New Publication for Maria Ramos-Nino

Anybody remember the Vermont Diabetes Information System? Back in the early 2000s, it was a large regional study of a quality improvement system that generated a big database of adults with diabetes. One of our old collaborators, Maria Ramos-Nino, PhD, now Associate Professor Microbiology, Immunology, and Pharmacology at St. George’s University in Grenada, West Indies remembers. She recently dug into the data to investigate the relationship between obesity and lung disease and the work was published this week. Congratulations, Maria!

Ramos-Nino, M.E., MacLean, C.D. & Littenberg, B. Association between prevalence of obstructive lung disease and obesity: results from The Vermont Diabetes Information System. Asthma Res and Pract 7, 6 (2021). https://doi.org/10.1186/s40733-021-00073-1

The association of obesity with the development of obstructive lung disease, namely asthma and/or chronic obstructive pulmonary disease, has been found to be significant in general population studies, and weight loss in the obese has proven beneficial in disease control. Obese patients seem to present with a specific obstructive lung disease phenotype including a reduced response to corticosteroids. Obesity is increasingly recognized as an important factor to document in obstructive lung disease patients and a critical comorbidity to report in diabetic patients, as it may influence disease management. This report presents data that contributes to establishing the relationship between obstructive lung disease in a diabetic cohort, a population highly susceptible to obesity.

A total of 1003 subjects in community practice settings were interviewed at home at the time of enrolment into the Vermont Diabetes Information System, a clinical decision support program. Patients self-reported their personal and clinical characteristics, including any history of obstructive lung disease. Laboratory data were obtained directly from the clinical laboratory, and current medications were obtained by direct observation of medication containers. We performed a cross-sectional analysis of the interviewed subjects to assess a possible association between obstructive lung disease history and obesity.

In a multivariate logistic regression model, a history of obstructive lung disease was significantly associated with obesity (body mass index ≥30) even after correcting for potential confounders including gender, low income (<$30,000/year), number of comorbidities, number of prescription medications, cigarette smoking, and alcohol problems (adjusted odds ratio (OR) = 1.58, P = 0.03, 95% confidence interval (CI) = 1.05, 2.37). This association was particularly strong and significant among female patients (OR = 2.18, P = < 0.01, CI = 1.27, 3.72) but not in male patients (OR = 0.97, P = 0.93, CI = 0.51, 1.83).

These data suggest an association between obesity and obstructive lung disease prevalence in patients with diabetes, with women exhibiting a stronger association. Future studies are needed to identify the mechanism by which women disproportionately develop obstructive lung disease in this population.

 

Clinical Research Oriented Workshop (CROW) Meeting: May 20, 2021

Present:   Levi Bonnell, Nancy Gell, Ben Littenberg, Adam Sprouse-Blum, Connie van Eeghen (5)

 1.                   Warm Up: It’s May… life is good in VT… maybe not so much in less vaccinated places

2.                   Adam’s K Award application: Reviewed Research Strategy

a.       Formatting:

                                                   i.      Watch for orphans

b.       Significance:

                                                   i.      Impact on people – how burdensome is migraine?

                                                 ii.      What is the additional burden of left sided headache?

                                               iii.      If bilateral is important, where does it belong in the Significance section, and why? Add to second paragraph. “Is bilateral migraine different from L/R or is bilateral the combination of L/R?”

c.       Language

                                                   i.      “I” or “We” – whatever you use, be consistent.  OK to be 1^st person singular in a K award.

d.       Analyses

                                                   i.      What to do about adjusting for multiple hypotheses? TBD

e.       Potential problems:

                                                   i.      Not generalizable to rare forms of migraine or other headache syndromes

                                                 ii.      Convenience sample: patients close to the Med Center and can come in without notice; may not be generalizable to other groups

                                               iii.      Small sample size

                                               iv.      Inadequate sample size is a threat to the study, however… (bridge to existing language in draft)

                                                 v.      Patients lost in prior study: 1 out of three not able to conduct study; able to retain when presented with their next attack

1.       Unable to tolerate scan – all 10 finished the migraine scan but, for the non-migraine scan, 1 was retriggered during the scan due to flashing lights (reaction to the protocol) – later returned to repeat scan without flashing lights; we will use such tactics going forward

f.        Other:

                                                   i.      Consider a conceptual model that drives migraine responsiveness in different hemispheres: are there structural differences in the brain that are reflected in migraine laterality, such as:

1.       Blood flow

2.       Neuronal activity

3.       Cerebral vascular reactivity

4.       Cognition based on what lights up

                                                 ii.      Or, are there intrinsic brain structures that influence laterality, or there is something else that affects laterality, are the symptoms primary to migraine?

                                               iii.      Include a list of outcomes

1.       Identify predictor: laterality of migraine

2.       Identify outcomes in relationship to predictor

3.                   Next week:  TBA