The use of hair detect (and monitor) chronic hyperglycaemia

Background: Diabetes mellitus is a major public health problem resulting in about 5 million deaths per year. This metabolic disorder is characterized by hyperglycaemia, which results in debilitating and life-threatening complications. It is, therefore, vital for diabetics to monitor and control their blood glucose levels in order to keep them below 7mmol/L while fasting and below 9mmol/L after meals. Chronic estimates of glucose control of 8-12 weeks are obtained using glycated haemoglobin A1 (HbA1c). Non-invasive, less expensive methods of monitoring long term glycaemic control may be useful. Since scalp hair consists of about 80% protein, which is subject to non-enzymatic glycation, and growing hair has a rich blood supply exposing it to free glucose, it is likely that hair can be used as an alternative substrate for monitoring chronic hyperglycaemia. Subjects and Methods: Scalp hair and a blood samples (for HbA1c) were collected from 46 diabetic and 46 healthy control subjects. There were 26 diabetic adults (30-70 years), recruited from the outpatient clinic at Groote Schuur hospital and 20 children (7-18 years) recruited from the diabetic clinic at the Red Cross children's hospital. There were 29 healthy control adults (26-65 years) and 17 children (7- 17 years) recruited from the Groote Schuur and Red Cross hospitals respectively. History of chemical hair treatment was recorded for each participant. Hair samples were washed using 1% sodium dodecyl sulphate and analysed using Fourier transform infrared- attenuated total reflection (ATR-FTIR) spectroscopy. Spectra were analysed using statistical software (SIMCA, Umetrics) to determine whether the hair of diabetics was distinguishable from hair of healthy controls as well as whether spectra correlated with HbA1c levels of participants. Hair amino acid concentrations were also analysed as it is known that circulating amino acid concentrations are altered in people with diabetes. Results and discussion: The Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) models between spectra obtained from hair of diabetic participants and spectraobtained from control hair show good separation and predictive ability. When ATR-FTIR spectra were analysed in four groups: children with natural hair, adults with natural hair, adults with dyed hair and adults with relaxed hair, the models yielded predictive abilities (Q2(cum)) ranging from 0.753 to 0.85 and classification abilities ranging from 97.97 to 100%. This demonstrated excellent separation and predictive ability for controls vs. diabetics. Partial least squares (PLS) revealed a good correlation between hair FTIR spectra and participant HbA1c levels (R2 ranging between 0.8067 and 0.9296). These results demonstrated the possibility to use ATR-FTIR alongside multivariate data analysis to detect hyperglycaemia and monitor blood glucose levels via prediction of HbA1c levels from the hair spectra. Amino acid analysis supported the OPLS-DA classifications, as the largest differences were seen between age and chemically treated hair groups. Amino acid results reinforced the necessity to classify spectra into groups in order to distinguish between hair spectra from diabetics or controls, as well as to predict HbA1c. Twelve amino acids (Asp, Glu, Pro, Gly, Met, Ile, His, Lys, Arg, Amm, Cys, Leu) were significantly different between hair from adults and children, eight amino acids (Ala, Cys, Val, Met, Lys, Amm, Ser, Tyr) were statistically significantly different between natural and dyed hair and only up to four amino acids (Gly, Val, Met, Ile in children or Val, Phe, Amm, Arg in adults) were significantly different between diabetic and control groups. Conclusion: There is a need for non-invasive means of monitoring chronic hyperglycaemia. This study demonstrated the ability to distinguish between the hair of diabetics and controls as well as the ability to predict HbA1c levels from hair using ATR-FTIR. However, factors such as age and chemical treatment, which affect the chemical properties of hair, like amino acid levels, should be considered first. This would lead to promising prospects for long term blood glucose monitoring, due to the ability to estimate hair growth rate, and greater insights into the timing and development of diabetic complications. ATR-FTIR relatively simple to use, requires minimal sample preparation and does not require the use of expensive consumables. This technology could, potentially, be adapted into a primary health point of care or home screening or monitoring device for long-term hyperglycaemia, which would assist in early detection and preventing the progression of debilitating complications