|Year : 2018 | Volume
| Issue : 2 | Page : 39-44
Cardiovascular risk stratification in new-onset diabetes by qrisk2 risk score and conventional risk score within 3 months of diagnosis of diabetes
Sujata Hiran, Anjala Singh, Pooja Sial
Department of Medicine, Jawaharlal Nehru Hospital and Research Centre Bhilai, Chhattisgarh, India
|Date of Web Publication||10-May-2018|
Dr. Sujata Hiran
8137-A, Rangoli Gardens, Kanakpura, Jaipur - 302 021, Rajasthan
Source of Support: None, Conflict of Interest: None
Aims: This study aims to assess the cardiovascular disease (CVD) risk by QRISK2 score and conventional risk score in new-onset diabetes without a history of heart disease or stroke, to find out if patients with diabetes have similar risk of coronary artery disease (CAD) as people with established CAD and to compare the conventional and QRISK2 score for the prediction of CVD. Materials and Methods: A cross-sectional study was conducted at Bhilai over 1 year in 183 newly detected diabetic patients (89 males and 94 females) aged 40–70 years. The probable risk factors were determined by cross-tabulation of cardiometabolic parameters with the 10-year cardiovascular risk level using the QRISK2-2016 and the conventional major risk markers. Results: The mean age in males was 53.5 ± 9.7 years and in females was 54.2 ± 10.1 signifying no gender differences. Mean body mass index in the most of the individuals in both sexes were either in the pre-obese or obese range. The mean value of high-density lipoprotein cholesterol (HDLc), low-density lipoprotein cholesterol (LDLc), non-HDLc, total cholesterol and HDLc (total cholesterol/HDLc) ratio was found to be higher in females than in the males. In the conventional risk group, 67.7% of individuals with new-onset diabetes were in high-risk category, 28.9% were in moderate-risk category and 3.2% in low-risk category. The QRISK2 score in new-onset diabetes was 68.8% in high-risk category, 31.1% were in moderate-risk category and none in the low-risk category. Conclusion: Risk stratification is essential for the primary prevention of CVD risks in patients with diabetes as patients with new-onset diabetes cannot be categorised as CAD risk equivalent.
Keywords: Cardiovascular disease, conventional risk markers, new-onset diabetes mellitus, QRISK2 score
|How to cite this article:|
Hiran S, Singh A, Sial P. Cardiovascular risk stratification in new-onset diabetes by qrisk2 risk score and conventional risk score within 3 months of diagnosis of diabetes. J Diabetol 2018;9:39-44
|How to cite this URL:|
Hiran S, Singh A, Sial P. Cardiovascular risk stratification in new-onset diabetes by qrisk2 risk score and conventional risk score within 3 months of diagnosis of diabetes. J Diabetol [serial online] 2018 [cited 2021 Jul 29];9:39-44. Available from: https://www.journalofdiabetology.org/text.asp?2018/9/2/39/232225
| Introduction|| |
The cardiovascular disease (CVD) rates increased steadily in India, with an estimated 3 million coronary artery disease (CAD) deaths in 2015. Ischemic heart disease (IHD) and stroke constitute the majority of CVD mortality in India (83%), with IHD being predominant 
Indians have a 2-fold risk of CVD and 3-fold risk of diabetes when adjusted for risk factors for these conditions. It is seen that Indians get the disease at an early age, have more severe disease and worse outcome as compared to Western counterparts.
Previously, it was thought that people with type 2 diabetes mellitus had a similar cardiovascular risk as people with CAD and was labelled as CAD risk equivalent. However, latest evidence suggests that, for those newly diagnosed or for those who have been living with type 2 diabetes mellitus for <10 years, the risk may not be as high. The risk approaches that of CAD approximately after 10 years. CVD risk in type 2 diabetes mellitus is not universally similar to the risk of patients with prior CVD but is highly heterogeneous. In a large, population-based cohort including 1,586,061 adults at ages 30–90 years, who were followed up for 10 years, the CAD risk was much lower among type 2 diabetes mellitus without CAD than in patients with CAD without diabetes. Of all the available risk score, QRISK 2 provides the most accurate estimates in Indians.
In our study, we have stratified the risks by conventional and QRISK2 score in new-onset diabetic population as per Lipid Association of India (LAI) guidelines. The study is done to assess whether diabetes is really CVD risk equivalent or not. A comparison between the two risk scores is also evaluated. There are similar studies from the other parts of India but none from Chhattisgarh.
| Materials and Methods|| |
A cross-sectional study was conducted at the Jawaharlal Nehru Hospital and Research Centre of Bhilai (JLNHRC), 860-bedded hospital, over 1 year. Bhilai is a small town in Chhattisgarh state situated in Central India. We screened 2050 patients who had attended the outpatient department of the hospital. The high-risk cases who could have diabetes mellitus were assessed by Indian Diabetes Risk Score and then were investigated for the presence of diabetes by doing fasting and 2-h post-glucose blood sugar. The present cross-sectional study included 183 diabetic patients (89 males and 94 females) aged 40–70 years. Parameters such as age, weight, myocardial infarction (MI), blood pressure, family history of CAD and lipid profile were evaluated. Most of the study participants were living in the Bhilai township or nearby areas, belonging to the different communities, states, caste and culture and almost same socioeconomic status. Sample size calculation was done with the sample size of 183, population size of 2050 and a confidence interval of 95%. The standard error was found to be 0.035 and relative error 7.08 with confidence interval 0.069.
Weight and height were measured with the participants wearing light clothes without shoes. Body mass index (BMI) was calculated by dividing observed weight by height in squared meter (kg/m 2). Blood pressure was measured in the right upper arm in sitting posture. Fasting blood sugar, total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDLc) and low-density lipoprotein cholesterol (LDLc) were checked using venous plasma glucose method. Blood pressure, systolic blood pressure as well as diastolic blood pressure, was recorded using an aneroid sphygmomanometer. Participants using antihypertensive medication were classified as hypertensive ascertained by the physician.
The probable risk factors were determined by cross-tabulation of cardiometabolic parameters with the 10-year cardiovascular risk level using the QRISK2-2016 algorithm. Ten-year risk is defined as the risk of developing a first CVD event, defined as non-fatal MI or CAD death, over a 10-year period among people free from CVD at the beginning of the period. The conventional risk markers were taken from the LAI Expert Consensus Statement on Management of Dyslipidaemia in Indians 2016. The following five conventional risk markers were taken to calculate the conventional risk score.
- Age ≥45 years in males and ≥55 years in females
- Family history of early atherosclerotic CVD (ASCVD) (<55 years of age in a male first-degree relative or <65 years of age in a female first-degree relative)
- Current cigarette smoking or tobacco use
- High blood pressure (≥140/90 mmHg or on blood pressure medication)
- Low HDLc (males <40 mg/dL and females <50 mg/dL).
The conventional markers were counted in each individual and the risk markers were added to find out the total conventional risk score.
The LAI considers diabetes mellitus only in high- or very high-risk group by conventional markers. LAI has not considered duration of diabetes mellitus as a parameter while classifying them in high- or very high-risk group. The risk of developing CVD is dependent on the duration of diabetes. The patients with diabetes now have not been considered as CAD equivalent due to high heterogeneity. Since the subjects in our study had new onset diabetes, we categorized them into three groups as follows:
High-risk diabetes with >2 other major ASCVD risk factors and no evidence of end-organ damageModerate-risk diabetes with >1 other major ASCVD risk factors and no evidence of end-organ damageLow-risk diabetes with no other major ASCVD risk factors and no evidence of end-organ damage.
QRISK2 (2016) is another score which we used to stratify the risk in new-onset Indian patients with diabetes. It included age, sex and ethnicity, history of smoking and family history of angina or heart attack in a first-degree relative <60 years, hypertension, BMI and TC/HDLc ratio.
QRISK2 (2016) score was calculated and classified into three risk categories as per LAI guidelines which they have recommended for general population.
- High-risk diabetes with QRISK2 score >15%
- Moderate-risk diabetes with QRISK2 score between 5% and 15% or lifetime CVD risk was >30%
- Low-risk diabetes with QRISK2 score <5% but lifetime risk assessment >30% or non-HDLc >160 mg/dL as moderate risk.
The lipid values were considered to be high as per recommendation of LAI
- TC/HDLc >4.5
- LDL-c >100 mg/dl calculated using the Friedewald equation
- Non-HDLc >130 mg/dL.
- BMI >23 kg/m 2.
- Established clinical CVD
- Presence of atrial fibrillation, rheumatoid arthritis
- Familial homozygous hypercholesterolemia.
- History of stroke or TIA
- CKD stage 4 or 5.
- Newly diagnosed diabetes (within 3 months of diagnosis)
- Age range 40–70 years.
This was done using statistic calculator Medcalc with P < 0.05 taken as statistically significant. The continuous data were expressed as mean ± standard deviation (SD) and 95% confidence interval (CI) for the difference was calculated. Comparisons between means were done by two sample t-test calculator. The categorical variables are represented in percentage. 'N-1' Chi-squared test was used to for comparison of percentages.
Approval was taken from the Institutional Ethical Committee. Informed consent was taken from study participants.
| Results|| |
A total of 183 individuals diagnosed to have diabetes mellitus for the first time were included in the study consisting of 89 males and 94 females. There were 9 individuals in the male group whose age was <45 years and 41 females who were <55 years. Hence, age, a non-modifiable conventional risk factor, was lacking in these cases.
[Table 1] describes the mean ± SD of age, BMI, fasting blood sugar, TC/HDLc ratio, conventional and QRISK2 score in patients aged 40-70 years. The mean age in males was 53.5 ± 9.7 years and in females was 54.2 ± 10.1 signifying no gender differences in the study group(p 0.63). The mean BMI in both the sexes were in the range of pre-obese signifying obesity is common in new-onset patient with diabetes with sex differentiation (p.0004). Fasting blood sugar at the time of diagnosis was higher in the males as compared to the females. The mean ± SD value of fasting blood sugar for males and females were 169.35 ± 16 and 163.28 + 16.45 (P = 0.0014.), respectively. The mean ± SD value in males and females for HDLc was 44.4 ± 0.9 and 44.7 ± 0.9 (P = 0.753), LDLc was 115.9 ± 6.9 and123.3 ± 5.2 (P< 0.0001) and non-HDLc was 143.6 ± 6.5 and 158.8 ± 3.3(p 0.103), respectively. TC/HDLc mean value ± SD was 4.343 ± 0.040 in males, slightly lower than the high value (>4.5), and in the females, it was higher 4.656 ± 0.182 (P< 0.0001). The conventional risk score by counting the number of risk markers was mean + SD 1.847 ± 0.054 for the males and 1.973 ± 0.756 for the females (P = 0.104). QRISK2 score mean + SD in males was 27.53% ± 11.95% and in females 18.36% ± 8.9% (P< 0.0001).
|Table 1: Descriptive value for anthropometric and biochemical characteristics of patients with diabetes|
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The overall percentage of patients (in all the age group) of TC/HDLc, HDLc, non-HDLc and BMI is described in [Table 2]. The LDLC, non-HDLc, TC/HDLc ratio and BMI in males and females were 67.1% and 77.4% (P = 0.121), 71.7% and 71.7% (P = 1.0), 44.9% and 61.7% (P = 0.023), 82.0% and 89.3% (P = 0.179), respectively.
|Table 2: Distribution of cardiovascular risk factors (not included in conventional markers) in percentage|
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[Table 3] describes the total number of males and females in high, medium and low risk category with percentage. The conventional risk score and QRISK2 score in the high-risk category was 67.7% and 68.8%, respectively. The moderate risk was 28.9% and 31.1% in the conventional and the QRISK2, respectively. In the conventional risk, 3.2% were in the low risk whereas none in the QRISK2.
|Table 3: Total number of the patients in the Conventional and Qrisk2 with percentage|
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| Discussion|| |
It is established that two-third of the patients with diabetes die of ASCVD; of these approximately 40% are from CAD, 15% from other heart disease, mainly from congestive heart failure and 10% from stroke. In 2015, over 0.9 million deaths in India were attributable to diabetes directly or indirectly. Reducing ASCVD burden in diabetes is imperative to reduce the premature death, improve the quality of life and reduce the economic burden. It is therefore essential to score risk factors and treat them both for primary and secondary prevention of CAD in diabetes.
The past studies have reported that adults with diabetes mellitus had the same risk for future MI as adults with previous MI and without diabetes mellitus. Consequently, the National Cholesterol Education Program Adult Treatment Panel III guidelines in 2001 recommended that all individuals with DM be considered as 'coronary heart disease risk equivalent'. This statement has been controversial since the publication of a systematic review and meta-analyses by Bulugahapitiya et al., in 2009. Recent studies indicate that CAD risk is not uniformly distributed among people, and there is a wide heterogeneity in patients with diabetes and risk stratification is required to individualise treatment. This is especially true in new-onset diabetes. The 2016 European Society of Cardiology  and the 2016 ADA standards of diabetes care, no longer consider diabetes as a coronary heart disease risk equivalent. The risk stratification not only allows the intensive risk care management but also a cost-effective in a resource-limited country like India. The knowledge of anticipated risks can improve patient's behaviour towards his health and compliance to interventions may improve.
In our study, risk stratification for CVD was done in 183 patients with new onset diabetes. The conventional risk markers, BMI, LDLc, non-HDLc and QRISK2 score were some of the parameters used to measure the CVD risks.
Of the various risk factors, obesity and hypertension were the most common risk factor in the males and obesity and low HDLc in the females. BMI >23 kg/m 2 was seen in 82.0% and 89.3% in males and females, respectively. This suggests that only 18% of males and 10.7% of females had normal weight, Jagannathan et al. has reported the incidence of overweight and obesity as >80% in the industrial workers. Recently, Mokta et al.; reported percentage BMI >23 kg/m 2 in 63.36% and 72.05% in diabetic males and females, respectively.
More than 50% of participants had the history of hypertension at the time of diagnosis of diabetes (54.1% vs. 51.06%). It is estimated that the prevalence of hypertension among diabetic patients varies from 33% to 70%. However, a study from Amritsar has reported a very high incidence of hypertension in newly diagnosed diabetes. In this study, 86.6% of the patients with diabetes were hypertensive according to 'JNC VII' criteria (males: 88.2%, females: 84.7%).
The tobacco chewing/smoking prevalence was high in males (8.5%) as compared to females (0%). The low prevalence of tobacco chewing/smoking in our study may be due to hesitancy in disclosing the actual history.
Family history of diabetes was present in 25.8% males and 13.8% females in our study. A similar data on family history of diabetes was found in 16.9% of patients with diabetes in the National Urban Diabetes Survey.
The lipid profile was estimated in our patients, but we have not evaluated TC and serum TGs. The American Association of clinical endocrinologists (2017) states that the clinical significance of fasting hypertriglyceridemia as an independent risk factor is disappeared when LDLc and HDLc concentrations are considered. The guidelines do not consider total cholesterol in the treatment of hyperlipidemia. Non-HDLc correlates better with apolipoprotein B100 (apoB) than LDLc, and its diagnostic value as a risk factor is similar or as high as apoB. The LAI recommends non-HDLc values to be kept within the desired goals regardless of the cholesterol levels. TC/HDLc ratio was required to calculate QRISK2 score. It is a powerful predictor of CVD.
In our study, the HDL value <50 mg/dL was present in 85.1% of females signifying that low HDL is a clear risk factor in females, whereas in males, HDL <40 was seen only in 16.8%. The ICMR-INDIAB study  suggests that low HDL is the most common lipid abnormality in Indians. In their study, 72.3% had low HDLc with regional disparity. Highest rate of low HDLc was seen in Jharkhand (76.8%). In our study, low HDLc in males was not an important risk factor for CVD. The LDLc in our study was >100 mg/dL in 67.1% males and 77.4% females. Parikh et al. reported the prevalence of dyslipidaemia (LDL-C ≥100 mg/dl or HDLc <40 mg/dl for males and <50 mg/dl for females) among patients with diabetes (n =788) from clinic records as 85.5% in males and 97.8% in females.
Non-HDLc can also be used as a predictor of CAD in individuals with and without high TG levels, in type 2 diabetes mellitus and metabolic syndrome. High non-HDLc covers, to some extent, the excess CAD risk imparted by the small, dense form of LDL, which is significantly more atherogenic than the normal large buoyant particles. LAI recommends non-HDLc as a coprimary target, as important as LDLc, for lipid-lowering therapy and keeping non-HDLc within 130 mg/dL. In our patients, non-HDLc >130 mg/dL was seen in 71.7% of the patients.
The conventional risk score was 67.7%, 28.9% and 3.2% in the high-risk, moderate-risk and low-risk category in studied population suggesting that not all diabetics are at high risk. More males were present in high-risk category than the females. In our study, 32% of the patients did not fall in high-risk category, emphasising that not all patients with diabetes are cardiovascular risk equivalent.
Another risk calculator QRISK2 score was used to stratify risk in new-onset diabetes. QRISK 2 which has built-in calibration of factor 1.5 for Indian men and 1.4 for Indian women has been advocated by the LAI. Nonetheless, of all the available risk scores, the risk calculator proposed by the Joint British Societies 3rd. Iteration (JBS3) appears to provide the most accurate risk estimates in Indians., The JBS released a new-risk calculator in 2014 which is based on the QRISK lifetime cardiovascular risk calculator. We, therefore, used the latest version QRISK2 2016 to estimate the CVD risk in our patients. It is important to note that differently from previous editions, the NICE guidelines recommend the use of QRISK2 score also for patients with type 2 diabetes, but not for type 1 diabetes. It also allows better quantification of risk of CVD for patients with type 2 diabetes, which is especially prevalent among south Asian patients. To compare the validity of available risk assessment models in Indians, Bansal et al. found that the JBS3 risk calculator was the most likely to identify the MI patients have 'high risk' (defined as 10-year risk >20%). We have defined 'high risk' as a 10-year risk >15% as per LAI guidelines for general population.
LAI has lowered the bar for statin therapy threshold to <5% risk and has recommended a lifetime risk estimation in all such individuals. They stated that those found to have lifetime risk >30% are categorised in the moderate risk and eligible for statin therapy. This is especially commendable as Indians are at high risk for CVD and the need to lower the threshold of intervention for statin therapy to compensate for the underestimation of CVD risk in Indians when QRISK2 is used. None of the patients as estimated by QRISK2 score were in the low-risk category in our study. Thus, with the QRISK2 score, our patients were in two risk categories high and moderate risk.
To assess the sex difference in CVD risks obesity, hypertension and family history of CAD was the most common risk factor in the males and obesity, hypertension and low HDLc in the females.
The common conditions co-existing with type 2 diabetes in our study were hypertension dyslipidaemia and obesity. These factors are easily modifiable. Of these, dyslipidaemia has the highest population attributable risk for MI, both because of its high prevalence and also because of its direct pathogenic association with atherosclerosis. Accordingly, effective management of dyslipidaemia remains one of the most important healthcare targets for prevention of CVD.
The risk stratification by conventional score is a simplified risk assessment by counting the number of risk factors. This method of risk assessment is ideal in India where a busy practitioner sees 200–300 patients/day. The estimation of CV risk by QRISK2 is time-consuming, but it is worthwhile as it provides more precise risk categorisation. In our study, the results by both the score were same, and hence either of the two methods can be used to stratify the risk. The risk stratification in new-onset diabetes with high-CVD risk will help us to reduce adverse CVD risks by use of serum glucose cotransporters (SGLT-2) inhibitors or Glucagon-like Peptide1 (GLP-1) receptor agonist class agents after metformin. This is in light of two recent trials EMPA-REG trial  and LEADER trial  These are Empagliflozin, and LIraglutide Cardiovascular Outcomes and Mortality trial in Type 2 Diabetes which suggests reduction in major cardiovascular event rates and early death.
The QRISK2 and the conventional risk markers in our study affirm that not all individuals with diabetes, especially new onset diabetic patients can be considered as CVD risk equivalent. Our study suggests that multivariable risk prediction to be significantly better than the classification of diabetes as a cardiovascular risk equivalent. The 2013 American College of Cardiology/American Heart Association of diabetes care risk assessment guidelines includes diabetes as a predictor rather than an automatic coronary heart disease risk equivalent. It emphasises consideration of risk assessment (e.g., with the Pooled Cohort Risk Calculator) to differentiate patients with Diabetes mellitus into higher- and low-risk category.
| Conclusion|| |
Risk stratification of patients with diabetes may improve quality of indication of treatment in patients with diabetes and motivate people to change their lifestyle and adhere to the treatment. Estimates of CVD risk can be useful for both clinicians and patients: for clinicians, it gives a prognostic information that can support them in the choice of therapeutic and preventive strategies; such as use of aspirin, statins and SGLT-inhibitors and GLP-1 receptor agonists, for patients, it can be a motivation tool to adopt healthy lifestyle measures and to observe prescribed risk-modifying treatments. It is sufficient to say that the Diabetes mellitus disease is high risk for CVD, especially men but not CVD risk equivalent, hence the individualised approach to treatment is mandatory.
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Conflicts of interest
There are no conflicts of interest.
| References|| |
Indrayan A. NCMH Background Papers-Burden of Disease in India (New Delhi, India). (Section II Non-communicable diseases). New Delhi, India: Ministry of Health & Family Welfare; 2005. p. 197-218. Available from: http://www.who.int/macrohealth/action/NCMH_Burden
. [Last accessed on 2016 Jul 12].
Shrivastava U, Misra A. Need for ethnic-specific guidelines for prevention, diagnosis, and management of type 2 diabetes in South Asians. Diabetes Technol Ther 2015;17:435-9.
Rao CR, Kamath VG, Shetty A, Kamath A. A cross-sectional analysis of obesity among a rural population in coastal Southern Karnataka, India. Australas Med J 2011;4:53-7.
Sattar N. Revisiting the links between glycaemia, diabetes and cardiovascular disease. Diabetologia 2013;56:686-95.
Rana JS, Liu JY, Moffet HH, Jaffe M, Karter AJ. Diabetes and prior coronary heart disease are not necessarily risk equivalent for future coronary heart disease events. J Gen Intern Med 2016;31:387-93.
Iyengar SS, Puri R, Narasingan SN, Wangnoo SK, Mohan V, Mohan JC, et al.
Lipid association of India expert consensus statement on management of dyslipidemia in Indians 2016: Part 1. J Assoc Physicians India 2016;64:7-52.
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;285:2486-97.
Bulugahapitiya U, Siyambalapitiya S, Sithole J, Idris I. Is diabetes a coronary risk equivalent? Systematic review and meta-analysis. Diabet Med 2009;26:142-8.
Bertoluci MC, Rocha VZ. Cardiovascular risk assessment in patients with diabetes. Diabetol Metab Syndr 2017;9:25.
Piepoli MF, Hoes AW, Agewall S, Albus C, Brotons C, Catapano AL, et al.
2016 European guidelines on cardiovascular disease prevention in clinical practice: The sixth joint task force of the European Society of Cardiology and other Societies on Cardiovascular Disease Prevention in Clinical Practice (constituted by representatives of 10 societies and by invited experts) Developed with the special contribution of the European Association for Cardiovascular Prevention & Rehabilitation (EACPR). Eur Heart J 2016;37:2315-81.
Standards of medical care in diabetes-2016: Summary of revisions. Diabetes Care 2016;39 Suppl 1:S4-5.
Jagannathan R, Nanditha A, Sundaram S, Simon M, Shetty AS, Snehalatha C, et al.
Screening among male industrial workers in India shows high prevalence of impaired glucose tolerance, undetected diabetes and cardiovascular risk clustering. J Assoc Physicians India 2014;62:312-5.
Mokta J, Mokta K, Ranjan A, Garg M. Prevalence of cardiovascular risk factors among diabetic population and awareness of diabetes among diabetic patients: A Population based Himalayan study. J Assoc Physicians India 2017;65:48-52.
K1aur N, Sidhu S. Prevalence of obesity and hypertension in newly diagnosed type 2 diabetes mellitus (T2dm) patients of Amritsar. J Exerc Sci Physiol 2012;8:113-8.
Ramachandran A, Snehalatha C, Kapur A, Vijay V, Mohan V, Das AK, et al.
High prevalence of diabetes and impaired glucose tolerance in India: National Urban Diabetes Survey. Diabetologia 2001;44:1094-101.
Joshi SR, Anjana RM, Deepa M, Pradeepa R, Bhansali A, Dhandania VK, et al.
Prevalence of dyslipidemia in urban and rural India: The ICMR-INDIAB study. PLoS One 2014;9:e96808.
Parikh RM, Joshi SR, Menon PS, Shah NS. Prevalence and pattern of diabetic dyslipidemia in Indian type 2 diabetic patients. Diabetes Metab Syndr 2010;4:10-2.
Kondru S, Thakur A. Role of Non-HDL cholesterol and LDLc/HDLc ratio to assess cardiovascular risk in type II diabetes patients. Int Res J Med Sci 2015;3:23-8.
Bansal M, Kasliwal RR, Trehan N. Relationship between different cardiovascular risk scores and measures of subclinical atherosclerosis in an Indian population. Indian Heart J 2015;67:332-40.
Kasliwal RR, Bansal M, Mehrotra R, Ahlawat K, Trehan N. Comparative diagnostic accuracy of different measures of preclinical atherosclerosis for detection of atherosclerotic coronary artery disease. J Clin Prev Cardiol 2014;3:36.
Enas EA, Dharmarajan TS. The lipid association of India expert consensus statement 2016: A sea change for management of dyslipidemia in Indians. J Clin Prev Cardiol 2016;5:62-6. [Full text]
DeMaria AN. Cardiology in India. J Am Coll Cardiol 2010;56:678-9.
Zinman B, Wanner C, Lachin JM, Fitchett D, Bluhmki E, Hantel S, et al.
Empagliflozin, cardiovascular outcomes, and mortality in type 2 diabetes. N
Engl J Med 2015;373:2117-28.
[Table 1], [Table 2], [Table 3]