Hostname: page-component-7c8c6479df-27gpq Total loading time: 0 Render date: 2024-03-28T14:17:50.055Z Has data issue: false hasContentIssue false

Gene polymorphisms and gene scores linked to low serum carotenoid status and their associations with metabolic disturbance and depressive symptoms in African-American adults

Published online by Cambridge University Press:  24 July 2014

May A. Beydoun*
Affiliation:
Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIH Biomedical Research Center, IRP 251, Bayview Boulevard, Suite 100, Room 04B118, Baltimore, MD21224, USA
Michael A. Nalls
Affiliation:
Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
J. Atilio Canas
Affiliation:
Pediatric Endocrinology, Diabetes and Metabolism, Nemours Children's Clinic, Jacksonville, FL, USA
Michele K. Evans
Affiliation:
Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIH Biomedical Research Center, IRP 251, Bayview Boulevard, Suite 100, Room 04B118, Baltimore, MD21224, USA
Alan B. Zonderman
Affiliation:
Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIH Biomedical Research Center, IRP 251, Bayview Boulevard, Suite 100, Room 04B118, Baltimore, MD21224, USA
*
*Corresponding author: Dr M. A. Beydoun, fax +1 410 558 8236, email baydounm@mail.nih.gov
Rights & Permissions [Opens in a new window]

Abstract

Gene polymorphisms provide a means to obtain unconfounded associations between carotenoids and various health outcomes. In the present study, we tested whether gene polymorphisms and gene scores linked to low serum carotenoid status are related to metabolic disturbance and depressive symptoms in African-American adults residing in Baltimore city, MD, using cross-sectional data from the Healthy Aging in Neighborhoods of Diversity across the Life Span study (age range 30–64 years, n 873–994). We examined twenty-four SNP of various gene loci that were previously shown to be associated with low serum carotenoid status (SNPlcar). Gene risk scores were created: five low specific-carotenoid risk scores (LSCRS: α-carotene, β-carotene, lutein+zeaxanthin, β-cryptoxanthin and lycopene) and one low total-carotenoid risk score (LTCRS: total carotenoids). SNPlcar, LSCRS and LTCRS were entered as predictors for a number of health outcomes. These included obesity, National Cholesterol Education Program Adult Treatment Panel III metabolic syndrome and its components, elevated homeostatic model assessment of insulin resistance, C-reactive protein, hyperuricaemia and elevated depressive symptoms (EDS, Center for Epidemiologic Studies-Depression score ≥ 16). Among the key findings, SNPlcar were not associated with the main outcomes after correction for multiple testing. However, an inverse association was found between the LTCRS and HDL-cholesterol (HDL-C) dyslipidaemia. Specifically, the α-carotene and β-cryptoxanthin LSCRS were associated with a lower odds of HDL-C dyslipidaemia. However, the β-cryptoxanthin LSCRS was linked to a higher odds of EDS, with a linear dose–response relationship. In summary, gene risk scores linked to low serum carotenoids had mixed effects on HDL-C dyslipidaemia and EDS. Further studies using larger African-American population samples are needed.

Type
Full Papers
Copyright
Copyright © The Authors 2014 

Oxidative stress, an imbalance between the production of reactive oxygen species and the ability of the cell to scavenge those species with various antioxidants, has been implicated in the pathogenesis of many chronic diseases, including type 2 diabetes mellitus, CVD, rheumatological disorders and carcinogenesis( Reference Soory 1 ). Potential beneficial effects have recently been ascribed to naturally occurring phytochemicals known as carotenoids which may reduce oxidative stress triggered by injury that characterises the pathogenesis of those chronic diseases( Reference Soory 1 ). Although the primary dietary sources of carotenoids are fruits and vegetables, they are also found in bread, eggs, beverages (e.g. carrot and tomato juices), fats and oils( Reference Rao and Rao 2 ). Among more than forty carotenoids in the human diet, only the following five carotenoids or groups of carotenoids have been shown to be consistently measurable in human serum: α-carotene, β-carotene, β-cryptoxanthin, lycopene and lutein+zeaxanthin (often combined together)( Reference Rao and Rao 2 ).

Some observational studies have shown inverse associations between carotenoids and CVD( Reference Voutilainen, Nurmi and Mursu 3 ), type 2 diabetes( Reference Montonen, Knekt and Jarvinen 4 Reference Reunanen, Knekt and Aaran 8 ) and the metabolic syndrome (MetS) in recent national surveys( Reference Ford, Mokdad and Giles 9 Reference Beydoun, Canas and Beydoun 11 ). Moreover, in two recent studies, using the National Health and Nutrition Examination Survey (NHANES) and Invecchiare in Chianti (InCHIANTI) data, serum total carotenoid level has been shown to be consistently inversely related to depressive symptoms( Reference Beydoun, Beydoun and Boueiz 12 , Reference Milaneschi, Bandinelli and Penninx 13 ). However, the findings are inconsistent with those reported by other studies( Reference Kataja-Tuomola, Sundell and Mannisto 14 Reference Wang, Liu and Manson 17 ). It is also worth noting that obesity and its related disorders have been shown to be associated with an increased level of depressive symptoms in a number of studies (e.g. Beydoun et al. ( Reference Beydoun, Kuczmarski and Mason 18 ), Kimura et al. ( Reference Kimura, Matsushita and Nanri 19 ), Akbaraly et al. ( Reference Akbaraly, Ancelin and Jaussent 20 )), suggesting co-morbidity between those conditions.

Importantly, it is unclear whether the observed inverse relationships between serum carotenoids and the MetS and/or depression are due to variations in carotenoid concentration or determined by other carotenoid-containing food constituents. To identify an unconfounded role of carotenoids in health and disease, surrogate measures such as genetic polymorphisms have been used in recent studies. In fact, genome-wide association studies (GWAS) and candidate gene studies have uncovered genetic polymorphisms in a number of genes that were significantly associated with serum carotenoid status. Genes carrying the specific SNP that have been commonly tested in the literature against serum carotenoid concentrations were either directly (e.g. β,β-carotene 15,15′-mono-oxygenase, BCMO1) or indirectly (e.g. ApoE) related to carotenoid metabolism( Reference Herron, McGrane and Waters 21 Reference Herbeth, Gueguen and Leroy 30 ). Many of these GWAS and candidate gene studies have been conducted among the individuals of European descent.

Therefore, the overall aim of the present study was to assess whether genetic polymorphisms involved in carotenoid absorption, intracellular trafficking and plasma transport are also related to a higher burden of metabolic disturbance and depressive symptoms. The present study focused on African-American adults who were part of the Healthy Aging in Neighborhoods of Diversity across the Life Span (HANDLS) study, providing the first opportunity to examine these relationships within this racial/ethnic group. The findings could elucidate whether metabolic disturbance and/or depressive symptoms are associated with genetic polymorphisms that are in turn related to low serum carotenoid status.

Materials and methods

Database and study population

Initiated in 2004 as an ongoing prospective cohort study, the HANDLS study used area probability sampling to recruit a socio-economically diverse and representative sample of African Americans and whites (30–64 years old) living in Baltimore, MD( Reference Evans, Lepkowski and Powe 31 ). The HANDLS protocol was approved by the Institutional Review Board of the National Institute on Aging. The present study used cross-sectional data from the baseline HANDLS study cohort.

A total of 3720 selected subjects participated in the household survey at phase 1 (sample 1). Of these selected subjects, 2436 (65·4 %) had complete baseline phase 2 examinations (sample 2). However, our data used a subset with complete genetic data on a sample of African-American participants of the HANDLS study (n 1024, sample 3). Of these subjects, 873 had complete depressive symptom data (sample 4a) and 910–961 had complete data on metabolic outcomes (sample 5a–5i).

Genetic data

Blood samples were collected from the participants for DNA extraction, and genome-wide genotyping was completed for 1024 participants of the HANDLS study using Illumina 1M SNP coverage. For a further description of the methods used, see online supplementary methods.

Selection of SNP of interest for the present analysis was solely based on those detected in previous GWAS and candidate gene studies as highly significant predictors of serum carotenoid status( Reference Borel 22 , Reference Hendrickson, Hazra and Chen 28 , Reference Lietz, Oxley and Leung 32 ). These SNP were extracted from high-quality imputed genotypes. Most of these selected SNP are available in our database, with the exception of two β,β-carotene-9′,10′-oxygenase (BCDO2) SNP (W80X: bovine SNP; c.196C>T: sheep SNP), which are not human SNP, and one scavenger receptor class B member 1 (SCARB1) SNP (SR-BI: intron 5)( Reference Borel 22 ). Other SNP (n 5) that were selected from one study( Reference Hendrickson, Hazra and Chen 28 ) were dropped for various reasons, the most common of which was high linkage disequilibrium with the other selected SNP. None of the remaining SNP was in strong linkage disequilibrium with each other. Consequently, twenty-four distinctive SNP with reliable values were chosen. A detailed description of these selected SNP is presented in online supplementary Table S1.

SNP for lower carotenoid status, low specific-carotenoid risk score and low total-carotenoid risk score

Of the twenty-four distinctive SNP, combinations that would allow the assessment of the effect of an increasing genetic risk of lower carotenoid level on the binary measures of metabolic disturbance and depression were created. First, we examined the independent effects of each SNP allele dosage that was previously shown to be associated with a lower specific carotenoid or a group of carotenoids. To this end, from these twenty-four SNP, twenty-four genetic exposure variables were created and termed SNPlcar (SNP for lower carotenoid status). SNP dosage was coded as is or reverse coded (0,1,2 or 2,1,0) depending on whether the minor allele was associated with lower carotenoid status or vice versa (for details, see online supplementary Table S1).

Moreover, to assess the collective associations of SNP linked to lower levels of specific and total carotenoids with the outcomes of interest, two risk scores were created: (1) low specific-carotenoid risk score (LSCRS), by summing the SNPlcar values together that pertained to that specific carotenoid; (2) low total-carotenoid risk score (LTCRS), by summing all SNPlcar values together, reflecting low levels of all carotenoids (see online supplementary Table S2 and Fig. S1). In the computation of the former score, a SNPlcar was entered into a LSCRS, when previously shown to have the most significant association with a specific carotenoid (smallest P value), particularly when multiple carotenoids were affected by the same SNPlcar. We assumed that each SNPlcar was associated with the levels of specific carotenoids based on previous findings in whites, despite potential ancestral differences in African Americans, particularly in terms of linkage disequilibrium patterns( Reference Reich, Cargill and Bolk 33 ). Since a direct way to estimate the effect size of each SNPlcar on the levels of serum carotenoids was not available for African Americans, we did not apply SNP-specific weights from previous studies on whites to account for SNP-specific differences in the effects on carotenoid status. Thus, we simply summed the risk alleles or the combinations of risk alleles together to obtain the LSCRS and LTCRS, as was done in a previous study( Reference Grimsby, Porneala and Vassy 34 ). In each LSCRS, SNPlcar included were specific to that particular carotenoid and were not double-counted in another LSCRS.

Anthropometric indices

Body weight and standing height were measured directly. BMI (weight/(height)2, kg/m2) was calculated for each participant. Waist circumference (cm) was measured using a tape measure starting from the hip bone and wrapping around the waist at the level of the navel. Obesity was defined as BMI ≥ 30 kg/m2, while central obesity was defined as a component of the MetS (see the ‘Metabolic syndrome’ section).

Metabolic outcome variables

Systolic and diastolic blood pressure

The average of the right and left sitting blood pressure values was taken to represent each of the systolic and diastolic blood pressure levels for the present analysis. Blood pressure was measured non-invasively using the brachial artery auscultation method with an aneroid manometer, a stethoscope and an inflatable cuff.

Other metabolic risk factors

Following an overnight fast, blood samples were drawn from an antecubital vein. Total cholesterol, HDL-cholesterol (HDL-C), TAG, uric acid and glucose concentrations were assessed using a spectrophotometer (Olympus 5400). Fasting serum insulin concentration was analysed with a standard immunoassay test (DPC Immulite 2000; Siemens), and C-reactive protein (CRP) concentration was analysed with an immunoturbidimetry method (Behring Nephelometer II; Siemens). Homeostatic model assessment of insulin resistance (HOMA-IR)( Reference Wallace, Levy and Matthews 35 ) was computed with a cut-off point of 2·61, reflecting a high insulin resistance level as has been suggested elsewhere( Reference Matthews, Hosker and Rudenski 36 ). Cut-off values for hyperuricaemia were >420 μmol/l (>7 mg/dl) in men and >360 μmol/l (>6 mg/dl) in women( 37 ), while elevated CRP was defined as >2·11 mg/l( Reference Ridker, Cushman and Stampfer 38 ).

Metabolic syndrome

Central obesity was defined by waist circumference ≥ 102 cm or 40 inches for men and ≥ 88 cm or 35 inches for women( 39 ). This is one of the five components in the main definition of the MetS according to the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) (2005)( 40 ). Using this definition, the MetS was positive when three or more of the following criteria screened were positive: (1) waist circumference >102 cm for men and >88 cm for women; (2) systolic blood pressure/diastolic blood pressure ≥ 130/85 mmHg; (3) fasting glucose ≥ 5·5 mmol/l ( ≥ 100 mg/dl); (4) TAG ≥ 1·7 mmol/l ( ≥ 150 mg/dl); (5) HDL-C < 1·04 mmol/l ( < 40 mg/dl) for men and < 1·3 mmol/l ( < 50 mg/dl) for women.

Assessment of depressive symptoms

Extensively trained psychometricians administered, among others, a baseline battery of cognitive and neuropsychological tests( Reference Lezak, Howeison and Loring 41 ) that included baseline depressive symptoms using the Center for Epidemiologic Studies-Depression scale, a twenty-item, self-report symptom rating scale that emphasises the affective, depressed mood component( Reference Radloff 42 ). The invariant factor structure of the Center for Epidemiologic Studies-Depression scale was recently shown using confirmatory factor analysis comparing NHANES I and HANDLS data( Reference Nguyen, Kitner-Triolo and Evans 43 ). A cut-off point of 16 was used to assess elevated depressive symptoms (EDS) in all analyses.

Covariates

Covariates considered as potential confounders included sex, age, education (below high school (grades 1–8), high school (grades 9–12), above high school (13+)), poverty income ratio (below v. at or above the poverty line), smoking status (current smoker v. non-smoker), drug use (current v. past or never), and ten principal components to control for any residual effects of the population structure (see online supplementary methods).

Statistical methods

Differences in means and associations of categorical variables across ‘genetic data completeness’ were tested by t and χ2 tests, respectively, using Stata 13.0 (StataCorp)( 44 ). Then, multiple logistic regression models were conducted to test the associations of SNPlcar (entered separately in each model), five LSCRS (entered simultaneously) and one LTCRS with ten binary outcomes (obesity, MetS and five components), elevated HOMA-IR, elevated CRP, hyperuricaemia and EDS. Adjusted OR and 95 % CI were estimated. Type I error was initially set at 0·05, with regression coefficients being assessed using the Wald test. Finally, to test linear dose–response relationships, quartiles of LSCRS and LTCRS were entered into the regression models as ordinal variables, and P values for trend were computed from the Wald test. Additionally, non-linear associations were tested for each quartile compared with the lowest quartile (Q1) as the common referent category. SNPlcar analyses were corrected for multiple testing by reducing type I error to α/k (k= 24 is the number of SNP tested for each phenotype). Thus, two-sided P values were presented uncorrected, with a significance level being set at 0·05/24 = 0·002.

Results

According to Table 1, the selected participants with complete genetic data were generally older, but had a few missing data on most sociodemographic and lifestyle variables compared with those without genetic data. All LSCRS (in their continuous form) were weakly to moderately correlated (R − 0·50 for lutein+zeaxanthin v. β-cryptoxanthin to +0·044 for lutein+zeaxanthin v. lycopene). Thus, it was possible to covary these gene scores in multiple logistic regression models. For descriptive purposes, mean dietary intakes of carotenoids (μg/4184 kJ per d (μg/1000 kcal per d)) are presented in online supplementary Fig. S2, stratified by sex and poverty income ratio categories. Comparisons were made between the categories, and key findings included a higher intake of β-carotene and lutein+zeaxanthin among women. However, the LTCRS was not correlated with total carotenoid intake (μg/4184 kJ per d (μg/1000 kcal per d)) (R − 0·03, P= 0·35; see online supplementary Fig. S3).

Table 1 Characteristics of the participants of the Healthy Aging in Neighborhood of Diversity across the Life Span (HANDLS) study by genetic data completeness (Number of participants and percentages; mean values and standard deviations)

HS, high school; CES-D, Center for Epidemiologic Studies-Depression; SBP, systolic blood pressure; DBP, diastolic blood pressure; HDL-C, HDL-cholesterol; NCEP ATP III, National Cholesterol Education Program Adult Treatment Panel III; MetS, metabolic syndrome; HOMA-IR, homeostatic model assessment-insulin resistance; CRP, C-reactive protein.

* P< 0·05 for null hypothesis of no difference by genetic data completeness (t or χ2 test).

Outliers with values of CRP >50 (n 12) were removed from this sample.

While examining each of the twenty-four SNPlcar in a separate model as a predictor for each of the outcomes of interest, controlling for key potential confounders (see Table 2 and online supplementary Table S3), a few associations emerged that were against the hypothesised direction. On the one hand, these included a putative inverse relationship between SNPlcar17(BCMO1,β-carotene) and obesity; SNPlcar2(APOB,β-carotene) and several phenotypes, indicative of inflammation (CRP), dyslipidaemia (low HDL-C) and, importantly, NCEP ATP III MetS; SNPlcar10(BCMO1,β-cryptoxanthin) and hypertension; SNPlcar19(CD36,lutein+zeaxanthin) and TAG dyslipidaemia; and SNPlcar23(LPL,α-carotene) and elevated HOMA-IR. On the other hand, a number of SNPlcar showed positive associations that were in line with the hypothesis, mainly within the BCMO1 locus. These included SNPlcar14(BCMO1,β-cryptoxanthin) and EDS; SNPlcar12(BCMO1,α-carotene) and central obesity; and SNPlcar14(BCMO1, β-carotene)/SNPlcar16(BCMO1,β-carotene) and hypertension. However, none of the key findings survived Bonferroni correction.

Table 2 Gene SNP related to low carotenoid status (SNPlcar) and their associations with selected binary metabolic outcomes (obesity, National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) metabolic syndrome (MetS)) and elevated depressive symptoms (EDS) among African-American adults assessed by multiple logistic regression analysis* (Odds ratios and 95 % confidence intervals)

ABCG5, ATP-binding cassette, subfamily G, member 5; BCMO1, β-carotene mono-oxygenase 1; CD36, thrombospondin receptor; LIPC, hepatic lipase; FABP2, fatty acid-binding protein 2; LPL, lipoprotein lipase; SCARB1, scavenger receptor class B member 1; CES-D, Center for Epidemiologic Studies-Depression.

* Each SNPlcar was entered in a separate multiple logistic regression model as the main predictor. SNP allele dosage was coded as is or reverse coded (0,1,2 or 2,1,0) depending on whether the minor allele was associated with lower carotenoid status or vice versa (for details, see online supplementary Table S1). Covariates entered as potential confounders included sex, age, poverty income ratio ( < 125 v. ≥ 125 %), education (below high school, high school or above high school), marital status (current, former, never or missing), smoking status (current, former, never or missing), drug use (current, past, never or missing) and ten principal components to adjust for population structure.

When combining SNPlcar into gene risk scores reflecting lower levels of specific carotenoids (i.e. LSCRS) and examining their associations with multiple outcomes (Table 3), several findings emerged. First, the α-carotene LSCRS was associated with a lower odds of HDL-C dyslipidaemia (Q4 (highest quartile) v. Q1 (lowest quartile): OR 0·65, 95 % CI 0·44, 0·97; P= 0·037, P for trend = 0·045), with a similar pattern being observed for the β-cryptoxanthin LSCRS (Q4 v. Q1: OR 0·61, 95 % CI 0·38, 0·96; P= 0·033, P for trend = 0·039). In contrast, this same LSCRS was associated with a higher odds of EDS (Q4 v. Q1: OR 1·83, 95 % CI 1·07, 3·12; P= 0·026, P for trend = 0·047).

Table 3 Low specific-carotenoid risk scores (LSCRS, quartiles (Q)) and their associations with selected binary metabolic outcomes (central obesity, National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) metabolic syndrome (MetS) and its components, elevated homeostatic model assessment-insulin resistance (HOMA-IR), elevated C-reactive protein (CRP) and hyperuricaemia) and elevated depressive symptoms (EDS) among African-American adults assessed by multiple logistic regression analysis* (Odds ratios and 95 % confidence intervals)

HDL-C, HDL-cholesterol; CES-D, Center for Epidemiologic Studies-Depression.

* All LSCRS were entered in the same multiple logistic regression model (as quartiles, with the first quartile being the reference category) as main predictors, to assess their net association with each of the metabolic outcomes and with EDS. Covariates entered as potential confounders were sex, age, poverty income ratio ( < 125 v. ≥ 125 %), education (below high school, high school or above high school), marital status (current, former, never or missing), smoking status (current, former, never or missing), drug use (current, past, never or missing) and ten principal components to adjust for population structure.

Moreover, a number of non-linear associations were also noted whereby a LSCRS was either inversely or positively associated with an outcome of interest when comparing one quartile with Q1, but not with others. For instance, a lower lutein+zeaxanthin gene risk score was associated with a lower odds of TAG dyslipidaemia only when comparing Q2 with Q1 (OR 0·51, 95 % CI 0·31, 0·83; P= 0·007). Thus, only the middle part of the distribution for lower lutein+zeaxanthin status was linked to the reduced odds of this type of dyslipidaemia, whereas the remaining part of the distribution (Q3 and Q4) showed a comparable odds of this outcome with Q1. Similarly, a lower odds of EDS was found when comparing Q2 with Q1 of the low α-carotene gene score, but not with others. In contrast, a gene score reflecting a low lycopene level was associated with a higher risk of central obesity only when comparing Q2 with Q1 (OR 2·81, 95 % CI 1·09, 7·26; P= 0·033), with the association weakening with each higher quartile comparison. Importantly, the lutein+zeaxanthin LSCRS was inversely related to the odds of having NCEP ATP III MetS, though only for Q2 and Q3 v. Q1, without a significant linear trend being observed.

As detailed in Table 4, the associations of the LTCRS with metabolic outcomes and EDS were assessed by a series of multiple logistic regression, using quartiles of the risk score as the main predictor and testing for linear trend in the association. Among the key findings, an inverse and linear association between the LTCRS and HDL-C dyslipidaemia indicated that a gene score associated with low carotenoid status was potentially protective against this outcome (Q4 v. Q1: OR 0·67, 95 % CI 0·45, 0·99; P= 0·046, P for trend = 0·046). Similarly, a non-linear association was found for elevated CRP (Q2 v. Q1: OR 0·63, 95 % CI 0·43, 0·91; P= 0·015).

Table 4 Low total-carotenoid risk scores (LTCRS, quartiles (Q)) and their associations with selected binary metabolic outcomes (central obesity, National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) metabolic syndrome (MetS) and its components, elevated homeostatic model assessment, insulin resistance (HOMA-IR), elevated C-reactive protein (CRP) and hyperuricaemia) and elevated depressive symptoms (EDS) among African-American adults assessed by multiple logistic regression analysis* (Odds ratios and 95 % confidence intervals)

HDL-C, HDL-cholesterol; CES-D, Center for Epidemiologic Studies-Depression.

* The LTCRS was entered in the multiple logistic regression model (as quartiles, with the first quartile being the reference category) as main predictors, to assess their net association with each of the metabolic outcomes and with EDS. Covariates entered as potential confounders were sex, age, poverty income ratio ( < 125 v. ≥ 125 %), education (below high school, high school or above high school), marital status (current, former, never or missing), smoking status (current, former, never or missing), drug use (current, past, never or missing) and ten principal components to adjust for population structure.

Discussion

In the present study, we examined the associations of gene polymorphisms related to low carotenoid status with various metabolic outcomes and EDS in an urban, socio-economically diverse sample of African-American adults. None of the key findings for SNP analyses survived correction for multiple testing. However, an inverse association was found between the LTCRS and HDL-C dyslipidaemia. The β-cryptoxanthin LSCRS was associated with a lower odds of HDL-C dyslipidaemia, but a higher odds of EDS.

Previous studies that examined SNP used in our SNPlcar focused on dyslipidaemia, type 2 diabetes, obesity and the MetS. Particularly, SNPlcar1(ABCG5,rs6720173:C/G,lutein+zeaxanthin) ( Reference Herron, McGrane and Waters 21 , Reference Borel 22 ) has been found to be unrelated to HDL-C dyslipidaemia or other lipids based on a study of Puerto Rican adults, while other associations were found for various other SNP studied on that gene locus( Reference Junyent, Tucker and Smith 45 ). The main function of ABCG5 (ATP-binding cassette, subfamily G, member 5) is to translocate various hydrophobic substrates including carotenoids and cholesterol across extra- and intracellular membranes( Reference Herron, McGrane and Waters 21 ).

Moreover, only a few studies directly examined the associations of SNPlcar2(ApoB-516,β-carotene) ( Reference Borel 22 , Reference Borel, Moussa and Reboul 23 ) with lipid profile and other metabolic disturbances. In one study( Reference Wojczynski, Gao and Borecki 46 ), while another ApoB SNP (rs676210) has been reported to be associated with the lowering of TAG, SNPlcar2 (rs934197:T/C) has not been found to be associated, a finding replicated by at least one other study( Reference Perez-Martinez, Perez-Jimenez and Ordovas 47 ). However, two recent studies have detected an association of the ‘T’ allele dosage of that SNP with a higher postprandial TAG level( Reference Perez-Martinez, Perez-Jimenez and Ordovas 48 ) and increased insulin resistance( Reference Perez-Martinez, Perez-Jimenez and Ordovas 49 ). In the present study, before correction for multiple testing (see Table 2 and online supplementary Table S2), the ApoB-516 ‘C’ allele dosage (SNPlcar2(ApoB)) yielded an inverse association with the MetS (OR 0·72, 95 % CI 0·52, 1·00; P= 0·048) and elevated CRP (OR 0·70, 95 % CI 0·51, 0·95; P= 0·022), which is consistent with previous studies. However, this finding was against the hypothesised direction that genetic polymorphisms linked to lower carotenoid status would be related to a higher odds of metabolic outcomes and EDS. ApoB is essential for the assembly and secretion of chylomicra and/or VLDL in the small intestine and the liver. It is also the main apo of LDL-C, a major carrier of carotenoids and TAG-rich lipoproteins( Reference Hammoud, Gastaldi and Maillot 50 ).

The main function of ApoA-IV is lipid absorption and modifying lipoprotein size( Reference Weinberg, Gallagher and Fabritius 51 ). Although no associations were detected in the present study with SNPlcar3 (ApoA-IV, rs675:A/T), previously linked to lower serum lycopene( Reference Borel 22 , Reference Borel, Moussa and Reboul 23 ), other studies have shown that this SNP was associated with the ability of fenofibrate to lower TAG levels among non-MetS patients( Reference Feitosa, An and Ordovas 52 ).

Moreover, in that same study( Reference Feitosa, An and Ordovas 52 ), one of the ApoE SNP included in SNPlcar4 (rs429358:C/T) was associated with increased LDL-C levels after fenofibrate treatment in the MetS group. ApoE2 has been reported to have established atheroprotective properties based on previous studies (e.g. Morabia et al. ( Reference Morabia, Cayanis and Costanza 53 )). However, we did not detect significant associations between SNPlcar4(ApoE) and any of the outcomes studied.

BCMO1 and BCDO2 are involved in symmetric and asymmetric carotenoid cleavage, respectively, and convert β-carotene and apocarotenals to retinal, thus influencing the circulatory levels of carotenoids( Reference Ziouzenkova, Orasanu and Sukhova 54 ). For two of the most highly studied SNP in the BCMO1 gene (SNPlcar5: rs6564851:G/T and SNPlcar6: rs6564851:T/G), with SNPlcar5(BCMO1,lutein+zeaxanthin) and SNPlcar6(BCMO1,β-cryptoxanthin), no relationship was observed with metabolic outcomes or EDS, in accordance with a meta-analysis suggesting that the loss of BCMO1 function was unrelated to a higher risk of type 2 diabetes( Reference Perry, Ferrucci and Bandinelli 55 ). Moreover, in a recent French-Canadian study( Reference Dastani, Pajukanta and Marcil 56 ), it has been demonstrated that SNPlcar7(BCMO1,β-carotene) and SNPlcar13(BCMO1,β-carotene) ( Reference Hendrickson, Hazra and Chen 28 ) had no association with a lower HDL-C level. No other SNPlcar on the BCMO1 gene locus have previously been studied in relation to metabolic disturbance or depressive symptoms. In the present study, although none of the associations remained significant after correction for multiple testing, among the notable associations before that correction, SNPlcar14(BCMO1,β-cryptoxanthin) ( Reference Hendrickson, Hazra and Chen 28 ) was associated with a higher odds of EDS (OR 2·05, 95 % CI 1·27, 3·31; P= 0·003; Table 2). Other associations detected were either in the expected direction (SNPlcar12(BCMO1,α-carotene) and central obesity; SNPlcar14(BCMO1,β-cryptoxanthin) and SNPlcar16(BCMO1,β-carotene) with hypertension) or against the hypothesised direction (SNPlcar17(BCMO1,β-carotene) and obesity; SNPlcar10(BCMO1,β-cryptoxanthin) and hypertension). Thus, further larger studies are needed to reconcile those inconsistent findings within that gene locus.

SNPlcar19(CD36,lutein+zeaxanthin) (thrombospondin receptor gene (rs13230419)) has been shown to increase the odds of the MetS by 29–40 % in the African-American population( Reference Love-Gregory, Sherva and Sun 57 ). CD36 codes for a membrane protein that facilitates the uptake and utilisation of fatty acids in key metabolic tissues. The present study found a similar putative effect, though there was only a significant or marginally significant association with the MetS before correction for multiple testing (OR 1·41, 95 % CI 0·99, 2·00; P= 0·06) and TAG dyslipidaemia (OR 0·66, 95 % CI 0·46, 0·94; P= 0·021). In contrast, the present study did not find any significant associations with SNPlcar18. SNPlcar18(CD36,low lutein+zeaxanthin with more ‘A’ alleles) ( Reference Borel 22 , Reference Borel, de Edelenyi and Vincent-Baudry 58 ) was related to the MetS in one previous case–control study of Egyptian adults, with the ‘G’ allele being more prevalent in cases (n 100) than in controls (n 100)( Reference Bayoumy, El-Shabrawi and Hassan 59 ). A similar finding was observed in another study of 317 African-American adults, in which the ‘A’ allele of CD36 (rs1761667:A/G) was associated with greater perceived creaminess regardless of the fat content of salad dressings (P< 0·01) and a higher mean acceptance of added fats and oils (P= 0·02) without a significant association with the obesity phenotype( Reference Keller, Liang and Sakimura 60 ).

Hepatic lipase (LIPC) hydrolyses TAG and phospholipids from HDL, intermediate-density lipoproteins and LDL, transforming them into smaller and denser particles, and promoting the cellular uptake of HDL-C( Reference Borel, Moussa and Reboul 61 ). For the two LIPC gene SNPlcar (both rs1800588:T/C), SNPlcar20 (TT v. others) has been previously shown to be associated with a low α-carotene level, while SNPlcar21 (C allele) has been linked to a low β-carotene level( Reference Borel 22 , Reference Borel, Moussa and Reboul 61 ). A study conducted among a large cohort of Chinese adults (n 4194) has shown that the ‘T’ allele was linked to a higher HDL-C level than the ‘C’ allele (P< 0·0001)( Reference Liu, Zhou and Zhang 62 ). The same pattern has been found in a large cohort study of Caucasian adults (n 4662), with the ‘C’ allele being associated with HDL-C dyslipidaemia (P< 0·0001)( Reference Lu, Dolle and Imholz 63 ). The present study failed to detect an association between LIPC SNPlcar and various outcomes of interest.

Fatty acid-binding protein 2 (FABP2)-related SNPlcar22 (rs1799883:A/G, GG v. others) was previously associated with a lower serum lycopene level( Reference Borel 22 , Reference Falush, Stephens and Pritchard 29 ). In a study of 315 elderly subjects with the MetS who were of European descent, the ‘G’ allele was linked to lower TAG and higher HDL-C levels (P< 0·05), indicative of lower risk for dyslipidaemia of both types( Reference Turkovic, Pizent and Dodig 64 ). There were no notable associations between this polymorphism and any of the outcomes of interest investigated in the present study. FABP2 is an intracellular protein expressed only in the intestine, which is involved in the absorption and intracellular transport of dietary long-chain fatty acids and carotenoids to their specific metabolic targets( Reference Borel, Moussa and Reboul 61 ).

For lipoprotein lipase (LPL)-related SNPlcar23 (rs328:G/C; GG v. CC, mainly low α-carotene level( Reference Borel 22 , Reference Herbeth, Gueguen and Leroy 30 )), two previous studies conducted among Caucasian adults also indicated that the ‘C’ allele was consistently linked to HDL-C dyslipidaemia( Reference Lu, Dolle and Imholz 63 , Reference Webster, Warrington and Weedon 65 ), with one of them observing an additional link to TAG dyslipidaemia( Reference Webster, Warrington and Weedon 65 ). However, a recent meta-analysis showed only a modest relationship between rs328:G/C and both types of dyslipidaemia( Reference Sagoo, Tatt and Salanti 66 ). Before correction for multiple testing, the present study was indicative of a consistent relationship in which the ‘G’ allele was associated with a lower odds of elevated HOMA-IR (OR 0·66, 95 % CI 0·44, 0·98; P= 0·037; see online supplementary Table S3). However, this SNPlcar was not found to be associated with any type of dyslipidaemia in the present study. LPL catalyses the hydrolysis of the TAG component of circulating chylomicrons and VLDL, in tissues other than the liver, and indirectly affects the concentration of carotenoids( Reference Herbeth, Gueguen and Leroy 30 ).

SCARB1 SNPlcar24 (SR-BI exon 1, rs61932577:A/G; GG v. others), previously linked to a lower level of β-cryptoxanthin( Reference Borel 22 , Reference Borel, Moussa and Reboul 23 ), was also studied in relation to lipid profiles among adults. SRBI has been shown to play a role in the metabolism of ApoB-containing lipoproteins in animal models and human subjects. In fact, SRBI constitutes a back-up pathway to the usual LDL receptor-mediated pathways for the catabolism of these lipoproteins. This is particularly relevant to adults with high ApoB-containing lipoproteins, commonly occurring in patients with familial hypercholesterolaemia( Reference Tai, Adiconis and Ordovas 67 ). Before correction for multiple testing, the present study found that SNPlcar24(SCARB1) (i.e. higher ‘G’ allele dosage) was linked to a higher odds of obesity, but no association was found with HDL-C or TAG dyslipidaemia. The associations of SCARB1 with HDL-C and TAG dyslipidaemia were investigated previously, with a higher dosage of the ‘A’ allele being related to higher HDL-C and lower LDL-C values in men, but not in women( Reference Acton, Osgood and Donoghue 68 ). This finding was replicated in a large study of US Caucasians (Framingham study: 2463 non-diabetic and 187 diabetic), in which diabetic subjects with the less common allele (allele A) had lower lipid concentrations, particularly LDL-C( Reference Osgood, Corella and Demissie 69 ). Those two studies had a consistent pattern of association found in the present study, though with different outcomes. However, two other studies found no associations of this SNPlcar with various lipid parameters( Reference Morabia, Cayanis and Costanza 53 , Reference McCarthy, Lehner and Reeves 70 ). Inconsistent with the pattern of findings from the present study and those of others, a study of seventy-seven subjects who were heterozygous for familial hypercholesterolaemia has found that the ‘A’ allele dosage of this SNP was associated with higher TAG( Reference Tai, Adiconis and Ordovas 67 ).

To our knowledge, the present study is the first to systematically examine genetic polymorphisms previously shown to be associated with the lower levels of serum carotenoids in relation to metabolic disturbance and depressive symptoms in an urban population of African-American adults, and to construct gene scores for that purpose. Despite its strengths, some limitations include a statistical power-limiting small sample size. Moreover, most GWAS yielding our SNPlcar short list came from studies of subjects of European ancestry. Finally, data on serum carotenoid concentrations were lacking, which prevented the direct assessment of SNPlcar associations with respective carotenoids and comparisons with previous studies of European ancestry subjects. Additionally, such data availability would have allowed the use of gene score weights depending on effect sizes of each SNPlcar on various carotenoids. Finally, in few gene loci included in of gene score computations, a SNP was related to multiple carotenoids, specifically BCMO1. However, to bypass this issue, the gene scores were made mutually exclusive by including only the most significant carotenoid for each of the carotenoid-specific gene score.

In conclusion, gene polymorphisms linked to low serum carotenoid status had mixed effects on metabolic disturbance and depression. Specifically, our findings do not support that gene polymorphisms associated with low carotenoid status will necessarily lead to a poorer metabolic and depressive symptom outcome. In fact, in most cases, the opposite trend was found, with the possible exception of the β-cryptoxanthin risk score and EDS. Therefore, there is a major discrepancy between what was found in studies linking serum carotenoids to metabolic disturbance and depressive symptoms and the present study that used gene polymorphisms linked to low carotenoid status as the main exposure. It is possible that different carotenoids may interact either synergistically or antagonistically with each other to affect these outcomes. Thus, similar studies on larger African-American samples are needed to test gene–gene (epistasis) interactions between these carotenoid-related gene polymorphisms.

Supplementary material

To view supplementary material for this article, please visit http://dx.doi.org/10.1017/S0007114514001706

Acknowledgements

The authors thank Dr Lori L. Beason-Held (NIA/NIH/IRP) for internally reviewing the manuscript and Dr Toshiko Tanaka (NIA/NIH/IRP) for additional help with the revision.

The present study was fully supported by the Intramural Research Program of the NIH, National Institute on Aging.

The contributions of the authors are as follows: M. A. B. had full access to the data, completed all the statistical analyses, wrote and revised the manuscript, planned the analysis, performed the data management and statistical analysis, and had primary responsibility for the final content; M. A. N. wrote and revised parts of the manuscript, participated in literature review, participated in data acquisition, plan of the analysis and statistical analysis; J. A. C. wrote and revised parts of the manuscript, and participated in literature review and plan of the analysis; M. K. E. wrote and revised parts of the manuscript and participated in data acquisition; A. B. Z. wrote and revised parts of the manuscript, and participated in data acquisition and plan of analysis. All authors read and approved the final version of the manuscript.

None of the authors has declared any conflict of interest.

References

1 Soory, M (2009) Relevance of nutritional antioxidants in metabolic syndrome, ageing and cancer: potential for therapeutic targeting. Infect Disord Drug Targets 9, 400414.CrossRefGoogle ScholarPubMed
2 Rao, AV & Rao, LG (2007) Carotenoids and human health. Pharmacol Res 55, 207216.CrossRefGoogle ScholarPubMed
3 Voutilainen, S, Nurmi, T, Mursu, J, et al. (2006) Carotenoids and cardiovascular health. Am J Clin Nutr 83, 12651271.CrossRefGoogle ScholarPubMed
4 Montonen, J, Knekt, P, Jarvinen, R, et al. (2004) Dietary antioxidant intake and risk of type 2 diabetes. Diabetes Care 27, 362366.CrossRefGoogle ScholarPubMed
5 Coyne, T, Ibiebele, TI, Baade, PD, et al. (2005) Diabetes mellitus and serum carotenoids: findings of a population-based study in Queensland, Australia. Am J Clin Nutr 82, 685693.CrossRefGoogle ScholarPubMed
6 Ford, ES, Will, JC, Bowman, BA, et al. (1999) Diabetes mellitus and serum carotenoids: findings from the Third National Health and Nutrition Examination Survey. Am J Epidemiol 149, 168176.CrossRefGoogle ScholarPubMed
7 Hozawa, A, Jacobs, DR Jr, Steffes, MW, et al. (2006) Associations of serum carotenoid concentrations with the development of diabetes and with insulin concentration: interaction with smoking: the Coronary Artery Risk Development in Young Adults (CARDIA) Study. Am J Epidemiol 163, 929937.CrossRefGoogle ScholarPubMed
8 Reunanen, A, Knekt, P, Aaran, RK, et al. (1998) Serum antioxidants and risk of non-insulin dependent diabetes mellitus. Eur J Clin Nutr 52, 8993.CrossRefGoogle ScholarPubMed
9 Ford, ES, Mokdad, AH, Giles, WH, et al. (2003) The metabolic syndrome and antioxidant concentrations: findings from the Third National Health and Nutrition Examination Survey. Diabetes 52, 23462352.CrossRefGoogle ScholarPubMed
10 Beydoun, MA, Shroff, MR, Chen, X, et al. (2011) Serum antioxidant status is associated with metabolic syndrome among U.S. adults in recent national surveys. J Nutr 141, 903913.CrossRefGoogle ScholarPubMed
11 Beydoun, MA, Canas, JA, Beydoun, HA, et al. (2012) Serum antioxidant concentrations and metabolic syndrome are associated among U.S. adolescents in recent national surveys. J Nutr 142, 16931704.CrossRefGoogle ScholarPubMed
12 Beydoun, MA, Beydoun, HA, Boueiz, A, et al. (2013) Antioxidant status and its association with elevated depressive symptoms among US adults: National Health and Nutrition Examination Surveys 2005–6. Br J Nutr 109, 17141729.CrossRefGoogle ScholarPubMed
13 Milaneschi, Y, Bandinelli, S, Penninx, BW, et al. (2012) The relationship between plasma carotenoids and depressive symptoms in older persons. World J Biol Psychiatry 13, 588598.CrossRefGoogle ScholarPubMed
14 Kataja-Tuomola, M, Sundell, JR, Mannisto, S, et al. (2008) Effect of alpha-tocopherol and beta-carotene supplementation on the incidence of type 2 diabetes. Diabetologia 51, 4753.CrossRefGoogle ScholarPubMed
15 Liu, S, Ajani, U, Chae, C, et al. (1999) Long-term beta-carotene supplementation and risk of type 2 diabetes mellitus: a randomized controlled trial. JAMA 282, 10731075.CrossRefGoogle ScholarPubMed
16 Wang, L, Liu, S, Pradhan, AD, et al. (2006) Plasma lycopene, other carotenoids, and the risk of type 2 diabetes in women. Am J Epidemiol 164, 576585.CrossRefGoogle ScholarPubMed
17 Wang, L, Liu, S, Manson, JE, et al. (2006) The consumption of lycopene and tomato-based food products is not associated with the risk of type 2 diabetes in women. J Nutr 136, 620625.CrossRefGoogle Scholar
18 Beydoun, MA, Kuczmarski, MT, Mason, MA, et al. (2009) Role of depressive symptoms in explaining socioeconomic status disparities in dietary quality and central adiposity among US adults: a structural equation modeling approach. Am J Clin Nutr 90, 10841095.CrossRefGoogle ScholarPubMed
19 Kimura, Y, Matsushita, Y, Nanri, A, et al. (2011) Metabolic syndrome and depressive symptoms among Japanese men and women. Environ Health Prev Med 16, 363368.CrossRefGoogle ScholarPubMed
20 Akbaraly, TN, Ancelin, ML, Jaussent, I, et al. (2011) Metabolic syndrome and onset of depressive symptoms in the elderly: findings from the three-city study. Diabetes Care 34, 904909.CrossRefGoogle ScholarPubMed
21 Herron, KL, McGrane, MM, Waters, D, et al. (2006) The ABCG5 polymorphism contributes to individual responses to dietary cholesterol and carotenoids in eggs. J Nutr 136, 11611165.CrossRefGoogle ScholarPubMed
22 Borel, P (2012) Genetic variations involved in interindividual variability in carotenoid status. Mol Nutr Food Res 56, 228240.CrossRefGoogle ScholarPubMed
23 Borel, P, Moussa, M, Reboul, E, et al. (2007) Human plasma levels of vitamin E and carotenoids are associated with genetic polymorphisms in genes involved in lipid metabolism. J Nutr 137, 26532659.CrossRefGoogle ScholarPubMed
24 Ortega, H, Castilla, P, Gomez-Coronado, D, et al. (2005) Influence of apolipoprotein E genotype on fat-soluble plasma antioxidants in Spanish children. Am J Clin Nutr 81, 624632.Google ScholarPubMed
25 Purcell, S, Neale, B, Todd-Brown, K, et al. (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81, 559575.Google ScholarPubMed
26 Epstein, MP, Duren, WL & Boehnke, M (2000) Improved inference of relationship for pairs of individuals. Am J Hum Genet 67, 12191231.CrossRefGoogle ScholarPubMed
27 Pritchard, JK, Stephens, M & Donnelly, P (2000) Inference of population structure using multilocus genotype data. Genetics 155, 945959.CrossRefGoogle ScholarPubMed
28 Hendrickson, SJ, Hazra, A, Chen, C, et al. (2012) β-Carotene 15,15′-monooxygenase 1 single nucleotide polymorphisms in relation to plasma carotenoid and retinol concentrations in women of European descent. Am J Clin Nutr 96, 13791389.CrossRefGoogle ScholarPubMed
29 Falush, D, Stephens, M & Pritchard, JK (2003) Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics 164, 15671587.CrossRefGoogle ScholarPubMed
30 Herbeth, B, Gueguen, S, Leroy, P, et al. (2007) The lipoprotein lipase serine 447 stop polymorphism is associated with altered serum carotenoid concentrations in the Stanislas Family Study. J Am Coll Nutr 26, 655662.CrossRefGoogle ScholarPubMed
31 Evans, MK, Lepkowski, JM, Powe, NR, et al. (2010) Healthy Aging in Neighborhoods of Diversity across the Life Span (HANDLS): overcoming barriers to implementing a longitudinal, epidemiologic, urban study of health, race, and socioeconomic status. Ethn Dis 20, 267275.Google ScholarPubMed
32 Lietz, G, Oxley, A, Leung, W, et al. (2012) Single nucleotide polymorphisms upstream from the β-carotene 15,15′-monoxygenase gene influence provitamin A conversion efficiency in female volunteers. J Nutr 142, 161S165S.CrossRefGoogle ScholarPubMed
33 Reich, DE, Cargill, M, Bolk, S, et al. (2001) Linkage disequilibrium in the human genome. Nature 411, 199204.CrossRefGoogle ScholarPubMed
34 Grimsby, JL, Porneala, BC, Vassy, JL, et al. (2012) Race-ethnic differences in the association of genetic loci with HbA1c levels and mortality in U.S. adults: the Third National Health and Nutrition Examination Survey (NHANES III). BMC Med Genet 13, 30.CrossRefGoogle ScholarPubMed
35 Wallace, TM, Levy, JC & Matthews, DR (2004) Use and abuse of HOMA modeling. Diabetes Care 27, 14871495.CrossRefGoogle ScholarPubMed
36 Matthews, DR, Hosker, JP, Rudenski, AS, et al. (1985) Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 28, 412419.CrossRefGoogle ScholarPubMed
37 Anonymous (2003) Handbook of Diagnostic Tests, 3rd ed. Philadelphia, PA: Lippincott, Williams & Wilkins.Google Scholar
38 Ridker, PM, Cushman, M, Stampfer, MJ, et al. (1997) Inflammation, aspirin, and the risk of cardiovascular disease in apparently healthy men. N Engl J Med 336, 973979.CrossRefGoogle ScholarPubMed
39 National Institute of Health (NIH), National Heart, Lung, and Blood Institute (NHLBI), North American Association for the Study of Obesity (NAASO) (2000) The Practical Guide: Identification, Evaluation, and Treatment of Overweight and Obesity in Adults, No. 00-4084. Bethesda, MD: NIH..Google Scholar
40 National Institute of Health (NIH) (2001) Third Report of the National Cholesterol Education Program Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). Executive Summary. NIH Publication No. 01-3670 . Bethesda, MD: National Institute of Health.Google Scholar
41 Lezak, MD, Howeison, DB & Loring, DW (2004) Neuropsychological Assessment, 4th ed. New York, NY: Oxford University Press.Google Scholar
42 Radloff, L (1977) The CES-D scale: a self-report depression scale for research in the general population. Appl Psychol Meas 1, 385401.CrossRefGoogle Scholar
43 Nguyen, HT, Kitner-Triolo, M, Evans, MK, et al. (2004) Factorial invariance of the CES-D in low socioeconomic status African Americans compared with a nationally representative sample. Psychiatry Res 126, 177187.CrossRefGoogle ScholarPubMed
44 STATA (2013) Statistics/Data Analysis: Release 13.0. College Station, TX: Stata Corporation.Google Scholar
45 Junyent, M, Tucker, KL, Smith, CE, et al. (2009) The effects of ABCG5/G8 polymorphisms on plasma HDL cholesterol concentrations depend on smoking habit in the Boston Puerto Rican Health Study. J Lipid Res 50, 565573.CrossRefGoogle ScholarPubMed
46 Wojczynski, MK, Gao, G, Borecki, I, et al. (2010) Apolipoprotein B genetic variants modify the response to fenofibrate: a GOLDN study. J Lipid Res 51, 33163323.CrossRefGoogle ScholarPubMed
47 Perez-Martinez, P, Perez-Jimenez, F, Ordovas, JM, et al. (2007) The APOB-516C/T polymorphism has no effect on lipid and apolipoprotein response following changes in dietary fat intake in a healthy population. Nutr Metab Cardiovasc Dis 17, 224229.CrossRefGoogle Scholar
48 Perez-Martinez, P, Perez-Jimenez, F, Ordovas, JM, et al. (2007) Postprandial lipemia is modified by the presence of the APOB-516C/T polymorphism in a healthy Caucasian population. Lipids 42, 143150.CrossRefGoogle Scholar
49 Perez-Martinez, P, Perez-Jimenez, F, Ordovas, JM, et al. (2007) The APOB-516C/T polymorphism is associated with differences in insulin sensitivity in healthy males during the consumption of diets with different fat content. Br J Nutr 97, 622627.CrossRefGoogle ScholarPubMed
50 Hammoud, A, Gastaldi, M, Maillot, M, et al. (2010) APOB-516 T allele homozygous subjects are unresponsive to dietary changes in a three-month primary intervention study targeted to reduce fat intake. Genes Nutr 5, 2937.CrossRefGoogle Scholar
51 Weinberg, RB, Gallagher, JW, Fabritius, MA, et al. (2012) ApoA-IV modulates the secretory trafficking of apoB and the size of triglyceride-rich lipoproteins. J Lipid Res 53, 736743.CrossRefGoogle ScholarPubMed
52 Feitosa, MF, An, P, Ordovas, JM, et al. (2011) Association of gene variants with lipid levels in response to fenofibrate is influenced by metabolic syndrome status. Atherosclerosis 215, 435439.CrossRefGoogle ScholarPubMed
53 Morabia, A, Cayanis, E, Costanza, MC, et al. (2003) Association of extreme blood lipid profile phenotypic variation with 11 reverse cholesterol transport genes and 10 non-genetic cardiovascular disease risk factors. Hum Mol Genet 12, 27332743.CrossRefGoogle ScholarPubMed
54 Ziouzenkova, O, Orasanu, G, Sukhova, G, et al. (2007) Asymmetric cleavage of beta-carotene yields a transcriptional repressor of retinoid X receptor and peroxisome proliferator-activated receptor responses. Mol Endocrinol 21, 7788.CrossRefGoogle ScholarPubMed
55 Perry, JR, Ferrucci, L, Bandinelli, S, et al. (2009) Circulating beta-carotene levels and type 2 diabetes-cause or effect? Diabetologia 52, 21172121.CrossRefGoogle ScholarPubMed
56 Dastani, Z, Pajukanta, P, Marcil, M, et al. (2010) Fine mapping and association studies of a high-density lipoprotein cholesterol linkage region on chromosome 16 in French-Canadian subjects. Eur J Hum Genet 18, 342347.CrossRefGoogle ScholarPubMed
57 Love-Gregory, L, Sherva, R, Sun, L, et al. (2008) Variants in the CD36 gene associate with the metabolic syndrome and high-density lipoprotein cholesterol. Hum Mol Genet 17, 16951704.CrossRefGoogle ScholarPubMed
58 Borel, P, de Edelenyi, FS, Vincent-Baudry, S, et al. (2011) Genetic variants in BCMO1 and CD36 are associated with plasma lutein concentrations and macular pigment optical density in humans. Ann Med 43, 4759.CrossRefGoogle ScholarPubMed
59 Bayoumy, NM, El-Shabrawi, MM & Hassan, HH (2012) Association of cluster of differentiation 36 gene variant rs1761667 (G>A) with metabolic syndrome in Egyptian adults. Saudi Med J 33, 489494.Google ScholarPubMed
60 Keller, KL, Liang, LC, Sakimura, J, et al. (2012) Common variants in the CD36 gene are associated with oral fat perception, fat preferences, and obesity in African Americans. Obesity (Silver Spring) 20, 10661073.CrossRefGoogle ScholarPubMed
61 Borel, P, Moussa, M, Reboul, E, et al. (2009) Human fasting plasma concentrations of vitamin E and carotenoids, and their association with genetic variants in apo C-III, cholesteryl ester transfer protein, hepatic lipase, intestinal fatty acid binding protein and microsomal triacylglycerol transfer protein. Br J Nutr 101, 680687.CrossRefGoogle ScholarPubMed
62 Liu, Y, Zhou, D, Zhang, Z, et al. (2011) Effects of genetic variants on lipid parameters and dyslipidemia in a Chinese population. J Lipid Res 52, 354360.CrossRefGoogle ScholarPubMed
63 Lu, Y, Dolle, ME, Imholz, S, et al. (2008) Multiple genetic variants along candidate pathways influence plasma high-density lipoprotein cholesterol concentrations. J Lipid Res 49, 25822589.CrossRefGoogle ScholarPubMed
64 Turkovic, LF, Pizent, A, Dodig, S, et al. (2012) FABP2 gene polymorphism and metabolic syndrome in elderly people of Croatian descent. Biochem Med (Zagreb) 22, 217224.CrossRefGoogle ScholarPubMed
65 Webster, RJ, Warrington, NM, Weedon, MN, et al. (2009) The association of common genetic variants in the APOA5, LPL and GCK genes with longitudinal changes in metabolic and cardiovascular traits. Diabetologia 52, 106114.CrossRefGoogle ScholarPubMed
66 Sagoo, GS, Tatt, I, Salanti, G, et al. (2008) Seven lipoprotein lipase gene polymorphisms, lipid fractions, and coronary disease: a HuGE association review and meta-analysis. Am J Epidemiol 168, 12331246.CrossRefGoogle ScholarPubMed
67 Tai, ES, Adiconis, X, Ordovas, JM, et al. (2003) Polymorphisms at the SRBI locus are associated with lipoprotein levels in subjects with heterozygous familial hypercholesterolemia. Clin Genet 63, 5358.CrossRefGoogle ScholarPubMed
68 Acton, S, Osgood, D, Donoghue, M, et al. (1999) Association of polymorphisms at the SR-BI gene locus with plasma lipid levels and body mass index in a white population. Arterioscler Thromb Vasc Biol 19, 17341743.CrossRefGoogle Scholar
69 Osgood, D, Corella, D, Demissie, S, et al. (2003) Genetic variation at the scavenger receptor class B type I gene locus determines plasma lipoprotein concentrations and particle size and interacts with type 2 diabetes: The Framingham Study. J Clin Endocrinol Metab 88, 28692879.CrossRefGoogle Scholar
70 McCarthy, JJ, Lehner, T, Reeves, C, et al. (2003) Association of genetic variants in the HDL receptor, SR-B1, with abnormal lipids in women with coronary artery disease. J Med Genet 40, 453458.CrossRefGoogle ScholarPubMed
Figure 0

Table 1 Characteristics of the participants of the Healthy Aging in Neighborhood of Diversity across the Life Span (HANDLS) study by genetic data completeness (Number of participants and percentages; mean values and standard deviations)

Figure 1

Table 2 Gene SNP related to low carotenoid status (SNPlcar) and their associations with selected binary metabolic outcomes (obesity, National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) metabolic syndrome (MetS)) and elevated depressive symptoms (EDS) among African-American adults assessed by multiple logistic regression analysis* (Odds ratios and 95 % confidence intervals)

Figure 2

Table 3 Low specific-carotenoid risk scores (LSCRS, quartiles (Q)) and their associations with selected binary metabolic outcomes (central obesity, National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) metabolic syndrome (MetS) and its components, elevated homeostatic model assessment-insulin resistance (HOMA-IR), elevated C-reactive protein (CRP) and hyperuricaemia) and elevated depressive symptoms (EDS) among African-American adults assessed by multiple logistic regression analysis* (Odds ratios and 95 % confidence intervals)

Figure 3

Table 4 Low total-carotenoid risk scores (LTCRS, quartiles (Q)) and their associations with selected binary metabolic outcomes (central obesity, National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) metabolic syndrome (MetS) and its components, elevated homeostatic model assessment, insulin resistance (HOMA-IR), elevated C-reactive protein (CRP) and hyperuricaemia) and elevated depressive symptoms (EDS) among African-American adults assessed by multiple logistic regression analysis* (Odds ratios and 95 % confidence intervals)

Supplementary material: File

Beydoun Supplementary Material

Table S1

Download Beydoun Supplementary Material(File)
File 183.8 KB
Supplementary material: File

Beydoun Supplementary Material

Table S2

Download Beydoun Supplementary Material(File)
File 34.3 KB
Supplementary material: File

Beydoun Supplementary Material

Table S3

Download Beydoun Supplementary Material(File)
File 399.9 KB
Supplementary material: File

Beydoun Supplementary Material

Figures S1-S3

Download Beydoun Supplementary Material(File)
File 196.1 KB