Geographical Variations in Metal Exposure and Its Impact on Metabolic Disorders and Hypertension: An Analysis of Chile's 2016–17 National Health Survey

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Local environmental factors may modulate these effects, emphasizing the importance of territorial disaggregation. This population-based study evaluated geographic variations in exposure to four metals and their associations with obesity, diabetes, metabolic syndrome, and hypertension in Chile. Method Data from 3,822 participants in the National Health Survey from 2016 to 2017 were analyzed. Biomarkers included inorganic arsenic, cadmium, mercury in urine, and lead in serum. Metal exposure was classified according to the 50th percentile distribution. Spatial simultaneous autoregressive models accounted for regional disaggregation and spatial dependencies, adjusting for age, sex, socioeconomic status, and smoking. Analyses were conducted at the national and subnational levels, incorporating sampling weights from the national survey complex design. Results A total of 42.4% of individuals were exposed to arsenic, 13.6% to lead, and 1.7% to mercury and cadmium. Regional analysis revealed elevated arsenic exposure in northern regions (e.g., Arica and Antofagasta), with lead exposure peaking at 29.9%. At the national level, adjusted models revealed no significant associations between metal exposure and metabolic disorders. However, geographical disaggregation revealed that arsenic exposure was linked to overweight and obesity across most areas and to diabetes and metabolic syndrome in the northern, southernmost, and central zones. Mercury exposure was associated with all conditions in the central macrozone, whereas cadmium exposure was exclusively linked to diabetes in southern regions. Conclusion These findings underscore critical regional differences in metal and metalloid exposure and metabolic disorders, highlighting the need for geographically targeted public health interventions that consider local environmental and contextual factors. Trial registration Not applicable Metal exposure metabolic disorders hypertension exposome geographical variation territorial disaggregation. Figures Figure 1 Figure 2 Background The increasing burden of metabolic disorders, including the persistent rise in obesity and diabetes mellitus, remains a pressing public health challenge despite notable advancements in prevention and treatment strategies (1–4). Traditional risk factors, such as poor diet, physical inactivity, genetic predisposition, and broader social determinants, are well-established drivers of these conditions. However, growing attention is being directed toward the exposome framework, which encompasses the cumulative environmental exposures experienced throughout an individual's lifetime, particularly emphasizing chemical contaminants (5–7) as emerging contributors to metabolic dysfunction. Integrating exposome-based research into public health strategies holds promising opportunities for identifying previously unrecognized risk factors, bridging knowledge gaps, and informing innovative interventions. Metalloids and nonessential heavy metals, such as arsenic and lead, which primarily stem from environmental exposures, are well documented for their harmful health effects. These include their mutagenic and carcinogenic properties and their capacity to induce oxidative stress (8). Similarly, synthetic chemicals used in industrial and agricultural processes, such as bisphenol A and phthalates, are recognized as endocrine agents, as environmental endocrine-disrupting chemicals (EDCs) (9) disrupt endocrine function and have been implicated in the development of noncommunicable diseases, including obesity, diabetes, metabolic disorders, and thyroid dysfunction (10,11). Recent evidence suggests that heavy metals, including mercury, cadmium, and lead, may also contribute to metabolic dysfunction by disrupting metabolic pathways, inducing oxidative stress, and impairing endocrine regulation. These mechanisms are thought to underlie conditions such as obesity and insulin resistance (7,12–14). Furthermore, emerging studies have linked heavy metal exposure to a broader spectrum of metabolic disorders, including obesity (15–17), diabetes (18–20), and metabolic syndrome (21). However, inconsistencies in findings across studies highlight the need for further research to clarify these associations. Most studies have focused on specific regions or national-level populations, often neglecting the potential variations within geographically diverse countries such as Chile. From an exposome perspective, local environmental, social, and genetic factors may modulate the effects of exposure, underscoring the importance of research incorporating territorial disaggregation approaches. These methods allow for a nuanced understanding of public health challenges and enable the development of equitable, context-specific interventions. The unique geographic and economic profile of Chile offers an ideal setting for exploring the health effects of heavy metal exposure. The country hosts over 200 active mining operations, which are located predominantly in the northern regions, whereas the central and southern zones are characterized by agricultural and fisheries activities, which are often accompanied by environmental pollution. Despite strict regulations on industrial metal emissions, evidence suggests that even low-level metal exposure may disrupt metabolic pathways (22). In addition to high diabetes rates (23), northern regions show a steep increase in obesity incidence (24), with a small case study linking urinary arsenic levels to glycemia (25), further suggesting a connection between environmental exposure and metabolic health. This population-based study aims to analyze the geographic variation in exposure to four metals and their associations with obesity, diabetes, and metabolic syndrome in Chile. Hypertension, a condition closely linked to metabolic dysfunction and highly prevalent in the country, was also included in the analysis, given emerging evidence suggesting its association with metal exposure (26–28). Understanding regional disparities in these exposures and outcomes can help identify areas at greatest risk and guide targeted public health interventions. Method Data source We used data from the most recent National Health Survey (ENS, its acronym in Spanish) conducted in Chile between August 2016 and March 2017. Despite Chile having conducted three ENSs, each approximately ten years apart, the latest survey included biological markers for metal exposure, making it a unique resource for environmental and metabolic population-based studies. The ENS 2016-17 included 6,233 participants aged 15 years or older, utilizing a multistage, stratified probabilistic sampling method to ensure national coverage, subnational, urban/rural areas, sex, and macrozone representativeness. By then, Chile was administratively and geographically divided into 15 regions spanning from north to south. We utilized these regions and macrozones to evaluate the main geographical variability at subnational levels. Owing to the small sample sizes for some metal exposures, such as mercury and cadmium, these regions were grouped into four macrozones according to the ENS criteria: 1) North: Arica and Parinacota, Tarapacá, Antofagasta, Atacama, and Coquimbo; 2) Center: Valparaíso, Libertador Bernardo O'Higgins, Maule, and Biobío; 3) South: La Araucanía, Los Ríos, Los Lagos, Aysén, and Magallanes and Chilean Antarctica; and 4) Metropolitan Region (RM): While geographically part of the central zone, the RM, which encompasses Santiago, Chile's capital and largest city, was categorized as a separate macrozone owing to its significant population density and unique characteristics. The ENS 2016-17 data collection involved four home visits by trained health professionals who combined clinical examinations and the administration of questionnaires with 576 items addressing health conditions and risk factors. Serum and urine samples were collected from a representative subsample of 3,822 individuals (61.3%) to determine heavy metal levels, among other biomarkers, ensuring coverage at the national level and in urban, rural, and macrozones. Heavy metal exposure ENS-2016-17 was used to determine inorganic arsenic, cadmium, and inorganic mercury levels in spot urine samples and lead levels in serum samples. Biological samples were transported to the National Institute of Public Health, Chile's national reference laboratory (ISP, its acronym in Spanish), for specialized analysis. For arsenic, inductively coupled plasma‒mass spectrometry (ICP-MS) was used, with a quantification limit of 5.00 µg/L and a detection limit of 0.56 µg/L. Mercury was analyzed via both ICP‒MS and cold vapor atomic absorption spectrophotometry, with a quantification limit of 2.00 µg/L and a detection limit between 0.39 and 0.54 µg/L. Cadmium and lead were also analyzed with ICP‒MS, with quantification limits of 1.00 µg/L for cadmium and 1.00 µg/dL for lead. These methods ensure accurate detection in the general population sample. The ENS imputed the minimum detectable value for values below the detection limit by default. Given that the risk cutoff points for metabolic conditions are still unclear and that including minimum detectable values could lead to harm, this study analyzed the correlations while considering the full range of data from the minimum detectable value. The 50th percentile distributions were used to classify individuals as exposed to better understand the exposure levels. This approach allowed for a more nuanced interpretation of the data while minimizing the risk of harm or misrepresentation. Metabolic conditions and hypertension In this study, overweight, obesity, diabetes, metabolic syndrome, and hypertension were analyzed. ENS measured weight in kilograms (kg), height in meters (m), waist circumference in centimeters (cm), and blood pressure. Additionally, a trained nurse collected blood samples to measure fasting glucose, triglyceride, high-density lipoprotein (HLD), and low-density lipoprotein (LDL) levels. These samples were sent to and analyzed by the ISP. For overweight and obesity, we calculated the body mass index (BMI) as weight divided by height squared (kg/m²). Overweight and obesity were defined according to the World Health Organization (WHO) criteria: a BMI of 25–29.9 kg/m² was classified as overweight, whereas a BMI ≥ 30 kg/m² was classified as obese. Diabetes was defined as both self-reported cases (self-reported diagnosis or under pharmacological treatment) and individuals with fasting glucose levels > 126 mg/dL, on the basis of the WHO cutoff. The ENS included questions for self-reported diseases and medical treatment. For example, for diabetes, it was "Has a doctor, nurse, or other healthcare professional ever told you that you have or have had diabetes (high blood sugar)?" and " What type of treatment are you undergoing?". Patients who answered "no" to these questions but did not have a glucose level to corroborate the absence of diabetes were excluded from the analysis, as were women who reported gestational diabetes. Metabolic syndrome was defined by adapting criteria used in ENS, which meet three or more of the following conditions: central obesity (Chile's recommended waist circumference cutoffs are ≥ 90 cm for men and ≥ 80 cm for women), elevated triglycerides (≥ 150 mg/dL, HDL cholesterol, < 40 mg/dL in men or < 50 mg/dL in women, or on treatment/self-reported diagnosis), average systolic blood pressure (SBP) ≥ 130 mmHg and diastolic blood pressure (DBP) ≥ 85 mmHg or diagnosis self-reported/medical treatment for hypertension or diabetes, and fasting glucose ≥ 100 mg/dL. Hypertension was defined on the basis of self-reports of receiving treatment for high blood pressure and the average of three blood pressure readings, with the criteria being SBP ≥ 140 mmHg and DBP ≥ 90 mmHg. Statistical analysis The national, regional, and macrozone-level prevalence rates were calculated via the expansion factor of the ENS sampling design, with 95% confidence intervals (95% CIs). Additionally, the age-adjusted mean was computed. The distribution of metals was described on the basis of the percentile (P) distribution, including P25, P50, and P75, as well as the percentage of individuals exposed according to the study's definition, which was above the median. The associations between metal exposure and various comorbidities were analyzed by estimating odds ratios (ORs) with corresponding 95% CIs via logistic regression models weighted for the complex sampling design of the ENS survey at the national, regional, and macrozone levels. Both crude and adjusted ORs were calculated, adjusting for sex, age, and educational level as proxies for socioeconomic status (SES) and smoking status as a dichotomous variable (smoker or nonsmoker) at the national level and across macrozones. For regional poststratification estimations, spatial relationships between regions were further incorporated via spatial simultaneous autoregressive (SAR) regression models estimated through maximum likelihood. This approach was employed to correct potential spatial autocorrelation biases, ensuring that regional estimates reflect local exposure data and spatial dependencies between neighboring regions. By accounting for spatial structure, SAR models provide more accurate and context-sensitive results, which are essential for identifying geographic patterns and tailoring public health interventions accordingly. All analyses were conducted via R statistical packages, specifically the "survey" package (version 4.0) for complex survey data analysis and the "spatialreg" package for spatial regression modeling. Results From the total of 3,822 biological samples, the ISP classified some as "insufficient sample," "not evaluated," or "nonadmissible." Consequently, the final sample size analyzed for metal exposure and its percentile distribution are described in Table 1 . At the national level, the median arsenic concentration (percentile 50) was 12.5, with values of 1.0 for lead and cadmium and 2 for mercury. The proportion of individuals with metal exposure above the median value was 42.4% for inorganic arsenic, 13.6% for lead, and 1.7% for mercury and cadmium. The prevalence of metabolic conditions, hypertension, and other sociodemographic variables, along with their 95% confidence intervals (95% CI), are also presented in Table 1 . Table 1 National distribution and prevalence of study variables, Chile 2016-17. Metal concentration levels within the sample* n Median (P25; P75) Arsenic µg/L 3,559 12.52 (6.98; 19.56) Lead µg/L 3,614 1.00 (1.00; 1.08) Mercury µg/L 3,514 2.00 (2.00; 20.00) Cadmium µg/L 3,560 1.00 (1.00; 2.27) Individuals exposed to levels exceeding the median Frequency Prevalence (95%CI) Arsenic 1,472 42.40 (39.59; 45.22) Lead 560 13.64 (11.54; 15.75) Mercury 63 1.73 (1.04; 2.43) Cadmium 101 1.66 (1.05; 2.27) Metabolic conditions and hypertension Overweight 2,087 39.8 (37.2; 42.4) Obesity 2,056 34.4 (32.1; 36.8) Diabetes 996 13.68 (12.3; 15.1) Metabolic Syndrome 1,649 42.1 (39.3; 45.0) Hypertension 2,601 34.9 (32.8; 37.1) Educational level (proxi of socioeconomic status) Less than 8 years 1,477 16.7 (14.7; 18.7) 8 to 12 years 3,323 56.6 (53.2; 59.9) 13 years or more 1,374 26.7 (23.5; 30.0) Sex Male 2,315 49.1 (46.5; 51.7) Female 3,918 50.9 (48.3; 53.5) Smoking status One or more cigarettes a day 1,327 25.8 (23.4; 28.2) Occasionally (less than once a day) 433 8.5 (6.7; 10.3) Stop smoking 1,456 23.7 (21.5: 25.9) Never 3,017 42.0 (39.4; 44.6) Age in years, mean 6,233 42.7 (41.7; 43.7) *Median, 25th Percentile, and 75th Percentile CI: Confidence Interval. The subnational analysis results for the metal exposures are displayed in Table 2 . Most regions presented median arsenic values below the national level, except for northern regions, such as Arica, Antofagasta, and Atacama, and the southernmost region, Magallanes. The pattern of the percentage of individuals exposed to this metal (above the median) was similar. No variations in the median levels were observed at the regional level for the other three metals. However, differences were noted in the percentage of individuals exposed. For example, in the case of lead, one northern region, Tarapacá, presented the highest percentage of individuals exposed to lead (29.9%). A similar pattern was observed for mercury and cadmium (Table 2 ). Table 2 Regional-level analysis of exposure to four heavy metals in Chile, 2016-17. Arsenic Lead Mercury Cadmium Region n Median µg/L Exposed % n Median µg/L Exposed % n Median µg/L Exposed % n Median µg/L Exposed % XV. Arica y Parinacota 177 27.56 83.60 181 1.00 15.47 174 2.00 4.02 177 1.00 5.08 I. Tarapacá 166 17.75 62.65 174 1.02 29.89 158 2.00 5,70 168 1.00 5.95 II. Antofagasta 185 14.29 50.81 187 1.00 11.76 182 2.00 3.85 186 1.00 4.84 III. Atacama 194 14.30 50.52 200 1.00 14.50 194 2.00 1.03 194 1.00 2.58 IV. Coquimbo 372 11.01 36.68 381 1.00 12.33 370 2.00 1.62 372 1.00 2.96 V. Valparaíso 198 12.05 40.40 193 1.00 13.47 188 2.00 2.13 197 1.00 2.03 XIII. Metropolitana 231 9.94 30.74 209 1.00 17.22 224 2.00 2.68 231 1.00 3.46 VI. L. Bdo. OHiggins 379 10.87 32.19 387 1.00 14.64 377 2.00 0.53 379 1.00 2.11 VII. Maule 178 10.64 37.64 185 1.00 11.35 180 2.00 0.55 178 1.00 0.00 VIII. Bíobío 186 12.31 40.01 191 1.00 18.20 185 2.00 0.00 185 1.00 1.08 IX. La Araucanía 196 10.80 37.24 197 1.00 12.18 196 2.00 0.00 197 1.00 3.55 XIV. Los Ríos 166 8.76 22.29 185 1.00 20.00 163 2.00 1.84 164 1.00 3.05 X. Los Lagos 547 10.56 35.10 551 1.00 13.97 548 2.00 2.19 548 1.00 2.19 XI. Aysén 186 7.27 18.82 185 1.00 23.78 184 2.00 0.54 186 1.00 2.69 XII. Magallanes y Antártica 198 22.13 74.75 208 1.00 10.58 191 2.00 1.57 198 1.00 2.03 At the national level, associations were observed between arsenic exposure and hypertension (OR: 0.64, 95% CI: 0.49; 0.78), lead exposure and both hypertension (OR: 1.55, 95% CI: 1.12; 2.14) and metabolic syndrome (OR: 1.47, 95% CI: 1.08; 2.00), and mercury exposure and both obesity (OR: 0.38, 95% CI: 0.16; 0.87) and diabetes (OR: 0.22, 95% CI: 0.07; 0.73). Nevertheless, these associations disappeared after adjusting for age, sex, socioeconomic status, and smoking status (Table 3 ). Table 3 National-level analysis of crude and adjusted Odds Ratios (OR) and 95% confidence intervals (95% CI) Arsenic Lead Mercury Cadmium Crude Adjusted 1 Crude Adjusted 1 Crude Adjusted 1 Crude Adjusted 1 Overweight 0.93 (0.72; 1.19) 0.95 (0.74; 1.23) 0.98 (0.70; 1.36) 0.80 (0.56; 1.14) 0.53 (0.24; 1.16) 0.60 (0.28; 1.29) 0.60 (0.29; 1.27) 0.54 (0.26; 1.14) Obesity 0.81 (0.63; 1.05) 0.96 (0.73; 1.26) 1.10 (0.78; 1.54) 1.11 (0.79; 1.57) 0.38 (0.16; 0.87) 0.44 (0.19; 1.02) 1.38 (0.63; 3.05) 1.24 (0.58; 2.69) Diabetes 0.74 (0.54; 1.03) 1.17 (0.80; 1.72) 1.00 (0.64; 1.55) 0.80 (0.50; 1.29) 0.22 (0.07; 0.73) 0.30 (0.09; 1.05) 0.47 (0.19; 1.16) 0.41 (0.16; 1.02) Metabolic Syndrome 1.00 (0.62; 1.02) 0.99 (0.77; 1.28) 1.47 (1.08; 2.00) 1.12 (0.81; 1.57) 0.56 (0.24; 1.31) 0.78 (0.33; 1.84) 1.65 (0.75; 3.59) 1.20 (0.55; 2.66) Hypertension 0.64 (0.49; 0.78) 1.03 (0.77; 1.37) 1.55 (1.12; 2.14) 0.90 (0.63; 1.28) 0.55 (0.25; 1.18) 1.44 (0.60; 3.47) 1.93 (0.97; 3.85) 1.32 (0.58; 2.97) 1 Adjusted by sex, age, socioeconomic and smoking status. With respect to subnational-level disaggregation, Supplementary Table 1 and Fig. 1 summarize the adjusted odds ratios (ORs) for arsenic exposure, whereas Supplementary Table 2 and Fig. 2 provide the corresponding data for lead exposure. Associations between arsenic exposure and overweight or obesity were observed in most regions of the country. For diabetes and metabolic syndrome, associations were noted in northern regions, southernmost regions, and three central regions. Similar patterns were observed for hypertension, although fewer regions presented significant associations (Fig. 1 and Supplementary Table 1). Similar geographic variability patterns were observed for lead exposure (Fig. 2 and Supplementary Table 2). In the case of mercury exposure, significant associations were observed in the central macrozone, which includes the Metropolitan Region, for obesity, overweight, diabetes, and hypertension. Conversely, a significant association between cadmium exposure and diabetes was found exclusively in the southern macrozone of the country (Table 4 ). Table 4 Macrozone-level analysis of adjusted Odds Ratios (OR) 1 and 95% confidence intervals (95% CI) for mercury and cadmium exposure. Overweight Obesity Diabetes Metabolic Syndrome Hypertension Mercury North 0.79 (0.20; 3.07) 1.61 (0.43; 6.10) 0.79 (0.15; 4.16) 2.38 (0.66; 8.61) 2.86 (0.68; 12.03) Central 2.00 (0.60; 6.71) 0.20 (0.05; 0.91) 0.08 (0.01; 0.63) 0.65 (0.14; 3.02) 1.90 (0.41; 8.81) South 0.66 (0.07; 6.32) 2.87 (0.31; 26.39) -- 5.48 (0.66; 45.44) 6.24 (0.55; 70.58) Metropolitan 0.10 (0.01; 0.89) 0.14 (0.02; 0.92) 0.15 (0.02; 1.35) 0.22 (0.03; 1.60) 0.16 (0.03; 0.74) Cadmium North 0.99 (0.35; 2.82) 0.82 (0.33; 2.02) 0.76 (0.19; 3.01) 1.76 (0.75; 4.12) 2.65 (0.63; 11.18) Central 0.39 (0.13; 1.14) 1.89 (0.65; 5.52) 0.50 (0.11; 2.26) 0.96 (0.32; 2.86) 0.86 (0.23; 3.24) South 1.24 (0.34; 4.58) 0.48 (0.12; 1.89) 0.03 (0.001; 0.23) 1.03 (0.22; 4.96) 0.48 (0.12; 1.89) Metropolitan 0.50 (0.10; 2.52) 0.87 (0.14; 5.36) 0.16 (0.02; 1.07) 1.23 (0.21; 7.32) 1.86 (0.40; 8.60) -- Without sufficient sample size. 1 Adjusted by sex, age, socioeconomic and smoking status. Discussion The results of this study highlight the importance of examining subnational geographic variations in metal exposure and their relationships with metabolic disorders and diseases. A key finding of our research reveals that Chile does not exhibit a uniform pattern of metal exposure across its population. Arsenic exposure is notably greater at the country's extremes, ranging from 83.6% of the population in Arica and Parinacota (north) to 18.82% in Aysén (south). In contrast, lead exposure shows less variation, ranging from 29.89% in Tarapacá to 10.58% in Magallanes and Antártica. These findings are based on the median exposure distribution; however, if stricter criteria were applied, the extent of metal exposure in Chile could pose an even greater challenge. Each geographic region's territorial, environmental, and industrial characteristics may explain this pattern, as may primary exposure pathways—water, air, soil, and food. While contamination through water and air is relatively well documented, other routes remain less understood and require deeper exploration. In particular, lead, mercury, and cadmium exposure pathways are less well characterized and necessitate further research. The prevalence of arsenic exposure has drawn our attention, as arsenic contamination in soil and groundwater is a global concern with significant impacts on human health. Studies have revealed widespread arsenic contamination in various regions worldwide, including India, Bangladesh, parts of the United States, Mexico, Chile, Argentina, and other European and Asian locations (26–29). The primary sources of arsenic are geogenic, resulting from the weathering of arsenic-bearing rocks and the reductive dissolution of iron oxides (30). Arsenic concentrations in groundwater often exceed the WHO's guideline of 10 µg/L, posing serious health risks (31), and vary geographically and with depth, affecting soil, water, and crops such as rice (32). Consequently, the continuous use of arsenic-contaminated water for irrigation can lead to soil contamination and bioaccumulation in plants (26), further impacting human health (28). Consistent with existing evidence (33–36), this study revealed an association between arsenic exposure and metabolic outcomes after territorial disaggregation. Notably, regions with high agricultural activity, such as Arica, the Metropolitan Region, and southern Chile, presented significant associations with overweight, obesity, and diabetes, even after we adjusted for smoking habits, socioeconomic status, age, and sex. These findings support the hypothesis that local environmental and behavioral factors—key components of the exposome—may shape cumulative arsenic exposure and its metabolic consequences. In agricultural areas, soil contamination and indirect arsenic intake through food consumption could contribute to increased exposure levels, increasing the risk of chronic dietary patterns and associated health effects. The proposed mechanisms include inflammation, oxidative stress, and altered adipose tissue function (36). However, a comprehensive exposome-based approach that integrates environmental, occupational, and lifestyle-related exposures is needed to better understand the complex interplay between metal contaminants and metabolic health (37). Another important finding regarding arsenic exposure and metabolic conditions was the opposing directions of the observed associations. For example, in Arica and Parinacota, the association with overweight was positive (OR > 1), whereas the association with obesity was inverse (OR < 1). From an epidemiological perspective, this pattern could be explained by potential pathophysiological mechanisms underlying the effects of arsenic and different stages of disease progression. Prolonged metal exposure may have a biphasic effect on body weight, initially leading to weight gain followed by progressive weight loss due to greater tissue damage at the cellular level. If this holds, it could reflect a transitional process from an epidemiological standpoint. That is, geographical areas where the OR for overweight is greater than one might represent earlier stages of arsenic-related metabolic disruption, whereas areas where the OR for overweight or obesity is less than one may reflect more advanced or prolonged exposure-related damage. Nevertheless, further longitudinal studies are needed to test these hypotheses, and exposure to multiple metals, including other metals such as cobalt, is needed (38). The prevalence of lead exposure has been associated with an increased risk of hypertension and metabolic syndrome (MS). Evidence indicates that lead exposure contributes to elevated blood pressure, altered heart rate variability, and a higher incidence of MS (39–42), as well as to recently published findings on vascular age (43). The underlying mechanisms involve oxidative stress, reduced nitric oxide availability, endothelial dysfunction, and impaired vascular responses (44). Additionally, lead exposure may exacerbate the effects of MS, although antioxidants such as lipoic acid and coenzyme Q10 have shown potential in mitigating these impacts (45). Early-life lead exposure may also influence MS risk indicators in children (46). However, conflicting findings persist, and establishing causality remains challenging owing to variations in study designs and confounding factors (47). Further research, particularly prospective studies with standardized methodologies, is necessary to clarify the causal relationship between lead exposure and MS-related outcomes. Regarding mercury exposure, Chile, like many other nations, is a signatory to the Minamata Convention, which aims to address contamination by this heavy metal. Despite these efforts, evidence of mercury exposure persists, particularly in the metropolitan area, which has the country's largest population. An association was observed between mercury exposure and all the metabolic alterations studied, even at very low doses. The evidence suggests a complex relationship between mercury exposure and metabolic disorders, particularly hypertension. Some studies report a positive association between mercury levels and hypertension risk (48,49), whereas others find no significant link (4). Additionally, mercury exposure has also been implicated in metabolic syndrome and diabetes, although the evidence remains inconsistent (50). Recent investigations have focused on the role of mercury in cardiovascular health, including its impact on atherosclerosis and hypertension (51). Maternal mercury exposure has been investigated for its potential association with hypertensive disorders of pregnancy, yielding mixed results (52). The mechanisms through which mercury and other heavy metals, including cadmium, contribute to metabolic syndrome likely involve oxidative stress, inflammation, and altered lipoprotein metabolism (53,54). Additionally, emerging research has highlighted the influence of the gut microbiota composition on the physiology of hedonic hunger (55). Studies have identified distinct microbial profiles in different populations, with Firmicutes and Bacteroidetes being the dominant phyla globally (56,57). Notably, gut microbial diversity decreases as populations transition from hunter-gatherers to industrialized urban lifestyles (58). Furthermore, exposure to toxic metals can significantly alter the composition and diversity of the gut microbiome (59). On the other hand, the gut microbiota plays a crucial role in limiting heavy metal absorption and dissemination in the body (60). Notably, the microbiome can both influence an individual's susceptibility to environmental toxicants and aid in their metabolism and excretion (61). Associations have been reported between childhood and perinatal blood metal levels and changes in the gut microbiome composition, including alterations in potentially pathogenic and beneficial species (62). While the gut microbiome appears to be a promising biomarker for metal exposure, further research is needed to fully understand the complex interactions between environmental pollutants and the gut microbiota (63). Early-life exposure to environmental toxins can have lasting effects on gut health, potentially influencing developmental outcomes. However, the dietary context in which these exposures occur is crucial, underscoring the importance of considering both environmental and dietary factors when assessing gut microbiome health. This study uses SAR models to analyze geographic variation robustly at the subnational level. SAR models allow us to account for spatial dependencies, improving the robustness of our estimates and reducing potential biases due to spatial autocorrelation. Additionally, the study benefits from a well-defined national dataset with high geographic resolution, enhancing the accuracy of our findings. A key strength is that prevalence estimates and association measures are weighted by the expansion factor of the complex survey design. This ensures that the results are representative of the target population, reducing selection bias and improving external validity. By focusing on associations where the 95% confidence intervals exclude the null effect, we ensure the statistical reliability of our reported results. Despite these strengths, limitations must be acknowledged. First, the study's observational nature precludes causal inference, limiting our ability to establish definitive cause‒and‒effect relationships. Nevertheless, this study provides a solid foundational framework for generating causal hypotheses and guiding future research. The identified associations can inform the design of longitudinal and experimental studies to investigate potential causal mechanisms further and refine risk assessment strategies. Additionally, the spatial patterns revealed in this study can help identify potential hot spots, which can be prioritized for targeted interventions aimed at mitigating health risks in high-exposure areas. Although SAR models mitigate spatial autocorrelation issues and efforts are made through adjusted analyses, residual confounding due to unmeasured environmental or socioeconomic factors cannot be entirely ruled out. Additionally, exposure assessment may be subject to measurement errors, particularly if data sources rely on regional estimates rather than individual-level exposure assessments. Finally, while our findings provide valuable insights into spatial patterns of association, their generalizability to other time periods or regions outside Chile requires further validation. Conclusion In conclusion, understanding geographical variations is essential for developing a comprehensive theory on complex illnesses, such as metabolic disorders. It also enables policymakers and healthcare professionals to design region-specific interventions effectively and preventive strategies to address metabolic disorders and hypertension. Abbreviations ENS (its acronym in Spanish) National Health Survey EDC Endocrine-disrupting chemical RM Metropolitan Region ICP-MS Inductively coupled plasma‒mass spectrometry HDL High-Density Lipoprotein LDL Low-Density Lipoprotein BMI Body mass index ISP (its acronym in Spanish) National Institute of Public Health of Chile WHO World Health Organization SBP Systolic blood pressure DBP Diastolic blood pressure CI confidence Interval OR Odds Ratio P Percentile SES Socioeconomic Status SAR Spatial simultaneous autoregressive regression Declarations Ethics approval and consent to participate Not applicable. This study is an observational secondary analysis based on anonymized and publicly available data. As such, it did not require approval from an ethics committee. Additionally, since all data were de-identified and freely accessible, obtaining individual consent to participate was not necessary. Consent for publication Not applicable Availability of data The article's data will be shared on reasonable request to the corresponding author. Competing interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Acknowledgements Not applicable Author contributions PM: Conceptualization; Methodology; Writing—review, editing. AS: Data curation; Formal analysis; Writing—review, editing. CU: Conceptualization; Methodology Writing—original draft, editing. References Chong B, Jayabaskaran J, Kong G, Chan YH, Chin YH, Goh R, et al. 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Association of obesity, diabetes, and hypertension with arsenic in drinking water in the Comarca Lagunera province (north-central Mexico). Sci Rep. 2023;13(1):9244. Maull EA, Ahsan H, Edwards J, Longnecker MP, Navas-Acien A, Pi J, et al. Evaluation of the Association between Arsenic and Diabetes: A National Toxicology Program Workshop Review. Environmental Health Perspectives. 2012;120(12):1658-70. Farkhondeh T, Samarghandian S, Azimi-Nezhad M. The role of arsenic in obesity and diabetes. Journal of Cellular Physiology. 2019;234(8):12516-29. Matus P, Sepúlveda-Peñaloza A, Page K, Rodríguez C, Urquidi C. The Chilean exposome-based system for ecosystems (CHiESS): a framework for national data integration and analytics platform. Front Public Health. 2024;12. Salcedo-Bellido I, Castillo Bueno H, Olmedo P, Gil F, Ocaña-Peinado FM, Rodrigo L, et al. Metal (loid) Exposure and Overweight and Obesity in 6–12-Year-Old Spanish Children. Expo Health. 2024;16(6):1471-83. Park SK, Schwartz J, Weisskopf M, Sparrow D, Vokonas PS, Wright RO, et al. Low-Level Lead Exposure, Metabolic Syndrome, and Heart Rate Variability: The VA Normative Aging Study. Environmental Health Perspectives. 2006;114(11):1718-24. Navas-Acien A, Guallar E, Silbergeld EK, Rothenberg SJ. Lead Exposure and Cardiovascular Disease—A Systematic Review. Environmental Health Perspectives. 2007;115(3):472 − 82. Rhee SY, Hwang YC, Woo J taek, Sinn DH, Chin SO, Chon S, et al. Blood lead is significantly associated with metabolic syndrome in Korean adults: an analysis based on the Korea National Health and Nutrition Examination Survey (KNHANES), 2008. Cardiovasc Diabetol. 2013;12(1):9. Zhang R, Zhou J, Huo P, Zhang H, Shen H, Huang Q, et al. Exposure to Multiple Metal(loid)s and Hypertension in Chinese Older Adults. Biol Trace Elem Res [Internet]. 2024; DOI: https://doi.org/10.1007/s12011-024-04388-x Feng Y, Liu C, Huang L, Qian J, Li N, Tan H, et al. Associations between heavy metal exposure and vascular age: a large cross-sectional study. J Transl Med. 2025;23(1):4. Vaziri ND. Mechanisms of lead-induced hypertension and cardiovascular disease. American Journal of Physiology-Heart and Circulatory Physiology. 2008;295(2):H454-65. Omar, Hany A., Waleed Hassan Almalki, Hanan A Shamardl, Abeer Yahia Mahdy and Hekma A. Abd El-Latif. Lipoic Acid and Coenzyme Q10 Protect Against Lead-induced Toxicity in Rats with Metabolic Syndrome. International Journal of Pharmacology. 2016;12:46–153. Muciño-Sandoval K, Ariza AC, Ortiz-Panozo E, Pizano-Zárate ML, Mercado-García A, Wright R, et al. Prenatal and Early Childhood Exposure to Lead and Repeated Measures of Metabolic Syndrome Risk Indicators From Childhood to Preadolescence. Front Pediatr. 2021;9. Planchart A, Green A, Hoyo C, Mattingly CJ. Heavy Metal Exposure and Metabolic Syndrome: Evidence from Human and Model System Studies. Curr Envir Health Rpt. 2018;5(1):110 − 24. Hu XF, Singh K, Chan HM. Mercury Exposure, Blood Pressure, and Hypertension: A Systematic Review and Dose–response Meta-analysis. Environmental Health Perspectives. 2018;126(7):076002. Yorifuji T, Tsuda T, Kashima S, Takao S, Harada M. Long-term exposure to methylmercury and its effects on hypertension in Minamata. Environmental Research. 2010;110(1):40 − 6. Roy C, Tremblay PY, Ayotte P. Is mercury exposure causing diabetes, metabolic syndrome and insulin resistance? A systematic review of the literature. Environmental Research. 2017;156:747 − 60. Queiroz RAM, Miranda GCN, D’Alessandro WB, Paiva MJM de, Herrera SDSC, Odorizzi VF, et al. Mercury and Cardiovascular Health: Exploring the Correlation between Atherosclerosis and Hypertension. Advances in Research. 2024;25(6):255 − 66. Dantas A de O, Castro T dos S da S de, Câmara V de M, Santos A de SE, Asmus CIRF, Vianna A dos S. Maternal Mercury Exposure and Hypertensive Disorders of Pregnancy: A Systematic Review. Rev Bras Ginecol Obstet. 2023;44:1126-33. Martins AC, Ferrer B, Tinkov AA, Caito S, Deza-Ponzio R, Skalny AV, et al. Association between Heavy Metals, Metalloids and Metabolic Syndrome: New Insights and Approaches. Toxics. 2023;11(8):670. Martins AC, Almeida Lopes ACB, Urbano MR, Carvalho M de FH, Silva AMR, Tinkov AA, et al. An updated systematic review on the association between Cd exposure, blood pressure and hypertension. Ecotoxicology and Environmental Safety. 2021;208:111636. Fasano A. The Physiology of Hunger. New England Journal of Medicine. 2025;392(4):372 − 81. Liu W, Wang Q, Song J, Xin J, Zhang S, Lei Y, et al. Comparison of Gut Microbiota of Yaks From Different Geographical Regions. Front Microbiol. 2021;12. Mobeen F, Sharma V, Tulika P. Enterotype Variations of the Healthy Human Gut Microbiome in Different Geographical Regions. Bioinformation. 2018;14(9):560 − 73. Gupta VK, Paul S, Dutta C. Geography, Ethnicity or Subsistence-Specific Variations in Human Microbiome Composition and Diversity. Front Microbiol. 2017;8:1162. Giambò F, Italia S, Teodoro M, Briguglio G, Furnari N, Catanoso R, et al. Influence of toxic metal exposure on the gut microbiota (Review). World Academy of Sciences Journal. 2021;3(2):1–1. Breton J, Daniel C, Dewulf J, Pothion S, Froux N, Sauty M, et al. Gut microbiota limits heavy metals burden caused by chronic oral exposure. Toxicology Letters. 2013;222(2):132-8. Santiago MSA, Avellar MCW, Perobelli JE. Could the gut microbiota be capable of making individuals more or less susceptible to environmental toxicants? Toxicology. 2024;503:153751. Shen Y, Laue HE, Shrubsole MJ, Wu H, Bloomquist TR, Larouche A, et al. Associations of Childhood and Perinatal Blood Metals with Children’s Gut Microbiomes in a Canadian Gestation Cohort. Environmental Health Perspectives. 2022;130(1):017007. Assefa S, Köhler G. Intestinal microbiome and metal toxicity. Current Opinion in Toxicology. 2020;19:21 − 7. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterials.doc Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6129933","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":423825194,"identity":"2d4e6c5b-a6cf-4b36-b670-06443dc14fa2","order_by":0,"name":"Patricia Matus","email":"","orcid":"","institution":"Universidad de los Andes","correspondingAuthor":false,"prefix":"","firstName":"Patricia","middleName":"","lastName":"Matus","suffix":""},{"id":423825195,"identity":"586a7839-ecae-46a0-acd6-4873edd8ad4f","order_by":1,"name":"Alejandro Sepúlveda-Peñaloza","email":"","orcid":"","institution":"Universidad de los Andes","correspondingAuthor":false,"prefix":"","firstName":"Alejandro","middleName":"","lastName":"Sepúlveda-Peñaloza","suffix":""},{"id":423825196,"identity":"3f43aceb-9d32-4b07-bed4-534655322332","order_by":2,"name":"Cinthya Urquidi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwElEQVRIiWNgGAWjYNACA2YefhDNAyLYidUi2QDTwkycNcwMBgeI1cI/I/fhbZ4CaxnjG8nPHrypqGPgbyagReJGurE1j0E6j9mNNHPDOWcOM0gcJqDFQCKNTZrH4DBQS4KZNG/bAaC/iNViPCP9mzTvvzoStBhI5ABtaWAmrEXizDNmyzlAv0iceVMmOefYYR6CfuFvT2O88eaPtT1/e/o2iTc1dXL87Q0E9IBsQubwEFaPrmUUjIJRMApGAQYAAOmvM44s5UP9AAAAAElFTkSuQmCC","orcid":"","institution":"Universidad de los Andes","correspondingAuthor":true,"prefix":"","firstName":"Cinthya","middleName":"","lastName":"Urquidi","suffix":""}],"badges":[],"createdAt":"2025-02-28 15:38:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6129933/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6129933/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78244889,"identity":"25820b56-0509-4b97-b78d-cb6ac8cb3327","added_by":"auto","created_at":"2025-03-11 09:21:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":89177,"visible":true,"origin":"","legend":"\u003cp\u003eGeographic Variation in the associations between arsenic exposure, metabolic disorders, and hypertension in Chile, 2016-17.\u003c/p\u003e\n\u003cp\u003eResults are based on Spatial Simultaneous Autoregressive (SAR) models.\u003c/p\u003e\n\u003cp\u003e*Associations with 95% confidence intervals that exclude the null effect.\u003c/p\u003e\n\u003cp\u003eXV. Arica y Parinacota I. Tarapacá. II. Antofagasta. III. Atacama. IV. Coquimbo. V. Valparaíso. XIII. Metropolitana. VI. L. Bdo. OHiggins. VII. Maule. VIII. Bíobío. IX. La Araucanía. XIV. Los Ríos. X. Los Lagos. XI. Aysén. XII. Magallanes y Antártica\u003c/p\u003e","description":"","filename":"Figure1Arsenic.png","url":"https://assets-eu.researchsquare.com/files/rs-6129933/v1/1ea967e34fe25bb4587b4d43.png"},{"id":78244890,"identity":"cf122ab7-159a-4cf2-80b1-f73331c0ffb7","added_by":"auto","created_at":"2025-03-11 09:21:34","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":87727,"visible":true,"origin":"","legend":"\u003cp\u003eGeographic Variation in the associations between lead exposure, metabolic disorders, and hypertension in Chile, 2016-17.\u003c/p\u003e\n\u003cp\u003eResults are based on Spatial Simultaneous Autoregressive (SAR) models.\u003c/p\u003e\n\u003cp\u003e*Associations with 95% confidence intervals that exclude the null effect.\u003c/p\u003e\n\u003cp\u003eXV. Arica y Parinacota I. Tarapacá. II. Antofagasta. III. Atacama. IV. Coquimbo. V. Valparaíso. XIII. Metropolitana. VI. L. Bdo. OHiggins. VII. Maule. VIII. Bíobío. IX. La Araucanía. XIV. Los Ríos. X. Los Lagos. XI. Aysén. XII. Magallanes y Antártica\u003c/p\u003e","description":"","filename":"Figure2Lead.png","url":"https://assets-eu.researchsquare.com/files/rs-6129933/v1/3ea8ac0f419abc931423e224.png"},{"id":78249091,"identity":"880c0fea-f02c-4bc5-ae15-ce2c5f8b9d62","added_by":"auto","created_at":"2025-03-11 09:45:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1103637,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6129933/v1/e16cd882-dc61-429d-b303-6d25c87907a4.pdf"},{"id":78244892,"identity":"d5841636-edcd-4c1d-aa4b-905bf9701431","added_by":"auto","created_at":"2025-03-11 09:21:34","extension":"doc","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":72704,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.doc","url":"https://assets-eu.researchsquare.com/files/rs-6129933/v1/8e2a232a20189ab5cb6af883.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"Geographical Variations in Metal Exposure and Its Impact on Metabolic Disorders and Hypertension: An Analysis of Chile's 2016–17 National Health Survey","fulltext":[{"header":"Background","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe increasing burden of metabolic disorders, including the persistent rise in obesity and diabetes mellitus, remains a pressing public health challenge despite notable advancements in prevention and treatment strategies (1\u0026ndash;4). Traditional risk factors, such as poor diet, physical inactivity, genetic predisposition, and broader social determinants, are well-established drivers of these conditions. However, growing attention is being directed toward the exposome framework, which encompasses the cumulative environmental exposures experienced throughout an individual's lifetime, particularly emphasizing chemical contaminants (5\u0026ndash;7) as emerging contributors to metabolic dysfunction. Integrating exposome-based research into public health strategies holds promising opportunities for identifying previously unrecognized risk factors, bridging knowledge gaps, and informing innovative interventions.\u003c/p\u003e \u003cp\u003eMetalloids and nonessential heavy metals, such as arsenic and lead, which primarily stem from environmental exposures, are well documented for their harmful health effects. These include their mutagenic and carcinogenic properties and their capacity to induce oxidative stress (8). Similarly, synthetic chemicals used in industrial and agricultural processes, such as bisphenol A and phthalates, are recognized as endocrine agents, as environmental endocrine-disrupting chemicals (EDCs) (9) disrupt endocrine function and have been implicated in the development of noncommunicable diseases, including obesity, diabetes, metabolic disorders, and thyroid dysfunction (10,11).\u003c/p\u003e \u003cp\u003eRecent evidence suggests that heavy metals, including mercury, cadmium, and lead, may also contribute to metabolic dysfunction by disrupting metabolic pathways, inducing oxidative stress, and impairing endocrine regulation. These mechanisms are thought to underlie conditions such as obesity and insulin resistance (7,12\u0026ndash;14). Furthermore, emerging studies have linked heavy metal exposure to a broader spectrum of metabolic disorders, including obesity (15\u0026ndash;17), diabetes (18\u0026ndash;20), and metabolic syndrome (21). However, inconsistencies in findings across studies highlight the need for further research to clarify these associations. Most studies have focused on specific regions or national-level populations, often neglecting the potential variations within geographically diverse countries such as Chile. From an exposome perspective, local environmental, social, and genetic factors may modulate the effects of exposure, underscoring the importance of research incorporating territorial disaggregation approaches. These methods allow for a nuanced understanding of public health challenges and enable the development of equitable, context-specific interventions.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe unique geographic and economic profile of Chile offers an ideal setting for exploring the health effects of heavy metal exposure. The country hosts over 200 active mining operations, which are located predominantly in the northern regions, whereas the central and southern zones are characterized by agricultural and fisheries activities, which are often accompanied by environmental pollution. Despite strict regulations on industrial metal emissions, evidence suggests that even low-level metal exposure may disrupt metabolic pathways (22). In addition to high diabetes rates (23), northern regions show a steep increase in obesity incidence (24), with a small case study linking urinary arsenic levels to glycemia (25), further suggesting a connection between environmental exposure and metabolic health.\u003c/p\u003e \u003cp\u003eThis population-based study aims to analyze the geographic variation in exposure to four metals and their associations with obesity, diabetes, and metabolic syndrome in Chile. Hypertension, a condition closely linked to metabolic dysfunction and highly prevalent in the country, was also included in the analysis, given emerging evidence suggesting its association with metal exposure (26\u0026ndash;28). Understanding regional disparities in these exposures and outcomes can help identify areas at greatest risk and guide targeted public health interventions.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData source\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eWe used data from the most recent National Health Survey (ENS, its acronym in Spanish) conducted in Chile between August 2016 and March 2017. Despite Chile having conducted three ENSs, each approximately ten years apart, the latest survey included biological markers for metal exposure, making it a unique resource for environmental and metabolic population-based studies.\u003c/p\u003e \u003cp\u003eThe ENS 2016-17 included 6,233 participants aged 15 years or older, utilizing a multistage, stratified probabilistic sampling method to ensure national coverage, subnational, urban/rural areas, sex, and macrozone representativeness. By then, Chile was administratively and geographically divided into 15 regions spanning from north to south. We utilized these regions and macrozones to evaluate the main geographical variability at subnational levels.\u003c/p\u003e \u003cp\u003eOwing to the small sample sizes for some metal exposures, such as mercury and cadmium, these regions were grouped into four macrozones according to the ENS criteria: 1) North: Arica and Parinacota, Tarapac\u0026aacute;, Antofagasta, Atacama, and Coquimbo; 2) Center: Valpara\u0026iacute;so, Libertador Bernardo O'Higgins, Maule, and Biob\u0026iacute;o; 3) South: La Araucan\u0026iacute;a, Los R\u0026iacute;os, Los Lagos, Ays\u0026eacute;n, and Magallanes and Chilean Antarctica; and 4) Metropolitan Region (RM): While geographically part of the central zone, the RM, which encompasses Santiago, Chile's capital and largest city, was categorized as a separate macrozone owing to its significant population density and unique characteristics.\u003c/p\u003e \u003cp\u003eThe ENS 2016-17 data collection involved four home visits by trained health professionals who combined clinical examinations and the administration of questionnaires with 576 items addressing health conditions and risk factors. Serum and urine samples were collected from a representative subsample of 3,822 individuals (61.3%) to determine heavy metal levels, among other biomarkers, ensuring coverage at the national level and in urban, rural, and macrozones.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eHeavy metal exposure\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eENS-2016-17 was used to determine inorganic arsenic, cadmium, and inorganic mercury levels in spot urine samples and lead levels in serum samples. Biological samples were transported to the National Institute of Public Health, Chile's national reference laboratory (ISP, its acronym in Spanish), for specialized analysis. For arsenic, inductively coupled plasma‒mass spectrometry (ICP-MS) was used, with a quantification limit of 5.00 \u0026micro;g/L and a detection limit of 0.56 \u0026micro;g/L. Mercury was analyzed via both ICP‒MS and cold vapor atomic absorption spectrophotometry, with a quantification limit of 2.00 \u0026micro;g/L and a detection limit between 0.39 and 0.54 \u0026micro;g/L. Cadmium and lead were also analyzed with ICP‒MS, with quantification limits of 1.00 \u0026micro;g/L for cadmium and 1.00 \u0026micro;g/dL for lead. These methods ensure accurate detection in the general population sample. The ENS imputed the minimum detectable value for values below the detection limit by default.\u003c/p\u003e \u003cp\u003eGiven that the risk cutoff points for metabolic conditions are still unclear and that including minimum detectable values could lead to harm, this study analyzed the correlations while considering the full range of data from the minimum detectable value. The 50th percentile distributions were used to classify individuals as exposed to better understand the exposure levels. This approach allowed for a more nuanced interpretation of the data while minimizing the risk of harm or misrepresentation.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eMetabolic conditions and hypertension\u003c/h3\u003e\n\u003cp\u003eIn this study, overweight, obesity, diabetes, metabolic syndrome, and hypertension were analyzed. ENS measured weight in kilograms (kg), height in meters (m), waist circumference in centimeters (cm), and blood pressure. Additionally, a trained nurse collected blood samples to measure fasting glucose, triglyceride, high-density lipoprotein (HLD), and low-density lipoprotein (LDL) levels. These samples were sent to and analyzed by the ISP.\u003c/p\u003e \u003cp\u003eFor overweight and obesity, we calculated the body mass index (BMI) as weight divided by height squared (kg/m\u0026sup2;). Overweight and obesity were defined according to the World Health Organization (WHO) criteria: a BMI of 25\u0026ndash;29.9 kg/m\u0026sup2; was classified as overweight, whereas a BMI\u0026thinsp;\u0026ge;\u0026thinsp;30 kg/m\u0026sup2; was classified as obese.\u003c/p\u003e \u003cp\u003eDiabetes was defined as both self-reported cases (self-reported diagnosis or under pharmacological treatment) and individuals with fasting glucose levels\u0026thinsp;\u0026gt;\u0026thinsp;126 mg/dL, on the basis of the WHO cutoff. The ENS included questions for self-reported diseases and medical treatment. For example, for diabetes, it was \u003cem\u003e\"Has a doctor, nurse, or other healthcare professional ever told you that you have or have had diabetes (high blood sugar)?\"\u003c/em\u003e and \"\u003cem\u003eWhat type of treatment are you undergoing?\".\u003c/em\u003e Patients who answered \"no\" to these questions but did not have a glucose level to corroborate the absence of diabetes were excluded from the analysis, as were women who reported gestational diabetes.\u003c/p\u003e \u003cp\u003eMetabolic syndrome was defined by adapting criteria used in ENS, which meet three or more of the following conditions: central obesity (Chile's recommended waist circumference cutoffs are \u0026ge;\u0026thinsp;90 cm for men and \u0026ge;\u0026thinsp;80 cm for women), elevated triglycerides (\u0026ge;\u0026thinsp;150 mg/dL, HDL cholesterol, \u0026lt;\u0026thinsp;40 mg/dL in men or \u0026lt;\u0026thinsp;50 mg/dL in women, or on treatment/self-reported diagnosis), average systolic blood pressure (SBP)\u0026thinsp;\u0026ge;\u0026thinsp;130 mmHg and diastolic blood pressure (DBP)\u0026thinsp;\u0026ge;\u0026thinsp;85 mmHg or diagnosis self-reported/medical treatment for hypertension or diabetes, and fasting glucose\u0026thinsp;\u0026ge;\u0026thinsp;100 mg/dL.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eHypertension was defined on the basis of self-reports of receiving treatment for high blood pressure and the average of three blood pressure readings, with the criteria being SBP\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg and DBP\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003e The national, regional, and macrozone-level prevalence rates were calculated via the expansion factor of the ENS sampling design, with 95% confidence intervals (95% CIs). Additionally, the age-adjusted mean was computed. The distribution of metals was described on the basis of the percentile (P) distribution, including P25, P50, and P75, as well as the percentage of individuals exposed according to the study's definition, which was above the median.\u003c/p\u003e \u003cp\u003e The associations between metal exposure and various comorbidities were analyzed by estimating odds ratios (ORs) with corresponding 95% CIs via logistic regression models weighted for the complex sampling design of the ENS survey at the national, regional, and macrozone levels. Both crude and adjusted ORs were calculated, adjusting for sex, age, and educational level as proxies for socioeconomic status (SES) and smoking status as a dichotomous variable (smoker or nonsmoker) at the national level and across macrozones.\u003c/p\u003e \u003cp\u003eFor regional poststratification estimations, spatial relationships between regions were further incorporated via spatial simultaneous autoregressive (SAR) regression models estimated through maximum likelihood. This approach was employed to correct potential spatial autocorrelation biases, ensuring that regional estimates reflect local exposure data and spatial dependencies between neighboring regions. By accounting for spatial structure, SAR models provide more accurate and context-sensitive results, which are essential for identifying geographic patterns and tailoring public health interventions accordingly.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eAll analyses were conducted via R statistical packages, specifically the \"survey\" package (version 4.0) for complex survey data analysis and the \"spatialreg\" package for spatial regression modeling.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFrom the total of 3,822 biological samples, the ISP classified some as \"insufficient sample,\" \"not evaluated,\" or \"nonadmissible.\" Consequently, the final sample size analyzed for metal exposure and its percentile distribution are described in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. At the national level, the median arsenic concentration (percentile 50) was 12.5, with values of 1.0 for lead and cadmium and 2 for mercury. The proportion of individuals with metal exposure above the median value was 42.4% for inorganic arsenic, 13.6% for lead, and 1.7% for mercury and cadmium. The prevalence of metabolic conditions, hypertension, and other sociodemographic variables, along with their 95% confidence intervals (95% CI), are also presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNational distribution and prevalence of study variables, Chile 2016-17.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetal concentration levels within the sample*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian (P25; P75)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArsenic \u0026micro;g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.52 (6.98; 19.56)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLead \u0026micro;g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (1.00; 1.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMercury \u0026micro;g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.00 (2.00; 20.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCadmium \u0026micro;g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (1.00; 2.27)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndividuals exposed to levels exceeding the median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrevalence (95%CI)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArsenic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.40 (39.59; 45.22)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.64 (11.54; 15.75)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMercury\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.73 (1.04; 2.43)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCadmium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.66 (1.05; 2.27)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetabolic conditions and hypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.8 (37.2; 42.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.4 (32.1; 36.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.68 (12.3; 15.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetabolic Syndrome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.1 (39.3; 45.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.9 (32.8; 37.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducational level (proxi of socioeconomic status)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than 8 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.7 (14.7; 18.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8 to 12 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.6 (53.2; 59.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13 years or more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.7 (23.5; 30.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.1 (46.5; 51.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.9 (48.3; 53.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOne or more cigarettes a day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.8 (23.4; 28.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccasionally (less than once a day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.5 (6.7; 10.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStop smoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.7 (21.5: 25.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.0 (39.4; 44.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge in years, mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.7 (41.7; 43.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e*Median, 25th Percentile, and 75th Percentile\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eCI: Confidence Interval.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe subnational analysis results for the metal exposures are displayed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Most regions presented median arsenic values below the national level, except for northern regions, such as Arica, Antofagasta, and Atacama, and the southernmost region, Magallanes. The pattern of the percentage of individuals exposed to this metal (above the median) was similar. No variations in the median levels were observed at the regional level for the other three metals. However, differences were noted in the percentage of individuals exposed. For example, in the case of lead, one northern region, Tarapac\u0026aacute;, presented the highest percentage of individuals exposed to lead (29.9%). A similar pattern was observed for mercury and cadmium (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRegional-level analysis of exposure to four heavy metals in Chile, 2016-17.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eArsenic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eLead\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eMercury\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003eCadmium\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003cp\u003e\u0026micro;g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExposed %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003cp\u003e\u0026micro;g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eExposed %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003cp\u003e\u0026micro;g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eExposed %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003cp\u003e\u0026micro;g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eExposed %\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXV. Arica y Parinacota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e5.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI. Tarapac\u0026aacute;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e29.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5,70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e5.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII. Antofagasta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e4.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII. Atacama\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIV. Coquimbo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV. Valpara\u0026iacute;so\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXIII. Metropolitana\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e3.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVI. L. Bdo. OHiggins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVII. Maule\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVIII. B\u0026iacute;ob\u0026iacute;o\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIX. La Araucan\u0026iacute;a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e3.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXIV. Los R\u0026iacute;os\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e3.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX. Los Lagos\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXI. Ays\u0026eacute;n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXII. Magallanes y Ant\u0026aacute;rtica\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAt the national level, associations were observed between arsenic exposure and hypertension (OR: 0.64, 95% CI: 0.49; 0.78), lead exposure and both hypertension (OR: 1.55, 95% CI: 1.12; 2.14) and metabolic syndrome (OR: 1.47, 95% CI: 1.08; 2.00), and mercury exposure and both obesity (OR: 0.38, 95% CI: 0.16; 0.87) and diabetes (OR: 0.22, 95% CI: 0.07; 0.73). Nevertheless, these associations disappeared after adjusting for age, sex, socioeconomic status, and smoking status (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNational-level analysis of crude and adjusted Odds Ratios (OR) and 95% confidence intervals (95% CI)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eArsenic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eLead\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eMercury\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eCadmium\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdjusted\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCrude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAdjusted\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCrude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAdjusted\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCrude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAdjusted\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003cp\u003e(0.72; 1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003cp\u003e(0.74; 1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003cp\u003e(0.70; 1.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003cp\u003e(0.56; 1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003cp\u003e(0.24; 1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003cp\u003e(0.28; 1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003cp\u003e(0.29; 1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003cp\u003e(0.26; 1.14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003cp\u003e(0.63; 1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003cp\u003e(0.73; 1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003cp\u003e(0.78; 1.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003cp\u003e(0.79; 1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.38\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.16; 0.87)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003cp\u003e(0.19; 1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003cp\u003e(0.63; 3.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003cp\u003e(0.58; 2.69)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003cp\u003e(0.54; 1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003cp\u003e(0.80; 1.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003cp\u003e(0.64; 1.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003cp\u003e(0.50; 1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.22\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.07; 0.73)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003cp\u003e(0.09; 1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003cp\u003e(0.19; 1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003cp\u003e(0.16; 1.02)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetabolic Syndrome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003cp\u003e(0.62; 1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003cp\u003e(0.77; 1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.47\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(1.08; 2.00)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003cp\u003e(0.81; 1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003cp\u003e(0.24; 1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003cp\u003e(0.33; 1.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.65\u003c/p\u003e \u003cp\u003e(0.75; 3.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003cp\u003e(0.55; 2.66)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.64\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.49; 0.78)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003cp\u003e(0.77; 1.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.55\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(1.12; 2.14)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003cp\u003e(0.63; 1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003cp\u003e(0.25; 1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003cp\u003e(0.60; 3.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.93\u003c/p\u003e \u003cp\u003e(0.97; 3.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003cp\u003e(0.58; 2.97)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003e1\u003c/sup\u003eAdjusted by sex, age, socioeconomic and smoking status.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWith respect to subnational-level disaggregation, Supplementary Table\u0026nbsp;1 and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarize the adjusted odds ratios (ORs) for arsenic exposure, whereas Supplementary Table\u0026nbsp;2 and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e provide the corresponding data for lead exposure. Associations between arsenic exposure and overweight or obesity were observed in most regions of the country. For diabetes and metabolic syndrome, associations were noted in northern regions, southernmost regions, and three central regions. Similar patterns were observed for hypertension, although fewer regions presented significant associations (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Supplementary Table\u0026nbsp;1). Similar geographic variability patterns were observed for lead exposure (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Supplementary Table\u0026nbsp;2).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the case of mercury exposure, significant associations were observed in the central macrozone, which includes the Metropolitan Region, for obesity, overweight, diabetes, and hypertension. Conversely, a significant association between cadmium exposure and diabetes was found exclusively in the southern macrozone of the country (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMacrozone-level analysis of adjusted Odds Ratios (OR)\u003csup\u003e1\u003c/sup\u003e and 95% confidence intervals (95% CI) for mercury and cadmium exposure.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eObesity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMetabolic Syndrome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMercury\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003cp\u003e(0.20; 3.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.61\u003c/p\u003e \u003cp\u003e(0.43; 6.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003cp\u003e(0.15; 4.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.38\u003c/p\u003e \u003cp\u003e(0.66; 8.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.86\u003c/p\u003e \u003cp\u003e(0.68; 12.03)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003cp\u003e(0.60; 6.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.20\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.05; 0.91)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.08\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.01; 0.63)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003cp\u003e(0.14; 3.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.90\u003c/p\u003e \u003cp\u003e(0.41; 8.81)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003cp\u003e(0.07; 6.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.87\u003c/p\u003e \u003cp\u003e(0.31; 26.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.48\u003c/p\u003e \u003cp\u003e(0.66; 45.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.24\u003c/p\u003e \u003cp\u003e(0.55; 70.58)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetropolitan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.10\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.01; 0.89)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.14\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.02; 0.92)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003cp\u003e(0.02; 1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003cp\u003e(0.03; 1.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.16\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.03; 0.74)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCadmium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003cp\u003e(0.35; 2.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003cp\u003e(0.33; 2.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003cp\u003e(0.19; 3.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.76\u003c/p\u003e \u003cp\u003e(0.75; 4.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.65\u003c/p\u003e \u003cp\u003e(0.63; 11.18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003cp\u003e(0.13; 1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.89\u003c/p\u003e \u003cp\u003e(0.65; 5.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003cp\u003e(0.11; 2.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003cp\u003e(0.32; 2.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003cp\u003e(0.23; 3.24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003cp\u003e(0.34; 4.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003cp\u003e(0.12; 1.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.03\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0.001; 0.23)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003cp\u003e(0.22; 4.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003cp\u003e(0.12; 1.89)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetropolitan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003cp\u003e(0.10; 2.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003cp\u003e(0.14; 5.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003cp\u003e(0.02; 1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003cp\u003e(0.21; 7.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.86\u003c/p\u003e \u003cp\u003e(0.40; 8.60)\u003c/p\u003e\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e-- Without sufficient sample size.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003e1\u003c/sup\u003eAdjusted by sex, age, socioeconomic and smoking status.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe results of this study highlight the importance of examining subnational geographic variations in metal exposure and their relationships with metabolic disorders and diseases. A key finding of our research reveals that Chile does not exhibit a uniform pattern of metal exposure across its population. Arsenic exposure is notably greater at the country's extremes, ranging from 83.6% of the population in Arica and Parinacota (north) to 18.82% in Ays\u0026eacute;n (south). In contrast, lead exposure shows less variation, ranging from 29.89% in Tarapac\u0026aacute; to 10.58% in Magallanes and Ant\u0026aacute;rtica. These findings are based on the median exposure distribution; however, if stricter criteria were applied, the extent of metal exposure in Chile could pose an even greater challenge.\u003c/p\u003e\u003cp\u003eEach geographic region's territorial, environmental, and industrial characteristics may explain this pattern, as may primary exposure pathways\u0026mdash;water, air, soil, and food. While contamination through water and air is relatively well documented, other routes remain less understood and require deeper exploration. In particular, lead, mercury, and cadmium exposure pathways are less well characterized and necessitate further research.\u003c/p\u003e\u003cp\u003eThe prevalence of arsenic exposure has drawn our attention, as arsenic contamination in soil and groundwater is a global concern with significant impacts on human health. Studies have revealed widespread arsenic contamination in various regions worldwide, including India, Bangladesh, parts of the United States, Mexico, Chile, Argentina, and other European and Asian locations (26\u0026ndash;29). The primary sources of arsenic are geogenic, resulting from the weathering of arsenic-bearing rocks and the reductive dissolution of iron oxides (30). Arsenic concentrations in groundwater often exceed the WHO's guideline of 10 \u0026micro;g/L, posing serious health risks (31), and vary geographically and with depth, affecting soil, water, and crops such as rice (32). Consequently, the continuous use of arsenic-contaminated water for irrigation can lead to soil contamination and bioaccumulation in plants (26), further impacting human health (28).\u003c/p\u003e\u003cp\u003eConsistent with existing evidence (33\u0026ndash;36), this study revealed an association between arsenic exposure and metabolic outcomes after territorial disaggregation. Notably, regions with high agricultural activity, such as Arica, the Metropolitan Region, and southern Chile, presented significant associations with overweight, obesity, and diabetes, even after we adjusted for smoking habits, socioeconomic status, age, and sex.\u003c/p\u003e\u003cp\u003eThese findings support the hypothesis that local environmental and behavioral factors\u0026mdash;key components of the exposome\u0026mdash;may shape cumulative arsenic exposure and its metabolic consequences. In agricultural areas, soil contamination and indirect arsenic intake through food consumption could contribute to increased exposure levels, increasing the risk of chronic dietary patterns and associated health effects. The proposed mechanisms include inflammation, oxidative stress, and altered adipose tissue function (36). However, a comprehensive exposome-based approach that integrates environmental, occupational, and lifestyle-related exposures is needed to better understand the complex interplay between metal contaminants and metabolic health (37).\u003c/p\u003e\u003cp\u003eAnother important finding regarding arsenic exposure and metabolic conditions was the opposing directions of the observed associations. For example, in Arica and Parinacota, the association with overweight was positive (OR\u0026thinsp;\u0026gt;\u0026thinsp;1), whereas the association with obesity was inverse (OR\u0026thinsp;\u0026lt;\u0026thinsp;1). From an epidemiological perspective, this pattern could be explained by potential pathophysiological mechanisms underlying the effects of arsenic and different stages of disease progression. Prolonged metal exposure may have a biphasic effect on body weight, initially leading to weight gain followed by progressive weight loss due to greater tissue damage at the cellular level. If this holds, it could reflect a transitional process from an epidemiological standpoint. That is, geographical areas where the OR for overweight is greater than one might represent earlier stages of arsenic-related metabolic disruption, whereas areas where the OR for overweight or obesity is less than one may reflect more advanced or prolonged exposure-related damage. Nevertheless, further longitudinal studies are needed to test these hypotheses, and exposure to multiple metals, including other metals such as cobalt, is needed (38).\u003c/p\u003e\u003cp\u003eThe prevalence of lead exposure has been associated with an increased risk of hypertension and metabolic syndrome (MS). Evidence indicates that lead exposure contributes to elevated blood pressure, altered heart rate variability, and a higher incidence of MS (39\u0026ndash;42), as well as to recently published findings on vascular age (43). The underlying mechanisms involve oxidative stress, reduced nitric oxide availability, endothelial dysfunction, and impaired vascular responses (44). Additionally, lead exposure may exacerbate the effects of MS, although antioxidants such as lipoic acid and coenzyme Q10 have shown potential in mitigating these impacts (45). Early-life lead exposure may also influence MS risk indicators in children (46). However, conflicting findings persist, and establishing causality remains challenging owing to variations in study designs and confounding factors (47). Further research, particularly prospective studies with standardized methodologies, is necessary to clarify the causal relationship between lead exposure and MS-related outcomes.\u003c/p\u003e\u003cp\u003eRegarding mercury exposure, Chile, like many other nations, is a signatory to the Minamata Convention, which aims to address contamination by this heavy metal. Despite these efforts, evidence of mercury exposure persists, particularly in the metropolitan area, which has the country's largest population. An association was observed between mercury exposure and all the metabolic alterations studied, even at very low doses.\u003c/p\u003e\u003cp\u003eThe evidence suggests a complex relationship between mercury exposure and metabolic disorders, particularly hypertension. Some studies report a positive association between mercury levels and hypertension risk (48,49), whereas others find no significant link (4). Additionally, mercury exposure has also been implicated in metabolic syndrome and diabetes, although the evidence remains inconsistent (50). Recent investigations have focused on the role of mercury in cardiovascular health, including its impact on atherosclerosis and hypertension (51). Maternal mercury exposure has been investigated for its potential association with hypertensive disorders of pregnancy, yielding mixed results (52). The mechanisms through which mercury and other heavy metals, including cadmium, contribute to metabolic syndrome likely involve oxidative stress, inflammation, and altered lipoprotein metabolism (53,54).\u003c/p\u003e\u003cp\u003eAdditionally, emerging research has highlighted the influence of the gut microbiota composition on the physiology of hedonic hunger (55). Studies have identified distinct microbial profiles in different populations, with Firmicutes and Bacteroidetes being the dominant phyla globally (56,57). Notably, gut microbial diversity decreases as populations transition from hunter-gatherers to industrialized urban lifestyles (58). Furthermore, exposure to toxic metals can significantly alter the composition and diversity of the gut microbiome (59). On the other hand, the gut microbiota plays a crucial role in limiting heavy metal absorption and dissemination in the body (60). Notably, the microbiome can both influence an individual's susceptibility to environmental toxicants and aid in their metabolism and excretion (61). Associations have been reported between childhood and perinatal blood metal levels and changes in the gut microbiome composition, including alterations in potentially pathogenic and beneficial species (62).\u003c/p\u003e\u003cp\u003eWhile the gut microbiome appears to be a promising biomarker for metal exposure, further research is needed to fully understand the complex interactions between environmental pollutants and the gut microbiota (63). Early-life exposure to environmental toxins can have lasting effects on gut health, potentially influencing developmental outcomes. However, the dietary context in which these exposures occur is crucial, underscoring the importance of considering both environmental and dietary factors when assessing gut microbiome health.\u003c/p\u003e\u003cp\u003eThis study uses SAR models to analyze geographic variation robustly at the subnational level. SAR models allow us to account for spatial dependencies, improving the robustness of our estimates and reducing potential biases due to spatial autocorrelation. Additionally, the study benefits from a well-defined national dataset with high geographic resolution, enhancing the accuracy of our findings. A key strength is that prevalence estimates and association measures are weighted by the expansion factor of the complex survey design. This ensures that the results are representative of the target population, reducing selection bias and improving external validity. By focusing on associations where the 95% confidence intervals exclude the null effect, we ensure the statistical reliability of our reported results.\u003c/p\u003e\u003cp\u003eDespite these strengths, limitations must be acknowledged. First, the study's observational nature precludes causal inference, limiting our ability to establish definitive cause‒and‒effect relationships. Nevertheless, this study provides a solid foundational framework for generating causal hypotheses and guiding future research. The identified associations can inform the design of longitudinal and experimental studies to investigate potential causal mechanisms further and refine risk assessment strategies. Additionally, the spatial patterns revealed in this study can help identify potential hot spots, which can be prioritized for targeted interventions aimed at mitigating health risks in high-exposure areas.\u003c/p\u003e\u003cp\u003eAlthough SAR models mitigate spatial autocorrelation issues and efforts are made through adjusted analyses, residual confounding due to unmeasured environmental or socioeconomic factors cannot be entirely ruled out. Additionally, exposure assessment may be subject to measurement errors, particularly if data sources rely on regional estimates rather than individual-level exposure assessments. Finally, while our findings provide valuable insights into spatial patterns of association, their generalizability to other time periods or regions outside Chile requires further validation.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn conclusion, understanding geographical variations is essential for developing a comprehensive theory on complex illnesses, such as metabolic disorders. It also enables policymakers and healthcare professionals to design region-specific interventions effectively and preventive strategies to address metabolic disorders and hypertension.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Abbreviations","content":" \u003cp\u003eENS (its acronym in Spanish) National Health Survey\u003c/p\u003e \u003cp\u003eEDC Endocrine-disrupting chemical\u003c/p\u003e \u003cp\u003eRM Metropolitan Region\u003c/p\u003e \u003cp\u003eICP-MS Inductively coupled plasma‒mass spectrometry\u003c/p\u003e \u003cp\u003eHDL High-Density Lipoprotein\u003c/p\u003e \u003cp\u003eLDL Low-Density Lipoprotein\u003c/p\u003e \u003cp\u003eBMI Body mass index\u003c/p\u003e \u003cp\u003eISP (its acronym in Spanish) National Institute of Public Health of Chile\u003c/p\u003e \u003cp\u003eWHO World Health Organization\u003c/p\u003e \u003cp\u003eSBP Systolic blood pressure\u003c/p\u003e \u003cp\u003eDBP Diastolic blood pressure\u003c/p\u003e \u003cp\u003eCI confidence Interval\u003c/p\u003e \u003cp\u003eOR Odds Ratio\u003c/p\u003e \u003cp\u003eP Percentile\u003c/p\u003e \u003cp\u003eSES Socioeconomic Status\u003c/p\u003e \u003cp\u003eSAR Spatial simultaneous autoregressive regression\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eThis study is an observational secondary analysis based on anonymized and publicly available data. As such, it did not require approval from an ethics committee. Additionally, since all data were de-identified and freely accessible, obtaining individual consent to participate was not necessary.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe article\u0026apos;s data will be shared on reasonable request to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) declared no potential conflicts of interest with respect to the\u003c/p\u003e\n\u003cp\u003eresearch, authorship, and/or publication of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePM: Conceptualization; Methodology; Writing\u0026mdash;review, editing. AS: Data curation; Formal analysis; Writing\u0026mdash;review, editing. CU: Conceptualization; Methodology Writing\u0026mdash;original draft, editing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChong B, Jayabaskaran J, Kong G, Chan YH, Chin YH, Goh R, et al. Trends and predictions of malnutrition and obesity in 204 countries and territories: an analysis of the Global Burden of Disease Study 2019. eClinicalMedicine. 2023;57.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu J, Ren ZH, Qiang H, Wu J, Shen M, Zhang L, et al. Trends in the incidence of diabetes mellitus: results from the Global Burden of Disease Study 2017 and implications for diabetes mellitus prevention. BMC Public Health. 2020;20(1):1415.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDai H, Alsalhe TA, Chalghaf N, Ricc\u0026ograve; M, Bragazzi NL, Wu J. 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Current Opinion in Toxicology. 2020;19:21\u0026thinsp;\u0026minus;\u0026thinsp;7.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Metal exposure, metabolic disorders, hypertension, exposome, geographical variation, territorial disaggregation.","lastPublishedDoi":"10.21203/rs.3.rs-6129933/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6129933/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eEvidence suggests that even low-level exposure to metals may disrupt metabolic pathways, contributing to metabolic disorders. Local environmental factors may modulate these effects, emphasizing the importance of territorial disaggregation. This population-based study evaluated geographic variations in exposure to four metals and their associations with obesity, diabetes, metabolic syndrome, and hypertension in Chile.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eData from 3,822 participants in the National Health Survey from 2016 to 2017 were analyzed. Biomarkers included inorganic arsenic, cadmium, mercury in urine, and lead in serum. Metal exposure was classified according to the 50th percentile distribution. Spatial simultaneous autoregressive models accounted for regional disaggregation and spatial dependencies, adjusting for age, sex, socioeconomic status, and smoking. Analyses were conducted at the national and subnational levels, incorporating sampling weights from the national survey complex design.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 42.4% of individuals were exposed to arsenic, 13.6% to lead, and 1.7% to mercury and cadmium. Regional analysis revealed elevated arsenic exposure in northern regions (e.g., Arica and Antofagasta), with lead exposure peaking at 29.9%. At the national level, adjusted models revealed no significant associations between metal exposure and metabolic disorders. However, geographical disaggregation revealed that arsenic exposure was linked to overweight and obesity across most areas and to diabetes and metabolic syndrome in the northern, southernmost, and central zones. Mercury exposure was associated with all conditions in the central macrozone, whereas cadmium exposure was exclusively linked to diabetes in southern regions.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThese findings underscore critical regional differences in metal and metalloid exposure and metabolic disorders, highlighting the need for geographically targeted public health interventions that consider local environmental and contextual factors.\u003c/p\u003e\u003ch2\u003eTrial registration\u003c/h2\u003e \u003cp\u003eNot applicable\u003c/p\u003e","manuscriptTitle":"Geographical Variations in Metal Exposure and Its Impact on Metabolic Disorders and Hypertension: An Analysis of Chile's 2016–17 National Health Survey","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-11 09:21:29","doi":"10.21203/rs.3.rs-6129933/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5e7ef52c-1c66-49d5-bcbb-798ba5b39598","owner":[],"postedDate":"March 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-03-11T09:21:32+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-11 09:21:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6129933","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6129933","identity":"rs-6129933","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}