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Published on: January 3, 2011
Temporal Trends in Distal Symmetric Polyneuropathy in Type 2 Diabetes: The Fremantle Diabetes Study
Wendy A Davis1,2, Emma Hamilton1,3, Timothy M E Davis1,2,3
1Medical School, University of Western Australia, Fremantle Hospital, Fremantle, WA 6959, Australia.
Context:
Macrovascular outcomes in type 2 diabetes have improved over recent decades. There are scant equivalent distal symmetric polyneuropathy (DSPN) data.
Objective:
This work aimed to characterize temporal changes in DSPN prevalence and incidence rates (IRs) in community-based Australians.
Methods:
An observational study was conducted among an urban population. Participants included individuals with type 2 diabetes from the Fremantle Diabetes Study phases I (FDS1; n = 1296 recruited 1993-1996) and II (FDS2; n = 1509 recruited 2008-2011). Main outcome measures included Michigan Neuropathy Screening Instrument (MNSI) clinical grading.
Results:
DSPN prevalence by 8-point MNSI was 30.8% (FDS1) and 58.9% (FDS2; P < .001), and by 6-point (excluding foot appearance) and 2-point (biothesiometry alone) MNSI was 37.5% and 35.7% (P = .336), and 33.8% and 38.7% (P = .011), respectively. Given between-phase changes in appearance assessment, 8-point MNSI data were not analyzed further. In multivariable analysis, FDS2 vs FDS1 participation was associated with 6-point (odds ratio (95% CI) 0.68 (0.56-0.83); P < .001) but not 2-point (0.90 (0.74-1.11); P = .326) MNSI DSPN prevalence. Four-year DSPN IRs (95% CI) for 6-point MNSI were 13.6 (12.0-15.4) and 17.6 (15.9-19.4)/100 person-years in FDS1 and FDS2, respectively (IR ratio [IRR] 1.31 [1.12-1.55]; P < .001), and for 2-point MNSI were 13.9 (12.3-15.8) and 7.4 (16.3-8.6/100 person-years; IRR 0.53 [0.43-0.64]; P < .001). FDS2 vs FDS1 independently predicted incident DSPN for 6-point (hazard ratio [95% CI] 1.25 [1.06-1.48]; P = .009) and 2-point (0.42 [0.33-0.55]; P < .001) MNSI.
Conclusion:
DSPN prevalence was lower or equivalent in FDS2 vs FDS1, and its incidence was greater or lower, in multivariable models depending on the MNSI features used.
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