Although most normal-weight adults with BMI between 18.5 and 25 kg/m² are seemingly healthy, a significant number of them may be affected by undiagnosed metabolic disorders such as insulin resistance, prediabetes, type 2 diabetes (T2D), and nonalcoholic fatty liver disease (NAFLD) (1). These individuals are classified as Normal-Weight Obese (NWO) because they usually have a high body fat mass but a normal BMI (2). Because of their increased risk of cardiometabolic morbidity and mortality, there is a growing interest in this group of subjects (2–4). The global prevalence of NWO ranges from 4.5 to 22% due to the wide variation in body fat percent cut-offs used to diagnose excess body fat in different populations (5). The exact etiology of NWO is unclear, but genetics, diet, and physical activity have all been associated with the condition. Compared to normal-weight lean (NWL) subjects with a normal BMI and body fat amount, the NOW subjects present changes in body composition, inflammation, and oxidative stress (2).

Screening for prediabetes and diabetes is recommended in overweight or obese adults (6). However, given the prevalence of NWO, these disorders may go undiagnosed in individuals with a seemingly normal weight. Prediabetes is a subclinical high-risk state that could lead to diabetes and conventional diabetes complications (7). Prediabetes is associated with the concomitant presence of insulin resistance and β-cell dysfunction, instigated before detectable glucose modifications (8). Prediabetes is defined as having a Hb1A_c level between 5.7 and 6.4% (39 and 47 mmol/mol), a fasting glucose concentration between 100 and 125 mg/dL (5.6 and 6.9 mmol/L), or a 2 h oral glucose tolerance test between 140 and 200 mg/dL (7.8–11.0 mmol/L) (6). Prediabetes affected 7.5% of the global population in 2019, and this figure is expected to rise to 8.6% by 2045 if no prompt actions are taken (Saeedi, 2019 #5).

According to previous prospective studies, the annualized conversion rate from prediabetes to diabetes is between 5 and 10% (8, 9). Additionally, persons with prediabetes have a 6-year risk of T2D at a rate of 33–65%, compared to 5% of those with normoglycemia (10).

Reports from the Middle East region show that, like T2D, prediabetes is highly prevalent in the region’s nations, with rates ranging from 20 to 40% (11, 12). Fortunately, many people with prediabetes can revert to normoglycemia and prevent the progression to T2D in response to sustained lifestyle changes and/or medication (13–16). Hence, identifying convenient clinical markers that can efficiently diagnose prediabetes would benefit from closer monitoring and early intervention to prevent T2D onset.

To date, the gold standard test for prediabetes diagnosis is the oral glucose tolerance test (OGTT), fasting plasma glucose (FPG), and HbA_1c levels (11, 12). However, several studies have shown discordance between HbA_1c and glycemia (17–21). This discordance could have a significant impact on clinical practice. Consequently, there is a need for improved and more reliable diagnostic tools for prediabetes and diabetes. Numerous obesity indices (22)such as waist circumference (WC) (23), waist-to-height ratio (WHR) (24), Visceral Adiposity Index (VAI) (12) and lipid accumulation product (LAP) (12, 22) have had their potential in predicting diabetes. However, many promising surrogate indices are being studied for predicting diabetes and performed better than the traditional markers identified hereinabove. These markers include the triglyceride glucose (TyG)-related parameters (TyG), TyG–Body mass index (TyG-BMI), TyG-WC, and TyG-WHTR (25–30). Some epidemiological studies also targeted a few surrogate indices in predicting prediabetes. Wen et al. found that TyG performed better as an indicator for prediabetes than the conventional markers in the Chinese Elderly (31). TyG also scored higher as a predictive index for prediabetes among reproductive-age women (32). Furthermore, the TyG index was useful as a surrogate tool to estimate dysglycemia in obese adolescents (33, 34).

To the best of our knowledge, no previous study has investigated the association of triglyceride glucose (TyG)-related indices with prediabetes in a population from the Middle East. Consequently, this cross-sectional study sought to investigate the association of TyG-glucose-related parameters with prediabetes, especially in normal-weight individuals, and evaluate their superiority over conventional dysglycemia risk factors.

The current study aimed to compare how well obesity and TyG-related indices might detect prediabetes in Qatari people who were normal-weight (NW). In NW men, TyG-WHTR had the highest predictive value compared to all other and most obesity indices in women. Furthermore, TyG-WHTR performed similarly to the conventionally adapted FPG index in normal-weight men and women, as well as in obese men, but was superior to FPG in obese women. These results suggest using the TyG-WHTR index as a potential predictor of prediabetes for general clinical usage in normal-weight men. When coupled with FPG results, TyG-WHTR can further determine prediabetes predisposition in both men and women. In addition, our results enabled us to determine optimal cut-off points of WC and WHTR for identifying prediabetes in normal-weight Qatari adults; WC ≥ 79.5 cm and WHTR ≥ 0.47 for men, and WC ≥ 71.5 cm and WHTR ≥ 0.45 for women.

According to the World Health Organization, the WC cut-off point for various metabolic disorders varies by ethnicity (40). Although, the present data facilitated the identification of the WC and WHTR cut-off points needed for predicting prediabetes risk in a Middle Eastern population, a person’s height can affect the predictive ability of WC (36). As a result, adopting the WHTR marker outweighed BMI and WC in predicting certain metabolic diseases, including diabetes (26, 41, 42). In line with these findings, our results revealed that the association of WHTR with prediabetes was stronger than WC among the normal-weight adult population.

Furthermore, WHTR had higher specificity in normal-weight men and women than WC and higher sensitivity in women. This result suggests that WHTR may have a better predictive ability than WC and may be used to screen for prediabetes. Although these anthropometric parameters performed poorly than the TyG-WHTR index, as indicated by the ROC curve analyses, their predictive value was equivalent to LAP, VAI, and the other TyG-related indices. These findings point to the inclusion of these predictors collectively to improve the accuracy of prediabetes diagnosis over anthropometric indices alone.

Obesity increases the risk of numerous chronic disease, such as T2D, metabolic syndrome, hypertension, dyslipidemia, hyperinsulinemia, coronary artery disease, cardiovascular disease, osteoarthritis, chronic kidney disease, and numerous cancers. It is also linked to non-alcoholic steatohepatitis, sleep apnea, depression and other psychiatric disorders. Studies have also shown that obesity may have an impact on cognitive function and that a higher BMI may increase the chance of dementia or other cognitive impairments in later life (43).

Obesity and being overweight are known diabetes and prediabetes risk factors. However, these disorders can affect people with lower BMIs as well. The fasting triglyceride and glucose parameters have been proposed as alternative surrogate markers for identifying insulin resistance and diabetes (22, 27–30). Our study supports this finding, and all TyG-related parameters predict individuals with prediabetes (AUC > 0.5). On a large scale, when the AUC is equal to 1, it indicates faultless predictive power, and an AUC ≤ 0.55 means that the predictive power of a parameter is not better than chance (44). TyG-BMI showed the highest OR for prediabetes occurrence in normal-weight men. However, as determined by the AUC, its predictive value was not the highest compared to the other indices. The TyG-WHTR had the highest AUC value in men and women and was selected as the primary index for predicting prediabetes. The cut-off points indicated sensitivity and specificity values between 66 and 76% for both sexes, thus reducing false-positive and false-negative cases. TyG-WHTR proved significantly different from all obesity-related parameters in men and was significantly higher than the remaining TyG-related indices. It also had the highest AUC and was significantly different from all other indices in the obese population (Supplementary Tables S1, S2). Therefore, TyG-WHTR could potentially be adopted for identifying prediabetes in normal-weight and Ow/Ob men. Many TyG-related parameters were assessed for the predictive value of prediabetes and diabetes. However, no evidence has been published regarding the relationship between TyG-WHTR and prediabetes in normal-weight individuals. However, in recently published studies, TyG-WHTR exceeded the commonly used anthropometric markers in predicting diabetes (45, 46).

The TyG-WHTR index’s capacity to detect prediabetes was contrasted with FPG’s to determine whether it could be a useful screening tool. The results demonstrated that the predictive abilities were all significantly improved. These results indicate that the TyG-WHTR index can act similarly to FPG. Hence, TyG-WHTR can, alongside FPG, function in screening individuals for prediabetes. Knowing that advanced age poses an additional risk for prediabetes (11), further age stratification of the participants demonstrated that TyG-WHTR proved higher predictive probability with advanced age in normal-weight men and obese men and women, highlighting the efficiency of TyG-WHTR in predicting prediabetes.

TyG-WC was previously reported as the best predictor of prediabetes or diabetes (47). TyG-BMI was also suggested as the best index for detecting prediabetes in adults of either sex (25). Further, TyG was suggested as a good index for predicting insulin resistance and prediabetes (29, 31). We have found the TyG-WHTR to be the most effective index for prediabetes prediction. These disparities could be attributed to the ethnic diversity of the populations studied. Nonetheless, the overall conclusion from our findings and previously published data is that triglyceride-glucose (TyG)-related parameters outperform obesity parameters alone. The clinical significance of TyG-WHTR rests in its ability to identify persons at risk of developing prediabetes before symptoms appear. By recognizing these patients early, healthcare providers can take steps to prevent or delay illness onset through lifestyle changes like diet and exercise, or medication interventions if necessary. Furthermore, TyG-WHTR can be used to assess the efficacy of programs targeted at lowering the risk of prediabetes in people of normal weight. A patient, for example, may begin an exercise and diet regimen in order to reduce their waist circumference and improve their glucose and lipid levels. Their TyG-WHTR score can be tracked over time to evaluate the intervention’s effectiveness and make any necessary modifications.

In some cases, the TyG-WHTR may be considered superior to fasting glucose or HbA1c because: (1) fasting glucose and HbA1c may only show abnormalities after significant metabolic damage has occurred, whereas TyG-WHTR can detect early metabolic changes when interventions are more likely to be effective. (2) TyG-WHTR ca be a better predictor of prediabetes than fasting glucose or HbA1c. This is due to the fact that TyG-WHTR considers both triglyceride and glucose levels, which are both independent risk factors for dysglycemia. (3) TyG-WHTR may be more responsive to metabolic state changes than fasting glucose or HbA1c. For example, if a patient improves their diet and exercise habits, their TyG-WHTR score may improve even if their fasting glucose or HbA1c levels stay unchanged. (4) TyG-WHTR is a straightforward computation that involves only basic laboratory tests and measures found in most healthcare facilities. HbA1c testing, on the other hand, might be more expensive, and not all healthcare settings have the necessary equipment or competence. One of the strengths of our study is the large sample size. Indeed, according to the Qatar Planning and Statistics Authority, the population of Qatar at the end of April 2022 was 2,773,598 people (accessed on 22nd of May, 2022),² with Qataris accounting for approximately 12% of the total (i.e., 333,000 individuals). Moreover, in 2015, individuals under 19 made up 47% of all Qatari nationals,³ and if this percentage did not change in 2022, approximately 176,500 individuals would be adults and would be eligible for our study. As a result, our study (6,000/176,500 = 0.03) has statistically significant power. Additionally, the data we used was obtained from a well-phenotyped cohort from the general population. Furthermore, our study is the first to compare the ability of obesity indices and TyG-related parameters to identify prediabetes in normal-weight individuals in a Middle Eastern population. It is worth noting that T2D is a major public health burden in the Middle East, and early detection of prediabetes in obese and normal-weight individuals is thus critical for implementing strategies to prevent its progression to T2D.

Given the shared environmental factors and lifestyle habits, as well as genetic background and ethnicity among many Middle Eastern countries, particularly the Gulf Cooperation Council nations (Qatar, Bahrain, Saudi Arabia, United Arab Emirates, Kuwait, and Oman), our findings may perform similarly in many of these countries.

The main limitation of our study is the cross-sectional design, which does not allow the use of the findings to predict future prediabetes. However, the QBB has recently started to call back the participants for a 5-year follow-up, which will open new avenues for assessing the predictive ability of the different indices longitudinally. We also did not adjust for parameters such as smoking status, medication, or physical activity. Finally, the findings of the present study may not be generalizable to all populations due to the ethnic and geographic characteristics of the study population.

Based on our results, factoring in waist-to-height ratio with simple biochemical measurements of triglyceride and glucose proved to be the best indicator of prediabetes in normal-weight, overweight and obese men, in addition to outperforming most obesity indices in women and having similar predictive effects to FPG in both normal-weight and overweight/obese men and women. We suggest that TyG-WHTR be used in clinical practice as part of routine check-ups as an asserting indicator to FPG for predicting prediabetes in men. Further studies are warranted to confirm the predictive value of these parameters across varying ethnicities.

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. Clinical, anthropometric, demographic and genetic data can be obtained from the Qatar biobank according to the applied rules.

The institutional review board approved the current project at the Qatar Biomedical Research Institute (IRB number: 2017–001) and QBB (IRB number: Ex‐2018‐Res‐ACC‐0123‐0067). All participants gave written informed consent for their data and biospecimens to be used in medical research. The patients/participants provided their written informed consent to participate in this study.

NA, EH, and AA performed the statistical analysis. NA and AA interpreted the results and wrote the manuscript. HB revised the statistical analysis. AA conceived and designed the study, was the guarantor of this work, had full access to all the data in the study, and took responsibility for the data’s integrity and the data’s accuracy. All authors reviewed the results, edited the manuscript, and approved the final version.

This project was funded by intermural grants from Qatar the Biomedical Research Institute to AA and from the Qatar Computing Research Institute to HB.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.