The association between arterial stiffness, visceral fat rating, and glycemic variability in non-obese adults with type 1 diabetes

The association between arterial stiffness, visceral fat rating, and glycemic variability in non-obese adults with type 1 diabetes | 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 Article The association between arterial stiffness, visceral fat rating, and glycemic variability in non-obese adults with type 1 diabetes Michal Kulecki, Dariusz Naskret, Maja Mietkiewska-Dolecka, Bartosz Lasowski, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7926850/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Adults with type 1 diabetes mellitus (T1DM) exhibit premature arterial stiffening, but the relative roles of visceral adiposity and glycemic variability remain unclear. We investigated these associations in a group of 120 non-obese adults with T1DM. Carotid–femoral pulse wave velocity (PWV) was measured oscillometrically, and visceral fat rating (VFR) was quantified by multifrequency bioimpedance. Ninety-day continuous glucose monitoring data provided glycemic metrics including mean glucose, time in range (TIR), time above range (TAR), glycemic risk index (GRI), mean amplitude of glycemic excursions (MAGE), mean of daily differences (MODD), and coefficient of variation (CV). Participants (median age 33.8 years, mean BMI 24.3 kg·m⁻²) had a mean PWV of 7.47 ± 1.43 m·s⁻¹. Age was the strongest correlate of PWV (ρ = 0.59, p < 0.001) followed by VFR (ρ = 0.48, p < 0.001). In multivariable models, each standard deviation increase in VFR was associated with a 0.22 m·s⁻¹ higher PWV (p = 0.01), comparable to the effects of systolic blood pressure and diabetes complications. MODD, MAGE, TAR, GRI, and lower TIR modestly improved model fit (ΔR² ≤ 0.08), yet none showed univariate associations. VFR and age are dominant correlates of arterial stiffness in T1DM, while glycemic variability plays a limited role. Health sciences/Diseases Health sciences/Endocrinology Health sciences/Medical research Biological sciences/Physiology Figures Figure 1 Figure 2 Introduction Type 1 diabetes mellitus (T1DM) is an autoimmune disease characterized by the destruction of pancreatic β-cells, leading to complete insulin deficiency and chronic hyperglycemia [ 1 ]. Despite advances in insulin therapy and glycemic monitoring, individuals with T1DM remain at heightened risk of cardiovascular complications, which are the leading cause of morbidity and mortality in this population [ 2 ]. Arterial stiffness (AS) is an independent predictor of cardiovascular risk [ 3 ]. Increased pulse wave velocity (PWV) reflects reduced arterial compliance and has been associated with cardiovascular events, cardiovascular mortality, and all-cause mortality in T1DM [ 4 – 6 ]. Several studies have indicated that AS develops earlier in young individuals with T1DM compared to the general population, and it correlates with both traditional risk factors and diabetes-specific variables, such as autonomic dysfunction and metabolic control [ 4 , 7 , 8 ]. Visceral fat rating (VFR)—a 0-to-59 score generated by the Tanita multi-frequency bio-impedance analyser—offers a rapid, radiation-free surrogate of intra-abdominal adiposity [ 9 ]. In adults with type 2 diabetes mellitus (T2DM), a greater visceral fat area (VFA) is independently linked to higher pulse-wave velocity [ 10 , 11 ]. Visceral fat also modifies the hemodynamic consequences of insulin resistance: mediation analyses indicate that more than half of the adverse effect of insulin resistance on AS and incident hypertension is channeled through excess intra-abdominal fat [ 12 ]. These associations emerge across the life-course—from adolescents with obesity or T2DM to non-obese Japanese adults whose normal BMI masks high visceral fat stores —highlighting the limitations of anthropometric cut-offs alone [ 10 , 11 ]. By contrast, studies with T1DM adults remain scarce, leaving it uncertain whether visceral fat confers the same incremental burden on AS in the context of autoimmune insulin deficiency. Addressing this gap could refine cardiovascular risk assessment in ostensibly lean adults with T1DM who might otherwise be overlooked by BMI-based screening. Hemoglobin A1c (HbA 1c ) is associated with AS in T2DM and T1DM [ 4 , 13 , 14 ]. However, some studies suggest that HbA 1c variability, rather than mean HbA 1c alone, is a more consistent predictor of AS in people with T1DM [ 15 – 17 ]. HbA 1c does not fully capture the dynamics of glycemic fluctuations that contribute to endothelial dysfunction and vascular aging. Recent studies suggest that glycemic variability (GV), encompassing acute fluctuations in glucose levels, may play a role in the development of AS in individuals with T2DM [ 18 ]. However, studies have not confirmed the relationship between glycemic variability and AS in individuals with T1DM [ 13 ]. Our study aimed to investigate the association between PWV, VFR, and glycemic parameters in adults with T1DM. Results Study population We analyzed 120 participants who exhibited a mean PWV of 7.47 ± 1.43 m/s and a median VFR of 4 [ 3 – 6 ] (Table 1 ). The sample showed balanced sex distribution – 50.8% men, a median age of 33.8 [24.5–41.2] years, and a median BMI of 24.3 [22.2–26.8] kg m⁻². Table 1 General characteristics of the study cohort (N = 120) Variable Whole group General characteristics Age (yrs) 33.8 [24.5–41.2] Sex – male 61 (50.8%) Height (m) 1.75 ± 0.1 Weight (kg) 75.1 ± 14.0 BMI (kg m⁻²) 24.3 [22.2–26.8] WHR 0.82 [0.73–0.91] Overweight (BMI 25–30 kg m⁻²)) 52 (43.3%) Daily insulin intake (U kg⁻¹) 0.51 [0.50–0.67] Diabetes duration (yrs) 14.0 [10.0–20.3] Body-composition metrics Body adipose tissue (%) 24.0 ± 8.5 Adipose-free mass (kg) 54.7 [46.7–67.5] Muscle mass (kg) 51.8 [42.6–64.2] Total body water (%) 54.7 [49.2–59.9] Visceral fat rating 4 [ 3 – 6 ] Impedance (Ω) 601 [537.8–662.8] Blood pressure & arterial stiffness Systolic BP (mm Hg) 125.6 ± 12.7 PWV (m s⁻¹) 7.47 ± 1.43 Laboratory parameters Leukocytes (10³ µl⁻¹) 6.23 [5.42–6.95] Erythrocytes (10⁶ µl⁻¹) 4.79 ± 0.45 Hemoglobin (g dl⁻¹) 14.3 [13.4–15.5] MCV (fl) 86.8 [83.7–89.6] ACR (mg g⁻¹) 3.00 [2.40–5.00] Total cholesterol (mg dl⁻¹) 176.0 [155.8–204.0] LDL-C (mg dl⁻¹) 93.0 [72.3–119.8] HDL-C (mg dl⁻¹) 64.0 [55.8–76.0] Triglycerides (mg dl⁻¹) 78.0 [62.0–111.0] Creatinine (mg dl⁻¹) 0.83 ± 0.15 eGFR (ml min⁻¹ 1.73 m⁻²) 106.4 ± 17.3 CRP (mg l⁻¹) 0.98 [0.60–1.95] CGM metrics (n = 100) HbA1c (%) 7.60 [7.08–8.40] Mean glucose (mg dl⁻¹) 177.5 [152.4–199.5] Glucose SD 69.0 ± 20.7 Glucose CV (%) 37.5 ± 5.9 TIR (%) 54.0 ± 18.9 TITR (%) 31.1 [24.6–42.4] GRI 53.8 [39.0–74.1] LBGI 0.59 [0.32–0.92] HBGI 9.97 [5.82–14.46] GMI 7.55 [6.96–8.08] MAGE (mg dl⁻¹) 125.5 [100.2–156.1] GRADE 10.7 [7.8–13.5] M100 28.4 [15.8–45.1] J-index 60.1 [42.7–80.7] MODD (mg dl⁻¹) 69.7 ± 21.4 TBR (%) 2.18 [0.84–3.81] TAR (%) 43.1 ± 20.1 TIR ≥ 70% 22 (22%) Treatment & complications Insulin-pump therapy 30 (25.0%) ≥ 1 diabetic complication 23 (19.2%) Peripheral neuropathy 8 (6.7%) Non-proliferative retinopathy 19 (15.8%) Proliferative retinopathy 2 (1.7%) Current smoker 21 (17.5%) Former smoker 26 (21.7%) eGDR (mg·kg⁻¹·min⁻¹) 9.66 [8.29–10.39] Continuous variables are shown as mean ± SD; median [IQR] . Categorical variables are n (%). p-values < 0.05 are in bold . Abbreviations: PWV – pulse-wave velocity; BP – blood pressure; BMI – body-mass index; WHR – waist-to-hip ratio; DII – daily insulin intake; eGFR – estimated glomerular filtration rate; eGDR – estimated glucose disposal rate; HbA1c – glycated hemoglobin; TIR – time-in-range; TAR/TBR – time-above/below range; GRI – glycemic-risk index; MAGE – mean amplitude of glycemic excursions; MODD – mean of daily differences; CV – coefficient of variation; GMI – glucose-management indicator; LBGI/HBGI – low/high blood-glucose indices; TITR – time-in-tight range. Visceral adiposity and arterial stiffness Visceral adiposity displayed a pronounced association with AS. Figure 2 presents the bivariate correlations; VFR correlated positively with PWV (ρ = +0.48, p < 0.001). When we split the cohort at the PWV median, the higher-PWV group had almost twice the VFR (3.5 [ 2 – 5 ] vs 6 [ 4 – 7 ], p < 0.0001; Table 2 ). Multivariable analysis supported an independent association. In the adjusted clinical models for sex, SBP, estimated glomerular filtration rate (eGFR), presence of diabetic complications, and smoking, each standard-deviation increase in VFR raised PWV by β ≈ +0.22 with p = 0.01 (Table 3 ). Substituting the extensive clinical covariate set with age alone yielded a comparable effect size (β ≈ +0.21, p = 0.02; Table 4 ). Table 2 Comparison of participants with pulse wave velocity above vs. below the median Variable PWV ≤ median (n = 60) PWV > median (n = 60) p Test General characteristics Age (yrs) 26.4 [21.2–34.7] 39.2 [32.4–46.6] < 0.0001 M-W Sex – male, n (%) 23 (38.3) 38 (63.3) 0.006 χ² Height (m) 1.7 [1.66–1.78] 1.8 [1.70–1.84] 0.0009 M-W Weight (kg) 70.3 ± 11.3 79.9 ± 14.9 0.0001 C-C BMI (kg m⁻²) 23.8 ± 2.70 25.2 ± 2.91 0.0069 t-st WHR 0.81 [0.76–0.84] 0.85 [0.82–0.93] 0.0001 M-W Overweight, n (%) 19 (31.7) 33 (55.0) 0.0099 χ² Daily insulin intake (U kg⁻¹) 0.55 [0.50–0.69] 0.50 [0.50–0.63] 0.55 M-W Diabetes duration (yrs) 12.5 [8.75–17.0] 17.0 [10.8–25.0] 0.0009 M-W Body-composition metrics Body adipose tissue percentage (%) 25.0 ± 9.25 23.0 ± 7.57 0.21 t-st Fat-free mass (kg) 50.3 [45.0–62.9] 63.6 [48.2–72.8] 0.0006 M-W Muscle mass (kg) 47.5 [42.6–58.1] 60.8 [45.8–69.6] 0.0003 M-W Total body water (kg) 52.6 [47.4–59.8] 56.0 [52.2–60.0] 0.051 M-W Visceral-fat rating 3.5 [ 2 – 5 ] 6 [ 4 – 7 ] < 0.0001 M-W Impedance (Ω) 647.7 ± 100.1 571.5 ± 80.5 < 0.0001 t-st Blood pressure & stiffness Systolic BP (mm Hg) 122.0 ± 12.5 129.1 ± 11.9 0.0019 t-st PWV (m s⁻¹) 6.40 [5.83–6.81] 8.43 [7.87–9.14] < 0.0001 M-W Laboratory parameters Leukocytes (10³ µl⁻¹) 6.45 [5.69–7.06] 6.15 [5.12–6.94] 0.15 M-W Erythrocytes (10⁶ µl⁻¹) 4.71 ± 0.41 4.87 ± 0.47 0.054 t-st Hemoglobin (g dl⁻¹) 14.0 ± 1.49 14.6 ± 1.58 0.031 t-st MCV (fl) 86.8 [84.2–89.6] 87.0 [83.5–89.6] 0.55 M-W Albumin/creatinine (mg g⁻¹) 3.00 [2.08–5.00] 3.00 [2.46–5.03] 0.75 M-W Total cholesterol (mg dl⁻¹) 170.5 [150.8–198.0] 180.0 [166.8–206.0] 0.08 M-W LDL-C (mg dl⁻¹) 88.1 [69.0–109.5] 103.0 [79.0–123.0] 0.07 M-W HDL-C (mg dl⁻¹) 65.0 [57.0–76.3] 61.5 [51.5–71.8] 0.18 M-W Triglycerides (mg dl⁻¹) 72.0 [56.7–100.0] 81.5 [68.0–116.5] 0.07 M-W Creatinine (mg dl⁻¹) 0.82 [0.73–0.91] 0.85 [0.78–0.97] 0.0022 M-W eGFR (ml min⁻¹ 1.73 m⁻²) 114.0 ± 16.6 98.7 ± 14.5 < 0.0001 t-st CRP (mg l⁻¹) 0.85 [0.60–2.15] 1.08 [0.60–1.88] 0.97 M-W Glycemic control & variability HbA 1c (%) 7.70 [7.10–8.90] 7.60 [7.00–8.13] 0.17 M-W HbA1c ≤ 6.5%, n (%) 6 (10.0) 9 (15.0) 0.41 χ² Mean glucose (mg dl⁻¹) 177.6 [151.3–208.6] 177.4 [153.2–197.6] 0.96 M-W Glucose SD 70.0 ± 23.1 68.1 ± 18.5 0.63 t-st Glucose CV (%) 37.4 ± 6.06 37.6 ± 5.75 0.89 t-st TIR (%) 53.4 ± 20.7 54.5 ± 17.4 0.78 t-st TITR (%) 33.0 ± 17.2 33.2 ± 13.7 0.95 t-st GRI 56.5 ± 25.8 54.5 ± 22.8 0.68 t-st LBGI 0.59 [0.31–1.15] 0.59 [0.32–0.90] 0.96 M-W HBGI 10.0 [5.76–16.1] 9.89 [6.03–14.2] 0.87 M-W GMI 7.56 [6.93–8.30] 7.55 [6.97–8.04] 0.96 M-W MAGE (mg dl⁻¹) 132.9 ± 45.2 128.8 ± 34.4 0.61 t-st GRADE 10.6 [7.73–14.4] 10.7 [8.01–13.3] 0.93 M-W M100 30.5 [15.9–50.8] 28.0 [15.7–44.4] 0.83 M-W J-index 63.0 [42.9–85.8] 59.4 [42.7–79.8] 0.88 M-W MODD (mg dl⁻¹) 69.5 ± 23.5 70.0 ± 19.5 0.92 t-st TBR (%) 2.26 [0.77–5.05] 2.11 [0.85–3.77] 0.95 M-W TAR (%) 43.5 ± 22.2 42.7 ± 18.3 0.83 t-st TIR ≥ 70%, n (%) 9 (19.2) 13 (24.5) 0.52 χ² Treatment & complications Insulin-pump therapy, n (%) 19 (31.7) 11 (18.3) 0.09 χ² ≥ 1 diabetic complication 7 (11.7) 16 (26.7) 0.037 χ² Peripheral neuropathy 3 (5.0) 5 (8.3) 0.71 χ² Yates Non-proliferative retinopathy 7 (11.7) 12 (20.0) 0.21 χ² Proliferative retinopathy 0 (0.0) 2 (3.3) 0.48 χ² Yates Current smoker 12 (20.0) 9 (15.0) 0.47 χ² Former smoker 11 (18.3) 15 (25.0) 0.38 χ² eGDR (mg·kg⁻¹·min⁻¹) 10.05 [8.89–10.77] 9.24 [7.63–10.08] 0.006 M-W Continuous variables are shown as mean ± SD; median [IQR] . Categorical variables are n (%). p-values < 0.05 are in bold . Abbreviations: t-st – Student’s t-test (equal variances); C-C – Cochran–Cox corrected t-test (unequal variances); M-W – Mann–Whitney U test; χ² – Pearson chi-square test; χ² Yates – χ² with Yates’ continuity correction; PWV – pulse-wave velocity; BP – blood pressure; BMI – body-mass index; WHR – waist-to-hip ratio; DII – daily insulin intake; eGFR – estimated glomerular filtration rate; eGDR – estimated glucose disposal rate; HbA1c – glycated hemoglobin; TIR – time-in-range; TAR/TBR – time-above/below range; GRI – glycemic-risk index; MAGE – mean amplitude of glycemic excursions; MODD – mean of daily differences; CV – coefficient of variation; GMI – glucose-management indicator; LBGI/HBGI – low/high blood-glucose indices; TITR – time-in-tight range. Table 3 Multivariable linear regression (dependent variable = pulse-wave velocity, PWV). Each column shows the base clinical model extended with a single glycemic parameter or variability metric. Predictor +HbA1c +TIR +GRI +TAR +MODD +CV +TBR +MAGE Clinical covariates (base model) Sex (male) 0.09 ( p = 0.28) 0.16 ( p = 0.064) 0.15 ( p = 0.076) 0.16 ( p = 0.062) 0.15 ( p = 0.083) 0.15 ( p = 0.07) 0.16 ( p = 0.059) 0.15 ( p = 0.071) Visceral-fat rating 0.22 ( p = 0.01) 0.19 ( p = 0.04) 0.20 ( p = 0.04) 0.19 ( p = 0.04) 0.22 ( p = 0.02) 0.17 ( p = 0.06) 0.15 ( p = 0.11) 0.20 ( p = 0.04) Systolic BP (mm Hg) 0.27 ( p = 0.001) 0.29 ( p = 0.001) 0.29 ( p = 0.001) 0.29 ( p = 0.001) 0.28 ( p = 0.001) 0.28 ( p = 0.001) 0.28 ( p = 0.001) 0.28 ( p = 0.001) eGFR (mL min⁻¹ 1.73 m⁻²) –0.29 ( p < 0.001) –0.31 ( p < 0.001) –0.30 ( p < 0.001) –0.31 ( p < 0.001) –0.31 ( p < 0.001) –0.30 ( p = 0.001) –0.32 ( p < 0.001) –0.31 ( p < 0.001) ≥ 1 diabetic complication 0.21 ( p = 0.01) 0.23 ( p = 0.01) 0.22 ( p = 0.01) 0.23 ( p = 0.01) 0.23 ( p = 0.01) 0.23 ( p = 0.01) 0.24 ( p = 0.003) 0.22 ( p = 0.01) Current smoker –0.10 ( p = 0.21) –0.09 ( p = 0.26) –0.09 ( p = 0.26) –0.09 ( p = 0.27) –0.08 ( p = 0.29) –0.05 ( p = 0.55) –0.05 ( p = 0.55) –0.08 ( p = 0.31) Added glycemic metric HbA1c (%) 0.09 ( p = 0.25) – – – – – – – TIR (%) – –0.17 ( p = 0.039) – – – – – – GRI – – 0.17 ( p = 0.042) – – – – – TAR (%) – – – 0.16 ( p = 0.047) – – – – MODD – – – – 0.19 ( p = 0.02) – – – CV (%) – – – – – 0.12 ( p = 0.13) – – TBR (%) – – – – – – –0.03 ( p = 0.71) – MAGE (mg dL⁻¹) – – – – – – – 0.16 ( p = 0.057) Model R² 0.42 0.49 0.49 0.49 0.50 0.48 0.46 0.48 Standardised β coefficients are shown; p-values appear in parentheses. All models include the same six clinical covariates (sex, visceral-fat rating, systolic BP, eGFR, ≥ 1 diabetic complication, smoking). Each column adds one glycemic metric to this base. R² indicates model fit. Bold marks p < 0.05. Abbreviations: PWV - pulse-wave velocity; HbA1c - hemoglobin A1c; TIR/TAR/TBR - time in/above/below range; GRI - glycemic risk index; MODD - mean of daily differences; CV - coefficient of variation; MAGE - mean amplitude of glycemic excursions; BP - blood pressure; eGFR - estimated glomerular filtration rate. Table 4 Multivariable linear regression (dependent variable = pulse-wave velocity, PWV). Each column shows the base model (age + visceral fat rating) extended with one glycemic parameter. Predictor +HbA1c +TIR +GRI +TAR +MODD +CV +TBR +MAGE Base covariates Age (yrs) 0.47 ( p < 0.001) 0.54 ( p < 0.001) 0.54 ( p < 0.001) 0.53 ( p < 0.001) 0.55 ( p < 0.001) 0.53 ( p < 0.001) 0.53 ( p < 0.001) 0.55 ( p < 0.001) Visceral-fat rating (score) 0.19 ( p = 0.043) 0.21 ( p = 0.03) 0.21 ( p = 0.01) 0.20 ( p = 0.03) 0.23 ( p = 0.01) 0.19 ( p = 0.04) 0.16 ( p = 0.08) 0.22 ( p = 0.02) Added glycemic metric HbA1c (%) 0.01 ( p = 0.92) – – – – – – – TIR (%) – –0.18 ( p = 0.03) – – – – – – GRI – – 0.21 ( p = 0.01) – – – – – TAR (%) – – – 0.16 ( p = 0.049) – – – – MODD – – – – 0.27 ( p < 0.01) – – – CV (%) – – – – – 0.18 ( p = 0.02) – – TBR (%) – – – – – – –0.03 ( p = 0.67) – MAGE (mg dL⁻¹) – – – – – – – 0.22 ( p < 0.01) Model R² 0.35 0.42 0.43 0.42 0.46 0.43 0.40 0.44 Standardised β coefficients are shown; p-values appear in parentheses. All models include the same two covariates—age and visceral-fat rating. Each column adds one glycemic metric to this base. R² indicates model fit. Bold marks p < 0.05. Abbreviations: PWV - pulse-wave velocity; HbA1c - haemoglobin A1c; TIR/TAR/TBR - time in/above/below range; GRI - glycemic risk index; MODD - mean of daily differences; CV - coefficient of variation; MAGE - mean amplitude of glycemic excursions. Age, blood pressure, and renal function Age produced the strongest individual relation with PWV. Correlation analysis revealed a correlation coefficient of ρ = +0.59 (p < 0.001), and regression coefficients ranged from + 0.47 to + 0.55 across all age-based models ( p < 0.001). Participants in the upper PWV half were a decade older on average than those below the median (26.4 [21.2–34.7] vs 39.2 [32.4–46.6]; Table 2 ). SBP followed closely (r = + 0.39, p < 0.001) and retained β ≈ +0.28 with p = 0.001 after full adjustment. Renal function moved in the opposite direction: lower eGFR associated with higher PWV (r = − 0.41, p < 0.001), and multivariable models confirmed this inverse link with β near − 0.31 and p < 0.001. Body composition Body composition traits other than VFR displayed heterogeneous patterns. Weight, fat-free mass, and muscle mass correlated positively with PWV (r ≈ + 0.29 to + 0.32, p ≤ 0.001), whereas whole-body impedance correlated negatively (ρ = − 0.39, p < 0.001). Despite higher absolute fat mass, the high-PWV group did not differ in total adipose tissue percentage (23.0 ± 7.6% vs 25.0 ± 9.3%, p = 0.21). Overweight status (BMI ≥ 25 kg m⁻²) appeared more often in the high-PWV group (55% vs 32%, p = 0.01). VFR showed very strong positive correlations with body-size measures—BMI (ρ = 0.72, p < 0.001) and weight (ρ = 0.71, p < 0.001)—and moderate positive associations with age (ρ = 0.55, p < 0.001), muscle mass (ρ = 0.47, p < 0.001), fat-free mass (ρ = 0.46, p < 0.001) and height (ρ = 0.42, p < 0.001), SBP (ρ = 0.33, p 70% had higher VFR than people with lower TIR (5,5 [ 4 – 7 ] vs 4 [ 2 – 6 ]; p = 0.01). VFR was positively related to TIR (ρ = 0.23, p = 0.021) and TITR. In contrast, VFR was negatively correlated with HbA1c (ρ = − 0.26, p = 0.004) and glycemic-variability indices such as MAGE (ρ = − 0.26, p = 0.010), MODD (ρ = − 0.25, p = 0.012) and the GRI (ρ = − 0.25, p = 0.014). The indirect parameter of insulin sensitivity – eGDR – correlated negatively with VFR and PWV. Glycemic parameters We next examined whether glycemic exposure associates with AS. HbA 1c failed to correlate with PWV (ρ = − 0.10, p = 0.28; Fig. 2 ). It also remained nonsignificant in multivariable models (β = +0.09, p = 0.25 in Table 3 ; β = +0.01, p = 0.92 in Table 4 ). Mean CGM glucose showed no relation (ρ = +0.05, p = 0.65), and the proportions of participants with HbA 1c achieving ≤ 6.5% or time-in-range (TIR) ≥ 70% did not differ between PWV groups (Table 2 ). We then evaluated CGM-based variability metrics; none of them were significantly associated with PWV. Some glycemic parameters became significant in multivariable linear regression models adjusted for VFR. TIR entered the complete clinical model with β = − 0.17 and p = 0.039, indicating a modest inverse association. Glycemic-risk index (GRI) showed β = +0.17 with p = 0.042, whereas TAR, when substituted separately, yielded β = +0.16 with p = 0.047, and MODD was also significant with β = 0.19 and p = 0.02 (Table 3 ). CV, TBR, and GMI produced nonsignificant coefficients (p ≥ 0.06). In age- and VFR-adjusted models, TIR, GRI, TAR, MODD, CV, and MAGE were significant (Table 4 ). TBR and other CGM-derived parameters did not contribute independently. Model performance Microvascular complications displayed a consistent but smaller effect. Having at least one complication increased the likelihood of falling into the higher-PWV category (27% vs 12%, p = 0.037) and entered all multivariable models with β ≈ +0.23 and p = 0.01. Smoking status and sex were not associated with PWV. We assessed model performance using explained variance. The base clinical model, which included VFR, accounted for 42% of PWV variability. Inserting any individual CGM metric changed R² by ≤ 0.08. MODD provided the most significant improvement, raising R² to 0.50. Age-based models started lower at R² = 0.35; adding MODD increased the explanation to 0.46, while most glycemic metrics increased R² by ≤ 0.07 (Table 4 ). Discussion Our cross-sectional study revealed that visceral adiposity, age, blood pressure, and eGFR are the main factors influencing PWV in adults with T1DM. These factors account for nearly half of the variance in PWV. HbA1c alone did not account for arterial stiffness. Although no direct bivariate links emerged between PWV and specific CGM-derived metrics, these measures contributed additional explanatory value once VFR was included, suggesting that the relationship between glycemia and stiffness is VFR-dependent. Notably, lower visceral fat was paradoxically associated with greater glycemic instability Across different populations, a VFA of about 100 cm² marks the beginning of multiple cardiometabolic risk factors, regardless of sex, age, or BMI [ 19 ] Bouchi et al. confirmed that Japanese patients with T2DM who had a normal BMI but VFA ≥ 100 cm² showed the steepest progression in PWV. People with a normal BMI but high visceral fat levels had significantly higher PWV than all other groups—including those who were overweight with either high or low visceral fat, and those with both normal BMI and low visceral fat [ 19 ]. Even after adjusting for other factors, having high visceral fat despite a normal BMI remained independently associated with higher PWV. The same pattern appears earlier in life. In youth aged 10–23, dual-energy X-ray-derived visceral fat more strongly explained carotid-femoral PWV (cf-PWV) than BMI or waist circumference, but only among individuals with obesity or T2DM [ 10 ]. Our data extend these observations to T1DM: each standard deviation increase in Tanita-generated VFR was linked to a clinically significant rise in cf-PWV despite a mean cohort BMI < 25 kg/m². The finding is consistent with extensive Mexican mediation analyses, which show that visceral adipose tissue accounts for approximately 57% of the adverse effect of insulin resistance on PWV and nearly 70% of the excess hypertension risk. In the multi-ethnic Asian population with T2DM, VFA measured by bioimpedance showed some link to aortic stiffness. Still, it did not surpass general adiposity measures, such as BMI, in predicting AS. After adjusting for confounders, BMI remained the most reliable and independent predictor across all ethnic groups. Notably, Indian participants had the strongest link between adiposity measures and AS, whereas the connection was weaker or not significant among Chinese and Malay individuals [ 20 ]. Visceral adiposity is closely related to insulin resistance, a known risk factor for cardiovascular disease and mortality in people with T1DM [ 21 ]. The synergistic interaction between visceral adiposity and insulin resistance described by Antonio-Villa et al. is biologically plausible: portal drainage of visceral fat deposits exposes the liver to excessive free fatty acids, diacylglycerol, and pro-inflammatory adipokines, thereby increasing hepatic insulin resistance and dyslipidemia, both of which speed up medial collagen cross-linking and smooth muscle dysfunction [ 12 ]. In our cohort, lower VFR was paradoxically associated with higher glycemic variability and worse glycemic control. These findings suggest that, in some individuals, the pursuit of glycemic targets may necessitate insulin doses high enough to promote visceral fat accumulation and worsen insulin sensitivity. However, prospective studies are needed to test this hypothesis. Notably, in Chinese adults with T2DM, lower BMI was associated with greater glycemic variability[ 22 ]. Consistent with prior research, we found that HbA 1c was not significantly associated with AS. This supports Tynjälä et al.’s findings, who showed that HbA 1c variability—rather than average HbA 1c —was associated with higher PWV in people with T1DM [ 5 ]. Their study emphasizes the limitations of relying only on HbA1 1c as a predictor of cardiovascular risk. It highlights GV as a more dynamic and sensitive marker of endothelial stress and arterial remodeling. Consistent with this pathophysiology, participants using continuous subcutaneous insulin infusion (CSII) in Rosenlund’s 601-patient cohort exhibited a 1.1 m s⁻¹ lower PWV than those on multiple daily injections after adjustment for conventional risk factors, despite comparable HbA 1c values [ 23 ]. Growing experimental and epidemiological evidence implicates short-term glucose fluctuations in vascular injury. In the Maastricht Study, a one-standard deviation higher CGM-derived coefficient of variation predicted a 0.13 m s⁻¹ higher cf-PWV after adjusting for age, sex, BMI, and mean arterial pressure [ 18 ]. Our data further showed that specific CGM-based variability metrics, including the TIR, MODD, CV, and MAGE, were associated with PWV after adjustment for VFR and age. These findings align closely with those of Foreman et al., who demonstrated that both low time in range (TIR) and higher MODD were associated with increased aortic stiffness in individuals with T2DM [ 18 ]. Specifically, MAGE and MODD represent within-day and between-day variability, respectively, and their contribution to vascular outcomes may be mediated by oxidative stress and inflammatory pathways. Monnier et al. demonstrated that glycemic variability strongly predicts oxidative stress in T2DM, a known mechanistic driver of arterial stiffening through endothelial dysfunction and collagen cross-linking [ 24 ]. Additionally, the glycemic risk index (GRI) is associated with carotid intima-media thickness, thereby extending the vascular implications of GV beyond stiffness to structural vessel changes [ 25 ]. Conversely, Helleputte et al. reported that none of the CGM metrics obtained during seven days correlated with cf-PWV in 54 adults with T1DM; only the current and 10-year mean HbA1c levels remained significant, highlighting the role of long-term chronic hyperglycemia [ 13 ]. The discrepancy may reflect limited statistical power and the brief monitoring window in the latter study. Our analysis showed that HbA 1c did not relate to AS. There was no correlation between PWV and any of the CGM-derived parameters. However, selected variability metrics, particularly MODD and MAGE, added modest explanatory power to logistic regression models including VFR. Our results differ from those of Gordin et al., who reported that high mean daily blood glucose, assessed during a 72-hour window, but not glucose variability, is associated with AS in individuals with T1DM [ 26 ]. In our cohort, SBP maintained a strong link with PWV across all models, highlighting its continued significance in cardiovascular risk management in T1DM. Additionally, microvascular complications such as neuropathy and retinopathy also predicted higher PWV, indicating that small-vessel disease and macrovascular stiffness may share similar pathogenic pathways. This finding aligns with observations by Jaiswal et al., who reported a correlation between reduced heart rate variability—a marker of autonomic neuropathy—and increased AS in young adults with T1DM [ 27 ]. The clinical significance of CGM-derived metrics is growing, with international guidelines now endorsing TIR and GV measures as actionable targets in diabetes care. Battelino et al. (2019) emphasized the prognostic importance of maintaining TIR ≥ 70% [ 28 ]. Our findings should be viewed considering several methodological and contextual limitations. First, the cross-sectional design prevents causal inference. Although we observed similar associations between VFR, CGM-derived variability, and carotid–femoral PWV, we cannot determine the temporal order. Similar cross-sectional studies in T1DM have produced conflicting results—Helleputte et al. found no relationship between 7-day CGM indices and cf-PWV [ 13 ]. Second, we measured visceral adiposity using a bioimpedance device. This tool improves on BMI but has limited spatial resolution, cannot distinguish intraperitoneal from deep subcutaneous fat, and is affected by hydration status [ 29 , 30 ]. Magnetic resonance, multi-slice CT, and densitometry would provide absolute volume estimates and fat-quality metrics, but they come with higher costs and radiation exposure. Bioimpedance devices are much more common in health centers, which makes it easier to translate the results into clinical practice. Third, AS was measured oscillometrically. Although brachial cuff–based cf-PWV correlates well with tonometric reference methods, its precision decreases in the lower velocity range and it is affected by brachial pulse-pressure amplification. Applanation tonometry with direct path-length correction remains the reference standard [ 31 ]. Fourth, the cohort was small and ethnically uniform. From the 120 recruited participants, 100 met the criterion of having at least 70% available CGM records. Although the study was designed to detect primary associations, wider confidence intervals around interaction terms might have hidden small but clinically essential modifiers. Validation in more diverse populations—especially those with higher visceral fat, such as South and East Asian adults—is necessary. Fifth, one could argue that adjusting for multiple comparisons is necessary in our study [ 32 ]. However, we believed that the increased risk of a type II error was undesirable, especially in CGM-based research, where sample sizes are often limited due to the relative invasiveness and cost of CGM [ 33 , 34 ]. Additionally, applying such adjustments based on the included determinants would likely be too conservative, since CGM-derived metrics are both conceptually and statistically interconnected [ 35 ]. These cross-sectional data from adults with BMI < 30 kg/m² suggest that VFR and selected CGM-derived variability indices may indicate higher aortic stiffness in T1DM. Because causality and direction cannot be determined, clinicians should view VFR as a supplementary risk signal rather than a validated therapeutic target. Similarly, the apparent added value of MODD, MAGE and other CGM-derived metrics became evident only after multivariable adjustment; their lack of bivariate association with PWV and the mixed evidence for HbA 1c ’s role mean that traditional glycemic measures remain vital to care until prospective trials clarify whether reducing glucose fluctuations truly slows vascular aging. Clinicians should individualize therapy to achieve glycemic targets while avoiding unnecessarily high insulin doses, which may promote insulin resistance and visceral adiposity. The combination of VFR, blood pressure, and renal function explained nearly half of pulse-wave velocity variability; aggressive management of each factor—including weight loss, renin–angiotensin blockade, and antihypertensive lifestyle strategies—may lessen early vascular aging. The assessment of VFR could be considered in the ongoing debate of GLP1 agonists' use in people with T1DM. Prospective intervention trials are needed to confirm whether decreasing visceral fat and glycemic variability slows arterial stiffening and reduces cardiovascular events. In non-obese adults with T1DM, higher VFR and age were the strongest predictors of increased pulse-wave velocity. VFR, age, blood pressure, and eGFR are the main factors influencing PWV in adults with T1DM, accounting for nearly half of the variation. HbA 1c and mean glucose levels were not associated with AS; MODD, GRI, TAR, and TIR showed moderate associations only after adjustment for VFR. Prospective trials are needed to determine if reducing visceral fat or stabilizing glucose fluctuations can slow vascular aging. Methods Participants We received ethical approval from our university’s Ethics Committee (No. 848/23) and obtained written informed consent from each participant before enrollment (Fig. 1 ). We followed the principles of the Declaration of Helsinki [ 36 ]. Between February 2024 and April 2025, we recruited 120 consecutive adults with T1DM. under the care of our department. To qualify, individuals had to be aged 18–50 years, have a confirmed T1DM diagnosis (based on diabetes-associated autoantibodies), and live with diabetes for at least five years. We excluded people with a BMI above 30 kg/m 2, a severe infection or active chronic disease, pregnant or lactating women, those with cardiovascular disease, hypertension, an estimated glomerular filtration rate (eGFR) below 60 mL/min/1.73 m², uncontrolled thyroid disorders, and a lack of a full dataset. Comprehensive baseline assessment We first administered a standardized questionnaire to each participant. This tool captured medical history, diabetes duration and management, co-existing illnesses, and any documented microvascular or macrovascular complications. We then measured height and weight with a stadiometer and calibrated scale, respectively, and calculated body mass index (BMI) as weight (kg) divided by the square of height (m²). Following a 10-minute seated rest, we measured brachial blood pressure three times on each arm with a mercury-validated aneroid sphygmomanometer, ensuring the cuff remained at heart level. We recorded the higher-pressure arm and used the mean of the three readings for analysis [ 37 ]. We screened for chronic diabetic complications. Spot urine was analyzed for albumin-to-creatinine ratio, and serum creatinine was assayed to estimate glomerular filtration rate using the CKD-EPI equation. Fundoscopy was performed by an experienced ophthalmologist following pharmacologic pupil dilation to evaluate diabetic eye disease. Peripheral neuropathy assessment included four modalities: 10 g monofilament for light touch, a 128 Hz tuning fork for vibration, a sterile single-use pinprick for nociception, and a dual-temperature rod for warm-cold differentiation [ 1 ]. The cardiac autonomic neuropathy score was estimated using Sudoscan. Bioelectrical impedance body composition analysis To characterize body composition, we used a multi-frequency segmental bioelectrical impedance analyzer (MC-780 MA N; Tanita, Tokyo, Japan). After entering sex, age, and measured height, we instructed participants—barefoot and wearing light clothing—to stand motionless so that both feet contacted the four footplates. The device delivered a 50 Hz current and, using proprietary equations, returned body-fat percentage, fat-free mass, skeletal-muscle mass, total body water, VFR (0–59 score), and whole-body impedance. We calculated absolute fat mass as body-fat percentage × weight ÷ 100 and derived fat-free mass as weight minus fat mass. The estimated glucose disposal rate (eGDR) highly correlates with the results of the euglycemic-hyperinsulinemic clamp, the gold standard for IR assessment in T1DM. The eGDR was derived from the following formula: eGDR [mg/kg/min] = 24.31 − (12.22 × WHR) − (3.29 × arterial hypertension) − (0.57 × HbA1c) where WHR is the waist-to-hip ratio, arterial hypertension is coded as 1 if present and 0 if not, and HbA1c is glycated hemoglobin [%]. Laboratory procedures We collected fasting venous blood between 07:00 and 08:00 h after 8–12 h of overnight abstinence from food and drink. Samples were drawn into dipotassium-EDTA tubes. Within two hours, a Sysmex XN-1000 analyser generated complete blood counts, and a Cobas Pure integrated platform (Roche Diagnostics) quantified total cholesterol, HDL-cholesterol, triglycerides, creatinine, high-sensitivity C-reactive protein, and HbA₁c. We calculated LDL-cholesterol using the Friedewald formula when triglycerides were ≤ 400 mg dL⁻¹; otherwise, we performed a direct measurement. All assays adhered to the manufacturer's quality-control procedures. Arterial-stiffness measurement We assessed aortic pulse-wave velocity (PWV) with a validated, cuff-based oscillometric device (Arteriograph 24; TensioMed, Budapest, Hungary) [ 38 ]. After at least 5 minutes of supine rest in a quiet, temperature-controlled room, we obtained three consecutive recordings. When within-subject SD exceeded 1 m s⁻¹, we repeated the set until this criterion was met. The mean of the three qualified measurements represented the participant’s PWV value. Continuous-glucose-monitoring metrics Real-time CGM systems (Dexcom G7 or FreeStyle Libre 2; Dexcom, San Diego, CA and Abbott Diabetes Care, Alameda, CA) logged interstitial glucose every 5–15 min and issued user-configurable alarms for hypo- and hyperglycemia. We downloaded the raw files and processed the data using Glyculator 3.0 [ 39 ]. For each individual, we analysed the 90 days preceding the vascular visit, evaluating the entire 24-h profiles. Primary CGM outcomes comprised: Mean glucose: the average glucose level over the monitoring period. Standard deviation (SD): the degree of absolute variability around the mean glucose. Coefficient of variation (CV): the relative variability (SD divided by mean, expressed as a percentage); values above 36% suggest unstable glycemic control. TIR, defined as the percentage of time glucose levels remain within the target range of 70–180 mg/d [ 28 ]. Time below range (TBR): time spent in hypoglycemia; 180 mg/dL [ 40 ]. Time in tight range (TITR): the percentage of time within a stricter range of 70–140 mg/dL, reflecting tighter glycemic control [ 41 ]. To better quantify risk and glycemic burden, several composite indices were calculated: M100: reflects the average deviation of glucose from the ideal value of 100 mg/dL (5.55 mmol/L); lower values indicate greater stability, while higher values signal greater variability [ 42 ]. J-index: integrates both mean glucose and SD, calculated as 0.001 × (mean + SD)²; higher values suggest poorer overall control [ 43 ]. GRADE (Glycemic Risk Assessment Diabetes Equation): assesses glycemic risk by assigning penalties for both hypo- and hyperglycemia [ 44 ]. LBGI (Low Blood Glucose Index) and HBGI (High Blood Glucose Index): risk scores for hypo- and hyperglycemia, respectively, with higher scores indicating greater risk of clinically significant excursions [ 45 ]. MAGE (Mean Amplitude of Glycemic Excursions) and MODD (Mean of Daily Differences): traditional measures of intraday and interday variability, respectively [ 42 ]. GRI (Glycemic Risk Index): a newer metric that condenses glucose distribution into a single risk-weighted score, validated against clinical outcomes [ 46 ]. All metrics followed international consensus definitions [ 28 ]. Statistical Analysis We expressed continuous data as mean ± SD or median [Q1–Q3] and presented categorical data as counts and percentages. We assessed normality with the Shapiro–Wilk test and homogeneity of variance with the Fisher–Snedecor test. When both assumptions held, we compared two independent groups with the Student t -test; we used the Cochran–Cox variant when variances differed. For non-normal or ordinal variables, we applied the Mann–Whitney U test. We examined associations between continuous variables with Pearson’s correlation if both distributions were normal and with Spearman’s rank correlation otherwise. We compared categorical variables with Pearson’s χ² test and adopted Yates’ continuity correction for 2 × 2 tables that violated Cochran’s rule. To identify independent determinants of carotid–femoral PWV, we constructed several multivariable linear-regression models. In every model, we standardized predictors and reported β-coefficients, two-tailed p-values, and the coefficient of determination (R²). One set of models incorporated the following covariates, chosen a priori for biological plausibility: sex (male = 1), VFR (Tanita scale), systolic blood pressure (SBP), estimated glomerular filtration rate (eGFR), presence of at least one microvascular diabetic complication (binary composite of retinopathy and neuropathy) and current smoking status (yes = 1). A second set contained age (which was the highest correlate of PWV and VFR). Into each base model, we entered one glycemic exposure or variability metric to quantify its incremental association with PWV. The metrics comprised laboratory HbA 1c , TIR, GRI, TAR, MODD, CV, TBR, and MAGE. CGM variables were summarized from 90 days of sensor data and were available for 100 participants. We defined statistical significance as p < 0.05. Because each regression addressed a distinct hypothesis, we did not adjust for multiple comparisons. All analyses were performed using the PQStat 1.8.6 (PQStat Software, Poznan, Poland). Abbreviations AS – Arterial stiffness BMI – Body mass index BP – Blood pressure CGM – Continuous glucose monitoring CV – Coefficient of variation eGDR – Estimated glucose disposal rate eGFR – Estimated glomerular filtration rate GMI – Glucose management indicator GRI – Glycemic risk index GV – Glycemic variability HbA1c – Hemoglobin A1c HBGI – High blood glucose index HDL – High-density lipoprotein IR – Insulin resistance J-index – J index of glycemic control LDL – Low-density lipoprotein LBGI – Low blood glucose index M100 – Mean glucose deviation from 100 mg/dL MAGE – Mean amplitude of glycemic excursions MODD – Mean of daily differences PWV – Pulse wave velocity SBP – Systolic blood pressure SD – Standard deviation T1DM – Type 1 diabetes mellitus T2DM – Type 2 diabetes mellitus TAR – Time above range TBR – Time below range TIR – Time in range TITR – Time in tight range VFA – Visceral fat area VFR – Visceral fat rating WHR – Waist-to-hip ratio Declarations Ethics approval and consent to participate This study was conducted in accordance with the decision of the Ethical Committee of Poznan University of Medical Sciences (approval No. 1245/18) and adhered to the principles outlined in the Declaration of Helsinki. All participants provided written informed consent. Competing Interests Statement The authors declare no conflicts of interest Funding This research was supported by Poznan University of Medical Sciences (grant number: 177/2025/DGB) Author Contribution MK conceived the study, analyzed and interpreted the data, and was a major contributor in writing the manuscript. DN contributed to study design, recruitment, and clinical interpretation. MMD contributed to data collection, statistical analysis, and manuscript preparation. BL, IA, SM, AL, and PH participated in data acquisition and analysis of continuous glucose monitoring records. AGW assisted in data collection and literature review. AU contributed to study conception, interpretation of findings, and critical revision of the manuscript. DZZ supervised the study, contributed to its conception and design, and critically revised the manuscript. AU and DZZ contributed equally. All authors read and approved the final manuscript. 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08:47:42","extension":"html","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":206397,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7926850/v1/0c71c1ee14b5872fb1ccc97d.html"},{"id":95806928,"identity":"b65cda7d-d60a-48d0-a383-4d474c614c87","added_by":"auto","created_at":"2025-11-13 08:48:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":28130,"visible":true,"origin":"","legend":"\u003cp\u003eStudy flowchart\u003c/p\u003e\n\u003cp\u003eAbbreviations:\u003c/p\u003e\n\u003cp\u003eCGM – continuous glucose monitoring\u003c/p\u003e\n\u003cp\u003eTSH – Thyroid-stimulating hormon\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7926850/v1/2796047cb37d095c45b5a792.png"},{"id":95806592,"identity":"efda3713-9e0a-4e6f-8184-7c0a1f8e1ebd","added_by":"auto","created_at":"2025-11-13 08:47:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":452384,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelations between pulse-wave velocity (A) and visceral fat rating (B) and clinical, anthropometric, and laboratory variables.\u003cbr\u003e\nHorizontal bars represent Pearson’s \u003cem\u003er\u003c/em\u003e (labelled “r =”) or Spearman’s ρ (“ρ =”), depending on the normality test for each pair of variables. Bars extending to the right indicate positive associations, those to the left negative. The numerical value of the coefficient (rounded to two decimals) is printed just outside each bar. Black asterisks placed at the far right mark correlations that reach conventional statistical significance (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05; two-tailed, unadjusted). Variables are ordered from weakest (bottom) to strongest absolute correlation (top);\u003c/p\u003e\n\u003cp\u003eAbbreviations:\u003c/p\u003e\n\u003cp\u003eAge: age (years)\u003c/p\u003e\n\u003cp\u003eBMI: body mass index (kg/m²)\u003c/p\u003e\n\u003cp\u003eVFR: visceral fat rating\u003c/p\u003e\n\u003cp\u003ePWV: carotid–femoral pulse-wave velocity (m/s)\u003c/p\u003e\n\u003cp\u003eSBP: systolic blood pressure (mmHg)\u003c/p\u003e\n\u003cp\u003eDiabetes duration: time since diagnosis (years)\u003c/p\u003e\n\u003cp\u003eWeight: body weight (kg)\u003c/p\u003e\n\u003cp\u003eFFM: fat-free mass (kg)\u003c/p\u003e\n\u003cp\u003eMuscle mass: skeletal muscle mass (kg)\u003c/p\u003e\n\u003cp\u003eTotal body water: TBW (L)\u003c/p\u003e\n\u003cp\u003eHeight: (cm)\u003c/p\u003e\n\u003cp\u003eImpedance: whole-body bioelectrical impedance (Ω)\u003c/p\u003e\n\u003cp\u003eeGFR: estimated glomerular filtration rate (mL/min/1.73 m²)\u003c/p\u003e\n\u003cp\u003eeGDR: estimated glucose disposal rate (mg·kg⁻¹·min⁻¹)\u003c/p\u003e\n\u003cp\u003eCreatinine: serum creatinine (µmol/L)\u003c/p\u003e\n\u003cp\u003eTotal cholesterol, Non-HDL-C, LDL-C, HDL-C, Triglycerides: lipid measures (mmol/L)\u003c/p\u003e\n\u003cp\u003eHemoglobin: (g/dL)\u003c/p\u003e\n\u003cp\u003eErythrocytes: red blood cell count (×10¹²/L)\u003c/p\u003e\n\u003cp\u003eLeukocytes: white blood cell count (×10⁹/L)\u003c/p\u003e\n\u003cp\u003eCRP: C-reactive protein (mg/L)\u003c/p\u003e\n\u003cp\u003eHbA1c: glycated hemoglobin (%)\u003c/p\u003e\n\u003cp\u003eMean glucose: average interstitial glucose (mmol/L)\u003c/p\u003e\n\u003cp\u003eGlucose CV: coefficient of variation of interstitial glucose (%)\u003c/p\u003e\n\u003cp\u003eGMI: glucose management indicator (%)\u003c/p\u003e\n\u003cp\u003eMAGE: mean amplitude of glycaemic excursions (mmol/L)\u003c/p\u003e\n\u003cp\u003eMODD: mean of daily differences (mmol/L)\u003c/p\u003e\n\u003cp\u003eGRI: glycaemic risk index (unitless)\u003c/p\u003e\n\u003cp\u003eGRADE: glycaemic risk assessment diabetes equation (unitless)\u003c/p\u003e\n\u003cp\u003eACR: albumin-to-creatinine ratio (mg/g)\u003c/p\u003e\n\u003cp\u003eTIR: time in range (% of time 70–180 mg/dL)\u003c/p\u003e\n\u003cp\u003eTBR: time below range (% \u0026lt; 70 mg/dL)\u003c/p\u003e\n\u003cp\u003eTAR: time above range (% \u0026gt; 180 mg/dL)\u003c/p\u003e\n\u003cp\u003eTITR: time in target range (alternative target window)\u003c/p\u003e\n\u003cp\u003eHBGI: high blood glucose index (unitless)\u003c/p\u003e\n\u003cp\u003eLBGI: low blood glucose index (unitless)\u003c/p\u003e\n\u003cp\u003eDII: daily insulin intake (units per day)\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7926850/v1/ee9bda1649ca36bf5d67bbfb.png"},{"id":106810901,"identity":"76db7238-d9c5-450d-9e80-2cf635fa282b","added_by":"auto","created_at":"2026-04-13 16:17:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2663249,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7926850/v1/1818ab4a-4278-41c7-a279-e15d32b36789.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The association between arterial stiffness, visceral fat rating, and glycemic variability in non-obese adults with type 1 diabetes","fulltext":[{"header":"Introduction","content":"\u003cp\u003eType 1 diabetes mellitus (T1DM) is an autoimmune disease characterized by the destruction of pancreatic β-cells, leading to complete insulin deficiency and chronic hyperglycemia [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Despite advances in insulin therapy and glycemic monitoring, individuals with T1DM remain at heightened risk of cardiovascular complications, which are the leading cause of morbidity and mortality in this population [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eArterial stiffness (AS) is an independent predictor of cardiovascular risk [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Increased pulse wave velocity (PWV) reflects reduced arterial compliance and has been associated with cardiovascular events, cardiovascular mortality, and all-cause mortality in T1DM [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Several studies have indicated that AS develops earlier in young individuals with T1DM compared to the general population, and it correlates with both traditional risk factors and diabetes-specific variables, such as autonomic dysfunction and metabolic control [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eVisceral fat rating (VFR)\u0026mdash;a 0-to-59 score generated by the Tanita multi-frequency bio-impedance analyser\u0026mdash;offers a rapid, radiation-free surrogate of intra-abdominal adiposity [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In adults with type 2 diabetes mellitus (T2DM), a greater visceral fat area (VFA) is independently linked to higher pulse-wave velocity [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Visceral fat also modifies the hemodynamic consequences of insulin resistance: mediation analyses indicate that more than half of the adverse effect of insulin resistance on AS and incident hypertension is channeled through excess intra-abdominal fat [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. These associations emerge across the life-course\u0026mdash;from adolescents with obesity or T2DM to non-obese Japanese adults whose normal BMI masks high visceral fat stores \u0026mdash;highlighting the limitations of anthropometric cut-offs alone [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. By contrast, studies with T1DM adults remain scarce, leaving it uncertain whether visceral fat confers the same incremental burden on AS in the context of autoimmune insulin deficiency. Addressing this gap could refine cardiovascular risk assessment in ostensibly lean adults with T1DM who might otherwise be overlooked by BMI-based screening.\u003c/p\u003e\u003cp\u003eHemoglobin A1c (HbA\u003csub\u003e1c\u003c/sub\u003e) is associated with AS in T2DM and T1DM [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, some studies suggest that HbA\u003csub\u003e1c\u003c/sub\u003e variability, rather than mean HbA\u003csub\u003e1c\u003c/sub\u003e alone, is a more consistent predictor of AS in people with T1DM [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. HbA\u003csub\u003e1c\u003c/sub\u003e does not fully capture the dynamics of glycemic fluctuations that contribute to endothelial dysfunction and vascular aging. Recent studies suggest that glycemic variability (GV), encompassing acute fluctuations in glucose levels, may play a role in the development of AS in individuals with T2DM [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. However, studies have not confirmed the relationship between glycemic variability and AS in individuals with T1DM [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOur study aimed to investigate the association between PWV, VFR, and glycemic parameters in adults with T1DM.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy population\u003c/h2\u003e\u003cp\u003eWe analyzed 120 participants who exhibited a mean PWV of 7.47\u0026thinsp;\u0026plusmn;\u0026thinsp;1.43 m/s and a median VFR of 4 [\u003cspan additionalcitationids=\"CR4 CR5\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The sample showed balanced sex distribution \u0026ndash; 50.8% men, a median age of 33.8 [24.5\u0026ndash;41.2] years, and a median BMI of 24.3 [22.2\u0026ndash;26.8] kg m⁻\u0026sup2;.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGeneral characteristics of the study cohort (N\u0026thinsp;=\u0026thinsp;120)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWhole group\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGeneral characteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (yrs)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33.8 [24.5\u0026ndash;41.2]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex \u0026ndash; male\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e61 (50.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeight (m)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeight (kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e75.1\u0026thinsp;\u0026plusmn;\u0026thinsp;14.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI (kg m⁻\u0026sup2;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24.3 [22.2\u0026ndash;26.8]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWHR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.82 [0.73\u0026ndash;0.91]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOverweight (BMI 25\u0026ndash;30 kg m⁻\u0026sup2;))\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52 (43.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDaily insulin intake (U kg⁻\u0026sup1;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.51 [0.50\u0026ndash;0.67]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes duration (yrs)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.0 [10.0\u0026ndash;20.3]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBody-composition metrics\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBody adipose tissue (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24.0\u0026thinsp;\u0026plusmn;\u0026thinsp;8.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdipose-free mass (kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e54.7 [46.7\u0026ndash;67.5]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMuscle mass (kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e51.8 [42.6\u0026ndash;64.2]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal body water (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e54.7 [49.2\u0026ndash;59.9]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVisceral fat rating\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 [\u003cspan additionalcitationids=\"CR4 CR5\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eImpedance (Ω)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e601 [537.8\u0026ndash;662.8]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBlood pressure \u0026amp; arterial stiffness\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSystolic BP (mm Hg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e125.6\u0026thinsp;\u0026plusmn;\u0026thinsp;12.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePWV (m s⁻\u0026sup1;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.47\u0026thinsp;\u0026plusmn;\u0026thinsp;1.43\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLaboratory parameters\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeukocytes (10\u0026sup3; \u0026micro;l⁻\u0026sup1;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.23 [5.42\u0026ndash;6.95]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eErythrocytes (10⁶ \u0026micro;l⁻\u0026sup1;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemoglobin (g dl⁻\u0026sup1;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.3 [13.4\u0026ndash;15.5]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMCV (fl)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e86.8 [83.7\u0026ndash;89.6]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eACR (mg g⁻\u0026sup1;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.00 [2.40\u0026ndash;5.00]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal cholesterol (mg dl⁻\u0026sup1;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e176.0 [155.8\u0026ndash;204.0]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDL-C (mg dl⁻\u0026sup1;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e93.0 [72.3\u0026ndash;119.8]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL-C (mg dl⁻\u0026sup1;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e64.0 [55.8\u0026ndash;76.0]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTriglycerides (mg dl⁻\u0026sup1;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e78.0 [62.0\u0026ndash;111.0]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCreatinine (mg dl⁻\u0026sup1;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eeGFR (ml min⁻\u0026sup1; 1.73 m⁻\u0026sup2;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e106.4\u0026thinsp;\u0026plusmn;\u0026thinsp;17.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCRP (mg l⁻\u0026sup1;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.98 [0.60\u0026ndash;1.95]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCGM metrics\u003c/b\u003e (n\u0026thinsp;=\u0026thinsp;100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHbA1c (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.60 [7.08\u0026ndash;8.40]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean glucose (mg dl⁻\u0026sup1;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e177.5 [152.4\u0026ndash;199.5]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGlucose SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e69.0\u0026thinsp;\u0026plusmn;\u0026thinsp;20.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGlucose CV (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTIR (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e54.0\u0026thinsp;\u0026plusmn;\u0026thinsp;18.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTITR (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31.1 [24.6\u0026ndash;42.4]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGRI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e53.8 [39.0\u0026ndash;74.1]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLBGI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.59 [0.32\u0026ndash;0.92]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHBGI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.97 [5.82\u0026ndash;14.46]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.55 [6.96\u0026ndash;8.08]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMAGE (mg dl⁻\u0026sup1;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e125.5 [100.2\u0026ndash;156.1]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGRADE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.7 [7.8\u0026ndash;13.5]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28.4 [15.8\u0026ndash;45.1]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJ-index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e60.1 [42.7\u0026ndash;80.7]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMODD (mg dl⁻\u0026sup1;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e69.7\u0026thinsp;\u0026plusmn;\u0026thinsp;21.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTBR (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.18 [0.84\u0026ndash;3.81]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTAR (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e43.1\u0026thinsp;\u0026plusmn;\u0026thinsp;20.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTIR\u0026thinsp;\u0026ge;\u0026thinsp;70%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22 (22%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTreatment \u0026amp; complications\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInsulin-pump therapy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30 (25.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;1 diabetic complication\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23 (19.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePeripheral neuropathy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8 (6.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-proliferative retinopathy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19 (15.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProliferative retinopathy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (1.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent smoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21 (17.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFormer smoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26 (21.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eeGDR (mg\u0026middot;kg⁻\u0026sup1;\u0026middot;min⁻\u0026sup1;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.66 [8.29\u0026ndash;10.39]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"2\"\u003eContinuous variables are shown as \u003cem\u003emean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD; median [IQR]\u003c/em\u003e. Categorical variables are \u003cem\u003en (%).\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"2\"\u003ep-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 are in \u003cb\u003ebold\u003c/b\u003e.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"2\"\u003eAbbreviations: PWV \u0026ndash; pulse-wave velocity; BP \u0026ndash; blood pressure; BMI \u0026ndash; body-mass index; WHR \u0026ndash; waist-to-hip ratio; DII \u0026ndash; daily insulin intake; eGFR \u0026ndash; estimated glomerular filtration rate; eGDR \u0026ndash; estimated glucose disposal rate; HbA1c \u0026ndash; glycated hemoglobin; TIR \u0026ndash; time-in-range; TAR/TBR \u0026ndash; time-above/below range; GRI \u0026ndash; glycemic-risk index; MAGE \u0026ndash; mean amplitude of glycemic excursions; MODD \u0026ndash; mean of daily differences; CV \u0026ndash; coefficient of variation; GMI \u0026ndash; glucose-management indicator; LBGI/HBGI \u0026ndash; low/high blood-glucose indices; TITR \u0026ndash; time-in-tight range.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eVisceral adiposity and arterial stiffness\u003c/h3\u003e\n\u003cp\u003eVisceral adiposity displayed a pronounced association with AS. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the bivariate correlations; VFR correlated positively with PWV (ρ = +0.48, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). When we split the cohort at the PWV median, the higher-PWV group had almost twice the VFR (3.5 [\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] vs 6 [\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Multivariable analysis supported an independent association. In the adjusted clinical models for sex, SBP, estimated glomerular filtration rate (eGFR), presence of diabetic complications, and smoking, each standard-deviation increase in VFR raised PWV by β \u0026asymp; +0.22 with \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Substituting the extensive clinical covariate set with age alone yielded a comparable effect size (β \u0026asymp; +0.21, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02; Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of participants with pulse wave velocity above vs. below the median\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePWV\u0026thinsp;\u0026le;\u0026thinsp;median (n\u0026thinsp;=\u0026thinsp;60)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePWV\u0026thinsp;\u0026gt;\u0026thinsp;median (n\u0026thinsp;=\u0026thinsp;60)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTest\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGeneral characteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (yrs)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26.4\u0026nbsp;[21.2\u0026ndash;34.7]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39.2\u0026nbsp;[32.4\u0026ndash;46.6]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eM-W\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex \u0026ndash; male, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23\u0026nbsp;(38.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38\u0026nbsp;(63.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeight (m)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.7\u0026nbsp;[1.66\u0026ndash;1.78]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.8\u0026nbsp;[1.70\u0026ndash;1.84]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.0009\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eM-W\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeight (kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e70.3\u0026nbsp;\u0026plusmn;\u0026nbsp;11.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e79.9\u0026nbsp;\u0026plusmn;\u0026nbsp;14.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.0001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eC-C\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI (kg m⁻\u0026sup2;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.8\u0026nbsp;\u0026plusmn;\u0026nbsp;2.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25.2\u0026nbsp;\u0026plusmn;\u0026nbsp;2.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.0069\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et-st\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWHR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.81\u0026nbsp;[0.76\u0026ndash;0.84]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.85\u0026nbsp;[0.82\u0026ndash;0.93]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.0001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eM-W\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOverweight, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19\u0026nbsp;(31.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33\u0026nbsp;(55.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.0099\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDaily insulin intake (U kg⁻\u0026sup1;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.55\u0026nbsp;[0.50\u0026ndash;0.69]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.50\u0026nbsp;[0.50\u0026ndash;0.63]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eM-W\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes duration (yrs)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.5\u0026nbsp;[8.75\u0026ndash;17.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17.0\u0026nbsp;[10.8\u0026ndash;25.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.0009\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eM-W\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBody-composition metrics\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBody adipose tissue percentage (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25.0\u0026nbsp;\u0026plusmn;\u0026nbsp;9.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.0\u0026nbsp;\u0026plusmn;\u0026nbsp;7.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et-st\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFat-free mass (kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50.3\u0026nbsp;[45.0\u0026ndash;62.9]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e63.6\u0026nbsp;[48.2\u0026ndash;72.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.0006\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eM-W\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMuscle mass (kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e47.5\u0026nbsp;[42.6\u0026ndash;58.1]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e60.8\u0026nbsp;[45.8\u0026ndash;69.6]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.0003\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eM-W\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal body water (kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52.6\u0026nbsp;[47.4\u0026ndash;59.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56.0\u0026nbsp;[52.2\u0026ndash;60.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.051\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eM-W\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVisceral-fat rating\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.5\u0026nbsp;[\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6\u0026nbsp;[\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eM-W\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eImpedance (Ω)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e647.7\u0026nbsp;\u0026plusmn;\u0026nbsp;100.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e571.5\u0026nbsp;\u0026plusmn;\u0026nbsp;80.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et-st\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBlood pressure \u0026amp; stiffness\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSystolic BP (mm Hg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e122.0\u0026nbsp;\u0026plusmn;\u0026nbsp;12.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e129.1\u0026nbsp;\u0026plusmn;\u0026nbsp;11.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.0019\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et-st\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePWV (m s⁻\u0026sup1;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.40\u0026nbsp;[5.83\u0026ndash;6.81]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.43\u0026nbsp;[7.87\u0026ndash;9.14]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eM-W\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLaboratory parameters\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeukocytes (10\u0026sup3; \u0026micro;l⁻\u0026sup1;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.45\u0026nbsp;[5.69\u0026ndash;7.06]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.15\u0026nbsp;[5.12\u0026ndash;6.94]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eM-W\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eErythrocytes (10⁶ \u0026micro;l⁻\u0026sup1;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.71\u0026nbsp;\u0026plusmn;\u0026nbsp;0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.87\u0026nbsp;\u0026plusmn;\u0026nbsp;0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.054\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et-st\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemoglobin (g dl⁻\u0026sup1;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.0\u0026nbsp;\u0026plusmn;\u0026nbsp;1.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.6\u0026nbsp;\u0026plusmn;\u0026nbsp;1.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.031\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et-st\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMCV (fl)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e86.8\u0026nbsp;[84.2\u0026ndash;89.6]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e87.0\u0026nbsp;[83.5\u0026ndash;89.6]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eM-W\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlbumin/creatinine (mg g⁻\u0026sup1;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.00\u0026nbsp;[2.08\u0026ndash;5.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.00\u0026nbsp;[2.46\u0026ndash;5.03]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eM-W\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal cholesterol (mg dl⁻\u0026sup1;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e170.5\u0026nbsp;[150.8\u0026ndash;198.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e180.0\u0026nbsp;[166.8\u0026ndash;206.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eM-W\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDL-C (mg dl⁻\u0026sup1;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e88.1\u0026nbsp;[69.0\u0026ndash;109.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e103.0\u0026nbsp;[79.0\u0026ndash;123.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eM-W\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL-C (mg dl⁻\u0026sup1;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65.0\u0026nbsp;[57.0\u0026ndash;76.3]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61.5\u0026nbsp;[51.5\u0026ndash;71.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eM-W\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTriglycerides (mg dl⁻\u0026sup1;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e72.0\u0026nbsp;[56.7\u0026ndash;100.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e81.5\u0026nbsp;[68.0\u0026ndash;116.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eM-W\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCreatinine (mg dl⁻\u0026sup1;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.82\u0026nbsp;[0.73\u0026ndash;0.91]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.85\u0026nbsp;[0.78\u0026ndash;0.97]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.0022\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eM-W\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eeGFR (ml min⁻\u0026sup1; 1.73 m⁻\u0026sup2;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e114.0\u0026nbsp;\u0026plusmn;\u0026nbsp;16.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e98.7\u0026nbsp;\u0026plusmn;\u0026nbsp;14.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et-st\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCRP (mg l⁻\u0026sup1;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.85\u0026nbsp;[0.60\u0026ndash;2.15]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.08\u0026nbsp;[0.60\u0026ndash;1.88]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eM-W\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGlycemic control \u0026amp; variability\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHbA\u003csub\u003e1c\u003c/sub\u003e (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.70\u0026nbsp;[7.10\u0026ndash;8.90]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.60\u0026nbsp;[7.00\u0026ndash;8.13]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eM-W\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHbA1c\u0026thinsp;\u0026le;\u0026thinsp;6.5%, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6\u0026nbsp;(10.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9\u0026nbsp;(15.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean glucose (mg dl⁻\u0026sup1;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e177.6\u0026nbsp;[151.3\u0026ndash;208.6]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e177.4\u0026nbsp;[153.2\u0026ndash;197.6]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eM-W\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGlucose SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e70.0\u0026thinsp;\u0026plusmn;\u0026thinsp;23.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e68.1\u0026thinsp;\u0026plusmn;\u0026thinsp;18.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et-st\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGlucose CV (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37.4\u0026nbsp;\u0026plusmn;\u0026nbsp;6.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37.6\u0026nbsp;\u0026plusmn;\u0026nbsp;5.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et-st\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTIR (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e53.4\u0026nbsp;\u0026plusmn;\u0026nbsp;20.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54.5\u0026nbsp;\u0026plusmn;\u0026nbsp;17.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et-st\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTITR (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33.0\u0026nbsp;\u0026plusmn;\u0026nbsp;17.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33.2\u0026nbsp;\u0026plusmn;\u0026nbsp;13.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et-st\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGRI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e56.5\u0026nbsp;\u0026plusmn;\u0026nbsp;25.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54.5\u0026nbsp;\u0026plusmn;\u0026nbsp;22.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et-st\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLBGI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.59\u0026nbsp;[0.31\u0026ndash;1.15]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.59\u0026nbsp;[0.32\u0026ndash;0.90]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eM-W\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHBGI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.0\u0026nbsp;[5.76\u0026ndash;16.1]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.89\u0026nbsp;[6.03\u0026ndash;14.2]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eM-W\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.56\u0026nbsp;[6.93\u0026ndash;8.30]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.55\u0026nbsp;[6.97\u0026ndash;8.04]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eM-W\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMAGE (mg dl⁻\u0026sup1;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e132.9\u0026nbsp;\u0026plusmn;\u0026nbsp;45.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e128.8\u0026nbsp;\u0026plusmn;\u0026nbsp;34.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et-st\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGRADE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.6\u0026nbsp;[7.73\u0026ndash;14.4]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.7\u0026nbsp;[8.01\u0026ndash;13.3]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eM-W\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30.5\u0026nbsp;[15.9\u0026ndash;50.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28.0\u0026nbsp;[15.7\u0026ndash;44.4]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eM-W\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJ-index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e63.0\u0026nbsp;[42.9\u0026ndash;85.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e59.4\u0026nbsp;[42.7\u0026ndash;79.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eM-W\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMODD (mg dl⁻\u0026sup1;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e69.5\u0026nbsp;\u0026plusmn;\u0026nbsp;23.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e70.0\u0026nbsp;\u0026plusmn;\u0026nbsp;19.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et-st\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTBR (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.26\u0026nbsp;[0.77\u0026ndash;5.05]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.11\u0026nbsp;[0.85\u0026ndash;3.77]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eM-W\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTAR (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e43.5\u0026nbsp;\u0026plusmn;\u0026nbsp;22.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42.7\u0026nbsp;\u0026plusmn;\u0026nbsp;18.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003et-st\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTIR\u0026thinsp;\u0026ge;\u0026thinsp;70%, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9\u0026nbsp;(19.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13\u0026nbsp;(24.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTreatment \u0026amp; complications\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInsulin-pump therapy, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19\u0026nbsp;(31.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11\u0026nbsp;(18.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;1 diabetic complication\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7\u0026nbsp;(11.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16\u0026nbsp;(26.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.037\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePeripheral neuropathy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u0026nbsp;(5.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u0026nbsp;(8.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2; Yates\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-proliferative retinopathy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7\u0026nbsp;(11.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12\u0026nbsp;(20.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProliferative retinopathy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u0026nbsp;(0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u0026nbsp;(3.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2; Yates\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent smoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12\u0026nbsp;(20.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9\u0026nbsp;(15.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFormer smoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11\u0026nbsp;(18.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15\u0026nbsp;(25.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eχ\u0026sup2;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eeGDR (mg\u0026middot;kg⁻\u0026sup1;\u0026middot;min⁻\u0026sup1;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.05\u0026nbsp;[8.89\u0026ndash;10.77]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.24\u0026nbsp;[7.63\u0026ndash;10.08]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eM-W\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eContinuous variables are shown as \u003cem\u003emean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD; median [IQR]\u003c/em\u003e. Categorical variables are \u003cem\u003en (%).\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003ep-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 are in \u003cb\u003ebold\u003c/b\u003e.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations: t-st \u0026ndash; Student\u0026rsquo;s t-test (equal variances); C-C \u0026ndash; Cochran\u0026ndash;Cox corrected t-test (unequal variances); M-W \u0026ndash; Mann\u0026ndash;Whitney U test; χ\u0026sup2; \u0026ndash; Pearson chi-square test; χ\u0026sup2; Yates \u0026ndash; χ\u0026sup2; with Yates\u0026rsquo; continuity correction; PWV \u0026ndash; pulse-wave velocity; BP \u0026ndash; blood pressure; BMI \u0026ndash; body-mass index; WHR \u0026ndash; waist-to-hip ratio; DII \u0026ndash; daily insulin intake; eGFR \u0026ndash; estimated glomerular filtration rate; eGDR \u0026ndash; estimated glucose disposal rate; HbA1c \u0026ndash; glycated hemoglobin; TIR \u0026ndash; time-in-range; TAR/TBR \u0026ndash; time-above/below range; GRI \u0026ndash; glycemic-risk index; MAGE \u0026ndash; mean amplitude of glycemic excursions; MODD \u0026ndash; mean of daily differences; CV \u0026ndash; coefficient of variation; GMI \u0026ndash; glucose-management indicator; LBGI/HBGI \u0026ndash; low/high blood-glucose indices; TITR \u0026ndash; time-in-tight range.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariable linear regression (dependent variable\u0026thinsp;=\u0026thinsp;pulse-wave velocity, PWV).\u003c/p\u003e\u003cp\u003eEach column shows the base clinical model extended with a single glycemic parameter or variability metric.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredictor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e+HbA1c\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e+TIR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+GRI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+TAR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e+MODD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e+CV\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e+TBR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e+MAGE\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClinical covariates (base model)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex (male)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.09 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.16 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.064)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.15 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.076)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.16 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.062)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.15 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.083)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.15 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.16 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.059)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.15 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.071)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVisceral-fat rating\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.22 (\u003c/b\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e=\u0026thinsp;0.01)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.19 (\u003c/b\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e=\u0026thinsp;0.04)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.20 (\u003c/b\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e=\u0026thinsp;0.04)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.19 (\u003c/b\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e=\u0026thinsp;0.04)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.22 (\u003c/b\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e=\u0026thinsp;0.02)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.17 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.15 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.20 (\u003c/b\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e=\u0026thinsp;0.04)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSystolic BP (mm Hg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.27 (\u003c/b\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e=\u0026thinsp;0.001)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.29 (\u003c/b\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e=\u0026thinsp;0.001)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.29 (\u003c/b\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e=\u0026thinsp;0.001)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.29 (\u003c/b\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e=\u0026thinsp;0.001)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.28 (\u003c/b\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e=\u0026thinsp;0.001)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.28 (\u003c/b\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e=\u0026thinsp;0.001)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.28 (\u003c/b\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e=\u0026thinsp;0.001)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.28 (\u003c/b\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e=\u0026thinsp;0.001)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eeGFR (mL min⁻\u0026sup1; 1.73 m⁻\u0026sup2;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e\u0026ndash;0.29 (\u003c/b\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u0026thinsp;0.001)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026ndash;0.31 (\u003c/b\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u0026thinsp;0.001)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026ndash;0.30 (\u003c/b\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u0026thinsp;0.001)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026ndash;0.31 (\u003c/b\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u0026thinsp;0.001)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026ndash;0.31 (\u003c/b\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u0026thinsp;0.001)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026ndash;0.30 (\u003c/b\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e=\u0026thinsp;0.001)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e\u0026ndash;0.32 (\u003c/b\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u0026thinsp;0.001)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e\u0026ndash;0.31 (\u003c/b\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u0026thinsp;0.001)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;1 diabetic complication\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.21 (\u003c/b\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e=\u0026thinsp;0.01)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.23 (\u003c/b\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e=\u0026thinsp;0.01)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.22 (\u003c/b\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e=\u0026thinsp;0.01)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.23 (\u003c/b\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e=\u0026thinsp;0.01)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.23 (\u003c/b\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e=\u0026thinsp;0.01)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.23 (\u003c/b\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e=\u0026thinsp;0.01)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.24 (\u003c/b\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e=\u0026thinsp;0.003)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.22 (\u003c/b\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e=\u0026thinsp;0.01)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent smoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;0.10 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;0.09 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;0.09 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;0.09 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;0.08 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026ndash;0.05 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026ndash;0.05 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026ndash;0.08 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.31)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAdded glycemic metric\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHbA1c (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.09 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTIR (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026ndash;0.17 (\u003c/b\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e=\u0026thinsp;0.039)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGRI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.17 (\u003c/b\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e=\u0026thinsp;0.042)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTAR (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.16 (\u003c/b\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e=\u0026thinsp;0.047)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMODD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.19 (\u003c/b\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e=\u0026thinsp;0.02)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCV (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.12 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTBR (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026ndash;0.03 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMAGE (mg dL⁻\u0026sup1;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.16 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.057)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eModel R\u0026sup2;\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eStandardised β coefficients are shown; p-values appear in parentheses. All models include the same six clinical covariates (sex, visceral-fat rating, systolic BP, eGFR, \u0026ge;\u0026thinsp;1 diabetic complication, smoking). Each column adds one glycemic metric to this base. R\u0026sup2; indicates model fit. \u003cb\u003eBold\u003c/b\u003e marks p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eAbbreviations: PWV - pulse-wave velocity; HbA1c - hemoglobin A1c; TIR/TAR/TBR - time in/above/below range; GRI - glycemic risk index; MODD - mean of daily differences; CV - coefficient of variation; MAGE - mean amplitude of glycemic excursions; BP - blood pressure; eGFR - estimated glomerular filtration rate.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariable linear regression (dependent variable\u0026thinsp;=\u0026thinsp;pulse-wave velocity, PWV).\u003c/p\u003e\u003cp\u003eEach column shows the base model (age\u0026thinsp;+\u0026thinsp;visceral fat rating) extended with one glycemic parameter.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredictor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e+HbA1c\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e+TIR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+GRI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+TAR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e+MODD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e+CV\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e+TBR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e+MAGE\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBase covariates\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (yrs)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.47 (\u003c/b\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u0026thinsp;0.001)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.54 (\u003c/b\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u0026thinsp;0.001)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.54 (\u003c/b\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u0026thinsp;0.001)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.53 (\u003c/b\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u0026thinsp;0.001)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.55 (\u003c/b\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u0026thinsp;0.001)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.53 (\u003c/b\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u0026thinsp;0.001)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.53 (\u003c/b\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u0026thinsp;0.001)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.55 (\u003c/b\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u0026thinsp;0.001)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVisceral-fat rating (score)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.19 (\u003c/b\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e=\u0026thinsp;0.043)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.21 (\u003c/b\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e=\u0026thinsp;0.03)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.21 (\u003c/b\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e=\u0026thinsp;0.01)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.20 (\u003c/b\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e=\u0026thinsp;0.03)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.23 (\u003c/b\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e=\u0026thinsp;0.01)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.19 (\u003c/b\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e=\u0026thinsp;0.04)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.16 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.22 (\u003c/b\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e=\u0026thinsp;0.02)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAdded glycemic metric\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHbA1c (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.01 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTIR (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026ndash;0.18 (\u003c/b\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e=\u0026thinsp;0.03)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGRI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.21 (\u003c/b\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e=\u0026thinsp;0.01)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTAR (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.16 (\u003c/b\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e=\u0026thinsp;0.049)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMODD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.27 (\u003c/b\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u0026thinsp;0.01)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCV (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.18 (\u003c/b\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e=\u0026thinsp;0.02)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTBR (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026ndash;0.03 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMAGE (mg dL⁻\u0026sup1;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.22 (\u003c/b\u003e\u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u003cb\u003e\u0026lt;\u0026thinsp;0.01)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eModel R\u0026sup2;\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eStandardised β coefficients are shown; p-values appear in parentheses. All models include the same two covariates\u0026mdash;age and visceral-fat rating. Each column adds one glycemic metric to this base. R\u0026sup2; indicates model fit. \u003cb\u003eBold\u003c/b\u003e marks p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eAbbreviations: PWV - pulse-wave velocity; HbA1c - haemoglobin A1c; TIR/TAR/TBR - time in/above/below range; GRI - glycemic risk index; MODD - mean of daily differences; CV - coefficient of variation; MAGE - mean amplitude of glycemic excursions.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eAge, blood pressure, and renal function\u003c/h3\u003e\n\u003cp\u003eAge produced the strongest individual relation with PWV. Correlation analysis revealed a correlation coefficient of ρ = +0.59 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and regression coefficients ranged from +\u0026thinsp;0.47 to +\u0026thinsp;0.55 across all age-based models (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Participants in the upper PWV half were a decade older on average than those below the median (26.4 [21.2\u0026ndash;34.7] vs 39.2 [32.4\u0026ndash;46.6]; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). SBP followed closely (r\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.39, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and retained β \u0026asymp; +0.28 with \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001 after full adjustment. Renal function moved in the opposite direction: lower eGFR associated with higher PWV (r = \u0026minus;\u0026thinsp;0.41, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and multivariable models confirmed this inverse link with β near \u0026minus;\u0026thinsp;0.31 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e\n\u003ch3\u003eBody composition\u003c/h3\u003e\n\u003cp\u003eBody composition traits other than VFR displayed heterogeneous patterns. Weight, fat-free mass, and muscle mass correlated positively with PWV (r\u0026thinsp;\u0026asymp;\u0026thinsp;+\u0026thinsp;0.29 to +\u0026thinsp;0.32, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.001), whereas whole-body impedance correlated negatively (ρ = \u0026minus;\u0026thinsp;0.39, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Despite higher absolute fat mass, the high-PWV group did not differ in total adipose tissue percentage (23.0\u0026thinsp;\u0026plusmn;\u0026thinsp;7.6% vs 25.0\u0026thinsp;\u0026plusmn;\u0026thinsp;9.3%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.21). Overweight status (BMI\u0026thinsp;\u0026ge;\u0026thinsp;25 kg m⁻\u0026sup2;) appeared more often in the high-PWV group (55% vs 32%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01).\u003c/p\u003e\u003cp\u003eVFR showed very strong positive correlations with body-size measures\u0026mdash;BMI (ρ\u0026thinsp;=\u0026thinsp;0.72, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and weight (ρ\u0026thinsp;=\u0026thinsp;0.71, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u0026mdash;and moderate positive associations with age (ρ\u0026thinsp;=\u0026thinsp;0.55, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), muscle mass (ρ\u0026thinsp;=\u0026thinsp;0.47, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), fat-free mass (ρ\u0026thinsp;=\u0026thinsp;0.46, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and height (ρ\u0026thinsp;=\u0026thinsp;0.42, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), SBP (ρ\u0026thinsp;=\u0026thinsp;0.33, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), serum creatinine (ρ\u0026thinsp;=\u0026thinsp;0.27, p\u0026thinsp;=\u0026thinsp;0.002) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003eSurprisingly, people achieving TIR\u0026thinsp;\u0026gt;\u0026thinsp;70% had higher VFR than people with lower TIR (5,5 [\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] vs 4 [\u003cspan additionalcitationids=\"CR3 CR4 CR5\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]; p\u0026thinsp;=\u0026thinsp;0.01). VFR was positively related to TIR (ρ\u0026thinsp;=\u0026thinsp;0.23, p\u0026thinsp;=\u0026thinsp;0.021) and TITR. In contrast, VFR was negatively correlated with HbA1c (ρ = \u0026minus;\u0026thinsp;0.26, p\u0026thinsp;=\u0026thinsp;0.004) and glycemic-variability indices such as MAGE (ρ = \u0026minus;\u0026thinsp;0.26, p\u0026thinsp;=\u0026thinsp;0.010), MODD (ρ = \u0026minus;\u0026thinsp;0.25, p\u0026thinsp;=\u0026thinsp;0.012) and the GRI (ρ = \u0026minus;\u0026thinsp;0.25, p\u0026thinsp;=\u0026thinsp;0.014). The indirect parameter of insulin sensitivity \u0026ndash; eGDR \u0026ndash; correlated negatively with VFR and PWV.\u003c/p\u003e\n\u003ch3\u003eGlycemic parameters\u003c/h3\u003e\n\u003cp\u003eWe next examined whether glycemic exposure associates with AS. HbA\u003csub\u003e1c\u003c/sub\u003e failed to correlate with PWV (ρ = \u0026minus;\u0026thinsp;0.10, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.28; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). It also remained nonsignificant in multivariable models (β = +0.09, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.25 in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; β = +0.01, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.92 in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Mean CGM glucose showed no relation (ρ = +0.05, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.65), and the proportions of participants with HbA\u003csub\u003e1c\u003c/sub\u003e achieving\u0026thinsp;\u0026le;\u0026thinsp;6.5% or time-in-range (TIR)\u0026thinsp;\u0026ge;\u0026thinsp;70% did not differ between PWV groups (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWe then evaluated CGM-based variability metrics; none of them were significantly associated with PWV. Some glycemic parameters became significant in multivariable linear regression models adjusted for VFR. TIR entered the complete clinical model with β = \u0026minus;\u0026thinsp;0.17 and p\u0026thinsp;=\u0026thinsp;0.039, indicating a modest inverse association. Glycemic-risk index (GRI) showed β = +0.17 with p\u0026thinsp;=\u0026thinsp;0.042, whereas TAR, when substituted separately, yielded β = +0.16 with p\u0026thinsp;=\u0026thinsp;0.047, and MODD was also significant with β\u0026thinsp;=\u0026thinsp;0.19 and p\u0026thinsp;=\u0026thinsp;0.02 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). CV, TBR, and GMI produced nonsignificant coefficients (p\u0026thinsp;\u0026ge;\u0026thinsp;0.06). In age- and VFR-adjusted models, TIR, GRI, TAR, MODD, CV, and MAGE were significant (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). TBR and other CGM-derived parameters did not contribute independently.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eModel performance\u003c/h2\u003e\u003cp\u003eMicrovascular complications displayed a consistent but smaller effect. Having at least one complication increased the likelihood of falling into the higher-PWV category (27% vs 12%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.037) and entered all multivariable models with β \u0026asymp; +0.23 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01. Smoking status and sex were not associated with PWV.\u003c/p\u003e\u003cp\u003eWe assessed model performance using explained variance. The base clinical model, which included VFR, accounted for 42% of PWV variability. Inserting any individual CGM metric changed R\u0026sup2; by \u0026le;\u0026thinsp;0.08. MODD provided the most significant improvement, raising R\u0026sup2; to 0.50. Age-based models started lower at R\u0026sup2; = 0.35; adding MODD increased the explanation to 0.46, while most glycemic metrics increased R\u0026sup2; by \u0026le;\u0026thinsp;0.07 (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur cross-sectional study revealed that visceral adiposity, age, blood pressure, and eGFR are the main factors influencing PWV in adults with T1DM. These factors account for nearly half of the variance in PWV. HbA1c alone did not account for arterial stiffness. Although no direct bivariate links emerged between PWV and specific CGM-derived metrics, these measures contributed additional explanatory value once VFR was included, suggesting that the relationship between glycemia and stiffness is VFR-dependent. Notably, lower visceral fat was paradoxically associated with greater glycemic instability\u003c/p\u003e\u003cp\u003eAcross different populations, a VFA of about 100 cm\u0026sup2; marks the beginning of multiple cardiometabolic risk factors, regardless of sex, age, or BMI [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eBouchi et al. confirmed that Japanese patients with T2DM who had a normal BMI but VFA\u0026thinsp;\u0026ge;\u0026thinsp;100 cm\u0026sup2; showed the steepest progression in PWV. People with a normal BMI but high visceral fat levels had significantly higher PWV than all other groups\u0026mdash;including those who were overweight with either high or low visceral fat, and those with both normal BMI and low visceral fat [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Even after adjusting for other factors, having high visceral fat despite a normal BMI remained independently associated with higher PWV. The same pattern appears earlier in life. In youth aged 10\u0026ndash;23, dual-energy X-ray-derived visceral fat more strongly explained carotid-femoral PWV (cf-PWV) than BMI or waist circumference, but only among individuals with obesity or T2DM [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOur data extend these observations to T1DM: each standard deviation increase in Tanita-generated VFR was linked to a clinically significant rise in cf-PWV despite a mean cohort BMI\u0026thinsp;\u0026lt;\u0026thinsp;25 kg/m\u0026sup2;. The finding is consistent with extensive Mexican mediation analyses, which show that visceral adipose tissue accounts for approximately 57% of the adverse effect of insulin resistance on PWV and nearly 70% of the excess hypertension risk.\u003c/p\u003e\u003cp\u003eIn the multi-ethnic Asian population with T2DM, VFA measured by bioimpedance showed some link to aortic stiffness. Still, it did not surpass general adiposity measures, such as BMI, in predicting AS. After adjusting for confounders, BMI remained the most reliable and independent predictor across all ethnic groups. Notably, Indian participants had the strongest link between adiposity measures and AS, whereas the connection was weaker or not significant among Chinese and Malay individuals [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eVisceral adiposity is closely related to insulin resistance, a known risk factor for cardiovascular disease and mortality in people with T1DM [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The synergistic interaction between visceral adiposity and insulin resistance described by Antonio-Villa et al. is biologically plausible: portal drainage of visceral fat deposits exposes the liver to excessive free fatty acids, diacylglycerol, and pro-inflammatory adipokines, thereby increasing hepatic insulin resistance and dyslipidemia, both of which speed up medial collagen cross-linking and smooth muscle dysfunction [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn our cohort, lower VFR was paradoxically associated with higher glycemic variability and worse glycemic control. These findings suggest that, in some individuals, the pursuit of glycemic targets may necessitate insulin doses high enough to promote visceral fat accumulation and worsen insulin sensitivity. However, prospective studies are needed to test this hypothesis. Notably, in Chinese adults with T2DM, lower BMI was associated with greater glycemic variability[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eConsistent with prior research, we found that HbA\u003csub\u003e1c\u003c/sub\u003e was not significantly associated with AS. This supports Tynj\u0026auml;l\u0026auml; et al.\u0026rsquo;s findings, who showed that HbA\u003csub\u003e1c\u003c/sub\u003e variability\u0026mdash;rather than average HbA\u003csub\u003e1c\u003c/sub\u003e\u0026mdash;was associated with higher PWV in people with T1DM [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Their study emphasizes the limitations of relying only on HbA1\u003csub\u003e1c\u003c/sub\u003e as a predictor of cardiovascular risk. It highlights GV as a more dynamic and sensitive marker of endothelial stress and arterial remodeling. Consistent with this pathophysiology, participants using continuous subcutaneous insulin infusion (CSII) in Rosenlund\u0026rsquo;s 601-patient cohort exhibited a 1.1 m s⁻\u0026sup1; lower PWV than those on multiple daily injections after adjustment for conventional risk factors, despite comparable HbA\u003csub\u003e1c\u003c/sub\u003e values [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGrowing experimental and epidemiological evidence implicates short-term glucose fluctuations in vascular injury. In the Maastricht Study, a one-standard deviation higher CGM-derived coefficient of variation predicted a 0.13 m s⁻\u0026sup1; higher cf-PWV after adjusting for age, sex, BMI, and mean arterial pressure [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Our data further showed that specific CGM-based variability metrics, including the TIR, MODD, CV, and MAGE, were associated with PWV after adjustment for VFR and age. These findings align closely with those of Foreman et al., who demonstrated that both low time in range (TIR) and higher MODD were associated with increased aortic stiffness in individuals with T2DM [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSpecifically, MAGE and MODD represent within-day and between-day variability, respectively, and their contribution to vascular outcomes may be mediated by oxidative stress and inflammatory pathways. Monnier et al. demonstrated that glycemic variability strongly predicts oxidative stress in T2DM, a known mechanistic driver of arterial stiffening through endothelial dysfunction and collagen cross-linking [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Additionally, the glycemic risk index (GRI) is associated with carotid intima-media thickness, thereby extending the vascular implications of GV beyond stiffness to structural vessel changes [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eConversely, Helleputte et al. reported that none of the CGM metrics obtained during seven days correlated with cf-PWV in 54 adults with T1DM; only the current and 10-year mean HbA1c levels remained significant, highlighting the role of long-term chronic hyperglycemia [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The discrepancy may reflect limited statistical power and the brief monitoring window in the latter study. Our analysis showed that HbA\u003csub\u003e1c\u003c/sub\u003e did not relate to AS. There was no correlation between PWV and any of the CGM-derived parameters. However, selected variability metrics, particularly MODD and MAGE, added modest explanatory power to logistic regression models including VFR.\u003c/p\u003e\u003cp\u003eOur results differ from those of Gordin et al., who reported that high mean daily blood glucose, assessed during a 72-hour window, but not glucose variability, is associated with AS in individuals with T1DM [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In our cohort, SBP maintained a strong link with PWV across all models, highlighting its continued significance in cardiovascular risk management in T1DM. Additionally, microvascular complications such as neuropathy and retinopathy also predicted higher PWV, indicating that small-vessel disease and macrovascular stiffness may share similar pathogenic pathways. This finding aligns with observations by Jaiswal et al., who reported a correlation between reduced heart rate variability\u0026mdash;a marker of autonomic neuropathy\u0026mdash;and increased AS in young adults with T1DM [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e The clinical significance of CGM-derived metrics is growing, with international guidelines now endorsing TIR and GV measures as actionable targets in diabetes care. Battelino et al. (2019) emphasized the prognostic importance of maintaining TIR\u0026thinsp;\u0026ge;\u0026thinsp;70% [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOur findings should be viewed considering several methodological and contextual limitations.\u003c/p\u003e\u003cp\u003eFirst, the cross-sectional design prevents causal inference. Although we observed similar associations between VFR, CGM-derived variability, and carotid\u0026ndash;femoral PWV, we cannot determine the temporal order. Similar cross-sectional studies in T1DM have produced conflicting results\u0026mdash;Helleputte et al. found no relationship between 7-day CGM indices and cf-PWV [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSecond, we measured visceral adiposity using a bioimpedance device. This tool improves on BMI but has limited spatial resolution, cannot distinguish intraperitoneal from deep subcutaneous fat, and is affected by hydration status [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Magnetic resonance, multi-slice CT, and densitometry would provide absolute volume estimates and fat-quality metrics, but they come with higher costs and radiation exposure. Bioimpedance devices are much more common in health centers, which makes it easier to translate the results into clinical practice.\u003c/p\u003e\u003cp\u003eThird, AS was measured oscillometrically. Although brachial cuff\u0026ndash;based cf-PWV correlates well with tonometric reference methods, its precision decreases in the lower velocity range and it is affected by brachial pulse-pressure amplification. Applanation tonometry with direct path-length correction remains the reference standard [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFourth, the cohort was small and ethnically uniform. From the 120 recruited participants, 100 met the criterion of having at least 70% available CGM records. Although the study was designed to detect primary associations, wider confidence intervals around interaction terms might have hidden small but clinically essential modifiers. Validation in more diverse populations\u0026mdash;especially those with higher visceral fat, such as South and East Asian adults\u0026mdash;is necessary.\u003c/p\u003e\u003cp\u003eFifth, one could argue that adjusting for multiple comparisons is necessary in our study [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. However, we believed that the increased risk of a type II error was undesirable, especially in CGM-based research, where sample sizes are often limited due to the relative invasiveness and cost of CGM [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Additionally, applying such adjustments based on the included determinants would likely be too conservative, since CGM-derived metrics are both conceptually and statistically interconnected [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThese cross-sectional data from adults with BMI\u0026thinsp;\u0026lt;\u0026thinsp;30 kg/m\u0026sup2; suggest that VFR and selected CGM-derived variability indices may indicate higher aortic stiffness in T1DM. Because causality and direction cannot be determined, clinicians should view VFR as a supplementary risk signal rather than a validated therapeutic target. Similarly, the apparent added value of MODD, MAGE and other CGM-derived metrics became evident only after multivariable adjustment; their lack of bivariate association with PWV and the mixed evidence for HbA\u003csub\u003e1c\u003c/sub\u003e\u0026rsquo;s role mean that traditional glycemic measures remain vital to care until prospective trials clarify whether reducing glucose fluctuations truly slows vascular aging. Clinicians should individualize therapy to achieve glycemic targets while avoiding unnecessarily high insulin doses, which may promote insulin resistance and visceral adiposity. The combination of VFR, blood pressure, and renal function explained nearly half of pulse-wave velocity variability; aggressive management of each factor\u0026mdash;including weight loss, renin\u0026ndash;angiotensin blockade, and antihypertensive lifestyle strategies\u0026mdash;may lessen early vascular aging. The assessment of VFR could be considered in the ongoing debate of GLP1 agonists' use in people with T1DM. Prospective intervention trials are needed to confirm whether decreasing visceral fat and glycemic variability slows arterial stiffening and reduces cardiovascular events.\u003c/p\u003e\u003cp\u003eIn non-obese adults with T1DM, higher VFR and age were the strongest predictors of increased pulse-wave velocity. VFR, age, blood pressure, and eGFR are the main factors influencing PWV in adults with T1DM, accounting for nearly half of the variation. HbA\u003csub\u003e1c\u003c/sub\u003e and mean glucose levels were not associated with AS; MODD, GRI, TAR, and TIR showed moderate associations only after adjustment for VFR. Prospective trials are needed to determine if reducing visceral fat or stabilizing glucose fluctuations can slow vascular aging.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eParticipants\u003c/h2\u003e\u003cp\u003eWe received ethical approval from our university\u0026rsquo;s Ethics Committee (No. 848/23) and obtained written informed consent from each participant before enrollment (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e). We followed the principles of the Declaration of Helsinki [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Between February 2024 and April 2025, we recruited 120 consecutive adults with T1DM. under the care of our department. To qualify, individuals had to be aged 18\u0026ndash;50 years, have a confirmed T1DM diagnosis (based on diabetes-associated autoantibodies), and live with diabetes for at least five years. We excluded people with a BMI above 30 kg/m\u003csup\u003e2,\u003c/sup\u003e a severe infection or active chronic disease, pregnant or lactating women, those with cardiovascular disease, hypertension, an estimated glomerular filtration rate (eGFR) below 60 mL/min/1.73 m\u0026sup2;, uncontrolled thyroid disorders, and a lack of a full dataset.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eComprehensive baseline assessment\u003c/h2\u003e\u003cp\u003eWe first administered a standardized questionnaire to each participant. This tool captured medical history, diabetes duration and management, co-existing illnesses, and any documented microvascular or macrovascular complications. We then measured height and weight with a stadiometer and calibrated scale, respectively, and calculated body mass index (BMI) as weight (kg) divided by the square of height (m\u0026sup2;).\u003c/p\u003e\u003cp\u003eFollowing a 10-minute seated rest, we measured brachial blood pressure three times on each arm with a mercury-validated aneroid sphygmomanometer, ensuring the cuff remained at heart level. We recorded the higher-pressure arm and used the mean of the three readings for analysis [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWe screened for chronic diabetic complications. Spot urine was analyzed for albumin-to-creatinine ratio, and serum creatinine was assayed to estimate glomerular filtration rate using the CKD-EPI equation. Fundoscopy was performed by an experienced ophthalmologist following pharmacologic pupil dilation to evaluate diabetic eye disease. Peripheral neuropathy assessment included four modalities: 10 g monofilament for light touch, a 128 Hz tuning fork for vibration, a sterile single-use pinprick for nociception, and a dual-temperature rod for warm-cold differentiation [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The cardiac autonomic neuropathy score was estimated using Sudoscan.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eBioelectrical impedance body composition analysis\u003c/h2\u003e\u003cp\u003eTo characterize body composition, we used a multi-frequency segmental bioelectrical impedance analyzer (MC-780 MA N; Tanita, Tokyo, Japan). After entering sex, age, and measured height, we instructed participants\u0026mdash;barefoot and wearing light clothing\u0026mdash;to stand motionless so that both feet contacted the four footplates. The device delivered a 50 Hz current and, using proprietary equations, returned body-fat percentage, fat-free mass, skeletal-muscle mass, total body water, VFR (0\u0026ndash;59 score), and whole-body impedance. We calculated absolute fat mass as body-fat percentage \u0026times; weight\u0026thinsp;\u0026divide;\u0026thinsp;100 and derived fat-free mass as weight minus fat mass.\u003c/p\u003e\u003cp\u003eThe estimated glucose disposal rate (eGDR) highly correlates with the results of the euglycemic-hyperinsulinemic clamp, the gold standard for IR assessment in T1DM. The eGDR was derived from the following formula:\u003c/p\u003e\u003cp\u003eeGDR [mg/kg/min]\u0026thinsp;=\u0026thinsp;24.31 \u0026minus; (12.22 \u0026times; WHR) \u0026minus; (3.29 \u0026times; arterial hypertension) \u0026minus; (0.57 \u0026times; HbA1c)\u003c/p\u003e\u003cp\u003ewhere WHR is the waist-to-hip ratio, arterial hypertension is coded as 1 if present and 0 if not, and HbA1c is glycated hemoglobin [%].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eLaboratory procedures\u003c/h2\u003e\u003cp\u003eWe collected fasting venous blood between 07:00 and 08:00 h after 8\u0026ndash;12 h of overnight abstinence from food and drink. Samples were drawn into dipotassium-EDTA tubes. Within two hours, a Sysmex XN-1000 analyser generated complete blood counts, and a Cobas Pure integrated platform (Roche Diagnostics) quantified total cholesterol, HDL-cholesterol, triglycerides, creatinine, high-sensitivity C-reactive protein, and HbA₁c. We calculated LDL-cholesterol using the Friedewald formula when triglycerides were \u0026le;\u0026thinsp;400 mg dL⁻\u0026sup1;; otherwise, we performed a direct measurement. All assays adhered to the manufacturer's quality-control procedures.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eArterial-stiffness measurement\u003c/h2\u003e\u003cp\u003eWe assessed aortic pulse-wave velocity (PWV) with a validated, cuff-based oscillometric device (Arteriograph 24; TensioMed, Budapest, Hungary) [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. After at least 5 minutes of supine rest in a quiet, temperature-controlled room, we obtained three consecutive recordings. When within-subject SD exceeded 1 m s⁻\u0026sup1;, we repeated the set until this criterion was met. The mean of the three qualified measurements represented the participant\u0026rsquo;s PWV value.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eContinuous-glucose-monitoring metrics\u003c/h2\u003e\u003cp\u003eReal-time CGM systems (Dexcom G7 or FreeStyle Libre 2; Dexcom, San Diego, CA and Abbott Diabetes Care, Alameda, CA) logged interstitial glucose every 5\u0026ndash;15 min and issued user-configurable alarms for hypo- and hyperglycemia. We downloaded the raw files and processed the data using Glyculator 3.0 [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. For each individual, we analysed the 90 days preceding the vascular visit, evaluating the entire 24-h profiles.\u003c/p\u003e\u003cp\u003ePrimary CGM outcomes comprised:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eMean glucose: the average glucose level over the monitoring period.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eStandard deviation (SD): the degree of absolute variability around the mean glucose.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eCoefficient of variation (CV): the relative variability (SD divided by mean, expressed as a percentage); values above 36% suggest unstable glycemic control.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eTIR, defined as the percentage of time glucose levels remain within the target range of 70\u0026ndash;180 mg/d [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eTime below range (TBR): time spent in hypoglycemia; \u0026lt; 70 mg/dL\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eTime above range (TAR): time spent in hyperglycemia, \u0026gt;180 mg/dL [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eTime in tight range (TITR): the percentage of time within a stricter range of 70\u0026ndash;140 mg/dL, reflecting tighter glycemic control [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eTo better quantify risk and glycemic burden, several composite indices were calculated:\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eM100: reflects the average deviation of glucose from the ideal value of 100 mg/dL (5.55 mmol/L); lower values indicate greater stability, while higher values signal greater variability [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eJ-index: integrates both mean glucose and SD, calculated as 0.001 \u0026times; (mean\u0026thinsp;+\u0026thinsp;SD)\u0026sup2;; higher values suggest poorer overall control [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eGRADE (Glycemic Risk Assessment Diabetes Equation): assesses glycemic risk by assigning penalties for both hypo- and hyperglycemia [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eLBGI (Low Blood Glucose Index) and HBGI (High Blood Glucose Index): risk scores for hypo- and hyperglycemia, respectively, with higher scores indicating greater risk of clinically significant excursions [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eMAGE (Mean Amplitude of Glycemic Excursions) and MODD (Mean of Daily Differences): traditional measures of intraday and interday variability, respectively [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eGRI (Glycemic Risk Index): a newer metric that condenses glucose distribution into a single risk-weighted score, validated against clinical outcomes [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eAll metrics followed international consensus definitions [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eWe expressed continuous data as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or median [Q1\u0026ndash;Q3] and presented categorical data as counts and percentages. We assessed normality with the Shapiro\u0026ndash;Wilk test and homogeneity of variance with the Fisher\u0026ndash;Snedecor test. When both assumptions held, we compared two independent groups with the Student \u003cem\u003et\u003c/em\u003e-test; we used the Cochran\u0026ndash;Cox variant when variances differed. For non-normal or ordinal variables, we applied the Mann\u0026ndash;Whitney U test. We examined associations between continuous variables with Pearson\u0026rsquo;s correlation if both distributions were normal and with Spearman\u0026rsquo;s rank correlation otherwise. We compared categorical variables with Pearson\u0026rsquo;s χ\u0026sup2; test and adopted Yates\u0026rsquo; continuity correction for 2 \u0026times; 2 tables that violated Cochran\u0026rsquo;s rule.\u003c/p\u003e\u003cp\u003eTo identify independent determinants of carotid\u0026ndash;femoral PWV, we constructed several multivariable linear-regression models. In every model, we standardized predictors and reported β-coefficients, two-tailed p-values, and the coefficient of determination (R\u0026sup2;). One set of models incorporated the following covariates, chosen a priori for biological plausibility: sex (male\u0026thinsp;=\u0026thinsp;1), VFR (Tanita scale), systolic blood pressure (SBP), estimated glomerular filtration rate (eGFR), presence of at least one microvascular diabetic complication (binary composite of retinopathy and neuropathy) and current smoking status (yes\u0026thinsp;=\u0026thinsp;1). A second set contained age (which was the highest correlate of PWV and VFR). Into each base model, we entered one glycemic exposure or variability metric to quantify its incremental association with PWV. The metrics comprised laboratory HbA\u003csub\u003e\u003cb\u003e1c\u003c/b\u003e\u003c/sub\u003e, TIR, GRI, TAR, MODD, CV, TBR, and MAGE. CGM variables were summarized from 90 days of sensor data and were available for 100 participants.\u003c/p\u003e\u003cp\u003eWe defined statistical significance as \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Because each regression addressed a distinct hypothesis, we did not adjust for multiple comparisons. All analyses were performed using the PQStat 1.8.6 (PQStat Software, Poznan, Poland).\u003c/p\u003e\u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAS – Arterial stiffness\u003c/p\u003e\n\u003cp\u003eBMI – Body mass index\u003c/p\u003e\n\u003cp\u003eBP – Blood pressure\u003c/p\u003e\n\u003cp\u003eCGM – Continuous glucose monitoring\u003c/p\u003e\n\u003cp\u003eCV – Coefficient of variation\u003c/p\u003e\n\u003cp\u003eeGDR – Estimated glucose disposal rate\u003c/p\u003e\n\u003cp\u003eeGFR – Estimated glomerular filtration rate\u003c/p\u003e\n\u003cp\u003eGMI – Glucose management indicator\u003c/p\u003e\n\u003cp\u003eGRI – Glycemic risk index\u003c/p\u003e\n\u003cp\u003eGV – Glycemic variability\u003c/p\u003e\n\u003cp\u003eHbA1c – Hemoglobin A1c\u003c/p\u003e\n\u003cp\u003eHBGI – High blood glucose index\u003c/p\u003e\n\u003cp\u003eHDL – High-density lipoprotein\u003c/p\u003e\n\u003cp\u003eIR – Insulin resistance\u003c/p\u003e\n\u003cp\u003eJ-index – J index of glycemic control\u003c/p\u003e\n\u003cp\u003eLDL – Low-density lipoprotein\u003c/p\u003e\n\u003cp\u003eLBGI – Low blood glucose index\u003c/p\u003e\n\u003cp\u003eM100 – Mean glucose deviation from 100 mg/dL\u003c/p\u003e\n\u003cp\u003eMAGE – Mean amplitude of glycemic excursions\u003c/p\u003e\n\u003cp\u003eMODD – Mean of daily differences\u003c/p\u003e\n\u003cp\u003ePWV – Pulse wave velocity\u003c/p\u003e\n\u003cp\u003eSBP – Systolic blood pressure\u003c/p\u003e\n\u003cp\u003eSD – Standard deviation\u003c/p\u003e\n\u003cp\u003eT1DM – Type 1 diabetes mellitus\u003c/p\u003e\n\u003cp\u003eT2DM – Type 2 diabetes mellitus\u003c/p\u003e\n\u003cp\u003eTAR – Time above range\u003c/p\u003e\n\u003cp\u003eTBR – Time below range\u003c/p\u003e\n\u003cp\u003eTIR – Time in range\u003c/p\u003e\n\u003cp\u003eTITR – Time in tight range\u003c/p\u003e\n\u003cp\u003eVFA – Visceral fat area\u003c/p\u003e\n\u003cp\u003eVFR – Visceral fat rating\u003c/p\u003e\n\u003cp\u003eWHR – Waist-to-hip ratio\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\u003cp\u003e This study was conducted in accordance with the decision of the Ethical Committee of Poznan University of Medical Sciences (approval No. 1245/18) and adhered to the principles outlined in the Declaration of Helsinki. All participants provided written informed consent.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eCompeting Interests Statement\u003c/h2\u003e\u003cp\u003eThe authors declare no conflicts of interest\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis research was supported by Poznan University of Medical Sciences (grant number: 177/2025/DGB)\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eMK conceived the study, analyzed and interpreted the data, and was a major contributor in writing the manuscript. DN contributed to study design, recruitment, and clinical interpretation. MMD contributed to data collection, statistical analysis, and manuscript preparation. BL, IA, SM, AL, and PH participated in data acquisition and analysis of continuous glucose monitoring records. AGW assisted in data collection and literature review. AU contributed to study conception, interpretation of findings, and critical revision of the manuscript. DZZ supervised the study, contributed to its conception and design, and critically revised the manuscript. AU and DZZ contributed equally. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003enone\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and analyzed during the current study are available from the corresponding author on reasonable request\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAraszkiewicz, A. et al. Standards of Care in Diabetes. The position of Diabetes Poland \u0026ndash; 2024. \u003cem\u003eCurr. Top. 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Technol.\u003c/em\u003e \u003cb\u003e17\u003c/b\u003e, 1226\u0026ndash;1242 (2022).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7926850/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7926850/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAdults with type 1 diabetes mellitus (T1DM) exhibit premature arterial stiffening, but the relative roles of visceral adiposity and glycemic variability remain unclear. We investigated these associations in a group of 120 non-obese adults with T1DM. Carotid\u0026ndash;femoral pulse wave velocity (PWV) was measured oscillometrically, and visceral fat rating (VFR) was quantified by multifrequency bioimpedance. Ninety-day continuous glucose monitoring data provided glycemic metrics including mean glucose, time in range (TIR), time above range (TAR), glycemic risk index (GRI), mean amplitude of glycemic excursions (MAGE), mean of daily differences (MODD), and coefficient of variation (CV). Participants (median age 33.8 years, mean BMI 24.3 kg\u0026middot;m⁻\u0026sup2;) had a mean PWV of 7.47\u0026thinsp;\u0026plusmn;\u0026thinsp;1.43 m\u0026middot;s⁻\u0026sup1;. Age was the strongest correlate of PWV (ρ\u0026thinsp;=\u0026thinsp;0.59, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) followed by VFR (ρ\u0026thinsp;=\u0026thinsp;0.48, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In multivariable models, each standard deviation increase in VFR was associated with a 0.22 m\u0026middot;s⁻\u0026sup1; higher PWV (p\u0026thinsp;=\u0026thinsp;0.01), comparable to the effects of systolic blood pressure and diabetes complications. MODD, MAGE, TAR, GRI, and lower TIR modestly improved model fit (ΔR\u0026sup2; \u0026le; 0.08), yet none showed univariate associations. VFR and age are dominant correlates of arterial stiffness in T1DM, while glycemic variability plays a limited role.\u003c/p\u003e","manuscriptTitle":"The association between arterial stiffness, visceral fat rating, and glycemic variability in non-obese adults with type 1 diabetes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-13 07:54:28","doi":"10.21203/rs.3.rs-7926850/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-21T03:42:15+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-17T13:59:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-10T15:24:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"143800020796836223069577943283028317219","date":"2025-11-06T06:57:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"218440523577237900892122409785221413383","date":"2025-10-31T18:49:06+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-31T16:30:31+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-29T03:00:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-23T12:05:35+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-23T12:04:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-10-22T22:23:25+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a24ef80b-2d03-4d39-90d7-08dd8f0f67fb","owner":[],"postedDate":"November 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":57746708,"name":"Health sciences/Diseases"},{"id":57746709,"name":"Health sciences/Endocrinology"},{"id":57746710,"name":"Health sciences/Medical research"},{"id":57746711,"name":"Biological sciences/Physiology"}],"tags":[],"updatedAt":"2026-04-13T16:16:23+00:00","versionOfRecord":{"articleIdentity":"rs-7926850","link":"https://doi.org/10.1038/s41598-026-47105-5","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-04-12 15:58:13","publishedOnDateReadable":"April 12th, 2026"},"versionCreatedAt":"2025-11-13 07:54:28","video":"","vorDoi":"10.1038/s41598-026-47105-5","vorDoiUrl":"https://doi.org/10.1038/s41598-026-47105-5","workflowStages":[]},"version":"v1","identity":"rs-7926850","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7926850","identity":"rs-7926850","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}