The mediating role of health behaviors in the association between depression, anxiety and cancer incidence: an individual participant data meta-analysis.

BACKGROUND: Although behavioral mechanisms in the association among depression, anxiety, and cancer are plausible, few studies have empirically studied mediation by health behaviors. We aimed to examine the mediating role of several health behaviors in the associations among depression, anxiety, and the incidence of various cancer types (overall, breast, prostate, lung, colorectal, smoking-related, and alcohol-related cancers). METHODS: Two-stage individual participant data meta-analyses were performed based on 18 cohorts within the Psychosocial Factors and Cancer Incidence consortium that had a measure of depression or anxiety (N = 319 613, cancer incidence = 25 803). Health behaviors included smoking, physical inactivity, alcohol use, body mass index (BMI), sedentary behavior, and sleep duration and quality. In stage one, path-specific regression estimates were obtained in each cohort. In stage two, cohort-specific estimates were pooled using random-effects multivariate meta-analysis, and natural indirect effects (i.e. mediating effects) were calculated as hazard ratios (HRs). RESULTS: Smoking (HRs range 1.04-1.10) and physical inactivity (HRs range 1.01-1.02) significantly mediated the associations among depression, anxiety, and lung cancer. Smoking was also a mediator for smoking-related cancers (HRs range 1.03-1.06). There was mediation by health behaviors, especially smoking, physical inactivity, alcohol use, and a higher BMI, in the associations among depression, anxiety, and overall cancer or other types of cancer, but effects were small (HRs generally below 1.01). CONCLUSIONS: Smoking constitutes a mediating pathway linking depression and anxiety to lung cancer and smoking-related cancers. Our findings underline the importance of smoking cessation interventions for persons with depression or anxiety.

Psychosocial factors, health behaviors and risk of cancer incidence: Testing interaction and effect modification in an individual participant data meta-analysis.

Depression, anxiety and other psychosocial factors are hypothesized to be involved in cancer development. We examined whether psychosocial factors interact with or modify the effects of health behaviors, such as smoking and alcohol use, in relation to cancer incidence. Two-stage individual participant data meta-analyses were performed based on 22 cohorts of the PSYchosocial factors and CAncer (PSY-CA) study. We examined nine psychosocial factors (depression diagnosis, depression symptoms, anxiety diagnosis, anxiety symptoms, perceived social support, loss events, general distress, neuroticism, relationship status), seven health behaviors/behavior-related factors (smoking, alcohol use, physical activity, body mass index, sedentary behavior, sleep quality, sleep duration) and seven cancer outcomes (overall cancer, smoking-related, alcohol-related, breast, lung, prostate, colorectal). Effects of the psychosocial factor, health behavior and their product term on cancer incidence were estimated using Cox regression. We pooled cohort-specific estimates using multivariate random-effects meta-analyses. Additive and multiplicative interaction/effect modification was examined. This study involved 437,827 participants, 36,961 incident cancer diagnoses, and 4,749,481 person years of follow-up. Out of 744 combinations of psychosocial factors, health behaviors, and cancer outcomes, we found no evidence of interaction. Effect modification was found for some combinations, but there were no clear patterns for any particular factors or outcomes involved. In this first large study to systematically examine potential interaction and effect modification, we found no evidence for psychosocial factors to interact with or modify health behaviors in relation to cancer incidence. The behavioral risk profile for cancer incidence is similar in people with and without psychosocial stress.

Depression, anxiety, and the risk of cancer: An individual participant data meta-analysis.

BACKGROUND: Depression and anxiety have long been hypothesized to be related to an increased cancer risk. Despite the great amount of research that has been conducted, findings are inconclusive. To provide a stronger basis for addressing the associations between depression, anxiety, and the incidence of various cancer types (overall, breast, lung, prostate, colorectal, alcohol-related, and smoking-related cancers), individual participant data (IPD) meta-analyses were performed within the Psychosocial Factors and Cancer Incidence (PSY-CA) consortium. METHODS: The PSY-CA consortium includes data from 18 cohorts with measures of depression or anxiety (up to N = 319,613; cancer incidences, 25,803; person-years of follow-up, 3,254,714). Both symptoms and a diagnosis of depression and anxiety were examined as predictors of future cancer risk. Two-stage IPD meta-analyses were run, first by using Cox regression models in each cohort (stage 1), and then by aggregating the results in random-effects meta-analyses (stage 2). RESULTS: No associations were found between depression or anxiety and overall, breast, prostate, colorectal, and alcohol-related cancers. Depression and anxiety (symptoms and diagnoses) were associated with the incidence of lung cancer and smoking-related cancers (hazard ratios [HRs], 1.06-1.60). However, these associations were substantially attenuated when additionally adjusting for known risk factors including smoking, alcohol use, and body mass index (HRs, 1.04-1.23). CONCLUSIONS: Depression and anxiety are not related to increased risk for most cancer outcomes, except for lung and smoking-related cancers. This study shows that key covariates are likely to explain the relationship between depression, anxiety, and lung and smoking-related cancers. PREREGISTRATION NUMBER: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=157677.

Pan-cancer classification of single cells in the tumour microenvironment.

Single-cell RNA sequencing can reveal valuable insights into cellular heterogeneity within tumour microenvironments (TMEs), paving the way for a deep understanding of cellular mechanisms contributing to cancer. However, high heterogeneity among the same cancer types and low transcriptomic variation in immune cell subsets present challenges for accurate, high-resolution confirmation of cells' identities. Here we present scATOMIC; a modular annotation tool for malignant and non-malignant cells. We trained scATOMIC on >300,000 cancer, immune, and stromal cells defining a pan-cancer reference across 19 common cancers and employ a hierarchical approach, outperforming current classification methods. We extensively confirm scATOMIC's accuracy on 225 tumour biopsies encompassing >350,000 cancer and a variety of TME cells. Lastly, we demonstrate scATOMIC's practical significance to accurately subset breast cancers into clinically relevant subtypes and predict tumours' primary origin across metastatic cancers. Our approach represents a broadly applicable strategy to analyse multicellular cancer TMEs.

Recombination affects allele-specific expression of deleterious variants in human populations.

How the genetic composition of a population changes through stochastic processes, such as genetic drift, in combination with deterministic processes, such as selection, is critical to understanding how phenotypes vary in space and time. Here, we show how evolutionary forces affecting selection, including recombination and effective population size, drive genomic patterns of allele-specific expression (ASE). Integrating tissue-specific genotypic and transcriptomic data from 1500 individuals from two different cohorts, we demonstrate that ASE is less often observed in regions of low recombination, and loci in high or normal recombination regions are more efficient at using ASE to underexpress harmful mutations. By tracking genetic ancestry, we discriminate between ASE variability due to past demographic effects, including subsequent bottlenecks, versus local environment. We observe that ASE is not randomly distributed along the genome and that population parameters influencing the efficacy of natural selection alter ASE levels genome wide.

Psychosocial factors and cancer incidence (PSY-CA): Protocol for individual participant data meta-analyses.

OBJECTIVES: Psychosocial factors have been hypothesized to increase the risk of cancer. This study aims (1) to test whether psychosocial factors (depression, anxiety, recent loss events, subjective social support, relationship status, general distress, and neuroticism) are associated with the incidence of any cancer (any, breast, lung, prostate, colorectal, smoking-related, and alcohol-related); (2) to test the interaction between psychosocial factors and factors related to cancer risk (smoking, alcohol use, weight, physical activity, sedentary behavior, sleep, age, sex, education, hormone replacement therapy, and menopausal status) with regard to the incidence of cancer; and (3) to test the mediating role of health behaviors (smoking, alcohol use, weight, physical activity, sedentary behavior, and sleep) in the relationship between psychosocial factors and the incidence of cancer. METHODS: The psychosocial factors and cancer incidence (PSY-CA) consortium was established involving experts in the field of (psycho-)oncology, methodology, and epidemiology. Using data collected in 18 cohorts (N = 617,355), a preplanned two-stage individual participant data (IPD) meta-analysis is proposed. Standardized analyses will be conducted on harmonized datasets for each cohort (stage 1), and meta-analyses will be performed on the risk estimates (stage 2). CONCLUSION: PSY-CA aims to elucidate the relationship between psychosocial factors and cancer risk by addressing several shortcomings of prior meta-analyses.

Interacting evolutionary pressures drive mutation dynamics and health outcomes in aging blood.

Age-related clonal hematopoiesis (ARCH) is characterized by age-associated accumulation of somatic mutations in hematopoietic stem cells (HSCs) or their pluripotent descendants. HSCs harboring driver mutations will be positively selected and cells carrying these mutations will rise in frequency. While ARCH is a known risk factor for blood malignancies, such as Acute Myeloid Leukemia (AML), why some people who harbor ARCH driver mutations do not progress to AML remains unclear. Here, we model the interaction of positive and negative selection in deeply sequenced blood samples from individuals who subsequently progressed to AML, compared to healthy controls, using deep learning and population genetics. Our modeling allows us to discriminate amongst evolutionary classes with high accuracy and captures signatures of purifying selection in most individuals. Purifying selection, acting on benign or mildly damaging passenger mutations, appears to play a critical role in preventing disease-predisposing clones from rising to dominance and is associated with longer disease-free survival. Through exploring a range of evolutionary models, we show how different classes of selection shape clonal dynamics and health outcomes thus enabling us to better identify individuals at a high risk of malignancy.

Score tests for scale effects, with application to genomic analysis.

Tests for variance or scale effects due to covariates are used in many areas and recently, in genomic and genetic association studies. We study score tests based on location-scale models with arbitrary error distributions that allow incorporation of additional adjustment covariates. Tests based on Gaussian and Laplacian double generalized linear models are examined in some detail. Numerical properties of the tests under Gaussian and other error distributions are examined. Our results show that the use of model-based asymptotic distributions with score tests for scale effects does not control type 1 error well in many settings of practical relevance. We consider simple statistics based on permutation distribution approximations, which correspond to well-known statistics derived by another approach. They are shown to give good type 1 error control under different error distributions and under covariate distribution imbalance. The methods are illustrated through a differential gene expression analysis involving breast cancer tumor samples.

Genetic association and transcriptome integration identify contributing genes and tissues at cystic fibrosis modifier loci.

Cystic Fibrosis (CF) exhibits morbidity in several organs, including progressive lung disease in all patients and intestinal obstruction at birth (meconium ileus) in ~15%. Individuals with the same causal CFTR mutations show variable disease presentation which is partly attributed to modifier genes. With >6,500 participants from the International CF Gene Modifier Consortium, genome-wide association investigation identified a new modifier locus for meconium ileus encompassing ATP12A on chromosome 13 (min p = 3.83x10(-10)); replicated loci encompassing SLC6A14 on chromosome X and SLC26A9 on chromosome 1, (min p<2.2x10(-16), 2.81x10(-11), respectively); and replicated a suggestive locus on chromosome 7 near PRSS1 (min p = 2.55x10(-7)). PRSS1 is exclusively expressed in the exocrine pancreas and was previously associated with non-CF pancreatitis with functional characterization demonstrating impact on PRSS1 gene expression. We thus asked whether the other meconium ileus modifier loci impact gene expression and in which organ. We developed and applied a colocalization framework called the Simple Sum (SS) that integrates regulatory and genetic association information, and also contrasts colocalization evidence across tissues or genes. The associated modifier loci colocalized with expression quantitative trait loci (eQTLs) for ATP12A (p = 3.35x10(-8)), SLC6A14 (p = 1.12x10(-10)) and SLC26A9 (p = 4.48x10(-5)) in the pancreas, even though meconium ileus manifests in the intestine. The meconium ileus susceptibility locus on chromosome X appeared shifted in location from a previously identified locus for CF lung disease severity. Using the SS we integrated the lung disease association locus with eQTLs from nasal epithelia of 63 CF participants and demonstrated evidence of colocalization with airway-specific regulation of SLC6A14 (p = 2.3x10(-4)). Cystic Fibrosis is realizing the promise of personalized medicine, and identification of the contributing organ and understanding of tissue specificity for a gene modifier is essential for the next phase of personalizing therapeutic strategies.

Aberrant PRDM9 expression impacts the pan-cancer genomic landscape.

The binding of PRDM9 to chromatin is a key step in the induction of DNA double-strand breaks associated with meiotic recombination hotspots; it is normally expressed solely in germ cells. We interrogated 1879 cancer samples in 39 different cancer types and found that PRDM9 is unexpectedly expressed in 20% of these tumors even after stringent gene homology correction. The expression levels of PRDM9 in tumors are significantly higher than those found in healthy neighboring tissues and in healthy nongerm tissue databases. Recurrently mutated regions located within 5 Mb of the PRDM9 loci, as well as differentially expressed genes in meiotic pathways, correlate with PRDM9 expression. In samples with aberrant PRDM9 expression, structural variant breakpoints frequently neighbor the DNA motif recognized by PRDM9, and there is an enrichment of structural variants at sites of known meiotic PRDM9 activity. This study is the first to provide evidence of an association between aberrant expression of the meiosis-specific gene PRDM9 with genomic instability in cancer.

Prediction of acute myeloid leukaemia risk in healthy individuals.

The incidence of acute myeloid leukaemia (AML) increases with age and mortality exceeds 90% when diagnosed after age 65. Most cases arise without any detectable early symptoms and patients usually present with the acute complications of bone marrow failure1. The onset of such de novo AML cases is typically preceded by the accumulation of somatic mutations in preleukaemic haematopoietic stem and progenitor cells (HSPCs) that undergo clonal expansion2,3. However, recurrent AML mutations also accumulate in HSPCs during ageing of healthy individuals who do not develop AML, a phenomenon referred to as age-related clonal haematopoiesis (ARCH)4-8. Here we use deep sequencing to analyse genes that are recurrently mutated in AML to distinguish between individuals who have a high risk of developing AML and those with benign ARCH. We analysed peripheral blood cells from 95 individuals that were obtained on average 6.3 years before AML diagnosis (pre-AML group), together with 414 unselected age- and gender-matched individuals (control group). Pre-AML cases were distinct from controls and had more mutations per sample, higher variant allele frequencies, indicating greater clonal expansion, and showed enrichment of mutations in specific genes. Genetic parameters were used to derive a model that accurately predicted AML-free survival; this model was validated in an independent cohort of 29 pre-AML cases and 262 controls. Because AML is rare, we also developed an AML predictive model using a large electronic health record database that identified individuals at greater risk. Collectively our findings provide proof-of-concept that it is possible to discriminate ARCH from pre-AML many years before malignant transformation. This could in future enable earlier detection and monitoring, and may help to inform intervention.

Testing Calibration of Cox Survival Models at Extremes of Event Risk.

Risk prediction models can translate genetic association findings for clinical decision-making. Most models are evaluated on their ability to discriminate, and the calibration of risk-prediction models is largely overlooked in applications. Models that demonstrate good discrimination in training datasets, if not properly calibrated to produce unbiased estimates of risk, can perform poorly in new patient populations. Poorly calibrated models arise due to missing covariates, such as genetic interactions that may be unknown or not measured. We demonstrate that models omitting interactions can lead to increased bias in predicted risk for patients at the tails of the risk distribution; i.e., those patients who are most likely to be affected by clinical decision making. We propose a new calibration test for Cox risk-prediction models that aggregates martingale residuals for subjects from extreme high and low risk groups with a test statistic maximum chosen by varying which risk groups are included in the extremes. To estimate the empirical significance of our test statistic, we simulate from a Gaussian distribution using the covariance matrix for the grouped sums of martingale residuals. Simulation shows the new test maintains control of type 1 error with improved power over a conventional goodness-of-fit test when risk prediction deviates at the tails of the risk distribution. We apply our method in the development of a prediction model for risk of cystic fibrosis-related diabetes. Our study highlights the importance of assessing calibration and discrimination in predictive modeling, and provides a complementary tool in the assessment of risk model calibration.

Gene-by-environment interactions in urban populations modulate risk phenotypes.

Uncovering the interaction between genomes and the environment is a principal challenge of modern genomics and preventive medicine. While theoretical models are well defined, little is known of the G × E interactions in humans. We used an integrative approach to comprehensively assess the interactions between 1.6 million data points, encompassing a range of environmental exposures, health, and gene expression levels, coupled with whole-genome genetic variation. From ∼1000 individuals of a founder population in Quebec, we reveal a substantial impact of the environment on the transcriptome and clinical endophenotypes, overpowering that of genetic ancestry. Air pollution impacts gene expression and pathways affecting cardio-metabolic and respiratory traits, when controlling for genetic ancestry. Finally, we capture four expression quantitative trait loci that interact with the environment (air pollution). Our findings demonstrate how the local environment directly affects disease risk phenotypes and that genetic variation, including less common variants, can modulate individual's response to environmental challenges.

A generalized Levene's scale test for variance heterogeneity in the presence of sample correlation and group uncertainty.

We generalize Levene's test for variance (scale) heterogeneity between k groups for more complex data, when there are sample correlation and group membership uncertainty. Following a two-stage regression framework, we show that least absolute deviation regression must be used in the stage 1 analysis to ensure a correct asymptotic χk-12/(k-1) distribution of the generalized scale (gS) test statistic. We then show that the proposed gS test is independent of the generalized location test, under the joint null hypothesis of no mean and no variance heterogeneity. Consequently, we generalize the recently proposed joint location-scale (gJLS) test, valuable in settings where there is an interaction effect but one interacting variable is not available. We evaluate the proposed method via an extensive simulation study and two genetic association application studies.

Cystic fibrosis gene modifier SLC26A9 modulates airway response to CFTR-directed therapeutics.

Cystic fibrosis is realizing the promise of personalized medicine. Recent advances in drug development that target the causal CFTR directly result in lung function improvement, but variability in response is demanding better prediction of outcomes to improve management decisions. The genetic modifier SLC26A9 contributes to disease severity in the CF pancreas and intestine at birth and here we assess its relationship with disease severity and therapeutic response in the airways. SLC26A9 association with lung disease was assessed in individuals from the Canadian and French CF Gene Modifier consortia with CFTR-gating mutations and in those homozygous for the common Phe508del mutation. Variability in response to a CFTR-directed therapy attributed to SLC26A9 genotype was assessed in Canadian patients with gating mutations. A primary airway model system determined if SLC26A9 shows modification of Phe508del CFTR function upon treatment with a CFTR corrector. In those with gating mutations that retain cell surface-localized CFTR we show that SLC26A9 modifies lung function while this is not the case in individuals homozygous for Phe508del where cell surface expression is lacking. Treatment response to ivacaftor, which aims to improve CFTR-channel opening probability in patients with gating mutations, shows substantial variability in response, 28% of which can be explained by rs7512462 in SLC26A9 (P = 0.0006). When homozygous Phe508del primary bronchial cells are treated to restore surface CFTR, SLC26A9 likewise modifies treatment response (P = 0.02). Our findings indicate that SLC26A9 airway modification requires CFTR at the cell surface, and that a common variant in SLC26A9 may predict response to CFTR-directed therapeutics.

A Joint Location-Scale Test Improves Power to Detect Associated SNPs, Gene Sets, and Pathways.

Gene-based, pathway, and other multivariate association methods are motivated by the possibility of GxG and GxE interactions; however, accounting for such interactions is limited by the challenges associated with adequate modeling information. Here we propose an easy-to-implement joint location-scale (JLS) association testing framework for single-variant and multivariate analysis that accounts for interactions without explicitly modeling them. We apply the JLS method to a gene-set analysis of cystic fibrosis (CF) lung disease, which is influenced by multiple environmental and genetic factors. We identify and replicate an association between the constituents of the apical plasma membrane and CF lung disease (p = 0.0099 and p = 0.0180, respectively) and highlight a role for the SLC9A3-SLC9A3R1/2-EZR complex in contributing to CF lung disease. Many association studies could benefit from re-analysis with the JLS method that leverages complex genetic architecture for SNP, gene, and pathway identification. Analytical verification, simulation, and additional proof-of-principle applications support our approach.

Variants in Solute Carrier SLC26A9 Modify Prenatal Exocrine Pancreatic Damage in Cystic Fibrosis.

OBJECTIVES: To test the hypothesis that multiple constituents of the apical plasma membrane residing alongside the causal cystic fibrosis (CF) transmembrane conductance regulator protein, including known CF modifiers SLC26A9, SLC6A14, and SLC9A3, would be associated with prenatal exocrine pancreatic damage as measured by newborn screened (NBS) immunoreactive trypsinogen (IRT) levels. STUDY DESIGN: NBS IRT measures and genome-wide genotype data were available on 111 subjects from Colorado, 37 subjects from Wisconsin, and 80 subjects from France. Multiple linear regression was used to determine whether any of 8 single nucleotide polymorphisms (SNPs) in SLC26A9, SLC6A14, and SLC9A3 were associated with IRT and whether other constituents of the apical plasma membrane contributed to IRT. RESULTS: In the Colorado sample, 3 SLC26A9 SNPs were associated with NBS IRT (min P=1.16×10(-3); rs7512462), but no SLC6A14 or SLC9A3 SNPs were associated (P>.05). The rs7512462 association replicated in the Wisconsin sample (P=.03) but not in the French sample (P=.76). Furthermore, rs7512462 was the top-ranked apical membrane constituent in the combined Colorado and Wisconsin sample. CONCLUSIONS: NBS IRT is a biomarker of prenatal exocrine pancreatic disease in patients with CF, and a SNP in SLC26A9 accounts for significant IRT variability. This work suggests SLC26A9 as a potential therapeutic target to ameliorate exocrine pancreatic disease.

Assessing the change in attitudes, knowledge, and perspectives of medical students towards chiropractic after an educational intervention.

Objective : We assessed the change in attitudes, knowledge, and perspectives of medical students towards chiropractic after a 1-hour educational intervention. Methods : A mixed-methods approach was used with a 52-item cross-sectional paper survey and 1 focus group of third-year medical students. The views of these medical students towards chiropractic were assessed previously in their second-year of medical school. ANOVA and the Wilcoxon rank-sum test were used to assess between-group differences between the medical students' views before and after the educational intervention. The constant comparative method for analyzing qualitative data was used to identify emergent themes from the focus group transcript. Results : Of 112 third-year medical students, 58 completed the survey (51.7% response rate). The focus group consisted of 6 medical students. Self-reported understanding of chiropractic and number of attitude-positive responses were significantly higher in the group after the educational session. The average number of correct responses assessing knowledge on chiropractic also was significantly higher. Focus group themes were that medical students wanted exposure to chiropractic in clinical settings, had negative attitudes towards chiropractic formed from hidden curriculum, had concerns regarding evidence and safety of chiropractic, and thought that timing of the session on chiropractic was too late in the curriculum. Conclusions : The attitudes and knowledge of medical students towards chiropractic improved immediately after a 1-hour educational intervention. Formally educating medical students on chiropractic may help minimize hidden curriculum issues regarding chiropractic, as identified by the medical students, and facilitate collaboration between medical and chiropractic providers.

Differentiating intraprofessional attitudes toward paradigms in health care delivery among chiropractic factions: results from a randomly sampled survey.

BACKGROUND: As health care has increased in complexity and health care teams have been offered as a solution, so too is there an increased need for stronger interprofessional collaboration. However the intraprofessional factions that exist within every profession challenge interprofessional communication through contrary paradigms. As a contender in the conservative spinal health care market, factions within chiropractic that result in unorthodox practice behaviours may compromise interprofessional relations and that profession's progress toward institutionalization. The purpose of this investigation was to quantify the professional stratification among Canadian chiropractic practitioners and evaluate the practice perceptions of those factions. METHODS: A stratified random sample of 740 Canadian chiropractors was surveyed to determine faction membership and how professional stratification could be related to views that could be considered unorthodox to current evidence-based care and guidelines. Stratification in practice behaviours is a stated concern of mainstream medicine when considering interprofessional referrals. RESULTS: Of 740 deliverable questionnaires, 503 were returned for a response rate of 68%. Less than 20% of chiropractors (18.8%) were aligned with a predefined unorthodox perspective of the conditions they treat. Prediction models suggest that unorthodox perceptions of health practice related to treatment choices, x-ray use and vaccinations were strongly associated with unorthodox group membership (X(2) =13.4, p = 0.0002). CONCLUSION: Chiropractors holding unorthodox views may be identified based on response to specific beliefs that appear to align with unorthodox health practices. Despite continued concerns by mainstream medicine, only a minority of the profession has retained a perspective in contrast to current scientific paradigms. Understanding the profession's factions is important to the anticipation of care delivery when considering interprofessional referral.

Evidence for a causal relationship between early exocrine pancreatic disease and cystic fibrosis-related diabetes: a Mendelian randomization study.

Circulating immunoreactive trypsinogen (IRT), a biomarker of exocrine pancreatic disease in cystic fibrosis (CF), is elevated in most CF newborns. In those with severe CF transmembrane conductance regulator (CFTR) genotypes, IRT declines rapidly in the first years of life, reflecting progressive pancreatic damage. Consistent with this progression, a less elevated newborn IRT measure would reflect more severe pancreatic disease, including compromised islet compartments, and potentially increased risk of CF-related diabetes (CFRD). We show in two independent CF populations that a lower newborn IRT estimate is associated with higher CFRD risk among individuals with severe CFTR genotypes, and we provide evidence to support a causal relationship. Increased loge(IRT) at birth was associated with decreased CFRD risk in Canadian and Colorado samples (hazard ratio 0.30 [95% CI 0.15-0.61] and 0.39 [0.18-0.81], respectively). Using Mendelian randomization with the SLC26A9 rs7512462 genotype as an instrumental variable since it is known to be associated with IRT birth levels in the CF population, we provide evidence to support a causal contribution of exocrine pancreatic status on CFRD risk. Our findings suggest CFRD risk could be predicted in early life and that maintained ductal fluid flow in the exocrine pancreas could delay the onset of CFRD.