https://orcid.org/0009-0004-5765-7414
Figures
Abstract
Objective
The purpose of this study was to investigate whether the combination of abnormal systemic immune-inflammation index (SII) levels and hyperglycemia increased the risk of cognitive function decline and reduced survival rate in the United States.
Methods
This cross-sectional study used data from the National Health and Nutrition Examination Survey (NHANES) database from 2011–2014 and enrolled 1,447 participants aged 60 years or older. Restricted cubic splines (RCS), linear regression and kaplan-meier(KM) curve were employed to explore the combined effects of abnormal SII and hyperglycemia on cognitive function and survival rate, and subgroup analysis was also conducted.
Results
The RCS analysis revealed an inverted U-shaped relationship between lgSII levels and cognitive function. Linear regression analysis indicated that neither abnormal SII nor diabetes alone significantly contributed to the decline in cognitive function compared to participants with normal SII levels and blood glucose. However, when abnormal SII coexisted with diabetes (but not prediabetes), it resulted to a significant decline in cognitive function. After adjusting for various confounding factors, these results remained significant in Delayed Word Recall (β:-0.76, P<0.05) and Digit Symbol Substitution tests (β:-5.02, P<0.05). Nevertheless, these results showed marginal significance in Total Word Recall test as well as Animal Fluency test. Among all subgroup analyses performed, participants with both abnormal SII levels and diabetes exhibited the greatest decline in cognitive function compared to those with only diabetes. Furthermore, KM curve demonstrated that the combination of abnormal SII levels and diabetes decreased survival rate among participants.
Citation: Chen W, Sun X, Han J, Wu X, Wang Q, Li M, et al. (2024) Joint effect of abnormal systemic immune-inflammation index (SII) levels and diabetes on cognitive function and survival rate: A population-based study from the NHANES 2011–2014. PLoS ONE 19(5): e0301300. https://doi.org/10.1371/journal.pone.0301300
Editor: Zahra Cheraghi, Hamadan University of Medical Sciences, School of Public Health, ISLAMIC REPUBLIC OF IRAN
Received: January 17, 2024; Accepted: March 13, 2024; Published: May 6, 2024
Copyright: © 2024 Chen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The data utilized in this study were obtained from the National Health and Nutrition Examination Survey (NHANES) database, administered by the Centers for Disease Control and Prevention’s National Center for Health Statistics (NCHS) for the purpose of evaluating the health and nutritional status of the United States population. The NHANES database can be accessed through the following link: https://www.cdc.gov/nchs/nhanes/index.htm.
Funding: This study was supported by the Clinical Research Award of the First Affiliated Hospital of Xi’an Jiaotong University, China(No. XJTU1AF2021CRF-003); the Clinical Research Award of the First Affiliated Hospital of Xi’an Jiaotong University, China(No. XJTU1AF-CRF-2020-015).
Competing interests: The authors have declared that no competing interests exist.
1. Introduction
The world’s population is aging rapidly, with virtually every country in the world reporting large increases in the number and proportion of older people [1]. This means that age-related cognitive decline has become an important social problem that concerns all of humanity and needs to be addressed urgently. The number of people with cognitive decline is increasing worldwide [2]. For example, in the United States, more than 6 million people over the age of 65 had been diagnosed with cognitive dysfunction by 2020, and that number is expected to increase to 13.85 million by 2060 [3]. The increase of cognitive impairment will lead to an increase in the incidence of various geriatric diseases, which will increase the investment of medical resources and cause a heavy socioeconomic burden [4,5]. Therefore, more effective prevention and control measures are urgently needed.
Cognitive decline is associated with chronic inflammation in the body, and older adults with high levels of inflammatory markers, including white blood cells, neutrophil-lymphocyte ratio (NLR), and neutrophil-albumin ratio (NAR), are at higher risk for cognitive impairment [6–8]. As simple markers of inflammation, their ability to predict cognitive impairment has been evaluated. But these markers involve only one or two types of immune inflammatory cells and may not accurately reflect the state of inflammation in the body. The systemic immune-inflammation index (SII) is a comprehensive novel inflammatory biomarker based on neutrophil, lymphocyte, and platelet counts that can represent different inflammatory and immune pathways in the body with greater stability [9]. SII is calculated as platelet count x neutrophil count/lymphocyte count [10]. It is now thought to accurately reflect inflammation [11]. Studies have shown that older adults with higher levels of dietary inflammation and SII are at higher risk for cognitive impairment [6]. Diabetes, a recognized risk factor for cognitive impairment, may be primarily due to vascular complications associated with abnormal blood sugar, including brain small vessel lesions and stroke [12,13]. Epidemiological review studies have proved that cognitive dysfunction is a complication of diabetes [14,15] and even prediabetes [16]. The Framingham Heart Study demonstrated that hyperglycemia is related to impaired attention and memory in people with undiagnosed diabetes and prediabetes [17]. Remarkably, there is a association between inflammatory biomarkers and the development of diabetes and its complications [18]. The study showed that older participants with lower cognitive functions have an increased risk for all-cause and cardiovascular disease mortality compared to older participants with a higher level of cognitive function [19].
Our hypothesis is that the co-occurrence of hyperglycemia and abnormal SII levels increases the risk of cognitive decline and decreases survival rate.
Therefore, we aimed to assess the combined effect of hyperglycemia and abnormal SII on cognitive function and survival rate in older adults using data from the National Health and Nutrition Examination Survey (NHANES) database from 2011–2014
2. Material methods
2.1 Study population
We obtained data from the NHANES database, which is conducted by the Centers for Disease Control and Prevention’s National Center for Health Statistics (NCHS) to assess the health and nutrition of the population of the United States.
All data analyzed in the present study are freely available online at the CDC website.
https://www.cdc.gov/nchs/nhanes/index.htm
The data survey was a combination of interview and physical examination. The survey included demographic, dietary, socioeconomic, and health-related questions. In addition, the examination includes medical, dental and physiological assessments, as well as laboratory analyses performed by highly skilled medical practitioners. This data is based on stratified, multi-stage probability sampling and is representative of the US population. NHANES research had been approved by the NCHS Research Ethics Review Committee. All participants in the survey signed informed consent forms. No ethical or administrative permission is required to access the NHANES database.
This analysis uses data from two survey cycles in the NHANES database (2011–2012, 2012–2014). Individuals younger than 60 years of age or missing values for key analytical variables were excluded. The inclusion criteria for this study are summarized in the flow chart shown in Fig 1
body mass index BMI.
2.2 Exposure and outcome definitions
Cognitive function assessment consisted of the following four tests [5]: Firstly, the Consortium to Establish a Registry for Alzheimer’s Disease Word Learning (CERAD-WL) test consisted of three consecutive learning experiments, which asked participants to immediately recall as many words as possible after reading 10 unrelated words aloud, with a total score of 30; Secondly, the CERAD Delayed Recall (CERAD-DR) test, which took place 10 minutes after the three continuous learning experiment, and participants were asked to recall words on the CERAD-WL test, is performed on a 10-point scale; The third is the animal fluency test, which assesses verbal fluency [20]. Participants were asked to name as many animals as they could within one minute, with each name receiving a share; Finally, the Digit Symbol Substitution Test (DSST) is a global measure of brain health, which measures processing speed, sustained attention, visual scanning and short-term memory [21]. The test required participants to draw the corresponding into a box next to a number within two minutes, totaling 133 boxes.
Diabetes is defined as follows: 1. diagnosed by a physician or health care professional; 2. HbA1c≥6.5%; 3.Fasting blood glucose> = 7.0mmol/L; 4. Random blood glucose> = 11.1mmol/L; 5. Two-hour OGTT blood glucose> = 11.1mmol/L; 6. Use of diabetes medication or insulin. Prediabetes is defined as follows: 1. Impaired fasting glycaemia: 6.11mmol/L< = fast glucose<7.0mmol/L; 2.Impaired glucose tolerance: 7.7mmol/L< = Two-hour OGTT blood glucose<11.1mmol/L. Diabetes and prediabetes are defined as hyperglycemia.
Blood specimens were measured at the NHANES Mobile Examination Centers (MECs). In 2011–2012 the Beckman Coulter MAXM was the hematology analyzer but in 2013–2014 the hematology analyzer used was the Beckman Coulter DXH 800. SII = platelet count x neutrophil count/lymphocyte count.
2.3 Variables
According to the available literature and clinical experience, variables with potential links to cognitive function decline were collected in this study. Demographic characteristics include age and sex (male/female), race (Mexican Americans, non-Hispanic Black, non-Hispanic white, other races), marital status (We put married/living with a partner in one category, widowed/divorced/separated in another, and never married in another), education levels (less than high school, high school, above high school) and poverty income ratio (divide into ≤1.2 and >1.2). In addition, examination include BMI. Laboratory test indicators include lymphocyte, neutrophil and platelet. Moreover, questionaire include the history of diabetes, hypertension, hypercholesterolemia, coronary heart disease, smoking and drinking, as well as daily energy intake, physical activity and depressive state. In addition, we also collected relevant survival data.
2.4 Statistical analysis
In this study, appropriate weights of NHANES samples were used for statistical analysis. Using RCS curve, the linear and nonlinear relationship between SII and cognitive function was studied after excluding confounding factors (age, sex, race, education level, marital status, poverty income ratio, smoking status, drinking status, hypercholesterolemia, hypertension, coronary heart disease, stroke, depressive state, Intake of energy and physical activity). However, SII was log-transformed when conducting RCS analysis, considering that these inflammatory markers were right-skewed distributed.Then SII was divided into 4 groups(Q1-Q4) according to the quartile, with Q1+Q4 as the abnormal group and Q2+Q3 as the normal group.
Continuous variables are expressed as the median (lower quartile-high quartile) and categorical variables are expressed as frequencies (percentages). For group comparisons of data with a normal distribution, we utilize weighted Student’s t-test or one-way ANOVA. For group comparisons of skewed variables, we used the weighted Wilcoxon test or Kruskal Wallis test. The weighted chi-square test was performed to analyze inter-group differences for categorical variables. We divided the participants into the following groups according to SII levels and blood glucose status: All no group, SII group, Prediabetes group, Diabetes group, SII+Predabetes, SII+Diabetes group (See Table 1 for detailed explanations), and then used weighted multiple linear regression analysis to explore the relationship between SII and blood glucose with cognitive function.
For continuous variables: Median (lower quartile-high quartile), P-value was by weighted one-way ANOVA or Kruskal Wallis test. For categorical variables: Frequencies (percentages), P-value was by weighted Chi-square test. PA_total_MET: Physical activity_total_metabolic equivalent.
Model 1 was adjusted for gender, age, race, education, marital status, and income. Model 2 added BMI, smoking, alcohol consumption, hyperlipidemia, hypertension, coronary heart disease and stroke to Model 1. Model 3 was adjusted for all covariables. Kaplan–Meier curves and the log-rank test were used to compare the cumulative risk of events according to abnormal SII levels and diabetes. The subgroup analyses were performed to evaluate the combined effect of abnormal SII levels and diabetes on cognitive function, considering age (≤ 70 or > 70/years), sex and BMI (< 25 or ≥ 25 kg/m2).
Data analyses were completed using R software version 4.2.0 The RCS was created with the “rms” package.
3. Results
3.1 Relationship between SII and cognitive function
As shown in Fig 2, The 3-knot RCS analysis reveal an inverted U-shaped relationship between lgSII levels and cognitive function (Pnon-liner<0.05). At a high levels of SII, cognitive function decreases with increasing SII levels. Furthermore, the results shown in Fig 3 show that the quartiles of SII show significant differences in cognitive function (P < 0.05). Based on the quartile and RCS results, SII is categorized into two groups: normal group (Q2+Q3) defined as 339.38 < SII ≤ 664.13, and abnormal group (Q1+Q4) defined as SII ≤ 339.38 or SII > 664.13.
The solid line represents β, and the shaded red represents 95% CI.
the first quartile: Q1; the second quartile: Q2; the third quartile: Q3; the fourth quartile:Q4. *P<0.05, ***P<0.001.
3.2 Baseline characteristics of the participants
Among 1447 participants included for analysis, 430 (29.72%) individuals had neither abnormal SII nor hyperglycemia, 442 (30.55%) had abnormal SII alone, 73 (5.4%) had prediabetes alone, 198 (13.68%) had diabetes alone, 74 (5.11%) had both abnormal SII and prediabetes and 230 (15.89%) had both abnormal SII and diabetes concurrently. Notably, those who experienced the coexistence of abnormal SII and diabetes tended to be less educated, poorer, more Non-Hispanic Black, higher BMI, lower total energy intake, less Physical activity and nondrinkers. Furthermore, significant differences in the prevalence rates of almost all chronic diseases were observed among these six groups except for age, sex, marital status, smoke, stroke and depression (all P<0.05) (Table 1).
3.3 Individual cognitive function in the different model
As illustrated in Fig 4, the four groups exhibited significant differences in various cognitive function tests. However, only in the delayed word recall test was there a significant difference between the Diabetes group and the SII+Diabetes group.
*P<0.05, ***P<0.001.
In the unadjusted, model1, model2 and model3, participants in SII+Diabetes group had significantly lower scores in the Delayed Word Recall test compared to the All no group (β: -1.13, -0.76, -0.78 and -0.76). Similar results were obtained in the Digit Symbol Substitution test (β: -11.62, -5.9,5.18 and -5.02). Both unadjusted and model1 analyses revealed that the SII+Diabetes group scored significantly lower than the All no group in Total Word Recall test (β: -1.9 and -0.97), but this association was not significant in model2 and model3. The same results were obtained in the Animal Fluency test (β: -2.78 and -1.15) (Table 2).
Model 1: Adjusted for age, sex, race, education level, marital status and poverty income ratio.
Model 2: Model 1 + BMI, smoking status, drinking status, hypercholesterolemia, hypertension, coronary heart disease and stroke.
Model3: Model2 + depressive state, Intake of energy and physical activity.
Overall, in the fully adjusted model, both the Diabetes and SII+Diabetes groups exhibited lower cognitive function scores in comparison to the All no group for all tests, with the SII+Diabetes group scoring even lower than the Diabetes group. While both Prediabetes and SII+Prediabetes groups showed decreased cognitive function scores compared to the All no group, there was no significant difference observed (Table 2).
3.4 Subgroup analysis
The subgroup analyses revealed that participants with concurrent abnormal SII levels and diabetes experienced more pronounced cognitive decline, although the extent of cognitive function reduction varied across different subgroups in different tests (Fig 5). Among all conducted subgroup analyses, participants with both abnormal SII levels and diabetes exhibited the greatest decline in cognitive function compared to those with only diabetes.
In the Delayed Word Recall test and Total Word Recall test, the decrease in cognitive function was more significant among participants ≤70 years old and male. Conversely, in the Animal Fluency test and Digit Symbol Substitution test, the decline in cognitive function was more noteworthy among participants > 70 years old and female. For any test, participants with a BMI <25kg/m2 demonstrated a more prominent cognitive decline (Fig 5).
3.5 Combined effects of abnormal SII and diabetes on survival rate
The Kaplan-Meier (KM) plot based on abnormal SII levels and hyperglycemia showed cumulative survival rate for participants (Fig 6). The difference in survival distribution among the six groups was found to be statistically significant (Log-rank test P<0.001); In compared to both the All no group and Diabetes group, the SII+Diabetes group exhibited a significantly lower survival rate. However, diabetes alone did not significantly increase participants’ risk of death.
4. Discussion
In this study of older adults in the United States, we observed an inverted U-shaped relationship between lgSII levels and cognitive function. Furthermore, our findings indicate that although abnormal SII and diabetes alone did not significantly contribute to cognitive decline compared to participants with All no group. However, the combined abnormal SII with diabetes (but not prediabetes) did cause a significant impairment in cognitive function compared to All no group. After adjusting for demographic data, health behavior, and past cardiovascular and cerebrovascular events, these results remained significant in the Delayed Word Recall and Digit Symbol Substitution tests. This revealed that the combination of abnormal SII and diabetes may impair delayed memory, executive function and processing speed in elderly Americans. These results were found in Total Word Recall and Animal Fluency tests when adjusting for demographic data, but only showed marginal significance after further adjusting for health behavioral and past cardiovascular and cerebrovascular events. This indicates that when abnormal SII was associated with diabetes, there was no strong correlation with participants’ immediate memory and verbal fluency. What’s more, across all subgroup analyses conducted, individuals with both abnormal SII levels and diabetes exhibited the most significant cognitive decline compared to those affected solely by diabetes. In addition, this study found that abnormal SII levels combined with diabetes further reduced survival rate in older Americans compared to those with normal SII and blood glucose or those diabetes alone.
Numerous studies have shown that chronic inflammation plays a crucial role in the pathogenesis of cognitive impairment in diabetic patients [6,22–24]. Systemic inflammation serves as a crucial mechanism underlying cognitive impairment [25,26]. Cognitive impairment, an important target of vascular damage in diabetes, is associated with both systemic and local brain tissue inflammation involving essential inflammatory cells and molecules. As simple indicators of inflammation, neutrophil to lymphocyte ratio (NLR) [23] and platelet-to-lymphocyte ratio (PLR) [24] have been found to be correlated with the development of cognitive impairment in diabetic patients. Platelets act as an atypical first-line inflammatory biomarker by adhering endothelial cells and leukocytes, thereby modulating the activity of inflammatory components of these cells. Multiple cytokines stem from activated platelets and regulate platelet function in the pathogenesis of cognitive impairment, such as IL-1 and interleukin-6 (IL-6) [25,27,28]. The main components of white blood cells are lymphocytes and neutrophils, which respectively mediate innate and adaptive immunity. Neutrophils, being the majority of white blood cells, play a crucial role in initiating and regulating inflammatory processes. On one hand, activated neutrophils release reactive oxygen species and neutrophilic elastase, which can directly cause damage to brain cells and contribute to the progression of cognitive impairment in diabetic patients [23,28]. On the other hand, activated neutrophils stimulate T lymphocytes by facilitating antigen presentation [23,29]. These T lymphocytes enter the central nervous system through the damaged blood-brain barrier, leading to their numbers within peripheral blood [30]. Besides, T lymphocytes further release tumor necrosis factor α (TNF-α), creating a vicious cycle [23]. The SII is calculated based on three types of circulating immune cells (neutrophils, lymphocytes, and platelets), which are not affected by fluid imbalances [10]. SII provides more valuable information about the relationship between systemic inflammation and cognitive impairment than one or two types of peripheral blood.
SII, which represents the balance of platelets, neutrophils, and lymphocytes, has been recognized as an index of inflammatory status in patients with multifarious chronic inflammatory diseases. Mahemuti N et al. [10] found a significant correlation between SII levels and hyperlipidemia. Topuz MF et al. [31] showed that the SII value was positively correlated with the severity of obstructive sleep apnea patients. Our study demonstrates an inverted U-shaped relationship between lgSII levels and cognitive function. At high levels of SII, cognitive function decreased with increasing SII levels. The reason for low cognitive function at low levels of SII may be attributed to the dual-sided effect of inflammation; excessive activation of neuroinflammation can damage brain cells and neurons, while an appropriate inflammatory state can maintain microenvironment stability [27].
Our study also highlights that diabetes does not increase the risk of cognitive decline in isolation, but rather within a broader spectrum of inflammatory and immune system diseases. Actually, we found that the combination of diabetes and abnormal SII significantly increased the risk of cognitive decline, while neither diabetes nor abnormal SII alone led to a significant risk of this outcome. These results suggest that SII affects cognitive function in a dose-dependent manner. This is similar to the findings of Dove A et al. [32], whose study explained that cognitive impairment is only associated with poorly controlled diabetes (i.e., HbA1c≥7.5%). This indicates that it is the degree of hyperglycemia, rather than diabetes itself, that negatively affects cognitive function. In summary, the effect of diabetes on cognitive impairment is caused by the severity of its complications. Furthermore, our study suggests that inflammation levels may play a role in influencing the impact of diabetes on cognitive function. This is supported by our observation regarding survival rates when considering both diabetes and SII levels together. We found that neither diabetes nor prediabetes status alone significantly reduced participants’ survival rate, only when both diabetes and abnormal SII coexisted did participants’ survival rate show a significantly reduction.
In conclusion, our findings suggest that the combination of abnormal SII levels and diabetes (but not prediabetes) is associated with a greater decline in cognitive function and lower survival rate among older Americans. Implementing interventions targeting these factors may be beneficial in preventing cognitive impairment and mortality. Therefore, further studies are warranted to evaluate the predictive value of abnormal SII levels and diabetes for cognitive impairment and mortality, as well as to conduct prospective studies with extend follow-up periods.
References
- 1.
United Nations. World Population Ageing 2019. United Nations; New York, NY, USA: 2019.
- 2. Dominguez LJ, Veronese N, Vernuccio L, Catanese G, Inzerillo F, Salemi G, Barbagallo M. Nutrition, Physical Activity, and Other Lifestyle Factors in the Prevention of Cognitive Decline and Dementia. Nutrients. 2021 Nov 15;13(11):4080. pmid:34836334
- 3. Rajan KB, Weuve J, Barnes LL, McAninch EA, Wilson RS, Evans DA. Population estimate of people with clinical Alzheimer’s disease and mild cognitive impairment in the United States (2020–2060). Alzheimers Dement. 2021 Dec;17(12):1966–1975. pmid:34043283
- 4. Ruangritchankul S, Peel NM, Shafiee Hanjani L, Gray LC. The Changes in Medication Prescribing Among the Older People with Cognitive Impairment in the Acute Care Setting. Clin Interv Aging. 2020 Jun 8;15:865–876. pmid:32606626
- 5. Li W, Li S, Shang Y, Zhuang W, Yan G, Chen Z, et al. Associations between dietary and blood inflammatory indices and their effects on cognitive function in elderly Americans. Front Neurosci. 2023 Feb 21;17:1117056. pmid:36895419
- 6. Wang X, Li T, Li H, Li D, Wang X, Zhao A, et al. Association of Dietary Inflammatory Potential with Blood Inflammation: The Prospective Markers on Mild Cognitive Impairment. Nutrients. 2022 Jun 10;14(12):2417. pmid:35745147
- 7. Hu M, Shu X, Feng H, Xiao LD. Sleep, inflammation and cognitive function in middle-aged and older adults: A population-based study. J Affect Disord. 2021 Apr 1;284:120–125. pmid:33592430
- 8. Miller AA, Spencer SJ. Obesity and neuroinflammation: a pathway to cognitive impairment. Brain Behav Immun. 2014 Nov;42:10–21. pmid:24727365
- 9. Ding P, Guo H, Sun C, Yang P, Kim NH, Tian Y, et al. Combined systemic immune-inflammatory index (SII) and prognostic nutritional index (PNI) predicts chemotherapy response and prognosis in locally advanced gastric cancer patients receiving neoadjuvant chemotherapy with PD-1 antibody sintilimab and XELOX: a prospective study. BMC Gastroenterol. 2022 Mar 14;22(1):121. pmid:35287591
- 10. Mahemuti N, Jing X, Zhang N, Liu C, Li C, Cui Z, et al. Association between Systemic Immunity-Inflammation Index and Hyperlipidemia: A Population-Based Study from the NHANES (2015–2020). Nutrients. 2023 Feb 26;15(5):1177. pmid:36904176
- 11. Hua X, Long ZQ, Zhang YL, Wen W, Guo L, Xia W, et al. Prognostic Value of Preoperative Systemic Immune-Inflammation Index in Breast Cancer: A Propensity Score-Matching Study. Front Oncol. 2020 Apr 21;10:580. pmid:32373539
- 12. Nanayakkara N, Curtis AJ, Heritier S, Gadowski AM, Pavkov ME, Kenealy T, et al. Impact of age at type 2 diabetes mellitus diagnosis on mortality and vascular complications: systematic review and meta-analyses. Diabetologia. 2021 Feb;64(2):275–287. pmid:33313987
- 13. Liu R, Li L, Shao C, Cai H, Wang Z. The Impact of Diabetes on Vascular Disease: Progress from the Perspective of Epidemics and Treatments. J Diabetes Res. 2022 Apr 8;2022:1531289. pmid:35434140
- 14. Ryan CM, van Duinkerken E, Rosano C. Neurocognitive consequences of diabetes. Am Psychol. 2016 Oct;71(7):563–576. pmid:27690485
- 15. Saedi E, Gheini MR, Faiz F, Arami MA. Diabetes mellitus and cognitive impairments. World J Diabetes. 2016 Sep 15;7(17):412–22. pmid:27660698
- 16. Buysschaert M, Medina JL, Bergman M, Shah A, Lonier J. Prediabetes and associated disorders. Endocrine. 2015 Mar;48(2):371–93. pmid:25294012
- 17. Weinstein G, Maillard P, Himali JJ, Beiser AS, Au R, Wolf PA, et al. Glucose indices are associated with cognitive and structural brain measures in young adults. Neurology. 2015 Jun 9;84(23):2329–37. pmid:25948725
- 18. Lontchi-Yimagou E, Sobngwi E, Matsha TE, Kengne AP. Diabetes mellitus and inflammation. Curr Diab Rep. 2013 Jun;13(3):435–44. pmid:23494755
- 19. Tamosiunas A, Sapranaviciute-Zabazlajeva L, Luksiene D, Virviciute D, Bobak M. Cognitive Function and Mortality: Results from Kaunas HAPIEE Study 2006–2017. Int J Environ Res Public Health. 2020 Apr 1;17(7):2397. pmid:32244660
- 20. Clark LJ, Gatz M, Zheng L, Chen YL, McCleary C, Mack WJ. Longitudinal verbal fluency in normal aging, preclinical, and prevalent Alzheimer’s disease. Am J Alzheimers Dis Other Demen. 2009 Dec-2010 Jan;24(6):461–8. pmid:19759254
- 21.
Wechsler D. WAIS Manual–Third Edition. In: Psychological Corporation, ed. New York,; 1997.
- 22. Muriach M, Flores-Bellver M, Romero FJ, Barcia JM. Diabetes and the brain: oxidative stress, inflammation, and autophagy. Oxid Med Cell Longev. 2014;2014:102158. pmid:25215171
- 23. Yu ZW, Wang Y, Li X, Tong XW, Zhang YT, Gao XY. Association between the neutrophil to lymphocyte ratio and mild cognitive impairment in patients with type 2 diabetes. Aging Clin Exp Res. 2023 Jun;35(6):1339–1345. pmid:37129710
- 24. Du L, Hu X, Zhang B, Miao X, Wang J, Shen J, et al. The relationship of platelet-to-lymphocyte ratio with cognitive decline in T2DM. Diabetol Metab Syndr. 2021 Dec 24;13(1):151. pmid:34952622
- 25. Paouri E, Georgopoulos S. Systemic and CNS Inflammation Crosstalk: Implications for Alzheimer’s Disease. Curr Alzheimer Res. 2019;16(6):559–574. pmid:30907316
- 26. Holmes C. Review: systemic inflammation and Alzheimer’s disease. Neuropathol Appl Neurobiol. 2013 Feb;39(1):51–68. pmid:23046210
- 27. Tian Z, Ji X, Liu J. Neuroinflammation in Vascular Cognitive Impairment and Dementia: Current Evidence, Advances, and Prospects. Int J Mol Sci. 2022 Jun 2;23(11):6224. pmid:35682903
- 28. Guo W, Song Y, Sun Y, Du H, Cai Y, You Q, et al. Systemic immune-inflammation index is associated with diabetic kidney disease in Type 2 diabetes mellitus patients: Evidence from NHANES 2011–2018. Front Endocrinol (Lausanne). 2022 Dec 6;13:1071465. pmid:36561561
- 29. Sayed A, Bahbah EI, Kamel S, Barreto GE, Ashraf GM, Elfil M. The neutrophil-to-lymphocyte ratio in Alzheimer’s disease: Current understanding and potential applications. J Neuroimmunol. 2020 Dec 15;349:577398. pmid:32977249
- 30. Liu YJ, Guo DW, Tian L, Shang DS, Zhao WD, Li B, et al. Peripheral T cells derived from Alzheimer’s disease patients overexpress CXCR2 contributing to its transendothelial migration, which is microglial TNF-alpha-dependent. Neurobiol Aging. 2010 Feb;31(2):175–88. pmid:18462836
- 31. Topuz MF, Ture N, Akdag G, Arik O, Gulhan PY. The importance of systemic immune-inflammation index in obstructive sleep apnea syndrome. Eur Arch Otorhinolaryngol. 2022 Oct;279(10):5033–5038. pmid:35091830
- 32. Dove A, Shang Y, Xu W, Grande G, Laukka EJ, Fratiglioni L, et al. The impact of diabetes on cognitive impairment and its progression to dementia. Alzheimers Dement. 2021 Nov;17(11):1769–1778. pmid:34636485