Association between obesity indices and type 2 diabetes mellitus among middle-aged and

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Table 1 Cross-sectional study: Association between obesity indices and type 2 diabetes mellitus among middle-aged and elderly people in Jinan, China: a cross-sectional study Critical appraisal question

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1. Were the criteria for inclusion in the sample clearly defined? Yes/No/Unclear

Evidence: Wang et al (2016) stated that the criteria of inclusion in the sample were clearly stated as people above the age of 50 with desired evidence were only to be included in the research. In this particular case study, a total of 3277 inhabitants over the age of 50 were eligible for this study, but 1563 individuals were disbarred since they ceased to provide the required anthropometric data, such as height, mass, waist measurement, waist-hip ratio, blood pressure systolic and diastolic fasting glucose metabolism, triglyceride, and cholesterol levels, in addition to details about their medication management use. Therefore, the final data included 1714 people.

2.Were the study subjects and the setting described in detail?

Evidence: The subject of the study was clearly defined. The aim of the study was to analyze how geographic position and racial composition influence the correlation between type 2 diabetes mellitus (T2DM) and obesity. Wang et al (2016) mentioned that the objective was to further emphasize and investigate the proportion of T2DM and the percentage of individuals without a diagnosis. In addition, to examine the correlations between various obesity indices and T2DM among middle-aged and elderly individuals from six areas in Jinan, China.

The setting seemed to be unclear. It is only stated in the article that the study participants were taken in a random way from blocks within each of Jinan, China’s six communities in a cross-sectional study done in 2011 and 2012.

3.Was the exposure measured in a valid and reliable way?

Evidence: Yes the exposure was measured invalid and in a proper way. The participants’ age, gender, tobacco consumption status, present alcohol intake, and daily exercise frequency were all inquired about on a questionnaire by trained interviewers. All participants had a comprehensive physical assessment that included standard anthropometric, medical, and laboratory tests after the overnight fast of a minimum of 12 hours.

Using a mercurial sphygmomanometer as well as a consistent protocol, trained examiners measured the person’s blood pressure (BP) in the right arm. The average of all of the measurements was chosen as the heart rate result for every subject after three heart rate measures were taken at intervals somewhere between 5 and fifteen minutes.

The participants had to take remove their shoes, any heavy clothing, and belts well before anthropometry was performed. Experienced nurses assessed each participant’s measurements, bodyweight, waist size, and hip circumference.

While the participants softly exhaled, the WC was recorded at the point midway between the iliac crest and the belly edge. Therefore these formalities suggest that a valid and reliable way was undertaken to measure the exposure.

4. Were objective, standard criteria used for measurement of the condition?

Evidence: The participants were picked randomly among blocks in six communities in Jinan, China, between 2011 and 2012. Minimum age of over 50 years and more than 6 months of residency in the designated localities within the previous year were the inclusion criteria for participants.

In this research, a total of 3277 residents over the age of 50 completed the questionnaire and 1563 people have been disqualified since they did fail to provide anthropometric data, such as height, weight, waist size, hip circumference, systolic and diastolic blood pressure, fasting plasma glucose, triglyceride, and cholesterol levels, or details regarding their medication management usages. The final data analysis encompasses a total of 1714 participants.

5. Were confounding factors identified?

Evidence: Yes the factors were identified. The original logistics regression model was calculated before continuous anthropometric parameters like the WC and BMI.

The different stages of the WSR and WHR were established using the three percentile cutoff levels of P25, P50, and P75, longevity, according to gender. The four logistic regression model brought a wide range of confounding factors into consideration.

6.Were strategies to deal with confounding factors stated?
Evidence: No particular strategy has been identified in the study that significantly dealt with the confounding factor.

7.Were the outcomes measured in a valid and reliable way?

Evidence: The outcomes were measured in a proper way. Aggregate statistics and comparisons of anthropometric measurements amongst individuals with and without diabetes, and by gender were enumerated through tabular representation. A statistical analysis method was stated.

As per gender and the existence or absence of diabetes, descriptive statistics for all variables were obtained. Depending on whether the distribution of the numerical data was standard or distorted as assessed by a graph, the numerical information was presented as means, median, and quadrant range.

The group differences were determined using the rank sum Wilcoxon test and the 2 t-tests. In order to match variances for categorical variables, a 2 testing was used to express categorical variables as proportions. Four multinomial logit models were devised using a variety of confounding factors. Four logistic regression models were constructed using a variety of confounding factors.

8.Was appropriate statistical analysis used?

Evidence: The statistical analysis was fruitful in generating reliable data about the objectives of the study. While model 2 just included an adjustment for age, the very first model did not contain any variable modifications. Adjustments for constant age, smoking, alcohol consumption, as well as daily exercise intensity 1-2 times each week, 3-4 times each week, 5-7 times each week, or 8 times per week, were included in the third model. The fourth model has included adjustments for high blood pressure, constant TG, and cholesterol. The control variables in the models were the categories BMI, WC, WHR, and WSR variables. Therefore it can be stated that statistical analysis has been instrumental in generating data.

Table 2 Case-control study: The association of body mass index with the risk of type 2 diabetes: a case-control study nested in an electronic health records system in the United States

1.Did the study address a clearly focused issue?

Evidence: It is clear from the objective of the study that the main issues of obesity and diabetes are being clearly addressed. To figure out the connection between the body mass index (BMI) as well as the probability of receiving a T2D diagnostic in the United States that study was conducted.

2. Did the authors use an appropriate method to answer their question?

Evidence: Yes, the method used by the author to collect data have been appropriate. The MedMining database, which incorporates electronic health data from Geisinger Healthcare System, was utilized to gather this information. Ganz et al (2014) stated that the Geisinger Health System is a fully integrated health system with 880 multi-specialty physicians’ practice settings, 5 hospital campuses, 72 basic and specialist clinic sites, and a health care plan that offers support to more than 4 million people throughout the state of Pennsylvania. As of 1996, Geisinger Health System has retained electronic health files for every patient. Such records involve demographic information, confront information from patients admitted, ambulatory care, and workplace settings including CPT-4 procedure codes and ICD-9-CM diagnosis codes, medication orders, lab tests, and precise expenses incurred by the Healthcare System for those encounters. The clinical and economic evaluations in this dataset have been commonly utilized to gather important data.

3. Were the cases recruited in an acceptable way?
Evidence: Cases were selected, from January 2004 through October 2011. Ganz et al (2014) mentioned that the initial diagnosis of type 2 diabetic (T2D) as evidenced by ICD-9-CM diagnoses or by a prescription for just an anti-diabetic medication, was received. Every person’s “index date,” which has been referred to as the expiry of their incident, or initially noticed, T2D identification in the MedMining database, served as the foundation for occurrences and measurements. Therefore it can be justified that the cases were selected in an acceptable way.

4. Were the controls selected in an acceptable way? Yes/No/Unclear

Evidence: Yes, The way the potential control were selected were acceptable.Two individuals without no diabetes history defined by ICD-9-CM clinical information or use of any anti-diabetic medication throughout the research period were randomly chosen for each instance as a group of possible controls. Depending on age group, sex, background of any cardiovascular comorbidities, approach to detect the condition, psychiatric drug usage, and testing phase narcotic use, possible controls were selected. A randomized index date between both the start and finish of the study years for the potential controls was chosen since they were never identified with T2D.

5. Was the exposure accurately measured to minimise bias?

Evidence: justification, compare and contrasting or/and providing solution
Yes, BMI values were extracted from self-reported weight and height in those earlier studies as opposed to just being clinically measured in the current study. The individuals’ self-reported weight and height significantly underplay their actual BMI, which might have weakened the link between obesity and the risk of T2D or distorted the estimated results.

6. Aside from the experimental intervention, were the groups treated equally?

Evidence: It is unclear in the study whether the groups were treated equally or not. It is only stated that the statistically significant difference between groups was ascertained. Using the chi-square test for categorical data, distinctions between individual characteristics and clinical traits, such as baseline BMI, among cases and controls have been made.

7. Have the authors taken account of the potential confounding factors in the design and/or in their analysis?

Evidence: No, the cofounding factors have not been taken precisely by the author in the analysis and design. There may have been unquantified and unprocessed confounding related to baseline attributes. The unavailability of Geisinger Health System data to capture medical services obtained from outside of the system is another disadvantage.

8. How large was the treatment effect?

Evidence: The effect of the treatment was substantially significant in a large proportion. Participants assigned to the lifestyle-modification treatment with a goal of at least a 7% fat loss of the initial body weight had a 58 percent lower chance of T2D than those compared to placebo. Furthermore, among study participants, weight loss was significantly and substantially linked to reductions in blood sugar from pre-diabetic to average levels.

9. How precise was the estimate of the treatment effect?

Evidence: The treatment seemed to be inclusive and detrimental relative to the bodyfat percentages. It significantly points out the implication related to high blood sugar levels.

10. Do you believe the results? Yes

Evidence: Yes the results have significantly highlighted the important perspective related to the role of body weight in obesity. When contrasting cases and control over the 12-month pre-index interval, cases were much more likely to be among younger males, who had used more medical resources. In fact, cases were more likely than control to have used diabetes or obesity-related medications and experienced comorbidities even during the 12-month pre-index period. The results have been indicated through a proper approach and highlight important perspectives.

11. Can the results be applied to the local population?

Evidence: The results can vary according to the areas and places therefore it cannot be justified whether the results in an area can significantly be applied to the local population. Further, the previous results cannot estimate the current health concerns of the people. Therefore it cannot be significantly determined whether the results can be applied to the local population.

12. Do the results of this study fit with other available evidence?

Evidence: It is unclear whether the results can suit the available evidence. The odds ratio of the individual characteristics, with the exception of BMI, did not constitute the main focus of this research because BMI was accounted for by including them in the regression. The research significantly highlights the other available evidential circumstance.

Table 3 Cohort study: Obesity and Body Fat Distribution in Relation to the Incidence of Non-insulin-dependent Diabetes Mellitus

1.Did the study address a clearly focused issue?

Evidence: Yes, the issues were clearly focused on in the study. Among a total of 1972 males participants in the Department of Veteran Affairs, the relation between the body fat accumulation in the belly, blood glucose level, overall obesity, and the likelihood of nob-insulin dependant diabetic were accessed prospectively. The issues related to diabetes were analyzed using the potential hazard model. The obesity classification was confirmed and measurable in the study. The study’s results supported earlier reports that indicate a possible connection between abdominal obesity and the risk of getting diabetes. These also offer promising evidence for a link between levels of blood glucose and both body fat composition and obesity.

2. Was the cohort recruited in an acceptable way?

Evidence: Yes, on the basis of the inclusion criteria, the participants were prospectively selected. 2,280 men who were enlisted in the Veterans Affairs Department Normative Ageing Study cohort in 1963 and spanned the ages of twenty and 80 composed the study’s population. The study’s details were already published elsewhere. All of the male volunteers who signed satisfied screening requirements based on their current medical status and medical history, and 98 percent of them were Caucasian vets. Only participants who were initially in good health would be included; for example, men who already had diabetes at screening weren’t included, thus there were no expected prevalent cases in the cohort at the base. Neither overweight nor the pattern of body fat was considered while the evaluation.

3. Was the exposure accurately measured to minimise bias?

Evidence: There was no exposure or social variables measured in this study. Perhaps this study’s risk evaluation of the indicators wasn’t purely objective. The indicators themselves should be considered as the “exposure” in this cohort research. Thus, the “exposure” is believed to have been precisely measured by the ELISA research method.

4.Was the outcome accurately measured to minimise bias?

Evidence: The initial investigation of the pattern of the correlation between anthropometric parameters and blood glucose levels utilized graphic approaches, incorporating smoothing methods. Cassano, Rosner, Vokonas, & Weiss (1992) stated that In an examination of the basic structure of multiple testing of blood glucose over time, Pearson’s product-moment correlates also were calculated. Although reliability statistics for the anthropometric measurements were not available, it may be assumed that any error would be randomized and consequently skew our results forward towards the null value. The subset of males lacking anthropometric data was contrasted to the majority of the cohort using data available at the baseline visit in order to rule out bias arising from deliberate reduction of the cohort.

5(a)Have the authors identified all-important confounding factors?
Evidence: Yes, the author has been successful in enumeration the confounding factors. Numerous factors, such as aging, smoking, and overall adiposity, may well be confounding. After accounting for the effects of fat distribution, gender, and smoking, overall adiposity, as measured by BMI, was also uniquely associated with diabetes risk. These factors were recognized.

5(b)Have they taken account of the confounding factors in the design and/or analysis?

Evidence: Yes, The data analysis has taken into consideration the important factors that are resulting in instances related to obesity and body fat distribution.

6(a)Was the follow up of subjects complete enough?

Evidence: The individuals were followed up after the baseline study examination with run get up to 18 years. The follow-up seemed to be complete with the resources available. A cohort study strategy was utilized in the investigation rather than a longer follow-up period. The availability of the resources to assist the long-term follow-up may be the cause of this.

6(b)Was the follow-up of subjects long enough?

Evidence: The subjects were followed for an average of 18 years in terms of their total body obesity, fat distribution, fasting glucose, and blood glucose two hours after an oral load of glucose. It was the longest the patients might be followed up upon.

7.What are the results of this study?

Evidence: Cassano, Rosner, Vokonas, & Weiss (1992) stated that three groups were created in view of all of the follow-up data gathered at all examinations: 1,312 (66.5%) men had generally normal blood sugar tolerance, 434 (22.0%) men had hyperglycemia at one or even more visits, and 226 (11.5%) men had NIDDM place at a single or more visit, based on the statistics. Men who were diagnosed with NIDDM were slightly older at entry, had much more total body adiposity, and had significantly higher blood sugar levels also after fasting and 2 hours compared to those with normal glycemic control.

8.How precise are the results?

Evidence: The results were accurate and precise as they gave perfect statistics. Because the age discrepancies between the 2 categories may have influenced a portion of the discrepancies in other factors classifications, age-adjusted averages were also taken into consideration. Little to no change, if any, happened in any considering age adjustment, of the norms.

9. Do you believe the results?

Evidence: Yes, an analysis of exploratory data was conducted to evaluate whether the relation between the glucose monitoring and the ratios is linear of hip width to the retroperitoneal circumference to effectively evaluate the research results.

10. Can the results be applied to the local population?

Evidence: The results can be applied to the local population as the results indicate prospective glucose and both fat percentage distributions and obesity and indicates the problems that are related to the local population.

11. Do the results of this study fit with other available evidence?

Evidence: The study results give an overview that is conjoined to the particular topic results indicate prospective proof of a link between Wood blood glucose and both fat percentage distributions and obesity, supporting prior studies of a prospective connection among abdominal obesity as well as the risk of diabetes.

12.What are the implications of this study for practice?

Evidence: The findings of this investigation offer additional prospective confirmation for a connection between overall body obesity and the probability of getting diabetes.

Table 4 Randomised controlled trial: (Insert the title of the paper you are appraising)

1. Did the trial address a clearly focused issue? Yes/No/Unclear
Evidence: justification, compare and contrasting or/and providing solution

2. Was the assignment of patients to treatments randomised?
Evidence: justification, compare and contrasting or/and providing solution
3. Were all of the patients who entered the trial properly accounted for at its conclusion?
Evidence: justification, compare and contrasting or/and providing solution
4.Were patients, health workers and study personnel ‘blind’ to treatment?
Evidence: justification, compare and contrasting or/and providing solution
5.Were the groups similar at the start of the trial?
Evidence: justification, compare and contrasting or/and providing solution
6.Aside from the experimental intervention, were the groups treated equally?
Evidence: justification, compare and contrasting or/and providing solution
7.How large was the treatment effect?
Evidence: justification, compare and contrasting or/and providing solution
8.How precise was the estimate of the treatment effect?
Evidence: justification, compare and contrasting or/and providing solution
9.Can the results be applied to the local population, or in your context?
Evidence: justification, compare and contrasting or/and providing solution
10.Were all clinically important outcomes considered?
Evidence: justification, compare and contrasting or/and providing solution
11.Are the benefits worth the harms and costs?
Evidence: justification, compare and contrasting or/and providing solution.

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