Prevalence Trends of Diabetes Subgroups in the United States: A Data-driven Analysis Spanning Three Decades From NHANES (1988-2018)

Antonio-Villa, Neftalí Eduardo; Fernández‐Chirino, Luisa; Vargas‐Vázquez, Arsenio; Aguilar-Salinas, Carlos A.; Bello‐Chavolla, Omar Yaxmehen

doi:10.1210/clinem/dgab762

Cited by 24 publications

(11 citation statements)

References 19 publications

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“…This population-based report suggests broad genetic influences. The findings in the current analysis were consistent and robust for the observed differences in age, sex, and ethnicity ( 24 , 28 – 31 ).…”

Section: Discussionsupporting

confidence: 80%

“…For demographic characteristics, reports have confirmed that the biological factors affecting the pathophysiology of diabetes differ from race or nationality, and may be influenced by social factors, such as different cultural backgrounds ( 17 – 24 ). Data from the Centers for Disease Control and Prevention shows that the probability of some racial and ethnic groups being diagnosed with diabetes in the USA is much higher than that of whites, which was reviewed in Lancet ( 25 ).…”

Section: Discussionmentioning

confidence: 99%

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Prevalence of diabetes in the USA from the perspective of demographic characteristics, physical indicators and living habits based on NHANES 2009-2018

Fang

Sheng²,

Tan³

et al. 2023

Front. Endocrinol.

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ObjectiveTo determine differences in DM in the U.S. population according to demographic characteristics, physical indicators and living habits.Methods23 546 participants in the 2009 to 2018 National Health and Nutrition Examination Survey (NHANES) who were 20 year of age or older and not pregnant. All analyses used weighted samples and considered the stratification and clustering of the design. Specific indicators include length of leg (cm), BMI (kg/cm2), TCHOL (mg/dL), fasting plasma glucose (mg/dL) and comparison of means and the proportion of participants with DM.ResultsThe prevalence of DM in the USA has been rising modestly in the past decade, and were consistent and robust for the observed differences in age, sex, and ethnicity. Compared with white participants, black participants and Mexican-American were both more likely (P<0.001) to have diabetes: 14.6% (CI, 13.6% to 15.6%) among black participants, 10.6% (CI, 9.9% to 11.3%) among white participants, and 13.5% (CI, 11.9% to 15.2%) among Mexican-American participants. The prevalence of diabetes is increasing with age, males peaked around the 60s, and women around the 70s. The overall mean leg length and TCHOL was lower in diabetics than in non-diabetics (1.07 cm, 18.67 mg/dL, respectively), while mean BMI were higher in diabetics than in non-diabetics (4.27 kg/cm2). DM had the greatest effect on decline of TCHOL in white participants (23.6 mg/dL), less of an effect in black participants (9.67 mg/dL), and the least effect in Mexican-American participants (8.25 mg/dL). Notably, smoking had great effect on percent increment of DM in whites (0.2%), and have little effect on black and Mexican-Americans.ConclusionsDM is more common in the general population than might be clinically recognized, and the prevalence of DM was associated to varying degrees with many indicators of demographic characteristics, physical indicators, and living habits. These indicators should be linked with medical resource allocation and scientific treatment methods to comprehensively implement the treatment of DM.

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Section: Discussionsupporting

confidence: 80%

Section: Discussionmentioning

confidence: 99%

Prevalence of diabetes in the USA from the perspective of demographic characteristics, physical indicators and living habits based on NHANES 2009-2018

Fang

Sheng²,

Tan³

et al. 2023

Front. Endocrinol.

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show abstract

“…In the abbreviated model, 4 of the 5 most influential features on an increased PTA were known risk factors for the development of HL: older age, male gender, cigarette smoking, and obesity 32‐36 . With the rising prevalence of obesity and diabetes mellitus, the impact of these conditions on HL remains an important consideration 37‐39 . These are health behavior and medical conditions that are routinely documented during clinic visits in primary and specialty care settings and available within the EMR.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Prediction of Objective Hearing Loss With Demographics, Clinical Factors, and Subjective Hearing Status

Gathman

Choi

Vasdev

et al. 2023

Otolaryngol.--head neck surg.

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ObjectiveHearing loss (HL) is highly prevalent, yet underrecognized and underdiagnosed. Lack of standardized screening, awareness, cost, and access to hearing testing present barriers to HL identification. To facilitate prescreening and selection of patients who warrant audiometric evaluation, we developed a machine learning (ML) model to predict speech‐frequency pure‐tone average (PTA).Study DesignCross‐sectional study.SettingNational Health and Nutrition Examination Survey (NHANES).MethodsThe cohort included 8918 adults (≥20 years) who completed audiometric testing with NHANES (2012‐2018). The primary outcome measure was the prediction of better hearing ear speech‐frequency PTA. Relevant predictors included demographics, medical conditions, and subjective assessment of hearing. Supervised ML with a tree‐based architecture was used. Regression performance was determined by the mean absolute error (MAE) with binary classification assessed with area under the receiver operating characteristic curve (AUC).ResultsUsing the full set of predictors, the test set MAE between the ML‐predicted and actual PTA was 5.29 dB HL (95% confidence interval [CI]: 4.97‐5.61). The 5 most influential predictors of higher PTA were increased age, worse subjective hearing, male gender, increased body mass index, and history of smoking. The 5‐factor abbreviated model performed comparably to the extended feature set with MAE 5.36 (95% CI: 5.03‐5.69) and AUC for PTA > 25 dB HL of 0.92 (95% CI: 0.90‐0.94).ConclusionThe ML model was able to predict PTA with patient demographics, clinical factors, and subjective hearing status. ML‐based prediction may be used to identify individuals who could benefit most from audiometric evaluation.

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“…The prevalence of T2D is 66% higher in US Hispanic/Latino persons than in their Non-Hispanic White (NHW) counterparts [ 1 ]. The age and sex-standardized prevalence for impaired fasting glucose (excluding those with a diagnosis of T2D) is 31.6% in Mexican-Americans compared to 26.1% in NHW.…”

Section: Introductionmentioning

confidence: 99%

Associations between avocado intake and measures of glucose and insulin homeostasis in Hispanic individuals with and without type 2 diabetes: Results from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL)

Senn,

Goodarzi,

Ramesh

et al. 2023

Nutrition, Metabolism and Cardiovascular Diseases

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Prevalence Trends of Diabetes Subgroups in the United States: A Data-driven Analysis Spanning Three Decades From NHANES (1988-2018)

Cited by 24 publications

References 19 publications

Prevalence of diabetes in the USA from the perspective of demographic characteristics, physical indicators and living habits based on NHANES 2009-2018

Prevalence of diabetes in the USA from the perspective of demographic characteristics, physical indicators and living habits based on NHANES 2009-2018

Machine Learning Prediction of Objective Hearing Loss With Demographics, Clinical Factors, and Subjective Hearing Status

Associations between avocado intake and measures of glucose and insulin homeostasis in Hispanic individuals with and without type 2 diabetes: Results from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL)

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