Variation in the plasma membrane monoamine transporter (PMAT) (encoded by SLC29A4) and organic cation transporter 1 (OCT1) (encoded by SLC22A1) and gastrointestinal intolerance to metformin in type 2 diabetes:An IMI direct study

OBJECTIVE Gastrointestinal adverse effects occur in 20–30% of patients with metformin-treated type 2 diabetes, leading to premature discontinuation in 5–10% of the cases. Gastrointestinal intolerance may reflect localized high concentrations of metformin in the gut. We hypothesized that reduced transport of metformin via the plasma membrane monoamine transporter (PMAT) and organic cation transporter 1 (OCT1) could increase the risk of severe gastrointestinal adverse effects. RESEARCH DESIGN AND METHODS The study included 286 severe metformin-intolerant and 1,128 metformin-tolerant individuals from the IMI DIRECT (Innovative Medicines Initiative: DIabetes REsearCh on patient straTification) consortium. We assessed the association of patient characteristics, concomitant medication, and the burden of mutations in the SLC29A4 and SLC22A1 genes on odds of intolerance. RESULTS Women (P < 0.001) and older people (P < 0.001) were more likely to develop metformin intolerance. Concomitant use of transporter-inhibiting drugs increased the odds of intolerance (odds ratio [OR] 1.72, P < 0.001). In an adjusted logistic regression model, the G allele at rs3889348 (SLC29A4) was associated with gastrointestinal intolerance (OR 1.34, P = 0.005). rs3889348 is the top cis-expression quantitative trait locus for SLC29A4 in gut tissue where carriers of the G allele had reduced expression. Homozygous carriers of the G allele treated with transporter-inhibiting drugs had more than three times higher odds of intolerance compared with carriers of no G allele and not treated with inhibiting drugs (OR 3.23, P < 0.001). Use of a genetic risk score derived from rs3889348 and SLC22A1 variants found that the odds of intolerance were more than twice as high in individuals who carry three or more risk alleles compared with those carrying none (OR 2.15, P = 0.01). CONCLUSIONS These results suggest that intestinal metformin transporters and concomitant medications play an important role in the gastrointestinal adverse effects of metformin

Predicting glycated hemoglobin levels in the non-diabetic general population:Development and validation of the DIRECT-DETECT prediction model - a DIRECT study

AIMS/HYPOTHESIS: To develop a prediction model that can predict HbA1c levels after six years in the non-diabetic general population, including previously used readily available predictors. METHODS: Data from 5,762 initially non-diabetic subjects from three population-based cohorts (Hoorn Study, Inter99, KORA S4/F4) were combined to predict HbA1c levels at six year follow-up. Using backward selection, age, BMI, waist circumference, use of anti-hypertensive medication, current smoking and parental history of diabetes remained in sex-specific linear regression models. To minimize overfitting of coefficients, we performed internal validation using bootstrapping techniques. Explained variance, discrimination and calibration were assessed using R2, classification tables (comparing highest/lowest 50% HbA1c levels) and calibration graphs. The model was externally validated in 2,765 non-diabetic subjects of the population-based cohort METSIM. RESULTS: At baseline, mean HbA1c level was 5.6% (38 mmol/mol). After a mean follow-up of six years, mean HbA1c level was 5.7% (39 mmol/mol). Calibration graphs showed that predicted HbA1c levels were somewhat underestimated in the Inter99 cohort and overestimated in the Hoorn and KORA cohorts, indicating that the model's intercept should be adjusted for each cohort to improve predictions. Sensitivity and specificity (95% CI) were 55.7% (53.9, 57.5) and 56.9% (55.1, 58.7) respectively, for women, and 54.6% (52.7, 56.5) and 54.3% (52.4, 56.2) for men. External validation showed similar performance in the METSIM cohort. CONCLUSIONS/INTERPRETATION: In the non-diabetic population, our DIRECT-DETECT prediction model, including readily available predictors, has a relatively low explained variance and moderate discriminative performance, but can help to distinguish between future highest and lowest HbA1c levels. Absolute HbA1c values are cohort-dependent

An internally validated prognostic model for success in revision stapes surgery for otosclerosis

Objectives/Hypothesis: To develop a prediction model that can accurately predict the chance of success following revision stapes surgery in patients with recurrent or persistent otosclerosis at 2- to 6-months follow-up and to validate this model internally. Study Design: A retrospective cohort study of prospectively gathered data in a tertiary referral center. Methods: The associations of 11 prognostic factors with treatment success were tested in 705 cases using multivariable logistic regression analysis with backward selection. Success was defined as a mean air-bone gap closure to 10 dB or less. The most relevant predictors were used to derive a clinical prediction rule to determine the probability of success. Internal validation by means of bootstrapping was performed. Model performance indices, including the Hosmer-Lemeshow test, the area under the receiver operating characteristics curve (AUC), and the explained variance were calculated. Results: Success was achieved in 57.7% of cases at 2- to 6-months follow-up. Certain previous surgical techniques, primary causes of failure leading up to revision stapes surgery, and positions of the prosthesis placed during revision surgery were associated with higher success percentages. The clinical prediction rule performed moderately well in the original dataset (Hosmer-Lemeshow P =.78; AUC = 0.73; explained variance = 22%), which slightly decreased following internal validation by means of bootstrapping (AUC = 0.69; explained variance = 13%). Conclusions: Our study established the importance of previous surgical technique, primary cause of failure, and type of the prosthesis placed during the revision surgery in predicting the probability of success following stapes surgery at 2- to 6-months follow-up. Level of Evidence: 2b. Laryngoscope, 128:2390–2396, 2018

Association between full monitoring of biomedical and lifestyle target indicators and HbA 1c level in primary type 2 diabetes care: An observational cohort study (ELZHA-cohort 1)

Objective Management of type 2 diabetes mellitus (T2DM) requires frequent monitoring of patients. Within a collective care group setting, doubts on the clinical effects of registration are a barrier for full adoption of T2DM registration in general practice. We explored whether full monitoring of biomedical and lifestyle-related target indicators within a care group approach is associated with lower HbA 1c levels. Design Observational, real-life cohort study. Setting Primary care data registry from the Hadoks (EerstelijnsZorggroepHaaglanden) care group. Exposure The care group provides general practitioners collectively with organisational support to facilitate structured T2DM primary care. Patients are offered quarterly medical and lifestyle-related consultation. Main outcome measure Full monitoring of each target indicator in patients with T2DM which includes minimally one measure of HbA 1c level, systolic blood pressure, LDL, BMI, smoking behaviour and physical exercise between January and December 2014; otherwise, patients were defined as 'incompletely monitored'. HbA 1c levels of 8137 fully monitored and 3958 incompletely monitored patients were compared, adjusted for the confounders diabetes duration, age and gender. Since recommended HbA 1c values depend on age, medication use and diabetes duration, analyses were stratified into three HbA 1c profile groups. Linear multilevel analyses enabled adjustment for general practice. Results Compared with incompletely monitored patients, fully monitored patients had significantly lower HbA 1c levels (95% CI) in the first (-2.03 [-2.53 to -1.52] mmol/mol) (-0.19% [-0.23% to -0.14%]), second (-3.36 [-5.28 to -1.43] mmol/mol) (-0.31% [-0.48% to -0.13%]) and third HbA 1c profile group (-1.89 [-3.76 to -0.01] mmol/mol) (-0.17% [-0.34% to 0.00%]). Conclusions/interpretation This study shows that in a care group setting, fully monitored patients had significantly lower HbA 1c levels compared with incompletely monitored patients. Since this difference might have considerable clinical impact in terms of T2DM-related risks, this might help general practices in care group settings to overcome barriers on adequate registration and thus improve structured T2DM primary care. From population health management perspective, we recommend a systematic approach to adjust the structured care protocol for incompletely monitored subgroups

Experiences with tailoring of primary diabetes care in well-organised general practices:a mixed-methods study

Abstract Background Dutch standard diabetes care is generally protocol-driven. However, considering that general practices wish to tailor diabetes care to individual patients and encourage self-management, particularly in light of current COVID-19 related constraints, protocols and other barriers may hinder implementation. The impact of dispensing with protocol and implementation of self-management interventions on patient monitoring and experiences are not known. This study aims to evaluate tailoring of care by understanding experiences of well-organised practices 1) when dispensing with protocol; 2) determining the key conditions for successful implementation of self-management interventions; and furthermore exploring patients’ experiences regarding dispensing with protocol and self-management interventions. Methods in this mixed-methods prospective study, practices (n = 49) were invited to participate if they met protocol-related quality targets, and their adult patients with well-controlled type 2 diabetes were invited if they had received protocol-based diabetes care for a minimum of 1 year. For practices, study participation consisted of the opportunity to deliver protocol-free diabetes care, with selection and implementation of self-management interventions. For patients, study participation provided exposure to protocol-free diabetes care and self-management interventions. Qualitative outcomes (practices: 5 focus groups, 2 individual interviews) included experiences of dispensing with protocol and the implementation process of self-management interventions, operationalised as implementation fidelity. Quantitative outcomes (patients: routine registry data, surveys) consisted of diabetes monitoring completeness, satisfaction, wellbeing and health status at baseline and follow-up (24 months). Results Qualitative: In participating practices (n = 4), dispensing with protocol encouraged reflection on tailored care and selection of various self-management interventions A focus on patient preferences, team collaboration and intervention feasibility was associated with high implementation fidelity Quantitative: In patients (n = 126), likelihood of complete monitoring decreased significantly after two years (OR 0.2 (95% CI 0.1–0.5), p < 0.001) Satisfaction decreased slightly (− 1.6 (95% CI -2.6;-0.6), p = 0.001) Non-significant declines were found in wellbeing (− 1.3 (95% CI -5.4; 2.9), p = 0.55) and health status (− 3.0 (95% CI -7.1; 1.2), p = 0.16). Conclusions To tailor diabetes care to individual patients within well-organised practices, we recommend dispensing with protocol while maintaining one structural annual monitoring consultation, combined with the well-supported implementation of feasible self-management interventions. Interventions should be selected and delivered with the involvement of patients and should involve population preferences and solid team collaborations

Reducing postprandial glucose in dietary intervention studies and the magnitude of the effect on diabetes-related risk factors: a systematic review and meta-analysis

Purpose: Reducing postprandial hyperglycemia has beneficial effects on diabetes-related risk factors, but the magnitude of the reduction needed to achieve such an effect is unknown. The purpose of the study was to quantify the relationship of acute glucose and insulin postprandial responses with longer-term effects on diabetes-related risk factors by performing a systematic review and meta-analysis of dietary intervention studies. Methods: We systematically searched EMBASE and MEDLINE. Dietary intervention studies among any human population aiming to reduce postprandial glycemia, with actual measures of postprandial glucose (PPG) and/or insulin (PPI) as acute exposures (incremental area under the curve, iAUC) as well as markers of glucose metabolism (fasting glucose, HbA1c) and insulin sensitivity (fasting insulin, HOMA-IR) after at least 4 weeks of diet intervention as outcomes were included. Meta-analyses were performed for the effects on acute exposures and on diabetes-related risk factors. The relationship between changes in acute exposures and changes in risk factor outcomes was estimated by meta-regression analyses. Results: Out of the 13,004 screened papers, 13 papers with 14 comparisons were included in the quantitative analysis. The dietary interventions acutely reduced mean PPG [mean difference (MD), − 0.27 mmol/l; 95% CI − 0.41 to − 0.14], but not mean PPI (MD − 7.47 pmol/l; 95% CI − 16.79 to 1.86). There were no significant overall effects on fasting glucose and insulin. HbA1c was reduced by − 0.20% (95% CI − 0.35 to − 0.05). Changes in acute PPG were significantly associated with changes in fasting plasma glucose (FPG) [per 10% change in PPG: β = 0.085 (95% CI 0.003, 0.167), k = 14], but not with fasting insulin [β = 1.20 (95% CI − 0.32, 2.71), k = 12]. Changes in acute PPI were not associated with changes in FPG [per 10% change in PPI: β = − 0.017 (95% CI − 0.056, 0.022), k = 11]. Conclusions: Only a limited number of postprandial glucose-lowering dietary intervention studies measured acute postprandial exposures to PPG/PPI during the interventions. In this small heterogeneous set of studies, an association was found between the magnitude of the acute postprandial responses and the change in fasting glucose, but no other outcomes. More studies are needed to quantify the relationship between acute postprandial changes and long-term effects on risk factors

Predicting Mortality in Patients Treated Differently: Updating and External Validation of a Prediction Model for Nursing Home Residents with Dementia and Lower Respiratory Infections

Objective To evaluate whether a model that was previously developed to predict 14-day mortality for nursing home residents with dementia and lower respiratory tract infection who received antibiotics could be applied to residents who were not treated with antibiotics. Specifically, in this same data set, to update the model using recalibration methods; and subsequently examine the historical, geographical, methodological and spectrum transportability through external validation of the updated model. Design 1 cohort study was used to develop the prediction model, and 4 cohort studies from 2 countries were used for the external validation of the model. Setting Nursing homes in the Netherlands and the USA. Participants 157 untreated residents were included in the development of the model; 239 untreated residents were included in the external validation cohorts. Outcome Model performance was evaluated by assessing discrimination: area under the receiver operating characteristic curves; and calibration: Hosmer and Lemeshow goodness-of-fit statistics and calibration graphs. Further, reclassification tables allowed for a comparison of patient classifications between models. Results The original prediction model applied to the untreated residents, who were sicker, showed excellent discrimination but poor calibration, underestimating mortality. Adjusting the intercept improved calibration. Recalibrating the slope did not substantially improve the performance of the model. Applying the updated model to the other 4 data sets resulted in acceptable discrimination. Calibration was inadequate only in one data set that differed substantially from the other data sets in case-mix. Adjusting the intercept for this population again improved calibration. Conclusions The discriminative performance of the model seems robust for differences between settings. To improve calibration, we recommend adjusting the intercept when applying the model in settings where different mortality rates are expected. An impact study may evaluate the usefulness of the two prediction models for treated and untreated residents and whether it supports decision-making in clinical practice

Predicting Mortality in Patients Treated Differently: Updating and External Validation of a Prediction Model for Nursing Home Residents with Dementia and Lower Respiratory Infections

Objective To evaluate whether a model that was previously developed to predict 14-day mortality for nursing home residents with dementia and lower respiratory tract infection who received antibiotics could be applied to residents who were not treated with antibiotics. Specifically, in this same data set, to update the model using recalibration methods; and subsequently examine the historical, geographical, methodological and spectrum transportability through external validation of the updated model. Design 1 cohort study was used to develop the prediction model, and 4 cohort studies from 2 countries were used for the external validation of the model. Setting Nursing homes in the Netherlands and the USA. Participants 157 untreated residents were included in the development of the model; 239 untreated residents were included in the external validation cohorts. Outcome Model performance was evaluated by assessing discrimination: area under the receiver operating characteristic curves; and calibration: Hosmer and Lemeshow goodness-of-fit statistics and calibration graphs. Further, reclassification tables allowed for a comparison of patient classifications between models. Results The original prediction model applied to the untreated residents, who were sicker, showed excellent discrimination but poor calibration, underestimating mortality. Adjusting the intercept improved calibration. Recalibrating the slope did not substantially improve the performance of the model. Applying the updated model to the other 4 data sets resulted in acceptable discrimination. Calibration was inadequate only in one data set that differed substantially from the other data sets in case-mix. Adjusting the intercept for this population again improved calibration. Conclusions The discriminative performance of the model seems robust for differences between settings. To improve calibration, we recommend adjusting the intercept when applying the model in settings where different mortality rates are expected. An impact study may evaluate the usefulness of the two prediction models for treated and untreated residents and whether it supports decision-making in clinical practice

The association between psychosocial stress and mortality is mediated by lifestyle and chronic diseases: The Hoorn Study

Psychosocial stress is associated with chronic disease. We evaluated whether in the general population the number of stressful life events is associated with risk of mortality and whether this association is mediated by behavioral factors and morbidities. We conducted this study in the Hoorn cohort; a population-based cohort study among older men and women. Our main variable of interest was the number of stressful life events experienced during the previous 5 years, which were assessed by questionnaire. We calculated Cox proportional hazard ratios (HRs) for all-cause and cause-specific mortality during follow-up for those who experienced stressful life events compared to those who did not. We included 2385 participants (46% male; 62 +/- 7 years). During 20 years of follow-up 834 (35%) participants died, of whom 239 (28.6%) died of cardiovascular disease. Compared to the group with no stressful life events, the age, sex and socioeconomic status adjusted HRs (with 95% confidence intervals) for all-cause mortality, for the groups who had 1 event, 2 events, 3 events and &gt;= 4 events were 0.89 (0.72-1.09), 1.01 (0.81-1.24), 1.29 (1.00-1.66) and 1.44 (1.08-1.92), respectively. Similar results were observed for cardiovascular mortality. Mediation analysis showed that smoking, prevalent type 2 diabetes and cardiovascular disease were statistically significant mediators of the association between the number of stressful life events and mortality. Having 3 or more stressful life events is associated with a significantly increased risk for mortality in an elderly population-based cohort. This association is mediated by smoking, type 2 diabetes and cardiovascular disease