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Low skin carotenoid concentration measured by resonance Raman spectroscopy is associated with metabolic syndrome in adults
N U T RI TI O N RE S E ARCH 3 4 ( 2 0 14 ) 8 2 1–8 26
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Low skin carotenoid concentration measured by resonance Raman spectroscopy is associated with metabolic syndrome in adults Edward W. Holt a,⁎, Esther K. Wei b , Nancy Bennett c , Laura M. Zhang d a
Department of Transplantation, Division of Hepatology, California Pacific Medical Center, San Francisco, CA Research Institute, California Pacific Medical Center, San Francisco, CA c Department of Nutrition, California Pacific Medical Center, San Francisco, CA d Department of Medicine, California Pacific Medical Center, San Francisco, CA b
ARTI CLE I NFO
A BS TRACT
Article history:
Oxidative stress is increased in patients with metabolic syndrome (MS). Antioxidants,
Received 9 May 2014
including carotenoids, are decreased in MS. We hypothesized that a low skin carotenoid
Revised 21 August 2014
score (SCS), calculated using resonance Raman spectroscopy, would correlate with the
Accepted 29 August 2014
presence of MS. We retrospectively reviewed consecutive patients referred for dietary assessment between 2010 and 2012. For each patient, a nutrition history, medical history,
Keywords:
and SCS were recorded. χ2 and Student t test were used to determine factors associated
Skin carotenoids
with MS. Multivariate logistic regression was used to identify factors associated with MS.
Resonance Raman spectroscopy
One hundred fifty-five patients were included. The mean age was 54.1 ± 13.1 years, and the
Antioxidant
mean body mass index was 28.3 ± 6.1 kg/m2. Metabolic syndrome was present in 43.9% of
Obesity
patients. The mean SCS was 28 084 ± 14 006 Raman counts (RC), including 23 058 ± 9812 RC
Metabolic syndrome
for patients with MS and 32 011 ± 15 514 RC for patients without MS (P = .0001). In a multivariate analysis, SCS less than 25 000 RC (odds ratio, 3.71; 95% confidence interval, 1.36-10.7; P = .01) was independently associated with MS. A higher number of MS components was associated with a progressively lower SCS (P = .004). In a consecutive sample of patients referred for dietary assessment, a noninvasively measured SCS was lower among patients with MS. © 2014 Elsevier Inc. All rights reserved.
1.
Introduction
Metabolic syndrome (MS), defined by obesity, glucose intolerance, dyslipidemia, and hypertension, has been linked to cardiovascular disease, cerebrovascular disease, and liver disease [1–6]. A recent report from the US Centers for Disease Control estimates that 34% of Americans have MS [7].
Oxidative stress, including hyperhomocysteinemia, contributes to vascular injury and other complications of MS [8]. Antioxidants reduce oxidative stress in MS by scavenging reactive oxygen species and preventing tissue injury [9–15]. Carotenoids, found in a wide variety of fruits and vegetables, are an important dietary source of antioxidants. Published data suggest that carotenoids may reduce the risk of
Abbreviations: BMI, body mass index; HDL-C, high-density lipoprotein cholesterol; MS, metabolic syndrome; RC, Raman counts; RRS, resonance Raman spectroscopy; SCS, skin carotenoid score. ⁎ Corresponding author. California Pacific Medical Center, 2340 Clay St, Room 312, San Francisco, CA 94115. Tel.: +1 415 600 1020; fax: +1 415 600 1200. E-mail address: [email protected] (E.W. Holt). http://dx.doi.org/10.1016/j.nutres.2014.08.017 0271-5317/© 2014 Elsevier Inc. All rights reserved.
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cardiovascular disease, cancer, and ocular disease [16,17]. Patients with MS have lower serum levels of antioxidants, including carotenoids. Likewise, a diet enriched with carotenoids is associated with a lower prevalence of MS [18]. Serum carotenoid levels have been used as a biomarker for oxidative stress [19]. However, their measurement requires phlebotomy and is therefore associated with discomfort and expense. Resonance Raman spectroscopy (RRS) allows for noninvasive, accurate, and reproducible measurement of carotenoids in human skin based on a recognizable pattern of scattered monochromatic light produced when carotenoids interacting with laser light [20–22]. Skin carotenoid levels measured by RRS have been shown to correlate with serum carotenoid levels [23–25]. In addition, measurement of skin carotenoids using RRS has been shown to correlate with fruit and vegetable intake [26,27]. Few studies have examined the association between MS and skin carotenoid levels. We hypothesized that a skin carotenoid score (SCS), calculated noninvasively using RRS, would correlate with the presence or absence of MS. We aimed to test the correlation between SCS and MS in patients with a broad spectrum of metabolic risk factors.
2.
Methods and materials
2.1.
Patient enrollment
The study was approved by our center's institutional review board; consent was waived due to the study's retrospective design. We reviewed the records of consecutive adult patients who were referred to our center for dietary assessment between December 2010 and June 2012. Our center is a local and tertiary referral center for patients in San Francisco and across Northern California. Patients in the study were referred to a private practice dietitian by their primary physician; data were collected for clinical purposes only. Laboratory, clinical, and spectroscopic data were retrospectively collected for each patient through medical record review.
2.2.
Resonance Raman spectroscopy
Each patient underwent a single measurement of skin carotenoids from the palm of the hand using the Biophotonic Scanner (Pharmanex; Nu Skin Enterprises, Provo, UT, USA). Patients were instructed to place their palm in front of the scanner's laser for up to 3 minutes while the local carotenoid concentration was measured. Blue laser light from the scanner produces excitation of the major carotenoids found in human skin, which scatter within a narrow range of wavelengths between 440 and 450 nm [28]. Resonance Raman spectroscopy calculation of skin carotenoid concentration is based on the peak absorbance signal generated by excitation of skin carotenoids. The SCS, reported in the nonstandardized unit Raman counts (RC), correlates directly with the concentration of carotenoid molecules in the skin. All measurements were taken by a single experienced operator (N.B.).
2.3.
Metabolic syndrome
A diagnosis of MS was made based on the National Cholesterol Education Program Adult Treatment Panel III [29]. Body mass index (BMI) was used in place of waist circumference, as has been previously reported [30]. Metabolic syndrome was defined as 3 or more of the following at the time of SCS measurement: (1) BMI ≥30 kg/m2, (2) triglycerides ≥150 mg/dL or on medication for hypertriglylceridemia, (3) high-density lipoprotein cholesterol (HDL-C) <40 mg/dL in men or <50 mg/dL in women or on medication for low HDL-C, (4) systolic blood pressure ≥130 mm Hg, diastolic blood pressure ≥85 mm Hg or on antihypertensive medication, (5) fasting glucose ≥100 mg/dL, or on diabetic medications. Criteria for diagnosis were established through medical record review.
2.4.
Nutrition history
A standard nutrition history was obtained during each clinical visit. Body mass index was calculated from measured weight and self-reported height. Patients were asked to quantify their fruit and vegetable intake and report “juicing” (extraction of juice from fruits or vegetables) or the use of nutritional supplements. Alcohol intake, activity level, and the use of tobacco and dietary supplements were self-reported using a standardized questionnaire. The cutoff for fruit and vegetable intake was set at 5 servings/d or greater, based on previously published data [31]. The cutoff for exercise was set at 3 h/wk or greater, the amount above which weight loss is likely to occur [32]. Laboratory values were included if they were obtained within 12 months of SCS measurement.
2.5.
Statistical analyses
χ2 and Student t test were used to determine factors associated with MS. Multivariate logistic regression was used to identify independent predictors of MS [33]. Variables entered into the multivariate analysis included age, sex, SCS, fruit and vegetable intake, and weekly exercise. Body mass index was not included as a predictor variable because it is part of the outcome variable, MS. Skin carotenoid score was tested both as a dichotomous and a continuous variable. When tested as a dichotomous variable, the median value for all patients was used as a cutoff: 25 000 RC. Additional analysis was performed to examine the relationship between SCS and the number of MS components. Differences were considered statistically significant if P < .05.
3.
Results
One hundred sixty-two consecutive patients underwent nutritional evaluation. Six subjects were excluded for insufficient data, and 1 was excluded because of age less than 18 years. One hundred fifty-five patients met the inclusion criteria and were included in the analysis. In the study cohort of 155 patients, 44% were male, 78% were white, 9% were African American, 10% were Asian, 3% were Hispanic, and less than 1% were Native American. The
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Table 1 – Patient characteristics and groups according to MS a Characteristic
All patients (N = 155)
Without MS (n = 87)
With MS (n = 68)
Pb
Age (y) Male White SCS (RC) BMI (kg/m2) Fasting glucose (n = 110; mg/dL) Triglyceride (mg/dL) HDL (mg/dL) LDL (n = 143; mg/dL) Mean alcohol intake (g/d) Creatinine (mg/dL) Albumin (g/dL) Total bilirubin (mg/dL) Aspartate aminotransferase (IU/L) Alanine aminotransferase (IU/L) Alkaline phosphatase (IU/L) Fruit/Vegetable intake (servings/d) Hypertension (%) Hypertriglyceridemia (%) Current smoker (%) Activity >3h/wk (%)
54.1 ± 13.1 68 121 28084 ± 14006 28.3 ± 6.1 93.3 ± 13.4 115.8 ± 66.4 57.0 ± 17.0 113.0 ± 34.3 6.0 ± 9.5 1.0 ± 0.2 4.0 ± 0.5 0.6 ± 0.3 24.7 ± 12.7 39.7 ± 20.8 82.4 ± 28.7 3.9 ± 2.6 45.8 45.5 6.6 9.9
50.5 ± 13.9 31 71 32011 ± 15514 25.7 ± 4.5 88.9 ± 12.1 90.4 ± 38.9 64.4 ± 17.2 121.1 ± 33.6 5.5 ± 6.8 0.9 ± 0.2 4.1 ± 0.3 0.6 ± 0.3 22.8 ± 10.0 36.6 ± 15.4 76.6 ± 21.0 4.2 ± 2.8 17.2 13.0 4.7 14.1
58.8 ± 10.3 37 50 23058 ± 9812 31.7 ± 6.3 100.5 ± 12.4 144.1 ± 78.6 48.6 ± 12.2 103.9 ± 32.9 6.6 ± 12.2 1.0 ± 0.4 3.99 ± 0.6 0.6 ± 0.3 26.9 ± 15.1 43.6 ± 25.6 89.3 ± 34.7 3.5 ± 2.3 82.4 82.4 9.1 4.5
.0001 .02 .23 .0001 <.0001 <.0001 <.0001 <.0001 .0025 .50 .004 .23 .91 .05 .04 .01 .11 <.001 <.001 .33 .06
All values represent means ± SD, unless otherwise indicated. Of the total 155 patients, 44 were male. MS defined as 3 or more of the following: (1) BMI >30 kg/m2, (2) triglycerides ≥150 mg/dL or on medication for hypertriglylceridemia, (3) HDL-C <40 mg/dL in men or <50 mg/dL in women or on medication for low HDL-C, (4) systolic blood pressure ≥130 mm Hg, diastolic blood pressure ≥85 mm Hg or on antihypertensive medication, (5) fasting glucose ≥100 mg/dL or use of diabetic medications. b Refers to the comparison between patients with and without MS. a
mean age (±SD) was 54.1 ± 13.1 years, and the mean BMI was 28.3 ± 6.1 kg/m2. Body mass index ≥30 kg/m2 was present in 33% of patients; 39% had impaired fasting glucose or diabetes mellitus type II and 46% had hypertension. In the study cohort, 68 patients (43.9%) had MS and 87 (56.1%) did not have MS. Additional patient characteristics are listed in Table 1.
3.1.
Factors associated with MS
Compared with patients without MS, those with MS were older, were more frequently male, and had lower SCS (Table 1). For women with and without MS, the mean SCSs were 23 065 ± 9764 RC and 31 930 ± 15 323 RC, respectively. For men with and without MS, the mean SCSs were 23 054 ± 986 RC and 32 484 ±
Table 2 – Odds ratios (ORs) and 95% confidence intervals (95% CIs) for MS by SCS (N = 155)
SCS <25 000 vs ≥25 000 RC Age-adjusted Multivariate model a Per −10 000 RC Age-adjusted model Multivariate model a a
OR
95% CI
P
4.55 3.71
1.75-12.5 1.36-10.7
.002 .01
1.69 1.50
1.23-2.34 1.04-2.17
.001 .03
Adjusted for age (continuous, years), sex, fruit/vegetable intake (continuous, servings/d), and physical activity (>3 vs ≤3 h/wk).
16 097 RC, respectively. Those with MS also had higher alanine aminotransferase (43.6 ± 25.6 IU/L vs 36.6 ± 15.4 IU/L, P = .04) and creatinine (1.0 ± 0.4 mg/dL vs 0.9 ± 0.2 mg/dL, P = .004) compared with those without MS (Table 1). There was no difference in fruit and vegetable intake or activity level between the groups with and without MS. In a multivariate analysis, SCS less than 25 000 RC was associated with MS (odds ratio, 3.71; 95% confidence interval, 1.36-10.7; P = .01; Table 2). In a separate analysis, the number of MS components (0-5 components) correlated with SCS, with lower scores seen among patients with more MS components (Figure).
3.2.
Factors associated with low SCS
Seventy-four patients (47.7%) had SCS less than 25 000 RC. Compared with those with SCS of 25 000 RC or greater, those with low SCS had a higher BMI (30.1 ± 7.1 kg/m2 vs 26.7 ± 4.6 kg/m2, P = .0003), higher serum alanine aminotransferases (43.3 ± 25.3 IU/L vs 36.2 ± 15.1 IU/L, P = .04), lower mean daily servings of fruits and vegetables (3 ± 2.1 servings/d vs 4.7 ± 2.7 servings/d, P < .0001), and higher proportions of MS (61% vs 28%, P = .001) and current smoking (13% vs 1%, P = .007).
4.
Discussion
In a retrospective cohort of 155 patients presenting for outpatient nutritional evaluation, we demonstrated that an elevated concentration of skin carotenoids, measured noninvasively using RRS, correlates with the presence of MS. The
824 Median SCS (Raman Counts)
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40,000 35,000 30,000 25,000 20,000 15,000 10,000 5,000 0 0 (n=34)
1 (n=35)
2 (n=18)
3 (n=31)
4 (n=26)
5 (n=11)
Number of Metabolic Syndrome Components Figure – Median SCS by number of MS components (N = 155). *P = .004 across categories by multivariate ordinal logistic regression including age, sex, fruit/vegetable intake, and physical activity.
technique used to measure serum carotenoids in this study has been previously validated and applied in other clinical contexts [25,34]. Our pilot study demonstrates the clinical relevance of this extraordinarily simple tool among individuals with MS. Few studies have shown such a correlation between low SCS and MS. A growing body of evidence confirms the relationships between dietary carotenoid intake and health outcomes. Donaldson recently proposed a carotenoid health index that established 5 risk categories based on plasma carotenoid concentration [35]. In this meta-analysis of 62 studies, he showed that patients with a plasma carotenoid concentration less than 1 μM have a higher risk of negative health outcomes, including death. Likewise, he found that plasma carotenoid concentration greater than 4 μM was associated with positive health outcomes. He emphasized that antioxidant-rich and carotenoid-rich foods, but not dietary supplements, should be used to improve health outcomes. This conclusion is supported by the findings of Czernichow et al., that risk of MS is associated with baseline dietary patterns but not with long-term antioxidant supplementation [36]. More recently, Hofe et al. showed that serum carotenoid concentration, representing fruit and vegetable intake, predicts risk of type 2 diabetes in US Adults [37]. As expected, we found that foods rich in carotenoids, namely, fruits and vegetables, were associated with a higher SCS. However, our data do not support an association between fruit and vegetable intake and the presence of MS. Carotenoids appear to reflect metabolic and physiologic changes caused by dietary interventions. In a single-arm study, Yeon et al. measured the effect of a diet high in fruits and vegetables in 22 overweight women [38]. In this study, a high-vegetable and fruit diet led to decreases in markers of oxidative stress, including interleukin-6, and increases in serum carotenoid levels. Future studies are needed to determine whether interventions that increase carotenoid levels—in serum or skin—lead directly to improvements in underlying diseases, including diabetes and MS. There is a precedent for using noninvasive measurements to provide a risk assessment for metabolic disease. Nakanishi et al demonstrated an association between brachial-ankle
pulse wave velocity and MS in Japanese men and women [39]. Panayiotou et al confirmed an association between MS and carotid intima media thickness using carotid ultrasound [40]. Others have used ultrasound elastography to risk stratify patients with nonalcoholic fatty liver disease. Roulot et al showed that MS was independently associated with elevated liver stiffness using transient elastography in a cohort of apparently healthy subjects [41]. The measurement of skin carotenoids using RRS requires less training than any of these noninvasive tests and could be widely disseminated across a spectrum of subspecialty care providers. Further validation of this clinical tool use is warranted; in particular, it remains to be seen whether SCS represents a risk of MS not already captured by BMI. To the best of our knowledge, only 1 other study has demonstrated an association between SCS and MS. Zhang et al produced findings similar to ours, showing a correlation between SCS and MS components, including a similar decrease in SCS for each additional component of MS [42]. Importantly, patients with low SCS who were given counseling on diet and exercise returned in 2 to 3 months and had higher SCS, although the magnitude and statistical significance of these changes have yet to be published. Our study replicated the association between SCS and MS and went a step further, demonstrating in a multivariate model that this association persists after adjustment for potential confounders. Our study is limited by several factors. We enrolled patients retrospectively and thus had little control over which predictive factors we could examine. Our data were collected for clinical purposes only, which may have affected our results in a number of ways. For example, only a single measurement of SCS was performed for each patient; however, there is recent evidence that a single measurement correlates well with multiple measurements of skin carotenoids using RRS (Κ = 0.80) [43]. We could not control for potential referral bias among our patients. In addition, there were missing values for some patients. The nutrition history was taken with a standardized questionnaire but not with a validated instrument. Our sample size was relatively small, which likely limited our ability to show a potential association between fruit and vegetable intake and the presence of MS.
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Future studies that can better measure longer-term dietary habits and control for dietary confounders will be important to establish the usefulness of this method. Despite these limitations, this proof-of-concept study provides strong evidence of the association between skin carotenoids and MS. Our preliminary findings suggest that RRS could be a new and useful tool in managing patients with MS. Based on our findings, we accept our original hypothesis that skin carotenoid concentration, measured by SCS, correlates with the presence or absence of MS. It remains to be seen whether this tool is sufficiently sensitive to detect the systemic inflammation and oxidative stress that accompany insulin resistance, or if SCS is merely a noninvasive marker of adiposity.
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Acknowledgment No internal or extramural funding or grant support was used to conduct this research. None of the authors have any relationships to disclose.
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Available online at www.sciencedirect.com
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Low skin carotenoid concentration measured by resonance Raman spectroscopy is associated with metabolic syndrome in adults Edward W. Holt a,⁎, Esther K. Wei b , Nancy Bennett c , Laura M. Zhang d a
Department of Transplantation, Division of Hepatology, California Pacific Medical Center, San Francisco, CA Research Institute, California Pacific Medical Center, San Francisco, CA c Department of Nutrition, California Pacific Medical Center, San Francisco, CA d Department of Medicine, California Pacific Medical Center, San Francisco, CA b
ARTI CLE I NFO
A BS TRACT
Article history:
Oxidative stress is increased in patients with metabolic syndrome (MS). Antioxidants,
Received 9 May 2014
including carotenoids, are decreased in MS. We hypothesized that a low skin carotenoid
Revised 21 August 2014
score (SCS), calculated using resonance Raman spectroscopy, would correlate with the
Accepted 29 August 2014
presence of MS. We retrospectively reviewed consecutive patients referred for dietary assessment between 2010 and 2012. For each patient, a nutrition history, medical history,
Keywords:
and SCS were recorded. χ2 and Student t test were used to determine factors associated
Skin carotenoids
with MS. Multivariate logistic regression was used to identify factors associated with MS.
Resonance Raman spectroscopy
One hundred fifty-five patients were included. The mean age was 54.1 ± 13.1 years, and the
Antioxidant
mean body mass index was 28.3 ± 6.1 kg/m2. Metabolic syndrome was present in 43.9% of
Obesity
patients. The mean SCS was 28 084 ± 14 006 Raman counts (RC), including 23 058 ± 9812 RC
Metabolic syndrome
for patients with MS and 32 011 ± 15 514 RC for patients without MS (P = .0001). In a multivariate analysis, SCS less than 25 000 RC (odds ratio, 3.71; 95% confidence interval, 1.36-10.7; P = .01) was independently associated with MS. A higher number of MS components was associated with a progressively lower SCS (P = .004). In a consecutive sample of patients referred for dietary assessment, a noninvasively measured SCS was lower among patients with MS. © 2014 Elsevier Inc. All rights reserved.
1.
Introduction
Metabolic syndrome (MS), defined by obesity, glucose intolerance, dyslipidemia, and hypertension, has been linked to cardiovascular disease, cerebrovascular disease, and liver disease [1–6]. A recent report from the US Centers for Disease Control estimates that 34% of Americans have MS [7].
Oxidative stress, including hyperhomocysteinemia, contributes to vascular injury and other complications of MS [8]. Antioxidants reduce oxidative stress in MS by scavenging reactive oxygen species and preventing tissue injury [9–15]. Carotenoids, found in a wide variety of fruits and vegetables, are an important dietary source of antioxidants. Published data suggest that carotenoids may reduce the risk of
Abbreviations: BMI, body mass index; HDL-C, high-density lipoprotein cholesterol; MS, metabolic syndrome; RC, Raman counts; RRS, resonance Raman spectroscopy; SCS, skin carotenoid score. ⁎ Corresponding author. California Pacific Medical Center, 2340 Clay St, Room 312, San Francisco, CA 94115. Tel.: +1 415 600 1020; fax: +1 415 600 1200. E-mail address: [email protected] (E.W. Holt). http://dx.doi.org/10.1016/j.nutres.2014.08.017 0271-5317/© 2014 Elsevier Inc. All rights reserved.
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cardiovascular disease, cancer, and ocular disease [16,17]. Patients with MS have lower serum levels of antioxidants, including carotenoids. Likewise, a diet enriched with carotenoids is associated with a lower prevalence of MS [18]. Serum carotenoid levels have been used as a biomarker for oxidative stress [19]. However, their measurement requires phlebotomy and is therefore associated with discomfort and expense. Resonance Raman spectroscopy (RRS) allows for noninvasive, accurate, and reproducible measurement of carotenoids in human skin based on a recognizable pattern of scattered monochromatic light produced when carotenoids interacting with laser light [20–22]. Skin carotenoid levels measured by RRS have been shown to correlate with serum carotenoid levels [23–25]. In addition, measurement of skin carotenoids using RRS has been shown to correlate with fruit and vegetable intake [26,27]. Few studies have examined the association between MS and skin carotenoid levels. We hypothesized that a skin carotenoid score (SCS), calculated noninvasively using RRS, would correlate with the presence or absence of MS. We aimed to test the correlation between SCS and MS in patients with a broad spectrum of metabolic risk factors.
2.
Methods and materials
2.1.
Patient enrollment
The study was approved by our center's institutional review board; consent was waived due to the study's retrospective design. We reviewed the records of consecutive adult patients who were referred to our center for dietary assessment between December 2010 and June 2012. Our center is a local and tertiary referral center for patients in San Francisco and across Northern California. Patients in the study were referred to a private practice dietitian by their primary physician; data were collected for clinical purposes only. Laboratory, clinical, and spectroscopic data were retrospectively collected for each patient through medical record review.
2.2.
Resonance Raman spectroscopy
Each patient underwent a single measurement of skin carotenoids from the palm of the hand using the Biophotonic Scanner (Pharmanex; Nu Skin Enterprises, Provo, UT, USA). Patients were instructed to place their palm in front of the scanner's laser for up to 3 minutes while the local carotenoid concentration was measured. Blue laser light from the scanner produces excitation of the major carotenoids found in human skin, which scatter within a narrow range of wavelengths between 440 and 450 nm [28]. Resonance Raman spectroscopy calculation of skin carotenoid concentration is based on the peak absorbance signal generated by excitation of skin carotenoids. The SCS, reported in the nonstandardized unit Raman counts (RC), correlates directly with the concentration of carotenoid molecules in the skin. All measurements were taken by a single experienced operator (N.B.).
2.3.
Metabolic syndrome
A diagnosis of MS was made based on the National Cholesterol Education Program Adult Treatment Panel III [29]. Body mass index (BMI) was used in place of waist circumference, as has been previously reported [30]. Metabolic syndrome was defined as 3 or more of the following at the time of SCS measurement: (1) BMI ≥30 kg/m2, (2) triglycerides ≥150 mg/dL or on medication for hypertriglylceridemia, (3) high-density lipoprotein cholesterol (HDL-C) <40 mg/dL in men or <50 mg/dL in women or on medication for low HDL-C, (4) systolic blood pressure ≥130 mm Hg, diastolic blood pressure ≥85 mm Hg or on antihypertensive medication, (5) fasting glucose ≥100 mg/dL, or on diabetic medications. Criteria for diagnosis were established through medical record review.
2.4.
Nutrition history
A standard nutrition history was obtained during each clinical visit. Body mass index was calculated from measured weight and self-reported height. Patients were asked to quantify their fruit and vegetable intake and report “juicing” (extraction of juice from fruits or vegetables) or the use of nutritional supplements. Alcohol intake, activity level, and the use of tobacco and dietary supplements were self-reported using a standardized questionnaire. The cutoff for fruit and vegetable intake was set at 5 servings/d or greater, based on previously published data [31]. The cutoff for exercise was set at 3 h/wk or greater, the amount above which weight loss is likely to occur [32]. Laboratory values were included if they were obtained within 12 months of SCS measurement.
2.5.
Statistical analyses
χ2 and Student t test were used to determine factors associated with MS. Multivariate logistic regression was used to identify independent predictors of MS [33]. Variables entered into the multivariate analysis included age, sex, SCS, fruit and vegetable intake, and weekly exercise. Body mass index was not included as a predictor variable because it is part of the outcome variable, MS. Skin carotenoid score was tested both as a dichotomous and a continuous variable. When tested as a dichotomous variable, the median value for all patients was used as a cutoff: 25 000 RC. Additional analysis was performed to examine the relationship between SCS and the number of MS components. Differences were considered statistically significant if P < .05.
3.
Results
One hundred sixty-two consecutive patients underwent nutritional evaluation. Six subjects were excluded for insufficient data, and 1 was excluded because of age less than 18 years. One hundred fifty-five patients met the inclusion criteria and were included in the analysis. In the study cohort of 155 patients, 44% were male, 78% were white, 9% were African American, 10% were Asian, 3% were Hispanic, and less than 1% were Native American. The
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Table 1 – Patient characteristics and groups according to MS a Characteristic
All patients (N = 155)
Without MS (n = 87)
With MS (n = 68)
Pb
Age (y) Male White SCS (RC) BMI (kg/m2) Fasting glucose (n = 110; mg/dL) Triglyceride (mg/dL) HDL (mg/dL) LDL (n = 143; mg/dL) Mean alcohol intake (g/d) Creatinine (mg/dL) Albumin (g/dL) Total bilirubin (mg/dL) Aspartate aminotransferase (IU/L) Alanine aminotransferase (IU/L) Alkaline phosphatase (IU/L) Fruit/Vegetable intake (servings/d) Hypertension (%) Hypertriglyceridemia (%) Current smoker (%) Activity >3h/wk (%)
54.1 ± 13.1 68 121 28084 ± 14006 28.3 ± 6.1 93.3 ± 13.4 115.8 ± 66.4 57.0 ± 17.0 113.0 ± 34.3 6.0 ± 9.5 1.0 ± 0.2 4.0 ± 0.5 0.6 ± 0.3 24.7 ± 12.7 39.7 ± 20.8 82.4 ± 28.7 3.9 ± 2.6 45.8 45.5 6.6 9.9
50.5 ± 13.9 31 71 32011 ± 15514 25.7 ± 4.5 88.9 ± 12.1 90.4 ± 38.9 64.4 ± 17.2 121.1 ± 33.6 5.5 ± 6.8 0.9 ± 0.2 4.1 ± 0.3 0.6 ± 0.3 22.8 ± 10.0 36.6 ± 15.4 76.6 ± 21.0 4.2 ± 2.8 17.2 13.0 4.7 14.1
58.8 ± 10.3 37 50 23058 ± 9812 31.7 ± 6.3 100.5 ± 12.4 144.1 ± 78.6 48.6 ± 12.2 103.9 ± 32.9 6.6 ± 12.2 1.0 ± 0.4 3.99 ± 0.6 0.6 ± 0.3 26.9 ± 15.1 43.6 ± 25.6 89.3 ± 34.7 3.5 ± 2.3 82.4 82.4 9.1 4.5
.0001 .02 .23 .0001 <.0001 <.0001 <.0001 <.0001 .0025 .50 .004 .23 .91 .05 .04 .01 .11 <.001 <.001 .33 .06
All values represent means ± SD, unless otherwise indicated. Of the total 155 patients, 44 were male. MS defined as 3 or more of the following: (1) BMI >30 kg/m2, (2) triglycerides ≥150 mg/dL or on medication for hypertriglylceridemia, (3) HDL-C <40 mg/dL in men or <50 mg/dL in women or on medication for low HDL-C, (4) systolic blood pressure ≥130 mm Hg, diastolic blood pressure ≥85 mm Hg or on antihypertensive medication, (5) fasting glucose ≥100 mg/dL or use of diabetic medications. b Refers to the comparison between patients with and without MS. a
mean age (±SD) was 54.1 ± 13.1 years, and the mean BMI was 28.3 ± 6.1 kg/m2. Body mass index ≥30 kg/m2 was present in 33% of patients; 39% had impaired fasting glucose or diabetes mellitus type II and 46% had hypertension. In the study cohort, 68 patients (43.9%) had MS and 87 (56.1%) did not have MS. Additional patient characteristics are listed in Table 1.
3.1.
Factors associated with MS
Compared with patients without MS, those with MS were older, were more frequently male, and had lower SCS (Table 1). For women with and without MS, the mean SCSs were 23 065 ± 9764 RC and 31 930 ± 15 323 RC, respectively. For men with and without MS, the mean SCSs were 23 054 ± 986 RC and 32 484 ±
Table 2 – Odds ratios (ORs) and 95% confidence intervals (95% CIs) for MS by SCS (N = 155)
SCS <25 000 vs ≥25 000 RC Age-adjusted Multivariate model a Per −10 000 RC Age-adjusted model Multivariate model a a
OR
95% CI
P
4.55 3.71
1.75-12.5 1.36-10.7
.002 .01
1.69 1.50
1.23-2.34 1.04-2.17
.001 .03
Adjusted for age (continuous, years), sex, fruit/vegetable intake (continuous, servings/d), and physical activity (>3 vs ≤3 h/wk).
16 097 RC, respectively. Those with MS also had higher alanine aminotransferase (43.6 ± 25.6 IU/L vs 36.6 ± 15.4 IU/L, P = .04) and creatinine (1.0 ± 0.4 mg/dL vs 0.9 ± 0.2 mg/dL, P = .004) compared with those without MS (Table 1). There was no difference in fruit and vegetable intake or activity level between the groups with and without MS. In a multivariate analysis, SCS less than 25 000 RC was associated with MS (odds ratio, 3.71; 95% confidence interval, 1.36-10.7; P = .01; Table 2). In a separate analysis, the number of MS components (0-5 components) correlated with SCS, with lower scores seen among patients with more MS components (Figure).
3.2.
Factors associated with low SCS
Seventy-four patients (47.7%) had SCS less than 25 000 RC. Compared with those with SCS of 25 000 RC or greater, those with low SCS had a higher BMI (30.1 ± 7.1 kg/m2 vs 26.7 ± 4.6 kg/m2, P = .0003), higher serum alanine aminotransferases (43.3 ± 25.3 IU/L vs 36.2 ± 15.1 IU/L, P = .04), lower mean daily servings of fruits and vegetables (3 ± 2.1 servings/d vs 4.7 ± 2.7 servings/d, P < .0001), and higher proportions of MS (61% vs 28%, P = .001) and current smoking (13% vs 1%, P = .007).
4.
Discussion
In a retrospective cohort of 155 patients presenting for outpatient nutritional evaluation, we demonstrated that an elevated concentration of skin carotenoids, measured noninvasively using RRS, correlates with the presence of MS. The
824 Median SCS (Raman Counts)
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40,000 35,000 30,000 25,000 20,000 15,000 10,000 5,000 0 0 (n=34)
1 (n=35)
2 (n=18)
3 (n=31)
4 (n=26)
5 (n=11)
Number of Metabolic Syndrome Components Figure – Median SCS by number of MS components (N = 155). *P = .004 across categories by multivariate ordinal logistic regression including age, sex, fruit/vegetable intake, and physical activity.
technique used to measure serum carotenoids in this study has been previously validated and applied in other clinical contexts [25,34]. Our pilot study demonstrates the clinical relevance of this extraordinarily simple tool among individuals with MS. Few studies have shown such a correlation between low SCS and MS. A growing body of evidence confirms the relationships between dietary carotenoid intake and health outcomes. Donaldson recently proposed a carotenoid health index that established 5 risk categories based on plasma carotenoid concentration [35]. In this meta-analysis of 62 studies, he showed that patients with a plasma carotenoid concentration less than 1 μM have a higher risk of negative health outcomes, including death. Likewise, he found that plasma carotenoid concentration greater than 4 μM was associated with positive health outcomes. He emphasized that antioxidant-rich and carotenoid-rich foods, but not dietary supplements, should be used to improve health outcomes. This conclusion is supported by the findings of Czernichow et al., that risk of MS is associated with baseline dietary patterns but not with long-term antioxidant supplementation [36]. More recently, Hofe et al. showed that serum carotenoid concentration, representing fruit and vegetable intake, predicts risk of type 2 diabetes in US Adults [37]. As expected, we found that foods rich in carotenoids, namely, fruits and vegetables, were associated with a higher SCS. However, our data do not support an association between fruit and vegetable intake and the presence of MS. Carotenoids appear to reflect metabolic and physiologic changes caused by dietary interventions. In a single-arm study, Yeon et al. measured the effect of a diet high in fruits and vegetables in 22 overweight women [38]. In this study, a high-vegetable and fruit diet led to decreases in markers of oxidative stress, including interleukin-6, and increases in serum carotenoid levels. Future studies are needed to determine whether interventions that increase carotenoid levels—in serum or skin—lead directly to improvements in underlying diseases, including diabetes and MS. There is a precedent for using noninvasive measurements to provide a risk assessment for metabolic disease. Nakanishi et al demonstrated an association between brachial-ankle
pulse wave velocity and MS in Japanese men and women [39]. Panayiotou et al confirmed an association between MS and carotid intima media thickness using carotid ultrasound [40]. Others have used ultrasound elastography to risk stratify patients with nonalcoholic fatty liver disease. Roulot et al showed that MS was independently associated with elevated liver stiffness using transient elastography in a cohort of apparently healthy subjects [41]. The measurement of skin carotenoids using RRS requires less training than any of these noninvasive tests and could be widely disseminated across a spectrum of subspecialty care providers. Further validation of this clinical tool use is warranted; in particular, it remains to be seen whether SCS represents a risk of MS not already captured by BMI. To the best of our knowledge, only 1 other study has demonstrated an association between SCS and MS. Zhang et al produced findings similar to ours, showing a correlation between SCS and MS components, including a similar decrease in SCS for each additional component of MS [42]. Importantly, patients with low SCS who were given counseling on diet and exercise returned in 2 to 3 months and had higher SCS, although the magnitude and statistical significance of these changes have yet to be published. Our study replicated the association between SCS and MS and went a step further, demonstrating in a multivariate model that this association persists after adjustment for potential confounders. Our study is limited by several factors. We enrolled patients retrospectively and thus had little control over which predictive factors we could examine. Our data were collected for clinical purposes only, which may have affected our results in a number of ways. For example, only a single measurement of SCS was performed for each patient; however, there is recent evidence that a single measurement correlates well with multiple measurements of skin carotenoids using RRS (Κ = 0.80) [43]. We could not control for potential referral bias among our patients. In addition, there were missing values for some patients. The nutrition history was taken with a standardized questionnaire but not with a validated instrument. Our sample size was relatively small, which likely limited our ability to show a potential association between fruit and vegetable intake and the presence of MS.
N U T RI TI O N RE S E ARCH 3 4 ( 2 0 14 ) 8 2 1–8 26
Future studies that can better measure longer-term dietary habits and control for dietary confounders will be important to establish the usefulness of this method. Despite these limitations, this proof-of-concept study provides strong evidence of the association between skin carotenoids and MS. Our preliminary findings suggest that RRS could be a new and useful tool in managing patients with MS. Based on our findings, we accept our original hypothesis that skin carotenoid concentration, measured by SCS, correlates with the presence or absence of MS. It remains to be seen whether this tool is sufficiently sensitive to detect the systemic inflammation and oxidative stress that accompany insulin resistance, or if SCS is merely a noninvasive marker of adiposity.
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Acknowledgment No internal or extramural funding or grant support was used to conduct this research. None of the authors have any relationships to disclose.
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