OBJECTIVE: To assess and compare the RQoL of the occupationally exposed (firefighters and traffic police) and the occupationally unexposed populations in Penang, Malaysia.
METHODS: We recruited male traffic police and firefighters from 5 districts of Penang by convenient sampling during June to September 2018. Participants completed the SGRQ. Scores (symptoms, activity, impacts, total) were derived using a scoring calculator. Higher scores indicate poorer RQoL. Univariate and multivariate linear regression models were fitted to explore the relationship of the independent predictive factors with participants' RQoL.
RESULTS: We recruited 706 participants---211 firefighters, 198 traffic police, and 297 from general population. Smokers had significantly higher scores than non-smokers in all SGRQ domains. Regardless of smoking status, the "occupationally exposed group" had higher symptoms score than the "occupationally unexposed group," who had higher activity and impact scores. Smoking status, comorbidity status and monthly income were significant independent predictors of SGRQ total score.
CONCLUSION: In comparison with the general population, firefighters and traffic police reported poorer RQoL; smoking further deteriorated their respiratory health. There is a need to strengthen preventive health measures against occupational disease and smoking cessation among firefighters and traffic police.
METHODS: We conducted a meta-analysis of four NPC GWAS among Chinese individuals (2,152 cases; 3,740 controls). Forty-three noteworthy findings outside the MHC region were identified and targeted for replication in a pooled analysis of four independent case-control studies across three regions in Asia (4,716 cases; 5,379 controls). A meta-analysis that combined results from the initial GWA and replication studies was performed.
RESULTS: In the combined meta-analysis, rs31489, located within the CLPTM1L/TERT region on chromosome 5p15.33, was strongly associated with NPC (OR = 0.81; P value 6.3 × 10(-13)). Our results also provide support for associations reported from published NPC GWAS-rs6774494 (P = 1.5 × 10(-12); located in the MECOM gene region), rs9510787 (P = 5.0 × 10(-10); located in the TNFRSF19 gene region), and rs1412829/rs4977756/rs1063192 (P = 2.8 × 10(-8), P = 7.0 × 10(-7), and P = 8.4 × 10(-7), respectively; located in the CDKN2A/B gene region).
CONCLUSIONS: We have identified a novel association between genetic variation in the CLPTM1L/TERT region and NPC. Supporting our finding, rs31489 and other SNPs in this region have been reported to be associated with multiple cancer sites, candidate-based studies have reported associations between polymorphisms in this region and NPC, the TERT gene has been shown to be important for telomere maintenance and has been reported to be overexpressed in NPC, and an EBV protein expressed in NPC (LMP1) has been reported to modulate TERT expression/telomerase activity.
IMPACT: Our finding suggests that factors involved in telomere length maintenance are involved in NPC pathogenesis.
METHODS AND FINDINGS: The association of metabolically defined body size phenotypes with colorectal cancer was investigated in a case-control study nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Metabolic health/body size phenotypes were defined according to hyperinsulinaemia status using serum concentrations of C-peptide, a marker of insulin secretion. A total of 737 incident colorectal cancer cases and 737 matched controls were divided into tertiles based on the distribution of C-peptide concentration amongst the control population, and participants were classified as metabolically healthy if below the first tertile of C-peptide and metabolically unhealthy if above the first tertile. These metabolic health definitions were then combined with body mass index (BMI) measurements to create four metabolic health/body size phenotype categories: (1) metabolically healthy/normal weight (BMI < 25 kg/m2), (2) metabolically healthy/overweight (BMI ≥ 25 kg/m2), (3) metabolically unhealthy/normal weight (BMI < 25 kg/m2), and (4) metabolically unhealthy/overweight (BMI ≥ 25 kg/m2). Additionally, in separate models, waist circumference measurements (using the International Diabetes Federation cut-points [≥80 cm for women and ≥94 cm for men]) were used (instead of BMI) to create the four metabolic health/body size phenotype categories. Statistical tests used in the analysis were all two-sided, and a p-value of <0.05 was considered statistically significant. In multivariable-adjusted conditional logistic regression models with BMI used to define adiposity, compared with metabolically healthy/normal weight individuals, we observed a higher colorectal cancer risk among metabolically unhealthy/normal weight (odds ratio [OR] = 1.59, 95% CI 1.10-2.28) and metabolically unhealthy/overweight (OR = 1.40, 95% CI 1.01-1.94) participants, but not among metabolically healthy/overweight individuals (OR = 0.96, 95% CI 0.65-1.42). Among the overweight individuals, lower colorectal cancer risk was observed for metabolically healthy/overweight individuals compared with metabolically unhealthy/overweight individuals (OR = 0.69, 95% CI 0.49-0.96). These associations were generally consistent when waist circumference was used as the measure of adiposity. To our knowledge, there is no universally accepted clinical definition for using C-peptide level as an indication of hyperinsulinaemia. Therefore, a possible limitation of our analysis was that the classification of individuals as being hyperinsulinaemic-based on their C-peptide level-was arbitrary. However, when we used quartiles or the median of C-peptide, instead of tertiles, as the cut-point of hyperinsulinaemia, a similar pattern of associations was observed.
CONCLUSIONS: These results support the idea that individuals with the metabolically healthy/overweight phenotype (with normal insulin levels) are at lower colorectal cancer risk than those with hyperinsulinaemia. The combination of anthropometric measures with metabolic parameters, such as C-peptide, may be useful for defining strata of the population at greater risk of colorectal cancer.
METHODS: Demographic, clinical and genotype data were determined for 1122 women (267 cases and 855 controls) recruited from the University of Malaya Medical Centre in the Klang Valley, Kuala Lumpur. Relevant articles were identified from Pubmed, Embase, MEDLINE, and Web of Science. Extraction of data was carried out and summary estimates of the association between rs780094 and GDM were examined.
RESULTS: The frequency of risk allele C was significantly higher in the cases than controls (OR 1.34, 95% CI 1.09-1.66, P = 0.006). The C allele was also associated with increased level of random 2-hour fasting plasma glucose and pregravid body mass index. Meta-analysis further confirmed the association of the GCKR rs780094 with GDM (OR 1.32, 95% CI 1.14-1.52, P = 0.0001).
CONCLUSION: This study strongly suggests that GCKR rs780094-C is associated with increased risk of GDM.
METHODS: An unmatched hospital based case-control study was conducted from October 2002 to December 2016 in Selangor, Malaysia. A total of 3,683 cases and 3,980 controls were included in this study. Unconditional logistic regressions, adjusted for potential confounding factors, were conducted. The breast cancer risk factors were compared across four birth cohorts by ethnicity.
RESULTS: Ever breastfed, longer breastfeeding duration, a higher soymilk and soy product intake, and a higher level of physical activity were associated with lower risk of breast cancer. Chinese had the lowest breastfeeding rate, shortest breastfeeding duration, lowest parity and highest age of first full term pregnancy.
CONCLUSIONS: Our study shows that breastfeeding, soy intake and physical activity are modifiable risk factors for breast cancer. With the increasing incidence of breast cancer there is an urgent need to educate the women about lifestyle intervention they can take to reduce their breast cancer risk.
Methods: A multi-centred matched case control study was conducted in five local hospitals. A total of 140 histologically confirmed CRC cases were matched with 280 cancer free controls. Mean value and prevalence of the components of metabolic syndrome between cases and controls were measured based on the three definitions. A multiple variable analysis using Cox regression was conducted to measure the strength of the association between the definitions of MetS, components of MetS and risk of CRC.
Results: Multiple variable analyses showed that metabolic syndrome significantly and independently increased the risk of CRC, with an odds ratio ranging from 1.79 to 2.61. This study identified that the definition of metabolic syndrome by the International Diabetes Federation is the most sensitive in predicting the risk of CRC, compared to metabolic syndrome as defined by the World Health Organization and National Cholesterol Education Program Adults Treatment Panel III. Abdominal obesity, low HDL-cholesterol, and hypertension were identified as the three core risk factors, which promote inflammatory signals that contribute to metabolic syndrome and an increased risk of CRC.
Conclusions: These data hypothesized that simple measurement of abdominal obesity, abnormal BP and HDL-cholesterol especially using International Diabetes Federation (IDF) definition of MetS for South Asians for to detect individuals at CRC risk may have higher clinical utility than applying other universal complex MetS definitions.