No abstract

The most prevalent form of cancer in females is breast cancer. Roughly 5%-10% of breast cancers are hereditary, and they are associated with Germline gene mutations, inherited from parents. Germline gene mutations increase the risk of developing cancer earlier in life compared to noninherited cases (sporadic cancer). BRCA1 and BRCA2 are well-studied tumour suppressor genes associated with hereditary breast cancer. Even though mutations in BRCA1 and BRCA2 are assumed to responsible the majority of hereditary breast cancers cases, many other breast cancer susceptibility genes have been identified in the last few decades. Identification of many germline mutations was possible due to advance sequencing technologies. Most of these genes are belongs to tumour suppressors and DNA damage repair gene families (DNA double-strand break repair and DNA mismatch repair). These genes play a vital role in genomic stability and cell cycle control suggesting that any alteration in these genes trigger uncontrolled growth and tumour formation. These genes are categorized according to the penetrance level, the proportion of carriers express the associated trait of the mutated gene. Mutations in high penetrance genes such as BRCA1, BRCA2, TP53, PTEN, and SKT11 greatly increase the risk of developing breast cancer. Moderate penetrance gene such as PALB2, ATM, CHEK2, BARD1, BRIP1 and low penetrance gene such as PARP4, CASP8, TOX3 confer moderate to low increase risk of developing breast cancer. Aim of this review is to summarize genes associated with hereditary breast cancer according to their penetrance level (high, moderate and low penetrance).

Resting metabolic rate (RMR) is the key determinant of the energy requirement of an individual. Measurement of RMR by indirect calorimetry is not feasible in field settings and therefore equation-based calculations are used. Since a valid equation is not available for Sri Lankans, it is important to develop a new population-specific equation for field use. The study objective was to develop a new equation for the prediction of RMR in healthy Sri Lankans using a reference method, indirect calorimetry. RMR data were collected from fifty-seven (male 27) adults aged 19 to 60 years. They were randomly assigned to validation (n = 28) and cross-validation (n = 19) groups using the statistical package R (version 3.6.3). Height, weight, and RMR were measured. Multivariable fractional polynomials (MFP) were used to determine explanatory variables and their functional forms for the model. A variable shrinkage method was used to find the best fit predictor coefficients of the equation. The developed equation was cross-validated on an independent group. Weight and sex code (male = 1; female = 0) were identified as reliable independent variables. The new equation developed was RMR (kcal/day) = 284.5 + (13.2 x weight) + (133.0 x sex code). Independent variables of the prediction equation were able to predict 88.5% of the variance. Root mean square error (RMSE) of the prediction equation in validation and cross-validation was 88.11 kcal/day and 79.03 kcal/day, respectively. The equation developed in this study is suitable for predicting RMR in Sri Lankan adults.