Differential pulse pressure and triglycerides predict incident type 2 diabetes in Chinese adults: a BMI- stratified cohort analysis

Differential pulse pressure and triglycerides predict incident type 2 diabetes in Chinese adults: a BMI- stratified cohort analysis | 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 Differential pulse pressure and triglycerides predict incident type 2 diabetes in Chinese adults: a BMI- stratified cohort analysis Liying Wang, Ling Sha, Tianchen Wu, Ming Li, Hui Yang, Wenwen Kong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6893931/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective: to investigate the relationship between differential pulse pressure, triglycerides, and the probability of type 2 diabetes mellitus (T2DM) incidence under different BMI conditions in the Chinese adult population. Methods: This study utilized a retrospective cohort study design, with data from 211,833 adults who underwent physical examinations at Ruicci Healthcare Group in China between 2010 and 2016. Based on the BMI index, the study participants were categorized into the BMI ≤23.9 kg/m² group and the BMI >23.9 kg/m² group. The two groups were matched 1:1 by propensity score matching (PSM) method using first fasting glucose as the matching factor to control for potential confounding bias. After successful matching, Cox proportional risk regression models were developed to analyze the relationship between pulse pressure difference, triglycerides, and the risk of developing T2DM. In addition, the restricted cubic spline curve (RCS) method was applied to fit the non-linear relationship between differential pulse pressure and triglycerides and the probability of developing T2DM. Results: In analyzing the relationship between pulse pressure difference and the risk of developing T2DM, each one mmHg increase in pulse pressure difference significantly increased the risk of developing T2DM in both groups. Specifically, the risk of developing T2DM increased by 1% in the non-obese group (HR=1.01, 95% CI: 1.00-1.02, P<0.05) and by 3% in the obese group (HR=1.03, 95% CI: 1.01-1.05, P36 mmHg had a T2DM with a progressively higher probability risk; in the obese group, the relationship was linear, with a more significant risk of developing T2DM with a pulse pressure difference greater than 33 mmHg. In the relationship between triglycerides and the probability of developing T2DM, for every 1 mmol/L increase in triglycerides in the non-obese group, the risk of developing T2DM increased by 21% (HR=1.21, 95% CI: 1.18-1.24); for every 1 mmol/L increase in triglycerides in the obese group, the risk of developing T2DM increased by 13% (HR=1.13, 95% CI: 1.10-1.16). 1.16). The relationship between triglycerides and the probability of developing T2DM in both groups showed a non-linear change in a fast and then a slow manner. At the same triglyceride level, the risk probability of developing T2DM was approximately twice as high in men as in women. Conclusion: The likelihood of incidence of differential pulse pressure and T2DM varies according to BMI, and blood pressure management and control of differential pulse pressure should be more stringent in people with large BMI. In terms of lipid management, the population with lower BMI may have a higher probability of developing T2DM than those with higher BMI, possibly due to differences in lipid tolerance. T2DM differential pulse pressure triglycerides restrictive triple spline Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Type 2 diabetes mellitus (T2DM) is a growing chronic disease globally, and its prevalence continues to rise, especially in developing countries. According to estimates, approximately 425 million adults aged 20 to 79 years worldwide had T2DM in 2019, and this number is expected to increase to 629 million by 2045 [ 1 ]. The prevalence of T2DM is even more significant in the Asian region, especially in countries with high economic development, and is showing a trend of increasing yearly. However, despite the high incidence, the diagnosis rate of T2DM is only 43% [ 2 ]. In addition, T2DM not only affects the health status of individuals but also imposes a significant socioeconomic burden. According to the World Health Organization, chronic diseases, including T2DM, are expected to cause 73% of global deaths in 2020 [ 3 ]. Therefore, epidemiologic studies and public health policy development for T2DM are critical to respond to this global health crisis effectively. In recent years, the relationship between differential pulse pressure and dyslipidemia and type 2 diabetes mellitus (T2DM) has received increasing attention. It has been shown that elevated differential pulse pressure is significantly associated with the risk of developing T2DM [ 4 ]. In addition, dyslipidemia, elevated considerably triglyceride levels, is a significant risk factor for T2DM. Studies have shown that elevated triglyceride levels are closely associated with increased insulin resistance, which further increases the risk of T2DM [ 5 – 6 ]. In China, with the westernization of lifestyle and population aging, the incidence of T2DM is increasing year by year, especially in the adult population. Epidemiologic studies have shown that body mass index (BMI) is positively associated with the risk of developing T2DM, but BMI thresholds vary in different ethnic groups [ 7 – 8 ]; for example, it has been found that elevated BMI is significantly associated with the occurrence of T2DM in Chinese adults, and this relationship may vary in different ethnic groups and regions [ 9 ]. However, current studies on the relationship between differential pulse pressure, triglycerides, and T2DM are still insufficient, especially when BMI stratification is considered. There is a relative paucity of data from large-scale studies, especially in the Chinese adult population. Therefore, the present study aimed to fill this gap by analyzing the relationship between differential pulse pressure and triglycerides and the probability of T2DM under different BMI conditions to provide a more targeted and scientific basis for the prevention and management of T2DM. Data and Methods 1. General information Medical data from adults who underwent physical examination at Ruicci Healthcare Group in China were used in this study. The study dataset and related copyrights have been shared and transferred to the Dryad public database ( https://datadryad.org/stash ) by Li et al. [ 10 ]. The analysis initially included a total of 685,277 subjects aged ≥ 20 years with at least two visits in 11 cities in China between 2010 and 2016, which was selected for the current study as the dataset with fasting glucose (FPG,mmol/L) final fasting glucose (FPG-Final-visit,mmol/L), age (Age, year) Gender (Gender,%) Systolic blood pressure (SBP,mmHg), systolic blood pressure (DBP,mmHg) Total cholesterol (Cholesterol,mmol/L) Triglyceride (Triglyceride,mmol/L) Glutamine transaminase (ALT,u/L) Blood urea nitrogen (BUN,mmol/L) Blood creatinine (Scr,µmol) /L) Family-history (Family-history,%). Inclusion criteria: 1. Fasting blood glucose < 7.00 mmol/L (venous blood sampling) and/or subjects who provided previous medical records to prove that they did not have T2DM. Subjects were examined at each physical examination, whichever of the above two criteria was met first (given a diagnosis of T2DM) 2. ≥2 years between visits. Exclusion criteria: 1. missing weight and height measurements at baseline 2. missing gender information 3. extreme BMI ( 55 kg/m2) 4. missing fasting blood glucose values 5. subjects with a baseline diagnosis of T2DM 6. subjects whose T2DM status was unknown at the time of follow-up visit. The Ethics Committee of Rui Ci Medical Group has approved the study of Li et al. The Ethics Committee of Nanjing Hospital of Traditional Chinese Medicine repeated the application for ethical approval, and informed consent was waived. 2 Statistical analysis and methods This study used R language (R-4.2.1 version) for data analysis. Count data were expressed as frequency (%), customarily distributed measures were described as (± s), and skewed data were expressed as median [M(P25, P75)]. For the comparison of differences between the two groups, two independent samples t-test was used for normally distributed data, a non-parametric Mann-Whitney U test was used for non-normal distribution, and the χ2 test was used for the comparison of differences between groups for count data. P < 0.05 (two-sided) was designed to be statistically significant. Missing value analysis and multiple interpolations of data were performed using the mice package to exclude variables with missing values > 10% [ 11 ]. Initial blood glucose was propensity scored (PSM) and plotted as standardized mean difference (SMD) using the MatchIt package [ 12 ], with the caliper value set at 0.01. Cox regression models were developed, K-M (Kaplan-Meier) survival curves were plotted, and the RCS curves (restricted cubic spline) [ 13 ] were used to study the trend between differential pulse pressure and lipids and the probability of T2DM development. The flow chart of the study (Fig. 1 ) Results 1. propensity score results and SMD plot After the total subjects were screened, 211,833 subjects were enrolled, and after data collation, a total of 210,529 were included in this study. In this study, the follow-up time was defined as the period from the beginning of the first physical examination to the end of the last physical examination. The time of the last blood glucose measurement was regarded as the end of follow-up, and the first fasting blood glucose was included in the propensity score matching process to control for differences in baseline blood glucose levels, to reduce potential confounding bias, and to ensure that the two groups were comparable in terms of blood glucose levels, to more accurately assess the independent associations of pulse pressure difference and triglycerides with the incidence of T2DM. The standardized mean difference (SMD) plot is shown in (Fig. 2 ). The PSM method for 1:1 matching is shown in ( Table 1 ), with 27,783 successful matches. There was no significant difference in fasting blood glucose at the time of enrollment (P > 0.05). Table 1 Baseline characteristics of subjects based on multiple interpolations Unmatched PSM Group(BMI) BMI ≦ 23.9 (N = 182552) BMI > 23.9 (N = 27977) P BMI ≦ 23.9 (N = 27783) BMI > 23.9 (N = 27783) P FPG* 〔M(P 25 -P 75 )mmol/〕 4.89 (4.50–5.24) 5.10 (4.70–5.52) < 0.001 5.10 (4.71–5.52) 5.10 (4.70–5.51) 0.051 Age 〔M(P 25 -P 75 ) year〕 38(32–49) 42.00 (34.00–53.00) < 0.001 41.00 (34.00–52.00) 42.00 (34.00–53.00) < 0.001 Gender (%) male 94051 (51.5) 21233 (75.9) < 0.001 15423 (55.5) 21067 (75.8) < 0.001 female 88501 (48.5) 6744 (24.1) 12360 (44.5) 6716 (24.2) SBP 〔M(P 25 -P 75 ) mmHg〕 116 (106–127) 128(117–138) < 0.001 118(108–129.00) 128(117–138) < 0.001 DBP 〔M(P 25 -P 75 ) mmHg〕 72 (66–80) 80.00 (72.00–88.00) < 0.001 74 (67–81) 80 (72–88) < 0.001 Cholesterol M(P 25 -P 75 ) mmol/L〕 4.58 (4.03–5.19) 4.90 (4.33 5.50) < 0.001 4.67 (4.10–5.30) 4.90 (4.32–5.50) < 0.001 Triglyceride 〔M(P 25 -P 75 )mmol/L〕 1.00 (0.70–1.48) 1.66 (1.18–2.39) < 0.001 1.05 (0.72–1.60) 1.66 (1.18–2.38) < 0.001 ALT 〔M(P 25 -P 75 ) u/L〕 17.00(12.20–25.00) 30.00 (20.20–46.60) < 0.001 18.00 (13.00-26.30) 30.00 (20.20–46.40) < 0.001 BUN M(P 25 -P 75 )-mmol/L〕 4.50 (3.78–5.31) 4.77 (4.05–5.60) < 0.001 4.60 (3.89–5.46) 4.77 (4.05–5.59) < 0.001 Scr M(P 25 -P 75 ) µmol/L〕 68.00(57.00–80.00) 75.40 (65.00–85.00) < 0.001 69.00 (57.80–80.00) 75.40 (65.00–85.00) < 0.001 Family-history (%) No 178871 (98.0) 27408 (98.0) 0.865 27067 (97.4) 27218 (98.0) < 0.001 Yes 3681 ( 2.0) 569 ( 2.0) 716 ( 2.6) 565 ( 2.0) Remarks: * factor variables set for this study that need to be matched 2. Cox regression model and subgroup analysis Using propensity scoring, this study first conducted a comprehensive assessment of all potential confounding variables, including age, gender, BMI, family history, and lifestyle factors. To minimize the influence of these confounding factors on the study results, we used stepwise regression to screen risk factors significantly impacting T2DM development, identifying six significant factors: Age, BMI, Gender, Pressure, Triglyceride, and ALT(P < 0.05; Fig. 3 ). The Cox regression model was established. The Cox regression model was established, and equal proportional risk determination was performed. The K-M (Kaplan-Meier) curves of the above six risk factors were not calibrated, and the K-M curves of the above six risk factors were not calibrated, suggesting that there was a crossover between the two curves (P > 0.05). None of them could satisfy the equal proportional risk determination(Fig. 4 a, Fig. 4 b). Subgroup analysis according to BMI is reasonable. Table 2 suggests that when analyzing the relationship between pulse pressure difference and the risk of developing T2DM, the risk of developing T2DM was significantly increased in both groups for every 1 mmHg increase in pulse pressure difference. Specifically, for the BMI ≦ 23.9kg/m² group, the risk of developing T2DM increased by 1% (HR = 1.01, 95% CI: 1.00-1.02, P 23.9kg/m² group, the risk of developing T2DM increased by 3% (HR = 1.03, 95% CI: 1.01–1.05, P < 0.001). This result suggests that an increase in differential pulse pressure is strongly associated with an increased risk of developing T2DM at all BMI levels and that this increased risk is more pronounced in the obese population. Table 2 Influence coefficient of pulse pressure difference and triglyceride on DM after controlling confounding factors by stratified analysis Group(BMI) BMI ≦ 23.9 (N = 27783) BMI > 23.9 (N = 27783) Variable Hazard ratio 95%CI P Hazard ratio 95%CI P Age 1.06 1.05–1.06 < 0.001 1.03 1.03–1.04 < 0.001 Gender (%) < 0.001 < 0.001 male ref ref ref ref female 0.52 0.43–0.62 0.60 0.49–0.73 Pulse-pressure 1.02 1.01–1.02 < 0.001 1.03 1.01–1.05 < 0.001 Triglyceride 1.21 1.18–1.24 < 0.001 1.13 1.10–1.16 < 0.001 ALT 1.01 1.01–1.02 < 0.001 1.01 1.00-1.01 < 0.001 3. Trend test analysis between differential pulse pressure and the development of T2DM In the BMI ≦ 23.9kg/m2 group, the relationship between pulse pressure difference and the probability of T2DM incidence, as shown in (Fig. 5 a), RCS (p for non-linear 23.9kg/m2 group, as shown in (Fig. 5 b), RCS (p for nonlinear = 0.404), the probability of disease showed a linear relationship, with an increase in the point of rise at about 33mmHg. 4. Trend test analysis between triglycerides and T2DM incidence Considering the differences in triglyceride changes by gender [ 14 ], group analysis was performed according to gender. In the BMI ≦ 23.9kg/m2 group(Fig. 6 a), RCS (p for non-linear 23.9kg/m2 group(Fig. 6 b), RCS (p for non-linear <0.001), the trend of incidence was about 4.68 in the male group and about 2.64 in the female group. Non-linear < 0.001), a non-linear trend with triglycerides of 2.34 mmol/L (the highest point of the slope), the probability of onset was about 2.36 in the male group and about 1.48 in the female group. Discussion In this study, the relationship between pulse pressure difference and triglycerides and the probability of developing T2DM was investigated based on retrospective cohort data from a Chinese adult population, with attention to differences in different BMI conditions. Using propensity score matching (PSM) and Cox proportional risk regression modelling, we found that both differential pulse pressure and triglycerides were significantly associated with the risk of T2DM incidence, and this relationship differed significantly across BMI conditions. Differential pulse pressure and risk of T2DM: Differential pulse pressure is the difference between systolic and diastolic blood pressure, reflecting the maximum and minimum circulating pressures during the cardiac cycle. Studies have shown that increased pulse pressure difference is strongly associated with the risk of developing type 2 diabetes mellitus (T2DM). In the non-obese group (BMI ≤ 23.9 kg/m²), each one mmHg increase in pulse pressure difference was associated with a 1% increase in the risk of developing T2DM (HR = 1.01, 95% CI: 1.00-1.02, P 23.9 kg/m²), this risk increased by 3% (HR = 1.03, 95% CI: 1.01–1.05, P < 0.001) These results suggest that an increase in differential pulse pressure is associated with an increased risk of developing T2DM across BMI levels, and that this increased risk is more pronounced in the obese population. This finding is consistent with previous studies and suggests that the management of differential pulse pressure has an essential role in the prevention of T2DM, especially in the obese population [ 15 – 16 ]. In addition, elevated differential pulse pressure has been associated with deterioration in cardiovascular health. Studies have shown that differential pulse pressure is not only a significant predictor of cardiovascular events but may also exacerbate the risk of related complications in diabetic patients [ 17 – 18 ]. Therefore, monitoring and management of differential pulse pressure may provide new ideas and strategies for the prevention and treatment of T2DM. Triglycerides and risk of developing T2DM: Elevated triglyceride levels are an essential risk factor for T2DM. In this study, the non-linear relationship between triglycerides and the probability of developing T2DM was further revealed by the restricted cubic spline curve (RCS) method. In the non-obese group, the risk of developing T2DM was significantly increased when the triglyceride level reached 2.84 mmol/L, while in the obese group, the risk of developing T2DM was similarly considerably increased when the triglyceride level reached 2.34 mmol/L, which was a similar finding to that of previous studies [ 19 – 20 ]. In addition, it was found that at the same triglyceride level, the risk of T2DM in men was approximately twice as high as that in women, which may be related to maladaptive lifestyle habits (e.g., smoking and alcohol consumption) as well as physiologic differences (e.g., hormone levels) in men [ 21 ]. These findings suggest that the management of triglyceride levels in populations with different BMIs and genders should involve more targeted interventions to reduce the risk of developing T2DM. The endogenous hormonal environment, such as serum follicle-stimulating hormone, estrogen, and other endogenous hormones, influences triglyceride and cholesterol levels in women. After menopause, changes in hormone levels in women cause dyslipidemia in women, and women appear to be at an increased risk for metabolic disorders such as diabetes mellitus and cardiovascular disease [ 22 – 24 ]. In addition, studies have shown that elevated triglyceride levels are closely related to insulin resistance, a mechanism that may be characterized differently in populations of different genders and body mass indexes [ 25 – 26 ]. Therefore, personalized intervention strategies for different populations can more effectively reduce T2DM risk. Future studies should continue to explore the role of differential pulse pressure and triglycerides in different populations to provide more precise strategies for the prevention and treatment of T2DM. Strengths and limitations of the study This study had a large sample size and used propensity score matching (PSM) and Cox proportional risk regression models to control confounding bias and ensure the scientific validity of the conclusions. The RCS curves revealed a non-linear relationship between differential pulse pressure and triglycerides and the probability of T2DM, and the gender-stratified analyses supported personalized management. However, the data relied on public databases, and the accuracy of follow-up may be limited, underestimating the incidence of T2DM. The study was limited in the extrapolation of conclusions by targeting only Chinese adults, and the retrospective design did not allow for a complete determination of causality. It did not address specific interventions, which need to be further explored in future studies. Conclusion The results of this study have clinical implications for the prevention and management of type 2 diabetes mellitus (T2DM). First, the management of differential pulse pressure should be considered an essential measure for the prevention of T2DM, especially in the obese population. Secondly, monitoring and management of triglyceride levels should vary among populations with different BMIs and gender. For the low BMI population, more attention needs to be paid to the changes in triglyceride levels, and timely interventions should be taken. In contrast, for the high BMI population, although the effect of triglyceride levels is still significant, the overall risk level is relatively lower than that of the low BMI population. Therefore, other risk factors should be taken into account for comprehensive management. Declarations Ethics approval and consent to participate Owing to the ethics committee of Rich Healthcare Group having approved the previous research, repeated application of the current study for ethical approval and informed consent were exempted by Nanjing Hospital of Traditional Chinese Medicine (ethical review No. 2021-067). We confirm that all methods are implemented following relevant guidelines and regulations. The designated agency approves all experimental protocols. Consent for publication The authors declare no financial or non-financial competing interests. Availability of data and materials The research data set and relevant copyright have been shared by Li et al. and transferred to the public database of Dryad(https://datadryad.org/stash). Competing interests The authors have declared that no competing interests exist. Funding This work was supported by the Nanjing Medical Science and Technology Development Special Fund Project (Grant No. YKK20170). Authors' contributions Study design: Wenwen Kong, Liying Wang, Ling Sha. Data analysis and interpretation: Liying Wang, Ling Sha, Wenwen Kong, Tianchen Wu, Hui Yang Important knowledge content: Tianchen Wu, Wenwen Kong, Ling Sha. Figure preparation and organization: Hui Yang, Tianchen Wu Critical modification: Wenwen Kong, Hui Yang. Manuscript Revision: Hui Yang, Tianchen Wu, Wenwen Kong, Ling Sha, Ming Li. Liying Wang and Ling Sha contributed equally to this work. Wenwen Kong and Tianchen Wu are the guarantors of this work, so they can fully access all the data in the study and are responsible for the integrity of the data and the accuracy of the data analysis. All authors have read and approved the final manuscript. Acknowledgements The authors gratefully acknowledge the support provided by the families of the main authors ( Wenquan Wu, Yue Dai, and Yijia Kong) during this research. References Khan MAB, Hashim MJ, King JK, Govender RD, Mustafa H, Al Kaabi J. Epidemiology of Type 2 Diabetes - Global Burden of Disease and Forecasted Trends. J Epidemiol Glob Health. 2020;10(1):107-111. doi:10.2991/jegh.k.191028.001. Zuhur, Şenay and Nurhan Özpancar. The Effect of Care Needs on Quality of Life and Chronic Disease Care in Patients with Diabetes.Turkish Journal of Family Medicine and Primary Care (2020);14(1): 56-65. DOI: 10.21763/tjfmpc.693078. Park, M.,&Heo, Y.J. Biosensing Technologies for Chronic Diseases. Biochip Journal , 2021; 15 (1). https://doi.org/10.1007/s13206-021-00014-3. QI Q, LIANG L, DORIA A, et al. Genetic predisposition to dyslipidemia and type 2 diabetes risk in two prospective cohorts[J]. Diabetes, 2012;61(3): 745-752. DOI: 10.2337/db11-1254. Qi Q, Liang L, Doria A, Hu FB, Qi L. Genetic predisposition to dyslipidemia and type 2 diabetes risk in two prospective cohorts. Diabetes. 2012;61(3):745-752. doi:10.2337/db11-1254. Rao, Dr. P. Hanumantha and Dr. G. Mohan Reddy. A cross-sectional study to determine the lipid profile derangement in newly diagnosed type-2 diabetic patients.International Journal of Advanced Research in Medicine.2019;1(1): 49-52. DOI: 10.22271/27069567.2019.v1.i1a.339. Reddy, Pillaram & Jayarama, N & Mahesh, V. Relation between waist-hip ratio and lipid profile in type 2 diabetes mellitus patients. Asian Journal of Medical Sciences. 2014;5(3): 51-53. DOI: 10.3126/ajms.v5i3.9407. Lu J, Lam SM, Wan Q, et al. High-Coverage Targeted Lipidomics Reveals Novel Serum Lipid Predictors and Lipid Pathway Dysregulation Antecedent to Type 2 Diabetes Onset in Normoglycemic Chinese Adults. Diabetes Care. 2019;42(11):2117-2126. doi:10.2337/dc19-0100. Joshi SR, Anjana RM, Deepa M, et al. Prevalence of dyslipidemia in urban and rural India: the ICMR-INDIAB study. PLoS One. 2014;9(5):e96808. Published 2014 May 9. doi:10.1371/journal.pone.0096808. Chen Y, Zhang XP, Yuan J, et al. Association of body mass index and age with incident diabetes in Chinese adults: a population-based cohort study. BMJ Open. 2018;8(9):e021768. Published 2018 Sep 28. doi:10.1136/bmjopen-2018-021768. Blazek K, van Zwieten A, Saglimbene V, Teixeira-Pinto A. A practical guide to multiple imputation of missing data in nephrology. Kidney Int. 2021;99(1):68-74. doi:10.1016/j.kint.2020.07.035. Fortin SP, Schuemie M. Indirect covariate balance and residual confounding: An applied comparison of propensity score matching and cardinality matching. Pharmacoepidemiol Drug Saf. 2022;31(12):1242-1252. doi:10.1002/pds.5510. Lee DH, Keum N, Hu FB, et al. Predicted lean body mass, fat mass, and all cause and cause specific mortality in men: prospective US cohort study. BMJ. 2018;362:k2575. Published 2018 Jul 3. doi:10.1136/bmj.k2575. Li X, Wu C, Lu J, et al. Cardiovascular risk factors in China: a nationwide population-based cohort study [published correction appears in Lancet Public Health. 2021 May;6(5):e271. doi: 10.1016/S2468-2667(21)00075-X.]. Lancet Public Health. 2020;5(12):e672-e681. doi:10.1016/S2468-2667(20)30191-2. Ledeganck KJ, Van Eyck A, Wouters K, et al. Urinary epidermal growth factor reflects vascular health in boys with either obesity or type 1 diabetes. A role for renin, or beyond? PLoS One. 2023;18(3):e0283716. Published 2023 Mar 30. doi:10.1371/journal.pone.0283716. Sharif S, Visseren FLJ, Spiering W, et al. Arterial stiffness as a risk factor for cardiovascular events and all-cause mortality in people with Type 2 diabetes. Diabet Med. 2019;36(9):1125-1132. doi:10.1111/dme.13954. Wu C, Ma D, Chen Y. Association of Pulse Pressure Difference and Diabetes Mellitus in Chinese People: A Cohort Study. Int J Gen Med. 2021;14:6601-6608. Published 2021 Oct 11. doi:10.2147/IJGM.S327841. Gordin D, Wadén J, Forsblom C, et al. Pulse pressure predicts incident cardiovascular disease but not diabetic nephropathy in patients with type 1 diabetes (The FinnDiane Study). Diabetes Care. 2011;34(4):886-891. doi:10.2337/dc10-2013. Lin SX, Berlin I, Younge R, et al. Does elevated plasma triglyceride level independently predict impaired fasting glucose?: the Multi-Ethnic Study of Atherosclerosis (MESA). Diabetes Care. 2013;36(2):342-347. doi:10.2337/dc12-0355. Wang Y. Higher fasting triglyceride predicts higher risks of diabetes mortality in US adults. Lipids Health Dis. 2021;20(1):181. Published 2021 Dec 20. doi:10.1186/s12944-021-01614-6. Lee EY, Yang HK, Lee J, et al. Triglyceride glucose index, a marker of insulin resistance, is associated with coronary artery stenosis in asymptomatic subjects with type 2 diabetes. Lipids Health Dis. 2016;15(1):155. Published 2016 Sep 15. doi:10.1186/s12944-016-0324-2. Bobker SM, Robbins MS. COVID-19 and Headache: A Primer for Trainees. Headache. 2020;60(8):1806-1811. doi:10.1111/head.13884. Maffei S, Guiducci L, Cugusi L, et al. Women-specific predictors of cardiovascular disease risk - new paradigms. Int J Cardiol. 2019;286:190-197. doi:10.1016/j.ijcard.2019.02.005. Guo Y, Zhao M, Bo T, et al. Blocking FSH inhibits hepatic cholesterol biosynthesis and reduces serum cholesterol. Cell Res. 2019;29(2):151-166. doi:10.1038/s41422-018-0123-6. Peters AL, Henry RR, Thakkar P, Tong C, Alba M. Diabetic Ketoacidosis With Canagliflozin, a Sodium-Glucose Cotransporter 2 Inhibitor, in Patients With Type 1 Diabetes. Diabetes Care. 2016;39(4):532-538. doi:10.2337/dc15-1995. Wang Y, Fang Y, Zhang X, Wu N-Q. Non-Fasting Plasma Triglycerides Are Positively Associated with Diabetes Mortality in a Representative US Adult Population. Targets . 2024; 2(2):93-103. https://doi.org/10.3390/targets2020006. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6893931","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":474599260,"identity":"571a1ca0-fec2-4d6f-898f-8eb1c476cb01","order_by":0,"name":"Liying Wang","email":"","orcid":"","institution":"Nanjing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Liying","middleName":"","lastName":"Wang","suffix":""},{"id":474599261,"identity":"51bca064-5258-4661-92ee-7e255c983040","order_by":1,"name":"Ling Sha","email":"","orcid":"","institution":"Nanjing Jiangning Maternal and Child Health and Family Planning Service Center","correspondingAuthor":false,"prefix":"","firstName":"Ling","middleName":"","lastName":"Sha","suffix":""},{"id":474599262,"identity":"0bd2d00e-5291-4e63-bbd2-85342596bb29","order_by":2,"name":"Tianchen Wu","email":"","orcid":"","institution":"Nanjing Hospital of Chinese Medicine, Nanjing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Tianchen","middleName":"","lastName":"Wu","suffix":""},{"id":474599263,"identity":"72a75284-aac5-41bd-bff0-b103ed116578","order_by":3,"name":"Ming Li","email":"","orcid":"","institution":"Nanjing Hospital of Chinese Medicine, Nanjing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Ming","middleName":"","lastName":"Li","suffix":""},{"id":474599264,"identity":"b8ed0e68-1101-4b6c-8950-f5208faa9b80","order_by":4,"name":"Hui Yang","email":"","orcid":"","institution":"Nanjing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Yang","suffix":""},{"id":474599265,"identity":"2cf7764e-0d48-421e-801b-a8b71943b48c","order_by":5,"name":"Wenwen Kong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAq0lEQVRIiWNgGAWjYBACCQaGhAMfKiTk+EnR8vDgjDMWxpINxGthfHyYt60icQPRWiRnJCcc4J0nwbiBgfnhoxvEaJGWSEs4ILlNgtmcgc3YOIcYLXLSOQkHDLdJsFk28LBJE6kl/8OBxDkSPAYHiNUiLZ2QcOBgg4QE8Vok5z9IONhwTMJAsplYv0icOZD8+U9NXX0/e/PDx0RpQQBm0pSPglEwCkbBKMAHAI34MZ/YN/GLAAAAAElFTkSuQmCC","orcid":"","institution":"Nanjing Hospital of Chinese Medicine, Nanjing University of Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Wenwen","middleName":"","lastName":"Kong","suffix":""}],"badges":[],"createdAt":"2025-06-14 12:23:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6893931/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6893931/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85347687,"identity":"7e7ecdb7-f81b-49dd-b6c9-a47a6ebd4142","added_by":"auto","created_at":"2025-06-25 02:21:28","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":396799,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of the research process.\u003c/p\u003e","description":"","filename":"Figure1flowchart.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6893931/v1/28175c3c07681ac9d7cbda0d.jpg"},{"id":85347688,"identity":"84cb227e-391f-456e-b270-2aba3693440c","added_by":"auto","created_at":"2025-06-25 02:21:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3996553,"visible":true,"origin":"","legend":"\u003cp\u003ePSM matching standardized mean difference SMD diagram.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-6893931/v1/a0b13d09a0070d6b19b35b5c.png"},{"id":85348798,"identity":"1ecd4551-6933-4906-a2db-01c9d4de2df3","added_by":"auto","created_at":"2025-06-25 02:29:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1195542,"visible":true,"origin":"","legend":"\u003cp\u003eForest map of multi-factor model variable screening after matching.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-6893931/v1/3e61f15e2af58db40e6940d6.png"},{"id":85347679,"identity":"e1ff21ce-fd0f-4cb2-8325-7c38a95d7eb7","added_by":"auto","created_at":"2025-06-25 02:21:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":518292,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea: \u003c/strong\u003eK-M curve of unadjusted covariates.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb: \u003c/strong\u003eK-M curve of adjusted covariates.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6893931/v1/4fe6b30234237290e272d3da.png"},{"id":85347685,"identity":"da4ddba6-a431-4bec-acc6-4da412556c10","added_by":"auto","created_at":"2025-06-25 02:21:28","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1821692,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea: \u003c/strong\u003eStudy the total population with BMI≦23.9kg/m2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb: \u003c/strong\u003eStudy the total population with BMI\u0026gt;23.9kg/m2.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6893931/v1/0df6c094aacc5c66266f1dc3.png"},{"id":85348800,"identity":"9ddc077a-a739-4507-80af-b0feb763722a","added_by":"auto","created_at":"2025-06-25 02:29:28","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":821222,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea: \u003c/strong\u003eThe relationship between triglyceride and DM incidence rate in the population (male and female) with BMI≦23.9 kg/m2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb: \u003c/strong\u003eThe relationship between triglyceride and DM incidence rate in the population (male and female) with BMI＞23.9 kg/m2.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6893931/v1/128c653fdd39b2c53685507b.png"},{"id":109356127,"identity":"c504b292-1d2b-4d9a-96f5-54c26f63cd30","added_by":"auto","created_at":"2026-05-16 05:40:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7392005,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6893931/v1/99c3ffce-caae-4f55-a4f0-2eedd0b6f169.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Differential pulse pressure and triglycerides predict incident type 2 diabetes in Chinese adults: a BMI- stratified cohort analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eType 2 diabetes mellitus (T2DM) is a growing chronic disease globally, and its prevalence continues to rise, especially in developing countries. According to estimates, approximately 425\u0026nbsp;million adults aged 20 to 79 years worldwide had T2DM in 2019, and this number is expected to increase to 629\u0026nbsp;million by 2045 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The prevalence of T2DM is even more significant in the Asian region, especially in countries with high economic development, and is showing a trend of increasing yearly. However, despite the high incidence, the diagnosis rate of T2DM is only 43% [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In addition, T2DM not only affects the health status of individuals but also imposes a significant socioeconomic burden. According to the World Health Organization, chronic diseases, including T2DM, are expected to cause 73% of global deaths in 2020 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Therefore, epidemiologic studies and public health policy development for T2DM are critical to respond to this global health crisis effectively.\u003c/p\u003e \u003cp\u003eIn recent years, the relationship between differential pulse pressure and dyslipidemia and type 2 diabetes mellitus (T2DM) has received increasing attention. It has been shown that elevated differential pulse pressure is significantly associated with the risk of developing T2DM [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In addition, dyslipidemia, elevated considerably triglyceride levels, is a significant risk factor for T2DM. Studies have shown that elevated triglyceride levels are closely associated with increased insulin resistance, which further increases the risk of T2DM [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e–\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn China, with the westernization of lifestyle and population aging, the incidence of T2DM is increasing year by year, especially in the adult population. Epidemiologic studies have shown that body mass index (BMI) is positively associated with the risk of developing T2DM, but BMI thresholds vary in different ethnic groups [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e–\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]; for example, it has been found that elevated BMI is significantly associated with the occurrence of T2DM in Chinese adults, and this relationship may vary in different ethnic groups and regions [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, current studies on the relationship between differential pulse pressure, triglycerides, and T2DM are still insufficient, especially when BMI stratification is considered. There is a relative paucity of data from large-scale studies, especially in the Chinese adult population. Therefore, the present study aimed to fill this gap by analyzing the relationship between differential pulse pressure and triglycerides and the probability of T2DM under different BMI conditions to provide a more targeted and scientific basis for the prevention and management of T2DM.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e\u003c/p\u003e"},{"header":"Data and Methods","content":"\u003cp\u003e1. General information\u003c/p\u003e\n\u003cp\u003eMedical data from adults who underwent physical examination at Ruicci Healthcare Group in China were used in this study. The study dataset and related copyrights have been shared and transferred to the Dryad public database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://datadryad.org/stash\u003c/span\u003e\u003c/span\u003e) by Li et al. [\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e]. The analysis initially included a total of 685,277 subjects aged\u0026thinsp;\u0026ge;\u0026thinsp;20 years with at least two visits in 11 cities in China between 2010 and 2016, which was selected for the current study as the dataset with fasting glucose (FPG,mmol/L) final fasting glucose (FPG-Final-visit,mmol/L), age (Age, year) Gender (Gender,%) Systolic blood pressure (SBP,mmHg), systolic blood pressure (DBP,mmHg) Total cholesterol (Cholesterol,mmol/L) Triglyceride (Triglyceride,mmol/L) Glutamine transaminase (ALT,u/L) Blood urea nitrogen (BUN,mmol/L) Blood creatinine (Scr,\u0026micro;mol) /L) Family-history (Family-history,%).\u003c/p\u003e\n\u003cp\u003eInclusion criteria: 1. Fasting blood glucose\u0026thinsp;\u0026lt;\u0026thinsp;7.00 mmol/L (venous blood sampling) and/or subjects who provided previous medical records to prove that they did not have T2DM. Subjects were examined at each physical examination, whichever of the above two criteria was met first (given a diagnosis of T2DM) 2. \u0026ge;2 years between visits.\u003c/p\u003e\n\u003cp\u003eExclusion criteria: 1. missing weight and height measurements at baseline 2. missing gender information 3. extreme BMI (\u0026lt;\u0026thinsp;15 kg/m2 or \u0026gt;\u0026thinsp;55 kg/m2) 4. missing fasting blood glucose values 5. subjects with a baseline diagnosis of T2DM 6. subjects whose T2DM status was unknown at the time of follow-up visit.\u003c/p\u003e\n\u003cp\u003eThe Ethics Committee of Rui Ci Medical Group has approved the study of Li et al. The Ethics Committee of Nanjing Hospital of Traditional Chinese Medicine repeated the application for ethical approval, and informed consent was waived.\u003c/p\u003e\n\u003cp\u003e2 Statistical analysis and methods\u003c/p\u003e\n\u003cp\u003eThis study used R language (R-4.2.1 version) for data analysis. Count data were expressed as frequency (%), customarily distributed measures were described as (\u0026plusmn;\u0026thinsp;s), and skewed data were expressed as median [M(P25, P75)]. For the comparison of differences between the two groups, two independent samples t-test was used for normally distributed data, a non-parametric Mann-Whitney U test was used for non-normal distribution, and the \u0026chi;2 test was used for the comparison of differences between groups for count data. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (two-sided) was designed to be statistically significant. Missing value analysis and multiple interpolations of data were performed using the mice package to exclude variables with missing values\u0026thinsp;\u0026gt;\u0026thinsp;10% [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e]. Initial blood glucose was propensity scored (PSM) and plotted as standardized mean difference (SMD) using the MatchIt package [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e], with the caliper value set at 0.01. Cox regression models were developed, K-M (Kaplan-Meier) survival curves were plotted, and the RCS curves (restricted cubic spline) [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e] were used to study the trend between differential pulse pressure and lipids and the probability of T2DM development. The flow chart of the study (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e1. propensity score results and SMD plot\u003c/p\u003e\n\u003cp\u003eAfter the total subjects were screened, 211,833 subjects were enrolled, and after data collation, a total of 210,529 were included in this study. In this study, the follow-up time was defined as the period from the beginning of the first physical examination to the end of the last physical examination. The time of the last blood glucose measurement was regarded as the end of follow-up, and the first fasting blood glucose was included in the propensity score matching process to control for differences in baseline blood glucose levels, to reduce potential confounding bias, and to ensure that the two groups were comparable in terms of blood glucose levels, to more accurately assess the independent associations of pulse pressure difference and triglycerides with the incidence of T2DM. The standardized mean difference (SMD) plot is shown in (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The PSM method for 1:1 matching is shown in ( Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e), with 27,783 successful matches. There was no significant difference in fasting blood glucose at the time of enrollment (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBaseline characteristics of subjects based on multiple interpolations\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eUnmatched\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePSM\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup(BMI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI\u0026thinsp;≦\u0026thinsp;23.9 (N\u0026thinsp;=\u0026thinsp;182552)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI\u0026thinsp;\u0026gt;\u0026thinsp;23.9\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;27977)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI\u0026thinsp;≦\u0026thinsp;23.9\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;27783)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI\u0026thinsp;\u0026gt;\u0026thinsp;23.9\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;27783)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFPG*\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e〔M(P\u003csub\u003e25\u003c/sub\u003e -P\u003csub\u003e75\u003c/sub\u003e)mmol/〕\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.89 (4.50\u0026ndash;5.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.10 (4.70\u0026ndash;5.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.10 (4.71\u0026ndash;5.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.10 (4.70\u0026ndash;5.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e〔M(P\u003csub\u003e25\u003c/sub\u003e -P\u003csub\u003e75\u003c/sub\u003e) year〕\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38(32\u0026ndash;49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.00 (34.00\u0026ndash;53.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.00 (34.00\u0026ndash;52.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.00 (34.00\u0026ndash;53.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003emale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94051 (51.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21233 (75.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15423 (55.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21067 (75.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003efemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88501 (48.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6744 (24.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12360 (44.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6716 (24.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSBP\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e〔M(P\u003csub\u003e25\u003c/sub\u003e -P\u003csub\u003e75\u003c/sub\u003e) mmHg〕\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e116 (106\u0026ndash;127)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e128(117\u0026ndash;138)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e118(108\u0026ndash;129.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e128(117\u0026ndash;138)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDBP\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e〔M(P\u003csub\u003e25\u003c/sub\u003e -P\u003csub\u003e75\u003c/sub\u003e) mmHg〕\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72 (66\u0026ndash;80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80.00 (72.00\u0026ndash;88.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74 (67\u0026ndash;81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80 (72\u0026ndash;88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCholesterol\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eM(P\u003csub\u003e25\u003c/sub\u003e -P\u003csub\u003e75\u003c/sub\u003e) mmol/L〕\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.58 (4.03\u0026ndash;5.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.90 (4.33 5.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.67 (4.10\u0026ndash;5.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.90 (4.32\u0026ndash;5.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTriglyceride\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e〔M(P\u003csub\u003e25\u003c/sub\u003e -P\u003csub\u003e75\u003c/sub\u003e)mmol/L〕\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00 (0.70\u0026ndash;1.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.66 (1.18\u0026ndash;2.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05 (0.72\u0026ndash;1.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.66 (1.18\u0026ndash;2.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eALT\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e〔M(P\u003csub\u003e25\u003c/sub\u003e -P\u003csub\u003e75\u003c/sub\u003e) u/L〕\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.00(12.20\u0026ndash;25.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.00 (20.20\u0026ndash;46.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.00 (13.00-26.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.00 (20.20\u0026ndash;46.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBUN\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eM(P\u003csub\u003e25\u003c/sub\u003e -P\u003csub\u003e75\u003c/sub\u003e)-mmol/L〕\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.50 (3.78\u0026ndash;5.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.77 (4.05\u0026ndash;5.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.60 (3.89\u0026ndash;5.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.77 (4.05\u0026ndash;5.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eScr\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eM(P\u003csub\u003e25\u003c/sub\u003e-P\u003csub\u003e75\u003c/sub\u003e) \u0026micro;mol/L〕\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68.00(57.00\u0026ndash;80.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75.40 (65.00\u0026ndash;85.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69.00 (57.80\u0026ndash;80.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75.40 (65.00\u0026ndash;85.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFamily-history\u003c/strong\u003e(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e178871 (98.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27408 (98.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.865\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27067 (97.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27218 (98.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3681 ( 2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e569 ( 2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e716 ( 2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e565 ( 2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eRemarks: * factor variables set for this study that need to be matched\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e2. Cox regression model and subgroup analysis\u003c/p\u003e\n\u003cp\u003eUsing propensity scoring, this study first conducted a comprehensive assessment of all potential confounding variables, including age, gender, BMI, family history, and lifestyle factors. To minimize the influence of these confounding factors on the study results, we used stepwise regression to screen risk factors significantly impacting T2DM development, identifying six significant factors: Age, BMI, Gender, Pressure, Triglyceride, and ALT(P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The Cox regression model was established. The Cox regression model was established, and equal proportional risk determination was performed. The K-M (Kaplan-Meier) curves of the above six risk factors were not calibrated, and the K-M curves of the above six risk factors were not calibrated, suggesting that there was a crossover between the two curves (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). None of them could satisfy the equal proportional risk determination(Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ea, Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eb). Subgroup analysis according to BMI is reasonable.\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e suggests that when analyzing the relationship between pulse pressure difference and the risk of developing T2DM, the risk of developing T2DM was significantly increased in both groups for every 1 mmHg increase in pulse pressure difference. Specifically, for the BMI\u0026thinsp;≦\u0026thinsp;23.9kg/m\u0026sup2; group, the risk of developing T2DM increased by 1% (HR\u0026thinsp;=\u0026thinsp;1.01, 95% CI: 1.00-1.02, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while for the BMI\u0026thinsp;\u0026gt;\u0026thinsp;23.9kg/m\u0026sup2; group, the risk of developing T2DM increased by 3% (HR\u0026thinsp;=\u0026thinsp;1.03, 95% CI: 1.01\u0026ndash;1.05, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This result suggests that an increase in differential pulse pressure is strongly associated with an increased risk of developing T2DM at all BMI levels and that this increased risk is more pronounced in the obese population.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eInfluence coefficient of pulse pressure difference and triglyceride on DM after controlling confounding factors by stratified analysis\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGroup(BMI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eBMI\u0026thinsp;≦\u0026thinsp;23.9\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;27783)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eBMI\u0026thinsp;\u0026gt;\u0026thinsp;23.9\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;27783)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHazard ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHazard ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05\u0026ndash;1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u0026ndash;1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003emale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003efemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.43\u0026ndash;0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.49\u0026ndash;0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePulse-pressure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u0026ndash;1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u0026ndash;1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTriglyceride\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.18\u0026ndash;1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.10\u0026ndash;1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eALT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u0026ndash;1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00-1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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\u003e3. Trend test analysis between differential pulse pressure and the development of T2DM\u003c/p\u003e\n\u003cp\u003eIn the BMI\u0026thinsp;≦\u0026thinsp;23.9kg/m2 group, the relationship between pulse pressure difference and the probability of T2DM incidence, as shown in (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ea), RCS (p for non-linear \u0026lt;0.001), showed a non-linear change, and the likelihood of disease appeared to increase when the pulse pressure difference exceeded 36mmHg. In the BMI\u0026thinsp;\u0026gt;\u0026thinsp;23.9kg/m2 group, as shown in (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eb), RCS (p for nonlinear\u0026thinsp;=\u0026thinsp;0.404), the probability of disease showed a linear relationship, with an increase in the point of rise at about 33mmHg.\u003c/p\u003e\n\u003cp\u003e4. Trend test analysis between triglycerides and T2DM incidence\u003c/p\u003e\n\u003cp\u003eConsidering the differences in triglyceride changes by gender [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e], group analysis was performed according to gender. In the BMI\u0026thinsp;≦\u0026thinsp;23.9kg/m2 group(Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ea), RCS (p for non-linear \u0026lt;0.001), the non-linear trend of triglyceride was 2.84mmol/L (the highest point of slope), the probability of incidence was about 4.68 in the male group and about 2.64 in the female group. In the BMI\u0026gt;23.9kg/m2 group(Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eb), RCS (p for non-linear \u0026lt;0.001), the trend of incidence was about 4.68 in the male group and about 2.64 in the female group. Non-linear\u0026thinsp;\u0026lt;\u0026thinsp;0.001), a non-linear trend with triglycerides of 2.34 mmol/L (the highest point of the slope), the probability of onset was about 2.36 in the male group and about 1.48 in the female group.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, the relationship between pulse pressure difference and triglycerides and the probability of developing T2DM was investigated based on retrospective cohort data from a Chinese adult population, with attention to differences in different BMI conditions. Using propensity score matching (PSM) and Cox proportional risk regression modelling, we found that both differential pulse pressure and triglycerides were significantly associated with the risk of T2DM incidence, and this relationship differed significantly across BMI conditions.\u003c/p\u003e \u003cp\u003eDifferential pulse pressure and risk of T2DM: Differential pulse pressure is the difference between systolic and diastolic blood pressure, reflecting the maximum and minimum circulating pressures during the cardiac cycle. Studies have shown that increased pulse pressure difference is strongly associated with the risk of developing type 2 diabetes mellitus (T2DM). In the non-obese group (BMI\u0026thinsp;\u0026le;\u0026thinsp;23.9 kg/m\u0026sup2;), each one mmHg increase in pulse pressure difference was associated with a 1% increase in the risk of developing T2DM (HR\u0026thinsp;=\u0026thinsp;1.01, 95% CI: 1.00-1.02, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), whereas in the obese group (BMI\u0026thinsp;\u0026gt;\u0026thinsp;23.9 kg/m\u0026sup2;), this risk increased by 3% (HR\u0026thinsp;=\u0026thinsp;1.03, 95% CI: 1.01\u0026ndash;1.05, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) These results suggest that an increase in differential pulse pressure is associated with an increased risk of developing T2DM across BMI levels, and that this increased risk is more pronounced in the obese population. This finding is consistent with previous studies and suggests that the management of differential pulse pressure has an essential role in the prevention of T2DM, especially in the obese population [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn addition, elevated differential pulse pressure has been associated with deterioration in cardiovascular health. Studies have shown that differential pulse pressure is not only a significant predictor of cardiovascular events but may also exacerbate the risk of related complications in diabetic patients [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Therefore, monitoring and management of differential pulse pressure may provide new ideas and strategies for the prevention and treatment of T2DM.\u003c/p\u003e \u003cp\u003eTriglycerides and risk of developing T2DM: Elevated triglyceride levels are an essential risk factor for T2DM. In this study, the non-linear relationship between triglycerides and the probability of developing T2DM was further revealed by the restricted cubic spline curve (RCS) method. In the non-obese group, the risk of developing T2DM was significantly increased when the triglyceride level reached 2.84 mmol/L, while in the obese group, the risk of developing T2DM was similarly considerably increased when the triglyceride level reached 2.34 mmol/L, which was a similar finding to that of previous studies [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In addition, it was found that at the same triglyceride level, the risk of T2DM in men was approximately twice as high as that in women, which may be related to maladaptive lifestyle habits (e.g., smoking and alcohol consumption) as well as physiologic differences (e.g., hormone levels) in men [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. These findings suggest that the management of triglyceride levels in populations with different BMIs and genders should involve more targeted interventions to reduce the risk of developing T2DM. The endogenous hormonal environment, such as serum follicle-stimulating hormone, estrogen, and other endogenous hormones, influences triglyceride and cholesterol levels in women. After menopause, changes in hormone levels in women cause dyslipidemia in women, and women appear to be at an increased risk for metabolic disorders such as diabetes mellitus and cardiovascular disease [\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In addition, studies have shown that elevated triglyceride levels are closely related to insulin resistance, a mechanism that may be characterized differently in populations of different genders and body mass indexes [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Therefore, personalized intervention strategies for different populations can more effectively reduce T2DM risk. Future studies should continue to explore the role of differential pulse pressure and triglycerides in different populations to provide more precise strategies for the prevention and treatment of T2DM.\u003c/p\u003e\n\u003ch3\u003eStrengths and limitations of the study\u003c/h3\u003e\n\u003cp\u003eThis study had a large sample size and used propensity score matching (PSM) and Cox proportional risk regression models to control confounding bias and ensure the scientific validity of the conclusions. The RCS curves revealed a non-linear relationship between differential pulse pressure and triglycerides and the probability of T2DM, and the gender-stratified analyses supported personalized management. However, the data relied on public databases, and the accuracy of follow-up may be limited, underestimating the incidence of T2DM. The study was limited in the extrapolation of conclusions by targeting only Chinese adults, and the retrospective design did not allow for a complete determination of causality. It did not address specific interventions, which need to be further explored in future studies.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe results of this study have clinical implications for the prevention and management of type 2 diabetes mellitus (T2DM). First, the management of differential pulse pressure should be considered an essential measure for the prevention of T2DM, especially in the obese population. Secondly, monitoring and management of triglyceride levels should vary among populations with different BMIs and gender. For the low BMI population, more attention needs to be paid to the changes in triglyceride levels, and timely interventions should be taken. In contrast, for the high BMI population, although the effect of triglyceride levels is still significant, the overall risk level is relatively lower than that of the low BMI population. Therefore, other risk factors should be taken into account for comprehensive management.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u0026nbsp;Owing to the ethics committee of Rich Healthcare Group having approved the previous research, repeated application of the current study for ethical approval and informed consent were exempted by Nanjing Hospital of Traditional Chinese Medicine (ethical review No. 2021-067). We confirm that all methods are implemented following relevant guidelines and regulations. The designated agency approves all experimental protocols.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no financial or non-financial competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research data set and relevant copyright have been shared by Li et al.\u0026nbsp;and transferred to the public database of Dryad(https://datadryad.org/stash).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have declared that no competing interests exist.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Nanjing Medical Science and Technology Development Special Fund Project (Grant No. YKK20170).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudy design: Wenwen Kong, Liying Wang, Ling Sha.\u003c/p\u003e\n\u003cp\u003eData analysis and interpretation: Liying Wang, Ling Sha, Wenwen Kong, Tianchen Wu, Hui Yang\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eImportant knowledge content: Tianchen Wu, Wenwen Kong, Ling Sha.\u003c/p\u003e\n\u003cp\u003eFigure preparation and organization: Hui Yang, Tianchen Wu\u003c/p\u003e\n\u003cp\u003eCritical modification: Wenwen Kong, Hui Yang.\u003c/p\u003e\n\u003cp\u003eManuscript Revision: Hui Yang, Tianchen Wu, Wenwen Kong, Ling Sha, Ming Li.\u003c/p\u003e\n\u003cp\u003eLiying Wang and Ling Sha contributed equally to this work.\u003c/p\u003e\n\u003cp\u003eWenwen Kong and Tianchen Wu are the guarantors of this work, so they can fully access all the data in the study and are responsible for the integrity of the data and the accuracy of the data analysis.\u003c/p\u003e\n\u003cp\u003eAll authors have read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors gratefully acknowledge the support provided by the families of the main authors ( Wenquan Wu, Yue Dai, and Yijia Kong) during this research.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKhan MAB, Hashim MJ, King JK, Govender RD, Mustafa H, Al Kaabi J. Epidemiology of Type 2 Diabetes - Global Burden of Disease and Forecasted Trends. J Epidemiol Glob Health. 2020;10(1):107-111. doi:10.2991/jegh.k.191028.001.\u003c/li\u003e\n\u003cli\u003eZuhur, Şenay and Nurhan \u0026Ouml;zpancar. The Effect of Care Needs on Quality of Life and Chronic Disease Care in Patients with Diabetes.Turkish Journal of Family Medicine and Primary Care (2020);14(1): 56-65. DOI: 10.21763/tjfmpc.693078.\u003c/li\u003e\n\u003cli\u003ePark, M.,\u0026amp;Heo, Y.J. Biosensing Technologies for Chronic Diseases. \u003cem\u003eBiochip Journal\u003c/em\u003e, 2021;\u003cem\u003e15\u003c/em\u003e(1). https://doi.org/10.1007/s13206-021-00014-3.\u003c/li\u003e\n\u003cli\u003eQI Q, LIANG L, DORIA A, et al. Genetic predisposition to dyslipidemia and type 2 diabetes risk in two prospective cohorts[J]. Diabetes, 2012;61(3): 745-752. DOI: 10.2337/db11-1254.\u003c/li\u003e\n\u003cli\u003eQi Q, Liang L, Doria A, Hu FB, Qi L. Genetic predisposition to dyslipidemia and type 2 diabetes risk in two prospective cohorts. Diabetes. 2012;61(3):745-752. doi:10.2337/db11-1254.\u003c/li\u003e\n\u003cli\u003eRao, Dr. P. Hanumantha and Dr. G. Mohan Reddy. A cross-sectional study to determine the lipid profile derangement in newly diagnosed type-2 diabetic patients.International Journal of Advanced Research in Medicine.2019;1(1): 49-52. DOI: 10.22271/27069567.2019.v1.i1a.339.\u003c/li\u003e\n\u003cli\u003eReddy, Pillaram \u0026amp; Jayarama, N \u0026amp; Mahesh, V. Relation between waist-hip ratio and lipid profile in type 2 diabetes mellitus patients. Asian Journal of Medical Sciences. 2014;5(3): 51-53. DOI: 10.3126/ajms.v5i3.9407.\u003c/li\u003e\n\u003cli\u003eLu J, Lam SM, Wan Q, et al. High-Coverage Targeted Lipidomics Reveals Novel Serum Lipid Predictors and Lipid Pathway Dysregulation Antecedent to Type 2 Diabetes Onset in Normoglycemic Chinese Adults. Diabetes Care. 2019;42(11):2117-2126. doi:10.2337/dc19-0100.\u003c/li\u003e\n\u003cli\u003eJoshi SR, Anjana RM, Deepa M, et al. Prevalence of dyslipidemia in urban and rural India: the ICMR-INDIAB study. PLoS One. 2014;9(5):e96808. Published 2014 May 9. doi:10.1371/journal.pone.0096808.\u003c/li\u003e\n\u003cli\u003eChen Y, Zhang XP, Yuan J, et al. Association of body mass index and age with incident diabetes in Chinese adults: a population-based cohort study. BMJ Open. 2018;8(9):e021768. Published 2018 Sep 28. doi:10.1136/bmjopen-2018-021768.\u003c/li\u003e\n\u003cli\u003eBlazek K, van Zwieten A, Saglimbene V, Teixeira-Pinto A. A practical guide to multiple imputation of missing data in nephrology. Kidney Int. 2021;99(1):68-74. doi:10.1016/j.kint.2020.07.035.\u003c/li\u003e\n\u003cli\u003eFortin SP, Schuemie M. Indirect covariate balance and residual confounding: An applied comparison of propensity score matching and cardinality matching. Pharmacoepidemiol Drug Saf. 2022;31(12):1242-1252. doi:10.1002/pds.5510.\u003c/li\u003e\n\u003cli\u003eLee DH, Keum N, Hu FB, et al. Predicted lean body mass, fat mass, and all cause and cause specific mortality in men: prospective US cohort study. BMJ. 2018;362:k2575. Published 2018 Jul 3. doi:10.1136/bmj.k2575.\u003c/li\u003e\n\u003cli\u003eLi X, Wu C, Lu J, et al. Cardiovascular risk factors in China: a nationwide population-based cohort study [published correction appears in Lancet Public Health. 2021 May;6(5):e271. doi: 10.1016/S2468-2667(21)00075-X.]. Lancet Public Health. 2020;5(12):e672-e681. doi:10.1016/S2468-2667(20)30191-2.\u003c/li\u003e\n\u003cli\u003eLedeganck KJ, Van Eyck A, Wouters K, et al. Urinary epidermal growth factor reflects vascular health in boys with either obesity or type 1 diabetes. A role for renin, or beyond? PLoS One. 2023;18(3):e0283716. Published 2023 Mar 30. doi:10.1371/journal.pone.0283716.\u003c/li\u003e\n\u003cli\u003eSharif S, Visseren FLJ, Spiering W, et al. Arterial stiffness as a risk factor for cardiovascular events and all-cause mortality in people with Type 2 diabetes. Diabet Med. 2019;36(9):1125-1132. doi:10.1111/dme.13954.\u003c/li\u003e\n\u003cli\u003eWu C, Ma D, Chen Y. Association of Pulse Pressure Difference and Diabetes Mellitus in Chinese People: A Cohort Study. Int J Gen Med. 2021;14:6601-6608. Published 2021 Oct 11. doi:10.2147/IJGM.S327841.\u003c/li\u003e\n\u003cli\u003eGordin D, Wad\u0026eacute;n J, Forsblom C, et al. Pulse pressure predicts incident cardiovascular disease but not diabetic nephropathy in patients with type 1 diabetes (The FinnDiane Study). Diabetes Care. 2011;34(4):886-891. doi:10.2337/dc10-2013.\u003c/li\u003e\n\u003cli\u003eLin SX, Berlin I, Younge R, et al. Does elevated plasma triglyceride level independently predict impaired fasting glucose?: the Multi-Ethnic Study of Atherosclerosis (MESA). Diabetes Care. 2013;36(2):342-347. doi:10.2337/dc12-0355.\u003c/li\u003e\n\u003cli\u003eWang Y. Higher fasting triglyceride predicts higher risks of diabetes mortality in US adults. Lipids Health Dis. 2021;20(1):181. Published 2021 Dec 20. doi:10.1186/s12944-021-01614-6.\u003c/li\u003e\n\u003cli\u003eLee EY, Yang HK, Lee J, et al. Triglyceride glucose index, a marker of insulin resistance, is associated with coronary artery stenosis in asymptomatic subjects with type 2 diabetes. Lipids Health Dis. 2016;15(1):155. Published 2016 Sep 15. doi:10.1186/s12944-016-0324-2.\u003c/li\u003e\n\u003cli\u003eBobker SM, Robbins MS. COVID-19 and Headache: A Primer for Trainees. Headache. 2020;60(8):1806-1811. doi:10.1111/head.13884.\u003c/li\u003e\n\u003cli\u003eMaffei S, Guiducci L, Cugusi L, et al. Women-specific predictors of cardiovascular disease risk - new paradigms. Int J Cardiol. 2019;286:190-197. doi:10.1016/j.ijcard.2019.02.005.\u003c/li\u003e\n\u003cli\u003eGuo Y, Zhao M, Bo T, et al. Blocking FSH inhibits hepatic cholesterol biosynthesis and reduces serum cholesterol. Cell Res. 2019;29(2):151-166. doi:10.1038/s41422-018-0123-6.\u003c/li\u003e\n\u003cli\u003ePeters AL, Henry RR, Thakkar P, Tong C, Alba M. Diabetic Ketoacidosis With Canagliflozin, a Sodium-Glucose Cotransporter 2 Inhibitor, in Patients With Type 1 Diabetes. Diabetes Care. 2016;39(4):532-538. doi:10.2337/dc15-1995.\u003c/li\u003e\n\u003cli\u003eWang Y, Fang Y, Zhang X, Wu N-Q. Non-Fasting Plasma Triglycerides Are Positively Associated with Diabetes Mortality in a Representative US Adult Population. \u003cem\u003eTargets\u003c/em\u003e. 2024; 2(2):93-103. https://doi.org/10.3390/targets2020006.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"T2DM, differential pulse pressure, triglycerides, restrictive triple spline","lastPublishedDoi":"10.21203/rs.3.rs-6893931/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6893931/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eto investigate the relationship between differential pulse pressure, triglycerides, and the probability of type 2 diabetes mellitus (T2DM) incidence under different BMI conditions in the Chinese adult population.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThis study utilized a retrospective cohort study design, with data from 211,833 adults who underwent physical examinations at Ruicci Healthcare Group in China between 2010 and 2016. Based on the BMI index, the study participants were categorized into the BMI ≤23.9 kg/m² group and the BMI \u0026gt;23.9 kg/m² group. The two groups were matched 1:1 by propensity score matching (PSM) method using first fasting glucose as the matching factor to control for potential confounding bias. After successful matching, Cox proportional risk regression models were developed to analyze the relationship between pulse pressure difference, triglycerides, and the risk of developing T2DM. In addition, the restricted cubic spline curve (RCS) method was applied to fit the non-linear relationship between differential pulse pressure and triglycerides and the probability of developing T2DM.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e In analyzing the relationship between pulse pressure difference and the risk of developing T2DM, each one mmHg increase in pulse pressure difference significantly increased the risk of developing T2DM in both groups. Specifically, the risk of developing T2DM increased by 1% in the non-obese group (HR=1.01, 95% CI: 1.00-1.02, P\u0026lt;0.05) and by 3% in the obese group (HR=1.03, 95% CI: 1.01-1.05, P\u0026lt;0.001).RCS analysis showed that the non-obese group with a pulse pressure difference of \u0026gt;36 mmHg had a T2DM with a progressively higher probability risk; in the obese group, the relationship was linear, with a more significant risk of developing T2DM with a pulse pressure difference greater than 33 mmHg. In the relationship between triglycerides and the probability of developing T2DM, for every 1 mmol/L increase in triglycerides in the non-obese group, the risk of developing T2DM increased by 21% (HR=1.21, 95% CI: 1.18-1.24); for every 1 mmol/L increase in triglycerides in the obese group, the risk of developing T2DM increased by 13% (HR=1.13, 95% CI: 1.10-1.16). 1.16). The relationship between triglycerides and the probability of developing T2DM in both groups showed a non-linear change in a fast and then a slow manner. At the same triglyceride level, the risk probability of developing T2DM was approximately twice as high in men as in women.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e The likelihood of incidence of differential pulse pressure and T2DM varies according to BMI, and blood pressure management and control of differential pulse pressure should be more stringent in people with large BMI. In terms of lipid management, the population with lower BMI may have a higher probability of developing T2DM than those with higher BMI, possibly due to differences in lipid tolerance.\u003c/p\u003e","manuscriptTitle":"Differential pulse pressure and triglycerides predict incident type 2 diabetes in Chinese adults: a BMI- stratified cohort analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-25 02:21:23","doi":"10.21203/rs.3.rs-6893931/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e4f21b4c-7ac5-46ee-b393-2a3046c3d779","owner":[],"postedDate":"June 25th, 2025","published":true,"recentEditorialEvents":[{"type":"decision","content":"Withdrawn","date":"2026-05-16T05:24:23+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-16T05:39:59+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-25 02:21:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6893931","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6893931","identity":"rs-6893931","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}