Tag Archives: #BMI

Study Finds an Association Between Increasing Body Mass Index (BMI) And The Risk of Testing Positive SARS-CoV-2 (Medicine)

New research presented at this year’s European Congress on Obesity (held online, 10-13 May) reveals an association between increasing body mass index (BMI) and the risk of testing positive SARS-CoV-2, the virus which causes COVID-19. The study is by Dr Hadar Milloh-Raz, The Chaim Sheba Medical Center, Tel-HaShomer, Ramat-Gan, Israel, and colleagues.

Obesity-related factors including changes to the innate and adaptive immune systems brought on by excess weight, are believed to be associated with an increased risk of contracting various viral diseases. This association between BMI and viral infection risk suggests that a similar relationship may also exist between an individual’s BMI and their risk of contracting SARS-CoV-2.

This study aimed to assess the relationship between BMI and likelihood testing positive in patients who were tested for SARS-CoV-2 at the largest medical centre in the Middle East. The team analysed the details of patients who had been tested for the virus during a 9-month period, collecting data on BMI, age, sex, and presence of comorbidities including congestive heart failure (CHF), diabetes mellitus (DM), hypertension (HTN), ischemic heart disease (IHD), stroke (CVA), and chronic kidney disease (CKD). The study did not look at COVID-19 mortality or outcomes, only the risk of testing positive.

At the start of the pandemic, the Chaim Sheba Medical Center introduced a policy in which all hospitalised patients were tested for COVID-19, regardless of their symptoms or reason for admission (whether they were suspected to have COVID-19, or for completely different reasons such as elective surgery, traffic accidents). In total 26,030 patients were tested across the study period (between March 16 and December 31, 2020), and 1,178 positive COVID-19 results were recorded. The numbers of patients in each BMI category and the proportion of positive tests varied as shown in the table:

The authors found that the odds of testing positive for SARS-CoV-2 were significantly higher in patients who were overweight or obese compared to those with a normal BMI. Those patients classed as overweight (BMI 25.0-29.9 kg/m2) were 22% more likely to test positive than those of normal weight (BMI 18.5-24.9 kg/m2).

The likelihood of testing positive was even higher in patients with obesity relative to their normal weight counterparts, and those odds rose with increasing BMI. Class I obesity (BMI 30.0-34.9 kg/m2) was linked to a 27% higher risk of testing positive, which increased to 38% for class II obesity (BMI 35.0-39.9 kg/m2), and an 86% higher risk in class III or morbid obesity (BMI ≥ 40.0 kg/m2).

The relationship between BMI and the probability of a patient testing positive remained significant even after adjusting for the age and sex of the patient and having accounted for any comorbidities that were present. The authors found that every 1 kg/m2 rise in a patient’s BMI was associated with an increase of around 2% in the risk of testing positive for SARS-CoV-2.

The study also found both positive and negative associations between the risk of testing positive and the presence of comorbidities linked to obesity. Diabetes was associated with a 30% higher likelihood of testing positive, while the risk of testing positive was almost 6 times greater in patients with hypertension. Conversely, the authors found that the odds of a positive test were 39%, 55%, and 45% lower among patients with a history of stroke, IHD, and CKD, respectively. The authors cannot provide an explanation for why patients with stroke, IHD or CKD would have a lower risk of testing positive for SARS-CoV-2.

The authors conclude: “As BMI rises above normal, the likelihood of a positive SARS-CoV-2 test result increases, even when adjusted for a number of patient variables. Furthermore, some of the comorbidities associated with obesity appear to either be associated with an increased risk of infection or to be protective.”

For full abstract, click here

For full poster, click here

Provided by EASO

Patient Education Program With Mental Health Component Reduces Cardiovascular Disease Risks (Medicine)

People who participated in a health education program that included both mental health and physical health information significantly reduced their risks of cardiovascular disease and other chronic diseases by the end of the 12-month intervention – and sustained most of those improvements six months later, researchers found.

People who participated in the integrated mental and physical health program maintained significant improvements on seven of nine health measures six months after the program’s conclusion. These included, on average, a 21% decrease in fasting blood sugar, a 17% decrease in low-density lipoprotein cholesterol and a 12% decrease in their body mass index.

However, patients in the group that focused only on physical health information maintained their improvements on just two risk factors – BMI and systolic blood pressure.

Data collected at the conclusion of the 12-month intervention indicated that patients in the program with the mental health component improved on eight of nine health measures, while their peers in the traditional program improved on just three.

“The gains achieved by patients in the integrated program were greater than those of their counterparts in the other group and had greater lasting effects,” said University of Illinois Urbana-Champaign social work professor Tara M. Powell, the first author of a study on the project, published in the journal Preventive Medicine Reports.

Social work professor Tara Powell found that people at risk of cardiovascular disease achieved significant improvements in their weight, blood pressure and other metrics that lasted six months after completing an informational program that included both physical health and mental health information. Photo by L. Brian Stauffer

Study participants were 213 Syrian refugees and 382 Jordanians who were patients of three health clinics in Irbid, Jordan, a border community that has experienced a large influx of people fleeing the civil war in Syria.

Powell conducted the research in partnership with the health-focused relief and development nonprofit organization Americares and the Royal Health Awareness Society, Jordan.

Powell’s group explored the efficacy of a health education intervention called the Healthy Community Clinic, delivered in clinics throughout Jordan to improve patients’ management of chronic conditions such as cardiovascular disease and diabetes, and reduce their risks of complications. Trained health educators or nurses led 20 interactive educational sessions that patients attended twice a month for one year.

Patients’ outcomes in the traditional HCC program were compared with those of peers who received routine health care only and with a group who participated in an expanded HCC program that integrated four additional sessions focused on mental health.

The mental health sessions included discussions of topics such as grief and physical and emotional traumatic stress reactions. Participants also learned tangible coping skills for reducing emotional distress such as deep-breathing exercises and walking.

“This study is among the first to illustrate how an integrated physical and mental health educational intervention can improve health outcomes and ultimately help reduce cardiovascular disease risk in refugees and low-income populations,” said co-principal investigator Dr. Shang-Ju Li, Americares’ senior director of monitoring and evaluation. “We are thrilled to share this groundbreaking research and look forward to making even more progress as we continue to look for ways to improve health outcomes for people affected by poverty or disaster.”

Additional co-authors of the study were Michelle Thompson, an associate director of emergency response, Americares; sociology graduate student Yuan Hsiao of the University of Washington; Aseel Farraj, a program manager of the Royal Health Awareness Society; Mariam Abdoh, a senior population and health advisor/project management specialist, USAID; and Dr. Rami Farraj, of the King Hussein Medical Center.

Based upon the findings of this research, the Royal Health Awareness Society has since deployed the HCC with the mental health component to public health centers across Jordan, Powell said.

In a prior study with the same participants that examined the impact of social support on mental and physical health, Powell and her colleagues found that more than half of the participants had experienced at least one traumatic event. Among Syrians, the most frequently reported traumatic experience was living in a war zone (73%), while among Jordanians it was witnessing a violent death (18%).

That study, published in PLOS ONE, was co-written by Li, Hsiao and U. of I. graduate student Oe Jin Shin.

“Because mental health conditions such as depression and anxiety often co-occur with chronic physical problems and with poverty, patient education programs that integrate mental and physical health information are critical for countries such as Jordan,” Powell said. “Making these integrated programs widely available can reduce the burden of noncommunicable diseases on marginalized populations and increase their access to care.”

Featured image: A mother and daughter participated in the patient education program at a clinic in Irbid, Jordan. Photo by Kathy Kukula, Americares

Reference: (1) The paper “An integrated physical and mental health awareness education intervention to reduce non-communicable diseases among Syrian refugees  and Jordanians in host communities: A natural experiment study” is available online DOI: 10.1016/j.pmedr.2021.101310  (2) The paper “Post-traumatic stress, social and physical health: A mediation and moderation analysis of Syrian refugees and Jordanians in a border community” is available online DOI: 10.1371/journal.pone.0241036

Provided by Illinois News Bureau

Computer Can Determine Whether You’ll Die From COVID (Medicine)

Using patient data, artificial intelligence can make a 90 percent accurate assessment of whether a person will die from COVID-19 or not, according to new research at the University of Copenhagen. Body mass index (BMI), gender and high blood pressure are among the most heavily weighted factors. The research can be used to predict the number of patients in hospitals, who will need a respirator and determine who ought to be first in line for a vaccination.

Artificial intelligence is able to predict who is most likely to die from the coronavirus. In doing so, it can also help decide who should be at the front of the line for the precious vaccines now being administered across Denmark. The result is from a newly published study by researchers at the University of Copenhagen’s Department of Computer Science. Since the COVID pandemic’s first wave, researchers have been working to develop computer models that can predict, based on disease history and health data, how badly people will be affected by COVID-19.  

Based on patient data from the Capital Region of Denmark and Region Zealand, the results of the study demonstrate that artificial intelligence can, with up to 90 percent certainty, determine whether an uninfected person who is not yet infected will die of COVID-19 or not if they are unfortunate enough to become infected. Once admitted to the hospital with COVID-19, the computer can predict with 80 percent accuracy whether the person will need a respirator.

“We began working on the models to assist hospitals, as during the first wave, they feared that they did not have enough respirators for intensive care patients. Our new findings could also be used to carefully identify who needs a vaccine,” explains Professor Mads Nielsen of the University of Copenhagen’s Department of Computer Science.

Older men with high blood pressure are highest at risk

The researchers fed a computer program with health data from 3,944 Danish COVID-19 patients. This trained the computer to recognize patterns and correlations in both patients’ prior illnesses and in their bouts against COVID-19.

“Our results demonstrate, unsurprisingly, that age and BMI are the most decisive parameters for how severely a person will be affected by COVID-19. But the likelihood of dying or ending up on a respirator is also heightened if you are male, have high blood pressure or a neurological disease,” explains Mads Nielsen.

The diseases and health factors that, according to the study, have the most influence on whether a patient ends up on a respirator after being infected with COVID-19 are in order of priority: BMI, age, high blood pressure, being male, neurological diseases, COPD, asthma, diabetes and heart disease.

“For those affected by one or more of these parameters, we have found that it may make sense to move them up in the vaccine queue, to avoid any risk of them becoming inflected and eventually ending up on a respirator,” says Nielsen. 

Predicting respiratory needs is a must

Researchers are currently working with the Capital Region of Denmark to take advantage of this fresh batch of results in practice. They hope that artificial intelligence will soon be able to help the country’s hospitals by continuously predicting the need for respirators.

“We are working towards a goal that we should be able to predict the need for respirators five days ahead by giving the computer access to health data on all COVID positives in the region,” says Mads Nielsen, adding:

“The computer will never be able to replace a doctor’s assessment, but it can help doctors and hospitals see many COVID-19 infected patients at once and set ongoing priorities.”

However, technical work is still pending to make health data from the region available for the computer and thereafter to calculate the risk to the infected patients. The research was carried out in collaboration with Rigshospitalet and Bispebjerg and Frederiksberg Hospital.


  • Data is processed on Computerome, a secure supercomputer for personal data, and under the permission of the Danish Patient Safety Authority, data owners and other relevant authorities.
  • Artificial intelligence predicts with 90 percent accuracy whether an infected patient will die of COVID-19.
  • Once a person is hospitalized with COVID-19, artificial intelligence can predict whether the person should be on a respirator with 80 percent accuracy.
  • BMI, age, high blood pressure, being male, neurological diseases, COPD, asthma, diabetes and heart disease are factors that artificial intelligence weigh`s most to with the risk of getting into the respirator.
  • The computer models are based on health data from 3,944 COVID-19 patients from the Capital Region and Region Zealand.
  • The article is published in the scientific journal Scientific Reports.
  • The study is supported by the Novo Nordisk Foundation and the Innovation Fund.

Featured image: The researchers fed a computer program with health data from 3,944 Danish COVID-19 patients. This trained the computer to recognize patterns and correlations in both patients’ prior illnesses and in their bouts against COVID-19. Photo: Getty

Reference: Jimenez-Solem, E., Petersen, T.S., Hansen, C. et al. Developing and validating COVID-19 adverse outcome risk prediction models from a bi-national European cohort of 5594 patients. Sci Rep 11, 3246 (2021). https://doi.org/10.1038/s41598-021-81844-x

Provided by University of Copenhagen

Artificial Intelligence Predicts Gestational Diabetes in Chinese women (Medicine)

Machine learning, a form of artificial intelligence, can predict which women are at high risk of developing gestational diabetes and lead to earlier intervention, according to a new study published in the Endocrine Society’s Journal of Clinical Endocrinology & Metabolism.

Gestational diabetes is a common complication during pregnancy that affects up to 15 percent of pregnant women. High blood sugar in the mother can be dangerous for the baby and lead to complications like stillbirth and premature delivery. Most women are diagnosed with gestational diabetes during the second trimester, but some women are at high risk and could benefit from earlier intervention.

“Our study leveraged artificial intelligence to predict gestational diabetes in the first trimester using electronic health record data from a Chinese hospital,” said study author He-Feng Huang Ph.D. of the Shanghai Jiao Tong University School of Medicine and the International Peace Maternity and Child Health Hospital in Shanghai, China. “These findings can help clinicians identify women at high risk of diabetes in early pregnancy and start interventions such as diet changes sooner. The artificial intelligence technology will continue to improve over time and help us better understand the risk factors for gestational diabetes.”

The researchers analyzed nearly 17,000 electronic health records from a hospital in China in 2017 with machine learning models to predict women at high risk for gestational diabetes. They compared their predictions with 2018 electronic health record data and found they were successful at identifying who would develop gestational diabetes. The prediction models also found an association between low body mass and gestational diabetes.

Other authors of the study include: Yan-Ting Wu, Chen-Jie Zhang, Cheng Li, Yu Wang, Jian-Xia Fan, and Lei Chen of the Shanghai Jiao Tong University School of Medicine and the International Peace Maternity and Child Health Hospital; Ben Willem Mol and Andrew Kawai of Monash University in Melbourne, Australia; Jian-Zhong Sheng of the Zhejiang University in Zhejiang, China; and Yi Shi of the Shanghai Jiao Tong University.

The manuscript received funding from the National Key Research and Development Program of China, the National Natural Science Foundation of China, the Foundation of Shanghai Municipal Commission of Health and Family Planning, the Clinical Skills Improvement Foundation of Shanghai Jiaotong University School of Medicine, the Natural Science Foundation of Shanghai, the Shanghai Shenkang Hospital Development Center, Clinical Technology Innovation Project, the Program of Shanghai Academic Research Leader, the CAMS Innovation Fund for Medical Sciences, and the Outstanding Youth Medical Talents of Shanghai Rising Stars of Medical Talent Youth Development Program.

The manuscript, “Early Prediction of Gestational Diabetes Mellitus in the Chinese Population Via Advanced Machine Learning,” was published online, ahead of print.

Reference: Yan-Ting Wu, PhD, Chen-Jie Zhang, MD, Ben Willem Mol, PhD, Andrew Kawai, Cheng Li, PhD, Lei Chen, Yu Wang, MD, Jian-Zhong Sheng, PhD, Jian-Xia Fan, PhD, Yi Shi, PhD, He-Feng Huang, PhD, Early prediction of gestational diabetes mellitus in the Chinese population via advanced machine learning, The Journal of Clinical Endocrinology & Metabolism, , dgaa899, https://doi.org/10.1210/clinem/dgaa899

Provided by Endocrine Society

Anorexia Nervosa Treatment: Patients Tolerate Rapid Weight Gain With Meal-Based Behavioral Support (Medicine)

A new study by Johns Hopkins Medicine researchers of adults hospitalized for the eating disorder anorexia nervosa has strengthened the case for promoting rapid weight gain as part of overall efforts for a comprehensive treatment plan. The study findings, after analyzing data regarding 149 adult inpatients with anorexia nervosa in the Johns Hopkins Eating Disorders Program, stand in contrast to long held beliefs that patients would not tolerate a faster weight gain plan because it would be too traumatic.

Credit: Getty Images

In a report on the work published online Oct. 7 in the International Journal of Eating Disorders, researchers say a majority of patients not only tolerated the regimen, they also met their weight gain goals in weeks rather than months, they would recommend the program to others and they would be willing to repeat it, if needed.

A form of self-starvation, anorexia nervosa is a serious psychiatric disorder in which people feel fat or fear gaining weight despite being very underweight. Over time, people with anorexia experience physical, psychological and social complications with a high risk of long-term consequences that can include heart, kidney and liver damage, bone loss, depression and self-harm. Anorexia has one of the highest mortality rates of any psychiatric condition.

The investigators say their findings also suggest that inpatient eating disorder programs that focus on rapid weight gain can minimize a patient’s time away from home, work and family, help curb treatment costs by reducing lengths of stay in a hospital or residential treatment program and be rated helpful by most patients.

“Treating anorexia is expensive due to the high cost of inpatient and residential treatment, and the cost of health care is important to both patients and health systems,” says Angela Guarda, M.D., director of the Eating Disorders Program at The Johns Hopkins Hospital. Guarda is also the Stephen and Jean Robinson Associate Professor of Psychiatry and Behavioral Sciences at the Johns Hopkins University School of Medicine. “Our findings suggest that a meal-based nutritional approach that emphasizes faster weight gain coupled with different types of behavioral therapy and meal support is well tolerated and achieves weight restoration in a majority of patients.”

Earlier work by Guarda and others countered the belief that patients with anorexia need to gain weight slowly to avoid a potentially life threatening condition called re-feeding syndrome, which is a metabolic imbalance that can occur when severely malnourished people take in too much food or drink. Despite these safety studies, clinicians are still reluctant to implement rapid re-feeding strategies, combined with behavioral treatment approaches, because they fear that patients won’t endure them. With the new study, Guarda and her team sought the patients’ perception of the Johns Hopkins rapid re-feeding program.

For the study, researchers analyzed information gathered on 134 women and 15 men, averaging 35 years of age, who were treated and discharged from the integrated inpatient-partial hospitalization eating disorders program at The Johns Hopkins Hospital between February 2014 and June 2017. They were underweight when admitted to the program and placed on a regimen emphasizing faster weight gain, balanced meals and behavioral therapies designed to prevent relapses. The program aims to normalize eating and weight control behaviors, encourage healthier eating habits and help patients overcome their anxieties about eating a variety of foods.

More than 70% of the patients in the study reached a healthy body mass index (BMI). BMI is a measure of body fat based on height and weight. For most adults, a healthy BMI is between 18.5 and 24.9. Patients in the study achieved an average BMI of at least 19, which is within the healthy range, compared to an average of 16.1 at the beginning of the program. The average hospital stay was just 39 days, and patients gained 4 pounds per week on average — “close to twice what many intensive treatment programs achieve, which means half as much hospital time is needed to reach a healthy weight,” says Guarda.

Upon hospital discharge, patients were invited to complete an anonymous questionnaire to rate their satisfaction with the treatment. Some 107 participants (72%) completed the questionnaire. Overall, 71% of respondents said they would come back if they needed help with their eating disorder in the future, while 83% would recommend the program to others.

Like faster weight gain, Guarda explains, behavioral management is often criticized by clinicians as poorly tolerable by patients. However, the program’s focus on behavior change was rated good or very good by 83% of the patients.

Participants also rated the degree to which they felt included in the treatment (78%), and their level of satisfaction with staff members (clinical nurses: 96%, occupational therapists: 99%, dietitians: 45%, social workers: 75%). Satisfaction with intervention factors (group therapies: 79%, family meetings and education: 63%) and environmental factors (comfort of units: 50%, presentation/taste of food: 36%) was also assessed.

“Our program is solely meal-based and does not employ tube feeding,” says Guarda. Occupational therapists and nursing and dietician staff members assist patients in preparing and portioning meals, and in eating food prepared by others in cafeteria and restaurant settings. “We want to help our patients translate what they’re learning here to a more real world environment so they can stay healthy once back at home.”

According to Guarda, most patients go to inpatient programs like the one at Johns Hopkins under pressure from family members, employers or a significant other, and they are often anxious and apprehensive about entering treatment. “At the beginning, they often don’t see the need to be here,” she says, “but these results show that for most patients, their overall perception is positive by the end of treatment.”

Guarda says she’s encouraged that the field of anorexia nervosa is gradually moving toward greater and more uniform accountability about outcomes. “The standard of care should be based on evidence. Uniform, transparent reporting of weight and behavioral outcomes by treatment programs is needed so that patients, their families and referring clinicians can be more informed about treatment programs,” she says.

According to the National Eating Disorders Association, 0.9% of women and 0.3% of men will develop anorexia during their lifetime.

Additional authors on the study include Marita Cooper, Allisyn Pletch, Lori Laddaran, Graham Redgrave and Colleen Schreyer. The authors have no conflicts to declare.

This research was supported in part by the Stephen and Jean Robinson Fund.

Provided by Johns Hopkins Medicine

AI Abdominal Fat Measure Predicts Heart Attack and Stroke (Medicine / Cardiology)

At A Glance

  • An automated AI measurement of visceral fat area on abdominal CT images predicts future heart attack or stroke risk better than overall weight or BMI.
  • The researchers studied 12,128 patients over 5 years.
  • Visceral fat area was independently associated with future heart attack and stroke. BMI was not associated with heart attack or stroke.

Automated deep learning analysis of abdominal CT images produces a more precise measurement of body composition and predicts major cardiovascular events, such as heart attack and stroke, better than overall weight or body mass index (BMI), according to a study presented today at the annual meeting of the Radiological Society of North America (RSNA).

Example of body composition analysis of an abdominal CT slice with subcutaneous fat in green, skeletal muscle in red, and visceral fat in yellow. ©Radiological Society of North America

“Established cardiovascular risk models rely on factors like weight and BMI that are crude surrogates of body composition,” said Kirti Magudia, M.D., Ph.D., an abdominal imaging and ultrasound fellow at the University of California San Francisco. “It’s well established that people with the same BMI can have markedly different proportions of muscle and fat. These differences are important for a variety of health outcomes.”

Unlike BMI, which is based on height and weight, a single axial CT slice of the abdomen visualizes the volume of subcutaneous fat area, visceral fat area and skeletal muscle area. However, manually measuring these individual areas is time intensive and costly.

As a radiology resident at Brigham and Women’s Hospital in Boston, Dr. Magudia was part of a multidisciplinary team of researchers, including radiologists, a data scientist and biostatistician, who developed a fully automated method using deep learning–a type of artificial intelligence (AI)–to determine body composition metrics from abdominal CT images.

“Abdominal CT scans that are routinely performed provide a more granular way of looking at body composition, but we’re not currently taking advantage of it,” Dr. Magudia said.

The study cohort was derived from the 33,182 abdominal CT outpatient exams performed on 23,136 patients at Partners Healthcare in Boston in 2012. The researchers identified 12,128 patients who were free of major cardiovascular and cancer diagnoses at the time of imaging. Mean age of the patients was 52 years, and 57% of patients were women.

The researchers selected the L3 CT slice (from the third lumbar spine vertebra) and calculated body composition areas for each patient. Patients were then divided into four quartiles based on the normalized values of subcutaneous fat area, visceral fat area and skeletal muscle area.

In this retrospective study, it was determined which of these 12,128 patients had a myocardial infarction (heart attack) or stroke within 5 years after their index abdominal CT scan. The researchers found 1,560 myocardial infarctions and 938 strokes occurred in this study group.

Statistical analysis demonstrated that visceral fat area was independently associated with future heart attack and stroke. BMI was not associated with heart attack or stroke.

Video 1. Dr. Kirti Magudia discusses her research on automated AI measurement of visceral fat area on abdominal CT images and the prediction of future heart attack or stroke risk better than overall weight or BMI.

“The group of patients with the highest proportion of visceral fat area were more likely to have a heart attack, even when adjusted for known cardiovascular risk factors,” said Dr. Magudia. “The group of patients with the lowest amount of visceral fat area were protected against stroke in the years following the abdominal CT exam.”

“These results demonstrate that precise measures of body muscle and fat compartments achieved through CT outperform traditional biomarkers for predicting risk for cardiovascular outcomes,” she added.

According to Dr. Magudia, this work demonstrates that fully automated and normalized body composition analysis could now be applied to large-scale research projects.

“This work shows the promise of AI systems to add value to clinical care by extracting new information from existing imaging data,” Dr. Magudia said. “The deployment of AI systems would allow radiologists, cardiologists and primary care doctors to provide better care to patients at minimal incremental cost to the health care system.”

This paper is the recipient of an RSNA 2020 Trainee Research Prize.

Co-authors are Christopher P. Bridge, D.Phil., Camden P. Bay, Ph.D., Florian J. Fintelmann, M.D., Ana Babic, Ph.D., Katherine P. Andriole, Ph.D., Brian M. Wolpin, M.D., and Michael H. Rosenthal, M.D., Ph.D.

For more information and images, visit RSNA.org/press20. Press account required to view embargoed materials.

RSNA is an association of radiologists, radiation oncologists, medical physicists and related scientists promoting excellence in patient care and health care delivery through education, research and technologic innovation. The Society is based in Oak Brook, Illinois. (RSNA.org)

Editor’s note: The data in these releases may differ from those in the published abstract and those actually presented at the meeting, as researchers continue to update their data right up until the meeting. To ensure you are using the most up-to-date information, please call the RSNA media relations team at Newsroom at 1-630-590-7762.

For patient-friendly information on abdominal CT, visit RadiologyInfo.org.

Provided by Radiological Society of North America

Obesity Increases the Risk Of Early Hip Fracture in Postmenopausal Women (Geriatrics / Medicine)

Obese women have an increased risk of hip fracture earlier than others, already well before the age of 70, a new study from the University of Eastern Finland shows. The study followed 12,715 women for a period of 25 years. The new findings from the Osteoporosis Risk Factor and Prevention (OSTPRE) study were published in Osteoporosis International.

Launched at the University of Eastern Finland in 1989, the OSTPRE study is a population-based cohort study that recruited all women born in Kuopio Province, Eastern Finland, between 1932 and 1941. In the 25-year follow-up, the researchers analyzed the association of body mass index (BMI) at the age of 58 with the risk of early hip fracture up until the age of 70. They also analyzed the association of body mass index at the age of 70 with the risk of hip fracture later in life, up until the age of 83. The risk of hip fracture was examined in groups of normal-weight, overweight and obese women. Data on hip fractures, mechanisms of injury, and mortality were obtained from national health registers.

Normal weight was defined as a BMI of 25 or less, overweight as a BMI of 25–29.9, and obesity as a BMI of 30 or over (kg/m2). At baseline, 39.6 percent of the women were normal-weight, 40 percent were overweight, and 19.9 percent were obese. A small fraction of the women (n=59, 0.5%) had a BMI below the normal range, i.e. less than 18.5 kg/m2. Aging was associated with some increase in the BMI: at 70 years of age, 33.4% of the women were normal-weight, 40.9% were overweight, and 25.7% were obese.

As expected, the risk of hip fracture increased with age in all of the groups; however, the risk of early hip fracture increased faster in obese women, and slower in overweight women, than in others. In obese women, the probability of hip fracture was at 1% already at the age of 66.7, while in overweight women the 1% probability was reached 5.1 years later, at the age of 71.8. Obese women had a 2% probability of hip fracture 2.1 years earlier than overweight women, and a 4% probability 1.3 years earlier. The differences between the groups became smaller with aging.

In obese women, hip fracture related mortality in five years after the incident was approximately 1.5 times higher than in others.

After around 75 years of age, the risk of hip fracture increased fastest in slender women whose BMI was at the lower end of normal weight. Women at the borderline between normal weight and overweight had the smallest risk all the way until old age.

The study also included a DXA bone density measurement of the hip and a related follow-up of a sub-sample of 3,136 women. At baseline, obese women had on average the highest bone density, but their bone loss was significantly faster than in others. Indeed, obese women in the lowest bone density tertile at baseline had a particularly high risk of hip fracture.

Some earlier studies have suggested that obesity could also be a factor that protects against hip fracture. However, the contradictory findings on the association of BMI with hip fracture seem to be dependent on which age group is being studied. Typically, follow-up times have been significantly shorter than those in the OSTPRE study.

“Based on this study, the risk of early hip fracture occurring before the age of 70 is clearly highest in obese women—and especially in obese women who have a below-average bone density. Later, after around 75 years of age, the risk increases fastest in slender women. Aging women at the borderline between normal weight and overweight seem to have the lowest risk,” Senior Researcher Toni Rikkonen from the University of Eastern Finland says.

References: T. Rikkonen et al. Obesity is associated with early hip fracture risk in postmenopausal women: a 25-year follow-up, Osteoporosis International (2020). DOI: 10.1007/s00198-020-05665-w https://link.springer.com/article/10.1007%2Fs00198-020-05665-w

Provided by University of Eastern Finland

Genetic Study Shows That The Risk of Pre-eclampsia is Related To Blood Pressure And BMI (Medicine)

An international study, coordinated by experts from the University of Nottingham, has revealed that the genetic risk of pre-eclampsia – a potentially dangerous condition in pregnancy – is related to blood pressure and body mass index.

© University of Nottingham

Pre-eclampsia, usually diagnosed by increased blood pressure and protein in urine, affects up to 5% of pregnant women. It contributes worldwide to the death of estimated 50 000 women and up to one million babies annually. The condition is also associated with an increased risk of cardiovascular diseases among mothers and their children later in life. There is an inherited risk, with women with a family history of pre-eclampsia at greater risk of developing the condition themselves.

In the InterPregGen study, researchers from the UK, Iceland, Finland, Norway, Denmark, Kazakhstan and Uzbekistan studied how maternal genetic variation influences the risk of pre-eclampsia. The team studied the genetic make-up of 9,515 pre-eclamptic women and 157,719 control individuals.

The results, reported today in Nature Communications, pinpointed DNA variants in the ZNF831 and FTO genes as risk factors for pre-eclampsia. These genes have previously been associated with blood pressure, and the FTO variant also with body mass index. Further analysis revealed other blood pressure related variants in the MECOM, FGF5 and SH2B3 genes also associating with pre-eclampsia. These variants increase the risk of pre-eclampsia by 10-15%.

The new insights from this study could form the basis for more effective prevention and treatment of pre-eclampsia in the future, and improve the outcome of pregnancy for mother and child. They could also encourage GPs to follow-up more closely women who have had pre-eclampsia.”, said Professor Fiona Broughton Pipkin.

The study also shows that overall genetic predisposition to hypertension is a major risk factor for preeclampsia and thus a large number of variants each with a small effect may also contribute to the risk. These current results complement the earlier findings of the same researchers, who showed that a variant near FLT1 gene in the fetal genome affects mothers’ risk of developing pre-eclampsia.

The genes identified so far fit hand-in-glove with other current knowledge of pre-eclampsia as hypertension and obesity are known maternal risk factors. This study shows that these associations are partly explained by inherited predispositions. However, they only explain a part of the pre-eclampsia risk. Whether the remaining unidentified factors act through the maternal or fetal genome, or both, remains to be seen.

The new insights from this study could form the basis for more effective prevention and treatment of pre-eclampsia in the future, and improve the outcome of pregnancy for mother and child.

The five-year study was coordinated by Dr Linda Morgan from the University of Nottingham’s School of Life Sciences; Nottingham Professors Emeritus Noor Kalsheker (Clinical Chemistry) and Fiona Broughton Pipkin (Obstetrics and Gynaecology) were among the collaborators.

The full study is published here and was funded by a 6 million Euro grant from the European Commission.

References: Steinthorsdottir, V., McGinnis, R., Williams, N.O. et al. Genetic predisposition to hypertension is associated with preeclampsia in European and Central Asian women. Nat Commun 11, 5976 (2020). https://doi.org/10.1038/s41467-020-19733-6

Provided by Nottingham University

Vegans, Vegetarians and Pescetarians May be at Higher Risk of Bone Fractures (Medicine)

Compared with people who ate meat, vegans with lower calcium and protein intakes on average, had a 43% higher risk of fractures anywhere in the body (total fractures), as well as higher risks of site-specific fractures of the hips, legs and vertebrae, according to a study published in the open access journal BMC Medicine. Vegetarians and people who ate fish but not meat had a higher risk of hip fractures, compared to people who ate meat. However, the risk of fractures was partly reduced once body mass index (BMI), dietary calcium and dietary protein intake were taken into account.

Dr Tammy Tong, Nutritional Epidemiologist at the Nuffield Department of Population Health, University of Oxford, and the lead author said: “This is the first comprehensive study on the risks of both total and site-specific fractures in people of different diet groups. We found that vegans had a higher risk of total fractures which resulted in close to 20 more cases per 1000 people over a 10-year period compared to people who ate meat. The biggest differences were for hip fractures, where the risk in vegans was 2.3 times higher than in people who ate meat, equivalent to 15 more cases per 1000 people over 10 years.”

A team of researchers at the Universities of Oxford and Bristol, UK analysed data from nearly 55,000 people in the EPIC-Oxford study, a prospective cohort of men and women living in the UK, who were recruited between 1993 and 2001, many of whom do not eat meat. Prospective cohort studies identify a group of people and follow them over a period of time to understand how certain factors (in this case diet) may affect certain outcomes (in this case fracture risk).

Out of the 54,898 participants included in the present study, 29,380 ate meat, 8,037 ate fish but not meat, 15,499 were vegetarians, and 1,982 were vegans when they were recruited. Their eating habits were assessed initially at recruitment, then again in 2010. Participants were followed continuously for 18 years on average, until 2016 for the occurrence of fractures. During the time of the study, 3,941 fractures occurred in total, including 566 arm, 889 wrist, 945 hip, 366 leg, 520 ankle and 467 fractures at other main sites, defined as the clavicle, ribs and vertebrae.

In addition to a higher risk of hip fractures in vegans, vegetarians and pescetarians than the meat eaters, vegans also had a higher risk of leg fractures and other main site fractures. The authors observed no significant differences in risks between diet groups for arm, wrist or ankle fractures once BMI was taken into account. The authors found that the differences in risk of total and site-specific fractures was partly reduced once BMI, dietary calcium and dietary protein intake had been taken into account.

Dr Tong said: “Previous studies have shown that low BMI is associated with a higher risk of hip fractures, and low intakes of calcium and protein have both been linked to poorer bone health. This study showed that vegans, who on average had lower BMI as well as lower intakes of calcium and protein than meat eaters, had higher risks of fractures at several sites. Well-balanced and predominantly plant-based diets can result in improved nutrient levels and have been linked to lower risks of diseases including heart disease and diabetes. Individuals should take into account the benefits and risks of their diet, and ensure that they have adequate levels of calcium and protein and also maintain a healthy BMI, that is, neither under nor overweight.”

The authors caution that they were unable to differentiate between fractures that were caused by poorer bone health (such as fractures due to a fall from standing height or less) and those that were caused by accidents because data on the causes of the fractures were not available. No data were available on differences in calcium supplement use between the different diet groups, and as in all dietary studies the estimates of nutrients such as dietary calcium or dietary protein are subject to measurement error. As the study predominantly included white European participants, generalisability to other populations or ethnicities may be limited, which could be important considering previously observed differences in bone mineral density and fracture risks by ethnicity, according to the authors.

More studies are needed from different populations, including from non-European populations, as well as cohorts with a larger proportion of men to explore possible differences in risk by sex, as around three-quarters of participants in the EPIC-Oxford cohort are women.

References: Tong, T.Y.N., Appleby, P.N., Armstrong, M.E.G. et al. Vegetarian and vegan diets and risks of total and site-specific fractures: results from the prospective EPIC-Oxford study. BMC Med 18, 353 (2020). https://bmcmedicine.biomedcentral.com/articles/10.1186/s12916-020-01815-3 https://doi.org/10.1186/s12916-020-01815-3

Provided by BMC Medicine