Association between Headache and Mild Cognitive Impairment (MCI) among Middle-aged and Elderly Individuals: Evidence from the China Health and Retirement Longitudinal Study (CHARLS)

doi:10.21203/rs.3.rs-5023855/v1

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Research Article

Association between Headache and Mild Cognitive Impairment (MCI) among Middle-aged and Elderly Individuals: Evidence from the China Health and Retirement Longitudinal Study (CHARLS)

https://doi.org/10.21203/rs.3.rs-5023855/v1

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Background

Studies investigating the association between headache and mild cognitive impairment (MCI) are lacking. This study evaluated the independent association between headache and MCI among middle-aged and older Chinese adults using data from the China Health and Retirement Longitudinal Study (CHARLS).

Methods

This prospective cohort study used headache and cognitive data from two waves of the CHARLS collected in 2011 (wave 1) and 2015 (wave 3). The study population consisted of individuals who are 45 years of age or older with complete data on headache and cognitive assessments. We used statistical methods such as linear regression and multiple logistic regression to analyze the data.

Results

The study initially enrolled 12,623 participants with a mean age of 58.9 years; 52.6% were female. Cognitive scores were lower in the headache group across all domains in the cross-sectional analysis (β= -0.39, 95% CI: -0.57~-0.21).

Longitudinally, the headache group had a higher risk of MCI at baseline (OR=2.30, 95% CI: 2.05 to 2.58) and follow-up (OR=2.46, 95% CI: 1.86 to 3.26), with persisting significance after adjustments (baseline adjusted OR=1.32, follow-up adjusted OR=1.42, both p<0.05). In the subgroup analyses, no significant interactions were observed between the groups (p > 0.05).

Conclusions

Participants with headache disorders had lower cognitive scores and had a higher risk of developing MCI in middle-aged and older Chinese population. As headache may increase the risk of MCI, headache prevention and treatment is of vital importance in preventing cognitive impairment, improving the quality of life in middle-aged and older population.

Headache

Cognitive Impairment

Middle Aged

Aged

CHARLS

With an exponential increase of prolonged life expectancy and the geriatric population in China, age-related morbidities have become pivotal public health concerns [1, 2]. Headache is a prevalent clinical complaint and a significant cause of disability. It is experienced ubiquitously throughout the lifespan [3, 4]. Epidemiological studies have reported a lifetime prevalence of approximately 90%, imposing a substantial burden on individuals, families and society [3, 4]. The global prevalence of headache disorders in the adult population is estimated to be approximately 50% [5]. Although the prevalence of headache decreases with age, headache remains a common symptom with an annual prevalence rate fluctuating between 21.8% and 51% in the elderly population aged 65 and above [6, 7]. Among the above individuals, 35% experienced frequent headaches with more than one episode per month [6].

Mild cognitive impairment (MCI) is a neurodegenerative process involving multiple domains, including attention, memory, execution, language, literacy, numeracy, reasoning, planning, and orientation [7]. It has been suggested that the incidence of MCI among individuals aged 60 and over is 15–20% [8]. The research conducted by Jia Jianping indicates that the prevalence of MCI in Chinese population is 15.54% [9]. Studies have demonstrated that cognitive impairment is associated with age, sex, apolipoprotein E (APOE) allele, a positive family history of dementia, and the concomitant existence of cardiovascular risk factors such as hypertension, hyperlipidemia and stroke [10–12].

Although headache disorders and MCI are not rare among middle-aged and elderly individuals, few studies elucidated the association between these two conditions. These studies were usually with small sizes and mostly lack of prospective evidence. The research results of these studies were controversial [13–19].

In the current study, we used China Health and Retirement Longitudinal Study (CHARLS) and provided both cross-sectional and longitudinal evidence of the association between headache disorders and MCI.

Study population

The CHARLS initiative is designed to gather a comprehensive and high-quality set of microdata pertaining to households and individuals who are 45 years of age or older in China, with the objective of examining the demographic phenomenon of aging within the Chinese population and fostering interdisciplinary research in gerontology. The national baseline survey of CHARLS employed a multi-stage probability to size (PPS) sampling methodology, which was a systematic approach to ensure representativeness in the sample selection. The survey encompassed 450 villages, 150 counties across 28 provinces, and included over 17,000 individuals from approximately 10,000 households. CHARLS is a longitudinal study with biennial to triennial assessments, and to date, it has released data from four distinct survey waves: the initial national baseline survey (Wave 1, 2011), the first follow-up survey (Wave 2, 2013), the second follow-up survey (Wave 3, 2015), and the third follow-up survey (Wave 4, 2018).

Extensive details regarding the CHARLS have been documented and published in the existing scholarly literature. The CHARLS survey project received ethical approval from the Biomedical Ethics Committee of Peking University(IRB00001052–11015), and all participants were mandated to provide informed consent prior to their involvement in the study.

This cohort study utilized two waves of CHARLS data collected in 2011 (wave 1) and 2015 (wave 3). The sample size of Wave 1 was 17,705, from which 5,082 individuals were excluded (including 1879 individuals with missing cognitive or headache data at wave 3, 2,605 individuals with missing covariates such as age, gender, and BMI and 668 individuals with missing CESD score). The cross-sectional analysis involved 12,623 participants. For the longitudinal analysis, 5,771 individuals were excluded, including 2,350 individuals with missing cognitive or headache data at wave 3, 2,328 individuals with mismatched headache information between wave 1 and wave 3, 1,331 individuals identified as MCI at wave 1, and 212 individuals with a history of memory-related disease and stroke. Ultimately, 6,852 individuals were included in the final analysis. The detailed flowchart of the sample selection process is shown in Fig. 1.

Measurement of cognitive function

Cognitive function was measured at two time points—the CHARLS 2011 baseline survey and the 2015 follow-up survey—using questionnaires adapted from the Telephone Interview for Cognitive Status. The assessment included episodic memory (score range: 0–10 points), orientation (score range: 0–5 points), calculation (score range: 0–5 points), and drawing (score range: 0–1 points). Episodic memory was measured through immediate and delayed recall. The total score range was from 0 to 21 points, with higher scores indicating better cognitive function.

There is no consensus on the diagnostic criteria of MCI. In our study, we adopted aging-associated cognitive decline (AACD) to define MCI, specifically defining MCI as cognitive performance that falls at least 1 standard deviation (SD) below the age-adjusted norm [25, 26]. All participants over 60 years old were grouped for every 5 years of age. Participants within each age cohort who satisfied the AACD criteria were classified as having MCI.

Measurement of headache

Headache was evaluated by asking the participants “Are You Often Troubled with Any Body Pains” and “Where Are The Body Pains”. Participants with “head (headache)” were considered having headache.

Covariates

According to prior knowledge, we also considered sociodemographic characteristics and health-related factors in our study. Sociodemographic characteristics included age, gender and marital status (married/unmarried; the term "unmarried" includes several marital statuses: "separated", "unmarried," "divorced" and "widowed"). Health-related factors included ever/current smoke, ever/current alcohol, nighttime sleep duration, poor sleep quality and 14 common co-morbidities (cancer, chronic lung diseases, heart disease, stroke, emotional and mental disorders, arthritis, dyslipidaemia, hepatic disease, kidney disease, digestive system disease, asthma, memory-related disease, hypertension, and hyperglycaemia). Poor sleep quality was assessed according to the response “my sleep was restless”, and divided into four groups according to the amount of time. Total nighttime sleep duration data was obtained from the question ‘‘During the past month, how many hours of actual sleep did you get at night (average hours for one night)?’’.Body mass index (BMI) was defined as the weight divided by the square of height (kg/m²). Depression was evaluated by the 10-item Center for Epidemiologic Studies Depression Scale (CESD-10), with a total score of 30.

Statistical analysis

Quantitative data with a normal distribution were described using the mean and standard deviation (SD), while non-normally distributed data were presented using the median (interquartile range). Qualitative data were reported as percentages. Group comparisons between the headache and non-headache groups were carried out using one-way analysis of variance and chi-square tests.

In cross-sectional study, a linear regression model was employed to investigate the association between headache and the total cognitive function score (Wave 1, 2011), and expressed as regression coefficients (β) and 95% confidence intervals (CI). Longitudinal data from 2011 and 2015 were analyzed using logistic regression models to explore the relationship between headache and MCI. Four different models with various combinations of covariates were utilized. Specifically, Model 1 included only cognitive function; Model 2 included age, gender and marital status; Model 3 further included education, smoking and alcohol history, nighttime sleep duration, poor sleep quality, life satisfaction, and insurance status; Model 4 further included comorbidities, BMI, and CESD score.

Additionally, we utilized a multivariable logistic regression model with subgroup based on gender (male and female), age (60 years and ≥ 60 years), marital status (married and unmarried), BMI (25 kg/m² and ≥ 25 kg/m²), nighttime sleep duration (< 6 hours and ≥ 6 hours), smoking status (never and former/current), and alcohol consumption status (never and former/current). The adjusted covariates were the same as in Model 4. Likelihood ratio tests were employed to examine interactions among the subgroups. Furthermore, sensitivity analyses were conducted to assess the robustness of our findings.

All statistical analyses were conducted using R software (version 4.4.0; R Foundation for Statistical Computing; http://www.R-proje ct.org) and Free Statistics software (version2.01; Beijing Free Clinical Medical Technology Co., Ltd)., with a significance level set at 0.05 for all tests.

Table 1 presented the baseline characteristics of the first wave of the study population. The average age of the 12,623 participants was 58.9 years (SD: 9.7 years), with 52.6% being female. A total of 1,720 (13.6%) participants had a history of headaches. Compared to non-headache group, those with headaches had a higher proportion of females, lower educational attainment, higher rates of smoking and alcohol consumption, lower life satisfaction, shorter sleep duration, poorer sleep quality, higher prevalence of co-morbidities, higher CESD scores, and lower BMI (p < 0.05). No significant differences were observed between the two groups in terms of age, marital status, and insurance (p > 0.05).

Table 1

Baseline characteristics of study population in 2011 (wave 1)
	Total (n = 12623)	Non-headache(n = 10903)	Headache(n = 1720)	P value
Age(years, M ± SD)	58.9 ± 9.7	58.9 ± 9.7	58.8 ± 9.5	0.807
Gender, n (%)				< 0.001
Female	6641 (52.6)	5443 (49.9)	1198 (69.7)
Male	5982 (47.4)	5460 (50.1)	522 (30.3)
Marital, n (%)				0.166
Married	11078 (87.8)	9586 (87.9)	1492 (86.7)
Unmarried	1545 (12.2)	1317 (12.1)	228 (13.3)
Education, n (%)				< 0.001
Illiterate	3364 (26.7)	2713 (24.9)	651 (37.8)
Primary school	5194 (41.2)	4444 (40.8)	750 (43.6)
Middle/high school	3835 (30.4)	3521 (32.3)	314 (18.3)
Junior college or above	229 ( 1.8)	224 (2.1)	5 (0.3)
Ever/current smoke, n (%)				< 0.001
No	7604 (60.2)	6391 (58.6)	1213 (70.5)
Yes	5018 (39.8)	4511 (41.4)	507 (29.5)
Ever/current drink, n (%)				< 0.001
No	8497 (67.3)	7196 (66)	1301 (75.6)
Yes	4126 (32.7)	3707 (34)	419 (24.4)
Insurance, n (%)				0.239
No	808 ( 6.4)	709 (6.5)	99 (5.8)
Yes	11815 (93.6)	10194 (93.5)	1621 (94.2)
Life satisfy, n (%)				< 0.001
Poor	1395 (11.1)	1107 (10.2)	288 (16.7)
Fair	8691 (68.9)	7492 (68.7)	1199 (69.7)
Good	2537 (20.1)	2304 (21.1)	233 (13.5)
Poor sleep quality, n (%)				< 0.001
Most or all of the time	2461 (19.5)	1824 (16.7)	637 (37)
Occasionally or a moderate amount of the time	1887 (14.9)	1513 (13.9)	374 (21.7)
Rarely or none of the time	6227 (49.3)	5789 (53.1)	438 (25.5)
Some or a little of the time	2048 (16.2)	1777 (16.3)	271 (15.8)
Co-morbidities
Hypertension, n (%)				< 0.001
No	9562 (76.2)	8333 (76.8)	1229 (72.1)
Yes	2994 (23.8)	2518 (23.2)	476 (27.9)
Dyslipidemia, n (%)				< 0.001
No	11268 (91.0)	9778 (91.4)	1490 (88.5)
Yes	1117 ( 9.0)	924 (8.6)	193 (11.5)
Diabetes, n (%)				< 0.001
No	11794 (94.3)	10220 (94.6)	1574 (92.5)
Yes	713 ( 5.7)	585 (5.4)	128 (7.5)
Cancer, n (%)				0.001
No	12446 (99.0)	10765 (99.1)	1681 (98.3)
Yes	124 ( 1.0)	95 (0.9)	29 (1.7)
Chronic lung disease, n (%)				< 0.001
No	11271 (89.6)	9869 (90.8)	1402 (81.9)
Yes	1303 (10.4)	994 (9.2)	309 (18.1)
Hepatic disease, n (%)				< 0.001
No	12044 (96.1)	10474 (96.7)	1570 (92.5)
Yes	490 ( 3.9)	363 (3.3)	127 (7.5)
Heart disease, n (%)				< 0.001
No	12044 (96.1)	10474 (96.7)	1570 (92.5)
Yes	490 ( 3.9)	363 (3.3)	127 (7.5)
Stroke, n (%)				< 0.001
No	12331 (97.9)	10682 (98.2)	1649 (96)
Yes	265 ( 2.1)	197 (1.8)	68 (4)
Kidney disease, n (%)				< 0.001
No	11767 (93.7)	10304 (95)	1463 (85.9)
Yes	786 ( 6.3)	545 (5)	241 (14.1)
Digestive system disease, n (%)				< 0.001
No	9689 (77.0)	8662 (79.6)	1027 (60.1)
Yes	2901 (23.0)	2219 (20.4)	682 (39.9)
Emotional/nervous/psychiatric problems n (%)				< 0.001
No	12429 (98.8)	10766 (99.1)	1663 (97.1)
Yes	149 ( 1.2)	99 (0.9)	50 (2.9)
Arthritis, n (%)				< 0.001
No	8316 (66.0)	7603 (69.8)	713 (41.6)
Yes	4286 (34.0)	3283 (30.2)	1003 (58.4)
Memory-related disease, n (%)				< 0.001
No	12441 (98.8)	10771 (99)	1670 (97.5)
Yes	149 ( 1.2)	107 (1)	42 (2.5)
Asthma, n (%)				< 0.001
No	12122 (96.4)	10522 (96.9)	1600 (93.3)
Yes	455 ( 3.6)	341 (3.1)	114 (6.7)
CESD, Mean ± SD	8.4 ± 6.3	7.6 ± 5.9	13.6 ± 6.6	< 0.001
Nighttime sleep duration, Mean ± SD	6.4 ± 1.9	6.5 ± 1.8	5.6 ± 2.2	< 0.001
BMI, Mean ± SD	23.5 ± 3.8	23.5 ± 3.8	23.1 ± 3.8	< 0.001
Abbreviations: BMI, body mass index; CESD, Center for Epidemiologic Studies Depression;;M± SD, mean ± standard deviation.

Table 2 presented the average total cognition score of the entire population as 10.4 ± 4.3, with median scores for orientation, memory, and computation being 3 (4, 5), 3.5 (2.0, 4.5), and 3 (1, 5) respectively. The accuracy rate for drawing was 64%. Compared to the non-headache group, the headache group exhibited a decline in various cognitive dimensions (p < 0.05).

Table 2

Cross-sectional association between Headache and cognitive score in 2011(wave1)
	Total	Non- headache	Headache	P value
Computation,Median(IQR)	3(1,5)	3(1,5)	1(0,5)	< 0.001
Drawing, n (%)				< 0.001
Wrong	4546 (36.0)	3689 (33.8)	857 (49.8)
Correct	8077 (64.0)	7214 (66.2)	863 (50.2)
Orientation, Median (IQR)	3(4,5)	3(4,5)	2(4,4)	< 0.001
Memory, Median (IQR)	3.5 (2.0, 4.5)	3.5 (2.0, 4.5)	3.0 (1.5, 4.0)	< 0.001
Cognition, Mean ± SD	10.4 ± 4.3	10.7 ± 4.2	8.7 ± 4.2	< 0.001
Abbreviations: Median(IQR), Median (Interquartile Range). Mean ± SD,, mean ± standard deviation.

Table 3 displays the cross-sectional association between headache and cognitive scores in 2011. In the crude model, the cognitive scores of the headache group (β=-1.94, 95% CI: -2.16, -1.73) were lower than those of the non-headache group. In the fully adjusted model accounting for age, gender, residential area, education level, insurance, marital status, past/current smoking, past/current alcohol consumption, life satisfaction, poor sleep quality, daily sleep duration, comorbidities, CESD score, and BMI, a similar pattern was also statistically significant (β= -0.39, 95% CI: -0.57~-0.21), (p < 0.001).

Table 3

Linear regression model on headache and cognitive score in 2011(wave1)
Models	No headache	Headache
Models	No headache	β (95% CI)	P-value
Model1	Reference	-1.94 (-2.16~-1.73)	< 0.001
Model2	Reference	-1.59 (-1.79~-1.39)	< 0.001
Model3	Reference	-0.72 (-0.89~-0.55)	< 0.001
Model4	Reference	-0.39 (-0.57~-0.21)	< 0.001
Notes: Model 1: No adjustment; Model 2: Adjusted for age, gender, and marital status; Model 3: Model 2 + education, ever/current smoke, ever/current alcohol, Nighttime sleep duration, poor sleep quality and lifesatisfy + insurance ; Model 4: Model 3 + comorbidities, BMI, and CESD score

Table 4 showed that in the baseline period (wave1), in the unadjusted model (model 1), the headache group had a higher risk of developing MCI compared to the non-headache group (OR = 2.30, 95% CI: 2.05, 2.58). This association remained statistically significant (OR = 1.32, 95% CI: 1.14, 1.54) even after comprehensive adjustment for potential confounders including age, gender, residential area, education level, insurance, marital status, smoking history, alcohol consumption, life satisfaction, poor sleep quality, daily sleep duration, comorbidities, CESD score, and BMI (all p < 0.001).

Table 4

logistic regression model on headache and MCI in 2011(wave1)
Models	No headache	Headache
Models	No headache	OR(95% CI)	P-value
Model1	Reference	2.30 (2.05 ~ 2.58)	< 0.001
Model2	Reference	1.98 (1.76 ~ 2.23)	< 0.001
Model3	Reference	1.48 (1.29 ~ 1.7)	< 0.001
Model4	Reference	1.32 (1.14 ~ 1.54)	< 0.001
Notes: Model 1: No adjustment; Model 2: Adjusted for age, gender, and marital status; Model 3: Model 2 + education, ever/current smoke, ever/current alcohol, Nighttime sleep duration, poor sleep quality and life-satisfy + insurance ; Model 4: Model 3 + comorbidities, BMI, and CESD score

Figure 2 presented the results of subgroup analyses in the cross-sectional study. In the headache group, the elderly (60 years or older) participants, female, participants with sufficient sleep (≥ 6 hours), and non-alcohol consumers exhibited a significantly higher odds ratio of MCI (p < 0.05). In contrast, there was no statistically significant difference in the odds ratio of MCI between headache participants and non-headache participants who were male, younger than 60 years old, with a sleep duration of less than 6 hours, and those with a history of alcohol consumption (p > 0.05). Furthermore, in the subgroups defined by smoking status and marital status, participants with headache demonstrated a significantly elevated odds ratio of MCI (p < 0.05). There were no significant interactions between the groups (p > 0.05).

Table 5 showed that at the end of the follow-up period (wave3), in the unadjusted model (model 1), the headache group had a higher risk of developing MCI compared to the non-headache group (OR = 2.46, 95% CI: 1.86, 3.26). This association remained statistically significant (OR = 1.42, 95% CI: 1.01, 1.98) even after comprehensive adjustment for potential confounders including age, gender, residential area, education level, insurance, marital status, smoking history, alcohol consumption, life satisfaction, poor sleep quality, daily sleep duration, comorbidities, CESD score, and BMI (all p < 0.05).

Table 5

logistic regression model on headache and MCI in 2015 (wave3)
Models	No headache	Headache
Models	No headache	OR(95% CI)	P-value
Model1	Reference	2.46 (1.86 ~ 3.26)	< 0.001
Model2	Reference	1.92 (1.44 ~ 2.57)	< 0.001
Model3	Reference	1.52 (1.11 ~ 2.08)	0.009
Model4	Reference	1.42 (1.01 ~ 1.98)	0.044
Notes: Model 1: No adjustment; Model 2: Adjusted for age, gender, and marital status; Model 3: Model 2 + education, ever/current smoke, ever/current alcohol, Nighttime sleep duration, poor sleep quality and life-satisfy + insurance ; Model 4: Model 3 + comorbidities, BMI, and CESD score

Figure3 showed that at the end of the follow-up period (wave 3), the subgroup analysis indicated that among individuals with lower BMI (BMI < 25), non-alcohol consumers, and ever/current smokers with headaches (p < 0.05), headache sufferers had higher odds ratio of developing MCI (p < 0.05). In contrast, there were no significant differences in the odds ratio of developing MCI between headache and non-headache individuals in other subgroups (p > 0.05). There were no significant interactions observed between the groups.

According to our cross-sectional study, the cognitive scores of the headache group were significantly lower than those of the non-headache group, and in our longitudinal study, the headache group had a higher risk of developing MCI compared to the non-headache group. Subgroup analysis revealed that age, gender, marital status, alcohol, smoking, sleep duration and BMI might be promoting/preventing factors to develop MCI.

Headache disorders, especially migraines, as a common neurological disorder, are considered to be potentially associated with white matter lesions, asymptomatic cerebral infarction, and an increased risk of stroke [20]. These studies challenged the previous view that most headache disorders are merely a benign condition. In recent years, the association between headache disorders and cognitive function has received widespread attention. However, the association between headache and cognition was controversial according to previous studies [13–19].

The mechanisms of the association of headache and cognition are complicated. One research indicated that the association between primary headaches and cognitive impairment went beyond simple comorbidity and might involve shared pathophysiological mechanisms [21]. There is a close link between the antinociceptive changes in primary headaches and the glutamate excitotoxicity caused by excessive excitation of NMDA receptors in dementia [21]. Furthermore, the severity of migraine and tension-type headache is strongly correlated with the severity of cognitive impairment [21]. A f-MRI study revealed that migraine patients had less task-related deactivation within the left dorsolateral prefrontal cortex (DLPFC) and left dorsal anterior midcingulate cortex (aMCC). These regions have decreased cortical thickness and cognitive-related deactivation and are also associated with pain regulation [22]. However, the sample size of this study is small. Other studies illustrating the mechanisms included functional thalamocortical disconnection, overload of the brain areas responsible for both cognition and pain processing and functional connectivity differences of fMRI in the hippocampus, parahippocampal gyrus, and orbitofrontal cortex which might affect cognitive processing [23,24]. Nevertheless, there is a lack of studies on the mechanisms of the association between headache and cognition [25], and the exact mechanism calls for further explanation.

Despite support from pathophysiology and functional imaging, the association between headache and cognitive impairment is inconsistent in population studies. In relatively young population, migraine is associated with deficits in attention, executive function, processing speed, and memory, but there is no evidence that migraine is associated with age-related dementia [26–32]. A cohort study of Women’s Health Study aged at least 65-year-old revealed that there was no difference in scores for verbal memory, language and executive function, and general cognitive function between the participants with or without migraine [15]. Another two studies in older population suggested an association between migraine and dementia in women only [33, 34]. Depression and cardiovascular diseases did not play a role in the association [33]. A systematic review and meta-analysis analyzed 18 researches, indicating that primary headaches were associated with an increase in dementia risk. Subgroup analysis found that migraine was associated with both a moderate increased risk of all-cause dementia as well as a moderate increased risk of Alzheimer’s disease [35]. Retrospective studies in Taiwan found that patients with tension-type headache are at a slightly higher risk of dementia [36, 37]. However, depression, anxiety, and insomnia which could be confounders of the association between tension type headache and cognitive impairment were not assessed [37]. A systematic review and meta-analysis indicated that there might not be an association between tension-type headache and cognitive impairment [35]. There is limited research on the association between cluster headache and cognitive impairment, which indicated that cluster headache was associated with executive dysfunction [38–41]. Most of the above studies were retrospective, cross-sectional and with small sample size. In our study, we applied both cross-sectional and longitudinal analysis in large population, offering more convincing evidence of the association between headache and cognition. Two studies in older population suggested an association between migraine and dementia in women only [33, 34]. Subgroup analysis was limited owing to small sample size. In the present study, we performed subgroup analysis and found that age, gender, marital status, alcohol, smoking, sleep duration and BMI might be promoting/preventing factors to develop MCI.

Strengths and limitations

The present study has some strengths. First, this study analyzed CHARLS data, which were nationally representative, and thus our findings reflected the association between headache disorders and cognitive status in middle-aged and older Chinese adults. Second, the sample size of the study was larger than many other studies. Third, the present study used both cross-sectional and longitudinal analysis, which was more persuative to illustrate the association between headache and MCI.

On the other hand, there are some limitations in this study. The present study was retrospective, which was difficult to adjust for all potential confounding variables, and a significant amount of data in CHARLS was missing or incomplete, which could lead to biases. Furthermore, we did not differentiate between types of headaches. Although we excluded conditions related to memory and cerebrovascular diseases, the possibility of secondary headaches could not be ruled out. Prospective cohort studies are called for to further explore the association between different types of headache disorders and cognitive function.

In the current study, we discovered that people with headache disorders had lower cognitive scores and had a higher risk of developing MCI in middle-aged and older Chinese population. As headache may increase the risk of MCI, headache prevention and treatment is of vital importance in preventing cognitive impairment, improving the quality of life in middle-aged and older population and reducing economic burden on families and society.

Acknowledgements

We thank the original data creators, depositors, copyright holders, and funders of the China Health and Retirement Longitudinal Study(CHARLS). We gratefully thank Dr. Jie Liu of Department of Vascular and Endovascular Surgery, Chinese PLA General Hospital for his contribution to the statistical support.

Author Contributions

Tianjiao Li and Bo Sun had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Tianjiao Li, Lingxuan Li, Bo Sun.

Acquisition, analysis, or interpretation of data: Tianjiao Li, Hongyang Xie.

Drafting of the manuscript: Tianjiao Li, Bo Sun.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Tianjiao Li.

Administrative, technical, or material support: Tianjiao Li, Rongyu Ping, Yane Guo, Dongmei Li, Yuwei Zhang, Bo Sun.

Supervision: , Xiujuan Bai, Bo Sun.

Conflict of Interest Disclosures: None reported.

Funding/Support: The study is not supported by any funding.

Additional Contributions: We thank the CHARLS team for sharing the data sets.

Clinical Implications:

1)Cross-sectionally, individuals with headache demonstrated significantly lower cognitive scores across all domains compared to those without headache.

2)Longitudinal analysis revealed a higher risk of MCI among headache sufferers at both baseline and follow-up, with the association remaining significant after adjustments.

3)The findings suggest that headache may be a risk factor for the development of MCI in middle-aged and older Chinese adults.

4)Effective headache prevention and management strategies could potentially contribute to preventing cognitive decline and improving quality of life in this population.

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Association between Headache and Mild Cognitive Impairment (MCI) among Middle-aged and Elderly Individuals: Evidence from the China Health and Retirement Longitudinal Study (CHARLS)

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Abstract

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Introduction

Methods

Study population

Measurement of cognitive function

Measurement of headache

Covariates

Statistical analysis

Results

Discussion

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Conclusion

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References

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Version 1