Neutrophil-Lymphocyte Ratio and Platelet-Lymphocyte Ratio in ADHD: A Systematic Scoping Review

Abstract

Objectives

Neutrophil-lymphocyte and platelet-lymphocyte ratio (NLR, PLR) are easily calculated from routine blood tests and are increasingly being used in research to assess disease severity in inflammatory, infectious, and psychiatric conditions. The goal of this systematic scoping review was to broadly examine the literature on NLR and PLR and attention-deficit/hyperactivity disorder (ADHD).

Methods

The PubMed/Medline, PsychInfo, Scopus, Embase, Cochrane Central Register of Controlled Trials, Cumulative Index to Nursing and Allied Health Literature, ClinicalTrials.gov, World Health Organization International Clinical Trials Registry Platform, International Standard Randomized Controlled Trial Number, Google Scholar, Directory of Open Access Journals, and ProQuest databases were searched.

Results

Among the 1542 articles evaluated, 15 were included. Fourteen studies evaluated children and adolescents with ADHD and one study evaluated adults. Study outcomes included the comparison of mean NLR and PLR values between ADHD and non-ADHD groups and the association of NLR and PLR values with measures of ADHD symptom severity. Four studies used receiver operating characteristic (ROC) curves to identify potential cut points for the diagnosis of ADHD. Assessing NLR and PLR is economical and minimally invasive.

Conclusions

Based on the 15 studies reviewed, findings suggest NLR and PLR may be promising biomarkers for ADHD. Additional research is justified to further investigate if routine incorporation of NLR and PLR into research and clinical practice could beneficially impact the diagnosis and treatment of patients with ADHD.

Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder of childhood, characterized by inattention, hyperactivity, and/or impulsivity, occurring in 5% of children and 2.5% of adults (American Psychiatric Association, 2013). The etiology of ADHD is complex with no single cause identified, but inflammation may play a considerable role in the pathophysiology of ADHD (Allred et al., 2017; Donfrancesco et al., 2020). Recent studies have assessed inflammation through the use of cytokines, finding multiple inflammatory cytokines elevated in individuals with ADHD compared to individuals without (Anand et al., 2017; Donfrancesco et al., 2020). Cytokine testing requires specialized testing which can cost upwards of $500 USD. A potentially more accessible way to assess inflammation in ADHD is via a complete blood count (CBC), which is widely used for health screening and diagnosis of a range of conditions (e.g., anemia, leukemia).

In contrast to measuring cytokines, complete blood counts are less expensive, around $50 USD, and are commonly obtained during routine preventative care visits, which are covered by many insurance policies. CBCs assess levels of red and white blood cells in the blood, including neutrophils, lymphocytes, and platelets amongst others. Neutrophils are the most abundant white blood cell, are the first white blood cell at the site of an injury, and are a hallmark of acute inflammation (Ocana et al., 2017). Lymphocytes include T cells and B cells, which create antibodies and defend the body against bacteria and viruses (Actor, 2012).

The ratio of neutrophils to lymphocytes “neutrophil-lymphocyte ratio” (NLR) can change with sickness or stress, and this ratio can be used to assess the level of overall inflammation (Song et al., 2021). NLR is easily obtained from routine blood count data by dividing the absolute neutrophil count by the absolute lymphocyte count. When NLR increases, it suggests that there has been activation of the systemic inflammatory response. NLR has been used as a measure of inflammation in patients with cancer, heart disease, and inflammatory bowel disease (IBD) for screening, diagnosis, and treatment guidance (Acarturk et al., 2015; Joshi et al., 2023; Langley et al., 2021; Ocana et al., 2017). NLR has been used to assess symptom severity in mood and psychiatric disorders such as depression, bipolar disorder, and schizophrenia, and a recent meta-analysis found NLR levels are elevated in autism spectrum disorder (ASD) (Arteaga-Henríquez et al., 2022; Bhikram & Sandor, 2022).

Platelets are cells that initiate and participate in inflammatory processes and help form blood clots after injury. Elevated platelet-lymphocyte ratio (PLR) indicates systemic inflammation and has been widely studied as a diagnostic biomarker in acute infection and cancer as well as a predictor of response to pharmaceutical treatments in individuals with ovarian cancer (Fang et al., 2020; Yin et al., 2019). PLR is elevated in mood disorders, including bipolar disorder (Mazza et al., 2018). PLR is also easily obtained from routine blood count data by dividing the absolute platelet count by the absolute lymphocyte count.

NLR and PLR are readily available biomarkers of the systemic inflammatory response and can be used to predict treatment response and disease outcomes in a variety of other conditions (Joshi et al., 2023; Langley et al., 2021; Ocana et al., 2017). One just-published systematic review and meta-analysis on NLR, PLR, and monocyte-lymphocyte ratio (MLR) in ADHD, found eight studies, four of which were meta-analyzed (Gędek et al., 2023). However, to the best of our knowledge, no prior review has systematically scoped the evidence on NLR and PLR in individuals with ADHD. In contrast to the recently published systematic review and meta-analysis which aimed to review the association between immune/inflammatory markers and ADHD using eight studies this scoping review is the first to search for and describe the entire body of published literature on NLR, PLR, and ADHD.

Methods

Protocol and Registration

This scoping review was conducted in accordance with the Joanna Briggs Institute methodology for scoping reviews and Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) (Peters et al., 2020; Tricco et al., 2018). The protocol was prospectively indexed online in the Open Science Forum database before the review began (https://osf.io/preprints/osf/4s6gx). Any important amendments made to the review process were documented and reported along with a description of the changes made, with rationale (Table SI).

Eligibility Criteria

Participants

To be considered for this review, participants in each study had a formal diagnosis of ADHD prior to the study, were diagnosed with ADHD as part of the study, or had elevated symptoms of ADHD per clinician-, parent-, teacher-, or self-report. Participants may have had additional diagnoses (i.e., ASD) as long as ADHD was one of the primary diagnoses of interest. There was no age limit placed on participants.

Outcomes

The Federal Drug Administration (FDA) requires many clinical studies of pharmacological or nutritional interventions in children with ADHD to include CBCs and other blood tests to determine study eligibility (i.e., to rule out pathology such as anemia) or for monitoring of participant safety (Johnstone et al., 2022). Studies had to include primary outcomes of blood data derived from a CBC and report at least one of the following ratios: NLR or PLR. Other relevant data were compiled and included if present in included studies that reported NLR and PLR.

Types of Studies

Observational and experimental study designs were eligible for inclusion, such as randomized controlled trials, non-randomized controlled trials, cohort studies, and case-control studies. Gray literature including theses, dissertations, book chapters, and conference presentations were considered if they otherwise met inclusion criteria. In addition, systematic reviews that met inclusion criteria were considered depending on the research question. Individual case studies, text, and opinion papers were excluded.

Information Sources

The following databases were searched: PubMed/Medline, PsychInfo, Scopus, Embase, Cochrane Central Register of Controlled Trials (Cochrane CENTRAL), Cumulative Index to Nursing and Allied Health Literature (CINAHL), ClinicalTrials.gov, World Health Organization International Clinical Trials Registry Platform (WHO ICTRP), International Standard Randomized Controlled Trial Number (ISCRTN), Google Scholar, and the Directory of Open Access Journals (DOAJ). The ProQuest database was searched for relevant theses or dissertations, and the PROSPERO database was searched for any other similar reviews, completed or in progress.

Studies published in any language were included; studies not available in English were translated via language translation software if possible. No date restrictions were put on the literature search. The reference lists of the studies included were also hand-searched for other relevant studies.

Search Strategy

The search strategy aimed to locate both published and unpublished studies. An initial search of Medline was undertaken to identify articles on the topic. The text words contained in the titles and abstracts of relevant articles, and the index terms used to describe the articles were used to develop a full search strategy. The search strategy, including all identified keywords and index terms, was adapted for each included database (Appendix I).

Selection of Sources of Evidence

After the literature search was completed, all identified articles were uploaded into Rayyan software and deduplicated (Ouzzani et al., 2016). The article screening was completed by two authors (AB, TM), independently and in duplicate. Articles that met the inclusion criteria for the review were retrieved in full, and the completed texts were assessed by two independent reviewers (AB, TM). Any disagreements were resolved by discussion or by consulting a third author (JJ, JR). Reasons for exclusion were documented to be included in a PRISMA flowchart.

Data Items

Data were extracted independently in duplicate by two authors (AB, TM), using a form made in Microsoft Excel (Appendix II). For articles missing data or with unclear methodology, up to three attempts were made to contact study authors for clarification. If studies reported a receiver operating characteristics (ROC) area under the curve (AUC) analysis to determine cut points for identifying ADHD, this data was also extracted. An AUC value of 0.5 suggests that the parameter (i.e., NLR) is not any better at identifying ADHD than random chance. An AUC of 1.0 suggests that the parameter is 100% accurate at identifying ADHD. Hosmer and Lemeshow’s criteria for AUC values were used, which are as follows: <0.5, useless; 0.5–0.7, poor; 0.7–0.8, acceptable; 0.8–0.9, excellent; and >0.9, outstanding (Hosmer et al., 2013).

Results

Study Selection

Of the 1542 articles identified through literature searches and hand-searching the reference lists of included studies, 1392 remained after removing duplicates. After titles and abstracts were screened in Rayyan, 1376 studies were excluded. Thirty full-text articles were assessed against the inclusion criteria and 15 were excluded with reasons noted in the PRISMA diagram (Figure 1). Fifteen studies met the full inclusion criteria and are summarized below.

Fig. 1.

PRISMA flowchart

One abstract described a study assessing NLR in children with ADHD and a comparison group without ADHD, but it was unclear if the abstract was from a published study, a conference presentation, or other material (Tunca et al., 2019). Two research librarians conducted searches but were unable to identify any associated full article, either published or unpublished. Multiple attempts to contact the authors were unsuccessful.

Three studies were only available in Turkish (Bilaç et al., 2021; Binici & Kutlu, 2018; Gündüz et al., 2018). Google Translate was used to translate each study into English for screening. Two of these studies were excluded because participants did not have ADHD (Bilaç et al., 2021; Gündüz et al., 2018). The third study met inclusion criteria, based on the translation, and was therefore included (Binici & Kutlu, 2018).

In the one identified systematic review and meta-analysis on NLR, PLR, and monocyte/lymphocyte ratio (MLR), 8 studies of children with ADHD, were included of which 4 were meta-analyzed (Akinci & Uzun, 2021; Aksu & Dağ, 2020; Gędek et al., 2023; Topal et al., 2021; Yektas et al., 2022). The review excluded studies in adults or studies not available in English (Akinci & Uzun, 2021; Aksu & Dağ, 2020; Alpay et al., 2020; Avcil, 2018; Fahiem & Mekkawy, 2022; Topal et al., 2021; Önder et al., 2021; Yektas et al., 2022). All studies referenced in this systematic review were included in this scoping review.

Characteristics of Included Studies

All 15 studies were published between 2018 and 2023 (details in Table 1). Fourteen of the 15 included studies were cross-sectional, evaluating children and adults with ADHD and a comparison group of individuals without ADHD. In addition, four of the 14 cross-sectional studies evaluated a third comparison group of individuals with ASD (Adıgüzel Akman & Esnafoglu, 2023; Alpay et al., 2020; Ferencova et al., 2023; Topal et al., 2021). Most studies allowed co-occurring diagnoses of oppositional defiant disorder or conduct disorder but excluded other psychiatric or mood disorders. One study included children with learning disorders (dyslexia, dysgraphia, and dyscalculia; n = 20) and children with learning disorders plus ADHD (n = 80), as well as a comparison group without ADHD; results were reported separately for each of the three groups.

Table 1.

			ADHD Group			Non-ADHD Group
Study	Year	Nation	n	% Female	Mean Age (Years)	n	% Female	Mean Age (Years)
Akinci	2021	Turkey	347	24	9.6	205	27	9.7
Akman	2023	Turkey	36	19	7.5*	31	39	7.0*
Aksu	2020	Turkey	169	23	9.6	59	16	10.3
Alpay	2020	Turkey	30	0	8.2	30	0	8.2
Avcil	2018	Turkey	82	21	8.9	70	14	9.2
Binici	2019	Turkey	65	38	7.8	65	38	7.8
Ceyhun	2022	Turkey	74	39	25.2	70	49	27.1
Esnafoglu	2023	Turkey	39	15	8.0*	39	15	7.0*
Fahiem	2021	Egypt	70	30	8.7	44	32	8.3
Ferencova	2023	Slovakia	20	25	13.4	20	25	13.2
Önder	2021	Turkey	100	17	9.8	99	17	10.2
Öz	2023	Turkey	22	36	8.3*	21	48	5.0*
Topal	2021	Turkey	61	25	8.7	70	30	8.4
Samei	2023	Egypt	50	32	8.3	50	48	9
Yektaş	2022	Turkey	80	32	9.1	75	33	9.3

ADHD, Attention-deficit/hyperactivity disorder

^*Median

Only one of the 15 studies was longitudinal (Öz et al., 2023). In terms of location, 12 studies were conducted in Turkey, two in Egypt (Abdel Samei et al., 2024; Akinci & Uzun, 2021), and one in Slovakia (Ferencova et al., 2023). Sample sizes ranged from 40 to 552 participants. Four studies excluded children who had a body mass index (BMI) over 30 (Akinci & Uzun, 2021; Aksu & Dağ, 2020; Binici & Kutlu, 2018; Ceyhun & Gürbüzer, 2022). Additional details of included studies, including diagnostic and medication status of participants, can be found in Table SII.

Reported Outcomes in Included Studies

All 15 studies compared mean NLR and/or PLR values between an ADHD and a non-ADHD group. In addition, many studies also reported the correlation between ADHD symptoms and NLR AND PLR values. Some studies included questionnaires on additional areas such as impulsivity, autism, learning disorders, and disruptive behavior (Table 2). Three studies reported additional blood test results including vitamin B₉, vitamin B₁₂, C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), ferritin, homocysteine, and vitamin D (Adıgüzel Akman & Esnafoglu, 2023; Topal et al., 2021; Öz et al., 2023). Nine studies reported mean platelet volume (MPV).

Table 2.

Relevant outcome measures in included studies

Study	Year	Blood outcomes	Behavioral outcome measures	Statistical comparisons
Akinci	2021	BLR, ELR, MLR, MPV, NLR, PLR	--	--
Akman	2023	NLR, PLR, CRP, PCT, CRP, ESR	K-SADS-PL-DSM-5-T, T-DSM-IV-S, CARS	Correlations between questionnaire and blood parameters
Aksu	2020	MLR, MPV, NLR, PLR	--	--
Alpay	2020	NLR	--	--
Avcil	2018	MLR, MPV, NLR, PLR	CTRS, T-DSM-IV-S	Correlations between questionnaire and blood parameters
Binici	2019	NLR, PLR	K-SADS-PL-DSM-5-T, WISC-R	Distribution of ADHD presentations compared
Ceyhun	2022	BLR, MLR, MPV, NLP, PLR, SII	BIS-11, SCID-5/CV	Correlations between questionnaire and blood parameters
Esnafoglu	2023	MLR, MPV, NLR, PLR; B9, B12, CRP, ESR, ferritin, homocysteine, vitamin D	T-DSM-IV-S	Correlations between questionnaire and blood parameters
Fahiem	2021	MLR, NLR, PLR	--	Comparison of ADHD presentations
Ferencova	2023	NLR, PLR, MLR, MVP, PDW, PMR, pro- and anti-inflammatory cytokines	--	--
Önder	2021	NLR, PLR	T-DSM-IV-S	Correlations between questionnaire and blood parameters
Öz	2023	NLR, PLR, MLR, CAR, SII, PIV, PDW, PCT, albumin, CRP, folate, B12		Lab values compared before and after atomoxetine dose
Topal	2021	MLR, MPV, NLR, PLR; B9, B12, ferritin	GARS-2, T-DSM-IV-S	Correlations between questionnaire and blood parameters
Samei	2023	NLR, PLR, MPV	MINI-KID, WISC, CPRS-L	Correlations between questionnaire and blood parameters
Yektaş	2022	MLR, MPV, NLR, PLR	T-DSM-IV-S, LDSC	Correlations between questionnaire and blood parameters

ADHD attention-deficit/hyperactivity disorder, B12 vitamin B₁₂, B9 vitamin B₉ (as folate), BIS-11 Barratt Impulsivity Scale, CARS child autism rating scale, CBC complete blood count, CRP C-reactive protein, CPRS-L Conners’ parent rating scale revised-long, CTRS Conners’ Teacher Rating Scale, ESR erythrocyte sedimentation rate, GARS-2 Gilliam Autism Rating Scale, K-SADS-PL-DSM-5-T Schedule for affective disorders and schizophrenia for school-age children-present and lifetime version DSM-5, LDSC Learning Disorders Symptom Checklist, MINI-KID mini-international neuropsychiatric interview for children and adolescents, SCID-5/CV Clinician Version Structured Clinical Interview, T-DSM-IV-S Turgay DSM-IV-Based Child and Adolescent Behavior Disorders Screening and Rating Scale, WISC-R Wechsler Intelligence Scale for Children-Re-vised; Wechsler Intelligence Scale for Children-New-Revised Form

Cut Points for ADHD

Three studies conducted an ROC analysis using the area under the curve (AUC); to generate cut points for ADHD using NLR and PLR to distinguish between children with and without ADHD (Table 3). The proposed cut points for NLR were 1.3 (sensitivity and specificity not reported) (Fahiem & Mekkawy, 2022), 1.62 (54% sensitivity, 70% specificity) (Avcil, 2018), and 3.07 (66% sensitivity, 84%. specificity) (Abdel Samei et al., 2024). The proposed cut points for PLR were 92.18 (sensitivity and specificity not reported) (Fahiem & Mekkawy, 2022), 115.7 (82% sensitivity, 52% specificity), and 130.55 (36% sensitivity, 72% specificity) (Avcil, 2018). Using Hosmer and Lemeshow’s criteria (Hosmer et al., 2013), the AUC values determined for NLR were poor (Avcil, 2018) and acceptable (Abdel Samei et al., 2024; Fahiem & Mekkawy, 2022), and the PLR AUC values were also poor (Abdel Samei et al., 2024; Avcil, 2018) and acceptable (Fahiem & Mekkawy, 2022). Additionally, studies also reported cut points for monocyte/lymphocyte ratio (MLR) (Avcil, 2018; Fahiem & Mekkawy, 2022), MPV (Akbayram et al., 2020; Garipardic et al., 2017), and basophils (Akbayram et al., 2020).

Table 3.

Proposed cutpoints for NLR, PLR, and other blood cell parameters

Parameters	Study	AUC	95% CI	Cut-off	Sensitivity	Specificity	p-value
Basophils	Aksu 2020	0.66	0.60–0.74	0.04	70%	54%	<0.001
MLR	Avcil 2018	0.57	0.48–0.66	0.23	45%	66%	0.046
	Fahiem 2021	0.75	0.67–0.84	0.13	--	--	<0.001
MPV	Aksu 2020	0.66	0.58–0.74	9.75	72%	56%	<0.001
	Avcil 2019	0.6	0.51–0.69	9.45	56%	59%	0.034
	Samei 2023	0.68	--	11.1	38%	98%	--
NLR	Avcil 2020	0.66	0.58–0.75	1.62	54%	70%	0.001
	Fahiem 2021	0.78	0.69–0.87	1.3	--	--	<0.001
	Samei 2023	0.78	--	3.07	66%	84%	--
PLR	Avcil 2021	0.6	0.51–0.69	130.55	36%	72%	0.036
	Fahiem 2023	0.79	0.70–0.87	93.18	--	--	<0.001
	Samei 2023	0.68	--	115.7	82%	52%	--

AUC area under the curve, CI confidence interval, MLR monocyte to lymphocyte ratio, MPV mean platelet volume, NLR neutrophil to lymphocyte ratio, PLR platelet to lymphocyte ratio

Additional Studies of Interest

Six relevant cross-sectional studies comparing red and white blood cell values in individuals with ADHD to those without ADHD did not report NLR and PLR, so were not included in analyses. For reference, they are in Table SIII (Akbayram et al., 2020; Garipardic et al., 2017; Metin, et al., 2018; Metin, et al., 2018; Unal et al., 2019; Yorbik et al., 2014). The studies reported other blood parameters included in a CBC including mean corpuscular hemoglobin (MCH), mean corpuscular volume (MCV), mean platelet volume (MPV), plateletcrit (platelet count × MPV), platelet distribution width (PDW), and red blood cell distribution width (RDW). Given that neutrophil, platelet, and lymphocyte values are part of a CBC, it is likely that these studies had data on NLR and PLR in their participants but did not report the values.

Discussion

This systematic scoping review identified 15 articles that investigated NLR and PLR in individuals with ADHD. Fourteen studies evaluated children and adolescents, and one study evaluated adults. Four studies reported potential cut points for identifying ADHD using NLR, PLR, MPV, and basophil count. All 15 studies were published between 2018 and 2023, suggesting a recent increase in interest in NLR and PLR as potential biomarkers for ADHD.

AUC cutpoints can be used to identify a diagnostic “cut-off.” Individuals with NLR or PLR scores above this cutoff may warrant further evaluation for ADHD. Three studies in this review (Abdel Samei et al., 2024; Avcil, 2018; Fahiem & Mekkawy, 2022) reported cut points via AUC analysis for NLR and PLR, though they were poor to acceptable for distinguishing individuals with ADHD. However, AUC analysis may be inaccurate in small samples, though there is no recognized minimum sample size (Hanley & McNeil, 1982). The poor/acceptable results in the included studies may be related to sample size (they had 100–150 participants each). Therefore, it is not clear whether NLR and PLR levels are an accurate tool to identify individuals who could benefit from further evaluation, thus further research is needed in larger and more diverse samples.

ADHD is a clinical diagnosis that does not have an established biomarker, though many attempts have been made to identify one. Previous studies have examined blood minerals, vitamins, cytokines, neurotransmitters, steroid hormones, and many other parameters for their potential as biomarkers for ADHD (Cortese et al., 2023). A recent systematic review of 780 studies failed to identify a single biomarker for ADHD that had sensitivity and specificity of at least 80%, but it included only two of the 15 studies on NLR and PLR identified by this review (Cortese et al., 2023). The accessibility of NLR and PLR values, through CBC tests, make them potential biomarkers for ADHD, though their use as a predictive tool needs further investigation.

Eight studies assessed the association between NLR and PLR and ADHD symptom severity. One study examined NLR and PLR in individuals with ADHD before and after administration of a 1-month atomoxetine intervention. It was found that there was an increase in inflammatory markers in individuals with ADHD and partial improvement after initiation of pharmaceutical treatment (Öz et al., 2023). Research on the impact of stimulant or non-stimulant medication on inflammation in ADHD is limited, though one study on pro- and anti-inflammatory cytokine levels in ADHD that medicated children showed a trend to normalization of interleukin levels when compared to participants who were medication-naïve (Oades et al., 2010).

NLR and PLR are readily available hematologic biomarkers of the systemic inflammatory response and can be used to predict treatment response and disease outcomes in other conditions including rheumatoid arthritis, cardiovascular disease, cerebrovascular accidents, various cancers, and SARS-CoV2 infections (Angkananard et al., 2018; Erre et al., 2019; Li et al., 2020; Mellor et al., 2018; Wang et al., 2019; Yin et al., 2019). Additionally, studies have suggested that there is a potential utility for NLR in the screening, diagnosis, and management of patients with IBD (Langley et al., 2021). ASD is a neurodevelopmental disorder that has some symptom overlap, and frequent co-occurrence with ADHD (Kaat et al., 2013). A recent meta-analysis on immune cells in individuals with ASD identified four studies which reported NLR values for a group with ASD and a comparison group without ASD (total N = 358) (Arteaga-Henríquez et al., 2022). The group with ASD had higher average NLR than the group without ASD (g = 0.69; 95% CI, 0.03–1.4; p = 0.04). The meta-analysis did not report on PLR.

Although normal ranges for NLR and PLR have not yet been clearly defined, the average NLR in healthy adults is approximately 1.70 (0.99–1.76 in children) (Forget et al., 2017; Moosazadeh et al., 2019; Moosmann et al., 2022). The average PLR in healthy adults is approximately 117 (61–199 in children) with no significant difference regarding sex for NLR or PLR (Forget et al., 2017; Moosazadeh et al., 2019; Moosmann et al., 2022).

Other inflammatory markers including pro-inflammatory cytokines are altered in individuals with ADHD and may contribute to altered NLR levels (Anand et al., 2017). The cytokines interleukin 1 (IL-1) and interleukin 6 (IL-6) can drive up neutrophil counts and/or prolong their lifespan, contributing to higher NLR values, and both IL-1 and IL-6 are higher in individuals with ADHD than those without, according to a review and a meta-analysis (Anand et al., 2017; Misiak et al., 2022). Cytokine gene polymorphisms may be partially responsible for this difference, as the review also found significant differences in the polymorphisms for IL-6 in those with ADHD versus without, and those IL-6 polymorphisms were associated with performance on tasks that assess attention. IL-10 may drive up lymphocyte counts, further increasing NLR values, but evidence on IL-10 levels in ADHD is mixed (Anand et al., 2017).

Four studies used BMI greater than 30 as an exclusion criterion for study participation. Notably, the 2023 report from the American Medical Association’s Council on Science and Public Health, criticized the BMI system for its inaccuracies. Using BMI as an exclusion criterion resulted in smaller sample sizes, as BMI does not account for the heterogeneity across racial and ethnic groups, sexes, and age span (“What’s Wrong With Overreliance on BMI?,” 2023). These concerns highlight the need to consider alternative methods, such as waist circumference measurement, to promote equity in inclusion and exclusion criteria.

Limitations and Future Research

Future directions for research on NLR and PLR in ADHD include a systematic review and meta-analysis on NLR and PLR in ADHD, including all 15 studies identified. A meta-analysis could identify the mean difference in NLR and PLR between individuals with and without ADHD and identify whether NLR and PLR are potential biomarkers for ADHD diagnosis, ADHD severity, or treatment response. Longitudinal studies examining change in NLR and PLR over time, including before and after medication administration, are also warranted, as are studies on NLR and PLR in individuals with ADHD which do not exclude participants due to high BMI.

The strengths of this scoping review include an extensive literature search that encompassed gray literature and a wide variety of medical databases and involved assistance from research librarians and contacting authors when necessary. Another strength was the translation of three papers from Turkish to English (one was included) using automated technology (Google Translate); although formal translation services would have been preferable. Additionally, we did not complete a formal assessment on the quality of the studies included such as a risk of bias assessment; however, the included sources of evidence in a scoping review are not typically critically appraised (Tricco et al., 2018).

Based on the 15 studies reviewed, NLR and PLR appear promising as biomarkers for identifying ADHD but require further investigation. Assessing NLR and PLR is economical, accessible, and minimally invasive as levels are easily obtained from routine blood tests. Additional research is justified to further investigate if routine incorporation of NLR and PLR into research and clinical practice could beneficially impact the diagnosis and treatment of patients with ADHD.