Hypertensive disorders in pregnancy (HPD) and abnormal patterns of growth of the fetus, such as small-for-gestational-age (SGA), are among the leading causes of perinatal and maternal morbidity and mortality in the world. These conditions often contribute to long-term negative effects on the mother and the unborn child. ^[1] Globally, gestational hypertension affects between 5% and 8% of all women. However, pregnancy-induced hypertension may occur at a frequency of up to 16.7% in developing countries. ^[2] In Poland, Lewandowska and Więckowska have reported a prevalence of up to 12.4% of pregnancy-induced hypertension. ^[3]

Hypertensive women are a high-risk pregnancy group who need close clinical monitoring. As such, early identification of the occurrence of gestational hypertension and SGA is critical for the monitoring of this group to facilitate the initiation of preventive measures. ^[4] The traditional approach to evaluating the risk of developing gestational hypertension in pregnant women involves the analysis of maternal demographic features and clinical presentations to predict risk factors. ^[5-10]

Pregnancy associated hypertensive disorders and intrauterine growth restriction are common complications responsible for fetal, neonatal, and maternal morbidity. Most current hypotheses regarding the path physiologic mechanisms of pregnancy-induced hypertension point to early placental abnormalities. ^[11]

Human placenta synthesizes steroid, protein, and glycoprotein hormones throughout gestation. The production of hCG by the placenta in early pregnancy is critical for implantation and maintenance of the blastocyst. Since it is postulated that preeclampsia is likely a trophoblastic disorder, it may be essential for understanding of this disease, to investigate the pathologic and secretary reaction of the placenta. Twin pregnancies and molar pregnancies produce higher levels of hCG and they are associated with a higher incidence of preeclampsia than uncomplicated singleton pregnancies. ^[12]

An association was reported between preeclampsia and elevated third trimester hCGlevels, whereas early experience with second trimester levels suggests a link between increased hCGand other adverse pregnancy outcomes. Considerable evidence suggests an association between serum hCG levels and preclampsia. ^[13]

This is prospective, Non-Randomized and observational study was conducted in the Department of Biochemistry, Index Medical College over a period of 2 year.

Inclusion criteria

First time pregnant women, who was aged 16- 40 years with singleton pregnancy, was included in the study.

Exclusion criteria

First time pregnant women who was aged less than 16 years and more than 40 years or had multiple pregnancies were excluded from the study.

Women at POG >22 weeks with Singleton pregnancy were included a one hundred thirty Hypertensive women as case group and control group include another one hundred thirty normotensive women after matching the parity and gestation age.

The gestational age at recruitment was 14-24 weeks (second trimester). Gestational age was estimated from the date of last menstrual period (LMP), if it was available, and confirmed from the abdomen/pelvic ultra sound scan. The abdomen/pelvic ultra sound scan also looked for fetal defects and multiple pregnancies. Gestational age was expressed in completed weeks (eg 12 weeks 6 days, is taken as 12 weeks).

At the time of recruitment, each subject completed a questionnaire with the help of the principal investigator. The questionnaire obtained information on maternal age, maternal weight, and height, smoking habits, alcohol intake, intake of drug supplements (such as iron, folic acid and vitamins), marital status, religion, tribe, educational background, occupation, blood group and Rhesus factor, family history of high BP and gestational hypertension.

The weight was measured to the nearest kilograms (kg) and height to the nearest centimeter (cm) to calculate the Body Mass Index (BMI). Blood pressure was taken after the subjects had rested for 15 minutes.

Venous blood sample was taken for measurement of hemoglobin (Hb) and for hCG assays at the second trimester. The recruitment gestational period (14-24 weeks) was chosen as a baseline to compare with the control data of the biochemical markers. The dip stick method of measuring urine protein was used to determine the protein levels in the urine of all the 260 women.

The information concerning pregnancy outcomes were obtained from medical records of the women.

4.1.4 Blood sample collection and storage

After recruitment 5ml of maternal blood was obtained from each pregnant woman into serum separator vacutainer tubes for determination of hCG. A portion was a liquated into EDTA vacutainer tubes for measurement of hemoglobin.

The samples in the serum separator vacutainer tubes were allowed to clot for 30 minutes before centrifugation for 15 minutes at 10000 x g. The resulting serum was aliquoted into Eppendorf tubes for storage at – 200C till analyzed.

4.5 Laboratory analysis

4.5.1 Hb and urine protein determination

Measurement of Hb was immediately performed after blood collection using ABx pentra 60 C+ automated hemocounter. Urine protein was also determined by the use of dip sticks.

4.5.2. hCG determination

Serum hCG was assayed using the quantitative sandwich ELISA technique using a commercial test kit obtained from GenWay Biotech on the Multiscan EX Microplate Photometer from Thermo Electron Corporation. The manufacturer’s instructions was followed for the analysis. All tests were performed in duplicate with variation not exceeding 10%.

4.5.2.1 Principle of the assay:

The assay system utilizes a unique monoclonal antibody directed against a distinct determinant on the hCG. Mouse monoclonal anti- hCG antibody was used for solid phase immobilization. A goat anti whole hCG antibody was added to the antibody- enzyme (horseradish peroxidase) conjugate solution. The test sample was allowed to react sequentially with the two antibodies, resulting in the hCG molecules being sandwiched between the solid phase and enzyme- linked antibodies. After two separate 30 minutes incubations at 370C, the wells were washed three (3) times to remove unbound labeled antibodies. A solution development was stopped with the addition of stop solution after one hour which changed the color to yellow. The concentration of hCG was directly proportional to the color intensity of the test sample. Absorbance was measured spectrophotometrically at 450 nm.

Statistical analyses

Microsoft Excel 2021 software was used for data storage and analyses. Data on maternal age, weight and height, BMI, Hb, Urine Protein, systolic and diastolic blood pressure was presented as mean with standard deviation, medians and ranges (min and max). Concentration of hCG was presented in mmol/ml and MoM respectively as mean with standard deviation, median and range (max and min).The results of biochemical markers were also expressed as the gestation- specific multiples of the median (MoM). MoM values were calculated by dividing the observed marker concentration by the median value for the gestational week at which the sample was obtained. Pearson’s correlation coefficient (r) was used for estimating correlations between age and concentrations of hCG.

Student’s t-test was used to test for differences between means of concentrations of hCG at recruitment and between mean concentrations of hCG in normal and adverse pregnancies. Means of Hb, SBP and DBP, UP, BMI, and age in the cases group was all compared with that of the controls. Statistical significance was determined at P< 0.05. The SPSS software version 29^th was used in all calculations to establish the significance

Table 1: Distribution of Age of the patient’s

Table 2: Distribution of patients according to β hCG level in PIH and normotensive patients.

In current study a significantly higher percentage of normotensive individuals have beta-hCG levels below 30,000 compared to the PIH group. This suggests that lower beta-hCG levels (<30,000) may be more common in normotensive pregnancies. 30,000–40,000 mIU/mL: The proportions in this range are relatively small, and the p-value (0.794) indicates no significant difference between the two groups. 40,000–50,000 mIU/mL: A notable difference is observed, as 12.3% of the PIH group falls into this range, but no normotensive individuals do. While no p-value is provided, this trend suggests that higher beta-hCG levels might be associated with PIH. A substantial proportion of the PIH group (22.3%) has beta-hCG levels above 50,000, whereas no normotensive individuals have such high levels. This clear difference suggests a strong association between very high beta-hCG levels and PIH.

Table 3: Distribution of patients according to β hCG level in gestation hypertension, pre eclemptic and eclemptic (N=130).

Eclampsia has no individuals in the <30000 mIU/ml, 30000-40000 mIU/ml, or 40000-50000 mIU/ml categories. The only category with Eclampsia cases is >50000 mIU/ml, where 8.4% of cases are present. Preeclampsia shows no individuals in the <30000 mIU/ml and 30000-40000 mIU/ml categories, but it has 10% in the 40000-50000 mIU/ml category and 7.6% in the >50000 mIU/ml category. Gestational Hypertension is the most common condition in the <30000 mIU/ml and 30000-40000 mIU/ml categories, with 60.1% and 10.7% respectively. It has a small percentage in 40000-50000 mIU/ml (0.7%) and >50000 mIU/ml (1.5%).

Table 4: Comparison of β hCG level in PIH and normotensive

Table 5: Comparison of β hCG level in PIH (Gestational hypertension, pre-eclampsia and eclampsia) (N=130).

In this study Gestational Hypertension: Mean beta-hCG: 25,206.19 mIU/mL, which is the lowest among the three groups. Standard deviation (SD): 3,696.9, indicating relatively low variability in beta-hCG levels. This suggests that gestational hypertension is associated with relatively moderate beta-hCG levels compared to the other, more severe conditions.

Preeclampsia: Mean beta-hCG: 61,697.67 mIU/mL, significantly higher than in gestational hypertension. Standard deviation (SD): 5,498.57, indicating moderate variability in beta-hCG levels within this group. The marked increase in beta-hCG levels compared to gestational hypertension highlights the association of higher beta-hCG levels with the severity of the condition.

Eclampsia: Mean beta-hCG: 84,106.38 mIU/mL, the highest among the three groups. Standard deviation (SD): 7,295.05, showing greater variability in beta-hCG levels within this group. The extremely elevated beta-hCG levels reflect the severity and critical nature of eclampsia. The p-value indicates a statistically significant difference in mean beta-hCG levels across the three groups. The difference in beta-hCG levels is unlikely to be due to cha .

In our study the percentage of individuals under 20 years is the same for both groups (5.3%). P-value = 0.679: This indicates no statistically significant difference in the prevalence of individuals under 20 between the PIH and normotensive groups. In this age group, the PIH group has 39 individuals (30%), while the normotensive group has 55 individuals (42.3%). P-value = 0.029: This is less than 0.05, indicating a statistically significant difference. This means that individuals in this age group are more likely to be normotensive compared to having PIH.

In this study, the distribution of individuals between rural and urban areas is slightly different for the PIH and normotensive groups. However, these differences are not statistically significant, as the p-value in both cases is greater than the typical threshold of 0.05. Rural Areas: A higher percentage of individuals with PIH (60.7%) live in rural areas compared to normotensive individuals (50.7%). However, this difference could be due to chance and is not significant. Urban Areas: A higher percentage of normotensive individuals (49.3%) live in urban areas compared to those with PIH (39.3%). Again, this difference is not statistically significant.

The beta-hCG blood levels were discovered to be a significant clinical diagnostic for predicting PE during the early stages of the second trimester. On the other hand, their predictive power was found to be restricted during the first trimester. ^[14] However, further study on the predictive ability of hCG in populations that are bigger and more diverse is required needed. ^[15] Similar to present study Kumari et al documented. When compared with normotensive women, hypertensive disorders of pregnancy are associated with greater levels of serum betahCG. The levels are also greater in patients who have severe preeclampsia as opposed to patients who have nonsevere preeclampsia, and they are higher in primigravida hypertension women as opposed to multigravida hypertensive women. ^[16]

In our study Gestational Hypertension: The majority of individuals with gestational hypertension have beta-hCG levels below 30,000 (60.1%). There is a steep decline in representation as beta-hCG levels increase, suggesting that lower beta-hCG levels are more typical of milder hypertensive disorders. Preeclampsia: Preeclampsia is most associated with beta-hCG levels of 40,000–50,000 (10%) and >50,000 (7.6%). This suggests that elevated beta-hCG levels are characteristic of more severe hypertensive disorders like Preeclampsia. Eclampsia: Eclampsia is exclusively associated with very high beta-hCG levels (>50,000) (8.4%). This reflects that extremely elevated beta-hCG levels are more likely linked to the most severe hypertensive disorder, Eclampsia.

In cases of early onset preeclampsia, it has also been noted that the levels of b-hCG in the serum are higher than normal. Therefore, determining the levels of b-hCG in the blood can potentially assist in the early identification of hypertensive disease of pregnancy, and it also has the potential to act as an indication of the degree to which the condition has progressed. ^[17] Because of its low sensitivity and the difficulty in determining where the cut off value should be, the serum b-hCG test has limited application as a diagnostic tool. ^[18] Serum beta-hCG estimate in primigravida patients at the middle of the first trimester (13-20 weeks) is an excellent predictor of PIH, and greater levels of beta-hCG are related with increased PIH severity. ^[19]

The current study revealed that maternal serum beta hCG estimate in the mid-trimester (13-20 weeks) is a good predictor of the development of hypertensive problems during pregnancy. ^[20] Raised ACR levels were shown to be associated with illness severity as well as a poor fetomaternal outcome in our investigation. ^[21] Amin et al demonstrated that the random urine protein: creatine ratio is a more accurate means of assessing proteinuria in hypertensive pregnant women than the dipstick approach. ^[22]

Clinical laboratories, on the other hand, should standardise the reference values for their setting. ^[23] Study showed that measuring second trimester beta-hCG levels is useful in clinical practice to identify women who will develop PIH in the same pregnancy. Also, higher levels of beta-hCG are associated with increase severity of PIH. The sample size for this study being small, necessitate the need of further large scale studies considering the importance of B-hCG in PIH prediction. ^[24]

In this study Gestational Hypertension: Mean beta-hCG: 25,206.19 mIU/mL, which is the lowest among the three groups. Standard deviation (SD): 3,696.9, indicating relatively low variability in beta-hCG levels. This suggests that gestational hypertension is associated with relatively moderate beta-hCG levels compared to the other, more severe conditions.

Preeclampsia: Mean beta-hCG: 61,697.67 mIU/mL, significantly higher than in gestational hypertension. Standard deviation (SD): 5,498.57, indicating moderate variability in beta-hCG levels within this group. The marked increase in beta-hCG levels compared to gestational hypertension highlights the association of higher beta-hCG levels with the severity of the condition.

Eclampsia: Mean beta-hCG: 84,106.38 mIU/mL, the highest among the three groups. Standard deviation (SD): 7,295.05, showing greater variability in beta-hCG levels within this group. The extremely elevated beta-hCG levels reflect the severity and critical nature of eclampsia. The p-value indicates a statistically significant difference in mean beta-hCG levels across the three groups. The difference in beta-hCG levels is unlikely to be due to chance.

This study showed that estimation of serum Beta HCG levels in early second trimester of pregnancy is a useful indicator to identify women who are likely to develop gestational hypertension in the same pregnancy. The level of beta HCG is strongly associated with development of GHT. This can be used as “POWERFUL PREDICTIVE TOOL” by the obstetricians for early identification and expert management of gestational hypertension. βHCG are low to be useful as a mass screening marker as a single tool and therefore it should be combined with other serum markers and ultrasound parameters like Doppler study of uterine vessels, which will help in improving its role as a screening tool.

1. Baschat AA, Madger LS, Doyle LE. Prediction of preeclampsia utilizing the first trimester screening examination. Am J Obstet Gynecol. 2014;211:524. doi: 10.1016/j.ajog.2014.04.018. et al. e1-524.e7. [DOI] [PubMed] [Google Scholar]
2. Sonek J, Krantz D, Carmichael J, Downing C, Jessup K, Haidar Z, Ho S, Hallahan T, Kliman HJ, McKenna D. First–trimester screening for early and late preeclampsia using maternal characteristics, biomarkers, and estimated placental volume. Am J Obstet Gynecol. 2017. [DOI] [PubMed]
3. Di Lorenzo G, Ceccarello M, Cecotti V, Ronfani L, Monasta L, Vecchi Brumatti L, Montico M, D’Ottavio G. First trimester maternal serum PIGF, free b-hCG, PAPPA, PP-13, uterine artery Doppler and maternal history for the prediction of preeclampsia. Placenta. 2014;33:495e501. doi: 10.1016/j.placenta.2012.03.003. [DOI] [PubMed] [Google Scholar]
4. Lagana AS, Giordano D, Loddo S, Zoccali G, Vitale SG, Santamaria A, Buemi M, D’Anna R. Decreased Endothelial Progenitor Cells (EPCs) and increased Natural Killer (NK) cells in peripheral blood as possible early markers of preeclampsia: a case-control analysis. Arch Gynecol Obstet. 2017;295:867. doi: 10.1007/s00404-017-4296-x. –. [DOI] [PubMed] [Google Scholar]
5. Park FJ, Leung CHY, Poon LCY, Williams PF, Rothwell SJ, Hyett JA. Clinical evaluation of a first trimester algorithm predicting the risk of hypertensive disease of pregnancy. ANZJOG. 2014;53:532. doi: 10.1111/ajo.12126. –. [DOI] [PubMed] [Google Scholar]
6. Sahai K, Saraswathy S, Yadav TP, Arora D, Krishnan M. Pre-eclampsia: Molecular events to biomarkers. Med J Armed Forces India. 2017;73:167. doi: 10.1016/j.mjafi.2016.09.001. –. [DOI] [PMC free article] [PubMed] [Google Scholar]
7. Paridaens H, Gruson D. Pre-eclampsia: overview on the role of biomarkers in 2016. Ann Biol Clin (Paris) 2017. 75:245. doi: 10.1684/abc.2017.1240. –. [DOI] [PubMed] [Google Scholar]
8. Yliniemi A, Makikallio K, Korpimaki T, Kouru H, Marttala J, Ryynanen M. Combination of PAPPA, fhCGβ, AFP, PlGF, sTNFR1, and maternal characteristics in prediction of early-onset preeclampsia. Clin Med Ins: Re - prod Health. 2015;9:13. doi: 10.4137/CMRH.S21865. –. [DOI] [PMC free article] [PubMed] [Google Scholar]
9. Poon L, Nicolaides K. First–trimester maternal factors and biomarker screening for preeclampsia. Prenatal Diagnosis. 2014;34:618. doi: 10.1002/pd.4397. –. [DOI] [PubMed] [Google Scholar]
10. Anderson UD, Olsson MG, Kristensen KH, Akerstrom B, Hansson SR. Review: biochemical markers to predict preeclampsia. Placenta. 2022;33:S42. doi: 10.1016/j.placenta.2011.11.021. –. [DOI] [PubMed] [Google Scholar]
11. Lambert-Messerlian G, Eklund EE, Chien EK, Rosene- Montella K, Neveux LM, Haddow HRM, Palomaki GE. Use of first or second trimester serum markers, or both, to predict preeclampsia. Pregnancy Hypertension: Int J Women’s Cardiovasc Health. 2014;4:271. doi: 10.1016/j.preghy.2014.07.001. –. [DOI] [PubMed] [Google Scholar]
12. Cuffe JSM, Holland O, Salomon C, Rice GE, Perkins AV. Review: Placental derived biomarkers of pregnancy disorders. Placenta. 2017;54:104e110. doi: 10.1016/j.placenta.2017.01.119. [DOI] [PubMed] [Google Scholar]
13. Ilekis JV, Tsilou E, Fisher S, Abrahams VM, Soares MJ, Cross JC, Zamudio S, Illsley NP, Myatt L, Colvis C, Costantine MM, Haas DM, Sadovsky Y, Weiner C, Rytting E, Bidwell G. Placental origins of adverse pregnancy out - comes: potential molecular targets: an Executive Work - shop Summary of the Eunice Kennedy Shriver National Institute of Child Health and Human Development. Am J Obstet Gynecol. 2016;215:S1. doi: 10.1016/j.ajog.2016.03.001. –. [DOI] [PMC free article] [PubMed] [Google Scholar]
14. Huppertz B, Kawaguchi R. First trimester serum markers to predict preeclampsia. Wien Med Wochenschr. 2014;162:191. doi: 10.1007/s10354-012-0072-5. –. [DOI] [PubMed] [Google Scholar]
15. Olsen RN, Woelkers D, Dunsmoor-Su R, LaCoursiere DY. Abnormal second-trimester serum analytes are more predictive of preterm preeclampsia. Am J Obstet Gynecol. 2014;207:228. doi: 10.1016/j.ajog.2012.06.006. e1-7. [DOI] [PubMed] [Google Scholar]
16. Ree PH, Hahn WB, Chang SW, Jung SH, Kang JH, Cha DH, Kang MS, Huh JY. Early detection of preeclampsia using inhibin a and other second-trimester serum markers. Fetal Diagn Ther. 2014;29:280. doi: 10.1159/000322742. –. [DOI] [PubMed] [Google Scholar]
17. Yefet E, Kuzmin O, Schwartz N, Basson F, Nachum Z. Predictive Value of Second-Trimester Biomarkers and Maternal Features for Adverse Pregnancy Outcomes. Fetal Diagn Ther. 2017. [DOI] [PubMed]
18. Fafula VR, Iefremova PU, Onufrovych KO, Maksymyuk VH, Besedina SA, Nakonechnyi AI, Vorobets ZD, Vorobets DZ. Alterations in Arginase-No-Synthase System of Spermatozoa in Human Subjects with Different Fertility Potential. J Med Biochem. 2018;37:134. doi: 10.1515/jomb-2017-0049. –. [DOI] [PMC free article] [PubMed] [Google Scholar]
19. Jelliffe-Pawlowski LL, Baer RJ, Currier RJ, Lyell DJ, Blumenfeld YJ, El-Sayed YY, Shaw GM, Druzin ML. Early-onset severe preeclampsia by first trimester pregnancy- associated plasma protein A and total human chorionic gonadotropin. Am J Perinatol. 2015;32:703. doi: 10.1055/s-0034-1396697. –. [DOI] [PubMed] [Google Scholar]
20. Revankar VM, Narmada L. Assessment of serum β- hCG and lipid profile in early second trimester as predictors of hypertensive disorders of pregnancy. Int J Gynecol Obstet. 2017;138:331. doi: 10.1002/ijgo.12225. –. [DOI] [PubMed] [Google Scholar]
21. Oliveira N, Magder LS, Blitzer MG, Baschat AA. Firsttrimester prediction of pre-eclampsia: external validity of algorithms in a prospectively enrolled cohort. Ultra – sound Obstet Gynecol. 2014;44:279. doi: 10.1002/uog.13435. –. [DOI] [PubMed] [Google Scholar]
22. Scazzocchio E, Figueras F, Crispi F, Meler E, Masoller N, Mula R, Gratacos E. Performance of a first-trimester screening of preeclampsia in a routine care low-risk setting. Am J Obstet Gynecol. 2014;208:203e, 1. doi: 10.1016/j.ajog.2012.12.016. –. [DOI] [PubMed] [Google Scholar]
23. Parra Cordero M, Rodrigo R, Barja P, Bosco C, Rencoret G, Sepulveda-Martinez A, Quezada S. Prediction of early and late pre–eclampsia from maternal characteristics, uterine artery Doppler and markers of vasculogenesis during first trimester of pregnancy. Ultrasound Obstet Gynecol. 2014;41:538. doi: 10.1002/uog.12264. –. [DOI] [PubMed] [Google Scholar]
24. Kuc S, Wortelboer EJ, van Rijn BB, Franx A, Visser GH, Schielen PC. Evaluation of 7 serum biomarkers and uterine artery Doppler ultrasound for first-trimester prediction of preeclampsia: a systematic review. Obstet Gynecol Surv. 2014;66:225. doi: 10.1097/OGX.0b013e3182227027. –. [DOI] [PubMed] [Google Scholar
25. isms of Morbidity and Mortality from Exposure to Ambient Air Particles." Research Report Health Effects Institute, no. 91, 2000, pp. 5-88, 89-103.
26. Mengesha, Yadeta A., and Assayie Bekele. "Relative Chronic Effects of Occupational Dusts on Respiratory Indices and Health of Workers in Three Ethiopian Factories." American Journal of Industrial Medicine, vol. 34, no. 4, 1998, pp. 373-380. DOI: 10.1002/(SICI)1097-0274(199810)34:4<373::AID-AJIM10>3.0.CO;2-0.
27. Wang, Zhen, et al. "Cancer Mortality among Silicosis in China’s Metallurgical Industry." International Journal of Epidemiology, vol. 25, no. 5, 1996, pp. 913-917. DOI: 10.1093/ije/25.5.913.
28. Garshick, Eric, et al. "Occupationally Induced Airway Obstruction." Medical Clinics of North America, vol. 80, no. 4, 1996, pp. 851-878. DOI: 10.1016/S0025-7125(05)70451-6.
29. Nij, Tjeert E., et al. "Dust Control Measures in the Construction Industry." Annals of Occupational Hygiene, vol. 47, no. 3, 2003, pp. 211-218. DOI: 10.1093/annhyg/meg029.
30. Cormier, Yves, Alain Merlaux, and Caroline Duchaine. "Respiratory Health Impact of Working in Sawmills in Eastern Canada." Archives of Environmental Health, vol. 55, no. 6, 2000, pp. 424-430. DOI: 10.1080/00039890009604036.
31. Purdue, Mark P., et al. "Impaired Lung Function and Lung Cancer Incidence in a Cohort of Swedish Construction Workers." Thorax, vol. 62, no. 1, 2007, pp. 51-56. DOI: 10.1136/thx.2006.061358.
32. Abrak, P., and C. Belgin. "Respiratory Symptoms and PFT among Furniture Decorators." Annals of Agricultural and Environmental Medicine, vol. 11, 2004, pp. 13-17.
33. Bussi, Marta, et al. "Study of Ethmoidal Mucosa in a Population at Occupational High Risk of Sinonasal Adenocarcinoma." Acta Oto-Laryngologica, vol. 122, no. 2, 2002, pp. 197-201. DOI: 10.1080/00016480252814174.
34. Osman, Erdem, and Kazim Pala. "Occupational Exposure to Wood Dust and Health Effects on the Respiratory System in a Minor Industrial Estate in Bursa/Turkey." International Journal of Occupational Medicine and Environmental Health, vol. 22, no. 1, 2009, pp. 43-50. DOI: 10.2478/v10001-009-0006-5.
35. Kamat, Shirish R., et al. "Distinguishing Byssinosis from Chronic Obstructive Pulmonary Disease: Results of a Prospective Five-Year Study of Cotton Mill Workers in India." American Review of Respiratory Disease, vol. 124, no. 1, 1981, pp. 31-40. DOI: 10.1164/arrd.1981.124.1.31.
36. Slywitch, E. Guide Feeding Vegetarian Diets for Adults. São Paulo: Sociedade Brasileira Vegetariana, 2012.
37. Acosta-Navarro, Jorge C., et al. "Blood Pressure, Lipid Profile, and Other Biochemical Parameters among Vegetarians, Semi-Vegetarians, and Omnivores Peruvians: The Lima Study." Anales Paulistas de Medicina e Cirurgia, vol. 125, 1998, pp. 87-101.
38. McEvoy, Claire T., Niall Temple, and Jayne V. Woodside. "Vegetarian Diets, Low-Meat Diets, and Health: A Review." Public Health Nutrition, vol. 15, no. 12, 2012, pp. 2287-2294. DOI: 10.1017/S1368980012000936.
39. Appel, Lawrence J., et al. "A Clinical Trial of the Effects of Dietary Patterns on Blood Pressure." New England Journal of Medicine, vol. 336, no. 16, 1997, pp. 1117-1124. DOI: 10.1056/NEJM199704173361601.
40. Liese, Angela D., et al. "Adherence to the DASH Diet Is Inversely Associated with Incidence of Type 2 Diabetes: The Insulin Resistance Atherosclerosis Study." Diabetes Care, vol. 32, no. 8, 2009, pp. 1434-1436. DOI: 10.2337/dc09-0228.
41. Campos, F. A. A. C., D. Cheavegatti, F. A. Martins, and L. A. Seixas. Enfermeiros no Cuidado da Saúde de Vegetarianos; 63 CBEn; 3 a 6 de Outubro de 2011; Macéo. Brasília: Aben, 2011.
42. Ball, Madeleine J., and Melissa A. Bartlett. "Dietary Intake and Iron Status of Australian Vegetarian Women." The American Journal of Clinical Nutrition, vol. 70, no. 3, 1999, pp. 353-358. DOI: 10.1093/ajcn/70.3.353.
43. Haddad, Ella H., and Joan S. Tanzman. "What Do Vegetarians in the United States Eat?" The American Journal of Clinical Nutrition, vol. 78, no. 3, 2003, pp. 626S-632S. DOI: 10.1093/ajcn/78.3.626S.
44. Fuhrman, Joel, and Deana M. Ferreri. "Fueling the Vegetarian (Vegan) Athlete." Current Sports Medicine Reports, vol. 9, no. 4, 2010, pp. 233-241. DOI: 10.1249/JSR.0b013e3181e93a6f.
45. Craig, Winston J. "Health Effects of Vegan Diets." The American Journal of Clinical Nutrition, vol. 89, no. 5, 2009, pp. 1627S-1633S. DOI: 10.3945/ajcn.2009.26736N.
46. Miller, Mark R., et al. "Standardisation of Spirometry." European Respiratory Journal, vol. 26, no. 2, 2005, pp. 319-338. DOI: 10.1183/09031936.05.00034805.
47. Crapo, Robert O., Ann H. Morris, and Reed M. Gardner. "Reference Spirometric Values Using Techniques and Equipment that Meet ATS Recommendations." American Review of Respiratory Disease, vol. 123, no. 6, 1981, pp. 659-664. DOI: 10.1164/arrd.1981.123.6.659.
48. Wanger, Jack, et al. "Standardisation of the Measurement of Lung Volumes." European Respiratory Journal, vol. 26, no. 3, 2005, pp. 511-522. DOI: 10.1183/09031936.05.00035005.
49. Fishwick, David, et al. "Chronic Obstructive Pulmonary Disease in Non-Smokers." Occupational Medicine, vol. 54, no. 1, 2004, pp. 12-15. DOI: 10.1093/occmed/kqg012.