Journal of Occupational
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Rafsanjan university Of medical sciences
Volume 14, Issue 3 (Summer 2025)
J Occup Health Epidemiol 2025, 14(3): 162-169 |
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Fitri A E R, Simanjorang C, Hanifah L. Prevalence and Risk Factors of Prediabetes in Indonesian Adults: A Cross-Sectional Study (2023). J Occup Health Epidemiol 2025; 14 (3) :162-169
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1- Public Health Student, Dept. of Public Health, Faculty of Health Sciences, National Development University, “Veteran” Jakarta, Indonesia.
2- Lecturer, Dept. of Public Health, Faculty of Health Sciences, National Development University, “Veteran” Jakarta, Indonesia. ,chandrayanis@upnvj.ac.id
3- Lecturer, Dept. of Public Health, Faculty of Health Sciences, National Development University, “Veteran” Jakarta, Indonesia.
2- Lecturer, Dept. of Public Health, Faculty of Health Sciences, National Development University, “Veteran” Jakarta, Indonesia. ,
3- Lecturer, Dept. of Public Health, Faculty of Health Sciences, National Development University, “Veteran” Jakarta, Indonesia.
Article history
Received: 2025/02/18
Accepted: 2025/05/17
ePublished: 2025/09/28
Accepted: 2025/05/17
ePublished: 2025/09/28
Subject:
Epidemiology
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| Abstract (HTML) (143 Views)
Table 2. Distribution of Prediabetes Determined Blood Glucose Measurement Results
Table 3. Distribution of Prediabetes Risk Factors among Adults
Bivariate analysis was performed using the Cox Regression test, with the majority of variables deemed eligible for multivariate analysis. Four variables had P-value>0.25 and were not included in the multivariate analysis: depression (P-value = 0.64), consumption of sweet foods (P-value = 0.45), consumption of sweet drinks (P-value = 0.71), and HDL (P-value = 0.44).
Multivariate analysis was carried out using the Cox Regression test. Eligible candidate variables were inputted into the model and subsequently removed if P-value>0.05 from the largest. The final results (fit model) of the multivariable analysis indicated that the risks linked tp prediabetes were age, education level, physical activity, BMI, central obesity, hypertension, LDL, and triglyceride levels. The final model of the multivariable analysis is reported in Table 4.
Table 4. Risk Factors for Prediabetes in Adults in Indonesia
Full-Text: (42 Views)
Introduction
Prediabetes and diabetes are significant global health concerns. Prediabetes is a risk factor for type 2 diabetes mellitus (T2DM). It also elevates the risk of macrovascular and microvascular disease [1]. Prediabetes, if untreated, can result in diabetes, carrying a risk of complications such as diabetic ketoacidosis (DKA) [2]. DKA is an acute metabolic disorder which can lead to life-threatening complications [3].
World Health Organization (WHO) has defined prediabetes as a condition of intermediate hyperglycemia [4]. Perkumpulan Endocrinology Indonesia (Perkeni) has adopted the same criteria for prediabetes as the American Diabetes Association (ADA). These criteria are specified using the parameters of Impaired Fasting Glucose (IFG) and/or Impaired Glucose Tolerance (IGT), with additional criteria based on hemoglobin A1c (HbA1c) of 5.7% to 6.4% [5, 6].
Knowing the risk factors of prediabetes is key to developing effective prevention strategies. Individuals who recognize these factors can proactively initiate preventive measures to avert severe complications. The risk factors of prediabetes include advanced age, obesity (especially with excess fat in the abdomen), and hypertension. [7]. Furthermore, other studies have explored the relationship between dietary patterns and physical inactivity, can augment the likelihood of prediabetes. [8]. Recognizing these risk factors is key to preventing progression from prediabetes to type 2 diabetes. Often referred to as a "golden period" for diabetes prevention, this time is a critical opportunity for individuals with prediabetes to make lifestyle changes that can significantly reduce the risk of developing diabetes [9].
Prediabetes is a serious condition that should not be overlooked. The prevalence of prediabetes has been reported to exceed that of diabetes mellitus. In 2021, the global prevalence of IGT was 10.6%, affecting 541 million, while the prevalence of IFG was 6.2%, affecting around 319 million. Recent data indicate that Indonesia has the fourth highest number of people with IGT worldwide, after China, India, and the USA. In 2021, there were 29.6 million cases in Indonesia. Projections reveal this will rise to 34.6 million by 2045 [10]. Concurrent with these findings, the Indonesian Health Survey (IHS) in 2023 found that the prevalence of IFG reached 13.4% and the prevalence of IGT rose to 18.6% in the population aged≥15 years [11].
The Indonesian population often develops prediabetes in their adult years, typically around age 30 already at risk of prediabetes [12]. Adult age starts from 19 years old to 59 years old [13]. Prolonged poor lifestyle choices have been demonstrated to heighten the risk of developing chronic diseases, including obesity and diabetes mellitus. A meta-analysis by Glechner et al. indicated that following lifestyle interventions, such as regular diet and physical activity instruction for one year and three years, can lower the risk of diabetes mellitus by 36-54% in adults with prediabetes [14].
As there is a lack of studies on prediabetes in Indonesia, particularly among adults, researchers will examine the risk factors for prediabetes across Indonesian adults using data from the Indonesian Health Survey (IHS) 2023.
Materials and Methods
The IHS 2023 data was employed in the study. The Ministry of Health of the Republic of Indonesia provided the data. The study utilized a cross-sectional design. The study population consisted of respondents aged 19-59 years who had never been diagnosed with diabetes mellitus in the IHS 2023. The sample was selected in cluster stages, starting with census blocks using the PPS method based on the number of families. This was followed by selecting households via systematic sampling with implicit stratification based on the education of the household head. The IHS 2023 data collection process entailed interviews, measurements, and examinations by trained officers.
The analysis was based on a sample of 11,645 respondents from IHS 2023 who fulfilled the following criteria: Fasting Blood Glucose (FBG) and/or Oral Glucose Tolerance Test (OGTT) test results, age 19 to 59 years, and no previous diagnosis of diabetes mellitus or undiagnosed diabetes with FBG≥126 mg/dL and/or OGTT≥200 mg/dL. Weighting was applied during data analysis to account for the complex sampling design of the Indonesian Health Survey (IHS 2023) and to ensure that the estimates accurately represented the national adult population. The weighting procedure was conducted using sampling weights provided in the IHS dataset. The implementation of weighting was facilitated by SPSS Statistics version 25, culminating in generation of a weighted sample of 14,832.
The criteria for prediabetes, as outlined by PERKENI and ADA, are as follows: (1) FBG level of 100-125 mg/dL, and/or (2) OGTT level of 140-199 mg/dL. Normal status was defined as FBG<100 mg/dL and OGTT<140 mg/dL.
The independent variables in this study included age, gender, education level, employment status, hypertension, BMI, central obesity, smoking behavior, alcohol drinking status, LDL cholesterol levels, HDL cholesterol levels, triglycerides, physical activity, food and drink consumption patterns, and depression. Hypertension was defined according to physician diagnosis. BMI was categorized based on the national classification issued by the Indonesian Ministry of Health, where a BMI≤25.0 kg/m2 was categorized as normal, and BMI>25.0 kg/m2 was categorized as overweight [11]. Central obesity was defined according to the criteria from the Indonesian Ministry of Health, with waist circumference ≥90cm for men and ≥80cm for women [11]. The analysis of smoking behavior variables was performed using the Brinkman index, calculated by the following formula: number of cigarettes smoked per day × length of smoking in years. The categorization of cholesterol levels followed established criteria: low HDL (<40 mg/dL), high LDL (≥100 mg/dL), and high triglyceride (≥150 mg/dL). The quantity of individual physical activity was measured in MET-minutes per week, with the category of sufficient if the combination of moderate and vigorous physical activity was ≥600 MET-minutes per week. This study employed the Mini International Neuropsychiatric Interview (MINI) to assess depression.
Complex sample analysis was applied to the IHS 2023 data following the recommendations of the BKPK of the Indonesian Ministry of Health. The IHS 2023 data were analyzed using the Cox Regression test to identify factors linked to prediabetes among adults in Indonesia. Bivariate analysis chose the independent variables for the model, and those with a p-value <0.25 were included in the multivariate model. The adjusted prevalence ratio was set at 0.05 with 95% CI. Weighting was implemented along the data analysis to ensure the representation of the population. All analyses were undertaken using SPSS software version 25.
Prediabetes and diabetes are significant global health concerns. Prediabetes is a risk factor for type 2 diabetes mellitus (T2DM). It also elevates the risk of macrovascular and microvascular disease [1]. Prediabetes, if untreated, can result in diabetes, carrying a risk of complications such as diabetic ketoacidosis (DKA) [2]. DKA is an acute metabolic disorder which can lead to life-threatening complications [3].
World Health Organization (WHO) has defined prediabetes as a condition of intermediate hyperglycemia [4]. Perkumpulan Endocrinology Indonesia (Perkeni) has adopted the same criteria for prediabetes as the American Diabetes Association (ADA). These criteria are specified using the parameters of Impaired Fasting Glucose (IFG) and/or Impaired Glucose Tolerance (IGT), with additional criteria based on hemoglobin A1c (HbA1c) of 5.7% to 6.4% [5, 6].
Knowing the risk factors of prediabetes is key to developing effective prevention strategies. Individuals who recognize these factors can proactively initiate preventive measures to avert severe complications. The risk factors of prediabetes include advanced age, obesity (especially with excess fat in the abdomen), and hypertension. [7]. Furthermore, other studies have explored the relationship between dietary patterns and physical inactivity, can augment the likelihood of prediabetes. [8]. Recognizing these risk factors is key to preventing progression from prediabetes to type 2 diabetes. Often referred to as a "golden period" for diabetes prevention, this time is a critical opportunity for individuals with prediabetes to make lifestyle changes that can significantly reduce the risk of developing diabetes [9].
Prediabetes is a serious condition that should not be overlooked. The prevalence of prediabetes has been reported to exceed that of diabetes mellitus. In 2021, the global prevalence of IGT was 10.6%, affecting 541 million, while the prevalence of IFG was 6.2%, affecting around 319 million. Recent data indicate that Indonesia has the fourth highest number of people with IGT worldwide, after China, India, and the USA. In 2021, there were 29.6 million cases in Indonesia. Projections reveal this will rise to 34.6 million by 2045 [10]. Concurrent with these findings, the Indonesian Health Survey (IHS) in 2023 found that the prevalence of IFG reached 13.4% and the prevalence of IGT rose to 18.6% in the population aged≥15 years [11].
The Indonesian population often develops prediabetes in their adult years, typically around age 30 already at risk of prediabetes [12]. Adult age starts from 19 years old to 59 years old [13]. Prolonged poor lifestyle choices have been demonstrated to heighten the risk of developing chronic diseases, including obesity and diabetes mellitus. A meta-analysis by Glechner et al. indicated that following lifestyle interventions, such as regular diet and physical activity instruction for one year and three years, can lower the risk of diabetes mellitus by 36-54% in adults with prediabetes [14].
As there is a lack of studies on prediabetes in Indonesia, particularly among adults, researchers will examine the risk factors for prediabetes across Indonesian adults using data from the Indonesian Health Survey (IHS) 2023.
Materials and Methods
The IHS 2023 data was employed in the study. The Ministry of Health of the Republic of Indonesia provided the data. The study utilized a cross-sectional design. The study population consisted of respondents aged 19-59 years who had never been diagnosed with diabetes mellitus in the IHS 2023. The sample was selected in cluster stages, starting with census blocks using the PPS method based on the number of families. This was followed by selecting households via systematic sampling with implicit stratification based on the education of the household head. The IHS 2023 data collection process entailed interviews, measurements, and examinations by trained officers.
The analysis was based on a sample of 11,645 respondents from IHS 2023 who fulfilled the following criteria: Fasting Blood Glucose (FBG) and/or Oral Glucose Tolerance Test (OGTT) test results, age 19 to 59 years, and no previous diagnosis of diabetes mellitus or undiagnosed diabetes with FBG≥126 mg/dL and/or OGTT≥200 mg/dL. Weighting was applied during data analysis to account for the complex sampling design of the Indonesian Health Survey (IHS 2023) and to ensure that the estimates accurately represented the national adult population. The weighting procedure was conducted using sampling weights provided in the IHS dataset. The implementation of weighting was facilitated by SPSS Statistics version 25, culminating in generation of a weighted sample of 14,832.
The criteria for prediabetes, as outlined by PERKENI and ADA, are as follows: (1) FBG level of 100-125 mg/dL, and/or (2) OGTT level of 140-199 mg/dL. Normal status was defined as FBG<100 mg/dL and OGTT<140 mg/dL.
The independent variables in this study included age, gender, education level, employment status, hypertension, BMI, central obesity, smoking behavior, alcohol drinking status, LDL cholesterol levels, HDL cholesterol levels, triglycerides, physical activity, food and drink consumption patterns, and depression. Hypertension was defined according to physician diagnosis. BMI was categorized based on the national classification issued by the Indonesian Ministry of Health, where a BMI≤25.0 kg/m2 was categorized as normal, and BMI>25.0 kg/m2 was categorized as overweight [11]. Central obesity was defined according to the criteria from the Indonesian Ministry of Health, with waist circumference ≥90cm for men and ≥80cm for women [11]. The analysis of smoking behavior variables was performed using the Brinkman index, calculated by the following formula: number of cigarettes smoked per day × length of smoking in years. The categorization of cholesterol levels followed established criteria: low HDL (<40 mg/dL), high LDL (≥100 mg/dL), and high triglyceride (≥150 mg/dL). The quantity of individual physical activity was measured in MET-minutes per week, with the category of sufficient if the combination of moderate and vigorous physical activity was ≥600 MET-minutes per week. This study employed the Mini International Neuropsychiatric Interview (MINI) to assess depression.
Complex sample analysis was applied to the IHS 2023 data following the recommendations of the BKPK of the Indonesian Ministry of Health. The IHS 2023 data were analyzed using the Cox Regression test to identify factors linked to prediabetes among adults in Indonesia. Bivariate analysis chose the independent variables for the model, and those with a p-value <0.25 were included in the multivariate model. The adjusted prevalence ratio was set at 0.05 with 95% CI. Weighting was implemented along the data analysis to ensure the representation of the population. All analyses were undertaken using SPSS software version 25.
Results
The final analytic sample consisted of 11,465 participants. The prevalence of prediabetes among adults in Indonesia was 47.5% (Table 1).
Among the total respondents with prediabetes, 15.6% had IFG, 21.8% had IGT, and 10.1% had a combination of IFG and IGT (Table 2).
The final analytic sample consisted of 11,465 participants. The prevalence of prediabetes among adults in Indonesia was 47.5% (Table 1).
Among the total respondents with prediabetes, 15.6% had IFG, 21.8% had IGT, and 10.1% had a combination of IFG and IGT (Table 2).
Table 1. Prevalence of Prediabetes in Adults in Indonesia
| Variables | n (11,465) | Percentage (%) | 95% CI (%) | |
| Prediabetes | No | 5,829 | 52.5 | 51,2 – 53,7 |
| Yes | 5,636 | 47.5 | 46,3 – 48,8 |
Table 2. Distribution of Prediabetes Determined Blood Glucose Measurement Results
| Variabel | n (11,465) | Percentage | 95% CI (%) |
| Normal | 5,829 | 52.5 | 51,2 – 53,7 |
| IFG | 1,855 | 15.6 | 14,8 – 16,5 |
| IGT | 2,524 | 21.8 | 20,8 – 22,8 |
| IFG + IGT | 1,257 | 10.1 | 9,5 – 10,8 |
The respondents were predominantly comprised of individuals aged <45 years (65.1%) and female (61.7%), with a low level of education (53.9%), and employed (60.5%). The majority of respondents had a BMI of ≤25 (57.5%), did not have central obesity (55.2%), were not depressed (98.6%), nor did they have hypertension (93.6%). Considering risk behaviors, the majority of respondents did not smoke (72.3%), engaged in sufficient physical activity (80.4%), consumed sweet foods 1-6 times per week (57.4%), consumed sweet drinks ≥1 time per day (47.1%), and did not use alcohol (97.5%). The majority of respondents revealed normal HDL levels (55.3%), high LDL levels (65.0%), and normal triglyceride levels (67.1%) (Table 3).
Table 3. Distribution of Prediabetes Risk Factors among Adults
| Variable | n (11,465) | Percentage (%) | |
| Age | <45 Years | 7,103 | 65.1 |
| ≥45 Years | 4,362 | 34.9 | |
| Gender | Male | 4,008 | 38.3 |
| Female | 7,457 | 61.7 | |
| Level of education | High | 5,235 | 46.1 |
| Low | 6,230 | 53.9 | |
| Job | Working | 6,873 | 60.5 |
| Not working | 4,592 | 39.5 | |
| Smoking behavior | Not smoking | 8,659 | 72.3 |
| Light smokers | 1,170 | 13.0 | |
| Moderate smokers | 910 | 8.2 | |
| Heavy smokers | 153 | 1.3 | |
| Don't know/forget | 573 | 5.2 | |
| Physical activity | Enough | 9,080 | 80.4 |
| Less | 2,385 | 19.6 | |
| BMI | BMI≤25,0 | 6,466 | 57.5 |
| BMI>25,0 | 4,999 | 42.5 | |
| Central obesity | No | 6,077 | 55.2 |
| Yes | 5,388 | 44.8 | |
| Hypertension | No | 10,690 | 93.6 |
| Yes | 775 | 6.4 | |
| Depression | No | 11,341 | 98.6 |
| Yes | 124 | 1.4 | |
| Consumption of Sweet Food | Never | 259 | 2.2 |
| ≤3 times the monthly | 940 | 8.4 | |
| 1 – 6 times per week | 6,557 | 57.4 | |
| ≥1 time per day | 3,709 | 32.0 | |
| Consumption of Sweet Drinks | Never | 397 | 3.4 |
| ≤3 times the monthly | 801 | 7.0 | |
| 1 – 6 times per week | 4,906 | 42.4 | |
| ≥1 time per day | 5,361 | 47.1 | |
| Consumption of Alcohol | No | 11,200 | 97.5 |
| Yes | 265 | 2.5 | |
| HDL | Normal (>40 mg/dL) | 6,409 | 55.3 |
| Low (≤40 mg/dL) | 5,056 | 44.7 | |
| LDL | Normal (<100mg/dL) | 3,832 | 35.0 |
| High (≥100 mg/dL) | 7,633 | 65.0 | |
| Triglyceride | Normal (<150 mg/dL) | 7,689 | 67.1 |
| High (≥150 mg/dL) | 3,776 | 32.9 | |
Bivariate analysis was performed using the Cox Regression test, with the majority of variables deemed eligible for multivariate analysis. Four variables had P-value>0.25 and were not included in the multivariate analysis: depression (P-value = 0.64), consumption of sweet foods (P-value = 0.45), consumption of sweet drinks (P-value = 0.71), and HDL (P-value = 0.44).
Multivariate analysis was carried out using the Cox Regression test. Eligible candidate variables were inputted into the model and subsequently removed if P-value>0.05 from the largest. The final results (fit model) of the multivariable analysis indicated that the risks linked tp prediabetes were age, education level, physical activity, BMI, central obesity, hypertension, LDL, and triglyceride levels. The final model of the multivariable analysis is reported in Table 4.
Table 4. Risk Factors for Prediabetes in Adults in Indonesia
| Variable | Prediabetes | Crude PR (95% CI) |
P-value | Adjusted PR (95% CI) | P-value | ||
| No (%) | Yes (%) | ||||||
| Age | <45 Years | 5,458 (56.5) | 4,203 (43.5) | Ref | Ref | Ref | Ref |
| ≥45 Years | 2,322 (44.9) | 2,849 (55.1) | 1.27 (1.21−1.33) | 0.001 | 1.19 (1.13−1.25) | 0.001 | |
| Gender | Male | 3,258 (57.3) | 2,427 (42.7) | Ref | Ref | ||
| Female | 4,522 (49.4) | 4,625 (50.6) | 1.18 (1.13−1.24) | 0.001 | |||
| Level of education | High | 3,786 (55.4) | 3,053 (44.6) | Ref | Ref | Ref | Ref |
| Low | 3,994 (50.0) | 3,999 (50.0) | 1.12 (1.07−1.18) | 0.001 | 1.08 (1.03−1.14) | 0.002 | |
| Job | Working | 4,793 (53.4) | 4,177 (46.6) | Ref | Ref | ||
| Not working | 2,987 (51.0) | 2,875 (49.0) | 1.05 (1.00−1.10) | 0.032 | |||
| Smoking behavior | Not smoking | 5,294 (49.3) | 5,434 (50.7) | Ref | Ref | ||
| Light smokers | 1,221 (63.3) | 709 (36.7) | 0.73 (0.67−0.78) | 0.001 | |||
| Moderate smokers | 753 (61.7) | 467 (38.3) | 0.76 (0.69−0.83) | 0.001 | |||
| Heavy smokers | 109 (57.4) | 81 (42.6) | 0.84 (0.68−1.05) | 0.124 | |||
| Don't know/forget | 403 (52.7) | 361 (47.3) | 0.93 (0.84−1.04) | 0.201 | |||
| Physical activity | Enough | 6,327 (53.0) | 5,602 (47.0) | Ref | Ref | Ref | Ref |
| Less | 1,453 (50.1) | 1,45 (49.9) | 1.06 (1.00−1.13) | 0.036 | 1.18 (1.08−1.28) | 0.001 | |
| BMI | BMI≤25,0 | 4,941 (57.9) | 3,589 (42.1) | Ref | Ref | Ref | Ref |
| BMI>25,0 | 2,839 (45.0) | 3,463 (55.0) | 1.31 (1.25−1.37) | 0.001 | 1.08 (1.02−1.14) | 0.010 | |
| Central obesity | No | 4,715 (57.6) | 3,473 (42.4) | Ref | Ref | Ref | Ref |
| Yes | 3,065 (46.1) | 3,579 (53.9) | 1.27 (1.21−1.33) | 0.001 | 1.20 (1.13−1.27) | 0.001 | |
| Hypertension | No | 7,439 (53.6) | 6,451 (46.4) | Ref | Ref | Ref | Ref |
| Yes | 341 (36.2) | 601 (63.8) | 1.37 (1.26−1.49) | 0.001 | 1.09 (1.03−1.16) | 0.004 | |
| Depression | No | 5,761 (50.8) | 5,580 (49.2) | Ref | Ref | ||
| Yes | 68 (54.8) | 56 (45.2) | 0.95 (0.77−1.17) | 0.638 | |||
| Consumption of sweet food | Never | 174 (54.2) | 147 (45.8) | Ref | Ref | ||
| ≤3 times the monthly | 636 (50.9) | 613 (49.1) | 1.07 (0.90−1.28) | 0.451 | |||
| 1 – 6 times per week | 4,479 (52.6) | 4,035 (47.4) | 1.04 (0.88−1.22) | 0.683 | |||
| ≥1 time per day | 2,491 (52.5) | 2,257 (47.5) | 1.04 (0.88−1.23) | 0.661 | |||
| Consumption of sweet drinks | Never | 266 (52.1) | 245 (47.9) | Ref | Ref | ||
| ≤3 times the monthly | 528 (50.7) | 514 (49.3) | 1.03 (0.88−1.20) | 0.714 | |||
| 1 – 6 times per week | 3,268 (51.9) | 3,023 (48.1) | 1.00 (0.88−1.14) | 0.973 | |||
| ≥1 time per day | 3,718 (53.2) | 3,27 (46.8) | 0.98 (0.86−1.11) | 0.714 | |||
| Consumption of alcohol | No | 7,562 (52.3) | 6,904 (47.7) | Ref | Ref | ||
| Yes | 218 (59.6) | 148 (40.4) | 0.85 (0.72−1.00) | 0.046 | |||
| HDL | Normal | 4,27 (52.1) | 3,932 (47.9) | Ref | Ref | ||
| Low | 3,51 (52.9) | 3,12 (47.1) | 0.98 (0.94−1.03) | 0.439 | |||
| LDL | Normal | 2,968 (57.3) | 2,216 (42.7) | Ref | Ref | Ref | Ref |
| High | 4,812 (49.9) | 4,836 (50.1) | 1.17 (1.12−1.23) | 0.001 | 1.10 (1.04−1.16) | 0.001 | |
| Triglyceride | Normal | 5,439 (54.7) | 4,508 (45.3) | Ref | Ref | Ref | Ref |
| High | 2,341 (47.9) | 2,544 (52.1) | 1.15 (1.10−1.21) | 0.001 | 1.07 (1.02−1.13) | 0.005 | |
Discussion
The objective of this study was to explore the prevalence and risk factors of prediabetes among Indonesian adults using nationally representative data. The findings offered important insights regarding the modifiable and non-modifiable factors contributing to
prediabetes. They can serve as a knowledge base for future community as well as public health policies and interventions.
The results of univariate analysis demonstrated that the prevalence of prediabetes among Indonesian adults was 47.5%, with a further breakdown indicating that 15.6% of individuals had Impaired Fasting Glucose (IFG), 21.8% had Impaired Glucose Tolerance (IGT), and 10.1% had prediabetes based on IFG and IGT. According to data from the IHS 2023, the prevalence of prediabetes in the population aged≥15 years based on IFG was 13.4%, and based on IGT was 18.6% [11]. The discrepancy in prevalence is accounted for by the high prevalence of prediabetes in certain age groups [15]. This high prevalence among adults is also thought to be closely linked to lifestyle changes and the growing prevalence of obesity among adults [16]. Further, a study using data from the Bogor Cohort found that the prevalence of prediabetes following a decade of observation was 50.3% [17]. The high prevalence of prediabetes can be assigned to changes in diet, especially the consumption of foods and beverages with elevated sugar levels, often accompanied by scant physical activity [18].
The findings of the multivariate analysis revealed that central obesity was the predominant factor in the incidence of prediabetes among adults in Indonesia. Those with central obesity had a risk of 1.2 times to experience prediabetes (95% CI: 1.13-1.27) compared to individuals without central obesity. These findings are in line with the research by Alkandari et al., who found that a high waist circumference ratio (≥0,9 for men and ≥0,85 for women) had a risk of prediabetes 1.29 times (95% CI: 1.00-1.65) compared to those with a normal waist circumference ratio [19]. Increased visceral adiposity has been identified as a precursor to the development of prediabetes and diabetes mellitus. A study indicated that visceral adiposity exerts an independent impact on the development of diabetes, independent of fasting insulin, insulin secretion, glycemia, total and regional adiposity, and family history of diabetes [20].
Adults aged≥45 years had a risk of 1.2 times (95% CI: 1.13-1.25) of having prediabetes compared to those aged<45 years. This finding aligns with the results of a study by Brož et al., which reported that the 45-54 years age group had a risk of 3.489 times (95% CI: 2.435-4.999) compared to the 24-44 years age group [21]. As the CDC has explained, age≥45 years are a primary risk factor for prediabetes [22]. However, studies demonstrate a growing prevalence of prediabetes among young adults. A study noted a higher prevalence of prediabetes in the 25-44 age group [23,24]. This is partly due to biological differences between the sexes, as well as behavioral risk factors including smoking, alcohol consumption, and lack of physical activity. Younger demographics tend to have less health awareness. This results in poor lifestyle choices and less medical care among the young [23,25].
The findings of the present study indicated that individuals with low educational levels had 1.1 times elevated risk of prediabetes (95% CI: 1.03-1.14) compared to those with high educational levels. A parallel finding emerged from research performed by Li et al., demonstrating that individuals with low education levels revealed a risk of 1.08 times higher (95% CI: 0.96-1.21) compared to those with high education levels (high school and above) [25]. Research suggests that the more educated, the more likely they are to seek and use health checkups. This results in better access to medical care and earlier detection of health conditions. Educational initiatives and other preventative measures can ameliorate health behaviors and health literacy [26]
Those with less physical activity had 1.18 times (95% CI: 1.08-1.28) the risk of developing prediabetes compared to individuals with sufficient physical activity. This finding aligns with the conclusions of previous studies conducted in adult populations, reporting that light physical activity augmented the risk of prediabetes in men by 1.55 times (95% CI: 1.17-2.05) compared to those engaging in moderate to vigorous physical activity [27]. Moderate to vigorous physical activity positively correlates with enhanced insulin sensitivity and improved β-cell function. Research indicates that aerobic and resistance training promotes insulin sensitivity in skeletal muscles and optimizes glucose transport, ensuring effective blood glucose level regulation. Thus, the maintenance of regular physical activity emerges as a critical component in the management of blood glucose levels as well as the enhancement of overall health [28, 29].
A Body Mass Index (BMI) >25 or overweight has been identified as a significant risk factor for prediabetes. Individuals with BMI>25.0 presented a risk of 1.1 times (95% CI: 1.02-1.14) to develop prediabetes compared to those with BMI≤25.0. This association has been identified in other studies, which found BMI is a risk factor for prediabetes with an aOR of 1.07 (95% CI: 1.04-1.1) [30].
A parallel set of findings was noted in Kuwait, people with a BMI>25 (overweight) have a 1.41 times greater risk (95% CI: 1.03-1.92) of developing prediabetes, compared to a normal BMI. The risk was 1.6 times higher (95% CI: 1.17–2.19) for obese individuals (BMI>27) (95% CI: 1.17-2.19) [19]. Obesity, a condition that can be modified through lifestyle changes, has been linked to augmented insulin demand and increased insulin resistance. This can result in prediabetes or hyperinsulinemia, and eventually T2DM. The underlying pathophysiology of this condition involves heightened insulin resistance from the accumulation of excess fat, leading to elevated plasma Free Fatty Acid (FFA) levels, which causes more insulin resistance in muscle tissues. The condition is further exacerbated by chronic physical and psychological stress, which cumulatively elevates the risk of developing prediabetes and T2DM [20].
Individuals diagnosed with hypertension reveal a 1.1 times higher risk (95% CI: 1.03-1.16) of developing prediabetes compared to those not diagnosed with hypertension. This finding is in line with the findings of a study conducted on a population of adults aged 25-64 years, which demonstrated that individuals with hypertension had 1.43 times (95% CI: 1.05-1.94) risk of developing prediabetes compared to those without hypertension [21]. Hypertension has been identified as a risk factor for glucose intolerance, given its association with reduced insulin sensitivity and impaired glucose absorption. High insulin levels promote sodium retention in renal tubules, potentially triggering hypertension. This finding highlights the notion that diabetes and hypertension are interconnected and reciprocally affect each other [31, 32].
The LDL cholesterol level variable revealed an adjusted PR value of 1.1 (95% CI: 1.04-1.16), suggesting that those with high LDL (≥100 mg/dL) had a 1.1 times greater risk of prediabetes compared to those with normal LDL (<100 mg/dL). A study by Li et al. also indicated that high LDL levels had a risk of 1.16 times (95% CI: 1.06-1.27) to experience prediabetes compared to individuals with normal LDL levels [25]. The acceleration of the development of prediabetes by elevated LDL levels can be attributed to the decline of maximum glucose-stimulated insulin secretion and the inhibition of human β-islet cell proliferation [33].
The analysis exhibited that those with elevated triglyceride levels (≥150 mg/dL) had 1.1 times (95% CI: 1.02-1.13) the risk of developing prediabetes compared to those with normal triglyceride levels (<150 mg/dL). The study by Gong et al. is in accordance with these findings, where triglycerides were identified as a risk factor for prediabetes and diabetes with an adjusted odds ratio (aOR) of 1.09 (95% CI: 1.02-1.16) [30]. The study also noted that elevated triglyceride levels have been shown to raise Free Fatty Acid (FFA) levels, which lower insulin sensitivity. This results in impaired glucose tolerance, speeding prediabetes, and T2DM development [34].
This study confirms a high prevalence of prediabetes among Indonesian adults, with central obesity, older age, low education, physical inactivity, high BMI, hypertension, and dyslipidemia identified as significant associated factors. Since many of these are modifiable, public health strategies should prioritize lifestyle interventions, health promotion, and early screening, especially targeting high-risk groups. Strengthening preventive measures could help mitigate the progression of prediabetes and lower the burden of type 2 diabetes mellitus (T2DM) in Indonesia.
This study had several limitations. The cross-sectional design of the study did not allow for determining cause-and-effect relationships between variables. The presence of missing data was observed on several variables, including waist circumference measurement, BMI, lipid profiles, and depression. The proportion of missing data for each variable was lower than 10%, these missing values could still result in selection bias and compromise the study's internal validity. Information bias may also occur owing to recall bias and respondents giving answers in accordance with the enumerator's expectations (Clever Hans Effect). Future studies should consider a longitudinal design to better ascertain causal relationships. It is imperative to explore further variables that may be implicated in this relationship, such as the family history of diabetes mellitus.
Conclusion
The prevalence of prediabetes among adults in Indonesia was found to be 47.5% (95% CI: 51.2% - 53.7%), with central obesity identified as the main risk factor, with an adjusted PR value of 1.196 (95% CI: 1.127 - 1.270). This study aimed to raise awareness about prediabetes prevention through lifestyle modifications, including dietary and regular physical activity. The rise in prediabetes can guide intervention program development, such as screening initiatives for young adults, digital tools for education and screening reminders, as well as collaborative efforts with the food industry to lower sugar content and promote healthy eating.
Acknowledgments
The authors would like to thank Badan Kebijakan Pembangunan Kesehatan (BKPK), and the Ministry of Health of Indonesia for the availability of data and the permission to use them in this research.
Conflict of interest
None declared.
Funding
We did not receive any external financial support for this study.
Ethical Considerations
The analysis was conducted using secondary data from the 2023 Indonesian Health Survey (SKI 2023), accessed with permission from the Ministry of Health of the Republic of Indonesia. All data were anonymized prior to analysis to ensure the confidentiality and privacy of participants.
Code of Ethics
Ethical approval was obtained from the Health Research Ethics Commission (KEPK) UPN Veteran Jakarta, on November 21, 2024, under letter number 450/XI/2024/KEP.
Authors' Contributions
Asti Elysia Rahmatul Fitri: Conceptualization, Methodology, Software, Formal analysis, Resources, Data curation, Writing - Original Draft, Visualization, Project administration; Chandrayani Simanjorang: Conceptualization, Methodology, Validation, Data curation, Writing - Review & Editing, Supervision; Laily Hanifah: Methodology, Validation, Writing - Review & Editing, Supervision.
The objective of this study was to explore the prevalence and risk factors of prediabetes among Indonesian adults using nationally representative data. The findings offered important insights regarding the modifiable and non-modifiable factors contributing to
prediabetes. They can serve as a knowledge base for future community as well as public health policies and interventions.
The results of univariate analysis demonstrated that the prevalence of prediabetes among Indonesian adults was 47.5%, with a further breakdown indicating that 15.6% of individuals had Impaired Fasting Glucose (IFG), 21.8% had Impaired Glucose Tolerance (IGT), and 10.1% had prediabetes based on IFG and IGT. According to data from the IHS 2023, the prevalence of prediabetes in the population aged≥15 years based on IFG was 13.4%, and based on IGT was 18.6% [11]. The discrepancy in prevalence is accounted for by the high prevalence of prediabetes in certain age groups [15]. This high prevalence among adults is also thought to be closely linked to lifestyle changes and the growing prevalence of obesity among adults [16]. Further, a study using data from the Bogor Cohort found that the prevalence of prediabetes following a decade of observation was 50.3% [17]. The high prevalence of prediabetes can be assigned to changes in diet, especially the consumption of foods and beverages with elevated sugar levels, often accompanied by scant physical activity [18].
The findings of the multivariate analysis revealed that central obesity was the predominant factor in the incidence of prediabetes among adults in Indonesia. Those with central obesity had a risk of 1.2 times to experience prediabetes (95% CI: 1.13-1.27) compared to individuals without central obesity. These findings are in line with the research by Alkandari et al., who found that a high waist circumference ratio (≥0,9 for men and ≥0,85 for women) had a risk of prediabetes 1.29 times (95% CI: 1.00-1.65) compared to those with a normal waist circumference ratio [19]. Increased visceral adiposity has been identified as a precursor to the development of prediabetes and diabetes mellitus. A study indicated that visceral adiposity exerts an independent impact on the development of diabetes, independent of fasting insulin, insulin secretion, glycemia, total and regional adiposity, and family history of diabetes [20].
Adults aged≥45 years had a risk of 1.2 times (95% CI: 1.13-1.25) of having prediabetes compared to those aged<45 years. This finding aligns with the results of a study by Brož et al., which reported that the 45-54 years age group had a risk of 3.489 times (95% CI: 2.435-4.999) compared to the 24-44 years age group [21]. As the CDC has explained, age≥45 years are a primary risk factor for prediabetes [22]. However, studies demonstrate a growing prevalence of prediabetes among young adults. A study noted a higher prevalence of prediabetes in the 25-44 age group [23,24]. This is partly due to biological differences between the sexes, as well as behavioral risk factors including smoking, alcohol consumption, and lack of physical activity. Younger demographics tend to have less health awareness. This results in poor lifestyle choices and less medical care among the young [23,25].
The findings of the present study indicated that individuals with low educational levels had 1.1 times elevated risk of prediabetes (95% CI: 1.03-1.14) compared to those with high educational levels. A parallel finding emerged from research performed by Li et al., demonstrating that individuals with low education levels revealed a risk of 1.08 times higher (95% CI: 0.96-1.21) compared to those with high education levels (high school and above) [25]. Research suggests that the more educated, the more likely they are to seek and use health checkups. This results in better access to medical care and earlier detection of health conditions. Educational initiatives and other preventative measures can ameliorate health behaviors and health literacy [26]
Those with less physical activity had 1.18 times (95% CI: 1.08-1.28) the risk of developing prediabetes compared to individuals with sufficient physical activity. This finding aligns with the conclusions of previous studies conducted in adult populations, reporting that light physical activity augmented the risk of prediabetes in men by 1.55 times (95% CI: 1.17-2.05) compared to those engaging in moderate to vigorous physical activity [27]. Moderate to vigorous physical activity positively correlates with enhanced insulin sensitivity and improved β-cell function. Research indicates that aerobic and resistance training promotes insulin sensitivity in skeletal muscles and optimizes glucose transport, ensuring effective blood glucose level regulation. Thus, the maintenance of regular physical activity emerges as a critical component in the management of blood glucose levels as well as the enhancement of overall health [28, 29].
A Body Mass Index (BMI) >25 or overweight has been identified as a significant risk factor for prediabetes. Individuals with BMI>25.0 presented a risk of 1.1 times (95% CI: 1.02-1.14) to develop prediabetes compared to those with BMI≤25.0. This association has been identified in other studies, which found BMI is a risk factor for prediabetes with an aOR of 1.07 (95% CI: 1.04-1.1) [30].
A parallel set of findings was noted in Kuwait, people with a BMI>25 (overweight) have a 1.41 times greater risk (95% CI: 1.03-1.92) of developing prediabetes, compared to a normal BMI. The risk was 1.6 times higher (95% CI: 1.17–2.19) for obese individuals (BMI>27) (95% CI: 1.17-2.19) [19]. Obesity, a condition that can be modified through lifestyle changes, has been linked to augmented insulin demand and increased insulin resistance. This can result in prediabetes or hyperinsulinemia, and eventually T2DM. The underlying pathophysiology of this condition involves heightened insulin resistance from the accumulation of excess fat, leading to elevated plasma Free Fatty Acid (FFA) levels, which causes more insulin resistance in muscle tissues. The condition is further exacerbated by chronic physical and psychological stress, which cumulatively elevates the risk of developing prediabetes and T2DM [20].
Individuals diagnosed with hypertension reveal a 1.1 times higher risk (95% CI: 1.03-1.16) of developing prediabetes compared to those not diagnosed with hypertension. This finding is in line with the findings of a study conducted on a population of adults aged 25-64 years, which demonstrated that individuals with hypertension had 1.43 times (95% CI: 1.05-1.94) risk of developing prediabetes compared to those without hypertension [21]. Hypertension has been identified as a risk factor for glucose intolerance, given its association with reduced insulin sensitivity and impaired glucose absorption. High insulin levels promote sodium retention in renal tubules, potentially triggering hypertension. This finding highlights the notion that diabetes and hypertension are interconnected and reciprocally affect each other [31, 32].
The LDL cholesterol level variable revealed an adjusted PR value of 1.1 (95% CI: 1.04-1.16), suggesting that those with high LDL (≥100 mg/dL) had a 1.1 times greater risk of prediabetes compared to those with normal LDL (<100 mg/dL). A study by Li et al. also indicated that high LDL levels had a risk of 1.16 times (95% CI: 1.06-1.27) to experience prediabetes compared to individuals with normal LDL levels [25]. The acceleration of the development of prediabetes by elevated LDL levels can be attributed to the decline of maximum glucose-stimulated insulin secretion and the inhibition of human β-islet cell proliferation [33].
The analysis exhibited that those with elevated triglyceride levels (≥150 mg/dL) had 1.1 times (95% CI: 1.02-1.13) the risk of developing prediabetes compared to those with normal triglyceride levels (<150 mg/dL). The study by Gong et al. is in accordance with these findings, where triglycerides were identified as a risk factor for prediabetes and diabetes with an adjusted odds ratio (aOR) of 1.09 (95% CI: 1.02-1.16) [30]. The study also noted that elevated triglyceride levels have been shown to raise Free Fatty Acid (FFA) levels, which lower insulin sensitivity. This results in impaired glucose tolerance, speeding prediabetes, and T2DM development [34].
This study confirms a high prevalence of prediabetes among Indonesian adults, with central obesity, older age, low education, physical inactivity, high BMI, hypertension, and dyslipidemia identified as significant associated factors. Since many of these are modifiable, public health strategies should prioritize lifestyle interventions, health promotion, and early screening, especially targeting high-risk groups. Strengthening preventive measures could help mitigate the progression of prediabetes and lower the burden of type 2 diabetes mellitus (T2DM) in Indonesia.
This study had several limitations. The cross-sectional design of the study did not allow for determining cause-and-effect relationships between variables. The presence of missing data was observed on several variables, including waist circumference measurement, BMI, lipid profiles, and depression. The proportion of missing data for each variable was lower than 10%, these missing values could still result in selection bias and compromise the study's internal validity. Information bias may also occur owing to recall bias and respondents giving answers in accordance with the enumerator's expectations (Clever Hans Effect). Future studies should consider a longitudinal design to better ascertain causal relationships. It is imperative to explore further variables that may be implicated in this relationship, such as the family history of diabetes mellitus.
Conclusion
The prevalence of prediabetes among adults in Indonesia was found to be 47.5% (95% CI: 51.2% - 53.7%), with central obesity identified as the main risk factor, with an adjusted PR value of 1.196 (95% CI: 1.127 - 1.270). This study aimed to raise awareness about prediabetes prevention through lifestyle modifications, including dietary and regular physical activity. The rise in prediabetes can guide intervention program development, such as screening initiatives for young adults, digital tools for education and screening reminders, as well as collaborative efforts with the food industry to lower sugar content and promote healthy eating.
Acknowledgments
The authors would like to thank Badan Kebijakan Pembangunan Kesehatan (BKPK), and the Ministry of Health of Indonesia for the availability of data and the permission to use them in this research.
Conflict of interest
None declared.
Funding
We did not receive any external financial support for this study.
Ethical Considerations
The analysis was conducted using secondary data from the 2023 Indonesian Health Survey (SKI 2023), accessed with permission from the Ministry of Health of the Republic of Indonesia. All data were anonymized prior to analysis to ensure the confidentiality and privacy of participants.
Code of Ethics
Ethical approval was obtained from the Health Research Ethics Commission (KEPK) UPN Veteran Jakarta, on November 21, 2024, under letter number 450/XI/2024/KEP.
Authors' Contributions
Asti Elysia Rahmatul Fitri: Conceptualization, Methodology, Software, Formal analysis, Resources, Data curation, Writing - Original Draft, Visualization, Project administration; Chandrayani Simanjorang: Conceptualization, Methodology, Validation, Data curation, Writing - Review & Editing, Supervision; Laily Hanifah: Methodology, Validation, Writing - Review & Editing, Supervision.
References
1. Zand A, Ibrahim K, Patham B. Prediabetes: Why Should We Care? Methodist Debakey Cardiovasc J. 2018;14(4):289-97. [DOI] [PMID] [PMCID]
2. Tian Y, Qiu Z, Wang F, Deng S, Wang Y, Wang Z, et al. Associations of Diabetes and Prediabetes With Mortality and Life Expectancy in China: A National Study. Diabetes Care. 2024;47(11):1969-77. [DOI] [PMID]
3. Lizzo JM, Goyal A, Gupta V. Adult Diabetic Ketoacidosis. In: StatPearls [Internet]. Treasure Island, Florida, United States: StatPearls Publishing; 2025 [PMID] [Bookshelf ID]
4. World Health Organization. Definition and Diagnosis of Diabetes Mellitus and Intermediate Hyperglycemia. Geneva, Switzerland: World Health Organization; 2006. [URL]
5. Soelistijo S, Lindarto D, Decroli E, Permana H, Sucipto KW, Kusnadi Y, et al. Pedoman Pengelolaan dan Pencegahan Diabetes Melitus Tipe 2 Dewasa di Indonesia 2021. Fontana, Wisconsin, United States: Global Initiative for Asthma. 2021.
6. American Heart Association. Diabetes Risk Factors. New York, United States: American Heart Association; 2024.
7. Andriani RN, Fitriadi Y, Danawati CW, Koesnanto H, Ikhsan R. Analysis of Prediabetes Risk Factors at Primary Health Care Centers. Rev Prim Care Pract Educ. 2022;5(2):526. [DOI]
8. Bahadoran Z, Mirmiran P, Shabani M, Azizi F. Higher daily physical activity levels may facilitate pre-diabetes regression to normoglycemia: A longitudinal study among an Iranian population. Prev Med Rep. 2023;34:102233. [DOI] [PMID] [PMCID]
9. Liberty IA, Kodim N. Assess Prediabetes Risk, As A Golden Period For Prevention Of Diabetes. Asian J Pharm Clin Res. 2017;10(6):349-53. [DOI]
10. International Diabetes Federation. The Diabetes Atlas. 10th ed. Brussels, Belgium: International Diabetes Federation; 2021.
11. Health Development Policy Agency, Ministry of Health. The 2023 Indonesian Health Survey (IHS) in Numbers. Jakarta, Indonesian: Ministry of Health; 2024.
12. Dany F, Dewi RM, Tjandrarini DH, Pradono J, Delima D, Sariadji K, et al. Urban-rural distinction of potential determinants for prediabetes in Indonesian population aged ≥15 years: a cross-sectional analysis of Indonesian Basic Health Research 2018 among normoglycemic and prediabetic individuals. BMC Public Health. 2020;20(1):1509. [DOI] [PMID] [PMCID]
13. Ministry of Health of the Republic of Indonesia. Adults 19-59 Years. Jakarta, Indonesian: Ministry of Health; 2024.
14. Glechner A, Keuchel L, Affengruber L, Titscher V, Sommer I, Matyas N, et al. Effects of lifestyle changes on adults with prediabetes: A systematic review and meta-analysis. Prim Care Diabetes. 2018;12(5):393-408. [DOI] [PMID]
15. Andes LJ, Cheng YJ, Rolka DB, Gregg EW, Imperatore G. Prevalence of Prediabetes Among Adult population and Young Adults in the United States, 2005-2016. JAMA Pediatr. 2020;174(2):e194498. [DOI] [PMID] [PMCID]
16. Lascar N, Brown J, Pattison H, Barnett AH, Bailey CJ, Bellary S. Type 2 diabetes in adult population and young adults. Lancet Diabetes Endocrinol. 2018;6(1):69-80. [DOI] [PMID]
17. Maghfiroh AA, Simanjorang C, Karima UQ. Factors Associated with the Incidence of Prediabetes in Bogor, Indonesia: A Cohort Study. J Res Health Sci. 2025;25(1):e00635. [DOI] [PMID] [PMCID]
18. Xie J, Wang M, Long Z, Ning H, Li J, Cao Y, et al. Global burden of type 2 diabetes in adult population and young adults, 1990-2019: systematic analysis of the Global Burden of Disease Study 2019. BMJ. 2022;379:e072385. [DOI] [PMID] [PMCID]
19. Alkandari A, Alarouj M, Elkum N, Sharma P, Devarajan S, Abu-Farha M, et al. Adult Diabetes and Prediabetes Prevalence in Kuwait: Data from the Cross-Sectional Kuwait Diabetes Epidemiology Program. J Clin Med. 2020;9(11):3420. [DOI] [PMID] [PMCID]
20. Sun J, Liu Z, Zhang Z, Zeng Z, Kang W. The Correlation of Prediabetes and Type 2 Diabetes with Adiposity in Adults. Front Nutr. 2022;9:818263. [DOI] [PMID] [PMCID]
21. Brož J, Malinovská J, Nunes MA, Kučera K, Rožeková K, Žejglicová K, et al. Prevalence of diabetes and prediabetes and its risk factors in adults aged 25–64 in the Czech Republic: A cross-sectional study. Diabetes Res Clin Pract. 2020;170:108470. [DOI] [PMID]
22. Centers for Disease Control and Prevention. The Surprising Truth About Prediabetes. Atlanta, Georgia, United States: Centers for Disease Control and Prevention; 2024.
23. Abdul Basit K, Fawwad A, Mustafa N, Davey T, Tahir B, Basit A. Changes in the prevalence of diabetes, prediabetes and associated risk factors in rural Baluchistan; a secondary analysis from repeated surveys (2002-2017). PLoS One. 2023;18(4):e0284441. [DOI] [PMID] [PMCID]
24. Aldossari KK, Aldiab A, Al-Zahrani JM, Al-Ghamdi SH, Abdelrazik M, Batais MA, et al. Prevalence of Prediabetes, Diabetes, and Its Associated Risk Factors among Males in Saudi Arabia: A Population-Based Survey. J Diabetes Res. 2018;2018:2194604. [DOI] [PMID] [PMCID]
25. Li Y, Teng D, Shi X, Qin G, Qin Y, Quan H, et al. Prevalence of diabetes recorded in mainland China using 2018 diagnostic criteria from the American Diabetes Association: national cross sectional study. BMJ. 2020;369:m997. [DOI] [PMID] [PMCID]
26. Liao YS, Tsai WC, Chiu LT, Kung PT. Educational attainment affects the diagnostic time in type 2 diabetes mellitus and the mortality risk of those enrolled in the diabetes pay-for-performance program. Health Policy. 2023;138:104917. [DOI] [PMID]
27. Park KS, Hwang SY. Lifestyle-related predictors affecting prediabetes and diabetes in 20-30-year-old young Korean adults. Epidemiol Health. 2020;42:e2020014. [DOI] [PMID] [PMCID]
28. Yang W, Wu Y, Chen Y, Chen S, Gao X, Wu S, et al. Different levels of physical activity and risk of developing type 2 diabetes among adults with prediabetes: a population-based cohort study. Nutr J. 2024;23(1):107. [DOI] [PMID] [PMCID]
29. Schweitzer GG, Ditzenberger GL, Hughey CC, Finck BN, Martino MR, Pacak CA, et al. Elevated liver glycogenolysis mediates higher blood glucose during acute exercise in Barth syndrome. PLoS One. 2023;18(8):e0290832. [DOI] [PMID] [PMCID]
30. Gong D, Chen X, Yang L, Zhang Y, Zhong Q, Liu J, et al. From normal population to prediabetes and diabetes: study of influencing factors and prediction models. Front Endocrinol (Lausanne). 2023;14:1225696. [DOI] [PMID] [PMCID]
31. Budiastutik I, Kartasurya MI, Subagio HW, Widjanarko B. High Prevalence of Prediabetes and Associated Risk Factors in Urban Areas of Pontianak, Indonesia: A Cross-Sectional Study. J Obes. 2022;2022:4851044. [DOI] [PMID] [PMCID]
32. Tsimihodimos V, Gonzalez-Villalpando C, Meigs JB, Ferrannini E. Hypertension and Diabetes Mellitus: Coprediction and Time Trajectories. Hypertension. 2018;71(3):422-8. [DOI] [PMID] [PMCID]
33. Kuang M, Peng N, Qiu J, Zhong Y, Zou Y, Sheng G. Association of LDL:HDL ratio with prediabetes risk: a longitudinal observational study based on Chinese adults. Lipids Health Dis. 2022;21(1):44. [DOI] [PMID] [PMCID]
34. Yang T, Liu Y, Li L, Zheng Y, Wang Y, Su J, et al. Correlation between the triglyceride-to-high-density lipoprotein cholesterol ratio and other unconventional lipid parameters with the risk of prediabetes and Type 2 diabetes in patients with coronary heart disease: a RCSCD-TCM study in China. Cardiovasc Diabetol. 2022;21(1):93. [DOI] [PMID] [PMCID]
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