Study finds bright nights raise risk for stroke and heart failure in adults over 40

 In your hospital, has your incompetent? doctor ensured night lighting is low enough?

 This just proves everyone's incompetence if nothing was done with any of this research!

Light exposure linked to mortality risk November 2024

Do you prefer your doctor, hospital and board of director's incompetence NOT KNOWING? OR NOT DOING?

Study finds bright nights raise risk for stroke and heart failure in adults over 40

Researchers have discovered that people exposed to brighter light at night face up to 50% higher risks of heart disease, while daytime light may protect the heart by reinforcing healthy circadian rhythms.

Study: Light Exposure at Night and Cardiovascular Disease Incidence. Image Credit: Krakenimages.com / Shutterstock

In a recent study published in JAMA Network Open, researchers explored whether being exposed to light at nighttime is associated with a higher risk of developing heart disease, particularly for those of a specific age, sex, or genetic makeup.

Their findings indicate that people over 40 exposed to bright lights at night face higher risks of heart disease, including stroke and heart failure. Associations were larger in females for heart failure and coronary artery disease, and in younger participants for heart failure and atrial fibrillation, with no clear modification for myocardial infarction or stroke.

Background

Healthy cardiovascular function relies on well-regulated circadian rhythms, which in turn influence vascular function, glucose tolerance, hormone levels, blood pressure, and heart rate. Disruption of these rhythms, through exposure to light or irregular sleep patterns, can elevate blood pressure and heart rate, increase inflammation, and reduce heart rate variability.

Animal studies show that prolonged circadian disruption can cause structural heart changes, such as hypertrophy and fibrosis. It worsens heart failure. Epidemiological evidence also links shift work, which disturbs these rhythms, to greater cardiovascular mortality, coronary heart disease, and heart failure.

Light exposure at night is a key source of circadian disruption and has been linked to higher rates of coronary artery disease and stroke, as well as conditions like obesity, diabetes, and hypertension, which are known cardiovascular risk factors. However, previous studies often relied on satellite-based measures of outdoor lighting or on small cohorts rather than on direct personal light-exposure data.

Using wrist-worn light sensors from about 89,000 UK Biobank participants, earlier research found that brighter nights were associated with higher cardiometabolic mortality and type 2 diabetes. Building on this, the present study examined whether individual day and night light exposures predict incident cardiovascular diseases over 9.5 years of follow-up.

About the Study

This large-scale cohort study used data from UK Biobank participants who wore wrist-worn light sensors for one week between 2013 and 2016. Participants’ light exposure was recorded continuously, processed to remove invalid data, and averaged into 24-hour profiles.

Factor analysis identified two main exposure periods: daytime (7:30 AM–8:30 PM) and nighttime (12:30 AM–6:00 AM). Participants were categorized into light-exposure percentiles, with the 0–50th percentile representing the darkest nights.

Cardiovascular outcomes, including stroke, atrial fibrillation, heart failure, myocardial infarction, and coronary artery disease, were identified using hospital, primary-care, and death-registry records. Individuals with pre-existing cardiovascular disease (CVD) were excluded.

Cox proportional-hazards models assessed the relationship between light exposure and disease risk, adjusting sequentially for demographic factors (ethnicity, age, and sex), socioeconomic variables (deprivation, education, and income), and lifestyle factors (urbanicity, diet, alcohol, smoking, and physical activity). Additional models were tested for potential interactions with genetic risk scores, age, and sex.

Key Findings

Researchers analyzed data from 88,905 UK Biobank participants, with an average age of 62.4 years and 57% female, over an average follow-up of 7.9 years. Participants were free of cardiovascular disease at baseline.

Nighttime light exposure showed a clear, dose-dependent association with a higher risk of heart disease, while daytime light exposure was linked to lower risks in minimally and socioeconomically adjusted models, but these associations were not significant after full lifestyle adjustment. When physical activity was excluded from the full model, inverse associations re-emerged for heart failure and stroke.

Compared with those in the darkest-night environment, participants with the brightest night exposure had significantly greater risks of coronary artery disease, myocardial infarction, heart failure, atrial fibrillation, and stroke after adjusting for lifestyle, demographic, and socioeconomic factors.

In contrast, an increase in night-light exposure by one standard deviation raised the risk of all five cardiovascular outcomes by about 5–8%. The associations were consistent across models and remained robust after adjustments. Sex and age showed selective modifying effects, with larger associations in females for heart failure and coronary artery disease, and in younger individuals for heart failure and atrial fibrillation, with no clear modification for myocardial infarction or stroke. Associations also remained after accounting for polygenic risk, suggesting gene–environment correlation is unlikely to explain the results.

Conclusions

This large prospective study demonstrates strong associations of higher nighttime light exposure with elevated cardiovascular risk, though causality cannot be inferred. The mechanisms underlying this association could include circadian disruption and sleep disturbance, leading to vascular and metabolic stress. Reduced melatonin secretion was not directly examined in this study.

In contrast, greater daytime light exposure may support cardiovascular health by reinforcing circadian rhythms.

Key strengths of this analysis include a large sample size, objective light measurements, and a long follow-up period. However, limitations include potential residual confounding, limited ethnic diversity (primarily White participants), lack of information on light sources, and the inability to infer causality. Sleep duration and efficiency were objectively measured and included in sensitivity analyses; short sleep partially attenuated some associations. Source information was unavailable, limiting the ability to adjust for behaviors correlated with light exposure.

Overall, these findings highlight artificial nighttime lighting as a potentially modifiable environmental risk factor for cardiovascular disease, underscoring the importance of maintaining dark nights and adequate daylight exposure in urban health strategies.

Journal reference:

• Windred, D.P., Burns, A.C., Rutter, M.K., Lane, J.M., Saxena, R., Scheer, F.A.J.L., Cain, S.W., Phillips, A.J.K. (2025). Light exposure at night and cardiovascular disease incidence. JAMA Network Open 8(10): e2539031. DOI: 10.1001/jamanetworkopen.2025.39031. https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2840489

AI Reveals Key Predictors of Lifelong Brain Health

 Why BMI, hasn't it been replaced by the waist-to-height ratio (WHtR) is: WHtR = Waist Circumference / Height?  My blood pressure is controlled by drugs. I don't see how blood pressure and BMI have any causation to brain health, correlation maybe, but scientists know not to depend on correlation.

 My BMI is 28.8 I think I'm pretty good. That problem is directly the result of my doctor COMPLETELY FAILING AT HAVING 100% RECOVERY PROTOCOLS!

 The formula for calculating the waist-to-height ratio (WHtR) is: WHtR = Waist Circumference / Height.

AI Reveals Key Predictors of Lifelong Brain Health

Summary: A new study used machine learning to pinpoint the lifestyle and health factors most strongly associated with cognitive performance across the lifespan. Among 374 adults aged 19 to 82, age, blood pressure, and BMI were the top predictors of success on a focus-and-speed-based attention test.

While diet and exercise played a smaller role, they were still associated with better outcomes, particularly in offsetting high BMI or blood pressure. This data-driven approach highlights how combining multiple factors provides a clearer picture of what supports brain health with age.

Key Facts:

• Top Predictors: Age, diastolic blood pressure, and BMI most strongly influenced cognitive performance.
• Diet + Exercise: Healthy eating and physical activity contributed modestly but positively to focus and reaction speed.
• Machine Learning Advantage: Advanced algorithms revealed nuanced relationships traditional statistics may miss.

Source: University of Illinois

A new study offers insight into the health and lifestyle indicators — including diet, physical activity and weight — that align most closely with healthy brain function across the lifespan.

The study used machine learning to determine which variables best predicted a person’s ability to quickly complete a task without becoming distracted.

They found that age was the most influential predictor of performance on the test, followed by diastolic blood pressure, BMI and systolic blood pressure. Credit: Neuroscience News

Reported in The Journal of Nutrition, the study found that age, blood pressure and body mass index were the strongest predictors of success on a test called the flanker task, which requires participants to focus on a central object without becoming distracted by flanking information.

Diet and exercise also played a smaller but relevant role in performance on the test, the team found, sometimes appearing to offset the ill effects of a high BMI or other potentially detrimental factors.

“This study used machine learning to evaluate a host of variables at once to help identify those that align most closely with cognitive performance,” said Naiman Khan, a professor of health and kinesiology at the University of Illinois Urbana-Champaign who led the work with kinesiology Ph.D. student Shreya Verma.

“Standard statistical approaches cannot embrace this level of complexity all at once.”

To build the model, the team used data collected from 374 adults 19 to 82 years of age. The data included participant demographics, such as age, BMI, blood pressure and physical activity levels, along with dietary patterns and performance on a flanker test that measured their processing speed and accuracy in determining the orientation of a central arrow flanked by other arrows that pointed in the same or opposite direction.

“This is a well-established measure of cognitive function that assesses attention and inhibitory control,” Khan said.

Previous studies have found that several factors are implicated in the preservation of cognitive function across the lifespan, Khan said.

“Adherence to the healthy eating index, a measure of diet quality, has been linked to superior executive function and processing speed in older adults,” he said. “Other studies have found that diets that are rich in antioxidants, omega-3 fatty acids and vitamins are associated with better cognitive function.”

The Dietary Approaches to Stop Hypertension, or DASH diet, the Mediterranean diet, and a diet that combines the two, called the MIND diet, all “have been linked to protective effects against cognitive decline and dementia,” the researchers wrote. Physical factors, such as BMI and blood pressure, along with increased physical activity also are strong predictors of cognitive health, or decline, in aging.

“Clearly, cognitive health is driven by a host of factors, but which ones are most important?” Verma said. “We wanted to evaluate the relative strength of each of these factors in combination with all the others.”

Machine learning “offers a promising avenue for analyzing large datasets with multiple variables and identifying patterns that may not be apparent through conventional statistical approaches,” the researchers wrote.

The team tested various machine learning algorithms to see which one best weighed the various factors to predict the speed of accurate responses in the flanker test. The researchers tested the predictive ability of each algorithm, using a variety of approaches to validate those that appeared to perform the best.

They found that age was the most influential predictor of performance on the test, followed by diastolic blood pressure, BMI and systolic blood pressure. Adherence to the healthy eating index was less predictive of cognitive performance than blood pressure or BMI but also correlated with better performance on the test.

“Physical activity emerged as a moderate predictor of reaction time, with results suggesting it may interact with other lifestyle factors, such as diet and body weight, to influence cognitive performance,” Khan said.

“This study reveals how machine learning can bring precision and nuance to the field of nutritional neuroscience,” he said.

“By moving beyond traditional approaches, machine learning could help tailor strategies for aging populations, individuals with metabolic risks or those seeking to enhance cognitive function through lifestyle changes.”

The Personalized Nutrition Initiative and National Center for Supercomputing Applications at the U. of I. supported this research.

Khan is a dietitian and an affiliate faculty member of the Division of Nutritional Sciences, the Neuroscience Program and the Beckman Institute for Advanced Science and Technology at Illinois.

About this AI and brain health research news

Author: Diana Yates
Source: University of Illinois
Contact: Diana Yates – University of Illinois
Image: The image is credited to Neuroscience News

Original Research: Open access.
“Predicting cognitive outcome through nutrition and health markers using supervised machine learning” by Naiman Khan et al. Journal of Nutrition

Waist-to-height ratio emerges as strong predictor of heart failure risk

Mine is 38/73 inches = 0.52054 My BMI is 28.2 I think I'm pretty good. The problem is directly the result of my doctor COMPLETELY FAILING AT HAVING 100% RECOVERY PROTOCOLS!

 The formula for calculating the waist-to-height ratio (WHtR) is: WHtR = Waist Circumference / Height. You should use the same unit of measurement for both waist circumference and height (e.g., inches or centimeters)

Waist-to-height ratio emerges as strong predictor of heart failure risk

European Society of CardiologyMay 18 2025

Waist-to-height ratio predicts heart failure incidence, according to research presented today at Heart Failure 2025, a scientific congress of the European Society of Cardiology (ESC). 

Obesity affects a substantial proportion of patients with heart failure (HF) and it has been reported that the risk of HF increases as body mass index (BMI) increases. Study presenter, Dr. Amra Jujic from Lund University, Malmö, Sweden, explained why the current analysis was carried out: "BMI is the most common measure of obesity, but it is influenced by factors such as sex and ethnicity, and does not take into account the distribution of body fat. Waist-to-height ratio (WtHR) is considered a more robust measure of central adiposity, the harmful deposition of fat around visceral organs. In addition, whereas BMI is associated with paradoxically good HF outcomes with high BMI, this is not seen with WtHR. We conducted this analysis to investigate the relationship between WtHR and the development of HF." 

The study population consisted of 1,792 participants from the Malmö Preventive Project. Participants were aged 45–73 years at baseline and were selected so that approximately one-third had normal blood glucose levels, one-third had impaired fasting glucose and one-third had diabetes. All participants were followed prospectively for incident HF. 

The study population had a mean age of 67 years and 29% were women. The median WtHR was 0.57 (interquartile range, 0.52–0.61). 

During the median follow-up of 12.6 years, 132 HF events occurred. Higher WtHR was associated with a significantly increased risk of incident HF (hazard ratio [HR] per one standard deviation increase 1.34; 95% confidence interval [CI] 1.12–1.61; p=0.001), independent of confounders. When WtHR was categorised into quartiles, individuals with the highest values of WtHR (median of 0.65) had a significantly higher risk of HF compared with individuals in the other three quartiles (HR 2.71; 95% CI 1.64–4.48; p<0.001). 

The median WtHR in our analysis was considerably higher than 0.5, the cut-off for increased cardiometabolic risk. Having a waist measurement that is less than half your height is ideal."

Dr. John Molvin, Study Co-Author, Lund University and Malmö University Hospital, Sweden

He concluded: "We found that WtHR was a significant predictor of incident HF and our results suggest that WtHR may be a better metric than BMI to identify patients with HF who could benefit from therapies for obesity. Our next step is to investigate whether WtHR predicts incident HF and also other cardiometabolic disorders in a larger cohort." 

Source:

European Society of Cardiology