Relationship of the TG/HDL Ratio With Cognition Function in Older Adults (≥ 60 Years): A Cross‐Sectional Analysis of NHANES 2011–2014

ABSTRACT

Background and Aim

The relationship between the TG/HDL‐C ratio and cognitive impairment in the general US elderly population aged 60 years and older is not well understood. This study aimed to investigate the association between TG/HDL‐C ratio and cognitive function in older adults in the US.

Methods

In this cross‐sectional study (NHANES 2011–2014), 1340 participants underwent cognitive assessments: CERAD (memory), Animal Fluency (verbal fluency), and DSST (processing speed). Global cognition was derived from standardized Z‐scores. Survey‐weighted multivariate linear regression evaluated associations between TG/HDL‐C (continuous/quartiles) and cognitive scores, adjusted for demographics, lifestyle, and comorbidities. Nonlinearity was tested via restricted cubic splines.

Results

Higher TG/HDL‐C correlated with poorer cognitive performance. Compared to Q1 (lowest), Q4 (highest) had significantly lower DSST (β = −2.1, 95% CI: −3.4 to −0.8) and global Z‐scores (β = −0.09, 95% CI: −0.15 to −0.03; p‐trend < 0.001). Each unit increase in TG/HDL‐C reduced DSST (β = −1.34, 95% CI: −2.09 to −0.59) and global scores (β = −0.06, 95% CI: −0.09 to −0.02). Nonlinear patterns were observed (p‐nonlinearity< 0.05). Subgroup analyses identified significant effect modifications by race (DSST: p‐interaction = 0.004) and age (global cognition: p‐interaction = 0.024).

Conclusion

Elevated TG/HDL‐C is independently associated with worse cognitive function in older US adults, particularly impacting processing speed and executive function. These findings underscore lipid dysregulation's role in cognitive aging, supporting targeted interventions for metabolic health.

1. Introduction

Cognitive function (CF) impairment (CFI) generally occurs with age and is caused by a combination of many factors. The global cost of dementia exceeds $800 billion per year [1]. Additionally, CF decline is linked to increased mortality, disability, and loss of independence among older persons [2]. Alzheimer's disease (AD) constitutes a frequent dementia form, liable for over 60% of all cases and affecting about 47 million people globally, a number predicted to triple by 2050 [3]. Typically, AD is characterized by degeneration of neurons and synapses, as well as intracellular neuro progenitor fiber tangles composed of aggregated amyloid‐β (Aβ) peptides and extracellular plaque deposition composed of hyperphosphorylated tau proteins [4, 5]. Therefore, there is a growing need for interventions aimed at slowing the progression of CFI.

Emerging evidence suggests that insulin plays a role in glucose metabolism and mitochondrial function in the brain [6, 7, 8]. Insulin resistance (IR) is defined by lowered insulin sensitivity and response of the body, as evidenced by decreased tissue glucose uptake and suppression of hepatic gluconeogenesis, ultimately leading to hyperglycemia [8, 9]. IR is a core pathophysiological process in metabolic syndrome, characterized by dysregulated lipid metabolism—specifically elevated TG and reduced HDL‐C. The TG/HDL‐C ratio is a well‐validated proxy for IR, with higher ratios reflecting impaired insulin signaling and ectopic lipid accumulation. There is growing evidence that brain glucose/energy metabolism and IR can significantly increase the risk of dementia and that impairments in insulin signaling pathways may be associated with the onset and progression of AD [10]. Factors associated with IR include (a) decreased neuronal glucose uptake, (b) decreased GLUT4 expression, (c) limited homeostatic or inflammatory response to insulin, and (d) then impaired neuroplasticity and cognition [11, 12, 13].

Studies have shown a relationship between the Insulin Resistance Index and CFIs. Both IR and CFI have multiple pathophysiologic mechanisms, with IR being a recognized risk factor for AD [14]. Observational studies have reported that individuals who have type 2 diabetes or IR are at higher risk for AD [15, 16].

As research has progressed, the main plasma biomarker for AD has been P‐tau 181, which is expensive and is a result of pathology rather than a causative factor. Chronic dysregulation of blood glucose and insulin has been shown to predict cognitive performance [17], suggesting that underlying common mechanisms determine pathological outcomes. The pathophysiologic relationship between IR and CFI is a complex area of research, and the sensitivity of IR to the prediction of CF is currently unknown. Our study addresses this gap by analyzing nationally representative National Health and Nutrition Examination Survey (NHANES) data to evaluate whether TG/HDL‐C, as an IR‐related metric, is associated with cognitive performance in adults. Therefore, this study looked at the effects of IR and CFI depending on the NHANES database.

2. Methods

2.1. Survey Description

The National Center for Health Statistics Research Ethics Review Board evaluated and approved the study protocol, with each participant signing informed consent. The generated/analyzed datasets are accessible from the NHANES website (https://www.cdc.gov/nchs/nhanes/index.html). Prespecified analyses included the primary association between TG/HDL ratio and global cognition (Global cognitive) and Covariate adjustment tiers. Post hoc exploratory analyses assessed TG/HDL subgroups and domain‐specific cognition and exploring threshold effects of TG/HDL ratios.

2.2. Study Population

The study population was selected from the NHANES, using a 2011–2012 and 2013–2014 NHANES data set of 19,931 participants, excluding participants aged < 60 years (n = 16,299). Individuals with (1) incomplete cognitive data (n = 688); (2) incomplete HDL‐C and TG data (n = 1588); (3) incomplete BMI data (n = 14); (4) incomplete education level data (n = 2) were excluded. Thus, a subset of 1340 participants remained eligible for further examination (see flow chart).

2.3. Independent Variable

A mathematical model was utilized for calculating the TG/HDL ratio = TG (mg/dL)/HDL (mg/dL).

2.4. Dependent Variable: CF

Using the Consortium to Establish a Registry for Alzheimer's Disease (CERAD), the Animal Fluency Test (AFT), and the Digit Symbol Substitution Test (DSST), a CF assessment was conducted. Both immediate and delayed recall of new verbal information were evaluated by the CERAD Word Learning subtest, which is generally used to measure memory and comprises three learning trials followed by a delayed recall. The highest possible score on the CERAD is 40 [18]. In addition to evaluating executive function, the AFT is primarily concerned with evaluating the lexical‐semantic components of the language domain. Scores on the AFT can range from 3 to 39 [19]. The DSST is an assessment of executive function, memory, attention, and brain reaction time [20], with scores ranging from 0 to 133 [21]. Due to the fact that these three tests are now being used, there exists no gold standard threshold that can be used to determine impaired CF. Herein, we determined the critical value using the lowest quartile of scores, which was based on earlier studies [22, 23, 24, 25]. In addition, we calculated a composite cognition score to provide a global measure of CF and reduce the impact of individual variability and the floor and ceiling effects. This score calculation was performed by summing the standardized z‐scores obtained from the CERAD, AFT, and DSST [18]. Calculating the z‐score follows this formula: z = (x − m)/σ, where x represents the individual's score, m is each test mean score, and σ denotes the standard deviation [26].

2.5. Covariate Information

Subgroups were defined a priori based on biological plausibility and prior literature. Subgroup analyses retained NHANES examination weights (WTMEC4YR), strata (SDMVSTRA), and clusters (SDMVPSU). Covariates in our study included age (60–69, 70–79, and 80+ years), gender (male or female), race (Mexican American, other Hispanic, non‐Hispanic white/black/Asian, other race—including Multi‐Racial), education (< 9th grade, 9–11th grade, high school graduate/GED or equivalent, some college or AA degree, college graduate or above), and BMI (Underweight [< 18.5]; Normal [18.5 to < 25]; Overweight [25 to < 30]; Obese [30 or greater]).

2.6. Statistical Analysis

Statistical analyses were performed through EmpowerStats (version 4.2; X&Y Solutions) and R software (version 4.2; R Foundation for Statistical Computing, Vienna, Austria) with the “survey” package (version 4.1‐1) for complex survey analyses. The proportion and pattern of missingness were evaluated for all key variables. Variables with > 20% missingness were excluded. NHANES survey weights incorporated to preserve population representativity. All analyses incorporated NHANES examination weights (WTMEC4YR) to account for the complex survey design, including stratification, clustering, and nonresponse [27]. Normality of continuous variables was assessed using the Shapiro–Wilk test with α = 0.05 criterion. Continuous variables conforming to normal distribution are expressed as mean ± standard deviation (SD); non‐normally distributed variables as median with interquartile range. Categorical variables are reported as frequencies with raw numerators/denominators (n/N). Study participants were stratified by TG/HDL quartiles (Q1–Q4). Between‐group differences were analyzed using: Weighted linear regression for continuous outcomes, Rao–Scott adjusted chi‐square tests for categorical variables, and Trends across ordered quartiles were tested by modeling category rank as a continuous variable in weighted linear regression. Multivariate linear regression (primary prespecified analysis) examined the relationship between TG/HDL ratio (continuous) and cognitive function, employing three hierarchical models: Model 1: Unadjusted; Model 2: Adjusted for age; Model 3: Further adjusted for sex, education, race, smoking status, and BMI. Sensitivity analyses included: Weighted generalized additive models for nonlinearity assessment and Subgroup interactions tested via Wald F‐tests. All statistical tests were specified as two‐sided with a priori significance level α = 0.05. Results are presented as regression coefficients (β) with 95% confidence intervals (95% CI). p‐values follow standard reporting conventions: p < 0.001, p = 0.003 (to nearest thousandth for 0.001–0.01), p = 0.02 (to nearest hundredth for ≥ 0.01), p > 0.99. Effect sizes take precedence over statistical significance in interpretation. This statistical reporting follows SAMPL guidelines [28].

3. Results

All prespecified analyses (determined before data inspection) are reported as primary findings. Post hoc exploratory analyses—including subgroup assessments by Education attainment/ethnicity—are explicitly identified as such.

3.1. Population Characteristics

Table 1 lists the baseline features of study participants (n = 1340), demonstrating the weighted characteristics of the participants depending on TG/HDL quartiles. Among them, the weighted mean age was 69.11 ± 6.58 years, and 55.48% were male, while the unweighted mean age was 69.61 ± 6.79 years and 51% were male. The results revealed significant differences in baseline features between TG/HDL quartiles except for age/marriage (Table 1). The average scores of AF, DSST, CERAD, and Global cognitive were 18.00, 51.91, 26.36, and 0.28 points, respectively. The highest TG/HDL quartile participants were more likely to be female and have a higher BMI, a lower AF score, a DSST score, and a Global cognitive score compared to other subgroups.

TABLE 1.

Weighted baseline characteristics of the study population (n = 1340).

Characteristics	Unweighted N = 1340	Weighted	TG/HDL‐C				p‐value
			Q1 (0.55–1.59)	Q2 (> 1.59–2.04)	Q3 (> 2.04–2.66)	Q4 (> 2.66–10.13)
Age (years)	69.61 ± 6.79	69.11 ± 6.58	68.85 ± 6.53	69.75 ± 6.75	68.53 ± 6.58	69.46 ± 6.35	0.0842*
Sex							< 0.001**
Female	656 (49)	44.52	28.43	38.35	53.33	63.11
Male	684 (51)	55.48	71.57	61.65	46.67	36.89
Race							0.0336**
Mexican American	123 (9.2)	3.62	2.87	2.73	3.43	5.94
Other Hispanic	142 (10.6)	3.60	1.23	4.01	4.43	5.41
Non‐Hispanic White	680 (50.7)	79.69	83.49	77.66	81.59	74.44
Non‐Hispanic Black	262 (19.6)	8.02	7.81	10.29	6.49	7.57
Other race‐Including multi‐racial	133 (9.9)	5.07	4.61	5.31	4.07	6.65
Education attainment							< 0.001**
Less than 9th grade	155 (11.6)	6.09	4.91	6.49	5.41	8.11
9–11th grade	192 (14.3)	10.75	7.78	8.44	11.33	16.84
High school grad/GED	321 (24.0)	22.93	18.02	24.96	25.42	24.29
Some college or AA degree	375 (28.0)	31.63	30.69	28.22	35.91	31.61
College graduate or above	297 (22.2)	28.60	38.59	31.89	21.94	19.15
Marriage							0.2363**
Married or living with partner	836 (62.4)	68.54	66.38	70.34	66.11	72.43
Unmarried	504 (37.6)	31.46	33.62	29.66	33.89	27.57
BMI	29.11 ± 6.37	29.21 ± 6.48	25.97 ± 5.03	29.04 ± 5.79	30.26 ± 6.07	32.62 ± 7.33	< 0.001*
Triglyceride (mg/dl)	122.33 ± 70.69	125.17 ± 73.64	82.58 ± 35.42	112.75 ± 52.32	143.73 ± 71.51	175.73 ± 95.09	< 0.001*
LDL (mg/dl)	110.94 ± 35.91	110.94 ± 35.91	115.68 ± 34.70	113.03 ± 32.52	115.28 ± 37.95	96.24 ± 34.98	< 0.001*
HDL (mg/dl)	55.69 ± 16.73	55.89 ± 16.85	76.65 ± 15.55	56.90 ± 6.68	47.98 ± 6.98	41.19 ± 8.66	< 0.001*
AF score	16.64 5 ± 5.44	18.00 ± 5.42	18.98 ± 5.63	17.83 ± 5.21	17.94 ± 5.41	16.91 ± 5.12	< 0.001*
DSST score	45.43 ± 17.39	51.91 ± 16.99	55.54 ± 17.13	52.70 ± 16.72	51.40 ± 17.17	46.60 ± 15.43	< 0.001*
CERAD score	24.93 ± 6.52	26.36 ± 6.19	27.58 ± 6.08	25.94 ± 6.09	26.28 ± 6.35	25.26 ± 5.95	< 0.001*
Global cognitive	0.00 ± 0.80	0.28 ± 0.79	0.47 ± 0.81	0.26 ± 0.75	0.26 ± 0.79	0.06 ± 0.73	< 0.001*

Note: Weighted by: WTSAF2YR/2.

Abbreviations: AF, animal fluency; CERAD, Consortium to Establish a Registry for Alzheimer's Disease; DSST, Digit Symbol Substitution Test; Q1–Q4, quartiles based on the median of TG to HDL‐C ratio; Mean ± SD for: Age BMI Triglyceride HDL AF DSST CERAD Global cognitive; % for: Sex Race Education attainment Marriage; Q1–Q4, quartiles on the basis of TG/HDL median.

*, Weighted ANOVA;

**, Weighted Rao–Scott χ² test.

3.2. Interaction Between TG/HDL and CF

Table 2 demonstrates the multivariate linear regression analysis results that examined relations between TG/HDL and AF, DSST, CERAD, and Global cognition score. In the unadjusted model, TG/HDL was negatively related to AF (β = –0.50, 95% CI: –0.79, –0.21, p < 0.001), DSST (β = –2.78, 95% CI: –3.68, –1.88, p < 0.001), CERAD (β = –0.73, 95% CI: –1.06, –0.40, p < 0.001), and Global cognition score (β = –0.12, 95% CI: –0.16, –0.08, p < 0.001) were negatively correlated. After adjusting for age confounders, this negative correlation remained for AF (β = –0.51, 95% CI: –0.78, –0.23, p < 0.001), DSST (β = –2.81, 95% CI: –3.62, –1.99, p < 0.001), CERAD (β = –0.74, 95% CI: –1.05, –0.43, p < 0.001), and Global cognition score (β = –0.12, 95% CI: –0.16, –0.09, p < 0.001). Fully adjusted, TG/HDL was negatively related to DSST score (β = –1.34, 95% CI: –2.09, –0.59, p < 0.001) and Global cognition score (β = –0.06, 95% CI: –0.09, –0.02, p = 0.003), p for trend < 0.001.

TABLE 2.

Multivariable linear regression of TG/HDL interconnection with AF, DSST, CERAD, and Global cognition.

Variable	β (95% CI)
	Model 1	p‐value	Model 2	p‐value	Model 3	p‐value
AF score
TG/HDL‐C	−0.50 (−0.79, −0.21)	< 0.001	−0.51 (−0.78, −0.23)	< 0.001	−0.27 (−0.56, 0.02)	0.068
TG/HDL‐C quartile
Q1	0 (Ref)		0 (Ref)		0 (Ref)
Q2	−1.15 (−1.97, −0.32)	0.006	−0.94 (−1.73, −0.15)	0.02	−0.62 (−1.37, 0.13)	0.103
Q3	−1.04 (−1.85, −0.23)	0.01	−1.11 (−1.89, −0.34)	0.005	−0.75 (−1.52, 0.01)	0.053
Q4	−2.07 (−2.93, −1.21)	< 0.001	−1.93 (−2.75, −1.11)	< 0.001	−1.21 (−2.08, −0.35)	0.006
p for trend		< 0.001		< 0.001		0.004
DSST score
TG/HDL‐C	−2.78 (−3.68, −1.88)	< 0.001	−2.81 (−3.62, −1.99)	< 0.001	−1.34 (−2.09, −0.59)	< 0.001
TG/HDL‐C quartile
Q1	0 (Ref)		0 (Ref)		0 (Ref)
Q2	−2.83 (−5.39, −0.28)	0.03	−1.88 (−4.20, 0.44)	0.113	0.09 (−1.86, 2.04)	0.929
Q3	−4.14 (−6.65, −1.62)	0.001	−4.49 (−6.77, −2.21)	0.001	−1.39 (−3.39, 0.60)	0.172
Q4	−8.94 (−11.61, −6.27)	< 0.001	−8.29 (−10.72, −5.87)	< 0.001	−3.20 (−5.45, −0.94)	0.006
P for trend		< 0.001		< 0.001		0.003
CERAD score
TG/HDL‐C	−0.73 (−1.06, −0.40)	< 0.001	−0.74 (−1.05, −0.43)	< 0.001	−0.26 (−0.59, 0.07)	0.128
TG/HDL‐C quartile
Q1	0 (Ref)		0 (Ref)		0 (Ref)
Q2	−1.63 (−2.57, −0.70)	< 0.001	−1.35 (−2.23, −0.47)	0.003	−0.74 (−1.60, 0.12)	0.092
Q3	−1.30 (−2.22, −0.38)	0.006	−1.41 (−2.27, −0.54)	0.002	−0.39 (−1.27, 0.49)	0.386
Q4	−2.31 (−3.29, −1.34)	< 0.001	−2.12 (−3.04, −1.20)	< 0.001	−0.49 (−1.48, 0.51)	0.336
P for trend		< 0.001		< 0.001		0.194
Global cognition
TG/HDL‐C	−0.12 (−0.16, −0.08)	< 0.001	−0.12 (−0.16, −0.09)	< 0.001	−0.06 (−0.09, −0.02)	0.003
TG/HDL‐C quartile
Q1	0 (Ref)		0 (Ref)		0 (Ref)
Q2	−0.21 (−0.33, −0.09)	< 0.001	−0.16 (−0.27, −0.06)	0.003	−0.07 (−0.17, 0.02)	0.120
Q3	−0.21 (−0.33, −0.09)	< 0.001	−0.23 (−0.33, −0.12)	< 0.001	−0.09 (−0.19, 0.00)	0.057
Q4	−0.42 (−0.54, −0.29)	< 0.001	−0.39 (−0.50, −0.27)	< 0.001	−0.16 (−0.27, −0.05)	0.004
p for trend		< 0.001		< 0.001		0.001

Abbreviations: AF, Animal Fluency; CERAD, Consortium to Establish a Registry for Alzheimer's Disease; DSST, Digit Symbol Substitution Test; Q1–Q4, quartiles based on the median of TG to HDL‐C ratio; 95% CI, 95% Confidence Interval. Model 1: non‐adjusted model. Model 2: adjusted for age. Model 3: adjusted for model 2, additionally adjusted for age, sex, race, education, and marriage.

3.3. Subgroup Analysis

For further assessment of the strength of the connection between TG/HDL and AF, DSST, CERAD, and Global cognition score, we also performed subgroup analysis. Race (P for interaction = 0.004, Figure 1B) for DSST, age (p for interaction = 0.035, Figure 1C) and gender (p for interaction = 0.038, Figure 1C) for CERAD, and age (p for interaction = 0.024, Figure 1D) and race (p for interaction = 0.010, Figure 1D) for Global cognition were significant.

FIGURE 1.

Subgroup Analyses of TG/HDL‐C Ratio and Cognitive Function. Description: Forest plot of subgroup‐specific β‐coefficients (95% CI) for TG/HDL‐C ratio (per 1‐unit increase) and global cognitive Z‐score. Interaction effects tested via weighted Wald tests. Reference lines: overall effect (β = −0.06). Subgroups stratified by age (60–69/70–79/ ≥ 80 yrs), sex, race (Mexican American/Other Hispanic/Non‐Hispanic White/Non‐Hispanic Black/Other Race‐Including Multi‐Racial), education (Less than 9th grade/9–11th grade/High school grad/GED/Some college or AA degree/College graduate or above), and Marriage. All models incorporate NHANES examination weights and adjust for covariates in Figure 1. (A) effect size of TG/HDL‐C on AF; (B) effect size of TG/HDL‐C on DSST; (C) effect size of TG/HDL‐C on CERAD; (D) effect size of TG/HDL‐C on Global cognitive.

3.4. Analysis of the Nonlinear Correlation Between TG/HDL and CF

The association between TG/HDL and AF was nonlinear rather than linear (nonlinear p = 0.045, Figure 2A). In addition, associations were also recognized between TG/HDL and DSST (nonlinear p = 0.001, Figure 2B) and global cognition (nonlinear p = 0.013, Figure 2D). However, a linear interplay existed between TG/HDL and CERAD (nonlinear p = 0.159, Figure 2C).

FIGURE 2.

Nonlinear relationship between TG/HDL‐C ratio and global cognitive Z‐score. Description: Survey‐weighted restricted cubic spline plot (3 knots) illustrating the nonlinear association between TG/HDL‐C ratio and standardized global cognitive function. Red line: adjusted β‐estimates; blue bars: 95% confidence interval. (A) AF; (B) DSST; (C) CERAD; (D) Global cognitive.

4. Discussion

This population‐based cross‐sectional study identified a negative correlation between TG/HDL and CF in 1340 older Americans. After adjusting for potential confounders, TG/HDL displayed a significant negative relation with AFT, DSST, CERAD, and overall global cognition scores (p < 0.05). Therefore, older adults with higher TG/HDL exhibited memory impairment and slower processing speed. A trend analysis using TG/HDL quartiles also showed a negative linear relation between TG/HDL and AFT, DSST, CERAD, and global cognition scores, further supporting this negative correlation.

The TG/HDL ratio strongly correlates with systemic insulin resistance (IR), which impairs brain glucose metabolism. Neurons reliant on steady glucose supply become vulnerable to energy deficits, leading to oxidative stress and synaptic dysfunction—hallmarks of Alzheimer's disease pathology [29]. Elevated TG drives inflammation via FFA‐induced activation of TLR4/NF‐κB signaling, while low HDL fails to suppress endothelial adhesion molecules (e.g., VCAM‐1). This chronic inflammation disrupts BBB integrity, allowing inflammatory cytokines to activate microglia and promote neurotoxicity [30, 31, 32]. Dyslipidemia accelerates atherosclerosis and cerebral small vessel disease, reducing perfusion in hippocampal and cortical regions critical for memory. The TG/HDL ratio predicts subclinical vascular damage (e.g., white matter hyperintensities) [33, 34]. HDL deficiency impairs Aβ clearance via apoE‐dependent mechanisms, while TG‐rich particles may increase Aβ production by altering lipid raft composition in neuronal membranes [35].

Prior research has indicated that a low TG/HDL is related to higher memory scores [36]. Plasma TG/HDL as a surrogate for insulin resistance. Lower HDL or higher TG may escalate the CFI risk in later life [37]. Using the TG/HDL as a predictor of future CFI can be beneficial. Implementing early interventions, such as maintaining a proper diet and engaging in appropriate exercise, to lower the ratio may possibly reduce CFI incidence. Previous studies have shown that hypercholesterolemia may elevate Aβ peptide production and deposition in the brain as well as promote neurotoxic protofibril formation and neuroinflammation [38, 39]. The mechanisms behind the association between TG/HDL and CF are unclear. The outcomes of subgroup analyses and interaction tests showcased that race, gender, and age may influence the impact of TG/HDL on CF. This suggests that the relation between TG/HDL levels and CFI differs across age groups, genders, and ethnicities. The study highlights that managing TG/HDL levels could potentially help improve CF in individuals aged ≥60 years.

There are high concentrations of insulin receptors in the hippocampus, entorhinal cortex, and frontal cortex, indicating that insulin is critical in the functional processes of these regions [5]. Within neurons, IR can be interpreted as an impairment in neural plasticity or the release of neurotransmitters. Tau hyperphosphorylation resulting from impaired glucose metabolism caused by IR [40]. Assessing IR requires complex techniques, which are not suitable for large‐scale research. The TG/HDL has previously been validated as an alternative measurement for evaluating HOMA‐IR [41, 42]. An elevated plasma TG/HDL is related to poorer cognitive outcomes in participants with MCI and dementia [43], aligning with the results of current studies on TG/HDL.

Here, we discovered a potential nonlinear relation between AF, DSST, and global cognitive scores. The current study performed an innovative analysis of TG/HDL and CF interplay. Before the inflection point, AF, DSST, and global cognitive scores progressively increased as TG/HDL levels rose. However, after the inflection point, there was a slight decline in AF, DSST, and global cognitive scores. Therefore, a lower TG/HDL does not necessarily indicate better CF. To our knowledge, there have been few studies reporting this nonlinear relationship before.

5. Conclusion

The present study elucidated a significant negative interconnection between TG/HDL and CF in older Americans. However, a lower TG/HDL does not necessarily indicate better CF. To our knowledge, there have been few studies reporting this nonlinear relationship before. The clinical implications of our findings suggest that determining TG/HDL in older adults may be able to assist in identifying individuals who are at a greater risk for CFI. Research in the future should investigate how lifestyle factors such as food, exercise, and other lifestyle factors that are known to alter blood lipids affect cognitive performance, with the goal of slowing or reversing the process of cognitive aging.

Author Contributions

Shanshan Li: conceptualization, writing – original draft, visualization, methodology. Boda Jiang: methodology. Yaxi Zhang: visualization. Mengdi Yu: validation. Xinlei Mao: writing – review & editing, resources, conceptualization, investigation.

Conflicts of Interest

All authors declare no conflicts of interest.

Study Strengths and Limitations

Our study results provide epidemiological proof to support the notion that TG/HDL is a potential predictor of CF. There are several restrictions that apply to this study. First, owing to being a cross‐sectional study, we could not demonstrate a causal connection between TG/HDL and CFI. Second, because the TG/HDL ratio is calculated based on a single blood sample taken at the beginning of the study, we are unable to evaluate its impact on CF over time. Finally, because our NHANES data set comprises cognitive test results only from 2011 to 2014, involving participants aged 60 and above, and the presence of missing covariate data, only 1340 subjects were ultimately included in the analysis, potentially resulting in biased research outcomes.

Transparency Statement

The lead author Xinlei Mao affirms that this manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.

Li S., Jiang B., Zhang Y., Yu M., and Mao X., “Relationship of the TG/HDL Ratio With Cognition Function in Older Adults (≥ 60 Years): A Cross‐Sectional Analysis of NHANES 2011–2014,” Health Science Reports 8 (2025): 1‐9. 10.1002/hsr2.71224.

Data Availability Statement

The data that support the findings of this study are openly available in the National Health and Nutrition Examination Survey at https://wwwn.cdc.gov/nchs/nhanes/. The data supporting this study are publicly available from the National Health and Nutrition Examination Survey (NHANES) database, managed by the Centers for Disease Control and Prevention (CDC). All datasets can be accessed at: https://wwwn.cdc.gov/nchs/nhanes/.