Monthly Archives: August 2021

Introduction

Diabetes mellitus (DM), a collective term for heterogeneous metabolic disorders whose main finding is chronic hyperglycemia, is a public health issue that has caused burdens of morbidity, mortality, and economic impact on individuals, families, health systems, and national economies.13 The global prevalence of DM in 2019 was estimated to be 9.3% (463 million people), with a projection of 10.2% (578 million people) for 2030 and 10.9% (700 million people) for 20452. Approximately, 90% of DM cases are attributed to type 2 diabetes mellitus (T2DM).2,3 Remarkably, China has witnessed one of the most dramatic rises in the prevalence of DM anywhere in the world and accounts for the largest number of individuals with 114 million cases of DM, mainly T2DM.46 Given that the cause of the rising prevalence of DM remains unclear, there is an urgent need to identify high-risk individuals and to implement targeted prevention to tackle diabetes.

Currently, obesity is a nearly global epidemic, affecting more than 20% of the general population.79 Metabolic syndrome and obesity frequently co-exist, allowing the categorization of obesity into different body mass index (BMI)-metabolic status phenotypes. It is suggested that 87% of overweight/obesity are not able to develop DM, but obtain some degree of fat-induced insulin resistance, which is not sufficient to cause diabetes.8 In contrast to metabolically unhealthy obesity (MUO), an estimated 1545% of individuals with obesity present insulin sensitivity, favorable glucose, blood pressure, hormone, lipid profiles, and inflammation levels despite excess adiposity; these are classified as the metabolically healthy obese (MHO).7,1012 In contrast, 530% of normal weight individuals do not present metabolically health, categorized as metabolically unhealthy-normal weight (MU-NW), which is defined as metabolically obese of normal weight.7 Obviously, taking the current epidemic of obesity and sub-phenotypes into consideration, one size fits all approaches to tackle obesity seem inappropriate.7 The association between BMI-metabolic status phenotypes and the risk of DM remains controversial, especially for MHO.1315 Several studies have reported that MHO individuals were not at increased risk for DM, in comparison with the metabolically healthy non-obese and may not benefit from lifestyle interventions.13,16 However, some studies have provided evidence that MHO is significantly associated with increased risk of incident DM.14,15

Overall, the evidence is inconsistent, especially for different ethnicities. The results of extant research may not be applicable to the Chinese population. Given that there are no universally and uniformly accepted standards to define obesity phenotypes,17 it is necessary to notice that BMI-metabolic status phenotypes can be identified by geographic regions of the word and ethnicity independently.7,9 Additionally, the classification of obese sub-phenotypes is not explicit, for some BMI categories of obesity and overweight were always combined in previous studies. Thus, to better understand the roles of different BMI-metabolic status phenotypes for incident DM, we measured the incidence density of DM by phenotypes and investigated the effects of metabolic health and obesity phenotypes on the risk of incident DM. We focused on repeated observations of the same individual at each follow-up examination in this longitudinal study to identify the high-risk and low-risk BMI-metabolic status phenotypes for early prevention of DM among the Chinese population.

A population-based prospective cohort study was established in Jiangsu province, China. The baseline survey was conducted between April and July 2017, and samples were selected using multistage-stratified sampling methods from Jurong Zhengjiang and Yandu Yancheng, two cities in Jiangsu province (Supplementary Figure S1). In the first stage, three district/town-level units were randomly chosen from Jurong and Yandu, respectively. Then eight to nine neighborhood/village-level units were randomly selected from every district/town-level unit and about 100 residents per neighborhood/village-level units were randomly sampled. Individuals aged 18 years and over at the time of enrollment, without mental disorders, pregnancy or a diagnosis of DM were eligible for this study. With the help and support of local community councils, residents living at their registered address and having permanent living records of Chinese nationality were invited. The final sample of 5318 participants fell within 50 neighborhoods/villages; 5250 participants with written informed consent were included with a response rate of 98.72%.

Overall, 5250 residents participated in the baseline survey which consisted of a questionnaire, physical and laboratory examination. Several participants with particular missing values at baseline were excluded: uncompleted questionnaires (n = 36), physical examination not done (n = 32) and no blood sample available (n = 36). We further excluded participants who were migrant workers and moved from city to city frequently (n = 71), those who had used antidiabetics before (or had a self-reported history of DM) (n = 186), as well as those who were newly diagnosed with diabetes at the baseline survey (n=637). According to the criteria of The Working Group on Obesity in China, the classification of BMI was defined as follows:18 underweight (BMI < 18.5 kg/m2), normal (18.5 BMI < 24.0 kg/m2), overweight (24.0 BMI < 28.0 kg/m2) and obesity (BMI 28.0 kg/m2). After removing the underweight individuals (n = 57) from baseline survey, 4195 participants were further classified into six groups according to the BMI-metabolic status phenotypes and received follow-ups (Supplementary Figure S1). Subsequently, two follow-up visits for data collection took place from July to August 2018 (re-investigated n = 3043) and from July to August, 2020 (re-investigated n = 3003), respectively. After excluding those participants without at least one follow-up examination and those who did not complete the physical or laboratory examination, with missing data preventing definition of BMI-metabolic status phenotypes and outcomes during follow-up (n = 554), we pooled data from the population study (baseline in April to July, 2017) which consisted of 3641 participants, and its two follow-ups examination (July to August, 2018: n=3007; July to August, 2020: n=2975) for our longitudinal data analysis. This resulted in a final analysis sample of 9623 observations across three time points (Figure 1). Thus, we obtained an analysis dataset with repeated observations, including 2345 participants with three observations and 1292 with two observations.

Figure 1 Flowchart for selection of study participants.

This study was conducted in accordance with the Declaration of Helsinki. The study was viewed and approved by the Ethics Review Committee of Zhongda Hospital, Southeast University and Jiangsu Provincial Centre for Disease Control and Prevention (JSJK2017-B003-02). Every participant provided written informed consent.

All enrolled participants completed an interview conducted by experienced local health workers using a standardized questionnaire to collected information regarding their demographic characteristics (age, gender, education level and self-reported drug history, history of chronic disease and family history of DM), as well as behavioral characteristics (smoking status, drinking status and regular physical activity). These characteristics were further categorized as follows: education level (junior high school or below/ senior high school or above), drug history (no/yes/unclear), history of chronic disease (no/yes) and family history of DM (no/yes/unclear). Smoking status was divided as follows: never (non-smoker) and ever (current smoker or ex-smoker), and drinking status as (never/ever). Regular exercise was grouped as follows: no and yes (recreational sports activities more than 30 minutes per time, more than three times a week). Weight was measured with a digital scale to the nearest 0.1kg with light clothes and height was measured to the nearest 0.1 cm using a wall-mounted stadiometer. BMI was calculated from weight and height (BMI=weight/height2), and normal weight, overweight and obesity were defined as 18.5 BMI < 24.0 kg/m2, 24.0 BMI < 28.0 kg/m2, and BMI 28 kg/m2, respectively.18 Participants were asked to have at least a 5-min rest in a seated position before their systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured using electronic sphygmomanometers. Waist circumference (WC) and hip circumference were measured using a tape with a metric scale under standardized procedures, with the participants in a standing position. The thresholds for WC were specified according to the International Diabetes Foundation (IDF) for the Chinese populations (WC > 80cm for females and WC > 90cm for males).19 For detecting central obesity in the Chinese adult population, a waist-to-height ratio 0.50 was considered as the optimal cut-off value.20 According to the World Health Organization (WHO), the cut-off values for metabolic complications for waist-tohip ratio are also defined (0.85 in women and 0.90 in men).19 Forearm venous blood samples were collected and measured for biochemical characteristics after overnight fasting for more than 8 hours before examination. Subsequently, standard oral glucose tolerance tests (OGTT) (75g anhydrous glucose) were administered and conducted without eating breakfast; 2 hours later, a second blood sample was taken for 2-hour plasma glucose (2hPG). Blood tests were all conducted in the same central laboratory (Adicon, Nanjing China) by the same method to maintain consistency, and included fasting plasma glucose (FPG), 2hPG, glycosylated hemoglobin (HbA1c), aspartate aminotransferase, alanine transaminase, gamma glutamyl transferase, serum total bilirubin, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol (HDL-C), total cholesterol and triglyceride (TG). The method of data collection and blood sample testing for the two follow-up examinations was exactly as same as for the baseline survey. All variables, including behavioral, anthropometric and biochemical characteristics, were assessed repeatedly at each follow-up examination (2018 and 2020).

The National Cholesterol Education Program Adult Treatment Panel III (ATP III), a standard operating protocol, was applied to define metabolic status.7,21 Metabolically healthy was defined as participants meeting fewer than three of the following five criteria:21 (1) Elevated WC with population-specific definitions (> 90 cm for men and > 80 cm for women);19 (2) elevated FPG (> 100 mg/dl) or using glucose-lowering agents; (3) reduced HDL-C (< 40 mg/dl for men or < 50 mg/dl for women); (4) elevated TG ( 150 mg/dl) or using lipid-lowing drugs; (5) elevated SBP ( 130 mmHg) or DBP ( 85 mmHg), or using anti-hypertensive drugs. Based on criteria for BMI of the Working Group on obesity in China, alongside the metabolic status, participants were grouped according to the following phenotypes:22 (1) metabolically healthy normal weight (MH-NW); (2) metabolically healthy overweight (MH-OW); (3) metabolically healthy obesity (MHO); (4) metabolically unhealthy normal weight (MU-NW); (5) metabolically unhealthy overweight (MU-OW); and (6) metabolically unhealthy obesity (MUO).

Diabetes was defined according to the diagnostic criteria of the WHO:2325 an FPG level 7.0mmol/L (126mg/dl) and/or a 2hPG value 11.1mmol/L (200mg/dl) and/or an HbA1c 6.5%.

We used meanstandard deviation(SD), median with inter quartile range, or numbers with percentages to present baseline characteristics of participants when appropriate. One-way analysis of variance, the KruskalWallis tests for continuous variables or two-sided 2 tests for categorical variables were introduced for statistical evaluations of demographic and behavioral characteristics, as well as anthropometric and biochemical characteristics among the six BMI-metabolic status phenotypes at baseline. Bonferroni corrections were applied for multiple comparisons. Person-year was calculated from the baseline survey until participants were lost to follow-up or at the end of the follow-up period (August, 2020), and the incidence density of diabetes was calculated per 100 person-years. Given the correlations between repetitive observations at each follow-up examination in the longitudinal study, which violated independence assumptions required for traditional regression procedures,26 and to increase the analytical power, robust generalized estimating equation (GEE) models with a binary distribution using a log link and exchange structure were applied for the pooled analysis sample. The correlation coefficients between two repetitive outcome measurements at each follow-up times were approximately equal (Supplementary Table S1). Risk ratios (RRs) and 95% confidence intervals (CIs) were adopted to estimate associations of BMI-metabolic status phenotypes with diabetes. We also carried out several analyses adjusted for potential confounding variables in multivariable models sequentially. Model a was adjusted for follow-up time, age, and gender. Model b was adjusted for follow-up time, demographic characteristics (age, gender, education level, drug history, history of chronic disease and family history of diabetes) and behavioral characteristics (smoking status, drinking status, and regular physical activity). Additionally, baseline characteristics were compared between participants in our cohort and those who were lost to follow-up (Supplementary Table S2). Sensitivity analyses were also undertaken to assess the stability and robustness of our findings. We re-ran the analysis for patients with unmodified BMI-metabolic status phenotype and we also excluded those with prediabetes who showed impaired fasting glycaemia (IFG) (6.1 mmol/L FPG level<7.0 mmol/L and/or 2hFPG level<7.8 mmol/L) and impaired glucose tolerance (IGT) (FPG level<7.0 mmol/L and/or 7.8 2hFPG level<11.1 mmol/L),23 to examine whether the results were consistent. Stata software (version 15.1, College Station, Texas) was applied for all the statistical analyses, with two-sided P-values < 0.05 indicating the statistical significance level.

Of 4195 participants invited to join the cohort, 3641 (86.79%) were included in our final analyses. Hence, a total sample of 9623 observations (April to July, 2017: n = 3641; July to August, 2018: n = 3007; July to August, 2020: n = 2975) were pooled for the longitudinal data analysis (Figure 1). Briefly, the mean (SD) age of the total 3641 individuals was 51.61 (8.75) years at study entry. The distribution of ATP III criteria for metabolic status between metabolic healthy and unhealthy was present in Supplementary Figure S2. The prevalence of MH-NW, MH-OW, MHO, MU-NW, MU-OW and MUO was 28.56%, 23.21%, 5.36%, 6.95%, 21.51% and 14.42% at baseline, respectively. The baseline demographic and behavioral characteristics, as well as anthropometric and biochemical characteristics, were compared among six BMI-metabolic status phenotypes and summarized in Table 1. There were significant differences detected for age, gender, drug history, history of chronic disease, drinking status and regular physical activity among different phenotypes (P < 0.05). Additionally, education level, family history of diabetes and smoking status were comparable (P > 0.05). The MH-OW, MHO, MU-NW, MU-OW and MUO phenotypes showed significantly higher values of all the anthropometric characteristics than MH-NW (Table 1). In terms of biochemical characteristics, there were significant differences among different phenotypes (P < 0.01) (Table 1).

Table 1 Baseline Characteristics of the Body Mass Index-Metabolic Status Phenotypes

The cohort with an average follow-up time of 1.64 years per person included 3641 participants and a total of 5982 person-years. During the follow-up period, 415 cases of new-onset diabetes occurred and the overall incidence density was 6.94% person-years. Among the six BMI-status phenotypes defined at baseline, MUO showed the highest incidence density of DM (12.08% person-years) at the end of follow-up (Table 2). The incidence density of DM increased with the BMI-metabolic status phenotypes transformation to metabolically unhealthy and greater BMI (Z=5.84, Ptrend < 0.001) (Table 2).

Table 2 Diabetes Incidence Density at the End of Follow-Up (Until August, 2020) Among Six Body Mass Index-Metabolic Status Phenotypes at Baseline

In the GEE model, associations of ATP III components, BMI categories and metabolic status with incident DM were analyzed and presented respectively in Table 3. All ATP III components were significantly associated with incident DM except LDL-C. Associations were detected between the number of metabolic unhealthy components found in an individual and DM (Table 3; Supplementary Figure S3). The RR for DM risk gradually increased with the increase in the number of components when compared with participants without any metabolically unhealthy components (Ptrend < 0.001) (Table 3).

Table 3 The Association of Body Mass Index, Metabolic Status and Adult Treatment Panel III Components with Diabetes

Compared with those of normal weight, only the obese group (Adjusted RR = 1.70; 95% CI=1.332.17; P < 0.001) presented an increased risk of DM. The metabolically unhealthy group showed an increased risk of incident DM when compared with metabolically healthy individuals (Adjusted RR = 2.48; 95% CI = 2.062.98; P < 0.001). Interaction analysis showed that BMI interacted multiplicatively with metabolic status to contribute to DM risk (Pinteraction = 0.015) (Table 3).

Interaction analyses failed to detect any significant association between BMI-metabolic status phenotypes and follow-up time (Pinteraction = 0.649). As is shown in Table 4, GEE analyses were further performed to detect the associations between BMI-metabolic status phenotypes and DM risk. It revealed that the MH-OW group were at a decreased risk of incident DM (Adjusted RR = 0.65; 95% CI = 0.470.90; P = 0.009) and no significant associations were found for MHO (Adjusted RR = 0.99; 95% CI = 0.631.53; P = 0.949) individuals when compared with the MH-NW phenotype. However, the MU-NW (Adjusted RR = 1.81; 95% CI = 1.282.55; P = 0.001), MU-OW (Adjusted RR = 2.02; 95% CI = 1.572.61; P < 0.001) and MUO phenotypes (Adjusted RR = 2.48; 95% CI = 1.893.26; P < 0.001) were associated with increased DM risk in comparison with MH-NW, among which the MUO phenotype showed the highest RR (Ptrend < 0.001) (Table 4).

Table 4 The Association Between Body Mass Index-Metabolic Status Phenotypes and Diabetes

In the stratified analyses, associations between BMI-metabolic phenotypes and DM risk were evaluated based on age and gender, separately. As is shown in Figure 2, only MU-OW (Adjusted RR = 2.11; 95% CI = 1.203.73; P = 0.010) and MUO (Adjusted RR = 2.37; 95% CI = 1.344.18; P = 0.003) phenotypes showed an increased diabetes risk in comparison with MH-NW among those individuals with age < 50 years. Those in the MU-NW (Adjusted RR = 2.12; 95% CI = 1.323.42; P = 0.002) MU-OW (Adjusted RR = 2.01; 95% CI = 1.352.99; P = 0.001) and MUO (Adjusted RR = 3.04; 95% CI = 2.024.56; P < 0.001) groups seemed more likely to suffer DM across all phenotypes among individuals aged 50 to 59 years. For the age groups 60, similar results were found to those for the main analysis of the total sample and no significant association was detected for the MU-NW phenotype (Figure 2; Supplementary Table S3). For male participants, MH-OW (Adjusted RR = 0.54; 95% CI = 0.350.82; P = 0.004) presented a decreased risk of DM compared with MH-NW, while, only the MU-OW (Adjusted RR = 1.54; 95% CI = 1.062.24; P = 0.025) and MUO (Adjusted RR = 1.73; 95% CI = 1.172.57; P = 0.006) phenotypes showed increased risk. Meanwhile, the associations of MU-NW, MU-OW and MUO with DM risk were more evident in female individuals (P < 0.001) (Figure 3; Supplementary Table S4).

Figure 2 The association of body mass index-metabolic status phenotypes with diabetes in an analysis stratified by age. Adjusted for follow-up time, demographic characteristics (gender, education level, drug history, history of chronic disease and family history of diabetes) and behavioral characteristics (smoking status, drinking status, regular physical activity).

Abbreviation: BMI, body mass index.

Figure 3 The association of body mass index-metabolic status phenotypes with diabetes in an analysis stratified by gender. Adjusted for follow-up time, demographic characteristics (age (numeric), education level, drug history, history of chronic disease and family history of diabetes) and behavioral characteristics (smoking status, drinking status, regular physical activity).

Abbreviation: BMI, body mass index.

A series of sensitivity analyses were undertaken to examine the robustness of our findings. The results of the analysis in patients with unmodified BMI-metabolic status phenotype were similar to the main results except that it failed to detect the association between MH-OW and DM risk (Supplementary Table S5). After removing participants with IFG/IGT, the results of sensitivity analysis were consistent with those outcomes reported in the main analysis (Supplementary Table S6). We also conducted sensitivity analyses of stratification based on age and gender, and the results did not essentially change (Supplementary Tables S7S10).

Furthermore, comparison of baseline characteristics was also carried out between the participants who were included in the analytic sample and those excluded because of loss to follow-up (Supplementary Table S2). No statistically significant difference was detected for the distribution of BMI-metabolic status phenotypes at baseline between the study population and individuals who were lost to follow-up (P > 0.05) (Supplementary Table S2).

Diabetes is a serious, long-term condition, which is considered as burdensome and costly disease.2 An epidemiological study reveals that 11% of Chinese people have suffered from DM, with a significant proportion remaining undiagnosed.5 As a result, the challenges for developing integrated care for DM and its complications are numerous in China.27 Hence, we should put emphasis on early screening of the high-risk population in the prevention of diabetes. In addition, obesity is expanding worldwide, and it has also become a major public health problem in China.2830 A previous study suggested that obesity is a condition which is related to multiple medical, psychological and social problems, and the most harmful could be DM.31 Considering that DM risk might vary among obese sub-phenotypes,28 stratification of obesity could be used to identify and target people at risk effectively, aiming at efficient distribution of limited financial resources. However, current guidelines for obesity management are not adequately supported by evidence from clinical studies among Chinese populations.32 In our study, we evaluated the effect of metabolic health status and obesity phenotypes on the DM risk in the Chinese population. MU-NW, MU-OW and MUO individuals were at increased risk for DM, in contrast to MH-OW and MHO phenotypes. As far as we know, this is the first study to use a GEE model, which took repeated data obtained at each follow-up in a longitudinal study into consideration, to examine the effect of metabolic health alongside obesity phenotypes on DM risk in the Chinese population.

The associations of obese phenotypes and DM risk have been evaluated by several studies, but the results remain controversial.13,15,33 Until now, available studies have focused on prospective or retrospective cohort studies, analyzing MHO phenotype and DM by applying a logistic regression model or Cox proportional hazards regression model,13,14 which only take the baseline information into consideration and cannot take advantage of the data from repetitive measurements at each follow-up to analyze changes in each variable. In addition, previous studies put emphasis on MHO; the diabetes risk, especially among MH-OW, MU-NW, MU-OW and MUO phenotypes in comparison with MH-NW, remains less examined, especially for the Chinese population. Recently, a study conducted by Barkas et al aimed to investigate the relationship between MHO and T2DM risk in statin-treated patients and suggested that the MHO phenotype might not significantly increase the T2DM risk and that the metabolically unhealthy non-obese presented an increased risk,13 which were similar to the findings in our study. However, their study focused on statin-treated patients and set non-obesity as comparison. In our study, the participants were from a community-based prospective cohort study and the categories of BMI-metabolic status phenotypes were explicit and appropriate to detect the association. A study carried out by Wang et al without a 2-h OGTT test suggested that significantly increased risk of DM was detected for all the individuals with baseline MHO, MU-NW, and MUO phenotypes, which was not consistent with our findings. No significant association was found between MHO and DM risk in our study.15 A recent network meta-analysis suggested that MUO groups posed the highest risk for diabetes and MH-OW/ MHO/ MU-NW/ MU-OW individuals also showed increased risk when compared with MH-NW,34 presenting partially similar results to our study. Taken together, the inconsistency of studies mainly concentrates on the MHO/MH-OW phenotypes. Therefore, the possible reasons for controversy may be as follows: First, ethnicity and population were diverse. Second, in our study, MH-NW was set as the comparison when exploring the effect of BMI-metabolic status phenotypes on DM risk instead of using overweight and normal weight group as a combination. Third, the criteria of BMI were inconsistent with our study and inappropriate for the Chinese population. At present, the BMI ranges recommend by WHO are mainly suitable for the Western populations, among which the BMI range of 2529.9 kg/m2 was defined as overweight and BMI30 kg/m2 as obesity. Moreover, the BMI criteria proposed by scientists in Asian countries (overweight: 2324.9 kg/m2; obesity: 25 kg/m2) are also inappropriate for the Chinese population, because the Asian criteria did not include the data from mainland China and Taiwan China.18,35 It is important and necessary to define the optimal range for overweight and obesity in DM prevention among the Chinese population.18 Thus, the definition of BMI in our study was in accordance with the criteria of The Working Group on Obesity in China.18

MHO is a novel concept obtained by stratifying individuals with obesity according to metabolic status.7,36 The findings of our study highlighted the effect of metabolic health status in predicting incident DM, as decreased risk was found for MH-OW and no significant association was detected for MHO phenotype. Moreover, metabolic abnormalities may play a much more important role in developing DM rather than exclusively BMI, as MU-NW, MU-OW and MUO all posed increased risk in our study. Interestingly, our study also showed that only in the older groups (more than 60 years) can the MH-OW phenotype be significantly associated with decreased risk of DM. A study suggested that obesity might not be diabetogenic, and not all metabolically healthy individuals with obesity were at the same risk of diabetes onset.34 It has been proposed that adipose tissue as an endocrine organ produces different adipocytokines, which could be associated with wide range of metabolic diseases.37 Therefore, the diagnosis of obesity phenotypes should remain an indication to initiate prevention, even though the individuals with certain phenotypes without DM at the time of diagnosis.7 In recent years, studies have explored the effectiveness of weight loss by stratifying individuals according to obesity phenotypes.38,39 A previous study also suggested that MHO individuals could not benefit to the same extent as those with unhealthy obesity from interventions involving losing weight.7 Given the frequently unsuccessful anti-obesity interventions and limitations of healthcare resources,7,9 it is a challenge of clinical practice to identify which individuals with obesity may be able to benefit the most from interventions, especially for the prevention of DM. Nevertheless, current guidelines recommended for weight loss are aimed at all obesity with no distinction according to BMI-metabolic status phenotypes.32 Notably, DM poses a huge health burden on China, and the substantial increase of diabetes-related burden shows an uninterrupted challenge as well.40 Therefore, novel strategies for targeted DM prevention need to be developed. Given that the prevalence of obesity continues to escalate, a strategy based on different obese phenotypes is imperative to enhance the efficacy and effectiveness of the prevention of DM,36 with promising methods to prioritize and identify the high-risk individuals who could benefit greatly and be the most suitable for intervention. It is worth mentioning that the MUO phenotype presented the highest risk, and needs to be accorded much more importance. Our findings highlighted the MU-OW and MU-NW phenotypes are also important groups for targeted prevention.

Several merits of this study deserve to be pointed out. First, our findings were based on a longitudinal study, making full use of repeated measurement data obtained at each follow-up examinations, which clarified the time sequence of correlation and took the time-independent variables into account by using GEE models. Second, both demographic and behavioral characteristics were adjusted to minimize potential confounding. Sensitivity analyses also showed the consistency and robustness of the results. Third, it is an advantage in the delimitation of obesity phenotypes in our study that the focus on BMI criteria for the Chinese population and the use of ATP III criteria of metabolic status were much more stringent and clinically relevant for the Chinese population.

However, there are some limitations. First, potential bias could not be avoided in our study, because the information on behavioral characteristics, drug history, history of chronic disease and family history of DM was self-reported. Second, because of the lack of repetitive investigations on dietary habits and nutritional information during follow-up examinations, we have not adjusted for them in our present study. Additionally, lack of test item of insulin resistance is also a limitation. Moreover, the majority of participants in this study were rural inhabitants. Thus, it may not be representative of the general population of China, which limits the generalizability of our findings.

In summary, in the Chinese population, the MH-OW and MHO phenotypes did not show an increased risk of incident diabetes. MU-NW, MU-OW and MUO were found to show a significantly increased risk of DM. Future studies are needed to extend the findings and elucidate whether some BMI-metabolic status phenotypes can be targeted for optimizing prevention and therapeutic strategies to mitigate the obviously increased prevalence of DM.

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

This research is supported by the National Key Research and Development Program of China (2016YFC1305700).

All authors declared that they have no conflict of interests.

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Metabolic health and obesity phenotype on risk of diabetes | DMSO - Dove Medical Press

Type 2 diabetes can cause serious complications if left untreated because uncontrolled blood sugar levels - the main threat - can cause a wave of destructive effects. Insulin is a hormone that's usually responsible for regulating blood sugar. However, this mechanism is impaired if you have type 2 diabetes so people have to find alternative ways to regulate blood sugar. According to research published in the British Journal of Nutrition, the timing of your morning coffee can influence blood sugar control.

The study found having a strong, black coffee to wake you up after a disturbed night's sleep could impair control of blood sugar levels.

Research from the Centre for Nutrition, Exercise & Metabolism at the University of Bath (UK) looked at the effect of broken sleep and morning coffee across a range of different metabolic markers.

The scientists showed that whilst one night of poor sleep has limited impact on our metabolism, drinking coffee as a way to rouse you from a slumber can have a negative effect on blood glucose (sugar) control.

To gather their findings, the physiologists at the University of Bath asked 29 healthy men and women to undergo three different overnight experiments in a random order:

READ MORE:Diabetes type 2 symptoms: Three subtle warning signs of high blood sugar in your hands

In each of these tests, blood samples from participants were taken following the glucose drink which in energy content (calories) mirrored what might typically be consumed for breakfast.

Their findings highlight that one night of disrupted sleep did not worsen participants' blood glucose/ insulin responses at breakfast, when compared to a normal night's sleep.

However, strong black coffee consumed before breakfast substantially increased the blood glucose response to breakfast by around 50 percent.

Although population-level surveys indicate that coffee may be linked to good health, past research has previously demonstrated that caffeine has the potential to cause insulin resistance.

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This new study therefore reveals that the common remedy of drinking coffee after a bad night's sleep may remedy the problem of feeling sleepy but could create another by limiting your body's ability to tolerate the sugar in your breakfast.

Professor James Betts, Co-Director of the Centre for Nutrition, Exercise and Metabolism at the University of Bath who oversaw the work, explained: "We know that nearly half of us will wake in the morning and, before doing anything else, drink coffee - intuitively the more tired we feel, the stronger the coffee.

"This study is important and has far-reaching health implications as up until now we have had limited knowledge about what this is doing to our bodies, in particular for our metabolic and blood sugar control.

"Put simply, our blood sugar control is impaired when the first thing our bodies come into contact with is coffee, especially after a night of disrupted sleep. We might improve this by eating first and then drinking coffee later if we feel we still need it. Knowing this can have important health benefits for us all."

Lead researcher, Harry Smith from the Department for Health at Bath added: "These results show that one night of disrupted sleep alone did not worsen participants' blood glucose/insulin response to the sugary drink compared to a normal night of sleep which will be reassuring to many of us. However, starting a day after a poor night's sleep with a strong coffee did have a negative effect on glucose metabolism by around 50 percent.

"As such, individuals should try to balance the potential stimulating benefits of caffeinated coffee in the morning with the potential for higher blood glucose levels and it may be better to consume coffee following breakfast rather than before.

"There is a lot more we need to learn about the effects of sleep on our metabolism, such as how much sleep disruption is necessary to impair our metabolism and what some of the longer-term implications of this are, as well as how exercise, for instance, could help to counter some of this."

Many people have type 2 diabetes without realising.

According to the NHS, this is because symptoms do not necessarily make you feel unwell.

Symptoms of type 2 diabetes include:

According to the NHS, you should see a GP if you have any of the symptoms of type 2 diabetes or you're worried you may have a higher risk of getting type 2 diabetes.

"You'll need a blood test, which you may have to go to your local health centre for if it cannot be done at your GP surgery."

Read more:
Diabetes type 2 warning: Study issues serious warning about having coffee in the morning - Daily Express

MORE people than ever are at risk of diabetes - and some may be developing it without a clue.

Charities have warned the UK is facing a diagnosis timebomb after many people missed vital GP appointments during the pandemic.

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Already some 4.9 million people in the UK are estimated to have diabetes, of which there are various forms.

But of those, some 850,000 people are living with type 2 diabetes but are yet to be diagnosed - a figure up 150,000 in the last year, according to Diabetes UK.

Staggering stats reveal people can live for up to 10 years with type 2 diabetes - the most common type - before being diagnosed.

On top of this, some 2.1 million people have a blood sugar level that is higher than normal, but not bad enough to be diagnosed with diabetes.

This is called prediabetes, or borderline diabetes, and means a person is on track to get a diagnosis if they dont act fast.

There are likely to be many more who are not aware they are in this category.

A further 13.6 million people in the UK are at risk of developing type 2 diabetes - but many will not know.

These people are overweight - the key driver of type 2 diabetes. Having a family member with the disease or being African-Carribean are also example of risk factors.

These people can start making changes, such as eating healthier, to prevent a diagnosis as early as possible.

Meanwhile, the pandemic has had a huge impact on people already living with a diagnosis.

Diabetes is a complex disease to manage. But concerning figures show nearly 2.5million people already with the condition missed vital checks over Covid.

GPs have performed 41 per cent fewer health checks in the last year, analysis by Diabetes UK found in June.

The backlog could be putting sufferers at risk of Covid death, heart attacks and strokes among other complications.

The total number of cases of diabetes is expected to reach 5.5 million people by 2030.

Diabetes UK chief executive Chris Askew said: We're sitting on a diabetes timebomb. Missed appointments and missed or delayed diagnoses can devastate lives.

Prediabetes is a grey area.

It is when someones blood glucose levels are higher than normal but not yet high enough to be classed as diabetes.

In the UK, around 7 million people are estimated to have prediabetes, according to Diabetes.co.uk.

People often dont notice the symptoms and put them down to something else, such as their stress levels or their age

Experts say prediabetes is a critical stage in the development of the disease because this is when people still have the ability to slow down, or even halt, the condition.

But prediabetes has no symptoms, Dr Prash Vas, consultant in Diabetes at London Bridge Hospital, told The Telegraph.

He said: [Prediabetes] has no symptoms. As your blood sugar levels go higher, the body will mount symptoms.

Even this group of individuals can have complications, such as a three times higher risk of developing eye problems such as diabetic retinopathy. There is also an increased chance of developing kidney problems and early nerve damage.

According to Dan Howarth, head of care at Diabetes UK, symptoms of diabetes wont kick in till blood sugar levels are around 11mmol/L, even though anything above 7.8mmol/L after eating is considered too high.

Even then, people often dont notice the symptoms and put them down to something else, such as their stress levels or their age, Dr Howarth said.

If your doctor is concerned you have pre-diabetes they will usually do either a HbA1c test, a fasting plasma glucose (FPG) test, or an OGTT test.

The NHS says type 2 diabetes is often diagnosed following blood or urine tests for something else.

Symptoms of diabetes can go completely missed because people think they are caused by something else.

Or their symptoms dont necessarily make them feel unwell, so they are not even acknowledged.

The most common early signs of type 2 diabetes are frequent urination, extreme thirst, and persistent hunger.

You should visit your GP if you are:

There are other symptoms that may alert you to this disease.

These include:

Factors such as age, family history and ethnicity can increase a persons likelihood of having diabetes.

The condition usually develops slowly when someone is over the age of 40 years old.

However, the risk starts increasing from the age of 25 if you are African-Caribbean, Black African, or South Asian.

Generally studies have found that people of African-Caribbean, black African or south Asian descent are two to four times more likely to develop type 2 diabetes than those from a white background.

Obesity is fuelling type 2 diabetes, accounting for 80 to 85 per cent of someones chances of developing the condition.

Just because you have one or more risk factors for diabetes, it doesnt mean you will get it.

However, its best to be wary so that you can prevent the condition now. Because the symptoms of type 2 diabetes are not always obvious, its really important to be aware of these risk factors, charities say.

Risk factors include if you:

You can take the "Know Your Risk" for type 2 diabetes quiz to find out your risk of getting type 2 diabetes.

The rapid quiz asks about age, weight, if someone in the family has diabetes and if you have been told you have high blood pressure, for example.

It will then give a score between zero and 47 points.

The higher the risk level, the more likely it is that person will develop type 2 diabetes in the next 10 years. For example, one out of four people with high risk will get type 2 diabetes in the next 10 years.

If someones score is moderate or high they can refer themselves to a local service for support remotely or online, without having to go through a healthcare professional.

Read more from the original source:
Do you have hidden diabetes? The 15 signs to watch out for... - The US Sun