Structural Brain Alterations in Metabolic Syndrome: A Comprehensive MRI Volumetric Analysis of Subcortical and Associated Structures

Abstract

Objective:

This study aims to elucidate the comprehensive effects of metabolic syndrome (MetS) on the structural integrity of subcortical brain regions and associated structures through high-resolution magnetic resonance imaging (MRI) volumetric analysis, thereby contributing to a deeper understanding of the neuroanatomical dimensions of MetS and its potential implications for cognitive functions and overall brain health.

Methods:

A cross-sectional design was implemented, involving 25 individuals diagnosed with MetS for at least one year and a healthy control group of 15 individuals at a tertiary hospital’s family medicine clinic in Eastern Turkey. Participants underwent a high-resolution MRI scan using a 1.5T Siemens Aera scanner. The MRICloud platform was employed for comprehensive segmentation and quantitative analysis of various brain structures.

Results:

The study revealed significant volumetric reductions in all measured subcortical brain regions among individuals with MetS compared to the control group (all P < 0.05). Notable differences were observed in key structures such as the substantia nigra, corpus callosum, and thalamus. In subcortical structures, the largest volumetric differences were noted in the basal ganglia L (1322.4 mm³), while the most significant percentage differences were seen in the substantia nigra R (25.24%) and caudate nucleus L (21.02%).

Conclusion:

The findings from this study underscore the significant neuroanatomical changes associated with MetS, manifesting as volumetric reductions in critical subcortical brain areas. These alterations underscore the necessity for further research into the comprehensive influence of MetS on cognitive processes and the potential for early therapeutic interventions.

Introduction

Metabolic syndrome (MetS), a multifaceted condition characterized by the convergence of cardiovascular risk factors such as insulin resistance, obesity, dyslipidemia, and hypertension, has been observed to significantly increase in prevalence on a global scale.¹ Comprehending the extensive effects of this syndrome on human health constitutes a primary focus in contemporary health research.² Given the concurrent global rise in both MetS and neurodegenerative conditions, the potential implications of this syndrome on neuroanatomy and cognitive functions have emerged as a critical area of investigation.^3
–5

In this context, studies examining the potential effects of MetS on brain structure and function hold significant importance.^6,7 The complex interaction between metabolic dysregulation and neural structural integrity reveals potential underlying mechanisms for an increased risk of neurodegenerative diseases.⁸ This connection is notably underscored by the criteria set by the American Heart Association (AHA) for MetS, which encompasses various metabolic irregularities capable of affecting cerebral perfusion and integrity.⁹ Furthermore, neuroimaging studies have consistently illuminated the subtle yet discernible effects of MetS components on brain structures, indicating a nuanced model of neural compromise.^10

–13 This underscores the significance of the relationship between metabolic dysregulation and neural alterations, implying profound consequences for individuals’ cognitive processes and overall neurobiological health.

Advancements in neuroimaging technologies have provided unprecedented opportunities to examine the brain’s landscape. High-resolution magnetic resonance imaging (MRI) techniques facilitate a detailed and noninvasive assessment of brain anatomy. The utilization of sophisticated analytical platforms such as MRICloud enhances the comprehensive segmentation and quantitative analysis of various brain structures, thereby increasing the precision of volumetric analyses.¹⁴

The intricate interplay between MetS and various health complications has been a focal point of extensive research. Although there are studies on the individual effects of the components of MetS on brain structure and function, evidence regarding the potential impact of MetS as a whole on brain structure is not sufficient. Upon review of the literature, it is evident that there is a paucity of research specifically examining the relationship between subcortical structures and MetS and its components. The objective of this study is to examine the potential effects of long-standing MetS on these brain structures in diagnosed individuals. This endeavor represents a significant step toward understanding the neuroanatomical dimensions of MetS and lays a foundation for potential therapeutic interventions.

Material and Methods

Study design

This research is a cross-sectional study conducted between January and December 2023 at the Family Medicine Clinic of a tertiary hospital located in eastern Turkey. The study comprises two groups: Patients who meet the criteria for MetS for at least one year and satisfy other inclusion criteria (25 individuals), and a healthy control group (15 individuals). The criteria proposed by the AHA for defining MetS were adhered to in this study (Table 1).⁹

Table 1.

Metabolic Syndrome Diagnostic Criteria and Cut-Off Points According to the AHA⁹

Criteria	Cut points
Elevated waist circumference	≥102 cm in men ≥88 cm in women
Elevated triglycerides	≥150 mg/dL (1.7 mmol/L)^a
Reduced HDL-C	<40 mg/dL (1.03 mmol/L) in men <50 mg/dL (1.3 mmol/L) in women^b
Elevated blood pressure	≥130 mmHg systolic or ≥85 mmHg diastolic
Elevated fasting glucose	≥100 mg/dL^c

Or drug treatment for elevated triglycerides.

Or drug treatment for reduced HDL-C.

Or drug treatment for elevated glucose.

AHA, American Heart Association; HDL-C, high-density lipoprotein-cholesterol.

Participant selection

Participants were recruited from a diverse group of individuals visiting the clinic for routine health assessments or minor health concerns, employing a detailed set of inclusion and exclusion criteria. Patients included in the MetS group had been under observation for at least one year and were evaluated for MetS components at each clinical visit. Similarly, patients were re-evaluated for MetS at the clinic visit closest to the MRI scan.

Inclusion criteria

Individuals between 18 and 65 years of age.

Only those who had undergone a comprehensive brain MRI scan no more than 30 days before their clinic visit.

Eligibility required recent measurements (no older than 30 days from the clinic visit) of serum triglycerides, high-density lipoprotein (HDL) cholesterol, blood pressure, fasting glucose, and waist circumference, enabling the precise evaluation of metabolic syndrome components.

Control group: Participants in the control group were selected based on the absence of any active chronic disease.

Exclusion criteria

Individuals exhibiting inadequate cognitive levels, a history of severe head trauma, current use of neurological or psychiatric medications, or conditions such as brain tumors, nervous system infections, or severe neurological diseases were excluded.

Those with current alcohol or substance dependence were also deemed ineligible, to avoid confounding the study outcomes.

Ethical principles and data collection

The research was reviewed and approved by the Clinical Research Ethics Committee of Erzincan Binali Yildirim University. Participants' rights were safeguarded at every stage of the research, and both verbal and written consents were obtained for the study. Data were processed anonymously and the principles of the Helsinki Declaration were adhered to in every phase of the research. Participants were surveyed regarding age, gender, education level, and the presence of chronic diseases.

MRI protocol and automatic brain structure segmentation with MRICloud

The MRI conducted within this study utilized a high-resolution magnet, specifically a 1.5T Siemens Aera scanner, engineered in Germany. This imaging process was executed using a T1-weighted 3D magnetization prepared rapid gradient echo (MPRAGE) sequence, which comprehensively encompasses the entire brain. The specific parameters for the imaging were as follows: A repetition time (TR) of 2200 ms, an echo time (TE) of 2.67 ms, a flip angle set at 8°, and an acquisition matrix of 256 × 246. The field of view (FOV) was established at 250 × 250 mm, with the acquisition duration being precisely 4 min and 59 sec. The imaging yielded 192 axial slices, each with a thickness of 1 mm and devoid of any intervening gap, ensuring detailed and continuous brain coverage.

For the postimaging analysis, the study utilized the MRICloud system (accessible at https://mricloud.org/), an online platform dedicated to MRI brain volumetry.¹⁴ MRICloud is an image processing software suite that encompasses a range of functionalities, including image viewing, volume calculation, diffusion tensor computation, formatting, three-dimensional visualization, image archiving, and marking of specified regions, as well as the generation of statistical analyses and image records. This innovative system, with the assistance of the ITK-SNAP application, as depicted in Figure 1, leverages a complex algorithm to offer a fully automated approach for the segmentation of brain structures. This cutting-edge technique was employed to meticulously process the MRI data, facilitating precise volumetric analyses of various cerebral structures, including the basal ganglia, caudate nucleus, putamen, globus pallidus, red nucleus, substantia nigra, amygdala, hippocampus, nucleus accumbens, cingulate gyrus, mammillary body, thalamus, hypothalamus, corpus callosum, claustrum, and limbic lobe. The utilization of MRICloud robust segmentation capacity ensured a thorough and accurate delineation of these intricate brain regions, contributing significantly to the depth and precision of the neuroanatomical insights derived from the study’s MRI data.

FIG. 1.

Automated 3D segmentation of brain structures.

Data analysis

During the data analysis phase, the normality of the dataset distribution was assessed using the Kolmogorov–Smirnov test. Descriptive statistics were utilized to examine the central tendency and distribution properties of continuous variables, with mean and standard deviation values reported for those adhering to a normal distribution. For the analysis of nominal scale data, frequencies (n) and percentages (%) were calculated. To calculate the percentage difference between the control and MetS groups, the average volume of the MetS group was subtracted from the average volume of the control group for each section, and the result was divided by the average volume of the MetS group. Relationships between two nominal variables were evaluated using the chi-squared test or, in cases where expected frequencies were less than five, Fisher’s exact test was used. The comparison of continuous variables between two independent groups was conducted using the independent samples t-test and Mann–Whitney U test. For the comparison between two continuous variables, Pearson correlation analysis was employed. The threshold for statistical significance was set at P < 0.05.

Results

The study included a total of 40 participants, with 25 individuals diagnosed with MetS and 15 forming the control group. Of these participants, 80% (n = 32) were female and 20% (n = 8) were male, with an average age of 37.13 ± 9.95 years. Education levels varied, with 30% (n = 12) holding a university degree, 32.5% (n = 13) having completed high school, and 37.5% (n = 15) finishing primary/middle school. No statistically significant difference was observed between the MetS group and the control group in terms of gender, age, or education level (P = 0.102, P = 0.334, and P = 0.736, respectively).

In the MetS group, all members were diagnosed with hypertension and were using at least one antihypertensive medication. There was no hypertensive patient in the control group. In the MetS group, the average waist circumference was 107.20 cm (min = 95.00, max = 122.00), the average triglyceride level was 237.08 mg/dL (min = 168, max = 491), the average HDL level was 44.44 ± 5.11 mg/dL, and the average fasting glucose was 119.88 ± 18.79 mg/dL. Comparatively, in the control group, these values were significantly lower for waist circumference at 74.73 cm (min = 60.00 and max = 90.00), triglycerides at 86.33 mg/dL (min = 67 and max = 102), HDL at 58.13 ± 5.87 mg/dL, and fasting glucose at 81.46 ± 4.83 mg/dL (all P < 0.001).

The comparison of the volumes of different brain regions (measured in mm³) between individuals with MetS and the control group is presented in Table 2 and Figure 2. According to the findings, the average volumes of all brain regions in the control group were significantly higher than those in the metabolic syndrome group, with the exception of the left claustrum (P = 0.119).

FIG. 2.

Mean values with standard deviation for brain regions in metabolic syndrome and control groups. MetS, metabolic syndrome.

Table 2.

Comparison of Volumes of Different Brain Regions According to Groups

	Met. Syndrome		Control
	Mean	S.D.	Mean	S.D.	t(38)	P value	Cohen’s d
Basal_Ganglia_L	8711.28	622.66	10,090	1020.89	−4.729	<0.001	−1.739
Basal_Ganglia_R	9225.40	686.58	10547.80	1027.52	−4.428	<0.001	−1.596
Caudate_Nucleus_L	2897.24	404.13	3506.13	463.78	−4.365	<0.001	−1.426
Caudate_Nucleus_R	2946.48	422.98	3431.20	497.67	−3.284	0.002	−1.073
Putamen_L	3676.12	324.41	4227.20	439.01	−4.551	<0.001	−1.486
Putamen_R	3925.64	419.81	4434	495.15	−3.466	0.001	−1.132
Globus_Pallidus_L	1214	94.19	1341.53	140.89	−3.436	0.001	−1.122
Globus_Pallidus_R	1264.32	102.20	1410.26	142.93	−3.760	0.001	−1.228
Red_Nucleus_L	286	35.78	338.66	25.20	−4.994	<0.001	−1.631
Red_Nucleus_R	317.92	33.61	369.53	53.22	−3.770	0.001	−1.231
Substantia_Nigra_L	302.0	33.48	357	27.80	−5.344	<0.001	−1.745
Substantia_Nigra _R	248.0	29.74	310.60	30.10	−6.375	<0.001	−2.082
Amygdala_L	1402.60	129.51	1626.80	131.64	−5.268	<0.001	−1.721
Amygdala_R	1595.32	159.11	1785.46	129.43	−3.911	<0.001	−1.277
Hippocampus_L	3387.12	263.57	3601.20	311.07	−2.324	0.026	−0.759
Hippocampus_R	3487.48	313.68	3789.06	300.42	−2.990	0.005	−0.976
Nucleus_Accumbens_L	575.96	55.98	661.60	55.98	−3.063	0.006	−1.144
Nucleus_Accumbens_R	731.48	74.43	858.40	117.87	−4.186	<0.001	−1.367
Cingulate_Gyrus_L	19177.32	1542.91	21691.46	2338.13	−4.104	<0.001	−1.341
Cingulate_Gyrus_R	20709.92	1611.36	23637.13	2383.35	−4.639	0.002	−1.515
Mammillary_Body_L	95.80	9.0	112.20	16.71	−3.508	0.002	−1.321
Mammillary_Body_R	74.28	10.31	85.20	17.39	−2.501	0.017	−0.817
Thalamus_L	4998.48	298.45	5621.73	384.77	−5.733	<0.001	−1.872
Thalamus_R	5066.36	274.77	5730.46	382.14	−6.383	<0.001	−2.085
Hypothalamus_L	508.32	51.55	568.26	81.92	−2.849	0.007	−0.930
Hypothalamus_R	585.84	57.82	644.86	73.35	−2.825	0.008	−0.922
Corpus_Callosum_L	9947.72	1578.82	12072.86	1488.18	−4.209	<0.001	−1.375
Corpus_Callosum_R	10495.0	1646.96	12209.53	1321.31	−3.420	0.002	−1.117
Claustrum_L	330.12	40.07	352.53	47.51	−1.597	0.119	−0.522
Claustrum_R	381.88	37.52	431.86	50.28	−3.587	0.001	−1.171
Limbic_Lobe_L	11545.48	866.01	13100.93	1209.90	−4.732	<0.001	−1.545
Limbic_Lobe_R	11587.16	888.81	12925.86	1130.44	−4.162	<0.001	−1.359

The most significant volumetric differences were observed in the cingulate gyrus R, amounting to 2927.21 mm³. Among the subcortical structures, the basal ganglia L exhibited the largest discrepancy, with a volume difference of 1322.4 mm³. In percentage terms, the largest difference between the groups was 25.24% in the substantia nigra R, followed by 21.36% in the corpus callosum L, with the smallest difference being 6.32% in the hippocampus L, and then 6.79% in the claustrum L. (Table 3, Fig. 3).

FIG. 3.

Comparative analysis of mean volumetric and percentage differences in brain regions between metabolic syndrome and control groups. MetS, metabolic syndrome.

Table 3.

Mean and Percentage Differences of Brain Region Volumes According to Groups

	Mean difference	% Difference	P value
Basal_Ganglia_L	1378.72	15.83	<0.001
Basal_Ganglia_R	1322.4	14.33	<0.001
Caudate_Nucleus_L	608.89	21.02	<0.001
Caudate_Nucleus_R	484.72	16.45	0.002
Putamen_L	551.08	14.99	<0.001
Putamen_R	508.36	12.95	0.001
Globus_Pallidus_L	127.53	10.50	0.001
Globus_Pallidus_R	145.94	11.54	0.001
Red_Nucleus_L	52.66	18.41	<0.001
Red_Nucleus_R	51.61	16.23	0.001
Substantia_Nigra_L	55	18.21	<0.001
Substantia_Nigra _R	62.6	25.24	<0.001
Amygdala_L	224.2	15.98	<0.001
Amygdala_R	190.14	11.92	<0.001
Hippocampus_L	214.08	6.32	0.026
Hippocampus_R	301.58	8.65	0.005
Nucleus_Accumbens_L	85.64	14.87	0.006
Nucleus_Accumbens_R	126.92	17.35	<0.001
Cingulate_Gyrus_L	2514.14	13.11	<0.001
Cingulate_Gyrus_R	2927.21	14.13	0.002
Mammillary_Body_L	16.4	17.12	0.002
Mammillary_Body_R	10.92	14.70	0.017
Thalamus_L	623.25	12.47	<0.001
Thalamus_R	664.1	13.11	<0.001
Hypothalamus_L	59.94	11.79	0.007
Hypothalamus_R	59.02	10.07	0.008
Corpus_Callosum_L	2125.14	21.36	<0.001
Corpus_Callosum_R	1714.53	16.34	0.002
Claustrum_L	22.41	6.79	0.119
Claustrum_R	49.98	13.09	0.001
Limbic_Lobe_L	1555.45	13.47	<0.001
Limbic_Lobe_R	1338.7	11.55	<0.001

This research also examined how volumes of brain regions were related to components of MetS. The findings are summarized in Table 4. It shows important connections between various brain regions and MetS factors such as waist size, triglycerides, HDL, and glucose levels. However, some areas, such as the hippocampus and claustrum, did not show significant associations with most MetS components.

Table 4.

Correlation Table of Brain Region Volumes and MetS Components

		Waist circumference	Triglyceride	HDL	Glucose
Basal_Ganglia_L	r	−0.530^***	−0.503^**	0.523^**	−0.500^**
Basal_Ganglia_R	r	−0.477^**	−0.469^**	0.490^**	−0.553^***
Caudate_Nucleus_L	r	−0.459^**	−0.342^*	0.513^**	−0.449^**
Caudate_Nucleus_R	r	−0.383^*	−0.251^ns	0.430^**	−0.482^**
Putamen_L	r	−0.483^**	−0.534^***	0.447^**	−0.440^**
Putamen_R	r	−0.348^*	−0.449^**	0.341^*	−0.414^**
Globus_Pallidus_L	r	−0.391^*	−0.403^**	0.325^*	−0.345^*
Globus_Pallidus_R	r	−0.389^*	−0.371^*	0.380^*	−0.379^*
Red_Nucleus_L	r	−0.420^**	−0.387^*	0.407^**	−0.673^***
Red_Nucleus_R	r	−0.386^*	−0.334^*	0.328^*	−0.493^**
Substantia_Nigra_L	r	−0.433^**	−0.556^***	0.402^**	−0.564^***
Substantia_Nigra _R	r	−0.538^***	−0.455^**	0.526^***	−0.646^***
Amygdala_L	r	−0.540^***	−0.556^***	0.495^**	−0.571^***
Amygdala_R	r	−0.485^**	−0.491^**	0.414^**	−0.617^***
Hippocampus_L	r	−0.298^ns	−0.439^**	0.203^ns	−0.322^*
Hippocampus_R	r	−0.362^*	−0.513^**	0.229 ^ns	−0.373^*
Nucleus_Accumbens_L	r	−0.450^**	−0.419^**	0.375^*	−0.491^**
Nucleus_Accumbens_R	r	−0.466^**	−0.438^**	0.434^**	−0.523^**
Cingulate_Gyrus_L	r	−0.383^*	−0.402^**	0.358^*	−0.601^***
Cingulate_Gyrus_R	r	−0.440^**	−0.427^**	0.445^**	−0.622^***
Mammillary_Body_L	r	−0.548^***	−0.514^**	0.402^**	−0.484^**
Mammillary_Body_R	r	−0.373^*	−0.264 ^ns	0.198 ^ns	−0.403^**
Thalamus_L	r	−0.474^**	−0.504^**	0.506^**	−0.556^***
Thalamus_R	r	−0.501^**	−0.549^***	0.519^**	−0.559^***
Hypothalamus_L	r	−0.325^*	−0.405^**	0.459^**	−0.368^*
Hypothalamus_R	r	−0.285^ns	−0.393^*	0.228 ^ns	−0.438^**
Corpus_Callosum_L	r	−0.377^*	−0.400^*	0.479^**	−0.442^**
Corpus_Callosum_R	r	−0.311^ns	−0.336^*	0.411^**	−0.434^**
Claustrum_L	r	−0.158^ns	−0.243 ^ns	0.162^ns	−0.229^ns
Claustrum_R	r	−0.375^*	−0.343^*	0.508^**	−0.429^**
Limbic_Lobe_L	r	−0.450^**	−0.506^**	0.453^**	−0.605^***
Limbic_Lobe_R	r	−0.398^*	−0.511^**	0.400^*	−0.551^***

P < 0.05.

P < 0.01.

***

P < 0.001.

MetS, metabolic syndrome; HDL, high-density lipoprotein; ns, not significant.

Discussion

Subcortical structures regulate numerous fundamental functions in the brain, including those related to motor activities and learning processes. The impacts of MetS on these structures could have significant implications for individuals' cognitive functions and overall brain health. This study underscores the volumetric reductions in almost all brain regions among individuals with MetS compared to a healthy control group, with notable differences in key subcortical structures. These outcomes highlight the detrimental effects of MetS on brain structure and underscore the necessity for further research into the potential implications of this syndrome on cognitive functions. The findings elucidate the need for a deeper understanding of MetS’s neuroanatomical impact, prompting additional investigation into its comprehensive influence on cognitive processes.

MetS, known as a multifaceted condition characterized by the convergence of various cardiovascular risk factors, is recognized globally.⁹ The increasing prevalence of MetS worldwide and the endeavor to comprehend the extensive impacts of this syndrome on human health have made it a significant focal point in contemporary health research.² Our study meticulously examined the differences in subcortical and associated brain volumes between individuals with MetS and a healthy control group. The findings indicate that the brain region volumes in the MetS group are significantly lower compared to the control group. These differences are particularly pronounced in areas such as the substantia nigra and corpus callosum. However, no significant difference was observed in the left claustrum region.

The literature indicates that MetS can contribute to alterations in brain metabolism and white matter abnormalities, and an increased risk of ischemic stroke.^15
–17 The SMART-MR study investigated the effects of MetS on brain structure in individuals with notable arterial disease. The study found that MetS is associated with a smaller brain tissue volume, even in patients without diabetes (n = 451).¹³ Various theories exist regarding the potential effects of MetS on brain structure. For instance, MetS can lead to vascular abnormalities such as arterial stiffness, hypertension, and endothelial dysfunction. These conditions can cause microvascular damage, ischemic changes, and atrophy in the brain.¹⁶ Moreover, chronic inflammation and oxidative stress associated with MetS can damage neuronal cells. Inflammatory cytokines and free radicals can lead to neuronal damage and, consequently, shrinkage of brain tissue.¹⁸ Another factor is associated with insulin resistance and disruptions in glucose metabolism found in MetS. Impairments in glucose metabolism can affect energy production and the health of neurons.¹⁹

Bender et al. conducted a prospective study in healthy adults, examining changes in white matter (WM) over a two-year period. This study revealed that the rate of change in WM varied across regions and individuals, but was exacerbated by metabolic risk factors.²⁰ On the other hand, adverse metabolic outcomes can accelerate the impact of MetS on brain structures, and significant effects can manifest even at a young age. In a study conducted by Yau et al. on adolescents, it was observed that adolescents with MetS had decreased fractional anisotropy in the corpus callosum, optic radiations, and medial longitudinal fasciculus compared to the control group.⁶ These findings indicate that negative metabolic states can adversely affect brain structure even at a young age and may lead to cognitive decline in later life. Therefore, the effects of MetS on the brain are independent of age and can lead to accelerated brain deformities.

Compared to the study by Song et al. (2015), which investigated the effects of MetS on cortical thickness and subcortical volumes, our study also shows that metabolic syndrome has a significant impact on brain morphology.²¹ The study by Song et al. demonstrated notable reductions in both cortical and subcortical areas, particularly in regions associated with body weight control and cognitive functions in patients with MetS.²¹ Unlike our study, not all subcortical areas were found to have significant reductions in the study by Song et al. However, the FreeSurfer software used in their research has certain limitations in the comprehensive evaluation of subcortical structures. The authors have acknowledged that not all subcortical regions could be effectively assessed. This limitation underscores the significance of the comprehensive segmentation and volumetric analysis capabilities provided by the MRICloud platform used in our study. Nevertheless, further research employing a combination of different software tools for brain volume analysis could be beneficial for a more precise assessment of the effects of MetS on the brain.

Our study utilized MRI to compare the volumes of brain structures between individuals diagnosed with MetS and a healthy control group. Our findings indicate that the volumes of subcortical brain structures in the MetS group are significantly lower compared to those in the healthy control group. This suggests that the implications of this syndrome may extend beyond the metabolic and cardiovascular systems, potentially affecting the central nervous system as well. Volume differences are particularly notable in regions such as the substantia nigra, corpus callosum, and thalamus. This finding raises considerations about potential impacts of MetS on various brain functions, including motor functions, cognitive processes, and emotional processing.

Moreover, our analysis revealed significant correlations not only between MetS as a whole but also between its individual components and brain volumes. This suggests that the influence of MetS on brain structure is not merely a cumulative effect of its presence but is also distinctly driven by the specific metabolic derangements it encompasses. Such findings underscore the importance of addressing each MetS component individually in clinical interventions to potentially mitigate its adverse effects on brain health.

Study limitations

When evaluating the outcomes of this study, it is crucial to consider certain limitations. First, the cross-sectional design of the study restricts the direct establishment of causality between MetS and alterations in brain volume. Second, the relatively small sample size may limit the reliability and statistical robustness of volume changes detected in specific brain regions. Another limitation is the absence of a long-term follow-up in the study, which constrains the understanding of the temporal dynamics between MetS and brain volume alterations. Future research should address these limitations and utilize longitudinal designs with broader and more diversified sample groups to more comprehensively examine the effects of MetS on brain structure.

This study highlights the profound impacts of MetS on brain structure, revealing significant volumetric reductions in all subcortical brain regions in individuals diagnosed with MetS compared to the healthy control group. The findings emphasize the intricate connection between metabolic disorders and neuroanatomical changes, providing compelling evidence that MetS significantly contributes to structural alterations, particularly in critical brain areas such as the substantia nigra, corpus callosum, and thalamus. These alterations imply potential effects on cognitive functions, emotional processing, and motor abilities. The research deepens our understanding of the neuroanatomical aspects of MetS and underscores the urgency of comprehensive strategies for managing and treating this syndrome.

Ethics Committee Approval

The research was reviewed and approved by the Clinical Research Ethics Committee of Erzincan Binali Yıldırım University, under the number 2023-22/10.

Footnotes

Author Contributions

H.C.: Conceptualization, data curation, formal analysis, funding acquisition, investigation, methodology, project administration, resources, software, supervision, validation, visualization, writing—original draft, and writing—review and editing. M.S.: Conceptualization, data curation, formal analysis, methodology, project administration, resources, supervision, and validation. S.A.: Formal analysis, investigation, methodology, project administration, software, visualization, writing—original draft, and writing—review and editing. O.K.C.: Conceptualization, data curation, investigation, methodology, project administration, supervision, validation, writing—original draft, and writing—review and editing. E.G.: Conceptualization, data curation, investigation, methodology, supervision, validation, writing—original draft, and writing—review and editing.

Author Disclosure Statement

There is no conflict of interest between the authors or family members of the authors. The authors do not have any consultancy, expertise, working conditions, shareholding, or similar situations that may lead to potential conflicts of interest in any company.

Funding Information

During this study, no financial or spiritual support was received neither from any pharmaceutical company directly connected with their search subject nor from a company that provides or produces medical instruments and materials that may negatively affect its evaluation process.

References

Saklayen

. The global epidemic of the metabolic syndrome. Curr Hypertens Rep, 2018; 20(2):12–18; doi: 10.1007/s11906-018-0812-z

Lemieux

, Després

J-P

. Metabolic syndrome: Past, present and future. Nutrients, 2020; 12(11):3501; doi: 10.3390/nu12113501

Solfrizzi

, Scafato

, Capurso

, et al. Metabolic syndrome, mild cognitive impairment, and progression to dementia. the italian longitudinal study on aging. Neurobiol Aging, 2011; 32(11):1932–1941; doi: 10.1016/j.neurobiolaging.2009.12.012

Raffaitin

, Gin

, Empana

J-P

, et al. Metabolic syndrome and risk for incident Alzheimer’s disease or vascular dementia: The three-city study. Diabetes Care, 2009; 32(1):169–174; doi: 10.2337/dc08-0272

Rojas‐Gutierrez

, Muñoz‐Arenas

, Treviño

, et al. Alzheimer’s disease and metabolic syndrome: A link from oxidative stress and inflammation to neurodegeneration. Synapse, 2017; 71(10):e21990; doi: 10.1002/syn.21990

Yau

, Castro

, Tagani

, et al. Obesity and metabolic syndrome and functional and structural brain impairments in adolescence. Pediatrics, 2012; 130(4):e856–e864; doi: 10.1542/peds.2012-0324

, Aziz

, Diers

, et al. Insulin resistance accounts for metabolic syndrome‐related alterations in brain structure. Hum Brain Mapp, 2021; 42(8):2434–2444; doi: 10.1002/hbm.25377

Cai

, Cong

W-N

, Ji

, et al. Metabolic dysfunction in Alzheimer’s disease and related neurodegenerative disorders. Curr Alzheimer Res, 2012; 9(1):5–17; doi: 10.2174/156720512799015064

Grundy

, Cleeman

, Daniels

, et al. Diagnosis and management of the metabolic syndrome: An American heart association/national heart, lung, and blood institute scientific statement. Circulation, 2005; 112(17):2735–2752; doi: 10.1161/CIRCULATIONAHA.105.169404

10.

Debette

, Seshadri

, Beiser

, et al. Midlife vascular risk factor exposure accelerates structural brain aging and cognitive decline. Neurology, 2011; 77(5):461–468; doi: 10.1212/WNL.0b013e318227b227

11.

Alfaro

, Gavrieli

, Saade-Lemus

, et al. White matter microstructure and cognitive decline in metabolic syndrome: A review of diffusion tensor imaging. Metabolism, 2018; 78:52–68; doi: 10.1016/j.metabol.2017.08.009

12.

Kotkowski

, Price

, Franklin

, et al. A neural signature of metabolic syndrome. Hum Brain Mapp, 2019; 40(12):3575–3588; doi: 10.1002/hbm.24617

13.

Tiehuis

, Van Der Graaf

, Mali

, et al. Metabolic syndrome, prediabetes, and brain abnormalities on mri in patients with manifest arterial disease: The SMART-MR study. Diabetes Care, 2014; 37(9):2515–2521; doi: 10.2337/dc14-0154

14.

Mori

, Wu

, Ceritoglu

, et al. MRICloud: Delivering high-throughput MRI neuroinformatics as cloud-based software as a service. Comput Sci Eng, 2016; 18(5):21–35; doi: 10.1109/MCSE.2016.93

15.

Yates

, Sweat

, Yau

, et al. Impact of metabolic syndrome on cognition and brain: A selected review of the literature. Arterioscler Thromb Vasc Biol, 2012; 32(9):2060–2067; doi: 10.1161/ATVBAHA.112.252759

16.

Bokura

, Yamaguchi

, Iijima

, et al. Metabolic syndrome is associated with silent ischemic brain lesions. Stroke, 2008; 39(5):1607–1609; doi: 10.1161/STROKEAHA.107.508630

17.

Segura

, Jurado

, Freixenet

, et al. Microstructural white matter changes in metabolic syndrome: A diffusion tensor imaging study. Neurology, 2009; 73(6):438–444; doi: 10.1212/WNL.0b013e3181b163cd

18.

Wang

, Norman

, Srinivasan

, et al. Metabolic, inflammatory, and microvascular determinants of white matter disease and cognitive decline. Am J Neurodegener Dis, 2016; 5(5):171–177.

19.

Galizzi

, Di Carlo

. Insulin and its key role for mitochondrial function/dysfunction and quality control: A shared link between dysmetabolism and neurodegeneration. Biology (Basel), 2022; 11(6):943; doi: 10.3390/biology11060943

20.

Bender

, Raz

. Normal-appearing cerebral white matter in healthy adults: Mean change over 2 years and individual differences in change. Neurobiol Aging, 2015; 36(5):1834–1848; doi: 10.1016/j.neurobiolaging.2015.02.001

21.

Song

S-W

, Chung

J-H

, Rho

, et al. Regional cortical thickness and subcortical volume changes in patients with metabolic syndrome. Brain Imaging Behav, 2015; 9(3):588–596; doi: 10.1007/s11682-014-9311-2