Chronic kidney disease (CKD) leads to end-stage renal failure and cardiovascular events. An attribute to these progressions is abnormalities in inflammation, which can be evaluated using the neutrophil-to-lymphocyte ratio (NLR). We aimed to investigate the association of NLR with the progression of end stage of renal disease (ESRD), cardiovascular disease (CVD) and all-cause mortality in Chinese patients with stages 1–4 CKD.Patients with stages 1–4 CKD (18–74 years of age) were recruited at 39 centers in 28 cities across 22 provinces in China since 2011. A total of 938 patients with complete NLR and other relevant clinical variables were included in the current analysis. Cox regression analysis was used to estimate the association between NLR and the outcomes including ESRD, CVD events or all-cause mortality.Baseline NLR was related to age, hypertension, serum triglycerides, total serum cholesterol, CVD history, urine albumin to creatinine ratio (ACR), chronic kidney disease-mineral and bone disorder (CKD-MBD), hyperlipidemia rate, diabetes, and estimated glomerular filtration rate (eGFR). The study duration was 4.55 years (IQR 3.52–5.28). Cox regression analysis revealed an association of NLR and the risk of ESRD only in patients with stage 4 CKD. We did not observe any significant associations between abnormal NLR and the risk of either CVD or all-cause mortality in CKD patients in general and CKD patients grouped according to the disease stages in particular.Our results suggest that NLR is associated with the risk of ESRD in Chinese patients with stage 4 CKD. NLR can be used in risk assessment for ESRD among patients with advanced CKD; this application is appealing considering NLR being a routine test.Trial registration ClinicalTrials.gov Identifier NCT03041987. Registered January 1, 2012. (retrospectively registered) The online version of this article (10.1186/s12967-019-1808-4) contains supplementary material, which is available to authorized users. Chronic kidney disease (CKD) is a growing health problem with an estimated prevalence of 10.8–16% in major developed and developing countries . IndividNonmicrobial inflammation contributes to CKD progression and fibrosis . The neuThe objective of this study was to evaluate whether NLR, a simple marker of chronic systemic inflammation, predicts the progression of ESRD, CVD and all-cause mortality among CKD1–4 patients in the Chinese Cohort Study of Chronic Kidney Disease (C-STRIDE), the first national prospective CKD cohort of the Chinese population.The design, methods, and baseline characteristics of the Chinese Cohort Study of Chronic Kidney Disease (C-STRIDE) study population has been published; this is a prospective investigation involving 39 clinical centers in 28 cities of 22 provinces in China . The desp value < 0.05 was regarded as statistically significant. Proportional hazards assumptions were verified by testing the interaction with time using the likelihood ratio test, which yielded non-significant p values. The results are presented as hazard ratios (HRs) with 95% confidence intervals (CIs). p values for trend were given by treating dichotomy NLR as a continuous variable. We hypothesized that the effect of NLR might be modified by the CKD stage. The interaction terms were generated between NLR and each of the eGFR levels . A stratified analysis in the prediction of ESRD events was conducted by the stages of CKD. A series of sequential models were fit to evaluate the effect of adding certain sets of covariates. All statistical analyses were performed using SPSS 24.0 for Windows .Demographic information and other baseline variables were described using mean ± SD or median and interquartile range for continuous variables and frequency and proportion for categorical variables. Chi-square or Fisher’s exact test was used for comparison of categorical variables between groups, while Mann–Whitney U test or unpaired Student’s t test was used for continuous variables. We identified the cutoff point for NLR level using maximally selected log-rank statistics. Prevalence of events according to NLR categorical variables and estimate survival time for each category are calculated with Kaplan–Meier Survival and time-to-event analysis of outcomes were performed using Cox proportional hazards model, including adjustment for potential confounding factors. Covariates for the models were selected based on prior knowledge about the factors that could be potential confounders of the associations of NLR with ESRD. The potential confounders including age (continuous), gender , smoking (yes vs. no), drinking more than once a day (yes vs. no), clinical characteristics , hemoglobin (continuous), eGFR (continuous), ACR categories . A −2; 345 (36.8%), and 233 (24.8%) patients were in CKD stage 3 and 4, respectively. Baseline demographics and biochemical measurements of CKD patients according to dichotomy of baseline NLR are summarized in Table 3− and eGFR between NLR group and non-NLR group, but there are no difference in age, sex, drinking, HGB, total serum cholesterol, cardiovascular disease (CVD), ACR, serum calcium (sCa) and diabetes.Among the total 938 CKD patients from C-STRIDE, the median follow-up time for the adverse outcomes was 4.55 (IQR 3.52–5.28) years. Baseline characteristics of the study population are described in Table p for log-rank test < 0.001). There were 57 CVD and 43 all-cause mortality events; the occurance of both events was not associated with NLR levels .The associations between NLR and ESRD are shown in Table The current study suggests that baseline NLR is associated with an increased risk of ESRD in pre-dialysis patients with stage 4 CKD; the association was independent of traditional risk factors of CKD including ACR and eGFR.In CKD patients, declines in glomerular filtration rate are associated with increases in the risk of CVD and rapid progression of CKD to end-stage renal disease and mortality , 16. CKDStudies with relatively small-scale have recently been conducted on whether NLR may be a predictor of CKD progression. The balance between the inflammatory and immune response is also reflected by NLR , which sOur study has several limitations. First, although most well-established risk factors of CKD progression were included in our multivariable regression models, the possibility of residual confounding still exists. Secondly, despite using a large patient cohort organized by multiple centers, risk factor exposures, CVD risk and deaths in some regions were affected by data shortages. Thirdly, the NLR only captured in 938 of 3358 patients, selection bias could not be excluded. Forthly, the cohort has a relatively short duration of follow-up and a limited number of cardiovascular events and death , which lNLR may independently predict the risk of ESRD in patients with stage 4 CKD. NLR is easily available, which adds valuable prognostic information to the well-established clinical and biochemical prognostic biomarkers. NLR could be used to improve risk-stratification of patients with stage 4 CKD. Future studies are warranted to see whether stage 4 CKD patients with elevated NLR levels will benefit from anti-inflammatory therapies and interventions.Additional file 1: Table S1. Baseline demographic characteristics of participants of C-STRIDE Study between population with NLR and without NLR. Table S2. Relationship between hsCRP and ESRD, CVD and all-cause mortality events rates.


Bronchial asthma (BA) and obstructive sleep apnea (OSA) are common respiratory obstructive diseases that may coexist. It would be interesting to study the possible influence of that coexistence on both diseases. Until now, reviews focused mainly on epidemiology. The aim of this study was to review the literature in relation to epidemiology, pathophysiology, consequences, screening of patients, and treatment of the coexistence of OSA and BA. We pooled studies from the PubMed database from 1986 to 2019. OSA prevalence in asthmatics was found to be high, ranging from19% to 60% in non-severe BA, reaching up to 95% in severe asthma. Prevalence was correlated with the duration and severity of BA, and increased dosage of steroids taken orally or by inhalation. This high prevalence of the coexistence of OSA and BA diseases could not be a result of just chance. It seems that this coexistence is based on the pathophysiology of the diseases. In most studies, OSA seems to deteriorate asthma outcomes, and mainly exacerbates them. CPAP (continuous positive airway pressure) treatment is likely to improve symptoms, the control of the disease, and the quality of life in asthmatics with OSA. However, almost all studies are observational, involving a small number of patients with a short period of follow up. Although treatment guidelines cannot be released, we could recommend periodic screening of asthmatics for OSA for the optimal treatment of both the diseases. It is characterized by bronchial hyper-responsiveness (BHR) and presents with variable symptoms including wheezing, shortness of breath, chest tightness, and cough that vary according to disease severity [lbutamol . The aimObstructive sleep apnea (OSA) is the most frequent sleep-related breathing disorder. OSA with excessive daytime sleepiness ranged in frequency between 3% and 18% in men and 1–17% in women . OSA is OSA and BA share some common characteristics. First, they are both obstructive respiratory diseases, but with different mechanisms and anatomy of obstruction. A patient with BA and OSA has both upper and lower airway obstruction during sleep. Second, both diseases share common co–morbidities like obesity, allergic rhinitis, and gastro-esophageal reflux (GER). Patients with OSA and BA also have poor quality of sleep and may have increased morbidity and mortality. When they coexist, a bidirectional relationship may additionally affect each other. Since 2013, the coexistence of OSA and BA was defined as alternative overlap syndrome (AOS) to distinguish it from the overlap syndrome that is referred to as chronic obstructive pulmonary disease (COPD) and OSA [A review of the literature was performed pooling studies in English from the PUBMED database from 1986 to 2019. The following search terms were used: obstructive sleep apnea, bronchial asthma, and alternative overlap syndrome. We decided to focus on studies addressing the prevalence, diagnosis, pathophysiology, clinical outcomes, treatment, and screening of patients with obstructive sleep apnea and bronchial asthma . During the search process, 673 articles emerged. Studies were excluded if not written in the English language or were carried out on a pediatric population (<18 years of age). Additionally, studies that were not relevant to our review were excluded. Finally, 92 articles were selected for this review.The vast majority of studies mainly referred to the prevalence of OSA in asthmatic populations. The prevalence was increased, ranging from 19% to 60% ,15,16, a1. The diagnostic method used for OSA diagnosis. Early studies used questionnaires regarding OSA symptoms like habitual snoring, witnessed apneas, and daytime sleepiness ,20,21,222. The cut-offs used for the determination of OSA diagnosis. Some studies used AHI≥5 ,33, othe3. The diagnostic method used for BA diagnosis. Few studies used spirometry or provocation tests ,33,35. T4. The heterogeneity of the populations in relation to age, smoking status, and weight.Early questionnaire-based, cross-sectional studies depicted a statistically significant higher prevalence of OSA symptoms, such as habitual snoring and witnessed apneas, in patients with BA as compared to the general population, independently of the body mass index (BMI), gender, age, or smoking status ,20,21,22In 2015, a large retrospective cohort study using data from the National Health Insurance of Taiwan, collected 38.840 newly diagnosed asthmatics between 2000 and 2010. Each patient matched to four people without asthma according to gender, age, and the date of the diagnosis. The occurrence of OSA was followed until the end of 2011. The overall incidence of OSA was 2.51-fold greater in the asthma cohort than in the comparison group (12.1 vs. 4.84 per person-years). Among asthmatics, the adjusted hazard ratio (HR) for OSA increased to 1.78 for asthma patients with one or less annual emergency room (ER) visits, and 23.8 for those who visited the ER more than once per year. Additionally, the adjusted HR in patients with inhaled steroids compared to those without receiving steroids was 1.33. The authors concluded that asthmatics had a greater risk for OSA, which was much greater when the asthma was more severe [The same year, in another large study, this time a prospective population-based study, 547 selected subjects free of OSA at baseline (AHI < 5) on two consecutive overnight-attended PSG studies were followed every four years for OSA incidence. The BA diagnosis was based on questionnaires. Patients with asthma had a 39% increase in the risk of developing incident OSA as compared with controls, independently of the baseline covariates such as BMI, AHI, and BMI change over time. The risk was duration-dependent: for each 5-year increment in asthma duration, the risk for incident OSA increased by 7%. An asthma duration >10 years increased the risk by 65% .2p < 0.004), and sleepiness  compared to asthmatics without OSA, while no difference was observed in FEV1 [In a recent meta–analysis of 26 studies (7675 patients), the prevalence/relative risk of OSA and the OSA risk (OSA diagnosis based on questionnaires) was 49.5%/2.64 and 27.5%/3.73, respectively, in asthmatics. Patients with BA and OSA had significantly greater BMI  was the only independent factor that could predict BA in OSA patients [While the vast majority of studies address asthmatic populations, Alhabri et al. studied the relation between OSA and BA in the opposite direction. They found that patients with OSA diagnosed with PSG had a 35% increased prevalence of BA and these patients had a greater BMI, AHI, and the lowest oxygen saturation during sleep compared with OSA patients without BA. It seems like BA deteriorated OSA. Obesity  is increased compared to healthy controls. However, the normal drop in FRC during sleep is exaggerated in asthmatics and specifically during Rapid Eye Movement (REM) sleep. This results in an increased airway resistance as an inverse relationship between lung volume and airway resistance is known to exist . MoreoveThe inflammatory infiltration of the upper airway in asthma , the incAdditionally, bronchial asthma possibly influences pharyngeal muscle function, either directly affecting neural sensor pathways because of inflammation or indirectly due to muscle weakness caused from the steroids that is the cornerstone of BA treatment . PharyngThe key factor seems to be the inflammation induced by OSA, both through the airway and systemic. The repeated episodes of a collapsed upper airway lead to hypoxia and eventually oxidative stress, which then cascades to systemic inflammation with increased serum levels of cytokines such as C-reactive protein, interleukin-6 (IL-6), and tumor necrosis factor (TNF-α). The distal airway inflammation seen in BA increases the probability of asthma attacks ,46.Additionally, in OSA, a neutrophil-predominant inflammation seems to begin from the nose, expands to the distal airways infiltrating the airway walls ,48, and 2-agonist use, healthcare utilization, and worse asthma quality of life. They also conducted sputum induction and found higher percentages of neutrophils in asthmatics’ sputum with a high OSA risk compared to those without a high OSA risk (p = 0.001), whereas sputum eosinophil percentages were similar (p = 0.66). There was a significant association of SA-SDQ with sputum neutrophils. Namely, each increase in SA-SDQ by its standard deviation (6.85 units) was associated with a rise in sputum neutrophils of 7.78% , independently of obesity and other confounding factors [Teodorescu et al. studied a large and objectively diagnosed sample of 255 asthmatic patients and estimated the risk of having OSA using the SA-SDQ questionnaire. A higher SA-SDQ score was associated with increased asthma symptoms, β factors . AuthorsThe same results were also confirmed in 55 patients with severe asthma and OSA diagnosed using cardiorespiratory polygraphy . FurtherMoreover, additional factors seen in OSA like GER, cardiac dysfunction, obesity, and increased levels of vascular endothelial growth factor (VEGF) and leptin have been proposed to increase BHR and deteriorate asthma outcomes ,52,53,54The overall pathophysiologic mechanisms involved in the BA–OSA interaction are depicted in There is strong clinical evidence to support the claim that OSA deteriorates BA outcomes.p < 0.0001 for each). A diagnosis of OSA was associated with both persistent daytime (p < 0.0001), in addition to night-time (p = 0.0008), asthma symptoms. The associations were retained in regression models where other known asthma aggravators, like obesity, were included. They concluded that unrecognized OSA may be the cause of persistent BA symptoms, both daytime and nocturnal [Namely, the coexistence of OSA in asthmatics causes an increase in persistent nocturnal asthmatic symptoms compared with those without OSA ,57,58. Locturnal .p < 0.001) in a prospective cross–sectional cohort study [OSA is the fifth independent risk factor (adjusted OR 3.4) among 13 clinical and environmental factors studied for recurrent exacerbations in difficult to treat asthma. The first four are psychological dysfunctioning, recurrent respiratory infections, GER, and severe chronic sinus disease . Therefort study .Regarding asthma control, studies using ACT and ACQ questionnaires showed worse asthma control in asthmatics with OSA, although this was not statistically significant in all studies ,60,61.1 decline in asthmatic patients has been reported to be 38–40.9 mL/year [1 decline in asthmatic patients are age, sex, smoking, acute exacerbations, obesity, and hypoxia [1per year compared to those without OSA in an AHI–dependent manner. Asthma patients with severe OSA (AHI > 30) had a decline in FEV1of 72.4 ± 61.7mL/year as compared to 41.9 ± 45.3 mL/year in those with mild to moderate OSA (5 < AHI ≤ 30) and 24.3 ± 27.5mL/year in those without OSA (AHI ≤ 5).The severity of OSA was the only independent factor for this decline after adjusting for other confounding factors like BMI, age, current smoking status, and number of emergency room visits/year. CPAP treatment significantly decreased the FEV1decline in the patients with severe OSA [1  [There is not much evidence addressing pulmonary function in asthmatic patients with OSA. The natural history of FEV mL/year . Known f hypoxia ,64,65. Ivere OSA . However = 0.32) .It is known that asthmatics have a low quality of sleep due to a variety of factors such as the increased frequency of nocturnal asthma, co-morbidities, bronchodilators, and corticosteroids ,66. As aThe European Sleep Apnea Database (ESADA) cohort addressed the differences between asthmatics and non-asthmatics referred to sleep centers. An interesting finding was that asthmatic patients are under-referred for sleep studies, meaning that physicians are not aware or underestimate the coexistence of OSA and asthma in clinical practice. In that cohort, asthmatic women (with or without OSA) were more obese and reported more daytime sleepiness (according to ESS) than the non-asthmatic ones. This shows that obesity is the main factor for sleep referral in asthmatic women but may give implications about the existence of a specific phenotype as well .1and N2, and less time in the REM stage than the control group with no asthma or OSA. The group with BA and OSA had a lower mean oxygen saturation  than the group with OSA alone and spent more time with oxygen saturation below 90% than the patients with OSA only. The results remained after adjusting for age, BMI, and smoking status. BA was independently associated with lower oxygen saturation, while OSA was not. Authors concluded that coexisting OSA and BA is associated with poorer sleep quality and more profound nocturnal hypoxemia than either of the diseases alone [There are very few reports investigating the objective parameters of sleep in subjects with AOS. In 2018, 384 adult women from the Sleep and Health Program in Sweden, an on-going community-based study, underwent overnight polysomnography. Women with both asthma and OSA had a longer sleep time in non-deep sleep stages Nes alone . Similares alone ,68.While studies have highlighted OSA among asthma patients in outpatient settings, such data in the inpatient setting was sparse until 2015 when Becerra et al., using a 2009–2011 U.S. Nationwide Inpatient Sample, studied the impact of two common asthma co-morbidities—OSA and obesity—on the length and cost of hospitalization and on the need for invasive mechanical ventilation. They reported that OSA and obesity significantly increased the length of stay  and the total hospital charges in both genders . Furthermore, when they coexisted, this led to multiplied increases in the length of stay  and hospital charges .OSA alone in asthmatics  or in coexistence with obesity  significantly increased the use of mechanical ventilation while obesity alone did not  .OSA and BA are associated with increased rates of systemic arterial hypertension ,71, withp < 0.0001). Authors concluded that the presence of sleep disorders in patients with asthma was associated with a high risk of mortality, as shown using Kaplan–Meier survival curves and a log rank test [The only study addressing the mortality in AOS is a retrospective study from South Korea. Kyu–Tae Han et al. used data from the National Health Insurance Service (NHIS) National Sample Cohort 2004–2013 in order to investigate the association between sleep disorders  and mortality in patients with newly diagnosed BA (ICD–10: J.45) during outpatient care between 2004 and 2013. They excluded patients with sleep disorders diagnosed prior to the BA diagnosis. They studied asthmatic patients that were difficult to be controlled, had increased sleep disorders prevalence, and frequently used health services. The study sample consisted of 186.491 asthmatic patients newly diagnosed with asthma. A sleep disorders diagnosis that followed the asthma diagnosis and mortality was studied in these patients. A total of 5179 patients died during the study period (2.78%) because of lung cancer (10.5%), senility (6.1%), COPD (4.5%), myocardial infarction (4.4%), and stomach cancer (3.7%). Asthmatic patients with coexisting sleep disorders had an increased risk of death compared with asthmatics without sleep disorders after adjusting for confounding variables like age, sex, gender, income, and co-morbidities . The mean duration between the asthma diagnosis and death was shorter in asthmatics with sleep disorders (mean duration: 103.85 months), compared with asthmatics without sleep disorders , having a short period of follow up (maximum 9 months), adherence to treatment was not based on objectives criteria, and there was a broad heterogeneity regarding the assessment of the results ,57,58.p < 0.001) without a significant change in the BMI [The first study investigating the impact of the long-term use of CPAP on asthma symptoms and on the control of the disease in a large number of patients was a questionnaire-based one. A survey questionnaire was distributed to all OSA patients using CPAP therapy in 2013. They received 1586 answers and 13% of them had BA. The mean duration of CPAP-usage was 5.7 years and their CPAP daily use was 6.3 h. Self-reported asthma severity  decreased significantly and the ACT score increased significantly from 15.35 to 19.8 ( the BMI .p = 0.015), a significant improvement in bronchial reversibility, symptoms of GER, rhinitis, and exhaled nitric oxide levels (eNO) . However, no significant changes were observed in the asthma medication used, drugs, body weight, and other asthma co-morbidities [The first prospective, multicenter study recently conducted in Spain examined asthma outcomes 6 months after CPAP use in 99 adult asthmatics with OSA. ACQ and the mini–asthma quality of life questionnaire (mini–AQLQ) was used for asthma control and quality of life, respectively, and objective measures of BA (spirometry) and OSA (PSG) as well. Both the control of the disease and the quality of life were significantly improved in general, but when the diseases were categorized according to their severity, only patients with moderate and severe BA and severe OSA, and patients who used CPAP >4 h/day, were significantly improved. There was a significant decrease in the percentage of asthma exacerbations seen in the six months after the use of CPAP , while neck circumference was not , although this is traditionally the most significant factor that predicts OSA [We therefore conclude that the optimal treatment practice for AOS is the best treatment of each disease separately and the recognition and treatment of the co-morbidities like obesity, GER. and more importantly allergic rhinitis, which is probably the basic reason of the high prevalence of OSA among the asthmatics. In a recent population-based study, a history of allergic rhinitis was associated with an increased risk of sleep-disordered breathing in the elderly and it was an independent risk factor of OSA in asthmatics  is likely to be much more accurate than questionnaires and better for identifying moderate to severe OSA, but it is not recommended in patients with severe cardiorespiratory disease and there are no special recommendations for asthmatic patients ,88.GivenThe prevalence of OSA in asthmatics is positively correlated with the severity and duration of asthma and the dosage of oral or inhaled steroids. It seems that asthma is a risk factor for the OSA occurrence, but in order for a causal relationship to confirmed, large prospective studies are needed with objective pulmonary function and sleep measurements.As the impact of OSA on asthma causes a neutrophilic inflammation, contributes to airway remodeling, increases nocturnal and diurnal persistent symptoms, worsens sleep quality and nocturnal hypoxemia increasing sleepiness the next day, deteriorates asthma outcomes, is an independent risk factor for asthma exacerbations, and increases the days of hospitalization and health resource utilization, OSA should not be seen as a simple co-morbidity. AOS is a new asthma phenotype that should be uncovered. Additionally, although it is clear that the co-existence of asthma and OSA increases asthma exacerbations, the association of the number of exacerbations with sleep parameters like AHI, ODI (oxygen desaturation index), and AI  has not yet been defined.Finally, since we lack data from double-blind studies, there are currently no available treatment guidelines for the combination of OSA and asthma, and the treatment of asthma and OSA is limited to each specific disease’s guidelines. In GINA guidelines urge physicians to look for OSA in asthmatics . HoweverWe conclude that there is an urgent need for the early diagnosis of OSA in asthmatic patients in order to effectively treat both diseases and lessen the economic burden.


Chronic kidney disease (CKD) poses a substantial health burden in sub-Saharan Africa, with risk factors ranging from communicable to non-communicable diseases. Hyperuricemia has been recently identified as a factor of progression of CKD. Identifying factors associated with hyperuricemia in CKD patients would help determine interventions to reduce CKD mortality, particularly in resources limited countries. We sought to determine the prevalence and factors associated with hyperuricemia in non-dialysed CKD adult patients in Cameroon.This was a cross-sectional study of non-dialysed CKD patients, conducted in 3 referral nephrology units in Cameroon. Relevant clinical and laboratory data were collected using interviewer-administered questionnaires. Serum uric acid, spot urine protein and spot urine creatinine were assessed. Associations between variables were assessed using multivariate analysis. Level of statistical significance was set at α < 0.05.A sample of 103 participants was included. Mean age of study participants was 55.78 ± 12.58 years, and 59.3% were men. Sixty-nine (67%) had hyperuricemia. Patient’s age , estimated glomerular filtration rate , spot urine protein-creatinine ratio , no hypertension , urate lowering therapy , loop diuretics , obesity  and no anaemia  were independently significantly associated with hyperuricemia.In this sample of non-dialysed CKD patients in Cameroon, about 7 out of 10 had hyperuricemia. Hyperuricemia was independently associated with patient’s age, estimated glomerular filtration rate, spot urine protein-creatinine ratio, hypertension, urate lowering therapy, loop diuretics, obesity and anaemia. More studies are required to establish causal relationships between these associations. Chronic kidney disease (CKD) poses a substantial health burden in sub-Saharan Africa (SSA), with an estimated prevalence of 13.9% . It is eHyperuricemia has been recently identified as a factor of progression of CKD –12. FindHyperuricemia in CKD has been mostly attributed to declining renal function . HoweverWe therefore sought to determine the prevalence and factors associated with hyperuricemia in non-dialysed adult CKD patients in Cameroon, in order to generate findings that would help determine interventions to reduce CKD mortality, particularly in resources limited countries.This was a cross sectional study carried out in the nephrology units of 3 hospitals in Cameroon  over a period of 6 months (01 February 2014 to 31 July 2014). DGH and YCH are level III  hospitals while YUTH is a level II (secondary) hospital in the Cameroon healthcare system. These sites are referral nephrology centres and receive patients with kidney diseases from all regions of the country.After prior ethical clearance from the Cameroon National Ethics Committee, we systematically sampled every non-dialysed CKD patients aged 20 years and above, followed up in the nephrology units of the 3 hospitals. We excluded patients with chronic liver diseases, haemoglobinopathies and/or cancers as reported in their medical charts, and patients who did not give consent.Socio-demographic data  and relevant clinical information  were recorded from the patient’s medical chart or obtained by interviewer-administered questionnaire. Anthropometric data including height and weight were obtained using a stadiometer  and a manual weighing scale  respectively. Blood pressure was measured according to World Health Organisation (WHO) guidelines  using anUric acid in the serum specimen was measured using the direct kinetic uricase method . A clean2 for more than 3 months [2), Stage 2/G2 (eGFR = 60–89 ml/min per 1.73 m2), Stage 3/G3a (eGFR = 45–59 ml/min per 1.73 m2), Stage 3b/G3b (eGFR = 30–44 ml/min per 1.73 m2), Stage 4/G4 (eGFR = 15–29 ml/min per 1.73 m2), Stage 5/G5 (eGFR< 15 ml/min per 1.73 m2) [Estimated glomerular filtration rate (eGFR) was computed from serum creatinine using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation . CKD was3 months . CKD was1.73 m2) . Protein1.73 m2) .Renal disease was Glomerular if manifesting with glomerular range proteinuria, and/or haematuria (deformed or red blood cells cast), associated with hypertension and/or oedema; Tubulo-interstitial if manifesting with sterile leucocyturia, and low urine specific gravity, associated with or without non-glomerular haematuria and proteinuria; Vascular if presence of hypertension, with moderate proteinuria and normal urine sediment. Mixed renal involvement was renal disease manifesting with a combination of 2 or more of glomerular, tubulo-interstitial and vascular involvement.2), normal weight (BMI = 18.5–24.9 kg/m2), overweight (BMI = 25–29.9 kg/m2) and obesity (BMI ≥ 30 kg/m2) [Hyperuricemia was defined as serum uric acid > 70 mg/l in males and > 60 mg/l in females . Hyperte0 kg/m2) . Anaemia0 kg/m2) . Dyslipi0 kg/m2) .Statistical analyses were done using Statistical Package for Social Sciences (SPSS), version 23 Inc., Chicago, Illinois, USA). Normality of continuous data was assessed using Shapiro-Wilk test. Continuous variables were summarised as means and standard deviations (SD), and medians and interquartile ranges (IQR) where appropriate. Categorical variables were summarised as frequencies and percentages.p < 0.02 were included as adjustment variables in multivariate analyses. Continuous data were selected over categorical data for variables with both continuous and categorical data. In order to avoid multicollinearity, bivariate correlations were assessed between the predictor variables prior to multivariate analyses. Predictor variables which were highly significantly (p < 0.01) on bivariate correlation were excluded from the multivariate analysis. Furthermore, collinearity diagnostics were done to ensure that none of the predictor variables had Tolerance < 0.3 and/or Variance Inflation Factor > 3. There were missing data in lipid profile indices  and logistic regression (using hyperuricemia as outcome variable). After bivariate analyses, variables with Sensitivity analyses were done, defining hyperuricemia as serum uric acid > 70 mg/l in males and > 60 mg/l in females or current use of ULT. The results were very similar to our current results.Out of the 198 non-dialysed CKD patients aged 20 years and above who attended our study sites during the study period, 2 patients were excluded for chronic liver disease (liver cirrhosis), one patient excluded for haemoglobinopathy (sickle cell disease), and one patient excluded for cancer (prostate cancer). Of the 194 eligible CKD patients approached for participation in the study, 103 patients gave consent and were included into study; thus a participation rate of 53.1%.2, and median (IQR) spot urine PCR ratio was 803.0 (1120.0) mg/g. More than two-thirds of participants were at an advanced stage of CKD with 48% at stage 4 and 25.2% at stage 5. The most common comorbidities identified were hypertension (87.4%), diabetes (34.0%) and gout (21.4%). The types of renal involvement included; glomerular (60.2%), vascular (19.4%), tubulo-insterstitial (11.7%), and mixed (8.7%). Forty-two (40.2%) participants were on ULT (Allopurinol), 32 (31.1%) were on loop diuretics (Furosemide), while 5 (4.9%) were on angiotensin receptor blockers (Losartan). The mean serum uric acid of the study participants was 76.03 ± 20.50 mg/l. Table Mean age of our study participants was 55.78 ± 12.58 years, and 61 (59.3%) were males. The median (IQR) eGFR was 17.00 (11.00) ml/min per 1.73mThe prevalence of hyperuricemia amongst our study participants was 67% (95% CI: 58.3–75.7%).p value < 0.001) Fig. p value = 0.006) Fig. On bivariate analysis, patient’s age , eGFR , CKD stage 4  or CKD stage 5 , spot urine PCR , severe proteinuria , no hypertension , systolic  and diastolic  blood pressures, diabetes , low protein diet , low-Carb diet , ULT , loop diuretics , body mass index  and no anaemia  were significantly associated with serum uric acid. Mean serum uric acid significantly differed across CKD stages , eGFR , CKD stage 4  or CKD stage 5 , spot urine PCR , severe proteinuria , no hypertension , systolic  and diastolic  blood pressures, low protein diet , low fat diet , ULT , loop diuretics , obesity  and no anaemia  were significantly associated with hyperuricemia.On multivariate analysis, after adjusting for patient’s age, eGFR, spot urine PCR, systolic blood pressure, ULT, loop diuretics and haemoglobin level, patient’s age , eGFR , CKD stage 4  or CKD stage 5 , spot urine PCR , severe proteinuria , no hypertension , systolic  and diastolic  blood pressures, ULT , loop diuretics  and no anaemia  were significantly associated with serum uric acid. Table On multivariate logistic regression, after adjusting for patient’s age, eGFR, spot urine PCR, systolic blood pressure, ULT, loop diuretics and haemoglobin level, patient’s age , GFR , CKD stage 4  or CKD stage 5 , spot urine PCR , severe proteinuria , no hypertension , systolic blood pressure , ULT , loop diuretics , obesity  and no anaemia  were significantly associated with hyperuricemia. Table Our study showed that more than half of CKD patients had hyperuricemia. Among a wide range of demographic, clinical and laboratory parameters evaluated, patient’s age, eGFR, spot urine PCR, hypertension, ULT, loop diuretics and anaemia were significantly associated with serum uric acid and hyperuricemia in CKD.Hyperuricemia is common in CKD and mostly results from decreased uric acid excretion . The kidWe found significant association between eGFR, spot urine PCR and serum uric acid. Patients with CKD stages 4 or 5 and had 24.66 and 7.73 times higher significant odds respectively, of having hyperuricemia compared to those with CKD stage 3b. Also, patients with severe proteinuria had 6.27 significant higher odds of having hyperuricemia compared to those with no or mild proteinuria. These findings were similar to the results of Krishnan . We also. also reported a significant association between hypertension and hyperuricemia in paediatric patients with CKD [. suggested that hypertension mediates the decline in renal function caused by hyperuricemia [We found a significant independent association between hypertension and hyperuricemia. Noone et al.with CKD . A growiwith CKD , 46. Uriwith CKD , 46. On with CKD . Also, uwith CKD . It is huricemia . FurtherSubjects who were obese had 6.12 times significant higher odds of having hyperuricemia. This was in accordance with findings by Noone et al, in their paediatric CKD population . HyperurIt is possible that the observed association between hyperuricemia and anaemia is just an incidental finding. Both conditions can be caused by the underlying CKD, and there may not be a direct relationship between the two. However, in our analysis, after adjusting for eGFR and spot urine PCR, we still found a significant association between both variables. CKD patients with no anaemia displayed 0.04 times lower odds of having hyperuricemia after relevant confounder adjustment. To the best of our knowledge, there has been no study to depict the relationship between hyperuricemia and anaemia in CKD. In the Artherosclerosis Risk in the Communities (ARIC) study, McAdams-DeMarco et al. , after fOur study had strengths and limitations. It was well designed to capture a wide range of demographic, clinical and biological variables that could influence hyperuricemia in CKD. Furthermore, important variables such as medications and diet that could influence our findings were taken into consideration and accounted for. However the small sample size may have limited statistical power of some analyses. Also, odds ratios on logistic regression overestimate relative risks when the prevalence is high. In addition, the cross-sectional design did not enable us carryout cause and effect analysis for the various associations seen. This was particularly true for the associations seen between hypertension, obesity and anaemia with hyperuricemia.In this sample of non-dialysed CKD followed-up in referral centres in Cameroon, about 7 out of 10 had hyperuricemia. Hyperuricemia was independently associated with patient’s age, eGFR, spot urine PCR, hypertension, ULT, loop diuretics, obesity and anaemia. More studies are required to establish causal relationships between these associations.


The correlation coefficient was 0.819 between UPCR and 24hUP. Prediction equation was derived as: Log1024hUP (g) = 0.814 x Log10UPCR (mg/mg) + 0.110 x Gender– 0.004 x Age + 0.004 x Body weight (kg) + 0.002 x CKD stage coefficient– 0.018, where CKD stage coefficient: CKD stage G1 = 1, G2 = 2, G3a = 3.1, G3b = 3.2, G4 = 4, G5 = 5. Correlation coefficient between measured and predicted 24hUP among derivation group and validation group is 0.866 and 0.915, respectively. However, the agreement of spot and daily estimates was less pronounced with proteinuria > 3g than lower values in Bland-Altman analysis. Spot UPCR can accurately predict 24hUP in patients with daily proteinuria below 3g. The development of this equation may facilitate estimation of 24hUP in the clinical practice.Daily urine protein (UP) loss is a cumbersome but important measurement to guide diagnosis and treatment of renal disease. Spot urine protein-creatinine ratio (UPCR) can been applied to estimate daily proteinuria. However, the correlations between spot and 24h proteinuria remain controversial. In this cross-sectional study, simultaneous collection of 24h and spot urines were performed from 1,039 (derivation cohort) and 204 CKD patients  of Chang Gung Memorial Hospital, from 2007 to 2017. The correlations between spot UPCR and 24h proteinuria were compared. The mean age of patients of derivation and validation cohort was 63 and 55 years and the mean estimated glomerular filtration rate was 62 ± 35 and 59 ± 36 mL/min/m The prevalence of Chronic Kidney Disease (CKD) is increasing worldwide and timely diagnosis is pivotal to preserve renal function . ProteinSeveral studies have investigated the correlations between UPCR and urine excretion among different renal disease groups, including lupus nephritis , pre-eclIn this study, we assessed the correlation between spot and 24hUP in patients with diverse degree of proteinuria (derivation cohort), developed prediction equation to estimate 24hUP from single spot urine and validated the equation in an independent second cohort.2  for > 3 months in two separate occasions [The study enrolled predialysis CKD patients aged greater than 20 years old who had visited nephrology outpatient department of Chang Gung Memorial Hospital at Keelung between 2007 January to 2017 January. Informed written consent were obtained from all these patients. CKD was defined if patients had kidney damage manifested by presence of proteinuria or estimated glomerular filtration rate (eGFR) < 60ml/min/1.73mccasions . PatientAll the subjects were instructed to start the 24h urine collections after their first voiding in the morning and to collect all urine continuously for 24h, including the last void at the end of the 24h period. The first early morning spot urine sample was collected immediately after the completion of 24h urine collection to estimate spot urine estimates. Both the 24h and spot urine specimen were assayed for volume, UP, albuminuria and urine creatinine (Ucr) concentration. UP concentration was assayed by using the Microprotein Reagent , which is based on a pyrogallol red-molybdate colorimetric method, and the Ucr level, by using the Beckman Synchron creatinine reagent according to the Jaffé kinetic alkaline picrate method. Serum total protein and creatinine concentrations were assayed using Beckman Synchron LX machine.p<0.05) in the univariate analysis were included in multivariate analysis by applying a multiple linear regression. We constructed an equation for the 24hUP by employing UPCR and other variables by linear regression from the validation cohort. Variance Inflation Factor (VIF) calculation was performed to address issue of collinearity by using principle component analysis. Validation of this equation was performed in the validation and derivation cohort by correlation coefficient and Bland-Altman analysis. Some variables were missing in minor proportion. Values of serum uric acid were missing in 36 cases (3.5%); body mass index and serum albumin , serum triglyceride  and body weight . The case listwise with missing value were excluded from analysis during linear regression analysis. The correlation between spot UPCR and 24hUP (in grams) was measured by Pearson’s correlation coefficients, and the degree of agreement by Bland-Altman analysis. Receiver-operating characteristic (ROC) analyses were done to assess the diagnostic utility of spot UPCR in predicting 24hUP excretion above specified clinically useful thresholds: >0.15 g, >0.3 g, >0.5 g, >1.0 g, >3.5 g, >4.0 g and >8.0 g. Discrimination was explored using the area under ROC (AUROC). Sensitivity, specificity, positive likelihood ratio and negative likelihood ratio were identified after best Youden’s index was calculated. Accuracy was defined as the proportion of patients correctly identified in the entire ROC analysis. Performance of our prediction has further compared with three previous published equation using derivation cohort [p-value < 0.05 was considered statistically significant. Data were analyzed using SPSS 21.0 .Descriptive variables were summarized using median (interquartile range), mean (standard deviation), frequencies (percentage) as appropriate. All variables were tested for normal distribution by Kolmogorov-Smirnov test. Student t test was employed to compare means of continuous variables and normally distributed data; otherwise, Mann-Whitney U test was employed. Categorical data were tested by chi-square test or Fisher exact test. Variables related to 24hUP excretion were assessed with univariate linear regression analysis, and variables that were statistically significant , respectively. 10UPCR as independent predictors to estimate 24hUP. A prediction equation best describing the relationship of 24hUP and UPCR was developed from the derivation cohort as: Log1024hUP (g) = 0.814 x Log10UPCR (mg/mg) + 0.110 x Gender– 0.004 x Age + 0.004 x Body weight (kg) + 0.002 x CKD stage coefficient– 0.018; where the CKD stage coefficients were: CKD stage G1 = 1, G2 = 2, G3a = 3.1, G3b = 3.2, G4 = 4, G5 = 5  in derivation cohort  compared with patients of other stages. The relative wide range of proteinuria and high variability of patients due to diverse disease etiology may contribute to this dissimilarity between patients of different CKD stages.Age, gender, body weight, Ucr and race are common variables used to estimate 24hUP –14, 25. Nephrotic-range proteinuria represents a critical clinical condition that warrants further diagnosis or treatment to preserve renal function. Rapid ascertainment of such degree of proteinuria may guide medical decision. Renal biopsies should mandatory recommended to confirm renal damage. On the other hand, daily proteinuria of 1g serves as indicator of prognosis or monitor of treatment response in glomerular disease. By using our prediction equation, spot UPCR of 0.9 in a CKD stage G1 man of 60 years and 70 kg corresponded to a daily proteinuria of 1.25g; in a CKD stage G3a woman with similar age and anthropometry may have a daily proteinuria of 979 mg. This may represent a practical way to prompt medical decisions.Here, we reported significant correlation between estimated 24hUP by using spot UPCR and the daily collected proteinuria. However, several limitations of the study should be addressed. First, the estimation has been developed from single ethnic population which limited the generalizability of study. Second, the information on the drug usage in these patients were unknown. Some antiproteinuric drugs, such as those drugs interfering with renin-angiotensinogen-aldosterone system or pentoxifylline, may influence on the protein excretion of participants. Finally, equation-based prediction of 24hUP in patients with severe proteinuria should be interpreted with caution. In spite of high accuracy to predict 24hUP using spot urine in patients with heavy proteinuria , the aff1024hUP (g) = 0.814 x Log10UPCR (mg/mg) + 0.110 x Gender– 0.004 x Age + 0.004 x Body weight (kg) + 0.002 x CKD stage coefficient– 0.018, may facilitate clinical practice for estimation of daily proteinuria. The exact role of this prediction equation in long-term patient outcome remains to be elucidated.In conclusion, spot UPCR can accurately predict 24hUP in patients with daily proteinuria below 3g. The development of a simple equation: LogS1 Table1024hUP (g) = 0.813 x Log10UPCR (mg/mg) + 0.104 x Gender– 0.004 x Age + 0.003 x CKD stage coefficient + 0.256; Model R2 = 0.79; male = 1, female = 0; *CKD stage coefficient: CKD stage G1 = 1, G2 = 2, G3a = 3.1, G3b = 3.2, G4 = 4, G5 = 5. Abbreviation: CKD, chronic kidney disease; UPCR, urine protein-creatinine ratio. BSA, body surface area.Prediction equation: Log(DOCX)Click here for additional data file.S1 Fig2 of model is 0.79.Scatter plot  and Bland-Altman analysis (B) comparing predicted with measured BSA-adjusted 24h urine protein from derivation cohort . Scatter plot  and Bland-Altman analysis (D) comparing predicted with measured BSA-adjusted 24-hour urine protein from validation cohort (n = 204). The R(TIFF)Click here for additional data file.S1 Dataset(XLSX)Click here for additional data file.


This paper aims to describe the effect of obesity on blood pressure. A descriptive cross-sectional study was conducted on 101 costaleros. The anthropometric measures were determined through segmental impedance. Cardiac recovery and anaerobic power were measured through the Ruffier–Dickson test and the Abalakov test, respectively. Blood pressure was measured when the individuals were at rest. The Kruskal–Wallis test was applied for of continuous parameters and the X2 test for dichotomous measures. Binary logistic regression models were used for the subsequent analysis with R-square and Receiver Operating Characteristic (ROC) curves. The average population was 28 years of age, 173.7 cm tall, and 82.59 Kg weigh. The excess of body fat was 11.27 Kg and Body Mass Index was 27.33 Kg/m2. 72.3% showed abnormal blood pressure and 68.2% were overweight. 32.7% had a waist-hip ratio higher than 0.94. The probability of presenting abnormal blood pressure was higher among the subjects whose fat content was higher and muscle content was lower.Preventive measures are a priority in those groups that perform intense physical efforts without physical preparation and that can also be overweight or obese. One of the groups that reflect these characteristics is the  Arterial hypertension or High Blood Pressure (HBP) is a silent and invisible disease significantly that besets on developed countries, enhancing the occurrence of cardiovascular and cerebrovascular diseases . The inc2, when considering overweight from 25 to 29.9 kg/m2 and defining obesity as a BMI ≥ 30 kg/m2 [Obesity is caused by the energy imbalance involving both dietary intake and patterns of physical activity , known a30 kg/m2 . A high 30 kg/m2 . Althoug30 kg/m2 . Another30 kg/m2 .Control of body weight, as well as the usual practice of physical activity, are essential pillars in what is known as the healthy lifestyle . Physica2 max) [2 max when studying parameters that are associated with cardiovascular diseases. The direct evaluation of VO2 max presents a number of limitations, such as an increased risk of adverse cardiovascular events, expensive equipment for the measurement of gases, the lack of motivation that the test has proven to show, and the reduction of tolerance to physical exercise [2 max [Cardio-respiratory fitness is explained through the maximum oxygen absorption (VO2 max)  and it h2 max) . Therefoexercise . In addiexercise , existinexercise . Among te [2 max .costaleros of the Holy Week of Andalusia, Spain, for making important physical efforts, of special characteristics, for a short period of time, and without prior preparation. Their work is characterised by rising, through anaerobic effort of explosive force and to the voice of a hammer sound, and carrying on their shoulders a metal or wooden structure of an approximate weight of 50 kg for a period longer than 5 h. The structure is raised explosively, being supported on the seventh cervical vertebra, as under the structure there are a few rounded beams that are designed to support the costalero’s neck. Subsequently, they have to walk and move the structure during periods of about 3 min, with breaks of 3–4 min in most cases.Preventive measures are a priority for the general population from the health point of view, but even more so if the collective studied performs intense physical efforts without physical preparation and even more when they can also be overweight and have a low CRF. One of the groups that meets these characteristics is the We believe that it is useful to determine the amount of body fat in this group and to correlate it with the effects that this causes in the coronary response to physical efforts through the estimation of CRF. The objective of the present study is to know the effect of obesity  on the blood pressure at rest, taking into account the consumption of tobacco, the level of physical fitness, the RDI, jump height, and percentage of muscle mass are useful.costaleros of the Holy Week of Huelva, Andalusia, Spain. Costaleros are the people who lift and carry on their shoulders a religious image in the Holy Week celebrations of Andalusia, Spain. The sample consisted only of men, with all belonging to a single team of costaleros. The costaleros population of the Holy Week does not have an official census on which calculations can be based, but by documentary sources it is estimated that it must not amount to more than 700, so our sample has important significance in terms of figures. The selection of the same was done while considering the group that greater weight supports and that longer distance covers during the performance of such activity.A descriptive cross-sectional study was conducted on 101 Information was collected on the bodily characteristics, demographic data and habits of life of the studied subjects on the previous week to Holy Week celebrations. In the same way, physical tests were carried out on each individual, highlighting the cardiac adaptation to the effort test (Ruffier–Dickson) .2; standard weight: 18.5–24.9 kg/m2; overweight: 25–29.9 kg/m2; and obese ≥ 30 kg/m2.Body mass index (BMI): with the participant standing upright, with bare feet, and with his head at the height level, it was calculated to the nearest 1mm using the portable stadiometer Seca 217 . The BMI is calculated by dividing the weight (in kilograms) by the height (in square metres) and categorising the sample according to the World Health Organization classification, being under-weight: <18.5 kg/mBody fat percentage and muscle mass percentage: Data output, calculated by the manufacturer’s algorithm, includes the amount of body fat measured in Kg as well as the percentage of tissue at body level. The normality data of this variable and the following ones were obtained according to the recommendations of the Spanish Society for the Study of Obesity (SEEDO) .For the WHR calculation, manual measurements of these circumferences were performed on all participants bare chested. Normality and non-normality figures were calculated through studies analysed in the previous revision .Our study independent variable, blood pressure at rest, was measured the day that the work was carried out, following the recommendations by Mancia et al. . Values It should be noted that all of the participants were in previous fasting of 3 h (including fluids), had urinated before the impedance analysis, and had not performed any physical activity before the test.The Ruffier–Dickson squat test consisted of a 45-s squat exercise (40 pushups/min), followed by a 3-min recovery period. The parameters assessed were: resting heart rate before the squats exercise, heart rate at the end of the exercise, and recovery heart rate after 1 and 3 min. In addition, the RDI was calculated according to the following equation: RDI = (P1 − 70) + 2 (P2 − P0)/10 . AccordiTo know the anaerobic power of the sample, the Abalakov test was performed . This teThe physical tests performed offered a general view of the individuals’ adaptation to physical efforts. For this, a physical test was performed for the assessment of the anaerobic resistance (Ruffier–Dickson) and another one for the anaerobic power :Behavioural measures include smoking status  and physical activity level, categorised as active , moderately active , or inactive (not active or inactive). The consumption of fruits and vegetables was evaluated with the question “How often do you eat fresh fruits or vegetables?”. The answers were given on a two-point scale: ≥1 fruit or vegetable/day, or <1 fruit or vegetable/day.costaleros, and the X2 test for dichotomous measures (tobacco use). Binary logistic regression models were built to identify the factors that were independently related to the presence of abnormal blood pressure. The variables included in the model were: BMI, WHR, percentage of fat, and percentage of muscle mass. Also, the Nagelkerke R-square was estimated to indicate the dependent variable’s part of the variance explained by the used model. The null hypothesis was rejected in each statistical test with a p < 0.05 value.The statistical analysis was carried out with the statistical programme SPSS Ver. 23 (IBM). The Kruskal-Wallis test was applied for the statistical significance of continuous parameters differences between groups of Non-parametric Receiver Operating Characteristic (ROC) curves were used to classify people with normal/abnormal blood pressure through the goodness-of-fit test by Hosmer and Lemeshow to confirm the proposed model adjustment.All the participants were informed of the study, prior to voluntary acceptance, and signed a consent. Data was recorded maintaining the anonymity of participants. The study is in line with the latest version of the Helsinki declaration. It has been approved by the Research Ethics Committee of Huelva, Spain (ID: PI 086/15).costalero prototype is a young man aged between 18–51 years of age (28.86 ± 8.63), with an average height and weight of 173.7 ± 5.74 cm and 82.59 ± 14.71 Kg, respectively. An excess of body fat of 11.27 kg, a BMI of 27.33 kg/m2, and a basal caloric consumption of 1677 kcal/day categorises this group as obese.The p = 0.001), BMI (H (3) = 16.831; p = 0.001), percentage of body fat (H (3) = 16.553; p = 0.001), percentage of skeletal muscle mass (H (3) = 17.028; p = 0.001), and WHR (H (3) = 10.148; p = 0.017). On the contrary, there have been no significant differences between the groups according to blood pressure levels with respect to the variables: age, size, smoker, RDI, and height of jump.72.3% (72) of the studied population showed abnormal blood pressure (Systolic Blood Pressure ≥ 120 or Diastolic Blood Pressure ≥ 80) at the time of data collection and 68.2% (69) showed overweight. 32.7% (33) had a WHR higher than 0.94. The amount of accumulated fat, adjusted to the age of the subjects, was above the recommended for their age in 67.3% (68) of the cases. In p < 0.009) and lower muscle mass percentage (0.10 (0.00–0.291); p < 0.007). However, BMI (p = 0.325) and WHR (p = 0.794) values were not significant in the model. According to Nagelkerke R-square, the fatty percentage and the muscle mass percentage could explain, in the studied population, 29.6% of people with abnormal blood pressure.Logistic regression showed that the probability of presenting abnormal blood pressure was worse among the subjects with higher fatty percentage (0.15 (0.001–0.347); p = 0.303)  showed an area of 79% (AUC = 0.79) under the ROC curve . We can interpret that, from an area of 0.7, the discrimination of the model is considered to be acceptable. On the other hand, the test of the Hosmer and Lemeshow test confirmed that the proposed model fits the observed values (= 0.303) .This study has allowed us to identify a group with a high percentage (72.3%) of abnormal blood pressure (SBP ≥ 120 or DBP ≥ 80) and a 59.4% of prehypertension. We see how, close to 30 years of age, there is a clear increase in blood pressure and that this increases with age, data that match the Di@bet.es study .Contrary to what is found in the literature, where tobacco consumption and high blood pressure  have beeHigh BMI and WHR are associated with an increased risk of cardiovascular disease and mortality , reveali2 per 1 raised kg). This shows the little usefulness of this index given that the analysed individuals are people who have a high muscle content (young and/or athletes).When relating the variables body fat and muscle mass with the BMI, we find that there is a direct relationship between them with respect to the index , of having a regular physical condition and taking care of their diet both during the year and during the time of carrying out the work, despite referring eating three times a day and despite the high figures of weight they present. In addition, and although they consider the work as hard (4 out of 5), they do not perform any previous preparation for its performance, or put in place appropriate dietary habits that will assure a less harmful execution of the task. They claim to smoke during the day of the task although they do not consider themselves habitual smokers. All of this presents an image of the One of the strengths of our study is the use of such data regarding the direct association between body fat and WHR and increased blood pressure figures by quantifying this increase by index unit. Thus, this process can be generally applied to any population, highlighting both variables as highly relevant cardiovascular risk factors.As for weaknesses, it is worth mentioning the fact that an exhaustive sampling for this study was not performed. This was not done so, because the complexity of the tests performed and the high number of participants in relation to the existing population led the study to focus on a single collective (one specific brotherhood) and select the one whose work was of greater intensity and physical strain.We can conclude that variables, such as body mass index, waist-hip ratio, and body fat level are good predictors of cardiovascular risk, as has already been published in previous studies.costaleros carry out and its characteristics, we can conclude that they are a group at cardiovascular risk.The increase in body fat is inversely correlated with the capacity of cardiac recovery after the effort. If we consider the type of work Increased body fat levels provoke an increase in blood pressure figures at rest, which confirms the importance of controlling adiposity levels as a risk factor for arterial stiffness in middle-aged adults.The use of BMI to identify cardiovascular risk groups can be interesting in general, but when dealing with people who have a high content of muscle mass, this can result in the emergence of false positives. These outcomes turn out to not be useful for the classification of people with coronary problems.costaleros is subjected to a high physical demand with little previous physical preparation, which increases the cardiovascular risk in the medium and long term.The studied group of Although the studied participants perceive their health status as good and believe that they take care of their diet, the facts do not seem to show a healthy lifestyle. All of this categorises them as a risk group from a cardiovascular point of view. The differences between real health and perceived health makes the modification of unhealthy habits more difficult, as it is really complex to achieve this objective if there is no real risk conception on the part of the individual.


Compared to age-matched normotensive adults, those with essential hypertension have been shown to have distinct arterial hemodynamic abnormalities consisting of increased peripheral resistance, pulse wave velocity, and wave reflection magnitude as well as decreased wave reflection time and aortic compliance. These abnormalities are further exacerbated by beta-adrenergic blockade. To see if there are similar hemodynamic abnormalities that antedate the onset of fixed hypertension, we compared age-matched normotensives with prehypertensives selected from patients undergoing diagnostic cardiac catheterization. Ascending aortic pressure and flow were measured with a micromanometer and flow velocity sensor in the baseline state and after beta-adrenergic blockade. In the baseline state the prehypertensive compared to the normotensive group had elevated blood pressure, resistance, left ventricular end-diastolic pressure (LVEDP), and wave reflections. Beta-adrenergic blockade increased resistance, LVEDP, and wave reflections in both groups. Some of these findings are the same as those we previously reported in young persons with established, essential hypertension. The differences in LVEDP and wave reflections, both in the baseline state and after beta-blockade, were still present in subgroups with no differences in blood pressure. Hence, the elevated wave reflections in prehypertensives do not appear to be directly related to the level of blood pressure. These results support the notion that the elevated blood pressure in hypertension may represent a later manifestation of an already abnormal vascular system rather than the vascular abnormalities resulting from hypertension. Consequently, even before blood pressure becomes elevated, early diagnosis and treatment of the vascular abnormalities in prehypertensives may be warranted. Acute administration of nonvasodilating adrenergic blockers and angiotensin converting enzyme inhibitors lowers blood pressure but does not normalize all the vascular properties. In contrast, administering a nonspecific smooth muscle dilator, nitroprusside, or a calcium-channel blocker lowered the blood pressure to the same extent but completely normalized the vascular abnormalities [Both invasive and noninvasive studies have documented distinct hemodynamic abnormalities in people with essential hypertension compared with age-matched normotensives –6. Althomalities . TherefoGiven the above observations, it is reasonable to ask whether the hemodynamic abnormalities are a consequence of or antedate the increased blood pressure. Answering this question is difficult once hypertension becomes manifest because acutely lowering the blood pressure may not be sufficient to reverse some of the longstanding neural or humoral vascular effects. If there is clear evidence of vascular abnormalities before the blood pressure becomes chronically elevated, this would help clarify this important “chicken vs. egg” issue. If chronically elevated blood pressure eventually causes vascular and target organ abnormalities, then treating the blood pressure in hopes of preventing, or even reversing, potential further damage makes sense. If, on the other hand, vascular changes antedate the onset of blood pressure increases, it might make sense to diagnose and perhaps treat the vascular abnormalities as early as possible so as to either prevent or ameliorate the later manifestations of increased blood pressure. In fact, treating the blood pressure at a later stage might be too late to reverse some of the early vascular abnormalities.Comparing vascular function in completely normal persons with that in prehypertensives who have a strong family history and likelihood of later developing fixed hypertension but who are not yet hypertensive is one way to directly address the “chicken or egg” issue. This is the rationale for the present study. We obtained acending aortic high-fidelity micromanometric pressure and flow data during cardiac catheterization in a group of prehypertensives and a group of age-matched normotensives. We compared R, aortic impedance, pulse wave reflection magnitude, wave reflection travel time, and compliance during baseline conditions and after acute beta-adrenergic blockade with intravenous propranolol. The results demonstrate that, compared to normotensives, prehypertensives have mildly but statistically significantly higher blood pressure, LVEDP, wave reflections, and R—abnormalities very similar to those previously found in established essential hypertension. More importantly, the elevated wave reflections and LVEDP are present even in subgroups with matched blood pressure. Hence, the presence of early vascular abnormalities in prehypertension together with the increasing recognition of its deleterious effects and predilection for progressing to hypertension , 8 suggeThe study population was selected from ethnic Chinese who were undergoing diagnostic cardiac catheterization for chest pain syndrome, evaluation of a systolic murmur, or electrophysiological evaluation. Exclusion criteria included the following: (1) evidence of congenital, coronary, or valvular heart disease; (2) age under 18; (3) pregnancy; (4) taking of medications that could affect blood pressure such as oral contraceptives, pain relievers, and antidepressants; (5) diabetes mellitus (based on fasting blood glucose level); (6) abnormal renal function based on renal arteriograms and abnormal levels of serum electrolytes, creatinine, blood urea nitrogen, and 24-hour creatinine clearance; (7) abnormal levels of cortisol, 17-ketosteroids, 17-hydroxycorticosterone, aldosterone, plasma renin activity, thyroid stimulating hormone, triiodothyronine, and free thyroxine. Based on multiple outpatient and in-hospital precatheterization standard syphgmomanometric blood pressure measurements, the patients were classified into the normotensive (N) or prehypertensive (P) main groups according to the 2003 Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure report (JNC 7) . Those iAll studies were performed as previously reported . Brieflyc, the frequency of the first zero crossing of the impedance phase angle (f0), total external power, oscillatory power, and the ratio of oscillatory to total power were calculated. We calculated the amplitudes of the forward (Pf) and backward (Pb) waves and used the ratio Pb/Pf as an index of wave reflection. Finally, since our previous studies have revealed concordant changes in compliance calculated at the different pressure values, for this study we restricted attention to the compliance at peak systolic pressure.Calculations and data analysis methods are identical to those previously reported . Briefly between-subjects factors being normotensive and prehypertensive and within-subjects factors being baseline and after propranolol. Statistical significance was considered to be P<0.05. We first examined all parameters in each main group for each condition for equality of variances using Levene's test and applied an appropriate correction for subsequent analysis only if the variances were not equal. Next, only those variables that exhibited significant between-subjects effects were subjected to further analysis, i.e., to discern if there were baseline differences, an effect of beta-blockade, and whether that effect differed between the main groups. Any parameter that had both significant between- and within-subjects effects was examined for an interaction effect. If no significant interaction was present, the simple main effects were compared. If there was a significant interaction, further analysis was performed to uncover any specific significantly different pairwise comparisons.For statistical analysis we used a two-way, mixed-factorial repeated measures analysis of variance (ANOVA) with theThe left section of  between-subjects effects, there are so many pairwise comparison results with differing levels of statistical significance that the results of the statistical analysis are presented in a separate table for clarity.The left section in  between-subjects effects are included. In the baseline state, compared to N, the P main group had significantly higher systolic and diastolic aortic blood pressure, R, LVEDP, Pb, and Pb/Pf. In both groups, as expected, beta-blockade significantly decreased heart rate and increased LVEDP and R. Beta-blockade also increased both Pb and Pb/Pf in both groups with the differences between groups remaining significant. A small but statistically significant increase in systolic blood pressure (SBP) occurred only in the P group after beta-blockade.The relevant results of the statistical analysis are shown in b, and Pb/Pf are significantly higher and HR is significantly lower in the P than in the N subgroup, both before and after beta-blockade.Because the P main group had slightly, but statistically significantly, higher blood pressures than the N main group, some or all of the above results might be attributable to the pressure differences. To directly examine this possibility within each of the N and P main groups we selected subgroups whose blood pressure was closer. Specifically, we separately analyzed those in the N group with peak SBP ≥115 and those in the P group with SBP ≤ 130. The results of the subgroups analyses are shown in the right sections of Tables 1b in the N and P patients. For the N patient the small Pb that rises throughout systole to a broad dome under both conditions adds to the dome-shaped Pf to produce a similar dome-shaped pressure wave that peaks in late systole. In contrast, for the P patient, the decrease in Pf beginning early in and continuing through the rest of systole is offset by a larger Pb that rises to a distinct peak resulting in a composite wave with a prominent late systolic peak, especially after propranolol.b/Pf was in the same range as we found [The novelty of this study is the detailed hemodynamic characterization of a group of young prehypertensives who have nearly normal levels of blood pressure and consequently have never been treated, either acutely or chronically, with any antihypertensive medications. There are three major findings which provide some new insights into prehypertension. Compared to pressure- and age-matched normotensives, prehypertensives have 1) elevated wave reflections, (2) no difference in wave reflection travel time, and (3) elevated LVEDP. This strongly suggests that these abnormalities are not directly attributable to the level of blood pressure. To our knowledge, this is the first direct evidence of the dissociation between wave reflections and blood pressure. Several previous studies provide corroborating indirect evidence of this dissociation. In more than 40 similarly aged ethnic Chinese with blood pressure much higher than in our P main group, the range of baseline P elevatedwe found . Anotherwe found .There is increasing evidence in muscular arteries of the important role of smooth muscle in modulating wave reflections. The fact that elevated wave reflections and central blood pressure augmentation can be normalized with dilating as compared with nondilating antihypertensive drugs , 13, 14 In the arterial system reflections arise from aortic tapering, branch points, and adjacent regions of differing stiffness. The specific site(s) of reflection are, however, difficult to pinpoint. There appears to be a major reflection near the renal arteries as well as near the iliac bifurcation . In cont0. This is not altogether surprising since the major determinants of f0 are arterial pulse wave velocity (PWV) and/or the effective wave reflecting site [0 and the mildly elevated blood pressure in the P main group strongly suggests that PWV was not substantially elevated in the prehypertensives. If either PWV were sufficiently increased or the reflecting site were sufficiently proximal in the presence of elevated wave reflections, we would have observed a more marked elevation of central systolic blood pressure.In the present study, unlike in fixed hypertensives , we founing site . Even thb/Pf [The small but significant increase above normal in LVEDP in both our P main group and subgroups is evidence for an early, subtle alteration in cardiac function. Since elevated blood pressure with its attendant increased stresses on the heart does not appear to be responsible, it is reasonable to ask how abnormally high wave reflection but normal wave reflection time can be deleterious. It is plausible that some daily activities—such as those involving isometric exercise or even mental stress—transiently increase blood pressure which in turn increases PWV and thereby sufficiently shortens wave reflection time to affect systolic function. Over a prolonged period, without needing to invoke chronic mechanisms such as fixed vascular damage or anatomic abnormalities, this transient loading could be deleterious. There is some indirect evidence supporting this contention. Handgrip exercise in both normotensives and hypertensives shortened wave reflection time without affecting Pb/Pf . PWV durb/Pf . In younb/Pf . In contb/Pf . The lacb/Pf . That stb/Pf .b—independent of heart rate, age, height, gender, or PWV—was found to be a strong predictor of long-term cardiovascular mortality.These short-term, reversible effects of wave reflections will also be affected by chronic effects. One example is aging which is well known to increase stiffness of the large arteries. The resultant increase in PWV shortens reflection time and thereby increases central arterial systolic and pulse pressure , 29. The0. However, because of the small differences in HR there is unlikely to be a discernible effect. In fact, we found no differences between any of the groups in f0. HR differences are unlikely to impact the other parameters which differed between the N and P groups. Resistance is independent of time (and hence HR) because it is a ratio of two factors, both of which are time dependent. Pb is a magnitude that is independent of time, and Pb/Pf is also a ratio. Although we do not know the reason(s) for the lower HR in the P group, it might be related to the fact that, compared to age-matched normotensive counterparts, male but not female prehypertensives have been found to have abnormal autonomic control of heart rate and increased sympathovagal imbalance [In addition to the parameters already discussed, we found the HRs of the P compared to the N main groups and subgroups to be slightly but significantly lower, both at baseline and after beta-blockade. This differs from other studies that found an increased HR in prehypertensives compared to age-matched normotensives , 31. Thembalance , 31. The0 because, in general, women tend to have shorter body lengths than men and body length is a factor potentially affecting wave reflection time. Indeed, of the five shortest body lengths in our population, four were women. In addition, unlike men, women prehypertensives did not have abnormal autonomic control of heart rate nor sympathovagal inhibition [0, as well as all the parameters listed in There are some limitations of our study that deserve discussion. First, we categorized patients based on the JNC 7 classification scheme in play at the time of the study . Accordihibition , 31. ConFinally, with respect to future directions and therapeutic implications, our findings of increased wave reflections and subtle cardiac effects in young prehypertensives could be only the tip of the iceberg. It seems clear that further large-scale studies in prehypertensives, focusing specifically on hemodynamics during provocations such as exercise, are warranted to more clearly elucidate the pathophysiology of this condition.Independent of our findings, there is also increasing evidence for deleterious effects of prehypertension. For example, one study found a familial disposition for hypertension across three generations, especially with early onset (< age 55) hypertension in grandparents . Among yCurrent guidelines do not suggest treating prehypertensives. However, in light of the findings discussed above and if our results are borne out by further studies, there will be more compelling evidence of a need to reconsider our approach to prehypertension. This is especially germane since it appears that the hemodynamic abnormalities in young persons with prehypertension or established essential hypertension are still reversible. In particular, it might be worth considering alterations in lifestyle and the early use of specific classes of antihypertensive drugs that act to reduce wave reflections.During diagnostic cardiac catheterization we measured detailed aortic hemodynamics in normotensives and age-matched prehypertensives in the baseline state and after acute beta-adrenergic blockade. In the baseline state the prehypertensives compared to the normotensives had elevated blood pressure, resistance, LVEDP, and wave reflections. Beta-adrenergic blockade increased resistance, LVEDP, and wave reflections in both groups. In subgroups selected so that there were no differences in blood pressure, the differences in LVEDP and wave reflections in the baseline state and after beta-blockade were still present. These baseline vascular abnormalities and responses to beta-blockade are very similar to those we previously reported in young persons with established, essential hypertension. Importantly, the elevated wave reflection in prehypertensives with the same blood pressure as normotensives suggests that this abnormality is not directly related to the level of blood pressure. Hence, these results support the notion that the elevation of blood pressure in hypertension may represent a later manifestation of an already abnormal vascular system rather than the vascular abnormalities being a result of the hypertension. Some implications for morbidity and treatment are discussed.


Current screening guidelines are based on simple, categorical rules developed for populations of patients. Although personalized diabetes risk calculators have been created, none are designed to predict current glycemic status using structured data commonly available in electronic health records (EHRs).Electronic, personalized clinical decision support tools to optimize glycated hemoglobin (HbA1c (≥5.7%) among patients with no history of hyperglycemia using readily available variables that will allow integration with EHR systems.The goal of this project was to create a mathematical equation for predicting the probability of current elevations in HbAThe reduced model was compared head-to-head with calculators created by Baan and Griffin. Ten-fold cross-validation was used to calculate the bias-adjusted prediction accuracy of the new model. Statistical analyses were performed in R version 3.2.5  using the rms (Regression Modeling Strategies) package.1c based on 22,635 patient records contained the following variables in order from most to least importance according to their impact on the discriminating accuracy of the model: age, body mass index, random glucose, race, serum non–high-density lipoprotein, serum total cholesterol, estimated glomerular filtration rate, and smoking status. The new model achieved a concordance statistic of 0.77 which was statistically significantly better than prior models. The model appeared to be well calibrated according to a plot of the predicted probabilities versus the prevalence of the outcome at different probabilities.The final model to predict an elevated HbA1c significantly outperformed the existing calculators. The personalized prediction model presented in this paper could improve the efficiency of HbA1c screening initiatives.The calculator created for predicting the probability of having an elevated HbA Many prediction tools have been created to assess the risk of undiagnosed diabetes and related outcomes such as impaired glucose tolerance, prediabetes, risk of future diabetes, and hyperinsulinemia. Most of these tools are not practical in the setting of the electronic health record (EHR) because they include predictor variables not readily available in structured formats -20. Exam2) plus another risk factor  at any age and for all patients beginning at 45 years of age at 3-year intervals [Current guidelines from the United States Preventive Services Task Force (USPSTF) recommend screening for abnormal blood glucose in adults aged 40 to 70 years who are overweight or obese. The USPSTF acknowledges that patients with other high-risk characteristics  may need to be screened sooner but this is left up to the physician’s discretion . The guintervals .Current approaches do not take advantage of advanced statistical modeling. Hyperglycemia risk prediction with simultaneous consideration of numerous independent variables and nonlinear effects is statistically ideal in the context of a multifactorial pathology. Creating strict cutoffs of individual variables or combinations of a limited number of variables for clinical guidelines does not take advantage of what can now be reasonably achieved. The USPSTF and ADA guidelines encourage physician judgment in the application of glucose screening but do not provide specific guidance. Simplified classification methods used in cancer have been notoriously poor at discriminating between high- and low-risk patients . Moreove1c) provide an easy and accurate method for determining current mean glycemia over the previous 8 to 12 weeks without the need for fasting. HbA1c testing is standardized according to specifications defined by the National Glycohemoglobin Standardization Program (NGSP). HbA1c levels are the primary blood marker used for guiding the management of type 2 diabetes, and the ADA has approved HbA1c testing for diabetes screening [1c as a screening tool in patients without prediabetes or diabetes provides data for prediction modeling from EHR records.Predicting the date of onset of hyperglycemia is difficult due to the lack of symptoms early in the course of the disease and inconsistent testing and/or documentation in clinical practice (particularly in a structured fashion). Previous studies indicate that the onset of type 2 diabetes frequently occurs more than 5 years before diagnosis ,28. In ccreening . The inc1c is important clinically despite previous studies that have not shown a mortality benefit from screening for diabetes [The authors strongly believe that the identification of patients with elevated HbAdiabetes . The earBehavioral counseling can lead to reductions in cardiovascular disease risk .Treatment of prediabetes, which affects approximately 35% of the adult population in the United States , has beeDiabetic-specific retinopathy is present in up to 21% of patients with newly diagnosed type 2 diabetes , while pEarly detection of diabetes allows for the allocation of proven preventive strategies  .Appropriate documentation of elevated blood sugar and diabetes allows health systems and payers to improve the risk stratification of patients and increases the potential pool of patients available for participation in clinical research.1c levels would constitute a major advancement in finding patients with the most probable need of screening interventions. To address this gap, we created a calculator for predicting the probability that a given patient with no history of diabetes or elevated blood sugar currently has an elevated HbA1c value (≥5.7%). This cutoff was chosen because it corresponds with the current guidelines published by the ADA that indicate values <5.7% are considered to be normal. Importantly, the calculator presented in this paper was restricted only to structured variables typically available in EHRs. This focus on common structured variables will enable the tool to be integrated into EHRs for implementation.Therefore, an accurate tool for predicting the current probability that a specific patient has an elevation in HbA1c testing prior to evidence of hyperglycemia (random blood sugar ≥200 mg/dL), any diabetes-related diagnostic code, or prescription for an antihyperglycemic medication. Data were extracted from the Epicare EHR at Wake Forest Baptist Medical Center in Winston-Salem, North Carolina, for the dates between September 2012 and September 2016. The study was approved by the institutional review board and granted a waiver of informed consent. Data were limited to structured data located in these areas of the EHR: encounter diagnoses, problem list, past medical history, procedures, prescriptions, vital signs, demographics, social history, and laboratory values. Candidate predictor variables were chosen based on their theoretical association with hyperglycemia. This study was conducted on all adult patients who have undergone HbA1c of interest. For missing continuous variables , the most recent prior value was used instead. Patients completely lacking values for independent variables were excluded. The investigators did not impute missing data because it was felt that imputation would not be appropriate at the point of implementation. Comorbidities were considered to be present if the patient had any structured instances of the diagnostic code on or before the date of the first HbA1c. Medications with start dates on or before the date of the HbA1c and end dates on or after the date of HbA1c were considered to be active. Medication order dates were used when the start dates were missing. Medications missing both start and order dates were excluded. Medication categories  were provided by First Databank Inc. Multiple logistic regression was used to model the association between the independent variables and the outcome of HbA1c ≥5.7%. Continuous variables were fit using restricted cubic splines with 3-knots. Due to collinearity, the model could not be fit with the simultaneous inclusion of serum non–high-density lipoprotein and high-density lipoprotein. Therefore, high-density lipoprotein was removed from the complete model. The model was reduced using Harrell’s model approximation method [Independent variables were defined on the date of the HbAn method . For parn method ,22. The n method . StatistLaboratory measurements:Serum triglyceridesRandom blood glucoseSerum non–high-density lipoproteinSerum high-density lipoprotein (dropped due to an inability to fit model)Serum total cholesterolEstimated glomerular filtration rate (estimated from serum creatinine using the modified Chronic Kidney Disease Epidemiology Collaboration formula )Active prescription medication categories:AntihypertensiveFirst generation antipsychoticSecond generation antipsychoticcoenzyme A reductase inhibitor (statin)3-hydroxy-3-methyl-glutaryl-FibrateValproic acidBeta-blockerThiazide diureticNiacinOral glucocorticoidProtease inhibitorNucleoside reverse transcriptase inhibitorOral contraceptiveInjectable medroxyprogesterone acetateCyclosporineSirolimusTacrolimusDiagnosis codes see :HypertensionIschemic heart diseasePeripheral vascular diseaseNeuropathyObesityHyperlipidemiaVital signs:Systolic blood pressureDiastolic blood pressureBody mass indexDemographics:RaceAgeGenderFamily history:Number of first degree relatives with diabetesSocial history:Smoking status1c (≥5.7%). The record search identified 22,635 patients for model building and validation of which 26% were found to have an elevated HbA1c results. As expected, patients found to have elevated HbA1c levels were older, had higher BMI, lower eGFR, and higher random glucose values.The final model included the following 8 variables ordered from most important to least important: age, BMI, random glucose, race, serum non–high-density lipoprotein, serum total cholesterol, estimated glomerular filtration rate (eGFR), and smoking status. 1c (≥5.7%) and sample calculations for 2 patient scenarios are shown in The coefficients along with instructions for calculating the probability of an elevated HbAThe 3 models were compared in their ability to accurately rank patients according to risk as measured by the concordance statistic (c-stat) and bias-adjusted using 10-fold cross-validation. The current model  demonstrated statistically significant improvements in discrimination when compared to the models created by Baan  and Griffin .The calibration curve shown in 1c (≥5.7%) without significant net harms above this threshold. This model confers a net benefit that is equal to or greater than the net benefit offered by the other models at all probability thresholds.Decision curves are displayed in 1c significantly outperformed the existing calculators. It should be noted that the calculators created by Baan and Griffin were designed for predicting current glucose tolerance test results and were not specifically calibrated to predict HbA1c values. However, any potential issues with calibration should not impact the ability to discriminate patients according to risk. The authors chose not to develop a simple risk score, which would be easier to calculate without a computer but would be less accurate and would not provide an absolute probability. One of the benefits of using multivariable regression over many machine learning methods is that the mathematical output of this model can be integrated into an EHR using common mathematical operations. In contrast, classification-based methods like random forest, artificial neural networks, and classification and regression trees would increase the complexity of implementation by requiring separate software outside the EHR to calculate probabilities. The movement of data into and out of the EHR also raises concerns about security and privacy.The calculator created for predicting the probability of having an elevated HbALimitations of the study include the lack of external validation. The model was validated internally using resampling and may not reflect the prediction accuracy that would be achieved in a prospective fashion at the current institution or when validated in a different health system. However, the authors used 10-fold cross-validation in which patients in the test data for each fold were not used to build the model. Another limitation pertains to the lack of data from outside health systems. Patients may have additional medication or laboratory results outside of the health system that could alter the predicted risk or change the patient’s status in terms of hyperglycemia. In order to ensure that patients have a minimal amount of data to guide the calculator’s creation, the investigators required that patients had at least one value for each of the independent variables. Future research and quality improvement projects may need to query patients about health history prior to implementation.1c tests were appropriate for use in model building. Some of the tests were obtained before the installation of a comprehensive EHR and, therefore, accompanying information like vital signs were not available. Many of the tests were obtained in patients who already had evidence of possible hyperglycemia, some of whom were already being treated with antidiabetic medication. These patients would be inappropriate to use for the creation of a model aimed at patients with unknown glucose status. Limiting the model building and validation dataset to patients with complete data further reduced the sample size from 32,872 to 22,635. The authors chose not to impute the missing values given the adequate number of patients with complete data. In addition, the authors are not convinced that imputation would be acceptable to patients and providers when the model is implemented into practice. Patients lacking common variables used in the model such as BMI and blood pressure values are probably very new to the health system or are seeking their usual care elsewhere. Serum creatinine and lipid measurements are routinely obtained in clinical practice, especially among older adults. Patients without any creatinine or lipid measurements are likely to be younger and less likely to be at risk for diabetes. The authors felt it was important to identify a population for model building that matches the future population where the model will be implemented. Despite the restrictions on data inclusion, the dataset contained >5000 patients with the outcome of interest. The size of this dataset is large compared to most similar studies conducted prior to the adoption of EHRs and is more than adequate for regression modeling. Harrel [A relatively small proportion of historical HbA. Harrel  has prop1c levels could have negatively impacted the model building and could decrease the prediction accuracy of the model upon implementation. Wake Forest Baptist Medical Center is not a certified member of the NGSP but maintains accreditation by the Clinical Laboratory Improvement Amendments Program (identification number 34D0664386). The Wake Forest Baptist Medical Center’s core laboratory performs HbA1c testing using ion exchange high performance liquid chromatography, which is highly precise and constituted the vast majority of HbA1c measurements used to create the data for this study. However, the investigators did not exclude HbA1c measurements obtained using different methods at other locations in the health system , which likely introduced variability in the HbA1c measurements. Comorbid conditions such as iron deficiency anemia can lead to HbA1c measurements that do not accurately reflect average blood glucose levels [1c measurements, the prediction model performed very well.Imprecision in the measurement of HbAe levels . Despite1c testing would provide a net benefit between probabilities of 0.01 to 0.71. The authors will conduct a targeted screening study in which patients with a predicted risk of an elevated HbA1c ≥50% will be notified directly regarding their elevated risk. Coupled with standing laboratory orders, this direct-to-patient design would enable patients to undergo HbA1c testing prior to a physician visit. This is particularly important for patients with infrequent in-person visits. The hope is that patients with subsequent elevations in HbA1c would be more likely to re-engage with the health system.Improving the efficiency of diabetes screening should be of great interest in the United States given the increased use of value-based care contracts. Health systems could use our model for diabetes screening initiatives in a variety of ways. The decision curves suggest that using the new algorithm to guide HbAIn summary, the risk equation created in this study is optimized for integration within an EHR and outperforms other similar models. Future research will attempt to integrate the risk calculator into clinical workflows, examine the ability of the calculator to predict risk in other health systems, and evaluate the potential economic savings of using this model for diabetes screening.