, Volume 53, Issue 5, pp 840–849 | Cite as

Association between glycated haemoglobin and the risk of lower extremity amputation in patients with diabetes mellitus—review and meta-analysis

  • A. I. Adler
  • S. Erqou
  • T. A. S. Lima
  • A. H. N. Robinson



Diabetes increases the risk of lower extremity amputation (LEA). Although epidemiological studies report positive associations between glycaemia and LEA, the magnitude of the risk is not adequately quantified and clinical trials to date have not provided conclusive evidence about glucose lowering and LEA risk. We synthesised the available prospective epidemiological data on the association between glycaemia measured by HbA1c and the risk of LEA in individuals with diabetes.


We searched electronic databases and reference lists of relevant articles. We considered prospective epidemiological studies that had measured HbA1c level and assessed LEA as an outcome among diabetic individuals without acute foot ulcerations or previous history of amputation. Of 2,548 citations identified, we included 14 studies comprising 94,640 participants and 1,227 LEA cases. We abstracted data using standardised forms and obtained data from investigators when required. Data included characteristics of study populations, HbA1c assay methods, outcome and covariates. Study-specific relative risk estimates were pooled using random-effects model meta-analysis; heterogeneity was explored with meta-regression analyses.


The overall RR for LEA was 1.26 (95% CI 1.16–1.36) for each percentage point increase in HbA1c. There was considerable heterogeneity across studies (I 2 76%, 67–86%; p < 0.001), which was not accounted for by recorded study characteristics. The estimated RR was 1.44 (95% CI 1.25–1.65) for type 2 diabetes and 1.18 (95% CI 1.02–1.38) for type 1 diabetes; however, the difference was not statistically significant (p = 0.09). We found no strong evidence for publication bias.


There is a substantial increase in risk of LEA associated with glycaemia in individuals with diabetes. In the absence of conclusive evidence from trials, this paper provides further epidemiological support for glucose-lowering as a strategy to reduce amputation in a population without acute foot ulceration or former amputation; it also provides disease modellers with estimates to assess the overall burden of hyperglycaemia.


Amputation Diabetes Disease modelling Epidemiology HbA1c Health economics Hyperglycaemia Meta-analysis Risk factor Systematic review 



Lower extremity amputation


UK Prospective Diabetes Study


Individuals with diabetes mellitus are at increased risk of macro- and microvascular complications, including, but not limited to, cardiovascular diseases, nephropathy and retinopathy. Observational studies in type 1 and type 2 diabetes have shown that these increased risks are related to the degree of glycaemic control [1, 2]. Findings from randomised trials in diabetes have confirmed that improving glycaemic control lowers the risk of microvascular complications [3, 4, 5]; however, whether it decreases the risk of cardiovascular disease is less clear [3, 6, 7, 8, 9].

Lower extremity amputation (LEA) is a serious complication of diabetes related to both macro- and micro-angiopathic changes [10, 11, 12]. Diabetic individuals have a markedly increased risk of LEA when compared with non-diabetic individuals [13, 14], with potentially grave consequences; thus those with LEAs die earlier on average than those without amputations [15]. Trials of glucose lowering that have assessed its effect on the incidence of diabetic complications have generally reported LEA as part of a composite endpoint. Regardless of this, because of the low incidence of LEA, trials have not had sufficient power to detect an effect [3, 5, 6, 7, 8, 16, 17, 18]. The PROactive and the UK Prospective Diabetes Study (UKPDS), two trials which reported on LEA as a separate endpoint, showed no difference in occurrence of LEA between groups randomised to more or less intensive glucose control (hazard ratio 1.01 [95% CI 0.58 to 1.73] and 0.70 [0.37–1.35], respectively) [3, 16, 17]. Prospective epidemiological studies, on the other hand, have suggested the presence of a graded relationship between level of glycaemia and LEA [2, 19], but individual studies did not have adequate power to estimate the magnitude of this association precisely. Although a meta-analysis of the relationship between glycaemia and cardiovascular disease from 2004 reported a positive association between glycosylated haemoglobin and peripheral vascular disease (including LEA), the estimate was based on only four studies involving fewer than 300 cases [1].

Epidemiological studies have generally used levels of fasting plasma glucose or HbA1c to measure glycaemia. HbA1c provides the better measure, as it reflects levels of blood glucose over several weeks, and is the main method of monitoring glycaemia in diabetes. Characterising the association between HbA1c and LEA, therefore, would help understand the relationship between glycaemia and LEA and, if found to be causal, inform clinical practice by allowing clinicians to estimate the magnitude of reduction in risk that could potentially be achieved by lowering blood glucose. It would provide individuals with diabetes an estimate of the size of this association. It would also provide useful estimates to disease modellers and health economists, who analyse the cost-effectiveness of diabetes-related interventions. We report a systematic review and study-level meta-analysis of prospective epidemiological data on the association between HbA1c and LEA in persons with type 1 or type 2 diabetes.



We systematically searched the electronic databases MEDLINE and EMBASE for studies published between January 1970 and July 2009 using key terms related to glycaemia and amputation. In the MEDLINE search, medical subject heading terms included ‘haemoglobin A, glycated’, ‘amputation’ and ‘diabetes mellitus’; key words in free text included ‘lower extremity’, ‘lower limb’, ‘amputation’, ‘HbA1c’, ‘glycated haemoglobin’ and ‘glycohaemoglobin A’. We supplemented this search by scanning reference lists of relevant articles. We corresponded with investigators of included studies if the published data were not sufficient to calculate relative risks.

Study selection

The search yielded 2,548 articles, which we assessed using titles, abstracts and/or full texts. The inclusion criteria were measurement of HbA1c at baseline and documentation of LEA outcome during follow-up in individuals with diabetes. We took the definition of diabetes in each study as that provided in the publications. To minimise bias from reverse causation arising from the effect of existing lower extremity pathology on levels of blood glucose, we excluded cross-sectional and retrospective case–control studies, and restricted the review to studies with prospective cohort and nested case–control designs that measured HbA1c at least an average of 6 months before occurrence of LEA. We excluded studies conducted in patients with acute foot ulcers, previous amputation or end-stage renal disease. When a study published more than one paper, we included the publication with the longest follow-up or largest sample size. In order to maximise the available information, we retained three studies [20, 21, 22] that combined endpoints such as peripheral vascular disease with amputations, assessing the effect of this inclusion through subgroup analysis. We selected 17 studies for inclusion and corresponded with the authors of five [21, 23, 24, 25, 26], of whom two [21, 23] provided data, enabling us to calculate relative risks for the 14 studies in this review (Fig. 1).
Fig. 1

Study flow diagram

Data abstraction

We abstracted data using standardised forms and obtained information on study design, study year, length of follow-up, average age of participants, percentage of men, whether type 1 or type 2 diabetes, duration of diabetes, method of measuring HbA1c, mean level of HbA1c in controls, values of relative risk for the association between HbA1c and LEA (along with the unit of comparison, e.g. top vs bottom fifths), and any covariates included in regression models (e.g. age and sex). When studies reported more than one HbA1c measurement, we chose the earliest to ensure the longest exposure. We rated a study’s quality using the Newcastle–Ottawa quality assessment scale, a system for rating the quality of non-randomised studies in meta-analyses based on three perspectives: selection, comparability of groups and ascertainment of exposure and/or outcome [27].

Data analyses

Because studies used different comparisons for HbA1c (e.g. top vs bottom fourth, increase of 1 SD), we converted the risk estimates into common metrics before combining them in meta-analysis. We present risk ratios for each one percentage point increase and for the top third vs the bottom third of HbA1c. We converted the risk ratios by assuming an approximately normal distribution of HbA1c and a log-linear relationship between HbA1c and the risk of LEA [28]. To obtain the conversion factors required to transform the relative risks, we determined the distance in SDs between the means of the quantiles using the standard normal curve. Accordingly, the log risk ratio of LEA among individuals in the top third vs the bottom third of HbA1c distribution was calculated as 2.18 times the log risk ratio for a 1 SD difference in HbA1c values or 2.18/2.54 times the log risk ratio for the comparison of the top and bottom fourths etc. We calculated the log ratio of the risk of a one percentage point increase in HbA1c levels similarly. When we could not obtain the SD from a published report, we assumed a SD of 1.8% obtained from pooled studies. To obtain a summary estimate, we combined the estimates of relative risk using a random-effects model meta-analysis [29]. We performed a fixed-effect meta-analysis for comparison.

We assessed heterogeneity between studies using Q and I 2 statistics. The I 2 statistic estimates the percentage of total variation across studies due to a true difference rather than chance [30]. In general, I 2 values greater than 60–70% indicate the presence of substantial heterogeneity. We explored sources of heterogeneity using meta-regression and subgroup analyses. Subgroups included duration of follow-up, diabetes type, level of adjustment for confounders, type of LEA outcome and average HbA1c level, as well as study design, year and quality. Data were insufficient to assess differences between subgroups defined by neuropathy, adiposity or smoking status. We assessed the presence of publication bias by comparing the combined risk estimates from larger- vs smaller-sized studies, and by using funnel plots and the Egger test of bias [31]. Descriptive statistics (e.g. age of participants, duration of follow-up etc.) are presented as ranges or weighted averages for studies that published these details. All analyses were performed using Stata release 9 (Stata, College Station, TX, USA). Statistical tests were two-sided and used a significance level of p < 0.05.

Funding sources were not involved in the design, conduct, analyses or write-up of this study.


Description of studies

Fourteen prospective studies [2, 10, 12, 20, 21, 22, 23, 32, 33, 34, 35, 36, 37, 38] involving 94,640 participants and 1,227 LEA cases were included. Details of study characteristics are provided (Table 1). The studies were North American and European, with the exception of two, which were conducted in Australia [38] and Jordan [37]. Three studies [10, 22, 37] had a nested case–control design, the rest had a cohort design. The proportion of men in the studies ranged between 33% and 98% (weighted average 85%). The average age of the participants by study ranged between 26 and 69 years (weighted average 49 years). Five studies were conducted in patients with type 1 diabetes, three in those with type 2 and the rest in mixed or unclassified diabetic populations. The average duration of diabetes in the studies ranged from 4 to 21 years. The participants were followed for an average of 1 to 14 years. Of the reported HbA1c assay methods, high performance liquid chromatography and micro-column techniques were the commonest. The average HbA1c level across studies was 9.5% in controls and 11.6% in cases.
Table 1

Characteristics of 14 prospective epidemiological studies included in the review of the association between HbA1c levels and the risk of lower extremity amputation

First author [reference]

Study name


Baseline year

Follow-up (years)

Participants (n)

Sex (% men)

Age, years (SD)

Diabetes type

Diabetes duration, years (SD)

Outcome assessed

Cases (n)

HbA1c assay

Average HbA1c in controls (%)

HbA1c SD in controls (%)

Quality scorea

Tseng [32]









Moss [33]







46 (12)


12 (8.5)







Resnick [34]












Lepore [23]







69 (11)

Type 2

21 (10)






Mühlhauser [35]







27.5 (9.5)

Type 1

10.5 (9.5)



Microcolumn, HPLC




Olson [20]







26 (8)

Type 1

18 (7)



Microcolumn, HPLC




Lehto [36]







58 (0.2)

Type 2

8 (0.1)



Affinity chromatography




Jbour [37]







56 (10)

Type 2

9 (7)







Davis [38]







64 (11)

Type 2






Stratton [2]







53 (8)

Type 2


LEA, PVD death






Adler [12]













Watts [10]





67 (10.5)






Roy [21]






28 (11)

Type 1

10 (9)







Coppini [22]















aFor quality assessment, the Newcastle–Ottawa quality assessment scale was used, maximum score 9

bType 1, 49%, type 2, 51%; ctype 1, 7%, type 2, 93%; dtype 1, 37%, type 2, 63%

FDS, Fremantle Diabetes Study; PEDCS, Pittsburgh Epidemiology of Diabetes Complications Study; PVD, peripheral vascular disease; SDFS, Seattle Diabetic Foot Study; SHS, Strong Heart Study; VHS, Large Veteran Health Survey & Diabetes Epidemiology Cohort; WESDR, Wisconsin Epidemiologic Study of Diabetic Retinopathy

Association of HbA1c with LEA

Based on a random-effects model meta-analysis, the combined risk ratio for LEA associated with a one percentage point increase in HbA1c was 1.26 (95% CI 1.16–1.36) (Fig. 2), with significant heterogeneity observed across studies (p < 0.001). The corresponding estimate using a fixed-effect model meta-analysis was 1.20 (95% CI 1.17–1.24). Among studies that reported the type of diabetic population, the estimates appeared stronger for type 2 diabetes (RR 1.44, 1.25–1.65) than for type 1 diabetes (RR 1.18, 1.02–1.38) (Fig. 3), but the difference was not statistically significant (p = 0.09). Comparing individuals in the top vs bottom third of the baseline distribution of HbA1c gave a pooled risk ratio for LEA of 2.51 (95% CI 1.93–3.25) (Fig. 4).
Fig. 2

Plot of RRs of amputation associated with a 1% increase in HbA1c among diabetic individuals in 14 studies. aOverall estimate was calculated using random-effects model meta-analysis. p < 0.001 for heterogeneity, I 2 75% (95% CI 63–84%). +, unadjusted estimates; ++, age- and sex-adjusted estimates only; +++, estimates adjusted for additional risk factors

Fig. 3

Plot of RRs of amputation associated with a 1% increase in HbA1c among diabetic individuals by type of diabetes. a Risk ratios were pooled within subgroups of diabetes type using random-effects model meta-analysis

Fig. 4

Plot of RRs of amputation comparing diabetic individuals in top vs bottom thirds of baseline HbA1c distribution in 14 studies. aOverall estimate was calculated using random-effects model meta-analysis. +, unadjusted estimates; ++, age- and sex-adjusted estimates only; +++, estimates adjusted for additional risk factors

Exploration of heterogeneity

Most of the variation observed was attributable to true heterogeneity rather than sampling error as indicated by an I 2 value of 76% (95% CI 67–86%). This heterogeneity was not explained by the characteristics available for subgroup analysis (Fig. 5). We also evaluated the role of each of absolute HbA1c level, duration of follow-up and duration of diabetes in meta-regression models as continuous variables, but found no significant differences. Sensitivity analyses excluding the single study that did not report a SD or the three nested case–control studies gave results consistent with the main analyses.
Fig. 5

Association between HbA1c and risk of LEA within subgroups defined by various characteristics. Subgroup risk estimates and heterogeneity p values were calculated using random-effects model. The relative risks were not significantly different between studies with higher or lower Newcastle–Ottawa scores (1.21 [95% CI 1.12–1.31] vs 1.37 [95% CI 1.13–1.67], p = 0.43 for heterogeneity). PVD, peripheral vascular disease

To assess the effect of potential publication bias, we plotted each study’s risk ratio against its standard error, which suggested that smaller studies gave more extreme results (Electronic supplementary material [ESM] Fig. 1). However, the Egger test did not reach statistical significance (p = 0.085). Comparison of the pooled estimate from larger studies (greater than the median number of cases) with that of smaller studies yielded no significant difference (Fig. 5).


The current data provide further support for a positive association between the risk of LEA and level of HbA1c. Each one percentage point increase in HbA1c was associated with a 26% increase in risk of LEA, but the increase may have been as large as 36%. The risk was not significantly different for individuals with type 1 or type 2 diabetes, although the point estimate was larger for type 2 diabetes. The relationship did not vary by the study quality or HbA1c concentrations, being similar in patients with moderately or extremely elevated HbA1c levels. Although the average HbA1c level in the present meta-analysis exceeds that of many modern diabetic populations, this suggests that the current findings may be equally true for individuals with different levels of glycaemic control. However, the analyses may not have detected a small difference or a non-linear association between HbA1c and LEA. At 26%, the magnitude of the risk increase was intermediate between that reported for cardiovascular complications (18%, 95% CI 10–26%) [1] and for microvascular complications (37%, 95% CI 33–41%) [2], while acknowledging overlapping confidence intervals and that different studies contributed to the estimates. This may reflect the notion that microvascular and macrovascular disease underlie the pathogenesis of LEA via microcirculatory defects, neuropathy and arterial disease. While many consider LEA to be a late-stage complication of diabetes, we found no differences in risk by duration of disease. We did find significant heterogeneity, which could influence the generalisability of results, but we did not find strong evidence for publication bias.

The relevance of these findings is increased by the fact that the scientific evidence for glycaemia and risk of LEA to date is not sufficient to translate into clinical practice. Data from observational studies in diabetes have documented positive associations of glycaemia with microvascular and macrovascular complications, suggesting a benefit from lowering blood glucose. The results of randomised controlled trials have confirmed this [3, 4] for microvascular complications, while results for macrovascular disease include the possibility of harm [6, 7, 39]. There are limited clinical trial data on the specific effect of glycaemic control on LEA. Trials to date have not been able to demonstrate unequivocally whether improving glycaemic control reduces the risk of LEA. The (DCCT) did not report the effect of glycaemic control on LEA [40] and the UKPDS showed no significant risk reduction associated with randomisation to intensive blood glucose lowering either during the main trial or afterwards [3, 9, 16]. The PROactive trial found no difference in risk of LEA between pioglitazone and placebo groups [17], and in the Kumamoto study no patient in either group had an LEA [5]. Neither the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial [6] nor the Veteran Affairs Diabetes Trial [41] included LEA in the published primary or secondary outcomes. The Rosiglitazone Evaluated for Cardiac Outcomes and Regulation (RECORD) study included LEA in the primary endpoint, but has not as yet reported how frequently it occurred [18]. The Action in Diabetes and Vascular Disease: Preterax and Diamicron Modified Release Controlled Evaluation (ADVANCE) trial found no difference in the incidence of ‘peripheral vascular events’ between groups (reduction in relative risk −6 [−9 to 19]) [7]. Although findings from a meta-analysis of clinical trials suggested possible reductions in the risk of peripheral vascular disease with intensified control of blood glucose, these data do not provide conclusive evidence as they were based on a small number of outcomes, of which LEA comprised only a small proportion [39].

Trials to date have either been inadequately powered to find a difference or lowering blood glucose may not in fact lessen the risk of LEA. If patients with higher HbA1c differ fundamentally from those with lower levels in ways that increase their risk of LEA, then lowering blood glucose may not lower the risk of amputation. While use of fenofibrate has recently been shown to lower risk of LEA [42], currently, referral of patients at high risk of amputation to a clinic providing foot care is the most effective preventive measure for major amputation [43].

The 10 year risk of LEA in diabetes varies widely from approximately 1% in Alaskan Natives [44] to 10% in Barbadians [45] and some UK sites [46]. If lowering blood glucose translates into a lower incidence of LEA, for a population with a 10 year incidence of LEA of 5% [47] and an average HbA1c of 9.5%, an improvement to an average HbA1c of 7.5% would reduce the rate of LEA to roughly 3%, all other things being equal.

Hyperglycaemia may increase the risk of LEA through various mechanisms. It damages tissue via glycation, activates protein kinase C, causes sorbitol to accumulate and increases activity of the hexosamine pathway [48]. This effect manifests as accelerated atherosclerosis and arterial disease, sensory neuropathy, infection and autonomic dysfunction, which deregulates blood flow. Foot deformity, trauma and oedema further contribute to amputation [49]. Improved glycaemic control can potentially modify the risk of sensory neuropathy [5, 50] and possibly the progression of peripheral arterial disease [8]. Other risk factors for LEA include increasing age and duration of diabetes, ethnicity, male sex, renal dysfunction, previous amputation or foot ulceration [34, 51] and, in some studies, smoking [52, 53].

As discussed, the increased risk associated with glycaemia and LEA is likely to be mediated by peripheral vascular disease and peripheral sensory neuropathy. Differences in diagnosis of these conditions may have accounted for some of the heterogeneity we observed. Even if possible, controlling for these factors, which are potentially on the causal pathway to amputation, might lead to statistical over-adjustment with resulting underestimation of the association between glycaemia and LEA [54].

In practical terms, healthcare providers probably already encourage good glycaemic control for patients with diabetes. However, patients may benefit from knowing the magnitude of risk of LEA associated with glycaemia, as LEA is an important complication of diabetes. In the UKPDS, participants rated their decrease in quality of life four times greater for amputation than for blindness in one eye [55].

Regarding potential biases, our inclusion of estimates from studies that adjusted inadequately for confounding (i.e. factors related to both glycaemia and LEA) may have inflated the summary estimate of risk reported by us. However, we found similar overall results for studies reporting crude (or age- and sex-adjusted) or multiply adjusted risk ratios. Misclassification of diabetes type, as in clinical practice, was likely. Yet, misclassification by diabetes type or status, if it occurred equally among those who did and did not have a LEA, is unlikely to have changed our main finding that hyperglycaemia is associated with an increased risk of LEA. However, if patients with (late-onset) type 1 diabetes were more likely to be diagnosed as type 2 diabetes than the reverse, then our study (p = 0.09) may have missed a real difference in the magnitude of risk associated with glycaemia between type 1 and type 2 diabetes. Another source of misclassification is in the assessment of exposure. Glycated haemoglobin moieties other than HbA1c may have been included in some of the measurements, potentially leading to between-study variations. However, this is also unlikely to have affected the results, as HbA1c is the main component of glycated haemoglobin and most studies stated specifically that they measured HbA1c levels. Due to limited data, we were unable to analyse separately the association between glycaemia and major vs minor amputation. Since LEA is not so much a complication of diabetes as a decision made by a patient advised by surgeons, it is possible that the included studies may not represent usual clinical practice, a possibility reduced by the fact that these studies originated from many areas. The relative risk we report would underestimate the true association between hyperglycaemia and risk of amputation if surgeons were reluctant to operate on chronically ill patients with poor glycaemic control. However, this too is unlikely, since amputation may be a necessary measure to treat an infected/non-healing foot ulcer. In addition, it is difficult to disentangle the contributions of hyperglycaemia and foot ulceration to the risk of amputation, in part because ulceration itself is on the causal pathway to LEA [56]. To diminish the acute effects on HbA1c of immobility and infection, we limited this review to prospective studies of people without acute ulceration or former amputations, but acknowledge that reverse causation may have occurred. Also, we do not know whether our estimates of risk apply to these excluded groups.

In relation to the possible limitations of literature-based meta-analyses, we did not find strong evidence of publication bias, i.e. the increased reporting of smaller studies with positive rather than negative results. Nonetheless, some degree of publication bias may have been present and exaggerated the risk ratios we report. While heterogeneity may limit the generalisability of our findings, it was not accounted for by the clinically relevant characteristics available. Individual-level data are required to assess the shape of the relationship between HbA1c and LEA, or to test differences between other clinical subgroups such as those with or without peripheral arterial disease or sensory neuropathy, or, notably, those with major or minor amputations. There is little reason to believe that glycaemia would increase minor, but not major amputation, or vice versa.

The current review highlights the potential importance of glycaemic control in the prevention of LEA. This study provides an assessment of risk to give to patients. It also provides health economists and planners with estimates to enable them to better model diabetes and its complications. The clinical significance of these observational data is further heightened by the probability that a trial on lowering blood glucose to prevent LEA is unlikely to be done because: (1) LEA occurs infrequently and would require a very large study; and (2) maintaining differences in glycaemia between groups could be unethical, since lowering blood glucose has already been proven to lower the incidence of other diabetic complications.

In conclusion, the present review shows a strong association between risk of LEA and increased levels of glycaemia in individuals with diabetes. If the association is causal, treatment of glycaemia in patients whose HbA1c remains far above target levels could translate into a large reduction in risk. While amputations occur less frequently than other cardiovascular complications, its consequences may be greater. In the absence of conclusive evidence from clinical trials, and assuming causality, this paper provides further epidemiological support for glucose lowering as a strategy for reducing the risk of LEA; it also provides modellers of diabetes with estimates to more accurately assess the overall burden of hyperglycaemia.



M. S. Roy (The Institute of Ophthalmology and Visual Science, University of Medicine and Dentistry of New Jersey, New Jersey Medical School, NJ, USA) and G. Lepore (Diabetes Unit, Hospital of Bergamo, A.O. Ospedali Riuniti Bergamo, Bergamo, Italy) kindly provided tabular data for the present analyses. S. Erqou is funded by the Gates Cambridge Scholarship and Overseas Research Studentship Award. A. Adler and A. Robinson are supported by the UK National Health Service (NHS).

Duality of interest

The authors declare that there is no duality of interest associated with this manuscript.

Supplementary material

125_2009_1638_MOESM1_ESM.pdf (26 kb)
ESM Fig. 1 (PDF 26 kb)


  1. 1.
    Selvin E, Marinopoulos S, Berkenblit G et al (2004) Meta-analysis: glycosylated hemoglobin and cardiovascular disease in diabetes mellitus. Ann Intern Med 141:421–431PubMedGoogle Scholar
  2. 2.
    Stratton IM, Adler AI, Neil HA et al (2000) Association of glycaemia with macrovascular and microvascular complications of type 2 diabetes (UKPDS 35): prospective observational study. BMJ 321:405–412CrossRefPubMedGoogle Scholar
  3. 3.
    UK Prospective Diabetes Study (UKPDS) Group (1998) Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33). Lancet 352:837–853CrossRefGoogle Scholar
  4. 4.
    The Diabetes Control and Complications Trial Research Group (1993) The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. N Engl J Med 329:977–986CrossRefGoogle Scholar
  5. 5.
    Shichiri M, Kishikawa H, Ohkubo Y, Wake N (2000) Long-term results of the Kumamoto Study on optimal diabetes control in type 2 diabetic patients. Diabetes Care 23(Suppl 2):B21–B29PubMedGoogle Scholar
  6. 6.
    Gerstein HC, Miller ME, Byington RP et al (2008) Effects of intensive glucose lowering in type 2 diabetes. N Engl J Med 358:2545–2559CrossRefPubMedGoogle Scholar
  7. 7.
    Patel A, MacMahon S, Chalmers J et al (2008) Intensive blood glucose control and vascular outcomes in patients with type 2 diabetes. N Engl J Med 358:2560–2572CrossRefPubMedGoogle Scholar
  8. 8.
    The Diabetes Control and Complications Trial Research Group (1995) Effect of intensive diabetes management on macrovascular events and risk factors in the Diabetes Control and Complications Trial. Am J Cardiol 75:894–903CrossRefGoogle Scholar
  9. 9.
    Holman RR, Paul SK, Bethel MA, Matthews DR, Neil HA (2008) 10-year follow-up of intensive glucose control in type 2 diabetes. N Engl J Med 359:1–13CrossRefGoogle Scholar
  10. 10.
    Watts SA, Daly B, Anthony M, McDonald P, Khoury A, Dahar W (2001) The effect of age, gender, risk level and glycosylated hemoglobin in predicting foot amputation in HMO patients with diabetes. J Am Acad Nurse Pract 13:230–235PubMedGoogle Scholar
  11. 11.
    Boulton AJ (1996) The pathogenesis of diabetic foot problems: an overview. Diabet Med 13(Suppl 1):S12–S16PubMedGoogle Scholar
  12. 12.
    Adler AI, Boyko EJ, Ahroni JH, Smith DG (1999) Lower-extremity amputation in diabetes. The independent effects of peripheral vascular disease, sensory neuropathy, and foot ulcers. Diabetes Care 22:1029–1035CrossRefPubMedGoogle Scholar
  13. 13.
    Most RS, Sinnock P (1983) The epidemiology of lower extremity amputations in diabetic individuals. Diabetes Care 6:87–91CrossRefPubMedGoogle Scholar
  14. 14.
    Morris AD, McAlpine R, Steinke D et al (1998) Diabetes and lower-limb amputations in the community. A retrospective cohort study. DARTS/MEMO Collaboration. Diabetes Audit and Research in Tayside Scotland/Medicines Monitoring Unit. Diabetes Care 21:738–743CrossRefPubMedGoogle Scholar
  15. 15.
    Schofield CJ, Libby G, Brennan GM et al (2006) Mortality and hospitalization in patients after amputation: a comparison between patients with and without diabetes. Diabetes Care 29:2252–2256CrossRefPubMedGoogle Scholar
  16. 16.
    UK Prospective Diabetes Study (UKPDS) Group (1998) Effect of intensive blood-glucose control with metformin on complications in overweight patients with type 2 diabetes (UKPDS 34). Lancet 352:854–865CrossRefGoogle Scholar
  17. 17.
    Dormandy JA, Charbonnel B, Eckland DJ et al (2005) Secondary prevention of macrovascular events in patients with type 2 diabetes in the PROactive Study (PROspective pioglitAzone Clinical Trial In macroVascular Events): a randomised controlled trial. Lancet 366:1279–1289CrossRefPubMedGoogle Scholar
  18. 18.
    Home PD, Pocock SJ, Beck-Nielsen H et al (2007) Rosiglitazone evaluated for cardiovascular outcomes—an interim analysis. N Engl J Med 357:28–38CrossRefPubMedGoogle Scholar
  19. 19.
    Moss SE, Klein R, Klein BE (1996) Long-term incidence of lower-extremity amputations in a diabetic population. Arch Fam Med 5:391–398CrossRefPubMedGoogle Scholar
  20. 20.
    Olson JC, Erbey JR, Forrest KY, Williams K, Becker DJ, Orchard TJ (2002) Glycemia (or, in women, estimated glucose disposal rate) predict lower extremity arterial disease events in type 1 diabetes. Metabolism 51:248–254CrossRefPubMedGoogle Scholar
  21. 21.
    Roy MS, Peng B (2008) Six-year incidence of lower extremity arterial disease and associated risk factors in type 1 diabetic African-Americans. Diabet Med 25:550–556CrossRefPubMedGoogle Scholar
  22. 22.
    Coppini DV, Young PJ, Weng C, Macleod AF, Sonksen PH (1998) Outcome on diabetic foot complications in relation to clinical examination and quantitative sensory testing: a case–control study. Diabet Med 15:765–771CrossRefPubMedGoogle Scholar
  23. 23.
    Lepore G, Maglio ML, Cuni C et al (2006) Poor glucose control in the year before admission as a powerful predictor of amputation in hospitalized patients with diabetic foot ulceration. Diabetes Care 29:1985CrossRefPubMedGoogle Scholar
  24. 24.
    Chaturvedi N, Abbott CA, Whalley A, Widdows P, Leggetter SY, Boulton AJ (2002) Risk of diabetes-related amputation in South Asians vs Europeans in the UK. Diabet Med 19:99–104CrossRefPubMedGoogle Scholar
  25. 25.
    Carrington AL, Shaw JE, van Schie CH, Abbott CA, Vileikyte L, Boulton AJ (2002) Can motor nerve conduction velocity predict foot problems in diabetic subjects over a 6-year outcome period? Diabetes Care 25:2010–2015CrossRefPubMedGoogle Scholar
  26. 26.
    Farnkvist LM, Lundman BM (2003) Outcomes of diabetes care: a population-based study. Int J Qual Health Care 15:301–307CrossRefPubMedGoogle Scholar
  27. 27.
    Wells GA, Shea B, O’Connell B et al (2009) The Newcastle–Ottawa quality assessment scale. Ottawa Health Research Institute, Ottawa. Available from (accessed 2 December 2009)
  28. 28.
    Danesh J, Collins R, Appleby P, Peto R (1998) Association of fibrinogen, C-reactive protein, albumin, or leukocyte count with coronary heart disease: meta-analyses of prospective studies. JAMA 279:1477–1482CrossRefPubMedGoogle Scholar
  29. 29.
    DerSimonian R, Laird N (1986) Meta-analysis in clinical trials. Control Clin Trials 7:177–188CrossRefPubMedGoogle Scholar
  30. 30.
    Higgins JP, Thompson SG, Deeks JJ, Altman DG (2003) Measuring inconsistency in meta-analyses. BMJ 327:557–560CrossRefPubMedGoogle Scholar
  31. 31.
    Egger M, Davey SG, Schneider M, Minder C (1997) Bias in meta-analysis detected by a simple, graphical test. BMJ 315:629–634PubMedGoogle Scholar
  32. 32.
    Tseng CL, Sambamoorthi U, Helmer D et al (2007) The association between mental health functioning and nontraumatic lower extremity amputations in veterans with diabetes. Gen Hosp Psychiatry 29:537–546CrossRefPubMedGoogle Scholar
  33. 33.
    Moss SE, Klein R, Klein BE (1999) The 14-year incidence of lower-extremity amputations in a diabetic population. The Wisconsin Epidemiologic Study of Diabetic Retinopathy. Diabetes Care 22:951–959CrossRefPubMedGoogle Scholar
  34. 34.
    Resnick HE, Carter EA, Sosenko JM et al (2004) Incidence of lower-extremity amputation in American Indians: the Strong Heart Study. Diabetes Care 27:1885–1891CrossRefPubMedGoogle Scholar
  35. 35.
    Muhlhauser I, Overmann H, Bender R, Jorgens V, Berger M (2000) Predictors of mortality and end-stage diabetic complications in patients with type 1 diabetes mellitus on intensified insulin therapy. Diabet Med 17:727–734CrossRefPubMedGoogle Scholar
  36. 36.
    Lehto S, Ronnemaa T, Pyorala K, Laakso M (1996) Risk factors predicting lower extremity amputations in patients with NIDDM. Diabetes Care 19:607–612CrossRefPubMedGoogle Scholar
  37. 37.
    Jbour AS, Jarrah NS, Radaideh AM et al (2003) Prevalence and predictors of diabetic foot syndrome in type 2 diabetes mellitus in Jordan. Saudi Med J 24:761–764PubMedGoogle Scholar
  38. 38.
    Davis WA, Norman PE, Bruce DG, Davis TM (2006) Predictors, consequences and costs of diabetes-related lower extremity amputation complicating type 2 diabetes: the Fremantle Diabetes Study. Diabetologia 49:2634–2641CrossRefPubMedGoogle Scholar
  39. 39.
    Stettler C, Allemann S, Juni P et al (2006) Glycemic control and macrovascular disease in types 1 and 2 diabetes mellitus: meta-analysis of randomized trials. Am Heart J 152:27–38CrossRefPubMedGoogle Scholar
  40. 40.
    Genuth S, Nathan DM, Engel S et al (2005) Intensive diabetes treatment and cardiovascular disease in patients with type 1 diabetes. N Engl J Med 353:2643–2653CrossRefPubMedGoogle Scholar
  41. 41.
    Duckworth W, Abraira C, Moritz T et al (2009) Glucose control and vascular complications in veterans with type 2 diabetes. N Engl J Med 360:129–139CrossRefPubMedGoogle Scholar
  42. 42.
    Rajamani K, Collman PG, Best JD et al (2009) Effect of fenofibrate on amputation events in people with type 2 diabetes mellitus (FIELD study): a prespecified analysis of randomised controlled trials. Lancet 373:1780–1788CrossRefPubMedGoogle Scholar
  43. 43.
    Hunt D (2009) Diabetes: foot ulcers and amputations. Clin Evid (pii: 0602)Google Scholar
  44. 44.
    Schraer CD, Adler AI, Mayer AM, Halderson KR, Trimble BA (1997) Diabetes complications and mortality among Alaska Natives: 8 years of observation. Diabetes Care 20:314–321CrossRefPubMedGoogle Scholar
  45. 45.
    Hennis AJ, Fraser HS, Jonnalagadda R, Fuller J, Chaturvedi N (2004) Explanations for the high risk of diabetes-related amputation in a Caribbean population of black african descent and potential for prevention. Diabetes Care 27:2636–2641CrossRefPubMedGoogle Scholar
  46. 46.
    The Global Lower Extremity Amputation Study Group (2000) Epidemiology of lower extremity amputation in centres in Europe. North America and East Asia. Br J Surg 87:328–337CrossRefGoogle Scholar
  47. 47.
    Krishnan S, Nash F, Baker N, Fowler D, Rayman G (2008) Reduction in diabetic amputations over 11 years in a defined U.K. population: benefits of multidisciplinary team work and continuous prospective audit. Diabetes Care 31:99–101CrossRefPubMedGoogle Scholar
  48. 48.
    Brownlee M (2005) The pathobiology of diabetic complications: a unifying mechanism. Diabetes 54:1615–1625CrossRefPubMedGoogle Scholar
  49. 49.
    Boulton AJ (2008) The diabetic foot: grand overview, epidemiology and pathogenesis. Diabetes Metab Res Rev 24(Suppl 1):S3–S6CrossRefPubMedGoogle Scholar
  50. 50.
    The Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications Research Group (2000) Retinopathy and nephropathy in patients with type 1 diabetes four years after a trial of intensive therapy. N Engl J Med 342:381–389CrossRefGoogle Scholar
  51. 51.
    Reiber GE, Pecoraro RE, Koepsell TD (1992) Risk factors for amputation in patients with diabetes mellitus. A case–control study. Ann Intern Med 117:97–105PubMedGoogle Scholar
  52. 52.
    Apelqvist J, Agardh CD (1992) The association between clinical risk factors and outcome of diabetic foot ulcers. Diabetes Res Clin Pract 18:43–53CrossRefPubMedGoogle Scholar
  53. 53.
    Gamba MA, Gotlieb SL, Bergamaschi DP, Vianna LA (2004) Lower extremity amputations in diabetic patients: a case–control study. Rev Saude Publica 38:399–404 (Article in Portuguese)CrossRefPubMedGoogle Scholar
  54. 54.
    Weinberg CR (1993) Toward a clearer definition of confounding. Am J Epidemiol 137:1–8PubMedGoogle Scholar
  55. 55.
    Clarke P, Gray A, Holman R (2002) Estimating utility values for health states of type 2 diabetic patients using the EQ-5D (UKPDS 62). Med Decis Making 22:340–349PubMedGoogle Scholar
  56. 56.
    Pecoraro RE, Reiber GE, Burgess EM (1990) Pathways to diabetic limb amputation. Basis for prevention. Diabetes Care 13:513–521CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • A. I. Adler
    • 1
  • S. Erqou
    • 2
  • T. A. S. Lima
    • 1
  • A. H. N. Robinson
    • 3
  1. 1.Wolfson Diabetes and Endocrine Clinic, Institute of Metabolic SciencesAddenbrooke’s Hospital, Cambridge University Foundation Hospital TrustCambridgeUK
  2. 2.Department of Public Health and Primary CareUniversity of CambridgeCambridgeUK
  3. 3.Department of Trauma and OrthopaedicsAddenbrooke’s HospitalCambridgeUK

Personalised recommendations