Serum metabolomic profiling and its association with 25-hydroxyvitamin D

Clinical Nutrition xxx (xxxx) xxx

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Original article

Serum metabolomic profiling and its association with 25-hydroxyvitamin D Raymond Y.H. Leung a, b, Gloria H.Y. Li a, Bernard M.Y. Cheung b, Kathryn C.B. Tan b, Annie W.C. Kung b, Ching-Lung Cheung a, c, * a b c

Department of Pharmacology and Pharmacy, The University of Hong Kong, Pokfulam, Hong Kong Department of Medicine, The University of Hong Kong, Pokfulam, Hong Kong State Key Laboratory, The University of Hong Kong, Pokfulam, Hong Kong

a r t i c l e i n f o

s u m m a r y

Article history: Received 12 September 2018 Accepted 27 April 2019

Introduction: The beneficial effect of vitamin D on the risk of non-musculoskeletal diseases has been investigated in observational studies and randomized clinical trials, but the findings were inconsistent. Identification of the metabolomic profile associated with vitamin D helps to identify novel biomarkers and increase the understanding of the biochemical and physiological role of vitamin D in different health conditions. Method: Serum metabolomic profiling was performed using liquid chromatography/tandem mass spectrometry [LC/MS] and their association with serum 25(OH)D was evaluated using multivariable linear regression in the baseline cohort of 316 participants (aged 20 or above; 92 men, 224 women; mean age±SD: 48.1 ± 15.8 years) and in the follow-up cohort of 275 participants (aged 20 or above; 12 men, 263 women; mean age: 56.2 ± 9.6) of the Hong Kong Osteoporosis Study. We discovered and validated potential metabolites; and by meta-analysis of these associations in two cohorts, we identified metabolites that were significantly associated with serum 25(OH)D levels. Results: Among 835 known metabolites, 102 metabolites showed significant correlation with 25(OH)D levels at baseline visit. Of these metabolites, 27 were validated in the follow-up visit. In meta-analysis of data from these two visits, 13 metabolites were highly correlated with 25(OH)D. The majority of metabolites identified were lipid in nature. Docosahexaenoylcarnitine and eicosapentaenoylcholine had the highest correlations, with effect estimates 0.2554 (p ¼ 9.60  109) and 0.1682 (p ¼ 4.94  107) respectively. Conclusion: In Hong Kong Chinese at least, serum vitamin D level is closely related to lipid metabolism. Our finding highlights an important new direction in the study of vitamin D in different health conditions. © 2019 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.

Keywords: Vitamin D 25-Hydroxyvitamin D Serum metabolites Metabolomics Epidemiological studies

1. Introduction Vitamin D, the sunshine vitamin, is a human steroid synthesized in our skin by UVB and is essential for human health [1]. It is transformed to the major circulating form of vitamin D, 25hydroxyvitamin D [25(OH)D] in the liver and then to the bioactive form 1,25-dihydroxyvitamin D [1,25(OH)D] in the kidney. 1,25(OH)D is a transcriptional factor and binds to VDR receptors to

* Corresponding author. Department of Pharmacology and Pharmacy, The University of Hong Kong, 21 Sassoon Road, Pokfulam, Hong Kong. Fax:þ852 2816 2095. E-mail address: [email protected] (C.-L. Cheung).

exert its biologic functions by regulating gene expressions. In in vitro cell studies, 1,25(OH)D has been shown to be associated with the expression of over 1000 genes [2]. Serum 25(OH)D level has been used as a clinical biomarker to define vitamin D status [3,4]. In epidemiological studies, circulating 25(OH)D has been found to be an important biomarker of bone and mineral metabolism, and other non-musculoskeletal disorders, including metabolic syndrome, diabetes, and cardiovascular diseases [5e7]. However, the findings are inconsistent [8e11]. On the other hand, there is still no conclusive evidence from randomized clinical trials showing the beneficial effect of vitamin D intervention in nonmusculoskeletal diseases. Therefore, there is a need to understand the role of vitamin D in human health.

https://doi.org/10.1016/j.clnu.2019.04.035 0261-5614/© 2019 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.

Please cite this article as: Leung RYH et al., Serum metabolomic profiling and its association with 25-hydroxyvitamin D, Clinical Nutrition, https://doi.org/10.1016/j.clnu.2019.04.035

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Metabolomics is the study of the metabolome, which is defined as the profile of the low molecular mass metabolites found in a biological system. It is a powerful technique to uncover the relationship between cell metabolism and health status, such as cancer, hypertension, cardiovascular diseases, and diabetes [12e15]. Since 25(OH)D affects cell metabolism, studying the metabolomics of serum 25(OH)D may provide new insights into the relationship between 25(OH)D, metabolism, and different health outcomes [16e18]. In this study, we utilized an untargeted metabolomics approach to identify metabolites associated with serum 25(OH)D levels in Hong Kong Chinese, and their relationship with biomarkers of diseases. 2. Methodology 2.1. Study design Study participants were from the Hong Kong Osteoporosis Study (HKOS) as previously described [19e21]. Briefly, HKOS is a prospective study on the relationship of bone-related traits with environmental and genetic factors in Southern Chinese living in Hong Kong. Baseline examination was conducted between 1995 and 2010, and a total of 9202 participants, aged over 20, were recruited from the community through road shows, health talks and exhibition. The baseline examination included anthropometric measurements, blood sampling, bone density measurement, dietary and lifestyle questionnaires. Lifestyle information including smoking and drinking status, and physical activities were recorded. Season of the visit was defined by the time of the blood sampling, as summer/autumn (May to October) and as winter/spring (November to April) [22]. Physical activities were defined as any regular exercise more than one hour per week. Education was defined as uneducated, primary, secondary, and tertiary or higher. Their electronic medical records in the public healthcare system, including demographics, admission, diagnosis, drug prescription records, were available in the Clinical Data Analysis and Reporting System (CDARS). The follow-up study started in 2015. The metabolomics sub-study was originally designed to investigate the relationship between metabolomics and bone mineral density (BMD) [23]. Untargeted metabolomic measurement was conducted in the serum samples from 340 (cohort 1: extreme BMD cohort; data collected from baseline examination) and 287 (cohort 2: random replication cohort; data collected from follow-up examination) independent participants. The participants in cohorts 1 and 2 did not overlap. Their diagnosis and drug prescription records were extracted from CDARS. They were defined to have a major chronic disease (such as coronary artery disease or hypertension, dyslipidemia, diabetes and thyroid disorders) if they were either diagnosed with the disease or prescribed drugs for treatment. The 340 participants in the extreme BMD cohort comprised 170 randomly-selected “high BMD” participants having BMD z-score þ1 at either spine or hip, and 170 “low BMD” participants having BMD z-score  -1.28 at either spine or hip. Among these 340 participants, 316 of them with serum 25(OH)D levels were included in the analysis. The serum samples from the baseline study have been stored at 80  C for more than 10 years (12.4 ± 2.3 years). Although this is an appropriate storage method, the effect of storage time on metabolite levels is unknown [24]. As it is possible that some metabolites might be degraded after prolonged storage even in 80  C [25,26], we included a “random replication cohort” using 287 serum samples collected during the follow up study and stored for less than 1 year at 80  C. Successful replication would indicate robustness of the findings [27]. Among these 287 participants, 275 of them with 25(OH)D levels were included. In total, 591 samples with 25(OH)D measurements were finally included in the whole

study. Ethical approval was obtained from Institutional Review Board, HKU/HA HKW, HKSAR, China. 2.2. Metabolomic analysis To avoid batch effect, all serum samples (extreme BMD cohort and random replication cohort) were sent to Metabolon Inc. (Durham, NC, USA) at the same time for metabolomic profiling using non-targeted ultrahigh performance liquid chromatography/ tandem mass spectrometry and gas chromatography/mass spectrometry. The details of this platform and procedures have been described previously [28]. In total, there were 1194 metabolites analyzed, with 359 unnamed compounds. All measurements were relatively quantified, expressed as a standardized unit, with mean of 0 and standard deviation of 1. After excluding the unnamed molecules, 835 named metabolites were included in our analysis. 2.3. Measurement on serum 25(OH)D and other covariates Serum 25(OH)D level was determined by enzymatic immunoassay EIA (IDS Ltd, UK) at baseline [29]. Quality controls were included in each batch of assay and the coefficient of variation for the assay was less than 10%. The minimum detectable level of 25(OH)D was 4.5 ng/ml. Serum calcium, phosphate and albumin were measured with a Hitachi 747 random access analyzer (Roche Molecular Biochemicals, Mannheim, Germany) in cohort 1 [30], and Ortho-Vitros Fusion 5.1 (Johnson & Johnson) in cohort 2. 2.4. Statistical analysis and pathway mapping Association between metabolites and serum 25(OH)D was analyzed using multivariable linear regression. The model was adjusted for sex, age, BMI, serum calcium, phosphate, albumin, season, and lifestyle factors including education, physical activities, smoking, and drinking habit. Physical activity was suggested as a confounding factor for serum 25(OH)D level as it relates to the exposure of UVB during outdoor activities. As we previously demonstrated that physical activities were significantly associated with the quantiles of the serum level of 25(OH)D in Hong Kong Chinese [31], we adjusted for it in the multivariable regression. Nominal significant association was defined as p < 0.05. Metabolites showing nominal significant association with serum 25(OH)D levels in cohort 1 were studied in the independent cohort 2 for replication. Inverse variance meta-analysis with fixed effect was conducted to combine the results from cohort 1 and 2. The p-value indicating significance after Bonferroni correction for multiple testing was 5.98  105 (0.05/835). Metabolites showing the same direction of association in cohorts 1 and 2, and with a p-value <5.98  105 were considered significant after correction for multiple testing. To gain more information from the key metabolites identified, metabolite set enrichment analysis and mapping were performed by Metabolite Set Enrichment Analysis (MSEA) software using Human Metabolome Database in MetaboAnalyst 4.0. The statistical program used was R (version 3.4.2). 3. Results 3.1. Subject characteristics The characteristics of HKOS cohorts 1 and 2 are shown in Table 1. The majority of the participants were female. Approximately half of the serum samples were collected during summer and autumn time and over 30% of the participants had an educational background of tertiary level or above. Mean serum 25(OH)D levels were similar in both cohorts (mean ± SD: 53.2 nmol/L ± 15.4 for cohort 1

Please cite this article as: Leung RYH et al., Serum metabolomic profiling and its association with 25-hydroxyvitamin D, Clinical Nutrition, https://doi.org/10.1016/j.clnu.2019.04.035

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and 52.9 nmol/L ± 16.1 for cohort 2). Among the participants, 22.5% had regular physical activities each week. 3.2. Metabolomics profiles and serum 25(OH)D The study design and flow of the current study is shown in Fig. 1. In the discovery stage, out of 835 metabolites analyzed in cohort 1, 102 metabolites showed nominal significant associations with 25(OH)D (Pnominal <0.05) (Supplementary Table 1) in multivariable linear regression, and the majority were lipids. Among these metabolites, the associations of cinnamoylglycine, 7-hydroxyindole sulfate, and ethyl glucuronide were significant after adjustment for multiple testing (p-value <5.98  105). In the replication stage, 27 of the 102 metabolites successfully replicated the association with serum 25(OH)D (p nominal <0.05) in cohort 2 (Supplementary Table 1 & Fig. 2). Among these 27 associations, 3 of them related to amino acid metabolism, 17 of them related to lipid and lipid metabolisms, and others related to xenobiotics and cofactors. All phospholipid metabolites showed inverse associations with 25(OH)D, while other lipid metabolites were positively associated with 25(OH)D (Fig. 3). Three drug xenobiotics showed positive association with 25(OH)D, while four other xenobiotics was inversely correlated with 25(OH)D (Fig. 2). To summarize the findings from cohorts 1 and 2, a fixed-effect inverse variance meta-analysis was conducted. 13 of the 27 replicated metabolites remained significantly associated with 25(OH), and 12 of them were lipids (Table 2 and Fig. 3), with docosahexaenoylcarnitine (C22:6) being the metabolite with the strongest association (standardized beta: 0.2554, P ¼ 9.86  109). Overall, nine metabolites showed positive association with 25(OH)D, with the beta-estimates ranging from 0.1053 (heneicosapentaenoate [21:5n3]) to 0.298 (isobutyrylcarnitine [C4]) SD increase in 25(OH) D per 1 SD increase in metabolite. On the other hand, four metabolites showed inverse association with 25(OH)D, with beta-

Table 1 Characteristics of the subjects at baseline (cohort 1) and follow-up (cohort 2) of Hong Kong Osteoporosis study (HKOS).

Age, years Female, N (%) Height, m Weight, kg Serum calcium, mmol/L Serum phosphate, mmol/L Serum albumin, g/L BMI, kg/m2 BMD (femoral neck), g/cm2 Current smoker, N (%) Current drinker, N (%) Education, N (%) uneducated primary secondary tertiary or above Physical activities Season, N (%) NovembereApril MayeOctober Serum 25(OH)D, nmol/L Coronary artery disease or hypertension, N (%) Dyslipidemia, N (%) Diabetes, N (%) Thyroid disorders, N (%) a b

Baseline (N ¼ 316)

Follow-up (N ¼ 275)

Cohort 1

Cohort 2

48.1 ± 15.8 224 (70.9%) 1.59 ± 0.08 57.67 ± 12.64 2.403 ± 0.086 1.13 ± 0.14 44.41 ± 2.68 22.78 ± 4.15 0.73 ± 0.194 22 (7.0%) 31 (9.8%)

56.29.6 263 (95.6%) 1.65 ± 0.06 56.82 ± 9.56 2.346 ± 0.104 1.31 ± 0.163 44.19 ± 2.84 23 ± 3.78 0.7 ± 0.127 6 (2.1%) 49 (17.8%)

17 (5.4%) 61 (19.3%) 137 (43.4%) 101 (32.0%) 71 (22.5%)a

5 (1.8%) 50 (18.2%) 158 (57.5%) 62 (22.5%) 8323.2 ± 6285.2b

39 (44.0%) 177 (56.0%) 53.28 ± 15.42 12 (3.8%)

109 (39.6%) 166 (60.4%) 52.94 ± 16.1 49 (17.8%)

2 (0.6%) 1 (0.3%) 0 (0.0%)

17 (6.2%) 3 (1.1%) 2 (0.7%)

Physical activities more than 1hr per week, N (%). kcal/week ± SD.

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estimates ranging from 0.2414 (1-stearoyl-2-linoleoyl-GPE [18:0/18:2]) to 0.3213 (1-linoleoyl-GPE (18:2]) SD decrease in 25(OH)D per 1 SD increase in metabolite. Of the 27 replicated metabolites, 14 were not significantly associated with 25(OH)D in the meta-analysis after correction for multiple testing (Table 3). 4. Discussion In this study, 13 metabolites showed robust associations with serum 25(OH)D in two independent cohorts from the HKOS, with different study designs and storage time of serum samples. The majority of these metabolites are lipids in nature, with docosahexaenoylcarnitine (C22:6) showing the most significant association. Isobutyrylcarnitine, an amino acid, was also found to be associated with 25(OH)D. Our study design indeed accounted for the possibility of spurious finding. It included two independent cohorts and the successful replication indicates that the findings observed in the current study are less likely to be false positives. There were retrospective studies using old samples for metabolomics profiling with vitamin D but the effect of storage time on the measurement of the metabolites was not evaluated in these studies, especially for long-term storage (>10 years) [16,32]. Indeed, our study design accounted for this potential bias; we included two cohorts, the serum samples from the first cohort were stored for >10 years at 80C, while the serum samples from the second cohort were stored ~1 year at 80C. All samples were aliquots without previous thawing. Consistent associations observed in both cohorts suggested that the significant metabolites we identified were unlikely to be affected by storage time. As we had included two cohorts in the meta-analysis, a fixed effect model was used. We also checked whether random effect would alter our findings, and our findings were essentially unchanged (data not shown). Our results are consistent with the findings in recent observational studies in Caucasian populations. Among the Finnish men in the ATBC study, the majority of the 25(OH)D-associated metabolites were lipids and it was demonstrated that CMPF and two polyunsaturated fatty acids, DHA and EPA, were associated with 25(OH)D with positive estimates [32]. These observations were similar to our findings in Hong Kong Chinese (Supplementary Table 2). Among the 25(OH)D-associated metabolites identified in the ATBC study, five of them also showed significant association with 25(OH)D in the current study, with the same direction of association (Supplementary Table 2). When comparing to a community-dwelling German population in the KOFA F4 Study [16], CMPF, isobutyrylcarnitine and urea were successfully replicated in the current study (Supplementary Table 3). In addition, heneicosapentaenoate, another polyunsaturated fatty acid [PUFA], was shown to be associated with 25(OH)D in our study population. These three omega-3 nutrients, n-3 long-chain polyunsaturated fatty acids [n-3 LCPUFA], were mostly from dietary intake, especially from fish or supplements. In Hong Kong, eating fish is common [33] and as fish oils are an abundant source of vitamin D, these may explain the correlation between omega-3 fatty acids and 25(OH)D. Of note, eicosapentaenoylcholine and 1docosahexaenoylglycerol, the EPA and DHA metabolites, were positive associated with vitamin D. These downstream metabolites of EPA and DHA produced by CYP1A1 were shown to contribute to the beneficial effects of the LCPUFAs but knowledge about underlying mechanisms is limited [34,35]. The dietary supplementation and fish consumption may be an important confounding factor for serum 25(OH)D level and its relationship with those omega-3 metabolites. As demonstrated in stratification analysis, higher association between 25(OH)D and metabolites were found in those with higher fish consumption in the Finnish population [32].

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Fig. 1. Flow chart of the identification of metabolites correlated with 25(OH)D in Hong Kong Chinese.

Unfortunately, data on dietary intake of vitamin D and omega-3 are lacking in the current study, therefore the effect of dietary intake of vitamin D and omega-3 on the results is unknown. Apart from adipose tissue, the circulating fatty acid composition and profile may contribute to lipotoxicity, which includes the elevation of plasma triglyceride, fatty acid, and cholesterol and the ectopic accumulation of lipid in organs, such as in pancreas, heart, liver and blood vessels walls [36]. These may lead to the metabolic syndrome and low-grade inflammation [37]. In the pathway analysis, significant enrichment ofa-linolenic acid metabolism and

beta-oxidation of long chain unsaturated fatty acid were observed in the 25(OH)D-associated metabolites in the discovery and validation stages (Supplementary Fig. 1). These suggest that 25(OH)D is closely correlated with lipid metabolism and hence affecting the risk of metabolic and cardiovascular disease. As obesity is closely associated with vitamin D deficiency, we performed additional analysis on the relationship between the 25(OH)D-associated metabolites themselves with BMI; and we observed that 1-linoleoylGPE was significantly associated with BMI in our study cohorts (Supplementary Table 4). As chronic diseases (such as artery

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Fig. 2. 27 metabolites showing nominal association with 25(OH)D in cohort 1 and 2 of HKOS.

disease, diabetes) may alter lipid metabolism, additional analysis with further adjustment of the chronic diseases on the multivariable model was also performed; and similar results were observed (Supplementary Table 6). Sex is another important confounding factor that affects vitamin D level and physiology and our previous study also demonstrated that there was a sex-specific serum 25(OH)D - parathyroid hormone (PTH) relationship in our study population of HKOS [29]. One weakness in our study was the selection bias due to the enrollment method of participants in HKOS resulting in a higher percentage of females in the cohort. To address the potential sex bias, we adjusted for sex in the multivariable model. Moreover, we additionally performed female-specific analysis and the results were similar to the original analysis with two new metabolites identified (Supplementary Table 5). Given the small sample size in male, no male-specific analysis was performed. 4.1. Fatty acid metabolism and acylcarntines Docosahexaenoylcarnitine, as well as pimeloylcarnitine (3methyladipoylcarnitine), were found to be highly correlated with 25(OH)D in both visits. They are long chain fatty acid acrylcarnitines and are important intermediates in fatty acid oxidation derived from the precursor, DHA and palmitic acid. Fatty acid oxidation [FAO] is not only an important process for energy homeostasis in the human body, but also the main energy source for cardiac and skeletal muscle and the kidneys. Long chain fatty acids once absorbed by cell through fatty acid transporter [FAT] are activated by esterification to acryl-CoA. Acylcarnitine are mostly

produced in the transport of long chain acryl-CoAs from cytosol into mitochondria for fatty acid ∞-oxidation by the carnitine shuttle system comprising carnitine palmitol-transferase I and II [CPT1 and CPT2] [38]. Although the increased formation of carnitines may be explained by the higher intake of their fatty acid precursors, e.g. DHA, together with vitamin D, the high correlation of 25(OH)D with EPA carnitine and pimeloyl-carnitine suggests that 25(OH)D may be closely related to pathways involved in fatty acid oxidation. In addition, tiglylcarnitine and isobutyrylcarnitine, which are short chain fatty acid carnitines, increased with serum 25(OH)D elevation, showing that 25(OH)D may be also related to the accumulation of acrylcaritines resulting from incomplete boxidation of FA. Together with the significant association of a metabolite of the TCA cycle, succinylcarnitine, with vitamin D, it may also suggest that 25(OH)D level increases with triglyceride mobilization from adipose tissues and increases fatty acid synthase activity, resulting in the elevated transport of acylcarnitinemediated long-chain fatty acid into the mitochondria for boxidation. Some studies demonstrated that accumulation of ∞-oxidation intermediates, such as ceramide, diacylglycerol, gangliosides, acylcaritines, and long chain fatty acid-derivatives in cytosol decreased insulin sensitivity by modulating the insulin signaling [39e41]. The serum level of carnitines reflects the efflux of carnitine into the circulation. The profile of serum acylcarnitine levels may vary due to different physiological status of the subjects, such as exercise or fasting; or the turnover rate in different tissues, e.g. skeletal or liver tissue; and the excretion in urine and bile [42e44]. Although the exact mechanism of the export of acylcarnitine

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Fig. 3. 13 metabolites showing significant associations with 25(OH)D in the meta-analysis.

through the cell membrane to plasma remains unclear, the efflux will result in preventing their accumulations in cells, and enhance the Co-A metabolic process without trapping Co-A [42]. The serum profile of acylcarnitines is an important biomarker for genetic disorders involving metabolism and has also been shown to be associated with different diseases, such as diabetes mellitus, liver and renal dysfunction [45]. The short chain fatty acid, isobutyrylcarnitine, was also positively associated with 25(O)D and it was previously shown to correlate with renal function and immunoactivation in different skeletal muscle density status [46,47]. As vitamin D is closely related to musculoskeletal function and muscle strength, which has been shown to be associated with 25(OH)D, these may account for the high correlation of these carnitines with 25(OH)D. In addition, we previously showed that calcium, which is closely regulated by vitamin D, was associated with the risk of diabetes in Hong Kong Chinese [30]. 4.2. Dicarboxylate: CMPF CMPF is a dicarboxlyate, a furry acid metabolite, and is produced when furry acid becomes incorporated into phospholipid, cholesterol ester and triglycerides. Furry acid is beneficial but CMPF is a uremic toxin. In animal models, CMPF was shown to decrease glucose utilization and impair insulin secretion. Plasma CMPF levels were found to be increased in patients with decreased glucose tolerance and in patients with end-stage kidney disease and glucose intolerance. The levels also depended on the intake of fish, fish oil and plants [48e50]. Degradation and hydrolysis of vitamin D and its derivatives to 25(OH)D to 24, 25 dihydroxyvitamin D [24,25(OH)D] are handled by a series of P450 cytochrome enzymes [CYP] [51] and it was suggested that this uremic toxin modulated and inhibited the expression of CYP3A4, the multifunctional nonspecific drug-metabolizing cytochrome enzyme. CMPF was shown to act on the activation of 1,25(OH)D through VDR and then the binding of the VDR-RXR complex to the pregnane X receptor

(PXR)-responsive elements of the CYP3A4 promoter for transcription [52e54]. Our results on the elevation of 25(OH)D with CMPF accumulation are highly consistent with the findings in Finnish men, and it is worth investigating CMPF together with 25(OH)D for other undiscovered pathways involving uremic toxin, renal function, and insulin resistance. 3-methyladipate, another dicarboxylate, is generated from the catabolism of phytanic acid during the subsequent cycles of low-capacity b-oxidation [55] and its positive correlation may be explained by the dietary intake of phytanic acid together with vitamin D [56]. 4.3. Other lipid metabolites and 25(OH)D Notably, phosphatidylethanolamines (PE) and lysophospholipids were significantly and inversely correlated with 25(OH)D. Phosphatidylcholine (PC) is a phospholipid. All are major components of the cell membrane and the raw material for cellular signaling intermediates and steroids, such as eicosanoid. However, we observed a null association of eicosanoids, such as prostacyclin, with 25(OH)D. Reduced circulating level of GPC has also been shown to be markers for insulin resistance and predictors for heart failure and related to cancers [14,45,57,58]. The lysophospholipid, 1-linoleoyl-GPE, is lysophosphatidylethanolamine and a minor glycophospholipid in cell membrane, and it was produced from the hydrolysis action of PLC-L from phosphatidylethanolamine. Its function remains unclear but it was shown to correlate with 1,25 dihydroxyvitamin D, the bioactive form of 25(OH)D, in the mechanisms of phospholipase action [59]. Our study has several strengths. First, it is the first study investigating the association of metabolomic profile with 25(OH)D in Chinese. Second, we included replication of the analysis using randomized cohorts from the same study to test the robustness of the analysis. However, there are limitations in this study. Firstly, there was no direct measurement on the 25(OH)D serum level. However, the assay employed in this study shows high correlation with free 25(OH)D [60]. Secondly, our cohort has an FFQ focusing

Please cite this article as: Leung RYH et al., Serum metabolomic profiling and its association with 25-hydroxyvitamin D, Clinical Nutrition, https://doi.org/10.1016/j.clnu.2019.04.035

Metabolites

Super pathway

isobutyrylcarnitine (C4) eicosapentaenoylcholine docosahexaenoylcarnitine (C22:6) pimeloylcarnitine/3-methyladipoylcarnitine (C7-DC) 3-carboxy-4-methyl-5-propyl-2-furanpropanoate (CMPF) 1-linoleoyl-GPE (18:2) 1-docosahexaenoylglycerol (22:6) 1-oleoyl-2-linoleoyl-GPE (18:1/18:2) 1-stearoyl-2-linoleoyl-GPE (18:0/18:2) 1-palmitoyl-2-linoleoyl-GPE (16:0/18:2) docosahexaenoate (DHA; 22:6n3) eicosapentaenoate (EPA; 20:5n3) heneicosapentaenoate (21:5n3)

Amino Acid Lipid

Sub pathway

Leucine, Isoleucine and Valine Metabolism Fatty Acid Metabolism (Acyl Choline) Fatty Acid Metabolism (Acyl Carnitine) Fatty Acid Metabolism (Acyl Carnitine) Fatty Acid, Dicarboxylate Lysophospholipid Monoacylglycerol Phosphatidylethanolamine (PE) Phosphatidylethanolamine (PE) Phosphatidylethanolamine (PE) Polyunsaturated Fatty Acid (n3 and n6) Polyunsaturated Fatty Acid (n3 and n6) Polyunsaturated Fatty Acid (n3 and n6)

Baseline (cohort 1)

Follow-up (cohort 2)

Meta-analysis

Effect

Effect

Effect

P

0.2780 0.1619 0.2693 0.1859 0.2219 0.2293 0.2111 0.1837 0.2192 0.2225 0.2514 0.1037 0.1064

nominal

0.004 <0.001 <0.001 0.003 <0.001 0.024 <0.001 0.018 0.014 0.008 <0.001 <0.001 <0.001

P

0.3201 0.1772 0.2451 0.3121 0.1856 0.4755 0.1688 0.2869 0.3212 0.2756 0.3518 0.2551 0.1037

nominal

0.002 <0.001 <0.001# <0.001 0.004 <0.001 0.016 <0.001# <0.001 0.001 0.003 0.001 0.006

P-value

0.2980 0.1682 0.2554 0.2324 0.2034 0.3213 0.1927 0.2690 0.2414 0.2489 0.2778 0.1221 0.1053

1.42 4.79 9.86 2.20 6.40 6.00 2.72 1.95 2.63 2.21 5.18 6.82 1.42

            

Direction 5

10 107 109 106 106 105 105 105 106 105 106 106 105

þþ þþ þþ þþ þþ e þþ e e e þþ þþ þþ

Effect: Standardized beta coefficient. p < 5.98  105 (0.05/835) threshold value after adjustment according to the Bonferroni method for multiple testing.

#

Table 3 14 metabolites that did not reach significance after Bonferroni correction in meta-analysis. Metabolites

Super pathway

Sub pathway

Baseline (cohort 1) Effect

P

tiglylcarnitine (C5:1-DC) urea docosahexaenoylcholine 3-methyladipate 1-oleoyl-GPE (18:1) 1,2-dilinoleoyl-GPC (18:2/18:2) 1,2-dilinoleoyl-GPE (18:2/18:2) 3-(N-acetyl-L-cystein-S-yl) acetaminophen 3-(cystein-S-yl)acetaminophen 2-methoxyacetaminophen glucuronide pyrraline 1,3,7-trimethylurate theobromine 3-methylxanthine

Amino Acid

Leucine, Isoleucine and Valine Metabolism Urea cycle; Arginine and Proline Metabolism Fatty Acid Metabolism (Acyl Choline) Fatty Acid, Dicarboxylate Lysophospholipid Phosphatidylcholine (PC) Phosphatidylethanolamine (PE) Drug Drug Drug Food Component/Plant Xanthine Metabolism Xanthine Metabolism Xanthine Metabolism

0.3165 0.4387 0.1093 0.1437 0.2026 0.3105 0.1602 0.1440 0.2117 0.1392 0.1533 0.0986 0.1273 0.1041

0.003 0.023 0.046 0.001 0.042 0.017 0.023 0.035 0.037 0.042 0.003 0.003 0.007 0.045

Lipid

Xenobiotics

nominal

Follow-up (cohort 2) Effect

P

0.3463 0.7252 0.5200 0.1639 0.2762 0.4147 0.2680 0.0736 0.1383 0.1970 0.1788 0.0854 0.0901 0.0995

0.010 0.002 <0.001 0.030 0.046 0.002 <0.001 0.015 0.027 0.028 0.037 0.035 0.031 0.035

nominal

Meta-analysis Effect

P-value

0.3281 0.5580 0.1830 0.1489 0.2278 0.3615 0.2067 0.0854 0.1586 0.1604 0.1599 0.0934 0.1064 0.1016

7.48 1.37 2.17 9.09 4.72 8.58 8.75 1.89 2.67 3.10 2.18 2.60 6.40 3.45

             

105 104 104 105 103 105 105 103 103 103 104 104 104 103

Direction þþ þþ þþ þþ e e e þþ þþ þþ e e e e

R.Y.H. Leung et al. / Clinical Nutrition xxx (xxxx) xxx

Please cite this article as: Leung RYH et al., Serum metabolomic profiling and its association with 25-hydroxyvitamin D, Clinical Nutrition, https://doi.org/10.1016/j.clnu.2019.04.035

Table 2 13 metabolites showing significant association with 25(OH)D in HKOS after Bonferroni correction in meta-analysis.

Effect: Standardized beta coefficient.

7

8

R.Y.H. Leung et al. / Clinical Nutrition xxx (xxxx) xxx

on calcium-rich diet but did not include complete record of fish intake [61]. We also acknowledge that we did not have complete data on fish consumption in the FFQ and there was a lack of data on omega-3/vitamin D supplementation. However, our previous study showed that only 2.6% of participants used vitamin D supplementation [62]. Moreover, it is well reported that the usual recommended dose (800 IU) of vitamin D supplementation has little or moderate impact on serum vitamin D concentration. Fish may be a vitamin D rich source, but the amount of vitamin D present in fish varies [63], and only wild salmon contains a high amount of vitamin D. Thus, this amount of vitamin D is expected to have little effect on serum vitamin D concentration. Given that our findings are largely in line with a previous metabolomic study in a cohort that controlled for dietary factors [32], we believed that the impact of dietary vitamin D intake/supplementation on our findings was minimal. Thirdly, we could not infer a causal relationship between 25(OH)D and the identified metabolites. Fourthly, the sample size in this study was relatively small. However, we can conduct a larger scale prospective study in the same cohort in the future to reexamine whether those identified key metabolites have any causal relationship with health outcome and vitamin D. In conclusion, serum 25(OH)D level was highly associated with metabolites the majority of which were lipid in nature. This association may suggest a relationship between vitamin D and lipid metabolism. Lipid metabolism is related to adiposity and lipotoxicity and these are the important confounding factors for cardiometabolic risk factors and diseases. This provides a new direction for further studies on vitamin D interacting with these metabolites to affect health outcomes. Large scale prospective investigations on the association of those metabolites with the risk of other diseases should be performed and will shed light on the possible metabolic pathways in which vitamin D may be involved. Financial support This work is supported by ECS grant funded by the Research Grants Council, HKSAR, China (27100416) and HMRF grant (12132451). Ethical standard disclosure The study was approved by the Institutional Review Board (IRB) of the University of Hong Kong and Hong Kong Hospital Authority, Hong Kong West Cluster (HKU/HA HKW IRB). Conflict of interest Authors declare no conflict of interest. CRediT authorship contribution statement Raymond Y.H. Leung: Data curation, Formal analysis, Investigation, Writing - original draft. Gloria H.Y. Li: Formal analysis, Investigation, Writing - original draft. Bernard M.Y. Cheung: Investigation, Writing - original draft. Kathryn C.B. Tan: Investigation, Writing - original draft. Annie W.C. Kung: Data curation, Investigation, Writing - original draft. Ching-Lung Cheung: Conceptualization, Data curation, Funding acquisition, Supervision, Investigation, Writing - original draft. Acknowledgement The authors gratefully acknowledge the support from staffs in Hong Kong Osteoporosis Study.

Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.clnu.2019.04.035.

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