- Email: [email protected]
Urinary metabolites and risk of coronary heart disease: A prospective investigation among urban Chinese adults
Journal Pre-proof Urinary Metabolites and Risk of Coronary Heart Disease: A prospective Investigation among Urban Chinese Adults Hyung-Suk Yoon, Jae Jeong Yang, Emilio S. Rivera, Xiao-Ou Shu, Yong-Bing Xiang, Marion W. Calcutt, Qiuyin Cai, Xianglan Zhang, Honglan Li, Yu-Tang Gao, Wei Zheng, Danxia Yu PII:
S0939-4753(19)30410-7
DOI:
https://doi.org/10.1016/j.numecd.2019.10.011
Reference:
NUMECD 2173
To appear in:
Nutrition, Metabolism and Cardiovascular Diseases
Received Date: 6 June 2019 Revised Date:
28 October 2019
Accepted Date: 29 October 2019
Please cite this article as: Yoon H-S, Yang JJ, Rivera ES, Shu X-O, Xiang Y-B, Calcutt MW, Cai Q, Zhang X, Li H, Gao Y-T, Zheng W, Yu D, Urinary Metabolites and Risk of Coronary Heart Disease: A prospective Investigation among Urban Chinese Adults, Nutrition, Metabolism and Cardiovascular Diseases, https://doi.org/10.1016/j.numecd.2019.10.011. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 The Italian Society of Diabetology, the Italian Society for the Study of Atherosclerosis, the Italian Society of Human Nutrition, and the Department of Clinical Medicine and Surgery, Federico II University. Published by Elsevier B.V. All rights reserved.
Urinary Metabolites and Risk of Coronary Heart Disease: A prospective Investigation among Urban Chinese Adults
Hyung-Suk Yoon1, Jae Jeong Yang1, Emilio S. Rivera2, Xiao-Ou Shu1, Yong-Bing Xiang3, Marion W. Calcutt2, Qiuyin Cai1, Xianglan Zhang4, Honglan Li3, Yu-Tang Gao3, Wei Zheng1, and Danxia Yu1
1
Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center,
Nashville, TN, USA 2
Department of Biochemistry and Mass Spectrometry Research Center, Vanderbilt University,
Nashville, TN, USA 3
State Key Laboratory of Oncogene and Related Genes & Department of Epidemiology,
Shanghai Cancer Institute, Renji Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China 4
Tennessee Department of Health, Nashville, TN, USA
Correspondence to: Danxia Yu, Vanderbilt University Medical Center, 2525 West End Avenue, Nashville, TN, USA 37203, Phone: (+1)615-936-7389, Fax: (+1)615-343-5938, Email: [email protected]
1
Abstract Background and Aims: Studies have linked several metabolites to the risk of coronary heart disease (CHD) among Western populations, but prospective studies among Asian populations on the metabolite-CHD association remain limited. Methods: We evaluated the association of urinary metabolites with CHD risk among Chinese adults in a nested case-control study of 275 incident cases and 275 matched controls (127 pairs of men and 148 pairs of women). Fifty metabolites were measured by a pre-defined metabolomics panel and adjusted using urinary creatinine. Conditional logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (95% CIs). Results: After adjusting for traditional CHD risk factors, urinary tryptophan showed a positive association with incident CHD: OR (95% CI) for the highest vs. lowest quartiles was 2.02 (1.153.56) among all study participants (p-trend=0.02). The tryptophan-CHD association was more evident among individuals with dyslipidemia than those without the condition (OR [95% CI] for the highest vs. lowest quartiles=3.90 [1.86-8.19] and 0.74 [0.26-2.06], respectively; pinteraction<0.01). Other metabolites did not show significant associations with CHD risk among all study participants. However, a positive association of methionine with CHD risk was observed only among women (OR [95% CI] for the highest vs. lowest quartiles=2.77 [1.17-6.58]; p-interaction=0.03), and an inverse association of inosine with CHD risk was observed only among men (OR [95% CI] for the highest vs. lowest quartiles=0.29 [0.11-0.81]; pinteraction=0.04). Conclusion: Elevated urinary tryptophan may be related to CHD risk among Chinese adults, especially for those with dyslipidemia. Keywords: Coronary heart disease; Chinese population; Metabolomics; Nested case-control study; Tryptophan metabolism 2
Introduction Coronary heart disease (CHD) is the leading cause of death worldwide [1,2]. Established risk factors for CHD include unhealthy lifestyle, poor diet, obesity, dyslipidemia, hypertension, diabetes, and metabolic syndrome [3], indicating that CHD is a disease of metabolic disturbance. Recently, metabolomics technology has been used in population-based studies, reporting some metabolites associated with risk of CHD or overall cardiovascular disease (CVD), including glycerophospholipids (e.g., lysophosphatidylcholines), acylcarnitines, unsaturated fatty acids, branched-chain amino acids (BCAAs, e.g., leucine, isoleucine, and valine), aromatic amino acids (AroAAs, e.g., phenylalanine and tyrosine), glutamate, and some gut microbiota-derived metabolites (e.g., trimethylamine N-oxide, TMAO) [4–14]. However, most previous metabolomics studies of CHD were conducted in Western populations. A few studies conducted in Asian populations used samples collected after CHD diagnosis [15,16], thus prone to reverse causation; others evaluated the associations of metabolites with CHD risk factors (e.g., obesity and diabetes) [17–24]. Furthermore, most previous studies measured blood metabolites, while the associations of urine metabolites with CHD risk remain largely unknown. Urine samples offer several advantages for metabolomics research and potential clinical translation, including a non-invasive sample collection and a simple sample processing before metabolomics profiling [25]. Urine contains various metabolic breakdown products of endogenous or exogenous compounds. Many urine metabolites have been found to be related to cardiometabolic disorders [12,15,26–28]. However, metabolites excreted in the urine might not be as biologically relevant to the cardiovascular system as the blood metabolites, and the associations for the same metabolite in blood and in urine may have different meanings [26,29].
3
In the current study, using a pre-defined metabolomics panel covering BCAAs, AroAAs, peptides, fatty acids, carboxylic acids, purines, and others, we examined the associations of urinary metabolites with incident CHD in a case-control study nested within two prospective cohorts of Chinese adults: the Shanghai Men's Health Study (SMHS) and the Shanghai Women's Health Study (SWHS).
Materials and Methods Study population The SMHS and SWHS are population-based, prospective cohort studies conducted in urban Shanghai, China. Detailed information on each cohort profile has been described [30,31]. Briefly, 74,941 women and 61,480 men, aged 40 to 74 years, were recruited between 1996 and 2000 for SWHS and between 2002 and 2006 for SMHS. After written informed consent was obtained, an in-person interview was conducted to collect information on sociodemographics, medical history, dietary habits, lifestyle factors, and anthropometrics. Biological samples were also donated from the study participants (75% for peripheral blood and 88% for spot urine). All samples were processed within six hours of collection and stored at −80 °C. Participants’ health status have been updated by follow-up surveys, every 2 to 4 years (follow-up rates >92%), and annual data linkages to the Shanghai Vital Statistic Registry (complete rates >99%). The study protocol for each cohort was approved by institutional review boards of the Shanghai Cancer Institute and Vanderbilt University. For the current nested case-control study, we selected incident CHD cases (nonfatal myocardial infarction or fatal CHD) and matched controls from CVD- and cancer-free cohort members who provided both blood and urine samples at baseline [12]. Fatal CHD cases were
4
screened based on death certificates (ICD-9 codes 410-414). Physicians’ medical record reviews confirmed the nonfatal myocardial infarction cases. Among a total of 377 incident CHD cases, we excluded 102 cases who reported a history of kidney disease (n=59) or showed a sign of kidney problems (n=31; i.e., having protein or blood in urine based on a dipstick test), or reported implausible calorie intake (n=12; <800 or >4200 kcal/day for men and <500 or >3500 kcal/day for women). Remaining 275 cases (127 from the SMHS and 148 from the SWHS) were matched to one randomly-selected control by sex, age (±2 years), date of sample collection (±30 days), time of sample collection (morning/afternoon), recent use of antibiotics (yes/no), and menopausal status (for women only).
Metabolites assays Along with a pre-defined metabolomics panel developed by the Vanderbilt University Mass Spectrometry Research Center, 50 metabolites were measured using ultra-performance liquid chromatography and mass spectrometry. The panel covers commonly measured metabolites including BCAAs, AroAAs, peptides, fatty acids, carboxy acids, purines, and others (Supplement Table S1). Details on metabolites assays have been described before [12]. Briefly, 20 µL of the urine sample was mixed with 80 µL acetonitrile. After centrifugation at 10,000g for 10 minutes, 90 µL of supernatant was loaded into the ultra performance liquid chromatography system with a Zic‐cHILIC column. Stable isotope-labelled internal standards (i.e., citrulline13
C1 2H4, arginine-15N4, tyrosine-phenol-d2, lactic acid-13C3, and glucose-6,6-d2) were used to
calculate the relative concentration of metabolites. Urinary creatinine was measured using a Roche-Cobas MiraPlus chemistry analyzer and used to adjust for urine dilution. To account for potential batch variation, samples of each case-control pair were allocated adjacently in the same
5
batch, and two quality control samples were analyzed along with the study samples in each batch. The laboratory person was blinded to the case-control status of samples. Statistical analysis Metabolites below the detection limit were assigned half of the minimum observed value. To normalize the distribution of metabolite data, before analyses, all metabolite concentrations were log-transformed and standardized by the mean and standard deviation. Baseline characteristics among CHD cases and matched controls were compared using the paired t-test for continuous variables and Cochran-Mantel-Haenszel test for categorical variables. Multivariable-adjusted conditional logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (95% CIs) for CHD risk associated with quartiles of each metabolite. Quartiles were determined by the sex-specific distribution among controls. Covariates were selected a priori based on literature and their associations with CHD risk in our study population, including enrollment age (continuous), body mass index (BMI, continuous), waist-hip ratio (WHR, continuous), smoking pack-years (continuous), history of hypertension and diabetes (no vs. yes), and time interval between the last meal and biospecimen collection (hours, continuous). Missing rates of covariate were all <2%, and missing data were imputed using the median (for continuous variables) or the mode (for categorical variables). P for trend was tested using a continuous variable with median values for each quartile of metabolites and corrected for multiple comparisons using the Benjamini‐Hochberg false discovery rate (FDR). To assess potential effect modification of the metabolite-CHD association by known CVD risk factors, we conducted stratified analyses by BMI (≤25 or >25 kg/m2), central obesity defined as WHR ≥0.95 in men and ≥0.85 in women according to WHO criteria for Asians [32,33], dyslipidemia defined as doctor diagnosis or lipid levels (total cholesterol >240 mg/dL, LDL
6
cholesterol >160 mg/dL, triglyceride >200 mg/dL, and HDL cholesterol <50 mg/dL among men and <40 mg/dL among women), hypertension, and high sensitivity C-reactive protein level (CRP, <1 or ≥1 mg/L, approximately the median value in our study). Stratified analyses were performed by adding an interaction term of metabolite levels and the stratified variable. The pvalue for interaction was obtained by the likelihood-ratio test; OR and 95% CI in each stratum were obtained by specifying the value of a stratified variable. All statistical analyses were conducted using the SAS Enterprise Guide 7.1 (SAS Institute, Cary, NC, USA).
Results In both men and women, CHD cases showed significantly higher WHR and higher prevalence of hypertension than controls. Male CHD cases also had higher BMI than controls, while female CHD cases had a higher prevalence of diabetes (Table 1, p<0.05). After controlling for potential confounders, urinary tryptophan showed a significant positive association with CHD risk (Table 2): OR [95% CI] in the highest vs. lowest quartiles of tryptophan was 2.02 [1.15-3.56] among total study participants, 3.86 [1.44-10.4] among men, and 1.51 [0.74-3.08] among women (p-interaction with sex=0.17). Guanosine, methionine, and valine were associated with increased CHD risk only among women (OR [95% CI] in the highest vs. lowest quartiles, p-interaction with sex: for guanosine: 3.13 [1.35-7.28], p-interaction=0.11; for methionine: 2.77 [1.17-6.58], p-interaction=0.03; for valine: 2.88 [1.19-6.97], pinteraction=0.13). Among men, hydroxyproline and asparagine were associated with increased CHD risk (OR [95% CI] in the highest vs. lowest quartiles, p-interaction with sex: for hydroxyproline: 2.50 [1.14-5.52], p-interaction=0.08; for asparagine: 2.47 [1.02-5.97], pinteraction=0.40), whereas uracil and inosine were associated with decreased risk (OR [95% CI]
7
in the highest vs. lowest quartiles, p-interaction with sex: for uracil: 0.39 [0.15-0.99], pinteraction=0.20; for inosine: 0.29 [0.11-0.81], p-interaction=0.04). The metabolite-CHD associations seemed more evident among participants with existing metabolic conditions (Table 3). The ORs [95% CIs] for tryptophan-CHD were 3.29 [1.45-7.46], 3.90 [1.86-8.19], 3.27 [1.40-7.64], and 2.39 [1.17-4.88] among participants with obesity, dyslipidemia, hypertension, and high CRP, respectively, although only the interaction between tryptophan level and dyslipidemia was statistically significant: p-interaction<0.01. The effect modification by CRP level was marginally significant for inosine: an inverse inosine-CHD association was found among individuals with CRP<1 mg/L (OR [95% CI] = 0.32 [0.12-0.84]; p-interaction=0.05). The associations between all metabolites and CHD risk are presented in the Supplement Tables S1-S8.
Discussion In this prospective investigation among Chinese men and women, we found that urinary levels of several metabolites were associated with CHD risk, even after adjusting for traditional CVD risk factors. Among those, tryptophan showed a positive association with CHD risk, especially among men, overweight/obese persons, and those with metabolic disorders, such as dyslipidemia, hypertension, and chronic inflammation. Guanosine, methionine, valine, hydroxyproline, and asparagine were also positively associated with CHD risk, but only among men or women or individuals with certain metabolic condition; while, uracil and inosine showed inverse associations among men or those with CRP level less than 1 mg/L. Previous metabolomics studies have suggested several circulating metabolites being associated with risk of CVD or CHD, including lysophosphatidylcholines, monoglycerides,
8
sphingomyelins, acylcarnitines, unsaturated fatty acids, BCAAs, AroAAs, glutamate, and some microbial metabolites such as TMAO and hippurate [4–14]. However, most previous studies were conducted among Western populations and used blood samples—little is known about the association of metabolites with CVD or CHD risk among non-white populations. In this study among Chinese adults, we observed a potential association of urinary tryptophan with CHD risk. Tryptophan, one of the essential amino acids, plays a crucial role in protein synthesis, and serves as a precursor of kynurenines, serotonin, and melatonin [34]. Tryptophan metabolism via the kynurenine pathway is closely related to inflammation [35]. Studies in Western populations have reported an inverse association of blood levels of tryptophan with CVD incidence or mortality [36,37]. Tryptophan catabolism via the kynurenine pathway (i.e., a low level of tryptophan and high levels of its metabolites) has been linked to adverse CVD events [38,39]. Seemingly contradictory to previous studies, we found a positive association of urinary tryptophan with incident CHD. The inconsistency may be attributed to the differences in the metabolome profile or dynamics between urine and blood samples. The potential racial/ethnic differences may also contribute to the controversial findings: a recent Chinese study has reported elevated circulating tryptophan for unstable angina [40], and other Chinese studies have reported positive associations of tryptophan levels with incident diabetes and metabolic disorders such as obesity, hypertension, and dyslipidemia [22–24]. To date, the role of tryptophan metabolism in CVD or other metabolic disease etiology remains poorly understood, especially for the racial/ethnicspecific or biospecimen-specific associations. Further studies are needed to elucidate the biological mechanism underlying the effect of tryptophan on the development of CVD among diverse populations.
9
BCAAs have attracted much attention due to their significant associations with cardiometabolic diseases [11,41–45]. High levels of blood and urinary BCAAs have been linked to increased risk of CVD and diabetes in both Western and Asian populations [11,44]. In the present study, we found a significant positive association of valine, one of BCAAs, with CHD risk among women, centrally obese individuals, or older participants, although not among total study participants. Thus, whether valine and other BCAAs are associated with CHD risk among Chinese needs to be evaluated in more studies. On the other hand, inosine, which is derived from plant foods, was associated with decreased risk of CHD, especially among men and individuals with low CRP level. This inverse association is in line with previous reports on the antiinflammatory and immunomodulatory effects of inosine [46]. Future investigations into the role of inosine and other plant food-derived metabolites in CVD etiology are warranted. The major strength of our study is its prospective design. All study participants were free from diseases such as CVD, cancer, and kidney problems at the time of sample collection. Our findings suggested the potential CHD-related metabolites for further investigations among Asian populations. Nevertheless, we acknowledge study limitations. First, we conducted a single measurement of metabolites using spot urine samples, which may not fully capture the usual level of metabolites, especially those with large within-person variations. Moreover, urinary metabolites may show different results from circulating metabolites—it should be very cautious when comparing our results with those from other studies that used blood samples. Second, we only measured a relatively small number of urinary metabolites, and thus, may overlook other important metabolites in the CHD etiology. Third, we consider the current study a pilot and thus included no replication. Larger prospective studies are needed to further examine metabolomic signatures of CHD in Asian populations.
10
Conclusions Our findings suggest that elevated urinary tryptophan may be associated with risk of CHD among Chinese adults, especially for individuals with dyslipidemia. Other amino acids and some plant food-derived metabolites may be also related to CHD risk. More prospective studies among Chinese populations are warranted. In addition, further functional studies will help understand the causal effect and/or biological relevance of those metabolites on CHD risk.
Conflicts of Interest The authors declare no conflict of interest. The sponsors had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. The contents of this article are solely the responsibility of the authors and do not represent the official views of the Tennessee Department of Health.
Supplementary Materials The following are available online, Supplement Table S1. Association between urinary metabolites and CHD risk; Supplement Table S2. Association between urinary metabolites and CHD risk by gender; Supplement Table S3. Association between urinary metabolites and CHD risk by age group; Supplement Table S4. Association between urinary metabolites and CHD risk by BMI; Supplement Table S5. Association between urinary metabolites and CHD risk by central obesity; Supplement Table S6. Association between urinary metabolites and CHD risk by dyslipidemia; Supplement Table S7. Association between urinary metabolites and CHD risk by 11
hypertension; Supplement Table S8. Association between urinary metabolites and CHD risk by CRP level
Author Contributions D.Y. conceived, designed, and supervised the study; H.S.Y. and J.J.Y. conducted statistical analysis; H.S.Y. and D.Y. drafted the manuscript; E.S.R., M.W.C., and Q.C. contributed to study design and metabolites data collection; X-O.S., Y.-B.X., Q.C., X.Z., H.L., Y.-T.G., and W.Z. contributed to study design and cohort data collection. All authors contributed to results interpretation, manuscript revision, and approved the final version of the manuscript.
Funding The parent cohort studies were supported by grants from the National Institutes of Health UM1 CA182910 to W.Z. and UM1 CA173640 to X-O.S. D.Y. was supported by Vanderbilt University Medical Center Faculty Research Scholars Program. The urine sample preparation was performed at the Survey and Biospecimen Shared Resource (Q.C.), which is supported in part by the Vanderbilt-Ingram Cancer Center (P30 CA068485). The bioanalytical methods development (E.S.R. and M.W.C.) was supported in part by a Vanderbilt University trans-Institutional Program Award.
References [1] World Health Organization. Global Health Estimates 2016: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2016. Geneva: 2018. [2] GBD 2016 Causes of Death Collaborators. Global, regional, and national age-sex specific mortality for 264 causes of death, 1980-2016: a systematic analysis for the Global Burden
12
[3]
[4]
[5]
[6]
[7]
[8]
[9]
[10]
[11]
[12]
[13]
[14]
of Disease Study 2016. Lancet Lond Engl 2017;390:1151–210. https://doi.org/10.1016/S0140-6736(17)32152-9. Benjamin EJ, Virani SS, Callaway CW, Chamberlain AM, Chang AR, Cheng S, et al. Heart Disease and Stroke Statistics-2018 Update: A Report From the American Heart Association. Circulation 2018;137:e67–492. https://doi.org/10.1161/CIR.0000000000000558. Ganna A, Salihovic S, Sundström J, Broeckling CD, Hedman AK, Magnusson PKE, et al. Large-scale metabolomic profiling identifies novel biomarkers for incident coronary heart disease. PLoS Genet 2014;10:e1004801. https://doi.org/10.1371/journal.pgen.1004801. Zheng Y, Yu B, Alexander D, Steffen LM, Nettleton JA, Boerwinkle E. Metabolomic patterns and alcohol consumption in African Americans in the Atherosclerosis Risk in Communities Study. Am J Clin Nutr 2014;99:1470–8. https://doi.org/10.3945/ajcn.113.074070. Vaarhorst AAM, Verhoeven A, Weller CM, Böhringer S, Göraler S, Meissner A, et al. A metabolomic profile is associated with the risk of incident coronary heart disease. Am Heart J 2014;168:45-52.e7. https://doi.org/10.1016/j.ahj.2014.01.019. Stegemann C, Pechlaner R, Willeit P, Langley SR, Mangino M, Mayr U, et al. Lipidomics profiling and risk of cardiovascular disease in the prospective population-based Bruneck study. Circulation 2014;129:1821–31. https://doi.org/10.1161/CIRCULATIONAHA.113.002500. Würtz P, Havulinna AS, Soininen P, Tynkkynen T, Prieto-Merino D, Tillin T, et al. Metabolite profiling and cardiovascular event risk: a prospective study of 3 populationbased cohorts. Circulation 2015;131:774–85. https://doi.org/10.1161/CIRCULATIONAHA.114.013116. Paynter NP, Balasubramanian R, Giulianini F, Wang DD, Tinker LF, Gopal S, et al. Metabolic Predictors of Incident Coronary Heart Disease in Women. Circulation 2018;137:841–53. https://doi.org/10.1161/CIRCULATIONAHA.117.029468. Kume S, Araki S, Ono N, Shinhara A, Muramatsu T, Araki H, et al. Predictive properties of plasma amino acid profile for cardiovascular disease in patients with type 2 diabetes. PloS One 2014;9:e101219. https://doi.org/10.1371/journal.pone.0101219. Tobias DK, Lawler PR, Harada PH, Demler OV, Ridker PM, Manson JE, et al. Circulating Branched-Chain Amino Acids and Incident Cardiovascular Disease in a Prospective Cohort of US Women. Circ Genomic Precis Med 2018;11:e002157. https://doi.org/10.1161/CIRCGEN.118.002157. Yu D, Shu X-O, Rivera ES, Zhang X, Cai Q, Calcutt MW, et al. Urinary Levels of Trimethylamine-N-Oxide and Incident Coronary Heart Disease: A Prospective Investigation Among Urban Chinese Adults. J Am Heart Assoc 2019;8:e010606. https://doi.org/10.1161/JAHA.118.010606. Guasch-Ferré M, Hu FB, Ruiz-Canela M, Bulló M, Toledo E, Wang DD, et al. Plasma Metabolites From Choline Pathway and Risk of Cardiovascular Disease in the PREDIMED (Prevention With Mediterranean Diet) Study. J Am Heart Assoc 2017;6. https://doi.org/10.1161/JAHA.117.006524. Wang Z, Klipfell E, Bennett BJ, Koeth R, Levison BS, Dugar B, et al. Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease. Nature 2011;472:57–63. https://doi.org/10.1038/nature09922.
13
[15] Feng Q, Liu Z, Zhong S, Li R, Xia H, Jie Z, et al. Integrated metabolomics and metagenomics analysis of plasma and urine identified microbial metabolites associated with coronary heart disease. Sci Rep 2016;6:22525. https://doi.org/10.1038/srep22525. [16] Li Y, Zhang D, He Y, Chen C, Song C, Zhao Y, et al. Investigation of novel metabolites potentially involved in the pathogenesis of coronary heart disease using a UHPLCQTOF/MS-based metabolomics approach. Sci Rep 2017;7:15357. https://doi.org/10.1038/s41598-017-15737-3. [17] Wang SM, Yang RY, Wang M, Ji FS, Li HX, Tang YM, et al. Identification of serum metabolites associated with obesity and traditional risk factors for metabolic disease in Chinese adults. Nutr Metab Cardiovasc Dis NMCD 2018;28:112–8. https://doi.org/10.1016/j.numecd.2017.09.009. [18] Yu D, Moore SC, Matthews CE, Xiang Y-B, Zhang X, Gao Y-T, et al. Plasma metabolomic profiles in association with type 2 diabetes risk and prevalence in Chinese adults. Metabolomics Off J Metabolomic Soc 2016;12. https://doi.org/10.1007/s11306-015-0890-8. [19] Tillin T, Hughes AD, Wang Q, Würtz P, Ala-Korpela M, Sattar N, et al. Diabetes risk and amino acid profiles: cross-sectional and prospective analyses of ethnicity, amino acids and diabetes in a South Asian and European cohort from the SABRE (Southall And Brent REvisited) Study. Diabetologia 2015;58:968–79. https://doi.org/10.1007/s00125-015-35178. [20] Tai ES, Tan MLS, Stevens RD, Low YL, Muehlbauer MJ, Goh DLM, et al. Insulin resistance is associated with a metabolic profile of altered protein metabolism in Chinese and Asian-Indian men. Diabetologia 2010;53:757–67. https://doi.org/10.1007/s00125-0091637-8. [21] Qiu G, Zheng Y, Wang H, Sun J, Ma H, Xiao Y, et al. Plasma metabolomics identified novel metabolites associated with risk of type 2 diabetes in two prospective cohorts of Chinese adults. Int J Epidemiol 2016;45:1507–16. https://doi.org/10.1093/ije/dyw221. [22] Lu Y, Wang Y, Liang X, Zou L, Ong CN, Yuan J-M, et al. Serum Amino Acids in Association with Prevalent and Incident Type 2 Diabetes in A Chinese Population. Metabolites 2019;9. https://doi.org/10.3390/metabo9010014. [23] Chen T, Zheng X, Ma X, Bao Y, Ni Y, Hu C, et al. Tryptophan Predicts the Risk for Future Type 2 Diabetes. PloS One 2016;11:e0162192. https://doi.org/10.1371/journal.pone.0162192. [24] Xie G, Ma X, Zhao A, Wang C, Zhang Y, Nieman D, et al. The metabolite profiles of the obese population are gender-dependent. J Proteome Res 2014;13:4062–73. https://doi.org/10.1021/pr500434s. [25] Bouatra S, Aziat F, Mandal R, Guo AC, Wilson MR, Knox C, et al. The human urine metabolome. PloS One 2013;8:e73076. https://doi.org/10.1371/journal.pone.0073076. [26] Elliott P, Posma JM, Chan Q, Garcia-Perez I, Wijeyesekera A, Bictash M, et al. Urinary metabolic signatures of human adiposity. Sci Transl Med 2015;7:285ra62. https://doi.org/10.1126/scitranslmed.aaa5680. [27] Holmes E, Loo RL, Stamler J, Bictash M, Yap IKS, Chan Q, et al. Human metabolic phenotype diversity and its association with diet and blood pressure. Nature 2008;453:396– 400. https://doi.org/10.1038/nature06882. [28] Zhang X, Gao Y-T, Yang G, Li H, Cai Q, Xiang Y-B, et al. Urinary isoflavonoids and risk of coronary heart disease. Int J Epidemiol 2012;41:1367–75. https://doi.org/10.1093/ije/dys130. 14
[29] Tynkkynen T, Wang Q, Ekholm J, Anufrieva O, Ohukainen P, Vepsäläinen J, et al. Proof of concept for quantitative urine NMR metabolomics pipeline for large-scale epidemiology and genetics. Int J Epidemiol 2019;48:978–93. https://doi.org/10.1093/ije/dyy287. [30] Shu X-O, Li H, Yang G, Gao J, Cai H, Takata Y, et al. Cohort Profile: The Shanghai Men’s Health Study. Int J Epidemiol 2015;44:810–8. https://doi.org/10.1093/ije/dyv013. [31] Zheng W, Chow W-H, Yang G, Jin F, Rothman N, Blair A, et al. The Shanghai Women’s Health Study: rationale, study design, and baseline characteristics. Am J Epidemiol 2005;162:1123–31. https://doi.org/10.1093/aje/kwi322. [32] World Health Organization. Waist circumference and waist-hip ratio: report of a WHO expert consultation, Geneva, 8-11 December 2008. Geneva: World Health Organization; 2011. [33] Yu D, Zheng W, Johansson M, Lan Q, Park Y, White E, et al. Overall and Central Obesity and Risk of Lung Cancer: A Pooled Analysis. J Natl Cancer Inst 2018;110:831–42. https://doi.org/10.1093/jnci/djx286. [34] Oxenkrug GF. Metabolic syndrome, age-associated neuroendocrine disorders, and dysregulation of tryptophan-kynurenine metabolism. Ann N Y Acad Sci 2010;1199:1–14. https://doi.org/10.1111/j.1749-6632.2009.05356.x. [35] Krause D, Suh H-S, Tarassishin L, Cui QL, Durafourt BA, Choi N, et al. The tryptophan metabolite 3-hydroxyanthranilic acid plays anti-inflammatory and neuroprotective roles during inflammation: role of hemeoxygenase-1. Am J Pathol 2011;179:1360–72. https://doi.org/10.1016/j.ajpath.2011.05.048. [36] Yu E, Ruiz-Canela M, Guasch-Ferré M, Zheng Y, Toledo E, Clish CB, et al. Increases in Plasma Tryptophan Are Inversely Associated with Incident Cardiovascular Disease in the Prevención con Dieta Mediterránea (PREDIMED) Study. J Nutr 2017;147:314–22. https://doi.org/10.3945/jn.116.241711. [37] Murr C, Grammer TB, Kleber ME, Meinitzer A, März W, Fuchs D. Low serum tryptophan predicts higher mortality in cardiovascular disease. Eur J Clin Invest 2015;45:247–54. https://doi.org/10.1111/eci.12402. [38] Sulo G, Vollset SE, Nygård O, Midttun Ø, Ueland PM, Eussen SJPM, et al. Neopterin and kynurenine-tryptophan ratio as predictors of coronary events in older adults, the Hordaland Health Study. Int J Cardiol 2013;168:1435–40. https://doi.org/10.1016/j.ijcard.2012.12.090. [39] Zuo H, Ueland PM, Ulvik A, Eussen SJPM, Vollset SE, Nygård O, et al. Plasma Biomarkers of Inflammation, the Kynurenine Pathway, and Risks of All-Cause, Cancer, and Cardiovascular Disease Mortality: The Hordaland Health Study. Am J Epidemiol 2016;183:249–58. https://doi.org/10.1093/aje/kwv242. [40] Fan Y, Li Y, Chen Y, Zhao Y-J, Liu L-W, Li J, et al. Comprehensive Metabolomic Characterization of Coronary Artery Diseases. J Am Coll Cardiol 2016;68:1281–93. https://doi.org/10.1016/j.jacc.2016.06.044. [41] Wang TJ, Larson MG, Vasan RS, Cheng S, Rhee EP, McCabe E, et al. Metabolite profiles and the risk of developing diabetes. Nat Med 2011;17:448–53. https://doi.org/10.1038/nm.2307. [42] Würtz P, Soininen P, Kangas AJ, Rönnemaa T, Lehtimäki T, Kähönen M, et al. Branchedchain and aromatic amino acids are predictors of insulin resistance in young adults. Diabetes Care 2013;36:648–55. https://doi.org/10.2337/dc12-0895.
15
[43] Ruiz-Canela M, Toledo E, Clish CB, Hruby A, Liang L, Salas-Salvadó J, et al. Plasma Branched-Chain Amino Acids and Incident Cardiovascular Disease in the PREDIMED Trial. Clin Chem 2016;62:582–92. https://doi.org/10.1373/clinchem.2015.251710. [44] Tobias DK, Mora S, Verma S, Lawler PR. Altered branched chain amino acid metabolism: toward a unifying cardiometabolic hypothesis. Curr Opin Cardiol 2018;33:558–64. https://doi.org/10.1097/HCO.0000000000000552. [45] Boulet MM, Chevrier G, Grenier-Larouche T, Pelletier M, Nadeau M, Scarpa J, et al. Alterations of plasma metabolite profiles related to adipose tissue distribution and cardiometabolic risk. Am J Physiol Endocrinol Metab 2015;309:E736-746. https://doi.org/10.1152/ajpendo.00231.2015. [46] Welihinda AA, Kaur M, Greene K, Zhai Y, Amento EP. The adenosine metabolite inosine is a functional agonist of the adenosine A2A receptor with a unique signaling bias. Cell Signal 2016;28:552–60. https://doi.org/10.1016/j.cellsig.2016.02.010.
16
Table 1. Baseline characteristics of the study participants
Age (Mean±SD) BMI (Mean±SD) WHR (Mean±SD) Ever smokers (N, %) a Pack-Years (Mean±SD) b Hypertension (N, %) No Yes Diabetes (N, %) No Yes Education c ≤ 12 years > 12 years Income (Yuan per month) < 500 500-3,000 ≥ 3,000 Alcohol consumption d None Moderate Heavy
Cases (N=127) 61.69 ± 8.62 24.77 ± 3.40 0.92 ± 0.06 85 (66.9) 32.30 ± 21.66
Men Controls (N=127) 61.79 ± 8.64 23.75 ± 3.34 0.90 ± 0.06 80 (63.0) 29.04 ± 18.95
Cases (N=148) 61.74 ± 7.29 25.74 ± 3.92 0.85 ± 0.06 16 (10.8) 18.90 ± 14.46
Women Controls (N=148) 61.54 ± 7.25 25.20 ± 3.50 0.83 ± 0.06 9 (6.1) 15.77 ± 12.72
50 (39.4) 77 (60.6)
83 (65.4) 44 (34.6)
< 0.01
73 (49.3) 75 (50.7)
106 (71.6) 42 (28.4)
< 0.01
111 (87.4) 16 (12.6)
111 (87.4) 16 (12.6)
-
124 (83.8) 24 (16.2)
136 (91.9) 12 (8.1)
0.03
93 (73.2) 34 (26.8)
95 (74.8) 32 (25.2)
0.78
134 (90.5) 14 (9.5)
135 (91.2) 13 (8.8)
0.84
9 (7.1) 105 (82.7) 13 (10.2)
17 (13.4) 105 (82.7) 5 (3.9)
0.05
35 (23.7) 106 (71.6) 7 (4.7)
36 (24.3) 106 (71.6) 6 (4.1)
0.96
88 (69.3) 23 (18.1) 16 (12.6)
89 (70.1) 18 (14.2) 20 (15.7)
0.59
144 (97.3) 3 (2.0) 1 (0.7)
145 (98.0) 3 (2.0) 0 (0.0)
0.61
a
p-value 0.39 0.01 < 0.01 0.51 < 0.01
Included were current and former smokers Estimated among ever smokers c ≤ 12 years including no formal education, elementary school, junior high school, high school and > 12 years including vocational/technical school, college or above d Moderate alcohol consumption defined >0 to 14 g/d for women and >0 to 28 g/d for men; heavy alcohol consumption defined >14 g/d for women and >28 g/d for men b
17
p-value 0.05 0.21 < 0.01 0.14 < 0.01
Table 2. Urinary metabolites a associated with risk of CHD in Chinese men and women Metabolites Tryptophan
Guanosine
Methionine
Valine
Hydroxyproline
Asparagine
Uracil
Inosine
Strata Total Men Women Total Men Women Total Men Women Total Men Women Total Men Women Total Men Women Total Men Women Total Men Women
Q1 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.)
Q2 1.23 (0.71-2.16) 1.84 (0.70-4.85) 1.07 (0.53-2.14) 0.74 (0.42-1.28) 0.50 (0.22-1.12) 1.12 (0.50-2.49) 0.85 (0.49-1.46) 0.63 (0.29-1.39) 1.19 (0.54-2.63) 0.96 (0.55-1.68) 0.82 (0.38-1.76) 1.16 (0.50-2.69) 0.76 (0.43-1.36) 1.51 (0.66-3.48) 0.39 (0.16-0.95) 1.05 (0.60-1.84) 1.23 (0.53-2.87) 0.99 (0.46-0.14) 1.53 (0.92-2.52) 1.39 (0.69-2.77) 1.62 (0.78-3.38) 0.96 (0.55-1.67) 0.85 (0.39-1.87) 0.97 (0.44-2.12)
Q3 1.17 (0.66-2.04) 2.55 (1.01-6.45) 0.70 (0.33-1.47) 1.00 (0.58-1.75) 0.60 (0.27-1.33) 1.74 (0.76-3.98) 0.71 (0.40-1.26) 0.32 (0.13-0.77) 1.54 (0.69-3.43) 1.25 (0.72-2.17) 0.73 (0.33-1.61) 2.16 (0.97-4.80) 1.12 (0.65-1.93) 1.60 (0.69-3.72) 0.89 (0.43-1.87) 1.02 (0.58-1.79) 1.69 (0.69-4.13) 0.73 (0.35-1.55) 0.78 (0.43-1.42) 0.75 (0.35-1.60) 0.92 (0.34-2.48) 1.08 (0.61-1.92) 0.61 (0.25-1.50) 1.56 (0.72-3.37)
a
Q4 2.02 (1.15-3.56) 3.86 (1.44-10.4) 1.51 (0.74-3.08) 1.54 (0.85-2.78) 0.72 (0.30-1.71) 3.13 (1.35-7.28) 1.26 (0.69-2.28) 0.58 (0.25-1.38) 2.77 (1.17-6.58) 1.51 (0.82-2.79) 0.83 (0.35-1.95) 2.88 (1.19-6.97) 1.33 (0.78-2.26) 2.50 (1.14-5.52) 0.76 (0.37-1.59) 1.50 (0.86-2.64) 2.47 (1.02-5.97) 1.12 (0.53-2.34) 0.77 (0.41-1.43) 0.39 (0.15-0.99) 1.40 (0.57-3.43) 0.81 (0.44-1.47) 0.29 (0.11-0.81) 1.45 (0.67-3.17)
p-trend b 0.02 < 0.01 0.52 0.10 0.39 < 0.01 0.52 0.10 < 0.01 0.11 0.55 < 0.01 0.16 0.03 0.63 0.16 0.03 0.98 0.26 0.01 0.46 0.60 0.01 0.28
p interaction 0.17
0.11
0.03
0.13
0.08
0.40
0.20
0.04
Based on the sex-specific quartiles among controls. Odds ratios (95% CIs) were adjusted for age, BMI, Waist-Hip Ratio, pack-years, fasting hour, and baseline disease of hypertension and diabetes. b Only metabolites with raw p-trend<0.05 in men or women are shown in the table. After FDR adjustment, all p-values >0.10.
18
Table 3. Urinary metabolites a associated with risk of CHD in Chinese men and women: stratified by CHD risk factors Stratification 2
BMI, kg/m
Metabolites Tryptophan Guanosine
Central obesity
Valine Methionine Tryptophan
Dyslipidemia
Tryptophan
Hypertension
Tryptophan Hypoxanthine
CRP level, mg/L
Hypoxanthine Tryptophan Guanosine Inosine
Strata ≤ 25 > 25 ≤ 25 > 25 No Yes No Yes No Yes No Yes No Yes No Yes <1 ≥1 <1 ≥1 <1 ≥1 <1 ≥1
Q1
Q2
Q3
Q4
p-trend b
p interaction
1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.)
0.82 (0.36-1.86) 1.73 (0.78-3.82) 0.59 (0.28-1.27) 0.92 (0.41-2.04) 0.90 (0.46-1.77) 0.96 (0.36-2.54) 0.86 (0.44-1.68) 0.81 (0.32-2.07) 1.46 (0.70-3.04) 0.92 (0.36-2.36) 0.37 (0.12-1.12) 2.26 (1.10-4.67) 1.29 (0.59-2.83) 1.12 (0.51-2.46) 2.67 (1.21-5.88) 0.91 (0.40-2.06) 1.14 (0.50-2.60) 2.13 (1.02-0.44) 0.69 (0.30-1.59) 2.00 (0.93-4.30) 0.79 (0.33-1.89) 0.73 (0.36-1.50) 0.86 (0.35-2.11) 0.93 (0.46-1.89)
0.77 (0.35-1.68) 1.62 (0.74-3.55) 0.69 (0.33-1.45) 1.63 (0.71-3.75) 1.17 (0.59-2.33) 1.65 (0.69-3.98) 0.57 (0.29-1.15) 1.17 (0.47-2.93) 1.12 (0.53-2.37) 1.31 (0.53-3.25) 0.18 (0.05-0.58) 2.57 (1.26-5.25) 0.81 (0.38-1.75) 1.75 (0.76-4.06) 1.59 (0.72-3.53) 1.33 (0.59-3.02) 0.70 (0.29-1.71) 2.00 (0.99-4.04) 0.70 (0.29-1.66) 1.71 (0.84-3.50) 0.57 (0.25-1.33) 1.56 (0.76-3.21) 1.13 (0.48-2.67) 1.06 (0.51-2.19)
1.32 (0.63-2.74) 3.29 (1.45-7.46) 0.83 (0.37-1.87) 2.69 (1.22-5.94) 1.13 (0.53-2.37) 2.82 (1.12-7.12) 0.80 (0.39-1.64) 2.80 (1.09-7.20) 2.09 (1.03-4.26) 1.88 (0.75-4.73) 0.74 (0.26-2.06) 3.90 (1.86-8.19) 1.43 (0.69-2.98) 3.27 (1.40-7.64) 2.45 (1.09-5.49) 1.04 (0.48-2.25) 0.75 (0.31-1.79) 2.43 (1.16-5.09) 1.44 (0.60-3.50) 2.39 (1.17-4.88) 1.07 (0.45-2.53) 2.21 (1.03-4.75) 0.32 (0.12-0.84) 1.42 (0.65-3.10)
0.38 < 0.01 0.54 < 0.01 0.25 < 0.01 0.61 < 0.01 0.87 0.74 0.47 < 0.01 0.92 < 0.01 0.39 0.33 0.43 0.02 0.61 0.97 0.46 0.01 0.41 0.23
0.34
a
0.15 0.34 0.07 0.78 < 0.01 0.20 0.20 0.20 0.24 0.16 0.05
Based on the sex-specific quartiles among controls. Odds ratios (95% CIs) were adjusted for age, BMI, Waist-Hip Ratio, pack-years, fasting hour, and baseline disease of hypertension and diabetes. b Only metabolites with raw p-trend<0.05 or p<0.01 for the highest quartile in any subgroups of subjects are shown in the table. After FDR adjustment, all p values >0.10.
19
Highlights
•
Prospective studies examining the associations between metabolites and risk of coronary heart disease among Asian populations remain limited.
•
Elevated urinary level of tryptophan was associated with risk of coronary heart disease among Chinese adults, especially for individuals with dyslipidemia.
•
This study also suggested potential associations of guanosine, methionine, valine, hydroxyproline, asparagine, uracil, and inosine with risk of coronary heart disease, which need to be further evaluated.
S0939-4753(19)30410-7
DOI:
https://doi.org/10.1016/j.numecd.2019.10.011
Reference:
NUMECD 2173
To appear in:
Nutrition, Metabolism and Cardiovascular Diseases
Received Date: 6 June 2019 Revised Date:
28 October 2019
Accepted Date: 29 October 2019
Please cite this article as: Yoon H-S, Yang JJ, Rivera ES, Shu X-O, Xiang Y-B, Calcutt MW, Cai Q, Zhang X, Li H, Gao Y-T, Zheng W, Yu D, Urinary Metabolites and Risk of Coronary Heart Disease: A prospective Investigation among Urban Chinese Adults, Nutrition, Metabolism and Cardiovascular Diseases, https://doi.org/10.1016/j.numecd.2019.10.011. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 The Italian Society of Diabetology, the Italian Society for the Study of Atherosclerosis, the Italian Society of Human Nutrition, and the Department of Clinical Medicine and Surgery, Federico II University. Published by Elsevier B.V. All rights reserved.
Urinary Metabolites and Risk of Coronary Heart Disease: A prospective Investigation among Urban Chinese Adults
Hyung-Suk Yoon1, Jae Jeong Yang1, Emilio S. Rivera2, Xiao-Ou Shu1, Yong-Bing Xiang3, Marion W. Calcutt2, Qiuyin Cai1, Xianglan Zhang4, Honglan Li3, Yu-Tang Gao3, Wei Zheng1, and Danxia Yu1
1
Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center,
Nashville, TN, USA 2
Department of Biochemistry and Mass Spectrometry Research Center, Vanderbilt University,
Nashville, TN, USA 3
State Key Laboratory of Oncogene and Related Genes & Department of Epidemiology,
Shanghai Cancer Institute, Renji Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China 4
Tennessee Department of Health, Nashville, TN, USA
Correspondence to: Danxia Yu, Vanderbilt University Medical Center, 2525 West End Avenue, Nashville, TN, USA 37203, Phone: (+1)615-936-7389, Fax: (+1)615-343-5938, Email: [email protected]
1
Abstract Background and Aims: Studies have linked several metabolites to the risk of coronary heart disease (CHD) among Western populations, but prospective studies among Asian populations on the metabolite-CHD association remain limited. Methods: We evaluated the association of urinary metabolites with CHD risk among Chinese adults in a nested case-control study of 275 incident cases and 275 matched controls (127 pairs of men and 148 pairs of women). Fifty metabolites were measured by a pre-defined metabolomics panel and adjusted using urinary creatinine. Conditional logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (95% CIs). Results: After adjusting for traditional CHD risk factors, urinary tryptophan showed a positive association with incident CHD: OR (95% CI) for the highest vs. lowest quartiles was 2.02 (1.153.56) among all study participants (p-trend=0.02). The tryptophan-CHD association was more evident among individuals with dyslipidemia than those without the condition (OR [95% CI] for the highest vs. lowest quartiles=3.90 [1.86-8.19] and 0.74 [0.26-2.06], respectively; pinteraction<0.01). Other metabolites did not show significant associations with CHD risk among all study participants. However, a positive association of methionine with CHD risk was observed only among women (OR [95% CI] for the highest vs. lowest quartiles=2.77 [1.17-6.58]; p-interaction=0.03), and an inverse association of inosine with CHD risk was observed only among men (OR [95% CI] for the highest vs. lowest quartiles=0.29 [0.11-0.81]; pinteraction=0.04). Conclusion: Elevated urinary tryptophan may be related to CHD risk among Chinese adults, especially for those with dyslipidemia. Keywords: Coronary heart disease; Chinese population; Metabolomics; Nested case-control study; Tryptophan metabolism 2
Introduction Coronary heart disease (CHD) is the leading cause of death worldwide [1,2]. Established risk factors for CHD include unhealthy lifestyle, poor diet, obesity, dyslipidemia, hypertension, diabetes, and metabolic syndrome [3], indicating that CHD is a disease of metabolic disturbance. Recently, metabolomics technology has been used in population-based studies, reporting some metabolites associated with risk of CHD or overall cardiovascular disease (CVD), including glycerophospholipids (e.g., lysophosphatidylcholines), acylcarnitines, unsaturated fatty acids, branched-chain amino acids (BCAAs, e.g., leucine, isoleucine, and valine), aromatic amino acids (AroAAs, e.g., phenylalanine and tyrosine), glutamate, and some gut microbiota-derived metabolites (e.g., trimethylamine N-oxide, TMAO) [4–14]. However, most previous metabolomics studies of CHD were conducted in Western populations. A few studies conducted in Asian populations used samples collected after CHD diagnosis [15,16], thus prone to reverse causation; others evaluated the associations of metabolites with CHD risk factors (e.g., obesity and diabetes) [17–24]. Furthermore, most previous studies measured blood metabolites, while the associations of urine metabolites with CHD risk remain largely unknown. Urine samples offer several advantages for metabolomics research and potential clinical translation, including a non-invasive sample collection and a simple sample processing before metabolomics profiling [25]. Urine contains various metabolic breakdown products of endogenous or exogenous compounds. Many urine metabolites have been found to be related to cardiometabolic disorders [12,15,26–28]. However, metabolites excreted in the urine might not be as biologically relevant to the cardiovascular system as the blood metabolites, and the associations for the same metabolite in blood and in urine may have different meanings [26,29].
3
In the current study, using a pre-defined metabolomics panel covering BCAAs, AroAAs, peptides, fatty acids, carboxylic acids, purines, and others, we examined the associations of urinary metabolites with incident CHD in a case-control study nested within two prospective cohorts of Chinese adults: the Shanghai Men's Health Study (SMHS) and the Shanghai Women's Health Study (SWHS).
Materials and Methods Study population The SMHS and SWHS are population-based, prospective cohort studies conducted in urban Shanghai, China. Detailed information on each cohort profile has been described [30,31]. Briefly, 74,941 women and 61,480 men, aged 40 to 74 years, were recruited between 1996 and 2000 for SWHS and between 2002 and 2006 for SMHS. After written informed consent was obtained, an in-person interview was conducted to collect information on sociodemographics, medical history, dietary habits, lifestyle factors, and anthropometrics. Biological samples were also donated from the study participants (75% for peripheral blood and 88% for spot urine). All samples were processed within six hours of collection and stored at −80 °C. Participants’ health status have been updated by follow-up surveys, every 2 to 4 years (follow-up rates >92%), and annual data linkages to the Shanghai Vital Statistic Registry (complete rates >99%). The study protocol for each cohort was approved by institutional review boards of the Shanghai Cancer Institute and Vanderbilt University. For the current nested case-control study, we selected incident CHD cases (nonfatal myocardial infarction or fatal CHD) and matched controls from CVD- and cancer-free cohort members who provided both blood and urine samples at baseline [12]. Fatal CHD cases were
4
screened based on death certificates (ICD-9 codes 410-414). Physicians’ medical record reviews confirmed the nonfatal myocardial infarction cases. Among a total of 377 incident CHD cases, we excluded 102 cases who reported a history of kidney disease (n=59) or showed a sign of kidney problems (n=31; i.e., having protein or blood in urine based on a dipstick test), or reported implausible calorie intake (n=12; <800 or >4200 kcal/day for men and <500 or >3500 kcal/day for women). Remaining 275 cases (127 from the SMHS and 148 from the SWHS) were matched to one randomly-selected control by sex, age (±2 years), date of sample collection (±30 days), time of sample collection (morning/afternoon), recent use of antibiotics (yes/no), and menopausal status (for women only).
Metabolites assays Along with a pre-defined metabolomics panel developed by the Vanderbilt University Mass Spectrometry Research Center, 50 metabolites were measured using ultra-performance liquid chromatography and mass spectrometry. The panel covers commonly measured metabolites including BCAAs, AroAAs, peptides, fatty acids, carboxy acids, purines, and others (Supplement Table S1). Details on metabolites assays have been described before [12]. Briefly, 20 µL of the urine sample was mixed with 80 µL acetonitrile. After centrifugation at 10,000g for 10 minutes, 90 µL of supernatant was loaded into the ultra performance liquid chromatography system with a Zic‐cHILIC column. Stable isotope-labelled internal standards (i.e., citrulline13
C1 2H4, arginine-15N4, tyrosine-phenol-d2, lactic acid-13C3, and glucose-6,6-d2) were used to
calculate the relative concentration of metabolites. Urinary creatinine was measured using a Roche-Cobas MiraPlus chemistry analyzer and used to adjust for urine dilution. To account for potential batch variation, samples of each case-control pair were allocated adjacently in the same
5
batch, and two quality control samples were analyzed along with the study samples in each batch. The laboratory person was blinded to the case-control status of samples. Statistical analysis Metabolites below the detection limit were assigned half of the minimum observed value. To normalize the distribution of metabolite data, before analyses, all metabolite concentrations were log-transformed and standardized by the mean and standard deviation. Baseline characteristics among CHD cases and matched controls were compared using the paired t-test for continuous variables and Cochran-Mantel-Haenszel test for categorical variables. Multivariable-adjusted conditional logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (95% CIs) for CHD risk associated with quartiles of each metabolite. Quartiles were determined by the sex-specific distribution among controls. Covariates were selected a priori based on literature and their associations with CHD risk in our study population, including enrollment age (continuous), body mass index (BMI, continuous), waist-hip ratio (WHR, continuous), smoking pack-years (continuous), history of hypertension and diabetes (no vs. yes), and time interval between the last meal and biospecimen collection (hours, continuous). Missing rates of covariate were all <2%, and missing data were imputed using the median (for continuous variables) or the mode (for categorical variables). P for trend was tested using a continuous variable with median values for each quartile of metabolites and corrected for multiple comparisons using the Benjamini‐Hochberg false discovery rate (FDR). To assess potential effect modification of the metabolite-CHD association by known CVD risk factors, we conducted stratified analyses by BMI (≤25 or >25 kg/m2), central obesity defined as WHR ≥0.95 in men and ≥0.85 in women according to WHO criteria for Asians [32,33], dyslipidemia defined as doctor diagnosis or lipid levels (total cholesterol >240 mg/dL, LDL
6
cholesterol >160 mg/dL, triglyceride >200 mg/dL, and HDL cholesterol <50 mg/dL among men and <40 mg/dL among women), hypertension, and high sensitivity C-reactive protein level (CRP, <1 or ≥1 mg/L, approximately the median value in our study). Stratified analyses were performed by adding an interaction term of metabolite levels and the stratified variable. The pvalue for interaction was obtained by the likelihood-ratio test; OR and 95% CI in each stratum were obtained by specifying the value of a stratified variable. All statistical analyses were conducted using the SAS Enterprise Guide 7.1 (SAS Institute, Cary, NC, USA).
Results In both men and women, CHD cases showed significantly higher WHR and higher prevalence of hypertension than controls. Male CHD cases also had higher BMI than controls, while female CHD cases had a higher prevalence of diabetes (Table 1, p<0.05). After controlling for potential confounders, urinary tryptophan showed a significant positive association with CHD risk (Table 2): OR [95% CI] in the highest vs. lowest quartiles of tryptophan was 2.02 [1.15-3.56] among total study participants, 3.86 [1.44-10.4] among men, and 1.51 [0.74-3.08] among women (p-interaction with sex=0.17). Guanosine, methionine, and valine were associated with increased CHD risk only among women (OR [95% CI] in the highest vs. lowest quartiles, p-interaction with sex: for guanosine: 3.13 [1.35-7.28], p-interaction=0.11; for methionine: 2.77 [1.17-6.58], p-interaction=0.03; for valine: 2.88 [1.19-6.97], pinteraction=0.13). Among men, hydroxyproline and asparagine were associated with increased CHD risk (OR [95% CI] in the highest vs. lowest quartiles, p-interaction with sex: for hydroxyproline: 2.50 [1.14-5.52], p-interaction=0.08; for asparagine: 2.47 [1.02-5.97], pinteraction=0.40), whereas uracil and inosine were associated with decreased risk (OR [95% CI]
7
in the highest vs. lowest quartiles, p-interaction with sex: for uracil: 0.39 [0.15-0.99], pinteraction=0.20; for inosine: 0.29 [0.11-0.81], p-interaction=0.04). The metabolite-CHD associations seemed more evident among participants with existing metabolic conditions (Table 3). The ORs [95% CIs] for tryptophan-CHD were 3.29 [1.45-7.46], 3.90 [1.86-8.19], 3.27 [1.40-7.64], and 2.39 [1.17-4.88] among participants with obesity, dyslipidemia, hypertension, and high CRP, respectively, although only the interaction between tryptophan level and dyslipidemia was statistically significant: p-interaction<0.01. The effect modification by CRP level was marginally significant for inosine: an inverse inosine-CHD association was found among individuals with CRP<1 mg/L (OR [95% CI] = 0.32 [0.12-0.84]; p-interaction=0.05). The associations between all metabolites and CHD risk are presented in the Supplement Tables S1-S8.
Discussion In this prospective investigation among Chinese men and women, we found that urinary levels of several metabolites were associated with CHD risk, even after adjusting for traditional CVD risk factors. Among those, tryptophan showed a positive association with CHD risk, especially among men, overweight/obese persons, and those with metabolic disorders, such as dyslipidemia, hypertension, and chronic inflammation. Guanosine, methionine, valine, hydroxyproline, and asparagine were also positively associated with CHD risk, but only among men or women or individuals with certain metabolic condition; while, uracil and inosine showed inverse associations among men or those with CRP level less than 1 mg/L. Previous metabolomics studies have suggested several circulating metabolites being associated with risk of CVD or CHD, including lysophosphatidylcholines, monoglycerides,
8
sphingomyelins, acylcarnitines, unsaturated fatty acids, BCAAs, AroAAs, glutamate, and some microbial metabolites such as TMAO and hippurate [4–14]. However, most previous studies were conducted among Western populations and used blood samples—little is known about the association of metabolites with CVD or CHD risk among non-white populations. In this study among Chinese adults, we observed a potential association of urinary tryptophan with CHD risk. Tryptophan, one of the essential amino acids, plays a crucial role in protein synthesis, and serves as a precursor of kynurenines, serotonin, and melatonin [34]. Tryptophan metabolism via the kynurenine pathway is closely related to inflammation [35]. Studies in Western populations have reported an inverse association of blood levels of tryptophan with CVD incidence or mortality [36,37]. Tryptophan catabolism via the kynurenine pathway (i.e., a low level of tryptophan and high levels of its metabolites) has been linked to adverse CVD events [38,39]. Seemingly contradictory to previous studies, we found a positive association of urinary tryptophan with incident CHD. The inconsistency may be attributed to the differences in the metabolome profile or dynamics between urine and blood samples. The potential racial/ethnic differences may also contribute to the controversial findings: a recent Chinese study has reported elevated circulating tryptophan for unstable angina [40], and other Chinese studies have reported positive associations of tryptophan levels with incident diabetes and metabolic disorders such as obesity, hypertension, and dyslipidemia [22–24]. To date, the role of tryptophan metabolism in CVD or other metabolic disease etiology remains poorly understood, especially for the racial/ethnicspecific or biospecimen-specific associations. Further studies are needed to elucidate the biological mechanism underlying the effect of tryptophan on the development of CVD among diverse populations.
9
BCAAs have attracted much attention due to their significant associations with cardiometabolic diseases [11,41–45]. High levels of blood and urinary BCAAs have been linked to increased risk of CVD and diabetes in both Western and Asian populations [11,44]. In the present study, we found a significant positive association of valine, one of BCAAs, with CHD risk among women, centrally obese individuals, or older participants, although not among total study participants. Thus, whether valine and other BCAAs are associated with CHD risk among Chinese needs to be evaluated in more studies. On the other hand, inosine, which is derived from plant foods, was associated with decreased risk of CHD, especially among men and individuals with low CRP level. This inverse association is in line with previous reports on the antiinflammatory and immunomodulatory effects of inosine [46]. Future investigations into the role of inosine and other plant food-derived metabolites in CVD etiology are warranted. The major strength of our study is its prospective design. All study participants were free from diseases such as CVD, cancer, and kidney problems at the time of sample collection. Our findings suggested the potential CHD-related metabolites for further investigations among Asian populations. Nevertheless, we acknowledge study limitations. First, we conducted a single measurement of metabolites using spot urine samples, which may not fully capture the usual level of metabolites, especially those with large within-person variations. Moreover, urinary metabolites may show different results from circulating metabolites—it should be very cautious when comparing our results with those from other studies that used blood samples. Second, we only measured a relatively small number of urinary metabolites, and thus, may overlook other important metabolites in the CHD etiology. Third, we consider the current study a pilot and thus included no replication. Larger prospective studies are needed to further examine metabolomic signatures of CHD in Asian populations.
10
Conclusions Our findings suggest that elevated urinary tryptophan may be associated with risk of CHD among Chinese adults, especially for individuals with dyslipidemia. Other amino acids and some plant food-derived metabolites may be also related to CHD risk. More prospective studies among Chinese populations are warranted. In addition, further functional studies will help understand the causal effect and/or biological relevance of those metabolites on CHD risk.
Conflicts of Interest The authors declare no conflict of interest. The sponsors had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. The contents of this article are solely the responsibility of the authors and do not represent the official views of the Tennessee Department of Health.
Supplementary Materials The following are available online, Supplement Table S1. Association between urinary metabolites and CHD risk; Supplement Table S2. Association between urinary metabolites and CHD risk by gender; Supplement Table S3. Association between urinary metabolites and CHD risk by age group; Supplement Table S4. Association between urinary metabolites and CHD risk by BMI; Supplement Table S5. Association between urinary metabolites and CHD risk by central obesity; Supplement Table S6. Association between urinary metabolites and CHD risk by dyslipidemia; Supplement Table S7. Association between urinary metabolites and CHD risk by 11
hypertension; Supplement Table S8. Association between urinary metabolites and CHD risk by CRP level
Author Contributions D.Y. conceived, designed, and supervised the study; H.S.Y. and J.J.Y. conducted statistical analysis; H.S.Y. and D.Y. drafted the manuscript; E.S.R., M.W.C., and Q.C. contributed to study design and metabolites data collection; X-O.S., Y.-B.X., Q.C., X.Z., H.L., Y.-T.G., and W.Z. contributed to study design and cohort data collection. All authors contributed to results interpretation, manuscript revision, and approved the final version of the manuscript.
Funding The parent cohort studies were supported by grants from the National Institutes of Health UM1 CA182910 to W.Z. and UM1 CA173640 to X-O.S. D.Y. was supported by Vanderbilt University Medical Center Faculty Research Scholars Program. The urine sample preparation was performed at the Survey and Biospecimen Shared Resource (Q.C.), which is supported in part by the Vanderbilt-Ingram Cancer Center (P30 CA068485). The bioanalytical methods development (E.S.R. and M.W.C.) was supported in part by a Vanderbilt University trans-Institutional Program Award.
References [1] World Health Organization. Global Health Estimates 2016: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2016. Geneva: 2018. [2] GBD 2016 Causes of Death Collaborators. Global, regional, and national age-sex specific mortality for 264 causes of death, 1980-2016: a systematic analysis for the Global Burden
12
[3]
[4]
[5]
[6]
[7]
[8]
[9]
[10]
[11]
[12]
[13]
[14]
of Disease Study 2016. Lancet Lond Engl 2017;390:1151–210. https://doi.org/10.1016/S0140-6736(17)32152-9. Benjamin EJ, Virani SS, Callaway CW, Chamberlain AM, Chang AR, Cheng S, et al. Heart Disease and Stroke Statistics-2018 Update: A Report From the American Heart Association. Circulation 2018;137:e67–492. https://doi.org/10.1161/CIR.0000000000000558. Ganna A, Salihovic S, Sundström J, Broeckling CD, Hedman AK, Magnusson PKE, et al. Large-scale metabolomic profiling identifies novel biomarkers for incident coronary heart disease. PLoS Genet 2014;10:e1004801. https://doi.org/10.1371/journal.pgen.1004801. Zheng Y, Yu B, Alexander D, Steffen LM, Nettleton JA, Boerwinkle E. Metabolomic patterns and alcohol consumption in African Americans in the Atherosclerosis Risk in Communities Study. Am J Clin Nutr 2014;99:1470–8. https://doi.org/10.3945/ajcn.113.074070. Vaarhorst AAM, Verhoeven A, Weller CM, Böhringer S, Göraler S, Meissner A, et al. A metabolomic profile is associated with the risk of incident coronary heart disease. Am Heart J 2014;168:45-52.e7. https://doi.org/10.1016/j.ahj.2014.01.019. Stegemann C, Pechlaner R, Willeit P, Langley SR, Mangino M, Mayr U, et al. Lipidomics profiling and risk of cardiovascular disease in the prospective population-based Bruneck study. Circulation 2014;129:1821–31. https://doi.org/10.1161/CIRCULATIONAHA.113.002500. Würtz P, Havulinna AS, Soininen P, Tynkkynen T, Prieto-Merino D, Tillin T, et al. Metabolite profiling and cardiovascular event risk: a prospective study of 3 populationbased cohorts. Circulation 2015;131:774–85. https://doi.org/10.1161/CIRCULATIONAHA.114.013116. Paynter NP, Balasubramanian R, Giulianini F, Wang DD, Tinker LF, Gopal S, et al. Metabolic Predictors of Incident Coronary Heart Disease in Women. Circulation 2018;137:841–53. https://doi.org/10.1161/CIRCULATIONAHA.117.029468. Kume S, Araki S, Ono N, Shinhara A, Muramatsu T, Araki H, et al. Predictive properties of plasma amino acid profile for cardiovascular disease in patients with type 2 diabetes. PloS One 2014;9:e101219. https://doi.org/10.1371/journal.pone.0101219. Tobias DK, Lawler PR, Harada PH, Demler OV, Ridker PM, Manson JE, et al. Circulating Branched-Chain Amino Acids and Incident Cardiovascular Disease in a Prospective Cohort of US Women. Circ Genomic Precis Med 2018;11:e002157. https://doi.org/10.1161/CIRCGEN.118.002157. Yu D, Shu X-O, Rivera ES, Zhang X, Cai Q, Calcutt MW, et al. Urinary Levels of Trimethylamine-N-Oxide and Incident Coronary Heart Disease: A Prospective Investigation Among Urban Chinese Adults. J Am Heart Assoc 2019;8:e010606. https://doi.org/10.1161/JAHA.118.010606. Guasch-Ferré M, Hu FB, Ruiz-Canela M, Bulló M, Toledo E, Wang DD, et al. Plasma Metabolites From Choline Pathway and Risk of Cardiovascular Disease in the PREDIMED (Prevention With Mediterranean Diet) Study. J Am Heart Assoc 2017;6. https://doi.org/10.1161/JAHA.117.006524. Wang Z, Klipfell E, Bennett BJ, Koeth R, Levison BS, Dugar B, et al. Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease. Nature 2011;472:57–63. https://doi.org/10.1038/nature09922.
13
[15] Feng Q, Liu Z, Zhong S, Li R, Xia H, Jie Z, et al. Integrated metabolomics and metagenomics analysis of plasma and urine identified microbial metabolites associated with coronary heart disease. Sci Rep 2016;6:22525. https://doi.org/10.1038/srep22525. [16] Li Y, Zhang D, He Y, Chen C, Song C, Zhao Y, et al. Investigation of novel metabolites potentially involved in the pathogenesis of coronary heart disease using a UHPLCQTOF/MS-based metabolomics approach. Sci Rep 2017;7:15357. https://doi.org/10.1038/s41598-017-15737-3. [17] Wang SM, Yang RY, Wang M, Ji FS, Li HX, Tang YM, et al. Identification of serum metabolites associated with obesity and traditional risk factors for metabolic disease in Chinese adults. Nutr Metab Cardiovasc Dis NMCD 2018;28:112–8. https://doi.org/10.1016/j.numecd.2017.09.009. [18] Yu D, Moore SC, Matthews CE, Xiang Y-B, Zhang X, Gao Y-T, et al. Plasma metabolomic profiles in association with type 2 diabetes risk and prevalence in Chinese adults. Metabolomics Off J Metabolomic Soc 2016;12. https://doi.org/10.1007/s11306-015-0890-8. [19] Tillin T, Hughes AD, Wang Q, Würtz P, Ala-Korpela M, Sattar N, et al. Diabetes risk and amino acid profiles: cross-sectional and prospective analyses of ethnicity, amino acids and diabetes in a South Asian and European cohort from the SABRE (Southall And Brent REvisited) Study. Diabetologia 2015;58:968–79. https://doi.org/10.1007/s00125-015-35178. [20] Tai ES, Tan MLS, Stevens RD, Low YL, Muehlbauer MJ, Goh DLM, et al. Insulin resistance is associated with a metabolic profile of altered protein metabolism in Chinese and Asian-Indian men. Diabetologia 2010;53:757–67. https://doi.org/10.1007/s00125-0091637-8. [21] Qiu G, Zheng Y, Wang H, Sun J, Ma H, Xiao Y, et al. Plasma metabolomics identified novel metabolites associated with risk of type 2 diabetes in two prospective cohorts of Chinese adults. Int J Epidemiol 2016;45:1507–16. https://doi.org/10.1093/ije/dyw221. [22] Lu Y, Wang Y, Liang X, Zou L, Ong CN, Yuan J-M, et al. Serum Amino Acids in Association with Prevalent and Incident Type 2 Diabetes in A Chinese Population. Metabolites 2019;9. https://doi.org/10.3390/metabo9010014. [23] Chen T, Zheng X, Ma X, Bao Y, Ni Y, Hu C, et al. Tryptophan Predicts the Risk for Future Type 2 Diabetes. PloS One 2016;11:e0162192. https://doi.org/10.1371/journal.pone.0162192. [24] Xie G, Ma X, Zhao A, Wang C, Zhang Y, Nieman D, et al. The metabolite profiles of the obese population are gender-dependent. J Proteome Res 2014;13:4062–73. https://doi.org/10.1021/pr500434s. [25] Bouatra S, Aziat F, Mandal R, Guo AC, Wilson MR, Knox C, et al. The human urine metabolome. PloS One 2013;8:e73076. https://doi.org/10.1371/journal.pone.0073076. [26] Elliott P, Posma JM, Chan Q, Garcia-Perez I, Wijeyesekera A, Bictash M, et al. Urinary metabolic signatures of human adiposity. Sci Transl Med 2015;7:285ra62. https://doi.org/10.1126/scitranslmed.aaa5680. [27] Holmes E, Loo RL, Stamler J, Bictash M, Yap IKS, Chan Q, et al. Human metabolic phenotype diversity and its association with diet and blood pressure. Nature 2008;453:396– 400. https://doi.org/10.1038/nature06882. [28] Zhang X, Gao Y-T, Yang G, Li H, Cai Q, Xiang Y-B, et al. Urinary isoflavonoids and risk of coronary heart disease. Int J Epidemiol 2012;41:1367–75. https://doi.org/10.1093/ije/dys130. 14
[29] Tynkkynen T, Wang Q, Ekholm J, Anufrieva O, Ohukainen P, Vepsäläinen J, et al. Proof of concept for quantitative urine NMR metabolomics pipeline for large-scale epidemiology and genetics. Int J Epidemiol 2019;48:978–93. https://doi.org/10.1093/ije/dyy287. [30] Shu X-O, Li H, Yang G, Gao J, Cai H, Takata Y, et al. Cohort Profile: The Shanghai Men’s Health Study. Int J Epidemiol 2015;44:810–8. https://doi.org/10.1093/ije/dyv013. [31] Zheng W, Chow W-H, Yang G, Jin F, Rothman N, Blair A, et al. The Shanghai Women’s Health Study: rationale, study design, and baseline characteristics. Am J Epidemiol 2005;162:1123–31. https://doi.org/10.1093/aje/kwi322. [32] World Health Organization. Waist circumference and waist-hip ratio: report of a WHO expert consultation, Geneva, 8-11 December 2008. Geneva: World Health Organization; 2011. [33] Yu D, Zheng W, Johansson M, Lan Q, Park Y, White E, et al. Overall and Central Obesity and Risk of Lung Cancer: A Pooled Analysis. J Natl Cancer Inst 2018;110:831–42. https://doi.org/10.1093/jnci/djx286. [34] Oxenkrug GF. Metabolic syndrome, age-associated neuroendocrine disorders, and dysregulation of tryptophan-kynurenine metabolism. Ann N Y Acad Sci 2010;1199:1–14. https://doi.org/10.1111/j.1749-6632.2009.05356.x. [35] Krause D, Suh H-S, Tarassishin L, Cui QL, Durafourt BA, Choi N, et al. The tryptophan metabolite 3-hydroxyanthranilic acid plays anti-inflammatory and neuroprotective roles during inflammation: role of hemeoxygenase-1. Am J Pathol 2011;179:1360–72. https://doi.org/10.1016/j.ajpath.2011.05.048. [36] Yu E, Ruiz-Canela M, Guasch-Ferré M, Zheng Y, Toledo E, Clish CB, et al. Increases in Plasma Tryptophan Are Inversely Associated with Incident Cardiovascular Disease in the Prevención con Dieta Mediterránea (PREDIMED) Study. J Nutr 2017;147:314–22. https://doi.org/10.3945/jn.116.241711. [37] Murr C, Grammer TB, Kleber ME, Meinitzer A, März W, Fuchs D. Low serum tryptophan predicts higher mortality in cardiovascular disease. Eur J Clin Invest 2015;45:247–54. https://doi.org/10.1111/eci.12402. [38] Sulo G, Vollset SE, Nygård O, Midttun Ø, Ueland PM, Eussen SJPM, et al. Neopterin and kynurenine-tryptophan ratio as predictors of coronary events in older adults, the Hordaland Health Study. Int J Cardiol 2013;168:1435–40. https://doi.org/10.1016/j.ijcard.2012.12.090. [39] Zuo H, Ueland PM, Ulvik A, Eussen SJPM, Vollset SE, Nygård O, et al. Plasma Biomarkers of Inflammation, the Kynurenine Pathway, and Risks of All-Cause, Cancer, and Cardiovascular Disease Mortality: The Hordaland Health Study. Am J Epidemiol 2016;183:249–58. https://doi.org/10.1093/aje/kwv242. [40] Fan Y, Li Y, Chen Y, Zhao Y-J, Liu L-W, Li J, et al. Comprehensive Metabolomic Characterization of Coronary Artery Diseases. J Am Coll Cardiol 2016;68:1281–93. https://doi.org/10.1016/j.jacc.2016.06.044. [41] Wang TJ, Larson MG, Vasan RS, Cheng S, Rhee EP, McCabe E, et al. Metabolite profiles and the risk of developing diabetes. Nat Med 2011;17:448–53. https://doi.org/10.1038/nm.2307. [42] Würtz P, Soininen P, Kangas AJ, Rönnemaa T, Lehtimäki T, Kähönen M, et al. Branchedchain and aromatic amino acids are predictors of insulin resistance in young adults. Diabetes Care 2013;36:648–55. https://doi.org/10.2337/dc12-0895.
15
[43] Ruiz-Canela M, Toledo E, Clish CB, Hruby A, Liang L, Salas-Salvadó J, et al. Plasma Branched-Chain Amino Acids and Incident Cardiovascular Disease in the PREDIMED Trial. Clin Chem 2016;62:582–92. https://doi.org/10.1373/clinchem.2015.251710. [44] Tobias DK, Mora S, Verma S, Lawler PR. Altered branched chain amino acid metabolism: toward a unifying cardiometabolic hypothesis. Curr Opin Cardiol 2018;33:558–64. https://doi.org/10.1097/HCO.0000000000000552. [45] Boulet MM, Chevrier G, Grenier-Larouche T, Pelletier M, Nadeau M, Scarpa J, et al. Alterations of plasma metabolite profiles related to adipose tissue distribution and cardiometabolic risk. Am J Physiol Endocrinol Metab 2015;309:E736-746. https://doi.org/10.1152/ajpendo.00231.2015. [46] Welihinda AA, Kaur M, Greene K, Zhai Y, Amento EP. The adenosine metabolite inosine is a functional agonist of the adenosine A2A receptor with a unique signaling bias. Cell Signal 2016;28:552–60. https://doi.org/10.1016/j.cellsig.2016.02.010.
16
Table 1. Baseline characteristics of the study participants
Age (Mean±SD) BMI (Mean±SD) WHR (Mean±SD) Ever smokers (N, %) a Pack-Years (Mean±SD) b Hypertension (N, %) No Yes Diabetes (N, %) No Yes Education c ≤ 12 years > 12 years Income (Yuan per month) < 500 500-3,000 ≥ 3,000 Alcohol consumption d None Moderate Heavy
Cases (N=127) 61.69 ± 8.62 24.77 ± 3.40 0.92 ± 0.06 85 (66.9) 32.30 ± 21.66
Men Controls (N=127) 61.79 ± 8.64 23.75 ± 3.34 0.90 ± 0.06 80 (63.0) 29.04 ± 18.95
Cases (N=148) 61.74 ± 7.29 25.74 ± 3.92 0.85 ± 0.06 16 (10.8) 18.90 ± 14.46
Women Controls (N=148) 61.54 ± 7.25 25.20 ± 3.50 0.83 ± 0.06 9 (6.1) 15.77 ± 12.72
50 (39.4) 77 (60.6)
83 (65.4) 44 (34.6)
< 0.01
73 (49.3) 75 (50.7)
106 (71.6) 42 (28.4)
< 0.01
111 (87.4) 16 (12.6)
111 (87.4) 16 (12.6)
-
124 (83.8) 24 (16.2)
136 (91.9) 12 (8.1)
0.03
93 (73.2) 34 (26.8)
95 (74.8) 32 (25.2)
0.78
134 (90.5) 14 (9.5)
135 (91.2) 13 (8.8)
0.84
9 (7.1) 105 (82.7) 13 (10.2)
17 (13.4) 105 (82.7) 5 (3.9)
0.05
35 (23.7) 106 (71.6) 7 (4.7)
36 (24.3) 106 (71.6) 6 (4.1)
0.96
88 (69.3) 23 (18.1) 16 (12.6)
89 (70.1) 18 (14.2) 20 (15.7)
0.59
144 (97.3) 3 (2.0) 1 (0.7)
145 (98.0) 3 (2.0) 0 (0.0)
0.61
a
p-value 0.39 0.01 < 0.01 0.51 < 0.01
Included were current and former smokers Estimated among ever smokers c ≤ 12 years including no formal education, elementary school, junior high school, high school and > 12 years including vocational/technical school, college or above d Moderate alcohol consumption defined >0 to 14 g/d for women and >0 to 28 g/d for men; heavy alcohol consumption defined >14 g/d for women and >28 g/d for men b
17
p-value 0.05 0.21 < 0.01 0.14 < 0.01
Table 2. Urinary metabolites a associated with risk of CHD in Chinese men and women Metabolites Tryptophan
Guanosine
Methionine
Valine
Hydroxyproline
Asparagine
Uracil
Inosine
Strata Total Men Women Total Men Women Total Men Women Total Men Women Total Men Women Total Men Women Total Men Women Total Men Women
Q1 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.)
Q2 1.23 (0.71-2.16) 1.84 (0.70-4.85) 1.07 (0.53-2.14) 0.74 (0.42-1.28) 0.50 (0.22-1.12) 1.12 (0.50-2.49) 0.85 (0.49-1.46) 0.63 (0.29-1.39) 1.19 (0.54-2.63) 0.96 (0.55-1.68) 0.82 (0.38-1.76) 1.16 (0.50-2.69) 0.76 (0.43-1.36) 1.51 (0.66-3.48) 0.39 (0.16-0.95) 1.05 (0.60-1.84) 1.23 (0.53-2.87) 0.99 (0.46-0.14) 1.53 (0.92-2.52) 1.39 (0.69-2.77) 1.62 (0.78-3.38) 0.96 (0.55-1.67) 0.85 (0.39-1.87) 0.97 (0.44-2.12)
Q3 1.17 (0.66-2.04) 2.55 (1.01-6.45) 0.70 (0.33-1.47) 1.00 (0.58-1.75) 0.60 (0.27-1.33) 1.74 (0.76-3.98) 0.71 (0.40-1.26) 0.32 (0.13-0.77) 1.54 (0.69-3.43) 1.25 (0.72-2.17) 0.73 (0.33-1.61) 2.16 (0.97-4.80) 1.12 (0.65-1.93) 1.60 (0.69-3.72) 0.89 (0.43-1.87) 1.02 (0.58-1.79) 1.69 (0.69-4.13) 0.73 (0.35-1.55) 0.78 (0.43-1.42) 0.75 (0.35-1.60) 0.92 (0.34-2.48) 1.08 (0.61-1.92) 0.61 (0.25-1.50) 1.56 (0.72-3.37)
a
Q4 2.02 (1.15-3.56) 3.86 (1.44-10.4) 1.51 (0.74-3.08) 1.54 (0.85-2.78) 0.72 (0.30-1.71) 3.13 (1.35-7.28) 1.26 (0.69-2.28) 0.58 (0.25-1.38) 2.77 (1.17-6.58) 1.51 (0.82-2.79) 0.83 (0.35-1.95) 2.88 (1.19-6.97) 1.33 (0.78-2.26) 2.50 (1.14-5.52) 0.76 (0.37-1.59) 1.50 (0.86-2.64) 2.47 (1.02-5.97) 1.12 (0.53-2.34) 0.77 (0.41-1.43) 0.39 (0.15-0.99) 1.40 (0.57-3.43) 0.81 (0.44-1.47) 0.29 (0.11-0.81) 1.45 (0.67-3.17)
p-trend b 0.02 < 0.01 0.52 0.10 0.39 < 0.01 0.52 0.10 < 0.01 0.11 0.55 < 0.01 0.16 0.03 0.63 0.16 0.03 0.98 0.26 0.01 0.46 0.60 0.01 0.28
p interaction 0.17
0.11
0.03
0.13
0.08
0.40
0.20
0.04
Based on the sex-specific quartiles among controls. Odds ratios (95% CIs) were adjusted for age, BMI, Waist-Hip Ratio, pack-years, fasting hour, and baseline disease of hypertension and diabetes. b Only metabolites with raw p-trend<0.05 in men or women are shown in the table. After FDR adjustment, all p-values >0.10.
18
Table 3. Urinary metabolites a associated with risk of CHD in Chinese men and women: stratified by CHD risk factors Stratification 2
BMI, kg/m
Metabolites Tryptophan Guanosine
Central obesity
Valine Methionine Tryptophan
Dyslipidemia
Tryptophan
Hypertension
Tryptophan Hypoxanthine
CRP level, mg/L
Hypoxanthine Tryptophan Guanosine Inosine
Strata ≤ 25 > 25 ≤ 25 > 25 No Yes No Yes No Yes No Yes No Yes No Yes <1 ≥1 <1 ≥1 <1 ≥1 <1 ≥1
Q1
Q2
Q3
Q4
p-trend b
p interaction
1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.) 1.00 (Ref.)
0.82 (0.36-1.86) 1.73 (0.78-3.82) 0.59 (0.28-1.27) 0.92 (0.41-2.04) 0.90 (0.46-1.77) 0.96 (0.36-2.54) 0.86 (0.44-1.68) 0.81 (0.32-2.07) 1.46 (0.70-3.04) 0.92 (0.36-2.36) 0.37 (0.12-1.12) 2.26 (1.10-4.67) 1.29 (0.59-2.83) 1.12 (0.51-2.46) 2.67 (1.21-5.88) 0.91 (0.40-2.06) 1.14 (0.50-2.60) 2.13 (1.02-0.44) 0.69 (0.30-1.59) 2.00 (0.93-4.30) 0.79 (0.33-1.89) 0.73 (0.36-1.50) 0.86 (0.35-2.11) 0.93 (0.46-1.89)
0.77 (0.35-1.68) 1.62 (0.74-3.55) 0.69 (0.33-1.45) 1.63 (0.71-3.75) 1.17 (0.59-2.33) 1.65 (0.69-3.98) 0.57 (0.29-1.15) 1.17 (0.47-2.93) 1.12 (0.53-2.37) 1.31 (0.53-3.25) 0.18 (0.05-0.58) 2.57 (1.26-5.25) 0.81 (0.38-1.75) 1.75 (0.76-4.06) 1.59 (0.72-3.53) 1.33 (0.59-3.02) 0.70 (0.29-1.71) 2.00 (0.99-4.04) 0.70 (0.29-1.66) 1.71 (0.84-3.50) 0.57 (0.25-1.33) 1.56 (0.76-3.21) 1.13 (0.48-2.67) 1.06 (0.51-2.19)
1.32 (0.63-2.74) 3.29 (1.45-7.46) 0.83 (0.37-1.87) 2.69 (1.22-5.94) 1.13 (0.53-2.37) 2.82 (1.12-7.12) 0.80 (0.39-1.64) 2.80 (1.09-7.20) 2.09 (1.03-4.26) 1.88 (0.75-4.73) 0.74 (0.26-2.06) 3.90 (1.86-8.19) 1.43 (0.69-2.98) 3.27 (1.40-7.64) 2.45 (1.09-5.49) 1.04 (0.48-2.25) 0.75 (0.31-1.79) 2.43 (1.16-5.09) 1.44 (0.60-3.50) 2.39 (1.17-4.88) 1.07 (0.45-2.53) 2.21 (1.03-4.75) 0.32 (0.12-0.84) 1.42 (0.65-3.10)
0.38 < 0.01 0.54 < 0.01 0.25 < 0.01 0.61 < 0.01 0.87 0.74 0.47 < 0.01 0.92 < 0.01 0.39 0.33 0.43 0.02 0.61 0.97 0.46 0.01 0.41 0.23
0.34
a
0.15 0.34 0.07 0.78 < 0.01 0.20 0.20 0.20 0.24 0.16 0.05
Based on the sex-specific quartiles among controls. Odds ratios (95% CIs) were adjusted for age, BMI, Waist-Hip Ratio, pack-years, fasting hour, and baseline disease of hypertension and diabetes. b Only metabolites with raw p-trend<0.05 or p<0.01 for the highest quartile in any subgroups of subjects are shown in the table. After FDR adjustment, all p values >0.10.
19
Highlights
•
Prospective studies examining the associations between metabolites and risk of coronary heart disease among Asian populations remain limited.
•
Elevated urinary level of tryptophan was associated with risk of coronary heart disease among Chinese adults, especially for individuals with dyslipidemia.
•
This study also suggested potential associations of guanosine, methionine, valine, hydroxyproline, asparagine, uracil, and inosine with risk of coronary heart disease, which need to be further evaluated.