This study was a multicenter observational investigation involving hospitalized patients with hypertension. The Affiliated Hospital of Shandong University of Traditional Chinese Medicine collaborated with nine hospitals located in Jinan, Weifang, Yantai, Tai’an, Dongying, and Jining. Hypertensive patients underwent comprehensive evaluation of: (a) general health status, (b) physical activity levels, (c) sleep quality, and (d) cognitive function. A total of 6,098 patients were enrolled from May 2022 to July 2024. The subjects of this study were patients with essential hypertension aged over 40 years. Patients with secondary hypertension, renal impairment, comorbid mental disorders, and those with a history of alcohol or psychotropic drug abuse were excluded. After matching patients with hypertension and cognitive decline to cognitively normal hypertensive patients by age and sex in a 1:1 ratio, 2,726 patients were included in the final analysis, comprising 1,363 patients with cognitive decline and 1,363 cognitively normal patients.

Among them, α is the type I error, β is the type II error, p₀, and p₁ represent the estimated exposure values of the control and exposed groups of the target population, respectively, q₁ = (1 − p₁), q₀ = (1 − p₀), and p₁ = p₀OR/ [1 + p₀(OR − 1)] (13).

In a previous meta-analysis of the relationship between hypertension and cognitive decline, the prevalence of cognitive decline in hypertensive patients was 30% (14), so p₀ = 0.30, then p₁ = 0.46, with an expected OR = 2.0, α = 0.05, and β = 0.10. Substituting into the above formula, and since this study used 1:1 for matching, a minimum of 186 case groups and 186 control samples. Considering the follow-up miss rate, a total of 2,726 patients were finally included in this study.

Hypertension is defined as a systolic blood pressure (SBP) of 140 mmHg or higher, a diastolic blood pressure (DBP) of 90 mmHg or higher, or the current use of antihypertensive medications (15). It is classified into three grades, Grade 1: SBP 140–159 mmHg and/or DBP 90–99 mmHg; Grade 2: SBP 160–179 mmHg and/or DBP 100–109 mmHg; Grade 3: SBP ≥ 180 mmHg and/or DBP ≥ 110 mmHg. Cognitive function was assessed using the Mini-Mental State Examination (MMSE), which evaluates five cognitive domains: orientation, attention, memory, language, and visuospatial abilities. The MMSE is a validated screening tool for cognitive decline and impairment that detects and differentiates between patients with cognitive decline and those with intact cognition (16, 17). The total score for the MMSE is 30, with higher scores reflecting better cognitive function. In this study, MMSE scores ranging from 18 to 27 were defined as early cognitive decline (18).

The WWI is determined by dividing the waist circumference (WC, measured in centimeters) by the square root of body weight (measured in kilograms) (10). Body weight and WC were assessed anthropometrically by trained healthcare professionals, whose proficiency was regularly verified. To ensure accuracy, subjects were advised to wear minimal clothing during weighing. Waist circumference was measured using a tape measured at specific anatomical landmarks.

Blood pressure was measured by a qualified nurse in a quiet environment. Additionally, fasting blood glucose (FBG), triglycerides (TG), total cholesterol (TC), high-density lipoproteins (HDL-C), low-density lipoproteins (LDL-C), and serum creatinine (Scr) levels were assessed in all subjects after an overnight fast.

Physical activity levels were measured using the Chinese version of the International Physical Activity Questionnaire Long Form (IPAQ-LC), which is reliable and shows adequate evidence of validity (19, 20).

Continuous variables collected from participants were tested for normality according to the characteristics of the data; data conforming to a normal distribution were expressed as mean ± standard deviation (SD), and data not conforming to a normal distribution were expressed as median and interquartile range (IQR). Qualitative variables were expressed as relative numbers or percentages. WWI was transformed from a continuous variable to a categorical variable (quartile), and differences between subjects grouped by quartiles of WWI were compared in a multivariate logistic regression using quartile (Q1) as the reference group: Model 1 was not adjusted for any confounding variables and represented a univariate analysis. Model 2 was adjusted for sex, age, education level, marital status, type of work, smoking and drinking. The primary physiological and biochemical indicators (SBP, DBP, BMI, FBG, TG, TC, HDL-C, LDL-C, Scr) were incorporated into Model 2 to develop Model 3. Considering the impact of sleep disorders on HCD, Model 4 included the prevalence of sleep disorders in hypertensive patients. Data processing and analysis were performed using R version 4.4.0, along with Zstats 1.0.¹

Table 1 summarizes the baseline characteristics of hypertensive patients with normal cognitive function and those with cognitive decline. The mean age of the participants in this study was 72 years. The gender distribution was 44.7% male and 55.3% female. The risk of cognitive decline was higher among the older adults with hypertensive patients (p < 0.05).

Table 2 categorizes the participants into quartiles based on their WWI values. Q1 corresponds to values ranging from 7.00 to 10.11, Q2 from 10.11 to 10.62, Q3 from 10.62 to 11.16, and Q4 from 11.16 to 16.76. The prevalence of cognitive decline significantly increased with higher WWI index among hypertensive patients (Q1: 41.75%; Q2: 50.07%; Q3: 54.37%; Q4: 53.64%, p < 0.001).

Figure 1 shows a significant negative correlation between age, WWI, PSQI, and MMSE scores in this study (p < 0.001). Additionally, significant positive correlations were observed between TC, LDL-C and MMSE scores (p < 0.001). Although the correlations between these variables and MMSE scores were relatively weak, the findings suggest that increases in age, WWI, and PSQI scores may negatively impact cognitive functioning. On the contrary, higher TC and LDL-C levels were associated with better cognitive function. This finding seems to be contradictory because TC and LDL-C are usually associated with adverse cardiovascular outcomes. Previous studies have also obtained conflicting data on the relationship between blood lipids and cognitive function (21–24), suggesting that the effects of TC and LDL-C on cognitive function may be dual.

Table 3 shows the relationship between WWI and HCD and the results of the multivariate logistic regression analysis. In the original model, there was a statistically significant positive correlation between WWI and HCD, with an OR of 1.13 (95% CI: 1.05 ~ 1.22; p < 0.001). In Model 2, after adjusting for factors such as age, sex, educational level, marital status, type of work, smoking and drinking, the positive association remained highly significant (OR = 1.11; 95% CI: 1.03 ~ 1.20; p < 0.001). In Model 3, even after adjusting for additional variables including BMI, FBG, TG, TC, HDL-C, LDL-C, and Scr, the correlation between WWI and HCD continued to be statistically significant (OR = 1.10; 95% CI: 1.02 ~ 1.19; p < 0.001). In Model 4, which was developed based on Model 3, this significant correlation persisted (OR = 1.12; 95% CI: 1.04 ~ 1.21; p < 0.001), even after accounting for sleep disorders. This indicates that the prevalence of cognitive decline among hypertensive patients increased by 12% with each unit increase in WWI. The statistical significance of the trend test showed that the strong correlation between WWI and cognitive decline in hypertensive patients persisted even after stratifying WWI into quartiles (p < 0.001). In Model 4, the prevalence of cognitive decline in hypertensive patients rose by 53% when comparing the highest quartile of WWI to the lowest quartile.

Our study is based on the hypertension follow-up system in Shandong Province, which is a hypertension sampling survey system in Shandong Province, following strict research programs and quality control measures. To enhance the reliability of our research results, we have adjusted multiple potential covariates to ensure the reliability of the results. Due to the simplicity and ease of calculation, WWI may become a practical tool for managing and intervening cognitive decline in patients with hypertension in clinical practice.

However, this study has several limitations. First, as a case-control study, it cannot establish a causal relationship between WWI and HCD. Second, although we adjusted to many important covariates, we could not completely exclude the effects of other potential confounders. While WWI predicted a decline in cognitive ability in our study, we recognized that other indicators derived from anthropometric indicators [e.g., TyG-body mass index (51)] may also complement cardiovascular risk prediction. Future studies should explore the combined utility of WWI and these indicators to better understand their influence on cognitive function in patients with hypertension.

Furthermore, we found that higher TC and LDL-C levels were associated with better cognitive performance. This finding seems contradictory, as high levels of these lipids are often associated with negative health outcomes, particularly cardiovascular risk (52, 53). However, combined with previous studies (21–24) and our findings, the relationship between TC, LDL-C, and cognitive function does not seem to be one-way but is influenced by a series of physiological and pathological factors. Therefore, the effect of blood lipid levels on cognitive function in patients with hypertension needs further study.

Finally, since the sample of this study is limited to individuals with hypertension in Shandong Province, the generalization of our results in the wider population of hypertension in China remains to be verified.