Skip to content
2000
  1. Home
  2. A-Z Publications
  3. Current Diabetes Reviews
  4. Volume 21, Issue 2
  5. Article
Volume 21, Issue 2
  • ISSN: 1573-3998
  • E-ISSN: 1875-6417

Abstract

Background

A condition that affects the circulatory system of the human body is referred to as a cardiovascular disease (CVD). Cardiovascular diseases (CVDs) are responsible for a significant number of fatalities globally. Annually, CVDs result in the demise of 17.9 million people, which accounts for 31% of all fatalities on a global scale.

Objective

The objective of the study was to assess the demographic profile of diabetic and non-diabetic patients suffering from cardiovascular disease. The aim of the study is to predict risk factors in relation to hyperlipidaemia using two different scales, the Framingham Risk Scale (FRS) and the Cholesterol Risk Calculator (CRC), and to determine the frequency of hypercholesterolemia in relation to CVD.

Methods

A cross-sectional study was conducted in Guru Gobind Singh Medical College and Hospital, Punjab, India.

Results

The mean age of patients was found to be M= (51.23), SD= (9.348) years, and among 331 patients (52.6%) were female patients. The mean of Framingham Risk Score was found to be (29.07%). The Framingham Risk Score was found significant with gender and calorie intake below the recommended dietary allowances of the patient (). The Framingham Risk Score was found significant with physical activity and employment status of the patients ( In linear regression, the Framingham Risk Score was found significant with the lipid profile of the patients (0.001) ., the higher the value of cholesterol level, the higher the Framingham Risk Score. The chi-square test showed a significant relation between Cholesterol Risk Score and employment status, physical activity, calorie intake, gender, and occupation of the patients (0.001, 0.001, 0.001, 0.004) respectively.

Conclusion

The present study demonstrated that patients with high Framingham risk score and cholesterol risk score are at increased risk of diabetes and cardiovascular disease. The present study concludes that the FRS is higher in patients below RDA, patients doing low physical activity, and sedentary workers. In order to provide proper assistance and counselling, healthcare professionals must continuously analyze each patient's risk factor for CVD and barriers to healthy and preventive behaviors. There is a lack of comprehensive studies comparing the effectiveness of the Framingham Risk Score and Cholesterol Risk Score in predicting hyperlipidemia and associated cardiovascular risks within the context of a tertiary care hospital setting.

© 2025 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/cdr/10.2174/0115733998284967240412104822
2024-07-10
2024-11-19

  • From This Site

    /content/journals/cdr/10.2174/0115733998284967240412104822
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. WangT. LiY. ZhengX. Association of socioeconomic status with cardiovascular disease and cardiovascular risk factors: a systematic review and meta-analysis.J Public Health20232018012345678911510.1007/s10389‑023‑01825‑4 36714072
     [Google Scholar]
  2. KunduJ. KunduS. Cardiovascular disease (CVD) and its associated risk factors among older adults in India: Evidence from LASI Wave 1.Clin. Epidemiol. Glob. Heal.20221310093710.1016/j.cegh.2021.100937
     [Google Scholar]
  3. ZubairF. NawazS.K. NawazA. NangyalH. AmjadN. KhanM.S. Prevalence of cardiovascular diseases in Punjab, Pakistan: a cross-sectional study.J Public Health201826552352910.1007/s10389‑018‑0898‑4
     [Google Scholar]
  4. Al-NohairS BabikerN Al-AhmariD Cross-sectional study of cardiovascular risk factors among male and female medical students in qassim university – college of medicine saudi arabia.J Med Sci20208E43944510.3889/oamjms.2020.4501
     [Google Scholar]
  5. MainiE. VenkateswarluB. MainiB. MarwahaD. Machine learning–based heart disease prediction system for Indian population: An exploratory study done in South India.Med. J. Armed Forces India202177330231110.1016/j.mjafi.2020.10.013 34305284
     [Google Scholar]
  6. MohdN. MohdZ. KrishnapillaiA. RahimiM. HassanC. Predictors of cardiovascular disease using framingham risk score in a rural population.Malaysian J Med Heal Sci202218June34034710.47836/mjmhs18.8.43
     [Google Scholar]
  7. BanitalebiE. Mardaniyan GhahfarrokhiM. FaramarziM. NasiriS. The effects of 10-week different exercise interventions on Framingham risk score and metabolic syndrome severity scores in overweight women with type 2 diabetes.Shahrekord Univ. Med. Sci. J.20182111810.34172/jsums.2019.01
     [Google Scholar]
  8. Al-ShamsiS. Performance of the Framingham coronary heart disease risk score for predicting 10-year cardiac risk in adult United Arab Emirates nationals without diabetes: a retrospective cohort study.BMC Fam. Pract.202021117510.1186/s12875‑020‑01246‑2 32847496
     [Google Scholar]
  9. TriantafyllidisA. KondylakisH. KatehakisD. Deep learning in mHealth for cardiovascular disease, diabetes, and cancer : systematic review.JMIR Mhealth Uhealth2022104e3234410.2196/32344 35377325
     [Google Scholar]
  10. MohanV. VenkatramanJ.V. PradeepaR. Epidemiology of cardiovascular disease in type 2 diabetes: the Indian scenario.J. Diabetes Sci. Technol.20104115817010.1177/193229681000400121 20167181
     [Google Scholar]
  11. PraveenD. PeirisD. MacMahonS. Cardiovascular disease risk and comparison of different strategies for blood pressure management in rural India.BMC Public Health2018181126410.1186/s12889‑018‑6142‑x 30442122
     [Google Scholar]
  12. Hernández-VásquezA. Vargas-FernándezR. Chacón-DiazM. Association between altitude and the framingham risk score: a cross-sectional study in the peruvian adult population.Int. J. Environ. Res. Public Health2022197383810.3390/ijerph19073838 35409522
     [Google Scholar]
  13. AggarwalP. SinhaS.K. KhanraD. Comparison of original and modified Q risk 2 risk score with Framingham risk score - An Indian perspective.Indian Heart J.202173335335810.1016/j.ihj.2021.01.016 34154755
     [Google Scholar]
  14. YadavR. YadavR.K. SarvottamK. NetamR. Framingham risk score and estimated 10-year.J. Altern. Complement. Med.201723973073710.1089/acm.2016.0309 28437144
     [Google Scholar]
  15. KumariN. VermaD.K. BinawaraB.K. A study to assess the future 10-year cardiovascular and8-year diabetic risk by using Framingham heartscale in prediabetic subjects.Int. J. Endocrinol.202218524525010.22141/2224‑0721.18.5.2022.1182
     [Google Scholar]
  16. JahangiryL. FarhangiM.A. RezaeiF. Framingham risk score for estimation of 10-years of cardiovascular diseases risk in patients with metabolic syndrome.J. Health Popul. Nutr.20173613610.1186/s41043‑017‑0114‑0 29132438
     [Google Scholar]
  17. Medindia teamCholesterol risk calculator. Medindia.2023Available from: https://www.medindia.net/patients/calculators/cholesterol-risk-calculator.asp Accessed January 19, 2023.
  18. NaingN.N. Determination of sample size.Malays. J. Med. Sci.20031028486 23386802
     [Google Scholar]
  19. WilsonP.W.F. D’AgostinoR.B. LevyD. BelangerA.M. SilbershatzH. KannelW.B. Prediction of coronary heart disease using risk factor categories.Circulation199897181837184710.1161/01.CIR.97.18.1837 9603539
     [Google Scholar]
  20. PletcherM.J. MoranA.E. Cardiovascular risk assessment.Med. Clin. North Am.2017101467368810.1016/j.mcna.2017.03.002 28577619
     [Google Scholar]
  21. JamalM. AbdulrahmanA. FarajallahA. Diabetes & metabolic syndrome: clinical research & reviews prevalence of dyslipidemia and factors affecting lipid pro fi le in patients with type 2 diabetes.Diabetes Metab. Syndr.20191342387239210.1016/j.dsx.2019.06.009 31405648
     [Google Scholar]
  22. SharmaA. BaldiA. Kumar SharmaD. Impact of physical activity and cooking oil amongst diabetes with coexisting hypertension patients on economic cost and length of stay: A 1914 patient’s observational study.Int. J. Clin. Pract.2021757e1416310.1111/ijcp.14163 33743546
     [Google Scholar]
  23. RamachandranA. MaryS. YamunaA. MurugesanN. SnehalathaC. High prevalence of diabetes and cardiovascular risk factors associated with urbanization in India.Diabetes Care200831589389810.2337/dc07‑1207 18310309
     [Google Scholar]
  24. ArabiyatS. TadrosO. Al-DaghastaniT. JaberD. Applying the framingham risk score for cardiovascular diseases in Jordan: a cross-sectional study.Trop. J. Pharm. Res.202221122667267510.4314/tjpr.v21i12.23
     [Google Scholar]
  25. Samaniyan BavarsadP. KheiriS. AhmadiA. Estimation of the 10-year risk of cardiovascular diseases : using the score, who/ish, and framingham models in the shahrekord cohort study in southwestern Iran.J. Tehran Univ. Heart Cent.202015310511210.18502/jthc.v15i3.4219 33552205
     [Google Scholar]
  26. MaharanaL. Sateesh Kumar ReddyK. PattnaikS. A prospective observational study on cardiovascular risk assessment in type-2 diabetes mellitus patients.J. Appl. Pharm. Sci.20199412613210.7324/JAPS.2019.90416
     [Google Scholar]
/content/journals/cdr/10.2174/0115733998284967240412104822
Loading
Prediction of Different Risk Factors in Relation to Hyperlipidemia Using Framingham Risk Score and Cholesterol Risk Score in a Tertiary Care Hospital
Curr. Diabetes Rev. 21, E100724231817 (2025); https://doi.org/10.2174/0115733998284967240412104822
/content/journals/cdr/10.2174/0115733998284967240412104822
/content/journals/cdr/10.2174/0115733998284967240412104822
Loading

Data & Media loading...

Supplements

Supplementary material is available on the publisher’s website along with the published article.

file format download Download

  • Article Type:     Research Article
Keyword(s): cholesterol risk score; CVD; diabetes; Framingham risk score; hypercholesterolemia; hyperlipidemia

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test