Impact of hyperhomocysteinemiaon carotid intima-media thickness in type 2 diabetes mellituswith or without hypertension: a retrospective observational study

Impact of hyperhomocysteinemiaon carotid intima-media thickness in type 2 diabetes mellituswith or without hypertension: a retrospective observational study | 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 Impact of hyperhomocysteinemiaon carotid intima-media thickness in type 2 diabetes mellituswith or without hypertension: a retrospective observational study Li Ao, Ling liu, Lixia Suo, Dongyu Liang, Jingya Niu, Qin Yang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3988060/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 Background: The influence of hyperhomocysteinemia on carotid intima-media thickness (CIMT) in patients with type 2 diabetes mellitus (T2DM) and its interplay with hypertension remains uncertain. This study aimed to investigate the impact of hyperhomocysteinemia on CIMT in patients with T2DM, both with and without coexisting hypertension. Methods: This retrospective observational study was conducted at Shanghai Jiading District Central Hospital, enrolling patients with type 2 diabetes mellitus (T2DM) between January 2019 and December 2020. Data on serum homocysteine levels and carotid intima-media thickness (CIMT) were collected. Multivariable logistic regression analysis was employed to investigate the potential risk factors associated with CIMT in the entire cohort of T2DM patients and within specific hypertension subgroups. Results: The study sample comprised 423 patients diagnosed with T2DM, with a mean age of 59.99±14.59 years, including 258 males (60.99%), 258 hypertension patients (60.99%). Multivariable logistic regression analysis revealed a significant association between hyperhomocysteinemia and carotid intima-media thickness (CIMT) in all T2DM patients (odds ratio [OR]=2.942, 95% confidence interval [CI]: 1.356-6.386, P=0.006) and among patients with coexisting hypertension (OR=2.840, 95%CI: 1.153-6.999, P=0.040). However, no significant association was observed between hyperhomocysteinemia and CIMT in patients without hypertension (OR=1.123, 95%CI: 0.329-3.383, P=0.194). Conclusion: The findings highlight hyperhomocysteinemia as an independent risk factor for CIMT thickening in patients with T2DM, particularly among those with concomitant hypertension. These results underscore the importance of managing hyperhomocysteinemia in T2DM patients, particularly those with hypertension, to mitigate the risk of CIMT thickening and subsequent cardiovascular events. type 2diabetes mellitus hyperhomocysteinemia carotid intima-media thickness hypertension INTRODUCTION The global prevalence of diabetes mellitus is about 536.6 million people, with type 2 diabetes mellitus (T2DM) accounting for above 90%(1). In China, 141 million individuals were suffering from T2DM from 2013 to 2021 (2). Hypertension, a common comorbidity in patients with T2DM, was found in 50% to 80% of adult T2DM patients (3).Hyperhomocysteinemia (HHCY) denotes increased concentrations of homocysteine, and its prevalence is estimated to be approximately 5% in the general population(4). HHCY can stem from heightened homocysteine production and/or reduced homocysteine clearance, often linked to various factors such as advancing age, smoking, coffee consumption, alcohol intake, chronic kidney disease, hepatic impairment, systemic lupus erythematosus, diabetes mellitus, and hypothyroidism, as well as vitamin deficiencies and genetic abnormalities(4). Macroangiopathy is a common complication and the main cause of death in patients with diabetes mellitus (DM), accounting for over 60% of the total mortality (5). Carotid intima-media thickness (CIMT) is an index for detecting subclinical atherosclerosis and was proven to have satisfying predictive value for the occurrence of atherosclerotic heart disease and stroke(6). Patients with DM have a thicker CIMT, representing a higher atherosclerotic burden (7-9). HHCY is an important risk factor for macroangiopathy(10, 11). Each 5-µmol/L increase in blood homocysteine levels is associated with a significant increase in cardiovascular risk (12), and homocysteine levels >15 µmol/L can help predict cardiovascular events in Chinese guidelines(13). HHCY is associated with DM (14)and hypertension (15), and HHCY and hypertension also have synergistic impacts on cardiovascular risk (16). Studies in Chinese patients revealed an interaction between HHCY and hypertension on CIMT (17)or early carotid artery atherosclerosis (18). The frequent association of both cardiovascular risk factors makes the joint analysis of the association interesting.Therefore, this study aimed to explore the impact of HHCY on CIMT in patients with T2DM and examine the impact of hypertension. METHODS Study design and patients This retrospective observational study enrolled patients with T2DM at the Department of Endocrinology of Shanghai Jiading District Central Hospital between January 2019 and December 2020. T2DM was diagnosed according to the 2013World Health Organization criteria for T2DM(19). Both CIMT and plasma homocysteine levels were routinely measured in with T2DM. The exclusion criteria were: 1) patients with diabetic ketosis or hyperosmolar coma; 2) patients with severe cardiac, hepatic, or renal insufficiency; 3) missing homocysteine levels; or 4) missing CIMT evaluation. The study was approved by the Ethics Committee of Shanghai Jiading District Central Hospital.The need for individual informed consent was waived by the committee owing to the retrospective nature of the study. Data collection The demographical data of the patients were collected, including age, sex, course of the disease, blood pressure, smoking history, history of hypertension, history of cerebral infarction, fasting and 2-h postprandial C-peptide levels, serum creatinine (SCR), plasma urea, urinary albumin creatinine ratio (UACR), glycosylated hemoglobin (HbA1c), uric acid (UA), serum cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), triglyceride (TG), and standard deviation of the blood sugar level (SDBG). Smoking history was defined as non-smoker when never tried smoking or never smoked a whole cigarette and as smoker when smoked regularly or tried smoking previous 30 days .SDBG was calculated according to the “Expert Consensus on Glycemic Fluctuation Management in Diabetic Patients”(20).CIMT was routinely measured by color Doppler flow imaging with a probe of 9-12 MHz (CDFI, GE Vivid E9, Norway). The bilateral carotid arteries were examined by a trained physician expert in ultrasound imaging for >15 years. The measurements were repeated three times, and the average value was recorded. CIMT of > 0.9 mm was considered abnormal according to the “The Guidelines for Prevention and Control of Hypertension”(21). Statistical analysis The statistical analysis was conducted utilizing SPSS 19.0 (IBM, Armonk, NY, USA). Continuous data following a normal distribution were expressed as means ± standard deviation (SD) and analyzed using Student’s t-test. Alternatively, data not adhering to a normal distribution were presented as medians (interquartile range [IQR]) and analyzed employing the Wilcoxon rank-sum test. Categorical data were presented as n (%) and evaluated using the chi-squared test or Fisher’s exact test, as appropriate. Multivariable logistic regression analysis was applied to examine the association between HHCY and carotid intima-media thickness (CIMT), with the backward stepwise method utilized for independent variable entry. P-values less than 0.05 were considered statistically significant, and all tests were two-sided. RESULTS A total of 423 patients (258 males) with T2DM were included in this study. The mean age was 59.99±14.59 years old. 132 (31.2%) were in the H-Hcy group, and 291 (168.8%) were in the Non-HHCY group, respectively. There were significant differences in gender, age, hypertension, cerebral infarction, postprandial plasma glucose , fasting C-peptide, 2-h C-peptide, blood urea , SCR , UA homocysteine , C reactive protein, CIMT , smoking between two groups .There were no significant differences in the course of disease, systolic blood pressure, diastolic blood pressure, body mass index, HbA1c, HDL-C,LDL-C, triglycerides (TG), total cholesterol (TC), UACR, postprandial glucose excursion (PPGE), standard deviation of blood glucose level (SDBG), and largest amplitude of glycemic excursions (LAGE) between the patients with vs. without HHCY (all P>0.05) ( Table 1 ). Among patients with hypertension, compared to patients without HHCY, those with HHCY were significantly older (67.32±12.82 vs. 62.82±13.11, P=0.007), showed more cases of cerebral infarction (26.26% vs. 12.58%, P=0.005), higher fasting C-peptide(2.56 (1.61-3.60) vs. 1.70 (1.04-2.50), P<0.001), higher 2-h C-peptide (5.94(3.58-9.94) vs. 3.87(2.53-6.23), P<0.001), higher blood urea (6.50(5.40-8.33) vs. 5.2(4.5-6.5), P<0.001), higher SCR (89.00(70.00-113.00) vs. 65.00(58.00-78.00), P<0.001), higher UA (354.19±44.96 vs. 314.88±47.75, P=0.001), higher homocysteine(18.54(16.50-20.91) vs. 11.56(9.84-13.10), P<0.001), higher CRP (4.00(2.08-11.13) vs. 2.85(1.50-6.18), P=0.028), and thicker CIMT (0.84±0.15 vs. 0.74±0.17, P<0.001). Among the patients without hypertension, compared to patients without HHCY, those with HHCY showed significantly more male (90.91% vs. 51.52%, P<0.001), higher fasting C-peptide (2.09(1.26-3.61) vs.1.36(0.83-2.08), P=0.004), higher 2-h C-peptide (5.23(3.41-7.61) vs. 3.57(1.90-5.74), P=0.019), more smokers (45.45% vs. 18.94%, P=0.001), higher blood urea (5.20(4.80-7.20) vs. 5.10(4.05-6.10), P=0.032), higher SCR(73.00(58.50-81.00) vs. 60.00(47.75-69.00), P=0.002), higher UA (327.59±125.66 vs. 274.17±86.49 P=0.006), and higher homocysteine (18.60(16.65-24.08) vs. 10.34(8.45-11.95), P<0.001)( Table 2 ). The multivariable logistic regression analysis showed that HHCY was significantly associated with CIMT in all patients (odds ratio [OR]=2.942, 95%confidence interval [CI]:1.356-6.386,P=0.006)and in patients with hypertension (OR=2.840,95%CI:1.153-6.999,P=0.040), while HHCY was not significantly associated with CIMT in patients without hypertension (OR=1.123,95%CI: 0.329-3.383,P=0.194).Age was associated with CIMT in all patients (OR=1.030, 95%CI:1.002-1.059, P=0.036). Fasting C-peptide was associated with CIMT in patients without hypertension (OR=1.777, 95%CI:1.005-3.143, P=0.048) ( Table 3 ). Supplementary Table S1 shows the comparison of the characteristics between patients with CIMT ≥0.9 vs. <0.9. DISCUSSION The study findings established HHCY as a risk factor for CIMT thickening in patients with T2DM, particularly among those concurrently dealing with hypertension. From a clinical perspective, it underscores the importance of monitoring blood homocysteine levels in patients with T2DM and hypertension and promptly initiating appropriate management strategies upon detection of elevated homocysteine levels. Studies have shown that the homocysteine levels of T2DM patients were higher than in non-T2DM patients (14, 22). A meta-analysis that included 4011 patients with T2DM and 4303 normal controls showed that the increase of homocysteine levels was causally related to the occurrence and development of DM(23), which was mainly related to poor blood glucose control of T2DM(24), insulin resistance(25), and dyslipidemia(26).Those results have clinical implications that T2DM is an independent factor of cardiovascular events(27, 28). HHCY is also independently associated with cardiovascular events (12, 13); therefore, the combination of T2DM and HHCY should additively or synergistically increase the risk of cardiovascular events. This study showed that HHCY was an independent risk factor of CIMT thickening in T2DM patients, consistent with the results of some previous studies (11, 29, 30).Indeed, Park et al. (11) showed that renal function impairments in patients with HHCY may worsen CIMT by increasing serum homocysteine levels. Kundi et al. (29) and Devasia et al. (30) showed that homocysteine levels were positively correlated with CIMT. The mechanisms are mainly because HHCY increases vascular endothelial injury, promotes vascular smooth muscle hypertrophy, exacerbates vessel wall inflammation, interferes with vasodilatation, increases thrombotic potential, and inhibits endothelial cell growth(31). The Hoorn study (32) showed that high plasma homocysteine level was a determinant of thicker CIMT in diabetic and non-diabetic individuals, which might help explain why HHCY is an especially strong risk factor for atherothrombosis amongT2DM patients.A study in obese subjects indicated that total homocysteine might be considered a component of insulin resistance syndrome(33).Nevertheless, a recent cross-sectional study in the middle-aged Chinese population found that increased homocysteine levels were not significantly associated with early carotid artery atherosclerosis(34).The discrepancy might be related to the different age groups of subjects included in the study, since the mean age of the patients in the present study was 60 years old, while previous studies may be younger or older (11, 32), the groups were age- and sex-matched (33), the patients had comorbid obesity, or the study population did not include Chinese patients, and so on. In a prospective study involving patients with type 1 diabetes mellitus, the findings suggested that monitoring homocysteine levels might not be necessary, as CIMT did not demonstrate progression(35).HHCY and the presence of cardiovascular disease were associated with CIMT in patients undergoing continuous ambulatory peritoneal dialysis (36). A recent study found a direct effect of homocysteine on the atherogenic process during metabolic syndrome with higher CIMT in patients with HHCY(37). The mechanisms leading to thicker CIMT in patients with HHCY are poorly understood. Nevertheless, it is hypothesized that the higher propensity for atherosclerosis observed in HHCY is due to endothelial dysfunction and injury followed by platelet activation and thrombus formation (30). Increased oxidative stress and decreased antioxidant defenses could also be involved (38).HHCY can also lead to NADPH oxidase activation, leading to oxidative vascular injury (39). This study further explored subgroups of patients with and without hypertension, which was a novelty of the present study. A previous study in patients with T2DM without hypercholesterolemia suggested that HHCY was correlated with carotid artery damage (40). Liu et al. (17) reported that the combination of prehypertension and HHCY was an independent risk factor of subclinical atherosclerosis in asymptomatic Chinese, but isolated prehypertension or HHCY was not. The present study showed that HHCY contributed to CIMT in patients with hypertension but not in those without hypertension, as supported by Liu et al. (17). On the other hand, in the non-hypertension subgroup, HHCY was not independently associated with CIMT, and only C-peptide was. HHCY can promote hypertension (17), and HHCY and hypertension have been reported to act synergistically to aggravate the cardiovascular risk (16).Since CIMT is a surrogate of the cardiovascular risk in T2DM(7), the combination of hypertension and HHCY contributed to a thicker CIMT, supported by Liu et al. (17), while HHCY without hypertension was not associated with CIMT, also as supported by Liu et al. (17). A study in patients with chronic kidney disease showed that HHCY with hypertension and hypertension alone led to thicker CIMT compared t o normotension with or without HHCY(41). A study showed that blood glucose fluctuations were independently associated with CIMT in T2DM but not C-peptide levels(21).Still, C-peptide participates in atherosclerosis since it induces vascular smooth cell proliferation (42, 43).The present study might suggest that the contribution of C-peptide to CIMT is weaker than hypertension and disappears in hypertensive patients. Additional studies are necessary to examine the impacts and interactions of HHCY,hypertension, and C-peptide on CIMT in patients with T2DM. Nevertheless, several limitations warrant acknowledgment in this study. Firstly, the study was conducted at a single center, and the sample size was comparatively small, potentially impacting the generalizability of the findings. Secondly, the retrospective nature of the study constrained the analyses to the data that were readily accessible in the patient charts, potentially restricting the scope of the investigation. Additionally,, the cross-sectional design of the study precluded the exploration of causal relationships between variables, limiting the ability to establish cause-and-effect associations. In conclusion, the study emphasizes HHCY as a risk factor for CIMT thickening in patients with T2DM, particularly those with comorbid hypertension. Nonetheless, the potential relevance of monitoring blood homocysteine levels in individuals with diabetes and hypertension warrants consideration. Future investigations should aim to elucidate the underlying mechanisms linking HHCY and CIMT in patients with T2DM, both with and without concurrent hypertension. Declarations Competing Interest All authors declared that they have no competing interests. Funding The study was supported by the Key Subjects of Jiading District (#2020-jdyxzdxk-04) , theShanghai Health Medical College Hundred Teachers Talent Project, and the Clinical Research Center of Shanghai University of Medicine & Health Sciences (#20MC2020004). Acknowledgments The authors acknowledged the members of the Nursing Department and Central Laboratory for their great help in the completion of this thesis. References Sun H, Saeedi P, Karuranga S, et al. IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. 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Basu A, Jenkins AJ, Stoner JA, Thorpe SR, Klein RL, Lopes-Virella MF, et al. Plasma total homocysteine and carotid intima-media thickness in type 1 diabetes: a prospective study. Atherosclerosis. 2014;236(1):188-95. Pawlak K, Mysliwiec M, Pawlak D. Hyperhomocysteinemia and the presence of cardiovascular disease are associated with kynurenic acid levels and carotid atherosclerosis in patients undergoing continuous ambulatory peritoneal dialysis. Thrombosis research. 2012;129(6):704-9. Piazzolla G, Candigliota M, Fanelli M, Castrovilli A, Berardi E, Antonica G, et al. Hyperhomocysteinemia is an independent risk factor of atherosclerosis in patients with metabolic syndrome. Diabetology & metabolic syndrome. 2019;11:87. Ostrakhovitch EA, Tabibzadeh S. Homocysteine in Chronic Kidney Disease. Adv Clin Chem. 2015;72:77-106. Zhang C, Hu JJ, Xia M, Boini KM, Brimson C, Li PL. Redox signaling via lipid raft clustering in homocysteine-induced injury of podocytes. Biochim Biophys Acta. 2010;1803(4):482-91. Liu M, Zhang H, Wang G. Hyperhomocysteinemia Promotes Carotid Artery Damage in Newly Diagnosed Type 2 Diabetic Patients Without Hypercholesterolemia. Metabolic syndrome and related disorders. 2021;19(10):575-80. Ye Z, Wang C, Zhang Q, Li Y, Zhang J, Ma X, et al. Prevalence of Homocysteine-Related Hypertension in Patients With Chronic Kidney Disease. J Clin Hypertens (Greenwich). 2017;19(2):151-60. Kim ST, Kim BJ, Lim DM, Song IG, Jung JH, Lee KW, et al. Basal C-peptide Level as a Surrogate Marker of Subclinical Atherosclerosis in Type 2 Diabetic Patients. Diabetes Metab J. 2011;35(1):41-9. Li Y, Zhao D, Li Y, Meng L, Enwer G. Serum C-peptide as a key contributor to lipid-related residual cardiovascular risk in the elderly. Arch Gerontol Geriatr. 2017;73:263-8. Tables Table 1. Basic characteristics and metabolic indexes between patients with vs. without HHCY. Variables Total (n=423) HHCY (n=132) Non-HHCY (n=291) P Male, n/% 258/60.99 98/74.24 160/50.98 <0.001 Age (years), Mean ± SD 59.99±14.59 63.81±14.43 58.44±14.25 <0.001 Course of disease(years), Median (IQR) 6.00(1.00-14.5) 7.00(1.00-15.00) 6.00(1.00-14.00) 0.789 SBP (mmHg), Mean ± SD 144.12±22.89 145.47±24.55 143.57±22.10 0.423 DBP (mmHg), Mean ± SD 84.65±11.49 84.26±12.04 84.74±11.17 0.691 BMI (mmHg), Mean ± SD 24.77±3.90 25.09±3.66 24.60±3.99 0.244 Medical history Hypertension,n (%) 258 (60.99) 99 (75.00) 159(54.64) <0.001 Cerebral infarction,n (%) 51(12.06) 27(20.45) 24(8.25) <0.001 Laboratory data HbAlc(%), Mean ± SD 9.85±2.51 9.70±2.58 9.90±2.46 0.436 FPG (mmol/L), Mean ± SD 10.49±4.13 10.25±4.22 10.59±4.09 0.451 PPG (mmol/L), Mean ± SD 16.30±5.24 15.49±4.78 16.67±5.41 0.046 FastingC-peptide(ng/ml), Median (IQR) 1.70(1.08-2.61) 2.37(1.54-3.54) 1.53(0.94-2.26) <0.001 2-h postprandial C-peptide(ng/ml), Median (IQR) 4.22(2.63-6.84) 5.66 (3.57-8.83) 3.76(2.28-6.08) <0.001 Smoking,n (%) 108 (25.53) 44 (33.33) 64 (21.99) 0.013 HDL-C (mmol/L), Mean ± SD 1.06±0.33 1.03±0.31 1.08±0.33 0.162 LDL-C(mmol/L), Mean ± SD 2.85±1.09 2.83±1.06 2.86±1.10 0.764 Blood urea(mmol/L), Median (IQR) 5.50(4.50-6.75) 6.30(5.10-8.10) 5.15(4.30-6.30) <0.001 SCr(μmol/L), Median (IQR) 68.00(57.00-81.00) 81.00(66.00-104.00) 63.00(53.25-75.00) <0.001 UA (μmol/L), Mean ± SD 313.53±98.57 347.65±103.41 297.52±92.09 <0.001 TG(mmol/L), Median (IQR) 1.50(1.07-2.30) 1.51(1.07-2.38) 1.49(1.06-2.22) 0.524 TC(mmol/L), Mean ± SD 4.89±1.23 4.47±1.23 4.49±1.23 0.851 Hcy(μmol/L), Median (IQR) 12.60(10.12-16.30) 18.57(16.50-21.28) 11.00(9.28-12.80) <0.001 UACR(mg/g), Median (IQR) 23.52(11.05-85.69) 29.30(11.90-185.72) 21.95(10.73-69.91) 0.051 CRP, Median (IQR) 3.35(1.80-9.00) 4.00(2.18-13.55) 3.00(1.60-7.50) 0.012 CIMT, Mean±SD 0.76±0.18 0.82±0.18 0.73±0.17 <0.001 PPGE(mmol/L), Mean ± SD 0.69±2.13 0.68±2.31 0.69±2.06 0.955 SDBG(mmol/L), Median (IQR) 2.45(1.72-3.37) 2.42(1.73-3.37) 2.45(1.71-3.35) 0.467 LAGEc(mmol/L),Mean ± SD 7.11±3.30 7.56±3.51 6.89±3.19 0.053 HHCY: hyperhomocysteinemia; SBP: systolic blood pressure; DBP: diastolic blood pressure; BMI: body mass index; HbA1c: glycosylated hemoglobin; FPG: fasting plasma glucose; PPG: postprandial plasma glucose; TC: total cholesterol; LDL-C:low-density lipoprotein cholesterol; HDL-C: high-density lipoprotein cholesterol; SCR: serum creatinine; UA: uric acid; TG: triglyceride; TC: total cholesterol; Hcy: homocysteine; UACR: urinary albumin creatinine ratio; CRP: C reactive protein;CIMT: carotid intima-media thickness; PPGE: postprandial glucose excursion; SDBG: standard deviation of blood glucose level;LAGE: largest amplitude of glycemic excursions. Table 2. Basic characteristics inT2DM patients with and without HHCYor hypertension. Patients with hypertension (n=258) Patients without hypertension (n=165) Variables HHCY (n=99) Non-HHCY (n=159) P HHCY (n=33) Non-HHCY (n=132) P Male, n/% 68/68.69% 92/57.86% 0.081 30/90.91% 68/51.52% <0.001 Age (years), Mean ± SD 67.32±12.82 62.82±13.11 0.007 53.27±14.00 53.12±13.81 0.954 Course of disease(years), Median (IQR) 10.00(2.00-15.00) 8.00(3.00-17.00) 0.619 3.00(0.20-10.50) 4.00(0.40-10.75) 0.630 SBP (mmHg), Mean ± SD 148.66±25.22 151.92±21.87 0.273 135.63±19.61 132.76±17.27 0.419 DBP (mmHg), Mean ± SD 84.44±11.80 87.27±11.70 0.061 83.72±12.94 81.45±9.58 0.359 BMI (mmHg), Mean ± SD 25.37±3.64 25.22±3.90 0.763 24.24±3.62 23.82±3.95 0.582 Medical history Cerebral infarction, n (%) 26 (26.26) 20 (12.58) 0.005 1 (3.03) 4 (3.03) 1.000 Laboratory data HbAlc(%), Mean ± SD 9.62±2.57 9.58±2.20 0.903 9.95±2.62 10.35±2.72 0.464 FPG (mmol/L),Mean ± SD 10.23±4.10 10.32±3.92 0.857 10.34±4.65 10.98±4.32 0.473 PPG (mmol/L), Mean ± SD 15.29±4.95 16.38±5.35 0.131 16.02±4.36 17.01±5.52 0.350 Fasting C-peptide(ng/mL), Mean ± SD 2.56 (1.61-3.60) 1.70 (1.04-2.50) <0.001 2.09 (1.26-3.61) 1.36 (0.83-2.08) 0.004 2-hour C-peptide(ng/mL), Mean ± SD 5.94 (3.58-9.94) 3.87 (2.53-6.23) <0.001 5.23 (3.41-7.61) 3.57 (1.90-5.74) 0.019 Smoking status, n (%) 29 (29.29) 39 (24.53) 0.398 15 (45.45) 25 (18.94) 0.001 HDL-C (mmol/L), Mean ± SD 1.04±0.32 1.06±0.35 0.589 0.99±0.30 1.09±0.31 0.096 LDL-C (mmol/L), Mean ± SD 2.83±1.12 2.71±1.15 0.389 2.80±0.88 3.07±1.01 0.167 Blood urea (mmol/L), Median (IQR) 6.50 (5.40-8.33) 5.2 (4.5-6.5) <0.001 5.20 (4.80-7.20) 5.10 (4.05-6.10) 0.032 SCR(μmol/L), Median (IQR) 89.00 (70.00-113.00) 65.00 (58.00-78.00) <0.001 73.00 (58.50-81.00) 60.00 (47.75-69.00) 0.002 UA (μmol/L), Mean ± SD 354.19±44.96 314.88±93.27 0.001 327.59±125.66 274.17±86.49 0.006 TG(mmol/L), Median (IQR) 1.59 (1.15-2.38) 1.50 (1.11-2.40) 0.816 1.43 (0.99-2.87) 1.41 (0.96-2.19) 0.677 TC(mmol/L), Mean ± SD 4.52±1.30 4.36±1.35 0.348 4.31±1.00 4.69±1.05 0.074 Hcy(μmol/L), Median (IQR) 18.54 (16.50-20.91) 11.56 (9.84-13.10) <0.001 18.60 (16.65-24.08) 10.34 (8.45-11.95) <0.001 UACR(mg/g), Median (IQR) 65.65 (14.10-256.10 30.90 (14.38-122.89) 0.622 13.80 (10.78-80.34) 15.06 (8.15-36.50) 0.373 CRP (mg/dL), Median (IQR) 4.00 (2.08-11.13) 2.85 (1.50-6.18) 0.028 6.85 (2.85-35.20) 3.20 (2.00-8.75) 0.072 CIMT,Mean ± SD 0.84±0.15 0.74±0.17 <0.001 0.75±0.23 0.71±0.18 0.398 PPGE(mmol/L), Mean ± SD 0.60±3.46 0.57±1.79l 0.874 0.88±2.29 0.83±2.29 0.916 SDBG(mmol/L), Median (IQR) 2.39 (1.70-3.28) 2.46 (1.66-3.29) 0.654 2.57 (1.71-3.92) 2.45 (1.74-3.40) 0.449 LAGE(mmol/L), Mean ± SD 7.59±3.46 6.83±3.10 0.072 7.50±3.71 6.99±3.33 0.444 HHCY: hyperhomocysteinemia; SBP: systolic blood pressure; DBP: diastolic blood pressure; BMI: body mass index; HbA1c: glycosylated hemoglobin; FPG: fasting plasma glucose; PPG: postprandial plasma glucose; TC: total cholesterol; LDL-C: low-density lipoprotein cholesterol; HDL-C: high-density lipoprotein cholesterol; SCR: serum creatinine; UA: uric acid; TG: triglyceride; TC: total cholesterol; Hcy: homocysteine; UACR: urinary albumin creatinine ratio; CRP: C reactive protein; CIMT: carotid intima-media thickness; PPGE: postprandial glucose excursion; SDBG: standard deviation of blood glucose level;LAGE: largest amplitude of glycemic excursions. Table 3. Multivariable logistic regression. Risk factors Total (n=423) Patients with hypertension (n=258) Patients without hypertension (n=165) Odds ratio (95%CI) P Odds ratio (95%CI) P Odds ratio(95%CI) P HHCY 2.942 (1.356-6.386) 0.006 2.840 (1.153-6.999) 0.040 1.123(0.329-3.383) 0.194 Gender, male/female 1.183 (0.541-2.587) 1.183 0.663(0.201-2.184) 0.499 Age 1.030 (1.002-1.059) 0.036 1.028 (0.993-1.063) 1.027 Cerebral infarction 0.598 (0.208-1.724) 0.341 0.591 (0.191-1.827) 0.450 PPG 0.985 (0.922-1.052) 0.652 FastingC-peptide 1.004 (0.744-1.354) 0.982 0.882 (0.635-1.224) 0.469 1.777(1.005-3.143) 0.048 2-h C-peptide 1.009 (0.914-1.114) 0.858 1.052 (0.943-1.016) 0.359 0.883(0.731-1.066) 0.194 Smoking status 1.347 (0.582-3.118) 1.347 1.469(0.466-4.631) 0.512 Blood urea 1.062 (0.881-1.279) 0.531 1.074 (0.845-1.366) 0.527 1.198(0.908-1.580) 0.202 SCR 1.001 (0.991-1.011) 0.816 1.001 (0.990-1.012) 0.895 1.017(0.995-1.040) 0.123 UA 0.999 (0.995-1.003) 0.536 0.999 (0.994-1.004) 0.752 1.000(0.994-1.005) 0.872 CRP 1.001 (0.987-1.015) 0.904 0.997 (0.979-1.016) 0.781 CI: confidence interval; HHCY: hyperhomocysteinemia; PPG: postprandial plasma glucose; SCR: serum creatinine; UA: uric acid; CRP: C reactive protein. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTableS1.docx 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-3988060","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":275591678,"identity":"fd57f0e6-00ec-459c-8bd0-a2bf87dae81a","order_by":0,"name":"Li Ao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAv0lEQVRIiWNgGAWjYBACNgbmhgMfKv7LMTATq4WfgbHh4IwzzMbEa5FsYGxg5mxjTmwg2mEGxxsbDzOwsaXPb+c9+IGhxiaasJYzBxsOF/Dw5G44zJcswXAsLZegdQY3EhsOz5CQyN3AzGMgwdhwmLAWe5AWHgODdPlmHuMfRGkB28KTkJDAcJjHjDhbQH45OOPAAcMNQC0WCUT55Xjz4Q8f/x2Ql+8/Y3zjQ40NYS2oIIE05aNgFIyCUTAKcAEAXApDedDY6zIAAAAASUVORK5CYII=","orcid":"","institution":"Jiading District Central Hospital","correspondingAuthor":true,"prefix":"","firstName":"Li","middleName":"","lastName":"Ao","suffix":""},{"id":275591679,"identity":"24c2581c-db1a-4ff3-994c-ec5cb9c9f89c","order_by":1,"name":"Ling liu","email":"","orcid":"","institution":"Jiading District Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ling","middleName":"","lastName":"liu","suffix":""},{"id":275591680,"identity":"3c25e8ec-823a-4c03-adb2-7c22337aa79b","order_by":2,"name":"Lixia Suo","email":"","orcid":"","institution":"Jiading District Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lixia","middleName":"","lastName":"Suo","suffix":""},{"id":275591681,"identity":"c37a5566-81eb-4d01-bb76-932d8dd12afa","order_by":3,"name":"Dongyu Liang","email":"","orcid":"","institution":"c、 Scientific research section, Jiading District Central Hospital, Shanghai Health Medical College，Shanghai","correspondingAuthor":false,"prefix":"","firstName":"Dongyu","middleName":"","lastName":"Liang","suffix":""},{"id":275591685,"identity":"5cbc4b33-212b-4099-9939-173bf6dc03db","order_by":4,"name":"Jingya Niu","email":"","orcid":"","institution":"Shanghai University of Medicine and Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Jingya","middleName":"","lastName":"Niu","suffix":""},{"id":275591687,"identity":"fc03b0ef-5767-4778-810f-1fb808ec2b44","order_by":5,"name":"Qin Yang","email":"","orcid":"","institution":"Jiading District Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Qin","middleName":"","lastName":"Yang","suffix":""},{"id":275591690,"identity":"aded638d-6579-4284-a16c-c39c10a23c16","order_by":6,"name":"Dongmei Ren","email":"","orcid":"","institution":"Jiading District Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Dongmei","middleName":"","lastName":"Ren","suffix":""}],"badges":[],"createdAt":"2024-02-25 13:14:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3988060/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3988060/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53849437,"identity":"e40610b7-b0fb-4d44-8fea-ff6a20dafe11","added_by":"auto","created_at":"2024-04-01 09:35:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":305553,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3988060/v1/9b7daa1b-96a6-4689-a710-20c03f4f5cc3.pdf"},{"id":52401298,"identity":"ce3a91fa-11d3-471b-b5fb-124c84e717c4","added_by":"auto","created_at":"2024-03-11 07:22:41","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":15917,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-3988060/v1/159deaadaa7cafd0b757f038.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of hyperhomocysteinemiaon carotid intima-media thickness in type 2 diabetes mellituswith or without hypertension: a retrospective observational study","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eThe global prevalence of diabetes mellitus is about 536.6 million people, with type 2 diabetes mellitus (T2DM) accounting for above 90%(1). In China, 141 million individuals were suffering from T2DM from 2013 to 2021\u0026nbsp;(2). Hypertension, a common comorbidity in patients with T2DM, was found in\u0026nbsp;\u0026nbsp;\u0026nbsp;50% to 80% \u0026nbsp;of adult T2DM patients\u0026nbsp;(3).Hyperhomocysteinemia (HHCY) denotes increased concentrations of homocysteine, and its prevalence is estimated to be approximately 5% in the general population(4). HHCY can stem from heightened homocysteine production and/or reduced homocysteine clearance, often linked to various factors such as advancing age, smoking, coffee consumption, alcohol intake, chronic kidney disease, hepatic impairment, systemic lupus erythematosus, diabetes mellitus, and hypothyroidism, as well as vitamin deficiencies and genetic abnormalities(4).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMacroangiopathy is a common complication and the main cause of death in patients with diabetes mellitus (DM), accounting for over 60% of the total mortality\u0026nbsp;(5). Carotid intima-media thickness (CIMT) is an index for detecting subclinical atherosclerosis and was proven to have satisfying predictive value for the occurrence of atherosclerotic heart disease and stroke(6). Patients with DM have a thicker CIMT, representing a higher atherosclerotic burden\u0026nbsp;(7-9).\u003c/p\u003e\n\u003cp\u003eHHCY is an important risk factor for macroangiopathy(10, 11). Each 5-µmol/L increase in blood homocysteine levels is associated with a significant increase in cardiovascular risk\u0026nbsp;(12), and homocysteine levels \u0026gt;15 µmol/L can help predict cardiovascular events in Chinese guidelines(13). HHCY is associated with DM\u0026nbsp;(14)and hypertension\u0026nbsp;(15), and HHCY and hypertension also have synergistic impacts on cardiovascular risk\u0026nbsp;(16). Studies in Chinese patients revealed an interaction between HHCY and hypertension on CIMT\u0026nbsp;(17)or early carotid artery atherosclerosis\u0026nbsp;(18).\u0026nbsp;The frequent association of both cardiovascular risk factors makes the joint analysis of the association interesting.Therefore, this study aimed to explore the impact of HHCY on CIMT in patients with T2DM and examine the impact of hypertension.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e\u003cstrong\u003eStudy design and patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective observational study enrolled patients with T2DM at the Department of Endocrinology of Shanghai Jiading District Central Hospital between January 2019 and\u0026nbsp;\u003ca href=\"javascript%3A;\"\u003eDecember\u003c/a\u003e 2020. T2DM was diagnosed according to the 2013World Health Organization criteria for T2DM(19). Both CIMT and plasma homocysteine levels were routinely measured in with T2DM. The exclusion criteria were: 1) patients with diabetic ketosis or hyperosmolar coma; 2) patients with severe cardiac, hepatic, or renal insufficiency; \u0026nbsp; 3) missing homocysteine levels; or 4) missing CIMT evaluation. The study was approved by the Ethics Committee of Shanghai Jiading District Central Hospital.The need for individual informed consent was waived by the committee owing to the retrospective nature of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe demographical data of the patients were collected, including age, sex, course of the disease, blood pressure, smoking history, history of hypertension, history of cerebral infarction, fasting and 2-h postprandial C-peptide levels, serum creatinine (SCR), plasma urea, urinary albumin creatinine ratio (UACR), glycosylated hemoglobin (HbA1c), uric acid (UA), serum cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), triglyceride (TG), and standard deviation of the blood sugar level (SDBG). Smoking history was defined as non-smoker when never tried smoking or never smoked a whole cigarette and as smoker when \u0026nbsp;smoked regularly or tried smoking previous 30 days .SDBG was calculated according to the “Expert Consensus on Glycemic Fluctuation Management in Diabetic Patients”(20).CIMT was routinely measured by color Doppler flow imaging with a probe of 9-12 MHz (CDFI, GE Vivid E9, Norway). The bilateral carotid arteries were examined by a trained physician expert in ultrasound imaging for \u0026gt;15 years. The measurements were repeated three times, and the average value was recorded. CIMT of\u003cu\u003e\u0026gt;\u003c/u\u003e0.9 mm was considered abnormal according to the “The Guidelines for Prevention and Control of Hypertension”(21).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe statistical analysis was conducted utilizing SPSS 19.0 (IBM, Armonk, NY, USA). Continuous data following a normal distribution were expressed as means ± standard deviation (SD) and analyzed using Student’s t-test. Alternatively, data not adhering to a normal distribution were presented as medians (interquartile range [IQR]) and analyzed employing the Wilcoxon rank-sum test. Categorical data were presented as n (%) and evaluated using the chi-squared test or Fisher’s exact test, as appropriate. Multivariable logistic regression analysis was applied to examine the association between HHCY and carotid intima-media thickness (CIMT), with the backward stepwise method utilized for independent variable entry. P-values less than 0.05 were considered statistically significant, and all tests were two-sided.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eA total of 423\u0026nbsp; patients\u0026nbsp;(258 males) with T2DM were included in this study. The mean age was 59.99±14.59 years old. 132 (31.2%) were in the H-Hcy group, and 291 (168.8%) were in the Non-HHCY group, respectively. There were significant differences in gender, age, hypertension, cerebral infarction, postprandial plasma glucose , fasting C-peptide, 2-h C-peptide, blood urea , SCR , UA homocysteine , C reactive protein, CIMT , smoking between two groups .There were no significant differences in the course of disease, systolic blood pressure, diastolic blood pressure, body mass index, HbA1c,\u0026nbsp;HDL-C,LDL-C, triglycerides\u0026nbsp;(TG),\u0026nbsp;total cholesterol (TC), UACR,\u0026nbsp;postprandial glucose excursion (PPGE), standard deviation of blood glucose level (SDBG), and largest amplitude of glycemic excursions (LAGE)\u0026nbsp;between the patients with vs. without HHCY (all P\u0026gt;0.05) (\u003cstrong\u003eTable 1\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eAmong patients with hypertension, compared to patients without HHCY, those with HHCY were significantly older (67.32±12.82 vs. 62.82±13.11, P=0.007), showed more cases of cerebral infarction (26.26% vs. 12.58%, P=0.005), higher fasting C-peptide(2.56 (1.61-3.60) vs. 1.70 (1.04-2.50), P\u0026lt;0.001), higher 2-h C-peptide (5.94(3.58-9.94) vs. 3.87(2.53-6.23), P\u0026lt;0.001), higher blood urea (6.50(5.40-8.33) vs. 5.2(4.5-6.5), P\u0026lt;0.001), higher SCR (89.00(70.00-113.00) vs. 65.00(58.00-78.00), P\u0026lt;0.001), higher UA (354.19±44.96 vs. 314.88±47.75, P=0.001), higher homocysteine(18.54(16.50-20.91) vs. 11.56(9.84-13.10),\u0026nbsp;P\u0026lt;0.001), higher CRP (4.00(2.08-11.13) vs. 2.85(1.50-6.18), P=0.028), and thicker CIMT (0.84±0.15 vs. 0.74±0.17,\u0026nbsp;P\u0026lt;0.001). Among the patients without hypertension, compared to patients without HHCY, those with HHCY showed significantly more male (90.91% vs. 51.52%, P\u0026lt;0.001), higher fasting C-peptide (2.09(1.26-3.61) vs.1.36(0.83-2.08), P=0.004), higher 2-h C-peptide (5.23(3.41-7.61) vs. 3.57(1.90-5.74), P=0.019), more smokers (45.45% vs. 18.94%, P=0.001), higher blood urea (5.20(4.80-7.20) vs. 5.10(4.05-6.10), P=0.032), higher SCR(73.00(58.50-81.00) vs. 60.00(47.75-69.00), P=0.002), higher UA (327.59±125.66 vs. 274.17±86.49 P=0.006), and higher homocysteine (18.60(16.65-24.08) vs. 10.34(8.45-11.95),\u0026nbsp;P\u0026lt;0.001)(\u003cstrong\u003eTable 2\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eThe multivariable logistic regression analysis showed that HHCY was significantly associated with CIMT in all patients (odds ratio [OR]=2.942, 95%confidence interval [CI]:1.356-6.386,P=0.006)and in patients with hypertension (OR=2.840,95%CI:1.153-6.999,P=0.040), while HHCY was not significantly associated with CIMT in patients without hypertension (OR=1.123,95%CI: 0.329-3.383,P=0.194).Age was associated with CIMT in all patients (OR=1.030, 95%CI:1.002-1.059, P=0.036). Fasting C-peptide was associated with CIMT in patients without hypertension (OR=1.777, 95%CI:1.005-3.143, P=0.048) (\u003cstrong\u003eTable 3\u003c/strong\u003e).\u003cstrong\u003eSupplementary Table S1\u0026nbsp;\u003c/strong\u003eshows the comparison of the characteristics between patients with CIMT ≥0.9 vs. \u0026lt;0.9.\u0026nbsp;\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe study findings established HHCY as a risk factor for CIMT thickening in patients with T2DM, particularly among those concurrently dealing with hypertension. From a clinical perspective, it underscores the importance of monitoring blood homocysteine levels in patients with T2DM and hypertension and promptly initiating appropriate management strategies upon detection of elevated homocysteine levels.\u003c/p\u003e\n\u003cp\u003eStudies have shown that the homocysteine levels of T2DM patients were higher than in non-T2DM patients\u0026nbsp;(14, 22). A meta-analysis that included 4011 patients with T2DM and 4303 normal controls showed that the increase of homocysteine levels was causally related to the occurrence and development of DM(23),\u0026nbsp;which was mainly related to poor blood glucose control of T2DM(24), insulin resistance(25), and dyslipidemia(26).Those results have clinical implications that T2DM is an independent factor of cardiovascular events(27, 28). HHCY is also independently associated with cardiovascular events\u0026nbsp;(12, 13); therefore, the combination of T2DM and HHCY should additively or synergistically increase the risk of cardiovascular events.\u003c/p\u003e\n\u003cp\u003eThis study showed that HHCY was an independent risk factor of CIMT thickening in T2DM patients, consistent with the results of some previous studies\u0026nbsp;(11, 29, 30).Indeed, Park et al.\u0026nbsp;(11)\u0026nbsp;showed that renal function impairments in patients with HHCY may worsen CIMT by increasing serum homocysteine levels. Kundi et al.\u0026nbsp;(29)\u0026nbsp;and Devasia et al.\u0026nbsp;(30)\u0026nbsp;showed that homocysteine levels were positively correlated with CIMT. The mechanisms are mainly because HHCY increases vascular endothelial injury, promotes vascular smooth muscle hypertrophy, exacerbates vessel wall inflammation, interferes with vasodilatation, increases thrombotic potential, and inhibits endothelial cell growth(31). The Hoorn study\u0026nbsp;(32)\u0026nbsp;showed that high plasma homocysteine level was a determinant of thicker CIMT in diabetic and non-diabetic individuals, which might help explain why HHCY is an especially strong risk factor for atherothrombosis amongT2DM patients.A study in obese subjects indicated that total homocysteine might be considered a component of insulin resistance syndrome(33).Nevertheless, a recent cross-sectional study in the middle-aged Chinese population found that increased homocysteine levels were not significantly associated with early carotid artery atherosclerosis(34).The discrepancy might be related to the different age groups of subjects included in the study, since the mean age of the patients in the present study was 60 years old, while previous studies may be younger or older\u0026nbsp;(11,\u0026nbsp;32), the groups were age- and sex-matched\u0026nbsp;(33), the patients had comorbid obesity, or the study population did not include Chinese patients, and so on.\u003c/p\u003e\n\u003cp\u003eIn a prospective study involving patients with type 1 diabetes mellitus, the findings suggested that monitoring homocysteine levels might not be necessary, as CIMT did not demonstrate progression(35).HHCY and the presence of cardiovascular disease were associated with CIMT in patients undergoing continuous ambulatory peritoneal dialysis\u0026nbsp;(36). A\u0026nbsp;recent study found a direct effect of homocysteine on the atherogenic process during metabolic syndrome with higher CIMT in patients with HHCY(37).\u003c/p\u003e\n\u003cp\u003eThe mechanisms leading to thicker CIMT in patients with HHCY are poorly understood. Nevertheless, it is hypothesized that the higher propensity for atherosclerosis observed in HHCY is due to endothelial dysfunction and injury followed by platelet activation and thrombus formation\u0026nbsp;(30). Increased oxidative stress and decreased antioxidant defenses could also be involved\u0026nbsp;(38).HHCY can also lead to NADPH oxidase activation, leading to oxidative vascular injury\u0026nbsp;(39).\u003c/p\u003e\n\u003cp\u003eThis study further explored subgroups of patients with and without hypertension, which was a novelty of the present study. A previous study in patients with T2DM without hypercholesterolemia suggested that HHCY was correlated with carotid artery damage\u0026nbsp;(40). Liu et al.\u0026nbsp;(17)\u0026nbsp;reported that the combination of prehypertension and HHCY was an independent risk factor of subclinical atherosclerosis in asymptomatic Chinese, but isolated prehypertension or HHCY was not. The present study showed that HHCY contributed to CIMT in patients with hypertension but not in those without hypertension, as supported by Liu et al.\u0026nbsp;(17). On the other hand, in the non-hypertension subgroup, HHCY was not independently associated with CIMT, and only C-peptide was. HHCY can promote hypertension\u0026nbsp;(17), and HHCY and hypertension have been reported to act synergistically to aggravate the cardiovascular risk\u0026nbsp;(16).Since CIMT is a surrogate of the cardiovascular risk in T2DM(7), the combination of hypertension and HHCY contributed to a thicker CIMT, supported by Liu et al.\u0026nbsp;(17), while HHCY without hypertension was not associated with CIMT, also as supported by Liu et al.\u0026nbsp;(17). A study in patients with chronic kidney disease showed that HHCY with hypertension and hypertension alone led to thicker CIMT compared t\u003cu\u003eo\u0026nbsp;\u003c/u\u003enormotension with or without HHCY(41). A study showed that blood glucose fluctuations were independently associated with CIMT in T2DM but not C-peptide levels(21).Still, C-peptide participates in atherosclerosis since it induces vascular smooth cell proliferation\u0026nbsp;(42, 43).The present study might suggest that the contribution of C-peptide to CIMT is weaker than hypertension and disappears in hypertensive patients. Additional studies are necessary to examine the impacts and interactions of HHCY,hypertension, and C-peptide on CIMT in patients with T2DM.\u003c/p\u003e\n\u003cp\u003eNevertheless, several limitations warrant acknowledgment in this study. Firstly, the study was conducted at a single center, and the sample size was comparatively small, potentially impacting the generalizability of the findings. Secondly, the retrospective nature of the study constrained the analyses to the data that were readily accessible in the patient charts, potentially restricting the scope of the investigation. Additionally,, the cross-sectional design of the study precluded the exploration of causal relationships between variables, limiting the ability to establish cause-and-effect associations.\u003c/p\u003e\n\u003cp\u003eIn conclusion, the study emphasizes HHCY as a risk factor for CIMT thickening in patients with T2DM, particularly those with comorbid hypertension. Nonetheless, the potential relevance of monitoring blood homocysteine levels in individuals with diabetes and hypertension warrants consideration. Future investigations should aim to elucidate the underlying mechanisms linking HHCY and CIMT in patients with T2DM, both with and without concurrent hypertension.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declared that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was supported by the Key Subjects of Jiading District (#2020-jdyxzdxk-04) , theShanghai Health Medical College Hundred Teachers Talent Project, and the Clinical Research Center of Shanghai University of Medicine \u0026amp; Health Sciences (#20MC2020004).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors acknowledged the members of the Nursing Department and Central Laboratory for their great help in the completion of this thesis.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSun H, Saeedi P, Karuranga S, et al. 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Arch Gerontol Geriatr. 2017;73:263-8.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003eBasic characteristics and metabolic indexes between patients with vs. without HHCY.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"803\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.755915317559154%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.929016189290163%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal (n=423)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.80074719800747%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHHCY\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=132)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.925280199252803%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-HHCY\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;(n=291)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.58904109589041%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.755915317559154%\" valign=\"top\"\u003e\n \u003cp\u003eMale, n/%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.929016189290163%\" valign=\"top\"\u003e\n \u003cp\u003e258/60.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.80074719800747%\" valign=\"top\"\u003e\n \u003cp\u003e98/74.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.925280199252803%\" valign=\"top\"\u003e\n \u003cp\u003e160/50.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.58904109589041%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.755915317559154%\" valign=\"top\"\u003e\n \u003cp\u003eAge (years),\u0026nbsp;Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.929016189290163%\" valign=\"top\"\u003e\n \u003cp\u003e59.99\u0026plusmn;14.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.80074719800747%\" valign=\"top\"\u003e\n \u003cp\u003e63.81\u0026plusmn;14.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.925280199252803%\" valign=\"top\"\u003e\n \u003cp\u003e58.44\u0026plusmn;14.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.58904109589041%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.755915317559154%\" valign=\"top\"\u003e\n \u003cp\u003eCourse of disease(years),\u0026nbsp;Median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.929016189290163%\" valign=\"top\"\u003e\n \u003cp\u003e6.00(1.00-14.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.80074719800747%\" valign=\"top\"\u003e\n \u003cp\u003e7.00(1.00-15.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.925280199252803%\" valign=\"top\"\u003e\n \u003cp\u003e6.00(1.00-14.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.58904109589041%\" valign=\"top\"\u003e\n \u003cp\u003e0.789\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.755915317559154%\" valign=\"top\"\u003e\n \u003cp\u003eSBP (mmHg),\u0026nbsp;Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.929016189290163%\" valign=\"top\"\u003e\n \u003cp\u003e144.12\u0026plusmn;22.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.80074719800747%\" valign=\"top\"\u003e\n \u003cp\u003e145.47\u0026plusmn;24.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.925280199252803%\" valign=\"top\"\u003e\n \u003cp\u003e143.57\u0026plusmn;22.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.58904109589041%\" valign=\"top\"\u003e\n \u003cp\u003e0.423\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.755915317559154%\" valign=\"top\"\u003e\n \u003cp\u003eDBP (mmHg),\u0026nbsp;Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.929016189290163%\" valign=\"top\"\u003e\n \u003cp\u003e84.65\u0026plusmn;11.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.80074719800747%\" valign=\"top\"\u003e\n \u003cp\u003e84.26\u0026plusmn;12.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.925280199252803%\" valign=\"top\"\u003e\n \u003cp\u003e84.74\u0026plusmn;11.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.58904109589041%\" valign=\"top\"\u003e\n \u003cp\u003e0.691\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.755915317559154%\" valign=\"top\"\u003e\n \u003cp\u003eBMI (mmHg),\u0026nbsp;Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.929016189290163%\" valign=\"top\"\u003e\n \u003cp\u003e24.77\u0026plusmn;3.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.80074719800747%\" valign=\"top\"\u003e\n \u003cp\u003e25.09\u0026plusmn;3.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.925280199252803%\" valign=\"top\"\u003e\n \u003cp\u003e24.60\u0026plusmn;3.99 \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.58904109589041%\" valign=\"top\"\u003e\n \u003cp\u003e0.244\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.755915317559154%\" valign=\"top\"\u003e\n \u003cp\u003eMedical history\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.929016189290163%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.80074719800747%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.925280199252803%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.58904109589041%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.755915317559154%\" valign=\"top\"\u003e\n \u003cp\u003eHypertension,n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.929016189290163%\" valign=\"top\"\u003e\n \u003cp\u003e258 (60.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.80074719800747%\" valign=\"top\"\u003e\n \u003cp\u003e99 (75.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.925280199252803%\" valign=\"top\"\u003e\n \u003cp\u003e159(54.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.58904109589041%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.755915317559154%\" valign=\"top\"\u003e\n \u003cp\u003eCerebral infarction,n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.929016189290163%\" valign=\"top\"\u003e\n \u003cp\u003e51(12.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.80074719800747%\" valign=\"top\"\u003e\n \u003cp\u003e27(20.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.925280199252803%\" valign=\"top\"\u003e\n \u003cp\u003e24(8.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.58904109589041%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.755915317559154%\" valign=\"top\"\u003e\n \u003cp\u003eLaboratory data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.929016189290163%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.80074719800747%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.925280199252803%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.58904109589041%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.755915317559154%\" valign=\"top\"\u003e\n \u003cp\u003eHbAlc(%),\u0026nbsp;Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.929016189290163%\" valign=\"top\"\u003e\n \u003cp\u003e9.85\u0026plusmn;2.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.80074719800747%\" valign=\"top\"\u003e\n \u003cp\u003e9.70\u0026plusmn;2.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.925280199252803%\" valign=\"top\"\u003e\n \u003cp\u003e9.90\u0026plusmn;2.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.58904109589041%\" valign=\"top\"\u003e\n \u003cp\u003e0.436\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.755915317559154%\" valign=\"top\"\u003e\n \u003cp\u003eFPG (mmol/L),\u0026nbsp;Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.929016189290163%\" valign=\"top\"\u003e\n \u003cp\u003e10.49\u0026plusmn;4.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.80074719800747%\" valign=\"top\"\u003e\n \u003cp\u003e10.25\u0026plusmn;4.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.925280199252803%\" valign=\"top\"\u003e\n \u003cp\u003e10.59\u0026plusmn;4.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.58904109589041%\" valign=\"top\"\u003e\n \u003cp\u003e0.451\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.755915317559154%\" valign=\"top\"\u003e\n \u003cp\u003ePPG (mmol/L),\u0026nbsp;Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.929016189290163%\" valign=\"top\"\u003e\n \u003cp\u003e16.30\u0026plusmn;5.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.80074719800747%\" valign=\"top\"\u003e\n \u003cp\u003e15.49\u0026plusmn;4.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.925280199252803%\" valign=\"top\"\u003e\n \u003cp\u003e16.67\u0026plusmn;5.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.58904109589041%\" valign=\"top\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.755915317559154%\" valign=\"top\"\u003e\n \u003cp\u003eFastingC-peptide(ng/ml),\u0026nbsp;Median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.929016189290163%\" valign=\"top\"\u003e\n \u003cp\u003e1.70(1.08-2.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.80074719800747%\" valign=\"top\"\u003e\n \u003cp\u003e2.37(1.54-3.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.925280199252803%\" valign=\"top\"\u003e\n \u003cp\u003e1.53(0.94-2.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.58904109589041%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.755915317559154%\" valign=\"top\"\u003e\n \u003cp\u003e2-h postprandial C-peptide(ng/ml),\u0026nbsp;Median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.929016189290163%\" valign=\"top\"\u003e\n \u003cp\u003e4.22(2.63-6.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.80074719800747%\" valign=\"top\"\u003e\n \u003cp\u003e5.66 (3.57-8.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.925280199252803%\" valign=\"top\"\u003e\n \u003cp\u003e3.76(2.28-6.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.58904109589041%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.755915317559154%\" valign=\"top\"\u003e\n \u003cp\u003eSmoking,n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.929016189290163%\" valign=\"top\"\u003e\n \u003cp\u003e108 (25.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.80074719800747%\" valign=\"top\"\u003e\n \u003cp\u003e44 (33.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.925280199252803%\" valign=\"top\"\u003e\n \u003cp\u003e64 (21.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.58904109589041%\" valign=\"top\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.755915317559154%\" valign=\"top\"\u003e\n \u003cp\u003eHDL-C (mmol/L),\u0026nbsp;Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.929016189290163%\" valign=\"top\"\u003e\n \u003cp\u003e1.06\u0026plusmn;0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.80074719800747%\" valign=\"top\"\u003e\n \u003cp\u003e1.03\u0026plusmn;0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.925280199252803%\" valign=\"top\"\u003e\n \u003cp\u003e1.08\u0026plusmn;0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.58904109589041%\" valign=\"top\"\u003e\n \u003cp\u003e0.162\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.755915317559154%\" valign=\"top\"\u003e\n \u003cp\u003eLDL-C(mmol/L),\u0026nbsp;Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.929016189290163%\" valign=\"top\"\u003e\n \u003cp\u003e2.85\u0026plusmn;1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.80074719800747%\" valign=\"top\"\u003e\n \u003cp\u003e2.83\u0026plusmn;1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.925280199252803%\" valign=\"top\"\u003e\n \u003cp\u003e2.86\u0026plusmn;1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.58904109589041%\" valign=\"top\"\u003e\n \u003cp\u003e0.764\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.755915317559154%\" valign=\"top\"\u003e\n \u003cp\u003eBlood urea(mmol/L),\u0026nbsp;Median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.929016189290163%\" valign=\"top\"\u003e\n \u003cp\u003e5.50(4.50-6.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.80074719800747%\" valign=\"top\"\u003e\n \u003cp\u003e6.30(5.10-8.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.925280199252803%\" valign=\"top\"\u003e\n \u003cp\u003e5.15(4.30-6.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.58904109589041%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.755915317559154%\" valign=\"top\"\u003e\n \u003cp\u003eSCr(\u0026mu;mol/L),\u0026nbsp;Median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.929016189290163%\" valign=\"top\"\u003e\n \u003cp\u003e68.00(57.00-81.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.80074719800747%\" valign=\"top\"\u003e\n \u003cp\u003e81.00(66.00-104.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.925280199252803%\" valign=\"top\"\u003e\n \u003cp\u003e63.00(53.25-75.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.58904109589041%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.755915317559154%\" valign=\"top\"\u003e\n \u003cp\u003eUA (\u0026mu;mol/L),\u0026nbsp;Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.929016189290163%\" valign=\"top\"\u003e\n \u003cp\u003e313.53\u0026plusmn;98.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.80074719800747%\" valign=\"top\"\u003e\n \u003cp\u003e347.65\u0026plusmn;103.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.925280199252803%\" valign=\"top\"\u003e\n \u003cp\u003e297.52\u0026plusmn;92.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.58904109589041%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.755915317559154%\" valign=\"top\"\u003e\n \u003cp\u003eTG(mmol/L),\u0026nbsp;Median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.929016189290163%\" valign=\"top\"\u003e\n \u003cp\u003e1.50(1.07-2.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.80074719800747%\" valign=\"top\"\u003e\n \u003cp\u003e1.51(1.07-2.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.925280199252803%\" valign=\"top\"\u003e\n \u003cp\u003e1.49(1.06-2.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.58904109589041%\" valign=\"top\"\u003e\n \u003cp\u003e0.524\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.755915317559154%\" valign=\"top\"\u003e\n \u003cp\u003eTC(mmol/L),\u0026nbsp;Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.929016189290163%\" valign=\"top\"\u003e\n \u003cp\u003e4.89\u0026plusmn;1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.80074719800747%\" valign=\"top\"\u003e\n \u003cp\u003e4.47\u0026plusmn;1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.925280199252803%\" valign=\"top\"\u003e\n \u003cp\u003e4.49\u0026plusmn;1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.58904109589041%\" valign=\"top\"\u003e\n \u003cp\u003e0.851\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.755915317559154%\" valign=\"top\"\u003e\n \u003cp\u003eHcy(\u0026mu;mol/L),\u0026nbsp;Median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.929016189290163%\" valign=\"top\"\u003e\n \u003cp\u003e12.60(10.12-16.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.80074719800747%\" valign=\"top\"\u003e\n \u003cp\u003e18.57(16.50-21.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.925280199252803%\" valign=\"top\"\u003e\n \u003cp\u003e11.00(9.28-12.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.58904109589041%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.755915317559154%\" valign=\"top\"\u003e\n \u003cp\u003eUACR(mg/g),\u0026nbsp;Median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.929016189290163%\" valign=\"top\"\u003e\n \u003cp\u003e23.52(11.05-85.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.80074719800747%\" valign=\"top\"\u003e\n \u003cp\u003e29.30(11.90-185.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.925280199252803%\" valign=\"top\"\u003e\n \u003cp\u003e21.95(10.73-69.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.58904109589041%\" valign=\"top\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.755915317559154%\" valign=\"top\"\u003e\n \u003cp\u003eCRP,\u0026nbsp;Median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.929016189290163%\" valign=\"top\"\u003e\n \u003cp\u003e3.35(1.80-9.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.80074719800747%\" valign=\"top\"\u003e\n \u003cp\u003e4.00(2.18-13.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.925280199252803%\" valign=\"top\"\u003e\n \u003cp\u003e3.00(1.60-7.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.58904109589041%\" valign=\"top\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.755915317559154%\" valign=\"top\"\u003e\n \u003cp\u003eCIMT, Mean\u0026plusmn;SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.929016189290163%\" valign=\"top\"\u003e\n \u003cp\u003e0.76\u0026plusmn;0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.80074719800747%\" valign=\"top\"\u003e\n \u003cp\u003e0.82\u0026plusmn;0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.925280199252803%\" valign=\"top\"\u003e\n \u003cp\u003e0.73\u0026plusmn;0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.58904109589041%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.755915317559154%\" valign=\"top\"\u003e\n \u003cp\u003ePPGE(mmol/L),\u0026nbsp;Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.929016189290163%\" valign=\"top\"\u003e\n \u003cp\u003e0.69\u0026plusmn;2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.80074719800747%\" valign=\"top\"\u003e\n \u003cp\u003e0.68\u0026plusmn;2.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.925280199252803%\" valign=\"top\"\u003e\n \u003cp\u003e0.69\u0026plusmn;2.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.58904109589041%\" valign=\"top\"\u003e\n \u003cp\u003e0.955\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.755915317559154%\" valign=\"top\"\u003e\n \u003cp\u003eSDBG(mmol/L),\u0026nbsp;Median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.929016189290163%\" valign=\"top\"\u003e\n \u003cp\u003e2.45(1.72-3.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.80074719800747%\" valign=\"top\"\u003e\n \u003cp\u003e2.42(1.73-3.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.925280199252803%\" valign=\"top\"\u003e\n \u003cp\u003e2.45(1.71-3.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.58904109589041%\" valign=\"top\"\u003e\n \u003cp\u003e0.467\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.755915317559154%\" valign=\"top\"\u003e\n \u003cp\u003eLAGEc(mmol/L),Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.929016189290163%\" valign=\"top\"\u003e\n \u003cp\u003e7.11\u0026plusmn;3.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.80074719800747%\" valign=\"top\"\u003e\n \u003cp\u003e7.56\u0026plusmn;3.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.925280199252803%\" valign=\"top\"\u003e\n \u003cp\u003e6.89\u0026plusmn;3.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.58904109589041%\" valign=\"top\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eHHCY: hyperhomocysteinemia; SBP: systolic blood pressure; DBP: diastolic blood pressure; BMI: body mass index; HbA1c: glycosylated hemoglobin; FPG: fasting plasma glucose; PPG: postprandial plasma glucose; TC: total cholesterol; LDL-C:low-density lipoprotein cholesterol; HDL-C: high-density lipoprotein cholesterol; SCR: serum creatinine; UA: uric acid; TG: triglyceride; TC: total cholesterol; Hcy: homocysteine; UACR: urinary albumin creatinine ratio; CRP: C reactive protein;CIMT: carotid intima-media thickness; PPGE: postprandial glucose excursion; SDBG: standard deviation of blood glucose level;LAGE: largest amplitude of glycemic excursions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003eBasic characteristics inT2DM patients with and without HHCYor hypertension.\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"981\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.120285423037718%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.75535168195719%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePatients with hypertension (n=258)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.1243628950051%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePatients without hypertension (n=165)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.09775967413442%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.39511201629328%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHHCY\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=99)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.39511201629328%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-HHCY\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=159)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.026476578411406%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.562118126272912%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHHCY\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=33)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.580448065173115%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-HHCY\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=132)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.942973523421589%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.09775967413442%\" valign=\"top\"\u003e\n \u003cp\u003eMale, n/%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.39511201629328%\" valign=\"top\"\u003e\n \u003cp\u003e68/68.69%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.39511201629328%\" valign=\"top\"\u003e\n \u003cp\u003e92/57.86%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.026476578411406%\" valign=\"top\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.562118126272912%\" valign=\"top\"\u003e\n \u003cp\u003e30/90.91%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.580448065173115%\" valign=\"top\"\u003e\n \u003cp\u003e68/51.52%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.942973523421589%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.09775967413442%\" valign=\"top\"\u003e\n \u003cp\u003eAge (years),\u0026nbsp;Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.39511201629328%\" valign=\"top\"\u003e\n \u003cp\u003e67.32\u0026plusmn;12.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.39511201629328%\" valign=\"top\"\u003e\n \u003cp\u003e62.82\u0026plusmn;13.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.026476578411406%\" valign=\"top\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.562118126272912%\" valign=\"top\"\u003e\n \u003cp\u003e53.27\u0026plusmn;14.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.580448065173115%\" valign=\"top\"\u003e\n \u003cp\u003e53.12\u0026plusmn;13.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.942973523421589%\" valign=\"top\"\u003e\n \u003cp\u003e0.954\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.09775967413442%\" valign=\"top\"\u003e\n \u003cp\u003eCourse of disease(years),\u0026nbsp;Median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.39511201629328%\" valign=\"top\"\u003e\n \u003cp\u003e10.00(2.00-15.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.39511201629328%\" valign=\"top\"\u003e\n \u003cp\u003e8.00(3.00-17.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.026476578411406%\" valign=\"top\"\u003e\n \u003cp\u003e0.619\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.562118126272912%\" valign=\"top\"\u003e\n \u003cp\u003e3.00(0.20-10.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.580448065173115%\" valign=\"top\"\u003e\n \u003cp\u003e4.00(0.40-10.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.942973523421589%\" valign=\"top\"\u003e\n \u003cp\u003e0.630\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.09775967413442%\" valign=\"top\"\u003e\n \u003cp\u003eSBP (mmHg),\u0026nbsp;Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.39511201629328%\" valign=\"top\"\u003e\n \u003cp\u003e148.66\u0026plusmn;25.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.39511201629328%\" valign=\"top\"\u003e\n \u003cp\u003e151.92\u0026plusmn;21.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.026476578411406%\" valign=\"top\"\u003e\n \u003cp\u003e0.273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.562118126272912%\" valign=\"top\"\u003e\n \u003cp\u003e135.63\u0026plusmn;19.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.580448065173115%\" valign=\"top\"\u003e\n \u003cp\u003e132.76\u0026plusmn;17.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.942973523421589%\" valign=\"top\"\u003e\n \u003cp\u003e0.419\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.09775967413442%\" valign=\"top\"\u003e\n \u003cp\u003eDBP (mmHg),\u0026nbsp;Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.39511201629328%\" valign=\"top\"\u003e\n \u003cp\u003e84.44\u0026plusmn;11.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.39511201629328%\" valign=\"top\"\u003e\n \u003cp\u003e87.27\u0026plusmn;11.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.026476578411406%\" valign=\"top\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.562118126272912%\" valign=\"top\"\u003e\n \u003cp\u003e83.72\u0026plusmn;12.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.580448065173115%\" valign=\"top\"\u003e\n \u003cp\u003e81.45\u0026plusmn;9.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.942973523421589%\" valign=\"top\"\u003e\n \u003cp\u003e0.359\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.09775967413442%\" valign=\"top\"\u003e\n \u003cp\u003eBMI (mmHg),\u0026nbsp;Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.39511201629328%\" valign=\"top\"\u003e\n \u003cp\u003e25.37\u0026plusmn;3.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.39511201629328%\" valign=\"top\"\u003e\n \u003cp\u003e25.22\u0026plusmn;3.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.026476578411406%\" valign=\"top\"\u003e\n \u003cp\u003e0.763\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.562118126272912%\" valign=\"top\"\u003e\n \u003cp\u003e24.24\u0026plusmn;3.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.580448065173115%\" valign=\"top\"\u003e\n \u003cp\u003e23.82\u0026plusmn;3.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.942973523421589%\" valign=\"top\"\u003e\n \u003cp\u003e0.582\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.09775967413442%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedical history\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.39511201629328%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.39511201629328%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.026476578411406%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.562118126272912%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.580448065173115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.942973523421589%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.09775967413442%\" valign=\"top\"\u003e\n \u003cp\u003eCerebral infarction, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.39511201629328%\" valign=\"top\"\u003e\n \u003cp\u003e26 (26.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.39511201629328%\" valign=\"top\"\u003e\n \u003cp\u003e20 (12.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.026476578411406%\" valign=\"top\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.562118126272912%\" valign=\"top\"\u003e\n \u003cp\u003e1 (3.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.580448065173115%\" valign=\"top\"\u003e\n \u003cp\u003e4 (3.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.942973523421589%\" valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.09775967413442%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLaboratory data\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.39511201629328%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.39511201629328%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.026476578411406%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.562118126272912%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.580448065173115%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.942973523421589%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.09775967413442%\" valign=\"top\"\u003e\n \u003cp\u003eHbAlc(%),\u0026nbsp;Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.39511201629328%\" valign=\"top\"\u003e\n \u003cp\u003e9.62\u0026plusmn;2.57 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.39511201629328%\" valign=\"top\"\u003e\n \u003cp\u003e9.58\u0026plusmn;2.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.026476578411406%\" valign=\"top\"\u003e\n \u003cp\u003e0.903\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.562118126272912%\" valign=\"top\"\u003e\n \u003cp\u003e9.95\u0026plusmn;2.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.580448065173115%\" valign=\"top\"\u003e\n \u003cp\u003e10.35\u0026plusmn;2.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.942973523421589%\" valign=\"top\"\u003e\n \u003cp\u003e0.464\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.09775967413442%\" valign=\"top\"\u003e\n \u003cp\u003eFPG (mmol/L),Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.39511201629328%\" valign=\"top\"\u003e\n \u003cp\u003e10.23\u0026plusmn;4.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.39511201629328%\" valign=\"top\"\u003e\n \u003cp\u003e10.32\u0026plusmn;3.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.026476578411406%\" valign=\"top\"\u003e\n \u003cp\u003e0.857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.562118126272912%\" valign=\"top\"\u003e\n \u003cp\u003e10.34\u0026plusmn;4.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.580448065173115%\" valign=\"top\"\u003e\n \u003cp\u003e10.98\u0026plusmn;4.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.942973523421589%\" valign=\"top\"\u003e\n \u003cp\u003e0.473\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.09775967413442%\" valign=\"top\"\u003e\n \u003cp\u003ePPG (mmol/L),\u0026nbsp;Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.39511201629328%\" valign=\"top\"\u003e\n \u003cp\u003e15.29\u0026plusmn;4.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.39511201629328%\" valign=\"top\"\u003e\n \u003cp\u003e16.38\u0026plusmn;5.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.026476578411406%\" valign=\"top\"\u003e\n \u003cp\u003e0.131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.562118126272912%\" valign=\"top\"\u003e\n \u003cp\u003e16.02\u0026plusmn;4.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.580448065173115%\" valign=\"top\"\u003e\n \u003cp\u003e17.01\u0026plusmn;5.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.942973523421589%\" valign=\"top\"\u003e\n \u003cp\u003e0.350\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.09775967413442%\" valign=\"top\"\u003e\n \u003cp\u003eFasting C-peptide(ng/mL),\u0026nbsp;Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.39511201629328%\" valign=\"top\"\u003e\n \u003cp\u003e2.56 (1.61-3.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.39511201629328%\" valign=\"top\"\u003e\n \u003cp\u003e1.70 (1.04-2.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.026476578411406%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.562118126272912%\" valign=\"top\"\u003e\n \u003cp\u003e2.09 (1.26-3.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.580448065173115%\" valign=\"top\"\u003e\n \u003cp\u003e1.36 (0.83-2.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.942973523421589%\" valign=\"top\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.09775967413442%\" valign=\"top\"\u003e\n \u003cp\u003e2-hour C-peptide(ng/mL),\u0026nbsp;Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.39511201629328%\" valign=\"top\"\u003e\n \u003cp\u003e5.94 (3.58-9.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.39511201629328%\" valign=\"top\"\u003e\n \u003cp\u003e3.87 (2.53-6.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.026476578411406%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.562118126272912%\" valign=\"top\"\u003e\n \u003cp\u003e5.23 (3.41-7.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.580448065173115%\" valign=\"top\"\u003e\n \u003cp\u003e3.57 (1.90-5.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.942973523421589%\" valign=\"top\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.09775967413442%\" valign=\"top\"\u003e\n \u003cp\u003eSmoking status, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.39511201629328%\" valign=\"top\"\u003e\n \u003cp\u003e29 (29.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.39511201629328%\" valign=\"top\"\u003e\n \u003cp\u003e39 (24.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.026476578411406%\" valign=\"top\"\u003e\n \u003cp\u003e0.398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.562118126272912%\" valign=\"top\"\u003e\n \u003cp\u003e15 (45.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.580448065173115%\" valign=\"top\"\u003e\n \u003cp\u003e25 (18.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.942973523421589%\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.09775967413442%\" valign=\"top\"\u003e\n \u003cp\u003eHDL-C (mmol/L),\u0026nbsp;Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.39511201629328%\" valign=\"top\"\u003e\n \u003cp\u003e1.04\u0026plusmn;0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.39511201629328%\" valign=\"top\"\u003e\n \u003cp\u003e1.06\u0026plusmn;0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.026476578411406%\" valign=\"top\"\u003e\n \u003cp\u003e0.589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.562118126272912%\" valign=\"top\"\u003e\n \u003cp\u003e0.99\u0026plusmn;0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.580448065173115%\" valign=\"top\"\u003e\n \u003cp\u003e1.09\u0026plusmn;0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.942973523421589%\" valign=\"top\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.09775967413442%\" valign=\"top\"\u003e\n \u003cp\u003eLDL-C (mmol/L),\u0026nbsp;Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.39511201629328%\" valign=\"top\"\u003e\n \u003cp\u003e2.83\u0026plusmn;1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.39511201629328%\" valign=\"top\"\u003e\n \u003cp\u003e2.71\u0026plusmn;1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.026476578411406%\" valign=\"top\"\u003e\n \u003cp\u003e0.389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.562118126272912%\" valign=\"top\"\u003e\n \u003cp\u003e2.80\u0026plusmn;0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.580448065173115%\" valign=\"top\"\u003e\n \u003cp\u003e3.07\u0026plusmn;1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.942973523421589%\" valign=\"top\"\u003e\n \u003cp\u003e0.167\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.09775967413442%\" valign=\"top\"\u003e\n \u003cp\u003eBlood urea (mmol/L),\u0026nbsp;Median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.39511201629328%\" valign=\"top\"\u003e\n \u003cp\u003e6.50 (5.40-8.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.39511201629328%\" valign=\"top\"\u003e\n \u003cp\u003e5.2 (4.5-6.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.026476578411406%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.562118126272912%\" valign=\"top\"\u003e\n \u003cp\u003e5.20 (4.80-7.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.580448065173115%\" valign=\"top\"\u003e\n \u003cp\u003e5.10 (4.05-6.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.942973523421589%\" valign=\"top\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.09775967413442%\" valign=\"top\"\u003e\n \u003cp\u003eSCR(\u0026mu;mol/L),\u0026nbsp;Median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.39511201629328%\" valign=\"top\"\u003e\n \u003cp\u003e89.00 (70.00-113.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.39511201629328%\" valign=\"top\"\u003e\n \u003cp\u003e65.00 (58.00-78.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.026476578411406%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.562118126272912%\" valign=\"top\"\u003e\n \u003cp\u003e73.00 (58.50-81.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.580448065173115%\" valign=\"top\"\u003e\n \u003cp\u003e60.00 (47.75-69.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.942973523421589%\" valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.09775967413442%\" valign=\"top\"\u003e\n \u003cp\u003eUA (\u0026mu;mol/L),\u0026nbsp;Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.39511201629328%\" valign=\"top\"\u003e\n \u003cp\u003e354.19\u0026plusmn;44.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.39511201629328%\" valign=\"top\"\u003e\n \u003cp\u003e314.88\u0026plusmn;93.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.026476578411406%\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.562118126272912%\" valign=\"top\"\u003e\n \u003cp\u003e327.59\u0026plusmn;125.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.580448065173115%\" valign=\"top\"\u003e\n \u003cp\u003e274.17\u0026plusmn;86.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.942973523421589%\" valign=\"top\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.09775967413442%\" valign=\"top\"\u003e\n \u003cp\u003eTG(mmol/L),\u0026nbsp;Median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.39511201629328%\" valign=\"top\"\u003e\n \u003cp\u003e1.59 (1.15-2.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.39511201629328%\" valign=\"top\"\u003e\n \u003cp\u003e1.50 (1.11-2.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.026476578411406%\" valign=\"top\"\u003e\n \u003cp\u003e0.816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.562118126272912%\" valign=\"top\"\u003e\n \u003cp\u003e1.43 (0.99-2.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.580448065173115%\" valign=\"top\"\u003e\n \u003cp\u003e1.41 (0.96-2.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.942973523421589%\" valign=\"top\"\u003e\n \u003cp\u003e0.677\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.09775967413442%\" valign=\"top\"\u003e\n \u003cp\u003eTC(mmol/L),\u0026nbsp;Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.39511201629328%\" valign=\"top\"\u003e\n \u003cp\u003e4.52\u0026plusmn;1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.39511201629328%\" valign=\"top\"\u003e\n \u003cp\u003e4.36\u0026plusmn;1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.026476578411406%\" valign=\"top\"\u003e\n \u003cp\u003e0.348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.562118126272912%\" valign=\"top\"\u003e\n \u003cp\u003e4.31\u0026plusmn;1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.580448065173115%\" valign=\"top\"\u003e\n \u003cp\u003e4.69\u0026plusmn;1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.942973523421589%\" valign=\"top\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.09775967413442%\" valign=\"top\"\u003e\n \u003cp\u003eHcy(\u0026mu;mol/L),\u0026nbsp;Median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.39511201629328%\" valign=\"top\"\u003e\n \u003cp\u003e18.54 (16.50-20.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.39511201629328%\" valign=\"top\"\u003e\n \u003cp\u003e11.56 (9.84-13.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.026476578411406%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.562118126272912%\" valign=\"top\"\u003e\n \u003cp\u003e18.60 (16.65-24.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.580448065173115%\" valign=\"top\"\u003e\n \u003cp\u003e10.34 (8.45-11.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.942973523421589%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.09775967413442%\" valign=\"top\"\u003e\n \u003cp\u003eUACR(mg/g),\u0026nbsp;Median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.39511201629328%\" valign=\"top\"\u003e\n \u003cp\u003e65.65 (14.10-256.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.39511201629328%\" valign=\"top\"\u003e\n \u003cp\u003e30.90 (14.38-122.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.026476578411406%\" valign=\"top\"\u003e\n \u003cp\u003e0.622\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.562118126272912%\" valign=\"top\"\u003e\n \u003cp\u003e13.80 (10.78-80.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.580448065173115%\" valign=\"top\"\u003e\n \u003cp\u003e15.06 (8.15-36.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.942973523421589%\" valign=\"top\"\u003e\n \u003cp\u003e0.373\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.09775967413442%\" valign=\"top\"\u003e\n \u003cp\u003eCRP (mg/dL),\u0026nbsp;Median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.39511201629328%\" valign=\"top\"\u003e\n \u003cp\u003e4.00 (2.08-11.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.39511201629328%\" valign=\"top\"\u003e\n \u003cp\u003e2.85 (1.50-6.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.026476578411406%\" valign=\"top\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.562118126272912%\" valign=\"top\"\u003e\n \u003cp\u003e6.85 (2.85-35.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.580448065173115%\" valign=\"top\"\u003e\n \u003cp\u003e3.20 (2.00-8.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.942973523421589%\" valign=\"top\"\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.09775967413442%\" valign=\"top\"\u003e\n \u003cp\u003eCIMT,Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.39511201629328%\" valign=\"top\"\u003e\n \u003cp\u003e0.84\u0026plusmn;0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.39511201629328%\" valign=\"top\"\u003e\n \u003cp\u003e0.74\u0026plusmn;0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.026476578411406%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.562118126272912%\" valign=\"top\"\u003e\n \u003cp\u003e0.75\u0026plusmn;0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.580448065173115%\" valign=\"top\"\u003e\n \u003cp\u003e0.71\u0026plusmn;0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.942973523421589%\" valign=\"top\"\u003e\n \u003cp\u003e0.398\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.09775967413442%\" valign=\"top\"\u003e\n \u003cp\u003ePPGE(mmol/L),\u0026nbsp;Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.39511201629328%\" valign=\"top\"\u003e\n \u003cp\u003e0.60\u0026plusmn;3.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.39511201629328%\" valign=\"top\"\u003e\n \u003cp\u003e0.57\u0026plusmn;1.79l\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.026476578411406%\" valign=\"top\"\u003e\n \u003cp\u003e0.874\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.562118126272912%\" valign=\"top\"\u003e\n \u003cp\u003e0.88\u0026plusmn;2.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.580448065173115%\" valign=\"top\"\u003e\n \u003cp\u003e0.83\u0026plusmn;2.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.942973523421589%\" valign=\"top\"\u003e\n \u003cp\u003e0.916\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.09775967413442%\" valign=\"top\"\u003e\n \u003cp\u003eSDBG(mmol/L),\u0026nbsp;Median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.39511201629328%\" valign=\"top\"\u003e\n \u003cp\u003e2.39 (1.70-3.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.39511201629328%\" valign=\"top\"\u003e\n \u003cp\u003e2.46 (1.66-3.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.026476578411406%\" valign=\"top\"\u003e\n \u003cp\u003e0.654\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.562118126272912%\" valign=\"top\"\u003e\n \u003cp\u003e2.57 (1.71-3.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.580448065173115%\" valign=\"top\"\u003e\n \u003cp\u003e2.45 (1.74-3.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.942973523421589%\" valign=\"top\"\u003e\n \u003cp\u003e0.449\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.09775967413442%\" valign=\"top\"\u003e\n \u003cp\u003eLAGE(mmol/L),\u0026nbsp;Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.39511201629328%\" valign=\"top\"\u003e\n \u003cp\u003e7.59\u0026plusmn;3.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.39511201629328%\" valign=\"top\"\u003e\n \u003cp\u003e6.83\u0026plusmn;3.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.026476578411406%\" valign=\"top\"\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.562118126272912%\" valign=\"top\"\u003e\n \u003cp\u003e7.50\u0026plusmn;3.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.580448065173115%\" valign=\"top\"\u003e\n \u003cp\u003e6.99\u0026plusmn;3.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.942973523421589%\" valign=\"top\"\u003e\n \u003cp\u003e0.444\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\u003eHHCY: hyperhomocysteinemia; SBP: systolic blood pressure; DBP: diastolic blood pressure; BMI: body mass index; HbA1c: glycosylated hemoglobin; FPG: fasting plasma glucose; PPG: postprandial plasma glucose; TC: total cholesterol; LDL-C: low-density lipoprotein cholesterol; HDL-C: high-density lipoprotein cholesterol; SCR: serum creatinine; UA: uric acid; TG: triglyceride; TC: total cholesterol; Hcy: homocysteine; UACR: urinary albumin creatinine ratio; CRP: C reactive protein; CIMT: carotid intima-media thickness; PPGE: postprandial glucose excursion; SDBG: standard deviation of blood glucose level;LAGE: largest amplitude of glycemic excursions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u0026nbsp;\u003c/strong\u003eMultivariable logistic regression.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"860\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.372093023255815%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eRisk factors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.41860465116279%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal (n=423)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.6046511627907%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePatients with hypertension (n=258)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.6046511627907%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePatients without hypertension (n=165)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.512820512820515%\" valign=\"top\"\u003e\n \u003cp\u003eOdds ratio (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.401709401709402%\" valign=\"top\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.216524216524217%\" valign=\"top\"\u003e\n \u003cp\u003eOdds ratio (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.826210826210826%\" valign=\"top\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.216524216524217%\" valign=\"top\"\u003e\n \u003cp\u003eOdds ratio(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.826210826210826%\" valign=\"top\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.372093023255815%\" valign=\"top\"\u003e\n \u003cp\u003eHHCY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.74418604651163%\" valign=\"top\"\u003e\n \u003cp\u003e2.942 (1.356-6.386)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.674418604651163%\" valign=\"top\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.767441860465116%\" valign=\"top\"\u003e\n \u003cp\u003e2.840 (1.153-6.999)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.837209302325581%\" valign=\"top\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.767441860465116%\" valign=\"top\"\u003e\n \u003cp\u003e1.123(0.329-3.383)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.837209302325581%\" valign=\"top\"\u003e\n \u003cp\u003e0.194\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.372093023255815%\" valign=\"top\"\u003e\n \u003cp\u003eGender, male/female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.74418604651163%\" valign=\"top\"\u003e\n \u003cp\u003e1.183 (0.541-2.587)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.674418604651163%\" valign=\"top\"\u003e\n \u003cp\u003e1.183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.767441860465116%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.837209302325581%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.767441860465116%\" valign=\"top\"\u003e\n \u003cp\u003e0.663(0.201-2.184)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.837209302325581%\" valign=\"top\"\u003e\n \u003cp\u003e0.499\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.372093023255815%\" valign=\"top\"\u003e\n \u003cp\u003eAge\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.74418604651163%\" valign=\"top\"\u003e\n \u003cp\u003e1.030 (1.002-1.059)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.674418604651163%\" valign=\"top\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.767441860465116%\" valign=\"top\"\u003e\n \u003cp\u003e1.028 (0.993-1.063)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.837209302325581%\" valign=\"top\"\u003e\n \u003cp\u003e1.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.767441860465116%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.837209302325581%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.372093023255815%\" valign=\"top\"\u003e\n \u003cp\u003eCerebral infarction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.74418604651163%\" valign=\"top\"\u003e\n \u003cp\u003e0.598 (0.208-1.724)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.674418604651163%\" valign=\"top\"\u003e\n \u003cp\u003e0.341\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.767441860465116%\" valign=\"top\"\u003e\n \u003cp\u003e0.591 (0.191-1.827)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.837209302325581%\" valign=\"top\"\u003e\n \u003cp\u003e0.450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.767441860465116%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.837209302325581%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.372093023255815%\" valign=\"top\"\u003e\n \u003cp\u003ePPG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.74418604651163%\" valign=\"top\"\u003e\n \u003cp\u003e0.985 (0.922-1.052)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.674418604651163%\" valign=\"top\"\u003e\n \u003cp\u003e0.652\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.767441860465116%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.837209302325581%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.767441860465116%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.837209302325581%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.372093023255815%\" valign=\"top\"\u003e\n \u003cp\u003eFastingC-peptide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.74418604651163%\" valign=\"top\"\u003e\n \u003cp\u003e1.004 (0.744-1.354)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.674418604651163%\" valign=\"top\"\u003e\n \u003cp\u003e0.982\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.767441860465116%\" valign=\"top\"\u003e\n \u003cp\u003e0.882 (0.635-1.224)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.837209302325581%\" valign=\"top\"\u003e\n \u003cp\u003e0.469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.767441860465116%\" valign=\"top\"\u003e\n \u003cp\u003e1.777(1.005-3.143)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.837209302325581%\" valign=\"top\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.372093023255815%\" valign=\"top\"\u003e\n \u003cp\u003e2-h C-peptide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.74418604651163%\" valign=\"top\"\u003e\n \u003cp\u003e1.009 (0.914-1.114)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.674418604651163%\" valign=\"top\"\u003e\n \u003cp\u003e0.858\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.767441860465116%\" valign=\"top\"\u003e\n \u003cp\u003e1.052 (0.943-1.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.837209302325581%\" valign=\"top\"\u003e\n \u003cp\u003e0.359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.767441860465116%\" valign=\"top\"\u003e\n \u003cp\u003e0.883(0.731-1.066)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.837209302325581%\" valign=\"top\"\u003e\n \u003cp\u003e0.194\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.372093023255815%\" valign=\"top\"\u003e\n \u003cp\u003eSmoking status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.74418604651163%\" valign=\"top\"\u003e\n \u003cp\u003e1.347 (0.582-3.118)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.674418604651163%\" valign=\"top\"\u003e\n \u003cp\u003e1.347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.767441860465116%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.837209302325581%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.767441860465116%\" valign=\"top\"\u003e\n \u003cp\u003e1.469(0.466-4.631)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.837209302325581%\" valign=\"top\"\u003e\n \u003cp\u003e0.512\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.372093023255815%\" valign=\"top\"\u003e\n \u003cp\u003eBlood urea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.74418604651163%\" valign=\"top\"\u003e\n \u003cp\u003e1.062 (0.881-1.279)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.674418604651163%\" valign=\"top\"\u003e\n \u003cp\u003e0.531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.767441860465116%\" valign=\"top\"\u003e\n \u003cp\u003e1.074 (0.845-1.366)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.837209302325581%\" valign=\"top\"\u003e\n \u003cp\u003e0.527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.767441860465116%\" valign=\"top\"\u003e\n \u003cp\u003e1.198(0.908-1.580)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.837209302325581%\" valign=\"top\"\u003e\n \u003cp\u003e0.202\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.372093023255815%\" valign=\"top\"\u003e\n \u003cp\u003eSCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.74418604651163%\" valign=\"top\"\u003e\n \u003cp\u003e1.001 (0.991-1.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.674418604651163%\" valign=\"top\"\u003e\n \u003cp\u003e0.816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.767441860465116%\" valign=\"top\"\u003e\n \u003cp\u003e1.001 (0.990-1.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.837209302325581%\" valign=\"top\"\u003e\n \u003cp\u003e0.895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.767441860465116%\" valign=\"top\"\u003e\n \u003cp\u003e1.017(0.995-1.040)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.837209302325581%\" valign=\"top\"\u003e\n \u003cp\u003e0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.372093023255815%\" valign=\"top\"\u003e\n \u003cp\u003eUA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.74418604651163%\" valign=\"top\"\u003e\n \u003cp\u003e0.999 (0.995-1.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.674418604651163%\" valign=\"top\"\u003e\n \u003cp\u003e0.536\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.767441860465116%\" valign=\"top\"\u003e\n \u003cp\u003e0.999 (0.994-1.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.837209302325581%\" valign=\"top\"\u003e\n \u003cp\u003e0.752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.767441860465116%\" valign=\"top\"\u003e\n \u003cp\u003e1.000(0.994-1.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.837209302325581%\" valign=\"top\"\u003e\n \u003cp\u003e0.872\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.372093023255815%\" valign=\"top\"\u003e\n \u003cp\u003eCRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.74418604651163%\" valign=\"top\"\u003e\n \u003cp\u003e1.001 (0.987-1.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.674418604651163%\" valign=\"top\"\u003e\n \u003cp\u003e0.904\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.767441860465116%\" valign=\"top\"\u003e\n \u003cp\u003e0.997 (0.979-1.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.837209302325581%\" valign=\"top\"\u003e\n \u003cp\u003e0.781\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.767441860465116%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.837209302325581%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eCI: confidence interval; HHCY: hyperhomocysteinemia; PPG: postprandial plasma glucose; SCR: serum creatinine; UA: uric acid; CRP: C reactive protein.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"type 2diabetes mellitus, hyperhomocysteinemia, carotid intima-media thickness, hypertension","lastPublishedDoi":"10.21203/rs.3.rs-3988060/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3988060/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eThe influence of hyperhomocysteinemia on carotid intima-media thickness (CIMT) in patients with type 2 diabetes mellitus (T2DM) and its interplay with hypertension remains uncertain. This study aimed to investigate the impact of hyperhomocysteinemia on CIMT in patients with T2DM, both with and without coexisting hypertension.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThis retrospective observational study was conducted at Shanghai Jiading District Central Hospital, enrolling patients with type 2 diabetes mellitus (T2DM) between January 2019 and December 2020. Data on serum homocysteine levels and carotid intima-media thickness (CIMT) were collected. Multivariable logistic regression analysis was employed to investigate the potential risk factors associated with CIMT in the entire cohort of T2DM patients and within specific hypertension subgroups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThe study sample comprised 423 patients diagnosed with T2DM, with a mean age of 59.99±14.59 years, including 258 males (60.99%), 258 hypertension patients (60.99%). Multivariable logistic regression analysis revealed a significant association between hyperhomocysteinemia and carotid intima-media thickness (CIMT) in all T2DM patients (odds ratio [OR]=2.942, 95% confidence interval [CI]: 1.356-6.386, P=0.006) and among patients with coexisting hypertension (OR=2.840, 95%CI: 1.153-6.999, P=0.040). However, no significant association was observed between hyperhomocysteinemia and CIMT in patients without hypertension (OR=1.123, 95%CI: 0.329-3.383, P=0.194).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e The findings highlight hyperhomocysteinemia as an independent risk factor for CIMT thickening in patients with T2DM, particularly among those with concomitant hypertension. These results underscore the importance of managing hyperhomocysteinemia in T2DM patients, particularly those with hypertension, to mitigate the risk of CIMT thickening and subsequent cardiovascular events.\u003c/p\u003e","manuscriptTitle":"Impact of hyperhomocysteinemiaon carotid intima-media thickness in type 2 diabetes mellituswith or without hypertension: a retrospective observational study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-11 07:22:37","doi":"10.21203/rs.3.rs-3988060/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":"f216a06d-775b-4af8-91f4-77e03b6f1b29","owner":[],"postedDate":"March 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-01T09:27:04+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-11 07:22:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3988060","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3988060","identity":"rs-3988060","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}