Study to assess whether waist circumference and changes in serum glucose and lipid profile are independent variables for the CETP gene

Variations in laboratory parameters in prechronic disease to determine disease occurrence at the molecular level

Aim: Change at a molecular level should occur before histopathological impairments. We refer to these changes as 'prechronic disease state.' Methods: Two blood samples were taken 1 day apart, 1 month apart and 2 years apart to determine the contribution of genetic and biological variations. Results: The 95% CI calculated quantitatively by the variability of genetic variation was very similar to the reference interval provided by the laboratory for most indicators, indicating that pathological variability had occurred without consideration of biological variation if an indicator's value lies outside of the reference interval. Conclusion: The physiological or predisease state can be distinguished directly using the laboratory reference interval.

Quantifying the effects of aging and urbanization on major gastrointestinal diseases to guide preventative strategies

BACKGROUND

This study aimed to quantify the effects of aging and urbanization on major gastrointestinal disease (liver cirrhosis, hepatitis B, diarrhea, liver cancer, stomach cancer, pancreas cancer, hepatitis C, esophagus cancer, colon/rectum cancer, gastrointestinal ulcers, diabetes, and appendicitis).

METHODS

We accessed 2004 and 2011 mortality statistics from the most developed cities and least developed rural areas in China using a retrospective design. The relative risk of death associated with urbanization and age was quantified using Generalized linear model (the exp.(B) from model is interpreted as the risk ratio; the greater the B, the greater the impact of urbanized factors or aging factor or effect of aging factor with urbanization). The interaction between region (cities and rural areas) and age was considered as indicator to assess role of age in mortality with urbanization.

RESULTS

Greater risk of disease with urbanization were, in ascending order, for diabetes, colon/rectum cancer, hepatitis C and pancreas cancer. Stronger the effect of aging with urbanization were, in ascending order, for stomach cancer, ulcer, liver cancer, colon/rectum cancer, pancreas cancer, diabetes, hepatitis C, appendicitis and diarrhea. When the effects of aging and urbanization on diseases were taken together as the dividing value, we were able to further divide the 12 gastrointestinal diseases into three groups to guide the development of medical strategies.

CONCLUSIONS

It was suggested that mortality rate for most gastrointestinal diseases was sensitive to urbanization and control of external risk factors could lead to the conversion of most gastrointestinal disease.

Effect of genetic and non-genetic factors, including aging, on waist circumference and BMI, and inter-indicator differences in risk assessment

OBJECTIVE

To assess the effect of genetic and non-genetic factors on indicators derived from waist circumference (WC) and body mass index (BMI) as well as inter-indicator differences in risk assessment age-related diseases including diabetes mellitus, coronary heart disease and liver cancer.

METHODS

Height, weight and WC were measured in 100 families (students and their two parents), 41 subjects with regular physical exercise routines, and 170 patients with diabetes mellitus, coronary heart disease or liver cancer. The BMI, waist-height ratio (WHtR) and waist circumference density index (WCDI) were calculated for each subject.

RESULTS

BMI was less affected by genetic factors, while WHtR and WCDI were greatly affected by genetic factors as revealed using multiple regression analysis. BMI, WHtR and WCDI were all sensitive to physical exercise according to ROC analysis; among these factors, the most sensitive indicator was WHtR. However, ROC analysis demonstrated that WCDI was more effective than BMI and WHtR for assessing the risk of three diseases.

CONCLUSIONS

WCDI more accurately reflects the roles of both genetic and non-genetic factors, including aging, which can better predict disease.