Combined Effect of Ambient Temperature and Relative Humidity on Skin Aging Phenotypes in the Era of Climate Change: Results From an Indian Cohort Study

Abstract

Background: There is no doubt that global warming, with its extreme heat events, is having an increasing impact on human health. Heat is not independent of ambient temperature but acts synergistically with relative humidity (RH) to increase the risk of several diseases, such as cardiovascular and pulmonary diseases. Although the skin is the organ in direct contact with the environment, it is currently unknown whether skin health is similarly affected. Objective: While mechanistic studies have demonstrated the mechanism of thermal aging, this is the first epidemiological study to investigate the effect of long-term exposure to heat index (HI) as a combined function of elevated ambient temperature and RH on skin aging phenotypes in Indian women. Methods: The skin aging phenotypes of 1510 Indian women were assessed using the Score of Intrinsic and Extrinsic Skin Aging (SCINEXA™) scoring tool. We used data on ambient temperature and RH, combined into an HI with solar ultraviolet radiation (UVR), and air pollution (particulate matter <2.5 µm [PM2.5]; nitrogen dioxide [NO2]) from secondary data sources with a 5-year mean residential exposure window. An adjusted ordinal multivariate logistic regression model was used to assess the effects of HI on skin aging phenotypes. Results: HI increased pigmentation such as hyperpigmented macula on the forehead (odds ratios [OR]: 1.31, 95% confidence interval [CI]: 1.12, 1.54) and coarse wrinkles such as crow's feet (OR: 1.17, 95% CI: 1.05, 1.30) and under-eye wrinkles (OR: 1.3, 95% CI: 1.15, 1.47). These associations were robust to the confounding effects of solar UVR and age. Prolonged exposure to extreme heat, as indicated by high HI, contributes to skin aging phenotypes. Conclusion: Thus, ambient temperature and RH are important factors in assessing the skin aging exposome.

Full text

Combined Effect of Ambient Temperature
and Relative Humidity on Skin Aging
Phenotypes in the Era of Climate Change:
Results From an Indian Cohort Study
Nidhi Singh, PhD,
a
Claudia Wigmann, PhD,
a
Prince Vijay, MTech,
b
Harish C. Phuleria, PhD,
b
Sara Kress, PhD,
a
Gopa Majmudar, PhD,
c
Rong Kong, PhD,
c
Jean Krutmann, MD,
ak
and Tamara Schikowski, PhD
ak
Abstract: Background: There is no doubt that global warming, with its extreme heat events, is having an
increasing impact on human health. Heat is not independent of ambient temperature but acts synergistically with
relative humidity (RH) to increase the risk of several diseases, such as cardiovascular and pulmonary diseases.
Although the skin is the organ in direct contact with the environment, it is currently unknown whether skin health is
similarly affected.
Objective: While mechanistic studies have demonstrated the mechanism of thermal aging, this is the first
epidemiological study to investigate the effect of long-term exposure to heat index (HI) as a combined function of
elevated ambient temperature and RH on skin aging phenotypes in Indian women.
Methods: The skin aging phenotypes of 1510 Indian women were assessed using the Score of Intrinsic and
Extrinsic Skin Aging (SCINEXA) scoring tool. We used data on ambient temperature and RH, combined into an HI
with solar ultraviolet radiation (UVR), and air pollution (particulate matter <2.5 mm [PM
2.5]
; nitrogen dioxide [NO
2
])
from secondary data sources with a 5-year mean residential exposure window. An adjusted ordinal multivariate
logistic regression model was used to assess the effects of HI on skin aging phenotypes.
a
From the IUF-Leibniz Research Institute for Environmental Medicine, D€
usseldorf,
Germany;
b
Environmental Science and Engineering Department, IIT Bombay,
Mumbai, India; and
c
Amway Corporation, Ada, Michigan, USA.
Address reprint requests to Jean Krutmann, MD, IUF-Leibniz Research
Institute for Environmental Medicine, Auf’m Hennekamp 50, 40225
D€
usseldorf, Germany, E-mail: jean.krutmann@iuf-duesseldorf.de
k
Shared last author.
City, state, and country in which the work was done: CASAI—Delhi,
Mumbai, and Bengaluru, India.
We are grateful to the participants of the CASAI study for their wholehearted
participation in the study. Furthermore, we would like to thank Khurshid Pia
Jahan for collecting the skin aging data on the Indian women (CASAI). We
also acknowledge the support of Delwin Varghese, Ritwik Raj, Anurag Gupta,
Uday Kumar, and Aviral Agarwal in the field monitoring and air pollution
data collection from the 3 Indian cities. We thank Dr. Patricia Ferrini
Rodrigues for assigning the ERA5 datasets.
The authors contributed to the article with the following responsibilities:
Conceptualization: N.S., T.S., and J.K. Data curation: N.S., C.W., P.V., and
S.K. Investigation: N.S., C.W., T.S., and J.K. Formal analysis: N.S. Project
administration: T.S., J.K., and H.P. Software: N.S. and P.V. Validation: N.S.
and P.V. Visualization: N.S., P.V., T.S., and J.K. Writing—original draft
preparation: N.S. Writing—review and editing: N.S., T.S., J.K., C.W., S.K.,
P.V., H.P., G.M., and R.K.
The data on skin aging scores are not available to be shared publicly due to the
data protection of study participants. The air pollution data for PM2.5 in
India are freely available to download from https://indiaaq.blog/2020/11/18/
satellite-pm-india/.TheNO
2
data for India are not available publicly. The
weather data for India are freely available to download from CPCB (https://
cpcb.nic.in/). Weather data from ERA 5 reanalysis data are freely available to
download from https://www.ecmwf.int/en/forecasts/dataset/ecmwf-
reanalysis-v5. The data on UVI are available from TEMIS for India (http://
www.temis.nl/index.php).
Procedures and questionnaires for standard surveys were reviewed and
approved by the local authorities. Additionally, the clinical examination was
conducted per the recommendations for research on human subjects, adopted
by the 18th World Medical Assembly, Declaration of Helsinki, and later
revisions.
1
All participants voluntarily provided written informed consent.
J.K. is serving as a scientific consultant to Amway. R.K. is an employee of
Amway. None of the other authors have any conflicts of interest.
The clinical part of the CASAI study was supported and funded by Amway
Corporation, Ada, Michigan. The funders had no role in the study design,
data collection and analysis, decision to publish, or preparation of the article.
The IUF is funded by the federal and state governments—the Ministry of
Culture and Science of North Rhine-Westphalia (MKW) and the Federal
Ministry of Education and Research (BMBF).
DOI: 10.1089/derm.2024.0301
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Results: HI increased pigmentation such as hyperpigmented macula on the forehead (odds ratios [OR]: 1.31, 95%
confidence interval [CI]: 1.12, 1.54) and coarse wrinkles such as crow’s feet (OR: 1.17, 95% CI: 1.05, 1.30) and under-
eye wrinkles (OR: 1.3, 95% CI: 1.15, 1.47). These associations were robust to the confounding effects of solar UVR
and age. Prolonged exposure to extreme heat, as indicated by high HI, contributes to skin aging phenotypes.
Conclusion: Thus, ambient temperature and RH are important factors in assessing the skin aging exposome.
Capsule Summary
•First epidemiological evidence linking ambient tempera-
ture and humidity to facial aging.
•Ambient temperature and humidity are associated with
forehead hyperpigmentation and coarse wrinkles in Indian
women.
•Associations were robust to confounding by UVR and age.
INTRODUCTION
From 1850–1900 to 2010–2019, the likely ranges of total
anthropogenic global surface temperature increase were
0.8–1.3C, with an average of 1.07C.
2
The Intergovernmental
Panel on Climate Change in its latest assessment report (AR6)
has stated in the strongest terms that this temperature increase
will lead to an unavoidable increase in multiple climate and
weather extremes, which will pose multiple risks to humanity.
3–5
Indeed, there is growing evidence that global warming poses a
major threat to human health.
6,7
Importantly, human health is
more severely affected by the combination of ambient tempera-
ture and relative humidity (RH) than by temperature alone.
8,9
Therefore, slightly different types of heat indices (HIs) have
been developed, to model the combined effect of ambient tem-
perature plus RH.
10,11
Increases in such HI have been found to
be associated with increased mortality and morbidity.
12
Strongly
affected health outcomes included infectious diseases, cardiovas-
cular and respiratory diseases, and mental health problems.
6,13
However, a less studied topic in epidemiology is the effect of
extreme heat on human skin aging phenotypes.
Environmental factors may act in concert to promote the
development of coarse wrinkles and pigmentary irregular-
ities.
14–16
Existing epidemiological evidence has reported the
potential effects of ultraviolet radiation (UVR) and air pollution
on skin aging phenotypes.
17–21
UVR has been reported to be the
most studied and most damaging environmental factor contrib-
uting to skin aging.
22
Recently, the skin aging exposome study
by Krutmann et al
22
proposed ambient temperature as a novel
exposomal factor inducing skin aging. However, epidemiolog-
ical evidence to support this hypothesis is lacking. Mechanistic
studies conducted in the past have proposed that heat contrib-
utes to the development of skin aging,
22,23
and in this context,
the term “thermal aging”was coined.
24
Thermal aging mainly
refers to studies in which acute heat shock responses (>40C
skin temperature) were induced in human skin, for example,
by short-term (90 minutes) exposure to heating pads at 43C.
Such treatments induced neovascularization and altered the com-
position of the extracellular matrix (reviewed in Krutmann
et al
22
) by mechanisms that probably involve the activation of
transient receptor potential vanilloid (TRPV-3) receptors in skin
cells.
25–27
However, it is not known whether long-term (ie, sev-
eral years of exposure to slightly elevated [up to 1C]) ambient
temperature, as is the case with global warming, can affect the
development of skin aging signs such as coarse wrinkles and
irregular pigmentation (age/pigment spots, lentigines) in the gen-
eral population.
Studies on the combined effect of ambient temperature and
RH on skin aging phenotypes are of particular interest to coun-
tries such as India, which has experienced an unprecedented
increase in extreme heat episodes over the past few decades,
28
making heat 1 of the leading risk factors for mortality in India.
29
Classified as a tropical monsoon climate due to its poor infra-
structure, high population exposure, and low adaptive capacity
to cope with extreme heat, India’s population is highly vulnera-
ble to the harmful combination of temperature and humidity.
At the same time, India’s dangerously high levels of air pollution
and UV index (UVI) put the population at even greater risk.
Therefore, it is important to assess the potential health effects of
prolonged high HI exposure in the Indian population. In the
present epidemiological study, we aimed to investigate the effect
of elevated HI in a 5-year mean exposure window on skin aging
phenotypes in Indian women in a cross-sectional study design
from 3 different geographical areas. The study regions consid-
ered in India are reported to be influenced by contrasting levels
of HI, high levels of air pollution, and solar UVR, and therefore
the choice of the Indian cohort seemed advantageous for the
comparison of effects.
MATERIALS AND METHODS
Study Cohort
For this analysis, we used data from the ongoing climate, air pol-
lution, and skin aging in Indian women (CASAI) cohort in
India. The cohort started in 2018 and recruited 1510 Indian
women aged 20–91 years between 2018 and 2019 from 3 metro-
politan cities in India: Delhi, Mumbai, and Bengaluru. Only
women with skin phototypes 3 and 4 were included. In each
city, approximately 500 women were recruited through local
clinical laboratories. The cities were selected based on their dif-
ferent levels of air pollution and different types of weather.
Standard survey procedures and questionnaires were reviewed
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and approved by local authorities in India. In addition, the clini-
cal examination was conducted in accordance with the recom-
mendations for research involving human subjects adopted by
the 18th World Medical Assembly, the Declaration of Helsinki,
and subsequent revisions.
1
All participants gave voluntary writ-
ten informed consent. The study was conducted in all 3 areas
during the dry season.
Study Region
The cities represent different levels of air pollution and different
types of weather. Delhi represents a highly polluted city. It has
distinct seasonal patterns, such as hot summers, cold and dry
winters and rainy seasons, and a short period of autumn and
spring. The temperature varies from 46C in summer to around
0C in winter. Mumbai is moderately polluted and moderately
hot with high humidity throughout the year with a distinct rainy
season (JJAS). The temperature varies from 36C in hot
summers to 19C in mild winters. Bengaluru is a less polluted
city and due to its high altitude, Bengaluru usually enjoys a
more moderate climate throughout the year with a distinct wet
and dry season, although occasional heat waves can make the
summer somewhat uncomfortable. Bengaluru receives rainfall
from both the northeast and southwest monsoons, with good
rainfall between May and October and the wettest month being
September, followed by October and August. Temperatures
range from 16C in January to 35C in April and May.
Skin Aging Assessment
Skin aging was assessed at 1 time-point using a validated score,
the Score of Intrinsic and Extrinsic Skin Aging (SCINEXA),
30
to assess extrinsic skin aging characteristics of the face. The
SCINEXA scores are based on the Tschachler and Morizot
31
photoreference scales. This tool has been used previously in sev-
eral epidemiological studies.
17,19,30,32,33
The number of pigment
spots (‡3 mm in diameter) on the forehead and cheeks was
scored as follows: 0 (no spots), 1 (1–10 spots), 2 (11–50 spots),
and 3 (>50 spots). The 6 major wrinkle signs (wrinkles on the
forehead, in the frown lines, in the crow’s feet, under the eyes,
on the upper lip, and in the nasolabial fold) were scored as fol-
lows: 0 (not present) to 6 (very severe). The score was originally
developed for Caucasians and was adapted for the Indian popu-
lation in this study by incorporating specific skin aging charac-
teristics of the Indian population according to the Skin Aging
Atlas by Bazin and Flament.
34
The assessment and scoring were
carried out by trained staff on-site using a standardized proto-
col.
30
In addition, facial photographs were taken of each woman
using Visia-CR (Canfield Scientific, Inc.). The skin aging scores
were re-categorized into 4 categories, with 0 indicating no signs
of skin aging, the original scores 1 and 2 being combined as low
severity, 3 and 4 as moderate severity, and 5 and 6 as high
severity. To illustrate the spectrum of severity of skin aging
phenotypes in women of this cohort, we have added some
photographs and corresponding gradings in Supplementary
Figure S2.
Environmental Exposures
Daily mean temperature (T
mean
) and RH were obtained from
the website of the Central Pollution Control Board (CPCB) of
India (https://cpcb.nic.in/). Individual exposures were modeled
based on the nearest monitoring station. Daily T
mean
and RH
data were obtained from ERA 5 reanalysis data at 10 ·10 km
(www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5)
35
as
observed data were missing for study participants in Mumbai.
The validation of the data is presented in Supplementary
Appendix SA1. Solar UVR exposure was determined by the
(UVI, which describes the expected daily peak of erythemal UV
irradiance at ground level. The UVI is based on the hour of
maximum UVR per day. The UVI includes both ultraviolet B
(UVB) and ultraviolet A (UVA) radiation but is mainly deter-
mined by the UVB dose. The spatio-temporal data of UVI have
been extracted from the Tropospheric Emission Monitoring
Internet Service (www.temis.nl/index.php) for each city with a
resolution of 25 ·25 km.
36
Daily data on particulate matter
with an aerodynamic diameter of <2.5 mm(PM
2.5
)were
obtained from a satellite-based ambient PM
2.5
database devel-
oped for India at a high resolution of 1 ·1km.
37
Ground-level
nitrogen dioxide (NO
2
) measurements were conducted at 60
outdoor sites in each of the 3 cities during the winter of 2019–
2020. Individual outdoor residential NO
2
exposures for partici-
pants in the 3 cities were estimated using the inverse distance-
weighted method.
38,39
In general, all data were extracted for the
last 5 years from the date of examination at the participant’s
coordinates, and values were averaged as a 5-year mean, with
some deviations to account for missing data.
For analysis, we used the HI developed by Rothfusz
10
as a
main exposure because it has been widely used in the past to
determine the risk of heat-related morbidity.
11,40,41
The HI was
calculated as
HI ¼8:7847 þ1:6114 ·T0:012308 ·T2
þ2:3385 0:14612 ·Tþ2:2117 ·103·T2
ðÞ
·RH
þ0:016425 þ7:2546 ·104·T3:582 ·T2
ðÞ
·RH2
where T is the temperature (C) and RH is the relative humidity
(%). A detailed description of the further adjustments made for
the HI computation is accessible elsewhere (www.wpc.ncep
.noaa.gov/html/heatindex_equation.shtml).
42
Statistical Analyses
All women for whom information on skin aging phenotypes
was available were included in the analysis, so our final study
sample comprised 1510 women with complete information on
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skin aging phenotypes. We performed a correlation analysis
between the different covariates used in the present analysis.
The choice of correlation test depends on the structure of the
variables, whether continuous, nominal, or ordinal.
43
A correla-
tion coefficient (r) >0.7 was considered to indicate high multi-
collinearity. Because of the ordinal nature of skin aging
outcomes, we analyzed the association of extrinsic skin aging
with long-term exposure to HI using a multivariate ordinal
logistic regression model, adjusting for potential confounders
such as UVI and age, which have been shown to be associated
with skin aging in previous studies.
17,18
UVI is considered an
important risk factor for skin aging in response to growing con-
cerns about the potential increase in UVR-induced skin damage
due to ozone depletion.
44
Failure to adjust for UV radiation
would therefore introduce uncertainty into the results. We used
1 IQR increment for UVI. Predictor variables were centered
around zero before fitting the model to reduce problems of mul-
ticollinearity. In addition, we assessed effect modification based
on passive smoking exposure and age (<50 years and >50 years)
to investigate whether passive smoking exposure and age modify
the association between HI and skin aging characteristics. We
performed some sensitivity analyses to test the robustness of the
model. First, we used T
mean
as the main exposure variable
instead of HI. Second, because UVI and air pollutants (NO
2
and
PM
2.5
) were highly correlated, the main model was not adjusted
for the potential effect of air pollutants, but we performed a sen-
sitivity analysis replacing UVI with PM
2.5
/NO
2
. Third, we per-
formed a separate analysis using only participants from Delhi,
who are exposed to a wide range of temperatures throughout
the year.
All results from the above analyses were presented as odds
ratios (ORs) with 95% CIs for a 1C increase in HI. The data
were analyzed using the R statistical software version 4.1.2; the
R package “MASS”(version 7.3-54) was used for subsequent
analysis.
45
RESULTS
Description of the Study Cohort
and SCINEXA Scores
Summary statistics are shown in Table 1. The analyses included
1510 women with a mean age of 45.5 years (SD –15.5). Most of
the participants were nonsmokers (99.7%), but about one-third
of the participants (35.2%) were exposed to passive smoking.
The skin aging characteristics are shown in Table 2. The partici-
pants showed the presence of both skin aging phenotypes,
facial pigmentation, and coarse wrinkles. Based on the SCI-
NEXA scores, the signs of skin aging, when present, were gen-
erally in the low to moderate category. However, some
participants had high severity signs of facial aging.
Climate and air pollution exposures at residence
Summary statistics of weather variables and exposure to air
pollutants at participants’addresses are shown in Table 1.
The long-term mean (–SD) HI, T
mean
,RH,andUVIfor
CASAI participants were 29.0C(–1.0), 26.5C(–0.6), 66.3%
(–8.0) and 10.3 (–1.5), respectively. The long-term mean
(–SD) PM
2.5
,andNO
2
concentrations were 104.7 (–40.7) lg/m
3
and 49.4 (–14.1) lg/m
3
, respectively, at the addresses of the
CASAI participants, which are much higher than the WHO and
Indian national air quality standards (PM
2.5
:40lg/m
3
;NO
2
:
50 lg/m
3
).
46
It should also be noted that the average annual
temperature and air pollution levels in the Indian subconti-
nent are higher than in most countries in the northern
hemisphere.
47,48
HI and Extrinsic Skin Aging Traits
The association between long-term HI and extrinsic skin aging
features is shown in Figure 1. The univariate model showed a
significant association between HI and hyperpigmented macules
on the forehead and coarse wrinkles (frown lines, crow’s feet,
under the eyes). Notably, the associations between HI and
extrinsic skin aging traits remained positive and significant after
adjusting the model for the confounding effects of solar UVI
and age. In the adjusted model, each 1C increase in HI was
associated with a 1.31-fold (95% CI: 1.12, 1.54) increase in the
odds of “hyperpigmented macula on the forehead,”a 1.17-fold
(95% CI: 1.05, 1.30) increase in the odds of “crow’s feet,”and a
1.3-fold (95% CI: 1.15, 1.47) increase in the odds of “eye
TABLE 1. Study Characteristics of CASAI Cohort
Participants
Individual Characteristics
a
CASAI (N = 1510)
Age 45.5 –15.5
Female 1510 (100.0%)
Smoking status
Never smoker 1506 (99.7%)
Ex-smoker 3 (0.2)
Smoker 1 (0.1)
Passive smoking 531 (35.2%)
Age >50 years 624 (41.3%)
HI (C) 29.0 –1.0
T
mean
(C) 26.5 –0.6
RH (%) 66.3 –8.0
UVI 10.3 –1.5
PM
2.5
(lg/m
3
) 104.7 –40.7
NO
2
(lg/m
3
) 49.4 –14.1
The values represent 5-year mean averages at the participant’s address
prior to investigation.
a
Mean –standard deviation is presented for continuous parameters, N (%)
is presented for categorical parameters.
CASAI, climate, air pollution, and skin aging in Indian women; HI, heat
index; NO
2
, nitrogen dioxide; PM2.5, particulate matter <2.5 mm; RH, relative
humidity; UVI, ultraviolet index.
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wrinkles.”Negative and significant associations were also found
between HI and pigmented spots on the cheeks and “nasolabial
wrinkles.”
Effect Modification
Smoking or exposure to passive smoking may increase the risk
of developing extrinsic signs of skin aging.
49
Therefore, we per-
formed effect modification to investigate whe-ther exposure to
passive smoking modified the association between HI and skin
aging. As most of the participants were nonsmokers, the analysis
was restricted to passive smoking exposure. The interaction
between HI and passive smoking was insignificant for all skin
aging. The effect modification by age over 50 years also shows
no significant interaction between HI and age.
Sensitivity Analysis
In order to determine the robustness of our core model, we con-
ducted several sensitivity analyses (Table 3). The results of
replacing HI with T
mean
as our main exposure variable show
similar results to the main model. Similarly, replacing UVI with
PM
2.5
/NO
2
in the main model gave similar results. Thus, the
sensitivity analysis results support that the association between
HI and skin aging is robust to the use of an alternative tempera-
ture metric or the inclusion of air pollutants as a potential con-
founding factor. In a third step, we restricted the analysis to
participants from Delhi. The results showed no significant asso-
ciations between HI and signs of skin aging, due to the low
power of the sample for the analysis.
DISCUSSION
To the best of our knowledge, this is the first epidemiological
study on the effect of high temperature and humidity on skin
aging phenotypes in Indian women. We found a positive associ-
ation of HI with facial pigmentation and coarse wrinkles in a
cohort of Indian women. It is worth noting that an increase in
ambient temperature is usually associated with an increase in
Figure 1. Association between HI with scores for extrinsic skin aging in the CASAI cohort. Adjusted—the estimates are adjusted for
UVI and age; * shows that the values are significant at P<0.05. CASAI, climate, air pollution, and skin aging in Indian women; HI, heat
index; UVI, ultraviolet index.
TABLE 2. Description of SCINEXA Scores in CASAI Cohort
Skin Aging Traits Absent N (%) Low N (%) Medium N (%) High N (%)
Pigmentation on face
Hyperpigmented maculae (forehead) 1231 (81.52) 272 (18.01) 7 (0.46) 0 (0.00)
Pigmented spots (cheeks) 678 (44.90) 599 (39.67) 216 (14.30) 17 (1.13)
Coarse wrinkles on the face
Wrinkles (forehead) 301 (19.93) 592 (39.21) 402 (26.62) 215 (14.24)
Wrinkles (frowlines) 715 (47.35) 487 (32.25) 267 (17.68) 41 (2.72)
Wrinkles (crow’s feet) 344 (22.78) 530 (35.10) 447 (29.60) 189 (12.52)
Wrinkles (under the eyes) 18 (1.19) 518 (34.30) 570 (37.75) 404 (26.75)
Wrinkles (upper lip) 445 (29.47) 723 (47.88) 234 (15.50) 108 (7.15)
Wrinkles (nasolabial fold) 97 (6.42) 571 (37.81) 616 (40.79) 226 (14.97)
Low score range from 1 to 2, medium from 3 to 4, and high from 5 to 6.
SCINEXA, Score of Intrinsic and Extrinsic Skin Aging.
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sun intensity. Given that sun exposure consists of harmful UVR,
which threatens the skin and is a major cause of skin aging, we
carefully assessed the potential confounding role of UVI.
50
The
results clearly show that the association between HI and facial
pigmentation (hyperpigmented macules on the forehead) and
coarse wrinkle signs (crow’s feet and under-eye wrinkles) was
robust, after accounting for the confounding effect of UVI and
age. The results show that temperature is an independent threat
to skin aging, along with solar UV and air pollution. Many peo-
ple living in high-temperature areas have high heat thresholds.
51
For example, the heat thresholds for people living in Delhi,
Mumbai, and Bengaluru (India) are 25.5–26.5C, 28.5–29.5C,
and 29.5–30.5C, respectively. This suggests that people exposed
to high temperatures for long periods of time gradually adapt to
warmer temperatures, and therefore may have a gradually atte-
nuated response to heat.
51
This may also explain the better
adaptability to heat and less severe skin aging in Indian women.
On the other hand, the high severity of certain skin aging char-
acteristics in the study population may be related to the higher
HI levels in India. In general, we found that Indian women are
exposed to higher levels of environmental exposures, such as
HI, UVI, PM2.5, and NO
2
. We have previously shown that SCI-
NEXA is significantly associated with both chronological age
and UV exposure (sunbed use) and that these associations were
not confounded by skin phototypes.
30
It is currently unknown
whether different skin phototypes have different susceptibilities
to the observed association between HI and skin aging pheno-
types. Effect modification showed no significant interaction
between HI and passive smoking for severe facial pigmentation
and coarse wrinkles in CASAI. This may be due to the low expo-
sure to passive smoking among CASAI participants. Smoking is
undoubtedly a potential risk factor for premature skin aging.
49
Tobacco smoke extract increases the degradation of collagen tis-
sue and elastic fibers through several pathways. Previous studies
have shown that smoking modifies the association between O
3
and coarse wrinkles,
21
but there is no prior evidence on whether
smoking modifies the association between temperature and skin
aging. Effect modification based on age, below and above
50 years, mostly resulted in insignificant associations. In con-
trast to our study, a study focusing on German and Chinese
women reported a higher effect of NO
2
on facial pigmentation
when the analysis was restricted to Chinese women above
50 years of age.
17
However, there is no evidence of whether age
modifies the association between temperature and skin aging.
As part of the sensitivity analysis, T
mean
was chosen as an
alternative temperature metric to show that temperature does
not independently affect the aging parameter. HI seems to be a
better choice than T
mean
. The main reason is that temperature
doesn’t affect health on its own. Instead, health effects often
result due to a combined function of temperature and humidity,
such that the combined effect of temperature and humidity is
often more detrimental than their individual effects.
41,52–54
Therefore, HI is the better temperature metric to show heat-
induced health effects. Similarly, previous studies have shown a
significant association between PM
2.5
and NO
2
(NO
2
is a more
specific indicator of traffic-related air pollution) and facial pig-
mentation in older German women,
17,18
so we used these pollu-
tants as potential confounders in this study. We accounted for
the potential bias in the association due to differences in HI
exposure (due to large seasonal differences in HI in New Delhi).
The analysis conducted after including participants from Delhi
only did not show significant associations due to low sample
size power.
As this is the first epidemiological evidence of the role of
environmental temperature expressed as HI, the biological
mechanism establishing a causal relationship between tempera-
ture and skin aging characteristics is lacking. A mechanistic
study based on animal models found that high skin temperature
upregulates tropoelastin, fibrillin, and matrix metalloproteinase
(MMP) expression
55,56
and induces oxidative DNA damage.
57
TABLE 3. Association of HI with Markers of Extrinsic Skin Aging in CASAI Cohort, Using Additional
Model Specifications
Extrinsic Skin Aging Signs
Core Model
HI +UVI +age
OR (LCL, UCL)
T
mean
+UVI +age
OR (LCL, UCL)
HI
+PM2.5 +age
OR (LCL, UCL)
HI
+NO
2
+age
OR (LCL, UCL)
Delhi
HI +UVI +age
OR (LCL, UCL)
Pigmentation on face
Hyperpigmented maculae (forehead) 1.31 (1.12, 1.54) 1.32 (1.02, 1.71) 1.40 (1.17, 1.67) 1.36 (1.10, 1.69) 1.22 (0.75, 1.98)
Pigmented spots (cheeks) 0.77 (0.68, 0.87) 0.74 (0.62, 0.88) 0.76 (0.66, 0.87) 0.77 (0.65, 0.90) 1.05 (0.76, 1.46)
Coarse wrinkles on the face
Wrinkles (forehead) 1.02 (0.91, 1.14) 1.11 (0.92, 1.33) 1.11 (0.98, 1.25) 1.29 (1.11, 1.51) 1.13 (0.82, 1.57)
Wrinkles (frowlines) 1.05 (0.93, 1.19) 1.02 (0.83, 1.24) 1.03 (0.90, 1.18) 1.05 (0.89, 1.24) 0.93 (0.66, 1.31)
Wrinkles (crow’s feet) 1.17 (1.05, 1.30) 1.26 (1.05, 1.50) 1.18 (1.05, 1.33) 1.19 (1.03, 1.37) 1.07 (0.80, 1.44)
Wrinkles (under the eyes) 1.3 (1.15, 1.47) 1.34 (1.10, 1.62) 1.29 (1.13, 1.47) 1.20 (1.02, 1.41) 0.79 (0.57, 1.09)
Wrinkles (upper lip) 1.07 (0.95, 1.20) 1.07 (0.88, 1.28) 1.09 (0.96, 1.23) 1.12 (0.96, 1.31) 0.89 (0.64, 1.24)
Wrinkles (nasolabial fold) 0.77 (0.68, 0.86) 0.74 (0.61, 0.89) 0.77 (0.68, 0.87) 0.79 (0.68, 0.92) 0.88 (0.64, 1.22)
Values in bold are significant at P <0.05.
OR, odds ratio; UCL, upper confidence interval; LCL, lower confidence interval.
6DERMATITIS, Vol 00 䊏No 0 䊏Month/Month 2024
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MMP is an extracellular matrix-degrading enzyme that causes the
degradation of extracellular matrix proteins such as type 1 and
type3collageninthedermis,contributingtoskinwrinkling.
58,59
The study has several strengths. First, it is the first epidemio-
logical evidence of the role of ambient temperature as HI on
extrinsic signs of skin aging in Indian women. Second, the study
uses a well-characterized cohort from 3 different geographical
regions with contrasting levels of exposure and different skin
aging traits, thus contributing to a better understanding of the
variation in the strength of association with different levels of
exposure. Third, the study uses the validated SCINEXA tool to
differentiate between extrinsic and intrinsic signs of skin
aging.
30
Fourth, we used a multiexposure model adjusting for
the strong confounding effect of solar UVR and PM
2.5
/NO
2
(in
a sensitivity analysis) to show that the observed associations of
HI with extrinsic signs of skin aging were independent of other
factors. Fifth, we performed sensitivity analyses to test the
robustness of the core model.
There are several caveats to this study. First, we performed the
exposure assessment partly based on modeled climate and air pol-
lution data, which may have led to misclassification of individual
exposure. Second, because we relied in part on modeled exposure
data, we cannot completely rule out uncertainty in the true rela-
tionship between HI and extrinsic signs of skin aging. Thirdly, due
to the lack of harmonized information on the personal characteris-
tics of the study participants, the model could not account for other
potential confounders and several effect modifications could not be
tested. For example, we have no information on indoor exposure
due to cooking with fossil fuels, which is a major problem in India.
Similarly, we do not have information about occupational expo-
sures and how they affect skin aging. For example, women who
have high occupational exposures would have worse SCINEXA
scores related to work-related chronic exposures. This type of infor-
mation is not available, and its absence is a pitfall of this study.
CONCLUSIONS
The study provides the first epidemiological evidence of an
adverse role of HI as a combined function of ambient tempera-
ture and RH on facial pigmentation and coarse wrinkling in
Indian women, independent of other known potential environ-
mental risk factors. As environmental factors are closely interre-
lated, future investigations of skin aging need to consider the
interactions of ambient temperature with solar UVR and air pol-
lutants. In addition, mechanistic studies are needed to further
establish a causal relationship between the long-term effects of
temperature and skin aging phenotypes.
SUPPLEMENTARY MATERIAL
Supplementary Figure S1
Supplementary Figure S2
Supplementary Appendix SA1
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