Abstract
Background
Higher vegetable intake is being promoted as an initiative to prevent lifestyle-related diseases. Carotenoids are yellow or red pigment components and are widely present in vegetables. Since ingested carotenoids accumulate in the skin, skin carotenoid levels are a quantitative indicator of vegetable intake. Recently, noninvasive optical sensors for assessing skin carotenoid levels were developed. We here examined the association between skin carotenoid scores measured using optical sensors and the presence of metabolic syndrome.
Methods
A total of 1618 individuals (604 men and 1014 women) aged ≥ 40 years (mean age 63.1 years) participated in the study. Skin carotenoid scores were determined using a noninvasive optical sensor based on multiple spatially resolved reflectance spectroscopy. Metabolic syndrome was defined based on the Joint Scientific Statement criteria developed by six international scientific societies. Multivariable-adjusted logistic regression models were used.
Results
The prevalence of metabolic syndrome was 31.3% (n = 506). A remarkably strong association was found between higher skin carotenoid scores and lower prevalence of metabolic syndrome after adjusting for confounders. The multivariable-adjusted odds ratio for the presence of metabolic syndrome in individuals with the highest quartile of skin carotenoid scores was 0.39 (95% confidence interval, 0.28–0.55) compared to those with the lowest quartile.
Conclusions
Our findings suggest that higher skin carotenoid scores measured by non-invasive optimal sensors are significantly associated with a lower likelihood of having metabolic syndrome in the general Japanese population.
Similar content being viewed by others
Introduction
Metabolic syndrome (MetS) is characterized by the clustering of metabolic abnormalities that include central obesity and insulin resistance, dyslipidemia, elevated blood pressure, and impaired glucose tolerance [1]. Although not all research findings are consistent [2], observational studies have found that increasing vegetable intake is effective in preventing various lifestyle-related diseases such as obesity and MetS [3]. Therefore, the assessment of vegetable intake is important in promoting the prevention of obesity and MetS, but it is difficult to determine whether vegetable intake is adequate [4]. There are several dietary survey methods for assessing vegetable intake, such as diet records, diet recall, diet history, the duplicate method, and the food frequency method, but these methods tend to be technically complex and costly to accurately implement [5]. Carotenoids are yellow or red-pigmented compounds widely present in plants, particularly vegetables [6]. Carotenoid levels in serum are known to be useful biomarkers for quantitatively assessing individual levels of vegetable intake [7, 8]. However, such biomarkers require blood sampling for measurement and thus are not suitable for frequent assessment.
A noninvasive optical sensor based on the resonance Raman spectroscopy (RRS) method has been developed for assessing carotenoid levels in skin; this sensor has high precision but is relatively expensive [9,10,11,12]. Recently, two techniques based on the reflective spectrophotometry (RS) method have been developed to measure carotenoid levels through the skin [9,10,11, 13, 14]: the multiple spatially resolved reflection spectroscopy [9, 10, 14] and the pressure-mediated reflection spectroscopy [11, 13]. The carotenoid levels measured by these techniques have been shown to be highly correlated with those estimated by the RRS-based device, as well as highly correlated with serum carotenoid levels [9, 13, 15]. Several epidemiological studies conducted in Western and Asian countries have reported that participants with higher skin carotenoid levels estimated using an RRS-based device are at lower risk of having MetS [16, 17]. However, there is limited epidemiological evidence of an association between skin carotenoid scores estimated by RS-based devices and MetS. Herein, we examined the association between skin carotenoid scores determined using a noninvasive optical sensor with the multiple spatially resolved RS method and the presence of metabolic syndrome in a community-dwelling Japanese population.
Methods
Study population
The present analysis was performed using the data from a cross-sectional survey of the Hisayama Study. The Hisayama Study is an ongoing population-based cohort study established in 1961 in the town of Hisayama, a suburb of the metropolitan area of Kyushu Island in Japan [18]. Since 1961, the age and occupational distributions and the nutrient intake of residents in this town have been similar to those of Japan as a whole based on data from the national census and nutrition survey [19]. Full community surveys of the health status of residents aged 40 and older have been repeated every one to two years since 1961 [20]. In 2019, a total of 2627 residents aged 40 and older underwent a screening examination. Of these, 2619 residents provided written consent to participate in the study. Skin carotenoid score measurement was included as an optional part of the screening examination, and 1743 participants (66.6%) who were interested in the carotenoid score measurement agreed to undergo this part of the screening. Then, after the exclusion of 114 participants in whom errors occurred in the carotenoid score measurement (113 participants for whom no measurement value was displayed in the first carotenoid score measurement and 1 for whom the skin carotenoid score exceeded the upper limit of 12.0) and 11 participants without a fasting blood test, the remaining 1618 participants (604 men and 1014 women) were enrolled in the present study (Fig. 1).
Ethical approval
This study was conducted in accordance with the Declaration of Helsinki and the approval of the Kyushu University Institutional Review Board for Clinical Research (Approval No. 2023–56), and written informed consent was obtained from all the participants. All procedures were carried out in accordance with the relevant ethical guidelines.
Measurement of skin carotenoid scores
To determine the skin carotenoid score, we used a multiple spatially resolved reflection spectroscopy sensor (Biozoom Services GmbH, Kassel, Germany) [9], because this sensor had been validated in a Japanese population at the time this study was conducted [15]. The sensor utilized 118 LED light emitters in 16 steps to provide light within the range of 350–1000 nm, while detecting the reflected light from 152 light-sensitive areas of the skin. To ensure the best possible correlation with the measured RRS values, an algorithm was developed to calculate the rank-order skin carotenoid score, which ranged from 0.1 to 12.0 [14]. Skin carotenoid scores determined by this system were reported to be significantly positively correlated with serum total carotenoid concentrations (r = 0.678) [15]. The skin carotenoid score was measured on the palm side of the thumb base, and the sensor was completely covered so that no stray light could enter. To ensure that the measurement conditions were the same for all participants, the values from the first measurement were used in this analysis.
Diagnosis of metabolic syndrome
The definition of MetS was based on a Joint Scientific Statement issued by six international scientific societies [21]. Specifically, abdominal obesity was defined as a waist circumference ≥ 90 cm in men and ≥ 80 cm in women according to a WHO expert consultation for Asian populations [22]. Elevated blood pressure was defined as average systolic/diastolic blood pressure of ≥ 130/85 mmHg and/or current use of antihypertensive medications. Elevated blood glucose level was defined as fasting blood glucose of ≥ 5.6 mmol/L and/or current use of antidiabetic medication. Hypertriglyceridemia was defined as serum triglycerides of ≥ 1.69 mmol/L. Low high-density lipoprotein (HDL) cholesterolemia was defined as serum HDL cholesterol levels of < 1.03 mmol/L in men and of < 1.29 mmol/L in women. MetS was defined as the presence of 3 or more of these 5 components [21]. In sensitivity analyses, we defined MetS using other established criteria, namely the Japanese criteria [23] and the criteria of the International Diabetes Federation for Asians [24] (Table S1).
Determination of other risk factors
All participants completed a self-administered questionnaire covering medical history, antidiabetic and antihypertensive medications, alcohol intake, smoking habits, and physical activity. Smoking habits and alcohol intake were categorized as current use or not. Regular exercise was defined as engaging in exercise ≥ 3 times per week during leisure time. Body height and weight were measured in light clothing without shoes and the body mass index (kg/m2) was calculated. The waist circumference was measured at the umbilical level in a standing position by a trained staff member. Blood pressure was measured three times using an automated sphygmomanometer with the participant seated after at least 5 min rest and the mean of the three measurements was calculated. Blood samples were collected from an antecubital vein after an overnight fast for the determination of blood glucose levels and lipids. Fasting blood glucose levels were measured by the hexokinase method. Serum concentrations of low-density lipoprotein (LDL) cholesterol, HDL cholesterol, and triglycerides were determined enzymatically.
Statistical analysis
Skin carotenoid scores were divided into four categories based on the sex-specific quartile distribution because skin carotenoid scores were higher in women than in men (Fig. 2). The trends in the mean values or the frequencies of cardiovascular risk factors across the sex-specific quartiles of skin carotenoid scores were tested by linear regression analysis or logistic regression analysis, respectively. For serum triglycerides, the median values and their interquartile ranges for each skin carotenoid score are shown due to the skewed distributions, and their trends across skin carotenoid scores were tested by a Jonckheere–Terpstra trend test. Logistic regression analysis was used to estimate adjusted odds ratios (ORs) with 95% confidence intervals (CIs) of MetS according to the skin carotenoid scores. The linear trends in the risk estimates were tested using a logistic regression model including the skin carotenoid quartiles represented as ordinal numbers (0, 1, 2, and 3) and the relevant covariates. We also used restricted cubic splines to show the shape of these associations with 5 knots placed at the 5th, 25th, 50th, 75th and 95th percentiles of skin carotenoid scores (3.8, 4.9, 5.8, 6.7 and 8.5, respectively) [25]. The 5th percentile was chosen as the reference value. To examine whether there was a need for cubic spline terms in addition to a linear term, the non-linearity for the association was tested by using a likelihood ratio test comparing the relevant model with only a linear term against the model with linear and cubic spline terms [25]. In the subgroup analysis of potential confounding factors, participants were divided into two groups by the sex-specific median values of skin carotenoid score, and the multivariable-adjusted ORs for the presence of MetS in the group with higher skin carotenoid score against the groups with lower scores were calculated for each subgroup. The heterogeneity in the associations across subgroups was tested by adding the multiplicative interaction term in the relevant logistic model. All statistical analyses were performed using SAS 9.4 software (SAS Institute, Cary, NC). In all analyses, two-sided p-values (P < 0.05) were considered to indicate statistical significance.
Results
A histogram of the skin carotenoid scores is shown in Fig. 2. In men, the skin carotenoid scores ranged from 1.4 to 11.2 (median, 5.1), with a mean of 5.3 and standard deviation of 1.3. In women, it ranged from 1.4 to 10.7 (median, 6.1), with a mean of 6.2 and a standard deviation of 1.3. The distribution of the carotenoid score was approximately normal, with skewness of 0.436, 0.785, and 0.365 and kurtosis of 0.610, 1.578, and 0.779 for the total study population (n = 1618), men (n = 604), and women (n = 1014), respectively. The clinical characteristics of participants according to the sex-specific quartiles of skin carotenoid scores are summarized in Table 1. Participants with higher skin carotenoid scores were more likely to be older. Higher skin carotenoid scores were associated with lower mean values of body mass index, waist circumference, diastolic blood pressure, and serum triglycerides, and the proportions of participants with current smoking and current alcohol drinking decreased with increasing skin carotenoid scores, whereas the mean values of serum HDL cholesterol and the proportions of participants with use of lipid-modifying agents and regular exercise increased with increasing skin carotenoid scores. Among the MetS components, the proportions of abdominal obesity and hypertriglyceridemia decreased significantly with higher skin carotenoid scores.
The overall prevalence of MetS (defined by the Joint Scientific Statement criteria) was 31.3% in the analyzed participants. As shown in Table 2, the age- and sex-adjusted OR for the presence of the MetS decreased significantly with increasing skin carotenoid scores (P-trend < 0.001). This significant downward association remained significant after adjusting for age, sex, serum LDL cholesterol, use of lipid-modifying agents, current smoking, current alcohol drinking, and regular exercise (P-trend < 0.001). The multivariable-adjusted OR for the presence of MetS was significantly lower in participants in the highest quartile of skin carotenoid scores than in those in the lowest quartile (OR 0.39; 95% CI, 0.28–0.55). With regard to MetS components, higher skin carotenoid scores were significantly associated with lower multivariable-adjusted ORs for the presence of abdominal obesity (P-trend < 0.001), elevated blood pressure (P-trend < 0.001), elevated blood glucose (P-trend < 0.001) low HDL cholesterolemia (P-trend = 0.048), and hypertriglyceridemia (P-trend = 0.004). When BMI was considered as an adjustment factor for MetS components other than abdominal obesity, the association of skin carotenoid levels with the presence of elevated blood pressure and elevated blood glucose was still significant, but the magnitude of the association was attenuated. In addition, the association of skin carotenoid levels with the presence of low HDL cholesterolemia and hypertriglyceridemia did not reach the level of statistical significance (Table 2). As a sensitivity analysis, we examined the association between skin carotenoid scores and metabolic syndrome using other criteria for MetS (i.e., the Japanese criteria and the International Diabetes Federation criteria). Both these criteria yielded inverse associations similar to those obtained using the Joint Scientific Statement criteria (Table S2).
As shown in Fig. 3, we observed a non-linear association of skin carotenoid scores with MetS by using a restricted cubic spline model (P non-linearity = 0.004). The multivariable-adjusted ORs for the presence of MetS tended to decrease at around skin carotenoid scores of 5. Compared to the reference value (3.8 for skin carotenoid score), the upper limits of the 95% confidence interval were below 1.0 at a carotenoid score of around 6. Finally, we compared the multivariable-adjusted ORs for the presence of MetS in the groups below the median of carotenoid score against the group above the median score across the subgroups of potential cofounding factors (Table 3). We found no evidence of significant heterogeneity in the associations between the subgroups of these factors (all P-heterogeneity > 0.26).
Discussion
The present cross-sectional study demonstrated that higher skin carotenoid levels as estimated by using a noninvasive optical sensor and multiple spatially resolved RS were significantly associated with a lower risk of the presence of MetS and its components in a community-dwelling Japanese population. Previous epidemiological studies have also reported a significant inverse association between skin carotenoid levels and the presence of MetS [16, 17, 26]. A hospital-based study in the United States [16] and a population-based study conducted in Singapore [17] showed that a lower skin carotenoid level estimated by the RRS method was associated with a higher prevalence of MetS. Another cross-sectional study of Japanese adults using health check-up data reported that skin carotenoid levels estimated by the RS method using the same optimal sensor as in the present study were positively correlated with vegetable intakes and negatively correlated with MetS components and cardiovascular risk factors [15], and the multivariable-adjusted OR for the presence of MetS decreased with higher skin carotenoid levels [26]. Both these previous findings and our present study indicated an association between skin carotenoid levels and a lower risk of the presence of MetS. Intriguingly, the present study showed that the OR for the presence of MetS started to decrease from a skin carotenoid score of around 5, which may suggest that a certain level of carotenoid intake is required in order to realize the risk reduction for developing MetS. Although to our knowledge there are no comparable previous studies on the association between MetS and skin carotenoid scores using this cut point, we believe that skin carotenoid levels estimated by the noninvasive optical sensor may be useful in assessing the daily vegetable intakes and in providing dietary guidance for individuals for the prevention of MetS.
The possible mechanisms underlying the significant inverse association between the skin carotenoid level and the presence of MetS in the present study should be discussed. Skin carotenoid levels have been reported to reflected serum total carotenoid levels [15]. Carotenoids are potent natural antioxidants [27]. Oxidative stress can be accumulated in the form of damage to proteins, lipids, and carbohydrates and is considered to disrupt redox signaling pathways in cells, leading to insulin resistance and consequently MetS [28]. Carotenoids can act as direct antioxidants, quenching singlet oxygen and reducing the formation of lipid peroxides [27, 29]. A study of 22 overweight Korean women demonstrated that a high-vegetable and fruit diet led to decreases in markers of oxidative stress, including interleukin-6, and increases in serum carotenoid levels [30]. Therefore, the antioxidant effect of carotenoids in the human body may contribute to a reduction in the risk of developing MetS. On the other hand, the present study found an association between skin carotenoid levels and the odds ratios for MetS components after adjustment for BMI. Skin carotenoid levels may reflect healthy dietary behaviors (e.g., diverse dietary patterns, regular eating habits, and slow eating speed), which are thought to prevent obesity and subsequent MetS, rather than a physiological response to carotenoids themselves [31,32,33].
Several limitations in the present study should be noted. First, because this is a cross-sectional study, the findings of the present study do not support the causality of the observed associations. Second, there is a possibility of selection bias due to non-participation. Approximately 30% of study individuals were excluded from the analysis because they did not wish to participate in the skin carotenoid score measurements. Third, participants in this study might have voluntarily measured their skin carotenoid scores prior to the study and received feedback on their scores. However, at the time this study was conducted, the instrument could not be used for anything other than research purposes, so this possibility is unlikely. Finally, it was difficult to fully address the influence of dietary factors (e.g., total energy intake and dietary intakes of vegetable and nutrients) on the association between skin carotenoid levels and prevalence of MetS because we did not conduct a dietary survey in the 2019 health examination. In addition, the possibility of residual confounding factors such as dietary behaviors and health literacy could not be ruled out, although there was no significant heterogeneity in the associations between subgroups of lifestyle factors, including current smoking, current alcohol drinking, and regular exercise.
Conclusions
Our findings suggest that higher skin carotenoid scores measured by non-invasive optimal sensors are significantly associated with a lower likelihood of having metabolic syndrome in the general Japanese population.
Data availability
The data described in the manuscript, the code book, and the analytic code will not be made available because they contain confidential clinical and demographic data of the study participants. However, further information about the datasets is available with the permission of the principal investigator of the Hisayama Study (TN) on reasonable request for purposes of replicating procedures and results.
References
Alberti KG, Zimmet P, Shaw J, for the IDF Epidemiology Task Force Consensus Group. The metabolic syndrome-a new worldwide definition. Lancet. 2005;366:1059–62. https://doi.org/10.1016/s0140-6736(05)67402-8
Kaiser KA, Brown AW, Brown MMB, Shikany JM, Mattre RD, Allison DB. Increased fruit and vegetable intake has no discernible effect on weight loss: a systematic review and meta-analysis. Am J Clin Nutr. 2014;100:567–76. https://doi.org/10.3945/ajcn.114.090548
Tian Y, Su L, Wang J, Duan X, Jiang X. Fruit and vegetable consumption and risk of the metabolic syndrome: a meta-analysis. Public Health Nutr. 2018;21:756–65. https://doi.org/10.1017/S136898001700310X
Schätzer M, Rust P, Elmadfa I. Fruit and vegetable intake in Austrian adults: intake frequency, serving sizes, reasons for and barriers to consumption, and potential for increasing consumption. Public Health Nutr. 2010;13:480–7. https://doi.org/10.1017/S136898000999142X
Willett W (eds). Nutritional Epidemiology, 3rd ed. Oxford University Press USA; 2013.
Eggersdorfer M, Wyss A. Carotenoids in human nutrition and health. Arch Biochem Biophys. 2018;652:18–26. https://doi.org/10.1016/j.abb.2018.06.001
Campbell DR, Gross MD, Martini MC, Grandits GA, Slavin JL, Potter JD. Plasma carotenoids as biomarkers of vegetable and fruit intake. Cancer Epidemiol Biomark Prev. 1994;3:493–500.
Couillard C, Lemieux S, Vohl MC, Couture P, Lamarche B. Carotenoids as biomarkers of fruit and vegetable intake in men and women. Br J Nutr. 2016;116:1206–15. https://doi.org/10.1017/S0007114516003056
Darvin ME, Magnussen B, Lademann J, Köcher W. Multiple spatially resolved reflection spectroscopy for in vivo determination of carotenoids in human skin and blood. Laser Phys Lett. 2016;13:095601. https://doi.org/10.1088/1612-2011/13/9/095601
Darvin ME, Sandhagen C, Koecher W, Sterry W, Lademann J, Meinke MC. Comparison of two methods for noninvasive determination of carotenoids in human and animal skin: Raman spectroscopy versus reflection spectroscopy. J Biophoton. 2012;5:550–8. https://doi.org/10.1002/jbio.201100080
Ermakov IV, Gellermann W. Dermal carotenoid measurements via pressure mediated reflection spectroscopy. J Biophoton. 2012;5:559–70. https://doi.org/10.1002/jbio.201100122
Hata TR, Scholz TA, Ermakov IV, McClane RW, Khachik F, Gellermann W, et al. Non-invasive raman spectroscopic detection of carotenoids in human skin. J Invest Dermatol. 2000;115:441–8. https://doi.org/10.1046/j.1523-1747.2000.00060.x
Ermakov IV, Ermakova M, Sharifzadeh M, Gorusupudi A, Farnsworth K, Bernstein P, et al. Optical assessment of skin carotenoid status as a biomarker of vegetable and fruit intake. Aech Buichem Biophys. 2018;15:46–54. https://doi.org/10.1016/j.abb.2018.03.033
Darvin ME, Meinke MC, Sterry W, Lademann J. Optical methods for noninvasive determination of carotenoids in human and animal skin. J Biomed Opt. 2013;18:61230 https://doi.org/10.1117/1.JBO.18.6.061230
Matsumoto M, Suganuma H, Shimizu S, Hayashi H, Sawada K, Tokuda I, et al. Skin carotenoid level as an alternative marker of serum total carotenoid concentration and vegetable intake correlates with biomarkers of circulatory diseases and metabolic syndrome. Nutrients. 2020;12:1825 https://doi.org/10.3390/nu12061825
Holt EW, Wei EK, Bennett N, Zhang L. Low skin carotenoid concentration measured by resonance Raman spectroscopy is associated with metabolic syndrome in adults. Nutr Res. 2014;34:821–6. https://doi.org/10.1016/j.nutres.2014.08.017
Toh DWK, Sutanto CN, Loh WW, Lee WY, Yao Y, Ong CN. Skin carotenoids status as a potential surrogate marker for cardiovascular disease risk determination in middle-aged and older adults. Nutr Metab Cardiovasc Dis. 2021;31:592–601. https://doi.org/10.1016/j.numecd.2020.10.016
Kubo M, Hata J, Doi Y, Tanizaki Y, Iida M, Kiyohara Y. Secular trends in the incidence of and risk factors for ischemic stroke and its subtypes in Japanese population. Circulation. 2008;118:2672–8. https://doi.org/10.1161/CIRCULATIONAHA.107.743211
Hata J, Ninomiya T, Hirakawa Y, Nagata M, Mukai N, Gotoh S, et al. Secular trends in cardiovascular disease and its risk factors in Japanese: half-century data from the Hisayama Study (1961–2009). Circulation. 2013;128:1198–205. https://doi.org/10.1161/CIRCULATIONAHA.113.002424
Ninomiya T. Japanese legacy cohort studies: the Hisayama study. J Epidemiol. 2018;28:444–51. https://doi.org/10.2188/jea.JE20180150
Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, et al. Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation. 2009;120:1640–5. https://doi.org/10.1161/CIRCULATIONAHA.109.192644
WHO Expert Consultation. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet. 2004;363:157–63. https://doi.org/10.1016/S0140-6736(03)15268-3
Committee to evaluate diagnostic standards for metabolic syndrome. Definition and the diagnostic standard for metabolic syndrome. Nippon Naika Gakkai Zasshi. 2005;94:794–809.
Alberti KG, Zimmet P, Shaw J. Metabolic syndrome-a new world-wide definition. A consensus statement from the International Diabetes Federation. Diabet Med. 2006;23:469–80. https://doi.org/10.1111/j.1464-5491.2006.01858.x
Durrleman S, Simon R. Flexible regression models with cubic splines. Stat Med. 1989;8:551–61. https://doi.org/10.1002/sim.4780080504
Takayanagi Y, Obana A, Muto S, Asaoka R, Tanito M, Ermakov IV, et al. Relationships between skin carotenoid levels and metabolic syndrome. Antioxid. 2022;11:14. https://doi.org/10.3390/antiox11010014
Bohn T. Carotenoids and markers of oxidative stress in human observational studies and intervention trials: implications for chronic diseases. Antioxid. 2019;8:179. https://doi.org/10.3390/antiox8060179
Vona R, Gambardella L, Cittadini C, Straface E, Pietraforte D. Biomarkers of oxidative stress in metabolic syndrome and associated diseases. Oxid Med Cell Longev. 2019;8267234. https://doi.org/10.1155/2019/8267234.
Krinsky NI, Johnson EJ. Carotenoid actions and their relation to health and disease. Mol Asp Med. 2005;26:459–516. https://doi.org/10.1016/j.mam.2005.10.001
Yeon JY, Kim HS, Sung MK. Diets rich in fruits and vegetables suppress blood biomarkers of metabolic stress in overweight women. Prev Med. 2012;54:S109–15. https://doi.org/10.1016/j.ypmed.2011.12.026. Suppl
Castro-Barquero S, Ruiz-León AM, Sierra-Pérez M, Estruch R, Casas R. Dietary strategies for metabolic syndrome: a comprehensive review. Nutrients. 2020;12. https://doi.org/10.3390/nu12102983.
Yokokawa H, Fukuda H, Yuasa M, Sanada H, Hisaoka T, Naito T. Association between health literacy and metabolic syndrome or healthy lifestyle characteristics among community-dwelling Japanese people. Diabetol Metab Syndr. 2016;8:30. https://doi.org/10.1186/s13098-016-0142-8
Świątkiewicz I, Woźniak A, Taub PR. Time-restricted eating and metabolic syndrome: current status and future perspectives. Nutrients. 2021;13:221. https://doi.org/10.3390/nu13010221
Acknowledgements
The authors thank the residents of the town of Hisayama for their participation in the survey and the staff of the Division of Health of Hisayama for their cooperation with this study. The authors would like to thank Hiroyuki Suganuma and Mai Matsumoto of the KAGOME CO., LTD. (Nagoya, Japan) for providing information on skin carotenoid score measurements. The statistical analyses were carried out using the computer resources offered under the category of General Projects by the Research Institute for Information Technology, Kyushu University. We would like to thank KN International for English proofreading.
Funding
This study was supported in part by the Ministry of Education, Culture, Sports, Science and Technology of Japan (JSPS KAKENHI Grant Number JP22K07421, JP22K17396, JP23K09692, JP23K09717, JP23K16330, JP23K06787, and JP23K09060); by the Health and Labour Sciences Research Grants of the Ministry of Health, Labour and Welfare of Japan (JPMH23FA1006, JPMH23FA1022, and JPMH24GB1002); by the Japan Agency for Medical Research and Development (JP24dk0207053, JP24km0405209, and JP24tm0524003); by Japan Science and Technology Agency (JPMJPF2210); and by funds from the KAGOME CO., LTD. (Nagoya, Japan). The funders had no role in the design of the study, the collection, analysis, and interpretation of data, or the writing of the manuscript.
Author information
Authors and Affiliations
Contributions
YK contributed to the study concept, data collection, interpretation of data, statistical analysis, and drafting of the manuscript. JH contributed to the study concept, data collection, interpretation of data, and revision of the manuscript. MS, TH, SS, YF, and EO contributed to the data collection and interpretation of data. TK contributed to the interpretation of data and revision of the manuscript. TN was the chief investigator of the Hisayama Study and contributed to the study concept, data collection, interpretation of data, and revision of the manuscript. All authors critically reviewed the manuscript and approved the final version. TN is the guarantor of this work and, as such, has full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Corresponding author
Ethics declarations
Competing interests
TN received research grants from KAGOME CO., LTD. (Nagoya, Japan). The other authors declare that they have no conflicts of interest to disclose.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Kimura, Y., Hata, J., Shibata, M. et al. Skin carotenoid scores and metabolic syndrome in a general Japanese population: the Hisayama study. Int J Obes (2024). https://doi.org/10.1038/s41366-024-01575-7
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41366-024-01575-7
- Springer Nature Limited