Skip to main content
Download PDF

Serum neurofilament light chain levels are associated with depression among US adults: a cross-sectional analysis among US adults, 2013–2014

BMC Psychiatry volume 24, Article number: 527 (2024) Cite this article

Abstract

Background

Serum neurofilament light chain (sNfL) has been identified as a biomarker for neurologic diseases. However, sNfL remains unknown to be responsible for depression.

Aims

The aim of this research was to explore the relationship between sNfL levels and depression in US adults.

Methods

In this cross-sectional survey of the general population, we investigated representative data involving 10,175 participants from the 2013–2014 cycle of the National Health and Nutrition Examination Survey (NHANES). Depression was diagnosed using the Patient Health Questionnaire-9 (PHQ-9). The effect of related factors on depression was analyzed by conducting a univariate analysis. Stratified analysis was utilized to detect the stability and sensitivity of the relationship. After adjusting for race, education, marital status, smoking status, body mass index (BMI), sleep duration, income, and a history of hypertension, sedentary behavior and stroke, multivariable linear regression was performed to demonstrate the correlation between sNfL and depression.

Results

A total of 1301 individuals between the ages of 20 and 75 were involved in this investigation, of which 108 (8.3%) were diagnosed with depression. A significant positive correlation between sNfL and depression among adults in the US was observed by conducting univariable analyses. After adjusting for confounding factors, the multivariate analyses indicated that elevated sNfL levels might play a pivotal role in the development of depression (odds ratio (OR) = 3.0; 95% confidence interval (CI): (1.5, 6.1), P = 0.002).

Conclusion

These results indicated that sNfL is closely linked to depression in a nationally representative individual. However, further studies are needed to confirm the biological mechanism as well as the clinical implications of sNfL and depression.

Peer Review reports

Introduction

Depression, the most prevalent psychiatric symptom, is distinguished by a diminished mood, absence of vitality, sorrow, sleeplessness, and an inability to derive pleasure from life [1]. Around 20% of the American population experiences depression at some point in their lives, leading to poor outcomes in psychosocial and quality of life [2, 3]. In addition, there is a strong link between depression and suicide. In America, the leading cause of suicides is mood disorder, especially depression, which accounts for almost a third of all deaths worldwide [4, 5]. Currently, treatment approaches for depression include pharmacologic treatments and nonpharmacologic therapies, such as psychotherapy, pharmacologic treatments, cognitive behavior therapy (CBT) and electroconvulsive therapy (ECT) [6,7,8]. However, up to one-third of individuals do not exhibit a positive response to those therapies. Additionally, among those who did respond, only a third achieved remission [9, 10]. Furthermore, it is impossible to escape the adverse reactions, which involve sexual dysfunction, decreased sex drive, headaches, digestive issues, feelings of unease, and restlessness [11]. ECT not only requires general anesthesia and must take comorbidities into account, but also has not examined evidence for treatment-resistant people [1]. Unlike many other neurologic diseases, there are no established blood biomarkers that can be used to predict the progression of depression, which makes diagnosis challenging. Therefore, it is urgent for us to discover effective biomarkers for the diagnosis and treatment of depression.

Neurofilament light chain (NfL) is a scaffolding protein of the neuronal cytoskeleton, and its elevated level reflects neuroaxonal damage [12]. As a marker of neurodegeneration, serum neurofilament light chain (sNfL) is released not only to cerebrospinal fluid (CSF) but also to blood [13, 14]. Thus, NfL levels in the blood are always be measured in the blood as well as CSF [15]. However, neurofilament levels in the blood always be measured by precise assay technology given that lumbar puncture is an invasive procedure [16]. Moreover, sNfL is well-established as a biomarker for disease prognosis and monitoring recurrences, including multiple sclerosis (MS), cognition decline, stroke, traumatic brain injury, Guillain-Barré syndrome (GBS) and primary psychiatric disorders (PPD) [17,18,19,20,21]. Therefore, the detection of NfL level in serum is widely utilized for evaluating neuroinflammatory and degenerative diseases.

However, based on our current understanding, there is no evidence indicating a connection between sNfL and depression within the entire population of the United States. It is therefore worthwhile to explore whether sNfL concentrations are a dependent predictor of depression progression. In this study, we presented the first known evidence for a correlation between sNfL and depression among the overall population of the United States from the 2013–2014 cycle of the NHANES database.

Materials and methods

Design and methods of the study

The NHANES program was created to investigate the health and nutritional status of the United States population by conducting a series of interviews, examinations and laboratory measurements, whose findings hold significant implications for discovering disease risk factors as well as effective interventions [22, 23].

For this investigation, the 2013–2014 continuous cycle of the NHANES dataset was obtained. After excluding 8104 participants who had missing sNfL data, 16 participants with missing BMI data, 537 participants with missing blood pressure data, 6 participants without sedentary behavior data, 48 participants at the threshold of a diabetes diagnosis, 91 participants with missing depression status data, 68 participants with missing income data, 1 participant without smoking status and education levels data, and 2 participants without sleep duration data, a total of 1301 participants were eligible for this study. Figure 1 illustrates the sample selection flowchart.

Fig. 1
figure 1

The workflow of the research. Abbreviations NHANES: National Health and Nutrition Examination Survey; BMI: body mass index; sNFL: serum neurofilament light chain

Full size image

Serum neurofilament light chain measurement

This study collected serum samples from individuals aged 20–75 years in the NHANES 2013–2014 cycle, who gave informed consent for the utilization of the remaining specimens in subsequent research. Initially, the specimens are cultured with acridinium-ester (AE)-labeled NfL antibody, followed by the introduction of paramagnetic particles (PMP) coated with capture antibody into the sample. Subsequently, the formation of antigen-AE-labeled antibodies and PMP complexes were acquired. Additionally, the sample is supplemented with paramagnetic particles (PMP) that have a coating of capture antibody to obtain paramagnetic particles. After the unbound AE-labeled antibodies were isolated and eliminated, the chemiluminescence was triggered and the emission of light was measured.

Assessment of depression symptoms

As a convenient tool, PHQ-9 was utilized to evaluate the frequency of depressive mood and anhedonia over a period of two weeks objectively [1, 24, 25]. The PHQ-9 assigned a score of 0 to 3 for each item (0 = none; 1 = some days; 2 = most days; 3 = almost every day), and the total scores for the PHQ-9 varied from 0 to 27 [26, 27]. In this study, participants were defined as having clinically significant depressive symptoms at a cutoff of ≥ 10, with a sensitivity and specificity of 74% and 91%, respectively [2, 25, 28].

Other covariates

The current research examined the age (20–34 years, 35–60 years, 61–75 years), gender, race/ethnicity (Mexican American, other Hispanic, non-Hispanic white, non-Hispanic black, and other races), body mass index (normal weight:<25.0, overweight: [25.0, 30.0), obesity:≥30.0 kg/m2), educational levels (≤ 9th grade, 9-11th grade/includes 12th grade with no diploma, high school graduate/GED or equivalent, some college or AA degree, and college graduate or above), marital status (married, living with partner, widowed, divorced, separated, and never married), family poverty income ratio (< 1, [1,3), ≥ 3), sleep duration (< 7, [7,9), ≥ 9 h), hypertension (systolic blood pressure ≥ 130 mmHg and/or diastolic blood pressure ≥ 80 mmHg), smoking status, congestive heart failure, sedentary behavior, coronary heart disease and stroke [29,30,31,32,33].

Statistical analysis

This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. R software (4.1.3, http://www.Rproject.org) and EmpowerStats (version 2.0, http://www.empowerstats.com) were utilized for all the analyses. An overview of the study population was statistically described by sNfL concentrations (Table 1). Participants’ characteristics based on quartiles of sNfL levels were compared through Rao–Scott χ2 tests. SNfL levels were logarithmically transformed to achieve a normal distribution. Then, univariate and multivariate analyses were performed to analyze the relationship between sNfL levels and depression, as well as other outcomes. The multivariate linear regression was built after taking those confounders into consideration: race, education, marital status, BMI, blood pressure, sedentary behavior, sleep duration and having a history of diabetes. P < 0.05 was considered statistically significant. We also conducted a smooth curve fitting and a threshold effect analysis for enhancing the correlation between sNfL and PHQ-9 scores, after taking all confounders into consideration. For the threshold effects analysis, a log-likehood ratio test of less than 0.05 was considered the criterion for the presence of a non-linear relationship. Model I was conducted using liner regression, and Model II was performed using a two-piecewise linear regression (Additional file 1 and 2).

Table 1 Characteristics of participants by quartiles of sNfL
Full size table

Results

The baseline characteristics of study population

Among the 10175 participants in the 2013–2014 NHANES, 1301 were involved (Fig. 1). The descriptive characteristics of the participants are displayed in Table 1, categorized by sNfL quartiles. The participants were divided into four groups based on their sNfL levels: Q1 (sNfL = 2.8–8.3 pg/mL), Q2 (sNfL = 8.4–12.3 pg/mL), Q3 (sNfL = 12.4–18.8 pg/mL) and Q4 (sNfL > 18.8 pg/mL). Participants ranged in age from 20 to 75, and 50% (n = 650) are male. Around 8.3% (n = 108) exhibited signs of depression (PHQ-9 score > = 10), while the remaining 91.7% (n = 1193) did not show any depressive symptoms (PHQ-9 score < 10). Interesting, there was a strong positive association between age and sNfL levels, which coincides with previous theories. Individuals with elevated sNfL levels were more likely to be male, older, Hispanic, married, obese and there was a significantly lower proportion of participants who had never smoked. Furthermore, participants exhibiting elevated sNfL levels demonstrated a higher occurrence of comorbidities, such as high blood pressure, sedentary behavior, diabetes, stroke, congestive heart failure and coronary heart disease, compared with lower sNfL levels.

Association of sNfL and the risk of depression

To examine the correlation between sNfL and depression, a univariate analysis was conducted (Table 2). The results showed that age (35–60 years: OR = 2.2, 95% CI: (1.2, 3.9), P = 0.010; 61–75 years: OR = 2.9 95% CI: (1.6, 5.4), P < 0.001), gender (female: OR = 1.8, 95% CI: (1.2, 2.7), P = 0.005), income (OR = 0.5, 95% CI: (0.3, 0.9) P = 0.03), smoking status (OR = 1.5, 95% CI: (1.0, 2.3), P = 0.030), had a disease history of stroke (OR = 2.6, 95% CI: (1.1, 6.6), P 0.037), congestive heart failure (OR = 4.4, 95% CI: (2.0, 9.8), P < 0.001), coronary heart disease (OR = 5.1, 95% CI: (2.4, 10.6), P < 0.001) and log-transformed serum NFL concentrations (OR = 3.4, 95% CI: (1.8, 6.4), P < 0.001) had a statistical difference with high prevalence of depression. Stratified analyses (Table 3) were conducted for age, gender, race, education level, marital status, income, BMI, smoking status, blood pressure, sedentary behavior, sleep duration and complications such as congestive heart failure, coronary heart disease, stroke and diabetes. It turned out that despite the OR values fluctuated among subgroups of the population, there was a good deal of consistency in our analysis (OR > 1), suggesting that the results were stable and sensitive. Additionally, it was relatively stable in some stratifications, especially in age, smoking status and sedentary behavior, although high heterogeneity was observed in terms of race, education, income, marital status, sleep duration, and relevant disease histories of diabetes, congestive heart failure, coronary heart disease and stroke.

Table 2 Univariate analysis of log-transformed sNfL and depression among American adults
Full size table
Table 3 Associations between log-transformed sNfL and depression by stratified analysis
Full size table

Multivariable-Adjusted Associations Between sNfL and depression

To evaluate the independent effects of log-transformed sNfL on depression, we conducted a multivariable analysis. Table 4 shows the significant predictors of depression included age (35–60 years: OR = 2.0, 95% CI: (1.0, 3.8), P = 0.038; 61–75 years: OR = 2.7, 95% CI: (1.3, 5.7), P = 0.010), sex (female: OR = 1.6, 95% CI: (1.0, 2.4), P = 0.042), income (median income: OR = 0.5, 95% CI: (0.3, 0.9), P = 0.032; high income: OR = 0.4, 95% CI: (0.2, 0.6), P < 0.001), congestive heart failure (OR = 3.2, 95% CI: (1.4, 7.6), P = 0.007), coronary heart disease (OR = 5.1, 95% CI: (2.3, 11.6), P < 0.001), and log-transformed sNfL levels (OR = 3.0, 95% CI: (1.5, 6.1), P = 0.002), and the result was stable after adjusted for race, marital status, education, BMI, sedentary behaviors, diabetes, hypertension, sleep duration. However, there was no significant difference in smoking status (OR = 1.3, 95% CI: (0.9, 2.0), P = 0.209) and stroke (OR = 1.9, 95% CI: (0.7, 5.0), P = 0.201).

Table 4 Multivariate logistic model of the predictors of depression
Full size table

Based on the results of regression analysis, a smooth curve fitting and a curve fitting analysis were performed to explore the non-liner relationship between the log10 transformation sNfL and PHQ-9 scores (Additional file 1 and 2). A. Figure 1 displayed a non-liner relationship between log-transformed serum NfL levels (pg/mL) and PHQ-9 scores (log-likelihood ratio < 0.001). A. Table 1 showed the threshold effect analysis of the log-transformed sNfL (pg/mL) on PHQ-9 scores. The results demonstrated that the turning point of log-transformed serum NfL levels was 1.5 pg/mL. When the log-transformed serum NfL levels less than 1.5 pg/mL, the effect value was 0.3 (95% CI = -2.0-2.5, P = 0.807). When the log-transformed serum NfL levels surpassed 1.5 pg/mL, the effect value was 8.9 (95% CI = 3.1–14.7, P = 0.003). The result showed the P-value of log-likehood ratio test (LR Test) < 0.001, indicating Model II is significant different from Model I. Moreover, from the overall smoothing curve, the relationship was still significant (β = 1.9, 95% CI = 0.1–3.8, P = 0.039).

Discussion

As the most abundant and soluble subunit of the class IV intermediate filament protein family (including neurofilament heavy protein, neurofilament medium protein, neurofilament light protein and alpha-internexin), NFL levels are responsible for the stability of the neuronal structure [19, 34, 35]. SNfL, the core of mature neurofilament, is released into not only CSF but also blood when it comes to neuroaxonal damage [36]. However, considering cerebrospinal fluid collection by lumbar puncture is an invasive and complex procedure, measures of NfL can be obtained from blood NfL with higher accuracy and safety. SNfL may emerged as a diagnostic and prognostic biomarker for neuronal damage, whose levels are positively related to recurrence or new magnetic resonance imaging (MRI) lesions and negatively related to effective treatment [14]. However, sNfL has never been explored as a potential therapeutic response, diagnostic, prognostic or monitoring biomarker in depression.

This is the first epidemiological study to examine the relationships between sNfL and depression among adult samples from the US general population. Firstly, we explored whether participants with high sNfL levels (n = 41, 12.5%) had significant greater risk of depression characterized compared with those with low sNfL levels (n = 19, 5.8%) (Table 1). In addition, based on the results, older people are more likely to suffer from depression, which is consistent with previous theories that sNfL concentration increases with age. Then, univariate analysis showed that log-transformed sNfL levels differed significantly in subjects with and without depression (OR = 3.4, 95% CI: (1.8, 6.4), P < 0.001; Table 2), which demonstrated that log-transformed serum NfL levels are positive-related with depression. Moreover, the relationship is stable after stratified analysis was conducted (Table 3). Besides, multivariate analysis showed that log-transformed sNfL levels can be an independent factor for the prevalence of depression after taking race, marital status, education, BMI, sedentary behaviors, diabetes, hypertension, sleep duration into consideration (OR = 2.8, 95% CI: (1.4, 5.6) p = 0.004; Table 4). Finally, a non-liner relationship between log-transformed sNfL pg/mL and PHQ-9 scores were discovered with a log-likehood ratio test less than 0.05 (A. Figure 1 and A. Table 1). The results demonstrated that individuals with high levels of sNfL had a greater risk of depression symptoms, which may be associated with neuronal functional pathways being inhibited or tricked after suffering neuroaxonal damage. However, a significant proportion of participants with high incomes had greater depressive episodes, which is contrary to previous research.

No clear mechanism underlies the biological processes for the sNfL levels in the pathogenesis of neurodegenerative diseases; however, the existing evidence may provide powerful clues to its mechanism. There is a broad agreement that the measurement of plasma or serum levels of NfL in neurological disorders or neurodegenerative diseases act as indirect indicator of the degree of axonal injury [37]. In addition, NfL levels can affect the hippocampal morphology in animals, thereby contribute to depression [38, 39]. Those potential mechanisms contribute to our understanding of the role of NfL in identifying new therapies for depression.

Our study has several strengths. This report provides public health researchers with a comprehensive population-based assessment of sNfL concentrations in the U.S. population. In addition, the data collection and survey procedures for NHANES followed standardized protocols and were rigorously inspected for quality assurance. Notably, the correlation between sNfL and depression remains statistically significant even after considering multiple confounding factors, revealing a novel discovery presented here for the first time. Additionally, the sNfL levels underwent a logarithmic transformation to attain a normal distribution. Moreover, the assessment for depression was conducted using the PHQ-9, which has been identified as an objective and sensitive screening tool [1].

Several limitations should also be acknowledged. Firstly, despite controlling for confounding factors through various methods, such as multivariable regression, we were unable to eliminate the possibility of residual and unmeasured confounding, including cognitive functions and physical activity as sample size limitations. Moreover, the ongoing trials will provide further insights into the correlation between alterations in NfL levels and depression. Besides, although the PHQ-9 has been identified as an effective tool for screening depression, subjective factors among participants may have some influence. We would perfect our results in conjunction with other approaches in the future. In addition, MRI-based clinical studies have shown a correlation between disease activity and elevated levels of NfL, such as MS [40]. However, the efficiency of NfL in monitoring depression progression is not yet defined and we hope to take more imaging features into consideration in the future. Besides, due to the nature of cross-section, causality cannot be implied.

Conclusion

Above all, in this large, population-based cross-sectional research, we provide evidence that sNfL was elevated in depression patients. Identifying the qualities of a delicate and minimally intrusive biomarker, such as neurofilament light protein, holds potential not only as a diagnostic biomarker for treatment-resistant major depression but also for monitoring disease progression and treatment effectiveness.

Data availability

The datasets generated and/or analyzed during the current study are available in the NHANES database (https://www.cdc.gov/nchs/nhanes/index.htm).

References

  1. McCarron RM, Shapiro B, Rawles J, Luo J, Depression. Ann Intern Med. 2021;174(80):Itc65–itc. https://doi.org/10.7326/aitc202105180.

    Article  PubMed  Google Scholar 

  2. Costantini L, et al. Screening for depression in primary care with Patient Health Questionnaire-9 (PHQ-9): a systematic review. J Affect Disord. 2021;279:473–83. https://doi.org/10.1016/j.jad.2020.09.131.

    Article  PubMed  Google Scholar 

  3. Malhi GS, Mann JJ, Depression. Lancet. 2018;392:2299–312. https://doi.org/10.1016/s0140-6736(18)31948-2.

    Article  PubMed  Google Scholar 

  4. Beck KE, et al. Improving primary care adolescent depression screening and initial management: a Quality Improvement Study. Pediatr Qual Saf. 2022;7:e549. https://doi.org/10.1097/pq9.0000000000000549.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Mann JJ, et al. Suicide prevention strategies: a systematic review. JAMA. 2005;294:2064–74. https://doi.org/10.1001/jama.294.16.2064.

    Article  CAS  PubMed  Google Scholar 

  6. Qaseem A, et al. Nonpharmacologic and pharmacologic treatments of adults in the Acute phase of major depressive disorder: a living clinical Guideline from the American College of Physicians. Ann Intern Med. 2023;176:239–52. https://doi.org/10.7326/m22-2056.

    Article  PubMed  Google Scholar 

  7. Brewin CR. Theoretical foundations of cognitive-behavior therapy for anxiety and depression. Annu Rev Psychol. 1996;47:33–57. https://doi.org/10.1146/annurev.psych.47.1.33.

    Article  CAS  PubMed  Google Scholar 

  8. Okuyama T, Akechi T, Mackenzie L, Furukawa TA. Psychotherapy for depression among advanced, incurable cancer patients: a systematic review and meta-analysis. Cancer Treat Rev. 2017;56:16–27. https://doi.org/10.1016/j.ctrv.2017.03.012.

    Article  PubMed  Google Scholar 

  9. Rush AJ, et al. Acute and longer-term outcomes in depressed outpatients requiring one or several treatment steps: a STAR*D report. Am J Psychiatry. 2006;163:1905–17. https://doi.org/10.1176/ajp.2006.163.11.1905.

    Article  PubMed  Google Scholar 

  10. Leighton SP, et al. Chemokines in depression in health and in inflammatory illness: a systematic review and meta-analysis. Mol Psychiatry. 2018;23:48–58. https://doi.org/10.1038/mp.2017.205.

    Article  CAS  PubMed  Google Scholar 

  11. Marwaha S, et al. Novel and emerging treatments for major depression. Lancet. 2023;401:141–53. https://doi.org/10.1016/s0140-6736(22)02080-3.

    Article  CAS  PubMed  Google Scholar 

  12. Disanto G, et al. Serum neurofilament light: a biomarker of neuronal damage in multiple sclerosis. Ann Neurol. 2017;81:857–70. https://doi.org/10.1002/ana.24954.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Gaetani L, et al. Neurofilament light chain as a biomarker in neurological disorders. J Neurol Neurosurg Psychiatry. 2019;90:870–81. https://doi.org/10.1136/jnnp-2018-320106.

    Article  PubMed  Google Scholar 

  14. Barro C, et al. Serum GFAP and NfL levels differentiate subsequent progression and disease activity in patients with Progressive multiple sclerosis. Neurol Neuroimmunol Neuroinflamm. 2023;10. https://doi.org/10.1212/nxi.0000000000200052.

  15. Ciardullo S, et al. Diabetes Mellitus is Associated with higher serum neurofilament light chain levels in the General US Population. J Clin Endocrinol Metab. 2023;108:361–7. https://doi.org/10.1210/clinem/dgac580.

    Article  PubMed  Google Scholar 

  16. Khalil M, et al. Neurofilaments as biomarkers in neurological disorders. Nat Rev Neurol. 2018;14:577–89. https://doi.org/10.1038/s41582-018-0058-z.

    Article  CAS  PubMed  Google Scholar 

  17. Benkert P, et al. Serum neurofilament light chain for individual prognostication of disease activity in people with multiple sclerosis: a retrospective modelling and validation study. Lancet Neurol. 2022;21:246–57. https://doi.org/10.1016/s1474-4422(22)00009-6.

    Article  PubMed  Google Scholar 

  18. Seiberl M, et al. Serum neurofilament light chain as biomarker for cladribine-treated multiple sclerosis patients in a Real-World setting. Int J Mol Sci. 2023;24. https://doi.org/10.3390/ijms24044067.

  19. Liu X, Chen J, Meng C, Zhou L, Liu Y. Serum neurofilament light chain and cognition decline in US elderly: a cross-sectional study. Ann Clin Transl Neurol. 2023. https://doi.org/10.1002/acn3.51929.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Altmann P, et al. Increased serum neurofilament light chain concentration indicates poor outcome in Guillain-Barré syndrome. J Neuroinflammation. 2020;17. https://doi.org/10.1186/s12974-020-01737-0.

  21. Zhao H, et al. Association of serum biomarker neurofilament light concentration with post-stroke depression: a preliminary study. Gen Hosp Psychiatry. 2020;64:17–25. https://doi.org/10.1016/j.genhosppsych.2020.01.006.

    Article  PubMed  Google Scholar 

  22. Patel CJ, et al. A database of human exposomes and phenomes from the US National Health and Nutrition Examination Survey. Sci Data. 2016;3:160096. https://doi.org/10.1038/sdata.2016.96.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Eke PI, Borgnakke WS, Genco RJ. Recent epidemiologic trends in periodontitis in the USA. Periodontol 2000. 2020;82:257–67. https://doi.org/10.1111/prd.12323.

    Article  PubMed  Google Scholar 

  24. Smarr KL, Keefer AL. Measures of Depression and depressive symptoms. Arthritis Care Res (Hoboken). 2020;72(10):608–29. https://doi.org/10.1002/acr.24191.

    Article  PubMed  Google Scholar 

  25. Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16:606–13. https://doi.org/10.1046/j.1525-1497.2001.016009606.x.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Manea L, Gilbody S, McMillan D. Optimal cut-off score for diagnosing depression with the Patient Health Questionnaire (PHQ-9): a meta-analysis. CMAJ. 2012;184:E191–196. https://doi.org/10.1503/cmaj.110829.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Wang K, et al. Higher HEI-2015 score is Associated with reduced risk of Depression: result from NHANES 2005–2016. Nutrients. 2021;13. https://doi.org/10.3390/nu13020348.

  28. Arroll B, et al. Validation of PHQ-2 and PHQ-9 to screen for major depression in the primary care population. Ann Fam Med. 2010;8:348–53. https://doi.org/10.1370/afm.1139.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Hicks CW, Wang D, Windham BG, Matsushita K, Selvin E. Prevalence of peripheral neuropathy defined by monofilament insensitivity in middle-aged and older adults in two US cohorts. Sci Rep. 2021;11:19159. https://doi.org/10.1038/s41598-021-98565-w.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. You Y, et al. Muscle quality index is associated with trouble sleeping: a cross-sectional population based study. BMC Public Health. 2023;23:489. https://doi.org/10.1186/s12889-023-15411-6.

    Article  PubMed  PubMed Central  Google Scholar 

  31. SSY AL, et al. Association between time since quitting smoking and periodontitis in former smokers in the National Health and Nutrition Examination Surveys (NHANES) 2009 to 2012. J Periodontol. 2019;90:16–25. https://doi.org/10.1002/jper.18-0183.

    Article  Google Scholar 

  32. Huang Y, et al. The effect of triglycerides in the associations between physical activity, sedentary behavior and depression: an interaction and mediation analysis. J Affect Disord. 2021;295:1377–85. https://doi.org/10.1016/j.jad.2021.09.005.

    Article  CAS  PubMed  Google Scholar 

  33. Dreimüller N, et al. Body mass index (BMI) in major depressive disorder and its effects on depressive symptomatology and antidepressant response. J Affect Disord. 2019;256:524–31. https://doi.org/10.1016/j.jad.2019.06.067.

    Article  PubMed  Google Scholar 

  34. Spanier S, Kilian HM, Meyer DM, Schlaepfer TE. Treatment resistance in major depression is correlated with increased plasma levels of neurofilament light protein reflecting axonal damage. Med Hypotheses. 2019;127:159–61. https://doi.org/10.1016/j.mehy.2019.03.022.

    Article  CAS  PubMed  Google Scholar 

  35. Petzold A. Neurofilament phosphoforms: surrogate markers for axonal injury, degeneration and loss. J Neurol Sci. 2005;233:183–98. https://doi.org/10.1016/j.jns.2005.03.015.

    Article  CAS  PubMed  Google Scholar 

  36. Jakobsson J, et al. Elevated concentrations of neurofilament light chain in the cerebrospinal fluid of bipolar disorder patients. Neuropsychopharmacology. 2014;39:2349–56. https://doi.org/10.1038/npp.2014.81.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Fitzgerald KC, et al. Contributors to serum NfL levels in people without neurologic disease. Ann Neurol. 2022;92:688–98. https://doi.org/10.1002/ana.26446.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Reinés A, Cereseto M, Ferrero A, Bonavita C, Wikinski S. Neuronal cytoskeletal alterations in an experimental model of depression. Neuroscience. 2004;129:529–38. https://doi.org/10.1016/j.neuroscience.2004.08.026.

    Article  CAS  PubMed  Google Scholar 

  39. Alexopoulos GS. Depression in the elderly. Lancet. 2005;365:1961–70. https://doi.org/10.1016/s0140-6736(05)66665-2.

    Article  PubMed  Google Scholar 

  40. Kapoor R, et al. Serum neurofilament light as a biomarker in progressive multiple sclerosis. Neurology. 2020;95:436–44. https://doi.org/10.1212/wnl.0000000000010346.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

National Natural Science Foundation of China (22067015), The Shenzhen Science and Technology Innovation Project [grant numbers: JCYJ20190822090801701; JCYJ20230807095124046] and Peking University Shenzhen Hospital - Ye Chenghai Charity Foundation provided funding for this project.

Author information

Authors and Affiliations

  1. Department of Neurology, Peking University Shenzhen Hospital, Shenzhen, 518036, China

    Ying Song, Huili Jiao, Qi Weng, Hang Liu & Li Yi

Authors
  1. Ying Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Huili Jiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qi Weng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Li Yi
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Li Yi conceptualized the study. Huili Jiao designed the study. Ying Song and Qi Weng conducted the analysis. Hang Liu wrote the article. The final draft was reviewed and approved by all authors.

Corresponding author

Correspondence to Li Yi.

Ethics declarations

Ethics approval and consent to participate

The protocol of NHANES is approved by the National Center for Health Statistics (NCHS) Research Ethics Review Board. Informed consent was obtained from patients before enrolment; otherwise, the consent was obtained from the patients’ closest relative.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Supplementary Material 2

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Song, Y., Jiao, H., Weng, Q. et al. Serum neurofilament light chain levels are associated with depression among US adults: a cross-sectional analysis among US adults, 2013–2014. BMC Psychiatry 24, 527 (2024). https://doi.org/10.1186/s12888-024-05964-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12888-024-05964-0

Keywords

BMC Psychiatry

ISSN: 1471-244X

Contact us