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Figure 1. Scatterplot of photodamage scores of twins (where 0 means no damage; 9, severe photodamage). The figure shows a strong relationship of photodamage between twins (overall) and within twin pairs of each type (A, Twin A of the pair and B, twin B of the pair). There was no significant difference of correlation between monozygotic (MZ) and dizygotic (DZ) twins (P = .60). The circles represent the photodamage scores from MZ twins, and the triangles represent the scores from DZ twins. The scatterplot is based on data from 65 intact twin pairs.

Figure 1. Scatterplot of photodamage scores of twins (where 0 means no damage; 9, severe photodamage). The figure shows a strong relationship of photodamage between twins (overall) and within twin pairs of each type (A, Twin A of the pair and B, twin B of the pair). There was no significant difference of correlation between monozygotic (MZ) and dizygotic (DZ) twins (P = .60). The circles represent the photodamage scores from MZ twins, and the triangles represent the scores from DZ twins. The scatterplot is based on data from 65 intact twin pairs.

Figure 2. Scatterplot of weight and photodamage. Increased weight significantly correlated with higher photodamage scores (where 0 means no damage; 9, severe photodamage) overall with r = 0.21 (P = .02). On the x-axis, 1 represents “very underweight”; 2, “slightly underweight”; 3, “about the right weight”; 4, “slightly overweight”; and 5, “very overweight.” Each open circle represents a data point (ie, the photodamage score for a certain weight).

Figure 2. Scatterplot of weight and photodamage. Increased weight significantly correlated with higher photodamage scores (where 0 means no damage; 9, severe photodamage) overall with r = 0.21 (P = .02). On the x-axis, 1 represents “very underweight”; 2, “slightly underweight”; 3, “about the right weight”; 4, “slightly overweight”; and 5, “very overweight.” Each open circle represents a data point (ie, the photodamage score for a certain weight).

Figure 3. Box plot of photodamage by history of skin cancer as a function of twin type. Participants with a personal history of skin cancer had higher photodamage scores (where 0 means no damage; 9, severe photodamage) than those who had no history of skin cancer. There was a significant difference between monozygotic (MZ) and dizygotic (DZ) twins with no skin cancer (P = .002).

Figure 3. Box plot of photodamage by history of skin cancer as a function of twin type. Participants with a personal history of skin cancer had higher photodamage scores (where 0 means no damage; 9, severe photodamage) than those who had no history of skin cancer. There was a significant difference between monozygotic (MZ) and dizygotic (DZ) twins with no skin cancer (P = .002).

Figure 4. Scatterplot of age and photodamage. Age was strongly linked to increased photodamage score (where 0 means no damage; 9, severe photodamage) with a correlation coefficient of 0.78 (P < .001), implicating a strong role for intrinsic aging in photoaging assessment. Age as a covariate in the multivariate analysis was excluded because of the problem of multicollinearity. Each open circle represents a data point (ie, photodamage score for a certain age).

Figure 4. Scatterplot of age and photodamage. Age was strongly linked to increased photodamage score (where 0 means no damage; 9, severe photodamage) with a correlation coefficient of 0.78 (P < .001), implicating a strong role for intrinsic aging in photoaging assessment. Age as a covariate in the multivariate analysis was excluded because of the problem of multicollinearity. Each open circle represents a data point (ie, photodamage score for a certain age).

Figure 5. Scatterplot of drinking level and photodamage. Less alcohol consumption significantly correlated with more photodamage with r = −0.26 (P = .003) (where 0 means no damage; 9, severe photodamage). The level of alcohol consumption ranged from 0 days to all 30 days in the past 30 days. On the x-axis, 0 represents “no drinking”; 1, “drinking 1 day”; 3, “drinking 3 to 5 days”; 4, “drinking 6 to 9 days”; 5, “drinking 10 to 19 days”; 6, “drinking 20 to 29 days”; and 7, “drinking all 30 days.” Each open circle represents a data point (ie, the photodamage score for a certain level of drinking alcohol).

Figure 5. Scatterplot of drinking level and photodamage. Less alcohol consumption significantly correlated with more photodamage with r = −0.26 (P = .003) (where 0 means no damage; 9, severe photodamage). The level of alcohol consumption ranged from 0 days to all 30 days in the past 30 days. On the x-axis, 0 represents “no drinking”; 1, “drinking 1 day”; 3, “drinking 3 to 5 days”; 4, “drinking 6 to 9 days”; 5, “drinking 10 to 19 days”; 6, “drinking 20 to 29 days”; and 7, “drinking all 30 days.” Each open circle represents a data point (ie, the photodamage score for a certain level of drinking alcohol).

Figure 6. Scatterplots of weight and photodamage, stratified by age group. A, Age older than 18 years but not older than 30 years; B, age older than 30 years but not older than 42 years; C, age older than 42 years but not older than 54 years; and D, age older than 54 years. Increased weight significantly correlated with higher photodamage scores (where 0 means no damage; 9, severe photodamage) for participants aged 30 to 42 years, with r = 0.65 (P = .004). For participants older than 54 years, increased weight was correlated with a decreased photodamage score, with r = −0.27 (P = .08). On the x-axis, 1 represents “very underweight”; 2, “slightly underweight”; 3, “about the right weight”; 4, “slightly overweight”; and 5, “very overweight.” Each open circle represents a data point (ie, the photodamage score for a certain weight).

Figure 6. Scatterplots of weight and photodamage, stratified by age group. A, Age older than 18 years but not older than 30 years; B, age older than 30 years but not older than 42 years; C, age older than 42 years but not older than 54 years; and D, age older than 54 years. Increased weight significantly correlated with higher photodamage scores (where 0 means no damage; 9, severe photodamage) for participants aged 30 to 42 years, with r = 0.65 (P = .004). For participants older than 54 years, increased weight was correlated with a decreased photodamage score, with r = −0.27 (P = .08). On the x-axis, 1 represents “very underweight”; 2, “slightly underweight”; 3, “about the right weight”; 4, “slightly overweight”; and 5, “very overweight.” Each open circle represents a data point (ie, the photodamage score for a certain weight).

Table. Questionnaire Including Responses by 130 Twins
Table. Questionnaire Including Responses by 130 Twins
1.
Gilchrest  BA Skin aging and photoaging: an overview.  J Am Acad Dermatol1989213, pt 2610613PubMedGoogle Scholar
2.
Guercio-Hauer  CMacfarlane  DFDeleo  VA Photodamage, photoaging and photoprotection of the skin.  Am Fam Physician1994502327332, 334PubMedGoogle Scholar
3.
Kappes  UPElsner  P Clinical and photographic scoring of skin aging.  Skin Pharmacol Appl Skin Physiol2003162100107PubMedGoogle Scholar
4.
Lober  CWFenske  NA Photoaging and the skin: differentiation and clinical response.  Geriatrics19904543640, 42PubMedGoogle Scholar
5.
Glogau  RG Physiologic and structural changes associated with aging skin.  Dermatol Clin1997154555559PubMedGoogle Scholar
6.
Scharffetter-Kochanek  KBrenneisen  PWenk  J  et al Photoaging of the skin from phenotype to mechanisms.  Exp Gerontol2000353307316PubMedGoogle Scholar
7.
Uitto  J The role of elastin and collagen in cutaneous aging: intrinsic aging versus photoexposure.  J Drugs Dermatol200872(suppl)s12s16PubMedGoogle Scholar
8.
Christensen  KIachina  MRexbye  H  et al “Looking old for your age”: genetics and mortality.  Epidemiology2004152251252PubMedGoogle Scholar
9.
Leung  WCHarvey  I Is skin ageing in the elderly caused by sun exposure or smoking?  Br J Dermatol2002147611871191PubMedGoogle Scholar
10.
Chung  JHLee  SHYoun  CS  et al Cutaneous photodamage in Koreans.  Arch Dermatol2001137810431051PubMedGoogle Scholar
11.
Kadunce  DPBurr  RGress  RKanner  RLyon  JLZone  JJ Cigarette smoking: risk factor for premature facial wrinkling.  Ann Intern Med199111410840844PubMedGoogle Scholar
12.
Daniell  HW Smoker's wrinkles  Ann Intern Med1971756873880PubMedGoogle Scholar
13.
Maier  TKorting  HC Sunscreens: which and what for?  Skin Pharmacol Physiol2005186253262PubMedGoogle Scholar
14.
Hatch  KLOsterwalder  U Garments as solar ultraviolet radiation screening materials.  Dermatol Clin200624185100PubMedGoogle Scholar
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Thompson  SCJolley  DMarks  R Reduction of solar keratoses by regular sunscreen use.  N Engl J Med19933291611471151PubMedGoogle Scholar
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Stern  RS Sunscreen Use and Non-melanoma Skin Cancer.  Vol 7. New York, NY: Marcel Dekker; 1990
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O’Hare  PMFleischer  AB  JrD'Agostino  RB  Jr  et al Tobacco smoking contributes little to facial wrinkling.  J Eur Acad Dermatol Venereol1999122133139PubMedGoogle Scholar
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Rexbye  HPetersen  IJohansens  MKlitkou  LJeune  BChristensen  K Influence of environmental factors on facial ageing.  Age Ageing2006352110115PubMedGoogle Scholar
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Griffiths  CEWang  TSHamilton  TAVoorhees  JJEllis  CN A photonumeric scale for the assessment of cutaneous photodamage.  Arch Dermatol19921283347351PubMedGoogle Scholar
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Diggle  PLiang  K-YZeger  SL Analysis of Longitudinal Data.  New York, NY: Oxford University Press; 1994
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Welsh  MMKaragas  MRApplebaum  KMSpencer  SKPerry  AENelson  HH A role for ultraviolet radiation immunosuppression in non-melanoma skin cancer as evidenced by gene-environment interactions.  Carcinogenesis2008291019501954PubMedGoogle Scholar
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Nan  HQureshi  AHunter  DJHan  J Interaction between p53 codon 72 polymorphism and melanocortin 1 receptor variants on suntan response and cutaneous melanoma risk.  Br J Dermatol20081592314321PubMedGoogle Scholar
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Aitken  JFGreen  ACMacLennan  RYoul  PMartin  NG The Queensland Familial Melanoma Project.  Melanoma Res199662155165PubMedGoogle Scholar
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Lindström  LSYip  BLichtenstein  PPawitan  YCzene  K Etiology of familial aggregation in melanoma and squamous cell carcinoma of the skin.  Cancer Epidemiol Biomarkers Prev200716816391643PubMedGoogle Scholar
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Koh  HK Cutaneous melanoma.  N Engl J Med19913253171182PubMedGoogle Scholar
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Kamb  AGruis  NWeaver-Feldhaus  J  et al A cell cycle regulator potentially involved in genesis of many tumor types.  Science19942645157436440PubMedGoogle Scholar
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Nobori  TMiura  KWu  DJLois  ATakabayashi  KCarson  DA Deletions of the cyclin-dependent kinase-4 inhibitor gene in multiple human cancers.  Nature19943686473753756PubMedGoogle Scholar
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Gailani  MRStahle-Backdahl  MLeffell  DJ  et al The role of the human homologue of Drosophila patched in sporadic basal cell carcinomas.  Nat Genet19961417881PubMedGoogle Scholar
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Hahn  HWicking  CZaphiropoulous  PG  et al Mutations of the human homolog of Drosophila patched in the nevoid basal cell carcinoma syndrome.  Cell1996856841851PubMedGoogle Scholar
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Bito  TUeda  MAhmed  NUNagano  TIchihashi  M Cyclin D and retinoblastoma gene product expression in actinic keratosis and cutaneous squamous cell carcinoma in relation to p53 expression.  J Cutan Pathol1995225427434PubMedGoogle Scholar
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Onodera  HNakamura  SSugai  T Cell proliferation and p53 protein expressions in cutaneous epithelial neoplasms.  Am J Dermatopathol1996186580588PubMedGoogle Scholar
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Beissert  SLoser  K Molecular and cellular mechanisms of photocarcinogenesis.  Photochem Photobiol20088412934PubMedGoogle Scholar
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Black  HS Influence of dietary factors on actinically-induced skin cancer.  Mutat Res19984221185190PubMedGoogle Scholar
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Sies  HStahl  W Nutritional protection against skin damage from sunlight.  Annu Rev Nutr200424173200PubMedGoogle Scholar
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Lahmann  CBergemann  JHarrison  GYoung  AR Matrix metalloproteinase-1 and skin aging in smokers.  Lancet20013579260935936PubMedGoogle Scholar
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Smith  JBFenske  NA Cutaneous manifestations and consequences of smoking.  J Am Acad Dermatol1996345, pt 1717732PubMedGoogle Scholar
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Reed  TPlassman  BLTanner  CMDick  DMRinehart  SANichols  WC Verification of self-report of zygosity determined via DNA testing in a subset of the NAS-NRC twin registry 40 years later.  Twin Res Hum Genet200584362367PubMedGoogle Scholar
Study
December 2009

Factors That Affect Skin Aging: A Cohort-Based Survey on Twins

Author Affiliations

Author Affiliations: Departments of Dermatology, University Hospitals Case Medical Center, Case Western Reserve School of Medicine (Ms Martires and Drs Fu, Cooper, and Baron), and Cleveland Clinic Foundation (Dr Polster), Cleveland, Ohio.

Arch Dermatol. 2009;145(12):1375-1379. doi:10.1001/archdermatol.2009.303
Abstract

Objective  To identify environmental factors that correlate with skin photoaging, controlling for genetic susceptibility by using a questionnaire administered to twins.

Design  The survey collected information about each participant's Fitzpatrick type, history of skin cancer, smoking and drinking habits, and weight from a cohort of twins. Clinicians then assigned a clinical photodamage score to each participant.

Setting  The annual Twins Days Festival in Twinsburg, Ohio.

Participants  A voluntary cohort of twins from the general community, mostly from Ohio, Pennsylvania, and the northeastern United States. The survey was completed on a voluntary basis by sets of monozygotic (MZ) and dizygotic (DZ) twins. A total of 130 surveys taken by 65 complete twin pairs were analyzed.

Main Outcome Measure  Skin aging was assessed using a validated photographic scale of photodamage, graded by such characteristics as wrinkling and pigmentation change.

Results  Photodamage scores among twins of a pair, whether MZ or DZ, were highly correlated (P = .92). Factors found to predict higher photodamage include history of skin cancer (P < .001), zygosity status (MZ vs DZ) (P = .001), weight (P = .02), and cigarette smoking (P = .046). Alcohol consumption was significantly associated with lower photodamage scores (P = .003).

Conclusions  The study of twins provides a unique opportunity to control for genetic susceptibility in order to elucidate environmental influences on skin aging. The relationships found between smoking, weight, sunscreen use, skin cancer, and photodamage in these twin pairs may help to motivate the reduction of risky behaviors.

Aging is attributed to both intrinsic and extrinsic processes.1 Photoaging—the most recognized form of extrinsic aging of the skin—describes changes brought about by long-term sun exposure, resulting in photodamage.2 Photodamage, therefore, refers to the physical and morphologic alterations secondary to solar UV exposure and is the main component of photoaging.3 Intrinsic aging of the skin is characterized by finely wrinkled skin, cherry hemangiomas, and seborrheic keratoses.4 Photodamage, however, includes other characteristics, such as coarsely wrinkled skin, hyperpigmentation, hypopigmentation, and telangiectasia,1,5,6 and has been associated with the development of malignant neoplasms.2

Up to 40% of changes that contribute to the aged appearance are the result of nongenetic factors.7,8 Studies have implicated smoking and sun exposure as contributory,9-12 whereas avoidance of UV2,13 and use of protective clothing and sunscreens14-16 have been shown to be protective. Some factors remain controversial. For example, some studies show that smoking had little or no contribution to skin wrinkling.17

The study of twins provides the opportunity to control for genetic susceptibility.18 Few epidemiological studies of twins have adequately examined associations between environmental factors and aging. The objective of this study is to determine the environmental factors that correlate with photoaging by obtaining a history (questionnaire format) and performing a skin examination on a cohort of monozygotic (MZ) and dizygotic (DZ) twins. Photoaging was assessed using a validated, photographic photodamage scale, examining such characteristics as wrinkling and pigmentation changes.19 Variables collected from the questionnaire (Table) were compared with photodamage scores, and statistical analyses were used to determine correlations.

Methods
Study design

A questionnaire was administered at the annual Twins Days Festival, August 2-3, 2002, in Twinsburg, Ohio, to 130 adult (ages ≥18 years) participants (65 twin pairs). The questions and responses from the survey are included in the Table. Each twin completed the survey separately in the presence of dermatology clinical faculty and residents. Specific questions from the survey are presented in the Table. Each twin also received a Fitzpatrick skin type score and clinical photodamage score (range, 0 to 9, with a score of “0” meaning no damage and “9” indicating severe photodamage). The scale was based on the photoaging classification system developed and validated by Griffiths et al.19 Scores were completed by 5 clinicians conducting the survey, and each score was overseen by 1 clinician (A.M.P.).

Statistical analysis

Statistical analyses examined associations between several factors surveyed and the outcome variable, photodamage score, using both univariate and multivariate methods. In the univariate analysis, the association of individual ordinal factors, including age, alcohol consumption, and weight, with photodamage was estimated using the Spearman correlation coefficient. Use of the Kruskal-Wallis test examined the median difference of scores of photodamage between or among the level of other variables such as smoking, type of twins, and skin cancer. In the multivariate analysis, multivariable linear regression with the forward model selection procedure was used. To take into account the possible correlation of photodamage outcome between each pair of twins, the generalized estimating equations (GEE) model was used for inference.20 When examining the correlations in photodamage between twins of a pair, Fisher z transformation was used to test for equality of 2 population correlations (the correlation of DZ twin pairs vs the correlation of MZ twin pairs). This method examined correlations in photodamage between twins of a pair. This was compared between MZ and DZ twins. All tests were 2-sided, and P < .05 was considered statistically significant.

Results

A total of 130 questionnaires were included in this study. Missing information was excluded from analysis. Participants ranged in age from 18 to 77 years and represented all regions of the United States, although most hailed from Ohio, Pennsylvania, and the northeastern states. Dizygotic twins comprised 52 twin pairs, MZ twins comprised 10, and 3 twin pairs marked “don't know” with regard to their zygosity status. The correlation in photodamage between twins of a pair was tested by using Fisher z transformation, comparing MZ vs DZ cohorts vs the entire cohort. As shown in Figure 1, the correlation between MZ twins was higher (r = 0.94) than that between DZ twins (r = 0.90), although the difference was not significant (P = .53). Overall, the correlation between twins of any pair was high (r = 0.92).

In the univariate analysis, age, frequency of alcohol consumption, and self-reported weight related significantly to photodamage score. Advanced age was strongly associated with higher photodamage scores, with r = 0.78 (P < .001), as was weight, with r = 0.21 (P = .02). However, more frequent consumption of alcohol was negatively associated with higher photodamage scores, with r = −0.26 (P = .003). Sunscreen use (r = −0.06) and Fitzpatrick type (r = −0.06) were also negatively correlated with photodamage. The influence of weight and alcohol consumption on photoaging was examined after stratifying age groups. For participants older than 54 years, higher weight predicted significantly lower photodamage scores than those who weighed less (r = −0.03; P = .08) (Figure 2). The finding for alcohol consumption in participants older than 54 years was consistent with the rest of the data in that more frequent consumption was negatively associated with photodamage. Differences of photodamage between or among the level of other factors using the Kruskal-Wallis test found significant associations of skin cancer and type of twins (MZ vs DZ) with photodamage score, and a significant association for quantity of cigarettes smoked (P < .12). A history of skin cancer (P < .001) and cigarette smoking (P = .12) positively correlated with higher photodamage scores. Dizygotic twins were found to have more photodamage than MZ twins (P = .001). Among 11 incidences of skin cancer, all were in DZ twins, and 8 of them were in 4 intact twin pairs. Box plots and scatterplots for factors with significant associations with photodamage are shown in Figures 2, 3, 4, 5, and 6.

Factors significantly associated from the univariate analyses were further examined using multivariable linear regression with the forward model selection procedure. Covariates included in the multiple linear regression were skin cancer history, Fitzpatrick type, weight, and alcohol consumption. The type of twins (MZ vs DZ) was also controlled for in the analysis. Factors found to predict photodamage include skin cancer, type of twins, and alcohol consumption. Participants with a history of skin cancer had photodamage scores that were an average of 1.39 points (P = .08) higher than scores of those who had never had skin cancer. Dizygotic twins had photodamage scores that were an average of 1.41 higher than those of MZ twins (P = .005), and those with more frequent alcohol consumption had scores that were 0.22 points lower than those with less frequent consumption (P = .06). Age as a covariate in the multivariate analysis was excluded because of the problem of multicollinearity. However, when age was included in the multivariate regression, age (P < .001) and type of twins (P = .003) were significantly correlated with photodamage.

Comment

We examined environmental factors associated with skin photoaging. The Twins Days Festival provides a rare opportunity to study a large number of twin pairs to control for genetic susceptibility. Among the most important results is that a history of skin cancer and photodamage are highly associated in a population that shares genetic commonalities. The relationship between differences in MZ vs DZ photodamage (as examined by the GEE logistic model), revealed similar correlation between twins of a pair, whether MZ or DZ.

Genetic and environmental factors are considered co-contributory in the development of melanoma and nonmelanoma skin cancer, as investigated by recent studies.21,22 However, analyses of the relative contributions of genetic and environmental factors have been limited.23 Lindström et al24 estimated the genetic components for melanoma and squamous cell cancer to be 18% and 8%, respectively. For melanoma, it has been documented that first-degree relatives of patients have an increased risk of developing melanoma,25 but the degree of genetic contribution remains unclear. The cell cycle regulator CDKN2A is implicated in the genetic component to melanoma,26,27 while PTCH28,29 and p5330,31 are involved in nonmelanoma skin cancers (NMSCs). As for the environmental component, UV plays an important role in inducing DNA mutations and suppressing cellular antitumor immune responses.32 Mitochondrial DNA is particularly susceptible to damage by UV owing to its small size and higher exposure to reactive oxygen species.33,34 Some studies treat skin cancer as a component of photoaging. However, this study addresses skin cancer as an independent factor. The persistent strength of correlation between the 2 thus confirms this relationship, implicating environmental and genetic factors that affect photoaging as components in the development of skin cancer.

Self-reported weight was found to positively correlate with photoaging score, contrast to the study of Danish twins by Rexbye et al,18 which reported body mass index to negatively correlate with facial aging. Previous animal experiments have suggested that high fat intake may increase skin sensitivity to UV damage and photocarcinogenesis.35 However, the relationship between fat intake and NMSC is complex, and lipids may potentially have an antioxidant and, thus, protective action.36 For participants 54 years or older, weight correlated negatively with photoaging, implying that although excess fat may increase skin's susceptibility to damage, it may help mask the appearance of wrinkles in older age.

Drinking alcohol was found to be negatively correlated with photodamage. In their study of Danish twins, Rexbye et al18 found no significant correlation between drinking and photodamage in either direction.18 Although our survey did not specify what type of alcohol was consumed, it is known that certain alcoholic beverages (eg, red wine) contain polyphenols such as resveratrol, which is an effective antioxidant.37,38 Finally, our finding that cigarette smoking is associated with higher photodamage (P = .046) is consistent with most of the data in the literature.9-12,18,39 Cigarette smoke induces matrix metalloproteinases in the skin and inhibits procollagen synthesis through alteration of transforming growth factor β.40-42

This study is one of the few twin studies examining the relationship between environmental factors and photodamage. As with all self-report methods, data may not be accurately reported by participants. The classification of zygosity was also based on self-report, which may have 96.8% accuracy.43 Finally, with clinical photodamage scales, there is room for interobserver discrepancy, which was minimized by having 1 senior resident (A.M.P.) oversee scores given, and by using a standardized, validated, photographic photoaging scale.19

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Article Information

Correspondence: Elma D. Baron, MD, Department of Dermatology, University Hospitals, Case Medical Center, Lakeside 3500, 11100 Euclid Ave, Cleveland, OH 44106 (edb4@case.edu).

Accepted for Publication: June 15, 2009.

Author Contributions: Dr Baron had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: Polster and Cooper. Acquisition of data: Polster and Cooper. Analysis and interpretation of data: Martires, Fu, Cooper, and Baron. Drafting of the manuscript: Martires. Critical revision of the manuscript for important intellectual content: Fu, Polster, Cooper, and Baron. Statistical analysis: Fu. Obtained funding: Cooper. Administrative, technical, and material support: Martires, Polster, and Cooper. Study supervision: Polster, Cooper, and Baron.

Financial Disclosure: None reported.

Additional Contributions: Catherine Demko, PhD, contributed to the questionnaire design, and Geeta Shah, MD, Radha Mikkilineni, MD, Sarolta Szabo, MD, Leila Ettefagh, MD, and Mary Veremis-Ley, DO, assisted at the Twins Days data collection booth.

References
1.
Gilchrest  BA Skin aging and photoaging: an overview.  J Am Acad Dermatol1989213, pt 2610613PubMedGoogle Scholar
2.
Guercio-Hauer  CMacfarlane  DFDeleo  VA Photodamage, photoaging and photoprotection of the skin.  Am Fam Physician1994502327332, 334PubMedGoogle Scholar
3.
Kappes  UPElsner  P Clinical and photographic scoring of skin aging.  Skin Pharmacol Appl Skin Physiol2003162100107PubMedGoogle Scholar
4.
Lober  CWFenske  NA Photoaging and the skin: differentiation and clinical response.  Geriatrics19904543640, 42PubMedGoogle Scholar
5.
Glogau  RG Physiologic and structural changes associated with aging skin.  Dermatol Clin1997154555559PubMedGoogle Scholar
6.
Scharffetter-Kochanek  KBrenneisen  PWenk  J  et al Photoaging of the skin from phenotype to mechanisms.  Exp Gerontol2000353307316PubMedGoogle Scholar
7.
Uitto  J The role of elastin and collagen in cutaneous aging: intrinsic aging versus photoexposure.  J Drugs Dermatol200872(suppl)s12s16PubMedGoogle Scholar
8.
Christensen  KIachina  MRexbye  H  et al “Looking old for your age”: genetics and mortality.  Epidemiology2004152251252PubMedGoogle Scholar
9.
Leung  WCHarvey  I Is skin ageing in the elderly caused by sun exposure or smoking?  Br J Dermatol2002147611871191PubMedGoogle Scholar
10.
Chung  JHLee  SHYoun  CS  et al Cutaneous photodamage in Koreans.  Arch Dermatol2001137810431051PubMedGoogle Scholar
11.
Kadunce  DPBurr  RGress  RKanner  RLyon  JLZone  JJ Cigarette smoking: risk factor for premature facial wrinkling.  Ann Intern Med199111410840844PubMedGoogle Scholar
12.
Daniell  HW Smoker's wrinkles  Ann Intern Med1971756873880PubMedGoogle Scholar
13.
Maier  TKorting  HC Sunscreens: which and what for?  Skin Pharmacol Physiol2005186253262PubMedGoogle Scholar
14.
Hatch  KLOsterwalder  U Garments as solar ultraviolet radiation screening materials.  Dermatol Clin200624185100PubMedGoogle Scholar
15.
Thompson  SCJolley  DMarks  R Reduction of solar keratoses by regular sunscreen use.  N Engl J Med19933291611471151PubMedGoogle Scholar
16.
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