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The associations between sugar-sweetened beverage (SSB) and artificially sweetened beverage (ASB) consumption and the risk of metabolic syndrome (MetS) remain controversial. A quantitative assessment of dose–response associations has not been reported. This study aims to assess the associations between the risk of MetS and SSB, ASB, and total sweetened beverage (TSB, the combination of SSB and ASB) consumption by reviewing population-based epidemiological studies.
We searched PubMed, Embase and Web of Science databases prior to 4 November 2019, for relevant studies investigating the SSB–MetS and ASB–MetS associations. A random effects model was used to estimate pooled relative risks (RR) and 95 % CI. Dose–response association was assessed using a restricted cubic splines model.
We identified seventeen articles (twenty-four studies, including 93 095 participants and 20 749 MetS patients).
The pooled RR for the risk of MetS were 1·51 (95 % CI 1·34, 1·69), 1·56 (1·32, 1·83) and 1·44 (1·19, 1·75) in high consumption group of TSB, SSB and ASB, respectively; and 1·20 (1·13, 1·28), 1·19 (1·11, 1·28) and 1·31 (1·05, 1·65) per 250 ml/d increase in TSB, SSB and ASB consumption, respectively. Additionally, we found evidence of non-linear, TSB–MetS and SSB–MetS dose–response associations and a linear ASB–MetS dose–response association.
TSB, SSB and ASB consumption was associated with the risk of MetS. The present findings provide evidence that supports reducing intake of these beverages to lower the TSB-, SSB- and ASB-related risk of MetS.
This paper deals with dynamic dimensional synthesis of the Delta robot using the pressure/transmission angle constraints. Two types of pressure/transmission angles are defined, with which the direct and indirect singularities can be identified in a straightforward manner. Two novel global dynamic metrics are proposed for minimisation, which are associated respectively with the inertial and centrifuge/Coriolis components of the driving torque. Various geometrical and performance constraints are taken into account in terms of workspace/machine volume ratio, pressure/transmission angles, etc. The effects of pressure/transmission angle constraints on the feasible domain of design variables are investigated in depth via an example, and a set of optimised dimensional parameters is obtained for achieving a good kinematic and dynamic performance throughout the entire task workspace.
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