Abstract
Objectives
Excessive accumulation of adipose tissue may accelerate brain aging, but the underlying mechanisms are poorly understood. Several adiposity indices were proposed to assess obesity, while their linkage with brain health in older adults remained unclear. Here we aimed to examine the associations of adiposity indices with global and regional cerebral blood flow (CBF) in older adults, while considering insulin resistance.
Design
This was a cross-sectional population-based study that included older adults derived from the baseline participants in the ongoing Multimodal Interventions to Delay Dementia and Disability in rural China (MIND-China) study.
Setting And Participants
The study included 103 Chinese rural-dwelling older adults (age≥60 years; 69.9% women) who underwent brain magnetic resonance imaging scans.
Methods
We estimated eight adiposity indices based on anthropometric measures. We automatically quantified global and regional CBF using the arterial spin labeling scans. Insulin resistance was assessed using the triglyceride-glucose index and then dichotomized into high and low levels according to the median. Data were analyzed using general linear model and voxel-wise analysis.
Results
Of the eight examined adiposity indices, only higher waist-to-height ratio (WHtR) and body roundness index (BRI) were associated with reduced global CBF (multivariable-adjusted β-coefficients and 95%CI: −1.76; −3.25, −0.27 and −1.77; −3.25, −0.30, respectively) and hypoperfusion in bilateral middle temporal gyri, angular gyri and superior temporal gyri, left middle cingulum and precuneus (P<0.05). There were statistical interactions of WHtR and BRI with levels of insulin resistance on CBF, such that the significant associations of higher WHtR and BRI with lower global and regional CBF existed only in people with high insulin resistance (P<0.05).
Conclusion
Higher WHtR and BRI are associated with cerebral hypoperfusion in older adults, especially in people with high insulin resistance. This may highlight the pathological role of visceral fat in vascular brain aging.
Key words: Adiposity indices, cerebral bloodflow, insulin resistance, population-based study
Introduction
Adiposity indices, as typical markers for body fat, have been widely used to reflect the nutritional and metabolic status (1). Currently, adiposity indices are mainly estimated based on different anthropometric measures (e.g., height, weight, and waist circumference) or levels of blood lipid proteins, thus, might differ in reflecting the amount and distribution of body fat. For example, theoretically, the adiposity indices, which take into account both waist circumference and height in estimation (e.g., waist-to-height ratio [WHtR] (2) and body roundness index [BRI] (3)), could more sensitively reflect the accumulation of visceral fat compared to other indices that do not (e.g., body mass index [BMI] (4)). Therefore, assessing multiple adiposity indices is crucial when investigating the possible health outcomes associated with body fat distribution and accumulation.
Previous studies have shown that a higher level of adiposity indices (e.g., BMI) is associated with measures of brain aging, such as reduced brain volume, accelerated cognitive decline, and even dementia (5, 6, 7, 8). In addition, data from the population-based Irish Longitudinal Study on Ageing suggested that a higher level of adiposity indices (e.g., BMI and waist circumference) was related with reduced cerebral blood flow (CBF) in the whole grey matter (GM) tissue among older adults (9). Whereas, the association of other adiposity indices with CBF in old age is still unclear in population-based studies. Given that CBF could sensitively reflect the cerebral hemodynamic and metabolic status in old age (10), studying the relationship of multiple adiposity indices with CBF might shed light on the pathophysiological role of different types of body fat accumulation and distribution as well as metabolic dysfunction in vascular brain aging. Moreover, investigating the association of different adiposity indices with CBF by brain regions may help in revealing the specific brain regions that are vulnerable to high adiposity indices.
It is widely accepted that high adiposity indices could trigger chronic low-grade systemic inflammation and further lead to insulin resistance (11). Thus, the insulin resistance levels might indicate the degree of metabolic dysfunction due to higher adiposity indices. In addition, it is also well-established that insulin resistance is closely related to brain aging (12). Taken together, investigating the possible role of insulin resistance in the association of higher adiposity indices with decreased CBF might help identify the populations who are vulnerable to higher adiposity indices in terms of the vascular brain health.
In the current study, we sought to investigate the associations of multiple adiposity indices with global and regional CBF in older adults, while taking into account the levels of insulin resistance. Our hypothesis was that a higher level of adiposity indices was associated with global and regional cerebral hypoperfusion in older adults and that these associations might vary by levels of insulin resistance.
Methods
Study design and participants
This was a cross-sectional population-based study. The study sample was derived from baseline participants in the ongoing Multimodal Interventions to Delay Dementia and Disability in rural China (MIND-China) study, as previously reported (13). In brief, the MIND-China study targeted people aged 60 years and older living in rural communities in Yanlou Town, Yanggu County, western Shandong Province. From March to September 2018, a total of 5765 individuals underwent the baseline examination of MIND-China study. From July to September 2019, a subsample of 137 participants in MIND-China were invited and finally agreed to undergo multimodal magnetic resonance imaging (MRI) scans including the pseudo-continuous arterial spin labeling (pcASL) scans. Of these, 34 participants were excluded due to missing or suboptimal quality of pcASL MRI scans (n=21), missing anthropometric data (n=9), diagnosis of dementia (n=1), or BMI ≤18.5 kg/m2 (n=3), leaving 103 participants for the current analysis. Compared with the participants who did not undergo MRI scans, participants who did were younger and more likely to be women.
The protocol of the MIND-China study was approved by the Ethics Committee of Shandong Provincial Hospital affiliated to Shandong University. Written informed consent was obtained from all participants, or if the participant was unable to provide the consent due to cognitive impairment, from a proxy (e.g., family member). The MIND-China study was registered at the Chinese Clinical Trial Registry (registration no.: ChiCTR1800017758).
Data collection and assessment
Trained staff collected data via face-to-face interviews, clinical examinations, and laboratory tests following standardized protocols, as previously reported (13, 14). The data included demographic factors (e.g., age, sex, and education), health-related behavior factors (e.g., smoking, drinking, and physical activity), clinical and anthropometric markers (e.g., blood pressure, fasting blood glucose, serum lipid, serum creatinine, height, weight, and waist circumference), and health history (e.g., coronary heart disease and stroke). Smoking and alcohol intake were classified as current versus noncurrent. We dichotomized physical exercise as at least weekly versus less than weekly. Hypertension was defined as systolic pressure ≥140 mm Hg or diastolic pressure ≥90 mm Hg or current use of antihypertensive medications (15); diabetes as fasting blood glucose ≥7.0 mmol/L or current use of antidiabetic medications (15); and dyslipidemia as total serum cholesterol ≥6.2 mmol/L, triglyceride ≥2.3 mmol/L, low-density lipoprotein cholesterol ≥4.1 mmol/L, high-density lipoprotein cholesterol <1.0 mmol/L, or current use of antilipemic medications (15). We used the Meso Scale Discovery V-PLEX Assays to measure serum inflammatory biomarkers, including interleukin-6 (IL-6), interleukin-8 (IL-8), interleukin-10 (IL-10), interferon gamma (IFN-γ), monocyte chemotactic protein-1 (MCP-1), tumor necrosis factor alpha (TNF-α), and soluble intercellular adhesion molecule-1 (sICAM-1) (13, 16). We generated a composite score for these serum inflammatory biomarkers following the methods described in previous studies (17), i.e., inflammatory biomarkers were log-transformed first, then the z-score of each of these biomarkers was calculated, and the z-scores were averaged and standardized again to yield a composite inflammatory score. Serum creatinine was measured following the methods described previously (13). The estimated glomerular filtration rate (eGFR) was calculated using a formula specifically for Chinese population: eGFR (mL/min/1.73 m2) = 186 × serum creatinine (mg/dL)−1.154 × age (years)−0.203 (women × 0.742) (18), and impaired kidney function was defined as eGFR<90 mL/min/1.73 m2 (19). The Berlin Questionnaire was used to screen people with high risk for obstructive sleep apnea, which divided people into 3 categories: cessation of breathing and snoring severity (category 1), symptoms of excessive daytime sleepiness (category 2), and hypertension and BMI≥28 kg/ m2 (category 3). Participants were defined as having a high risk for obstructive sleep apnea when having positive scores in two or more categories (20). Global cognitive function was assessed with the Mini-Mental State Examination. Weight and height were measured with participants wearing lightweight clothes and without shoes. Waist circumference was measured with a non-stretchable measuring tape to the nearest 1 cm. We assessed insulin resistance using the triglyceride-glucose index, which was estimated using the following formula: triglyceride-glucose index=Ln[fasting triglyceride (mg/dl)×fasting glucose (mg/dl)/2] (21). Then, we dichotomized the levels of insulin resistance into high and low according to the median of the triglyceride-glucose index (median=8.59).
Measurements of adiposity indices
Except the waist circumference, all the other seven adiposity indices were calculated using the formulas as described in the literature (3, 22, 23, 24, 25):
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Body mass index= weight/(height)2.
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Waist-to-height ratio= waist circumference/height.
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Body roundness index= 364.2−365.5×(1−(((waist circumfe rence/2n)2)/((0.5×height)2)))0.5 (3).
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A body shape index= waist circumference/(((BMI(2,3))×(height(1,2))) (22).
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The Chinese visceral adiposity index= −267.93+0.68xage+0.03×BMI+4.00xwaist circumference+22.00×Log10triglyceride−16.32×high density lipoprotein (males) or = −187.32+1.71×age+4.23×BMI+1.12×waist circumference+39.76×Log10triglyceride − 11.66×high density lipoprotein (females) (23).
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Conicity index= waist circumference/(0.109×(weight/height)0.5) (24).
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Weight-adjusted waist index= waist circumference×100/(weight0.5) (25).
MRI acquisition and processing
All participants underwent the brain MRI scans on a GE 3.0-T Discover MR750w MRI system (General Electric Company, Waukesha, WI, USA) with a 24-channel head coil at Liaocheng People's Hospital, Shandong. A comfortable foam padding was applied to stabilize the head and minimize movement. During scanning, all participants were instructed to be relaxed, keep their eyes closed, think of nothing particularly, and not to fall asleep or move. Resting-state CBF was evaluated using a pcASL sequence with three-dimensional fast spin-echo acquisition, following the protocols recommended in the ASL white paper (26). The scan parameters were as follows: repetition time/echo time=4852/10.7 ms; post-label delay=2025 ms; spiral in readout of 8 arms with 512 sample points; flip angle=111°; field of view=240 mm×240 mm; reconstruction matrix=128×128; slice thickness=4 mm, no gap; 36 axial slices; number of excitation=2. To quantify CBF, a proton-density-weighted image was acquired with the same image slab location as the pcASL, but without radio frequency labeling.
Estimation of the global grey matter cerebral blood flow
We automatically estimated the CBF according to the protocols in previous literature (27). Briefly, the ASL difference images were calculated by subtracting the label images from the control images. Subsequently, the absolute CBF maps (ml/100 g/min) were derived from the ASL difference images and proton-density-weighted images. The preprocessing of CBF maps was performed using Statistical Parametric Mapping 8 (Institute of Neurology, London, UK) on MATLAB R2013b. The CBF images of all participants were co-registered to a Positron Emission Computed Tomography template in the Montreal Neurological Institute (MNI) space by non-linear transformation. After co-registration, spatially standardized CBF maps were generated and removed of inhomogeneities in shape and position. Each MNI-standardized CBF map was processed to eliminate non-brain tissues and spatially smoothed with a Gaussian kernel of 6 mm×6 mm×6 mm full-width at half maximum in SPM8 software. Finally, a GM mask in the MNI space was applied to extracting the global grey matter CBF (CBFGM) using the DPABI toolbox (28).
Statistical analysis
First, we compared the characteristics of study participants by levels of insulin resistance using the two-sample t-test for continuous variables and the chi-square test for categorical variables. Then, we used the general linear regression models to estimate the standardized ²-coefficient and 95% confidence interval of global CBFGM associated with various adiposity indices. We selected the adiposity indices which were significantly associated with global CBFGM to investigate their associations with regional CBF, using a voxel-wise multiple regression analysis in Statistical Parametric Mapping 8 software. Multiple comparisons were corrected using a cluster-level family-wise error (FWE) method, with a voxel threshold P<0.001 and FWE-corrected P<0.05. The regional CBF that was significantly associated with above selected adiposity indices was extracted for further analyses using the region-of-interest method in the DPABI toolbox, based on the Automated Anatomical Labeling atlas (28). Accordingly, we estimated the standardized β-coefficient and 95% confidence interval of above extracted regional CBF with adiposity indices using the general linear regression models. Finally, we examined the statistical interaction of insulin resistance levels (low vs. high, according to the median of triglyceride-glucose index) with adiposity indices on global CBF using the general linear regression model; for the adiposity indices that were significantly associated with global CBF in the total sample, we further examined their statistical interactions with levels of insulin resistance on regional CBF, using the general linear regression model. We did not examine the statistical interactions of all adiposity indices with insulin resistance on regional CBF for minimizing the possible influence of multiple comparisons. We considered P for interaction <0.10 as having potential statistical interaction due to relatively small sample size of our study, as recommended in previous studies (29). Once a potentially statistical interaction was detected, we further assessed the direction and magnitude of the interaction by performing stratifying analysis by levels of insulin resistance. In the analyses of associations between various adiposity indices and global CBFGM, we reported the results from 2 models: model 1 was controlled for age, sex, education, physical activity, alcohol consumption, smoking status, hypertension, diabetes, dyslipidemia, coronary heart disease, and stroke, and model 2 was further controlled for high risk for obstructive sleep apnea, impaired kidney function, and low-grade systemic inflammation. In all other association analyses, we controlled for all the examined confounders. IBM SPSS Statistics 26.0 for Windows (IBM Corp., Armonk, NY, USA) and Statistical Parametric Mapping 8 (SPM8, Institute of Neurology, London, UK) were used for above analyses.
Results
Characteristics of the study participants
The mean age of the 103 participants was 68.47 years (SD, 3.97; range, 60–79 years), 69.90% were women, and the average years of education was 3.09 years (SD, 3.29; range, 0–13 years; 39.81% illiteracy) (Table 1). Compared with participants with low insulin resistance, those with a high level of insulin resistance were younger, more likely to have dyslipidemia and diabetes, and less likely to have a history of stroke. In addition, participants with a high level of insulin resistance had a higher BMI, waist circumference, WHtR, BRI, and the Chinese visceral adiposity index than those with low insulin resistance (P<0.05), but the two groups had no significant difference in the mean Mini-Mental State Examination score (Table 1).
Table 1.
Characteristics of study participants in total sample and by levels of insulin resistance
| Characteristics |
Total sample |
Levels of insulin resistancea |
||
|---|---|---|---|---|
| (n = 103) | Low (n = 52) | High (n = 51) | P value | |
| Age (years) | 68.47 (3.97) | 69.63 (3.93) | 67.27 (3.67) | 0.002 |
| Education (years) | 3.09 (3.29) | 3.21 (3.33) | 2.96 (3.27) | 0.701 |
| Physical activity (weekly), n (%) | 64 (62.14) | 35 (67.31) | 29 (56.86) | 0.275 |
| Current smoking, n (%) | 21 (20.39) | 14 (26.92) | 7(13.73) | 0.096 |
| Alcohol intake, n (%) | 28 (27.18) | 17 (32.69) | 11 (21.57) | 0.205 |
| Hypertension, n (%) | 73 (70.87) | 38 (73.08) | 35 (68.63) | 0.619 |
| Dyslipidemia, n (%) | 27 (26.21) | 7 (13.46) | 20 (39.22) | 0.003 |
| Diabetes, n (%) | 16 (15.53) | 1 (1.92) | 15 (29.41) | <0.001 |
| Coronary heart disease, n (%) | 13 (12.62) | 6 (11.54) | 7(13.73) | 0.738 |
| Stroke, n (%) | 7 (6.80) | 7 (13.46) | 0 (0.00) | 0.020 |
| High risk for obstructive sleep apnea, n (%) | 35 (33.98) | 17 (32.69) | 18 (35.29) | 0.780 |
| Impaired kidney function, n (%) | 27 (26.21) | 14 (26.92) | 13 (25.49) | 0.869 |
| Composite inflammatory score | 0 (0.97) | 0.02 (1.11) | -0.02 (0.82) | 0.820 |
| MMSE score | 21.79 (5.79) | 22.21 (5.54) | 21.35 (6.07) | 0.455 |
| Triglyceride-glucose index | 8.63 (0.52) | 8.24 (0.24) | 9.03 (0.42) | <0.001 |
| Body mass index | 24.51 (3.16) | 23.82 (2.92) | 25.22 (3.25) | 0.023 |
| Waist circumference | 87.10 (7.74) | 85.21 (7.18) | 89.02 (7.88) | 0.012 |
| Waist-to-height ratio | 0.55 (0.05) | 0.54 (0.05) | 0.57 (0.04) | 0.004 |
| Body roundness index | 4.46 (1.01) | 4.19 (1.02) | 4.75 (0.93) | 0.004 |
| A body shape index | 0.08 (0.01) | 0.08 (0.005) | 0.08 (0.004) | 0.451 |
| Chinese visceral adiposity index | 116.78 (29.65) | 105.35 (25.0) | 128.44 (29.68) | <0.001 |
| Conicity index | 1.29 (0.07) | 1.28 (0.08) | 1.30 (0.06) | 0.087 |
| Weight-adjusted waist index | 11.21 (0.70) | 11.09 (0.79) | 11.32 (0.57) | 0.083 |
Abbreviations: MMSE, Mini-Mental State Examination. Data were mean (standard deviation), unless otherwise specified. a. The insulin resistance level was divided into high and low levels according to the median (i.e., 8.59) of the triglyceride-glucose index.
Associations of adiposity indices with global CBFGM and regional CBF
Higher WHtR and BRI were significantly associated with reduced global CBFGM in both model 1 and model 2 (P<0.05, Table 2). Furthermore, the voxel-wise analysis revealed three clusters of brain regions, in which a higher WHtR was significantly associated with reduced CBF, and three additional clusters, in which a higher BRI was significantly associated with cerebral hypoperfusion, after controlling for all the examined confounders (Supplementary Table 1). The largest cluster of brain regions that showed reduced CBF associated with greater WHtR and BRI was located in the left lateral temporal lobe, including the left middle temporal gyrus, left superior temporal gyrus, and left angular gyrus. The second largest cluster of brain regions was located in the right lateral temporal lobe, including the right middle temporal gyrus, right superior temporal gyrus, and right angular gyrus. The smallest cluster of brain regions was located in the medial surface of the left hemisphere, including left middle cingulum and left precuneus. Higher WHtR and BRI were significantly associated with reduced CBF in the similar brain regions (Supplementary Table 1). Figure 1 shows the three-dimensional rendered view and multi-slice view of the results of voxel-wise analyses.
Table 2.
Associations of adiposity indices with global grey matter cerebral blood flow
| Adiposity index [per 1-SD increase] |
β-coefficient (95% CI), CBFGM[ml/100 g/min] |
|
|---|---|---|
| Model 1a | Model 2a | |
| Body mass index | −0.83 (−2.24, 0.58) | −1.22 (−2.64, 0.20) |
| Waist circumference | −1.01 (−2.53, 0.50) | −1.11 (−2.61, 0.39) |
| Waist-to-height ratio | −1.56 (−3.05, -0.07)* | −1.76 (−3.25, −0.27)* |
| Body roundness index | −1.58 (−3.06, -0.09)* | −1.77 (−3.25, −0.30)* |
| A body shape index | −0.40 (−1.85, 1.05) | −0.05 (−1.48, 1.38) |
| Chinese visceral adiposity index | −1.17 (−2.65, 0.31) | −1.36 (−2.82, 0.10) |
| Conicity index | −0.85 (−2.34, 0.64) | −0.58 (−2.05, 0.88) |
| Weight-adjusted waist index | −1.23 (−2.81, 0.35) | −1.01 (−2.56, 0.54) |
Abbreviations: SD, standard deviation; CI, confidence interval; CBFGM, grey matter cerebral blood flow. a. Model 1 was controlled for age, sex, education, physical activity, alcohol consumption, smoking status, hypertension, diabetes, dyslipidemia, coronaiy heart disease, and stroke, and model 2 was further controlled for high risk for obstructive sleep apnea, impaired kidney function, and low-grade systemic inflammation. * P <0.05.
Figure 1.
Voxel-wise analysis of the associations of regional cerebral blood flow with waist-to-height ratio (A) and body roundness index (B), at voxel threshold p<0.001 and cluster-level family-wise error corrected p<0.05
The analyses were controlled for age, sex, education, ever smoking, alcohol intake, physical activity, hypertension, diabetes mellitus, dyslipidemia, coronary heart disease, stroke, high risk for obstructive sleep apnea, impaired kidney function, and low-grade systemic inflammation. The shade of color of the overlays reflects the T value, and the red numbers of the slices show the order of the slices based on the original point in MNI space.
We extracted the values of CBF in above brain regions that were significantly related to WHtR and BRI for further analyses. Both higher WHtR and higher BRI were significantly associated with reduced CBF in the bilateral middle temporal gyri, bilateral angular gyri, bilateral superior temporal gyri, left middle cingulum, and left precuneus (Table 3).
Table 3.
Associations of waist-to-height ratio and body roundness index with cerebral blood flow in regions of interest in the total sample and by levels of insulin resistance
| Regions of interest | β-coefficient (95% confidence interval), regional cerebral blood flow [ml/100 g/min]a | |||
|---|---|---|---|---|
| Total sample, n=103 | Levels of insulin resistance (range: 7.63–10.54) | |||
| Low, n=52 (range: 7.63–8.59) | High, n=51 (range: 8.59–10.54) | P for interaction | ||
| Waist-to-height ratio [per 1-standard deviation increase] | ||||
| Left middle temporal gyrus | −2.61 (−4.23, −0.98)** | −1.59 (−4.72, 1.55) | −3.54 (−6.03, −1.06)** | 0.071 |
| Left angular gyrus | −3.08 (−5.12, −1.04)** | - | - | 0.348 |
| Left superior temporal gyrus | −2.02 (−3.63, −0.42)* | −1.33 (−4.56, 1.90) | −3.06 (−5.49, −0.63)* | 0.064 |
| Left middle cingulum | −2.53 (−4.29, −0.76)** | −1.44 (−4.69, 1.80) | −3.84 (−6.67, −1.01)** | 0.071 |
| Left precuneus | −3.07 (−5.13, −1.01)** | - | - | 0.229 |
| Right middle temporal gyrus | −2.17 (−3.92, −0.42)* | −0.69 (−3.92, 2.53) | −3.05 (−5.74, −0.36)* | 0.094 |
| Right angular gyrus | −2.93 (−5.11, −0.76)** | - | - | 0.195 |
| Right superior temporal gyrus | −1.88 (−3.69, −0.08)* | −0.69 (−4.11, 2.73) | −3.01 (−5.85, −0.18)* | 0.090 |
| Body roundness index [per 1-standard deviation increase] | ||||
| Left middle temporal gyrus | −2.62 (−4.24, −1.00)** | −1.68 (−4.83, 1.47) | −3.51 (−5.95, −1.06)** | 0.090 |
| Left angular gyrus | −3.08 (−5.11, −1.06)** | - | - | 0.398 |
| Left superior temporal gyrus | −2.04 (−3.64, −0.44)* | −1.44 (−4.68, 1.81) | −3.00 (−5.40, −0.61)* | 0.081 |
| Left middle cingulum | −2.52 (−4.28, −0.77)** | −1.48 (−4.75, 1.78) | −3.82 (−6.60, −1.04)** | 0.086 |
| Left precuneus | −3.06 (−5.11, −1.01)** | - | - | 0.258 |
| Right middle temporal gyrus | −2.23 (−3.96, −0.49)* | - | - | 0.128 |
| Right angular gyrus | −2.97 (−5.13, −0.81)** | - | - | 0.249 |
| Right superior temporal gyrus | −1.92 (−3.72, −0.13)* | - | - | 0.118 |
a. β-coefficients and 95% confidence intervals were controlled for age, sex, education, physical activity, alcohol consumption, smoking status, hypertension, diabetes, dyslipidemia, coronary heart disease, stroke, high risk for obstructive sleep apnea, impaired kidney function, and low-grade systemic inflammation. * P < 0.05, ** P < 0.01.
Interactions of adiposity indices with insulin resistance on CBF
Controlling for multiple potential confounders, we detected statistical interactions of insulin resistance with WHtR, BRI, and the Chinese visceral adiposity index on global CBFGM (P for all interactions <0.10) (Figure 2). Stratifying analysis by insulin resistance levels suggested that among participants with a low level of insulin resistance, none of the eight examined adiposity indices was significantly associated with CBFGM, while among those with a high level of insulin resistance, higher WHtR and BRI were significantly associated with reduced CBFGM (P<0.05) (Figure 2).
Figure 2.
Interactions of insulin resistance levels with adiposity indices on global grey matter cerebral blood flow
A. Associations of waist-to-height ratio with global grey matter cerebral blood flow by insulin resistance levels; B. Associations of body roundness index with global grey matter cerebral blood flow by insulin resistance levels; and C. Associations of Chinese visceral adiposity index with global grey matter cerebral blood flow by insulin resistance levels. The β-coefficients and 95% confidence intervals were controlled for age, sex, education, physical activity, alcohol consumption, smoking status, hypertension, diabetes, dyslipidemia, coronary heart disease, stroke, high risk for obstructive sleep apnea, impaired kidney function, and low-grade systemic inflammation. * P <0.05. Abbreviation: CBFGM, grey matter cerebral blood flow.
We further detected statistical interactions of insulin resistance with WHtR on reduced CBF in the bilateral middle temporal gyri, bilateral superior temporal gyri, and left middle cingulum, and statistical interactions of insulin resistance with BRI on reduced CBF in the left middle temporal gyrus, left superior temporal gyrus, and left middle cingulum when adjusting for multiple confounders (P for all interactions <0.10). Among participants with a high level of insulin resistance, a higher WHtR was significantly associated with reduced CBF in the bilateral middle temporal gyri, bilateral superior temporal gyri, and left middle cingulum (P<0.05), while a higher BRI was significantly associated with decreased cerebral perfusion in the left middle temporal gyrus, left superior temporal gyrus, and left middle cingulum. However, these associations were not significant among those with low insulin resistance (Table 3).
Discussion
In this population-based study among rural-dwelling Chinese older adults who underwent brain pcASL scans, we found that higher WHtR and BRI were associated with decreased global CBFGM as well as reduced CBF in the lateral temporal lobe and the medial surface of the left hemisphere. In addition, the association of cerebral hypoperfusion with higher WHtR and BRI was evident in individuals with high insulin resistance, but not in those with low insulin resistance.
We found that adiposity indices that reflect visceral fat (i.e., WHtR and BRI) were related to cerebral hypoperfusion in older adults, while the associations of BMI and waist circumference with CBF were not detected in our study. However, the findings from the Irish Longitudinal Study on Ageing highlighted the role of higher BMI and waist circumference in cerebral hypoperfusion (9). The inconsistency might be due partly to differences in demographic features of the study sample (e.g., education, socioeconomic position, and ethnicity) and the mean BMI (28.0 kg/m2 in the Irish study vs. 24.5 kg/m2 in our study). Among these adiposity indices, BMI is the most frequently used measure for adiposity, but BMI could not differentiate bone and muscle tissue from adipose tissue, and thus, performs poorly for quantifying visceral fat (4). Waist circumference could not independently reflect the body shape due to ignoring the influence of heights (30). Of the other adiposity indices, BRI, WHtR, the Chinese visceral adiposity index, a body shape index, weight-adjusted waist index, and conicity index can reflect the body shape (3, 22–25). However, the Chinese visceral adiposity index, conicity index, weight-adjusted waist index, and a body shape index are derived mainly from the weight or BMI, which could not differentiate bone or muscle tissue from adipose tissue either (31). Therefore, the correlations between these adiposity indices and cerebral perfusion may be underestimated. By contrast, BRI and WHtR could reflect individual body shape and visceral fat accumulation independently of height, weight, and BMI (3). A large-scale hospital-based study of Chinese adults (age ≥18 years) showed strong correlations of BRI and WHtR with arterial stiffness (32), which is in line with our findings of the association of increased BRI and WHtR with cerebral hypoperfusion. However, the associations of BRI and WHtR with arterial stiffness have not been reported in population-based studies. A meta-analysis of over 300,000 adults from multiple ethnic populations suggested that WHtR was more sensitive than BMI and waist circumference in detecting metabolic status (4), which also supported the prominent role of WHtR in cerebral perfusion. Of note, both WHtR and BRI are derived from waist circumference and height, and they showed a perfect correlation (Spearman rank correlation r=1, p<0.001), and thus, both indices were similarly associated with reduced regional CBF.
The majority of the well-designed population-based studies have so far targeted young or middle-aged individuals (33–35). However, cerebral hypoperfusion in the brain regions associated with high adiposity indices appeared to differ among young, middle-aged, and older adults. Brain regions that showed hypoperfusion associated with obesity in older adults are mainly supplied by internal carotid arteries, especially in the lateral and superior temporal lobes (36), whereas the brain regions with hypoperfusion associated with obesity in younger people mainly include the thalamus, visual cortex, and brain regions of the default mode network and salience network, which are not consistent with the distribution of internal carotid arterial territories (33, 34, 35). One possible reason is that cerebral hypoperfusion in older adults with higher adiposity indices is mainly due to arterial diseases such as atherosclerosis (37), which is more common in the carotid system than the vertebral-basilar system (38). Further large-scale population-based studies are warranted to investigate the pathological mechanisms linking adiposity indices with cerebral hypoperfusion, especially in older adults (e.g., via affecting vascular hemodynamics of carotid system).
It is known that higher adiposity indices are associated with insulin resistance via chronic systemic inflammation caused by glycerol, fatty acids, hormones, and pro-inflammatory cytokines, which are released by adipose tissues (39). The insulin resistance accompanied with higher levels of adiposity indices can be associated with endothelial dysfunction (40), arterial wall hypertrophy and fibrosis (41), and oxidative radicals (42), which can eventually result in atherosclerosis (40), and reduced brain blood perfusion. Previous research has suggested that the beneficial effect of weight loss on the vascular endothelial function is evident only in people with high insulin resistance (43), indicating that insulin resistance levels may modify the association between adiposity indices and vascular hemodynamics. Of note, we found that the association between high adiposity indices and reduced CBF was present independent of systemic inflammatory biomarkers, suggesting that mechanisms other than inflammation (e.g., cerebral insulin signaling pathway) might play a part in the relationships of obesity, insulin resistance, and cerebral perfusion (44). In addition, low insulin resistance indicates a metabolically healthy status, in which the subcutaneous fat, instead of the visceral fat, may constitute the dominant part of adipose tissue (45). The subcutaneous fat is less likely to be associated with vascular dysfunction compared to visceral fat (46). Therefore, it is plausible that the association between adiposity indices and cerebral perfusion is not evident in individuals with low insulin resistance. Taken together, our study highlighted the potential modifying role of insulin resistance in the adiposity-related cerebral hypoperfusion and the importance of controlling insulin resistance in maintaining cerebral vascular health. Prospective cohort studies are needed to verify our findings.
The major strengths of our study included the population-based design that engaged rural-dwelling older adults, the use of non-invasive and quantitative pcASL MRI technique to assess global and regional CBF, and the combination of multiple adiposity indices with the triglyceride-glucose index (as a surrogate marker of insulin resistance). This was possible using our unique database, in which epidemiological and clinical data were integrated with data derived from the state-of-the-art pcASL sequence. However, our study also has limitations. Firstly, our study sample was relatively small, and thus, the statistical power might be limited. Secondly, the MRI subsample was relatively younger and healthier compared with the MIND-China total sample, which should be kept in mind when generalizing our results to other populations. Finally, the cross-sectional design of the current study did not allow us to determine the causal relationship for any of the observed associations. These potential limitations should be taken into consideration when interpreting the main findings from this study.
Conclusions
In conclusion, this cross-sectional population-based MRI study among older adults living in rural China suggests that higher WHtR and BRI are associated with global and regional cerebral hypoperfusion (especially in the lateral temporal lobe), and that these associations are mainly detectable in people with high insulin resistance. These findings contribute to our understanding of the pathophysiological mechanisms linking visceral fat with vascular brain aging. Future large-scale population-based longitudinal studies are warranted to explore the interplay of adiposity indices and insulin resistance on the structural and functional vascular brain aging.
Acknowledgments
We would like to thank all the participants in the MIND-China study as well as the MIND-China Research Group for their collaboration in data collection and management.
Contributor Information
Lin Song, Dr., Email: zzusonglin@163.com.
Yifeng Du, Prof., Email: duyifeng2013@163.com.
Electronic supplementary material
Supplementary material is available for this article at https://doi.org/10.1007/s12603-023-1894-2 and is accessible for authorized users.
Supplementary Table 1. Characteristics of the clusters of brain regions in which regional blood perfusion was associated with waist-to-height ratio and body roundness index
Authors' contributions: Conceptualization, Du YF, Qiu CX, Song L, Li YJ, and Han XD; methodology, Song L, Li YJ, and Han XD; software, Han XD; validation, Du YF, Qiu CX, and Song L; formal analysis, Song L and Han XD; investigation, all authors.; resources, all authors.; data curation, Song L, Hou TT, and Han XD; writing—original draft, Han XD and Song L.; writing—review and editing, Du YF, Qiu CX, Song L, Li YJ, and Han XD; visualization, Han XD; study supervision, Du YF, Qiu CX, and Song L; project administration, Du YF, Qiu CX, and Song L; funding acquisition, Du YF, Qiu CX, and Song L. All authors read and approved the final version of the manuscript.
Funding: MIND-China was financially supported by the National Key R&D Program of China (grant no. 2017YFC1310100) and by additional grants from the National Nature Science Foundation of China (grants no. 82171175, 82011530139, and 82001120), the Academic Promotion Program of Shandong First Medical University (grant no. 2019QL020 and 2020RC009), and the Taishan Scholar Program of Shandong Province (grant no. ts20190977 and Tsqn201909182). This work was further supported by additional grants from the STI2030-Major Projects (grant no.: 2021ZD0201808), the Nature Science Foundation of Shandong Province (grant no.: ZR2020QH098), and the Integrated Traditional Chinese and Western Medicine Program in Shandong Province (YXH2019ZXY008). C Qiu received grants from the Swedish Research Council (grant no.: 2017-05819 and 2020-01574) for the Sino-Sweden Network and Research Projects, the Swedish Foundation for International Cooperation in Research and Higher Education (STINT) (grant no.: CH2019-8320) for the Joint China-Sweden Mobility program, and the Karolinska Institutet, Stockholm, Sweden.
Ethical standards: The protocol of the MIND-China study was approved by the Ethics Committee of Shandong Provincial Hospital affiliated to Shandong University. Written informed consent was obtained from all participants, or if the participant was unable to provide the consent due to cognitive impairment, from a proxy (e.g., family member). The MIND-China study was registered at the Chinese Clinical Trial Registry (registration no.: ChiCTR1800017758).
Conflict of interest: All authors declare no conflicts of interest.
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Associated Data
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Supplementary Materials
Supplementary Table 1. Characteristics of the clusters of brain regions in which regional blood perfusion was associated with waist-to-height ratio and body roundness index