Skip to main content
Download PDF

Exploring the relationship between lipid metabolism and cognition in individuals living with stable-phase Schizophrenia: a small cross-sectional study using Olink proteomics analysis

BMC Psychiatry volume 24, Article number: 593 (2024) Cite this article

Abstract

Background

Cognitive impairment is a core symptom of schizophrenia. Metabolic abnormalities impact cognition, and although the influence of blood lipids on cognition has been documented, it remains unclear. We conducted a small cross-sectional study to investigate the relationship between blood lipids and cognition in patients with stable-phase schizophrenia. Using Olink proteomics, we explored the potential mechanisms through which blood lipids might affect cognition from an inflammatory perspective.

Methods

A total of 107 patients with stable-phase schizophrenia and cognitive impairment were strictly included. Comprehensive data collection included basic patient information, blood glucose, blood lipids, and body mass index. Cognitive function was assessed using the Montreal Cognitive Assessment (MoCA) and the MATRICS Consensus Cognitive Battery (MCCB). After controlling for confounding factors, we identified differential metabolic indicators between patients with mild and severe cognitive impairment and conducted correlation and regression analyses. Furthermore, we matched two small sample groups of patients with lipid metabolism abnormalities and used Olink proteomics to analyze inflammation-related differential proteins, aiming to further explore the association between lipid metabolism abnormalities and cognition.

Results

The proportion of patients with severe cognitive impairment (SCI) was 34.58%. Compared to patients with mild cognitive impairment (MCI), those with SCI performed worse in the Attention/Alertness (t = 2.668, p = 0.009) and Working Memory (t = 2.496, p = 0.014) cognitive dimensions. Blood lipid metabolism indicators were correlated with cognitive function, specifically showing that higher levels of TG (r = -0.447, p < 0.001), TC (r = -0.307, p = 0.002), and LDL-C (r = -0.607, p < 0.001) were associated with poorer overall cognitive function. Further regression analysis indicated that TG (OR = 5.578, P = 0.003) and LDL-C (OR = 5.425, P = 0.001) may be risk factors for exacerbating cognitive impairment in individuals with stable-phase schizophrenia. Proteomics analysis revealed that, compared to individuals with stable-phase schizophrenia and normal lipid metabolism, those with hyperlipidemia had elevated levels of 10 inflammatory proteins and decreased levels of 2 inflammatory proteins in plasma, with these changes correlating with cognitive function. The differential proteins were primarily involved in pathways such as cytokine-cytokine receptor interaction, chemokine signaling pathway, and IL-17 signaling pathway.

Conclusion

Blood lipids are associated with cognitive function in individuals with stable-phase schizophrenia, with higher levels of TG, TC, and LDL-C correlating with poorer overall cognitive performance. TG and LDL-C may be risk factors for exacerbating cognitive impairment in these patients. From an inflammatory perspective, lipid metabolism abnormalities might influence cognition by activating or downregulating related proteins, or through pathways such as cytokine-cytokine receptor interaction, chemokine signaling pathway, and IL-17 signaling pathway.

Peer Review reports

Introduction

Schizophrenia is a prevalent clinical psychiatric disorder characterized by positive symptoms, negative symptoms, and cognitive impairments [1]. Among these, cognitive impairment is recognized as a core symptom that significantly affects the quality of life and prognosis for individuals living with schizophrenia. Nearly all individuals living with schizophrenia (approximately 98%) experience cognitive deficits [2]. Compared to healthy controls, people living with schizophrenia exhibit impairments across multiple cognitive domains, including memory [3], executive function [4], processing speed [5], verbal fluency [6], and social cognition [7]. These deficits vary in severity and may be influenced by factors such as age, substance use, untreated illness duration, symptom dimensions, treatment regimens, and childhood trauma [8]. In addition, metabolic abnormalities are also key factors affecting cognition, though conclusions in this area remain inconsistent. This topic has garnered increasing attention in recent years for further exploration.

Metabolic abnormalities are risk factors for cardiovascular and cerebrovascular diseases [9]. It is noteworthy that severe metabolic abnormalities can affect cognition [10], a phenomenon widely reported in non-psychiatric patients [11, 12]. The executive function of patients with hypertension, for example, tends to be poorer [13], and fluctuations in BMI can impact cognitive function [14]. Certainly, the close relationship between cognitive impairment and metabolic abnormalities should similarly apply to patients with schizophrenia. Existing research has shown that individuals living with schizophrenia who have metabolic abnormalities exhibit poorer cognitive function compared to those without such abnormalities [15]. This impairment specifically manifests in attention, memory, and reasoning tasks, and typically develops after the onset of the illness [16, 17]. Moreover, antipsychotic medications are the cornerstone of schizophrenia treatment. However, research indicates that approximately 50% of patients experience metabolic side effects after using antipsychotics, especially second-generation antipsychotics [18]. These side effects can include weight gain, dyslipidemia, insulin resistance, and elevated prolactin levels [19]. This further increases the risk of metabolic syndrome in patients with schizophrenia. Certainly, we believe that besides focusing on the relationship between metabolic syndrome and cognition, the relationships between specific individual aspects such as lipid levels, body weight, and blood glucose with cognition require further exploration.

Given the potential influence of various confounding factors such as the illness course, recovery of general psychiatric symptoms, and the type and dosage of antipsychotic medications on cognition, current research on the relationship between metabolism and cognition predominantly focuses on individuals experiencing their first episode of schizophrenia [20]. It is well known that in the short term, metabolic abnormalities alone may not immediately translate into cognitive impairment [21]. However, as the condition stabilizes and the disease progresses, the cumulative effects of metabolic abnormalities, combined with the "catalytic" effect of schizophrenia itself, may make it easier to detect the relationship between metabolic abnormalities and cognition. Therefore, exploring the relationship between metabolic abnormalities and cognition during the stable phase and implementing comprehensive interventions targeting potential risk factors in a timely manner may bring significant benefits to patients on long-term stable antipsychotic medication.

Recent studies have gradually linked lipid dysregulation, inflammation, and cognition. C-reactive protein (CRP), a reliable biomarker of inflammatory status, has been shown to predict cognitive improvement when low CRP levels are combined with high levels of HDL-C [22]. Genetic variations associated with CRP and plasma lipids (including HDL, LDL-C, and TG) have also been linked to an increased risk of Alzheimer's disease [23]. Peripheral inflammation and chronic low-grade inflammation can affect the central nervous system [24], as increases in circulating pro-inflammatory factors and free fatty acids may alter the permeability of the blood–brain barrier, potentially leading to changes in hippocampal function [25]. Variations in inflammation levels may also impact cognitive function by influencing plasma phospholipids [26]. In studies related to psychiatric disorders, patients with bipolar disorder have higher levels of apolipoprotein B compared to those with unipolar depression, and this may represent a risk factor for cognitive impairment [27]. Elucidating the role of inflammation as a potential bridge could deepen our understanding of the pathophysiological mechanisms by which lipids impact cognition.

In recent years, high-throughput omics technologies have rapidly advanced, providing new perspectives for a deeper understanding of physiological and pathological mechanisms. Proteomics is a systems biology approach that serves as a "bridge," reflecting upstream DNA or RNA abnormalities and predicting changes in various downstream metabolites [28]. Blood plasma, characterized by its safety and easy accessibility, is a stable and ideal sample used in research for various diseases. Multiple quantifiable proteins in plasma can serve as biomarkers for the diagnosis and prediction of complex diseases, reflecting various biological processes such as signal transduction, immune inflammation, and transport [29]. This study utilizes Olink proteomics technology to analyze inflammation-related protein changes in the blood plasma of individuals clinically diagnosed with stable-phase schizophrenia. From an inflammatory perspective, it aims to preliminarily explore the potential association and molecular mechanisms between lipid metabolism abnormalities and cognitive impairment, providing a basis for further clinical and experimental research.

In this study, we conducted a small cross-sectional analysis of individuals previously diagnosed with stable-phase schizophrenia, aiming to comprehensively gather their basic information and metabolic markers. These included blood glucose, lipid profiles (TG, TC, LDL, HDL), body mass index (BMI), as well as cognitive-related scores segmented into various dimensions. Taking into account the influence of confounding factors and controlling for gender, age, and illness duration, we observed differential metabolic markers between the two patient groups. Furthermore, through regression analysis, we identified lipid metabolism abnormalities as risk factors for cognitive impairment. Following this, we strictly matched two small sample groups of patients with lipid metabolism abnormalities based on gender, age, illness duration, and educational level. We utilized Olink proteomics analysis to investigate differential inflammatory-related proteins, aiming to further explore the association between lipid metabolism abnormalities and cognition. In summary, our primary objective is to identify risk factors for cognitive impairment in individuals living with stable-phase schizophrenia under specific conditions and to elucidate potential mechanisms linking lipid metabolism abnormalities with cognitive dysfunction.

Methods

Participants

The research subjects are patients from the closed management ward of the Mental Neurological Disease Hospital in Heilongjiang Province, China. They meet the diagnostic criteria for schizophrenia according to the International Classification of Diseases, 10th Revision (ICD-10). Diagnosis was confirmed upon admission by two experienced psychiatrists. Inclusion criteria: (1) Age between 25–65 years, Han Chinese ethnicity. (2) The patient's condition has been stable for at least 6 months, with the Positive and Negative Syndrome Scale (PANSS) of ≤ 60, and scores of ≤ 3 on the items delusions, conceptual disorganization, hallucinatory behavior, blunted affect, social withdrawal, lack of spontaneity, and mannerisms/posturing [30].(3) Stable dose of monotherapy with antipsychotic medication (olanzapine) for at least 6 months [31]. (4) Education level of ≥ 6 years. (5) Montreal Cognitive Assessment (MoCA) score less than 25 [32]. Exclusion criteria: (1) Hypertension (systolic blood pressure ≥ 140 mmHg or diastolic blood pressure ≥ 90 mmHg) or current use of antihypertensive medications. (2) History of head injury resulting in neurological sequelae, epilepsy, or neurosurgical history. (3) Presence of schizoaffective disorder, depressive disorders, bipolar affective disorder, or organic mental disorders. (4) History of drug abuse or alcohol abuse. (5) The presence of malignant tumors, severe cardiovascular and cerebrovascular diseases, and serious physical illnesses resulting from liver or kidney failure. Additionally, all patients are managed uniformly with a low-salt, low-fat diet, tobacco restriction, and daily centralized exercise training.

This study adheres to the Helsinki Declaration and is conducted under the auspices of the Heilongjiang Academy of Chinese Medicine. It has received approval from the ethics committee. Following an explanation of the study's nature, all patients and their relatives have provided informed consent.

Measurements

Clinical assessments

Gender, age, years of education (year), and illness duration (year) are collected from patient medical records. Current height and weight are measured to calculate Body Mass Index (BMI) using the formula: BMI = weight (kg) / height squared (m2). Psychopathology is assessed using the PANSS, which comprises 30 items including scales for positive symptoms (7 items), negative symptoms (7 items), and general psychopathology (16 items). The PANSS is well-established for evaluating recent-week psychiatric symptoms with good reliability and validity. It is administered by two experienced psychiatrists, with inter-rater reliability coefficients exceeding 0.8.

Blood samples

Patients are not permitted to engage in vigorous physical activity for 8 h prior to blood collection. The following morning, between 6:30 and 7:30, trained nurses centrally collect fasting blood samples from patients. Each patient is required to provide two tubes of blood. (1) For biochemical analysis: Whole blood is collected using vacuum clot activator tubes. After standing at room temperature, serum is obtained by centrifugation at 3000 rpm for 10 min. Metabolic parameters including fasting blood glucose (FBG), total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) levels are measured using an automatic biochemical analyzer (TBA-2000FR Biochemical analyzer). FBG, TC, TG, LDL-C were elevated at 6.2 mmol/L, 5.2 mmol/L, 1.7 mmol/L, and 3.4 mmol/L, respectively, while HDL-C was low at 1.0 mmol/L. (2) For Olink analysis: Whole blood from patients is collected using standard venipuncture technique into tubes containing EDTA. Within 30 min, the tubes are centrifuged at 3000 rpm for 10 min at 4 °C to remove blood cells. Plasma is then transferred to clean aliquot tubes and stored at -80 °C until analysis. Subsequent inflammatory markers were measured using Olink proteomics technology. All assays were conducted according to the manufacturer's protocols, as detailed in the "Olink Analysis" section.

Cognitive

Cognitive assessment is conducted using the MoCA scale to swiftly screen patients' cognitive functions. This scale has a maximum score of 30 points, with higher scores indicating better cognitive function. It demonstrates high reliability and validity across diverse populations and has been widely used for cognitive screening in patients with schizophrenia. Research suggests that a score below 25 indicates mild cognitive impairment, while a score below 23 indicates severe impairment [33]. An additional point is added to the total score if the years of education are ≤ 12 years (In this study, participants with 6–12 years of education had 1 point added to their total MoCA score). Further cognitive function assessment is performed using the Chinese version of the MATRICS Consensus Cognitive Battery (MCCB) [34]. This battery is specifically developed for cognitive assessment in schizophrenia and primarily includes seven domains: processing speed, attention/ Alertness, working memory, verbal learning, visual learning, problem solving, and social cognition. Finally, demographic data (including age, sex, education level, city of upbringing, and current city of residence) were used to convert raw scores from each test into Chinese standardized T-scores for statistical analysis. The T-scores have a mean of 50 and a standard deviation of 10. For domains with more than one test (working memory and processing speed), the T-scores were summed and then re-standardized. After obtaining T-scores for seven domains, they were summed and then standardized to create an overall composite score. All data collection is completed within 7 days.

Olink analysis

According to the manufacturer's instructions, protein levels are measured using the Olink® Inflammation Panel (Olink Proteomics AB, Uppsala, Sweden). The selection of the 92 biomarkers in the inflammation panel is predetermined by Olink Proteomics and cannot be customized. The Proximity Extension Assay (PEA) technology used in the Olink protocol is well-described and allows for the simultaneous analysis of 92 analytes using only 1 μL of each sample [35]. In brief, paired antibody probes labeled with oligonucleotides bind to their target proteins. If the two probes are in close proximity, the oligonucleotides will hybridize in a paired manner. The addition of DNA polymerase leads to a proximity-dependent DNA polymerization event, producing unique PCR target sequences. Subsequently, the obtained DNA sequences are detected and quantified using a microfluidic real-time PCR instrument (Biomark HD, Fluidigm). Internal extension controls and plate-to-plate controls are then used for data quality control and normalization to adjust for intra-run and inter-run variations. The final detection readings are expressed as Normalized Protein eXpression (NPX) values, which are arbitrary units on a log2 scale where higher values correspond to higher protein expression levels. All assay validation data, including detection limits, intra-assay and inter-assay precision data, can be found on the manufacturer's website at www.olink.com.

Data analysis

We used SPSS 26.0 software to conduct statistical analysis of demographic characteristics, clinical features, and cognitive functions. Specifically, categorical variables such as gender were represented using frequencies and percentages, and chi-square tests were conducted. We assessed the normality of data distribution using the Shapiro–Wilk test. For normally distributed continuous variables, t-tests were used, and results were represented using mean (Mean) and standard deviation (SD). For non-normally distributed data, non-parametric tests were used, and results were presented using median M (P25, P75). After controlling for factors other than differential metabolic indicators, partial correlation analysis was used to preliminarily explore the relationship between differential metabolic indicators and cognition. Furthermore, backward logistic regression was used to iteratively remove the least contributory variables from all potential predictors affecting cognition, in order to identify the optimal subset of predictor variables. The conditional likelihood ratio test was employed to determine which variables to exclude. Ultimately, the most influential predictors for the dependent variable were retained, and the logistic regression results are presented as odds ratios (ORs) with 95% confidence intervals (CIs). All statistical tests were two-tailed, with a significance level set at P < 0.05.

Results

Participant characteristics

In the final dataset, complete information was collected from 107 patients, including 53 males and 54 females, resulting in a nearly equal gender ratio of approximately 1:1. The patients had an average age of (46.28 ± 8.24) years. The duration of illness ranged from 5 to 26 years, with a median duration of 12 years. The average years of education were 8.11 [6.87, 9.52] years. The average PANSS total score was (46.63 ± 5.95). Among them, 37 patients (34.6%) with stable schizophrenia exhibited severe cognitive impairment. There were no significant differences in age, illness duration, years of education, or PANSS scores between these patients and those with mild cognitive impairment in schizophrenia (all p > 0.05). The BMI of patients ranged from 21 to 31.25 kg/m2. The average BMI for the two groups was (24.87 ± 2.16) kg/m2 and (25.18 ± 1.88) kg/m2, both indicating overweight status. However, the difference was not statistically significant (t = -0.737, p = 0.463). Meanwhile, FBG and HDL-C did not show significant differences (t = -1.511, p = 0.134; Z = -1.838, p = 0.066). In contrast, the severe cognitive impairment group exhibited significantly higher levels of TG, TC, and LDL-C compared to the mild cognitive impairment group (Z = -5.535, p < 0.001; t = -2.604, p = 0.011; Z = -6.400, p < 0.001) (Table 1).

Table 1 Demographics, clinical characteristics, and cognitive function of patients with schizophrenia
Full size table

Cognitive function

The MoCA scores of the 107 participants were 22 [16, 24], with a maximum of 24 and a minimum of 14. The MCCB scores were 33.09 ± 6.47, with a maximum of 48 and a minimum of 19. When categorized, the MCCB score ranges for the mild cognitive impairment and severe cognitive impairment groups were MCCB (21–48; 19–45), respectively. There were significant differences in MoCA and MCCB scores between the mild cognitive impairment group and the severe cognitive impairment group (Z = -8.632, p < 0.001; t = 3.52, p = 0.001). In terms of MCCB dimensions, compared to the mild cognitive impairment group, the severe cognitive impairment group performed worse in the Attention/Alertness and Working Memory dimensions, with statistically significant differences (t = 2.668, p = 0.009; t = 2.496, p = 0.014). However, no significant differences were observed between the two groups in other cognitive domains (all p > 0.05) (Table 1).

The correlation between blood lipids and cognition, and regression analysis

After controlling for factors other than the differential metabolic indicators, partial correlation analysis was conducted across the entire sample to examine the relationship between differential metabolic indicators (TG, TC, and LDL-C) and MoCA scores. The results showed that TC (r = -0.307, p = 0.002), TG (r = -0.447, p < 0.001), and LDL-C (r = -0.607, p < 0.001) were all negatively correlated with MoCA scores. In the regression model, variables such as age, sex, illness duration, BMI, years of education, and HDL-C were gradually excluded, leaving FBG, TC, TG, and LDL-C as the optimal predictors. Among these, only elevated TG (OR = 5.578, P = 0.003) and LDL-C (OR = 5.425, P = 0.001) were associated with a decline in cognitive function, as shown in Table 2.

Table 2 Regression analysis
Full size table

Oink proteomics

The above results suggest that TG and LDL-C may be risk factors for exacerbating cognitive impairment. To further explore the impact of blood lipids on cognition, we matched 20 cases of hyperlipidemia (defined according to Chinese lipid management guidelines as LDL-C ≥ 3.4 mmol/L and TG ≥ 1.7 mmol/L for a diagnosis of mixed hyperlipidemia) with 19 cases of individuals living with schizophrenia who had normal lipid metabolism, based on demographic characteristics. Using the Olink proteomics method, we screened for differential proteins and investigated their correlation with cognition, aiming to further explore the relationship between blood lipids and cognition at the molecular level. The hyperlipidemia group had significantly higher TG and LDL-C levels compared to the control group, with lower cognitive function scores. Other demographic and metabolic indicators were similar between the two groups, as shown in the Supplementary Material 1.

In the panel of 92 proteins related to inflammation, differential expression analysis revealed that 10 proteins were significantly higher in the SZHL (schizophrenia with hyperlipidemia) group, while 2 proteins were lower. MCP-3 has the largest logfold change in protein expression (logFC = 0.78, p < 0.001), followed closely by IFN-gamma (logFC = 1.64, p = 0.001), and nearly alongside is CD8A (logFC = 0.65, p = 0.001). Additionally, the following 7 proteins showed increased expression: IL10 (logFC = 0.58, p = 0.003), FGF-21 (logFC = 1.56, p = 0.005), CXCL11 (logFC = 0.65, p = 0.008), EN-RAGE (logFC = 0.97, p = 0.022), CCL3 (logFC = 0.42, p = 0.023), CXCL10 (logFC = 0.73, p = 0.027), and CXCL6 (logFC = 0.69, p = 0.049). In contrast, TWEAK (logFC = -0.27, p = 0.032) and FGF-5 (logFC = -0.18, p = 0.025) were lower compared to the control group. The differential protein data is presented in Table 3. The volcano plot depicting differential protein expression is shown in Fig. 1, while the box plot is displayed in Fig. 2. Supplementary Material 2 provide detailed results for all samples. In addition, the correlation results between differential proteins and MoCA scores are presented in Table 4. Except for EN-RAGE and FGF-5, all other differential proteins show significant correlations with cognitive function (all p < 0.05). The GO Enrichment ScatterPlot is depicted in Fig. 3, and the KEGG Enrichment ScatterPlot is shown in Fig. 4.

Table 3 Differential Protein Data
Full size table
Fig. 1
figure 1

Volcano plot

Full size image
Fig. 2
figure 2

Box plot

NPX, Normalized Protein eXpression; SZHL, schizophrenia with hyperlipidemia; SZ, schizophrenia. *p < 0.05, **p < 0.01

Full size image
Table 4 Differential Protein-MoCA Correlation
Full size table
Fig. 3
figure 3

GO Enrichment ScatterPlot

Full size image
Fig. 4
figure 4

KEGG Enrichment ScatterPlot

Full size image

Discussion

This study explored the relationship between lipid abnormalities and cognition in stable schizophrenia. The main findings indicate that individuals living with schizophrenia in a stable phase exhibit widespread cognitive impairments. Compared to patients with mild cognitive impairment, those with severe impairment perform worse in the cognitive dimensions of Attention/Alertness and working memory. Lipid metabolic indicators show correlations with cognitive function, with higher levels of TG, TC, and LDL-C correlating with poorer overall cognitive performance. Further regression analysis suggests that TG and LDL-C may be risk factors exacerbating cognitive impairment in individuals living with schizophrenia in a stable phase. Proteomics analysis reveals that, compared to individuals living with schizophrenia with normal lipid metabolism, those with hyperlipidemia exhibit elevated levels of 10 inflammatory proteins and decreased levels of 2, which correlate with cognitive function. The differential proteins are primarily involved in pathways such as Cytokine-cytokine receptor interaction, Chemokine signaling, and IL-17 signaling.

A twenty-year cohort study has indicated that midlife lipid levels correlate more strongly with cognition than those in later life. This suggests that lipid levels may have a direct impact on cognition that surpasses the cognitive decline associated with aging. Lipids may thus function as independent risk factors for cognitive decline. Moreover, elevated levels of TC, TG, and LDL-C are significantly associated with substantial declines in attention. Additionally, higher levels of total cholesterol and triglycerides are linked to significant declines in memory [36]. In our study, we similarly found significant differences in TG, TC, and LDL-C levels between two groups of patients with distinct cognitive functions. These differences in cognitive function were primarily observed in memory and attention. Additionally, abnormalities in lipid levels, particularly elevated TG and LDL-C, may act as risk factors exacerbating cognitive impairment in individuals living with schizophrenia. In both groups in our study, we did not observe significant differences in HDL-C levels, which aligns with findings from similar research [37]. Interestingly, in elderly individuals aged 75 and older, HDL-C has been found to correlate with cognition [38]. This suggests that the protective role of HDL-C in brain function may be less apparent in middle-aged and older patients.

Lipids can directly affect neurodegeneration, and alterations in brain cholesterol homeostasis may be similar to the neuropathology observed in Alzheimer's disease. Specifically, elevated levels of LDL-C, TC, and TG may be closely associated with increased β-amyloid protein and hippocampal atrophy [39]. Lipid abnormalities can disrupt brain network integrity, exacerbate cognitive decline, and increase the risk of Alzheimer's disease [40]. However, conclusions from studies on the relationship between lipid levels and cognition have been inconsistent. For example, a study conducted in three cities found that hypertriglyceridemia and low LDL-C were associated with declines in MMSE scores [41]. Additionally, a seven-year follow-up study found no correlation between lipid levels and cognition [42]. Possible explanations for these inconsistent results include selection bias due to differences in regions, ethnicities, and lifestyles, variations in the duration of follow-up periods [43], or a reverse causality between lipid levels and cognition [44, 45]. Additionally, a cross-sectional study in China reported that high TG levels may reduce the risk of cognitive impairment in urban men, while high LDL-C levels increase the risk in urban women. This suggests that there may also be gender and urban–rural differences in the relationship between lipid levels and cognition [46]. Although conclusions are inconsistent, the general trend acknowledges that high lipid levels can affect cognition. From a treatment perspective, lipid-lowering agents (LLAs) can slow cognitive decline in Alzheimer's disease and have neuroprotective effects, which may provide strong support for this association [47].

In a survey on the prevalence of dementia among individuals living with schizophrenia in the United States, it was found that 21% of the patients had severe cognitive impairment [48]. In a large cohort study involving 8,011,773 individuals, it was found that 27.9% of elderly people living with schizophrenia were diagnosed with dementia. In contrast, the dementia diagnosis rate was only 1.3% among those without psychosis [49]. The high prevalence of dementia among people living with schizophrenia can be explained by a decline in cognitive reserve in this specific population, compounded by the cumulative effects of various metabolic risk factors. These factors may push them beyond clinical risk thresholds, thereby accelerating the progression of dementia [50]. In this study, the prevalence of severe cognitive impairment among people living with schizophrenia included was 34.58%, slightly higher than the aforementioned research. We speculate this might be due to the limitation of a smaller sample size. Furthermore, we did not track the baseline cognitive levels at the time of schizophrenia diagnosis. Additionally, the patients included in our study were all long-term hospitalized under closed management, leading to relatively limited lifestyles and recreational activities, potentially accelerating the progression of dementia.

Schizophrenia has often been referred to as a cognitive disorder, with patients frequently exhibiting multidimensional cognitive impairments compared to healthy controls [51]. In our preliminary findings, patients with severe cognitive impairment in the stable phase of schizophrenia showed poorer performance in working memory and attention/alertness, while no significant differences were observed in other domains. Similar findings have been reported in a comparative study investigating schizophrenia with metabolic disturbances and metabolic syndrome, where patients with metabolic syndrome exhibited worse working memory performance, with minimal differences in other areas [52]. Additionally, a study on healthy individuals found that subtle changes in lipid profiles could lead to reduced hippocampal integrity, resulting in cognitive impairment [53]. These phenomena suggest that memory might be more sensitive to cognitive impairment induced by metabolic abnormalities, or that metabolic disturbances may specifically affect certain cognitive domains. However, the exact mechanisms remain to be confirmed. Working memory and attention are closely related to cognitive control, which functionally involves the frontal lobe [54]. Higher medial frontal gamma-aminobutyric acid (GABA) concentrations are associated with better working memory performance [55]. Current research indicates a close relationship between blood lipids and GABA [56, 57], suggesting that lipids may influence the function of specific brain regions by affecting related neurotransmitters.

Monocyte chemotactic protein (MCP)-3 is a chemokine involved in attracting monocytes and neutrophils. Elevated levels of MCP-3 can be observed in patients with increased body fat [58]. CC chemokine receptor (CCR2) is the best-known receptor for MCP-3. MCP-3 stimulates CCR2 located on monocytes and macrophages, which is associated with the pathogenesis of atherosclerosis [59]. A decrease in MCP-3 levels may lead to a loss of its chemotactic effect on leukocytes, resulting in reduced recruitment of inflammatory cells. Conversely, an increase in MCP-3 levels may lead to increased inflammation [60]. Although direct clinical associations between MCP-3 and cognition have not been established, studies in rats with traumatic brain injury have shown that MCP-3 is upregulated within 24 h post-injury. Other inflammatory factors occur later and remain relatively stable, suggesting that MCP-3 may play a crucial role in rapid inflammatory responses and induction of long-term brain damage and neuronal dysfunction. This imbalance in excitatory and inhibitory neurons in the hippocampus could ultimately affect cognitive function [61]. In addition, early systemic lupus erythematosus often presents with neuropsychiatric symptoms. In mouse models, cognitive dysfunction has been observed alongside elevated plasma MCP-3 levels [62]. Therefore, based on this limited evidence, it can be inferred that the elevation of chemotactic factors may play a regulatory role in subtle changes in brain function.

It is generally believed that interferon-γ (IFN-γ) can promote inflammation in microglial cells. Recent studies have shown that IFN-γ plays a unique role in the activation of microglial cells, and its role in driving neuroinflammation in cognitive impairment is increasingly being recognized [63]. IFN-γ can exacerbate synaptic damage and even promote the release of nitric oxide, which is sufficient to impair synaptic signaling and cognitive function [64]. In mice, injections of IFN-γ inhibit the proliferation of neural stem cells and progenitor cells, and induce apoptosis of immature neurons, ultimately leading to impaired neurogenesis in the adult hippocampus [65]. In mice, IFN-γ has been shown to cross the blood–brain barrier intact and enter the central nervous system parenchyma via transport systems. This phenomenon is particularly pronounced when the blood–brain barrier is compromised under pathological conditions. IFN-γ can enter the central nervous system parenchyma extensively and uncontrollably during conditions such as bacterial and viral infections, Alzheimer's disease, and systemic inflammation [66]. Specifically, in cognitively impaired APP/PS1 mice, anti-IFN-γ antibody therapy has been shown to improve these cognitive-related neuroimmunological changes [67]. This suggests that increased levels of IFN-γ can contribute to cognitive impairment.

CD8 + T cells are a subset of T cells characterized by the surface expression of the CD8α and CD8β heterodimer. CD8 + T cells are the predominant T cell type in cognitive-related brain structures [68, 69]. The development of cognitive impairment is associated with the infiltration of CD8 + T cells into cognitive-related brain structures [70, 71]. In cognitively impaired elderly individuals, overexpression of the CD8β chain has been found [72]. CD8 + T cells may also contribute to neuronal damage and cognitive impairment through the release of IFN-γ [73]. In peripheral blood diagnosed with mild cognitive impairment, more CD8 + TEMRA cells producing IFN-γ were found [70]. These cytokines increase the permeability of the blood–brain barrier, promoting the migration of T cells into the central nervous system parenchyma, which may gradually catalyze cognitive impairment.

FGF-21 is an important member of the fibroblast growth factor family [74]. Recent studies indicate that increased levels of FGF-21 in non-elderly metabolic syndrome patients are associated with cognitive decline, suggesting that FGF-21 may serve as a risk factor for cognitive decline [75]. In our study, we observed similar findings where individuals living with schizophrenia and hyperlipidemia had higher levels of FGF-21, which were negatively correlated with cognitive function. However, contrasting findings suggest that FGF-21 may act as a neuroprotective factor with potential to alleviate neurodegenerative diseases. For instance, FGF-21 treatment has been shown to effectively increase synaptic plasticity in the hippocampus, reduce neuronal apoptosis, and improve cognitive impairment in insulin-resistant rats [76]. Intracerebroventricular injection of FGF-21 can reshape brain glucose and neurotransmitter metabolism, exerting neuroprotective effects against cognitive impairment [77]. Based on this, we should expect to find decreased levels of FGF-21 in the hyperlipidemia group rather than increased levels. One possible explanation is that elevated levels of FGF-21 may indicate more severe cognitive impairment under feedback regulation. Specific mechanisms require further research.

Similar to FGF-21, IL-10 is an anti-inflammatory cytokine. Most studies have found that an increase in IL-10 is often associated with better cognitive function [78, 79]. However, contrasting this, scientists have discovered unexpected negative effects of IL-10 on cognition and Aβ protein homeostasis in APP mouse models expressing IL-10 [80]. Coincidentally, in an aging study conducted in Berlin, higher levels of IL-10 were significantly associated with poorer executive function in elderly individuals [81]. In our study, we also found that higher levels of IL-10 may be associated with poorer cognition in individuals living with stable-phase schizophrenia. Future research may need to explore blocking IL-10 to further elucidate its effects on cognition in specific disease models.

Chemokines and their receptors play roles in the central nervous system, being present on both glial cells and neurons, and participating in intercellular communication [82]. The important physiological and pathological roles of CXCL10 and CXCL11 in the central nervous system are gradually being elucidated [83]. For example, in dementia patients, CXCL10 levels are positively correlated with β-amyloid protein [84]. Cerebrospinal fluid concentrations of CXCL10 in subjects with mild cognitive impairment are significantly higher compared to controls [85]. In a comparative study between mild cognitive impairment and depression, levels of CXCL6 and CXCL11 are higher in mild cognitive impairment patients than in elderly depression patients [86]. Furthermore, CCL3 has been found to be highly expressed in adult Alzheimer's disease patients and elevated in epileptic mouse models [87, 88]. These findings strengthen the potential role of chemokines as mediators in communication with neurological disorders.

EN-RAGE is also commonly known as S100-A12. The S100 protein family has previously been shown to be associated with cognition [89]. In a large prospective cohort study using Olink inflammation proteomics, EN-RAGE was found to be associated with overall dementia and Alzheimer's disease incidence [90]. In this study, although EN-RAGE showed differential expression between groups, no statistical correlation with cognition was found. This could potentially be attributed to the influence of a small sample size, indicating the need for further research in future studies.

Among the downregulated proteins, TWEAK is notable. TWEAK is a TNF family ligand that exerts pleiotropic effects through its receptor Fn14, including stimulating the production of inflammatory cytokines and inducing neuronal death [91]. In a large cohort study on peripheral inflammation biomarkers and cognition, higher levels of TWEAK were found to be associated with better memory scores and lower risk of dementia [92]. This suggests a potentially protective role of TWEAK on cognition, which could potentially explain the findings in the user's study where TWEAK levels were lower in people living with schizophrenia with severe cognitive impairment compared to those with mild cognitive impairment. Meanwhile, another downregulated protein, FGF-5, has not yet been found to have a close association with cognition and requires further investigation.

The strength of this study lies in its first-time exploration of the relationship between cognition and lipid metabolism in patients with stable-phase schizophrenia. Using omics approaches, the study preliminarily detected plasma differential proteins in two small sample groups of patients with different lipid metabolism levels and examined their associations with cognition. We included patients managed in a closed ward, who had similar dietary, exercise, and daily living habits, with strict restrictions on the use of tobacco and alcohol. The limitations of this study include the following aspects: (1) As a small cross-sectional study, it can partially reflect the associations between different variables, but it cannot establish causal relationships. Future research should involve long-term follow-up and longitudinal studies within the same individuals to further elucidate the complex relationship between lipid metabolism and cognition in people living with schizophrenia. (2) We used the MoCA to define different levels of cognitive impairment, with cutoffs based on currently limited research. Although MoCA is a reliable tool for detecting cognitive impairment, it is not specifically designed for schizophrenia populations and may not be fully sensitive to the specific cognitive deficits observed in schizophrenia. As a result, the generalizability of the current findings may be limited. Future studies should consider using specialized cognitive assessment tools designed for psychiatric disorders in larger-scale studies. (3) Due to time constraints, we only included patients with cognitive impairment and did not include non-cognitively impaired patients as controls. Future research should more comprehensively include patients with varying levels of cognitive function, as well as healthy individuals, in larger-scale cross-sectional comparisons to address the limitations of the current small sample size. (4) We only measured and compared routine blood lipid levels, including TG, TC, LDL-C, and HDL-C. Future studies should conduct more comprehensive examinations of other lipid metabolism indicators, including different lipoprotein levels, and perform detailed subgroup analyses to explore the associations between various types of lipid metabolism abnormalities and cognition. (5) Different types and combinations of antipsychotic medications can affect both metabolism and cognition. To control for confounding effects, we included only participants who were using a single antipsychotic drug, but did not take into account the specific types and combinations of antipsychotic medications used by the participants. In future research, we plan to include the type and combination of antipsychotic medications as variables for more in-depth analysis. (6) We did not systematically assess other potential clinical comorbidities beyond the exclusion criteria. Future research should include comprehensive evaluations of comorbidities to better understand their impact on the relationship between metabolic abnormalities and cognitive impairment in schizophrenia.

Conclusion

Lipid levels in patients with stable-phase schizophrenia are associated with cognitive function, specifically showing that higher levels of TG, TC, and LDL-C are linked to poorer overall cognitive function. TG and LDL-C may be risk factors for exacerbating cognitive impairment in these patients. From an inflammatory perspective, preliminary proteomics in a small sample suggest that lipid metabolism abnormalities may influence cognition through the activation or downregulation of related proteins, potentially involving pathways such as cytokine-cytokine receptor interaction, chemokine signaling pathway, and IL-17 signaling pathway. Nonetheless, our study suggests that improving lipid management may benefit cognitive rehabilitation in people living with schizophrenia. The complex relationship between lipid levels and cognition, as well as the precise mechanisms involved, require further research to be confirmed.

Availability of data and materials

The key data is provided within the manuscript and supplementary information files. Detailed data supporting this study can be obtained by directly contacting the authors. However, the availability of this data is restricted as it was obtained and used under the permission of Heilongjiang Academy of Chinese Medicine and Heilongjiang Provincial Hospital of Neurology and Psychiatry, and thus is not publicly available. Access can be granted upon reasonable request and with the approval of Heilongjiang Academy of Chinese Medicine.

Abbreviations

MCI:

Mild cognitive impairment

SCI:

Severe cognitive impairment

MoCA:

Montreal Cognitive Assessment

PANSS:

Positive and Negative Syndrome Scale

BMI:

Body mass index

FBG:

Fasting blood glucose

TG:

Triglyceride

TC:

Total cholesterol

LDL-C:

Low-density lipoprotein cholesterol

HDL-C:

High-density lipoprotein cholesterol

MCCB:

MATRICS Consensus Cognitive Battery

References

  1. Jauhar S, Johnstone M, McKenna PJ. Schizophrenia. Lancet (London, England). 2022;399(10323):473–86. https://doi.org/10.1016/s0140-6736(21)01730-x.

    Article  PubMed  CAS  Google Scholar 

  2. Keefe RS, Eesley CE, Poe MP. Defining a cognitive function decrement in schizophrenia. Biol Psychiatry. 2005;57(6):688–91. https://doi.org/10.1016/j.biopsych.2005.01.003.

    Article  PubMed  Google Scholar 

  3. Panov G, Dyulgerova S, Panova P. Cognition in Patients with Schizophrenia: Interplay between Working Memory, Disorganized Symptoms, Dissociation, and the Onset and Duration of Psychosis, as Well as Resistance to Treatment. Biomedicines. 2023;11(12):3114. https://doi.org/10.3390/biomedicines11123114.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Thai ML, Andreassen AK, Bliksted V. A meta-analysis of executive dysfunction in patients with schizophrenia: Different degree of impairment in the ecological subdomains of the Behavioural Assessment of the Dysexecutive Syndrome. Psychiatry Res. 2019;272:230–6. https://doi.org/10.1016/j.psychres.2018.12.088.

    Article  PubMed  Google Scholar 

  5. Knowles EE, David AS, Reichenberg A. Processing speed deficits in schizophrenia: reexamining the evidence. Am J Psychiatry. 2010;167(7):828–35. https://doi.org/10.1176/appi.ajp.2010.09070937.

    Article  PubMed  Google Scholar 

  6. Henry JD, Crawford JR. A meta-analytic review of verbal fluency deficits in schizophrenia relative to other neurocognitive deficits. Cogn Neuropsychiatry. 2005;10(1):1–33. https://doi.org/10.1080/13546800344000309.

    Article  PubMed  Google Scholar 

  7. Javed A, Charles A. The Importance of Social Cognition in Improving Functional Outcomes in Schizophrenia. Front Psychiatry. 2018;9:157. https://doi.org/10.3389/fpsyt.2018.00157.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Gebreegziabhere Y, Habatmu K, Mihretu A, Cella M, Alem A. Cognitive impairment in people with schizophrenia: an umbrella review. Eur Arch Psychiatry Clin Neurosci. 2022;272(7):1139–55. https://doi.org/10.1007/s00406-022-01416-6.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Hennekens CH, Hennekens AR, Hollar D, Casey DE. Schizophrenia and increased risks of cardiovascular disease. Am Heart J. 2005;150(6):1115–21. https://doi.org/10.1016/j.ahj.2005.02.007.

    Article  PubMed  Google Scholar 

  10. Kostadinova PS, Atanasova GN, Kostadinov SD, Kostadinov SS. Detection of cognitive decline in metabolic syndrome. Eur J Pub Health. 2020;30:ckaa166–1087.

    Article  Google Scholar 

  11. Petersen M, Hoffstaedter F, Nägele FL, Mayer C, Schell M, Rimmele DL, et al. A latent clinical-anatomical dimension relating metabolic syndrome to brain structure and cognition. eLife. 2024;12:RP93246. https://doi.org/10.7554/eLife.93246.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Yates KF, Sweat V, Yau PL, Turchiano MM, Convit A. Impact of metabolic syndrome on cognition and brain: a selected review of the literature. Arterioscler Thromb Vasc Biol. 2012;32(9):2060–7. https://doi.org/10.1161/atvbaha.112.252759.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. Paradela R, Martino L, Torres L, Ferreira N, Cabella B, Detogni A, et al. Time of Hypertension Is Differently Associated with Cognitive Impairment. J Am Coll Cardiol. 2020;75(11):2023.

    Article  Google Scholar 

  14. Beeri MS, Tirosh A, Lin HM, Golan S, Boccara E, Sano M, et al. Stability in BMI over time is associated with a better cognitive trajectory in older adults. Alzheimer’s Dementia. 2022;18(11):2131–9. https://doi.org/10.1002/alz.12525.

    Article  PubMed  CAS  Google Scholar 

  15. Zheng W, Jiang WL, Zhang X, Cai DB, Sun JW, Yin F, et al. Use of the RBANS to Evaluate Cognition in Patients with Schizophrenia and Metabolic Syndrome: a Meta-Analysis of Case-Control Studies. Psychiatry Q. 2022;93(1):137–49. https://doi.org/10.1007/s11126-021-09889-9.

    Article  Google Scholar 

  16. Li C, Zhan G, Rao S, Zhang H. Metabolic syndrome and its factors affect cognitive function in chronic schizophrenia complicated by metabolic syndrome. J Nerv Ment Dis. 2014;202(4):313–8. https://doi.org/10.1097/nmd.0000000000000124.

    Article  PubMed  Google Scholar 

  17. Lindenmayer JP, Khan A, Kaushik S, Thanju A, Praveen R, Hoffman L, et al. Relationship between metabolic syndrome and cognition in patients with schizophrenia. Schizophr Res. 2012;142(1–3):171–6. https://doi.org/10.1016/j.schres.2012.09.019.

    Article  PubMed  Google Scholar 

  18. MacKenzie NE, Kowalchuk C, Agarwal SM, Costa-Dookhan KA, Caravaggio F, Gerretsen P, et al. Antipsychotics, Metabolic Adverse Effects, and Cognitive Function in Schizophrenia. Front Psychiatry. 2018;9:622. https://doi.org/10.3389/fpsyt.2018.00622.

    Article  PubMed  PubMed Central  Google Scholar 

  19. McEvoy JP, Meyer JM, Goff DC, Nasrallah HA, Davis SM, Sullivan L, et al. Prevalence of the metabolic syndrome in patients with schizophrenia: baseline results from the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) schizophrenia trial and comparison with national estimates from NHANES III. Schizophr Res. 2005;80(1):19–32. https://doi.org/10.1016/j.schres.2005.07.014.

    Article  PubMed  Google Scholar 

  20. Deng X, Lu S, Li Y, Fang X, Zhang R, Shen X, et al. Association between increased BMI and cognitive function in first-episode drug-naïve male schizophrenia. Front Psychiatry. 2024;15:1362674. https://doi.org/10.3389/fpsyt.2024.1362674.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Miola A, Alvarez-Villalobos NA, Ruiz-Hernandez FG, De Filippis E, Veldic M, Prieto ML, et al. Insulin resistance in bipolar disorder: A systematic review of illness course and clinical correlates. J Affect Disord. 2023;334:1–11. https://doi.org/10.1016/j.jad.2023.04.068.

    Article  PubMed  CAS  Google Scholar 

  22. Pikalov A, Miller B, Siu C, Tocco M, Tsai J, Harvey P, et al. Inflammatory Markers and Cognitive Performance in Patients with Schizophrenia Treated with Lurasidone. Schizophr Bull. 2018;44:S242–3. https://doi.org/10.1093/schbul/sby017.591.

    Article  PubMed Central  Google Scholar 

  23. Lutz MW, Casanova R, Saldana S, Kuchibhatla M, Plassman BL, Hayden KM. Analysis of pleiotropic genetic effects on cognitive impairment, systemic inflammation, and plasma lipids in the Health and Retirement Study. Neurobiol Aging. 2019;80:173–86. https://doi.org/10.1016/j.neurobiolaging.2018.10.028.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. Calsolaro V, Edison P. Neuroinflammation in Alzheimer’s disease: Current evidence and future directions. Alzheimer’s Dementia. 2016;12(6):719–32. https://doi.org/10.1016/j.jalz.2016.02.010.

    Article  PubMed  Google Scholar 

  25. Hargrave SL, Davidson TL, Zheng W, Kinzig KP. Western diets induce blood-brain barrier leakage and alter spatial strategies in rats. Behav Neurosci. 2016;130(1):123–35. https://doi.org/10.1037/bne0000110.

    Article  PubMed  CAS  Google Scholar 

  26. Liu TT, Pang SJ, Jia SS, Man QQ, Li YQ, Song S, et al. Association of Plasma Phospholipids with Age-Related Cognitive Impairment: Results from a Cross-Sectional Study. Nutrients. 2021;13(7):2185. https://doi.org/10.3390/nu13072185.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. Zhang SF, Chen HM, Xiong JN, Liu J, Xiong J, Xie JZ, et al. Comparison of cognitive impairments with lipid profiles and inflammatory biomarkers in unipolar and bipolar depression. J Psychiatr Res. 2022;150:300–6. https://doi.org/10.1016/j.jpsychires.2022.04.002.

    Article  PubMed  Google Scholar 

  28. Aslam B, Basit M, Nisar MA, Khurshid M, Rasool MH. Proteomics: Technologies and Their Applications. J Chromatogr Sci. 2017;55(2):182–96. https://doi.org/10.1093/chromsci/bmw167.

    Article  PubMed  CAS  Google Scholar 

  29. Sun BB, Maranville JC, Peters JE, Stacey D, Staley JR, Blackshaw J, et al. Genomic atlas of the human plasma proteome. Nature. 2018;558(7708):73–9. https://doi.org/10.1038/s41586-018-0175-2.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. Andreasen NC, Carpenter WT Jr, Kane JM, Lasser RA, Marder SR, Weinberger DR. Remission in schizophrenia: proposed criteria and rationale for consensus. Am J Psychiatry. 2005;162(3):441–9. https://doi.org/10.1176/appi.ajp.162.3.441.

    Article  PubMed  Google Scholar 

  31. Misiak B, Piotrowski P, Samochowiec J. Assessment of interrelationships between cognitive performance, symptomatic manifestation and social functioning in the acute and clinical stability phase of schizophrenia: insights from a network analysis. BMC Psychiatry. 2023;23(1):774. https://doi.org/10.1186/s12888-023-05289-4.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Gil-Berrozpe GJ, Sánchez-Torres AM, García de Jalón E, Moreno-Izco L, Fañanás L, Peralta V, et al. Utility of the MoCA for cognitive impairment screening in long-term psychosis patients. Schizophr Res. 2020;216:429–34. https://doi.org/10.1016/j.schres.2019.10.054.

    Article  PubMed  Google Scholar 

  33. Yang Z, Abdul Rashid NA, Quek YF, Lam M, See YM, Maniam Y, et al. Montreal Cognitive Assessment as a screening instrument for cognitive impairments in schizophrenia. Schizophr Res. 2018;199:58–63. https://doi.org/10.1016/j.schres.2018.03.008.

    Article  PubMed  Google Scholar 

  34. Shi C, Kang L, Yao S, Ma Y, Li T, Liang Y, et al. The MATRICS Consensus Cognitive Battery (MCCB): Co-norming and standardization in China. Schizophr Res. 2015;169(1–3):109–15. https://doi.org/10.1016/j.schres.2015.09.003.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Assarsson E, Lundberg M, Holmquist G, Björkesten J, Thorsen SB, Ekman D, et al. Homogenous 96-plex PEA immunoassay exhibiting high sensitivity, specificity, and excellent scalability. PLoS ONE. 2014;9(4):e95192. https://doi.org/10.1371/journal.pone.0095192.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. Power MC, Rawlings A, Sharrett AR, Bandeen-Roche K, Coresh J, Ballantyne CM, et al. Association of midlife lipids with 20-year cognitive change: A cohort study. Alzheimer’s Dementia. 2018;14(2):167–77. https://doi.org/10.1016/j.jalz.2017.07.757.

    Article  PubMed  Google Scholar 

  37. McFarlane O, Kozakiewicz M, Kędziora-Kornatowska K, Gębka D, Szybalska A, Szwed M, et al. Blood Lipids and Cognitive Performance of Aging Polish Adults: A Case-Control Study Based on the PolSenior Project. Frontiers in aging neuroscience. 2020;12: 590546. https://doi.org/10.3389/fnagi.2020.590546.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  38. van Vliet P, van de Water W, de Craen AJ, Westendorp RG. The influence of age on the association between cholesterol and cognitive function. Exp Gerontol. 2009;44(1–2):112–22. https://doi.org/10.1016/j.exger.2008.05.004.

    Article  PubMed  CAS  Google Scholar 

  39. Wolf H, Hensel A, Arendt T, Kivipelto M, Winblad B, Gertz HJ. Serum lipids and hippocampal volume: the link to Alzheimer’s disease? Ann Neurol. 2004;56(5):745–8. https://doi.org/10.1002/ana.20289.

    Article  PubMed  CAS  Google Scholar 

  40. Wang Q, Zang F, He C, Zhang Z, Xie C. Dyslipidemia induced large-scale network connectivity abnormality facilitates cognitive decline in the Alzheimer’s disease. J Transl Med. 2022;20(1):567. https://doi.org/10.1186/s12967-022-03786-w.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. Raffaitin C, Féart C, Le Goff M, Amieva H, Helmer C, Akbaraly TN, et al. Metabolic syndrome and cognitive decline in French elders: the Three-City Study. Neurology. 2011;76(6):518–25. https://doi.org/10.1212/WNL.0b013e31820b7656.

    Article  PubMed  CAS  Google Scholar 

  42. Reitz C, Luchsinger J, Tang MX, Manly J, Mayeux R. Impact of plasma lipids and time on memory performance in healthy elderly without dementia. Neurology. 2005;64(8):1378–83. https://doi.org/10.1212/01.Wnl.0000158274.31318.3c.

    Article  PubMed  CAS  Google Scholar 

  43. Weuve J, Proust-Lima C, Power MC, Gross AL, Hofer SM, Thiébaut R, et al. Guidelines for reporting methodological challenges and evaluating potential bias in dementia research. Alzheimer’s Dementia. 2015;11(9):1098–109. https://doi.org/10.1016/j.jalz.2015.06.1885.

    Article  PubMed  Google Scholar 

  44. Mielke MM, Zandi PP, Shao H, Waern M, Östling S, Guo X, et al. The 32-year relationship between cholesterol and dementia from midlife to late life. Neurology. 2010;75(21):1888–95. https://doi.org/10.1212/WNL.0b013e3181feb2bf.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  45. Power MC, Tchetgen EJ, Sparrow D, Schwartz J, Weisskopf MG. Blood pressure and cognition: factors that may account for their inconsistent association. Epidemiology. 2013;24(6):886–93. https://doi.org/10.1097/EDE.0b013e3182a7121c.

    Article  PubMed  Google Scholar 

  46. Yu Y, Yan P, Cheng G, Liu D, Xu L, Yang M, et al. Correlation between serum lipid profiles and cognitive impairment in old age: a cross-sectional study. General psychiatry. 2023;36(2): e101009. https://doi.org/10.1136/gpsych-2023-101009.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  47. Masse I, Bordet R, Deplanque D, Al Khedr A, Richard F, Libersa C, et al. Lipid lowering agents are associated with a slower cognitive decline in Alzheimer’s disease. J Neurol Neurosurg Psychiatry. 2005;76(12):1624–9. https://doi.org/10.1136/jnnp.2005.063388.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  48. Brown MT, Wolf DA. Estimating the Prevalence of Serious Mental Illness and Dementia Diagnoses Among Medicare Beneficiaries in the Health and Retirement Study. Res Aging. 2018;40(7):668–86. https://doi.org/10.1177/0164027517728554.

    Article  PubMed  Google Scholar 

  49. Stroup TS, Olfson M, Huang C, Wall MM, Goldberg T, Devanand DP, et al. Age-Specific Prevalence and Incidence of Dementia Diagnoses Among Older US Adults With Schizophrenia. JAMA Psychiat. 2021;78(6):632–41. https://doi.org/10.1001/jamapsychiatry.2021.0042.

    Article  Google Scholar 

  50. Kirkpatrick B, Messias E, Harvey PD, Fernandez-Egea E, Bowie CR. Is schizophrenia a syndrome of accelerated aging? Schizophr Bull. 2008;34(6):1024–32. https://doi.org/10.1093/schbul/sbm140.

    Article  PubMed  Google Scholar 

  51. Kahn RS, Keefe RS. Schizophrenia is a cognitive illness: time for a change in focus. JAMA Psychiat. 2013;70(10):1107–12. https://doi.org/10.1001/jamapsychiatry.2013.155.

    Article  Google Scholar 

  52. Chen S, Xia X, Deng C, Wu X, Han Z, Tao J, et al. The correlation between metabolic syndrome and neurocognitive and social cognitive performance of patients with schizophrenia. Psychiatry Res. 2020;288: 112941. https://doi.org/10.1016/j.psychres.2020.112941.

    Article  PubMed  CAS  Google Scholar 

  53. Sanjana F, Delgorio PL, Hiscox LV, DeConne TM, Hobson JC, Cohen ML, et al. Blood lipid markers are associated with hippocampal viscoelastic properties and memory in humans. Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism. 2021;41(6):1417–27. https://doi.org/10.1177/0271678x20968032.

    Article  PubMed  CAS  Google Scholar 

  54. Ullsperger M, Danielmeier C, Jocham G. Neurophysiology of performance monitoring and adaptive behavior. Physiol Rev. 2014;94(1):35–79. https://doi.org/10.1152/physrev.00041.2012.

    Article  PubMed  CAS  Google Scholar 

  55. Rowland LM, Krause BW, Wijtenburg SA, McMahon RP, Chiappelli J, Nugent KL, et al. Medial frontal GABA is lower in older schizophrenia: a MEGA-PRESS with macromolecule suppression study. Mol Psychiatry. 2016;21(2):198–204. https://doi.org/10.1038/mp.2015.34.

    Article  PubMed  CAS  Google Scholar 

  56. Radwan RA, Abuelezz NZ, Abdelraouf SM, Bakeer EM, Rahman A. Decreased Serum Level of Gamma-amino Butyric Acid in Egyptian Infertile Females with Polycystic Ovary Syndrome is Correlated with Dyslipidemia, Total Testosterone and 25(OH) Vitamin D Levels. Journal of medical biochemistry. 2019;38(4):512–8. https://doi.org/10.2478/jomb-2018-0051.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  57. Dong P, Wang H, Li Y, Yu J, Liu X, Wang Y, et al. Active peptides from Eupolyphaga sinensis walker attenuates experimental hyperlipidemia by regulating the gut microbiota and biomarkers in rats with dyslipidemia. Biomed Pharmacother. 2024;170:116064. https://doi.org/10.1016/j.biopha.2023.116064

    Article  PubMed  CAS  Google Scholar 

  58. Pang Y, Kartsonaki C, Lv J, Fairhurst-Hunter Z, Millwood IY, Yu C, et al. Associations of Adiposity, Circulating Protein Biomarkers, and Risk of Major Vascular Diseases. JAMA cardiology. 2021;6(3):276–86. https://doi.org/10.1001/jamacardio.2020.6041.

    Article  PubMed  Google Scholar 

  59. Colin S, Chinetti-Gbaguidi G, Staels B. Macrophage phenotypes in atherosclerosis. Immunol Rev. 2014;262(1):153–66. https://doi.org/10.1111/imr.12218.

    Article  PubMed  CAS  Google Scholar 

  60. Noels H, Weber C, Koenen RR. Chemokines as Therapeutic Targets in Cardiovascular Disease. Arterioscler Thromb Vasc Biol. 2019;39(4):583–92. https://doi.org/10.1161/atvbaha.118.312037.

    Article  PubMed  CAS  Google Scholar 

  61. Almeida-Suhett CP, Li Z, Marini AM, Braga MF, Eiden LE. Temporal course of changes in gene expression suggests a cytokine-related mechanism for long-term hippocampal alteration after controlled cortical impact. J Neurotrauma. 2014;31(7):683–90. https://doi.org/10.1089/neu.2013.3029.

    Article  PubMed  PubMed Central  Google Scholar 

  62. Li Y, Eskelund AR, Zhou H, Budac DP, Sánchez C, Gulinello M. Behavioral Deficits Are Accompanied by Immunological and Neurochemical Changes in a Mouse Model for Neuropsychiatric Lupus (NP-SLE). Int J Mol Sci. 2015;16(7):15150–71. https://doi.org/10.3390/ijms160715150.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  63. Guerrero A, De Strooper B, Arancibia-Cárcamo IL. Cellular senescence at the crossroads of inflammation and Alzheimer’s disease. Trends Neurosci. 2021;44(9):714–27. https://doi.org/10.1016/j.tins.2021.06.007.

    Article  PubMed  CAS  Google Scholar 

  64. Brown GC. Nitric oxide and neuronal death. Nitric Oxide Biol Chem. 2010;23(3):153–65. https://doi.org/10.1016/j.niox.2010.06.001.

    Article  CAS  Google Scholar 

  65. Zhang J, He H, Qiao Y, Zhou T, He H, Yi S, et al. Priming of microglia with IFN-γ impairs adult hippocampal neurogenesis and leads to depression-like behaviors and cognitive defects. Glia. 2020;68(12):2674–92. https://doi.org/10.1002/glia.23878.

    Article  PubMed  Google Scholar 

  66. van de Haar HJ, Burgmans S, Jansen JF, van Osch MJ, van Buchem MA, Muller M, et al. Blood-Brain Barrier Leakage in Patients with Early Alzheimer Disease. Radiology. 2016;281(2):527–35. https://doi.org/10.1148/radiol.2016152244.

    Article  PubMed  Google Scholar 

  67. Browne TC, McQuillan K, McManus RM, O’Reilly JA, Mills KH, Lynch MA. IFN-γ Production by amyloid β-specific Th1 cells promotes microglial activation and increases plaque burden in a mouse model of Alzheimer’s disease. J Immunol. 2013;190(5):2241–51. https://doi.org/10.4049/jimmunol.1200947.

    Article  PubMed  CAS  Google Scholar 

  68. Unger MS, Marschallinger J, Kaindl J, Klein B, Johnson M, Khundakar AA, et al. Doublecortin expression in CD8+ T-cells and microglia at sites of amyloid-β plaques: A potential role in shaping plaque pathology? Alzheimer’s Dementia. 2018;14(8):1022–37. https://doi.org/10.1016/j.jalz.2018.02.017.

    Article  PubMed  Google Scholar 

  69. Monsonego A, Imitola J, Petrovic S, Zota V, Nemirovsky A, Baron R, et al. Abeta-induced meningoencephalitis is IFN-gamma-dependent and is associated with T cell-dependent clearance of Abeta in a mouse model of Alzheimer’s disease. Proc Natl Acad Sci U S A. 2006;103(13):5048–53. https://doi.org/10.1073/pnas.0506209103.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  70. Gate D, Saligrama N, Leventhal O, Yang AC, Unger MS, Middeldorp J, et al. Clonally expanded CD8 T cells patrol the cerebrospinal fluid in Alzheimer’s disease. Nature. 2020;577(7790):399–404. https://doi.org/10.1038/s41586-019-1895-7.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  71. Laurent C, Dorothée G, Hunot S, Martin E, Monnet Y, Duchamp M, et al. Hippocampal T cell infiltration promotes neuroinflammation and cognitive decline in a mouse model of tauopathy. Brain : a journal of neurology. 2017;140(1):184–200. https://doi.org/10.1093/brain/aww270.

    Article  PubMed  Google Scholar 

  72. Esgalhado AJ, Reste-Ferreira D, Albino SE, Sousa A, Amaral AP, Martinho A, et al. CD45RA, CD8β, and IFNγ Are Potential Immune Biomarkers of Human Cognitive Function. Front Immunol. 2020;11: 592656. https://doi.org/10.3389/fimmu.2020.592656.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  73. Villegas-Mendez A, Greig R, Shaw TN, de Souza JB, Gwyer Findlay E, Stumhofer JS, et al. IFN-γ-producing CD4+ T cells promote experimental cerebral malaria by modulating CD8+ T cell accumulation within the brain. J Immunol. 2012;189(2):968–79. https://doi.org/10.4049/jimmunol.1200688.

    Article  PubMed  CAS  Google Scholar 

  74. Adams AC, Cheng CC, Coskun T, Kharitonenkov A. FGF21 requires βklotho to act in vivo. PLoS ONE. 2012;7(11): e49977. https://doi.org/10.1371/journal.pone.0049977.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  75. Phrommintikul A, Sa-Nguanmoo P, Sripetchwandee J, Vathesatogkit P, Chattipakorn N, Chattipakorn SC. Factors associated with cognitive impairment in elderly versus nonelderly patients with metabolic syndrome: the different roles of FGF21. Sci Rep. 2018;8(1):5174. https://doi.org/10.1038/s41598-018-23550-9.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  76. Sa-Nguanmoo P, Tanajak P, Kerdphoo S, Satjaritanun P, Wang X, Liang G, et al. FGF21 improves cognition by restored synaptic plasticity, dendritic spine density, brain mitochondrial function and cell apoptosis in obese-insulin resistant male rats. Horm Behav. 2016;85:86–95. https://doi.org/10.1016/j.yhbeh.2016.08.006.

    Article  PubMed  CAS  Google Scholar 

  77. Zhang X, Zheng H, Ni Z, Shen Y, Wang D, Li W, et al. Fibroblast growth factor 21 alleviates diabetes-induced cognitive decline. Cerebral Cortex. 2024;34(2):bhad502. https://doi.org/10.1093/cercor/bhad502.

    Article  PubMed  PubMed Central  Google Scholar 

  78. Guillot-Sestier MV, Doty KR, Gate D, Rodriguez J Jr, Leung BP, Rezai-Zadeh K, et al. Il10 deficiency rebalances innate immunity to mitigate Alzheimer-like pathology. Neuron. 2015;85(3):534–48. https://doi.org/10.1016/j.neuron.2014.12.068.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  79. Cho JA, Park SH, Cho J, Kim JO, Yoon JH, Park E. Exercise and Curcumin in Combination Improves Cognitive Function and Attenuates ER Stress in Diabetic Rats. Nutrients. 2020;12(5):1309. https://doi.org/10.3390/nu12051309.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  80. Chakrabarty P, Li A, Ceballos-Diaz C, Eddy JA, Funk CC, Moore B, et al. IL-10 alters immunoproteostasis in APP mice, increasing plaque burden and worsening cognitive behavior. Neuron. 2015;85(3):519–33. https://doi.org/10.1016/j.neuron.2014.11.020.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  81. Tegeler C, O’Sullivan JL, Bucholtz N, Goldeck D, Pawelec G, Steinhagen-Thiessen E, et al. The inflammatory markers CRP, IL-6, and IL-10 are associated with cognitive function–data from the Berlin Aging Study II. Neurobiol Aging. 2016;38:112–7. https://doi.org/10.1016/j.neurobiolaging.2015.10.039.

    Article  PubMed  CAS  Google Scholar 

  82. Ramesh G, MacLean AG, Philipp MT. Cytokines and chemokines at the crossroads of neuroinflammation, neurodegeneration, and neuropathic pain. Mediators Inflamm. 2013;2013: 480739. https://doi.org/10.1155/2013/480739.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  83. Koper OM, Kamińska J, Sawicki K, Kemona H. CXCL9, CXCL10, CXCL11, and their receptor (CXCR3) in neuroinflammation and neurodegeneration. Adv Clin Exp Med. 2018;27(6):849–56. https://doi.org/10.17219/acem/68846.

    Article  PubMed  Google Scholar 

  84. Corrêa JD, Starling D, Teixeira AL, Caramelli P, Silva TA. Chemokines in CSF of Alzheimer’s disease patients. Arq Neuropsiquiatr. 2011;69(3):455–9. https://doi.org/10.1590/s0004-282x2011000400009.

    Article  PubMed  Google Scholar 

  85. Galimberti D, Schoonenboom N, Scarpini E, Scheltens P. Chemokines in serum and cerebrospinal fluid of Alzheimer’s disease patients. Ann Neurol. 2003;53(4):547–8. https://doi.org/10.1002/ana.10531.

    Article  PubMed  Google Scholar 

  86. Simon NM, McNamara K, Chow CW, Maser RS, Papakostas GI, Pollack MH, et al. A detailed examination of cytokine abnormalities in Major Depressive Disorder. European neuropsychopharmacology : the journal of the European College of Neuropsychopharmacology. 2008;18(3):230–3. https://doi.org/10.1016/j.euroneuro.2007.06.004.

    Article  PubMed  CAS  Google Scholar 

  87. Xia MQ, Qin SX, Wu LJ, Mackay CR, Hyman BT. Immunohistochemical study of the beta-chemokine receptors CCR3 and CCR5 and their ligands in normal and Alzheimer’s disease brains. Am J Pathol. 1998;153(1):31–7. https://doi.org/10.1016/s0002-9440(10)65542-3.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  88. Guzik-Kornacka A, Sliwa A, Plucinska G, Lukasiuk K. Status epilepticus evokes prolonged increase in the expression of CCL3 and CCL4 mRNA and protein in the rat brain. Acta Neurobiol Exp. 2011;71(2):193–207. https://doi.org/10.55782/ane-2011-1840.

    Article  Google Scholar 

  89. Cristóvão JS, Gomes CM. S100 Proteins in Alzheimer’s Disease. Front Neurosci. 2019;13:463. https://doi.org/10.3389/fnins.2019.00463.

    Article  PubMed  PubMed Central  Google Scholar 

  90. Trares K, Bhardwaj M, Perna L, Stocker H, Petrera A, Hauck SM, et al. Association of the inflammation-related proteome with dementia development at older age: results from a large, prospective, population-based cohort study. Alzheimer’s research & therapy. 2022;14(1):128. https://doi.org/10.1186/s13195-022-01063-y.

    Article  CAS  Google Scholar 

  91. Wen J, Chen CH, Stock A, Doerner J, Gulinello M, Putterman C. Intracerebroventricular administration of TNF-like weak inducer of apoptosis induces depression-like behavior and cognitive dysfunction in non-autoimmune mice. Brain Behav Immun. 2016;54:27–37. https://doi.org/10.1016/j.bbi.2015.12.017.

    Article  PubMed  CAS  Google Scholar 

  92. Chen J, Doyle MF, Fang Y, Mez J, Crane PK, Scollard P, et al. Peripheral inflammatory biomarkers are associated with cognitive function and dementia: Framingham Heart Study Offspring cohort. Aging Cell. 2023;22(10): e13955. https://doi.org/10.1111/acel.13955.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

Download references

Acknowledgements

We sincerely thank all the patients for their generous contributions. We also extend our heartfelt gratitude to the medical staff at Heilongjiang Academy of Chinese Medicine and Heilongjiang Provincial Hospital of Neurology and Psychiatry for their support.

Clinical trial number

Not applicable.

Statement

This paper has not been previously published. The authors are responsible for all content in the article and have the authority to prepare the manuscript and decide to submit it for publication. All listed authors have agreed to submit the manuscript to the journal.

Funding

This work was supported by the National Key Specialty Construction Project (030104–254-02).

Author information

Authors and Affiliations

  1. The First Clinical Medical College, Heilongjiang University of Chinese Medicine, Harbin, China

    Yingkang Zheng, Xiaojun Cai, Yinxiong He, Jiarui Li & Juan Li

  2. Department of Endocrinology, Heilongjiang Academy of Chinese Medicine, Harbin, China

    Xiaojun Cai, Dezhong Wang, Xinghai Chen, Tao Wang, Yanpeng Xie, Haojing Li & Tong Wang

Authors
  1. Yingkang Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaojun Cai
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dezhong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xinghai Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Tao Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yanpeng Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Haojing Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Tong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Yinxiong He
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Jiarui Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Juan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Y.Z. made the drafting. X.C. and Y.Z. designed the study. D.W. and X.C. conducted the investigation and collected the data. T.W., Y.X., H.L., and T.W. managed the data. Y.H., J.L., and J.L. reviewed and revised the draft. All authors approved the final submitted version.

Corresponding author

Correspondence to Xiaojun Cai.

Ethics declarations

Ethics approval and consent to participate

The study was approved by the Ethics Committee of Heilongjiang Academy of Chinese Medicine (Approval Number: 2023–050-01). Informed consent was obtained from all participants and their families.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zheng, Y., Cai, X., Wang, D. et al. Exploring the relationship between lipid metabolism and cognition in individuals living with stable-phase Schizophrenia: a small cross-sectional study using Olink proteomics analysis. BMC Psychiatry 24, 593 (2024). https://doi.org/10.1186/s12888-024-06054-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12888-024-06054-x

Keywords

BMC Psychiatry

ISSN: 1471-244X

Contact us