Study shows that rheumatoid arthritis is associated with a 23% increased risk of developing diabetes

A new study presented at this year’s annual meeting of the European Association for the Study of Diabetes (EASD), held online this year, shows that rheumatoid arthritis (RA) is associated with a 23% increased risk of type 2 diabetes (T2D), and may indicate that both diseases are linked to the body’s inflammatory response. The research was conducted by Zixing Tian and Dr. Adrian Heald, University of Manchester, UK, and colleagues.

Inflammation has emerged as a key factor in the onset and progression of T2D, and RA is an autoimmune and inflammatory disease. The team suggest that the systemic inflammation associated with RA might therefore contribute to the risk of an individual developing diabetes in the future.

The team conducted a comprehensive search of a range of medical and scientific databases up to 10 March 2020, for cohort studies comparing the incidence of T2D among people with RA to the diabetes risk within the general population. Statistical analyses were performed to calculate the relative risks, as well as to test for possible publication bias (in which the outcome of research influences the decision whether to publish it or not). The eligible studies identified comprised a total of 1,629,854 participants. Most of the studies were population-based and one was hospital-based, while no evidence was found for publication bias in any of them.

The authors found that having RA was associated with a 23% higher chance of developing T2D, compared to the diabetes risk within the general population. They conclude that: “This finding supports the notion that inflammatory pathways are involved in the pathogenesis of diabetes.”

Source: Read Full Article

Interim data from early US COVID-19 hotspot show mortality of disease were not associated with race/ethnicity

A study of interim data from two hospitals in an early US COVID-19 hotspot, to be presented at the ESCMID Conference on Coronavirus Disease (ECCVID, held online 23-25 September), shows that race and ethnicity were not significantly associated with higher in-hospital COVID-19 mortality, and that rates of moderate, severe, and critical forms of COVID-19 were similar between racial and ethnic groups.

The study, by Dr. Daniel Chastain (University Of Georgia College Of Pharmacy, Albany, GA, U.S.) and colleagues included data from adult patients hospitalised between March 10 and and May 22 with COVID-19, defined by laboratory-detected severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, in Southwest Georgia.

The authors compared severity of illness categories on presentation to the hospital between patients from different racial and ethnic groups based on criteria from the US National Institutes of Health (NIH) COVID-19 treatment guidelines. They also studied outcomes including comorbidities, laboratory values, vital signs, and in-hospital mortality.

A total of 164 randomly selected non-consecutive patients were included with a median age of 61.5 years. These consisted of 119 African American patients, 36 Caucasian patients, and 9 Latinx patients. Thus the majority were African American (73%) and 51% were female. Rates of moderate, severe, and critical COVID-19 did not significantly differ between African American (9%, 56%, and 35%), Caucasian (0%, 69%, and 31%), and Latinx patients (0%, 56%, and 44%). In-hospital mortality was not statistically significantly different between groups but was highest among Caucasians (31%) followed by Latinx (22%) and African Americans (16%).

Caucasian patients had significantly higher Charlson comorbidity index scores (meaning more underlying conditions) (4.5) compared to African American (4) and Latinx (2) patients, while median BMI was significantly higher in African Americans (33.7 kg/m2) than in Caucasians (26.9) or Latinx patients (25.9).

Duration of time from symptom onset to admission was similar between groups, whereas median temperature on admission was significantly higher in African Americans (38.3C) than in Caucasians (37.9) or Latinx patients (37.8)

Source: Read Full Article

Smartphones can predict brain function associated with anxiety and depression

Information on social activity, screen time and location from smartphones can predict connectivity between regions of the brain that are responsible for emotion, according to a study from Dartmouth College.

In the research, data from phone usage was analyzed alongside results from fMRI scans to confirm that passively collected information can mirror activity in the brain linked to traits such as anxiety. Predictions based solely on the phone data matched the brain scans with 80 percent accuracy.

The study, presented at ACM UbiComp, an annual conference on pervasive and ubiquitous computing, represents the first time researchers have been able to predict connectivity between specific brain regions solely based on passive data from smartphones.

“Simple information about how someone is using their smartphone can provide a peek into the complex functioning of the human brain,” said Mikio Obuchi, a Ph.D. student in the Department of Computer Science at Dartmouth and lead author of the study. “Although this research is just beginning, combining data from smartphones—rather than fMRI alone—will hopefully accelerate research to understand better how the human brain works.”

According to the research, how often and how long an individual uses their phone provides information about the functioning between the ventromedial prefrontal cortex and the amygdala—two key centers of the brain related to emotional state.

The ventromedial prefrontal cortex is responsible for self-control, decision making, and risk evaluation. The amygdala triggers the fight or flight response and helps individuals determine the emotions of others.

In addition to data on social activity, screen time and location, information on exercise and sleep patterns was also collected for the study.

The research found that more screen time, regular exercise, earlier bedtimes, higher social interaction and certain location patterns passively inferred from phone data matched a state of higher functional connectivity between the brain regions. This increased activity indicates a more positive emotional state.

“We are not suggesting that phones should replace technology like fMRI, but they can help individuals and health providers learn more about behavior patterns from everyday observations,” said Jeremy Huckins, a lecturer on psychological and brain sciences at Dartmouth and a co-author of the study.

The research result aligns with clinical evidence showing that stronger connectivity between the ventromedial prefrontal cortex and the amygdala to be associated with lower levels of anxiety and depression. Weaker functional connectivity, on the other hand, represents a more negative emotional state.

Anonymous fMRI data from volunteer participants were placed into two categories divided by low and high brain connectivity levels. By matching phone data against the fMRI results the researchers were able to predict which research subjects had higher or lower connectivity between brain regions with 80 percent accuracy.

According to the research team, the use of passive information from a smartphone can help eliminate the subjectivity that often complicates other information-gathering techniques on emotional well-being such as personal interviews and self-reporting on questionnaires.

The phone information allowed researchers to predict the emotional state of individuals at any given time without intrusive data collection. The data also support predictions of the long-term emotional traits in individuals.

“Hopefully, this study shows how mobile sensing can provide deep longitudinal human behavioral data to complement brain scans,” said Andrew Campbell, the Albert Bradley 1915 Third Century Professor of computer science at Dartmouth and the senior researcher on the study. “This could offer new insights into the emotional well-being of subjects that would just not be possible without continuous sensing.”

Source: Read Full Article