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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Hum. Neurosci.</journal-id>
<journal-title-group>
<journal-title>Frontiers in Human Neuroscience</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Hum. Neurosci.</abbrev-journal-title>
</journal-title-group>
<issn pub-type="epub">1662-5161</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fnhum.2026.1775215</article-id>
<article-version article-version-type="Version of Record" vocab="NISO-RP-8-2008"/>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Original Research</subject>
</subj-group>
</article-categories>
<title-group>
<article-title>U-shaped association of the non-HDL/HDL ratio with cognitive impairment identified by conventional analyses and machine learning in health examination participants in Liuyang</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author" equal-contrib="yes">
<name>
<surname>Chen</surname>
<given-names>Xiaoyi</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="author-notes" rid="fn0001"><sup>&#x2020;</sup></xref>
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</contrib>
<contrib contrib-type="author" equal-contrib="yes">
<name>
<surname>Lin</surname>
<given-names>Runzhui</given-names>
</name>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
<xref ref-type="author-notes" rid="fn0001"><sup>&#x2020;</sup></xref>
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</contrib>
<contrib contrib-type="author">
<name>
<surname>Zhao</surname>
<given-names>Le</given-names>
</name>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
<uri xlink:href="https://loop.frontiersin.org/people/2600064"/>
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</contrib>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>He</surname>
<given-names>Yong</given-names>
</name>
<xref ref-type="aff" rid="aff4"><sup>4</sup></xref>
<xref ref-type="corresp" rid="c001"><sup>&#x002A;</sup></xref>
<uri xlink:href="https://loop.frontiersin.org/people/3078605"/>
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<contrib contrib-type="author" corresp="yes">
<name>
<surname>Zhu</surname>
<given-names>Tieshi</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="corresp" rid="c001"><sup>&#x002A;</sup></xref>
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<aff id="aff1"><label>1</label><institution>Department of Neurology, Zhanjiang Central Hospital, Guangdong Medical University</institution>, <city>Zhanjiang</city>, <state>Guangdong</state>, <country country="cn">China</country></aff>
<aff id="aff2"><label>2</label><institution>Department of Hepatobiliary, Pancreatic and Splenic Surgery, Second Affiliated Hospital of Shantou University Medical College</institution>, <city>Shantou</city>, <country country="cn">China</country></aff>
<aff id="aff3"><label>3</label><institution>Department of Medical Affairs, Central Hospital of Guangdong Provincial Nongken</institution>, <city>Zhanjiang</city>, <state>Guangdong</state>, <country country="cn">China</country></aff>
<aff id="aff4"><label>4</label><institution>Department of Neurology, Liuyang Jili Hospital</institution>, <city>Changsha</city>, <state>Hunan</state>, <country country="cn">China</country></aff>
<author-notes>
<corresp id="c001"><label>&#x002A;</label>Correspondence: Yong He, <email xlink:href="mailto:277475748@qq.com">277475748@qq.com</email>; Tieshi Zhu, <email xlink:href="mailto:Zhuts1994@163.com">Zhuts1994@163.com</email></corresp>
<fn fn-type="equal" id="fn0001"><label>&#x2020;</label><p>These authors have contributed equally to this work</p></fn>
</author-notes>
<pub-date publication-format="electronic" date-type="pub" iso-8601-date="2026-02-18">
<day>18</day>
<month>02</month>
<year>2026</year>
</pub-date>
<pub-date publication-format="electronic" date-type="collection">
<year>2026</year>
</pub-date>
<volume>20</volume>
<elocation-id>1775215</elocation-id>
<history>
<date date-type="received">
<day>25</day>
<month>12</month>
<year>2025</year>
</date>
<date date-type="rev-recd">
<day>26</day>
<month>01</month>
<year>2026</year>
</date>
<date date-type="accepted">
<day>26</day>
<month>01</month>
<year>2026</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#x00A9; 2026 Chen, Lin, Zhao, He and Zhu.</copyright-statement>
<copyright-year>2026</copyright-year>
<copyright-holder>Chen, Lin, Zhao, He and Zhu</copyright-holder>
<license>
<ali:license_ref start_date="2026-02-18">https://creativecommons.org/licenses/by/4.0/</ali:license_ref>
<license-p>This is an open-access article distributed under the terms of the <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution License (CC BY)</ext-link>. The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p>
</license>
</permissions>
<abstract>
<sec>
<title>Background</title>
<p>The non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) is associated with cardiovascular risk, but its relationship with cognitive impairment has not been well studied.</p>
</sec>
<sec>
<title>Methods</title>
<p>In this cross-sectional study, 1,103 adults (median age, 68&#x202F;years; 54.7% women) from Liuyang were included. Cognitive function was assessed by Mini-Mental State Examination. Restricted cubic splines were used to evaluate associations of NHHR with cognitive impairment. Breakpoint regression identified inflection points. Discrimination was compared using area under the curve (AUC). Machine learning with SHapley Additive exPlanations (SHAP) was applied to assess the relative importance of NHHR and to further explore its relationship with cognitive impairment.</p>
</sec>
<sec>
<title>Results</title>
<p>Overall, 241 participants (21.9%) had cognitive impairment. NHHR demonstrated a significant U-shaped association with cognitive impairment (overall and nonlinearity <italic>p</italic>&#x202F;&#x003C;&#x202F;0.001). Breakpoint regression identified an inflection point at 2.772; NHHR &#x2265;2.772 was associated with increased risk (odds ratio, 3.36; 95% CI, 2.23&#x2013;5.05; <italic>p</italic>&#x202F;&#x003C;&#x202F;0.001). Compared with LDL, HDL, and non-HDL, NHHR had the greatest AUC for discriminating cognitive impairment. SHAP analysis confirmed the U-shaped relationship and identified NHHR as the most influential lipid-related predictor.</p>
</sec>
<sec>
<title>Conclusion</title>
<p>In this cross-sectional analysis, NHHR was associated with cognitive impairment in a U-shaped pattern and demonstrated better discrimination than individual lipid measures. These findings suggest that NHHR may serve as an alternative lipid-related index in studies of cognitive health, although longitudinal studies are needed to clarify its predictive value.</p>
</sec>
</abstract>
<kwd-group>
<kwd>Chinese rural residents</kwd>
<kwd>cognitive function</kwd>
<kwd>cross-sectional study</kwd>
<kwd>lipid management</kwd>
<kwd>machine learning</kwd>
</kwd-group>
<funding-group>
<award-group id="gs1">
<funding-source id="sp1">
<institution-wrap>
<institution>Health Commission of Changsha</institution>
</institution-wrap>
</funding-source>
<award-id rid="sp1">KJ-A2023024</award-id>
</award-group>
<award-group id="gs2">
<funding-source id="sp2">
<institution-wrap>
<institution>Natural Science Foundation of Changsha</institution>
</institution-wrap>
</funding-source>
<award-id rid="sp2">kq2202498</award-id>
</award-group>
<funding-statement>The author(s) declared that financial support was received for this work and/or its publication. This study was supported by the Natural Science Foundation of Changsha (No. kq2202498) and the Health Commission of Changsha (No. KJ-A2023024).</funding-statement>
</funding-group>
<counts>
<fig-count count="4"/>
<table-count count="2"/>
<equation-count count="1"/>
<ref-count count="37"/>
<page-count count="8"/>
<word-count count="5683"/>
</counts>
<custom-meta-group>
<custom-meta>
<meta-name>section-at-acceptance</meta-name>
<meta-value>Brain Health and Clinical Neuroscience</meta-value>
</custom-meta>
</custom-meta-group>
</article-meta>
</front>
<body>
<sec sec-type="intro" id="sec1">
<label>1</label>
<title>Introduction</title>
<p>Cognitive impairment, particularly dementia, poses a growing global public health concern, with substantial implications for individuals, families, and healthcare systems (<xref ref-type="bibr" rid="ref22">Lee et al., 2023</xref>; <xref ref-type="bibr" rid="ref30">Reuben et al., 2024</xref>). In 2019, an estimated 57.4 million individuals worldwide were affected by dementia, and this number is projected to rise to 152.8 million by 2050 (<xref ref-type="bibr" rid="ref11">GBD Dementia Forecasting Collaborators, 2022</xref>). Dementia not only substantially diminishes patients&#x2019; quality of life but also places a considerable economic burden on families and healthcare systems (<xref ref-type="bibr" rid="ref18">Kalaria et al., 2024</xref>; <xref ref-type="bibr" rid="ref31">Sommerlad et al., 2023</xref>). Given the current challenges in dementia treatment, identifying and addressing modifiable risk factors is crucial for developing effective prevention strategies (<xref ref-type="bibr" rid="ref4">Budd Haeberlein et al., 2022</xref>; <xref ref-type="bibr" rid="ref35">van Dyck et al., 2023</xref>).</p>
<p>Recent studies increasingly suggest a strong association between dyslipidemia and cognitive decline (<xref ref-type="bibr" rid="ref26">Pan et al., 2024</xref>). As modifiable biological markers, lipid profiles offer promising targets for interventions aimed at preventing cognitive dysfunction (<xref ref-type="bibr" rid="ref28">Petek et al., 2023</xref>). The non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) has emerged as a comprehensive lipid indicator that reflects both atherogenic and protective lipoproteins (<xref ref-type="bibr" rid="ref37">Zhen et al., 2023</xref>). NHHR has been recognized for its utility in cardiovascular risk assessment (<xref ref-type="bibr" rid="ref25">Liu et al., 2024</xref>). Compared with traditional lipid parameters, NHHR may provide a more holistic reflection of lipid-related vascular risk, yet evidence supporting its role in cognitive health is limited. Therefore, this study aimed to investigate the association between NHHR and cognitive function using data from the Liuyang Jili Hospital Physical Examination Center. We hypothesized that NHHR would exhibit a nonlinear relationship with cognitive function, providing new insights into the potential utility of lipid ratios in strategies for cognitive health maintenance and dementia prevention.</p>
</sec>
<sec sec-type="methods" id="sec2">
<label>2</label>
<title>Methods</title>
<sec id="sec3">
<label>2.1</label>
<title>Population</title>
<p>The study population comprised individuals from Liuyang Jili Hospital. Data were obtained from the Physical Examination Center (FY2022) and included 1,103 individuals who underwent Mini-Mental State Examination (MMSE) assessment, with a median age of 68&#x202F;years.</p>
</sec>
<sec id="sec4">
<label>2.2</label>
<title>Cognitive assessments</title>
<p>Cognitive assessment was conducted using the MMSE, which evaluates multiple cognitive domains, including orientation, memory, numerical ability, and language skills (<xref ref-type="bibr" rid="ref10">Fiorenzato et al., 2024</xref>). Cognitive impairment was defined as an MMSE score of &#x2264;17 for individuals with no formal education, &#x2264;19 for those with a primary school education, and &#x2264;24 for those with a junior high school education or higher (<xref ref-type="bibr" rid="ref15">He et al., 2025</xref>).</p>
</sec>
<sec id="sec5">
<label>2.3</label>
<title>Blood sampling and laboratory measurements</title>
<p>Venous blood samples were collected in the morning after an overnight fast (&#x2265;8&#x202F;h). Serum was separated and analyzed in the certified clinical laboratory of Liuyang Jili Hospital. Total cholesterol (TC), triglycerides (TG), and HDL were measured using enzymatic methods on an automated clinical chemistry analyzer with manufacturer-provided reagents. Routine internal quality control was performed daily using commercial control materials, and the analyzer was calibrated regularly according to the manufacturer&#x2019;s protocol. The laboratory also participated in external quality assessment/proficiency testing programs.</p>
</sec>
<sec id="sec6">
<label>2.4</label>
<title>NHHR</title>
<p>The NHHR is calculated using the following equation (<xref ref-type="bibr" rid="ref14">Guo et al., 2024</xref>; <xref ref-type="bibr" rid="ref29">Qing et al., 2024</xref>):</p>
<disp-formula id="E1">
<mml:math id="M1">
<mml:mtext>NHHR</mml:mtext>
<mml:mo>=</mml:mo>
<mml:mo stretchy="true">(</mml:mo>
<mml:mi>TC</mml:mi>
<mml:mo>&#x2212;</mml:mo>
<mml:mi>HDL</mml:mi>
<mml:mo stretchy="true">)</mml:mo>
<mml:mo>/</mml:mo>
<mml:mi>HDL</mml:mi>
</mml:math>
</disp-formula>
</sec>
<sec id="sec7">
<label>2.5</label>
<title>Covariate</title>
<p>Covariates included age, gender, body mass index (BMI), TC, low-density lipoprotein cholesterol (LDL), high-density lipoprotein cholesterol (HDL), education level, physical activity, dietary habits, smoking status, alcohol consumption, hypertension, diabetes mellitus (DM), and ischemic stroke. Among these, TC, LDL, and HDL levels were obtained from the hospital&#x2019;s Laboratory Department, while the remaining covariates were collected through self-report.</p>
</sec>
<sec id="sec8">
<label>2.6</label>
<title>Statistic</title>
<p>Data were analyzed using R, version 4.4.1 (R Foundation for Statistical Computing). Baseline characteristics are presented as median [interquartile range (IQR)] for continuous variables and as number (percentage) for categorical variables. Restricted cubic splines (RCS) with 3 knots at the 10th, 50th, and 90th percentiles were used to evaluate nonlinear associations between the NHHR, cognitive impairment, and log-transformed MMSE scores [log(MMSE+1)]. Breakpoint regression was applied to examine the association of NHHR with cognitive impairment before and after the estimated inflection point, which was determined by model self-fitting. The RCS-derived turning point indicates the exposure range where the association starts to strengthen under a smooth functional form, whereas the breakpoint from two-piecewise (segmented) regression represents a single data-driven cut-point that optimizes model fit for a piecewise linear approximation; thus, the two estimates may differ. To compare the discriminative ability of lipid measures, logistic regression models were fitted separately for NHHR, non-high-density lipoprotein cholesterol (NHDL), LDL, and HDL. Receiver operating characteristic (ROC) curves were generated, and the areas under the curve (AUCs) were compared using the DeLong test. Covariates included age, sex, BMI, educational attainment, exercise, dietary, smoking status, alcohol use, hypertension, DM, and history of ischemic stroke. Multicollinearity was evaluated with the variance inflation factor (VIF); values greater than 5 indicated collinearity. All covariates had VIF values less than 5 (<xref ref-type="sec" rid="sec21">Supplementary Table S1</xref>). Two-sided <italic>p</italic>&#x202F;&#x003C;&#x202F;0.05 was considered statistically significant.</p>
<p>In addition, we used an extreme gradient boosting (XGBoost) classifier to further evaluate the predictive contribution of NHHR to cognitive outcomes. Participants were randomly split into a training set (70%) and a test set (30%). Categorical variables were one-hot encoded based on the training set design matrix to ensure consistent feature representation in the test set, and missing values were handled internally by XGBoost without imputation. The XGBoost model was trained in the training set with stratified 5-fold cross-validation to determine the optimal number of boosting rounds using early stopping (50 rounds; maximum 2,000 rounds), and the final model was refit using the selected number of iterations. The model hyperparameters were prespecified as follows: maximum tree depth&#x202F;=&#x202F;3, learning rate (eta)&#x202F;=&#x202F;0.06, subsample&#x202F;=&#x202F;0.85, colsample by tree&#x202F;=&#x202F;0.85, minimum child weight&#x202F;=&#x202F;1, L2 regularization (lambda)&#x202F;=&#x202F;1, and scale pos weight was set according to the ratio of negative to positive samples in the training set. NHHR, LDL, HDL, and the covariates listed above were included as predictors. Model discrimination was assessed using ROC curves and AUC with 95% confidence intervals, calibration was evaluated using calibration plots, and clinical utility was assessed using decision curve analysis. The optimal probability threshold was determined in the training set using the Youden index and then applied to the test set; sensitivity, specificity, accuracy, positive predictive value, negative predictive value, <italic>F</italic><sub>1</sub>-score, and the selected threshold were reported. Model interpretability was assessed using SHapley Additive exPlanations (SHAP, TreeSHAP); feature importance was summarized as mean absolute SHAP values, and 95% confidence intervals were obtained using bootstrap resampling (1,000 resamples). SHAP dependency plots were further generated to examine the dependency of NHHR and its interactions with LDL and HDL.</p>
</sec>
</sec>
<sec sec-type="results" id="sec9">
<label>3</label>
<title>Results</title>
<sec id="sec10">
<label>3.1</label>
<title>Baseline characteristics of the study population</title>
<p>A total of 1,103 participants (median age, 68&#x202F;years; 54.7% women) were included, of whom 241 (21.9%) had cognitive impairment and 862 (78.1%) had normal cognition. Compared with participants with normal cognition, those with cognitive impairment had lower MMSE scores, lower HDL levels, higher NHDL levels, higher NHHR, higher educational attainment, lower rates of regular exercise, a higher prevalence of ischemic stroke, and a greater proportion of nondrinkers (<italic>p</italic> all &#x003C;0.05). No significant differences were observed between groups in age, sex, BMI, TC, LDL, dietary, hypertension, or DM (<italic>p</italic> all &#x003E;0.05). Smoking status showed a borderline association (<italic>p</italic>&#x202F;=&#x202F;0.05) (<xref ref-type="table" rid="tab1">Table 1</xref>).</p>
<table-wrap position="float" id="tab1">
<label>Table 1</label>
<caption>
<p>Baseline characteristics of participants stratified by cognitive impairment status.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Variables</th>
<th align="center" valign="top">Total <italic>n</italic> =&#x202F;1,103</th>
<th align="center" valign="top">Yes <italic>n</italic> =&#x202F;241</th>
<th align="center" valign="top">No <italic>n</italic> =&#x202F;862</th>
<th align="center" valign="top">
<italic>p</italic>
</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top">Age (year)</td>
<td align="center" valign="middle">68.0 (66.0, 72.0)</td>
<td align="center" valign="middle">69.0 (66.0, 74.0)</td>
<td align="center" valign="middle">68.0 (66.0, 72.0)</td>
<td align="center" valign="middle">0.101</td>
</tr>
<tr>
<td align="left" valign="top">Female</td>
<td align="center" valign="middle">603 (54.7%)</td>
<td align="center" valign="middle">139 (57.7%)</td>
<td align="center" valign="middle">464 (53.8%)</td>
<td align="center" valign="top">0.323</td>
</tr>
<tr>
<td align="left" valign="top">BMI (kg/m<sup>2</sup>)</td>
<td align="center" valign="middle">24.4 (22.3, 26.3)</td>
<td align="center" valign="middle">24.7 (22.1, 26.6)</td>
<td align="center" valign="middle">24.4 (22.3, 26.2)</td>
<td align="center" valign="middle">0.533</td>
</tr>
<tr>
<td align="left" valign="top">MMSE</td>
<td align="center" valign="middle">25.0 (21.0, 28.0)</td>
<td align="center" valign="middle">19.0 (15.0, 23.0)</td>
<td align="center" valign="middle">26.0 (24.0, 28.0)</td>
<td align="center" valign="middle">&#x003C;0.001</td>
</tr>
<tr>
<td align="left" valign="top">TC (mmol/L)</td>
<td align="center" valign="middle">5.09 (4.32, 5.77)</td>
<td align="center" valign="middle">5.20 (4.44, 5.85)</td>
<td align="center" valign="middle">5.04 (4.29, 5.75)</td>
<td align="center" valign="middle">0.055</td>
</tr>
<tr>
<td align="left" valign="top">LDL (mmol/L)</td>
<td align="center" valign="middle">2.96 (2.36, 3.52)</td>
<td align="center" valign="middle">3.05 (2.34, 3.59)</td>
<td align="center" valign="middle">2.94 (2.36, 3.52)</td>
<td align="center" valign="middle">0.749</td>
</tr>
<tr>
<td align="left" valign="top">HDL (mmol/L)</td>
<td align="center" valign="middle">1.44 (1.23, 1.67)</td>
<td align="center" valign="middle">1.37 (1.20, 1.60)</td>
<td align="center" valign="middle">1.46 (1.25, 1.68)</td>
<td align="center" valign="middle">0.004</td>
</tr>
<tr>
<td align="left" valign="top">NHDL (mmol/L)</td>
<td align="center" valign="middle">3.59 (2.91, 4.23)</td>
<td align="center" valign="middle">3.76 (3.00, 4.46)</td>
<td align="center" valign="middle">3.55 (2.88, 4.16)</td>
<td align="center" valign="middle">0.008</td>
</tr>
<tr>
<td align="left" valign="top">NHHR</td>
<td align="center" valign="middle">2.50 (1.99, 3.05)</td>
<td align="center" valign="middle">2.64 (2.00, 3.57)</td>
<td align="center" valign="middle">2.47 (1.98, 2.94)</td>
<td align="center" valign="middle">&#x003C;0.001</td>
</tr>
<tr>
<td align="left" valign="middle">Education</td>
<td/>
<td/>
<td/>
<td align="center" valign="middle">&#x003C;0.001</td>
</tr>
<tr>
<td align="left" valign="middle">&#x00A0;&#x00A0;&#x00A0;&#x00A0;&#x003E;Elementary school</td>
<td align="center" valign="middle">328 (29.7%)</td>
<td align="center" valign="middle">105 (43.6%)</td>
<td align="center" valign="middle">223 (25.9%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">&#x00A0;&#x00A0;&#x00A0;&#x00A0;Elementary school</td>
<td align="center" valign="middle">584 (52.9%)</td>
<td align="center" valign="middle">98 (40.7%)</td>
<td align="center" valign="middle">486 (56.4%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">&#x00A0;&#x00A0;&#x00A0;&#x00A0;Illiterate</td>
<td align="center" valign="middle">191 (17.3%)</td>
<td align="center" valign="middle">38 (15.8%)</td>
<td align="center" valign="middle">153 (17.7%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">Exercise</td>
<td/>
<td/>
<td/>
<td align="center" valign="middle">0.003</td>
</tr>
<tr>
<td align="left" valign="middle">&#x00A0;&#x00A0;&#x00A0;&#x00A0;Never</td>
<td align="center" valign="middle">494 (44.8%)</td>
<td align="center" valign="middle">131 (54.4%)</td>
<td align="center" valign="middle">363 (42.1%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">&#x00A0;&#x00A0;&#x00A0;&#x00A0;Sometime</td>
<td align="center" valign="middle">49 (4.4%)</td>
<td align="center" valign="middle">7 (2.90%)</td>
<td align="center" valign="middle">42 (4.9%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">&#x00A0;&#x00A0;&#x00A0;&#x00A0;Everyday</td>
<td align="center" valign="middle">560 (50.8%)</td>
<td align="center" valign="middle">103 (42.7%)</td>
<td align="center" valign="middle">457 (53.0%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">Diet</td>
<td/>
<td/>
<td/>
<td align="center" valign="middle">0.147</td>
</tr>
<tr>
<td align="left" valign="middle">&#x00A0;&#x00A0;&#x00A0;&#x00A0;Unbalance</td>
<td align="center" valign="middle">25 (2.3%)</td>
<td align="center" valign="middle">2 (0.8%)</td>
<td align="center" valign="middle">23 (2.7%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">&#x00A0;&#x00A0;&#x00A0;&#x00A0;Balance</td>
<td align="center" valign="middle">1,078 (97.7%)</td>
<td align="center" valign="middle">239 (99.2%)</td>
<td align="center" valign="middle">839 (97.3%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">Smoke</td>
<td/>
<td/>
<td/>
<td align="center" valign="middle">0.050</td>
</tr>
<tr>
<td align="left" valign="middle">&#x00A0;&#x00A0;&#x00A0;&#x00A0;Never</td>
<td align="center" valign="middle">855 (77.5%)</td>
<td align="center" valign="middle">195 (80.9%)</td>
<td align="center" valign="middle">660 (76.6%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">&#x00A0;&#x00A0;&#x00A0;&#x00A0;Former</td>
<td align="center" valign="middle">48 (4.4%)</td>
<td align="center" valign="middle">14 (5.8%)</td>
<td align="center" valign="middle">34 (3.9%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">&#x00A0;&#x00A0;&#x00A0;&#x00A0;Current</td>
<td align="center" valign="middle">200 (18.1%)</td>
<td align="center" valign="middle">32 (13.3%)</td>
<td align="center" valign="middle">168 (19.5%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">Drink</td>
<td/>
<td/>
<td/>
<td align="center" valign="middle">0.021</td>
</tr>
<tr>
<td align="left" valign="middle">&#x00A0;&#x00A0;&#x00A0;&#x00A0;Never</td>
<td align="center" valign="middle">996 (90.3%)</td>
<td align="center" valign="middle">225 (93.4%)</td>
<td align="center" valign="middle">771 (89.4%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">&#x00A0;&#x00A0;&#x00A0;&#x00A0;Sometime</td>
<td align="center" valign="middle">69 (6.3%)</td>
<td align="center" valign="middle">6 (2.5%)</td>
<td align="center" valign="middle">63 (7.3%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">&#x00A0;&#x00A0;&#x00A0;&#x00A0;Everyday</td>
<td align="center" valign="middle">38 (3.4%)</td>
<td align="center" valign="middle">10 (4.1%)</td>
<td align="center" valign="middle">28 (3.3%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="top">Hypertension</td>
<td align="center" valign="middle">545 (49.4%)</td>
<td align="center" valign="middle">130 (53.9%)</td>
<td align="center" valign="middle">415 (48.1%)</td>
<td align="center" valign="top">0.129</td>
</tr>
<tr>
<td align="left" valign="top">Diabetes</td>
<td align="center" valign="middle">167 (15.1%)</td>
<td align="center" valign="middle">40 (16.6%)</td>
<td align="center" valign="middle">127 (14.7%)</td>
<td align="center" valign="top">0.540</td>
</tr>
<tr>
<td align="left" valign="top">Ischemic stroke</td>
<td align="center" valign="middle">21 (1.9%)</td>
<td align="center" valign="middle">11 (4.6%)</td>
<td align="center" valign="middle">10 (1.2%)</td>
<td align="center" valign="top">0.002</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>Data are presented as median (Q1, Q3) or <italic>n</italic> (%). Q1, 1st quartile; Q3, 3rd quartile; MMSE, Mini-Mental State Examination; BMI, body mass index; TC, total cholesterol; LDL, low-density lipoprotein cholesterol; HDL, high-density lipoprotein cholesterol; NHDL, non-HDL; NHHR, non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio. Cognitive impairment was defined using education-stratified MMSE cutoffs: MMSE &#x2264;17 for illiterate participants, &#x2264;19 for primary school education, and &#x2264;24 for junior high school education or above.</p>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="sec11">
<label>3.2</label>
<title>NHHR has U-shaped relationship with risk of cognitive impairment</title>
<p>RCS analyses demonstrated significant nonlinear associations of NHHR with both cognitive impairment and cognitive function (overall <italic>p</italic>&#x202F;&#x003C;&#x202F;0.001; nonlinearity <italic>p</italic>&#x202F;&#x003C;&#x202F;0.001), with U-shaped curves and turning points near 2.361 and 2.209, respectively (<xref ref-type="fig" rid="fig1">Figures 1A</xref>,<xref ref-type="fig" rid="fig1">B</xref>). In AUC comparisons from logistic regression models, NHHR showed the greatest discriminative performance for cognitive impairment compared with NHDL, LDL, and HDL (<xref ref-type="fig" rid="fig2">Figure 2</xref>). Breakpoint regression further supported a U-shaped association, identifying a best-fitting threshold at NHHR&#x202F;=&#x202F;2.772. Below this threshold, NHHR was not significantly associated with cognitive impairment [odds ratio (OR), 0.73; 95% CI, 0.48&#x2013;1.09; <italic>p</italic>&#x202F;=&#x202F;0.124]; at or above the threshold, higher NHHR was associated with substantially increased risk (OR, 3.36; 95% CI, 2.23&#x2013;5.05; <italic>p</italic>&#x202F;&#x003C;&#x202F;0.001) (<xref ref-type="table" rid="tab2">Table 2</xref> and <xref ref-type="fig" rid="fig3">Figure 3</xref>).</p>
<fig position="float" id="fig1">
<label>Figure 1</label>
<caption>
<p>Restricted cubic spline analysis of the association of NHHR with cognitive impairment and log(MMSE+1). <bold>(A)</bold> Adjusted association of NHHR with cognitive impairment based on restricted cubic spline models. <bold>(B)</bold> Adjusted association of NHHR with log(MMSE+1) based on restricted cubic spline models. Solid lines indicate fitted values, and shaded areas indicate 95% confidence intervals.</p>
</caption>
<graphic xlink:href="fnhum-20-1775215-g001.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Panel A shows a nonlinear association between NHHR and odds ratio (OR) with a sharp increase after the reference value 2.361; panel B shows a nonlinear decline in &#x03B2; coefficient after the reference value 2.209. Both graphs display confidence intervals and significant p-values for overall association and nonlinearity.</alt-text>
</graphic>
</fig>
<fig position="float" id="fig2">
<label>Figure 2</label>
<caption>
<p>Receiver operating characteristic curves comparing NHHR, NHDL, LDL, and HDL for predicting cognitive impairment. Receiver operating characteristic (ROC) curves for NHHR, NHDL, LDL, and HDL in predicting cognitive impairment. The area under the curve (AUC) for each marker is shown. <italic>p</italic>-values were obtained using DeLong&#x2019;s test for differences between AUCs.</p>
</caption>
<graphic xlink:href="fnhum-20-1775215-g002.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Receiver operating characteristic (ROC) curve compares NHR, NHDL, LDL, and HDL as predictors, with AUC values of 0.712, 0.692, 0.686, and 0.693, respectively; DeLong test p-values indicate significant differences between NHR and others.</alt-text>
</graphic>
</fig>
<table-wrap position="float" id="tab2">
<label>Table 2</label>
<caption>
<p>Breakpoint regression analysis of the association between NHHR and cognitive impairment.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Variables</th>
<th align="center" valign="top">OR</th>
<th align="center" valign="top">95% CI</th>
<th align="center" valign="top"><italic>p</italic></th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top">NHHR &#x003C;2.772</td>
<td align="center" valign="top" char=".">0.726</td>
<td align="center" valign="top" char=",">(0.482, 1.092)</td>
<td align="center" valign="top" char=".">0.124</td>
</tr>
<tr>
<td align="left" valign="top">NHHR &#x2265;2.772</td>
<td align="center" valign="top" char=".">3.357</td>
<td align="center" valign="top" char=",">(2.233, 5.047)</td>
<td align="center" valign="top" char=".">&#x003C;0.001</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>NHHR, non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio.</p>
</table-wrap-foot>
</table-wrap>
<fig position="float" id="fig3">
<label>Figure 3</label>
<caption>
<p>Breakpoint regression analysis of the association between NHHR and cognitive impairment. The fitted curve from breakpoint regression analysis showing the association between NHHR and the risk of cognitive impairment. A breakpoint was identified at NHHR&#x202F;=&#x202F;2.772. The solid line represents the estimated effect of NHHR, and tick marks along the <italic>x</italic>-axis represent the distribution of participants.</p>
</caption>
<graphic xlink:href="fnhum-20-1775215-g003.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Line graph showing the effect of NHHR on the vertical axis and NHHR values on the horizontal axis, with a red line depicting a sharp increase after a minimum point around NHHR of three. Black marks under the x-axis indicate data distribution.</alt-text>
</graphic>
</fig>
</sec>
<sec id="sec12">
<label>3.3</label>
<title>Machine learning analysis</title>
<p>The study population was randomly divided into a training dataset (<italic>n</italic>&#x202F;=&#x202F;773) and a test dataset (<italic>n</italic>&#x202F;=&#x202F;330), with no significant differences in baseline clinical characteristics between groups (<xref ref-type="sec" rid="sec21">Supplementary Table S2</xref>). An XGBoost model was developed in the training dataset and internally validated in the test dataset. Model performance was acceptable, as demonstrated by receiver operating characteristic curves, calibration plots, and related metrics (<xref ref-type="supplementary-material" rid="SM1">Supplementary Figures S1A,B,D,E</xref> and <xref ref-type="sec" rid="sec21">Supplementary Table S3</xref>). Decision curve analysis indicated clinical utility within specific threshold probabilities (<xref ref-type="supplementary-material" rid="SM1">Supplementary Figures S1C,F</xref>). SHAP analysis identified NHHR as the variable with the highest mean SHAP value, exceeding those of LDL and HDL (<xref ref-type="fig" rid="fig4">Figure 4A</xref> and <xref ref-type="sec" rid="sec21">Supplementary Table S4</xref>). Moreover, SHAP dependence plots again revealed a U-shaped association between NHHR and cognitive impairment, with the lowest SHAP values observed at NHHR around 2.5. SHAP values increased at higher NHHR levels, with greater values in the presence of elevated LDL and lower values with higher HDL (<xref ref-type="fig" rid="fig4">Figures 4B</xref>,<xref ref-type="fig" rid="fig4">C</xref>).</p>
<fig position="float" id="fig4">
<label>Figure 4</label>
<caption>
<p>SHAP analysis of the contribution of NHHR and other variables to cognitive impairment prediction. <bold>(A)</bold> Ranking of variables by mean SHAP values, showing their relative importance in predicting cognitive impairment. <bold>(B)</bold> SHAP dependence plot of NHHR with LDL levels overlaid. <bold>(C)</bold> SHAP dependence plot of NHHR with HDL levels overlaid. Each point represents an individual participant.</p>
</caption>
<graphic xlink:href="fnhum-20-1775215-g004.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Grouped data visualization with three panels: Panel A is a horizontal bar chart showing mean SHAP values for 14 variables, ranking NHHR highest, followed by education and exercise, with all values indicated; Panels B and C are scatterplots of SHAP value for NHHR versus NHHR, color-coded by LDL (panel B) and HDL (panel C), respectively, showing clear trends and color gradients.</alt-text>
</graphic>
</fig>
</sec>
</sec>
<sec sec-type="discussion" id="sec13">
<label>4</label>
<title>Discussion</title>
<p>In this cross-sectional study of a community-based population in China, we identified a U-shaped association between the NHHR and cognitive impairment. Breakpoint regression analysis suggested an inflection point at 2.772, above which NHHR was significantly associated with increased risk of cognitive impairment. Machine learning analysis confirmed this U-shaped relationship and indicated that NHHR contributed more strongly to the model than individual lipid markers. Collectively, these findings suggest that NHHR may better reflect lipid-related risk for cognitive impairment than single lipid measures and support its potential role as an alternative index in studies of cognitive health.</p>
<p>NHHR reflects the ratio of NHDL cholesterol to HDL cholesterol and is inversely related to HDL levels while positively correlated with NHDL levels. Both lipoproteins have been shown to be strongly associated with cognitive function, with HDL cholesterol demonstrating a U-shaped relationship with cognitive outcomes. For example, a recent study reported that individuals with HDL cholesterol levels exceeding 80&#x202F;mg/dL had a 27% higher risk of dementia compared with those with HDL levels of 40&#x2013;60&#x202F;mg/dL (<xref ref-type="bibr" rid="ref17">Hussain et al., 2024</xref>). Similarly, another study found that HDL levels above 65&#x202F;mg/dL were associated with a 15% increased risk of dementia compared with the median level (53.7&#x202F;mg/dL), while HDL levels between 11 and 41&#x202F;mg/dL were linked to a 7% increased risk (<xref ref-type="bibr" rid="ref9">Ferguson et al., 2023</xref>). Collectively, these findings suggest that both excessively high and low HDL cholesterol levels may increase dementia risk, aligning with the U-shaped association observed in the present study. In contrast, NHDL cholesterol primarily comprises LDL and remnant cholesterol (RC), both of which, when elevated, have been associated with cognitive decline. A large population-based study from the United Kingdom found that a 1-standard deviation increase in LDL cholesterol (39&#x202F;mg/dL) was associated with a 5% increased risk of dementia in the general population. Additionally, higher concentrations of RC were linked to greater risks of all-cause dementia, Alzheimer&#x2019;s disease, and vascular dementia. Compared with participants in the lowest quartile of RC, those in the highest quartile had hazard ratios of 1.11 (95% CI, 1.09&#x2013;1.13) for all-cause dementia, 1.11 (95% CI, 1.08&#x2013;1.13) for Alzheimer&#x2019;s disease, and 1.15 (95% CI, 1.09&#x2013;1.21) for vascular dementia. Notably, a recent cross-sectional study also reported a U-shaped relationship between NHDL cholesterol and cognitive function (<xref ref-type="bibr" rid="ref24">Li et al., 2024</xref>). Taken together, these findings suggest that the right side of the U-shaped curve observed in the present study may reflect the adverse effects of elevated NHDL cholesterol and reduced HDL cholesterol, while the left side of the curve may be attributable to the detrimental impact of excessively high HDL cholesterol levels.</p>
<p>Our study identified a U-shaped association between NHHR and cognitive function, which may be explained by several potential biological mechanisms. At higher NHHR levels, reflecting an increased burden of NHDL lipoproteins, there is a greater propensity for the development of atherosclerosis, cerebral microvascular injury, chronic cerebral hypoperfusion, blood&#x2013;brain barrier disruption, intracerebral inflammation, and oxidative stress (<xref ref-type="bibr" rid="ref3">Bolanle et al., 2025</xref>; <xref ref-type="bibr" rid="ref5">Chen et al., 2024</xref>; <xref ref-type="bibr" rid="ref6">Chung et al., 2023</xref>; <xref ref-type="bibr" rid="ref7">Dias et al., 2015</xref>; <xref ref-type="bibr" rid="ref16">Huang et al., 2019</xref>; <xref ref-type="bibr" rid="ref21">Lara-Guzman et al., 2018</xref>). These pathophysiological processes can ultimately result in neuronal damage and cognitive decline. In parallel, a decrease in HDL levels may weaken its protective functions, including anti-inflammatory, antioxidant, and &#x03B2;-amyloid clearance activities, thereby further exacerbating the risk of cognitive impairment (<xref ref-type="bibr" rid="ref13">Grao-Cruces et al., 2022</xref>; <xref ref-type="bibr" rid="ref34">Tziomalos et al., 2019</xref>; <xref ref-type="bibr" rid="ref36">Zhang et al., 2022</xref>). Conversely, low NHHR is often accompanied by elevated HDL levels; however, excessively high HDL does not necessarily confer additional neuroprotection. Emerging evidence suggests that dysfunctional HDL particles may be enriched with lipids, exhibit reduced antioxidant and anti-inflammatory capacity, and display impaired reverse cholesterol transport. Such dysfunctional HDL may result in insufficient cholesterol delivery to the brain, negatively impacting neuronal synaptic plasticity and neurotransmitter synthesis (<xref ref-type="bibr" rid="ref19">Kjeldsen et al., 2021</xref>, <xref ref-type="bibr" rid="ref20">2022</xref>; <xref ref-type="bibr" rid="ref32">Suleiman et al., 2020</xref>). Additionally, reduced NHDL cholesterol levels may reflect suboptimal metabolic or nutritional conditions, which could impair brain homeostasis through insufficient provision of substrates required for neuronal maintenance and repair processes (<xref ref-type="bibr" rid="ref1">An et al., 2019</xref>; <xref ref-type="bibr" rid="ref12">Gong et al., 2022</xref>; <xref ref-type="bibr" rid="ref23">Li et al., 2020</xref>). Moreover, extreme deviations in NHHR may disrupt reverse cholesterol transport. At very low NHHR levels, although HDL concentrations are high, its functional capacity to accept and transport cholesterol may be diminished. Conversely, at very high NHHR levels, the excessive accumulation of NHDL lipoproteins may overwhelm the clearance capacity of HDL, leading to cholesterol retention within the arterial wall, thereby promoting the progression of atherosclerotic plaque (<xref ref-type="bibr" rid="ref19">Kjeldsen et al., 2021</xref>; <xref ref-type="bibr" rid="ref2">Bhale et al., 2024</xref>; <xref ref-type="bibr" rid="ref8">Durrington et al., 2025</xref>; <xref ref-type="bibr" rid="ref33">Tosheska Trajkovska and Topuzovska, 2017</xref>). In summary, elevated NHHR likely contributes to cognitive impairment through mechanisms related to vascular injury and inflammation, whereas low NHHR is more likely linked to HDL dysfunction and cerebral cholesterol deficiency. Both extremes may disrupt cognitive health, underscoring the potential importance of maintaining NHHR within an appropriate range for preserving cognitive function.</p>
<p>The present study found that cognitive function appeared to be optimal when NHHR was maintained within the range of approximately 2.8, a finding that is generally consistent with existing lipid management guidelines (<xref ref-type="bibr" rid="ref27">Patel et al., 2025</xref>). When NHDL cholesterol is &#x003C;130&#x202F;mg/dL and HDL cholesterol is &#x003E;40&#x202F;mg/dL, NHHR naturally falls within this range. Maintaining NHHR within the range of approximately 2.8 provides clinicians with a simple and intuitive target for lipid management. Compared to focusing solely on LDL or HDL levels, NHHR offers a more comprehensive assessment of lipid metabolism, facilitating the development of more individualized intervention strategies.</p>
<p>This study has several limitations. First, as a cross-sectional study, it can only establish associations rather than causality. Second, although multiple confounding variables were adjusted for, our analysis did not account for potential residual confounders such as APOE genotype and psychological factors including depression and anxiety, which are known to influence cognitive function. Third, lipid levels were assessed based on a single measurement, whereas lipid profiles may fluctuate over time; dynamic lipid monitoring may provide a more accurate reflection of long-term lipid status. Fourth, the study population was derived from a hospital-based sample, which may introduce selection bias and limit the generalizability of findings to the broader community population. Survival bias may also be present, particularly in older adults, as individuals with severe comorbidities or cognitive impairment may be underrepresented due to early mortality or inability to participate in examinations.</p>
</sec>
<sec sec-type="conclusions" id="sec14">
<label>5</label>
<title>Conclusion</title>
<p>In this community-based cross-sectional study, NHHR demonstrated a significant U-shaped association with cognitive impairment, with risk increasing when NHHR exceeded an inflection point of approximately 2.8. Compared with single lipid measures, NHHR appeared to provide superior discrimination of cognitive impairment. These findings suggest that NHHR may serve as a useful lipid-related index in studies of cognitive health, although further longitudinal research is needed to clarify its predictive value and clinical implications.</p>
</sec>
</body>
<back>
<sec sec-type="data-availability" id="sec15">
<title>Data availability statement</title>
<p>The data analyzed in this study is subject to the following licenses/restrictions: the data used in this study were obtained from the Liuyang Jili Hospital Medical Examination Center, and their use was contingent upon obtaining ethical approval from the hospital. Without this approval, the data could not be used, and therefore cannot be made publicly available. The authors do not have permission to share data. Requests to access these datasets should be directed to YH, <email xlink:href="mailto:277475748@qq.com">277475748@qq.com</email>.</p>
</sec>
<sec sec-type="ethics-statement" id="sec16">
<title>Ethics statement</title>
<p>The studies involving humans were approved by the Ethics Committee of Liuyang Jili Hospital (Approval Number: 2025032502). The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required from the participants or the participants&#x2019; legal guardians/next of kin in accordance with the national legislation and institutional requirements.</p>
</sec>
<sec sec-type="author-contributions" id="sec17">
<title>Author contributions</title>
<p>XC: Conceptualization, Formal analysis, Investigation, Methodology, Software, Visualization, Writing &#x2013; review &#x0026; editing. RL: Conceptualization, Formal analysis, Investigation, Methodology, Software, Writing &#x2013; review &#x0026; editing. LZ: Conceptualization, Formal analysis, Investigation, Software, Writing &#x2013; original draft. YH: Conceptualization, Data curation, Funding acquisition, Project administration, Supervision, Writing &#x2013; review &#x0026; editing. TZ: Conceptualization, Formal analysis, Methodology, Project administration, Supervision, Validation, Writing &#x2013; review &#x0026; editing.</p>
</sec>
<sec sec-type="COI-statement" id="sec18">
<title>Conflict of interest</title>
<p>The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec sec-type="ai-statement" id="sec19">
<title>Generative AI statement</title>
<p>The author(s) declared that Generative AI was not used in the creation of this manuscript.</p>
<p>Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.</p>
</sec>
<sec sec-type="disclaimer" id="sec20">
<title>Publisher&#x2019;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
<sec sec-type="supplementary-material" id="sec21">
<title>Supplementary material</title>
<p>The Supplementary material for this article can be found online at: <ext-link xlink:href="https://www.frontiersin.org/articles/10.3389/fnhum.2026.1775215/full#supplementary-material" ext-link-type="uri">https://www.frontiersin.org/articles/10.3389/fnhum.2026.1775215/full#supplementary-material</ext-link></p>
<supplementary-material xlink:href="Image_1.TIF" id="SM1" mimetype="image/tiff" xmlns:xlink="http://www.w3.org/1999/xlink">
<label>SUPPLEMENTARY FIGURE S1</label>
<caption>
<p>Performance of the prediction model in training and test sets. <bold>(A&#x2013;C)</bold> Model performance in the training set: <bold>(A)</bold> receiver operating characteristic (ROC) curve with the area under the curve (AUC), <bold>(B)</bold> calibration curve comparing predicted and observed probabilities, and <bold>(C)</bold> decision curve analysis (DCA) showing net clinical benefit. <bold>(D&#x2013;F)</bold> Model performance in the test set: <bold>(D)</bold> ROC curve, <bold>(E)</bold> calibration curve, and <bold>(F)</bold> DCA.</p>
</caption>
</supplementary-material>
<supplementary-material xlink:href="Table_1.DOCX" id="SM2" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document" xmlns:xlink="http://www.w3.org/1999/xlink"/>
<supplementary-material xlink:href="Table_2.DOCX" id="SM3" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document" xmlns:xlink="http://www.w3.org/1999/xlink"/>
<supplementary-material xlink:href="Table_3.DOCX" id="SM4" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document" xmlns:xlink="http://www.w3.org/1999/xlink"/>
<supplementary-material xlink:href="Table_4.DOCX" id="SM5" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document" xmlns:xlink="http://www.w3.org/1999/xlink"/>
</sec>
<ref-list>
<title>References</title>
<ref id="ref1"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>An</surname><given-names>Y.</given-names></name> <name><surname>Zhang</surname><given-names>X.</given-names></name> <name><surname>Wang</surname><given-names>Y.</given-names></name> <name><surname>Wang</surname><given-names>Y.</given-names></name> <name><surname>Liu</surname><given-names>W.</given-names></name> <name><surname>Wang</surname><given-names>T.</given-names></name> <etal/></person-group>. (<year>2019</year>). <article-title>Longitudinal and nonlinear relations of dietary and serum cholesterol in midlife with cognitive decline: results from EMCOA study</article-title>. <source>Mol. Neurodegener.</source> <volume>14</volume>:<fpage>51</fpage>. doi: <pub-id pub-id-type="doi">10.1186/s13024-019-0353-1</pub-id>, <pub-id pub-id-type="pmid">31888696</pub-id></mixed-citation></ref>
<ref id="ref2"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Bhale</surname><given-names>A. S.</given-names></name> <name><surname>Meilhac</surname><given-names>O.</given-names></name> <name><surname>D&#x2019;Hellencourt</surname><given-names>C. L.</given-names></name> <name><surname>Vijayalakshmi</surname><given-names>M. A.</given-names></name> <name><surname>Venkataraman</surname><given-names>K.</given-names></name></person-group> (<year>2024</year>). <article-title>Cholesterol transport and beyond: illuminating the versatile functions of HDL apolipoproteins through structural insights and functional implications</article-title>. <source>Biofactors</source> <volume>50</volume>, <fpage>922</fpage>&#x2013;<lpage>956</lpage>. doi: <pub-id pub-id-type="doi">10.1002/biof.2057</pub-id>, <pub-id pub-id-type="pmid">38661230</pub-id></mixed-citation></ref>
<ref id="ref3"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Bolanle</surname><given-names>I. O.</given-names></name> <name><surname>de Liedekerke Beaufort</surname><given-names>G. C.</given-names></name> <name><surname>Weinberg</surname><given-names>P. D.</given-names></name></person-group> (<year>2025</year>). <article-title>Transcytosis of LDL across arterial endothelium: mechanisms and therapeutic targets</article-title>. <source>Arterioscler. Thromb. Vasc. Biol.</source> <volume>45</volume>, <fpage>468</fpage>&#x2013;<lpage>480</lpage>. doi: <pub-id pub-id-type="doi">10.1161/ATVBAHA.124.321549</pub-id>, <pub-id pub-id-type="pmid">40013359</pub-id></mixed-citation></ref>
<ref id="ref4"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Budd Haeberlein</surname><given-names>S.</given-names></name> <name><surname>Aisen</surname><given-names>P. S.</given-names></name> <name><surname>Barkhof</surname><given-names>F.</given-names></name> <name><surname>Chalkias</surname><given-names>S.</given-names></name> <name><surname>Chen</surname><given-names>T.</given-names></name> <name><surname>Cohen</surname><given-names>S.</given-names></name> <etal/></person-group>. (<year>2022</year>). <article-title>Two randomized phase 3 studies of aducanumab in early Alzheimer&#x2019;s disease</article-title>. <source>J. Prev Alzheimers Dis.</source> <volume>9</volume>, <fpage>197</fpage>&#x2013;<lpage>210</lpage>. doi: <pub-id pub-id-type="doi">10.14283/jpad.2022.30</pub-id>, <pub-id pub-id-type="pmid">35542991</pub-id></mixed-citation></ref>
<ref id="ref5"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Chen</surname><given-names>M.</given-names></name> <name><surname>Chu</surname><given-names>Y.</given-names></name> <name><surname>Yu</surname><given-names>W.</given-names></name> <name><surname>You</surname><given-names>Y.</given-names></name> <name><surname>Tang</surname><given-names>Y.</given-names></name> <name><surname>Pang</surname><given-names>X.</given-names></name> <etal/></person-group>. (<year>2024</year>). <article-title>Serum LDL promotes microglial activation and exacerbates demyelinating injury in neuromyelitis optica spectrum disorder</article-title>. <source>Neurosci. Bull.</source> <volume>40</volume>, <fpage>1104</fpage>&#x2013;<lpage>1114</lpage>. doi: <pub-id pub-id-type="doi">10.1007/s12264-023-01166-y</pub-id>, <pub-id pub-id-type="pmid">38227181</pub-id></mixed-citation></ref>
<ref id="ref6"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Chung</surname><given-names>D. W.</given-names></name> <name><surname>Platten</surname><given-names>K.</given-names></name> <name><surname>Ozawa</surname><given-names>K.</given-names></name> <name><surname>Adili</surname><given-names>R.</given-names></name> <name><surname>Pamir</surname><given-names>N.</given-names></name> <name><surname>Nussdorfer</surname><given-names>F.</given-names></name> <etal/></person-group>. (<year>2023</year>). <article-title>Low-density lipoprotein promotes microvascular thrombosis by enhancing von Willebrand factor self-association</article-title>. <source>Blood</source> <volume>142</volume>, <fpage>1156</fpage>&#x2013;<lpage>1166</lpage>. doi: <pub-id pub-id-type="doi">10.1182/blood.2023019749</pub-id>, <pub-id pub-id-type="pmid">37506337</pub-id></mixed-citation></ref>
<ref id="ref7"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Dias</surname><given-names>H. K. I.</given-names></name> <name><surname>Brown</surname><given-names>C. L. R.</given-names></name> <name><surname>Polidori</surname><given-names>M. C.</given-names></name> <name><surname>Lip</surname><given-names>G. Y. H.</given-names></name> <name><surname>Griffiths</surname><given-names>H. R.</given-names></name></person-group> (<year>2015</year>). <article-title>LDL-lipids from patients with hypercholesterolaemia and Alzheimer&#x2019;s disease are inflammatory to microvascular endothelial cells: mitigation by statin intervention</article-title>. <source>Clin. Sci.</source> <volume>129</volume>, <fpage>1195</fpage>&#x2013;<lpage>1206</lpage>. doi: <pub-id pub-id-type="doi">10.1042/CS20150351</pub-id>, <pub-id pub-id-type="pmid">26399707</pub-id></mixed-citation></ref>
<ref id="ref8"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Durrington</surname><given-names>P. N.</given-names></name> <name><surname>Bashir</surname><given-names>B.</given-names></name> <name><surname>Soran</surname><given-names>H.</given-names></name></person-group> (<year>2025</year>). <article-title>How does HDL participate in atherogenesis? Antioxidant activity versus role in reverse cholesterol transport</article-title>. <source>Antioxidants</source> <volume>14</volume>:<fpage>430</fpage>. doi: <pub-id pub-id-type="doi">10.3390/antiox14040430</pub-id>, <pub-id pub-id-type="pmid">40298833</pub-id></mixed-citation></ref>
<ref id="ref9"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Ferguson</surname><given-names>E. L.</given-names></name> <name><surname>Zimmerman</surname><given-names>S. C.</given-names></name> <name><surname>Jiang</surname><given-names>C.</given-names></name> <name><surname>Choi</surname><given-names>M.</given-names></name> <name><surname>Swinnerton</surname><given-names>K.</given-names></name> <name><surname>Choudhary</surname><given-names>V.</given-names></name> <etal/></person-group>. (<year>2023</year>). <article-title>Low- and high-density lipoprotein cholesterol and dementia risk over 17 years of follow-up among members of a large health care plan</article-title>. <source>Neurology</source> <volume>101</volume>, <fpage>e2172</fpage>&#x2013;<lpage>e2184</lpage>. doi: <pub-id pub-id-type="doi">10.1212/WNL.0000000000207876</pub-id>, <pub-id pub-id-type="pmid">37793911</pub-id></mixed-citation></ref>
<ref id="ref10"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Fiorenzato</surname><given-names>E.</given-names></name> <name><surname>Cauzzo</surname><given-names>S.</given-names></name> <name><surname>Weis</surname><given-names>L.</given-names></name> <name><surname>Garon</surname><given-names>M.</given-names></name> <name><surname>Pistonesi</surname><given-names>F.</given-names></name> <name><surname>Cianci</surname><given-names>V.</given-names></name> <etal/></person-group>. (<year>2024</year>). <article-title>Optimal MMSE and MoCA cutoffs for cognitive diagnoses in Parkinson&#x2019;s disease: a data-driven decision tree model</article-title>. <source>J. Neurol. Sci.</source> <volume>466</volume>:<fpage>123283</fpage>. doi: <pub-id pub-id-type="doi">10.1016/j.jns.2024.123283</pub-id>, <pub-id pub-id-type="pmid">39471638</pub-id></mixed-citation></ref>
<ref id="ref11"><mixed-citation publication-type="journal"><person-group person-group-type="author"><collab id="coll1">GBD Dementia Forecasting Collaborators</collab></person-group> (<year>2022</year>). <article-title>Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: an analysis for the Global Burden of Disease Study 2019</article-title>. <source>Lancet Public Health</source> <volume>7</volume>, <fpage>e105</fpage>&#x2013;<lpage>e125</lpage>. doi: <pub-id pub-id-type="doi">10.1016/S2468-2667(21)00249-8</pub-id>, <pub-id pub-id-type="pmid">34998485</pub-id></mixed-citation></ref>
<ref id="ref12"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Gong</surname><given-names>J.</given-names></name> <name><surname>Harris</surname><given-names>K.</given-names></name> <name><surname>Peters</surname><given-names>S. A. E.</given-names></name> <name><surname>Woodward</surname><given-names>M.</given-names></name></person-group> (<year>2022</year>). <article-title>Serum lipid traits and the risk of dementia: a cohort study of 254,575 women and 214,891 men in the UK Biobank</article-title>. <source>EClinicalMedicine</source> <volume>54</volume>:<fpage>101695</fpage>. doi: <pub-id pub-id-type="doi">10.1016/j.eclinm.2022.101695</pub-id>, <pub-id pub-id-type="pmid">36247924</pub-id></mixed-citation></ref>
<ref id="ref13"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Grao-Cruces</surname><given-names>E.</given-names></name> <name><surname>Lopez-Enriquez</surname><given-names>S.</given-names></name> <name><surname>Martin</surname><given-names>M. E.</given-names></name> <name><surname>Montserrat-De La Paz</surname><given-names>S.</given-names></name></person-group> (<year>2022</year>). <article-title>High-density lipoproteins and immune response: a review</article-title>. <source>Int. J. Biol. Macromol.</source> <volume>195</volume>, <fpage>117</fpage>&#x2013;<lpage>123</lpage>. doi: <pub-id pub-id-type="doi">10.1016/j.ijbiomac.2021.12.009</pub-id></mixed-citation></ref>
<ref id="ref14"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Guo</surname><given-names>X.</given-names></name> <name><surname>Chu</surname><given-names>H.</given-names></name> <name><surname>Xu</surname><given-names>H.</given-names></name> <name><surname>Wang</surname><given-names>S.</given-names></name> <name><surname>He</surname><given-names>J.</given-names></name> <name><surname>Wang</surname><given-names>Y.</given-names></name></person-group> (<year>2024</year>). <article-title>Association of non-high-density lipoprotein cholesterol-to-high-density lipoprotein cholesterol ratio (NHHR) with gout prevalence: a cross-sectional study</article-title>. <source>Front. Nutr.</source> <volume>11</volume>:<fpage>1480689</fpage>. doi: <pub-id pub-id-type="doi">10.3389/fnut.2024.1480689</pub-id>, <pub-id pub-id-type="pmid">39512523</pub-id></mixed-citation></ref>
<ref id="ref15"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>He</surname><given-names>Y.</given-names></name> <name><surname>Zhu</surname><given-names>T.</given-names></name> <name><surname>Bei</surname><given-names>E.</given-names></name> <name><surname>Xiang</surname><given-names>G.</given-names></name> <name><surname>Xi</surname><given-names>D.</given-names></name> <name><surname>Meng</surname><given-names>H.</given-names></name> <etal/></person-group>. (<year>2025</year>). <article-title>Hyperuricemia reduces the risk of MCI but not dementia: a cross-sectional study in Liuyang</article-title>. <source>Front. Neurol.</source> <volume>16</volume>:<fpage>1555587</fpage>. doi: <pub-id pub-id-type="doi">10.3389/fneur.2025.1555587</pub-id>, <pub-id pub-id-type="pmid">40166637</pub-id></mixed-citation></ref>
<ref id="ref16"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Huang</surname><given-names>L.</given-names></name> <name><surname>Chambliss</surname><given-names>K. L.</given-names></name> <name><surname>Gao</surname><given-names>X.</given-names></name> <name><surname>Yuhanna</surname><given-names>I. S.</given-names></name> <name><surname>Behling-Kelly</surname><given-names>E.</given-names></name> <name><surname>Bergaya</surname><given-names>S.</given-names></name> <etal/></person-group>. (<year>2019</year>). <article-title>SR-B1 drives endothelial cell LDL transcytosis via DOCK4 to promote atherosclerosis</article-title>. <source>Nature</source> <volume>569</volume>, <fpage>565</fpage>&#x2013;<lpage>569</lpage>. doi: <pub-id pub-id-type="doi">10.1038/s41586-019-1140-4</pub-id>, <pub-id pub-id-type="pmid">31019307</pub-id></mixed-citation></ref>
<ref id="ref17"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Hussain</surname><given-names>S. M.</given-names></name> <name><surname>Robb</surname><given-names>C.</given-names></name> <name><surname>Tonkin</surname><given-names>A. M.</given-names></name> <name><surname>Lacaze</surname><given-names>P.</given-names></name> <name><surname>Chong</surname><given-names>T. T.</given-names></name> <name><surname>Beilin</surname><given-names>L. J.</given-names></name> <etal/></person-group>. (<year>2024</year>). <article-title>Association of plasma high-density lipoprotein cholesterol level with risk of incident dementia: a cohort study of healthy older adults</article-title>. <source>Lancet Reg. Health West. Pac.</source> <volume>43</volume>:<fpage>100963</fpage>. doi: <pub-id pub-id-type="doi">10.1016/j.lanwpc.2023.100963</pub-id>, <pub-id pub-id-type="pmid">38456089</pub-id></mixed-citation></ref>
<ref id="ref18"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Kalaria</surname><given-names>R.</given-names></name> <name><surname>Maestre</surname><given-names>G.</given-names></name> <name><surname>Mahinrad</surname><given-names>S.</given-names></name> <name><surname>Acosta</surname><given-names>D. M.</given-names></name> <name><surname>Akinyemi</surname><given-names>R. O.</given-names></name> <name><surname>Alladi</surname><given-names>S.</given-names></name> <etal/></person-group>. (<year>2024</year>). <article-title>The 2022 symposium on dementia and brain aging in low- and middle-income countries: highlights on research, diagnosis, care, and impact</article-title>. <source>Alzheimers Dement.</source> <volume>20</volume>, <fpage>4290</fpage>&#x2013;<lpage>4314</lpage>. doi: <pub-id pub-id-type="doi">10.1002/alz.13836</pub-id>, <pub-id pub-id-type="pmid">38696263</pub-id></mixed-citation></ref>
<ref id="ref19"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Kjeldsen</surname><given-names>E. W.</given-names></name> <name><surname>Nordestgaard</surname><given-names>L. T.</given-names></name> <name><surname>Frikke-Schmidt</surname><given-names>R.</given-names></name></person-group> (<year>2021</year>). <article-title>HDL cholesterol and non-cardiovascular disease: a narrative review</article-title>. <source>Int. J. Mol. Sci.</source> <volume>22</volume>:<fpage>4547</fpage>. doi: <pub-id pub-id-type="doi">10.3390/ijms22094547</pub-id>, <pub-id pub-id-type="pmid">33925284</pub-id></mixed-citation></ref>
<ref id="ref20"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Kjeldsen</surname><given-names>E. W.</given-names></name> <name><surname>Thomassen</surname><given-names>J. Q.</given-names></name> <name><surname>Juul Rasmussen</surname><given-names>I.</given-names></name> <name><surname>Nordestgaard</surname><given-names>B. G.</given-names></name> <name><surname>Tybjaerg-Hansen</surname><given-names>A.</given-names></name> <name><surname>Frikke-Schmidt</surname><given-names>R.</given-names></name></person-group> (<year>2022</year>). <article-title>Plasma high-density lipoprotein cholesterol and risk of dementia: observational and genetic studies</article-title>. <source>Cardiovasc. Res.</source> <volume>118</volume>, <fpage>1330</fpage>&#x2013;<lpage>1343</lpage>. doi: <pub-id pub-id-type="doi">10.1093/cvr/cvab164</pub-id>, <pub-id pub-id-type="pmid">33964140</pub-id></mixed-citation></ref>
<ref id="ref21"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Lara-Guzman</surname><given-names>O. J.</given-names></name> <name><surname>Gil-Izquierdo</surname><given-names>A.</given-names></name> <name><surname>Medina</surname><given-names>S.</given-names></name> <name><surname>Osorio</surname><given-names>E.</given-names></name> <name><surname>Alvarez-Quintero</surname><given-names>R.</given-names></name> <name><surname>Zuluaga</surname><given-names>N.</given-names></name> <etal/></person-group>. (<year>2018</year>). <article-title>Oxidized LDL triggers changes in oxidative stress and inflammatory biomarkers in human macrophages</article-title>. <source>Redox Biol.</source> <volume>15</volume>, <fpage>1</fpage>&#x2013;<lpage>11</lpage>. doi: <pub-id pub-id-type="doi">10.1016/j.redox.2017.11.017</pub-id>, <pub-id pub-id-type="pmid">29195136</pub-id></mixed-citation></ref>
<ref id="ref22"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Lee</surname><given-names>J.</given-names></name> <name><surname>Meijer</surname><given-names>E.</given-names></name> <name><surname>Langa</surname><given-names>K. M.</given-names></name> <name><surname>Ganguli</surname><given-names>M.</given-names></name> <name><surname>Varghese</surname><given-names>M.</given-names></name> <name><surname>Banerjee</surname><given-names>J.</given-names></name> <etal/></person-group>. (<year>2023</year>). <article-title>Prevalence of dementia in India: national and state estimates from a nationwide study</article-title>. <source>Alzheimers Dement.</source> <volume>19</volume>, <fpage>2898</fpage>&#x2013;<lpage>2912</lpage>. doi: <pub-id pub-id-type="doi">10.1002/alz.12928</pub-id>, <pub-id pub-id-type="pmid">36637034</pub-id></mixed-citation></ref>
<ref id="ref23"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Li</surname><given-names>J.</given-names></name> <name><surname>Cao</surname><given-names>Y.</given-names></name> <name><surname>Xiao</surname><given-names>C.</given-names></name></person-group> (<year>2020</year>). <article-title>Subgroup analysis of the influence of body mass index on the association between serum lipids and cognitive function in Chinese population</article-title>. <source>Lipids Health Dis.</source> <volume>19</volume>:<fpage>130</fpage>. doi: <pub-id pub-id-type="doi">10.1186/s12944-020-01314-7</pub-id>, <pub-id pub-id-type="pmid">32513187</pub-id></mixed-citation></ref>
<ref id="ref24"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Li</surname><given-names>L.</given-names></name> <name><surname>Zhuang</surname><given-names>L.</given-names></name> <name><surname>Xu</surname><given-names>Z.</given-names></name> <name><surname>Jiang</surname><given-names>L.</given-names></name> <name><surname>Zhai</surname><given-names>Y.</given-names></name> <name><surname>Liu</surname><given-names>D.</given-names></name> <etal/></person-group>. (<year>2024</year>). <article-title>U-shaped relationship between non-high-density lipoprotein cholesterol and cognitive impairment in Chinese middle-aged and elderly: a cross-sectional study</article-title>. <source>BMC Public Health</source> <volume>24</volume>:<fpage>1624</fpage>. doi: <pub-id pub-id-type="doi">10.1186/s12889-024-19164-8</pub-id>, <pub-id pub-id-type="pmid">38890653</pub-id></mixed-citation></ref>
<ref id="ref25"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Liu</surname><given-names>J.</given-names></name> <name><surname>Oorloff</surname><given-names>M. D.</given-names></name> <name><surname>Nadella</surname><given-names>A.</given-names></name> <name><surname>Guo</surname><given-names>P.</given-names></name> <name><surname>Ye</surname><given-names>M.</given-names></name> <name><surname>Wang</surname><given-names>X.</given-names></name> <etal/></person-group>. (<year>2024</year>). <article-title>Association between the non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) and cardiovascular outcomes in patients undergoing percutaneous coronary intervention: a retrospective study</article-title>. <source>Lipids Health Dis.</source> <volume>23</volume>:<fpage>324</fpage>. doi: <pub-id pub-id-type="doi">10.1186/s12944-024-02309-4</pub-id>, <pub-id pub-id-type="pmid">39354522</pub-id></mixed-citation></ref>
<ref id="ref26"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Pan</surname><given-names>Y.</given-names></name> <name><surname>Liang</surname><given-names>J.</given-names></name> <name><surname>Zhang</surname><given-names>W.</given-names></name> <name><surname>Gao</surname><given-names>D.</given-names></name> <name><surname>Li</surname><given-names>C.</given-names></name> <name><surname>Xie</surname><given-names>W.</given-names></name> <etal/></person-group>. (<year>2024</year>). <article-title>Association between age at diagnosis of hyperlipidemia and subsequent risk of dementia</article-title>. <source>J. Am. Med. Dir. Assoc.</source> <volume>25</volume>:<fpage>104960</fpage>. doi: <pub-id pub-id-type="doi">10.1016/j.jamda.2024.01.029</pub-id>, <pub-id pub-id-type="pmid">38453136</pub-id></mixed-citation></ref>
<ref id="ref27"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Patel</surname><given-names>S. B.</given-names></name> <name><surname>Wyne</surname><given-names>K. L.</given-names></name> <name><surname>Afreen</surname><given-names>S.</given-names></name> <name><surname>Belalcazar</surname><given-names>L. M.</given-names></name> <name><surname>Bird</surname><given-names>M. D.</given-names></name> <name><surname>Coles</surname><given-names>S.</given-names></name> <etal/></person-group>. (<year>2025</year>). <article-title>American Association of Clinical Endocrinology Clinical Practice guideline on pharmacologic management of adults with dyslipidemia</article-title>. <source>Endocr. Pract.</source> <volume>31</volume>, <fpage>236</fpage>&#x2013;<lpage>262</lpage>. doi: <pub-id pub-id-type="doi">10.1016/j.eprac.2024.09.016</pub-id>, <pub-id pub-id-type="pmid">39919851</pub-id></mixed-citation></ref>
<ref id="ref28"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Petek</surname><given-names>B.</given-names></name> <name><surname>Habel</surname><given-names>H.</given-names></name> <name><surname>Xu</surname><given-names>H.</given-names></name> <name><surname>Villa-Lopez</surname><given-names>M.</given-names></name> <name><surname>Kalar</surname><given-names>I.</given-names></name> <name><surname>Hoang</surname><given-names>M. T.</given-names></name> <etal/></person-group>. (<year>2023</year>). <article-title>Statins and cognitive decline in patients with Alzheimer&#x2019;s and mixed dementia: a longitudinal registry-based cohort study</article-title>. <source>Alzheimers Res. Ther.</source> <volume>15</volume>:<fpage>220</fpage>. doi: <pub-id pub-id-type="doi">10.1186/s13195-023-01360-0</pub-id>, <pub-id pub-id-type="pmid">38115091</pub-id></mixed-citation></ref>
<ref id="ref29"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Qing</surname><given-names>G.</given-names></name> <name><surname>Deng</surname><given-names>W.</given-names></name> <name><surname>Zhou</surname><given-names>Y.</given-names></name> <name><surname>Zheng</surname><given-names>L.</given-names></name> <name><surname>Wang</surname><given-names>Y.</given-names></name> <name><surname>Wei</surname><given-names>B.</given-names></name></person-group> (<year>2024</year>). <article-title>The association between non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) and suicidal ideation in adults: a population-based study in the United States</article-title>. <source>Lipids Health Dis.</source> <volume>23</volume>:<fpage>17</fpage>. doi: <pub-id pub-id-type="doi">10.1186/s12944-024-02012-4</pub-id>, <pub-id pub-id-type="pmid">38218917</pub-id></mixed-citation></ref>
<ref id="ref30"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Reuben</surname><given-names>D. B.</given-names></name> <name><surname>Kremen</surname><given-names>S.</given-names></name> <name><surname>Maust</surname><given-names>D. T.</given-names></name></person-group> (<year>2024</year>). <article-title>Dementia prevention and treatment: a narrative review</article-title>. <source>JAMA Intern. Med.</source> <volume>184</volume>, <fpage>563</fpage>&#x2013;<lpage>572</lpage>. doi: <pub-id pub-id-type="doi">10.1001/jamainternmed.2023.8522</pub-id></mixed-citation></ref>
<ref id="ref31"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Sommerlad</surname><given-names>A.</given-names></name> <name><surname>Kivimaki</surname><given-names>M.</given-names></name> <name><surname>Larson</surname><given-names>E. B.</given-names></name> <name><surname>Rohr</surname><given-names>S.</given-names></name> <name><surname>Shirai</surname><given-names>K.</given-names></name> <name><surname>Singh-Manoux</surname><given-names>A.</given-names></name> <etal/></person-group>. (<year>2023</year>). <article-title>Social participation and risk of developing dementia</article-title>. <source>Nat. Aging</source> <volume>3</volume>, <fpage>532</fpage>&#x2013;<lpage>545</lpage>. doi: <pub-id pub-id-type="doi">10.1038/s43587-023-00387-0</pub-id>, <pub-id pub-id-type="pmid">37202513</pub-id></mixed-citation></ref>
<ref id="ref32"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Suleiman</surname><given-names>S.</given-names></name> <name><surname>Coughlan</surname><given-names>J. J.</given-names></name> <name><surname>Maher</surname><given-names>V.</given-names></name></person-group> (<year>2020</year>). <article-title>Quality over quantity: a case based review of HDL function and dysfunction</article-title>. <source>Int. J. Clin. Cardiol.</source> <volume>7</volume>:<fpage>176</fpage>. doi: <pub-id pub-id-type="doi">10.23937/2378-2951/1410176</pub-id></mixed-citation></ref>
<ref id="ref33"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Tosheska Trajkovska</surname><given-names>K.</given-names></name> <name><surname>Topuzovska</surname><given-names>S.</given-names></name></person-group> (<year>2017</year>). <article-title>High-density lipoprotein metabolism and reverse cholesterol transport: strategies for raising HDL cholesterol</article-title>. <source>Anatol. J. Cardiol.</source> <volume>18</volume>, <fpage>149</fpage>&#x2013;<lpage>154</lpage>. doi: <pub-id pub-id-type="doi">10.14744/AnatolJCardiol.2017.7608</pub-id>, <pub-id pub-id-type="pmid">28766509</pub-id></mixed-citation></ref>
<ref id="ref34"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Tziomalos</surname><given-names>K.</given-names></name> <name><surname>Katrini</surname><given-names>K.</given-names></name> <name><surname>Papagianni</surname><given-names>M.</given-names></name> <name><surname>Christou</surname><given-names>K.</given-names></name> <name><surname>Gkolfinopoulou</surname><given-names>C.</given-names></name> <name><surname>Angelopoulou</surname><given-names>S.</given-names></name> <etal/></person-group>. (<year>2019</year>). <article-title>Impaired antioxidative activity of high-density lipoprotein is associated with more severe acute ischemic stroke</article-title>. <source>Metabolism</source> <volume>98</volume>, <fpage>49</fpage>&#x2013;<lpage>52</lpage>. doi: <pub-id pub-id-type="doi">10.1016/j.metabol.2019.06.004</pub-id>, <pub-id pub-id-type="pmid">31202834</pub-id></mixed-citation></ref>
<ref id="ref35"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>van Dyck</surname><given-names>C. H.</given-names></name> <name><surname>Swanson</surname><given-names>C. J.</given-names></name> <name><surname>Aisen</surname><given-names>P.</given-names></name> <name><surname>Bateman</surname><given-names>R. J.</given-names></name> <name><surname>Chen</surname><given-names>C.</given-names></name> <name><surname>Gee</surname><given-names>M.</given-names></name> <etal/></person-group>. (<year>2023</year>). <article-title>Lecanemab in early Alzheimer&#x2019;s disease</article-title>. <source>N. Engl. J. Med.</source> <volume>388</volume>, <fpage>9</fpage>&#x2013;<lpage>21</lpage>. doi: <pub-id pub-id-type="doi">10.1056/NEJMoa2212948</pub-id>, <pub-id pub-id-type="pmid">36449413</pub-id></mixed-citation></ref>
<ref id="ref36"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Zhang</surname><given-names>H.</given-names></name> <name><surname>Jiang</surname><given-names>W.</given-names></name> <name><surname>Zhao</surname><given-names>Y.</given-names></name> <name><surname>Song</surname><given-names>T.</given-names></name> <name><surname>Xi</surname><given-names>Y.</given-names></name> <name><surname>Han</surname><given-names>G.</given-names></name> <etal/></person-group>. (<year>2022</year>). <article-title>Lipoprotein-inspired nanoscavenger for the three-pronged modulation of microglia-derived neuroinflammation in Alzheimer&#x2019;s disease therapy</article-title>. <source>Nano Lett.</source> <volume>22</volume>, <fpage>2450</fpage>&#x2013;<lpage>2460</lpage>. doi: <pub-id pub-id-type="doi">10.1021/acs.nanolett.2c00191</pub-id>, <pub-id pub-id-type="pmid">35271279</pub-id></mixed-citation></ref>
<ref id="ref37"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Zhen</surname><given-names>R.</given-names></name> <name><surname>Ban</surname><given-names>J.</given-names></name> <name><surname>Jia</surname><given-names>Z.</given-names></name> <name><surname>Liu</surname><given-names>Y.</given-names></name> <name><surname>Li</surname><given-names>Z.</given-names></name> <name><surname>Chen</surname><given-names>S.</given-names></name></person-group> (<year>2023</year>). <article-title>The relationship between non-HDL-C /HDL-C ratio (NHHR) and vitamin D in type 2 diabetes mellitus</article-title>. <source>Diabetes Metab. Syndr. Obes.</source> <volume>16</volume>, <fpage>2661</fpage>&#x2013;<lpage>2673</lpage>. doi: <pub-id pub-id-type="doi">10.2147/DMSO.S414673</pub-id>, <pub-id pub-id-type="pmid">37670851</pub-id></mixed-citation></ref>
</ref-list>
<fn-group>
<fn fn-type="custom" custom-type="edited-by" id="fn0002">
<p>Edited by: <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/1608754/overview">Sheng Luo</ext-link>, The Second Affiliated Hospital of Guangzhou Medical University, China</p>
</fn>
<fn fn-type="custom" custom-type="reviewed-by" id="fn0003">
<p>Reviewed by: <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/1358838/overview">Chunyan Hao</ext-link>, First Hospital of Shanxi Medical University, China</p>
<p><ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/2196691/overview">Jia Wang</ext-link>, Guangdong Medical University, China</p>
</fn>
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