Detection of C-peptide in human hair and nail: a comparison between healthy persons and persons with type 1 diabetes

Identification of diagnostic biomarkers used in the diagnosis of cardiovascular diseases and diabetes mellitus: A systematic review of quantitative studies

AIMS

To perform a systematic review of studies that sought to identify diagnostic biomarkers for the diagnosis of cardiovascular diseases (CVDs) and diabetes mellitus (DM), which could be used in low- and middle-income countries (LMICs) where there is a lack of diagnostic equipment, treatments and training.

MATERIALS AND METHODS

Papers were sourced from six databases: the British Nursing Index, Google Scholar, PubMed, Sage, Science Direct and Scopus. Articles published between January 2002 and January 2023 were systematically reviewed by three reviewers and appropriate search terms and inclusion/exclusion criteria were applied.

RESULTS

A total of 18 studies were yielded, as well as 234 diagnostic biomarkers (74 for CVD and 160 for DM). Primary biomarkers for the diagnosis of CVDs included growth differentiation factor 15 and neurogenic locus notch homologue protein 1 (Notch1). For the diagnosis of DM, alpha-2-macroglobulin, C-peptides, isoleucine, glucose, tyrosine, linoleic acid and valine were frequently reported across the included studies. Advanced analytical techniques, such as liquid chromatography mass spectrometry, enzyme-linked immunosorbent assays and vibrational spectroscopy, were also repeatedly reported in the included studies and were utilized in combination with traditional and alternative matrices such as fingernails, hair and saliva.

CONCLUSIONS

While advanced analytical techniques are expensive, laboratories in LMICs should carry out a cost-benefit analysis of their use. Alternatively, laboratories may want to explore emerging techniques such as infrared, Fourier transform-infrared and near-infrared spectroscopy, which allow sensitive noninvasive analysis.

Application of urine C-peptide creatinine ratio in type 2 diabetic patients with different levels of renal function

OBJECTIVE

This study aims to investigate the effect of single urine C peptide/creatinine (UCPCR) in assessing the islet β Cell function of type 2 diabetes mellitus (T2DM) patients with different renal function.

METHODS

A total of 85 T2DM patients were recruited in this study, all the patients were assigned to one group with estimated glomerular filtration rate (eGFR)≤60 ml·min^-1·1.73 m^-2 and another group complicated with eGFR>60 ml·min^-1·1.73 m^-2. Serum creatinine, urine creatinine, serum fasting C-peptide (FCP), fasting blood glucose (FBG), glycosylated hemoglobin (HbA1C) and 24-hour urinary C-peptide (24hUCP) were measured. The modified homeostasis model assessment-islet β cell function [HOMA-islet (CP-DM)], the modified homeostasis model assessment-insulin resistance [HOMA-IR(CP)] and UCPCR were calculated.

RESULTS

When compared with group eGFR ≤60 ml·min^-1·1.73 m^-2, the levels of UCPCR, FCP, the modified HOMA-IR(CP) and HOMA-islet (CP-DM) were promoted and the concentrations of HbA1C, FPG, creatinine were decreased in the patients of eGFR>60 ml·min^-1·1.73 m^-2 (P<0.05); FCP was uncorrelated with 24hUCP while associated with UCPCR in the patients of eGFR ≤ 60 ml·min^-1·1.73 m^-2; UCPCR was positively correlated with FCP and HOMA-IR(CP) in the T2DM patients with different levels of renal function; the cut-off (UCPCR ≤ 1.13 nmol/g) had 88.37% sensitivity and 95.24% specificity [95% confidence interval (CI):0.919-0.997] for identifying severe insulin deficiency in T2DM patients[area under the curve (AUC) 0.978].

CONCLUSION

UCPCR can be used to evaluate islets β Cell function in T2DM patients with different renal function status.

Scalp hair sweating as a predictor of hair cortisol level in human compared to obesity and other confounders

Inconsistent results were found throughout the literature regarding factors affecting hair cortisol levels. Hair cortisol level in humans was not studied for its associations to scalp hair sweating or hair wash frequency in a patient-based way. Factors affecting hair cortisol levels must be precisely known in order to interpret the results correctly. The aims of the study are to assess if BMI, Perceived Stress Scale (PSS), hair wash frequency, and sweating with scalp hair affect hair cortisol levels. It will assess which of these factors are more significant predictors of hair cortisol levels. In a study on healthy adults, information about history, socio-demographics, PSS, hair wash frequency, hair treatment, and scalp hair sweating were collected, and hair samples were taken and analyzed for their hair cortisol level. Associations of hair cortisol levels with each of the variables were investigated and significant predictors of hair cortisol levels among the variables were found. Mean hair cortisol level in the study participants was 16.84 pg/mg hair. Hair cortisol has a significant positive association with weight, BMI, PSS, and scalp hair sweating, p < 0.05. Scalp hair sweating significantly predicts hair cortisol levels by 12.3%, while other variables did not significantly predict hair cortisol levels, p < 0.05. Scalp hair sweating significantly predicts hair cortisol levels. Age, hair wash frequency, hair treatment, and stressful events have no associations with hair cortisol levels. Although BMI and PSS are associated with hair cortisol levels, they do not significantly predict it. Obesity is significantly associated with profuse sweating, thus the increase in hair cortisol levels in obese individuals could partly be the result of a higher incidence of sweating in these individuals. Thus, scalp hair sweating should be taken into consideration during the study and interpretation of hair cortisol levels.