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Haptoglobin genotype as a risk factor for postmenopausal osteoporosis
  1. Gian Piero Pescarmonaa,
  2. Patrizia D'Ameliob,
  3. Emanuella Morraa,
  4. Gian Carlo Isaiab
  1. aDepartment of Genetic, Biology and Biochemistry, University of Torino, Italy, bDepartment of Internal Medicine, University of Torino, Italy
  1. Professor Isaia, UOADU Medicina Malattie Metaboliche dell'Osso, Dipartimento di Medicina Interna, Facoltà di Medicina e Chirurgia, Università di Torino, Corso Dogliotti 14, 10126 Torino, Italy, isaia{at}molinette.unito.it

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Editor—Some epidemiological and experimental data have shown a correlation between iron metabolism and calcium, phosphate, and magnesium turnover.1 2 In particular, previous reports have shown that iron availability can play a fundamental role in bone metabolism and that iron depletion can lead to bone demineralisation. For example, in patients who underwent gastrectomy3-5 or in rats treated similarly,6 osteoporosis was accompanied by laboratory and clinical signs of iron deficiency and was prevented by the administration of fructo-oligosaccharides, a substance that promotes iron absorption from the gut. In oophorectomised rats (a condition mimicking the oestrogen levels commonly found in the menopause), a wide range of cells, including osteoblasts, displayed a reduced number of transferrin receptors and hence a reduced iron uptake.7 In humans, it has been assessed that out of 14 nutrients tested (including calcium), iron was the best positive predictor of BMD in the femoral neck,8 and furthermore a negative correlation between ascorbic acid content of the diet and osteoporosis has been found9 10; it is notable that ascorbic acid in the diet affects iron absorption increasing it by a factor of 2-3. A severe nutritional iron deficiency anaemia provokes significant alterations in the metabolism of calcium, phosphorus, and magnesium in rats with a noticeable degree of bone demineralisation, even in the presence of normal serum levels of calcium, phosphorus, and magnesium.1

On the basis of the above evidence, we searched for a genetic marker of iron disposal (haptoglobin genotype) as a risk factor for postmenopausal osteoporosis.

Only about 5% of daily iron turnover comes from intestinal absorption, most of it coming from haemoglobin turnover, which requires three proteins, haemopexin, haptoglobin, and haem oxygenase. We focused our attention on haptoglobin since it is the only one with a well known polymorphism.

Haptoglobin (HP) is a serum α2 glycoprotein that exists as a tetramer, composed of two smaller identical alpha (α) and two larger identical beta (β) chains. At present, three main different genotypes of haptoglobin in normal adult plasma have been identified. Differences among the three haptoglobin genotypes are given by light alpha subunit structures: type 1.1, type 2.2, which has homozygous α1 (9 kDa) and α2 (18 kDa) subunits, and type 2.1, which has heterozygous α1 and α2 subunits, with a shared β subunit in all three genotypes (38 kDa). The β chain is a glycoprotein which does not exhibit polymorphism but only some rare variants.

The main function of haptoglobin is to bind free haemoglobin in a stable complex, which is later cleared from the plasma by the liver reticuloendothelial system. Haemoglobin binding capacity depends on the genetic haptoglobin type, on the amount of haptoglobin, and on the number of polymers.11 12

Functional differences between haptoglobin genotypes have been described12; type 1.1 has the highest haemoglobin carrying ability, while type 2.2 is almost unable to carry it because the Hb binding site is buried by the polymerisation process.

In European populations, the genotype distribution is as follows: about 16% has genotype 1.1, about 48% genotype 2.1, and the remaining 36% genotype 2.2.13 Several authors have studied the haptoglobin haplotype frequency in different populations and different pathologies,13 14 and various haptoglobin genotypes have also been correlated with the serum iron.12 In spite of the large number of published reports on the topic, no study has been performed to investigate a possible difference in the incidence of haptoglobin genotypes in osteoporotic patients and non-osteoporotic subjects.

In order to investigate the possible correlations between postmenopausal osteoporosis and frequencies of haptoglobin genotypes, we studied a group of women affected by postmenopausal osteoporosis and a control group of non-osteoporotic postmenopausal women.

Methods

The osteoporotic group consisted of 135 subjects (age range 40-73 years, postmenopausal age range 6 months-26 years) and the osteoporosis was diagnosed using the Double EmissionX ray Absorptiometry (DXA) technique with a Hologic QDR4500 densitometer (Hologic Inc, Waltam, MA, USA). In particular, we considered as osteoporotic patients with a T score value of 2.5 SD or less, according to WHO (WHO Technical Report Series No 843 “Assessment of fracture risk and the application to screening for postmenopausal osteoporosis”, 1994). Secondary osteoporosis was excluded by history, physical examination, and measurement of calcium, phosphorus, and bone alkaline phosphatase (BAP) in the blood.

The control group consisted of 65 non-osteoporotic women (age range 47-76 years, postmenopausal age range 6 months-33 years) (T score >−1 SD).

The HP genotypes of patients and controls were analysed with SDS-PAGE electrophoresis (fig 1). Data were analysed by the χ2test using the Statistical Analysis System (SAS Institute Inc). The odds ratio and the corresponding confidence interval were also calculated for genotype 1.1 versus genotype 2.2 and 2.1. To avoid possible selection bias, we compared the patients and the control group for age, postmenopausal age, body mass index (BMI), and T score (table1). The controls were, on average, older than the patients (p=0.01) with a longer postmenopausal period (p=0.05).

Figure 1

Electrophoretic runs of haptoglobin: type 1 appears as a single band furthest from the origin, while types 2.1 (200 kDa) and 2.2 (400 kDa) appear as a series of bands nearer to the origin.

Table 1

Characteristics of the patients compared to the controls (age, postmenopausal age, BMI, T score). Shown are the mean values, the standard deviation (SD), and the result of Student's t test

Results

The frequencies of the three haptoglobin genotypes are 32.6% for 2.2, 55.5% for 2.1, and 11.9% for 1.1 in the patient group, while in the control group they are 47.7%, 50.8%, and 1.5%, respectively, with significant differences between the two groups (p=0.0076, χ2 test). The odds ratio between genotype 1.1 and genotype 2.2 was 12 (confidence interval = 1.34-106.7). The odds ratio between genotype 1.1 and genotype 2.1 was 7.04 (confidence interval = 1.21-2.9).

Discussion

It is well known that advancing age, a prolonged period of amenorrhoea, and low BMI are risk factors for osteoporosis. Any possible bias in selection of subjects was excluded as the controls were on average significantly older and the BMI of the two groups was not significantly different.

Our data show that the presence of haptoglobin genotype 1.1 is an important risk factor for postmenopausal osteoporosis. The functional differences between haptoglobin genotypes, namely the fact that type 1.1 has the highest haemoglobin carrying ability and, hence, the highest elimination rate through the liver, while type 2.2 is almost unable to carry it to the liver, can account on a molecular basis for the increased risk for osteoporosis linked to the presence of haptoglobin genotype 1.1. In normal subjects, the amount of iron stores (namely ferritin) is significantly correlated with the HP genotype.12

The daily requirement of iron intake to keep the body iron store stable is therefore strongly dependent on the ability of the organism to store iron (HP 2.2) or to waste it (HP 1.1) through the liver.

The finding that HP genotype may play an important role as a risk factor for osteoporosis may be useful in clinical practice to identify in advance the women who will probably develop postmenopausal osteoporosis and so allow primary prevention of the disease and reduce the social cost of its consequences.

Acknowledgments

This work was supported by a grant from the Ministero dell'Università e della Ricerca Scientifica e Tecnologica (MURST, 60%) of Italy.

References

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