BackgroundHexavalent chromium is a known carcinogen when inhaled, but its carcinogenic potential when orally ingested remains controversial. Water contaminated with hexavalent chromium is a worldwide problem, making this a question of significant public health importance.MethodsWe conducted an ecological mortality study within the Oinofita region of Greece, where water has been contaminated with hexavalent chromium. We calculated gender, age, and period standardized mortality ratios (SMRs) for all deaths, cancer deaths, and specific cancer types of Oinofita residents over an 11-year period (1999 - 2009), using the greater prefecture of Voiotia as the standard population.ResultsA total of 474 deaths were observed. The SMR for all cause mortality was 98 (95% CI 89-107) and for all cancer mortality 114 (95% CI 94-136). The SMR for primary liver cancer was 1104 (95% CI 405-2403, p-value < 0.001). Furthermore, statistically significantly higher SMRs were identified for lung cancer (SMR = 145, 95% CI 100-203, p-value = 0.047) and cancer of the kidney and other genitourinary organs among women (SMR = 368, 95% CI 119-858, p-value = 0.025). Elevated SMRs for several other cancers were also noted (lip, oral cavity and pharynx 344, stomach 121, female breast 134, prostate 128, and leukaemias 168), but these did not reach statistical significance.ConclusionsElevated cancer mortality in the Oinofita area of Greece supports the hypothesis of hexavalent chromium carcinogenicity via the oral ingestion pathway of exposure. Further studies are needed to determine whether this association is causal, and to establish preventive guidelines and public health recommendations.

In this article, three alternative Bayesian hierarchical latent factor models are described for spatially and temporally correlated multivariate health data. The fundamentals of factor analysis with ideas of space- time disease mapping to provide a flexible framework for the joint analysis of multiple-related diseases in space and time with a view to estimating common and disease-specific trends in cancer risk are combined. The models are applied to area-level mortality data on six diet-related cancers for Greece over the 20-year period from 1980 to 1999. The aim of this study is to uncover the spatial and temporal patterns of any latent factor(s) underlying the cancer data that could be interpreted as reflecting some aspects of the habitual diet of the Greek population.

Gestational diabetes mellitus (GDM) is associated with increased risk of pregnancy complications and adverse perinatal outcomes. GDM often reoccurs and is associated with increased risk of subsequent diagnosis of type 2 diabetes (T2D). To improve our understanding of the aetiological factors and molecular processes driving the occurrence of GDM, including the extent to which these overlap with T2D pathophysiology, the GENetics of Diabetes In Pregnancy (GenDIP) Consortium assembled genome-wide association studies (GWAS) of diverse ancestry in a total of 5485 women with GDM and 347 856 without GDM. Through multi-ancestry meta-analysis, we identified five loci with genome-wide significant association (p &lt; 5x10−8) with GDM, mapping to/near MTNR1B (p = 4.3x10−54), TCF7L2 (p = 4.0x10−16), CDKAL1 (p = 1.6 × 10−14), CDKN2A-CDKN2B (p = 4.1x10−9) and HKDC1 (p = 2.9x10−8). Multiple lines of evidence pointed to the shared pathophysiology of GDM and T2D: (i) four of the five GDM loci (not HKDC1) have been previously reported at genome-wide significance for T2D; (ii) significant enrichment for associations with GDM at previously reported T2D loci; (iii) strong genetic correlation between GDM and T2D; and (iv) enrichment of GDM associations mapping to genomic annotations in diabetes-relevant tissues and transcription factor binding sites. Mendelian randomisation analyses demonstrated significant causal association (5% false discovery rate) of higher body mass index on increased GDM risk. Our results provide support for the hypothesis that GDM and T2D are part of the same underlying pathology but that, as exemplified by the HKDC1 locus, there are genetic determinants of GDM that are specific to glucose regulation in pregnancy.

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