Abstract

Collagen, a protein, calcium salts, and other minerals are found in our skeletal system. Osteoporosis arises when the elements that collectively make up the bone’s framework turn weak, making bones brittle and prone to breaking. Every bone is composed of an in thickness outer shell called cortical bone and a strong inner mesh of trabecular bone, which resembles a honeycomb with blood and bone marrow in between the struts of bone. Osteoporosis is typically associated with postmenopausal women, but it can also afflict men, younger women, children, and expectant mothers. Hypoestrinism, inadequate dietary intake (low calcium, vitamin D, and K intake), inactivity, “frailty” syndrome, nicotinism, or excessive alcohol use are among the known reasons of poor bone density. This research project involved determining the correlation between the beginning of osteoporosis and the Parathyriod hormone level.The physiologic findings indicated significant variations (p < 0.05) in the following group of study variables: soft drink, parathyroid (PTH), vitamin D, and P mineral, marital status, number of births, menopause state, medicine taken, and other disorders.

Keywords: Physiological parameters, Osteoporosis, Hypoestrinism

Introduction

          Collagen, calcium salts, and a few other minerals are found in bones. Because the bone is made up of a robust inner net of trabecular bone, a thick outer shell called cortical bone, and bone marrow in the spaces between the arches of bone, osteoporosis arises when the struts that support this structure lose out, making bones brittle and prone to breaking. Although postmenopausal women are typically thought to have osteoporosis, other groups may also be impacted, including men, younger women, children, and pregnant women. Bones prone to osteoporosis fracture after a small knock or accident. Osteoporosis does not indicate bone breakage, rather, it indicates a high risk of bone resorption. Although fractured bones can cause pain and lead to other issues, thin, weak bones do not hurt. A widespread, dangerous condition with a high rate of death and morbidity is osteoporosis.When the amount of bone, as assessed on the scan, is determined to be significantly lower than typical, the Bone Density Exanimate machine diagnoses osteoporosis. Right now, the most precise and trustworthy technique is dual energy x-ray absorptiometry (DEXA) scanning. It is a straightforward, pleasant process that requires extremely minimal radiation dosages. (National osteoporosis society, 2015).

            The amount patients with glucocorticoid-induced osteoporosis along with other immunosuppressive pharmaceuticals, the use of anti-epileptics and anti-coagulant agents, and a growing number of autoimmune, rheumatic, and various illnesses are all contributing factors to the rise in osteoporosis cases. Osteoporosis is an inherited illness, according to research findings from families impacted. It is well established that daughters or granddaughters of women with a low density of bone minerals are more likely than those of women with a typical bone density to experience osteoporosis. (Obermayer-Pietsch 2006, Abed Karkosh, 2016).

            The macrophage/monocyte lineage of hematopoietic stem cells gives rise to osteoclasts. Research indicates that the control of particular genes mediates the immediate effect of estrogen on osteoclasts in a number of species. Additionally, estrogen causes osteoclasts to undergo apoptosis, reducing the longevity of these cells that break down bone.There is likely a larger range of effects for the regulatory function of estrogen on the skeleton. Estrogen-related genes affect bone density not just directly but also indirectly, by binding proteins for sex steroids or producing precursors of sex steroids. Therefore, when estrogen levels decline in time after menopause, women lose bone density more quickly. Bone fractures and osteoporosis may result from this. (Lorraine A. Fitzpatrick, 2006, Abed Karkosh, 2016).

            In contrast, intermittent administration of parathyroid hormone (PTH) results in net bone growth (deposition) and net bone loss (resorption) when given continuously. Endocrinologists are perplexed by the contradictory behavior of PTH, therefore, a simulation of bone resorption or deposition that depends on the timing of PTH administration would help to clarify what is happening and serve as the foundation for a rational medication formulation. Focusing on the differing effects of PTH on the osteoblastic and osteoclastic cell populations, it created a mathematical model that incorporates net bone formation with intermittent PTH administration and total reduction in bone with continual PTH administration. (Abed Karkosh, 2016).The aim of this study is to determine the physiological effect related with osteoporosis in Iraq.

Materials and methods

Study population

          Seventy-two female patients in the Merjan Teaching Hospital patient group, ranging in age from 20 to 82, made up the study subjects. In the control group, a total of thirty female participants in the age range of 20–71 years were found to be in good health. Written consent was obtained from each subject before to their involvement in the study.

Control group

          A control group of thirty people with ages that ranged from 20 to 71 was selected. Groups in which age distinctions across the sick and healthy groups were statistically significant (p > 0.05).All of the experiment’s healthy group subjects gave their consent after being informed of the purpose and benefits of the studyEvery participant in the control group got an in-depth physical exam and history, covering factors such as age, smoking status, past

medical history, and prescription history. Factors such as age, occupation, length of illness,

place of residence, family history, BMI, symptoms, marital status, number of births and abortions, smoking habit, consumption of carbonated drinks, menopause, and laboratory tests.

Body Mass Index (BMI)

          Body mass index, or BMI, is a typical way to categorize weight as healthy or unhealthy. It is calculated as the weight of an individual divided by their height. BMI determined according to Whitlock et al. (2005):  Weight (kg) / (height)2 (m2) equals BMI (kg/m2)                                   .

There are four BMI categories (Sturm, 2007):

• BMI fewer than 18.5 are considered underweight.
• BMI values between 18.5 and 24.9 are considered normal or healthy weight.
• BMI values between 25 and 29.9 are considered overweight.
• BMI 30 and above are considered obese (BMI 30-39.9 considered severely obese, BMI 40-49.9 considered morbidly obese, BMI>50considered super obese).

Results and discussion

Study Groups distribution by Socio-Demographic Characteristics

          The patient groups had an average age of (20–82) years, while the control groups had an average age of (20–71) years.The results of Park, J. J. and his colleagues indicate that individuals over 50 have a higher risk of developing osteoporosis. (Park et. al,2010, Ouzzif, et.al., 2012, Baccaro et.al.,2013).

            This outcome can be explained by the postmenopausal changes in how cells function balance as well as the aging process.

The distribution of research groups according to sociodemographic traits is displayed in Table (1). The research found that while there was no significant difference in between the two groups’ residence, there was an important distinction in their occupational state (p ≤ 0.05). The patient group in urban areas had a higher incidence of osteoporosis (61.43%) than the control group that was healthy (60%), while the patient group in rural areas had a lower incidence (38.57%) than the  control  group (40%).

            The current findings indicate that there is no statistically significant difference in the rate of osteoporosis between those who resided in rural and urban areas (p<0.05). This could potentially be attributed to the sample size or sample ethics.However, amongst those who resided in metropolitan settings, the odds ratio seemed to indicate a high risk factor (O.R.) for osteoporosis. This may be the result of a genuine rural community that has never resided in a city living their entire existence in a city.The disparity in sunlight intake and physical activity levels between the two groups could be the cause of the difference.

            The current findings indicate that there is no statistically significant difference in the rate of osteoporosis between those who resided in rural and urban areas (p<0.05). This could potentially be attributed to the sample size or sample ethics.However, amongst those who resided in metropolitan settings, the odds ratio seemed to indicate a high risk factor (O.R.) for osteoporosis. This may be the result of a genuine rural community that has never resided in a city living their entire existence in a city.The disparity in sunlight intake and physical activity levels between the two groups could be the cause of the difference. ( Jónsson, et. al.,1993).

Table1: Study distribution groups by socio-demographic characteristics

*(p  ≤ 0.05)mean significant ,N.S mean not significant

Study groups distribution by BMI

          According to Table (2), there are notable variations in BMI across the healthy  and treatment study groups, which are further subdivided into four age categories: <18, 18.5-24..9, 25-29.9, and >30 years old. Between the group serving as the control and patient study groups, there is a significant difference in value (p = 0.000) (p < 0.05).

            The greater incidence of osteoporosis in individuals with high body mass index can be explained as follows. Higher BMI individuals may experience greater load on their bones. (Lau, E. M., et. al., 2006).

            Another explanation for why osteoporosis and obesity are widespread conditions that impact millions of individuals. Obesity would lead to low serum levels of vitamin D. The hypothesis gained traction lately when a research paper evaluating the direction and causation of the relationship across serum vitamin D levels and BMI was published. (Vimaleswaran, K.S., et al., 2013).

            The drop in blood vitamin D is caused by high body mass index (BMI), and low serum vitamin D levels can lower calcium levels in the blood and create secondary hyperparathyroidism, which can influence the probability of illness. (Cunningha et. al., 2011).

Table2: Study groups distribution by BMI

Study groups distribution by mother history

          Table (3) demonstrates that there are no statistically significant variations in mother background across the control and patient study groups (p= 0.138) (p < 0.05). Table (3) demonstrates that there are not any statistically significant differences in mother history between the placebo and patient study groups (p= 0.138) (p < 0.05). this can be the result of sample ethics or quantity of collection. However, the OR showed that there is a chance that individuals with a mother’s medical history will be more likely to have a sickness than those without one.

Table3: Study groups distribution by mother history

Study groups distribution by marital state and number of birth

          Table 4 demonstrates that there are significant variations in the subsequent characteristics between the two study groups (control and patient): number of births (p=0.000) and marital status (p=0.017) (p ≤ 0.05). Research indicates that women who have a higher child count are at a higher risk of developing osteoporosis compared to those who have fewer children.This might be attributed to the fact that a fully developed neonate accumulates regarding 30g of calcium during pregnancy, and has a skeleton containing about ninety-five percent of this calcium. Therefore, calcium loss negatively affects BMD, and if the mother’s bone mineral were the only source of calcium, the mother’s skeleton would lose roughly three percent (30g/1000g) of its mineral per pregnancy. The consequence may be particularly significant in cases of numerous deliveries and prolonged lactation. (Gur, A.,et. al., 2003).

            Because of our society’s rapid fertility rate and close spacing among pregnancies (the area where the current study was conducted), a rise in the number of births had a negative impact on BMD.Other workers corroborate this clarification. (Keramat, et. al., 2008,  El Maghraoui, et. al., 2009, El Maghraoui, et. al., 2013).

Table4: Study groups distribution by marital state and number of birth

Conclusions

    Maternal history, marital status, number of children, BMI, and women’s occupation are significant disease risk factors.

Acknowledgment

This work was financially supported by Aliaa Saad Abed Ph.D thesis, Aliaa Saad Abed Karkosh and Ali Al-kazzaz. Molecular and physiological study in women with osteoporosis in Babylon province\Iraq. Submitted to the Council of College of Sciences Babylon University in Partial Fulfillment of the Requirements for the Degree of Doctorate of Philosophy in Biology- Biotechnology.

References

• Abed Karkosh ,A. S.(2016). Molecular and physiological study in women with osteoporosis in Babylon province\Iraq. Submitted to the Council of College of Sciences Babylon University in Partial Fulfillment of the Requirements for the Degree of Doctorate of Philosophy in Biology- Biotechnology.
• Baccaro, L. F., Machado,V. S. S., Costa-Paiva, L., Sousa, M. H. , Osis, M. J. and Pinto-Neto,A. M. (2013). Factors associated with osteoporosis in Brazilian women: a population-based household survey. Arch Osteoporos (8).138.
• Cunningham, J., Locatelli, F., Rodriguez, M. (2011). Secondary hyperparathyroidism: Pathogenesis,disease progression, and therapeutic options. Clin. J. Am. Soc. Nephrol., 6, 913–921.
• El Maghraoui, A., Morjane, F., Nouijai, A., Achemlal, L., Bezza, A. and Ghozlani, I. (2009). Vertebral fracture assessment in Moroccan women: Prevalence and risk factors. Maturitas , 62:171–175
• El Maghraoui, A., Rezqi, A., Mounach, A., L. Achemlal, L., Bezza, A. and Ghozlani, I. (2013). Systematic vertebral fracture assessment in asymptomatic postmenopausal women.Bone, 52: 176–180.
• Gur, A., Nas, K.,Cevik, R., Sarac, A. J., Ataoglu, S. and Karakoc, M. (2003). Influence of number of pregnancies on bone mineral density in postmenopausal women of different age groups. J Bone Mineral Metabolism, 21:234–241.
• Jónsson, B., Gärdsell, P., Johnell, O., Sernbo, I. and Gullberg, B. (1993). Life-style and different fracture prevalence: a cross-sectional comparative population-based study. Calcif Tissue Int., 52(6): 425-33.
• Keramat, A., Patwardhan, B., Larijani, B., Chopra, A., Mithal, A., Chakravarty, D., Adibi, H. and Khosravi, A. (2008). The assessment of osteoporosis risk factors in Iranian women compared with Indian women BMC Musculoskeletal Disorders, 9: 28.
• Lau, E. M., Leung, P. C., Kwok, T., Woo, J., Lynn, H., Orwoll, E., Cummings, S. and Cauley, J. (2006) The determinants of bone mineral density in Chinese men—results from Mr. Os (Hong Kong), the first cohort study on osteoporosis in Asian men. Osteoporos Int., 17:297–303.
• Lorraine A Fitzpatrick, (2006). Estrogen therapy for postmenopausal osteoporosis. PMID: 17117296, DOI: 10.1590/s0004-27302006000400016.
• National osteoporosis society(2015) Camerton, Bath, BA2 0PJ General: info@nos.org.uk, 2015.
• Obermayer-Pietsch (2006) B. Genetics of osteoporosis. Wien Med Wochenschr 156: 162-163, 2006.
• Ouzzif, Z., Oumghar, K., Sbai , K., Mounach, A., Derouiche, E. and El Maghraoui, A. (2012). Relation of plasma total homocysteine, folate and vitamin B12 levels to bone mineral density in Moroccan healthy postmenopausal women. Rheumatol Int., 32:123–128.
• Park, J. J., Shin, J., Youn, Y., Champagne, C., Jin, E., Hong, S., Jung, K., Lee, S. and Yeom, S.(2010). Bone mineral density, body mass index, postmenopausal period and outcomes of low back pain treatment in Korean postmenopausal women. Eur Spine J, 19:1942–1947.
• Riancho, JزA, Herandeze, J.L.(2012) Pharmacogenomics of osteoporosis: a pathway approach. Pharmacogenomics 13: 815-829.
• Vimaleswaran, K.S., Berry, D.J., Lu, C., Tikkanen, E., Pilz, S., Hiraki, L.T., Cooper, J.D., Dastani, Z., Li, R., Houston, D.K., (2013). Causal relationship between obesity and vitamin D status: Bi-directional mendelian randomization analysis of multiple cohorts. PLoS Med.,10, 1–13.