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Association of waist-to-hip ratio adjusted for body mass index with cognitive impairment in middle-aged and elderly patients with type 2 diabetes mellitus: a cross-sectional study

BMC Public Health volume 24, Article number: 2424 (2024) Cite this article

Abstract

Background

Numerous reports indicate that both obesity and type 2 diabetes mellitus (T2DM) are factors associated with cognitive impairment (CI). The objective was to assess the relationship between abdominal obesity as measured by waist-to-hip ratio adjusted for body mass index (WHRadjBMI) and CI in middle-aged and elderly patients with T2DM.

Methods

A cross-sectional study was conducted, in which a total of 1154 patients with T2DM aged ≥ 40 years were included. WHRadjBMI was calculated based on anthropometric measurements and CI was assessed utilizing the Montreal Cognitive Assessment (MoCA). Participants were divided into CI group (n = 509) and normal cognition group (n = 645). Correlation analysis and binary logistic regression were used to explore the relationship between obesity-related indicators including WHRadjBMI, BMI as well as waist circumference (WC) and CI. Meanwhile, the predictive power of these indicators for CI was estimated by receiver operating characteristic (ROC) curves.

Results

WHRadjBMI was positively correlated with MoCA scores, independent of sex. The Area Under the Curve (AUC) for WHRadjBMI, BMI and WC were 0.639, 0.521 and 0.533 respectively, and WHRadjBMI had the highest predictive power for CI. Whether or not covariates were adjusted, one-SD increase in WHRadjBMI was significantly related to an increased risk of CI with an adjusted OR of 1.451 (95% CI: 1.261–1.671). After multivariate adjustment, the risk of CI increased with rising WHRadjBMI quartiles (Q4 vs. Q1 OR: 2.980, 95%CI: 2.032–4.371, P for trend < 0.001).

Conclusions

Our study illustrated that higher WHRadjBMI is likely to be associated with an increased risk of CI among patients with T2DM. These findings support the detrimental effects of excess visceral fat accumulation on cognitive function in middle-aged and elderly T2DM patients.

Peer Review reports

Background

Obesity has been recognized as a public health issue affecting human health worldwide, and obesity has been of particular interest in relation to cognition, specifically with cognitive impairment (CI). Globally, the number of people developing dementia is estimated to climb from 57 million in 2019 to 153 million in 2050 as the population increases and ages rapidly [1], which will place a tremendous burden on families, society and the economy. Increasing evidence has established that obesity can affect cognitive function. Obesity is also a risk factor for prediabetes and type 2 diabetes mellitus (T2DM), due to the fact that obesity leads to insulin resistance and β cell dysfunction [2, 3]. In addition, individuals with diabetes typically exhibit poorer cognitive function and a higher risk of dementia [4,5,6]. According to a cross-sectional study conducted in China, the prevalence of mild cognitive impairment (MCI) in T2DM patients was 21.8% [7]. Moreover, CI was more pronounced in obese T2DM patients than in non-obese T2DM patients [8]. Therefore, increasing attention is being focused on investigating whether obesity and T2DM act synergistically to contribute to the development of CI.

The impact of obesity on cognitive function is believed to be inconclusive. Body mass index (BMI), a routinely used indicator of generalized obesity, is complex regarding the relationship between BMI and CI as well as dementia. Some evidence suggests that elevated BMI is associated with poorer cognitive function [9, 10], whereas there are a couple of studies that showed that being overweight and obese in late-life seemed to be associated with better cognitive performance [11, 12], i.e., the “obesity paradox”. The reasons for such contradictory results can be partly explained by differences in the age distribution of the participants and by the fact that BMI does not distinguish between fat mass and non-fat mass, nor does it reflect the body fat distribution [13, 14]. Relying exclusively on BMI as the sole determinant of obesity may be insufficient in comprehensively capturing the complex association between obesity and cognitive function. In recent years, numerous innovative obesity-related metrics, such as abdominal volume index, body adiposity index, body shape index, body roundness index and weight-adjusted-waist index, have been suggested to be related to cognitive function [15, 16]. It is increasingly acknowledged that fat distribution plays a more pivotal role than the total amount of fat in the occurrence of obesity-related complications [17]. Notably, fat distribution, in particular excess visceral adipose tissue, was associated with cognitive decline [18]. Recent research indicates a J-shaped correlation between central visceral obesity measured by weight-adjusted-waist index and the risk of cardiovascular disease, with similar trends also observed in cardiovascular disease subtypes [19]. This highlights the critical importance of addressing visceral obesity. Compared to generalized obesity, visceral fat accumulation and abdominal obesity demonstrate superior predictive capacity in evaluating cardiovascular diseases [20, 21] and cognitive decline [22]. Waist circumference (WC), a surrogate indicator of abdominal fat distribution, has been found to be associated with cognitive dysfunction in patients with T2DM [23]. However, WC does not fully differentiate between visceral and subcutaneous fat and is insensitive to height [14, 24], so searching for a better proxy for visceral fat is necessary.

Waist-to-hip ratio adjusted for body mass index (WHRadjBMI) can estimate intra-abdominal fat accumulation, which more accurately reflects the distribution of visceral and subcutaneous fat than BMI and WC, with higher WHRadjBMI indicating higher visceral fat burden [25]. WHRadjBMI, an indicator derived from BMI and WHR, can compensate for the limitations of BMI and WC to a certain extent and is commonly used in epidemiologic and genetic studies [26,27,28]. Hitherto, the relationship between WHRadjBMI and cognitive function in T2DM patients has not been reported. Hence, we carried out a cross-sectional study and sought to evaluate the relationship between WHRadjBMI and CI in middle-aged and elderly T2DM patients. A better awareness of the association between abdominal obesity and CI could facilitate the identification of high-risk individuals for CI and provide proactive prevention strategies.

Methods

Study population

The present study was designed as a cross-sectional analysis with sample size estimation using PASS V.15 software. Based on the findings reported in prior literature, the prevalence of CI in patients with T2DM was 35.8% [29]. Using the Exact (Clooper-Pearson) method for sample size estimation with the confidence level of 0.95 and the allowable error of 0.0358, we calculated that a minimum of 715 patients were required. To minimize selection bias and enhance the robustness of the results, we recruited 1154 consecutive patients with T2DM who were admitted to the Endocrinology Department of Tianjin Union Medical Center from July 2018 to July 2022. All participants met the diagnostic criteria for T2DM according to 1999 WHO criteria. Exclusion criteria of the participants were aged < 40 years; severe hearing, vision, reading, speech impairments or other reasons for not being able to complete the cognitive function assessment; anemia (hemoglobin ≤ 90 g/L), severe hepatic or renal insufficiency, abnormal thyroid function, history of hyperthyroidism or hypothyroidism; history of Parkinson's disease, epilepsy, traumatic brain injury, encephalitis, brain tumor, schizophrenia, depression or vascular dementia. Repeat enrollments were excluded and only data from their initial enrollment were retained. Those who had incomplete data on critical variables such as missing anthropometric measurements were excluded, too.

The process of the inclusion and exclusion of participants is shown in the Supplementary Fig. 1. All participants signed informed consent forms, and the study protocol was approved by the Medical Ethics Committee of Tianjin Union Medical Center and is in compliance with the principles of the Declaration of Helsinki.

Covariates

Face-to-face interviews conducted by trained interviewers, such as physicians or postgraduate medical students, using a structured questionnaire to obtain clinical information. The interviews were conducted during 8.00–11.00 am and lasted around 20 min per participant to finish. The interviews consisted of demographic variables and health information, including sex (female and male), age, ethnicity (Han nationality or others), years of education, smoking status, alcohol consumption status, physical exercise, nutrition management, diabetes duration, comorbidities (cardiovascular disease, cerebrovascular disease, hypertension), medication use (antidiabetic agents, antihypertensive agents, lipid-lowering agents). Smoking status was classified as ‘current smoking’ or ‘no current smoking’. Alcohol consumption status was categorized into ‘current alcohol consumption’ or ‘no current alcohol consumption’. Physical exercise was defined as engaging in a minimum of 150 min of moderate intensity aerobic exercise per week [30]. Nutritional management was defined as adhering to a rational and balanced intake of calories and essential nutrients required under the guidance of a diabetes manager [30]. The uses of medication were defined as an affirmative response to the following question: ‘Do you regularly take antidiabetic, antihypertensive or lipid-lowering medications?’.

After resting for at least 5 min, systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured by using an automatic electronic sphygmomanometer (AC-05C, Ling Qian, China). Participants were asked to fast overnight for more than 8 h prior to drawing venous blood samples. Blood biochemical examination mainly included fasting plasma glucose (FPG), total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-c), low-density lipoprotein cholesterol (LDL-c) and uric acid (UA). Glycated hemoglobin (HbA1c) was measured with an automatic glycosylated hemoglobin analyzer (HA-8180, ARKRAY, Japan). Eventually, the clinical characteristics of the participants were extracted from the electronic medical record system.

Anthropometric measurements

Height and weight measurements of participants in the study were performed by a health professional investigator utilizing an automated height and weight measuring device (DST-600, DONGHUAYUAN, China). WC was measured with a soft tape, to the nearest 0.1 cm, between the lower rib margin and iliac crest. Using a soft tape, the hip's widest point at the level of the greater trochanter was measured to determine the hip circumference (HC), to the nearest 0.1 cm. All anthropometric measurements were performed while wearing light clothing. BMI was calculated by dividing weight by height squared (kg/m2). WHR was computed by dividing a person’s WC by his/her HC. The calculation of WHRadjBMI involved generating predicted residuals from a linear regression of WHR on BMI [31], and due to differences in fat distribution between sexes, WHRadjBMI was calculated separately according to sex.

Cognitive evaluation

The Montreal Cognitive Assessment (MoCA) is a straightforward cognitive screening tool with exceptional sensitivity [32]. In this study, CI status of the participants was determined using the Chinese version of MoCA [33], which took approximately 10 min to administer. Briefly, MoCA includes 8 important cognitive domains: visuospatial, executive function, naming ability, delayed recall, attention, language, abstraction and orientation, with scores ranging from 0—30, and scores of < 26 were denoted CI [32]. This test was performed based on face-to-face interviews in a quiet room. Finally, the participants were categorized into two groups: CI group and normal cognition group.

Statistical analysis

Clinical characteristics of participants were expressed as mean ± standard deviation (SD) or median (25th, 75th percentile) for continuous variables and n (%) for categorical variables. The number of participants with missing data on HbA1c, nutrition management, physical exercise, SBP, and DBP were 5 (0.43%), 4 (0.35%), 3 (0.26%), 2 (0.17%) and 2 (0.17%), respectively. Despite the small proportion of missing data in this study, in order to minimize bias due to missing data and to maintain the largest possible sample size, we used multiple imputation via Statistical Product and Service Solutions (SPSS) software to address missing data. We assumed that the type of missing values was missing at random. The imputation methods used were logistic regression for binary data and predictive mean matching for continuous variables. The imputation model included sex, age, education, current smoking status, current alcohol consumption status, physical exercise, nutrition management, cardiovascular disease, cerebrovascular disease, hypertension, diabetes duration, antidiabetic agents, antihypertensive agents, lipid-lowering agents, SBP, DBP, TC, TG and WC. To compare clinical characteristics between the two groups, student t-tests (normally distributed continuous variables) or Mann–Whitney U test (non-normally distributed continuous variables) or the Chi square test (categorical variables) were used.

Correlations between obesity-related indicators (WHRadjBMI, BMI and WC) and MoCA scores were ascertained by the Spearman correlation analysis. The predictive power of BMI, WC, and WHRadjBMI for CI was investigated using receiver operating characteristic (ROC) curve analysis, and DeLong’s test was used to compare the area under the ROC curve (AUC) of WHRadjBMI with BMI and WC, respectively. Using logistic regression models, we estimated the association between different obesity-related indicators (WHRadjBMI, BMI and WC) as continuous variables and CI based on per SD increments, first unadjusted and then fitting three models for multivariate logistic regression analysis. In Model 1, we adjusted for demographic characteristics (age, sex, ethnicity, years of education) and lifestyle (current smoking status, current alcohol consumption status, physical exercise and nutrition management). In Model 2, we further adjusted for comorbidities (cardiovascular disease, cerebrovascular disease and hypertension), medication use (antidiabetic agents, antihypertensive agents and lipid-lowering agents) and diabetes duration. In Model 3 (fully adjusted model), we further adjusted for blood pressure (SBP and DBP) and biochemical indicators (FPG, TC, TG, HDL-c, LDL-c, UA and HbA1c).

To further explore the relationship between WHRadjBMI and CI, we performed quartile transformation of WHRadjBMI. Using either linear polynomial contrasts in ANOVA (normally distributed variables) or the Jonckheere-Terpstra test (non-normally distributed variables), trends in continuous variables between groups were evaluated. Trends in dichotomous variables were examined between groups using the Cochran-Armitage trend test. On the basis of univariate logistic regression analysis, we utilized three multivariate logistic regression models to adjust different covariates and to explain the effects of WHRadjBMI on CI. Model 1 was adjusted by age, sex, ethnicity, years of education, current smoking status, current alcohol consumption status, physical exercise and nutrition management. Model 2 was additionally adjusted by cardiovascular disease, cerebrovascular disease, hypertension, diabetes duration, antidiabetic agents, antihypertensive agents and lipid-lowering agents based on model 1 and Model 3 (fully adjusted model) was additionally adjusted by SBP, DBP, FPG, TC, TG, HDL-c, LDL-c, UA and HbA1c based on model 2. We also conducted stratified analyses to categorize participants into different subgroups based on sex (male, female) and age (< 60 y, ≥ 60 y).

Several sensitivity analyses were performed. To clarify the effects of factors that may influence cognitive function on relationships between WHRadjBMI and CI, we also conducted subgroup analyses based on diabetes duration (≤ 10y, > 10y), HbA1c (< 7%, ≥ 7%), cerebrovascular disease (yes, no), metformin (yes, no) and insulin therapy (yes, no). Based on the results of univariate analyses, logistic regression was used to examine the association between subgroups and CI, adjusting for key covariates including sex, age, years of education, current smoking status, physical exercise, nutrition management, cerebrovascular disease, diabetes duration and HbA1c. Also, an interaction analysis of stratification factors and WHRadjBMI on CI was performed. Additionally, we repeated the main analyses after excluding samples with missing data.

Statistical analyses were performed using SPSS version 26.0 or R version 4.3.1, and P values were two-sided, with differences considered significant when P < 0.05. All graphs were plotted in GraphPad Prism version 9.0 for Windows.

Results

Clinical characteristics

Among the 1,154 participants with T2DM (n = 529 [45.8%] female), the mean age was 61.9 years, 509 patients were diagnosed with CI (44.1%) and 645 patients with T2DM were normal cognition (55.9%). The clinical characteristics of participants are shown in Table 1. The mean age of the CI group was 63.6 years (SD = 7.6) higher than that of the normal cognition group, which was 60.7 years (SD = 7.6). The proportion of females (49.3% vs 43.1%) and cerebrovascular disease (35% vs 21.7%) in the CI group was higher than that of the normal cognition group. Participants with CI were more likely to have higher WC, WHRadjBMI, and were more likely to be less educated and less nutrition management.

Table 1 Clinical characteristics of participants included in the study
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Obesity-related Indicators (WHRadjBMI, BMI, and WC) and CI

Regarding the correlation analysis of obesity-related indicators (BMI, WC and WHRadjBMI) with MoCA scores, it was found that WHRadjBMI was negatively correlated with MoCA scores both in the total population (r = -0.279, P < 0.001), males (r = -0.262, P < 0.001), and females (r = -0.303, P < 0.001). Whereas BMI was positively associated with MoCA scores only statistically significant in the total population (r = 0.067, P = 0.023) and males (r = 0.106, P = 0.008), WC was negatively associated with MoCA scores statistically significant in the total population (r = -0.069, P = 0.019) and females (r = -0.156, P < 0.001) (Supplementary Fig. 2). It can be seen that the correlation between WHRadjBMI and MoCA scores was stronger compared with BMI and WC and was not dependent on sex.

Table 2 presents logistic regression analyses of obesity-related indicators associated with CI unadjusted and after adjustment by controlling covariates. When not adjusting for covariates, it was observed that one SD increase in WC (OR: 1.173, 95% CI: 1.044–1.319, P = 0.008) and WHRadjBMI (OR: 1.625, 95% CI: 1.431–1.846, P < 0.001) levels was positively associated with the risk of CI, whereas there was no significant correlation between BMI and CI. In a fully adjusted model that higher levels of WHRadjBMI were associated with a greater risk of CI. An increase of one SD in WHRadjBMI resulted in a 45.1% increased risk of CI (OR: 1.451, 95% CI: 1.261–1.167, P < 0.001). For a one SD increase in BMI, the OR for CI was 0.853 (95% CI: 0.744–0.979, P = 0.024). However, a one SD increase in WC was not associated with CI (OR:1.100, 95% CI: 0.959–1.261, P = 0.173). Among these obesity-related indicators, WHRadjBMI resulted as an independent risk factor for CI, while BMI was a protective factor.

Table 2 Associations between obesity-related indicators and CI
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The performance of obesity-related indicators (BMI, WC and WHRadjBMI) in predicting CI was demonstrated by ROC curves with AUC of 0.521 (P = 0.220), 0.533 (P = 0.057) and 0.639 (P < 0.001); WHRadjBMI had the biggest AUC compared with BMI and WC (Fig. 1). Furthermore, the DeLong’s test demonstrated that the differences between AUC of WHRadjBMI and that of BMI and WC were all significant (P < 0.001).

Fig. 1
figure 1

ROC curves of obesity-related indicators for predicting CI. Abbreviations: WC, waist circumference; BMI: body mass index; WHRadjBMI: waist-to-hip ratio adjusted for body mass index; AUC: Area Under the Curve

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WHRadjBMI and CI

All of participants were divided into four subgroups based on WHRadjBMI quartiles (quartile 1 [Q1]: ≤ -0.03971; quartile 2 [Q2]: -0.03970 to ≤ -0.00210; quartile 3 [Q3]: -0.00209 to ≤ 0.03714; quartile 4 [Q4]: ≥ 0.03715) and linear trends in clinical characteristics were analyzed between different subgroups (Supplementary Table 1). There was an increasing trend across quartiles of WHRadjBMI for age, physical exercise, comorbidities (cardiovascular disease, cerebrovascular disease and hypertension), diabetes duration, antihypertensive agents use and SBP, whereas proportion of nutrition management, HDL and MoCA scores tended to decrease (all P for trend < 0.05). The prevalence of CI increased with ascending quartiles of WHRadjBMI (28.4% in Q1, 39.2% in Q2, 48.8% in Q3, 60.1% in Q4, P for trend < 0.001) (Fig. 2).

Fig. 2
figure 2

Prevalence of CI according to quartiles of WHRadjBMI

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We compared the risk of CI among quartiles of WHRadjBMI by multivariate logistic regression analyses using the first quartile (Q1) as the reference, and the results are shown in the Table 3. When not adjusting for covariates, the ORs for CI progressively increased across increasing quartiles of WHRadjBMI (Q4 vs. Q1 OR: 3.798, 95% CI: 1.704–3.394, P for trend < 0.001). After an adjustment for different covariates, the positive association between WHRadjBMI and the risk of CI was still observed as a quartile variable, Q4 vs Q1, OR (95% CI): 2.980 (2.032—4.371), P for trend < 0.001.

Table 3 Associations between quartiles of WHRadjBMI and CI
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Stratified analyses

As age is an independent risk factor for cognitive dysfunction and aging is accompanied by changes in the brain [34], we stratified participants according to age in order to further illustrate the effect of age on the association between WHRadjBMI and CI. In participants aged < 60 years, the risk of CI was increased for the fourth quartile (OR: 2.878, 95% CI: 1.573—5.263, P for trend = 0.002). In elderly individuals aged ≥ 60 years, the risk of CI increased almost with increasing WHRadjBMI (Q2 vs Q1 OR: 1.890, 95% CI: 1.212 -2.946; Q3 vs Q1 OR: 2.843, 95% CI: 1.814—4.456; Q4 vs Q1 OR: 2.790, 95% CI: 1.783—4.367; P for trend < 0.001) (Fig. 3).

Fig. 3
figure 3

Associations between quartiles of WHRadjBMI and CI, stratified by sexa and ageb. aAdjusted for age, years of education, current smoking status, physical exercise, nutrition management, cerebrovascular disease, diabetes duration, HbA1c. bAdjusted for sex, years of education, current smoking status, physical exercise, nutrition management, cerebrovascular disease, diabetes duration, HbA1c. *P < 0.05

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Females are also at risk for CI and dementia [35], possibly due to reduced estrogen and related hormones after menopause and differences in brain structure [36]. In addition, there are significant differences in fat distribution between males and females. As a result, we carried out stratified analyses according to sex, observing that the risk of CI increased with increasing WHRadjBMI and that this relationship was not modified by sex (Fig. 3).

We also performed interaction analyses of WHRadjBMI with sex and age, respectively, which suggested that there was no interaction between WHRadjBMI and either sex (P for interaction = 0.490) or age (P for interaction = 0.844).

Sensitivity analyses

Considering that diabetes duration, glycemic control, and cerebrovascular disease are also important factors of the development of CI [37], we stratified participants by diabetes duration, HbA1c, and cerebrovascular disease as described above, and also tested for interactions between WHRadjBMI and stratification factors on the risk of CI. Moreover, we stratified the analyses according to whether metformin or insulin therapy was applied. In the stratified analyses, the results were largely consistent with the primary outcome except for HbA1c < 7% (Supplementary Fig. 3–4). We considered this could be attributed to the small sample size of this subgroup with HbA1c < 7%. Furthermore, we observed no significant interaction effect of diabetes duration (P for interaction = 0.953), HbA1c (P for interaction = 0.713), cerebrovascular disease (P for interaction = 0.363), metformin (P for interaction = 0.716) and insulin therapy (P for interaction = 0.529) with WHRadjBMI for CI. After excluding missing data, the risk of CI increased with increasing WHRadjBMI in the fully adjusted model (Supplementary Table 2). The robustness of the results of the main analysis was verified.

Discussion

In present study, we focused on the association between abdominal obesity, as measured by WHRadjBMI, with CI among Chinese T2DM patients aged ≥ 40 years. It was demonstrated that higher WHRadjBMI was significantly associated with an increased risk of CI, independently of age, sex, diabetes duration, cerebrovascular disease, metformin and insulin therapy even after adjustment for covariates. Furthermore, WHRadjBMI could be a relatively stronger parameter to predict poor cognitive function compared with BMI and WC. Our findings suggest that patients with T2DM and abdominal obesity may face a heightened risk of CI. Therefore, early assessment of cognitive function and prompt interventions in individuals with abdominal obesity and T2DM are crucial for achieving optimal public health gains.

There has been some research on the relationship between obesity and the risk of CI, but the results have not been completely consistent. The present study observed a positive correlation between BMI and MoCA scores in total participants, suggesting that high BMI appears to be a protective factor for cognitive function in patients with T2DM. Consistent with this finding, a post-hoc analysis of the Action to Control Cardiovascular Risk in Diabetes-Memory in Diabetes (ACCORD-MIND) study showed that higher BMI was associated with better cognitive function in T2DM patients [38]. This result may be explained by the following reasons. First, lower BMI may be the result of poor nutritional status leading to reduced muscle mass for older individuals. Second, weight loss begins several years before diagnosis of dementia [39]. Deng et al. found that adult BMI showed a significant U-shaped relationship with dementia, and further revealed that obesity may increase the risk of dementia by affecting metabolism, inflammation and brain structure [40]. However, a recent epidemiological and Mendelian randomization study in Asian populations showed a causal relationship between increased BMI and cognitive decline [10]. Elevated BMI is associated with reduced brain volume [41], which leads to decreased regional cerebral blood flow in the prefrontal cortex [42]. Therefore, there remains no consensus on the relationship between obesity and CI, with variations in findings possibly affected by sample characteristics, study design, confounding variables and intricate interactions. In any case, the complicated relationship between BMI and cognitive function in T2DM patients requires further investigation and substantiation through additional research.

Compared to generalized obesity, abdominal obesity warrants greater attention due to its association with elevated health hazards. Abdominal obesity is capable of predicting a heightened risk of CI [43]. A cohort study involving 872,082 participants revealed that WC adjusted for BMI was significantly and positively associated with the prevalence of dementia, whereas BMI adjusted for WC was negatively associated with the risk of dementia in older adults [44], this result emphasized the importance of abdominal fat in accurately predicting dementia. In the current study, we found that WC was associated with an increased risk of CI; however, as shown in Model 3, this association was attenuated to a non-significant level after adjustment for covariates. Like this study, some previous studies did not find a significant association between WC and cognitive function [45, 46]. Nevertheless, there exist some studies that are inconsistent with the results of the present study and support the association of elevated WC with cognitive decline. Abbatecola et al. reported that elevated WC was associated with cognitive decline in older patients with T2DM [47]. Similarly, a systematic review and meta-analysis found that higher WC was associated with an increased risk of CI and dementia (HR: 1.1, 95% CI: 1.05–1.15). Further stratified by sex, the results showed a significant correlation between high WC and CI and dementia in women, while no correlation was observed in men [48]. In this study, we also demonstrated that WC was negatively associated with MoCA scores in women (r = -0.156, P < 0.001) and did not observe this relationship in men. Additionally, it has also been suggested that individuals with increased WC have a reduced risk of developing Alzheimer's disease, considering that it may be mediated by reverse causation [49]. The relationship between conventional indicators of abdominal obesity and cognitive function has shown inconsistent results across studies, potentially due to variations in study populations, methods for evaluating cognitive function and covariate adjustments. It should be noted that WC is limited in accurately identifying visceral fat. To date, several studies have reported varying results concerning the relationship between Visceral fat area (VFA) and cognitive function. A cross-sectional study revealed an association between higher VFA and poorer cognitive function in older Asians with T2DM [45]. Interestingly, a 10-year community-based longitudinal study indicated gender differences in the impact of VFA on cognitive function, which might be explained by the influence of estrogen [50]. However, the gold standards for measuring VFA are Computed Tomography (CT) and Magnetic Resonance Imaging (MRI), which are commonly used in research but have limited application in clinical practice [51] due to cost and radiation exposure. Therefore, given the limitations of both traditional surrogate indicators of abdominal obesity and VFA, it is essential to explore appropriate surrogate measures for abdominal obesity that can be used in large-scale investigations of populations at risk of CI.

In contrast to the traditional indicators of abdominal obesity, WHRadjBMI has the advantage of being not only readily available, but also a more accurate reflection of the distribution of visceral and subcutaneous fat [25]. Therefore, it seems to be more applicable to populations than traditional indicators from the perspective of public health. Our current study suggests that WHRadjBMI is a better predictor of CI in middle-aged and elderly T2DM patients compared with BMI and WC, and higher WHRadjBMI is associated with poorer cognitive function in T2DM patients. It provides evidence that excess visceral fat accumulation is related to CI. However, the literature to evaluate the relationship between WHRadjBMI and cognitive function remains scarce. A trans-ethnic Mendelian randomization study found that abdominal obesity as measured by WHRadjBMI was associated with poor cognitive performance [52]. An explanation suggests a causal relationship between WHRadjBMI and brain cortical structure [53]. In addition, consistent with prior studies [54, 55], we also observed that individuals with abdominal obesity are prone to increased obesity-related comorbidities including cardiovascular disease, hypertension and dyslipidemia, which were linked to a higher risk of CI [56, 57]. The mechanisms underlying the relationship between abdominal obesity and CI are complex and not fully understood. Possible mechanisms include abdominal visceral fat accumulation is associated with increased levels of blood adipocytokines, which can affect metabolic status and are harmful to the brain [58,59,60]. Some cytokines may promote oxidative stress by stimulating the production of reactive oxygen species by macrophages and monocytes, which may lead to chronic neuroinflammation, neuronal degeneration and ultimately CI [61]. Abdominal obesity implies the accumulation of visceral fat, which can result in insulin resistance and further impair cognitive function [62]. Nevertheless, another Mendelian randomization study showed no causal association between WHRadjBMI and Alzheimer's disease [63], possibly due to the usage of different outcomes and databases, and the biological mechanisms require further research. In our study, higher WHRadjBMI was linked to worse cognitive function, and this association remained consistent even after stratifying for age, sex, diabetes duration, cerebrovascular disease, HbA1c, metformin and insulin therapy. Consequently, WHRadjBMI appears to hold significant clinical value in predicting the risk of CI in patients with T2DM.

This study has some strengths. First, to the best of our knowledge, it is the first clinical study on the relationship between WHRadjBMI and CI in patients with T2DM. Second, this study differs from some previous studies in which obesity and cognitive function had inconsistent results across age groups, as our findings were consistent across middle-aged and older adults. Third, the alternative visceral fat accumulation indicator used in this study, WHRadjBMI, does not need to be measured by CT or MRI and is more likely to be generalized to the general population.

However, this study has several limitations. First, it was a retrospective analysis, which precludes the ability to establish a causal relationship between abdominal obesity and cognitive impairment; therefore, future longitudinal studies are needed. Second, this study was conducted as a single-center study focused on hospitalized patients with T2DM, resulting in a participant population predominantly comprising those with poorly controlled blood glucose levels; this may limit the generalizability of the findings to other regions and diverse demographics. Third, this study utilized the MoCA test as a screening tool for global cognitive function. Despite its sensitivity compared to the Mini-Mental State Examination [64], the MoCA may not address all cognitive domains, potentially leading to an incomplete evaluation of participants’ cognitive status. Incorporating a variety of neuropsychological tests and specific cognitive domain assessments for cognitive domains in future research could enhance the comprehensiveness and reliability of the findings, as different assessments may identify various types of cognitive issues that the MoCA alone may overlook. Fourth, while WHRadjBMI offers a more accurate reflection of abdominal obesity, this study lacks comparative evidence between WHRadjBMI and the imaging techniques used for direct measurement of visceral fat. Lastly, although we have adjusted for a range of confounding variables including demographic data, diabetes-related confounders and biochemical markers, additional factors such as insulin resistance and oxidative stress markers related to obesity and cognitive function were not included.

Conclusions

In conclusion, WHRadjBMI, as a surrogate for abdominal obesity, exhibited a positive association with the risk of CI in patients with T2DM. Our findings suggest that screening for cognitive function in individuals with abdominal obesity and T2DM may be particularly important for preventing cognitive decline. Further longitudinal studies are needed to evaluate the predictive capability of abdominal obesity in CI.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

AUC :

Area Under the Curve

BMI :

Body mass index

CI :

Cognitive impairment

CT :

Computed Tomography

DBP :

Diastolic blood pressure

FPG:

Fasting plasma glucose

HbA1c:

Glycated hemoglobin

HC:

Hip circumference

HDL-c:

High-density lipoprotein cholesterol

LDL-c:

Low-density lipoprotein cholesterol

MCI :

Mild cognitive impairment

MRI :

Magnetic Resonance Imaging

OR:

Odds ratio

ROC :

Receiver operating characteristic

SBP:

Systolic blood pressure

SD:

Standard deviation

SPSS :

Statistical Product and Service Solutions

TC:

Total cholesterol

TG :

Triglycerides

T2DM :

Type 2 diabetes mellitus

UA:

Uric acid

VFA:

Visceral fat area

WC :

Waist circumference

WHR:

Waist-to-hip ratio

WHRadjBMI :

Waist-to-hip ratio adjusted for body mass index

95% CI:

95% Confidence interval

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Acknowledgements

The authors would like to thank all participants in the study.

Funding

This work was supported by the Science and Technology Project of the Tianjin Municipal Health Commission (J.N.L, Grant No. ZD20006) and the Tianjin Science and Technology Committee (J.N.L, Grant No. 18ZXDBSY00120).

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Authors and Affiliations

  1. School of Medicine, Nankai University, Tianjin, China

    Tong Chen & Jing-Na Lin

  2. Department of Endocrinology, Tianjin Union Medical Center, Nankai University Affiliated Hospital, 190 Jieyuan Road, Hongqiao District, Tianjin, 300121, China

    Tong Chen, Yan-Lan Liu, Fang Li, Hui-Na Qiu, Fan Wu, Jing-Bo Li & Jing-Na Lin

  3. School of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Nahal Haghbin

  4. Tianjin Union Medical Center, Tianjin Medical University, Tianjin, China

    Yao-Shuang Li, Chen-Ying Lin & Long-Fei Xia

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Contributions

TC contributed to conception, design, data analysis, interpretation of data, manuscript drafting. YLL contributed to conception, design, data analysis and revision process. FL, HNQ, NH, YSL, CYL, FW, LFX were involved in data collection and revision of the manuscript. JBL and JNL contributed to conception, research design, supervision and writing - review and editing. All authors reviewed the manuscript.

Corresponding authors

Correspondence to Jing-Bo Li or Jing-Na Lin.

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Ethics approval and consent to participate

The protocol was approved by the Medical Ethics Committee of Tianjin Union Medical Center and all participants provided informed consent form before participating in the study. The approval number is (2018) Expedited Review (C08).

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The authors declare no competing interests.

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Chen, T., Liu, YL., Li, F. et al. Association of waist-to-hip ratio adjusted for body mass index with cognitive impairment in middle-aged and elderly patients with type 2 diabetes mellitus: a cross-sectional study. BMC Public Health 24, 2424 (2024). https://doi.org/10.1186/s12889-024-19985-7

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