The Association Between Neutrophil-Lymphocyte Ratio and Variability with New-Onset Dementia: A Population-Based Cohort Study

Abstract

Background:

Previous studies identified that neutrophil-to-lymphocyte ratio (NLR) may be a predictor of dementia. However, the associations between NLR and dementia at the population level were less explored.

Objective:

This retrospective population-based cohort study was designed to identify the associations between NLR and dementia among patients visiting for family medicine consultation in Hong Kong.

Methods:

The patients were recruited from January 1, 2000, to December 31, 2003, and followed up until December 31, 2019. The demographics, prior comorbidities, medications, and laboratory results were collected. The primary outcomes were Alzheimer’s disease and related dementia and non-Alzheimer’s dementia. Cox regression and restricted cubic spline were applied to identify associations between NLR and dementia.

Results:

A cohort of 9,760 patients (male: 41.08% ; baseline age median: 70.2; median follow-up duration: 4756.5 days) with complete NLR were included. Multivariable Cox regression identified that patients with NLR >5.44 had higher risks of developing Alzheimer’s disease and related dementia (hazard ratio [HR]: 1.50, 95% Confidence interval [CI]: 1.17–1.93) but not non-Alzheimer’s dementia (HR: 1.33; 95% CI: 0.60–2.95). The restricted cubic splines demonstrated that higher NLR was associated with Alzheimer’s disease and related dementia. The relationship between the NLR variability and dementia was also explored; of all the NLR variability measures, only the coefficient of variation was predictive of non-Alzheimer’s dementia (HR: 4.93; 95% CI: 1.03–23.61).

Conclusion:

In this population-based cohort, the baseline NLR predicts the risks of developing dementia. Utilizing the baseline NLR during family medicine consultation may help predict the risks of dementia.

Keywords

Alzheimer’s disease dementia inflammation neuro-inflammation non-Alzheimer’s dementia

INTRODUCTION

Due to the demographic changes in the 21st century, dementia is among the greatest threats to global health [1 –3]. Dementia was responsible for 1.62 million deaths in 2019 globally, and greater numbers are expected in the coming decades [4]. Early diagnosis and treatment may help alleviate the symptoms and prevent the progression of dementia [5, 6]. There was a need to identify novel biomarkers for dementia.

Inflammation plays an important role in the pathogenesis of dementia [7, 8]. It promotes injury to neurons through neurotransmission, apoptosis, and activation of astrocytes and microglia [9, 10]. Alongside the production of reactive oxygen species and extravascular neutrophil traps, neutrophil hyperactivation is also associated with cognitive decline in Alzheimer’s disease [11]. Furthermore, inflammation may also alter the kynurenine pathway, resulting in neurodegeneration [12].

The neutrophil-to-lymphocyte ratio (NLR) was proposed to be a readily available indicator of inflammation. NLR has been applied to predict the risks of cancer [13], cardiovascular diseases [14, 15], and other adverse outcomes [16 –18]. Some studies suggested that elevated NLR were associated with dementia [19 –23] and cognitive dysfunction [24] among specific populations. However, whether baseline NLR may act as a clinically relevant predictor for dementia in the general population remained unclear.

This study aimed to investigate the associations between NLR and the risks of dementia among patients attending family medicine clinics in Hong Kong.

METHODS

Study design and population

This study was approved by the Institutional Review Board of the University of Hong Kong/Hospital Authority Hong Kong West Cluster (Reference No. UW 20–250) and Joint Chinese University of Hong Kong–New Territories East Cluster Clinical Research Ethics Committee (Reference No. 2018.309, 2018.643). This is a retrospective cohort study of patients attending family medicine clinics between January 1, 2000, to December 31, 2003, who have both neutrophil and lymphocyte measurements in the records. They were followed up until December 31, 2019. The patients were identified from the Clinical Data Analysis and Reporting System (CDARS), a territory-wide database that centralizes anonymized patient information from individual local hospitals. There was no adjudication of the outcomes as this relied on the International Classification of Diseases Ninth Edition (ICD-9) coding or a record on the death registry. The coding was performed by the clinicians or administrative staff, who were not involved in the model development. No blinding was performed for the predictor as the values were obtained from the electronic health records. The system has been used by both our team and other teams in Hong Kong [25 –27]. Prior comorbidities at baseline were extracted. Charlson’s standard comorbidity index was also calculated. ICD-9 codes used to identify prior comorbidities and the outcomes are provided in Supplementary Table 1. Baseline NLR is calculated based on the absolute neutrophil and lymphocyte count on the initial admission date (from January 1, 2000, to December 31, 2003). The formula to calculate the NLR variability measures are presented in Supplementary Table 2.

Statistical analysis and primary outcomes

The primary outcomes were Alzheimer’s disease and related dementia and non-Alzheimer’s dementia [28, 29] (Supplementary Table 1). Mortality data were obtained from the Hong Kong Death Registry, a population-based official government registry with the registered death records of all Hong Kong citizens. Mortality was recorded using the International Classification of Diseases Tenth Edition (ICD-10) coding.

Descriptive statistics were used to summarise the baseline and clinical characteristics of patients. Continuous variables were presented as median (95% confidence interval [CI] or interquartile range [IQR]) and categorical variables were presented as count (%). The Mann-Whitney U test was used to compare continuous variables. The χ² test with Yates’ correction was used for 2×2 contingency data. We divided the cohorts according to stratified NLR into four quantiles: <1.87, 1.87–2.85, 2.85–5.44, and >5.44. Univariable and multivariable Cox regression was used to explore the possible relationships between NLR and adverse outcomes. Weibull model was conducted as a sensitivity analysis to demonstrate the relationship with the consideration of survival time and censoring. Restricted cubic spline was used to delineate the relationship between the NLR and the outcomes after adjustments, and patients with the top or bottom 5% NLR were trimmed. Hazard ratios (HRs) with corresponding 95% CIs and p values were reported. All significance tests were two-tailed and considered significant if p values were equal to or less than 0.05. No imputation was performed for missing data. Data analyses were performed using R-Studio software (Version: 1.1.456), STATA (Version: 16.1) and Python (Version: 3.6).

RESULTS

Basic characteristics

The cohort composed of an overall 9760 patients (41.08% males, median age: 70.2 years old [IQR: 56.68–77.78]; median follow-up duration: 4756.5 days [IQR: 1972.5–6636.5]) after excluding 655 patients with a prior diagnosis of Alzheimer’s disease and related dementia, non-Alzheimer’s dementia, steroid use, infection within a week at baseline, baseline haematological malignancies, and immunodeficiencies (Fig. 1) [30].

Fig. 1

Flow chart for the identification, inclusion and exclusion of study subjects. IR, incidence rate; NLR, neutrophil-lymphocyte ratio.

Among the included main cohort (N = 9760), 529 patients developed Alzheimer’s disease and related dementia (incidence rate [IR]: 5.72%), and 56 patients developed, non-Alzheimer’s dementia (IR: 0.61%) and 6,121 patients passed away (IR: 62.71%). The characteristics of patients were stratified into four NLR quantiles: <1.87 (N = 2,422), 1.87–2.85 (N = 2,428), 2.85–5.44 (N = 2,453), and >5.44 (N = 2,457) (Table 1). The number of patients with Alzheimer’s disease and related dementia increased as the NLR increased, however, the number of patients with non-Alzheimer’s dementia was not different. The characteristics of the patients who developed adverse outcomes are presented in Supplementary Table 3.

Table 1

The number of adverse events, baseline, and clinical characteristics of patients in the study cohorts of four NLR quantiles

	All (N = 9,760) Median (IQR); N or Count (%)	NLR quantiles
Characteristics		<1.87 (N = 2,422) Median (IQR); N or Count (%)	1.87–2.85 (N = 2,428) Median (IQR); N or Count (%)	2.85–5.44 (N = 2,453) Median (IQR); N or Count (%)	>5.44 (N = 2,457) Median (IQR); N or Count (%)	p ^#
Adverse events
Alzheimer’s disease and related dementia	529 (5.42%)	100 (4.12%)	104 (4.28%)	150 (6.11%)	175 (7.12%)	<0.0001^***
Non-Alzheimer’s dementia	56 (0.57%)	16 (0.66%)	12 (0.49%)	13 (0.52%)	15 (0.61%)	0.9024
Demographics
Male gender	4010 (41.08%)	768 (31.70%)	959 (39.49%)	1,069 (43.57%)	1,214 (49.40%)	<0.0001^***
Female gender	5750 (58.91%)	1,654 (68.29%)	1,469 (60.50%)	1,384 (56.42%)	1,243 (50.59%)	<0.0001^***
Baseline age, y	70.2 (56.68–77.78); n = 9,760	62.27 (49.9–72.07); n = 2,422	67.69 (52.94–76.13); n = 2,428	72.65 (63.14–79.19); n = 2,453	74.99 (67.6–80.83); n = 2,457	<0.0001^***
Past comorbidities
Charlson standard comorbidity index	3.0 (1.0–3.0); n = 9,760	2.0 (1.0–3.0); n = 2,422	2.0 (1.0–3.0); n = 2,428	3.0 (2.0–4.0); n = 2,453	3.0 (2.0–4.0); n = 2,457	<0.0001^***
Diabetes mellitus	134 (1.37%)	23 (0.94%)	33 (1.35%)	36 (1.46%)	42 (1.70%)	0.1254
Hypertension	915 (9.37%)	159 (6.56%)	202 (8.31%)	274 (11.16%)	280 (11.39%)	0.0004^***
Renal failure	165 (1.69%)	16 (0.66%)	35 (1.44%)	67 (2.73%)	47 (1.91%)	0.3785
Stroke/TIA	589 (6.03%)	92 (3.79%)	135 (5.56%)	167 (6.80%)	195 (7.93%)	<0.0001^***
Heart failure	296 (3.03%)	30 (1.23%)	48 (1.97%)	111 (4.52%)	107 (4.35%)	<0.0001^***
Acute myocardial infarction	47 (0.48%)	12 (0.49%)	7 (0.28%)	17 (0.69%)	11 (0.44%)	0.912
Atrial fibrillation	167 (1.71%)	27 (1.11%)	34 (1.40%)	44 (1.79%)	62 (2.52%)	0.0006^***
Chronic obstructive pulmonary disease	156 (1.59%)	8 (0.33%)	20 (0.82%)	37 (1.50%)	91 (3.70%)	<0.0001^***
Ischemic heart disease	383 (3.92%)	60 (2.47%)	85 (3.50%)	121 (4.93%)	117 (4.76%)	0.0208^*
Baseline cancer	197 (2.01%)	30 (1.23%)	47 (1.93%)	52 (2.11%)	68 (2.76%)	0.0037^**
Medications
ACEI	2,543 (26.05%)	472 (19.48%)	602 (24.79%)	717 (29.22%)	752 (30.60%)	<0.0001^***
ARB	126 (1.29%)	32 (1.32%)	25 (1.02%)	38 (1.54%)	31 (1.26%)	0.9653
Calcium channel blockers	3,539 (36.26%)	636 (26.25%)	853 (35.13%)	988 (40.27%)	1,062 (43.22%)	<0.0001^***
Beta blockers	2,853 (29.23%)	713 (29.43%)	747 (30.76%)	730 (29.75%)	663 (26.98%)	0.0373^*
Diuretics for heart failure	1,148 (11.76%)	134 (5.53%)	209 (8.60%)	367 (14.96%)	438 (17.82%)	<0.0001^***
Diuretics for hypertension	1,527 (15.64% )	346 (14.28% )	359 (14.78% )	431 (17.57% )	391 (15.91% )	0.7419
Nitrates	1,985 (20.33% )	395 (16.30% )	453 (18.65% )	550 (22.42% )	587 (23.89% )	<0.0001^***
Antihypertensive drugs	1,650 (16.90% )	248 (10.23% )	358 (14.74% )	515 (20.99% )	529 (21.53% )	<0.0001^***
Anti-diabetic agents	2,924 (29.95% )	647 (26.71% )	765 (31.50% )	795 (32.40% )	717 (29.18% )	0.4897
Statins and fibrates	1,514 (15.51% )	408 (16.84% )	385 (15.85% )	405 (16.51% )	316 (12.86% )	0.0003^***
Anticoagulants	135 (1.38% )	20 (0.82% )	24 (0.98% )	53 (2.16% )	38 (1.54% )	0.4899
Laboratory testings
Baseline NLR	2.87 (1.88–5.47); n = 9,760	1.46 (1.21–1.67); n = 2,422	2.3 (2.07–2.55); n = 2,428	3.74 (3.23–4.4); n = 2,453	9.56 (7.0–15.56); n = 2,457	<0.0001^***
NLR variance	0.12 (0.04–0.28); n = 4,547	0.13 (0.04–0.4); n = 800	0.12 (0.04–0.28); n = 910	0.09 (0.03–0.23); n = 1,231	0.12 (0.04–0.29); n = 1,606	0.1161
NLR SD	0.34 (0.2–0.53); n = 4,547	0.36 (0.21–0.63); n = 800	0.35 (0.21–0.53); n = 910	0.31 (0.18–0.48); n = 1,231	0.34 (0.21–0.54); n = 1,606	0.1166
SD/Initial-NLR	0.24 (0.14–0.38); n = 4,547	0.17 (0.09–0.27); n = 800	0.19 (0.11–0.28); n = 910	0.22 (0.13–0.33); n = 1,231	0.38 (0.22–0.67); n = 1,606	<0.0001^***
NLR CV	0.22 (0.11–0.35); n = 4,547	0.15 (0.07–0.29); n = 800	0.17 (0.09–0.28); n = 910	0.19 (0.1–0.32); n = 1,231	0.29 (0.18–0.41); n = 1,606	<0.0001^***
Neutrophil, ×10^∧9/L	4.8 (3.59–7.1); n = 9,760	3.2 (2.6–3.9); n = 2,422	4.2 (3.5–5.1); n = 2,428	5.5 (4.4–6.8); n = 2,453	9.2 (7.0–12.17); n = 2,457	<0.0001^***
Lymphocyte, ×10^∧9/L	1.6 (1.12–2.1); n = 9,760	2.3 (1.9–2.7); n = 2,422	1.9 (1.5–2.2); n = 2,428	1.5 (1.2–1.8); n = 2,453	0.9 (0.6–1.2); n = 2,457	<0.0001^***
White blood count, ×10^∧9/L	7.42 (6.0–9.5); n = 9,760	6.3 (5.3–7.6); n = 2,422	6.93 (5.8–8.4); n = 2,428	7.7 (6.3–9.5); n = 2,453	9.9 (7.6–13.1); n = 2,457	<0.0001^***
Albumin, g/L	40.0 (37.0–43.0); n = 9,064	42.0 (39.0–44.0); n = 2,197	41.0 (38.0–43.0); n = 2,203	39.2 (36.08–42.0); n = 2,310	38.0 (34.45–41.0); n = 2,354	<0.0001^***
Creatinine, umol/L	89.0 (75.0–110.0); n = 9,281	83.0 (71.0–98.0); n = 2,249	87.0 (73.0–106.0); n = 2,281	93.0 (77.0–115.0); n = 2,359	97.0 (79.0–121.0); n = 2,392	<0.0001^***

^*p≤0.05, ^**p≤0.01, ^***p≤0.001; IQR: interquartile range, ACEI, angiotensin-converting-enzyme inhibitors; ARB, angiotensin II receptor blockers; CV, coefficient of variation; NLR, neutrophil to lymphocyte ratio; SD, standard deviation; TIA, transient ischemic attacks. ^#indicates the differences between the patients in four NLR quantiles.

The association between NLR and dementia

Univariable Cox regression identified the risk factors for new onset Alzheimer’s disease and related dementia and non-Alzheimer’s dementia (Supplementary Table 4). After adjusting for the significant covariates in the univariate analysis (demographics, prior comorbidities, and medications), the patients with NLR >5.44 were associated with Alzheimer’s disease and related dementia (HR: 1.50, 95% CI: 1.17–1.93) but not non-Alzheimer’s dementia (HR: 1.33; 95% CI: 0.60–2.95) (Table 2). The result remained consistent in the Weibull model as a sensitivity analysis (Table 3).

Table 2

Multivariable Cox models assessing the relationships between NLR quartiles and Alzheimer’s disease and related dementia and non-Alzheimer’s dementia

Model 1: unadjusted
Characteristics	Alzheimer’s disease and related dementia HR (95% CI); p	Non-Alzheimer’s dementia HR (95% CI); p
NLR overall	1.02 (1.01–1.03);<0.0001^***	1.01 (0.98–1.04); 0.4920
NLR quartiles
<1.87	0.51 (0.41–0.64);<0.0001^***	0.91 (0.51–1.62); 0.7409
1.87–2.85	1.0 (Reference)	1.0 (Reference)
2.85–5.44	1.35 (1.12–1.63); 0.0018^**	1.01 (0.55–1.89); 0.9641
>5.44	2.15 (1.79–2.58);<0.0001^***	1.54 (0.85–2.79); 0.1571
Model 2: adjusted by significant demographics
Characteristics	Alzheimer’s disease and related dementia HR (95% CI); p	Non-Alzheimer’s dementia HR (95% CI); p
NLR overall	1.01 (1.00–1.02); 0.0114^*	0.99 (0.95–1.03); 0.693
NLR quartiles
<1.87	1.01 (0.77–1.34); 0.9204	1.33 (0.52–2.83); 0.4660
1.87–2.85	1 [Reference]	1 (Reference)
2.85–5.44	1.32 (1.03–1.70); 0.0297^*	1.07 (0.48–2.36); 0.8705
>5.44	1.50 (1.17–1.92); 0.0014^**	1.17 (0.54–2.56); 0.6940
Model 3: adjusted by significant demographics, prior comorbidities, and medications
Characteristics	Alzheimer’s disease and related dementia HR (95% CI); p	Non-Alzheimer’s dementia HR (95% CI); p
NLR overall	1.01 (1.00–1.02); 0.0141^*	1.00 (0.96–1.04);0.8271
NLR quartiles
<1.87	1.04 (0.79–1.38); 0.7632	1.29 (0.60–2.78); 0.5084
1.87–2.85	1 (Reference)	1 (Reference)
2.85–5.44	1.33 (1.03–1.71); 0.0273^*	1.08 (0.48–2.42); 0.8527
>5.44	1.50 (1.17–1.93); 0.0014^**	1.33 (0.60–2.95); 0.4796

^*p≤0.05, ^**p≤0.01, ^***p≤0.001; NLR, neutrophil to lymphocyte ratio; HR, hazard ratio; CI, confidence interval.

Table 3

Weibull model for assessing the relationships between NLR quartiles and Alzheimer’s disease and related dementia and non-Alzheimer’s dementia

Characteristics	Alzheimer’s disease and related dementia HR (95% CI); p	Non-Alzheimer’s dementia HR (95% CI); p
NLR overall	1.01 (1.00–1.02); 0.012^*	1.00 (0.96–1.04); 0.821
NLR quartiles
<1.87	1.10 (0.83–1.45); 0.498	1.51 (0.70–3.23); 0.283
1.87–2.85	1 (Reference)	1 (Reference)
2.85–5.44	1.28 (0.99–1.66); 0.051	1.04 (0.47–2.30); 0.916
>5.44	1.55 (1.21–1.99); <0.001^**	1.20 (0.56–2.60); 0.641

The cumulative hazard of Alzheimer’s disease and related dementia was higher among patients with higher NLR (Fig. 2). This trend was also consistent if the NLR were stratified by the clinical NLR quantiles (Supplementary Figure 1). The restricted cubic splines models demonstrated that as the NLR increased, the HR of Alzheimer’s disease and related dementia also increased (Fig. 3). In the subgroup analysis, the association remained significant across gender and age (Supplementary Figures 2 and 3). Across different subgroups above 50 years old, NLR >5.44 were associated with Alzheimer’s disease and related dementia (all p < 0.05). Besides, among patients above 60 years old, NLR >5.44 were associated with non-Alzheimer’s dementia (all p < 0.05) (Supplementary Table 5).

Fig. 2

Cumulative incidence curves stratified by NLR quantiles to predict Alzheimer’s disease and related dementia and non-Alzheimer’s dementia. NLR, neutrophil-lymphocyte ratio.

Fig. 3

Restricted cubic spline curves for predicting Alzheimer’s disease and related dementia and non-Alzheimer’s dementia.

The relationship between NLR variability and dementia was also explored. The NLR variability measures, including NLR variance, NLR standard deviation [SD], SD/initial-NLR and NLR coefficient of variation (CV) were not associated with Alzheimer’s disease and related dementia following adjustments. However, NLR CV, but not other NLR variability measures, was associated with increased risks of non-Alzheimer’s dementia (HR: 4.93; 95% CI: 1.03–23.61) (Table 4).

Table 4

Multivariable Cox models assessing the relationships between NLR variability and Alzheimer’s disease and related dementia and non-Alzheimer’s dementia

Characteristics	Alzheimer’s disease and related dementia HR (95% CI); p	Non-Alzheimer’s dementia HR (95% CI); p
NLR variance	1.00 (0.98–1.01); 0.8980	0.97 (0.54–1.74); 0.9175
NLR SD	1.00 (0.85–1.18); 0.9927	1.05 (0.76–1.45); 0.7716
SD/Initial-NLR	0.97 (0.79–1.18); 0.7480	0.83 (0.34–2.03); 0.6768
NLR CV	1.68 (0.86–3.29); 0.1311	4.93 (1.03–23.61); 0.0459^*

CV, coefficient of variation; NLR, neutrophil-lymphocyte ratio; SD, standard deviation. Adjusted by significant demographics, prior comorbidities, and medications.

DISCUSSION

In this population-based cohort study, higher NLR was associated with Alzheimer’s and related dementia in the patients attending the family medicine clinic; the NLR CV was also predictive of non-Alzheimer’s dementia in our studied population. To the best of our knowledge, the present study is the first to demonstrate the use of NLR and NLR variability measures to predict dementia in the general population.

Comparison with previous studies

Generally, NLR seems to have a role in predicting dementia in the future. Positive associations between NLR and dementia risk were demonstrated in several studies [23, 31]. Similarly, studies in the past have shown that patients with Alzheimer’s disease had raised NLR as compared to the healthy controls [32 –34]. Similarly, an association between NLR and cognitive impairment in community-dwelling older patients was also demonstrated in one study [19]. Additionally, there is some evidence for the prognostic value of NLR for dementia. Halazun et al. suggested that patients undergoing carotid endarterectomy with NLR ≥5 had a higher risk of dementia post-operation [35].

Our results support the use of NLR as a predictor for Alzheimer’s and related dementia, which are consistent with several published studies that demonstrated higher NLRs in patients with Alzheimer’s disease compared with healthy controls [32 , 37]. Moreover, Zhang et al. also found patients with higher NLR were at greater risk of developing Alzheimer’s dementia [23]. On the other hand, we did not find an association between NLR and non-Alzheimer’s dementia. Although this contradicts the findings of Zhang et al. that higher NLR is related to an increased risk of vascular dementia, our results seem to be consistent with existing literature [23]. Furthermore, Schröder et al. did not find significant differences between the NLRs of patients with different types of dementia (Alzheimer’s disease, vascular dementia, mixed dementia, and frontotemporal dementia) compared to those without dementia [38]. From the evidence gathered, NLR could be a good predictor of Alzheimer’s and related dementia. However, the role of NLR remains uncertain for its predictive value for non-Alzheimer’s dementia.

Inflammation and dementia

Several mechanisms is responsible for the association of NLR with dementia. Recent evidence suggests that all forms of dementia, including Alzheimer’s disease, and non-Alzheimer’s dementia, are characterized by neuroinflammation [39, 40]. Patients with dementia had higher levels of proinflammatory cytokines and lower levels of antioxidants [41].

Neutrophils play an important role in the development of dementia. Glial cells activate in response to neutrophil granule proteins, resulting in deterioration of Alzheimer’s disease [42 –44]. Meanwhile, higher levels of lymphocytes, particularly regulatory T cells, may help suppress the inflammatory response, hence, dampening the progression of Alzheimer’s disease [45]. As such, the NLR may reflect the imbalance of immune response involved in dementia.

Atherosclerosis may also explain the association between NLR and dementia. Some studies have shown that NLR is an independent predictor for atherosclerosis [46, 47]. Additionally, an association between dementia and atherosclerosis of intracranial arteries has been established [48, 49]. An efficient blood supply to the brain is necessary for its normal function, thus, reduced blood flow is a contributor to the development of dementia.

Clinical relevance

This study provided substantial evidence to support the inclusion of NLR into the risk assessment of dementia within the family medicine patients cohort. Our study design included all adult patients that attend family medicine clinics in Hong Kong. Thus, the results of this study may be reflective of daily practice and applicable to clinical practice. NLR and its variability markers are emerging marker that contains valuable information on both neutrophils and lymphocytes, and are not altered by fluid imbalance in comparison to single components of the white blood cells [50]. These measures may also serve as inexpensive and quick markers that could be easily incorporated into risk stratification assessments. NLR may predict the development of Alzheimer’s disease and related dementia, particularly at early stages. Early diagnosis of dementia is crucial, as it helps delay the progression and reduce the severity of dementia. Our findings provided further evidence regarding the role of inflammation in the development of dementia, supporting the need for future research to evaluate the role of anti-inflammatory therapy for the prevention and treatment of dementia.

Limitations

Several limitations are present in this study which should be acknowledged. Firstly, the derived populations may differ from others due to comorbidities and demographic differences. The model should be externally validated using patient data from other regions. Secondly, given the retrospective nature of this study, residual or unmeasured confounders were not addressed. However, the established risk factors of dementia were included in the multivariable regression models. Besides, the observational study was also subjected to bias secondary to under-coding and documentation errors. As such, some covariates associated with dementia, such as smoking, alcohol uses, obesity, and substance use, were not included in this study. Moreover, the patients that were lost to follow-up apart from mortality were not documented in the study. Furthermore, as NLR was not routinely recorded in clinical practice, thus, variations between the frequency and timings of NLR measurements were not standardised in all patients. Last but not least, this retrospective study could only demonstrate the association but not the causation relationship between NLR and dementia.

Conclusions

This cohort study demonstrated that the baseline NLR was associated with the risks of Alzheimer’s disease and related dementia among the family medicine patients. Baseline NLR may allow earlier diagnosis of dementia, and patients may benefit from prompt intervention.

Footnotes

ACKNOWLEDGMENTS

The authors have no acknowledgments to report.

FUNDING

The authors have no funding to report.

CONFLICT OF INTEREST

The authors have no conflict of interest to report.

DATA AVAILABILITY

The data that support the findings of this study were provided by the Hong Kong Hospital Authority, but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of Hong Kong Hospital Authority.

The supplementary material is available in the electronic version of this article: .

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