Early evaluation of patient risk for substantial weight gain during olanzapine treatment for schizophrenia, schizophreniform, or schizoaffective disorder

BACKGROUND: To make well informed treatment decisions for their patients, clinicians need credible information about potential risk for substantial weight gain. We therefore conducted a post-hoc analysis of clinical trial data, examining early weight gain as a predictor of later substantial weight gain. METHODS: Data from 669 (Study 1) and 102 (Study 2) olanzapine-treated patients diagnosed with schizophrenia, schizophreniform, or schizoaffective disorder were analyzed to identify and validate weight gain cut-offs at Weeks 1–4 that were predictive of substantial weight gain (defined as an increase of ≥ 5, 7, 10 kg or 7% of baseline weight) after approximately 30 weeks of treatment. Baseline characteristics alone, baseline characteristics plus weight change from baseline to Weeks 1, 2, 3 or 4, and weight change from baseline to Weeks 1, 2, 3, or 4 alone were evaluated as predictors of substantial weight gain. Similar analyses were performed to determine BMI increase cut-offs at Weeks 1–4 of treatment that were predictive of substantial increase in BMI (1, 2 or 3 kg/m(2 )increase from baseline). RESULTS: At Weeks 1 and 2, predictions based on early weight gain plus baseline characteristics were more robust than those based on early weight gain alone. However, by Weeks 3 and 4, there was little difference between the operating characteristics associated with these two sets of predictors. The positive predictive values ranged from 30.1% to 73.5%, while the negative predictive values ranged from 58.1% to 89.0%. Predictions based on early BMI increase plus baseline characteristics were not uniformly more robust at any time compared to those based on early BMI increase alone. The positive predictive values ranged from 38.3% to 83.5%, while negative predictive values ranged from 42.1% to 84.7%. For analyses of both early weight gain and early BMI increase, results for the validation dataset were similar to those observed in the primary dataset. CONCLUSION: Results from these analyses can be used by clinicians to evaluate risk of substantial weight gain or BMI increase for individual patients. For instance, negative predictive values based on data from these studies suggest approximately 88% of patients who gain less than 2 kg by Week 3 will gain less than 10 kg after 26–34 weeks of olanzapine treatment. Analysis of changes in BMI suggests that approximately 84% of patients who gain less than .64 kg/m(2 )in BMI by Week 3 will gain less than 3 kg/m(2 )in BMI after 26–34 weeks of olanzapine treatment. Further research in larger patient populations for longer periods is necessary to confirm these results

Early evaluation of patient risk for substantial weight gain during olanzapine treatment for schizophrenia, schizophreniform, or schizoaffective disorder

Abstract Background To make well informed treatment decisions for their patients, clinicians need credible information about potential risk for substantial weight gain. We therefore conducted a post-hoc analysis of clinical trial data, examining early weight gain as a predictor of later substantial weight gain. Methods Data from 669 (Study 1) and 102 (Study 2) olanzapine-treated patients diagnosed with schizophrenia, schizophreniform, or schizoaffective disorder were analyzed to identify and validate weight gain cut-offs at Weeks 1–4 that were predictive of substantial weight gain (defined as an increase of ≥ 5, 7, 10 kg or 7% of baseline weight) after approximately 30 weeks of treatment. Baseline characteristics alone, baseline characteristics plus weight change from baseline to Weeks 1, 2, 3 or 4, and weight change from baseline to Weeks 1, 2, 3, or 4 alone were evaluated as predictors of substantial weight gain. Similar analyses were performed to determine BMI increase cut-offs at Weeks 1–4 of treatment that were predictive of substantial increase in BMI (1, 2 or 3 kg/m2 increase from baseline). Results At Weeks 1 and 2, predictions based on early weight gain plus baseline characteristics were more robust than those based on early weight gain alone. However, by Weeks 3 and 4, there was little difference between the operating characteristics associated with these two sets of predictors. The positive predictive values ranged from 30.1% to 73.5%, while the negative predictive values ranged from 58.1% to 89.0%. Predictions based on early BMI increase plus baseline characteristics were not uniformly more robust at any time compared to those based on early BMI increase alone. The positive predictive values ranged from 38.3% to 83.5%, while negative predictive values ranged from 42.1% to 84.7%. For analyses of both early weight gain and early BMI increase, results for the validation dataset were similar to those observed in the primary dataset. Conclusion Results from these analyses can be used by clinicians to evaluate risk of substantial weight gain or BMI increase for individual patients. For instance, negative predictive values based on data from these studies suggest approximately 88% of patients who gain less than 2 kg by Week 3 will gain less than 10 kg after 26–34 weeks of olanzapine treatment. Analysis of changes in BMI suggests that approximately 84% of patients who gain less than .64 kg/m2 in BMI by Week 3 will gain less than 3 kg/m2 in BMI after 26–34 weeks of olanzapine treatment. Further research in larger patient populations for longer periods is necessary to confirm these results.</p

Stable warfarin dose prediction in sub-Saharan African patients: A machine-learning approach and external validation of a clinical dose-initiation algorithm

Warfarin remains the most widely prescribed oral anticoagulant in sub‐Saharan Africa. However, because of its narrow therapeutic index, dosing can be challenging. We have therefore (a) evaluated and compared the performance of 21 machine‐learning techniques in predicting stable warfarin dose in sub‐Saharan Black‐African patients and (b) externally validated a previously developed Warfarin Anticoagulation in Patients in Sub‐Saharan Africa (War‐PATH) clinical dose–initiation algorithm. The development cohort included 364 patients recruited from eight outpatient clinics and hospital departments in Uganda and South Africa (June 2018–July 2019). Validation was conducted using an external validation cohort (270 patients recruited from August 2019 to March 2020 in 12 outpatient clinics and hospital departments). Based on the mean absolute error (MAE; mean of absolute differences between the actual and predicted doses), random forest regression (12.07 mg/week; 95% confidence interval [CI], 10.39–13.76) was the best performing machine‐learning technique in the external validation cohort, whereas the worst performing technique was model trees (17.59 mg/week; 95% CI, 15.75–19.43). By comparison, the simple, commonly used regression technique (ordinary least squares) performed similarly to more complex supervised machine‐learning techniques and achieved an MAE of 13.01 mg/week (95% CI, 11.45–14.58). In summary, we have demonstrated that simpler regression techniques perform similarly to more complex supervised machine‐learning techniques. We have also externally validated our previously developed clinical dose–initiation algorithm, which is being prospectively tested for clinical utility

A genome-wide association study of plasma concentrations of warfarin enantiomers and metabolites in sub-Saharan black-African patients.

Diversity in pharmacogenomic studies is poor, especially in relation to the inclusion of black African patients. Lack of funding and difficulties in recruitment, together with the requirement for large sample sizes because of the extensive genetic diversity in Africa, are amongst the factors which have hampered pharmacogenomic studies in Africa. Warfarin is widely used in sub-Saharan Africa, but as in other populations, dosing is highly variable due to genetic and non-genetic factors. In order to identify genetic factors determining warfarin response variability, we have conducted a genome-wide association study (GWAS) of plasma concentrations of warfarin enantiomers/metabolites in sub-Saharan black-Africans. This overcomes the issue of non-adherence and may have greater sensitivity at genome-wide level, to identify pharmacokinetic gene variants than focusing on mean weekly dose, the usual end-point used in previous studies. Participants recruited at 12 outpatient sites in Uganda and South Africa on stable warfarin dose were genotyped using the Illumina Infinium H3Africa Consortium Array v2. Imputation was conducted using the 1,000 Genomes Project phase III reference panel. Warfarin/metabolite plasma concentrations were determined by high-performance liquid chromatography with tandem mass spectrometry. Multivariable linear regression was undertaken, with adjustment made for five non-genetic covariates and ten principal components of genetic ancestry. After quality control procedures, 548 participants and 17,268,054 SNPs were retained. CYP2C9*8, CYP2C9*9, CYP2C9*11, and the CYP2C cluster SNP rs12777823 passed the Bonferroni-adjusted replication significance threshold (p CYP2C9*8, passed the Bonferroni-adjusted genome-wide significance threshold (p CYP2C19 intron variant, p = 1.55E-17). Approximately 69% of these SNPs were in linkage disequilibrium (r 2 > 0.8) with CYP2C9*8 (n = 216) and rs12777823 (n = 8). Using a pharmacokinetic approach, we have shown that variants other than CYP2C9*2 and CYP2C9*3 are more important in sub-Saharan black-Africans, mainly due to the allele frequencies. In exploratory work, we conducted the first warfarin pharmacokinetics-related GWAS in sub-Saharan Africans and identified novel SNPs that will require external replication and functional characterization before they can be considered for inclusion in warfarin dosing algorithms

DataSheet2_A genome-wide association study of plasma concentrations of warfarin enantiomers and metabolites in sub-Saharan black-African patients.XLSX

Diversity in pharmacogenomic studies is poor, especially in relation to the inclusion of black African patients. Lack of funding and difficulties in recruitment, together with the requirement for large sample sizes because of the extensive genetic diversity in Africa, are amongst the factors which have hampered pharmacogenomic studies in Africa. Warfarin is widely used in sub-Saharan Africa, but as in other populations, dosing is highly variable due to genetic and non-genetic factors. In order to identify genetic factors determining warfarin response variability, we have conducted a genome-wide association study (GWAS) of plasma concentrations of warfarin enantiomers/metabolites in sub-Saharan black-Africans. This overcomes the issue of non-adherence and may have greater sensitivity at genome-wide level, to identify pharmacokinetic gene variants than focusing on mean weekly dose, the usual end-point used in previous studies. Participants recruited at 12 outpatient sites in Uganda and South Africa on stable warfarin dose were genotyped using the Illumina Infinium H3Africa Consortium Array v2. Imputation was conducted using the 1,000 Genomes Project phase III reference panel. Warfarin/metabolite plasma concentrations were determined by high-performance liquid chromatography with tandem mass spectrometry. Multivariable linear regression was undertaken, with adjustment made for five non-genetic covariates and ten principal components of genetic ancestry. After quality control procedures, 548 participants and 17,268,054 SNPs were retained. CYP2C9*8, CYP2C9*9, CYP2C9*11, and the CYP2C cluster SNP rs12777823 passed the Bonferroni-adjusted replication significance threshold (p 2 > 0.8) with CYP2C9*8 (n = 216) and rs12777823 (n = 8). Using a pharmacokinetic approach, we have shown that variants other than CYP2C9*2 and CYP2C9*3 are more important in sub-Saharan black-Africans, mainly due to the allele frequencies. In exploratory work, we conducted the first warfarin pharmacokinetics-related GWAS in sub-Saharan Africans and identified novel SNPs that will require external replication and functional characterization before they can be considered for inclusion in warfarin dosing algorithms.</p