Antipsychotics, Weight Gain and Metabolic Risk

2020 ◽  
pp. 619-619
Author(s):  
Stephen J. Cooper ◽  
Gavin P. Reynolds
Keyword(s):  
Weight Gain ◽  
Metabolic Risk

scholarly journals Gestational weight gain in normal weight women and offspring cardio-metabolic risk factors at 20 years of age

2014 ◽  
Vol 39 (4) ◽  
pp. 671-676 ◽  
Author(s):  
L Hrolfsdottir ◽  
D Rytter ◽  
S F Olsen ◽  
B H Bech ◽  
E Maslova ◽  
...  
Keyword(s):  
Risk Factors ◽  
Weight Gain ◽  
Gestational Weight Gain ◽  
Normal Weight ◽  
Metabolic Risk Factors ◽  
Metabolic Risk ◽  
Gestational Weight

The weight change impact on metabolic syndrome: a 17-year follow-up study

Open Medicine ◽  
2011 ◽  
Vol 6 (6) ◽  
pp. 788-794 ◽  
Author(s):  
Magdalena Kwaśniewska ◽  
Dorota Kaleta ◽  
Anna Jegier ◽  
Tomasz Kostka ◽  
Elżbieta Dziankowska-Zaborszczyk ◽  
...  
Keyword(s):  
Metabolic Syndrome ◽  
Weight Loss ◽  
Weight Gain ◽  
Weight Change ◽  
Metabolic Risk ◽  
Adjusted Relative Risk ◽  
Prospective Longitudinal ◽  
The Impact

AbstractIntroduction: Data on long-term patterns of weight change in relation to the development of metabolic syndrome (MetS) are scarce. The aim of the study was to evaluate the impact of weight change on the risk of MetS in men. Material and Methods: Prospective longitudinal observation (17.9 ± 8.1 years) of apparently healthy 324 men aged 18–64 years. Metabolic risk was assessed in weight gain (⩾ 2.5 kg), stable weight (> −2.5 kg and < 2.5 kg) and weight loss (⩽ −2.5 kg) groups. Adjusted relative risk (RR) of MetS was analyzed using multivariate logistic regression. Results: The prevalence of MetS over follow-up was 22.5%. There was a strong relationship between weight gain and worsening of MetS components among baseline overweight men. Long-term increase in weight was most strongly related with the risk of abdominal obesity (RR=7.26; 95% CI 2.98–18.98), regardless of baseline body mass index (BMI). Weight loss was protective against most metabolic disorders. Leisure-time physical activity (LTPA) with energy expenditure > 2000 metabolic equivalent/min/week was associated with a significantly lower risk of MetS. Conclusions: Reducing weight among overweight and maintaining stable weight among normal-weight men lower the risk of MetS. High LTPA level may additionally decrease the metabolic risk regardless of BMI.

scholarly journals Cardio-Metabolic Benefits of Walnuts in Type 2 Diabetes Mellitus: A Literature Review

2021 ◽  
pp. 111-124
Author(s):  
Valentine Y Njike ◽  
Genevieve Cecile Kela ◽  
Nisar Khan ◽  
Rockiy Ayettey ◽  
Maxime Tindong ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Risk Factors ◽  
Type 2 Diabetes ◽  
At Risk ◽  
Type 2 Diabetes Mellitus ◽  
Weight Gain ◽  
Glycaemic Control ◽  
Metabolic Risk Factors ◽  
Metabolic Risk

Diabetes and its complications are main causes of morbidity and mortality among adults in the USA. An increase in the number of individuals with diabetes is primarily attributed to changes in dietary patterns including increased consumption of obesogenic foods and beverages. Many individuals who are overweight and obese show signs of insulin resistance and are at increased risk of Type 2 diabetes mellitus (T2DM) and cardiovascular disease. Lifestyle interventions (i.e., physical activity and nutrition) are the cornerstone of T2DM management and prevention. Prior research attests to the health benefits of consuming nuts, which have a substantial amount of mono- and polyunsaturated fatty acids, for individuals at risk for or with T2DM, and walnuts appear to be particularly promising. Walnuts are rich in nutrients, minerals, antioxidants, and vitamins that can contribute to improved cardio-metabolic risk factors in individuals at risk for or with T2DM. This review assesses the cardio-metabolic benefits of walnuts in T2DM. The authors’ review indicates that the reported effects of walnuts on glycaemic control have been inconclusive, with several studies showing association with improved glycaemic control while others show no effect. Despite their high energy density and potential to contribute to weight gain, the authors’ review suggests that walnuts can contribute to satiety without association with weight gain. This review also suggests that walnut consumption has been associated with improved low-density lipoprotein cholesterol levels and endothelial function but has not been associated with blood pressure improvement. Meta-analyses are warranted to quantitatively assess impact of walnut consumption on these cardio-metabolic risk factors in T2DM.

Promotion of Weight Gain in Early Childhood Does Not Increase Metabolic Risk in Adolescents: A 15-Year Follow-Up of a Cluster-Randomized Controlled Trial

2015 ◽  
Vol 145 (12) ◽  
pp. 2749-2755 ◽  
Author(s):  
Iná S Santos ◽  
Alicia Matijasevich ◽  
Maria Cecília F Assunção ◽  
Neiva CJ Valle ◽  
Bernardo L Horta ◽  
...  
Keyword(s):  
Randomized Controlled Trial ◽  
Early Childhood ◽  
Weight Gain ◽  
Controlled Trial ◽  
Cluster Randomized Controlled Trial ◽  
Metabolic Risk ◽  
Randomized Controlled ◽  
Cluster Randomized

scholarly journals The alteration of ­­­­­­metabolic profiling in the second trimester of GDM and high risk pregnancy, and a nomogram for predicting macrosomia

2019 ◽  
Author(s):  
Man Sun ◽  
Baihui Zhao ◽  
Sainan He ◽  
Ruopeng Weng ◽  
Binqiao Wang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Weight Gain ◽  
High Risk ◽  
Metabolic Profiling ◽  
Metabolic Risk Factors ◽  
Metabolic Risk ◽  
Second Trimester ◽  
Risk Pregnancy ◽  
Independent Risk Factors ◽  
Maternal And Newborn

Abstract Objective Dyslipidemia in the second trimester and associated gestational diabetes are increasing worldwide. Carnitine plays a key role in lipid metabolism. We aim to describe metabolic profiling in the second trimester based on carnitine related metabolomics in GDM and high risk pregnancy, and to find the potential risk factors in GDM and candidate metabolites for diagnosing GDM induced macrosomia.Methods We have randomly investigated 450 pregnant women and their neonates in this retrospective study and 56 (12.4%) GDM cases were diagnosed. We used LC-MS/MS performing metabolic profiling about 12 amino acids and 31 acylcarnitines (containing C0) to assess circulating metabolites concentration in different subgroup according maternal and newborn clinical characteristic. We also calculated the correlation coefficient between maternal and newborn. GDM potential metabolic risk factors were screened by PLS-DA. Multivariate regression analyses were used in identifying independent risk factors for GDM and macrosomia. Based on these carnitine-related factors, a nomogram for estimating macrosomia was developed.Results We found 14 AA (Ala, Arg, Met, BCAA, AAA) and AC (C0, C2, C3, C4DC+C5OH, C5, C16, C18, C18:1) were increased in Age > 35 group, BMI ≥ 30, weight gain > 20 kg group, using assistant reproductive technology group, but the level of C0, Gly were decreased. In fetal clinical data, we obtained AA and AC level in fetuses are higher than their mothers and the metabolic trend was similar with maternal result. PLS-DA showed 15 metabolism(C0, LEU+ILE+PRO-OH, Phe, C18, TYR, etc)play main roles in class separation of GDM. Multivariate analysis showed pre-pregnancy BMI, weight gain, LEU+ILE+PRO-OH, TYR, C0/acylcarnitine, C0, C3, C16, C18 are independent risk factors associated with GDM. Finally, we developed a nomogram predicting macrosomia based on carnitine-related metabolic variables.Conclusion Metabolomics was proved as a powerful tool in identifying the metabolic alteration during the second trimester. These metabolic risk factors in GDM may help understanding of the underlying biochemical pathology of GDM and help physician diagnosing macrosomia.

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