Ng et al.
Journal of Physiological Anthropology(2024) 43:14
https://doi.org/10.1186/s40101-024-00361-8

ORIGINAL ARTICLE

Journal of
Physiological Anthropology

Open AccessComparisons between wrinkles
and photo‑ageing detected and self‑reportedby the participant or identified by trained
assessors reveal insights from Chinese individuals
in the Singapore/Malaysia Cross‑sectional
Genetics Epidemiology Study (SMCGES) cohort
Jun Yan Ng1 , Hongyu Zhou1 , Tianqi Li1   and Fook Tim Chew1,2*   

Abstract
Background Changes develop on the facial skin as a person ages. Other than chronological time, it has been discovered that gender, ethnicity, air pollution, smoking, nutrition, and sun exposure are notable risk factors that influence
the development of skin ageing phenotypes such as wrinkles and photo-ageing. These risk factors can be quantified
through epidemiological collection methods. We previously studied wrinkles and photo-ageing in detail using photonumeric scales. The analysis was performed on the ethnic Chinese skin by three trained assessors. Recent studies
have shown that it is possible to use self-reported data to identify skin-related changes including skin colour and skincancer. In order to investigate the association between risk factors and skin ageing phenotypic outcomes in largescale epidemiological studies, it would be useful to evaluate whether it is also possible for participants to self-reportsigns of ageing on their skin.
Aim We have previously identified several validated photo-numeric scales for wrinkling and photo-ageing to use
on ethnic Chinese skin. Using these scales, our trained assessors grade wrinkling and photo-ageing with moderately
high inter-assessor concordance and agreement. The main objective of this study involves letting participants grade
self-reported wrinkling and photo-ageing using these same scales. We aim to compare the concordance and agreement between signs of skin ageing by the participant and signs of ageing identified by our assessors.
Method Three trained assessors studied facial photo-ageing on 1081 ethnic Chinese young adults from the Singapore/Malaysia Cross-sectional Genetics Epidemiology Study (SMCGES) cohort. Self-reported facial photo-ageing databy the same 1081 participants were also collated and the two sets of data are compared.
Results Here, we found that self-reported signs of photo-ageing are concordant with photo-ageing detectedby our assessors. This finding is consistent whether photo-ageing is evaluated through studying wrinkle variations(Spearman’s rank correlation (ρ) value: 0.246–0.329) or through studying dyspigmentation patterns (Spearman’s*Correspondence:
Fook Tim Chew
dbscft@nus.edu.sg
Full list of author information is available at the end of the article© The Author(s) 2024. 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 theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother 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
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Ng et al. Journal of Physiological Anthropology(2024) 43:14

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rank correlation (ρ) value 0.203–0.278). When studying individual wrinkles, both participants and assessors often
detect the presence of the same wrinkle (Spearman’s rank correlation (ρ) value 0.249–0.366). A weak-to-fair levelof agreement between both participants and assessors (Cohen’s kappa (κ) values: 0.041–0.233) persists and is statistically significant after accounting for agreements due to chance. Both the participant and the assessor are largely
consistent in evaluating the extent of photo-ageing (area under curve (AUC) values 0.689–0.769) and in discerning
between the presence or absence of a given facial wrinkle (area under curve (AUC) values 0.601–0.856).
Conclusion When we analyse the overall appearance of the face, our results show that signs of photo-ageing identified by the participant are concordant with signs of photo-ageing identified by our assessors. When we focused ouranalysis on specific areas of the face, we found that participants were more likely to identify and self-report the same
wrinkles that our assessors have also detected. Here, we found that self-reported signs of skin ageing provide a satisfactory approximation to the signs of skin ageing identified by our assessors. The ability to use self-reported signsof skin ageing should also be evaluated on scales beyond the ones discussed in this study. Currently, there are
not as many photo-numeric scales for quantifying dyspigmentation patterns as there are for quantifying wrinkle variations. As Chinese skin is known to become dyspigmented more easily with age, more photo-numeric scales need
to be developed and properly validated.

Introduction
Skin ageing can take many forms. We have previously
discussed wrinkles (Ng JY, & Chew FT. Comparisons
between Caucasian validated photo-numeric scales andChinese validated photo-numeric scales for assessing
facial wrinkles: Crow’s Feet wrinkles, forehead wrinkles,
glabellar frowns, and nasolabial folds. Insights from theSingapore/Malaysia Cross-sectional Genetics Epidemiology Study (SMCGES) cohort. Manuscript submitted
for publication in Scientific Reports) and photo-ageing(Ng JY, & Chew FT. Comparisons between Caucasianvalidated photo-numeric scales and Korean validated
photo-numeric scales for photo-ageing. Insights from theSingapore/Malaysia Cross-sectional Genetics Epidemiology Study (SMCGES) cohort. Manuscript submitted for
publication in Skin Research and Technology) in detail.
In our two earlier studies, we identified validated photonumeric scales suitable to be used on the ethnic Chinese.
Using these scales, all three of our trained assessors consistently grade signs of ageing on the skin in similar ways.
We recently published a meta-analysis [23] where we
reviewed that age, gender, ethnicity, air pollution, smoking, nutrition, and sun exposure are notable risk factorsthat influence the development of skin ageing phenotypes
such as wrinkles and photo-ageing. Meanwhile, recent
studies have found that it is possible to use self-reported
data to identify skin-related changes such as skin colour[15] and skin cancer [3, 4].
We wonder if the methodology of using self-reported
data to report skin characteristics can be extended to skinageing phenotypes. This is because using self-reported
skin ageing data can potentially improve the scalability of
our findings in large-scale epidemiological studies. Thiscan in turn allow us to explore the associations between
risk factors and different forms of skin ageing in a larger

population. Therefore, the main objective of this study is
to examine how the photo-numeric scales perform using
self-reported data as compared to using data evaluatedby our trained assessors. Using the results of these comparisons, we seek to evaluate whether self-reported signsof skin ageing provide a satisfactory approximation to thesigns of skin ageing identified by our assessors.

Methods
Participant recruitment

We recruited from the Singapore/Malaysia Cross-section
Genetics Epidemiology Study (SMCGES) cohort for thisstudy. The SMCGES cohort consists of participants fromSingapore and Malaysia and has been previously studiedin epidemiological and genetic studies of allergic diseases. We have previously described this cohort in detailelsewhere [12, 24, 25].
Using emails and posters, we recruited participants forthe previous study (i.e., the epidemiological and genetic
study) from the National University of Singapore, Singapore (2005 to 2023), Universiti Tunku Abdul Rahman(UTAR) Campus, Malaysia (2016 to 2018), and Sunway
University, Malaysia (2019 and 2022). These participants
were recruited through walk-ins and participated in ourstudy voluntarily.
Some participants consent to be re-contacted. We
invited these participants to participate in this present
study (i.e., the skin ageing study). A total of 10,248 participants were invited to participate in the skin ageing study,of which 3365 completed the study. The demographicsof the respondents and non-respondents are similar andcan be found elsewhere (Ng JY, & Chew FT. Comparisons between Caucasian validated photo-numeric scales
and Chinese validated photo-numeric scales for assessing
facial wrinkles: Crow’s Feet wrinkles, forehead wrinkles,

Ng et al. Journal of Physiological Anthropology(2024) 43:14

glabellar frowns, and nasolabial folds. Insights from theSingapore/Malaysia Cross-sectional Genetics Epidemiology Study (SMCGES) cohort. Manuscript submitted
for publication in Scientific Reports) (Ng JY, & Chew
FT. Comparisons between Caucasian validated photonumeric scales and Korean validated photo-numericscales for photo-ageing. Insights from the Singapore/
Malaysia Cross-sectional Genetics Epidemiology Study(SMCGES) cohort. Manuscript submitted for publicationin Skin Research and Technology). All 3365 participants
constitute our study population. From this study population, we randomly selected a subset of 1081 ethnic Chinese young adult participants (Table 1). This subset has
been previously studied for their facial wrinkles (Crow’s
Feet wrinkles, forehead wrinkles, glabellar frowns, andnasolabial folds) (Ng JY, & Chew FT. Comparisons
between Caucasian validated photo-numeric scales andChinese validated photo-numeric scales for assessing
facial wrinkles: Crow’s Feet wrinkles, forehead wrinkles,
glabellar frowns, and nasolabial folds. Insights from theSingapore/Malaysia Cross-sectional Genetics Epidemiology Study (SMCGES) cohort. Manuscript submitted
for publication in Scientific Reports) and photo-ageing (Ng JY, & Chew FT. Comparisons between Caucasian

Table 1 Summary table for ­demographicsa drawn froma population of young Singapore ethnic Chinese adults
recruited from the Singapore/Malaysia Cross-section Genetics
Epidemiology Study (SMCGES) cohort
Demographic factor

Subset evaluated All participantsParticipants

1081 (100%)

3365 (100%)

Mean age (years) ± SD

26.15 ± 8.11

26.30 ± 6.92

Mean height (cm) ± SD

166.03 ± 8.60

165.37 ± 8.69

Mean weight (kg) ± SD

60.96 ± 12.60

60.86 ± 13.27

BMI (kg/m2) ± SD

22.02 ± 3.99

22.19 ± 4.93

Male

420 (38.85%)

1227 (36.46%)

Female661 (61.15%)

2138 (63.54%)

Chinese1081 (100%)

2885 (85.74%)

Low

117 (10.82%)

386 (11.47%)

Moderate

275 (25.44%)774 (23.00%)

High

226 (20.91%)

792 (23.54%)

Very high446 (41.26%)

1396 (41.49%)

Missing/invalid

17 (1.57%)

17 (0.51%)

Public housing

664 (61.42%)

1794 (53.31%)

Condominium/private apartment 320 (29.60%)

852 (25.32%)

Landed property

76 (7.03%)

698 (20.74%)

Missing/invalid

21 (1.94%)

21 (0.62%)

Abbreviations: SD standard deviation, SGD Singapore dollars, HDB (public
housing) Public housing constructed by the Singapore Housing Development
Board, BMI body mass index, cm, centimetres, m2 squared metres, kg kilogramsa

The values after ± are standard deviation values. Missing/Invalid refers to
responses that are either left blank or otherwise invalid

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validated photo-numeric scales and Korean validated
photo-numeric scales for photo-ageing. Insights from theSingapore/Malaysia Cross-sectional Genetics Epidemiology Study (SMCGES) cohort. Manuscript submitted for
publication in Skin Research and Technology).
The study was conducted in accordance with the Declaration of Helsinki and Good Clinical Practices.
Survey data collection

The SMCGES cohort participants have previously completed a set of investigator-administered, validated
International Study of Asthma and Allergies in Childhood (ISAAC) questionnaires. These questionnaires collated sociodemographic data, personal lifestyle data, andfamilial and personal medical history data.
After completing the ISAAC questionnaires, most of
the participants agreed to take part in a separate study onskin ageing (i.e. this present study). All these participants
signed an informed consent form to participate in theskin ageing study which contains an investigator-administered skin ageing questionnaire. In this questionnaire,participants self-reported various skin ageing phenotypes
on their skin; this will be elaborated on in the “Evaluating skin ageing” section. After completing the self-evaluation, participants completed more personal lifestylequestions on the questionnaire.
Further details on the consent process can be obtained
from the “Ethics approval and consent to participate”
section.
Image data collection

Investigators acquire photographic documentation of allparticipants. Photographs are taken with the same camera (Canon EOS 6DII Body with an EF85 f/1.4L IS USMlens) and tripod positioned one metre away from the participant. Photographs taken meet the standards of otherrecent and similar studies [2, 6]. In brief, photographs
are taken with identical camera settings, lighting, andpositioning at five angles–en face, 45° oblique, and 90°side profiles. A total of 5 photos are collected from eachparticipant.
Evaluating skin ageing

Photo-ageing is evaluated by (a) the participant, and(b) three trained assessors on multiple validated photonumeric scales reported in Table S1. The same photonumeric scales are used by all participants and assessors.
A dressing table mirror was provided for each participant. With the aid of the mirror, each participant independently evaluates different signs of ageing on theirskin. These signs of skin ageing include wrinkling phenotypes (Crow’s Feet wrinkles, forehead wrinkles, glabellar frowns, and nasolabial folds) and photo-ageingNg et al. Journal of Physiological Anthropology(2024) 43:14

phenotypes. Other skin ageing phenotypes are reported
in the supplementary materials (Tables S1 and S2).
The three trained assessors evaluate skin ageing phenotypes from photographs and the assessment scores arecalibrated before use. The calibration process has been
described previously (Ng JY, & Chew FT. Comparisons
between Caucasian validated photo-numeric scales andChinese validated photo-numeric scales for assessing
facial wrinkles: Crow’s Feet wrinkles, forehead wrinkles,
glabellar frowns, and nasolabial folds. Insights from theSingapore/Malaysia Cross-sectional Genetics Epidemiology Study (SMCGES) cohort. Manuscript submitted
for publication in Scientific Reports) (Ng JY, & Chew
FT. Comparisons between Caucasian validated photonumeric scales and Korean validated photo-numericscales for photo-ageing. Insights from the Singapore/
Malaysia Cross-sectional Genetics Epidemiology Study(SMCGES) cohort. Manuscript submitted for publication in Skin Research and Technology). Briefly, 30 participants were randomly selected, their photographs were
openly discussed, and a consensus among all three assessors was reached. Except for this small handful of participants (n = 30), all the 1081 participants are assessed threetimes independently.
Statistical analysis

All the photo-numeric scales are standardised.
Two-tailed bivariate correlations for Spearman’s rankcorrelation (ρ) are calculated using Version 25 of the IBM
Statistical Package for Social Scientists (SPSS/PC) andreported in Tables 2 and S2. Qualitative interpretationsof the Spearman’s Rank correlation (ρ) values follow thenaming practices for the strength of correlation coefficients used in healthcare and related fields [1, 5]. The
strengths are interpreted as follows: 0.00: no correlation,
0.01–0.20: weak, 0.21–0.50: fair, 0.51–0.70: moderate,
0.71–0.90: very strong, 0.91–1.00: perfect.
Cohen’s kappa (κ) values report the levels of interrater agreement after accounting for agreements due tochance. Cohen’s kappa (κ) is calculated using Version 25of the IBM Statistical Package for Social Scientists (SPSS/
PC) and reported in Tables 2 and S2. The strength of
Cohen’s kappa is interpreted as follows: 0.00–0.19: weak,
0.20–0.39: fair, 0.40–0.59: moderate, 0.60–0.79: verystrong, and 0.80–1.00: perfect.
Bubble plots compare the grades for wrinkles (Fig. 1)
and photo-ageing (Fig. 2) evaluated by our trained assessors or self-reported by the participant.
The area under the curve (AUC) of the Receiver Operator Characteristic (ROC) curve is computed using SPSS.
AUC curves compare the grades for wrinkles (Figs. 3 and4) and photo-ageing (Fig. 5) evaluated by our trained
assessors or self-reported by the participant. The strength

Page 4 of 22of the AUC is interpreted as follows: 0.51–0.59: very
weak, 0.60–0.69: weak, 0.70–0.79: fair, 0.80–0.89: strong,
and 0.90–1.00: very strong.
Chi-square tests are performed to evaluate the proportion of participants with skin ageing phenotypes at different severity levels (Figs. 6, 7, and 8). Chi-square trend
tests are performed to evaluate whether the changes inthese proportions follow a significant trend with chronological age in both sexes.

Results
Participant demographics

We studied a group of 1081 participants which can provide a representative overview of the epidemiology of skinageing in the Singapore ethnic Chinese young adult population. This group has been studied in detail in our previous works (Ng JY, & Chew FT. Comparisons between
Caucasian validated photo-numeric scales and Chinese
validated photo-numeric scales for assessing facial wrinkles: Crow’s Feet wrinkles, forehead wrinkles, glabellarfrowns, and nasolabial folds. Insights from the Singapore/
Malaysia Cross-sectional Genetics Epidemiology Study(SMCGES) cohort. Manuscript submitted for publicationin Scientific Reports) (Ng JY, & Chew FT. Comparisons
between Caucasian validated photo-numeric scales andKorean validated photo-numeric scales for photo-ageing.
Insights from the Singapore/Malaysia Cross-sectional
Genetics Epidemiology Study (SMCGES) cohort. Manuscript submitted for publication in Skin Research andTechnology). Briefly, this group consists of more females(n = 661, 61.15%) than males. The average age of the participants is 26.15 ± 8.11 years old. Our participants are
166.03 ± 8.60 cm in height, 60.96 ± 12.60 kg in weight,
and have a BMI of 22.02 ± 3.99 kg/m2. Most participants
have a total monthly family income per capita of ≥ 6000
Singapore dollars (SGD) (n = 446, 41.26%) and most participants stay in HDB public housing (n = 664, 61.42%)(Table 1).
Validated photo‑numeric scales for wrinklingand photo‑ageing

We have previously shown that Caucasian scales forfour types of facial wrinkles (Crow’s Feet wrinkles, forehead wrinkles, glabellar frowns, and nasolabial folds)(Table S1) are suitable photo-numeric scales to evaluatefacial wrinkles on Chinese skin (Ng JY, & Chew FT. Comparisons between Caucasian validated photo-numericscales and Chinese validated photo-numeric scales forassessing facial wrinkles: Crow’s Feet wrinkles, foreheadwrinkles, glabellar frowns, and nasolabial folds. Insightsfrom the Singapore/Malaysia Cross-sectional Genetics Epidemiology Study (SMCGES) cohort. Manuscript
submitted for publication in Scientific Reports). We

Ng et al. Journal of Physiological Anthropology(2024) 43:14

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Table 2 Comparison between skin ageing phenotypes grades by assessors and participants evaluated using Spearman correlation,
Cohen’s kappa, and area under the receiver operating characteristic (ROC) curve (AUC)
Category

Phenotype

Test

The self-reported phenotype by the participant is comparedagainst:An investigatoradministeredgrading by ourassessors (lax­definitiona)
Value

WrinklesCrow’s Feet wrinkles

Spearman’s rank correlation (ρ)

0.331

Cohen’s kappa (κ)

0.101
d

Area under curve (AUC) (assessor)

0.671
e

Area under curve (AUC) (participant)
WrinklesForehead wrinkles

Spearman’s rank correlation (ρ)

0.286

Cohen’s kappa (κ)

0.134Area under curve (AUC) (assessor)

d

0.671
e

Area under curve (AUC) (participant)
WrinklesWrinklesPhoto-ageing

Glabellar frowns

Nasolabial folds

Wrinkling constituentof photo-ageing as measured by the Larnier scale

4.65 × ­10−16

9.98 × ­10−11

1.79 × ­10−13

1.45 × ­10−32

3.64 × ­10−20

6.71 × ­10−11

3.41 × ­10

Value
0.281
0.180
0.798
0.659
0.348
0.233
0.828

p valueValuep value

−21

0.346

1.04 × ­10−31

−15

0.109

1.00 × ­10−11

−15

0.772

1.09 × ­10−11

−7

0.731

1.34 × ­10−14

−32

0.307

4.90 × ­10−25

−25

0.105

2.75 × ­10−12

−18

0.765

2.60 × ­10−13

−12

0.704

5.66 × ­10−14

4.64 × ­10

4.42 × ­10
5.28 × ­10
1.16 × ­10
4.34 × ­10
5.79 × ­10
7.36 × ­10−121.60 × ­10

0.686

7.01 × ­10

Spearman’s rank correlation (ρ)

0.273

5.42 × ­10−20

0.289

2.92 × ­10−220.249

9.85 × ­10−170.170

5.74 × ­10−17

0.113

6.98 × ­10−16

0.065

6.27 × ­10−13

Area under curve (AUC) (assessor) d

0.795

8.70 × ­10−5

0.911

1.21 × ­10−7

0.840

5.10 × ­10−5

Area under curve (AUC) (participant)e

0.615

2.09 × ­10−3

0.614

2.30 × ­10−3

0.601

6.85 × ­10−3

Spearman’s rank correlation (ρ)

0.339

1.55 × ­10−30

0.351

1.25 × ­10−32

0.366

1.42 × ­1035

Cohen’s kappa (κ)

0.053

9.65 × ­10−4

0.190

2.58 × ­10−25

0.136

2.07 × ­10−13

Area under curve (AUC) (assessor) d

0.771

3.17 × ­10−14

0.847

7.40 × ­10−16

0.771

3.17 × ­10−14

Area under curve (AUC) (participant)e

0.839

3.49 × ­10−9

0.877

4.75 × ­10−110.856

5.34 × ­10−10

Spearman’s rank correlation (ρ)

0.246

2.24 × ­10−16

0.383

4.86 × ­10−39

0.329

1.09 × ­10−28

0.041

−5

0.149

−25

0.181

1.01 × ­10−24

−10

0.769

3.00 × ­10−6

−14

0.750

5.63 × ­10−10

−20

0.226

5.54 × ­10−14

−22

0.130

2.28 × ­10−12

−4

0.716

6.65 × ­10−7

−14

0.689

4.27 × ­10−11

Cohen’s kappa (κ)
Area under curve (AUC) (assessor)

d

0.769

Area under curve (AUC) (participant)

a

0.692

−29

An investigatoradministeredgrading by ourassessors (strict­definitionc)

Cohen’s kappa (κ)

ePhoto-ageing

0.722

p value

An investigatoradministeredgrading by
our assessors(moderately-strict­definitionb)

Spearman’s rank correlation (ρ)
Dyspigmentation constituent of photo-ageing
Cohen’s kappa (κ)
as measured by the Korean
Area under curve (AUC) (assessor) dscale
Area under curve (AUC) (participant)e

0.750
0.203
0.099
0.716
0.683

9.80 × ­10−63.00 × ­10−10

5.63 × ­10−11

1.63 × ­10−10

1.09 × ­10−7

6.65 × ­10−10

1.79 × ­10

0.853
0.806
0.278
0.155
0.727
0.722

5.41 × ­10
7.47 × ­10
3.02 × ­10
1.44 × ­10
8.34 × ­10
6.89 × ­10
1.18 × ­10

Lax definition: a phenotype is determined to be present if any of the three assessors identify itbModerately strict definition: a phenotype is determined to be present if at least two of the three assessors identify itcStrict definition: a phenotype is determined to be present only if all three assessors identify itdArea under curve (assessor): area under the receiver operating characteristic (ROC) curve (AUC) when the grading by the assessor is treated as the putative gold
standarde
Area under curve (participant): area under the receiver operating characteristic (ROC) curve (AUC) when the grading by the participant is treated as the putative gold
standard

have also previously identified that the Larnier scale anda Korean dyspigmentation scale (Table S1) are the mostsuitable scales currently available to identify photo-ageing on ethnic Chinese skin (Ng JY, & Chew FT. Comparisons between Caucasian validated photo-numeric scales
and Korean validated photo-numeric scales for photoageing. Insights from the Singapore/Malaysia Cross-sectional Genetics Epidemiology Study (SMCGES) cohort.
Manuscript submitted for publication in Skin Research

and Technology). Here, we will study these phenotypes
further and assess how these scales perform when usedby a trained assessor and an untrained participant.
Adjusting for potential variations in evaluation standards

We designed a three-tiered stringency-based classification model for the identification of wrinkles and photoageing (Table 2). This is done to introduce flexibility andaccount for variation between assessors. At the laxest

Ng et al. Journal of Physiological Anthropology(2024) 43:14

level of stringency, a skin ageing phenotype (e.g., Crow’s
Feet wrinkles) is determined to be present if any of thethree assessors identify it. At the other end of the spectrum, the strictest level of stringency requires all threeassessors to identify the phenotype in order to treat thephenotype as present. We also examined the middle
ground (i.e., a moderately strict level of stringency) in
which a skin ageing phenotype is determined to be present when at least two of the three assessors identify it.
WrinklesOur main objective is to use the validated photonumeric scale identified in “Validated photo-numericscales for wrinkling and photo-ageing” section and compare whether there are differences when wrinkles andphoto-ageing are identified by trained assessors andby untrained participants. Here, we report the results
for four wrinkle types: Crow’s Feet wrinkles, foreheadwrinkles, glabellar frowns, and nasolabial folds. The
concordance between self-reported Crow’s Feet wrinkles and Crow’s Feet wrinkles identified by our trained
assessors is fair (Spearman correlation = 0.281–0.346, pvalue ≤ 4.64 × ­10−21) (Table 2, Fig. 1i). After adjusting for
agreements due to chance, we still find a significant levelof agreement between the participant and our assessors(Cohen’s kappa = 0.101–0.180, p value ≤ 1.00 × ­10−11)(Table 2). When the evaluation by our trained assessors
is treated as the gold standard, the positive identificationof Crow’s Feet wrinkles by our participants ranges fromweak to fair (AUC = 0.671–0.798, p value ≤ 1.79 × ­10−11).
Taking the evaluation by the participant as the gold
standard, our assessors identified Crow’s Feet wrinkles
similarly well (AUC = 0.659–0.731, p value ≤ 1.16 × ­10−7)(Table 2, Fig. 3).
The concordance (Spearman correlation = 0.286–0.348,
p value ≤ 4.90 × ­10−25) (Table 2, Fig. 1ii) observed between
the identification of forehead wrinkles by our participants and by our assessors is also fair and is very similar

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to the results for Crow’s Feet wrinkles reported above.
Here, the level of agreement between the participant andour assessors is weak-to-fair (Cohen’s kappa = 0.105–
0.233, p value ≤ 2.75 × ­10−12) (Table 2). When taking theevaluation by our assessors as the gold standard, thecapability of our participants to identify forehead wrinkles is on average, fairly strong (AUC = 0.671–0.828, p
value ≤ 3.41 × ­10−11). Using the self-reported foreheadwrinkles as the gold standard, our assessors identified
forehead wrinkles similarly well (AUC = 0.686–0.704, p
value ≤ 7.01 × ­10−12) (Table 2, Fig. 3).
A fair level of concordance is observed between
glabellar frowns identified by our participants andglabellar frowns identified by our assessors (Spearman correlation = 0.249–0.289, p value ≤ 9.85 × ­10−17)(Table 2, Fig. 1iii). The level of agreement for glabellar frowns is weaker (Cohen’s kappa = 0.065–0.170, pvalue ≤ 6.27 × ­10−13) than the level of agreement for
Crow’s Feet wrinkles and forehead wrinkles (Table 2).
However, different grades of glabellar frowns can be verystrongly distinguished apart from one another by ourparticipants (AUC = 0.795 − 0.911, p value ≤ 8.70 × ­10−5)
and weakly by our assessors (AUC = 0.601 − 0.615, p
value ≤ 6.85 × ­10−3) (Table 2, Fig. 4).
Lastly, the concordance between self-reported andassessor-evaluated nasolabial folds is the highest (Spearman correlation = 0.339–0.366, p value ≤ 1.55 × ­10−30)(Table 2, Fig. 1iv). The level of agreement for nasolabial folds is similar to that for glabellar frowns (Cohen’s
Kappa = 0.053–0.190, p value ≤ 9.65 × ­10−4. Both the participant (AUC = 0.839 − 0.877, p-value ≤ 3.49 × ­10−9) andthe assessors (AUC = 0.771–0.847, p value ≤ 3.17 × ­10−14)
can strongly distinguish apart different severity grades ofnasolabial folds when either of them is taken as the gold
standard (Table 2, Fig. 4).
Overall, we interpret our results to indicate that
self-reported wrinkles (Crow’s Feet wrinkles, forehead wrinkles, glabellar frowns, and nasolabial folds)(See figure on next page.)
Fig. 1 i Bubble plots compare the evaluation of Crow’s Feet wrinkles by our three trained assessors and the self-reported evaluationby the participant. For the evaluation performed by our assessors, the phenotype is determined to be present only if all three assessors identify
it to be present. Larger circles indicate greater concordance between the two scales. Numbers in the circles are the number of concordances.
The sample size of each plot is 1081 participants. ii Bubble plots compare the evaluation of forehead wrinkles by our three trained assessors
and the self-reported evaluation by the participant. For the evaluation performed by our assessors, the phenotype is determined to be present
only if all three assessors identify it to be present. Larger circles indicate greater concordance between the two scales. Numbers in the circles
are the number of concordances. The sample size of each plot is 1081 participants. iii Bubble plots compare the evaluation of glabellar frowns
by our three trained assessors and the self-reported evaluation by the participant. For the evaluation performed by our assessors, the phenotype
is determined to be present only if all three assessors identify it to be present. Larger circles indicate greater concordance between the two scales.
Numbers in the circles are the number of concordances. The sample size of each plot is 1081 participants. iv Bubble plots compare the evaluationof nasolabial folds by our three trained assessors and the self-reported evaluation by the participant. For the evaluation performed by ourassessors, the phenotype is determined to be present only if all three assessors identify it to be present. Larger circles indicate greater concordance
between the two scales. Numbers in the circles are the number of concordances. The sample size of each plot is 1081 participantsNg et al. Journal of Physiological Anthropology(2024) 43:14

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Fig. 1 (See legend on previous page.)evaluated through Caucasian scales satisfactorily
approximate the wrinkle grades evaluated by ourassessors with a fair level of concordance.

Photo‑ageing

We found that self-reported photo-ageing is concordant with photo-ageing identified by our trained assessors

Ng et al. Journal of Physiological Anthropology(2024) 43:14

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Fig. 2 i Bubble plots compare photo-ageing as evaluated through studying wrinkle variations. Evaluations conducted by our three trained
assessors are compared with the self-reported evaluation by the participant. For the evaluation performed by our assessors, the phenotype
is determined to be present only if all three assessors identify it to be present. Larger circles indicate greater concordance between the two
scales. Numbers in the circles are the number of concordances. The sample size of each plot is 1081 participants. ii Bubble plots compare
photo-ageing as evaluated through studying dyspigmentation patterns. Evaluations conducted by our three trained assessors are comparedwith the self-reported evaluation by the participant. For the evaluation performed by our assessors, the phenotype is determined to be present
only if all three assessors identify it to be present. Larger circles indicate greater concordance between the two scales. Numbers in the circles are
the number of concordances. The sample size of each plot is 1081 participants(Table 2, Fig. 2). We have earlier shown that photo-ageing is best evaluated separately through the wrinkling
constituent and the dyspigmentation constituent (NgJY, & Chew FT. Comparisons between Caucasian validated photo-numeric scales and Korean validated photonumeric scales for photo-ageing. Insights from theSingapore/Malaysia Cross-sectional Genetics Epidemiology Study (SMCGES) cohort. Manuscript submitted for
publication in Skin Research and Technology).
We find that the level of concordance for the wrinkling constituent of photo-ageing between self-reported
data and data identified by our trained assessors (Spearman correlation = 0.246–0.383, p value ≤ 2.24 × ­10−16)
is fair and significant. Our participants and assessors also significantly agree with one another (Cohen’s
kappa = 0.041–0.181, p value ≤ 9.80 × ­10−5). When we
take the evaluation by our assessors as the gold standard,
our participants are fair-to-strongly capable of discerning the different levels of photo-ageing and identifying
the most appropriate level of severity which corresponds
to their skin (AUC = 0.769–0.853, p value ≤ 3.00 × ­10−6)(Table 2, Fig. 5). When we take the evaluation by theparticipants as the gold standard instead, we find similar results (AUC = 0.750–0.806, p value ≤ 5.63 × ­10−10)(Table 2, Fig. 5). This evidence suggests that the photoageing scale used to assess the wrinkling constituent ofphoto-ageing is sufficiently flexible to be used either by atrained assessor or an untrained participant.
Meanwhile, our participants and our assessors are alsoconcordant with one another in grading the dyspigmentation constituent of photo-ageing (Spearman correlation = 0.203–0.278, p value ≤ 1.63 × ­10−11). The level ofconcordance between the participants and assessors forthe dyspigmentation constituent of photo-ageing is both
fair and significant. As above, our participants and assessors also significantly agree with one another (Cohen’s
kappa = 0.099–0.155, p value ≤ 1.09 × ­10−10). Taking theevaluation by our assessors as the gold standard, participants are fairly capable of distinguishing apart different
grades of dyspigmentation and locating the severity level
which best resembles their skin (AUC = 0.716–0.727, pvalue ≤ 6.89 × ­10−4) (Table 2, Fig. 5). Likewise, when using
the evaluation by our participants as the gold standard, we find that our assessors often identify the sameNg et al. Journal of Physiological Anthropology(2024) 43:14

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Fig. 3 i Receiver operator characteristic (ROC) curves treat the evaluation by our three trained assessors as the gold standard and compare itwith the self-reported evaluation by the participant. AUC refers to the area under curve values of the corresponding ROC curves. ROC curves
describe grading data for Crow’s Feet wrinkles. ii Receiver operator characteristic (ROC) curves treat the evaluation by our three trained
assessors as the gold standard and compare it with the self-reported evaluation by the participant. AUC refers to the area under curve valuesof the corresponding ROC curves. ROC curves describe grading data for forehead wrinkles. iii Receiver operator characteristic (ROC) curves treat
the self-reported evaluation by the participant as the gold standard and compare it with the evaluation by our three trained assessors. AUC refers
to the area under curve values of the corresponding ROC curves. ROC curves describe grading data for Crow’s Feet wrinkles. iv Receiver Operator
Characteristic (ROC) curves treat the self-reported evaluation by the participant as the gold standard and compare it with the evaluation by ourthree trained assessors. AUC refers to the area under curve values of the corresponding ROC curves. ROC curves describe grading data for foreheadwrinklesseverity grade as the participants (AUC = 0.683–0.722, p
value ≤ 1.79 × ­10−10) (Table 2, Fig. 5).
Overall, our results lead us to interpret that selfreported photo-ageing levels on the Larnier scale
and a Korean dyspigmentation scale give satisfactory
approximations to the photo-ageing grades evaluatedby our assessors as both datasets have a fair level ofconcordance.

Discussion
Participant demographics

Singapore and Malaysia are racially diverse and composed of people of different ethnicities. Tapping on
this diversity, we were able to recruit participants fromdifferent ethnicities. Nonetheless, there is an unequal

proportion and sampling probability among some races,with only ethnic Chinese remaining as the major racial
group with a large enough sample size. In this study,
we focus on the ethnic Chinese; however, future analyses and descriptions of skin ageing in other races (e.g.,
Malays and Indians) will be conducted when the number of participants of the other races grows sufficiently
large through progressive, annual recruitment drives toexpand the Singapore/Malaysia Cross-section Genetics
Epidemiology Study (SMCGES) cohort.
Comparing self‑reported phenotypes with the evaluations
from our trained assessors

Skin ageing is a continuous process. One of the ways
to quantify skin ageing uses a series of validated

Ng et al. Journal of Physiological Anthropology(2024) 43:14

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Fig. 4 i Receiver operator characteristic (ROC) curves treat the evaluation by our three trained assessors as the gold standard and compare
it with the self-reported evaluation by the participant. AUC refers to the area under curve values of the corresponding ROC curves. ROC
curves describe grading data for glabellar frowns. ii Receiver operator characteristic (ROC) curves treat the evaluation by our three trained
assessors as the gold standard and compare it with the self-reported evaluation by the participant. AUC refers to the area under curve valuesof the corresponding ROC curves. ROC curves describe grading data for nasolabial folds. iii Receiver operator characteristic (ROC) curves treat
the self-reported evaluation by the participant as the gold standard and compare it with the evaluation by our three trained assessors. AUC refers
to the area under curve values of the corresponding ROC curves. ROC curves describe grading data for glabellar frowns. iv Receiver operator
characteristic (ROC) curves treat the self-reported evaluation by the participant as the gold standard and compare it with the evaluation by ourthree trained assessors. AUC refers to the area under curve values of the corresponding ROC curves. ROC curves describe grading data for nasolabialfolds

photo-numeric scales. Each photo-numeric scale consists of a set of photographs and successive photographs
show increased intensities of ageing either on a specificarea of the skin or on the overall appearance of theskin. Our previous work on wrinkles (Ng JY, & Chew
FT. Comparisons between Caucasian validated photonumeric scales and Chinese validated photo-numericscales for assessing facial wrinkles: Crow’s Feet wrinkles, forehead wrinkles, glabellar frowns, and nasolabial
folds. Insights from the Singapore/Malaysia Crosssectional Genetics Epidemiology Study (SMCGES)
cohort. Manuscript submitted for publication in Scientific Reports) and photo-ageing (Ng JY, & Chew FT.
Comparisons between Caucasian validated photonumeric scales and Korean validated photo-numericscales for photo-ageing. Insights from the Singapore/
Malaysia Cross-sectional Genetics Epidemiology Study(SMCGES) cohort. Manuscript submitted for publication in Skin Research and Technology) have shownthat the same skin is consistently evaluated to have thesame severity grade on the same photo-numeric scale
regardless of who is making the assessment. This led
us to wonder how the signs of skin ageing which are
identified by the participant compare with those which
are identified by our trained assessors. As participants
are not specially trained to identify the signs of ageingon their skin, it is possible to encounter considerable
variations in the assessments made by different participants. Well-designed photo-numeric scales would bebetter equipped to address such variations.

Ng et al. Journal of Physiological Anthropology(2024) 43:14

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Fig. 5 i Receiver operator characteristic (ROC) curves treat the evaluation by our three trained assessors as the gold standard and compares itwith the self-reported evaluation by the participant. AUC refers to the area under curve values of the corresponding ROC curves. ROC curves
describe grading data for photo-ageing as evaluated through studying wrinkle variations. ii Receiver operator characteristic (ROC) curves
treat the evaluation by our three trained assessors as the gold standard and compare it with the self-reported evaluation by the participant.
AUC refers to the area under curve values of the corresponding ROC curves. ROC curves describe grading data for photo-ageing as evaluatedthrough studying dyspigmentation patterns. iii Receiver operator characteristic (ROC) curves treat the self-reported evaluation by the participant
as the gold standard and compare it with the evaluation by our three trained assessors. AUC refers to the area under curve valuesof the corresponding ROC curves. ROC curves describe grading data for photo-ageing as evaluated through studying wrinkle variations. iv Receiver
operator characteristic (ROC) curves treat the self-reported evaluation by the participant as the gold standard and compare it with the evaluation
by our three trained assessors. AUC refers to the area under curve values of the corresponding ROC curves. ROC curves describe grading datafor photo-ageing as evaluated through studying dyspigmentation patterns

Our main objective here is to examine how photonumeric scales perform using self-reported data as
compared to using data evaluated by our trained
assessors. By making these comparisons, we aim tounderstand whether self-reported signs of skin ageing provide a satisfactory approximation to the signsof skin ageing identified by our assessors. To make thecomparisons fair across the entire spectrum of participants, we decided that it is not as meaningful to compare the self-reported data with the mean gradings
given by our trained assessors. Instead, we adapted thescores from our trained assessors into a three-tiered
stringency-based classification model (Table 2). The
three tiers are lax, moderately strict, and strict. Eachtier had been described earlier in the “Adjusting forpotential variations in evaluation standards” section.
Briefly, three datasets are derived from the assessors
and each dataset corresponds to one of the three tiers.
The three datasets use different thresholds to determine whether a phenotype is present or absent. In thedataset with the laxest level of stringency, a skin ageing
phenotype (e.g., Crow’s Feet wrinkles) is determined to
be present if any of the three assessors identify it. The
moderately-strict dataset is considerably more stringent in which a skin ageing phenotype is determined
to be present if at least two of the three assessors identify it. Lastly, the strict dataset gives the highest levelof stringency because here, a skin ageing phenotype

Ng et al. Journal of Physiological Anthropology(2024) 43:14

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Fig. 6 a Severity of forehead wrinkles stratified by age and sex. Forehead wrinkles are quantified as a grade on a validated photo-numeric scale.
The chi-square test p values for all the pair-wise comparisons conducted were corrected for multiple testing through the Bonferroni correction
method. The photo-numeric scale used for assessing forehead wrinkles can be found in Supplementary Table S1. b Presence or absence of pigment
spots stratified by age and sex. Pigment spots are quantified as a binary grade. Photos of pigment spots are obtained from Ferri’s Fast Facts
in Dermatology. Participants are deemed to have pigment spots if their skin appear similar to the photos from Ferri’s Fast Facts in Dermatology. The
chi-square test p values for all the pair-wise comparisons conducted were corrected for multiple testing through the Bonferroni correction method.
The citation for Ferri’s Fast Facts in Dermatology, in which photos used for assessing pigment spots are available, can be found in Supplementary
Table S1

is determined to be present only if all three assessors
identify it (Table 2).
We found that taking a moderately strict approach toidentifying signs of photo-ageing delivers the best results:
Spearman correlation between self-reported data and theevaluation by our trained assessors is fairly strong (Spearman correlation ρ = 0.383, p = 4.86 × ­10−39) for the wrinkling constituent of photo-ageing and is also fairly strong(Spearman correlation ρ = 0.278, p = 1.44 × ­10−20) for thedyspigmentation constituent of photo-ageing (Table 2).
Taking a moderately strict approach is also more effectivein identifying forehead wrinkles (Spearman correlation
ρ = 0.348, p = 4.34 × ­10−32) and glabellar frowns (Spearman correlation ρ = 0.289, p = 2.92 × ­10−22). Meanwhile,
Crow’s Feet wrinkles (Spearman correlation ρ = 0.346,
p = 1.04 × ­10−31) and nasolabial folds (Spearman correlation ρ = 0.366, p = 1.42 × ­10−35) are more concordant with
self-reported data when they are graded using a strictNg et al. Journal of Physiological Anthropology(2024) 43:14

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Fig. 7 a Distribution of Fitzpatrick Skin Types I to VI across participants with different severities of Crow’s Feet wrinkles. Crow’s Feet wrinkles
are quantified as a grade on a validated photo-numeric scale. The chi-square test p values for all the pair-wise comparisons conducted werecorrected for multiple testing through the Bonferroni correction method. The photo-numeric scale used for this phenotypic assessment can befound in Supplementary Table S1. b Distribution of Fitzpatrick Skin Types I to VI across participants with different severities of forehead wrinkles.
Forehead wrinkles are quantified as a grade on a validated photo-numeric scale. The chi-square test p values for all the pair-wise comparisonsconducted were corrected for multiple testing through the Bonferroni correction method. The photo-numeric scale used for this phenotypicassessment can be found in Supplementary Table S1. c Distribution of Fitzpatrick Skin Types I to VI across participants with different severities
of glabellar frowns. Glabellar frowns are quantified as a grade on a validated photo-numeric scale. The chi-square test p values for all the pair-wisecomparisons conducted were corrected for multiple testing through the Bonferroni correction method. The photo-numeric scale used for thisphenotypic assessment can be found in Supplementary Table S1. d Distribution of Fitzpatrick Skin Types I to VI across participants with different
severities of nasolabial folds. Nasolabial folds are quantified as a grade on a validated photo-numeric scale. The chi-square test p values for all
the pair-wise comparisons conducted were corrected for multiple testing through the Bonferroni correction method. The photo-numeric scale
used for this phenotypic assessment can be found in Supplementary Table S1. e Distribution of Fitzpatrick Skin Types I to VI across participants
with different severities of the wrinkling constituent of photo-ageing. The wrinkling constituent of photo-ageing is quantified as a gradeon a validated photo-numeric scale. The Chi-square test p-values for all the pair-wise comparisons conducted were corrected for multiple testing
through the Bonferroni correction method. The photo-numeric scale used for this phenotypic assessment can be found in Supplementary Table S1.
f Distribution of Fitzpatrick Skin Types I to VI across participants with different severities of the dyspigmentation constituent of photo-ageing. The
dyspigmentation constituent of photo-ageing is quantified as a grade on a validated photo-numeric scale. The chi-square test p values for all
the pair-wise comparisons conducted were corrected for multiple testing through the Bonferroni correction method. The photo-numeric scale
used for this phenotypic assessment can be found in Supplementary Table S1

Ng et al. Journal of Physiological Anthropology(2024) 43:14

approach (Table 2). We extended our stringency-basedclassification model to other signs of skin ageing (e.g.,wrinkling in other areas, sagging, dyspigmentation) andobserved similar results (Table S2). We found that themoderately strict and strict levels of stringency in identifying a phenotype yield the highest concordance with
self-reported signs of skin ageing.
Overall, we interpret our findings to be indicative thatparticipants often recognise and report that they possess
the same skin ageing phenotype identified by our assessors. We found that the behaviour in which participants
report the presence of a given sign of ageing on theirskin can be best captured by the middle tier of our threetiered stringency-based classification model. Putting it
another way, most participants are self-aware of ageing
signs on their skin early on, especially so when such signs
are noticeable by more than one independent assessor.
While the specific behaviour of the data varies fromphenotype to phenotype, the bottom line remains thesame: signs of skin ageing identified by the participant

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are concordant with signs of skin ageing identified by
our assessors using the same photo-numeric scale. These
concordances are significant and persist even after we
use methods to account for any differences in evaluation
standards. Self-reported signs of skin ageing thus provide
a satisfactory approximation to the signs of skin ageing
identified by our assessors.
Interpreting the statistical indices

We attempted to evaluate six different facets of skin ageing by comparing self-reported data with assessor-evaluated data for six different skin ageing phenotypes. Our
primary goal is to characterise how the ascertainment ofskin ageing phenotypes differs among different people.
The six skin ageing phenotypes were evaluated using
photo-numeric scales. Each of the four wrinkle phenotypes (Crow’s Feet wrinkles, forehead wrinkles, glabellarfrowns, nasolabial folds) and two photo-ageing phenotypes (the wrinkling constituent of photo-ageing, and the(See figure on next page.)
Fig. 8 a Distribution of Fitzpatrick Skin Types I to VI across participants with different cheek laxities. Cheek laxity is quantified as a gradeon a validated photo-numeric scale. The chi-square test p values for all the pair-wise comparisons conducted were corrected for multiple testing
through the Bonferroni correction method. The photo-numeric scale used for this phenotypic assessment can be found in Supplementary Table S1.
b Distribution of Fitzpatrick Skin Types I to VI across participants with different amounts of fat tissue. The amount of fat tissue is quantified as a gradeon a validated photo-numeric scale. The chi-square test p values for all the pair-wise comparisons conducted were corrected for multiple testing
through the Bonferroni correction method. The photo-numeric scale used for this phenotypic assessment can be found in Supplementary Table S1.
c Distribution of Fitzpatrick Skin Types I to VI across participants with different severities of solar lentigines. Solar lentigines are quantified as a gradeon a validated photo-numeric scale. The chi-square test p values for all the pair-wise comparisons conducted were corrected for multiple testing
through the Bonferroni correction method. The photo-numeric scale used for this phenotypic assessment can be found in Supplementary Table S1.
d Distribution of Fitzpatrick Skin Types I to VI across participants with different severities of wrinkles under the eyes. Wrinkles under the eyes arequantified as a grade on a validated photo-numeric scale. The chi-square test p values for all the pair-wise comparisons conducted were corrected
for multiple testing through the Bonferroni correction method. The photo-numeric scale used for this phenotypic assessment can be foundin Supplementary Table S1. e Distribution of Fitzpatrick Skin Types I to VI across participants with different eyebrow positioning heights. Eyebrow
positioning heights are quantified as a grade on a validated photo-numeric scale. The chi-square test p values for all the pair-wise comparisonsconducted were corrected for multiple testing through the Bonferroni correction method. The photo-numeric scale used for this phenotypicassessment can be found in Supplementary Table S1. f Distribution of Fitzpatrick Skin Types I to VI across participants with different severities
of eyebags. Eyebags are quantified as a grade on a validated photo-numeric scale. The chi-square test p values for all the pair-wise comparisonsconducted were corrected for multiple testing through the Bonferroni correction method. The photo-numeric scale used for this phenotypicassessment can be found in Supplementary Table S1. g Distribution of Fitzpatrick Skin Types I to VI across participants with different severities
of droopy eyelids. Droopy eyelids are quantified as a grade on a validated photo-numeric scale. The chi-square test p values for all the pair-wisecomparisons conducted were corrected for multiple testing through the Bonferroni correction method. The photo-numeric scale used for thisphenotypic assessment can be found in Supplementary Table S1. h Distribution of Fitzpatrick Skin Types I to VI across participants with different
severities of perioral wrinkles. Perioral wrinkles are quantified as a grade on a validated photo-numeric scale. The chi-square test p values for all
the pair-wise comparisons conducted were corrected for multiple testing through the Bonferroni correction method. The photo-numeric scale
used for this phenotypic assessment can be found in Supplementary Table S1. i Distribution of Fitzpatrick Skin Types I to VI across participants
with different severities of Melomental folds. Melomental folds are quantified as a grade on a validated photo-numeric scale. The chi-square
test p values for all the pair-wise comparisons conducted were corrected for multiple testing through the Bonferroni correction method. The
photo-numeric scale used for this phenotypic assessment can be found in Supplementary Table S1. j Distribution of Fitzpatrick Skin Types I to VI
across participants with different severities of jawline sagging. Jawline sagging is quantified as a grade on a validated photo-numeric scale. The
chi-square test p values for all the pair-wise comparisons conducted were corrected for multiple testing through the Bonferroni correction method.
The photo-numeric scale used for this phenotypic assessment can be found in Supplementary Table S1. k Distribution of Fitzpatrick Skin Types I
to VI across participants with different severities of sagging and/or wrinkling of the neck skin. Sagging and/or wrinkling of the neck skin is quantified
as a grade on a validated photo-numeric scale. The chi-square test p values for all the pair-wise comparisons conducted were corrected for multiple
testing through the Bonferroni correction method. The photo-numeric scale used for this phenotypic assessment can be found in Supplementary
Table S1

Ng et al. Journal of Physiological Anthropology

Fig. 8 (See legend on previous page.)(2024) 43:14

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Ng et al. Journal of Physiological Anthropology(2024) 43:14

dyspigmentation constituent of photo-ageing) were evaluated on a dedicated scale.
As skin ageing is a multi-faceted phenomenon, a thorough understanding of skin ageing requires us to takea multi-pronged approach. Here, we will discuss threestatistical tools used in our current assessment to bettercharacterise and understand how the ascertainment ofskin ageing phenotypes differs among different people.
Spearman correlation coefficientsFirst, we investigated the correlation between Crow’s Feetwrinkles ascertained by our assessors and self-reported
by our participants (Fig. 1i). We have established in previous work that photo-numeric scales are the superior
method for ascertaining skin ageing phenotypes [18]. By
their nature, photo-numeric scales comprise a sequenceof photographs showing a stepwise increase in the severity of skin ageing phenotypes (e.g., Crow’s Feet wrinkles)
between successive photographs. Spearman correlation
measures the association between two categorical ordinalvariables. Since the ascertainment of Crow’s Feet wrinkles is done by both the assessor and the participant on
the same photo-numeric scale, both evaluations are categorical and ordinal in nature. Spearman correlation is anappropriate tool to numerically describe this association.
The numerical value of Spearman’s ρ ranges from 0 to 1,
in which 0 implies no correlation and 1 implies complete
correlation. While it is understood that values closer to
0 show a weak correlation and values closer to 1 show astrong correlation, the naming practices for the strengthof Spearman correlation coefficients vary from field tofield [1, 5]. We applied the qualitative interpretations
used in healthcare and related fields to interpret Spearman correlation values on skin ageing phenotypes. The
strengths are interpreted as follows: 0.00: no correlation,
0.01–0.20: weak, 0.21–0.50: fair, 0.51–0.70: moderate,
0.71–0.90: very strong, 0.91–1.00: perfect. Our results
indicate that the strength of Spearman correlations
between self-reported data and assessor-evaluated data is
fair for the six different facets of skin ageing studied inthis paper.
Next, we take a closer look at the bubble plots illustrating the concordance between phenotypes ascertained by
the assessor and the participant for Crow’s Feet wrinkles(Fig. 1i), forehead wrinkles (Fig. 1ii), and glabellar frowns(Fig. 1iii). Bubbles that lie on the (0,0), (1,1), (2,2), (3,3),and (4,4) coordinates indicate agreements in the severityof the phenotype. For instance, a value of 435 at the (0,0)
coordinate for Crow’s Feet wrinkles indicates that 435
participants self-reported that they do not have Crow’s
Feet wrinkles. Crow’s Feet wrinkles were also ascertained
as absent by our trained assessors in the same 435 participants (Fig. 1i).

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Bubbles at all the other coordinates indicate a difference in judgement between the severity of the phenotype. The larger the difference between the two
numbers (e.g., (0,2) compared to (0,1)), the greater
the disparity in the assessments. We report 412 participants at the (0,1) coordinate. This means that 412
participants self-reported that they have Crow’s Feetwrinkles, but these Crow’s Feet wrinkles are just barely
present. Our trained assessors ascertained that Crow’s
Feet wrinkles were absent in the same 412 participants.
Crow’s Feet wrinkles are found near the eyes. As
explained in our previous work [18], participants havea very keen awareness of the state of the skin aroundtheir eyes. This could explain why a large number ofparticipants picked up on very fine and barely visible
Crow’s Feet wrinkles when the assessors did not. Thisillustrates one of the key strengths of self-reported data:
it enables phenotypes to be swiftly detected as soon as
they emerge. The main trade-off is that these 412 evaluations lower the Spearman correlation coefficient.
In contrast to 412 participants at the (0,1) coordinate,
there are only 43 participants at the (0,2) coordinate, 3participants at the (0,3) coordinate and 2 participants atthe (0,4) coordinate. This indicates that once a wrinkle
has become somewhat visible, it will almost certainly
be detected by our assessors and graded accordingly.
In other words, our assessors reliably detect the majority of wrinkles, with the only exception being very fine,barely present wrinkles on the verge of emergence.
Upon looking at the bubble plots illustrating the concordance between phenotypes ascertained by the assessor and the participant for nasolabial folds (Fig. 1iv),
the wrinkling constituent of photo-ageing (Fig. 2i)and the dyspigmentation constituent of photo-ageing(Fig. 2ii), we see that the assessors are stricter than theparticipants. Using the dyspigmentation constituent ofphoto-ageing as an example, there are 176 participantsat the (1,0) coordinate compared to just 92 participantsat the (0,1) coordinate. This means that our trained
assessors ascertained that there is some dyspigmentation in 176 participants. The same 176 participants
self-reported that they do not have dyspigmentation(Fig. 2ii). One possible explanation of our results isthat the photo-numeric scale for the dyspigmentation
constituent of photo-ageing puts equal emphasis on
both the number and the size of pigment spots. Participants may have believed that they had pigment spots
only if such spots exceeded a certain quantity or size.
As a result, the number of participants ascertained tohave dyspigmentation patterns is higher than the selfreported numbers. One method to potentially resolve
this issue is to separate the evaluation of dyspigmentation patterns into two parts: quantity and size.

Ng et al. Journal of Physiological Anthropology(2024) 43:14

In summary, a close evaluation of the data shows thatthe main contributing factor that lowered the Spearman
correlation coefficient for wrinkling phenotypes is a fineline between whether a wrinkle is absent or just barely
present and on the verge of emergence. The main contributing factor that lowered the Spearman correlation
coefficient for the dyspigmentation constituent of photoageing is a decision call on whether many small pigment
spots and a few large pigment spots should be classified
as the same grade.
Beyond Spearman correlation, we also looked at otherstatistical indices so that our data can be described in amore thorough manner.
Cohen’s kappa coefficients

The second statistical tool we used to characterise how
the ascertainment of skin ageing phenotypes differs
among different people is Cohen’s kappa (κ). Cohen’s
kappa is a more robust measure of inter-rater agreement.
It is superior to calculating the percentage of agreement
between two raters because Cohen’s kappa accounts foragreements due to chance [14]. The numerical value of
Cohen’s kappa ranges from 0 to 1, in which 0 implies noagreement and 1 implies complete agreement.
The Cohen’s kappa for the six different facets of skinageing studied in this paper lies between weak and fair.
The behaviour of this data can be understood using
Crow’s Feet wrinkles as an example. As the bulk of ourparticipants are in their mid-twenties (Table 1) where
wrinkles are just beginning to emerge, it is challenging to discern between very fine, barely visible wrinkles,
and no wrinkles at all. Upon a closer look at the bubble
plot for Crow’s Feet wrinkles (Fig. 1i), most of our participants self-reported either no Crow’s Feet wrinkles, orvery fine, barely visible Crow’s Feet wrinkles. These twogroups have roughly equal proportions. In contrast, ourtrained assessors are more cautious and evaluated thatmost of our participants have no Crow’s Feet wrinkles.
By superimposing these two distribution charts, we get
perfect agreement on the absence of Crow’s Feet wrinkles
for approximately half of our participants, and a small
discrepancy (i.e., a fine line between no wrinkles and very
fine, barely visible wrinkles) for approximately the otherhalf of our participants.
The value of Cohen’s kappa decreases as long as there
is a discrepancy, be it a small discrepancy or a large discrepancy. From the perspective of this metric, agreements and disagreements happen in a roughly 1-to-1
ratio. As a result, the value of Cohen’s kappa is small.
At first glance, it may be tempting to interpret that
there is a weak agreement between skin ageing phenotypes ascertained by the participant and the assessors. However, a closer look reveals that the scale ofPage 17 of 22

the disagreements is mostly minuscule. In summary, aclose evaluation of the data shows that the main contributing factor to a low Cohen’s Kappa is still just a fineline between whether a wrinkle is absent or just barely
present.
Area under the curve (AUC) of the receiver operator
characteristic (ROC) curve

To be thorough in our analysis, we also looked at the
AUC of the ROC curve. The numerical value of AUC
ranges from 0.51 to 1.00. The closer the value is to 1.00,
the higher the sensitivity and specificity.
Our results indicate that the AUC curves for selfreported data and assessor-evaluated data are similar no
matter whether the assessors or the participant is treatedas the gold standard.
We interpret that sensitivity and specificity for theascertainment of Crow’s Feet wrinkles (AUC = 0.659–
0.731, p value ≤ 1.16 × 10 − 7), forehead wrinkles(AUC = 0.686–0.704, p value ≤ 7.01 × ­10−12), glabellarfrowns (AUC = 0.601–0.615, p value ≤ 6.85 × ­10−3), nasolabial folds (AUC = 0.771–0.847, p value ≤ 3.17 × ­10−14),
the wrinkling constituent of photo-ageing (AUC = 0.750–
0.806, p value ≤ 5.63 × ­10−10), and the dyspigmentation constituent of photo-ageing (AUC = 0.683–0.722,
p value ≤ 1.79 × ­10−10) can be summarised qualitatively as fair, fairly strong, weak, strong, strong, and fairrespectively.
The AUC measures the accuracy of diagnostic tests.
In our earlier analyses of the other statistical indices,
we have established that our assessors reliably detect
the majority of wrinkles, except very fine, barely present
wrinkles on the verge of emergence. This is represented
graphically by the ROC in which the sensitivity and specificity of detecting a wrinkle increases with the severityof the wrinkle. The size and shape of the AUC changes intandem with the ROC.
Our data shows that except for glabellar frowns, treating self-reported data as the gold standard for different
facets of skin ageing is a suitable approach because thisapproach has high sensitivity and high specificity.
In summary, we ascertained six different facets of skinageing by comparing self-reported data with assessorevaluated data. We derived three statistical indices tosummarise the behaviour of the data. Each of the three
indices provides a different perspective on the behaviourof data. While some skin ageing phenotypes do not perform that well on one index (e.g., Spearman correlation
values), they perform well on another index (e.g., AUC).
Hence, it is necessary to look at the data from multiple
perspectives in order to give a balanced assessment of thesuitability of using self-reported skin ageing data.

Ng et al. Journal of Physiological Anthropology(2024) 43:14

Cross‑validating the self‑reported dataset with existing
literature

To further explore the suitability of using self-reported
skin ageing data, we also cross-validated age- and sexdependent trends from our self-reported dataset withexisting literature. To enable a common basis for comparison, we looked specifically at the literature on Asianskin. Here, we will demonstrate our comparisons using
two morphologically distinct skin ageing phenotypes:
forehead wrinkles (Fig. 6a) and pigment spots (Fig. 6b).
We found that the trends from our self-reported dataset
and the trends from the literature are the same.
It has been reported that Japanese men below theage of 65 develop more severe forehead wrinkles thanage-matched Japanese women [22]. We report thatthe ascertainment of forehead wrinkles through selfreported means also returns the same trend in our
SMCGES cohort. Overall, 62% of Chinese males in ourcurrent assessment report some degree of foreheadwrinkles while just 43% of Chinese females report thesame (p value = 6.60 × ­10−30) (Fig. 6a). When we stratify the data by age, we observe that males in each age
group have significantly more severe forehead wrinkles than age-matched females. Males aged 18 − 20 (pvalue = 7.62 × ­10−8), aged 21 − 25 (p value = 1.76 × ­10−20),
aged 26 − 30 (p value = 1.64 × ­10−5), aged 31 − 35 (pvalue = 2.38 × ­10−13), aged 36 − 40 (p value = 2.11 × ­10−8),
and aged above 40 (p value = 2.18 × ­10−61) have significantly more severe forehead wrinkles than age-matchedfemales (Fig. 6a). Wrinkle severity shows a trend that isstrongly dependent on age in both males and females.
The proportion of males with forehead wrinkles experiences a persistent stepwise increase from 48% in males
aged 18 − 20, to 59% in males aged 21 − 25, 59% in males
aged 26 − 30, 72% in males aged 31 − 35, 79% in males
aged 36 − 40, and 93% in males aged above 40 (chi-square
test for trend p value < 0.001) (Fig. 6a). The proportion of females with forehead wrinkles also experiencesa persistent stepwise increase from 33% in females aged
18 − 20, to 37% in females aged 21 − 25, 44% in females
aged 26 − 30, 53% in females aged 31 − 35, 75% in females
aged 36 − 40, and 94% in females aged above 40 (chisquare test for trend p value < 0.001) (Fig. 6a). Our results
cross-validated the available literature on how foreheadwrinkles are more severe in males below the age of 65 ascompared to age-matched females.
A different study reported that Japanese women below
the age of 30 develop pigment spots [9] and a Korean
study observed that hyperpigmented macules are themain form of pigmentary changes with age in women[7]. We report that the ascertainment of pigment spots
through self-reported means also returns the same
trend in our SMCGES cohort. Overall, 16% of Chinese

Page 18 of 22

females in our current assessment report having pigment spots while just 13% of Chinese males report thesame (p value = 3.47 × ­10−3) (Fig. 6b). Next, to align ourcomparison with the available literature, we focusedon age-matched comparisons for age groups below theage of 30. Significantly more females aged 18 − 20 (pvalue = 9.82 × ­10−6), aged 21 − 25 (p value = 3.92 × ­10−1),
and aged 26 − 30 (p value = 5.13 × ­10−4) have pigment
spots than age-matched males (Fig. 6b). 24% of females
aged 18 − 20 have pigment spots while just 19% of males
report the same. We observe the same trend for participants aged 21 − 25–16% of females aged 21 − 25 havepigment spots while just 14% of males report the same.
Lastly, 11% of females aged 26 − 30 and 9% of males aged
26 − 30 have pigment spots (Fig. 6b). Our results crossvalidated the available literature on how age-related pigmentary changes are prevalent in women below the ageof 30, more so than in men.
All in all, our data demonstrates that the ascertainmentof skin ageing phenotypes through self-reported means
returns age- and sex-specific trends that are consistent
with existing literature. More importantly, our results
indicate that while our Spearman correlation values are
weak, they do not hamper our ability to use self-reported
phenotypic data to attain meaningful insights.
Understanding and interpreting our results in the context
of a sunny tropical environment

The participants in our SMCGES cohort are recruitedand assessed in Singapore and Malaysia, both of which
are sunny tropical countries. Based on Singapore’s
National Environmental Agency [16], the Ultraviolet
Index is graded as ‘Extreme’ from 1 to 3 pm on a typical day. Consequentially, participants from both countries are regularly exposed to large amounts of ultraviolet(UV) radiation. UV radiation is a well-studied risk factor
for skin ageing. Hence, the skin ageing results from the
SMCGES cohort should be interpreted in the context ofan environment with high UV radiation exposure.
Meanwhile, as a result of evolutionary adaptation toenvironments with different amounts of UV radiation,some variation exists between the skin of people of different ethnicities. The effect of skin variation in otherraces (e.g., Malays and Indians) will be analysed when thenumber of participants of these races grows sufficiently
large through ongoing efforts to expand the SMCGES
cohort.
Despite analysing just the ethnic Chinese participants,
we still observe a wide diversity in skin types. Skin typeis measured using the Fitzpatrick Skin Type classification
system. This is a classification system on whether the skinburns or tans in response to prolonged sun exposure.
Participants with Fitzpatrick Skin Types III, IV, V, or VI

Ng et al. Journal of Physiological Anthropology(2024) 43:14

are better protected from ultraviolet radiation from the
Sun than participants with Fitzpatrick Skin Types I or II[10].
We wondered whether the six skin ageing scales studied in this paper considered the effects of skin type when
designing their respective photo-numeric scales. Using
Crow’s Feet wrinkles as an example, the proportion ofparticipants with no Crow’s Feet wrinkles (i.e., photo 0
on the photo-numeric scale) and having Fitzpatrick Skin
Types I through VI are treated as the reference group.
Participants with few Crow’s Feet wrinkles (i.e., photo1) on the photo-numeric scale have a significantly different composition of Fitzpatrick Skin Types as comparedto the reference group (chi-square p value = 1.95 × ­10−5).
Similar comparisons were made between the reference group and participants with moderate Crow’s Feetwrinkles (i.e., photo 2) (chi-square p value = 3.98 × ­10−4),
severe Crow’s Feet wrinkles (i.e., photo 3) (chi-square pvalue = 2.02 × ­10−3), and very severe Crow’s Feet wrinkles(i.e., photo 4) (chi-square p value = 2.33 × ­10−3) (Fig. 7a).
Similar observations were made using the scale forforehead wrinkles. The proportion of participants withno forehead wrinkles (i.e., photo 0) and Fitzpatrick Skin
Types I through VI are treated as the reference group. Statistically significant comparisons were observed between
the reference group and participants with few foreheadwrinkles (i.e., photo 1) (chi-square p value = 3.02 × ­10−4),
moderate forehead wrinkles (i.e., photo 2) (chi-square
p value = 4.31 × ­10−10), severe forehead wrinkles (i.e.,
photo 3) (chi-square p value = 9.47 × ­10−9), and very
severe forehead wrinkles (i.e., photo 4) (chi-square pvalue = 1.48 × ­10−5) (Fig. 7b).
Evaluations for glabellar frowns returned similar
results. The proportion of participants with no glabellar frowns (i.e., photo 0) and Fitzpatrick Skin Types I
through VI are treated as the reference group. Statistically significant comparisons were observed between
the reference group and participants with few glabellarfrowns (i.e., photo 1) (chi-square p value = 2.25 × ­10−6),
moderate glabellar frowns (i.e., photo 2) (chi-square pvalue = 1.30 × ­10−5), severe glabellar frowns (i.e., photo 3)(chi-square p value = 4.86 × ­10−4), and very severe glabellar frowns (i.e., photo 4) (chi-square p value = 2.67 × ­10−4)(Fig. 7c).
Similar results emerged from evaluations for nasolabial folds. The proportion of participants with no nasolabial folds (i.e., photo 0) and Fitzpatrick Skin Types I
through VI are treated as the reference group. Statistically significant comparisons were observed between
the reference group and participants with few nasolabial
folds (i.e., photo 1) (chi-square p value = 4.92 × ­10−5),
moderate nasolabial folds (i.e., photo 2) (chi-square pvalue = 1.06 × ­10−6), severe nasolabial folds (i.e., photo 3)

Page 19 of 22(chi-square p value = 1.02 × ­10−9), and very severe nasolabial folds (i.e., photo 4) (chi-square p value = 4.10 × ­10−10)(Fig. 7d).
Next, for the wrinkling constituent of photo-ageing,
we treat participants with grade 1 photo-ageing on thephoto-ageing scale and Fitzpatrick Skin Types I throughVI as the reference group. Statistically significant comparisons were observed between the reference groupand participants with grade 2 photo-ageing (chi-square
p value = 3.59 × ­10−7), grade 3 photo-ageing (chi-square
p value = 5.68 × ­10−11), grade 4 photo-ageing (chi-square
p value = 4.06 × ­10−4), grade 5 photo-ageing (chi-square pvalue = 3.94 × ­10−3), and grade 6 photo-ageing (chi-square
p value = 3.48 × ­10−4) (Fig. 7e).
Lastly, when the dyspigmentation constituent of photoageing is evaluated, similar results also emerge. The proportion of participants with photo 0 photo-ageing on thisphoto-ageing scale and Fitzpatrick Skin Types I throughVI are treated as the reference group. Statistically significant comparisons were observed between the reference group and participants with photo 1 photo-ageing(chi-square p value = 1.01 × ­10−6), photo 2 photo-ageing(chi-square p value = 3.76 × ­10−11), photo 3 photo-ageing(chi-square p value = 8.24 × ­10−7), photo 4 photo-ageing(chi-square p value = 5.11 × ­10−10), and photo 5 photoageing (chi-square p value = 1.55 × ­10−5) (Fig. 7f ).
Our data shows that each of the six skin ageing scales
studied in this paper has incorporated key elements ofthe six Fitzpatrick Skin Types in their respective scale
design process. This is because the proportion of Fitzpatrick Skin Types I through VI in one photo-numeric grade
is significantly different from the proportion of Fitzpatrick Skin Types I through VI in another photo-numeric
grade. Thus, the fact that the participants from the
SMCGES cohort have differences in skin variation does
not appear to complicate the ability of the six scales studied in this paper to grade wrinkling and photo-ageing
effectively.
The effect of skin variation on other skin ageing
phenotypes

Using the same method, we also studied 24 other skinageing phenotypes using either photo-numeric scales orphotographs (Table S1). We wondered whether the otherskin ageing scales also considered the effects of skin typewhen designing their respective scale. We found that thephoto-numeric scales for eleven other skin ageing phenotypes have also incorporated key elements of the six
Fitzpatrick Skin Types in their respective scale design
process. These eleven phenotypes can be broadly categorised based on their anatomical locations: ageing-relatedchanges to the skin around the cheek (Fig. 8a–c), theeyes (Fig. 8d–g), and the lower face (Fig. 8h–k). The 13

Ng et al. Journal of Physiological Anthropology(2024) 43:14

remaining phenotypes should be thoroughly evaluatedagain with scales designed to better capture key elementsof the six Fitzpatrick Skin Types (Figures S1i–S1xiii).
Future work

Our observation that self-reported skin ageing data iscomparable to the evaluations done by our trained assessors contributes to discussions in the field on the best
ways to evaluate skin characteristics. A recent study
found that self-reported skin colour using a photonumeric scale is concordant with objective measurements [15]. Localised changes in skin pigmentation are
one of the key hallmarks of ageing skin [17]. Furthermore,
Chinese skin is known to become dyspigmented moreeasily with age [21]. We therefore see a potential future
direction to validate self-reported skin dyspigmentation
patterns using suitable photo-numeric scales. This future
work can be conducted when more photo-numeric scales
for dyspigmentation patterns are developed and properly
validated.
The main weakness of this validation study is that while
we have used a large sample size of 1081 ethnic Chineseparticipants, scaling this further up to our full dataset of3365 participants is labour-intensive. As a result, it will
be challenging to deliver any findings in a timely manner. Meanwhile, a meta-analysis published by our team
[23] uncovered significant epidemiological risk factors(e.g., age, gender, ethnicity, air pollution, smoking, nutrition, and sun exposure) associated with skin ageing phenotypes. The ability to use self-reported skin ageing data
enables us to scale up our findings in large-scale epidemiological studies, such as in a population of 3365 participants, in a reasonable time frame. This in turn enables
us to explore the association between various risk factors
and different forms of skin ageing in a larger population.
At present, there are a growing number of studies using
self-reported skin data [3, 4, 8, 11, 13, 15, 19, 20]. Here,
we showed that self-reported signs of skin ageing provide a satisfactory approximation to the signs of skin ageing identified by our assessors. Using self-reported signsof skin ageing improves the scalability of our findings
in large-scale epidemiological studies. Future work will
focus on investigating the associations between risk factors and a variety of skin ageing phenotypes.

Conclusion
In conclusion, our study contributes to ongoing discussions on different methods to evaluate skin ageing. We
examined the suitability of using self-reported skin ageing data and compared it to skin ageing assessments
evaluated by our trained assessors. Here, we demonstratethat self-reported data on wrinkling and photo-ageing isconcordant with assessments conducted by our trained

Page 20 of 22

assessors. The same skin is consistently evaluated to havethe same severity grade on the same photo-numeric scale
regardless of whether the participant or one of the assessors is making the assessment. These findings underscore
the reliability and validity of self-reported skin ageing
data, providing a valuable tool for large-scale epidemiological studies. Our future work will focus on exploring
the associations between risk factors and different formsof skin ageing to advance our understanding of this complex ageing phenomenon.
Abbreviations
SMCGES	Singapore/Malaysia Cross-section Genetics Study
ISAAC​	
International Study of Asthma and Allergies inChildhood
IBM SPSS/PC	International Business Machines Corporation Statistical Package for Social Scientists Personal Computer(SPSS/PC)
EOS	Electro-Optical System
IS	Image stabilisation
USM	Ultrasonic motorEF	Electronic focus
SD	Standard deviation
SGD	Singapore dollars
HDB (Public housing)	Public housing constructed by the Singapore Housing Development Board
BMI	Body mass indexcm	Centimetresm2	Squared metreskg	Kilograms
ROC	Receiver operator characteristic
AUC​	Area under the curve

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s40101-​024-​00361-8.
Additional file 1: Figure S1i: Distribution of Fitzpatrick Skin Types I to VI
across participants with different appearance of cheek pores. Whether
cheek pores appear larger is quantified as a binary grade. Photos of large
cheek pores are obtained from Volume 2 of the Skin Ageing Atlas.
Participants are deemed to have cheek pores that appear larger if theirskin cheek pores appear similar to the photos from Volume 2 of the Skin
Ageing Atlas. The Chi-square test p-values for all the pair-wise comparisons conducted were corrected for multiple testing through the
Bonferroni Correction method. The citation for Volume 2 of the Skin
Ageing Atlas, in which photos used for assessing whether cheek pores
appear larger are available, can be found in Supplementary Table S1.
Figure S1ii: Distribution of Fitzpatrick Skin Types I to VI across participants
with different severities of cheek folds. Cheek folds are quantified as a
grade on a validated photonumeric scale. The Chi-square test p-values for
all the pair-wise comparisons conducted were corrected for multiple
testing through the Bonferroni Correction method. The photonumericscale used for this phenotypic assessment can be found in Supplementary
Table S1. Figure S1iii: Distribution of Fitzpatrick Skin Types I to VI acrossparticipants with or without permanent erythema. Permanent erythema isquantified as a binary grade. Photos of permanent erythema are obtained
from Volume 2 of Dermatology (Third Edition). Participants are deemed tohave permanent erythema if the skin of their cheeks appears similar to thephotos from Volume 2 of Dermatology (Third Edition). The Chi-square test
p-values for all the pair-wise comparisons conducted were corrected for
multiple testing through the Bonferroni Correction method. The citation
for Volume 2 of Dermatology (Third Edition), in which photos used forassessing permanent erythema are available, can be found in Supplementary Table S1. Figure S1iv: Distribution of Fitzpatrick Skin Types I to VI acrossparticipants with different severities of telangiectasias. Telangiectasias areNg et al. Journal of Physiological Anthropology(2024) 43:14

quantified as a grade on a validated photo-numeric scale. The
Chi-square test p-values for all the pair-wise comparisons conducted
were corrected for multiple testing through the Bonferroni Correction
method. The photo-numeric scale used for this phenotypic assessment
can be found in Supplementary Table S1. Figure S1v: Distribution of
Fitzpatrick Skin Types I to VI across participants with or without
horizontal interocular wrinkles. Horizontal interocular wrinkles arequantified as a binary grade. Photos of horizontal interocular wrinkles
are obtained from Volume 2 of the Skin Ageing Atlas. Participants are
deemed to have horizontal interocular wrinkles if their skin appear
similar to the photos from Volume 2 of the Skin Ageing Atlas. The
Chi-square test p-values for all the pair-wise comparisons conducted
were corrected for multiple testing through the Bonferroni Correction
method. The citation for Volume 2 of the Skin Ageing Atlas, in whichphotos used for assessing horizontal interocular wrinkles are available,
can be found in Supplementary Table S1. Figure S1vi: Distribution of
Fitzpatrick Skin Types I to VI across participants with different fullness of
the upper lip. Upper lip fullness is quantified as a grade on a validated
photonumeric scale. The Chi-square test p-values for all the pair-wisecomparisons conducted were corrected for multiple testing throughthe Bonferroni Correction method. The photonumeric scale used forthis phenotypic assessment can be found in Supplementary Table S1.
Figure S1vii: Distribution of Fitzpatrick Skin Types I to VI acrossparticipants with different fullness of the lower lip. Lower lip fullness isquantified as a grade on a validated photonumeric scale. The
Chi-square test p-values for all the pair-wise comparisons conducted
were corrected for multiple testing through the Bonferroni Correction
method. The photonumeric scale used for this phenotypic assessment
can be found in Supplementary Table S1. Figure S1viii: Distribution of
Fitzpatrick Skin Types I to VI across participants with or without milia.
Milia are quantified as a binary grade. Photos of milia are obtained from
Skin Disease Diagnosis and Treatment. Participants are deemed to havemilia if their skin appear similar to the photos from Skin Disease
Diagnosis and Treatment. The Chi-square test p-values for all thepair-wise comparisons conducted were corrected for multiple testing
through the Bonferroni Correction method. The citation for Skin
Disease Diagnosis and Treatment, in which photos used for assessing
milia are available, can be found in Supplementary Table S1. Figure S1ix:
Distribution of Fitzpatrick Skin Types I to VI across participants with orwithout freckles. Freckles are quantified as a binary grade. Photos of
freckles are obtained from Ferri’s Fast Facts in Dermatology. Participants
are deemed to have freckles if their skin appear similar to the photos
from Ferri’s Fast Facts in Dermatology. The Chi-square test p-values for
all the pair-wise comparisons conducted were corrected for multiple
testing through the Bonferroni Correction method. The citation for
Ferri’s Fast Facts in Dermatology, in which photos used for assessing
freckles are available, can be found in Supplementary Table S1. Figure
S1x: Distribution of Fitzpatrick Skin Types I to VI across participants withor without solar comedones. Solar comedones are quantified as abinary grade. Photos of solar comedones are obtained from Roxburgh’s
Common Skin Diseases and Skin Disease Diagnosis and Treatment.
Participants are deemed to have solar comedones if their skin appear
similar to the photos from Roxburgh’s Common Skin Diseases and Skin
Disease Diagnosis and Treatment. The Chi-square test p-values for all
the pair-wise comparisons conducted were corrected for multiple
testing through the Bonferroni Correction method. The citations for
Roxburgh’s Common Skin Diseases and Skin Disease Diagnosis andTreatment, in which photos used for assessing solar comedones areavailable, can be found in Supplementary Table S1. Figure S1xi:
Distribution of Fitzpatrick Skin Types I to VI across participants with orwithout uneven skin pigmentation. Uneven skin pigmentation isquantified as a binary grade. Photos of uneven skin pigmentation are
obtained from Ferri’s Fast Facts in Dermatology. Participants are
deemed to have uneven skin pigmentation if their skin appear similar
to the photos from Ferri’s Fast Facts in Dermatology. The Chi-square test
p-values for all the pair-wise comparisons conducted were corrected
for multiple testing through the Bonferroni Correction method. The
citation for Ferri’s Fast Facts in Dermatology, in which photos used forassessing uneven skin pigmentation are available, can be found in
Supplementary Table S1. Figure S1xii: Distribution of Fitzpatrick Skin

Page 21 of 22

Types I to VI across participants with or without sebaceous hyperplasia.
Sebaceous hyperplasia is quantified as a binary grade. Photos ofsebaceous hyperplasia are obtained from Diagnosis of Aging Skin
Diseases, Roxburgh’s Common Skin Diseases, and Skin Disease Diagnosis
and Treatment. Participants are deemed to have sebaceous hyperplasia if
their skin appear similar to the photos from Diagnosis of Aging Skin
Diseases, Roxburgh’s Common Skin Diseases, and Skin Disease Diagnosis
and Treatment. The Chi-square test p-values for all the pair-wisecomparisons conducted were corrected for multiple testing through the
Bonferroni Correction method. The citations for Diagnosis of Aging Skin
Diseases, Roxburgh’s Common Skin Diseases, and Skin Disease Diagnosis
and Treatment, in which photos used for assessing sebaceous hyperplasia
are available, can be found in Supplementary Table S1. Figure S1xiii:
Distribution of Fitzpatrick Skin Types I to VI across participants with orwithout seborrheic keratosis. Seborrheic keratosis is quantified as a binary
grade. Photos of seborrheic keratosis are obtained from Volume 2 ofDermatology (Third Edition), Handbook of Skin Diseases, and Skin Disease
Diagnosis and Treatment. Participants are deemed to have seborrheic
keratosis if their skin appear similar to the photos from Volume 2 ofDermatology (Third Edition), Handbook of Skin Diseases, and Skin Disease
Diagnosis and Treatment. The Chi-square test p-values for all the pair-wisecomparisons conducted were corrected for multiple testing through the
Bonferroni Correction method. The citations for Volume 2 of Dermatology(Third Edition), Handbook of Skin Diseases, and Skin Disease Diagnosis
and Treatment, in which photos used for assessing seborrheic keratosis
are available, can be found in Supplementary Table S1. Table S1: Sources ofthe validated photo-numeric scales. Table S2: Comparison between skinageing phenotypes grades by assessors and participants evaluated using
Spearman’s Rank Correlation, Cohen’s Kappa, sensitivity, and specificity.
Acknowledgements
All validated photo-numeric scales used in the analysis were collected frompublications in Table S1. We extend our sincerest gratitude to all participants
for their contributions to this study.
Authors’ contributions
F.T.C. conceived and supervised the current review study. J.Y.N. conducted theliterature review, analysed and interpreted the data, and wrote the manuscript.
The three trained assessors are J.Y.N., H.Y.Z, and T.Q.L. J.Y.N., H.Y.Z, and T.Q.L analysed the data. J.Y.N, H.Y.Z., and T.Q.L. assisted in recruiting study participants
and data collation. All authors read and approved the final manuscript.
Funding
F.T.C. received grants from the National University of Singapore (N-154–000038–001), Singapore Ministry of Education Academic Research Fund (R-154–
000-191–112; R-154–000-404–112; R-154–000-553–112; R-154–000-565–112;
R-154–000-630–112; R-154–000-A08-592; R-154–000-A27-597; R-154–
000-A91-592; R-154–000-A95-592; R154-000-B99-114), Biomedical ResearchCouncil (BMRC) (Singapore) (BMRC/01/1/21/18/077; BMRC/04/1/21/19/315;
BMRC/APG2013/108), Singapore Immunology Network (SIgN-06–006;
SIgN-08–020), National Medical Research Council (NMRC) (Singapore)(NMRC/1150/2008; OFIRG20nov-0033), National Research Foundation (NRF)(Singapore) (NRF-MP-2020–0004), Singapore Food Agency (SFA) (SFS_RND_
SUFP_001_04; W22W3D0006), and the Agency for Science Technology andResearch (A*STAR) (Singapore) (H17/01/a0/008; and APG2013/108). The
funding agencies had no role in the study design, data collection and analysis,
decision to publish, or preparation of the manuscript.
Availability of data and materials
All data used and included in this study are available from the corresponding
author (F.T.C.).

Declarations
Ethics approval and consent to participate
This study was conducted in accordance with the principles of the Declarationof Helsinki and Good Clinical Practices and in compliance with local regulatory
requirements. The cross-sectional studies in Singapore were conducted on
the National University of Singapore (NUS) campus annually between 2005

Ng et al. Journal of Physiological Anthropology(2024) 43:14

and 2023 with the approval of the Institutional Review Board (Reference
Code: NUS-07–023, NUS-09–256, NUS-10–445, NUS-13–075, NUS-14–150, andNUS-18–036) and by the Helsinki declaration, of which, participants between
2011 and 2022 participated in the current skin ageing study (Reference Code:NUS-2020–495). The cross-sectional studies in Malaysia were held at Universiti
Tunku Abdul Rahman (UTAR) Campus and Sunway University. Ethical approval
was granted from the Scientific and Ethical Review Committee of UTAR
(Reference Code: U/SERC/03/2016) and the Sunway University Research Ethics
Committee (Reference Code: SUREC 2019/029 and SUREC 2022/049). Before
the data collection, all participants involved signed an informed consent form.
Consent for publication
All authors have read and consented to the publication of this manuscript.
Competing interests
F.T.C reports grants from the National University of Singapore, Singapore Ministry of Education Academic Research Fund, Singapore Immunology Network,
National Medical Research Council (NMRC) (Singapore), Biomedical ResearchCouncil (BMRC) (Singapore), National Research Foundation (NRF) (Singapore),
Singapore Food Agency (SFA), and the Agency for Science Technology andResearch (A*STAR) (Singapore), during the conduct of the study; and consulting fees from Sime Darby Technology Centre; First Resources Ltd; Genting
Plantation, Olam International, and Syngenta Crop Protection, outside thesubmitted work. The other authors declare no other competing interests.
Author details
1
Department of Biological Sciences, Faculty of Science, National Universityof Singapore, Singapore 117543, Singapore. 2Allergy and Molecular Immunology
Laboratory, Lee Hiok Kwee Functional Genomics Laboratories, Block S2, Level 5,
14 Science Drive 4, Lower Kent Ridge Road, Singapore 117543, Singapore.
Received: 26 October 2023 Accepted: 4 May 2024

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