{"paper_id":"43260f4b-accf-4e20-8e9e-9891dafaae7d","body_text":"Sex Differences in Multifactorial Target Attainment in Type 2 Diabetes: A Primary Care Cohort of 40,211 Adults (PROMETEA) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Sex Differences in Multifactorial Target Attainment in Type 2 Diabetes: A Primary Care Cohort of 40,211 Adults (PROMETEA) Chema Fernández- Rodríguez Lacín, José María Enguita, Ignacio Diaz, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8462163/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background To identify sex-specific, actionable gaps in metabolic control and multifactorial target attainment in adults with type 2 diabetes Methods Cross-sectional analysis of 40,211 adults with type 2 diabetes from a primary care registry (46,3% women). Outcomes were HbA1c, LDL-cholesterol (LDL-C), systolic/diastolic blood pressure (SBP/DBP), body mass index (BMI), estimated glomerular filtration rate (eGFR), and urine albumin-to-creatinine ratio (UACR). Targets were HbA1c < 7%, LDL-C < 100 mg/dL, BP < 140/90 mmHg, and a composite triple target. Sex differences were assessed with multivariable linear regression (HbA1c) and robust Poisson regression (target attainment), adjusted for age, diabetes duration, and BMI, and including sex-by-age and sex-by-duration interaction terms. Results Women were older than men (73.9 ± 11.7 vs 70.2 ± 11.3 years) and had higher mean LDL-C (103.0 ± 32.8 vs 95.7 ± 32.4 mg/dL) and BMI (31.1 ± 6.9 vs 30.3 ± 6.6 kg/m²) (all p < 0.001). Female sex was independently associated with higher HbA1c (adjusted β + 0.075, 95% CI + 0.050 to + 0.100; p < 0.001), and the sex gap in HbA1c widened at older ages and longer diabetes duration (both interaction p < 0.001). Men more frequently had albuminuria (UACR ≥ 30 mg/g: 24.5% vs 17.3%), whereas women more often had reduced renal function (eGFR < 60 mL/min/1.73 m²: 24.9% vs 17.3%) (both p < 0.001). Overall, only 17.9% achieved the triple target (men 19.5% vs women 16.2%, p < 0.001). Compared with women, men were 3% more likely to achieve the HbA1c target (adjusted prevalence ratio (aPR) 1.03, 95% CI 1.01–1.04), 22% more likely to reach the LDL-C target (aPR 1.22, 1.20–1.24), and 21% more likely to attain the composite triple target (aPR 1.21, 1.16–1.26), but 4% less likely to achieve the BP target (aPR 0.96, 0.95–0.98). Equalizing women’s composite attainment to men’s would translate into ≈ 614 additional women meeting all three targets. Conclusions In contemporary primary care,women with type 2 diabetes have consistent shortfalls in LDL-C control and composite multifactorial target attainment, and their glycemic disadvantage increases with age and longer diabetes duration. These actionable patterns support sex-aware implementation strategies—prioritizing lipid-lowering optimization in women across ages and tailoring glycemic intensification for older and long-duration women—to close treatment gaps and improve risk factor control. Type 2 diabetes Sex difference Primary Care Treatment goals HbA1c target Figures Figure 1 Background Type 2 diabetes is a major global health problem, affecting 537 million adults worldwide in 2021; these estimates are projected to rise to 783 million by 2045 ( 1 ). In Spain, the prevalence estimates vary: 7.8% according to the National Health Survey, and 13.8% in the population-based [email protected] study, which also reported that 43.5% of cases were undiagnosed previously ( 2 , 3 ). These figures highlight the high burden of type 2 diabetes. Type 2 diabetes is associated with several major complications, including cardiovascular disease and chronic kidney disease (CKD), which account for much of its excess morbidity and mortality ( 4 ). Landmark studies such as the UK Prospective Diabetes Study established that multifactorial control, including glycemia, BP, and LDL-cholesterol (LDL-C), substantially reduces complication risk ( 5 , 6 ). Nonetheless, real-world studies consistently show that only a minority of patients achieve the recommended targets ( 7 ). Sex-related differences play a key role in the development, management, and outcomes of type 2 diabetes ( 8 – 11 ). Men generally develop diabetes at a younger age and lower body mass index (BMI), whereas women often present later, with greater adiposity and a higher burden of risk factors. Once diabetes develops, women lose their premenopausal cardiovascular protection and face a disproportionately higher relative risk of developing cardiovascular disease than men ( 10 – 12 ). Several studies in Spain and other countries have reported that women with type 2 diabetes are less likely than men to achieve glycemic and lipid targets ( 13 – 15 ). However, most analyses have been limited in scope, and comprehensive assessments across multiple domains of metabolic control—including glycemia, lipids, BP, obesity, and renal function—are scarce in large contemporary primary-care cohorts. Spain´s universal healthcare system, which with fully digitalized electronic health records (EHR) and near-universal primary-care registration, offers a unique opportunity to address this gap and generate high-quality real-world data with broad external validity. In Asturias, the regional diabetes registry encompasses all patients with a coded diagnosis of type 2 diabetes, enabling population-based studies of unprecedented scope. In this study, we analyzed the cases of > 40,000 adults with type 2 diabetes from the Asturias primary care registry. Our objective was to quantify sex differences in key metabolic parameters (HbA1c, LDL-C, BP, BMI, estimated glomerular filtration rate (eGFR), and albuminuria) and in the attainment of individual targets and a triple composite, and to assess modification by age, BMI, and diabetes duration. Methods Study Design and Setting The study was conducted within the framework of the PROMETEA project (Spanish acronym for “Project to Improve Care for Patients with Chronic Conditions in Primary Care Clinics in Asturias”), an initiative launched in 2015 by the Asturian Society of Family and Community Medicine (SAMFYC). PROMETEA focuses on the care of patients with chronic conditions attended in primary care centers belonging to the Principality of Asturias Health Service (SESPA), aiming to describe current practice in real-world family medicine and to identify opportunities for quality improvement. The project is structured into four disease-specific subprojects—coronary heart disease, chronic obstructive pulmonary disease (COPD), prostate cancer, and, for the present analysis, type 2 diabetes mellitus (T2D). All PROMETEA research protocols were approved in 2015 by the Clinical Research Ethics Committee of the Principality of Asturias and is formally endorsed by the Directorate-General for Planning of the Regional Ministry of Health of the Principality of Asturias, as well as by the management of SESPA. Cross-sectional analysis of anonymized electronic health records (EHRs) from the Asturias regional diabetes registry (~ 1M inhabitants). Primary care acts as gatekeeper; their electronic records (OMI-ECAP system) records demographics, clinical data, labs, and diagnoses for all patients with longitudinal coverage. The registry used in this study represents real world data with high external validity, encompassing all individuals with type 2 diabetes followed in the public health system. Study Population Adults with a diagnosis of type 2 diabetes recorded in OMI-ECAP between January 2014 and December 2018. Type 2 diabetes was defined according to the International Classification of Primary Care, Second Edition (ICPC-2) code T90 (non–insulin-dependent diabetes mellitus). Patients with ICPC-2 code T89 (insulin-dependent diabetes mellitus) were excluded. We identified 88,800 unique patients with type 2 diabetes. After applying the data-cleaning procedures described below, the final analytic sample comprised 40,211 unique patients (21,600 men and 18,611 women). Data Sources and Structure Data were drawn from three main EHR tables: Last EHR: general demographic and clinical information (age, sex, BMI, BP, smoking and alcohol status, and comorbidities). Five-year EHR: laboratory data (HbA1c, LDL-C, eGFR, microalbuminuria), BP, and BMI measured between 2014 and 2018. Episodes: ICPC-2 diagnoses and dates (for diabetes duration) Records were merged by a unique patient ID; for analyses we retained the latest available values per patient. Data Cleaning and Standardization The dataset underwent rigorous curation to ensure accuracy and consistency: Categorical fields were harmonized; numeric entries standardized and out-of-range values set to missing. Ages/durations recalculated with 1 Jan 2019 as reference; earliest diabetes date used to derive duration. After integration, the dataset included 294,000 patient-year records corresponding to 88,800 unique patients. For each patient, only the latest available values were retained. Exclusion of patients with type 1 diabetes and incomplete or invalid records yielded the final study cohort of 40,211 patients with type 2 diabetes. Variables and Outcomes The primary biomarkers were HbA1c (%), LDL-C (mg/dL), systolic (SBP) and diastolic BP (DBP) (mmHg), BMI (kg/m²), eGFR (mL/min/1.73 m², by CKD-EPI equation), and urine albumin-to-creatinine ratio (UACR) (mg/g). Target attainment was defined: HbA1c < 7%, LDL-C < 100 mg/dL, BP < 140/90 mmHg ( 16 , 17 ), and a triple composite. Statistical Analysis The baseline characteristics were summarized as means ± standard deviations (SD), medians (interquartile ranges), or percentages. Sex differences were tested using student’s t-test, Mann–Whitney U test, or χ² test as appropriate. Analyses were stratified by age (< 60 years, 60–79 years, ≥ 80 years), BMI (< 25 kg/m², 25–29.9 kg/m², ≥ 30 kg/m²), and diabetes duration (< 5 years, 5–14 years, ≥ 15 years). HbA1c predictors were assessed using univariate and multivariable linear regression models adjusted for sex, age, diabetes duration, SBP/DBP, and BMI. Interaction terms (sex × age, sex × duration) were tested. Robust Poisson regression models were fitted to estimate the adjusted prevalence ratios (aPRs) for each target, as well as the composite target, adjusting for age, duration, and BMI. All analyses were conducted using Python version 3.13, using packages Pandas 2.3.0, Numpy 2.3, SciPy 1.15.3 and Statsmodels 0.14.4. Two-sided p-values of < 0.05 were considered statistically significant. Results Study Population We included 40,211 patients: women 46.3% (n = 18,611). Women were older than men (73.9 ± 11.7 years vs 70.2 ± 11.3 years) and had slightly longer diabetes duration (11.0 ± 6.3 years vs 10.6 ± 6.4 years; both p < 0.001) (Table 1 ). Table 1 Baseline characteristics of the overall cohort, with comparison by sex (women vs men). Variable Overall (n = 40,211) Women (n = 18,611) Men (n = 21,600) P-value Age, years (mean ± SD) 71.9 ± 11.6 73.9 ± 11.7 70.2 ± 11.3 < 0.001 Diabetes duration, years 10.8 ± 6.4 11.0 ± 6.3 10.6 ± 6.4 < 0.001 BMI, kg/m² 30.6 ± 6.8 31.1 ± 6.9 30.3 ± 6.6 < 0.001 SBP, mmHg 137.2 ± 16.3 137.4 ± 16.4 137.1 ± 16.2 0.052 DBP, mmHg 77.1 ± 9.5 76.7 ± 9.2 77.5 ± 9.7 < 0.001 LDL-C, mg/dL 99.1 ± 32.8 103.0 ± 32.8 95.7 ± 32.4 < 0.001 HbA1c, % 6.98 ± 1.27 7.01 ± 1.25 6.96 ± 1.28 < 0.001 UACR, mg/g, Median [IQR] 7.6 [3.8–22.8] 7.0 [3.8–18.0] 8.0 [3.9–28.6] < 0.001 eGFR, mL/min/1.73 m² 73.1 ± 18.1 71.0 ± 18.6 74.9 ± 17.5 < 0.001 Albuminuria ≥ 30 mg/g,% 21.2% 17.3% 24.5% < 0.001 eGFR < 60 mL/min/1.73 m²,% 20.81% 24.9% 17.3% < 0.001 Albuminuria ≥ 30 and eGFR < 60 mL/min/1.73 m²,% 7.73% 7.4% 8.0% 0.03 Overall Sex Differences in Metabolic Control In unadjusted comparisons of key metabolic parameters, women showed slightly higher mean HbA1c levels (7.01 ± 1.25% vs 6.96 ± 1.28%, p < 0.001), higher mean LDL-C levels (103.0 ± 32.8 mg/dL vs 95.7 ± 32.4 mg/dL, p < 0.001) and BMI (31.1 ± 6.9 kg/m 2 vs 30.3 ± 6.6 kg/m 2 , p < 0.001) than men. Men had a slightly higher DBP than women (77.5 ± 9.7 mmHg vs 76.7 ± 9.2 mmHg, p < 0.001), higher mean eGFR (74.9 ± 17.5 mL/min/1.73 m² vs 71.0 ± 18.6 mL/min/1.73 m², p < 0.001) and a higher median UACR than women (8 [IQR 3.9–28.6] mg/g vs 7 [3.8–18.0] mg/g, p < 0.001). Consistent with this, overt albuminuria (UACR ≥ 30 mg/g) was more frequent in men (24.5% vs 17.3%, p < 0.001), whereas reduced renal function (eGFR < 60 mL/min/1.73 m²) was more prevalent in women (24.9% vs 17.3%, p < 0.001). (Table 1 ) Age-Stratified Sex Differences Women aged 60–79 years had higher mean HbA1c levels than men (6.97% vs 6.89%, p < 0.001); a similar trend was observed in those aged ≥ 80 years (7.01% vs 6.93%, p < 0.001). Women consistently had higher LDL-C levels than men across all ages ( ~ + 8–9 mg/dL, p < 0.001 for each). Women also had a higher BMI in each age category (on average + 1 kg/m 2 vs men, p < 0.001 for each comparison). (Table 2 ). Table 2 Clinical and biochemical parameters by age and sex Variable < 60 year Women (n = 2285) < 60 year Men (n = 3919) 60–79 year Women (n = 9879) 60–79 year Men (n = 13099) ≥ 80 year Women (n = 6447) ≥ 80 year Men (n = 4582) SBP, mmHg 130.0 ± 16.2 133.5 ± 15.1 * 137.5 ± 15.6 137.9 ± 15.9 † 140.0 ± 16.9 137.6 ± 17.3 * DBP, mmHg 80.2 ± 9.7 82.3 ± 9.5 * 77.6 ± 8.9 77.8 ± 9.2 74.0 ± 8.8 72.8 ± 9.0 * BMI, kg/m² 32.8 ± 7.0 31.6 ± 7.1 * 31.7 ± 6.9 30.4 ± 6.2 * 29.5 ± 6.7 28.7 ± 6.7 * LDL-C, mg/dL 112.6 ± 34.7 104.7 ± 34.3 * 102.8 ± 32.5 94.6 ± 32.1 * 99.9 ± 31.8 91.0 ± 29.9 * HbA1c, % 7.17 ± 1.60 7.21 ± 1.55 6.97 ± 1.19 6.89 ± 1.22 * 7.01 ± 1.19 6.93 ± 1.17 * UACR, mg/g, median [IQR] 5.9 [3.1–3.2] 6.0 [3.0–17.0] 6.0 [3.4–13.3] 7.7 [3.7–26.0] * 10.3 [4.9–29.5] 14.0 [5.3–53.9] * eGFR, mL/min/1.73 m² 86.4 ± 14.6 86.1 ± 14.2 74.9 ± 16.1 76.1 ± 15.9 * 59.4 ± 16.8 62.0 ± 16.7 * * p < 0.001, † p < 0.05 BP patterns crossed with age. In patients aged < 60 years, men had higher SBP and DBP (SBP 133.5 ± 15.1 mmHg vs 130.0 ± 16.2 mmHg; DBP 82.3 ± 9.5 mmHg vs 80.2 ± 9.7 mmHg; both p < 0.001). In the 60–79-year-old group, only SBP remained higher in men (137.9 mmHg vs 137.5 mmHg, p = 0.011). Among patients aged ≥ 80 years, women had higher SBP (140.0 ± 16.9 mmHg vs 137.6 ± 17.3 mmHg, p < 0.001), and DBP (74.0 ± 8.8 mmHg vs 72.8 ± 9.0 mmHg, p < 0.001). (Table 2 ). Women aged 60–79 years had lower eGFR (74.9 ± 16.1 mL/min/1.73 m² vs 76.1 ± 15.9 mL/min/1.73 m², p < 0.001). In the ≥ 80-year-old group, the women’s eGFR averaged was 59.4 ± 16.8 mL/min/1.73 m² vs 62.0 ± 16.7 mL/min/1.73 m² in men (p < 0.001). The median UACR was higher in men than women in both the 60–79-year-old group (median 7.7 mg/g vs 6 mg/g, p < 0.001) and ≥ 80-year-old group (median 14.0 mg/g vs 10.3 mg/g, p < 0.001). (Table 2 ). BMI-Stratified Sex Differences In normal-weight patients, women had slightly higher SBP (134.7 ± 17.5 mmHg vs 133.3 ± 17.0 mmHg, p = 0.002). Regarding DBP, in overweight and obese men was higher (76.6 ± 9.3 mmHg vs 75.5 ± 8.8 mmHg, p < 0.001) (79.5 ± 9.7 mmHg vs 78.3 ± 9.2 mmHg, p < 0.001). (Table 3 ). Table 3 Parameters by BMI category and sex Variable Women < 25 kg/m 2 (n = 2739) Men < 25 kg/m 2 (n = 2643) Women 25–29.9 kg/m 2 (n = 6200) Men 25–29.9 kg/m 2 (n = 8919) Women ≥ 30 kg/m 2 (n = 9672) Men ≥ 30 kg/m 2 (n = 10038) SBP, mmHg 134.7 ± 17.5 133.3 ± 17.0 † 137.1 ± 16.2 136.6 ± 16.0 138.4 ± 16.1 138.5 ± 15.9 DBP, mmHg 73.5 ± 8.8 73.4 ± 9.2 75.5 ± 8.8 76.6 ± 9.3 * 78.3 ± 9.2 79.5 ± 9.7 * LDL-C, mg/dL 103.1 ± 31.8 94.2 ± 31.0 * 102.5 ± 32.3 95.8 ± 32.4 * 103.3 ± 33.3 96.0 ± 32.7 * HbA1c, % 6.92 ± 1.27 6.98 ± 1.35 6.97 ± 1.20 6.90 ± 1.23 * 7.05 ± 1.27 7.00 ± 1.31 † UACR, mg/g, median [IQR] 7.8 [4.0-18.5] 8.3 [4.0–33.0] * 7.0 [3.7–17.5] 7.9 [3.75–25.3] * 7.0 [3.77–18.26] 8.4 [3.9–30.6] * eGFR, mL/min/1.73 m² 71.5 ± 18.3 73.8 ± 18.5 * 70.2 ± 18.3 74.2 ± 17.4 * 71.3 ± 18.9 75.9 ± 17.3 * * p < 0.001, † p < 0.005 Among overweight patients, women had higher HbA1c (6.97 ± 1.2% vs 6.9 ± 1.2% (p < 0.01). In the obese group, women’s mean HbA1c was also higher (7.05 ± 1.27% vs 7.0 ± 1.31%, p < 0.01). Meanwhile, women maintained higher LDL-C levels than men within each BMI stratum (all p < 0.001). Men had an eGFR higher (by ~ 2–4 mL/min/1.73 m², p < 0.001 in each BMI stratum), and UACR also higher in all BMI categories, p < 0.001). (Table 3 ). Sex Differences by Diabetes Duration At < 5 years, women had slightly lower HbA1c levels (6.73 ± 1.17% vs 6.79 ± 1.33%, p = 0.020). This relationship reversed with longer disease: in the 5–14-year-old group, women’s mean HbA1c level was marginally higher than men’s (6.94% vs 6.91%, p = 0.027), and for a duration of ≥ 15 years, the difference widened (7.34 ± 1.28% vs 7.22 ± 1.26%, p < 0.001). (Table 4 ). Table 4 Parameters by diabetes duration and sex Variable Women < 5 yr (n = 3731) Men < 5 yr (n = 4589) Women 5–14 yr (n = 9898) Men 5–14 yr (n = 11863) Women ≥ 15 yr (n = 4982) Men ≥ 15 yr (n = 5148) SBP, mmHg 135.6 ± 16.2 135.9 ± 15.7 137.3 ± 16.3 137.2 ± 15.9 138.9 ± 16.6 138.0 ± 17.1 † DBP, mmHg 78.5 ± 9.2 79.9 ± 9.7 * 77.1 ± 9.1 77.9 ± 9.5 * 74.5 ± 8.9 74.7 ± 9.4 LDL-C, mg/dL 109.9 ± 34.2 104.2 ± 34.2 * 103.1 ± 32.4 95.1 ± 32.0 * 97.6 ± 31.4 89.4 ± 29.9 * HbA1c, % 6.73 ± 1.17‡ 6.79 ± 1.33 6.94 ± 1.23 6.91 ± 1.25 7.34 ± 1.28 7.22 ± 1.26 * UACR, mg/g, median [IQR] 5.8 [3.3–13.2] 6.0 [3.1–17.0] § 7.0 [3.8–16.7] 8.0 [3.8–26.0] * 8.9 [4.3–26.0] 12.0 [4.8–52.4] * eGFR, mL/min/1.73 m² 75.7 ± 17.3 79.4 ± 16.0 * 71.7 ± 18.4 75.2 ± 17.2 * 66.1 ± 18.9 70.3 ± 18.5 * BMI, kg/m² 32.04 ± 6.48 31.25 ± 6.31 * 31.19 ± 6.58 30.36 ± 6.37 * 30.06 ± 7.41 * 29.16 ± 6.97 * * p < 0.001, † p < 0.005, ‡ p < 0.05, § p < 0.051 For patients with < 5 years of diabetes, men had higher DBP (79.9 ± 9.7mmHg vs 78.5 ± 9.2 mmHg, p < 0.001). In the 5–14-year diabetes duration group, men continued to have higher DBP (77.9 ± 9.5 vs 77.1 ± 9.1mmHg, p < 0.001). Among those with ≥ 15 years of diabetes, women had a higher SBP (138.9 ± 16.6 mmHg vs 138.0 ± 17.1 mmHg, p = 0.004). The average BMI difference was ~ 0.8–0.9 kg/m² in favor of women in each duration category (p < 0.001 for each). Similarly, women’s LDL-C levels exceeded men’s at all durations (by ~ 6–8 mg/dL, all p < 0.001). Men had significantly higher eGFR and albuminuria than women in all strata. Predictors of HbA1c: Regression and Interaction Analyses We performed univariate and multivariate linear regression analyses to further examine the factors associated with HbA1c (Supplemental Table 2). In the multivariable linear regression model (including sex, age, diabetes duration, SBP, DBP, and BMI simultaneously), all predictors except DBP remained independently associated with HbA1c. Female sex was independently associated with HbA1c (β =+0.075, 95% CI + 0.050 - +0.100, p < 0.001). (Table 5 ). These six variables together explained a relatively small fraction of the variance in HbA1c levels (model R²≈0.04), suggesting that other unmeasured factors contribute to glycemic control. Figure 1 summarizes the predictors of HbA1c in a forest plot. Table 5 Predictors of HbA1c-linear regression Predictor Univariate β (95% CI) Multivariate β (95% CI) P-value Female sex (ref = male) + 0.051 (0.026–0.076) + 0.075 (0.050–0.100) < 0.001 Age (per yr) -0.0056 (–0.007 to − 0.004) -0.0124 (–0.014 to − 0.011) < 0.001 Diabetes duration (per yr) + 0.033 (0.031–0.035) + 0.040 (0.038–0.042) < 0.001 SBP (per mmHg) + 0.00285 (0.002–0.004) + 0.00274 (0.002–0.004) < 0.001 DBP (per mmHg) + 0.00319 (0.002–0.004) + 0.00082 (–0.001 to 0.002) 0.32 BMI (per kg/m²) + 0.00578 (0.004–0.008) + 0.00550 (0.004–0.007) < 0.001 Finally, we assessed interaction effects and performed ANCOVA to explore whether the influence of sex on HbA1c depended on age or other factors. A significant sex–age interaction was observed in relation to HbA1c levels (p ≤ 0.001), consistent with the stratified results showing that the sex gap in HbA1c is minimal at younger ages and enlarges in older populations. In the ANCOVA model adjusted for age, diabetes duration, SBP, DBP, and BMI, the main effect of sex on HbA1c remained significant (F > 25, p < 0.001). The sex-by-age interaction term (in the subsequent OLS model) was also significant, indicating that the slope of HbA1c vs age differed by sex. Similarly, an interaction between sex and diabetes duration was evident (p < 0.001), reflecting the reversal of sex differences in HbA1c with longer disease duration described above. Meeting Treatment Goals Only 17.9% of patients achieved the triple goal. Notably, men were more likely to meet all three goals than women (19.5% vs 16.2%, p < 0.001). Equalizing women’s attainment to men’s would translate into ≈ 614 additional women meeting the composite target (absolute difference 3.3 percentage points × 18,611 women). Robust Poisson modelling (adjusted for age, diabetes duration, and BMI) showed that, compared with women, men were 3% more likely to achieve an HbA1c level of < 7% (aPR 1.03, 95% CI 1.01–1.04), 22% more likely to reach an LDL-C level of < 100 mg/dL (aPR 1.22, 1.20–1.24), 4% less likely to have a BP of < 140/90 mmHg (aPR 0.96, 0.95–0.98), and 21% more likely to attain the composite triple goal (aPR 1.21, 1.16–1.26). (Table 6 ). Table 6 Achievement of key treatment targets by sex Treatment goal Women Men Absolute Δ (W–M) P-value HbA1c < 7% 59.5% 60.5% -1.0 pp < 0.001 LDL-C < 100 mg/dL 47.1% 55.1% -8.0 pp < 0.001 BP < 140/90 mmHg 60.6% 62.4% -1.8 pp 0.07 Composite triple goal 16.2% 19.5% -3.3 pp < 0.001 Among non-achievers, average excess over goal was ~ + 1.14 percentage points for HbA1c (no sex difference; p = 0.52), + 26.1 vs + 28.4 mg/dL for LDL-C in men vs women (Δ=+2.3 mg/dL; p < 0.001), ~+11 mmHg for SBP with a negligible sex difference (Δ=+0.3 mmHg; p = 0.04), and ~ + 4 mmHg for DBP (p = 0.053). Discussion In a large, contemporary primary-care cohort of more than forty thousand adults with type 2 diabetes, women showed slightly worse glycemic control, higher BMI, and consistently higher LDL-C than men, while BP differences were small and age dependent. Kidney profiles diverged by sex: men had more albuminuria, whereas women more often had reduced eGFR. Only about one in five patients achieved the triple target (HbA1c < 7%, LDL-C < 100 mg/dL, BP < 140/90 mmHg), and men were more likely than women to meet all three goals. Beyond confirming known gaps, our analysis highlights actionable patterns: a stable shortfall in LDL-C target attainment among women, and a growing female disadvantage in HbA1c with older age and longer duration. These findings support sex-aware implementation in primary care—emphasizing lipid management across all women and prioritizing older women and those with long-standing diabetes for glycemic optimization—to advance equity and cardiovascular risk reduction. Our findings align with Spanish data showing lower attainment of HbA1c and LDL-C goals in women ( 13 , 14 ) and with reports from other settings ( 15 , 18 , 19 ). Recent Canadian primary care data ( 20 ) likewise found fewer women meeting all three targets (HbA1c, BP, LDL-C) simultaneously. In our cohort, the absolute difference in triple-goal attainment was 3.3 percentage points, equivalent to approximately nineteen thousand additional women per one hundred thousand patients not achieving targets compared with men. Although some studies report not sex differences in HbA1c ( 21 , 22 ), the direction of the lipid gap—women less likely to reach LDL-C goals—has been remarkably consistent across populations ( 13 – 15 , 18 – 22 ). Elevated LDL-C levels in women with T2D significantly contributes to their disproportionately higher relative risk of cardiovascular disease. Meta-analyses have confirmed that diabetes confers ~ 40–50% greater relative risk of coronary heart disease in women than in men ( 23 , 24 ). Although mean sex differences were modest in our population, a ~ 7 mg/dL higher LDL-C in women corresponds to ~ 4% higher relative risk of cardiovascular disease (per 38.7 mg/dL ≈ 19% risk reduction) ( 25 ). Systolic BP gaps by sex were negligible overall; however, in those aged ≥ 80 years, even 1–2 mmHg differences could have aggregate impact given the stroke reductions seen per 5–10 mmHg SBP decrease; supporting careful BP optimization in very old women ( 26 ). Biological, behavioral, and system factors likely contribute. Women generally develop type 2 diabetes at an older age and at higher levels of adiposity ( 27 ). This greater obesity contributes to insulin resistance and complicates subsequent metabolic control. In our study, women had higher BMI across all strata of age, diabetes duration, and BMI category, supporting this observation. Differences in treatment intensity and persistence probably play a role. Evidence suggests less timely therapy intensification and lower use of high-intensity statins ( 27 , 28 ) or newer cardiometabolic-agents in women ( 29 ), though not all studies agree ( 30 ). The greater difficulty in reaching the targets can be related to sex physiopathological difference. On this issue, a study documented that statin therapy after an acute myocardial infarction is associated with reduced rates of all-cause and cardiac mortality, but the degree of risk reduction is lower for women than for men ( 31 ). In contrast, a recent network meta-analyses show no clinically meaningful sex differences in the efficacy of SGLT2 inhibitors, GLP-1 receptor agonists, or DPP4 inhibitors for glycemic or cardiovascular outcomes ( 32 ). Sex-differences in adverse-event reporting (statin myalgias and gastrointestinal side effects with metformin use) may reduce adherence ( 33 , 34 ). The kidney phenotype also differs by sex- earlier and more frequent albuminuria in men and lower eGFR in women ( 30 ), consistent with our results. In addition, psychosocial factors—particularly depression and diabetes-related distress—are more prevalent in women ( 35 ) and are associated with poorer glycemic control ( 36 ), which could contribute to the observed disparities. Notably, the small R² (~ 0.04) in our HbA1c model underscores that unmeasured driver—medication class/intensity, adherence, smoking, kidney disease severity, socioeconomic and psychosocial factors—likely mediate much of the variance. Evidence on whether intensive multifactorial control fully offsets the excess cardiovascular risk associated with diabetes is mixed ( 37 , 38 ). More recent data ( 39 ) suggest that stringent control of risk factors (HbA1c < 7%, BP < 140/90, LDL-c < 100 mg/dl, non-current smoking and absence of microalbuminuria) is associated with lower incident cardiovascular disease, with a stronger association in women, reinforcing the need to raise target attainment in both sexes, especially in women. Strengths include the very large real-world primary-care cohort and the use of high-quality, contemporaneous data, supporting broad external validity, and stratified/interaction analyses across age, BMI, and duration, with robust Poisson models for target attainment. Limitations include the cross-sectional design, residual confounding, and lack of granular medication data (class, dosing, intensity, adherence, and prior cardiovascular disease). We used routinely collected data from 2014–2018, a window chosen a priori for high completeness and stable measurement workflows and to avoid pandemic-related disruptions; thus, our estimates provide a robust pre-COVID benchmark for sex differences under standard care. Generalizability beyond our largely European-ancestry region may be limited, although congruent findings in other countries support broader relevance. Conclusions In this large primary-care cohort, women were less likely than men to achieve LDL-C and composite multifactorial targets, and their disadvantage in glycemic control increased with older age and longer diabetes duration. These findings support sex-aware quality-improvement approaches in routine care, including systematic lipid-lowering optimization for women, age- and duration-tailored glycemic intensification for higher-risk women, and focused kidney and blood pressure monitoring where each sex shows the greatest unmet need. Embedding sex-stratified audit metrics in primary care may help close treatment gaps and reduce downstream cardiovascular and kidney complications. Abbreviations • aPR adjusted prevalence ratio • ANCOVA analysis of covariance • BMI body mass index • BP blood pressure • CI confidence interval • CKD chronic kidney disease • CKD-EPI Chronic Kidney Disease Epidemiology Collaboration equation • CVD cardiovascular disease • DBP diastolic blood pressure • DPP-4 dipeptidyl peptidase-4 • EHR electronic health record • eGFR estimated glomerular filtration rate • ESH/ESC European Society of Hypertension / European Society of Cardiology • GLP-1 RA glucagon-like peptide-1 receptor agonist • HbA1c glycated hemoglobin • ICPC-2 International Classification of Primary Care, Second Edition • IDF International Diabetes Federation • IQR interquartile range • LDL-C low-density lipoprotein cholesterol • OMI-ECAP Asturias primary care electronic health record system • OLS ordinary least squares • PROMETEA Project to Improve Care for Patients with Chronic Conditions in Primary Care Clinics in Asturias • SAMFYC Asturian Society of Family and Community Medicine • SD standard desviation • SESPA Principality of Asturias Health Service • SGLT2 sodium-glucose cotransporter 2 • T2D type 2 diabetes (mellitus) • UACR urine albumin-to-creatinine ratio • UKPDS UK Prospective Diabetes Study Declarations Ethics approval and consent to participate The study used anonymized EHR data from routine clinical practice. No direct patient contact occurred, and individual informed consent was not required. The study protocol was approved by the Research Ethics Committee of the Principality of Asturias (Estudio-cod CEImPA 2023.499). Consent for publication Not applicable. Availability of data and materials The data are available from the corresponding author upon reasonable request . Competing interests The authors declare that they have no competing interests. Funding This work was supported in part by the Ministerio de Ciencia e Innovación/Agencia Estatal de Investigación (MCIN/AEI/10.13039/501100011033) under Grant PID2020-115401GB-I00. Authors´ Contributions: Ch.R-L. and N.V. acquired the data, conceptualization, interpretation of data, wrote the first draft of the manuscript and reviewed it. J.E., I.B., and D.G. data curation and statistical analysis, reviewed and edited the manuscript. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-8462163\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":570238080,\"identity\":\"888224d0-441e-41bc-9186-1c123ac5f7ae\",\"order_by\":0,\"name\":\"Chema Fernández- Rodríguez Lacín\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Instituto de Investigación Sanitaria del Principado de Asturias\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Chema\",\"middleName\":\"Fernández- 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06:43:01\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":91075,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eMultivariable linear regression for HbA1c predictors (forest plot).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8462163/v1/f5f13240b9b660bf847071ae.png\"},{\"id\":100356184,\"identity\":\"85ad5af6-7dd6-4562-80d9-7155558e06b6\",\"added_by\":\"auto\",\"created_at\":\"2026-01-16 06:56:01\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1117235,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8462163/v1/f0e8c8f1-258e-4931-97b6-230caa7bb655.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Sex Differences in Multifactorial Target Attainment in Type 2 Diabetes: A Primary Care Cohort of 40,211 Adults (PROMETEA)\",\"fulltext\":[{\"header\":\"Background\",\"content\":\"\\u003cp\\u003eType 2 diabetes is a major global health problem, affecting 537\\u0026nbsp;million adults worldwide in 2021; these estimates are projected to rise to 783\\u0026nbsp;million by 2045 (\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e). In Spain, the prevalence estimates vary: 7.8% according to the National Health Survey, and 13.8% in the population-based di@bet.es study, which also reported that 43.5% of cases were undiagnosed previously (\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e). These figures highlight the high burden of type 2 diabetes.\\u003c/p\\u003e \\u003cp\\u003eType 2 diabetes is associated with several major complications, including cardiovascular disease and chronic kidney disease (CKD), which account for much of its excess morbidity and mortality (\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e). Landmark studies such as the UK Prospective Diabetes Study established that multifactorial control, including glycemia, BP, and LDL-cholesterol (LDL-C), substantially reduces complication risk (\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e). Nonetheless, real-world studies consistently show that only a minority of patients achieve the recommended targets (\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eSex-related differences play a key role in the development, management, and outcomes of type 2 diabetes (\\u003cspan additionalcitationids=\\\"CR9 CR10\\\" citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e). Men generally develop diabetes at a younger age and lower body mass index (BMI), whereas women often present later, with greater adiposity and a higher burden of risk factors. Once diabetes develops, women lose their premenopausal cardiovascular protection and face a disproportionately higher relative risk of developing cardiovascular disease than men (\\u003cspan additionalcitationids=\\\"CR11\\\" citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e). Several studies in Spain and other countries have reported that women with type 2 diabetes are less likely than men to achieve glycemic and lipid targets (\\u003cspan additionalcitationids=\\\"CR14\\\" citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e). However, most analyses have been limited in scope, and comprehensive assessments across multiple domains of metabolic control\\u0026mdash;including glycemia, lipids, BP, obesity, and renal function\\u0026mdash;are scarce in large contemporary primary-care cohorts.\\u003c/p\\u003e \\u003cp\\u003eSpain\\u0026acute;s universal healthcare system, which with fully digitalized electronic health records (EHR) and near-universal primary-care registration, offers a unique opportunity to address this gap and generate high-quality real-world data with broad external validity. In Asturias, the regional diabetes registry encompasses all patients with a coded diagnosis of type 2 diabetes, enabling population-based studies of unprecedented scope.\\u003c/p\\u003e \\u003cp\\u003eIn this study, we analyzed the cases of \\u0026gt;\\u0026thinsp;40,000 adults with type 2 diabetes from the Asturias primary care registry. Our objective was to quantify sex differences in key metabolic parameters (HbA1c, LDL-C, BP, BMI, estimated glomerular filtration rate (eGFR), and albuminuria) and in the attainment of individual targets and a triple composite, and to assess modification by age, BMI, and diabetes duration.\\u003c/p\\u003e\"},{\"header\":\"Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStudy Design and Setting\\u003c/h2\\u003e \\u003cp\\u003e The study was conducted within the framework of the PROMETEA project (Spanish acronym for \\u0026ldquo;Project to Improve Care for Patients with Chronic Conditions in Primary Care Clinics in Asturias\\u0026rdquo;), an initiative launched in 2015 by the Asturian Society of Family and Community Medicine (SAMFYC). PROMETEA focuses on the care of patients with chronic conditions attended in primary care centers belonging to the Principality of Asturias Health Service (SESPA), aiming to describe current practice in real-world family medicine and to identify opportunities for quality improvement. The project is structured into four disease-specific subprojects\\u0026mdash;coronary heart disease, chronic obstructive pulmonary disease (COPD), prostate cancer, and, for the present analysis, type 2 diabetes mellitus (T2D). All PROMETEA research protocols were approved in 2015 by the Clinical Research Ethics Committee of the Principality of Asturias and is formally endorsed by the Directorate-General for Planning of the Regional Ministry of Health of the Principality of Asturias, as well as by the management of SESPA.\\u003c/p\\u003e \\u003cp\\u003e Cross-sectional analysis of anonymized electronic health records (EHRs) from the Asturias regional diabetes registry (~\\u0026thinsp;1M inhabitants). Primary care acts as gatekeeper; their electronic records (OMI-ECAP system) records demographics, clinical data, labs, and diagnoses for all patients with longitudinal coverage. The registry used in this study represents real world data with high external validity, encompassing all individuals with type 2 diabetes followed in the public health system.\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eStudy Population\\u003c/h3\\u003e\\n\\u003cp\\u003eAdults with a diagnosis of type 2 diabetes recorded in OMI-ECAP between January 2014 and December 2018. Type 2 diabetes was defined according to the International Classification of Primary Care, Second Edition (ICPC-2) code T90 (non\\u0026ndash;insulin-dependent diabetes mellitus). Patients with ICPC-2 code T89 (insulin-dependent diabetes mellitus) were excluded. We identified 88,800 unique patients with type 2 diabetes. After applying the data-cleaning procedures described below, the final analytic sample comprised 40,211 unique patients (21,600 men and 18,611 women).\\u003c/p\\u003e\\n\\u003ch3\\u003eData Sources and Structure\\u003c/h3\\u003e\\n\\u003cp\\u003eData were drawn from three main EHR tables:\\u003c/p\\u003e \\u003cp\\u003e \\u003cul\\u003e \\u003cli\\u003e \\u003cp\\u003eLast EHR: general demographic and clinical information (age, sex, BMI, BP, smoking and alcohol status, and comorbidities).\\u003c/p\\u003e \\u003c/li\\u003e \\u003cli\\u003e \\u003cp\\u003eFive-year EHR: laboratory data (HbA1c, LDL-C, eGFR, microalbuminuria), BP, and BMI measured between 2014 and 2018.\\u003c/p\\u003e \\u003c/li\\u003e \\u003cli\\u003e \\u003cp\\u003eEpisodes: ICPC-2 diagnoses and dates (for diabetes duration)\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/ul\\u003e \\u003c/p\\u003e \\u003cp\\u003eRecords were merged by a unique patient ID; for analyses we retained the latest available values per patient.\\u003c/p\\u003e\\n\\u003ch3\\u003eData Cleaning and Standardization\\u003c/h3\\u003e\\n\\u003cp\\u003eThe dataset underwent rigorous curation to ensure accuracy and consistency: Categorical fields were harmonized; numeric entries standardized and out-of-range values set to missing. Ages/durations recalculated with 1 Jan 2019 as reference; earliest diabetes date used to derive duration.\\u003c/p\\u003e \\u003cp\\u003eAfter integration, the dataset included 294,000 patient-year records corresponding to 88,800 unique patients. For each patient, only the latest available values were retained. Exclusion of patients with type 1 diabetes and incomplete or invalid records yielded the final study cohort of 40,211 patients with type 2 diabetes.\\u003c/p\\u003e\\n\\u003ch3\\u003eVariables and Outcomes\\u003c/h3\\u003e\\n\\u003cp\\u003eThe primary biomarkers were HbA1c (%), LDL-C (mg/dL), systolic (SBP) and diastolic BP (DBP) (mmHg), BMI (kg/m\\u0026sup2;), eGFR (mL/min/1.73 m\\u0026sup2;, by CKD-EPI equation), and urine albumin-to-creatinine ratio (UACR) (mg/g). Target attainment was defined: HbA1c\\u0026thinsp;\\u0026lt;\\u0026thinsp;7%, LDL-C\\u0026thinsp;\\u0026lt;\\u0026thinsp;100 mg/dL, BP\\u0026thinsp;\\u0026lt;\\u0026thinsp;140/90 mmHg (\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e), and a triple composite.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStatistical Analysis\\u003c/h2\\u003e \\u003cp\\u003eThe baseline characteristics were summarized as means\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;standard deviations (SD), medians (interquartile ranges), or percentages. Sex differences were tested using student\\u0026rsquo;s t-test, Mann\\u0026ndash;Whitney U test, or χ\\u0026sup2; test as appropriate. Analyses were stratified by age (\\u0026lt;\\u0026thinsp;60 years, 60\\u0026ndash;79 years, \\u0026ge;\\u0026thinsp;80 years), BMI (\\u0026lt;\\u0026thinsp;25 kg/m\\u0026sup2;, 25\\u0026ndash;29.9 kg/m\\u0026sup2;, \\u0026ge;\\u0026thinsp;30 kg/m\\u0026sup2;), and diabetes duration (\\u0026lt;\\u0026thinsp;5 years, 5\\u0026ndash;14 years, \\u0026ge;\\u0026thinsp;15 years).\\u003c/p\\u003e \\u003cp\\u003eHbA1c predictors were assessed using univariate and multivariable linear regression models adjusted for sex, age, diabetes duration, SBP/DBP, and BMI. Interaction terms (sex \\u0026times; age, sex \\u0026times; duration) were tested. Robust Poisson regression models were fitted to estimate the adjusted prevalence ratios (aPRs) for each target, as well as the composite target, adjusting for age, duration, and BMI. All analyses were conducted using Python version 3.13, using packages Pandas 2.3.0, Numpy 2.3, SciPy 1.15.3 and Statsmodels 0.14.4. Two-sided p-values of \\u0026lt;\\u0026thinsp;0.05 were considered statistically significant.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStudy Population\\u003c/h2\\u003e \\u003cp\\u003eWe included 40,211 patients: women 46.3% (n\\u0026thinsp;=\\u0026thinsp;18,611). Women were older than men (73.9\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;11.7 years vs 70.2\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;11.3 years) and had slightly longer diabetes duration (11.0\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;6.3 years vs 10.6\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;6.4 years; both p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e).\\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\\u003eBaseline characteristics of the overall cohort, with comparison by sex (women vs men).\\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=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" 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\\u003eOverall (n\\u0026thinsp;=\\u0026thinsp;40,211)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eWomen (n\\u0026thinsp;=\\u0026thinsp;18,611)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eMen (n\\u0026thinsp;=\\u0026thinsp;21,600)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eP-value\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAge, years\\u003c/p\\u003e \\u003cp\\u003e(mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;SD)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e71.9\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;11.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e73.9\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;11.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e70.2\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;11.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDiabetes duration, years\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e10.8\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;6.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e11.0\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;6.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e10.6\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;6.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\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\\u003e30.6\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;6.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e31.1\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;6.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e30.3\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;6.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSBP, mmHg\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e137.2\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;16.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e137.4\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;16.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e137.1\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;16.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.052\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDBP, mmHg\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e77.1\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;9.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e76.7\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;9.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e77.5\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;9.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLDL-C, mg/dL\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e99.1\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;32.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e103.0\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;32.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e95.7\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;32.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\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\\u003e6.98\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.27\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e7.01\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.25\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e6.96\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.28\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eUACR, mg/g,\\u003c/p\\u003e \\u003cp\\u003eMedian [IQR]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e7.6 [3.8\\u0026ndash;22.8]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e7.0 [3.8\\u0026ndash;18.0]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e8.0 [3.9\\u0026ndash;28.6]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eeGFR, mL/min/1.73 m\\u0026sup2;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e73.1\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;18.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e71.0\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;18.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e74.9\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;17.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAlbuminuria\\u003c/p\\u003e \\u003cp\\u003e\\u0026ge;\\u0026thinsp;30 mg/g,%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e21.2%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e17.3%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e24.5%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eeGFR\\u0026thinsp;\\u0026lt;\\u0026thinsp;60 mL/min/1.73 m\\u0026sup2;,%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e20.81%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e24.9%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e17.3%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAlbuminuria\\u0026thinsp;\\u0026ge;\\u0026thinsp;30\\u003c/p\\u003e \\u003cp\\u003eand eGFR\\u0026thinsp;\\u0026lt;\\u0026thinsp;60 mL/min/1.73 m\\u0026sup2;,%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e7.73%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e7.4%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e8.0%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.03\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eOverall Sex Differences in Metabolic Control\\u003c/h2\\u003e \\u003cp\\u003eIn unadjusted comparisons of key metabolic parameters, women showed slightly higher mean HbA1c levels (7.01\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.25% vs 6.96\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.28%, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), higher mean LDL-C levels (103.0\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;32.8 mg/dL vs 95.7\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;32.4 mg/dL, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) and BMI (31.1\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;6.9 kg/m\\u003csup\\u003e2\\u003c/sup\\u003e vs 30.3\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;6.6 kg/m\\u003csup\\u003e2\\u003c/sup\\u003e, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) than men. Men had a slightly higher DBP than women (77.5\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;9.7 mmHg vs 76.7\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;9.2 mmHg, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), higher mean eGFR (74.9\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;17.5 mL/min/1.73 m\\u0026sup2; vs 71.0\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;18.6 mL/min/1.73 m\\u0026sup2;, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) and a higher median UACR than women (8 [IQR 3.9\\u0026ndash;28.6] mg/g vs 7 [3.8\\u0026ndash;18.0] mg/g, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). Consistent with this, overt albuminuria (UACR\\u0026thinsp;\\u0026ge;\\u0026thinsp;30 mg/g) was more frequent in men (24.5% vs 17.3%, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), whereas reduced renal function (eGFR\\u0026thinsp;\\u0026lt;\\u0026thinsp;60 mL/min/1.73 m\\u0026sup2;) was more prevalent in women (24.9% vs 17.3%, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eAge-Stratified Sex Differences\\u003c/h2\\u003e \\u003cp\\u003eWomen aged 60\\u0026ndash;79 years had higher mean HbA1c levels than men (6.97% vs 6.89%, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001); a similar trend was observed in those aged\\u0026thinsp;\\u0026ge;\\u0026thinsp;80 years (7.01% vs 6.93%, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). Women consistently had higher LDL-C levels than men across all ages (\\u0026thinsp;~\\u0026thinsp;+\\u0026thinsp;8\\u0026ndash;9 mg/dL, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001 for each). Women also had a higher BMI in each age category (on average\\u0026thinsp;+\\u0026thinsp;1 kg/m\\u003csup\\u003e2\\u003c/sup\\u003e vs men, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001 for each comparison). (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\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\\u003eClinical and biochemical parameters by age and sex\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"7\\\"\\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 \\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\\u003e\\u0026lt;\\u0026thinsp;60\\u0026nbsp;year Women (n\\u0026thinsp;=\\u0026thinsp;2285)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;60\\u0026nbsp;year Men (n\\u0026thinsp;=\\u0026thinsp;3919)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e60\\u0026ndash;79\\u0026nbsp;year Women (n\\u0026thinsp;=\\u0026thinsp;9879)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e60\\u0026ndash;79\\u0026nbsp;year Men (n\\u0026thinsp;=\\u0026thinsp;13099)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026ge;\\u0026thinsp;80\\u0026nbsp;year Women (n\\u0026thinsp;=\\u0026thinsp;6447)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026ge;\\u0026thinsp;80\\u0026nbsp;year Men (n\\u0026thinsp;=\\u0026thinsp;4582)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSBP, mmHg\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e130.0\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;16.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e133.5\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;15.1\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e137.5\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;15.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e137.9\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;15.9\\u003csup\\u003e\\u0026dagger;\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e140.0\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;16.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e137.6\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;17.3\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDBP, mmHg\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e80.2\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;9.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e82.3\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;9.5\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e77.6\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;8.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e77.8\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;9.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e74.0\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;8.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e72.8\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;9.0\\u003csup\\u003e*\\u003c/sup\\u003e\\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\\u003e32.8\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;7.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e31.6\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;7.1\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e31.7\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;6.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e30.4\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;6.2\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e29.5\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;6.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e28.7\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;6.7\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLDL-C, mg/dL\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e112.6\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;34.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e104.7\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;34.3\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e102.8\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;32.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e94.6\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;32.1\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e99.9\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;31.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e91.0\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;29.9\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e \\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\\u003e7.17\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.60\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e7.21\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.55\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e6.97\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.19\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e6.89\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.22\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e7.01\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.19\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e6.93\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.17\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eUACR, mg/g,\\u003c/p\\u003e \\u003cp\\u003emedian [IQR]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5.9\\u003c/p\\u003e \\u003cp\\u003e[3.1\\u0026ndash;3.2]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6.0\\u003c/p\\u003e \\u003cp\\u003e[3.0\\u0026ndash;17.0]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e6.0\\u003c/p\\u003e \\u003cp\\u003e[3.4\\u0026ndash;13.3]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e7.7\\u003c/p\\u003e \\u003cp\\u003e[3.7\\u0026ndash;26.0] \\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e10.3\\u003c/p\\u003e \\u003cp\\u003e[4.9\\u0026ndash;29.5]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e14.0\\u003c/p\\u003e \\u003cp\\u003e[5.3\\u0026ndash;53.9] \\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eeGFR, mL/min/1.73 m\\u0026sup2;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e86.4\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;14.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e86.1\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;14.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e74.9\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;16.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e76.1\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;15.9\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e59.4\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;16.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e62.0\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;16.7\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"7\\\"\\u003e* p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001, \\u0026dagger; p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eBP patterns crossed with age. In patients aged\\u0026thinsp;\\u0026lt;\\u0026thinsp;60 years, men had higher SBP and DBP (SBP 133.5\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;15.1 mmHg vs 130.0\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;16.2 mmHg; DBP 82.3\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;9.5 mmHg vs 80.2\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;9.7 mmHg; both p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). In the 60\\u0026ndash;79-year-old group, only SBP remained higher in men (137.9 mmHg vs 137.5 mmHg, p\\u0026thinsp;=\\u0026thinsp;0.011). Among patients aged\\u0026thinsp;\\u0026ge;\\u0026thinsp;80 years, women had higher SBP (140.0\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;16.9 mmHg vs 137.6\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;17.3 mmHg, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), and DBP (74.0\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;8.8 mmHg vs 72.8\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;9.0 mmHg, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eWomen aged 60\\u0026ndash;79 years had lower eGFR (74.9\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;16.1 mL/min/1.73 m\\u0026sup2; vs 76.1\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;15.9 mL/min/1.73 m\\u0026sup2;, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). In the \\u0026ge;\\u0026thinsp;80-year-old group, the women\\u0026rsquo;s eGFR averaged was 59.4\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;16.8 mL/min/1.73 m\\u0026sup2; vs 62.0\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;16.7 mL/min/1.73 m\\u0026sup2; in men (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). The median UACR was higher in men than women in both the 60\\u0026ndash;79-year-old group (median 7.7 mg/g vs 6 mg/g, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) and \\u0026ge;\\u0026thinsp;80-year-old group (median 14.0 mg/g vs 10.3 mg/g, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eBMI-Stratified Sex Differences\\u003c/h2\\u003e \\u003cp\\u003eIn normal-weight patients, women had slightly higher SBP (134.7\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;17.5 mmHg vs 133.3\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;17.0 mmHg, p\\u0026thinsp;=\\u0026thinsp;0.002). Regarding DBP, in overweight and obese men was higher (76.6\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;9.3 mmHg vs 75.5\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;8.8 mmHg, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) (79.5\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;9.7 mmHg vs 78.3\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;9.2 mmHg, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\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\\u003eParameters by BMI category and sex\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"7\\\"\\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 \\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\\u003eWomen\\u0026thinsp;\\u0026lt;\\u0026thinsp;25 kg/m\\u003csup\\u003e2\\u003c/sup\\u003e\\u003c/p\\u003e \\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;2739)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eMen\\u003c/p\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;25 kg/m\\u003csup\\u003e2\\u003c/sup\\u003e\\u003c/p\\u003e \\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;2643)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eWomen\\u003c/p\\u003e \\u003cp\\u003e25\\u0026ndash;29.9 kg/m\\u003csup\\u003e2\\u003c/sup\\u003e\\u003c/p\\u003e \\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;6200)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eMen\\u003c/p\\u003e \\u003cp\\u003e25\\u0026ndash;29.9 kg/m\\u003csup\\u003e2\\u003c/sup\\u003e\\u003c/p\\u003e \\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;8919)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eWomen\\u003c/p\\u003e \\u003cp\\u003e\\u0026ge;\\u0026thinsp;30 kg/m\\u003csup\\u003e2\\u003c/sup\\u003e\\u003c/p\\u003e \\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;9672)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eMen\\u003c/p\\u003e \\u003cp\\u003e\\u0026ge;\\u0026thinsp;30 kg/m\\u003csup\\u003e2\\u003c/sup\\u003e\\u003c/p\\u003e \\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;10038)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSBP, mmHg\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e134.7\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;17.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e133.3\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;17.0\\u003csup\\u003e\\u0026dagger;\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e137.1\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;16.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e136.6\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;16.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e138.4\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;16.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e138.5\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;15.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDBP, mmHg\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e73.5\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;8.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e73.4\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;9.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e75.5\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;8.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e76.6\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;9.3\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e78.3\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;9.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e79.5\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;9.7\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLDL-C, mg/dL\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e103.1\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;31.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e94.2\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;31.0\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e102.5\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;32.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e95.8\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;32.4\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e103.3\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;33.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e96.0\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;32.7\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e \\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\\u003e6.92\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.27\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6.98\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.35\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e6.97\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.20\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e6.90\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.23\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e7.05\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.27\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e7.00\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.31\\u003csup\\u003e\\u0026dagger;\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eUACR, mg/g,\\u003c/p\\u003e \\u003cp\\u003emedian [IQR]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e7.8\\u003c/p\\u003e \\u003cp\\u003e[4.0-18.5]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e8.3\\u003c/p\\u003e \\u003cp\\u003e[4.0\\u0026ndash;33.0] \\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e7.0\\u003c/p\\u003e \\u003cp\\u003e[3.7\\u0026ndash;17.5]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e7.9\\u003c/p\\u003e \\u003cp\\u003e[3.75\\u0026ndash;25.3] \\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e7.0\\u003c/p\\u003e \\u003cp\\u003e[3.77\\u0026ndash;18.26]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e8.4\\u003c/p\\u003e \\u003cp\\u003e[3.9\\u0026ndash;30.6] \\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eeGFR, mL/min/1.73 m\\u0026sup2;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e71.5\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;18.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e73.8\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;18.5\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e70.2\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;18.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e74.2\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;17.4\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e71.3\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;18.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e75.9\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;17.3\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"7\\\"\\u003e* p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001, \\u0026dagger; p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.005\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eAmong overweight patients, women had higher HbA1c (6.97\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.2% vs 6.9\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.2% (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01). In the obese group, women\\u0026rsquo;s mean HbA1c was also higher (7.05\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.27% vs 7.0\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.31%, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01).\\u003c/p\\u003e \\u003cp\\u003eMeanwhile, women maintained higher LDL-C levels than men within each BMI stratum (all p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001).\\u003c/p\\u003e \\u003cp\\u003eMen had an eGFR higher (by ~\\u0026thinsp;2\\u0026ndash;4 mL/min/1.73 m\\u0026sup2;, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001 in each BMI stratum), and UACR also higher in all BMI categories, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eSex Differences by Diabetes Duration\\u003c/h2\\u003e \\u003cp\\u003eAt \\u0026lt;\\u0026thinsp;5 years, women had slightly lower HbA1c levels (6.73\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.17% vs 6.79\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.33%, p\\u0026thinsp;=\\u0026thinsp;0.020). This relationship reversed with longer disease: in the 5\\u0026ndash;14-year-old group, women\\u0026rsquo;s mean HbA1c level was marginally higher than men\\u0026rsquo;s (6.94% vs 6.91%, p\\u0026thinsp;=\\u0026thinsp;0.027), and for a duration of \\u0026ge;\\u0026thinsp;15 years, the difference widened (7.34\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.28% vs 7.22\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.26%, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\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\\u003eParameters by diabetes duration and sex\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"7\\\"\\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 \\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\\u003eWomen\\u003c/p\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;5 yr\\u003c/p\\u003e \\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;3731)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eMen\\u003c/p\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;5 yr\\u003c/p\\u003e \\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;4589)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eWomen\\u003c/p\\u003e \\u003cp\\u003e5\\u0026ndash;14 yr\\u003c/p\\u003e \\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;9898)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eMen\\u003c/p\\u003e \\u003cp\\u003e5\\u0026ndash;14 yr\\u003c/p\\u003e \\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;11863)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eWomen\\u003c/p\\u003e \\u003cp\\u003e\\u0026ge;\\u0026thinsp;15 yr\\u003c/p\\u003e \\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;4982)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eMen\\u003c/p\\u003e \\u003cp\\u003e\\u0026ge;\\u0026thinsp;15 yr\\u003c/p\\u003e \\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;5148)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSBP, mmHg\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e135.6\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;16.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e135.9\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;15.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e137.3\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;16.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e137.2\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;15.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e138.9\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;16.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e138.0\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;17.1\\u003csup\\u003e\\u0026dagger;\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDBP, mmHg\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e78.5\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;9.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e79.9\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;9.7\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e77.1\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;9.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e77.9\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;9.5\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e74.5\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;8.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e74.7\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;9.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLDL-C, mg/dL\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e109.9\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;34.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e104.2\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;34.2\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e103.1\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;32.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e95.1\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;32.0\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e97.6\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;31.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e89.4\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;29.9\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e \\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\\u003e6.73\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.17\\u0026Dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6.79\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.33\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e6.94\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.23\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e6.91\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.25\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e7.34\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.28\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e7.22\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.26\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eUACR, mg/g,\\u003c/p\\u003e \\u003cp\\u003emedian [IQR]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5.8\\u003c/p\\u003e \\u003cp\\u003e[3.3\\u0026ndash;13.2]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6.0\\u003c/p\\u003e \\u003cp\\u003e[3.1\\u0026ndash;17.0] \\u003csup\\u003e\\u0026sect;\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e7.0\\u003c/p\\u003e \\u003cp\\u003e[3.8\\u0026ndash;16.7]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e8.0\\u003c/p\\u003e \\u003cp\\u003e[3.8\\u0026ndash;26.0] \\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e8.9\\u003c/p\\u003e \\u003cp\\u003e[4.3\\u0026ndash;26.0]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e12.0\\u003c/p\\u003e \\u003cp\\u003e[4.8\\u0026ndash;52.4] \\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eeGFR,\\u003c/p\\u003e \\u003cp\\u003emL/min/1.73 m\\u0026sup2;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e75.7\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;17.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e79.4\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;16.0\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e71.7\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;18.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e75.2\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;17.2\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e66.1\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;18.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e70.3\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;18.5\\u003csup\\u003e*\\u003c/sup\\u003e\\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\\u003e32.04\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;6.48\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e31.25\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;6.31\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e31.19\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;6.58\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e30.36\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;6.37\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e30.06\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;7.41\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e29.16\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;6.97\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"7\\\"\\u003e* p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001, \\u0026dagger; p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.005, \\u0026Dagger; p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05, \\u0026sect; p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.051\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eFor patients with \\u0026lt;\\u0026thinsp;5 years of diabetes, men had higher DBP (79.9\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;9.7mmHg vs 78.5\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;9.2 mmHg, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). In the 5\\u0026ndash;14-year diabetes duration group, men continued to have higher DBP (77.9\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;9.5 vs 77.1\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;9.1mmHg, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). Among those with \\u0026ge;\\u0026thinsp;15 years of diabetes, women had a higher SBP (138.9\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;16.6 mmHg vs 138.0\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;17.1 mmHg, p\\u0026thinsp;=\\u0026thinsp;0.004).\\u003c/p\\u003e \\u003cp\\u003eThe average BMI difference was ~\\u0026thinsp;0.8\\u0026ndash;0.9 kg/m\\u0026sup2; in favor of women in each duration category (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001 for each). Similarly, women\\u0026rsquo;s LDL-C levels exceeded men\\u0026rsquo;s at all durations (by ~\\u0026thinsp;6\\u0026ndash;8 mg/dL, all p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001).\\u003c/p\\u003e \\u003cp\\u003eMen had significantly higher eGFR and albuminuria than women in all strata.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003ePredictors of HbA1c: Regression and Interaction Analyses\\u003c/h2\\u003e \\u003cp\\u003eWe performed univariate and multivariate linear regression analyses to further examine the factors associated with HbA1c (Supplemental Table\\u0026nbsp;2). In the multivariable linear regression model (including sex, age, diabetes duration, SBP, DBP, and BMI simultaneously), all predictors except DBP remained independently associated with HbA1c. Female sex was independently associated with HbA1c (β =+0.075, 95% CI\\u0026thinsp;+\\u0026thinsp;0.050 - +0.100, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e). These six variables together explained a relatively small fraction of the variance in HbA1c levels (model R\\u0026sup2;\\u0026asymp;0.04), suggesting that other unmeasured factors contribute to glycemic control. Figure\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e summarizes the predictors of HbA1c in a forest plot.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab5\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 5\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003ePredictors of HbA1c-linear regression\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"4\\\"\\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=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\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\\u003eUnivariate β (95% CI)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eMultivariate β (95% CI)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eP-value\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFemale sex (ref\\u0026thinsp;=\\u0026thinsp;male)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e+\\u0026thinsp;0.051 (0.026\\u0026ndash;0.076)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e+\\u0026thinsp;0.075 (0.050\\u0026ndash;0.100)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAge (per yr)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-0.0056 (\\u0026ndash;0.007 to \\u0026minus;\\u0026thinsp;0.004)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-0.0124\\u003c/p\\u003e \\u003cp\\u003e(\\u0026ndash;0.014 to \\u0026minus;\\u0026thinsp;0.011)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDiabetes duration\\u003c/p\\u003e \\u003cp\\u003e(per yr)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e+\\u0026thinsp;0.033 (0.031\\u0026ndash;0.035)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e+\\u0026thinsp;0.040 (0.038\\u0026ndash;0.042)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSBP (per mmHg)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e+\\u0026thinsp;0.00285 (0.002\\u0026ndash;0.004)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e+\\u0026thinsp;0.00274 (0.002\\u0026ndash;0.004)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDBP (per mmHg)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e+\\u0026thinsp;0.00319 (0.002\\u0026ndash;0.004)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e+\\u0026thinsp;0.00082\\u003c/p\\u003e \\u003cp\\u003e(\\u0026ndash;0.001 to 0.002)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.32\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBMI (per kg/m\\u0026sup2;)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e+\\u0026thinsp;0.00578 (0.004\\u0026ndash;0.008)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e+\\u0026thinsp;0.00550 (0.004\\u0026ndash;0.007)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eFinally, we assessed interaction effects and performed ANCOVA to explore whether the influence of sex on HbA1c depended on age or other factors. A significant sex\\u0026ndash;age interaction was observed in relation to HbA1c levels (p\\u0026thinsp;\\u0026le;\\u0026thinsp;0.001), consistent with the stratified results showing that the sex gap in HbA1c is minimal at younger ages and enlarges in older populations. In the ANCOVA model adjusted for age, diabetes duration, SBP, DBP, and BMI, the main effect of sex on HbA1c remained significant (F\\u0026thinsp;\\u0026gt;\\u0026thinsp;25, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). The sex-by-age interaction term (in the subsequent OLS model) was also significant, indicating that the slope of HbA1c vs age differed by sex. Similarly, an interaction between sex and diabetes duration was evident (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), reflecting the reversal of sex differences in HbA1c with longer disease duration described above.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec16\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eMeeting Treatment Goals\\u003c/h2\\u003e \\u003cp\\u003eOnly 17.9% of patients achieved the triple goal. Notably, men were more likely to meet all three goals than women (19.5% vs 16.2%, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). Equalizing women\\u0026rsquo;s attainment to men\\u0026rsquo;s would translate into \\u0026asymp;\\u0026thinsp;614 additional women meeting the composite target (absolute difference 3.3 percentage points \\u0026times; 18,611 women).\\u003c/p\\u003e \\u003cp\\u003eRobust Poisson modelling (adjusted for age, diabetes duration, and BMI) showed that, compared with women, men were 3% more likely to achieve an HbA1c level of \\u0026lt;\\u0026thinsp;7% (aPR 1.03, 95% CI 1.01\\u0026ndash;1.04), 22% more likely to reach an LDL-C level of \\u0026lt;\\u0026thinsp;100 mg/dL (aPR 1.22, 1.20\\u0026ndash;1.24), 4% less likely to have a BP of \\u0026lt;\\u0026thinsp;140/90 mmHg (aPR 0.96, 0.95\\u0026ndash;0.98), and 21% more likely to attain the composite triple goal (aPR 1.21, 1.16\\u0026ndash;1.26). (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab6\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 6\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eAchievement of key treatment targets by sex\\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=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" 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=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTreatment goal\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eWomen\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eMen\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eAbsolute Δ (W\\u0026ndash;M)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eP-value\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHbA1c\\u0026thinsp;\\u0026lt;\\u0026thinsp;7%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e59.5%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e60.5%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-1.0 pp\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLDL-C\\u0026thinsp;\\u0026lt;\\u0026thinsp;100 mg/dL\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e47.1%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e55.1%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-8.0 pp\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBP\\u0026thinsp;\\u0026lt;\\u0026thinsp;140/90 mmHg\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e60.6%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e62.4%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-1.8 pp\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.07\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eComposite triple goal\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e16.2%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e19.5%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-3.3 pp\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eAmong non-achievers, average excess over goal was ~\\u0026thinsp;+\\u0026thinsp;1.14 percentage points for HbA1c (no sex difference; p\\u0026thinsp;=\\u0026thinsp;0.52), +\\u0026thinsp;26.1 vs\\u0026thinsp;+\\u0026thinsp;28.4 mg/dL for LDL-C in men vs women (Δ=+2.3 mg/dL; p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), ~+11 mmHg for SBP with a negligible sex difference (Δ=+0.3 mmHg; p\\u0026thinsp;=\\u0026thinsp;0.04), and ~\\u0026thinsp;+\\u0026thinsp;4 mmHg for DBP (p\\u0026thinsp;=\\u0026thinsp;0.053).\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eIn a large, contemporary primary-care cohort of more than forty thousand adults with type 2 diabetes, women showed slightly worse glycemic control, higher BMI, and consistently higher LDL-C than men, while BP differences were small and age dependent. Kidney profiles diverged by sex: men had more albuminuria, whereas women more often had reduced eGFR. Only about one in five patients achieved the triple target (HbA1c\\u0026thinsp;\\u0026lt;\\u0026thinsp;7%, LDL-C\\u0026thinsp;\\u0026lt;\\u0026thinsp;100 mg/dL, BP\\u0026thinsp;\\u0026lt;\\u0026thinsp;140/90 mmHg), and men were more likely than women to meet all three goals.\\u003c/p\\u003e \\u003cp\\u003eBeyond confirming known gaps, our analysis highlights actionable patterns: a stable shortfall in LDL-C target attainment among women, and a growing female disadvantage in HbA1c with older age and longer duration. These findings support sex-aware implementation in primary care\\u0026mdash;emphasizing lipid management across all women and prioritizing older women and those with long-standing diabetes for glycemic optimization\\u0026mdash;to advance equity and cardiovascular risk reduction.\\u003c/p\\u003e \\u003cp\\u003eOur findings align with Spanish data showing lower attainment of HbA1c and LDL-C goals in women (\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e) and with reports from other settings (\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e). Recent Canadian primary care data (\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e) likewise found fewer women meeting all three targets (HbA1c, BP, LDL-C) simultaneously. In our cohort, the absolute difference in triple-goal attainment was 3.3 percentage points, equivalent to approximately nineteen thousand additional women per one hundred thousand patients not achieving targets compared with men. Although some studies report not sex differences in HbA1c (\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e), the direction of the lipid gap\\u0026mdash;women less likely to reach LDL-C goals\\u0026mdash;has been remarkably consistent across populations (\\u003cspan additionalcitationids=\\\"CR14\\\" citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e, \\u003cspan additionalcitationids=\\\"CR19 CR20 CR21\\\" citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e). Elevated LDL-C levels in women with T2D significantly contributes to their disproportionately higher relative risk of cardiovascular disease. Meta-analyses have confirmed that diabetes confers\\u0026thinsp;~\\u0026thinsp;40\\u0026ndash;50% greater relative risk of coronary heart disease in women than in men (\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e). Although mean sex differences were modest in our population, a\\u0026thinsp;~\\u0026thinsp;7 mg/dL higher LDL-C in women corresponds to ~\\u0026thinsp;4% higher relative risk of cardiovascular disease (per 38.7 mg/dL\\u0026thinsp;\\u0026asymp;\\u0026thinsp;19% risk reduction) (\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e). Systolic BP gaps by sex were negligible overall; however, in those aged\\u0026thinsp;\\u0026ge;\\u0026thinsp;80 years, even 1\\u0026ndash;2 mmHg differences could have aggregate impact given the stroke reductions seen per 5\\u0026ndash;10 mmHg SBP decrease; supporting careful BP optimization in very old women (\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eBiological, behavioral, and system factors likely contribute. Women generally develop type 2 diabetes at an older age and at higher levels of adiposity (\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e). This greater obesity contributes to insulin resistance and complicates subsequent metabolic control. In our study, women had higher BMI across all strata of age, diabetes duration, and BMI category, supporting this observation.\\u003c/p\\u003e \\u003cp\\u003eDifferences in treatment intensity and persistence probably play a role. Evidence suggests less timely therapy intensification and lower use of high-intensity statins (\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e) or newer cardiometabolic-agents in women (\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e), though not all studies agree (\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e). The greater difficulty in reaching the targets can be related to sex physiopathological difference. On this issue, a study documented that statin therapy after an acute myocardial infarction is associated with reduced rates of all-cause and cardiac mortality, but the degree of risk reduction is lower for women than for men (\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e). In contrast, a recent network meta-analyses show no clinically meaningful sex differences in the efficacy of SGLT2 inhibitors, GLP-1 receptor agonists, or DPP4 inhibitors for glycemic or cardiovascular outcomes (\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e). Sex-differences in adverse-event reporting (statin myalgias and gastrointestinal side effects with metformin use) may reduce adherence (\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e). The kidney phenotype also differs by sex- earlier and more frequent albuminuria in men and lower eGFR in women (\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e), consistent with our results. In addition, psychosocial factors\\u0026mdash;particularly depression and diabetes-related distress\\u0026mdash;are more prevalent in women (\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e) and are associated with poorer glycemic control (\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e), which could contribute to the observed disparities. Notably, the small R\\u0026sup2; (~\\u0026thinsp;0.04) in our HbA1c model underscores that unmeasured driver\\u0026mdash;medication class/intensity, adherence, smoking, kidney disease severity, socioeconomic and psychosocial factors\\u0026mdash;likely mediate much of the variance.\\u003c/p\\u003e \\u003cp\\u003eEvidence on whether intensive multifactorial control fully offsets the excess cardiovascular risk associated with diabetes is mixed (\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e). More recent data (\\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e) suggest that stringent control of risk factors (HbA1c\\u0026thinsp;\\u0026lt;\\u0026thinsp;7%, BP\\u0026thinsp;\\u0026lt;\\u0026thinsp;140/90, LDL-c\\u0026thinsp;\\u0026lt;\\u0026thinsp;100 mg/dl, non-current smoking and absence of microalbuminuria) is associated with lower incident cardiovascular disease, with a stronger association in women, reinforcing the need to raise target attainment in both sexes, especially in women.\\u003c/p\\u003e \\u003cp\\u003eStrengths include the very large real-world primary-care cohort and the use of high-quality, contemporaneous data, supporting broad external validity, and stratified/interaction analyses across age, BMI, and duration, with robust Poisson models for target attainment. Limitations include the cross-sectional design, residual confounding, and lack of granular medication data (class, dosing, intensity, adherence, and prior cardiovascular disease). We used routinely collected data from 2014\\u0026ndash;2018, a window chosen a priori for high completeness and stable measurement workflows and to avoid pandemic-related disruptions; thus, our estimates provide a robust pre-COVID benchmark for sex differences under standard care. Generalizability beyond our largely European-ancestry region may be limited, although congruent findings in other countries support broader relevance.\\u003c/p\\u003e\"},{\"header\":\"Conclusions\",\"content\":\"\\u003cp\\u003eIn this large primary-care cohort, women were less likely than men to achieve LDL-C and composite multifactorial targets, and their disadvantage in glycemic control increased with older age and longer diabetes duration. These findings support sex-aware quality-improvement approaches in routine care, including systematic lipid-lowering optimization for women, age- and duration-tailored glycemic intensification for higher-risk women, and focused kidney and blood pressure monitoring where each sex shows the greatest unmet need. Embedding sex-stratified audit metrics in primary care may help close treatment gaps and reduce downstream cardiovascular and kidney complications.\\u003c/p\\u003e\"},{\"header\":\"Abbreviations\",\"content\":\"\\u003cdiv class=\\\"DefinitionList\\\"\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003e\\u0026bull; aPR\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eadjusted prevalence ratio\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003e\\u0026bull; ANCOVA\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eanalysis of covariance\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003e\\u0026bull; BMI\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003ebody mass index\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003e\\u0026bull; BP\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eblood pressure\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003e\\u0026bull; CI\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003econfidence interval\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003e\\u0026bull; CKD\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003echronic kidney disease\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003e\\u0026bull; CKD-EPI\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eChronic Kidney Disease Epidemiology Collaboration equation\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003e\\u0026bull; CVD\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003ecardiovascular disease\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003e\\u0026bull; DBP\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003ediastolic blood pressure\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003e\\u0026bull; DPP-4\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003edipeptidyl peptidase-4\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003e\\u0026bull; EHR\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eelectronic health record\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003e\\u0026bull; eGFR\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eestimated glomerular filtration rate\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003e\\u0026bull; ESH/ESC\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eEuropean Society of Hypertension / European Society of Cardiology\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003e\\u0026bull; GLP-1 RA\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eglucagon-like peptide-1 receptor agonist\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003e\\u0026bull; HbA1c\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eglycated hemoglobin\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003e\\u0026bull; ICPC-2\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eInternational Classification of Primary Care, Second Edition\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003e\\u0026bull; IDF\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eInternational Diabetes Federation\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003e\\u0026bull; IQR\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003einterquartile range\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003e\\u0026bull; LDL-C\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003elow-density lipoprotein cholesterol\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003e\\u0026bull; OMI-ECAP\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eAsturias primary care electronic health record system\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003e\\u0026bull; OLS\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eordinary least squares\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003e\\u0026bull; PROMETEA\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eProject to Improve Care for Patients with Chronic Conditions in Primary Care Clinics in Asturias\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003e\\u0026bull; SAMFYC\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eAsturian Society of Family and Community Medicine\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003e\\u0026bull; SD\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003estandard desviation\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003e\\u0026bull; SESPA\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003ePrincipality of Asturias Health Service\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003e\\u0026bull; SGLT2\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003esodium-glucose cotransporter 2\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003e\\u0026bull; T2D\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003etype 2 diabetes (mellitus)\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003e\\u0026bull; UACR\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eurine albumin-to-creatinine ratio\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003e\\u0026bull; UKPDS\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eUK Prospective Diabetes Study\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003c/div\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cem\\u003eEthics approval and consent to participate\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe study used anonymized EHR data from routine clinical practice. No direct patient contact occurred, and individual informed consent was not required. The study protocol was approved by the Research Ethics Committee of the Principality of Asturias (Estudio-cod CEImPA 2023.499).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eConsent for publication\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eAvailability of data and materials\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe data are available from the corresponding author upon reasonable request\\u003cem\\u003e.\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eCompeting interests\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare that they have no competing interests.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eFunding\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis work was supported in part by the Ministerio de Ciencia e Innovaci\\u0026oacute;n/Agencia Estatal de Investigaci\\u0026oacute;n (MCIN/AEI/10.13039/501100011033) under Grant PID2020-115401GB-I00.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eAuthors\\u0026acute; Contributions:\\u0026nbsp;\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eCh.R-L. and N.V. acquired the data, conceptualization, interpretation of data, wrote the first draft of the manuscript and reviewed it. J.E., I.B., and D.G. data curation and statistical analysis, reviewed and edited the manuscript. E.FS. and M.Ch. contributed to analysis and interpretation of data, discussion, reviewed and edited the manuscript. All authors approved the final version of the manuscript.\\u003c/p\\u003e\\n\\u003ch4\\u003eAcknowledgements\\u003c/h4\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eSun H, Saeedi P, Karuranga S, Pinkepank M, Ogurtsova K, Duncan BB, et alIDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res Clin Pract. 2022; 183:109-119. https://doi.org/10.1016/j.diabres.2021.109119 \\u003c/li\\u003e\\n\\u003cli\\u003eMinisterio de Sanidad. Encuesta Nacional de Salud de Espa\\u0026ntilde;a 2017. Madrid: Ministerio de Sanidad; 2018. https://www.sanidad.gob.es/estadEstudios/estadisticas/encuestaNacional/encuestaNac2017/ENSE2017_notatecnica.pdf.\\u003c/li\\u003e\\n\\u003cli\\u003eSoriguer F, Goday A, Bosch-Comas A, Bordi\\u0026uacute; E, Calle-Pascual A, Carmena R, et al. Prevalence of diabetes mellitus and impaired glucose regulation in Spain: the Di@bet.es Study. Diabetologia.2012; 55(1):88\\u0026ndash;93. https://doi.org/10.1007/s00125-011-2336-9 \\u003c/li\\u003e\\n\\u003cli\\u003eShah AD, Langenberg C, Rapsomaniki E, Denaxas S, Pujades-Rodriguez M, Gale CP, et al. Type 2 diabetes and incidence of cardiovascular diseases: a cohort study in 1.9 million people. Lancet Diabetes Endocrino. 2015; 3(2):105\\u0026ndash;113. https://doi.org/10.1016/S2213-8587(14)70219-0\\u003c/li\\u003e\\n\\u003cli\\u003eUK Prospective Diabetes Study (UKPDS) Group. Effect of intensive blood-glucose control with metformin on complications in overweight patients with type 2 diabetes. Lancet. 1998; 352(9131):854\\u0026ndash;865. https://doi.org/10.1016/S0140-6736(05)60425-4 \\u003c/li\\u003e\\n\\u003cli\\u003eGaede P, Lund-Andersen H, Parving HH, Pedersen O. Effect of a multifactorial intervention on mortality in type 2 diabetes. N Engl J Med. 2008; 358:580\\u0026ndash;591. https://doi.org/10.1056/NEJMoa0706245 \\u003c/li\\u003e\\n\\u003cli\\u003eFang M, Wang D, Coresh J, Selvin E.Trends in diabetes treatment and control in U.S. adults, 1999\\u0026ndash;2018. N Engl J Med. 2021;384(23):2219\\u0026ndash;2228. https://doi.org/10.1056/NEJMsa2032271 \\u003c/li\\u003e\\n\\u003cli\\u003eKautzky-Willer A, Harreiter J, Pacini G. Sex and gender differences in risk, pathophysiology and complications of type 2 diabetes mellitus. Endocr Rev. 2016; 37(3):278\\u0026ndash;316. https://doi.org/10.1210/er.2015-1137\\u003c/li\\u003e\\n\\u003cli\\u003eTramunt B, Smati S, Grandgeorge N, Lenfant F, Arnal JF, Montagner A, Gourdy P. Sex differences in metabolic regulation and diabetes susceptibility. Diabetologia. 2020; 63(3):453\\u0026ndash;461. https://doi.org/10.1007/s00125-019-05040-3 \\u003c/li\\u003e\\n\\u003cli\\u003ede Jong M, Woodward M, Peters SAE. Diabetes, glycated hemoglobin, and the risk of myocardial infarction in women and men: a prospective cohort study of the UK Biobank. Diabetes Care. 2020; 43:2050\\u0026ndash;2059. https://doi.org/10.2337/dc19-2363\\u003c/li\\u003e\\n\\u003cli\\u003eKautzky-Willer A, Leutner M, Harreiter J.Sex differences in type 2 diabetes. \\u003cem\\u003eDiabetologia\\u003c/em\\u003e. 2023; 66(6):986\\u0026ndash;1002. https://doi.org/10.1007/s00125-023-05891-x\\u003c/li\\u003e\\n\\u003cli\\u003eGnatiuc L, Herrington WG, Halsey J, et al. Sex-specific relevance of diabetes to occlusive vascular and other mortality: a collaborative meta-analysis of individual data from 980,793 adults from 68 prospective studies. \\u003cem\\u003eLancet Diabetes Endocrinol\\u003c/em\\u003e\\u003cem\\u003e. \\u003c/em\\u003e2018; 6(7):538\\u0026ndash;546. https://doi.org/10.1016/S2213-8587(18)30079-2 \\u003c/li\\u003e\\n\\u003cli\\u003eCambra K, Galbete A, Forga L, Lecea O, Ariz MJ, Moreno-Iribas C, Aizpuru F, Iba\\u0026ntilde;ez B. Sex and age differences in the achievement of control targets in patients with type 2 diabetes: results from a population-based study in a South European region. \\u003cem\\u003eBMC Fam Pract. 2016;\\u003c/em\\u003e 17:144. https://doi.org/10.1186/s12875-016-0533-9 \\u003c/li\\u003e\\n\\u003cli\\u003eRam\\u0026iacute;rez-Morros A, Franch-Nadal J, Real J, Gratac\\u0026ograve;s M, Mauricio D. Sex differences in cardiovascular prevention in type 2: diabetes in a real-world practice database. \\u003cem\\u003eJ Clin Med.2022;\\u003c/em\\u003e11(8):2196. https://doi.org/10.3390/jcm11082196 \\u003c/li\\u003e\\n\\u003cli\\u003ede Jong M, Oskam MJ, Sep SJS, Ozcan B, Rutters F, Sijbrands EJG, et al. Sex differences in cardiometabolic risk factors, pharmacological treatment, and risk factor control in type 2 diabetes: findings from the Dutch Diabetes Pearl cohort. BMJ Open Diabetes Res Care. 2020; 8(1):e001365. https://doi.org/10.1136/bmjdrc-2020-001365\\u003c/li\\u003e\\n\\u003cli\\u003eAmerican Diabetes Association; Standards of Medical Care in Diabetes\\u0026mdash;2014. Diabetes Care 1 January 2014; 37 (Supplement_1): S14\\u0026ndash;S80. https://doi.org/10.2337/dc14-S014 \\u003c/li\\u003e\\n\\u003cli\\u003eESH/ESC Task Force for the Management of Arterial Hypertension. 2013 Practice guidelines for the management of arterial hypertension of the European Society of Hypertension (ESH) and the European Society of Cardiology (ESC): ESH/ESC Task Force for the Management of Arterial Hypertension. J Hypertens. 2013 Oct;31(10):1925-38. https://doi.org/10.1093/eurheartj/eht151 \\u003c/li\\u003e\\n\\u003cli\\u003eWexler DJ, Grant RW, Meigs JB, Nathan DM, Cagliero E. Sex disparities in treatment of cardiac risk factors in patients with type 2 diabetes. Diabetes Care. 2005 Mar;28(3):514-20 https://doi.org/10.2337/diacare.28.3.514 \\u003c/li\\u003e\\n\\u003cli\\u003eKautzky-Willer A, Kamyar MR, Gerhat D, Handisurya A, Stemer G, Hudson S, Luger A, Lemmens-Gruber R. Sex-specific differences in metabolic control, cardiovascular risk, and interventions in patients with type 2 diabetes mellitus. Gend Med. 2010 Dec;7(6):571-83 https://doi.org/10.1016/j.genm.2010.12.001 \\u003c/li\\u003e\\n\\u003cli\\u003eNandiwada S, Manca DP, Yeung RO, Lau D. Achievement of treatment targets among patients with type 2 diabetes in 2015 and 2020 in Canadian primary care. CMAJ.2023; 195(1): E1\\u0026ndash;E9. https://doi.org/10.1503/cmaj.220673 \\u003c/li\\u003e\\n\\u003cli\\u003eFranch-Nadal J, Mata-Cases M, Vinagre I, Patitucci F, Hermosilla E, Casellas A, Bolivar B, Mauricio D. Differences in the cardiometabolic control in type 2 diabetes according to gender and the presence of cardiovascular disease: results from the eControl Study. Int J Endocrinol. 2014;131709. https://doi.org/10.1155/2014/131709 \\u003c/li\\u003e\\n\\u003cli\\u003eGouni-Berthold I, Berthold HK, Mantzoros CS, et al. Sex disparities in the treatment and control of cardiovascular risk factors in type 2 diabetes. Diabetes Care. 2008; 31:1389\\u0026ndash;1391. https://doi.org/10.2337/dc08-0194\\u003c/li\\u003e\\n\\u003cli\\u003ePeters SAE, Huxley RR, Woodward M. Diabetes as risk factor for incident coronary heart disease in women compared with men: a systematic review and meta-analysis of 64 cohorts including 858,507 individuals and 28,203 coronary events. Diabetologia. 2014;57:1542\\u0026ndash;1551. https://doi.org/10.1007/s00125-014-3260-6 \\u003c/li\\u003e\\n\\u003cli\\u003eMalmborg M, Schmiegelow MDS, N\\u0026oslash;rgaard CH, et al. Does type 2 diabetes confer higher relative rates of cardiovascular events in women compared with men? Eur Heart J.2020; 41(13):1346\\u0026ndash;1353. https://doi.org/10.1093/eurheartj/ehz913 \\u003c/li\\u003e\\n\\u003cli\\u003eWang N, Fulcher J, Abeysuriya N, Park L, Kumar S, Di Tanna GL, et al. Intensive LDL cholesterol-lowering treatment beyond current recommendations for the prevention of major vascular events: a systematic review and meta-analysis of randomised trials including 327,037 participants. Lancet Diabetes Endocrinol. 2020; 8(1):36\\u0026ndash;49. https://doi.org/10.1016/S2213-8587(19)30388-2 \\u003c/li\\u003e\\n\\u003cli\\u003eBlood Pressure Lowering Treatment Trialists\\u0026rsquo; Collaboration. Age-stratified and blood-pressure-stratified effects of blood-pressure-lowering pharmacotherapy for the prevention of cardiovascular disease and death: an individual participant-level data meta-analysis. Lancet. 2021; 398(10305):1053\\u0026ndash;1064. https://doi.org/10.1016/S0140-6736(21)01921-8 \\u003c/li\\u003e\\n\\u003cli\\u003eKautzky-Willer A, Leutner M, Harreiter J. Sex differences in type 2 diabetes. Diabetologia. 2023; 66(6):986\\u0026ndash;1002. https://doi.org/10.1007/s00125-023-05891-x \\u003c/li\\u003e\\n\\u003cli\\u003eFerrannini G, De Bacquer D, De Backer G, et al.Gender differences in screening and risk factor management in patients with dysglycaemia and coronary heart disease: results from the EUROASPIRE IV and V surveys. Cardiovasc Diabetol. 2021; 20(1):96. https://doi.org/10.1186/s12933-021-01233-6 \\u003c/li\\u003e\\n\\u003cli\\u003eFunck KL, Bjerg L, Isaksen AA, Sandb\\u0026aelig;k A, Grove EL. Gender disparities in time-to-initiation of cardioprotective glucose-lowering drugs in patients with type 2 diabetes and cardiovascular disease: a Danish nationwide cohort study. Cardiovasc Diabetol. 2022; 21(1):279. doi: 10.1186/s12933-022-01713-3. \\u003c/li\\u003e\\n\\u003cli\\u003eRossi MC, Cristofaro MR, Gentile S, Lucisano G, Manicardi V, Mulas MF, et al. Sex disparities in the quality of diabetes care: biological and cultural factors may play a different role for different outcomes: a cross-sectional observational study from the AMD Annals initiative. Diabetes Care. 2013; 36(10):3162\\u0026ndash;3168. https://doi.org/10.2337/dc13-0184\\u003c/li\\u003e\\n\\u003cli\\u003eKarp I, Chen SF, Pilote L. Sex differences in the effectiveness of statins after myocardial infarction. CMAJ. 2007; 176:333\\u0026ndash;338. https://doi.org/10.1503/cmaj.060627 \\u003c/li\\u003e\\n\\u003cli\\u003eHanlon P, Butterly E, Wei L, et al. Age and sex differences in efficacy of treatments for type 2 diabetes: a network meta-analysis. JAMA.2025; 333(12):1062\\u0026ndash;1073. https://doi.org/10.1001/jama.2024.27402\\u003c/li\\u003e\\n\\u003cli\\u003eSkilving I, Eriksson M, Rane A, Ovesj\\u0026ouml; ML. Statin-induced myopathy in a usual care setting: a prospective observational study of gender differences. Eur J Clin Pharmacol.2016; 72(10):1171\\u0026ndash;1176. https://doi.org/10.1007/s00228-016-2105-2 \\u003c/li\\u003e\\n\\u003cli\\u003eFranconi F, Brunelleschi S, Steardo L, Cuomo V. Gender differences in drug responses. Pharmacol Res. 2007; 55(2):81\\u0026ndash;5. https://doi.org/10.1016/j.phrs.2006.11.001 \\u003c/li\\u003e\\n\\u003cli\\u003eAnderson RJ, Freedland KE, Clouse RE, Lustman PJ.The prevalence of comorbid depression in adults with diabetes: a meta-analysis. Diabetes Care. 2001; 24(6):1069\\u0026ndash;1078. https://doi.org/10.2337/diacare.24.6.1069 \\u003c/li\\u003e\\n\\u003cli\\u003eWojujutari Ajele K, Sunday Idemudia E.The role of depression and diabetes distress in glycemic control: a meta-analysis. Diabetes Res Clin Pract. 2025; 221:112014. https://doi.org/10.1016/j.diabres.2025.112014 \\u003c/li\\u003e\\n\\u003cli\\u003eRawshani A, Rawshani A, Franz\\u0026eacute;n S, et al. Risk factors, mortality, and cardiovascular outcomes in patients with type 2 diabetes. N Engl J Med. 2018; 379:633\\u0026ndash;644. https://doi.org/10.1056/NEJMoa1800256 \\u003c/li\\u003e\\n\\u003cli\\u003eWright AK, Suarez-Ortegon MF, Read SH, Kontopantelis E, Buchan I, Emsley R, et al. Risk factor control and cardiovascular event risk in people with type 2 diabetes in primary and secondary prevention settings. Circulation. 2020; 142(20):1925\\u0026ndash;1936. https://doi.org/10.1161/CIRCULATIONAHA.120.046783 \\u003c/li\\u003e\\n\\u003cli\\u003eWang X, Ma H, Li X, Liang Z, Fonseca V, Qi L. Risk factor control and incident cardiovascular disease in patients with diabetes: sex-specific relations. Diabetes Obes Metab. 2024; 26(4):1421\\u0026ndash;1429. https://doi.org/10.1111/dom.15443\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":true,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Type 2 diabetes, Sex difference, Primary Care, Treatment goals, HbA1c target\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-8462163/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-8462163/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003e\\u003cb\\u003eBackground\\u003c/b\\u003e\\u003c/p\\u003e \\u003cp\\u003eTo identify sex-specific, actionable gaps in metabolic control and multifactorial target attainment in adults with type 2 diabetes\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eMethods\\u003c/b\\u003e\\u003c/p\\u003e \\u003cp\\u003eCross-sectional analysis of 40,211 adults with type 2 diabetes from a primary care registry (46,3% women). Outcomes were HbA1c, LDL-cholesterol (LDL-C), systolic/diastolic blood pressure (SBP/DBP), body mass index (BMI), estimated glomerular filtration rate (eGFR), and urine albumin-to-creatinine ratio (UACR). Targets were HbA1c\\u0026thinsp;\\u0026lt;\\u0026thinsp;7%, LDL-C\\u0026thinsp;\\u0026lt;\\u0026thinsp;100 mg/dL, BP\\u0026thinsp;\\u0026lt;\\u0026thinsp;140/90 mmHg, and a composite triple target. Sex differences were assessed with multivariable linear regression (HbA1c) and robust Poisson regression (target attainment), adjusted for age, diabetes duration, and BMI, and including sex-by-age and sex-by-duration interaction terms.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eResults\\u003c/b\\u003e\\u003c/p\\u003e \\u003cp\\u003eWomen were older than men (73.9\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;11.7 vs 70.2\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;11.3 years) and had higher mean LDL-C (103.0\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;32.8 vs 95.7\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;32.4 mg/dL) and BMI (31.1\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;6.9 vs 30.3\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;6.6 kg/m\\u0026sup2;) (all p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). Female sex was independently associated with higher HbA1c (adjusted β\\u0026thinsp;+\\u0026thinsp;0.075, 95% CI\\u0026thinsp;+\\u0026thinsp;0.050 to +\\u0026thinsp;0.100; p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), and the sex gap in HbA1c widened at older ages and longer diabetes duration (both interaction p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). Men more frequently had albuminuria (UACR\\u0026thinsp;\\u0026ge;\\u0026thinsp;30 mg/g: 24.5% vs 17.3%), whereas women more often had reduced renal function (eGFR\\u0026thinsp;\\u0026lt;\\u0026thinsp;60 mL/min/1.73 m\\u0026sup2;: 24.9% vs 17.3%) (both p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). Overall, only 17.9% achieved the triple target (men 19.5% vs women 16.2%, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). Compared with women, men were 3% more likely to achieve the HbA1c target (adjusted prevalence ratio (aPR) 1.03, 95% CI 1.01\\u0026ndash;1.04), 22% more likely to reach the LDL-C target (aPR 1.22, 1.20\\u0026ndash;1.24), and 21% more likely to attain the composite triple target (aPR 1.21, 1.16\\u0026ndash;1.26), but 4% less likely to achieve the BP target (aPR 0.96, 0.95\\u0026ndash;0.98). Equalizing women\\u0026rsquo;s composite attainment to men\\u0026rsquo;s would translate into \\u0026asymp;\\u0026thinsp;614 additional women meeting all three targets.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eConclusions\\u003c/b\\u003e\\u003c/p\\u003e \\u003cp\\u003eIn contemporary primary care,women with type 2 diabetes have consistent shortfalls in LDL-C control and composite multifactorial target attainment, and their glycemic disadvantage increases with age and longer diabetes duration. These actionable patterns support sex-aware implementation strategies\\u0026mdash;prioritizing lipid-lowering optimization in women across ages and tailoring glycemic intensification for older and long-duration women\\u0026mdash;to close treatment gaps and improve risk factor control.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Sex Differences in Multifactorial Target Attainment in Type 2 Diabetes: A Primary Care Cohort of 40,211 Adults (PROMETEA)\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2026-01-07 06:42:57\",\"doi\":\"10.21203/rs.3.rs-8462163/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"0a3dd762-6b37-46e1-8cf2-6a099bdf1dc4\",\"owner\":[],\"postedDate\":\"January 7th, 2026\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-01-08T13:35:32+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2026-01-07 06:42:57\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-8462163\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-8462163\",\"identity\":\"rs-8462163\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}