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Gain in weight is common after heart transplantation but the magnitude of usual weight gain and whether this varies by country is unknown. We systematically reviewed all relevant studies to quantify weight change among heart transplant recipients (HTRs) in the years after transplantation and assess variation with geographic location. We searched PubMed, Cumulative Index to Nursing and Allied Health Literature, and Excerpta Medica Database databases to September 2020. Eligible studies reported adult HTRs’ mean/median weight and/or body mass index (BMI) up to time of transplantation (baseline) and posttransplantation in any language. Weighted mean differences (WMDs) (95% confidence intervals [CIs]) of weight/BMI from baseline to posttransplantation were estimated using a random-effects model. Ten studies met the inclusion criteria. Pooled analysis showed weight gain of 7.1 kg (95% CI, 4.4–9.8 kg) in HTRs 12 months posttransplant, with corresponding BMI increase of 1.69 kg/m2(95% CI, 0.83–2.55 kg/m2). Greatest weight gain at 12 months posttransplant occurred in US HTRs (WMD weight 10.42 kg, BMI 3.25 kg/m2) and least, in European HTRs (WMD weight 3.10 kg, BMI 0.78 kg/m2). In conclusion, HTRs gain substantial weight in the years after transplantation, but varying widely by geographic location.

Epidemiological data suggest that cancer patients have a reduced risk of subsequent Parkinson’s disease (PD) development, but the prevalence of PD in melanoma patients is often reported to be increased. Causal relationships between cancers and PD have not been fully explored. To study causal relationship between different cancers and PD. We used GWAS summary statistics of 15 different types of cancers and two-sample Mendelian randomization to study the causal relationship with PD. There was no evidence to support a causal relationship between the studied cancers and PD. We also performed reverse analyses between PD and cancers with available full summary statistics (melanoma, breast, prostate, endometrial and keratinocyte cancers) and did not find evidence of causal relationship. We found no evidence to support a causal relationship between cancer and PD and the previously reported associations could be a result of genetic pleiotropy, shared biology or biases.

Genome-wide association studies (GWAS) have identified a number of risk loci for cutaneous melanoma. Cutaneous melanoma shares overlapping genetic risk (genetic correlation) with a number of other traits, including its risk factors such as sunburn propensity. This genetic correlation can be exploited to identify additional cutaneous melanoma risk loci by multitrait analysis of GWAS (MTAG). We used bivariate linkage disequilibrium–score regression score regression to identify traits that are genetically correlated with clinically confirmed cutaneous melanoma and then used publicly available GWAS for these traits in a multitrait analysis of GWAS. Multitrait analysis of GWAS allows GWAS to be combined while accounting for sample overlap and incomplete genetic correlation. We identified a total of 74 genome-wide independent loci, 19 of them were not previously reported in the input cutaneous melanoma GWAS meta-analysis. Of these loci, 55 were replicated (P< 0.05/74, Bonferroni-corrected P-value in two independent cutaneous melanoma replication cohorts from Melanoma Institute Australia and 23andMe, Inc. Among the, to our knowledge, previously unreported cutaneous melanoma loci are ones that have also been associated with autoimmune traits including rs715199 near LPPand rs10858023 near AP4B1. Our analysis indicates genetic correlation between traits can be leveraged to identify new risk genes for cutaneous melanoma.