Arsenic has been known for a very long time to have carcinogenic and toxic effects [1, 2]. Some adverse health effects include cancers, high blood pressure, chronic bronchitis, and a range of skin diseases. In several countries, including Australia, Argentina, Bangladesh, China, India, Mexico, Taiwan, and the United States of America, arsenic (As) poisoning of groundwater is a major concern [3,4]. Arsenic in drinking water is a public health concern since it has been associated with several different types of cancer, including skin, bladder, lung, and liver cancers and cardiovascular disease [5-7]. Additionally, it has been speculated that arsenic exposure is linked to the onset of T2DM [8]. According to a study conducted in the Mexican state of Coahuila, humans who have been exposed to arsenic over a prolonged period of time are more likely to develop T2DM [9]. In addition to arsenic exposure from drinking water, research has linked chronic occupational arsenic exposure to T2DM as well. Patients with diabetes who have been exposed to arsenic at work have a higher risk of complications and death compared to the general population or to workers who have not been exposed [10, 11]. Whereas earlier research suggested a link between T2DM and chronic drinking water arsenic absorption [12], some of these findings are inconclusive, and the validity of the data has been questioned [13]. Arsenic in drinking water is suspected of contributing to the rise of T2DM in Bangladesh; hence a thorough evaluation of the issue is crucial from a public health perspective.  The purpose of this study was to determine whether or not there was a connection between chronic arsenic exposure and T2DM in certain locations of Bangladesh, taking into account demographic, socioeconomic, and medical risk factors as potential confounding factors.

Study procedure and subjects

This research is a cross-sectional survey conducted in a rural area in Bangladesh. Using a stratified multistage random sample method, 128 participants aged 12 and up were chosen. Our sample size of 128 was adequate to test all of our hypotheses with 80% power (=0.20) at the 5% significance level. Chronic illness patients (such as those with hypothyroidism), pregnant women, people using hormone replacement therapy, and people with preexisting disorders (such as ischemic heart disease) were all disqualified from participation in the study. Consenting participants' responses were included in the study. Responses from respondents 12 and above were included in the analysis. Participants who did not want their responses made public were disqualified. We followed the WHO's (modified) STEPS approach, which entails a step-by-step collection of risk factor statistics based on a standard questionnaire covering demographics, somatic diseases, somatic and mental symptomatology, medications, lifestyle, and health-related behavior (step 1), basic physical measures (step 2), and basic biochemical investigations, like blood glucose and cholesterol (step 3). Gabtali Upazilla was the source for the participants. They came from four different villages (sub-district of Bogura). First, the communities were selected (by division, district, Upazila, and then by village), and then the respondents were chosen at random from within those communities. In a nutshell, we made loudspeaker announcements across town and knocked on people's doors to enlist them. Participants were interviewed and given physical exams and blood tests in mobile health clinics to collect data. This research followed the guidelines set forth in the Helsinki Declaration and was sanctioned by the HDRCRP Ethical Committee in Dhaka, Bangladesh. In addition, before to inclusion in the study, all subjects provided written informed permission.

Anthropometric measurements

Trained anthropometric assessors took the following measurements of the subjects while they were in minimal clothing and barefoot: Subjects' waist circumferences were taken at the end of normal expiration from the narrowest point between the lower borders of the rib cage and the iliac crest, also to the nearest 0.1 cm, and their body mass indices were calculated by dividing their kilogram body weight by their meter squared height. To the nearest 2 mmHg, the subject's right arm blood pressure was measured twice while seated using a standard mercury manometer and cuff; the first reading was taken at least 5 minutes (min) after the person was comfortably seated, and the second measurement was taken after a 15-min gap. Then, the mean blood pressures were calculated, both systolic and diastolic.

Biochemical analysis

After having fasted for 10-12 hours overnight, blood was drawn for biochemical examination. The blood sample was taken in accordance with the accepted protocol for taking blood samples. The blood samples were sent to the HDRCRP, Bangladesh, for biochemical evaluation as soon as they were collected and labeled. Triglycerides (TG), total cholesterol (TC), and high-density lipoprotein cholesterol (HDL-C) fractions were measured in the plasma immediately after blood was centrifuged to isolate the serum for analysis (HDL-C). Plasma glucose levels during the fasting state were also evaluated. VLDL-cholesterol was either measured directly (after ultracentrifugation) or estimated as 0.456 x total triglyceride concentration expressed in mmol/l to determine LDL-cholesterol levels (Friedewald).

Definition of MetS and risk factors

MetS was classified as having three or more of the following five conditions: a) high blood pressure (130/85 mmHg) or subjects diagnosed with hypertension; b) elevated fasting blood glucose (110 mg/dl or 6.1 mmol/l) or patients diagnosed with diabetes; c) elevated triglycerides (150 mg/dl or 1.7 mmol/l); d) High-density lipoprotein cholesterol (HDL-C) levels of 40 mg/dl or 1.03 mmol/l for males and 50 mg/dl or 1.29 mmol/l for women; and e) abdominal obesity as indicated by a waist circumference of 102 cm for men and 88 cm for women [14, 15]. Participants who reported being on anti-hypertensive or anti-diabetic drugs (insulin or oral treatments) at the time of the trial were also considered to have high blood pressure or abnormal fasting blood glucose.

Results

A total of 128 people were chosen from all over rural Bangladesh. While talking about the demographics, the average age was 42.5 ± 16.9 years. About 79.5% of the people who take part are married. 60.3% of the participants are stay-at-home moms. The rest are self-employed (8.7%), work for the government (1.6%), or run their own businesses (1.6%). Among these participants, 8.2% of these people smoke every day, and 14.1% use tobacco in some other way. The percentage of illiterate was 41.7%. Talking about income, 65.8% of the people who took part made less than 10,000 BDT per month, while 34.2% made 10,000 BDT or more per month. All tube well water contained arsenic above the minimum detection limit (3 microgram) and 95.4% of the people taking part use their own tube wells (the rest of the part use Government tube wells) (Table 1). 42.3% of the population drank water from a tube well with a high level of arsenic (Well water arsenic concentration between 300-399 µg/L) (Figure-1A). Most cases (70.7%) don't know how to keep arsenic out of the water (Figure-1B). Only 11.8% of people with arsenic are getting treatment for it (Figure-1C).

Table 1: Sociodemographic characteristics of study subjects.

Values are presented as mean ± SD for continuous variables and percentage (%) for categorical variables. SD: standard deviation.

Figure 1

The Co-morbidity variable showed that 13.2% of patients have diabetes (Figure-2). While evaluating the characteristics between diabetics and non-diabetics patients, it is found that diabetic group is older (mean age 52.6 years ± 13.2; p=0.022), has higher waist circumference (89.5 cm ± 11.0, p=0.003), and higher systolic blood pressure (136.7mmHg ± 26.7, p=0.049), than the non-diabetic group. 

Figure 2

The arsenic concentration levels in the tube well of diabetic group is also higher (0.5 mg/l ± 0.1, p=0.013) than the non-diabetic group (Table 2). Our data shows that arsenic-affected communities have a greater rate of diabetes than non-arsenic-affected populations (Figure-3A) in aligned with the data that shows people in Bangladesh who have been exposed to arsenic are getting more and more likely to get diabetes every year (Figure-3B).

Table 2: Clinical and laboratory characteristics of subjects corresponding to with and without diabetes.

Values are presented as mean ± SD for continuous variables and percentage (%) for categorical variables.  SD: standard deviation; HDL: high density lipoprotein; LDL: low density lipoprotein. Significant at p<0.05*. Based on T-test for continuous variable and Pearson Chi-square test for categorical variable.

Figure 3

(Table 3) shows the clinical and lab trails of people with and without high blood pressure. In the group with high blood pressure, the average age is higher (49.9 years ± 14.3; p<0.001) than in the group without high blood pressure (36.8 years ± 16.7). Statistically, the hypertensive group has a higher weight, body mass index, waist circumference, triglyceride, fasting blood sugar, systolic blood pressure, and diastolic blood pressure than the non-hypertensive group. While talking about the arsenic exposure variables, the number of skin spots and physical weakness is much higher in the group with high blood pressure than in the group without high blood pressure (HTN vs. non-HTN: skin lesion, 86.7% vs. 65.9%, p=0.046; physical weakness, 61.1% vs. 29.7%). But arsenic does not come out to be significant between both groups.

Table 3: Clinical and laboratory characteristics of subjects corresponding to with and without hypertension.

Values are presented as mean ± SD for continuous variables and percentage (%) for categorical variables.  SD: standard deviation; HDL: high density lipoprotein; LDL: low density lipoprotein. Significant at p<0.05*. Based on T-test for continuous variable and Pearson Chi-square test for categorical variable.

Similar findings were seen between Metabolic and non-metabolic group when we compared both groups (Table 4). Shows the clinical and laboratory characteristics of people with metabolic syndrome and people who don't have it. In the group with metabolic syndrome, again, the average age is higher (50.3 years ± 9.9; p=0.014) than in the group without metabolic syndrome (40.0 years ± 18.0). People with metabolic syndrome had much higher levels of all metabolic factors. Talking about arsenic exposure variables, more people in the metabolic syndrome group have skin lesions, feel weak, and drink water from a red color tube well, compared to the group without metabolic syndrome. Whereas the arsenic concentration was significant among both groups being higher in group with metabolic syndrome (0.4 mg/l ± 0.1 p = 0.052).  Diabetes is present in 42.9% of people with metabolic syndrome.

Table 4: Clinical and laboratory characteristics of subjects corresponding to with and without metabolic syndrome.

Values are presented as mean ± SD for continuous variables and percentage (%) for categorical variables.  SD: standard deviation; HDL: high density lipoprotein; LDL: low density lipoprotein; MS: metabolic syndrome. Significant at p<0.05*. Based on T-test for continuous variable and Pearson Chi-square test for categorical variable.

The association of skin lesions shows the number of skin lesions is higher in the group with a higher arsenic level than in the group with a lower arsenic level (Figure-4A). Overall, 75% of the people taking part in the study have skin lesions (Table 1). Also, the number of people with high blood pressure is higher in the group with skin lesions than in the other group (Figure-4B). But this trend shows inverse relation with higher monthly income as in the higher monthly income group, the number of skin lesions is heading down (Figure-4C).

Figure 4

Apart from arsenic concentration, we also evaluated the duration of exposure dividing two groups with a history of arsenic exposure lesser or greater than 10 years. In the >=10 years arsenic-affected group, more people have hypertriglyceridemia than in the <10 years arsenic-affected group (Figure-5A). Similarly, in the >=10 years arsenic-affected group, almost twice as many people are obese as in the <10 years arsenic-affected group (Figure-5B).

(Table 5) shows that there is a statistically significant link between age, body mass index, waist circumference, total cholesterol, triglyceride, HDL-cholesterol, LDL-cholesterol, hypertension, and length of arsenic exposure (years). Metabolic syndrome had an OR of 0.75 (CI = 0.32-1.78, p = 0.514), and duration of arsenic affected (years) had an OR of 0.86 (CI = 0.80-0.93, p <0.001) (Table 5).

Figure 5

Table 5: Univariate logistic regression analysis corresponding to arsenic exposure variables by diabetes status (non-diabetes vs. diabetes.

CI: confidence interval; HDL: high density lipoprotein; LDL: low density lipoprotein. Significant at p<0.05*. Based on binary logistic regression.

We looked at how likely it was that people drinking arsenic-tainted water in Bangladesh would develop type 2 diabetes, also talking about the risk of hypertension and metabolic syndrome. The study was not only focused on the arsenic concentration but we also evaluated the effects of duration on the arsenic affected individuals. These findings suggest further investigation into the link between arsenic in drinking water and type 2 diabetes. Arsenic's dangers are often greater when the exposure is prolonged. Our data shows that arsenic-affected communities have a greater rate of diabetes than non-arsenic-affected populations (Fig-3A) in aligned with the data that shows people in Bangladesh who have been exposed to arsenic are getting more and more likely to get diabetes every year (Fig-3B). Wang et al. [16] recently showed that greater hair arsenic levels were associated with higher plasma glucose levels and the incidence of metabolic syndrome in a sample of 660 adults exposed to relatively low arsenic levels in drinking water in Taiwan. Subjects with total urine as (U-As) >75 g/g creatinine were found to have a 2-fold greater risk of T2D compared with individuals whose U-As were 35 g/g creatinine, even after controlling for relevant confounders [17].

It is true that epidemiological studies attempting to connect arsenic exposure with diabetes found conflicting results across communities and exposure pathways. Consistently, those in Taiwan and Bangladesh who drank water with high levels of arsenic had a higher incidence of diabetes. In the workplace, diabetes has been linked to an increase (and a decrease) in death rates, depending on the study. In conclusion, four studies, including populations other than those in Taiwan and Bangladesh, showed no correlation between the two. However, methodological difficulties, particularly in assessing arsenic exposure and diabetes outcomes, lowered the overall quality of this epidemiologic data. There are evidences from experiments and mechanisms shows that arsenic may contribute to the development of diabetes. When sodium arsenite (1.7 mg/kg) was gavaged into rats for 90 days, the animals exhibited higher glucose and insulin levels, decreased glucose-to-insulin ratio, and enhanced homeostasis model evaluation of insulin resistance compared to controls [18]. Molecular processes might involve inhibiting insulin signal transmission or repressing gene transcription factors [19]. Finally, it has been shown that arsenic exposure is associated with the development of diabetes and that oxidative stress, inflammation, and apoptosis may all play a role in this [20]. More research is needed to identify pathways that may be changed by arsenic at lower and intermediate exposure levels [21], as most mechanistic investigations have been conducted at high concentrations of arsenic.

While comparing the metabolic disease group, it is found that the arsenic concentration was significant among both groups being higher in group with metabolic syndrome. Other arsenic exposure variables were also observed such as more people in the metabolic syndrome group have skin lesions, feel weak, and drink water from a red color tube well, compared to the group without metabolic syndrome. These results lend to the hypothesis that arsenic exposure and arsenic metabolism have opposing and separate correlations with metabolic outcomes that may contribute to the risk of diabetes in the general population [22].

We not only focused on concentration but also compared the duration of arsenic exposure between groups suggesting those have exposure greater than 10 years has more metabolic components such as hypertriglyceridemia and obesity in support to the link of chronic arsenic exposure with hypertriglyceridemia [23].

While this work has significant public health and scientific implications, a few cautions should be taken into account. The absence of arsenic exposure biomarker measurements is a significant caveat of this study. The robustness of our results is further demonstrated by the fact that we did not include persons with preexisting diseases that might be exacerbated by arsenic exposure.

Since the available water samples only represented a snapshot in time and not the historical exposure, it would have been preferable to have direct measurement data on individual exposure over time. It was necessary to infer that arsenic contents from the tube wells had been essentially stable over time due to the lack of any reliable information on previous exposure. Since shallow groundwater is more likely to experience fluctuations in arsenic concentration than water from a deeper well, this issue is of special concern. Nonetheless, we anticipate that the study subjects will be exposed to the same degree of harm from any change in arsenic concentrations.

Our results add to the growing body of research that shows a link between long-term exposure to arsenic and T2DM. They also show a relation with metabolic syndrome and arsenic exposure. Such an effect could help us learn more about how diabetes develops and how it can be prevented or treated in the future. It also calls for future, larger, prospective studies. Since T2DM is becoming more common in rural Bangladesh, it is very important that programs clean up drinking water continue to reduce arsenic exposure. Also, if people in Bangladesh try to quit smoking and keep their BMI at a healthy level, this could help lower the risk of T2DM.

We declare that we have no conflict of interest.

This research has been partly supported by a grant from the Ministry of Education and Science (21K07354) in Japan

All study participants provided written informed consent.

SJ has designed and executed the study. SJ has also drafted the manuscript. SK, FS, AR, AAM, MMI, YM, TS, NY, NS and MM have assisted in sample collection and analysis. SJ and AM supervised this manuscript preparation and provided critical editing.

1. Bousquet A. Examining the Impact of Preconception Inorganic Arsenic Exposure on the Mouse Offspring Epigenome. Carolina Digital Repository. 2022.
2. Piyushbhai MK, Binesh A, Shanmugam SA, Venkatachalam, K. Exposure to low-dose arsenic caused teratogenicity and upregulation of proinflammatory cytokines in zebrafish embryos. Biological Trace Element Research. 2022.
3. Adeloju SB, Khan S, Patti AF. Arsenic Contamination of Groundwater and Its Implications for Drinking Water Quality and Human Health in Under-Developed Countries and Remote Communities—A Review. Applied Sciences. 2021; 11: 1926.
4. Bundschuh J, Litter MI, Parvez F, Román-Ross G, Nicolli HB, López D, et al. One century of arsenic exposure in Latin America: A review of history and occurrence from 14 countries. Science of the Total Environment.2012; 429: 2-35.
5. Pal L, Jenei T, McKee M, Kovács N, Vargha M, Bufa-D?rr Z, et al. Health and economic gain attributable to the introduction of the World Health Organization's drinking water standard on the arsenic level in Hungary: A nationwide retrospective study on cancer occurrence and ischemic heart disease mortality. Science of the Total Environment. 2022; 851: 158305.
6. Martínez-Castillo M, García-Montalvo EA, Arellano-Mendoza MG, Izquierdo-Vega JA, Valenzuela OL, Hernández-Zavala A. et al. Arsenic exposure and non-carcinogenic health effects. Human & Experimental Toxicology. 2021; 40: S826-S850.
7. Zierold KM, Knobeloch L, Anderson H. Prevalence of Chronic Diseases in Adults Exposed to Arsenic-Contaminated Drinking Water. American Journal of Public Health. 2004; 94: 1936-1937.
8. Prakash S, Verma AK. Arsenic: It’s Toxicity and Impact on Human Health. International Journal of Biological Innovations. 2021; 3: 38-47.
9. Coronado-González JA, Del Razo LM, García-Vargas G, Sanmiguel-Salazar F, Escobedo-de la Peña J. Inorganic arsenic exposure and type 2 diabetes mellitus in Mexico. Environmental Research. 2007; 104: 383-389.
10. Rahman M, Wingren G, Axelson O. Diabetes mellitus among Swedish art glass workers -- an effect of arsenic exposure? Scandinavian Journal of Work, Environment & Health. 1996; 22: 146–149.
11. Jensen GE, Hansen ML. Occupational arsenic exposure and glycosylated haemoglobin†. The Analyst. 1998; 123: 77-80.
12. Lai M-S, Hsueh Y-M, Chen C-J, Shyu M-P, Chen S-Y, Kuo T-L, et al. Ingested Inorganic Arsenic and Prevalence of Diabetes Mellitus. American Journal of Epidemiology. 1994; 139: 484-492.
13. Longnecker MP, Daniels JL. Environmental contaminants as etiologic factors for diabetes. Environmental Health Perspectives. 2001; 109: 871-876.
14. Huang PL. A comprehensive definition for metabolic syndrome. Dis Models Mech. 2009; 2: 231-237.
15. Jolliffe CJ, Janssen I. Development of age-specificadolescent metabolic syndrome criteria that are linkedto the adult treatment panel III and International Diabetes Federation criteria. J Am Coll Cardiol. 2007; 49: 891-898.
16. Wang S-L, Chang F-H, Liou S-H, Wang H-J, Li W-F, Hsieh DPH. Inorganic arsenic exposure and its relation to metabolic syndrome in an industrial area of Taiwan. Environment International. 2007; 33: 805-811.
17. Chen J-W, Chen H-Y, Li W-F, Liou S-H, Chen C-J, Wu J-H, et al. The association between total urinary arsenic concentration and renal dysfunction in a community-based population from central Taiwan. Chemosphere. 2011; 84: 17-24.
18. Izquierdo-Vega JA, Soto CA, Sanchez-Peña LC, Vizcaya-Ruiz AD, Razo LMD. Diabetogenic effects and pancreatic oxidative damage in rats subchronically exposed to arsenite. Toxicology Letters. 2006; 160: 135-142.
19. Elrick LJ, Docherty K. Phosphorylation-Dependent Nucleocytoplasmic Shuttling of Pancreatic Duodenal Homeobox-1. Diabetes. 2001; 50: 2244-2252.
20. Farkhondeh T, Samarghandian S, Azimi-Nezhad M. The role of arsenic in obesity and diabetes. Journal of cellular physiology. 2019; 234: 12516-12529.
21. Grau-Perez M, Kuo C-C, Gribble MO, Balakrishnan P, Spratlen MJ, Vaidya D, et al. Association of Low-Moderate Arsenic Exposure and Arsenic Metabolism with Incident Diabetes and Insulin Resistance in the Strong Heart Family Study. Environmental Health Perspectives. 2017; 125: 127004.
22. Spratlen MJ, Grau-Perez M, Best LG, Yracheta J, Lazo M, Vaidya D, et al. The Association of Arsenic Exposure and Arsenic Metabolism with the Metabolic Syndrome and Its Individual Components: Prospective Evidence from the Strong Heart Family Study. American journal of epidemiology. 2018; 187: 1598-1612.
23. Mendez MA, González-Horta C, Sánchez-Ramírez B, Ballinas-Casarrubias L, Cerón RH, Morales DV, et al. Chronic Exposure to Arsenic and Markers of Cardiometabolic Risk: A Cross-Sectional Study in Chihuahua, Mexico. Environmental health perspectives. 2016; 124: 104-111.