Association Between Lymphocyte-High Density Lipoprotein Ratio and Cognitive Impairment in Chinese Older Adults: A Population-Based Cross-Section Study

Abstract

Objective

To explore the association between lymphocyte-high density lipoprotein ratio (LHR) and cognitive impairment in Chinese older adults.

Methods

This study included participants aged ≥65 years from the 2008 to 2014 waves of the Chinese Longitudinal Healthy Longevity Survey (CLHLS). Cognitive function was assessed using the Mini-Mental State Examination, with a score of less than 24 points considered indicative of cognitive impairment. Multiple logistic regression analysis and restricted cubic spline were applied to investigate the association between LHR and cognitive impairment.

Results

Of 3556 participants, 1625 (45.7%) suffered from cognitive impairment. Multivariate logistic regression analysis indicated that low LHR was associated with cognitive impairment [odds ratio (OR) = 1.34; 95% confidence interval (CI) = 1.11-1.62, P = 0.002].

Conclusions

Lower LHR was associated with cognitive impairment in Chinese older people. This finding suggests LHR could serve as a novel and accessible biomarker for identifying high-risk aging populations.

Keywords

lymphocyte-high density lipoprotein ratio cognitive impairment Chinese older adults cross-section study linear relation

Introduction

Cognitive impairment remains a significant public health challenge globally, particularly in aging populations.¹ It is characterized by dysfunctions in memory, language, executive function, attention and other cognitive functions. Previous studies have shown that, the prevalence of cognitive impairment among older people in China ranges from 5-28%, in which 50% progressing to dementia within 5 years.^2,3 Which imposes a heavy public health burden.^4-6 Given the lack of effective treatment for cognitive impairment currently, it is crucial to identify potential risk factors associated with cognitive impairment, especially in fast-aging developing countries such as China.⁷

Previous study has shown that advanced age and age-related hearing loss were associated with cognitive impairment.⁸ In addition, a recent observational study showed that lifestyle-related (lower cognitive activities), psychosocial (higher loneliness and absence of meaning in life), as well as health-related (functional impairment) factors associated with cognitive impairment in community-dwelling and institutionalized older individuals from Germany.⁹ However, there are still under-explored risk factors for cognitive impairment.

Research has demonstrated that systemic inflammation plays a pathogenic role in the development of cognitive impairment and Alzheimer’s disease.^10,11 Lymphocytes, a crucial component of the immune system, have been reported to correlate with cognitive dysfunction in many clinical settings, including stoke, diabetes and Parkinson’s Disease.^12-15 In addition, previous studies have found that neutrophil-lymphocyte ratio (NLR) was independently associated with cognitive impairment.^16,17 Moreover, increasing evidence suggests serum high density lipoprotein (HDL) is a potentially negative predictor of cognitive impairment in older adults.^18,19 However, the relationship between lymphocyte-high density lipoprotein ratio (LHR) and cognitive impairment has not been thoroughly investigated in Chinese older adults.

In the current study, we explored the association between LHR and cognitive impairment in older adults from Chinese Longitudinal Healthy Longevity Survey (CLHLS). Our study provided a novel avenue for early detection, risk stratification, and targeted interventions to mitigate cognitive decline and improve the quality of life for older adults.

Methods

Data Source and Participants

We extracted data of participants aged over 65 years from CLHLS. The CLHLS was established in 1998 and implemented recent follow-up surveys in 2018.²⁰ Totally, 22 provinces in China were randomly recruited in the CLHLS, which covering about 85% of total population in China. Biomarker-based studies were performed in 2008, 2012 and 2014. The CLHLS study was approved by the Biomedical Ethics Committee of Peking University (IRB00001052-13074) and all participants have signed informed consent.

To screen participants surveyed for this cross-sectional study, participants who met the criteria below were included: first survey from 2008 to 2014, age ≥65 years. Participants with missing data on lymphocyte or HDL were excluded. Multiple imputation was used to deal with variables of the missing data less than 10% except for lymphocyte count and high-density lipoprotein.

A total of 4166 participants were included in the survey from 2008, 2012, 2014 waves of the CLHLS. We further excluded 281 participants with age <65 years, 329 participants with missing lymphocyte or high-density lipoprotein. Finally, a total of 3556 participants were included in our analysis. A detailed description of the flowchart is shown in Figure 1.

Figure 1.

Flowchart of Study Population

Outcome Variables

The Chinese version of the Mini-Mental State Examination (CMMSE) was used to measure cognitive function, which evaluated 5 dimensions with 24 items: orientation, registration, attention and calculation, recall and language.²¹ Previous study has validated the reliability of MMSE.²² The total score of CMMSE ranges from 0 to 30 with higher scores indicating better cognitive function. Consistent with previous study, MMSE score of 24 was identified as the cut-off point to distinguish cognitive impairment (<24), including individuals with dementia or normal cognitive function (≥24) in our study.²³

Covariates

Based on existing literature and clinical practice,² we extracted the following variables from the CLHLS database in 2008, 2012, 2014. Demographic and comorbidities included: age, sex, BMI, central obesity, education, ethnic, residence of birth, hypertension, diabetes, heart disease and stroke or cerebrovascular disease. Lifestyle included: current smoking, current drinking, regular exercise, total sleep time and limited in at least one activity of daily living (ADL). Dietary habits included: fruits, vegetables, animal oil, meat, fish, eggs, food made from beans, salt-preserved vegetables, sugar, garlic, milk products, nut products, mushroom or algae, vitamins, and medicinal plants. We recorded the dietary habits as “eat” or “don’t eat” according to the previous study.²⁴ Laboratory results included: lymphocyte counts, high-density lipoprotein, creatinine, triglyceride, total cholesterol, hemoglobin, platelet. In addition, we extracted the household income, marriage status and medical service of the participants. LHR was calculated using lymphocyte count (10⁹/L) / high-density lipoprotein (mmol/L).

Statistical Analysis

All participants were divided into 2 groups based on cognitive status. Continuous variables with normally distributed data were described as mean ± SD and compared using Student’s t-test. Mann-Whitney U test was used to compare continuous variables with non-normal distribution data described as medians (IQR). Categorical variables were presented as frequency (%) and analyzed using Chi-square test.

Multiple logistic regression models were used to calculate the odds ratios (ORs) and 95% confidence intervals (CIs) for investigating the association between LHR and cognitive impairment. We selected variables with P < 0.05 in univariate analysis for model adjustment. Model I adjusted for nothing; model II adjusted for age, sex, marriage status, ethnic, total sleep time, education, central obesity, stroke or cerebrovascular disease and limited in at least one ADL; model III adjusted for model II, current smoking, current drinking, regular exercise, fruits, vegetables, meat, fish, salt-preserved vegetables, sugar, garlic, milk products, nut products, mushroom or algae, and medicinal plants; In model IV, hemoglobin, creatinine, and triglyceride were further adjusted. In addition, we also performed receiver operating characteristic curves to compare the predictive power of LHR and lymphocyte for cognitive impairment. Meanwhile, we also performed the logistic regression analysis based on complete cases data as sensitive analysis to test the consistency of our results.

We used restricted cubic splines with four spline knots at 5%, 35%, 65% and 95% based on the Regression Modeling Strategies²⁵ and the sample in our study, to explore the potential nonlinear relationship between cognitive impairment and LHR. Stratified and interaction analyses were applied according to age (<80 or ≥80 years), sex (male or female), ethnic (Han or others), married (yes or no), limited in at least one ADL (yes or no) in the multiple logistic regression model adjusted for all variables with P < 0.05, excluding stratification factor itself.

R 4.2.2 software (https://www.Rproject.org; The R Foundation, Vienna, Austria) and Free Statistics software version 1.7.1 (Beijing Free Clinical Medical Technology Co, Ltd, Beijing, China) were used to conduct all statistical analyses. Statistical differences were considered significant at P < 0.05.

Results

Participants Characteristics

The baseline characteristics of all the participants are shown in Table 1. The prevalence of cognitive impairment was 45.7%. The mean age of the participants was 87.15 ± 11.47 years. The median baseline LHR was 1.29 (0.94∼1.76). Compared to participants with normal cognitive function, participants with cognitive impairment were more likely to be older, unmarried status, Han ethnic, non-smoking status, non-drinking status, central obesity, limited in at least one ADL, and were more likely to eat sugar, milk products as well as were less likely to eat fruits, vegetables, meat, fish, salt-preserved vegetables, garlic, nut products, mushroom or algae, medicinal plants. (all P < 0.05). Besides, they also tend to suffer from stroke or cerebrovascular diseases. Unlike participants with normal cognitive function, participants with cognitive impairment tend to have longer sleep time, lower education level, and decreased physical exercise (all P < 0.001). Moreover, they also have lower LHR, creatinine, triglyceride, hemoglobin and lymphocyte (all P < 0.05).

Table 1.

Characteristics of the Study Participants by Cognitive Status at 2008-2014 Waves of CLHLS

Variables	Total (N = 3556)	Normal cognitive function (N = 1931)	Cognitive impairment (N = 1625)	P
Age (year)	87.15 ± 11.47	81.66 ± 10.60	93.68 ± 8.74	<0.001
Sex				<0.001
Male	1502 (42.24)	1068 (55.31)	434 (26.71)
Female	2054 (57.76)	863 (44.69)	1191 (73.29)
BMI (kg/m²)	22.93 ± 17.98	23.35 ± 18.92	22.43 ± 16.78	0.126
Married				<0.001
Yes	1201 (33.77)	965 (49.97)	236 (14.52)
No	2355 (66.23)	966 (50.03)	1389 (85.48)
Total sleep time (h)	8.00 (6.00∼9.00)	8.00 (6.00∼8.00)	8.00 (6.00∼10.00)	<0.001
Education (year)	0.00 (0.00∼3.00)	0.00 (0.00∼5.00)	0.00 (0.00∼0.00)	<0.001
Ethnic				<0.001
Han	2901 (81.58)	1499 (77.63)	1402 (86.28)
Others	655 (18.42)	432 (22.37)	223 (13.72)
Residence of birth				0.491
Urban	279 (7.85)	146 (7.56)	133 (8.18)
Rural	3277 (92.15)	1785 (92.44)	1492 (91.82)
Current smoking				<0.001
Yes	594 (16.70)	440 (22.79)	154 (9.48)
No	2962 (83.30)	1491 (77.21)	1471 (90.52)
Current drinking				<0.001
Yes	572 (16.09)	381 (19.73)	191 (11.75)
No	2984 (83.91)	1550 (80.27)	1434 (88.25)
Regular exercise				<0.001
Yes	552 (15.52)	396 (20.51)	156 (9.60)
No	3004 (84.48)	1535 (79.49)	1469 (90.40)
Fruits				<0.001
Yes	1433 (40.30)	848 (43.92)	585 (36.00)
No	2123 (59.70)	1083 (56.08)	1040 (64.00)
Vegetables				<0.001
Yes	3087 (86.81)	1740 (90.11)	1347 (82.89)
No	469 (13.19)	191 (9.89)	278 (17.11)
Animal oil				0.430
Yes	588 (16.54)	328 (16.99)	260 (16.00)
No	2968 (83.46)	1603 (83.01)	1365 (84.00)
Meat				<0.001
Yes	2576 (72.44)	1496 (77.47)	1080 (66.46)
No	980 (27.56)	435 (22.53)	545 (33.54)
Fish				<0.001
Yes	1752 (49.27)	1020 (52.82)	732 (45.05)
No	1804 (50.73)	911 (47.18)	893 (54.95)
Eggs				0.957
Yes	2307 (64.88)	1252 (64.84)	1055 (64.92)
No	1249 (35.12)	679 (35.16)	570 (35.08)
Food made from beans				0.171
Yes	1747 (49.13)	969 (50.18)	778 (47.88)
No	1809 (50.87)	962 (49.82)	847 (52.12)
Salt-preserved vegetables				0.032
Yes	1288 (36.22)	730 (37.8)	558 (34.34)
No	2268 (63.78)	1201 (62.2)	1067 (65.66)
Sugar				<0.001
Yes	1231 (34.62)	621 (32.16)	610 (37.54)
No	2325 (65.38)	1310 (67.84)	1015 (62.46)
Garlic				<0.001
Yes	1242 (34.93)	733 (37.96)	509 (31.32)
No	2314 (65.07)	1198 (62.04)	1116 (68.68)
Milk products				0.045
Yes	844 (23.73)	433 (22.42)	411 (25.29)
No	2712 (76.27)	1498 (77.58)	1214 (74.71)
Nut products				<0.001
Yes	293 (8.24)	199 (10.31)	94 (5.78)
No	3263 (91.76)	1732 (89.69)	1531 (94.22)
Mushroom or algae				0.036
Yes	254 ( 7.14)	154 (7.98)	100 (6.15)
No	3302 (92.86)	1777 (92.02)	1525 (93.85)
Vitamins				0.645
Yes	173 ( 4.87)	91 (4.71)	82 (5.05)
No	3383 (95.13)	1840 (95.29)	1543 (94.95)
Medicinal plants				<0.001
Yes	53 (1.49)	41 (2.12)	12 (0.74)
No	3503 (98.51)	1890 (97.88)	1613 (99.26)
Central obesity				<0.001
Yes	1700 (47.81)	861 (44.59)	839 (51.63)
No	1856 (52.19)	1070 (55.41)	786 (48.37)
Hypertension				0.104
Yes	808 (22.72)	459 (23.77)	349 (21.48)
No	2748 (77.28)	1472 (76.23)	1276 (78.52)
Diabetes				0.063
Yes	84 (2.36)	54 (2.80)	30 (1.85)
No	3472 (97.64)	1877 (97.20)	1595 (98.15)
Heart disease				0.172
Yes	264 (7.42)	154 (7.98)	110 (6.77)
No	3292 (92.58)	1777 (92.02)	1515 (93.23)
Stroke or cerebrovascular disease				<0.001
Yes	225 (6.33)	93 (4.82)	132 (8.12)
No	3331 (93.67)	1838 (95.18)	1493 (91.88)
Household income (yuan)				0.317
<10 000	1469 (41.31)	785 (40.65)	684 (42.09)
10 000-30000	1554 (43.70)	841 (43.55)	713 (43.88)
>30 000	533 (14.99)	305 (15.79)	228 (14.03)
Adequate medical service				0.202
Yes	3329 (93.62)	1817 (94.10)	1512 (93.05)
No	227 (6.38)	114 (5.90)	113 (6.95)
Limited in at least one ADL				<0.001
Yes	2421 (68.08)	931 (48.21)	1490 (91.69)
No	1135 (31.92)	1000 (51.79)	135 (8.31)
Creatinine (mmol/L)	77.00 (65.00∼94.00)	79.00 (67.00∼94.00)	75.00 (63.00∼93.00)	<0.001
Triglyceride (mmol/L)	0.94 (0.71∼1.30)	0.95 (0.71∼1.34)	0.92 (0.71∼1.24)	0.030
Total cholesterol (mmol/L)	4.24 (3.57∼4.97)	4.27 (3.59∼5.00)	4.18 (3.54∼4.92)	0.072
Hemoglobin (g/L)	124.00 (112.00∼138.00)	128.00 (114.00∼142.00)	121.00 (109.00∼133.00)	<0.001
Platelet (10⁹/L)	191.00 (147.00∼250.00)	193.00 (149.00∼244.20)	190.00 (145.00∼258.00)	0.835
Lymphocyte (10⁹/L)	1.60 (1.24∼2.10)	1.70 (1.30∼2.20)	1.60 (1.20∼2.00)	<0.001
High density lipoprotein (mmol/L)	1.26 (1.04∼1.52)	1.25 (1.03∼1.52)	1.28 (1.06∼1.52)	0.114
LHR	1.29 (0.94∼1.76)	1.36 (1.00∼1.86)	1.23 (0.89∼1.68)	<0.001

Data are presented as mean ± SD or median (IQR) and frequency (%). BMI, body mass index; ADL, activity of daily living; LHR, lymphocyte-high density lipoprotein ratio; NA, missing value.

Association Between LHR and Cognitive Impairment

The cut-off point of LHR was 1.29 calculated by the median. The linear correlation between LHR and cognitive impairment is shown in Figure 2. Multivariate Logistic regression analysis indicated that LHR was related to cognitive impairment regarding of continuous variable (OR = 0.79, 95% CI = 0.72∼0.86) or categorical variable (OR = 1.44, 95% CI = 1.26∼1.64) in the unadjusted model. Moreover, the relationship of LHR with cognitive impairment was not affected after adjusting for confounding factors. LHR as a continuous variable was negatively associated with cognitive impairment (OR = 0.86, 95% CI = 0.75∼0.98) in model IV. Meanwhile, as a categorical variable, low LHR was also independently related to cognitive impairment (OR = 1.34, 95% CI = 1.11∼1.62) in model IV (Table 2). Sensitive analysis after excluding participants with missing data also showed the similar results (Supplemental Table 1). In addition, we found that the area under the curve of LHR (0.714, 95% CI = 0.698∼0.731) was higher than lymphocyte (0.563, 95% CI = 0.544∼0.582) (Supplemental Figure 1).

Figure 2.

Curve Fitting of LHR and Cognitive Impairment in Chinese Older Adults. Data Were Fit by a Logistic Regression Model Based on Restricted Cubic Splines. LHR was Entered as Continuous Variable. Data Were Adjusted for the Factors (Age, Sex, Married, Ethnic, Total Sleep Time, Education, Central Obesity, Stroke or Cerebrovascular Disease, Limited in at Least One ADL, Current Smoking, Current Drinking, Regular Exercise, Fruits, Vegetables, Meat, Fish, Salt-Preserved Vegetables, Sugar, Garlic, Milk Products, Nut Products, Mushroom or Algae, and Medicinal Plants, Hemoglobin, Creatinine, and Triglyceride). The Curves Line and Shaded Areas Around Depict the Estimated Values and Their Corresponding 95% Confidence Intervals. Curves Below Depict the Distribution of the LHR and Only 99% of the Data is Displayed. LHR, Lymphocyte-High Density Lipoprotein Ratio; ADL, Activity of Daily Living

Table 2.

Univariable and Multivariable Logistic Regression Analysis to Assess the Association Between LHR and Cognitive Impairment

Variable	Model I		Model II		Model III		Model IV
Variable	OR (95% CI)	P	OR (95% CI)	P	OR (95% CI)	P	OR (95% CI)	P
LHR	0.79 (0.72∼0.86)	<0.001	0.88 (0.79∼0.98)	0.022	0.89 (0.80∼0.99)	0.040	0.86 (0.76∼0.97)	0.011
≥1.29	Rf		Rf		Rf		Rf
<1.29	1.44 (1.26∼1.64)	<0.001	1.26 (1.07∼1.49)	0.005	1.24 (1.05∼1.47)	0.012	1.29 (1.09∼1.53)	0.003

Model I, adjusted for nothing; model II, adjusted for age, sex, married status, ethnic, total sleep time, education, central obesity, stroke or cerebrovascular disease, limited in at least one ADL; model III, adjusted for model II, current smoking, current drinking, regular exercise, fruits, vegetables, meat, fish, salt-preserved vegetables, sugar, garlic, milk products, nut products, mushroom or algae, and medicinal plants; model IV, adjusted for model III, hemoglobin, creatinine, and triglyceride. LHR, lymphocyte-high density lipoprotein ratio; ADL, activity of daily living; OR, odds ratio; CI, confidence interval; Rf, reference.

Subgroup Analysis

Subgroup analysis revealed an interaction between LHR and limited in at least one ADL on cognitive impairment (P for interaction = 0.018). A forest plot of the results also indicated that low LHR was independently associated with cognitive impairment in participants aged ≥88 (OR = 1.42, 95% CI = 1.11∼1.81), those who were female (OR = 1.39, 95% CI = 1.10∼1.76), Han ethnic (OR = 1.30, 95% CI = 1.07∼1.59), unmarried (OR = 1.34, 95% CI = 1.08∼1.66) and limited in at least one ADL (OR = 1.42, 95% CI = 1.15∼1.75) (Figure 3).

Figure 3.

Association Between LHR and Cognitive Impairment in Sub-groups for Chinese Older Adults. High LHR was the Reference for Low Lymphocyte. Each Stratification Adjusted for the Factors (Age, Sex, Married, Ethnic, Total Sleep Time, Education, Central Obesity, Stroke or Cerebrovascular Disease, Limited in at Least One ADL, Current Smoking, Current Drinking, Regular Exercise, Fruits, Vegetables, Meat, Fish, Salt-Preserved Vegetables, Sugar, Garlic, Milk Products, Nut Products, Mushroom or Algae, and Medicinal Plants, Hemoglobin, Creatinine, and Triglyceride) in the Multivariable Logistic Regression, Except for the Stratification Factor Itself. LHR, Lymphocyte-High Density Lipoprotein Ratio; ADL, Activity of Daily Living; OR, Odds Ratio; CI, Confidence Interval

Discussion

The present study included 3556 Chinese older adults, and the prevalence of cognitive impairment was 45.7%. We found a linear relationship between LHR and cognitive impairment. Meanwhile, we revealed that lower LHR levels were associated with cognitive impairment in Chinese older adults. In addition, subgroup analysis showed that lower LHR levels were independently related to cognitive impairment in individuals with aged over 88 years old and those who were female, Han ethnic, unmarried and limited in at least one ADL. Furthermore, there was an interaction between LHR and limited in at least one ADL on cognitive impairment.

Previous studies have shown that systematic inflammation has emerged as a significant contributor to cognitive impairment.^26,27 The animal study found that inflammation and oxidative stress in brain play a vital role in the pathophysiological mechanisms of age-related cognitive impairment.²⁸ Lymphocytes are components of the adaptive immune system and play a major role in the body’s immune inflammation response, including pathogen identification, immunologic surveillance, immune defenses and the regulation of inflammation.²⁹ In addition, decreased lymphocyte was associated with chronic inflammation in clinical settings,^30,31 which was the mechanism of the cognitive impairment. Moreover, several studies have demonstrated that decreased levels of lymphocytes were associated with cognitive decline,^32,33 which was consistent with our study.

HDL, as a component of lipid profile, not only has anti-thrombosis, anti-inflammation effects, but also has antioxidant properties.^34,35 HDL also can exhibit immunomodulation by interacting with neutrophil, inhibiting inflammation-induced cell aggregation and infiltration.³⁶ Recent study reported that HDL is increasingly recognized for its neuroprotective benefits.³⁷ In addition, previous study has shown that low HDL was linked to cognitive impairment.¹⁹ Our study found that there appeared to be an increasing trend in HDL level among older adults with cognitive impairment compared to cognitively normal patients, but further investigation is needed due to lack of statistical significance.

LHR was a composite indicator derived from lymphocyte and HDL. Therefore, low LHR may be associated with the chronic inflammation status in the older adults. Previous study has shown that LHR was associated with some mental disorders, including schizophrenia, bipolar disorder and depression.³⁸ In addition, recent study uncovered a significant association between LHR and depression using a representative sample of 4216 Americans from NHANES data.³⁹ Consistent with previous studies, we found that lower LHR levels were associated with cognitive impairment in Chinese older adults.

However, the definite causes and pathophysiological mechanisms underlying the relationship between LHR and cognitive impairment remain unclear. Based on these above data, it can be said that the LHR is a comprehensive body reserve involving inflammation and oxidative stress and may be more useful than one variable in assessing cognitive impairment. Moreover, LHR is simple and easy to calculate, and is convenient for clinical use, which is not affected by individual subjectivity.

This study offers several clinical implications and strengths. First, we performed the investigation of the association between the LHR and cognitive impairment for the first time, using a nationally representative sample among Chinese older adults. Additionally, the robustness of this study is bolstered by adjustment of multiple critical covariates and sensitive analysis, which enhances the reality of conclusions. Moreover, to verify the consistency of the association between LHR and cognitive impairment, we conducted the analyses in different subgroups.

However, there were several limitations in this study. First, the present study failed to elucidate causality due to the nature of the cross-sectional study. Therefore, additional longitudinal studies are necessary to clarify the association between dynamic changes in LHR and cognitive impairment in the future. Second, we failed to investigate the association between NLR and cognitive impairment due to the inability to obtain neutrophil count in the database. Third, cognitive function was only assessed by MMSE score, which can lead to bias in definition of cognitive impairment. Forth, our study focused on a Chinese population, which may limit the generalizability of our findings. Future research should validate this relationship in diverse populations due to the influence of ethnicity and culture on that. Lastly, our study design did not control for ApoE genotyping, which was associated with cognitive function in older adults. Future studies incorporating genetic data would be valuable to further explore these associations. Despite these limitations, it is the first study to investigate the link between LHR and cognitive impairment in Chinese older adults.

Conclusion

This nationally representative cross-sectional study indicated a significant correlation between low LHR levels and cognitive impairment in Chinese older adults, uncovering LHR’s potential as a novel biomarker for cognitive impairment. Further larger-scale prospective investigations are in need to establish a causal relationship between LHR and cognitive impairment in older adults.

Supplemental Material

Supplemental Material - Association Between Lymphocyte-High Density Lipoprotein Ratio and Cognitive Impairment in Chinese Older Adults: A Population-Based Cross-Section Study

Supplemental Material for Association Between Lymphocyte-High Density Lipoprotein Ratio and Cognitive Impairment in Chinese Older Adults: A Population-Based Cross-Section Study by Jinhui Yang, Wenbin Lu, Hao Wang, Xiaofei Li, Liangliang Lu, Jinjun Bian and Xiaoming Deng in American Journal of Alzheimer's Disease & Other Dementias.

Footnotes

Acknowledgments

We thank all investigators and participants who conducting and participating the Chinese Longitudinal Healthy Longevity Survey.

ORCID iDs

Jinhui Yang

Wenbin Lu

Jinjun Bian

Ethical Consideration

The studies concerning human participants were reviewed and approved by the ethics committee of Peking University. The participants participated in the study by providing written informed consent.

Author Contributions

Xiaoming Deng and Jinjun Bian conceived and designed the study; Hao Wang and Xiaofei Li collected and processed the data; Liangliang Lu interpreted and analyzed the data; Wenbin Lu and Jinhui Yang drafted the manuscript; Xiaoming Deng and Jinjun Bian revised the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The study was funded by the National Natural Science Foundation of China (82272205).

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The data supporting the findings of this study can be obtained from the corresponding author according to reasonable request, and the corresponding author/s can be directly contacted for further inquiry.*

Supplemental Material

Supplemental material for this article is available online.

References

Fowler

Partrick

Taylor

, et al. Implementing early detection of cognitive impairment in primary care to improve care for older adults. J Intern Med. 2025;298(1):31-45. doi:10.1111/joim.20098

Ran

. Data-driven clustering approach to identify novel clusters of high cognitive impairment risk among Chinese community-dwelling elderly people with normal cognition: a national cohort study. J Glob Health. 2024;14:04088. doi:10.7189/jogh.14.04088

Jin

Miao

, et al. Sociodemographic factors predict incident mild cognitive impairment: a brief review and empirical study. J Am Med Dir Assoc. 2023;24(12):1959-1966.e7. doi:10.1016/j.jamda.2023.08.016

Tang

Ren

. The dilution effect of healthy lifestyles on the risk of cognitive function attributed to socioeconomic status among Chinese older adults: a nation-wide prospective cohort study. J Glob Health. 2024;14:04010. doi:10.7189/jogh.14.04010

Zhang

Chen

, et al. Traffic-related air pollution, adherence to healthy lifestyles, and risk of cognitive impairment: a nationwide population-based study. Ecotoxicol Environ Saf. 2023;262:115349. doi:10.1016/j.ecoenv.2023.115349

Pérez

Trejo Ortega

Joshi

. Cognitive impairment in older adults: epidemiology, diagnosis, and treatment. Psychiatr Clin. 2022;45(4):639-661. doi:10.1016/j.psc.2022.07.010

Huang

Deng

Liu

. Sex differences in cognitive function among Chinese older adults using data from the Chinese longitudinal healthy longevity survey: a cross-sectional study. Front Public Health. 2023;11:1182268. doi:10.3389/fpubh.2023.1182268

Połtyn-Zaradna

Pazdro-Zastawny

Szcześniak

, et al. Age-related hearing loss associated with cognitive impairment in the polish cohort of the PURE study. Front Aging Neurosci. 2025;17:1540803. doi:10.3389/fnagi.2025.1540803

Hajek

Gyasi

Chen

Peltzer

König

. Factors associated with mild cognitive impairment and dementia amongst the oldest old: findings based on the nationally representative “old age in Germany (D80+)” study. Aging Clin Exp Res. 2025;37(1):110. doi:10.1007/s40520-025-03022-7

10.

Hung

Liu

, et al. Association between the neutrophil-to-lymphocyte ratio and cognitive impairment: a meta-analysis of observational studies. Front Endocrinol. 2023;14:1265637. doi:10.3389/fendo.2023.1265637

11.

Cantero-Fortiz

Boada

. The role of inflammation in neurological disorders: a brief overview of multiple sclerosis, Alzheimer’s, and Parkinson’s disease. Front Neurol. 2024;15:1439125. doi:10.3389/fneur.2024.1439125

12.

Hou

Zhang

, et al. Correlation between neutrophil/lymphocyte ratio and cognitive impairment in cerebral small vessel disease patients: a retrospective study. Front Neurol. 2022;13:925218. doi:10.3389/fneur.2022.925218

13.

Contaldi

Magistrelli

Cosentino

Marino

Comi

. Lymphocyte count and neutrophil-to-lymphocyte ratio are associated with mild cognitive impairment in Parkinson’s disease: a single-center longitudinal study. J Clin Med. 2022;11(19):5543. doi:10.3390/jcm11195543

14.

Sayed

Bahbah

Kamel

Barreto

Ashraf

Elfil

. The neutrophil-to-lymphocyte ratio in Alzheimer’s disease: current understanding and potential applications. J Neuroimmunol. 2020;349:577398. doi:10.1016/j.jneuroim.2020.577398

15.

Lee

Lim

Kim

, et al. High neutrophil-lymphocyte ratio predicts post-stroke cognitive impairment in acute ischemic stroke patients. Front Neurol. 2021;12:693318. doi:10.3389/fneur.2021.693318

16.

Lucero

Gurnani

Weinberg

Shih

. Neutrophil-to-lymphocyte ratio and longitudinal cognitive performance in Parkinson’s disease. Ann Clin Transl Neurol. 2024;11(9):2301-2313. doi:10.1002/acn3.52144

17.

Chen

Gao

, et al. The neutrophil-to-lymphocyte ratio is associated with mild cognitive impairment in community-dwelling older women aged over 70 years: a population-based cross-sectional study. Front Aging Neurosci. 2023;15:1261026. doi:10.3389/fnagi.2023.1261026

18.

Hong

Zheng

Agus

, et al. High-density lipoprotein changes in Alzheimer’s disease are APOE genotype-specific. Biomedicines. 2022;10(7):1495. doi:10.3390/biomedicines10071495

19.

Svensson

Sawada

Mimura

Nozaki

Shikimoto

Tsugane

. The association between midlife serum high-density lipoprotein and mild cognitive impairment and dementia after 19 years of follow-up. Transl Psychiatry. 2019;9:26. doi:10.1038/s41398-018-0336-y

20.

Shi

Zhang

Yang

Wang

Hong

. The relationship between cognitive function and muscle mass in older adults: a longitudinal study based on CLHLS. Front Psychiatr. 2025;16:1595625. doi:10.3389/fpsyt.2025.1595625

21.

Liang

Turner-McGrievy

, et al. Association of body mass index and plant-based diet with cognitive impairment among older Chinese adults: a prospective, nationwide cohort study. Nutrients. 2022;14(15):3132. doi:10.3390/nu14153132

22.

Liu

. Cognitive impairment and mortality among the oldest-old Chinese. Int J Geriatr Psychiatr. 2016;31(12):1345-1353. doi:10.1002/gps.4442

23.

Qin

Wang

. Increased intake of vegetables and fruits improves cognitive function among Chinese oldest old: 10-year follow-up study. Nutrients. 2023;15(9):2147. doi:10.3390/nu15092147

24.

Wei

Lin

Yang

. Dietary habits and depression in community-dwelling Chinese older adults: cross-sectional analysis of the moderating role of physical exercise. Nutrients. 2024;16(5):740. doi:10.3390/nu16050740

25.

Andersen

. Regression modeling Strategies with applications to linear models, logistic regression and survival analysis. Stat Med. 2003;22(15):2531-2532. doi:10.1002/sim.1497

26.

Guo

Zheng

Geng

, et al. Unveiling the link between systemic inflammation markers and cognitive performance among older adults in the US: a population-based study using NHANES 2011–2014 data. J Clin Neurosci. 2024;119:45-51. doi:10.1016/j.jocn.2023.11.004

27.

Yang

Ran

, et al. New insight into neurological degeneration: inflammatory cytokines and blood–brain barrier. Front Mol Neurosci. 2022;15:1013933. doi:10.3389/fnmol.2022.1013933

28.

Zuo

Chen

Liu

. Poliumoside inhibits apoptosis, oxidative stress and neuro-inflammation to prevent intracerebroventricular Streptozotocin-induced cognitive dysfunction in Sprague-Dawley Rats: in in-vivo, in-vitro and in-silico study. Folia Morphol. 2024;84(2):359-370. doi:10.5603/fm.101463. Published online October 24.

29.

Zhang

Liu

Yan

T . lymphocyte-related immune response and immunotherapy in gastric cancer. Oncol Lett. 2024;28(5):537. doi:10.3892/ol.2024.14670. (Review).

30.

Zhang

Liu

Chen

. Expression analysis of lymphocyte subsets and lymphocyte-to-monocyte ratio: reveling immunosuppression and chronic inflammation in breast cancer. J Cancer Res Clin Oncol. 2024;150(1):28. doi:10.1007/s00432-023-05508-1

31.

Zhou

Shao

Zou

Chen

. Association between monocyte-to-lymphocyte ratio and inflammation in chronic kidney disease: a cross-sectional study. Kidney Blood Press Res. 2024;49(1):1066-1074. doi:10.1159/000542625

32.

Dong

Nao

Shi

Zheng

. Predictive value of routine peripheral blood biomarkers in Alzheimer’s disease. Front Aging Neurosci. 2019;11:332. doi:10.3389/fnagi.2019.00332

33.

Zhou

, et al. Association of neutrophil-lymphocyte ratio with mild cognitive impairment in elderly Chinese adults: a case-control study. Curr Alzheimer Res. 2019;16(14):1309-1315. doi:10.2174/1567205017666200103110521

34.

Bonacina

Pirillo

Catapano

Norata

. HDL in immune-inflammatory responses: implications beyond cardiovascular diseases. Cells. 2021;10(5):1061. doi:10.3390/cells10051061

35.

Mazzuferi

Bacchetti

Islam

Ferretti

. High density lipoproteins and oxidative stress in breast cancer. Lipids Health Dis. 2021;20(1):143. doi:10.1186/s12944-021-01562-1

36.

Tao

Wang

. Anti-inflammatory mechanism of apolipoprotein A-I. Front Immunol. 2024;15:1417270. doi:10.3389/fimmu.2024.1417270

37.

Tran-Dinh

Levoye

Couret

, et al. High-density lipoprotein therapy in stroke: evaluation of endothelial SR-BI-dependent neuroprotective effects. Int J Mol Sci. 2020;22(1):106. doi:10.3390/ijms22010106

38.

Wei

Wang

, et al. Investigation of systemic immune-inflammation index, neutrophil/high-density lipoprotein ratio, lymphocyte/high-density lipoprotein ratio, and monocyte/high-density lipoprotein ratio as indicators of inflammation in patients with schizophrenia and bipolar disorder. Front Psychiatr. 2022;13:941728. doi:10.3389/fpsyt.2022.941728

39.

Chen

Huang

. The association between lymphocyte to high density lipoprotein ratio and depression: data from NHANES 2015–2018. Brain Behav. 2024;14(3):e3467. doi:10.1002/brb3.3467

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