Cardiovascular Investigations: Open Access Open Access

Environmental Exposures Lead to Premature Age-Related Diseases

Ziyi Yang^* and Huanyu You Department of Cardiology, Veritas Collegiate Academy Chesapeake, USA
^*Correspondence: Ziyi Yang, Department of Cardiology, Veritas Collegiate Academy Chesapeake, USA, Email:

Received: 29-Dec-2022, Manuscript No. IPCIOA-22-15355; Editor assigned: 02-Jan-2023, Pre QC No. IPCIOA-22-15355 (PQ); Reviewed: 16-Jan-2023, QC No. IPCIOA-22-15355; Revised: 23-Jan-2023, Manuscript No. IPCIOA-22-15355 (R); Published: 30-Jan-2023, DOI: 10.36648/09768610.23.7.001

Abbreviations

(URXDAZ) Daidzein (ng/mL); (URXDMA) O-Desmethylangolensin (O-DMA)(ng/mL); (URXEQU) Equol (ng/mL); (URXETD) Enterodiol (ng-mL); (URXETL) Enterolactone (ng/mL); (URXGNS) Genistein (ng/mL); (URXUCR) Creatinine, urine (mg/dl); (URXUAS) Urinary total Arsenic (ug/L); (URXUAB) Urinary Arsenobetaine (ug/L); (URXUMMA) Urinary Monomethylarsonic acid (ug/L); (URXUDMA) Urinary Dimethylarsinic acid (ug/L); (URXUHG) Mercury, urine (ng/mL); (URXUSB) Antimony, urine (ng/ mL); (URXUCD) Cadmium, urine (ng/mL); (URXUCO) C o b a l t , urine (ng/mL); (URXUCS) Cesium, urine (ng/mL); (URXUMO) Molybdenum, urine (ng/mL); (URXUPB) Lead, urine (ng/mL); (URXUTL) Thallium, urine (ng/mL); (URXUTU) Tungsten, urine (ng/mL): (URXUUR) Uranium, urine (ng/mL); (LBXBCD) Cadmium (ug/L); (LBXBPB) Lead (ug/dL); (LBXTHG) Mercury, total (ug/L); (URXCOTT) Total Cotinine, urine (ng/mL); (URXHCTT) Total Hydroxycotinine, urine (ng/mL); (URXANBT) Anabasine, urine (ng/mL); (URXCOXT) Cotinine-n-oxide, urine (ng/mL); (URXNICT) Nicotine, urine (ng/mL); (URXNNCT) Nornicotine, urine (ng/mL); (URXNOXT) Nicotine-1 N-oxide, urine (ng/mL); (URDTNE2) TNE-2 (nmol/mL); (URDTNE3) TNE-3 (nmol/mL); (URDTNE6) TNE-6 (nmol/mL); (URX1TB) 2, 4, 5-trichlorophenol (ug/L); (URX14D) 2, 5-dichlorophenol (ug/L); (URXDCB) 2, 4-dichlorophenol (ug/L); (URX3TB) 2, 4, 6-trichlorophenol (ug/L); (LBCTCD) 2, 3, 7, 8-Tetrachloro-p-dioxn (tcdd) (fg/g); (LBCTCDLA) 2, 3, 7, 8-tcdd lipid adjusted (pg/g); (URX24D) 2, 4-D (ug/L); (URX25T) 2, 4, 5-T (ug/L); (URXUBA) Barium, urine (ng/mL)

INTRODUCTION

With aging the human body becomes worse; the elderly have a higher probability of getting more diseases than the young. Atherosclerosis is a disease with erectile dysfunction, heart attack and strokes. People with stroke lack motor skills and have facial paralysis or numbness. People with macular degeneration might have blurred vision and partial loss of vision. Many age-related diseases have their own specific symptoms. Heavy metals are linked to many diseases. For cadmium, it is ingested by human from the contaminated foods and inhalation of air. The ingested cadmium is taken by gastrointestinal tract and lung and enters to the blood stream [1]. One research which uses the post-mortem brain tissues found that the brain which has Alzheimer’s disease had higher concentration of cadmium than the comparing group which do not have this disease [2]. Moreover, not only Alzheimer’s disease, but also cadmium could cause osteoarthritis by diminishing skeletal growth, which could result osteopenia [3]. Another harmful element like 2,3,7,8-Tetrachloro-p-dioxin (TCDD) which is mainly used in herbicide is also connected with many diseases. A research which had used the zebrafish to be the test group revealed that TCDD exposure could not only attack the liver, kidney, gut and pancreas but also it could influence the eyes by disrupting the angiogenesis and vessel pruning of vascular development [4,5]. These examples show that excessive concentration of heavy metals or harmful elements may cause many different diseases. However, in the NHANES, there are no numeric relationships suggesting that the same metal is associated with multiple diseases. Although there are many studies claiming that people with excessive heavy metals may have their own symptoms. There is little literature that can conclude that different heavy metals are associated with different diseases. To solve this problem, in this paper, the heavy metals from 2003-2004 to 2017-2018 and the survey sample of the nationally representative age-related diseases are selected. There are around 43 harmful elements including the heavy metals and herbicide and 10 aging-associated diseases. To get the relationship between these harmful elements and aging associated diseases, we use the P-value to calculate them. After the P-value between the heavy metals and diseases is calculated, the lower P-value is chosen to demonstrate the relationships between heavy metals and age-related diseases. After that, these diseases are collected to make a comparison.

Materials and Methods

Data Source

The data used to calculate the P-value come from the National Health and Nutrition Examination Survey (NHANES) from 2003- 2004 to 2017-2018. This data is sponsored by a division of the Centers for Disease Control of the National Center for Health Statistics. The NHANES includes the health and nutritional status of both the children and adults in the United States. And in this study, the persons aged 20-150 are included to examine the association between harmful elements and age-related diseases.

Study Design and Participant Sample

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Measures

This research aims to demonstrate that environmental exposures lead to premature age-related diseases, and the conclusion of this research presents a convincing result that metals and pesticides are associated with age-related diseases. For the judgment of the disease, two conditions needed to be estimated. One was the variable for the disease, which could be directly judged by the specific value. This variable description is as follows: doctors told the participants whether they had those diseases or not. In this question, the participants could answer “yes”, “no”, “I don’t know” and “refuse”. Those who responded with “I don’t know” and “refuse” were not suitable for the measurement, so they were excluded. In the case that no doctors told the respondents, some diseases like the Parkinson’s disease were judged by whether that respondent had taken a series of medicines or not and some diseases like Alzheimer’s were judged by the cognitive functions in different modules in the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD), the Animal Fluency Test (AFT), and the Digit Symbol Substitution Test (DSST) [6]. For Parkinson’s, 0 and 1 were set to discover whether the respondent suffered from it not [7]. In which, 0 represented that the male/female had not previously taken any medication for Parkinson’s. As a result, they did not suffer from Parkinson’s. Whereas, 1 represented the opposite meaning. For Alzheimer’s, there were four modules to judge, and the values of the four modules could influence the results, so we did not set the obvious variable which indicated the person had Alzheimer’s. In this case, any lower P-value between the harmful elements and the four modules indicated that a certain relationship between them was present. After data cleaning, the data for the persons who have age-related diseases and the data for these harmful elements are the parameters to calculate P-value by using R Studio.

Statistical Analysis

In this research, we set the P-value to evaluate the relationship between harmful elements and the age-related diseases. If the P<0.05, we can define that there is a relationship between them. However, if it is larger than 0.05, this disease is excluded.

Results

In our research, more than 43 harmful elements are measured in the urine and blood of the respondents, as given in Table 1.

Table 1: The age-related diseases and their symptoms After all the data cleaning, we put the data into RStudio and calculate the P-value. Then, the data with P-value less than 0.05 are selected to be shown in Table 2.

Some are the heavy metals, and others are from the pesticide or the herbicides like 2, 4-dichlorophenoxyacetic acid (2, 4-D) or 2, 4, 5-trichlorophen-oxyacetic acid (2, 4, 5-T). The age-related diseases are listed in Tables 2 and 3 (Figure 1).

Table 2: The age-related diseases associated with the harmful elements.

Table 3: The harmful elements associated with different diseases.

Figure 1: A number of harmful elements related to aging-associated diseases

Discussion

In this paper, we find that the concentration of cadmium and lead have the most widely effects on the age-related diseases, with 9 kinds of diseases apart from macular degeneration for cadmium and Parkinson’s for lead associating with these two harmful elements. Moreover, the concentration of Molybdenum in urine is just associated with 2 diseases. One is the Arthritis, and the other is the diabetes. Corresponding results are noticeable between these results and other researches. For example, in this research, the concentration of molybdenum in urine relates to two aging associated diseases. One is Arthritis and the other is diabetes. This result is also mentioned in other studies. In Kot’s research, the researchers showed that the patients who did not have Arthritis had a lower concentration of molybdenum at 0.403 mg/kg dw, than the patients who had Arthritis at 0.998 mg/kg dw. Noteworthily, in Menke’s research, the concentration of molybdenum also relates to diabetes with a consistently positive association [8]. Apropos the concentration of creatine is also mentioned in many times in different research. In the study from Liu, it shows that the group who have osteoporosis has a higher concentration of creatine than the normal group [9]. Also, measuring the concentration of creatine is a traditional way to detect the kidney tubular injury [10]. So based on this, we could deduce that the concentration of creatine also relates to the chronic kidney disease. Not only these two diseases, but also included in many other diseases. According to the other research, the patients who have macular degeneration, stroke or atherosclerosis also exit relationships within creatine [11-13]. These studies’ results also correspond to our result. The current research presents a reliable evidence and visualized graph to show the complex relationship between pollutants and age-related diseases. However, there are still many limitations in our research. First, there is a little data on some harmful elements, such as macular degeneration. There are just 2,136 heavy metal samples, much smaller than other samples, such as 35,346 samples of heavy metals in the blood of patients with stroke [14]. Meanwhile, it is unavailable to link all the 43 harmful elements to Alzheimer’s. In this case, we could just link 18 elements to the age-related disease, and the number of samples is also too small. The metals in urine contain only 972 pollutants. However, metals in the blood also just cover 2,148. Hence, based on such conditions, we cannot guarantee that there is no relationship with other harmful elements that are not included in these two, and the P-value in this also might be influenced [15]. The future work would more focus on the datasets used in the research extension, such as adding more harmful elements and including all 43 harmful elements that are not fully included in the original research.

Conclusion

In summary, we set the P-value to judge the relationship between 43 harmful elements and 10 age-related diseases. The P-value is an index that indicates cadmium, lead, 2, 3, 7, 8- Tetrachloro- p-dioxn(tcdd), creatine, and mercury have a strong relationship with the age-related diseases, and other harmful elements like 2, 4-dichlorophenol are just associated with one disease. This research has important public health implications because people can try to avoid the age-related diseases by avoiding these harmful elements.

Acknowledgement

Thank you to Dr. Alterovitz for mentorship on this project.

Conflict of Interest

None.

References

1. DeMarco EC, Al-Hammadi N, Hinyard L (2021) Exploring treatment for depression in parkinson's patients: A cross-sectional analysis. Int J Environ Res Public Health. 18(16):8596.

   [Crossref] [Google Scholar] [PubMed]

2. Taylor MK, Mahnken JD, Sullivan DK (2020) NHANES 2011–2014 Reveals cognition of us older adults may benefit from better adaptation to the mediterranean diet. Nutrients. 12(7):1929.

   [Crossref] [Google Scholar] [PubMed]

3. National Academies of Sciences, Engineering, and Medicine. Veterans and agent orange: Update 11 (2018). Washington, DC: The National Academies Press.

   [Crossref] [Google Scholar] [PubMed]

4. Bakulski KM, Seo YA, Hickman RC, Brandt D, Vadari HS, et al. (2020) Heavy metals exposure and alzheimer's disease and related dementias. J Alzheimers Dis. 76(4):1215-1242.

   [Crossref] [Google Scholar] [PubMed]

5. Szabo ST, Harry GJ, Hayden KM, Szabo DT, Birnbaum L (2016) Comparison of metal levels between postmortem brain and ventricular fluid in alzheimer’s disease and non-demented elderly controls. Toxicol Sci. 150(2):292-300.

   [Crossref] [Google Scholar] [PubMed]

6. Chen L, Zhao Y, Liu F, Chen H, Tan T, et al. (2022) Biological aging mediates the associations between urinary metals and osteoarthritis among U.S. adults. BMC Med. 20(1):207.

   [Crossref] [Google Scholar] [PubMed]

7. Andreasen EA, Spitsbergen JM, Tanguay RL, Stegeman JJ, Heideman W, et al. (2002) Tissue-specific expression of AHR2, ARNT2, and CYP1A in zebrafish embryos and larvae: Effects of developmental stage and 2,3,7,8-tetrachlorodibenzo-p-dioxin exposure. Toxicol Sci. 68(2):403-19.

   [Crossref] [Google Scholar] [PubMed]

8. Yue MS, Martin SE, Martin NR, Taylor MR, Plavicki JS (2021) 2,3,7,8- Tetrachlorodibenzo-p-dioxin exposure disrupts development of the visceral and ocular vasculature. Aquat Toxicol. 234:105786

   [Crossref] [Google Scholar] [PubMed]

9. Kot K, Kosik-Bogacka D, Ziętek P, Karaczun M, Ciosek Ż, et al. (2020) Impact of varied factors on iron, nickel, molybdenum and vanadium concentrations in the knee joint. Int J Environ Res Public Health. 17(3):813

   [Crossref] [Google Scholar] [PubMed]

10. Menke A, Guallar E, Cowie CC (2016) Metals in urine and diabetes in u.s. adults. Diabetes. 65(1):164-71.

    [Crossref] [Google Scholar] [PubMed]

11. Liu C, Li H (2019) Correlation of the severity of chronic kidney disease with serum inflammation, osteoporosis and vitamin D deficiency. Exp Ther Med. 17(1):368-372.

    [Crossref] [Google Scholar] [PubMed]

12. Obert LA, Elmore SA, Ennulat D, Frazier KS (2021) A review of specific biomarkers of chronic renal injury and their potential application in nonclinical safety assessment studies. Toxicol Pathol. 49(5):996-1023.

    [Crossref] [Google Scholar] [PubMed]

13. Chong EW, Guymer RH, Klein R, Klein BE, Cotch MF, et al. (2014) Is renal function associated with early age-related macular degeneration? Optom Vis Sci. 91(8):860-4.

    [Crossref] [Google Scholar] [PubMed]

14. Sarfo FS, Mobula LM, Sarfo-Kantanka O, Adamu S, Plange-Rhule J, et al. (2019) Estimated glomerular filtration rate predicts incident stroke among Ghanaians with diabetes and hypertension. J Neurol Sci. 396:140-147.

    [Crossref] [Google Scholar] [PubMed]

15. Liu S, Niu J, Wu S, Xin Z, Zhao Z, et al. (2021) Urinary albumin-to-creatinine ratio levels are associated with subclinical atherosclerosis and predict CVD events and all-cause deaths: A prospective analysis. BMJ Open. 11(3):e040890.

    [Crossref] [Google Scholar] [PubMed]