Trajectories of body mass index and waist circumference before the onset of diabetes among people with prediabetes

Hu, Huan; Kawasaki, Yohei; Kuwahara, Keisuke; Nakagawa, Tohru; Honda, Takeshi; Yamamoto, S.; Eguchi, M.; Kochi, Takeshi; Nishihara, Akiko; Imai, Toshio; Yamamoto, Makoto; Okazaki, Hiroko; Miyamoto, Toshiaki; Tomita, Kentaro; Uehara, Akihiko; Ogasawara, Takayuki; Sasaki, Nozomu; Hori, Akihiro; Nagahama, Satsue; Shimizu, Makiko; Murakami, Taizo; Chen, Sanmei; Kabe, Isamu; Mizoue, Tetsuya; Sone, Toshio; Dohi, Seitaro

doi:10.1016/j.clnu.2019.12.023

Cited by 18 publications

(14 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hence, decreasing WC and thus abdominal obesity could favour T2DM control. Indeed, in our study, participants who lost > 5 cm WC during follow-up were three times more likely to be controlled, although this association was not observed with HbA 1 c. A similar finding was reported in a Japanese study, where obese participants with prediabetes who lost WC returned to normoglycaemia ( Hu et al, 2019 ). Overall, our results suggest that decreasing WC might be beneficial for T2DM management.…”

Section: Discussionsupporting

confidence: 87%

“…Our findings are in agreement with other studies, where increasing WC was found among subjects with T2DM ( De Backer et al, 2016 ). Increased WC has been shown to be a major determinant of T2DM incidence ( Hu et al, 2019 , Jeon et al, 2019 ) and control ( Hameed and AbdulQahar, 2019 , Mamo et al, 2019 ). Hence, decreasing WC and thus abdominal obesity could favour T2DM control.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Do Weight trajectories influence diabetes control? A prospective study in Switzerland (CoLaus study)

Ducraux

Waeber

Marques‐Vidal

2021

Preventive Medicine Reports

View full text Add to dashboard Cite

Objective Identify anthropometric trajectories among subjects with type 2 diabetes mellitus (T2DM), and associate them with glycaemic control. Methods Prospective study including 268 community-dwelling participants with T2DM (34% women, mean age 68.7 ± 8.9 years) followed for 10.7 years (range: 8.8–13.6 years). T2DM control was considered for 1) fasting plasma glucose (FPG) < 7.0 mmol/L, or 2) HbA 1 c < 7.0% (53 nmol/mol). Changes in weight or waist and weight variability were considered. Results One half (FPG) and one third (HbA 1 c) of participants presented with uncontrolled T2DM. Half of the participants presented with obesity and 75% with abdominal obesity. During follow-up, half of the participants maintained their weight, 25% gained > 5 kg, and 25% lost < 5 kg; almost half increased their waist by > 5 cm. Using FPG as criterion, participants who lost > 5 cm waist were more likely to be controlled: multivariable-adjusted odds ratio (OR) and 95% confidence interval (CI): 3.10 (1.23–7.78). Participants with controlled T2DM also presented with a higher weight variability: multivariable adjusted mean ± standard error 4.8 ± 0.3 vs. 3.9 ± 0.3 kg, p = 0.028. Using HbA 1 c as criterion, participants who lost > 5 kg were less likely to be controlled: OR and (95% CI): 0.35 (0.18–0.66). Similar findings were obtained when restricting the analysis to participants who were diabetic throughout the whole study period. Conclusion In a Swiss community-based sample of participants with T2DM, T2DM control rates could be implemented. Neither weight nor waist variability was significantly and consistently associated with T2DM control.

show abstract

Section: Discussionsupporting

confidence: 87%

Section: Discussionmentioning

confidence: 99%

Do Weight trajectories influence diabetes control? A prospective study in Switzerland (CoLaus study)

Ducraux

Waeber

Marques‐Vidal

2021

Preventive Medicine Reports

View full text Add to dashboard Cite

show abstract

“…Third, most of the studies did not consider the effects of additional confounders on the association between hearing loss and stroke. As a result, in this study, the role of smoking 53 , 54 , obesity 55 , physical activity 56 , occupational and environmental exposures such as shift work and noise, air pollution, all being associated with both stroke and hearing loss, has not been addressed. Fourth, most of the included cohort studies had a retrospective method.…”

Section: Discussionmentioning

confidence: 99%

Sensorineural hearing loss and risk of stroke: a systematic review and meta-analysis

Khosravipour

Rajati

2021

Sci Rep

View full text Add to dashboard Cite

The aim of this systematic review and meta-analysis study was to clarify the effects of sensorineural hearing loss (SNHL) on the incidence of stroke. In line with this, PubMed, Scopus, Web of Science, and ScienceDirect databases were searched using related keywords and MeSH terms from inception to March 1, 2020. Out of the 1961 initial records, eight cohort studies comprising 4,564,202 participants were included, and their qualities were assessed using the Newcastle-Ottawa Scale (NOS). Then, the random-effects model was used to pool HR (95% CI) for risk of stroke; and heterogeneity was presented with I2 index. Subgroup analysis and publication bias tests were performed, and the pooled HR (95% CI) of stroke in SNHL was estimated as 1.31 (1.08, 1.53) for the unadjusted model and 1.33 (1.18, 1.49) for the adjusted model. Subgroup analysis indicates a significantly higher risk of stroke in patients with sudden SNHL (SSNHL) in comparison to age-related HL (ARHL) both in the unadjusted model, [HR = 1.46; 95% CI (1.08, 1.63)] versus [HR = 1.14; 95% CI (0.64, 1.65)], and in the adjusted model, [HR = 1.44; 95% CI (1.15, 1.74)] versus [HR = 1.29; 95% CI (1.24, 1.34)]. Our study showed that patients with SNHL face a higher risk of stroke than those without SNHL. It is necessary to perform hematologic and neurological examinations to help clinicians detect patients who are potentially at risk for stroke.

show abstract

“…Numerous clinical and epidemiologic studies have demonstrated that type 2 diabetes mellitus (T2DM) is correlated with obesity and blood pressure (BP) (12)(13)(14)(15)(16)(17). In addition to the absolute level of these risk factors, increasing evidence indicates that their longitudinal growth trajectory also plays a considerable role in the incident T2DM (18)(19)(20)(21)(22). However, most previous studies focused on the body mass index (BMI) and/or systolic BP in a single measurement or only paid attention to the association between the trajectories of BMI and incident diabetes, ignoring the dynamic trends of SBP in the life course.…”

Section: Introductionmentioning

confidence: 99%

Life-course blood pressure trajectories and incident diabetes: A longitudinal cohort in a Chinese population

Liu

Chen

et al. 2022

Front. Endocrinol.

View full text Add to dashboard Cite

BackgroundBlood pressure levels are correlated with diabetes among middle-aged or older adults. However, longitudinal trajectories of blood pressure during young adulthood and their impact on diabetes have been insufficiently studied.MethodsThe longitudinal cohort consisted of 4,625 adults who had blood pressure and body mass index (BMI) repeatedly measured five to nine times during 18–60 years of age. Distinct systolic blood pressure (SBP) trajectories were identified by a group-based trajectory model. Logistic regression analyses were used to investigate the association between trajectory patterns or quartiles of area under the curve values of SBP trajectories and incident diabetes, respectively.ResultsFour distinct trajectory groups were identified for SBP: normotensive-stable (n = 761, 16.5%), prehypertension-stable (n = 2,381, 51.5%), stage I hypertension-increasing (n = 1,231, 26.6%), and stage II hypertension-increasing (n = 251, 5.4%). Compared with subjects who remained at SBP <120 mmHg in the normotensive-stable group, individuals in the prehypertension-stable trajectory exhibited a normal SBP range (<140 mmHg), and they still had a significantly higher risk of diabetes (adjusted OR = 1.82, p = 0.029). Individuals had a greater risk of diabetes in the stage I hypertension-increasing group (adjusted OR = 2.31, p = 0.006) and the highest risk in the stage II hypertension-increasing group (adjusted OR = 3.91, p < 0.001) relative to the normotensive-stable group. Furthermore, compared with the first quartile, adjusted ORs (95% CIs) of the fourth quartile of SBP incremental and total AUC were 2.50 (1.61–3.97) and 1.82 (1.15–2.94), respectively.ConclusionsLong-term SBP trajectory is a significant predictor for incident diabetes, which is independent of baseline SBP and body weight, attaching importance to maintaining optimal blood pressure levels and controlling changing slopes of SBP for preventing diabetes.

show abstract

Trajectories of body mass index and waist circumference before the onset of diabetes among people with prediabetes

Cited by 18 publications

References 21 publications

Do Weight trajectories influence diabetes control? A prospective study in Switzerland (CoLaus study)

Do Weight trajectories influence diabetes control? A prospective study in Switzerland (CoLaus study)

Sensorineural hearing loss and risk of stroke: a systematic review and meta-analysis

Life-course blood pressure trajectories and incident diabetes: A longitudinal cohort in a Chinese population

Contact Info

Product

Resources

About