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

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 California, San Francisco Indian Diaspora: 26 million 1

U.S. Immigration History 2015 Estimates v 1 st wave: 1899-1914; mostly Sikh, west coast v 2 nd wave: 1965-1980 after INS Act of 1965 Total v 3 rd wave: 1995-current South Asians: 5,081,457 24% of all “Asians” 1.8% of all Americans US Census Bureau, 2015 ACS U.S. Census Bureau Mortality from CHD Highest in Kaiser: California study Asian Indians: CA data v 126,905 members v health exam, 1978-1985 v 7 race/ethnic groups v followed for 17.4 years PMR v Outcome: CAD hospital. v Adjusted for age, sex, smoking, alcohol, BMI, education, marital status, cardiorespiratory composite, SBP, total cholesterol, glucose, WBC Palaniappan, Annals Epi, 2004 Hajra, JACC, 2013 2

Why study South Asians? South Asian research gap v Cardiovascular disease is more • No longitudinal cohort data in prevalent at younger ages in SA South Asians worldwide (in 2009)… v High diabetes prevalence, but that doesn’t explain the high CVD risk v Shed light on etiology of ASCVD v 20-25% of the world’s population Study Design MASALA Study Aims Establish a prospective community-based MASALA MESA cohort of South Asians in the Unites States to: • Ages 40-84 years • Ages 45-84 years 1. Determine the traditional, socio-cultural, • N = 900 • N = 6,500 behavioral, and novel risk factors • Two sites (UCSF and • 6 sites (Columbia, associated with subclinical atherosclerosis NWU) Hopkins, NWU, and type 2 diabetes. Minnesota, UCLA, • Pilot study (n=150; 2. Compare the adjusted prevalence of CVD Wake Forest) 2006-2007) risk factors to the four race/ethnic groups • Started in 2000: • Baseline exam: exam 5 ended in in MESA. 2010-2013 2012 3

Birth Country Roadmap 1. Demographic and behavioral characteristics 5% 1% 2. Overweight/obesity 1% 84% 3. Diabetes 4. Subclinical Atherosclerosis 3% Africa 2% U.S. 2% Fiji 5. CVD events 2% other Diaspora country 1% Years and Percent Time in U.S. Religious affiliation Overall Men Women 27±11 years or 49±18% of life n=906 n=486 n=420 Hinduism 69% 69% 69% Sikhism 8 8 8 Islam 7 7 7 Jainism 6 7 6 Christianity 4 3 5 Buddhism 1 1 1 None 7 4 9 0-10 11-20 21-30 31-40 >40 0-20 21-40 41-60 61-80 >80 4

SES: MESA Comparison Behavioral Factors SA White Black Latino Chin. N=906 n=2622 n=1893 n=1496 n=803 SA White Black Latino Chin. 3% 12%* 18%* 14%* 6% Current Smoker N=906 n=2622 n=1893 n=1496 n=803 Educ. ≥ BS 88% 51%* 34%* 10%* 39%* Alcohol 33% 64%* 51%* 47%* 21%* 1+ drinks/week Income Exercise 73% 36%* 17%* 7%* 17%* 945 1852* 1654* 1200* 1230* >$75K MET-min/week TV watching 7 14* 14* 14* 12* * p<0.001 in comparison to South Asians, adjusted by sex and age Hours/week * p<0.001 in comparison to South Asians, adjusted by sex and age Three Major Dietary Patterns: South Asian foods Fried snacks, sweets, and Animal protein high fat dairy Fruits, vegetables, nuts, and legumes 5

Three Major Dietary Patterns: Characteristics by Dietary Pattern butter, Sweets & 33 33 Meat, eggs, Fruits & Animal ghee, Refined p pasta, pizza, Veggies protein fried snacks, Grains refined grains, high-fat dairy, Western diet alcohol, low-fat Female sex 58 46 37 <0.001 refined grains, dairy, coffee rice, potatoes Years in the U.S. 28 ± 10 25 ± 11 28 ± 11 0.67 Fruits and Vegetables fresh fruit, Income >$75K 77 66 78 0.001 vegetables, legumes, Sweets and Refined Religion: Hinduism 37 36 27 <0.001 low-fat dairy, nuts, Grains Islam 8 26 66 <0.001 whole grains Sikhism 28 35 37 0.01 33 Gadgil, J Nutrition , 2015 Gadgil, J Nutrition , 2015 Dietary Pattern and Metabolic Risk Sweets & Fruits & Animal Refined Veggies protein Grains 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 6

Definitions for BMI Category by Race/Ethnicity 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 Yudkin-Yajnik Paradox Hidden Fat Stores Lancet, 2004 7

Metabolic Status by BMI and Race/Ethnicity South Asians store fat in all of the wrong places Shah, Intl J Obes , 2016 Race/ethnic-specific BMI values associated with same Prevalence Ratios of the Metabolically Abnormal prevalence of metabolic abnormalities as Whites with Phenotype Among Normal Weight Individuals BMI 25 100 South Asian 90 Chinese Prevalence Ratio (95% CI) Hispanic 80 African American Multivariate Unadjusted White Adjusted* 70 Prevalence (%) Race/Ethnicity 60 White 1.00 (Reference) 1.00 (Reference) 50 South Asian 2.07 (1.69, 2.55) 2.53 (1.99, 3.22) 40 Chinese 1.53 (1.25, 1.88) 1.27 (1.02, 1.59) 30 African American 1.48 (1.20, 1.82) 1.66 (1.35, 2.04) 20 Hispanic 1.83 (1.49, 2.24) 1.56 (1.26, 1.92) 10 * adjusted for age, sex, education, alcohol use, smoking status, physical activity, daily caloric intake, hepatic fat attenuation, and pericardial fat volume 0 Gujral, 19.6 20.921.5 22.9 25 Annals Int BMI (kg/m 2 ) Gujral, Annals Int Med , 2017 Med , 2017 8

Asian Americans with newly diagnosed Optimizing BMI Type 2 Diabetes by Body Mass Index v So much ectopic fat in South Asians, Men 16% Proportion who would which cannot be measured with simple 14% Women not be screened clinical tools 12% 37% women and 10% 21% men v Waist is not easy to measure clinically, 8% with T2DM had and is only marginally better than BMI BMI <25 kg/m 2 6% v Need to optimize the BMI cut-points 4% for diabetes screening guidelines 2% 0% 16 18 20 22 24 26 28 30 32 34 36 38 40 49 BMI (kg/m 2 ) Araneta, Diabetes Care , 2015 Screen Asian Americans Roadmap at BMI ≥ 23 (not 25) 1. Demographic and behavioral characteristics 2. Overweight/obesity 3. Diabetes 4. Subclinical Atherosclerosis 5. CVD events Hsu, Diabetes Care, 2015 9

Comparison of 5 Race/Ethnic groups MASALA Diabetes Prevalence MEN WOMEN 100% 4.7 6.9 12.2 14.2 15.4 14.1 18.4 8.8 17.3 20.2 12.5 29.8 13.8 80% 12.4 12.2 20.3 16.2 17.9 29.0 26% 42% Normal 60% PreDM 37.1 86.5 T2DM 40% 80.6 73.5 74.0 70.5 65.4 65.5 61.9 55.6 33% 20% 33.1 0% Normal IFG DM Adjusted prevalence * Higher levels of ** ** ** Insulin Resistance South African Chinese White Latino Asian American American n=2,611 n=1,493 n=799 n=1,879 n=801 Crude 21.1 6.0* 17.7** 17.7** 13.1* ** prevalence, % (18.3-24.0) (5.1-7.0) (15.9-19.4) (15.7-19.6) (10.8-15.4) ** ** ** Lower levels of Beta cell function 26.7 6.3* 16.4** 14.4* 16.0* Fully adjusted† (21.2-32.3) (5.3-7.3) (14.5-18.3) (12.6-16.3) (12.9-19.1) * p<0.05; **p<0.001 compared to South Asians Excludes those on diabetes meds †adjusted for age, sex, clinical site, education, family income, smoking, alcohol use, exercise, BMI, Adjusted for age, sex, BMI, waist waist circumference, HDL-cholesterol, triglycerides, hypertension, and fasting insulin * p<0.05 **p<0.001 compared to SA Kanaya, Diabetes Care , 2014 10

U.S. vs. India comparison Results – Age Adjusted MASALA CARRS • Ages 40-84 years • Ages 40-84 years U.S. India • N = 757 • N = 2,305 Diabetes 24% 38% • SF & Chicago • Chennai, India • 2010-2013 • 2010-2011 Prediabetes 33% 24% Indians in the U.S. have lower rates of diabetes and higher rates of prediabetes than those in India. Vs. Gujral, Diabetes Care , 2015 Coronary Artery Calcium Subclinical Atherosclerosis Men Women 100 Coronary Artery Calcium (CAC) 400 300 50 200 100 0 0 40 50 60 70 80 40 50 60 70 80 Age (years) South Asians Whites African Americans Latinos Chinese 11

MV model*: any CAC CAC progression is greater South Asians compared to each MESA group Change in CAC score per year *adjusted for sex, age, site, education, MEN smoking, BMI, DM, hypertension, HDL, LDL, and cholesterol medication use P-for-interaction by sex = 0.002 WOMEN Kanaya, Atherosclerosis , 2014 Kanaya, AHA moderated poster, 2017 CVD events Conclusions (so far) 1. SA have higher prevalence of several v Approximately 45 hard events to date cardiometabolic risk factors. v Traditional risk factors: 2. An adverse body composition may be partially culprit. • Age, sex, diabetes, HTN, cholesterol 3. Remember to screen, regardless of BMI. v Novel risk factors: 4. Several modifiable risk factors include: • CAC: volume, density, and progression • a dietary pattern with fruits, veggies, nuts, whole grains, and low-fat dairy • Pericardial fat volume • More exercise • Fasting insulin • Moderate traditional cultural beliefs v Stay tuned! • Anxiety and stress reduction 12