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Salivary metabolite fingerprint of type 1 diabetes in young children Livia Roberta Piedade de Oliveira, Carla Martins, Tatiana Kelly Silva Fidalgo, Liana Bastos Freitas-Fernandes, Rafaela de Oliveira Torres, Aline Laignier Soares, Fabio C.L. Almeida, Ana Paula Valente, and Ivete Pomarico Ribeiro de Souza J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.6b00007 • Publication Date (Web): 16 Jun 2016 Downloaded from http://pubs.acs.org on June 22, 2016

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Journal of Proteome Research

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Salivary metabolite fingerprint of type 1 diabetes in young children 4 5 6 7 8 10

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Livia Roberta Piedade de Oliveira† 12

1

Carla Martins† 14

13

Tatiana Kelly Silva Fidalgo† 16

15

Liana Bastos Freitas-Fernandes† 17

Rafaela de Oliveira Torres† 19

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Aline Laignier Soares† 21 2

Fabio C. L. Almeida‡ 23

Ana Paula Valente*,‡ 24

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Ivete Pomarico Ribeiro de Souza*,† 26

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28

27 †

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Department of Pediatric Dentistry and Orthodontics, School of Dentistry, Federal

University of Rio de Janeiro, Brazil; 32

‡

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31

National Center for Nuclear Magnetic Resonance – Jiri Jonas, Medical Biochemistry

Institute, Federal University of Rio de Janeiro, Brazil; 35 36 37 39

38

*Correspondence to: 41

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Dr Ivete Pomarico Ribeiro de Souza, Disciplina de Odontopediatria da FO-UFRJ, Caixa 42

Postal: 68066 - Cidade Universitária - CCS, CEP: 21941-971 - Rio de Janeiro – RJ – 4

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Brazil, Phone: + 55 21 25622101. E-mail: [email protected] 46

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Dr Ana Paula Valente, Centro Nacional de Resonancia Magnética Nuclear, Bioquímica 48

Médica, Cidade Universitária - CCS, CEP: 21.941-590 - Rio de Janeiro – RJ – Brazil, 50

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Phone: + 55 21 2566789. E-mail: [email protected] 51 52 53 54 5 56 57 58 59 60

ACS Paragon Plus Environment


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ABSTRACT 4 6

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Metabolomics is an important tool for the evaluation of the human condition, both in 8

7

health or disease. This study analyzed the salivary components of type I diabetic 10

children (DM1) under six years of age, to assess oral health related to diabetes control, 12

1

9

as well as metabolite profiling using NMR. Partial Least Squared Discriminant Analysis 13

(PLS-DA) was used to compare healthy (HG) and uncontrolled DM1 subjects that 15

14

demonstrated a separation between the groups with classificatory performance of ACC 17

= 0.80, R2 = 0.92, Q2 = 0.02 and for DM1 children with glycemia > 200 mg/dL of ACC 18

16

= 0.74, R2 = 0.91, Q2 = 0.06. The metabolites that mostly contributed to the distinction 20

19

between the groups in the loading factor were acetate, n-acetyl-sugar, lactate and sugar. 2

21

The univariate analysis showed a decreased salivary concentration of succinic acid and 24

23

increased levels of lactate, acetate and sucrose in uncontrolled and DM1 children with 26

glycemia > 200 mg/dL. The present study demonstrates that the salivary profile of DM1 28

27

25

differs from that of HG children. It appears that diabetes status control has an important 29

effect on the salivary composition. 30 32

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Keywords: Type 1 diabetes mellitus, saliva, children, metabolomics, NMR. 3 34 35 36 37 38 39 40 41 42 43 4 45 46 47 48 49 50 51 52 53 54 5 56 57 58 59 60


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INTRODUCTION 4 6

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Diabetes mellitus type 1 (DM1) is a common endocrine-metabolic disorder of childhood 8

7

that causes significant organ damage, particularly the eyes, kidneys, nerves and blood 10

vessels with severe consequences in terms of foot wounds and ulcers that commonly 1

9

lead to amputation.1 Patients with uncontrolled diabetes mellitus, either type 1 or 2, are 13

12

at higher risk of periodontal disease.2 These patients have high levels of IL-1β, PGE2 15

14

and TNF when compared with non-diabetic patients with similar periodontal disease 17

severity.3 Chronic hyperglycemia results in the non-enzymatic glycosylation of several 19

18

16

proteins, leading to a severe accumulation of final advanced glycation end products 20

(AGE) in diabetic patients.4, 5 AGE bind to monocytes and macrophages, disrupting 2

21

their function. Regarding dental caries, there is no consensus as to whether diabetic 24 25

subjects are more susceptible.6, 7 The most frequent oral mucosa findings in diabetic 26

patients are tongue alterations (geographic tongue) and candidiasis lesions.8, 28

27

23

9

It has

already been observed that the rate of tooth eruption is accelerated among diabetes 29

patients.10, 11 In adult diabetics, salivary glucose follows the blood levels.12, 13 These 31

30

patients also present a lower flow rate of saliva due to abnormalities in the salivary 3

glands14-16 that lead to alterations in buffer capacity, which influences salivary pH.17 34

32

35

Metabolomics is an important tool for the evaluation of human healthy or 36

disease states and to understand the biochemical features of disease.18-21 Metabolomics 38

37

is a comprehensive assessment of endogenous and exogenous low molecular weight 40

39

metabolites in a biological sample. Analytical methods such as NMR spectroscopy and 42

mass spectrometry are capable of measuring the concentrations of a large number of 43

41

metabolites in body fluids.22 4 45

Metabolomics analysis of urine samples from DM1 subjects demonstrated 47

46

changes in numerous metabolic components, such as alanine, valine, pyruvate, acetate, 49

lactate 51

50

48

and

hydroxybutyric

acid.22-24

Elevated

salivary

ketone

bodies

(α-

hydroxybutyrate) and a decrease in 1,5-anhydroglucitol, an exogenous metabolite of 52

food, have been also associated with diabetes in adults.25, 26 Urine and plasma from a 54

53

juvenile population with diabetes have also been evaluated, demonstrating similar 56

changes to those seen in the adult population, such as increased alanine, lactate and 58

57

5

acetate levels.27-30 60

59

Here, we used nuclear magnetic resonance (NMR) spectroscopy, biochemical tests on salivary samples and clinical parameters to understand the oral health of



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children with type 1 diabetes. Our aim was to evaluate the salivary biochemical profile 5

4

and the oral health of young children with and without type 1 diabetes. 6 7 9

8

EXPERIMENTAL PROCEDURES 1

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Study population 12

This cross-sectional study enrolled pediatric male and female subjects from the 14

13

metropolitan Rio de Janeiro area and was conducted in accordance with the Helsinki 16

Declaration. The Institutional Review Board Ethical Committee at the Federal 18

17

15

University of Rio de Janeiro approved the protocol, including the clinical examination 19

and collection of saliva samples. All subjects’ parents/legal guardians read and signed a 21

20

written informed consent document before their children enrolled in the study. The 23

2

inclusion criteria were: children under 6 years of age in primary dentition, subjects 25

24

diagnosed with type 1 diabetes mellitus (DM1) and gender/age-matched children with 26

good general health. Exclusion criteria included: inability or unwillingness to sign the 28

27

informed consent form, diagnosis of a medical condition which required medication and 30

29

a history of uncharacterized systemic disease, immune compromised individuals, 32

bleeding oral lesions and antibiotic intake within three months prior to saliva collection. 34

3

31

Children were divided into the diabetic group (DM1), which was constituted of 35

type 1 diabetic children in treatment at the Diabetes Ambulatory of the Pediatric 37

36

Hospital from Federal University of Rio de Janeiro (UFRJ) and the healthy group (HG), 39

which consisted of children from the Dental Clinic of the Department of Pediatric 41

40

38

Dentistry and Orthodontics at UFRJ. 42 4

43

Data collection 46

45

Clinical examination 48

Oral mucosal lesions: a complete intra-oral soft tissue examination was performed by a 50

49

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single pediatric dentist with a dental mirror and a gauze square. An assistant recorded 51

all the information. The diagnosis of any oral soft tissue condition was established 53

52

based on onset, duration, oral habits, clinical appearance, history of trauma and previous 5

episodes. The locations and descriptions of the lesions were also recorded and 57

56

54

photographed. 58 59 60


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Dental caries: the condition of each surface was recorded using the decayed, 5

4

missing, filled index (dmf-t). The diagnosis criteria followed those proposed by WHO 7

6

(1997). 8 9 1

10

Oral findings 12

The parents/legal guardians were requested to provide information about oral health 14

13

perception (dry mouth sensation and breath acetone). The subjective experience of 16

xerostomia (dry mouth sensation) was related by the question: “Has your child ever had 18

17

15

dry mouth before?/Have you ever noticed dry mouth in your child?/Has your child ever 19

complained of dry mouth before?”31 The experience of breath acetone was measured 21

20

through the following questions: “Have you ever noticed a sweet, fruity smell on the 23

breath of your child?” and “How often does your child have bad breath?”32 24

2

25 26

Diabetes-related variables 28

27

In order to assess the diabetic status of the patient, medical data related to DM1 was 30

29

obtained from the medical records, such as: 1. years since diagnosis; 2. the child’s age 32

at the time of DM1 diagnosis; 3. the history of complications associated with diabetes; 34

3

31

4. the insulin regimen (multiple daily insulin injections or continuous subcutaneous 35

insulin infusion) and 5. data on the most recent glycosylated hemoglobin (HbA1c) tests 37

36

(not exceeding three months prior), whereby HbA1c 8.5% uncontrolled. The capillary glucose value was recorded before saliva 41

40

38

collection and was measured by the parent/legal guardian at the moment of 42

examination, grouping the children into: normal (< 120 mg/dL), moderate (120-200 4

43

mg/dL) and high levels of blood glucose (< 200 mg/dL). 45 46 48

47

Saliva collection 50

49

Subjects were requested to refrain from eating or drinking one hour before the dental 51

appointment (excluding water), as well as to not brush their teeth and entire mouth for 53

52

the same period of time before saliva collection. Saliva samples from all children were 5

taken at the same time of day (8:00 am to 10:00 am) to avoid fluctuation in the results 56

54

due to the circadian saliva cycle. Salivette® (Sarstaedt, Germany) was used for saliva 58

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collection. Every 30 seconds, a Salivette® was placed in and then removed from the 60

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child’s oral cavity and pressed inside a needleless syringe to extract saliva directly into



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a sterile polypropylene tube, until a volume of 1 mL of unstimulated saliva was 5

4

obtained.33 Each sample was identified with the code of the respective patient. 7

6

Immediately after that, saliva samples were centrifuged at 12,000 g for 1 hour at 4°C 9

(Cientec, CT - 15,000R, Brazil). Prior to analysis, saliva samples were stored at -80°C 10

8

to avoid metabolite degradation and to provide reproducible spectra (data not shown). 34, 12

1

35

13 14 16

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Biochemical parameters 18

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Glucose concentration 19

The salivary glucose level, expressed as mg/dL, was determined using the glucose 21

20

oxidase-peroxidase (GOD-POD) method through a salivary glucose assay (BIOCLIN® 23

2

kits, MG/Brazil) on a spectrophotometer (UV-VIS BECKHAM DU 640) at 500 nm, 25

24

following the manufacturer’s recommendations. 26 28

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Calcium concentration 30

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In order to determine the salivary calcium concentration, expressed in mM, a calcium 32

assay kit was used (QuantiChromTM Calcium Assay Kit - DICA500, BioAssay Systems, 34

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31

Medibena Life Science and Diagnostic Solutions, Austria) on a spectrophotometer (UV35

VIS BECKHAM DU 640) at 612 nm, following the manufacturer’s recommendations. 36 37 39

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Total protein 41

40

The total protein concentration expressed in mg/mL was determined using established 42

colorimetric methods with the use of a spectrophotometer by reading duplicate samples 4

43

at 595 nm on a spectrophotometer (Beckman Coulter Inc., USA). Bovine serum 46

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albumin was used for calibration purposes following the Bradford method. 47 48 50

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Metabolite profiling through nuclear magnetic resonance (NMR) 51

For NMR analysis, 300 µL of each salivary sample was mixed with 300 µL of sodium 53

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phosphate buffer (pH 7.0) and added to 60 µL of deuterium water (D2O; Cambridge 5

Isotope Laboratories Inc., USA) and 10 µL of 20 mM 4,4-dimethyl-4-silapentane-157

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sulfonic acid (DSS; Sigma-Aldrich, Milwaukee, USA). D2O was used as lock to the 58

magnetic field with the sample (lock) and DSS was used as the chemical shift reference 60

59

(δ= 0.00 ppm).


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The spectra were obtained using a 500 MHz NMR spectrometer (Bruker 5

4

Biospin, Rheinstetten, Germany) at 25°C. We used the CPMG (Carr-Purcell-Meiboom7 8

Gill) pulse sequence for 1H spectrum, as described by Fidalgo et al,34, 35 and water 9

suppression using excitation sculpting with gradients with 1024 scans. To avoid 1

10

6

extreme intensity differences between spectra we acquired all spectra with similar 12

signal-to-noise ratio values. 1H-1H-TOCSY experiments with acquisition parameters of 14

13

70 ms mixing time and 2048 X 256 points was performed in order to confirm signatures 16

and assess ambiguities. 17

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18 19

Data and statistical analysis 21

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Children’s data, such as demographic data, diabetes related variables and oral clinical 23

2

findings were described and analyzed through SPSS 20.0 software (IBM, Chicago, 25

24

USA). First, diabetic and healthy children were compared and then controlled and 26

uncontrolled diabetic children were compared regarding their diabetes status using 28

27

unadjusted Fisher’s exact test and two independent sample t-tests. The absolute and 30

29

relative frequencies, as well as the mean and standard deviation were described. The 32

confidence interval was set at 95%. Salivary assay biochemical parameters 34

3

31

(concentrations of glucose, calcium and total protein) and frequencies were also 35

tabulated in SPSS 20.0 software (IBM, Chicago, USA). Additionally, the mean and 37

36

standard deviation were described, as well as the sample distribution. Values from 39

HbA1c and capillary glycemia were correlated with salivary glucose concentrations 41

40

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using paired Student’s t-tests, p < 0.05 was considered statistically significant. 42

The CPMG spectra with water suppression using excitation sculpting with 4

43

gradients and T2 filter from healthy subjects and DM1 were analyzed using the 46

45

statistical software AMIX (Bruker Biospin, Rheinstetten, Germany). After spectral 48

acquisition, edge effects were evaluated by overlaying all spectra using Topspin® 50

49

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(Bruker Biospin, Rheinstetten, Germany). The spectra that could not be corrected for 51

phase and baseline were excluded from the analysis. Resonance assignments were made 53

52

based on 1H-1H-TOCSY experiments and confirmed using the Human Metabolome 5

54

database (http://www.hmdb.ca/). Figure S1 (see the Supporting Information) shows the 57

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assignment of metabolites that were important for the difference among groups. Each 58

NMR spectrum was analyzed by integrating the bucket size regions of 0.04 ppm 60

59

without the water region (5.00-4.50 ppm). The regions 6.54-5.66, 3.78-3.34 and 1.26-



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0.86 were also excluded due to edge effects. The datasets were stored in a matrix with 5

4

the rows representing the saliva samples from subjects and the columns the chemical 7

6

shifts (157 buckets). We used DSS as the internal reference. Data were normalized by 9

the sum of intensities and also using Pareto scaling before applying the PLS-DA 10

8

method.36 The PLS-DA was performed using the initial input variables of each group 1 12

that was stored in the y-table with two columns (0 and 1). 14

13

Using Metaboanalyst 3.0,37 we obtained the predictive performance of the 16

15

models was evaluated using the Q2 (fraction of y variation predicted by model X) and 17

R2 (fraction of X variation modeled) parameters, and the accuracy (ACC) of the models 19

18

was evaluated using a cross-validation based on van Valzen et al. (2008) for each 21

20

model.38 The permutation was performed using the leave in-leave out method with 1000 23

2

permutations for each model. The receiver operating characteristic (ROC) curve was 25

24

generated by Monte-Carlo cross-validation (MCCV) using balanced subsampling. In 26

each MCCV, two thirds of the samples were used to evaluate the feature importance. 28

27

The area under the ROC curve (AUC) and the prediction matrix were obtained to 30

29

evaluate the sensitivity and the specificity. The 95% confidence interval was used for 32

the models. 34

3

31

The loading factor was obtained for each model, and the correspondent 35

metabolites were analyzed using peak intensity and univariate statistical analysis. We 37

36

also used all the buckets for a double check using univariate statistical analysis. 39

Statistical significance was assessed using univariate non-parametric Kruskal-Wallis 41

40

38

and Mann-Whitney tests for the comparisons between the groups (SPSS 20.0 software, 42

IBM, Chicago, USA). A p value < 0.05 was considered statistically significant 4

43

difference. 45 46 48

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RESULTS 50

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A total of 68 children participated in this study, including 34 children with DM1 (DM1) 51

and 34 healthy children (HG) aged from 2 to 6 years old. Half of each group was female 53

52

(n = 17). Table 1 shows the characterization of the study subjects, comparing DM1 and 5

HG, and also includes the controlled and uncontrolled diabetic children, according to 57

56

54

the glycated hemoglobin (HbA1C) levels. The uncontrolled patients corresponded to 58

20% of the diabetic children. The data were also assessed based on blood glycemia at 59 60


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the moment of saliva collection, grouping the children into normal (< 120 mg/dL); 5

4

moderate (120-200 mg/dL) and high levels of blood glucose (< 200 mg/dL). 6 7 9

8

Salivary biochemical parameters 1

10

Figure 1 shows the correlation between salivary glucose values and capillary glucose of 12

DM1 (A) or glycohemoglobin (HbA1c) (B) (p < 0.05). The comparison of salivary with 14

13

capillary glucose showed correlations ranging from 0.29 to 0.43. The correlation 16

between salivary glucose and glycohemoglobin (HbA1c) was 0.61. 18

17

15

Biochemical analysis of total protein (Figure 2A), calcium (Figure 2B) and 19

glucose (Figure 2C-D) in saliva was performed. Figure 2A shows total salivary protein 21

20

in children with and without diabetes, with a similar distribution between both groups (p 23

2

= 0.488). The diabetic children showed slightly less protein in saliva than the healthy 25

24

children. 26

Figure 2B shows that the calcium concentration presented a similar distribution 28

27

in both groups (p = 0.471). The population with higher salivary calcium levels, healthy 30

29

and diabetic, were the youngest children, i.e. less than 3 years old. We believe that these 32

higher calcium levels were related to their higher milk intake. 34

3

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Figure 2C shows the distribution plot of the salivary glucose of healthy and 35

diabetic children and Figure 2D shows the same glucose data separated based on the 37

36

HbA1c results. The data show that DM1 children presented higher levels of salivary 39

glucose when the HbA1c level was above 8.5. Therefore, the controlled diabetic 41

40

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children exhibited similar glucose salivary concentrations. 42 4

43

NMR data 46

Figure 3 shows the representative NMR spectra of salivary samples from uncontrolled 47

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(A), controlled (B) and healthy (C) children according to the HbA1c level upon 49

48

enrollment in this study. We were able to observe differences between each spectrum, 51

50

mainly in the sugar region. These differences were then analyzed using PLS-DA. In 53

Figure 4A, the PLS-DA demonstrates a better distinction of the salivary profiles from 5

54

52

healthy and uncontrolled diabetic children considering HbA1c > 8.5 (4 PC were used; 56

PC1 = 33.02% and PC2 = 27.14%) with ACC of 0.80, R2 of 0.92, and this model 58

57 59

presented an AUC of 0.72 (Table 2). Similar results were obtained when the children 60

were assessed based on the capillary glycemia values obtained at the moment of saliva



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collection. Figure 4C shows the PLS-DA and Figure 4D refers to the loading factors 5

4

comparing healthy and DM1 children with glycemia > 200 mg/dL (4 PC were used; 7 8

PC1 = 28.98% and PC2 = 19.85%) with ACC of 0.74, R2 of 0.91 and AUC of 0.72 9

(Table 2). Figure 4B and D show the loading factor important for the distinction 1

10

6

between groups. The analysis showed that the metabolites acetate, n-acetyl-sugar, 12

lactate, sucrose and glucose are responsible for the distinction between groups (Table 14

13

3). 16

15

When comparing healthy, controlled and uncontrolled children (11 PC were 18

17

used; PC1 = 32.47% and PC2 = 23.18%) in the PLS-DA analysis (Figure 5A), no 19

separation was observed between the groups with an ACC of 0.63 and R2 of 0.72 (Table 21

20

2). Figure 5C shows also no separation between healthy x controlled group (11 PC were 23

used; PC1 = 35.02% and PC2 = 27.14%) with an ACC of 0.70, R2 of 0.87 and AUC of 25

24

2

0.56. Figure 5E shows the similarity between controlled and uncontrolled diabetic 26

children (9 PC were used; PC1 = 40.78% and PC2 = 15.37%) with an ACC of 0.79, R2 28

27

of 0.86 and AUC of 0.52. 30

29

Besides the multivariate analysis, we performed an univariate analysis using 32

non-parametric tests for the evaluation of metabolite intensity. Table 3 demonstrates 34

3

31

that lactate, acetate, succinic acid, sucrose and an ambiguous peak presented statistical 35

differences when comparing healthy and uncontrolled DM1 children and DM1 children 37

36

with glycemia > 200 mg/dL. The succinic acid and ambiguous peak were smaller in 39

DM1 children, while we found an increase in lactate, acetate and sucrose in these 41

40

38

subjects. Although the glucose peak is located in an ambiguous region, we were able to 42

assign these peaks unambiguously using TOSCY spectra. The glucose peak was 4

43

statistically increased in uncontrolled DM1 children and DM1 children with glycemia > 46

45

200 mg/dL in comparison to healthy and controlled children (p < 0.05; Mann-Whitney 48

test). It was not possible to localize citrate in our saliva samples. Pathway analysis 50

49

47

showed that the identified metabolites are related to pyruvate metabolism (Figure 6). 51 53

52

Oral findings 5

To evaluate the importance of oral condition regarding the results obtained in this study, 57

56

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we assessed the clinical parameters of diabetic and healthy children and no correlation 58

was found. The most significant oral findings were geographic tongue (35.3%), reported 60

59

breath acetone (47.1%) and xerostomia (52.9%) among DM1 children. The mean


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duration of disease was 1.29±1.1 and 1.77±1.01 years for the controlled and 5

4

uncontrolled diabetic patients, respectively. Age at diagnosis was around 3 years of age 7

6

for those who were controlled and 2 years of age for those who were not controlled. 8 9 1

10

DISCUSSION 12

This study analyzed the salivary components of type I diabetic children under six years 14

13

of age, using biochemical assays to assess oral health related to diabetes control, as well 16

as metabolite profiling. Our studies show that glucose was detected in the saliva of 18

17

15

healthy and type 1 diabetic subjects, along with a significant correlation between 19

salivary glucose concentrations and HbA1c values..9, 39 We also observed a positive 21

20

correlation between salivary and capillary glucose in diabetic patients. Interestingly, 23

2

statistically increased levels of sucrose were observed in uncontrolled DM1 children 25

24

and DM1 children with glycemia > 200 mg/dL, suggesting that these children ingest 26

larger quantities of sucrose in comparison with healthy subjects. 28

27

Salivary calcium concentration levels may play an important role in caries 30

development.40 In our study, we did not find differences in the calcium concentration 32

31

29

between groups. Furthermore, no difference was observed in the salivary total protein 34

3

level in diabetics compared to healthy patients, regardless of the HbA1c value. These 35

results are in accordance with the literature.39, 41 37

36

Breath acetone is a common oral manifestation of type 1 diabetes, especially in 39

patients with ketoacidosis, and it was also observed in our study.42 It should be pointed 41

40

38

out that an appropriate evaluation of salivary clinical parameters should be emphasized 42

to the dental practitioner when assisting diabetic children.25, 26 4

43

The metabolomic approach has been previously used in type 1 diabetic children, 46

45

using serum and urine as biofluids, but not saliva. An increase in lactate and acetate was 48

described by Zuppi et al.30 in urinary samples from diabetic children and adolescents 50

49

47

with high HbA1c levels using NMR. In our study, we also observed statistically higher 51

levels of lactate and acetate in saliva, in uncontrolled and DM1 children with glycemia 53

52

> 200 mg/dL compared to the healthy ones. Lactic acid may contribute to metabolic 5

acidosis in patients with diabetic ketoacidosis.43 56

54

Our findings also corroborate those of Culledu et al.,27 who highlighted the 58

57

differences of low concentration metabolites between healthy and type 1 diabetic 60

59

children, and noted that the most intense signals are from glucose, other sugars,



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creatinine, creatine and citrate in urinary samples. When the children were grouped and 5

4

analyzed based on capillary glycemia levels, the PLS-DA improved the separation 7

6

between diabetic children with high capillary glycemia levels and healthy subjects. The 9

role of diabetes status control among type 1 diabetic children and adolescents, also 1

10

8

using the urinary metabolite profile obtained by NMR, was emphasized by Stanislaw 12

Deja et al.28 who showed higher levels of alanine, pyruvate and branched amino acids 14

13

among diabetes type1 patients with a high HbA1c level. 16

15

Succinate was found in saliva and at lower levels in uncontrolled DM1 children 18

17

and DM1 children with glycemia > 200 mg/dL when compared with healthy subjects. 19

Succinate is an important intermediate metabolite of the Krebs cycle with a central role 21

20

in energy metabolism; it also participates in signaling with insulinotropic properties 23

related to insulin release.44 According to the succinate mechanism, pyruvate metabolism 25

24

2

is enhanced in the hepatic mitochondria of diabetics, resulting in increased production 26

of hydroxymethylglutaryl (HMG)-CoA, mevalonate and cholesterol.45 Our results are in 28

27

accordance with this hypothesis and show a decrease in succinate levels in uncontrolled 30

29

diabetic patients. Pathway analysis performed with Metabonalist 3.0 demonstrated that 32

the metabolites identified in our work are related to pyruvate metabolism. This opens up 34

3

31

an excellent opportunity for further investigation. 35

We also found increased levels of formate in the saliva of uncontrolled patients. 37

36

Formate is commonly found in saliva and is related to the oxidation of carbohydrates.46 39

38

Since the glucose level was higher in these patients, oxidation was higher as well, 41

40

therefore increasing formate levels. 42

The challenge of interpreting metabolic profiling data in a pediatric population is 4

43

the influence of different environmental factors, lifestyle and genetic background. 46

45

Despite this, we were able to obtain similar profiles within each group. The current 48

study is a preliminary investigation in this type 1 diabetic children population. The 50

49

47

modest number of volunteers reflects the precise eligibility inclusion criteria adopted in 51

this study that excluded patients with other systemic disorders, young children aged 53

52

from 20 to 71 months and the presence of primary teeth, as well as children who had not 5

received proper care or regular medical checkups. In addition, all children that fulfilled 57

56

54

the inclusion criteria were recruited from a pediatric reference center for type I diabetes. 58 59 60


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In conclusion, this study demonstrates that the salivary profile of diabetic 5

4

children differs slightly from the salivary profile of healthy children, probably because 7

6

they receive parental care full-time. Therefore, diabetes metabolic control plays an 9

important role in the salivary composition, since children with uncontrolled diabetes 1

10

8

showed more saliva alterations than controlled children. 12 14

13

ASSOCIATED CONTENT 16

Supporting Information 17

15

Figure S1 shows a 1H-1H-TOCSY representative spectrum of saliva sample used in this 19

18

study 20 21 23

2

AUTHOR INFORMATION 25

Corresponding Author 26

24

Phone/Fax: + 55 21 25622101. E-mail: [email protected] 28

27

Phone/Fax: + 55 21 2566789. E-mail: [email protected] 29 30 32

31

Notes 34

3

The authors declare no competing financial interest. 35 37

36

ACKNOWLEDGMENTS 39

The authors acknowledge Karen Santos and Gileno Santos for the technical support. We 41

40

38

also thank the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq 42

– 303785/2014-4, 306214/2013-0 and 150151/2014-4), Fundação Carlos Chagas Filho 4

43

de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ – E-26/202902/2015, E46

45

26/110490/2014, 2014/2-209396 and E-26/201278/2014), Centro Nacional de 48

Ressonância Magnética Nuclear (CNRMN), Instituto Nacional de Ciência e Tecnologia 50

49

47

de Biologia Estrutural e Bioimagem (InBEB) for the financial support. 51 53

52

REFERENCES 54

60

59

58

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56

5

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(12). Wong DT. Salivary Diagnostics powered by nanotechnologies, proteomics and genomics. J Am Dent Assoc. 2006; 137 (3): 313-321. 50

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(13). Yan W, Apweiler R, Balgley BM, Boontheung P, Bundy JL, Cargile BJ, et al. Systematic comparison of the human saliva and plasma proteomes. Proteomics Clin Appl. 2009; 3 (1): 116-134. 5

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(14). Jurysta C, Bulur N, Oguzhan B, Satman I, Yilmaz TM, Malaisse WJ, et al. Salivary glucose concentration and excretion in normal and diabetic subjects. J Biomed Biotechnol. 2009; 2009 (2009): 430426. 58

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(15). Karjalainen KM, Knuuttila ML, Kaar ML. Relationship between caries and level of metabolic balance in children and adolescents with insulin-dependent diabetes mellitus. Caries Res. 1997; 31 (1): 13-18. 8

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(16). Malicka B, Kaczmarek U, Skoskiewicz-Malinowska K. Selected antibacterial factors in the saliva of diabetic patients. Arch Oral Biol. 2015; 60 (3): 425-431. 10

9 1

(17). Mata AD, Marques D, Rocha S, Francisco H, Santos C, Mesquita MF, et al. Effects of diabetes mellitus on salivary secretion and its composition in the human. Mol Cell Biochem. 2004; 261 (1-2): 137-142. 16

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(18). Aimetti M, Cacciatore S, Graziano A, Tenori L. Metabonomic analysis of saliva reveals generalized chronic periodontitis signature. Metabolomics. 2012; 8 (3): 465-474. 18

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(19). Bertram HC, Eggers N, Eller N. Potential of human saliva for nuclear magnetic resonance-based metabolomics and for health-related biomarker identification. Anal Chem. 2009; 81 (21): 9188-9193. 24

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(20). Zhang L, Xiao H, Karlan S, Zhou H, Gross J, Elashoff D, et al. Discovery and preclinical validation of salivary transcriptomic and proteomic biomarkers for the noninvasive detection of breast cancer. PLoS One. 2010; 5 (12): e15573. 27

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(21). Darpolor MM, Basu SS, Worth A, Nelson DS, Clarke-Katzenberg RH, Glickson JD, et al. The aspartate metabolism pathway is differentiable in human hepatocellular carcinoma: transcriptomics and (13) C-isotope based metabolomics. NMR Biomed. 2014; 27 (4): 381-389. 34

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(22). Lanza IR, Zhang S, Ward LE, Karakelides H, Raftery D, Nair KS. Quantitative metabolomics by H-NMR and LC-MS/MS confirms altered metabolic pathways in diabetes. PLoS One. 2010; 5 (5): e10538. 39

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(23). Fujii S, Maeda T, Noge I, Kitagawa Y, Todoroki K, Inoue K, et al. Determination of acetone in saliva by reversed-phase liquid chromatography with fluorescence detection and the monitoring of diabetes mellitus patients with ketoacidosis. Clin Chim Acta. 2014; 20 (430): 140-144. 43

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(24). Tsutsui H, Mochizuki T, Maeda T, Noge I, Kitagawa Y, Min JZ, et al. Simultaneous determination of DL-lactic acid and DL-3-hydroxybutyric acid enantiomers in saliva of diabetes mellitus patients by high-throughput LC-ESI-MS/MS. Anal Bioanal Chem. 2012; 404 (6-7): 1925-1934. 49

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(25). Barnes VM, Kennedy AD, Panagakos F, Devizio W, Trivedi HM, Jonsson T, et al. Global metabolomic analysis of human saliva and plasma from healthy and diabetic subjects, with and without periodontal disease. PLoS One. 2014; 9 (8): e105181. 5

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(26). Suhre K, Meisinger C, Doring A, Altmaier E, Belcredi P, Gieger C, et al. Metabolic footprint of diabetes: a multiplatform metabolomics study in an epidemiological setting. PLoS One. 2010; 5 (11): e13953. 58

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(27). Culeddu N, Chessa M, Porcu MC, Fresu P, Tonolo G, Virgilio G, et al. NMRbased metabolomic study of type 1 diabetes. Metabolomics. 2012; 8 (6): 1162-1169.



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(28). Deja S, Barg E, Mlynarz P, Basiak A, Willak-Janc E. 1H NMR-based metabolomics studies of urine reveal differences between type 1 diabetic patients with high and low HbAc1 values. J Pharm Biomed Anal. 2013; 83 (1): 43-48. 8

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(29). Oresic M, Simell S, Sysi-Aho M, Nanto-Salonen K, Seppanen-Laakso T, Parikka V, et al. Dysregulation of lipid and amino acid metabolism precedes islet autoimmunity in children who later progress to type 1 diabetes. J Exp Med. 2008; 205 (13): 29752984. 12

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(30). Zuppi C, Messana I, Tapanainen P, Knip M, Vincenzoni F, Giardina B, et al. Proton nuclear magnetic resonance spectral profiles of urine from children and adolescents with type 1 diabetes. Clin Chem. 2002; 48 (4): 660-662. 17

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(31). Fox PC, Busch KA, Baum BJ. Subjective reports of xerostomia and objective measures of salivary gland performance. J Am Dent Assoc. 1987; 115 (4): 581-584. 20

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(32). Al-Zahrani MS, Zawawi KH, Austah ON, Al-Ghamdi HS. Self reported halitosis in relation to glycated hemoglobin level in diabetic patients. Open Dent J. 2011; 5: 154157. 25

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(33). Martins C, Castro GF, Siqueira MF, Xiao Y, Yamaguti PM, Siqueira WL. Effect of dialyzed saliva on human enamel demineralization. Caries Res. 2013; 47 (1): 56-62. 28

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(34). Fidalgo TKS, Freitas-Fernandes LB, Almeida FL, Valente AP, Souza IPR. Longitudinal evaluation of salivary profile from children with dental caries before and after treatment. Metabolomics. 2015; 11 (3): 780-785. 34

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(35). Fidalgo TKS, Freitas-Fernandes LB, Angeli R, Muniz AMS, Gonsalves E, Santos R, et al. Salivary metabolite signatures of children with and without dental caries lesions. Metabolomics. 2013; (9): 657-666. 39

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(36). Ramadan Z, Jacobs D, Grigorov M, Kochhar S. Metabolic profiling using principal component analysis, discriminant partial least squares, and genetic algorithms. Talanta. 2006; 68 (5): 1683-1691. 42

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(37). Xia J, Sinelnikov IV, Han B, Wishart DS. MetaboAnalyst 3.0--making metabolomics more meaningful. Nucleic Acids Res. 2015; 43 (W1): W251-257. 47

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(38). van Velzen EJ, Westerhuis JA, van Duynhoven JP, van Dorsten FA, Hoefsloot HC, Jacobs DM, et al. Multilevel data analysis of a crossover designed human nutritional intervention study. J Proteome Res. 2008; 7 (10): 4483-4491. 50

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(39). Ben-Aryeh H, Serouya R, Kanter Y, Szargel R, Laufer D. Oral health and salivary composition in diabetic patients. J Diabetes Complications. 1993; 7 (1): 57-62. 5

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(40). do Amaral FM, Ramos PG, Ferreira SR. [Study on the frequency of caries and associated factors in type 1 diabetes mellitus]. Arq Bras Endocrinol Metabol. 2006; 50 (3): 515-522. 58

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(41). Lasisi TJ, Fasanmade AA. Comparative analysis of salivary glucose and electrolytes in diabetic individuals with periodontitis. Ann Ib Postgrad Med. 2012; 10 (1): 25-30. 8

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(42). Blaikie TP, Edge JA, Hancock G, Lunn D, Megson C, Peverall R, et al. Comparison of breath gases, including acetone, with blood glucose and blood ketones in children and adolescents with type 1 diabetes. J Breath Res. 2014; 8 (4): 046010. 1

10

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(43). Brouwers MC, Ham JC, Wisse E, Misra S, Landewe S, Rosenthal M, et al. Elevated lactate levels in patients with poorly regulated type 1 diabetes and glycogenic hepatopathy: a new feature of Mauriac syndrome. Diabetes Care. 2015; 38 (2): e11-12. 17

16

15

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(44). Fahien LA, MacDonald MJ, Kmiotek EH, Mertz RJ, Fahien CM. Regulation of insulin release by factors that also modify glutamate dehydrogenase. J Biol Chem. 1988; 263 (27): 13610-13614. 20

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2

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(45). Ertel NH. Diabetes and cholesterol metabolism: the succinate hypothesis. Diabetes Care. 2003; 26 (2): 549-550. 25

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(46). Grootveld M, Silwood CJ. 1H NMR analysis as a diagnostic probe for human saliva. Biochem Biophys Res Commun. 2005; 329 (1): 1-5. 27

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Page 18 of 27

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18 2 3

TABLES 4 5 7

6

Table1: Characterization of study participants. 10

9

8

Uncontrolled - DM1 n=7

p value

62.5

85.7

0.05

0.05

1.61±2.99

0.38±1.06

0.19

9%

0.001

29.2

57.1

0.001

47.1

0%

0.001

41.7

71.4

0.001

Reported xerostomia (%)

52.9

0%

0.001

45.8

71.4

0.001

Hb1AC

7.78±1.29

--

---

7.23±0.95

9.37±0.67

0.05

Capillary glucose level (mg/dL)

203.16±98

--

---

178±75.99

293±110.45

0.05

Parameters

DM1 n= 34

HG n=34

p value

17

16

15

14

13

12

Caries-free children (%)

64.7

31.3%

0.001

dmf-t index

1.38±0.46

5.30±0.94

Presence of oral manifestations (%)

35.3

Reported breath acetone (%)

1

Controlled DM1 n=24

28

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25

24

23

2

21

20

19

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Table 2: Performances indexes of the classifications performed with PLS-DA methods for salivary metabolites from healthy children, controlled DM1, uncontrolled DM1 children with and children with glycemia > 200 mg/dL. 8

7

6

10

9

13

12

1

Model performances ACC R2 Q2 Permutation AUC Sensitivity Specificity 21

20

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17

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15

14

Healthy x Uncontrolled DM1 0.80 0.92 0.02 0.57 0.72 0.67 0.79

Healthy x Glycemia > 200 mg/dL 0.74 0.91 0.06 0.52 0.72 0.70 0.79

Healthy x Controlled DM1 x Uncontrolled DM1 0.63 0.72 0.08 0.39 -

2 23 24 25 26 27 28 29 30 31 32 3 34 35 36 37 38 39 40 41 42 43 4 45 46 47 48 49 50 51 52 53 54 5 56 57 58 59 60


Healthy x Controlled DM1 0.70 0.87 0.17 0.04 0.56 0.50 0.63

Controlled DM1 x Uncontrolled DM1 0.79 0.86 0.08 0.61 0.52 0.61 0.50


Page 20 of 27

Table 3: Metabolite intensities shown as the mean (arbitrary units), confidence interval, bucket region (ppm) and statistical analysis from multivariate 20 analysis using the 1loading factor and 2univatiate analysis. A B C D Healthy Controlled DM1 Uncontrolled DM1 Glycemia > 200 mg/dL Metabolites ppm region p-value 5

4

3

2

1

Mean (CI 95%)

6

1

10

9

8

7

Lactate1,2

1.30 - 1.38

Acetate1,2

1.90 - 1.94

16

15

14

13

n-Acetyl sugar1

2.02 - 2.06

Succinic acid2

2.42 - 2.46

Ambiguous peak1,2

3.14 - 3.18

Glucose1,2

3.86 - 3.90

Glucose1,2

3.90 - 3.94

Sucrose1,2

4.06 - 4.10

Lactate1,2

4.10 - 4.18

Formate1

8.42 - 8.46

12

19

18

17

24

23

2

21

25 27

26 28 30

29 31

34

3

32

2.23 x 10-2 (1.05 x

10-2 -

(4.27 x

10-2

Mean (CI 95%)

2.97 x 10-2 10-2)

4.89 x

12.49 x 10-2 - 24.2 x

10-2)

(0.71 x

10-2

- 6.59 x

3.72 x 10-2 10-2)

19.55 x 10-2 (1.04 x

10-2

- 55.7 x

Mean (CI 95%)

10-2)

(1.83 x

10-2 -

8.29 x

3.94 x 10-2 10-2)

14.46 x 10-2 (5.90 x

10-2 -

22.4 x

10-2)

(1.67 x

10-2 -

8.40 x

10-2)

14.20 x 10-2 (5.90 x 10-2 - 24.4 x 10-2)

3.96 x 10-2

3.37 x 10-2

2.80 x 10-2

3.01 x 10-2

(1.25 x 10-2 - 6.11 x 10-2)

(2.33 x 10-2 - 4.51 x 10-2)

(2.28 x 10-2 - 5.47 x 10-2)

(2.28 x 10-2 - 5.47 x 10-2)

1.34 x 10-2

1.03 x 10-2

1.01 x 10-2

1.02 x 10-2

(0.90 x 10-2 - 2.49 x 10-2)

(0.45 x 10-2 - 1.60 x 10-2)

(0.86 x 10-2 - 1.20 x 10-2)

(0.79 x 10-2 - 1.49 x 10-2)

0.38 x 10-2

0.35 x 10-2

0.17 x 10-2

1.19 x 10-2

(0.12 x

20

Mean (CI 95%)

10-2

- 0.79 x

10-2)

(0.00 x

10-2

- 0.06 x

10-2)

(0.02 x

10-2

- 0.42 x

10-2)

(0.67 x 10-2 - 1.69 x 10-2)

3.31 x 10-2

3.45 x 10-2

3.98 x 10-2

3.90 x 10-2

(2.28 x 10-2 - 5.42 x 10-2)

(1.52 x 10-2 - 5.08 x 10-2)

(3.28 x 10-2 - 6.53 x 10-2)

(2.66 x 10-2 - 6.53 x 10-2)

3.62 x 10-2

3.36 x 10-2

4.21 x 10-2

4.10 x 10-2

(2.29 x

10-2

- 5.48 x

10-2)

(1.36 x

10-2

- 4.78 x

10-2)

(3.53 x

10-2

- 6.29 x

10-2)

(2.81 x 10-2 - 6.29 x 10-2)

1.47 x 10-2

1.73 x 10-2

2.09 x 10-2

1.94 x 10-2

(0.73 x 10-2 - 2.12 x 10-2)

(0.33 x 10-2 - 2.73 x 10-2)

(1.33 x 10-2 - 3.81 x 10-2)

(0.87 x 10-2 - 3.81 x 10-2)

1.19 x 10-2

1.27 x 10-2

1.68 x 10-2

1.48 x 10-2

(0.67 x

10-2

- 1.69 x

10-2)

0.22 x 10-2 (0.00 x

10-2

- 1.21 x

(0.27 x

10-2 -

1.83 x

10-2)

0.10 x 10-2 10-2)

(0.00 x

10-2

- 1.39 x

(0.97 x

10-2 -

2.43 x

10-2)

0.09 x 10-2 10-2)

(0.00 x

10-2

- 0.91 x

(0.72 x

10-2 -

2.27 x

10-2)

0.08 x 10-2 10-2)

(0.05 x 10-2 - 0.91 x 10-2)

0.014AC 0.016AD 0.004AB NSD 0.011AC 0.011AD 0.043AC 0.025BC 0.010BC 0.043AC 0.021AD 0.043AC 0.021AD NSD

The Kruskal-Wallis test and Mann-Whitney test were performed (p < 0.05) for the statistical analysis. ABCD indicate statistical comparisons among groups; CI = confidence interval; 1 metabolite from loading factor analysis and 2 metabolite from unvaried non-parametric analysis; NSD = no statistical difference. The absence of statistical analysis values means absence of significant difference (p > 0.05). 38

37

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39 40 41 42 4

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FIGURES 4 5 6 7 8 9 10 1 12 13 14 15 16 17 18 19 21

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Figure 1: A- Correlation between salivary glucose and capillary glycemia for normal (red), moderate (blue) and high (purple) values in diabetic patients. B-Correlation between salivary glucose and HbA1c values. 24

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Figure 2: A- Number of children and the salivary total protein concentration distribution comparing HG and DM1 (3 mg/mL) B- Number of children and the salivary calcium concentration distribution comparing HG and DM1. C- Number of children and the salivary glucose concentration distribution, comparing HG and DM1. D- Number of diabetic children and the salivary glucose concentration distribution, comparing healthy, controlled (HbA1c < 8.5) and uncontrolled children (HbA1c > 8.5). *means no statistical difference and ***means a statistical difference (p < 0.05). 45

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Figure 3: Representative 1H-NMR spectra of total saliva from uncontrolled diabetic children (A), controlled (B) and healthy children (C). Resonance assignments of specific metabolites are identified. 3

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Figure 4: A- PLS-DA showing the sample separation of saliva from healthy (green) and uncontrolled DM1 children (black); B- Loading factor comparing healthy and uncontrolled DM1 children; C- PLS-DA showing the sample grouping of saliva from healthy children (green) and DM1 children with high capillary glycemia levels (>200 mg/dL) (red); D- Loading factor comparing healthy and children with high capillary glycemia levels (>200 mg/dL); 49

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Figure 5: A- PLS-DA of saliva from healthy (green), controlled diabetic (blue) and uncontrolled diabetic children related to HbA1c (black); B- Loading factor comparing healthy, controlled and uncontrolled diabetic children; C- PLS-DA showing the sample saliva from healthy children (green) and controlled DM1 children (blue); D- Loading factor comparing healthy and controlled DM1children; E- PLS-DA showing the sample saliva from controlled (blue) and uncontrolled DM1 children (black); FLoading factor comparing controlled and uncontrolled DM1 children. 58

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Figure 6: Pathway impact resulted from the principals metabolites obtained from Metabonalist 3.0. 30

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3 TOC 200x165mm (150 x 150 DPI)

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Salivary Metabolite Fingerprint of Type 1 Diabetes ... - ACS Publications

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