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Contents lists available at SciVerse ScienceDirect

Primary Care Diabetes

j o u r n a l h o m e p a g e : h t t p : / / w w w . e l s e v i e r . c o m / l o c a t e / p c d

riginal research

isk assessment of Pakistani individuals for diabetesRAPID)

usarrat Riaza, Abdul Basita,∗, Muhammad Zafar Iqbal Hydrieb,c, Fariha Shaheend,khtar Hussaine, Rubina Hakeemf,d, Abdus Samad Sherag

Department of Medicine, Baqai Institute of Diabetology and Endocrinology, Baqai Medical University, Plot No. 1-2, II-B, Block 2,azimabad, Karachi 74600, PakistanDepartment of International Health, Institute of General Practice and Community Medicine, Faculty of Medicine, University of Oslo,orwayBaqai Institute of Diabetology and Endocrinology, Plot 1-2, II-B, Block 2, Nazimabad-2, Karachi 74600, PakistanResearch Department, Baqai Institute of Diabetology and Endocrinology, Baqai Medical University, Plot No. 1-2, II-B, Block 2,azimabad, Karachi 74600, PakistanUniversity of Oslo, Faculty of Medicine, Institute of General Practice and Community Medicine, Department of International Health,niversity of Oslo, Norway, P.O. Box 1130 Blindern, N-0317 Oslo, NorwayRana Liaquat Ali Khan Government College of Home Economics, Stadium Road Karachi, PakistanDiabetic Association of Pakistan, 5-E/3, Nazimabad, Karachi 74600, Pakistan

r t i c l e i n f o

rticle history:

eceived 30 June 2011

eceived in revised form

5 March 2012

ccepted 9 April 2012

vailable online 3 May 2012

eywords:

ype 2 diabetes

isk score

a b s t r a c t

Objective: To develop and evaluate a risk score to predict people at high risk of developing

type 2 diabetes in Pakistan.

Methodology: Cross sectional data regarding primary prevention of diabetes in Pakistan. Dia-

betes risk score was developed by using simple parameters namely age, waist circumference,

and family history of diabetes. Odds ratios of the model were used to assign a score value

for each variable and the diabetes risk score was calculated as the sum of those scores.

Results: We externally validated the score using two data from 1264 subjects and 856 subjects

aged 25 years and above from two separate studies respectively. Validating this score using

the first data from the second screening study gave an area under the receive operator

characteristics curve [AROC] of 0.758. A cut point of 4 had a sensitivity of 47.0% and specificity

akistan of 88% and in the second data AROC is 0.7 with 44% sensitivity and 89% specificity.

Conclusions: A simple diabetes risk score, based on a set of variables can be used for the iden-

tification of high risk individuals for early intervention to delay or prevent type 2 diabetes

in Pakistani population.

ry Care Diabetes Europe. Published by Elsevier Ltd. All rights reserved.

© 2012 Prima

∗ Corresponding author. Tel.: +92 21 36688897/08565/707179; fax: +92 21 36608568.E-mail address: research@bideonline.com (A. Basit).

751-9918/$ – see front matter © 2012 Primary Care Diabetes Europe. Published by Elsevier Ltd. All rights reserved.ttp://dx.doi.org/10.1016/j.pcd.2012.04.002

e t e s

ple diabetes risk score was calculated for each participantfor the exploratory cohort. The second validation cohort wasfrom lyari survey. This study included 856 subjects aged 25

298 p r i m a r y c a r e d i a b

1. Introduction

Type 2 diabetes mellitus (T2DM) is a complex metabolic dis-order and gives rise to significant morbidity, mortality andfinancial burden [1]. International Diabetes Federation (IDF)estimates that between 2010 and 2030 the number of peoplewith diabetes will increase by more than 50%, about 285–438million [2]. The estimated number of people with diabetes inPakistan is 7.1 million in 2010 and is expected to increase to13.8 million by the year 2030 [3]. Diabetes is usually undiag-nosed for long periods of time during which micro vascularcomplications can develop [4]. Thus screening programs at pri-mary care level may help in earlier diagnosis and preventionof type 2 diabetes [5] which may improve outcome [6,7].

Randomized controlled trials have shown that throughlifestyle modifications or pharmacological interventions, type2 DM can be prevented in high risk groups [8–11]. The majorchallenge is how to identify those high risk individuals. The75 g oral glucose tolerance test [OGTT] is considered to bethe gold standard for detecting abnormal glucose regulation.However it is costly, time consuming and requires physicianinterpretation of results [12]. Various risk scores have beendeveloped for the purpose of screening undiagnosed type 2 DM[13–17]. Most of these have been developed among Caucasians[13,15] with few scoring systems tested in Asian populations[18,19].

We present the derivation and validation of a new risk pre-diction algorithm for assessing the risk of developing type 2DM among Pakistani population by the name of RAPID (riskassessment of Pakistani individuals for diabetes. We designedthis scoring system based on variables that are readily avail-able to people without needing laboratory tests and physicianinterpretation thereby making it cost effective for nationalscreening programmes at primary care level.

2. Methodology

2.1. Patients and methods

To develop this risk assessment score we used data from 1822participants aged 25 years and above who participated in aprospective diabetes primary prevention program in Pakistan.Screening camps were organized at various institutions, unioncouncil offices, schools and factories in and around the city ofKarachi, Pakistan as a part of this program. On the first dayof the program diabetes awareness lectures were delivered tothe general public and employees by health professionals ofthe prevention team, while on the second day, subjects iden-tified as high risk on the basis of a questionnaire about age,weight, positive family history of diabetes, physical inactivityand dietary habits without prior diagnosis of diabetes receivedan oral glucose tolerance test [OGTT] using 75 g of glucose afteran overnight fast. Type 2 DM was defined according to WHOdiagnostic criteria of RBS ≥ 200 mg/dl [5].

Demographic and socioeconomic information was also

collected through a structured questionnaire. Height wasmeasured in centimeters and weight in light clothing mea-sured to the nearest 0.1 kg. Body mass index [BMI] was

6 ( 2 0 1 2 ) 297–302

defined as weight in kilograms divided by height in meterssquared.

2.2. Variables considered

All those variables that could be completed on a self-assessment by the participant like age, gender, weight, BMI,smoking status, family history of diabetes, physical activityand history of hypertension were considered for inclusionin the scoring system, excluding variables based on labora-tory results or requiring assessment by a physician. Initiallyage, body mass index, hypertension (BP 130/85 mmHg), gender,physical activity, waist hip ratio and smoking were assessedin the stepwise modeling; however, in the final analysisage, waist circumference and family history of diabetes wereincluded.

2.3. Modeling and internal validation

It was carried out using logistic regression with type 2 DMas the dependent variables with p value ≤0.05 reflectingstatistical significance. A full stepwise approach was takenfor variable selection, with significance level set at p ≥ 0.05for variable removed and p ≤ 0.049 for inclusion into themodel.

2.4. At each step, the area under the receiver operatorcurve [ROC] was calculated

A scoring system was developed for the simple model: pointswere assigned to each variable based on the magnitude of itsregression coefficient and odds ratio. A total diabetes risk scorefor each individual was calculated as the sum of points foreach variable; this score was related to the diagnosis of DM. Areceiver operating characteristic curve and area under curvewere produced.

Sensitivity and specificity were calculated for each cutoffscore. The cut off score that gave the maximum sum of sensi-tivity and specificity was taken as optimum [20].

2.5. Validation

The performance of the risk score was evaluated in two othervalidation cohorts of 1264 and 856 subjects aged 25 years andabove.

The baseline survey of the validation cohorts was con-ducted in Hub [Baluchistan] and Lyari [Karachi]. The Hubsurvey included 1264 subjects aged 25 and above. The vari-ables collected and methods used were similar to those ofthe exploratory set, except that diabetes was diagnosed onmeasurement of fasting glucose only [≥126 mg/dl]. The sim-

and above. The diabetes was diagnosed on the basis of fastingblood glucose ≥126 mg/dl. The diabetes risk score was calcu-lated as in the previous cohort.

p r i m a r y c a r e d i a b e t e s 6 ( 2 0 1 2 ) 297–302 299

Table 1 – Baseline characteristics of the cross sectional and validation data.

Cross-sectional data Validation data 1 Validation data 2

n = 1822 n = 1264 n = 856

Age (years) 41.47 ± 9.48 42.27 ± 12.53 40.77 ± 14.15Weight (kg) 71.66 ± 13.93 67.11 ± 14.85 60.57 ± 15.98Height (cm) 165.27 ± 9.63 162.71 ± 8.06 156.87 ± 9.22Body mass index (kg/m2) 26.39 ± 5.3 25.52 ± 7.46 24.69 ± 6.51Waist circumference (cm) 92.34 ± 13.86 79.66 ± 30.37 86.09 ± 15.49Systolic blood pressure (mmHg) 119.72 ± 17.16 129.27 ± 15.8 124.84 ± 22.62Diastolic blood pressure (mmHg) 83.29 ± 11.2 84.13 ± 9.57 81.29 ± 32.28Cholesterol (mg/dl) 181.94 ± 50.23 165.2 ± 45.96 179.51 ± 44.33Triglyceride (mg/dl) 158.48 ± 103.96 171.82 ± 104.8 142.59 ± 83.07High density lipoprotein (mg/dl) 38.62 ± 10.44 41.21 ± 15.3 41.44 ± 12.14Low density lipoprotein (mg/dl) 117.03 ± 23.4 91.7 ± 34.54 114.65 ± 32.1Family history of diabetes 50.84% 32.75% 9.6%Waist circumference > cutoffa 54.72% 56.4% 59.92%Male 70.77% 66.37% 26.98%Female 29.23% 33.62% 73.01%Hypertensionb 59.6% 53.16% 43.1%Abnormal glucose Tolerance 32.82% 24.95% 4.20%Smoking 6.1% 51.89% 4.9%

Data presented in the form of mean ± sd and percentages.

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a Cutoff = 90 cm for males and 80 cm for females.b Hypertension≥130/85 mmHg.

. Results

here was no significant difference between the ages of crossectional and validation data 1 and 2 [Table 1]. Overall 598f the 1822 study subjects had abnormal glucose tolerance

males 67.9%, females 32.1%].

The odds ratio was 1.39 for the age 40–50 years, 2.82 for >50

ears compared with age <40 years, 2.08 for waist circumfer-nce and 1.43 for family history of diabetes [Table 2].

ig. 1 – Receiver operating characteristics curve for the final modectional data and (b) validation data.

ROCs were obtained for this risk assessment tool and testedfor newly diagnosed diabetes using WHO criteria. The AUC forthe ROC was 0.658 for cross-sectional data, 0.758 and 0.7 fortwo validation data (Fig. 1).

Table 3 provides the sensitivity and specificity of differ-ent cutoffs for diagnosis of diabetes using WHO criteria.A value ≥4 had the optimum sensitivity and specificity

for determining diabetes. The positive predictive valuewas 54.5% for cross-sectional data and 59.6% and 39%for validation data while negative predictive value for

el using cross sectional and validation data (a) cross

300 p r i m a r y c a r e d i a b e t e s 6 ( 2 0 1 2 ) 297–302

Table 2 – Logistic regression model for Type II DM/impaired glucose regulation and scoring system.

Risk factors Univariate model Multivariate model Score

Beta scores OR (95%CI) Beta scores OR (95%CI)

Age40–50 0.386 1.472 (1.159–1.869) 0.332 1.393 (1.086–1.787) 1>50 1.043 2.838 (2.139–3.767) 1.039 2.827 (2.098–3.81) 3Waist circumference ≥ cutoffa 0.917 2.502 (2.034–3.077) 0.732 2.087 (1.64–2.635) 2Family history of diabetes 0.287 1.332 (1.081–1.642) 0.362 1.436 (1.154–1.787) 1

OR, odds ratio; CI, confidence interval.a Waist circumference cutoff = 80 cm for female and 90 cm for males.

cross-sectional data was 70.1%, 82.7% and 91.3% for validationdata.

4. Discussion

In this study we present simplified diabetes risk score [RAPID]a screening tool for the early detection of type 2 DM in Pak-istani population.

The traditional method for identifying patients atincreased risk of type 2 diabetes involves the use of oralglucose tolerance test which is costly inconvenient and timeconsuming. Targeted screening of higher risk groups has beenproposed as a more cost effective solution [21]. The main aimof the present study was to derive a simplified diabetes riskscore to be used at national level in Pakistan for screening ofdiabetes.

Various diabetes risk score have been developed based onanthropometric, demographic and behavioral factors to detect

undiagnosed type 2 DM [15,18,19]. However they are devel-oped and validated in a predominantly Caucasian population.Furthermore cut points for obesity and overweight are lowerin south Asian people like Pakistan, and these differences

Table 3 – Sensitivity, specificity, positive and negative predictivefor predicting Type II diabetes.

Scores AROC Sensitivity (95%C

Primary prevention data (n = 1822)≥1 40.6 94 (91–95)

≥2 44.7 83 (79–85)

≥3 54.8 68 (64–71)

≥4 65.8 33 (29–37)

≥5 55.8 16 (14–20)

≥6 65.5 7 (5–9)

Validation data 1 (n = 1264)≥1 41.8 96 (94–98)

≥2 35.3 87 (83–90)

≥3 61.7 77 (72–81)

≥4 75.8 47 (41–52)

≥5 80.3 32 (27–37)

≥6 75.9 10 (7–14)

Validation data 2 (n = 856)≥1 0.413 94 (81–98)

≥2 0.466 94 (81–98)

≥3 0.555 69 (53–82)

≥4 0.7 44 (29–60)

≥5 0.675 41 (27–58)

≥6 0.887 5(1.5–18)

AROC, area under the curve; CI, confidence interval; PPV, positive predictiv

are usually not taken into account [22]. Thus the risk scoresneed to be developed specifically for the population in whichthey will be used. The studies done in Caucasians includedfactors like biochemical parameters, use of antihypertensivemedicines, use of steroids, fruits and vegetables consump-tion [12–15] for deriving their risk scores. Pakistan being adeveloping country lacks data regarding standardization offood portions. Similarly majority of patients are not awareof their hypertension and hence not using any hypertensivemedicines so we avoided using these factors for risk assess-ment. Similarly these scores have also used physical activityas a risk factor but in Pakistan due to lack of correct catego-rization and different interpretation of physical activity levelamong general public we did not consider it in our regressionmodel.

Central obesity is most easily assessed by simply mea-suring the waist circumference which many significantlyinfluence insulin sensitivity and other cardiovascular risk fac-tors in South Asian populations. Therefore risk score like

RAPID which include waist circumference many also help inidentifying people with undiagnosed metabolic syndrome aswell as type 2 diabetes mellitus. Although this score has beendeveloped using data on a preselected high risk group – not a

values with 95% confidence interval on the RAPID scores

I) Specificity (95%CI) PPV NPV

16 (14–18) 37.9 83.535 (32–38) 41. 7956 (53–59) 45.9 76.585 (82–87) 54.5 70.193 (92–94) 58.4 67.596 (95–97) 55.3 65.8

22 (19–24) 30.5 95.437 (34–40) 33 89.667 (64–70) 45.5 89.388 (86–90) 59.6 82.793 (92–95) 64.5 79.897 (96–98) 60.3 75.6

27 (22–33) 16.7 9734 (28–40) 18.2 97.665 (59–71) 23.6 93.389 (84–92) 39 91.390 (85–93) 39.5 90.999 (97–99) 66.7 87.3

e value; NPV, negative predictive value.

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opulation based cohort but we expect that this will be help-ul in general population as well because the prevalence of

etabolic syndrome and obesity is quite high in Pakistani pop-lation [23,24]. The risk tool may not perform as well whensed in general Pakistani population. The risk tool will haveo be tested when data from a representative sample of theakistani population becomes available. Since older age, fam-ly history of diabetes and large waist circumference increaseisk of diabetes, people with high score should be referred forurther testing or appropriate interventions. Even though thisisk tool has been developed from a high risk population notepresentative of the Pakistani population, it can be used inhe general population until we have representative data toest the risk tool or when a better risk tool for this populationecomes available.

This score is intended for use by lay person for assessmentf their own personal risk. This study uses four simple easilybtainable factors for predicting the high risk population andan be used as a mass screening tool for undiagnosed type 2M in Pakistan.

Although this is the first self-assessment score to be appli-able in Pakistani population, there are some limitations andreas for further exploration. This score has been developedn a cross sectional data set. This means that it gives an indi-ation of prevalent type 2 DM and at present cannot be usedo establish the risk of developing the condition in the future.uture work will involve testing this score on a prospectiveata set to determine its validity in predicting future type 2M. Although the definition of diabetes in the external val-

dation sets is different than the development data set, weope that it will not significantly affects the results as bothefinitions are used to diagnose diabetes.

In conclusion, there is strong evidence that diabetes can berevented through identification of high risk individuals whoan be subjected to life style modifications [4,6] or pharma-ological interventions [4,6–8]. This simple diabetes risk scorean be applied in primary care settings and by the generalublic as a self-assessment tool which is simple, non-invasivend convenient to identify people at high risk of diabetes. Peo-le with a high score should be referred for further testing orppropriate interventions.

ontributions

onception and design or acquisition of data or analysis andnterpretation of data were done by Mussarat Riaz, Abdul Basit,

. Zafar Iqbal Hydrie, Fariha Shaheen.Drafting the article or revising it critically for important

ntellectual content was done by Mussarat Riaz, Abdul Basit,. Zafar Iqbal Hydrie, Akhtar Hussain, Rubina Hakeem, A.

amad Shera.Final approval of the version to be published was done

y Mussarat Riaz, Abdul Basit, M. Zafar Iqbal Hydrie, Farihahaheen, Akhtar Hussain, Rubina Hakeem, A. Samad Shera.

onflict of interest

he authors state that they have no conflict of interest.

( 2 0 1 2 ) 297–302 301

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