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Postmenopausal Depression and Weight Gain Linked to Chronic Disease
In the study, published in the American Journal of Public Health, University of Massachusetts Medical School researchers investigated whether elevated depressive symptoms and antidepressant use are associated with biomarkers for glucose dysregulation and inflammation, BMI, and waist circumference.
The three main findings indicate that both elevated depressive symptoms and antidepressant use are each significantly associated with higher BMI and waist circumference.
Elevated depressive symptoms are associated with increased levels of insulin and insulin resistance. Antidepressant use is associated with higher leves of C-reactive protein (CRP), a marker of inflammation which increases the risk of type-2 diabetes and cardiovascular disease.
“It may be prudent to monitor post-menopausal women who have elevated depression symptoms or are taking antidepressant medication to prevent diabetes and cardiovascular disease,” said Yunsheng Ma, PhD, MD, MPH, lead researcher.
Postmenopausal women were recruited into the study from 1993 to 1998, and data for this analysis were collected at regular intervals through 2005. Using data from 1,953 women who completed all relevant assessments, the study found that elevated depressive symptoms were discovered to be significantly associated with increased insulin levels and measures of insulin resistance.
Researchers found that throughout the entire 7.6 years, women enrolled in the study with depressive symptoms (or taking antidepressants) had a higher BMI and waist measurements than those without depressive symptoms, with the strongest association for waist circumference.
Analysis of data from 2,242 women showed that both elevated depressive symptoms and antidepressant use were associated with higher CRP levels.
“Identifying these markers in women is important for diabetes prevention because they can be monitored for possible action before progression to full-blown diabetes,” said Ma.
Few studies have examined the association of BMI, waist circumference, and biomarkers of glucose dysregulation and inflammation with depression, antidepressant medication use, or both.
The current study included a large, racially and ethnically diverse sample of post-menopausal women.
Because the analysis was epidemiological, it could not determine a causal relationship, so further study is needed to confirm the results through clinical trials.
Source: University of Massachusetts Medical School
New Brain Imaging Technique Helps Diagnose Parkinson’s
Researchers from the University of Florida believe a diffusion tensor imaging technique could allow clinicians to assess people earlier than is possible today, leading to improved treatment interventions and therapies for patients.
The three-year study looked at 72 patients, each with a clinically defined movement disorder diagnosis. The new technique allowed researchers to successfully separate the patients into disorder groups with a high degree of accuracy.
The research will be published in the journal Movement Disorders.
“The purpose of this study is to identify markers in the brain that differentiate movement disorders which have clinical symptoms that overlap, making [the disorders] difficult to distinguish,” said David Vaillancourt, associate professor and the study’s principal investigator.
“No other imaging, cerebrospinal fluid or blood marker has been this successful at differentiating these disorders,” he said. “The results are very promising.”
Movement disorders such as Parkinson’s disease, essential tremor, multiple system atrophy and progressive supranuclear palsy exhibit similar symptoms in the early stages, which can make it challenging to assign a specific diagnosis.
Vaillancourt said that often the original diagnosis changes as the disease progresses.
Diffusion tensor imaging, known as DTI, is a non-invasive method that examines the diffusion of water molecules within the brain. It can identify key areas that have been affected as a result of damage to gray matter and white matter in the brain.
Vaillancourt and his team measured areas of the basal ganglia and cerebellum in individuals, and used a statistical approach to predict group classification.
By asking different questions within the data and comparing different groups to one another, they were able to show distinct separation among disorders.
“Our goal was to use these measures to accurately predict the original disease classification,” Vaillancourt said, “the idea being that if a new patient came in with an unknown diagnosis, you might be able to apply this algorithm to that individual.”
He compared the process to a cholesterol test.
“If you have high cholesterol, it raises your chances of developing heart disease in the future,” he said.
“There are tests like those that give a probability or likelihood scenario of a particular disease group. We’re going a step further and trying to utilize information to predict the classification of specific tremor and Parkinsonian diseases.”
Source: University of Florida
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