Research Spotlights: April 2018

research spotlight

The Great Recession raised Americans’ blood pressure and glucose levels

The 2008–2010 Great Recession negatively impacted the cardiovascular health of American adults, according to a new study funded by NHLBI.

Data from 4,600 adults participating in the Multi-Ethnic Study of Atherosclerosis in 2000–2012 were used for this study. Primary outcomes included measured blood pressure and blood glucose levels and changes in the use of antihypertensive and antiglycemic medications. Expected age-related changes in blood pressure and glucose were modeled and compared to observed changes at the end of the recession.

Results revealed that employed individuals taking medications saw systolic blood pressure and glucose levels increase 13 mmHg and 11 percent higher than expected, respectively. Older adults’ (> 65y) systolic blood pressure and blood glucose levels rose 8 mm Hg and 6 percent higher than expected, respectively. Substantial declines in medication use were seen, with a 17 percent reduction in antihypertensives and 13 percent reduction antiglycemic medications for older adults. In younger adults, the use of antihypertensives dropped by 6 percent and 13 percent for antiglycemics.

These findings demonstrate the deleterious effects of economic instability on public health. These results also highlight the differential impact of the Great Recession on different population subgroups.

Seeman T, Thomas D, Merkin SS, Moore K, Watson K, Karlamangla A. 2018. The Great Recession worsened blood pressure and blood glucose levels in American adults. Proc Natl Acad Sci. doi:10.1073/pnas.1710502115.

How you smile can induce physiological stress in others

Physiological responses to verbal feedback have been well documented; however, less is known about non-verbal cues. A recent NIMH-funded study examined whether different smiles received during a speech resulted in physiological changes.

The study included 90 male undergraduate students who were randomly assigned to one of three smile conditions (i.e., dominance, affiliation, reward). In each condition, the participant completed three 2-minute speech tasks and were led to believe a male evaluator was watching via webcam. After each speech, participants were shown facial expressions of the evaluator corresponding to their assigned smile condition. Throughout the experiment, EKG was recorded and cortisol samples were taken at baseline and in 10-minute intervals post-experiment.

Results revealed the overall cortisol response was significantly higher for participants that received dominance smiles (19.4 mmol/L) compared to those who received reward (1.21 mmol/L) or affiliation smiles (2.43 mmol/L). Cortisol remained significantly higher for participants receiving dominance smiles 30 minutes post-speech, whereas those who received reward or affiliation smiles returned to their baseline levels. These findings suggest that smiles with different social functions result in distinct physiological changes. This research provides the basis for further exploration of the effect of non-verbal cues on the physiological responses of those at risk for depression and anxiety disorders.

Martin JD, Abercrombie HC, Gilboa-Schechtman E, Niedenthal PM. 2018. Functionally distinct smiles elicit different physiological responses in an evaluative context. Sci Rep 8(1):3558.

Predicting chronic opioid use with electronic health record data

Over 200 million opioid prescriptions are written each year in the United States, yet research determining which patients will develop chronic opioid use is limited. Predictive tools provide a novel way for clinicians to identify individuals at risk for developing opioid use disorders. This was the focus of a recent study by NIDA-supported researchers.

The study used electronic health record (EHR) data from over 27,000 hospitalized patients between 2008 and 2014. The main outcome, chronic opioid therapy (COT), was classified as being prescribed a 90-day supply of opioids with a less than 30-day gap in supply over a 180-day period, or as receiving 10 or more opioid prescriptions in one year. Predictors extracted from the EHR included demographics, insurance status, mental health diagnoses and substance use disorders, and past medication use.

The analytical model created with the EHR data predicted 79 percent of future COT and 78 percent of individuals without COT. Highly predictive risk factors in the model included having two or more opioid prescriptions filled in the year preceding hospitalization and being prescribed more than 10 mg of morphine equivalents per day while hospitalized. Developing and integrating predictive models into EHRs is a promising method for clinicians to identify and intervene in patients at risk of developing opioid use disorders.

Calcaterra SL, Scarbro S, Hull ML, Forber AD, Binswanger IA, Colborn KL. 2018. Prediction of future chronic opioid use among hospitalized patients. J Gen Intern Med. doi:10.1007/s11606-018-4335-8.