Next Article in Journal
A Narrative Review on Gut Microbiome Disturbances and Microbial Preparations in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: Implications for Long COVID
Previous Article in Journal
Effects of Hydrolyzed Collagen as a Dietary Supplement on Fibroblast Activation: A Systematic Review
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Nutrients
Volume 16
Issue 11
10.3390/nu16111544
nutrients-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

The Tryptophan Index Is Associated with Risk of Ischemic Stroke: A Community-Based Nested Case–Control Study

by
Dong Liu
1,2,
Yan Hong
2,
Zhenting Chen
3,
Yifan Ma
3,
Shangyu Xia
3,
Shujun Gu
4 and
Hui Zuo
1,5,6,*
1
School of Public Health, Suzhou Medical College of Soochow University, 199 Ren’ai Rd., Suzhou 215123, China
2
School of Public Health, Nantong University, Nantong 226019, China
3
Suzhou Medical College of Soochow University, Suzhou 215123, China
4
Department of Chronic Disease Control and Prevention, Changshu Center for Disease Control and Prevention, Suzhou 215501, China
5
Jiangsu Key Laboratory of Preventive and Translational Medicine for Major Chronic Non-Communicable Diseases, Suzhou Medical College of Soochow University, Suzhou 215123, China
6
MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou 215123, China
*
Author to whom correspondence should be addressed.
Nutrients 2024, 16(11), 1544; https://doi.org/10.3390/nu16111544
Submission received: 22 April 2024 / Revised: 18 May 2024 / Accepted: 20 May 2024 / Published: 21 May 2024
(This article belongs to the Section Nutritional Epidemiology)
Browse Figures
Review Reports Versions Notes

Abstract

:
Background: The relative availability of the essential amino acid tryptophan in the brain, as indicated by the tryptophan index, which is the ratio of tryptophan to its competing amino acids (CAAs) in circulation, has been related to major depression. However, it remains unknown whether tryptophan availability is involved in the pathogenesis of ischemic stroke. Aims: We aimed to investigate the relationship between the tryptophan index and the risk of ischemic stroke. Methods: We performed a nested case–control study within a community-based cohort in eastern China over the period 2013 to 2018. The analysis included 321 cases of ischemic stroke and 321 controls matched by sex and date of birth. The plasma levels of tryptophan and CAAs, including tyrosine, valine, phenylalanine, leucine, and isoleucine, were measured by ultra-high-performance liquid chromatography–tandem mass spectrometry. Conditional logistic regression analyses were employed to determine incidence rate ratios (IRRs) and their 95% confidence intervals (CIs). Results: After adjustment for body mass index, current smoking status, educational attainment, physical activity, family history of stroke, hypertension, diabetes, hyperlipidemia, and estimated glomerular filtration rate, an elevated tryptophan index was significantly associated with a reduced risk of ischemic stroke in a dose–response manner (IRR, 0.76; 95% CI, 0.63–0.93, per standard deviation increment). The plasma tryptophan or CAAs were not separately associated with the risk of ischemic stroke. Conclusions: The tryptophan index was inversely associated with the risk of ischemic stroke. Our novel observations suggest that the availability of the essential amino acid tryptophan in the brain is involved in the pathogenesis of ischemic stroke.

1. Introduction

Stroke continues to be the leading cause of mortality in China, with the incidence rate reaching to 276.7 per 100,000 people in 2019 [1]. About 71% of total strokes worldwide are ischemic, usually manifesting as a cerebrovascular infarction [2]. The blood–brain barrier (BBB), serving as the guardian of the central nervous system, plays a crucial role in nutrient transportation, brain hemodynamics, paracellular permeability, and the development of neurological dysfunction [3].
As an essential amino acid, tryptophan must be acquired through dietary sources [4]. Notably, the availability of circulating tryptophan after penetrating the BBB determines the rate of synthesis of the neurotransmitter serotonin, also known as 5-hydroxytryptamine (5-HT), in the brain [5]. Once arrived, nearly half of the tryptophan is utilized for serotonin synthesis, whose shortage is considered to be associated with disorders of the central nervous system [6,7]. However, during its transport from circulation via the large neutral amino acid transporter system, tryptophan faces competition from other amino acids (competing amino acids, CAAs), namely tyrosine, valine, phenylalanine, leucine, and isoleucine [5,8,9]. Therefore, a higher tryptophan index (calculated as the ratio tryptophan/total CAAs) has been used as an indicator for increased tryptophan availability in the brain [5]. An animal study observed that mice being fed a diet with the lowest ratio of tryptophan to branched-chain amino acids (BCAAs: valine, leucine, and isoleucine) had central serotonin depletion and hyperphagia, which was reversed by the intervention of tryptophan or a serotonin reuptake inhibitor (SSRI) [10]. As shown in Figure 1, the initial and rate-limiting stage involves the conversion of tryptophan to the short-lived 5-hydroxytryptophan (5-HTP), followed by amino acid decarboxylation to produce 5-HT [11]. Previous studies have linked a reduced tryptophan index and decreased brain serotonin levels with depression and suicide [7,12]. A cross-sectional study suggested that tryptophan and the tryptophan index was significantly lower among acute ischemic stroke patients than their controls [13]. Notably, SSRIs are the most widely prescribed antidepressants with the ability to increase serotonin levels in synapses, compared to non-SSRIs [14]. Rasha et al. recently reported that the use of SSRIs was inversely associated with non-cardioembolic ischemic stroke risk compared with other antidepressants [15].
Despite advancements in understanding the brain’s tryptophan/serotonin completing theory published in Science, 1972 [5], there remains a knowledge gap regarding prospective association between tryptophan availability and stroke risk. Therefore, it motivated us to examine the associations of plasma tryptophan, CAAs, and the tryptophan index with the incidence of ischemic stroke using a case–control study nested within a community-based cohort.

2. Material and Methods

2.1. Study Participants

Details of the Prospective Follow-up Study on Cardiovascular Morbidity and Mortality in China (PFS-CMMC), including study strategy and enrollment, have been previously described [16,17]. In summary, after excluding individuals with severe cancer, severe disability, and/or severe psychiatric disturbance, a total of 16,457 residents aged 35 to 74 years in Changshu (Jiangsu province, China) were recruited in 2013. In the baseline survey, well-trained staff conducted face-to-face interviews with participants to collect information on demographic characteristics, education, lifestyle factors, and history of diseases using standard questionnaires. Written informed consent was obtained from each participant. Ethical approval for record linkage between the baseline survey data and study outcomes was acquired from the Ethics Committee of Soochow University (No.: SUDA20210127H01, Suzhou, China).

2.2. Follow-Up and Definition of Ischemic Stroke

The follow-up began with the baseline survey conducted in 2013, and continued until the date of death, occurrence of stroke, or 31 December 2018, whichever occurred first. Hospital records of discharge diagnoses and the Cause of Death Registry were used to accurately obtain the data linkage of disease or cause of death. The International Classification of Diseases, Tenth Revision (ICD-10 codes: I63 except for I63.9) was applied to define ischemic stroke [17]. If a participant experienced more than one stroke event during the follow-up period, only the first event was taken into consideration.

2.3. Selection of Cases and Controls

As shown in Figure 2, a total of 137 participants were excluded due to stroke history prior to enrolment, as well as 207 participants with missing baseline information. Over a median follow-up of 5.3 years, a total of 321 incident cases of ischemic stroke were identified. For each case, one control was matched by date of birth (±1 year) and sex using the incidence density sampling method [18].

2.4. Measurements of Biomarkers

Venous blood samples were collected from participants after a minimum of 8 h fasting overnight [16,17]. Procedures of plasma separation and preservation have been mentioned in a previous study [19]. Plasma tryptophan, tyrosine, valine, phenylalanine, leucine, isoleucine, and cotinine were detected by ultra-high-performance liquid chromatography–tandem mass spectrometry (UHPLC–MS/MS) [20,21]. All laboratory staff were blinded to the plasma case/control status. The within-day and between-day coefficients of variation were 0.70–3.50% (tryptophan: 1.85%, tyrosine: 3.50%, valine: 0.70%, phenylalanine: 1.70%, leucine: 1.40%, isoleucine: 1.25%, and cotinine: 2.47%) and 2.45–17.3% (tryptophan: 4.81%, tyrosine: 4.65%, valine: 3.70%, phenylalanine: 4.88%, leucine: 2.68%, isoleucine: 17.3%, and cotinine: 2.45%), respectively. Serum glucose levels were determined using the oxidase enzymatic method; creatinine was measured via the picric acid method; and total cholesterol (TC), triglycerides (TG), and high-density lipoprotein cholesterol (HDL-C) were assessed using enzymatic methods [22,23].

2.5. Covariates

We selected the covariates mainly following the basic criteria of confoundment and based on the current literature [24,25]. Body mass index (BMI) was calculated as body weight divided by the square of height, kg/m2. Smoking status was determined by self-report and plasma cotinine levels. Briefly, individuals with plasma cotinine levels of 85 nmol/L or above were classified as current smokers, regardless of their self-reported status [16]. Physical activity was assessed using a standardized questionnaire and estimated as metabolic equivalent-hours/day (MET-h/d) [26], including occupation, transportation, home activity exercise, etc. [27]. Educational attainment was classified into three groups: 0 years (no formal education), 1–5 years (primary school), and ≥6 years (middle school or higher). Hypertension was identified by self-report, readings of mean systolic (diastolic) blood pressure ≥140 (90) mm Hg, or the use of antihypertensive drugs. Diabetes was determined by self-report, the use of insulin/glucose-lowering drugs, or a fasting blood glucose level of ≥7.0 mmol/L. Hyperlipidemia was determined by self-report, the use of lipid-lowering treatment, or laboratory measurement (TC > 6.20 mmol/L, TG > 2.30 mmol/L, or HDL-C < 1.00 mmol/L) [28]. In addition, the equation from Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) was used to compute the estimated glomerular filtration rate (eGFR, mL/min/1.73 m2) [29].

2.6. Statistical Analyses

Continuous variables are presented as medians (interquartile ranges), while categorical variables are presented as numbers (percentages). We assessed differences between cases and their matched controls using the Wilcoxon test for continuous variables and the Chi-squared test for categorical variables. To evaluate p trends across quartiles of biomarkers or the tryptophan index at baseline, we used the Jonckheere–Terpstra test for all continuous variables due to their skewed distributions. For binary categorical variables, we employed the Cochran–Armitage Trend test, and the Cochran–Mantel–Haenszel test for other categorical variables.
Conditional logistic regression models were applied to assess the associations of tryptophan, its CAAs, and the tryptophan index with the risk of ischemic stroke, separately. In a nested case–control study using incidence density sampling, this method estimates incidence rate ratios (IRRs) [30]. IRRs and corresponding 95% confidence intervals (CIs) were calculated across quartiles according to the distribution of controls or per standard deviation (SD) increment of natural-log-transformed biomarkers. We constructed three models. Model 1 was an unadjusted model conditioning on the individual case set. Model 2 was adjusted for educational attainment (0 years, 1–5 years, or ≥6 years), current smoking status (yes or no), BMI (continuous), and physical activity (quartiles). Model 3 was adjusted for family history of stroke (yes or no), hypertension (yes or no), diabetes (yes or no), hyperlipidemia (yes or no), and eGFR (continuous). Moreover, we additionally included total CAAs in the multivariable model for tryptophan to check for a potential confounding effect.
Moreover, we conducted stratified analyses by sex (male or female), age (median), BMI (median), physical activity (median), hypertension (yes or no), hyperlipidemia (yes or no), and eGFR (<90 or ≥90 mL/min/1.73 m2) using unconditional logistic regression [16,31,32,33]. Statistical interaction was evaluated at the multiplicative scale [34]. Meanwhile, those cases that developed stroke and their matching controls in the first year of follow-up were excluded to check for potential reverse causation bias and the robustness of the primary findings. Furthermore, we calculated the E-value to examine the potential impact of unmeasured confounders [35]. A two-sided p-value less than 0.05 was considered to indicate statistical significance. All analyses of the data were conducted using R software, version 4.1.2 (available at https://www.r-project.org, accessed on 1 November 2021).

3. Results

3.1. Baseline Characteristics

The characteristics at baseline of 321 ischemic stroke cases and their controls are illustrated in Table 1. There was no statistical difference between the two groups in BMI, current smoking status, physical activity, or educational attainment (p > 0.1). The cases were more likely to have diabetes (p = 0.004) and hypertension (p < 0.001), but not hyperlipidemia. Furthermore, these participants with incident ischemic stroke had lower baseline levels of the tryptophan index (p = 0.010) and a higher fasting glucose (p = 0.013) and isoleucine (p = 0.038) than the controls, whereas no significant difference was observed in other amino acids, eGFR, lipid profiles, or total CAAs (all p > 0.05).
As presented in Supplementary Table S1, participants possessing a higher tryptophan index were generally younger (p trend = 0.010) and more likely to be female (p trend = 0.032). Moreover, no significant difference was observed in current smoking status, physical activity levels, educational attainment, or prevalence of hypertension across quartiles (all p trend > 0.05). Notably, individuals with higher quartiles of the tryptophan index showed a lower prevalence of diabetes (p trend < 0.001) and hyperlipidemia (p trend = 0.003), a decreased trend in TG (p trend = 0.017) and fasting glucose (p trend < 0.001), and an elevated trend in HDL-C and eGFR (p trend < 0.001).

3.2. The Tryptophan Index, Its Components, and the Risk of Ischemic Stroke

As illustrated in Table 2, the tryptophan index was found to be inversely associated with the risk of ischemic stroke yielding an adjusted IRR of 0.76 (95% CI: 0.63–0.93, p = 0.006) per one SD increment of log-transformed level and E-value of 1.96 (1.36). Likewise, compared with the first quartile (Q1), the adjusted IRR for the highest quartile (Q4) of the tryptophan index was 0.53 (95% CI: 0.31–0.88, p trend = 0.008) and the corresponding E-value was 3.18 (1.53). In contrast, there was no significant association of tryptophan with ischemic stroke risk, either as a continuous variable or by quartile. The same was true for total CAAs and the individual amino acids tyrosine, valine, phenylalanine, isoleucine, and leucine after multivariable adjustment (Supplementary Table S2). However, the association between tryptophan and the risk for ischemic stroke became significant after further adjustment for total CAAs. Compared with the Q1, the IRR was 1.02 (95% CI: 0.61–1.71) for the second quartile (Q2) [E-value: 1.16 (1.00)], 0.51 (95% CI: 0.29–0.88) for the third quartile (Q3) [E-value: 3.33 (1.53)], and 0.57 (95% CI: 0.30–1.07) for Q4 [E-value: 2.90 (1.00)]. The corresponding IRR was 0.75 (95% CI: 0.59–0.94, p = 0.015) per one SD increment of log-transformed level, and the E-value was 2.00 (1.32).

3.3. Sensitivity Analyses

After exclusion of the cases that developed ischemic stroke within the first year, the significant association of the tryptophan index with ischemic stroke risk was still essentially unchanged (adjusted IRR: 0.51 (95% CI: 0.29–0.88), p trend = 0.012, Q4 vs. Q1; 0.71 (95% CI: 0.55–0.91), p = 0.007, per one SD increment).

3.4. Stratified Analyses

The findings remained consistent across various stratifications, including sex (male or female), age (median), BMI (median), physical activity (median), hypertension (yes or no), hyperlipidemia (yes or no), and eGFR (<90 or ≥90 mL/min/1.73 m2). No significant effect modification was observed among these stratified factors for the association between the tryptophan index and the risk of ischemic stroke (Figure 3).

4. Discussion

4.1. Principal Findings

In the present nested case–control study, we found that an elevated tryptophan index, indicating a higher availability of tryptophan in the brain, was significantly associated with a lower risk of ischemic stroke. The individual CAAs were not associated with the risk after multivariable adjustment.

4.2. The Tryptophan Index and Ischemic Stroke Risk

Amino acid neurotransmitters, including excitatory amino acids (such as glutamate and aspartate) and non-excitatory amino acids (such as glycine, serine, and threonine), have been suggested to contribute the progression of ischemia [17,36]. Certainly, the homeostasis of extracellular amino acids in the central nervous system is essential for maintaining brain function. As illustrated by our recent study, the elevated ratio of glycine to lysine was associated with the decreased risk of ischemic stroke [17]. However, there is still a lack of prospective studies to systematically investigate the association between the neurotransmitter-related amino acids and stroke risk, especially for tryptophan and its CAAs. The established evidence has shown that the decreased utilization of tryptophan due to elevated circulating levels of CAAs may decrease serotonin concentration in the brain [37]. In the present study, upon additional adjustment for total CAAs, the initially observed association between tryptophan and ischemic risk became significant. Therefore, the protective effect of tryptophan on ischemic stroke might largely be offset by CAAs across the BBB. Also, our findings suggested that the elevated tryptophan index, which means a relatively high availability of tryptophan in the brain, was mainly driven by higher plasma levels of tryptophan when it was linked with the risk of ischemic stroke. These findings support dietary recommendations to increase the intake of tryptophan-rich foods, which could enhance brain health and reduce stroke risk. These insights encourage further interdisciplinary research to explore nutritional strategies for stroke prevention and treatment.

4.3. Mechanisms

It has been reported that the tryptophan index was a marker of immune-inflammatory response [38] and positively related to the level of anti-inflammatory interleukin-10, which may play a neuroprotective role and inhibit the reduced synthesis of 5-hydroxytryptamine in the brain [13]. Given the close relationship with serotonin, the decreased tryptophan index blunted the neurotransmitter synthesis that was associated with depression [39,40]. In addition, serotonin as a strong vasoconstrictor could innervate cerebral arteries, arterioles, and veins [41,42], whereas a weak vasoconstrictive effect due to a decreased tryptophan index might increase blood flow [42]. Moreover, the higher brain serotonin levels could attenuate platelet activation and improve the thrombotic pathway, which could decrease the risk of myocardial infarction [43], and may also relate to a reduced risk of stroke [44].

4.4. Strengths and Limitations

The main strengths include a comprehensive measurement of tryptophan and its CAAs, a complete follow-up, and high generalizability by recruiting community residents, which could minimize selection bias. However, the present study also has several limitations. Firstly, the residual confoundment by unknown or untested covariates cannot be excluded. For instance, we lacked data on the use of antidepressants or prior diagnoses of depression before blood sampling. Both factors are known to influence the tryptophan index [7,12,45]. However, we have excluded those residents with severe psychiatric disturbance, which may help to reduce this potential confounding effect. In addition, statin treatment may also have affected our observed associations, because of its potential regulating roles in both tryptophan metabolism and cholesterol reduction [46]. Secondly, our study did not assess the relationship between the tryptophan index and specific subtypes of ischemic stroke due to the absence of relevant data. Thirdly, we did not measure tryptophan metabolites involved in the serotonin pathway, such as serotonin itself, which restricts further investigative possibilities.

5. Conclusions

In the present study, we observed that an elevated tryptophan index, mainly driven by increased plasma tryptophan, was independently associated with a lower risk of ischemic stroke in community residents. In contrast, the CAAs tyrosine, valine, phenylalanine, leucine, and isoleucine were not associated. Our novel findings underscore the critical role of the availability of the essential amino acid tryptophan in the brain in the pathogenesis of ischemic stroke.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/nu16111544/s1, Supplementary Table S1 Demographic and clinical characteristics by quartiles of tryptophan index at baseline (n = 642). Supplementary Table S2 Incidence rate ratio (IRRs) and 95% confidence intervals (CI) for ischemic risk by conditional logistic regression models.

Author Contributions

Funding acquisition, H.Z.; investigation, S.G.; methodology, D.L.; resources, S.G. and H.Z.; software, D.L., Y.H., Y.M. and S.X.; supervision, H.Z.; visualization, D.L. and Z.C.; writing—original draft, D.L.; writing—review and editing, H.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research received funding from the National Natural Science Foundation of China under grant numbers 81973122 and 82273719.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of Soochow University (No.: SUDA20210127H01, Suzhou, China, 27 January 2021).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data, codebook, and analytical code described in this manuscript will be accessible upon request, subject to approval by the corresponding author.

Acknowledgments

We express our gratitude to all participants and staff involved in the Study.

Conflicts of Interest

The authors report no conflicts of interest.

Abbreviations

BBB, blood–brain barrier; BMI, body mass index; CAAs, competing amino acids; CKD-EPI, Chronic Kidney Disease Epidemiology Collaboration; CIs, confidence intervals; eGFR, estimated glomerular filtration rate; MET, metabolic equivalent; NRI, net reclassification improvement; HDL-C, high-density lipoprotein cholesterol; ICD, International Classification of Diseases; IDI, integrated discrimination improvement; IRR, incidence rate ratio; PFS-CMMC, Prospective Follow-up Study on Cardiovascular Morbidity and Mortality in China; Q1, first quartile; Q3, third quartile; Q4, highest quartile; SSRIs, selective serotonin reuptake inhibitors; SD, standard deviation; TC, total cholesterol; TG, triglyceride; UHPLC–MS/MS, ultra-high-performance liquid chromatography–tandem mass spectrometry; 5-HT, 5-hydroxytryptamine; 5-HTP, 5-hydroxytryptophan.

References

  1. Ma, Q.; Li, R.; Wang, L.; Yin, P.; Wang, Y.; Yan, C.; Ren, Y.; Qian, Z.; Vaughn, M.G.; McMillin, S.E.; et al. Temporal trend and attributable risk factors of stroke burden in China, 1990–2019: An analysis for the Global Burden of Disease Study 2019. Lancet Public Health 2021, 6, e897–e906. [Google Scholar] [CrossRef] [PubMed]
  2. Campbell, B.C.V.; De Silva, D.A.; Macleod, M.R.; Coutts, S.B.; Schwamm, L.H.; Davis, S.M.; Donnan, G.A. Ischaemic stroke. Nat. Rev. Dis. Primers 2019, 5, 70. [Google Scholar] [CrossRef] [PubMed]
  3. Yang, C.; Hawkins, K.E.; Dore, S.; Candelario-Jalil, E. Neuroinflammatory mechanisms of blood-brain barrier damage in ischemic stroke. Am. J. Physiol. Cell Physiol. 2019, 316, C135–C153. [Google Scholar] [CrossRef] [PubMed]
  4. Le Floc’h, N.; Otten, W.; Merlot, E. Tryptophan metabolism, from nutrition to potential therapeutic applications. Amino Acids 2011, 41, 1195–1205. [Google Scholar] [CrossRef] [PubMed]
  5. Fernstrom, J.D.; Wurtman, R.J. Brain serotonin content: Physiological regulation by plasma neutral amino acids. Science 1972, 178, 414–416. [Google Scholar] [CrossRef] [PubMed]
  6. Moir, A.T.; Eccleston, D. The effects of precursor loading in the cerebral metabolism of 5-hydroxyindoles. J. Neurochem. 1968, 15, 1093–1108. [Google Scholar] [CrossRef]
  7. Ormstad, H.; Dahl, J.; Verkerk, R.; Andreassen, O.A.; Maes, M. Increased plasma levels of competing amino acids, rather than lowered plasma tryptophan levels, are associated with a non-response to treatment in major depression. Eur. Neuropsychopharmacol. 2016, 26, 1286–1296. [Google Scholar] [CrossRef]
  8. Ruddick, J.P.; Evans, A.K.; Nutt, D.J.; Lightman, S.L.; Rook, G.A.; Lowry, C.A. Tryptophan metabolism in the central nervous system: Medical implications. Expert Rev. Mol. Med. 2006, 8, 1–27. [Google Scholar] [CrossRef]
  9. Fernstrom, J.D. Role of precursor availability in control of monoamine biosynthesis in brain. Physiol. Rev. 1983, 63, 484–546. [Google Scholar] [CrossRef]
  10. Solon-Biet, S.M.; Cogger, V.C.; Pulpitel, T.; Wahl, D.; Clark, X.; Bagley, E.; Gregoriou, G.C.; Senior, A.M.; Wang, Q.P.; Brandon, A.E.; et al. Branched chain amino acids impact health and lifespan indirectly via amino acid balance and appetite control. Nat. Metab. 2019, 1, 532–545. [Google Scholar] [CrossRef]
  11. Berger, M.; Gray, J.A.; Roth, B.L. The expanded biology of serotonin. Annu. Rev. Med. 2009, 60, 355–366. [Google Scholar] [CrossRef]
  12. Moller, S.E.; De Beurs, P.; Timmerman, L.; Tan, B.K.; Leijnse-Ybema, H.J.; Stuart, M.H.; Petersen, H.E. Plasma tryptophan and tyrosine ratios to competing amino acids in relation to antidepressant response to citalopram and maprotiline. A preliminary study. Psychopharmacology 1986, 88, 96–100. [Google Scholar] [CrossRef] [PubMed]
  13. Ormstad, H.; Verkerk, R.; Aass, H.C.; Amthor, K.F.; Sandvik, L. Inflammation-induced catabolism of tryptophan and tyrosine in acute ischemic stroke. J. Mol. Neurosci. 2013, 51, 893–902. [Google Scholar] [CrossRef]
  14. Kim, S.W.; Park, S.Y.; Hwang, O. Up-Regulation of Tryptophan Hydroxylase Expression and Serotonin Synthesis by Sertraline. Mol. Pharmacol. 2002, 61, 778–785. [Google Scholar] [CrossRef]
  15. Alqdwah-Fattouh, R.; Rodriguez-Martin, S.; Barreira-Hernandez, D.; Izquierdo-Esteban, L.; Gil, M.; Gonzalez-Bermejo, D.; Fernandez-Anton, E.; Rodriguez-Miguel, A.; Garcia-Lledo, A.; Bolumar, F.; et al. Selective serotonin reuptake inhibitors and risk of noncardioembolic ischemic stroke: A nested case-control study. Stroke 2022, 53, 1560–1569. [Google Scholar] [CrossRef]
  16. Liu, D.; Gu, S.; Zhou, Z.; Ma, Z.; Zuo, H. Associations of plasma TMAO and its precursors with stroke risk in the general population: A nested case-control study. J. Intern. Med. 2022, 293, 110–120. [Google Scholar] [CrossRef]
  17. Liu, D.; Wang, J.; Xiao, L.; Gu, S.; Ma, Z.; Zhou, Z.; Gu, S.; Zuo, H. Associations of plasma carnitine, lysine, trimethyllysine and glycine with incident ischemic stroke: Findings from a nested case-control study. Clin. Nutr. 2022, 41, 1889–1895. [Google Scholar] [CrossRef] [PubMed]
  18. Richardson, D.B. An incidence density sampling program for nested case-control analyses. Occup. Environ. Med. 2004, 61, e59. [Google Scholar] [CrossRef] [PubMed]
  19. Zhang, Y.; Liu, D.; Ma, Z.; Wang, C.; Gu, S.; Zhou, Z.; Zuo, H. Plasma beta-Alanine is Positively Associated With Risk of Ischemic Stroke: A Nested Case-Control Study. J. Nutr. 2023, 153, 1162–1169. [Google Scholar] [CrossRef]
  20. Lewis, M.R.; Pearce, J.T.; Spagou, K.; Green, M.; Dona, A.C.; Yuen, A.H.; David, M.; Berry, D.J.; Chappell, K.; Horneffer-van der Sluis, V.; et al. Development and Application of Ultra-Performance Liquid Chromatography-TOF MS for Precision Large Scale Urinary Metabolic Phenotyping. Anal. Chem. 2016, 88, 9004–9013. [Google Scholar] [CrossRef]
  21. Wong, J.M.; Malec, P.A.; Mabrouk, O.S.; Ro, J.; Dus, M.; Kennedy, R.T. Benzoyl chloride derivatization with liquid chromatography-mass spectrometry for targeted metabolomics of neurochemicals in biological samples. J. Chromatogr. A 2016, 1446, 78–90. [Google Scholar] [CrossRef] [PubMed]
  22. Zhang, Y.; Wang, C.; Liu, D.; Zhou, Z.; Gu, S.; Zuo, H. Association of total pre-existing comorbidities with stroke risk: A large-scale community-based cohort study from China. BMC Public Health 2021, 21, 1910. [Google Scholar] [CrossRef] [PubMed]
  23. Zhang, Y.; Gu, S.; Wang, C.; Liu, D.; Zhang, Q.; Yang, M.; Zhou, Z.; Zuo, H. Association between fasting blood glucose levels and stroke events: A large-scale community-based cohort study from China. BMJ Open 2021, 11, e050234. [Google Scholar] [CrossRef] [PubMed]
  24. Jager, K.J.; Zoccali, C.; MacLeod, A.; Dekker, F.W. Confounding: What it is and how to deal with it. Kidney Int. 2008, 73, 256–260. [Google Scholar] [CrossRef] [PubMed]
  25. Chowdhury, M.Z.I.; Turin, T.C. Variable selection strategies and its importance in clinical prediction modelling. Fam. Med. Community Health 2020, 8, e000262. [Google Scholar] [CrossRef] [PubMed]
  26. Liu, Q.; Liu, F.C.; Huang, K.Y.; Li, J.X.; Yang, X.L.; Wang, X.Y.; Chen, J.C.; Liu, X.Q.; Cao, J.; Shen, C.; et al. Beneficial effects of moderate to vigorous physical activity on cardiovascular disease among Chinese adults. J. Geriatr. Cardiol. 2020, 17, 85–95. [Google Scholar] [CrossRef]
  27. Ainsworth, B.E.; Haskell, W.L.; Herrmann, S.D.; Meckes, N.; Bassett, D.R., Jr.; Tudor-Locke, C.; Greer, J.L.; Vezina, J.; Whitt-Glover, M.C.; Leon, A.S. 2011 Compendium of Physical Activities: A second update of codes and MET values. Med. Sci. Sports Exerc. 2011, 43, 1575–1581. [Google Scholar] [CrossRef] [PubMed]
  28. Joint Committee for Guideline Revision. 2016 Chinese guidelines for the management of dyslipidemia in adults. J. Geriatr. Cardiol. 2018, 15, 1–29. [Google Scholar] [CrossRef] [PubMed]
  29. Levey, A.S.; Stevens, L.A.; Schmid, C.H.; Zhang, Y.L.; Castro, A.F., 3rd; Feldman, H.I.; Kusek, J.W.; Eggers, P.; Van Lente, F.; Greene, T.; et al. A new equation to estimate glomerular filtration rate. Ann. Intern. Med. 2009, 150, 604–612. [Google Scholar] [CrossRef]
  30. Labrecque, J.A.; Hunink, M.M.G.; Ikram, M.A.; Ikram, M.K. Do Case-Control Studies Always Estimate Odds Ratios? Am. J. Epidemiol. 2021, 190, 318–321. [Google Scholar] [CrossRef]
  31. Nie, J.; Xie, L.; Zhao, B.X.; Li, Y.; Qiu, B.; Zhu, F.; Li, G.F.; He, M.; Wang, Y.; Wang, B.; et al. Serum trimethylamine N-oxide concentration is positively associated with first stroke in hypertensive patients. Stroke 2018, 49, 2021–2028. [Google Scholar] [CrossRef] [PubMed]
  32. Kim, S.Y.; Lee, C.H.; Yoo, D.M.; Min, C.; Choi, H.G. Association between Asthma and Meniere’s Disease: A Nested Case-Control Study. Laryngoscope 2022, 132, 864–872. [Google Scholar] [CrossRef] [PubMed]
  33. Jia, C.; Zhang, S.; Cheng, X.; An, J.; Zhang, X.; Li, P.; Li, W.; Wang, X.; Yuan, Y.; Zheng, H.; et al. Association between serum pyrethroid insecticide levels and incident type 2 diabetes risk: A nested case-control study in Dongfeng-Tongji cohort. Eur. J. Epidemiol. 2022, 37, 959–970. [Google Scholar] [CrossRef] [PubMed]
  34. VanderWeele, T.J.; Knol, M.J. A Tutorial on Interaction. Epidemiol. Methods 2014, 3, 33–72. [Google Scholar] [CrossRef]
  35. VanderWeele, T.J.; Ding, P. Sensitivity Analysis in Observational Research: Introducing the E-Value. Ann. Intern. Med. 2017, 167, 268–274. [Google Scholar] [CrossRef] [PubMed]
  36. Zhu, Z.; Yang, P.; Jia, Y.; Wang, Y.; Shi, M.; Zhong, C.; Peng, H.; Sun, L.; Guo, D.; Xu, Q.; et al. Plasma Amino Acid Neurotransmitters and Ischemic Stroke Prognosis: A Multicenter Prospective Study. Am. J. Clin. Nutr. 2023, 118, 754–762. [Google Scholar] [CrossRef] [PubMed]
  37. Fernstrom, J.D.; Faller, D.V.; Shabshelowitz, H. Acute reduction of brain serotonin and 5-HIAA following food consumption: Correlation with the ratio of serum tryptophan to the sum of competing amino acids. J. Neural Transm. 1975, 36, 113–121. [Google Scholar] [CrossRef] [PubMed]
  38. Maes, M.; Verkerk, R.; Vandoolaeghe, E.; Van Hunsel, F.; Neels, H.; Wauters, A.; Demedts, P.; Scharpé, S. Serotonin-immune interactions in major depression: Lower serum tryptophan as a marker of an immune-inflammatory response. Eur. Arch. Psychiatry Clin. Neurosci. 1997, 247, 154–161. [Google Scholar] [CrossRef] [PubMed]
  39. Wu, C.S.; Wu, H.T.; Tsai, Y.T.; Huang, Y.W.; Tsai, H.J. Use of antidepressants and risk of hospitalization for acute myocardial infarction: A nationwide case-crossover study. J. Psychiatr. Res. 2017, 94, 7–14. [Google Scholar] [CrossRef] [PubMed]
  40. Glymour, M.M.; Gibbons, L.E.; Gilsanz, P.; Gross, A.L.; Mez, J.; Brewster, P.W.; Marden, J.; Zahodne, L.B.; Nho, K.; Hamilton, J.; et al. Initiation of antidepressant medication and risk of incident stroke: Using the Adult Changes in Thought cohort to address time-varying confounding. Ann. Epidemiol. 2019, 35, 42–47.e41. [Google Scholar] [CrossRef]
  41. Steinbusch, H.W. Distribution of serotonin-immunoreactivity in the central nervous system of the rat-cell bodies and terminals. Neuroscience 1981, 6, 557–618. [Google Scholar] [CrossRef] [PubMed]
  42. Van Donkelaar, E.L.; Blokland, A.; Ferrington, L.; Kelly, P.A.; Steinbusch, H.W.; Prickaerts, J. Mechanism of acute tryptophan depletion: Is it only serotonin? Mol. Psychiatry 2011, 16, 695–713. [Google Scholar] [CrossRef]
  43. Sauer, W.H.; Berlin, J.A.; Kimmel, S.E. Effect of Antidepressants and Their Relative Affinity for the Serotonin Transporter on the Risk of Myocardial Infarction. Circulation 2003, 108, 32–36. [Google Scholar] [CrossRef] [PubMed]
  44. Boyanpally, A.; Cutting, S.; Furie, K. Acute Ischemic Stroke Associated with Myocardial Infarction: Challenges and Management. Semin. Neurol. 2021, 41, 331–339. [Google Scholar] [CrossRef] [PubMed]
  45. Møller, S.E. Tryptophan to competing amino acids ratio in depressive disorder: Relation to efficacy of antidepressive treatments. Acta Psychiatr. Scand. Suppl. 1985, 325, 3–31. [Google Scholar] [CrossRef]
  46. Zinellu, A.; Sotgia, S.; Mangoni, A.A.; Sanna, M.; Satta, A.E.; Carru, C. Impact of cholesterol lowering treatment on plasma kynurenine and tryptophan concentrations in chronic kidney disease: Relationship with oxidative stress improvement. Nutr. Metab. Cardiovasc. Dis. 2015, 25, 153–159. [Google Scholar] [CrossRef]
Nutrients 16 01544 g001
Figure 1. Tryptophan’s competing pathways from blood to brain. Abbreviations: 5-HT, 5-hydroxytryptamine; 5-HTP, 5-hydroxytryptophan; BBB, blood–brain barrier; CAAs: competing amino acids; TPH, tryptophan hydroxylase.
Figure 1. Tryptophan’s competing pathways from blood to brain. Abbreviations: 5-HT, 5-hydroxytryptamine; 5-HTP, 5-hydroxytryptophan; BBB, blood–brain barrier; CAAs: competing amino acids; TPH, tryptophan hydroxylase.
Nutrients 16 01544 g001
Nutrients 16 01544 g002
Figure 2. Flowchart for the nested case–control study conducted within the Prospective Follow-up Study on Cardiovascular Morbidity and Mortality in China (PFS-CMMC). Abbreviations: PFS-CMMC, the Prospective Follow-up Study on Cardiovascular Morbidity and Mortality in China; IS, ischemic stroke.
Figure 2. Flowchart for the nested case–control study conducted within the Prospective Follow-up Study on Cardiovascular Morbidity and Mortality in China (PFS-CMMC). Abbreviations: PFS-CMMC, the Prospective Follow-up Study on Cardiovascular Morbidity and Mortality in China; IS, ischemic stroke.
Nutrients 16 01544 g002
Nutrients 16 01544 g003
Figure 3. Stratified analyses for the association between tryptophan index and risk of ischemic stroke. The model was adjusted for age (continuous), sex (male or female), BMI (continuous), current smoking status (yes or no), educational attainment (0 year, 1–5 years, or ≥6 years), physical activity (quartiles), family history of stroke (yes or no), hypertension (yes or no), diabetes (yes or no), hyperlipidemia (yes or no), and eGFR (continuous). Abbreviations: BMI, body mass index; eGFR, estimated glomerular filtration rate; MET, metabolic equivalent.
Figure 3. Stratified analyses for the association between tryptophan index and risk of ischemic stroke. The model was adjusted for age (continuous), sex (male or female), BMI (continuous), current smoking status (yes or no), educational attainment (0 year, 1–5 years, or ≥6 years), physical activity (quartiles), family history of stroke (yes or no), hypertension (yes or no), diabetes (yes or no), hyperlipidemia (yes or no), and eGFR (continuous). Abbreviations: BMI, body mass index; eGFR, estimated glomerular filtration rate; MET, metabolic equivalent.
Nutrients 16 01544 g003
Table 1. Demographic and clinical characteristics at baseline between ischemic stroke cases and controls (n = 642).
Table 1. Demographic and clinical characteristics at baseline between ischemic stroke cases and controls (n = 642).
Cases (n = 321)Controls (n = 321)p-Value
Age (years)69.5 (63.2, 75.2)69.6 (63.4, 75.1)
Male (%)142 (44.2)142 (44.2)
BMI (kg/m2)23.6 (21.4, 25.9)23.3 (21.4, 25.7)0.53
Currently smoking (%)83 (25.9)80 (24.9)0.79
Physical activity (MET-h/d)20.4 (12.3, 34.8)21.8 (13.5, 34.4)0.37
Educational attainment (%) 0.85
 0 year157 (49.1)153 (47.7)
 1–5 years121 (37.8)121 (37.7)
 ≥6 years42 (13.1)47 (14.6)
TC (mmol/L)4.95 (4.30, 5.61)4.84 (4.21, 5.51)0.24
TG (mmol/L)1.32 (0.95, 1.88)1.23 (0.92, 1.70)0.23
HDL-C (mmol/L)1.40 (1.16, 1.69)1.48 (1.21, 1.71)0.05
Fasting glucose (mmol/L)5.54 (5.07, 6.23)5.40 (4.98, 5.96)0.013
Diabetes (%)61 (19.0)35 (10.9)0.004
Hypertension (%)260 (81.0)214 (66.7)<0.001
eGFR (mL/min/1.73 m2)84.5 (72.4, 91.0)85.4 (74.3, 93.2)0.06
Hyperlipidemia (%)89 (27.7)80 (24.9)0.42
Tryptophan (μmol/L)76.6 (66.2, 87.9)79.2 (67.0, 90.3)0.19
Tyrosine (μmol/L)89.5 (77.3, 106)91.4 (80.0, 107)0.23
Valine (μmol/L)268 (237, 301)264 (233, 296)0.20
Phenylalanine (μmol/L)69.3 (61.4, 80.1)69.6 (62.2, 79.9)0.83
Isoleucine (μmol/L)86.8 (73.3, 106)83.7 (71.1, 99.4)0.038
Leucine (μmol/L)115 (100, 134)114 (100, 129)0.29
Total CAAs (μmol/L)636 (569, 700)626 (561, 696)0.28
Tryptophan index (×100)12.1 (10.7, 13.6)12.5 (11.3, 13.8)0.010
The differences between cases and controls were assessed using Chi-squared tests for categorical variables, presented as a number and percentage, and Wilcoxon test for continuous variables, presented as a median and interquartile range. Abbreviations: BMI, body mass index; eGFR, estimated glomerular filtration rate; HDL-C, high-density lipoprotein cholesterol; MET, metabolic equivalent; TC, total cholesterol; TG, triglyceride.
Table 2. Incidence rate ratio (IRR) and 95% confidence intervals (CIs) for ischemic risk by conditional logistic regression models.
Table 2. Incidence rate ratio (IRR) and 95% confidence intervals (CIs) for ischemic risk by conditional logistic regression models.
Cases/ControlsModel 1Model 2Model 3
IRR (95% CI)IRR (95% CI)IRR (95% CI)
Tryptophan index (×100)
 Q1 (<11.2)101/811.00 (Ref)1.00 (Ref)1.00 (Ref)
 Q2 (11.3, 12.5)88/810.87 (0.56, 1.34)0.86 (0.55, 1.33)0.91 (0.58, 1.45)
 Q3 (12.6, 13.7)68/780.68 (0.43, 1.07)0.62 (0.39, 1.01)0.63 (0.38, 1.05)
 Q4 (>13.8)64/810.62 (0.40, 0.98)0.56 (0.35, 0.90)0.53 (0.31, 0.88)
p for trend 0.0230.0080.008
Continuous321/3210.79 (0.67, 0.93)0.76 (0.64, 0.90)0.76 (0.63, 0.93)
p-value 0.0050.0020.006
Tryptophan (μmol/L)
 Q1 (<67.0)89/811.00 (Ref)1.00 (Ref)1.00 (Ref)
 Q2 (67.1, 79.1)99/801.16 (0.75, 1.79)1.16 (0.74, 1.82)1.22 (0.76, 1.79)
 Q3 (79.2, 90.3)60/800.70 (0.45, 1.09)0.67 (0.43, 1.05)0.63 (0.39, 1.03)
 Q4 (>90.4)73/800.82 (0.52, 1.29)0.75 (0.47, 1.22)0.72 (0.44, 1.21)
p for trend 0.130.060.05
Continuous321/3210.89 (0.76, 1.05)0.85 (0.72, 1.10)0.83 (0.69, 1.00)
p-value 0.170.070.05
Model 1: Unadjusted model, based on individual case set; Model 2: adjusted for BMI (continuous), current smoking status (yes or no), educational attainment (0 year, 1–5 years, or ≥6 years), and physical activity (by quartiles), based on individual case set; Model 3: further adjusted for family history of stroke (yes or no), hypertension (yes or no), diabetes (yes or no), hyperlipidemia (yes or no), and eGFR (continuous). To normalize the distribution of the continuous tryptophan index, a natural logarithmic (log) transformation was applied. Abbreviations: BMI, body mass index; eGFR, estimated glomerular filtration rate.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Liu, D.; Hong, Y.; Chen, Z.; Ma, Y.; Xia, S.; Gu, S.; Zuo, H. The Tryptophan Index Is Associated with Risk of Ischemic Stroke: A Community-Based Nested Case–Control Study. Nutrients 2024, 16, 1544. https://doi.org/10.3390/nu16111544

AMA Style

Liu D, Hong Y, Chen Z, Ma Y, Xia S, Gu S, Zuo H. The Tryptophan Index Is Associated with Risk of Ischemic Stroke: A Community-Based Nested Case–Control Study. Nutrients. 2024; 16(11):1544. https://doi.org/10.3390/nu16111544

Chicago/Turabian Style

Liu, Dong, Yan Hong, Zhenting Chen, Yifan Ma, Shangyu Xia, Shujun Gu, and Hui Zuo. 2024. "The Tryptophan Index Is Associated with Risk of Ischemic Stroke: A Community-Based Nested Case–Control Study" Nutrients 16, no. 11: 1544. https://doi.org/10.3390/nu16111544

APA Style

Liu, D., Hong, Y., Chen, Z., Ma, Y., Xia, S., Gu, S., & Zuo, H. (2024). The Tryptophan Index Is Associated with Risk of Ischemic Stroke: A Community-Based Nested Case–Control Study. Nutrients, 16(11), 1544. https://doi.org/10.3390/nu16111544

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop