Investigating the use of PEth, CDT and MCV to evaluate alcohol consumption in a cohort of homeless individuals- A comparison of different alcohol biomarkers

Stability of PEth 16:0/18:1, 16:0/18:2, 16:0/20:4, 18:0/18:1, 18:0/18:2, and 18:1/18:1 in authentic whole blood samples (at room temperature)

Phosphatidylethanol (PEth) is a direct alcohol biomarker to monitor individuals' drinking behavior that has gained recognition in clinical and forensic settings. The increasing application of the marker makes investigation of the preanalytical handling necessary, and analyte stability deserves major attention. This study was conducted to investigate the change of six PEth homologues' concentration, stored in authentic samples of EDTA blood over a course of 30 days at room temperature (n = 62). The stability criterion of concentration being ±15% of the original concentration was fulfilled at mean for 10, 3, 2, 5, 2, and 7 days for PEth 16:0/18:1, 16:0/18:2, 16:0/20:4, 18:0/18:1, 18:0/18:2, and 18:1/18:1, respectively. Regarding all homologues, there were samples in which concentration had declined by >15% or by more than the critical difference on day 1. Overall, calculated concentration declines were very inhomogeneous, with inter-sample differences of 43%-73% after 30 days. PEth 16:0/18:2, 16:0/20:4, and 18:0/18:2 declined to a greater extent than PEth 16:0/18:1. Blood alcohol concentration was measured >0.1‰ in 25 samples. Three of the six samples that exceeded 115% of initial concentrations were positive for blood alcohol. The study results add to the previously reported information on PEth stability and firstly look at six homologues in comparison. Due to the high scatter of stability among the samples and the observed poor stabilities in some, it can be concluded that transportation and storage times, especially if cooling cannot be provided, must be kept short. If analyzing from dried blood, spotting should preferably be conducted at the site of sampling.

The complementarity of phosphatidylethanol in whole blood and ethyl glucuronide in hair as biomarkers for the monitoring of alcohol use

Monitoring long-term alcohol use and/or abstinence is essential in clinical and medico-legal cases. Analysis of ethyl glucuronide (EtG) in hair provides information on alcohol consumption over several months. However, there is a lag time between ethanol consumption, incorporation of EtG in the hair bulb and hair growing out of the scalp. Phosphatidylethanol (PEth) 16:0/18:1 analysis in whole blood has a detection window of 2-4 weeks, allowing for the detection of recent alcohol consumption. In this study, 2340 paired samples (of hair and venous whole blood from 1170 individuals) were analysed for EtG in hair (hEtG) and PEth 16:0/18:1 in venous whole blood. PEth 16:0/18:1 and hEtG results were subdivided into three categories according to the consensus of SoHT (hEtG) and PEth-NET (PEth): abstinence/low, moderate or excessive alcohol consumption. For hEtG analysis, 446 individuals presented abstinence/low alcohol consumption, of which 2% were classified as excessive alcohol users through PEth 16:0/18:1 analysis. This suggests excessive alcohol consumption in the weeks before sample collection. Out of 483 individuals classified as heavy alcohol users based on hEtG analysis, 14% showed abstinence/low alcohol consumption for PEth 16:0/18:1 analysis, implying that these subjects stopped drinking 2-4 weeks before sample collection. Our results show that the analysis of the two different biomarkers can lead to a more accurate categorisation of individuals. Therefore, we emphasize that for the retrospective investigation of alcohol use, it is necessary to include two alcohol use biomarkers with different detection windows.

[Health of homeless individuals during the COVID-19 pandemic]

The living situation and health of homeless people differs from the general population in many ways. It is reasonable to assume that the homeless population has been particularly vulnerable during the coronavirus disease 2019 (COVID-19) pandemic. This narrative review will summarize the current literature on the health and care of homeless people during the COVID-19 pandemic. The literature research was performed between December 2022 and February 2023. In addition to the current national and international literature, findings from the "National Survey on the Psychiatric and Somatic Health of Homeless Individuals" (NAPSHI study) will be synopsized, examining psychiatric and somatic diseases as well as the care for homeless people in Germany.Homeless individuals are often mentally and physically ill and have limited access to the regular medical care system. Facilities with group rooms and dormitories pose a risk for outbreaks during the COVID-19 pandemic. As suspected, evidence of Severe Acute Respiratory Syndrome Coronavirus Type 2 (SARS-CoV‑2) infections emerged more frequently in homeless individuals than in the general population during the pandemic. Many of the infected individuals were asymptomatic. High rates of those unknowingly infected homeless individuals may have contributed to the spread of the viral disease. However, uncontrolled COVID-19 outbreaks, as feared by some researchers at the beginning of the pandemic, were not observed.

PEth 16:0/18:1 and 16:0/18:2 after consumption of low doses of alcohol-A contribution to cutoff discussion

Phosphatidylethanol in blood has gained recognition as a direct alcohol biomarker. Although different cutoffs have been suggested, there is no consensus for differentiating abstinence from alcohol consumption. In this study, 75 participants (72% female) consumed 20 g of ethanol on three consecutive evenings. Blood was sampled on each following day and PEth 16:0/18:1 and 16:0/18:2 were determined. PEth 16:0/18:1 ranged from 8.9-21.5, 8.7-19.3, and 8.8-42.3 ng/ml and PEth 16:0/18:2 from 8.7-31.7, 9.0-39.3, and 9.4-43.0 ng/ml after the respective days of ethanol consumption. PEth 16:0/18:1 yielded a sensitivity of 25%, 45%, and 49% and PEth 16:0/18:2 of 40%, 61%, and 68% for the consumption days, respectively (cutoff 10 ng/ml). PEth 16:0/18:1 reached >20 ng/ml in five samples overall. Sensitivity of PEth 16:0/18:2 > 20 ng/ml was better with 35% after the three drinking days. Overall, PEth 16:0/18:1 was >35 ng/ml in one sample and PEth 16:0/18:2 in three samples. Significantly, more women had PEth 16:0/18:1 > 10 ng/ml after the third day of consuming 20 g of alcohol (p = 0.02) and PEth 16:0/18:2 > 10 ng/ml after the second (p = 0.023) and the third (p = 0.002) consumption, which can be led back to the higher blood alcohol concentration women reach after consuming the same alcohol amount as men. Although the response rates of PEth to alcohol uptake are subject to strong interindividual differences, results suggest that PEth cutoff should be lowered for better detection of consumption of low to medium amounts of alcohol. Furthermore, it is advantageous to analyze both PEth 16:0/18:2 and 16:0/18:1.

Common Laboratory Parameters Are Useful for Screening for Alcohol Use Disorder: Designing a Predictive Model Using Machine Learning

The diagnosis of alcohol use disorder (AUD) remains a difficult challenge, and some patients may not be adequately diagnosed. This study aims to identify an optimum combination of laboratory markers to detect alcohol consumption, using data science. An analytical observational study was conducted with 337 subjects (253 men and 83 women, with a mean age of 44 years (10.61 Standard Deviation (SD)). The first group included 204 participants being treated in the Addictive Behaviors Unit (ABU) from Albacete (Spain). They met the diagnostic criteria for AUD specified in the Diagnostic and Statistical Manual of mental disorders fifth edition (DSM-5). The second group included 133 blood donors (people with no risk of AUD), recruited by cross-section. All participants were also divided in two groups according to the WHO classification for risk of alcohol consumption in Spain, that is, males drinking more than 28 standard drink units (SDUs) or women drinking more than 17 SDUs. Medical history and laboratory markers were selected from our hospital's database. A correlation between alterations in laboratory markers and the amount of alcohol consumed was established. We then created three predicted models (with logistic regression, classification tree, and Bayesian network) to detect risk of alcohol consumption by using laboratory markers as predictive features. For the execution of the selection of variables and the creation and validation of predictive models, two tools were used: the scikit-learn library for Python, and the Weka application. The logistic regression model provided a maximum AUD prediction accuracy of 85.07%. Secondly, the classification tree provided a lower accuracy of 79.4%, but easier interpretation. Finally, the Naive Bayes network had an accuracy of 87.46%. The combination of several common biochemical markers and the use of data science can enhance detection of AUD, helping to prevent future medical complications derived from AUD.