficulties in daily activities. The goal was to see how much of their life was healthy and active. To make the findings more accurate, factors like age, gender, and other health issues were also considered using a statistical method called linear regression. Results: The average (standard deviation [SD]) scores for the social network were 32.3 ± 6.8 points, and for social support, they were 8.3 ± 2.4 points. When the social network score increased by one standard deviation (1-SD), the adjusted life expectancy of participants increased by 0.40 years (95% CI 0.22-0.58; p<0.0001), and disability-free life expectancy increased by 0.35 years (95% CI 0.18-0.53; p<0.0001). The impact on life expectancy varied with the age of participants (p<0.001), but it remained significant even for those aged 75 years and older (3 months per SD; 95% CI 0.1-6 months, p = 0.04). Even after considering frailty, the association with life expectancy remained unchanged. The social support score did not show a significant association with years of life or years of active life after adjusting for the social network score, and neither measure was linked to the compression of disability. Conclusion: In older adults, having stronger social networks is directly linked to longer life expectancy and more years lived without disabilities. This connection remains consistent in a linear dose-response pattern, even when considering factors such as age, race, sex, and other health issues. However, figuring out the best ways to use these findings to enhance the scope and strength of social networks in older adults—ultimately leading to longer and healthier lives—will need further investigation and research in the future. Impact: According to the study, the impact on life expectancy exhibited some variability depending on the age of participants. Nevertheless, it remained notably significant, even for individuals aged 75 years and older. Furthermore, there is perpetual room for ongoing research and exploration of innovative ideas and their consequences.
Quality of YouTube videos on meningioma treatment using the DISCERN instrument.
Published on: March 07, 2023
Original author: Paulina Śledzińska, Marek G. Bebyn, et.al. (2021) (DOI: 10.1016/j.wneu.2021.06.072)
Meningiomas are the most common type of primary brain tumor that arises from the brain or the spinal cord. Although the majority of meningiomas are benign, these tumors can cause problems as they grow and compress against vital parts of the brain or the spinal cord. The risk of meningioma increases with age. The most preferred treatment for meningioma is surgery. The complex nature of meningioma has led to a number of myths and misunderstandings about the disease. Patients generally want to learn about their illness and treatment choices, and a well-informed patient can play a more active role in decision-making and consequently experience less anxiety. However, healthcare specialists may provide insufficient information due to a lack of consulting time or by providing information in such a way that the patient cannot understand. Therefore, many patients now browse the internet for readily accessible and available medical information, with YouTube being one of the most popular sources of patient information on the internet. Although patients can access medical information via YouTube, not everyone can assess the quality, reliability, and accuracy of this information. Biased or conflicting advice can not only lower the credibility of the clinicians but also harm the patients, especially when discussing different treatment choices. Hence, it is important to assess the accuracy of patient information on meningioma on YouTube. The foremost objective of the present study was to evaluate the quality of YouTube videos as a source of patient education. A secondary objective was to examine quantitative data including the video length, source of upload (physician, hospital, health information channel, and educational channel), popularity (number of views, views per day, number of likes, dislikes, and comments), and their associations with the video quality. A search was performed on YouTube on January 14, 2021, using the keywords “meningioma treatment,” “meningeal tumor treatment,” “meningioma brain tumor treatment,” “meningioma cures,” and “meningioma therapy.” “Relevance-Based Ranking” was used to sort the YouTube results, and the first 3 results pages of each search were assessed noting that 95% of users conducting a YouTube search watch no more than the first 60 videos retrieved in the search results. The review included videos reporting how the treatment works and the benefits, risk factors, and possible treatment choices for meningioma. All duplicate or irrelevant videos, where the latter were defined as videos not containing any information about meningioma treatment, were removed. Out of 150 records identified through YouTube searching, 83 duplicates were removed. Out of the remaining 67 videos, 6 of them were excluded because of irrelevance, and one video was for not being in the English language. Video contents were independently evaluated by 2 fifth-year medical students using the DISCERN scoring system for video quality analysis which is a tool to assess the quality of the videos on YouTube. The further set of statistics was evaluated using the like ratio and Video Power Index (VPI). It recorded whether videos included the following features: symptoms of meningioma; risk factors during treatment; results of treatment; steps on how to perform the procedure; discussion of the prognosis; an animation; a diagram; radiological findings; a brain anatomy explanation; a patient experience; and whether or not the speaker was a doctor. Results & discussion According to DISCERN groupings, 34.4% of the YouTube videos were classified as very poor, 32.8% as poor, 11.5% as fair, 16.4% as good, and 4.9% as excellent. Most videos were provided by the health category followed by the hospital, physician, and educational categories. There were no significant differences between videos with and without radiological findings; results of treatment; steps on how to perform the procedure; a brain anatomy explanation; patient experience; or whether the speaker was a doctor. The DISCERN scores indicated that the quality of information on meningioma treatment on YouTube is poor, and the video content requires improvement. The videos on YouTube poorly described areas of uncertainty, risks associated with meningioma treatment, and what would happen in the absence of treatment. Moreover, they provided little support for shared decision making but the YouTube videos were fair at describing how treatments work. Conclusion It was concluded that YouTube tended to be a poor source of patient information on meningioma treatment, with less than 5% of videos of excellent quality and over two-thirds of poor quality. The impact of inaccurate YouTube videos on patients’ understanding of meningioma treatment must be recognized by healthcare professionals. Physicians can be aware of the possible effect of inaccurate or unreliable YouTube videos on patients’ comprehension of the disease as well as the impact on the patient-physician relationship. Guidelines can be created for guiding patients to credible sources of information which may be one solution for overcoming the issue of misleading advice in YouTube videos. Moreover, healthcare providers may warn patients against searching for information about meningioma treatment on YouTube which lacks credibility.
