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These tools have the capability to allow us to start to identify the many small molecular building blocks that make us unique as a species," Reilly said. James Xue of the Broad Institute is lead author of the study.
The researchers used a technology called Massively Parallel Reporter Assays (MPRA), which can simultaneously screen and measure the function of thousands of genetic changes among species.
[Such deletions] can tweak the meaning of the instructions of how to make a human slightly, helping explain our bigger brains and complex cognition," he said.
The deletion of this genetic information, Reilly said, had an effect that was the equivalent of removing three characters -- "n't" -- from the word "isn't" to create a new word, "is."
In their study, the Yale team found that some genetic sequences found in the genomes of most other mammal species, from mice to whales, vanished in humans. But rather than disrupt human biology, they say, some of these deletions created new genetic encodings that eliminated elements that would normally turn genes off.
The paper was one of several published in Science from the Zoonomia Project, an international research collaboration that is cataloging the diversity in mammalian genomes by comparing DNA sequences from 240 species of mammals that exist today.
Often we think new biological functions must require new pieces of DNA, but this work shows us that deleting genetic code can result in profound consequences for traits make us unique as a species," said Steven Reilly, an assistant professor of genetics at Yale School of Medicine and senior author of the paper.
The fact that these genetic deletions became conserved in all humans, the authors say, attests to their evolutionary importance, suggesting that they conferred some biological advantage.
These 10,000 missing pieces of DNA -- which are present in the genomes of other mammals -- are common to all humans, the Yale team found.
For the new study researchers used an even deeper genomic dive into primate DNA to show that the loss of about 10,000 bits of genetic information -- most as small as a few base pairs of DNA -- over the course of our evolutionary history differentiate humans from chimpanzees, our closest primate relative. Some of those "deleted" pieces of genetic information are closely related to genes involved in neuronal and cognitive functions, including one associated with the formation of cells in the developing brain.
The new findings, published April 28 in the journal *Science*, fill an important gap in what is known about historical changes to the human genome. While a revolution in the capacity to collect data from genomes of different species has allowed scientists to identify additions that are specific to the human genome -- such as a gene that was critical for humans to develop the ability to speak -- less attention has been paid to what's missing in the human genome.
What the human genome is lacking compared with the genomes of other primates might have been as crucial to the development of humankind as what has been added during our evolutionary history, according to a new study led by researchers at Yale and the Broad Institute of MIT and Harvard.
Gratitude is extended to the community members and the governing body of the participating reservation. Funding for this study was provided by the National Institutes of Health National Institute of Diabetes and Digestive and Kidney Diseases Healthy People 2010 project (F31 DK059286-01), with support from the Local Service Unit of the Indian Health Service. We gratefully acknowledge Rachel Schaperow of MedStar Health Research Institute for editing the manuscript.
Despite several limitations, the data suggest that there may be clinical utility to using an MI approach among American Indians with type 2 diabetes. In this pilot study, we saw improvement in several areas, including depressive symptoms, fatalistic thinking, and quality of life, and individuals appeared to benefit from participating. In addition, a number of psychosocial and other predictors of improvement in glycemic control were identified. Thus, this preliminary study can serve as a template for a larger trial.
The findings of the present study also are limited by the collection of data in only one American Indian community. There are more than 500 American Indian tribes in the United States, each having a unique culture and background. The results of this project may not be applicable to other tribal populations. Replication of this study with other American Indian tribes is needed, and cross-validation should be a priority in future research in this area.
This was a small pilot study with limited resources, which restricted our ability to recruit participants and adequately follow up or reschedule missed appointments. A larger study with a longer follow-up period and designated staff to focus on recruitment and follow-up is needed.
In conclusion, this is the first study to use MI techniques in brief counseling sessions as an intervention among American Indian participants with type 2 diabetes. This study's strengths include the quality of the intervention and the systematic collection of a wide range of physiological and psychological data. Limitations include the lack of a control group, the lack of coding of the intervention sessions to ensure fidelity to the MI model, and the small sample size that limited the power and precluded holding out a subsample to cross-validate the results of the regression model.
The only demographic variable associated with improved A1C was total blood quantum. This finding should be interpreted with caution because it is potentially a proxy variable for a wide range of social and cultural variables (e.g., degree of genetic risk for diabetes, degree of traditionalism, level of education, degree of discrimination experienced, acculturation stress, exposure to historical trauma, age, and fear and distrust of medical providers), some of which have been shown to affect glycemic control in other minority populations.26,27 The degree to which these factors are interrelated may make it difficult to analyze them separately. This finding must be explored with sensitivity to the potential for misrepresentation and misuse of data regarding such variables.
No evidence of selective attrition was observed; comparison of baseline data of the 20 participants who completed the study to that of the 6 participants who did not complete the study showed no significant differences on demographic variables (age, sex, education, tribal enrollment, length of diabetes history, and BMI), baseline physiological measures (A1C and random glucose), baseline psychological measures (DC Fatalism, Diabetes Locus of Control, Diabetes Quality of Life, Beck Depression Inventory**–**II, and 5-Item Stage of Change), or measures of health-related behaviors (exercise and dietary intake)
The three missing random blood glucose values from 6 months before baseline were imputed using a regression equation based on the participants' A1C and average estimated daily blood glucose 6 months before baseline. The two missing baseline random blood glucose measures were imputed from the participants' baseline average estimated daily blood glucose and baseline A1C values. Finally, the post-intervention values for the six participants who did not complete the study were imputed based on their baseline measures and the relationship between baseline and post-intervention data observed in the complete data set.
The analyses were conducted using SPSS 12.0 software for Windows, with an alpha level of 0.05 for all tests of significance. Examination of the data set revealed the following missing data: three participants had medical charts that were missing the random blood glucose measurement taken 6 months before baseline; two other participants were missing baseline random blood glucose measures due to lab error; and six participants completed the baseline assessment but had no post-intervention data because of attrition. Imputation15,16 was used to estimate the missing values.
The development of discrepancies technique was used when appropriate, as well. Information exchange was used in cases where participants would ask for specific information on activities or behaviors such as exercise, smoking cessation, and dietary changes (e.g., participants frequently asked whether eating less fried food would make a difference in their diabetes management). As described for this intervention and elsewhere,13,14 MI lends itself well to addressing behavioral change on an individual basis, which is an important component in diabetes management.
The intervention focused on behaviors raised either through participant responses or from direct questions the participant asked the interventionist. Behaviors targeted were primarily those associated with improved overall health and diabetes management: exercise, diet, smoking cessation, SMBG, adherence to medication/diabetes treatment plan, and management of mood. Through the continued use of directive open-ended questions, issues explored were those the participants chose to discuss in relation to their management of diabetes. Additionally, individual participants' level of readiness for considering behavior change, confidence in their ability to initiate or maintain behavior change, and rating of the importance of behavioral change in improving individual diabetes management were elicited and discussed.13 This type of conversation regularly led to exploration of the pros and cons of the behavior change under discussion.
The goal of the intervention was to address behaviors raised by the participant and then follow up with further directive open-ended questions. An example from one such encounter is, “You mention walking as something that is often helpful for improved blood sugar management. What would it be like for you to use walking in your management of diabetes?”
Sessions of MI began with a brief discussion of how the management of diabetes may differ for each individual. A standardized introductory script was followed. Participants were told, “I would like to learn what it has been like for you to have diabetes and what if anything you would like to do to address your overall management of diabetes.” Use of this open-ended question frequently led to personal histories and stories from the participants.
The interventionist in this study was trained in MI, was supervised by a member of the Motivational Interviewing Network of Trainers, and attended training in MI skill coding to strengthen working knowledge of MI.
The next two meetings consisted of 30-minute sessions of MI occurring within 3 weeks of the participants' baseline appointments. The aim of the counseling sessions was to address type 2 diabetes management at the individual level and elicit behavioral change by helping individuals resolve ambivalence regarding their lifestyle habits (e.g., diet, amount of exercise, and adherence to treatment regimens). Three months after the intervention, participants returned to again complete the physiological and psychological measures.
The dietary intake questionnaire was a self-reported tally of the number of food servings consumed during the previous 24 hours from a list of healthy and unhealthy food choices. This questionnaire yielded scores ranging from 0 to 16; higher scores reflected greater consumption of that type of food (either healthy or unhealthy). The change score on this measure was computed by comparing the number of servings of foods quantified as healthy versus nonhealthy in the previous 24-hour period at baseline and at study completion.
Additionally, exercise/physical activity was recorded using a nonstandardized self-report questionnaire. Participants were asked to estimate the amount of time (in 30-minute increments) they spent during the previous 7 days engaging in exercise/physical activity, including culturally specific forms of activity such as pow-wow dancing. The data were expressed as total hours for the 7-day period, and change scores were derived by comparing baseline and end-of-study totals.
Sessions of MI began with a brief discussion of how the management of diabetes may differ for each individual. A standardized introductory script was followed. Participants were told, “I would like to learn what it has been like for you to have diabetes and what if anything you would like to do to address your overall management of diabetes.” Use of this open-ended question frequently led to personal histories and stories from the participants.
The interventionist in this study was trained in MI, was supervised by a member of the Motivational Interviewing Network of Trainers, and attended training in MI skill coding to strengthen working knowledge of MI.
The next two meetings consisted of 30-minute sessions of MI occurring within 3 weeks of the participants' baseline appointments. The aim of the counseling sessions was to address type 2 diabetes management at the individual level and elicit behavioral change by helping individuals resolve ambivalence regarding their lifestyle habits (e.g., diet, amount of exercise, and adherence to treatment regimens). Three months after the intervention, participants returned to again complete the physiological and psychological measures.
The dietary intake questionnaire was a self-reported tally of the number of food servings consumed during the previous 24 hours from a list of healthy and unhealthy food choices. This questionnaire yielded scores ranging from 0 to 16; higher scores reflected greater consumption of that type of food (either healthy or unhealthy). The change score on this measure was computed by comparing the number of servings of foods quantified as healthy versus nonhealthy in the previous 24-hour period at baseline and at study completion.
Additionally, exercise/physical activity was recorded using a nonstandardized self-report questionnaire. Participants were asked to estimate the amount of time (in 30-minute increments) they spent during the previous 7 days engaging in exercise/physical activity, including culturally specific forms of activity such as pow-wow dancing. The data were expressed as total hours for the 7-day period, and change scores were derived by comparing baseline and end-of-study totals.
**5-Item Stage of Change**, which measures an individual's assessment of his or her readiness to change behaviors that are related specifically to diabetes management. This five-item measure uses a Likert scale and is based on the Transtheoretical Model Stage of Change.
**Beck Depression Inventory–II,** which measures depressive symptoms.
**Diabetes Quality of Life,** which measures five domains of quality of life related to living with diabetes.10
**iabetes Locus of Control,** which measures individual perception regarding control over health issues specifically related to diabetes.
**DC Fatalism Questionnaire,** which measures fatalistic thinking regarding the inevitability of the onset of diabetes and its complications. This test was developed among American Indian participants and has shown acceptable factor structure.8 It yields scores ranging in value from 36 to 180, with higher scores indicating increased levels of fatalism.
Participants met with the PI a total of four times. They attended an initial baseline appointment to complete a demographic questionnaire, physiological measures (random glucose testing and A1C), psychological self-report instruments, and exercise and dietary intake questionnaires. The psychological measures included the following:
Approximately 96 individuals were invited in person to participate in the study. Of these, 83 (86%) expressed an interest in participating and scheduled initial appointments. Of those who expressed interest, 26 (31%) attended the initial appointment for baseline assessment. Those who had expressed interest but did not attend their scheduled initial appointment were contacted and rescheduled a minimum of two and a maximum of three times; a fourth missed appointment resulted in no further contact. Twenty-six participants completed the baseline measures, and 20 of those individuals (77%) returned for their first session of counseling. Every participant (100%) who completed the initial session of counseling completed the entire study, including the 3-month follow-up appointment.
This project used a within-subjects pre-post design. Participants were tribal members who were ≥ 18 years of age, residing on a Northern Plains reservation in the western United States, diagnosed with type 2 diabetes, and receiving their diabetes medical treatment at their local Indian Health Service (IHS) clinic. Participants were selected through chart review and collaboration with the physicians and the diabetes coordinators employed at the local IHS clinics. The primary investigator (PI) as well as IHS staff (diabetes educators, behavioral health specialists, and medical staff) attended semimonthly clinics and met with participants to inform them of the study and invite them to participate. The study was approved by the institutional review boards of the University of Montana and the area IHS office and by the governing tribal councils.
This study is the first to assess the use of MI in American Indians diagnosed with diabetes and living in a reservation community. The aim of the MI sessions was to facilitate change in the participants' health-related behaviors and improve their psychological well-being. The investigators' previous experience has shown a relationship between these factors and diabetes management, and it was hypothesized that change on measures of diet, exercise, depression, locus of control, fatalism, and quality of life would be predictive of improvement in A1C from baseline to post-intervention.
MI techniques are well suited to assist patients with diabetes in changing the behaviors associated with increased risk of secondary complications. The available literature on the use of MI in patients with diabetes has demonstrated significantly enhanced adherence to treatment recommendations, increased weight loss, and improved glycemic control.6,7 Even brief interventions using this technique have shown positive outcomes with regard to health-related behaviors, including those relevant to type 2 diabetes
The technique of motivational interviewing (MI), used in psychological counseling, can inspire behavioral changes such as patients' efforts to improve their diets and increase their level of exercise. MI was developed in the treatment of substance abuse.4 This technique has been described as particularly useful for individuals who are reluctant to change and ambivalent about doing so
Obesity is the leading risk factor for type 2 diabetes, and improvements in health-related behaviors such as diet, exercise, smoking cessation or reduction, and self-monitoring of blood glucose (SMBG) have been shown to delay the onset of the disease and to minimize secondary complications. Studies have shown that lifestyle changes such as weight management and increased physical activity can prevent the development of type 2 diabetes and improve insulin sensitivity and glycemic control in those who already have the disease.
An estimated 17 million people in the United States have been diagnosed with type 2 diabetes.1 Prevalence rates are highest among ethnic minority populations. Among American Indians, type 2 diabetes has reached epidemic proportions, with occurrence rates more than three times that of the general U.S. population.2 Diabetes is an expensive disease to treat and manage; in 2007, the annual estimated economic cost was $174 billion, with an estimated one in every five health care dollars being spent on this chronic condition.1 Thus, studies that may lead to better prevention and treatment are needed.
Significant improvements in participants' self-reported depressive symptoms, genetic/racial fatalism, treatment satisfaction, and social/vocational worry were observed. Stepwise regression revealed seven predictors of change in A1C from baseline to study end: completion of the study, total blood quantum, change in A1C from 6 months before baseline to baseline, and change in provider trust, treatment acceptance, depression, and reported hours of exercise per week from baseline to study completion. The final analysis had an *R*2 value of 0.896, accounting for 89.6% of the variance in A1C change. This pilot study provides preliminary support for the utility of MI techniques in diabetes care among American Indians