Abstract P390: Using Unbiased Autopsy Data to Study Temporal Trends in Coronary Artery Disease: A 3-Decade Population-Based Study
Authors: Leibson Cynthia L , Nemetz Peter N , Bailey Kent R , Smith Carin Y , Emerson Jane A , Roger Veronique L 
Published: July 2021

The importance of coronary artery disease (CAD) surveillance, as recently underscored by the American Heart Association, is heightened by the obesity/diabetes epidemics. The need for surveillance is especially great for subclinical CAD, as the greatest increases in obesity/diabetes are occurring among younger age groups, where clinically-recognized CAD is infrequent. Yet few methods for such population-based surveillance exist. Using one such method, unbiased heart autopsy data, we previously showed that declines in grade of CAD observed since 1981 ended in the mid-1990s and may have reversed. In order to assess and interpret the factors underlying these observations, it is necessary to first examine trends in CAD risk factors among heart autopsied decedents and investigate the extent to which they mirror trends in the general population. Rochester Epidemiology Project (REP) medical records linkage resources were used to identify 1) all Olmsted County residents age 16-64 years who died from non-natural causes and went to autopsy 1981-2009 (96% autopsy rate) and 2) all Olmsted County residents age 16-64 years 1981-2009. We randomly selected two population subjects per decedent, stratified by calendar year. All REP inpatient/ ambulatory records were reviewed for CAD risk factors. Compared to population subjects, decedents were older and more likely to be male, smokers, alcohol abusers, and treated for hypertension; however, risk factor trends for both groups were generally of similar magnitude and direction, i.e., smoking, alcohol use, and blood pressure declined and anti-hypertensive medication use, diagnosed hyperlipidemia, and body mass index increased (
Table
). These population-based longitudinal data indicate that temporal trends in risk factors among autopsied decedents parallel those for the general population, supporting the concept that data on autopsied decedents can provide insight into trends in CAD risk and the changing burden of atherosclerosis within the source population.

Characteristics of all 1) non-elderly adult Olmsted County residents who died non-natural deaths and were autopsied and 2) non-elderly adult Olmsted County residents with respect to CAD risk factors and calendar year trends

CAD Risk Factor
Autopsied Decedents (N=315)
Population Subjects (N=672)
P Value
Autopsied Decedents, Temporal Trend
Population Subjects, Temporal Trend

N
Calendar Year Estimate
P value
N
Calendar Year Estimate
P Value

Age at Index, Mean (SD)
37.5 (14.0)
34.1 (13.6)

<0.01

1

315
0.172

0.06

1

672
−0.059

0.33

1

Sex, % Male
236 (75%)
339 (50%)

<0.01

2

315
0.008

0.60

2

672
−0.008

0.39

2

Race, % Non-White
29 (9%)
85 (13%)

0.14

3

305
0.054

0.04

3

589
0.023

0.12

3

Of Ever Smokers, Current = Yes
128/184 (70%)
118/210 (56%)

0.01

3

184
−0.040

0.048

3

210
−0.052

<0.01

3

Of Ever Alcohol Use, Current = Yes
175/238 (74%)
284/364 (78%)

0.08

3

238
−0.065

<0.01

3

364
−0.027

0.09

3

Mention of Alcoholism, Yes
80 (25%)
41 (6%)

<0.01

3

315
0.014

0.35

3

672
0.042

0.04

3

Closest Systolic BP, Mean (SD)
124.1 (16.6)
120.3 (15.7)

0.34

3

285
−0.179

0.09

3

600
−0.156

0.02

3

Closest Diastolic BP, Mean (SD)
76.2 (11.6)
74.0 (10.5)

0.53

3

285
−0.194

0.01

3

600
−0.112

0.02

3

Closest BP on Medication, Yes
44 (14%)
36 (5%)

<0.01

3

283
0.046

0.03

3

599
0.063

<0.01

3

Hyperlipidemia Diagnosis, Yes
32 (10%)
65 (10%)

0.32

3

315
0.114

<0.01

3

672
0.078

<0.01

3

Closest Fasting Glucose, Mean (SD)
95.8 (26.0)
95.0 (27.6)

0.63

3

177
0.270

0.23

3

335
0.505

<0.01

3

Closest BMI, Mean (SD)
26.1 (5.8)
25.9 (5.4)

0.52

3

280
0.080

0.04

3

593
0.091

<0.01

3

1

Unadjusted, Chi-square;

2

Unadjusted, Wilcoxon rank sum test;

3

Adjusted for age and sex, linear or logistic regression;

BP = Blood Pressure; BMI = Body Mass Index