Efficacy and safety of acupuncture for painful diabetic neuropathy: a systematic review and meta-analysis

The rising burden of type 2 diabetes is a major concern in healthcare worldwide. This research aimed to analyze the global epidemiology of type 2 diabetes. We analyzed the incidence, prevalence, and burden of suffering of diabetes mellitus based on epidemiological data from the Global Burden of Disease (GBD) current dataset from the Institute of Health Metrics, Seattle. Global and regional trends from 1990 to 2017 of type 2 diabetes for all ages were compiled. Forecast estimates were obtained using the SPSS Time Series Modeler. In 2017, approximately 462 million individuals were affected by type 2 diabetes corresponding to 6.28% of the world’s population (4.4% of those aged 15–49 years, 15% of those aged 50–69, and 22% of those aged 70+), or a prevalence rate of 6059 cases per 100,000. Over 1 million deaths per year can be attributed to diabetes alone, making it the ninth leading cause of mortality. The burden of diabetes mellitus is rising globally, and at a much faster rate in developed regions, such as Western Europe. The gender distribution is equal, and the incidence peaks at around 55 years of age. Global prevalence of type 2 diabetes is projected to increase to 7079 individuals per 100,000 by 2030, reflecting a continued rise across all regions of the world. There are concerning trends of rising prevalence in lower-income countries. Urgent public health and clinical preventive measures are warranted.

Acupuncture has been accepted to effectively treat chronic pain by inserting needles into the specific "acupuncture points" (acupoints) on the patient's body. During the last decades, our understanding of how the brain processes acupuncture analgesia has undergone considerable development. Acupuncture analgesia is manifested only when the intricate feeling (soreness, numbness, heaviness and distension) of acupuncture in patients occurs following acupuncture manipulation. Manual acupuncture (MA) is the insertion of an acupuncture needle into acupoint followed by the twisting of the needle up and down by hand. In MA, all types of afferent fibers (Abeta, Adelta and C) are activated. In electrical acupuncture (EA), a stimulating current via the inserted needle is delivered to acupoints. Electrical current intense enough to excite Abeta- and part of Adelta-fibers can induce an analgesic effect. Acupuncture signals ascend mainly through the spinal ventrolateral funiculus to the brain. Many brain nuclei composing a complicated network are involved in processing acupuncture analgesia, including the nucleus raphe magnus (NRM), periaqueductal grey (PAG), locus coeruleus, arcuate nucleus (Arc), preoptic area, nucleus submedius, habenular nucleus, accumbens nucleus, caudate nucleus, septal area, amygdale, etc. Acupuncture analgesia is essentially a manifestation of integrative processes at different levels in the CNS between afferent impulses from pain regions and impulses from acupoints. In the last decade, profound studies on neural mechanisms underlying acupuncture analgesia predominately focus on cellular and molecular substrate and functional brain imaging and have developed rapidly. Diverse signal molecules contribute to mediating acupuncture analgesia, such as opioid peptides (mu-, delta- and kappa-receptors), glutamate (NMDA and AMPA/KA receptors), 5-hydroxytryptamine, and cholecystokinin octapeptide. Among these, the opioid peptides and their receptors in Arc-PAG-NRM-spinal dorsal horn pathway play a pivotal role in mediating acupuncture analgesia. The release of opioid peptides evoked by electroacupuncture is frequency-dependent. EA at 2 and 100Hz produces release of enkephalin and dynorphin in the spinal cord, respectively. CCK-8 antagonizes acupuncture analgesia. The individual differences of acupuncture analgesia are associated with inherited genetic factors and the density of CCK receptors. The brain regions associated with acupuncture analgesia identified in animal experiments were confirmed and further explored in the human brain by means of functional imaging. EA analgesia is likely associated with its counter-regulation to spinal glial activation. PTX-sesntive Gi/o protein- and MAP kinase-mediated signal pathways as well as the downstream events NF-kappaB, c-fos and c-jun play important roles in EA analgesia.

Background Most meta-analyses in systematic reviews, including Cochrane ones, do not have sufficient statistical power to detect or refute even large intervention effects. This is why a meta-analysis ought to be regarded as an interim analysis on its way towards a required information size. The results of the meta-analyses should relate the total number of randomised participants to the estimated required meta-analytic information size accounting for statistical diversity. When the number of participants and the corresponding number of trials in a meta-analysis are insufficient, the use of the traditional 95% confidence interval or the 5% statistical significance threshold will lead to too many false positive conclusions (type I errors) and too many false negative conclusions (type II errors). Methods We developed a methodology for interpreting meta-analysis results, using generally accepted, valid evidence on how to adjust thresholds for significance in randomised clinical trials when the required sample size has not been reached. Results The Lan-DeMets trial sequential monitoring boundaries in Trial Sequential Analysis offer adjusted confidence intervals and restricted thresholds for statistical significance when the diversity-adjusted required information size and the corresponding number of required trials for the meta-analysis have not been reached. Trial Sequential Analysis provides a frequentistic approach to control both type I and type II errors. We define the required information size and the corresponding number of required trials in a meta-analysis and the diversity (D2) measure of heterogeneity. We explain the reasons for using Trial Sequential Analysis of meta-analysis when the actual information size fails to reach the required information size. We present examples drawn from traditional meta-analyses using unadjusted naïve 95% confidence intervals and 5% thresholds for statistical significance. Spurious conclusions in systematic reviews with traditional meta-analyses can be reduced using Trial Sequential Analysis. Several empirical studies have demonstrated that the Trial Sequential Analysis provides better control of type I errors and of type II errors than the traditional naïve meta-analysis. Conclusions Trial Sequential Analysis represents analysis of meta-analytic data, with transparent assumptions, and better control of type I and type II errors than the traditional meta-analysis using naïve unadjusted confidence intervals.