BACKGROUND: Glycemic traits-such as hyperinsulinemia, hyperglycemia, and type 2 diabetes-have been associated with higher colorectal cancer risk in observational studies; however, causality of these associations is uncertain. We used Mendelian randomization (MR) to estimate the causal effects of fasting insulin, 2-hour glucose, fasting glucose, glycated hemoglobin (HbA1c), and type 2 diabetes with colorectal cancer. METHODS: Genome-wide association study summary data were used to identify genetic variants associated with circulating levels of fasting insulin (n = 34), 2-hour glucose (n = 13), fasting glucose (n = 70), HbA1c (n = 221), and type 2 diabetes (n = 268). Using 2-sample MR, we examined these variants in relation to colorectal cancer risk (48 214 case patient and 64 159 control patients). RESULTS: In inverse-variance models, higher fasting insulin levels increased colorectal cancer risk (odds ratio [OR] per 1-SD = 1.65, 95% confidence interval [CI] = 1.15 to 2.36). We found no evidence of any effect of 2-hour glucose (OR per 1-SD = 1.02, 95% CI = 0.86 to 1.21) or fasting glucose (OR per 1-SD = 1.04, 95% CI = 0.88 to 1.23) concentrations on colorectal cancer risk. Genetic liability to type 2 diabetes (OR per 1-unit increase in log odds = 1.04, 95% CI = 1.01 to 1.07) and higher HbA1c levels (OR per 1-SD = 1.09, 95% CI = 1.00 to 1.19) increased colorectal cancer risk, although these findings may have been biased by pleiotropy. Higher HbA1c concentrations increased rectal cancer risk in men (OR per 1-SD = 1.21, 95% CI = 1.05 to 1.40), but not in women. CONCLUSIONS: Our results support a causal effect of higher fasting insulin, but not glucose traits or type 2 diabetes, on increased colorectal cancer risk. This suggests that pharmacological or lifestyle interventions that lower circulating insulin levels may be beneficial in preventing colorectal tumorigenesis.

• MeSH
• Genome-Wide Association Study MeSH
• Diabetes Mellitus, Type 2 * complications   epidemiology   genetics   MeSH
• Glycated Hemoglobin analysis   MeSH
• Hyperinsulinism * complications   genetics   MeSH
• Insulin MeSH
• Polymorphism, Single Nucleotide MeSH
• Colorectal Neoplasms * complications   epidemiology   genetics   MeSH
• Blood Glucose analysis   genetics   MeSH
• Humans MeSH
• Mendelian Randomization Analysis MeSH
• Risk Factors MeSH
• Check Tag
• Humans MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

Reproductive longevity is essential for fertility and influences healthy ageing in women1,2, but insights into its underlying biological mechanisms and treatments to preserve it are limited. Here we identify 290 genetic determinants of ovarian ageing, assessed using normal variation in age at natural menopause (ANM) in about 200,000 women of European ancestry. These common alleles were associated with clinical extremes of ANM; women in the top 1% of genetic susceptibility have an equivalent risk of premature ovarian insufficiency to those carrying monogenic FMR1 premutations3. The identified loci implicate a broad range of DNA damage response (DDR) processes and include loss-of-function variants in key DDR-associated genes. Integration with experimental models demonstrates that these DDR processes act across the life-course to shape the ovarian reserve and its rate of depletion. Furthermore, we demonstrate that experimental manipulation of DDR pathways highlighted by human genetics increases fertility and extends reproductive life in mice. Causal inference analyses using the identified genetic variants indicate that extending reproductive life in women improves bone health and reduces risk of type 2 diabetes, but increases the risk of hormone-sensitive cancers. These findings provide insight into the mechanisms that govern ovarian ageing, when they act, and how they might be targeted by therapeutic approaches to extend fertility and prevent disease.

• MeSH
• Alleles MeSH
• Genome-Wide Association Study MeSH
• Checkpoint Kinase 1 genetics   MeSH
• Checkpoint Kinase 2 genetics   MeSH
• Diabetes Mellitus, Type 2 MeSH
• Diet MeSH
• Longevity genetics   MeSH
• Adult MeSH
• Fertility genetics   MeSH
• Genetic Predisposition to Disease MeSH
• Bone and Bones metabolism   MeSH
• Middle Aged MeSH
• Humans MeSH
• Menopause genetics   MeSH
• Mice, Inbred C57BL MeSH
• Mice MeSH
• Ovary metabolism   MeSH
• Menopause, Premature genetics   MeSH
• Primary Ovarian Insufficiency genetics   MeSH
• Fragile X Mental Retardation Protein genetics   MeSH
• Aging genetics   MeSH
• Uterus MeSH
• Healthy Aging genetics   MeSH
• Animals MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Mice MeSH
• Female MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH
• Geographicals
• Asia, Eastern MeSH
• Europe MeSH

Importance: Cardiometabolic disorders often occur concomitantly with psychosis and depression, contribute to high mortality rates, and are detectable from the onset of the psychiatric disorders. However, it is unclear whether longitudinal trends in cardiometabolic traits from childhood are associated with risks for adult psychosis and depression. Objective: To examine whether specific developmental trajectories of fasting insulin (FI) levels and body mass index (BMI) from early childhood were longitudinally associated with psychosis and depression in young adults. Design, Setting, and Participants: A cohort study from the Avon Longitudinal Study of Parents and Children, a prospective study including a population-representative British cohort of 14 975 individuals, was conducted using data from participants aged 1 to 24 years. Body mass index and FI level data were used for growth mixture modeling to delineate developmental trajectories, and associations with psychosis and depression were assessed. The study was conducted between July 15, 2019, and March 24, 2020. Exposures: Fasting insulin levels were measured at 9, 15, 18, and 24 years, and BMI was measured at 1, 2, 3, 4, 7, 9, 10, 11, 12, 15, 18, and 24 years. Data on sex, race/ethnicity, paternal social class, childhood emotional and behavioral problems, and cumulative scores of sleep problems, average calorie intake, physical activity, smoking, and alcohol and substance use in childhood and adolescence were examined as potential confounders. Main Outcomes and Measures: Psychosis risk (definite psychotic experiences, psychotic disorder, at-risk mental state status, and negative symptom score) depression risk (measured using the computerized Clinical Interview Schedule-Revised) were assessed at 24 years. Results: From data available on 5790 participants (3132 [54.1%] female) for FI levels and data available on 10 463 participants (5336 [51.0%] female) for BMI, 3 distinct trajectories for FI levels and 5 distinct trajectories for BMI were noted, all of which were differentiated by mid-childhood. The persistently high FI level trajectory was associated with a psychosis at-risk mental state (adjusted odds ratio [aOR], 5.01; 95% CI, 1.76-13.19) and psychotic disorder (aOR, 3.22; 95% CI, 1.29-8.02) but not depression (aOR, 1.38; 95% CI, 0.75-2.54). A puberty-onset major increase in BMI was associated with depression (aOR, 4.46; 95% CI, 2.38-9.87) but not psychosis (aOR, 1.98; 95% CI, 0.56-7.79). Conclusions and Relevance: The cardiometabolic comorbidity of psychosis and depression may have distinct, disorder-specific early-life origins. Disrupted insulin sensitivity could be a shared risk factor for comorbid cardiometabolic disorders and psychosis. A puberty-onset major increase in BMI could be a risk factor or risk indicator for adult depression. These markers may represent targets for prevention and treatment of cardiometabolic disorders in individuals with psychosis and depression.

• MeSH
• Depressive Disorder epidemiology   MeSH
• Child MeSH
• Adult MeSH
• Risk Assessment MeSH
• Body Mass Index * MeSH
• Insulin blood   MeSH
• Cardiometabolic Risk Factors * MeSH
• Infant MeSH
• Humans MeSH
• Longitudinal Studies MeSH
• Adolescent MeSH
• Young Adult MeSH
• Child, Preschool MeSH
• Psychotic Disorders epidemiology   MeSH
• Check Tag
• Child MeSH
• Adult MeSH
• Infant MeSH
• Humans MeSH
• Adolescent MeSH
• Young Adult MeSH
• Male MeSH
• Child, Preschool MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Geographicals
• United Kingdom MeSH