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Body iron stores and glucose intolerance in premenopausal women: role of hyperandrogenism, insulin resistance, and genomic variants related to inflammation, oxidative stress, and iron metabolism.

Martínez-García MA, Luque-Ramírez M, San-Millán JL, Escobar-Morreale HF - Diabetes Care (2009)

Bottom Line: We aimed to study the determinants of serum ferritin levels in premenopausal women among indexes of insulin resistance, adiposity, hyperandrogenism, and genotypes pertaining to inflammation, oxidative stress, and iron metabolism.A stepwise multivariate linear regression analysis (R(2) = 0.18, P < 0.0001) retained menstrual dysfunction (beta = 0.14, P = 0.035), free testosterone (beta = 0.14, P = 0.052), insulin sensitivity index (beta = -0.12, P = 0.012), the His63Asp variant in HFE (beta = 0.16, P = 0.008), and abnormal glucose tolerance (beta = 0.15, P = 0.015) as significant predictors of the logarithm of ferritin levels, whereas CRP, haptoglobin, waist-to-hip ratio, or variants in the TNFalpha, TNFRSF1B, IL6, IL6ST, IL6Ralpha, PON1, and HFE Cys282Tyr mutation exerted no influence.CONCLUSIONS Androgen excess (partly because of hyperandrogenemia and partly because of menstrual dysfunction), insulin resistance, abnormal glucose tolerance, and the HFE His63Asp variant correlate with ferritin levels in premenopausal women.

View Article: PubMed Central - PubMed

Affiliation: Department of Endocrinology, Hospital Universitario Ramón y Cajal and Universidad de Alcala, Madrid, Spain.

ABSTRACT
OBJECTIVE Increased serum ferritin levels and iron stores may be involved in the development of abnormal glucose tolerance in women presenting with obesity and/or polycystic ovary syndrome (PCOS). We aimed to study the determinants of serum ferritin levels in premenopausal women among indexes of insulin resistance, adiposity, hyperandrogenism, and genotypes pertaining to inflammation, oxidative stress, and iron metabolism. RESEARCH DESIGN AND METHODS A total of 257 premenopausal women, classified depending on the presence or absence of PCOS, obesity, and/or abnormal glucose tolerance, underwent a complete metabolic evaluation, serum ferritin, haptoglobin, and C-reactive protein (CRP) measurements, and genotyping for proinflammatory and prooxidant variants and mutations in the HFE gene. RESULTS Serum ferritin concentrations were increased in women presenting with PCOS and/or abnormal glucose tolerance, independent of obesity. A stepwise multivariate linear regression analysis (R(2) = 0.18, P < 0.0001) retained menstrual dysfunction (beta = 0.14, P = 0.035), free testosterone (beta = 0.14, P = 0.052), insulin sensitivity index (beta = -0.12, P = 0.012), the His63Asp variant in HFE (beta = 0.16, P = 0.008), and abnormal glucose tolerance (beta = 0.15, P = 0.015) as significant predictors of the logarithm of ferritin levels, whereas CRP, haptoglobin, waist-to-hip ratio, or variants in the TNFalpha, TNFRSF1B, IL6, IL6ST, IL6Ralpha, PON1, and HFE Cys282Tyr mutation exerted no influence. CONCLUSIONS Androgen excess (partly because of hyperandrogenemia and partly because of menstrual dysfunction), insulin resistance, abnormal glucose tolerance, and the HFE His63Asp variant correlate with ferritin levels in premenopausal women.

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Multiple linear regression analysis of the logarithm of serum ferritin concentrations. The squares are the standardized regression coefficients (β, the change in terms of SDs in the dependent variable that results from a change of 1 SD in an independent variable), and the error bars indicate the 95% CI of β. Menstrual history and genomic variants were coded as dummy variables: regular menstruation was coded 0, 1 was used for oligomenorrhea, and 2 was used for amenorrhea. Variants in TNFα, TNFRSF1B, IL6, IL6ST, HFE, and PON1 loci were coded as 0 for homozygosity for wild-type alleles, 1 for heterozygosity, and 2 for homozygosity for mutant alleles. The IL6Rα polymorphism was coded 0 for homozygosity for 149-bp alleles, 1 for subjects carrying only one 149-bp allele, and 2 for subjects carrying two non–149-bp alleles. Finally, HFE His63Asp/Cys282Tyr double heterozygotes were coded 1 and subjects without double heterozygosity were coded 0.
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Figure 2: Multiple linear regression analysis of the logarithm of serum ferritin concentrations. The squares are the standardized regression coefficients (β, the change in terms of SDs in the dependent variable that results from a change of 1 SD in an independent variable), and the error bars indicate the 95% CI of β. Menstrual history and genomic variants were coded as dummy variables: regular menstruation was coded 0, 1 was used for oligomenorrhea, and 2 was used for amenorrhea. Variants in TNFα, TNFRSF1B, IL6, IL6ST, HFE, and PON1 loci were coded as 0 for homozygosity for wild-type alleles, 1 for heterozygosity, and 2 for homozygosity for mutant alleles. The IL6Rα polymorphism was coded 0 for homozygosity for 149-bp alleles, 1 for subjects carrying only one 149-bp allele, and 2 for subjects carrying two non–149-bp alleles. Finally, HFE His63Asp/Cys282Tyr double heterozygotes were coded 1 and subjects without double heterozygosity were coded 0.

Mentions: Because serum ferritin levels were not distributed normally, their logarithm was introduced as a dependent variable in multiple linear regression models using age; BMI; glucose tolerance (codified as normal or abnormal); menstrual cycles (codified as regular menstruation, oligomenorrhea, or amenorrhea); serum free testosterone, CRP, and haptoglobin levels; the insulin sensitivity index; and the genomic variants related to iron metabolism, inflammation, and oxidative stress as independent variables. The model that considered all independent variables explained 22% of the variability in the logarithm of serum ferritin concentrations (R2 = 0.22, P < 0.0001) (Fig. 2 ).


Body iron stores and glucose intolerance in premenopausal women: role of hyperandrogenism, insulin resistance, and genomic variants related to inflammation, oxidative stress, and iron metabolism.

Martínez-García MA, Luque-Ramírez M, San-Millán JL, Escobar-Morreale HF - Diabetes Care (2009)

Multiple linear regression analysis of the logarithm of serum ferritin concentrations. The squares are the standardized regression coefficients (β, the change in terms of SDs in the dependent variable that results from a change of 1 SD in an independent variable), and the error bars indicate the 95% CI of β. Menstrual history and genomic variants were coded as dummy variables: regular menstruation was coded 0, 1 was used for oligomenorrhea, and 2 was used for amenorrhea. Variants in TNFα, TNFRSF1B, IL6, IL6ST, HFE, and PON1 loci were coded as 0 for homozygosity for wild-type alleles, 1 for heterozygosity, and 2 for homozygosity for mutant alleles. The IL6Rα polymorphism was coded 0 for homozygosity for 149-bp alleles, 1 for subjects carrying only one 149-bp allele, and 2 for subjects carrying two non–149-bp alleles. Finally, HFE His63Asp/Cys282Tyr double heterozygotes were coded 1 and subjects without double heterozygosity were coded 0.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2713652&req=5

Figure 2: Multiple linear regression analysis of the logarithm of serum ferritin concentrations. The squares are the standardized regression coefficients (β, the change in terms of SDs in the dependent variable that results from a change of 1 SD in an independent variable), and the error bars indicate the 95% CI of β. Menstrual history and genomic variants were coded as dummy variables: regular menstruation was coded 0, 1 was used for oligomenorrhea, and 2 was used for amenorrhea. Variants in TNFα, TNFRSF1B, IL6, IL6ST, HFE, and PON1 loci were coded as 0 for homozygosity for wild-type alleles, 1 for heterozygosity, and 2 for homozygosity for mutant alleles. The IL6Rα polymorphism was coded 0 for homozygosity for 149-bp alleles, 1 for subjects carrying only one 149-bp allele, and 2 for subjects carrying two non–149-bp alleles. Finally, HFE His63Asp/Cys282Tyr double heterozygotes were coded 1 and subjects without double heterozygosity were coded 0.
Mentions: Because serum ferritin levels were not distributed normally, their logarithm was introduced as a dependent variable in multiple linear regression models using age; BMI; glucose tolerance (codified as normal or abnormal); menstrual cycles (codified as regular menstruation, oligomenorrhea, or amenorrhea); serum free testosterone, CRP, and haptoglobin levels; the insulin sensitivity index; and the genomic variants related to iron metabolism, inflammation, and oxidative stress as independent variables. The model that considered all independent variables explained 22% of the variability in the logarithm of serum ferritin concentrations (R2 = 0.22, P < 0.0001) (Fig. 2 ).

Bottom Line: We aimed to study the determinants of serum ferritin levels in premenopausal women among indexes of insulin resistance, adiposity, hyperandrogenism, and genotypes pertaining to inflammation, oxidative stress, and iron metabolism.A stepwise multivariate linear regression analysis (R(2) = 0.18, P < 0.0001) retained menstrual dysfunction (beta = 0.14, P = 0.035), free testosterone (beta = 0.14, P = 0.052), insulin sensitivity index (beta = -0.12, P = 0.012), the His63Asp variant in HFE (beta = 0.16, P = 0.008), and abnormal glucose tolerance (beta = 0.15, P = 0.015) as significant predictors of the logarithm of ferritin levels, whereas CRP, haptoglobin, waist-to-hip ratio, or variants in the TNFalpha, TNFRSF1B, IL6, IL6ST, IL6Ralpha, PON1, and HFE Cys282Tyr mutation exerted no influence.CONCLUSIONS Androgen excess (partly because of hyperandrogenemia and partly because of menstrual dysfunction), insulin resistance, abnormal glucose tolerance, and the HFE His63Asp variant correlate with ferritin levels in premenopausal women.

View Article: PubMed Central - PubMed

Affiliation: Department of Endocrinology, Hospital Universitario Ramón y Cajal and Universidad de Alcala, Madrid, Spain.

ABSTRACT
OBJECTIVE Increased serum ferritin levels and iron stores may be involved in the development of abnormal glucose tolerance in women presenting with obesity and/or polycystic ovary syndrome (PCOS). We aimed to study the determinants of serum ferritin levels in premenopausal women among indexes of insulin resistance, adiposity, hyperandrogenism, and genotypes pertaining to inflammation, oxidative stress, and iron metabolism. RESEARCH DESIGN AND METHODS A total of 257 premenopausal women, classified depending on the presence or absence of PCOS, obesity, and/or abnormal glucose tolerance, underwent a complete metabolic evaluation, serum ferritin, haptoglobin, and C-reactive protein (CRP) measurements, and genotyping for proinflammatory and prooxidant variants and mutations in the HFE gene. RESULTS Serum ferritin concentrations were increased in women presenting with PCOS and/or abnormal glucose tolerance, independent of obesity. A stepwise multivariate linear regression analysis (R(2) = 0.18, P < 0.0001) retained menstrual dysfunction (beta = 0.14, P = 0.035), free testosterone (beta = 0.14, P = 0.052), insulin sensitivity index (beta = -0.12, P = 0.012), the His63Asp variant in HFE (beta = 0.16, P = 0.008), and abnormal glucose tolerance (beta = 0.15, P = 0.015) as significant predictors of the logarithm of ferritin levels, whereas CRP, haptoglobin, waist-to-hip ratio, or variants in the TNFalpha, TNFRSF1B, IL6, IL6ST, IL6Ralpha, PON1, and HFE Cys282Tyr mutation exerted no influence. CONCLUSIONS Androgen excess (partly because of hyperandrogenemia and partly because of menstrual dysfunction), insulin resistance, abnormal glucose tolerance, and the HFE His63Asp variant correlate with ferritin levels in premenopausal women.

Show MeSH
Related in: MedlinePlus