Dr. Sara Espinoza's presentation from Texas Public Radio's Think Science event, held on Friday, August 16, 2019.

1. Clinical Research to
Improve Health with Aging
Sara E. Espinoza, MD, MSc
Associate Professor, Department of Medicine
Sam and Ann Barshop Institute for
Longevity & Aging Studies

2. Aging in the U.S.
U.S. Census Bureau

3. Aging is Unique for Every
Person
• Aging is a spectrum, and individualized
– Environmental
– Genetic
• Unfortunately, many older adults will
– Be frail
– Have multiple chronic diseases
– Become disabled
• Goal is to prevent or delay if possible and do what
we can to enhance quality of life for all adults as they
age

4. “Healthy Aging”
• Minimize the number of years with:
–Disease
–Disability
–Depending on others
• Want to be active and independent for as
long as possible

5. Heterogeneity with Aging
Independent Dependent
Few health
problems,
active and
robust Some
health
problems Multiple
medical
problems
Frail,
vulnerable
J Walston

6. How important is aging?
Smoking Alcohol Diet Infection
PercentIncrease
0
10
20
30
40
Risk Factors for Cancer

7. Aging overwhelms all other
risk factors for Cancer
Smoking Alcohol Diet Infection Aging
PercentIncrease
0
1000
2000
3000
4000
5000
6000

8. Aging as a therapeutic target
AGINGStroke
Arthritis
Sarcopenia
Heart
Disease
Type II
Diabetes
Cancer
Neurodegenerative diseases
(Alzheimer’s, Parkinson’s, ALS)
Osteoporosis
Adapted from: Biochim. Biophys. Acta (2009) 1790: 1067-1074.

9. The Barshop Institute Mission
• To understand the basic biology of aging
• To discover the therapies that will treat diseases of aging
• To educate and train our future scientists and clinicians
• To promote public awareness of age-related issues

10. Clinical Studies
• UT Health Science Center and
the SA GRECC partner to
conduct clinical research trials
• These studies help further the
medical knowledge of specific
treatments and preventions of
various age-related diseases
• Participants can help make a
difference in the care and
treatment of future patients

11. Barshop Institute Clinical Studies
CURRENTLY RECRUITING:
• Cognitive improvement
• Healthy eating and physical activity
• Improving the immune system
• Loss of muscle & strength
• Metformin for the prevention of frailty
• South Texas Aging Registry & Repository
COMING SOON:
• MOTRPAC – Exercise
The Molecular Transducers of Physical
Activity Consortium

12. Barshop Institute Clinical Studies
COMPLETED:
• Aspirin for the prevention of events in the
elderly
• Effect of rapamycin on clinical outcomes,
function and cognition
• Healthy weight
• Effect of Microbiome on Metabolism
• Mild cognitive impairment
• Sarcopenic obesity
• Senolytics for older patients with IPF

13. Metformin for Frailty
Prevention in Older Adults
with Pre-Diabetes

14. Age-adjusted Prevalence of Obesity and Diagnosed
Diabetes Among US Adults
Obesity (BMI ≥30 kg/m2)
Diabetes
1994
1994
2000
2000
No Data <14.0% 14.0%–17.9% 18.0%–21.9% 22.0%–25.9% > 26.0%
No Data <4.5% 4.5%–5.9% 6.0%–7.4% 7.5%–8.9% >9.0%
CDC’s Division of Diabetes Translation. United States Surveillance System available at
http://www.cdc.gov/diabetes/data
2015
2015

15. Source: NIDDK, Diabetes in America,
3rd edition; niddk.nih.gov

16. Aging Consequences of Diabetes
• Diabetic sequelae
− Microvascular: Renal, eye,
− Macrovascular: heart disease, stroke,
peripheral vascular disease
− Peripheral neuropathy
• Cognitive impairment & Dementia
• Poor muscle strength/quality
− Disability
− Frailty

17. Frailty
• Clinical, geriatric syndrome
• Poor tolerance to stressors
• Vulnerable to decline
• At risk population for poor outcomes
–Falls, hospitalization, disability, death

18. Fried Model
• Weight loss
• Exhaustion
• Low physical activity
• Weak hand grip
• Slow walking speed
Fried LP, Tangen CM, Walston J. J Geron Med Sci 56:M146-M157 (2001)
Trichotomous: 0=Not frail, 1 or2 = Pre-Frail, 3 or more=Frail
Dichotomous: < 3 = Not frail, 3 or more= Frail
Cardiovascular Health Study, N = 5,317

19. SALSA - San Antonio
Longitudinal Study of Aging
• Longitudinal, observational study
• Baseline Exam (1992-96) & 3 Follow-up Exams
(2000-05)
– Comprehensive assessment of the disablement process
– Frailty classified using Fried criteria
• Original cohort characteristics:
– Unique, bi-ethnic: 394 MAs, 355 EAs
– Sociocultural variation among the MAs

20. Frailty is Associated with Obesity
and Diabetes in SALSA
Diabetes defined by ADA criteria: fasting blood glucose ≥126 mg/dL and/or taking
glucose lowering medications
N = 671
Non-frail
N = 249
Pre-frail
N = 356
Frail
N = 66
P-
value
BMI, kg/m2 27.6 ±4.2 28.7 ±5.6 30.1 ±6.8 .0018
Waist
circumference, cm
97.5 ±11.7 99.8 ±14.3 104.3 ±16 .0013
N (%) N (%) N (%)
Diabetes 29 (12.7) 77 (24.3) 27 (44.3) <.001

21. Diabetes Predicts Onset of any
One Frailty Characteristic
Covariate OR (95% CI) P-value
Diabetes 2.15 (1.18-3.94) 0.01
Ethnicity (MA vs. EA) 0.66 (0.38-1.13) 0.13
Age (1-year increments) 1.07 (1-1.14) 0.06
Sex (male vs. female) 3.38 (2.07-5.52) <0.001
Income (1-category increment) 0.87 (0.79-0.96) 0.006
Education (1-category
increment)
1.02 (0.96-1.09) 0.53
Comorbidity (not including
diabetes)
1.3 (0.83-2.05) 0.26
Using GEE analysis, average follow-up of 6.5 years.
N=466
Espinoza, Jung & Hazuda, JAGS, 2012

22. Insulin Resistance & Inflammation
Predict Frailty
Frailty
HR (95% CI)
IR-HOMA 1.15 (1.02-1.31)
Metabolic Syndrome 1.05 (0.92-1.19)
CRP 1.16 (1.02-1.32)
Barzilay et al., Arch Intern Med, 2007
Multivariable analysis adjusting for age, sex, smoking, SES, BMI, depression,
cognition, incident diabetes, heart disease, stroke, and cancer.
N = 2,826
~10 yr f/u
IR-HOMA: insulin sensitivity based on fasting insulin and glucose levels
using nonlinear statistical modeling
Cardiovascular Health Study

23. Glycoprotein Biomarkers
0
10
20
30
40
50
60
70
80
90
100
Non-frail Pre-frail Frail
FibrinogenConc.g/L
Frailty category
Plasma Fibrinogen
P < .0001
0
10
20
30
40
50
60
70
80
Non-frail Pre-frail Frail
Transferrinconc.ng/ml
Frailty category
Plasma Transferrin
P < .001
N = 65
Remained significant after age and
sex adjustment
Haptoglobin did not significantly differ
by frailty
Darvin et al., J Geron Biol Sci, 2013

24. Conceptual Model/Rationale
Insulin
Resistance
Aging & Obesity
Inflammation
MetforminMetformin
Frailty

25. Inclusion Criteria
• Age 65+
• Non-frail or Pre-frail
• Community-dwelling
• Impaired glucose tolerant
(OGTT)*
Exclusion Criteria
• Chronic disabling neurologic,
heart, pulmonary,
rheumatologic disease
* 2 hour values of 140- 199 mg/dL
Study Design
Screening
Frailty Status
OGTT
Eligibility
Baseline
Measures
Insulin Sensitivity
Inflammation
Randomize
Follow 2 years
Metformin Placebo

26. Outcomes
Primary
Outcomes
Frailty
Category
Frailty
Score
Secondary
Outcomes
Gait speed, Grip Strength, SPPB
Systemic inflammation
IL-6, CRP, TNFα, IL-1RA, TNFs R1 and 2, fibrinogen, transferrin
Muscle Inflammation
IL-1β, IL-6, MCP-1, and TNFα mRNA; MAPK & NFκB activation
Muscle Insulin Signaling
AMPK and ACC phosphorylation, PGC-1α expression, and insulin
(IRS-1, Akt , AS160, mTOR and S6K) signaling
Body composition
DEXA
Glucose Tolerance & Insulin Sensitivity
OGTT, Insulin clamp

27. Metformin for Frailty Prevention
• Metformin targets underlying mechanisms
of frailty
• The results of this trial may lead to a novel
way to prevent frailty
• Positive results will have major
implications for clinical frailty screening
and early intervention

28. Thank You, Questions?
Acknowledgements
San Antonio Pepper Center
San Antonio Nathan Shock Center
Robert Wood Johnson Foundation
NIH, 1KL2RR025766-01 (San Antonio CTSA)
VISN 17 New Investigator Award
San Antonio Area Foundation
Research Support: