Optical screening of diabetes mellitus using non-invasive Fourier-transform infrared spectroscopy technique for human lip

Use of a Miniaturized Near-Infrared Spectroscopy Device for Type 2 Diabetes Mellitus Screening: Pooled Analysis of the Pilot ANODE01 and ANODE02 Studies

BACKGROUND

Current diabetes screening methods are complex, inefficient, and inconvenient, requiring resource-intensive blood sampling. With the increasing prevalence of underdiagnosed type 2 diabetes mellitus (T2DM) worldwide, particularly in low-resource settings and underserved populations, affordable and sustainable mass-screening tools are crucial.

METHODS

The accuracy and safety of the miniaturized near-infrared (NIR), full-spectrum spectroscopy Glyconics-DS System in detecting T2DM risk status was assessed by pooling data from two independent pilot studies: ANODE01 and ANODE02. Rapid NIR assessments of glycated nail keratin in 60 repeated spectral readings of fingernails from individuals with or without T2DM focused on detecting dichotomized diabetes risk status (glycated hemoglobin [HbA1c] <6.5%) based on chemometric prediction models, clinical specificity/sensitivity, and true/false positive outcomes. An HbA1c point-of-care assay served as an internal control.

RESULTS

Over 12 000 NIR spectral readings were collected in a female-dominant (58.5%), mostly non-smoking (80.0%), diverse cohort of 200 participants (n = 100 with/n = 100 without T2D). The selected chemometrics prediction model on a diagnostic HbA1c cut-off of 6.5% showed a specificity of 92.9% (95% confidence interval [CI] = 88.5-97.4) and a sensitivity of 34.2% (95% CI = 23.4-45.1), with 71.5% concordance. Chemometric predictions were consistent and reproducible with no relevant impact of anthropometric variables, concomitant conditions/medications, smoking status, and number of spectral assessments/nail or hand dominance on NIR assessment. No adverse events or suspected de novo T2D cases were reported.

CONCLUSIONS

This pooled analysis of two independent studies demonstrates the clinical feasibility and high specificity of rapid NIR spectral assessment of T2DM risk, with potential for screening, early detection, and sustainable management across health care settings.

Effects of Skin Blood Flow Fluctuations on Non-Invasive Glucose Measurement and a Feasible Blood Flow Control Method

In non-invasive blood glucose measurement (NBGM) based on near-infrared spectroscopy, fluctuations in blood flow represent a primary source of interference. This paper proposes a local blood flow pre-stimulation method in which the local skin is heated to dilate blood vessels and increase blood flow. This approach aims to mitigate the impact of environmental temperature variations, emotional fluctuations, and insulin secretion on blood flow, thereby enhancing the accuracy of glucose measurement. To evaluate the effectiveness of this method, a blood flow interference experiment was conducted to compare the stability of the measured spectra with and without blood flow pre-stimulation. The results demonstrated that the pre-stimulation method presents good anti-interference capabilities. Furthermore, 45 volunteers underwent oral glucose tolerance tests (OGTTs) as a part of the validation experiments. In these tests, the forearm skin blood flow of 24 volunteers was pre-stimulated using elevated temperature, while the skin of the remaining 21 subjects was maintained at a natural temperature level without stimulation. The results indicate that compared to the non-stimulated condition, the correlation between the optical signal at 1550 nm and blood glucose levels was significantly enhanced under the pre-stimulation condition. Furthermore, the root mean square error (RMSE) of the linear prediction model was reduced to just 0.92 mmol/L. In summary, this paper presents a feasible blood flow control strategy that effectively stabilizes internal blood flow, thereby improving the accuracy of NBGM.

Exploration of macromolecular phenotype of human skeletal muscle in diabetes using infrared spectroscopy

Introduction

The global burden of diabetes mellitus is escalating, and more efficient investigative strategies are needed for a deeper understanding of underlying pathophysiological mechanisms. The crucial role of skeletal muscle in carbohydrate and lipid metabolism makes it one of the most susceptible tissues to diabetes-related metabolic disorders. In tissue studies, conventional histochemical methods have several technical limitations and have been shown to inadequately characterise the biomolecular phenotype of skeletal muscle to provide a holistic view of the pathologically altered proportions of macromolecular constituents.

Materials and methods

In this pilot study, we examined the composition of five different human skeletal muscles from male donors diagnosed with type 2 diabetes and non-diabetic controls. We analysed the lipid, glycogen, and collagen content in the muscles in a traditional manner with histochemical assays using different staining techniques. This served as a reference for comparison with the unconventional analysis of tissue composition using Fourier-transform infrared spectroscopy as an alternative methodological approach.

Results

A thorough chemometric post-processing of the infrared spectra using a multi-stage spectral decomposition allowed the simultaneous identification of various compositional details from a vibrational spectrum measured in a single experiment. We obtained multifaceted information about the proportions of the different macromolecular constituents of skeletal muscle, which even allowed us to distinguish protein constituents with different structural properties. The most important methodological steps for a comprehensive insight into muscle composition have thus been set and parameters identified that can be used for the comparison between healthy and diabetic muscles.

Conclusion

We have established a methodological framework based on vibrational spectroscopy for the detailed macromolecular analysis of human skeletal muscle that can effectively complement or may even serve as an alternative to histochemical assays. As this is a pilot study with relatively small sample sets, we remain cautious at this stage in drawing definitive conclusions about diabetes-related changes in skeletal muscle composition. However, the main focus and contribution of our work has been to provide an alternative, simple and efficient approach for this purpose. We are confident that we have achieved this goal and have brought our methodology to a level from which it can be successfully transferred to a large-scale study that allows the effects of diabetes on skeletal muscle composition and the interrelationships between the macromolecular tissue alterations due to diabetes to be investigated.

Prediction of type 1 diabetes with machine learning algorithms based on FTIR spectral data in peripheral blood mononuclear cells

The incidence of autoimmunity is increasing, to ensure timely and comprehensive treatment, there must be a diagnostic method or markers that would be available to the general public. Fourier-transform infrared spectroscopy (FTIR) is a relatively inexpensive and accurate method for determining metabolic fingerprint. The metabolism, molecular composition and function of blood cells vary according to individual physiological and pathological conditions. Thus, by obtaining autoimmune disease-specific metabolic fingerprint markers in peripheral blood mononuclear cells (PBMC) and subsequently using machine learning algorithms, it might be possible to create a tool that will allow the diagnosis of autoimmune diseases. In this preliminary study, it was found that the peak shift at 1545 cm-1 could be considered specific for autoimmune disease type 1 diabetes (T1D), while the shifts at 1070 and 1417 cm-1 could be more attributed to the autoimmune condition per se. The prediction of T1D, despite the small number of participants in the study, showed an inverse AUC = 0.33 ± 0.096, n = 15, indicating a stable trend in the prediction of T1D based on FTIR metabolic fingerprint data in the PBMC.

Fourier-transform infrared spectroscopy: a universal optical sensing technique with auspicious application prospects in the diagnosis and management of autoimmune diseases

Autoimmune diseases (AIDs) are poorly understood clinical syndromes due to breakdown of immune tolerance towards specific types of self-antigens. They are generally associated with an inflammatory response mediated by lymphocytes T, autoantibodies or both. Ultimately, chronic inflammation culminates in tissue damages and clinical manifestations. AIDs affect 5% of the world population, and they represent the main cause of fatality in young to middle-aged females. In addition, the chronic nature of AIDs has a devastating impact on the patient's quality of life. It also places a heavy burden on the health care system. Establishing a rapid and accurate diagnosis is considered vital for an ideal medical management of these autoimmune disorders. However, for some AIDs, this task might be challenging. Vibrational spectroscopies, and more particularly Fourier-transform infrared (FTIR) spectroscopy, have emerged as universal analytical techniques with promising applications in the diagnosis of various types of malignancies and metabolic and infectious diseases. The high sensitivity of these optical sensing techniques and their minimal requirements for test reagents qualify them to be ideal analytical techniques. The aim of the current review is to explore the potential applications of FTIR spectroscopy in the diagnosis and management of most common AIDs. It also aims to demonstrate how this technique has contributed to deciphering the biochemical and physiopathological aspects of these chronic inflammatory diseases. The advantages that can be offered by this optical sensing technique over the traditional and gold standard methods used in the diagnosis of these autoimmune disorders have also been extensively discussed.

Live-cell ATR-FTIR spectroscopy as a novel bioanalytical tool for cell glucose metabolism research

Current novel drug developments for the treatment of diabetes require multiple bioanalytical assays to interrogate the cellular metabolism, which are costly, laborious and time-consuming. Fourier-transform infrared (FTIR) spectroscopy is a nondestructive, label-free, sensitive and low-cost technique that is recently found to be suitable for studying living cells. The aim of this study is to demonstrate that live-cell FTIR can be applied to study the differences in glucose metabolism in cells in normal culturing medium and cells treated in high glucose (a diabetes model) in order to highlight the potential of the technique in diabetes research. Live HepG2 cells were treated in normal glucose (3.8 mM; control) or high glucose (25 mM) medium and were measured directly using the FTIR approach. Principal component analysis was used to highlight any possible correlated changes 24, 48 and 72 h after treatments. FTIR spectra of live cell treated in normal and high glucose medium have shown significant differences (p < 0.05) for all treatment time. The control cells have seen an increased in the absorbance at 1088, 1240 and 1400 cm^-1, which are associated with phosphate stretching mode vibrations from phosphorylated proteins and DNA back bone; and symmetric stretching mode vibration of COO^- from fatty acids, amino acids, lipids and carbohydrate metabolites. However, the high glucose treated cells have shown a different changes in the 1000-1200 cm^-1 region, which is linked to the glycogen and ATP:ADP ratio. In conclusion, live-cell FTIR can be a low-cost method for the studies of metabolic changes in cells.

Retinal oxidative stress at the onset of diabetes determined by synchrotron FTIR widefield imaging: towards diabetes pathogenesis

Diabetic retinopathy is a microvascular complication of diabetes that can lead to blindness. In the present study, we aimed to determine the nature of diabetes-induced, highly localized biochemical changes in the neuroretina at the onset of diabetes. High-resolution synchrotron Fourier transform infrared (s-FTIR) wide field microscopy coupled with multivariate analysis (PCA-LDA) was employed to identify biomarkers of diabetic retinopathy with spatial resolution at the cellular level. We compared the retinal tissue prepared from 6-week-old Ins2^Akita/+ heterozygous (Akita/+, N = 6; a model of diabetes) male mice with the wild-type (control, N = 6) mice. Male Akita/+ mice become diabetic at 4-weeks of age. Significant differences (P < 0.001) in the presence of biomarkers associated with diabetes and segregation of spectra were achieved. Differentiating IR bands attributed to nucleic acids (964, 1051, 1087, 1226 and 1710 cm^-1), proteins (1662 and 1608 cm^-1) and fatty acids (2854, 2923, 2956 and 3012 cm^-1) were observed between the Akita/+ and the WT samples. A comparison between distinctive layers of the retina, namely the photoreceptor retinal layer (PRL), outer plexiform layer (OPL), inner nucleus layer (INL) and inner plexiform layer (IPL) suggested that the photoreceptor layer is the most susceptible layer to oxidative stress in short-term diabetes. Spatially-resolved chemical images indicated heterogeneities and oxidative-stress induced alterations in the diabetic retina tissue morphology compared with the WT retina. In this study, the spectral biomarkers and the spatial biochemical alterations in the diabetic retina and in specific layers were identified for the first time. We believe that the conclusions drawn from these studies will help to bridge the gap in our understanding of the molecular and cellular mechanisms that contribute to the pathobiology of diabetic retinopathy.

Comparison of FTIR transmission and transfection substrates for canine liver cancer detection

FTIR spectroscopy is a widely used technique that provides insights into disease processes at the molecular level.