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| ORIGINAL ARTICLE |
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| Year : 2019 | Volume
: 8
| Issue : 1 | Page : 18-24 |
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Distribution of conventional lipids in Indians with premature coronary artery disease: A substudy of the premature coronary artery disease registry
Rahul S Patil DM 1, TR Raghu DM 1, CN Manjunath DM 1, Santu Ghosh PhD 2, Laxmi H Shetty DM 1
1 Department of Cardiology, Sri Jayadeva Institute of Cardiovascular Sciences and Research, Bengaluru, Karnataka, India
2 Department of Biostatistics, St. John's Medical College, Bengaluru, Karnataka, India
| Date of Web Publication | 11-Feb-2019 |
Correspondence Address:
Rahul S Patil
PCAD Room, 7th Floor, North Block, Sri Jayadeva Institute of Cardiovascular Sciences and Research, Bannerghatta Road, Jayanagar 9th Block, Bengaluru - 560 069, Karnataka
India
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/JCPC.JCPC_33_18
Context: Conventional lipids in young Indians with Coronary Artery Disease. Aims: To study distribution of conventional lipids and their quantification in demographic subgroups of Indians with Premature Coronary Artery Disease (PCAD). Settings and Design: PCAD Registry is a Prospective Multisite Descriptive Observational study of Indians aged below 40 years with Coronary Artery Disease. This Lipid sub study is based on preliminary data of first year of the PCAD registry. Methods and Material: Of 1380 patients registered in PCAD registry, 1061 satisfied entry criteria. Conventional lipids were estimated using commercially available kits. Each of risk factor subgroups were compared by statistical analysis of lipid values. Statistical significance was derived by independent t-test or one-way ANOVA wherever appropriate. The distribution of different lipid profile parameters was visualised by nonparametric density plot. The data was analysed by statistical software R version 3.5.0. Results: A total of 1380 patients were registered. of which 1061 patients satisfied the entry criteria and were enrolled for the lipid analysis study. The mean age of all patients registered was 34.27 (±4.30) years. Mean total cholesterol of entire study population was 171.95 ± 47.11, LDL was 116.39 ± 84.81 mg/dl, HDL was 34.50 ± 9.64, TG was 165.18 ± 87.11, non-HDL was 138.09 ± 46.18. Conclusions: Among all the conventional lipid parameters, low HDL-C along with high TGs seems to be more relevant for premature coronary artery in Indians. Primary cardiovascular disease prevention for Young Indians cannot be solely on the basis of LDL-C. All risk factors should be considered together. Larger sample population studies are needed to draw population specific cutoff values for risk factors and to discover novel risk factors (CTRI/2018/03/012544).
Keywords: Low-density lipoprotein-cholesterol, premature coronary artery disease, prospective observational study
How to cite this article:
Patil RS, Raghu T R, Manjunath C N, Ghosh S, Shetty LH. Distribution of conventional lipids in Indians with premature coronary artery disease: A substudy of the premature coronary artery disease registry. J Clin Prev Cardiol 2019;8:18-24 |
How to cite this URL:
Patil RS, Raghu T R, Manjunath C N, Ghosh S, Shetty LH. Distribution of conventional lipids in Indians with premature coronary artery disease: A substudy of the premature coronary artery disease registry. J Clin Prev Cardiol [serial online] 2019 [cited 2023 Jun 8];8:18-24. Available from: https://www.jcpconline.org/text.asp?2019/8/1/18/252011 |
| Introduction | |  |
Premature coronary artery disease (PCAD) is defined by its occurrence at a younger age (before the age of 55 years in men and 65 years in women).[1] In its severe form, PCAD occurs below the age of 40 years.[2]
Cardiovascular disease (CVD) is the leading cause (28%) of death in India.[3] The annual CVD mortality in India was predicted to rise to 4.77 million[4] making India the CVD capital of the world by 2020. The risk of coronary artery disease (CAD) in Asian Indians is 4 times Caucasians, 6 times Chinese, and 20 times Japanese.[5] Indians are prone to CAD at a much younger age.[6] Approximately 50% of first heart attacks occur before 55 years and 25% occur before 40 years of age.[7] The problem in India is the incomplete detection, treatment, and control of CAD risk factors. The nonavailability of population-specific normal ranges and guidelines for various risk factors such as low-density lipoprotein-cholesterol (LDL-C) hampers treatment. The result is that treatment of the risk factor is denied. Once the guidelines for cutoff values for Asians for the two sexes are available, a true picture of the prevalence of these risk factors in this population will be available. Appropriate guidelines for identifying and treating an individual at risk of developing CAD could then be established.
The key steps in the initiation of atherosclerotic CVD are the accumulation of cholesterol-rich apoB-containing lipoproteins within the arterial intima.[8] LDL and other apoB lipoproteins <70 nm in diameter (including very LDL [VLDL], intermediate-density lipoprotein, and Lipoprotein a) efficiently enter the arterial intima.[9] As the concentration of LDL-C in blood increases above the physiological level (20–40 mg/dl – as in new-born), the probability of initiation and progression of atherosclerotic plaque increases in a dose-dependent manner. LDL particles constitute 90% of circulating apoB-containing lipoproteins in fasting blood. Under most conditions, LDL-C concentration and LDL particle number are highly correlated, and therefore, plasma LDL-C is a good surrogate for LDL particle concentration. However, in certain conditions (e.g., the metabolic syndrome, diabetes, and hypertriglyceridemia), plasma LDL-C and LDL particle concentration can become discordant as a result of the predominance of small, dense cholesterol-poor LDL, and therefore, plasma LDL-C may not accurately reflect LDL particle concentration or its effect on cardiovascular risk.
Several large meta-analyses have consistently reported a continuous log-linear association between the plasma LDL-C levels and the risk of CVD.[9] Numerous outcome trials of intensive statin therapy have corroborated a causal role for LDL-C in atherogenesis and the safety and the benefits of lowering LDL-C.[10] Abnormal LDL has been used for many years as the risk factors for CAD. Based on the evidence and current National Cholesterol Education Program-Adult Treatment Panel (NCEP-ATP) III guidelines, LDL-C level is the primary lipid target to lower risk of coronary heart disease (CHD).
However, in many cases, the conventional lipid parameters (such as LDL-C) fail to explain the higher occurrence, prematurity, or severity of CAD in Indians. Most Indian patients with PCAD are seen to have LDL almost within the normal range but have derangement of other lipid fractions, most commonly low levels of high-density lipoprotein cholesterol (HDLC). Risk assessment that considers the entire lipid profile will identify more high-risk individuals than evaluating LDLC alone. Some studies suggest that instead of measuring the cholesterol in LDL or HDL, measuring their respective apolipoproteins, apoB100 and apoAI, may improve CHD risk assessment; similarly, ratios of lipids and/or apolipoproteins have been better predictors of CHD risk than levels of anyone lipid fraction. Trials of lipid-modifying therapy also suggest that apolipoproteins and ratios may provide improved targets for therapy beyond LDLC, but optimal values have not been established.
All of the above findings ultimately point toward the need for undertaking large sample prospective studies in India to better understand the role of lipids in PCAD in Indians.
| Subjects and Methods | | |
The PCAD registry is a prospective multisite descriptive observational study examining a cohort of young Indian adults aged ≤40 years with CAD from the point of index admission till a period of 5 years. This is registered under the Clinical Trials Registry of India (CTRI/2018/03/012544).
Inclusion criteria
(1) Patients aged ≤40 years and (2) all patients with index admission for ischemic heart disease, as proven by (i) documented episode of acute coronary syndrome and (ii) Chronic stable angina with documented evidence of CAD were included in the study.
Exclusion criteria
Patients (1) with myocarditis, cardiomyopathies, and pulmonary embolism; (2) who previously diagnosed case of CAD or on medications such as antiplatelets and statins; and (3) with chronic kidney disease, liver failure, consumption of oral contraceptives, and steroids were excluded from the study.
Once admitted into the hospital, patients who satisfied the entry criteria were selected. About 5 ml of patients' venous blood sample was collected in plain vacutainers even before the first dose of cardiac drugs were administered and sent for assessment. The blood was subjected to centrifugation at 3500 rpm for 10 min, and the separated serum was used for the estimation of routine lipid profile. Total cholesterol and triglycerides (TGs) were estimated using commercially available kits (Accurex Biomedical Pvt. Ltd., Mumbai, Maharashtra, India). Measurement of direct LDL-C was done by enzymatic homogeneous colorimetric assay using Cobas Gen3. C502 analyzer.
Statistical method
The qualitative data were summarized by count and percentage, while quantitative data were tabulated by descriptive statistics such as mean, median, standard deviation (SD), interquartile range, minimum, and maximum. Some extreme values of lipid profile were excluded (above the 99th percentile) to prevent the data from asymmetric shape. The distribution of the parameters of lipid profile was compared across all risk factors by mean and SD. Statistical significance was derived by independent t-test or one-way ANOVA wherever appropriate. The distribution of different lipid profile parameters was visualized by nonparametric density plot. The data were analysis by R statistical analysis and computing language version 3.5.1 (R core team,2018) which is released under the GNU General Public License (GPL), version 2, published by the Free Software Foundation.
Finally, all significant risk factors based on completeness of the data were put into a multivariate linear regression equation, and the slope with 95% confidence interval (CI) is reported in the table. P < 0.05 or 95% CI with both the limits is of the same sign and is considered to be statistically significant at 5% level of significance.
| Results | | |
A total of 1380 patients were registered under the PCAD registry during the 1st year, of which a total of 1061 patients satisfied the entry criteria and were enrolled for the lipid analysis study.
A majority of 980 (92.37%) of the patients were males. About 820 (77.58%) of them were aged above 30 years, 540 (50.90%) were smokers, 142 (13.38%) were diabetics, and 126 (11.88%) were hypertensives. About 147 (13.85%) had a family history of PCAD. About 582 (54.85%) were staying in urban places. About 983 (92.65%) were nonvegetarians, and 284 (26.77%) gave a history of alcohol consumption, of which 85 (8.01%) were chronic alcoholics, while the remaining 199 (18.76%) had only social alcohol consumption. About 893 (84.17%) were Hindus by religion, 154 (14.51%) Muslims, and remaining 14 were Christians [Table 1].
About 418 (39.43%) patients were overweight, while 99 (9.34%) were obese as per the calculated body mass index (BMI). About 799 (75.31%) had abdominal obesity (as per their waist-to-hip ratio [WHR]). The mean age of all patients registered was 34.27 (±4.30) years.
Coming to individual lipid parameters [Figure 1], 742 (69.9%) patients had optimal total cholesterol, 196 (18.4%) had levels in the borderline range, while only 103 (9.7%) patients were in the high range. About 470 (44.3%) patients had LDL in the optimal range, 253 (23.8%) were in optimal range, 189 (17.8%) were in borderline range, while 150 (14.13%) patients were in the high range. About 176 (16.58%) patients had HDL-C in the normal range, 401 (37.79%) were in borderline range, while 483 (45.5%) had low HDL-C. About 564 (53.15%) had TGs in optimal range, 192 (18.09%) were in borderline range, while 304 (28.65%) had high TGs. In TC: HDL ratio, 573 (54%) were in normal range, 416 (39.20%) were in borderline range, while 72 (6.78%) were in high range.
Mean total cholesterol of entire study population [Figure 2] was 171.95 ± 47.11 [Table 2], LDL was 116.39 ± 84.81 mg/dl, HDL was 34.50 ± 9.64, TG was 165.18 ± 87.11, non-HDL was 138.09 ± 46.18, mean TC/HDL ratio was 5.41 ± 1.89, mean BMI was 23.52 kg/m2, and WHR was 0.92.
Statistical analysis
After excluding the outliers above 99 percentiles, the distribution of individual lipid parameters along the given population was found to be symmetric [Figure 3].
Statistically significant intergroup differences was observed between normal versus overweight/obese by BMI and abdominal obesity with respect to total cholesterol (160.06 ± 40.2 vs. 185.17 ± 45.62; P < 0.001), LDL-C (100.11 ± 31.97 vs. 124.4 ± 42.77; P < 0.001) [Figure 4] and [Table 3], TGs (156.61 ± 84.87 vs. 172.3 ± 85.59; P= 0.001), non-HDL (126.76 ± 39.94 vs. 151.27 ± 47.92; P < 0.001), and TC: HDL ratio (5.11 ± 1.7 vs. 5.72 ± 1.96; P < 0.001). For HDL-C, the intergroup difference was nearing significance for males versus females (34.38 ± 9.67 vs. 36.01 ± 9.22; P= 0.03) and for vegetarian versus nonvegetarians (32.71 ± 9.74 vs. 34.65 ± 9.62; P= 0.09), while it was insignificant for smokers, hypertension, and religion. | Figure 4: Linear correlation of body mass index and waist-to-hip ratio with low-density lipoprotein-cholesterol
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Multivariate analysis was done by applying stepwise linear equation select appropriate predictors. With reference to LDL-C, after adjusting other factors, subgroups such as male sex showed to have 11.4 units of LDL higher than females, while people with abdominal obesity had 22.3 units of LDL higher than nonabdominal obese.
The same subgroups had positive correlation with non-HDL (males = 13.9 units and abdominal obesity = 25.6 units) and TC: HDL ratio (males = 0.76 units and abdominal obese = 0.72 units).
| Discussion | | |
Abnormal lipid parameters have been used for many years as the risk factors for CAD. Based on a preponderance of the evidence and current NCEP-ATP III guidelines, LDL-C level is the primary lipid target to lower risk of CHD, resulting in significant reductions in nonfatal and fatal CHD events.[11] However, in many cases, the conventional lipid parameters fail to explain the higher occurrence or severity of CAD in the Indian population.
The presence of high total cholesterol, TG, and LDL-C and low level of HDL-C in PCAD patients was seen in earlier studies done in the Western population.[12] Many studies have shown that increase in the HDL levels and decrease in the TGs level lead to the improvement in the outcome of CAD.
This lipid study is a part of the preliminary 1st-year report of the PCAD registry which is designed to be the largest sample and longest follow-up study of PCAD in Indians. This study had a predominant male and a nonvegetarian population, with a mean age of 34.27 years. This population was roughly proportionally distributed among smokers/nonsmokers (49.1% vs. 50.9%) and among urban/rural (45.15% vs. 54.85%).
The profile of this population reflects the unique nature of Indian PCAD, wherein almost 50% had a normal BMI, and only 9% were obese (remaining 41% being overweight). However, the same group by WHR criteria, 75.3% had abdominal obesity (average BMI: 23.52 and average WHR: 1.1).
With respect to entire study population as a whole, LDL as an independent entity did not seem to be a strong risk factor with a mean LDL of 116.39 ± 84.81 mg/dl. Mean total cholesterol of entire study population [Figure 1] was 171.95 ± 47.11, HDL was 34.50 ± 9.64, TG was 165.18 ± 87.11, non-HDL was 138.09 ± 46.18, mean TC/HDL ratio was 5.41 ± 1.89, mean BMI was 23.52 kg/m2, and WHR was 0.92.
Hence, among all the conventional lipid parameters, low HDL-C along with high TGs seems to be the main contributing factor for premature coronary artery in Indians.
Furthermore, risk assessment that considers the entire lipid profile will identify more high-risk individuals than evaluating LDL-C alone. Some epidemiologic data suggest that instead of measuring the cholesterol in LDL or HDL, measuring their respective apolipoproteins, apoB-100 and apoA-I, ratios of lipids and/or apolipoproteins have been better predictors of CHD risk.[13] Studies show that VLDL-C is as much if not more atherogenic than LDL-C.[14] The combined risk from LDL-C and VLDL-C is best assessed by calculating non-HDL-C, which contains all the apoB-containing atherogenic lipoproteins.[14] It is estimated by simply subtracting HDL-C from the total cholesterol and can be done even from nonfasting blood. High non-HDL-C level has been shown to be a strong predictor of severity of coronary atherosclerosis and major adverse cardiac event, particularly in patients who have elevations in TGs.[15]
Asian Indians have the highest risk of PCAD and diabetes. When compared with Whites, Asian Indians have double the risk of CAD and triple the risk of diabetes mellitus. A cause of concern to developing countries such as India is the incomplete detection, treatment, and control of CAD risk factors. The nonavailability of guidelines for various risk factors with particular reference to Indians hampers treatment. In the absence of population-specific upper and lower extreme values for a risk factor, a clinician is left with an “action level” for a risk factor that is perhaps more appropriate for Western populations. The result is that treatment at lower levels of the risk factor is denied.
Once the guidelines for cutoff values for Asians for different risk factors at different ages and BMI for the two sexes are available, a true picture of the prevalence of these risk factors in this population will be available. Small sample Indian studies support the use of statin therapy for primary prevention in Asian Indians at a younger age and with lower targets for LDL-C and non-HDL-C, than those currently recommended for Americans and Europeans. Early and aggressive statin therapy offers the greatest potential for reducing the continuing epidemic of CAD among Indians.
Thus, the key to finding a solution to the CAD epidemic in Asians in general and Indians, in particular, lies in getting accurate data for CAD risk factors in native populations using guideline values relevant to these populations. Primary CVD prevention for Young Indians cannot be solely on the basis of LDL-C. All risk factors should be considered together. Larger sample population studies are needed to draw population specific cutoff values for risk factors and to discover novel risk factors.
Acknowledgment
We would like to thank Research Coordinator, Mrs. Rani B J, and Research Assistant, Mr. Prateesh, for technical help.
Financial support and sponsorship
This study was financially supported by Sri Jayadeva Institute of Cardiovascular Sciences and Research.
Conflicts of interest
There are no conflicts of interest.
| References | | |
| 1. | Farmer JA, Grotto AN. Dyslipidaemia and other risk factors for coronary artery disease. In: Braunwald E, editor. Braunwald's Heart Disease: A Text Book of Cardiovascular Disease. Vol. 5. Philadelphia: W.B. Saunders and Co.; 1997. p. 1126-60.  |
| 2. | Bansal SK, Agarwal S, Daga MK. Advanced atherogenic index for the assessment of consolidated lipid risk in premature coronary artery disease patients in India. J Lab Physicians 2016;8:77-84. [ PUBMED] [Full text] |
| 3. | Enas EA, Yusuf S. Third meeting of the international working group on coronary artery disease in South Asians 29 March 1998, Atlanta, USA. Indian Heart J 1999;51:99-103. |
| 4. | Gupta R, Guptha S, Sharma KK, Gupta A, Deedwania P. Regional variations in cardiovascular risk factors in India: India heart watch. World J Cardiol 2012;4:112-20. |
| 5. | Enas EA, Garg A, Davidson MA, Nair VM, Huet BA, Yusuf S, et al. Coronary heart disease and its risk factors in first-generation immigrant Asian Indians to the United States of America. Indian Heart J 1996;48:343-53. |
| 6. | Janus ED, Postiglione A, Singh RB, Lewis B. The modernization of Asia. Implications for coronary heart disease. Council on Arteriosclerosis of the International Society and Federation of Cardiology. Circulation 1996;94:2671-3. |
| 7. | Enas EA. How to Beat the Heart Disease Epidemic among South Asians: A Prevention and Management Guide for Asian Indians and their Doctors. Downers Grove: Advanced Heart Lipid Clinic USA, 2007. |
| 8. | Tabas I, Williams KJ, Borén J. Subendothelial lipoprotein retention as the initiating process in atherosclerosis: Update and therapeutic implications. Circulation 2007;116:1832-44. |
| 9. | Grundy SM, Cleeman JI, Merz CN, Brewer HB Jr., Clark LT, Hunninghake DB, et al. Implications of recent clinical trials for the national cholesterol education program adult treatment panel III guidelines. Circulation 2004;110:227-39. |
| 10. | Emerging Risk Factors Collaboration, Di Angelantonio E, Gao P, Pennells L, Kaptoge S, Caslake M, et al. Lipid-related markers and cardiovascular disease prediction. JAMA 2012;307:2499-506. |
| 11. | National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). 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) final report. Circulation 2002;106:3143-421. |
| 12. | Ridker PM, Rifai N, Cook NR, Bradwin G, Buring JE. Non-HDL cholesterol, apolipoproteins A-I and B100, standard lipid measures, lipid ratios, and CRP as risk factors for cardiovascular disease in women. JAMA 2005;294:326-33. |
| 13. | Ballantyne CM, Hoogeveen RC. Role of lipid and lipoprotein profiles in risk assessment and therapy. Am Heart J 2003;146:227-33. |
| 14. | Liu J, Sempos C, Donahue RP, Dorn J, Trevisan M, Grundy SM, et al. Joint distribution of non-HDL and LDL cholesterol and coronary heart disease risk prediction among individuals with and without diabetes. Diabetes Care 2005;28:1916-21. |
| 15. | Pischon T, Girman CJ, Sacks FM, Rifai N, Stampfer MJ, Rimm EB, et al. Non-high-density lipoprotein cholesterol and apolipoprotein B in the prediction of coronary heart disease in men. Circulation 2005;112:3375-83. |
[Figure 1], [Figure 2], [Figure 3], [Figure 4]
[Table 1], [Table 2], [Table 3]
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