
==== Front
Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

39251672
71423
10.1038/s41598-024-71423-1
Article
New insight into primary hyperparathyroidism using untargeted metabolomics
Wielogórska-Partyka Marta 1
https://orcid.org/0000-0002-9477-057X
Godzien Joanna joanna.godzien@umb.edu.pl

2
Podgórska-Golubiewska Beata 1
Sieminska Julia 2
Mamani-Huanca Maricruz 3
Mocarska Karolina 1
Stępniewska Marta 1
Supronik Jakub 1
Pomichter Bartosz 1
Lopez-Gonzalvez Angeles 3
Kozłowska Gabryela 4
Buczyńska Angelika 4
Popławska-Kita Anna 1
Adamska Agnieszka 1
Szelachowska Małgorzata 1
Barbas Coral 3
Ciborowski Michal 2
Siewko Katarzyna 1
Krętowski Adam 124
1 https://ror.org/00y4ya841 grid.48324.39 0000 0001 2248 2838 Department of Endocrinology, Diabetology and Internal Medicine, Medical University of Bialystok, 15-276 Białystok, Poland
2 grid.48324.39 0000000122482838 Metabolomics and Proteomics Laboratory, Clinical Research Centre, Medical University of Bialystok, Skłodowskiej 24a, 15-276 Białystok, Poland
3 https://ror.org/00tvate34 grid.8461.b 0000 0001 2159 0415 Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, 28660 Boadilla del Monte, Spain
4 grid.48324.39 0000000122482838 Clinical Research Centre, Medical University of Bialystok, 15-276 Białystok, Poland
9 9 2024
9 9 2024
2024
14 2098720 4 2024
28 8 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Primary Hyperparathyroidism (PHPT) is characterized by excessive parathormone (PTH) secretion and disrupted calcium homeostasis. Untargeted metabolomics offers a valuable approach to understanding the complex metabolic alterations associated with different diseases, including PHPT. Plasma untargeted metabolomics was applied to investigate the metabolic profiles of PHPT patients compared to a control group. Two complementary liquid-phase separation techniques were employed to comprehensively explore the metabolic landscape in this retrospective, single-center study. The study comprised 28 female patients diagnosed following the current guidelines of PHPT diagnosis and a group of 30 healthy females as a control group. To evaluate their association with PHPT, we identified changes in plasma metabolic profiles in patients with PHPT compared to the control group. The primary outcome measure included detecting plasma metabolites and discriminating PHPT patients from controls. The study unveiled specific metabolic imbalances that may link l-amino acids with peptic ulcer disease, gamma-glutamyls with oxidative stress, and asymmetric dimethylarginine (ADMA) with cardiovascular complications. Several metabolites, such as gamma-glutamyls, caffeine, sex hormones, carnitine, sphingosine-1-phosphate (S-1-P), and steroids, were connected with reduced bone mineral density (BMD). Metabolic profiling identified distinct metabolic patterns between patients with PHPT and healthy controls. These findings provided valuable insights into the pathophysiology of PHPT.

Keywords

Primary hyperparathyroidism
Calcium
Untargeted metabolomics
Bone mineral density
Subject terms

Biochemistry
Endocrinology
issue-copyright-statement© Springer Nature Limited 2024
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pmcIntroduction

Metabolomics is an expanding field in medical sciences that aims to comprehensively analyze the complete set of small molecule metabolites in blood, urine, or tissue samples. This approach enables the identification of global metabolic profiles and may provide valuable information about an organism’s metabolic state and health status, as well as reveal disease mechanisms. Metabolomics techniques involve various analytical platforms, including mass spectrometry (MS) and nuclear magnetic resonance (NMR) for detection, along with liquid (LC), gas (GC) chromatography, and capillary electrophoresis (CE) for separation, to identify and quantify metabolites1.

Primary hyperparathyroidism (PHPT) is one of the most common endocrine disorders characterized by excessive production of parathormone (PTH). PTH plays a crucial role in maintaining calcium and phosphate homeostasis. Hence, the overproduction of PTH disrupts their balance, leading to hypercalcemia and hypophosphatemia2.

Applying metabolomics to PHPT may lead to the discovery of relevant biomarkers which may support early diagnosis, prognosis assessment, and treatment monitoring. However, to achieve this, the first mechanisms involved in the pathogenesis of PHPT must be uncovered and fully understood.

Despite the potency of metabolomics, so far, it has been applied only in a few research studies with PHPT3,4. In this study, we aimed to use plasma untargeted metabolomics to identify metabolites associated with PHPT. This research represents a pioneering effort in investigating this widespread endocrine disorder using metabolomics. We examined disparities between individuals with PHPT and healthy controls. Following this, we stratified both groups of patients based on their bone density. According to our analyses, we identified several metabolic alterations that could provide new insights into disease-related mechanisms. The preliminary results of this study were presented in a poster session at the European Congress of Endocrinology5.

Results

Baseline characteristic

The mean age of patients in the study group was 56 years (± 13.2 years), whereas the average age in the control group was 53 years (± 6.8 years). Patients in the study group exhibited significantly elevated PTH levels, with a mean of 231.4 pg/mL, compared to the control group's mean of 36.7 pg/mL. Calcium levels in the study group were also markedly higher, with a mean of 2.7 mmol/L, compared to the control group’s 2.2 mmol/L mean, as well as vitamin D3 level, with a mean of 28.5 ng/mL for PHPT patients and 23.0 ng/mL for controls. The mean creatinine concentration in the study group was 0.74 mg/dL, whereas the average concentration in the control group was 0.77 mg/dL. Table 1 summarizes the clinical data of the study group. Patients with malignancies were excluded from the study. Table 1 Clinical characteristics of PHPT patients and healthy controls.

Parameter (Ref. Range)	Study group (n = 28)	Control group (n = 30)	
Age	55.82 ± 13.21	53.00 ± 6.82	
BMI (18.50–24.99 kg/m2)	26.38 ± 3.94	26.15 ± 3.32	
Creatinine (0.55–1.02 mg/dL)	0.74 ± 0.13	0.77 ± 0.14	
Calcium total (2.1–2.6 mmol/L)	2.73 ± 0.22	2.24 ± 0.09	
Parathormone (15.0–68.3 pg/mL)	231.38 ± 100.62	36.65 ± 9.56	
Vitamin D3 (30–40 ng/mL)	28.46 ± 12.79	22.99 ± 15.23	
Hypertension	13 patients	6 controls	
Bone Mineral Density	Healthy: 4 patients

Osteopenia: 12 patients

Osteoporosis: 9 patients

	Healthy: 16 controls

Osteopenia: 13 controls

Osteoporosis: 1 control

	
Calcium ionized (1.15–1.29 mol/L)	1.46 ± 0.09	NA	
24-h urine calcium (2.5–7.5 mmol/L)	8.37 ± 2.51	NA	
Phosphate (0.74–1.52 mmol/L)	0.85 ± 0.11	NA	
Data are presented as mean ± standard deviation.

Ref. Range, reference range for each parameter; NA, not available.

Patients and controls were also evaluated in terms of medication. Information about taken medication was revised, focusing on the drugs affecting calcium, vitamin D3, and lipid profile (bisphosphonates, statins, glucocorticosteroids (GCS), and sex hormones). We did not observe significant differences between patients with and without medication: we built the OPLS-DA models built to compare these two groups, but the model parameters were not acceptable (negative Q2).

Metabolic profiles

Acquired data was inspected to evaluate their quality. No outliers were observed. Therefore, the complete data set was forwarded for processing. Processing of LC–MS + data resulted in a matrix of 995 features, among which 746 passed the QA procedure. For LC–MS- data, we obtained 1240 features, reducing this number to 1030 signals after the QA check. For CE-MS data, we obtained 1246 features. However, many corresponded to the multiple signals formed by a single compound ionized with different adducts and clusters. After manual data curation, we limited this number to 169 features, among which 155 passed the QA filtration.

Statistical analyses were performed for each data set independently, resulting in the selection of 91 statistically significant metabolites for LC–MS+ analysis, 166 discriminating metabolites for LC–MS− analysis, and 38 significant metabolites for CE-MS analysis. Metabolite annotation allowed us to assign 40 ions to the metabolites for the LC–MS+ data, 77 for LC–MS− data, and 18 for the CE-MS data. This resulted in 135 annotated ions. However, some corresponded to the in-source formed fragments or ions of the same metabolite formed with different adducts, giving 106 unique metabolites.

Because the majority of selected metabolites were lipids, we decided to inspect the potential confounding effects of lipid-modifying medication. To address this, we performed statistical analyses excluding samples collected from the patients under medication. In this way, from control group we excluded 3 participants and from PHPT group 8 patients. In this analysis 15 lipids lost significance: ST 21:0;O2;S, Glycochenodeoxycholate sulfate, trihydroxy-octadecenoic acid TriHOME FA 18:1;O3, Tetradecadiencarnitine CAR 14:2, CAR 24:0, Carboxyheptadecenoylcarnitine CAR 18:2;O2, Ketosphingosine-1-phosphate SPBP 18:2;O2, PE 38:4, PE 18:0/20:4, PE 44:12, PC O-38:6/PC P-38:5, LPC 20:4, LPE 22:6, LPE 22:5, and LPI 20:4. Loss of significance might be related to the effect of medication on the lipid profiles, or due to the lowered number of tested samples, what weaken the power of the analysis. These lipids were excluded from the data interpretation.

During new analyses we selected 30 statistically significant metabolites for LC–MS+ analysis, 36 discriminating metabolites for LC–MS− analysis, and 16 significant metabolites for CE-MS analysis. The complete list of these metabolites is provided in Table 2, while in the supplementary table we provided percentage of change calculated for all participants and after excluding those on medication. Table 2 Statistically significant and annotated metabolites across three data sets discriminating PHPT patients from controls.

Category	Sub class	Compound	p-value	Change [%]	
Control
vs
PHPT	Control
vs
post-PHPT	PHPT
vs
post-PHPT	
Organic acids and derivatives	Amino acids, peptides and analogues	l-asparagine	2.74E−02	22.1	− 0.6	− 15.3	
l-glutamine	7.34E−05	15.5	3.3	− 11.4	
l-histidine	1.99E−02	6.5	3.7	− 4.6	
l-cystine	2.96E−02	38.3	134.1	61.4	
Methyl-l-proline	5.51E−02	− 45.8	− 57.5	− 8.5	
Methyl-l-glutamic acid	2.85E−02	− 12.2	− 6.0	7.9	
Sarcosine (Methyl-l-glycine)	3.07E−02	13.7	0.8	− 9.4	
Dimethyl-l-arginine	4.79E−02	10.4	10.1	1.8	
Proline betaine	3.19E−02	− 42.3	− 47.5	12.3	
Gamma-glutamyl-l-glycine	2.89E−03	48.5	0.7	− 44.9	
Gamma-glutamyl-l-alanine	1.84E−02	24.3	− 1.5	− 20.6	
Gamma-glutamyl-l-ornithine	3.87E−03	19.5	13.7	− 3.0	
Gamma-glutamyl-l-glutamine	2.82E−04	23.5	2.0	− 19.2	
Gamma-glutamyl-l-hydroxyproline	9.75E−04	40.8	− 7.6	− 35.0	
Xantnes	Caffeine	4.26E−03	− 72.7	− 35.7	117.0	
Arylsulfates	Ethylphenyl sulfate	3.07E−02	− 38.4	− 6.8	91.9	
Pterins and derivatives	Methenyltetrahydrofolic acid	1.02E−03	− 38.7	− 16.8	57.9	
Monoterpenoids	Thymol sulfate	1.01E−02	31.8	66.3	51.5	
Sterol lipids	Sulfates	5alpha-androstane-3alpha-ol-17-one sulfate

5alpha-Dihydrotestosterone sulfate

Etiocholanolone sulfate

ST 19:1;O2;S iso 1

	2.30E−03	− 46.1	− 46.6	9.5	
ST 19:1;O2;S iso 2	2.53E−04	− 50.1	− 48.1	13.7	
ST 19:1;O2;S iso 3	7.12E−03	− 29.9	− 26.6	9.8	
Testosterone sulfate

Epitestosterone sulfate

Dehydroepiandrosterone sulfate

ST 19:2;O2;S

	2.53E−04	− 45.9	− 40.2	15.7	
Pregnanolone sulfate

ST 21:1;O2;S iso 1

	1.27E−03	− 42.1	− 34.2	17.3	
ST 21:1;O2;S iso2	1.21E−04	− 43.2	− 45.6	− 0.5	
ST 19:1;O3;S	2.66E−04	− 43.4	− 3.8	85.6	
Dihydroxyandrostenone sulfate

ST 19:2;O3;S

	1.84E−03	− 46.6	− 26.9	54.8	
ST 20:2;O5;S	1.63E−02	− 57.1	− 41.8	− 0.4	
Cholesterol and derivaties	Hydroxy-4beta-methyl-5alpha-cholest-7-ene-4alpha-carboxylic acid	2.14E−03	− 25.6	− 3.3	40.1	
Cholesterol	3.99E−02	− 9.9	− 3.8	3.9	
7-oxo-cholestenone

ST 27:3;O2

	2.49E−03	− 27.1	− 19.8	0.8	
Cholesteryl acetate

ST 29:2;O2

	2.63E−02	− 10.2	− 3.1	4.5	
Glucuronides	3-alpha-androstanediol glucuronide

hydroxyandrostane-3-glucuronide

ST 19:0;O2;GlcA

	9.74E−03	− 31.4	− 22.4	12.2	
testolic acid glucuronide

ST 19:2;O4;GlcA

	3.10E−03	− 76.2	− 34.5	148.9	
Vitamin D3 and derivatives	1alpha,25-dihydroxy-2alpha-(3-hydroxypropoxy)vitamin D3	2.62E−03	− 36.1	− 5.1	37.7	
Hydroxygeminivitamin D3	4.96E−02	− 35.2	− 13.7	19.1	
Fatty acyls	Amino fatty acids	Amino-octanoic acid	5.93E−03	− 46.4	− 29.4	36.4	
Amino-undecanoic acid	2.95E−05	29.1	25.8	− 2.1	
Oxidized fatty acids	Oxo-amino-nonanoic acid	3.36E−04	− 21.9	− 17.3	7.0	
Hydroxy-pentacosanoic acid

FA 25:0;O

	8.91E−03	− 39.8	− 31.6	11.2	
Hydroxyhexacosanoic acid

FA 26:0;O

	2.53E−02	− 35.1	− 31.7	0.6	
dihydroxy-eicosapentaenoic acid

di-HEPE

FA 20:5;O2

	2.93E−02	− 63.6	− 63.6	− 18.4	
Dicarboxylic acids	Hexacosanedioic acid

FA 26:1;O2

	1.21E−03	− 34.9	− 15.5	27.8	
Heterocyclic fatty acids	Dimethyl-carboxyethyl-furanpentanoic acid

FA 14:4;O3

	2.25E−02	− 21.8	1.7	15.2	
Fatty acyl carnitines	Undecanoylcarnitine

CAR 11:0

	1.32E−02	− 33.4	2.1	44.9	
CAR 11:1	4.92E−03	− 31.7	− 12.5	19.7	
Dodecanoylcarnitine

CAR 12:0

	2.85E−02	− 30.8	9.5	47.5	
Tridecanoylcarnitine

CAR 13:0

	3.17E−04	− 55.4	− 38.2	24.2	
CAR 13:1	6.06E−05	− 49.7	− 26.3	32.4	
Tetradecanoylcarnitine

CAR 14:0

	3.44E−02	− 25.9	5.1	34.2	
Hydroxymyristoylcarnitine

CAR 14:0;O

	2.08E−02	− 34.6	− 18.0	22.1	
Sphingolipids	N-acylsphingosines

(ceramides)

	Cer d18:1/16:0

Cer 34:1;O2

	3.44E−02	− 9.1	− 3.7	3.2	
Ceramide phosphocholines

(sphingomyelins)

	SM d32:0

SM 32:0;O2

	3.32E−02	− 26.7	4.4	32.7	
SM d32:1

SM 32:1;O2

	1.92E−02	− 22.7	0.7	21.9	
SM d34:1	3.19E−02	13.8	1.5	− 8.0	
SM d34:2

SM 34:2;O2

	3.71E−02	− 21.3	− 11.8	4.0	
SM 34:1;O3	1.49E−03	− 24.9	− 5.0	18.8	
SM 32:1;O3	3.19E−02	− 13.1	2.0	16.9	
Sphingoid base phosphates	Sphinganine-1-phosphate

SPBP 18:0;O2

	2.53E−02	− 18.0	− 12.4	3.4	
Sphingosine-1-phosphate

SPBP 18:1;O2

	3.54E−02	− 11.8	− 13.9	− 3.4	
Glycerophospholipids	Diacylglycerophosphocholines	PC 34:1	1.13E−02	10.5	4.5	− 4.7	
PC 16:0/18:2

PC 34:2

	2.96E−02	19.7	− 3.2	− 17.4	
PC 36:2	1.16E−02	− 10.2	− 4.0	6.0	
PC 36:3	2.34E−02	33.2	31.7	7.4	
PC 36:4	3.07E−02	11.4	2.0	− 5.8	
PC 16:0/22:5 & PC 18:1/20:4

PC 38:5 iso 1

	3.35E−03	22.0	14.9	− 2.3	
PC 38:5 iso 2	6.28E−04	36.4	− 3.9	24.4	
PC 16:0/22:6 & PC 18:2/20:4

PC 38:6 iso 2

	2.35E−02	7.9	− 0.9	− 8.1	
1-(1Z-alkenyl),

2-acylglycerophosphocholines

	PC O-34:3/PC P-34:2	2.26E−02	− 19.8	− 18.7	− 0.2	
PC O-34:3/PC P-34:2	2.08E−02	− 24.7	− 20.7	1.6	
PC O-36:5/PC P-36:4	1.56E−02	− 26.7	− 21.7	2.7	
PC O-38:5/PC P-38:4	6.28E−04	− 19.8	− 9.2	9.8	
1-(1Z-alkenyl),

2-acylglycerophosphoethanolamines

	PE O-16:1/20:5

PE O-36:6 iso 1

	1.04E−02	− 22.9	− 20.7	1.1	
PE O-36:6/ PE P-36:5 iso 2	1.04E−02	− 24.1	− 19.6	1.3	
PE O-18:2/20:4 & PE O-16:1/22:5

PE O-38:6

	3.66E−03	− 22.9	− 9.8	13.9	
PE P-18:0/20:5

PE P-38:5

	4.26E−03	− 32.7	− 14.5	21.0	
PE O-18:2/20:5

PE O-38:7

	4.26E−03	− 30.7	− 37.6	− 5.9	
Oxidized

glycerophosphocholines

	PC 16:0/18:2 OH	6.80E−03	− 17.2	0.0	20.0	
PC 39:3 OH	3.32E−02	11.2	3.6	− 5.6	
Monoacyl-

glycerophosphoethanolamines

	LPE 18:0	3.52E−03	20.9	15.1	− 6.0	
LPE 20:4	1.43E−02	14.9	22.1	6.5	
Monoacyl-

glycerophosphoinositols

	LPI 18:1	1.10E−05	− 38.9	− 28.8	19.6	
LPI 22:6	4.13E−02	− 45.7	− 46.4	− 14.7	
p-values were computed using Mann-Whithey U-test. Results are shown as a percentage change. A “+” means the average amount of metabolite is higher in the PHPT group than in the control group, and a “−” means a lower amount in the PHPT group than in the control group. “&” means that in the MS/MS spectrum, we observed fragments originating from two isomeric and co-eluting lipids.

Cumulatively, this resulted in 64 lipidic molecules and 18 polar metabolites. 28% of all selected metabolites were classified as glycerophospholipids, 18% as fatty acyls (FA), 11% as sphingolipids, 21% as sterol lipids, and 22% as small polar metabolites. Figure 1 graphically represents this distribution.Fig. 1 Distribution of discriminating metabolites according to their classification. The pie charts were built based on the number of metabolites assigned to a given class. FA, fatty acid; oxFA, oxidized fatty acid; CAR, fatty acyl carnitine; PC, diacylglycerophosphocholines; PE, diacylglycerophosphoethanolamines; LPC, monoacylglycerophosphocholines LPE, monoacylglycerophosphoethanolamines; LPI, monoacylglycerophosphoinositols; oxPC, oxidiszed glycerophosphocholines; PC O-/PC P-, 1-alkyl,2-acylglycerophosphocholines/1-(1Z-alkenyl),2-acylglycerophosphocholines; PE O-/PE P-, 1-alkyl,2-acylglycerophosphoethanolamines/1-(1Z-alkenyl),2-acylglycerophosphoethanolamines; SM, sphingomyelins; Cer, ceramides.

Glycerophospholipids were represented by eight diacylglycerophosphocholines (PC) (35%) and ether glycerophospholipids (phospholipids with alkyl or alkenyl linkage in the sn-1 position) such as four 1-alkyl,2-acylglycerophosphocholines PC O- and/or 1-(1Z-alkenyl),2-acylglycerophosphocholines PC P-, and five 1-alkyl,2-acylglycerophosphoethanolamines PE O- and/or 1-(1Z-alkenyl),2-acylglycerophosphoethanolamines PE P-. They were followed by two lyso-groups represented by two monoacylglycerophosphoethanolamines (LPE) and two monoacylglycerophosphoinositoles (LPI).

Among fatty acyls, the biggest group was fatty acylcarnitines (CAR), corresponding to 47% of all fatty acyls, followed by oxidized fatty acids (oxFA) (27%) and amino fatty acids (13%). We also found one statistically significant dicarboxylic fatty acid, and one heterocyclic fatty acid. We also found two oxidized diacylglycerophosphocholines oxPC.

Sphingolipids were represented by three classes: one ceramide (Cer), six ceramide phosphocholines known as sphingomyelins (SM), and two Sphingoid base 1-phosphates. Among sterol lipids, we found two vitamin D3 derivatives, two glucuronides, cholesterol, three cholesterol derivatives, and nine steroid sulfates.

Polar metabolites were represented by amino acids and their derivatives (78%), one xanthine, one arylsulfate, one pterin derivative, and one monoterpenoid.

To bring a broader context to selected metabolites, we performed pathway analysis. For polar metabolites, such as amino acids or organic acids, we performed Pathway Analysis via MetaboAnalyst, where HMDB identifiers of metabolites of interest were matched against pathways. In case of lipids very few compounds have identifiers, and even if they are available, even fewer can be matched against pathways. Therefore, to visualize interactions between significant lipids, we used LIPID MAPS® reaction explorer in which lipids are considered by the classes rather than as individual species. The results of these analyses are shown in Fig. 2.Fig. 2 Panel (a) the pathway analysis results rank the pathways according to their significance (gamma-adjusted p-values for permutation per pathway) on the y-axis and the total number of hits per pathway on the x-axis. As the values of both x and y increase, the color transitions smoothly from white to yellow, orange, and red. Panel (b) the lipid reactions linking different lipid classes. Classes found as altered in PHPT patients are marked with red-dotted circles, while direction of alterations is indicated by arrow.

To visualize the separation between the samples collected from PHPT patients and controls, we used OPLS-DA models. We built one model per data set (Fig. 3). As can be seen, we obtained nice separation regardless of the data source; however, considering the quality of the models, the weakest parameters were for LC/MS+ data.Fig. 3 OPLS-DA models illustrating the separation between PHPT patients (blue circles) and controls (green circles) for LC/MS+ dataset (panel a), LC/MS− dataset (panel b), CE/MS dataset (panel c) and for all discriminating and annotated metabolites (panel d), where we predicted samples collected from patients after the surgery. PLS-DA models illustrating the separation between samples considering BMD for PHPT patients (panel e) and controls (panel f), where circles correspond to the PHPT patients, while squares illustrate controls; green corresponds to controls (regardless of BMD), yellow to participants with normal BMD, blue to participants with osteopenia and red to participants with osteoporosis (regardless of classification). Parameters of model from panel (a) R2cum: 0.753 and Q2cum: 0.117, p-value 5.89E-02; from panel (b) R2cum: 0.846 and Q2cum: 0.451, p-value 6.52E−06; from panel (c) R2cum: 0.719 and Q2cum: 0.359, p-value 1.19E−05; from panel (d) R2cum: 0.811 and Q2cum: 0.653, p-value 7.00E−12; from panels e and f: R2cum: 0.383 and Q2cum: 0.192, p-value 1.32E−05. All OPLS-DA models were log-transformed and Par-scalled, while the PLS-DA model was log-transformed and UV-scalled.

During data interpretation, BMD and calcium metabolism appeared to be the most important factors influencing the levels of selected metabolites. Therefore, we inspected the data considering the BMD to make our analysis more complete. To do so, we used the PLS-DA model: the model was built by merging data from all three datasets, stratifying samples for PHPT patients (circles) and controls (squares) (Fig. 3, panels e and f). Then, we colored the samples considering the BMD for PHPT patients (Fig. 3, panel e) and controls (Fig. 3, panel f) for normal BMD (blue), osteopenia (yellow), and osteoporosis (red). As can be seen, the first and strongest component stratifies samples according to the classification for PHPT and control samples (left and right part of the plot). In contrast, the second component stratifies samples according to the BMD to normal and reduced BMD (up and down part of the plot). Moreover, samples were stratified according to the BMD only in the case of a PHPT patient, where three distinct regions marked with blue, yellow, and red circles barely overlapped (Fig. 3, panel e). In contrast, for control samples, these circles are significantly overlapping, which could indicate that for controls, differences in BMD do not cause noticeable metabolic changes (Fig. 3, panel f).

Although this study aimed at underlying clinical manifestations of PHPT, rather than a selection of biomarkers, we decided to perform ROC analysis. The idea behind this decision was to evaluate the potential of selected discriminating metabolites as markers. Based on the AUC, we divided potential markers into two categories: excellent with an AUC above 0.9, and good with an AUC between 0.8 and 0.9. Classical ROC curve analysis for individual biomarkers allowed the selection of 6 good markers (two isomers of LPI 18:1 with AUC of 0.839 and 0.830, amino-undecanoic acid with AUC of 0.817 and 5alpha-androstane-3-alpha-ol-17-one sulfate with AUC of 0.811, CAR 13:1 with AUC of 0.805 and l-glutamine with AUC of 0.801) but no excellent markers. To judge the power of metabolic discriminators, we also performed ROC analysis for clinical parameters, obtaining the best AUC for PTH level: 0.896 and Ca2+ concentration: 0.891. Although clinical parameters had stronger values than metabolic ones, none of the tested parameters was classified as excellent marker. Therefore, we decided to implement a combinatorial approach where several preselected parameters are used to build a single classifier. We tested different combinations of parameters classified as good markers. Finally, we obtained an excellent classifier with an AUC of 0.99 (0.954–1), using which only one control sample was wrongly classified as a PHPT sample. The ROCs of individual and combinatorial parameters are depicted in Fig. 4.Fig. 4 ROC for individual parameters and combinatorial one, being combination of all 6 individual parameters. Lowest line represents the ROC for combinatorial discriminator along with probabilistic view and confusion matrix.

Discussion

Untargeted metabolomics is a commonly known and valuable approach for assessing metabolic changes in various disorders. Prior analyses of PHPT focused on evaluating metabolomic profiles in parathyroid tissue samples highlighted six main categories of altered metabolites: carboxylic acids and derivatives, dihydrofurans, hydroxy acids and derivatives, organic sulfonic acids and derivatives, organonitrogen compounds and organooxygen compounds. Within the category of carboxylic acids, amino acids and their derivatives were the predominant metabolites1. In our study, we employed untargeted metabolomics to detect alterations in plasma metabolic profiles within the group of patients with PHPT in comparison to the control group. Using two complementary liquid-phase separation techniques enabled us to identify a comprehensive array of metabolites. After obtaining preliminary results, we further enhanced the analysis by comparing metabolic profiles before and after surgical treatment. Moreover, considering disrupted calcium homeostasis in PHPT, we also evaluated our results in relation to BMD. Our study mainly brought new potential insights into the mechanisms underlying the clinical manifestations of PHPT that can unveil new therapeutic targets and interventions. To our knowledge, this is the first study that comprehensively assesses the plasma metabolomic profile of patients with PHPT. A summary of altered pathways is presented in Fig. 2, while metabolic alterations and their possible associations with the PHPT pathology is depicted in Fig. 5.Fig. 5 Altered metabolite and their association with the PHPT and/or clinical manifestation of PHPT.

Calcium is crucial in numerous physiological processes, such as muscle contraction, nerve transmission, blood clotting, and bone formation. In our study, we observed a significant increase in the concentrations of specific amino acids (l-asparagine: 22.1%, l-glutamine: 15.5%, l-histidine: 6.5%, and l-cysteine: 38.3%) in PHPT patients compared to the control group with normocalcemia. This finding prompted us to explore the connection between calcium and amino acids in biological systems. Cells use l-amino acid sensing to regulate cellular activity such as hormone release and protein synthesis6. The modulation of calcium-sensing receptor (CaSR) by plasma amino acid level changes may explain these interactions. CaSR is a G-protein coupled receptor playing a significant role in regulating the amount of calcium in the blood7. It binds a variety of multiple physiologically relevant ligands. Ca2+ are type-I calcimimetics, also known as full agonists of the receptor, since they activate numerous downstream signaling pathways. On the other hand, type-II calcimimetics are typically uncharged organic compounds. The intracellular Ca2+ mobilization with CaSR activation is associated, among others, with the presence of the N-terminal Venus Fly Trap domain, a specific amino acid binding site on the CaSR. Various amino acids belonging to different subclasses are considered potent activators of this receptor3,5. Meanwhile, aliphatic amino acids such as l-glutamine and l-cysteine exhibited a wide range of effects on overall metabolism as robust activators of the CaSR8. Furthermore, l-amino acids may modulate the CaSR in various compartments regarding their relative concentrations in selected areas. Notably, higher levels of l-amino acids are observed in the gut lumen and may play a role in luminal amino acid-induced gastrin secretion following the CaSR activation9. For instance, l-histidine serves as a substrate for enzymatic conversion by l-histidine decarboxylase. The resulting l-histamine, as the effector of the gastrin-histamine axis, directly stimulates the parietal cell to secrete hydrochloric acid10. These findings suggest a potential link between elevated l-amino acid levels in PHPT patients and its clinical manifestations like peptic ulcers. Interestingly, our analysis of plasma l-amino acid concentrations before and after parathyroidectomy revealed a decrease in their concentrations following surgery, except l-cysteine, which exhibited an increase (Fig. 6). There is no clear explanation for the rise in l-cysteine concentration following parathyroidectomy. Indeed, all presented results should be validated in larger patient cohorts.Fig. 6 l-amino acids altered in PHPT with their averaged abundance in control, PHPT, and post-surgery PHPT samples. The y-axes in the graph represent the abundance of metabolites. The notch represents the 95% confidence interval around the median of each group, which is defined as ± 1.58 times the interquartile range divided by the square root of the number of samples.

Gamma-glutamyl transferase (GGT) is an enzyme involved in the metabolism of gamma-glutamyl compounds, including glutathione, an important antioxidant. The circulating GGT activity measurement is commonly used for identifying liver and obstructive biliary diseases and as a marker of the extent of alcohol intake. Previously, GGT activity has been linked to cardiovascular diseases such as arterial hypertension, coronary heart disease, congestive heart failure, embolic disease, arrhythmias, stroke, and diabetes11. Glutathione is a small tripeptide with glutamic acid, cysteine, and glycine. In the initial phase of the gamma-glutamyl cycle, GGT catalyzes the transfer of the gamma-glutamyl group from glutathione to various acceptors, including amino acids12,13. Some studies have already suggested that GGT activity may be related to calcium metabolism and that elevated GGT levels may be associated with decreased BMD and an increased risk of osteoporosis14. Gamma-glutamyl amino acids were significantly elevated in our study group with PHPT, reflecting increased GGT activity. Moreover, gamma-glutamyl residue attached to a glycine molecule showed the highest percentage of change among the analyzed metabolites (82.7%) (Table 2). Further analysis of post-parathyroidectomy samples demonstrated a decrease in gamma-glutamyl amino acid concentrations, consistent with reduced oxidative stress post-surgery (Fig. 7). An explanation for this phenomenon might be the calcium-reactive oxygen species (ROS) cycle, wherein the reduction of calcium-induced ROS formation is observed following parathyroidectomy as calcium concentration becomes normalized15. Only gamma-glutamyl-l-glycine did not show a concentration decrease in the postoperative period. However, this observation requires verification in a larger sample size. To our knowledge, our study is the first research highlighting the association between GGT and PHPT.Fig. 7 Gamma-glutamyl-amino acids altered in PHPT with their averaged abundance in control, PHPT, and post-surgery PHPT samples. The y-axes in the graph represent the abundance of metabolites. The notch represents the 95% confidence interval around the median of each group, which is defined as ± 1.58 times the interquartile range divided by the square root of the number of samples.

The effects of asymmetric dimethylarginine (ADMA), a subtype of dimethyl-l-arginine (DMA), have been demonstrated to have an indirect association with calcium through its effects on endothelial function and nitric oxide (NO) production. ADMA is an endogenous inhibitor of nitric oxide synthase (NOS), which is responsible for NO production. NO is an important signaling molecule that helps regulate vascular tone and blood flow, among other functions. Inhibition of NOS by ADMA can lead to the reduced NO production and impaired endothelial function, which can contribute to a range of cardiovascular and metabolic disorders, including hypertension, atherosclerosis, and diabetes. At the same time, calcium is involved in the activation of NOS and the production of NO by endothelial cells16. One of the clinical manifestations of PHPT is cardiovascular diseases, including hypertension. We have found significantly higher concentrations of ADMA in our study group compared to the control group. Interestingly, we have also noted an increase instead of a decrease in ADMA in the patient group after parathyroidectomy. While earlier research had suggested that hypertension resolved following successful parathyroidectomy, more recent findings indicate that in patients with PHPT, arterial hypertension is irreversible17. Therefore, the current guidelines do not include isolated arterial hypertension as an indication for surgical intervention unless accompanied by other symptoms of PHPT18. Nevertheless, our additional analyses comparing the hypertensive study group, both in the preoperative and postoperative periods, with the hypertensive control group did not yield statistically significant results in correlation with ADMA. It is worth considering conducting further analyses on a larger study group. Moreover, there is a growing interest in exploring the potential of vitamin D receptor activation as an innovative approach to reducing the accumulation of ADMA in chronic kidney disease. Unfortunately, a double-blind, randomized trial failed to confirm paricalcitol's positive impact on ADMA and SDMA levels16. Furthermore, our study demonstrated a statistically significant positive correlation between the concentration of DMA and vitamin D3 in the study group (corr: 0.423, p-value: 0.022). Therefore, the supposed beneficial effect of paricalcitol on cardiovascular outcomes is not supported by its ability to reduce the levels of methylarginines via vitamin D receptor activation.

In recent studies, the secretion of PTH was found to be suppressed by high doses of caffeine, and the authors of these studies suggested that a decline in the intracellular level of cAMP might explain this effect. They concluded that caffeine could be considered as an adjuvant therapy in parathyroid adenomas19,20. In our study, we observed a deficiency in caffeine levels in patients with PHPT compared to the control group. Moreover, following the reduction in PTH levels due to parathyroidectomy, we noted a significant increase in plasma caffeine concentration. Patients with osteoporosis in the PHPT group also had markedly lower caffeine concentrations compared to the control group. This observed association between decreased caffeine levels, reduced BMD, and increased PTH concentrations warrants further investigation. It is important to emphasize that the relationship between caffeine deficiency and the development of PHPT is not well-established. Further research is required, primarily to determine whether a deficiency in caffeine contributes to the onset of PHPT and to explore any potential therapeutic benefits of caffeine in parathyroid disorders and osteoporosis.

Metabolic bone disease, characterized by decreased BMD, particularly at cortical sites, and high bone turnover, is another well-known clinical manifestation of PHPT. Meanwhile, the involvement of sex steroid hormones in bone homeostasis is commonly known. For instance, dehydroepiandrosterone (DHEA), significantly decreased in our study group, can enhance osteoblast activity21. Osteoblasts express receptor activators of nuclear factor kappa-Β ligand (RANKL) and its decoy receptor osteoprotegrin (OPG). DHEA treatment may lead to the upregulation of OPG expression and a decrease in the RANKL/OPG ratio, thereby reducing the resorptive activity of osteoclasts in the presence of osteoblasts. The administration of DHEA was related to a rise in cortical thickness and BMD. Notably, reduced DHEA levels were linked to a higher risk of fracture and an increased risk of fall, which might be related to muscle weakness22.

In another study, Almqvist et al. demonstrated that the recovery of BMD subsequent to parathyroidectomy is a multifactorial phenomenon associated with decreased PTH concentration and sex steroid hormone concentration. The increase of bioavailable testosterone after parathyroidectomy was a primary factor of change in BMD in both the hip and spine. Interestingly, the authors highlighted the decline in sex hormone binding globulin (SHBG) after surgery as the cause of the increase of bioavailable sex hormones, and the study was focused on postmenopausal women23. Our study exclusively comprised female participants, and most of them were postmenopausal. We carefully selected control subjects based on matching variables of interest, such as age and sex. However, the average age of patients in the control group was slightly lower. Given this disparity, we decided to analyze sex hormone levels before and after parathyroidectomy. The results validated an increase in sex hormone concentrations during the postoperative period. When analyzing the results concerning patient age, it is essential to note that the postoperative group had an even higher average age than the preoperative group. Notably, the lowest average hormone concentrations were noted in patients with osteoporosis, followed by higher concentrations in those with osteopenia and the highest concentrations in the control group. Our study is consistent with previous reports, confirming the association between sex hormones and BMD. However, it is worth considering further research on the relationship between sex hormone levels and the pathomechanism of PHPT development, especially given its prevalence among postmenopausal women.

Mitochondrial dysfunction and oxidative stress are also important factors in developing bone fragility. Our analysis revealed significant deviations in fatty acyl carnitine levels in a study group compared to a control group. Specifically, our study demonstrated a reduction in fatty acyl carnitine levels in the PHPT patient group compared to the control group and a significant increase in their concentrations during the postoperative period. Fatty acyl carnitines are involved in a reaction facilitated by carnitine acyltransferase II (CAT-II) within the mitochondrial matrix leading to the production of fatty acyl coenzyme A (CoA) and carnitine24. l-carnitine, a significant cofactor in lipid metabolism with potent antioxidant properties, has been extensively studied for its role in enhancing osteoblastic proliferation and activity. It is involved in long-chain fatty acids transport to the inner mitochondrial membrane, maintains the balance between acyl CoA and CoA, and increases oxidation of branched amino acids. l-carnitine in Terruzzi’s study has increased the activity of kinases involved in osteoblast differentiation, including Ca2+/calmodulin-dependent protein kinase II (CAMKII), ERK1/2, and AKT. Moreover, it has upregulated the expression of osteogenic-related genes, such as RUNX2, osterix (OSX), bone sialoprotein (BSP), and osteopontin (OPN)25. A shortage of l-carnitine can limit the availability of energy, which can affect the balance of bone remodeling and lead to a reduction in bone mass during each remodeling cycle26–28. Interestingly, disturbances in l-carnitine metabolism were already described in metabolic syndrome, sepsis, or other endocrine impairments29. Nevertheless, diet is one of the sources of l-carnitine, while its absorption and bioavailability are higher in vegetarians. Notably, neither the study nor the control group was selected based on diet, and the patients’ diets were not analyzed, which may be a limitation of the presented research.

Our analysis also revealed significant disturbances in sphingolipids. Most of them were decreased in our study group compared to the control group. Besides their role in energy metabolism, sphingolipids and their byproducts play various functions in bone metabolism30. Sphingosine-1-phosphate (S-1-P) is a bioactive sphingolipid signaling molecule with significant bone anabolic functions and the potential to be a novel bone biomarker and therapeutic target. It is produced by converting ceramide to sphingosine, which is then phosphorylated by either sphingosine kinase isoforms, SPHK1 or SPHK2. Specifically, SPHK2 role in skeletal homeostasis was proved. Sphingosine-1-phosphate stimulates the differentiation, survival, and function of osteoblasts. Moreover, its interaction with the S-1-P receptor induces the recruitment of osteoclast precursors to resorption sites. Hence, it also acts as an osteoclast-osteoblast coupling factor. Most studies suggest that increased levels of S-1-P in the bloodstream could be considered an independent risk factor for fractures related to osteoporosis31,32. Interestingly, PTH does not influence these associations since there was an inverse relationship between S-1-P and PTH levels32. We have confirmed this inverse association, achieving statistically significant results. In the most extensive study, authors concluded that S-1-P can stimulate bone formation and act as a regulatory mechanism when BMD decreases, indicating that S-1-P is positively linked to bone formation33. These differences in various studies, indicating a correlation between elevated S-1-P concentration and increased fracture risk, as well as the positive association of S-1-P with bone formation, might be attributed to the fact that plasma S-1-P concentrations may not always reflect the overall situation in the body. They depend on the local environment and may be much higher in the bloodstream than in other tissues, including bone. Moreover, none of the presented studies has focused on patients with PHPT. Nevertheless, a negative correlation between the levels of S-1-P and PTH is consistent with our study results (corr: − 0.383, p-value: 0.040).

A growing number of studies suggest that bioactive lipids are an essential subgroup of osteokines and myokines, with their plasma alterations linked to aging and the development of osteoporosis34. Previous metabolomics studies revealed alterations in glycerophospholipids, glycerolipids, and sphingolipids in postmenopausal women with decreased BMD35. In our study, a relationship between lipid metabolism and PHPT was easily noticeable. Specifically, glycerophospholipids were significantly dysregulated in patients with PHPT compared to healthy controls. Furthermore, our study group observed a negative correlation between most glycerophospholipids and calcium, age, and PTH. It is noteworthy that these correlations were not identified within the control group. When we consider the decreased levels of a selected subclass of lipids in the plasma in our study group patients, we should remember the potential fat accumulation in patients’ bone tissue with decreased BMD. Indeed, identifying lipid molecules unaffected by confounding factors would enhance the validity of the results. Taken together, our study’s results align with previous untargeted metabolomics studies showing a major role of lipids in bone metabolism and mineralization.

The greatest advantage of our study was its pioneering use of metabolomics to deepen our understanding of the pathophysiology of PHPT. Moreover, metabolomics techniques used in this study are highly sensitive and can detect even trace amounts of metabolites. We presented a novel set of metabolites that can serve as a valuable tool for enhancing our understanding of the pathomechanism of PHPT, thereby offering the potential to influence future considerations in therapeutic strategies. The study was performed with only non-invasive analysis methods, all metabolomics procedures were performed on plasma samples. Metabolomics already has clinical applications in various medical branches, and this study contributes to the expansion of the application of metabolomics in endocrinology by opening many new directions for further studies in this medical area. The main limitation of this study was a small study group providing subtle changes in the levels of metabolites. The study protocol did not include any other dietary limitations besides an overnight fasting requirement. Moreover, although we did not observe the impact of medication use, lifestyle variables, and other confounding factors on metabolite profiles, these possible associations were not further evaluated. Furthermore, we captured metabolite levels only once due to the study design. At the same time, multiple sampling after the surgery is crucial to capture long-term changes in the metabolite profiles. Additionally, direct correlations with ulcer disease, oxidative stress, or atherosclerosis were not established through the evaluation of individual complications within the study group. Another limitation was the lack of densitometry results from the distal 1/3 part of the radius bone, which is particularly significant for patients with PHPT. Furthermore, due to the lack of consent for surgical treatment from some patients in the study group and the lack of complete histopathological examination results, we cannot determine the group's homogeneity in terms of histopathological diagnosis. Therefore, further validation in larger cohorts regarding presented limitations is required during the study planning process.

Despite the study’s limitations and the exploratory character of this project, we obtained an excellent discriminator, allowing for almost 100% correct sample classification. The fact that such an excellent classifier was possible by combining clinical parameters with discriminating metabolites confirms the discovery power of untargeted metabolomics and also the need to discuss metabolomics data in association with clinical data. Although we could not directly connect these metabolites with the clinical manifestation of PHPT, we decided to report them since, in the future, they might be linked to different PHPT manifestations.

PHPT is one of the most commonly diagnosed endocrine disorders. The increased accessibility of diagnostic tools has led to a growing recognition of asymptomatic forms of this condition, including normocalcemic hyperparathyroidism. While searching for biomarkers supporting early disease detection may have limited clinical significance, unraveling the mechanisms contributing to disease development and its various complications is crucial. Moreover, the differences in the acquired panels of metabolites for PHPT suggested the necessity of comparing the metabolic content among different subtypes of PHPT causes, including multiglandular hyperplasia, parathyroid adenoma, and atypical parathyroid adenomas, to determine their specific patterns. An additional clinical challenge lies in the diagnosis of parathyroid cancer, which accounts for 1% of all PHPT cases. In our study, we utilized untargeted metabolomics. Subsequent research endeavors should leverage our preliminary findings for targeted analyses, benefiting from the advantages of targeted metabolomics, including enhanced interlaboratory reproducibility of tests. Hence, we consider our finding as an initial step towards broadening our understanding of the pathophysiology and complications of PHPT. Furthermore, the results presented may be used to develop therapeutic targets and dietary approaches to manage PHPT.

Materials and methods

Study participant

The experimental design initially focused on comparing metabolic profiles between PHPT patients and healthy controls. However, we also included some samples from PHPT patients who underwent surgical intervention. The inclusion of postoperative samples was not planned initially but stemmed from the unique opportunity afforded by our biobank resources. Consequently, these postoperative samples were analyzed to elucidate any metabolic reversions that may indicate the healing effects associated with surgical intervention.

This retrospective, single-center study used clinical data from 28 female patients with PHPT who were qualified for parathyroidectomy and 30 female patients from the control group. Moreover, we included samples from 10 PHPT patients who underwent successful surgical intervention entailing the excision of pathologically altered parathyroid glands, and histopathological examination confirmed the presence of parathyroid adenoma. The effectiveness of the surgery was confirmed in each case by demonstrating normalization of calcium concentrations. The mean calcium concentration was 2.4 mmol/L after successful parathyroidectomy. Despite the initial qualification, not all patients in the study group consented to surgical treatment.

We grouped patients based on the severity of BMD disorders using the results of dual-energy X-ray absorptiometry (DXA). We relied on the T-score measured in the lumbar spine, femoral neck, and total hip area during the interpretation. None of the patients reported a low-energy fracture in their past medical history. Based on DXA measurement, 4 patients were classified with normal BMD, 12 showed osteopenia, and 9 exhibited osteoporosis. Three patients were excluded from this analysis due to the lack of DXA results. In the control group, 16 patients were found to have normal BMD, 13 showed osteopenia, and 1 presented osteoporosis according to the same criteria as applied in the PHPT group (Table 1).

Patients were also stratified according to the presence of hypertension as a marker of cardiovascular complications. In the study cohort, 13 patients had hypertension. Among the subset of post-operative patients, 6 were diagnosed with hypertension, while the control group included 6 patients with hypertension. A group of study participants with hypertension was selected based on their medical history at the time of inclusion in the study.

All patients were diagnosed at the outpatient clinic affiliated with the Department of Endocrinology, Diabetology, and Internal Medicine at the University Clinical Hospital in Bialystok between 2016 and 2020.

This study complied with the Declaration of Helsinki and was approved by the Ethical Committee of Bialystok (no. APK.002.331.2020). All patients provided informed consent before they participated in the study.

Chemicals and reagents

Ultrapure water was used to prepare all aqueous solutions and was obtained using a Milli-Q Integral 3 system (Millipore SAS, Molsheim, France).

LC/MS-grade formic acid, methanol (MeOH), acetonitrile (ACN), and analytical-grade ethanol (EtOH) were purchased from Sigma-Aldrich Chemie GmbH (Steinheim, Germany), as well as zomepirac sodium salt (used as the internal standard (IS) for LC/MS analysis) and methionine sulfone (used as the IS for CE/MS analysis).

Centrifree® Ultrafiltration Centrifugal Filters with regenerated cellulose membrane with a cut-off of 30 kDa were purchased from Millipore (Ireland, Eire).

Plasma sample collection

Fasting blood was collected in vacuum system tubes with EDTA as an anticoagulant. After gentle mixing, the plasma was separated by centrifugation at 1137 × g for 15 min at room temperature. Plasma fractions (0.5 mL each) were collected in Eppendorf tubes and stored at − 80 °C until analysis.

Biochemical parameters

Calcium, PTH, and creatinine levels were measured during the preoperative period as part of the treatment qualification process and within the first 7 months during postoperative observation. These measurements were determined alongside other canonical biochemical parameters at the Clinical Hospital of the Medical University of Bialystok diagnostic laboratory.

LC/QTOF-MS analyses

The sample preparation process was based on deproteinization and liquid–liquid metabolite extraction based on the previously described protocol36. 100 µL of plasma was mixed with 400 µL of cold (− 20 °C) MeOH:EtOH (1:1, v/v). Samples were vortex-mixed for 1 min, incubated on ice for 5 min, and centrifuged for 20 min at 16,000 × g at 4 °C. The resulting supernatant was then transferred directly to a vial for analysis.

Samples were analyzed on a UHPLC system 1290 Infinity II (Agilent Technologies, Waldbronn, Germany), which includes a multisampler, a binary pump, and a thermostated autosampler, coupled with 6545B QTOF (Agilent Technologies). For the separation, 0.5 µL of the extracted sample was injected onto a Zorbax Extended-C18 Rapid Resolution column (Agilent Technologies, 50 × 2.1 mm, 1.8 μm) thermostated at 60 °C. The flow rate was 0.6 mL/min with a mobile phase composed of water with 0.1% formic acid for the aqueous phase and ACN with 0.1% formic acid for the organic phase. The chromatography gradient started from 5% B for the first minute, increasing to 80% B in 6.0 min, then to 100% by 11.5 min, and the starting condition was returned in 0.5 min, allowing re-equilibration until 15.0 min.

Data were collected in positive and negative ESI ionization modes in separate runs and operated in the range from m/z 100 to 1000 for MS analysis and m/z 40–1000 for MS/MS analysis. The nozzle voltage was set to 1000 V, and the capillary voltage was 3000 V with a scan rate of 1.5 scans/s in positive mode and − 4000 V with a scan rate of 1.0 scan/s in negative mode. The drying gas was heated to 250 °C and flowed at 12 L/min under a pressure of 52.0 psi. Additional heating was applied using sheath gas heated up to 370 °C and flowed at a rate of 11 L/min.

For internal mass correction during data acquisition, two reference compounds were infused continuously to the system throughout the whole analysis: m/z 121.0509 (protonated purine) and m/z 922.0098 protonated hexakis (1H,1H,3H-tetrafluoropropoxy)phosphazine (HP-0921) for the positive ionization mode; and m/z 112.9856 (proton-abstracted trifluoroacetic acid (TFA) anion) and m/z 966.0007 (formate adduct of HP-921) for the negative mode.

Samples were randomly analysed throughout the run. To control the system performance and to determine reproducibility, several replicates were analyzed from a homogeneous pool containing a small aliquot of all the samples (Quality Controls, QCs). These QCs were treated like the rest of the samples and injected at the beginning of the batch (10 injections) to equilibrate the system and every eight samples to further monitor the stability of the analysis37.

For the MS/MS analyses, experiments were re-run under identical chromatographic conditions to the primary analysis. The ions were targeted for collision-induced dissociation (CID) fragmentation, based on the previously determined accurate mass and retention time (RT) in MS, using a narrow isolation width (approx. 1.3 Da).

CE/TOF-MS analyses

On the day of analysis, plasma samples were thawed on ice and prepared using the described extraction method38. Briefly, 100 µL of plasma sample was mixed with 100 µL of the solution containing 0.2 M formic acid, 5% (v/v) ACN, and 0.4 mM methionine sulfone. Each sample was vortex-mixed for 1 min, and then transferred to a Centrifree ultracentrifugation device for deproteinization by centrifugation (2000 × g, 70 min, 4 °C). The filtrate was then transferred directly to a vial for analysis.

Samples were analyzed by the CE Capillary Electrophoresis System (7100, Agilent Technologies) coupled with a TOF–MS detector (6224, Agilent Technologies) with an electrospray source. An isocratic pump (1200 Agilent) was used to supply sheath liquid.

The metabolite separation was carried out in a 100 cm long and 50 μm internal diameter fused-silica capillary (Agilent Technologies). Before each analysis, the capillary was flushed for 5 min (950 mbar) with a background electrolyte (BGE) of 1.0 M formic acid solution in 10% MeOH (v/v) at pH 2.0.

Before each analysis, the BGE was automatically replaced. The capillary was then rinsed for 5 min (950 mbar) with BGE, and a voltage of 30 kV was applied for 10 s to displace the BGE ions. After this time, the sample was hydrodynamically injected by applying 50 mbar for 50 s, and for the stacking, the BGE was introduced for 20 s at 100 mbar. The separation voltage was 30 kV, with internal pressure of 25 mbar. The analysis of a single sample was carried out for 40 min. The sheath liquid used for detection in normal polarity consisted of 50% MeOH, 50% water, and reference standards (purine and HP-0921), continuously infused at 6 μL/min.

Data was collected in ESI positive ion mode in separate runs on a TOF operated in full-scan mode from m/z 60 to 1000 with a scan rate of 1.41 scans per second. The drying gas flow rate was 10 L/min, the nebulizer was 10 psi, the voltage was 3500 V, the gas temperature was 200 °C, the skimmer was 65 V, and the octopole was 750 V. The fragmentor voltage was set to 125 V for MS analysis and 200 V for pseudo-MS/MS analysis to induce in-source fragmentation39.

QC samples were employed, similar to the LC/MS analyses. The analysis started with injecting five QCs, and a QC sample was injected at the beginning of the run and, later, every six real samples.

Data processing

LC/MS and CE/MS data were processed following the same scheme and software.

MassHunter Qualitative Analysis software (B.07.00, Agilent Technologies) was used to examine the quality of acquired chromatograms and spectra. The initial inspection covered a chromatogram examination to inspect the signal and retention time (RT, for LC/MS) and migration time (MT, for CE/MS) reproducibility and to estimate potential RT/MT shift. Pressure curves were checked for LC/MS data, and the current was examined for CE/MS data. Moreover, reference mass chromatograms were evaluated to ensure stable ionization conditions.

Data files were processed with MassHunter Profinder software (B.10.00, Agilent Technologies), applying recursive feature extraction. First, the features were created using the “Find Compounds by Molecular Feature” (MFE) algorithm. It finds correlated, co-eluting ions corresponding to the same molecule and merges them into a single value (feature) described by mass, RT, and abundance. The software also eliminated background noise, grouped correlated ions, and aligned the chromatograms. The algorithm used the accurate mass to group the related ions by the state of charge, the isotopic distribution, and the presence of adducts and dimers. The background noise was set to 200 counts for CE/MS data and 600 counts for LC/MS data. To find co-eluting adducts of the same feature following adduct settings were applied: [M+H]+, [M+Na]+, and [M+K]+ in positive ion mode and [M–H]−, [M+HCOO]−, and [M + Cl]− in negative ion mode for LC/MS data, and [M + H]+, [M+Na]+ for CE/MS data. Dehydratation-neutral losses were allowed for all data sets; for CE-MS data processing, the MT range was restricted to 4.0–17.0 min, while the mass range was restricted to m/z 70–600. The RT range was restricted to 1.0–11.5 min for LC/MS data processing, with no mass range restriction.

The features were aligned by RT and mass across all samples, creating a list of unique features through binning. Alignment allowed 1% and 0.1 min of RT variation for LC/MS data, 1% and 0.5 min for CE/MS data, and 20 ppm for measured mass deviation for LC/MS and CE/MS data. The data matrix was forwarded for preliminary examination, where signals common between the blank and biological samples were eliminated, with low-frequency signals detected only in a few samples. Second, the RT/MT and mass data pairs of pre-selected signals were used as input criteria to find the features using “Find by Ion” more accurately. For this targeted search, feature finding, and alignment parameters were the same as for the MFE search. The integration of all extracted peaks was manually curated and corrected if necessary. Finally, the list of ions characterized by the exact monoisotopic mass, RT/MT, and the peak area was created.

Statistical analyses

Before statistical analysis was obtained, LC/MS and CE/MS matrices were forwarded for the quality assurance (QA) procedure. This procedure also included evaluating the data and determining the necessity of data normalization. Different normalization strategies were examined, and the best method was selected to reduce the batch effect. Finally, CE/MS and LC/MS positive data matrices were normalized to the internal standard. In contrast, the LC/MS negative data matrix was normalized to the total signal.

Then, data was filtered based on the signal frequency and quality of the QC samples. To achieve this, only signals detected across at least 75% of QC samples were kept, and subsequently, only features with RSD of the signal across all QC samples below 30% were selected and forwarded for further statistical analysis.

To evaluate the quality of the data, filtered datasets were for Principal Component Analysis (PCA), where clustering of the QC samples was inspected. To visualize the strength and quality of the sample’s separations, orthogonal partial least squares discriminant analysis (OPLS-DA) was used (SIMCA-P+, 13.0, Sartorius, Germany) for two sample group comparison and partial least squares discriminant analysis (PLS-DA) for multiple group comparison.

To select metabolites differentiating between controls and PHPT patients, we used the Mann–Whitney U-test (p-value ≤ 0.05), employing an in-house built script (Matlab, version 2020a, Mathworks). Variations between groups were inspected by calculating the percentage of change. For this, the abundance of each feature was averaged for PHPT patients and controls. The variation was calculated as follows: (Average for controls−Average for controls)/Average for PHPT patients, in %, where a positive value means an increase of the averaged amount of given metabolite in PHPT patients compared to controls. In contrast, a negative value indicates a decrease in the average amount in PHPT patients. Furthermore, we calculated the median, Q1, and Q3 for the level of each metabolite for each comparison (Table 1S in Supplementary Material). Percentage of change, median, Q1, and Q3 were computed using Excel (Microsoft).

We performed a correlation analysis of metabolomics data and clinical metadata to bring clinical context to our results. For this purpose, we used Spearman correlation computing correlation value (corr) and p-value. These calculations were made using an in-house built script (Matlab, version 2020a, Mathworks), and the results of these correlations are presented in the supplementary file.

A receiver operating characteristic (ROC) analysis was performed in MetaboAnalyst 6.0 (http://www.metaboanalyst.ca/), to calculate area under curve (AUC). Optimal cutoffs were shown using the “closest to top-left corner” method. Apart from the classical univariate ROC curve analysis, we performed a combinatorial analysis of manually preselected markers. This analysis was performed using a linear SVM algorithm.

Metabolite annotation

For LC–MS data, metabolite annotation utilized fragmentation spectra obtained through DIA-MS/MS. Ions selected through statistical analysis were specifically isolated and targeted using CID. This approach involved a comprehensive study of accurate mass and isotopic distributions for precursor and product ions. Comparison with spectral data available in open-source databases like HMDB (https://hmdb.ca), METLIN (https://metlincloud2.massconsortium.com), and LIPID MAPS (https://www.lipidmaps.org) was conducted. Metabolites lacking MS/MS spectra in these databases underwent manual annotation, where spectral inspection aided lipid structural elucidation based on established lipid fragmentation patterns40,41.

For CE-MS data, metabolite annotation relied on pseudo-fragmentation spectra generated via in-source fragmentation. To achieve this, several samples were re-analyzed intentionally, enhancing the fragment or voltage and, therefore, inducing in-source fragmentation. Obtained fragmentation spectra were compared with existing data in databases such as HMDB (https://hmdb.ca), METLIN (https://metlincloud2.massconsortium.com), and an in-house library. Moreover, we determined Relative Migration Time (RMT) by dividing the MT of each measured ion by that of the IS. This RMT was compared with the RMT of authentic standards in the in-house library, which allowed for a more confident annotation of measured ions, enhancing the accuracy of metabolite identification.

Metabolites were classified into main categories and sub-classes based on the official LipidMaps classification for lipidic compounds and HMDB classification for polar and ionic molecules42,43. Names for lipids were provided according to the structural information we obtained based on the MS/MS spectra. Apart from their metabolite name, we also provided an abbreviation for lipids. In some cases where insufficient evidence for unambiguous annotation was available, we reported solely the abbreviation instead of using the exact metabolite's name. These abbreviations correspond to the bulk structure of the lipid, indicating the number of carbons and double-bond equivalents but not chain positions or double-bond regiochemistry and geometry.

Pathway analysis

To analyze the impact of particular compounds on biochemical pathways the Pathway Analysis was performed using MetaboAnalyst 6.0 (http://www.metaboanalyst.ca/). Matching the metabolites into the pathways was performed based on the HMDB ID. For 60 metabolites, the HMDB identifiers were found, while 37 were matched in the MetaboAnalyst. Homo sapiens pathways were used; for visualization scatter plot was chosen; for enrichment analysis hypergeometric test was employed; and for topology analysis relative-betweenness centrality was selected.

To explore and visualize connections between discriminating lipids, we employed LIPID MAPS® reaction explorer. Connections from the explorer were completed by reactions reported in the literature.

Conclusions

To our knowledge, this is the first metabolomics study evaluating differences between PHPT patients and healthy controls. Overall, untargeted metabolomics has revealed a distinct metabolic pattern between PHPT patients and healthy controls, offering promising discriminatory capabilities. We have successfully identified potential associations between specific groups of metabolites deviating in PHPT patients and clinical symptoms typical for PHPT, such as peptic ulcer disease, hypertension, and decreased BMD. However, the connection between most altered metabolites and calcium metabolism requires more research. The next step should involve conducting further studies in larger cohorts, using a combination of untargeted and targeted metabolomics, to evaluate changes in plasma metabolites in patients with PHPT before and after parathyroidectomy. Another interesting area may be the use of metabolomics to differentiate between benign and malignant causes of PHPT. In particular, support for diagnosing parathyroid carcinoma, which accounts for 1% of PHPT cases, may have significant clinical importance for practicing physicians. Additionally, the continuation of the study using targeted metabolomics on a cohort selected based on specific clinical criteria could yield highly insightful results. This approach is crucial for developing new tools to detect potential biomarkers and track the development of diseases in individuals diagnosed with PHPT.

Supplementary Information

Supplementary Table 1.

Abbreviations

ACN Acetonitrile

ADMA Asymmetric-dimethyl-l-arginine

AUC Area under curve

BGE Background electrolyte

BMD Bone mineral density

BSP Bone sialoprotein

CAMKII Ca2+/calmodulin-dependent protein kinase II

CAR Carnitines

CaSR Calcium-sensing receptor

CAT-II Carnitine acyltransferase II

CE Capillary electrophoresis

Cer Ceramides

CID Collision induced dissociation

CoA Coenzyme A

Corr Correlation value

DHEA Dehydroepiandrosterone

DMA Dimethyl-l-arginine

DXA Dual-energy X-ray absorptiometry

eGFR Estimated glomerular filtration rate

ESI Electrospray ionization

EtOH Ethanol

FA Fatty acyls

GC Gas chromatography

GGT Gamma-glutamyl transferase

IS Internal Standard

LC Liquid chromatography

LPA Monoacylglycerophosphates

LPC Monoacylglycerophosphocholines

LPE Monoacylglycerophosphoethanolamines

LPI Monoacylglycerophosphoinositoles

MeOH Methanol

MFE Find compounds by molecular feature

MS Mass spectrometry

MT Migration time

NMR Nuclear magnetic resonance

NO Nitrix oxide

NOS Nitric oxide synthase

OPG Osteoprotegrin

OPLS-DA Orthogonal partial least squares discriminant analysis

OPN Osteopontin

OSX Osterix

oxFA Oxidized fatty acids

oxPC Oxidized diacylglycerophosphocholines

PC Diacylglycerophosphocholines

PC O- 1-alkyl,2-acylglycerophosphocholines

PC P- 1-(1Z-alkenyl),2-acylglycerophosphocholines

PCA Principal component analysis

PE Diacylglycerophosphoethanolamines

PE O- 1-alkyl,2-acylglycerophosphoethanolamines

PE P- 1-(1Z-alkenyl),2-acylglycerophosphoethanolamines

PHPT Primary hyperparathyroidism

PLS-DA Partial least squares discriminant analysis

PTH Parathormone

Q1 First quartile

Q3 Third quartile

QCs Quality controls

QA Quality assurance

QTOF Quadrupole time of flight

RANKL Receptor activator of nuclear factor kappa-Β ligand

RMT Relative migration time

ROC Receiver operating characteristic

ROS Reactive oxygen species

RT Retention time

S-1-P Sphingosine-1-phosphate

SDMA Symmetric-dimethyl-l-arginine

SHBG Sex hormone binding globuline

SM Sphingomyelins

SPHK Sphingosine kinase isoforms

TFA Trifluoroacetic acid

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-024-71423-1.

Author contributions

Conceptualization— M.W.P., B.P.G., J.G., M.C., K.S.; Methodology— J.G., J.Si., A.L.G., M.W.P, K.S; Software— J.G., J.Si.; Formal analysis— J.G., J.Si., B.P.G, M.W.P.; Investigation— J.G., J.Si., M.M.H., M.W.P, B.P.G., K.S, M.S.; Data curation— J.G., J.Si.; Writing—original draft— J.G., M.W.P., M.C.; Writig—review and editing—B.P.G., J.Si., M.M.H., K.M., M.S., J.Su., B.P.G., B.P., A.L.G., G.K., A.B., A.P.K., A.A., M.Sz., C.B., K.S., A.K.; Visualization—J.G., M.W.P.; Supervision— A.L.G., C.B., M.C., K.S., A.K.

Data availability

Data is provided within the manuscript or supplementary information files.

Competing interests

The authors declare no competing interests.

Publisher's note

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

These authors contributed equally: Michal Ciborowski, Katarzyna Siewko and Adam Jacek Krętowski.
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