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Bruno A, Prabu P, Vedala K, Sethuraman S, Nichols FT. Distribution of cerebral age-related white matter changes in relation to risk factors in stroke patients. Clin Neurol Neurosurg 2023; 235:108018. [PMID: 37924721 DOI: 10.1016/j.clineuro.2023.108018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 10/20/2023] [Accepted: 10/21/2023] [Indexed: 11/06/2023]
Abstract
INTRODUCTION The distribution of cerebral age-related white matter changes (ARWMC) may be indicative of the underlying etiology and could suggest optimal interventions. We aimed to determine if left ventricular hypertrophy (LVH), a marker of uncontrolled hypertension, along with additional risk factors are associated with the distribution of cerebral ARWMC. METHODS We analyzed data of 172 patients from a hospital stroke registry who had acute stroke and brain MRI. We classified lesion location as superficial (frontal, parieto-occipital, or temporal) or deep (basal nuclei) using the ARWMC scale. We defined a superficial ARWMC index as the superficial minus the deep score. We excluded infratentorial lesions and patients with bilateral strokes. Regression analysis analyzed LVH and other relevant clinical factors for independent association with the superficial ARWMC index. RESULTS The superficial ARWMC scores ranged from 0 to 6, the deep scores from 0 to 3, and the superficial ARWMC index from -2 to 6. We categorized the superficial ARWMC index as -2 to 1 (n = 65), 2 (n = 50), and 3 - 6 (n = 57). In bivariate analysis, ARWMC distribution was significantly associated with older age, lower household income (HI), and lower serum triglyceride (TG) levels. In multiple logistic regression analysis, higher superficial ARWMC index was significantly associated with lower HI (OR 10.72, 95 % CI 2.30-49.85), lower serum low density cholesterol (LDL) (OR 0.86, 95 % CI 0.75-0.98, per 10 mg/dL), and lower serum TG levels (OR 0.91, 95 % CI 0.85-0.99, per 10 mg/dL). The area under the curve in receiver operating characteristic analysis (95 % CI) for HI was 0.63 (0.49-0.76), LDL level 0.64 (0.51-0.77), and TG level 0.77 (0.65-0.88). CONCLUSION In this study, LVH was not associated with the distribution of cerebral ARWMC. Using an alternate classification of ARWMC distribution and analyzing additional risk factors in larger studies may yield further discoveries.
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Affiliation(s)
- Askiel Bruno
- Department of Neurology, Medical College of Georgia at Augusta University, Augusta, GA, United States.
| | - Pranav Prabu
- Medical College of Georgia, Augusta, GA, United States
| | | | - Sankara Sethuraman
- Department of Mathematics, Augusta University, Augusta, GA, United States
| | - Fenwick T Nichols
- Department of Neurology, Medical College of Georgia at Augusta University, Augusta, GA, United States
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Rajabi F, Rahimi S, Maracy MR, Barekatain M. Association between cognitive capacity and metabolic indices in patients with neuropsychiatric disorders. JOURNAL OF EDUCATION AND HEALTH PROMOTION 2023; 12:292. [PMID: 37849862 PMCID: PMC10578545 DOI: 10.4103/jehp.jehp_82_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 03/11/2023] [Indexed: 10/19/2023]
Abstract
BACKGROUND Although previous studies suggested the relationship between metabolic indices and cognitive capacity, results have been conflicting. The prevalence of metabolic and cognitive disorders is high in patients with neuropsychiatric disorders. We aimed to assess the relationship between laboratory metabolic indices and specific areas of cognitive capacity. MATERIALS AND METHODS This was a retrospective review of the medical records of 423 from 452 patients with neuropsychiatric disorders who were admitted to the neuropsychiatry unit, Ayatollah Kashani Hospital, Isfahan, Iran, from September 1, 2018, to September 30, 2022. We extracted demographic factors, laboratory metabolic indices, and scores of the Neuropsychiatry Unit Cognitive Assessment tool (NUCOG). We utilized a generalized linear model (GLM) to demonstrate the effect of metabolic indices on the risk of reduction in cognitive domains. Due to the presence of missing data in the metabolic indices, we used the multiple imputation method. RESULTS The regression coefficient of NUCOG total score and subscale scores for metabolic indices using GLM after multiple imputation method demonstrated that among the metabolic indicators, fasting blood sugar (FBS) had the reverse relationship with the total score of NUCOG (β = -.05). Among the NUCOG subscales, executive functioning had the strongest relationship with FBS (β = -.01). Also, there was a negative relationship between patients' age and the total score of NUCOG (β = -.38). Educational level had a positive relationship with the total NUCOG score (β =10.2). CONCLUSIONS The main metabolic factors that might reduce cognitive capacity were higher FBS.
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Affiliation(s)
- Fatemeh Rajabi
- Department of Psychiatry, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Somayyeh Rahimi
- Department of Psychiatry, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Mohammad R. Maracy
- Department of Epidemiology and Biostatistics, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Majid Barekatain
- Department of Psychiatry, Isfahan University of Medical Sciences, Isfahan, Iran
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Maglinger B, Harp JP, Frank JA, Rupareliya C, McLouth CJ, Pahwa S, Sheikhi L, Dornbos D, Trout AL, Stowe AM, Fraser JF, Pennypacker KR. Inflammatory-associated proteomic predictors of cognitive outcome in subjects with ELVO treated by mechanical thrombectomy. BMC Neurol 2023; 23:214. [PMID: 37280551 PMCID: PMC10243077 DOI: 10.1186/s12883-023-03253-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Accepted: 05/18/2023] [Indexed: 06/08/2023] Open
Abstract
BACKGROUND Emergent Large Vessel Occlusion (ELVO) stroke causes devastating vascular events which can lead to significant cognitive decline and dementia. In the subset of ELVO subjects treated with mechanical thrombectomy (MT) at our institution, we aimed to identify systemic and intracranial proteins predictive of cognitive function at time of discharge and at 90-days. These proteomic biomarkers may serve as prognostic indicators of recovery, as well as potential targets for novel/existing therapeutics to be delivered during the subacute stage of stroke recovery. METHODS At the University of Kentucky Center for Advanced Translational Stroke Sciences, the BACTRAC tissue registry (clinicaltrials.gov; NCT03153683) of human biospecimens acquired during ELVO stroke by MT is utilized for research. Clinical data are collected on each enrolled subject who meets inclusion criteria. Blood samples obtained during thrombectomy were sent to Olink Proteomics for proteomic expression values. Montreal Cognitive Assessments (MoCA) were evaluated with categorical variables using ANOVA and t-tests, and continuous variables using Pearson correlations. RESULTS There were n = 52 subjects with discharge MoCA scores and n = 28 subjects with 90-day MoCA scores. Several systemic and intracranial proteins were identified as having significant correlations to discharge MoCA scores as well as 90-day MoCA scores. Highlighted proteins included s-DPP4, CCL11, IGFBP3, DNER, NRP1, MCP1, and COMP. CONCLUSION We set out to identify proteomic predictors and potential therapeutic targets related to cognitive outcomes in ELVO subjects undergoing MT. Here, we identify several proteins which predicted MoCA after MT, which may serve as therapeutic targets to lessen post-stroke cognitive decline.
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Affiliation(s)
- Benton Maglinger
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Jordan P Harp
- Department of Neurology, University of Kentucky, Lexington, KY, USA
- Center for Advanced Translational Stroke Science, University of Kentucky, Lexington, KY, USA
| | - Jacqueline A Frank
- Department of Neurology, University of Kentucky, Lexington, KY, USA
- Center for Advanced Translational Stroke Science, University of Kentucky, Lexington, KY, USA
| | | | | | - Shivani Pahwa
- Department of Neurosurgery, University of Kentucky, Lexington, KY, USA
- Department of Radiology, University of Kentucky, Lexington, KY, USA
| | - Lila Sheikhi
- Department of Neurology, University of Kentucky, Lexington, KY, USA
- Department of Neurosurgery, University of Kentucky, Lexington, KY, USA
- Department of Radiology, University of Kentucky, Lexington, KY, USA
| | - David Dornbos
- Department of Neurosurgery, University of Kentucky, Lexington, KY, USA
- Department of Radiology, University of Kentucky, Lexington, KY, USA
| | - Amanda L Trout
- Department of Neurology, University of Kentucky, Lexington, KY, USA
- Center for Advanced Translational Stroke Science, University of Kentucky, Lexington, KY, USA
| | - Ann M Stowe
- Department of Neurology, University of Kentucky, Lexington, KY, USA
- Center for Advanced Translational Stroke Science, University of Kentucky, Lexington, KY, USA
- Department of Neuroscience, University of Kentucky, Lexington, KY, USA
| | - Justin F Fraser
- Department of Neurology, University of Kentucky, Lexington, KY, USA
- Center for Advanced Translational Stroke Science, University of Kentucky, Lexington, KY, USA
- Department of Neurosurgery, University of Kentucky, Lexington, KY, USA
- Department of Radiology, University of Kentucky, Lexington, KY, USA
- Department of Neuroscience, University of Kentucky, Lexington, KY, USA
| | - Keith R Pennypacker
- Department of Neurology, University of Kentucky, Lexington, KY, USA.
- Center for Advanced Translational Stroke Science, University of Kentucky, Lexington, KY, USA.
- Department of Neuroscience, University of Kentucky, Lexington, KY, USA.
- Department of Neurology and Neuroscience, Center for Advanced Translational Stroke Science, University of Kentucky, Building BBSRB, Office B383, Lexington, KY, 40536, USA.
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Kaddumukasa MN, Kaddumukasa M, Katabira E, Sewankambo N, Namujju LD, Goldstein LB. Prevalence and predictors of post-stroke cognitive impairment among stroke survivors in Uganda. BMC Neurol 2023; 23:166. [PMID: 37098461 PMCID: PMC10127321 DOI: 10.1186/s12883-023-03212-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Accepted: 04/15/2023] [Indexed: 04/27/2023] Open
Abstract
BACKGROUND Little is known about the characteristics and determinants of post-stroke cognitive impairment in residents of low- and middle-income countries. The objective of this study was to determine the frequencies, patterns, and risk factors for cognitive impairment in a cross-sectional study of consecutive stroke patients cared for at Uganda's Mulago Hospital, located in sub-Saharan Africa. METHODS 131 patients were enrolled a minimum of 3-months after hospital admission for stroke. A questionnaire, clinical examination findings, and laboratory test results were used to collect demographic information and data on vascular risk factors and clinical characteristics. Independent predictor variables associated with cognitive impairment were ascertained. Stroke impairments, disability, and handicap were assessed using the National Institute of Health Stroke Scale (NIHSS), Barthel Index (BI), and modified Rankin scale (mRS), respectively. The Montreal Cognitive Assessment (MoCA) was used to assess participants' cognitive function. Stepwise multiple logistic regression was used to identify variables independently associated with cognitive impairment. RESULTS The overall mean MoCA score was 11.7-points (range 0.0-28.0-points) for 128 patients with available data of whom 66.4% were categorized as cognitively impaired (MoCA < 19-points). Increasing age (OR 1.04, 95% CI 1.00-1.07; p = 0.026), low level of education (OR 3.23, 95% CI 1.25-8.33; p = 0.016), functional handicap (mRS 3-5; OR 1.84, 95% CI 1.28-2.63; p < 0.001) and high LDL cholesterol (OR 2.74, 95% CI 1.14-6.56; p = 0.024) were independently associated with cognitive impairment. CONCLUSIONS Our findings highlight the high burden and need for awareness of cognitive impairment in post stroke populations in the sub-Saharan region and serve to emphasize the importance of detailed cognitive assessment as part of routine clinical evaluation of patients who have had a stroke.
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Affiliation(s)
- Martin N Kaddumukasa
- Department of Medicine, School of Medicine, College of Health Sciences, Makerere University, Kampala, Uganda
| | - Mark Kaddumukasa
- Department of Medicine, School of Medicine, College of Health Sciences, Makerere University, Kampala, Uganda
| | - Elly Katabira
- Department of Medicine, School of Medicine, College of Health Sciences, Makerere University, Kampala, Uganda
| | - Nelson Sewankambo
- Department of Medicine, School of Medicine, College of Health Sciences, Makerere University, Kampala, Uganda
| | - Lillian D Namujju
- Department of Electrical and Computer Engineering, College of Engineering, Design, Art and Technology, Makerere University, Kampala, Uganda
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Comparing the Relationships of Genetically Proxied PCSK9 Inhibition With Mood Disorders, Cognition, and Dementia Between Men and Women: A Drug‐Target Mendelian Randomization Study. J Am Heart Assoc 2022; 11:e026122. [DOI: 10.1161/jaha.122.026122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Background
PCSK9 (proprotein convertase subtilisin/kexin type 9) inhibitors are important therapeutic options for reducing cardiovascular disease risk; however, questions remain regarding potential differences in the neuropsychiatric impact of long‐term PCSK9 inhibition between men and women.
Methods and Results
Using PCSK9 gene single‐nucleotide polymorphisms from European ancestry–based genome‐wide association studies of low‐density lipoprotein cholesterol (N=1 320 016), circulating PCSK9 protein levels (N=10 186), tissue‐specific PCSK9 gene expression, sex‐specific genome‐wide association studies of anxiety, depression, cognition, insomnia, and dementia (ranging from 54 321 to 194 174), we used drug‐target inverse variance–weighted Mendelian randomization (MR) and complementary MR methods (MR Egger, weighted median, and weighted mode) to investigate potential neuropsychiatric consequences of genetically proxied PCSK9 inhibition in men and women. We failed to find evidence surpassing correction for multiple comparisons of relationships between genetically proxied PCSK9 inhibition and the risk for the 12 neuropsychiatric end points in either men or women. Drug‐target analyses were generally well‐powered to detect effect estimates at several hypothesized thresholds for both combined‐sex and sex‐specific end points, especially analyses using PCSK9 instruments derived from protein and expression quantitative trait loci. Further, MR estimates across complementary MR methods and additional models using genetic instruments derived from circulating PCSK9 protein levels and tissue‐specific
PCSK9
expression were in alignment, strengthening causal inference.
Conclusions
Genetically proxied PCSK9 inhibition showed a neutral neuropsychiatric side effect profile with no major sex‐specific differences. Given statistical power considerations, replication with larger samples, as well as data from other ancestral populations, are necessary. These findings may have important clinical implications for lipid‐lowering drug‐prescribing practices and side effect monitoring of approved and future PCSK9 therapies.
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Zaciragic A, Dervisevic A, Valjevac A, Fajkic A, Spahic S, Hasanbegovic I, Jadric R, Lepara O. Difference in the Standard and Novel Lipid Profile Parameters Between Patients With Alzheimer's Disease and Vascular Dementia Stratified by the Degree of Cognitive Impairment. Mater Sociomed 2022; 34:100-106. [PMID: 36199842 PMCID: PMC9478525 DOI: 10.5455/msm.2022.34.100-106] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 02/24/2022] [Indexed: 11/18/2022] Open
Abstract
Background Lipids and lipoproteins are significantly involved in maintaining structural and functional components of the human brain and neurons, but their role in the development of Alzheimer's disease (AD) and vascular dementia (VD) remains unclear. Objective The aim of the present study was to explore the differences in the standard and novel lipid profile parameters in patients with AD and VD, stratified by the degree of cognitive impairment (CI). Methods Present study included 66 patients with AD, 50 patients with VD, and 60 control subjects. For an evaluation of the global cognitive function the Montreal Cognitive Assessment (MoCA) test was used. In order to distinguish patients with VD from those with AD the Hachinski ischemic score was used. Plasma total cholesterol (TC), high-density lipoprotein -cholesterol (HDL-C), and triglycerides (TG) levels were determined using standard enzymatic colorimetric techniques, whereas the Friedewald formula was used to calculate low-density lipoprotein-cholesterol (LDL-C) levels. The non-traditional lipid indices such as TG/HDL-C, TC/HDL-C, and LDL-C/HDL-C ratio were separately calculated. The differences between the groups were analyzed with the Kruskal Wallis test followed by the Mann-Whitney test or with ANOVA followed by the Tuckey posthoc test. Results Results of the conducted study have found that the patients in AD group with moderate CI and patients in AD group with severe CI exhibited significantly lower levels of serum TC, TG, LDL-C, VLDL-C, Non- HDL-C, atherogenic index, TG/HDL-C, TC/HDL-C and LDL-C/HDL-C compared to cognitively normal control subjects. Moreover, patients in VD group with severe and moderate CI had significantly lower level of TG compared to control group of subjects. Our results have also shown that patients in AD group with moderate CI had significantly lower level of TC, TG, LDL-C, Non-HDL-C, atherogenic index, TG/HDL-C, TC/HDL-C compared to VD patients with moderate CI. In addition, patients in AD group with severe CI had significantly lower level of TC, LDL-C, Non-HDL-C and TC/HDL-C compared to VD patients with severe CI. Conclusion The results of this study have shown dysregulation of lipid metabolism in AD and VD patients with different degree of CI. In both moderate and in severe CI, patients with AD had lower levels of majority of standard and novel lipid parameters compared to patients with VD. Further larger prospective studies are required to elucidate the accuracy of standard and novel lipid parameters in the assessment of different degree of CI in AD and VD.
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Affiliation(s)
- Asija Zaciragic
- Department of Human Physiology, Faculty of Medicine, University of Sarajevo, Sarajevo, Bosnia and Herzegovina
| | - Amela Dervisevic
- Department of Human Physiology, Faculty of Medicine, University of Sarajevo, Sarajevo, Bosnia and Herzegovina
| | - Amina Valjevac
- Department of Human Physiology, Faculty of Medicine, University of Sarajevo, Sarajevo, Bosnia and Herzegovina
| | - Almir Fajkic
- Department of Pathophysiology, Faculty of Medicine, University of Sarajevo, Sarajevo, Bosnia and Herzegovina
| | - Selma Spahic
- Faculty of Medicine, University of Sarajevo, Sarajevo, Bosnia and Herzegovina
| | - Ilvana Hasanbegovic
- Department of Anatomy, Faculty of Medicine, University of Sarajevo, Sarajevo, Bosnia and Herzegovina
| | - Radivoj Jadric
- Department of Medical Biochemistry, Faculty of Medicine, University of Sarajevo, Sarajevo, Bosnia and Herzegovina
| | - Orhan Lepara
- Department of Human Physiology, Faculty of Medicine, University of Sarajevo, Sarajevo, Bosnia and Herzegovina
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Li Y, Li M, Zhao K, Wang Y, Tan X, Qin C, Rao Y, Sun Z, Ge L, Cao Z, Liang Y, Qiu S. Altered dynamic functional architecture in type 2 diabetes mellitus. Front Endocrinol (Lausanne) 2022; 13:1117735. [PMID: 36760808 PMCID: PMC9903314 DOI: 10.3389/fendo.2022.1117735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 12/29/2022] [Indexed: 01/25/2023] Open
Abstract
INTRODUCTION Type 2 diabetes mellitus (T2DM) can accelerate cognitive decline and even dementia so that the underlying mechanism deserves further exploration. In the resting state, brain function is still changing dynamically. At present, it is still unknown whether the dynamic functional connectivity (dFC) between various brain regions is in a stable state. It is necessary to interpret brain changes from a new perspective, that is, the stability of brain architecture. METHODS In this study, we used a fixed dynamic time scale to explore the stability of dynamic functional architecture in T2DM, then the dynamic effective connectivity (dEC) was used to further explain how information flows through dynamically fluctuating brain architecture in T2DM. RESULT Two brain regions with decreased stability were found including the right supra-marginal gyrus (SMG) and the right median cingulate gyrus (MCG) in T2DM subjects. The dEC variation has increased between the left inferior frontal gyrus (IFG) and the right MCG. The direction of causal flow is from the right MCG to the left IFG. CONCLUSION The combination of stability and dEC can not only show the stability of dynamic functional architecture in brain but also reflect the fluidity of brain information, which is an innovative and interesting attempt in the field of neuroimaging. The changes of dynamic architecture in T2DM patients may present an innovative perspective and explanation for their cognitive decline.
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Affiliation(s)
- Yifan Li
- First Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Mingrui Li
- First Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
- Department of Magnetic Resonance Imaging, Zhanjiang First Hospital of Traditional Chinese Medicine, Zhanjiang, China
| | - Kui Zhao
- First Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Yan Wang
- First Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Xin Tan
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Chunhong Qin
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Yawen Rao
- First Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Zhizhong Sun
- First Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Limin Ge
- First Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Zidong Cao
- First Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Yi Liang
- First Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
- *Correspondence: Shijun Qiu, ; Yi Liang,
| | - Shijun Qiu
- First Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
- *Correspondence: Shijun Qiu, ; Yi Liang,
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Association between Serum Lipid Parameters and Cognitive Performance in Older Adults. J Clin Med 2021; 10:jcm10225405. [PMID: 34830687 PMCID: PMC8617666 DOI: 10.3390/jcm10225405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2021] [Revised: 11/16/2021] [Accepted: 11/17/2021] [Indexed: 11/23/2022] Open
Abstract
(1) Background: Previous studies have suggested the association between lipid profiles and cognitive function in older adults. However, they generated inconsistent results. We aim to determine the relationship between lipid profiles and cognitive performance in older adults. (2) Methods: We used the 2011–2014 National Health and Nutrition Examination Survey. This study included 2215 participants who were aged more than 60 years old and free of coronary heart disease or stroke. Lipid profiles included total cholesterol (TC), low density lipid cholesterol (LDL), high density lipid cholesterol (HDL), and triglyceride (TG). Cognitive function was assessed using the digit symbol substitution test (DSST). (3) Results: Positive correlations of DSST were observed with TC (r = 0.111; p < 0.0001), HDL (r = 0.127; p < 0.0001), and LDL (r = 0.107; p = 0.0005). However, there was no significant relationship between TG and DSST. A one-unit increase in HDL was associated with an increase in DSST score (beta coefficient: 0.036; p = 0.018); but the association was not significant for LDL, TG, and TC. In the categorical analysis, the high HDL group had a higher DSST score than the low HDL group (beta = 3.113; p < 0.0001) and the low TG group was more likely to show a lower DSST score than the high TC group (beta = −1.837; p = 0.0461). However, LDL and TC showed no statistically significant associations. Moreover, HDL was only associated with a 0.701 times increased risk of cognitive impairment (95% CI = 0.523–0.938) in the logistic regression analysis. (4) Conclusions: Higher blood concentrations of HDL levels were positively associated with DSST scores in older adults. We suggest that the high levels of HDL may be a protective factor against cognitive impairment.
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