Acetyl-L-carnitine for the treatment of diabetic polyneuropathy

  • Protocol
  • Intervention



This is the protocol for a review and there is no abstract. The objectives are as follows:

To evaluate the effectiveness and safety of ALC for the treatment of diabetic polyneuropathy.


Description of the condition

Diabetic polyneuropathy (DPN) is one of the most common chronic complications of diabetes mellitus (DM), affecting 50% of all individuals with diabetes (Tesfaye 2010). An internationally accepted definition of DPN for clinical practice is “the presence of symptoms and/or signs of peripheral nerve dysfunction in people with diabetes after the exclusion of other causes" (Boulton 2005).

DPN is frequently asymptomatic but it may be clinically evident through a set of positive and negative symptoms. The positive symptoms are often painful and the negative are abnormalities associated with lack of sensation or, less commonly, with weakness. Chronic neuropathic pain, depression, balance disorders, foot ulcerations, Charcot arthropathy, osteomyelitis, and amputations are some examples of complications associated with progressively advanced stages of DPN (Callaghan 2012). In DPN, pain is typically distal, symmetrical, and worsens at night. The patient usually describes the sensation as tingling, a deep ache, sharp shooting or burning. On examination it is common to find hyperalgesia (abnormal sensitivity to painful stimuli) and allodynia (heightened sensitivity to non-noxious stimuli).

The combination of neuropathic symptoms, signs, and abnormal electrical diagnostic studies provides researchers with the most accurate diagnosis of polyneuropathy (England 2005; Tesfaye 2010). However, in clinical practice, the diagnosis of DPN can be made after a careful clinical examination with at least two neurological tests. For example, the combination of temperature and vibratory tests (on neurological examination) provides 87% sensitivity in detecting DPN (Boulton 2005). The most important differential diagnoses in the assessment of DPN include spinal stenosis, hypothyroidism, vitamin B12 and thiamine deficiency, and other nutritional polyneuropathies such as alcohol-related neuropathy. Furthermore, polyneuropathy may be associated with uraemia, cancer, peripheral arterial disease, hepatitis, neurotoxic drugs, and toxins. Additionally, rarer possibilities are human immunodeficiency disease, vasculitis, connective tissue diseases, amyloidosis, and inherited neuropathies, such as Charcot-Marie-Tooth disease.

Neuropathic pain is frequently encountered in diabetics with and without diagnosed neuropathy, but its prevalence is difficult to ascertain, as definitions vary enormously among studies. It is thought that between 16% and 24% of people with DPN may experience chronic neuropathic pain (Boulton, 2010). In clinical trials, the severity of pain is evaluated through pain scales (visual analogue scale (VAS) and 11-point Likert scale) and outcomes must be evaluated through validated instruments (for example, Brief Pain Inventory, McGill Pain Questionnaire, and Neuro-Qol) (Cruccu 2004). The usual criteria for including a patient in such trials are the presence of DPN associated with neuropathic pain lasting for six months or longer, and a mean weekly pain on a zero to 10 VAS of between four and 10. The usual reasons to exclude a patient from a painful DPN trial are proximal neuropathies, pain of central origin, non-neuropathic pain, and pain that is not DPN-related.

A long period of non-controlled hyperglycaemia, metabolic imbalances, such as oxidative stress, increased polyol flux, accumulation of advanced glycation end-products, and dyslipidaemia (increase in low density lipoprotein (LDL) and triglycerides) are the main factors associated with DPN development. However, total hyperglycaemic exposure seems to be the most important factor associated with DPN (Tesfaye 2010). The elucidation of metabolic disruptions related to hyperglycaemia remains the foremost target for research, with the aim of reversing or minimising these homeostatic imbalances and eventually reducing complications and negative impact on quality of life.

Apart from tight blood glucose control, no treatments have shown any capacity to arrest DPN progression. In addition, the pharmacological treatment of painful DPN remains a challenge for physicians and the ability of the individual to tolerate treatment remains a major consideration in any treatment decision (Ziegler 2009). A multifaceted treatment approach to chronic neuropathic pain in DPN is reasonable, but results have been modest so far. Currently, there are many treatments for painful DPN, but quite a few have adverse effects that limit their utility (Bril 2011), and few papers have studied the effects of treatment on function and quality of life.

Description of the intervention

Acetyl-L-carnitine (ALC) is an ester of L-carnitine, which is a naturally occurring amino acid. ALC is produced in the kidneys, central nervous system (CNS) and liver via the action of ALC-transferase and is stored in skeletal muscle. ALC plays an essential role in the transfer of long-chain fatty acids into the mitochondria for β-oxidation (Sima 2007). ALC supports cell metabolism when there is hypoxia, for example during reduced circulation, or due to genetic metabolic defects. ALC binds organic acids and fragmented free fatty acids in order to expel them from the cell, and to prevent the harmful effects that fatty acid fragments can have on cell and on tissue structures.

In addition, ALC is able to reduce tumour necrosis factor alpha (TNFα) concentration and has an antioxidant effect on mitochondrial DNA, whilst stimulating mitochondrial DNA synthesis. Therefore, ALC not only assists in the transportation of long-chain fatty acids through the inner mitochondrial membrane for β-oxidation, but also helps energy availability and prevents toxic accumulation of long-chain fatty acids (Williamson 1992).

Administration of ALC can be oral, intravenous (IV) or intramuscular (IM). Oral doses range from 1.5 g to 3.0 g by day, in divided doses. IV and IM doses range from 1.0 g to 2.0 g daily.

How the intervention might work

Recent clinical trials, usually in groups of patients with an advanced stage of DPN, have shown disappointing outcomes from experimental treatments based on vitamin supplementation, aldose reductase inhibitors and protein kinase C inhibitors. In these studies, each different drug has targeted a single underlying pathogenetic factor. By contrast, substitution of ALC in DPN targets several mechanisms and could be a more advantageous therapy (Sima 2007).

In experimental as well as human diabetic neuropathy, ALC is depleted in peripheral nerves (Scarpini 1996). Replenishment of ALC enhances regional blood flow, increases myo-inositol and free carnitine levels, and reduces malonyl dialdehyde (that is, reduces lipid peroxidation). Another mechanism to explain the effects of ALC in DPN could be via diabetic dyslipidaemia, as oral L-carnitine supplementation in people with diabetes significantly reduces plasma lipoprotein A levels by as much as 20.9% after six months (Derosa 2003). On the other hand, one theoretical role of ALC in peripheral neuropathy is the reduction of pain. ALC may abate the overexcitability of Aδ and C fibres in the dorsal root ganglion and regulate their connections with interneurons in the spinal medulla (Chiechio 2006).

Why it is important to do this review

Polyneuropathy is a frequent, heterogeneous, polymorphic and devastating complication of diabetes mellitus (Rolim 2009) because of the debilitating symptoms it causes and the associated higher risk of other complications, in particular those involving the cardiovascular system and the foot. The World Health Organization (WHO) estimates that "someone somewhere in the world loses a limb due to DM every 30 seconds" (Boulton 2005) .

Currently, it is estimated that there are 366 million adults with diabetes in the world (Vidal-Casariego 2013). Of these, approximately half have some phenotype of neuropathy and 16% to 24% have chronic painful DPN. Of those with chronic painful DPN, up to 39% have never received any kind of treatment for their pain (Daousi 2004). There is therefore an enormous worldwide burden of disease, and an unmet need for prevention and treatment of the diabetes, the neuropathy and the resultant painful symptoms.

Recently, at least four systematic reviews and meta-analyses have been published about ALC or L-carnitine and its effects on different conditions: type 2 diabetes mellitus (Vidal-Casariego 2013), secondary prevention of cardiovascular disease (DiNicolantonio 2013), hepatic encephalopathy (Jiang 2013), and end-stage renal failure under haemodialysis (Chen 2014), with positive results in all except renal failure. ALC has also shown promise as a therapeutic agent for DPN in small numbers of studies but the published data have not been considered as a whole and subjected to meta-analysis. This review will systematically evaluate the evidence on the effectiveness and safety of ALC for this highly prevalent condition.


To evaluate the effectiveness and safety of ALC for the treatment of diabetic polyneuropathy.


Criteria for considering studies for this review

Types of studies

Randomised controlled trials (RCTs) and quasi-RCTs with parallel or cross-over designs. Quasi-RCTs are studies in which participants are allocated to intervention groups based on methods that are not truly random, such as hospital number or date of birth.

Types of participants

The participants will be individuals of any sex and age (adults, including those over 60, and children), and with both forms of diabetes (type 1 or type 2). Furthermore, participants will be diabetics with any severity of DPN (for example, stages from Grade 0 to Grade 5 described by Dyck 1988) and with any three definitions of minimal criteria for typical DPN recommended by the Toronto Consensus (Tesfaye 2010), that is, probable, confirmed or subclinical DPN. The participants with only symptoms or signs of DPN (that is, with 'possible DPN') will be considered as providing a lower quality of evidence and could be excluded at a later date from this review.

Types of interventions

The intervention will be ALC compared with placebo, other treatment or no intervention. Where additional treatments are given these should be matched equally in the intervention and control groups.

Types of outcome measures

Primary outcomes

Pain (using a validated scoring system such as a VAS or numerical rating scale) (Cruccu 2004). The pain outcome will be the proportion of participants with a 30% or 50% decrease in pain, as well as the magnitude of that pain on a VAS or Likert scale.

Secondary outcomes
  1. Functional impairment score: the neuropathy impairment score (NIS) (Dyck 1988) or the neuropathy disability score (NDS) (or one of the modified NDS scores) in the lower extremities (Young 1993). Additionally, we will consider these functional disability outcomes as continuous variables (for example, zero to 10 for the modified NDS).

  2. Measures of impairment of sensation: quantitative sensory testing (vibration perception threshold and thermal threshold) with an appropriate time of assessment (at least six months after treatment).

  3. Quality of life using a validated scoring system such as the Short Form-36 Health Survey (SF-36) or the NeuroQol (Vileikyte L 2003).

  4. Neurophysiological measures: changes in sural and peroneal nerve conduction velocities (NCV) after an appropriate time of therapy (one year or more). Similarly, changes in amplitude of either the sensory nerve action potential (SNAP) or the compound muscle action potential (CMAP) from the ulnar, peroneal, and tibial nerves.

  5. Adverse events: any adverse event, those adverse events requiring withdrawal, and serious adverse events (those which are fatal, life-threatening, or which require prolonged hospitalisation).

  6. Validated screening questionnaires about symptom quality and severity: NSS (neuropathy symptom score) or MNSI (Michigan Neuropathy Screening Instrument). These instruments will be used for continuous variables (e.g. from zero to nine for NSS) (Young 1993).

  7. Sural nerve biopsy parameters.

All outcomes will be assessed before treatment and at six months (at minimum) after treatment and, when possible, at a year or even two or more years after treatment.

These outcomes are not eligibility criteria for the review but are the outcomes of interest in whichever studies are included.

Outcomes for inclusion in a 'Summary of findings' table

We will create a 'Summary of findings' table using the following outcomes.

  • Number of patients with 30% or greater reduction in pain assessed by a validated scoring system, such as a VAS or Likert scale, at six months or more.

  • Change in a functional impairment score (NIS or NDS) at six months or more.

  • Change in measures of impairment from quantitative sensory testing - vibration perception threshold at six months or more.

  • Change in measures of impairment from quantitative sensory testing - thermal threshold at six months or more.

  • Change in data from validated screening questionnaires about symptom quality and severity (NSS, MNSI) at six months or more.

  • Adverse events: any adverse event, those requiring withdrawal, and serious adverse events.

We will use the five GRADE considerations (study limitations, consistency of effect, imprecision, indirectness and publication bias) to assess the quality of a body of evidence (studies that contribute data for the prespecified outcomes). We will use methods and recommendations described in Chapters 11 and 12 of the Cochrane Handbook for Systematic Reviews of Interventions (Schünemann 2011a; Schünemann 2011b) using GRADEpro software. We will justify all decisions to down- or up-grade the quality of studies using footnotes and we will make comments to aid reader's understanding of the review where necessary.

Search methods for identification of studies

Electronic searches

We will search the Cochrane Neuromuscular Disease Group Specialized Register, The Cochrane Central Register of Controlled Trials (CENTRAL) (Current Issue inThe Cochrane Library), MEDLINE (1966 to present) and EMBASE (1980 present) and LILACs (1982 to present). The search strategy will be composed of the MeSH terms 'acetylcarnitine' and 'diabetic neuropathies'. We will search with both subject headings and free-text words The detailed search strategies are in the appendices: MEDLINE (Appendix 1); EMBASE Appendix 2.

We will also search trial registries, including ( and the World Health Organization International Clinical Trials Registry Platform (ICTRP) ( portal for ongoing trials.

We will search the DARE (Database of Abstracts and Reviews of Effects) and HTA (Health Technology Assessments) databases to identify reviews and assessments to give context to our review. We will search NHS EED (NHS Economic Evaluation Database) for cost information (for inclusion in the Discussion).

We will impose no restriction on language of publication.

Searching other resources

We will check all references in the identified trials and contact trial authors to identify any additional published or unpublished data. We will check references in review articles.

We will search the websites of relevant manufacturers for trial information.

We will search for errata or retractions from included studies published in full-text on PubMed ( and report the date this was done within the review.

Data collection and analysis

Selection of studies

Two review authors (LCR and SAD) will independently screen the trials identified by the literature search for inclusion. We will retrieve the full-text study reports/publication.The same two review authors (LCR and SAD) will independently screen the full text and identify studies for inclusion, and identify and record reasons for exclusion of the ineligible studies. We will consult a third author (EMKS) if there is any disagreement arises (at this or at any other stage listed below); we will not include data from the trials in question until we reach a consensus. We will identify and exclude duplicates and collate multiple reports of the same study so that each study rather than each report is the unit of interest in the review. We will record the selection process in sufficient detail to complete a PRISMA flow diagram and 'Characteristics of excluded studies' table.

Data extraction and management

Two review authors (LCR and SAD) will independently extract data. They will resolve discrepancies in the results by discussion. We will use a standard form to extract the following information.

  1. Methods: study design, total duration of study, details of any 'run in' period, number of study centres and location, study setting, withdrawals, and date of study.

  2. Participants: number, mean age, age range, gender, severity of condition, diagnostic criteria, baseline characteristics, inclusion criteria, and exclusion criteria.

  3. Interventions: intervention, comparison, concomitant medications, and excluded medications.

  4. Outcomes: primary and secondary outcomes specified and collected, and time points reported, as described above.

  5. Notes: funding for trial, and notable conflicts of interest of trial authors.

  6. Outcome data.

We will collect continuous and dichotomous data to reflect changes recorded by the primary investigators. If the paper does not report outcome data in a usable way, we will note this in the 'Characteristics of included studies' table. One review author (LCR) will transfer data into Review Manager (RevMan 2012). A second author (WRK) will check the outcome data entries. A third review author (EMKS) will spot-check study characteristics for accuracy against the trial report.

When reports require translation, the translator will extract data directly using a data extraction form, or authors will extract data from the translation provided. After data entry into the review, where possible, a review author will check numerical data from translated trials against the study report.

Assessment of risk of bias in included studies

Two review authors will independently assess the risk of bias in the studies that are included in the review, according to the criteria described in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011a).

Where information on risk of bias relates to unpublished data or correspondence with a trialist, we will note this in the 'Risk of bias' table.

When considering treatment effects, we will take into account the risk of bias for the studies that contribute to that outcome.

Assesment of bias in conducting the systematic review

We will conduct the review according to this published protocol and report any deviations from it in the 'Differences between protocol and review' section of the systematic review.

Measures of treatment effect

For dichotomous variables, we will calculate the risk ratio (RR) and 95% confidence intervals (CIs). For continuous outcomes, we will calculate the mean difference (MD) and 95% CIs when studies report their results using the same variables measured with the same units of measure. If continuous data relate to the same variable but are measured with different instruments (different and not interchangeable units of measure), we will pool these data using the standardised mean difference (SMD). Where possible, we will analyse continuous scales together. When it is not possible to perform such a combination, we will dichotomise results to improvement versus no improvement or worsening, and analyse them as risk ratios (RRs). In the event that authors do not make available the necessary information, we will insert any data from primary studies which are not parametric (for example, effects reported as medians, quartiles) or without sufficient statistical information (for example, standard deviations, number of patients) into an 'Additional table'.

Where multiple trial arms are reported in a single trial, we will include only the relevant arms. If two comparisons (e.g. drug A versus placebo and drug B versus placebo) are combined in the same meta-analysis, we will halve the control group to avoid double-counting.

Unit of analysis issues

The unit of analysis is based on the individual participant (unit to be randomised for interventions to be compared), that is the number of observations in the analysis should match the number of individuals randomised. For trials with a cross-over design, we will use only first period data (before participants have crossed over the treatments) (Elbourne 2002).

Dealing with missing data

Irrespective of the type of data, we will report dropout rates in the 'Characteristics of included studies' table and we will use intention-to-treat analysis (Higgins 2011b).

We do not plan to contact trial authors for missing data.

Assessment of heterogeneity

We will qualify inconsistency among the pooled estimates using the I2 = [(Q - df)/Q] x 100% test, where Q is the Chi2 statistic and df its degrees of freedom. This illustrates the percentage of the variability in effect estimates resulting from heterogeneity rather than sampling error (Higgins 2003; Higgins 2011a). The thresholds for the interpretation of I2 will be as follows: 0% to 25% low heterogeneity, 25% to 75% moderate and more than 75% significant heterogeneity (Higgins 2003).

Assessment of reporting biases

We will assess publication bias by drawing a funnel plot (trial effect versus trial size), if a sufficient number of studies (more than 10) are included in the review.

Data synthesis

Qualitative information

We will synthesise qualitative information relative to methods, risk of bias, description of participants and outcomes measures and insert this information in the 'Characteristics of included studies' table. Qualitative (non-randomised) studies will not be included in the analysis.

Quantitative information

If no significant heterogeneity is identified, we will compute pooled estimates of the treatment effect for each outcome under a fixed-effect model. Otherwise, if we identify significant heterogeneity, we will perform a random-effects analysis.

If the review includes more than one comparison, which cannot be included in the same analysis, we will report the results for each comparison separately.

Subgroup analysis and investigation of heterogeneity

If we find significant heterogeneity, we will investigate the possible causes by exploring the impact of risk of bias and the condition of the individuals. If we find sources of heterogeneity, and if there are sufficient data, we will conduct meta-analysis by subgroups (for example, by types of dosage, age of the individuals and disease severity).

Sensitivity analysis

If there are an adequate number of studies, we will perform a sensitivity analysis to explore causes of heterogeneity and the robustness of the results.

We plan to carry out the following sensitivity analyses.

  1. Repeat the analysis excluding unpublished studies (if there were any).

  2. Repeat the analysis excluding studies at high risk of bias, for example studies with participants with only symptoms or signs of DPN (possible DPN criteria as described by Tesfaye 2010) or studies without any useful pre-trial data or without outcomes assessed at a minimum of six months after therapy.

  3. Effect of type of diabetes (insulin-dependent or late onset) on outcomes.

  4. Effect of age, exploring adults and children under 18 years of age separately.

  5. Repeat the analysis excluding other types of study with lower quality evidence:

    1. studies without a changed outcome score after six months' therapy (at minimum) or without any useful pre-trial data (with only the end score);

    2. studies without minimal criteria for typical DPN recommended by the Toronto Consensus (Tesfaye 2010), that is, probable, confirmed or subclinical DPN;

    3. studies with low doses of ALC (< 1.5 g daily for oral doses and < 1.0 g daily for IV or IM doses).

Economics issues

Where data on cost are available we will report them in the 'Discussion'.


Editorial support from the Cochrane Neuromuscular Disease Group is funded by the MRC Centre for Neuromuscular Diseases. The Trials Search Co-ordinator developed the search strategy in collaboration with the review authors.


Appendix 1. MEDLINE via PubMed (OvidSP) search strategy

Database: Ovid MEDLINE(R) <1946 to March Week 3 2013>
Search Strategy:
1 randomized controlled (343749)
2 controlled clinical (85478)
3 randomized.ab. (246632)
4 placebo.ab. (136427)
5 drug therapy.fs. (1590966)
6 randomly.ab. (176808)
7 trial.ab. (253988)
8 groups.ab. (1151170)
9 or/1-8 (2968418)
10 exp animals/ not (3784285)
11 9 not 10 (2522067)
12 exp Diabetes Mellitus/ (291837)
13 diabet$.mp. (404443)
14 12 or 13 (405613)
15 exp Peripheral Nervous System Diseases/ (117446)
16 15 or (neuropath$ or polyneuropath$).mp. (171968)
17 14 and 16 (18755)
18 Diabetic Neuropathies/ (11492)
19 17 or 18 (18755)
20 Acetylcarnitine/ (994)
21 (acetyl carnitine or acetylcarnitine or acetyl l carnitine).tw. (1240)
22 (7)
23 or/20-22 (1428)
24 11 and 19 and 23 (19)

Appendix 2. EMBASE (OvidSP) search strategy

Database: Embase <1980 to 2013 Week 13>
Search Strategy:
1 (36535)
2 double-blind (113825)
3 single-blind (17167)
4 randomized controlled (339521)
5 (random$ or crossover$ or cross over$ or placebo$ or (doubl$ adj blind$) or allocat$).tw,ot. (942309)
6 trial.ti. (142943)
7 or/1-6 (1074052)
8 (animal/ or nonhuman/ or animal experiment/) and human/ (1249164)
9 animal/ or nonanimal/ or animal experiment/ (3371230)
10 9 not 8 (2789523)
11 7 not 10 (985119)
12 limit 11 to embase (769918)
13 exp diabetes mellitus/ (528901)
14 13 or diabet$.tw. (618575)
15 exp peripheral neuropathy/ (46842)
16 (neuropath$ or polyneuropath$ or peripheral nervous system disease$).mp. (200532)
17 15 or 16 (200863)
18 14 and 17 (31909)
19 diabetic neuropathy/ (16727)
20 18 or 19 (31909)
21 acetylcarnitine/ (923)
22 acetly (0)
23 (596)
24 acetyl l (777)
25 (1045)
26 or/21-25 (2204)
27 12 and 20 and 26 (14)
28 remove duplicates from 27 (14)

Contributions of authors

Conceiving the review: Luiz Clemente Rolim and Edina Mariko Koga da Silva were responsible for the conception of this protocol

Designing the review: Luiz Clemente Rolim and Edina Mariko Koga da Silva

Co-ordinating the review: Sérgio Atala Dib

Undertaking manual searches: William Ricardo Komatsu

Screening search results: Luiz Clemente Rolim and William Ricardo Komatsu

Organising retrieval of papers: William Ricardo Komatsu

Screening retrieved papers against inclusion criteria: Luiz Clemente Rolim and William Ricardo Komatsu

Appraising quality of papers: Luiz Clemente Rolim and William Ricardo Komatsu

Abstracting data from papers: Luiz Clemente Rolim and William Ricardo Komatsu

Writing to authors of papers for additional information: Sérgio Atala Dib

Providing additional data about papers: Sérgio Atala Dib and Luiz Clemente Rolim

Obtaining and screening data on unpublished studies: Luiz Clemente Rolim

Data management for the review: Luiz Clemente Rolim and William Ricardo Komatsu

Entering data into Review Manager (RevMan 5.0):Luiz Clemente Rolim and Edina Mariko Koga da Silva

Double entry of data: (data entered by person one:Luiz Clemente Rolim ; data entered by person two: Edina Mariko Koga da Silva)

Interpretation of data: Luiz Clemente Rolim and  Sérgio Atala Dib

Writing the review: Luiz Clemente Rolim

Providing guidance on the review: Marc Abreu

Performing previous work that was the foundation of the present study: None

Guarantor for the review (one author): Luiz Clemente Rolim

Person responsible for reading and checking review before submission: Marc Abreu

Declarations of interest

None known.