Methylation marks of prenatal exposure to maternal smoking and risk of cancer in adulthood

Associations of maternal and personal smoking with all-cause and cause-specific mortality risk and life expectancy: a prospective cohort study

OBJECTIVES

The aim of this study was to evaluate the individual and combined effects of maternal smoking during pregnancy (MSDP) and personal smoking on mortality and life expectancy.

STUDY DESIGN

A prospective cohort study based on the UK Biobank, with a median follow-up of 12.47 years.

METHODS

This study employed multivariate Cox regression to determine the relative risks of mortality from all causes and specific diseases according to maternal and/or personal smoking status and pack-years of smoking (0, 1-20, 21-30, >30). Additionally, this study estimated the additive interaction between the two exposures. Life table analyses were performed using the estimated age-specific mortality rates to forecast life expectancy.

RESULTS

Results indicated that MSDP elevated the risk of all-cause mortality (HR = 1.12, 95% CI: 1.09-1.15) and mortality due to neoplasms (HR = 1.10, 95% CI: 1.06-1.12), circulatory (HR = 1.13, 95% CI: 1.06-1.19), respiratory (HR = 1.27, 95% CI: 1.16-1.40) and digestive system diseases (HR = 1.22, 95% CI: 1.08-1.38). Notably, both multiplicative and additive interactions were observed between maternal and personal smoking, with Relative Excess Risk due to Interaction (RERI) values for mortality from all causes, neoplasms, circulatory, and respiratory diseases being 0.21, 0.22, 0.16, and 0.76, respectively. This study also found a trend towards shorter gained life expectancy when maternal smoking and increasing pack-years of personal smoking were combined.

CONCLUSIONS

In this cohort study of UK Biobank, MSDP was associated with an increased risk of all-cause mortality and reduced life expectancy, suggesting that quitting smoking during pregnancy might have health and longevity benefits for both generations.

Long-term health risk of offspring born from assisted reproductive technologies

Since the world's first in vitro fertilization baby was born in 1978, there have been more than 8 million children conceived through assisted reproductive technologies (ART) worldwide, and a significant proportion of them have reached puberty or young adulthood. Many studies have found that ART increases the risk of adverse perinatal outcomes, including preterm birth, low birth weight, small size for gestational age, perinatal mortality, and congenital anomalies. However, data regarding the long-term outcomes of ART offspring are limited. According to the developmental origins of health and disease theory, adverse environments during early life stages may induce adaptive changes and subsequently result in an increased risk of diseases in later life. Increasing evidence also suggests that ART offspring are predisposed to an increased risk of non-communicable diseases, such as malignancies, asthma, obesity, metabolic syndrome, diabetes, cardiovascular diseases, and neurodevelopmental and psychiatric disorders. In this review, we summarize the risks for long-term health in ART offspring, discuss the underlying mechanisms, including underlying parental infertility, epigenetic alterations, non-physiological hormone levels, and placental dysfunction, and propose potential strategies to optimize the management of ART and health care of parents and children to eliminate the associated risks. Further ongoing follow-up and research are warranted to determine the effects of ART on the long-term health of ART offspring in later life.

In Utero and Childhood/Adolescence Exposure to Tobacco Smoke, Genetic Risk, and Cancer Incidence in Adulthood: A Prospective Cohort Study

OBJECTIVE

To evaluate the association of early-life tobacco smoke exposure, especially interacting with cancer genetic variants, with adult cancer.

PARTICIPANTS AND METHODS

We examined the associations of in utero tobacco smoke exposure, age of smoking initiation, and their interaction with genetic risk levels with cancer incidence in 393,081 participants from the UK Biobank. Information on tobacco exposure was obtained by self-reported questionnaires. A cancer polygenic risk score was constructed by weighting and integrating 702 genome-wide association studies-identified risk variants. Cox proportional hazards regression models were used to calculate hazard ratios (HRs) for overall cancer and organ-specific cancer incidence.

RESULTS

During 11.8 years of follow-up, 23,450 (5.97%) and 23,413 (6.03%) incident cancers were included in the analyses of in utero exposure and age of smoking initiation, respectively. The HR (95% CI) for incident cancer in participants with in utero exposure to tobacco smoke was 1.04 (1.01-1.07) for overall cancer, 1.59 (1.44-1.75) for respiratory cancer, and 1.09 (1.03-1.17) for gastrointestinal cancer. The relative risk of incident cancer increased with earlier smoking initiation (Ptrend<.001), with the HR (95% CI) of 1.44 (1.36-1.51) for overall cancer, 13.28 (11.39-15.48) for respiratory cancer, and 1.72 (1.54-1.91) for gastrointestinal cancer in smokers with initiation in childhood compared with never smokers. Importantly, a positive additive interaction between age of smoking initiation and genetic risk was observed for overall cancer (Padditive=.04) and respiratory cancer (Padditive=.003) incidence.

CONCLUSION

In utero exposure and earlier smoking initiation are associated with overall and organ-specific cancer, and age of smoking initiation interaction with genetic risk is associated with respiratory cancer.

Methylation scores for smoking, alcohol consumption and body mass index and risk of seven types of cancer

Methylation marks of exposure to health risk factors may be useful markers of cancer risk as they might better capture current and past exposures than questionnaires, and reflect different individual responses to exposure. We used data from seven case-control studies nested within the Melbourne Collaborative Cohort Study of blood DNA methylation and risk of colorectal, gastric, kidney, lung, prostate and urothelial cancer, and B-cell lymphoma (N cases = 3123). Methylation scores (MS) for smoking, body mass index (BMI), and alcohol consumption were calculated based on published data as weighted averages of methylation values. Rate ratios (RR) and 95% confidence intervals for association with cancer risk were estimated using conditional logistic regression and expressed per SD increase of the MS, with and without adjustment for health-related confounders. The contribution of MS to discriminate cases from controls was evaluated using the area under the curve (AUC). After confounder adjustment, we observed: large associations (RR = 1.5-1.7) with lung cancer risk for smoking MS; moderate associations (RR = 1.2-1.3) with urothelial cancer risk for smoking MS and with mature B-cell neoplasm risk for BMI and alcohol MS; moderate to small associations (RR = 1.1-1.2) for BMI and alcohol MS with several cancer types and cancer overall. Generally small AUC increases were observed after inclusion of several MS in the same model (colorectal, gastric, kidney, urothelial cancers: +3%; lung cancer: +7%; B-cell neoplasms: +8%). Methylation scores for smoking, BMI and alcohol consumption show independent associations with cancer risk, and may provide some improvements in risk prediction.

In Utero and Childhood/Adolescence Exposure to Tobacco Smoke, Genetic Risk, and Lung Cancer Incidence and Mortality in Adulthood

Rationale: The individual effects of early-life tobacco smoke exposure and its interactions with genetic factors on lung cancer in adulthood remain unclear. Objectives: To investigate the associations of early-life tobacco exposures as well as their interactions with polygenic risk scores (PRSs) with lung cancer incidence and mortality. Methods: A total of 432,831 participants from the UK Biobank study were included. We estimated the associations of in utero exposure to tobacco smoke, the age of smoking initiation and their interactions with PRSs with lung cancer incidence and mortality in adulthood using Cox proportional hazard models. Measurements and Main Results: Lung cancer incidence (hazard ratio [HR]: 1.59, 95% confidence interval [CI], 1.44-1.76) increased among participants with in utero tobacco exposure. Multivariable-adjusted HRs (with 95% CIs) of lung cancer incidence for smoking initiation in adulthood, adolescence, and childhood (versus never-smokers) were 6.10 (5.25-7.09), 9.56 (8.31-11.00), and 15.15 (12.90-17.79) (P_trend < 0.001). Similar findings were observed in lung cancer mortality. Participants with high PRSs and in utero tobacco exposure (versus low PRSs participants without in utero exposure) had an HR of 2.35 for lung cancer incidence (95% CI, 1.97-2.80, P_interaction = 0.089) and 2.43 for mortality (95% CI, 2.05-2.88, P_interaction = 0.032). High PRSs with smoking initiation in childhood (versus never-smokers with low PRSs) had HRs of 18.71 for incidence (95% CI, 14.21-24.63, P_interaction = 0.004) and 19.74 for mortality (95% CI, 14.98-26.01, P_interaction = 0.033). Conclusions: In utero and childhood/adolescence exposure to tobacco smoke and its interaction with genetic factors may substantially increase the risks of lung cancer incidence and mortality in adulthood.

Epigenetics as a Biomarker for Early-Life Environmental Exposure

PURPOSE OF REVIEW

There is interest in evaluating the developmental origins of health and disease (DOHaD) which emphasizes the role of prenatal and early-life environments on non-communicable health outcomes throughout the life course. The ability to rigorously assess and identify early-life risk factors for later health outcomes, including those with childhood onset, in large population samples is often limited due to measurement challenges such as impractical costs associated with prospective studies with a long follow-up duration, short half-lives for some environmental toxicants, and lack of biomarkers that capture inter-individual differences in biologic response to external environments.

RECENT FINDINGS

Epigenomic patterns, and DNA methylation in particular, have emerged as a potential objective biomarker to address some of these study design and exposure measurement challenges. In this article, we summarize the literature to date on epigenetic changes associated with specific prenatal and early-life exposure domains as well as exposure mixtures in human observational studies and their biomarker potential. Additionally, we highlight evidence for other types of epigenetic patterns to serve as exposure biomarkers. Evidence strongly supports epigenomic biomarkers of exposure that are detectable across the lifespan and across a range of exposure domains. Current and future areas of research in this field seek to expand these lines of evidence to other environmental exposures, to determine their specificity, and to develop predictive algorithms and methylation scores that can be used to evaluate early-life risk factors for health outcomes across the life span.

Does genetic predisposition modify the effect of lifestyle-related factors on DNA methylation?

Lifestyle-related phenotypes have been shown to be heritable and associated with DNA methylation. We aimed to investigate whether genetic predisposition to tobacco smoking, alcohol consumption, and higher body mass index (BMI) moderates the effect of these phenotypes on blood DNA methylation. We calculated polygenic scores (PGS) to quantify genetic predisposition to these phenotypes using training (N = 7,431) and validation (N = 4,307) samples. Using paired genetic-methylation data (N = 4,307), gene-environment interactions (i.e., PGS × lifestyle) were assessed using linear mixed-effects models with outcomes: 1) methylation at sites found to be strongly associated with smoking (1,061 CpGs), alcohol consumption (459 CpGs), and BMI (85 CpGs) and 2) two epigenetic ageing measures, PhenoAge and GrimAge. In the validation sample, PGS explained ~1.4% (P = 1 × 10-14), ~0.6% (P = 2 × 10-7), and ~8.7% (P = 7 × 10-87) of variance in smoking initiation, alcohol consumption, and BMI, respectively. Nominally significant interaction effects (P < 0.05) were found at 61, 14, and 7 CpGs for smoking, alcohol consumption, and BMI, respectively. There was strong evidence that all lifestyle-related phenotypes were positively associated with PhenoAge and GrimAge, except for alcohol consumption with PhenoAge. There was weak evidence that the association of smoking with GrimAge was attenuated in participants genetically predisposed to smoking (interaction term: -0.022, standard error [SE] = 0.012, P = 0.058) and that the association of alcohol consumption with PhenoAge was attenuated in those genetically predisposed to drink alcohol (interaction term: -0.030, SE = 0.015, P = 0.041). In conclusion, genetic susceptibility to unhealthy lifestyles did not strongly modify the association between observed lifestyle behaviour and blood DNA methylation. Potential associations were observed for epigenetic ageing measures, which should be replicated in additional studies.

Methylation-based markers of aging and lifestyle-related factors and risk of breast cancer: a pooled analysis of four prospective studies

BACKGROUND

DNA methylation in blood may reflect adverse exposures accumulated over the lifetime and could therefore provide potential improvements in the prediction of cancer risk. A substantial body of research has shown associations between epigenetic aging and risk of disease, including cancer. Here we aimed to study epigenetic measures of aging and lifestyle-related factors in association with risk of breast cancer.

METHODS

Using data from four prospective case-control studies nested in three cohorts of European ancestry participants, including a total of 1,655 breast cancer cases, we calculated three methylation-based measures of lifestyle factors (body mass index [BMI], tobacco smoking and alcohol consumption) and seven measures of epigenetic aging (Horvath-based, Hannum-based, PhenoAge and GrimAge). All measures were regression-adjusted for their respective risk factors and expressed per standard deviation (SD). Odds ratios (OR) and 95% confidence intervals (CI) were calculated using conditional or unconditional logistic regression and pooled using fixed-effects meta-analysis. Subgroup analyses were conducted by age at blood draw, time from blood sample to diagnosis, oestrogen receptor-positivity status and tumour stage.

RESULTS

None of the measures of epigenetic aging were associated with risk of breast cancer in the pooled analysis: Horvath 'age acceleration' (AA): OR per SD = 1.02, 95%CI: 0.95-1.10; AA-Hannum: OR = 1.03, 95%CI:0.95-1.12; PhenoAge: OR = 1.01, 95%CI: 0.94-1.09 and GrimAge: OR = 1.03, 95%CI: 0.94-1.12, in models adjusting for white blood cell proportions, body mass index, smoking and alcohol consumption. The BMI-adjusted predictor of BMI was associated with breast cancer risk, OR per SD = 1.09, 95%CI: 1.01-1.17. The results for the alcohol and smoking methylation-based predictors were consistent with a null association. Risk did not appear to substantially vary by age at blood draw, time to diagnosis or tumour characteristics.

CONCLUSION

We found no evidence that methylation-based measures of aging, smoking or alcohol consumption were associated with risk of breast cancer. A methylation-based marker of BMI was associated with risk and may provide insights into the underlying associations between BMI and breast cancer.

Inflammation and Epigenetic Aging Are Largely Independent Markers of Biological Aging and Mortality

Limited evidence exists on the link between inflammation and epigenetic aging. We aimed to (a) assess the cross-sectional and prospective associations of 22 inflammation-related plasma markers and a signature of inflammaging with epigenetic aging and (b) determine whether epigenetic aging and inflammaging are independently associated with mortality. Blood samples from 940 participants in the Melbourne Collaborative Cohort Study collected at baseline (1990-1994) and follow-up (2003-2007) were assayed for DNA methylation and 22 inflammation-related markers, including well-established markers (eg, interleukins and C-reactive protein) and metabolites of the tryptophan-kynurenine pathway. Four measures of epigenetic aging (PhenoAge, GrimAge, DunedinPoAm, and Zhang) and a signature of inflammaging were considered, adjusted for age, and transformed to Z scores. Associations were assessed using linear regression, and mortality hazard ratios (HR) and 95% confidence intervals (95% CI) were estimated using Cox regression. Cross-sectionally, most inflammation-related markers were associated with epigenetic aging measures, although with generally modest effect sizes (regression coefficients per SD ≤ 0.26) and explaining altogether between 1% and 11% of their variation. Prospectively, baseline inflammation-related markers were not, or only weakly, associated with epigenetic aging after 11 years of follow-up. Epigenetic aging and inflammaging were strongly and independently associated with mortality, for example, inflammaging: HR = 1.41, 95% CI = 1.27-1.56, p = 2 × 10-10, which was only slightly attenuated after adjustment for 4 epigenetic aging measures: HR = 1.35, 95% CI = 1.22-1.51, p = 7 × 10-9). Although cross-sectionally associated with epigenetic aging, inflammation-related markers accounted for a modest proportion of its variation. Inflammaging and epigenetic aging are essentially nonoverlapping markers of biological aging and may be used jointly to predict mortality.

Association of FOXO3 Blood DNA Methylation with Cancer Risk, Cancer Survival, and Mortality

Genetic variants in FOXO3 are associated with longevity. Here, we assessed whether blood DNA methylation at FOXO3 was associated with cancer risk, survival, and mortality. We used data from eight prospective case–control studies of breast (n = 409 cases), colorectal (n = 835), gastric (n = 170), kidney (n = 143), lung (n = 332), prostate (n = 869), and urothelial (n = 428) cancer and B-cell lymphoma (n = 438). Case–control pairs were matched on age, sex, country of birth, and smoking (lung cancer study). Conditional logistic regression was used to assess associations between cancer risk and methylation at 45 CpGs of FOXO3 included on the HumanMethylation450 assay. Mixed-effects Cox models were used to estimate hazard ratios (HR) and 95% confidence intervals (CI) for associations with cancer survival (total n = 2286 deaths). Additionally, using data from 1088 older participants, we assessed associations of FOXO3 methylation with overall and cause-specific mortality (n = 354 deaths). Methylation at a CpG in the first exon region of FOXO3 (6:108882981) was associated with gastric cancer survival (HR = 2.39, 95% CI: 1.60–3.56, p = 1.9 × 10⁻⁵). Methylation at three CpGs in TSS1500 and gene body was associated with lung cancer survival (p < 6.1 × 10⁻⁵). We found no evidence of associations of FOXO3 methylation with cancer risk and mortality. Our findings may contribute to understanding the implication of FOXO3 in longevity.

Multi-Omics Analysis of the Effects of Smoking on Human Tumors

Comprehensive studies on cancer patients with different smoking histories, including non-smokers, former smokers, and current smokers, remain elusive. Therefore, we conducted a multi-omics analysis to explore the effect of smoking history on cancer patients. Patients with smoking history were screened from The Cancer Genome Atlas database, and their multi-omics data and clinical information were downloaded. A total of 2,317 patients were included in this study, whereby current smokers presented the worst prognosis, followed by former smokers, while non-smokers showed the best prognosis. More importantly, smoking history was an independent prognosis factor. Patients with different smoking histories exhibited different immune content, and former smokers had the highest immune cells and tumor immune microenvironment. Smokers are under a higher incidence of genomic instability that can be reversed following smoking cessation in some changes. We also noted that smoking reduced the sensitivity of patients to chemotherapeutic drugs, whereas smoking cessation can reverse the situation. Competing endogenous RNA network revealed that mir-193b-3p, mir-301b, mir-205-5p, mir-132-3p, mir-212-3p, mir-1271-5p, and mir-137 may contribute significantly in tobacco-mediated tumor formation. We identified 11 methylation driver genes (including EIF5A2, GBP6, HGD, HS6ST1, ITGA5, NR2F2, PLS1, PPP1R18, PTHLH, SLC6A15, and YEATS2), and methylation modifications of some of these genes have not been reported to be associated with tumors. We constructed a 46-gene model that predicted overall survival with good predictive power. We next drew nomograms of each cancer type. Interestingly, calibration diagrams and concordance indexes are verified that the nomograms were highly accurate for the prognosis of patients. Meanwhile, we found that the 46-gene model has good applicability to the overall survival as well as to disease-specific survival and progression-free intervals. The results of this research provide new and valuable insights for the diagnosis, treatment, and follow-up of cancer patients with different smoking histories.