Association of Weight Waist Adjustment Index (WWI) and A Body Mass Index (ABSI) with Serum Neurofilament Light Chain Levels in a national study of US adults

doi:10.21203/rs.3.rs-4718881/v1

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Research Article

Association of Weight Waist Adjustment Index (WWI) and A Body Mass Index (ABSI) with Serum Neurofilament Light Chain Levels in a national study of US adults

https://doi.org/10.21203/rs.3.rs-4718881/v1

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Objective

This study explores how the Weight-Adjusted Waist Index (WWI) and A Body Shape Index (ABSI) relate to serum neurofilament light chain (sNfL) levels among US adults. We aim to clarify sNfL that serves key roles in neuronal injury in neurological diseases, given its understudied connection to obesity.

Methods

Focusing on people with complete information on the Weight-Adjusted Waist Index (WWI), A Body Shape Index (ABSI), and serum neurofilament light chain (sNfL), we used cross-sectional data from the 2013–2014 National Health and Nutrition Examination Survey (NHANES). Multiple linear regression analysis let us investigate the separate connections of WWI, ABSI, and sNfL. To assess any nonlinear correlations, we also performed threshold effect analysis and smoothed curve fitting. Moreover, interaction testing and subgroups analysis were done to improve the general validity of our results.

Results

1,995 people were finally enrolled for this study. WWI and ABSI were positively linked with sNfL (WWI: β = 0.05, 95% CI: 0.01–0.09; ABSI: β = 1.65, 95% CI: 3.53–13.72). There was no clear reliance in this association according to subgroup analysis and interaction tests. Furthermore, studies of smoothed curve fitting and threshold effects revealed a positive connection of WWI and ABSI with reference to sNfL.

Conclusion

In the adult American population, while WWI and ABSI are linearly positively correlated with serum neurofilament light protein (sNfL), the effect size is more pronounced for ABSI. This correlation provides fresh evidence connecting obesity to neurological conditions, deepening our comprehension of the extensive health impacts associated with obesity.

Level of Evidence: Level I, experimental studies

serum neurofilament light chain1

Weight-Adjusted Waist Index

A Body Shape Index

Obesity

NHANES

Emerging as a vital sign of neuronal injury for a range of neurological diseases is the biomarker serum neurofilament light chain (sNfL)[1]. Neurofilaments are intracellular cytoskeletal proteins comprising light, medium, and heavy chain types, categorized by their molecular weight. These proteins are released into the extracellular milieu as a result of neuronal injury, and they may subsequently infiltrate the cerebrospinal fluid and blood circulation. With advancing research, sNfL has been frequently termed the "neurologist's C-reactive protein" [2]. What's more, the detection of sNfL can facilitate the early diagnosis of neurodegenerative diseases, and there is substantial evidence supporting its role in assessing disease activity and progression in multiple sclerosis, Alzheimer's disease, and ischemic stroke [3–5]. Therefore, sNfL holds significant potential for a wide range of clinical applications.

Given the elevated all-cause mortality rates linked to obesity[6], there has been increasing focus on the relationship between obesity and cognitive health. Nevertheless, studies investigating the link between obesity and serum ferritin concentrations are limited and inconclusive. Evidence suggests that sNfL is negatively correlated with body mass index (BMI)[7]. According to other reports, there is no correlation between BMI and sNfL overall. However, BMI is the biggest predictor of sNfL among Americans under 60 years old[8]. The Body Mass Index (BMI), which has been used historically to characterize obesity, has come under scrutiny in recent years for its limitations in distinguishing between different distributions of visceral fat. Additionally, the introduction of the obesity paradox has further deepened doubts about the validity of this indicator [9]. Thus, the relationship of obesity and sNfL remains questionable and uncertain.

With the introduction of new obesity indices, such as the Weight-Adjusted Waist Index (WWI) and A Body Shape Index (ABSI), exploration of the associations between obesity and various diseases has become more in-depth. WWI, an anthropometric indicator focusing on central obesity, evaluates waist circumference to offer a more precise assessment of health risks[10, 11]. Research indicates that the obesity paradox, which relates BMI to mortality, does not extend to the association between WWI and mortality[11]. A Body Shape Index (ABSI) formulated using mortality statistics serves as an abdominal obesity metric[12]. And ABSI stands out as the sole abdominal obesity index unaffected by the obesity paradox[13]. But few existing studies examined the correlations between WWI and ABSI in relation to sNfL. Therefore, we examined potential links between these parameters using NHANES data.

2.1 Data source

Under the direction of the Centers for Disease Control and Prevention (CDC), the National Center for Health Statistics (NCHS) conducts the ongoing transversal survey known as the National Health and Nutrition Examination Survey (NHANES). It offers a wealth of resources related to operational manuals, estimation techniques, consent documentation, and analytical protocols. To get comprehensive information on NHANES operational guidelines, estimating techniques, permission forms, and data analysis processes, visit the NHANES website at https://wwwn.cdc.gov/nchs/nhanes/. The NCHS Ethics Review Board has authorized NHANES' ethical conduct, and each participant gave written consent to take part in the study.

2.2 Participants

Given the scarcity of sNfL data, our analytical focus was narrowed down to the 2013-2014 iteration of NHANES. From the original cohort of 10,175 participants in this survey, we first excluded those with missing serum neurofilament light protein (sNfL) data (n =8,104). We determined the Weight-to-Waist Index (WWI) using weight and waist circumference measurements and excluded individuals lacking complete WWI data (n = 72). Participants lacking ABSI information were additionally excluded (n = 2). Participants missing covariate information were also excluded, though the sample size was small. Missing information included self-reported smoking status (n = 1) and congestive heart failure (n = 1). Ultimately, 1,995 participants were enrolled in the research. The process of participants selection is schematically represented in Figure 1.

2.3 Independent and dependent variables

Measurements made during physical examinations yielded data that was used to identify WWI and ABSI as predictor factors. Upon review, all gathered data underwent scrutiny, and any unrealistic values were excluded from the records. All initial data were preserved in their original form during collection and did not incorporate approximated values. The raw data for WWI and ABSI was not directly provided by the NHANES program. Rather, waist circumference (cm) was divided by weight (kg) squared to get WWI[14]. Calculating ABSI involved dividing WC by (BMI^(2/3) × height^(1/2)) [12].

The dependent variable was designated as serum neurofilament light protein (sNfL). In alignment with prior research protocols[15],serum samples gathered between 2013 and 2014 were subjected to neurofilament light protein (NfL) analysis using a Siemens Healthineers-developed immunoassay that boasts exceptional sensitivity. This analysis was efficiently conducted on the fully automated Atellica immunoassay platform. For comprehensive information and detailed methodological protocols, kindly refer to the specified URL:https://wwwn.cdc.gov/Nchs/Nhanes/2013-2014/SSSNFL_H.htm.

2.4 Covariates

In order to account for these modifications, we incorporated numerous confounding variables into the model, which may affect the correlation within the two types of variables, based on prior research[16, 17]. In general, these covariates were obtained from demographic information, self-reported survey data, and laboratory test results. Demographic data encompassed age, gender, race, and the poverty-to-income ratio (PIR). Self-reported questionnaire data included information on stroke, congestive heart failure, diabetes, and smoking status (current, former, and never). The Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation was utilized to estimate the glomerular filtration rate (eGFR) [18]. Blood volume for males and females was calculated using Nadler's formula based on weight and height.

2.5 Statistical analysis

EmpowerStats (http://www.empowerstats.com) and R (http://www.R-project.org) were used for any statistical evaluations and executive data . Demographic representativeness was guaranteed using NHANES procedures by means of sampling weights (WTSSNH2Y). Whereas categorical variables were shown as frequencies with their associated percentages, variables reflecting normal distribution were given as mean values with accompanying standard deviations (SD). Natural logarithm transformation (ln-transformation) enabled sNfL levels to reach approximation normalcy[17]. To evaluate the linkage between the predictor variables and the outcome variable, multivariable linear regression analysis was performed with adjustments for covariates in the model. Given the continuous nature of variables, subjects were initially categorized into Group 1 (sNfL level < 2.49 pg/ml) and Group 2 (sNfL level > 2.49 pg/ml), based on the median ln-transformed sNfL level (2.49 pg/ml) [19] . Various models were developed depending on adjusted covariates, including Model 1 (unmodified covariates), Model 2 (modified for age, gender, and race), and Model 3 (modified for age, gender, and race, PIR, smoking status, stroke status, congestive heart failure, estimated glomerular filtration rate, and blood volume). This allowed for revealing the particular influence between the predictor and the result variable. To confirm the robustness of the correlation of the predictor and the outcome variable, the independent variables were divided into quartiles, and a trend test (P for trend) was conducted. Subsequently, subgroup studies were carried out to identify sensitive subgroups for the interaction of the independent and response variables. In conclusion, in order to confirm the validity of a non-linear interaction of the two sets of variables, we implemented threshold effect analyses and smoothing curve fitting techniques. These approaches offered further insights into the nature of the correlation.

3.1 Characteristics of participants

1,995 subjects were included in the research. As shown in Figure 2, it was observed that sNfL levels did not adhere to a normal distribution; instead, on the logarithmic scale, they displayed a log-normal distribution. When grouped by the median ln-transformed sNfL level (2.49pg/ml), participants with higher ln-transformed sNfL levels also exhibited higher WWI (11.10 ± 0.79) and ABSI (0.41 ± 0.04) (p < 0.001). Additionally, participants with higher ln-transformed sNfL levels indicated a greater propensity towards being male (52.17%) and older (51.92 ± 14.51 years). Table 1. displays participant characteristics, emphasizing statistically significant differences observed between the two tertiles in sex, age, ethnicity, smoking status, stroke, congestive heart failure, eGFR, WWI and ABSI.

3.2 Association of WWI and ABSI with sNfL levels

Applying a multivariable linear regression analysis, one could determine the predictive value within WWI, ABSI, and ln-transformed sNfL levels. In all three models, adjustments were made for different covariates. In the fully tuned model, an affirmative correlation was discerned between WWI and serum neurofilament light protein (sNfL) (β = 0.05, 95% CI: 0.01-0.09). This suggested that the ln-transformed sNfL level raised by 0.05 units for every single boost in WWI. However, ABSI showed a stronger positive correlation with ln-transformed sNfL levels in the completely corrected model (β = 1.65, 95% CI: 3.53–13.72). This indicates that each incremental unit increase in ABSI correlates with a 1.65-unit elevation in the ln-transformed sNfL level. Furthermore, when WWI and ABSI were further transformed from continuous to categorical variables (expressed in quartiles), greater quartiles of WWI and ABSI were shown to correlate with higher ln-transformed sNfL levels than the lowest quartile category. The top percentile of WWI had a 0.13unit higher ln-transformed sNfL level than the lowest quartile (β = 0.13, 95% CI: 0.05-0.22), while ABSI's highest quartile group had 0.17 units more ln-transformed sNfL than the lowest (β = 0.17, 95% CI: 0.08-0.27). It revealed that as WWI and ABSI increase, their positive associations with ln-transformed sNfL levels became more pronounced (P for trend < 0.01). Table 2 displayed all of the outcomes.

3.3 Results of subgroup analysis

Confirming the long-term stability of the beneficial linking of WWI, ABSI, and ln-transformed sNfL levels across different cohorts was carried out by subgroup studies. The differences in the correlation between WWI, ABSI, and ln-transformed sNfL levels across different strata were not statistically significant, indicating that this positive correlation does not significantly depend on gender, age, race, stroke, smoking, or congestive heart failure (P for interaction > 0.05). Specifically, among those under 60 years old, a unit escalation in WWI was linked to a 0.12-unit boost in logarithmically modified sNfL levels (β = 0.12, 95% CI = 0.08-0.16), while ABSI exhibited a 3.44-unit increase (β = 3.44, 95% CI = 2.42-4.46). among those 60 years of age and above, ABSI explained a 3.16-unit a boost (β = 3.16, 95% CI = 1.35-4.96) whereas a unit surge in WWI linked with a 0.12-unit elevation in logarithmically converted sNfL levels (β = 0.12, 95% CI = 0.05-0.22). However, the interaction terms failed to demonstrate a substantial impact of age on the correlation within WWI, ABSI, and logarithmically transformed sNfL levels (WWI: P for interaction > 0.05; ABSI: P for interaction > 0.05). Table 3 and 4 displayed all of the outcomes.

3.4 Validation of the linear relationship of WWI and ABSI with sNfl levels

In order to evaluate the potential nonlinear interactions among the predictor variables and ln-transformed sNfL levels, smoothing curve fitting and threshold effect analysis were undertaken. There is a curvilinear correlation with WWI and ln-transformed sNfL levels in Figure 3. Subsequently, the threshold effect analysis revealed a negative correlation prior to the inflection value of 10.38 and a positive relationship there after (LLR < 0.001). As Figure 4 showed, a non-linear correlation with ABSI and ln-transformed sNfL was observed, when ABSI was treated as the independent factor. This non-linear relationship was further validated through threshold effect study (LLR = 0.002) in Table 5.

There was still a dearth of information on the link between obesity and serum neurofilament light protein (sNfL), particularly when it came to the interplay of sNfL, WWI, and ABSI. Initially, we established a positive association of WWI, ABSI, and sNfL levels in our research. Additionally, this positive correlation showed no significant dependence on gender, age, race, stroke, smoking, or congestive heart failure. Furthermore, we illustrated that WWI and ABSI, as novel obesity-related indices, offer improved explanation of this positive association. To our understanding, this work represents the first to investigate the connection of WWI, ABSI, and sNfL, offering preliminary evidence on the influence of obesity on neurodegenerative conditions.

What is already known on this subject?

Reviewing previous studies, various cross-sectional studies have explained the associations between neural and non-neural factors with sNfL. Neurodegenerative diseases or traumatic impacts can cause neuronal damage, leading to increased sNfL levels [20]. Among non-neural factors, models predicting sNfL concentrations showed significantly increased explanatory power when considering aging, cardiovascular factors, and kidney function [21]. Recent research indicates that increases in sNfL are associated with environmental exposure to glyphosate (a herbicide), phthalates (plastic additives), and smoke[17, 22, 23].

What this study adds?

To determine the connection of new obesity indices and sNfL in this study, we considered non-neural body factors and environmental influences, using a multivariable linear regression technique to adjust for pertinent hazards such as hypertension, heart failure, hypercholesterolemia, and smoke, to mitigate potential biases related to neurological, cardiovascular diseases and environmental exposure. Therefore, the association between new obesity indices and sNfL levels further enhances the understanding of the complexity of interactions between physiological, pathological, and toxicological factors with sNfL levels. Given recent research on sNfL, more comprehensive and extensive exploration of this advanced biomarker for neurodegenerative changes may aid clinical decision-making and treatment.

Moderate and chronic inflammation is often linked to obesity[24, 25]. The body's production of inflammatory mediators[26] has verified a positive correlation in NfL and inflammatory cytokines (IL-1β and IL-6) [27]. This finding aligns with prior animal studies indicating elevated a great quantity of inflammatory mediators (IL-2, IL-4, IL-6, and TNF-α) and cultivated CSF NfL levels link to chronic neuroinflammation and immune responses[28]. This cross-section study demonstrated a positive correlation of WWI, ABSI, and sNfL levels, this further corroborates the potential mechanisms linking obesity with sNfL levels. In summary, WWI and ABSI, as effective tools for more accurately reflecting body composition, are closely associated with sNfL levels.

sNfL has been proposed as a valuable indicator for various neurological disorders, especially predicting cognitive decline and the severity of brain lesions [29]. sNfL has a clear role in providing reliable measurements of neuroaxonal damage caused by demyelination processes and the neuroprotective effects of drugs[30]. Numerous neurodegenerative illnesses, including Alzheimer's and Parkinson's, are at threat due to obesity and obesity-related glucose homeostasis problems[31]. Large-scale, long-term, prospective studies have shown that BMI and plasma NfL increase rapidly over time[32], and that middle-aged obesity is significantly link to the risk of dementia and structural brain changes [33, 34]. Its potential role in guiding clinical innovations for neurodegenerative diseases warrants further exploration. Recent studies suggest that reduced NO production and related insulin resistance in obesity might contribute to cognitive impairment and neurodegeneration [35]. And the role of miRNA in obese phenotypes [36] and neurodegenerative diseases[37, 38] has been confirmed. Additionally, recent progress in elucidating the molecular mechanisms of obesity and neurodegenerative diseases have identified astrocytes and endoplasmic reticulum stress as crucial links bridging obesity and neurodegenerative pathologies[39]. Current advancements suggest that neuroinflammation serves as an alterable route connecting obesity and Alzheimer's disease[40]. This increased the possibility of using innovative clinical approaches to treat and prevent neurodegenerative illnesses. Therefore, this research suggests that the correlation between obesity and sNfL provides guidance for clinical interventions in high-risk populations for neurodegenerative diseases. Although the specific mechanisms linking obesity and neurodegenerative diseases require further investigation, effective weight management, maintaining physical health, and regular health check-ups provide valuable insights for clinical guidelines.

Strength and limits

NHANES, a broadly representative cross-sectional survey, is our study's primary strength. Sampling weights facilitate the evaluation of WWI, ABSI, and sNfL relationships. To ensure study accuracy, the authors controlled for confounding factors. Subgroup analyses additionally occurred to determine the reliability of WWI, ABSI, and sNfL correlations across populations. Furthermore, our study examines how obesity indices affect neuro-related biomarkers. This extensive study sheds light on how neuro-related indicators affect obesity's negative impacts.

However, we acknowledge the limitations of our investigation. First, our cross-section study restricts our causal inferences despite a strong correlation of WWI, ABSI, and sNfL levels. Therefore, further validation is needed in prospective cohort studies. Secondly, the sNfL data utilized in this study were confined to the NHANES 2013–2014 cycle, where sNfL measurements were available. Given the absence of sNfL data in other NHANES cycles, our findings may not fully represent the cross-section profiles of these patients. Consequently, further large-scale longitudinal cohort studies are warranted to validate our initial observations. Moreover, although we accounted for numerous covariates, we recognize the potential for biases stemming from unmeasured confounding variables. These may involve drug usage, the existence of long-term illness including diabetes, renal disease, and hepatic disorders, among others. Finally, despite rigorous methodological approaches, our study is hampered by the relative population size for subgroup estimation. In order to yield more dependable and definitive outcomes in future experimental subgroups, it is imperative to expand the sample size significantly. This method will help to clarify the examined events in more whole sense.

Conflicts of Interest

The authors have no relevant financial or non-financial interests to disclose.

Fundings

This study was funded by Natural Science Foundation of Sichuan Province (No. 2023NSFSC1819), National Natural Science Foundation of China (No. 82205286 & 82074556), the regional cooperation program of the National Natural Science Foundation of China (No. U21A20404), and Joint Innovation Fund between School and Hospital: Hospital Special Fund(LH202402005).

Author Contribution

Z.Y.: Conceptualization, Data curation, Software, Writing – original draft, Writing – review & editing. Q.G.: Conceptualization, Data curation, Software, Writing – original draft, Writing – review & editing. H.Y.: Writing – original draft, Writing – review & editing, Funding acquisition. M.Y.: Writing – original draft, Writing – review & editing. X.W.: Writing – original draft, Writing – review & editing. F.L.: Writing – original draft, Writing – review & editing, Funding acquisition. D.C.: Conceptualization,Supervision, Writing – review & editing. W.Q.: Conceptualization, Funding acquisition, Supervision, Writing – review & editing.

Acknowledgement

The authors thank all the NHANES study participants for their assistance.

Data Availability

Publicly available datasets were analyzed in this study. This data can be found here: https://wwwn.cdc.gov/nchs/nhanes/

Khalil, M., et al., CSF neurofilament and N-acetylaspartate related brain changes in clinically isolated syndrome. Mult Scler, 2013. 19(4): p. 436–42.
Giovannoni, G., Peripheral blood neurofilament light chain levels: the neurologist's C-reactive protein? Brain, 2018. 141(8): p. 2235–2237.
Hampel, H., et al., Blood-based biomarkers for Alzheimer disease: mapping the road to the clinic. Nat Rev Neurol, 2018. 14(11): p. 639–652.
Rosengren, L.E., et al., Patients with amyotrophic lateral sclerosis and other neurodegenerative diseases have increased levels of neurofilament protein in CSF. J Neurochem, 1996. 67(5): p. 2013–8.
Tiedt, S., et al., Serum neurofilament light: A biomarker of neuroaxonal injury after ischemic stroke. Neurology, 2018. 91(14): p. e1338-e1347.
Flegal, K.M., et al., Association of all-cause mortality with overweight and obesity using standard body mass index categories: a systematic review and meta-analysis. Jama, 2013. 309(1): p. 71–82.
Manouchehrinia, A., et al., Confounding effect of blood volume and body mass index on blood neurofilament light chain levels. Ann Clin Transl Neurol, 2020. 7(1): p. 139–143.
Koini, M., et al., Factors influencing serum neurofilament light chain levels in normal aging. Aging (Albany NY), 2021. 13(24): p. 25729–25738.
Antonopoulos, A.S. and D. Tousoulis, The molecular mechanisms of obesity paradox. Cardiovasc Res, 2017. 113(9): p. 1074–1086.
Kim, J.Y., et al., Associations between Weight-Adjusted Waist Index and Abdominal Fat and Muscle Mass: Multi-Ethnic Study of Atherosclerosis. Diabetes Metab J, 2022. 46(5): p. 747–755.
Kim, N.H., et al., Weight-adjusted waist index reflects fat and muscle mass in the opposite direction in older adults. Age Ageing, 2021. 50(3): p. 780–786.
Krakauer, N.Y. and J.C. Krakauer, A new body shape index predicts mortality hazard independently of body mass index. PLoS One, 2012. 7(7): p. e39504.
Nagayama, D., et al., A Body Shape Index (ABSI) as a Variant of Conicity Index Not Affected by the Obesity Paradox: A Cross-Sectional Study Using Arterial Stiffness Parameter. J Pers Med, 2022. 12(12).
Park, Y., et al., A novel adiposity index as an integrated predictor of cardiometabolic disease morbidity and mortality. Sci Rep, 2018. 8(1): p. 16753.
Fitzgerald, K.C., et al., Contributors to Serum NfL Levels in People without Neurologic Disease. Ann Neurol, 2022. 92(4): p. 688–698.
Liu, X., et al., Serum neurofilament light chain and cognition decline in US elderly: A cross-sectional study. Ann Clin Transl Neurol, 2024. 11(1): p. 17–29.
Zhu, N., et al., Correlation analysis between smoke exposure and serum neurofilament light chain in adults: a cross-sectional study. BMC Public Health, 2024. 24(1): p. 353.
Levey, A.S., et al., A new equation to estimate glomerular filtration rate. Ann Intern Med, 2009. 150(9): p. 604–12.
Han, H., et al., The association of body mass index and weight waist adjustment index with serum ferritin in a national study of US adults. Eur J Med Res, 2023. 28(1): p. 374.
Shahim, P., et al., Serum neurofilament light as a biomarker for mild traumatic brain injury in contact sports. Neurology, 2017. 88(19): p. 1788–1794.
Ramanathan, M., Non-neurological factors associated with serum neurofilament levels in the United States population. Ann Clin Transl Neurol, 2024. 11(5): p. 1347–1358.
Yang, A.M., et al., Association between urinary glyphosate levels and serum neurofilament light chain in a representative sample of US adults: NHANES 2013–2014. J Expo Sci Environ Epidemiol, 2024. 34(2): p. 287–293.
Yang, A.M., et al., Urinary di-(2-ethylhexyl) phthalate metabolites are independently related to serum neurofilament light chain, a biomarker of neurological diseases, in adults: results from NHANES 2013–2014. Environ Sci Pollut Res Int, 2023. 30(24): p. 66417–66425.
Fantuzzi, G., Adipose tissue, adipokines, and inflammation. J Allergy Clin Immunol, 2005. 115(5): p. 911-9; quiz 920.
Gregor, M.F. and G.S. Hotamisligil, Inflammatory mechanisms in obesity. Annu Rev Immunol, 2011. 29: p. 415–45.
Rogero, M.M. and P.C. Calder, Obesity, Inflammation, Toll-Like Receptor 4 and Fatty Acids. Nutrients, 2018. 10(4).
Jing, X., et al., Serum neurofilament light chain and inflammatory cytokines as biomarkers for early detection of mild cognitive impairment. Sci Rep, 2024. 14(1): p. 9072.
Hsiao, T.-C., et al., Serum neurofilament light polypeptide is a biomarker for inflammation in cerebrospinal fluid caused by fine particulate matter. Aerosol and Air Quality Research, 2020. 20(7): p. 1665–1674.
Lee, E.H., et al., Serum neurofilament light chain level as a predictor of cognitive stage transition. Alzheimers Res Ther, 2022. 14(1): p. 6.
Ghezzi, A. and R.F. Neuteboom, Neurofilament Light Chain in Adult and Pediatric Multiple Sclerosis: A Promising Biomarker to Better Characterize Disease Activity and Personalize MS Treatment. Neurol Ther, 2023. 12(6): p. 1867–1881.
Kueck, P.J., J.K. Morris, and J.A. Stanford, Current Perspectives: Obesity and Neurodegeneration - Links and Risks. Degener Neurol Neuromuscul Dis, 2023. 13: p. 111–129.
Beydoun, M.A., et al., BMI and Allostatic Load Are Directly Associated with Longitudinal Increase in Plasma Neurofilament Light among Urban Middle-Aged Adults. J Nutr, 2022. 152(2): p. 535–549.
Beydoun, M.A., H.A. Beydoun, and Y. Wang, Obesity and central obesity as risk factors for incident dementia and its subtypes: a systematic review and meta-analysis. Obes Rev, 2008. 9(3): p. 204–18.
Driscoll, I., et al., Midlife obesity and trajectories of brain volume changes in older adults. Hum Brain Mapp, 2012. 33(9): p. 2204–10.
Buie, J.J., et al., Obesity-related cognitive impairment: The role of endothelial dysfunction. Neurobiol Dis, 2019. 132: p. 104580.
Zhang, J.Y., et al., Role of MicroRNAs in Dietary Interventions for Obesity and Obesity-Related Diseases. J Agric Food Chem, 2023. 71(40): p. 14396–14412.
Salama, II, et al., Risk Factors Associated with Mild Cognitive Impairment among Apparently Healthy People and the Role of MicroRNAs. Open Access Maced J Med Sci, 2019. 7(19): p. 3253–3261.
Yuen, S.C., et al., Prediction of differentially expressed microRNAs in blood as potential biomarkers for Alzheimer's disease by meta-analysis and adaptive boosting ensemble learning. Alzheimers Res Ther, 2021. 13(1): p. 126.
Martin-Jiménez, C.A., et al., Astrocytes and endoplasmic reticulum stress: A bridge between obesity and neurodegenerative diseases. Prog Neurobiol, 2017. 158: p. 45–68.
Ly, M., et al., Neuroinflammation: A Modifiable Pathway Linking Obesity, Alzheimer's disease, and Depression. Am J Geriatr Psychiatry, 2023. 31(10): p. 853–866.

Table 1 to 5 are available in the Supplementary Files section.

No competing interests reported.

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You are reading this latest preprint version

Association of Weight Waist Adjustment Index (WWI) and A Body Mass Index (ABSI) with Serum Neurofilament Light Chain Levels in a national study of US adults

Status:

Version 1

Abstract

Figures

1 Introduction

2 Materials and methods

2.1 Data source

2.2 Participants

2.3 Independent and dependent variables

2.4 Covariates

2.5 Statistical analysis

3 Results

3.1 Characteristics of participants

3.2 Association of WWI and ABSI with sNfL levels

3.3 Results of subgroup analysis

3.4 Validation of the linear relationship of WWI and ABSI with sNfl levels

4 Discussion

Declarations

Conflicts of Interest

Fundings

Author Contribution

Acknowledgement

Data Availability

References

Tables

Additional Declarations

Supplementary Files

Status:

Version 1