F1000Research F1000Research 2046-1402 F1000 Research Limited London, UK 10.12688/f1000research.150600.2 Research Article Articles Prevalence, Characteristics and Risk Factors Analysis of Prediabetes: A Cross-Sectional Study

[version 2; peer review: 1 approved, 1 approved with reservations, 1 not approved]

 Amelia Rina Conceptualization Data Curation Investigation Methodology Supervision Writing – Original Draft Preparation Writing – Review & Editing https://orcid.org/0000-0002-0419-9622 a 1 Harahap Juliandi Data Curation Formal Analysis Investigation Methodology Resources Software Supervision Writing – Review & Editing https://orcid.org/0000-0002-1090-2003 1 Wijaya Hendri Conceptualization Funding Acquisition Investigation Resources Supervision Validation Writing – Original Draft Preparation Writing – Review & Editing https://orcid.org/0000-0002-7309-8227 2 Pase M. Aron Formal Analysis Investigation Methodology Project Administration Software Supervision Validation 3 Suryani Widjaja Sry Data Curation Formal Analysis Funding Acquisition Investigation Validation Visualization Writing – Original Draft Preparation Writing – Review & Editing https://orcid.org/0000-0001-9738-9339 4 Saktioto Saktioto Supervision Validation Visualization Writing – Original Draft Preparation Writing – Review & Editing https://orcid.org/0000-0001-9200-8998 5 Department of Community Medicine, Universitas Sumatera Utara, Medan, North Sumatra, 20155, Indonesia Department of Pediatrics, Universitas Sumatera Utara, Medan, North Sumatra, 20155, Indonesia Department of Internal Medicine, Universitas Sumatera Utara, Medan, North Sumatra, 20155, Indonesia Department of Biochemistry, Universitas Sumatera Utara, Medan, North Sumatra, 20155, Indonesia Physics Department, Math and Natural Sciences, Universitas Riau, Pekanbaru, Riau, 28293, Indonesia rina2@usu.ac.id

No competing interests were disclosed.

 21 10 2024 2024 13 843 9 10 2024 Copyright: © 2024 Amelia R et al. 2024 This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background

Prediabetes, a reversible condition before the onset of diabetes, is a significant concern in healthcare globally. The global prediabetes epidemic has emerged and has considerably impacted health expenditures. Various risk factors play important roles in the progression of prediabetes to diabetes. Intensive lifestyle and pharmacological interventions can significantly reduce the risk of diabetes progression.

 Objective

This study aimed to determine the prevalence, characteristics, and risk factors of prediabetes state of Medan in August 2023.

 Methods

The sample consisted of 89 participants. This was an analytical cross-sectional study in the community that met the inclusion and exclusion criteria. The determination of prediabetes is based on the results of blood tests, namely, the examination of fasting blood sugar levels (FBGL), 2-hour postprandial oral glucose tolerance test (OGTT), and hemoglobin A1c (HbA1C). Other examinations included lipid profiling (total cholesterol, HDL-C, LDL-C, and triglycerides). Data processing was performed using SPSS via univariate and bivariate analyses (chi-square test).

 Results

Of the 89 participants, the prevalence of prediabetes based on HbA1c, FBGL and 2-hours OGTT levels was 28.1%, 50.6%, and 28.1%, respectively. 82% of the participants were female, and 53.9% were overweight or obese based on body mass index (BMI). The risk factors for prediabetes were age >64 years, female, physical inactivity, and diastolic blood pressure ≥90 mmHg ( p<0.05). Other risk factors such age <45-64 years, consumption of vegetables/fruits, BMI, HDL, LDL, trygliceride, total cholesterol, systolic blood pressure, achantosis nigricans, and waist-hip circumference did not associate significantly ( p>0.05).

 Conclusion

This study found that the prevalence of prediabetes was 67.4% in Medan. Age >64 years, female, physical inactivity, and diastolic blood pressure ≥90 mmHg were the most important risk factors for prediabetes.

 macrovascular complication HbA1c diabetes type 2 Prediabetes Risk Factors Directorate of Research, Technology and Community Service (DRTPM) Directorate General of Higher Education, Research and Technology (Ditjen Diktiristek) Ministry of Education, Culture, Research and Technology Indonesia, and TALENTA Universitas Sumatera Utara, Medan .Indonesia This research was funded by the Directorate of Research, Technology and Community Service (DRTPM) Directorate General of Higher Education, Research and Technology (Ditjen Diktiristek) Ministry of Education, Culture, Research and Technology Indonesia, and TALENTA Universitas Sumatera Utara, Medan .Indonesia The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Amendments from Version 1

In this revised manuscript, I have added the results of the multivariate test suggested by Reviewer 2 to examine overall risk factors. Table 4 was corrected as suggested by Reviewer 1. Likewise, the discussion has been added to adapt to the results of multivariate tests, and the bibliography has also changed.

 Introduction

Diabetes is a group of metabolic diseases and a serious, long-term (chronic) condition which characterized by hyperglycemia resulting from defects in insulin secretion, insulin action, or both. 1 – 3 Based on the 10th edition of the International Diabetic Federation (IDF) Atlas, the prevalence of diabetes is estimated to be 537 million adults aged 20–79 years worldwide (10.5%). This includes both type 1 and type 2 diabetes as well as diagnosed and undiagnosed diabetes. The adult population with diabetes aged between 20-79 years is estimated to be 19,465,100 in Indonesia. Instead, the prevalence of diabetes among the ages–20-79 years is 10.6% (of the total adult population aged 20-79 years is 179,720,500). In other words, one in nine people in Indonesia had diabetes. 2 T2DM is one of the most important causes of morbidity and mortality worldwide. 4

Prediabetes is a condition that results in high BGL and often leads to T2DM. 5 People with prediabetes have high BGL (are below the amount needed to be diagnosed with diabetes, but they are at a higher risk of getting diabetes. 6 According to the World Health Organization (WHO), prediabetes is an intermediate level of high blood sugar. They use two specific tests to define it: impaired FBGL, which means BGL of 6.1 to 6.9 mmol/L (110–125 mg/dL) before a meal, or impaired OGTT, which mean OGTT of 7.8 to 11.0 mmol/L (140–199 mg/dL) two hours after eating 75 g of oral glucose. 7

According to the American Diabetes Association (ADA), the criterion for identifying impaired FBGL between 5.6 and 6.9 mmol/L (100-125 mg/dL), impaired OGTT between 7.8 and 11.0 mmol/L (140-199 mg/dL), and HbA1c levels between 5.7% and 6.4% (39-47 mmol/mol). 8 , 9 Both definitions rely on FBGL measurements, 2-hour plasma glucose concentrations during an OGTT, and HbA1c concentrations. 10 In Indonesia, the diagnostic criteria for prediabetes align with those established by the ADA. 11 The significance of Impaired FBGL and impaired 2-hour postprandial OGTT is threefold: they signal an elevated chance of developing T2DM in the future, indicative of an existing heightened risk of cardiovascular disease (CVD), and identifying therapies that can prevent the onset of T2DM. 2

Individuals with Impaired FBGL and impaired OGTT are at a high risk of developing T2DM, with up to 50% within five years. 8 , 9 Untreated T2DM for a prolonged time can lead to complications such as retinopathy, neuropathy, CVD, or stroke. 12 – 14 These chronic implications contribute to diabetes distress and health expenditures. 15 , 16 Diabetes distress is a hidden emotional burden in DM. 17 Healthcare expenditure for people with diabetes is expected to reach 1,054 billion USD by 2045. 18 The cost of managing individuals with T2DM and complications is two times higher than that for individuals without complications. 19 , 20

Risk factors for prediabetes include BMI, waist circumference, ethnicity, family history, and sex. 2 , 9 , 21 Other risk factors include hypertension, low levels of HDL cholesterol, smoking, and low levels of education and income. 22 According to the RISKESDAS (National Basic Health Research) Indonesia, the increase in diabetes data is in line with the rise in obesity rates, a risk factor for diabetes, from 14.8% to 21.8% in 2013-2018. In addition, it is also in line with the increase in BMI from 11.5% to 13.6% and central obesity from 26.6% to 31%. 23 , 24 Intensive lifestyle and pharmacological intervention can significantly reduce the risk of progression to diabetes in patients with impaired FBGL or impaired OGTT. 25 , 26

This study aimed to investigate the prevalence, characteristics, and risk factors for prediabetes in Medan, Indonesia.

 Methods Study design and selection criteria

A cross-sectional study of a community that fulfilled the eligibility criteria was conducted in Medan, Indonesia. The participants were people who were at least 18 years old. Participants who had been diagnosed with diabetes or were pregnant were excluded criteria. A day before the study, all participants were reminded to fast for 8 hours and were only allowed to drink plain water before we assessed their FBGL. The minimum number of participants was determined using the Slovin formula. This formula allows calculation of the minimum sample size based on an acceptable margin of error. 27

Data collection

Data were collected in August 2023 in Medan, Indonesia. Participation in this study is voluntary; participants are not obligated to participate. Earlier, the researcher provided an explanation regarding the ongoing research and their active participation in it. Subsequently, patients who gave their consent would sign the informed consent form. Participants then provided their background information, physical activity, consumption of vegetables or fruits, and history of high blood glucose (during pregnancy or medical checkups). Physical activity is defined as physical activity during work or leisure time (including daily activities) for at least 30 min. We also obtained information about a nigricans to identify additional risk factors for prediabetes. The study instrument was filled in by the participants themselves, followed by the measurement of their height, weight, waist circumference, hip circumference, systolic and diastolic blood pressure (SBP and DBP), lipid profile, FBGL, HbA1c, and 2-hour postprandial OGTT levels.

 Ethical statement

The research design was approved by the Ethics Committee of the Faculty of Medicine, Universitas Sumatera Utara. The approval number is 896/KEPK/USU/2023 (Approval date: 21 August 2023). Patient participation is voluntary; patients have no compulsion to participate in this research. Previously, the researcher explained the research protocol that would be carried out. If the patient agreed, they signed informed consent.

 Data measurement

Body Height, body weight, waist circumference, and hip circumference were measured by trained research assistants. While weighing, we asked participants to take off their footwear and only wear loose clothing. Waist circumference and hip circumference were measured using a non-stretchable tape. Patients were defined as centrally obese if they had a waist circumference of >90 cm in men and >80 cm in women. The blood pressure was measured using a digital blood pressure monitor (Omron™). FBGL, HbA1c, and 2-hour postprandial OGTT levels were measured using venous blood. The process of collecting blood was conducted in two distinct phases. The initial phase was conducted following an 8-hour fasting period by the patient, the examination included measuring the patient’s FBGL, HbA1C, and lipid profile. Subsequently, the patient was administered 75 grams of glucose (sugar dissolved in water) for the OGTT assessment, and the 2 hours post-prandial was monitored. The lipid profile was checked using the enzymatic colorimetric method (Thermo Scientific™ Indiko™ Plus Clinical Chemistry Analyzer). 28 The hexokinase method (NIPRO Premier S Blood Glucose Monitoring System GM01IAA) was used to find the FBGL and 2-hour post-meal OGTT levels. 29 High-performance liquid chromatography (HPLC) was used to determine HbA1c levels (BIORAD D-10 Hemoglobin Testing System). 30

Statistical analysis

We conducted univariate analysis to determine the prevalence and demographic characteristics. Bivariate analysis was used to analyze the risk factors for prediabetes in Medan, Indonesia, using the Chi Square Test ( p<0.05). The multivariate analysis uses Poisson regression with a stepwise method, which involves entering qualified variables with a p-value of less than 0.25 into the next analysis to obtain the ratio prevalence value. It is statistically significant if p value < 0.05. Statistical analyses were performed using the Statistical Package for the Social Sciences (SPSS Inc.).

 Results Characteristics of patients

Table 1 shows that the majority of the 89 participants were housewives (69.7%) and women (82%). 40.4 Of the participants, 40.4% had a high school education, 31.5% were age range–45-54 years old, 56.2% were never engaged in physical activity, and 51.7% consumed vegetables/fruits.

Frequency distribution based on demographic characteristics.
Characteristics Frequency (n = 89) Percentage (%)
Gender
 Men 16 18
 Women 73 82
Age (years)
 <45 26 29.2
 45-54 28 31.5
 55-64 23 25.8
 >64 12 13.5
Education
 Elementary School 26 29.2
 Junior High School 13 14.6
 Senior High School 36 40.4
 Diploma 2 2.2
 Bachelor 11 12.4
 Doctoral degree 1 1.1
Employment
 Housewife 62 69.7
 Student 1 1.1
 Entrepreneur 16 18
 Civil servants 6 6.7
 Private employee 4 4.5
Physical activity
 Never 39 43.8
 More than 30 min 50 56.2
Consuming vegetables/fruits
 Everyday 46 51.7
 Not everyday 43 48.3

Note. n; sample size.

A total of 25 people had prediabetes based on HbA1c measurement, 45 had prediabetes based on FBGL measurement, and 25 had prediabetes based on 2-hour postprandial OGTT levels measurement. The prevalence of prediabetes based on HbA1c, FBG, and 2-hour postprandial OGTT was 28.1%, 50.6%, and 28.1%, respectively ( Table 2).

Prevalence of prediabetes based on HbA1c, FBGL and 2-h postprandial OGTT examination.
Measurement Frequency (n= 89) Percentage (%)
HbA1c Levels
 Normal 50 56.2
 Prediabetes 25 28.1
 Diabetes 14 15.7
Fasting Blood Glucose Levels
 Normal 30 33.7
 Prediabetes 45 50.6
 Diabetes 14 15.7
2 Hours Postprandial OGTT Levels
 Normal 49 55.1
 Prediabetes 25 28.1
 Diabetes 15 16.9

Note. n; sample size, OGTT; oral glucose tolerance test.

Table 3 shows that the median value of BMI and waist-hip circumference ratio were 26.14 kg/m 2 and 2, respectively. Based on the blood pressure examination, the median value of systolic and diastolic blood pressures were 142 and 86 mmHg, respectively. Based on lipid profile laboratory results, the median value of serum total cholesterol, HDL, LDL, and triglycerides levels were 206 mg/dL, 53 mg/dL, 122 mg/dl, and 126 mg/dl, respectively. Based on the glucose level test, the median value of FBGL, 2-hours postprandial OGTT, and HbA1c were 106 mg/dl, 136 mg/dl, and 5.6%, respectively.

The result of physical examination and laboratory.
Characteristics Mean Median 95% Confidence Interval (CI) Minimum-Maximum
BMI 26.61 kg/m 2 26.14 kg/m 2 25.53-27.68 13.23-41.87 (kg/m 2)
Waist-hip ratio 1.74 2.00 1.65-1.83 1-2
Systolic Blood Pressure 141.03 mmHg 142 mmHg 136.42-145.65 100-204 (mmHg)
Diastolic Blood Pressure 87.12 mmHg 86 mmHg 84.60-89.64 66-131 (mmHg)
Total Cholesterol 209.66 mg/dL 206 mg/dL 198.76-220.57 127-471 (mg/dL)
HDL 54.48 mg/dL 53 mg/dL 51.70-57.27 27-89 (mg/dL)
LDL 124.87 mg/dL 122 mg/dL 116.53-133.20 30-238 (mg/dL)
Triglycerides 142.87 mg/dL 126 mg/dL 123.87-161.86 51-738 (mg/dL)
Fasting blood glucose 116.64 mg/dL 106 mg/dL 108.24-125.04 77-311 (mg/dL)
2-hours postprandial OGTT 157.70 mg/dL 136 mg/dL 143.44-172.03 75-521 (mg/dL)
HbA1c 6.08% 5.60% 5.72-6.45 4.5-13%

Note. VEGF, vascular endothelial growth factor; IGF-1; Insulin-like growth factor-1; BMI, body mass index; HDL, high-density lipoprotein; LDL, low-density lipoprotein; OGTT, oral glucose tolerance test.

As shown in Table 4, the risk factors for prediabetes were significantly correlated if the p-value was <0.05. According to chi square analysis, age >64 years, gender, daily exercise, and triglyceride levels had a significant relationship with prediabetes events ( p<0.05). There was no significant relationship between age <45-64 years, consumption of vegetables/fruits, BMI, HDL, LDL, total cholesterol, systolic and diastolic blood pressure, achantosis nigricans, and waist-hip circumference ratio in prediabetes patients.

Analysis risk factors of prediabetes.
Risk Factors Prediabetes Non-Prediabetes Unadjusted Prevalence Ratio (PR) 95% CI PR p-value
Frequency (n) Percentage (%) Frequency (n) Percentage (%)
Age (years) 0.005
 <45 9 34.6 17 65.4 Ref.
 45-54 10 35.7 18 64.3 1.032 0.498 – 2.140 0.933
 55-64 69 39.1 14 69.9 1.130 0.540 – 2.365 0.745
 >64 11 91.7 1 8.3 2.648 1.515 – 4.628 0.001
Gender
 Male 13 81.3 3 18.7 Ref.
 Female 26 35.6 47 64.4 0.438 0.297 – 0.646 0.001
Daily Exercise
 More than 30 min 17 34.0 33 66.0 Ref.
 Never 22 56.4 17 43.6 1.659 1.032 – 2.667 0.034
Consuming vegetables/fruits
 Everyday 20 43.5 26 56.5 Ref.
 Not everyday 19 44.2 24 55.8 1.016 0.635 – 1.627 0.946
Body Mass Index
 Normoweight 7 36.8 12 63.2 Ref.
 Obesity 32 45.7 38 54.3 1.241 0.653 – 2.357 0.489
High Density Lipoprotein (mg/dL)
 ≥60 12 40.0 18 60.0 Ref.
 <60 27 45.8 32 54.2 1.144 0.681 – 1.922 0.604
Low Density Lipoprotein (mg/dL)
 <100 11 47.8 12 52.2 Ref.
 ≥100 28 42.4 38 57.6 0.887 0.532 – 1.479 0.653
Trygliceride (mg/dL)
 <150 18 32.1 38 67.9 Ref.
 ≥150 21 63.6 12 36.4 1.980 1.250 – 3.135 0.004
Total Cholesterol (mg/dL)
 <200 14 37.8 23 62.2 Ref.
 ≥200 25 48.1 27 51.9 1.271 0.770 – 2.096 0.337
2-h Postprandial OGTT (mg/dL) 0.0001
 <140 14 28.6 35 71.4 Ref.
 140-199 10 40.0 15 60.0 1.400 0.726 – 2.700 0.315
 ≥200 15 100 0 0 3.500 2.242 – 5.463 0.0001
Fasting Blood Glucose (mg/dL) 0.0001
 <100 8 26.7 22 73.3 Ref.
 100-125 18 39.1 28 60.9 1.467 0.730 – 2.950 0.282
 ≥126 13 100 0 0 3.750 2.065 – 6.811 0.0001
Systolic Blood Pressure (mmHg)
 <140 16 38.1 26 61.9 Ref.
 ≥140 23 48.9 24 51.1 1.285 0.792 – 2.084 0.303
Diastolic Blood Pressure (mmHg)
 <90 22 37.9 36 62.1 Ref.
 ≥90 17 54.8 14 41.2 1.446 0.914 – 2.287 0.126
Achantosis Nigricans
 No 37 42.5 50 57.5 Ref.
 Yes 2 100 0 0 2.351 0.567 – 9.756 0.105
Waist-hip Ratio
 Normal 12 52.2 11 47.8 Ref.
 Obesity 27 40.9 39 59.1 0.784 0.482 – 1.276 0.384

Note. N, sample size; p, probability less than 0.05; OGTT, oral glucose tolerance test.

Table 5 explains that based on multivariate data analysis using the Poisson regression test with the stepwise method, it was found that the factors that were purely risk factors for prediabetes were age >64 years, female, never doing daily exercise, and diastolic blood pressure ≥90 mmHg ( p<0.05).

Multivarite.
Risk Factors Adjusted Prevalence Ratio (PR) 95% CI PR p-value
Age (years)
 >64 3.130 1.787 – 5.481 0.0001
Gender
 Female 0.404 0.244 – 0.669 0.0001
Daily Exercise
 Never 1.949 1.227 – 3.096 0.005
Low Density Lipoprotein (mg/dL)
 ≥100 0.777 0.510 – 1.183 0.240
2-h Postprandial OGTT (mg/dL)
 140-199 1.527 0.857 – 2.722 0.151
 ≥200 1.460 0.701 – 3.042 0.312
Fasting Blood Glucose (mg/dL)
 ≥126 2.661 1.172 – 6.041 0.019
Systolic Blood Pressure (mmHg)
 ≥140 0.642 0.379 – 1.088 0.100
Diastolic Blood Pressure (mmHg)
 ≥90 1.805 1.125 – 2.897 0.014
Waist-hip Ratio
 Obesity 0.582 0.328 – 1.031 0.064
Discussion

Our study demonstrated that the prevalence of individuals with prediabetes in Medan, Indonesia, using HbA1c, FBGL, and 2-hours postprandial OGTT were 28.1%, 50.6%, and 28.1%, respectively. The prevalence of prediabetes based on FBGL examinations in 33 provinces in Indonesia was 10%. 31 In this study, the prevalence of prediabetes using FBGL was one-fifth of that in the previous study. Another study found that the prevalence of prediabetes in Pontianak with an FBGL > 100 mg/dL was 76.5%. A significant increase in the prevalence of prediabetes has also been reported in the US, Europe, North America, the Caribbean, Africa, West Iran, and Malaysia. 32 , 33 The global prevalence of prediabetes using FBGL and 2-hours postprandial OGTT is estimated to increase to 6.5% and 10%, respectively, in 2045. 8

The American Diabetes Association (ADA) defines impaired fasting glucose (IFG) as having a fasting plasma glucose (FPG) level of 100–125 mg/dL, impaired glucose tolerance (IGT) as having a 2-hour postprandial glucose of 140–199 mg/dL, or elevated HbA1c (5.7%–6.4%) as having “prediabetes,” or intermediate hyperglycemia, and advises this population to make preventative efforts. 9

In this study, we found that most patients with prediabetes were 55-64 years old (n=69, 39.1%), but the prevalence was higher among age >64 years (n=11, 91.7%). In this study, we also found that age >64 years was significantly associated with the incidence of prediabetes ( p=0.001, 95% CI; 1.515-4.628) based on chi square test, and was a risk factor of prediabetes ( p=0.0001, 95% CI; 1.787-5.481) based on poisson regression test. Respondents aged >64 years were 3.13 times more at risk of experiencing prediabetes. Similar to the study by Andriani et al., the majority of prediabetic patients were >50 years old. A previous study also found a significant relationship between age and incidence of prediabetes ( p=0.029). 22 Numerous additional factors that may impact the etiology of prediabetes are also associated with advanced age. Peripheral insulin resistance is increasing in tandem with these processes. with a poor diet, little exercise, or obesity. Hyperglycemia results if this process occurs in people who are at risk of developing prediabetes. The degree of environmental exposure and lifestyle choices have a significant impact on the rate and timing of development. 22

In this study, we found that 82% of the respondents were female and the most patients with prediabetes were female (n=26, 35.6%). Female was significantly associated with the incidence of prediabetes ( p=0.001, 95% CI; 0.297-4.0.646) based on Chi square test, and was a risk factor of prediabetes ( p=0.0001, 95% CI; 0.244-0.669) based on poisson regression test. Female was 0.4 times more at risk of experiencing prediabetes. This study is consistent with research conducted in Pontianak, showing that female are more prevalent among people with prediabetes. 22 Women of reproductive age are less susceptible to cardiovascular disease because of the protective effects of estrogen. Estrogen commonly reduces triglyceride and LDL-C circulating levels, while increasing HDL-C levels. However, some studies have mentioned the development of cardiovascular disease in women with lower blood glucose levels than in men. 34 – 37

The prevalence of physical inactivity in the subjects diagnosed with prediabetes in this study was 56.4%. It was significantly associated with the incidence of prediabetes ( p=0.034, 95% CI; 1.032-4.2.667) based on chi square test, and was a risk factor of prediabetes ( p=0.005, 95% CI; 1.227-3.096) based on poisson regression test. Peoples that never do daily exercise were 1.94 times more at risk of experiencing prediabetes. This is inline with previous research which states that a sedentary lifestyle influences the development of prediabetes and diabetes. Exercise helps to avoid obesity and increases insulin sensitivity. Compared to people who exercise, those who do not exercise may be more susceptible to developing prediabetes and diabetes, 38 and physical activity is known to be protective against the onset of type 2 diabetes. 39 Program-intensive lifestyle interventions from the DPP were to achieve and maintain a minimum weight loss of 7% and a physical activity of 150 min per week identical in intensity to brisk walking. The goal of physical activity was to approximate at least 700 kcal/week of physical activity. 40

The prevalence of prediabetes among groups who do not consume vegetables/fruits every day is higher (44.2%), compared to those who consume vegetables/fruits every day (43.5%). But, we did not find an association between consuming vegetables/fruits every day and prediabetes. Consuming fruits and vegetables has been linked to the prevention of a number of chronic conditions, such as diabetes and prediabetes. These benefits have been attributed to the high nutrient content and low energy of fruits and vegetables. Fruit consumption up to 200 g/day was associated with a lower relative risk of type 2 diabetes; intakes beyond this threshold were associated with an increased risk of diabetes, possibly due to the increased intake of fructose from fruit, which has been associated with a reduction in insulin sensitivity. On the other hand, results from future research have been mixed. In one study, fruit and vegetable intake was compared to prediabetes risk in 150 prediabetes cases and 150 controls. The results showed an inverse relationship. There could be a connection between these discrepancies and the nutritional evaluation technique that was employed. 41 The 12-week intervention consisted of four nutrition visits and instructions on a high-carbohydrate diet (60% to 70% daily calories), high-fiber diet, and low-fat diet (<7% calories from saturated fat). The results showed 5% weight loss. 42 In a study of participants at a high risk of diabetes, dietary fiber intake lowered postprandial blood glucose and insulin resistance. The recommended dietary fibre intake recommendation is 3.0 g or 1,000 kcal of total energy per day to prevent T2DM. 16

The pravelence of obesity based on BMI and waist-hip circumference ratio in the subjects diagnosed with prediabetes in this study was 45.7% and 40.9%, respectively. The prevalence of obesity based on BMI was higher than normoweight subjects, but the prevalence of obesity based on waist-hip circumference ratio was lower than normoweight subject. However, in this study, we did not find an association between BMI, waist-hip circumference ratio and prediabetes. This study is consistent with research conducted in Pontianak, showing that overweight or obesity are more prevalent among people with prediabetes. 22 BMI is a simple anthropometric measure commonly used to measure general adiposity. 43 Asian populations have more visceral fat than Caucasian populations do. This results in metabolic disorders, lipotoxicity, and insulin resistance. In addition, limited insulin secretory capacity and genetic predisposition play important roles in the development of insulin resistance. Several studies have reported that there is no relationship between BMI and the obesity paradox, and BMI acts as a simple indicator for evaluating the risk of blood glucose and lipid metabolism. 42 Maintaining a normal weight BMI is essential in the education of patients with prediabetes and is a concern for physicians. 43

Our study found that low HDL (<60 mg/dl), high LDL (≥100 mg/dl), high trygliceride (≥150 mg/dl), and high total cholesterol (≥200 mg/dl) were more frequent among prediabetes patients. Among all of lipid profiles, high trygliceride (≥150 mg/dl) was significantly associated with the incidence of prediabetes ( p=0.004, 95% CI; 1.250-3.135) based on chi square test. This is consistent with a study by Li et al. from 2024, which also revealed that, after controlling for confounding variables, standard lipid measures showed trygliceride to be an independent risk factor for prediabetes, while HDL and LDL seemed to be possibly protective. There is evidence that a common dyslipidemia feature in prediabetic patients is hypertriglyceridemia. Increased free fatty acids from elevated trygliceride levels stimulate changes in pancreatic α cell insulin signaling and excessive glucagon release, which ultimately culminates in insulin resistance. On the other hand, insulin resistance increases trygliceride levels by blocking trygliceride lipolysis, which raises free fatty acids in the liver and lowers HDL by lowering the expression of apolipoprotein A-I, which is required for HDL synthesis. The “vicious circle” of diabetes development is aided by the causal link that exists between trygliceride and insulin resistance. Out of all the conventional lipid measures, trygliceride was found to be the most significant factor linked to prediabetes in the current investigation. However, from the results of the multivariate analysis, none of the lipid profiles were risk factors for prediabetes in this study. 44

In this study, we found that high blood pressure was more frequent among prediabetes patients. High diastolic blood pressure (≥90 mmHg) was a risk factor of prediabetes ( p=0.014, 95% CI; 1.125-2.897) based on poisson regression test. Peoples with high diastolic blood pressure (≥90 mmHg) were 1.8 times more at risk of experiencing prediabetes. This is consistent with a study by Huang et al. from 2020, which also stated that, one of the most prevalent chronic illnesses, hypertension, is a significant modifiable risk factor for metabolic and cardiovascular disorders, including prediabetes and diabetes. In populations with a reduced risk of cardiovascular disease, hypertension was linked to a higher risk of mortality. When prediabetes was added, this risk increased even further. 45

In this study, we also found that the prevalence of prediabetes was higher among groups who have achantosis nigricans (100%). But, it was not statistically significant ( p=0.105). This contradict the claim that achantosis nigricans was found to be associated with insulin resistance. Achantosis nigricans is a dermatosis that usually affects the neck and intertriginous surfaces. It is characterized by velvety, papillomatous, brownish-black, hyperkeratotic plaques. 46 Additionally, this study also contradicts a 2020 study by Alvarez that found people with normoglycemia, prediabetes, and type 2 diabetes had significant (>85%) achantosis nigricans specificity and positive predictive value. Achantosis nigricans’s positive predictive value for insulin resistance were high across nearly all categories of carbohydrate tolerance. This implies that in those who are euglycemic or have prediabetes, achantosis nigricans is a reliable and early clinical indicator of insulin resistance. 47

In this study, of 13 risk factors, only 4 risk factors have significant correlation with prediabetes in Medan ( p<0.05), namely age >64 years, female, never doing daily exercise, and diastolic blood pressure ≥90 mmHg. Preventing type 2 diabetes mellitus provides significant public health benefits, including lower rates of complications. 42 Implementing lifestyle counselling in clinical practice is feasible and cost-effective. 33 Holistic and integrated coordination is needed to assess the disease, including early detection in high-risk factor populations, targeted treatment, and intensive lifestyle modification.

 Conclusion

This study found that the prevalence of prediabetes based on HbA1c, FBGL and 2-hours OGTT levels was 28.1%, 50.6%, and 28.1%, respectively in Medan. Age >64 years, female, physical inactivity, and diastolic blood pressure ≥90 mmHg were the most important risk factors for prediabetes. Early detection is necessary to assess high-risk factors, targeted treatment, and intensive lifestyle modifications.

Ethical statement

The research design was approved by the Ethics Committee of the Faculty of Medicine, Universitas Sumatera Utara. The approval number is 896/KEPK/USU/2023 (Approval date: 21 August 2023). Patient participation is voluntary; patients are not compelled to participate in this research. Before being included in the research, patients are given an informed consent sheet containing information about research procedures, blood examinations, the discomfort they will experience when taking blood, and other matters related to the research. If the patient understands and is willing to participate, they must sign the informed consent sheet.

 Data availability Underlying data

Figshare: Prediabetes data, https://doi.org/10.6084/m9.figshare.25612098.v1. 48

This project contains the following underlying data:

Data file: Prediabetes Data

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0)

 References Kharroubi AT Darwish HM : Diabetes mellitus: The epidemic of the century. World J. Diabetes. 2015;6(6):850–867. 26131326 10.4239/wjd.v6.i6.850 PMC4478580 Magliano DJ Boyko EJ : IDF Diabetes Atlas 10th edition scientific committee. IDF Diabetes Atlas. 10th ed. Brussels: International Diabetes Federation;2021 Amelia R Harahap J Zulham : Educational Model and Prevention on Prediabetes: A Systematic Review. Curr. Diabetes Rev. 2024;20:e101023221945. 37818560 10.2174/0115733998275518231006074504 PMC10909827 Lee CMY Colagiuri S Woodward M : Comparing different definitions of prediabetes with subsequent risk of diabetes: an individual participant data meta-analysis involving 76 513 individuals and 8208 cases of incident diabetes. BMJ Open Diabetes Res. Care. 2019;7(1):e000794. 31908797 10.1136/bmjdrc-2019-000794. eCollection 2019 PMC6936411 Vicks WS Lo JC Guo L : Prevalence of prediabetes and diabetes vary by ethnicity among U.S. Asian adults at healthy weight, overweight, and obesity ranges: an electronic health record study. BMC Public Health. 2022;22(1):1954. 36273116 10.1186/s12889-022-14362-8 PMC9587616 Khan RMM Chua ZJY Tan JC : From Pre-Diabetes to Diabetes: Diagnosis, Treatments and Translational Research. Medicina (Kaunas). 2019;55(9):546. 31470636 10.3390/medicina55090546 PMC6780236 Bansal N : Prediabetes diagnosis and treatment: A review. World J. Diabetes. 2015;6(2):296–303. 25789110 10.4239/wjd.v6.i2.296 PMC4360422 Rooney MR Fang M Ogurtsova K : Global Prevalence of Prediabetes. Diabetes Care. 2023;46(7):1388–1394. 37196350 10.2337/dc22-2376 PMC10442190 American Diabetes Association: 2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes—2021. Diabetes Care. 2021;44(Supplement1):S15–S33. 33298413 10.2337/dc21-S002 Beulens J Rutters F Rydén L : Risk and management of pre-diabetes. Eur. J. Prev. Cardiol. 2019;26(2):47–54. 10.1177/2047487319880041 Soelestijo S Sanusi H Sasiarini L : Pedoman Pengelolaan dan Pencegahan Diabetes Melitus Tipe 2 Dewasa di Indonesia. PB PERKENI [Internet]. 7th ed. Indonesia: Perkumpulan Endokrinologi Indonesia;2021. Amelia R Sari Wahyuni A Yunanda Y : Atherosclerotic Cardiovascular Disease in Diabetes Patients. Curr. Diabetes Rev. 2023;19(8):e060223213457. 10.2174/1573399819666230206124638 Amelia R Wijaya H Rusdiana R : Risk of Cardiovascular Complication Among Type 2 Diabetes Mellitus Patients in Medan, Indonesia. A Cross-sectional Study. Med. Arch. 2022 Oct;76(5):324–328. 36545452 10.5455/medarh.2022.76.324-328 PMC9760233 Amelia R Harahap J Lelo A : Risk analysis for cardiovascular complication based on the atherogenic index of plasma of type 2 diabetes mellitus patients in Medan, Indonesia. Family Med. Prim. Care Rev. 2020;22(3):197–201. 10.5114/fmpcr.2020.98242 Hostalek U : Global epidemiology of prediabetes - present and future perspectives. Clin. Diabetes Endocrinol. 2019;5:5. 31086677 10.1186/s40842-019-0080-0 PMC6507173 Feldman EL Callaghan BC Pop-Busui R : Diabetic neuropathy. Nat. Rev. Dis. Prim. 2019;5:42. 10.1038/s41572-019-0092-1 Amelia R Sahbudin DKNSB Yamamoto Z : Stress level and self-concept among type 2 diabetes mellitus patients in Indonesia. Family Med. Prim. Care Rev. 2020;22(2):111–115. 10.5114/fmpcr.2020.95313 Sun H Saeedi P Karuranga S : IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res. Clin. Pract. 2022;183:109119. 34879977 10.1016/j.diabres.2021.109119 PMC11057359 Hidayat B Ramadani RV Rudijanto A : Direct Medical Cost of Type 2 Diabetes Mellitus and Its Associated Complications in Indonesia. Value Health Reg. Issues. 2022;28:82–89. 34839111 10.1016/j.vhri.2021.04.006 Amelia R : The Model of Self Care Behaviour and the Relationship with Quality Of Life, Metabolic Control and Lipid Control of Type 2 Diabetes Mellitus Patients in Binjai City, Indonesia. Open Access Maced. J. Med. Sci. 2018 Sep 21;6(9):1762–1767. 30338004 10.3889/oamjms.2018.363 PMC6182544 Amelia R Harahap J Wijaya H : The role of physical activity on glucose transporter-4, fasting blood glucose level and glycate hemoglobin in type 2 diabetes mellitus patients in Medan, Indonesia. Family Med. Prim. Care Rev. 2021;23(3):274–278. 10.5114/fmpcr.2021.108188 Budiastutik I Kartasurya MI Subagio HW : High Prevalence of Prediabetes and Associated Risk Factors in Urban Areas of Pontianak, Indonesia: A Cross-Sectional Study. J. Obes. 2022;2022:4851044–4851049. 36536959 10.1155/2022/4851044 PMC9759381 Kementerian Kesehatan Republik Indonesia: InfoDATin 2020 Diabetes Melitus. Pusdatin. 2020 Amelia R Burhan B Lelo A : The Relationship between Anthropometry and Ankle-Brachial Index with Blood Glucose Level in Patients with Type 2 Diabetes Mellitus at Community Health Center in Medan, Indonesia. Family Med. Prim. Care Rev. 2018;20(4):307–312. 10.5114/fmpcr.2018.79339 Amelia R Lelo A Lindarto D : Analysis of factors affecting the self-care behaviors of diabetes mellitus type 2 patients in Binjai, North Sumatera-Indonesia. Asian J. Microbiol., Biotechnol. Environ. Sci. 2018;20(2):361–367. Rusdiana SM Amelia R : The Effect of Diabetes Self-Management Education on Hba1c Level and Fasting Blood Sugar in Type 2 Diabetes Mellitus Patients in Primary Health Care in Binjai City of North Sumatera, Indonesia. Open Access Maced. J. Med. Sci. 2018 Apr 12;6(4):715–718. 29731946 10.3889/oamjms.2018.169 PMC5927509 Anoyke MA : Sample Size Determination in Survey Research. J. Sci. Res. Rep. 2020;26(5):90–97. 10.9734/jsrr/2020/v26i530263 Kathak RR Sumon AH Molla NH : The association between elevated lipid profile and liver enzymes: a study on Bangladeshi adults. Sci. Rep. 2022;12(1):1711. 35110625 10.1038/s41598-022-05766-y PMC8810783 Sonagra A Zubair M Motiani A : Hexokinase method. StatPearls;2024. Zechmeister B Erden T Kreutzig B : Analytical interference of 33 different hemoglobin variants on HbA1c measurements comparing high-performance liquid chromatography with whole blood enzymatic assay: a multi-center study. Clin. Chim. Acta. 2022;531:145–151. 35378091 10.1016/j.cca.2022.03.028 Soewondo P Pramono LA : Prevalence, Characteristics, and Predictors Of Pre-diabetes In Indonesia. Med. J. Indones. 2011;20(4):283–294. 10.13181/mji.v20i4.465 Center for Disease Control and Prevention: Prevalence of Prediabetes Among Adults. The United States of America (The US): CDC;2022 [updated 2022; cited 2023 Sept 06]. Prevalence of Prediabetes Among Adults|Diabetes|CDC. Moradpour F Rezaei S Piroozi B : Prevalence of prediabetes, diabetes, diabetes awareness, treatment, and its socioeconomic inequality in west of Iran. Sci. Rep. 2022;12(1):17892. 36284227 10.1038/s41598-022-22779-9 PMC9596718 Akhtar S Nasir JA Ali A : Prevalence of type-2 diabetes and prediabetes in Malaysia: A systematic review and meta-analysis. PLoS One. 2022;17(1):e0263139. 35085366 10.1371/journal.pone.0263139 PMC8794132 Bernabe-Ortiz A Perel P Miranda JJ : Diagnostic accuracy of the Finnish Diabetes Risk Score (FINDRISC) for undiagnosed T2DM in Peruvian population. Prim. Care Diabetes. 2018;12(6):517–525. 30131300 10.1016/j.pcd.2018.07.015 PMC6249987 Siddiqui S Zainal H Harun SN : Gender differences in the modifiable risk factors associated with the presence of prediabetes: A systematic review. Diabetes Metab. Syndr. Clin. Res. Rev. 2020;14(5):1243–1252. 32688241 10.1016/j.dsx.2020.06.069 Rao G Powell-Wiley TM Ancheta I : Identification of obesity and cardiovascular risk in ethnically and racially diverse populations: a scientific statement from the american heart association. Circulation. 2015;132(5):457–472. 26149446 10.1161/CIR.0000000000000223 Andriani RN Fitriadi Y Danawati CW : Analysis of prediabetes risk factors at primary health care centers. RevPrim. Care Prac. Educ. 2022;5(2):52–56. 10.22146/rpcpe.74552 Galaviz KI Narayan KMV Lobelo F : Lifestyle and the Prevention of Type 2 Diabetes: A Status Report. Am. J. Lifestyle Med. 2018;12(1):4–20. 30202378 10.1177/1559827615619159 PMC6125024 American Diabetes Association: Standards of Medical Care in Diabetes—2020 Abridged for Primary Care Providers. Clin. Diabetes. 2020;38(1):10–38. 31975748 10.2337/cd20-as01 PMC6969656 Barouti AA Tynelius P Lager A : Fruit and vegetable intake and risk of prediabetes and type 2 diabetes: results from a 20-year long prospective cohort study in Swedish men and women. Eur. J. Nutr. 2022;61(6):3175–3187. 35435501 10.1007/s00394-022-02871-6 PMC9363331 Diabetes Canada Clinical Practice Guidelines Expert Committee Lipscombe L Booth G : Pharmacologic Glycemic Management of Type 2 Diabetes in Adults. Can. J. Diabetes 2018;42(Suppl 1):S88–S103. 10.1016/j.jcjd.2017.10.034 Yoshida Y Jia W Yuanhao Z : Rising Prediabetes, Undiagnosed Diabetes, and Risk Factors in Young Women. Am. J. Prev. Med. 2023;64(3):423–427. 36437142 10.1016/j.amepre.2022.10.001 PMC9974837 Li M Zhang W Zhang M : Nonlinear relationship between untraditional lipid parameters and the risk of prediabetes: a large retrospective study based on Chinese adults. Cardiovasc. Diabetol. 2024;23(1):12. 38184606 10.1186/s12933-023-02103-z PMC10771669 Huang YQ Liu L Huang JY : Prediabetes and risk for all-cause and cardiovascular mortality based on hypertension status. Ann. Transl. Med. 2020;8(23):1580. 33437779 10.21037/atm-20-1155 PMC7791204 Singh SK Agrawal NK Vishwakarma AK : Association of Acanthosis Nigricans and Acrochordon with Insulin Resistance: A Cross-Sectional Hospital-Based Study from North India. Indian J. Dermatol. 2020;65(2):112–117. 32180596 10.4103/ijd. IJD_646_18 PMC7059470 Álvarez-Villalobos NA Rodríguez-Gutiérrez R González-Saldivar G : Acanthosis nigricans in middle-age adults: A highly prevalent and specific clinical sign of insulin resistance. Int. J. Clin. Pract. 2020;74(3): e13453. 31769902 10.1111/ijcp.13453 Amelia R : Prediabetes Data. 10.6084/m9.figshare.25612098.v1 10.5256/f1000research.173232.r370535 Reviewer response for version 2 Dany Frans 1 Referee https://orcid.org/0000-0003-1412-5894 National Research and Innovation Agency (BRIN), Cibinong, Indonesia

Competing interests: No competing interests were disclosed.

 20 3 2025 Copyright: © 2025 Dany F 2025 This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. recommendation approve-with-reservations

Overall, similar studies of prediabetes risk factor evaluation are already available and considerable flaws are encountered in the methods. However, it seems that the authors would like raise awareness of acanthosis nigricans as potential prediabetes determinants, which deserves further consideration.

Title

1. By nature, cross-sectional approach is not appropriate for risk factor identification in community-based studies. Cohort design is more suitable so the title should be changed by adding 'potential' word before risk factors.

2. You should also put your location of study in the title, in this case in Medan.

3. In which settings was your study conducted? In community-based such as Posyandu or Puskesmas (primary health care), referral clinics or hospital-based? Please be more specific and include it in the title as well.

Introduction

4. Please describe why did you choose Medan as your study location? Is there any unusual spike of prediabetes cases in that area compared to other regions in North Sumatera or other strong justifications?

5. Please highlight the importance of bringing up this issue in your background. In addition to risk of diabetes and its increasing trend, is there any particular interests to conduct this study? for example, rise of diabetes-related complications in Medan and the need to allocate enough resources to mitigate its increasing prevalence from local health authority (Dinkes) perspectives, development of software to assess risk of diabetes etc.? If yes, please provide the data or references to support your reasonings.

Methods

6. Please explain why did you choose the Slovin formula to calculate sample size while prediabetes prevalence estimation is known from Riskesdas or perhaps P2PTM data? You can track the data at the district (Kabupaten) level. Slovin formula is usually used when we don't have enough estimates of variability or expected proportions of a particular condition (prediabetes in this case).

7. Please describe how you get 89 participants by using the Slovin formula. Please specifiy the population size (N) and margin of error threshold (e). Please provide the data/reference to support your N (population size) and margin of error value.  

8. Please explain more on how you recruited participants. How did you choose particular regions in Medan to collect the data? Did you involve local statistical agency (BPS) to help you pick the sample area?

9. Did you perform randomization (simplified or stratified) and blinding? Sample randomization and blinding during data collection are important to minimize bias in epidemiological studies. If you didn't perform one or both, state your solid rationale and your effort to minimize sampling bias.

10. How did you get the information of acanthosis nigricans? Did you involve dermatologists, internists or clinicians to assess it? If yes, did they receive proper training prior to data collection in the field?

11. Acanthosis nigricans is actually a manifestation of insulin resistance. You might consider this variable as the outcome instead of prediabetes or you may complement your findings to make your study 'different' from the rest. There are already many studies of prediabetes risk factors with bigger sample size and determinants in different settings so you would have to make your study standout. If you are interested in analyzing acanthosis nigricans as the outcome variable then you should add your justifications in the Introduction as well.

12. Since the study instrument was filled in by the participants, how did you ensure their validity?

13. Did any external validator check their answers? External validators are usually experts who don't have any conflict of interests and are not involved in your study, but they will assess the validity of your research and/or data collection.

14. Please attach the study instrument (questionnaires) both in Bahasa Indonesia and English version so readers can get an overview of your data collection process.

15. Did any participant take medications (antihypertensive agents, antidyslipidemia drugs etc.) or herbal medicines prior to data collection? If yes, describe them in detail as this can affect your measurements. Short fasting (one day) is not enough to eliminate drug effects. If your participants went fasting only for 8 hours, please describe your effort to minimize bias of data analysis. Did you exclude them or else?

16. Please describe the instrument brand name for the anthropometric measurement (particularly, weighing scale and stadiometer for height)

17. Did you perform calibration on your anthropometric measurement tools (weighing scale, stadiometer)? If yes, please describe.

18. How many times did you measure participant blood pressure and how did you decide the final number to be included in your data if there was any deviation between measurements?

19. Did you process and use the blood samples for glucose and lipid profiling in the same day? If not, how did you store them? Please specify.

20. Please explain on how you validated the clinical chemistry (glucose and lipid profile) measurements. Did you use control beads/calibrators before and after sample measurements? Were any clinical pathologists involved in the validation?

21. Which cutoff criteria did you choose for BMI calculation? WHO in general or Asia-Pacific criteria since both have different threshold to define overweight and obesity.

22. Triglyceride is the most variable among common lipid parameters and usually fasting period for its reliable measurement lasts for minimum 12 hours, preferrably up to 14 hours. Did you measure the lipid profile more than once? If the measurement was only once, then you should refer to other reliable literature or references instead of exisiting consensus/guidelines to define hypertriglyceridemia.

23. Please mention if there was any participant drop out and their proportion from the total samples.  

24. Please provide a separate table for operational definition for each variable and their respective citation.

Statistical Analysis

25. Chi square test is appropriate when you have only categorical data. Several numerical variables might loss some information if they are aggregated into a composite categorical variable. Since some predictors are of continuous numerical type, you could perform binary logistic regression as an 'early screening' for potential risk factors although bigger sample size is preferred (rule of thumb minimum 10 samples per predictor variable).

26. Poisson regression is usually not for risk factor prediction, but to estimate how many events (of prediabetes in this case or number of participant visits to primary health care facilities) or case rates (could be incidence or prevalence rates) instead of binary outcome prediction (disease vs normal). Logistic regression is more appropriate for this purpose although the sample size in this study was not adequate. You might need to change your main objectives of this study (as well as in the title) from 'risk factor prediction' to 'prevalence rate estimation' if you keep on using Poisson regression.

27. Please use the different cutoff of HDL level for women and men since both have different physiological/hormonal conditions. You might refer to NCEP ATP III Guidelines or national consensus (PERKENI).  

28. As mentioned earlier, triglyceride cutoff might be different if the fasting period is less than 12 hours. Please check other references for this regard.

Results

29. Please also add the proportion of prediabetes as a whole (combining FBGl, OGTT and HbA1c) in Table 2 since there is a possibility of an individual have more than one abnormal blood glucose profile (e.g., have abnormal OGTT and HbA1c value)

30. In Table 3, why did you choose waist-hip-ratio over waist circumference? Waist circumference is preferred to predict abdominal obesity and metabolic syndrome risk as it is more reproducible and practical in clinical settings (already included in the NCEP ATP III Guidelines). Please note that measuring hip circumference can be more difficult than measuring waist circumference only. If the person is not accustomed to measuring hip circumference, this could be the source of errors in waist-hip ratio calculation. Unless you are properly trained and utilize specific scales that have the ability to measure lean body mass, fat mass, non-lean mass and fat-free mass accurately, waist-hip ratio calculation can be more prone to errors than waist circumference measurement.

You could refer to Waist Circumference and Waist-Hip Ratio: Report of a WHO Expert Consultation in this matter.

31. It is advisable not to include both BMI and waist circumference/waist-hip ratio in multivariate analysis as this may result in collinearity issue when performing the regression test. BMI can be omitted in this analysis.

32. Please add the binary logistic regression test results for the bivariate analysis.

Discussion

33. As mentioned earlier, BMI poses bigger bias than waist circumference and waist-hip ratio despite its practicability. Please revise this in your discussion.

34. Please add more discussions of HDL level difference between men and women and their influence on risk of prediabetes and acanthosis nigricans (if you also choose to evaluate this as outcome variables).

35. Please review the influence of fasting period on triglyceride level fluctuation / variability and the implication of measurement stability and cutoff determination.

36. As described earlier, Poisson regression is performed to estimate number of events or case rates instead of risk factors. Please revise this part in your discussion.

37. Please elaborate more on acanthosis nigricans and its risk factors, notably when associating it with predictors in your study.

38. Please describe the limitations of your study, any effort to minimize biases and further suggestions to improve your research.

Conclusion

39. Please adjust accordingly by considering the changes made in earlier sections.

Abstracts

40. Please adjust accordingly by considering the changes made in the main sections of manuscript.

41. In conclusion part in Abstract, it is said that the prevalence of prediabetes was 67.4% in Medan. How did you get this number? Is this the proportion of prediabetes as a whole (combining FBGl, OGTT and HbA1c)? If yes, you should state this in other sections (Methods, Results, Discussion)

Data Availability

42. Please check again the supplementary data. Both Excel files (45686667_Prediabetes data.xlsx and 47638837_Prediabetes data.xlsx) have the same contents (same predictor and outcome variables). There is no data of acanthosis nigricans and other measurements (blood pressure, anthropometric parameters) yet. Please complete the additional information.

43. As notified in the Methods. please attach the study instrument (questionnaires) both in Bahasa Indonesia and English version.

Is the work clearly and accurately presented and does it cite the current literature?

No

If applicable, is the statistical analysis and its interpretation appropriate?

Partly

Are all the source data underlying the results available to ensure full reproducibility?

Partly

Is the study design appropriate and is the work technically sound?

No

Are the conclusions drawn adequately supported by the results?

No

Are sufficient details of methods and analysis provided to allow replication by others?

No

Reviewer Expertise:

Diabetes and non-communicable diseases, stem cells, bioinformatics (notably molecular modeling and metabolomics).

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

References : Update on the NCEP ATP-III emerging cardiometabolic risk factors. BMC Med .2014;12: 10.1186/1741-7015-12-115 115 25154373 10.1186/1741-7015-12-115 Consultation WE. Waist circumference and waist-hip ratio. Report of a WHO Expert Consultation. Geneva: World Health Organization.2008; Baioumi AY. Comparing measures of obesity: waist circumference, waist-hip, and waist-height ratios. InNutrition in the Prevention and Treatment of Abdominal Obesity 2019 Jan 1 (pp. 29-40). Academic Press.2019; Sathiyakumar V, Park J, Golozar A, Lazo M, Quispe R, Guallar E, Blumenthal RS, Jones SR, Martin SS. Fasting versus nonfasting and low-density lipoprotein cholesterol accuracy. Circulation. 2018 Jan 2;137(1):10-9. : Comparing performance between log-binomial and robust Poisson regression models for estimating risk ratios under model misspecification. BMC Med Res Methodol .2018;18(1) : 10.1186/s12874-018-0519-5 63 29929477 10.1186/s12874-018-0519-5 : Association between Three Waist Circumference-Related Obesity Metrics and Estimated Glomerular Filtration Rates. J Clin Med .2022;11(10) : 10.3390/jcm11102876 35629005 10.3390/jcm11102876 10.5256/f1000research.173232.r333445 Reviewer response for version 2 Pradeepa Rajendra 1 Referee https://orcid.org/0000-0002-4909-3733 Madras Diabetes Research Foundation, Tamil Nadu, India

Competing interests: No competing interests were disclosed.

 11 11 2024 Copyright: © 2024 Pradeepa R 2024 This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. recommendation approve

I am fine with the revision, and I approve the current manuscript.

Is the work clearly and accurately presented and does it cite the current literature?

Yes

If applicable, is the statistical analysis and its interpretation appropriate?

No

Are all the source data underlying the results available to ensure full reproducibility?

Yes

Is the study design appropriate and is the work technically sound?

No

Are the conclusions drawn adequately supported by the results?

Yes

Are sufficient details of methods and analysis provided to allow replication by others?

Yes

Reviewer Expertise:

NA

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

 10.5256/f1000research.165188.r317377 Reviewer response for version 1 Pradeepa Rajendra 1 Referee https://orcid.org/0000-0002-4909-3733 Madras Diabetes Research Foundation, Tamil Nadu, India

Competing interests: No competing interests were disclosed.

 18 9 2024 Copyright: © 2024 Pradeepa R 2024 This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. recommendation reject

Thank you for asking me to review the article entitled “Prevalence, Characteristics and Risk Factors Analysis of Prediabetes: A Cross-Sectional Study” conducted in Medan, Indonesia. The study aimed to determine the prevalence, characteristics, and risk factors of prediabetes state of Medan in August 2023 among 89 participants. The authors have reported the prevalence of prediabetes based on HbA1c, FBG and 2-hours OGTT levels as 28.1%, 50.6%, and 28.1%, respectively. They have provided the clinical and biochemical characteristics and risk factors assessed in the study population.

 The study has been conducted in one month with a very small sample size of 89 participants. The authors could also assess the prevalence using combinations of criteria such as using both HbA1c and FBG etc. The statistical analysis done is very basic, suggest to include multivariate regression to assess the association of risk factors with prediabetes.

This manuscript needs major modification

Is the work clearly and accurately presented and does it cite the current literature?

Yes

If applicable, is the statistical analysis and its interpretation appropriate?

No

Are all the source data underlying the results available to ensure full reproducibility?

Yes

Is the study design appropriate and is the work technically sound?

No

Are the conclusions drawn adequately supported by the results?

Yes

Are sufficient details of methods and analysis provided to allow replication by others?

Yes

Reviewer Expertise:

Epidemiology of diabetes and associated complications

I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above.

 10.5256/f1000research.165188.r313859 Reviewer response for version 1 Abdallah Hanaa Reyad 1 Referee National Research Centre, Cairo, Egypt

Competing interests: No competing interests were disclosed.

 30 8 2024 Copyright: © 2024 Abdallah HR 2024 This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. recommendation reject

This manuscript gives information about the prevalence of pre-diabetes in  Medan state in Indonesia which was diagnosed by three methods; HbA1c, OGTT and FBG. the authors also investigated factors associated with pre-diabetes occurrence in those people. Here are my comments about this manuscript:

1- The title: please remove the word "analysis".

2- Methods:

- I think the sample size is small for a community based study representing a state like Medan.

-  The anthropometric measures needs to be in more details and reference is needed and BMI calculation and classification is shoes insufficiency.

- The statistical analysis: the statistical tests were not used appropriately as follows:

 - The Chi square test is not used to detect associated factors; it is used to compare two groups according to qualitative data. so you should calculate the percentages in each group ( prediabetes group and normal group) separately.

- The correlation was not assessed accurately as you should use the tests of correlation e.g Pearson's correlation coefficient test.

- The risk factors associated with the occurrence of prediabetes should be assessed by Multiple logistic regression analysis not Chi square test.

3- Results:

- The prevalence of prediabetes was mentioned by three percentages according to the method used to diagnose it then latter in the text it was mentioned in only one percentage which differed completely from these findings.

- there is discrepancies in results in the tables than those in the text so please revise your results accurately.

-  HbA1c, FBGL, and 2-hours postprandial OGTT, are not risk factors for prediabetes, they are diagnostic tests  used to detect prediabetes.

4- The conclusion:

the prevalence of prediabetes mentioned was completely different than this in the results.

5-The legends of tables contains some abbreviations which was not present in the tables as IGF1 & VEGF.

Is the work clearly and accurately presented and does it cite the current literature?

Partly

If applicable, is the statistical analysis and its interpretation appropriate?

Partly

Are all the source data underlying the results available to ensure full reproducibility?

Yes

Is the study design appropriate and is the work technically sound?

Yes

Are the conclusions drawn adequately supported by the results?

Partly

Are sufficient details of methods and analysis provided to allow replication by others?

Yes

Reviewer Expertise:

Child growth and development, Diabetes, NAFLD, Obesity, nutrition, stunted growth, Autism, Down Syndrome, Oxidative stress, Gut microbiota.

I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above.