Anand SS, Yusuf S, Jacobs R, et al. Risk factors, atherosclerosis, and cardiovascular disease ...... Reitan RM. Validity of the trail making test as an indicator of ...
VASCULAR AND METABOLIC RISK FACTORS, CAROTID ATHEROSCLEROSIS AND VASCULAR COGNITIVE IMPAIRMENT IN A FIRST NATIONS POPULATION by
Jennifer Hope Fergenbaum
A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Dalla Lana School of Public Health University of Toronto
© Jennifer Hope Fergenbaum (2009)
VASCULAR AND METABOLIC RISK FACTORS, CAROTID ATHEROSCLEROSIS AND VASCULAR COGNITIVE IMPAIRMENT IN A FIRST NATIONS POPULATION Jennifer Hope Fergenbaum Doctor of Philosophy Dalla Lana School of Public Health University of Toronto 2009 ABSTRACT The objectives of the thesis were to examine the associations between vascular and metabolic risk factors, carotid atherosclerosis and cognitive function in a Canadian First Nations population. Eligible individuals were ≥18 years and with First Nations status who had undergone cognitive function assessment by the Clock Drawing Test (CDT) and the Trail Making Test Parts A and B. Parts A and B were combined into an executive function score (TMT-exec). Anthropometric, vascular and metabolic risk factors were assessed by interview, clinical examinations and blood tests. Doppler ultrasonography assessed carotid atherosclerosis (carotid stenosis, plaque volume). For the 190 individuals with TMT-exec scores, obese individuals were at a 4-fold increased risk for lowered cognitive performance compared to those who were not obese (odds ratio [OR]: 3.77, 95% confidence interval [CI]: 1.46-9.72). Those having an increased waist circumference also had 5 times the risk compared to those without an increased waist circumference (OR: 5.41, 95% CI: 1.83-15.99). Individuals having the metabolic syndrome were at a 4-fold increased risk compared to those without the metabolic syndrome (OR: 3.67, 95% CI: 1.34-10.07). No other cardiovascular risk factors were associated and no associations were shown for the CDT. For TMT-exec
ii
only, individuals with elevated levels of left (LCS) and total carotid stenosis (TCS) were less likely to demonstrate lowered cognitive performance (LCS, OR: 0.47, 95% CI: 0.24-0.96; TCS, OR: 0.40, 95% CI: 0.20-0.80). In structural equation modeling, for every 1-unit change in the anthropometric factor in kg/m2, there was a 0.86-fold decrease in the percent of TCS (p88 cm (female), and a low risk group for those with a waist circumference: c)
b y
x a
z'
c
Figure 4. Total Effects (z' + xy) and Mediation (z-z')
3.4.2 Principal Components Analysis Principal components analysis (PCA) is used when there are a large number of observed or measured variables (e.g. more than 10) that are highly correlated to one another and there is an interest to examine these variables simultaneously using regression methods. To circumvent the regression modeling limitations of SEM or classical regression methods due to multicollinearity of these variables, a principal components analysis can be used that reduces the number of variables to a smaller subset, which are referred to as factors or components. These factors or components are then interpreted to represent the clustering of the original variables among the factors or components(23). Unlike factor analysis, PCA does not assume an underlying causal structure between the factors or components and observed variables. Following from that, PCA uses an unadjusted correlation matrix in which all observed variables are standardized (e.g. σ2=1) and the retained
70 components represent the partitioning of the total amount of variance provided by the observed variables, which has been maximized. Factor analysis uses an adjusted correlation matrix with communalities on the diagonal that have been estimated using prior communality estimates (h2). In this way, factor analysis differs from PCA in that it accounts for the common variance of an observed variable and leaves the unique variance, otherwise known as the systematic or random error unanalyzed. Therefore, factor analysis and PCA have different functions although overlapping methodology(23). Standard methods of PCA follow a sequence of analytical steps. In preliminary analysis, each continuous variable should be examined for its distribution and normality, since PCA assumes a normal distribution for the variables to be analyzed. A principal components analysis is then performed to identify the number of components that account for the maximum amount of total variance. Determination of the number of components to be retained is multi-fold and includes the following: (1) eigenvalue criterion, with components having an eigenvalue >1 such that a retained component accounts for a greater amount of variance than contributed by one variable alone; (2) scree plot analysis, with components above the eigenvalue break point (Figure 5); (3) proportion of total variance >5% for each component and calculated as the eigenvalue for a given component divided by the total number of observed variables being analyzed, and the highest cumulative total variance; (4) interpretability criterion. Determination or "extraction" of the components follows a one or two-step process. In the first step, components are extracted uncorrelated, known as a varimax rotation. For some datasets, PCA stops here and the results are interpreted. However, a promax or oblique rotation may be used which allows the extracted components to be correlated if there is belief that the observed variables are interrelated. Therefore, in a subsequent analysis step, the orthogonality or
71 uncorrelated requirement may be relaxed and the results are generated for a promax or oblique rotation. The results of a promax rotation compared to a varimax rotation should indicate greater differences for the factor loadings of an observed variable across the different components. Standard regression coefficients are interpreted for a promax rotation whereas results for the rotated factor pattern are interpreted for a varimax rotation. Determination of the number of components to be retained is multi-fold and includes the criteria mentioned above.
Figure 5. Scree Plot Analysis Example
4 3
Eigenvalue
Break point
2 1 0 1
2
3
4
5
Number of observed variables
3.5 REFERENCES
1. Bruce SG, Young KT. Prevalence and risk factors for neuropathy in a Canadian First Nation Community. Diabetes Care. 2008;31:1837-1841. 2. Okura Y, Urban LH, Mahoney DW, et al. Agreement between self-report questionnaires and medical record data was substantial for diabetes, hypertension, myocardial infarction and stroke but not for heart failure. J Clin Epidemiol. 2004;57(10):1096-103.
72 3. Heckbert SR, Kooperberg C, Safford MM, et al. Comparison of self-report, hospital discharge codes, and adjudication of cardiovascular events in the Women's Health Initiative. Am J Epidemiol. 2004;160(12):1152-8.
4. Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults. Executive Summary of the Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). JAMA. 2001;285(19):2486-97.
5. National Institutes of Health. Identification, Evaluation and Treatment of Overweight and Obesity in Adults. Bethesda, MD, 2000.
6. Friedewald WT, Levy RI, Fredrickson DS. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem. 1972;18(6):499-502.
7. Matthews DR, Hosker JP, Rudenski AS, et al. Homeostasis model assessment: Insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia. 1985;28(7):412-9.
8. Wallace TM, Levy JC, Matthews DR. Use and abuse of HOMA modeling. Diabetes Care. 2004;27(6):1487-95.
9. Caraballo RS, Giovino GA, Pechacek TF, et al. Factors associated with discrepancies between self-reports on cigarette smoking and measured serum cotinine levels among persons
73 aged 17 years or older: Third National Health and Nutrition Examination Survey, 1988-1994. Am J Epidemiol. 2001;153(8):807-14.
10. McDowell I. Measuring health: A Guide to Rating Scales and Questionnaires, 3rd edition. London, UK: Oxford University Press, 2006.
11. Watson YI, Arfken CL, Birge SJ. Clock completion: An objective screening test for dementia. J Am Geriatr Soc. 1993;41(11):1235-40.
12. Arbuthnott K, Frank J. Trail making test, part B as a measure of executive control: Validation using a set-switching paradigm. J Clin Exp Neuropsychol. 2000;22(4):518-28.
13. Royall DR, Lauterbach EC, Cummings JL, et al. Executive control function: A review of its promise and challenges for clinical research. A report from the committee on research of the American Neuropsychiatric Association. J Neuropsychiatry Clin Neurosci. 2002;14(4):377-405.
14. Reitan RM. Validity of the trail making test as an indicator of organic brain damage. Percept Mot Skills. 1958;8:271-276.
15. Landry A, Fenster A. Theoretical and experimental quantification of carotid plaque volume measurements made by three-dimensional ultrasound using test phantoms. Med Phys. 2002;29(10):2319-27.
16. Landry A, Spence JD, Fenster A. Measurement of carotid plaque volume by 3-dimensional ultrasound. Stroke. 2004;35(4):864-9.
74 17. Grant EG, Benson CB, Moneta GL, Alexandrov AV, Baker JD, Bluth EI, Carroll BA, Eliasziw M, Gocke J, Hertzberg BS, Katanick S, Needleman L, Pellerito J, Polak JF, Rholl KS, Wooster DL, Zierler RE. Carotid artery stenosis: Gray-scale and Doppler US diagnosis--Society of Radiologists in Ultrasound Consensus Conference. Radiology. 2003;229:340-346. 18. Bursell SE, Cavallerano JD, Cavallerano AA, Clermont AC, Birkmire-Peters D, Aiello LP, Aiello LM, and the Joslin Vision Network Research Team. Stereo nonmydriatic digital-video color retinal imaging compared with early treatment diabetic retinopathy study seven standard field 35-mm stereo color photos for determining level of diabetic retinopathy. Ophthalmology. 2001;108(3):572-85.
19. Hubbard LD, Brothers RJ, King WN, Clegg LX, Klein R, Cooper LS, Sharrett RA, Davis MD, Cai J, Atherosclerosis Risk in Communities Study Group. Methods for evaluation of retinal microvascular abnormalities associated with hypertension/sclerosis in the Atherosclerosis Risk in Communities Study. Ophthalmology. 1999;106:2269-2280.
20. Fundus photographic risk factors for progression of diabetic retinopathy. ETDRS report number 12. Early Treatment Diabetic Retinopathy Study Research Group. Ophthalmology. 1991;98(Suppl 5):823-833.
21. Wong TY. Is retinal photography useful in the measurement of stroke risk? Lancet Neurol 2004;3:179-183. 22. Schumacker RE, Lomax RG. A Beginner's Guide to Structural Equation Modeling, 2nd edition. Mahwah, NJ, USA: Lawrence Erlbaum Associates, 2004.
75 23. Hatcher H. A Step-by-Step Approach to Using SAS for Factor Analysis and Structural Equation Modeling. Cary, NC, USA: SAS Press, 1994.
24. Tu K. Commentary: Is structural equation modeling a step forward for epidemiologists? Int J Epidemiol 2009;1-3 [epub ahead of print].
25. Aneshensel CS. Theory-Based Data Analysis for the Social Sciences. Thousand Oaks, CA, USA: Sage Publications Inc, 2002.
26. Kenny DA. Mediation. Available: http://davidakenny.net/cm/mediate.htm (accessed January, 2009).
76
CHAPTER 4: DATA MANAGEMENT
4.1 PRELIMINARY DATA ANALYSIS Preliminary data analysis was used to guide the operational definition of decreased cognitive function for Trail Making Test Parts A and B (TMT-A and TMT-B). The preliminary analysis included examining the distribution of times to completion for TMT-A (A) and TMT-B (B), and the linear relationship between the two tests. Derived scores were calculated and their distributions were also examined. The derived scores included: [B-A] (Difference), [B/A] (Ratio), and [(B-A)/A] (Combined), (TMT-D, TMT-R and TMT-C, respectively). The relationships between age and TMT-A and TMT-B, and the derived scores [B-A], [B/A] and [(B-A)/A] were examined. All descriptive statistics were calculated in SAS v. 9.1 (SAS Institute, NC). Figure 6a-b displays the distributions for times to completion for the TMT-A and TMTB. Among 203 individuals with times to completion for TMT-A, the average time was 28.4 seconds (Standard deviation [SD]: 13.0), and more than 50% of individuals had times greater than 24 seconds (Range: 10.0-97.0 seconds). Among 191 individuals with times to completion for TMT-B, the average time was 90.3 seconds (SD: 55.8), and more than 50% of individuals had times greater than 78 seconds (Range: 14.0-494.0 seconds). The relationship between TMTA and TMT-B was strong. The Spearman correlation was 0.54, and very highly statistically significant (p
