Additive Interaction Between Insulin Resistance, Chronic Low-Grade Inflammation and Vitamin D Deficiency on the Risk of Type 2 Diabetes Mellitus: A Cohort Study.

OBJECTIVES: The aim of this study was to explore, on an additive scale, the combined effect of the association between insulin resistance (IR), chronic low-grade inflammation (CLGI) and vitamin D deficiency (VDD) on the risk of type 2 Diabetes Mellitus (T2DM). METHODS: This is a cohort study, including 1484 non-diabetic subjects, followed for a period of four years. 25 hydroxy-vitamin D (25OHD), hypersensitive C-reactive protein (HsCRP) and triglyceride-glucose index were assessed. Based on VDD and CLGI, the population was subdivided into 4 exposure groups. Analysis was performed both in the case of IR and without IR. Cox proportional regression and additive interaction were applied to explore cumulative effects of exposure. RESULTS: At follow-up, 162 newly diagnosed cases of T2DM were identified. TYG index (RR = 4.0[2.8-5.6]), HsCRP (RR = 1.6 [1.4-1.7]) and 25OHD (RR = 0.96 [0.39-0.98]) were all significantly associated with the risk of T2DM (p < 0.01). The highest excess risk was recorded in patients cumulating simultaneously IR, CLGI and VDD (RR= 8.4[3.6-19.8], p < 0.0001). The additive interaction was significant, the excess risk linked to the interaction RERI = 10.5[1.43-19.7], the proportion attributable to the combined effect: AP = 0.61[0.37-0.85], and the interaction was synergistic: synergy index: 2.8[1.42-5.69]. CONCLUSION: Baseline levels of TYG index, 25OHD and HsCRP are strongly predictive of future T2DM, and their joint effects are additive and synergistic. Interventional studies are therefore warranted in order to evaluate whether vitamin D supplementation, combined with appropriate anti-inflammatory therapies, is effective as a preventive strategy to reduce the risk of T2DM.

Prediction of body fat percentage: Development and validation of new anthropometric equations.

INTRODUCTION: The aim of this study was to test the validity of existing equations, retrieved from the literature, in the Algerian adult population. To develop, and validate, new predictive equations for body fat percentage (%BF) using simple and easy-to-measure anthropometric parameters. METHODS: This is a cross-sectional study including 877 Algerian adults who underwent a body composition assessment by the direct segmental multi-frequency bioelectrical impedance technique (Inbody-770). Participants were randomly divided into two groups: the development group (n = 577) and the validation group (n = 300). To develop the equations, multiple linear regression models were analyzed. The predictive performance of the developed equations was compared with the direct technique. The following validation tests were used: Student's t-test for paired samples, correlation, and Bland-Altman diagram. Diagnostic accuracy has also been assessed. RESULTS: Four existing equations were tested, and all showed statically significant bias. Four new equations were developed; all had satisfactory predictive performance, with a correlation coefficient ranging from 0.72 to 0.94 in men and 0.87 to 0.93 in women. The best-fitting equation was based on body mass index, waist-to-hip ratio, and chest circumference. The diagnostic accuracy of this equation was 96.7% in men and 95.3% in women. CONCLUSION: The newly developed equations based on anthropometric parameters can serve as a simple tool for the accurate prediction of BF% in adult subjects, at both individual and epidemiological levels.

Reconsidering vitamin D optimal values based on parathyroid hormone levels in a North Algerian cohort: stratification by gender and season.

"Health-based threshold value" is used to define the optimal cutoff of vitamin D. This approach is based on the hypothesis of a secondary hyperparathyroidism associated with hypovitaminosis D. We define the optimal values in a North Algerian population. The optimal value is 25.0 ng/ml in men and 30.0 ng/ml in women. PURPOSE/INTRODUCTION: There is no consensus defining the vitamin D optimal values. The aim of this study is to establish vitamin D optimal values in the Northern Algerian population, based on its skeletal effects as represented by the inverse relationship between 25-hydroxy vitamin D (25(OH) D) and parathyroid hormone (PTH). METHODS: 451 healthy volunteers of both genders, aged 19 to 79 years, were enrolled in a cross-sectional study conducted at the medical analysis laboratory of the University Hospital of Blida, Algeria. 25(OH) D was assessed by a sequential competitive immuno-fluoroassay technique. Determination of vitamin D optimal values was performed based on the kinetic relationship between 25(OH) D and PTH, as explored by inverse nonlinear regression on a spline plots curve. The optimal value represents the 25(OH) D level at which PTH ceases to increase and reaches a virtual plateau. RESULTS: In men and women, respectively, the 25 (OH) D thresholds are estimated at 25.0 ng/ml and 30 ng/ml, above this value, PTH stabilizes in a virtual plateau, estimated at 22.3 pg/ml and 26.8 pg/ml. In warm and cold seasons, respectively, the 25 (OH) D cut-offs are estimated at 30.0 ng/ml and 25.0 ng/ml, from these values, the PTH stabilizes in a virtual plateau, estimated at 21.5 pg/ml and 27.7 pg/ml. CONCLUSION: In this study, the optimal values of 25(OH) D were defined for the first time in a North Algerian adult population. The optimal value is 25.0 ng/ml in men and 30.0 ng/ml in women.

Combined effect of obesity and metabolic profile on glomerular dysfunction in hypertensive subjects.

AIM: The aim of this study is to explore the individual and combined effects of obesity and metabolic profile on the impairment of glomerular function among hypertensive subjects. METHODS: This is a cross-sectional study enrolling 499 hypertensive subjects. Based on body mass index values and metabolic profile, they were assigned to one of four metabolic phenotype groups: MHNO: metabolically healthy non-obese, MHO: metabolically healthy but obese, MUHNO: metabolically unhealthy but non-obese, and MUHO: metabolically unhealthy and obese. The effect of the interaction between obesity and metabolic profile was tested on an additive scale, for both microalbuminuria and reduced estimated glomerular filtration rate (eGFR). RESULTS: After adjustment for confounding factors, the highest risk of both microalbuminuria and decreased eGFR was found among patients of the MUHO group (OR = 6.0 [2.13], p < 0.0001, OR = 5.4 [1.26], p = 0.03, respectively). Analysis of the additive interaction indicates that 51% and 53% of the risk of microalbuminuria and its combination with low eGFR respectively is explained by the co-occurrence of obesity and metabolic disorder. The mechanism of this interaction is synergistic (synergy index = 2.6, [1.5.3]). CONCLUSION: The decline of glomerular function in hypertensive subjects is significantly exacerbated by the interaction between obesity and metabolic disorders. The management of such high-risk subjects requires, in addition to the therapeutic regimen, an adequate dietary and physical program in order to preserve glomerular function.

Development and validation of a laboratory risk score for the early prediction of COVID-19 severity and in-hospital mortality.

BACKGROUND AND AIMS: Coronavirus Disease 2019 is characterized by a spectrum of clinical severity. This study aimed to develop a laboratory score system to identify high-risk individuals, to validate this score in a separate cohort, and to test its accuracy in the prediction of in-hospital mortality. METHODS: In this cohort study, biological data from 330 SARS-CoV-2 infected patients were used to develop a risk score to predict progression toward severity. In a second stage, data from 240 additional COVID-19 patients were used to validate this score. Accuracy of the score was measured by the area under the receiver operating characteristic curve (AUC). RESULTS: In the development cohort, a step-wise decrease in the average survival duration was noted with the increment of the risk score (pANOVA < 0.0001). A similar trend was confirmed when analyzing this association in the validation cohort (p < 0.0001). The AUC was 0.74 [0.66-0.82] and 0.90 [0.87-0.94], p < 0.0001, respectively for severity and mortality prediction. CONCLUSION: This study provides a useful risk score based on biological routine parameters assessed at the time of admission, which has proven its effectiveness in predicting both severity and short-term mortality of COVID-19. Improved predictive scores may be generated by including other clinical and radiological features.

Usefulness of biological markers in the early prediction of corona virus disease-2019 severity.

Coronavirus Disease 2019 is a very fast-spreading infectious disease. Severe forms are marked by a high mortality rate. The objective of this study is to identify routine biomarkers that can serve as early predictors of the disease progression. This is a prospective, single-center, cohort study involving 330 SARS-CoV-2 infected patients who were admitted at the University Hospital of Blida, Algeria in the period between the 27th of March and 22nd of April 2020. The ROC curve was used to evaluate the predictive performance of biomarkers, assessed at admission, in the early warning of progression toward severity. Multivariate logistic regression was used to quantify the independent risk for each marker. After an average follow-up period of 13.9 ± 3.5 days, 143 patients (43.3%) were classified as severe cases. Six biological abnormalities were identified as potential risk markers independently related to the severity: elevated urea nitrogen (>8.0 mmol/L, OR = 9.3 [2.7-31.7], p < .00001), elevated CRP (>42mg/L, OR = 7.5 [2.4-23.3], p = .001), decreased natremia (<133. 6 mmol/L, OR = 6.0 [2.0-17.4], p = .001), decreased albumin (<33.5 g/L, OR = 5.2 [1.7-16.6], p = .003), elevated LDH (>367 IU/L, OR = 4.9 [1.7-14.2], p = .003) and elevated neutrophil to lymphocyte ratio (>7.99, OR = 4.2, [1.4-12.2], p = .009). These easy-to-measure, time-saving and very low-cost parameters have been shown to be effective in the early prediction of the COVID-19 severity. Their use at the early admission stage can improve the risk stratification and management of medical care resources in order to reduce the mortality rate.