1. Introduction
Prediabetes is an intermediate glycemic state in which blood glucose concentrations are higher than normal but not of a magnitude that would correspond to the diagnosis of type 2 diabetes (Consortium, 2014). The incidence of prediabetes is increasing globally and is expected to affect 472 million people by the end of 2030 (Tabák et al., 2012). Subjects with prediabetes have a profound risk of developing type 2 diabetes. If prediabetes is not treated, it accounts for 37% of cases of diabetes in succeeding years (Tuso, 2014). Prediabetes also imposes the potential burden of mortality upon the affected individuals due to its association with cardiovascular complications (Siddiqui et al., 2020). However, changes in the lifestyle can reduce or delay the appearance of diabetes among people with prediabetes (Tuomilehto et al., 2001). Thus, it is important to recognize individuals presenting with a higher probability of impaired glucose metabolism and related risk factors to reduce disease progression as well as financial burden on the health care system (Díaz-Redondo et al., 2015).
Adolescent is a stage of life between childhood and adulthood, from ages 10 to 19. This evolutionary period may be the right sequence for human progress and the necessary time to create great welfare facilities. The emerging increase in the prevalence of prediabetes in children and adolescents has paralleled the observed increase in childhood obesity over the past three decades. The recorded prevalence of prediabetes depends on the conditions and tools chosen for screening, the population studied, pubertal status, and-importantly-the definitions used. Impaired fasting glucose is defined as above 5·6 mmol/L by the American Diabetes Association (ADA) and above 6·1 mmol/L by the World Health Organization (WHO). As a result, the recorded prevalence of impaired glucose tolerance in obese children and adolescents ranges from 1% to 30%, whereas the prevalence of impaired fasting glucose has a broader range in not only obese children, but also the general adolescent population that is not specifically within at-risk groups. For example, the prevalence of impaired fasting glucose in obese children ranges from 1% in Italy (Brufani et al., 2010) and 4% in Germany, to 17% in Sweden (all with the use of ADA criteria) (Hagman et al., 2014). In the USA, reported prevalence ranges from 2%-9% (WHO criteria) to 15%-47% (ADA criteria) (Di Bonito et al., 2017). Impaired fasting glucose was reported in a small proportion (<5%) of the obese pediatric population in Mexico (Yamamoto-Kimura et al., 2006) India (Narayanappa et al., 2011) and mainland China (Yang et al., 2015) compared with 28% of obese adolescents in Taiwan (Chen et al., 2014). In the United Arab Emirates, as one of the countries with the highest prevalence of type 2 diabetes in adults worldwide, fasting glucose is impaired in 12% of overweight and obese children (Al Amiri et al., 2015). Several challenges arise when attempting to compare and interpret these prevalence data, since the sample populations have been recruited from different settings (e.g., whole population vs obesity clinic). Additionally, in epidemiological studies involving large samples, some participants might not have truly fasted when fasting glucose is measured, causing an overestimation of the prevalence. Similarly, the presence of impaired glucose tolerance can vary across repeated sampling within a timeframe of weeks (Libman et al., 2008), possibly because whole-body insulin sensitivity can be affected by participant characteristics, such as minor infection or phases of the menstrual cycle. However, arguably, a single detection of impaired glucose tolerance, even if not repeated, already indicates that the β cells of the individual have inadequate function and pronounced insulin resistance.
Several studies have been conducted previously, to investigate the association of these risk factors with the risk of prediabetes (Hadaegh et al., 2015; Lee et al., 2015; Geva et al., 2019); However the findings remained inconsistent. Conclusive evidence to support or refute differences in risk factor association across the wide range of studies is lacking and has never been studied systematically previously. Summarized information is needed because it may help to develop or implement age-appropriate preventive strategies for adolescents that can be used as a key to self-care to improve quality of life among prediabetic individuals. Therefore, this systematic review was conducted to briefly address and summarize the available literature for the evidence of age-specific modifiable and non-modifiable risk factors among adolescent population and answer the question: which risk factors are most associated with the incidence of prediabetes in adolescents? 

2. Materials and Methods
We performed a systematic review of the literature consisting of observational or cross-sectional studies, which reported the risk factors of pre-diabetes among adolescent by following the preferred reporting items for systematic reviews and meta-analysis (PRISMA) 2020 guidelines (Page et al., 2021).
Information sources and search strategy
We performed a literature search through PubMed, ScienceDirect, Scopus, and Web of Science databases in the time frame of 2010 to 2021. A careful manual search of reference lists of identified papers was also done by all authors to identify studies that were not detected by the database search. All the retrieved articles were then sent to Mendeley to remove duplicates. Screening of titles and abstract based on the pre-defined inclusion and exclusion criteria was done to get relevant citations that were included in the present review. Keywords used were “Modifiable Risk Factors” OR “Non-modifiable Risk Factors” OR “Factors associated” OR “Factors related” AND “Prediabetes” OR “Prediabetic” AND “Adolescent” OR “Youth” OR “Teen” OR “Young people” (Table 1). 


Eligibility criteria 
Eligibility criteria were determined based on the (population, intervention, comparison and outcomes) PICOS guidelines. The participants consisted of adolescent in any age (10-19 years old). No intervention was needed to be considered in this review, but the laboratory tests and diagnostic procedures to identify pre-diabetes were considered as exposure. Comparison was considered as a difference in demographic variables such as age, sex, smoking, race, and family history of diabetes. The outcomes were significant modifiable and non-modifiable factors involved in occurrence of pre-diabetes. In terms of research design, cross-sectional and observational studies were considered and those published in English in the time range of 2010 to 2021 were included in the review. We excluded preprint articles, meta-analytical and systematic reviews, case reports, non-English articles, articles without pertinent data, non-research articles, and articles without full-text availability. In case of duplicate reports from the same study (Díaz-Redondo et al., 2015; Ehrhart et al., 2017), the study which had evaluated the higher number of modifiable and non-modifiable risk factors was included in this review (Díaz-Redondo et al., 2015).

Data extraction and synthesis
Two authors carried out data extraction based on study objectives, design, participants, risk factors, measurement methods, and main findings. A narrative synthesis was done considering the significant levels of the study results. Disagreement between the two reviewers was referred to a third author for final decision. 

Quality assessment
The quality of included studies was assessed by using the National Institute of Health (NIH) Quality assessment tool for observational, cohort and cross-sectional studies National Heart, Lung, and Blood Institute (NHLBI). Several reviews have previously used this tool to critically appraise the quality of reviewed studies (Bayfield et al., 2018; Uloko et al., 2018). The related checklist has 14 questions relating to the research question, selection of study subjects, statistical analysis and measurement and selection of timeframe between exposure and outcome to see an effect. The quality of studies was graded as good (G) if the overall score was at least 70%, fair (F) if the article score was at least 50%, and poor (P) if it was less than 50%. (Uloko et al., 2018). We only included studies of Fair and Good quality.

3. Results
The initial search strategy identified 3982 potentially relevant articles, of which 849 remained after removing duplicates and other reasons (Figure 1).

After abstract and title screening, 815 studies were excluded because the sample used did not include adolescents. A total of 34 articles remained for full text screening, of which 10 articles were included in this review that met the pre-defined eligibility criteria. 


Study characteristics
The major characteristics of 10 studies were summarized in chronological order in Table 2.

The studies included in this review came from a number of countries in various parts of the world, including the US (n=4), and one study each from Qatar, Ecuador, Indonesia, India, China, and UAE. The number of subjects who participated in the reviewed studies ranged from 131 to 125,375 adolescent. All the studies utilized cross-sectional design except one study which used retrospective study design (Propst et al., 2015). 

Risk of bias
The quality of included studies was evaluated by using appraisal tool for observational cross-sectional studies (AXIS) (Young & Solomon, 2009). This assessment tool has 20 questions and sections to record answers as “yes”, “no” or “don’t know” (Table 3).

Based on the result of risk of bias assessment, all of the studies included were in the moderate to low risk of bias category.


Pre-diabetes reports
Assessment of prediabetes in the participants of included studies was carried out using Fasting Blood Glucose (Al Amiri et al., 2015; Casapulla et al., 2017; Mamtani et al., 2014; Propst et al., 2015; Nagarajan et al., 2017), glycated hemoglobin (HbA1c) (Al Amiri et al., 2015; Casapulla et al., 2017), fasting plasma glucose (Ratliff et al., 2019; Sylvia & Ardiyani, 2019; Taranikanti, Khan & Tabassum, 2014; Luo et al., 2020) and sweetened beverage (SSB) intake (Duke, 2021). 
The standard criteria for prediabetes based on the measurement results of fasting blood glucose is 5.5 mmol/L (Mamtani et al., 2014). The standard percentage of HbA1c is 5.7% to 6.4% (Luo et al., 2020; Propst et al., 2015), 5.7% (39 mmol/L) (Nagarajan et al., 2017). While indications of prediabetes through Fasting plasma glucose (FPG) examination are 100-125mg/dl, and/or 2-h plasma glucose from OGTT at 140-199mg/dl (Sylvia & Ardiyani, 2019), 140 to 126 mg/dl (Taranikanti et al., 2014), 100 mg/dl and less than 125 mg/dl (Luo et al., 2020). One study did not report standard criteria for prediabetes (Casapulla et al., 2017).

Modifiable factors
Modifiable factors included in the reviewed studies were BMI, smoking, physical inactivity, poor diet, and cardio-metabolic factors. Some studies analyzed the physical activity of the participants which was considered to have a relationship with prediabetes. Daily exercise, when compared with less than daily exercise, had a significant beneficial effect on several key anthropometric factors such as weight (P=0.003), BMI z –score (P=0.02), waist-to-height ratio (P=0.0003), waist z-score (P=0.0004) and obesity (P=0.003) (Mamtani et al., 2014). In Indonesia, students who had physical activity<3 times a week were 1.9 times at risk of prediabetes than students who engaged in physical activity ≥3 times in a week. Although, the risk is not proven statistically (P>0.05) (Sylvia & Ardiyani, 2019). A study in US showed that adolescents with vigorous physical activity had a significant and negative odds of prediabetes/diabetes (OR=0.74) (Nagarajan et al., 2017). In UEA, no significant correlation was found between glycemic status and level of physical activity, presence of acanthosis nigricans or symptoms of diabetes, except for weight loss (P=0.032) (Al Amiri et al., 2015). In Duke’s study, it was found that for each additional day of being physically active for at least 60 min, the odds of student reported prediabetes was significantly decreased, by approximately 7%-8% (OR: 0.93-OR: 0.92) (Duke, 2021).
A study in the United States by Ratliff and colleagues concluded that consuming 1 serving/d of either low-calorie sweetened beverage (LCSB) or water is not associated with an increased risk for prediabetes (Ratliff et al., 2019). Another study found that households with high levels of healthy food access had a lower prevalence of prediabetes or diabetes in adolescents compared to those with low healthy food availability (4.05% vs 3.69%, P=0.08). However, the prevalence of adolescent prediabetes or diabetes was not significantly lower in households with high vs low supermarket spending proportion (3.77 vs 3.97%, p=0.46). The association of higher healthy food availability showed negative odds of adolescent prediabetes/diabetes (OR=0.78) (Nagarajan et al., 2017). Luo et al., in their study of analyzing the urine of adolescents found that urinary organophosphate esters (OPEs)  metabolites were associated with prediabetes and indices of glucose homeostasis (Luo et al., 2020). Another study on adolescent consumption patterns stated that increasing frequency of SSB intake was significantly associated with the odds of youth reported diagnosis of prediabetes (Duke, 2021).
Duke and colleagues in the United States reported that for each additional hour of sleep, the odds of reported prediabetes were significantly reduced, by approximately 14%-15% (OR:0.86—OR:0.85) (Duke, 2021).
Cardiometabolic factors such as obesity, hypertension, and dyslipidemia were also reported in several studies included in this review. Mamtani et al. reported that an elevated waist-to-height ratio were each independently predictive of prediabetes (P=0.03) (Mamtani et al., 2014). Meanwhile, a study in Indonesia reported that thinner students have 0.9 times at risk of having high impaired glucose tolerance levels compared to students with normal BMI (95% CI:0.3-2.4). Meanwhile, obese students had a 2.2 times higher risk of developing prediabetes than students with normal BMI (95% CI:0.7-6.8). However, it was not proven statistically (P>0.05) (Sylvia & Ardiyani, 2019). Studies in Ecuador also prove that waist circumference, BMI, as well as systolic blood pressure (SBP) and diastolic blood pressure (DBP) significantly affect the condition of prediabetes (Casapulla et al., 2017). In Alabama, morbidly obese adolescents had a higher prevalence of pre-diabetes (19.5% of obese versus 27.3% of morbidly obese; P<0.0001) and type 2 diabetes (39.8% of obese versus 52.4% of morbidly obese; P<0.0001) (Propst et al., 2015). A study in India proved that there is no significant correlation between FPG and BMI or waist to hip ratio (Taranikanti et al., 2014). Al Amiri and colleagues stated that high levels of triglycerides were independent risk factors for abnormal glycemic testing (P<0.05). Abnormal blood glucose testing was 2.28 times more common among students with high levels of triglycerides (Al Amiri et al., 2015). In another study in the US, BMI exhibited the strongest association with student reports of prediabetes diagnosis (OR:4.62-OR:4.67). BMI of students in 85% of cases was associated with more than four times the probability of prediabetes diagnosis (Duke, 2021). 

Non-modifiable factors
Non-modifiable factors included in the reviewed studies were gender, race, and family history. Male gender, and a positive family history of diabetes were each independently predictive of prediabetes (Mamtani et al., 2014). The study in Indonesia found that gender and a history of DM affect the occurrence of prediabetes in students in all four public high schools. The obtained value was 0.130, which means that these variables can explain the incidence of prediabetes in high school students (Sylvia & Ardiyani, 2019). In Ecuador, gender and location were significant predictors of the six cardiometabolic risk factors such as HbA1c, TC, HDL, TG, LDL and VLDL (P=0.001) (Casapulla et al., 2017). In their analysis, Nagarajan and colleagues found that fully adjusted models for age, sex, and race had a significant and negative odds of adolescent prediabetes/diabetes (OR=0.74) (Nagarajan et al., 2017). Another study found no significant correlation between prediabetic status and family history of DM (Taranikanti et al., 2014). In the study of Liu and colleagues, the unadjusted prevalence of pre-diabetes was higher among boys than  girls (P<0.001), lower among non-Hispanic blacks than non-Hispanic whites (P<0.05) and Mexican Americans (P<0.05) (Liu et al., 2012). In UEA study, family history of T2D in first-degree relatives (OR 1.87 P=0.028) was statistically correlated with abnormal glycemic testing based on fasting blood glucose and 2-h post-prandial blood glucose (OGTT) (Al Amiri et al., 2015). 

4. Discussion
In this review, we found convergence of findings from retrospective and cross-sectional studies, demonstrating physical inactivity, poor diet, cardio-metabolic factors, and unhealthy lifestyles such as smoking habit as a modifiable risk factor. Meanwhile, gender, race, and family history of T2D presented as a non-modifiable risk factors. Between the two categories, modifiable risk factors have a strong relationship with the incidence of prediabetes reported by adolescents in various countries in the studies included in this review.

Modifiable risk factors of prediabetes in adolescents
Smoking was a significant risk factor of prediabetes in one study (Sylvia & Ardiyani, 2019). Previous studies show that smoking is a significant predictor of dyslipidemia, large waist circumference, and high insulin resistance (Awadalla et al., 2018; Yu et al., 2014). Pronounced frequency of smoking among men makes them more prone to its adverse metabolic effect as compared to women (Díaz-Redondo et al., 2015; Song et al., 2016). A prior experimental study have shown that cigarette smoking directly decreased insulin action and increased insulin resistance (Bergman et al., 2012). The higher scores on the Fagerström questionnaire-a well validated tool of nicotine dependence-were strongly associated with pre-diabetes, even after exclusion of cumulative smoking exposure from the score. These data suggest that the nicotinergic system and nicotine dependence may play a role in smoking related hyperglycemia, which is in line with a small experimental study showing that the long-term use of nicotine containing chewing gums was associated with insulin resistance and hyperinsulinemia (Haj Mouhamed et al., 2016; Yu et al., 2014). Furthermore, the majority of inhaled nicotine is catabolized into cotinine byanenzymatic activity, which is mediated by Cytochrome P450 2A6 (CYP2A6). Previous studies have shown that heavy smokers with a slow or poor metabolizer genotype were more susceptible to develop type 2 diabetes (T2D) compared to heavy smokers with a fast metabolizer genotype (Liu et al., 2012; Tanner et al., 2017; Wu et al., 2020). 
In vitro studies have shown that nicotine in the presence of palmitate enhanced the production of reactive oxygen species and impaired glucose uptake in skeletal myocytes (Bhattacharjee et al., 2016; Wang et al., 2022; Yang et al., 2015). Fetal and neonatal nicotine exposure in rats resulted in dysglycaemia, pancreatic beta cell dysfunction, increased apoptosis, and loss of beta cell mass (Clair et al., 2015; Xia et al., 2019).
According to the analysis of the reviewed studies, it seems that physical activity is one of the strongest predictors related to the occurrence of prediabetes among adolescents (Al Amiri et al., 2015; Duke, 2021; Mamtani et al., 2014; Nagarajan et al., 2017). Daily exercise, when compared with less than daily exercise, has a significant beneficial effect on several key anthropometric factors such as weight, BMI z–score, waist-to-height ratio, waist z-score and obesity which will directly affect metabolic conditions of glucose and insulin production (Kawasaki et al., 2018; Peirson et al., 2015). Individuals who lack activity tend to be obese. Obesity conditions will trigger an increase in insulin resistance in the muscles so that blood glucose cannot be used optimally by muscles as energy (Kahn & Jeffrey, 2000; Venkatasamy et al., 2013; Wondmkun, 2020).
In the studies included in this review, poor diet patterns were mainly related to low-calorie sweetened beverages, the sugar-sweetened drinks, and energy-dense snacks such as salty snacks (Duke, 2021; Nagarajan et al., 2017; Ratliff et al., 2019). Consumption of some carbohydrate-containing beverages is linked to increased caloric intake at the expense of providing nutritive value; thus, jeopardizing consumption of appropriate ratios of carbohydrate, protein, and fat, which raises concerns about risks to healthy growth and development (Khanferyan et al., 2018). Further, drinking fruit juice and juice drinks, sports and energy drinks has been linked to excess caloric intake and inappropriate weight gain (Larson et al., 2015). Sugar sweetened beverages disrupt metabolism via multiple mechanisms, including increasing caloric load devoid of nutrients with associated weight gain and increasing the risk for the development of obesity, increasing glycemic load resulting in insulin resistance and pancreatic beta cell dysfunction, contributing to a rise in inflammatory biomarkers signaling oxidative stress, and promoting accumulation of visceral fat tissue relating to liver lipogenesis (Malik & Hu, 2022; Schiano et al., 2021; Uloko et al., 2018). 
Studies included in this review have pointed to cardio-metabolic factors including overweight and obesity as predictors of prediabetes (Casapulla et al., 2017; Propost et al., 2015; Taranikanti et al., 2014). The relationship between cardio-metabolic factors and the occurrence of pre-diabetes has been described in previous studies, and obesity has been mainly measured through BMI. Studies have also been conducted to analyze the relationship between BMI and prediabetes with the occurrence of diabetes (Pandey et al., 2017). Hyperglycemia has been associated with high levels of triglycerides. Improving glycemic control in individuals with moderate to severe hyperglycemia regardless of type of treatment is associated with improvement in lipid values (Arthur et al., 2016).
Non-modifiable risk factors of prediabetes in adolescents
The studies included in this review showed that there were different reports of prediabetes in various races. Nagarajan and colleagues in their study in the US tried to analyze the relationship between race and the incidence of prediabetes in adolescents, where African Americans had both lower healthy food availability and lower monthly super market spending levels, which corresponded to their higher mean HbA1C levels and prevalence of prediabetes and diabetes. However, non-Hispanic black adolescents are known to have higher HbA1C values than Mexican Americans and non-Hispanic whites; despite higher HbA1C levels in black and Hispanic adolescents compared to whites, there is no increased risk of hyperglycemia (Nagarajan et al., 2017). 
A study in Qatar found that male gender is a major factor for the occurrence of prediabetes in adolescent (Mamtani et al., 2014). The same result was also obtained by Nagarajan and colleagues, where prediabetes was generally found in males compared to females (Nagarajan et al., 2017). This effect can be explained by differences in sex hormones among men and women (Kautzky-Willer et al., 2016). Estrogen generally reduces TG and LDL circulating levels, while increases HDL-C level thus, resulting in inherited cardioprotective effect in women as compared to men (Shahwan et al., 2019). The difference in body fat distribution may also explain the discrepancy in the association of dyslipidemia in prediabetic men and women. Women metabolically inclined to store fat in subcutaneous tissues rather than in abdominal region. Men, in contrast tend to store fat preferentially in visceral tissue in abdominal region. High proportion of fat as visceral adipose tissue is generally known as a significant predictor of dyslipidemia (Chang et al., 2018; Link & Reue, 2017) thus, portray male gender more prone to dyslipidemia as compared to women.
Family history of T2D also has major contribution in the risk of prediabetes in adolescent as presented in the results of the included studies (Al amiri et al., 2015;  Mamtani et al., 2014; Taranikanti et al., 2014). A previous study found that family history was more associated with a perception of risk for type 2 diabetes than was genetic risk testing (Wu et al., 2017). Other cases may show no association between family history and risk of prediabetes. What can be explained is the possibility of the age of the parents who are still under 40 years old, where the manifestations of DM may not be clearly appears (Taranikanti et al., 2014). 
Considering the prominent role of modifiable risk factors in the occurrence of prediabetes in adolescents, it seems necessary to include lifestyle modification programs for this age group. It is also important to pay attention to gender as a non-modifiable factor in the development of prediabetes in adolescents.

5. Conclusion 
Based on a total of 10 studies reviewed, the association between risk factors and adolescents’ prediabetes appeared to be strongest among modifiable factors such as physical activity, poor diet, smoking, and cardiometabolic factors. Several non-modifiable risk factors also support the severity of prediabetes among adolescents. The most significant modifiable factor was BMI. For non-modifiable factors, the most significant was gender. This review provides a definite picture of the importance of controlling weight as a major factor in the incidence of prediabetes in adolescents, especially by gender.  Conducting systematic reviews focusing on the impact of interventional studies on the control of prediabetes in adolescents as well as studies on genetic modification of the incidence of prediabetes and diabetes are suggested. 

Limitations
Although a comprehensive literature search was conducted, some articles relevant to this review might have missed out. The focus of this review is limited and only grouped relevant data for adolescent in both modifiable and non-modifiable risk factors. Data was not pooled for meta-analysis owing to the heterogeneity in study design, exposure evaluated, and study outcome that limits the possibility to fully compare review findings. No attempts were made to contact the author to obtain full papers of eligible abstracts that could not be found as free full text on the web search. Finally, the search was limited to English language.

Ethical Considerations
Compliance with ethical guidelines
The authors of this article have all provided valuable help in conducting this study and have approved the final version. “Guest authorship” and “ghost authorship” were avoided. All duplicate evidence was removed. Data were extracted independently by two of the authors and discrepancies were resolved. Decisions on which data to include are also agreed upon by all authors.

Funding
This research did not receive any grant from funding agencies in the public, commercial, or non-profit sectors.  


Authors' contributions
Structured review questions, compiling the manuscript, literature search, screening: Arvida Bar; Literature search, extracting, screening: Agusriani; Literature search, extracting, screening: Halimahtussa’ diyah.

Conflict of interest
The authors declare no conflict of interests.

Acknowledgments
We would like to express our gratitude to the chief of Poltekkes Kemenkes Jambi for the support in preparing the study.


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