|Year : 2018 | Volume
| Issue : 2 | Page : 39-44
Appraising risk of development of cardiovascular disease in patients with type 2 diabetes mellitus
Shadan Modaresahmadi1, Shobha Rani R Hiremath1, T Vithya1, Shankar Prasad2, Amy Bobbins3, Sunitha Srinivas4
1 Department of Pharmacy Practice, Al-Ameen College of Pharmacy, Bengaluru, Karnataka, India
2 Department of General Medicine, St. Philomena's Hospital, Bengaluru, Karnataka, India
3 Department of Pharmacy, Nelson Mandela University, Port Elizabeth, Grahamstown, South Africa
4 Department of Pharmacy Practice, Rhodes University, Grahamstown, South Africa
|Date of Web Publication||19-Jun-2018|
No. 4, Mother Theresa Road, Near Life Style, Vivek Nagar Post, St. Philomena's Hospital, Bengaluru, Karnataka
Source of Support: None, Conflict of Interest: None
Background: Research shows a strong relationship between type 2 diabetes mellitus (T2DM) and cardiovascular diseases (CVD). Most commonly, diabetic participants experience increased morbidity and mortality due to CVD complications. This study aims to determine the proportion of CVD prevalence and to evaluate the risk factors for developing CVD among T2DM participants and to evaluate the CVD risk factor for the next 10 years using Joint British Societies recommendations on the the Prevention of Cardiovascular Disease (JBS3) scale.
Materials and Methods: In this hospital-based observational study, data including serum creatinine, blood urea, high-density lipoprotein (HDL), low-density lipoprotein (LDL), very LDL, echocardiography, and ECHO readings were collected from a random sample of 106 participants, both diabetic and nondiabetic. The risk of developing CVD in participants with DM in the next 10 years was evaluated using the JBS3 risk calculator through analysis of collected data.
Results: Among 106 participants, there were 72 participants with DM, and the majority of these participants had comorbidities, including hypertension and other CVDs. According to the data collected from DM participants, it was observed that 77.77% had elevated serum creatinine value, 27.77% had elevated blood urea level, 61.11% had abnormal HDL value, 65.27% had elevated LDL value, and 26.38% had elevated VLDL value. Total triglycerides level was also observed to be high in 54.16% of the DM participants. Moreover, the results indicated that CVD was present in 77.78% of type 2 diabetic patients, which was comparatively higher than in nondiabetic participants of whom 32.35% had CVD.
Conclusion: Based on the data collected and results obtained from the JBS3 risk calculator, it was found that the participants with DM were at higher risk of developing CVD.
Keywords: Cardiovascular disease, low-density lipoproteins, type 2 diabetes mellitus, very low-density lipoproteins
|How to cite this article:|
Modaresahmadi S, R Hiremath SR, Vithya T, Prasad S, Bobbins A, Srinivas S. Appraising risk of development of cardiovascular disease in patients with type 2 diabetes mellitus. Heart India 2018;6:39-44
|How to cite this URL:|
Modaresahmadi S, R Hiremath SR, Vithya T, Prasad S, Bobbins A, Srinivas S. Appraising risk of development of cardiovascular disease in patients with type 2 diabetes mellitus. Heart India [serial online] 2018 [cited 2018 Sep 23];6:39-44. Available from: http://www.heartindia.net/text.asp?2018/6/2/39/234664
| Introduction|| |
Noncommunicable diseases (NCDs) are currently the world's greatest public health challenge, with cardiovascular diseases (CVD), cancers, chronic respiratory diseases, and diabetes resulting in 63% deaths globally. Diabetes in adults (20–79 years) is estimated to increase to 439 million (7.7%) by 2013, with an estimated 20% increase in developing countries alone, largely accredited to population growth, increased life expectancy, fluctuation in aging populations and urbanization, and associated lifestyle changes. In the long-term, diabetes may lead to complications that result in premature death; most commonly involving heart attacks, kidney failure, stroke, limb amputation, nerve damage, and vision loss.
India has a triple burden of disease, with the rapid rise in NCDs threatening sustainable development of the health system and economy as a whole. In 2010, NCDs resulted in approximately 235 million disability-adjusted life years (DALYs), with the increased diabetes burden having resulted in an estimated 8 million DALYs in 2010. In 2012, 63 million Indians were living with diabetes mellitus (DM), with an estimated 33% of adults estimated to be undiagnosed by the International Diabetes Federation in 2012. The alarmingly high rate of DM in India has resulted in the country being named the “diabetes capital of the world” and will have an estimated 87.0 million adults with DM in 2030.,
Most commonly, diabetic participants experience a death due to CVD complications, more than any other related cause, such as ketoacidosis or hypoglycemia. CVD and DM develop earlier in South Asians with complications arising more frequently compared to those of European descent., The reason for a higher CVD risk for those of South Asian descent is unclear; however, it is thought that it may be due to a higher prevalence of insulin resistance and adherence risk factors prevalent in the population., Other factors previously mentioned, such as urban lifestyle (inadequate diet and lack of physical activity), further drives this link between insulin resistance and consequent CVS in South Asian populations. There is a noted low amount of research focused on South Asian communities with regard to the diabetes and CVS link, thus knowledge gaps are prevalent and are a barrier to successful health promotion interventions regarding the diabetes and CVD association. Thus, the inequality in the availability of health promotion and education, together with the unequal access to healthcare and effective management of diabetes and CVD also drive the prevalence of the diabetes and CVD link, exacerbated by contextual cultural or traditional health beliefs an relative socioeconomic status. The increase in diabetes in India and other developing countries threatens strides made to combat the high prevalence of CVD in low- and middle-income (LMIC) countries.
The purpose of the study was to assess the risk of development of CVD among DM participants in the next 10 years using the Joint British Societies recommendations on the Prevention of Cardiovascular Disease (JBS3) CVD risk calculator (JBS3), and to identify associated risk factors in the development of CVD in these participants. CVD and associated outcomes are often determined by assessing a combination of risk factors often co-existing in participants, with prevention of CVD requiring the integrated approach targeting different organ systems and related conditions. An estimation of CVD risk is desirable; however, previous strategies were based on quantification of a 10-year risk (considered short-term risk) with pharmacological treatments being employed if this risk surpasses a threshold value. This “risk-based” approach is employed to direct treatment to those of highest absolute benefit; however, concerns have since arisen, as there is a “continuum” of CVD risk and most CVD events are actually reported in those with an intermediate CVD risk (20% of 10 years risk). Thus, the new JBS3 risk calculator has been intended to evaluate individuals in the population that are at low short-term risk, but high lifetime risk, with novel metrics being used to quantify heart age and CVD event-free survival displayed together with the 10-year risk. Long-term consequences of the lifestyle of an individual are evaluated, thus CVD risk factors and the lowering of these can be modified earlier on where necessary by the use of appropriate drug therapies. In addition, this risk estimation helps facilitate dialogue between health professional and patient to ensure that risk factors can be minimized earlier on in participants life through the evaluation of CVD risk more long term.
| Materials and Methods|| |
This observational-based study was conducted from June to November 2016, with data being collected during the first 4 months of the study to appropriately estimate the prevalence of CVD among DM participants at Tertiary Care Hospital, Bengaluru. The study was approved by the Institutional Ethical Review Board committee, and the study was explained to participants in their own language, with their written informed consent being obtained before participation in the study.
The analysis of lipid profile tests was carried out for participants with medical histories of DM and CVD. The details regarding medical and medication histories were collected from the case sheets and bedside interviews of participants. The data were analyzed statistically to assess the risk factors of CVD among type 2 DM (T2DM) subjects, using the JBS3 risk calculator to estimate both 10-year risk and lifetime risk of CVD in all individuals, except those with existing CVD. There is a relationship between cholesterol levels and CVD risk with the absolute benefit of cholesterol reduction being related to the baseline CVD risk. Thus, CVD risk can be determined by values of nonfasting blood samples to measure total cholesterol and high-density lipoprotein (HDL) level, both in participants with and without CVD. The JBS3 risk calculator allows the entry of these values to calculate the CVD risk in participants.
Normally distributed variables were reported using mean ± standard deviation and categorical variables were reported using numbers and percentages. Continuous variables, which were normally distributed, were compared between categories using an Independent t-test and the Mann–Whitney U-test used for the comparison. The association between the categorical variables was examined using the Chi-square test or Fisher's exact test as appropriate. All the analyses were done using SPSS version 24 (IBM Corporation, Armonk, New York, United States of America).
| Results|| |
A total number of 106 participants were included in this study, out of which 54 were male and 52 were female. [Table 1] shows the demographic details, risk factors, and clinical features of the study population. From [Table 1], it can also be understood that the study population had both diabetic males and diabetic females. Risk factors such as hypertension and body mass index (BMI) (>25 kg/m 2) were prevalent in individuals of the diabetic population. Similarly, HbA1c (>6.5), elevated blood urea, and elevated serum creatinine were significantly higher in the diabetic population compared to the non-DM population. [Table 1] also represents the abnormal electrocardiogram and abnormal ECHO in CVD participants. The overall age group of participants ranged from 22 to 89 years old. [Figure 1] shows that majority of the participants were between the age range of 51–60 years in both the DM and non-DM Group.
|Table 1: Demographic details, risk factors, and clinical feature of the study population (n=106)|
Click here to view
Serum creatinine, total triglyceride and low-density lipoproteins (LDL) values were significantly elevated in participants with DM (P< 0.001) and in CVD participants; however, HDL values were decreased in DM participants, although not proven as statistically significant (P = 0.100), and in participants with CVD (P = 0.489) when compared to non-DM participants [Table 2]. Very LDLs value was significantly elevated in participants with DM (P = 0.037) compared to non-DM participants, but not significantly proved (P = 0.0587) among CVD participants [Table 2]. [Table 3] shows the presence of CVD among DM participants, which was high and significantly proven (P< 0.001) when compared to non-DM participants. The JBS3 risk calculation score was carried out among all non-CVD participants to assess their risk ratio to develop CVD for the next 10 years. It was observed that the risk ratio was higher for the DM participants when compared with non-DM participants [Figure 2].
|Table 2: Distribution of patients with respect to elevated biochemistry values among diabetes mellitus, nondiabetes mellitus, and cardiovascular diseases|
Click here to view
|Table 3: Distribution of patients with respect to the presence of cardiovascular disease|
Click here to view
|Figure 2: Distribution of patients based on 10-year cardiovascular diseases risk category|
Click here to view
| Discussion|| |
It was observed that 77.78% of DM participants had CVD; and with respect to the JBS3 risk calculation score, it was found that the risk ratio was high for the DM participants when compared with non-DM participants. Similarly, in other study carried out by Tungdim et al. based on gender-specific CVD risk scores, approximately one-third of the participants recruited for the study will have cardiovascular complications in the next 10 years, with 38.3% being high risk, 37% moderate risk, and 24.7% low risk. Participants with diabetes are at 2-fold to 4-fold risk of developing CVD, such as coronary artery disease (CAD), as diabetes and insulin resistance increase this risk, but also hypertension, obesity, physical inactivity, and age (the risk factors for diabetes) are also risk factors of the development of CVD., This is also coherent with the results of the Chennai Urban Population Study, whereby 11% of respondents in LMIC groups in Chennai, South India, had CAD, 1.2% had a myocardial infarction, 1.5% with ST-segment changes, 7% with T-wave abnormalities, and 1.3% with Q-wave abnormalities. The study not only indicated 10 times the prevalence rate of diabetes in urban areas since 1970 but also that diabetic respondents had 21.4% CAD (compared to 9.1% with no diabetes or glucose intolerance).,
The study reports a higher prevalence of DM in female participants. Currently, in India studies surrounding gender distribution of DM is inconclusive, as some studies from the North, including that by Misra et al. report a prevalence in women and others from the South including Ramachandran et al. report a prevalence of men, while some find no prevalence, such as that by Tripathy et al. However, the rate of diabetes in women could be widely unreported in India due to traditional gender roles, socioeconomic position of women, decision-making abilities of women in families, and control of resources of the individual. Fifty-five percent of early diagnoses of T2DM are males. Analyses have shown that diabetes contributes for a more intensive cardiovascular risk profile, with women having higher levels of blood pressure and lipids than men., The prevalence of treatment bias is evident in many contexts in both developing and developed countries, with men with diabetes or diagnosed with CVD more likely to receive statin, aspirin, or antihypertensive over women with DM.,
In this study, it was observed that hypertension is more predominant in the diabetic population by 72.22%. The prevalence of hypertension and diabetes varies between the urban and rural population, due to urbanized living contributing to the epidemiological transition that underpins the spread of NCDs, of which hypertension is a part.,, There is an estimated 33% hypertension in urbanized populations and 25% rural population, with diabetes showing a similar trend with 12.9% urban Indians with diabetes and 6.5% in rural India.
In 2008, the WHO stated that the mean BMI of people in India was around 21 kg/m 2. However, in our study, 68.05% of diabetic population had a BMI more than 25 kg/m 2. Overwhelming evidence shows the link between obesity and DM and CVD, with the rapid urbanization and westernization of Indian communities resulting in more consumption of calorie dense, high sugar, processed foods, and paired with less physical activity of this increasingly sedentary lifestyle., Like many other LMICs, India has a double burden of malnutrition, with undernutrition and obesity being prevalent in communities in both adults and children. In 2014, the ICMR-INDIAB study estimated that obesity was found to be greatest in Chandigarh, the region with the highest income per capita, that is mostly urban with peri-urban surrounding areas, further emphasizing the link between obesity and urbanized living.,,
The JBS3 risk calculator recommends the reduction of risk factors associated with the development of CVD, involving lifestyle modification to be introduced as early as possible and to be sustained to ensure the avoidance of CVD and other diabetic complications. The JBS3 calculator is valuable because of its ability to introduce interactive communication between patient and health professional, to ensure the “investment in health” concept, and health promotion that may influence health behaviors. To ensure the maximum benefit of the patient, this health promotion needs to take place to ensure that participants understand the link between their lifestyle and health, and particularly the importance in lifestyle modification in the prevention and control of diabetes and the prevention of future CVD events.
Evidently, from the uncontrolled results of the HBA1C and lipid profiles (88.88% of DM participants had HbA1C values above 6.5%, and the majority of participants with CVD had elevated biochemistry values) participants are not empowered with appropriate health promotion and medicine-related information to adherently ensure the adequate maintenance of their chronic conditions. Monitoring and maintaining the participants' glucose and cholesterol levels could prevent the complications of DM and risk of future CVD events; and can largely be influenced by the active input of a health-care professional, such as a pharmacist, in ensuring that the disease condition is understood, that the patient knows how to test their blood or where to go to attain the means to do so and how to adequately use medicines to ensure the maintenance of chronic therapy.,
In addition, strengthened health systems involving several comprehensive and integrated patient-centered care programs have shown that focusing on contextual counseling, facilitating self-care, and medication adherence of these participants would improve their quality of life of participants, ensuring that chronic illnesses can be easily managed. Multidisciplinary health-care teams consisting of interdisciplinary health professionals, including clinical pharmacists, could contribute toward optimized outcomes for participants who need chronic care, while contributing to a stronger health system, more favorable to ensuring the success of chronic care of participants., Furthermore, the use of an Expanded Chronic Care Model is pertinent in the LMIC context, to ensure that prevention efforts identify social determinants of health and optimize community participation in the attainment of better community health. The empowerment of the patient is crucial going forward, to ensure the adherence to chronic therapy and true commitment to meaningful lifestyle modification.
Limitations of the study
There were less number of lipid profile tests prescribed for the participants.
| Conclusion|| |
From the results obtained, it was clearly observed that DM participants are more likely to develop CVD compared to non-DM participants. This mirrors similar studies conducted within India, indicating the interdependence of NCDs on one another, and the heightened burden of disease that is caused by comorbidities. The readiness of the health system and the multidisciplinary health-care team needs to be optimized to involve patient-centered and health promotion interventions to curb the epidemic rise of diabetes and CVS in Indian communities.
The authors would like to acknowledge the co-operation of the participants, nurses, resident doctors, and the senior physicians of medicine Department of St. Philomena's Hospital, Bangalore for their valuable support and guidance.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Institute for Health Metrics and Evaluation. Seattle: India Country Profile; c2017. Available from: http://www.healthdata.org/india
. [Last accessed on 2017 Nov 22].
Bloom DE, Cafiero-Fonseca ET, Candeias V, Adashi E, Bloom L, Gurfein L, et al.
Economics of Non-Communicable Diseases in India: The Costs and Returns on Investment of Interventions to Promote Healthy Living and Prevent, Treat, and Manage NCDs. World Economic Forum, Harvard School of Public Health; 2014. Available from: http://www3.weforum.org/docs/WEF_EconomicNonCommunicableDiseasesIndia_Report_2014.pdf
. [Last accessed on 2017 Nov 20].
Tungdim MG, Ginzaniang T, Kabui GP, Verma D, Kapoor S. Risk of cardiovascular disease among diabetic patients in Manipur, Northeast India. J Anthropol 2014;2014:1-9.
Mather HM, Chaturvedi N, Fuller JH. Mortality and morbidity from diabetes in South Asians and Europeans: 11-year follow-up of the Southall Diabetes Survey, London, UK. Diabet Med 1998;15:53-9.
Forouhi NG, Sattar N, Tillin T, McKeigue PM, Chaturvedi N. Do known risk factors explain the higher coronary heart disease mortality in South Asian compared with European men? Prospective follow-up of the Southall and Brent studies, UK. Diabetologia 2006;49:2580-8.
McKeigue PM, Shah B, Marmot MG. Relation of central obesity and insulin resistance with high diabetes prevalence and cardiovascular risk in South Asians. Lancet 1991;337:382-6.
Gammon BD, Gunarathne A. It's time to reappraise recruitment of South Asians to clinical trials. BMJ 2008;336:46.
Khunti K, Kumar S, Bordie J. Diabetes UK and South Asian Health Foundation Recommendations on Diabetes Research Priorities for British South Asians. 1st
ed. London: Diabetes UK; 2009. p. 1-123.
JBS3 Board. Joint British Societies' consensus recommendations for the prevention of cardiovascular disease (JBS3). Heart 2014;100 Suppl 2:ii1-67.
Haffner SM, Lehto S, Rönnemaa T, Pyörälä K, Laakso M. Mortality from coronary heart disease in subjects with type 2 diabetes and in nondiabetic subjects with and without prior myocardial infarction. N
Engl J Med 1998;339:229-34.
Kannel WB. Metabolic risk factors for coronary heart disease in women: Perspective from the Framingham study. Am Heart J 1987;114:413-9.
Mohan V, Deepa R, Rani SS, Premalatha G; Chennai Urban Population Study (CUPS No. 5). Prevalence of coronary artery disease and its relationship to lipids in a selected population in South India: The Chennai Urban Population Study (CUPS No 5). J Am Coll Cardiol 2001;38:682-7.
Arvind K, Pradeepa R, Deepa R, Mohan V. Diabetes and coronary artery disease. Indian J Med Res 2002;116:163-76.
Misra A, Pandey RM, Devi JR, Sharma R, Vikram NK, Khanna N, et al.
High prevalence of diabetes, obesity and dyslipidaemia in urban slum population in Northern India. Int J Obes Relat Metab Disord 2001;25:1722-9.
Ramachandran A, Snehalatha C, Vijay V, King H. Impact of poverty on the prevalence of diabetes and its complications in urban Southern India. Diabet Med 2002;19:130-5.
Tripathy JP, Thakur JS, Jeet G, Chawla S, Jain S, Pal A, et al.
Prevalence and risk factors of diabetes in a large community-based study in North India: Results from a STEPS survey in Punjab, India. Diabetol Metab Syndr 2017;9:8.
Wingard DL, Barrett-Connor EL, Ferrara A. Is insulin really a heart disease risk factor. Diabetes Care 1995;18:1299-304.
Fuller JH, Keen H, Jarrett RJ, Omer T, Meade TW, Chakrabarti R, et al.
Haemostatic variables associated with diabetes and its complications. Br Med J 1979;2:964-6.
Tonstad S, Rosvold EO, Furu K, Skurtveit S. Undertreatment and overtreatment with statins: The Oslo Health Study 2000-2001. J Intern Med 2004;255:494-502.
Cull CA, Neil HA, Holman RR. Changing aspirin use in patients with type 2 diabetes in the UKPDS. Diabet Med 2004;21:1368-71.
Bhansali A, Dhandania VK, Deepa M, Anjana RM, Joshi SR, Joshi PP, et al.
Prevalence of and risk factors for hypertension in urban and rural India: The ICMR-INDIAB study. J Hum Hypertens 2015;29:204-9.
Anchala R, Kannuri NK, Pant H, Khan H, Franco OH, Di Angelantonio E, et al.
Hypertension in India: A systematic review and meta-analysis of prevalence, awareness, and control of hypertension. J Hypertens 2014;32:1170-7.
Gulati S, Misra A. Sugar intake, obesity, and diabetes in India. Nutrients 2014;6:5955-74.
Hanlon JT, Weinberger M, Samsa GP, Schmader KE, Uttech KM, Lewis IK, et al.
Arandomized, controlled trial of a clinical pharmacist intervention to improve inappropriate prescribing in elderly outpatients with polypharmacy. Am J Med 1996;100:428-37.
Leape LL, Cullen DJ, Clapp MD, Burdick E, Demonaco HJ, Erickson JI, et al.
Pharmacist participation on physician rounds and adverse drug events in the Intensive Care Unit. JAMA 1999;282:267-70.
Barr VJ, Robinson S, Marin-Link B, Underhill L, Dotts A, Ravensdale D, et al.
The expanded chronic care model: An integration of concepts and strategies from population health promotion and the chronic care model. Hosp Q 2003;7:73-82.
[Figure 1], [Figure 2]
[Table 1], [Table 2], [Table 3]