| Diabetes is a multifactorial complicating disease resulting from environmental factors,genetic factors and their interaction and has become a serious public health problem worldwide.By 2015,there are 415 million diabetic patients in the world.China has 196 million diabetic patients,making it a country with the most diabetic patients,with $ 51 billion spent on diabetes.The epidemic of diabetes caused a heavy burden to Chinese medication and economy.About 90%of all people with diabetes are type 2 diabetes.Besides,diabetes and long-term chronic hyperglycemia can result in cardiovascular and cerebrovascular complications,as well as diabetic nephrology,ophthalmopathy and nerve system damage.Diabetes and its complications have tremendous deleterious influence on people's health.In order to lower prevalence and incidence of diabetes and improve its prevention and treatment,it is significant to establish risk prediction models.From a population perspective,diabetes risk score systems enable the identification of individuals at high risk of developing diabetes for enrolment into intervention program.They can also help individuals calculate their risk of developing diabetes and prevent or postpone its development.With rapid economy development and urbanization,aging problem is unneglectable.Middle-aged and elderly people are more vulnerable and susceptible to chronic diseases.Therefore,efforts are urgent to improve health quality for middle-aged and elderly population.There have been various country-specific diabetes risk scores developed,comprising varying sets of predictors besides traditional risk factors such as age,obesity,hypertension,diet and physical activity.However,risk scores have limited power when directly applied to other populations other than the original populations.That's why risk scores developed from Caucasian data may not perform well in Asian populations.Furthermore,very few prediction models are suitable for middle-aged and elderly populations and most of them were developed on cross-sectional studies.Moreover,although multiple diabetes risk factors were discovered,many of them are difficult to measure or access,and they may not be suitable for risk prediction.It is important to identify new risk factors and establish diabetes risk prediction model among middle-aged and elderly populations and improve its predictive ability.In order to answer above questions,based on the 2008 baseline data and 2013 follow up data of Dongfeng-Tongji(DFTJ)cohort,we study the relationship between helicobacter pylori,nighttime sleeping,napping and diabetes.Besides,we establish diabetes risk prediction model including traditional risk factors,which are easily accessible.Moreover,we explore whether the addition of sleeping,napping and helicobacter pylori can improve the predictive ability of aforementioned risk prediction model.The main contents were shown as follows:Part 1 Association of sleep duration and afternoon napping with the incidence of type 2 diabetes—the prospective study Objective:To examine the association between sleep duration and afternoon napping and the incidence of type 2 diabetes in a middle-aged and elderly Chinese population.Methods:We selected participants with available data of questionnaire and biochemical assay from the DFTJ cohort at baseline.Those with diabetes at baseline,and those with self-reported history of coronary heart disease,stroke and cancer were further excluded.A total of 16,399 participants were included in the current study.Basic information about demographic data,disease history,family history of disease,and life style were collected by questionnaire.Plasma glucose and lipid profiles were measured.Type 2 diabetes was defined as having fasting plasma glucose(FPG)? 7.0 mmol/L or having self-reported doctor-diagnosed diabetes or taking antidiabetic medications.Cox proportional hazards regression models were used.Results:In the longitudinal analysis of DFTJ cohort(1,123 incident diabetes cases during 4.5 years of follow-up among 16,399 diabetes-free participants at baseline),Compared with referential sleeping group,subjects sleeping>10 h had a 42%higher risk of developing diabetes(95%CI,1.08-1.87;P quadratic trend = 0.013).The HR was 1.28 for napping>90 min when compared with no napping(95%CI,1.03-1.59;P quadratic trend = 0.002).Combined effects of long sleep duration and afternoon napping were further identified.Individuals with both sleep duration>10 h and napping>60 min had a 72%higher risk of incident diabetes than those with sleeping 7?8 h and napping 0 min(HR = 1.72,95%CI,1.03-2.85;P = 0.037).Conclusion:In this prospective study,both long sleep duration and afternoon napping were independently and jointly associated with higher risk of incident diabetes.Larger prospective studies among other populations are warranted to confirm the associations of sleep duration and afternoon napping and risk of type 2 diabetes.Part 2 Association between helicobacter pylori and type 2 diabetes risk—cross-sectional studyObjective:To examine the association between helicobacter pylori(H.pylori)and the prevalence of type 2 diabetes among middle-aged and elderly Chinese population.Methods:We selected participants with available data of 14C,plasma glucose from the DFTJ cohort at 2013 follow-up.Basic information about demographic data,disease history,family history of disease,and life style were collected by questionnaire.Plasma glucose and lipid profiles were measured.A total of 30,810 participants were included in association study.Type 2 diabetes was defined as having fasting plasma glucose(FPG)? 7.0 mmol/L or having self-reported doctor-diagnosed diabetes or taking antidiabetic medications.Binary logistic regression models were used.Results:The prevalence of H.pylori infection was 49.6%in the total sample.Prevalence of type 2 diabetes was significantly different between H.pylori negative and positive individuals(20.2%versus 21.3%,P = 0.026).Diabetic subjects had higher H.pylori infection prevalence compared with the controls(50.9%versus 49.3%,P ?0.026).After adjustment for age,sex,BMI,smoking,drinking,family history of diabetes,metabolic equivalent hours and use of antibiotics,individuals who were infected with H.pylori had higher ORs of type 2 diabetes compared with those who did not(OR = 1.08,95%Cl,1.02-1.14;P = 0.008).We also assessed the association between H.pylori infection and type 2 diabetes risk according to diabetes diagnosis standard of the HbAlc test with a threshold of>6.5%and results were consistent(OR=1.07,95%CI,1.01-1.14;P = 0.022).Conclusion:In the present cross-sectional study,we found a positive association between H.pylori infection and the risk of type 2 diabetes in a middle-age and old-age Chinese population.Further prospective cohort studies are warranted to establish the role of serum H.pylori infection in the development of type 2 diabetes.Part 3 Development of a scoring system to predict 5-year incident diabetes risk in middle-aged and elderly Chinese populationObjective:To develop a new risk score system to predict 5-year incident diabetes risk among middle-aged and elderly Chinese population.Methods:This prospective study included 17,690 individuals derived from the Dongfeng-Tongji cohort.Participants were recruited in 2008 and were followed until October 2013.Incident diabetes was defined as self-reported clinician diagnosed diabetes,fasting glucose>7.0 mmol/L,or the use of insulin or oral hypoglycemic agent.A total of 1,390 incident diabetic cases were diagnosed during the follow-up period.(3-Coefficients were derived from a multiple Logistic regression model and were used to calculate the diabetes risk score.Sleeping and napping were added to see whether they could improve the predictive ability.Age and sex matched case-control study including 6,732 diabetic cases and 6,732 controls was performed to investigate whether the addition of H.pylori could improve the predictive ability of diabetes risk model.Results:The new diabetes risk score includes BMI,waist circumference,fasting glucose,hypertension,hyperlipidemia,smoking,and family history of diabetes.The score ranges from 0 to 40.The area under the receiver operating curve of the score was 0.749(95%CI:0.735-0.762).At the optimal cutoff value of 17,the sensitivity and specificity of the score were 67.7%and 71.5%,respectively.Based upon these risk factors,the diabetes risk prediction model had the highest discrimination(0.763,95%CI:0.749-0.776)compared with several commonly used diabetes prediction models.The addition of sleeping,napping and helicobacter pylori did not improve the predictive ability of the risk model.Conclusion:The newly established diabetes risk score with seven parameters appears to be a reliable screening tool to predict 5-year risk of incident diabetes in a middle-aged and elderly Chinese population. |
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