Elevated body iron stores are associated with morbidity and mortality due to oxidative stress. Hereditary hemochromatosis, a common condition caused by HFE gene mutations, can lead to excess iron storage and disease but clinical penetrance of HFE gene mutations is low and many people with elevated iron stores lack HFE mutations. We analyzed data from the Hemochromatosis and Iron Overload Screening Study to assess the relationship among HFE genotype (individuals with either homozygous or compound heterozygous status for C282Y and/or H63D HFE mutations were defined as genotype positive, or G+), elevated iron phenotype (individuals exceeding gender-specific transferrin saturation and serum ferritin threshold levels were considered phenotype positive, or P+), and leukocyte telomere length, a marker of biological aging and cumulative oxidative stress. In unadjusted analyses in comparison to individuals who were G−P−, G+P− were not significantly different (OR 0.74; 95% CI 0.26–2.04), while the G+P+ (OR 2.03; 95% CI 1.15–3.56), and G−P+ (OR 2.24; 95% CI 1.5–3.29) had increased risk of short telomeres (<=25th percentile) rather than long telomeres (>=75th percentile). In analyses adjusting for age, gender, and race/ethnicity, the effect of individuals with elevated iron phenotypes having short telomeres persisted with G+P+ individuals (OR 1.94; 95% CI 1.02–3.72), and G−P+ individuals (OR 2.17; 95% CI 1.39–3.39) being significantly different from the G−P− group. In conclusion, elevated iron phenotype, but not HFE genotype, was associated with shortened telomeres. Further studies will be needed to determine whether telomere length provides a marker for morbidities specifically associated with iron overload.
Introduction
Certain inherited genetic mutations are associated with hereditary hemochromatosis (HH), a condition of excess body iron [1,2]. Type 1 HH, or HFE hemochromatosis, is one of the most common autosomal recessive disorders in the US [3,4]. Mutations on the HFE gene, especially the C282Y homozygous variant found most often in populations of northern European descent, can lead to excessive dietary iron absorption and progressive accumulation of iron in the body, potentially reaching toxic levels by middle life [4,5]. Other HFE mutations, like H63D, and non-HFE HH mutations (Types 2,3, and 4 HH) are likely more common in non-northern European populations than the C282Y mutation [1,6–15]. The excess iron is deposited in multiple organs, causing oxidative tissue damage which can lead to health conditions, including cirrhosis of the liver, cancer, and cardiomyopathy [16–21]. However, once the diagnosis is made, liver and heart function can be improved with periodic phlebotomy or erythrocytapheresis to remove excess iron [22–24].
Despite the common usage of HFE mutation identification to assess risk of clinical iron overload in at-risk individuals with potential iron overload, not all persons with elevated body iron stores have mutations in the HFE gene and elevated body iron stores are still associated with an increased risk of morbidity (cancer, cardiovascular disease, inflammation, and dementia) and mortality [1,2,25–30].
Excess iron deposition is associated with biomolecular oxidative damage and mimics physiologic changes that occur with aging and leads to age-related conditions [31–35]. Telomere length has emerged as a marker for cumulative oxidative stress and biological aging, which is the key to age-related morbidity [36–38]. Shortened telomere length has been associated with shorter life span as well as a wide variety of age-related diseases and conditions, including cardiovascular disease, diabetes, insulin resistance, and hypertension [39–41].
Because not everyone with HFE gene mutations develops elevated iron stores and not everyone with elevated body iron stores has HFE gene mutations, the relationship among HFE gene mutations, elevated body iron, and cumulative oxidative stress represented by telomere length is uncertain. The purpose of this study was to examine the relationship among HFE genotypes, elevated iron phenotypes, and telomere length.
Methods
This study used existing data included in the Hemochromatosis and Iron Overload Screening (HEIRS) Study and computed additional assays of the linked, stored specimens. The HEIRS Study evaluated a multicenter, multiracial-ethnic sample of 101,168 primary care adults 25 years of age or older in the United States and Canada. Interview data were obtained from initial screening of all participants and a subsequent Comprehensive Clinical Exam (CCE) for C282Y homozygotes, non-C282Y homozygote participants with elevations of serum biochemical tests of iron status, and control subjects. DNA specimens collected from each participant during the CCE were obtained from the Biologic Specimen and Data Repository Information Coordinating Center (BioLINCC) at the National Heart Lung and Blood Institute and used for the telomere assays. The data obtained in the present study were then merged with the other variables contained in the HEIRS data sets.
Details of study design and sampling methods have been published and can be found in the HEIRS Protocol [42,43].
Subjects
Of the 1,157 subject DNA specimens sent to the investigators by BioLINCC, 1,146 samples had verified telomere values, of which, 137 (12% of the original sample) were excluded due to potential risk of misclassification due to phlebotomy treatment. Individuals from the CCE who could be classified into one of four groups representing both HFE genotype and phenotypic expression of elevated body iron (elevated transferrin saturation (TS), and serum ferritin (SF) levels) were identified. Our final sample for analysis consisted of 1,009 subjects.
Elevated iron phenotype
Subjects’ phenotype status was determined by gender-specific threshold values for serum biochemical tests for iron status. Males were considered “phenotype positive” (P+) for the elevated iron phenotype if their TS level was 50% or above and SF level was above 300 ng/mL. Females were considered “phenotype positive” (P+) for the elevated iron phenotype if their TS level was 45% or above and SF level was above 200 ng/mL. Individuals who had SF and TS levels below these gender-specific thresholds were considered “phenotype negative” (P−) for the elevated iron phenotype.
HFE genotype
Subjects were considered to be “genotype positive” (G+) for HFE genotype if they were homozygous or compound heterozygous for HFE gene mutations, expressed as C282Y/C282Y, H63D/H63D, and C282Y/H63D alleles, respectively. H63D homozygotes were considered G+ as the genotype has been associated with elevated body iron indicators in certain race-ethnicities [7–9]. Compound heterozygotes were considered G+ as the genotype has been associated with elevated body iron indicators relative to wild-type [44]. All other subjects were defined as “genotype negative” (G−).
Analysis groups
The first of four groups consisted of individuals who were G+P+. The second group consisted of individuals that were G−P+. These were individuals with either one copy of either the C282Y or H63D allele (heterozygous carriers of HFE mutation) or neither allele affected by the two common HFE gene mutations and were included in the CCE because they had both elevated TS and elevated SF.
The third group consisted of individuals who were G+P−. This group was composed of persons homozygous or compound heterozygous for the C282Y or H63D alleles with normal TS (<50% in men and <45% in women) and SF (<300 ng/mL in men, and <200 ng/mL in women).
The fourth group consisted of individuals who were G−P−. These were individuals who had no allele mutations and normal TS and SF. They participated in the CCE as control subjects. We randomly selected individuals from all of the groups except for the G+P− group which had a small sample size. For the G+P− group, we used the specimens from all available subjects.
Telomere length via real time PCR analysis
Leukocyte telomere length was measured with a quantitative PCR-based technique (qPCR) that compares telomere repeat sequence copy number to single-copy gene (36b4) copy number in a given sample [45]. Triplicate DNA samples were amplified in parallel in 20 μL reaction using SsoFast EvaGreen real-time PCR supermix (Bio-Rad, Hercules, USA) containing 20 ng of sample DNA. The telomere-specific reaction included 500 nM of telomere-specific primers (forward: 5′GGGTTTGTTTGGGTTTGGGTTTGGGTTTGGGTTTGGGT T3′; reverse: 5′GGCTTGCCTTACCCTTACCCTTACCCTTACCCTTACC CT3′). The 36b4 reaction included 300 nM of the forward (5′CAGCAAG TGGGAAGGTGTAATCC3′) and reverse (5′CCCATTCTATCATCAACGG GTACAA3′) primers. The qPCR/primer supermix (19 uL) was aliquoted into PCR multiwell plates using an EpMotion 5070 robotic liquid handling unit (Eppendorf, Germany), and then 1 uL of sample DNA (20 ng) was added to each well. All qPCR reactions were run using a CFX96 real-time thermal cycler (Bio-Rad). The thermal cycling profile for both amplicons began with 95°C incubation for 3 min and then 30 cycles of 10 sec at 95°C and 1 min at 58°C. The specificity of all amplifications was determined by melting curve analysis. A total of 14 study samples and 2 calibrator samples (all in triplicate) were processed per plate.
Analysis of qPCR data
Analysis of sample telomere length and 36b4 expression levels was done using the PCR Miner algorithm developed by Zhao and Fernald [46]. Values derived for telomere (T) were normalized for each sample with the corresponding expression of 36b4 gene (S) as T/S ratio.
Telomere length status was defined according to intra-sample telomere length percentiles as the bottom, or “short,” quartile (<=25th percentile), middle quartiles (26–74th percentile), and top, or “long,” quartile (>=75th percentile). The “short” quartile was considered to have more cumulative oxidative stress than the “long” quartile [37,38].
CovariatesDemographics
Age is associated with both increasing body iron stores and shorter telomeres [47,48]. For analysis, age was analyzed as a continuous variable in the logistic regression model. Subjects’ race-ethnicity was categorized as either Non-Hispanic White or Other, as hemochromatosis-related genetic mutations differ by race-ethnicity [6–8,11,14,15]. Subjects’ gender was categorized as either male or female, as iron stores and telomere length may be associated with gender [49,50]. Subjects’ health insurance status was categorized as either “insured” or “uninsured” and education attainment as “less than high school,” “high school,” or “more than high school.”
Statistical analysis
Analyses were conducted using SAS 9.2 (SAS Institute, Cary NC). Bivariate analyses with Chi-square and Fisher’s exact test for rare outcomes were conducted to compare genotype-phenotype groups. An adjusted multivariate logistic regression model (N = 511) for the odds of having “Short telomere” versus “Long telomere” was generated to compare genotype-phenotype status after controlling for significant differences in covariates among groups (age, race-ethnicity, and gender). Intra-sample telomere quartiles (< =25th percentile and > =75th percentile) were compared to examine extremes in telomere length to detect an association between genotype-phenotype group and telomere quartile if one were present. Age was included in the model because it is linked to biological aging and telomere length [47,48]. Although education and insurance were examined to describe the individuals, they were not entered into the multivariate model because of their lack of previously identified relationship with telomere length.
An additional subgroup analysis was conducted of only the C282Y homozygotes. In this subgroup analysis, we examined elevated iron phenotype with telomere length through several strategies. First, t-tests were conducted comparing mean telomere length between individuals who had the elevated iron phenotype and those who did not have elevated iron. Second, we computed t-tests comparing individuals with SF > 1000 ng/mL with those with non-elevated SF (<300 ng/mL in men, and <200 ng/mL in women).
Results
Demographic characteristics of the groups under investigation are featured in Table I. Genotype-phenotype groups were significantly different by gender, race-ethnicity, and health insurance. Groups carrying the HFE mutations were much more likely to be Non-Hispanic White than groups without HFE mutations. No differences were observed between genotype-phenotype groups in regards to education status.
In a sub-analysis of the C282Y/C282Y subgroup (n = 82) (data not shown), although the mean telomere length was shorter in the elevated iron group (272.2) than the phenotype negative group (290.1), the relationship in this small subgroup was not statistically significant (P = 0.44). Similarly, among individuals with SF levels >1,000 ng/mL (n = 11) telomere length was shorter (255.3) than those with non-elevated SF (<300 ng/mL in men, and <200 ng/mL in women) (n = 29) whose mean telomere length was 292.5 but once again this did not reach statistical significance (P = 0.29).
Elevated iron phenotype, but not HFE genotype, was associated with shorter telomeres in bivariate analyses (Table II). Elevated iron phenotype, but not hemochromatosis genotype, was associated with shorter telomeres in both crude and adjusted logistic regression models (Table III). Adjusting for age, gender, and race-ethnicity attenuated the relationship slightly but both groups with elevated iron phenotype (G+P+ and G−P+) remained significantly different from the control group (G−P−) in terms of the likelihood of having short telomeres.
Discussion
The results of this study showed that elevated serum biochemical tests of iron status were associated with shorter telomere length. To the best of our knowledge, this is the first study to examine telomere length in relation to HFE genotypes or elevated serum tests of iron status. The result was independent of HFE gene mutations and existed even after controlling for age, gender, and race-ethnicity. HFE mutations were not significantly associated with telomere length in this sample of adults.
Regarding HFE genotype, the results further support the idea of gene–environment interactions in that the HFE genotype appears to primarily impact health through the pathway of elevated body iron and subsequently increased oxidative stress [2]. It is unclear whether lifestyle variables may increase the propensity for phenotypic expression among these individuals. Finding interventions to minimize the expression of the genes and control iron levels is a reasonable approach to health promotion in individuals with HFE genotypes.
The results also indicate that individuals who did not have the HFE gene mutation but had elevated iron had shorter telomeres. This suggests that there are other precursors to elevated iron in the general population that need to be monitored beyond the HFE gene mutations. Previous research indicated that although women who used multivitamins had longer telomere length than those who didn’t, women who used iron supplements had a shorter telomere length than nonusers [51]. There is some evidence that certain non-HFE gene mutations associated with elevated iron stores are more common in different ethnic groups [1,6–12]. It is possible that other unidentified genotypes associated with elevated iron were present in the genotype negative group and that these groups attenuated the relationship between genotype and telomere length.
Though C282Y homozygosity is associated with more severely elevated body iron stores and greater incidence of clinical iron overload than other HFE mutations, many populations with low or no occurrence of this mutation, especially non-northern Europeans, experience elevated body iron stores [6–15]. It is possible that other HFE mutations, such as H63D homozygosity, and non-HFE mutations, such as Types 2,3, and 4 HH (related to gene mutations on the hepcidin, TfR2, and Fpn genes, respectively), are contributing to elevated iron storage in persons of non-northern European descent [1,2]. However, given that elevated body iron, even below clinical iron overload, is associated with greater morbidity, mortality, and shorter telomeres, it is important to discover environmental factors contributing to elevated iron storage in multiracial-ethnic groups [25–32].
These results in humans focusing on leukocyte telomere length are somewhat different from evidence from a rat model examining liver cell telomere length. In a rat model of iron overload examining liver cell telomere length, iron overloaded rats had significantly increased telomerase activity but no difference in telomere length [52]. As a response to iron overload, enhanced telomerase activity may be a response to iron-induced damage in specific organs.
There are several limitations to this study. First, although the H63D HFE gene mutation is commonly used as an indicator of hemochromatosis, its relationship to iron overload is less clear than that of the C282Y HFE gene mutation [7,44]. In this study, both genotypes were evaluated together as well as separately, in relationship to elevated iron with the results suggesting that combining the two did not lead to a bias in the results. Second, this study is cross-sectional and thus allows us to only impute associations. Telomere length evaluates cumulative exposure to oxidative stress and in this study is significantly associated with currently elevated iron. Future studies will increase our knowledge regarding changes in iron levels and changes in the slope of telomere shortening. Third, we did not control for lifestyle variables and some outcome conditions like diabetes, which may be associated with both elevated iron and shorter telomere length. These variables may be in the causal pathway between elevated iron and telomere length [37,53,54].
In conclusion, this study suggests that the presence of HFE genotypes does not have an inherent physiologic impact on telomere length, whereas elevated body iron test results are associated with shorter telomere length. Given the consistent association observed between elevated body iron and morbidity and mortality, it is important to discover the physiological and environmental factors contributing to iron loading to prevent future disease [25–30].
Samples were provided by Biologic Specimen and Data Repository Information Coordinating Center (BioLNCC).
Contract grant sponsor: Centers for Disease Control and Prevention (CDC); Contract grant number: 1U01DD000754.
Contract grant sponsor: National Heart Lung and Blood Institute (NHLBI).
Contract grant sponsor: NIH/NCRR South Carolina COBRE for Cardiovascular Disease; Contract grant number: NIH/NCRR P20 RR016434.
Contract grant sponsor: South Carolina IDeA Networks of Biomedical Research Excellence (INBRE); Contract grant number: NIH/NCRR P20 RR16461.
Conflict of interest: Nothing to report.
CDC disclaimer: The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention.
WallaceDFSubramaniamVNNon- HFE haemochromatosisWorld J Gastroenterol2007134690469817729390van BokhovenMAvan DeursenCTBMSwinkelsDWDiagnosis and management of hereditary hemochromatosisBMJ2011342218223SteinbergKKCogswellMEChangJCPrevalence of C282Yand H63D Mutations in the Hemochromatosis (HFE) Gene in the United StatesJAMA20012852216222211325323AdamsPCReboussinDMBartonJCHemochromatosis and iron-overload screening in a racially diverse populationN Engl J Med20053521769177815858186McLarenGDNathansonMHJacobsARegulation of intestinal iron absorption and mucosal iron kinetics in hereditary hemochromatosisJ Lab Clin Med19911173904012019794MilićSRistićSStarčević-ČizmarevićNLow frequency of HFE gene mutations in Croatian patients suspected of having hereditary hemochromatosisMed Sci Monit201117CR552CR55621959608DulgerACEsenRMeteRThe prevalence of hereditary hemochromatosis in some men from the Eastern part of Turkey and the effects of H63D mutations on iron studiesClin Chem Lab Med20121422718642Nieves-SantiagoPCancelDCanalesDPresence of hemochromatosis-associated mutations in Hispanic patients with iron overloadP R Health Sci J20113013513821932715PedersenPMilmanNGenetic screening for HFE hemochromatosis in 6,020 Danish men: penetrance of C282Y, H63D, and S65C variantsAnn Hematol20098877578419159930PipernoASampietroMPietrangeloAHeterogeneity of hemochromatosis in ItalyGastroenterology199811499610029558289HayashiHWakusawaSMotonishiSGenetic background of primary iron overload syndromes in JapanIntern Med2006451107111117106152ElmrghniSDixonRAWilliamsDRFrequencies of HFE gene mutations associated with haemochromatosis in the population of Libya living in BenghaziInt J Clin Exp Med2011420020421977233PietrangeloACaleffiACorradiniENon-HFE hepatic iron overloadSemin Liver Dis20113130231821901660GordeukVRAfrican iron overloadSemin Hematol20023926326912382201Aguilar-MartinezPPicotMCBeckerFPrevalence of HFE mutations in people from North Africa living in southern FranceBr J Haematol200111491491611564085GordeukVRMcLarenGDSamowitzWEtiologies, consequences, and treatment of iron overloadCrit Rev Clin Lab Sci199431891337917009GujjaPRosingDRTripodiDJIron overload cardiomyopathy: Better understanding of an increasing disorderJ Am Coll Cardiol2010561001101220846597ElmbergMHultcrantzRSimardJFRisk of ischaemic heart disease and cardiomyopathy in patients with haemochromatosis and in their first-degree relatives: A nationwide, population-based studyJ Intern Med2012272455422026548FracanzaniALConteDFraquelliMIncreased cancer risk in a cohort of 230 patients with hereditary hemochromatosis in comparison to matched control patients with non-iron-related chronic liver diseaseHepatology20013364765111230745National Center for Biotechnology InformationUS National Library of MedicineNational Institutes of HealthADAM Medical Encyclopedia: Hemochromatosis/Iron OverloadRevised342012Retrieved from: http://www.ncbi.nlm.nih.gov/pubmedhealth/PMH0001368/. Accessed on Oct 31, 2012.FalizeLGuillygomarchAPerrinMReversibility of hepatic fibrosis in treated genetic hemochromatosis: A study of 36 casesHepatology20064447247716871557DabestaniAChildJSHenzeEPrimary hemochromatosis: Anatomic and physiologic characteristics of the cardiac ventricles and their response to phlebotomyAm J Cardiol1984541531606741807NiederauCFischerRSonnenbergASurvival and causes of death in cirrhotic and in noncirrhotic patients with primary hemochromatosisN Engl J Med1985313125612624058506Rombout-SestrienkovaENiemanFHMEssersBABErythrocytapheresis versus phlebotomy in the initial treatment of HFE hemochromatosis patients: Results from a randomized trialTransfusion20125247047721848963MainousAGIIIWellsBJKoopmanRJIron, lipids and risk of cancer in the Framingham Offspring CohortAm J Epidemiol20051611115112215937020MainousAGIIIDiazVAKnollMETransferrin saturation and hospital length of stay and mortality in Medicare beneficiariesJ Am Geriatr Soc20136113213623205743MainousAGIIIEschenbachSLWellsBJCholesterol, transferrin saturation, and the development of dementia and Alzheimer’s disease: Results from an 18-year population-based cohortFam Med200537364215619154MainousAGIIIGillJMCarekPJElevated serum transferrin saturation and mortalityAnn Fam Med2004213313815083853WellsBJMainousAGIIIKingDEThe combined effect of transferrin saturation and low density lipoprotein on mortalityFam Med20043632432915129378MainousAGIIIWellsBJEverettCJAssociation of ferritin and lipids with C-reactive proteinAm J Cardiol20049355956214996579PraDFrankeSIRHenriquesJAPFenechMIron and genome stability: An updateMutat Res2012733929922349350XuJMarzettiESeoAYThe emerging role of iron dyshomeostasis in the mitochondrial decay of agingMech Ageing Dev201013148749320434480ZeccaLCasellaLAlbertiniANeuromelanin can protect against iron-mediated oxidative damage in system modeling iron overload of brain aging and Parkinson’s diseaseJ Neurochem20081061866187518624918DunaiefJLIron induced oxidative damage as a potential factor in age-related macular degeneration: The Cogan LectureInvest Ophthalmol Vis Sci2006474660466417065470QuintanaCBellefqihSLavalJYStudy of the localization of iron, ferritin, and hemosiderin in Alzheimer’s disease hippocampus by analytical microscopy at the subcellular levelJ Struct Biol2006153425416364657Von ZglinickiTRole of oxidative stress in telomere length regulation and replicative senescenceAnn NY Acad Sci20009089911010911951ShammasMATelomeres, lifestyle, cancer, and agingCurr Opin Clin Nutr Metab Care201114283421102320BabizhayevMASavelyevaELMoskvinaSNTelomere length is a biomarker of cumulative oxidative stress, biologic age, and an independent predictor of survival and therapeutic treatment requirement associated with smoking behaviorAm J Therapeutics201118e209e226MainousAGIIICoddVDiazVALeukocyte telomere length and coronary artery calcificationAtherosclerosis201021026226719945703AdaikalakoteswariABalasubramanyamMMohanVTelomere shortening occurs in Asian Indian type 2 diabetic patientsDiabetic Med2005221151115616108841DemissieSLevyDBenjaminEJInsulin resistance, oxidative stress, hypertension, and leukocyte telomere length in men from the Framingham Heart StudyAging Cell2006532533016913878McLarenCEBartonJCAdamsPCHemochromatosis and iron overload screening (HEIRS) study design for an evaluation of 100,000 primary care-based adultsAm J Med Sci2003325536212589228The Hemochromatosis and Iron Overload Study Research InvestigatorsHemochromatosis and iron overload screening study study design/protocolRetrieved from https://biolincc.nhlbi.nih.gov/static/studies/heirs/Heirs_Protocol.pdf. Accessed on Oct 30, 2012.GurrinLCBertalliNADaltonGWHFE C282Y/H63D compound heterozygotes are at low risk of hemochromatosis-related morbidityHepatology2009509410119554541CawthonRMTelomere measurement by quantitative PCRNucleic Acids Res200230e4712000852ZhaoSFernaldRDComprehensive algorithm for quantitative real-time polymerase chain reactionJ Comput Biol20051210451062FrenckRWJrBlackburnEHShannonKMThe rate of telomere sequence loss in human leukocytes varies with ageProc Natl Acad Sci USA199895560756109576930MontonenJBoeingHSteffenABody iron stores and risk of type 2 diabetes: Results from the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam studyDiabetologia2012552613262122752055HansonEHImperatoreGBurkeWHFE gene and hereditary hemochromatosis: A HuGE reviewAm J Epidemiol200115419320611479183NordfjallKEliassonMStegmayrBTelomere length is associated with obesity parameters but with a gender differenceObesity2008162682268918820651XuQParksCGDeRooLAMultivitamin use and telomere length in womenAm J Clin Nutr2009891857186319279081BrownKEMathahsMMBroadhurstKAIncreased hepatic telomerase activity in a rat model of iron overload: A role for altered thiol redox state?Free Radic Biol Med20074222823517189828DekkerLHNicolaouMvan der ADLSex Differences in the association between serum ferritin and fasting glucose in type 2 diabetes among South Asian Surinamese, African Surinamese, and Ethnic Dutch: The population-based SUNSET studyDiabetes Care20133696597123172974WilsonJGLindquistJHGrambowSCPotential role of increased iron stores in diabetesAm J Med Sci200332533233912811229
Covariates by Genotype–Phenotype Status (%)
Genotype+ Phenotype+
Genotype+ Phenotype−
Genotype− Phenotype+
Genotype− Phenotype−
P-value
Sample size = 1,009
110
45
397
457
–
Total (%)
10.9
4.5
39.3
45.3
–
Gender (%)
<0.001
Men
46.4
28.9
58.2
50.1
Women
53.6
71.1
41.8
49.9
Race-ethnicity (%)
<0.0001
White, non-Hispanic
88.2
91.1
26.7
53.6
Other
11.8
8.9
73.3
46.4
Education (%)
0.13
Less than HS
9.8
17.9
16.3
10.1
High school
18.3
25.0
26.2
23.5
More than HS
71.9
57.1
57.5
66.3
Health insurance
<0.001
Insured
92.7
78.6
72.8
84.4
Uninsured
7.3
21.4
27.2
15.6
Genotype or Phenotype Status by Telomere Length (%)
Short telomere (< =25th pctl)
Long telomere (> =75th pctl)
P-value
Sample size = 511
257
254
–
Total
50.3
49.7
–
HFE genotype
0.67
Positive (n = 82)
52.4
47.6
Negative (n = 429)
49.9
50.1
Elevated iron phenotype
<0.0001
Positive (n = 270)
59.6
40.4
Negative (n = 241)
39.8
60.2
Logistic Regression Predicting Short Telomere Length by Genotype Phenotype Group (n = 511)
Crude model
Odds ratio
95% Confidence interval
Genotype–henotype
G+ P+ (n = 64)
2.03
1.15–3.56
G+ P− (n = 18)
0.74
0.26–2.04
G− P+ (n = 206)
2.24
1.5–3.29
G− P− (n = 223)
–
Reference
Adjusted modela
Odds ratio
95% Confidence interval
Genotype–henotype
G+ P+ (n = 64)
1.94
1.02–3.72
G+ P− (n = 18)
1.27
0.42–3.89
G− P+ (n = 206)
2.17
1.39–3.39
G− P− (n = 223)
–
Reference
Adjusted for age group, gender, and race-ethnicity.