Comparative Study of FINDRISC and IDRS in Predicting Prediabetes and Diabetes Mellitus in a Young Adult Yemeni Population

Comparative Study of FINDRISC and IDRS in Predicting Prediabetes and Diabetes Mellitus in a Young Adult Yemeni Population | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Comparative Study of FINDRISC and IDRS in Predicting Prediabetes and Diabetes Mellitus in a Young Adult Yemeni Population Abdulsalam Al-Shami, Lina Qasem, Mufeed Baddah, Sami Kahtan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7781850/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Apr, 2026 Read the published version in Diabetology & Metabolic Syndrome → Version 1 posted 13 You are reading this latest preprint version Abstract Background Diabetes mellitus (DM) is a leading cause of death worldwide. To address the rising of DM, it might be more effective to create and validate a targeted risk scoring system for specific populations. This study aimed to evaluate and compare the diagnostic accuracy of the Finnish Diabetes Risk Score (FINDRISC) and the Indian Diabetes Risk Score (IDRS) in assessing the risk of developing T2DM and to identify the factors associated with T2DM risk among healthy medical students at the Faculty of Medicine, Taiz University, Yemen. Methods A cross-sectional study was conducted among 200 students at Taiz University. The IDRS and FINDRISC questionnaires were used to assess the diabetes risk score for developing T2DM within 10 years. Fasting blood sugar was measured. Descriptive statistics and the chi-square test were used, with P < 0.05 regarded as statistically significant. The diagnostic accuracy of FINDRISC and IDRS was compared using the area under the receiver operating characteristic curve (AUC-ROC). Sensitivity, specificity, Youden index, likelihood ratio, positive and negative predictive values were calculated for both tools. Results Out of 200 participants, 10.5% and 1.5% were diagnosed with prediabetes and T2DM, respectively, where females had a higher prevalence than males for both outcomes ( P < 0.001). The AUC-ROC for both scores in identifying participants with diabetes differed (P < 0.001); for FINDRISC, it was larger (0.782; 95% CI: 0.68–0.88; P < 0.001) compared to that of IDRS (0.671; 95% CI: 0.56–0.78). For FINDRISC at 9 as the best cutoff (sensitivity = 67.0%, specificity = 80.1%, and Youden index = 0.44); whereas for IDRS at 45 as the best cutoff (sensitivity = 46.0%, specificity = 80.0% and Youden index = 0.25). Bland-Altman plot suggested fair agreement between both scores in assessing diabetes risk. Conclusion FINDRISC serves as a simple and effective screening tool to detect subjects at high risk for prediabetes and T2DM among young adults in Yemen. Diagnostic accuracy and clinical utility of FINDRISC is better than that of IDRS. Type 2 diabetes mellitus FINDRISC IDRS Risk scores Prediabetes Yemen Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Diabetes mellitus (DM) is a group of metabolic disorders characterized by high blood glucose levels due to impairments in insulin production (type 1 diabetes), or insulin action (type2 diabetes) [ 1 , 2 ]. Type 2 Diabetes Mellitus (T2DM) is the most prevalent type of diabetes, which is responsible for almost 90% of the total cases of diabetes worldwide [ 3 ]. DM has been increasingly identified as a serious, global public health challenge[ 4 ]. The International Diabetes Federation (IDF) estimates that the number of individuals with diabetes mellitus worldwide are currently 463 million, and it is expected to rise to 592 million and 700 million individuals by 2035 and 2045, respectively, with the highest proportion observed in low- and middle-income countries [ 5 , 6 ]. The increasing worldwide prevalence of DM represents substantial economic burdens. Globally, health care costs estimate in 2019 was 760 billion USD, and this number is predicated to be increased to 845 billion USD by 2045 [ 7 ]. Unfortunately, besides a rapidly increased incidence among young individuals, more than 50% of the diabetics in the world remain unaware of their illness status, which contributes to the disease burden by increasing a public health risk and preventing immediate interventions [ 5 , 8 ]. In Yemen, our knowledge about the prevalence of DM remains poor with little data available. Although it has been found that the prevalence of DM in Yemen was 4.6% by 2004, the number increased to 10.4% in 2008 [ 9 , 10 ]. The rise in DM in Yemen can be attributed to the genetic predisposition, urbanization, the sedentary life-styles and the changing food habits. Given that DM is a chronic disease associated with a high rate of mortality, and health care expenditures that have been explained by long-term vascular complications, thus managing DM represents one of the biggest worldwide health concerns at the present time [ 11 , 12 ]. Hence, an urgent need has emerged to develop a simple, fast, cost-effective and non-invasive screening tool for early identification of individuals at higher risk of developing T2DM in the future [ 13 , 14 ]. Previous studies demonstrated that early screening and detection, diagnosis and management of the risk of T2DM could alleviate the rapidly growing socioeconomic burdens of T2DM, thus delaying or preventing the development of the illness and reducing serious complications [ 15 , 16 , 17 ]. Considering the targeted interventions such as lifestyle modification and exercise or medications, lifestyle modifications is proven to be beneficial to avoid DM and lessen its burden, thus improving health care outcomes and the quality of life [ 18 ]. Recently, various diabetic risk scores have been developed to assess individuals with undiagnosed T2DM (prevalent), or those who are at risk of developing T2DM (incident) [ 19 ]. Some diabetic assessment models have been validated in selected populations, prompting their use in other countries. However, recent studies have shown that these risk scoring systems derived from the same populations may not be appropriate for other ethnic groups [ 20 ]; therefore, there is a need to establish a diabetes risk score for the Yemeni population. To the best of our knowledge, no previous studies have been conducted to compare two different existing diabetes risk screening tools in the adult Yemeni population during wartime. Hence, the current study was designed to evaluate and compare the diagnostic accuracy of the Finnish Diabetes Risk Score (FINDRISC) and the Indian Diabetes Risk Score (IDRS) in assessing the risk of developing T2DM among healthy medical students at the Faculty of Medicine and Health Sciences, Taiz University, Yemen. We also determine the factors associated with the risk of T2DM among study subjects. Methods Study design and setting A cross-sectional study was conducted among 200 medical students during a three-month period (January to March 2024) at the Faculty of Medicine and Health Sciences, Taiz University, Taiz governorate, Yemen. This governorate is the third largest city in the country and is located in the central of its southwestern highlands region, 1400 metres (4,600 ft) above sea level. Study population During the study period, third- and fourth-year medical students were recruited using a convenience sampling technique at the Faculty of Medicine and Health Sciences, Taiz University. Only students who had T2DM, were not present during data collection, refused to participate, or did not compete the questionnaire were excluded from the study. Sample Size The sample size was calculated using the following formula: N = Z 2 P q/d 2 Where N = Sample size, a 95% confidence level (Z = 1.96), a percentage of failure (q = 1 – p), and an estimated prevalence (P) of 50% was used due to lacking of available data on the prevalence of prediabetes mellitus (PDM) among Yemeni medical students with a 5% margin of sampling error ( d = 5%). The formula was applied, resulting in N = (1.96) 2 X 0.5 X 0.5 / (0.05) 2 = 384. To account for the finite population size, the Adjusted Sample Size formula was then applied. The population size (S) was set to 346, representing the total number of second and third-year medical students during the study period. The adjusted sample size was calculated as N = [(384) / (1 + (384–1) / (346)] = 181. Considering a 5% non-response rate, the minimum recommended sample size for the study was determined to be 190. Study Tool There has not been a previously validated risk scoring system for the Yemeni population. After a review of validated risk assessment scoring systems for different countries [ 19 , 20 ]. The current study used two diabetes risk scores: Finnish Diabetes Risk Score (FINDRISC) and Indian Diabetes Risk Score (IDRS) for assessing the risk of developing T2DM within the next 10 years. The Indian Diabetes Risk Score (IDRS) considers four risk factors; age, waist circumference (WC), physical activity and family history to assess the risk of developing T2DM [ 21 ]. Based on the total score, participants are classified into different risk levels: a score of < 30 indicates low risk, 30 to 59 denotes moderate risk, and a score exceeding 60 represents high risk of developing diabetes (Table 3 ) [ 22 ]. The FINDRISC tool is a validated and frequently used questionnaire that consists of eight variables; age, BMI, WC, physical activity, daily consumption of fruit and vegetables, high blood pressure or antihypertensive medication, high fasting blood sugar level, and family history of diabetes [ 13 , 23 ]. Participants with FINDRISC score of < 7 was categorized as low risk (estimated 1 in 100{1%} will develop T2DM), 7–11 as slightly elevated risk (estimated 1 in 25{4%} will develop T2DM), 12 to 14 as moderate risk (estimated 1 in 6{16%} will develop T2DM), 15 to 20 as high risk (estimated 1 in 3{33%} will develop T2DM) and those with a score > 20 as very high risk for developing diabetes (estimated 1 in 2{50%} will develop T2DM) (Table 4 ) [ 24 ]. Data collection The participant’s data was obtained by using a predesigned, one page structured questionnaire containing participant’s name, age, gender, contact number, and different risk factors of T2DM that are essential to estimate the predicted risk for developing T2DM in the following 10 years among the study participants according to FINDRISC and IDRS models. After asking all participants to stand barefoot wearing light clothing, weight and height were measured in kilograms (to the nearest 0.1 kg) using a standard weighing scale, and in centimeters (to the nearest 0.5 cm) using a wall-mounted measuring tape respectively. BMI (kg/m 2 ) was calculated by dividing weight (in kilograms) by height (in meters squared). Waist circumference was measured using a flexible measuring tape in centimeters (to nearest 0.1 cm) at halfway between the lowest rib and the superior edge of the iliac crest. Hypertension was defined as a systolic blood pressure ≥ 140 mmHg and/or diastolic blood pressure ≥ 90 mmHg [ 25 ].The blood pressure was measured by a manual sphygmomanometer in standard conditions (measured 2 times after a 5-min rest between each measurement [ 26 ]. Blood glucose measurement All students were invited to attend the Laboratory of Clinical Biochemistry at the Faculty of Medicine and Health Sciences, Taiz University. In the early morning after an overnight fast (at least 8 hours), fasting blood samples were collected by finger-pricking using a sterilized lancet, and fasting blood sugar (FBS) levels were measured on the glucose test strip using a standard glucometer (Accu-Chek Active, Roche Mannheim, Germany). Participants were classified based on FBS levels (mg/dl) as follows: < 100 (normal); 100–125 (PDM); ≥126 (DM) [ 27 ]. Data analysis The collected data were entered into a computer database, and the statistical analysis was performed using SPSS version 26.0 software (SPSS Inc., Chicago, IL, USA). In order to describe the characteristics of the study participants and the prevalence of diabetes risk factors, descriptive statistics, such as frequencies, proportions, means, and standard deviations, were generated. For categorical data, Pearson's chi-square and for continuous variables, student t-tests were performed. P 0.05 was set as the significant level. Diagnostic accuracy of the FINDRISC and IDRS was also assessed using area under the ROC curve as well as sensitivity, specificity, Youden index (sensitivity + specificity − 1), positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio, and Mitchell’s clinical utility indices (CUIs) were calculated for each risk tool. Agreement between the different scores in predicting the risk of diabetes mellitus was analyzed by using the Bland–Altman approach (B-A plot). Results Characteristics of enrolled study subjects A total of 200 participants were included, of whom more than half (110; 55%) were females and 90 (45%) were males. The mean (± SD) age of the study subjects was 21.33 ± 1.75 years (females: 21.3 ± 1.95; males: 21.3 ± 1.48; P = 0.919) with no statistically significant difference between females and males. Of note, female participants had higher body mass index (BMI) of 22.6 ± 4.49 vs. 21.3 ± 3.43; P = 0.019), fasting blood sugar (FBS) of 85.1 ± 15.59 vs. 82.4 ± 13.66; P = 0.192), IDRS risk score (37.6 ± 17.51 vs. 24.2 ± 13.23; P < 0.001) and FINDRISC risk score (7.01 ± 3.53 vs.4.1 ± 2.93; P < 0.001) mean (± SD) values when compared with those of male participants. On the other hand, the male participants had a higher mean (± SD) value of the waist circumference (WC; 85.3 ± 8.94 vs. 86.0 ± 11.44; P = 0.588) relative to the females (Table 1 ). These results also indicated that no significant differences regarding the systolic blood pressure ( SBP;111.1 ± 10.64 vs. 110.3 ± 9.82; P = 0.583) and diastolic blood pressure ( DBP;76.0 ± 7.57 vs.77.0 ± 6.61; P = 0.326) were found among participants of both sexes as presented in Table 1 . Table 1 Baseline characteristics of study subjects* Variable Total (n = 200) Females (n = 110) Male (n = 90) P-value Age 21.3 ± 1.75 21.3 ± 1.95 21.3 ± 1.48 0.919 BMI (kg/m 2 ) 22.0 ± 4. 10 22.7 ± 4. 50 21.3 ± 3. 42 0.019 WC (cm) 85.6 ± 10.11 85.3 ± 8.97 86.0 ± 11.39 0.588 FBS (mg/dl) 83. 9 ± 14.78 85.1 ± 15.59 82.4 ± 13.66 0.192 SBP (mmHg) 110.8 ± 10.26 111. 1 ± 10.64 110.3 ± 9.82 0.583 DBP (mmHg) 76. 5 ± 7.15 76.0 ± 7.57 77.0 ± 6.61 0.326 FINDRISC score 5.7 ± 3.61 7.0 ± 3.53 4.1 ± 2.93 < 0.001 IDRS scores 32.1 ± 17.06 37.6 ± 17.51 24.2 ± 13.23 < 0.001 * Total number of study subjects enrolled in the study was 200..Results are expressed as mean ± standard deviation. Age (years); BMI- Body mass index; WC -Waist circumstances; FBS -Fasting blood sugar; SBP- Systolic blood pressure; DBP- Diastolic Blood pressure; FINDRISC- Finnish Diabetes Risk Score ; IDRS- Indian Diabetes Risk Score. Risk assessment factors of FINDRISC The distribution of FINDRIS diabetic risk factors among study subjects are presented in Table 2 . According to FINDRISC tool, 159 (79.5%) were normal healthy weight (BMI 18.0–24.9 kg /m 2 ), while 32 (16.0%) and 9 (4.5%) were overweight (BMI 25.0–29.9 kg/m 2 ) and obese (BMI ≥ 30 kg/m 2 ) respectively. Taken together, 41 (20.5%) participants were either overweight or obese (BMI ≥ 25 kg/m 2 ). The distribution of participants’ BMI by sex (Table 3 ) shows that 83 (75.5%) females and 76 (84.4%) males had a healthy weight while 27 (24.6%) females and 14 (15.5%) males were either overweight or obese. It is worth noting that the BMI difference observed between genders was statistically significant and higher in females than in males; ( P = 0.009). In relation to waist circumference (WC) distribution, out of 200 study subjects, 31 (28.2%) of females and 65 (72.2%) males had WC less than 80 cm and less than 94 cm, respectively. Of 38 (34.5%) females and 20 (22.2%) males who found to have WC ranging from 80 to 88 cm and 94 to102 cm respectively. Also, 41 (37.3%) females and 5.0 (5.6%) males had WC above the threshold value of less than 80 cm (for female) and less than 94 cm (for male). Summarily, 104 (52.0%) of participants were with WC greater than 88 cm (for female) and 102 cm (for male), indicating a central obesity; ( P < 0.001). About half of the participants 113 (56.5%) had a minimum of 30 minutes of daily physical activity during either work or leisure time ( P < 0.001). Table 2 Distribution of FINDRISC diabetic risk factors among 200 medical students at Taiz University, Yemen Variable FIDRISC Points Total (%) Females (%) Male (%) p-value Body Mass index (BMI; kg/m 2 ) < 25 0 159 (79.5) 83 (75.5) 76 (84.4) 0.009 25-29.9 1 32 (16.0) 20 (18.2) 12 (13.3) ≥ 30 3 9.0 (4.5) 7.0 (6.4%) 2.0 (2.2) Waist circumference (cm; female/ male) < 80/94 0 96 (48.0) 31 (28.2) 65 (72.2) 88 /102 3 46 (23.0) 41 (37.3) 5.0 (5.6) Daily physical activity (30 minutes) Yes 0 113 (56.5) 37 (33.6%) 76 (84.4%) < 0.001 No 2 87 (43.5) 73 (66.4%) 14 (15.6%) Eating of vegetables or fruits Everyday 0 64 (32.3) 35 (31.8%) 29 (32.2%) 0.231 Not everyday 1 136 (68.0) 75 (68.2%) 61 ( 67.8%) FBS < 100 mg/dl 0 176 (88.0) 94 (85.5%) 82 (91.1%) < 0.001 100–125 or higher 5 24 (12.0) 16 (14.5%) 8.0 (8.9%) Family History of Diabetes No 0 116 (58.0) 62 (56.4%) 54 (60.0%) < 0.001 Yes (Second degree) 3 52 (26.0) 27 (24.5%) 25 (27.8%) Yes (First degree) 5 32 (16.0) 21 (19.1%) 11 (12.2%) * Data are presented as number (%). Chi-Square p < 0.05 considered as significant value Furthermore, it was observed that none of the participants had a history of anti- hypertensive medication anytime in their life. It was also observed that out of the 64 (32.3%) participants who were eating vegetables or fruits every day, 35 (31.8%) and 29 (32.2%) were females and male, respectively, with no statistically significant difference ( P = 0.231) as presented in Table 3 . Regarding fasting blood sugar (FBS) levels; Table 3 shows that 176 (88.0%) participants had a FBS levels (< 100 mg/dl), while 24 (12%) Had a FBS (100–125 mg/dl or higher). Of 24 (12.0%) participants, 16 (14.5%) were females and 8 (8.9%) were males but there was a significant difference in FBS levels between genders; ( P < 0.001). Also, it was reported that the prevalence of a family history of DM was high in this young population; overall, 84 (42.0%) had either first or second-degree relatives with diabetes. Out of 84 (42.0%) study population, 52 (26.0%) of them having a parent, sister, brother, or child with diabetes, while 33 (16.5%) having a grandparent, uncle, aunt, or first cousin with diabetes. A significantly higher proportion of 48 (43.6%) females had at least one first or second relative compared to that of 36 (40.0%) males (Table 2 ). Risk assessment factors of IDRS The distribution of IDRS diabetic risk factors among study subjects are presented in Table 3 . Based on IDRS tool, all participants were young adults below the age of 35 years ( P = 0.919). Regarding the waist circumference grading, the abdominal obesity was higher in females compared to males (37.3% vs. 5.6%; P < 0.001). It was interesting to note that though 57.5% (female: 34.5% vs. male: 85.5%; P < 0.001) of participants did regular vigorous regular exercise or physical activity at home or workplace. From this young population, 23% of participants had at least one parent with diabetes and in 9% of participants, both parents had diabetes ( P < 0.001) (Table 3 ). Table 3 Distribution of IDRS diabetic risk factors among 200 medical students at Taiz University, Yemen Variable IDRS Points Total (%) Females (%) Male (%) p-value Waist circumferences (cm; female/ male) < 80/94 0 96 (48.0%) 31 (28.2%) 65 (72.2%) 88 /102 20 46 (23.0%) 41 (37.3%) 5.0 (05.6%) Daily Physical Activity (30 minutes) Regular Vigorous Exercise 0 01 (0.5%) 00 (0.0%) 01(1.1%) < 0.001 Regular Mild Exercise 20 114 (57%) 38 (34.5%) 76 (84.4%) No exercise or sedentary activities 30 85 (42.5%) 72 (65.5%) 13 (14.4%) Family History of Diabetes No 0 168 (84.0%) 89 (80.9%) 79 (87.8%) < 0.001 Yes -Either parent -Both parents 10 20 23 (11.5%) 09 (4.5%) 15 (13.6%) 06 (05.5%) 08 (08.9%) 03(03.3%) * Data are presented as number (%). Chi-Square p < 0.05 considered as significant value Further, the risk of developing T2DM among the study subjects was assessed using FINDRIS and IDRS scoring system to define the different grades of risk of diabetes in our study population (Table 4 ). Considering the FINDRISC, from Table 4 and Fig. 2 , 121 (60.5%) had low risk (a score < 7), 65 (32.5%) slightly elevated risk (a score 7–11), 13 (6.5%) moderate (a score 12–14) elevated risk, and 1 (0.5%) had high risk (a score 15–20) for developing diabetes. Taken together, out of the 14 (7.0%) students found to have a moderately elevated to high risk ≥ 12) for developing T2DM in the following 10 years as shown in Fig. 1 . The risk of developing T2DM was significantly higher in females compared to males (FINDRISC score mean: 7.01 versus 5.04; P < 0.001) (Tables 1 and 4 ). Table 4 Comparison of risk of developing T2DM among 110 female and 90 male participants according to FINDRISC and IDRS scoring system Risk Total (%) Females (%) Male (%) p-value FINDRISC score < 7 Low 121 (60.5%) 49 (40.5%) 72 (59.5%) < 0.001 7–11 Slightly elevated 65 (32.5%) 48 (73.8%) 17 (26.2%) 12–14 Moderate 13 (6.5%) 12 (10.9%) 1 ( 1.1%) 15–20 High 1 (0.5%) 1.0 (0.5%) 0 (0.0%) IDRS score < 30 Low 83 (41.5%) 29(26.4%) 54 (60.0%) < 0.001 30–59 Moderate 91 (45.5%) 58(52.7%) 33 (36.7%) ≥ 60 High 26 (13.0%) 23 (20.9%) 3 (3.3%) * Results are expressed as number (%).Chi-Square, p < 0.05 considered as significant value With regard to the IDRS score, 83(41.5%) students were found to be at low risk (a score < 30), 91(45.5%) had a score between 30 and 59 indicating a moderate risk, and 26 (13%) scored 60 or above which indicates a high risk of diabetes depicted in Table 4 and Fig. 2 . Taken together, out of the 117 (91.1%) students found to have a moderate to high risk (IDRS score ≥ 30) for developing T2DM in the following 10 years as shown in Fig. 2 . As reported by IDRS, female subjects had a higher mean risk score compared to males; (37.6 vs. 24.2; P < 0.001) (Table 1 ). Diagnostic accuracy of risk score models for undiagnosed T2DM We further analyzed the data to validate the IDRS score and FINDRISC against increased fasting blood sugar levels in diagnosing T2DM (either FBS: 100–125 or ≥ 126 mg/dl). When assessing the diagnostic accuracy of the FINDRISC score and IDRS score for stratifying the risk of diabetes (Table 5 ), the area under the ROC curve was 0.78 (95% CI: 0. 68–0.88), with an optimal cut-off of ≥ 9, sensitivity and specificity of 67% and 81%, respectively; whereas the area under the ROC curve for the IDRS was 0.67 (95% CI: 0.56–0.78), with a cut-off of ≥ 45, a sensitivity and specificity of 46% and 80%, respectively. There was a significant difference between AUC’s of both scores (P = 0.000). As shown in Fig. 3 , AUC in our study was largest for FINDRISC (0.782) when compared with IDRS. In comparison to the IDRS sensitivity and specificity levels, FINDRISC had the highest sensitivity (67%), whereas the IDRS risk score had a high specificity (80%) as same as that of FINFRISC, but also had the lowest sensitivity between both of tools. Furthermore, the FINDRISC had higher positive predictive value (32% vs. 23%) and negative predictive value (95% vs.92%) relative to IDRS. Notably, positive predictive values of both scores were below 40%, while negative predictive values were above 90%. The probability that the FINDRISC score is positive for the high risk subjects was PPV; 32% ,while it was less (PPV 23%) for those by IDRS. Positive likelihood ratio (3.5 vs.2.3) than IDRS. Hence, those who were at high‑risk group according to Finnish score were 3.5 times more likely to be diabetic than non-diabetics relative to IDRS. Table 5 Comparison of performance of the FINDRISC and IDRS scores Screening characteristic FINDRISC score IDRS score AUC (95%CI) 0.782 0.671 Youden index 0.44 0.25 Optimum cut off 9.0 45.0 Sensitivity (%) 67.0 46.0 Specificity (%) 81.0 80.0 PPV (%) 32.0 23.0 NPV (%) 95.0 92.0 Positive likelihood ratio 3.50 2.30 Negative likelihood ratio 0.41 0.68 Accuracy(%) 79.0 75.5 CUI + 0.21 0.11 CUI − 0.80 0.73 FINDRISC-Finnish Diabetes Risk Score; IDRS-Indian Diabetes Risk Score; AUC- area under the receiver-operating curve; CI, 95% confidence interval; LR- likelihood ratio. Results are expressed as percentage, except for AU, NPV- negative predictive value; PPV- positive predictive value; CUI -clinical utility index. When comparing accuracy (79% for FINDRISC vs.75% for IDRS) and clinical utility indices (CUI) of both the score models, CUI was found to be very poor in case finding, but the clinical utility of FINDRISC was "very good" when compared with IDRS (CUI − : 0.80 vs. 0.73) in ruling out T2DM in the study population. In addition, we measured the level of agreement between FINDRISC and IDRS scores by using Bland-Altman plot (B-A plot) for stratifying risk of diabetes (Fig. 4 ). As shown in B‑A plot, differences were increasing as mean was increasing. There was no perfect agreement in these two scores. There was a proportionality bias as the majority of observations were lying well above the 0 (y‑axis). Differences can be regressed on mean but as these values were not following normal curve, it was not plotted. Except few, majority of values in the graph lies in CIs (within 5th − 95th percentiles); indicating a fair agreement. We also compared the applicability of both two risk score models in detecting undiagnosed T2DM (UT2DM) and prediabetes (PDM) among the study subjects using cutoff value ≥ 12 and IDRS cutoff value > 60, which is routinely in practice (Table 6 ). According to the FINDRISC score, among 14 (7%) subjects who were categorized in the high risk category (FINDRISC score ≥ 12), 7 had dysglycemia. Only 1 out of 7 dysglycemic subjects had a FINDRISC score ≥ 15. While using the FINDRISC cut-off value ≥ 9, resulted in detecting the highest incidence of dysglycemia 17(8.5%) among the total population, compared to 7 (3.5%) with the FINDRISC cut-off value ≥ 12. In contrast, we found that there was no significant difference between IDRS score with cut-off values ≥ 45 or ≥ 60 in detecting dysglycemia (p = 0.06). Although the highest IDRS cut-off ≥ 45 value was observed for screening PDM compared to IDRS cut-off ≥ 60 (Table 6 ). Table 6 Distribution of FINDRISC and IDRS cutoff risk scores for diagnosis of dysglycemia (prediabetes, and diabetes) among 200 study subjects Non-diabetes < 100 mg/dl Prediabetes 100–125 mg/dl Diabetes ≥ 126 p-value FINDRISC score < 9 143(81.2%) 7.0 (33.3%) 0.0 (0.0%) 0.000 ≥ 9 33 (18.8%) 14 (66.7%) 3.0 (100%) < 12 168 (95.5) 15 (71.4%) 2.0 (66.7%) ≥ 12 8.0 (4.5%) 6.0 (28.6) 1.0 (33.3) IDRS score < 45 140 (79.5%) 11 (234%) 2.0 (66.7%) 0.060 ≥ 45 36 (20.5%) 10 (47.6%) 1.0 (33.3) < 60 157(89.2%) 15 (71.4%) 2.0 (66.7%) ≥ 60 19 (10.8) 6.0 (28.6%) 1.0 (33.3) *Results are expressed as number (%).Chi-Square p < 0.05 considered as significant value Summarily, at a cutoff point of FINDRISC ≥ 9, out of these17 (8.5%) dysglycemic participants among the total population,14 (7%) were prediabetes and 3 (1.5%) were diabetes whereas at a cutoff point of IDRS ≥ 45, of11(5.5%) dysglycemic participants, 10 (5%) were PDM and only1(0.5%) was DM. Discussion Diabetes is increasingly identified as an important cause of mortality and a critical public health issue worldwide [ 5 , 28 ]. An important burden for diabetes, besides a health burden, is financial burden to individuals and society. This is attributed to the high prevalence of diabetes and the high cost per individual with DM [ 28 ]. It has been reported that early identification of individuals at high risk for the development of diabetes using easy, noninvasive, cost effective and reliable tool can help to reduce or prevent the development and the cardio metabolic associated complications of T2DM through dietary and lifestyle interventions [ 29 , 30 ]. For this purpose, several diabetes risk screening models have been developed to be easily and effectively used in clinical practice with minimum cost[ 31 ]. However, it is not ensured whether these risk score tools can be applied in local populations. Data suggest that some tools developed in a selected population do not always perform well in other populations [ 32 ]. Thus, the applicability of these models in a different population is limited and can be misleading. Therefore, various countries need to continue to explore diabetes assessment systems that are suitable and applicable for local populations [ 33 ]. In Yemen, despite the widespread use of diabetes risk scores, no previous study, to the best of our knowledge, has studied the discriminatory accuracy of different assessment scores for diabetes screening in a healthy Yemeni population. Hence, in this study, the screening effectiveness of two frequently used less costly validated risk scores such as FINDRISC and IDRS are compared in a young population in Taiz city, Yemen. The main reasons for T2DM screening in this population group include the sedentary life they have, the increase in the incidence rate of diabetes in the younger population and limited studies conducted among them. Based on the data of our study, the overall prevalence of dysglycemia was (12%). This finding is consistent with previous studies by Viitasalo et al. and Sapkota et al. who found a prevalence of dysglycemia of 12.9% and 11.3%, respectively, among their study participants [ 34 , 35 ]. However, a recent study carried out in Yemen found that the prevalence of dysglycemia was (5.7%) [ 36 ]. In contrary to our finding; studies conducted in different countries. For example, studies by Al-Shudifat et al., Ali et al., D’Souza et al., and Nnamudi et al. conducted among adult population reported a lower and a higher prevalence of 3.8%, 8.2%, 26.9% and 32.8% respectively [ 37 , 38 , 39 , 40 ]. This difference could be due to the high number of participants at a high risk of diabetes, that is, older and obese, and along with the use of a cutoff value of hyperglycemia to define PDM and T2DM, besides impaired fasting glucose, and diabetes, also comprised oral glucose tolerance test. A noteworthy finding in our study was the low prevalence of PDM (10.5%) and UT2DM (1.5%) among the participants. This finding is inconsistent with the results of a study by Farag et al. who found a prevalence of PDM and UT2DM of 21.7% and 5% respectively among their study participants[ 41 ]. Compared to our study, a study done in the Lebanese University showed the prevalence of PDM and UT2DM was 22.9% and 7.6% respectively[ 42 ]. Similar to our finding, studies in different countries reported a lower prevalence of PDM among young adult students[ 43 , 44 , 45 ]. Nevertheless, increased awareness of the high prevalence of PDM worldwide as alarming, is crucial for young subjects to implement preventive measures that may reduce the risk of diabetes. Regarding undiagnosed T2DM, it was well demonstrated that about 1.5% of the subjects with newly UT2DM are unaware of their disease. The disparity and inconsistency in PDM and T2DM prevalence could be because of differences in the study participant characteristics (age, sex, ethnicity, etc.), sampling technique, the diagnostic criteria, lifestyle choices, unhealthy dietary habits, socioeconomic status and genetic factors. It is worth mentioning that there was a significant difference in the risk of developing T2DM between both sexes (P < 0.001). This could be explained by the higher frequency of overweight and obesity that may be associated with a sedentary lifestyle, limited physical activity and nutritional behaviors among young adult females in our region, contributing to the high risk of T2DM.This finding is consistent with similar studies by Ahmad et al. and Evcimen et al. which also found a significantly higher diabetic risk among females [ 43 , 44 ]. In contrast to our findings, a previous study done by Sezer et al., which found no significant difference between both sexes and reported no statistically significant difference between females and males in terms of their risk of developing T2DM [ 45 ]. This discrepancy may be partly due to differences in sample sizes, risk factors included in study's tool, genetic factors, or variations in the involved populations. It should be noted that the prevalence and risk factors of diabetes in a population assess the applicability of a risk score. In this study, the prevalence of DM increased with increasing FINDRISC and IDRS scores. Several cross-sectional studies have tested the FINDRISC and IDRS as screening tools for T2DM. Despite the widespread use of FINDRISC scores, the discriminatory performance of the IDRS versus the FINDRISC score has not been evaluated in detecting T2DM in a Yemeni population. Both the risk score in our study were similar because they include common variables for Asian populations [ 46 ]. Regarding the accuracy and the validation of FINDRISC and IDRS in our study, their performance is generally analyzed based on their sensitivity, specificity, and ROC curves. For IDRS, the best cut‑off was ≥ 45 for identifyingUT2MD, with a sensitivity of 46.0%, specificity of 80.0%, and Youden index of 0.25 (Table 5 ). This finding is in agreement with a study from central India that showed that the optimum cut-off for IDRS was ≥ 40, with a sensitivity (higher than that of the current study), a specificity, and a Youden index of 60.4%, 70.7%, and 0.31, respectively [ 47 ]. Pawar et al. found sensitivity 78.95%, specificity 56.14% at optimal cutoff point of 60 [ 48 ]. This disparity might be attributed to ethnic diversity, differences in the populations studied, and/or unclear categorization of daily physical exercises across various studies. Of note, the low sensitivity of the IDRS in our study, coupled with high specificity, suggests that while the test accurately identifies subjects who do not have diabetes (high true negatives and low false positives), it may be missing a significant number of subjects who do have diabetes (high false negatives). This indicates that the IDRS is good at ruling out diabetes with a low level of accuracy (at Youden index of 0.25) but not as good at identifying those who have diabetes. On the other hand, FINDRISC had shown optimum cut-off of ≥ 9 for identifying T2DM, with a sensitivity, specificity and Youden index of 67.0%, 81.0% and 0.44 respectively (Table 5 ). This indicates that at a cut-off of ≥ 9, the FINDRISC correctly identifies about 67% of subjects with T2DM, while also correctly identifying about 81% of those without T2DM with a moderate level of accuracy (at Youden index of 0.44). This finding aligns with other studies that also found best cut-off of FINDRISC > 9 to detect T2DM [ 13 , 49 ]. In two retrospective cohorts, Bhowmik et al. found a sensitivity of 62.4% and 75.7%, and specificity of 67.4% and 61.6% at a risk score of > 9 [ 49 ]. Variations in the study populations could be the possible reason for differences in the sensitivity and specificity values at the same cut-off point across various studies [ 50 ]. With respect to AUC, the FINDRISC had a larger area under the ROC curve (AUC of 0.78, 95% CI: 0.68–0.88) compared to IDRS (AUC of 0.67, 95% CI: 0.56–0.78). This suggests that FINDRISC has fairly good discriminatory accuracy in screening diabetes. Our study indicates that FINDRISC is superior to the IDRS at identifying diabetes in the Yemeni population. This could be due to FINDRISC including more diabetes risk factors in its screening system than IDRS [ 48 ]. Finally, this study found that FINDRISC had higher sensitivity, specificity and PPV compared to IDRS. Statistically, higher sensitivity and specificity improve the PPV, especially with a higher disease prevalence in the studied population. This explains the higher prevalence of PDM and DM in the FINDRISC model (7% and 1.5% vs. 5% and 0.5%) compared to IDRS (Table 6 ). Similar to our study, Schmid et al. indicated that the number of subjects in the high-risk category for developing diabetes varies based on the risk assessment [ 51 ]. Furthermore, our data showed that FINDRISC was effective at detecting diabetes (AUC = 0.78) and had moderate diagnostic accuracy (at a cut-off ≥ 9 with a Youden index of 0.44). This suggests a reasonably good level of accuracy in identifying those at risk for T2DM. Conclusion In conclusion, FINDRISC can serve as a simple and effective screening tool to identify individuals at high risk for T2DM and PDM among young adults in Yemen. Our data showed that with a cut-off score ≥ 9, the FINDRISC had moderate diagnostic accuracy (with a sensitivity of 67.0%, specificity of 81.0%, and AUC of 0.78). Diagnostic accuracy of FINDRISC is relatively good compared to that of IDRS. To address the growing socioeconomic burden of diabetes, it may be more beneficial to develop and validate a more specific risk scoring system for a young adult Yemeni population. Abbreviations AOR Adjusted odds ratio AUC Area Under Curve B-A plot Bland–Altman Plot BMI Body Mass Index CI Confidence interval CUIs Clinical Utility Indices FBS Fasting blood sugar FINDRISC Finnish Diabetes Risk Score IDF International Diabetes Federation IDRS Indian Diabetes Risk Score PDM Prediabetes ROC Receiver Operating Characteristic Curve SD Standard deviation SPSS Statistical Package for the Social Sciences T2DM Type 2 Diabetes Mellitus UT2DM Undiagnosed Type 2 Diabetes Mellitus WC Waist Circumference WHO World Health Organization Declarations Acknowledgments The authors thank the students who gave of their consent and time to fill out the questionnaire and participate in this study. We are also grateful for the administration of Faculty of Medicine and Health Sciences for allowing us to conduct this study and the staff of the Laboratory Department of the faculty for their assistance. Authors’ contributions AA developed the study idea and collected data. AA, LQ, and MB designed the study. AA and LQ drafted the manuscript. AA and SK analyzed the study data. AA and LQ contributed to the data interpretation and revised the manuscript draft. AA and MB collected data and supervised data collection. All authors read and approved the final draft of the manuscript. Funding Not applicable. Availability of data and materials Data are available from the corresponding author upon reasonable request. Ethics approval and consent to participate The current study was carried out in accordance with the Declaration of Helsinki for studies on human subjects . The Ethical approval for the study was obtained from the Research Ethics Committee of the Faculty of Medicine and Health Sciences, Taiz University, Taiz, Yemen. Written informed consent was taken from every participant after a full illustration of the purpose of the study. Each participant was interviewed and completed a standardized questionnaire that included family history, habits, diets, uses of drugs and history of disease like diabetes, hypertension and cardiac diseases. They were told that all the data will be confidential and will be used only for research purposes. Consent for publication Not applicable. Competing interests The authors declare no competing interests. References Akash MS, Rehman K, Liaqat A. Tumor necrosis factor‐alpha: role in development of insulin resistance and pathogenesis of type 2 diabetes mellitus J. Cell. Biochem. 2018;119(1):105-10. https://doi.org/10.1002/jcb.26174. World Health Organization. Diabetes. Geneva: World Health Organization; 2021. Available from: https://www.who.int/ news-room/fact-sheets/detail/diabetes. [accessed on 2021 Jun 08]. Jaacks LM, Siegel KR, Gujral UP, Narayan KV. Type 2 diabetes: A 21st century epidemic. Best Pract Res Clin Endocrinol Metab. 2016 Jun;30(3):331-43. https://doi.org/10.1016/j.beem.2016.05.003. Wild S, Roglic G, Green A, Sicree R, King H. Global prevalence of diabetes estimates for the year 2000 and projections for 2030. Diabetes Care 2004;27:1047–53. https://doi.org/10.2337/diacare.27.5.1047. Saeedi P, Petersohn I, Salpea P, Malanda B, Karuranga S, Unwin N, et al. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas. Diabetes Res. Clin. Pract. 157:107843. https://doi.org/10.1016/j.diabres.2019.107843. Guariguata L, Whiting DR, Hambleton I, Beagley J, Linnenkamp U, Shaw JE. Global estimates of diabetes prevalence for 2013 and projections for 2035. Diabetes Res Clin Pract . 2014 Feb 1;103(2):137-49. https://doi.org/10.1016/j.diabres.2013.11.002. Williams R, Karuranga S, Malanda B, Saeedi P, Basit A, Besançon S, et al. Global and regional estimates and projections of diabetes-related health expenditure: Results from the International Diabetes Federation Diabetes Atlas. Diabetes Res Clin Pract. 2020; 162:108072. https://doi.org/10.1016/j.diabres.2020.108072. Beagley J, Guariguata L, Weil C, Motala AA. Global estimates of undiagnosed diabetes in adults. Diabetes Res. Clin. Pract. 2014 Feb 1;103(2):150-60. https://doi.org/10.1016/j.diabres.2013.11.001. Al-Habori M, Al-Mamari M, Al-Meeri A. Type II Diabetes Mellitus and impaired glucose tolerance in Yemen: prevalence, associated metabolic changes and risk factors. Diabetes Res. Clin. Pract. 2004 Sep 1;65(3):275-81. https://doi.org/10.1016/j.diabres.2004.02.001. Gunaid AA, Assabri AM. Prevalence of type 2 diabetes and other cardiovascular risk factors in a semirural area in Yemen. Eastern Mediterranean Health Journal. 2008 Jan 1;14(1):42-56. Ali MK, Pearson-Stuttard J, Selvin E, Gregg EW. Interpreting global trends in type 2 diabetes complications and mortality. Diabetologia. 2022 Jan;65(1):3-13. https://doi.org/10.1007/s00125-021-05585-2. Ziyab AH, Mohammad A, Maclean E, Behbehani K, Carballo M. Diabetes: a fast evolving epidemic. Kuwait Med J. 2015 Nov 25;47(4):291-301.URL: http://www.kma.org.kw/uploads/versions/OBNMZHFMDVCUTNOYYKOYAXBV.pdf. Lindstrom J, Tuomilehto J. The diabetes risk score: a practical tool to predict type 2 diabetes risk. Diabetes care. 2003 Mar 1;26(3):725-31. https://doi.org/10.2337/diacare.26.3.725. Phillips CM, Kearney PM, McCarthy VJ, Harrington JM, Fitzgerald AP, Perry IJ. Comparison of diabetes risk score estimates and cardiometabolic risk profiles in a middle-aged Irish population. PLoS one. 2013 Nov 13;8(11):e78950. https://doi.org/10.1371/journal.pone.0078950. Lin J, Thompson TJ, Cheng YJ, Zhuo X, Zhang P, Gregg E, et al. Projection of the future diabetes burden in the United States through 2060. Popul Health Metr. 2018 Dec;16:1-9. https://doi.org/10.1186/s12963-018-0166-4. Meneghini LF, editor. Medical management of type 2 diabetes. 8th ed. Alexandria: American Diabetes Association; 2020. World Health Organization. Screening for type 2 diabetes: report of a World Health Organization and International Diabetes Federation meeting. Geneva, 2003. (Available from: http://www.who.int/iris/handle/10665/68614) [4 August 2007]. Kumar D, Prakash B, Subhash CBJ, Kadkol PS, Arun V, Thomas JJ. An android smartphone-based randomized intervention improves the quality of life in patients with type 2 diabetes in Mysore, Karnataka, India. Diabetes Metab Syndr. 2020;14(5):1327-32. https://doi.org/10.1016/j.dsx.2020.07.025. Noble D, Mathur R, Dent T, Meads C, Greenhalgh T. Risk models and scores for type 2 diabetes: systematic review. BMJ 2011;343:d7163. https://doi.org/10.1136/bmj.d7163. Mann DM, Bertoni AG, Shimbo D, Carnethon MR, Chen H, Jenny NS, et al. Comparative validity of 3 diabetes mellitus risk prediction scoring models in a multiethnic US cohort: the Multi-Ethnic Study of Atherosclerosis. Am J Epidemiol. 2010; 171:980-88. https://doi.org/10.1093/aje/kwq030. Mohan V, Sandeep S, Deepa M, Gokulakrishnan K, Datta M, Deepa R. A diabetes risk score helps identify metabolic syndrome and cardiovascular risk in Indians - the Chennai Urban Rural Epidemiology Study (CURES-38). Diabetes Obes Metab.2007; 9(3):337-43. https://doi.org/10.1111/j.1463-1326.2006.00612.x. Saleem SM, Raina AF, Khan SM, Jan SS. Indian diabetic risk score-A tool for predicting risk of undiagnosed type 2 diabetes mellitus. Int J Curr Res Rev. 2017 Feb 1;9:39-42. Available from:https://ijcrr.com/uploads/96_pdf.pdf. Meijnikman AS, De Block CE, Verrijken A, Mertens I, Van Gaal LF. Predicting type 2 diabetes mellitus: a comparison between the FINDRISC score and the metabolic syndrome. Diabetol Metab Syndr. 2018 Dec;10:1-6. https://doi.org/10.1186/s13098-018-0310-0. Bernabe-Ortiz A, Perel P, Miranda JJ, Smeeth L. Diagnostic accuracy of the Finnish Diabetes Risk Score (FINDRISC) for undiagnosed T2DM in Peruvian population. Prim Care Diabetes. 2018;12(6):517–25. https://doi.org/10.1016/j.pcd.2018.07.015. Herman WH, Smith PJ, Thompson TJ, Engelgau MM, Aubert RE. A new and simple questionnaire to identify people at increased risk for undiag¬nosed diabetes. Diabetes Care 1995; 18(3): 382–7. https://doi.org/10.2337/diacare.18.3.382. Smith Liz. New AHA recommendations for blood pressure measurement: American Heart Association Practice Guide¬lines. Am Fam Physician 2005; 72(7): 1391–8. Available from: https://www.aafp.org/afp/2005/1001/p1391.html. American Diabetes Association. 2. Classification and Diagnosis of Diabetes. Diabetes Care. 2017 Jan;40 Suppl 1:S11-S24. https://doi.org/10.2337/dc17-S005. Lin X, Xu Y, Pan X, Xu J, Ding Y, Sun X, Song X, Ren Y, Shan PF. Global, regional, and national burden and trend of diabetes in 195 countries and territories: an analysis from 1990 to 2025. Sci Rep. 2020 Sep 8;10(1):14790. https://doi.org/10.1038/s41598-020-71908-9. ElSayed NA, Aleppo G, Aroda VR, Bannuru RR, Brown FM, Bruemmer D, et al.1. Improving care and promoting health in populations: standards of care in diabetes—2023. Diabetes care. 2023 Jan 1;46 Suppl 1:S10-8. https://doi.org/10.2337/dc23-S001. Osman OA, Saeed AA, Mousnad MA, Hamid A. Assessment of The Risk Of Type 2 Diabetes Among Healthy without Diabetes in Sudan Using the FINDRISC Tool. Univers. J. Pharm. Res. 2020, 5, 37–41. https://doi.org/10.22270/ujpr.v5i4.438. Buijsse B, Simmons RK, Griffin SJ, Schulze MB. Risk assessment tools for identifying individuals at risk of developing type 2 diabetes. Epidemiol Rev. 2011 Jul 1;33(1):46-62. https://doi.org/10.1093/epirev/mxq019. Bhowmik B, Akhter A, Ali L, Ahmed T, Pathan F, Mahtab H, et al. Simple risk score to detect rural AsianIndian (Bangladeshi) adults at high risk for type 2 diabetes. J Diabetes Investig. 2015; 6:670–7. https://doi.org/10.1111/jdi.12344. Gray BJ, Bracken RM, Turner D, Morgan K, Williams M, Rice S, et al. Different type 2 diabetes risk assessments predict dissimilar numbers at ‘high risk’:a retrospective analysis of diabetes risk-assessment tools. Br J Gen Pract. 2015; http://doi.org/10.3399/bjgp15X687661. Viitasalo K, Lindström J, Hemiö K, Puttonen S, Koho A, Härmä M, et al. Occupational health care identifies risk for type 2 diabetes and cardiovascular disease. Prim Care Diabetes. 2012; 6(2):95-102. https://doi.org/10.1016/j.pcd.2012.01.003. Sapkota M, Timilsina A, Shakya M, Thapa TB, Shrestha S, Pokhrel S, et al. Metabolic syndrome and diabetes risk among young adult students in the health sciences from Kathmandu, Nepal. Drug Healthc Patient Saf. 2020;12:125–33. https://doi.org/10.2147/DHPS.S258331. Al-Dolae MH, Salah MG, Al-Najjar AM, Al-Rassas SH, Al-Madwami MA, Al-khadher MA. Assessment of Diabetes Risk Among The Fifth and Sixth Years Medical students in Thamar University, Yemen, A Cross-sectional Study. Zagazig University Medical Journal. 2024 Jun 1;30(1.4):385-94. https://doi.org/10.21608/ZUMJ.2023.231878.2863. Al-Shudifat AE, Al-Shdaifat A, Al-Abdouh AA, Aburoman MI, Otoum SM, Sweedan AG, et al. Diabetes risk score in a young student population in Jordan: a cross‐sectional study. J Diabetes Res. 2017;2017(1):8290710. https://doi.org/10.1155/2017/8290710. Ali N, Samadder M, Shourove JH, Taher A, Islam F. Prevalence and factors associated with metabolic syndrome in university students and academic staff in Bangladesh. Sci Rep. 2023 Nov 14;13(1):19912. https://doi.org/10.1038/s41598-023-46943-x. D’Souza MS, Amirtharaj A, Venkatesaperumal R, Isac C, Maroof S. Risk-assessment score for screening diabetes mellitus among Omani adults. SAGE Open Med.2013;1. https://doi.org/10.1177/2050312113508390. Nnamudi AC, Orhue NE, Ijeh II. Assessment of the FINDRISC tool in predicting the risk of developing type 2 diabetes mellitus in a young adult Nigerian population. Bull Natl Res Cent. 2020 Nov 3;44(1):186. https://doi.org/10.1186/s42269-020-00440-7. Farag HF, Elrewany E, Abdel-Aziz BF, Sultan EA. Prevalence and predictors of undiagnosed type 2 diabetes and pre-diabetes among adult Egyptians: a community-based survey. BMC Public Health. 2023 May 25;23(1):949. https://doi.org/10.1186/s12889-023-15819-0. Abdallah M, Sharbaji S, Sharbaji M, Daher Z, Faour T, Mansour Z, et al. Diagnostic accuracy of the Finnish Diabetes Risk Score for the prediction of undiagnosed type 2 diabetes, prediabetes, and metabolic syndrome in the Lebanese University. Diabetol Metab Syndr. 2020 Sep 30;12(1):84. https://doi.org/10.1186/s13098-020-00590-8. Ahmad T, Tauqir A, Tariq H, Qureshi A, Jalaluddin S, I. Khan U, et al. Sex differences in risk of developing Type 2 Diabetes Mellitus (T2DM): A feasibility assessment of FINDRISC scoring and barriers to disease management in a low-income settlement of Rawalpindi, Pakistan. PLOS Global Public Health. 2025 Jul 16;5(7):e0003087. https://doi.org/10.1371/journal.pgph.0003087. Evcimen H, Ayyıldız Nİ, Doğan U. Diabetes Risk Score of Adult Applications to Primary Health Care Center: A Cross-Sectional Study. Van Sağlık Bilimleri Dergisi. 2023;16(1):53-9. https://doi.org/10.52976/vansaglik.1162785. Sezer Ö, Lafçi NÖ, Korkmaz S, Dağdeviren HN. Prediction of a 10-year risk of type 2 diabetes mellitus in the Turkish population: A cross-sectional study. Medicine. 2021 Nov 5;100(44):e27721. https://doi.org/10.1097/MD.0000000000027721. Zahid N, Shi Z, Claussen B, Hussain A. Prevalence and risk factors for diabetes, comparison of rural populations in Bangladesh, China and Pakistan. Diabetes Metab Syndr Clin Res Rev. 2009 Jun 1;3(2):109-12. https://doi.org/10.1016/j.dsx.2009.04.006. Bhadoria A, Kasar P, Toppo N. Validation of Indian diabetic risk score in diagnosing type 2 diabetes mellitus against high fasting blood sugar levels among adult population of central India. Biomed J. 2015;38:359–60. https://doi.org/10.4103/2319-4170.143508 . Pawar SD, Naik JD, Prabhu P, Jatti GM, Jadhav SB, Radhe BK. Comparative evaluation of Indian Diabetes Risk Score and Finnish Diabetes Risk Score for predicting risk of diabetes mellitus type II: A teaching hospital-based survey in Maharashtra. J. Fam. Med. Prim. Care. 2017 Jan 1;6(1):120-5. https://doi.org/10.4103/2249-4863.214957. Bhowmik B, Akhter A, Ali L, Ahmed T, Pathan F, Mahtab H, et al. Simple risk score to detect rural Asian Indian (Bangladeshi) adults at high risk for type 2 diabetes. J Diabetes Investig. 2015;6(6):670–7. https://doi.org/10.1111/jdi.12344. Vandersmissen GJ, Godderis L. Evaluation of the Finnish Diabetes Risk Score (FINDRISC) for diabetes screening in occupational health care. Int. J. Occup. Med. Environ. Health. 2015 Jan 1;28(3):587-91. https://doi.org/10.13075/ijomeh.1896.00407. Schmid R, Vollenweider P, Waeber G, Marques-Vidal P. Estimating the risk of developing type 2 diabetes: a comparison of several risk scores: the Cohorte Lausannoise study. Diabetes Care. 2011 Aug 1;34(8):1863-8. https://doi.org/10.2337/dc11-0206 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 02 Apr, 2026 Read the published version in Diabetology & Metabolic Syndrome → Version 1 posted Editorial decision: Revision requested 18 Dec, 2025 Reviews received at journal 15 Dec, 2025 Reviewers agreed at journal 10 Dec, 2025 Reviewers agreed at journal 08 Dec, 2025 Reviewers agreed at journal 07 Dec, 2025 Reviewers agreed at journal 06 Dec, 2025 Reviewers agreed at journal 05 Dec, 2025 Reviews received at journal 20 Oct, 2025 Reviewers agreed at journal 14 Oct, 2025 Reviewers invited by journal 13 Oct, 2025 Editor assigned by journal 06 Oct, 2025 Submission checks completed at journal 06 Oct, 2025 First submitted to journal 04 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7781850","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":534155308,"identity":"9e267ce0-2e32-4a5c-9596-e2801b20c686","order_by":0,"name":"Abdulsalam Al-Shami","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIiWNgGAWjYDCCAwyMBxgbGBjY5N9/fADk8/ARoYUBooUhwdgApIWNaC0MDAlmEiCKoBa+24cfHPy5wy6aj+FAWuXXHDsZNgbmh49u4NEieS7N4DDvmeTcNsaGY7dltyUDHcZmbJyDR4vBGQaDw4xtzLltzIxttyW3MQO18LBJ49fC/uHgz7b63DY2ZrZiyW31xGjhMTjA23Y4t42HjY3x47bDhLVInuEpOMzbdjy3TYKHWZpx23EeoG34/cJ3hn3jw59t1bnzZ/Awfvy5rdqen7354WN8WlAAMw+YJFY5CDD+IEX1KBgFo2AUjBgAALXFSS7nBCYVAAAAAElFTkSuQmCC","orcid":"","institution":"Taiz University","correspondingAuthor":true,"prefix":"","firstName":"Abdulsalam","middleName":"","lastName":"Al-Shami","suffix":""},{"id":534155309,"identity":"85f5d975-a851-49eb-b63c-05150f5c8203","order_by":1,"name":"Lina Qasem","email":"","orcid":"","institution":"Amran University","correspondingAuthor":false,"prefix":"","firstName":"Lina","middleName":"","lastName":"Qasem","suffix":""},{"id":534155310,"identity":"3c05c10a-80bc-4946-9ab6-14cb47afc2af","order_by":2,"name":"Mufeed Baddah","email":"","orcid":"","institution":"Taiz University","correspondingAuthor":false,"prefix":"","firstName":"Mufeed","middleName":"","lastName":"Baddah","suffix":""},{"id":534155311,"identity":"a6dda30c-f88f-4122-bef8-983251b73a58","order_by":3,"name":"Sami Kahtan","email":"","orcid":"","institution":"University of Aden","correspondingAuthor":false,"prefix":"","firstName":"Sami","middleName":"","lastName":"Kahtan","suffix":""}],"badges":[],"createdAt":"2025-10-04 20:53:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7781850/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7781850/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13098-026-02123-1","type":"published","date":"2026-04-02T15:58:58+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":94480895,"identity":"bdb534c4-046f-4935-91ae-902224bfe494","added_by":"auto","created_at":"2025-10-27 16:12:11","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":165940,"visible":true,"origin":"","legend":"","description":"","filename":"FINDRISCandIDRSmanuscriptBMCFinal.docx","url":"https://assets-eu.researchsquare.com/files/rs-7781850/v1/6adf3e6b30c47853db1fa3ba.docx"},{"id":94481233,"identity":"87e1f7d5-23e3-460e-9d5b-f08bf56ab1b8","added_by":"auto","created_at":"2025-10-27 16:12:55","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":6467,"visible":true,"origin":"","legend":"","description":"","filename":"a639379cad8043ba810975169ddcb818.json","url":"https://assets-eu.researchsquare.com/files/rs-7781850/v1/1744c10f7ad9da7abbe9c204.json"},{"id":94481444,"identity":"72185beb-5423-4c14-b5ab-23877627923a","added_by":"auto","created_at":"2025-10-27 16:13:23","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":167244,"visible":true,"origin":"","legend":"","description":"","filename":"a639379cad8043ba810975169ddcb8181enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7781850/v1/94ebbaf5cfedd025a39b2293.xml"},{"id":94480498,"identity":"bc610450-70c2-450e-a2f7-ac8e0f7c3f25","added_by":"auto","created_at":"2025-10-27 16:11:18","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":20468,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7781850/v1/aa062d8e02ce9771874420a6.png"},{"id":94480976,"identity":"37874641-401f-47b9-b8fb-630c0ed064ba","added_by":"auto","created_at":"2025-10-27 16:12:21","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":9646,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7781850/v1/f945366f80158eaf52821a9e.png"},{"id":94480889,"identity":"e5c1fe1e-725c-4a36-b4b5-94f0a95fec06","added_by":"auto","created_at":"2025-10-27 16:12:10","extension":"xml","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":165679,"visible":true,"origin":"","legend":"","description":"","filename":"a639379cad8043ba810975169ddcb8181structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7781850/v1/0ab866f53d3c30a994047bfe.xml"},{"id":94481400,"identity":"2e360077-f9ea-4f0d-bbd0-ae19b60f7c3d","added_by":"auto","created_at":"2025-10-27 16:13:16","extension":"html","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":174676,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7781850/v1/7d7f88801a8d56e74e0689d7.html"},{"id":94481448,"identity":"a9fcdf68-dd46-4ff6-94b4-bb68f76642c4","added_by":"auto","created_at":"2025-10-27 16:13:23","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":23334,"visible":true,"origin":"","legend":"\u003cp\u003eRisk of developing of T2DM based on FINDRISC score among 200 medical students\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7781850/v1/bc4243b232148eab4731fa44.png"},{"id":94481627,"identity":"0ef4838c-9c6b-4177-af1d-46267541a57f","added_by":"auto","created_at":"2025-10-27 16:13:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":20587,"visible":true,"origin":"","legend":"\u003cp\u003eRisk of developing T2DM based on IDRS score among 200 medical students\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7781850/v1/2a07c2204204a1324dea3b6b.png"},{"id":94481017,"identity":"d32fc6d1-1718-4cb1-8c0d-61c7096e7acf","added_by":"auto","created_at":"2025-10-27 16:12:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":64325,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of area under the ROC curve (AUROC) for the FINDRISC score and IDRS score\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7781850/v1/6feba1a50de4dfe3050ed7f3.png"},{"id":94481121,"identity":"0a230a62-d6bf-41ec-a9c5-e8b6b6d82289","added_by":"auto","created_at":"2025-10-27 16:12:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":22230,"visible":true,"origin":"","legend":"\u003cp\u003eBland-Altman plot for assessing the agreement between Finnish Diabetes \u0026nbsp;Risk Score (FINDRISC) and Indian Diabetes Risk Score (IRDS\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7781850/v1/aba09d6fe6f8236257b47543.png"},{"id":106343985,"identity":"0276d2c4-46a1-4959-b5cc-7786b9e27c6e","added_by":"auto","created_at":"2026-04-07 16:11:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1209246,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7781850/v1/30d18e31-d768-4f82-9f51-575ee0635130.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparative Study of FINDRISC and IDRS in Predicting Prediabetes and Diabetes Mellitus in a Young Adult Yemeni Population","fulltext":[{"header":"Background","content":"\u003cp\u003eDiabetes mellitus (DM) is a group of metabolic disorders characterized by high blood glucose levels due to impairments in insulin production (type 1 diabetes), or insulin action (type2 diabetes) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Type 2 Diabetes Mellitus (T2DM) is the most prevalent type of diabetes, which is responsible for almost 90% of the total cases of diabetes worldwide [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDM has been increasingly identified as a serious, global public health challenge[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The International Diabetes Federation (IDF) estimates that the number of individuals with diabetes mellitus worldwide are currently 463\u0026nbsp;million, and it is expected to rise to 592\u0026nbsp;million and 700\u0026nbsp;million individuals by 2035 and 2045, respectively, with the highest proportion observed in low- and middle-income countries [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The increasing worldwide prevalence of DM represents substantial economic burdens. Globally, health care costs estimate in 2019 was 760\u0026nbsp;billion USD, and this number is predicated to be increased to 845\u0026nbsp;billion USD by 2045 [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Unfortunately, besides a rapidly increased incidence among young individuals, more than 50% of the diabetics in the world remain unaware of their illness status, which contributes to the disease burden by increasing a public health risk and preventing immediate interventions [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn Yemen, our knowledge about the prevalence of DM remains poor with little data available. Although it has been found that the prevalence of DM in Yemen was 4.6% by 2004, the number increased to 10.4% in 2008 [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The rise in DM in Yemen can be attributed to the genetic predisposition, urbanization, the sedentary life-styles and the changing food habits. Given that DM is a chronic disease associated with a high rate of mortality, and health care expenditures that have been explained by long-term vascular complications, thus managing DM represents one of the biggest worldwide health concerns at the present time [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Hence, an urgent need has emerged to develop a simple, fast, cost-effective and non-invasive screening tool for early identification of individuals at higher risk of developing T2DM in the future [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePrevious studies demonstrated that early screening and detection, diagnosis and management of the risk of T2DM could alleviate the rapidly growing socioeconomic burdens of T2DM, thus delaying or preventing the development of the illness and reducing serious complications [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Considering the targeted interventions such as lifestyle modification and exercise or medications, lifestyle modifications is proven to be beneficial to avoid DM and lessen its burden, thus improving health care outcomes and the quality of life [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRecently, various diabetic risk scores have been developed to assess individuals with undiagnosed T2DM (prevalent), or those who are at risk of developing T2DM (incident) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Some diabetic assessment models have been validated in selected populations, prompting their use in other countries. However, recent studies have shown that these risk scoring systems derived from the same populations may not be appropriate for other ethnic groups [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]; therefore, there is a need to establish a diabetes risk score for the Yemeni population.\u003c/p\u003e\u003cp\u003eTo the best of our knowledge, no previous studies have been conducted to compare two different existing diabetes risk screening tools in the adult Yemeni population during wartime. Hence, the current study was designed to evaluate and compare the diagnostic accuracy of the Finnish Diabetes Risk Score (FINDRISC) and the Indian Diabetes Risk Score (IDRS) in assessing the risk of developing T2DM among healthy medical students at the Faculty of Medicine and Health Sciences, Taiz University, Yemen. We also determine the factors associated with the risk of T2DM among study subjects.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy design and setting\u003c/h2\u003e\u003cp\u003eA cross-sectional study was conducted among 200 medical students during a three-month period (January to March 2024) at the Faculty of Medicine and Health Sciences, Taiz University, Taiz governorate, Yemen. This governorate is the third largest city in the country and is located in the central of its southwestern highlands region, 1400 metres (4,600 ft) above sea level.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eStudy population\u003c/h3\u003e\n\u003cp\u003eDuring the study period, third- and fourth-year medical students were recruited using a convenience sampling technique at the Faculty of Medicine and Health Sciences, Taiz University. Only students who had T2DM, were not present during data collection, refused to participate, or did not compete the questionnaire were excluded from the study.\u003c/p\u003e\n\u003ch3\u003eSample Size\u003c/h3\u003e\n\u003cp\u003eThe sample size was calculated using the following formula:\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;Z\u003csup\u003e2\u003c/sup\u003e P q/d\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eWhere N\u0026thinsp;=\u0026thinsp;Sample size, a 95% confidence level (Z\u0026thinsp;=\u0026thinsp;1.96), a percentage of failure (q\u0026thinsp;=\u0026thinsp;1 \u0026ndash; p), and an estimated prevalence (P) of 50% was used due to lacking of available data on the prevalence of prediabetes mellitus (PDM) among Yemeni medical students with a 5% margin of sampling error ( d\u0026thinsp;=\u0026thinsp;5%). The formula was applied, resulting in N = (1.96)\u003csup\u003e2\u003c/sup\u003e X 0.5 X 0.5 / (0.05)\u003csup\u003e2\u003c/sup\u003e = 384. To account for the finite population size, the Adjusted Sample Size formula was then applied. The population size (S) was set to 346, representing the total number of second and third-year medical students during the study period. The adjusted sample size was calculated as N = [(384) / (1 + (384\u0026ndash;1) / (346)]\u0026thinsp;=\u0026thinsp;181. Considering a 5% non-response rate, the minimum recommended sample size for the study was determined to be 190.\u003c/p\u003e\n\u003ch3\u003eStudy Tool\u003c/h3\u003e\n\u003cp\u003eThere has not been a previously validated risk scoring system for the Yemeni population. After a review of validated risk assessment scoring systems for different countries [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The current study used two diabetes risk scores: Finnish Diabetes Risk Score (FINDRISC) and Indian Diabetes Risk Score (IDRS) for assessing the risk of developing T2DM within the next 10 years.\u003c/p\u003e\u003cp\u003eThe Indian Diabetes Risk Score (IDRS) considers four risk factors; age, waist circumference (WC), physical activity and family history to assess the risk of developing T2DM [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Based on the total score, participants are classified into different risk levels: a score of \u0026lt;\u0026thinsp;30 indicates low risk, 30 to 59 denotes moderate risk, and a score exceeding 60 represents high risk of developing diabetes (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe FINDRISC tool is a validated and frequently used questionnaire that consists of eight variables; age, BMI, WC, physical activity, daily consumption of fruit and vegetables, high blood pressure or antihypertensive medication, high fasting blood sugar level, and family history of diabetes [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Participants with FINDRISC score of \u0026lt;\u0026thinsp;7 was categorized as low risk (estimated 1 in 100{1%} will develop T2DM), 7\u0026ndash;11 as slightly elevated risk (estimated 1 in 25{4%} will develop T2DM), 12 to 14 as moderate risk (estimated 1 in 6{16%} will develop T2DM), 15 to 20 as high risk (estimated 1 in 3{33%} will develop T2DM) and those with a score\u0026thinsp;\u0026gt;\u0026thinsp;20 as very high risk for developing diabetes (estimated 1 in 2{50%} will develop T2DM) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eThe participant\u0026rsquo;s data was obtained by using a predesigned, one page structured questionnaire containing participant\u0026rsquo;s name, age, gender, contact number, and different risk factors of T2DM that are essential to estimate the predicted risk for developing T2DM in the following 10 years among the study participants according to FINDRISC and IDRS models.\u003c/p\u003e\u003cp\u003eAfter asking all participants to stand barefoot wearing light clothing, weight and height were measured in kilograms (to the nearest 0.1 kg) using a standard weighing scale, and in centimeters (to the nearest 0.5 cm) using a wall-mounted measuring tape respectively. BMI (kg/m\u003csup\u003e2\u003c/sup\u003e) was calculated by dividing weight (in kilograms) by height (in meters squared). Waist circumference was measured using a flexible measuring tape in centimeters (to nearest 0.1 cm) at halfway between the lowest rib and the superior edge of the iliac crest.\u003c/p\u003e\u003cp\u003eHypertension was defined as a systolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg and/or diastolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].The blood pressure was measured by a manual sphygmomanometer in standard conditions (measured 2 times after a 5-min rest between each measurement [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eBlood glucose measurement\u003c/h2\u003e\u003cp\u003eAll students were invited to attend the Laboratory of Clinical Biochemistry at the Faculty of Medicine and Health Sciences, Taiz University. In the early morning after an overnight fast (at least 8 hours), fasting blood samples were collected by finger-pricking using a sterilized lancet, and fasting blood sugar (FBS) levels were measured on the glucose test strip using a standard glucometer (Accu-Chek Active, Roche Mannheim, Germany). Participants were classified based on FBS levels (mg/dl) as follows: \u0026lt; 100 (normal); 100\u0026ndash;125 (PDM); \u0026ge;126 (DM) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eData analysis\u003c/h2\u003e\u003cp\u003eThe collected data were entered into a computer database, and the statistical analysis was performed using SPSS version 26.0 software (SPSS Inc., Chicago, IL, USA). In order to describe the characteristics of the study participants and the prevalence of diabetes risk factors, descriptive statistics, such as frequencies, proportions, means, and standard deviations, were generated. For categorical data, Pearson's chi-square and for continuous variables, student t-tests were performed. P 0.05 was set as the significant level. Diagnostic accuracy of the FINDRISC and IDRS was also assessed using area under the ROC curve as well as sensitivity, specificity, Youden index (sensitivity\u0026thinsp;+\u0026thinsp;specificity \u0026minus;\u0026thinsp;1), positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio, and Mitchell\u0026rsquo;s clinical utility indices (CUIs) were calculated for each risk tool. Agreement between the different scores in predicting the risk of diabetes mellitus was analyzed by using the Bland\u0026ndash;Altman approach (B-A plot).\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eCharacteristics of enrolled study subjects\u003c/h2\u003e\u003cp\u003eA total of 200 participants were included, of whom more than half (110; 55%) were females and 90 (45%) were males. The mean (\u0026plusmn;\u0026thinsp;SD) age of the study subjects was 21.33\u0026thinsp;\u0026plusmn;\u0026thinsp;1.75 years (females: 21.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.95; males: 21.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.48; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.919) with no statistically significant difference between females and males. Of note, female participants had higher body mass index (BMI) of 22.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.49 vs. 21.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.43; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.019), fasting blood sugar (FBS) of 85.1\u0026thinsp;\u0026plusmn;\u0026thinsp;15.59 vs. 82.4\u0026thinsp;\u0026plusmn;\u0026thinsp;13.66; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.192), IDRS risk score (37.6\u0026thinsp;\u0026plusmn;\u0026thinsp;17.51 vs. 24.2\u0026thinsp;\u0026plusmn;\u0026thinsp;13.23; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and FINDRISC risk score (7.01\u0026thinsp;\u0026plusmn;\u0026thinsp;3.53 vs.4.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.93; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) mean (\u0026plusmn;\u0026thinsp;SD) values when compared with those of male participants.\u003c/p\u003e\u003cp\u003eOn the other hand, the male participants had a higher mean (\u0026plusmn;\u0026thinsp;SD) value of the waist circumference (WC; 85.3\u0026thinsp;\u0026plusmn;\u0026thinsp;8.94 vs. 86.0\u0026thinsp;\u0026plusmn;\u0026thinsp;11.44; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.588) relative to the females (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These results also indicated that no significant differences regarding the systolic blood pressure ( SBP;111.1\u0026thinsp;\u0026plusmn;\u0026thinsp;10.64 vs. 110.3\u0026thinsp;\u0026plusmn;\u0026thinsp;9.82; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.583) and diastolic blood pressure ( DBP;76.0\u0026thinsp;\u0026plusmn;\u0026thinsp;7.57 vs.77.0\u0026thinsp;\u0026plusmn;\u0026thinsp;6.61; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.326) were found among participants of both sexes as presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline characteristics of study subjects*\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;200)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFemales (n\u0026thinsp;=\u0026thinsp;110)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMale (n\u0026thinsp;=\u0026thinsp;90)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e21.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e21.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e21.3 \u0026plusmn; 1.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.919\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e22.0\u0026thinsp;\u0026plusmn;\u0026thinsp;4. 10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e22.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4. 50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e21.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3. 42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWC (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e85.6\u0026thinsp;\u0026plusmn;\u0026thinsp;10.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e85.3\u0026thinsp;\u0026plusmn;\u0026thinsp;8.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e86.0\u0026thinsp;\u0026plusmn;\u0026thinsp;11.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.588\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFBS (mg/dl)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e83. 9\u0026thinsp;\u0026plusmn;\u0026thinsp;14.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e85.1\u0026thinsp;\u0026plusmn;\u0026thinsp;15.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e82.4\u0026thinsp;\u0026plusmn;\u0026thinsp;13.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.192\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSBP (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e110.8\u0026thinsp;\u0026plusmn;\u0026thinsp;10.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e111. 1\u0026thinsp;\u0026plusmn;\u0026thinsp;10.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e110.3\u0026thinsp;\u0026plusmn;\u0026thinsp;9.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.583\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDBP (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e76. 5\u0026thinsp;\u0026plusmn;\u0026thinsp;7.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e76.0\u0026thinsp;\u0026plusmn;\u0026thinsp;7.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e77.0\u0026thinsp;\u0026plusmn;\u0026thinsp;6.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.326\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFINDRISC score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e5.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e7.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e4.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIDRS scores\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e32.1\u0026thinsp;\u0026plusmn;\u0026thinsp;17.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e37.6\u0026thinsp;\u0026plusmn;\u0026thinsp;17.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e24.2\u0026thinsp;\u0026plusmn;\u0026thinsp;13.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e* \u003cem\u003eTotal number of study subjects enrolled in the study was 200..Results are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. Age (years); BMI- Body mass index; WC -Waist circumstances; FBS -Fasting blood sugar; SBP- Systolic blood pressure; DBP- Diastolic Blood pressure; FINDRISC- Finnish Diabetes Risk Score ; IDRS- Indian Diabetes Risk Score.\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eRisk assessment factors of FINDRISC\u003c/h2\u003e\u003cp\u003eThe distribution of FINDRIS diabetic risk factors among study subjects are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. According to FINDRISC tool, 159 (79.5%) were normal healthy weight (BMI 18.0\u0026ndash;24.9 kg /m\u003csup\u003e2\u003c/sup\u003e), while 32 (16.0%) and 9 (4.5%) were overweight (BMI 25.0\u0026ndash;29.9 kg/m\u003csup\u003e2\u003c/sup\u003e) and obese (BMI\u0026thinsp;\u0026ge;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e) respectively. Taken together, 41 (20.5%) participants were either overweight or obese (BMI\u0026thinsp;\u0026ge;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e). The distribution of participants\u0026rsquo; BMI by sex (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) shows that 83 (75.5%) females and 76 (84.4%) males had a healthy weight while 27 (24.6%) females and 14 (15.5%) males were either overweight or obese. It is worth noting that the BMI difference observed between genders was statistically significant and higher in females than in males; (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009).\u003c/p\u003e\u003cp\u003eIn relation to waist circumference (WC) distribution, out of 200 study subjects, 31 (28.2%) of females and 65 (72.2%) males had WC less than 80 cm and less than 94 cm, respectively. Of 38 (34.5%) females and 20 (22.2%) males who found to have WC ranging from 80 to 88 cm and 94 to102 cm respectively. Also, 41 (37.3%) females and 5.0 (5.6%) males had WC above the threshold value of less than 80 cm (for female) and less than 94 cm (for male). Summarily, 104 (52.0%) of participants were with WC greater than 88 cm (for female) and 102 cm (for male), indicating a central obesity; (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). About half of the participants 113 (56.5%) had a minimum of 30 minutes of daily physical activity during either work or leisure time (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDistribution of FINDRISC diabetic risk factors among 200 medical students at Taiz University, Yemen\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eFIDRISC Points\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTotal (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eFemales (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMale (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eBody Mass index (BMI; kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e159 (79.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e83 (75.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e76 (84.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e25-29.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32 (16.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20 (18.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e12 (13.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.0 (4.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.0 (6.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e2.0 (2.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eWaist circumference (cm; female/ male)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;80/94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e96 (48.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e31 (28.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e65 (72.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e80\u0026ndash;88/94 \u0026minus;\u0026thinsp;102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e58(29.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e38 (34.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e20 (22.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;88 /102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46 (23.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e41 (37.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e5.0 (5.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDaily physical activity (30 minutes)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e113 (56.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e37 (33.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e76 (84.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e87 (43.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e73 (66.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e14 (15.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eEating of vegetables or fruits\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEveryday\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e64 (32.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e35 (31.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e29 (32.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.231\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot everyday\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e136 (68.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e75 (68.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e61 ( 67.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eFBS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;100 mg/dl\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e176 (88.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e94 (85.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e82 (91.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e100\u0026ndash;125 or higher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24 (12.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16 (14.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e8.0 (8.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFamily History of Diabetes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e116 (58.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e62 (56.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e54 (60.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eYes (Second degree)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e52 (26.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e27 (24.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e25 (27.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eYes (First degree)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32 (16.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21 (19.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e11 (12.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003e*\u003cem\u003eData are presented as number (%).\u003c/em\u003e Chi-Square p\u0026thinsp;\u003cem\u003e\u0026lt;\u0026thinsp;0.05 considered as significant value\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFurthermore, it was observed that none of the participants had a history of anti- hypertensive medication anytime in their life. It was also observed that out of the 64 (32.3%) participants who were eating vegetables or fruits every day, 35 (31.8%) and 29 (32.2%) were females and male, respectively, with no statistically significant difference (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.231) as presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eRegarding fasting blood sugar (FBS) levels; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows that 176 (88.0%) participants had a FBS levels (\u0026lt;\u0026thinsp;100 mg/dl), while 24 (12%) Had a FBS (100\u0026ndash;125 mg/dl or higher). Of 24 (12.0%) participants, 16 (14.5%) were females and 8 (8.9%) were males but there was a significant difference in FBS levels between genders; (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003e Also, it was reported that the prevalence of a family history of DM was high in this young population; overall, 84 (42.0%) had either first or second-degree relatives with diabetes. Out of 84 (42.0%) study population, 52 (26.0%) of them having a parent, sister, brother, or child with diabetes, while 33 (16.5%) having a grandparent, uncle, aunt, or first cousin with diabetes. A significantly higher proportion of 48 (43.6%) females had at least one first or second relative compared to that of 36 (40.0%) males (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eRisk assessment factors of IDRS\u003c/h2\u003e\u003cp\u003eThe distribution of IDRS diabetic risk factors among study subjects are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Based on IDRS tool, all participants were young adults below the age of 35 years (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.919). Regarding the waist circumference grading, the abdominal obesity was higher in females compared to males (37.3% vs. 5.6%; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). It was interesting to note that though 57.5% (female: 34.5% vs. male: 85.5%; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) of participants did regular vigorous regular exercise or physical activity at home or workplace. From this young population, 23% of participants had at least one parent with diabetes and in 9% of participants, both parents had diabetes (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDistribution of IDRS diabetic risk factors among 200 medical students at Taiz University, Yemen\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIDRS Points\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFemales (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMale (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003eWaist circumferences (cm; female/ male)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;80/94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e96 (48.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31 (28.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e65 (72.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e80\u0026ndash;88/94 \u0026minus;\u0026thinsp;102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58 (29.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38 (34.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20 (22.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;88 /102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46 (23.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e41 (37.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.0 (05.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDaily Physical Activity (30 minutes)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegular Vigorous Exercise\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e01 (0.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e00 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e01(1.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegular Mild Exercise\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e114 (57%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38 (34.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e76 (84.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo exercise or sedentary activities\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e85 (42.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e72 (65.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13 (14.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFamily History of Diabetes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e168 (84.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e89 (80.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e79 (87.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes -Either parent\u003c/p\u003e\u003cp\u003e-Both parents\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23 (11.5%)\u003c/p\u003e\u003cp\u003e09 (4.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15 (13.6%)\u003c/p\u003e\u003cp\u003e06 (05.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e08 (08.9%)\u003c/p\u003e\u003cp\u003e03(03.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e*\u003cem\u003eData are presented as number (%).\u003c/em\u003e Chi-Square p\u0026thinsp;\u003cem\u003e\u0026lt;\u0026thinsp;0.05 considered as significant value\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFurther, the risk of developing T2DM among the study subjects was assessed using FINDRIS and IDRS scoring system to define the different grades of risk of diabetes in our study population (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eConsidering the FINDRISC, from Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, 121 (60.5%) had low risk (a score\u0026thinsp;\u0026lt;\u0026thinsp;7), 65 (32.5%) slightly elevated risk (a score 7\u0026ndash;11), 13 (6.5%) moderate (a score 12\u0026ndash;14) elevated risk, and 1 (0.5%) had high risk (a score 15\u0026ndash;20) for developing diabetes. Taken together, out of the 14 (7.0%) students found to have a moderately elevated to high risk\u0026thinsp;\u0026ge;\u0026thinsp;12) for developing T2DM in the following 10 years as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The risk of developing T2DM was significantly higher in females compared to males (FINDRISC score mean: 7.01 versus 5.04; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of risk of developing T2DM among 110 female and 90 male participants according to FINDRISC and IDRS scoring system\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRisk\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFemales (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMale (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFINDRISC score\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e121 (60.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e49 (40.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e72 (59.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u0026ndash;11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSlightly elevated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e65 (32.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48 (73.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17 (26.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e12\u0026ndash;14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13 (6.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12 (10.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 ( 1.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15\u0026ndash;20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (0.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.0 (0.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIDRS score\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e83 (41.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29(26.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e54 (60.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e30\u0026ndash;59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e91 (45.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e58(52.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e33 (36.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26 (13.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23 (20.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3 (3.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e* Results are expressed as number (%).Chi-Square, p\u0026thinsp;\u003cem\u003e\u0026lt;\u0026thinsp;0.05 considered as significant value\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWith regard to the IDRS score, 83(41.5%) students were found to be at low risk (a score\u0026thinsp;\u0026lt;\u0026thinsp;30), 91(45.5%) had a score between 30 and 59 indicating a moderate risk, and 26 (13%) scored 60 or above which indicates a high risk of diabetes depicted in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Taken together, out of the 117 (91.1%) students found to have a moderate to high risk (IDRS score\u0026thinsp;\u0026ge;\u0026thinsp;30) for developing T2DM in the following 10 years as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. As reported by IDRS, female subjects had a higher mean risk score compared to males; (37.6 vs. 24.2; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eDiagnostic accuracy of risk score models for undiagnosed T2DM\u003c/h2\u003e\u003cp\u003eWe further analyzed the data to validate the IDRS score and FINDRISC against increased fasting blood sugar levels in diagnosing T2DM (either FBS: 100\u0026ndash;125 or \u0026ge;\u0026thinsp;126 mg/dl). When assessing the diagnostic accuracy of the FINDRISC score and IDRS score for stratifying the risk of diabetes (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), the area under the ROC curve was 0.78 (95% CI: 0. 68\u0026ndash;0.88), with an optimal cut-off of \u0026ge;\u0026thinsp;9, sensitivity and specificity of 67% and 81%, respectively; whereas the area under the ROC curve for the IDRS was 0.67 (95% CI: 0.56\u0026ndash;0.78), with a cut-off of \u0026ge;\u0026thinsp;45, a sensitivity and specificity of 46% and 80%, respectively. There was a significant difference between AUC\u0026rsquo;s of both scores (P\u0026thinsp;=\u0026thinsp;0.000). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, AUC in our study was largest for FINDRISC (0.782) when compared with IDRS.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn comparison to the IDRS sensitivity and specificity levels, FINDRISC had the highest sensitivity (67%), whereas the IDRS risk score had a high specificity (80%) as same as that of FINFRISC, but also had the lowest sensitivity between both of tools. Furthermore, the FINDRISC had higher positive predictive value (32% vs. 23%) and negative predictive value (95% vs.92%) relative to IDRS. Notably, positive predictive values of both scores were below 40%, while negative predictive values were above 90%. The probability that the FINDRISC score is positive for the high risk subjects was PPV; 32% ,while it was less (PPV 23%) for those by IDRS. Positive likelihood ratio (3.5 vs.2.3) than IDRS. Hence, those who were at high‑risk group according to Finnish score were 3.5 times more likely to be diabetic than non-diabetics relative to IDRS.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of performance of the FINDRISC and IDRS scores\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eScreening characteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFINDRISC score\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIDRS score\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAUC (95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.782\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.671\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYouden index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOptimum cut off\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSensitivity (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e67.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpecificity (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e81.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e80.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePPV (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNPV (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e95.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e92.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePositive likelihood ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNegative likelihood ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAccuracy(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e79.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e75.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCUI\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCUI\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eFINDRISC-Finnish Diabetes Risk Score; IDRS-Indian Diabetes Risk Score; AUC- area under the receiver-operating curve; CI, 95% confidence interval; LR- likelihood ratio. Results are expressed as percentage, except for AU, NPV- negative predictive value; PPV- positive predictive value; CUI -clinical utility index.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhen comparing accuracy (79% for FINDRISC vs.75% for IDRS) and clinical utility indices (CUI) of both the score models, CUI was found to be very poor in case finding, but the clinical utility of FINDRISC was \"very good\" when compared with IDRS (CUI\u003csup\u003e\u0026minus;\u003c/sup\u003e : 0.80 vs. 0.73) in ruling out T2DM in the study population.\u003c/p\u003e\u003cp\u003eIn addition, we measured the level of agreement between FINDRISC and IDRS scores by using Bland-Altman plot (B-A plot) for stratifying risk of diabetes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). As shown in B‑A plot, differences were increasing as mean was increasing. There was no perfect agreement in these two scores. There was a proportionality bias as the majority of observations were lying well above the 0 (y‑axis). Differences can be regressed on mean but as these values were not following normal curve, it was not plotted. Except few, majority of values in the graph lies in CIs (within 5th \u0026minus;\u0026thinsp;95th percentiles); indicating a fair agreement.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWe also compared the applicability of both two risk score models in detecting undiagnosed T2DM (UT2DM) and prediabetes (PDM) among the study subjects using cutoff value\u0026thinsp;\u0026ge;\u0026thinsp;12 and IDRS cutoff value\u0026thinsp;\u0026gt;\u0026thinsp;60, which is routinely in practice (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). According to the FINDRISC score, among 14 (7%) subjects who were categorized in the high risk category (FINDRISC score\u0026thinsp;\u0026ge;\u0026thinsp;12), 7 had dysglycemia. Only 1 out of 7 dysglycemic subjects had a FINDRISC score\u0026thinsp;\u0026ge;\u0026thinsp;15. While using the FINDRISC cut-off value\u0026thinsp;\u0026ge;\u0026thinsp;9, resulted in detecting the highest incidence of dysglycemia 17(8.5%) among the total population, compared to 7 (3.5%) with the FINDRISC cut-off value\u0026thinsp;\u0026ge;\u0026thinsp;12. In contrast, we found that there was no significant difference between IDRS score with cut-off values\u0026thinsp;\u0026ge;\u0026thinsp;45 or \u0026ge;\u0026thinsp;60 in detecting dysglycemia (p\u0026thinsp;=\u0026thinsp;0.06). Although the highest IDRS cut-off \u0026ge;\u0026thinsp;45 value was observed for screening PDM compared to IDRS cut-off \u0026ge;\u0026thinsp;60 (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDistribution of FINDRISC and IDRS cutoff risk scores for diagnosis of dysglycemia (prediabetes, and diabetes) among 200 study subjects\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-diabetes\u003c/p\u003e\u003cp\u003e\u0026lt;\u0026thinsp;100 mg/dl\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePrediabetes\u003c/p\u003e\u003cp\u003e100\u0026ndash;125 mg/dl\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDiabetes\u0026thinsp;\u0026ge;\u0026thinsp;126\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFINDRISC score\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e143(81.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.0 (33.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33 (18.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14 (66.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.0 (100%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e168 (95.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15 (71.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.0 (66.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.0 (4.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.0 (28.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.0 (33.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIDRS score\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e140 (79.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11 (234%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.0 (66.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.060\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36 (20.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (47.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.0 (33.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e157(89.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15 (71.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.0 (66.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19 (10.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.0 (28.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.0 (33.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e*Results are expressed as number (%).Chi-Square p\u0026thinsp;\u003cem\u003e\u0026lt;\u0026thinsp;0.05 considered as significant value\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSummarily, at a cutoff point of FINDRISC\u0026thinsp;\u0026ge;\u0026thinsp;9, out of these17 (8.5%) dysglycemic participants among the total population,14 (7%) were prediabetes and 3 (1.5%) were diabetes whereas at a cutoff point of IDRS\u0026thinsp;\u0026ge;\u0026thinsp;45, of11(5.5%) dysglycemic participants, 10 (5%) were PDM and only1(0.5%) was DM.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eDiabetes is increasingly identified as an important cause of mortality and a critical public health issue worldwide [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. An important burden for diabetes, besides a health burden, is financial burden to individuals and society. This is attributed to the high prevalence of diabetes and the high cost per individual with DM [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. It has been reported that early identification of individuals at high risk for the development of diabetes using easy, noninvasive, cost effective and reliable tool can help to reduce or prevent the development and the cardio metabolic associated complications of T2DM through dietary and lifestyle interventions [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. For this purpose, several diabetes risk screening models have been developed to be easily and effectively used in clinical practice with minimum cost[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. However, it is not ensured whether these risk score tools can be applied in local populations. Data suggest that some tools developed in a selected population do not always perform well in other populations [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Thus, the applicability of these models in a different population is limited and can be misleading. Therefore, various countries need to continue to explore diabetes assessment systems that are suitable and applicable for local populations [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn Yemen, despite the widespread use of diabetes risk scores, no previous study, to the best of our knowledge, has studied the discriminatory accuracy of different assessment scores for diabetes screening in a healthy Yemeni population. Hence, in this study, the screening effectiveness of two frequently used less costly validated risk scores such as FINDRISC and IDRS are compared in a young population in Taiz city, Yemen. The main reasons for T2DM screening in this population group include the sedentary life they have, the increase in the incidence rate of diabetes in the younger population and limited studies conducted among them.\u003c/p\u003e\u003cp\u003eBased on the data of our study, the overall prevalence of dysglycemia was (12%). This finding is consistent with previous studies by Viitasalo et al. and Sapkota et al. who found a prevalence of dysglycemia of 12.9% and 11.3%, respectively, among their study participants [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. However, a recent study carried out in Yemen found that the prevalence of dysglycemia was (5.7%) [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. In contrary to our finding; studies conducted in different countries. For example, studies by Al-Shudifat et al., Ali et al., D\u0026rsquo;Souza et al., and Nnamudi et al. conducted among adult population reported a lower and a higher prevalence of 3.8%, 8.2%, 26.9% and 32.8% respectively [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. This difference could be due to the high number of participants at a high risk of diabetes, that is, older and obese, and along with the use of a cutoff value of hyperglycemia to define PDM and T2DM, besides impaired fasting glucose, and diabetes, also comprised oral glucose tolerance test.\u003c/p\u003e\u003cp\u003eA noteworthy finding in our study was the low prevalence of PDM (10.5%) and UT2DM (1.5%) among the participants. This finding is inconsistent with the results of a study by Farag et al. who found a prevalence of PDM and UT2DM of 21.7% and 5% respectively among their study participants[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Compared to our study, a study done in the Lebanese University showed the prevalence of PDM and UT2DM was 22.9% and 7.6% respectively[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Similar to our finding, studies in different countries reported a lower prevalence of PDM among young adult students[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Nevertheless, increased awareness of the high prevalence of PDM worldwide as alarming, is crucial for young subjects to implement preventive measures that may reduce the risk of diabetes. Regarding undiagnosed T2DM, it was well demonstrated that about 1.5% of the subjects with newly UT2DM are unaware of their disease. The disparity and inconsistency in PDM and T2DM prevalence could be because of differences in the study participant characteristics (age, sex, ethnicity, etc.), sampling technique, the diagnostic criteria, lifestyle choices, unhealthy dietary habits, socioeconomic status and genetic factors.\u003c/p\u003e\u003cp\u003eIt is worth mentioning that there was a significant difference in the risk of developing T2DM between both sexes (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This could be explained by the higher frequency of overweight and obesity that may be associated with a sedentary lifestyle, limited physical activity and nutritional behaviors among young adult females in our region, contributing to the high risk of T2DM.This finding is consistent with similar studies by Ahmad et al. and Evcimen et al. which also found a significantly higher diabetic risk among females [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. In contrast to our findings, a previous study done by Sezer et al., which found no significant difference between both sexes and reported no statistically significant difference between females and males in terms of their risk of developing T2DM [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. This discrepancy may be partly due to differences in sample sizes, risk factors included in study's tool, genetic factors, or variations in the involved populations.\u003c/p\u003e\u003cp\u003eIt should be noted that the prevalence and risk factors of diabetes in a population assess the applicability of a risk score. In this study, the prevalence of DM increased with increasing FINDRISC and IDRS scores. Several cross-sectional studies have tested the FINDRISC and IDRS as screening tools for T2DM. Despite the widespread use of FINDRISC scores, the discriminatory performance of the IDRS versus the FINDRISC score has not been evaluated in detecting T2DM in a Yemeni population. Both the risk score in our study were similar because they include common variables for Asian populations [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRegarding the accuracy and the validation of FINDRISC and IDRS in our study, their performance is generally analyzed based on their sensitivity, specificity, and ROC curves. For IDRS, the best cut‑off was \u0026ge;\u0026thinsp;45 for identifyingUT2MD, with a sensitivity of 46.0%, specificity of 80.0%, and Youden index of 0.25 (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). This finding is in agreement with a study from central India that showed that the optimum cut-off for IDRS was \u0026ge;\u0026thinsp;40, with a sensitivity (higher than that of the current study), a specificity, and a Youden index of 60.4%, 70.7%, and 0.31, respectively [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Pawar et al. found sensitivity 78.95%, specificity 56.14% at optimal cutoff point of 60 [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. This disparity might be attributed to ethnic diversity, differences in the populations studied, and/or unclear categorization of daily physical exercises across various studies. Of note, the low sensitivity of the IDRS in our study, coupled with high specificity, suggests that while the test accurately identifies subjects who do not have diabetes (high true negatives and low false positives), it may be missing a significant number of subjects who do have diabetes (high false negatives). This indicates that the IDRS is good at ruling out diabetes with a low level of accuracy (at Youden index of 0.25) but not as good at identifying those who have diabetes.\u003c/p\u003e\u003cp\u003eOn the other hand, FINDRISC had shown optimum cut-off of \u0026ge;\u0026thinsp;9 for identifying T2DM, with a sensitivity, specificity and Youden index of 67.0%, 81.0% and 0.44 respectively (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). This indicates that at a cut-off of \u0026ge;\u0026thinsp;9, the FINDRISC correctly identifies about 67% of subjects with T2DM, while also correctly identifying about 81% of those without T2DM with a moderate level of accuracy (at Youden index of 0.44). This finding aligns with other studies that also found best cut-off of FINDRISC\u0026thinsp;\u0026gt;\u0026thinsp;9 to detect T2DM [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. In two retrospective cohorts, Bhowmik et al. found a sensitivity of 62.4% and 75.7%, and specificity of 67.4% and 61.6% at a risk score of \u0026gt;\u0026thinsp;9 [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Variations in the study populations could be the possible reason for differences in the sensitivity and specificity values at the same cut-off point across various studies [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWith respect to AUC, the FINDRISC had a larger area under the ROC curve (AUC of 0.78, 95% CI: 0.68\u0026ndash;0.88) compared to IDRS (AUC of 0.67, 95% CI: 0.56\u0026ndash;0.78). This suggests that FINDRISC has fairly good discriminatory accuracy in screening diabetes. Our study indicates that FINDRISC is superior to the IDRS at identifying diabetes in the Yemeni population. This could be due to FINDRISC including more diabetes risk factors in its screening system than IDRS [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFinally, this study found that FINDRISC had higher sensitivity, specificity and PPV compared to IDRS. Statistically, higher sensitivity and specificity improve the PPV, especially with a higher disease prevalence in the studied population. This explains the higher prevalence of PDM and DM in the FINDRISC model (7% and 1.5% vs. 5% and 0.5%) compared to IDRS (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Similar to our study, Schmid et al. indicated that the number of subjects in the high-risk category for developing diabetes varies based on the risk assessment [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Furthermore, our data showed that FINDRISC was effective at detecting diabetes (AUC\u0026thinsp;=\u0026thinsp;0.78) and had moderate diagnostic accuracy (at a cut-off \u0026ge;\u0026thinsp;9 with a Youden index of 0.44). This suggests a reasonably good level of accuracy in identifying those at risk for T2DM.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, FINDRISC can serve as a simple and effective screening tool to identify individuals at high risk for T2DM and PDM among young adults in Yemen. Our data showed that with a cut-off score\u0026thinsp;\u0026ge;\u0026thinsp;9, the FINDRISC had moderate diagnostic accuracy (with a sensitivity of 67.0%, specificity of 81.0%, and AUC of 0.78). Diagnostic accuracy of FINDRISC is relatively good compared to that of IDRS. To address the growing socioeconomic burden of diabetes, it may be more beneficial to develop and validate a more specific risk scoring system for a young adult Yemeni population.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAOR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Adjusted odds ratio\u003c/p\u003e\n\u003cp\u003eAUC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Area Under Curve\u003c/p\u003e\n\u003cp\u003eB-A plot\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Bland\u0026ndash;Altman Plot\u003c/p\u003e\n\u003cp\u003eBMI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Body Mass Index\u003c/p\u003e\n\u003cp\u003eCI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Confidence interval\u003c/p\u003e\n\u003cp\u003eCUIs \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Clinical Utility Indices\u003c/p\u003e\n\u003cp\u003eFBS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Fasting blood sugar\u003c/p\u003e\n\u003cp\u003eFINDRISC\u0026nbsp; \u0026nbsp; \u0026nbsp;Finnish Diabetes Risk Score\u003c/p\u003e\n\u003cp\u003eIDF\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;International Diabetes Federation\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIDRS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Indian Diabetes Risk Score\u003c/p\u003e\n\u003cp\u003ePDM\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Prediabetes\u003c/p\u003e\n\u003cp\u003eROC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Receiver Operating Characteristic Curve\u003c/p\u003e\n\u003cp\u003eSD\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Standard deviation\u003c/p\u003e\n\u003cp\u003eSPSS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Statistical Package for the Social Sciences\u003c/p\u003e\n\u003cp\u003eT2DM \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Type 2 Diabetes Mellitus\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUT2DM\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Undiagnosed Type 2 Diabetes Mellitus\u003c/p\u003e\n\u003cp\u003eWC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Waist Circumference\u003c/p\u003e\n\u003cp\u003eWHO \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;World Health Organization\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the students who gave of their consent and time to fill out the questionnaire and participate in this study. We are also grateful for the administration of Faculty of Medicine and Health Sciences for allowing us to conduct this study and the staff of the Laboratory Department of the faculty for their assistance.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAA developed the study idea and collected data. AA, LQ, and MB designed the study. AA and LQ drafted the manuscript. AA and SK analyzed the study data. AA and LQ contributed to the data interpretation and revised the manuscript draft. AA and MB collected data and supervised data collection. All authors read and approved the final draft of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe current study was carried out\u0026nbsp;in accordance with the Declaration of Helsinki for studies on human subjects\u003cins cite=\"mailto:Rashad%20Abdul-Ghani\" datetime=\"2025-08-24T22:23\"\u003e.\u003c/ins\u003e The Ethical approval for the study was obtained from the Research Ethics Committee of the Faculty of Medicine and Health Sciences, Taiz University, Taiz, Yemen. Written informed consent was taken from every participant after a full illustration of the purpose of the study. Each participant was interviewed and completed a standardized questionnaire that included family history, habits, diets, uses of drugs and history of disease like diabetes, hypertension and cardiac diseases. They were told that all the data will be confidential and will be used only for research purposes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAkash MS, Rehman K, Liaqat A. Tumor necrosis factor‐alpha: role in development of insulin resistance and pathogenesis of type 2 diabetes mellitus J. Cell. Biochem. 2018;119(1):105-10. https://doi.org/10.1002/jcb.26174.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. Diabetes. Geneva: World Health Organization; 2021. Available from: https://www.who.int/ news-room/fact-sheets/detail/diabetes. [accessed on 2021 Jun 08].\u003c/li\u003e\n\u003cli\u003eJaacks LM, Siegel KR, Gujral UP, Narayan KV. Type 2 diabetes: A 21st century epidemic. Best Pract Res Clin Endocrinol Metab. 2016 Jun;30(3):331-43. https://doi.org/10.1016/j.beem.2016.05.003. \u003c/li\u003e\n\u003cli\u003eWild S, Roglic G, Green A, Sicree R, King H. Global prevalence of diabetes estimates for the year 2000 and projections for 2030. Diabetes Care 2004;27:1047\u0026ndash;53. https://doi.org/10.2337/diacare.27.5.1047. \u003c/li\u003e\n\u003cli\u003eSaeedi P, Petersohn I, Salpea P, Malanda B, Karuranga S, Unwin N, et al. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas. Diabetes Res. Clin. Pract. 157:107843. https://doi.org/10.1016/j.diabres.2019.107843.\u003c/li\u003e\n\u003cli\u003eGuariguata L, Whiting DR, Hambleton I, Beagley J, Linnenkamp U, Shaw JE. Global estimates of diabetes prevalence for 2013 and projections for 2035. Diabetes Res Clin Pract . 2014 Feb 1;103(2):137-49. https://doi.org/10.1016/j.diabres.2013.11.002.\u003c/li\u003e\n\u003cli\u003eWilliams R, Karuranga S, Malanda B, Saeedi P, Basit A, Besan\u0026ccedil;on S, et al. Global and regional estimates and projections of diabetes-related health expenditure: Results from the International Diabetes Federation Diabetes Atlas. Diabetes Res Clin Pract. 2020; 162:108072. https://doi.org/10.1016/j.diabres.2020.108072.\u003c/li\u003e\n\u003cli\u003eBeagley J, Guariguata L, Weil C, Motala AA. Global estimates of undiagnosed diabetes in adults. Diabetes Res. Clin. Pract. 2014 Feb 1;103(2):150-60. https://doi.org/10.1016/j.diabres.2013.11.001.\u003c/li\u003e\n\u003cli\u003eAl-Habori M, Al-Mamari M, Al-Meeri A. Type II Diabetes Mellitus and impaired glucose tolerance in Yemen: prevalence, associated metabolic changes and risk factors. Diabetes Res. Clin. Pract. 2004 Sep 1;65(3):275-81. https://doi.org/10.1016/j.diabres.2004.02.001. \u003c/li\u003e\n\u003cli\u003eGunaid AA, Assabri AM. Prevalence of type 2 diabetes and other cardiovascular risk factors in a semirural area in Yemen. Eastern Mediterranean Health Journal. 2008 Jan 1;14(1):42-56.\u003c/li\u003e\n\u003cli\u003eAli MK, Pearson-Stuttard J, Selvin E, Gregg EW. Interpreting global trends in type 2 diabetes complications and mortality. Diabetologia. 2022 Jan;65(1):3-13. https://doi.org/10.1007/s00125-021-05585-2.\u003c/li\u003e\n\u003cli\u003eZiyab AH, Mohammad A, Maclean E, Behbehani K, Carballo M. Diabetes: a fast evolving epidemic. Kuwait Med J. 2015 Nov 25;47(4):291-301.URL: http://www.kma.org.kw/uploads/versions/OBNMZHFMDVCUTNOYYKOYAXBV.pdf.\u003c/li\u003e\n\u003cli\u003eLindstrom J, Tuomilehto J. The diabetes risk score: a practical tool to predict type 2 diabetes risk. Diabetes care. 2003 Mar 1;26(3):725-31. https://doi.org/10.2337/diacare.26.3.725. \u003c/li\u003e\n\u003cli\u003ePhillips CM, Kearney PM, McCarthy VJ, Harrington JM, Fitzgerald AP, Perry IJ. Comparison of diabetes risk score estimates and cardiometabolic risk profiles in a middle-aged Irish population. PLoS one. 2013 Nov 13;8(11):e78950. https://doi.org/10.1371/journal.pone.0078950.\u003c/li\u003e\n\u003cli\u003eLin J, Thompson TJ, Cheng YJ, Zhuo X, Zhang P, Gregg E, et al. Projection of the future diabetes burden in the United States through 2060. Popul Health Metr. 2018 Dec;16:1-9. https://doi.org/10.1186/s12963-018-0166-4. \u003c/li\u003e\n\u003cli\u003eMeneghini LF, editor. Medical management of type 2 diabetes. 8th ed. Alexandria: American Diabetes Association; 2020.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. Screening for type 2 diabetes: report of a World Health Organization and International Diabetes Federation meeting. Geneva, 2003. (Available from: http://www.who.int/iris/handle/10665/68614) [4 August 2007].\u003c/li\u003e\n\u003cli\u003eKumar D, Prakash B, Subhash CBJ, Kadkol PS, Arun V, Thomas JJ. An android smartphone-based randomized intervention improves the quality of life in patients with type 2 diabetes in Mysore, Karnataka, India. Diabetes Metab Syndr. 2020;14(5):1327-32. https://doi.org/10.1016/j.dsx.2020.07.025. \u003c/li\u003e\n\u003cli\u003eNoble D, Mathur R, Dent T, Meads C, Greenhalgh T. Risk models and scores for type 2 diabetes: systematic review. BMJ 2011;343:d7163. https://doi.org/10.1136/bmj.d7163.\u003c/li\u003e\n\u003cli\u003eMann DM, Bertoni AG, Shimbo D, Carnethon MR, Chen H, Jenny NS, et al. Comparative validity of 3 diabetes mellitus risk prediction scoring models in a multiethnic US cohort: the Multi-Ethnic Study of Atherosclerosis. Am J Epidemiol. 2010; 171:980-88. https://doi.org/10.1093/aje/kwq030.\u003c/li\u003e\n\u003cli\u003eMohan V, Sandeep S, Deepa M, Gokulakrishnan K, Datta M, Deepa R. A diabetes risk score helps identify metabolic syndrome and cardiovascular risk in Indians - the Chennai Urban Rural Epidemiology Study (CURES-38). Diabetes Obes Metab.2007; 9(3):337-43. https://doi.org/10.1111/j.1463-1326.2006.00612.x.\u003c/li\u003e\n\u003cli\u003eSaleem SM, Raina AF, Khan SM, Jan SS. Indian diabetic risk score-A tool for predicting risk of undiagnosed type 2 diabetes mellitus. Int J Curr Res Rev. 2017 Feb 1;9:39-42. Available from:https://ijcrr.com/uploads/96_pdf.pdf.\u003c/li\u003e\n\u003cli\u003eMeijnikman AS, De Block CE, Verrijken A, Mertens I, Van Gaal LF. Predicting type 2 diabetes mellitus: a comparison between the FINDRISC score and the metabolic syndrome. Diabetol Metab Syndr. 2018 Dec;10:1-6. https://doi.org/10.1186/s13098-018-0310-0. \u003c/li\u003e\n\u003cli\u003eBernabe-Ortiz A, Perel P, Miranda JJ, Smeeth L. Diagnostic accuracy of the Finnish Diabetes Risk Score (FINDRISC) for undiagnosed T2DM in Peruvian population. Prim Care Diabetes. 2018;12(6):517\u0026ndash;25. https://doi.org/10.1016/j.pcd.2018.07.015.\u003c/li\u003e\n\u003cli\u003eHerman WH, Smith PJ, Thompson TJ, Engelgau MM, Aubert RE. A new and simple questionnaire to identify people at increased risk for undiag\u0026not;nosed diabetes. Diabetes Care 1995; 18(3): 382\u0026ndash;7. https://doi.org/10.2337/diacare.18.3.382. \u003c/li\u003e\n\u003cli\u003eSmith Liz. New AHA recommendations for blood pressure measurement: American Heart Association Practice Guide\u0026not;lines. Am Fam Physician 2005; 72(7): 1391\u0026ndash;8. Available from: https://www.aafp.org/afp/2005/1001/p1391.html.\u003c/li\u003e\n\u003cli\u003eAmerican Diabetes Association. 2. Classification and Diagnosis of Diabetes. Diabetes Care. 2017 Jan;40 Suppl 1:S11-S24. https://doi.org/10.2337/dc17-S005. \u003c/li\u003e\n\u003cli\u003eLin X, Xu Y, Pan X, Xu J, Ding Y, Sun X, Song X, Ren Y, Shan PF. Global, regional, and national burden and trend of diabetes in 195 countries and territories: an analysis from 1990 to 2025. Sci Rep. 2020 Sep 8;10(1):14790. https://doi.org/10.1038/s41598-020-71908-9. \u003c/li\u003e\n\u003cli\u003eElSayed NA, Aleppo G, Aroda VR, Bannuru RR, Brown FM, Bruemmer D, et al.1. Improving care and promoting health in populations: standards of care in diabetes\u0026mdash;2023. Diabetes care. 2023 Jan 1;46 Suppl 1:S10-8. https://doi.org/10.2337/dc23-S001.\u003c/li\u003e\n\u003cli\u003eOsman OA, Saeed AA, Mousnad MA, Hamid A. Assessment of The Risk Of Type 2 Diabetes Among Healthy without Diabetes in Sudan Using the FINDRISC Tool. Univers. J. Pharm. Res. 2020, 5, 37\u0026ndash;41. https://doi.org/10.22270/ujpr.v5i4.438. \u003c/li\u003e\n\u003cli\u003eBuijsse B, Simmons RK, Griffin SJ, Schulze MB. Risk assessment tools for identifying individuals at risk of developing type 2 diabetes. Epidemiol Rev. 2011 Jul 1;33(1):46-62. https://doi.org/10.1093/epirev/mxq019.\u003c/li\u003e\n\u003cli\u003eBhowmik B, Akhter A, Ali L, Ahmed T, Pathan F, Mahtab H, et al. Simple risk score to detect rural AsianIndian (Bangladeshi) adults at high risk for type 2 diabetes. J Diabetes Investig. 2015; 6:670\u0026ndash;7. https://doi.org/10.1111/jdi.12344.\u003c/li\u003e\n\u003cli\u003eGray BJ, Bracken RM, Turner D, Morgan K, Williams M, Rice S, et al. Different type 2 diabetes risk assessments predict dissimilar numbers at \u0026lsquo;high risk\u0026rsquo;:a retrospective analysis of diabetes risk-assessment tools. Br J Gen Pract. 2015; http://doi.org/10.3399/bjgp15X687661. \u003c/li\u003e\n\u003cli\u003eViitasalo K, Lindstr\u0026ouml;m J, Hemi\u0026ouml; K, Puttonen S, Koho A, H\u0026auml;rm\u0026auml; M, et al. Occupational health care identifies risk for type 2 diabetes and cardiovascular disease. Prim Care Diabetes. 2012; 6(2):95-102. https://doi.org/10.1016/j.pcd.2012.01.003.\u003c/li\u003e\n\u003cli\u003eSapkota M, Timilsina A, Shakya M, Thapa TB, Shrestha S, Pokhrel S, et al. Metabolic syndrome and diabetes risk among young adult students in the health sciences from Kathmandu, Nepal. Drug Healthc Patient Saf. 2020;12:125\u0026ndash;33. https://doi.org/10.2147/DHPS.S258331.\u003c/li\u003e\n\u003cli\u003eAl-Dolae MH, Salah MG, Al-Najjar AM, Al-Rassas SH, Al-Madwami MA, Al-khadher MA. Assessment of Diabetes Risk Among The Fifth and Sixth Years Medical students in Thamar University, Yemen, A Cross-sectional Study. Zagazig University Medical Journal. 2024 Jun 1;30(1.4):385-94. https://doi.org/10.21608/ZUMJ.2023.231878.2863.\u003c/li\u003e\n\u003cli\u003eAl-Shudifat AE, Al-Shdaifat A, Al-Abdouh AA, Aburoman MI, Otoum SM, Sweedan AG, et al. Diabetes risk score in a young student population in Jordan: a cross‐sectional study. J Diabetes Res. 2017;2017(1):8290710. https://doi.org/10.1155/2017/8290710.\u003c/li\u003e\n\u003cli\u003eAli N, Samadder M, Shourove JH, Taher A, Islam F. Prevalence and factors associated with metabolic syndrome in university students and academic staff in Bangladesh. Sci Rep. 2023 Nov 14;13(1):19912. https://doi.org/10.1038/s41598-023-46943-x. \u003c/li\u003e\n\u003cli\u003eD\u0026rsquo;Souza MS, Amirtharaj A, Venkatesaperumal R, Isac C, Maroof S. Risk-assessment score for screening diabetes mellitus among Omani adults. SAGE Open Med.2013;1. https://doi.org/10.1177/2050312113508390.\u003c/li\u003e\n\u003cli\u003eNnamudi AC, Orhue NE, Ijeh II. Assessment of the FINDRISC tool in predicting the risk of developing type 2 diabetes mellitus in a young adult Nigerian population. Bull Natl Res Cent. 2020 Nov 3;44(1):186. https://doi.org/10.1186/s42269-020-00440-7. \u003c/li\u003e\n\u003cli\u003eFarag HF, Elrewany E, Abdel-Aziz BF, Sultan EA. Prevalence and predictors of undiagnosed type 2 diabetes and pre-diabetes among adult Egyptians: a community-based survey. BMC Public Health. 2023 May 25;23(1):949. https://doi.org/10.1186/s12889-023-15819-0. \u003c/li\u003e\n\u003cli\u003eAbdallah M, Sharbaji S, Sharbaji M, Daher Z, Faour T, Mansour Z, et al. Diagnostic accuracy of the Finnish Diabetes Risk Score for the prediction of undiagnosed type 2 diabetes, prediabetes, and metabolic syndrome in the Lebanese University. Diabetol Metab Syndr. 2020 Sep 30;12(1):84. https://doi.org/10.1186/s13098-020-00590-8. \u003c/li\u003e\n\u003cli\u003eAhmad T, Tauqir A, Tariq H, Qureshi A, Jalaluddin S, I. Khan U, et al. Sex differences in risk of developing Type 2 Diabetes Mellitus (T2DM): A feasibility assessment of FINDRISC scoring and barriers to disease management in a low-income settlement of Rawalpindi, Pakistan. PLOS Global Public Health. 2025 Jul 16;5(7):e0003087. https://doi.org/10.1371/journal.pgph.0003087.\u003c/li\u003e\n\u003cli\u003eEvcimen H, Ayyıldız Nİ, Doğan U. Diabetes Risk Score of Adult Applications to Primary Health Care Center: A Cross-Sectional Study. Van Sağlık Bilimleri Dergisi. 2023;16(1):53-9. https://doi.org/10.52976/vansaglik.1162785.\u003c/li\u003e\n\u003cli\u003eSezer \u0026Ouml;, Laf\u0026ccedil;i N\u0026Ouml;, Korkmaz S, Dağdeviren HN. Prediction of a 10-year risk of type 2 diabetes mellitus in the Turkish population: A cross-sectional study. Medicine. 2021 Nov 5;100(44):e27721. https://doi.org/10.1097/MD.0000000000027721. \u003c/li\u003e\n\u003cli\u003eZahid N, Shi Z, Claussen B, Hussain A. Prevalence and risk factors for diabetes, comparison of rural populations in Bangladesh, China and Pakistan. Diabetes Metab Syndr Clin Res Rev. 2009 Jun 1;3(2):109-12. https://doi.org/10.1016/j.dsx.2009.04.006.\u003c/li\u003e\n\u003cli\u003eBhadoria A, Kasar P, Toppo N. Validation of Indian diabetic risk score in diagnosing type 2 diabetes mellitus against high fasting blood sugar levels among adult population of central India. Biomed J. 2015;38:359\u0026ndash;60. https://doi.org/10.4103/2319-4170.143508\u003cu\u003e. \u003c/u\u003e\u003c/li\u003e\n\u003cli\u003ePawar SD, Naik JD, Prabhu P, Jatti GM, Jadhav SB, Radhe BK. Comparative evaluation of Indian Diabetes Risk Score and Finnish Diabetes Risk Score for predicting risk of diabetes mellitus type II: A teaching hospital-based survey in Maharashtra. J. Fam. Med. Prim. Care. 2017 Jan 1;6(1):120-5. https://doi.org/10.4103/2249-4863.214957. \u003c/li\u003e\n\u003cli\u003eBhowmik B, Akhter A, Ali L, Ahmed T, Pathan F, Mahtab H, et al. Simple risk score to detect rural Asian Indian (Bangladeshi) adults at high risk for type 2 diabetes. J Diabetes Investig. 2015;6(6):670\u0026ndash;7. https://doi.org/10.1111/jdi.12344.\u003c/li\u003e\n\u003cli\u003eVandersmissen GJ, Godderis L. Evaluation of the Finnish Diabetes Risk Score (FINDRISC) for diabetes screening in occupational health care. Int. J. Occup. Med. Environ. Health. 2015 Jan 1;28(3):587-91. https://doi.org/10.13075/ijomeh.1896.00407.\u003c/li\u003e\n\u003cli\u003eSchmid R, Vollenweider P, Waeber G, Marques-Vidal P. Estimating the risk of developing type 2 diabetes: a comparison of several risk scores: the Cohorte Lausannoise study. Diabetes Care. 2011 Aug 1;34(8):1863-8. https://doi.org/10.2337/dc11-0206\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"diabetology-and-metabolic-syndrome","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dims","sideBox":"Learn more about [Diabetology \u0026 Metabolic Syndrome](http://dmsjournal.biomedcentral.com/)","snPcode":"13098","submissionUrl":"https://submission.nature.com/new-submission/13098/3","title":"Diabetology \u0026 Metabolic Syndrome","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Type 2 diabetes mellitus, FINDRISC, IDRS, Risk scores, Prediabetes, Yemen","lastPublishedDoi":"10.21203/rs.3.rs-7781850/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7781850/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eDiabetes mellitus (DM) is a leading cause of death worldwide. To address the rising of DM, it might be more effective to create and validate a targeted risk scoring system for specific populations. This study aimed to evaluate and compare the diagnostic accuracy of the Finnish Diabetes Risk Score (FINDRISC) and the Indian Diabetes Risk Score (IDRS) in assessing the risk of developing T2DM and to identify the factors associated with T2DM risk among healthy medical students at the Faculty of Medicine, Taiz University, Yemen.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA cross-sectional study was conducted among 200 students at Taiz University. The IDRS and FINDRISC questionnaires were used to assess the diabetes risk score for developing T2DM within 10 years. Fasting blood sugar was measured. Descriptive statistics and the chi-square test were used, with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 regarded as statistically significant. The diagnostic accuracy of FINDRISC and IDRS was compared using the area under the receiver operating characteristic curve (AUC-ROC). Sensitivity, specificity, Youden index, likelihood ratio, positive and negative predictive values were calculated for both tools.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eOut of 200 participants, 10.5% and 1.5% were diagnosed with prediabetes and T2DM, respectively, where females had a higher prevalence than males for both outcomes (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The AUC-ROC for both scores in identifying participants with diabetes differed (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001); for FINDRISC, it was larger (0.782; 95% CI: 0.68\u0026ndash;0.88; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to that of IDRS (0.671; 95% CI: 0.56\u0026ndash;0.78). For FINDRISC at 9 as the best cutoff (sensitivity\u0026thinsp;=\u0026thinsp;67.0%, specificity\u0026thinsp;=\u0026thinsp;80.1%, and Youden index\u0026thinsp;=\u0026thinsp;0.44); whereas for IDRS at 45 as the best cutoff (sensitivity\u0026thinsp;=\u0026thinsp;46.0%, specificity\u0026thinsp;=\u0026thinsp;80.0% and Youden index\u0026thinsp;=\u0026thinsp;0.25). Bland-Altman plot suggested fair agreement between both scores in assessing diabetes risk.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eFINDRISC serves as a simple and effective screening tool to detect subjects at high risk for prediabetes and T2DM among young adults in Yemen. Diagnostic accuracy and clinical utility of FINDRISC is better than that of IDRS.\u003c/p\u003e","manuscriptTitle":"Comparative Study of FINDRISC and IDRS in Predicting Prediabetes and Diabetes Mellitus in a Young Adult Yemeni Population","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-27 15:25:30","doi":"10.21203/rs.3.rs-7781850/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-19T01:39:02+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-16T02:34:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"135971298712957613791407053249166042489","date":"2025-12-10T19:45:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"35357192148232258062076311207777799891","date":"2025-12-08T09:33:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"286914285432586161283104522379579601257","date":"2025-12-07T22:20:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"247501585143530339108939723693887882724","date":"2025-12-06T12:15:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"186380272121117735806723901379975666482","date":"2025-12-05T20:39:57+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-20T08:29:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"74059121761420753598664607133454418923","date":"2025-10-14T13:49:43+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-13T20:46:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-06T06:07:22+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-06T06:05:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"Diabetology \u0026 Metabolic Syndrome","date":"2025-10-04T20:39:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"diabetology-and-metabolic-syndrome","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dims","sideBox":"Learn more about [Diabetology \u0026 Metabolic Syndrome](http://dmsjournal.biomedcentral.com/)","snPcode":"13098","submissionUrl":"https://submission.nature.com/new-submission/13098/3","title":"Diabetology \u0026 Metabolic Syndrome","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3b956017-3234-4ce7-8f95-853a571ffed5","owner":[],"postedDate":"October 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-07T16:07:01+00:00","versionOfRecord":{"articleIdentity":"rs-7781850","link":"https://doi.org/10.1186/s13098-026-02123-1","journal":{"identity":"diabetology-and-metabolic-syndrome","isVorOnly":false,"title":"Diabetology \u0026 Metabolic Syndrome"},"publishedOn":"2026-04-02 15:58:58","publishedOnDateReadable":"April 2nd, 2026"},"versionCreatedAt":"2025-10-27 15:25:30","video":"","vorDoi":"10.1186/s13098-026-02123-1","vorDoiUrl":"https://doi.org/10.1186/s13098-026-02123-1","workflowStages":[]},"version":"v1","identity":"rs-7781850","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7781850","identity":"rs-7781850","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}