Long-term effects of weight-reducing drugs in hypertensive patients

Siebenhofer, Andrea; Horvath, Karl; Jeitler, Klaus; Berghold, Andrea; Stich, Anne Kathrin; Matyas, Eva; Pignitter, Nicole; Siering, Ulrich

doi:10.1002/14651858.cd007654

Cited by 13 publications

(13 citation statements)

References 6 publications

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“…In one of these meta-analyses, the beneficial effect of orlistat on blood pressure was not seen in the 3three studies examining only patients with diabetes [37]. In another more recent meta-analysis examining 31323,132 people with hypertension from 4four randomized, double-blinded, placebo-controlled studies, participants using orlistat had a placebo-subtracted reductions in weight of 3 [39].…”

Section: Blood Pressurepressurementioning

confidence: 99%

Benefit-Risk Assessment of Orlistat in the Treatment of Obesity

Sumithran

Proietto

2014

Drug Saf

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Orlistat, an inhibitor of intestinal lipase, has been available for the treatment of obesity for nearly two decades. In conjunction with a hypocaloric diet, orlistat treatment results in a placebo-subtracted reduction in body weight of around 3 kg at 1 year, and increases the likelihood of achieving clinically significant (≥5%) weight loss by around 20%. Orlistat-induced weight loss also confers modest improvements in systolic and diastolic blood pressure, low-density lipoprotein (LDL) cholesterol, glycemic parameters, and progression to diabetes in people with impaired glucose tolerance. Overall, it has a good safety profile, and serious adverse events (including reports of severe kidney and liver injury) are rare. However, a high rate of gastrointestinal side effects limits adherence to treatment.

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Section: Blood Pressurepressurementioning

confidence: 99%

Benefit-Risk Assessment of Orlistat in the Treatment of Obesity

Sumithran

Proietto

2014

Drug Saf

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“…The most commonly experienced side effects of orlistat are gastrointestinal and include diarrhoea, flatulence, bloating, abdominal pain and dyspepsia [5,23,24]. In addition, long-term use of orlistat can result in a deficiency of the fat-soluble vitamins; adequate vitamin supplementation may therefore be needed for patients on orlistat [9].…”

Section: Orlistatmentioning

confidence: 99%

Combination of very low energy diets and pharmacotherapy in the treatment of obesity: meta‐analysis of published data

Koutroumanidou

Pagonopoulou

2014

Diabetes Metabolism Res

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Obesity has reached epidemic proportions globally, with more than 1 billion adults overweight - at least 300 million of them clinically obese - and is a major contributor to the global burden of chronic disease (heart disease and diabetes) and disability. The aim of the study was to perform a systematic review and meta-analysis of published data on the combination of very low energy diets also known as very low calorie diets and pharmacotherapy for its effectiveness in the treatment of obesity. A MEDLINE (Pubmed) search from 1970 to 2009 using multiple combinations of the relevant terms was carried out; the matching articles were also searched for additional references. Meta-analysis tools were used to summarize results. Only randomized controlled trials that compared pharmacotherapy with placebo after a very low energy diet period were selected, and six articles were finally considered to be appropriate for evaluation. The combination of very low energy diet and pharmacotherapy was found to be effective for people with obesity in clinical trials. The net effect of 6.1 kg placebo subtracted weight loss after 1 year represents a clinically meaningful result that is comparable with the effect of drugs given at the start of a weight loss programme. The present meta-analysis contributes to the understanding that combination therapies are expected to achieve greater weight loss than monotherapy; grasping this understanding, researcher has introduced newer anti-obesity pharmacological approaches have embraced combination therapies.

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“…Orlistat (tetrahydrolipstatin) is a pancreatic and gastric lipase inhibitor whose primary effect is to reduce the absorption of fat and therefore calories. Long-term use is also associated with reductions in blood pressure (6). It is one of few pharmacologic treatment options available to help patients with type 2 diabetes reduce body weight and improve glycaemic control (7).…”

Section: Introductionmentioning

confidence: 99%

Effect of orlistat on glycaemic control in overweight and obese patients with type 2 diabetes mellitus: a systematic review and meta‐analysis of randomized controlled trials

et al. 2015

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Abstract-We study a novel solution to executing aggregation (and specifically COUNT) queries over large-scale data. The proposed solution is generally applicable, in the sense that it can be deployed in environments in which data owners may or may not restrict access to their data and allow only 'aggregation operators' to be executed over their data. For this, it is based on predictive analytics, driven by queries and their results. We propose a machine learning (ML) framework for the task (which can be adapted for different aggregates as well). We focus on the widely used set-cardinality (i.e., COUNT) aggregation operator, as it is a fundamental operator for both internal data system optimisations and for aggregation-query analytics. We contribute a novel, query-driven ML model whose goals are to: (i) learn the query space (access patterns), (ii) associate (complex) aggregation queries with the cardinality of their results, (iii) define query similarity and use it to predict the cardinality of the answer set of an ad-hoc incoming query. Our ML model incorporates incremental learning algorithms for ensuring high prediction accuracy even when both the querying patterns and the underlying data change. The significance of contribution lies in that it (i) is the only query-driven solution applicable over general environments which include restrictedaccess data, (ii) offers incremental learning adjusted for arriving ad-hoc queries, which is well suited for big data analytics, and (iii) offers a performance (in terms of prediction accuracy and time, and memory requirements) that is superior to datacentric approaches. We provide a comprehensive performance evaluation of our model, evaluating its sensitivity and comparative advantages versus acclaimed data-centric methods (self-tuning histograms, sampling, and multidimensional histograms).

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Long-term effects of weight-reducing drugs in hypertensive patients

Cited by 13 publications

References 6 publications

Benefit-Risk Assessment of Orlistat in the Treatment of Obesity

Benefit-Risk Assessment of Orlistat in the Treatment of Obesity

Combination of very low energy diets and pharmacotherapy in the treatment of obesity: meta‐analysis of published data

Effect of orlistat on glycaemic control in overweight and obese patients with type 2 diabetes mellitus: a systematic review and meta‐analysis of randomized controlled trials

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