Whether or not drinking alcohol directly raises the risk of getting diabetes, the sophisticated lives of people throughout the globe have led alcohol usage to grow [1, 2 ] considerably. This has coincided with a similarly rapid rise in the prevalence of diabetes. Whether or not drinking alcohol directly raises the risk of developing diabetes is not known. According to the findings of a study [3] conducted by the Madras Diabetes Research Foundation, Goa, Puducherry, and Kerala had the highest incidence of diabetes (about 25%).

This research was supported financially by the Indian Council of Medical Research and the Union Health Ministry. 11% of people in India have diabetes, 15% of people in India have pre-diabetes, 35% of people in India have hypertension, 28% of people in India have generalized obesity, 39% of people in India have abdominal obesity, and 24% of people in India have hypercholesterolemia. In addition, about 100 million Indians have been identified as having diabetes, 136 million Indians have been identified as having pre-diabetes, 315 Indians have been identified as having hypertension, 254 million Indians have been identified as having generalized obesity, and 354 million Indians have been identified as having abdominal obesity[3]. Previous research[1, 2, 4, 5] has shown a correlation between the use of alcoholic beverages, insulin resistance (IR), and obesity.

On the other hand, the effects of alcohol on the cellular creation of insulin in the body are still not entirely known. The objective of this study was to determine whether or not excessive drinking is associated with reduced cell function in urban Indian men and whether or not weight increase complicates the relationship between the two factors. Additionally, a higher level of body fat was investigated to see whether or not this variable had any impact on the findings.

Participants in the research were locals from Puri, Odisha, India, aged 25 to 80. In-person interviews were conducted with 476 participants who were selected from a random pool. The objectives and procedures of the research were explained to the participants. Everyone who took part in the study gave their written agreement to participate. The Institutional Ethics Committee gave this research project the go-light. Women were not allowed to participate because of their low alcohol use and self-reported histories of diabetes, cardiovascular sickness, pancreatitis, liver disease, and renal disease. Men were allowed to participate. Overall, 225 men took part in the research.

A standardized questionnaire was used to gather information on drinking histories, frequency, and average intake of beer, wine, hard liquor (more than 38% vol/vol), and light liquor (38% vol/vol). Beer, wine, hard liquor (defined as having a volume-to-proof ratio of more than 38%), and light liquor (defined as having a v/v ratio of less than 38%) were all included in the survey. The following recommendations for alcohol consumption, recommended by the Indian Food Composition [6], are stated in grams per day: There are 31.36 grams of sugar in every 640-milliliter bottle of beer, 5.2 grams in every 50-milliliter glass of wine and 21.85 grams in every 50-milliliter glass of hard liquor. The body mass index (BMI) was used to categorize study participants as either abstainers, teetotalers, light drinkers (0.1-20 g/day), moderate drinkers (21.0-40 g/day), or heavy drinkers (41 g/day). Abstainers were defined as those who did not consume any alcohol at all. The following is a list of the classes: A body mass index of more than 25 kg per square meter.

The use of clinical questionnaires allowed for the collection of information on demographics as well as risk factors. In order to get the most reliable data possible from each participant, we requested that they abstain from eating or engage in strenuous physical activity for a minimum of 12 hours before the examination. While the patient was fasting, anthropometric measures and blood samples were collected using a procedure that is considered to be standard practice. The patient’s vitals were measured and recorded, including their blood pressure, waist circumference, height, and weight. The body mass index is calculated by taking the individual’s weight in kilograms and dividing that number by the square of their height in meters. An automated biochemical analyzer was used to assess the following components of the plasma: fasting blood glucose (FBG), total cholesterol (TC), high-density lipoprotein (HDL), low-density lipoprotein (LDL), and triglyceride (TAG). A radioimmunoassay was used to determine the amounts of insulin present during the fasting state. Both beta-cell function (HOMA- = 20 minus fasting insulin divided by FBG-3.5) and insulin resistance (HOMA-IR = fasting blood glucose minus fasting insulin divided by 22.5) were evaluated with the use of the homeostasis model[7].

Statistical Analysis

Version 20.0 of SPSS was used to conduct the statistical analysis. Every collection of numerical data has a mean value and a standard deviation value. We analyzed the study’s variables among groups of people who consumed diverse quantities of alcohol by using variance, chi-square, and covariance. Each group of people consumed a different quantity of alcohol. Multiple regression analysis was utilized to determine the extent to which possible confounding factors, such as fasting blood glucose, homeostasis model assessment, and insulin resistance (abbreviated as FBG, HOMA-, and HOMA-IR, respectively) impacted the study’s results. A P value of 0.05 or less would indicate a statistically significant difference. This was a consensus among the researchers. The Bonferroni correction was used to compare the results of many tests.

Table 1 contains an in-depth description of the unique qualities possessed by each individual. Drinking was reported by 52% of the persons who took part in the research. Among those drinkers, 19% reported drinking more than 41 grams of alcohol daily. Most heavy drinkers were young men under the age of 34, with the average age being 34. It was discovered that only 47.2 percent of the population now engaged in smoking, whereas 11.8 percent of the population had smoked in the past. There were 42.1% current smokers in the light group, 51.8% in the intermediate group, and 34.2% in the heavy group. The proportion of current smokers was highest in the light group. There was a large disparity in attributes across the different categories of people. Even after considering other factors, such as age and smoking, it was shown that people who drank moderately to heavily had greater levels of FBG. It was discovered that moderate users had considerably greater levels of both TG and TC than light users. HOMA- levels were lower in those whose drinking ranged from moderate to excessive. The percentage of individuals whose fasting blood glucose was 110 mg/dl [8] ranged from its greatest point among those who did not drink alcohol at all (21%) to its lowest point among those who consumed just moderate amounts of alcohol (7.5%). 61.2% of the individuals were overweight, and those who used moderate amounts of alcohol had a considerably higher likelihood of being overweight than those who did not consume any alcohol at all (68.5% versus 51.5%).

Abstainers were not included in the study’s analysis because the researchers wanted to illustrate the connection between drinking alcohol and fasting blood glucose, the homeostatic model assessment index, and the homeostatic model assessment index. After taking into account the participants’ ages and whether or not they smoked, researchers found that those with a body mass index (BMI) of 25 \(kg/m^{2}\) and those who drank too much alcohol had elevated levels of fasting blood glucose. Although alcoholics had a lower HOMA-IR than nonalcoholics, the difference was not large enough to be considered statistically significant. The HOMA- score was lower in all groups that admitted to drinking alcohol, regardless of the participants’ body mass index. Most comparisons still showed statistically significant differences, most notably between people who never drink and those who drink frequently (p 0.05), even if the results of many tests were considered.

The outcome measures of interest were fasting blood sugar, the homeostasis model assessment index, and the homeostasis model assessment index. The variables that had a role in explaining the results are listed in Table 2, which can be accessed here. High fasting blood glucose, total cholesterol, and fat levels were shown to have a significant and independent connection among patients with BMIs lower than 25 \(kg/m^{2}\) despite these patients having lower BMIs. Those individuals with a body mass index of less than 25 exhibited a link between age and total cholesterol and elevated fasting blood glucose levels. If a patient had a history of alcohol consumption, their HOMA-IR was substantially and independently lowered, even if they had a body mass index (BMI) that was lower than 25.

On the other hand, consumption of alcoholic beverages was significantly and independently related to a lower HOMA-IR among those whose BMI was less than 25 \(kg/m^{2}\). Those individuals with body mass indexes (BMIs) lower than 25 \(kg/m^{2}\) demonstrated a significant and independent connection between growing waistlines and increasing HOMA-IR values. It was revealed that HOMA-IR has a statistically significant link with uric acid, and this relationship exists independently. It was discovered that the levels of HOMA-a were lower in those individuals who not only routinely consumed alcohol but were also older. On the other hand, researchers found a correlation between greater HOMA-a concentrations and bigger waist widths. It was discovered that the body mass index did not influence this association at all.

The findings of a multiple stepwise regression analysis are shown in Table 3. HOMA-IR and HOMA- were used as the dependent variables in this study, and FBG was used as the grouping technique for the explanatory factors. The findings of the study are going to be provided in the parts that are listed below. Alcohol consumption and age were both significantly and independently associated with lower HOMA-IR and HOMA- values in the patient whose fasting blood glucose level was less than 110 mg/dl. On the other hand, a bigger body mass index was significantly and independently linked to higher HOMA- values. The patient’s fasting blood glucose level was less than 110 mg/dl. This was the case even though drinking and becoming older are both factors that are predictive of lower HOMA-IR and HOMA-values. The patient’s blood sugar was 109.9 mg/dl while fasting. Patients who were either overweight or had a big waist circumference were shown to have higher levels of both HOMA and HOMA-IR. On the other hand, the patient’s history of alcohol use and consumption (in years) was related to lower levels of HOMA and HOMA-IR.

The inability of beta cells to perform their normal functions is recognized as one of the most important contributors to the development of type 2 diabetes [9, 10, 11]. The most important finding that the researchers found was that consuming alcohol did not have any effect on HOMA levels and that this result was unrelated to the individual’s BMI. This was a shocking finding. It was discovered that there was a U-shaped relationship between the HOMA-IR score and the amount of alcohol consumed, with moderate drinkers having the lowest average score.

Several other tests, such as the hyperglycemic clamp, the intravenous glucose tolerance test, the oral glucose tolerance test, and the pulsatile insulin secretion test, are utilized to examine beta-cell activity these days. Additional methods include a glucose tolerance test that is administered intravenously, as well as a glucose tolerance test that is administered orally. These approaches are impractical for deployment in large-scale clinical studies[12] due to their complexity and the enormous number of needed participants.

FBG is more straightforward to measure, and its connection to cellular function is more fundamental[12]. There is a correlation between age, gender, body mass index (BMI), and waist circumference and a reduction in insulin production, a decrease in insulin sensitivity, and an increase in blood glucose when fasting[13, 14]. According to the findings of a study carried out in China [15], higher fasting plasma glucose levels may signal a bigger breakdown in the function of individual cells. The scholars that looked into this discovered this in [15]. This discovery could have repercussions for using various treatment techniques in the foreseeable future. The fact that these findings support the assumption that FBG may act as a helpful signal for revealing cellular activity lends validity to the idea. More than half of the people who took part in this study experiment had an unhealthy relationship with alcohol, and 11.4% of the patients had abnormally high levels of fasting blood glucose (6.1 mmol/L). This reveals that alcoholism is quite prevalent in the whole of this community. Those individuals whose fasting blood glucose levels were 6.1% or lower than the national average had the lowest risk of developing a moderate drinking pattern over time. Alcohol use, a high total cholesterol level, and a high percentage of belly fat were shown to be the most important risk factors for impaired fasting blood glucose levels. On the other hand, the coefficient of determination (R2) was just 0.0109, which indicates that other risk factors, including genetics, the environment, and lifestyle, may have a more substantial impact on fasting blood glucose levels. It was shown that both moderate and heavy drinkers of Korean origin had an increased risk of acquiring impaired glucose tolerance or type 2 diabetes[11]. This was the case regardless of the amount of alcohol consumed. Men of Korean ancestry made up the whole of the sample population. According to the findings of this study, those who consume alcohol in moderation have lower amounts of glucose in their blood when they are fasting compared to heavy drinkers. Statistically speaking, however, there was not a distinguishable difference that could be shown between the two groups. The fact that the majority of people who drank were minors and that the total number of people who drank heavily was relatively modest is probably to blame.

"Light drinkers displayed lower levels of FBG, TAG, and TC and greater levels of HOMA, while moderate to heavy drinkers exhibited a broad array of features. Moderate to heavy drinkers were also more likely to smoke. Individuals who consumed less alcohol had lower levels of FAT, TAG, and TC, respectively. People who consumed alcohol, even in moderation, had higher HOMA values than individuals who did not drink alcohol at all. Eating sensibly could be an effective way to enhance metabolic indicators.

To this day, there hasn’t been a lot of research done to look into whether or not drinking alcohol is linked to cellular malfunction. Even though the never-drinkers had a higher body mass index, the findings indicated that the HOMA- level was lower in all four groups of drinkers as compared to the never-drinkers. According to these studies, alcohol use may have an effect on the secretion process of cells. In a multivariate regression study, the researchers discovered that drinking alcohol considerably raised the probability of having dysfunctional cells in the body. This was shown to be the case regardless of the person’s body mass index (BMI). A meta-analysis[16] discovered that greater alcohol intake was linked to decreased insulin secretion. This impact was actually distinct from insulin sensitivity, as higher alcohol consumption was related to lower insulin secretion. The researchers arrived at this result even though they took a variety of characteristics into account, including age, gender, ethnicity, nutrition, and body weight. These findings lend credence to the findings and conclusions of the research. Alcohol consumption was also revealed to have a strong link with cell dysfunction in individuals whose fasting blood glucose levels were 6.1 mmol/L. The findings of the study showed this. Throughout the trial, the participants’ blood glucose levels were within the normal range. In order to accomplish this goal, several distinct categories were applied to the FBG levels. This gives credibility to the theory that alcoholic use is linked to an increased risk of acquiring prediabetes and diabetes. Consumption of alcoholic beverages may result in the death of cells in the body in several different ways. Abuse of alcohol is related to chronic pancreatitis in 50-70% of patients[16], accelerates pancreatic fibrosis [17], and has been tied to optical and structural abnormalities of -cells[18]. These factors may contribute, at least in part, to -cell functional disruption. Alcohol abuse is also associated with pancreatic cancer [19]. Continuous alcohol use is responsible for the development of chronic pancreatitis in 50-70% of patients[16]. There is a correlation between alcohol use and chronic pancreatitis in between 50 and 70 percent of patients [16]. On the other hand, there is a need for further research in the humanities.

Research on the relationship between alcohol use and insulin resistance, as well as the progression to type 2 diabetes, has made steady progress over the years. In several pieces of study [19, 20, 21, 22], the letters "U" and "J" are connected. In contrast, neither the "U" curve nor the "J" curve were found in any of the primary sources[23, 24]. Consumption of alcoholic beverages may be detrimental to a person’s health in a variety of different ways. Consuming large amounts of alcohol frequently was shown to be connected with increased insulin resistance as well as impaired glucose absorption by insulin. In order to accomplish these findings, the expression of Gs was increased in isolated rat skeletal muscle[25], while the expression of GLUT4 was downregulated in rat adipocytes [26, 27]. Despite this effect being seen, the exact mechanism whereby alcohol could lower insulin resistance is yet unclear. An increase in the ratio of lactate to pyruvate, as well as NADH (nicotinamide adenine dinucleotide) to NAD (nicotinamide adenine dinucleotide), has been linked to alcohol use [29]. Nicotinamide adenine dinucleotide is referred to by its acronym NADH.

According to the study, participants who drank in moderation had the lowest levels of HOMA-IR, as shown by the "U"-shaped curve. From a statistical point of view, there was not a detectable difference between the two groups. The results of our multivariate investigation of the connection between alcohol intake and HOMA-IR in obese males indicated that the link is negatively correlated. Men who did not have weight problems and who did not have excessive drinking histories made more considerable donations. In the earlier studies, the only factor that was considered for calculating HOMA-IR was the total amount of alcohol consumed; the researchers paid little attention to how alcohol usage changed over time. According to the results of our investigation, each of these elements had a significant role in the development of HOMA-IR. Individuals with a BMI of 25 \(kg/m^{2}\) consumed alcohol more often (16.6% as opposed to 14.1%) and had higher HOMA-IR values [30]. The patient’s body mass index significantly influences the development of insulin resistance [30]. Individuals with a body mass index of 25 \(kg/m^{2}\) or more had a higher HOMA-IR than those with lower BMIs. The graph presents both sets of data in the formats that are appropriate for each set. This has the effect of amplifying the influence that alcohol consumption has on HOMA-IR. However, in order to measure the effect of alcohol on people with a BMI of 25 \(kg/m^{2}\), a more extended research time is required.

Obesity is a risk factor in and of itself, independent of whether or not insulin resistance is present[31]. Even though confounders such as alcohol use, smoking, blood lipid levels, and uric acid levels were investigated, this research indicated that a higher IR was considerably connected with a wider waist circumference. This was the case even when the study looked at the levels of uric acid and blood lipids. This was still the case after considering the amounts of uric acid and lipids in the blood. This was especially true for frequent drinkers who consumed moderate to substantial amounts regularly. More than half of the population had a weight considered unhealthy for their height and build. This would imply that the use of alcoholic beverages has a significant influence on the prevalence of obesity among males in India. Drinking alcohol was linked to higher levels of visceral fat and insulin resistance in the study’s group of obese persons who were also overweight. This result implies that drinking alcohol may increase visceral fat, which runs counter to the widespread idea that waist circumference[31, 32] is the most reliable predictor of this disease.

It was shown that alcohol intake had a U-shaped connection with HOMA-IR in recruited groups. After accounting for age, risk variables such as smoking, serum uric acid, lipids, alcohol use, and obesity remained significant. Since beta-cell dysfunction is a major contributor to the development of type 2 diabetes, investigating how alcohol consumption impacts insulin secretion is prudent.

This research paper received no external funding.

The authors declare no conflicts of interest.

All authors contributed equally to this paper. They have all read and approved the final version.

Informed consent was obtained from all participates in the study as needed.

1. Yan, Y., Wu, T., Zhang, M., Li, C., Liu, Q. & Li, F. (2022). Prevalence, awareness and control of type 2 diabetes mellitus and risk factors in Chinese elderly population. \emph{BMC Public Health, 22}(1), 1-6.
2. Delgado-Velandia, M., Gonzalez-Marrachelli, V., Domingo-Relloso, A., Galvez-Fernandez, M., Grau-Perez, M., Olmedo, P., ... & Sotos-Prieto, M. (2022). Healthy lifestyle, metabolomics and incident type 2 diabetes in a population-based cohort from Spain. International \emph{Journal of Behavioral Nutrition and Physical Activity, 19}(1), 8.
3. Perappadan, B. S. (2022). 31 million more Indians became diabetic between 2019-2021, says study. The Hindu. \url{https://www.thehindu.com/sci-tech/health/31-million-more-indians-became-diabetic-between-2019-2021-says-study/article66949970.ece}.
4. Yang, J., Qian, F., Chavarro, J. E., Ley, S. H., Tobias, D. K., Yeung, E., ... & Zhang, C. (2022). Modifiable risk factors and long term risk of type 2 diabetes among individuals with a history of gestational diabetes mellitus: prospective cohort study. \emph{BMJ}, 378, e070312.
5. Nanda, M., Sharma, R., Mubarik, S., Aashima, A., & Zhang, K. (2022). Type-2 Diabetes Mellitus (T2DM): spatial-temporal patterns of incidence, mortality and attributable risk factors from 1990 to 2019 among 21 world regions. \emph{Endocrine, 77}(3), 444-454.
6. Longvah, T., An\_antan\_, I., Bhaskarachary, K., Venkaiah, K., & Longvah, T. (2017). Indian food composition tables (pp. 2-58). Hyderabad: National Institute of Nutrition, Indian Council of Medical Research.
7. Fosam, A., Bansal, R., Ramanathan, A., Sarcone, C., Iyer, I., Murthy, M., ... & Muniyappa, R. (2023). Lipoprotein insulin resistance index: a simple, accurate method for assessing insulin resistance in South Asians. Journal of the Endocrine Society, 7(3), bvac189.
8. Szychlinska, M., Gontarz-Nowak, K., Matuszewski, W., Myszka-Podg\'orska, K., & Bandurska-Stankiewicz, E. (2020). Diagnostic criteria for metabolic syndrome: A historical overview. Polish Annals of Medicine, 27(2), 244-249.
9. Onikanni, S. A., Lawal, B., Oyinloye, B. E., Ajiboye, B. O., Ulziijargal, S., Wang, C. H., ... & Simal-Gandara, J. (2023). Mitochondrial defects in pancreatic beta-cell dysfunction and neurodegenerative diseases: Pathogenesis and therapeutic applications. Life Sciences, 312, 121247.
10. Qiu, R., Wu, B., He, Y., Huang, S., Wang, S., Li, H., & Zheng, F. (2023). Age-related adiposity and beta-cell function: impact on prediabetes and diabetes prevalence in middle-aged and older Han Chinese adults. Journal of Endocrinological Investigation, 46(2), 405-413.
11. Roh, W. G., Shin, H. C., Choi, J. H., Lee, Y. J., & Kim, K. (2009). Alcohol consumption and higher incidence of impaired fasting glucose or type 2 diabetes in obese Korean men. Alcohol, 43(8), 643-648.
12. Manco, M., Panunzi, S., Macfarlane, D. P., Golay, A., Melander, O., Konrad, T., ... & Relationship between Insulin Sensitivity and Cardiovascular Risk (RISC) Consortium. (2010). One-hour plasma glucose identifies insulin resistance and $\beta$-cell dysfunction in individuals with normal glucose tolerance: Cross-sectional data from the relationship between insulin sensitivity and cardiovascular risk (RISC) study. Diabetes Care, 33(9), 2090-2097.
13. Lakshmi, L. J., Dash, A., Muhamed, F., Choudhary, A., Madhura, Q., Rathore, S., & Khan, F. N. (2022). To determine the relationship between chemerin and melatonin levels in type 2 diabetes mellitus obese subjects. \emph{Journal of Cardiovascular Disorders, 13}(5), 2470-79.
14. Madhura, A. D., Lakshmi, L. J., Choudhary, A., & Doddigarla Zephy, Q. (2022). Determination of random blood sugar, alkaline phosphatase and zinc levels in individuals with leprosy. European Journal of Molecular & Clinical Medicine, 9(7), 2048-2056.
15. Lin, J. D., Chen, Y. L., Hsu, C. H., Wu, C. Z., Hsieh, A. T., Hsieh, C. H., ... & Pei, D. (2013). Beta-cell function and insulin sensitivity at various degrees of glucose tolerance in Chinese subjects. Diabetes Research and Clinical Practice, 100(3), 391-397.
16. Lai, Y. J., Hu, H. Y., Lee, Y. L., Ko, M. C., Ku, P. W., Yen, Y. F., & Chu, D. (2019). Frequency of alcohol consumption and risk of type 2 diabetes mellitus: a nationwide cohort study. Clinical Nutrition, 38(3), 1368-1372.
17. Baliunas, D. O., Taylor, B. J., Irving, H., Roerecke, M., Patra, J., Mohapatra, S., & Rehm, J. (2009). Alcohol as a risk factor for type 2 diabetes: a systematic review and meta-analysis. Diabetes Care, 32(11), 2123-2132.
18. Knott, C., Bell, S., & Britton, A. (2015). Alcohol consumption and the risk of type 2 diabetes: a systematic review and dose-response meta-analysis of more than 1.9 million individuals from 38 observational studies. \emph{Diabetes Care, 38}(9), 1804-1812.
19. Koppes, L. L., Dekker, J. M., Hendriks, H. F., Bouter, L. M., & Heine, R. J. (2005). Moderate alcohol consumption lowers the risk of type 2 diabetes: a meta-analysis of prospective observational studies. Diabetes Care, 28(3), 719-725.
20. Heianza, Y., Arase, Y., Saito, K., Tsuji, H., Fujihara, K., Hsieh, S. D., ... & Sone, H. (2013). Role of alcohol drinking pattern in type 2 diabetes in Japanese men: the Toranomon Hospital Health Management Center Study 11 (TOPICS 11). The American Journal of Clinical Nutrition, 97(3), 561-568.
21. Liu, C., Yu, Z., Li, H., Wang, J., Sun, L., Qi, Q., & Lin, X. (2010). Associations of alcohol consumption with diabetes mellitus and impaired fasting glycemia among middle-aged and elderly Chinese. BMC Public Health, 10(1), 1-9.
22. Carlsson, S., Hammar, N., & Grill, V. (2005). Alcohol consumption and type 2 diabetes: meta-analysis of epidemiological studies indicates a U-shaped relationship. Diabetologia, 48, 1051-1054.
23. Wannamethee, S. G., Shaper, A. G., Perry, I. J., & Alberti, K. G. M. M. (2002). Alcohol consumption and the incidence of type II diabetes. Journal of Epidemiology & Community Health, 56(7), 542-548.
24. Conigrave, K. M., Hu, B. F., Camargo, C. A., Stampfer, M. J., Willett, W. C., & Rimm, E. B. (2001). A prospective study of drinking patterns in relation to risk of type 2 diabetes among men. Diabetes, 50(10), 2390-2395.
25. Rachdaoui, N., Sebastian, B. M., & Nagy, L. E. (2003). Chronic ethanol feeding impairs endothelin-1-stimulated glucose uptake via decreased Ga11 expression in rat adipocytes. American Journal of Physiology-Endocrinology and Metabolism, 285(2), E303-E310.
26. Poirier, L. A., Rachdaoui, N., & Nagy, L. E. (2001). GLUT4 vesicle trafficking in rat adipocytes after ethanol feeding: regulation by heterotrimeric G-proteins. Biochemical Journal, 354(2), 323-330.
27. Yu, H. C., Li, S. Y., Cao, M. F., Jiang, X. Y., Feng, L., Zhao, J. J., & Gao, L. (2010). Effects of chronic ethanol consumption on levels of adipokines in visceral adipose tissues and sera of rats. Acta Pharmacologica Sinica, 31(4), 461-469.
28. Tian, C., Jin, X., Ye, X., Wu, H., Ren, W., Zhang, R., ... & Ying, C. (2014). Long term intake of 0.1\% ethanol decreases serum adiponectin by suppressing PPAR? expression via p38 MAPK pathway. Food and Chemical Toxicology, 65, 329-334.
29. Kim, S. J., & Kim, D. J. (2012). Alcoholism and diabetes mellitus. Diabetes & Metabolism Journal, 36(2), 108-115.
30. Khosravi-Boroujeni, H., Sarrafzadegan, N., Sadeghi, M., Roohafza, H., Talaei, M., Ng, S. K., ... & Ahmed, F. (2017). Secular trend of metabolic syndrome and its components in a cohort of Iranian adults from 2001 to 2013. Metabolic Syndrome and Related Disorders, 15(3), 137-144.
31. Deedwania, P. C., Gupta, R., Sharma, K. K., Achari, V., Gupta, B., Maheshwari, A., & Gupta, A. (2014). High prevalence of metabolic syndrome among urban subjects in India: a multisite study. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 8(3), 156-161.
32. Venugopal, V., Dongre, A. R., & Saravanan, S. (2019). Prevalence and determinants of metabolic syndrome among the rural adult population of Puducherry. Indian Journal of Community Medicine: Official Publication of Indian Association of Preventive & Social Medicine, 44(1), 21-25.