Association Between Platelet-to-High-Density Lipoprotein Cholesterol Ratio and Cognitive Function in Older Americans: Insights from a Cross-Sectional Study

doi:10.21203/rs.3.rs-4907926/v1

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Research Article

Association Between Platelet-to-High-Density Lipoprotein Cholesterol Ratio and Cognitive Function in Older Americans: Insights from a Cross-Sectional Study

https://doi.org/10.21203/rs.3.rs-4907926/v1

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Aims

The main aim of this study was to explore the correlation between the platelet/high-density lipoprotein cholesterol ratio (PHR) and cognitive function in elderly individuals from the United States.

Methods

This investigation leveraged data encompassing 2299 participants, all aged 60 years and above, from the National Health and Nutrition Examination Survey conducted from 2011 to 2014. Inclusion criteria were based on the availability of complete datasets for PHR and cognitive function assessments. The analytical approach incorporated multivariate logistic regression to discern the association between PHR and cognitive impairment. Additionally, the study employed restricted cubic splines (RCS) to explore potential non-linear relationships and subgroup analyses to identify variations in the observed associations across different demographic and clinical subgroups.

Results

In the fully adjusted model, an increment of 10 units in PHR was associated with a decline of 0.014 in cognitive scores (β=-0.014, 95% CI: -0.025, -0.002; P<0.05). Compared to the lowest quartile, participants in the highest quartile exhibited a 38.4% increased prevalence of cognitive impairment per one-unit increase in PHR (OR=1.384, 95% CI: 1.012, 1.893; P<0.05). Subgroup analysis revealed consistent results regarding the relationship between PHR and cognitive impairment across all subgroups. A non-linear relationship between PHR and cognitive impairment was observed using RCS, indicating that an increase in PHR above 111.49 significantly elevated the incidence of cognitive impairment (P<0.05).

Conclusions

Our study proves that higher PHR is linked to a greater risk of cognitive decline in the U.S. elderly population, warranting consideration in clinical assessments and interventions.

Cognitive Neuroscience

PHR

Cognitive impairment

NHANES

Cross-sectional study

Cognitive function refers to the mental capabilities of an individual to process information, encompassing a variety of faculties such as memory, attention, language, perception, and decision-making. Cognitive decline, one of the most evident signs of aging, is characterized by a progressive deterioration in this cognitive domain; with advancing age, the risk and rate of cognitive decline increase, potentially culminating in cognitive disorders[1]. Cognitive disorders encompass a spectrum of cognitive abnormalities, ranging from mild cognitive impairment to dementia[2]. These disorders are prevalent across various continents, with varying degrees of incidence, and are particularly common among individuals aged 60 years and older[3]. It is shown that cognitive impairment due to cognitive decline and the prevalence of dementia are closely related and that the number of people with dementia worldwide will reach 150 million in 2020 as global aging increases[4–6]. This also means that more older adults may be at risk of cognitive impairment. There is an urgent need to slow down the development of cognitive impairment in older adults as much as possible and even to control the transition from cognitive impairment to dementia as early as possible. The cognitive damage caused by normal aging cannot be reversed, but reducing an individual's exposure to harmful risk factors is beneficial in preventing and slowing the onset and progression of cognitive impairment and dementia [7]. Engaging in regular physical activity, adopting an anti-inflammatory and antioxidant-rich diet, and managing symptoms of depression, diabetes, and cardiovascular diseases have all been shown to reduce the risk of cognitive impairment in individuals[8–11]. These interventions can potentially slow the progression of cognitive decline and contribute to the overall cognitive health of the elderly population.

The platelet (PLT)/high-density lipoprotein cholesterol (HDL-C) ratio (PHR) is a newly discovered biomarker that indicates both inflammation and increased blood clotting. It is also linked to the severity of metabolic syndrome [12]. PLT is a disc-shaped fragment of small cells, produced and released into the bloodstream by megakaryocytes in the bone marrow, whose role is not limited to hemostasis but also participates in the inflammatory response and wound healing and plays a vital role in blood circulation [13, 14]. HDL-C is an important biomarker of cardiovascular health. As a beneficial type of cholesterol, it can protect against cardiovascular disease, especially atherosclerosis, through cholesterol retrograde transport and antioxidant and anti-inflammatory properties[15, 16]. Abnormal expression of PLT and HDL-C can significantly impact the function of the brain and the nervous system. Overactive PLT can lead to thrombosis, increasing the risk of ischemic stroke, while a deficiency in PLT raises the risk of hemorrhagic stroke [17].Furthermore, excessive PLT activation can compromise the integrity of the blood-brain barrier, heightening the risk of brain inflammation and injury [18]. Conversely, elevated levels of HDL-C are associated with a protective effect against stroke. In contrast, low levels of HDL-C can diminish its anti-inflammatory, antioxidant, and endothelial repair functions, thereby disrupting its protective role in the brain [19, 20].

Previous studies have indicated a correlation between the PHR and the risk of kidney stone formation [21]. This suggests that PHR not only reflects the body's inflammatory and coagulation status but also holds the potential as an indicator for assessing the risk of certain diseases. Given that alterations in PLT and HDL-C levels can impact normal brain function, PHR could serve as a metric for evaluating the risk of cognitive impairment. This hypothesis forms the primary objective of the present study.

2.1 Date Source and Participants

The study data were derived from the National Health and Nutrition Examination Survey (NHANES), a nationwide health and nutrition survey program conducted jointly by the National Institutes of Health and the Centers for Disease Control and Prevention. NHANES is specifically created to evaluate the health status and nutritional intake of the U.S. population, providing a scientific basis for formulating public health policies and health interventions. The research protocol of the NHANES database has been approved by the National Center for Health Statistics Institutional Review Board, and all participants have provided informed consent; the NHANES data are publicly accessible and can be directly used for analysis [22, 23]. For more detailed information about NHANES, please visit the website (https://www.cdc.gov/nchs/nhanes/about_nhanes.htm).

For this study, we included 3,632 participants aged 60 and above who had undergone cognitive function assessment from the NHANES database between 2011 and 2014. Participants with incomplete cognitive function assessments, missing PLT, HDL-C, or other covariate data were eliminated, leaving a total sample size of 2299.

The participant selection process is depicted in Fig. 1.

2.2 Cognitive Function Assessment

The study included participants whose cognitive functioning was established by the word learning and recall modules from the Consortium to Establish a Registry for Alzheimer's Disease (CERAD), the Animal Fluency Test (AFT), and the Digit Symbol Substitution Test (DSST). The CERAD consists of three consecutive word learning trials and one delayed word recall after AFT and DSST. The word learning and delayed memory modules of CERAD can monitor the cognitive functions of the patients, and they play an essential role in the monitoring and early screening of Alzheimer's disease (AD), which is conducive to early diagnosis and intervention of AD [24, 25]. The AFT is a neuropsychological assessment tool widely used in different populations to assess memory and cognitive functioning, and it evaluates a subject's cognitive status by requiring them to verbally list as many animal names as they can within a one-minute timeframe [26–28]. DSST assesses multiple cognitive collaterals, including processing speed, attention, and executive function, by measuring how quickly and accurately an individual matches numbers to symbols within a limited time frame, and the results can help physicians and researchers identify and monitor the progression of cognitive impairment. The DSST can detect cognitive impairments in multiple affected domains, and people with AD generally exhibit subpar performance on mental tasks that require intricate information processing, such as the DSST [29, 30].

In alignment with previous studies [31, 32], this investigation employed a composite "Cognitive score" to quantify participants' cognitive performance. This score integrated the results from the CERAD, AFT, and DSST assessments, thereby mitigating the discrepancies that might arise from different cognitive scales and controlling for extreme values to prevent biases due to floor or ceiling effects in cognitive evaluations. The formula for calculating the "Cognitive score" is as follows: "Cognitive score"=\(\:{\sum\:}_{1}^{3}(x-Mean)/SD\), where \(\:x\) represents the score of the respective cognitive test, Mean is the average test score, and SD is the standard deviation. Given the absence of a gold standard for diagnosing cognitive impairment, this study defined individuals with "Cognitive scores" below the 25th percentile (P₂₅) as having cognitive impairment, with the P₂₅ value for "Cognitive scores" being − 1.68 [33, 34].

2.3 Assessment of PHR

PHR = PLT/HDL-C. The participants' PLT and HDL-C biochemical characteristics were acquired from blood samples collected at the mobile screening centre.

2.4 Covariates

The covariates included in this study include gender (male and female), age, race (Mexican American, other Hispanic, Non-Hispanic White, Non-Hispanic Black, and other races), educational level (below high school, high school or GED and above high school), poverty-income ratio(PIR), body mass index (BMI) (normal: <25 kg/m², overweight: 25 to < 30 kg/m², obese: ≥30 kg/m²), alcohol use, smoking status. A medical history of hypertension, diabetes, or stroke was determined based on whether or not the individual received a diagnosis from a doctor or other healthcare provider. Alcohol consumption status was categorized into three grades: 1) Never: <12 drinks in lifetime; 2) Former: ≥12 drinks in lifetime, but no drinks in last year; 3) Now: ≥12 drinks in last year. Smoking status was categorized into two classes: 1) Never: no more than 100 cigarettes in a lifetime; 2) Now: more than 100 cigarettes in a lifetime.

2.5 Statistical Analysis

The participants' initial characteristics were evaluated using chi-square tests and Student's t-tests. Categorical data were presented as numbers (%) and continuous variables as mean ± standard deviation. The study employed multifactor logistic regression to evaluate the association between PHR, PHR quartiles, and Cognitive scores, as well as the relationship between PHR, PHR quartiles, and cognitive impairment. In order to exclude confounding factors from interfering with the study results, three adjusted models were developed. Model 1 was a basic model without any adjustments. Model 2 included adjustments for the variables of gender, age, and race of the participants. Model 3 included adjustments for a more comprehensive set of variables: age, gender, race, education level, PIR, alcohol usage, smoking status, BMI, hypertension, stroke, and diabetes.

The dose-response association between PHR and cognitive impairment was examined using a Restricted Cubic Spline (RCS), and inflection points in the RCS images were assessed by threshold effect analysis. The interaction between PHR and cognitive impairment was tested by stratifying by age, gender, smoking status, drinking status, whether stroke, diabetes, or hypertension, and forest plots were used to reflect the final results. The analyses were conducted using EmpowerStats (version: 4.1) and R software (version: 4.3.3). The association between PHR and cognitive function scores was assessed using β and 95% confidence interval (CI). The relationship between PHR and cognitive impairment was evaluated using Odds ratio (OR) and 95% CI. A statistically significant difference was defined as P < 0.05.

3.1 Population Characteristics

A total of 2299 participants were included in the study, and the study population was categorized into four groups by PHR quartiles, with four PHR ranges from smallest to largest: Q1 (14.75–124.70), Q2 (124.70-160.13), Q3 (160.13–204.70), and Q4 (204.70–908.00). As shown in Table 1, the differences in all variables included in this study were statistically significant (P < 0.05) across the four populations, except for stroke. The Q4 population mainly consisted of low-income Non-Hispanic White males aged < 70 years and had lower cognitive scores and a higher prevalence of cognitive impairment in the Q4 population compared to the Q1 population.

Table 1

Baseline characteristics of participants (N = 2299) by PHR
	PHR categories
Characteristic	Q1(14.75–124.70)	Q2(124.70–160.13)	Q3(160.13–204.70)	Q4(204.70–908.00)	P-Value
N	575	574	575	575
Age (years) (%)
< 70	264 (45.91%)	324 (56.45%)	324 (56.35%)	344 (59.83%)	< 0.001
≥ 70	311 (54.09%)	250 (43.55%)	251 (43.65%)	231 (40.17%)
Gender (%)					< 0.001
Male	252 (43.83%)	271 (47.21%)	283 (49.22%)	319 (55.48%)
Female	323 (56.17%)	303 (52.79%)	292 (50.78%)	256 (44.52%)
Race (%)					< 0.001
Mexican American	31 (5.39%)	41 (7.14%)	56 (9.74%)	61 (10.61%)
Other Hispanic	36 (6.26%)	54 (9.41%)	63 (10.96%)	70 (12.17%)
Non-Hispanic White	306 (53.22%)	291 (50.70%)	293 (50.96%)	277 (48.17%)
Non-Hispanic Black	150 (26.09%)	125 (21.78%)	119 (20.70%)	116 (20.17%)
Other Race	52 (9.04%)	63 (10.98%)	44 (7.65%)	51 (8.87%)
Education level (%)					< 0.001
Below high school	105 (18.26%)	121 (21.09%)	127 (22.80%)	159 (27.65%)
High school or GED	119 (20.70%)	127 (22.13%)	148 (25.74%)	153 (26.61%)
Above high school	351 (61.04%)	326 (56.78%)	300 (52.18%)	263 (45.74%)
PIR	2.81 ± 1.59	2.78 ± 1.58	2.59 ± 1.59	2.36 ± 1.60	< 0.001
Alcohol use (%)
Never	76 (13.22%)	98 (17.07%)	88 (15.30%)	85 (14.78%)	< 0.001
Former	135 (23.48%)	140 (24.39%)	170 (29.57%)	193 (33.57%)
Now	364 (63.30%)	336 (58.54%)	317 (55.13%)	297 (51.65%)
Smoking status (%)					0.012
Never	278 (48.35%)	281 (48.95%)	292 (50.78%)	328 (57.04%)
Now	297 (51.65%)	293 (51.05%)	283 (49.22%)	247 (42.96%)
Stroke (%)					0.312
Yes	39 (6.78%)	44 (7.67%)	29 (5.04%)	41 (7.13%)
No	536 (93.22%)	530 (92.33%)	546 (94.96%)	534 (92.87%)
Diabetes (%)					< 0.001
Yes	95 (16.52%)	122 (21.25%)	155 (26.96%)	189 (32.87%)
No	480 (83.48%)	452 (78.75%)	420 (73.04%)	386 (67.13%)
Hypertension (%)					< 0.001
Yes	321 (55.83%)	343 (59.76%)	371 (64.52%)	394 (68.52%)
No	254 (44.17%)	231 (40.24%)	204 (35.48%)	181 (31.48%)
BMI (%)					< 0.001
Normal	232 (40.35%)	159 (27.70%)	128 (22.26%)	93 (16.17%)
Overweight	200 (34.78%)	208 (36.24%)	194 (33.74%)	220 (38.26%)
Obese	143 (24.87%)	207 (36.06%)	253 (44.00%)	262 (45.57%)
PLT (1000 cells/uL)	180.10 ± 41.12	208.26 ± 42.20	231.27 ± 43.85	275.72 ± 67.17	< 0.001
HDL-C (mmol/L)	1.84 ± 0.45	1.46 ± 0.30	1.29 ± 0.25	1.07 ± 0.23	< 0.001
CERAD	25.10 ± 6.57	25.42 ± 6.30	25.34 ± 6.71	24.54 ± 6.25	0.028
AFT	16.91 ± 5.42	17.12 ± 5.42	17.11 ± 5.83	16.03 ± 5.23	< 0.001
DSST	47.61 ± 17.23	47.87 ± 16.72	47.85 ± 17.94	42.97 ± 16.51	< 0.001
Cognitive scores	0.08 ± 2.42	0.19 ± 2.29	0.17 ± 2.58	-0.43 ± 2.27	< 0.001
Cognitive impairment (%)					0.005
Yes	136 (23.65%)	125 (21.78%)	139 (24.17%)	175 (30.43%)
No	439 (76.35%)	449 (78.22%)	436 (75.83%)	400 (69.57%)
Mean ± SE for continuous variable, number (%) for categorical variables.

3.2 Relationship between PHR and Cognitive

The relationships between PHR and cognitive scores and PHR and cognitive impairment were described by multifactor logistic regression; Model 1 represented the unadjusted model, Model 2 represented the partially adjusted model, and Model 3 represented the fully adjusted model. The results of Model 3, fully adjusted for confounders, showed that there was a significant negative correlation between PHR and cognitive scores (β=-0.014; 95% CI: -0.025, -0.002; P < 0.05), for every 10-unit increase in cognitive scores there was a 0.014 decrease in cognitive scores, and for every 1-unit increase in PHR in the Q4 population, compared to Q1, a consequent decrease in cognitive scores of − 0.35 (β=-0.35; 95% CI: -0.579, -0.120; P < 0.01). Whereas there was no significant correlation between PHR and cognitive impairment (OR = 1.012; 95% CI: 0.996, 1.028; P > 0.05), there was a 38.4% increase in the chance of suffering from cognitive impairment for every 1-unit increase in PHR in the Q4 population compared to that of Q1 (OR = 1.384; 95% CI: 1.012, 1.893; P < 0.05), indicating that a rise in PHR by itself does not reliably indicate a heightened likelihood of cognitive decline, and that only if a specific threshold of PHR is surpassed does its growth indicate an increased risk of cognitive impairment.

Table 2

Relationship of PHR to Cognitive Scores and Cognitive Impairment
PHR	Model 1	Model 2	Model 3
Overall	β (95% CI)/OR (95% CI)	β (95% CI)/OR (95% CI)	β (95% CI)/OR (95% CI)
Cognitive scores
PHR# continuous	-0.020 (-0.034, -0.007) **	-0.033(-0.045, -0.021)*******	-0.014 (-0.025, -0.002) *
PHR categories
Q1	0	0	0
Q2	0.105 (-0.172, 0.381)	-0.046 (-0.286, 0.194)	0.075 (-0.145, 0.295)
Q3	0.089 (-0.188, 0.365)	-0.123 (-0.364, 0.118)	0.095 (-0.129, 0.319)
Q4	-0.515 (-0.792, -0.239) *******	-0.728(-0.971, -0.485)*******	-0.350 (-0.579, -0.120) **
Cognitive impairment
PHR# continuous	1.013 (1.000, 1.026)	1.024 (1.009, 1.038) **	1.012 (0.996, 1.028)
PHR categories
Q1	1	1	1
Q2	0.899 (0.682, 1.185)	1.009 (0.750, 1.358)	0.907 (0.661, 1.243)
Q3	1.029 (0.785, 1.349)	1.222 (0.910, 1.640)	1.036 (0.755, 1.422)
Q4	1.412 (1.087, 1.835) **	1.741 (1.305, 2.322) *******	1.384 (1.012, 1.893) *
PHR# represents 10 units of PHR; PHR# continuous represents a 10-unit continuous rise in PHR Model 1 adjusted for: none. Model 2 adjusted for: age, gender, race. Model 3 adjusted for: age, gender, race, education level, PIR, alcohol use, smoking status, BMI, hypertension, stroke, diabetes. P < 0.05; P < 0.01; ******P < 0.001

Based on fully adjusted for confounders, the RCS analysis showed a nonlinear relationship between PHR and cognitive impairment (P for nonlinear = 0.06) (Fig. 2). The threshold analysis revealed that the inflexion point of the RCS was 111.49. Above this inflexion point, there was an elevated risk of cognitive impairment with PHR, as indicated by an OR of 1.012 (95% CI: 1.000, 1.024; P < 0.001) (Table 3). However, in the case of PHR < 111.49, the results of RCS analysis and threshold analysis indicated that there was no significant negative correlation between PHR and cognitive impairment (OR = 0.990, 95% CI: 0.979–1.001; P > 0.05) (Fig. 2, Table 3).

Table 3

Threshold effect analysis of PHR and cognitive impairment
PHR	OR (95%CI)	P-Value
< 111.49	0.990 (0.979, 1.001)	> 0.05
≥ 111.49	1.012 (1.000, 1.024)	< 0.001
log-likelihood ratio test		< 0.05

3.3 Subgroup Analysis

To assess the reliability of the association between PHR and cognitive impairment, we analyzed the association according to age, gender, drinking habits, smoking status, hypertension, diabetes, and stroke, performed subgroup analyses, and assessed whether there was a significant interaction between the different subgroups. The results showed (see Fig. 3 for details) that all subgroups showed a positive but not significant association between PHR and cognitive impairment (P > 0.05), and no significant interaction was observed in all subgroups (P for interaction > 0.05).

This study, which examined 2299 Americans aged 60 and older, aimed to explore the association between PHR and cognitive performance. The results showed a notable inverse correlation between PHR and cognitive scores. A prominent and exciting finding was a unique "U-shaped" relationship between PHR and cognitive impairment. This finding could account for the lack of a significant positive connection identified in the multivariate logistic regression analysis. The RCS study revealed that when the PHR surpasses 111.49, a notable and positive association exists between the PHR and the likelihood of experiencing cognitive impairment. The subgroup analysis findings indicate that demographic characteristics do not impact the correlation between PHR and cognitive impairment, suggesting a stable link across diverse population groups. These findings indicate that a rise in PHR, particularly when PHR exceeds 111.49, is linked to decreased cognitive scores and a higher likelihood of cognitive impairment. This correlation is probably related to a rise in PLT count and a decline in HDL-C levels.

PLT play a key role in repairing vascular damage and modulating inflammatory pathways, and due to their role in thrombosis, immune response regulation, and vascular homeostasis, PLT are thought to be implicated in the pathogenesis of several diseases including atherosclerosis, cancer, and stroke [35]. PLT counts and activation levels also affect cognitive function, with increased PLT counts often associated with increased PLT activation [36]. Excessive PLT activation can lead to vascular inflammation, carotid artery disease, excessive release of Beta-amyloid (Aβ), multiple microemboli or infarcts, and ultimately may lead to cognitive impairment [37, 38]. A recent study utilizing resting-state functional magnetic resonance imaging (fMRI) has observed the impact of PLT on cognitive function in patients with mild cognitive impairment (MCI) [39]. The findings indicate a negative correlation between PLT count and cognitive performance in MCI patients, with elevated PLT levels affecting executive functions within the left precuneus region [39]. Platelets are recognized as a primary source of peripheral blood Aβ, and an increase in PLT count and activity can lead to enhanced cerebral Aβ deposition, exacerbating cognitive impairment and promoting the onset and progression of AD [40, 41]. Other neurodegenerative conditions have noted similar associations between elevated PLT counts and cognitive decline [42]. These observations suggest that platelets play a significant role in the genesis and progression of cognitive impairment. Further research in this domain could elucidate the underlying mechanisms and offer novel insights for the early diagnosis and intervention of cognitive disorders.

HDL-C exerts multifaceted roles within the central nervous system, extending beyond reverse cholesterol transport to include antioxidant, anti-inflammatory, endothelial function promotion, antithrombotic, and immune function modulation activities, all of which are crucial for maintaining cognitive function [43]. Higher levels of HDL-C are linked to better cognitive performance in older individuals, whereas lower HDL levels increase the likelihood of future memory deterioration in middle-aged and older populations [44]. Furthermore, low HDL-C levels are significantly correlated with reduced total white matter volume and hippocampal volume, increased cognitive impairment, and a heightened likelihood of MCI in the elderly [45]. A prospective study indicates that having greater levels of HDL-C in middle age is linked to a lower risk of developing MCI and dementia later in life, therefore supporting the idea that HDL-C has a protective effect on cognitive function [46]. In the context of AD, HDL-C is implicated in the clearance of cerebral Aβ and the reduction of amyloid plaque formation associated with AD, with higher HDL-C levels potentially reducing the risk of AD [47]. High levels of HDL-C may facilitate cholesterol efflux from neurons, reduce neuroinflammation, and protect neurons from oxidative stress, which could be the primary mechanisms underlying its protective effect on cognition [48]. This study focuses on the relationship between PHR and cognition, with results suggesting that low HDL levels may exacerbate cognitive impairment, consistent with the findings of the studies mentioned earlier.

While the present study suggests that elevated PHR may impair cognition, it does not imply that higher levels of HDL-C and lower PLT counts are invariably beneficial. It is important to note that excessively high levels of HDL-C may lead to disruptions in cholesterol transport and metabolism, potentially affecting cholesterol homeostasis within the central nervous system and resulting in neuronal dysfunction and a decline in cognitive abilities [49]. Elevated levels of HDL-C are linked to a higher likelihood of acquiring AD and any dementia in individuals [50]. Studies have shown that long-term increases in HDL-C are associated with the risk of developing cognitive decline and memory loss, and that there is an inverted U-shaped relationship between HDL-C levels and cognition, with either too high or too low HDL-C levels impairing cognition [51–53]. This suggests that excessively high HDL-C levels, likewise, impair cognition. Although high PLT counts impair cognition, low PLT counts are associated with coagulation disorders, spontaneous bleeding, etc. PLT promotes brain homeostasis in physiological states [54], PLT is the largest peripheral store of brain-derived neurotrophic factor (BDNF), and low levels of BDNF have been associated with poorer cognitive performance, whereas high levels of BDNF play a role in the prevention of the neurological syndromes [55–57].

The findings of this study emphasize the possibility of PHR as a potential biomarker for assessing the risk of cognitive impairment, especially when PHR levels are high. The results of the stratified analysis also confirmed that there was a positive association between PHR and cognitive impairment in both unused populations, especially in the stroke population, which may be due to the fact that both high PLT count and low HDL-C are risk factors for cardiovascular disease [37, 58]. However, the study's cross-sectional design limits inferences of causality, and the sample was limited to an older American population, which may affect the generalizability of the results. Future studies should adopt a longitudinal design to expand the sample and explore the interactions among PHR, PLT, and HDL-C and their specific mechanisms of influence on cognitive function. In addition, studies should consider including additional biomarkers and environmental factors to more fully understand the complexity of cognitive decline. Through these efforts, we hope to provide more effective strategies for the prevention and treatment of cognitive impairment.

Although this study provides preliminary evidence of PHR as a predictor of cognitive impairment risk, the establishment of causality needs to be validated by further prospective studies. In addition, because the sample was limited to the older American population, future studies should consider populations of different ethnic, regional, and cultural backgrounds to enhance the generalizability and extrapolation of the findings.

Acknowledgments

We thank all those who participated in this study.

Author Contributions

Tianyi. Wang and Ruwen Zheng: writing the paper and statistical analysis of the results; Siqi Zhang, Shuai Ma, Hong Jin, Hongyu Qin, Yubo Teng: collecting and processing the data; Miao Zhang: supervising and reviewing the manuscript.

Funding

This study was supported by the Natural Science Foundation Program of Heilongjiang Province. (No.LH2019H047)

Data availability statement

The publicly available datasets presented in this study can be found in online repositories. These data can be found here: https://www.cdc.gov/nchs/nhanes/.

Ethics approval and consent to participate

This study protocol was reviewed and approved by the research ethics review board of the U.S. Centers for Disease Control and the National Center for Health Statistics. All participants in NHANES provided written informed consent to participate. The approval numbers for the presented surveys years are : Protocol #2011-17 (NHANES 2011-2012), Continuation of Protocol #2011-17 (NHANES 2013-2014). More information about the NHANES database can be

found at https://www.cdc.gov/nchs/nhanes/irba98.htm.

Competing interests

The authors declare that there are no competing interests

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The authors declare no competing interests.

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Association Between Platelet-to-High-Density Lipoprotein Cholesterol Ratio and Cognitive Function in Older Americans: Insights from a Cross-Sectional Study

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Abstract

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1. Introduction

2. Materials and Methods

2.1 Date Source and Participants

2.2 Cognitive Function Assessment

2.3 Assessment of PHR

2.4 Covariates

2.5 Statistical Analysis

3. Results

3.1 Population Characteristics

3.2 Relationship between PHR and Cognitive

3.3 Subgroup Analysis

4. Discussion

5. Conclusions

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References

Additional Declarations

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