Skip to main content

Advertisement

SpringerLink
Log in

Screening score for early detection of cardio-metabolic risk in Indian adults

  • Original Article
  • Published:
International Journal of Public Health

Abstract

Objectives

To develop and to evaluate efficacy of screening score for early detection of cardio-metabolic risk (CMR) in adults.

Methods

Cross-sectional data on anthropometry, lipids, sugar levels, diet, and physical activity were collected on 720 adults (361 men, 35–50 year) using standardized techniques. Screening score was developed using regression analysis—cluster of risk conditions (blood pressure, lipids, and sugar levels) was dependent variable against age, sex, waist, diet, and physical activity as independent variables. Odd ratios were added to obtain final score and receiver-operating characteristic (ROC) curves were constructed to identify cut-off value of CMR score.

Results

Mean age and BMI were 42.7 ± 9.4 years and 25.7 ± 5.0 kg/m2. Analysis showed age, male sex, waist, lack of fruits, green leafy vegetables, and lack of physical activity were independent predictors for increased CMR (p < 0.05). Total score ranged from 0 to 20. Area under the curve for ROC was 0.728 [95% (CI) 0.67–0.78]. Criterion value >8 had sensitivity (76%) and specificity (56%) for screening cases with CMR.

Conclusions

Screening score is a pragmatic way of identifying individuals with CMR without performing biochemical tests. Cost-effective community screening programs may be planned.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  • Barden A, Singh R, Walters B, Phillips M, Beilin LJ (2013) A simple scoring method using cardiometabolic risk measurements in pregnancy to determine 10 year risk of type 2 diabetes in women with gestational diabetes. Nutr Diabetes 3:e72

    Article  CAS  Google Scholar 

  • Bhowmik B, Akhter A, Ali L, Ahmed T, Pathan F, Mahtab H, Khan AK, Hussain A (2015) Simple risk score to detect rural Asian Indian (Bangladeshi) adults at high risk for type 2 diabetes. J Diabetes Investig 6(6):670–677

    Article  Google Scholar 

  • Carroll SJ, Paquet C, Howard NJ, Adams RJ, Taylor AW, Daniel M (2014) Validation of continuous clinical indices of cardiometabolic risk in cohort of Australian adults. BMC Cardiovasc Disord 14:27

    Article  Google Scholar 

  • Chiplonkar SA, Agte VV, Mengale SS, Tarwade KV (2001) Are lifestyle factors good predictors of retinol and vitamin C deficiency in apparently healthy adults? Eur J Clin Nutr 56:96–104

    Article  Google Scholar 

  • Chiplonkar SA, Agte VV, Tarwadi KV, Paknikar KM, Diwate UP (2004) Micronutrient deficiencies as predisposing factors for hypertension in lacto-vegetarian Indian adults. J Am Coll Nutr 23:239–247

    Article  CAS  Google Scholar 

  • D’Agostino RB, Grundy S, Sullivan LM, Wilson P (2001) Validation of the Framingham coronary heart disease prediction scores: results of a multiple ethnic groups investigation. JAMA 286(2):180–187

    Article  Google Scholar 

  • Ferrario M, Chiodini P, Chambless LE et al (2005) Prediction of coronary events in a low incidence population. Assessing accuracy of the CUORE Cohort Study prediction equation. Int J Epidemiol 34:413–421

    Article  Google Scholar 

  • Forouhi NG, Sattar N (2006) CVD risk factors and ethnicity: a homogeneous relationship? Atheroscler Suppl 7:11–19

    Article  Google Scholar 

  • Glumer C, Carstensen B, Sandbaek A, Lauritzen T, Jorgensen T, Borch-Johnsen K (2004) Inter99 study. A Danish diabetes risk score for targeted screening: the Inter99 study. Diabetes Care 27:727–733

    Article  Google Scholar 

  • Gray LJ, Taub NA, Khunti K, Gardiner E, Hiles S, Webb DR, Srinivasan BT, Davies MJ (2010) The Leicester risk assessment score for detecting undiagnosed Type 2 diabetes and impaired glucose regulation for use in a multiethnic UK setting. Diabet Med 27(8):887–895

    Article  CAS  Google Scholar 

  • Gupta R, Joshi P, Mohan V, Reddy KS, Yusuf S (2008) Epidemiology and causation of coronary heart disease and stroke in India. Heart 94:16–26

    Article  CAS  Google Scholar 

  • Hu FB, Manson JE, Stampfer MJ, Colditz G, Liu S, Solomon CG, Willet WC (2001) Diet, lifestyle, and the risk of type 2 diabetes mellitus in women. N Engl J Med 345:790–797

    Article  CAS  Google Scholar 

  • Kaur J (2014) A comprehensive review on metabolic syndrome. Cardiol Res Pract 2014:943162

    PubMed  PubMed Central  Google Scholar 

  • Lindstrom J, Tuomilehto J (2003) The diabetes risk score: a practical tool to predict type 2 diabetes risk. Diabetes Care 26:725–731

    Article  Google Scholar 

  • Lynch J, Helmrich S, Lakka TA, Kaplan GA, Cohen RD, Salonen R, Salonen JT (2005) Moderately intense physical activities an high levels of cardio-respiratory fitness reduce the risk of non-insulin-dependent diabetes mellitus in middle-aged men. Arch Intern Med 156:1307–1314

    Article  Google Scholar 

  • Maruthur NM, Wang N, Appel LJ (2009) Lifestyle interventions to reduce coronary heart disease risk: results from the PREMIER trial. Circulation 119:2026–2031

    Article  Google Scholar 

  • Misra A (2003) Revisions of cutoffs of body mass index to define overweight and obesity are needed for the Asian-ethnic groups. Int J Obes 27:1294–1296. doi:10.1038/sj.ijo.0802412

    Article  CAS  Google Scholar 

  • Misra A, Vikram NK (2004) Insulin resistance syndrome (metabolic syndrome) and obesity in Asian Indians: evidence and implications. Nutrition 20(5):482–491

    Article  CAS  Google Scholar 

  • Mohan V, Deepa R, Deepa M, Somannavar S, Datta M (2005) A simplified Indian Diabetes Risk Score for screening for undiagnosed diabetic subjects. JAPI 53:759–763

    CAS  PubMed  Google Scholar 

  • Murray CJ, Lopez AD (1997) Alternative projections of mortality and disability by cause 1990–2020: global burden of disease study. Lancet 349:1498–1504

    Article  CAS  Google Scholar 

  • Raiko Juho RH, Magnussen CG, Kivimäki Mika, Taittonen Leena, Laitinen Tomi, Kähönen Mika et al (2010) Cardiovascular risk scores in the prediction of subclinical atherosclerosis in young adults: evidence from the cardiovascular risk in young Finns study. Eur J Cardiovasc Prev Rehabil 17(5):549–555

    Article  CAS  Google Scholar 

  • Ramachandran A, Snehalatha C, Vijay C et al (2005) Derivation and validation of diabetes risk score for urban Asian Indians. Diabetes Res Clin Pract 70:63–70

    Article  CAS  Google Scholar 

  • Repas TB, DO (2007) Challenges and strategies in managing cardio-metabolic risk. J Am Osteopath Assoc 107(suppl 2):S4–S11

  • Riaz M, Basit A, Hydrie MZ, Shaheen F, Hussain A, Hakeem R, Shera AS (2012) Risk assessment of Pakistani individuals for diabetes (RAPID). Prim Care Diabetes 6(4):297–302

    Article  Google Scholar 

  • Saaristo T, Peltonen M, Lindström J, Saarikoski L, Sundvall J, Eriksson Gunnar J, Tuomilehto Jaakko (2005) Cross-sectional evaluation of the Finnish Diabetes Risk Score: a tool to identify undetected type 2 diabetes, abnormal glucose tolerance and metabolic syndrome. Diabetes Vasc Dis Res 2:67

    Article  Google Scholar 

  • Sample Registration System (2007) Million death study: preliminary report on causes of death in India 2001–2003. Registrar General of India, New Delhi

    Google Scholar 

  • Spijkerman AM, Yuyun MF, Griffin SJ, Dekker JM, Nijpels G, Wareham NJ (2004) The performance of a risk score as a screening test for undiagnosed hyperglycemia in ethnic minority groups: Data from the 1999 health survey for England. Diabetes Care 27:116–122

    Article  Google Scholar 

  • The Diabetes Prevention Program Research Group (2003) Costs associated with the primary prevention of T2DM mellitus in the diabetes prevention program. Diabetes Care 26:36–47

    Article  Google Scholar 

  • Wilson PW, D’Agostino RB, Levy D, Belanger AM, Silbershatz H, Kannel WB (1998) Prediction of coronary heart disease using risk factor categories. Circulation 97:1837–1847

    Article  CAS  Google Scholar 

  • Yajnik CS, Lubree HG, Rege SS, Naik SS, Deshpande JA, Deshpande SS et al (2002) Adiposity and hyperinsulinemia in Indians are present at birth. J Clin Endocrinol Metab 87:5575–5580

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Hirabai Cowasji Jehangir Medical Research Institute, Jehangir Hospital, 32, Sassoon Road, Pune, 411 001, India

    Deepa Pandit-Agrawal, Anuradha Khadilkar, Shashi Chiplonkar & Vivek Patwardhan

  2. Jehangir Hospital, Pune, India

    Vaman Khadilkar

Authors
  1. Deepa Pandit-Agrawal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anuradha Khadilkar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shashi Chiplonkar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Vaman Khadilkar
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Vivek Patwardhan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anuradha Khadilkar.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pandit-Agrawal, D., Khadilkar, A., Chiplonkar, S. et al. Screening score for early detection of cardio-metabolic risk in Indian adults. Int J Public Health 62, 787–793 (2017). https://doi.org/10.1007/s00038-017-0994-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00038-017-0994-1

Keywords

Search

Navigation