Alcohol Consumption Patterns and Biologic Markers of Glycemic Control

Glycemic control is a crucial determinant of blood glucose levels. Over the years, there have been significant cases of abnormal glycemic indices, which results in a high mortality rate and the chances of contracting type 2 diabetes. Several studies reveal that glycemic index is maintained by proper consumption of foods that regulate blood sugar levels. For instance, consuming a low carbohydrate diet can significantly reduce glucose levels, maintaining a balanced glycemic index. Other than diet, there are several determinants of reasonable glycemic control. For instance, regular physical exercise, total adherence to proper medication and reduced alcohol consumption can significantly help maintain the required glycemic index. In this article, Kroenke et al. conducted a cross-sectional study on the link between alcohol consumption patterns and glycemic control among 459 nurses, published in 2003. (1) They aimed to evaluate the relationship between insulin resistance and biological markers and moderate alcohol consumption in young women. The authors employed a large sample and validated assessment tools which necessitated a thriving establishment that alcohol consumption habits such as consuming a moderate amount on selected days of the week can significantly maintain a standard glycemic index.

The authors examine how various drinking habits relate to glycemic control markers among women. Previous studies suggest that regular mild drinking decreases the likelihood of getting type 2 diabetes, while excessive and irregular alcohol drinking elevates the probability of type 2 diabetes. Therefore, the researchers sought to establish the association between patterns of alcohol intake such as daily and weekly consumption, drinking with meals, with signs of insulin resistance among young females. The cross-sectional study involved 459 female nurses aged between 33 and 50, including normal-weight and overweight, chosen randomly from the Nurses’ Health Study II. Blood samples and questionnaires on lifestyle and dietary factors were used to collect data, with the main measures being insulin, HbA1c, and C-peptide. Findings showed that medium alcohol intake was negatively associated with HbA1c after adjusting for smoking, BMI, age, and physical exercise. Moreover, there was an inverse relationship between insulin resistance and alcohol consumption, but only among females with a BMI of 25 kg/m2 or more. Additionally, episodic drinkers were found to have the least insulin levels while drinking alcohol with food, and the type of alcohol consumed did not affect insulin resistance. In summary, the study found that mild alcohol consumption (1-2 drinks) helps with insulin sensitivity. Although the study had strong alcohol and dietary tools and oversampled extreme drinking patterns, its cross-sectional nature limited the investigation of causality. Lastly, the study suggests further studies into drinking patterns among post-menopausal women.

Alcohol consumption is significantly linked to glycemic index and insulin levels in the body. Besides, the drinking patterns of individuals determines the effect on the glycemic index. While moderate, frequent alcohol consumption helps in glycemic control, resulting in minor type 2 diabetes risk, excess alcohol consumption contributes to diabetes and other chronic infections. Still, there are limited glycemic control assessments relating to women with moderate alcohol consumption patterns. Although there may be few available studies on this subject, this study’s findings portray practical and robust evaluation, characterized by a large sample size and the use of validated assessment tools. The researchers employed a sufficiently large US women population to examine the impact of moderate alcohol consumption on the glycemic index. For instance, the fasting insulin, HbA1c and C-peptide were measured from a sample of 459 US women. Besides, the women ages alongside their drinking patterns were well distributed to cover an extensive outcome coverage, translating to a significant representation of the entire middle-aged women. In addition, the authors oversampled the extreme drinking patterns for successful and more accurate detection of the association between the independent variables. According to Frieden, a sufficiently large sample is ideal for reliable generalization and application. (2) As such, the use of a large sample ascertained more accurate and reliable findings. Next, the authors examined the relationship between alcohol consumption and glycemic control using validated assessment mechanisms. For instance, they independently established the correlation between alcohol consumption patterns and intake quantity. Besides, they eliminated episordic drinkers from their analysis and performed other sub-analyses to eliminate participants who quit alcohol consumption and those with family diabetes history. On the other hand, alcohol consumption was tested separately on each dietary and lifestyle variable to provide accurate and specified results. Eglseer et al. argue that validated assessment tools are associated with better and reliable outcomes. (3) Therefore, this study is reliable, and its findings can be generalized. Of course, many will disagree with this assertion that the findings of this study are reliable due to the large sample and use of validated assessment tools. Riley et al., in their article; Calculating the Sample Size Required for Developing a Clinical Prediction Model, suggest that a large sample results into a complicated analytical model, which may be challenging to interpret. (4) However, Chen et al., experts in medical information and evaluation, strongly emphasize the importance of conducting reliable and accurate medical research using appropriate sample size and validated tools since the outcomes are likely to be used by other doctors and medical experts globally. (5) As such, the claims that a small sample with no consideration for validated tools reduces work-load must be eliminated, especially in medical studies. All in all, this study’s findings are reliable and hence can be generalized to the entire US women population.

In conclusion, Kroenke et al.’s study revealed that moderate alcohol consumption is commendable for glycemic control in women using a relatively large sample and validated assessment tools. The large selection ensured that all the required demographic variables were collected from the general population, promoting generalization power. In addition, the validated tools enabled accurate and reliable findings, which also boosted generalization. Despite the claims that a small sample is easy to administer and analyze, medical research should extensively ensure that the sample size effectively and sufficiently represents the entire population’s required assessments. Therefore, women, especially those with overweight and obese conditions, should embrace moderate alcohol consumption habits to maintain a standard glycemic index. Since there is little research on this topic, more research is vital to evaluate further the link between alcohol consumption and glycemic control in women.

References

1. Kroenke CH, Chu NF, Rifai N, Spiegelman D, Hankinson SE, Manson JE, Rimm EB. A cross-sectional study of alcohol consumption patterns and biologic markers of glycemic control among 459 women. Diabetes Care. 2003 Jul 1;26(7):1971-8.
2. Frieden TR. Evidence for health decision making—beyond randomized, controlled trials. New England Journal of Medicine. 2017 Aug 3;377(5):465-75. Available from: https://www.nejm.org/doi/full/10.1056/nejmra1614394
3. Eglseer D, Halfens RJ, Lohrmann C. Is the presence of a validated malnutrition screening tool associated with better nutritional care in hospitalized patients?. Nutrition. 2017 May 1;37:104-11. Available from: https://pubmed.ncbi.nlm.nih.gov/28359355/
4. Riley RD, Ensor J, Snell KI, Harrell FE, Martin GP, Reitsma JB, Moons KG, Collins G, Van Sweden M. Calculating the sample size required for developing a clinical prediction model. BMJ. 2020 Mar 18;368. Available from: https://www.bmj.com/content/368/bmj.m441.full
5. Chen YY, Li CM, Liang JC, Tsai CC. Health information obtained from the internet and changes in medical decision making: questionnaire development and cross-sectional survey. Journal of medical Internet research. 2018;20(2):e47. Available from: https://www.jmir.org/2018/2/e47/