Self-Directed Biological Transformation Initiative

SPECIFIC AIMS

1. To examine the effects of the Self-Directed Biological Transformation Initiative (SBTI)
course participants compared to control group participants on key biochemical and
physiological markers.

2. To examine the effects of the SBTI course participants compared to control group
participants on short- and long-term changes in heart rate variability, level of
activity, and sleep quality.

3. To examine the effects of the SBTI course participants as compared to control group
participants on mood and wellbeing.

BACKGROUND AND SIGNIFICANCE It has long been thought that ancient practices can promote
longevity and wellbeing but there is little empirical evidence and it is difficult to test
this under controlled or experimental conditions. Recently, however, there have been several
cellular-based markers that may index rate of biological aging. The rate of telomere
shortening, as indexed by change in telomere length and telomerase activity, predicts both
cellular and human longevity (Lin et al, 2012). It is related to malleable factors,
lifestyle and psychological state (Puterman & Epel, 2012). A secondary measure that shows
promise of understanding rate of cellular aging is examination of gene expression,
particularly genes related to aging. Preliminary evidence suggests that mind-body practices
may slow the rate of cellular aging by improving the telomere/telomerase maintenance system.
This has been examined in four small studies so far.

In the first study of its kind, a four month intensive lifestyle modification program,
including yoga and group support was associated with an increase in telomerase in 39 men
with prostate cancer (in preparation). There was no control group in this study. However,
those with the greatest decreases in distressing thoughts about having cancer showed the
biggest increases in telomerase activity (Ornish et al, 2008). In a second study, healthy
men and women were randomized to a 3-month intensive in-residence meditation group or wait
list control group. The researchers examined telomerase only post intervention and found
that the meditation group had 30% higher telomerase, and further increases in wellbeing were
associated with increases in telomerase (Jacobs et al, 2011). In another study with an
active control group, examined 39 elderly high stress dementia caregivers. They randomized
half the group to Yoga Nidra, listening to a 15 minute tape each day of Yoga Nidra, which
includes instruction on breathing and hand movements and half to a control group which
listened to a relaxation tape for 15 minutes each day. Eight weeks later, they found greater
telomerase increases in the Yoga Nidra group, and across the sample, decreases in depressive
symptoms were associated with increases in telomerase activity (Lavretsky et al, 2013).

Similar studies have reported positive effects of such practices on inflammatory profiles
and gene expression (Tang, Ma et al. 2009, Bhasin, Dusek et al. 2013, Saatcioglu 2013).
Another well studied parameter for practices such as meditation and yoga is heart rate
variability (HRV) as it provides a broad measure of autonomic nervous system activity.
Observational studies report HRV to be associated with stress in the workplace,(Jarczok,
Jarczok et al. 2013) depressive and anxiety disorders (Gorman and Sloan 2000), and in
individuals with chronic somatic complaints (Tak, Riese et al. 2009). Meditation and yoga
interventions to improve HRV can lead to improved physiologic and clinical outcomes (Wheat
and Larkin 2010, Papp, Lindfors et al. 2013). Other parameters can also be of potential
benefit in evaluating an individual's overall wellbeing. For example, sleep duration and
quality, as well as general activity levels can both contribute to and reflect overall
wellbeing. To date, however, lack of compelling data around objective measures of wellbeing
is in large part due to challenges associated with long term monitoring of monitoring of
appropriate patient populations. However, recent advances in biosensor technologies have
overcome this limitation and now allow for the non-obtrusive and passive monitoring of
individuals for long periods of time. These new data streams of real-time physiologic data,
couple with sophisticated and individualized data analytics can potentially identify novel
measures of individual wellness, which will allow for the development of personalized
therapeutic interventions to improve wellbeing.

Overall, while the results of many of the studies of traditional practices are compelling,
in reality, and according the more whole system approaches such as Ayurveda and Chines
Medicine, traditional practices are rarely practiced singularly, i.e, typically yoga asanas
are practiced with meditation as well as a form of pranayama (breathing). With this
consideration, few scientific studies have taken a more comprehensive "whole systems"
approach that is simultaneously inclusive of numerous practices. Regarding outcomes, this
study will take a systems biology approach to examining the biochemical, physiological, and
psychosocial effects of the intervention. It is anticipated that the findings will
demonstrate the value of taking a more inclusive and comprehensive whole systems approach to
improving wellbeing and improved health.

Inclusion Criteria:

1. Men and women between the ages of 40-80 years

Exclusion Criteria:

2. Self-reported diagnosis of a major medical condition, such as cancer (including those
who have received past radiation or chemotherapy treatment), heart disease,
autoimmune disease, or diabetes, as these can affect the cell aging system and
possibly the ability for telomerase to change in short periods

3. Individuals taking antidepressant medication will be excluded since such medication
appears to increase telomerase (Wolkowitz et al, 2010)

4. Individuals with diagnosed PTSD will be excluded; there is evidence that those with
PTSD may have lower telomere length as compared to those without PTSD (O'Donovan et
al, 2011). It is currently unknown how PTSD may impact telomerase levels

5. Estrogen use is excluded as it increases telomerase (Lin et al, 2011)

6. Smokers will be excluded since smoking decreases telomerase. We will base smoking
status on self report. If participants have not smoked regularly for the past 6
months, they will be considered a 'non-smoker

7. Pregnant women are excluded since the cell aging system changes during pregnancy in
ways that have not been studied

8. Participants with a Body Mass Index (BMI) of 35 or greater will be excluded due to
differences in telomerase activity in obese women

9. Potential eligible participants who are unable to secure the week off from work or
other responsibilities will not be enrolled

10. Known atrial fibrillation or other chronic dysrhythmia