Exploring lifestyle confounding in the association between tattoos and cancer: A latent class analysis of tattooing and cervical cancer risk within the CRABAT study

Exploring lifestyle confounding in the association between tattoos and cancer: A latent class analysis of tattooing and cervical cancer risk within the CRABAT study | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Exploring lifestyle confounding in the association between tattoos and cancer: A latent class analysis of tattooing and cervical cancer risk within the CRABAT study View ORCID Profile Rachel D McCarty , Liacine Bouaoun , View ORCID Profile Marie Zins , View ORCID Profile Marcel Goldberg , Céline Ribet , Sofiane Kab , Khaled Ezzedine , Joachim Schüz , Milena Foerster doi: https://doi.org/10.1101/2025.09.24.25336531 Rachel D McCarty 1 Environment and Lifestyle Epidemiology Branch, International Agency for Research on Cancer , World Health Organization, Lyon, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Rachel D McCarty For correspondence: mccartyr{at}iarc.who.int Liacine Bouaoun 1 Environment and Lifestyle Epidemiology Branch, International Agency for Research on Cancer , World Health Organization, Lyon, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Marie Zins 2 Université Paris Cité, Université Paris-Saclay, UVSQ, INSERM, Epidemiological Population Cohorts Unit (UMS 011) , France 3 Université Paris Cité and Université Sorbonne Paris Nord, Inserm, INRAE, Centre for Research in Epidemiology and Statistics (CRESS) , Paris, France 4 Centre d’Epidémiologie Clinique , Hôpital Hôtel Dieu, AP-HP, Paris, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Marie Zins Marcel Goldberg 2 Université Paris Cité, Université Paris-Saclay, UVSQ, INSERM, Epidemiological Population Cohorts Unit (UMS 011) , France Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Marcel Goldberg Céline Ribet 2 Université Paris Cité, Université Paris-Saclay, UVSQ, INSERM, Epidemiological Population Cohorts Unit (UMS 011) , France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sofiane Kab 2 Université Paris Cité, Université Paris-Saclay, UVSQ, INSERM, Epidemiological Population Cohorts Unit (UMS 011) , France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Khaled Ezzedine 5 Department of Dermatology, Henri-Mondor University Hospital , Créteil, France 6 Epidemiology in Dermatology and Evaluation of Therapeutics (EpiDermE) , EA 7379, Paris-Est Créteil University, Créteil, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Joachim Schüz 1 Environment and Lifestyle Epidemiology Branch, International Agency for Research on Cancer , World Health Organization, Lyon, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Milena Foerster 1 Environment and Lifestyle Epidemiology Branch, International Agency for Research on Cancer , World Health Organization, Lyon, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Background Recent studies report increased hematologic cancer risk with tattooing, an exposure highly correlated with sociodemographic and lifestyle patterns. To assess potential confounding by such factors in associations between tattooing and cancer, we studied cervical cancer, a cancer unlikely to be causally related to tattooing. We applied latent class analysis (LCA) to group women into latent lifestyle profiles based on sociodemographic, lifestyle, and health-related variables, and assessed how such profiles may confound associations between tattooing and cervical cancer Methods We analyzed data from 50,769 women in the Cancer Risk Associated with the Body Art of Tattooing (CRABAT) study nested within the French national Constances cohort. Logistic regression models estimated associations between tattoos and cervical cancer, adjusting for confounders and latent lifestyle profile. Additional models were stratified by latent lifestyle profile. Results We selected a five-profile LCA model to balance model fit and interpretability. In minimally adjusted models, women with tattoos had a higher cervical cancer risk (OR=1.54 [95% CI 1.15–2.05]). Associations were attenuated after adjusting for known confounders (1.33 [0.99–1.77]), and a bit further after adjusting for latent profile (1.29 [0.95–1.73]). Stratified models showed strong variations, with ORs ranging from 0.99 [0.37–2.61] in the younger profile to 2.56 [1.23–5.34] in the socially-advantaged profile. Conclusions While elevated risk among tattooed individuals persisted after adjustment for known confounders, LCA was able to differentiate risk patterns between latent lifestyle profiles. LCA may be useful when complex confounding is likely, such as in studies of tattoos and cancer risk. Key messages We examined the influence of complex, interrelated sociodemographic and lifestyle factors on associations between tattooing and cancer, using cervical cancer as an example and latent class analysis (LCA) to identify and adjust for latent lifestyle profiles. Cervical cancer risk was elevated among tattooed women, but attenuated after accounting for latent lifestyle profiles, with substantial variation in risk across profiles. These results highlight the potential of LCA to examine residual confounding in observational cancer research, particularly for exposures which are correlated with lifestyle factors. Background Due to potential systemic lifetime exposure to carcinogens in tattoo inks, tattooing is being studied as a new potential lifestyle risk factor for cancer. 1 Initial studies have reported positive associations between tattooing and risk of hematologic cancers 2 , 3 and mixed findings regarding its association with skin cancers. 4 While these analyses adjusted for some major risk factors like smoking and education, the relationship of tattooing exposure with cancer risk is particularly complex because of the many sociodemographic and lifestyle factors that are correlated with both tattooing exposure and cancer risk, and therefore potentially confound associations. 5 Indeed, first analyses of our Cancer Risk Associated with the Body Art of Tattooing (CRABAT) cohort, showed clear differences between the tattooed and non-tattooed population regarding many established cancer risk factors such as education, income, and smoking, in addition to risk of hepatitis B (HBV)/hepatitis C (HCV) infections and related sexual risk factors. 6 These relationships may further be inter-dependent and time-changing. Therefore, multivariable adjustment may be insufficient to fully account for complex lifestyle-related confounding structures in the tattoo-cancer relationship, as residual confounding may remain and influence observed associations. To capture multidimensional, correlated, and complex confounding, we applied latent class analysis (LCA) to group women into profiles based on patterns of sociodemographic, lifestyle, and health-related variables. LCA is a statistical method that uses probabilistic modeling to identify unobserved subgroups, or latent classes, within a population based on patterns of observed variables. 7 This approach can be particularly useful when distinct unmeasured patterns between variables are suspected and has been previously applied to adjust for complex confounding, e.g. when disentangling mobile phone usage from radiation. 8 By accounting for interrelated lifestyle factors, LCA allows for more comprehensive adjustment for lifestyle confounding than examining individual variables separately. Therefore, we applied LCA to address complex sociodemographic, lifestyle, and health-related confounding affecting tattooing and cancer risk in the CRABAT study, using cervical cancer as an example. Cervical cancer was selected because its etiology is well established; nearly all cases are attributable to cervical infection with mucosal human papillomavirus (HPV). 9 In addition, lifestyle factors such as tobacco smoking are known to act as cofactors that increase cervical cancer risk. 10 While there is little (if not none) biological rationale to suggest a causal effect of tattooing on cervical cancer risk, we hypothesized that individuals with tattoos may have a higher risk due to associations between tattooing and known HPV risk factors which would therefore be associated to certain latent profiles more than others. Methods Study population The ongoing French Constances cohort includes almost 220,000 participants, sampled to reflect the sociodemographic characteristics of the metropolitan French population ( doi.org/10.13143/inserm_constances ) . 11 The cohort collects extensive data on occupational and social factors as well as health outcomes, and was established as a research infrastructure for the epidemiologic research community. 11 Individuals were randomly sampled from adults aged 18 to 69, insured through the French General Health Insurance Fund which covers approximately 85% of the population. 12 Baseline data were collected via self-administered questionnaires between 2012 and 2020. 12 In addition to follow-up questionnaires completed each year, Constances receives medical data through linkage to the French national health database (Système National des Données de Santé, SNDS) which contains records of all medical services reimbursed by the national public health insurance system. 6 Written informed consent was obtained from all participants prior to their inclusion in the study. The Constances study was approved by the institutional review board (IRB) of the French Institute of Health (Inserm) (Opinion n°01-011, then n°21-842), and authorized by the French Data Protection Authority (“Commission Nationale de l’Informatique et des Libertés”, CNIL) (Authorization #910486). CRABAT consists of over 110,000 Constances participants. As previously described, 6 during the annual 2020–2021 follow-up, participants were asked “Do you have a tattoo?” Respondents to this question were enrolled in CRABAT. Between July and December 2023, individuals with tattoos were sent the validated Epidemiological Tattoo Assessment Tool (EpiTAT) questionnaire, which collected more detailed information on tattoo characteristics. 13 The CRABAT study was approved by the International Agency for Research on Cancer (IARC) Ethics Committee (IEC 22-02) and was authorized by the Commission Nationale de l’Informatique et des Libertés (CNIL, #22015584). The study population was restricted to women below age 70 at first tattoo assessment in 2020 because of the very low tattoo prevalence in older age groups. We excluded women without SNDS data (n=2,809), women diagnosed with cervical cancer prior to 2005 as this was more than 15 years prior to the first tattoo questions (n=11), and women who were known to have received their first tattoo the year of or after cancer diagnosis to ensure that exposure preceded the outcome (n=8) ( Figure 1 ). A total of 50,769 women were included in analyses (7,881 with tattoos and 42,888 without tattoos). Download figure Open in new tab Figure 1. Flowchart of participant inclusion and exclusion Tattoo exposure data Data on ever receiving a tattoo (yes/no) were obtained from the 2020–2021 Constances follow-up questionnaire. Other tattoo variables retrieved from the 2023 EpiTAT questionnaire were: total tattooed body surface area (never tattooed, < one hand palm, one hand palm or more); timing of tattoos (never tattooed, less than 10 years ago, 10 or more years ago); and the context in which tattoos were received (i.e., by an experienced artist in a tattoo studio; by an experienced artist in other circumstances; or by a non-experienced person, various occasions. For statistical analyses these categories were collapsed to tattoos received by a professional artist in a studio versus other settings). Because participants could have multiple tattoos acquired at different times, timing of tattoos was assessed using a multiple-choice format with the following categories: within the last year, more than one year to five years ago, more than five years to 10 years ago, more than 10 years to 15 years ago, more than 15 years ago. The midpoint of the earliest indicated range was used to estimate the date of first tattoo. In case of a first tattoo reported more than 15 years ago, date of first tattoo was set to 20 years prior to the date the questionnaire was completed. Outcome data The outcome cervical cancer diagnosis was ascertained through linkage to the SNDS database. Eligible diagnoses were identified using ICD-10 codes, specifically C53 (malignant neoplasm of the cervix) and D06 (carcinoma in situ of the cervix). Cases were defined as those diagnosed between 2005 and 2021, as long as they completed the first tattoo assessment prior to diagnosis. Sociodemographic, lifestyle and health-related covariates Data on covariates used for latent profile analysis model fitting and logistic regression were primarily obtained from the Constances baseline and 2020/2021 follow-up questionnaires. In general, the variable assessed closest to the 2020/2021 follow-up was used. However, if a more detailed assessment was available in an earlier questionnaire (e.g., the alcohol use score in the baseline questionnaire), that version was used instead of less detailed follow-up data. Geographic location was obtained from the most recent geocode. HPV vaccination and cervical smears were obtained from SNDS data between 2007 (the first-year data are available) and 2021. Sociodemographic variables were age (< 30, 30–39, 40–49, 50–59, 60–69); education (no diploma, high school diploma, bachelor’s diploma, master’s diploma, or higher); monthly income in euros (< 1500, 1500 to < 2800, 2800 to < 4200, ≥ 4200); marital status (never married, married/civil partnership, was married—separated divorced or widowed); urban/rural residence (suburban, central city, isolated city, rural); and healthcare access (at least one instance of forgoing healthcare due to financial problems in the past year or not). Lifestyle variables were tobacco smoking (never, former, current); cannabis use (never, former, current); physical activity outside of work (low, moderate, or high); lifetime number of sexual partners (none, one, two to five, six or more, no answer); and alcohol use (abstinence, abuse or dependence, moderate use). Health-related variables were body mass index (BMI, underweight, < 18.5 kg/m2; normal weight, 18.5–24.9; overweight, 25– 29.9; obese, 30+); a general health score (good, OK, bad); ever being diagnosed with depression (yes/no); HPV vaccination (yes/no), and regular Pap smears (received two or more Pap smears during the 2008–2021 period or not). Details on each questionnaire-derived covariate, including the specific questionnaire and answer categories, are provided in Supplemental Table 1. Statistical analysis First, demographic characteristics of tattooed and non-tattooed women were described using frequency distributions and compared using standardized mean differences (SMDs), a common measure of effect size in observational studies. We considered an SMD of 0.20 or greater as indicative of a meaningful difference. The statistical modeling analysis was comprised of two parts. First latent lifestyle profiles of women sharing similar observed sociodemographic, lifestyle, and health-related patterns were identified using LCA analysis. Second, we examined how associations between tattoos and cervical cancer varied when accounting for latent lifestyle profile. LCA Initially all sociodemographic, lifestyle, and health-related covariates were considered in the LCA models. However, after examining the distribution of these variables across profiles, healthcare access, HPV vaccination, and regular Pap smears were deemed uninformative and excluded from the final models. We implemented LCA models with 2–8 latent lifestyle profiles and determined the optimal number of latent profiles based on model goodness-of-fit criteria, using the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC) while also considering interpretability. Item response probabilities were calculated for each response variable category within each latent lifestyle profile. To improve readability of results, we assigned descriptive labels to each latent lifestyle profile based on the predominant characteristics of women within that profile. These labels are intended as general summaries rather than precise definitions. Individual posterior probabilities of profile membership were calculated for each participant, and participants were assigned to the profile for which they had the highest posterior membership probability. Logistic regression models Logistic regression models were fit to evaluate associations between tattoo exposures and cervical cancer risk: Odds ratios (ORs) and 95% confidence intervals (CIs) of these associations were calculated. We fit three logistic regression models: A minimally adjusted model (Model 1) was adjusted for age, ever smoking, and education; (Model 2) a model further adjusted for number of lifetime sexual partners, HPV vaccination, health seeking, and regular Pap smears; and (Model 3) a model further adjusted for latent profile using the largest profile as the reference group. Notably, some covariates included in the models (e.g., age, ever smoking, education, and number of lifetime sexual partners) were also used to fit the latent profile model. These variables were retained in the regression models to address potential residual confounding due to variation within latent profiles. Subsequently we also fit logistic regression models stratified by latent profile and adjusted for age, number of lifetime sexual partners, and regular Pap smears. This reduced set of covariates was selected to maintain model parsimony given the smaller sample sizes within strata. Sensitivity analyses To understand how the latent lifestyle profiles related to the outcome, we examined associations between profile membership and cervical cancer risk, both excluding and including women with tattoos using logistic regression adjusted for age, HPV vaccination, healthcare access, and regular Pap smears. To understand how the latent lifestyle profiles related to the exposure, we also examined associations between profile membership and ever receiving a tattoo using logistic regression, adjusted for age, ever smoking, and education level. We further fit models associating tattoos with cervical cancer diagnosis restricted to women who were receiving regular Pap smears. All analyses were performed in R (v4.3.1; R core team; Vienna, Austria) and a two-sided p-value < 0.05 was considered as statistically significant. Results Demographics of tattooed and non-tattooed women Among 50,769 women included in the analyses, 7,881 (16%) reported having at least one tattoo and 42,888 (84%) reported no tattoos. Those with tattoos tended to be younger, had lower levels of education, lower income, were less likely to be married, and were more likely to have ever smoked cigarettes, formerly or currently use cannabis, and report a higher number of lifetime sexual partners than those without tattoos ( Table 1 ). Tattooed women were also more likely to have received at least one dose of the HPV vaccine, likely reflecting their younger age. Tattooed and non-tattooed women were well balanced in terms of, alcohol use, BMI, and receiving regular Pap smears. View this table: View inline View popup Download powerpoint Table 1. Demographic characteristics of women under age 70 within the CRABAT cohort, overall and by tattoo status Description of latent profiles representing sociodemographic and lifestyle confounding patterns We selected a five-profile model as it provided an optimal balance between model goodness-of-fit, well-defined lifestyle pattern groups, and maintaining sufficient sample sizes for subsequent stratified analyses. Item response probabilities for the sociodemographic, lifestyle, and health-related variables within each latent profile are shown in Table 2 . The five latent profiles were characterized as follows: Profile 1 (“Older, not married) included older women who were not married–either never married or separated, divorced, or widowed–and who were unlikely to have used cannabis; Profile 2 (“Younger”) included women primarily under age 50 who were likely to have never smoked tobacco and likely to have never been married; Profile 3 (“Older, married”) comprised older, married women who were unlikely to have used cannabis; these women also tended to have lower educational attainment and lived in more rural areas; Profile 4 (“Risk-takers”) included women who were the most likely to smoke tobacco, use cannabis, meet criteria for alcohol abuse or dependence, and report a higher number of sexual partners; Profile 5 (“Socially-advantaged”) included women with higher income and education levels, and a greater likelihood of never or formerly smoking tobacco or using cannabis. Based on maximum-probability assignment, individuals were distributed across the five latent profiles as follows: 14% in Profile 1 (“Older, not married”), 12% in Profile 2 (“Younger”), 20% in Profile 3 (“Older, married”), 22% in Profile 4 (“Risk-takers”), and 31% in Profile 5 (“Socially-advantaged”). Profile 5 was selected as the reference group as it was the largest among the identified profiles. View this table: View inline View popup Download powerpoint Table 2. Item response probabilities for sociodemographic, lifestyle, and health-related variables within each latent lifestyle profile The distribution of sociodemographic and lifestyle characteristics of women varied substantially across latent lifestyle profiles (Supp Table 2). Tattoo exposures and risk of cervical cancer Women with tattoos had a 54% higher risk of cervical cancer (OR=1.54 [95% CI 1.15– 2.05]) ( Table 3 , Model 1). This association was attenuated with a 33% increased risk after additional adjustment for number of lifetime sexual partners, HPV vaccination, healthcare access, and regular Pap smears (OR=1.33 [95% CI 0.99–1.77]) (Model 2). In models further adjusted for latent profile, the association was only slightly more attenuated to a 29% increased risk (OR=1.29 [95% CI 0.95–1.73]) (Model 3). View this table: View inline View popup Download powerpoint Table 3. Associations between tattoos and cervical cancer risk The highest risk of cervical cancer was observed among women with a tattooed body surface area of one hand palm or more and those tattooed 10 or more years prior to questionnaire completion, each with approximately a 90% increased risk compared with women who were never tattooed (Model 1). These associations were reduced to roughly 60% elevated risk after adjustment for additional confounders (Model 2). The associations were further reduced to 58% and 43% respectively, and no longer statistically significant after adjusting for latent lifestyle profile (Model 3). Tattoo exposures and risk of cervical cancer stratified by latent profile Cervical cancer risk associated with tattooing varied considerably across latent lifestyle profiles, though some estimates were statistically imprecise ( Figure 2 ). The strongest association with ever receiving a tattoo, with roughly a two and a half-fold increase in risk, was observed within the socially-advantaged group (Profile 5, OR=2.56 [95% CI 1.23–5.34]) compared with never getting tattooed, followed by the older, married group (Profile 3, OR=2.14 [95% CI 1.01–4.52]). The association was weaker within the risk-taker group (Profile 4, OR=1.21 [95% CI 0.77–1.90]) and close to null within the older, not married (Profile 1, OR=1.07 [95% CI 0.53–2.15]) and the younger group (Profile 2, OR=0.99 [95% CI 0.37–2.61]). Download figure Open in new tab Download figure Open in new tab Figure 2. Odds ratios and 95% confidence intervals for associations between tattoo characteristics and cervical cancer, by latent class Having a tattooed body surface area of one hand palm or more was associated with more than a six-fold increased risk of cervical cancer in the socially-advantaged group (Profile 5). Associations were also in the same positive direction among the older, not married group (Profile 1) and the older, married group (Profile 3), though they were statistically imprecise. Risk was slightly elevated but imprecise in the risk-taker group (Profile 4). A decreased risk was observed in the younger group (Profile 2), albeit the confidence interval was wide. Analyses of tattoo context were limited by small sample sizes, preventing clear patterns from emerging. Receiving a first tattoo 10 or more years prior was associated with a nearly three-fold increased risk of cervical cancer in the socially-advantaged profile (Profile 5), and a 70% increased risk among the risk-taker profile (Profile 4). No increased risk was observed in the older, not married (Profile 1) or older, married (Profile 3) groups. Sensitivity analyses Ever receiving a tattoo was also associated with latent lifestyle profile membership with approximately 65% higher odds among the risk-taker group (Profile 4) and the older, not married group (Profile 1), compared with the socially-advantaged group as the referent group (Supp Table 3). Compared to the socially-advantaged group, risk of cervical cancer was higher in all other latent lifestyle profiles, while risks were somewhat lower when including vs excluding tattooed individuals from the analysis in the younger, and risk-taker profiles (Supp Table 4). In analyses between tattoos and cervical cancer risk restricted to individuals receiving regular Pap smears, adjustment for HPV risk factors (Model 2) had little influence on the estimate while risks were slightly more attenuated when additionally adjusting for latent profiles (Model 3, Supp Table 5) Discussion In this novel application of LCA, we explored the impact of underlying sociodemographic and lifestyle patterns on associations between tattoos and cervical cancer, a cancer unlikely to be causally related to tattooing, but plausibly associated due to risk factors being more common among tattooed women. As expected, we initially observed an elevated overall risk of cervical cancer associated with being tattooed. However, stratified analyses revealed that this association varied across latent lifestyle profiles suggesting that tattooing may serve as a proxy for underlying lifestyle factors associated with increased cervical cancer risk. The strongest association was observed in the socially-advantaged profile, potentially reflecting a greater correlation of being tattooed with unmeasured lifestyle factors associated with HPV/cervical cancer risk compared to other profiles. In contrast, associations were weaker and sometimes close to null among three other latent class profiles: i) the older, not married; ii) younger; and iii) risk-taking groups, likely because tattoo status does not distinguish individuals within these groups on other cancer-related lifestyle factors. These findings suggest that the persisting association between tattoos and cervical cancer even after adjustment for known confounders, is likely due to residual confounding. More broadly, our results highlight the importance of accounting for complex interdependency of sociodemographic and lifestyle factors in studies of tattooing and cancer, particularly when residual confounding by smoking or other risk-taking behaviors which increase cancer risk may be present. 14 , 15 Earlier cited studies of tattoos and hematologic cancer adjusted for some known confounders, including education, tobacco smoking and BMI. 2 – 4 , 16 Compared to cervical cancer, a biological mechanism between tattooing and hematological cancers is more biologically plausible. However, residual confounding including from differential smoking history, infections, or other lifestyle factors associated with tattooing and cancer risk, remains a concern. Considering the strong influence of the latent lifestyle profiles on tattoo-associated cervical cancer risks in this study, we suggest using LCA to examine residual confounding even for cancer sites with weaker associations to sociodemographic and lifestyle factors. Recent studies of tattoos and skin cancer risk have been mixed with both increased and decreased risk of skin cancer associated with tattoo exposures. 4 , 17 As skin cancer risk varies by both lifestyle and dermal factors such as skin type, sunburn history, sun exposure, and sun safety measures, which vary by tattoo status and even by tattoo size, LCA could be used to define profiles that differ in both skin cancer risk and likelihood of being tattooed. Strengths and limitations Strengths of this study include the rich and detailed data on exposure, sociodemographic, and lifestyle (smoking, alcohol use, sexual behavior) factors which are available in CRABAT through its linkage to the French Constances cohort and which allowed the creation of distinct latent profiles. Moreover, the focus on cervical cancer which has a well-established etiology associated with lifestyle factors, (i.e., HPV and smoking) made it an ideal candidate for evaluating related confounding. Limitations include the cross-sectional nature of our analysis, and small sample sizes within stratified analyses which impacted the statistical precision of estimates. In addition, lifestyle variables may be under- or mis-reported due to social desirability bias leading to measurement error or misclassification which could have impacted latent profile assignment. Despite these limitations, this study provided a novel application of LCA to examine how sociodemographic and lifestyle variables may affect associations between tattoos and cancer. Future work could incorporate methods that account for uncertainty in profile membership assignments (e.g., weighting by posterior probabilities) to improve robustness of the findings. Conclusions This study demonstrates that associations between tattooing and cancer may be observed even for cancer types without a biologically plausible causal link, likely due to the complex sociodemographic and lifestyle factors associated with tattooing. Notably, such associations can persist despite adjustment for these factors. Our findings suggest that LCA is a useful approach to address residual confounding by unmeasured sociodemographic, lifestyle, and health-related factors that can substantially influence observed associations between tattoos and cancer risk. These results also underline the need for research on tattooing and cancer to carefully collect detailed data on lifestyle and sociodemographic characteristics as underlying confounding structures are particularly complex. Beyond tattooing, LCA may be a valuable tool for evaluating confounding in studies of other exposures and cancer risk, particularly for cancer types with complex lifestyle and behavioral risks. Conflicts of interest None to report Sources of funding The CRABAT study is supported by the French National Cancer Institute (INCa; grant No 2021-137). The Constances study is supported by the French national health insurance fund (“Caisse nationale d’assurance maladie”, Cnam). Constances is also supported by the French national agency for research (ANR-11-INBS-0002) and by industrial companies, including in the healthcare sector, within the framework of Public-Private Partnerships (PPP). Data availability For data security and participant confidentiality, the data used in this research is hosted on the secured Centre d’Accès Sécurisé aux Données (CASD) platform. Access is strictly regulated. All data access requests must be formally addressed to the Constances and CRABAT study teams via the procedures outlined on the Constances website (constances.fr). Ethics The Constances study was approved by the French Institute of Health institutional review board (IRB) (Opinion n°01-011, then n°21-842), and was authorized by the French Data Protection Authority (“Commission Nationale de l’Informatique et des Libertés”, CNIL) (Authorization #910486). The CRABAT study was approved by the International Agency for Research on Cancer (IARC) Ethic Committee (IEC 22-02) and was authorized by the Commission Nationale de l’Informatique et des Libertés (CNIL, #22015584). Disclaimer Where authors are identified as personnel of the International Agency for Research on Cancer or the World Health Organization, the authors alone are responsible for the views expressed in this paper; the views do not necessarily represent the decisions, policy, or views of the International Agency for Research on Cancer or the World Health Organization. Acknowledgements The authors thank the team of the Epidemiological Population Cohorts Unit in France (UMS 011 – Inserm, Universités Paris Cité, Paris Saclay, Versailles Saint-Quentin), which designed and manages the Constances cohort. They also acknowledge the French national health insurance fund (Caisse nationale d’assurance maladie, CNAM) and its Health examination centers for collecting a large part of the data, as well as the French national old-age insurance fund (Caisse nationale d’assurance vieillesse, CNAV) for their contribution to the establishment of the cohort, and ClinSearch for performing data quality control. 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