Prostate cancer is one of the most common cancers in men and the global burden of this disease is rising. Lifestyle modifications like smoking cessation, exercise and weight control offer opportunities to decrease the risk of developing prostate cancer. Early detection of prostate cancer by PSA screening remains controversial; yet, changes in PSA threshold, frequency of screening, and addition of other biomarkers have potential to minimise overdiagnosis associated with PSA screening. Several new biomarkers appear promising in individuals with elevated PSA levels or those diagnosed with prostate cancer, these are likely to guide in separating individuals who can be spared of aggressive treatment from those who need it. Several pharmacological agents like 5α-reductase inhibitors, aspirin etc. have a potential to prevent development of prostate cancer. In this review, we discuss the current evidence and research questions regarding prevention, early detection of prostate cancer and management of men either at high risk of prostate cancer or diagnosed with low-grade prostate cancer.

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Objective Autoantibodies recognizing 3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGCR) are associated with statin exposure, the HLA allele DRB1*11:01, and necrotizing muscle biopsies in adult myositis patients. The aim of this study was to characterize the features of pediatric anti-HMGCR-positive myositis patients. Methods The sera of 440 juvenile myositis patients were screened for anti-HMGCR autoantibodies. Demographic and clinical features, responses to therapy, and HLA alleles were assessed. The features of anti-HMGCR-positive patients were compared to those of previously described adult patients with this autoantibody and to children with other myositis-specific autoantibodies (MSAs). Results Five (1.1%) of 440 patients were anti-HMGCR-positive; none had taken statin medications. Three patients had rashes characteristic of juvenile dermatomyositis and two patients had immune-mediated necrotizing myopathies. The median highest creatine kinase (CK) level of anti-HMGCR-positive subjects was 17,000 IU/L. All patients had severe proximal muscle weakness, distal weakness, muscle atrophy, joint contractures, and arthralgias, which were all more prevalent in HMGCR-positive subjects compared to MSA-negative patients or those with other MSAs. Anti-HMGCR-positive patients had only partial responses to multiple immunosuppressive medications and often a chronic course. The DRB1*07:01allele was present in all 5 patients compared to 26.25% of healthy controls (Pcorrected=0.01); none of the 5 pediatric patients had DRB1*11:01. Conclusions Compared to children with other MSAs, muscle disease appeared to be more severe in those with anti-HMGCR autoantibodies. Like adults, children with anti-HMGCR autoantibodies have severe weakness and high CK levels. In contrast to adults, anti-HMGCR-positive children have a strong association with HLA DRB1*07:01.

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HCC incidence remains high in the Indigenous people of the NT. More resources are needed for HCC surveillance and management programs in this population.

Genome Wide Association Studies have identified several Single Nucleotide Polymorphisms (SNPs) that are independently associated with small increments in risk of prostate cancer, opening up the possibility for using such variants in risk prediction. Using segregation analysis of population-based samples of 4390 families of prostate cancer patients from the UK and Australia, and assuming all familial aggregation has genetic causes, we previously found that the best model for the genetic susceptibility to prostate cancer was a mixed model of inheritance that included both a recessive major gene component and a polygenic component (P) that represents the effect of a large number of genetic variants each of small effect, where P∼Nfalse(0,σP2false). Based on published studies of 26 SNPs that are currently known to be associated with prostate cancer, we have extended our model to incorporate these SNPs by decomposing the polygenic component into two parts: a polygenic component due to the known susceptibility SNPs, PK∼Nfalse(0,σK2false), and the residual polygenic component due to the postulated but as yet unknown genetic variants, PU∼Nfalse(0,σU2false). The resulting algorithm can be used for predicting the probability of developing prostate cancer in the future based on both SNP profiles and explicit family history information. This approach can be applied to other diseases for which population-based family data and established risk variants exist.

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BACKGROUND Identification of acute kidney injury (AKI) is predominantly based on changes in plasma creatinine concentration, an insensitive marker. Alternative biomarkers have been proposed. The reference change value (RCV), the point at which biomarker change can be inferred to have occurred with statistical certainty, provides an objective assessment of change in serial tests results in an individual. METHODS In 80 patients with chronic kidney disease, weekly measurements of blood and urinary biomarker concentrations were undertaken over 6 weeks. Variability was determined and compared before and after adjustment for urinary creatinine and across subgroups stratified by level of kidney function, proteinuria, and presence or absence of diabetes. RESULTS RCVs were determined for whole blood, plasma, and urinary neutrophil gelatinase-associated lipocalin (111%, 59%, and 693%, respectively), plasma cystatin C (14%), creatinine (17%), and urinary kidney injury molecule 1 (497%), tissue inhibitor of metalloproteinases 2 (454%), N-acetyl-β-d-glucosaminidase (361%), interleukin-18 (819%), albumin (430%), and α1-microglobulin (216%). Blood biomarkers exhibited lower variability than urinary biomarkers. Generally, adjusting urinary biomarker concentrations for creatinine reduced (P < 0.05) within-subject biological variability (CVI). For some markers, variation differed (P < 0.05) between subgroups. CONCLUSIONS These data can form a basis for application of these tests in clinical practice and research studies and are applicable across different levels of kidney function and proteinuria and in the presence or absence of diabetes. Most of the studied biomarkers have relatively high CVI (noise) but also have reported large concentration changes in response to renal insult (signal); thus progressive change should be detectable (high signal-to-noise ratio) when baseline data are available.