Acute Myocardial Infarction and Periodontal Disease

4532 words (18 pages) Essay in Biology

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Research Findings

The study examined the association between acute myocardial infarction and periodontal diseases using cross sectional design. The analysis was carried out using the SPSS/PC Windows version 21.0 software package (IBM, Inc.). The sample size taken for the study was 80 (Cases=40, Control=40). The bivariate association between the studied variables, acute MI and periodontitis (dichotomized) was analyzed with the appropriate test. A significance level of p≤0.05 was considered significant and the odds ratios with 95% confidence intervals were calculated. Further, conditional logistic regression analysis/cox regression analysis (1:1 matched pairs) was used to assess the independent contribution of periodontal diseases to the risk of acute myocardial infarction and also to find the relationship between AMI and other possible explanatory variables. The risk factors such as tobacco habit, smoking, dietary habits, family history of diabetes, were forced into the model. The following section presents the results.

Descriptive Statistics and Preliminary Analyses

Association between acute myocardial infarction and study variables. The table below presents the association between Acute Myocardial Infarction and study variables. The results showed that odds of outcome (AMI) were significantly higher in subjects with periodontitis, smoking habits, hypertension and mixed dietary habits. Out of the total 80 patients, the majority of the periodontitis patients (82.5%) were present within the case group (AMI patients) (p=0.026). Similarly, the prevalence of smoking (52.5% vs. 27.5%, p=0.031) and hypertension (52.5% vs. 47.9%, p<0.05) was higher in cases than in controls. Further, the controls consumed significantly more vegetarian foods in comparison to the cases (85% Vs 55% p=0.04). In contrast, there was no statistically significant association between acute myocardial infarction and tobacco habits, alcohol drinking, family history of cardiovascular disease, regular exercise, diabetes, socioeconomic status, marital status or body mass index (BMI).

Table 6: Association between Acute Myocardial Infarction and study variables

Variables	Controls (n=40)	Cases (n=40)	Odds ratio (95% CI)	P-value
Periodontitis status, n (%)				0.026*
Absent	16 (40.0)	7 (17.5)
Present	24 (60.0)	33 (82.5)	3.14 (0.113-0.893)
Smoking habits				0.031*
Non-smoker	11 (27.5)	11 (27.5)	0.524 (0.173-1.588)
Former smoker	18 (45.0)	8 (20.0)	0.233 (0.077-0.704)
Smoker	11 (27.5)	21 (52.5)
Smokeless Tobacco habits				0.962
Current User	13 (32.5)	14 (35.0)	1.077 (0.374-3.101)
Former-Users	13 (32.5)	12 (30.0)	0.923 (0.314-2.716)
Non-Users	14 (35.0)	14 (35.0)
Alcohol drinking				0.569
Non-drinker	16 (40.0)	12 (30.0)
Current drinker	17 (42.5)	18 (45.0)	1.411 (0.519-3.836)
Irregular abstainer	7 (17.5)	10 (25.0)	1.9084 (0.5612-6.464)
Family history of Cardiovascular disease				0.485
No	27 (67.5)	24 (60.0)
Yes	13 (32.5)	16 (40.0)	1.385(0.289-1.804)
Exercise regularly				0.820
No	23 (57.5)	24 (60.0)
Yes	17 (42.5)	16 (40.0)	0.901 (0.455-2.701)
Hypertension				0.0008*
No	34 (85.0)	19 (47.5)
Yes	6 (15.0)	21 (52.5)	6.25 (2.1549-18.20)
Diabetes				0.431
No	32 (80.0)	29 (72.5)
Yes	8 (20.0)	11 (27.5)	1.517(0.233-1.865)
Socioeconomic status				0.576
Upper	3 (7.5)	3 (7.5)	0.7143 (0.099-5.1184)
Upper middle	19 (47.5)	14 (35.0)	0.5263 (0.1379-2.009)
Lower middle	7 (17.5)	5 (12.5)	0.5102 (0.1007-2.585)
Upper lower	6 (15.0)	11 (27.5)	1.309 (0.2868-5.980)
Lower	5 (12.5)	7 (17.5)
Marital status				0.388
Married	32 (80.0)	27 (67.5)
Unmarried	6 (15.0)	11 (27.5)	2.1728 (0.709- 6.6512)
Divorced	2 (5.0)	2 (5.0)	1.185 (0.1563- 8.986)
Dietary habits				0.040*
Vegetarian	34 (85.0)	22 (55.0)
Mixed	6 (15.0)	18 (45.0)	4.62 (0.074-0.628)
BMI (Kg/m²)	24.2±3.7	25.9±4.3		0.056

*p<0.05; BMI are represented as Mean ± SD

Association between periodontitis and study variables. The table below presents the association between Periodontitis and study variables. The results showed that odds of outcome (periodontitis) were significantly higher in subjects with smoking habits, hypertension and alcohol drinking (p<.05), suggesting higher chances of periodontitis disease occurrence among smokers, alcohol drinkers and hypertensive patients. Almost half (47.4%) of the patients with periodontitis were current smokers and alcohol drinkers (52.6%). Further, hypertension events were also found to be significantly higher in periodontitis group (43.9%) in contrast to non-periodontitis subjects (8.7%) (p<.01) However, there was no statistically significant association between periodontitis and smokeless tobacco habits, family history of cardiovascular disease, exercise regularly, diabetes, socioeconomic status, marital status and dietary habits.

Table 7: Association between Periodontitis and study variables

Variables	Absent (n=23)	Present (n=57)	Odds ratio (95% CI)	P-value
Smoking habits, n (%)				0.027*
Non-smoker	10 (43.5)	12 (21.1)	0.2222 (0.062-0.792)
Former smoker	8 (34.8)	18 (31.6)	0.417 (0.117-1.479)
Smoker	5 (21.7)	27 (47.4)
Smokeless Tobacco habits				0.122
Current User	4 (17.4)	23 (40.4)	3.721 (1.009-13.718)
Former-Users	8 (34.8)	17 (29.8)	1.375 (0.443-4.265)
Non-Users	11 (47.8)	17 (29.8)
Alcohol drinking				0.041*
Non-drinker	11 (47.8)	17 (29.8)
Current drinker	5 (21.7)	30 (52.6)	3.882 (1.1541-13.059)
Irregular abstainer	7 (30.4)	10 (17.5)	0.924 (0.2707-3.156)
Family history of Cardiovascular disease				0.492
No	16 (69.6)	35 (61.4)
Yes	7 (30.4)	22 (38.6)	1.4367 (0.247-1.961)
Exercise regularly				0.448
No	12 (52.2)	35 (61.4)	1.458 (0.549-3.872)
Yes	11 (47.8)	22 (38.6)
Hypertension				0.0075**
No	21 (91.3)	32 (56.1)
Yes	2 (8.7)	25 (43.9)	8.203 (1.7553-38.337)
Diabetes				0.163
No	20 (87.0)	41 (71.9)
Yes	3 (13.0)	16 (28.1)	2.602 (0.679-9.976)
Socioeconomic status				0.427
Upper	2 (8.7)	4 (7.0)	0.400 (0.041-3.900)
Upper middle	13 (56.5)	20 (35.1)	0.308 (0.058-1.636)
Lower middle	3 (13.0)	9 (15.8)	0.600 (0.081-4.447)
Upper lower	3 (13.0)	14 (24.6)	0.933 (0.131-6.657)
Lower	2 (8.7)	10 (17.5)
Marital status				0.552
Married	18 (78.3)	41 (71.9)
Unmarried	4 (17.4)	13 (22.8)	1.4268 (0.4087-4.981)
Divorced	1 (4.3)	3 (5.3)	1.317 (0.128-13.5378)
Dietary habits				0.957
Vegetarian	16 (69.6)	40 (70.2)
Mixed	7 (30.4)	17 (29.8)	0.971 (0.359-2.953)

Conditional Logistic Regression Analysis Using Cox Proportional Hazard Model

Following the preliminary analysis’ cox regression analyses were used to assess the independent contribution of periodontal diseases to the risk of acute myocardial infarction and also to find the relationship between an AMI event and possible explanatory variables. To control the effects of multiple potential confounders, multivariate model were also fitted by modeling periodontitis as a time varying covariant in a model.

Cox proportional hazard analysis allowed the researcher to include the predictor variables (covariates) one by one into the subsequent models. This provided estimated coefficients for each of the covariates and allowed the researcher to assess the impact of multiple covariates in the same model. We can also use Cox regression to examine the effect of continuous covariates such as BMI. The following recoding was done to examine the association between AMI and periodontitis. Socio economic status=0 (Reference category): Lower; 1=Upper Lower; 2=Lower middle; 3=Upper middle; 4=Upper: Family history=0 (Reference category): No; 1=Yes; Exercise=0 (Reference category): Yes; 1=No ; Hyper tension=0 (Reference category): No; 1=Yes: Diabetes=0 (Reference category): No; 1=Yes: Dietary habit=0 (Reference category): Vegetarian; 1=Mixed: Smoking habit=0 (Reference category): Non-smoker; 1= Former smoker; 2=Smoker: Smokeless tobacco habit=0 (Reference category): Non-users; 1= Former user; 2=Current user: Alcohol drinking=0 (Reference category): Non-drinker; 1= Current drinker; 2=Irregular abstainer: Marital status=0 (Reference category): Unmarried; 1= Married; 2=Divorced. The conditional logistic regression estimates the odds ratio, and an exact 95% confidence interval. Table 3, below presents the association between AMI and Periodontitis using Cox regression.

Table 8: Cox Regression

Variables in the Equation
	Model Constant B	SE(B)	Wald Statistic	df	Sig.	PHR, Exp(B)	95.0% CI for Exp(B)
Lower	Upper
Periodontitisn1	1.358	.505	2.501	1	.039	3.430	1.531	4.848
smoking_new			.801	2	.670
smoking_new(1)	.415	.533	1.006	1	.436	.660	.232	1.877
smoking_new(2)	.011	.464	.001	1	.981	.989	.398	2.457
hypertension_new	1.651	.389	2.800	1	.026	4.117	2.894	5.109
diethabit_new	1.680	.389	3.058	1	.048	2.507	1.636	4.086
BMI	1.245	.046	1.934	1	.034	2.046	1.255	2.945
family_history	.098	.383	.065	1	.799	1.103	.521	2.335
diabetes_new	.134	.435	.095	1	.758	.875	.373	2.052
tobaccohabit_new			.578	2	.749
tobaccohabit_new(1)	.346	.457	.573	1	.449	.708	.289	1.732
tobaccohabit_new(2)	.153	.453	.115	1	.735	.858	.353	2.084
SES_new			.689	4	.953
SES_new(1)	-.213	.639	.111	1	.739	.808	.231	2.826
SES_new(2)	-.561	.715	.616	1	.432	.571	.141	2.316
SES_new(3)	.282	.516	.299	1	.585	.754	.274	2.075
SES_new(4)	-.318	.788	.163	1	.687	.728	.155	3.410
exercise_new	-.134	.364	.135	1	.713	1.143	.560	2.334
marstatus_new			.251	2	.882
marstatus_new(1)	.235	.524	.201	1	.654	1.265	.453	3.532
marstatus_new(2)	-.030	.926	.001	1	.974	.970	.158	5.960

Dependent variable: Acute Myocardial Infarction

Conditional logistic regression analysis outcomes indicated the presence of a significant association between AMI and periodontitis (Beta=1.358, p= .039 < .05, PHR=3.430) even after introducing all other study variables (tobacco habit, smoking, dietary habits, family history of diabetes etc.) in to subsequent blocks. Periodontitis was found to be associate with hazard ratio of 3.430, which indicates that chances or probability of occurring a hazardous outcome (AMI event) is 3.43 times higher in subjects with periodontitis than the subjects without periodontitis. Nevertheless, hypertension (Exp (B) = 4.117, p (.026) <0.05), dietary habits (Exp (B) =2.507, p (.048) <0.05) and BMI (Exp (B) = 2.046, p (.034) <0.05) were also found to be significantly increasing the chances of hazardous outcome (AMI).

In all the stages, for regular exercise the beta value is negative which means it is a protective factor or is inversely related as acute myocardial events, however this association is not statistically significant to report.

Statistical Analysis of Other Clinical Parameters (DMFT, CPI and LOA Scores)

Test for normality. To test the assumption of normality, the study used the Kolmogorov-Smirnov and Shapiro-Wilks test. From this test, the Sig. (p) value was compared to the priori alpha level (level of significance for the statistic) – and a determination was made as to reject (p < α) or retain (p > α) the null hypothesis. The Table 1 below shows that where α = 0.001, given that p <0.001 and p<0.05 for the each of the levels of the Independent Variable. Hence, we can conclude that the variables were not normally distributed. Therefore, the assumption of normality was not met for this sample.

Table 9: Test for Normality

Case/Control	Kolmogorov-Smirnov	Shapiro-Wilk
Statistic	df	Sig.	Statistic	df	Sig.
Case	DMFT_S	.170	40	.005	.915	40	.005
CPI_S	.308	40	.000	.734	40	.000
LOA_S	.315	40	.000	.747	40	.000
Control	DMFT_S	.145	40	.034	.936	40	.025
CPI_S	.194	40	.001	.865	40	.000
LOA_S	.254	40	.000	.816	40	.000

Test for homogeneity of variance (equality of variances). Further, to test the assumption of homogeneity of variance, where the null hypothesis assumes no difference between the two group’s variances (H0: 2 σ 1 = 2 σ 2), a non-parametric Levene’s test for equality of variances is the most commonly used statistic to verify the equality of variances in the samples (homogeneity of variance) especially for non-normally distributed data. Therefore, Kruskal Wallis one-way analysis Leven’s test was applied. The Levene’s test uses the level of significance set a priori for the t test analysis (e.g., α = .05) to test the assumption of homogeneity of variance. However, in SPSS it’s challenging to execute Leven’s test for non-normally distributed data in one step. Hence steps were applied to create three new variables such as ranked data, group mean ranks and deviation from mean ranks. Finally, the differences were computed using ANOVA and the p value was found to be < 0.05, and hence null hypothesis is rejected and assumption was made that the differences in variances between the groups were statistically significant. Hence, homogeneity of variance was not met.

Table 10: Test Statistics

	Indivi_DMFT	Indivi_CPI	Indivi_LOA
Chi-Square	.777	.999	4.026
Df	1	1	1
Asymp. Sig.	.378	.318	.045
a. Kruskal Wallis Test
b. Grouping Variable: Case/Control

Test of Homogeneity of Variances
	Levene Statistic	df1	df2	Sig.
Indivi_DMFT	.292	1	78	.059
Indivi_CPI	39.444	1	78	.000
Indivi_LOA	1.450	1	78	.032

Mann-Whitney U and Wilcoxon W test : comparing medians. As the data is non-homogenous and non-normally distributed, Mann-Whitney U and Wilcoxon W tests were used to compare the median scores of DMFT, CPI and LOA scores, and also to check the significance of differences.

Null Hypothesis: Median score of DMFT, CPI and LOA is same for both case and control.

Alternative hypothesis: Median score of DMFT, CPI and LOA differs between case and control.

Table 11: Test Statistics

DMFT Score

CPI Score

LOA Score

Mann-Whitney U

403.500

340.500

374.500

Wilcoxon W

1223.500