INTRODUCTION
Alzheimer’s disease (AD), the most prevalent form of dementia, is a neurodegenerative condition characterized by cognitive decline and progressive memory impairment [1]. Projections from the World Health Organization indicate that in the forthcoming decades, neurodegenerative disorders will surpass cardiovascular diseases as the leading cause of mortality in developed nations [2]. Notably, women are disproportionately affected by AD, constituting nearly two-thirds of AD cases in the United States [3-5]. The wealth of preclinical and clinical data underscores a heightened intrinsic risk of AD among women. Thus, further investigations into the underlying biological mechanisms driving this sex disparity are warranted to enhance our understanding of AD pathogenesis across sex [6].
The major neuropathological hallmark of AD is the extracellular deposits of amyloid protein and neurofibrillary tangles. Current clinical diagnosis of AD focuses on the measurement of total tau or phosphorylated tau and amyloid-β in cerebrospinal fluid (CSF), positron emission tomography (PET) imaging of brain amyloid, or inflammation, structural imaging of the hippocampus with magnetic resonance imaging (MRI), and neuropsychological tests [7,8]. However, both PET imaging and CSF measurements have significant hurdles. They are expensive, time-consuming, invasive, and have limited availability. Therefore, there is an excellent need for easily accessible, non-invasive, and cost-effective biomarkers, preferably blood tests.
Blood is a complex tissue containing many organ proteins and is easily collected via non-invasive and safe procedures. Therefore, blood is a valid potential source for screening biomarkers [9-11]. Dysfunction of blood-brain barrier permeability has been reported in AD, implying that protein alterations may occur between the brain and peripheral bloodstream [12]. Cerebral lipids comprise at least 50% of dry brain weight and are the essential structural components of neuronal cell membranes [13]. Accumulating evidence has demonstrated that cerebral lipid peroxidation was an early event in AD. Abundant lipid particles are present in the brain glial cells of AD patients, indicating abnormal lipid metabolism [14]. Numerous blood metabolomic studies have highlighted the role of lipid compounds, such as phosphatidylcholines, in AD. Blood metabolites have been surveyed intensively as promising AD biomarkers and may help identify new therapeutic targets [8,15-17].
The lipidomics and metabolomics approach in biomedical research can be based on diverse analytical platforms for lipid/metabolite profiling, the two leading analytical strategies being nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) [18]. NMR spectroscopy has accurately measured the subclass distribution, size, and number of circulating lipoprotein particles [19,20]. NMR spectroscopy has rarely been used in “omic” AD-related studies [21-23].
To our knowledge, there is still a lack of studies on sex differences in metabolites associated with lipid subfractions in AD. Therefore, in this study, we aimed to present our NMR studies of human blood plasma samples obtained from AD patients and healthy controls, emphasizing the disease-specific sex differences in plasma lipid metabolism levels. In particular, we hypothesized that AD patients would have unique lipid metabolism changes than healthy controls.
METHODS
Participants
We recruited older people with AD from Tianjin Anding Hospital (Tianjin Mental Health Center) and geriatric health from communities in Tianjin City. Participants were interviewed for diagnosis, demographic and clinical data, laboratory examination, and cognitive tests. The experimental group included 35 cases, and the health control included 35 cases. The inclusion and exclusion criteria for the participants were as follows: the experimental group was defined by 1) the initial diagnosis of AD is based on the clinical criteria of the National Institute on Aging and Alzheimer’s Association [24]. The clinical standard is mainly based on the core clinical symptoms of patients, combined with a physical examination of the nervous system and mental and psychological examinations for diagnosis, 2) age ≥50 years, male and female, 3) mini-mental state examination (MMSE) total score <24, and 4) informed consent signed by the patient or guardian. Exclusion criteria were 1) severe or acute medical illness within the three months preceding the study and 2) neurological disorders such as delirium, Parkinson’s disease, aphasia, or multiple sclerosis.
The health control criteria included 1) age ≥50 years, male and female, 2) MMSE total score ≥24, and 3) capacity to provide informed consent. Exclusion criteria were 1) severe or acute medical illness within the 3 months preceding the study, 2) neurological disorders such as dementia, delirium, Parkinson’s disease, aphasia, or multiple sclerosis.
The study had received ethics approval from the Tianjin Anding Hospital (Approval Number: 2020-01), and all participants or guardians provided written informed consent.
Blood collection
Fasting morning peripheral blood samples were collected from all participants using ethylenediaminetetraacetic acid anticoagulant tube. Plasma was obtained by centrifugation at 1,000 rpm for 10 min. All samples were stored at -80°C until NMR analysis.
NMR spectroscopic measurements
NMR samples were allowed to thaw for approximately an hour on ice and transferred to 3 mm (600 MHz) NMR tubes (Bruker Biospin, Billerica, MA, USA) for NMR analysis. All blood samples collected were tested for 114 lipid indicators. The detection process was standard operating procedure [25,26]. NMR data were obtained at 4°C under specific spectral conditions (cycle time 5 s, 100 Carr-Purcell-Meiboom-Gill [CPMG] p pulses, total mixing time 55.56 ms, 16 K data points, total acquisition time 0.85 s, spectral width 16 ppm) and rotation at 3,600 Hz rate and rotor synchronization collected by CPMG method.
Statistical analysis
Results were displayed as the mean±standard deviation and analyzed using SPSS Statistics 23 (IBM Corp., Armonk, NY, USA). The Student’s t-test analyzed the two groups’ demographic information and clinical features. The receiver operating characteristic (ROC) curves were utilized to investigate the area under the curve (AUC) values for lipid metabolites in plasma from AD patients and healthy controls. We performed a false discovery rate correction to adjust for multiple tests. No metabolites passed the false discovery rate correction. All p-values were two two-tailed at the significance level of <0.05.
RESULTS
Sample and baseline characteristics
Clinical data of the participants were provided in Table 1. There were no statistically significant differences in age, body mass index (BMI), and total MMSE scores between males and females in the healthy control group (all p>0.05). There were also no differences in age and BMI between males and females in the AD patient group, except for the MMSE total score (p<0.05). Table 2 provided an overview of the clinical characteristics between AD patients and healthy controls, separated by sex: 21 were male and 49 were female. There were no significant differences in age and BMI between healthy controls and AD patients (all p>0.05). In addition, the MMSE total scores of AD patients were significantly lower than those of healthy controls (both p<0.05).
Sex-associated differences in plasma lipid metabolism levels
First, to identify sex-related differences, statistical comparisons of lipid metabolite levels were examined between males and females in AD patients and healthy controls, respectively (Table 3). The levels of the three metabolites in the healthy controls differed significantly by sex. As shown in Table 3, the levels of 2 high-density lipoprotein (HDL) subfractions (HDL-2 cholesterol [H2CH] and HDL-3 apolipoprotein-A1 [H3A1]) were higher in females than in males, while low-density lipoprotein cholesterol/high-density lipoprotein cholesterol (LDHD) levels were just the opposite, with males higher than females. The levels of the four metabolites-HDL-3 phospholipids (H3PL), HDL-4 phospholipids (H4PL), H3A1, and HDL-3 apolipoprotein-A2 (H3A2) differed significantly by sex in the AD patients. All four above metabolites were present in higher levels in females than males, and all belonged to HDL subfractions.
Disease-specific sex differences in plasma lipid metabolism levels
Disease-specific sex differences in lipid metabolite levels were performed by comparing males and females. Of the metabolites detected, 9 (8%) showed sex-related differences in males (total apolipoprotein-A2 [TPA2], HDL apolipoprotein-A2 [HDA2], VLDL-5 triglycerides [V5TG], HDL-1 cholesterol [H1CH], HDL-4 cholesterol [H4CH], HDL-1 free cholesterol [H1FC], H4PL, HDL-4 apolipoprotein-A1 [H4A1], HDL-4 apolipoprotein-A2 [H4A2]). As shown in Table 4, the levels of 1 very low-density lipoprotein (VLDL) subfraction (VLDL-5 triglycerides [V5TG]) and 2 HDL subfractions (H1CH and H1FC) were higher in AD patients than in healthy controls. In contrast, the remaining 6 metabolites, including TPA2 and 5 HDL subfractions (HDA2, H4CH, H4PL, H4A1, and H4A2), were significantly lower in AD patients than in healthy controls. In females, as shown in Table 4, 23 (21%) metabolites showed sex-related differences (LDL-cholesterol [LDCH], LDHD, LDL particle number [LDPN], LDL-1 particle number [L1PN], LDL-2 particle number [L2PN], LDL-3 particle number [L3PN], LDL free cholesterol [LDFC], LDL phospholipids [LDPL], LDL apolipoprotein-B [LDAB], LDL-2 triglycerides [L2TG], LDL-3 triglycerides [L3TG], LDL-1 cholesterol [L1CH], LDL-2 cholesterol [L2CH], LDL-3 cholesterol [L3CH], LDL-1 free cholesterol [L1FC], LDL-2 free cholesterol [L2FC], LDL-3 free cholesterol [L3FC], LDL-1 phospholipids [L1PL], LDL-2 phospholipids [L2PL], LDL-3 phospholipids [L3PL], LDL-1 apolipoprotein-B [L1AB], LDL-2 apolipoprotein-B [L2AB], LDL-3 apolipoprotein-B [L3AB]). More notably, these metabolites were all associated with LDL and higher in AD patients than in healthy controls. On the other hand, it should be noted that no significant metabolites overlapped between the male and the female groups.
Correlation and clustering analysis
The correlation analysis using the BioCPR-A tool [27], after sex stratification, was shown in Supplementary Figures 1 and 2. Moreover, the metabolic combinations of diagnostic potential were negatively associated with MMSE scores in both males and females (Supplementary Table 1). Further, the 36 differential metabolites were further analyzed for clustering analysis. The results suggested that these compounds were mainly divided into 4 clusters (Figure 1). The differential metabolites in cluster I was composed of LDPN, LDAB, LDCH, LDPL, LDFC, LDHD, L3PN, L3AB, L3CH, L3PL, L3FC, L2PN, L2AB, L2CH, L2PL, L2FC, L1PN, L1AB, L1PL, L1CH, L1FC, L2TG, and L3TG. The metabolites of cluster II include TPA2, HDA2, H4A1, H4A2, H4CH, and H4PL. The metabolites of cluster III include H1CH, H1FC, H2CH, H3PL, H3A1, and H3A2. Moreover, cluster IV only consisted of V5TG.
The ROC curve analysis of lipid metabolites
ROC curves were analyzed to evaluate the diagnostic value of lipid metabolites for AD. ROC curves of 8 lipid metabolites in males were shown in Figure 2A. We found an AUC of 0.82 (95% confidence interval [CI] is 0.63 to 1.00) for V5TG levels with a p-value of 0.016 (Figure 2A and Supplementary Table 2). Except for V5TG, no individual ROC curves provided an acceptable AUC. We thus decided to determine whether the combination of the lipid metabolites could reveal more accurate diagnostic and better predictive estimates than each metabolite marker itself. We tested apolipoprotein (apo)-associated metabolites (TPA2, HDA2, H4A1, and H4A2) and HDL cholesterol-associated metabolites (H1CH, H4CH, and H1FC), as shown in Supplementary Figure 3A and Supplementary Table 2, the AUC were 0.92 (95% CI, 0.80-1.00) and 0.90 (95% CI, 0.77-1.00), respectively.
ROC curves for each lipid metabolite from the females were performed, and all the individual ROC curves provided an acceptable AUC (Figure 2B and Supplementary Table 3). L3TG showed the best performance, with an AUC of 0.79 (95% CI 0.65-0.92). We also tested the combinations of the lipid metabolites, including LDL apo-B-associated metabolites (LDAB, L1AB, L2AB, and L3AB), LDL cholesterol-associated metabolites (LDCH, L1CH, L2CH, and L3CH), LDL free cholesterol-associated metabolites (LDFC, L1FC, L2FC, and L3FC), LDL cholesterol and free cholesterol-associated metabolites (LDCH, L1CH, L2CH, L3CH, LDFC, L1FC, L2FC, and L3FC), LDL triglycerides -associated metabolites (L2TG and L3TG), LDL particle number-associated indicators (LDPN, L1PN, L2PN, and L3PN) and LDL phospholipids-associated metabolites (LDPL, L1PL, L2PL, and L3PL) to determine whether those achieved higher AUCs (Supplementary Figure 3B). As shown in Supplementary Table 3, the combination of multiple indicators improved the AUC, and LDL cholesterol plus free cholesterol-associated metabolites showed the best performance. For this composite biomarker, the AUC was 0.82 (95% CI, 0.71-0.94, p<0.001). All these data from the ROC curve showed that HDL subfractions in males and LDL subfractions in females might be prospective indicative parameters for AD.
DISCUSSION
To our best knowledge, this study is the first to conduct lipidomic analysis using NMR spectroscopy to reveal the disease-specific sex differences in AD and elderly control plasma samples. The main findings of our study were as follows: 1) the HDL-related subfractions levels were significantly higher in females than in males, 2) 7 HDL-related subfractions, 1 VLDL-related subfraction and apo-A2 were significantly different between AD patients and elderly controls in males, 3) 23 LDL-related subfractions were significantly higher in AD patients than in elderly controls in females, and 4) the combination of lipid metabolites had good diagnostic values.
Our study found that the HDL-related subfractions were significantly higher in females than in males in AD patients and elderly controls. There are several differences in brain anatomy between males and females, with males having greater CSF, lateral ventricles, sulcal volumes, a higher proportion of white matter, and approximately 10% higher head size and brain volume than females [28-31]. In contrast, females have more gray matter and cerebral blood flow [32,33]. Sex differences in brain structure and function may lead to differences in biofluid results. For example, studies of CSF neurofilament light chain, a marker of large-caliber subcortical axonal degeneration, have consistently shown higher levels in males than in females [34,35], even among cognitively unimpaired individuals without a neurodegenerative disease. The sex dimorphism of AD can be explained by many different biological risk factors, including genetic background (e.g., apolipoprotein E), the deviation of brain structure, inflammation, gliosis, and immune module (e.g., Triggering Receptor Expressed on Myeloid Cells 2 [TREM2]), and sex differences at the single cell level [5,6,36,37]. HDL particles include a variety of lipoprotein complexes that differ in size, structure, and function [38,39]. The brain makes HDL-like particles, but these particles have not been fully characterized.
Interestingly, we found 7 HDL-related subfractions, 1 VLDL-related subfraction, and apo-A2 significantly differed between AD patients and elderly controls in males. VLDL is a class of triglyceride-rich lipoproteins, and elevated VLDL may lead to hypertriglyceridemia or excess triglycerides in the blood, which puts one at risk for AD [40]. Different lipids in VLDL correlated differently with AD progression between males and females [41]. However, no relevant studies on sex differences in HDL-related subfraction and apo-A2 have been reported. Our analysis indicated that 23 LDL-related subfractions were significantly higher in AD patients than in elderly controls in females. As mentioned before, LDL, specifically LDL cholesterol content, poses a risk of developing cognitive impairment [42].
Moreover, previous studies have found that small and medium LDL was associated with AD and mild cognitive impairment progression in females [41]. Interestingly, there was no significant metabolite overlap between the male and female groups, suggesting that lipoprotein metabolism in AD differs in females and males. Our findings highlighted the complexity of the role of HDL, VLDL, and LDL lipid content in AD and the sex-specific role it may play.
In addition, we demonstrated that the AUC areas of the combination of the lipid metabolites were all above 0.70, all of which had good diagnostic values. One such study from 2018 with 22,623 participants used NMR or MS metabolomics analysis to identify metabolites significantly correlated with dementia. This cohort study found that two VLDL-specific lipoprotein lipid subclasses, one VLDL (total cholesterol to total lipids ratio in very large VLDL) lipoprotein lipid subclass, and one HDL (the concentration of cholesterol esters relative to total lipids in large HDL), were associated with dementia risk [43]. Another study published in 2019 quantified 58 metabolites in each of the 311 samples. The results suggested that individuals with dementia and pre-diagnostic dementia were discriminated from controls using threonine and its relevant metabolic pathways [44]. Proitsi et al. [45] showed that a combination of blood lipid metabolites could help predict AD progression. Lipoproteins and their subfraction profiles have been associated with diverse diseases. Thus, there has been a great demand for measuring and quantifying lipoprotein profiles validly and accurately. NMR spectroscopy was sensitive to lipid chemistry and lipid micelle physics, enabling it to nondestructively measure the lipoprotein profiles of blood samples [46]. This present NMR-based metabolomics study found several metabolites significantly correlated with AD compared to controls, which may contribute to developing blood-based AD diagnostic and treatment tools.
Limitations of this study included the small sample size, which indicated an important direction for future studies. A larger trial may find more subtle differences in the concentration and particle size of lipoprotein subfractions caused by sex in AD. These differences could be due to chance, differences in dietary patterns, or genetic risk, suggesting that this area deserves further scrutiny, including possible gene-environment interaction [47]. The extent to which dysregulation of lipid metabolites in AD patients contributes to the occurrence and development of AD remains to be tested. Measurements of blood metabolomics in AD need to be cross-evaluated with measurements from different sources, such as those quantified from CSF [48-51], saliva [52], and brain tissues [53]. Metabolomics determined using NMR methods should also be compared with those assessed by other techniques, including in vivo MRI/magnetic resonance spectroscopy [54] and MS [55], to construct complete AD-characterized metabolomics. In addition, this study with the cross-sectional design cannot lead to causality. Although our study found that the blood plasma samples using NMR hold promise in distinguishing between AD patients and healthy controls, these results cannot show their causal relationship and further longitudinal studies are needed.
In conclusion, our current study was among the first to conduct lipidomic analysis using NMR spectroscopy to reveal the disease-specific sex differences in AD and elderly control plasma samples. In addition, there was no significant metabolite overlap between the male and female groups. Last, ROC curve analysis was used to assess the diagnostic value of each differential lipoprotein metabolite and lipid metabolite combination, and the results showed that the AUC areas of the combinations of lipid metabolites were above 0.70, all of which had good diagnostic value. This study has important clinical implications because it demonstrates the potential to diagnose AD from a simple, non-invasive blood sample.
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