scholarly journals Erratum. Differential Health Care Use, Diabetes-Related Complications, and Mortality Among Five Unique Classes of Patients With Type 2 Diabetes in Singapore: A Latent Class Analysis of 71,125 Patients. Diabetes Care 2020;43:1048–1056

Diabetes Care ◽  
2020 ◽  
Vol 43 (8) ◽  
pp. 1976-1976
Author(s):  
Jun Jie Benjamin Seng ◽  
Yu Heng Kwan ◽  
Vivian Shu Yi Lee ◽  
Chuen Seng Tan ◽  
Sueziani Binte Zainudin ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Health Care ◽  
Latent Class Analysis ◽  
Diabetes Care ◽  
Latent Class ◽  
Health Care Use ◽  
Class Analysis ◽  
Complications And Mortality

scholarly journals Differential Health Care Use, Diabetes-Related Complications, and Mortality Among Five Unique Classes of Patients With Type 2 Diabetes in Singapore: A Latent Class Analysis of 71,125 Patients

Diabetes Care ◽  
2020 ◽  
Vol 43 (5) ◽  
pp. 1048-1056 ◽  
Author(s):  
Jun Jie Benjamin Seng ◽  
Yu Heng Kwan ◽  
Vivian Shu Yi Lee ◽  
Chuen Seng Tan ◽  
Sueziani Binte Zainudin ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Health Care ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Health Care Use ◽  
Class Analysis ◽  
Complications And Mortality

Not all type-2-diabetes patients increase BMI after initiating insulin - Results of latent class analysis from the DPV registry

2021 ◽  
Author(s):  
Nicole Prinz ◽  
Anke Schwandt ◽  
Bernadette Borgert ◽  
Bettina Hartmann ◽  
Hans-Peter Kempe ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Class Analysis ◽  
Diabetes Patients

scholarly journals Day-Time Patterns of Carbohydrate Intake in Adults by Non-Parametric Multi-Level Latent Class Analysis—Results from the UK National Diet and Nutrition Survey (2008/09–2015/16)

Nutrients ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 2476
Author(s):  
Wang ◽  
Almoosawi ◽  
Palla
Keyword(s):  
Type 2 Diabetes ◽  
Latent Class Analysis ◽  
Total Energy ◽  
Energy Intake ◽  
Latent Class ◽  
Eating Patterns ◽  
Class Analysis ◽  
Nutrition Survey ◽  
The Uk

This study aims at combining time and quantity of carbohydrate (CH) intake in the definition of eating patterns in UK adults and investigating the association of the derived patterns with type 2 diabetes (T2D). The National Diet and Nutrition Survey (NDNS) Rolling Program included 6155 adults in the UK. Time of the day was categorized into 7 pre-defined time slots: 6–9 am, 9–12 noon, 12–2 pm, 2–5 pm, 5–8 pm, 8–10 pm, and 10 pm–6 am. Responses for CH intake were categorized into: no energy intake, CH <50% or ≥50% of total energy. Non-parametric multilevel latent class analysis (MLCA) was applied to identify eating patterns of CH consumption across day-time, as a novel method accounting for the repeated measurements of intake over 3–4 days nested within individuals. Survey-designed multivariable regression was used to assess the associations of CH eating patterns with T2D. Three CH eating day patterns (low, high CH percentage and frequent CH intake day) emerged from 24,483 observation days; based on which three classes of CH eaters were identified and characterized as: low (28.1%), moderate (28.8%) and high (43.1%) CH eaters. On average, low-CH eaters consumed the highest amount of total energy intake (7985.8 kJ) and had higher percentages of energy contributed by fat and alcohol, especially after 8 pm. Moderate-CH eaters consumed the lowest amount of total energy (7341.8 kJ) while they tended to have their meals later in the day. High-CH eaters consumed most of their carbohydrates and energy earlier in the day and within the time slots of 6–9 am, 12–2 pm and 5–8 pm, which correspond to traditional mealtimes. The high-CH eaters profile had the highest daily intake of CH and fiber and the lowest intake of protein and fat. Low-CH eaters had greater odds than high-CH eaters of having T2D in self-reported but not in previously undiagnosed diabetics. Further research using prospective longitudinal studies is warranted to ascertain the direction of causality in the association of CH patterns with type 2 diabetes.

scholarly journals P2-94 Types of smokers, depression and disability in type 2 diabetes: a latent class analysis

2011 ◽  
Vol 65 (Suppl 1) ◽  
pp. A246-A246
Author(s):  
G. Gariepy ◽  
A. Malla ◽  
J. Wang ◽  
L. Messier ◽  
I. Strychar ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Class Analysis

scholarly journals Identifying Patterns of Lifestyle Behaviors among People with Type 2 Diabetes in Tianjin, China: A Latent Class Analysis

2017 ◽  
Vol 8 (6) ◽  
pp. 1379-1392 ◽  
Author(s):  
Xuying Wang ◽  
Jiageng Chen ◽  
Xiaoqian Liu ◽  
Fei Gao ◽  
Haozuo Zhao ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Class Analysis ◽  
Lifestyle Behaviors

scholarly journals Social determinants of hypertension and type-2 diabetes in Kenya: A latent class analysis of a nationally representative sample

PLoS ONE ◽  
2019 ◽  
Vol 14 (8) ◽  
pp. e0221257 ◽  
Author(s):  
Rahma S. Mkuu ◽  
Tamika D. Gilreath ◽  
Caroline Wekullo ◽  
Gabriela A. Reyes ◽  
Idethia S. Harvey
Keyword(s):  
Type 2 Diabetes ◽  
Latent Class Analysis ◽  
Social Determinants ◽  
Representative Sample ◽  
Latent Class ◽  
Class Analysis ◽  
Nationally Representative

Which obese youth are at increased risk for type 2 diabetes? Latent class analysis and comparison with diabetic youth

2011 ◽  
Vol 13 (2) ◽  
pp. 181-188 ◽  
Author(s):  
Fenella Greig ◽  
Sharon Hyman ◽  
Elizabeth Wallach ◽  
Tom Hildebrandt ◽  
Robert Rapaport
Keyword(s):  
Type 2 Diabetes ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Class Analysis ◽  
Obese Youth ◽  
Increased Risk
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