Dysregulated cerebral blood flow, rather than gray matter Volume, exhibits stronger correlations with blood inflammatory and lipid markers in depression

Highlights

• •Bilateral temporal subcortical cerebral blood flow contributes to predicting depression.
• •The dysregulated cerebral blood flow is closely associated with inflammation and lipid markers.
• •Dysregulated cerebral blood flow, rather than gray matter volume, exhibits stronger correlations with blood inflammatory and lipid markers.

Abstract

Arterial spin labeling (ASL) can be used to detect differences in perfusion for multiple brain regions thought to be important in major depressive disorder (MDD). However, the potential of cerebral blood flow (CBF) to predict MDD and its correlations between the blood lipid levels and immune markers, which are closely related to MDD and brain function change, remain unclear. The 451 individuals − 298 with MDD and 133 healthy controls who underwent MRI at a single time point with arterial spin labelling and a high resolution T1-weighted structural scan. A proportion of MDD also provided blood samples for analysis of lipid and immune markers. We performed CBF case-control comparisons, random forest model construction, and exploratory correlation analyses. Moreover, we investigated the relationship between gray matter volume (GMV), blood lipids, and the immune system within the same sample to assess the differences in CBF and GMV. We found that the left inferior parietal but supramarginal and angular gyrus were significantly different between the MDD patients and HCs (voxel-wise P < 0.001, cluster-wise FWE correction). And bilateral inferior temporal (ITG), right middle temporal gyrus and left precentral gyrus CBF predict MDD (the area under the receiver operating characteristic curve of the random forest model is 0.717) and that CBF is a more sensitive predictor of MDD than GMV. The left ITG showed a positive correlation trend with immunoglobulin G (r = 0.260) and CD4 counts (r = 0.283). The right ITG showed a correlation trend with Total Cholesterol (r = −0.249) and tumour necrosis factor-alpha (r = −0.295). Immunity and lipids were closely related to CBF change, with the immunity relationship potentially playing a greater role. The interactions between CBF, plasma lipids and immune index could therefore represent an MDD pathophysiological mechanism. The current findings provide evidence for targeted regulation of CBF or immune properties in MDD.

1. Introduction

Major depressive disorder (MDD) rates among young people have risen sharply and are associated with recurrence, the onset of other psychiatric disorders, and wider, protracted impairments in interpersonal, social, educational, and occupational functioning (Thapar,et al., 2022). The detrimental impact of depression is due to sequelae that extend beyond mental health, increasing the risk for the development of metabolic syndrome and immune-related diseases (Penninx,et al., 2013). In particular, MDD has a high prevalence in individuals with autoimmune disorders and metabolic syndrome, and it is becoming clear that the dysregulation of homeostasis-maintaining actions in immune cells and energy balance are associated with MDD onset and could be reliable markers (Brydges et al., 2022, Gold, 2015, Millar et al., 2017, Vancampfort et al., 2015). Recently, in the Netherlands Study of Depression and Anxiety cohort of depressed adults, metabolism and immunity were found to be associated with immunometabolic dysregulations, as well as with atypical, energy-related symptoms (Lamers et al., 2018, Alshehri et al., 2019, Guo et al., 2023). Yuri et al.’s study also demonstrated that immunometabolic dysregulations vary as a function of MDD heterogeneity by illustrating that such biological dysregulations map more consistently to atypical behavioural symptoms reflecting altered energy intake/expenditure balance (hyperphagia, weight gain, hypersomnia, fatigue, and leaden paralysis) and may moderate the antidepressant effects of standard or novel therapeutic approaches (Milaneschi,et al., 2020). One-third of the cases exhibiting a relationship between depression and elevated arterial stiffness index levels during midlife may be accounted for by combined metabolic syndrome and inflammatory processes (Dregan,et al., 2020). Alterations in inflammatory, metabolic and bioenergetic biological pathways may represent the underlying pathophysiological mechanism of depression and are, therefore, a promising target for intervention (Milaneschi,et al., 2020).

Arterial spin labelling (ASL) is a noninvasive neuroimaging technique that provides a quantifiable measure of blood perfusion within a given region and the derived cerebral blood flow (CBF), revealing differences in perfusion between brain region functions in MDD and highlighting the potential role of perfusion as a biosignature of MDD (Alsop et al., 2015, Cooper et al., 2020). A case-control meta- analysis and meta-regression revealed increased CBF in the inferior parietal lobule, striatum, and bilateral thalamus in all patients with MDD relative to healthy controls, but decreased CBF was observed in the inferior frontal gyrus, insula, middle occipital gyrus and bilateral superior temporal gyrus in patients with MDD (Wang and Yang, 2022). Another study demonstrated that MDD participants exhibited decreased CBF in the inferior parietal lobule, highlighting shifts in cognitive networks and revealing that HCs have consistently increased perfusion relative to MDD participants (Cooper,et al., 2020). The majority of the clinical-perfusion relationships were chronicity-based MDD features, such as current episode, length of illness, age of onset, and number of MDEs, which indicated that levels of perfusion in MDD could be markers of trait or long-term effects of MDD (Cooper,et al., 2020).

The close relationship between immunity and blood lipids and brain measure has been gradually revealed. In MDD, inflammation is associated with the disruption of functional connectivity within a brain network deemed critical for interoceptive signaling, such as accurate communication of peripheral immune states to the brain, with implications for the pathogenesis of neuroinflammation and inflammation-linked depression (Aruldass et al., 2021, Brasanac et al., 2022). Higher glucose levels and lower levels of three small high-density lipoprotein (HDL) particles are associated with brain atrophy (de Leeuw,et al., 2021). A longitudinal follow-up study at 3.5 years revealed that higher total cholesterol and LDL cholesterol levels were associated with a greater reduction in functional connectivity in the default-mode network over time (Kobe,et al., 2021). The relationship between CBF and blood lipids has been recognized in other diseases. For example, high-density lipoprotein cholesterol and apolipoprotein A-I are associated with greater cerebral perfusion in multiple sclerosis (Jakimovski,et al., 2020). CBF may be used as a potential biomarker to predict the onset of MDD and is closely related to blood lipids and immunity. But whether there is a relationship with blood lipids and immunity in CBF related to the onset of depression is still unclear and needs further exploration.

Multimodal magnetic resonance imaging can help identify the underlying neuropathophysiological mechanisms of MDD from different aspect (Zhang,et al., 2023). The smaller GMV relative to those of controls in the right dorsolateral prefrontal cortex and left hippocampus, along with cerebellar, temporal and parietal regions, were substantial in MDD, which could potentially inform the development of diagnostic biomarkers for these conditions (Wise,et al., 2017). Some studies have shown associations between GMV and interleukin (IL)-6, high-density lipoprotein (HDL), as well as clinical phenotypes such as pleasure deficits (Lu et al., 2022, Zhang et al., 2020). However, systematic studies regarding the relationship between GMV, immune factors, lipid-related factors, and whether the relationship between GMV and CBF differs with biomarkers remain unclear.

The interactions between cerebral perfusion, plasma lipids and the immune index could therefore represent an MDD pathophysiological mechanism. However, the associations among them in MDD have not been investigated. First, we measured CBF changes in MDD patients. We also investigated associations of lipid (and apolipoprotein) and immune indicators (serum levels of cytokines, cellular immune index, immunoglobulin, complement and blood immune cells) with global and cortical perfusion measures. What’s more, we collected T1-weighted structural imaging and investigated the relationship between GMV, blood lipids, and the immune system within the same sample to assess the differences in CBF and GMV.

2. Methods

2.1. Participants

451 participants were recruited in Early-Warning System and Comprehensive Intervention for Depression (ESCID) study in Renmin Hospital of Wuhan University from April 2020 to August 2022, including 298 MDD patients and 133 healthy controls (HCs) (Kang,et al., 2021). The MDD patients were all diagnosed by two experienced psychiatrists and met the Diagnostic and Statistical Manual of Mental Disorders, fifth edition, diagnostic criteria for MDD and were screened with the Mini-International Neuropsychiatric Interview (MINI) (Amorim et al., 1998, Arbanas, 2015). MDD patients were excluded if they were ≤ 18 years of age or ≥ 40 years of age, had major neurological or other psychiatric disorders, had magnetic resonance imaging abnormalities, or history of substance or alcohol dependence, metabolic or cardiovascular disorders, autoimmune, or had contraindications to MRI.

Some MDD participants had peripheral blood drawn. These participants needed to meet the following exclusion criteria: serious and unstable physical or autoimmune diseases; taking glucocorticoids, nonsteroidal anti-inflammatory drugs and immunomodulators within two weeks; vaccination within one month before the study; a heavy oil diet 8 h prior to blood collection; and alcohol and vigorous exercise 24 h prior to blood collection.

The inclusion criteria for HCs were the following: absence of any psychiatric disorder or neurological disorders, no contraindications to MRI, and no history of substance or alcohol dependence. HCs participants were recruited from the college and the local community.

This experiment was reviewed and approved by the Ethics Committee of the Renmin Hospital of Wuhan University, and all participants were informed and agreed to participate in this study.

2.2. Clinical assessment

The sociodemographic and basic clinical characteristics questionnaires section specified the age, gender, education level, age of depression onset, and duration of depression symptoms. The 17-item Hamilton Depression Scale (HAMD-17) was used to assess the severity of depressive symptoms in the past week by clinicians (HAMILTON, 1960).

2.3. MRI acquisition and analysis

MRI scans were obtained using a General Electric Company 3.0 T scanner (GE Discovery MR750 3.0 T) at the Renmin Hospital of Wuhan University. Pseudocontinuous arterial spin labeling (pCASL) was performed in whole brain (scan plane: axial; freq FOV: 240 mm; slice thickness = 3 mm; slice gap = 3 mm; repetition time = 5070 ms; TE = 11.5 ms; voxel resolution = 1.88*1.88*3mm3; post-label delay = 2025 ms; Scan Locs number = 50; points: 512; scan time: 4mins 54 s). All ASL data were transferred to the AW4.6 workstation (GE Healthcare) to generate CBF maps by using GE functool 4.6 software. The CBF map was preprocessed by using statistical parametric mapping 8 (SPM, https://www.fil.ion.ucl.ac.uk/spm/software/spm8) and DPABI based on the MATLAB 2013a platform. The steps of CBF map preprocessing were as follows: spatial normalization through a PET template, standardization by mean regression and smoothing by an 8-mm full-width at half-maximum (FWHM) smoothing kernel.

A localizer sequence was acquired to position subsequent scans, followed by a high-resolution 3-dimensional T1-weighted structural scan (repetition time = 8.5 ms; TE = 3.2 ms; echo time = 3.2 ms; FOV = 25.6*25.6 cm; slice thickness = 1.0 mm; matrix = 256 * 256 mm²; slice thickness: 1 mm; Locs per Slab = 180; scan time: 4mins 41 s). The SPM 8 and the VBM 8 toolbox (https://dbm.neuro.unijena.de/vbm) were used for voxel-based morphometry analysis (VBM). T1 images were reoriented to have the same point of origin, spatial orientation, and nonlinear deformation field. They were also segmented (grey matter, white matter, and cerebrospinal fluid), checked, normalized to a template space, smoothed (8-mm FWHM), and obtained a grey matter volume (GMV) map.

2.4. Blood biomarkers

Whole blood was collected from the forearm vein. Blood lipids were test in Enzymatic method (siemens ADVIA 2400), including low-density lipoprotein (LDL), HDL, triglyceride (TG), total cholesterol (TC). The lipoprotein A1 (ApoA1), lipoprotein B (ApoB), and lipoprotein E (ApoE) were test in Immunoturbidimetry method (siemens ADVIA 2400). Immune indicators included serum levels of cytokines (interferon-gamma (IFN-γ), IL-2, IL-4, IL-6, IL-10, and tumour necrosis factor-alpha (TNF-α)) (flow cytometry, siemens BD FACSCalibur), immunoglobulin (IgA, IgG and IgM) (nephelometry, siemens BNII), cellular immune index (CD3+, CD4+, CD8+, CD19+, CD16 + 56 + ) (flow cytometry, siemens BD FACSCalibur), complement (C3, C4) (nephelometry, siemens BNII) and number of blood immune cells (neutrophils (Neu), eosinophils (EOS), basophils (BASO), lymphocytes (LYM), monocytes (Mono)) (Resistance method, fluorescent staining method and light scattering method, Hysenmecon XN-10).

2.5. Statistical methods

IBM SPSS Statistics Version 23.0 was used to compare the sociodemographic characteristics. The continuous variable data, which follows a normal distribution, is analyzed using a two-sample t-test to compare differences between groups. The medians, quartile ranges and Wilcoxon rank sum test were used for data not conforming to the normal distribution. The data of categorical variables were compared using the chi-square test or Wilcoxon rank sum test. And P value less than 0.05 indicated that a difference was statistically significant.

SPM8 was used to compare the CBF and GMV by two-sample t tests in whole gray matter voxels. And sex, education level, and current age were controlled as covariates. The brain regions with statistically significant differences have a voxel-wise P < 0.001, corrected for family-wise error (FWE) in cluster-wise at P < 0.05. In order not to miss any other potentially differing brain regions, a voxel-wise P < 0.001 and cluster size greater than 20 were also reported.

To compare the potential differences of CBF and GMV in predicting MDD. We constructed a 6-mm regions of interest (ROIs) centred on the peak point coordinates of the above different brain regions (P < 0.001 and cluster size > 20), calculated the average CBF within the ROIs, and constructed a random forest model (using R software package 4.0.2, by generating 500 trees) (Breiman, 2001). Ten-fold cross-validation was used, and the best area under the receiver operating characteristic curve (AUC) and importance orders Mean Decrease Gini were obtained (Rodriguez,et al., 2010). A descending order of the mean decrease Gini coefficient represents the decreasing importance of factors in the model, from large to small. Following a similar process, we applied the method to construct two random forests separately for GMV-only and CBF-GMV-combined.

In the MDD sample only, Spearman’s correlations were used to analyse the relationships between the average CBF/GMV value of the 6-mm globule around the peak point and blood lipid and immune indices. To control the false positive error rate, statistically significant differences are indicated by Bonferroni-corrected P < 0.05 in each brain region in lipid and immune-related investigations.

3. Results

3.1. Sample characteristics

A total of 451 participants were initially recruited for this study. Of these, 20 participants were excluded from the analysis for the following reasons: abnormal brain lesions (n = 18), did not complete all assessments or were unwilling to continue participating in the study (n = 2). This left a final sample of 431 participants (MDD:298, HCs:133) who underwent ASL, 3-dimensional T1-weighted structural scan data analysis and clinical sample correlation. Among the MDD participants, 236 participants obtained all immune markers, LDL, HDL, TG, and TC, with 66 participants receiving additional ApoA1, ApoB, and ApoE. Sociodemographic and clinical variables are summarized in Table 1. There was no difference in the proportion of men versus women in the MDD and HC groups, but there were significant differences in age and education level.

Table 1.

Demographic data of MDD and HCs groups (Categorical variables (frequency in percentage), continuous variables(means ± SD)).

		MDD(n = 298)	HCs(n = 133)	P
sex	man	76(0.255)	36(0.271)	0.732
	female	222(0.745)	97(0.729)
education level	below bachelor	16(0.054)	2(0.015)	<0.001
	bachelor	239(0.802)	84(0.632)
	high bachelor	43(0.144)	47(0.353)
Age (year)		24.01 + 4.674	24.83 + 3.347	<0.001
oneset age (year)		19.74 ± 5.401	NA
depression course (month)		31.47 ± 39.855	NA
HAMD-17		18.43 ± 7.545	NA

MDD: major depressive disorder, HCs: healthy controls; HAMD-17: Hamilton Depression Scale-17;

3.2. Case-control differences in CBF

The bilateral inferior temporal gyrus (ITG), bilateral middle temporal gyrus (MTG), bilateral superior dorsolateral frontal gyrus (SFGdor), and left precentral gyrus (PreCG) showed different trends in CBF between the MDD patients and HCs (voxel-wise P < 0.001 and cluster size > 20) (Table 2; Fig. 1 clearly show different directions of differences in CBF). The left inferior parietal but supramarginal and angular gyrus were significantly different between the MDD patients and HCs (voxel-wise P < 0.001, cluster-wise FWE correction) (Fig. 1; Table 2).

Table 2.

Regional CBF differences and meandecreseGini.

number	region	cluster size	x	y	z	t	meandecreseGini
1	right inferior temporal gyrus	37	38	−4	−40	3.5983	17.56488
2	left inferior temporal gyrus	145	−44	−20	–22	3.9326	17.70442
3	right middle temporal gyrus	158	48	−52	0	4.1215	16.37548
4	left Middle frontal gyrus	133	−24	52	4	4.305	15.51116
	left Superior frontal gyrus dorsolateral	52
5	left Superior frontal gyrus dorsolateral	70	−14	58	24	3.9762	13.7449
6	right Superior frontal gyrus dorsolateral	107	16	54	30	3.6221	14.3113
7	left Middle frontal gyrus	86	−34	24	40	3.8387	13.93117
8	left Superior frontal gyrus dorsolateral	28	−18	46	38	3.5114	14.91894
9	left inferior parietal, but supramarginal and angular gyri	379	−40	−42	50	4.1156	13.32019
10	left precental gyrus	26	−28	−20	54	3.7048	15.84296
11	left Middle frontal gyrus	69	−36	8	60	3.8159	15.77365
12	left precental gyrus	29	−24	−12	72	3.4969	14.65869

Fig. 1.

Case-control differences in CBF.

The bilateral PreCG, left MFG, left inferior and middle frontal gyrus orbital parts, left superior medial frontal gyrus, left lingual gyrus, left median cingulate and paracingulate gyrus, and right superior occipital gyrus GMV showed different trends between MDD vs. HC (voxel-wise P < 0.001 and cluster size > 20, did not survive cluster-wise FWE correction) (eTable 1).

3.3. Pattern classification analysis

In CBF-only random forest model, the AUC of the above factors in predicting MDD or HCs was 0.717, with a specificity of 0.5 and a sensitivity of 0.933. The importance ranking of the above factors in predicting MDD is shown in Table 2. The CBF of the bilateral ITG, right MTG and left PreCG was more important than the other factors.

In GMV-only random forest model, the AUC of the GMV in predicting MDD or HCs was 0.573, with a specificity of 0.214 and a sensitivity of 0.931. In CBF-GMV-combined random forest model, the AUC of the GMV and CBF in predicting MDD or HCs was 0.654, with a specificity of 0.308 and a sensitivity of 1.000. The importance ranking of the above factors in predicting MDD is shown in the eTable 1.

3.4. Association between CBF and lipid and immunity profiling indicators in MDD

We next examined the relationships between blood biomarkers and CBF within the mask defined by the case-control analysis. The right MTG CBF was significantly positively correlated with TG (r = 0.410), ApoB (r = 0.355), and CD16 + 56 (r = 0.394) (Bonferroni correction). The left SFGdor was significantly correlated with IgG (r = −0.412) and ApoE (r = 0.443) (Bonferroni correction). The left PreCG was significantly correlated with TG (r = −0.412) and LYM (r = 0.390) (Bonferroni correction). The MFG was significantly positively correlated with CD3 (r = 0.429), CD4 (r = 0.422), and LYM (r = 0.482) (Bonferroni correction). The left ITG showed a positive correlation trend with IgG (r = 0.260) and CD4 counts (r = 0.283) (did not pass Bonferroni correction). The right ITG showed a correlation trend with TCh (r = −0.249) and TNF-α (r = −0.295) (did not pass Bonferroni correction). Correlations between CBF and immune and lipid markers are presented in Fig. 2 and eTable 2. Correlations between GMV and immune and lipid factors did not Bonferroni correction, as shown in eFigure 1 and eTable 3.

Fig. 2.

Association between CBF and lipid and immunity profiling indicators in MDD. C1-C12 represent the statistically significant brain regions with differences in CBF between MDD and HC as shown in Table 2.

4. Discussion

In our study, we compared regional CBF in patients with MDD and a group of HC participants. We also explored the relationship among regional CBF and GMV and dyslipidaemia and immunometabolic markers in MDD patients. CBF differences in the bilateral ITG, right MTG, and left PreCG were important illness markers. In particular, bilateral ITG CBF, which was the best predictor of MDD, was associated with with immune measures. And right MTG and left PreCG CBF, were associated with both immunity and metabolism.

Our study highlights the importance of CBF in the ITG. The ITG is part of the ventral stream of the visual system, and is involved in the perception of objects, including human facial features and scenes. The ITG is also involved in multimodal sensory integration and visual perception (Hickok and Poeppel, 2007). In MDD, it is common for complex network abnormalities to be associated with emotional regulation and multisensory integration (Li,et al., 2021). A large multicentre dataset from the PsyMRI consortium demonstrated that the network organization of cortical networks involved in processing sensory information may be a more stable neuroimaging marker for MDD than higher-order neural networks such as the default mode and frontoparietal networks (Javaheripour,et al., 2021). The altered CBF in the ITG may underlie the deficits in some cognitive function or posttraumatic symptoms, especially attention changes (Li,et al., 2020). Our study also provides evidence that changes in the CBF of the ITG also support an important role of the sensory processing system in the pathogenesis of MDD. Considering the varying impact of disease duration on CBF, selecting CBF indicators more closely associated with the duration of the illness may enhance the accuracy of using CBF to predict MDD (Cooper,et al., 2020). Meanwhile, we used the same method to examine changes in GMV. Our study revealed that CBF was found to be a potentially more sensitive biomarker than VBM, highlighting its importance.

Our study focused on a younger population, and previous studies have shown a strong relationship between age and immunity in MDD. Earlier age of onset is associated with a proinflammatory state in MDD, and higher serum levels of IL-1β and TNFα may be associated with the earlier onset subgroup of MDD patients (Anzolin,et al., 2022). Early trauma and peripheral inflammatory responses play an important role in the pathophysiology of early-onset MDD (Li,et al., 2022). Compared with controls, increased circulating inflammatory markers in MDD patients were most consistent for IL-6 and TNF-α in both peripheral blood and cerebrospinal fluid (Enache et al., 2019, Hiles et al., 2017). Anti-inflammatory factors, cellular immunity, humoral immunity, and the complement system coordinate and coregulate the immune mechanism in the organism from different levels. Our study is comprehensive, as it examined different levels of immunity indicators. Our study did not examine changes in immune markers between MDD and HCs individuals, but our study also supports a close relationship between CBF changes in ITG, which is most associated with MDD, and TNF-α. This shows the close relationship between MDD and immune indicators and suggests that CBF changes in the ITG may play an important mediating role. Unfortunately, there are still few studies in this area, and the relationship between CBF and immunity needs to be further explored. Previous studies have shown that the mechanisms of immune and brain effects on MDD may be multifaceted. Proinflammatory cytokines have unique and specific actions on neurons and circuits within the central nervous system, influencing microglial activation, signalling molecules in neurotransmission, memory, glucocorticoid function, and activity control (Stertz,et al., 2013). Inflammatory mediators may alter monoamine and glutamate neurotransmission, glucocorticoid receptor resistance, and hippocampal neurogenesis (Zunszain,et al., 2012). Lipid metabolism in the CNS and peripheral tissues and brain insulin signalling may underlie this protection (Elhaik,et al., 2018). Although our study comprehensively reviewed different immune indicators, research on the relationship and mechanism between immunity and brain CBF is still limited, and further analysis is needed.

As previously mentioned regarding the role of immunity in young people, similarly, regarding blood lipids, the blood lipid pattern of young people also seems to be very specific. Given the multifactorial cascade of metabolic, inflammatory, and neurodegenerative processes in ageing, the unique contribution of cardiovascular risk to brain outcomes may be attenuated or even altered with age (Suri,et al., 2019). Some studies also suggest that CBF may be more sensitive to earlier stages of atherosclerosis, as white matter hyperintensity may develop after the long-term presence of atherosclerosis, while reductions in CBF may be apparent more immediately (Aljondi,et al., 2020). Our study also investigated the association of lipid profiles with ASL in young MDD patients and supports their role in the young population. The relationship between cardiovascular risk and CBF has been confirmed in the literature (Hafdi,et al., 2022). The intimal penetration and retention of LDL represents a key step in the pathophysiological process of atherogenesis (Borén,et al., 2020). A 2016 Alzheimer’s Disease Neuroimaging Initiative study revealed that the earliest pathological event in Alzheimer’s progression was reduced CBF, and cerebrovascular dysregulation may therefore precede and even accelerate neurodegeneration (Iturria-Medina,et al., 2016). The lipid composition indicators in MDD subjects showed a shift toward less HDL and more very-LDL and triglyceride particles in depression, which is in accordance with a higher metabolic syndrome profile in MDD and is more likely related to atypical symptoms (Bot et al., 2020, Brailean et al., 2020). Some studies support the effect of blood lipids on MDD through changes in brain structure (Wang,et al., 2021). However, the effect size of these lipids needs to be further explored. In older adults, low HDL may be involved in structural brain ageing and cognitive dysfunction, but the association of low HDL with cognitive ageing phenotypes appears to not be mediated by brain structure (Wang,et al., 2021). Our research has also made some contributions in this area. In our study, HDL was the most important indicator of blood lipids, which was correlated with right MTG and left PreCG CBF. Higher serum HDL cholesterol was associated with more favourable changes in memory and executive function in older people with diabetes mellitus (Wu,et al., 2022).

Our study also has certain limitations. Our findings have emphasized that circulating lipids and immunity are related to brain CBF changes that are characteristic of MDD. We note that our findings are difficult to interpret in terms of causality. Whether an identified association is a cause or consequence of CBF changes cannot be studied with the cross‐sectional design of this study, although we used a larger sample size. Research evaluating immunity/lipid–brain CBF associations prospectively and whether lipid optimization has salutary effects on brain CBF is necessary. And the combination of genetic sex, pubertal sex hormones, and other factors defines differential metabolic homeostasis between males and females (Mauvais, 2024). Future studies should be performed to validate our immunity and lipid findings and indicate whether they are related to brain CBF changes and can be used as treatment targets or to stratify patients for intervention trials, with due consideration to gender differences. The associations between metabolites, immunity and brain measures we found in this study do indicate subtle effects with moderate P‐values, which is a common observation when studying associations between peripheral metabolites and brain diseases. Further experiments and longitudinal studies are needed to explore the causality, correlation and affect pathway.

5. Conclusions

This work emphasizes the importance of CBF in the bilateral ITG, right MTG and left PreCG in MDD. In addition, immunity and lipids are closely related to CBF, with the immunity relationship potentially playing a greater role.

6. Funding statement

This work was supported by grants from the National Natural Science Foundation of China (U21A20364), the National Key R&D Program of China (2018YFC1314600), the Natural Science Foundation of Hubei Province (2023AFB213), the Fundamental Research Funds for the Central Universities (2042023kf0014).

7. Ethics approval statement

This experiment was examined and approved by the ethics committee of the Renmin Hospital of Wuhan University, and all the participants were informed and agreed to participate in this study.

8. Patient consent statement

Not applicable.

9. Permission to reproduce material from other sources

Not applicable.

10. Clinical trial registration

Not applicable.

CRediT authorship contribution statement

Lijun Kang: Writing – review & editing, Writing – original draft, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation. Wei Wang: Writing – review & editing, Writing – original draft, Methodology, Investigation. Zhaowen Nie: Software, Methodology, Investigation, Formal analysis. Qian Gong: Writing – review & editing, Writing – original draft, Visualization, Software, Project administration, Methodology, Investigation. Lihua Yao: Methodology, Investigation. Dan Xiang: Methodology, Investigation. Nan Zhang: Project administration, Methodology, Investigation. Ning Tu: Software, Methodology. Hongyan Feng: Methodology. Xiaofen Zong: Project administration, Methodology, Investigation. Hanping Bai: Project administration, Investigation, Conceptualization. Gaohua Wang: Supervision, Conceptualization. Fei Wang: Writing – review & editing, Writing – original draft, Methodology, Investigation. Lihong Bu: Writing – review & editing, Writing – original draft, Supervision, Conceptualization. Zhongchun Liu: Writing – review & editing, Writing – original draft, Supervision, Project administration, Methodology, Funding acquisition, Conceptualization.

Appendix A. Supplementary data

The following are the Supplementary data to this article:

Supplementary figure 1.

Data availability

Data will be made available on request.