The association between the AIP and undiagnosed diabetes in ACS patients with different body mass indexes and LDL-C levels: findings from the CCC-ACS project.

BACKGROUND: The atherogenic index of plasma (AIP) has been demonstrated to be significantly associated with the incidence of prediabetes and diabetes. This study aimed to investigate the association between the AIP and undiagnosed diabetes in acute coronary syndrome (ACS) patients. METHODS: Among 113,650 ACS patients treated with coronary angiography at 240 hospitals in the Improving Care for Cardiovascular Disease in China-ACS Project from 2014 to 2019, 11,221 patients with available clinical and surgical information were included. We analyzed these patients' clinical characteristics after stratification according to AIP tertiles, body mass index (BMI) and low-density lipoprotein cholesterol (LDL-C) levels. RESULTS: The AIP was independently associated with a greater incidence of undiagnosed diabetes. The undiagnosed diabetes was significantly greater in the T3 group than in the T1 group after adjustment for confounders [T3 OR 1.533 (1.199-1.959) p < 0.001]. This relationship was consistent within normal weight patients and patients with an LDL-C level ≥ 1.8 mmol/L. In overweight and obese patients, the AIP was significantly associated with the incidence of undiagnosed diabetes as a continuous variable after adjustment for age, sex, and BMI but not as a categorical variable. The area under the receiver operating characteristic curve (AUC) of the AIP score, triglyceride (TG) concentration, and HDL-C concentration was 0.601 (0.581-0.622; p < 0.001), 0.624 (0.603-0.645; p < 0.001), and 0.493 (0.472-0.514; p = 0.524), respectively. A nonlinear association was found between the AIP and the incidence of undiagnosed diabetes in ACS patients (p for nonlinearity < 0.001), and this trend remained consistent between males and females. The AIP may be a negative biomarker associated with undiagnosed diabetes ranging from 0.176 to 0.738. CONCLUSION: The AIP was significantly associated with the incidence of undiagnosed diabetes in ACS patients, especially in those with normal weight or an LDL-C level ≥ 1.8 mmol/L. A nonlinear relationship was found between the AIP and the incidence of undiagnosed diabetes, and this trend was consistent between male and female patients. The AIP may be a negative biomarker associated with undiagnosed diabetes and ranges from 0.176 to 0.738.

Burden of undiagnosed and suboptimally controlled diabetes in selected regions of India: Results from the SMART India population-level diabetes screening study.

AIMS: To estimate the prevalence of undiagnosed diabetes and suboptimally controlled diabetes and the associated risk factors by community screening in India. METHODS: In this multi-centre, cross-sectional study, house-to-house screening was conducted in people aged ≥40 years in urban and rural areas across 10 states and one union territory in India between November 2018 and March 2020. Participants underwent anthropometry, clinical and biochemical assessments. Capillary random blood glucose and point-of-care glycated haemoglobin (HbA1c ) were used to diagnose diabetes. The prevalence of undiagnosed diabetes and suboptimal control (HbA1c ≥53 mmol/mol [≥7%]) among those with known diabetes was assessed. RESULTS: Among the 42,146 participants screened (22,150 urban, 19,996 rural), 5689 had known diabetes. The age-standardised prevalence of known diabetes was 13.1% (95% CI 12.8-13.4); 17.2% in urban areas and 9.4% in rural areas. The age-standardised prevalence of undiagnosed diabetes was 6.0% (95% CI 5.7-6.2); similar in both urban and rural areas with the highest proportions seen in the East (8.0%) and South (7.8%) regions. When we consider all people with diabetes in the population, 22.8% of individuals in urban areas and 36.7% in rural areas had undiagnosed diabetes. Almost 75% of the individuals with known diabetes had suboptimal glycaemic control. CONCLUSIONS: High prevalence of undiagnosed diabetes and suboptimally controlled diabetes emphasises the urgent need to identify and optimally treat people with diabetes to reduce the burden of diabetes.

Clinical significance of prediabetes, undiagnosed diabetes and diagnosed diabetes on critical outcomes in COVID-19: Integrative analysis from the Japan COVID-19 task force.

AIM: Diabetes mellitus (DM) is a known risk factor for severe coronavirus disease 2019 (COVID-19), but the clinical impact of undiagnosed diabetes and prediabetes in COVID-19 are unclear particularly in Japan. We clarify the difference in clinical characteristics, including age, sex, body mass index and co-morbidities, laboratory findings and critical outcomes, in a large Japanese COVID-19 cohort without diabetes, with prediabetes, undiagnosed diabetes and diagnosed diabetes, and to identify associated risk factors. MATERIALS AND METHODS: This multicentre, retrospective cohort study used the Japan COVID-19 Task Force database, which included data on 2430 hospitalized COVID-19 patients from over 70 hospitals from February 2020 to October 2021. The prevalence of prediabetes, undiagnosed diabetes and diagnosed diabetes were estimated based on HbA1c levels or a clinical diabetes history. Critical outcomes were defined as the use of high-flow oxygen, invasive positive-pressure ventilation or extracorporeal membrane oxygenation, or death during hospitalization. RESULTS: Prediabetes, undiagnosed diabetes and diagnosed diabetes were observed in 40.9%, 10.0% and 23.0%, respectively. Similar to diagnosed diabetes, prediabetes and undiagnosed diabetes were risk factors for critical COVID-19 outcomes (adjusted odds ratio [aOR] [95% CI]: 2.13 [1.31-3.48] and 4.00 [2.19-7.28], respectively). HbA1c was associated with COVID-19 severity in prediabetes patients (aOR [95% CI]: 11.2 [3.49-36.3]), but not other groups. CONCLUSIONS: We documented the clinical characteristics and outcomes of Japanese COVID-19 patients according to HbA1c levels or diabetes co-morbidity. As well as undiagnosed and diagnosed diabetes, physicians should be aware of prediabetes related to COVID-19 severity.

Prognostic value of resting heart rate in predicting undiagnosed diabetes in adults: Korean National Health and Nutrition Examination Survey 2008-2018.

BACKGROUND AND AIM: Although resting heart rate (RHR) is associated with prevalence and incidence of diabetes, whether it is associated with undiagnosed diabetes is still unclear. We aimed to investigate whether the RHR is associated with the prevalence of undiagnosed diabetes in a large Korean national dataset. METHODS AND RESULTS: The Korean National Health and Nutrition Examination Survey data from 2008 to 2018 were used. After screening, 51,637 participants were included in this study. The odds ratios and 95% confidence intervals (CIs) for undiagnosed diabetes were calculated using multivariable-adjusted logistic regression analyses. Analyses showed that participants with a RHR of ≥90 bpm showed a 4.00- (95% CI: 2.77-5.77) and 3.21-times (95% CI: 2.01-5.14) higher prevalence of undiagnosed diabetes for men and women, respectively, than those with a RHR of <60 bpm. The linear dose-response analyses showed that each 10-bpm increment in RHR was associated with a 1.39- (95% CI: 1.32-1.48) and 1.28-times (95% CI: 1.19-1.37) higher prevalence of undiagnosed diabetes for men and women, respectively. In the stratified analyses, the positive association between RHR and the prevalence of undiagnosed diabetes was tended to be stronger among those who were younger (age: <40 years) and lean (BMI: <23 kg/m2). CONCLUSIONS: Elevated RHR was significantly associated with a higher prevalence of undiagnosed diabetes in Korean men and women, independent of demographic, lifestyle, and medical factors. Accordingly, the value of RHR as a clinical indicator and health marker, especially in reducing the prevalence of undiagnosed diabetes, is suggestible.

Evaluation of Madras Diabetes Research Foundation-Indian Diabetes Risk Score in detecting undiagnosed diabetes in the Indian population: Results from the Indian Council of Medical Research-INdia DIABetes population-based study (INDIAB-15).

Background & objectives: Screening of individuals for early detection and identification of undiagnosed diabetes can help in reducing the burden of diabetic complications. This study aimed to evaluate the performance of Madras Diabetes Research Foundation (MDRF)-Indian Diabetes Risk Score (IDRS) to screen for undiagnosed type 2 diabetes in a large representative population in India. Methods: Data were acquired from the Indian Council of Medical Research-INdia DIABetes (ICMR-INDIAB) study, a large national survey that included both urban and rural populations from 30 states/union territories in India. Stratified multistage design was followed to obtain a sample of 113,043 individuals (94.2% response rate). MDRF-IDRS used four simple parameters, viz. age, waist circumference, family history of diabetes and physical activity to detect undiagnosed diabetes. Receiver operating characteristic (ROC) with area under the curve (AUC) was used to assess the performance of MDRF-IDRS. Results: We identified that 32.4, 52.7 and 14.9 per cent of the general population were under high-, moderate- and low-risk category of diabetes. Among the newly diagnosed individuals with diabetes [diagnosed by oral glucose tolerance test (OGTT)], 60.2, 35.9 and 3.9 per cent were identified under high-, moderate- and low-risk categories of IDRS. The ROC-AUC for the identification of diabetes was 0.697 (95% confidence interval: 0.684-0.709) for urban population and 0.694 (0.684-0.704) for rural, as well as 0.693 (0.682-0.705) for males and 0.707 (0.697-0.718) for females. MDRF-IDRS performed well when the population were sub-categorized by state or by regions. Interpretation & conclusions: Performance of MDRF-IDRS is evaluated across the nation and is found to be suitable for easy and effective screening of diabetes in Asian Indians.

The association between undiagnosed diabetes and cognitive function: findings from the China health and retirement longitudinal study.

BACKGROUND: The cognitive function of people with diabetes has gained an increasing interest in recent years, and this study focuses on exploring the relationship between undiagnosed diabetes and cognitive function among the middle-aged and elderly people in China. METHODS: The data came from the China Health and Retirement Longitudinal Study (CHARLS) which was conducted between July and October 2015. 9855 subjects were enrolled in the study. Executive function and episodic memory were used to assess cognitive function. The subjects were divided into three groups: no diabetes, diagnosed diabetes, and undiagnosed diabetes, and weighted multiple linear regression models were established to evaluate the association of undiagnosed diabetes with cognitive function. RESULTS: After controlling for covariates, undiagnosed diabetes was statistically associated with executive function (ß = -0.215, P < 0.01). In the age group of ≥65 years, undiagnosed diabetes was statistically associated with executive function (ß = -0.358, P < 0.01) and episodic memory (ß = -0.356, P < 0.01). When adjusting for confounders, no statistically significant associations were found between diagnosed diabetes and cognitive function except in 45-54 age group (ß = 0.374, P < 0.05). CONCLUSIONS: The cross-sectional study suggested that undiagnosed diabetes was linked to poor cognitive function, especially in the elderly population. Timely diagnosis and active treatment of diabetes are important to reduce the occurrence of cognitive impairment. Further prospective cohort studies are required to articulate the association between undiagnosed diabetes and cognitive function.

Diabetes, even newly defined by HbA1c testing, is associated with an increased risk of in-hospital death in adults with COVID-19.

BACKGROUND: Diabetes is associated with poor coronavirus disease 2019 (COVID-19) outcomes. However, little is known on the impact of undiagnosed diabetes in the COVID-19 population. We investigated whether diabetes, particularly undiagnosed diabetes, was associated with an increased risk of death from COVID-19. METHODS: This retrospective study identified adult patients with COVID-19 admitted to Tongji Hospital (Wuhan) from January 28 to April 4, 2020. Diabetes was determined using patients' past history (diagnosed) or was newly defined if the hemoglobin A1c (HbA1c) level at admission was ≥6.5% (48 mmol/mol) (undiagnosed). The in-hospital mortality rate and survival probability were compared between the non-diabetes and diabetes (overall, diagnosed, and undiagnosed diabetes) groups. Risk factors of mortality were explored using Cox regression analysis. RESULTS: Of 373 patients, 233 were included in the final analysis, among whom 80 (34.3%) had diabetes: 44 (55.0%) reported a diabetes history, and 36 (45.0%) were newly defined as having undiagnosed diabetes by HbA1c testing at admission. Compared with the non-diabetes group, the overall diabetes group had a significantly increased mortality rate (22.5% vs. 5.9%, p <  0.001). Moreover, the overall, diagnosed, and undiagnosed diabetes groups displayed lower survival probability in the Kaplan-Meier survival analysis (all p <  0.01). Using multivariate Cox regression, diabetes, age, quick sequential organ failure assessment score, and D-dimer ≥1.0 µg/mL were identified as independent risk factors for in-hospital death in patients with COVID-19. CONCLUSIONS: The prevalence of undiagnosed pre-existing diabetes among patients with COVID-19 is high in China. Diabetes, even newly defined by HbA1c testing at admission, is associated with increased mortality in patients with COVID-19. Screening for undiagnosed diabetes by HbA1c measurement should be considered in adult Chinese inpatients with COVID-19.

Screening of undiagnosed hyperglycaemia in the dental setting: The DiabetRisk study. A field trial.

AIM: To evaluate the efficacy of different screening protocols for undiagnosed hyperglycaemia in a Research Network of Dental Clinics coordinated by the Spanish Society of Periodontology (SEPA). MATERIAL AND METHODS: A total of 1143 patients were included in the study. Participants filled a questionnaire considering diabetes risk factors (FINDRISC) and received a periodontal screening examination. Patients with a slightly elevated score according to the Findrisc (≥7), received a point-of-care HbA1c and were eventually referred to their physician for confirmatory diagnosis. Receiver Operating Characteristic (ROC) curves were used to assess the performance of various predictive models with confirmed hyperglycaemia as outcome. RESULTS: From this population, 97 (8.5%) were finally diagnosed of diabetes (n = 28; 2.5%) or prediabetes (n = 69; 6.0%). When only including the results from the FINDRISC questionnaire, the model reported an area under the curve (AUC) of 0.866 (95% confidence interval - CI [0.833; 0.900]). This model significantly improved when a basic periodontal examination (EPB Code; AUC = 0.876; 95% CI [0.845: 0.906]; p = .042) or a point-of-care HbA1c were added (AUC = 0.961; 95% CI [0.941; 0.980]; p < .001). CONCLUSIONS: The tested protocol, combining the FINDRISC questionnaire and a point-of-care HbA1c, showed to be feasible when carried out in a dental clinic setting and was efficient to identify subjects with undiagnosed diabetes or prediabetes.

Diabetes diagnosis from administrative claims and estimation of the true prevalence of diabetes among 4.2 million individuals of the Veneto region (North East Italy).

BACKGROUND AND AIMS: Diabetes can often remain undiagnosed or unregistered in administrative databases long after its onset, even when laboratory test results meet diagnostic criteria. In the present work, we analyse healthcare data of the Veneto Region, North East Italy, with the aims of: (i) developing an algorithm for the identification of diabetes from administrative claims (4,236,007 citizens), (ii) assessing its reliability by comparing its performance with the gold standard clinical diagnosis from a clinical database (7525 patients), (iii) combining the algorithm and the laboratory data of the regional Health Information Exchange (rHIE) system (543,520 subjects) to identify undiagnosed diabetes, and (iv) providing a credible estimate of the true prevalence of diabetes in Veneto. METHODS AND RESULTS: The proposed algorithm for the identification of diabetes was fed by administrative data related to drug dispensations, outpatient visits, and hospitalisations. Evaluated against a clinical database, the algorithm achieved 95.7% sensitivity, 87.9% specificity, and 97.6% precision. To identify possible cases of undiagnosed diabetes, we applied standard diagnostic criteria to the laboratory test results of the subjects who, according to the algorithm, had no diabetes-related claims. Using a simplified probabilistic model, we corrected our claims-based estimate of known diabetes (6.17% prevalence; 261,303 cases) to account for undiagnosed cases, yielding an estimated total prevalence of 7.50%. CONCLUSION: We herein validated an algorithm for the diagnosis of diabetes using administrative claims against the clinical diagnosis. Together with rHIE laboratory data, this allowed to identify possibly undiagnosed diabetes and estimate the true prevalence of diabetes in Veneto.

Factors associated with undiagnosed type II diabetes mellitus, undiagnosed impaired fasting glucose and these patients' quality of life in Lebanon.

OBJECTIVE: To assess the association between undiagnosed impaired fasting glucose (UIFG) and diabetes (UD), their risk factors and the patients' knowledge about diabetes and their quality of life (QOL). METHODS: A cross-sectional study was conducted between May and September 2017 in four laboratories enrolling 495 patients. We used the Diabetes Knowledge and SF-12 questionnaires to assess the patients' knowledge about diabetes and QOL. This study meets the Wilson and Jungner criteria for disease screening. RESULTS: A higher knowledge score (ORa = 0.85) was significantly associated with the absence of diabetes compared to UPD, whereas an increased age (ORa = 1.2) and a positive family history of diabetes (ORa = 1.81) were significantly associated with higher odds of UPD. An increased knowledge score (ORa = 0.55) was significantly associated with an absence of diabetes compared to UD, whereas an increased BMI (ORa = 1.40) and an increased age (ORa = 1.07) were associated with increased odds of having UD.UD (Beta = -5.799) was significantly associated with decreased total physical component QOL score, whereas increased diabetes knowledge score (Beta = 0.415) was significantly associated with increased physical QOL. A higher diabetes knowledge score (Beta = 0.459) were associated with increased mental QOL. CONCLUSION: Seeing the relatively high percentage of UD and UIFG, we propose that screening should be considered and subject to proper assessment in the light of the findings of this study.

Burden of prediabetes, undiagnosed, and poorly or potentially sub-controlled diabetes: Lolland-Falster health study.

BACKGROUND: This study aimed to investigate prevalence and risk factors for prediabetes, undiagnosed diabetes mellitus, poorly and potentially sub-controlled diabetes in a rural-provincial general adult population in Denmark. METHODS: Using cross-sectional data from the Lolland-Falster Health Study, we examined a total of 10,895 individuals aged 20 years and above. RESULTS: Prevalence of prediabetes was 5.8% (men: 6.1%; women: 5.5%); of undiagnosed diabetes 0.8% (men: 1.0%; women: 0.5%); of poorly controlled diabetes 1.2% (men: 1.5%; women: 0.8%); and of potentially sub-controlled diabetes 2% (men: 3.0%; women: 1.3%). In total, 9.8% of all participants had a diabetes-related condition in need of intervention; men at a higher risk than women; RR 1.41 (95% CI 1.26-1.58); person aged + 60 years more than younger; RR 2.66 (95% CI 2.34-3.01); obese more than normal weight person, RR 4.51 (95% CI 3.79-5.38); smokers more than non-smokers, RR 1.38 (95% CI 1.19-1.62); persons with self-reported poor health perception more than those with good, RR 2.59 (95% CI 2.13-3.15); low leisure time physical activity more than those with high, RR 2.64 (95% CI 2.17-3.22); and persons with self-reported hypertension more than those without, RR 3.28 (95% CI 2.93-3.68). CONCLUSIONS: In the Lolland-Falster Health Study, nearly 10% of participants had prediabetes, undiagnosed diabetes, poorly controlled, or potentially sub-controlled diabetes. The risk of these conditions was more than doubled in persons with self-reported poor health perception, self-reported hypertension, low leisure time physical activity, or measured obesity, and a large proportion of people with diabetes-related conditions in need of intervention can therefore be identified relatively easily.

Undiagnosed diabetes, hypertension, and hypercholesterolaemia in an overweight or obese population: implications for cardiovascular disease risk screening programme.

INTRODUCTION: Establishing the burden of undiagnosed CVD risk factors is critical to monitoring public health efforts related to screening and diagnosis. OBJECTIVE: To assess the proportion and determinants of undiagnosed diabetes, hypertension, and hypercholesterolaemia, among overweight or obese adults. METHODS: A sample of 1200 participants aged 35-64 years with a BMI ≥25 kg/m2 was selected from the Colombo district. Data were collected through a questionnaire, anthropometry, blood pressure measurement, and blood sampling for fasting plasma glucose, HbA1c, and lipid profile. Undiagnosed diabetes, hypertension, and hypercholesterolaemia were defined as fasting plasma glucose ≥126 mg/dL or HbA1c ≥6.5%; systolic blood pressure ≥140 mmHg or diastolic blood pressure ≥90 mmHg; total cholesterol ≥240 mg/dl respectively, in a person without a previous diagnosis. Multiple logistic regression analyses were carried out to identify determinants. RESULTS: The prevalence (95%CI) of diabetes was 28% (25.5, 30.5), hypertension, 33.4% (30.7, 36.1) and hypercholesterolaemia, 31.9% (29.2, 34.5). The proportion of undiagnosed diabetes was 13.8% (11.9, 15.8), undiagnosed hypertension 11.3% (9.5, 13.1), and undiagnosed hypercholesterolaemia 17.8% (15.6, 19.9). Undiagnosed cases accounted for almost half of all diabetes cases, one-third of all hypertension cases, and more than half (56%) of all high cholesterol cases. The key determinants for undiagnosed CVD risk were: male sex, low or middle income, rural residence, and relatively younger age. CONCLUSION: CVD screening programmes should be tailored to target populations based on these determinants and provide basic diagnostic facilities in all health centres. The 'proportion undiagnosed' in the population may be a useful indicator to evaluate their effectiveness.

Undiagnosed diabetes among Hispanic and white adults with elevated haemoglobin A1c levels.

AIMS: The aim of this study is to compare the frequency and correlates of undiagnosed diabetes between Hispanic and white American adults ages 20 and older with known diabetes or elevated HbA1c . METHODS: Using 2007 to 2012 National Health and Nutrition Survey data, 1792 white and Hispanic Americans age 20 and older with known diabetes or elevated HbA1c (≥6.5%) were included in the analysis. Undiagnosed diabetes was defined as elevated HbA1c more than or equal to 6.5% without prior diabetes diagnosis. Covariates included demographics, health care access, and survey language. RESULTS: Hispanics were younger (54.92 vs 61.45 y, p < .001) and had a higher rate of undiagnosed diabetes (28.2% vs 18.0%, p < .001) than their white counterparts. For Hispanics, those with undiagnosed diabetes had higher mean HbA1c than those with known diabetes (p = .03), but no significant difference was found between diagnosed and undiagnosed diabetes among whites. Covariates that decreased the odds of undiagnosed diabetes among Hispanic Americans included having a routine place to receive health care. For whites, covariates that decreased the odds of undiagnosed diabetes were a higher ratio of income to poverty level, having health insurance and having a routine place for health care. CONCLUSION: The high proportion of unknown diabetes among Hispanic Americans suggests the need for a culturally competent health campaign to improve community diabetes awareness. Emphasizing the importance of having a routine place of health care for regular health check-ups and expanded community resources for diabetes early detection and prevention among this population could decrease the burden of diabetes.

Pre-operative optimisation of the surgical patient with diagnosed and undiagnosed diabetes: a practical review.

Peri-operative hyperglycaemia, whether the cause is known diabetes, undiagnosed diabetes or stress hyperglycaemia, is a risk factor for harm, increased length of stay and death. There is increasing evidence that peri-operative hyperglycaemia is a modifiable risk factor, and many of the interventions required to improve the outcome of surgery must be instituted before the actual surgical admission. These interventions depend on communication and collaboration within the multidisciplinary team along each stage of the patient journey to ensure that integration of care occurs across the whole of the patient-centred care pathway.

Spousal cardiometabolic risk factors and incidence of type 2 diabetes: a prospective analysis from the English Longitudinal Study of Ageing.

AIMS/HYPOTHESIS: In the UK, more than one million people have undiagnosed diabetes and an additional five million are at high risk of developing the disease. Given that early identification of these people is key for both primary and secondary prevention, new screening approaches are needed. Since spouses resemble each other in cardiometabolic risk factors related to type 2 diabetes, we aimed to investigate whether diabetes and cardiometabolic risk factors in one spouse can be used as an indicator of incident type 2 diabetes in the other spouse. METHODS: We analysed data from 3649 men and 3478 women from the English Longitudinal Study of Ageing with information on their own and their spouse's diabetes status and cardiometabolic risk factors. We modelled incidence rates and incidence rate ratios with Poisson regression, using spousal diabetes status or cardiometabolic risk factors (i.e. BMI, waist circumference, systolic and diastolic BP, HDL- and LDL-cholesterol and triacylglycerols) as exposures and type 2 diabetes incidence in the index individual as the outcome. Models were adjusted for two nested sets of covariates. RESULTS: Spousal BMI and waist circumference were associated with incident type 2 diabetes, but with different patterns for men and women. A man's risk of type 2 diabetes increased more steeply with his wife's obesity level, and the association remained statistically significant even after adjustment for the man's own obesity level. Having a wife with a 5 kg/m2 higher BMI (30 kg/m2 vs 25 kg/m2) was associated with a 21% (95% CI 11%, 33%) increased risk of type 2 diabetes. In contrast, the association between incident type 2 diabetes in a woman and her husband's BMI was attenuated after adjusting for the woman's own obesity level. Findings for waist circumference were similar to those for BMI. Regarding other risk factors, we found a statistically significant association only between the risk of type 2 diabetes in women and their husbands' triacylglycerol levels. CONCLUSIONS/INTERPRETATION: The main finding of this study is the sex-specific effect of spousal obesity on the risk of type 2 diabetes. Having an obese spouse increases an individual's risk of type 2 diabetes over and above the effect of the individual's own obesity level among men, but not among women. Our results suggest that a couples-focused approach may be beneficial for the early detection of type 2 diabetes and individuals at high risk of developing type 2 diabetes, especially in men, who are less likely than women to attend health checks. DATA AVAILABILITY: Data were accessed via the UK Data Service under the data-sharing agreement no. 91400 ( https://discover.ukdataservice.ac.uk/catalogue/?sn=5050&type=Data%20catalogue ).

The contribution of family history to the burden of diagnosed diabetes, undiagnosed diabetes, and prediabetes in the United States: analysis of the National Health and Nutrition Examination Survey, 2009-2014.

PURPOSE: Given the importance of family history in the early detection and prevention of type 2 diabetes, we quantified the public health impact of reported family health history on diagnosed diabetes (DD), undiagnosed diabetes (UD), and prediabetes (PD) in the United States. METHODS: We used population data from the National Health and Nutrition Examination Survey 2009-2014 to measure the association of reported family history of diabetes with DD, UD, and PD. RESULTS: Using polytomous logistic regression and multivariable adjustment, family history prevalence ratios were 4.27 (confidence interval (CI): 3.57, 5.12) for DD, 2.03 (CI: 1.56, 2.63) for UD, and 1.26 (CI: 1.09, 1.44) for PD. In the United States, we estimate that 10.1 million DD cases, 1.4 million UD cases, and 3.9 million PD cases can be attributed to having a family history of diabetes. CONCLUSION: These findings confirm that family history of diabetes has a major public health impact on diabetes in the United States. In spite of the recent interest and focus on genomics and precision medicine, family health history continues to be an integral component of public health campaigns to identify persons at high risk for developing type 2 diabetes and early detection of diabetes to prevent or delay complications.

Performance of the 2015 US Preventive Services Task Force Screening Criteria for Prediabetes and Undiagnosed Diabetes.

BACKGROUND: In 2015, The US Preventive Services Task Force (USPSTF) recommended screening for prediabetes and undiagnosed diabetes (collectively called dysglycemia) among adults aged 40-70 years with overweight or obesity. The recommendation suggests that clinicians consider screening earlier in people who have other diabetes risk factors. OBJECTIVE: To compare the performance of limited and expanded screening criteria recommended by the USPSTF for detecting dysglycemia among US adults. DESIGN: Cross-sectional analysis of survey and laboratory data collected from nationally representative samples of the civilian, noninstitutionalized US adult population. PARTICIPANTS: A total of 3643 adults without diagnosed diabetes who underwent measurement of hemoglobin A1c (A1c), fasting plasma glucose (FPG), and 2-h plasma glucose (2-h PG). MAIN MEASURES: Screening eligibility according to the limited criteria was based on age 40 to 70 years old and overweight/obesity. Screening eligibility according to the expanded criteria was determined by meeting the limited criteria or having ≥ 1 of the following risk factors: family history of diabetes, history of gestational diabetes or polycystic ovarian syndrome, and non-white race/ethnicity. Dysglycemia was defined by A1c ≥ 5.7%, FPG ≥ 100 mg/dL, and/or 2-h PG ≥ 140 mg/dL. KEY RESULTS: Among the US adult population without diagnosed diabetes, 49.7% had dysglycemia. Screening based on the limited criteria demonstrated a sensitivity of 47.3% (95% CI, 44.7-50.0%) and specificity of 71.4% (95% CI, 67.3-75.2%). The expanded criteria yielded higher sensitivity [76.8% (95% CI, 73.5-79.8%)] and lower specificity [33.8% (95% CI, 30.1-37.7%)]. Point estimates for the sensitivity of the limited criteria were lower in all minority groups and significantly different for Asians compared to non-Hispanic whites [29.9% (95% CI, 23.4-37.2%) vs. 49.8% (95% CI, 45.9-53.7%); P < .001]. CONCLUSIONS: Diabetes screening that follows the limited USPSTF criteria will identify approximately half of US adults with dysglycemia. Screening other high-risk subgroups defined in the USPSTF recommendation would improve detection of dysglycemia and may reduce associated racial/ethnic disparities.

Undiagnosed Diabetes Mellitus in Community-Acquired Pneumonia: A Prospective Cohort Study.

BACKGROUND: Diabetes mellitus is an important risk factor for community-acquired pneumonia, whereas the prevalence of undiagnosed diabetes mellitus and prediabetes in patients with community-acquired pneumonia is largely unknown. We aimed to determine the prevalence of prediabetes, undiagnosed diabetes mellitus, and risk factors associated with undiagnosed diabetes mellitus in a large European community-acquired pneumonia cohort. METHODS: This was a multicenter prospective cohort study of hospitals and private practices in Germany and Austria encompassing 1961 adults with community-acquired pneumonia included in the German Community-Acquired Pneumonia Competence Network (CAPNETZ) study between 2007 and 2014. The prevalence of undiagnosed diabetes mellitus and prediabetes was estimated based on hemoglobin A1c measurements. Logistic regression was used to assess risk factors for undiagnosed diabetes mellitus. RESULTS: Fifteen percent of patients had known diabetes mellitus. Among patients without known diabetes mellitus, 5.0% had undiagnosed diabetes mellitus and 37.5% had prediabetes. Male sex (odds ratio [OR], 2.45 [95% confidence interval {CI}, 1.35-4.45]), body mass index ≥25 kg/m2 (OR, 2.64 [95% CI, 1.48-4.72]), and hyperglycemia at admission (6-11 mM: OR, 2.93 [95% CI, 1.54-5.60] and ≥11 mM: OR, 44.76 [95% CI, 17.58-113.98]) were associated with undiagnosed diabetes mellitus. Patients with undiagnosed diabetes mellitus had a higher 180-day mortality rate compared to patients without diabetes mellitus (12.1% vs 3.8%, respectively; P = .001). CONCLUSIONS: Undiagnosed diabetes mellitus was prevalent among community-acquired pneumonia. Male sex, overweight, and hyperglycemia at admission were associated with undiagnosed diabetes mellitus. The long-term mortality among patients with undiagnosed diabetes mellitus was high compared to patients without diabetes mellitus.

A comparison of three different diabetes screening methods among dental patients in Turkey.

OBJECTIVE: This study was conducted to describe the frequency of diabetes in dental patients, and to compare three different screening methods: the random finger plasma glucose (RFPG) test, the Finnish diabetes risk score (FINDRISC) survey and a special clinical guideline developed for dental patients. METHODS: The study design was cross-sectional, descriptive and comparative. The data were collected between August 2011 and February 2012. A total of 702 dental patients participated in this study. The screening tools were RFPG, FINDRISC and a clinical guideline. Data were analyzed using the Chi-squared test, the t test, analysis of variance, and the Pearson correlation test. RESULTS: The frequency of diabetes was 8.3% for the participants. The prevalence of participants at risk for undiagnosed diabetes was 20.1% according to the RFPG test, 29.9% according to the FINDRISC, and 29.8% according to the clinical guideline. Correlation analysis showed a significant positive correlation between the screening methods (p<0.001 for each). CONCLUSION: The overall frequency of diabetes was 8.3%. It was found that the three screening methods used in this study were statistically similar. However, FINDRISC and clinical guideline as the questionnaire screening tools indicated a little larger group than RFPG with respect to diabetes risk.