Disclosures Cardiometabolic Disease among South Asians I have no - - PDF document

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Disclosures Cardiometabolic Disease among South Asians I have no conflicts to disclose. Mediators of Atherosclerosis in South Asians Living in America Alka M. Kanaya, M.D. Professor of Medicine, Epidemiology & Biostatistics University of

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Cardiometabolic Disease among South Asians

Alka M. Kanaya, M.D.

Professor of Medicine, Epidemiology & Biostatistics University of California, San Francisco Mediators of Atherosclerosis in South Asians Living in America

Disclosures

I have no conflicts to disclose.

Indian Diaspora: 26 million

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U.S. Immigration History

v1st wave: 1899-1914; mostly Sikh, west coast v2nd wave: 1965-1980 after INS Act of 1965 v3rd wave: 1995-current

U.S. Census Bureau

2015 Estimates

US Census Bureau, 2015 ACS

Total South Asians: 5,081,457 24% of all “Asians” 1.8% of all Americans

Mortality from CHD Highest in Asian Indians: CA data

Palaniappan, Annals Epi, 2004

PMR

Kaiser: California study

v 126,905 members v health exam, 1978-1985 v 7 race/ethnic groups v followed for 17.4 years v Outcome: CAD hospital. v Adjusted for age, sex, smoking, alcohol, BMI, education, marital status, cardiorespiratory composite, SBP, total cholesterol, glucose, WBC

Hajra, JACC, 2013

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Why study South Asians?

v Cardiovascular disease is more prevalent at younger ages in SA vHigh diabetes prevalence, but that doesn’t explain the high CVD risk vShed light on etiology of ASCVD v20-25% of the world’s population

South Asian research gap

  • No longitudinal cohort data in

South Asians worldwide (in 2009)… MASALA Study Aims

Establish a prospective community-based cohort of South Asians in the Unites States to: 1. Determine the traditional, socio-cultural, behavioral, and novel risk factors associated with subclinical atherosclerosis and type 2 diabetes. 2. Compare the adjusted prevalence of CVD risk factors to the four race/ethnic groups in MESA.

Study Design

MASALA

  • Ages 40-84 years
  • N = 900
  • Two sites (UCSF and

NWU)

  • Pilot study (n=150;

2006-2007)

  • Baseline exam:

2010-2013

MESA

  • Ages 45-84 years
  • N = 6,500
  • 6 sites (Columbia,

Hopkins, NWU, Minnesota, UCLA, Wake Forest)

  • Started in 2000:

exam 5 ended in 2012

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Roadmap

  • 1. Demographic and behavioral

characteristics

  • 2. Overweight/obesity
  • 3. Diabetes
  • 4. Subclinical Atherosclerosis
  • 5. CVD events

Birth Country

84% 5% 1% 1% 1%

3% Africa 2% U.S. 2% Fiji 2% other Diaspora country

Years and Percent Time in U.S.

0-10 11-20 21-30 31-40 >40 0-20 21-40 41-60 61-80 >80

27±11 years or 49±18% of life

Religious affiliation

Overall n=906 Men n=486 Women n=420 Hinduism Sikhism Islam Jainism Christianity Buddhism None 69% 8 7 6 4 1 7 69% 8 7 7 3 1 4 69% 8 7 6 5 1 9

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SES: MESA Comparison

SA N=906 White n=2622 Black n=1893 Latino n=1496 Chin. n=803

Educ.≥BS 88% 51%* 34%* 10%* 39%* Income >$75K 73% 36%* 17%* 7%* 17%*

* p<0.001 in comparison to South Asians, adjusted by sex and age

Behavioral Factors

SA N=906 White n=2622 Black n=1893 Latino n=1496 Chin. n=803 Current Smoker

3% 12%* 18%* 14%* 6%

Alcohol

1+ drinks/week

33% 64%* 51%* 47%* 21%*

Exercise

MET-min/week

945 1852* 1654* 1200* 1230*

TV watching

Hours/week

7 14* 14* 14* 12*

* p<0.001 in comparison to South Asians, adjusted by sex and age

South Asian foods Three Major Dietary Patterns:

Animal protein Fried snacks, sweets, and high fat dairy Fruits, vegetables, nuts, and legumes

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33 33 33

Western diet Fruits and Vegetables Sweets and Refined Grains Meat, eggs, pasta, pizza, refined grains, alcohol, low-fat dairy, coffee butter, ghee, fried snacks, high-fat dairy, refined grains, rice, potatoes fresh fruit, vegetables, legumes, low-fat dairy, nuts, whole grains

Three Major Dietary Patterns:

Gadgil, J Nutrition, 2015

Characteristics by Dietary Pattern

Fruits & Veggies Sweets & Refined Grains Animal protein p Female sex 58 46 37 <0.001 Years in the U.S. 28 ± 10 25 ± 11 28 ± 11 0.67 Income >$75K 77 66 78 0.001 Religion: Hinduism Islam Sikhism 37 8 28 36 26 35 27 66 37 <0.001 <0.001 0.01

Gadgil, J Nutrition, 2015

Dietary Pattern and Metabolic Risk

Fruits & Veggies Sweets & Refined Grains Animal protein BMI

(reference) NS 0.92, p<0.05

WHR

  • NS

0.02, p<0.001

HOMA-IR

  • 1.13, p<0.05

NS

HDL

  • 2.5, p<0.05

NS

TG

  • NS

NS

Fasting Glucose

  • NS

NS

*adjusted for age, sex, site, and total caloric intake

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7 Definitions for Overweight/Obesity

WHO-general WHO-Asian Underweight <18.5 <18.5 Normal weight 18.5 – 24.9 18.5 – 22.9 Overweight 25.0 – 29.9 23.0 – 27.5 Obese ≥30.0 ≥27.5

Lancet, WHO expert panel, 2004

BMI Category by Race/Ethnicity

Yudkin-Yajnik Paradox

Lancet, 2004

Hidden Fat Stores

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Shah, Intl J Obes, 2016

South Asians store fat in all of the wrong places

Metabolic Status by BMI and Race/Ethnicity

Prevalence Ratio (95% CI) Unadjusted Multivariate Adjusted* Race/Ethnicity White 1.00 (Reference) 1.00 (Reference) South Asian 2.07 (1.69, 2.55) 2.53 (1.99, 3.22) Chinese 1.53 (1.25, 1.88) 1.27 (1.02, 1.59) African American 1.48 (1.20, 1.82) 1.66 (1.35, 2.04) Hispanic 1.83 (1.49, 2.24) 1.56 (1.26, 1.92)

Prevalence Ratios of the Metabolically Abnormal Phenotype Among Normal Weight Individuals

* adjusted for age, sex, education, alcohol use, smoking status, physical activity, daily caloric intake, hepatic fat attenuation, and pericardial fat volume Gujral, Annals Int Med, 2017

Race/ethnic-specific BMI values associated with same prevalence of metabolic abnormalities as Whites with BMI 25

South Asian Chinese Hispanic African American White

BMI (kg/m2)

25 19.6 20.921.5 22.9

Prevalence (%)

100 90 80 70 60 50 40 30 20 10

Gujral, Annals Int Med, 2017

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Optimizing BMI

vSo much ectopic fat in South Asians, which cannot be measured with simple clinical tools vWaist is not easy to measure clinically, and is only marginally better than BMI vNeed to optimize the BMI cut-points for diabetes screening guidelines

Asian Americans with newly diagnosed Type 2 Diabetes by Body Mass Index

0% 2% 4% 6% 8% 10% 12% 14% 16% 16 18 20 22 24 26 28 30 32 34 36 38 40 49

Men Women

BMI (kg/m2)

37% women and 21% men with T2DM had BMI <25 kg/m2

Araneta, Diabetes Care, 2015

Proportion who would not be screened

Screen Asian Americans at BMI ≥23 (not 25)

Hsu, Diabetes Care, 2015

Roadmap

  • 1. Demographic and behavioral

characteristics

  • 2. Overweight/obesity
  • 3. Diabetes
  • 4. Subclinical Atherosclerosis
  • 5. CVD events
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10 MASALA Diabetes Prevalence

33% 42% 26%

Normal PreDM T2DM

Comparison of 5 Race/Ethnic groups

33.1 80.6 65.4 61.9 65.5 55.6 86.5 70.5 73.5 74.0 37.1 12.5 16.2 17.9 20.3 29.0 8.8 12.2 12.4 13.8 29.8 6.9 18.4 20.2 14.2 15.4 4.7 17.3 14.1 12.2 0% 20% 40% 60% 80% 100%

Normal IFG DM

MEN WOMEN

Adjusted prevalence

South Asian n=799 White n=2,611 African American n=1,879 Latino n=1,493 Chinese American n=801 Crude prevalence, %

21.1 (18.3-24.0) 6.0* (5.1-7.0) 17.7** (15.9-19.4) 17.7** (15.7-19.6) 13.1* (10.8-15.4)

Fully adjusted†

26.7 (21.2-32.3) 6.3* (5.3-7.3) 16.4** (14.5-18.3) 14.4* (12.6-16.3) 16.0* (12.9-19.1)

* p<0.05; **p<0.001 compared to South Asians †adjusted for age, sex, clinical site, education, family income, smoking, alcohol use, exercise, BMI, waist circumference, HDL-cholesterol, triglycerides, hypertension, and fasting insulin

Kanaya, Diabetes Care, 2014

Higher levels of Insulin Resistance

** ** ** *

Excludes those on diabetes meds Adjusted for age, sex, BMI, waist * p<0.05 **p<0.001 compared to SA

Lower levels of Beta cell function

** ** ** **

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U.S. vs. India comparison

MASALA

  • Ages 40-84 years
  • N = 757
  • SF & Chicago
  • 2010-2013

CARRS

  • Ages 40-84 years
  • N = 2,305
  • Chennai, India
  • 2010-2011

Vs.

Results – Age Adjusted

U.S. India

Diabetes 24% 38% Prediabetes 33% 24%

Indians in the U.S. have lower rates of diabetes and higher rates of prediabetes than those in India.

Gujral, Diabetes Care, 2015

Subclinical Atherosclerosis

Coronary Artery Calcium (CAC)

Coronary Artery Calcium

100 200 300 400 50 100 40 50 60 70 80 40 50 60 70 80

Men Women South Asians Whites African Americans Latinos Chinese Age (years)

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12 MV model*: any CAC

South Asians compared to each MESA group *adjusted for sex, age, site, education, smoking, BMI, DM, hypertension, HDL, LDL, and cholesterol medication use

P-for-interaction by sex = 0.002

Kanaya, Atherosclerosis, 2014

MEN WOMEN

CAC progression is greater

Kanaya, AHA moderated poster, 2017

Change in CAC score per year

CVD events

vApproximately 45 hard events to date vTraditional risk factors:

  • Age, sex, diabetes, HTN, cholesterol

v Novel risk factors:

  • CAC: volume, density, and progression
  • Pericardial fat volume
  • Fasting insulin

v Stay tuned!

Conclusions (so far)

  • 1. SA have higher prevalence of several

cardiometabolic risk factors.

  • 2. An adverse body composition may be partially

culprit.

  • 3. Remember to screen, regardless of BMI.
  • 4. Several modifiable risk factors include:
  • a dietary pattern with fruits, veggies, nuts, whole

grains, and low-fat dairy

  • More exercise
  • Moderate traditional cultural beliefs
  • Anxiety and stress reduction
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13 Current and Future Directions

1. Analyzing social networks data 2. 2nd clinical exam underway 3. Multi-omics measurements 4. Second generation study 5. New immigrants from South Asia 6. Cognition with aging in South Asians 7. Diabetes and prediabetes complications 8. Environmental exposures (POPs)

Check out our website: masalastudy.org

Email: alka.kanaya@ucsf.edu Website: www.masalastudy.org Twitter: @masala_study

Thank you to the participants, study staff, investigators and collaborators of MASALA!

Are psychological symptoms linked to atherosclerosis?

Psychological factors

  • Anger
  • Anxiety
  • Depression
  • Current Stress
  • Chronic Stress
  • Everyday hassles

Carotid wall thickness

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14 Results differ by sex

Men vAnxiety and CIMT*:

0.023 (0.004-0.41, p=0.02)

vDepression and CIMT:

0.022 (0.003-0.042, p=0.03)

Women vCurrent stress & CIMT*:

0.018 (0.002-0.035; p=0.03)

vChronic stress & CIMT*:

0.017 (0.001-0.034; p=0.04)

*Even after accounting for all other risk factors, diet and exercise

Shah B, J Immigr Minor Health, 2016

“Acculturation”

Cultural items collected:

  • 1. Length of US residency
  • 2. English language proficiency
  • 3. Food eaten at home
  • 4. Food eaten outside of home
  • 5. Frequency of social, cultural,

religious engagements

  • 6. Frequency of shopping at SA

markets

  • 7. Race/ethnicity of friends
  • 8. Traditional cultural beliefs

Traditional Cultural Beliefs

In the future, how much do you wish these traditions would be practiced in America?

  • 1. Performing religious ceremonies/rituals
  • 2. Serving SA sweets at ceremonies/rituals
  • 3. Fasting on specific occasions
  • 4. Living in a joint family
  • 5. Having an arranged marriage
  • 6. Having a staple diet of chapattis, rice, daal, veg.
  • 7. Using spices for healing and health

Traditional Cultural Beliefs

%

Cronbach’s α: 0.83

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15 Acculturation and Subclinical Atherosclerosis

Carotid Intima Media Thickness (CIMT):

v Traditional cultural beliefs:

  • Weak (reference)
  • Moderate: -0.04 (-0.08 to -0.01), p=0.007
  • Strong: -0.02 (-0.05 to 0.01), p=0.22

v Independent of age, sex, years lived in the US, study site, alcohol use, waist, HDL, physical activity, total caloric intake, and cholesterol medication use

Coronary artery calcium:

v Years lived in the U.S.:

  • 2-4% risk of higher CAC for each additional year of US

residence

Kanaya, J Clin Exp Res Cardiol, 2014