Tag: Heart

Research identifies optimal body weight to reduce cardiovascular risk in diabetes patients

[ad_1]

New research being presented at this year’s European Congress on Obesity (ECO) in Venice, Italy (12-15 May), identifies the optimum body weight range for adults with type 2 diabetes to minimize their risk of dying from any cardiovascular disease, including heart failure, heart disease, stroke, and chronic kidney disease.

The findings, based on health data from the UK Biobank, indicate that for adults aged 65 years or younger, maintaining a body mass index (BMI) within the normal range of 23–25 kg/m² was associated with the lowest risk of dying from cardiovascular disease. But for those over 65 years old, being moderately overweight with a BMI of 26–28 kg/m², had the lowest risk.

Maintaining a healthy weight is crucial for reducing the risk of cardiovascular diseases, particularly for people with type 2 diabetes who are predisposed to cardiovascular disease and death. However, it’s not clear whether the optimal BMI range for people with type 2 diabetes varies by age.

To plug these knowledge gaps, researchers explored the age differences in the association between BMI and risk of cardiovascular death in 22,874 UK Biobank participants with a previous diagnosis of type 2 diabetes at the time they enrolled between 2006 and 2010. Patients with prior cardiovascular diseases were not excluded.

The average age of all the participants was 59 years, and around 59% were women. Their cardiovascular health was tracked, using linked health records, for nearly 13 years during which time 891 participants died from cardiovascular diseases.

Researchers analyzed data in two age groups-;the elderly (over 65 years) and the middle-aged (age 65 years or younger)-;and assessed the relationship between variables such as BMI, waist circumference, and waist-to-height ratio and the risk of cardiovascular death.

The optimal BMI cut-off point was also calculated in different age groups and the findings were adjusted for traditional cardiometabolic risk factors and other factors associated with adverse cardiovascular outcomes including age, sex, smoking history, alcohol consumption, level of physical exercise, and history of cardiovascular diseases.

The analyses found that in the middle-aged group, having a BMI in the overweight range range (25 kg/m² to 29.9 kg/m²) was associated with a 13% greater risk of dying from cardiovascular disease than those with a BMI in the normal range (less than 25.0 kg/m²).

However, in the elderly group, having a BMI in the overweight range (25 kg/m² to 29.9 kg/m²) was associated with an 18% lower risk of dying compared to having a BMI in the normal range (less than 25.0 kg/m²).

The relationship between BMI and cardiovascular death risk exhibited a U-shaped pattern, even after stratification by age, so the optimal BMI cut-off point was different in the elderly and middle-aged groups. For the middle-aged group, the optimal BMI cut-off was 24 kg/m², whereas for the elderly group, it was 27 kg/m². Consequently, personalized treatment plans can be developed in clinical settings by tailoring recommendations to different age groups.

The researchers also found a positive relationship between both waist circumference and waist-to-height ratio and the risk of cardiovascular death. As waist circumference increased, the risk of cardiovascular death also showed a corresponding rise. When the study population was divided into older and middle-aged categories, this upward trend remained consistent. Similar patterns were observed for the waist-to-height ratio. However, no significant BMI cut-off point was identified.

Importantly, we demonstrate that optimal BMI for people with type 2 diabetes varies by age, independent of traditional cardiometabolic risk factors. Our findings suggest that for older individuals who are moderately overweight but not obese, maintaining rather than losing weight may be a more practical way of reducing their risk of dying from cardiovascular disease.”

Dr Shaoyong Xu, lead author from Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, China

He adds, “Our findings also indicate that adiposity may offer some protection against fatal diseases to some extent. The possible biological mechanisms that explain this ‘obesity survival paradox’ in elderly people may be associated with a lower rate of bone mass loss, which reduces the effects of fall and trauma episodes, and greater nutritional reserves to accommodate periods of acute stress.”

The authors say that in the future, measures of central obesity, such as waist circumference, would be used to further refine the risk.

This is an observational study, and as such, can’t establish cause. And the researchers acknowledge various limitations to their findings, including small numbers of cardiovascular deaths and no information on type of cardiovascular disease or specific treatments. They also note that most of the UK Biobank study participants are White, so the findings might not apply to people of other ethnic backgrounds. Also, the nature of the cohort study may create potential classification errors that could partially affect the conclusions, because anthropometric measurements were only assessed at the start of the study, and body weight may change during the follow-up period.

[ad_2]

Source link

March 30, 2024
Research highlights health risks posed by 2,6-DHNPs in drinking water

[ad_1]

2,6-DHNPs, a group of disinfection byproducts (DBPs), are raising alarm bells for public health. These bad actors in the water world are tougher and more toxic than many other pollutants, making it hard for typical water cleaning methods to get rid of them. They pack a powerful punch, being significantly more harmful to marine life and cells than similar pollutants. Found in places like sewage, swimming pools, and our drinking taps, 2,6-DHNPs are everywhere, signaling a pressing need for better ways to clean our water and keep us safe.

A new study (DOI: 10.1016/j.eehl.2024.02.004), published in Eco-Environment & Health on 4 March 2024, has uncovered the severe cardiotoxic impacts 2,6-DHNPs have on zebrafish embryos, serving as a model for potential human health risks.

2,6-DHNPs, a group of DBPs resistant to traditional water purification methods like boiling and filtration. These DBPs pose a significant risk, showing a toxicity level 248 times higher than the known regulated DBPs, dichloroacetic acid, in zebrafish embryos. Using zebrafish as a biological model due to their genetic similarity to humans the study meticulously detailed how these emerging contaminants wreak havoc on cardiac health. The zebrafish embryos exposed to 2,6-DHNPs suffered from severe heart damage characterized by increased production of harmful reactive oxygen species, cell death (apoptosis), and disrupted heart development.

The study revealed that 2,6-DCNP and 2,6-DBNP, two types of DBPs, exhibited significant resistance to removal in drinking water treatment plants. Boiling and filtration were found to be the most effective household water treatment methods, reducing 2,6-DCNP and 2,6-DBNP levels by 47% and 52%, respectively. Exposure to 2,6-DHNPs caused heart failure in zebrafish embryos through increased production of harmful reactive oxygen species (ROS) and delayed heart development. Notably, the antioxidant N-acetyl-L-cysteine was able to mitigate the cardiotoxic effects induced by 2,6-DHNPs.

Dr. Hongjie Sun, a leading researcher in the study, stated, “The cardiotoxic potential of 2,6-DHNPs at low concentrations significantly challenges our current understanding of water safety and highlights the need for urgent reassessment of drinking water treatment methods.”

Dr. Peng Gao, the corresponding author, added, “Our findings underscore the importance of evaluating the health impacts of disinfection byproducts that may form during water treatment and being resistant to household treatment. We need to prioritize the development of advanced water purification technologies to effectively remove these concerning pollutants and safeguard public health.

This research underscores a critical environmental and public health issue: the contaminants that survive water treatment processes can lead to severe health outcomes in exposed organisms, hinting at the possible public health risks faced by these persistent waterborne chemicals.

Source:

Journal reference:

Sun, H., et al. (2024). Dihalogenated nitrophenols in drinking water: Prevalence, resistance to household treatment, and cardiotoxic impact on zebrafish embryo. Eco-Environment & Health. doi.org/10.1016/j.eehl.2024.02.004

[ad_2]

Source link

March 29, 2024
Having a ruff day? Study says spending time with dogs can help

[ad_1]

In a recent study published in PLoS ONE, researchers examined how interacting with dogs affects the emotions and psychophysiology of humans using an electroencephalogram (EEG).

Their results indicate that people are significantly less stressed when they interact with dogs, with specific activities showing associations with heightened emotional stability, relaxation, creativity, concentration, and attention.

Study: Psychophysiological and emotional effects of human–Dog interactions by activity type: An electroencephalogram study. Image Credit: 4 PM production/Shutterstock.com

Background

Since the domestication of dogs more than 30,000 years ago, they have been humans’ constant companions, providing invaluable assistance for herding livestock, guarding people and property, hunting, and working in many other domains.

The emotional and physiological benefits of interacting with animals, particularly dogs, are now well-known, as they reduce levels of cortisol and, thus, behavioral distress, anxiety, and stress reactivity and improve mental and cardiovascular health.

This has led to the use of animal-assisted interventions (AAI) and animal-assisted activities (AAA) for various domains such as social work, education, and health.

However, the effects of these interventions have usually been assessed using before-after or experiment-control comparisons, and evidence on the underlying mechanisms, such as changes in brain activity, is lacking.

About the study

In this study, researchers investigated people’s psychophysiological responses while interacting with dogs through various activities.

Participants were recruited from an animal grooming academy and pet salons in South Korea, excluding those with cynophobia or dog allergies and a medical history of conditions like unstable angina, high blood pressure, heart surgery, heart attack, or psychopathological disease.

Individuals receiving medication for these conditions or pregnant or lactating were also excluded. During the study, participants avoided stimulants and did not drink caffeinated beverages or smoke.

The dog they interacted with was a four-year-old female poodle who had been trained in aggression, sociability, manners, and basic obedience. She was healthy and vaccinated, and a professional handler was available to ensure her safety.

Participants engaged in eight activities in a single session: meeting, walking, hugging, photography, grooming, massage, feeding, and play. These activities were chosen to prioritize direct interactions and take place over an hour.

Their responses were measured using EEGs for three minutes during each activity, while their emotional responses were assessed using self-reported mood questionnaires such as the Stress Numeric Rating Scale (Stress NRS), the Profile of Mood State (POMS), and the Semantic Differential Method (SDM). Demographic information like weight, height, sex, and age were also collected.

The collected data were analyzed using descriptive statistics, one-way analysis of variance (ANOVA), statistical testing, and Duncan’s post-hoc analysis.

Findings

The study included 30 individuals, of whom 15 were male and 15 were female, and they were 27.9 years old on average.

Results from the EEG analysis showed that playing with the dog significantly increased activity in both frontal lobes and the left prefrontal lobe.

Walking also boosted activity in both prefrontal lobes. Playing correlated with heightened activation in both frontal lobes, while walking specifically activated the right prefrontal lobe.

Massage and grooming activities are elevated in the prefrontal and left frontal lobes. Massage also activated the right frontal lobe. However, no significant differences were found across activities in the parietal and occipital lobes.

Playing with the dog showed consistent activation in multiple brain regions, including those associated with focused attention, meditative states, and cognitive and sensory processing areas.

The subjective reports of emotional states were analyzed across six categories, namely depression-dejection, fatigue, tension-anxiety, confusion, vigor, and anger-hostility.

Individuals reported lower levels of depression, fatigue, and stress during AAAs than they did while resting. They also experienced more vigor when they engaged in feeding, relaxed when they engaged in massaging the dog, and comfortable when they walked her.

Participants experienced more positive mood states when they hugged, massaged, or fed her.

Conclusions

The findings from this study, which included both pet owners and those without pets, support the use of AAI for stress management and emotional relaxation, with different activities eliciting different brainwave reactions.

Responses in the prefrontal lobe are significant as this region plays important roles in regulating memory, language, emotional, behavioral, and cognitive functioning while the parietal lobe is essential for perceiving stimuli, motor function control, spatial orientation comprehension, and sensory information integration.

In addition to the limited sample size, another possible limitation of this study is that participants in AAI are more likely to be those who are not afraid of animals and enjoy being with them.

People without a pre-existing fondness for animals are likely to respond very differently to AAA, and this difference could lead to a selection bias in the findings.

Further studies are needed to validate these findings and shed more light on the mechanisms that make interacting with dogs and other animals so beneficial for humans.

[ad_2]

Source link

March 29, 2024
Study reveals obesity’s link to increased risk of multiple sclerosis and ischemic stroke

[ad_1]

In a recent study published in Scientific Reports, researchers from China used Mendelian randomization (MR) to assess the genetic relationship between body mass index (BMI) and multiple neurological diseases.

They found that BMI shows a genetic causal relationship with multiple sclerosis (MS) and ischemic stroke (IS), but not with Parkinson’s disease (PD), Alzheimer’s disease (AD), amyotrophic lateral sclerosis (ALS), and epilepsy (EP).

Study: Genetic causal role of body mass index in multiple neurological diseases. Image Credit: New Africa/Shutterstock.com

Background

BMI is widely used for obesity assessment owing to its simplicity and sensitivity. Economic changes and lifestyle shifts have increased obesity risk globally. Elevated BMI is linked to various diseases and higher mortality rates, including type 2 diabetes, hypertension, coronary heart disease, musculoskeletal disorders, and neoplastic growth.

Neurological diseases cover a broad spectrum of nervous system conditions, including neurodegenerative, cerebrovascular, infectious, oncological, and hereditary disorders.

While PD is characterized by dopamine concentration changes and Lewy body presence, AD is linked to β-amyloid deposition and tau protein phosphorylation. ALS affects motor neurons, while MS is a demyelinating disease mediated by the immune system.

IS is associated with various risk factors like hypertension and diabetes, and EP arises from synchronized neuronal discharges due to genetic or structural abnormalities.

MR is a method to assess causal relationships between exposures and outcomes using genetic instrumental variables, including single nucleotide polymorphisms (SNPs). The method is robust against the effects of confounders and reverse causation.

Therefore, researchers in the present study investigated the genetic links between BMI and neurological diseases using MR analysis, aiming to inform disease management strategies.

About the study

The present study used SNPs from a genome-wide association study (GWAS) dataset as instrumental variables to explore genetic causality between exposure and outcome factors.

The study followed stringent criteria for MR studies, ensuring robust correlations between instrumental variables and exposure factors while controlling for potential confounders.

Data on BMI indicators were obtained from the Integrative Epidemiology Unit (IEU) database, comprising nearly one million participants of European ancestry, with measurements for over seven million SNPs.

Data for various neurological diseases were sourced from the IEU database, including PD, AD, MS, ALS, IS, EP cases, and respective control groups.

The participants were predominantly of European origin, except for ALS and EP, which comprised individuals of multiple races and regions.

Quality control procedures were implemented for all disease data. SNPs significantly associated with BMI were subjected to cluster analysis to exclude redundant effects. SNPs causally linked to PD, AD, MS, ALS, IS, EP, and those related to disease confounders were excluded.

Two-sample MR analysis was employed, with inverse variance weighting (IVW) as the primary analytical approach, supported by weighted median, MR Egger, simple mode, and weighted mode. Further, the sensitivity analysis employed the MR-Egger method, Cochran Q test, and leave-one-out method to assess horizontal pleiotropy, heterogeneity, and robustness of the causal relationship between BMI indicators and neurological diseases.

Results and discussion

As per the study, significant genome-wide associations were found between BMI indicators and SNPs for PD (42), AD (42), MS (39), ALS (42), IS (42), and EP (31). The IVW analysis showed no genetic causality between BMI and PD, AD, ALS, and epilepsy (P > 0.05).

However, a positive genetic causality was found between BMI and MS (P = 0.035) and IS (P = 0.000). The findings suggest that a higher BMI is associated with increased risk for MS and IS.

Further, the weighted median analysis showed causal relationships between BMI and MS, IS, while the simple mode suggested a relationship with IS alone. Interestingly, MR Egger and weighted mode analyses showed no causal relationship between BMI and the studied diseases.

Results of the sensitivity analysis corroborated with the main findings. No significant heterogeneity or pleiotropy was found, and the findings were confirmed to be stable and reliable.

The findings are strengthened with the use of robust instrumental SNPs derived from the most comprehensive GWAS database so far.

However, the study is limited by its focus on patients of European ancestry, potential incomplete control of all neurological disorder risk factors, and reliance solely on BMI, without considering other body composition metrics.

Future studies involving waist circumference, waist-to-hip ratio, body fat percentage, and bioelectrical impedance could potentially reduce the bias in the results.

Conclusion

In conclusion, the study demonstrates MR analysis’s utility in exploring genetic causal links between BMI and neurological diseases.

While no causal relationship was found with PD, AD, ALS, or EP, a genetic causal association of BMI was identified with MS and IS, suggesting that an increased BMI may increase the risk of MS and IS.

These findings highlight obesity’s potential role as a risk factor in neurological disorders, paving the way for prevention and treatment strategies for improved health outcomes.

[ad_2]

Source link

March 29, 2024
AI tool predicts lethal heart rhythm with 80% accuracy

[ad_1]

In a Leicester study that looked at whether artificial intelligence (AI) can be used to predict whether a person was at risk of a lethal heart rhythm, an AI tool correctly identified the condition 80 per cent of the time.

The findings of the study, led by Dr Joseph Barker working with Professor Andre Ng, Professor of Cardiac Electrophysiology and Head of Department of Cardiovascular Sciences at the University of Leicester and Consultant Cardiologist at the University Hospitals of Leicester NHS Trust, have been published in the European Heart Journal – Digital Health.

Ventricular arrhythmia (VA) is a heart rhythm disturbance originating from the bottom chambers (ventricles) where the heart beats so fast that blood pressure drops which can rapidly lead to loss of consciousness and sudden death if not treated immediately.

NIHR Academic Clinical Fellow Dr Joseph Barker co-ordinated the multicentre study at the National Institute for Health and Care Research (NIHR) Leicester Biomedical Research Centre,  and co-developed an AI tool with Dr Xin Li, Lecturer in Biomedical Engineering, School of Engineering. The tool examined Holter electrocardiograms (ECGs) of 270 adults taken during their normal daily routine at home.  

These adults had the Holter ECGs taken as part of their NHS care between 2014 and 2022. Outcomes for these patients were known, and 159 had sadly experienced lethal ventricular arrhythmias, on average 1.6 years following the ECG.

The AI tool, VA-ResNet-50, was used to retrospectively examine ‘normal for patient’ heart rhythms to see if their heart was capable of the lethal arrythmias.

Current clinical guidelines that help us to decide which patients are most at risk of going on to experience ventricular arrhythmia, and who would most benefit from the life-saving treatment with an implantable cardioverter defibrillator are insufficiently accurate, leading to a significant number of deaths from the condition.

Ventricular arrhythmia is rare relative to the population it can affect, and in this study we collated the largest Holter ECG dataset associated with longer term VA outcomes.

We found the AI tool performed well compared with current medical guidelines, and correctly predicted which patient’s heart was capable of ventricular arrhythmia in 4 out of every 5 cases.

If the tool said a person was at risk, the risk of lethal event was three times higher than normal adults.

These findings suggest that using artificial intelligence to look at patients’ electrocardiograms while in normal cardiac rhythm offers a novel lens through which we can determine their risk, and suggest appropriate treatment; ultimately saving lives.”

Professor Andre Ng, Professor of Cardiac Electrophysiology and Head of Department of Cardiovascular Sciences at the University of Leicester

He added: “This is important work, which wouldn’t have been possible without an exceptional team in Dr Barker and Dr Xin Li, and their belief and dedication to novel methods of analysis of historically disregarded data.”

Dr Barker’s work has been recognized with a van Geest Foundation Award and Heart Rhythm Society Scholarship and more research will be carried out to develop the work further.

For the full paper, please visit  https://academic.oup.com/ehjdh/advance-article/doi/10.1093/ehjdh/ztae004/7591810

The NIHR Leicester BRC is part of the NIHR and hosted by the University Hospitals of Leicester NHS Trust in partnership with the University of Leicester, Loughborough University and the University Hospitals of Northamptonshire NHS Group.

Source:

University Hospitals of Leicester NHS Trust

Journal reference:

Barker, J., et al. (2024). Artificial intelligence for ventricular arrhythmia capability using ambulatory electrocardiograms. European Heart Journal. doi.org/10.1093/ehjdh/ztae004.

[ad_2]

Source link

March 28, 2024
Google Street View predicts heart disease risk based on neighborhood features

[ad_1]

Researchers have used Google Street View to study hundreds of elements of the built environment, including buildings, green spaces, pavements and roads, and how these elements relate to each other and influence coronary artery disease in people living in these neighborhoods.

Their findings, published in the European Heart Journal today (Thursday), show that these factors can predict 63% of the variation in the risk of coronary heart disease from one area to another.

Coronary heart disease, where a build-up of fatty substances in the coronary arteries interrupts the blood supply to the heart, is one of the most common forms of cardiovascular disease.

Researchers say that using Google Street View can help provide an overview of physical environmental risk factors in the built and natural environments that could help not only in understanding risk factors in these environments, but ultimately help towards building or adapting towns and cities to make them healthier places to live.

The study was led by Prof. Sadeer Al-Kindi and Prof. Sanjay Rajagopalan from University Hospitals Harrington Heart & Vascular Institute and Case Western Reserve University, Ohio, USA, and Dr. Zhuo Chen, a post-doctoral fellow in Prof. Rajagopalan’s laboratory.

We have always been interested in how the environment, both the built and natural environment, influences cardiovascular disease. Here in America, they say that the zip code is a better predictor of heart disease than even personal measures of health. However, to investigate how the environment influences large populations in multiple cities is no mean task. Hence, we used machine vision-based approaches to assess the links between the built environment and coronary heart disease prevalence in US cities.”

Prof. Sanjay Rajagopalan from University Hospitals Harrington Heart & Vascular Institute and Case Western Reserve University, Ohio, USA

The study included more than half a million Google Street View images of Detroit, Michigan; Kansas City, Missouri; Cleveland, Ohio; Brownsville, Texas; Fremont, California; Bellevue, Washington State; and Denver, Colorado. Researchers also collected data on rates of coronary heart disease according to ‘census tracts’. These are areas smaller than a US zip code that are home to an average of 4,000 people. The researchers used an approach called a convolutional neural network; a type of artificial intelligence that can recognize and analyze patterns in images to make predictions.

The research revealed that features of the built environment visible on Google Street View images could predict 63% of the variation in coronary heart disease between these small regions of US cities.

Prof. Al-Kindi added: “We also used an approach called attention mapping, which highlights some of the important regions in the image, to provide a semi-qualitative interpretation of some of the thousands of features that may be important in coronary heart disease. For instance, features like green space and walkable roads were associated with lower risk, while other features, such as poorly paved roads, were associated with higher risk. However, these findings need further investigation.

“We’ve shown that we can use computer vision approaches to help identify environmental factors influencing cardiovascular risk and this could play a role in guiding heart-healthy urban planning. The fact that we can do this at scale is something that is absolutely unique and important for urban planning.”

“With upcoming challenges including climate change and a shifting demographic, where close to 70% of the world’s population will live in urban environments, there is a compelling need to understand urban environments at scale, using computer vision approaches that can provide exquisite detail at an unparalleled level,” said Prof. Rajagopalan.

In an accompanying editorial, Dr. Rohan Khera from Yale University School of Medicine, USA said: “The association of residential location with outcomes often supersedes that of known biological risk factors. This is often summarised with the expression that a person’s postal code is a bigger determinant of their health than their genetic code. However, our ability to appropriately classify environmental risk factors has relied on population surveys that track wealth, pollution, and community resources.

“The study by Chen and colleagues presents a novel and more comprehensive evaluation of the built environment. This work creatively leverages Google’s panoramic street-view imagery that supplements its widely used map application.

“… an AI-enhanced approach to studying the physical environment and its association with cardiovascular health highlights that across our communities, measures of cardiovascular health are strongly encoded in merely the visual appearance of our neighborhoods. It is critical to use this information wisely, both in defining strategic priorities for identifying vulnerable communities and in redoubling efforts to improve cardiovascular health in communities that need it most.”

Source:

European Society of Cardiology

[ad_2]

Source link

March 28, 2024
Evolving brain sizes from 1930 to 1970 could signal decreased dementia risk, researchers say

[ad_1]

The development and upkeep of the human brain are influenced by both genetic factors and environmental conditions, which may subsequently impact the risk of dementia later in life. Thus, a recent study published in JAMA Neurology assessed whether there were changes in skull and brain size, as well as the thickness of the cortex, across individuals born between the years 1930 and 1970.

Study: Trends in Intracranial and Cerebral Volumes of Framingham Heart Study Participants Born 1930 to 1970. Image Credit: Gorodenkoff/Shutterstock.com

Background

The health of the American populace has improved significantly due to advancements in healthcare diagnostics and treatment strategies, resulting in an extended average lifespan. However, this increase in longevity also brings a higher likelihood of encountering Alzheimer’s disease and other forms of dementia, as well as various conditions prevalent in older age.

Fortunately, the dementia incidence is decreasing, perhaps in part because of more education and better preventive measures for cardiovascular risk. Another important contributor may be the early environment.

The Framingham Heart Study cohort (FHS) includes many generations of people, followed up over decades. The difference between the earliest and latest subjects to be enrolled in the cohort spans over 80 years.

This led the researchers in the current study to draw their cohort from this study group, examining trends in cardiovascular and brain health in successive generations.

The aim was to look for a predicted increase in brain development in the US population due to changing early life environment trends. This would reflect in larger brain volumes.

About the study

All participants were born between 1925 and 1968. None had been diagnosed with dementia or stroke, and all had undergone magnetic resonance imaging (MRI) between 1999 and 2019. The mean age at MRI varied with the decade of birth but with overlap between decades.

What were the findings?

There were over 3,200 participants, the mean age at MRI being 58 years. The images revealed that multiple brain volume measurements showed an upward trajectory with the later birth cohorts.

The investigators measured intracranial volume (ICV), hippocampal volume (HV), cortical surface area (CSA), cortical gray matter volume (CGMV), and white matter volume (WMV). Females were observed to be 5.5 inches shorter on average, with lower HV, CGMV, and WMV.

The difference in hippocampal volume was by -0.64 mL, while males had ~54 mL and 63 mL greater volumes for gray and white matter, respectively.

The 1930s birth cohort had a mean height of 66 inches vs 68 inches for the 1970s birth cohort. The average ICV increased by over 6%, at 1321 mL in the 1970s vs 1234 mL in the 1930s cohort, respectively. This was after compensating for confounding factors like age, sex, and height.

Regional measures also varied with the birth cohort, showing a definite trend. Both HV and WMV went up with the decade of birth. So did the CSA, while the cortical thickness decreased, implying cortical atrophy.

Comparing the 1930s to the 1970s cohort, the largest increase was for CSA, which increased by 15%. The WMV and HV increased by 8% and 6%, respectively, but CGMV by 2%. The cortical thickness declined by over a fifth, from 2.3 mm to 1.9 mm, respectively. There was no significant difference between the sexes.

Even after limiting the analysis to only those born in the 1940s and aged 55 to 65 years, the same trends were observed, though the differences were attenuated. For instance, the increase in WMV and CGMV were only 0.2% and 0.1%, respectively.

What are the implications?

The study results indicate that later generations are experiencing increased brain volume, both overall and regional. The difference was greatest for ICV, WMV, and HV, when the 1930s and 1940s cohorts were compared.

“We hypothesize that larger brain volumes indicate larger brain development and potentially greater “brain reserve” that could explain the declining incidence of dementia.”

The ICV reflects normal brain development and does not go down with aging or diseases affecting the volume. In fact, the adult ICV predicts cognitive levels in old age and provides a reliable and consistent biomarker for brain size.

HV loss may occur early in neurodegenerative conditions, including Alzheimer’s,

The larger cortical WMV in later cohorts might be the result of greater gyrification, leading to larger CSA. The increased WMV indicates higher neuronal connectivity while reducing the effects of brain tissue loss with aging. The increase in CSA with a reduction in cortical thickness supports this explanation.

The presence of gyri in the brain increases the brain CSA by 1,700 times compared to the brain of a shrew but limits the increase in cortical thickness to six times. Genes regulate different brain regions differently to develop gyri to various extents.

The increase in larger brain structures is due to changes in early life experiences, including better education, social environment, and health status. The better preventive measures for cardiovascular disease may be responsible as well. Thus, modifying these factors could also improve resistance to late-life dementia.

At the population level, these effects may be very important, helping to optimize brain development and building cognitive resilience over the decades.

[ad_2]

Source link

March 27, 2024
Study reveals risk factors for faster aging in the brain

[ad_1]

The researchers had previously identified a ‘weak spot’ in the brain, which is a specific network of higher-order regions that not only develop later during adolescence, but also show earlier degeneration in old age. They showed that this brain network is also particularly vulnerable to schizophrenia and Alzheimer’s disease.

In this new study, published in Nature Communications, they investigated the genetic and modifiable influences on these fragile brain regions by looking at the brain scans of 40,000 UK Biobank participants aged over 45.

The researchers examined 161 risk factors for dementia, and ranked their impact on this vulnerable brain network, over and above the natural effects of age. They classified these so-called ‘modifiable’ risk factors – as they can potentially be changed throughout life to reduce the risk of dementia – into 15 broad categories: blood pressure, cholesterol, diabetes, weight, alcohol consumption, smoking, depressive mood, inflammation, pollution, hearing, sleep, socialization, diet, physical activity, and education.

Prof. Gwenaëlle Douaud, who led this study, said: ‘We know that a constellation of brain regions degenerates earlier in aging, and in this new study we have shown that these specific parts of the brain are most vulnerable to diabetes, traffic-related air pollution – increasingly a major player in dementia – and alcohol, of all the common risk factors for dementia.’

‘We have found that several variations in the genome influence this brain network, and they are implicated in cardiovascular deaths, schizophrenia, Alzheimer’s and Parkinson’s diseases, as well as with the two antigens of a little-known blood group, the elusive XG antigen system, which was an entirely new and unexpected finding.’

In fact, two of our seven genetic findings are located in this particular region containing the genes of the XG blood group, and that region is highly atypical because it is shared by both X and Y sex chromosomes. This is really quite intriguing as we do not know much about these parts of the genome; our work shows there is benefit in exploring further this genetic terra incognita.”

Prof. Lloyd Elliott, co-author from Simon Fraser University in Canada

Importantly, as Prof. Anderson Winkler, a co-author from the National Institutes of Health and The University of Texas Rio Grande Valley in the US, points out: ‘What makes this study special is that we examined the unique contribution of each modifiable risk factor by looking at all of them together to assess the resulting degeneration of this particular brain ‘weak spot’. It is with this kind of comprehensive, holistic approach – and once we had taken into account the effects of age and sex – that three emerged as the most harmful: diabetes, air pollution, and alcohol.’

This research sheds light on some of the most critical risk factors for dementia, and provides novel information that can contribute to prevention and future strategies for targeted intervention.

Source:

Journal reference:

Manuello, J., et al. (2024). The effects of genetic and modifiable risk factors on brain regions vulnerable to ageing and disease. Nature Communications. doi.org/10.1038/s41467-024-46344-2.

[ad_2]

Source link

March 27, 2024
Environmental and social adversities double heart disease risk
[ad_1]
People who live in areas with social and environmental adversities may have up to twice the increased risk for developing heart disease and stroke, according to new research published today in the Journal of the American Heart Association, an open access, peer-reviewed journal of the American Heart Association.

In this study, environmental adversities included air and water pollution, potentially hazardous or toxic sites, few recreational parks, and high traffic roads, railways or airports. Social vulnerabilities were defined as racial and ethnic minority status; socioeconomic factors such as income, education and employment levels; housing status; and access to internet and health care.

“Our study is one of the first to examine the impact of both social and environmental factors in combination and looked at the complex interplay between them,” said the study’s senior author Sarju Ganatra, M.D., a cardiologist and vice chair in the department of medicine for research and director of the Cardio-Oncology Program and South Asian Cardio-Metabolic Program at Lahey Hospital and Medical Center in Burlington, Massachusetts.

This study used the Environmental Justice Index -; developed with data from the U.S. Census Bureau, the U.S. Environmental Protection Agency, U.S. Mine Safety and Health Administration and the U.S. Centers for Disease Control and Prevention -; to rate environmental disadvantages across all U.S. census tracts.

The analysis found:
- People living in the most environmentally vulnerable neighborhoods had 1.6 times the rate of blocked arteries and more than twice the rate of stroke compared to people living in the least environmentally vulnerable neighborhoods.
- Cardiovascular disease risk factors were higher in the most vulnerable areas with twice the rate of Type 2 diabetes, 1.8 times higher rates of chronic kidney disease, and 1.5 times higher incidence of high blood pressure and obesity.
- About 30% of all U.S. residents aged 18-44, 21% of Black adults and most Hispanic adults resided in places with alarmingly high environmental burdens.
“I was amazed to see the tight links and complex interplay between social and environmental factors on health outcomes. We were able to demonstrate their ‘dual hit’ on health outcomes. And beyond that, we were more amazed by the fact that even after adjusting for socioeconomic factors, environmental factors played a crucial and independent role in determining various heart disease and other related health outcomes,” Ganatra said.

According to researchers, reversing the impact of social and environmental disadvantages will require a multi-pronged approach with interventions to reduce pollution exposure and policies that address the causes of poverty, urban revitalization, high quality public education, job creation programs and affordable housing, along with steps to ensure universal access to quality health care.

Our aim is to empower the health care community to better inform patients about environmental factors they encounter daily. Patients, in turn, gain the ability to reduce their exposure to harmful environmental conditions, such as exposure to harmful chemicals and air pollutants to minimize health hazards and mitigate health risks.”

Sarju Ganatra, M.D., cardiologist and vice chair in the department of medicine for research and director of the Cardio-Oncology Program and South Asian Cardio-Metabolic Program at Lahey Hospital and Medical Center in Burlington, Massachusetts

A March 2024 American Heart Association Policy Statement, Adapting cities for heart-healthy, sustainable living requires integrated public policies, addresses the impact of urban provisioning systems – those that provide energy, mobility, housing, green infrastructure, water and waste management – on the cardiovascular and metabolic health of people who live in urban communities nationwide.

Study background and details:
- The study used the 2022 Environmental Justice Index, socio-environmental justice index, and an environmental burden module to rank census tracts from least vulnerable to most vulnerable to determine the cumulative impact of environmental injustice for more than 71,000 census tracts in the U.S.
- Age-adjusted rate ratios of blocked arteries, strokes and various health measures reported in the Prevention Population-Level Analysis and Community Estimates (PLACES) data were compared between the index and module rankings. The population estimates were taken from the 2015-2019 American community survey.
Study limitations include that it cannot prove cause and effect due to the nature of the database. However, the study’s findings show strong associations.

Source:

American Heart Association

Journal reference:

Khadke, S., et al. (2024) Association of Environmental Injustice and Cardiovascular Diseases and Risk Factors in the United States. Journal of the American Heart Association. doi.org/10.1161/JAHA.123.033428.
[ad_2]

Source link
March 27, 2024
Multiomic approach boosts disease prediction accuracy beyond traditional methods

[ad_1]

In a recent study published in the journal Nature Aging, researchers assessed the added predictive value of integrating polygenic risk scores (PRSs) and gut microbiome scores with conventional risk factors for common diseases in a long-term cohort study.

Analysis: Integration of polygenic and gut metagenomic risk prediction for common diseases. Image Credit: remotevfx.com / Shutterstock

Background

Multiomic technologies are transforming disease prediction by integrating genomic and microbiomic data, offering new insights into age-related conditions like heart disease, diabetes, and cancer. Previously, risk assessments relied mainly on demographic, lifestyle, and clinical metrics. Now, the integration of PRSs and gut microbiome analysis into risk models promises to improve predictive accuracy beyond traditional factors. PRSs provide a cost-effective genetic predisposition metric, while the gut microbiome adds a novel dimension to understanding disease risk. This emerging approach necessitates further research to refine its accuracy and ensure its effectiveness across various populations and healthcare systems.

About the study

The FINRISK 2002 cohort, part of a series of Finnish surveys aimed at exploring chronic disease risk factors since 1972, served as the foundation for this study, focusing on the interplay between gut microbiota and health outcomes. Spanning six Finnish regions, this cohort engaged 8,783 participants from a pool of 13,498 invitees, including a diverse demographic aged 25–74. Under stringent ethical guidelines, these participants underwent comprehensive health examinations and contributed biological samples, including blood and stool.

This research, grounded in detailed baseline data collection, aimed to explore the predictive power of genetic and microbiomic factors alongside traditional risk indicators for diseases like coronary artery disease (CAD), type 2 diabetes (T2D), Alzheimer’s disease (AD), and prostate cancer. Through careful sample handling and state-of-the-art genomic and metagenomic analyses, the study capitalized on advanced multiomic technologies to build predictive models. These models were refined through rigorous statistical methods, evaluating their predictive performance against conventional risk assessment tools.

Study results

In the FINRISK 2002 cohort, a longitudinal study spanning over 17.8 years and including electronic health records (EHRs), 579 of T2D, 333 cases of CAD, 273 of AD, and 141 of prostate cancer were identified among participants with both imputed genotypes and gut metagenomic sequencing. The baseline clinical risk factors exhibited significant differences between incident cases and non-cases for CAD, T2D, and AD, with certain factors like smoking for T2D and sex, diastolic blood pressure (DBP), and High-Density Lipoprotein (HDL) for AD not differing significantly. Prostate cancer cases differed significantly from non-cases in terms of baseline age and smoking habits.

PRSs and conventional risk factors were assessed for their predictive performance in incident diseases through Cox regression models. The analysis revealed that PRSs, when assessed individually or in combination with conventional risk factors, significantly correlated with incident diseases, enhancing the predictive performance beyond baseline clinical risk factors alone. Notably, for diseases like CAD, T2D, and prostate cancer, PRSs offered a distinct advantage over traditional family history indicators, emphasizing their potential to complement existing risk assessment models.

Subanalyses exploring additional risk factors, such as glucose levels determined through nuclear magnetic resonance (NMR) for T2D, consistently supported the PRSs’ predictive value. The gut microbiome also emerged as a significant factor, with its composition at baseline correlating with incident diseases. The study delved into the gut microbiome’s diversity and its association with disease incidence, finding specific patterns that could potentially enhance disease prediction models.

The research underscored the potential of integrating polygenic, metagenomic, and conventional factors into a cohesive model for predicting incident diseases. Such a model, which combines PRSs and gut microbiome scores with conventional risk factors, showed a marked improvement in predictive accuracy for CAD, T2D, AD, and prostate cancer. This integrative approach illustrates the promise of multiomic data in refining disease prediction and tailoring preventive measures more effectively.

Subgroup analyses reaffirmed the significant associations between PRSs, gut microbiome scores, and disease incidence, highlighting these factors’ contributions across different conditions.

Conclusions

To summarize, this study contrasts the predictive power of well-established PRSs, baseline gut microbiome, and traditional risk factors across a median follow-up of 17.8 years. Findings reveal that while age stands as the most influential individual risk factor for CAD, AD, and prostate cancer, the inclusion of PRSs and gut microbiome scores notably enhances predictive accuracy. PRSs alone significantly correlate with higher disease incidence, underscoring their potential to augment conventional risk assessments. Furthermore, the study suggests that PRSs can refine predictions for CAD, T2D, and prostate cancer, even beyond family history’s established risk implications. Although the gut microbiome’s predictive contribution appears modest, it shows promise in enhancing disease forecasts when combined with conventional factors. The analysis points to a subtle role of the gut microbiome across different conditions, suggesting that its predictive value may vary due to the complex interplay between host aging and microbial changes.

[ad_2]

Source link

March 27, 2024