Assignments for resonances present in the 60 MHz 1H NMR spectra shown in Figure 1 (coupling patterns for these are also provided). Resonances highlighted in red are visible in both LF (60 MHz) and HF (400 MHz) 1H NMR profiles, whereas those in blue are observable but not readily quantifiable at the lower operating frequency in view of overlap with or close localization to other biomolecule signals, or being below the lower limits of quantification (LLOQ) values for their assigned metabolites. The identities of selected signals were confirmed via reference to .
Implementations of high-field nuclear magnetic resonance (NMR) facilities into metabolomics studies are unfortunately restricted by their large dimensions, high costings, and specialist technical staff requirements. Therefore, here the application and practical advantages offered by low-field (60 MHz), compact NMR spectrometers for probing the metabolic profiles of human saliva was explored, as was their value in salivary metabolomics studies. Saliva samples were collected from cigarette smoking (n = 11) and non-smoking (n = 31) human participants. 1H NMR spectra were acquired on both low-field (60 MHz) and medium-field (400 MHz) spectrometers. Metabolomics analyses were employed to evaluate the consistencies of salivary metabolite levels determined, and their abilities to distinguish between smokers and non-smokers. Low-field 1H NMR analysis detected up to 15, albeit permitted the reliable quantification of 5, potentially key diagnostic biomolecules simultaneously (LLOQ values 250–400 μmol/L), although these were limited to those with the most prominent resonances. Such low-field profiles were also found to be suitable for salivary metabolomics investigations, which confirmed the successful discrimination between smoking and non-smoking participant sample donors. Differences observed between these groups were largely ascribable to upregulated salivary levels of methanol, and its metabolite formate, in the smoking group, but higher smoking-mediated concentrations of acetate, propionate and glycine may arise from a diminished salivary flow-rate in these participants. In conclusion, determination of salivary biomolecules using low-field, benchtop 1H NMR analysis techniques were found to be valuable for bioanalytical and metabolomics investigations. Future perspectives for the applications of this non-stationary NMR technique, for example for the on-site ‘point-of-care’ testing of saliva samples for diagnostic oral disease screening purposes at dental surgeries and community pharmacies, are considered.
- compact low-field NMR analysis
- NMR spectroscopy
- NMR-linked metabolomics
- bioanalytical chemistry
- salivary biomarkers
- oral diseases
- tobacco smoking
- salivary biomarkers for smoking
‘State-of-the-art’ developments of novel devices and facilities for the metabolic screening of biofluids through ‘omics’ strategies provide an encouraging and thoroughly emerging outlook for future healthcare management prospects, including those focused on the diagnosis of diseases and/or their prognostic stratification . Indeed, high-field (HF) nuclear magnetic resonance (NMR) facilities are routinely used for metabolomics investigations in order to rapidly recognize unusual metabolic patterns in patients suffering from a wide range of diseases. However, deployments in healthcare settings have been prohibitively impacted by the large sizes and costs of the instrumentation required for these purposes .
Imbalances in the human metabolome have long been associated with health-mediated disturbances, with ancient societies employing very crude methods to assess biofluids , including the smell and taste of urine samples to detect urinary ketone bodies and glucose, respectively, in cases of diabetes, for example. However, more recently there have been many notable developments in this research area, and these have given rise to the development of multicomponent metabolic profile analysis by many researchers since the early 1970s [4, 5, 6, 7]. Whilst tandem liquid-chromatographic- and gas-chromatographic-mass spectrometric methods (LC-MS and GC-MS, respectively) can be employed for the reliable and sensitive determination of low concentrations of metabolites in biofluids, these approaches suffer from issues associated with matrix effects, and both are destructive techniques, which usually require a substantial knowledge of sample composition, and the likely identities of key biomarker analytes, prior to analysis . Conversely, high-resolution NMR-based metabolomics analysis offers a highly selective means of simultaneously identifying a wide range of biomolecules present in complex biofluids at a minimal detectable concentration of ≤5 μM, providing an untargeted methodology ideal for the analysis of a very large number of biomolecules simultaneously  (up to 120 or so and
Although previous applications of low-field (LF) rather than HF NMR spectroscopy to the multicomponent analysis of intact or near-intact biofluids, or other biological media, have been severely limited, our laboratory has paved the way forward for the performance of such studies. Indeed, one of our recent investigations explored the ability of a LF (60 MHz) NMR spectrometer to provide valuable urinary metabolite data for the monitoring of type 2 diabetes (T2D) in humans . Indeed, this application displayed a high level of chemopathological classification success, although this is perhaps not completely unexpected, since uncontrolled or poorly controlled T2D samples all contain quite high levels of glucose (both 1H NMR-distinguishable α- and β-anomers), along with the ketone bodies acetoacetate, acetone and 3-d-hydroxybutyrate, whereas little or none of these biomolecules are normally detectable in healthy control samples. However, one of the major advantages of the 1H NMR technique is that it can simultaneously detect and monitor abnormal levels of a range of further metabolites involved in human disease pathology and associated co-morbidities, for example excessive urinary creatinine concentrations arising from kidney dysfunction and damage in T2D, together with hypoglycaemic drugs such as metformin .
Saliva serves as a multifunctional biofluid which plays important roles in facilitating the chewing, swallowing and tasting of foods, and also the regulation of oral flora; hence, it is of much importance for the maintenance of overall health in humans. Indeed, human saliva comprises an agglomerate hypertonic ‘solution’ which contains oral mucosal exudates, salivary acini and gingival crevicular fluid (GCF) . Since this biofluid is readily accessible, it offers much potential as a medium for the identification and monitoring of established or potential biomarkers for human diseases, particularly oral health conditions, but not exclusively so. Indeed, it’s collection can be self-performed by participants with minimal training and without clinical supervision. Therefore, here we have employed optimized LF NMR-based metabolic profiling strategies for the global analysis of human saliva in order to assess the viabilities of these compact instruments for biomedical applications in metabolic profiling, metabolomics, oral health assessments, and potentially future screenings of the efficacies of oral healthcare products. Coupling of this LF salivary analysis technique to MV analysis strategies may indeed serve to facilitate the development of favourable outcome strategies for such investigations, and therefore here we also demonstrate, for the first time, the applications of LF NMR-based metabolomics protocols to the distinction of saliva samples collected from non-smoking and tobacco cigarette-smoking subjects. The future clinical monitoring applications of this novel technique are discussed.
2. Materials and methods
2.1 Saliva sample collection from human participants
Whole mouth saliva samples (n = 61) were collected from healthy human participants (n = 42, age range 21–65 years, 14 male/28 female), of whom 31 were non-smokers, and 11 were regular ‘mild-to-heavy’ smokers of tobacco cigarettes (an average of 3 to ≥20 cigarettes per day, with 44% of these smoking ≥20 per day). These non-smoking and tobacco-smoking participant groups were age-matched, with their mean ± SEM ages being 42.52 ± 2.25 and 41.91 ± 2.70 years respectively. All ethical considerations were in accord with those of the Declaration of Helsinki 1975 (7th amendment made in 2013). All samples were collected with informed consent and approved by the Faculty of Health and Life Sciences Research Ethics Committee, De Montfort University, Leicester, UK (reference no. 1082). Participants were fasted for a 12-h period prior to providing saliva specimens. All participants were requested to refrain from all oral activities, including eating, drinking, tooth-brushing and smoking, etc. throughout this period, including the short,
2.2 Sample preparation and 1H NMR analysis
All reagents and chemicals were purchased from Sigma-Aldrich (Gillingham, UK) unless otherwise stated. Aliquots (500 μL) of HSS samples were treated with 60 μL of pH 7.00 phosphate buffer (1.00 mol/L) containing 0.04% (w/v) sodium azide, and 50 μL of 2H2O containing 0.05% (w/v) sodium 3-(trimethylsilyl)propionate-2,2,3,3-d4 (TSP) (final added HSS concentration 238 μmol/L). TSP served as an internal chemical shift reference and quantitative calibration standard; sodium azide acted as a microbicidal preservative in order to protect against the artefactual generation and/or consumption of microbial catabolites during the sample transport and preparation stages; phosphate buffer served to control sample pH values; and 2H2O acted as a field frequency lock. Admixtures were then homogenized and transferred to 5-mm diameter NMR tubes (Norell, Morganton, NC, USA). LF 1H NMR spectra were acquired on a 60 MHz Magritek Spinsolve Ultra Benchtop spectrometer (Magritek GmbH, Philipsstr. 852068, Aachen, Nordrhein-Westfalen, Germany) with 64 and/or 384 scans, acquisition and repetition times of 6.4, and 10 or 15 s respectively, and a pulse angle of 90
1H NMR resonances present in each HSS spectrum acquired were routinely assigned by a consideration of chemical shift values, coupling patterns and coupling constants with reference to literature sources, and where required, two-dimensional 1H-1H correlation and total correlation (COSY and TOCSY respectively) spectra were acquired to confirm these assignments. Median 1H NMR signal-to-noise (STN) ratios were determined from the formula STN = 2.50A/Npp, where A represents resonance height, and Npp the highest peak-to-peak noise difference determined at each chemical shift region selected. Lower limits of detection and quantification (LLOD and LLOQ respectively) values were computed as 3- and 10-times these median STN values. HSS spectral resonances were manually-bucketed, and their intensities determined using
Calibration and Bland-Altman dominance plots of the 1H NMR-determined concentrations of acetate, propionate, formate, glycine and methanol featured matched analysis sample datasets, with determinations made on these salivary metabolites at both 60 and 400 MHz operating frequencies. All determinations which were found to have none detectable (nd, specifically those with values <LLOD) at both operating frequencies utilized, were removed from the datasets. As recommended , corresponding 1H NMR profiles of blank samples, which were prepared as outlined above, but with HPLC-grade water in place of HSSs, were acquired, and their ‘noise’ intensities at the appropriate δ values were included in these calibration plots. Spectra were acquired on replicate (n = 3) preparations of such blank samples for these purposes.
2.3 UV and MV statistical and metabolomics analyses
2.3.1 UV analysis
A paired sample t-test was applied to test for any differences between LF 60 MHz spectra acquired with relaxation delays of 10 or 15 s, and an
An analysis-of-covariance (ANCOVA) experimental design was employed to test the statistical significance of the ‘between-smoking status’ source of variation, along with the potential effects of essential demographic variables recorded on participant sample donors, on salivary acetate, formate, propionate, glycine and methanol concentrations (Eq. (1)). Overall, this model evaluated the influences of the ‘between-participant’ (random) effect P(
An additional ANCOVA model explored the significance of any differences between the two operating frequencies in the non-smoking group only, and in this experimental design, the ‘between-ages’, ‘between-genders’ and ‘between-participants’ sources of variation were also evaluated, as was the age × gender first-order interaction effect (Eq. (2)). In this design, O
2.3.2 MV metabolomics analysis
Principal component analysis (PCA) was primarily employed to identify any possible outlier samples present in the 60 MHz operating frequency 1H NMR dataset, and in total 8 of these were found and classified as such, and then subsequently removed prior to the performance of further MV analysis. PCA was then employed to determine the reproducibility of replicate salivary metabolite determinations made on the LF benchtop spectrometer, and this check was performed with n = 9 duplicated salivary samples randomly selected from the smoking group of participants, with all the above five metabolite variables included, and not non-smokers in view of the restricted availability of data on salivary methanol and, to a lesser extent, formate concentrations above their LLOQ indices. For this PCA analysis, salivary metabolite concentrations were not constant sum-normalized (CSN), nor transformed, and nor auto- or Pareto-scaled.
For the major objective of this study, a LF benchtop 1H NMR-based metabolomics investigation featured a comparison of saliva specimens collected from the non-smoking and tobacco-smoking participants, and for this purpose all the above 5 potential predictor variables, determined via TSP-normalization as described above, were incorporated. For these purposes, the dataset was product quotient normalized (PQN), generalised log10 (glog)-transformed, and Pareto-scaled prior to MV analysis, which involved PCA, partial least squares-discriminatory analysis (PLS-DA), orthogonal partial least squares-discriminatory analysis (OPLS-DA), random forest (RF) and agglomerative hierarchical clustering (AHC) techniques (
PQN converts 1H NMR metabolomics profiles according to an overall estimate of the most probable ‘dilution’ influence , and for saliva this includes reductions in salivary flow-rate (SFR), which has been reported to be significantly reduced in cigarette smokers . This strategy usually involves the subtraction of a mean or median column-bucketed reference spectral profile from those of either all or a sub-set of study samples, and for this investigation the non-smoking control group was employed for this purpose.
At an operating frequency of 60 MHz, some missing data in the MV datasets were unavoidable in view of resonance overlap complications. Therefore, for this metabolomics model, these randomly missing values,
Multivariate ANOVA (MANOVA) was performed using
3.1 Outline of 1H NMR analysis results: detection and quantitative determination of salivary biomolecules at 60 MHz operating frequency
Figure 1 shows the 60 MHz 1H NMR profile of a typical human salivary supernatant (HSS) sample obtained with 384 scans, a process which took 60 min to acquire. This spectrum contains clear 1H resonances assignable to acetate (signal 7) and methanol (signal 14), which are both ascribable to their ▬C
|Resonance code||Chemical shift (||Assignment|
Acceptable benchtop 60 MHz spectra were also obtained with only 64 scans, which involved a 10 min acquisition time, although all 1H NMR resonances therein were, however, notably affected by significantly lower signal-to-noise (STN) parameters, as expected. All carboxylic acid anions detectable represent oral microbial catabolites, and the simultaneous 1H NMR measurement of their salivary concentrations in this manner may offer significant potential regarding the provision of valuable diagnostic and prognostic screening information for dental surgeons and oral healthcare specialists alike, especially those regarding conditions such as dental caries and periodontal diseases, as previously noted in studies performed with conventional specialist NMR laboratory-based medium- and high-resolution 400 and 600 MHz operating frequency spectrometers respectively [19, 20]. Moreover, amino acids detectable such as glycine are potentially derived from the actions of proteolytic bacteria on salivary proteins, although there are, of course, also host sources of this metabolite , as indeed there are for some of the organic acid anions, such as acetate.
Although the 60 MHz spectral profiles of HSS specimens investigated are largely dominated by the highest intensity resonances therein (i.e. those assigned to biomolecules of the highest salivary concentrations such as acetate and propionate), this technique also lent itself to the assignment of lower intensity signals, and the quantification of salivary metabolites present at significantly lower concentrations. Indeed, the singlet resonances of formate, methanol and glycine were clearly resolved from potential overlapping signals, and therefore appeared suitable for quantification purposes. As previously documented , one major source of salivary methanol in humans is the ingestion of cigarette smoke. However, in some HSS specimens, the lactate-CH3 function doublet resonance at δ = 1.33 ppm was also clearly visible in 60 MHz spectra, most especially those with quite high millimolar salivary levels. Although highly variable, reported mean levels for salivary lactate in adults are 0.1–20.3 mmol/L . However, our previously reported overall mean lactate concentration in saliva samples collected from a pre-fasting healthy control population was found to be 13.3 mmol/L, although those of replicate samples from n = 20 participants also varied substantially, i.e. from 0.08 to 100.9 mmol/L .
TSP was present in HSS analyte solutions at an added level of only 238 μmol/L (i.e. a 238/9 = 26.44 μmol/L single 1H nucleus equivalent value), and because this was also one of the most intense signals present in the spectra acquired (
The relaxation delay employed for acquisition of LF 60 MHz 1H NMR spectra, i.e. 10 or 15 s, did not give rise to any differences in the estimated salivary concentrations of acetate, propionate, formate, glycine and methanol (paired sample t-tests performed on untransformed datasets, n ≥ 10 matched spectra for each biomolecule). These data demonstrated that a relaxation delay of 10 s was sufficient for full T1 relaxation of the 1H environments involved.
3.2 Comparative evaluations of the 1H NMR profiles of human salivary supernatants at 60 and 400 MHz operating frequencies
All resonances detectable in the above 60 MHz 1H NMR profiles were, of course, also readily detectable in corresponding 400 MHz spectra, and as expected, median resonance STN values were much greater, and hence corresponding LLOQ and LLOD parameters were substantially lower with this MF spectrometer. Indeed, signals arising from metabolites with detectable but not quantifiable signals in the 60 MHz profiles acquired, for example the malodorous amines DMA and TMA, and the amino acids phenylalanine and tyrosine, were readily quantifiable at the 400 MHz operating frequency. Additionally, in view of the much improved spectral resolution and quality, metabolites which were only detectable but unquantifiable, or completely undetectable at 60 MHz, were also detectable and predominantly quantifiable at MF, and these included leucine (▬C
Plots of salivary acetate, glycine and methanol concentrations determined on the LF 60 MHz NMR facility
In order to explore the significant LF spectral overestimations of propionate further, the complete chemical shift span of its -CH3 function triplet at 60 MHz (0.92–1.18 ppm) was integrated in the corresponding 400 MHz spectra acquired, and apparent propionate concentrations obtained in this manner were then plotted against those determined at 60 MHz (Figure 2). As expected, this plot was found to display a much-improved agreement between the two estimated concentration values, with 95% CI values for the regression coefficient and y-intercept both covering unity and 0.00 respectively (r = 0.985). However, a comparison of these bioanalytical calibration plots indicated only a relatively marginal interference from the above potentially overlapping, albeit minor signals for the direct determinations of propionate at 60 MHz when its salivary level was
Nevertheless, a novel statistical approach was employed for determining the maximal concentration determinable at this operating frequency. Firstly. an alternative ANCOVA model (model 2) was designed and employed for statistical analysis of the non-smoking participant group only (Eq. (2)), and this included a consideration of variance contributions from differential ages, genders, participants and spectrometer operating frequencies, plus the age × gender first-order interaction effect. Secondly,
3.3 PCA and MANOVA assessments of the bioanalytical precision of metabolite determinations at 60 MHz operating frequency
Subsequently, principal component analysis (PCA) was utilized to monitor the precision of duplicate, ‘between-assay’ sample analyses with this facility in a model containing 5 LF NMR-detectable salivary metabolites with quantifiable concentrations (acetate, propionate, formate, glycine and methanol). For this purpose, n = 9 duplicate sets of samples were randomly drawn from the tobacco-smoking group, and analyzed in different assay batches conducted with LF 60 MHz spectral acquisitions made on separate work-days. With the exception of two sets of matched analyses, there was a good agreement of both PC1 and PC2 scores obtained for all duplicate samples analyzed (Figure 4). Moreover, MV analysis-of-variance (MANOVA) of these experimental data found that the ‘between-replicates’ and the participant × replicate interaction effects were both statistically insignificant (
3.4 UV evaluations of metabolic differences between smoking and non-smoking participants
Mean ± SEM salivary levels of acetate, propionate, formate, glycine and methanol determined using the LF 60 MHz benchtop spectrometer for both the non-smoking and smoking groups are provided in Table 2. Univariate statistical analyses of these data revealed that the salivary concentrations of methanol, glycine and acetate were all significantly higher in smokers, the substantial upregulation in methanol observed being concordant with results previously acquired by Percival et al. .
|Metabolite||Non-smokers (mmol/L)||Smokers (mmol/L)|
|Acetate||5.54 ± 0.95 (range 0–30.92)||11.20 ± 2.00 (range 0–30.20)*|
|PropionateϮ||1.89 ± 0.18 (range 0–5.59)||2.43 ± 0.35 (range 0–4.95)|
|Formate||0.14 ± 0.04 (range 0–0.95)||0.37 ± 0.09 (range 0–1.27)|
|Glycine||0.34 ± 0.04 (range 0–1.04)||0.80 ± 0.17 (0–2.49)*|
|MethanolϮϮ||0.00 ± 0.00 (range 0–0.20)||0.48 ± 0.11 (0–1.58)**|
The above model 1 experimental design featured the ‘between-age (A
|Age × gender||Fixed||ns||ns||ns||ns||ns|
|Age × Smoking Status||Fixed||ns||ns||ns||ns||ns|
|Gender × Smoking Status||Fixed||<0.05||ns||ns||<0.01||ns|
3.5 Distinction between non-smoking and smoking participants using LF NMR-based MV metabolomics analysis
1H NMR-based metabolomics analysis was, for the first time, utilized to determine the value of LF benchtop NMR analysis to discriminate between the salivary 1H NMR profiles of the cigarette smoking
Similarly, AHC analysis confirmed an at least partial distinction between these two HSS sample classifications (Figure 5(c)). However, despite a reasonable-to-good level of discrimination, 2 of the samples donated by non-smoking participants appeared within the smoking group cluster, and 5 of the smoking ones are clustered with the non-smoking cohort. This is possibly explicable by self-reporting bias in the former case, but for the latter, it is possible that the participants concerned smoked their last cigarette some considerable period of time prior to sample collection.
Finally, an RF model demonstrated that 88 and 95% of the non-smoking and smoking participant donor samples were correctly classified (91% classification success rate overall). Hence, these MV comparisons demonstrate, for the first time, an important 1H NMR-based metabolomics application which employs a non-stationary LF benchtop spectrometer. Four of the non-smoking participants were misclassified as smokers, whereas only one of the smokers was classified as a non-smoker with this analysis strategy.
3.6 Potential clinical and diagnostic significance of salivary metabolite tracking with LF benchtop NMR devices
In this study we have demonstrated the rapid, virtually non-invasive analysis of human saliva using a compact, LF 60 MHz benchtop NMR spectrometer. The major aim of the pilot investigations described here was to establish the abilities of LF NMR spectrometers to effectively perform the simultaneous quantitative analysis of a series of biomolecules in human saliva, and to consider their potential future value as trackable agents for the monitoring of selected oral diseases. We also critically examined limitations of the applications of this technique and their potential outcomes. Moreover, for the first time we have also applied ‘state-of-the art’ NMR-linked metabolomics techniques to distinguish between saliva samples donated by both non-smokers and tobacco cigarette-smoking participants in a case study.
Overall, we have shown that 60 MHz 1H NMR measurements can be employed to reliably determine selected salivary metabolite concentrations, with potentially much scope for future diagnostic and prognostic applications to oral health conditions. The authors are fully aware of issues related to resonance overlap at low magnetic fields in view of the dependence of resonance frequencies on static magnetic field strength, in which 1H NMR signals appear to be broader, and with a spectrally-wider chemical shift range for all multiplets, with decreasing spectrometer operating frequencies. Such studies are therefore highly challenging in view of these inherent analyte selectivity considerations, along with potential sensitivity issues expected at such lower magnetic field strengths. However, we found that complications arising from overlapping resonances in complex biofluid samples such as saliva were minimal, or were circumventable, for the most common prominent resonances, and also for those located within relatively interference-free spectral regions. The ability of this LF NMR technique to detect exogenous agents present in this biofluid should also be considered, for example the detection of drugs and other xenobiotics in saliva within specified time zones following their oral ingestion by humans.
Intriguingly, human saliva may afford a transference-dependent ‘
In 2002, the study reported by Silwood et al. , was described in Ref.  as the very first untargeted metabolomics investigation of human saliva. This unique investigation successfully identified a total of 63 biomolecules therein using 600 MHz 1H NMR analysis, and quantified 11 key microbial-derived catabolites, 9 of which displayed very highly significant ‘between-participant’ components of variance. These markers included acetate and lactate, with excessive levels of their corresponding acids being viewed as primary end-point biomarkers involved in the aetiology of dental caries . However, formic and pyruvic acids (both present at millimolar or near-millimolar concentrations in human saliva, the former being higher in smokers as found here) are stronger acids than lactic acid, and therefore may also exert pro-cariogenic activities. Hence, the LF 60 MHz NMR detection and quantification of salivary formate demonstrated in the current study may indeed offer some diagnostic and/or prognostic monitoring potential. However, propionate, along with
Furthermore, whilst modifications in salivary formate concentrations have been previously linked to between-gender differences (i.e. elevated concentrations in males) , which we did not find here, our data provides evidence that one potential source for it is the oxidative metabolism of methanol as an ingested and/or inhaled environmental toxin. Indeed, salivary methanol levels are markedly upregulated via tobacco smoke inhalation , and/or alternative exogenous sources such as dietary ones. In our study, although salivary formate levels were
Interestingly, the study reported in  found that salivary citrate, lactate, pyruvate and sucrose levels were significantly upregulated in saliva samples collected from smokers over those of non-smokers, and formate was downregulated therein. Moreover, although salivary methanol concentrations were
Likewise, citrate is only very rarely 1H NMR-detectable in our HSS samples collected according to our rigorous overnight fasting protocol, and therefore its direct derivation from human diets (which serve as rich sources of this metabolite), and insufficient periods of fasting in Ref. , remains a strong possibility.
4. Limitations of the study
One major limitation of the application of LF salivary 1H NMR analysis is inherent resonance overlap problems experienced at this operating frequency, which is much lower than those of more traditional MF or HF spectrometers coupled with restrictively-sized superconducting magnets (for example, those of 400–750 MHz operating frequencies). Hence, these resonance superimposition problems clearly give rise to major analytical limitations, notably in complex multianalyte biofluid spectra. Therefore, for future prospective studies involving the quantification of salivary biomolecules and/or xenobiotics, at least some level of caution should be applied when employing such devices. Unfortunately, these complications increase substantially when integrating resonances of higher first-order and more complex coupling patterns, which may markedly hinder such intensity determinations. Nevertheless, with the exception of the propionate-CH3 resonance, these interference problems may be considered minimal for the determination of major, high concentration salivary metabolites, specifically those with prominent resonances in LF spectra obtained. Indeed, these signals have only low or negligible levels of superimposition with lower intensity signals, or appear in relatively ‘spectroscopically clean’ regions of the spectra acquired, for example formate. Therefore, although the LF 60 MHz 1H NMR profiles of HSSs are largely commanded by resonances of the highest intensity and with simple coupling patterns and orders, and/or those arising from metabolites of high concentrations, in principle this novel NMR strategy potentially offers valuable quantitative information for those detectable at lower levels, most notably with the advent of higher-field compact benchtop instruments which operate at frequencies of 80 or 100 MHz.
As observed and further explored in Ref. , an additional limitation of 1H NMR-based metabolomics studies featuring LF NMR spectrometers is the intensity-diminishing effects of the H2O/HOD signal presaturation protocol, notably for signals located close to its chemical shift value (δ = 4.8 ppm). However, although such effects substantially influence the C1-
Finally, for the
In this study, we have evaluated the viability of low-field (LF) benchtop 1H NMR analysis technologies for metabolomics investigations of human saliva. This novel, convenient and near-portable technique was used to detect and/or potentially quantify and hence monitor up to 15 potentially healthcare-impacting oral metabolites in healthy human saliva, a strategy prospectively offering much potential for the direct ‘on-site’ testing of biofluids from patients affected by oral health or related conditions at clinical locations.
We report the detection of typical salivary metabolites, including propionate, acetate, succinate, glycine, dimethylamine, trimethylamine, methanol, formate and aromatic amino acids all at an operating frequency of only 60 MHz. However, quantification of the salivary levels of biomolecules was limited to only five of those with the most prominent 1H NMR signals, although succinate (singlet signal, δ = 2.405 ppm) could also be considered if not significantly quantitatively impacted by salivary pyruvate (
This study also demonstrated for the first time that LF 1H NMR-linked metabolomics analysis could be employed to discriminate between the salivary biomolecular profiles of tobacco-smoking and non-smoking participants. When detectable at sufficient salivary concentrations, these analyses may be transferable to the detection and perhaps quantification of exogenous agents such as ingested drugs in this biofluid. Moreover, the approach outlined here may indeed offer some forensic applications involving the identification of illicit drugs in human saliva samples collected at crime scenes, provided that such analytes have resonances present in spectrally-clear regions unaffected by overlapping signal interferences arising from endogenous species.
Notwithstanding, currently the authors recommend that future LF NMR investigations of human saliva and oral diseases should focus on the more prominent resonances present in spectra acquired, most notably those with relatively simple first-order coupling patterns (i.e., singlets, doublets, triplets, etc.) for quantification purposes, with the exception of those in which the targeted analyte has resonances(s) located in ‘spectroscopically-clear’ regions. However, future developments in LF benchtop NMR technologies operating at higher frequencies may serve to provide effective solutions to these issues.
The authors also suggest that this technique may also serve valuable diagnostic and/or prognostic tracking purposes in a clinical context for a range of human diseases. In principle, this non-stationary multi-analytical technique may be employed as a sensitive means of monitoring the salivary metabolic status of patients suffering from oral diseases, and potentially also physiologically-remote conditions, directly at dental surgery and primary healthcare sites, hospitals and hospital laboratories, and perhaps also community pharmacies. In this manner, such patient-contact sites may offer significant diagnostic and monitoring potential for oral health practitioners.
The authors wish to express the highest level of thanks to all project participants for the kind donation of their saliva samples for 1H NMR analysis. We are also very grateful to Magritek GmbH, Aachen, Germany for the academic loan of a 60 MHz Magritek Spinsolve Ultra Benchtop NMR Spectrometer for research purposes.
Conflict of interest
The authors declare no conflict of interest.
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