The Body mass index-glucose Index as a New Tool for Early Detection of the Risk of Dysglycemia in Patients with Hypertension and Obstructive Sleep Apnea

The Body mass index-glucose Index as a New Tool for Early Detection of the Risk of Dysglycemia in Patients with Hypertension and Obstructive Sleep Apnea | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Body mass index-glucose Index as a New Tool for Early Detection of the Risk of Dysglycemia in Patients with Hypertension and Obstructive Sleep Apnea Wenbo Yang, Xintian Cai, Mulalibieke Heizhati, Qing Zhu, Xiaoguang Yao, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6446894/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Mar, 2026 Read the published version in BMC Endocrine Disorders → Version 1 posted 12 You are reading this latest preprint version Abstract Purpose: Currently, there is a lack of early biomarkers to identify the risk of dysglycemia in patients with concurrent hypertension and obstructive sleep apnea (OSA). The aim of our study is to evaluate the efficacy of the recently proposed Body Mass Index (BMI)-Glucose (ByG) index in identifying the risk of dysglycemia in patients with hypertension and OSA. Methods: A retrospective cohort study of 1579 adults with hypertension and OSA from the Urumqi Research on Sleep Apnea and Hypertension study (UROSAH) was conducted. Cox proportional hazards models were used to assess the associations between the ByG index and new-onset dysglycemia, diabetes, and prediabetes. Time-dependent receiver operating characteristic (ROC) curves to compare the efficacy of the ByG index with traditional insulin resistance indicators. Results: During a median follow-up of 7.25 years, 212 cases of dysglycemia (157 diabetes, 55 prediabetes) were identified. Participants in the highest ByG tertile had a significantly increased risk of dysglycemia (HR 3.07; 95% CI: 2.03–4.67), diabetes (HR 3.34; 95% CI: 2.01–5.57), and prediabetes (HR 2.60; 95% CI: 1.24–5.43) compared to the lowest tertile, after full adjustment. Time-dependent ROC showed the ByG index was more discriminative in predicting dysglycemia (including diabetes and prediabetes) events at 3, 5 and 7 years compared to BMI, TyG and TyG-BMI indices. Conclusion: The ByG index demonstrates a significant association with the risk of new-onset dysglycemia, encompassing both diabetes and prediabetes, in patients with hypertension and OSA. This straightforward tool can facilitate the early identification of high-risk individuals and provide individualized dysglycemia prevention. Trial registration : Not applicable. Hypertension Obstructive sleep apnea Dysglycemia Body mass index–glucose index Retrospective cohort Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Diabetes affects approximately 530 million adults worldwide with a prevalence of 10.5% in people aged 20–79 years, posing a significant burden on global health [ 1 – 3 ] . Remarkably, hypertension and obstructive sleep apnoea (OSA) significantly increase the risk of developing diabetes [ 4 – 6 ] . Patients with OSA have a 1.4 times higher risk of developing diabetes, whereas those with hypertension have a 1.61 times higher risk [ 7 , 8 ] . The prevalence of diabetes is notably higher among patients with both hypertension and OSA. Additionally, these individuals face a greater burden of cardiovascular disease after developing diabetes 9–11 . Therefore, to optimise diabetes management strategies, it is essential to find reliable and easy-to-use biomarkers for early identification of the risk of dysglycemia in this high-risk population. Nonetheless, few studies focus on predicting the risk of dysglycemia in patients with both hypertension and OSA, and there are currently no simple and convenient biomarkers available for the early identification of people at high risk of dysglycemia. Insulin resistance is widely regarded as an early detection indicator of the development of dysglycemia and also is considered to be one of the main pathological mechanisms underlying the development of dysglycemia in patients with hypertension and OSA [ 12 – 14 ] . The hyperinsulin glucose clamp is the gold standard for detecting insulin resistance [ 15 ] ; however, its complexity, time consumption, and invasiveness limit its widespread use. Recently, a new, simple, and promising indicator for evaluating insulin resistance has recently been proposed and is known as Body mass index (BMI)-glucose (ByG) index [ 16 ] . Defined as Ln [1/2 BMI (kg/m²) × fasting plasma glucose (FPG) (mg/dL)], the ByG index demonstrated good predictive value in the general population and outperformed traditional markers like BMI, triglyceride glucose (TyG) and triglyceride glucose-BMI (TyG-BMI) in predicting diabetes risk [ 16 ] . Given its simplicity, convenience, cost-effectiveness, and predictive value, coupled with the lack of early identification indicators for dysglycemia in patients with OSA and hypertension, it is imperative to study the relationship between these factors. Therefore, this study utilized data from the Urumqi Research on Sleep Apnea and Hypertension study (UROSAH) to explore the association between the ByG index and dysglycemia, including diabetes and prediabetes, in individuals with hypertension and OSA. In addition, we also compared the diagnostic capabilities of the ByG index with traditional classic indicators BMI, TyG, and TYG-BMI to identify dysglycemia at different time points. Materials and Methods Study Population This study used data from the UROSAH cohort, which has been previously detailed [ 17] . Briefly, the UROSAH cohort was a single-center observational study conducted at the Hypertension Center of Xinjiang Uygur Autonomous Region People's Hospital, a provincial tertiary care hospital. The aim of the UROSAH study was to investigate the association between OSA and long-term cardiovascular outcomes in patients with hypertension. This retrospective cohort study enrolled 3605 consecutive hypertensive patients with suspected OSA. In this analysis, 744 participants were excluded because their apnea-hypopnea index (AHI) was less than 5 events per hour, as measured by polysomnography. Moreover, 826 individuals were excluded based on the presence of diabetes or prediabetes at baseline, or due to missing baseline values for FPG or BMI. A total of 1,759 participants were included in this study (Figure 1). Ethical Approval Approval for this research was granted by the Ethics Committee at the People's Hospital in the Xinjiang Uygur Autonomous Region (reference: 2019030662). The research followed the ethical principles specified in the Declaration of Helsinki. Consent was obtained from every participant, ensuring that the collection and use of data were conducted ethically throughout the duration of the study. Data Collection Experienced researchers systematically collected baseline data including participant demographics, medical history, and laboratory findings. Height and weight were measured using standardized protocols. BMI was using the equation: body weight in kilograms divided by the square of height in meters (kg/m²). Skilled professionals recorded blood pressure measurements using standardized methods referenced in previous studies [ 17] . Healthcare professionals compiled each participant's medical history including demographic details, lifestyle factors, medication use, and previous health conditions. Laboratory analysis encompassed a range of blood tests, including FPG, low-density lipoprotein, triglyceride, high-density lipoprotein (HDL), total cholesterol, aspartate aminotransferase， alanine aminotransferase, and serum creatinine. These tests were conducted on peripheral venous blood samples collected after a 12-hour fasting period. The glomerular filtration rate was determined through the application of the Chronic Kidney Disease Epidemiology Collaboration-derived equations [ 18] . Additionally, all participants underwent overnight polysomnography in a controlled laboratory environment, following the detailed protocols provided in the Supplemental material. Definitions at baseline The ByG index was determined through the equation ByG = Ln [1/2 × BMI (kg/m²) × FPG (mg/dL)] [ 16] . TyG = Ln [(FPG (mg/ dL)/2) × triglyceride (mg/dL)]. TyG-BMI = BMI × TyG [ 16] . In alignment with the Chinese health industry standard WS/T 428–2013, obesity was classified as a BMI of 28 kg/m² or higher, overweight was defined as a BMI ranging from 24 to 28 kg/m². Hypertension was identified based on the 2010 Chinese Hypertension Prevention and Treatment Guidelines, which specify a resting blood pressure of 140/90 mmHg or higher or the current use of antihypertensive medications. OSA diagnosis was established when the AHI exceeded five events per hour. The severity of OSA was categorized as mild for an AHI between 5 and less than 15 events per hour, moderate for an AHI of 15 to less than 30 events per hour, and severe for an AHI exceeding 30 events per hour [ 19] . Adherence to regular continuous positive airway pressure (CPAP) therapy was defined as using CPAP for more than 70% of nights and at least four hours per night during the follow-up period [ 20,21] . Smoking and alcohol consumption were grouped into two categories: "Current" for individuals who currently smoke or consume alcohol or who ceased within the past year, and "Never or Former" for those who have never engaged in these habits or who discontinued them more than one year prior. Follow-up and outcome A comprehensive follow-up process was implemented, encompassing outpatient visits, inpatient medical records, and telephone interviews. Participants were monitored for the development of dysglycemia, a term encompassing both diabetes and prediabetes, until the conclusion of the follow-up in January 2021. Diabetes was diagnosed when fasting plasma glucose levels were equal to or greater than 7.0 mmol/L and/or 2-hour plasma glucose levels reached or exceeded 11.1 mmol/L during a oral glucose tolerance test, or if an individual was utilizing antidiabetic medications [ 22] . Prediabetes was defined as two conditions: impaired fasting glucose and impaired glucose tolerance. Impaired fasting glucose was defined as an FPG level between 6.1 and 6.9 mmol/L, with a 2-hour postprandial glucose level of less than 7.8 mmol/L. Impaired glucose tolerance was defined as a 2-hour postprandial glucose level between 7.8 and 11.0 mmol/L 22 . All events were verified using medical records and confirmed by the clinical event committee in accordance with the protocols detailed in prior studies [ 17,23] . Statistical Analysis Descriptive analyses were performed to characterize the dataset, with continuous variables presented as mean values accompanied by their standard deviations, while categorical variables were reported as both frequency counts and corresponding percentages. Comparative analyses of participant characteristics across ByG tertiles were conducted using appropriate statistical tests, including the one-way analysis of variance, Fisher's exact test, Kruskal-Wallis test, and chi-square tests. Visualize the unadjusted cumulative risk using Kaplan-Meier analysis and determine significance using the Log-rank test. Multicollinearity among predictor variables was assessed through variance inflation factor calculations, with variables exhibiting variance inflation factor values greater than 5 being excluded from subsequent analyses (Supplemental Table 1). To evaluate the association between ByG (analyzed both continuously and by tertiles) and the new-onset of dysglycemic, a Cox proportional hazards regression model was implemented, providing hazard ratios (HR) with corresponding 95% confidence intervals (CI) for diabetes, prediabetes, and overall dysglycemia outcomes. Three analytical models were constructed: The initial model (Model 1) incorporated demographic factors including sex and age. The subsequent model (Model 2) extended the first model by incorporating alcohol consumption, smoking status, diastolic and systolic blood pressure, and coronary heart disease and stroke history. The most comprehensive model (Model 3) further augmented Model 2 by integrating biochemical markers (aminotransferase levels, estimated glomerular filtration rate, HDL, and triglycerides), pharmacological interventions (angiotensin receptor blockers, ACE inhibitors, diuretics, beta-blockers, calcium channel blockers, and statins), sleep-related parameters (AHI, nadir oxygen saturation, mean oxygen saturation), and therapeutic interventions (continuous positive airway pressure treatment). To assess potential trends, the median values of each tertile were assigned to participants and analyzed as continuous variables within the Cox proportional hazards regression framework. In order to explore the potential for nonlinear associations between the ByG index and dysglycemia, diabetes and prediabetes, we performed restricted cubic spline analyses using Cox regression models. These analyses were conducted after the correction of all confounders in model 3. A range of three to seven node configurations were evaluated, and the configuration yielding the lowest Akaike Information Criterion value was selected for the final analysis. For dysglycemia and diabetes, four nodes were positioned at the 5th, 35th, 65th, and 95th percentiles, while for prediabetes, five nodes were placed at the 5th, 28th, 50th, 72nd, and 95th percentiles. Restricted cubic spline analysis identified an inflection point that segmented the ByG index into two parts, thus allowing the modeling of distinct association patterns between the ByG index and outcomes using segmented Cox regression. In addition, to compare the efficacy of the ByG index with traditional classic insulin resistance indicators (including BMI, TyG, and TyG-BMI) for diagnosing dysglycemia, diabetes, and prediabetes at different time points, we constructed time-dependent receiver operating characteristic (ROC) curves and calculated the area under the ROC curve (AUC). Multiple sensitivity analyses were performed to verify the robustness of our findings. Initially, individuals who were current smokers or drinkers were excluded to assess the impact of residual confounding factors. Secondly, participants who received regular OSA treatment were excluded to evaluate the possible treatment-related confounding factors. Thirdly, a one-year lag analysis was conducted, which excluded patients who experienced dysglycemia during the first year of follow-up. Fourthly, we separately excluded individuals using diuretics and those with a history of stroke at baseline due to the potential effects of diuretics on glucose metabolism and the baseline differences in stroke rates between the groups. Stratified and interaction analyses were also carried out based on several key factors: age (<45 or ≥45 years), gender, drinking status, smoking status, AHI (<15, 15-30, or ≥30 events/h), BMI (<28 or ≥28 kg/m²), diastolic blood pressure (DBP) (<90 or ≥90 mmHg), systolic blood pressure (SBP) (<140 or ≥140 mmHg), angiotensin-converting enzyme inhibitors(ACEIs) /angiotensin receptor blockers (ARBs) use, and statin use. All statistical tests were two-sided, with a significance threshold of P < 0.005. Analyses were carried out using the statistical software R version 4.2.2. Results Baseline characteristics The baseline analysis involved 1,759 participants, 69.0% of whom were male, with an average age of 48.79 years. Participants were stratified into three groups based on the ByG index: tertile 1 (≤7.01), tertile 2 (7.01 < ByG ≤ 7.16), and tertile 3 (≥7.16). Participants in the highest ByG tertile were generally younger, had a lower incidence of baseline stroke, and had a higher likelihood of being male. Additionally, this group showed a higher prevalence of obesity and an increased likelihood of alcohol consumption. Clinically, participants in the high ByG group exhibited slightly elevated levels of aminotransferase. As expected, triglyceride and FPG levels rose with higher ByG tertiles, HDL levels significantly decreased. Regarding medication use, the prevalence of ACEI and CCB was higher in the high ByG group, while statin use was consistent across all groups. Additionally, participants with a higher ByG index exhibited more severe OSA, indicated by higher AHI values and lower nocturnal minimum and mean oxygen saturation levels. Association of ByG index with the risk of new-onset dysglycemia, diabetes, and prediabetes Over a median follow-up period of 7.25 years, that equates to 11,662.1 person-years, 212 new cases of dysglycemia were observed, included 157 cases of diabetes (8.93%) and 55 cases of prediabetes (3.13%). The analysis of cumulative risk curves indicated that participants with higher ByG indices experienced a significant increase in the incidence of dysglycemia, diabetes, and prediabetes (log-rank P < 0.001; Figure 2A-C). Table 2 presents the relationship between ByG levels and the development of dysglycemia, diabetes, and prediabetes. When ByG was analyzed as a continuous variable, each 1 SD increase in ByG was associated with a 53% elevated risk of incident dysglycemia (95% CI: 1.32–1.78), a 62% higher risk of incident diabetes (95% CI: 1.35–1.92), and a 35% increased risk of incident prediabetes (95% CI: 1.01–1.82). In the tertile-based analysis, participants in the highest tertile exhibited significantly higher risks of dysglycemia (HR: 3.07; 95% CI: 2.03–4.67), diabetes (HR: 3.34; 95% CI: 2.01–5.55), and prediabetes (HR: 2.60; 95% CI: 1.24–5.43) compared to those in the lowest tertile, after adjusting for potential confounders (Model 3). The middle tertile also demonstrated a significantly elevated risk for these outcomes relative to the lowest tertile. Furthermore, a trend test indicated a dose-dependent increase in risk across the tertile groups. Figure 3 shows that the restricted cubic spline analysis revealed significant non-linear relationship between the ByG index and the risks of dysglycemia (Figure 3A) and prediabetes (Figure 3C) (P-nonlinear < 0.001). In contrast, the risk of diabetes showed a linear relationship (Figure 3B, P-nonlinear = 0.239). Significant changes in the risk of dysglycemia and prediabetes were observed when the ByG index was approximately 7.04. Segmented Cox regression analysis at this inflection point showed that the risk of dysglycemia was lower for the ByG index below 7.04, yet significantly elevated at 7.04 and above (Supplemental Table 2). However, the risk of prediabetes remained statistically unchanged before and after the inflection point (Supplemental Table 3). Comparison of the ByG Index and traditional indicators for early prediction capability Time-dependent receiver operating characteristic curve analyses showed that the ByG index had superior discriminatory ability to predict dysglycemia events (new-onset diabetes, prediabetes) at 3, 5, and 7 years compared to the BMI, TyG, and TyG-BMI indices, with higher AUC values at all of the above time points (Supplementary Figure 1-3). Sensitivity Analysis Results remained consistent after sensitivity analyses excluding current smokers (Supplementary Table 4) and alcohol drinkers (Supplementary Table 5). Excluding patients receiving OSA therapy (Supplemental Table 6), participants who developed dysglycemia (both diabetic and prediabetic) within the first year of follow-up (Supplemental Table 7), those using diuretics (Supplemental Table 8), and those with a history of stroke (Supplemental Table 9) did not show significant changes, suggesting that the study results were robust. Subgroup Analysis Subgroup analysis was conducted to further explore the association between ByG index and the incidence of new-onset dysglycemia (Figure 4A), diabetes (Figure 4B), and prediabetes (Figure 4C). The stratification variables included gender, age, BMI, blood pressure parameters (SBP and DBP), AHI, smoking status, drinking status, and use of statins and ACEIs/ARBs. The results showed that the association between ByG index and dysglycemia was consistent across various subgroups (Supplemental Tables 10-12). Notably, individuals with DBP greater than 90 mmHg exhibited a significantly higher risk of developing diabetes. Additionally, participants who consumed alcohol had an elevated risk of progressing to prediabetes. Discussion The present retrospective cohort study found a significant association between the ByG index and the risk of new-onset dysglycemia (including diabetes and prediabetes) in patients with hypertension and OSA. Furthermore, the ByG index demonstrated superior early predictive capability for dysglycemia (including diabetes and prediabetes) compared to traditional predictive indicators. This simple and practical tool aids in the early identification of high-risk populations and provides personalized preventive measures for glycemic abnormalities. Hypertension and OSA tend to coexist, with a prevalence of OSA ranging from 30% to 50% in hypertensive patients [ 24] and a prevalence of hypertension ranging from 59% to 67% in patients with OSA [ 25] . This unique subgroup, characterized by the intertwining of both conditions and a high prevalence rate, presents a complex metabolic foundation that significantly increases the risk of glucose abnormalities [ 7,8,26,27] . However, limited research has addressed the early identification of abnormal glucose metabolism risk in this population. A previous study indicated that the non-dipping blood pressure pattern during 24-hour ambulatory blood pressure monitoring is a risk factor for the onset of diabetes in patients with hypertension and OSA, providing a novel approach for the management of diabetes [ 23] . However, due to the complexity of diabetes and the multitude of risk factors, relying solely on changes in nighttime blood pressure patterns may not effectively identify high-risk subgroups of diabetes. Moreover, the cost-effectiveness of the widespread application of ambulatory blood pressure monitoring hinders its utility and dissemination for risk assessment. Our research findings demonstrate a strong association between the ByG index, which integrates BMI and blood glucose, and the risk of new-onset of dysglycemia (including diabetes and prediabetes), highlighting the effectiveness of ByG as an early risk predictor. This offers a practical, cost-effective, and widely applicable method to identify individuals at high risk for dysglycemia with both hypertension and OSA, facilitating the development of personalized prevention and management strategies. The ByG index was initially validated for its association with diabetes risk in a Japanese community-based cohort [ 16] , and no further studies have been conducted to reveal its association with abnormal glucose metabolism. The present study investigated the association between the index and new-onset dysglycemia in hypertensive patients with OSA, confirming the strong association between the index and new-onset diabetes, and further finding its efficacy in identifying the risk of prediabetes. Our study broadens the application of the ByG index for predicting diabetes in various populations and stages. We also uncovered a non-linear correlation between the ByG index and the new-onset of prediabetes and dysglycemia. Once the ByG index surpassed 7.04, the risk of dysglycemia escalated significantly. Intriguingly, we observed a counterintuitive reduction in the risk of prediabetes when the ByG index exceeded 7.27. This phenomenon may be ascribed to the temporal progression from prediabetes to diabetes, with a heightened risk of diabetes development associated with prolonged follow-up and higher ByG index values. This trend may be reversed in a sufficiently large sample size. After excluding drinkers, our sensitivity analysis revealed a weak association between the ByG index and abnormal blood glucose levels. Subgroup analyses indicated that individuals who consumed alcohol and those with DBP > 90 mmHg had a significantly elevated risk of dysglycemia, particularly among prediabetics. Previous epidemiological studies have highlighted a link between alcohol consumption and the onset of diabetes, and alcoholism is generally recognized as a risk factor for new-onset diabetes [ 28,29] . Additionally, the established association between hypertension and glucose metabolism disorders further supported these findings [ 30] . These results underscore the potential of the ByG index in identifying high-risk subgroups that could benefit from intervention strategies aimed at reducing blood pressure or abstaining from alcohol. In time-dependent ROC curve analysis, while AUC values exhibit restricted discriminative capacity at the individual level, the superior performance of the ByG index in comparison to traditional indicators underscores its promise as a predictive instrument. The development of dysglycemia involves a complex interplay of genetic and environmental factors, rendering precise early-stage diagnosis challenging for a singular index [ 31,32] . Meanwhile, our multivariate adjusted Cox proportional hazards regression analysis indicates that the ByG index has the ability to independently predict the risk of dysglycemia, highlighting its potential as a personalized risk assessment method and its clinical applicability. The efficacy of ByG metrics in detecting the risk of dysglycemia may be attributable to the interplay between hypertension, obstructive sleep apnoea (OSA), and obesity [ 30,33] . Obesity is commonly associated with OSA in individuals with hypertension, resulting in endothelial dysfunction and increased sympathetic activity that impede glucose uptake [ 27] . Intermittent nocturnal hypoxia and oxidative stress exacerbate metabolic stress and impair insulin signal-ling [ 34] . The integration of BMI and fasting glucose levels in the ByG metric has been shown to summarise the synergistic metabolic effects of obesity, hypertension, and OSA [ 12,33,35,36] . As expected, this index is valuable for early prediction of the risk of glucose abnormalities in this high-risk population. In this study, the long-term follow-up with clearly defined cohorts enhanced the credibility of the results. Comprehensive baseline clinical and laboratory data were beneficial for adjusting potential confounding factors, thereby improving the robustness of the findings. Nevertheless, this study has several limitations. The retrospective design is susceptible to inherent biases such as selection bias, which may constrain the generalizability of the findings beyond specific hypertensive OSA risk cohorts. Despite adjusting for numerous confounders, unmeasured variables (e.g., dietary patterns, physical activity levels, and genetic predisposition) could still impact the observed correlations, warranting consideration in future investigations. Furthermore, the reliance on a single center in this study may constrain the external validity of the outcomes, future studies should include multiple centers to enhance the external validity of the findings. Conclusion The ByG index is an independent predictor of dysglycemia, encompassing diabetes and prediabetes, among hypertensive individuals with OSA. As a simple, accessible, and reliable indicator, it will help to develop personalised diabetes prevention strategies for this high-risk group. Abbreviations ACEIs: angiotensin-converting enzyme inhibitors；AHI: apnea-hypopnea index; ARBs: angiotensin receptor blockers; BMI: Body mass index; ByG: Body mass index -glucose; CCBs: calcium channel blockers；CI: confidence intervals; CPAP：continuous positive airway pressure; DBP: diastolic blood pressure; FPG: Fasting plasma glucose; HDL: high-density lipoprotein；OSA: Obstructive sleep apnea (OSA); SaO2：oxygen saturation, SBP: systolic blood pressure; SD: standard deviation; TyG: triglyceride glucose; TyG-BMI: triglyceride glucose-body mass index; UROSAH: Urumqi Research on Sleep Apnea and Hypertension; Declarations Ethics approval and consent to participate Xinjiang Uygur Autonomous Region People's Hospital's Ethics Committee approved this study (reference: 2019030662), and written informed consent was provided by all study participants. Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Funding This research was supported by the Tianshan Talent Training Program - Science and Technology Innovation Team [grant number 2023TSYCTD0016], and the People's Hospital of Xinjiang Uygur Autonomous Region Level Funded Projects [project number 20210101]. The funding sources had no involvement in study design, data collection, analysis, interpretation, report writing, or the decision to submit the article for publication. Conflict of Interest The authors declare that they have no competing interests. Author contributions WY: Conceptualization, Data Curation, Formal Analysis, Writing – original draft. XC, MH, QZ, XY, WW, DS, JH, JH: Data Curation, Formal Analysis. NL: Conceptualization, Project Administration, Supervision, Data Curation. All authors contributed to the article and approved the submitted version. 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The Burden of Obesity on Diabetes in the United States: Medical Expenditure Panel Survey, 2008 to 2012. Value Health. 2017;20(1):77–84. Ryan S. Adipose tissue inflammation by intermittent hypoxia: mechanistic link between obstructive sleep apnoea and metabolic dysfunction. J Physiol. 2017;595(8):2423–30. Bell JA, Kivimaki M, Hamer M. Metabolically healthy obesity and risk of incident type 2 diabetes: a meta-analysis of prospective cohort studies. Obes Rev. 2014;15(6):504–15. He L, Zheng W, Li Z, Chen L, Kong W, Zeng T. J-shape relationship between normal fasting plasma glucose and risk of type 2 diabetes in the general population: results from two cohort studies. J Transl Med. 2023;21(1):175. Tables Table 1. Baseline characteristics of participants by ByG index tertiles. Characteristics Overall ByG Tertile 1 ByG Tertile 2 ByG Tertile 3 P-value (<7.01) (≥7.01 to <7.16) (≥7.16) Participants, n (%) 1759 586 587 586 Demographic characteristics Age, years 48.79 ± 10.68 49.95 ± 11.05 48.83 ± 10.63 47.58 ± 10.24 <0.001 Male, n (%) 1214 (69.0%) 368 (62.8%) 426 (72.6%) 420 (71.7%) <0.001 Current smokers, n (%) 769 (43.7%) 237 (40.4%) 275 (46.8%) 257 (43.9%) 0.086 Current drinkers, n (%) 709 (40.3%) 200 (34.1%) 254 (43.3%) 255 (43.5%) <0.001 BMI, kg/m 2 28.06 ± 3.71 25.17 ± 2.50 27.99± 2.51 31.01 ± 3.42 0.002 SBP, mmHg 139.56 ± 19.75 139.61 ± 19.77 138.58 ± 19.28 140.48 ± 20.19 0.257 DBP, mmHg 92.01 ± 14.01 91.52 ± 13.91 91.51 ± 14.00 93.00 ± 14.11 0.113 Baseline CHD, n (%) 176(10.0%) 51(8.7%) 63(10.7%) 62(10.0%) 0.435 Baseline Stroke, n (%) 365(20.8%) 138(23.5%) 131(22.3%) 96(16.4%) 0.005 Clinical laboratory measurements AST, U/L 22.26 ± 15.57 22.84 ± 23.29 21.11± 8.02 22.84 ± 11.06 0.093 ALT, U/L 27.86 ± 20.17 25.98 ± 23.01 26.32± 16.42 31.26 ± 20.84 <0.001 eGFR, ml/min/1.73 m 2 95.59 ± 20.89 94.81 ± 20.48 95.49 ± 21.32 96.47 ± 20.86 0.394 TC, mmol/L 4.52 ± 1.11 4.52 ± 1.19 4.47 ± 1.05 4.58 ± 1.10 0.260 TG, mmol/L 2.02 ± 1.41 1.82 ± 1.33 2.03 ± 1.39 2.20 ± 1.50 <0.001 HDL-C, mmol/L 1.12 ± 0.30 1.19 ± 0.32 1.10 ± 0.27 1.07 ± 0.27 <0.001 LDL-C, mmol/L 2.66 ± 0.78 2.65 ± 0.81 2.63 ± 0.77 2.68 ± 0.76 0.323 FPG, mmol/L 4.80 ± 0.62 4.35 ± 0.50 4.79 ± 0.40 5.27 ± 0.58 <0.001 ByG 7.08 ± 0.19 6.88 ± 0.14 7.09 ± 0.04 7.28 ± 0.10 <0.001 Prescribed medication, n (%) ACEIs/ARBs users, n (%) 843 (47.9%) 252 (43.0%) 281 (47.9%) 310 (52.9%) 0.003 β-blockers users, n (%) 171 (9.7%) 52 (8.9%) 62 (10.6%) 57 (9.7%) 0.621 CCBs users, n (%) 1285 (73.1%) 395 (67.4%) 437 (74.4%) 453 (77.3%) <0.001 Diuretics users, n (%) 303 (17.2%) 94 (16.0%) 105 (17.9%) 104 (17.7%) 0.648 Statins users, n (%) 942 (53.6%) 313 (53.4%) 324 (55.2%) 305 (52.0%) 0.806 PSG parameters AHI, events/h 23.48 ± 18.11 20.05 ± 15.23 23.21 ± 17.12 27.18 ± 20.83 <0.001 Moderate-severe OSA, n (%) 1062（60.4%） 317 (51.1%) 357 (60.8%) 388 (66.2%) <0.001 Nadir SaO 2 , % 78.25 ± 8.69 79.97 ± 7.45 78.64 ± 7.60 76.16 ± 10.29 <0.001 Mean SaO 2 , % 91.97 ± 3.37 92.59 ± 2.60 91.85 ± 4.17 91.46 ± 3.05 <0.001 Regular CPAP treatment, n (%) 39 (2.2%) 8 (1.4%) 15 (2.6%) 16 (2.7%) 0.225 Values of continuous variables are expressed as medians (twenty-fifth percentile - seventy-fifth percentile) or means (standard deviation). Categorical variables are expressed as no. (%). ALT, alanine aminotransferase; AST, aspartate aminotransferase; BMI, body mass index; DBP, diastolic blood pressure; FPG, fasting plasma glucose; SBP, systolic blood pressure; HDL-C, high-density lipoprotein cholesterol; CHD, coronary cardiovascular disease; eGFR, estimated glomerular filtration rate; LDL-C, lowdensity lipoprotein cholesterol; TC, total cholesterol; AHI, apnea hypopnea index; TG, triglyceride; ACEIs, angiotensin-converting enzyme inhibitors; ARBs, angiotensin receptor blockers; CCBs, calcium channel blockers; PSG, polysomnography; OSA,obstructive sleep apnea; CPAP, continuous positive airway pressure; Sao 2 , oxygen saturation; ByG,body mass index - glucose index Table 2. Hazard ratios (95% CI) of dysglycemia, diabetes, and prediabetes stratified by ByG index. Exposure Model 1 Model 2 Model 3 (HR, 95% CI) P-value (HR, 95% CI) P-value (HR, 95% CI) P-value Dysglycemia Per SD increment 1.58 (1.38, 1.81) <0.001 1.62 (1.42, 1.86) <0.001 1.53 (1.32, 1.78) <0.001 ByG Tertiles T1 Reference Reference Reference T2 1.79 (1.17, 2.74) 0.007 1.83 (1.20, 2.79) 0.005 1.63 (1.04, 2.54) 0.032 T3 3.40 (2.31, 4.99) <0.001 3.62 (2.46, 5.32) <0.001 3.07 (2.03, 4.67) <0.001 P for trend <0.001 <0.001 <0.001 Diabetes Per SD increment 1.64 (1.40, 1.92) <0.001 1.68 (1.43, 1.96) <0.001 1.62 (1.35, 1.92) <0.001 ByG Tertiles T1 Reference Reference Reference T2 2.13 (1.29, 3.52) 0.003 2.15 (1.31, 3.55) 0.003 2.00 (1.18, 3.41) 0.011 T3 3.66 (2.20, 5.82) <0.001 3.77 (2.36, 6.00) <0.001 3.34 (2.01, 5.55) <0.001 P for trend <0.001 <0.001 <0.001 Prediabetes Per SD increment 1.42 (1.08, 1.85) 0.012 1.49 (1.14, 1.94) 0.003 1.35 (1.01, 1.82) 0.046 ByG Tertiles T1 Reference Reference Reference T2 1.10 (0.48, 2.49) 0.827 1.14 (0.50, 2.59) 0.758 0.94 (0.40, 2.20) 0.880 T3 2.86 (1.43, 5.70) 0.003 3.30 (1.65, 6.63) <0.001 2.60 (1.24, 5.43) 0.011 P for trend <0.001 <0.001 0.005 Model 1: adjusted for age and sex. Model 2: adjusted for variables in model 1 plus drinking status, baseline CHD, baseline stroke, smoking status, DBP, and SBP. Model 3: adjusted for variables in model 2 plus ALT, eGFR, TG, HDL-C, ACEIs/ARBs, β-Blockers, CCBs, diuretics, statins, AHI, nadir SaO 2 , mean SaO 2 , and regular CPAP treatment. HR, hazard ratio; CI, confidence interval. Other abbreviations appear in Table 1. Additional Declarations No competing interests reported. Supplementary Files SupplementalMaterial.docx Cite Share Download PDF Status: Published Journal Publication published 19 Mar, 2026 Read the published version in BMC Endocrine Disorders → Version 1 posted Editorial decision: Revision requested 25 Sep, 2025 Reviews received at journal 18 Jul, 2025 Reviewers agreed at journal 18 Jul, 2025 Reviews received at journal 01 Jul, 2025 Reviewers agreed at journal 15 Jun, 2025 Reviews received at journal 16 May, 2025 Reviewers agreed at journal 16 May, 2025 Reviewers invited by journal 14 May, 2025 Editor invited by journal 17 Apr, 2025 Editor assigned by journal 17 Apr, 2025 Submission checks completed at journal 17 Apr, 2025 First submitted to journal 14 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Center of People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi","correspondingAuthor":false,"prefix":"","firstName":"Xintian","middleName":"","lastName":"Cai","suffix":""},{"id":456657103,"identity":"751574d5-767e-4545-ac1d-3433a9c777e3","order_by":2,"name":"Mulalibieke Heizhati","email":"","orcid":"","institution":"Hypertension Center of People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi","correspondingAuthor":false,"prefix":"","firstName":"Mulalibieke","middleName":"","lastName":"Heizhati","suffix":""},{"id":456657104,"identity":"55e48907-66a5-4a52-bccd-89790902a9cc","order_by":3,"name":"Qing Zhu","email":"","orcid":"","institution":"Hypertension Center of People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi","correspondingAuthor":false,"prefix":"","firstName":"Qing","middleName":"","lastName":"Zhu","suffix":""},{"id":456657105,"identity":"461eb54e-e519-4c68-bc8c-32ddc5e993f8","order_by":4,"name":"Xiaoguang 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Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIiWNgGAWjYBACPhBRAcT8zIyNDz4wSBDWwgYizgCxZHtzs+EMkrQYnDneJsxDjMPYpJufPThQc8eu4UZiG7PNH4s8/gbmh49u4NMic8zc4MCxZ8mNMxLbHue2SRRLHGAzNs7Bp0UiwUz6A9vhZGaJxHbj3AaJxIYDPGzS+LWkf5M48O9wMptEYpu0xR+JxPmEteSYSRxsO2zHw3OwTRrITdxAhJYyiYN9hxMk2BubDXvbJBI3HibgF36J9G0SB74dtrc/zP7wwY8/dYnzjjc/fIxPCwwkNsCZzEQoBwF7ItWNglEwCkbBSAQALs1PMpI7qVIAAAAASUVORK5CYII=","orcid":"","institution":"Hypertension Center of People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi","correspondingAuthor":true,"prefix":"","firstName":"Nanfang","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-04-14 14:23:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6446894/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6446894/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12902-026-02226-w","type":"published","date":"2026-03-19T15:59:35+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":83028682,"identity":"04d65251-d0f1-4013-b6f7-59e06d68ea0d","added_by":"auto","created_at":"2025-05-19 08:47:38","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1590687,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of selected participants\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6446894/v1/7ba4d0c22646f881fa88fcea.jpg"},{"id":83030091,"identity":"59c303d3-419a-4c0d-b95d-de1d805206e9","added_by":"auto","created_at":"2025-05-19 08:55:38","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2087561,"visible":true,"origin":"","legend":"\u003cp\u003eCumulative hazard curves for new-onset of dysglycemia (2A), diabetes (2B), and prediabetes (2C), all stratified by ByG.\u003c/p\u003e","description":"","filename":"Figure2A.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6446894/v1/b9daee7972ffe6d8d7a8460d.jpg"},{"id":83028683,"identity":"2fd4c3f5-75ac-4150-ab85-b8215ecb66da","added_by":"auto","created_at":"2025-05-19 08:47:38","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1739090,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between ByG and dysglycemia (2A), diabetes (2B), and prediabetes (2C) using the restricted cubic spline function.\u003c/p\u003e\n\u003cp\u003eThe Y-axis represents the HR for survival based on any ByG value compared to individuals with the reference value (50th percentile) of ByG. The cox regression was adjusted for age, sex, drinking status, baseline IHD, baseline stroke, smoking status, DBP, SBP, ALT, eGFR, TG, HDL-C, ACEIs/ARBs, β-Blockers, CCBs, diuretics, statins, AHI, nadir SaO2, mean SaO2, and regular CPAP treatment.\u003c/p\u003e","description":"","filename":"Figure3A.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6446894/v1/04e8066f84935440b48b579f.jpg"},{"id":83028684,"identity":"6d610b2f-fc4d-47fe-a45e-582c0147344c","added_by":"auto","created_at":"2025-05-19 08:47:38","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2873255,"visible":true,"origin":"","legend":"\u003cp\u003eAssociations of ByG with the risk of new-onset (A) dysglycemia , (B) diabetes, and (C) prediabetes in different subgroups.\u003c/p\u003e","description":"","filename":"Figure4A.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6446894/v1/0a646d6a8f3c7cc2ffc85d28.jpg"},{"id":105224798,"identity":"29440296-b4b3-4889-9c50-50bb022f7f8e","added_by":"auto","created_at":"2026-03-23 16:16:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9250243,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6446894/v1/74a0590b-2054-47fe-a189-1bb304aaab85.pdf"},{"id":83028690,"identity":"28e8450a-0e67-4bd5-8f71-6d8f53dc9879","added_by":"auto","created_at":"2025-05-19 08:47:38","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":538590,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-6446894/v1/2b2f30d21b7389b552d2f874.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Body mass index-glucose Index as a New Tool for Early Detection of the Risk of Dysglycemia in Patients with Hypertension and Obstructive Sleep Apnea","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDiabetes affects approximately 530\u0026nbsp;million adults worldwide with a prevalence of 10.5% in people aged 20\u0026ndash;79 years, posing a significant burden on global health\u003csup\u003e[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Remarkably, hypertension and obstructive sleep apnoea (OSA) significantly increase the risk of developing diabetes\u003csup\u003e[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Patients with OSA have a 1.4 times higher risk of developing diabetes, whereas those with hypertension have a 1.61 times higher risk\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. The prevalence of diabetes is notably higher among patients with both hypertension and OSA. Additionally, these individuals face a greater burden of cardiovascular disease after developing diabetes \u003csup\u003e9\u0026ndash;11\u003c/sup\u003e. Therefore, to optimise diabetes management strategies, it is essential to find reliable and easy-to-use biomarkers for early identification of the risk of dysglycemia in this high-risk population. Nonetheless, few studies focus on predicting the risk of dysglycemia in patients with both hypertension and OSA, and there are currently no simple and convenient biomarkers available for the early identification of people at high risk of dysglycemia.\u003c/p\u003e \u003cp\u003eInsulin resistance is widely regarded as an early detection indicator of the development of dysglycemia and also is considered to be one of the main pathological mechanisms underlying the development of dysglycemia in patients with hypertension and OSA\u003csup\u003e[\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. The hyperinsulin glucose clamp is the gold standard for detecting insulin resistance\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e; however, its complexity, time consumption, and invasiveness limit its widespread use. Recently, a new, simple, and promising indicator for evaluating insulin resistance has recently been proposed and is known as Body mass index (BMI)-glucose (ByG) index\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Defined as Ln [1/2 BMI (kg/m\u0026sup2;) \u0026times; fasting plasma glucose (FPG) (mg/dL)], the ByG index demonstrated good predictive value in the general population and outperformed traditional markers like BMI, triglyceride glucose (TyG) and triglyceride glucose-BMI (TyG-BMI) in predicting diabetes risk\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Given its simplicity, convenience, cost-effectiveness, and predictive value, coupled with the lack of early identification indicators for dysglycemia in patients with OSA and hypertension, it is imperative to study the relationship between these factors.\u003c/p\u003e \u003cp\u003eTherefore, this study utilized data from the Urumqi Research on Sleep Apnea and Hypertension study (UROSAH) to explore the association between the ByG index and dysglycemia, including diabetes and prediabetes, in individuals with hypertension and OSA. In addition, we also compared the diagnostic capabilities of the ByG index with traditional classic indicators BMI, TyG, and TYG-BMI to identify dysglycemia at different time points.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStudy Population\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study used data from the UROSAH cohort, which has been previously detailed\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e17]\u003c/sup\u003e. Briefly, the UROSAH cohort was a single-center observational study conducted at the Hypertension Center of Xinjiang Uygur Autonomous Region People\u0026apos;s Hospital, a provincial tertiary care hospital. The aim of the UROSAH study was to investigate the association between OSA and long-term cardiovascular outcomes in patients with hypertension. This retrospective cohort study enrolled 3605 consecutive hypertensive patients with suspected OSA. In this analysis, 744 participants were excluded because their apnea-hypopnea index (AHI) was less than 5 events per hour, as measured by polysomnography. Moreover, 826 individuals were excluded based on the presence of diabetes or prediabetes at baseline, or due to missing baseline values for FPG or BMI. A total of 1,759 participants were included in this study (Figure 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthical Approval\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eApproval for this research was granted by the Ethics Committee at the People\u0026apos;s Hospital in the Xinjiang Uygur Autonomous Region (reference: 2019030662). The research followed the ethical principles specified in the Declaration of Helsinki. Consent was obtained from every participant, ensuring that the collection and use of data were conducted ethically throughout the duration of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eData Collection\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExperienced researchers systematically collected baseline data including participant demographics, medical history, and laboratory findings. Height and weight were measured using standardized protocols. BMI was using the equation: body weight in kilograms divided by the square of height in meters (kg/m\u0026sup2;). Skilled professionals recorded blood pressure measurements using standardized methods referenced in previous studies\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e17]\u003c/sup\u003e. Healthcare professionals compiled each participant\u0026apos;s medical history including demographic details, lifestyle factors, medication use, and previous health conditions.\u003c/p\u003e\n\u003cp\u003eLaboratory analysis encompassed a range of blood tests, including FPG, low-density lipoprotein, triglyceride, high-density lipoprotein (HDL), total cholesterol, aspartate aminotransferase， alanine aminotransferase, and serum creatinine. These tests were conducted on peripheral venous blood samples collected after a 12-hour fasting period. The glomerular filtration rate was determined through the application of the Chronic Kidney Disease Epidemiology Collaboration-derived equations\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e18]\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAdditionally, all participants underwent overnight polysomnography in a controlled laboratory environment, following the detailed protocols provided in the Supplemental material.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDefinitions at baseline\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ByG index was determined through the equation ByG = Ln [1/2 \u0026times; BMI (kg/m\u0026sup2;) \u0026times; FPG (mg/dL)]\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e16]\u003c/sup\u003e. TyG = Ln [(FPG (mg/ dL)/2) \u0026times; triglyceride (mg/dL)]. TyG-BMI = BMI \u0026times; TyG\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e16]\u003c/sup\u003e. In alignment with the Chinese health industry standard WS/T 428\u0026ndash;2013, obesity was classified as a BMI of 28 kg/m\u0026sup2; or higher, overweight was defined as a BMI ranging from 24 to 28 kg/m\u0026sup2;. Hypertension was identified based on the 2010 Chinese Hypertension Prevention and Treatment Guidelines, which specify a resting blood pressure of 140/90 mmHg or higher or the current use of antihypertensive medications. OSA diagnosis was established when the AHI exceeded five events per hour. The severity of OSA was categorized as mild for an AHI between 5 and less than 15 events per hour, moderate for an AHI of 15 to less than 30 events per hour, and severe for an AHI exceeding 30 events per hour\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e19]\u003c/sup\u003e. Adherence to regular continuous positive airway pressure (CPAP) therapy was defined as using CPAP for more than 70% of nights and at least four hours per night during the follow-up period\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e20,21]\u003c/sup\u003e. Smoking and alcohol consumption were grouped into two categories: \u0026quot;Current\u0026quot; for individuals who currently smoke or consume alcohol or who ceased within the past year, and \u0026quot;Never or Former\u0026quot; for those who have never engaged in these habits or who discontinued them more than one year prior.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFollow-up and outcome\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA comprehensive follow-up process was implemented, encompassing outpatient visits, inpatient medical records, and telephone interviews. Participants were monitored for the development of dysglycemia, a term encompassing both diabetes and prediabetes, until the conclusion of the follow-up in January 2021. Diabetes was diagnosed when fasting plasma glucose levels were equal to or greater than 7.0 mmol/L and/or 2-hour plasma glucose levels reached or exceeded 11.1 mmol/L during a oral glucose tolerance test, or if an individual was utilizing antidiabetic medications\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e22]\u003c/sup\u003e. Prediabetes was defined as two conditions: impaired fasting glucose and impaired glucose tolerance. Impaired fasting glucose was defined as an FPG level between 6.1 and 6.9 mmol/L, with a 2-hour postprandial glucose level of less than 7.8 mmol/L. Impaired glucose tolerance was defined as a 2-hour postprandial glucose level between 7.8 and 11.0 mmol/L\u0026nbsp;\u003csup\u003e22\u003c/sup\u003e. All events were verified using medical records and confirmed by the clinical event committee in accordance with the protocols detailed in prior studies\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e17,23]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStatistical Analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDescriptive analyses were performed to characterize the dataset, with continuous variables presented as mean values accompanied by their standard deviations, while categorical variables were reported as both frequency counts and corresponding percentages. Comparative analyses of participant characteristics across ByG tertiles were conducted using appropriate statistical tests, including the one-way analysis of variance, Fisher\u0026apos;s exact test, Kruskal-Wallis test, and chi-square tests.\u003c/p\u003e\n\u003cp\u003eVisualize the unadjusted cumulative risk using Kaplan-Meier analysis and determine significance using the Log-rank test. Multicollinearity among predictor variables was assessed through variance inflation factor calculations, with variables exhibiting variance inflation factor values greater than 5 being excluded from subsequent analyses (Supplemental Table 1). To evaluate the association between ByG (analyzed both continuously and by tertiles) and the new-onset of dysglycemic, a Cox proportional hazards regression model was implemented, providing hazard ratios (HR) with corresponding 95% confidence intervals (CI) for diabetes, prediabetes, and overall dysglycemia outcomes. Three analytical models were constructed: The initial model (Model 1) incorporated demographic factors including sex and age. The subsequent model (Model 2) extended the first model by incorporating alcohol consumption, smoking status, diastolic and systolic blood pressure, and coronary heart disease and stroke history. The most comprehensive model (Model 3) further augmented Model 2 by integrating biochemical markers (aminotransferase levels, estimated glomerular filtration rate, HDL, and triglycerides), pharmacological interventions (angiotensin receptor blockers, ACE inhibitors, diuretics, beta-blockers, calcium channel blockers, and statins), sleep-related parameters (AHI, nadir oxygen saturation, mean oxygen saturation), and therapeutic interventions (continuous positive airway pressure treatment). To assess potential trends, the median values of each tertile were assigned to participants and analyzed as continuous variables within the Cox proportional hazards regression framework.\u003c/p\u003e\n\u003cp\u003eIn order to explore the potential for nonlinear associations between the ByG index and dysglycemia, diabetes and prediabetes, we performed restricted cubic spline analyses using Cox regression models. These analyses were conducted after the correction of all confounders in model 3. A range of three to seven node configurations were evaluated, and the configuration yielding the lowest Akaike Information Criterion value was selected for the final analysis. For dysglycemia and diabetes, four nodes were positioned at the 5th, 35th, 65th, and 95th percentiles, while for prediabetes, five nodes were placed at the 5th, 28th, 50th, 72nd, and 95th percentiles. Restricted cubic spline analysis identified an inflection point that segmented the ByG index into two parts, thus allowing the modeling of distinct association patterns between the ByG index and outcomes using segmented Cox regression. In addition, to compare the efficacy of the ByG index with traditional classic insulin resistance indicators (including BMI, TyG, and TyG-BMI) for diagnosing dysglycemia, diabetes, and prediabetes at different time points, we constructed time-dependent receiver operating characteristic (ROC) curves and calculated the area under the ROC curve (AUC).\u003c/p\u003e\n\u003cp\u003eMultiple sensitivity analyses were performed to verify the robustness of our findings. Initially, individuals who were current smokers or drinkers were excluded to assess the impact of residual confounding factors. Secondly, participants who received regular OSA treatment were excluded to evaluate the possible treatment-related confounding factors. Thirdly, a one-year lag analysis was conducted, which excluded patients who experienced dysglycemia during the first year of follow-up. Fourthly, we separately excluded individuals using diuretics and those with a history of stroke at baseline due to the potential effects of diuretics on glucose metabolism and the baseline differences in stroke rates between the groups. Stratified and interaction analyses were also carried out based on several key factors: age (\u0026lt;45 or \u0026ge;45 years), gender, drinking status, smoking status, AHI (\u0026lt;15, 15-30, or \u0026ge;30 events/h), BMI (\u0026lt;28 or \u0026ge;28 kg/m\u0026sup2;), diastolic blood pressure (DBP) (\u0026lt;90 or \u0026ge;90 mmHg), systolic blood pressure (SBP) (\u0026lt;140 or \u0026ge;140 mmHg), angiotensin-converting enzyme inhibitors(ACEIs) /angiotensin receptor blockers\u0026nbsp;(ARBs) use, and statin use.\u003c/p\u003e\n\u003cp\u003eAll statistical tests were two-sided, with a significance threshold of P \u0026lt; 0.005. Analyses were carried out using the statistical software R version 4.2.2.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eBaseline characteristics\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe baseline analysis involved 1,759 participants, 69.0% of whom were male, with an average age of 48.79 years. Participants were stratified into three groups based on the ByG index: tertile 1 (\u0026le;7.01), tertile 2 (7.01 \u0026lt; ByG \u0026le; 7.16), and tertile 3 (\u0026ge;7.16).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eParticipants in the highest ByG tertile were generally younger, had a lower incidence of baseline stroke, and had a higher likelihood of being male. Additionally, this group showed a higher prevalence of obesity and an increased likelihood of alcohol consumption.\u003c/p\u003e\n\u003cp\u003eClinically, participants in the high ByG group exhibited slightly elevated levels of aminotransferase. As expected, triglyceride and FPG levels rose with higher ByG tertiles, HDL levels significantly decreased. Regarding medication use, the prevalence of ACEI and CCB was higher in the high ByG group, while statin use was consistent across all groups. Additionally, participants with a higher ByG index exhibited more severe OSA, indicated by higher AHI values and lower nocturnal minimum and mean oxygen saturation levels.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAssociation of ByG index with the risk of\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003enew-onset dysglycemia, diabetes, and prediabetes\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOver a median follow-up period of 7.25 years, that equates to 11,662.1 person-years, 212 new cases of dysglycemia were observed, included 157 cases of diabetes (8.93%) and 55 cases of prediabetes (3.13%).\u0026nbsp;The analysis of cumulative risk curves indicated that participants with higher ByG indices experienced a significant increase in the incidence of dysglycemia, diabetes, and prediabetes (log-rank P \u0026lt; 0.001; Figure 2A-C).\u003c/p\u003e\n\u003cp\u003eTable 2 presents the relationship between ByG levels and the development of dysglycemia, diabetes, and prediabetes. When ByG was analyzed as a continuous variable, each 1 SD increase in ByG was associated with a 53% elevated risk of incident dysglycemia (95% CI: 1.32\u0026ndash;1.78), a 62% higher risk of incident diabetes (95% CI: 1.35\u0026ndash;1.92), and a 35% increased risk of incident prediabetes (95% CI: 1.01\u0026ndash;1.82). In the tertile-based analysis, participants in the highest tertile exhibited significantly higher risks of dysglycemia (HR: 3.07; 95% CI: 2.03\u0026ndash;4.67), diabetes (HR: 3.34; 95% CI: 2.01\u0026ndash;5.55), and prediabetes (HR: 2.60; 95% CI: 1.24\u0026ndash;5.43) compared to those in the lowest tertile, after adjusting for potential confounders (Model 3). The middle tertile also demonstrated a significantly elevated risk for these outcomes relative to the lowest tertile. Furthermore, a trend test indicated a dose-dependent increase in risk across the tertile groups.\u003c/p\u003e\n\u003cp\u003eFigure 3 shows that the restricted cubic spline analysis revealed significant non-linear relationship between the ByG index and the risks of dysglycemia (Figure 3A) and prediabetes (Figure 3C) (P-nonlinear \u0026lt; 0.001). In contrast, the risk of diabetes showed a linear relationship (Figure 3B, P-nonlinear = 0.239). Significant changes in the risk of dysglycemia and prediabetes were observed when the ByG index was approximately 7.04. Segmented Cox regression analysis at this inflection point showed that the risk of dysglycemia was lower for the ByG index below 7.04, yet significantly elevated at 7.04 and above (Supplemental Table 2). However, the risk of prediabetes remained statistically unchanged before and after the inflection point (Supplemental Table 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eComparison of the ByG Index and traditional indicators for early prediction capability\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTime-dependent receiver operating characteristic curve analyses showed that the ByG index had superior discriminatory ability to predict dysglycemia events (new-onset diabetes, prediabetes) at 3, 5, and 7 years compared to the BMI, TyG, and TyG-BMI indices, with higher AUC values at all of the above time points (Supplementary Figure 1-3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSensitivity Analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResults remained consistent after sensitivity analyses excluding current smokers (Supplementary Table 4) and alcohol drinkers (Supplementary Table 5). Excluding patients receiving OSA therapy (Supplemental Table 6), participants who developed dysglycemia (both diabetic and prediabetic) within the first year of follow-up (Supplemental Table 7), those using diuretics (Supplemental Table 8), and those with a history of stroke (Supplemental Table 9) did not show significant changes, suggesting that the study results were robust.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSubgroup Analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSubgroup analysis was conducted to further explore the association between ByG index and the incidence of new-onset dysglycemia (Figure 4A), diabetes (Figure 4B), and prediabetes (Figure 4C). The stratification variables included gender, age, BMI, blood pressure parameters (SBP and DBP), AHI, smoking status, drinking status, and use of statins and ACEIs/ARBs. The results showed that the association between ByG index and dysglycemia was consistent across various subgroups (Supplemental Tables 10-12). Notably, individuals with DBP greater than 90 mmHg exhibited a significantly higher risk of developing diabetes. Additionally, participants who consumed alcohol had an elevated risk of progressing to prediabetes.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present retrospective cohort study found a significant association between the ByG index and the risk of new-onset dysglycemia (including diabetes and prediabetes) in patients with hypertension and OSA. Furthermore, the ByG index demonstrated superior early predictive capability for dysglycemia (including diabetes and prediabetes) compared to traditional predictive indicators. This simple and practical tool aids in the early identification of high-risk populations and provides personalized preventive measures for glycemic abnormalities.\u003c/p\u003e\n\u003cp\u003eHypertension and OSA tend to coexist, with a prevalence of OSA ranging from 30% to 50% in hypertensive patients\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e24]\u003c/sup\u003e and a prevalence of hypertension ranging from 59% to 67% in patients with OSA\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e25]\u003c/sup\u003e. This unique subgroup, characterized by the intertwining of both conditions and a high prevalence rate, presents a complex metabolic foundation that significantly increases the risk of glucose abnormalities\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e7,8,26,27]\u003c/sup\u003e. However, limited research has addressed the early identification of abnormal glucose metabolism risk in this population. A previous study indicated that the non-dipping blood pressure pattern during 24-hour ambulatory blood pressure monitoring is a risk factor for the onset of diabetes in patients with hypertension and OSA, providing a novel approach for the management of diabetes\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e23]\u003c/sup\u003e. However, due to the complexity of diabetes and the multitude of risk factors, relying solely on changes in nighttime blood pressure patterns may not effectively identify high-risk subgroups of diabetes. Moreover, the cost-effectiveness of the widespread application of ambulatory blood pressure monitoring hinders its utility and dissemination for risk assessment. Our research findings demonstrate a strong association between the ByG index, which integrates BMI and blood glucose, and the risk of new-onset of dysglycemia (including diabetes and prediabetes), highlighting the effectiveness of ByG as an early risk predictor. This offers a practical, cost-effective, and widely applicable method to identify individuals at high risk for dysglycemia with both hypertension and OSA, facilitating the development of personalized prevention and management strategies.\u003c/p\u003e\n\u003cp\u003eThe ByG index was initially validated for its association with diabetes risk in a Japanese community-based cohort\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e16]\u003c/sup\u003e, and no further studies have been conducted to reveal its association with abnormal glucose metabolism. The present study investigated the association between the index and new-onset dysglycemia in hypertensive patients with OSA, confirming the strong association between the index and new-onset diabetes, and further finding its efficacy in identifying the risk of prediabetes. Our study broadens the application of the ByG index for predicting diabetes in various populations and stages. We also uncovered a non-linear correlation between the ByG index and the new-onset of prediabetes and dysglycemia. Once the ByG index surpassed 7.04, the risk of dysglycemia escalated significantly. Intriguingly, we observed a counterintuitive reduction in the risk of prediabetes when the ByG index exceeded 7.27. This phenomenon may be ascribed to the temporal progression from prediabetes to diabetes, with a heightened risk of diabetes development associated with prolonged follow-up and higher ByG index values. This trend may be reversed in a sufficiently large sample size.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAfter excluding drinkers, our sensitivity analysis revealed a weak association between the ByG index and abnormal blood glucose levels. Subgroup analyses indicated that individuals who consumed alcohol and those with DBP \u0026gt; 90 mmHg had a significantly elevated risk of dysglycemia, particularly among prediabetics. Previous epidemiological studies have highlighted a link between alcohol consumption and the onset of diabetes, and alcoholism is generally recognized as a risk factor for new-onset diabetes\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e28,29]\u003c/sup\u003e. Additionally, the established association between hypertension and glucose metabolism disorders further supported these findings\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e30]\u003c/sup\u003e\u003csup\u003e.\u003c/sup\u003e These results underscore the potential of the ByG index in identifying high-risk subgroups that could benefit from intervention strategies aimed at reducing blood pressure or abstaining from alcohol.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn time-dependent ROC curve analysis, while AUC values exhibit restricted discriminative capacity at the individual level, the superior performance of the ByG index in comparison to traditional indicators underscores its promise as a predictive instrument. The development of dysglycemia involves a complex interplay of genetic and environmental factors, rendering precise early-stage diagnosis challenging for a singular index\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e31,32]\u003c/sup\u003e. Meanwhile, our multivariate adjusted Cox proportional hazards regression analysis indicates that the ByG index has the ability to independently predict the risk of dysglycemia, highlighting its potential as a personalized risk assessment method and its clinical applicability.\u003c/p\u003e\n\u003cp\u003eThe efficacy of ByG metrics in detecting the risk of dysglycemia may be attributable to the interplay between hypertension, obstructive sleep apnoea (OSA), and obesity\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e30,33]\u003c/sup\u003e. Obesity is commonly associated with OSA in individuals with hypertension, resulting in endothelial dysfunction and increased sympathetic activity that impede glucose uptake\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e27]\u003c/sup\u003e. Intermittent nocturnal hypoxia and oxidative stress exacerbate metabolic stress and impair insulin signal-ling\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e34]\u003c/sup\u003e. The integration of BMI and fasting glucose levels in the ByG metric has been shown to summarise the synergistic metabolic effects of obesity, hypertension, and OSA \u003csup\u003e[\u003c/sup\u003e\u003csup\u003e12,33,35,36]\u003c/sup\u003e. As expected, this index is valuable for early prediction of the risk of glucose abnormalities in this high-risk population.\u003c/p\u003e\n\u003cp\u003eIn this study, the long-term follow-up with clearly defined cohorts enhanced the credibility of the results. Comprehensive baseline clinical and laboratory data were beneficial for adjusting potential confounding factors, thereby improving the robustness of the findings. Nevertheless, this study has several limitations. The retrospective design is susceptible to inherent biases such as selection bias, which may constrain the generalizability of the findings beyond specific hypertensive OSA risk cohorts. Despite adjusting for numerous confounders, unmeasured variables (e.g., dietary patterns, physical activity levels, and genetic predisposition) could still impact the observed correlations, warranting consideration in future investigations. Furthermore, the reliance on a single center in this study may constrain the external validity of the outcomes, future studies should include multiple centers to enhance the external validity of the findings.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe ByG index is an independent predictor of dysglycemia, encompassing diabetes and prediabetes, among hypertensive individuals with OSA. As a simple, accessible, and reliable indicator, it will help to develop personalised diabetes prevention strategies for this high-risk group.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eACEIs: angiotensin-converting enzyme inhibitors；AHI: apnea-hypopnea index; ARBs: angiotensin receptor blockers;\u0026nbsp;BMI: Body mass index; ByG: Body mass index -glucose; CCBs: calcium channel blockers；CI: confidence intervals; CPAP：continuous positive airway pressure; DBP: diastolic blood pressure; FPG: Fasting plasma glucose; HDL: high-density lipoprotein；OSA: Obstructive sleep apnea (OSA); SaO2：oxygen saturation, SBP: systolic blood pressure; SD: standard deviation; TyG: triglyceride glucose; TyG-BMI: triglyceride glucose-body mass index; UROSAH: Urumqi Research on Sleep Apnea and Hypertension;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXinjiang Uygur Autonomous Region People\u0026apos;s Hospital\u0026apos;s Ethics Committee approved this study (reference: 2019030662), and written informed consent was provided by all study participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the Tianshan Talent Training Program - Science and Technology Innovation Team [grant number 2023TSYCTD0016], and the People\u0026apos;s Hospital of Xinjiang Uygur Autonomous Region Level Funded Projects [project number 20210101]. The funding sources had no involvement in study design, data collection, analysis, interpretation, report writing, or the decision to submit the article for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWY: Conceptualization, Data Curation, Formal Analysis, Writing \u0026ndash; original draft.\u003c/p\u003e\n\u003cp\u003eXC, MH, QZ, XY, WW, DS, JH, JH: Data Curation, Formal Analysis.\u003c/p\u003e\n\u003cp\u003eNL: Conceptualization, Project Administration, Supervision, Data Curation.\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all participants and staff of the UROSAH study for their important contributions.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGBD 2021 Diabetes Collaborators. Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021. Lancet (London England). 2023;402(10397):203\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMagliano DJ, Boyko EJ. IDF Diabetes Atlas 10th edition scientific committee. \u003cem\u003eIDF DIABETES ATLAS\u003c/em\u003e. 10th ed. International Diabetes Federation; 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYe J, Wu Y, Yang S, et al. The global, regional and national burden of type 2 diabetes mellitus in the past, present and future: a systematic analysis of the global burden of disease study 2019. Front Endocrinol. 2023;14:1192629.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoumie CL, Hung AM, Russell GB, et al. Blood pressure control and the association with diabetes mellitus incidence: results from SPRINT randomized trial. Hypertens (Dallas Tex : 1979). 2020;75(2):331\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYildiz M, Esenboğa K, Oktay AA. Hypertension and diabetes mellitus: highlights of a complex relationship. Curr Opin Cardiol. 2020;35(4):397\u0026ndash;404.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWANG X, BI Y, ZHANG Q, PAN F. Obstructive sleep apnoea and the risk of type 2 diabetes: A meta-analysis of prospective cohort studies. Respirology. 2013;18(1):140\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQie R, Zhang D, Liu L, et al. Obstructive sleep apnea and risk of type 2 diabetes mellitus: A systematic review and dose-response meta‐analysis of cohort studies. J Diabetes. 2020;12(6):455\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJung JY, Oh CM, Ryoo JH, et al. The influence of prehypertension, hypertension, and glycated hemoglobin on the development of type 2 diabetes mellitus in prediabetes: the korean genome and epidemiology study (KoGES). Endocrine. 2018;59(3):593\u0026ndash;601.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTao M, Dong X, Tu J, Fang Q, Shao C. Symptom and comorbidity burden in hypertensive patients with obstructive sleep apnea. Front Endocrinol. 2024;15:1361466.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLabarca G, Dreyse J, Salas C, et al. Risk of mortality among patients with moderate to severe obstructive sleep apnea and diabetes mellitus: results from the SantOSA cohort. Sleep Breath. 2021;25(3):1467\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQiu M, Shen W, Song X, et al. Effects of prediabetes mellitus alone or plus hypertension on subsequent occurrence of cardiovascular disease and diabetes mellitus: longitudinal study. Hypertens (Dallas Tex : 1979). 2015;65(3):525\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJia G, Sowers JR. Hypertension in Diabetes: An Update of Basic Mechanisms and Clinical Disease. Hypertension. 2021;78(5):1197\u0026ndash;205.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReutrakul S, Mokhlesi B. Obstructive sleep apnea and diabetes: a state of the art review. Chest. 2017;152(5):1070\u0026ndash;86.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGalicia-Garcia U, Benito-Vicente A, Jebari S, et al. Pathophysiology of type 2 diabetes mellitus. Int J Mol Sci. 2020;21(17):6275.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYe F, Tao R, Cong W, Tian J, Liu Q. Utilization of fluorescence tracer in hyperinsulinemic-euglycemic clamp test in mice. J Biochem Bioph Methods. 2008;70(6):978\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao X, Yao T, Song B, et al. The combination of body mass index and fasting plasma glucose is associated with type 2 diabetes mellitus in Japan: a secondary retrospective analysis. Front Endocrinol. 2024;15:1355180.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCai X, Li N, Hu J, et al. Nonlinear relationship between chinese visceral adiposity index and new-onset myocardial infarction in patients with hypertension and obstructive sleep apnoea: insights from a cohort study. JIR. 2022;15:687\u0026ndash;700.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLevey AS, Inker LA, Coresh J. GFR Estimation: From Physiology to Public Health. Am J Kidney Dis. 2014;63(5):820\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChang JL, Goldberg AN, Alt JA, et al. International Consensus Statement on Obstructive Sleep Apnea. Int Forum Allergy Rhinol. 2023;13(7):1061\u0026ndash;482.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChirinos JA, Gurubhagavatula I, Teff K, et al. CPAP, Weight Loss, or Both for Obstructive Sleep Apnea. N Engl J Med. 2014;370(24):2265\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCollen J, Lettieri C, Kelly W, Roop S. Clinical and Polysomnographic Predictors of Short-Term Continuous Positive Airway Pressure Compliance. Chest. 2009;135(3):704\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCosentino F, Grant PJ, Aboyans V, et al. 2019 ESC Guidelines on diabetes, pre-diabetes, and cardiovascular diseases developed in collaboration with the EASD. Eur Heart J. 2020;41(2):255\u0026ndash;323.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuo Q, Li N, Zhu Q, et al. Non-dipping blood pressure pattern is associated with higher risk of new-onset diabetes in hypertensive patients with obstructive sleep apnea: UROSAH data. Front Endocrinol. 2023;14:1083179.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTietjens JR, Claman D, Kezirian EJ, et al. Obstructive sleep apnea in cardiovascular disease: a review of the literature and proposed multidisciplinary clinical management strategy. J Am Heart Assoc. 2019;8(1):e010440.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSalman LA, Shulman R, Cohen JB. Obstructive sleep apnea, hypertension, and cardiovascular risk: epidemiology, pathophysiology, and management. Curr Cardiol Rep. 2020;22(2):6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsimihodimos V, Gonzalez-Villalpando C, Meigs JB, Ferrannini E. Hypertension and Diabetes Mellitus: Coprediction and Time Trajectories. Hypertension. 2018;71(3):422\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFerrannini E, Cushman WC. Diabetes and hypertension: the bad companions. Lancet. 2012;380(9841):601\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi MJ, Ren J, Zhang WS, et al. Association of alcohol drinking with incident type 2 diabetes and pre-diabetes: the guangzhou biobank cohort study. Diabetes Metab Res Rev. 2022;38(6):e3548.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan T, Zhang S, Duan W, et al. Eighteen-year alcohol consumption trajectories and their association with risk of type 2 diabetes and its related factors: the China health and nutrition survey. Diabetologia. 2019;62(6):970\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin CH, Wei JN, Fan KC, et al. Different cutoffs of hypertension, risk of incident diabetes and progression of insulin resistance: A prospective cohort study. J Formos Med Assoc. 2022;121(1):193\u0026ndash;201.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrotz AK, Gloyn AL, Thomsen SK. Prioritising causal genes at type 2 diabetes risk loci. Curr Diabetes Rep. 2017;17(9):76.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKolb H, Martin S. Environmental/lifestyle factors in the pathogenesis and prevention of type 2 diabetes. BMC Med. 2017;15(1):131.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeung MYM, Carlsson NP, Colditz GA, Chang SH. The Burden of Obesity on Diabetes in the United States: Medical Expenditure Panel Survey, 2008 to 2012. Value Health. 2017;20(1):77\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRyan S. Adipose tissue inflammation by intermittent hypoxia: mechanistic link between obstructive sleep apnoea and metabolic dysfunction. J Physiol. 2017;595(8):2423\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBell JA, Kivimaki M, Hamer M. Metabolically healthy obesity and risk of incident type 2 diabetes: a meta-analysis of prospective cohort studies. Obes Rev. 2014;15(6):504\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe L, Zheng W, Li Z, Chen L, Kong W, Zeng T. J-shape relationship between normal fasting plasma glucose and risk of type 2 diabetes in the general population: results from two cohort studies. J Transl Med. 2023;21(1):175.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"769\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 769px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eTable 1. Baseline characteristics of participants by ByG index tertiles.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 207px;\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 112px;\"\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003eByG Tertile 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eByG Tertile 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003eByG Tertile 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 90px;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e(\u0026lt;7.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e(\u0026ge;7.01 to \u0026lt;7.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e(\u0026ge;7.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eParticipants, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e1759\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e587\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 769px;\"\u003e\n \u003cp\u003eDemographic characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eAge, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e48.79 \u0026plusmn; 10.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e49.95 \u0026plusmn; 11.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e48.83 \u0026plusmn; 10.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e47.58 \u0026plusmn; 10.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eMale, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e1214 (69.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e368 (62.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e426 (72.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e420 (71.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eCurrent smokers, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e769 (43.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e237 (40.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e275 (46.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e257 (43.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eCurrent drinkers, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e709 (40.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e200 (34.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e254 (43.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e255 (43.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e28.06 \u0026plusmn; 3.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e25.17 \u0026plusmn; 2.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e27.99\u0026plusmn; 2.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e31.01 \u0026plusmn; 3.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eSBP, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e139.56 \u0026plusmn; 19.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e139.61 \u0026plusmn; 19.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e138.58 \u0026plusmn; 19.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e140.48 \u0026plusmn; 20.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.257\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eDBP, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e92.01 \u0026plusmn; 14.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e91.52 \u0026plusmn; 13.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e91.51 \u0026plusmn; 14.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e93.00 \u0026plusmn; 14.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eBaseline CHD, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e176(10.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e51(8.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e63(10.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e62(10.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.435\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eBaseline Stroke, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e365(20.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e138(23.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e131(22.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e96(16.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 769px;\"\u003e\n \u003cp\u003eClinical laboratory measurements\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eAST, U/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e22.26 \u0026plusmn; 15.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e22.84 \u0026plusmn; 23.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e21.11\u0026plusmn; 8.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e22.84 \u0026plusmn; 11.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eALT, U/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e27.86 \u0026plusmn; 20.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e25.98 \u0026plusmn; 23.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e26.32\u0026plusmn; 16.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e31.26 \u0026plusmn; 20.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eeGFR, ml/min/1.73 m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e95.59 \u0026plusmn; 20.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e94.81 \u0026plusmn; 20.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e95.49 \u0026plusmn; 21.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e96.47 \u0026plusmn; 20.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.394\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTC, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e4.52 \u0026plusmn; 1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e4.52 \u0026plusmn; 1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e4.47 \u0026plusmn; 1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e4.58 \u0026plusmn; 1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.260\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTG, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e2.02 \u0026plusmn; 1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e1.82 \u0026plusmn; 1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e2.03 \u0026plusmn; 1.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e2.20 \u0026plusmn; 1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eHDL-C, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e1.12 \u0026plusmn; 0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e1.19 \u0026plusmn; 0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e1.10 \u0026plusmn; 0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e1.07 \u0026plusmn; 0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eLDL-C, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e2.66 \u0026plusmn; 0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e2.65 \u0026plusmn; 0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e2.63 \u0026plusmn; 0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e2.68 \u0026plusmn; 0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.323\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eFPG, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e4.80 \u0026plusmn; 0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e4.35 \u0026plusmn; 0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e4.79 \u0026plusmn; 0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e5.27 \u0026plusmn; 0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eByG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e7.08 \u0026plusmn; 0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e6.88 \u0026plusmn; 0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e7.09 \u0026plusmn; 0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e7.28 \u0026plusmn; 0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 769px;\"\u003e\n \u003cp\u003ePrescribed medication, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eACEIs/ARBs users, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e843 (47.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e252 (43.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e281 (47.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e310 (52.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003e\u0026beta;-blockers users, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e171 (9.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e52 (8.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e62 (10.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e57 (9.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.621\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eCCBs users, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e1285 (73.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e395 (67.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e437 (74.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e453 (77.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eDiuretics users, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e303 (17.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e94 (16.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e105 (17.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e104 (17.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.648\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eStatins users, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e942 (53.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e313 (53.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e324 (55.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e305 (52.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.806\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 769px;\"\u003e\n \u003cp\u003ePSG parameters\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eAHI, events/h\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e23.48 \u0026plusmn; 18.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e20.05 \u0026plusmn; 15.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e23.21 \u0026plusmn; 17.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e27.18 \u0026plusmn; 20.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eModerate-severe OSA, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e1062（60.4%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e317 (51.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e357 (60.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e388 (66.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eNadir SaO\u003csub\u003e2\u003c/sub\u003e, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e78.25 \u0026plusmn; 8.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e79.97 \u0026plusmn; 7.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e78.64 \u0026plusmn; 7.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e76.16 \u0026plusmn; 10.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eMean SaO\u003csub\u003e2\u003c/sub\u003e, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e91.97 \u0026plusmn; 3.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e92.59 \u0026plusmn; 2.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e91.85 \u0026plusmn; 4.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e91.46 \u0026plusmn; 3.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eRegular CPAP treatment, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e39 (2.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e8 (1.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e15 (2.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e16 (2.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.225\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 769px;\"\u003e\n \u003cp\u003eValues of continuous variables are expressed as medians (twenty-fifth percentile - seventy-fifth percentile) or means (standard deviation). Categorical variables are expressed as no. (%).\u003c/p\u003e\n \u003cp\u003eALT, alanine aminotransferase; AST, aspartate aminotransferase; BMI, body mass index; DBP, diastolic blood pressure; FPG, fasting plasma glucose; SBP, systolic blood pressure; HDL-C, high-density lipoprotein cholesterol; CHD, coronary cardiovascular disease; eGFR, estimated glomerular filtration rate; LDL-C, lowdensity lipoprotein cholesterol; TC, total cholesterol; AHI, apnea hypopnea index; TG, triglyceride; ACEIs, angiotensin-converting enzyme inhibitors; ARBs, angiotensin receptor blockers; CCBs, calcium channel blockers; PSG, polysomnography; OSA,obstructive sleep apnea; CPAP, continuous positive airway pressure; Sao\u003csub\u003e2\u003c/sub\u003e, oxygen saturation; ByG,body mass index - glucose index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Hazard ratios (95% CI) of dysglycemia, diabetes, and prediabetes stratified by ByG\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eindex.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"764\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 164px;\"\u003e\n \u003cp\u003eExposure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 199px;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 187px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 214px;\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e(HR, 95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e(HR, 95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e(HR, 95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 764px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDysglycemia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003ePer SD increment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e1.58 (1.38, 1.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e1.62 (1.42, 1.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e1.53 (1.32, 1.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eByG Tertiles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eT1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eT2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e1.79 (1.17, 2.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e1.83 (1.20, 2.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e1.63 (1.04, 2.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e3.40 (2.31, 4.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e3.62 (2.46, 5.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e3.07 (2.03, 4.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eP for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 764px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003ePer SD increment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e1.64 (1.40, 1.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e1.68 (1.43, 1.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e1.62 (1.35, 1.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eByG Tertiles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eT1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eT2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e2.13 (1.29, 3.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e2.15 (1.31, 3.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e2.00 (1.18, 3.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eT3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e3.66 (2.20, 5.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e3.77 (2.36, 6.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e3.34 (2.01, 5.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eP for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrediabetes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003ePer SD increment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e1.42 (1.08, 1.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e1.49 (1.14, 1.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e1.35 (1.01, 1.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eByG Tertiles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eT1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eT2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e1.10 (0.48, 2.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.827\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e1.14 (0.50, 2.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e0.94 (0.40, 2.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.880\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eT3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e2.86 (1.43, 5.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e3.30 (1.65, 6.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e2.60 (1.24, 5.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eP for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 764px;\"\u003e\n \u003cp\u003eModel 1: adjusted for age and sex. Model 2: adjusted for variables in model 1 plus drinking status, baseline CHD, baseline stroke, smoking status, DBP, and SBP. Model 3: adjusted for variables in model 2 plus ALT, eGFR, TG, HDL-C, ACEIs/ARBs, \u0026beta;-Blockers, CCBs, diuretics, statins, AHI, nadir SaO\u003csub\u003e2\u003c/sub\u003e, mean SaO\u003csub\u003e2\u003c/sub\u003e, and regular CPAP treatment.\u003c/p\u003e\n \u003cp\u003eHR, hazard ratio; CI, confidence interval. Other abbreviations appear in Table 1.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-endocrine-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bend","sideBox":"Learn more about [BMC Endocrine Disorders](http://bmcendocrdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bend/default.aspx","title":"BMC Endocrine Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Hypertension, Obstructive sleep apnea, Dysglycemia, Body mass index–glucose index, Retrospective cohort","lastPublishedDoi":"10.21203/rs.3.rs-6446894/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6446894/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose: \u003c/strong\u003eCurrently, there is a lack of early biomarkers to identify the risk of dysglycemia in patients with concurrent hypertension and obstructive sleep apnea (OSA). The aim of our study is to evaluate the efficacy of the recently proposed Body Mass Index (BMI)-Glucose (ByG) index in identifying the risk of dysglycemia in patients with hypertension and OSA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eA retrospective cohort study of 1579 adults with hypertension and OSA from the Urumqi Research on Sleep Apnea and Hypertension study (UROSAH) was conducted. Cox proportional hazards models were used to assess the associations between the ByG index and new-onset dysglycemia, diabetes, and prediabetes. Time-dependent receiver operating characteristic (ROC) curves to compare the efficacy of the ByG index with traditional insulin resistance indicators.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eDuring a median follow-up of 7.25 years, 212 cases of dysglycemia (157 diabetes, 55 prediabetes) were identified. Participants in the highest ByG tertile had a significantly increased risk of dysglycemia (HR 3.07; 95% CI: 2.03–4.67), diabetes (HR 3.34; 95% CI: 2.01–5.57), and prediabetes (HR 2.60; 95% CI: 1.24–5.43) compared to the lowest tertile, after full adjustment. Time-dependent ROC showed the ByG index was more discriminative in predicting dysglycemia (including diabetes and prediabetes) events at 3, 5 and 7 years compared to BMI, TyG and TyG-BMI indices.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThe ByG index demonstrates a significant association with the risk of new-onset dysglycemia, encompassing both diabetes and prediabetes, in patients with hypertension and OSA. This straightforward tool can facilitate the early identification of high-risk individuals and provide individualized dysglycemia prevention.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrial registration\u003c/strong\u003e: Not applicable.\u003c/p\u003e","manuscriptTitle":"The Body mass index-glucose Index as a New Tool for Early Detection of the Risk of Dysglycemia in Patients with Hypertension and Obstructive Sleep Apnea","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-19 08:47:33","doi":"10.21203/rs.3.rs-6446894/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-25T12:11:41+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-19T03:20:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"20978766703589141923068298361772091115","date":"2025-07-19T02:32:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-01T15:32:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"218617400264932393160863487443133098251","date":"2025-06-15T13:30:27+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-16T15:51:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"32339679367425476628585596598874643079","date":"2025-05-16T15:39:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-14T12:35:37+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-04-17T07:49:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-17T04:07:24+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-17T04:05:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Endocrine Disorders","date":"2025-04-14T14:13:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-endocrine-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bend","sideBox":"Learn more about [BMC Endocrine Disorders](http://bmcendocrdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bend/default.aspx","title":"BMC Endocrine Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"68e22b9f-08ba-46a8-863a-26b21e9eadb9","owner":[],"postedDate":"May 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-23T16:14:42+00:00","versionOfRecord":{"articleIdentity":"rs-6446894","link":"https://doi.org/10.1186/s12902-026-02226-w","journal":{"identity":"bmc-endocrine-disorders","isVorOnly":false,"title":"BMC Endocrine Disorders"},"publishedOn":"2026-03-19 15:59:35","publishedOnDateReadable":"March 19th, 2026"},"versionCreatedAt":"2025-05-19 08:47:33","video":"","vorDoi":"10.1186/s12902-026-02226-w","vorDoiUrl":"https://doi.org/10.1186/s12902-026-02226-w","workflowStages":[]},"version":"v1","identity":"rs-6446894","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6446894","identity":"rs-6446894","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}