Exploring the Association Between Hemoglobin Glycation Index and Cognitive Function in Older Adults with Hypertension: A Cross-Sectional Study

Exploring the Association Between Hemoglobin Glycation Index and Cognitive Function in Older Adults with Hypertension: A Cross-Sectional Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Exploring the Association Between Hemoglobin Glycation Index and Cognitive Function in Older Adults with Hypertension: A Cross-Sectional Study Hong Ding, Tingyue Kang, Wenbo Gao, Qi Wang, Shu Liu, Xiaowei Zhang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5736468/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 May, 2025 Read the published version in BMC Geriatrics → Version 1 posted 4 You are reading this latest preprint version Abstract Background The Hemoglobin Glycation Index (HGI) quantifies the difference between the actual and expected values of glycosylated hemoglobin (HbA1c), a marker that has been closely linked to various adverse health outcomes. Nonetheless, a significant gap exists in the current literature concerning the association between HGI and cognitive function. This study aims at testing such association in older adults with hypertension, a topic that has not yet been extensively investigated. Methods A linear regression model between glycated hemoglobin A1c (HbA1c) levels and fasting plasma glucose (FPG) was constructed for the calculation of the HGI. The cross-sectional study focused on evaluating the cognitive function of hypertensive individuals (≥ 60 years old), based on the data from the 2011–2014 National Health and Nutrition Examination Survey (NHANES), by using a series of standardized tests, including the Word List Learning (CERAD-WL) and Delayed Recall (CERAD-DR) tests from the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD), the Animal Fluency Test (AFT), and the Digit Symbol Substitution Test (DSST). Weighted logistic and linear regression models served for evaluating the effect of HGI on hypertensive patients’ cognitive function. Restricted cubic spline (RCS) curves assisted in detecting the underlying nonlinear associations between HGI and cognitive outcomes. Furthermore, subgroup analyses and interaction tests were performed to gain deeper insights into these associations. Results The study included 1023 participants ≥ 60 years old from 2011–2014 NHANES. Higher HGI was accompanied by lower DSST score (P = 0.009). In the fully adjusted model, participants in the highest quartile (Q4) of HGI possessed a lower DSST score (β = 0.01, 95% CI 0.00–0.41) versus the lowest quartile (Q1), and were more likely to exhibit low cognitive function as evaluated by the DSST (OR = 2.21, 95% CI 0.98–5.03). According to the results from RCS analysis, HGI presented a linear relevance to cognitive function scores in older adults with hypertension. No significant statistical interaction was detected between these variables. Conclusion High HGI was an important risk factor leading to reduced cognitive performance in hypertensive patients, ensuring HGI to be used for effectively predicting patients’ cognitive decline. Hypertension Hemoglobin glycation index (HGI) Cognitive function NHANES Cross-sectional study Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Hypertension is a prevalent condition and also a leading risk factor for cardiovascular disease and stroke, especially among the elderly [ 1 ] . According to the China Patient-Centered Evaluative Assessment of Cardiac Events (PEACE) Million Persons Project (2014–2017), approximately 50% of the participants aged 35–75 years could be affected by hypertension, with the prevalence increasing progressively with age [ 2 ] . The brain can be easily affected by hypertension, which is a major cause of the vascular cognitive impairment and late-life dementia [ 3 ] . Apart from aging, hypertension stands out as the most critical risk factor for cerebrovascular pathology leading to cognitive decline [ 4 ] . Cognitive impairment is a significant global contributor to death and disability [ 5 ] . The World Health Organization's 2021 Global Status Report on dementia estimated that there were approximately 55.2 million of dementia cases in 2019, with the number expected to rise to 78 million and 139 million by 2030 and 2050, respectively [ 6 ] . Obviously, midlife hypertension significantly adds the possibility of developing cognitive decline in late life, independent of genetic predisposition to cognitive impairment [ 7 ] . Therefore, understanding the mechanisms linking hypertension to cognitive impairment remains a critical area of research. Although some studies suggest the possible effect of effective blood pressure (BP) management on lowering the risk of cognitive decline, they fail to yield conclusive results [ 8 , 9 ] . Additionally, previous studies have not well elucidated whether specific classes of antihypertensive drugs can offer superior cognitive benefits [ 10 , 11 ] . There is an urgent need for new discoveries and innovative therapeutic targets to safeguard hypertensive patients’ cognitive function. Identifying individuals at risk in early stage also could benefit the retardation or prevention of the progression to dementia. Glycosylated hemoglobin (HbA1c) is widely used in diagnosing and managing diabetes mellitus, providing an estimate of the mean blood glucose levels of an individual over the past three months [ 12 ] . At present, it is the most commonly used surrogate marker for evaluating the effectiveness of glucose-lowering interventions [ 13 ] . However, evidence indicates that HbA1c levels may consistently differ from fasting plasma glucose (FPG) levels, being either higher or lower in certain populations [ 14 ] , affected by various factors such as erythrocyte lifespan difference [ 15 ] , cell membrane glucose transmembrane gradients [ 16 ] , enzyme abnormalities [ 17 ] , and genetic factors [ 18 ] . As a result, HbA1c measurement may not fully capture an individual's blood glucose metabolic status. The hemoglobin glycation index (HGI) is to quantify the variable relationship between HbA1c and plasma glucose levels [ 19 ] . Calculation of HGI followed a linear regression equation based on FPG, referring to the difference between the observed and the predicted HbA1c [ 20 ] . Numerous studies have shown that HGI is a predictor of diabetes-related complications, such as mortality [ 21 , 22 ] , cardiovascular disease [ 23 ] , and microvascular complications [ 24 ] . In particular, a high HGI has been strongly associated with major adverse cardiovascular events in the populations studied [ 25 ] . Previous studies have indicated that HGI can serve as a relatively intuitive indicator of glycemic variability in patients [ 26 ] . However, there is limited research on glycemic variability in patients with cognitive impairment. Investigating the relevance of HGI to the prognosis of individuals developing cognitive impairment assists in understanding the significance of glycemic variability for long-term outcomes from new perspectives. Materials and methods Study population The National Health and Nutrition Examination Survey (NHANES), a population-based study, is conducted by the National Center for Health Statistics (NCHS) using a complex, multistage design. This survey, which releases data in two-year cycles, monitors the nutritional and health status pertaining to noninstitutionalized civilians in the United States. Detailed descriptions of the NHANES design and operations have been previously published. Our study analyzed data from the NHANES cycles spanning 2011 to 2014, conducting cognitive testing on participants ≥ 60 years old, which has been described previously [ 27 ] . Initially, data from 19,931 participants were collected, but 16,299 were excluded due to being younger than 60 years, 698 due to incomplete cognitive impairment data, 1,528 due to incomplete HGI data, and 383 because they were not diagnosed with hypertension. Ultimately, the study included data from 1,023 participants ≥ 60 years old. The selection process for the study sample is illustrated in Fig. 1 . Definition of hypertension Three to four blood pressure measurements were taken following standard procedures. For analysis, the mean of all measurements, excluding the first, was used when multiple readings were available. Hypertension refers to the disease situation with a mean SBP of ≥ 140 mmHg, a mean DBP of ≥ 90 mmHg, and/or the use of prescribed antihypertensive medications or a prior diagnosis by a physician [ 28 ] . HGI calculation HbA1c and FPG values were combined to calculate HGI, thereby estimating the inter-individual difference in the HbA1c level. We determined the predicted HbA1c through a regression equation based on baseline FPG and HbA1c measurements: Predicted HbA1c = 3.412 + 0.416 × FPG (mmol/L), as shown in Fig. 2 . HGI = measured HbA1c − predicted HbA1c [ 20 ] . The study population fell into 4 HGI quartiles: Q1 (-3.29 to -0.35), Q2 (-0.35 to -0.05), Q3 (-0.05 to 0.25), and Q4 (0.25 to 3.69). Cognitive function assessment Participants ≥ 60 years old were administered a cognitive battery comprising four tests in the Mobile Examination Center (MEC): AFT [ 29 ] , DSST [ 30 ] , CERAD-WL test and CERAD-DR test [ 31 ] . The CERAD test includes 3 consecutive learning trials and 1 delayed recall task. In the AFT, testers required participants to name as many animals as they could in one minute to complete the verbal fluency assessment, with the score determined by the total number of animals named. Testers set a cut-off score of less than fourteen for the identification of potential cognitive impairment, as previously established in peer-reviewed research. The DSST, part of the Wechsler Adult Intelligence Scale, is designed to measure cognitive functions (sustained attention, working memory, and information processing speed). Participants were given a set of symbols paired with a corresponding key and asked to accurately draw as many symbols as possible within 120 seconds, with a threshold score of less than 40, as suggested by a prior NCHS report accounting for the "Flynn effect." The CERAD battery is widely used for diagnosing dementia associated with Alzheimer’s disease, evaluating abilities of new learning, recognition memory, and delayed recall. The CERAD-WL test involves 3 consecutive learning trials, requiring participants to recite a list of distinct words and recall as many as possible. The maximum score is 30, with the trials featuring different word orders. After a 8–10 min interval, CERAD-DR test was conducted, requiring participants to recall the 10 words from the previous test. Threshold scores of less than 17 for the CERAD Word Learning and less than 5 for the CERAD Delayed Recall were selected based on existing scientific literature. Selection of covariates We included covariates associated with HGI or cognitive function identified in previous studies, while addressing concerns of collinearity. These covariates included sex (male/female), age (years), race/ethnicity (Mexican American/Other Hispanic/Non-Hispanic White/Non-Hispanic Black/Other race), education level (below high school/high school/above high school), alcohol consumption, BMI (body mass index), diastolic blood pressure (DBP), systolic blood pressure (SBP), antihypertensive medication use, BP classification, smoking status, and diabetes. Trained medical professionals administered the questionnaires and collected all data through standardized interviews, physical examinations, and laboratory tests. Statistical analysis The complex survey design adopted specific sample weights, in accordance with NHANES analytic standards. Experimenters are arranged to collect all data specific to each cycle in a single interview. For the weighted participants, baseline characteristics were presented as means ± SD and % for continuous variables and categorical variables, respectively. HGI was analyzed both as a continuous variable and in quartiles. Weighted linear regression analyses engaged in calculating the β coefficients and 95% CI for the confirmation of the possible associations between HGI and scores on the three cognitive tests. Logistic regression analyses served for calculating the odds ratios (OR) and CIs for the exploration of the possible associations between HGI and low cognitive function. The crude model and Model 1 did not adjust for any covariates. Model 2 involved age adjustment, while Model 3 involved adjustments for sex, age, race/ethnicity, education level, alcohol consumption, poverty-to-income ratio (PIR), BMI, DBP, SBP, antihypertensive medication use, BP classification, and smoking status. Subgroup analyses involved gender, age, BMI, smoking status, and alcohol consumption. Data analysis relied on the R software (version 4.2.2). P < 0.05 reported statistical significance. Results Characteristics of study population Our study included 1023 hypertensive patients in total, with a mean age of 69.9 ± 6.8 years, with 538 female patients (55%) and 485 male patients (45%). Sex, race, alcohol consumption, diabetes status, and DSST scores were obviously different in statistical level in the HGI quartiles (Table 1 ). Table 1 Characteristics of participants stratified by quartile of hemoglobin glycation index Characteristic Overall, N = 1023 1 Q1, N = 256 1 Q2, N = 257 1 Q3, N = 254 1 Q4, N = 256 1 p-value 2 Age (years) 69.9 (6.8) 69.6 (7.2) 70.1 (6.7) 70.2 (6.6) 70.0 (6.5) 0.7 Age, (%) > 0.9 60–69 years 459 (47%) 119 (49%) 109 (47%) 115 (46%) 116 (44%) 70–79 years 310 (30%) 70 (26%) 81 (29%) 80 (32%) 79 (32%) 80 + years 254 (24%) 67 (24%) 67 (24%) 59 (22%) 61 (24%) Sex, (%) 0.021 female 538 (55%) 109 (46%) 136 (55%) 155 (64%) 138 (56%) male 485 (45%) 147 (54%) 121 (45%) 99 (36%) 118 (44%) BMI, (%) 0.015 Underweight (< 18.5) 9 (1.0%) 3 (0.5%) 2 (1.0%) 3 (1.5%) 1 (1.0%) Normal (18.5 to < 25) 232 (22%) 51 (18%) 66 (27%) 61 (23%) 54 (19%) Overweight (25 to < 30) 346 (35%) 93 (32%) 100 (41%) 91 (41%) 62 (23%) Obese (30 or greater) 424 (42%) 107 (50%) 86 (31%) 95 (35%) 136 (57%) Race, (%) < 0.001 Mexican American 82 (3.2%) 24 (3.8%) 20 (2.5%) 17 (2.7%) 21 (4.1%) Other Hispanic 93 (3.5%) 23 (3.1%) 20 (2.5%) 22 (3.2%) 28 (6.0%) Non-Hispanic White 500 (78%) 133 (83%) 155 (86%) 131 (79%) 81 (60%) Non-Hispanic Black 249 (9.0%) 54 (6.7%) 34 (4.4%) 64 (9.1%) 97 (19%) Other/multiracial 99 (6.1%) 22 (3.4%) 28 (4.9%) 20 (6.3%) 29 (12%) Alcohol, (%) 0.010 1–5 drinks/month 481 (46%) 114 (45%) 117 (42%) 121 (46%) 129 (54%) 5–10 drinks/month 36 (4.8%) 8 (4.4%) 9 (4.5%) 13 (8.2%) 6 (1.1%) 10 + drinks/month 157 (21%) 55 (30%) 49 (24%) 30 (17%) 23 (10%) Non-drinker 332 (28%) 73 (21%) 80 (29%) 84 (29%) 95 (34%) Smoke, (%) 0.13 Current smoker 120 (11%) 26 (9.5%) 34 (11%) 26 (8.5%) 34 (16%) Former smoker 391 (42%) 104 (50%) 88 (36%) 103 (45%) 96 (36%) Never smoker 512 (47%) 126 (41%) 135 (52%) 125 (46%) 126 (48%) Education, (%) 0.056 Less Than 9th Grade 109 (6.5%) 24 (4.2%) 17 (5.4%) 25 (6.3%) 43 (12%) 9-11th Grade 159 (12%) 45 (12%) 31 (11%) 44 (12%) 39 (12%) High School Grad/GED 254 (24%) 53 (20%) 66 (25%) 60 (22%) 75 (31%) Some College or AA degree 287 (32%) 74 (36%) 84 (33%) 61 (27%) 68 (34%) College Graduate or above 213 (25%) 60 (28%) 59 (25%) 64 (32%) 30 (11%) Unknown 1 (< 0.1%) 0 (0%) 0 (0%) 0 (0%) 1 (0.1%) PIR 3.04 (1.56) 3.28 (1.50) 3.14 (1.60) 2.96 (1.53) 2.68 (1.53) 0.054 SBP, mmHg 135 (20) 137 (19) 135 (21) 134 (17) 135 (20) 0.4 DBP, mmHg 67 (15) 68 (14) 67 (14) 66 (17) 66 (13) 0.4 Antihypertensive drug, (%) 0.9 Yes 835 (95%) 205 (94%) 196 (94%) 206 (95%) 228 (97%) No 41 (5.0%) 13 (5.4%) 15 (5.9%) 7 (5.0%) 6 (2.9%) Unknown 1 (< 0.1%) 1 (< 0.1%) 0 (0%) 0 (0%) 0 (0%) Hypertension Grade, (%) 0.6 Grade 1 hypertension 31 (2.5%) 11 (3.6%) 9 (3.3%) 5 (1.7%) 6 (1.1%) Grade 2 hypertension 4 (0.4%) 2 (0.2%) 1 (0.4%) 1 (0.8%) 0 (0%) Grade 3 hypertension 1 (< 0.1%) 0 (0%) 0 (0%) 1 (0.1%) 0 (0%) Normal 957 (97%) 239 (96%) 235 (96%) 240 (97%) 243 (99%) Heart failure, (%) 0.3 Yes 93 (9.6%) 24 (8.3%) 15 (6.3%) 25 (10%) 29 (15%) No 929 (90%) 232 (92%) 241 (94%) 229 (90%) 227 (85%) Diabetes, (%) < 0.001 Yes 270 (23%) 64 (19%) 30 (11%) 54 (18%) 122 (56%) No 705 (72%) 185 (78%) 219 (86%) 180 (73%) 121 (41%) CERAD - WL 19.4 (4.6) 19.3 (4.7) 19.8 (4.7) 19.5 (4.4) 18.9 (4.4) 0.4 CERAD - DR 6.18 (2.28) 6.19 (2.23) 6.24 (2.21) 6.32 (2.41) 5.87 (2.26) 0.4 AFT 17.5 (5.7) 17.6 (5.5) 18.2 (5.7) 17.8 (6.0) 16.0 (5.1) 0.060 DSST 50 (17) 51 (16) 52 (18) 50 (17) 43 (15) 0.009 1 Mean (SD); n (unweighted) (%) 2 Wilcoxon rank-sum test for complex survey samples; chi-squared test with Rao & Scott's second-order correction Abbreviation: HGI, hemoglobin glycation index; BMI, body mass index; RIP, poverty–income ratio; DBP, diastolic blood pressure; SBP, systolic blood pressure; CERAD-WL/DR, the Consortium to Establish a Registry for Alzheimer’s Disease Word Learning subtest for assessing learning and memory; AFT, the Animal Fluency Test for verbal fluency; and DSST, the Digit Symbol Substitution Test; Q, quartile Association between the HGI and cognition function Upon the adjustment for confounding factors, a strong correlation was observed between HGI (as a continuous variable) and DSST test scores [β = 0.08 (95% CI: 0.01, 0.69)]. However, no significant correlations were identified between HGI and the CERAD or AFT test scores. Similarly, when analyzing HGI by quartiles, the fourth quartile of HGI showed a strong correlation with DSST test scores [β = 0.01 (95% CI: 0.00, 0.41)], while no significant associations were found with CERAD or AFT test scores (Table 2 ). Table 2 Associations between HGI with CERAD – WL, CERAD -DR, AFT, and DSST Characteristic Model 1 Model 2 Model 3 β (95% CI) β (95% CI) β (95% CI) CERAD – WL HGI (continuous) 0.75(0.41, 1.38) 0.68(0.40, 1.16) 0.73(0.48, 1.11) HGI (categories) Q1 Reference Reference Reference Q2 1.60 (0.52, 4.94) 1.78 (0.59, 5.39) 1.49 (0.47, 4.71) Q3 1.28 (0.43, 3.83) 1.46 (0.54, 3.94) 0.88 (0.22, 3.58) Q4 0.67 (0.24, 1.82) 0.73 (0.28, 1.95) 0.98 (0.47, 2.01) CERAD -DR HGI (continuous) 0.87(0.67, 1.13) 0.83(0.65, 1.06) 0.90(0.72, 1.11) HGI (categories) Q1 Reference Reference Reference Q2 1.06 (0.60, 1.87) 1.11 (0.64, 1.95) 1.28 (0.71, 2.30) Q3 1.15 (0.74, 1.78) 1.23 (0.84, 1.80) 1.01 (0.58, 1.78) Q4 0.73 (0.45, 1.19) 0.77 (0.48, 1.22) 0.98 (0.61, 1.59) AFT HGI (continuous) 0.58(0.16, 2.04) 0.52(0.18, 1.55) 0.82(0.32, 2.10) HGI (categories) Q1 Reference Reference Reference Q2 1.88 (0.59, 5.97) 2.12 (0.73, 6.13) 5.06 (1.14, 22.3) Q3 1.29 (0.25, 6.57) 1.49 (0.32, 6.93) 2.09(0.31, 14.0) Q4 0.21 (0.03, 1.58) 0.23 (0.04, 1.45) 0.67(0.10, 4.45) DSST HGI (continuous) 0.02(0.00, 0.75) 0.01(0.00, 0.21) 0.08(0.01, 0.69) HGI (categories) Q1 Reference Reference Reference Q2 1.52 (0.06, 35.9) 2.55 (0.14, 47.0) 3.14 (0.08, 125) Q3 0.32 (0.01, 10.8) 0.60 (0.03, 10.7) 1.00 (0.04, 26.4) Q4 0.00 (0.00, 0.03) 0.00 (0.00, 0.02) 0.01 (0.00, 0.41) Model 1 was adjusted for none. Model 2 was adjusted for age. Model 3 was adjusted for sex, age, race, education level, PIR, BMI, smoking status, alcohol consumption, SBP, DBP, antihypertensive drug use and BP classification. Abbreviation: HGI, hemoglobin glycation index; CERAD-WL/DR, the Consortium to Establish a Registry for Alzheimer’s Disease Word Learning subtest for assessing learning and memory; AFT, the Animal Fluency Test for verbal fluency and DSST, the Digit Symbol Substitution Test; Q, quartile. Potential nonlinear relationship between HGI and cognition function According to the RCS analysis, there were no significant nonlinear relationships between HGI and the outcome indicators ( P > 0.05). In fully adjusted weighted linear regression models, CREAD, AFT, and DSST test scores showed a roughly linear decline with increasing HGI levels (CREAD-WL: P-nonlinear = 0.7927, CREAD-DR: P-nonlinear = 0.2900, AFT: P-nonlinear = 0.5779, DSST: P-nonlinear = 0.4311) (Fig. 3 ). Association between the HGI and low cognition function Upon the full adjustment for confounding factors, participants in the 4th quartile of HGI more tended to present low cognitive function as measured by the DSST test compared to those in the first quartile ( P = 0.029). No significant associations were found between HGI quartiles and low cognitive function from the CERAD and AFT tests (Fig. 4 ). Subgroup analyses Similarly, HGI presented different degrees of correlation with cognitive impairment in different subgroups, as illustrated in Fig. 5 . Within subgroups under the stratification of sex, age, BMI, alcohol consumption, and smoking status, the HGI presented a closer association with cognitive impairment among females, individuals aged 60–69 and 80+, those with normal or overweight BMI, and those who consumed alcohol. In particular, the association was statistically significant for DSST scores among normal and overweight female participants aged 60 to 80 years who consumed alcohol. According to the interaction tests, sex, age, BMI, alcohol use, or smoking status failed to exert a remarkable impact on the association (P for interaction > 0.05). Discussion Our study is the first one that conducts a large-scale investigation on the HGI-cognitive impairment relationship in a hypertensive population. According to the cross-sectional analysis, increased HGI resulted in a higher risk of cognitive impairment, even after the adjustment for covariates such as sex, age, race, education level, PIR, BMI, smoking status, alcohol consumption, SBP, DBP, antihypertensive medication use, and BP classification. A linear relationship between the two parameters was ascertained in the smooth curve fitting analysis. No significant statistical interactions were detected between these variables. Hence, HGI indicates the risk of cognitive impairment in hypertensive individuals, thereby benefiting the relevant assessment. Cognitive impairment together with the subsequent onset of dementia primarily account for the morbidity and mortality in the elderly population [ 14 ] . The prevalence among individuals aged 60 and older is 10.12% in China [ 32 ] . There is growing evidence that hypertension is closely related to older adults’ cognitive impairment [ 4 ] . The backdrop that hypertension prevails worldwide due to the aged tendency of population and cognitive decline detrimentally influences people’s life quality highlights the necessity to well ascertain the hypertension-cognitive impairment relationship. This knowledge is essential for improving hypertension management and reducing the risk of cognitive decline. HbA1c is produced through the nonenzymatic reaction between intracellular HbA1 and glucose [ 33 ] . Discrepancies between actual and predicted HbA1c levels exist, despite the unclear underlying mechanisms. Significant interindividual variation in the relationship between HbA1c and FPG can arise from factors influencing glucose metabolism, and the hemoglobin HGI quantifies this variability [ 20 ] . HGI appears to capture glycemic variability across different populations, serving as a crucial indicator for the risk of microvascular complications [ 34 ] , which may contribute to their development. According to studies, HGI elevation is related to reduced telomere length [ 35 ] and increased inflammation and oxidative stress biomarkers [ 36 ] . There are some factors influencing the HGI-hypertension connection: insulin resistance [ 37 ] , a pivotal mechanism affecting the hypertension; inflammation, a significant contributor to hypertension [ 38 ] ; and oxidative stress, which crucially affects hypertension development [ 39 ] . By reflecting cumulative glycemic exposure, HGI, which also indicates the cardiovascular risk, may provide valuable insights into metabolic health for hypertensive patients in the long run. While existing studies fail to specifically examine the relationship between the HGI and cognitive impairment in hypertensive populations, there is evidence linking hypertension to impaired glucose metabolism and insulin resistance [ 40 ] . Glycemic dysregulations are accompanied by worsening cognitive function in the short term among individuals at high cardiovascular risk [ 41 ] . The precise mechanisms underlying the relationship remain unclear, but several potential mechanisms have been proposed. First, insulin plays a critical role in regulating brains’ learning and memory functions [ 42 ] . Insulin resistance in the brain can impair these functions meanwhile weakening insulin transport across the blood-brain barrier (BBB) [ 43 ] , potentially leading to poorer cognitive outcomes [ 44 ] . Additionally, peripheral insulin resistance might decrease cerebral glucose metabolism, which could correlate with worse memory function [ 45 ] . The study indicated that insulin resistance is inversely associated with cognitive performance [ 46 ] . Inflammation is another significant factor. Individuals with prolonged elevated levels of inflammatory proteins from middle age tend to exhibit poorer cognitive function in older age [ 47 ] . Elevated inflammatory markers in the blood can elevate the risk of cognitive impairment decades later [ 48 ] . In preclinical models, peripheral inflammation activates microglia, which produce excess IL-1β and TNF-α, leading to neuroinflammation and cognitive impairment [ 49 ] . Cytokines play a central role in cognitive processes by affecting synaptic plasticity, neurogenesis, and neuromodulation [ 50 ] . These cytokines can influence cholinergic [ 51 ] and dopaminergic [ 52 ] pathways and may contribute to neurodegeneration or regeneration. Some evidence suggests the ability of peripheral cytokines to cross the BBB [ 53 ] , either through less protected circumventricular regions or via vagal nerve stimulation [ 54 ] . Emerging evidence also points to oxidative stress as a key mechanism in cognitive aging [ 55 ] . Oxidative stress impairs mitochondrial function and damages various body systems, particularly the central nervous system [ 56 ] . Free radicals are capable of inducing brain chronic inflammation via releasing proinflammatory cytokines, which causes cell and synapse damage, synaptic function disruption [ 57 ] , and microglial cell activation [ 58 ] , ultimately resulting in neuronal damage. Furthermore, research has shown that HGI correlates with advanced glycation end products (AGEs) [ 59 ] . Hypertension-induced oxidative stress in cerebral vessels increases the expression of receptor for AGEs (RAGE) [ 60 ] , which binds to Aβ and is involved in its transport across the BBB. This interaction exacerbates the accumulation of Aβ and ROS in the brain, worsening cognitive impairment. Our study offers several advantages over previous research. Firstly, the large sample size and application of weighted data analysis enhance the robustness of our findings. Secondly, the use of smoothed fitted curves based on a fully adjusted model allowed us to identify potential linear relationships between variables. Lastly, our subgroup analyses account for various covariates, which helps in assessing the stability of the results. However, the study has many limitations. As the NHANES database is based on cross-sectional data, we can only examine correlations between the HGI and cognitive impairment, without establishing causality. Future research will focus on conducting prospective studies to explore causal relationships. Additionally, despite our efforts to include a broad range of covariates, there are still other potential confounding factors that can influence the analysis results. Conclusion Taken together, our study suggests a negative correlation between the HGI and cognitive impairment in hypertensive adults ≥ 60 years old in the United States. To fully understand the mechanisms underlying this relationship, further prospective studies are necessary. Declarations Acknowledgements The authors express their gratitude to the participants and staff of the NHANES for their invaluable contributions to this study. Authors’ contributions The study design was conceived by HD, TYK, WBG, QW, SL, XWZ and JY. HD and LS organized the data, conducted the analyses, and wrote and edited the manuscript. XWZ and JY contributed to the interpretation of the results, revision, and finalization of the manuscript. All authors have reviewed and approved the final version of the manuscript. Funding This work was supported by the National Natural Science Foundation of China (NSFC 82160089,82460084), Special Fund Project for Doctoral Training of the Lanzhou University Second Hospital (YJS-BD-24), CuiYing Scientific and Technological Innovation Program of Lanzhou University Second Hospital (CY2022-QN-A17). This study was also supported by Provincial Talent Project in 2024 (Gan Group Tongzi [2024] No. 4), Lanzhou University Student Innovation and Entrepreneurship Action Plan Project (20240050067) and Cuiying Scientific Training Program for Undergraduates of The Second Hospital & Clinical Medical School, Lanzhou University (CYXZ2024-20). Availability of data and materials The data were obtained from publicly available sources, as previously stated. Ethical approval The National Center for Health Statistics and Ethics Review Board approved the protocol for NHANES, and all participants provided written informed consent. The authors have disclosed no conflicts of interest. Competing interests The authors declared that they had no competing interests. Author details Department of Cardiovascular Medicine, Lanzhou University Second Hospital, Lanzhou 730030, China. Clinical trial number : not applicable. Clinical trial number : not applicable. Consent to Participate declaration : not applicable. References KIM JH. Hypertension in an ageing population: Diagnosis, mechanisms, collateral health risks, treatments, and clinical challenges [J]. Ageing Res Rev. 2024;98:102344. LU J, XUAN S, DOWNING N S, et al. Protocol for the China PEACE (Patient-centered Evaluative Assessment of Cardiac Events) Million Persons Project pilot [J]. BMJ open. 2016;6(1):e010200. BAGGEROER C E, CAMBRONERO F E, SAVAN N A, et al. Basic Mechanisms of Brain Injury and Cognitive Decline in Hypertension [J]. Hypertension. 2024;81(1):34–44. SANTISTEBAN M M, IADECOLA C, Hypertension CARNEVALED. Neurovascular Dysfunct Cogn Impairment [J] Hypertens. 2023;80(1):22–34. YAMAMOTO K, AKASAKA H, YASUNOBE Y, et al. Clinical characteristics of older adults with hypertension and unrecognized cognitive impairment [J]. Hypertens research: official J Japanese Soc Hypertens. 2022;45(4):612–9. ORGANIZATION. W H. Global status report on the public health response to dementia [J]. World Health Organization; 2021. UNGVARI Z, TOTH P. Hypertension-induced cognitive impairment: from pathophysiology to public health [J]. Nat Rev Nephrol. 2021;17(10):639–54. WILLIAMSON JD, PAJEWSKI N M, AUCHUS A P, et al. Effect of Intensive vs Standard Blood Pressure Control on Probable Dementia: A Randomized Clinical Trial [J]. JAMA. 2019;321(6):553–61. HUGHES D, JUDGE C, MURPHY R, et al. Association of Blood Pressure Lowering With Incident Dementia or Cognitive Impairment: A Systematic Review and Meta-analysis [J]. JAMA. 2020;323(19):1934–44. YANG W, LUO H, MA Y, et al. Effects of Antihypertensive Drugs on Cognitive Function in Elderly Patients with Hypertension: A Review [J]. Aging Dis. 2021;12(3):841–51. ROUCH L, CESTAC P, HANON O, et al. Antihypertensive drugs, prevention of cognitive decline and dementia: a systematic review of observational studies, randomized controlled trials and meta-analyses, with discussion of potential mechanisms [J]. CNS Drugs. 2015;29(2):113–30. YIMING PAN L M. Inflammatory Markers Associated with Physical Frailty and Cognitive Impairment [J]. aging and disease, 2024. DAVIDSON M B, SCHRIGER D L, PETERS A L, et al. Glycosylated hemoglobin as a diagnostic test for type 2 diabetes mellitus [J]. JAMA. 2000;283(5):606–7. (NCD-RISC)* N R F C. Global variation in diabetes diagnosis and prevalence based on fasting glucose and hemoglobin A1c [J]. Nat Med. 2023;29(11):2885–901. WANG J, ZHANG L, BAI Y, et al. The influence of shorter red blood cell lifespan on the rate of HbA1c target achieved in type 2 diabetes patients with a HbA1c detection value lower than 7 [J]. J diabetes. 2023;15(1):7–14. KHERA P K, JOINER C H, CARRUTHERS A, et al. Evidence for interindividual heterogeneity in the glucose gradient across the human red blood cell membrane and its relationship to hemoglobin glycation [J]. Diabetes. 2008;57(9):2445–52. CHAO G, ZHU Y. CHEN L. Role and Risk Factors of Glycosylated Hemoglobin Levels in Early Disease Screening [J]. Journal of diabetes research, 2021, 2021: 6626587. BENJAFIELD A V, GLENN C L, WANG X L, et al. TNFRSF1B in genetic predisposition to clinical neuropathy and effect on HDL cholesterol and glycosylated hemoglobin in type 2 diabetes [J]. Diabetes Care. 2001;24(4):753–7. NAYAK A U, SINGH B M DUNMORESJ. Potential Clinical Error Arising From Use of HbA1c in Diabetes: Effects of the Glycation Gap [J]. Endocr Rev. 2019;40(4):988–99. HEMPE JM, LIU S, MYERS L, et al. The hemoglobin glycation index identifies subpopulations with harms or benefits from intensive treatment in the ACCORD trial [J]. Diabetes Care. 2015;38(6):1067–74. AHN C H, MIN S H, LEE D H, et al. Hemoglobin Glycation Index Is Associated With Cardiovascular Diseases in People With Impaired Glucose Metabolism [J]. J Clin Endocrinol Metab. 2017;102(8):2905–13. WANG Y, LIU H, HU X, et al. Association between hemoglobin glycation index and 5-year major adverse cardiovascular events: the REACTION cohort study [J]. Chin Med J. 2023;136(20):2468–75. RHEE E J, CHO J H, KWON H, et al. Association Between Coronary Artery Calcification and the Hemoglobin Glycation Index: The Kangbuk Samsung Health Study [J]. J Clin Endocrinol Metab. 2017;102(12):4634–41. MCCARTER R J, HEMPE JM, GOMEZ R, et al. Biological variation in HbA1c predicts risk of retinopathy and nephropathy in type 1 diabetes [J]. Diabetes Care. 2004;27(6):1259–64. WANG S, GU L, CHEN J, et al. Association of hemoglobin glycation index and glycation gap with cardiovascular disease among US adults [J]. Diabetes Res Clin Pract. 2022;190:109990. KLEIN K R, FRANEK E. MARSO S, et al. Hemoglobin glycation index, calculated from a single fasting glucose value, as a prediction tool for severe hypoglycemia and major adverse cardiovascular events in DEVOTE [J]. Volume 9. BMJ open diabetes research & care; 2021. 2. PEERI N C, EGAN K M, CHAI W, et al. Association of magnesium intake and vitamin D status with cognitive function in older adults: an analysis of US National Health and Nutrition Examination Survey (NHANES) 2011 to 2014 [J]. Eur J Nutr. 2021;60(1):465–74. EGAN B M, LI J, SHATAT I F, et al. Closing the gap in hypertension control between younger and older adults: National Health and Nutrition Examination Survey (NHANES) 1988 to 2010 [J]. Circulation. 2014;129(20):2052–61. CLARK L J, GATZ M. Longitudinal verbal fluency in normal aging, preclinical, and prevalent Alzheimer's disease [J]. Am J Alzheimer's Dis Other dement. 2009;24(6):461–8. MACDONALD S W, HULTSCH D F, STRAUSS E et al. Age-related slowing of digit symbol substitution revisited: what do longitudinal age changes reflect? [J]. The journals of gerontology Series B, Psychological sciences and social sciences, 2003, 58(3): P187–94. MORRIS JC, HEYMAN A, MOHS R C, et al. The Consortium to Establish a Registry for Alzheimer's Disease (CERAD). Part I. Clinical and neuropsychological assessment of Alzheimer's disease [J]. Neurology. 1989;39(9):1159–65. CHEN H, YE K X FENGQ, et al. Trends in the prevalence of cognitive impairment at old age in China, 2002–2018 [J]. Alzheimer's Dement J Alzheimer's Assoc. 2024;20(2):1387–96. SU Y, XIA C, ZHANG H, et al. Emerging biosensor probes for glycated hemoglobin (HbA1c) detection [J]. Mikrochim Acta. 2024;191(6):300. IBARRA-SALCE R, POZOS-VARELA F J, MARTINEZ-ZAVALA N, et al. Endocr practice: official J Am Coll Endocrinol Am Association Clin Endocrinologists. 2023;29(3):162–7. Correlation Between Hemoglobin Glycation Index Measured by Continuous Glucose Monitoring With Complications in Type 1 Diabetes [J]. LYU L, YU J, LIU Y, et al. High Hemoglobin Glycation Index Is Associated With Telomere Attrition Independent of HbA1c, Mediated by TNFα [J]. J Clin Endocrinol Metab. 2022;107(2):462–73. LYU L, YU J, LIU Y, et al. Dietary patterns, oxidative Stress, inflammation and biological variation in hemoglobin A1c: Association and Mediation analysis in a rural community in north China [J]. Volume 194. Diabetes research and clinical practice; 2022. p. 110154. WANG R, CHEN C, XU G, et al. Association of triglyceride glucose-body mass index and hemoglobin glycation index with heart failure prevalence in hypertensive populations: a study across different glucose metabolism status [J]. Lipids Health Dis. 2024;23(1):53. GUZIK TJ, NOSALSKI R, MAFFIA P, et al. Immune and inflammatory mechanisms in hypertension [J]. Nat reviews Cardiol. 2024;21(6):396–416. GUZIK T J, TOUYZ RM. Oxidative Stress, Inflammation, and Vascular Aging in Hypertension [J]. Hypertension. 2017;70(4):660–7. DA SILVA A A, DO CARMO J M, LI X, et al. Role of Hyperinsulinemia and Insulin Resistance in Hypertension: Metabolic Syndrome Revisited [J]. Can J Cardiol. 2020;36(5):671–82. GóMEZ-MARTíNEZ C, BABIO N, JúLVEZ J, et al. Glycemic Dysregulations Are Associated With Worsening Cognitive Function in Older Participants at High Risk of Cardiovascular Disease: Two-Year Follow-up in the PREDIMED-Plus Study [J]. Front Endocrinol. 2021;12:754347. AGRAWAL R, RENO C M, SHARMA S, et al. Insulin action in the brain regulates both central and peripheral functions [J]. Am J Physiol Endocrinol metabolism. 2021;321(1):E156–63. HENI M, KULLMANN S, PREISSL H, et al. Impaired insulin action in the human brain: causes and metabolic consequences [J]. Nat reviews Endocrinol. 2015;11(12):701–11. KULLMANN S, HENI M, HALLSCHMID M, et al. Brain Insulin Resistance at the Crossroads of Metabolic and Cognitive Disorders in Humans [J]. Physiol Rev. 2016;96(4):1169–209. DEERY H A LIANGE, DI PAOLO R et al. Peripheral insulin resistance attenuates cerebral glucose metabolism and impairs working memory in healthy adults [J]. npj Metabolic Health Disease, 2024, 2(1). KIM AB. Insulin resistance, cognition, and Alzheimer disease [J]. Obes (Silver Spring Md). 2023;31(6):1486–98. FINGER C E, MORENO-GONZALEZ I, GUTIERREZ A, et al. Age-related immune alterations and cerebrovascular inflammation [J]. Mol Psychiatry. 2022;27(2):803–18. SIM W L POHL, JO D G, et al. The role of inflammasomes in vascular cognitive impairment [J]. Mol neurodegeneration. 2022;17(1):4. BELARBI K, JOPSON T. TNF-α protein synthesis inhibitor restores neuronal function and reverses cognitive deficits induced by chronic neuroinflammation [J]. J Neuroinflamm. 2012;9:23. CORNELL J, SALINAS S, HUANG H Y, et al. Microglia regulation of synaptic plasticity and learning and memory [J]. Neural regeneration Res. 2022;17(4):705–16. SHAPIRA-LICHTER I, BEILIN B, OFEK K, et al. Cytokines and cholinergic signals co-modulate surgical stress-induced changes in mood and memory [J]. Brain Behav Immun. 2008;22(3):388–98. LIU Z, QIU A W, HUANG Y, et al. IL-17A exacerbates neuroinflammation and neurodegeneration by activating microglia in rodent models of Parkinson's disease [J]. Brain Behav Immun. 2019;81:630–45. OSIPOVA E D, SEMYACHKINA-GLUSHKOVSKAYA O V, MORGUN A V, et al. Gliotransmitters and cytokines in the control of blood-brain barrier permeability [J]. Rev Neurosci. 2018;29(5):567–91. BESEDOVSKY H O, DEL REY A. Central and peripheral cytokines mediate immune-brain connectivity [J]. Neurochem Res. 2011;36(1):1–6. LANE H Y, WANG S H, LIN C H. Differential relationships of NMDAR hypofunction and oxidative stress with cognitive decline [J]. Psychiatry Res. 2023;326:115288. NETTO M B, DE OLIVEIRA JUNIOR A N, GOLDIM M et al. Oxidative stress and mitochondrial dysfunction contributes to postoperative cognitive dysfunction in elderly rats [J]. Brain, behavior, and immunity, 2018, 73: 661–9. MASSAAD CA, KLANN E. Reactive oxygen species in the regulation of synaptic plasticity and memory [J]. Antioxid Redox Signal. 2011;14(10):2013–54. ROJO A I, MCBEAN G, CINDRIC M, et al. Redox control of microglial function: molecular mechanisms and functional significance [J]. Antioxid Redox Signal. 2014;21(12):1766–801. FELIPE D L, HEMPE J M, LIU S, et al. Skin intrinsic fluorescence is associated with hemoglobin A(1c)and hemoglobin glycation index but not mean blood glucose in children with type 1 diabetes [J]. Diabetes Care. 2011;34(8):1816–20. HARTOG J W, VAN DE WAL R M, SCHALKWIJK C G, et al. Advanced glycation end-products, anti-hypertensive treatment and diastolic function in patients with hypertension and diastolic dysfunction [J]. Eur J Heart Fail. 2010;12(4):397–403. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 13 May, 2025 Read the published version in BMC Geriatrics → Version 1 posted Editorial decision: Revision requested 10 Jan, 2025 Editor assigned by journal 10 Jan, 2025 Submission checks completed at journal 03 Jan, 2025 First submitted to journal 30 Dec, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5736468","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":397341026,"identity":"c073cb46-1ec3-4f4c-89d9-d7691ad9ebfb","order_by":0,"name":"Hong Ding","email":"","orcid":"","institution":"Lanzhou University Second Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hong","middleName":"","lastName":"Ding","suffix":""},{"id":397341027,"identity":"50365b75-0e6c-4cc7-b33c-f95bc4e48697","order_by":1,"name":"Tingyue Kang","email":"","orcid":"","institution":"Lanzhou University Second Hospital","correspondingAuthor":false,"prefix":"","firstName":"Tingyue","middleName":"","lastName":"Kang","suffix":""},{"id":397341028,"identity":"5414dc89-9073-4eec-9798-2dbec59616de","order_by":2,"name":"Wenbo Gao","email":"","orcid":"","institution":"Lanzhou University Second Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wenbo","middleName":"","lastName":"Gao","suffix":""},{"id":397341029,"identity":"f856e899-4583-492f-9386-9a4f9907a578","order_by":3,"name":"Qi Wang","email":"","orcid":"","institution":"Lanzhou University Second Hospital","correspondingAuthor":false,"prefix":"","firstName":"Qi","middleName":"","lastName":"Wang","suffix":""},{"id":397341030,"identity":"31ca84c5-2218-4fda-be43-5074416c0dc1","order_by":4,"name":"Shu Liu","email":"","orcid":"","institution":"Lanzhou University Second Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shu","middleName":"","lastName":"Liu","suffix":""},{"id":397341031,"identity":"0dbed6e1-a04f-4b0c-bde5-a3eb831bd703","order_by":5,"name":"Xiaowei Zhang","email":"","orcid":"","institution":"Lanzhou University Second Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiaowei","middleName":"","lastName":"Zhang","suffix":""},{"id":397341032,"identity":"98968272-fe4f-4670-be9f-26c888f5fd35","order_by":6,"name":"Jing Yu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIie3RsQrCMBCA4SuB6BDNmiLoK5w4iHTwVeqURaFTZ6dMxbngS+gbVLKGzg4OfYSOggoG7dymm2B+OLjhvukAfL5fTQBMx5+N9iAL2o/YNsqZ8IlBWCktFbdLnWrgx307CQ9bhFDpnQKDQV5qELeinaBhDQkyJCOlAUXcTtYNkZQwJC8XguxLYkotCVyIMDQBUcq5YjS5ZKVk4tpBeEZORKTRjHN9ru5pNOV5BwEYLh9h847CDuu6tw2qoH463Pl8Pt//9gYCbzcsIiRJuQAAAABJRU5ErkJggg==","orcid":"","institution":"Lanzhou University Second Hospital","correspondingAuthor":true,"prefix":"","firstName":"Jing","middleName":"","lastName":"Yu","suffix":""}],"badges":[],"createdAt":"2024-12-30 15:08:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5736468/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5736468/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12877-025-05999-2","type":"published","date":"2025-05-13T15:57:09+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":73086701,"identity":"93247edf-d760-4325-ace5-7ca88265e98a","added_by":"auto","created_at":"2025-01-06 14:54:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":42792,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the sample selection from the NHANES 2011–2014 assessment of cognitive functioning\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5736468/v1/507025f5db2ad7e2f4625071.png"},{"id":73086665,"identity":"4fc2e7d4-ba08-4d7c-b5ed-92b0b39fa196","added_by":"auto","created_at":"2025-01-06 14:54:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":24454,"visible":true,"origin":"","legend":"\u003cp\u003eThe correlation between HbA1c and FBG\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5736468/v1/f59e0dd23dd1ed0d9636d6b8.png"},{"id":73086697,"identity":"ca3872ff-14bd-4e29-9b57-eaeef7db461f","added_by":"auto","created_at":"2025-01-06 14:54:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":28507,"visible":true,"origin":"","legend":"\u003cp\u003eResults of restrictive cubic spline analysis. A. CERAD-WL; B. CERAD-DR; C. AFT; D. DSST. Adjusted for sex, age, race, education level, PIR, BMI, smoking status, alcohol consumption, SBP, DBP, antihypertensive drug use and BP classification. The solid line and blue area represent the estimated values and their corresponding 95% CIs, respectively (HGI, hemoglobin glycation index; CERAD-WL/DR, the Consortium to Establish a Registry for Alzheimer’s Disease Word Learning subtest for assessing learning and memory; AFT, the Animal Fluency Test for verbal fluency and DSST, the Digit Symbol Substitution Test)\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5736468/v1/46bcbc52c54a35d08d93f586.png"},{"id":73086705,"identity":"81fbc09d-1893-4d50-9874-f65777e4a3c9","added_by":"auto","created_at":"2025-01-06 14:54:37","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":21842,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation of HGI with low cognitive function. A. CERAD-WL; B. CERAD-DR; C. AFT; D. DSST. Adjusted for sex, age, race, education level, PIR, BMI, smoking status, alcohol consumption, SBP, DBP, antihypertensive drug use and BP classification. The solid symbols and error bars represent the odds ratios and their corresponding 95% confidence intervals. (HGI, hemoglobin glycation index; CERAD-WL/DR, the Consortium to Establish a Registry for Alzheimer’s Disease Word Learning subtest for assessing learning and memory; AFT, the Animal Fluency Test for verbal fluency and DSST, the Digit Symbol Substitution Test).\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5736468/v1/ada17927c83a1171ab805dca.png"},{"id":73086703,"identity":"5629c1fa-61ce-484a-ac73-ef82f47c5d20","added_by":"auto","created_at":"2025-01-06 14:54:37","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":70857,"visible":true,"origin":"","legend":"\u003cp\u003eResults of subgroup analysis. A. CERAD-WL; B. CERAD-DR; C. AFT; D. DSST.\u003c/p\u003e","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-5736468/v1/bd4da4b8cf4ee31ef64cb11e.png"},{"id":83067708,"identity":"8c9d70e6-ec3b-4208-afb2-b0caac2476fa","added_by":"auto","created_at":"2025-05-19 16:04:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1494419,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5736468/v1/a76f95a7-d439-4ed2-8821-33f13660d8aa.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring the Association Between Hemoglobin Glycation Index and Cognitive Function in Older Adults with Hypertension: A Cross-Sectional Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHypertension is a prevalent condition and also a leading risk factor for cardiovascular disease and stroke, especially among the elderly\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. According to the China Patient-Centered Evaluative Assessment of Cardiac Events (PEACE) Million Persons Project (2014\u0026ndash;2017), approximately 50% of the participants aged 35\u0026ndash;75 years could be affected by hypertension, with the prevalence increasing progressively with age\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. The brain can be easily affected by hypertension, which is a major cause of the vascular cognitive impairment and late-life dementia\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Apart from aging, hypertension stands out as the most critical risk factor for cerebrovascular pathology leading to cognitive decline\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCognitive impairment is a significant global contributor to death and disability\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. The World Health Organization's 2021 Global Status Report on dementia estimated that there were approximately 55.2\u0026nbsp;million of dementia cases in 2019, with the number expected to rise to 78\u0026nbsp;million and 139\u0026nbsp;million by 2030 and 2050, respectively \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Obviously, midlife hypertension significantly adds the possibility of developing cognitive decline in late life, independent of genetic predisposition to cognitive impairment\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Therefore, understanding the mechanisms linking hypertension to cognitive impairment remains a critical area of research. Although some studies suggest the possible effect of effective blood pressure (BP) management on lowering the risk of cognitive decline, they fail to yield conclusive results\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Additionally, previous studies have not well elucidated whether specific classes of antihypertensive drugs can offer superior cognitive benefits \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. There is an urgent need for new discoveries and innovative therapeutic targets to safeguard hypertensive patients\u0026rsquo; cognitive function. Identifying individuals at risk in early stage also could benefit the retardation or prevention of the progression to dementia.\u003c/p\u003e \u003cp\u003eGlycosylated hemoglobin (HbA1c) is widely used in diagnosing and managing diabetes mellitus, providing an estimate of the mean blood glucose levels of an individual over the past three months\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. At present, it is the most commonly used surrogate marker for evaluating the effectiveness of glucose-lowering interventions\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. However, evidence indicates that HbA1c levels may consistently differ from fasting plasma glucose (FPG) levels, being either higher or lower in certain populations\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e, affected by various factors such as erythrocyte lifespan difference \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e, cell membrane glucose transmembrane gradients\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e, enzyme abnormalities\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e, and genetic factors\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. As a result, HbA1c measurement may not fully capture an individual's blood glucose metabolic status.\u003c/p\u003e \u003cp\u003eThe hemoglobin glycation index (HGI) is to quantify the variable relationship between HbA1c and plasma glucose levels\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Calculation of HGI followed a linear regression equation based on FPG, referring to the difference between the observed and the predicted HbA1c\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Numerous studies have shown that HGI is a predictor of diabetes-related complications, such as mortality\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e, cardiovascular disease\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e, and microvascular complications\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. In particular, a high HGI has been strongly associated with major adverse cardiovascular events in the populations studied\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. Previous studies have indicated that HGI can serve as a relatively intuitive indicator of glycemic variability in patients\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. However, there is limited research on glycemic variability in patients with cognitive impairment. Investigating the relevance of HGI to the prognosis of individuals developing cognitive impairment assists in understanding the significance of glycemic variability for long-term outcomes from new perspectives.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eThe National Health and Nutrition Examination Survey (NHANES), a population-based study, is conducted by the National Center for Health Statistics (NCHS) using a complex, multistage design. This survey, which releases data in two-year cycles, monitors the nutritional and health status pertaining to noninstitutionalized civilians in the United States. Detailed descriptions of the NHANES design and operations have been previously published. Our study analyzed data from the NHANES cycles spanning 2011 to 2014, conducting cognitive testing on participants\u0026thinsp;\u0026ge;\u0026thinsp;60 years old, which has been described previously\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. Initially, data from 19,931 participants were collected, but 16,299 were excluded due to being younger than 60 years, 698 due to incomplete cognitive impairment data, 1,528 due to incomplete HGI data, and 383 because they were not diagnosed with hypertension. Ultimately, the study included data from 1,023 participants\u0026thinsp;\u0026ge;\u0026thinsp;60 years old. The selection process for the study sample is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDefinition of hypertension\u003c/h3\u003e\n\u003cp\u003eThree to four blood pressure measurements were taken following standard procedures. For analysis, the mean of all measurements, excluding the first, was used when multiple readings were available. Hypertension refers to the disease situation with a mean SBP of \u0026ge;\u0026thinsp;140 mmHg, a mean DBP of \u0026ge;\u0026thinsp;90 mmHg, and/or the use of prescribed antihypertensive medications or a prior diagnosis by a physician\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eHGI calculation\u003c/h3\u003e\n\u003cp\u003eHbA1c and FPG values were combined to calculate HGI, thereby estimating the inter-individual difference in the HbA1c level. We determined the predicted HbA1c through a regression equation based on baseline FPG and HbA1c measurements: Predicted HbA1c\u0026thinsp;=\u0026thinsp;3.412\u0026thinsp;+\u0026thinsp;0.416 \u0026times; FPG (mmol/L), as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. HGI\u0026thinsp;=\u0026thinsp;measured HbA1c\u0026thinsp;\u0026minus;\u0026thinsp;predicted HbA1c\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. The study population fell into 4 HGI quartiles: Q1 (-3.29 to -0.35), Q2 (-0.35 to -0.05), Q3 (-0.05 to 0.25), and Q4 (0.25 to 3.69).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eCognitive function assessment\u003c/h3\u003e\n\u003cp\u003eParticipants\u0026thinsp;\u0026ge;\u0026thinsp;60 years old were administered a cognitive battery comprising four tests in the Mobile Examination Center (MEC): AFT \u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e, DSST\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e, CERAD-WL test and CERAD-DR test\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. The CERAD test includes 3 consecutive learning trials and 1 delayed recall task. In the AFT, testers required participants to name as many animals as they could in one minute to complete the verbal fluency assessment, with the score determined by the total number of animals named. Testers set a cut-off score of less than fourteen for the identification of potential cognitive impairment, as previously established in peer-reviewed research. The DSST, part of the Wechsler Adult Intelligence Scale, is designed to measure cognitive functions (sustained attention, working memory, and information processing speed). Participants were given a set of symbols paired with a corresponding key and asked to accurately draw as many symbols as possible within 120 seconds, with a threshold score of less than 40, as suggested by a prior NCHS report accounting for the \"Flynn effect.\" The CERAD battery is widely used for diagnosing dementia associated with Alzheimer\u0026rsquo;s disease, evaluating abilities of new learning, recognition memory, and delayed recall. The CERAD-WL test involves 3 consecutive learning trials, requiring participants to recite a list of distinct words and recall as many as possible. The maximum score is 30, with the trials featuring different word orders. After a 8\u0026ndash;10 min interval, CERAD-DR test was conducted, requiring participants to recall the 10 words from the previous test. Threshold scores of less than 17 for the CERAD Word Learning and less than 5 for the CERAD Delayed Recall were selected based on existing scientific literature.\u003c/p\u003e\n\u003ch3\u003eSelection of covariates\u003c/h3\u003e\n\u003cp\u003eWe included covariates associated with HGI or cognitive function identified in previous studies, while addressing concerns of collinearity. These covariates included sex (male/female), age (years), race/ethnicity (Mexican American/Other Hispanic/Non-Hispanic White/Non-Hispanic Black/Other race), education level (below high school/high school/above high school), alcohol consumption, BMI (body mass index), diastolic blood pressure (DBP), systolic blood pressure (SBP), antihypertensive medication use, BP classification, smoking status, and diabetes. Trained medical professionals administered the questionnaires and collected all data through standardized interviews, physical examinations, and laboratory tests.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe complex survey design adopted specific sample weights, in accordance with NHANES analytic standards. Experimenters are arranged to collect all data specific to each cycle in a single interview. For the weighted participants, baseline characteristics were presented as means\u0026thinsp;\u0026plusmn;\u0026thinsp;SD and % for continuous variables and categorical variables, respectively. HGI was analyzed both as a continuous variable and in quartiles. Weighted linear regression analyses engaged in calculating the β coefficients and 95% CI for the confirmation of the possible associations between HGI and scores on the three cognitive tests. Logistic regression analyses served for calculating the odds ratios (OR) and CIs for the exploration of the possible associations between HGI and low cognitive function. The crude model and Model 1 did not adjust for any covariates. Model 2 involved age adjustment, while Model 3 involved adjustments for sex, age, race/ethnicity, education level, alcohol consumption, poverty-to-income ratio (PIR), BMI, DBP, SBP, antihypertensive medication use, BP classification, and smoking status. Subgroup analyses involved gender, age, BMI, smoking status, and alcohol consumption. Data analysis relied on the R software (version 4.2.2). P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 reported statistical significance.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of study population\u003c/h2\u003e \u003cp\u003eOur study included 1023 hypertensive patients in total, with a mean age of 69.9\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8 years, with 538 female patients (55%) and 485 male patients (45%). Sex, race, alcohol consumption, diabetes status, and DSST scores were obviously different in statistical level in the HGI quartiles (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\u003eCharacteristics of participants stratified by quartile of hemoglobin glycation index\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\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall, N\u0026thinsp;=\u0026thinsp;1023\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ1, N\u0026thinsp;=\u0026thinsp;256\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ2, N\u0026thinsp;=\u0026thinsp;257\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ3, N\u0026thinsp;=\u0026thinsp;254\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ4, N\u0026thinsp;=\u0026thinsp;256\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003csup\u003e2\u003c/sup\u003e\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 \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69.9 (6.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.6 (7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70.1 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70.2 (6.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e70.0 (6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u0026ndash;69 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e459 (47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119 (49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e109 (47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e115 (46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e116 (44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e70\u0026ndash;79 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e310 (30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70 (26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81 (29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e80 (32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e79 (32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e80\u0026thinsp;+\u0026thinsp;years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e254 (24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67 (24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67 (24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e61 (24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e538 (55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e109 (46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e136 (55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e155 (64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e138 (56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e485 (45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e147 (54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e121 (45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e99 (36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e118 (44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderweight (\u0026lt;\u0026thinsp;18.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (1.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (0.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (1.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (1.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (1.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal (18.5 to \u0026lt;\u0026thinsp;25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e232 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51 (18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66 (27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e61 (23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e54 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight (25 to \u0026lt;\u0026thinsp;30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e346 (35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93 (32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100 (41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e91 (41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e62 (23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese (30 or greater)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e424 (42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e107 (50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86 (31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95 (35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e136 (57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace, (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (3.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (2.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17 (2.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21 (4.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93 (3.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (3.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (2.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28 (6.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e500 (78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e133 (83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e155 (86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e131 (79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e81 (60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e249 (9.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34 (4.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64 (9.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e97 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther/multiracial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99 (6.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (3.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28 (4.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20 (6.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol, (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;5 drinks/month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e481 (46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114 (45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e117 (42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e121 (46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e129 (54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u0026ndash;10 drinks/month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36 (4.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (4.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (4.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13 (8.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6 (1.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u0026thinsp;+\u0026thinsp;drinks/month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e157 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55 (30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49 (24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23 (10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-drinker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e332 (28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80 (29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84 (29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95 (34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoke, (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e120 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (9.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26 (8.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e34 (16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormer smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e391 (42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104 (50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88 (36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e103 (45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e96 (36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e512 (47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e126 (41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e135 (52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e125 (46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e126 (48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation, (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess Than 9th Grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e109 (6.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (4.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (5.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25 (6.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e43 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9-11th Grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e159 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e39 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh School Grad/GED\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e254 (24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e75 (31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSome College or AA degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e287 (32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74 (36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84 (33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e61 (27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e68 (34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege Graduate or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e213 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60 (28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64 (32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (\u0026lt;\u0026thinsp;0.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (0.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePIR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.04 (1.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.28 (1.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.14 (1.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.96 (1.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.68 (1.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.054\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\u003e135 (20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e137 (19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e135 (21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e134 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e135 (20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.4\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\u003e67 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e66 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntihypertensive drug, (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e835 (95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e205 (94%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e196 (94%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e206 (95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e228 (97%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41 (5.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (5.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (5.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7 (5.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6 (2.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (\u0026lt;\u0026thinsp;0.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (\u0026lt;\u0026thinsp;0.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension Grade, (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrade 1 hypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (2.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (3.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6 (1.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrade 2 hypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (0.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrade 3 hypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (\u0026lt;\u0026thinsp;0.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (0.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e957 (97%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e239 (96%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e235 (96%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e240 (97%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e243 (99%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart failure, (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93 (9.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (6.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25 (10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e929 (90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e232 (92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e241 (94%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e229 (90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e227 (85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes, (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e270 (23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54 (18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e122 (56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e705 (72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e185 (78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e219 (86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e180 (73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e121 (41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCERAD - WL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.4 (4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.3 (4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.8 (4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.5 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.9 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCERAD - DR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.18 (2.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.19 (2.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.24 (2.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.32 (2.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.87 (2.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAFT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.5 (5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.6 (5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.2 (5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.8 (6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.0 (5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDSST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52 (18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e43 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e1\u003c/sup\u003eMean (SD); n (unweighted) (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e2\u003c/sup\u003eWilcoxon rank-sum test for complex survey samples; chi-squared test with Rao \u0026amp; Scott's second-order correction\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eAbbreviation: HGI, hemoglobin glycation index; BMI, body mass index; RIP, poverty\u0026ndash;income ratio; DBP, diastolic blood pressure; SBP, systolic blood pressure; CERAD-WL/DR, the Consortium to Establish a Registry for Alzheimer\u0026rsquo;s Disease Word Learning subtest for assessing learning and memory; AFT, the Animal Fluency Test for verbal fluency; and DSST, the Digit Symbol Substitution Test; Q, quartile\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\u003eAssociation between the HGI and cognition function\u003c/h2\u003e \u003cp\u003eUpon the adjustment for confounding factors, a strong correlation was observed between HGI (as a continuous variable) and DSST test scores [β\u0026thinsp;=\u0026thinsp;0.08 (95% CI: 0.01, 0.69)]. However, no significant correlations were identified between HGI and the CERAD or AFT test scores.\u003c/p\u003e \u003cp\u003eSimilarly, when analyzing HGI by quartiles, the fourth quartile of HGI showed a strong correlation with DSST test scores [β\u0026thinsp;=\u0026thinsp;0.01 (95% CI: 0.00, 0.41)], while no significant associations were found with CERAD or AFT test scores (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\u003eAssociations between HGI with CERAD \u0026ndash; WL, CERAD -DR, AFT, and DSST\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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eβ (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eβ (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCERAD \u0026ndash; WL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHGI (continuous)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.75(0.41, 1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.68(0.40, 1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.73(0.48, 1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHGI (categories)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.60 (0.52, 4.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.78 (0.59, 5.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.49 (0.47, 4.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.28 (0.43, 3.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.46 (0.54, 3.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.88 (0.22, 3.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.67 (0.24, 1.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.73 (0.28, 1.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98 (0.47, 2.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCERAD -DR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHGI (continuous)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.87(0.67, 1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.83(0.65, 1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.90(0.72, 1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHGI (categories)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.06 (0.60, 1.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.11 (0.64, 1.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.28 (0.71, 2.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.15 (0.74, 1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.23 (0.84, 1.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.01 (0.58, 1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.73 (0.45, 1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.77 (0.48, 1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98 (0.61, 1.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAFT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHGI (continuous)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.58(0.16, 2.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.52(0.18, 1.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.82(0.32, 2.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHGI (categories)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.88 (0.59, 5.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.12 (0.73, 6.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e5.06 (1.14, 22.3)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.29 (0.25, 6.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.49 (0.32, 6.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.09(0.31, 14.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.21 (0.03, 1.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.23 (0.04, 1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.67(0.10, 4.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDSST\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHGI (continuous)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.02(0.00, 0.75)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.01(0.00, 0.21)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.08(0.01, 0.69)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHGI (categories)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.52 (0.06, 35.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.55 (0.14, 47.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.14 (0.08, 125)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.32 (0.01, 10.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.60 (0.03, 10.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (0.04, 26.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.00 (0.00, 0.03)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.00 (0.00, 0.02)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.01 (0.00, 0.41)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel 1 was adjusted for none.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel 2 was adjusted for age.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel 3 was adjusted for sex, age, race, education level, PIR, BMI, smoking status, alcohol consumption, SBP, DBP, antihypertensive drug use and BP classification.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviation: HGI, hemoglobin glycation index; CERAD-WL/DR, the Consortium to Establish a Registry for Alzheimer\u0026rsquo;s Disease Word Learning subtest for assessing learning and memory; AFT, the Animal Fluency Test for verbal fluency and DSST, the Digit Symbol Substitution Test; Q, quartile.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePotential nonlinear relationship between HGI and cognition function\u003c/h2\u003e \u003cp\u003eAccording to the RCS analysis, there were no significant nonlinear relationships between HGI and the outcome indicators (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). In fully adjusted weighted linear regression models, CREAD, AFT, and DSST test scores showed a roughly linear decline with increasing HGI levels (CREAD-WL: P-nonlinear\u0026thinsp;=\u0026thinsp;0.7927, CREAD-DR: P-nonlinear\u0026thinsp;=\u0026thinsp;0.2900, AFT: P-nonlinear\u0026thinsp;=\u0026thinsp;0.5779, DSST: P-nonlinear\u0026thinsp;=\u0026thinsp;0.4311) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAssociation between the HGI and low cognition function\u003c/h2\u003e \u003cp\u003eUpon the full adjustment for confounding factors, participants in the 4th quartile of HGI more tended to present low cognitive function as measured by the DSST test compared to those in the first quartile (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029). No significant associations were found between HGI quartiles and low cognitive function from the CERAD and AFT tests (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup analyses\u003c/h2\u003e \u003cp\u003eSimilarly, HGI presented different degrees of correlation with cognitive impairment in different subgroups, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Within subgroups under the stratification of sex, age, BMI, alcohol consumption, and smoking status, the HGI presented a closer association with cognitive impairment among females, individuals aged 60\u0026ndash;69 and 80+, those with normal or overweight BMI, and those who consumed alcohol. In particular, the association was statistically significant for DSST scores among normal and overweight female participants aged 60 to 80 years who consumed alcohol. According to the interaction tests, sex, age, BMI, alcohol use, or smoking status failed to exert a remarkable impact on the association (P for interaction\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study is the first one that conducts a large-scale investigation on the HGI-cognitive impairment relationship in a hypertensive population. According to the cross-sectional analysis, increased HGI resulted in a higher risk of cognitive impairment, even after the adjustment for covariates such as sex, age, race, education level, PIR, BMI, smoking status, alcohol consumption, SBP, DBP, antihypertensive medication use, and BP classification. A linear relationship between the two parameters was ascertained in the smooth curve fitting analysis. No significant statistical interactions were detected between these variables. Hence, HGI indicates the risk of cognitive impairment in hypertensive individuals, thereby benefiting the relevant assessment.\u003c/p\u003e \u003cp\u003eCognitive impairment together with the subsequent onset of dementia primarily account for the morbidity and mortality in the elderly population\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. The prevalence among individuals aged 60 and older is 10.12% in China \u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. There is growing evidence that hypertension is closely related to older adults\u0026rsquo; cognitive impairment\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. The backdrop that hypertension prevails worldwide due to the aged tendency of population and cognitive decline detrimentally influences people\u0026rsquo;s life quality highlights the necessity to well ascertain the hypertension-cognitive impairment relationship. This knowledge is essential for improving hypertension management and reducing the risk of cognitive decline.\u003c/p\u003e \u003cp\u003eHbA1c is produced through the nonenzymatic reaction between intracellular HbA1 and glucose\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. Discrepancies between actual and predicted HbA1c levels exist, despite the unclear underlying mechanisms. Significant interindividual variation in the relationship between HbA1c and FPG can arise from factors influencing glucose metabolism, and the hemoglobin HGI quantifies this variability\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. HGI appears to capture glycemic variability across different populations, serving as a crucial indicator for the risk of microvascular complications\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e, which may contribute to their development. According to studies, HGI elevation is related to reduced telomere length\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e and increased inflammation and oxidative stress biomarkers\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. There are some factors influencing the HGI-hypertension connection: insulin resistance\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e, a pivotal mechanism affecting the hypertension; inflammation, a significant contributor to hypertension\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e; and oxidative stress, which crucially affects hypertension development\u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. By reflecting cumulative glycemic exposure, HGI, which also indicates the cardiovascular risk, may provide valuable insights into metabolic health for hypertensive patients in the long run.\u003c/p\u003e \u003cp\u003eWhile existing studies fail to specifically examine the relationship between the HGI and cognitive impairment in hypertensive populations, there is evidence linking hypertension to impaired glucose metabolism and insulin resistance\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e. Glycemic dysregulations are accompanied by worsening cognitive function in the short term among individuals at high cardiovascular risk\u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. The precise mechanisms underlying the relationship remain unclear, but several potential mechanisms have been proposed. First, insulin plays a critical role in regulating brains\u0026rsquo; learning and memory functions \u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e. Insulin resistance in the brain can impair these functions meanwhile weakening insulin transport across the blood-brain barrier (BBB)\u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e, potentially leading to poorer cognitive outcomes\u003csup\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e. Additionally, peripheral insulin resistance might decrease cerebral glucose metabolism, which could correlate with worse memory function\u003csup\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e. The study indicated that insulin resistance is inversely associated with cognitive performance\u003csup\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e. Inflammation is another significant factor. Individuals with prolonged elevated levels of inflammatory proteins from middle age tend to exhibit poorer cognitive function in older age\u003csup\u003e[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/sup\u003e. Elevated inflammatory markers in the blood can elevate the risk of cognitive impairment decades later\u003csup\u003e[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/sup\u003e. In preclinical models, peripheral inflammation activates microglia, which produce excess IL-1β and TNF-α, leading to neuroinflammation and cognitive impairment\u003csup\u003e[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/sup\u003e. Cytokines play a central role in cognitive processes by affecting synaptic plasticity, neurogenesis, and neuromodulation\u003csup\u003e[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/sup\u003e. These cytokines can influence cholinergic\u003csup\u003e[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]\u003c/sup\u003e and dopaminergic\u003csup\u003e[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]\u003c/sup\u003e pathways and may contribute to neurodegeneration or regeneration. Some evidence suggests the ability of peripheral cytokines to cross the BBB\u003csup\u003e[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]\u003c/sup\u003e, either through less protected circumventricular regions or via vagal nerve stimulation\u003csup\u003e[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]\u003c/sup\u003e. Emerging evidence also points to oxidative stress as a key mechanism in cognitive aging\u003csup\u003e[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]\u003c/sup\u003e. Oxidative stress impairs mitochondrial function and damages various body systems, particularly the central nervous system\u003csup\u003e[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]\u003c/sup\u003e. Free radicals are capable of inducing brain chronic inflammation via releasing proinflammatory cytokines, which causes cell and synapse damage, synaptic function disruption \u003csup\u003e[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]\u003c/sup\u003e, and microglial cell activation\u003csup\u003e[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]\u003c/sup\u003e, ultimately resulting in neuronal damage. Furthermore, research has shown that HGI correlates with advanced glycation end products (AGEs)\u003csup\u003e[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]\u003c/sup\u003e. Hypertension-induced oxidative stress in cerebral vessels increases the expression of receptor for AGEs (RAGE)\u003csup\u003e[\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]\u003c/sup\u003e, which binds to Aβ and is involved in its transport across the BBB. This interaction exacerbates the accumulation of Aβ and ROS in the brain, worsening cognitive impairment.\u003c/p\u003e \u003cp\u003eOur study offers several advantages over previous research. Firstly, the large sample size and application of weighted data analysis enhance the robustness of our findings. Secondly, the use of smoothed fitted curves based on a fully adjusted model allowed us to identify potential linear relationships between variables. Lastly, our subgroup analyses account for various covariates, which helps in assessing the stability of the results. However, the study has many limitations. As the NHANES database is based on cross-sectional data, we can only examine correlations between the HGI and cognitive impairment, without establishing causality. Future research will focus on conducting prospective studies to explore causal relationships. Additionally, despite our efforts to include a broad range of covariates, there are still other potential confounding factors that can influence the analysis results.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eTaken together, our study suggests a negative correlation between the HGI and cognitive impairment in hypertensive adults\u0026thinsp;\u0026ge;\u0026thinsp;60 years old in the United States. To fully understand the mechanisms underlying this relationship, further prospective studies are necessary.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors express their gratitude to the participants and staff of the NHANES for their invaluable contributions to this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study design was conceived by HD, TYK, WBG, QW, SL, XWZ and JY. HD and LS organized the data, conducted the analyses, and wrote and edited the manuscript. XWZ and JY contributed to the interpretation of the results, revision, and finalization of the manuscript. All authors have reviewed and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (NSFC 82160089,82460084), Special Fund Project for Doctoral Training of the Lanzhou University Second Hospital (YJS-BD-24), CuiYing Scientific and Technological Innovation Program of Lanzhou University Second Hospital (CY2022-QN-A17). This study was also supported by Provincial Talent Project in 2024 (Gan Group Tongzi [2024] No. 4), Lanzhou University Student Innovation and Entrepreneurship Action Plan Project (20240050067) and Cuiying Scientific Training Program for Undergraduates of The Second Hospital \u0026amp; Clinical Medical School, Lanzhou University (CYXZ2024-20).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data were obtained from publicly available sources, as previously stated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe National Center for Health Statistics and Ethics Review Board approved the protocol for NHANES, and all participants provided written informed consent. The authors have disclosed no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declared that they had no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDepartment of Cardiovascular Medicine, Lanzhou University Second Hospital, Lanzhou 730030, China.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e: not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e: not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate declaration\u003c/strong\u003e: not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKIM JH. Hypertension in an ageing population: Diagnosis, mechanisms, collateral health risks, treatments, and clinical challenges [J]. Ageing Res Rev. 2024;98:102344.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLU J, XUAN S, DOWNING N S, et al. Protocol for the China PEACE (Patient-centered Evaluative Assessment of Cardiac Events) Million Persons Project pilot [J]. BMJ open. 2016;6(1):e010200.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBAGGEROER C E, CAMBRONERO F E, SAVAN N A, et al. Basic Mechanisms of Brain Injury and Cognitive Decline in Hypertension [J]. Hypertension. 2024;81(1):34\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSANTISTEBAN M M, IADECOLA C, Hypertension CARNEVALED. Neurovascular Dysfunct Cogn Impairment [J] Hypertens. 2023;80(1):22\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYAMAMOTO K, AKASAKA H, YASUNOBE Y, et al. Clinical characteristics of older adults with hypertension and unrecognized cognitive impairment [J]. Hypertens research: official J Japanese Soc Hypertens. 2022;45(4):612\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eORGANIZATION. W H. Global status report on the public health response to dementia [J]. World Health Organization; 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUNGVARI Z, TOTH P. Hypertension-induced cognitive impairment: from pathophysiology to public health [J]. Nat Rev Nephrol. 2021;17(10):639\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWILLIAMSON JD, PAJEWSKI N M, AUCHUS A P, et al. Effect of Intensive vs Standard Blood Pressure Control on Probable Dementia: A Randomized Clinical Trial [J]. JAMA. 2019;321(6):553\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHUGHES D, JUDGE C, MURPHY R, et al. Association of Blood Pressure Lowering With Incident Dementia or Cognitive Impairment: A Systematic Review and Meta-analysis [J]. JAMA. 2020;323(19):1934\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYANG W, LUO H, MA Y, et al. Effects of Antihypertensive Drugs on Cognitive Function in Elderly Patients with Hypertension: A Review [J]. Aging Dis. 2021;12(3):841\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eROUCH L, CESTAC P, HANON O, et al. Antihypertensive drugs, prevention of cognitive decline and dementia: a systematic review of observational studies, randomized controlled trials and meta-analyses, with discussion of potential mechanisms [J]. CNS Drugs. 2015;29(2):113\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYIMING PAN L M. Inflammatory Markers Associated with Physical Frailty and Cognitive Impairment [J]. aging and disease, 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDAVIDSON M B, SCHRIGER D L, PETERS A L, et al. Glycosylated hemoglobin as a diagnostic test for type 2 diabetes mellitus [J]. JAMA. 2000;283(5):606\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e(NCD-RISC)* N R F C. Global variation in diabetes diagnosis and prevalence based on fasting glucose and hemoglobin A1c [J]. Nat Med. 2023;29(11):2885\u0026ndash;901.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWANG J, ZHANG L, BAI Y, et al. The influence of shorter red blood cell lifespan on the rate of HbA1c target achieved in type 2 diabetes patients with a HbA1c detection value lower than 7 [J]. J diabetes. 2023;15(1):7\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKHERA P K, JOINER C H, CARRUTHERS A, et al. Evidence for interindividual heterogeneity in the glucose gradient across the human red blood cell membrane and its relationship to hemoglobin glycation [J]. Diabetes. 2008;57(9):2445\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCHAO G, ZHU Y. CHEN L. Role and Risk Factors of Glycosylated Hemoglobin Levels in Early Disease Screening [J]. Journal of diabetes research, 2021, 2021: 6626587.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBENJAFIELD A V, GLENN C L, WANG X L, et al. TNFRSF1B in genetic predisposition to clinical neuropathy and effect on HDL cholesterol and glycosylated hemoglobin in type 2 diabetes [J]. Diabetes Care. 2001;24(4):753\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNAYAK A U, SINGH B M DUNMORESJ. Potential Clinical Error Arising From Use of HbA1c in Diabetes: Effects of the Glycation Gap [J]. Endocr Rev. 2019;40(4):988\u0026ndash;99.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHEMPE JM, LIU S, MYERS L, et al. The hemoglobin glycation index identifies subpopulations with harms or benefits from intensive treatment in the ACCORD trial [J]. Diabetes Care. 2015;38(6):1067\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAHN C H, MIN S H, LEE D H, et al. Hemoglobin Glycation Index Is Associated With Cardiovascular Diseases in People With Impaired Glucose Metabolism [J]. J Clin Endocrinol Metab. 2017;102(8):2905\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWANG Y, LIU H, HU X, et al. Association between hemoglobin glycation index and 5-year major adverse cardiovascular events: the REACTION cohort study [J]. Chin Med J. 2023;136(20):2468\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRHEE E J, CHO J H, KWON H, et al. Association Between Coronary Artery Calcification and the Hemoglobin Glycation Index: The Kangbuk Samsung Health Study [J]. J Clin Endocrinol Metab. 2017;102(12):4634\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMCCARTER R J, HEMPE JM, GOMEZ R, et al. Biological variation in HbA1c predicts risk of retinopathy and nephropathy in type 1 diabetes [J]. Diabetes Care. 2004;27(6):1259\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWANG S, GU L, CHEN J, et al. Association of hemoglobin glycation index and glycation gap with cardiovascular disease among US adults [J]. Diabetes Res Clin Pract. 2022;190:109990.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKLEIN K R, FRANEK E. MARSO S, et al. Hemoglobin glycation index, calculated from a single fasting glucose value, as a prediction tool for severe hypoglycemia and major adverse cardiovascular events in DEVOTE [J]. Volume 9. BMJ open diabetes research \u0026amp; care; 2021. 2.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePEERI N C, EGAN K M, CHAI W, et al. Association of magnesium intake and vitamin D status with cognitive function in older adults: an analysis of US National Health and Nutrition Examination Survey (NHANES) 2011 to 2014 [J]. Eur J Nutr. 2021;60(1):465\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEGAN B M, LI J, SHATAT I F, et al. Closing the gap in hypertension control between younger and older adults: National Health and Nutrition Examination Survey (NHANES) 1988 to 2010 [J]. Circulation. 2014;129(20):2052\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCLARK L J, GATZ M. Longitudinal verbal fluency in normal aging, preclinical, and prevalent Alzheimer's disease [J]. Am J Alzheimer's Dis Other dement. 2009;24(6):461\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMACDONALD S W, HULTSCH D F, STRAUSS E et al. Age-related slowing of digit symbol substitution revisited: what do longitudinal age changes reflect? [J]. The journals of gerontology Series B, Psychological sciences and social sciences, 2003, 58(3): P187\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMORRIS JC, HEYMAN A, MOHS R C, et al. The Consortium to Establish a Registry for Alzheimer's Disease (CERAD). Part I. Clinical and neuropsychological assessment of Alzheimer's disease [J]. Neurology. 1989;39(9):1159\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCHEN H, YE K X FENGQ, et al. Trends in the prevalence of cognitive impairment at old age in China, 2002\u0026ndash;2018 [J]. Alzheimer's Dement J Alzheimer's Assoc. 2024;20(2):1387\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSU Y, XIA C, ZHANG H, et al. Emerging biosensor probes for glycated hemoglobin (HbA1c) detection [J]. Mikrochim Acta. 2024;191(6):300.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIBARRA-SALCE R, POZOS-VARELA F J, MARTINEZ-ZAVALA N, et al. Endocr practice: official J Am Coll Endocrinol Am Association Clin Endocrinologists. 2023;29(3):162\u0026ndash;7. Correlation Between Hemoglobin Glycation Index Measured by Continuous Glucose Monitoring With Complications in Type 1 Diabetes [J].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLYU L, YU J, LIU Y, et al. High Hemoglobin Glycation Index Is Associated With Telomere Attrition Independent of HbA1c, Mediated by TNFα [J]. J Clin Endocrinol Metab. 2022;107(2):462\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLYU L, YU J, LIU Y, et al. Dietary patterns, oxidative Stress, inflammation and biological variation in hemoglobin A1c: Association and Mediation analysis in a rural community in north China [J]. Volume 194. Diabetes research and clinical practice; 2022. p. 110154.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWANG R, CHEN C, XU G, et al. Association of triglyceride glucose-body mass index and hemoglobin glycation index with heart failure prevalence in hypertensive populations: a study across different glucose metabolism status [J]. Lipids Health Dis. 2024;23(1):53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGUZIK TJ, NOSALSKI R, MAFFIA P, et al. Immune and inflammatory mechanisms in hypertension [J]. Nat reviews Cardiol. 2024;21(6):396\u0026ndash;416.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGUZIK T J, TOUYZ RM. Oxidative Stress, Inflammation, and Vascular Aging in Hypertension [J]. Hypertension. 2017;70(4):660\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDA SILVA A A, DO CARMO J M, LI X, et al. Role of Hyperinsulinemia and Insulin Resistance in Hypertension: Metabolic Syndrome Revisited [J]. Can J Cardiol. 2020;36(5):671\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eG\u0026oacute;MEZ-MART\u0026iacute;NEZ C, BABIO N, J\u0026uacute;LVEZ J, et al. Glycemic Dysregulations Are Associated With Worsening Cognitive Function in Older Participants at High Risk of Cardiovascular Disease: Two-Year Follow-up in the PREDIMED-Plus Study [J]. Front Endocrinol. 2021;12:754347.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAGRAWAL R, RENO C M, SHARMA S, et al. Insulin action in the brain regulates both central and peripheral functions [J]. Am J Physiol Endocrinol metabolism. 2021;321(1):E156\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHENI M, KULLMANN S, PREISSL H, et al. Impaired insulin action in the human brain: causes and metabolic consequences [J]. Nat reviews Endocrinol. 2015;11(12):701\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKULLMANN S, HENI M, HALLSCHMID M, et al. Brain Insulin Resistance at the Crossroads of Metabolic and Cognitive Disorders in Humans [J]. Physiol Rev. 2016;96(4):1169\u0026ndash;209.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDEERY H A LIANGE, DI PAOLO R et al. Peripheral insulin resistance attenuates cerebral glucose metabolism and impairs working memory in healthy adults [J]. npj Metabolic Health Disease, 2024, 2(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKIM AB. Insulin resistance, cognition, and Alzheimer disease [J]. Obes (Silver Spring Md). 2023;31(6):1486\u0026ndash;98.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFINGER C E, MORENO-GONZALEZ I, GUTIERREZ A, et al. Age-related immune alterations and cerebrovascular inflammation [J]. Mol Psychiatry. 2022;27(2):803\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSIM W L POHL, JO D G, et al. The role of inflammasomes in vascular cognitive impairment [J]. Mol neurodegeneration. 2022;17(1):4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBELARBI K, JOPSON T. TNF-α protein synthesis inhibitor restores neuronal function and reverses cognitive deficits induced by chronic neuroinflammation [J]. J Neuroinflamm. 2012;9:23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCORNELL J, SALINAS S, HUANG H Y, et al. Microglia regulation of synaptic plasticity and learning and memory [J]. Neural regeneration Res. 2022;17(4):705\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSHAPIRA-LICHTER I, BEILIN B, OFEK K, et al. Cytokines and cholinergic signals co-modulate surgical stress-induced changes in mood and memory [J]. Brain Behav Immun. 2008;22(3):388\u0026ndash;98.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLIU Z, QIU A W, HUANG Y, et al. IL-17A exacerbates neuroinflammation and neurodegeneration by activating microglia in rodent models of Parkinson's disease [J]. Brain Behav Immun. 2019;81:630\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOSIPOVA E D, SEMYACHKINA-GLUSHKOVSKAYA O V, MORGUN A V, et al. Gliotransmitters and cytokines in the control of blood-brain barrier permeability [J]. Rev Neurosci. 2018;29(5):567\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBESEDOVSKY H O, DEL REY A. Central and peripheral cytokines mediate immune-brain connectivity [J]. Neurochem Res. 2011;36(1):1\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLANE H Y, WANG S H, LIN C H. Differential relationships of NMDAR hypofunction and oxidative stress with cognitive decline [J]. Psychiatry Res. 2023;326:115288.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNETTO M B, DE OLIVEIRA JUNIOR A N, GOLDIM M et al. Oxidative stress and mitochondrial dysfunction contributes to postoperative cognitive dysfunction in elderly rats [J]. Brain, behavior, and immunity, 2018, 73: 661\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMASSAAD CA, KLANN E. Reactive oxygen species in the regulation of synaptic plasticity and memory [J]. Antioxid Redox Signal. 2011;14(10):2013\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eROJO A I, MCBEAN G, CINDRIC M, et al. Redox control of microglial function: molecular mechanisms and functional significance [J]. Antioxid Redox Signal. 2014;21(12):1766\u0026ndash;801.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFELIPE D L, HEMPE J M, LIU S, et al. Skin intrinsic fluorescence is associated with hemoglobin A(1c)and hemoglobin glycation index but not mean blood glucose in children with type 1 diabetes [J]. Diabetes Care. 2011;34(8):1816\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHARTOG J W, VAN DE WAL R M, SCHALKWIJK C G, et al. Advanced glycation end-products, anti-hypertensive treatment and diastolic function in patients with hypertension and diastolic dysfunction [J]. Eur J Heart Fail. 2010;12(4):397\u0026ndash;403.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Hypertension, Hemoglobin glycation index (HGI), Cognitive function, NHANES, Cross-sectional study","lastPublishedDoi":"10.21203/rs.3.rs-5736468/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5736468/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe Hemoglobin Glycation Index (HGI) quantifies the difference between the actual and expected values of glycosylated hemoglobin (HbA1c), a marker that has been closely linked to various adverse health outcomes. Nonetheless, a significant gap exists in the current literature concerning the association between HGI and cognitive function. This study aims at testing such association in older adults with hypertension, a topic that has not yet been extensively investigated.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA linear regression model between glycated hemoglobin A1c (HbA1c) levels and fasting plasma glucose (FPG) was constructed for the calculation of the HGI. The cross-sectional study focused on evaluating the cognitive function of hypertensive individuals (\u0026ge;\u0026thinsp;60 years old), based on the data from the 2011\u0026ndash;2014 National Health and Nutrition Examination Survey (NHANES), by using a series of standardized tests, including the Word List Learning (CERAD-WL) and Delayed Recall (CERAD-DR) tests from the Consortium to Establish a Registry for Alzheimer\u0026rsquo;s Disease (CERAD), the Animal Fluency Test (AFT), and the Digit Symbol Substitution Test (DSST). Weighted logistic and linear regression models served for evaluating the effect of HGI on hypertensive patients\u0026rsquo; cognitive function. Restricted cubic spline (RCS) curves assisted in detecting the underlying nonlinear associations between HGI and cognitive outcomes. Furthermore, subgroup analyses and interaction tests were performed to gain deeper insights into these associations.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe study included 1023 participants\u0026thinsp;\u0026ge;\u0026thinsp;60 years old from 2011\u0026ndash;2014 NHANES. Higher HGI was accompanied by lower DSST score (P\u0026thinsp;=\u0026thinsp;0.009). In the fully adjusted model, participants in the highest quartile (Q4) of HGI possessed a lower DSST score (β\u0026thinsp;=\u0026thinsp;0.01, 95% CI 0.00\u0026ndash;0.41) versus the lowest quartile (Q1), and were more likely to exhibit low cognitive function as evaluated by the DSST (OR\u0026thinsp;=\u0026thinsp;2.21, 95% CI 0.98\u0026ndash;5.03). According to the results from RCS analysis, HGI presented a linear relevance to cognitive function scores in older adults with hypertension. No significant statistical interaction was detected between these variables.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eHigh HGI was an important risk factor leading to reduced cognitive performance in hypertensive patients, ensuring HGI to be used for effectively predicting patients\u0026rsquo; cognitive decline.\u003c/p\u003e","manuscriptTitle":"Exploring the Association Between Hemoglobin Glycation Index and Cognitive Function in Older Adults with Hypertension: A Cross-Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-06 14:54:16","doi":"10.21203/rs.3.rs-5736468/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-01-10T10:50:51+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-01-10T10:42:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-01-03T14:31:31+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Geriatrics","date":"2024-12-30T14:54:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"21b4435f-9ad6-412e-92d4-fd7d354b2e9b","owner":[],"postedDate":"January 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-05-19T15:59:15+00:00","versionOfRecord":{"articleIdentity":"rs-5736468","link":"https://doi.org/10.1186/s12877-025-05999-2","journal":{"identity":"bmc-geriatrics","isVorOnly":false,"title":"BMC Geriatrics"},"publishedOn":"2025-05-13 15:57:09","publishedOnDateReadable":"May 13th, 2025"},"versionCreatedAt":"2025-01-06 14:54:16","video":"","vorDoi":"10.1186/s12877-025-05999-2","vorDoiUrl":"https://doi.org/10.1186/s12877-025-05999-2","workflowStages":[]},"version":"v1","identity":"rs-5736468","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5736468","identity":"rs-5736468","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}